Grounding DINO")
- gr.Markdown("Open-World Detection with Grounding DINO")
- gr.Markdown("Note the model runs on CPU, so it may take a while to run the model.")
-
- with gr.Row():
- with gr.Column():
- input_image = gr.Image(source='upload', type="pil")
- grounding_caption = gr.Textbox(label="Detection Prompt")
- run_button = gr.Button(label="Run")
- with gr.Accordion("Advanced options", open=False):
- box_threshold = gr.Slider(
- label="Box Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
- )
- text_threshold = gr.Slider(
- label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
- )
-
- with gr.Column():
- gallery = gr.outputs.Image(
- type="pil",
- # label="grounding results"
- ).style(full_width=True, full_height=True)
- # gallery = gr.Gallery(label="Generated images", show_label=False).style(
- # grid=[1], height="auto", container=True, full_width=True, full_height=True)
-
- run_button.click(fn=run_grounding, inputs=[
- input_image, grounding_caption, box_threshold, text_threshold], outputs=[gallery])
- gr.Examples(
- [["watermelon.jpg", "watermelon", 0.25, 0.25]],
- inputs = [input_image, grounding_caption, box_threshold, text_threshold],
- outputs = [gallery],
- fn=run_grounding,
- cache_examples=True,
- label='Try this example input!'
- )
- block.launch(share=True, show_api=False, show_error=True)
-
diff --git a/spaces/Audio-AGI/WavJourney/README.md b/spaces/Audio-AGI/WavJourney/README.md
deleted file mode 100644
index 6514202f6a3a92fe09c96516f15d4e7848d85484..0000000000000000000000000000000000000000
--- a/spaces/Audio-AGI/WavJourney/README.md
+++ /dev/null
@@ -1,111 +0,0 @@
----
-title: WavJourney
-emoji: 🔥
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
-license: cc-by-nc-4.0
----
-# 🎵 WavJourney: Compositional Audio Creation with LLMs
-[](https://arxiv.org/abs/2307.14335) [](https://github.com/Audio-AGI/WavJourney/) [](https://audio-agi.github.io/WavJourney_demopage/) [](https://huggingface.co/spaces/Audio-AGI/WavJourney)
-
-
-This repository contains the official implementation of ["WavJourney: Compositional Audio Creation with Large Language Models"](https://audio-agi.github.io/WavJourney_demopage/WavJourney_arXiv.pdf).
-
-Starting with a text prompt, WavJourney can create audio content with engaging storylines encompassing personalized speakers, lifelike speech in context, emotionally resonant music compositions, and impactful sound effects that enhance the auditory experience. Check the audio examples in the [Project Page](https://audio-agi.github.io/WavJourney_demopage/)!
-
-
-
-
-
-
-## Preliminaries
-1. Install the environment:
-```bash
-bash ./scripts/EnvsSetup.sh
-```
-2. Activate the conda environment:
-```bash
-conda activate WavJourney
-```
-
-3. (Optional) You can modify the default configuration in `config.yaml`, check the details described in the configuration file.
-4. Pre-download the models (might take some time):
-```bash
-python scripts/download_models.py
-```
-
-5. Set the WAVJOURNEY_OPENAI_KEY in the environment variable for accessing [GPT-4 API](https://platform.openai.com/account/api-keys) [[Guidance](https://help.openai.com/en/articles/7102672-how-can-i-access-gpt-4)]
-```bash
-export WAVJOURNEY_OPENAI_KEY=your_openai_key_here
-```
-
-6. Set environment variables for using API services
-```bash
-# Set the port for the WAVJOURNEY service to 8021
-export WAVJOURNEY_SERVICE_PORT=8021
-
-# Set the URL for the WAVJOURNEY service to 127.0.0.1
-export WAVJOURNEY_SERVICE_URL=127.0.0.1
-
-# Limit the maximum script lines for WAVJOURNEY to 999
-export WAVJOURNEY_MAX_SCRIPT_LINES=999
-```
-
-
-7. Start Python API services (e.g., Text-to-Speech, Text-to-Audio)
-```bash
-bash scripts/start_services.sh
-```
-
-## Web APP
- ```bash
-bash scripts/start_ui.sh
- ```
-
-## Commandline Usage
- ```bash
- python wavjourney_cli.py -f --input-text "Generate a one-minute introduction to quantum mechanics"
- ```
-
-
-## Kill the services
-You can kill the running services via this command:
- ```bash
-python scripts/kill_services.py
- ```
-
-## (Advanced features) Speaker customization
-You can add voice presets to WavJourney to customize the voice actors. Simply provide the voice id, the description and a sample wav file, and WavJourney will pick the voice automatically based on the audio script. Predefined system voice presets are in `data/voice_presets`.
-
-You can manage voice presets via UI. Specifically, if you want to add voice to voice presets. Run the script via command line below:
-```bash
-python add_voice_preset.py --id "id" --desc "description" --wav-path path/to/wav --session-id ''
-```
-What makes for good voice prompt? See detailed instructions here.
-## Hardware requirement
-- The VRAM of the GPU in the default configuration should be greater than 16 GB.
-- Operation system: Linux.
-
-## Citation
-If you find this work useful, you can cite the paper below:
-
- @article{liu2023wavjourney,
- title = {WavJourney: Compositional Audio Creation with Large Language Models},
- author = {Liu, Xubo and Zhu, Zhongkai and Liu, Haohe and Yuan, Yi and Huang, Qiushi and Liang, Jinhua and Cao, Yin and Kong, Qiuqiang and Plumbley, Mark D and Wang, Wenwu},
- journal = {arXiv preprint arXiv:2307.14335},
- year = {2023}
- }
-
-[](https://www.buymeacoffee.com/liuxubo)
-
-## Appreciation
-- [Bark](https://github.com/suno-ai/bark) for a zero-shot text-to-speech synthesis model.
-- [AudioCraft](https://github.com/facebookresearch/audiocraft) for state-of-the-art audio generation models.
-
-## Disclaimer
-We are not responsible for audio generated using semantics created by this model. Just don't use it for illegal purposes.
-
diff --git a/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py b/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py
deleted file mode 100644
index be58431ed446bf6283c17274a41e37ec3eb24409..0000000000000000000000000000000000000000
--- a/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import os
-import asyncio
-import random
-from datetime import date, datetime, timezone, timedelta
-from ast import literal_eval
-
-import streamlit as st
-import openai
-
-from autoagents.utils.constants import MAIN_HEADER, MAIN_CAPTION, SAMPLE_QUESTIONS
-from autoagents.agents.search import ActionRunner
-
-from langchain.chat_models import ChatOpenAI
-
-
-async def run():
- output_acc = ""
- st.session_state["random"] = random.randint(0, 99)
- if "task" not in st.session_state:
- st.session_state.task = None
- if "model_name" not in st.session_state:
- st.session_state.model_name = "gpt-3.5-turbo"
-
- st.set_page_config(
- page_title="Search Agent",
- page_icon="🤖",
- layout="wide",
- initial_sidebar_state="expanded",
- )
-
- st.title(MAIN_HEADER)
- st.caption(MAIN_CAPTION)
-
- with st.form("my_form", clear_on_submit=False):
- st.markdown("", unsafe_allow_html=True)
- user_input = st.text_input(
- "You: ",
- key="input",
- placeholder="Ask me anything ...",
- label_visibility="hidden",
- )
-
- submitted = st.form_submit_button(
- "Search", help="Hit to submit the search query."
- )
-
- # Ask the user to enter their OpenAI API key
- if (api_key := st.sidebar.text_input("OpenAI api-key", type="password")):
- api_org = None
- else:
- api_key, api_org = os.getenv("OPENAI_API_KEY"), os.getenv("OPENAI_API_ORG")
- with st.sidebar:
- model_dict = {
- "gpt-3.5-turbo": "GPT-3.5-turbo",
- "gpt-4": "GPT-4 (Better but slower)",
- }
- st.radio(
- "OpenAI model",
- model_dict.keys(),
- key="model_name",
- format_func=lambda x: model_dict[x],
- )
-
- time_zone = str(datetime.now(timezone(timedelta(0))).astimezone().tzinfo)
- st.markdown(f"**The system time zone is {time_zone} and the date is {date.today()}**")
-
- st.markdown("**Example Queries:**")
- for q in SAMPLE_QUESTIONS:
- st.markdown(f"*{q}*")
-
- if not api_key:
- st.warning(
- "API key required to try this app. The API key is not stored in any form. [This](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key) might help."
- )
- elif api_org and st.session_state.model_name == "gpt-4":
- st.warning(
- "The free API key does not support GPT-4. Please switch to GPT-3.5-turbo or input your own API key."
- )
- else:
- outputq = asyncio.Queue()
- runner = ActionRunner(outputq,
- ChatOpenAI(openai_api_key=api_key,
- openai_organization=api_org,
- temperature=0,
- model_name=st.session_state.model_name),
- persist_logs=True) # log to HF-dataset
-
- async def cleanup(e):
- st.error(e)
- await st.session_state.task
- st.session_state.task = None
- st.stop()
-
- placeholder = st.empty()
-
- if user_input and submitted:
- if st.session_state.task is not None:
- with placeholder.container():
- st.session_state.task.cancel()
- st.warning("Previous search aborted", icon="⚠️")
-
- st.session_state.task = asyncio.create_task(
- runner.run(user_input, outputq)
- )
- iterations = 0
- with st.expander("Search Results", expanded=True):
- while True:
- with st.spinner("Wait for it..."):
- output = await outputq.get()
- placeholder.empty()
- if isinstance(output, Exception):
- if isinstance(output, openai.error.AuthenticationError):
- await cleanup(f"AuthenticationError: Invalid OpenAI API key.")
- elif isinstance(output, openai.error.InvalidRequestError) \
- and output._message == "The model: `gpt-4` does not exist":
- await cleanup(f"The free API key does not support GPT-4. Please switch to GPT-3.5-turbo or input your own API key.")
- elif isinstance(output, openai.error.OpenAIError):
- await cleanup(output)
- elif isinstance(output, RuntimeWarning):
- st.warning(output)
- continue
- else:
- await cleanup("Something went wrong. Please try searching again.")
- return
- try:
- output_fmt = literal_eval(output)
- st.json(output_fmt, expanded=False)
- st.write("---")
- iterations += 1
- except:
- output_acc += "\n" + output
- st.markdown(f" {output} ",
- unsafe_allow_html=True)
- if iterations >= runner.agent_executor.max_iterations:
- await cleanup(
- f"Maximum iterations ({iterations}) exceeded. You can try running the search again or try a variation of the query."
- )
- return
- if "Final Answer:" in output:
- break
- # Found the answer
- final_answer = await st.session_state.task
- final_answer = final_answer.replace("$", "\$")
- # st.success accepts md
- st.success(final_answer, icon="✅")
- st.balloons()
- st.session_state.task = None
- st.stop()
-
-if __name__ == "__main__":
- loop = asyncio.new_event_loop()
- loop.set_debug(enabled=False)
- loop.run_until_complete(run())
diff --git a/spaces/BAAI/vid2vid-zero/Dockerfile b/spaces/BAAI/vid2vid-zero/Dockerfile
deleted file mode 100644
index 9eaa78f8facc5066f018183f108824842f11b7e8..0000000000000000000000000000000000000000
--- a/spaces/BAAI/vid2vid-zero/Dockerfile
+++ /dev/null
@@ -1,57 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update && \
- apt-get upgrade -y && \
- apt-get install -y --no-install-recommends \
- git \
- git-lfs \
- wget \
- curl \
- # ffmpeg \
- ffmpeg \
- x264 \
- # python build dependencies \
- build-essential \
- libssl-dev \
- zlib1g-dev \
- libbz2-dev \
- libreadline-dev \
- libsqlite3-dev \
- libncursesw5-dev \
- xz-utils \
- tk-dev \
- libxml2-dev \
- libxmlsec1-dev \
- libffi-dev \
- liblzma-dev && \
- apt-get clean && \
- rm -rf /var/lib/apt/lists/*
-
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:${PATH}
-WORKDIR ${HOME}/app
-
-RUN curl https://pyenv.run | bash
-ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ENV PYTHON_VERSION=3.10.9
-RUN pyenv install ${PYTHON_VERSION} && \
- pyenv global ${PYTHON_VERSION} && \
- pyenv rehash && \
- pip install --no-cache-dir -U pip setuptools wheel
-
-RUN pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1
-COPY --chown=1000 requirements.txt /tmp/requirements.txt
-RUN pip install --no-cache-dir -U -r /tmp/requirements.txt
-
-COPY --chown=1000 . ${HOME}/app
-# RUN cd Tune-A-Video && patch -p1 < ../patch
-ENV PYTHONPATH=${HOME}/app \
- PYTHONUNBUFFERED=1 \
- GRADIO_ALLOW_FLAGGING=never \
- GRADIO_NUM_PORTS=1 \
- GRADIO_SERVER_NAME=0.0.0.0 \
- GRADIO_THEME=huggingface \
- SYSTEM=spaces
-CMD ["python", "app.py"]
diff --git a/spaces/Bala2-03-2003/MygenvioceAI/app.py b/spaces/Bala2-03-2003/MygenvioceAI/app.py
deleted file mode 100644
index ca8b6d40b4ab898c70da92f4a4298de2baf703dc..0000000000000000000000000000000000000000
--- a/spaces/Bala2-03-2003/MygenvioceAI/app.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import os
-import re
-import requests
-import json
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-PLAY_HT_API_KEY=os.getenv('PLAY_HT_API_KEY')
-PLAY_HT_USER_ID=os.getenv('PLAY_HT_USER_ID')
-
-PLAY_HT_VOICE_ID=os.getenv('PLAY_HT_VOICE_ID')
-play_ht_api_get_audio_url = "https://play.ht/api/v2/tts"
-
-
-template = """You are a helpful assistant to answer user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
- input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
- llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
- prompt=prompt,
- verbose=True,
- memory=memory,
-)
-
-headers = {
- "accept": "text/event-stream",
- "content-type": "application/json",
- "AUTHORIZATION": "Bearer "+ PLAY_HT_API_KEY,
- "X-USER-ID": PLAY_HT_USER_ID
-}
-
-
-def get_payload(text):
- return {
- "text": text,
- "voice": PLAY_HT_VOICE_ID,
- "quality": "medium",
- "output_format": "mp3",
- "speed": 1,
- "sample_rate": 24000,
- "seed": None,
- "temperature": None
- }
-
-def get_generated_audio(text):
- payload = get_payload(text)
- generated_response = {}
- try:
- response = requests.post(play_ht_api_get_audio_url, json=payload, headers=headers)
- response.raise_for_status()
- generated_response["type"]= 'SUCCESS'
- generated_response["response"] = response.text
- except requests.exceptions.RequestException as e:
- generated_response["type"]= 'ERROR'
- try:
- response_text = json.loads(response.text)
- if response_text['error_message']:
- generated_response["response"] = response_text['error_message']
- else:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["type"]= 'ERROR'
- generated_response["response"] = response.text
- return generated_response
-
-def extract_urls(text):
- # Define the regex pattern for URLs
- url_pattern = r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+[/\w\.-]*'
-
- # Find all occurrences of URLs in the text
- urls = re.findall(url_pattern, text)
-
- return urls
-
-def get_audio_reply_for_question(text):
- generated_audio_event = get_generated_audio(text)
- #From get_generated_audio, you will get events in a string format, from that we need to extract the url
- final_response = {
- "audio_url": '',
- "message": ''
- }
- if generated_audio_event["type"] == 'SUCCESS':
- audio_urls = extract_urls(generated_audio_event["response"])
- if len(audio_urls) == 0:
- final_response['message'] = "No audio file link found in generated event"
- else:
- final_response['audio_url'] = audio_urls[-1]
- else:
- final_response['message'] = generated_audio_event['response']
- return final_response
-
-def download_url(url):
- try:
- # Send a GET request to the URL to fetch the content
- final_response = {
- 'content':'',
- 'error':''
- }
- response = requests.get(url)
- # Check if the request was successful (status code 200)
- if response.status_code == 200:
- final_response['content'] = response.content
- else:
- final_response['error'] = f"Failed to download the URL. Status code: {response.status_code}"
- except Exception as e:
- final_response['error'] = f"Failed to download the URL. Error: {e}"
- return final_response
-
-def get_filename_from_url(url):
- # Use os.path.basename() to extract the file name from the URL
- file_name = os.path.basename(url)
- return file_name
-
-def get_text_response(user_message):
- response = llm_chain.predict(user_message = user_message)
- return response
-
-def get_text_response_and_audio_response(user_message):
- response = get_text_response(user_message) # Getting the reply from Open AI
- audio_reply_for_question_response = get_audio_reply_for_question(response)
- final_response = {
- 'output_file_path': '',
- 'message':''
- }
- audio_url = audio_reply_for_question_response['audio_url']
- if audio_url:
- output_file_path=get_filename_from_url(audio_url)
- download_url_response = download_url(audio_url)
- audio_content = download_url_response['content']
- if audio_content:
- with open(output_file_path, "wb") as audio_file:
- audio_file.write(audio_content)
- final_response['output_file_path'] = output_file_path
- else:
- final_response['message'] = download_url_response['error']
- else:
- final_response['message'] = audio_reply_for_question_response['message']
- return final_response
-
-def chat_bot_response(message, history):
- text_and_audio_response = get_text_response_and_audio_response(message)
- output_file_path = text_and_audio_response['output_file_path']
- if output_file_path:
- return (text_and_audio_response['output_file_path'],)
- else:
- return text_and_audio_response['message']
-
-demo = gr.ChatInterface(chat_bot_response,examples=["How are you doing?","What are your interests?","Which places do you like to visit?"])
-
-if __name__ == "__main__":
- demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/BartPoint/VoiceChange/app_multi.py b/spaces/BartPoint/VoiceChange/app_multi.py
deleted file mode 100644
index 67890355f12e2d2d5420ce8e31a722020f349b24..0000000000000000000000000000000000000000
--- a/spaces/BartPoint/VoiceChange/app_multi.py
+++ /dev/null
@@ -1,469 +0,0 @@
-from typing import Union
-
-from argparse import ArgumentParser
-
-import asyncio
-import json
-import hashlib
-from os import path, getenv
-
-import gradio as gr
-
-import torch
-
-import numpy as np
-import librosa
-
-import edge_tts
-
-import config
-import util
-from infer_pack.models import (
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono
-)
-from vc_infer_pipeline import VC
-
-# Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21 # noqa
-in_hf_space = getenv('SYSTEM') == 'spaces'
-
-# Argument parsing
-arg_parser = ArgumentParser()
-arg_parser.add_argument(
- '--hubert',
- default=getenv('RVC_HUBERT', 'hubert_base.pt'),
- help='path to hubert base model (default: hubert_base.pt)'
-)
-arg_parser.add_argument(
- '--config',
- default=getenv('RVC_MULTI_CFG', 'multi_config.json'),
- help='path to config file (default: multi_config.json)'
-)
-arg_parser.add_argument(
- '--api',
- action='store_true',
- help='enable api endpoint'
-)
-arg_parser.add_argument(
- '--cache-examples',
- action='store_true',
- help='enable example caching, please remember delete gradio_cached_examples folder when example config has been modified' # noqa
-)
-args = arg_parser.parse_args()
-
-app_css = '''
-#model_info img {
- max-width: 100px;
- max-height: 100px;
- float: right;
-}
-
-#model_info p {
- margin: unset;
-}
-'''
-
-app = gr.Blocks(
- theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate"),
- css=app_css,
- analytics_enabled=False
-)
-
-# Load hubert model
-hubert_model = util.load_hubert_model(config.device, args.hubert)
-hubert_model.eval()
-
-# Load models
-multi_cfg = json.load(open(args.config, 'r'))
-loaded_models = []
-
-for model_name in multi_cfg.get('models'):
- print(f'Loading model: {model_name}')
-
- # Load model info
- model_info = json.load(
- open(path.join('model', model_name, 'config.json'), 'r')
- )
-
- # Load RVC checkpoint
- cpt = torch.load(
- path.join('model', model_name, model_info['model']),
- map_location='cpu'
- )
- tgt_sr = cpt['config'][-1]
- cpt['config'][-3] = cpt['weight']['emb_g.weight'].shape[0] # n_spk
-
- if_f0 = cpt.get('f0', 1)
- net_g: Union[SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono]
- if if_f0 == 1:
- net_g = SynthesizerTrnMs768NSFsid(
- *cpt['config'],
- is_half=util.is_half(config.device)
- )
- else:
- net_g = SynthesizerTrnMs768NSFsid_nono(*cpt['config'])
-
- del net_g.enc_q
-
- # According to original code, this thing seems necessary.
- print(net_g.load_state_dict(cpt['weight'], strict=False))
-
- net_g.eval().to(config.device)
- net_g = net_g.half() if util.is_half(config.device) else net_g.float()
-
- vc = VC(tgt_sr, config)
-
- loaded_models.append(dict(
- name=model_name,
- metadata=model_info,
- vc=vc,
- net_g=net_g,
- if_f0=if_f0,
- target_sr=tgt_sr
- ))
-
-print(f'Models loaded: {len(loaded_models)}')
-
-# Edge TTS speakers
-tts_speakers_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) # noqa
-
-
-# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer-web.py#L118 # noqa
-def vc_func(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- if input_audio is None:
- return (None, 'Please provide input audio.')
-
- if model_index is None:
- return (None, 'Please select a model.')
-
- model = loaded_models[model_index]
-
- # Reference: so-vits
- (audio_samp, audio_npy) = input_audio
-
- # https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49
- # Can be change well, we will see
- if (audio_npy.shape[0] / audio_samp) > 320 and in_hf_space:
- return (None, 'Input audio is longer than 60 secs.')
-
- # Bloody hell: https://stackoverflow.com/questions/26921836/
- if audio_npy.dtype != np.float32: # :thonk:
- audio_npy = (
- audio_npy / np.iinfo(audio_npy.dtype).max
- ).astype(np.float32)
-
- if len(audio_npy.shape) > 1:
- audio_npy = librosa.to_mono(audio_npy.transpose(1, 0))
-
- if audio_samp != 16000:
- audio_npy = librosa.resample(
- audio_npy,
- orig_sr=audio_samp,
- target_sr=16000
- )
-
- pitch_int = int(pitch_adjust)
-
- resample = (
- 0 if resample_option == 'Disable resampling'
- else int(resample_option)
- )
-
- times = [0, 0, 0]
-
- checksum = hashlib.sha512()
- checksum.update(audio_npy.tobytes())
-
- output_audio = model['vc'].pipeline(
- hubert_model,
- model['net_g'],
- model['metadata'].get('speaker_id', 0),
- audio_npy,
- checksum.hexdigest(),
- times,
- pitch_int,
- f0_method,
- path.join('model', model['name'], model['metadata']['feat_index']),
- feat_ratio,
- model['if_f0'],
- filter_radius,
- model['target_sr'],
- resample,
- rms_mix_rate,
- 'v2'
- )
-
- out_sr = (
- resample if resample >= 16000 and model['target_sr'] != resample
- else model['target_sr']
- )
-
- print(f'npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s')
- return ((out_sr, output_audio), 'Success')
-
-
-async def edge_tts_vc_func(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- if input_text is None:
- return (None, 'Please provide TTS text.')
-
- if tts_speaker is None:
- return (None, 'Please select TTS speaker.')
-
- if model_index is None:
- return (None, 'Please select a model.')
-
- speaker = tts_speakers_list[tts_speaker]['ShortName']
- (tts_np, tts_sr) = await util.call_edge_tts(speaker, input_text)
- return vc_func(
- (tts_sr, tts_np),
- model_index,
- pitch_adjust,
- f0_method,
- feat_ratio,
- filter_radius,
- rms_mix_rate,
- resample_option
- )
-
-
-def update_model_info(model_index):
- if model_index is None:
- return str(
- '### Model info\n'
- 'Please select a model from dropdown above.'
- )
-
- model = loaded_models[model_index]
- model_icon = model['metadata'].get('icon', '')
-
- return str(
- '### Model info\n'
- ''
- '**{name}**\n\n'
- 'Author: {author}\n\n'
- 'Source: {source}\n\n'
- '{note}'
- ).format(
- name=model['metadata'].get('name'),
- author=model['metadata'].get('author', 'Anonymous'),
- source=model['metadata'].get('source', 'Unknown'),
- note=model['metadata'].get('note', ''),
- icon=(
- model_icon
- if model_icon.startswith(('http://', 'https://'))
- else '/file/model/%s/%s' % (model['name'], model_icon)
- )
- )
-
-
-def _example_vc(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- (audio, message) = vc_func(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
- )
- return (
- audio,
- message,
- update_model_info(model_index)
- )
-
-
-async def _example_edge_tts(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- (audio, message) = await edge_tts_vc_func(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method,
- feat_ratio, filter_radius, rms_mix_rate, resample_option
- )
- return (
- audio,
- message,
- update_model_info(model_index)
- )
-
-
-with app:
- gr.Markdown(
- '## A simplistic Web interface\n'
- 'RVC interface, project based on [RVC-WebUI](https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI)' # thx noqa
- 'A lot of inspiration from what\'s already out there, including [zomehwh/rvc-models](https://huggingface.co/spaces/zomehwh/rvc-models) & [DJQmUKV/rvc-inference](https://huggingface.co/spaces/DJQmUKV/rvc-inference).\n ' # thx noqa
- )
-
- with gr.Row():
- with gr.Column():
- with gr.Tab('Audio conversion'):
- input_audio = gr.Audio(label='Input audio')
-
- vc_convert_btn = gr.Button('Convert', variant='primary')
-
- with gr.Tab('TTS conversion'):
- tts_input = gr.TextArea(
- label='TTS input text'
- )
- tts_speaker = gr.Dropdown(
- [
- '%s (%s)' % (
- s['FriendlyName'],
- s['Gender']
- )
- for s in tts_speakers_list
- ],
- label='TTS speaker',
- type='index'
- )
-
- tts_convert_btn = gr.Button('Convert', variant='primary')
-
- pitch_adjust = gr.Slider(
- label='Pitch',
- minimum=-24,
- maximum=24,
- step=1,
- value=0
- )
- f0_method = gr.Radio(
- label='f0 methods',
- choices=['pm', 'harvest', 'crepe'],
- value='pm',
- interactive=True
- )
-
- with gr.Accordion('Advanced options', open=False):
- feat_ratio = gr.Slider(
- label='Feature ratio',
- minimum=0,
- maximum=1,
- step=0.1,
- value=0.6
- )
- filter_radius = gr.Slider(
- label='Filter radius',
- minimum=0,
- maximum=7,
- step=1,
- value=3
- )
- rms_mix_rate = gr.Slider(
- label='Volume envelope mix rate',
- minimum=0,
- maximum=1,
- step=0.1,
- value=1
- )
- resample_rate = gr.Dropdown(
- [
- 'Disable resampling',
- '16000',
- '22050',
- '44100',
- '48000'
- ],
- label='Resample rate',
- value='Disable resampling'
- )
-
- with gr.Column():
- # Model select
- model_index = gr.Dropdown(
- [
- '%s - %s' % (
- m['metadata'].get('source', 'Unknown'),
- m['metadata'].get('name')
- )
- for m in loaded_models
- ],
- label='Model',
- type='index'
- )
-
- # Model info
- with gr.Box():
- model_info = gr.Markdown(
- '### Model info\n'
- 'Please select a model from dropdown above.',
- elem_id='model_info'
- )
-
- output_audio = gr.Audio(label='Output audio')
- output_msg = gr.Textbox(label='Output message')
-
- multi_examples = multi_cfg.get('examples')
- if (
- multi_examples and
- multi_examples.get('vc') and multi_examples.get('tts_vc')
- ):
- with gr.Accordion('Sweet sweet examples', open=False):
- with gr.Row():
- # VC Example
- if multi_examples.get('vc'):
- gr.Examples(
- label='Audio conversion examples',
- examples=multi_examples.get('vc'),
- inputs=[
- input_audio, model_index, pitch_adjust, f0_method,
- feat_ratio
- ],
- outputs=[output_audio, output_msg, model_info],
- fn=_example_vc,
- cache_examples=args.cache_examples,
- run_on_click=args.cache_examples
- )
-
- # Edge TTS Example
- if multi_examples.get('tts_vc'):
- gr.Examples(
- label='TTS conversion examples',
- examples=multi_examples.get('tts_vc'),
- inputs=[
- tts_input, model_index, tts_speaker, pitch_adjust,
- f0_method, feat_ratio
- ],
- outputs=[output_audio, output_msg, model_info],
- fn=_example_edge_tts,
- cache_examples=args.cache_examples,
- run_on_click=args.cache_examples
- )
-
- vc_convert_btn.click(
- vc_func,
- [
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_rate
- ],
- [output_audio, output_msg],
- api_name='audio_conversion'
- )
-
- tts_convert_btn.click(
- edge_tts_vc_func,
- [
- tts_input, model_index, tts_speaker, pitch_adjust, f0_method,
- feat_ratio, filter_radius, rms_mix_rate, resample_rate
- ],
- [output_audio, output_msg],
- api_name='tts_conversion'
- )
-
- model_index.change(
- update_model_info,
- inputs=[model_index],
- outputs=[model_info],
- show_progress=False,
- queue=False
- )
-
-app.queue(
- concurrency_count=1,
- max_size=20,
- api_open=args.api
-).launch()
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md b/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md
deleted file mode 100644
index 231c36d39005d9275c61ccc88067d084ca775635..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md
+++ /dev/null
@@ -1,134 +0,0 @@
-
-Aparcamiento de coches multijugador Sudáfrica Edición APK Descargar: Una guía para los usuarios de Android
- Si usted está buscando un juego de estacionamiento de coches realista y divertido que ofrece más que solo estacionamiento, es posible que desee echa un vistazo a Parking Multijugador. Y si usted está en Sudáfrica o desea experimentar la cultura del automóvil de Sudáfrica, es posible que desee probar el Aparcamiento Multijugador South áfrica Edition APK. En este artículo, te diremos qué es el Car Parking Multiplayer, qué hace que South áfrica Edition sea diferente, cómo descargarlo e instalarlo en tu dispositivo Android, y algunos consejos y trucos para jugarlo.
- ¿Qué es el Aparcamiento Multijugador?
- Car Parking Multiplayer es un juego que puede engañarte con su nombre bastante engañoso. Pero, es mucho más que solo estar aparcando tu coche. Es una experiencia de mundo abierto donde se puede conducir gratis y sí, todavía trabajar en ese aparcamiento si lo desea. Incluso puedes saltar de tu coche y caminar. Hay diferentes áreas que se pueden explorar en el juego. Cada una es como su propio mundo abierto. Puedes optar por jugar en modo de un solo jugador o en modo online si quieres una escena más caótica (de forma divertida) .
-aparcamiento de coches multijugador sudáfrica edición apk descargar
DOWNLOAD === https://bltlly.com/2v6IE9
- Características de Aparcamiento Multijugador
- Aparcamiento de coches multijugador tiene las siguientes características :
-
-- Modo multijugador de mundo abierto
- - Caminar libremente
- - Mundo abierto libre con gasolineras reales y servicios de automóviles
- - Compite contra jugadores reales en las carreras multijugador
- - Intercambiar coches con jugadores reales
- - Miles de jugadores reales cada día
- - Lista de amigos
- - Chat de voz
- - Modo de policía
-
-
-- Personalización del coche
- - Suspensión ajustable, ángulo de rueda y más
- - Ajuste del motor: motor de intercambio, turbo, caja de cambios y escape
- - Visual auto tungs: Vynils dinámico, partes del cuerpo del coche
-
-
-- Mundo abierto de alta calidad
-
- - 100 coches con el interior real
- - 16 pieles de jugador
- - Edificios con interior
-
-
-- Juego interesante
- - 82 desafíos de estacionamiento y conducción en la vida real
- - Diferentes vehículos: Grúa, camioneta, camiones, coches deportivos y clásicos
-
-
-
- Opiniones de Aparcamiento multijugador
- Aparcamiento de coches multijugador ha recibido en su mayoría críticas positivas de los usuarios en Google Play Store y App Store. Tiene una calificación de 4.4 de 5 estrellas en Google Play Store y una calificación de 4.3 de 5 estrellas en App Store . Estos son algunos de los comentarios de los usuarios:
- "Increíble juego! No hay errores en este juego o retrasos ect. (base en el dispositivo que utiliza) Me encantan los gráficos y los coches. Los coches son tan realistas y los sonidos son increíbles. Me encanta cómo puedes personalizar tu coche y hacer que se vea genial. El modo multijugador es increíble. Puedes chatear con otros jugadores y competir con ellos. También puedes unirte a un clan o crear tu propio clan. Este juego es muy divertido y adictivo. Recomiendo este juego a todos los que aman los coches y los juegos de estacionamiento."
- "Este juego es muy bueno pero necesita algunas mejoras como agregar más coches, más mapas, más personalizaciones, más modos de juego, más desafíos, etc. Además, el juego se bloquea a veces y los controles no son muy suaves. Los gráficos son agradables, pero pueden ser mejores. El juego es divertido de jugar con amigos, pero se vuelve aburrido después de un tiempo. Espero que los desarrolladores actualicen el juego pronto y lo hagan más agradable."
-
- ¿Qué es el aparcamiento multijugador South áfrica Edition?
- Car Parking Multijugador South áfrica Edition es una versión modificada de Car Parking Multijugador que está especialmente diseñado para los usuarios sudafricanos o los fans de la cultura del coche de Sudáfrica. No es una versión oficial del juego, sino un archivo APK hecho por fans que se puede descargar e instalar en dispositivos Android .
- Diferencias entre Aparcamiento de coches multijugador y Aparcamiento de coches multijugador Sudáfrica Edición
- Car Parking Multijugador South áfrica Edition tiene algunas diferencias con el juego original Car Parking Multijugador, como :
-
-- Más coches que son populares en Sudáfrica, como BMW E30, VW Golf Mk1, Toyota Corolla, Nissan 1400, etc.
-- Más personalizaciones que reflejan la cultura del automóvil sudafricano, como llantas giratorias, escapes fuertes, pegatinas, banderas, etc.
-- Más mapas que se basan en ubicaciones reales en Sudáfrica, como Ciudad del Cabo, Johannesburgo, Durban, etc.
-- Más música inspirada en la escena musical sudafricana, como kwaito, gqom, amapiano, etc.
-- Más idiomas que se hablan en Sudáfrica, como afrikaans, zulú, xhosa, etc.
-
- Beneficios de Aparcamiento Multijugador South áfrica Edition
- Car Parking Multijugador South áfrica Edition tiene algunos beneficios para los usuarios que quieren disfrutar del juego con un toque sudafricano, como :
-
-- Más variedad y diversidad en términos de coches, personalizaciones, mapas, música e idiomas
-- Más diversión y emoción en términos de jugabilidad, gráficos, efectos de sonido e interacciones
-- Más conexión y comunidad con otros jugadores que comparten el mismo interés y pasión por la cultura del automóvil de Sudáfrica
-- Más soporte y actualizaciones de los desarrolladores que se dedican a mejorar el juego y agregar nuevas características
-
- Cómo descargar e instalar el estacionamiento de coches multijugador South áfrica Edition APK en dispositivos Android?
-
- Requisitos para descargar e instalar el estacionamiento de coches multijugador South áfrica Edition APK
- Para descargar e instalar Aparcamiento Multijugador South áfrica Edition APK en su dispositivo Android, es necesario tener :
-
-
-- Un dispositivo Android que se ejecuta en Android 4.1 o superior
-- Una conexión a Internet estable
-- Una aplicación de administrador de archivos que puede acceder al archivo APK
-- Una cantidad suficiente de espacio de almacenamiento en su dispositivo
-- Un permiso para instalar aplicaciones de fuentes desconocidas en la configuración del dispositivo
-
- Pasos para Descargar e Instalar Aparcamiento Multijugador South áfrica Edition APK
- Para descargar e instalar Aparcamiento Multijugador South áfrica Edition APK en su dispositivo Android, es necesario seguir estos pasos :
-
-- Ir a un sitio web de confianza que proporciona el enlace para descargar Car Parking Multi jugador South áfrica Edition APK. Por ejemplo, puede visitar [este sitio web] para obtener la última versión del archivo APK.
-- Haga clic en el botón de descarga y espere a que el archivo APK se descargue en su dispositivo.
-- Una vez que se complete la descarga, localizar el archivo APK en su dispositivo utilizando una aplicación de administrador de archivos. Puede encontrarlo en la carpeta Descargas o en cualquier otra carpeta donde lo haya guardado.
-- Toque en el archivo APK y seleccione Instalar. Es posible que vea un mensaje de advertencia que dice "Para su seguridad, el teléfono no está permitido instalar aplicaciones desconocidas de esta fuente". Si ves este mensaje, ve a la configuración del dispositivo y habilita la opción de instalar aplicaciones de fuentes desconocidas. Esta opción puede estar en Seguridad, Privacidad o Aplicaciones dependiendo del modelo de dispositivo y la versión de Android.
-
-- Espere a que termine el proceso de instalación. Puede tardar unos minutos dependiendo del rendimiento del dispositivo y la velocidad de Internet.
-- Una vez que se hace la instalación, se puede abrir el juego y disfrutar de aparcamiento multijugador South áfrica Edition en su dispositivo Android.
-
- Consejos y Trucos para Jugar Aparcamiento Multijugador South áfrica Edition
- Ahora que ha descargado e instalado Car Parking Multiplayer South áfrica Edition en su dispositivo Android, es posible que desee conocer algunos consejos y trucos para jugarlo. Estos son algunos de ellos:
- Cómo seleccionar un coche y un jugador
- Para seleccionar un coche y un jugador en Car Parking Multiplayer South áfrica Edition, debe hacer lo siguiente:
-
-- Toque en el icono de menú en la esquina superior izquierda de la pantalla.
-- Toque en Garaje para ver la lista de coches que usted posee o puede comprar.
-- Toque en el coche que desea utilizar y luego toque en Select.
-- Toque en Volver para volver al menú.
-- Toque en Perfil para ver la lista de jugadores entre los que puede elegir.
-- Toque en el reproductor que desea utilizar y luego toque en Select.
-- Toque en Volver para volver al menú.
-- Toque en Jugar para comenzar a jugar el juego con el coche y el jugador seleccionado.
-
- Cómo ajustar su coche y ajustar su relación de engranajes
- Para afinar su coche y ajustar su relación de engranajes en Car Parking Multiplayer South áfrica Edition, debe hacer lo siguiente:
-
-- Toque en el icono de menú en la esquina superior izquierda de la pantalla.
-- Toque en Garaje para ver la lista de coches que usted posee o puede comprar.
-- Toque en el coche que desea sintonizar y luego toque en Tune.
-- Verá cuatro pestañas: Motor, Suspensión, Ruedas y Cuerpo. Puede deslizar hacia la izquierda o hacia la derecha para cambiar entre ellas.
-
-- Para ajustar su relación de cambios, toque en Caja de cambios en la pestaña Motor. Verá un gráfico que muestra cómo cambia su velocidad con diferentes marchas. Puede arrastrar los puntos en el gráfico para cambiar la relación de transmisión para cada marcha. También puede pulsar en Auto o Manual para cambiar entre modos de transmisión automática o manual.
-- Una vez que haya terminado de ajustar su coche, toque en Guardar para aplicar los cambios.
-
- Cómo Deriva, Donut, y Burnout
- Para la deriva, donut, y burnout en el estacionamiento de coches multijugador South áfrica Edition, es necesario hacer lo siguiente:
-
-- Para la deriva, debe usar una combinación de dirección, acelerador, freno y freno de mano. Puede usar los controles de inclinación o táctiles para la dirección. Para iniciar la deriva, es necesario acelerar y girar bruscamente en una esquina. Luego, debe aplicar el freno de mano brevemente para hacer que sus ruedas traseras pierdan tracción. Después de eso, debe equilibrar el acelerador y la dirección para mantener el ángulo de deriva y la dirección. También puede utilizar el freno de mano o freno de mano de nuevo si necesita ajustar su deriva. Para terminar con la deriva, es necesario soltar el acelerador y la dirección y enderezar el coche.
-- Para la rosquilla, debe usar una combinación de dirección, acelerador y freno de mano. Puede usar controles de inclinación o táctiles para la dirección. Para comenzar a donar, debe acelerar y girar bruscamente en una dirección. Entonces, es necesario aplicar el freno de mano para hacer que su coche gire alrededor de su centro. Después de eso, es necesario mantener el acelerador y la dirección constante para mantener el círculo de la dona. También puede cambiar la dirección de su dona cambiando la dirección. Para terminar donuting, es necesario soltar el acelerador y el freno de mano y enderezar su coche.
-
-
- Conclusión
- Car Parking Multijugador South áfrica Edition es un juego que ofrece una experiencia de aparcamiento realista y divertido con un toque sudafricano. Tiene más coches, personalizaciones, mapas, música e idiomas que reflejan la cultura del automóvil sudafricano. También tiene un modo de mundo abierto, un modo de ajuste de coche, y un modo multijugador que hacen el juego más agradable y emocionante. Puede descargar e instalar Parking Multijugador South áfrica Edition APK en su dispositivo Android siguiendo los pasos en este artículo. También puedes usar los consejos y trucos de este artículo para mejorar tu juego y divertirte más.
- Preguntas frecuentes
- Aquí están algunas de las preguntas más frecuentes sobre Car Parking Multiplayer South áfrica Edition:
- ¿Es gratis Car Parking Multijugador South áfrica Edition?
- Sí, Parking Multijugador South áfrica Edition es gratis para descargar y jugar. Sin embargo, puede contener algunas compras en la aplicación que requieren dinero real.
- ¿Es seguro el aparcamiento multijugador South áfrica Edition?
- Sí, Parking Multijugador South áfrica Edition es seguro, siempre y cuando se descarga desde un sitio web de confianza. Sin embargo, siempre debe tener cuidado al descargar e instalar aplicaciones de fuentes desconocidas, ya que pueden contener virus o malware que pueden dañar su dispositivo.
- ¿Es compatible Car Parking Multiplayer South áfrica Edition con mi dispositivo?
- Car Parking Multijugador South áfrica Edition es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 4.1 o superior. Sin embargo, algunos dispositivos pueden tener diferentes especificaciones o problemas de rendimiento que pueden afectar la calidad o funcionalidad del juego.
- ¿Cómo puedo actualizar Car Parking Multijugador South áfrica Edition?
-
- ¿Cómo puedo contactar a los desarrolladores de Car Parking Multiplayer South áfrica Edition?
- Para contactar a los desarrolladores de Car Parking Multiplayer South áfrica Edition, puedes visitar su página de Facebook o su canal de YouTube y dejar un comentario o un mensaje.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md b/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md
deleted file mode 100644
index 2001fb4da4e341236f8b4ef59c584c7ee860ae80..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md
+++ /dev/null
@@ -1,76 +0,0 @@
-
-Nitro asfalto 9 leyendas Mod Apk: Una guía para los aficionados a las carreras
-Si eres un fan de los juegos de carreras, es posible que hayas oído hablar de Asphalt Nitro 9 Legends, uno de los juegos más populares y emocionantes del género. Este juego le permite tomar el volante de los coches reales de gama alta de renombrados fabricantes de automóviles legendarios, como Ferrari, Porsche, Lamborghini, y W Motors, entre muchas otras marcas internacionales. Puedes conducir, impulsar y realizar acrobacias en ubicaciones dinámicas de la vida real en modo individual o multijugador.
-Pero ¿y si quieres disfrutar del juego sin limitaciones ni restricciones? ¿Qué pasa si quieres tener dinero ilimitado y fichas, desbloquear todos los coches y pistas, personalizar sus vehículos y actualizarlos, y competir con otros jugadores en línea o fuera de línea? Bueno, hay una manera de hacerlo. Puede descargar e instalar Asphalt Nitro 9 Leyendas Mod Apk, una versión modificada del juego que le da acceso a todas estas características y más.
-asfalto nitro 9 leyendas mod apk
Download File ===== https://bltlly.com/2v6LmQ
-En este artículo, le guiará a través de las características, proceso de descarga, pasos de instalación, consejos y trucos, pros y contras, y la conclusión de Asphalt Nitro 9 Leyendas Mod Apk. Sigue leyendo para saber más.
- Características de Asphalt Nitro 9 Leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es una versión modificada del juego original que le da recursos ilimitados y características que mejoran su experiencia de juego. Estas son algunas de las características que puedes disfrutar con este mod apk:
-
-- Dinero ilimitado y fichas: Con este apk mod, nunca se quedará sin dinero o fichas para comprar nuevos coches, pistas, mejoras, o artículos. Puedes gastar todo lo que quieras sin preocuparte por tu presupuesto.
-
-- Personalizar sus vehículos y actualizarlos: Con este apk mod, puede personalizar sus vehículos de acuerdo a su preferencia. Puedes cambiar su color, pintura, calcomanías, ruedas, etc. También puedes mejorar su rendimiento mejorando su motor, transmisión, suspensión, frenos, nitro boost, etc.
-- Disfrutar de gráficos realistas y efectos de sonido: Con este apk mod, se puede disfrutar de los impresionantes gráficos y efectos de sonido del juego. El juego utiliza un motor de física realista que simula el movimiento y el comportamiento de los coches. El juego también cuenta con efectos de sonido de alta calidad que te hacen sentir como si estuvieras en una carrera real.
-- Compite con otros jugadores en línea o fuera de línea: Con este apk mod, puede competir con otros jugadores en línea o fuera de línea. Puedes unirte a carreras multijugador online y desafiar a tus amigos u otros jugadores de todo el mundo. También puedes jugar offline en modo carrera y completar varias misiones y desafíos.
-
- Cómo descargar e instalar Asphalt Nitro 9 leyendas Mod Apk
-Si desea descargar e instalar Asphalt Nitro 9 leyendas Mod Apk, es necesario seguir estos sencillos pasos:
-
-- Paso 1: Descargar el archivo apk mod de una fuente de confianza. Usted puede encontrar muchos sitios web que ofrecen el archivo apk mod para Asphalt Nitro 9 Leyendas, pero no todos ellos son seguros y fiables. Debes asegurarte de que el archivo que descargas esté libre de virus, malware o cualquier otro contenido dañino. Puede utilizar este enlace para descargar el archivo apk mod de forma segura.
-- Paso 2: Habilitar fuentes desconocidas en la configuración del dispositivo. Antes de poder instalar el archivo apk mod, debe habilitar fuentes desconocidas en la configuración de su dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a la configuración del dispositivo, luego a la seguridad, luego a fuentes desconocidas y conéctela.
-
-- Paso 4: Disfruta del juego con recursos y funciones ilimitadas. Ahora que ha instalado Asphalt Nitro 9 Leyendas Mod Apk, se puede disfrutar del juego con dinero ilimitado, fichas, coches, pistas, opciones de personalización, y más. También puedes competir con otros jugadores online o offline y divertirte.
-
- Consejos y trucos para jugar Asphalt Nitro 9 leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es un juego divertido y emocionante que le mantendrá entretenido durante horas. Sin embargo, si quieres mejorar tus habilidades y rendimiento en el juego, puedes seguir estos consejos y trucos:
-
-- Elija el coche adecuado para cada pista y modo. El juego ofrece una variedad de coches de diferentes marcas y categorías, pero no todos ellos son adecuados para cada pista o modo. Es necesario tener en cuenta la velocidad, aceleración, manejo, nitro, y otros factores de cada coche antes de elegir uno. Por ejemplo, si usted está jugando en una pista con curvas, es posible que desee elegir un coche con buen manejo y nitro. Si usted está jugando en una pista recta, es posible que desee elegir un coche con alta velocidad y aceleración.
-- Utilice nitro boost sabiamente y estratégicamente. Nitro boost es una de las características más importantes del juego, ya que puede darte una ventaja sobre tus oponentes. Sin embargo, necesitas usarlo sabiamente y estratégicamente. No debes usarlo todo de una vez ni desperdiciarlo en momentos innecesarios. Debes guardarlo para momentos cruciales, como adelantar a tus rivales, escapar de obstáculos o colisiones, o llegar a la meta. También debe usarlo en combinación con acrobacias y derivas para ganar más puntos y recompensas.
-
-- Evitar obstáculos y colisiones con otros coches. El juego cuenta con muchos obstáculos y desafíos que pueden obstaculizar su progreso o dañar su coche. Estos incluyen coches de tráfico, coches de policía, helicópteros, barricadas, picos, etc. Usted debe evitar estos obstáculos tanto como sea posible, ya que pueden ralentizar o dañar su coche. También debes evitar colisiones con otros coches, especialmente tus rivales, ya que pueden sacarte de la pista o hacerte perder tu posición. Usted debe tratar de esquivar o escapar de ellos, o utilizar su impulso nitro para alejarse de ellos.
-- Utilice diferentes ángulos de cámara y controles para adaptarse a sus preferencias. El juego ofrece diferentes ángulos de cámara y controles que puedes usar para jugar el juego. Puede elegir entre vista en primera persona, en tercera persona o de arriba hacia abajo, según su preferencia. También puede elegir entre los controles de inclinación, toque o toque, dependiendo de su comodidad. Puede cambiar estos ajustes en el menú de opciones del juego.
-
- Pros y contras de Asphalt Nitro 9 Leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es un gran juego que ofrece muchos beneficios y ventajas para los aficionados a las carreras. Sin embargo, también tiene algunos inconvenientes y desventajas que usted debe tener en cuenta. Estos son algunos de los pros y los contras de Asphalt Nitro 9 Leyendas Mod Apk:
-
-
-Pros
-Contras
-
-
-
-- Divertido, adictivo y desafiante juego
-- Dinero ilimitado y tokens
-- Desbloquear todos los coches y pistas
-- Personalizar sus vehículos y actualizarlos
-- Disfruta de gráficos realistas y efectos de sonido
-- Compite con otros jugadores online o offline
-
-- Puede causar retraso o estrellarse en algunos dispositivos
-- Puede no ser compatible con algunos dispositivos o versiones
-- No se puede actualizar regularmente o con frecuencia
-- No puede ser soportado por los desarrolladores o editores oficiales
-
-- Puede exponer su dispositivo a riesgos o amenazas de seguridad
-
-
- Conclusión
-En conclusión, Asfalto Nitro 9 Leyendas Mod Apk es un juego fantástico que le dará horas de diversión y emoción. Es una versión modificada del juego original que le da recursos ilimitados y características que mejoran su experiencia de juego. Puede descargarlo e instalarlo de forma fácil y segura siguiendo los pasos que proporcionamos en este artículo. También puedes seguir los consejos y trucos que compartimos para mejorar tus habilidades y rendimiento en el juego. Sin embargo, también debe ser consciente de los pros y los contras de Asphalt Nitro 9 Leyendas Mod Apk, y decidir si vale la pena jugar o no.
-Si usted es un fanático de las carreras, le recomendamos que pruebe Asphalt Nitro 9 Leyendas Mod Apk y ver por sí mismo lo increíble que es. No te arrepentirás.
- Preguntas frecuentes (preguntas frecuentes)
-Aquí están algunas de las preguntas y respuestas más comunes sobre Asphalt Nitro 9 Legends Mod Apk:
-
- Q: ¿Es libre de Asphalt Nitro 9 Leyendas Mod Apk?
-A: Sí, Asphalt Nitro 9 Leyendas Mod Apk es gratis para descargar y jugar. Usted no necesita pagar nada para disfrutar del juego.
- Q: ¿Es seguro Asphalt Nitro 9 Legends Mod Apk?
-A: Sí, Asfalto Nitro 9 Leyendas Mod Apk es seguro para descargar e instalar. Sin embargo, siempre debe descargarlo de una fuente de confianza y habilitar fuentes desconocidas en la configuración del dispositivo antes de instalarlo.
- Q: ¿Es legal Asphalt Nitro 9 Legends Mod Apk?
-A: No, Asphalt Nitro 9 Leyendas Mod Apk no es legal. Es una versión modificada del juego original que viola los términos y condiciones de los desarrolladores y editores oficiales. También puede infringir los derechos de propiedad intelectual del juego original.
- Q: Es Asphalt Nitro 9 Leyendas Mod Apk en línea o fuera de línea?
-
- Q: ¿Cómo puedo actualizar Asphalt Nitro 9 Legends Mod Apk?
-A: Para actualizar Asphalt Nitro 9 leyendas Mod Apk, es necesario descargar e instalar la última versión del archivo apk mod de una fuente de confianza. Es posible que tenga que desinstalar la versión anterior del archivo apk mod antes de instalar el nuevo.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md b/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md
deleted file mode 100644
index d872bc888664198a5fb77f20f85945d9c05ae2ef..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md
+++ /dev/null
@@ -1,76 +0,0 @@
-
-Camioneros de Europa 3 APK 36.6: Un juego de simulador de camiones realista y divertido
-Si eres un fan de los juegos de simuladores de camiones, es posible que hayas oído hablar de Truckers of Europe 3, un juego popular que te permite experimentar la vida de un conductor de camiones en Europa. En este artículo, te contaremos todo lo que necesitas saber sobre este juego, incluyendo qué es, cuáles son sus características, cómo descargarlo e instalarlo, cómo jugarlo y por qué deberías jugarlo. ¡Así que abróchate el cinturón y prepárate para un emocionante viaje!
-Introducción
-¿Qué es Camioneros de Europa 3?
-Truckers of Europe 3 es un juego de simulador de camioneros desarrollado por Jerryisgaming, un canal de YouTube que crea videos de juegos. El juego fue lanzado en 2020 y ha sido actualizado regularmente con nuevas características y mejoras. La última versión del juego es 0.36.6, que fue lanzado el 30 de septiembre de 2021.
-camioneros de europa 3 apk 36.6
Download Zip ❤❤❤ https://bltlly.com/2v6LwC
-¿Cuáles son las características de Camioneros de Europa 3?
-Truckers of Europe 3 tiene muchas características que lo convierten en uno de los mejores juegos de simuladores de camiones en el mercado. Algunas de estas características son:
-
-- Un gran mapa de Europa con más de 50 ciudades y países para explorar.
-- Una variedad de camiones y remolques para elegir, cada uno con diferentes especificaciones y rendimiento.
-- Un sistema de conducción realista con transmisión manual, volante, pedales, indicadores, espejos, luces, bocina, limpiaparabrisas, etc.
-- Un sistema de tráfico realista con coches, autobuses, camiones, motocicletas, policía, ambulancia, etc.
-- Un sistema meteorológico realista con ciclo de día y noche, lluvia, nieve, niebla, etc.
-- Un sistema de daños realista con desgaste de neumáticos, consumo de combustible, sobrecalentamiento del motor, etc.
-- Un modo de carrera con misiones, contratos, entrega de carga, ingresos, gastos, etc.
-- Un sistema de habilidades con niveles, puntos, ventajas, etc.
-- Un sistema de personalización con trabajos de pintura, accesorios, calcomanías, etc.
-
-
-Cómo descargar e instalar camioneros de Europa 3 APK 36.6?
-Requisitos
-Para descargar e instalar Camioneros de Europa 3 APK 36.6 en su dispositivo Android, es necesario cumplir con los siguientes requisitos:
-
-- Tu dispositivo debe tener la versión de Android 4.4 o superior.
-- Su dispositivo debe tener al menos 1 GB de RAM y 500 MB de espacio de almacenamiento libre.
-- Su dispositivo debe tener una conexión a Internet estable.
-- Su dispositivo debe permitir la instalación desde fuentes desconocidas. Puede habilitar esta opción yendo a Configuración > Seguridad > Fuentes desconocidas.
-
-Pasos
-Para descargar e instalar Camioneros de Europa 3 APK 36.6 en su dispositivo Android, debe seguir estos pasos:
-
-- Ir a este enlace y descargar el archivo APK.
-- Localice el archivo descargado en el administrador de archivos de su dispositivo y toque en él para iniciar el proceso de instalación.
-- Siga las instrucciones en la pantalla y conceda los permisos necesarios a la aplicación.
-- Espere a que la instalación se complete y luego inicie la aplicación desde el cajón de aplicaciones o la pantalla de inicio.
-- Disfruta jugando Camioneros de Europa 3 en tu dispositivo Android!
-
-Cómo jugar Camioneros de Europa 3?
-Elige tu camión y remolque
-Lo primero que tienes que hacer cuando empiezas a jugar Truckers of Europe 3 es elegir tu camión y remolque. Puede hacer esto yendo al menú del garaje y navegando a través de las opciones disponibles. También puede comprar nuevos camiones y remolques con el dinero que gana de sus misiones. Cada camión y remolque tiene diferentes atributos, como velocidad, potencia, capacidad de combustible, peso de carga, etc. Debe elegir el que se adapte a sus preferencias y necesidades.
-Conduce por toda Europa y entrega carga
-
-Mientras conduce, tendrá que seguir las reglas de tráfico y regulaciones, tales como límites de velocidad, semáforos, señales, etc. También tendrá que lidiar con condiciones de tráfico realistas, como automóviles, autobuses, camiones, motocicletas, policía, ambulancia, etc. También tendrá que enfrentarse a condiciones meteorológicas realistas, como lluvia, nieve, niebla, etc. También tendrá que controlar el estado de su camión, como el nivel de combustible, la temperatura del motor, la presión de los neumáticos, etc. También tendrá que prestar atención al estado de su conductor, como el nivel de fatiga, el nivel de hambre, etc. Tendrá que parar en gasolineras, áreas de descanso, restaurantes, etc. para llenar su tanque de combustible, descansar a su conductor, comer algo de comida, etc.
-Cuando llegue a su destino, tendrá que aparcar su camión y remolque en el lugar designado y descargar la carga. A continuación, recibirá su recompensa y comentarios sobre su rendimiento. También ganarás puntos de experiencia que te ayudarán a subir de nivel y desbloquear nuevas habilidades y beneficios.
-Personaliza tu camión y mejora tus habilidades
-Otro aspecto divertido de Truckers of Europe 3 es que puedes personalizar tu camión y mejorar tus habilidades. Puedes hacer esto yendo al menú del taller y gastando algo de dinero en varios artículos y servicios. Puede cambiar el color de su camión, agregar algunos accesorios, aplicar algunas calcomanías, etc. También puede reparar su camión si está dañado o afinarlo si no funciona bien.
-También puedes ir al menú de habilidades y gastar algunos puntos en varias habilidades y beneficios que mejorarán tus habilidades y beneficios como conductor de camión. Puede mejorar su eficiencia de combustible, manejo de carga, seguridad de conducción, habilidades de negociación, etc. También puede desbloquear nuevos tipos de carga, contratos, camiones, remolques, etc.
-
-¿Por qué deberías jugar Camioneros de Europa 3?
-Gráficos realistas y física
-
-Tráfico y clima diversos y dinámicos
-Otra razón por la que deberías jugar a Truckers of Europe 3 es porque tiene un tráfico diverso y dinámico y el clima hará que tu experiencia de conducción sea más desafiante y agradable. El juego tiene un gran mapa de Europa con más de 50 ciudades y países para explorar. Cada ciudad y país tiene sus propias características únicas, tales como monumentos, edificios, carreteras, señales, etc. El juego también tiene un sistema de tráfico diverso que incluye automóviles, autobuses, camiones, motocicletas, policía, ambulancia, etc. Cada vehículo tiene su propio comportamiento, velocidad, dirección, etc. El juego también tiene un sistema de tiempo dinámico que incluye ciclo de día y noche, lluvia, nieve, niebla, etc. Cada condición climática tiene su propio efecto en la visibilidad, tracción, manejo, etc. del camión y el remolque.
-Misiones y logros desafiantes y gratificantes
-Otra razón por la que deberías jugar a Truckers of Europe 3 es porque tiene misiones desafiantes y gratificantes y logros que te mantendrán motivado y entretenido. El juego tiene un modo de carrera que le permite comenzar como un conductor de camión novato y trabajar su camino hasta convertirse en un camionero profesional. Puede hacerlo completando varias misiones y contratos que implican la entrega de carga a diferentes destinos en toda Europa. También puede ganar dinero de sus entregas y gastarlo en la compra de nuevos camiones y remolques o la personalización de los existentes. También puede ganar puntos de experiencia de sus entregas y gastarlos en mejorar sus habilidades y beneficios. También puedes desbloquear nuevos tipos de carga, contratos, camiones, remolques, etc. a medida que avanzas en el juego.
-
-Conclusión
-Truckers of Europe 3 es un juego de simulador de camiones realista y divertido que te permite experimentar la vida de un conductor de camiones en Europa. Puede elegir su camión y remolque, conducir por toda Europa y entregar carga, personalizar su camión y mejorar sus habilidades, y ganar dinero y logros. El juego tiene gráficos y física realistas, tráfico y clima diversos y dinámicos, y misiones y logros desafiantes y gratificantes. Si usted está buscando un juego de simulador de camiones que le mantendrá enganchado durante horas, definitivamente debe probar Camioneros de Europa 3 APK 36.6.
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre los camioneros de Europa 3:
-
-- Q: ¿Los camioneros de Europa 3 son libres de jugar?
-- A: Sí, Truckers of Europe 3 es gratis. Puedes descargarlo e instalarlo desde el enlace proporcionado en este artículo. Sin embargo, el juego puede contener algunas compras en la aplicación que pueden mejorar su experiencia de juego.
-- Q: ¿Es seguro descargar e instalar Truckers of Europe 3?
-- A: Sí, Truckers of Europe 3 es seguro para descargar e instalar. El archivo APK proporcionado en este artículo es verificado y probado por nuestro equipo. Sin embargo, siempre debe descargar e instalar archivos APK de fuentes confiables solamente.
-- Q: ¿Es Truckers of Europe 3 compatible con mi dispositivo?
-- A: Camioneros de Europa 3 es compatible con la mayoría de los dispositivos Android que tienen la versión Android 4.4 o superior. Sin embargo, algunos dispositivos pueden no ser compatibles con el juego debido a limitaciones de hardware o software.
-- P: ¿Cómo puedo contactar al desarrollador de Truckers of Europe 3?
-- A: Puedes contactar al desarrollador de Truckers of Europe 3 visitando su canal de YouTube Jerryisgaming o su página de Facebook Jerryisgaming. También puedes dejar tus comentarios, sugerencias o consultas en la sección de comentarios de sus videos o publicaciones.
-- P: ¿Cómo puedo apoyar el desarrollo de Camioneros de Europa 3?
-
-
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md b/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md
deleted file mode 100644
index 516d398df61b62811ac5d43e8205d838c0714cbc..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md
+++ /dev/null
@@ -1,80 +0,0 @@
-
-Cómo descargar Genshin Impact Cloud y disfrutar del juego en cualquier lugar
-Genshin Impact es uno de los juegos más populares y exitosos de 2020, con millones de jugadores en todo el mundo. El juego es un RPG de acción de mundo abierto que te permite explorar un mundo vasto y hermoso llamado Teyvat, donde puedes conocer a varios personajes, luchar contra enemigos, reunir recursos y completar misiones. El juego está disponible en múltiples plataformas, como PC, PlayStation 4, PlayStation 5, iOS y Android. Sin embargo, si quieres jugar el juego en tu dispositivo móvil, puedes enfrentarte a algunos desafíos, como espacio de almacenamiento limitado, bajo rendimiento o dispositivos incompatibles. Ahí es donde entra Genshin Impact Cloud.
-¿Qué es la nube de impacto Genshin y por qué debe probarlo
-Genshin Impact Cloud es un servicio que te permite jugar el juego en tu dispositivo móvil sin descargar el juego entero
-Genshin Impact Cloud es una nueva característica que fue introducida por miHoYo, el desarrollador de Genshin Impact, en abril de 2021. Es un servicio de juegos en la nube que le permite transmitir el juego desde un servidor a su dispositivo móvil a través de Internet. Esto significa que no necesitas descargar o instalar el juego en tu dispositivo, lo que puede ahorrarte mucho espacio de almacenamiento. El tamaño del juego de Genshin Impact en dispositivos móviles es de alrededor de 5,2 GB, pero con Genshin Impact Cloud, solo necesitas descargar una aplicación de 56 MB de tamaño. Esto también puede ayudarle a evitar largos tiempos de carga o actualizaciones.
-descargar estrellas pelea hack ios
Download ===> https://bltlly.com/2v6KEj
-Genshin Impact Cloud tiene muchos beneficios, como ahorrar espacio de almacenamiento, mejorar el rendimiento y admitir cross-save
-Además de ahorrar espacio de almacenamiento, Genshin Impact Cloud también tiene otras ventajas que pueden mejorar su experiencia de juego. Por ejemplo:
-
-
-- Genshin Impact Cloud soporta cross-save y cross-play. Esto significa que puedes acceder a tu progreso y datos existentes desde otras plataformas iniciando sesión con tu cuenta miHoYo. También puedes jugar con tus amigos que usan diferentes dispositivos, como PC o PlayStation.
-- Genshin Impact Cloud es de uso gratuito. No es necesario pagar tarifas o suscripciones adicionales para utilizar el servicio. También puede hacer compras en la aplicación como de costumbre, y disfrutar de todas las actualizaciones y eventos del juego.
-
-Con Genshin Impact Cloud, puede disfrutar del juego en cualquier lugar y en cualquier momento, siempre y cuando tenga una conexión a Internet estable y un dispositivo compatible.
-Cómo descargar Genshin Impact Cloud en dispositivos Android
-Genshin Impact Cloud está disponible actualmente solo para usuarios de Android en Malasia y Singapur como prueba beta
-Genshin Impact Cloud todavía está en sus primeras etapas de desarrollo, y todavía no está disponible para todas las regiones y plataformas. Actualmente, el servicio solo está abierto para los usuarios de Android en Malasia y Singapur como una prueba beta. Esto significa que solo un número limitado de jugadores puede acceder al servicio, y puede haber algunos errores o errores durante el juego. miHoYo no ha anunciado cuándo se ampliará el servicio a otras regiones y plataformas, pero es probable que lo hagan en el futuro.
-Para descargar Genshin Impact Cloud, necesita tener una cuenta miHoYo y registrarse para la prueba beta en el sitio web oficial
-Si eres un usuario de Android en Malasia o Singapur, y quieres probar Genshin Impact Cloud, debes seguir estos pasos:
-
-- Crea una cuenta miHoYo si aún no tienes una. Puede hacer esto visitando https://account.mihoyo.com/#/register y llenando la información requerida.
-- Visite None:
- """
- Raise an option parsing error using parser.error().
-
- Args:
- parser: an OptionParser instance.
- option: an Option instance.
- msg: the error text.
- """
- msg = f"{option} error: {msg}"
- msg = textwrap.fill(" ".join(msg.split()))
- parser.error(msg)
-
-
-def make_option_group(group: Dict[str, Any], parser: ConfigOptionParser) -> OptionGroup:
- """
- Return an OptionGroup object
- group -- assumed to be dict with 'name' and 'options' keys
- parser -- an optparse Parser
- """
- option_group = OptionGroup(parser, group["name"])
- for option in group["options"]:
- option_group.add_option(option())
- return option_group
-
-
-def check_dist_restriction(options: Values, check_target: bool = False) -> None:
- """Function for determining if custom platform options are allowed.
-
- :param options: The OptionParser options.
- :param check_target: Whether or not to check if --target is being used.
- """
- dist_restriction_set = any(
- [
- options.python_version,
- options.platforms,
- options.abis,
- options.implementation,
- ]
- )
-
- binary_only = FormatControl(set(), {":all:"})
- sdist_dependencies_allowed = (
- options.format_control != binary_only and not options.ignore_dependencies
- )
-
- # Installations or downloads using dist restrictions must not combine
- # source distributions and dist-specific wheels, as they are not
- # guaranteed to be locally compatible.
- if dist_restriction_set and sdist_dependencies_allowed:
- raise CommandError(
- "When restricting platform and interpreter constraints using "
- "--python-version, --platform, --abi, or --implementation, "
- "either --no-deps must be set, or --only-binary=:all: must be "
- "set and --no-binary must not be set (or must be set to "
- ":none:)."
- )
-
- if check_target:
- if dist_restriction_set and not options.target_dir:
- raise CommandError(
- "Can not use any platform or abi specific options unless "
- "installing via '--target'"
- )
-
-
-def _path_option_check(option: Option, opt: str, value: str) -> str:
- return os.path.expanduser(value)
-
-
-def _package_name_option_check(option: Option, opt: str, value: str) -> str:
- return canonicalize_name(value)
-
-
-class PipOption(Option):
- TYPES = Option.TYPES + ("path", "package_name")
- TYPE_CHECKER = Option.TYPE_CHECKER.copy()
- TYPE_CHECKER["package_name"] = _package_name_option_check
- TYPE_CHECKER["path"] = _path_option_check
-
-
-###########
-# options #
-###########
-
-help_: Callable[..., Option] = partial(
- Option,
- "-h",
- "--help",
- dest="help",
- action="help",
- help="Show help.",
-)
-
-debug_mode: Callable[..., Option] = partial(
- Option,
- "--debug",
- dest="debug_mode",
- action="store_true",
- default=False,
- help=(
- "Let unhandled exceptions propagate outside the main subroutine, "
- "instead of logging them to stderr."
- ),
-)
-
-isolated_mode: Callable[..., Option] = partial(
- Option,
- "--isolated",
- dest="isolated_mode",
- action="store_true",
- default=False,
- help=(
- "Run pip in an isolated mode, ignoring environment variables and user "
- "configuration."
- ),
-)
-
-require_virtualenv: Callable[..., Option] = partial(
- Option,
- "--require-virtualenv",
- "--require-venv",
- dest="require_venv",
- action="store_true",
- default=False,
- help=(
- "Allow pip to only run in a virtual environment; "
- "exit with an error otherwise."
- ),
-)
-
-override_externally_managed: Callable[..., Option] = partial(
- Option,
- "--break-system-packages",
- dest="override_externally_managed",
- action="store_true",
- help="Allow pip to modify an EXTERNALLY-MANAGED Python installation",
-)
-
-python: Callable[..., Option] = partial(
- Option,
- "--python",
- dest="python",
- help="Run pip with the specified Python interpreter.",
-)
-
-verbose: Callable[..., Option] = partial(
- Option,
- "-v",
- "--verbose",
- dest="verbose",
- action="count",
- default=0,
- help="Give more output. Option is additive, and can be used up to 3 times.",
-)
-
-no_color: Callable[..., Option] = partial(
- Option,
- "--no-color",
- dest="no_color",
- action="store_true",
- default=False,
- help="Suppress colored output.",
-)
-
-version: Callable[..., Option] = partial(
- Option,
- "-V",
- "--version",
- dest="version",
- action="store_true",
- help="Show version and exit.",
-)
-
-quiet: Callable[..., Option] = partial(
- Option,
- "-q",
- "--quiet",
- dest="quiet",
- action="count",
- default=0,
- help=(
- "Give less output. Option is additive, and can be used up to 3"
- " times (corresponding to WARNING, ERROR, and CRITICAL logging"
- " levels)."
- ),
-)
-
-progress_bar: Callable[..., Option] = partial(
- Option,
- "--progress-bar",
- dest="progress_bar",
- type="choice",
- choices=["on", "off"],
- default="on",
- help="Specify whether the progress bar should be used [on, off] (default: on)",
-)
-
-log: Callable[..., Option] = partial(
- PipOption,
- "--log",
- "--log-file",
- "--local-log",
- dest="log",
- metavar="path",
- type="path",
- help="Path to a verbose appending log.",
-)
-
-no_input: Callable[..., Option] = partial(
- Option,
- # Don't ask for input
- "--no-input",
- dest="no_input",
- action="store_true",
- default=False,
- help="Disable prompting for input.",
-)
-
-keyring_provider: Callable[..., Option] = partial(
- Option,
- "--keyring-provider",
- dest="keyring_provider",
- choices=["auto", "disabled", "import", "subprocess"],
- default="auto",
- help=(
- "Enable the credential lookup via the keyring library if user input is allowed."
- " Specify which mechanism to use [disabled, import, subprocess]."
- " (default: disabled)"
- ),
-)
-
-proxy: Callable[..., Option] = partial(
- Option,
- "--proxy",
- dest="proxy",
- type="str",
- default="",
- help="Specify a proxy in the form scheme://[user:passwd@]proxy.server:port.",
-)
-
-retries: Callable[..., Option] = partial(
- Option,
- "--retries",
- dest="retries",
- type="int",
- default=5,
- help="Maximum number of retries each connection should attempt "
- "(default %default times).",
-)
-
-timeout: Callable[..., Option] = partial(
- Option,
- "--timeout",
- "--default-timeout",
- metavar="sec",
- dest="timeout",
- type="float",
- default=15,
- help="Set the socket timeout (default %default seconds).",
-)
-
-
-def exists_action() -> Option:
- return Option(
- # Option when path already exist
- "--exists-action",
- dest="exists_action",
- type="choice",
- choices=["s", "i", "w", "b", "a"],
- default=[],
- action="append",
- metavar="action",
- help="Default action when a path already exists: "
- "(s)witch, (i)gnore, (w)ipe, (b)ackup, (a)bort.",
- )
-
-
-cert: Callable[..., Option] = partial(
- PipOption,
- "--cert",
- dest="cert",
- type="path",
- metavar="path",
- help=(
- "Path to PEM-encoded CA certificate bundle. "
- "If provided, overrides the default. "
- "See 'SSL Certificate Verification' in pip documentation "
- "for more information."
- ),
-)
-
-client_cert: Callable[..., Option] = partial(
- PipOption,
- "--client-cert",
- dest="client_cert",
- type="path",
- default=None,
- metavar="path",
- help="Path to SSL client certificate, a single file containing the "
- "private key and the certificate in PEM format.",
-)
-
-index_url: Callable[..., Option] = partial(
- Option,
- "-i",
- "--index-url",
- "--pypi-url",
- dest="index_url",
- metavar="URL",
- default=PyPI.simple_url,
- help="Base URL of the Python Package Index (default %default). "
- "This should point to a repository compliant with PEP 503 "
- "(the simple repository API) or a local directory laid out "
- "in the same format.",
-)
-
-
-def extra_index_url() -> Option:
- return Option(
- "--extra-index-url",
- dest="extra_index_urls",
- metavar="URL",
- action="append",
- default=[],
- help="Extra URLs of package indexes to use in addition to "
- "--index-url. Should follow the same rules as "
- "--index-url.",
- )
-
-
-no_index: Callable[..., Option] = partial(
- Option,
- "--no-index",
- dest="no_index",
- action="store_true",
- default=False,
- help="Ignore package index (only looking at --find-links URLs instead).",
-)
-
-
-def find_links() -> Option:
- return Option(
- "-f",
- "--find-links",
- dest="find_links",
- action="append",
- default=[],
- metavar="url",
- help="If a URL or path to an html file, then parse for links to "
- "archives such as sdist (.tar.gz) or wheel (.whl) files. "
- "If a local path or file:// URL that's a directory, "
- "then look for archives in the directory listing. "
- "Links to VCS project URLs are not supported.",
- )
-
-
-def trusted_host() -> Option:
- return Option(
- "--trusted-host",
- dest="trusted_hosts",
- action="append",
- metavar="HOSTNAME",
- default=[],
- help="Mark this host or host:port pair as trusted, even though it "
- "does not have valid or any HTTPS.",
- )
-
-
-def constraints() -> Option:
- return Option(
- "-c",
- "--constraint",
- dest="constraints",
- action="append",
- default=[],
- metavar="file",
- help="Constrain versions using the given constraints file. "
- "This option can be used multiple times.",
- )
-
-
-def requirements() -> Option:
- return Option(
- "-r",
- "--requirement",
- dest="requirements",
- action="append",
- default=[],
- metavar="file",
- help="Install from the given requirements file. "
- "This option can be used multiple times.",
- )
-
-
-def editable() -> Option:
- return Option(
- "-e",
- "--editable",
- dest="editables",
- action="append",
- default=[],
- metavar="path/url",
- help=(
- "Install a project in editable mode (i.e. setuptools "
- '"develop mode") from a local project path or a VCS url.'
- ),
- )
-
-
-def _handle_src(option: Option, opt_str: str, value: str, parser: OptionParser) -> None:
- value = os.path.abspath(value)
- setattr(parser.values, option.dest, value)
-
-
-src: Callable[..., Option] = partial(
- PipOption,
- "--src",
- "--source",
- "--source-dir",
- "--source-directory",
- dest="src_dir",
- type="path",
- metavar="dir",
- default=get_src_prefix(),
- action="callback",
- callback=_handle_src,
- help="Directory to check out editable projects into. "
- 'The default in a virtualenv is "
/src". '
- 'The default for global installs is "/src".',
-)
-
-
-def _get_format_control(values: Values, option: Option) -> Any:
- """Get a format_control object."""
- return getattr(values, option.dest)
-
-
-def _handle_no_binary(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- existing = _get_format_control(parser.values, option)
- FormatControl.handle_mutual_excludes(
- value,
- existing.no_binary,
- existing.only_binary,
- )
-
-
-def _handle_only_binary(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- existing = _get_format_control(parser.values, option)
- FormatControl.handle_mutual_excludes(
- value,
- existing.only_binary,
- existing.no_binary,
- )
-
-
-def no_binary() -> Option:
- format_control = FormatControl(set(), set())
- return Option(
- "--no-binary",
- dest="format_control",
- action="callback",
- callback=_handle_no_binary,
- type="str",
- default=format_control,
- help="Do not use binary packages. Can be supplied multiple times, and "
- 'each time adds to the existing value. Accepts either ":all:" to '
- 'disable all binary packages, ":none:" to empty the set (notice '
- "the colons), or one or more package names with commas between "
- "them (no colons). Note that some packages are tricky to compile "
- "and may fail to install when this option is used on them.",
- )
-
-
-def only_binary() -> Option:
- format_control = FormatControl(set(), set())
- return Option(
- "--only-binary",
- dest="format_control",
- action="callback",
- callback=_handle_only_binary,
- type="str",
- default=format_control,
- help="Do not use source packages. Can be supplied multiple times, and "
- 'each time adds to the existing value. Accepts either ":all:" to '
- 'disable all source packages, ":none:" to empty the set, or one '
- "or more package names with commas between them. Packages "
- "without binary distributions will fail to install when this "
- "option is used on them.",
- )
-
-
-platforms: Callable[..., Option] = partial(
- Option,
- "--platform",
- dest="platforms",
- metavar="platform",
- action="append",
- default=None,
- help=(
- "Only use wheels compatible with . Defaults to the "
- "platform of the running system. Use this option multiple times to "
- "specify multiple platforms supported by the target interpreter."
- ),
-)
-
-
-# This was made a separate function for unit-testing purposes.
-def _convert_python_version(value: str) -> Tuple[Tuple[int, ...], Optional[str]]:
- """
- Convert a version string like "3", "37", or "3.7.3" into a tuple of ints.
-
- :return: A 2-tuple (version_info, error_msg), where `error_msg` is
- non-None if and only if there was a parsing error.
- """
- if not value:
- # The empty string is the same as not providing a value.
- return (None, None)
-
- parts = value.split(".")
- if len(parts) > 3:
- return ((), "at most three version parts are allowed")
-
- if len(parts) == 1:
- # Then we are in the case of "3" or "37".
- value = parts[0]
- if len(value) > 1:
- parts = [value[0], value[1:]]
-
- try:
- version_info = tuple(int(part) for part in parts)
- except ValueError:
- return ((), "each version part must be an integer")
-
- return (version_info, None)
-
-
-def _handle_python_version(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- """
- Handle a provided --python-version value.
- """
- version_info, error_msg = _convert_python_version(value)
- if error_msg is not None:
- msg = "invalid --python-version value: {!r}: {}".format(
- value,
- error_msg,
- )
- raise_option_error(parser, option=option, msg=msg)
-
- parser.values.python_version = version_info
-
-
-python_version: Callable[..., Option] = partial(
- Option,
- "--python-version",
- dest="python_version",
- metavar="python_version",
- action="callback",
- callback=_handle_python_version,
- type="str",
- default=None,
- help=dedent(
- """\
- The Python interpreter version to use for wheel and "Requires-Python"
- compatibility checks. Defaults to a version derived from the running
- interpreter. The version can be specified using up to three dot-separated
- integers (e.g. "3" for 3.0.0, "3.7" for 3.7.0, or "3.7.3"). A major-minor
- version can also be given as a string without dots (e.g. "37" for 3.7.0).
- """
- ),
-)
-
-
-implementation: Callable[..., Option] = partial(
- Option,
- "--implementation",
- dest="implementation",
- metavar="implementation",
- default=None,
- help=(
- "Only use wheels compatible with Python "
- "implementation , e.g. 'pp', 'jy', 'cp', "
- " or 'ip'. If not specified, then the current "
- "interpreter implementation is used. Use 'py' to force "
- "implementation-agnostic wheels."
- ),
-)
-
-
-abis: Callable[..., Option] = partial(
- Option,
- "--abi",
- dest="abis",
- metavar="abi",
- action="append",
- default=None,
- help=(
- "Only use wheels compatible with Python abi , e.g. 'pypy_41'. "
- "If not specified, then the current interpreter abi tag is used. "
- "Use this option multiple times to specify multiple abis supported "
- "by the target interpreter. Generally you will need to specify "
- "--implementation, --platform, and --python-version when using this "
- "option."
- ),
-)
-
-
-def add_target_python_options(cmd_opts: OptionGroup) -> None:
- cmd_opts.add_option(platforms())
- cmd_opts.add_option(python_version())
- cmd_opts.add_option(implementation())
- cmd_opts.add_option(abis())
-
-
-def make_target_python(options: Values) -> TargetPython:
- target_python = TargetPython(
- platforms=options.platforms,
- py_version_info=options.python_version,
- abis=options.abis,
- implementation=options.implementation,
- )
-
- return target_python
-
-
-def prefer_binary() -> Option:
- return Option(
- "--prefer-binary",
- dest="prefer_binary",
- action="store_true",
- default=False,
- help="Prefer older binary packages over newer source packages.",
- )
-
-
-cache_dir: Callable[..., Option] = partial(
- PipOption,
- "--cache-dir",
- dest="cache_dir",
- default=USER_CACHE_DIR,
- metavar="dir",
- type="path",
- help="Store the cache data in .",
-)
-
-
-def _handle_no_cache_dir(
- option: Option, opt: str, value: str, parser: OptionParser
-) -> None:
- """
- Process a value provided for the --no-cache-dir option.
-
- This is an optparse.Option callback for the --no-cache-dir option.
- """
- # The value argument will be None if --no-cache-dir is passed via the
- # command-line, since the option doesn't accept arguments. However,
- # the value can be non-None if the option is triggered e.g. by an
- # environment variable, like PIP_NO_CACHE_DIR=true.
- if value is not None:
- # Then parse the string value to get argument error-checking.
- try:
- strtobool(value)
- except ValueError as exc:
- raise_option_error(parser, option=option, msg=str(exc))
-
- # Originally, setting PIP_NO_CACHE_DIR to a value that strtobool()
- # converted to 0 (like "false" or "no") caused cache_dir to be disabled
- # rather than enabled (logic would say the latter). Thus, we disable
- # the cache directory not just on values that parse to True, but (for
- # backwards compatibility reasons) also on values that parse to False.
- # In other words, always set it to False if the option is provided in
- # some (valid) form.
- parser.values.cache_dir = False
-
-
-no_cache: Callable[..., Option] = partial(
- Option,
- "--no-cache-dir",
- dest="cache_dir",
- action="callback",
- callback=_handle_no_cache_dir,
- help="Disable the cache.",
-)
-
-no_deps: Callable[..., Option] = partial(
- Option,
- "--no-deps",
- "--no-dependencies",
- dest="ignore_dependencies",
- action="store_true",
- default=False,
- help="Don't install package dependencies.",
-)
-
-ignore_requires_python: Callable[..., Option] = partial(
- Option,
- "--ignore-requires-python",
- dest="ignore_requires_python",
- action="store_true",
- help="Ignore the Requires-Python information.",
-)
-
-no_build_isolation: Callable[..., Option] = partial(
- Option,
- "--no-build-isolation",
- dest="build_isolation",
- action="store_false",
- default=True,
- help="Disable isolation when building a modern source distribution. "
- "Build dependencies specified by PEP 518 must be already installed "
- "if this option is used.",
-)
-
-check_build_deps: Callable[..., Option] = partial(
- Option,
- "--check-build-dependencies",
- dest="check_build_deps",
- action="store_true",
- default=False,
- help="Check the build dependencies when PEP517 is used.",
-)
-
-
-def _handle_no_use_pep517(
- option: Option, opt: str, value: str, parser: OptionParser
-) -> None:
- """
- Process a value provided for the --no-use-pep517 option.
-
- This is an optparse.Option callback for the no_use_pep517 option.
- """
- # Since --no-use-pep517 doesn't accept arguments, the value argument
- # will be None if --no-use-pep517 is passed via the command-line.
- # However, the value can be non-None if the option is triggered e.g.
- # by an environment variable, for example "PIP_NO_USE_PEP517=true".
- if value is not None:
- msg = """A value was passed for --no-use-pep517,
- probably using either the PIP_NO_USE_PEP517 environment variable
- or the "no-use-pep517" config file option. Use an appropriate value
- of the PIP_USE_PEP517 environment variable or the "use-pep517"
- config file option instead.
- """
- raise_option_error(parser, option=option, msg=msg)
-
- # If user doesn't wish to use pep517, we check if setuptools and wheel are installed
- # and raise error if it is not.
- packages = ("setuptools", "wheel")
- if not all(importlib.util.find_spec(package) for package in packages):
- msg = (
- f"It is not possible to use --no-use-pep517 "
- f"without {' and '.join(packages)} installed."
- )
- raise_option_error(parser, option=option, msg=msg)
-
- # Otherwise, --no-use-pep517 was passed via the command-line.
- parser.values.use_pep517 = False
-
-
-use_pep517: Any = partial(
- Option,
- "--use-pep517",
- dest="use_pep517",
- action="store_true",
- default=None,
- help="Use PEP 517 for building source distributions "
- "(use --no-use-pep517 to force legacy behaviour).",
-)
-
-no_use_pep517: Any = partial(
- Option,
- "--no-use-pep517",
- dest="use_pep517",
- action="callback",
- callback=_handle_no_use_pep517,
- default=None,
- help=SUPPRESS_HELP,
-)
-
-
-def _handle_config_settings(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- key, sep, val = value.partition("=")
- if sep != "=":
- parser.error(f"Arguments to {opt_str} must be of the form KEY=VAL") # noqa
- dest = getattr(parser.values, option.dest)
- if dest is None:
- dest = {}
- setattr(parser.values, option.dest, dest)
- if key in dest:
- if isinstance(dest[key], list):
- dest[key].append(val)
- else:
- dest[key] = [dest[key], val]
- else:
- dest[key] = val
-
-
-config_settings: Callable[..., Option] = partial(
- Option,
- "-C",
- "--config-settings",
- dest="config_settings",
- type=str,
- action="callback",
- callback=_handle_config_settings,
- metavar="settings",
- help="Configuration settings to be passed to the PEP 517 build backend. "
- "Settings take the form KEY=VALUE. Use multiple --config-settings options "
- "to pass multiple keys to the backend.",
-)
-
-build_options: Callable[..., Option] = partial(
- Option,
- "--build-option",
- dest="build_options",
- metavar="options",
- action="append",
- help="Extra arguments to be supplied to 'setup.py bdist_wheel'.",
-)
-
-global_options: Callable[..., Option] = partial(
- Option,
- "--global-option",
- dest="global_options",
- action="append",
- metavar="options",
- help="Extra global options to be supplied to the setup.py "
- "call before the install or bdist_wheel command.",
-)
-
-no_clean: Callable[..., Option] = partial(
- Option,
- "--no-clean",
- action="store_true",
- default=False,
- help="Don't clean up build directories.",
-)
-
-pre: Callable[..., Option] = partial(
- Option,
- "--pre",
- action="store_true",
- default=False,
- help="Include pre-release and development versions. By default, "
- "pip only finds stable versions.",
-)
-
-disable_pip_version_check: Callable[..., Option] = partial(
- Option,
- "--disable-pip-version-check",
- dest="disable_pip_version_check",
- action="store_true",
- default=False,
- help="Don't periodically check PyPI to determine whether a new version "
- "of pip is available for download. Implied with --no-index.",
-)
-
-root_user_action: Callable[..., Option] = partial(
- Option,
- "--root-user-action",
- dest="root_user_action",
- default="warn",
- choices=["warn", "ignore"],
- help="Action if pip is run as a root user. By default, a warning message is shown.",
-)
-
-
-def _handle_merge_hash(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- """Given a value spelled "algo:digest", append the digest to a list
- pointed to in a dict by the algo name."""
- if not parser.values.hashes:
- parser.values.hashes = {}
- try:
- algo, digest = value.split(":", 1)
- except ValueError:
- parser.error(
- "Arguments to {} must be a hash name " # noqa
- "followed by a value, like --hash=sha256:"
- "abcde...".format(opt_str)
- )
- if algo not in STRONG_HASHES:
- parser.error(
- "Allowed hash algorithms for {} are {}.".format( # noqa
- opt_str, ", ".join(STRONG_HASHES)
- )
- )
- parser.values.hashes.setdefault(algo, []).append(digest)
-
-
-hash: Callable[..., Option] = partial(
- Option,
- "--hash",
- # Hash values eventually end up in InstallRequirement.hashes due to
- # __dict__ copying in process_line().
- dest="hashes",
- action="callback",
- callback=_handle_merge_hash,
- type="string",
- help="Verify that the package's archive matches this "
- "hash before installing. Example: --hash=sha256:abcdef...",
-)
-
-
-require_hashes: Callable[..., Option] = partial(
- Option,
- "--require-hashes",
- dest="require_hashes",
- action="store_true",
- default=False,
- help="Require a hash to check each requirement against, for "
- "repeatable installs. This option is implied when any package in a "
- "requirements file has a --hash option.",
-)
-
-
-list_path: Callable[..., Option] = partial(
- PipOption,
- "--path",
- dest="path",
- type="path",
- action="append",
- help="Restrict to the specified installation path for listing "
- "packages (can be used multiple times).",
-)
-
-
-def check_list_path_option(options: Values) -> None:
- if options.path and (options.user or options.local):
- raise CommandError("Cannot combine '--path' with '--user' or '--local'")
-
-
-list_exclude: Callable[..., Option] = partial(
- PipOption,
- "--exclude",
- dest="excludes",
- action="append",
- metavar="package",
- type="package_name",
- help="Exclude specified package from the output",
-)
-
-
-no_python_version_warning: Callable[..., Option] = partial(
- Option,
- "--no-python-version-warning",
- dest="no_python_version_warning",
- action="store_true",
- default=False,
- help="Silence deprecation warnings for upcoming unsupported Pythons.",
-)
-
-
-# Features that are now always on. A warning is printed if they are used.
-ALWAYS_ENABLED_FEATURES = [
- "no-binary-enable-wheel-cache", # always on since 23.1
-]
-
-use_new_feature: Callable[..., Option] = partial(
- Option,
- "--use-feature",
- dest="features_enabled",
- metavar="feature",
- action="append",
- default=[],
- choices=[
- "fast-deps",
- "truststore",
- ]
- + ALWAYS_ENABLED_FEATURES,
- help="Enable new functionality, that may be backward incompatible.",
-)
-
-use_deprecated_feature: Callable[..., Option] = partial(
- Option,
- "--use-deprecated",
- dest="deprecated_features_enabled",
- metavar="feature",
- action="append",
- default=[],
- choices=[
- "legacy-resolver",
- ],
- help=("Enable deprecated functionality, that will be removed in the future."),
-)
-
-
-##########
-# groups #
-##########
-
-general_group: Dict[str, Any] = {
- "name": "General Options",
- "options": [
- help_,
- debug_mode,
- isolated_mode,
- require_virtualenv,
- python,
- verbose,
- version,
- quiet,
- log,
- no_input,
- keyring_provider,
- proxy,
- retries,
- timeout,
- exists_action,
- trusted_host,
- cert,
- client_cert,
- cache_dir,
- no_cache,
- disable_pip_version_check,
- no_color,
- no_python_version_warning,
- use_new_feature,
- use_deprecated_feature,
- ],
-}
-
-index_group: Dict[str, Any] = {
- "name": "Package Index Options",
- "options": [
- index_url,
- extra_index_url,
- no_index,
- find_links,
- ],
-}
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py
deleted file mode 100644
index fd80d8c1129722b84771bd6a0f6ccfd57f5cf78e..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import re
-from ast import literal_eval
-from operator import attrgetter
-from typing import Callable, Iterable, List, Match, NamedTuple, Optional, Tuple, Union
-
-from ._emoji_replace import _emoji_replace
-from .emoji import EmojiVariant
-from .errors import MarkupError
-from .style import Style
-from .text import Span, Text
-
-RE_TAGS = re.compile(
- r"""((\\*)\[([a-z#/@][^[]*?)])""",
- re.VERBOSE,
-)
-
-RE_HANDLER = re.compile(r"^([\w.]*?)(\(.*?\))?$")
-
-
-class Tag(NamedTuple):
- """A tag in console markup."""
-
- name: str
- """The tag name. e.g. 'bold'."""
- parameters: Optional[str]
- """Any additional parameters after the name."""
-
- def __str__(self) -> str:
- return (
- self.name if self.parameters is None else f"{self.name} {self.parameters}"
- )
-
- @property
- def markup(self) -> str:
- """Get the string representation of this tag."""
- return (
- f"[{self.name}]"
- if self.parameters is None
- else f"[{self.name}={self.parameters}]"
- )
-
-
-_ReStringMatch = Match[str] # regex match object
-_ReSubCallable = Callable[[_ReStringMatch], str] # Callable invoked by re.sub
-_EscapeSubMethod = Callable[[_ReSubCallable, str], str] # Sub method of a compiled re
-
-
-def escape(
- markup: str,
- _escape: _EscapeSubMethod = re.compile(r"(\\*)(\[[a-z#/@][^[]*?])").sub,
-) -> str:
- """Escapes text so that it won't be interpreted as markup.
-
- Args:
- markup (str): Content to be inserted in to markup.
-
- Returns:
- str: Markup with square brackets escaped.
- """
-
- def escape_backslashes(match: Match[str]) -> str:
- """Called by re.sub replace matches."""
- backslashes, text = match.groups()
- return f"{backslashes}{backslashes}\\{text}"
-
- markup = _escape(escape_backslashes, markup)
- return markup
-
-
-def _parse(markup: str) -> Iterable[Tuple[int, Optional[str], Optional[Tag]]]:
- """Parse markup in to an iterable of tuples of (position, text, tag).
-
- Args:
- markup (str): A string containing console markup
-
- """
- position = 0
- _divmod = divmod
- _Tag = Tag
- for match in RE_TAGS.finditer(markup):
- full_text, escapes, tag_text = match.groups()
- start, end = match.span()
- if start > position:
- yield start, markup[position:start], None
- if escapes:
- backslashes, escaped = _divmod(len(escapes), 2)
- if backslashes:
- # Literal backslashes
- yield start, "\\" * backslashes, None
- start += backslashes * 2
- if escaped:
- # Escape of tag
- yield start, full_text[len(escapes) :], None
- position = end
- continue
- text, equals, parameters = tag_text.partition("=")
- yield start, None, _Tag(text, parameters if equals else None)
- position = end
- if position < len(markup):
- yield position, markup[position:], None
-
-
-def render(
- markup: str,
- style: Union[str, Style] = "",
- emoji: bool = True,
- emoji_variant: Optional[EmojiVariant] = None,
-) -> Text:
- """Render console markup in to a Text instance.
-
- Args:
- markup (str): A string containing console markup.
- emoji (bool, optional): Also render emoji code. Defaults to True.
-
- Raises:
- MarkupError: If there is a syntax error in the markup.
-
- Returns:
- Text: A test instance.
- """
- emoji_replace = _emoji_replace
- if "[" not in markup:
- return Text(
- emoji_replace(markup, default_variant=emoji_variant) if emoji else markup,
- style=style,
- )
- text = Text(style=style)
- append = text.append
- normalize = Style.normalize
-
- style_stack: List[Tuple[int, Tag]] = []
- pop = style_stack.pop
-
- spans: List[Span] = []
- append_span = spans.append
-
- _Span = Span
- _Tag = Tag
-
- def pop_style(style_name: str) -> Tuple[int, Tag]:
- """Pop tag matching given style name."""
- for index, (_, tag) in enumerate(reversed(style_stack), 1):
- if tag.name == style_name:
- return pop(-index)
- raise KeyError(style_name)
-
- for position, plain_text, tag in _parse(markup):
- if plain_text is not None:
- # Handle open brace escapes, where the brace is not part of a tag.
- plain_text = plain_text.replace("\\[", "[")
- append(emoji_replace(plain_text) if emoji else plain_text)
- elif tag is not None:
- if tag.name.startswith("/"): # Closing tag
- style_name = tag.name[1:].strip()
-
- if style_name: # explicit close
- style_name = normalize(style_name)
- try:
- start, open_tag = pop_style(style_name)
- except KeyError:
- raise MarkupError(
- f"closing tag '{tag.markup}' at position {position} doesn't match any open tag"
- ) from None
- else: # implicit close
- try:
- start, open_tag = pop()
- except IndexError:
- raise MarkupError(
- f"closing tag '[/]' at position {position} has nothing to close"
- ) from None
-
- if open_tag.name.startswith("@"):
- if open_tag.parameters:
- handler_name = ""
- parameters = open_tag.parameters.strip()
- handler_match = RE_HANDLER.match(parameters)
- if handler_match is not None:
- handler_name, match_parameters = handler_match.groups()
- parameters = (
- "()" if match_parameters is None else match_parameters
- )
-
- try:
- meta_params = literal_eval(parameters)
- except SyntaxError as error:
- raise MarkupError(
- f"error parsing {parameters!r} in {open_tag.parameters!r}; {error.msg}"
- )
- except Exception as error:
- raise MarkupError(
- f"error parsing {open_tag.parameters!r}; {error}"
- ) from None
-
- if handler_name:
- meta_params = (
- handler_name,
- meta_params
- if isinstance(meta_params, tuple)
- else (meta_params,),
- )
-
- else:
- meta_params = ()
-
- append_span(
- _Span(
- start, len(text), Style(meta={open_tag.name: meta_params})
- )
- )
- else:
- append_span(_Span(start, len(text), str(open_tag)))
-
- else: # Opening tag
- normalized_tag = _Tag(normalize(tag.name), tag.parameters)
- style_stack.append((len(text), normalized_tag))
-
- text_length = len(text)
- while style_stack:
- start, tag = style_stack.pop()
- style = str(tag)
- if style:
- append_span(_Span(start, text_length, style))
-
- text.spans = sorted(spans[::-1], key=attrgetter("start"))
- return text
-
-
-if __name__ == "__main__": # pragma: no cover
-
- MARKUP = [
- "[red]Hello World[/red]",
- "[magenta]Hello [b]World[/b]",
- "[bold]Bold[italic] bold and italic [/bold]italic[/italic]",
- "Click [link=https://www.willmcgugan.com]here[/link] to visit my Blog",
- ":warning-emoji: [bold red blink] DANGER![/]",
- ]
-
- from pip._vendor.rich import print
- from pip._vendor.rich.table import Table
-
- grid = Table("Markup", "Result", padding=(0, 1))
-
- for markup in MARKUP:
- grid.add_row(Text(markup), markup)
-
- print(grid)
diff --git a/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat b/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat
deleted file mode 100644
index 62a39a7584f4d7c5fbc31758e3e9e7eff700276d..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat
+++ /dev/null
@@ -1,22 +0,0 @@
-@echo off
-
-if defined _OLD_VIRTUAL_PROMPT (
- set "PROMPT=%_OLD_VIRTUAL_PROMPT%"
-)
-set _OLD_VIRTUAL_PROMPT=
-
-if defined _OLD_VIRTUAL_PYTHONHOME (
- set "PYTHONHOME=%_OLD_VIRTUAL_PYTHONHOME%"
- set _OLD_VIRTUAL_PYTHONHOME=
-)
-
-if defined _OLD_VIRTUAL_PATH (
- set "PATH=%_OLD_VIRTUAL_PATH%"
-)
-
-set _OLD_VIRTUAL_PATH=
-
-set VIRTUAL_ENV=
-set VIRTUAL_ENV_PROMPT=
-
-:END
diff --git a/spaces/CAMP-ViL/Xplainer/inference.py b/spaces/CAMP-ViL/Xplainer/inference.py
deleted file mode 100644
index 2ab6b61c2b1fa8039af58a5f5c4823b6c2f56cab..0000000000000000000000000000000000000000
--- a/spaces/CAMP-ViL/Xplainer/inference.py
+++ /dev/null
@@ -1,116 +0,0 @@
-import argparse
-import gc
-from pathlib import Path
-
-import torch
-from torch.utils.data import DataLoader
-from tqdm import tqdm
-
-from chestxray14 import ChestXray14Dataset
-from chexpert import CheXpertDataset
-from descriptors import disease_descriptors_chexpert, disease_descriptors_chestxray14
-from model import InferenceModel
-from utils import calculate_auroc
-
-torch.multiprocessing.set_sharing_strategy('file_system')
-
-
-def inference_chexpert():
- split = 'test'
- dataset = CheXpertDataset(f'data/chexpert/{split}_labels.csv') # also do test
- dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=lambda x: x, num_workers=0)
- inference_model = InferenceModel()
- all_descriptors = inference_model.get_all_descriptors(disease_descriptors_chexpert)
-
- all_labels = []
- all_probs_neg = []
-
- for batch in tqdm(dataloader):
- batch = batch[0]
- image_paths, labels, keys = batch
- image_paths = [Path(image_path) for image_path in image_paths]
- agg_probs = []
- agg_negative_probs = []
- for image_path in image_paths:
- probs, negative_probs = inference_model.get_descriptor_probs(image_path, descriptors=all_descriptors)
- agg_probs.append(probs)
- agg_negative_probs.append(negative_probs)
- probs = {} # Aggregated
- negative_probs = {} # Aggregated
- for key in agg_probs[0].keys():
- probs[key] = sum([p[key] for p in agg_probs]) / len(agg_probs) # Mean Aggregation
-
- for key in agg_negative_probs[0].keys():
- negative_probs[key] = sum([p[key] for p in agg_negative_probs]) / len(agg_negative_probs) # Mean Aggregation
-
- disease_probs, negative_disease_probs = inference_model.get_diseases_probs(disease_descriptors_chexpert, pos_probs=probs,
- negative_probs=negative_probs)
- predicted_diseases, prob_vector_neg_prompt = inference_model.get_predictions_bin_prompting(disease_descriptors_chexpert,
- disease_probs=disease_probs,
- negative_disease_probs=negative_disease_probs,
- keys=keys)
- all_labels.append(labels)
- all_probs_neg.append(prob_vector_neg_prompt)
-
- all_labels = torch.stack(all_labels)
- all_probs_neg = torch.stack(all_probs_neg)
-
- # evaluation
- existing_mask = sum(all_labels, 0) > 0
- all_labels_clean = all_labels[:, existing_mask]
- all_probs_neg_clean = all_probs_neg[:, existing_mask]
- all_keys_clean = [key for idx, key in enumerate(keys) if existing_mask[idx]]
-
- overall_auroc, per_disease_auroc = calculate_auroc(all_probs_neg_clean, all_labels_clean)
- print(f"AUROC: {overall_auroc:.5f}\n")
- for idx, key in enumerate(all_keys_clean):
- print(f'{key}: {per_disease_auroc[idx]:.5f}')
-
-
-def inference_chestxray14():
- dataset = ChestXray14Dataset(f'data/chestxray14/Data_Entry_2017_v2020_modified.csv')
- dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=lambda x: x, num_workers=1)
- inference_model = InferenceModel()
- all_descriptors = inference_model.get_all_descriptors(disease_descriptors_chestxray14)
-
- all_labels = []
- all_probs_neg = []
- for batch in tqdm(dataloader):
- batch = batch[0]
- image_path, labels, keys = batch
- image_path = Path(image_path)
- probs, negative_probs = inference_model.get_descriptor_probs(image_path, descriptors=all_descriptors)
- disease_probs, negative_disease_probs = inference_model.get_diseases_probs(disease_descriptors_chestxray14, pos_probs=probs,
- negative_probs=negative_probs)
- predicted_diseases, prob_vector_neg_prompt = inference_model.get_predictions_bin_prompting(disease_descriptors_chestxray14,
- disease_probs=disease_probs,
- negative_disease_probs=negative_disease_probs,
- keys=keys)
- all_labels.append(labels)
- all_probs_neg.append(prob_vector_neg_prompt)
- gc.collect()
-
- all_labels = torch.stack(all_labels)
- all_probs_neg = torch.stack(all_probs_neg)
-
- existing_mask = sum(all_labels, 0) > 0
- all_labels_clean = all_labels[:, existing_mask]
- all_probs_neg_clean = all_probs_neg[:, existing_mask]
- all_keys_clean = [key for idx, key in enumerate(keys) if existing_mask[idx]]
-
- overall_auroc, per_disease_auroc = calculate_auroc(all_probs_neg_clean[:, 1:], all_labels_clean[:, 1:])
- print(f"AUROC: {overall_auroc:.5f}\n")
- for idx, key in enumerate(all_keys_clean[1:]):
- print(f'{key}: {per_disease_auroc[idx]:.5f}')
-
-
-if __name__ == '__main__':
- # add argument parser
- parser = argparse.ArgumentParser()
- parser.add_argument('--dataset', type=str, default='chexpert', help='chexpert or chestxray14')
- args = parser.parse_args()
-
- if args.dataset == 'chexpert':
- inference_chexpert()
- elif args.dataset == 'chestxray14':
- inference_chestxray14()
diff --git a/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h b/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h
deleted file mode 100644
index e00d3ef054bd740b801e47cc1344e38621d8c055..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h
+++ /dev/null
@@ -1,253 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-
-namespace thrust
-{
-
-namespace detail
-{
-
-
-// adapted from http://www.boost.org/doc/libs/1_37_0/libs/iterator/doc/iterator_facade.html#iterator-category
-//
-// in our implementation, R need not be a reference type to result in a category
-// derived from forward_XXX_iterator_tag
-//
-// iterator-category(T,V,R) :=
-// if(T is convertible to input_host_iterator_tag
-// || T is convertible to output_host_iterator_tag
-// || T is convertible to input_device_iterator_tag
-// || T is convertible to output_device_iterator_tag
-// )
-// return T
-//
-// else if (T is not convertible to incrementable_traversal_tag)
-// the program is ill-formed
-//
-// else return a type X satisfying the following two constraints:
-//
-// 1. X is convertible to X1, and not to any more-derived
-// type, where X1 is defined by:
-//
-// if (T is convertible to forward_traversal_tag)
-// {
-// if (T is convertible to random_access_traversal_tag)
-// X1 = random_access_host_iterator_tag
-// else if (T is convertible to bidirectional_traversal_tag)
-// X1 = bidirectional_host_iterator_tag
-// else
-// X1 = forward_host_iterator_tag
-// }
-// else
-// {
-// if (T is convertible to single_pass_traversal_tag
-// && R is convertible to V)
-// X1 = input_host_iterator_tag
-// else
-// X1 = T
-// }
-//
-// 2. category-to-traversal(X) is convertible to the most
-// derived traversal tag type to which X is also convertible,
-// and not to any more-derived traversal tag type.
-
-
-template
- struct iterator_facade_default_category;
-
-
-// Thrust's implementation of iterator_facade_default_category is slightly
-// different from Boost's equivalent.
-// Thrust does not check is_convertible because Reference
-// may not be a complete type at this point, and implementations of is_convertible
-// typically require that both types be complete.
-// Instead, it simply assumes that if is_convertible,
-// then the category is input_iterator_tag
-
-
-// this is the function for standard system iterators
-template
- struct iterator_facade_default_category_std :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if< // XXX note we differ from Boost here
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_std
-
-
-// this is the function for host system iterators
-template
- struct iterator_facade_default_category_host :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if< // XXX note we differ from Boost here
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_host
-
-
-// this is the function for device system iterators
-template
- struct iterator_facade_default_category_device :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value, // XXX note we differ from Boost here
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_device
-
-
-// this is the function for any system iterators
-template
- struct iterator_facade_default_category_any
-{
- typedef thrust::detail::iterator_category_with_system_and_traversal<
- typename iterator_facade_default_category_std::type,
- thrust::any_system_tag,
- Traversal
- > type;
-}; // end iterator_facade_default_category_any
-
-
-template
- struct iterator_facade_default_category
- // check for any system
- : thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_any,
-
- // check for host system
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_host,
-
- // check for device system
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_device,
-
- // if we don't recognize the system, get a standard iterator category
- // and combine it with System & Traversal
- thrust::detail::identity_<
- thrust::detail::iterator_category_with_system_and_traversal<
- typename iterator_facade_default_category_std::type,
- System,
- Traversal
- >
- >
- >
- >
- >
-{};
-
-
-template
- struct iterator_facade_category_impl
-{
- typedef typename iterator_facade_default_category<
- System,Traversal,ValueParam,Reference
- >::type category;
-
- // we must be able to deduce both Traversal & System from category
- // otherwise, munge them all together
- typedef typename thrust::detail::eval_if<
- thrust::detail::and_<
- thrust::detail::is_same<
- Traversal,
- typename thrust::detail::iterator_category_to_traversal::type
- >,
- thrust::detail::is_same<
- System,
- typename thrust::detail::iterator_category_to_system::type
- >
- >::value,
- thrust::detail::identity_,
- thrust::detail::identity_ >
- >::type type;
-}; // end iterator_facade_category_impl
-
-
-template
- struct iterator_facade_category
-{
- typedef typename
- thrust::detail::eval_if<
- thrust::detail::is_iterator_category::value,
- thrust::detail::identity_, // categories are fine as-is
- iterator_facade_category_impl
- >::type type;
-}; // end iterator_facade_category
-
-
-} // end detail
-} // end thrust
-
diff --git a/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py b/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py
deleted file mode 100644
index a4f5ed44caefb1612df67785b1f4f0d9ec46ee93..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import torch.nn as nn
-from mmcv.cnn import ConvModule, xavier_init
-
-from ..builder import NECKS
-
-
-@NECKS.register_module()
-class ChannelMapper(nn.Module):
- r"""Channel Mapper to reduce/increase channels of backbone features.
-
- This is used to reduce/increase channels of backbone features.
-
- Args:
- in_channels (List[int]): Number of input channels per scale.
- out_channels (int): Number of output channels (used at each scale).
- kernel_size (int, optional): kernel_size for reducing channels (used
- at each scale). Default: 3.
- conv_cfg (dict, optional): Config dict for convolution layer.
- Default: None.
- norm_cfg (dict, optional): Config dict for normalization layer.
- Default: None.
- act_cfg (dict, optional): Config dict for activation layer in
- ConvModule. Default: dict(type='ReLU').
-
- Example:
- >>> import torch
- >>> in_channels = [2, 3, 5, 7]
- >>> scales = [340, 170, 84, 43]
- >>> inputs = [torch.rand(1, c, s, s)
- ... for c, s in zip(in_channels, scales)]
- >>> self = ChannelMapper(in_channels, 11, 3).eval()
- >>> outputs = self.forward(inputs)
- >>> for i in range(len(outputs)):
- ... print(f'outputs[{i}].shape = {outputs[i].shape}')
- outputs[0].shape = torch.Size([1, 11, 340, 340])
- outputs[1].shape = torch.Size([1, 11, 170, 170])
- outputs[2].shape = torch.Size([1, 11, 84, 84])
- outputs[3].shape = torch.Size([1, 11, 43, 43])
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size=3,
- conv_cfg=None,
- norm_cfg=None,
- act_cfg=dict(type='ReLU')):
- super(ChannelMapper, self).__init__()
- assert isinstance(in_channels, list)
-
- self.convs = nn.ModuleList()
- for in_channel in in_channels:
- self.convs.append(
- ConvModule(
- in_channel,
- out_channels,
- kernel_size,
- padding=(kernel_size - 1) // 2,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg))
-
- # default init_weights for conv(msra) and norm in ConvModule
- def init_weights(self):
- """Initialize the weights of ChannelMapper module."""
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- xavier_init(m, distribution='uniform')
-
- def forward(self, inputs):
- """Forward function."""
- assert len(inputs) == len(self.convs)
- outs = [self.convs[i](inputs[i]) for i in range(len(inputs))]
- return tuple(outs)
diff --git a/spaces/CVPR/regionclip-demo/datasets/README.md b/spaces/CVPR/regionclip-demo/datasets/README.md
deleted file mode 100644
index 0eb44cc3b23beeb1755ab8d12002d26f13434235..0000000000000000000000000000000000000000
--- a/spaces/CVPR/regionclip-demo/datasets/README.md
+++ /dev/null
@@ -1,140 +0,0 @@
-# Use Builtin Datasets
-
-A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog)
-for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc).
-This document explains how to setup the builtin datasets so they can be used by the above APIs.
-[Use Custom Datasets](https://detectron2.readthedocs.io/tutorials/datasets.html) gives a deeper dive on how to use `DatasetCatalog` and `MetadataCatalog`,
-and how to add new datasets to them.
-
-Detectron2 has builtin support for a few datasets.
-The datasets are assumed to exist in a directory specified by the environment variable
-`DETECTRON2_DATASETS`.
-Under this directory, detectron2 will look for datasets in the structure described below, if needed.
-```
-$DETECTRON2_DATASETS/
- coco/
- lvis/
- cityscapes/
- VOC20{07,12}/
-```
-
-You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`.
-If left unset, the default is `./datasets` relative to your current working directory.
-
-The [model zoo](https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md)
-contains configs and models that use these builtin datasets.
-
-## Expected dataset structure for [COCO instance/keypoint detection](https://cocodataset.org/#download):
-
-```
-coco/
- annotations/
- instances_{train,val}2017.json
- person_keypoints_{train,val}2017.json
- {train,val}2017/
- # image files that are mentioned in the corresponding json
-```
-
-You can use the 2014 version of the dataset as well.
-
-Some of the builtin tests (`dev/run_*_tests.sh`) uses a tiny version of the COCO dataset,
-which you can download with `./datasets/prepare_for_tests.sh`.
-
-## Expected dataset structure for PanopticFPN:
-
-Extract panoptic annotations from [COCO website](https://cocodataset.org/#download)
-into the following structure:
-```
-coco/
- annotations/
- panoptic_{train,val}2017.json
- panoptic_{train,val}2017/ # png annotations
- panoptic_stuff_{train,val}2017/ # generated by the script mentioned below
-```
-
-Install panopticapi by:
-```
-pip install git+https://github.com/cocodataset/panopticapi.git
-```
-Then, run `python datasets/prepare_panoptic_fpn.py`, to extract semantic annotations from panoptic annotations.
-
-## Expected dataset structure for [LVIS instance segmentation](https://www.lvisdataset.org/dataset):
-```
-coco/
- {train,val,test}2017/
-lvis/
- lvis_v0.5_{train,val}.json
- lvis_v0.5_image_info_test.json
- lvis_v1_{train,val}.json
- lvis_v1_image_info_test{,_challenge}.json
-```
-
-Install lvis-api by:
-```
-pip install git+https://github.com/lvis-dataset/lvis-api.git
-```
-
-To evaluate models trained on the COCO dataset using LVIS annotations,
-run `python datasets/prepare_cocofied_lvis.py` to prepare "cocofied" LVIS annotations.
-
-## Expected dataset structure for [cityscapes](https://www.cityscapes-dataset.com/downloads/):
-```
-cityscapes/
- gtFine/
- train/
- aachen/
- color.png, instanceIds.png, labelIds.png, polygons.json,
- labelTrainIds.png
- ...
- val/
- test/
- # below are generated Cityscapes panoptic annotation
- cityscapes_panoptic_train.json
- cityscapes_panoptic_train/
- cityscapes_panoptic_val.json
- cityscapes_panoptic_val/
- cityscapes_panoptic_test.json
- cityscapes_panoptic_test/
- leftImg8bit/
- train/
- val/
- test/
-```
-Install cityscapes scripts by:
-```
-pip install git+https://github.com/mcordts/cityscapesScripts.git
-```
-
-Note: to create labelTrainIds.png, first prepare the above structure, then run cityscapesescript with:
-```
-CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createTrainIdLabelImgs.py
-```
-These files are not needed for instance segmentation.
-
-Note: to generate Cityscapes panoptic dataset, run cityscapesescript with:
-```
-CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createPanopticImgs.py
-```
-These files are not needed for semantic and instance segmentation.
-
-## Expected dataset structure for [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/index.html):
-```
-VOC20{07,12}/
- Annotations/
- ImageSets/
- Main/
- trainval.txt
- test.txt
- # train.txt or val.txt, if you use these splits
- JPEGImages/
-```
-
-## Expected dataset structure for [ADE20k Scene Parsing](http://sceneparsing.csail.mit.edu/):
-```
-ADEChallengeData2016/
- annotations/
- annotations_detectron2/
- images/
- objectInfo150.txt
-```
-The directory `annotations_detectron2` is generated by running `python datasets/prepare_ade20k_sem_seg.py`.
diff --git a/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py b/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py
deleted file mode 100644
index ee1f6f1bcfa2fc3437fb6d5f579640482f1ad5f4..0000000000000000000000000000000000000000
--- a/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py
+++ /dev/null
@@ -1,397 +0,0 @@
-# -*- coding: utf-8 -*-
-import re
-from os.path import join, abspath, dirname
-from collections import defaultdict
-import epitran
-
-epi = epitran.Epitran("deu-Latn-nar")
-
-
-def mode_type(mode_in):
- """In the case of "sql", this will return an sqlite cursor."""
- if mode_in.lower() == "sql":
- import sqlite3
- conn = sqlite3.connect(join(abspath(dirname(__file__)), "./Resources/de.db"))
- return conn.cursor()
-
-
-#TESTS
-#NUMBERS ARE TOO HARD!
-
-
-
-def preprocess(words):
- """Returns a string of words stripped of punctuation"""
- punct_str = '!"#$%&\'()*+,-./:;<=>/?@[\\]^_`{|}~«» '
- return ' '.join([w.strip(punct_str).lower() for w in words.split()])
-
-
-def preserve_punc(words):
- """converts words to IPA and finds punctuation before and after the word."""
- words_preserved = []
- for w in words.split():
- punct_list = ["", preprocess(w), ""]
- before = re.search("^([^A-Za-z0-9]+)[A-Za-z]", w)
- after = re.search("[A-Za-z]([^A-Za-z0-9]+)$", w)
- if before:
- punct_list[0] = str(before.group(1))
- if after:
- punct_list[2] = str(after.group(1))
- words_preserved.append(punct_list)
- return words_preserved
-
-
-
-def apply_punct(triple, as_str=False):
- """places surrounding punctuation back on center on a list of preserve_punc triples"""
- if type(triple[0]) == list:
- for i, t in enumerate(triple):
- triple[i] = str(''.join(triple[i]))
- if as_str:
- return ' '.join(triple)
- return triple
- if as_str:
- return str(''.join(t for t in triple))
- return [''.join(t for t in triple)]
-
-
-def _punct_replace_word(original, transcription):
- """Get the IPA transcription of word with the original punctuation marks"""
- for i, trans_list in enumerate(transcription):
- for j, item in enumerate(trans_list):
- triple = [original[i][0]] + [item] + [original[i][2]]
- transcription[i][j] = apply_punct(triple, as_str=True)
- return transcription
-
-
-def fetch_words(words_in, db_type="sql"):
- """fetches a list of words from the database"""
- asset = mode_type(db_type)
- f_result = []
- if db_type.lower() == "sql":
- for word in words_in:
- asset.execute("SELECT Words, phonemes FROM De_words WHERE Words IN (?)", (word,))
- result = asset.fetchall()
- flag = True
- try:
- f_result.append(result.pop())
- flag = False
- except IndexError:
- pass
- if result == [] and flag is True:
- result = epi.transliterate(word)
- f_result.append((word, result))
- f_result = list(filter(None,f_result))
- f_set = set(f_result)
- d = defaultdict(list)
- for k, v in f_set:
- d[k].append(v)
- return list(d.items())
-
-def get_deu(tokens_in, db_type="sql"):
- """query the SQL database for the words and return the phonemes in the order of user_in"""
- result = fetch_words(tokens_in, db_type)
- ordered = []
- for word in tokens_in:
- this_word = [[i[1] for i in result if i[0] == word]][0]
- if this_word:
- ordered.append(this_word[0])
- else:
- ordered.append(["__IGNORE__" + word])
- return ordered
-
-
-def deu_to_ipa(deu_list, mark=True):
- """converts the deu word lists into IPA transcriptions"""
- symbols = {}
- ipa_list = [] # the final list of IPA tokens to be returned
- for word_list in deu_list:
- ipa_word_list = [] # the word list for each word
- for word in word_list:
- if re.sub("\d*", "", word.replace("__IGNORE__", "")) == "":
- pass # do not delete token if it's all numbers
- else:
- word = re.sub("[0-9]", "", word)
- ipa_form = ''
- if word.startswith("__IGNORE__"):
- ipa_form = word.replace("__IGNORE__", "")
- # mark words we couldn't transliterate with an asterisk:
-
- if mark:
- if not re.sub("\d*", "", ipa_form) == "":
- ipa_form += "*"
- else:
- for piece in word.split(" "):
- marked = False
- unmarked = piece
- if piece[0] in ["ˈ", "ˌ"] or piece[0] is None:
- marked = True
- mark = piece
- unmarked = piece[1:]
-
- if unmarked in symbols:
- if marked:
- ipa_form += mark + symbols[unmarked]
- else:
- ipa_form += symbols[unmarked]
-
- else:
- ipa_form += piece
- swap_list = [["ˈər", "əˈr"], ["ˈie", "iˈe"]]
- for sym in swap_list:
- if not ipa_form.startswith(sym[0]):
- ipa_form = ipa_form.replace(sym[0], sym[1])
- ipa_word_list.append(ipa_form)
- ipa_list.append(sorted(list(set(ipa_word_list))))
- return ipa_list
-
-
-def get_top(ipa_list):
- """Returns only the one result for a query. If multiple entries for words are found, only the first is used."""
- return ' '.join([word_list[-1] for word_list in ipa_list])
-
-
-def get_all(ipa_list):
- """utilizes an algorithm to discover and return all possible combinations of IPA transcriptions"""
- final_size = 1
- for word_list in ipa_list:
- final_size *= len(word_list)
- list_all = ["" for s in range(final_size)]
- for i in range(len(ipa_list)):
- if i == 0:
- swtich_rate = final_size / len(ipa_list[i])
- else:
- swtich_rate /= len(ipa_list[i])
- k = 0
- for j in range(final_size):
- if (j+1) % int(swtich_rate) == 0:
- k += 1
- if k == len(ipa_list[i]):
- k = 0
- list_all[j] = list_all[j] + ipa_list[i][k] + " "
- return sorted([sent[:-1] for sent in list_all])
-
-
-def ipa_list(words_in, keep_punct=True, db_type="sql"):
- """Returns a list of all the discovered IPA transcriptions for each word."""
- if type(words_in) == str:
- words = [preserve_punc(w.lower())[0] for w in words_in.split()]
- else:
- words = [preserve_punc(w.lower())[0] for w in words_in]
- deu = get_deu([w[1] for w in words], db_type=db_type)
- ipa = deu_to_ipa(deu)
- if keep_punct:
- ipa = _punct_replace_word(words, ipa)
- return ipa
-
-
-def isin_deu(word, db_type="sql"):
- """checks if a word is in the deu dictionary. Doesn't strip punctuation.
- If given more than one word, returns True only if all words are present."""
- if type(word) == str:
- word = [preprocess(w) for w in word.split()]
- results = fetch_words(word, db_type)
- as_set = list(set(t[0] for t in results))
- return len(as_set) == len(set(word))
-
-def replace_number(text):
- text = text.replace("1","eins ")
- text = text.replace("2","zwei ")
- text = text.replace("3","drei ")
- text = text.replace("4","vier ")
- text = text.replace("5","fünf ")
- text = text.replace("6","sechs ")
- text = text.replace("7","sieben ")
- text = text.replace("8","acht ")
- text = text.replace("9","neun ")
- text = text.replace("0","null ")
- return text
-
-
-
-def convert(text, retrieve_all=False, keep_punct=True, mode="sql"):
- """takes either a string or list of German words and converts them to IPA"""
- text = replace_number(text)
- ipa = ipa_list(
- words_in=text,
- keep_punct=keep_punct,
- db_type=mode)
- if retrieve_all:
- return get_all(ipa)
- return get_top(ipa)
-
-
-
-_decimal_number_re = re.compile(r'\d+\,\d+')
-_euros_pre = re.compile(r'€([0-9\,]*[0-9]+)')
-_euros_re = re.compile(r'([0-9\,]*[0-9]+)€')
-_ordinal_re = re.compile(r'(der |die |das )([0-9]+)\.')
-_clock_re=re.compile(r'\d{1,2}\:\d{2}')
-_number_re = re.compile(r'[0-9]+')
-
-def base(text):
- text = text.replace("1", "eins ")
- text = text.replace("2", "zwei ")
- text = text.replace("3", "drei ")
- text = text.replace("4", "vier ")
- text = text.replace("5", "fünf ")
- text = text.replace("6", "sechs ")
- text = text.replace("7", "sieben ")
- text = text.replace("8", "acht ")
- text = text.replace("9", "neun ")
- text = text.replace("0", "null ")
- return text
-
-def tens_to_word(num):
- tens = num[0]
- ones = num[1]
- ones_word = base(ones)
-
- if num =="10":
- return "zehn"
- elif num=="11":
- return "elf"
- elif num=="12":
- return "zwölf"
-
- if tens == "1":
- if ones == "6":
- ones_word = ones_word[:-1]
- elif ones == "7":
- ones_word = ones_word[:-2]
- return ones_word + "zehn"
- else:
- tens_word = base(tens)
- if ones == "1":
- ones_word = ones_word[:-1]
- if tens == "2":
- tens_word = "zwan"
- elif tens == "6":
- tens_word = tens_word[:-1]
- elif tens == "7":
- tens_word = tens_word[:-2]
- if tens == "3":
- tens_word += "ßig"
- else:
- tens_word += "zig"
- if ones == "0":
- return tens_word
- else:
- return ones_word + " und " + tens_word
-
-def huns_to_word(num):
- huns = num[0]
- tens = num[1]
-
- if huns == "1":
- huns_word= "hundert"
- else:
- huns_word = base(huns)+" hundert"
-
- remain = num_to_word(num[1:])
- if remain != "":
- remain = " " + remain
- return huns_word + remain
-
-def thos_to_word(num):
- thos = num[0]
- if thos == "1":
- thos_word= "tausend"
- else:
- thos_word = base(thos)+" tausend"
- remain=num_to_word(num[1:])
- if remain!="":
- remain=" "+remain
- return thos_word+remain
-
-def num_to_word(num):
- num=num.lstrip("0")
- if num=="":
- return("")
- digit=len(num)
- if digit==1:
- return base(num)
- elif digit==2:
- return tens_to_word(num)
- elif digit == 3:
- return huns_to_word(num)
- elif digit == 4:
- return thos_to_word(num)
- else:
- return base(num)
-
-def number_to_words(m):
- m=m.group(0).lstrip("0")
- if m=="":
- return"null"
- return num_to_word(m)
-
-def _expand_ordinal(m):
-
- pre=m.group(1)
- m = m.group(2).lstrip("0")
-
- if m=="":
- return"NULL"
- num=int(m)
- if num<=19 & num>=1:
- if num ==1:
- return "erste"
- elif num==3:
- return "dritte"
- elif num==7:
- return "siebte"
- elif num==8:
- return "achte"
- else:
- return pre + num_to_word(m) + "te"
- else:
- return pre + num_to_word(m) + "ste"
-
-def _expand_decimal(m):
- match=m.group(0)
- parts = match.split(',')
- if int(parts[0])==0:
- return '%s komma %s' % ("null", base(parts[1]))
- return '%s komma %s' % (num_to_word(parts[0]),base(parts[1]))
-
-def _expand_euros(m):
- match = m.group(1)
- parts = match.split(',')
- if len(parts) > 2:
- return match + ' euro' # Unexpected format
- euros = int(parts[0]) if parts[0] else 0
- cents = int(parts[1])*10 if len(parts) > 1 and parts[1] else 0
- if euros and cents:
- return '%s euro %s' % (euros, cents)
- elif euros:
- return '%s euro' % (euros)
- elif cents:
- return '%s cent' % (cents)
- else:
- return 'null euro'
-
-def _expand_clock(m):
- match = m.group(0)
- parts = match.split(':')
- if int(parts[0]) == 0:
- return '%s Uhr %s' % ("null",num_to_word(parts[1]))
- elif int(parts[0]) == 1:
- return '%s Uhr %s' % ("ein", num_to_word(parts[1]))
- return '%s Uhr %s' % (num_to_word(parts[0]),num_to_word(parts[1]))
-
-def normalize_numbers(text):
- text = re.sub(_euros_pre, _expand_euros, text)
- text = re.sub(_euros_re, _expand_euros, text)
- text = re.sub(_clock_re, _expand_clock, text)
- text = re.sub(_decimal_number_re, _expand_decimal, text)
- text = re.sub(_ordinal_re, _expand_ordinal, text)
- text = re.sub(_number_re, number_to_words, text)
- text=text.replace(" "," ")
- return text
-
-def collapse_whitespace(text):
- return re.sub(r'\s+', ' ', text)
-
-def mark_dark_l(text):
- return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
diff --git a/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py b/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py
deleted file mode 100644
index 1519b3095bc5250016066754ebe6c0328773f24a..0000000000000000000000000000000000000000
--- a/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from utils import randomize_seed_fn
-
-
-def create_demo(process, max_images=12, default_num_images=3):
- with gr.Blocks() as demo:
- with gr.Row():
- with gr.Column():
- image = gr.Image()
- prompt = gr.Textbox(label='Prompt')
- run_button = gr.Button('Run')
- with gr.Accordion('Advanced options', open=False):
- preprocessor_name = gr.Radio(label='Preprocessor',
- choices=['NormalBae', 'None'],
- type='value',
- value='NormalBae')
- num_samples = gr.Slider(label='Images',
- minimum=1,
- maximum=max_images,
- value=default_num_images,
- step=1)
- image_resolution = gr.Slider(label='Image resolution',
- minimum=256,
- maximum=512,
- value=512,
- step=256)
- preprocess_resolution = gr.Slider(
- label='Preprocess resolution',
- minimum=128,
- maximum=512,
- value=384,
- step=1)
- num_steps = gr.Slider(label='Number of steps',
- minimum=1,
- maximum=100,
- value=20,
- step=1)
- guidance_scale = gr.Slider(label='Guidance scale',
- minimum=0.1,
- maximum=30.0,
- value=9.0,
- step=0.1)
- seed = gr.Slider(label='Seed',
- minimum=0,
- maximum=1000000,
- step=1,
- value=0,
- randomize=True)
- randomize_seed = gr.Checkbox(label='Randomize seed',
- value=True)
- a_prompt = gr.Textbox(
- label='Additional prompt',
- value='best quality, extremely detailed')
- n_prompt = gr.Textbox(
- label='Negative prompt',
- value=
- 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
- )
- with gr.Column():
- result = gr.Gallery(label='Output', show_label=False).style(
- columns=2, object_fit='scale-down')
- inputs = [
- image,
- prompt,
- a_prompt,
- n_prompt,
- num_samples,
- image_resolution,
- preprocess_resolution,
- num_steps,
- guidance_scale,
- seed,
- preprocessor_name,
- ]
- prompt.submit(
- fn=randomize_seed_fn,
- inputs=[seed, randomize_seed],
- outputs=seed,
- ).then(
- fn=process,
- inputs=inputs,
- outputs=result,
- )
- run_button.click(
- fn=randomize_seed_fn,
- inputs=[seed, randomize_seed],
- outputs=seed,
- ).then(
- fn=process,
- inputs=inputs,
- outputs=result,
- api_name='normal',
- )
- return demo
-
-
-if __name__ == '__main__':
- from model import Model
- model = Model(task_name='NormalBae')
- demo = create_demo(model.process_normal)
- demo.queue().launch()
diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py
deleted file mode 100644
index 0a44b37fc5563deea8406c6ddf109860a56e9485..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from PIL.Image import Image as IMG
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.utils import save_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def knock(images: List[BuildImage], texts, args):
- img = images[0].convert("RGBA").square()
- # fmt: off
- locs = [(60, 308, 210, 195), (60, 308, 210, 198), (45, 330, 250, 172), (58, 320, 218, 180),
- (60, 310, 215, 193), (40, 320, 250, 285), (48, 308, 226, 192), (51, 301, 223, 200)]
- # fmt: on
- frames: List[IMG] = []
- for i in range(8):
- frame = BuildImage.open(img_dir / f"{i}.png")
- x, y, w, h = locs[i]
- frame.paste(img.resize((w, h)), (x, y), below=True)
- frames.append(frame.image)
- return save_gif(frames, 0.04)
-
-
-add_meme("knock", knock, min_images=1, max_images=1, keywords=["敲"])
diff --git a/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py b/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py
deleted file mode 100644
index 37f310fadd0b48b2f364877158fb2105d645fc03..0000000000000000000000000000000000000000
--- a/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import locale
-import json
-import os
-
-
-def load_language_list(language):
- with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f:
- language_list = json.load(f)
- return language_list
-
-
-class I18nAuto:
- def __init__(self, language=None):
- if language in ["Auto", None]:
- language = locale.getdefaultlocale()[
- 0
- ] # getlocale can't identify the system's language ((None, None))
- if not os.path.exists(f"./i18n/{language}.json"):
- language = "en_US"
- self.language = language
- # print("Use Language:", language)
- self.language_map = load_language_list(language)
-
- def __call__(self, key):
- return self.language_map.get(key, key)
-
- def print(self):
- print("Use Language:", self.language)
diff --git a/spaces/Codecooker/rvcapi/src/mdx.py b/spaces/Codecooker/rvcapi/src/mdx.py
deleted file mode 100644
index 575c456fbdf9e5b5955401f41a3a58ddc27267b3..0000000000000000000000000000000000000000
--- a/spaces/Codecooker/rvcapi/src/mdx.py
+++ /dev/null
@@ -1,287 +0,0 @@
-import gc
-import hashlib
-import os
-import queue
-import threading
-import warnings
-
-import librosa
-import numpy as np
-import onnxruntime as ort
-import soundfile as sf
-import torch
-from tqdm import tqdm
-
-warnings.filterwarnings("ignore")
-stem_naming = {'Vocals': 'Instrumental', 'Other': 'Instruments', 'Instrumental': 'Vocals', 'Drums': 'Drumless', 'Bass': 'Bassless'}
-
-
-class MDXModel:
- def __init__(self, device, dim_f, dim_t, n_fft, hop=1024, stem_name=None, compensation=1.000):
- self.dim_f = dim_f
- self.dim_t = dim_t
- self.dim_c = 4
- self.n_fft = n_fft
- self.hop = hop
- self.stem_name = stem_name
- self.compensation = compensation
-
- self.n_bins = self.n_fft // 2 + 1
- self.chunk_size = hop * (self.dim_t - 1)
- self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(device)
-
- out_c = self.dim_c
-
- self.freq_pad = torch.zeros([1, out_c, self.n_bins - self.dim_f, self.dim_t]).to(device)
-
- def stft(self, x):
- x = x.reshape([-1, self.chunk_size])
- x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True, return_complex=True)
- x = torch.view_as_real(x)
- x = x.permute([0, 3, 1, 2])
- x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 4, self.n_bins, self.dim_t])
- return x[:, :, :self.dim_f]
-
- def istft(self, x, freq_pad=None):
- freq_pad = self.freq_pad.repeat([x.shape[0], 1, 1, 1]) if freq_pad is None else freq_pad
- x = torch.cat([x, freq_pad], -2)
- # c = 4*2 if self.target_name=='*' else 2
- x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 2, self.n_bins, self.dim_t])
- x = x.permute([0, 2, 3, 1])
- x = x.contiguous()
- x = torch.view_as_complex(x)
- x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True)
- return x.reshape([-1, 2, self.chunk_size])
-
-
-class MDX:
- DEFAULT_SR = 44100
- # Unit: seconds
- DEFAULT_CHUNK_SIZE = 0 * DEFAULT_SR
- DEFAULT_MARGIN_SIZE = 1 * DEFAULT_SR
-
- DEFAULT_PROCESSOR = 0
-
- def __init__(self, model_path: str, params: MDXModel, processor=DEFAULT_PROCESSOR):
-
- # Set the device and the provider (CPU or CUDA)
- self.device = torch.device(f'cuda:{processor}') if processor >= 0 else torch.device('cpu')
- self.provider = ['CUDAExecutionProvider'] if processor >= 0 else ['CPUExecutionProvider']
-
- self.model = params
-
- # Load the ONNX model using ONNX Runtime
- self.ort = ort.InferenceSession(model_path, providers=self.provider)
- # Preload the model for faster performance
- self.ort.run(None, {'input': torch.rand(1, 4, params.dim_f, params.dim_t).numpy()})
- self.process = lambda spec: self.ort.run(None, {'input': spec.cpu().numpy()})[0]
-
- self.prog = None
-
- @staticmethod
- def get_hash(model_path):
- try:
- with open(model_path, 'rb') as f:
- f.seek(- 10000 * 1024, 2)
- model_hash = hashlib.md5(f.read()).hexdigest()
- except:
- model_hash = hashlib.md5(open(model_path, 'rb').read()).hexdigest()
-
- return model_hash
-
- @staticmethod
- def segment(wave, combine=True, chunk_size=DEFAULT_CHUNK_SIZE, margin_size=DEFAULT_MARGIN_SIZE):
- """
- Segment or join segmented wave array
-
- Args:
- wave: (np.array) Wave array to be segmented or joined
- combine: (bool) If True, combines segmented wave array. If False, segments wave array.
- chunk_size: (int) Size of each segment (in samples)
- margin_size: (int) Size of margin between segments (in samples)
-
- Returns:
- numpy array: Segmented or joined wave array
- """
-
- if combine:
- processed_wave = None # Initializing as None instead of [] for later numpy array concatenation
- for segment_count, segment in enumerate(wave):
- start = 0 if segment_count == 0 else margin_size
- end = None if segment_count == len(wave) - 1 else -margin_size
- if margin_size == 0:
- end = None
- if processed_wave is None: # Create array for first segment
- processed_wave = segment[:, start:end]
- else: # Concatenate to existing array for subsequent segments
- processed_wave = np.concatenate((processed_wave, segment[:, start:end]), axis=-1)
-
- else:
- processed_wave = []
- sample_count = wave.shape[-1]
-
- if chunk_size <= 0 or chunk_size > sample_count:
- chunk_size = sample_count
-
- if margin_size > chunk_size:
- margin_size = chunk_size
-
- for segment_count, skip in enumerate(range(0, sample_count, chunk_size)):
-
- margin = 0 if segment_count == 0 else margin_size
- end = min(skip + chunk_size + margin_size, sample_count)
- start = skip - margin
-
- cut = wave[:, start:end].copy()
- processed_wave.append(cut)
-
- if end == sample_count:
- break
-
- return processed_wave
-
- def pad_wave(self, wave):
- """
- Pad the wave array to match the required chunk size
-
- Args:
- wave: (np.array) Wave array to be padded
-
- Returns:
- tuple: (padded_wave, pad, trim)
- - padded_wave: Padded wave array
- - pad: Number of samples that were padded
- - trim: Number of samples that were trimmed
- """
- n_sample = wave.shape[1]
- trim = self.model.n_fft // 2
- gen_size = self.model.chunk_size - 2 * trim
- pad = gen_size - n_sample % gen_size
-
- # Padded wave
- wave_p = np.concatenate((np.zeros((2, trim)), wave, np.zeros((2, pad)), np.zeros((2, trim))), 1)
-
- mix_waves = []
- for i in range(0, n_sample + pad, gen_size):
- waves = np.array(wave_p[:, i:i + self.model.chunk_size])
- mix_waves.append(waves)
-
- mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(self.device)
-
- return mix_waves, pad, trim
-
- def _process_wave(self, mix_waves, trim, pad, q: queue.Queue, _id: int):
- """
- Process each wave segment in a multi-threaded environment
-
- Args:
- mix_waves: (torch.Tensor) Wave segments to be processed
- trim: (int) Number of samples trimmed during padding
- pad: (int) Number of samples padded during padding
- q: (queue.Queue) Queue to hold the processed wave segments
- _id: (int) Identifier of the processed wave segment
-
- Returns:
- numpy array: Processed wave segment
- """
- mix_waves = mix_waves.split(1)
- with torch.no_grad():
- pw = []
- for mix_wave in mix_waves:
- self.prog.update()
- spec = self.model.stft(mix_wave)
- processed_spec = torch.tensor(self.process(spec))
- processed_wav = self.model.istft(processed_spec.to(self.device))
- processed_wav = processed_wav[:, :, trim:-trim].transpose(0, 1).reshape(2, -1).cpu().numpy()
- pw.append(processed_wav)
- processed_signal = np.concatenate(pw, axis=-1)[:, :-pad]
- q.put({_id: processed_signal})
- return processed_signal
-
- def process_wave(self, wave: np.array, mt_threads=1):
- """
- Process the wave array in a multi-threaded environment
-
- Args:
- wave: (np.array) Wave array to be processed
- mt_threads: (int) Number of threads to be used for processing
-
- Returns:
- numpy array: Processed wave array
- """
- self.prog = tqdm(total=0)
- chunk = wave.shape[-1] // mt_threads
- waves = self.segment(wave, False, chunk)
-
- # Create a queue to hold the processed wave segments
- q = queue.Queue()
- threads = []
- for c, batch in enumerate(waves):
- mix_waves, pad, trim = self.pad_wave(batch)
- self.prog.total = len(mix_waves) * mt_threads
- thread = threading.Thread(target=self._process_wave, args=(mix_waves, trim, pad, q, c))
- thread.start()
- threads.append(thread)
- for thread in threads:
- thread.join()
- self.prog.close()
-
- processed_batches = []
- while not q.empty():
- processed_batches.append(q.get())
- processed_batches = [list(wave.values())[0] for wave in
- sorted(processed_batches, key=lambda d: list(d.keys())[0])]
- assert len(processed_batches) == len(waves), 'Incomplete processed batches, please reduce batch size!'
- return self.segment(processed_batches, True, chunk)
-
-
-def run_mdx(model_params, output_dir, model_path, filename, exclude_main=False, exclude_inversion=False, suffix=None, invert_suffix=None, denoise=False, keep_orig=True, m_threads=2):
- device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
-
- device_properties = torch.cuda.get_device_properties(device)
- vram_gb = device_properties.total_memory / 1024**3
- m_threads = 1 if vram_gb < 8 else 2
-
- model_hash = MDX.get_hash(model_path)
- mp = model_params.get(model_hash)
- model = MDXModel(
- device,
- dim_f=mp["mdx_dim_f_set"],
- dim_t=2 ** mp["mdx_dim_t_set"],
- n_fft=mp["mdx_n_fft_scale_set"],
- stem_name=mp["primary_stem"],
- compensation=mp["compensate"]
- )
-
- mdx_sess = MDX(model_path, model)
- wave, sr = librosa.load(filename, mono=False, sr=44100)
- # normalizing input wave gives better output
- peak = max(np.max(wave), abs(np.min(wave)))
- wave /= peak
- if denoise:
- wave_processed = -(mdx_sess.process_wave(-wave, m_threads)) + (mdx_sess.process_wave(wave, m_threads))
- wave_processed *= 0.5
- else:
- wave_processed = mdx_sess.process_wave(wave, m_threads)
- # return to previous peak
- wave_processed *= peak
- stem_name = model.stem_name if suffix is None else suffix
-
- main_filepath = None
- if not exclude_main:
- main_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav")
- sf.write(main_filepath, wave_processed.T, sr)
-
- invert_filepath = None
- if not exclude_inversion:
- diff_stem_name = stem_naming.get(stem_name) if invert_suffix is None else invert_suffix
- stem_name = f"{stem_name}_diff" if diff_stem_name is None else diff_stem_name
- invert_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav")
- sf.write(invert_filepath, (-wave_processed.T * model.compensation) + wave.T, sr)
-
- if not keep_orig:
- os.remove(filename)
-
- del mdx_sess, wave_processed, wave
- gc.collect()
- return main_filepath, invert_filepath
diff --git a/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py b/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py
deleted file mode 100644
index 76dce7e41c803a8055f3627cccb98deb51419b09..0000000000000000000000000000000000000000
--- a/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import argparse
-import glob
-import os
-
-
-def main(args):
- txt_file = open(args.meta_info, 'w')
- # sca images
- img_paths_gt = sorted(glob.glob(os.path.join(args.input[0], '*')))
- img_paths_lq = sorted(glob.glob(os.path.join(args.input[1], '*')))
-
- assert len(img_paths_gt) == len(img_paths_lq), ('GT folder and LQ folder should have the same length, but got '
- f'{len(img_paths_gt)} and {len(img_paths_lq)}.')
-
- for img_path_gt, img_path_lq in zip(img_paths_gt, img_paths_lq):
- # get the relative paths
- img_name_gt = os.path.relpath(img_path_gt, args.root[0])
- img_name_lq = os.path.relpath(img_path_lq, args.root[1])
- print(f'{img_name_gt}, {img_name_lq}')
- txt_file.write(f'{img_name_gt}, {img_name_lq}\n')
-
-
-if __name__ == '__main__':
- """This script is used to generate meta info (txt file) for paired images.
- """
- parser = argparse.ArgumentParser()
- parser.add_argument(
- '--input',
- nargs='+',
- default=['datasets/DF2K/DIV2K_train_HR_sub', 'datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub'],
- help='Input folder, should be [gt_folder, lq_folder]')
- parser.add_argument('--root', nargs='+', default=[None, None], help='Folder root, will use the ')
- parser.add_argument(
- '--meta_info',
- type=str,
- default='datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt',
- help='txt path for meta info')
- args = parser.parse_args()
-
- assert len(args.input) == 2, 'Input folder should have two elements: gt folder and lq folder'
- assert len(args.root) == 2, 'Root path should have two elements: root for gt folder and lq folder'
- os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)
- for i in range(2):
- if args.input[i].endswith('/'):
- args.input[i] = args.input[i][:-1]
- if args.root[i] is None:
- args.root[i] = os.path.dirname(args.input[i])
-
- main(args)
diff --git a/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py b/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py
deleted file mode 100644
index eb453a809d17e6dee04e158d1c68dc807478edef..0000000000000000000000000000000000000000
--- a/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Onodofthenorth/SD_PixelArt_SpriteSheet_Generator").launch()
\ No newline at end of file
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py
deleted file mode 100644
index bafc43a19d432290867a5c08b9820f2e4f79aea3..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py
+++ /dev/null
@@ -1,97 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# GD file handling
-#
-# History:
-# 1996-04-12 fl Created
-#
-# Copyright (c) 1997 by Secret Labs AB.
-# Copyright (c) 1996 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-"""
-.. note::
- This format cannot be automatically recognized, so the
- class is not registered for use with :py:func:`PIL.Image.open()`. To open a
- gd file, use the :py:func:`PIL.GdImageFile.open()` function instead.
-
-.. warning::
- THE GD FORMAT IS NOT DESIGNED FOR DATA INTERCHANGE. This
- implementation is provided for convenience and demonstrational
- purposes only.
-"""
-
-
-from . import ImageFile, ImagePalette, UnidentifiedImageError
-from ._binary import i16be as i16
-from ._binary import i32be as i32
-
-
-class GdImageFile(ImageFile.ImageFile):
- """
- Image plugin for the GD uncompressed format. Note that this format
- is not supported by the standard :py:func:`PIL.Image.open()` function. To use
- this plugin, you have to import the :py:mod:`PIL.GdImageFile` module and
- use the :py:func:`PIL.GdImageFile.open()` function.
- """
-
- format = "GD"
- format_description = "GD uncompressed images"
-
- def _open(self):
- # Header
- s = self.fp.read(1037)
-
- if i16(s) not in [65534, 65535]:
- msg = "Not a valid GD 2.x .gd file"
- raise SyntaxError(msg)
-
- self.mode = "L" # FIXME: "P"
- self._size = i16(s, 2), i16(s, 4)
-
- true_color = s[6]
- true_color_offset = 2 if true_color else 0
-
- # transparency index
- tindex = i32(s, 7 + true_color_offset)
- if tindex < 256:
- self.info["transparency"] = tindex
-
- self.palette = ImagePalette.raw(
- "XBGR", s[7 + true_color_offset + 4 : 7 + true_color_offset + 4 + 256 * 4]
- )
-
- self.tile = [
- (
- "raw",
- (0, 0) + self.size,
- 7 + true_color_offset + 4 + 256 * 4,
- ("L", 0, 1),
- )
- ]
-
-
-def open(fp, mode="r"):
- """
- Load texture from a GD image file.
-
- :param fp: GD file name, or an opened file handle.
- :param mode: Optional mode. In this version, if the mode argument
- is given, it must be "r".
- :returns: An image instance.
- :raises OSError: If the image could not be read.
- """
- if mode != "r":
- msg = "bad mode"
- raise ValueError(msg)
-
- try:
- return GdImageFile(fp)
- except SyntaxError as e:
- msg = "cannot identify this image file"
- raise UnidentifiedImageError(msg) from e
diff --git a/spaces/Dana19/ImageRecognition_FaceCount/README.md b/spaces/Dana19/ImageRecognition_FaceCount/README.md
deleted file mode 100644
index 8afdea6aa817a1ec3b056820a882b63deedc6772..0000000000000000000000000000000000000000
--- a/spaces/Dana19/ImageRecognition_FaceCount/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Headcount
-emoji: 💻
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.3
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/DemoLou/moe-tts/text/thai.py b/spaces/DemoLou/moe-tts/text/thai.py
deleted file mode 100644
index 998207c01a85c710a46db1ec8b62c39c2d94bc84..0000000000000000000000000000000000000000
--- a/spaces/DemoLou/moe-tts/text/thai.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import re
-from num_thai.thainumbers import NumThai
-
-
-num = NumThai()
-
-# List of (Latin alphabet, Thai) pairs:
-_latin_to_thai = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', 'เอ'),
- ('b','บี'),
- ('c','ซี'),
- ('d','ดี'),
- ('e','อี'),
- ('f','เอฟ'),
- ('g','จี'),
- ('h','เอช'),
- ('i','ไอ'),
- ('j','เจ'),
- ('k','เค'),
- ('l','แอล'),
- ('m','เอ็ม'),
- ('n','เอ็น'),
- ('o','โอ'),
- ('p','พี'),
- ('q','คิว'),
- ('r','แอร์'),
- ('s','เอส'),
- ('t','ที'),
- ('u','ยู'),
- ('v','วี'),
- ('w','ดับเบิลยู'),
- ('x','เอ็กซ์'),
- ('y','วาย'),
- ('z','ซี')
-]]
-
-
-def num_to_thai(text):
- return re.sub(r'(?:\d+(?:,?\d+)?)+(?:\.\d+(?:,?\d+)?)?', lambda x: ''.join(num.NumberToTextThai(float(x.group(0).replace(',', '')))), text)
-
-def latin_to_thai(text):
- for regex, replacement in _latin_to_thai:
- text = re.sub(regex, replacement, text)
- return text
diff --git a/spaces/Detomo/AI-Galary/README.md b/spaces/Detomo/AI-Galary/README.md
deleted file mode 100644
index 5772a1eb261aa38b6407c07b0633d4a12566eccf..0000000000000000000000000000000000000000
--- a/spaces/Detomo/AI-Galary/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AI Galary
-emoji: ⚡
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Detomo/generate_wifi_qrcode/app.py b/spaces/Detomo/generate_wifi_qrcode/app.py
deleted file mode 100644
index e6681ad55d07851290ef2f0f04f1e026a95b917a..0000000000000000000000000000000000000000
--- a/spaces/Detomo/generate_wifi_qrcode/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import wifi_qrcode_generator
-import gradio as gr
-
-examples = [['Home wifi', 'very complicated password', 'WPA']]
-
-def infer(wifi_name, wifi_pw, authentication_type):
- image = wifi_qrcode_generator.wifi_qrcode(
- wifi_name, False, authentication_type, wifi_pw
- )
- image.save('qrcode_wifi.png')
- return 'qrcode_wifi.png'
-
-description= "Generate code for accessing WIFI. Fill your wifi name, wifi password and select authentication type. Then click submit and enjoy 😎"
-
-iface = gr.Interface(
- fn=infer,
- title="Generate QR code for accessing WIFI 🥃",
- description=description,
- inputs=[
- gr.Textbox(
- label="Wifi name",
- lines=1,
- ),
- gr.Textbox(
- label="Wifi password",
- lines=1,
- ),
- gr.Dropdown(
- label='authentication_type',
- choices=["WPA", "WEP", "nopass"],
- value='WPA'
- ),
- ],
- outputs="image",
- examples=examples,
- article="Author: Vu Minh Chien", cache_examples=True).launch(enable_queue=True)
diff --git a/spaces/DiffusionArtco/scifi-art-creator/app.py b/spaces/DiffusionArtco/scifi-art-creator/app.py
deleted file mode 100644
index 236417b745e1681f1a9e1ae644d75254da2de370..0000000000000000000000000000000000000000
--- a/spaces/DiffusionArtco/scifi-art-creator/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import gradio as gr
-import requests
-from PIL import Image
-from io import BytesIO
-import base64
-
-api_url = "https://5cb20b40-572c-426f-9466-995256f9b6eb.id.repl.co/generate_image"
-
-def generate_image(model="Sci-Fi Diffusion", prompt="", seed=0, negative_prompt="", sampler="k_dpmpp_2s_a", steps=50):
- data = "?model=" + model + "&prompt=" + prompt + "&seed=" + str(seed) + "&negative_prompt=" + negative_prompt + "&sampler=" + sampler + "&steps=" + str(steps)
- response = requests.post(api_url + data, timeout=400)
- if response.status_code == 200:
- img_base64 = response.json()["url"]
- img_bytes = base64.b64decode(img_base64)
- img = Image.open(BytesIO(img_bytes))
- return img
- else:
- return None
-
-inputs = [
- gr.inputs.Dropdown([ 'Experience', 'FKing SciFi', 'Future Diffusion', 'JWST Deep Space Diffusion', 'Protogen Infinity', 'RCNZ Dumb Monkey', 'RealBiter', 'Robo-Diffusion', 'Sci-Fi Diffusion'], label="Model", default="Sci-Fi Diffusion"),
- gr.inputs.Textbox(label="Prompt"),
- gr.inputs.Number(label="Seed", default=0),
- gr.inputs.Textbox(label="Negative Prompt", default=""),
- gr.inputs.Dropdown(["k_lms", "k_heun", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "DDIM", "k_dpm_fast", "k_dpm_adaptive", "k_dpmpp_2m", "k_dpmpp_2s_a", "k_dpmpp_sde"], label="Sampler", default="k_dpmpp_2s_a"),
- gr.inputs.Number(label="Steps", default=50)
-]
-
-outputs = gr.outputs.Image(label="Generated Image", type="pil")
-
-interface = gr.Interface(generate_image, inputs, outputs, title="",
- description="
-#include
-#include
-#include "filtered_lrelu.h"
-
-//------------------------------------------------------------------------
-
-static std::tuple filtered_lrelu(
- torch::Tensor x, torch::Tensor fu, torch::Tensor fd, torch::Tensor b, torch::Tensor si,
- int up, int down, int px0, int px1, int py0, int py1, int sx, int sy, float gain, float slope, float clamp, bool flip_filters, bool writeSigns)
-{
- // Set CUDA device.
- TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
- const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-
- // Validate arguments.
- TORCH_CHECK(fu.device() == x.device() && fd.device() == x.device() && b.device() == x.device(), "all input tensors must reside on the same device");
- TORCH_CHECK(fu.dtype() == torch::kFloat && fd.dtype() == torch::kFloat, "fu and fd must be float32");
- TORCH_CHECK(b.dtype() == x.dtype(), "x and b must have the same dtype");
- TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat, "x and b must be float16 or float32");
- TORCH_CHECK(x.dim() == 4, "x must be rank 4");
- TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
- TORCH_CHECK(x.numel() > 0, "x is empty");
- TORCH_CHECK((fu.dim() == 1 || fu.dim() == 2) && (fd.dim() == 1 || fd.dim() == 2), "fu and fd must be rank 1 or 2");
- TORCH_CHECK(fu.size(0) <= INT_MAX && fu.size(-1) <= INT_MAX, "fu is too large");
- TORCH_CHECK(fd.size(0) <= INT_MAX && fd.size(-1) <= INT_MAX, "fd is too large");
- TORCH_CHECK(fu.numel() > 0, "fu is empty");
- TORCH_CHECK(fd.numel() > 0, "fd is empty");
- TORCH_CHECK(b.dim() == 1 && b.size(0) == x.size(1), "b must be a vector with the same number of channels as x");
- TORCH_CHECK(up >= 1 && down >= 1, "up and down must be at least 1");
-
- // Figure out how much shared memory is available on the device.
- int maxSharedBytes = 0;
- AT_CUDA_CHECK(cudaDeviceGetAttribute(&maxSharedBytes, cudaDevAttrMaxSharedMemoryPerBlockOptin, x.device().index()));
- int sharedKB = maxSharedBytes >> 10;
-
- // Populate enough launch parameters to check if a CUDA kernel exists.
- filtered_lrelu_kernel_params p;
- p.up = up;
- p.down = down;
- p.fuShape = make_int2((int)fu.size(-1), fu.dim() == 2 ? (int)fu.size(0) : 0); // shape [n, 0] indicates separable filter.
- p.fdShape = make_int2((int)fd.size(-1), fd.dim() == 2 ? (int)fd.size(0) : 0);
- filtered_lrelu_kernel_spec test_spec = choose_filtered_lrelu_kernel(p, sharedKB);
- if (!test_spec.exec)
- {
- // No kernel found - return empty tensors and indicate missing kernel with return code of -1.
- return std::make_tuple(torch::Tensor(), torch::Tensor(), -1);
- }
-
- // Input/output element size.
- int64_t sz = (x.dtype() == torch::kHalf) ? 2 : 4;
-
- // Input sizes.
- int64_t xw = (int)x.size(3);
- int64_t xh = (int)x.size(2);
- int64_t fut_w = (int)fu.size(-1) - 1;
- int64_t fut_h = (int)fu.size(0) - 1;
- int64_t fdt_w = (int)fd.size(-1) - 1;
- int64_t fdt_h = (int)fd.size(0) - 1;
-
- // Logical size of upsampled buffer.
- int64_t cw = xw * up + (px0 + px1) - fut_w;
- int64_t ch = xh * up + (py0 + py1) - fut_h;
- TORCH_CHECK(cw > fdt_w && ch > fdt_h, "upsampled buffer must be at least the size of downsampling filter");
- TORCH_CHECK(cw <= INT_MAX && ch <= INT_MAX, "upsampled buffer is too large");
-
- // Compute output size and allocate.
- int64_t yw = (cw - fdt_w + (down - 1)) / down;
- int64_t yh = (ch - fdt_h + (down - 1)) / down;
- TORCH_CHECK(yw > 0 && yh > 0, "output must be at least 1x1");
- TORCH_CHECK(yw <= INT_MAX && yh <= INT_MAX, "output is too large");
- torch::Tensor y = torch::empty({x.size(0), x.size(1), yh, yw}, x.options(), x.suggest_memory_format());
-
- // Allocate sign tensor.
- torch::Tensor so;
- torch::Tensor s = si;
- bool readSigns = !!s.numel();
- int64_t sw_active = 0; // Active width of sign tensor.
- if (writeSigns)
- {
- sw_active = yw * down - (down - 1) + fdt_w; // Active width in elements.
- int64_t sh = yh * down - (down - 1) + fdt_h; // Height = active height.
- int64_t sw = (sw_active + 15) & ~15; // Width = active width in elements, rounded up to multiple of 16.
- TORCH_CHECK(sh <= INT_MAX && (sw >> 2) <= INT_MAX, "signs is too large");
- s = so = torch::empty({x.size(0), x.size(1), sh, sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
- }
- else if (readSigns)
- sw_active = s.size(3) << 2;
-
- // Validate sign tensor if in use.
- if (readSigns || writeSigns)
- {
- TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
- TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
- TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
- TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
- TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
- TORCH_CHECK(s.size(2) <= INT_MAX && s.size(3) <= INT_MAX, "signs is too large");
- }
-
- // Populate rest of CUDA kernel parameters.
- p.x = x.data_ptr();
- p.y = y.data_ptr();
- p.b = b.data_ptr();
- p.s = (readSigns || writeSigns) ? s.data_ptr() : 0;
- p.fu = fu.data_ptr();
- p.fd = fd.data_ptr();
- p.pad0 = make_int2(px0, py0);
- p.gain = gain;
- p.slope = slope;
- p.clamp = clamp;
- p.flip = (flip_filters) ? 1 : 0;
- p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
- p.yShape = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
- p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3), (int)s.size(2)) : make_int2(0, 0); // Width is in bytes. Contiguous.
- p.sOfs = make_int2(sx, sy);
- p.swLimit = (sw_active + 3) >> 2; // Rounded up to bytes.
-
- // x, y, b strides are in bytes.
- p.xStride = make_longlong4(sz * x.stride(3), sz * x.stride(2), sz * x.stride(1), sz * x.stride(0));
- p.yStride = make_longlong4(sz * y.stride(3), sz * y.stride(2), sz * y.stride(1), sz * y.stride(0));
- p.bStride = sz * b.stride(0);
-
- // fu, fd strides are in elements.
- p.fuStride = make_longlong3(fu.stride(-1), fu.dim() == 2 ? fu.stride(0) : 0, 0);
- p.fdStride = make_longlong3(fd.stride(-1), fd.dim() == 2 ? fd.stride(0) : 0, 0);
-
- // Determine if indices don't fit in int32. Support negative strides although Torch currently never produces those.
- bool index64b = false;
- if (std::abs(p.bStride * x.size(1)) > INT_MAX) index64b = true;
- if (std::min(x.size(0) * p.xStride.w, 0ll) + std::min(x.size(1) * p.xStride.z, 0ll) + std::min(x.size(2) * p.xStride.y, 0ll) + std::min(x.size(3) * p.xStride.x, 0ll) < -INT_MAX) index64b = true;
- if (std::max(x.size(0) * p.xStride.w, 0ll) + std::max(x.size(1) * p.xStride.z, 0ll) + std::max(x.size(2) * p.xStride.y, 0ll) + std::max(x.size(3) * p.xStride.x, 0ll) > INT_MAX) index64b = true;
- if (std::min(y.size(0) * p.yStride.w, 0ll) + std::min(y.size(1) * p.yStride.z, 0ll) + std::min(y.size(2) * p.yStride.y, 0ll) + std::min(y.size(3) * p.yStride.x, 0ll) < -INT_MAX) index64b = true;
- if (std::max(y.size(0) * p.yStride.w, 0ll) + std::max(y.size(1) * p.yStride.z, 0ll) + std::max(y.size(2) * p.yStride.y, 0ll) + std::max(y.size(3) * p.yStride.x, 0ll) > INT_MAX) index64b = true;
- if (s.numel() > INT_MAX) index64b = true;
-
- // Choose CUDA kernel.
- filtered_lrelu_kernel_spec spec = { 0 };
- AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_cuda", [&]
- {
- if constexpr (sizeof(scalar_t) <= 4) // Exclude doubles. constexpr prevents template instantiation.
- {
- // Choose kernel based on index type, datatype and sign read/write modes.
- if (!index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if (!index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if (!index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- }
- });
- TORCH_CHECK(spec.exec, "internal error - CUDA kernel not found") // This should not happen because we tested earlier that kernel exists.
-
- // Launch CUDA kernel.
- void* args[] = {&p};
- int bx = spec.numWarps * 32;
- int gx = (p.yShape.x - 1) / spec.tileOut.x + 1;
- int gy = (p.yShape.y - 1) / spec.tileOut.y + 1;
- int gz = p.yShape.z * p.yShape.w;
-
- // Repeat multiple horizontal tiles in a CTA?
- if (spec.xrep)
- {
- p.tilesXrep = spec.xrep;
- p.tilesXdim = gx;
-
- gx = (gx + p.tilesXrep - 1) / p.tilesXrep;
- std::swap(gx, gy);
- }
- else
- {
- p.tilesXrep = 0;
- p.tilesXdim = 0;
- }
-
- // Launch filter setup kernel.
- AT_CUDA_CHECK(cudaLaunchKernel(spec.setup, 1, 1024, args, 0, at::cuda::getCurrentCUDAStream()));
-
- // Copy kernels to constant memory.
- if ( writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
- else if (!writeSigns && readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
- else if (!writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
-
- // Set cache and shared memory configurations for main kernel.
- AT_CUDA_CHECK(cudaFuncSetCacheConfig(spec.exec, cudaFuncCachePreferShared));
- if (spec.dynamicSharedKB) // Need dynamically allocated shared memory?
- AT_CUDA_CHECK(cudaFuncSetAttribute(spec.exec, cudaFuncAttributeMaxDynamicSharedMemorySize, spec.dynamicSharedKB << 10));
- AT_CUDA_CHECK(cudaFuncSetSharedMemConfig(spec.exec, cudaSharedMemBankSizeFourByte));
-
- // Launch main kernel.
- const int maxSubGz = 65535; // CUDA maximum for block z dimension.
- for (int zofs=0; zofs < gz; zofs += maxSubGz) // Do multiple launches if gz is too big.
- {
- p.blockZofs = zofs;
- int subGz = std::min(maxSubGz, gz - zofs);
- AT_CUDA_CHECK(cudaLaunchKernel(spec.exec, dim3(gx, gy, subGz), bx, args, spec.dynamicSharedKB << 10, at::cuda::getCurrentCUDAStream()));
- }
-
- // Done.
- return std::make_tuple(y, so, 0);
-}
-
-//------------------------------------------------------------------------
-
-static torch::Tensor filtered_lrelu_act(torch::Tensor x, torch::Tensor si, int sx, int sy, float gain, float slope, float clamp, bool writeSigns)
-{
- // Set CUDA device.
- TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
- const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-
- // Validate arguments.
- TORCH_CHECK(x.dim() == 4, "x must be rank 4");
- TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
- TORCH_CHECK(x.numel() > 0, "x is empty");
- TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat || x.dtype() == torch::kDouble, "x must be float16, float32 or float64");
-
- // Output signs if we don't have sign input.
- torch::Tensor so;
- torch::Tensor s = si;
- bool readSigns = !!s.numel();
- if (writeSigns)
- {
- int64_t sw = x.size(3);
- sw = (sw + 15) & ~15; // Round to a multiple of 16 for coalescing.
- s = so = torch::empty({x.size(0), x.size(1), x.size(2), sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
- }
-
- // Validate sign tensor if in use.
- if (readSigns || writeSigns)
- {
- TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
- TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
- TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
- TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
- TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
- TORCH_CHECK(s.size(2) <= INT_MAX && (s.size(3) << 2) <= INT_MAX, "signs tensor is too large");
- }
-
- // Initialize CUDA kernel parameters.
- filtered_lrelu_act_kernel_params p;
- p.x = x.data_ptr();
- p.s = (readSigns || writeSigns) ? s.data_ptr() : 0;
- p.gain = gain;
- p.slope = slope;
- p.clamp = clamp;
- p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
- p.xStride = make_longlong4(x.stride(3), x.stride(2), x.stride(1), x.stride(0));
- p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3) << 2, (int)s.size(2)) : make_int2(0, 0); // Width is in elements. Contiguous.
- p.sOfs = make_int2(sx, sy);
-
- // Choose CUDA kernel.
- void* func = 0;
- AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_act_cuda", [&]
- {
- if (writeSigns)
- func = choose_filtered_lrelu_act_kernel();
- else if (readSigns)
- func = choose_filtered_lrelu_act_kernel();
- else
- func = choose_filtered_lrelu_act_kernel();
- });
- TORCH_CHECK(func, "internal error - CUDA kernel not found");
-
- // Launch CUDA kernel.
- void* args[] = {&p};
- int bx = 128; // 4 warps per block.
-
- // Logical size of launch = writeSigns ? p.s : p.x
- uint32_t gx = writeSigns ? p.sShape.x : p.xShape.x;
- uint32_t gy = writeSigns ? p.sShape.y : p.xShape.y;
- uint32_t gz = p.xShape.z * p.xShape.w; // Same as in p.sShape if signs are in use.
- gx = (gx - 1) / bx + 1;
-
- // Make sure grid y and z dimensions are within CUDA launch limits. Kernel loops internally to do the rest.
- const uint32_t gmax = 65535;
- gy = std::min(gy, gmax);
- gz = std::min(gz, gmax);
-
- // Launch.
- AT_CUDA_CHECK(cudaLaunchKernel(func, dim3(gx, gy, gz), bx, args, 0, at::cuda::getCurrentCUDAStream()));
- return so;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
- m.def("filtered_lrelu", &filtered_lrelu); // The whole thing.
- m.def("filtered_lrelu_act_", &filtered_lrelu_act); // Activation and sign tensor handling only. Modifies data tensor in-place.
-}
-
-//------------------------------------------------------------------------
diff --git a/spaces/EronSamez/RVC_HFmeu/gui_v0.py b/spaces/EronSamez/RVC_HFmeu/gui_v0.py
deleted file mode 100644
index 88c3cf9eb1eaa0fa812b32ae4d3750b4ce0a8699..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/gui_v0.py
+++ /dev/null
@@ -1,786 +0,0 @@
-import os, sys, traceback, re
-
-import json
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from configs.config import Config
-
-Config = Config()
-import PySimpleGUI as sg
-import sounddevice as sd
-import noisereduce as nr
-import numpy as np
-from fairseq import checkpoint_utils
-import librosa, torch, pyworld, faiss, time, threading
-import torch.nn.functional as F
-import torchaudio.transforms as tat
-import scipy.signal as signal
-import torchcrepe
-
-# import matplotlib.pyplot as plt
-from lib.infer_pack.models import (
- SynthesizerTrnMs256NSFsid,
- SynthesizerTrnMs256NSFsid_nono,
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono,
-)
-from i18n import I18nAuto
-
-i18n = I18nAuto()
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-current_dir = os.getcwd()
-
-
-class RVC:
- def __init__(
- self, key, f0_method, hubert_path, pth_path, index_path, npy_path, index_rate
- ) -> None:
- """
- 初始化
- """
- try:
- self.f0_up_key = key
- self.time_step = 160 / 16000 * 1000
- self.f0_min = 50
- self.f0_max = 1100
- self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
- self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
- self.f0_method = f0_method
- self.sr = 16000
- self.window = 160
-
- # Get Torch Device
- if torch.cuda.is_available():
- self.torch_device = torch.device(
- f"cuda:{0 % torch.cuda.device_count()}"
- )
- elif torch.backends.mps.is_available():
- self.torch_device = torch.device("mps")
- else:
- self.torch_device = torch.device("cpu")
-
- if index_rate != 0:
- self.index = faiss.read_index(index_path)
- # self.big_npy = np.load(npy_path)
- self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
- print("index search enabled")
- self.index_rate = index_rate
- model_path = hubert_path
- print("load model(s) from {}".format(model_path))
- models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
- [model_path],
- suffix="",
- )
- self.model = models[0]
- self.model = self.model.to(device)
- if Config.is_half:
- self.model = self.model.half()
- else:
- self.model = self.model.float()
- self.model.eval()
- cpt = torch.load(pth_path, map_location="cpu")
- self.tgt_sr = cpt["config"][-1]
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- self.if_f0 = cpt.get("f0", 1)
- self.version = cpt.get("version", "v1")
- if self.version == "v1":
- if self.if_f0 == 1:
- self.net_g = SynthesizerTrnMs256NSFsid(
- *cpt["config"], is_half=Config.is_half
- )
- else:
- self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
- elif self.version == "v2":
- if self.if_f0 == 1:
- self.net_g = SynthesizerTrnMs768NSFsid(
- *cpt["config"], is_half=Config.is_half
- )
- else:
- self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
- del self.net_g.enc_q
- print(self.net_g.load_state_dict(cpt["weight"], strict=False))
- self.net_g.eval().to(device)
- if Config.is_half:
- self.net_g = self.net_g.half()
- else:
- self.net_g = self.net_g.float()
- except:
- print(traceback.format_exc())
-
- def get_regular_crepe_computation(self, x, f0_min, f0_max, model="full"):
- batch_size = 512
- # Compute pitch using first gpu
- audio = torch.tensor(np.copy(x))[None].float()
- f0, pd = torchcrepe.predict(
- audio,
- self.sr,
- self.window,
- f0_min,
- f0_max,
- model,
- batch_size=batch_size,
- device=self.torch_device,
- return_periodicity=True,
- )
- pd = torchcrepe.filter.median(pd, 3)
- f0 = torchcrepe.filter.mean(f0, 3)
- f0[pd < 0.1] = 0
- f0 = f0[0].cpu().numpy()
- return f0
-
- def get_harvest_computation(self, x, f0_min, f0_max):
- f0, t = pyworld.harvest(
- x.astype(np.double),
- fs=self.sr,
- f0_ceil=f0_max,
- f0_floor=f0_min,
- frame_period=10,
- )
- f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
- f0 = signal.medfilt(f0, 3)
- return f0
-
- def get_f0(self, x, f0_up_key, inp_f0=None):
- # Calculate Padding and f0 details here
- p_len = x.shape[0] // 512 # For Now This probs doesn't work
- x_pad = 1
- f0_min = 50
- f0_max = 1100
- f0_mel_min = 1127 * np.log(1 + f0_min / 700)
- f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
- f0 = 0
- # Here, check f0_methods and get their computations
- if self.f0_method == "harvest":
- f0 = self.get_harvest_computation(x, f0_min, f0_max)
- elif self.f0_method == "reg-crepe":
- f0 = self.get_regular_crepe_computation(x, f0_min, f0_max)
- elif self.f0_method == "reg-crepe-tiny":
- f0 = self.get_regular_crepe_computation(x, f0_min, f0_max, "tiny")
-
- # Calculate f0_course and f0_bak here
- f0 *= pow(2, f0_up_key / 12)
- # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- tf0 = self.sr // self.window # 每秒f0点数
- if inp_f0 is not None:
- delta_t = np.round(
- (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
- ).astype("int16")
- replace_f0 = np.interp(
- list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
- )
- shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
- f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
- # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- f0bak = f0.copy()
- f0_mel = 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
- f0_mel_max - f0_mel_min
- ) + 1
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > 255] = 255
- f0_coarse = np.rint(f0_mel).astype(np.int)
- return f0_coarse, f0bak # 1-0
-
- def infer(self, feats: torch.Tensor) -> np.ndarray:
- """
- 推理函数
- """
- audio = feats.clone().cpu().numpy()
- assert feats.dim() == 1, feats.dim()
- feats = feats.view(1, -1)
- padding_mask = torch.BoolTensor(feats.shape).fill_(False)
- if Config.is_half:
- feats = feats.half()
- else:
- feats = feats.float()
- inputs = {
- "source": feats.to(device),
- "padding_mask": padding_mask.to(device),
- "output_layer": 9 if self.version == "v1" else 12,
- }
- torch.cuda.synchronize()
- with torch.no_grad():
- logits = self.model.extract_features(**inputs)
- feats = (
- self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
- )
-
- ####索引优化
- try:
- if (
- hasattr(self, "index")
- and hasattr(self, "big_npy")
- and self.index_rate != 0
- ):
- npy = feats[0].cpu().numpy().astype("float32")
- score, ix = self.index.search(npy, k=8)
- weight = np.square(1 / score)
- weight /= weight.sum(axis=1, keepdims=True)
- npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
- if Config.is_half:
- npy = npy.astype("float16")
- feats = (
- torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
- + (1 - self.index_rate) * feats
- )
- else:
- print("index search FAIL or disabled")
- except:
- traceback.print_exc()
- print("index search FAIL")
- feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
- torch.cuda.synchronize()
- print(feats.shape)
- if self.if_f0 == 1:
- pitch, pitchf = self.get_f0(audio, self.f0_up_key)
- p_len = min(feats.shape[1], 13000, pitch.shape[0]) # 太大了爆显存
- else:
- pitch, pitchf = None, None
- p_len = min(feats.shape[1], 13000) # 太大了爆显存
- torch.cuda.synchronize()
- # print(feats.shape,pitch.shape)
- feats = feats[:, :p_len, :]
- if self.if_f0 == 1:
- pitch = pitch[:p_len]
- pitchf = pitchf[:p_len]
- pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
- pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
- p_len = torch.LongTensor([p_len]).to(device)
- ii = 0 # sid
- sid = torch.LongTensor([ii]).to(device)
- with torch.no_grad():
- if self.if_f0 == 1:
- infered_audio = (
- self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
- .data.cpu()
- .float()
- )
- else:
- infered_audio = (
- self.net_g.infer(feats, p_len, sid)[0][0, 0].data.cpu().float()
- )
- torch.cuda.synchronize()
- return infered_audio
-
-
-class GUIConfig:
- def __init__(self) -> None:
- self.hubert_path: str = ""
- self.pth_path: str = ""
- self.index_path: str = ""
- self.npy_path: str = ""
- self.f0_method: str = ""
- self.pitch: int = 12
- self.samplerate: int = 44100
- self.block_time: float = 1.0 # s
- self.buffer_num: int = 1
- self.threhold: int = -30
- self.crossfade_time: float = 0.08
- self.extra_time: float = 0.04
- self.I_noise_reduce = False
- self.O_noise_reduce = False
- self.index_rate = 0.3
-
-
-class GUI:
- def __init__(self) -> None:
- self.config = GUIConfig()
- self.flag_vc = False
-
- self.launcher()
-
- def load(self):
- (
- input_devices,
- output_devices,
- input_devices_indices,
- output_devices_indices,
- ) = self.get_devices()
- try:
- with open("values1.json", "r") as j:
- data = json.load(j)
- except:
- # Injecting f0_method into the json data
- with open("values1.json", "w") as j:
- data = {
- "pth_path": "",
- "index_path": "",
- "sg_input_device": input_devices[
- input_devices_indices.index(sd.default.device[0])
- ],
- "sg_output_device": output_devices[
- output_devices_indices.index(sd.default.device[1])
- ],
- "threhold": "-45",
- "pitch": "0",
- "index_rate": "0",
- "block_time": "1",
- "crossfade_length": "0.04",
- "extra_time": "1",
- }
- return data
-
- def launcher(self):
- data = self.load()
- sg.theme("DarkTeal12")
- input_devices, output_devices, _, _ = self.get_devices()
- layout = [
- [
- sg.Frame(
- title="Proudly forked by Mangio621",
- ),
- sg.Frame(
- title=i18n("Load model"),
- layout=[
- [
- sg.Input(
- default_text="hubert_base.pt",
- key="hubert_path",
- disabled=True,
- ),
- sg.FileBrowse(
- i18n("Hubert Model"),
- initial_folder=os.path.join(os.getcwd()),
- file_types=(("pt files", "*.pt"),),
- ),
- ],
- [
- sg.Input(
- default_text=data.get("pth_path", ""),
- key="pth_path",
- ),
- sg.FileBrowse(
- i18n("Select the .pth file"),
- initial_folder=os.path.join(os.getcwd(), "weights"),
- file_types=(("weight files", "*.pth"),),
- ),
- ],
- [
- sg.Input(
- default_text=data.get("index_path", ""),
- key="index_path",
- ),
- sg.FileBrowse(
- i18n("Select the .index file"),
- initial_folder=os.path.join(os.getcwd(), "logs"),
- file_types=(("index files", "*.index"),),
- ),
- ],
- [
- sg.Input(
- default_text="你不需要填写这个You don't need write this.",
- key="npy_path",
- disabled=True,
- ),
- sg.FileBrowse(
- i18n("Select the .npy file"),
- initial_folder=os.path.join(os.getcwd(), "logs"),
- file_types=(("feature files", "*.npy"),),
- ),
- ],
- ],
- ),
- ],
- [
- # Mangio f0 Selection frame Here
- sg.Frame(
- layout=[
- [
- sg.Radio(
- "Harvest", "f0_method", key="harvest", default=True
- ),
- sg.Radio("Crepe", "f0_method", key="reg-crepe"),
- sg.Radio("Crepe Tiny", "f0_method", key="reg-crepe-tiny"),
- ]
- ],
- title="Select an f0 Method",
- )
- ],
- [
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Input device")),
- sg.Combo(
- input_devices,
- key="sg_input_device",
- default_value=data.get("sg_input_device", ""),
- ),
- ],
- [
- sg.Text(i18n("Output device")),
- sg.Combo(
- output_devices,
- key="sg_output_device",
- default_value=data.get("sg_output_device", ""),
- ),
- ],
- ],
- title=i18n("Audio device (please use the same type of driver)"),
- )
- ],
- [
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Response threshold")),
- sg.Slider(
- range=(-60, 0),
- key="threhold",
- resolution=1,
- orientation="h",
- default_value=data.get("threhold", ""),
- ),
- ],
- [
- sg.Text(i18n("Pitch settings")),
- sg.Slider(
- range=(-24, 24),
- key="pitch",
- resolution=1,
- orientation="h",
- default_value=data.get("pitch", ""),
- ),
- ],
- [
- sg.Text(i18n("Index Rate")),
- sg.Slider(
- range=(0.0, 1.0),
- key="index_rate",
- resolution=0.01,
- orientation="h",
- default_value=data.get("index_rate", ""),
- ),
- ],
- ],
- title=i18n("General settings"),
- ),
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Sample length")),
- sg.Slider(
- range=(0.1, 3.0),
- key="block_time",
- resolution=0.1,
- orientation="h",
- default_value=data.get("block_time", ""),
- ),
- ],
- [
- sg.Text(i18n("Fade length")),
- sg.Slider(
- range=(0.01, 0.15),
- key="crossfade_length",
- resolution=0.01,
- orientation="h",
- default_value=data.get("crossfade_length", ""),
- ),
- ],
- [
- sg.Text(i18n("Extra推理时长")),
- sg.Slider(
- range=(0.05, 3.00),
- key="extra_time",
- resolution=0.01,
- orientation="h",
- default_value=data.get("extra_time", ""),
- ),
- ],
- [
- sg.Checkbox(i18n("Input noise reduction"), key="I_noise_reduce"),
- sg.Checkbox(i18n("Output noise reduction"), key="O_noise_reduce"),
- ],
- ],
- title=i18n("Performance settings"),
- ),
- ],
- [
- sg.Button(i18n("开始音频Convert"), key="start_vc"),
- sg.Button(i18n("停止音频Convert"), key="stop_vc"),
- sg.Text(i18n("Inference time (ms):")),
- sg.Text("0", key="infer_time"),
- ],
- ]
- self.window = sg.Window("RVC - GUI", layout=layout)
- self.event_handler()
-
- def event_handler(self):
- while True:
- event, values = self.window.read()
- if event == sg.WINDOW_CLOSED:
- self.flag_vc = False
- exit()
- if event == "start_vc" and self.flag_vc == False:
- if self.set_values(values) == True:
- print("using_cuda:" + str(torch.cuda.is_available()))
- self.start_vc()
- settings = {
- "pth_path": values["pth_path"],
- "index_path": values["index_path"],
- "f0_method": self.get_f0_method_from_radios(values),
- "sg_input_device": values["sg_input_device"],
- "sg_output_device": values["sg_output_device"],
- "threhold": values["threhold"],
- "pitch": values["pitch"],
- "index_rate": values["index_rate"],
- "block_time": values["block_time"],
- "crossfade_length": values["crossfade_length"],
- "extra_time": values["extra_time"],
- }
- with open("values1.json", "w") as j:
- json.dump(settings, j)
- if event == "stop_vc" and self.flag_vc == True:
- self.flag_vc = False
-
- # Function that returns the used f0 method in string format "harvest"
- def get_f0_method_from_radios(self, values):
- f0_array = [
- {"name": "harvest", "val": values["harvest"]},
- {"name": "reg-crepe", "val": values["reg-crepe"]},
- {"name": "reg-crepe-tiny", "val": values["reg-crepe-tiny"]},
- ]
- # Filter through to find a true value
- used_f0 = ""
- for f0 in f0_array:
- if f0["val"] == True:
- used_f0 = f0["name"]
- break
- if used_f0 == "":
- used_f0 = "harvest" # Default Harvest if used_f0 is empty somehow
- return used_f0
-
- def set_values(self, values):
- if len(values["pth_path"].strip()) == 0:
- sg.popup(i18n("Select the pth file"))
- return False
- if len(values["index_path"].strip()) == 0:
- sg.popup(i18n("Select the index file"))
- return False
- pattern = re.compile("[^\x00-\x7F]+")
- if pattern.findall(values["hubert_path"]):
- sg.popup(i18n("The hubert model path must not contain Chinese characters"))
- return False
- if pattern.findall(values["pth_path"]):
- sg.popup(i18n("The pth file path must not contain Chinese characters."))
- return False
- if pattern.findall(values["index_path"]):
- sg.popup(i18n("The index file path must not contain Chinese characters."))
- return False
- self.set_devices(values["sg_input_device"], values["sg_output_device"])
- self.config.hubert_path = os.path.join(current_dir, "hubert_base.pt")
- self.config.pth_path = values["pth_path"]
- self.config.index_path = values["index_path"]
- self.config.npy_path = values["npy_path"]
- self.config.f0_method = self.get_f0_method_from_radios(values)
- self.config.threhold = values["threhold"]
- self.config.pitch = values["pitch"]
- self.config.block_time = values["block_time"]
- self.config.crossfade_time = values["crossfade_length"]
- self.config.extra_time = values["extra_time"]
- self.config.I_noise_reduce = values["I_noise_reduce"]
- self.config.O_noise_reduce = values["O_noise_reduce"]
- self.config.index_rate = values["index_rate"]
- return True
-
- def start_vc(self):
- torch.cuda.empty_cache()
- self.flag_vc = True
- self.block_frame = int(self.config.block_time * self.config.samplerate)
- self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate)
- self.sola_search_frame = int(0.012 * self.config.samplerate)
- self.delay_frame = int(0.01 * self.config.samplerate) # 往前预留0.02s
- self.extra_frame = int(self.config.extra_time * self.config.samplerate)
- self.rvc = None
- self.rvc = RVC(
- self.config.pitch,
- self.config.f0_method,
- self.config.hubert_path,
- self.config.pth_path,
- self.config.index_path,
- self.config.npy_path,
- self.config.index_rate,
- )
- self.input_wav: np.ndarray = np.zeros(
- self.extra_frame
- + self.crossfade_frame
- + self.sola_search_frame
- + self.block_frame,
- dtype="float32",
- )
- self.output_wav: torch.Tensor = torch.zeros(
- self.block_frame, device=device, dtype=torch.float32
- )
- self.sola_buffer: torch.Tensor = torch.zeros(
- self.crossfade_frame, device=device, dtype=torch.float32
- )
- self.fade_in_window: torch.Tensor = torch.linspace(
- 0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32
- )
- self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
- self.resampler1 = tat.Resample(
- orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
- )
- self.resampler2 = tat.Resample(
- orig_freq=self.rvc.tgt_sr,
- new_freq=self.config.samplerate,
- dtype=torch.float32,
- )
- thread_vc = threading.Thread(target=self.soundinput)
- thread_vc.start()
-
- def soundinput(self):
- """
- 接受音频输入
- """
- with sd.Stream(
- channels=2,
- callback=self.audio_callback,
- blocksize=self.block_frame,
- samplerate=self.config.samplerate,
- dtype="float32",
- ):
- while self.flag_vc:
- time.sleep(self.config.block_time)
- print("Audio block passed.")
- print("ENDing VC")
-
- def audio_callback(
- self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
- ):
- """
- 音频处理
- """
- start_time = time.perf_counter()
- indata = librosa.to_mono(indata.T)
- if self.config.I_noise_reduce:
- indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate)
-
- """noise gate"""
- frame_length = 2048
- hop_length = 1024
- rms = librosa.feature.rms(
- y=indata, frame_length=frame_length, hop_length=hop_length
- )
- db_threhold = librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold
- # print(rms.shape,db.shape,db)
- for i in range(db_threhold.shape[0]):
- if db_threhold[i]:
- indata[i * hop_length : (i + 1) * hop_length] = 0
- self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata)
-
- # infer
- print("input_wav:" + str(self.input_wav.shape))
- # print('infered_wav:'+str(infer_wav.shape))
- infer_wav: torch.Tensor = self.resampler2(
- self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav)))
- )[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to(
- device
- )
- print("infer_wav:" + str(infer_wav.shape))
-
- # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
- cor_nom = F.conv1d(
- infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
- self.sola_buffer[None, None, :],
- )
- cor_den = torch.sqrt(
- F.conv1d(
- infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
- ** 2,
- torch.ones(1, 1, self.crossfade_frame, device=device),
- )
- + 1e-8
- )
- sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
- print("sola offset: " + str(int(sola_offset)))
-
- # crossfade
- self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
- self.output_wav[: self.crossfade_frame] *= self.fade_in_window
- self.output_wav[: self.crossfade_frame] += self.sola_buffer[:]
- if sola_offset < self.sola_search_frame:
- self.sola_buffer[:] = (
- infer_wav[
- -self.sola_search_frame
- - self.crossfade_frame
- + sola_offset : -self.sola_search_frame
- + sola_offset
- ]
- * self.fade_out_window
- )
- else:
- self.sola_buffer[:] = (
- infer_wav[-self.crossfade_frame :] * self.fade_out_window
- )
-
- if self.config.O_noise_reduce:
- outdata[:] = np.tile(
- nr.reduce_noise(
- y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
- ),
- (2, 1),
- ).T
- else:
- outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
- total_time = time.perf_counter() - start_time
- self.window["infer_time"].update(int(total_time * 1000))
- print("infer time:" + str(total_time))
- print("f0_method: " + str(self.config.f0_method))
-
- def get_devices(self, update: bool = True):
- """获取设备列表"""
- if update:
- sd._terminate()
- sd._initialize()
- devices = sd.query_devices()
- hostapis = sd.query_hostapis()
- for hostapi in hostapis:
- for device_idx in hostapi["devices"]:
- devices[device_idx]["hostapi_name"] = hostapi["name"]
- input_devices = [
- f"{d['name']} ({d['hostapi_name']})"
- for d in devices
- if d["max_input_channels"] > 0
- ]
- output_devices = [
- f"{d['name']} ({d['hostapi_name']})"
- for d in devices
- if d["max_output_channels"] > 0
- ]
- input_devices_indices = [
- d["index"] if "index" in d else d["name"]
- for d in devices
- if d["max_input_channels"] > 0
- ]
- output_devices_indices = [
- d["index"] if "index" in d else d["name"]
- for d in devices
- if d["max_output_channels"] > 0
- ]
- return (
- input_devices,
- output_devices,
- input_devices_indices,
- output_devices_indices,
- )
-
- def set_devices(self, input_device, output_device):
- """设置输出设备"""
- (
- input_devices,
- output_devices,
- input_device_indices,
- output_device_indices,
- ) = self.get_devices()
- sd.default.device[0] = input_device_indices[input_devices.index(input_device)]
- sd.default.device[1] = output_device_indices[
- output_devices.index(output_device)
- ]
- print("input device:" + str(sd.default.device[0]) + ":" + str(input_device))
- print("output device:" + str(sd.default.device[1]) + ":" + str(output_device))
-
-
-gui = GUI()
diff --git a/spaces/EuroPython2022/clickbaitonator/fudge/main.py b/spaces/EuroPython2022/clickbaitonator/fudge/main.py
deleted file mode 100644
index e8c2299b2449b6dd07d26c7ae678732b1dabca88..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/clickbaitonator/fudge/main.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import os
-import random
-import time
-import pickle
-import math
-from argparse import ArgumentParser
-
-from tqdm import tqdm
-import numpy as np
-import torch
-import torch.nn as nn
-
-from data import Dataset
-from model import Model
-from util import save_checkpoint, ProgressMeter, AverageMeter, num_params, pad_mask
-from constants import *
-
-
-def train(model, dataset, optimizer, criterion, epoch, args, data_start_index):
- model.train()
- if data_start_index == 0:
- dataset.shuffle('train', seed=epoch + args.seed)
- if args.epoch_max_len is not None:
- data_end_index = min(data_start_index + args.epoch_max_len, len(dataset.splits['train']))
- loader = dataset.loader('train', num_workers=args.num_workers, indices=list(range(data_start_index, data_end_index)))
- data_start_index = data_end_index if data_end_index < len(dataset.splits['train']) else 0
- else:
- loader = dataset.loader('train', num_workers=args.num_workers)
- loss_meter = AverageMeter('loss', ':6.4f')
- total_length = len(loader)
- progress = ProgressMeter(total_length, [loss_meter], prefix='Training: ')
- for batch_num, batch in enumerate(tqdm(loader, total=len(loader))):
- batch = [tensor.to(args.device) for tensor in batch]
- inputs, lengths, future_words, log_probs, labels, classification_targets, syllables_to_go, future_word_num_syllables, rhyme_group_index = batch
- if args.task not in ['formality', 'iambic']:
- if not args.debug and len(inputs) != args.batch_size: # it'll screw up the bias...?
- continue
- scores = model(inputs, lengths, future_words, log_probs, syllables_to_go, future_word_num_syllables, rhyme_group_index, run_classifier=True)
- if args.task == 'formality': # we're learning for all positions at once. scores are batch x seq
- expanded_labels = classification_targets.unsqueeze(1).expand(-1, scores.shape[1]) # batch x seq
- length_mask = pad_mask(lengths).permute(1, 0) # batch x seq
- loss = criterion(scores.flatten()[length_mask.flatten()==1], expanded_labels.flatten().float()[length_mask.flatten()==1])
- elif args.task in ['iambic', 'newline']:
- use_indices = classification_targets.flatten() != -1
- loss = criterion(scores.flatten()[use_indices], classification_targets.flatten().float()[use_indices])
- else: # topic, rhyme
- loss = criterion(scores.flatten(), labels.flatten().float())
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
- loss_meter.update(loss.detach(), len(labels))
- if batch_num % args.train_print_freq == 0:
- progress.display(batch_num)
- progress.display(total_length)
- return data_start_index
-
-
-def validate(model, dataset, criterion, epoch, args):
- model.eval()
- random.seed(0)
- loader = dataset.loader('val', num_workers=args.num_workers)
- loss_meter = AverageMeter('loss', ':6.4f')
- total_length = len(loader)
- progress = ProgressMeter(total_length, [loss_meter], prefix='Validation: ')
- with torch.no_grad():
- for batch_num, batch in enumerate(tqdm(loader, total=len(loader))):
- batch = [tensor.to(args.device) for tensor in batch]
- inputs, lengths, future_words, log_probs, labels, classification_targets, syllables_to_go, future_word_num_syllables, rhyme_group_index = batch
- if args.task not in ['formality', 'iambic']: # topic predictor
- if not args.debug and len(inputs) != args.batch_size:
- continue
- scores = model(inputs, lengths, future_words, log_probs, syllables_to_go, future_word_num_syllables, rhyme_group_index, run_classifier=True)
- if args.task == 'formality': # we're learning for all positions at once. scores are batch x seq
- expanded_labels = classification_targets.unsqueeze(1).expand(-1, scores.shape[1]) # batch x seq
- length_mask = pad_mask(lengths).permute(1, 0) # batch x seq
- loss = criterion(scores.flatten()[length_mask.flatten()==1], expanded_labels.flatten().float()[length_mask.flatten()==1])
- elif args.task in ['iambic', 'newline']:
- use_indices = classification_targets.flatten() != -1
- loss = criterion(scores.flatten()[use_indices], classification_targets.flatten().float()[use_indices])
- else: # topic, rhyme
- loss = criterion(scores.flatten(), labels.flatten().float())
- loss_meter.update(loss.detach(), len(labels))
- if batch_num % args.train_print_freq == 0:
- progress.display(batch_num)
- progress.display(total_length)
- return loss_meter.avg
-
-
-def main(args):
- dataset = Dataset(args)
- os.makedirs(args.save_dir, exist_ok=True)
- with open(os.path.join(args.save_dir, 'dataset_info'), 'wb') as wf:
- pickle.dump(dataset.dataset_info, wf)
- if args.task == 'rhyme':
- with open(os.path.join(args.save_dir, 'rhyme_info'), 'wb') as wf:
- pickle.dump(dataset.rhyme_info, wf)
- if args.ckpt:
- checkpoint = torch.load(args.ckpt, map_location=args.device)
- start_epoch = checkpoint['epoch'] + 1
- best_val_metric = checkpoint['best_metric']
- model_args = checkpoint['args']
- model = Model(model_args, dataset.gpt_pad_id, len(dataset.index2word), rhyme_group_size=len(dataset.index2rhyme_group) if args.task == 'rhyme' else None) # no need to get the glove embeddings when reloading since they're saved in model ckpt anyway
- model.load_state_dict(checkpoint['state_dict'])
- model = model.to(args.device)
- optimizer = torch.optim.Adam(model.parameters(), lr=model_args.lr)
- optimizer.load_state_dict(checkpoint['optimizer'])
- data_start_index = checkpoint['data_start_index']
- print("=> loaded checkpoint '{}' (epoch {})"
- .format(args.ckpt, checkpoint['epoch']))
- # NOTE: just import pdb after loading the model here if you want to play with it, it's easy
- # model.eval()
- # import pdb; pdb.set_trace()
- else:
- model = Model(args, dataset.gpt_pad_id, len(dataset.index2word), rhyme_group_size=len(dataset.index2rhyme_group) if args.task == 'rhyme' else None, glove_embeddings=dataset.glove_embeddings)
- model = model.to(args.device)
- optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
- best_val_metric = 1e8 # lower is better for BCE
- data_start_index = 0
- print('num params', num_params(model))
- criterion = nn.BCEWithLogitsLoss().to(args.device)
-
- if args.evaluate:
- epoch = 0
- validate(model, dataset, criterion, epoch, args)
- return
- for epoch in range(args.epochs):
- print("TRAINING: Epoch {} at {}".format(epoch, time.ctime()))
- data_start_index = train(model, dataset, optimizer, criterion, epoch, args, data_start_index)
- if epoch % args.validation_freq == 0:
- print("VALIDATION: Epoch {} at {}".format(epoch, time.ctime()))
- metric = validate(model, dataset, criterion, epoch, args)
-
- if not args.debug:
- if metric < best_val_metric:
- print('new best val metric', metric)
- best_val_metric = metric
- save_checkpoint({
- 'epoch': epoch,
- 'state_dict': model.state_dict(),
- 'best_metric': best_val_metric,
- 'optimizer': optimizer.state_dict(),
- 'data_start_index': data_start_index,
- 'args': args
- }, os.path.join(args.save_dir, 'model_best.pth.tar'))
- save_checkpoint({
- 'epoch': epoch,
- 'state_dict': model.state_dict(),
- 'best_metric': metric,
- 'optimizer': optimizer.state_dict(),
- 'data_start_index': data_start_index,
- 'args': args
- }, os.path.join(args.save_dir, 'model_epoch' + str(epoch) + '.pth.tar'))
-
-
-if __name__=='__main__':
- parser = ArgumentParser()
-
- # DATA
- parser.add_argument('--task', type=str, required=True, choices=['iambic', 'rhyme', 'newline', 'topic', 'formality', 'clickbait'])
- parser.add_argument('--data_dir', type=str, required=True)
- parser.add_argument('--glove_file', type=str, help='glove embedding init, for topic task')
-
- # SAVE/LOAD
- parser.add_argument('--save_dir', type=str, required=True, help='where to save ckpts')
- parser.add_argument('--ckpt', type=str, default=None, help='load ckpt from file if given')
- parser.add_argument('--dataset_info', type=str, help='saved dataset info')
- parser.add_argument('--rhyme_info', type=str, help='saved dataset rhyme info, for a ckpt with task==rhyme')
-
- # TRAINING
- parser.add_argument('--batch_size', type=int, default=128)
- parser.add_argument('--epochs', type=int, default=100)
- parser.add_argument('--epoch_max_len', type=int, default=None, help='max batches per epoch if set, for more frequent validation')
- parser.add_argument('--validation_freq', type=int, default=1, help='validate every X epochs')
- parser.add_argument('--lr', type=float, default=1e-3, help='Adam learning rate')
- parser.add_argument('--seed', type=int, default=1, help='random seed')
- parser.add_argument('--device', type=str, default='cuda', choices=['cpu', 'cuda'])
- parser.add_argument('--num_workers', type=int, default=20, help='num workers for data loader')
- parser.add_argument('--evaluate', action='store_true', default=False)
- parser.add_argument('--debug', action='store_true', default=False)
-
- # PRINTING
- parser.add_argument('--train_print_freq', type=int, default=100, help='how often to print metrics (every X batches)')
-
- args = parser.parse_args()
-
- random.seed(args.seed)
- np.random.seed(args.seed)
- torch.manual_seed(args.seed)
- if args.evaluate:
- assert args.ckpt is not None
-
- main(args)
\ No newline at end of file
diff --git a/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts b/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts
deleted file mode 100644
index 9ba0fe30a092c73c8321a6bb2502750471b133f2..0000000000000000000000000000000000000000
--- a/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts
+++ /dev/null
@@ -1,279 +0,0 @@
-import {
- ChatInterface,
- ChatModule,
- ChatRestModule,
- ChatWorkerClient,
-} from "@mlc-ai/web-llm";
-
-function getElementAndCheck(id: string): HTMLElement {
- const element = document.getElementById(id);
- if (element == null) {
- throw Error("Cannot find element " + id);
- }
- return element;
-}
-
-const appConfig = {
- model_list: [
- {
- model_url:
- "https://huggingface.co/Felladrin/mlc-chat-Mistral-7B-OpenOrca-q4f32_1/resolve/main/params/",
- local_id: "Mistral-7B-OpenOrca-q4f32_1",
- },
- ],
- model_lib_map: {
- "Mistral-7B-OpenOrca-q4f32_1":
- "https://huggingface.co/Felladrin/mlc-chat-Mistral-7B-OpenOrca-q4f32_1/resolve/main/Mistral-7B-OpenOrca-q4f32_1-webgpu.wasm",
- },
- use_web_worker: true,
-};
-
-class ChatUI {
- private uiChat: HTMLElement;
- private uiChatInput: HTMLInputElement;
- private uiChatInfoLabel: HTMLLabelElement;
- private chat: ChatInterface;
- private localChat: ChatInterface;
- private config = appConfig;
- private selectedModel: string;
- private chatLoaded = false;
- private requestInProgress = false;
- // We use a request chain to ensure that
- // all requests send to chat are sequentialized
- private chatRequestChain: Promise = Promise.resolve();
-
- constructor(chat: ChatInterface, localChat: ChatInterface) {
- // use web worker to run chat generation in background
- this.chat = chat;
- this.localChat = localChat;
- // get the elements
- this.uiChat = getElementAndCheck("chatui-chat");
- this.uiChatInput = getElementAndCheck("chatui-input") as HTMLInputElement;
- this.uiChatInfoLabel = getElementAndCheck(
- "chatui-info-label"
- ) as HTMLLabelElement;
- // register event handlers
- getElementAndCheck("chatui-reset-btn").onclick = () => {
- this.onReset();
- };
- getElementAndCheck("chatui-send-btn").onclick = () => {
- this.onGenerate();
- };
- // TODO: find other alternative triggers
- getElementAndCheck("chatui-input").onkeypress = (event) => {
- if (event.keyCode === 13) {
- this.onGenerate();
- }
- };
-
- const modelSelector = getElementAndCheck(
- "chatui-select"
- ) as HTMLSelectElement;
- for (let i = 0; i < this.config.model_list.length; ++i) {
- const item = this.config.model_list[i];
- const opt = document.createElement("option");
- opt.value = item.local_id;
- opt.innerHTML = item.local_id;
- opt.selected = i == 0;
- modelSelector.appendChild(opt);
- }
- // Append local server option to the model selector
- const localServerOpt = document.createElement("option");
- localServerOpt.value = "Local Server";
- localServerOpt.innerHTML = "Local Server";
- modelSelector.append(localServerOpt);
- this.selectedModel = modelSelector.value;
- modelSelector.onchange = () => {
- this.onSelectChange(modelSelector);
- };
- }
- /**
- * Push a task to the execution queue.
- *
- * @param task The task to be executed;
- */
- private pushTask(task: () => Promise) {
- const lastEvent = this.chatRequestChain;
- this.chatRequestChain = lastEvent.then(task);
- }
- // Event handlers
- // all event handler pushes the tasks to a queue
- // that get executed sequentially
- // the tasks previous tasks, which causes them to early stop
- // can be interrupted by chat.interruptGenerate
- private async onGenerate() {
- if (this.requestInProgress) {
- return;
- }
- this.pushTask(async () => {
- await this.asyncGenerate();
- });
- }
-
- private async onSelectChange(modelSelector: HTMLSelectElement) {
- if (this.requestInProgress) {
- // interrupt previous generation if any
- this.chat.interruptGenerate();
- }
- // try reset after previous requests finishes
- this.pushTask(async () => {
- await this.chat.resetChat();
- this.resetChatHistory();
- await this.unloadChat();
- this.selectedModel = modelSelector.value;
- await this.asyncInitChat();
- });
- }
-
- private async onReset() {
- if (this.requestInProgress) {
- // interrupt previous generation if any
- this.chat.interruptGenerate();
- }
- // try reset after previous requests finishes
- this.pushTask(async () => {
- await this.chat.resetChat();
- this.resetChatHistory();
- });
- }
-
- // Internal helper functions
- private appendMessage(kind, text) {
- if (kind == "init") {
- text = "[System Initalize] " + text;
- }
- if (this.uiChat === undefined) {
- throw Error("cannot find ui chat");
- }
- const msg = `
-
-
- ${text}
-
-
- `;
- this.uiChat.insertAdjacentHTML("beforeend", msg);
- this.uiChat.scrollTo(0, this.uiChat.scrollHeight);
- }
-
- private updateLastMessage(kind, text) {
- if (kind == "init") {
- text = "[System Initalize] " + text;
- }
- if (this.uiChat === undefined) {
- throw Error("cannot find ui chat");
- }
- const matches = this.uiChat.getElementsByClassName(`msg ${kind}-msg`);
- if (matches.length == 0) throw Error(`${kind} message do not exist`);
- const msg = matches[matches.length - 1];
- const msgText = msg.getElementsByClassName("msg-text");
- if (msgText.length != 1) throw Error("Expect msg-text");
- if (msgText[0].innerHTML == text) return;
- const list = text.split("\n").map((t) => {
- const item = document.createElement("div");
- item.textContent = t;
- return item;
- });
- msgText[0].innerHTML = "";
- list.forEach((item) => msgText[0].append(item));
- this.uiChat.scrollTo(0, this.uiChat.scrollHeight);
- }
-
- private resetChatHistory() {
- const clearTags = ["left", "right", "init", "error"];
- for (const tag of clearTags) {
- // need to unpack to list so the iterator don't get affected by mutation
- const matches = [...this.uiChat.getElementsByClassName(`msg ${tag}-msg`)];
- for (const item of matches) {
- this.uiChat.removeChild(item);
- }
- }
- if (this.uiChatInfoLabel !== undefined) {
- this.uiChatInfoLabel.innerHTML = "";
- }
- }
-
- private async asyncInitChat() {
- if (this.chatLoaded) return;
- this.requestInProgress = true;
- this.appendMessage("init", "");
- const initProgressCallback = (report) => {
- this.updateLastMessage("init", report.text);
- };
- this.chat.setInitProgressCallback(initProgressCallback);
-
- try {
- if (this.selectedModel != "Local Server") {
- await this.chat.reload(this.selectedModel, undefined, this.config);
- }
- } catch (err) {
- this.appendMessage("error", "Init error, " + err.toString());
- console.log(err.stack);
- this.unloadChat();
- this.requestInProgress = false;
- return;
- }
- this.requestInProgress = false;
- this.chatLoaded = true;
- }
-
- private async unloadChat() {
- await this.chat.unload();
- this.chatLoaded = false;
- }
-
- /**
- * Run generate
- */
- private async asyncGenerate() {
- await this.asyncInitChat();
- this.requestInProgress = true;
- const prompt = this.uiChatInput.value;
- if (prompt == "") {
- this.requestInProgress = false;
- return;
- }
-
- this.appendMessage("right", prompt);
- this.uiChatInput.value = "";
- this.uiChatInput.setAttribute("placeholder", "Generating...");
-
- this.appendMessage("left", "");
- const callbackUpdateResponse = (step, msg) => {
- if (msg.length === 0) return this.chat.interruptGenerate();
- this.updateLastMessage("left", msg);
- };
-
- try {
- if (this.selectedModel == "Local Server") {
- await this.localChat.generate(prompt, callbackUpdateResponse);
- this.uiChatInfoLabel.innerHTML =
- await this.localChat.runtimeStatsText();
- } else {
- await this.chat.generate(prompt, callbackUpdateResponse);
- this.uiChatInfoLabel.innerHTML = await this.chat.runtimeStatsText();
- }
- } catch (err) {
- this.appendMessage("error", "Generate error, " + err.toString());
- console.log(err.stack);
- await this.unloadChat();
- }
- this.uiChatInput.setAttribute("placeholder", "Enter your message...");
- this.requestInProgress = false;
- }
-}
-
-const useWebWorker = appConfig.use_web_worker;
-let chat: ChatInterface;
-let localChat: ChatInterface;
-
-if (useWebWorker) {
- chat = new ChatWorkerClient(
- new Worker(new URL("./worker.ts", import.meta.url), { type: "module" })
- );
- localChat = new ChatRestModule();
-} else {
- chat = new ChatModule();
- localChat = new ChatRestModule();
-}
-new ChatUI(chat, localChat);
diff --git a/spaces/Francesco/torch-cam-transformers/app.py b/spaces/Francesco/torch-cam-transformers/app.py
deleted file mode 100644
index 6c5bf7ea8344430235dfcc553c112be801348a50..0000000000000000000000000000000000000000
--- a/spaces/Francesco/torch-cam-transformers/app.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import streamlit as st
-
-import sys
-
-
-def main():
- # Wide mode
- st.set_page_config(layout="wide")
-
- # Designing the interface
- st.title(sys.version)
-
-
-main()
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py
deleted file mode 100644
index bf18572329019d7a8f1df01799eda207c16dd7ff..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py
+++ /dev/null
@@ -1,284 +0,0 @@
-import os
-import random
-import re
-import numpy as np
-import librosa
-import torch
-import random
-from utils import repeat_expand_2d
-from tqdm import tqdm
-from torch.utils.data import Dataset
-
-def traverse_dir(
- root_dir,
- extensions,
- amount=None,
- str_include=None,
- str_exclude=None,
- is_pure=False,
- is_sort=False,
- is_ext=True):
-
- file_list = []
- cnt = 0
- for root, _, files in os.walk(root_dir):
- for file in files:
- if any([file.endswith(f".{ext}") for ext in extensions]):
- # path
- mix_path = os.path.join(root, file)
- pure_path = mix_path[len(root_dir)+1:] if is_pure else mix_path
-
- # amount
- if (amount is not None) and (cnt == amount):
- if is_sort:
- file_list.sort()
- return file_list
-
- # check string
- if (str_include is not None) and (str_include not in pure_path):
- continue
- if (str_exclude is not None) and (str_exclude in pure_path):
- continue
-
- if not is_ext:
- ext = pure_path.split('.')[-1]
- pure_path = pure_path[:-(len(ext)+1)]
- file_list.append(pure_path)
- cnt += 1
- if is_sort:
- file_list.sort()
- return file_list
-
-
-def get_data_loaders(args, whole_audio=False):
- data_train = AudioDataset(
- filelists = args.data.training_files,
- waveform_sec=args.data.duration,
- hop_size=args.data.block_size,
- sample_rate=args.data.sampling_rate,
- load_all_data=args.train.cache_all_data,
- whole_audio=whole_audio,
- extensions=args.data.extensions,
- n_spk=args.model.n_spk,
- spk=args.spk,
- device=args.train.cache_device,
- fp16=args.train.cache_fp16,
- use_aug=True)
- loader_train = torch.utils.data.DataLoader(
- data_train ,
- batch_size=args.train.batch_size if not whole_audio else 1,
- shuffle=True,
- num_workers=args.train.num_workers if args.train.cache_device=='cpu' else 0,
- persistent_workers=(args.train.num_workers > 0) if args.train.cache_device=='cpu' else False,
- pin_memory=True if args.train.cache_device=='cpu' else False
- )
- data_valid = AudioDataset(
- filelists = args.data.validation_files,
- waveform_sec=args.data.duration,
- hop_size=args.data.block_size,
- sample_rate=args.data.sampling_rate,
- load_all_data=args.train.cache_all_data,
- whole_audio=True,
- spk=args.spk,
- extensions=args.data.extensions,
- n_spk=args.model.n_spk)
- loader_valid = torch.utils.data.DataLoader(
- data_valid,
- batch_size=1,
- shuffle=False,
- num_workers=0,
- pin_memory=True
- )
- return loader_train, loader_valid
-
-
-class AudioDataset(Dataset):
- def __init__(
- self,
- filelists,
- waveform_sec,
- hop_size,
- sample_rate,
- spk,
- load_all_data=True,
- whole_audio=False,
- extensions=['wav'],
- n_spk=1,
- device='cpu',
- fp16=False,
- use_aug=False,
- ):
- super().__init__()
-
- self.waveform_sec = waveform_sec
- self.sample_rate = sample_rate
- self.hop_size = hop_size
- self.filelists = filelists
- self.whole_audio = whole_audio
- self.use_aug = use_aug
- self.data_buffer={}
- self.pitch_aug_dict = {}
- # np.load(os.path.join(self.path_root, 'pitch_aug_dict.npy'), allow_pickle=True).item()
- if load_all_data:
- print('Load all the data filelists:', filelists)
- else:
- print('Load the f0, volume data filelists:', filelists)
- with open(filelists,"r") as f:
- self.paths = f.read().splitlines()
- for name_ext in tqdm(self.paths, total=len(self.paths)):
- name = os.path.splitext(name_ext)[0]
- path_audio = name_ext
- duration = librosa.get_duration(filename = path_audio, sr = self.sample_rate)
-
- path_f0 = name_ext + ".f0.npy"
- f0,_ = np.load(path_f0,allow_pickle=True)
- f0 = torch.from_numpy(np.array(f0,dtype=float)).float().unsqueeze(-1).to(device)
-
- path_volume = name_ext + ".vol.npy"
- volume = np.load(path_volume)
- volume = torch.from_numpy(volume).float().unsqueeze(-1).to(device)
-
- path_augvol = name_ext + ".aug_vol.npy"
- aug_vol = np.load(path_augvol)
- aug_vol = torch.from_numpy(aug_vol).float().unsqueeze(-1).to(device)
-
- if n_spk is not None and n_spk > 1:
- spk_name = name_ext.split("/")[-2]
- spk_id = spk[spk_name] if spk_name in spk else 0
- if spk_id < 0 or spk_id >= n_spk:
- raise ValueError(' [x] Muiti-speaker traing error : spk_id must be a positive integer from 0 to n_spk-1 ')
- else:
- spk_id = 0
- spk_id = torch.LongTensor(np.array([spk_id])).to(device)
-
- if load_all_data:
- '''
- audio, sr = librosa.load(path_audio, sr=self.sample_rate)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio)
- audio = torch.from_numpy(audio).to(device)
- '''
- path_mel = name_ext + ".mel.npy"
- mel = np.load(path_mel)
- mel = torch.from_numpy(mel).to(device)
-
- path_augmel = name_ext + ".aug_mel.npy"
- aug_mel,keyshift = np.load(path_augmel, allow_pickle=True)
- aug_mel = np.array(aug_mel,dtype=float)
- aug_mel = torch.from_numpy(aug_mel).to(device)
- self.pitch_aug_dict[name_ext] = keyshift
-
- path_units = name_ext + ".soft.pt"
- units = torch.load(path_units).to(device)
- units = units[0]
- units = repeat_expand_2d(units,f0.size(0)).transpose(0,1)
-
- if fp16:
- mel = mel.half()
- aug_mel = aug_mel.half()
- units = units.half()
-
- self.data_buffer[name_ext] = {
- 'duration': duration,
- 'mel': mel,
- 'aug_mel': aug_mel,
- 'units': units,
- 'f0': f0,
- 'volume': volume,
- 'aug_vol': aug_vol,
- 'spk_id': spk_id
- }
- else:
- path_augmel = name_ext + ".aug_mel.npy"
- aug_mel,keyshift = np.load(path_augmel, allow_pickle=True)
- self.pitch_aug_dict[name_ext] = keyshift
- self.data_buffer[name_ext] = {
- 'duration': duration,
- 'f0': f0,
- 'volume': volume,
- 'aug_vol': aug_vol,
- 'spk_id': spk_id
- }
-
-
- def __getitem__(self, file_idx):
- name_ext = self.paths[file_idx]
- data_buffer = self.data_buffer[name_ext]
- # check duration. if too short, then skip
- if data_buffer['duration'] < (self.waveform_sec + 0.1):
- return self.__getitem__( (file_idx + 1) % len(self.paths))
-
- # get item
- return self.get_data(name_ext, data_buffer)
-
- def get_data(self, name_ext, data_buffer):
- name = os.path.splitext(name_ext)[0]
- frame_resolution = self.hop_size / self.sample_rate
- duration = data_buffer['duration']
- waveform_sec = duration if self.whole_audio else self.waveform_sec
-
- # load audio
- idx_from = 0 if self.whole_audio else random.uniform(0, duration - waveform_sec - 0.1)
- start_frame = int(idx_from / frame_resolution)
- units_frame_len = int(waveform_sec / frame_resolution)
- aug_flag = random.choice([True, False]) and self.use_aug
- '''
- audio = data_buffer.get('audio')
- if audio is None:
- path_audio = os.path.join(self.path_root, 'audio', name) + '.wav'
- audio, sr = librosa.load(
- path_audio,
- sr = self.sample_rate,
- offset = start_frame * frame_resolution,
- duration = waveform_sec)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio)
- # clip audio into N seconds
- audio = audio[ : audio.shape[-1] // self.hop_size * self.hop_size]
- audio = torch.from_numpy(audio).float()
- else:
- audio = audio[start_frame * self.hop_size : (start_frame + units_frame_len) * self.hop_size]
- '''
- # load mel
- mel_key = 'aug_mel' if aug_flag else 'mel'
- mel = data_buffer.get(mel_key)
- if mel is None:
- mel = name_ext + ".mel.npy"
- mel = np.load(mel)
- mel = mel[start_frame : start_frame + units_frame_len]
- mel = torch.from_numpy(mel).float()
- else:
- mel = mel[start_frame : start_frame + units_frame_len]
-
- # load f0
- f0 = data_buffer.get('f0')
- aug_shift = 0
- if aug_flag:
- aug_shift = self.pitch_aug_dict[name_ext]
- f0_frames = 2 ** (aug_shift / 12) * f0[start_frame : start_frame + units_frame_len]
-
- # load units
- units = data_buffer.get('units')
- if units is None:
- path_units = name_ext + ".soft.pt"
- units = torch.load(path_units)
- units = units[0]
- units = repeat_expand_2d(units,f0.size(0)).transpose(0,1)
-
- units = units[start_frame : start_frame + units_frame_len]
-
- # load volume
- vol_key = 'aug_vol' if aug_flag else 'volume'
- volume = data_buffer.get(vol_key)
- volume_frames = volume[start_frame : start_frame + units_frame_len]
-
- # load spk_id
- spk_id = data_buffer.get('spk_id')
-
- # load shift
- aug_shift = torch.from_numpy(np.array([[aug_shift]])).float()
-
- return dict(mel=mel, f0=f0_frames, volume=volume_frames, units=units, spk_id=spk_id, aug_shift=aug_shift, name=name, name_ext=name_ext)
-
- def __len__(self):
- return len(self.paths)
\ No newline at end of file
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py
deleted file mode 100644
index 3bdb70ba9357e95ff05853dcc06437c3401ef3be..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import os
-from functools import lru_cache
-from typing import Union
-
-import ffmpeg
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-from .utils import exact_div
-
-from librosa.filters import mel as librosa_mel_fn
-
-# hard-coded audio hyperparameters
-SAMPLE_RATE = 16000
-N_FFT = 400
-N_MELS = 80
-HOP_LENGTH = 160
-CHUNK_LENGTH = 30
-N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk
-N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH) # 3000: number of frames in a mel spectrogram input
-
-
-def load_audio(file: str, sr: int = SAMPLE_RATE):
- """
- Open an audio file and read as mono waveform, resampling as necessary
-
- Parameters
- ----------
- file: str
- The audio file to open
-
- sr: int
- The sample rate to resample the audio if necessary
-
- Returns
- -------
- A NumPy array containing the audio waveform, in float32 dtype.
- """
- try:
- # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
- # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
- out, _ = (
- ffmpeg.input(file, threads=0)
- .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
- .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
- )
- except ffmpeg.Error as e:
- raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
-
- return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
-
-
-def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
- """
- Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
- """
- if torch.is_tensor(array):
- if array.shape[axis] > length:
- array = array.index_select(dim=axis, index=torch.arange(length, device=array.device))
-
- if array.shape[axis] < length:
- pad_widths = [(0, 0)] * array.ndim
- pad_widths[axis] = (0, length - array.shape[axis])
- array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
- else:
- if array.shape[axis] > length:
- array = array.take(indices=range(length), axis=axis)
-
- if array.shape[axis] < length:
- pad_widths = [(0, 0)] * array.ndim
- pad_widths[axis] = (0, length - array.shape[axis])
- array = np.pad(array, pad_widths)
-
- return array
-
-
-@lru_cache(maxsize=None)
-def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
- """
- load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
- Allows decoupling librosa dependency; saved using:
-
- np.savez_compressed(
- "mel_filters.npz",
- mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
- )
- """
- assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
- return torch.from_numpy(librosa_mel_fn(sr=SAMPLE_RATE,n_fft=N_FFT,n_mels=n_mels)).to(device)
-
-
-def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS):
- """
- Compute the log-Mel spectrogram of
-
- Parameters
- ----------
- audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
- The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
-
- n_mels: int
- The number of Mel-frequency filters, only 80 is supported
-
- Returns
- -------
- torch.Tensor, shape = (80, n_frames)
- A Tensor that contains the Mel spectrogram
- """
- if not torch.is_tensor(audio):
- if isinstance(audio, str):
- audio = load_audio(audio)
- audio = torch.from_numpy(audio)
-
- window = torch.hann_window(N_FFT).to(audio.device)
- stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
- magnitudes = stft[..., :-1].abs() ** 2
-
- filters = mel_filters(audio.device, n_mels)
- mel_spec = filters @ magnitudes
-
- log_spec = torch.clamp(mel_spec, min=1e-10).log10()
- log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
- log_spec = (log_spec + 4.0) / 4.0
- return log_spec
diff --git a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py b/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py
deleted file mode 100644
index 6817392ba8a9eb830351de89fb7afc5ad72f5e42..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py
+++ /dev/null
@@ -1,188 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
- 1. "english_cleaners" for English text
- 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
- the Unidecode library (https://pypi.python.org/pypi/Unidecode)
- 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
- the symbols in symbols.py to match your data).
-'''
-
-
-# Regular expression matching whitespace:
-
-
-import re
-import inflect
-from unidecode import unidecode
-import eng_to_ipa as ipa
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
-_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
-_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
-_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
-_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
-_number_re = re.compile(r'[0-9]+')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
- ('mrs', 'misess'),
- ('mr', 'mister'),
- ('dr', 'doctor'),
- ('st', 'saint'),
- ('co', 'company'),
- ('jr', 'junior'),
- ('maj', 'major'),
- ('gen', 'general'),
- ('drs', 'doctors'),
- ('rev', 'reverend'),
- ('lt', 'lieutenant'),
- ('hon', 'honorable'),
- ('sgt', 'sergeant'),
- ('capt', 'captain'),
- ('esq', 'esquire'),
- ('ltd', 'limited'),
- ('col', 'colonel'),
- ('ft', 'fort'),
-]]
-
-
-# List of (ipa, lazy ipa) pairs:
-_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('æ', 'e'),
- ('ɑ', 'a'),
- ('ɔ', 'o'),
- ('ð', 'z'),
- ('θ', 's'),
- ('ɛ', 'e'),
- ('ɪ', 'i'),
- ('ʊ', 'u'),
- ('ʒ', 'ʥ'),
- ('ʤ', 'ʥ'),
- ('ˈ', '↓'),
-]]
-
-# List of (ipa, lazy ipa2) pairs:
-_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('ð', 'z'),
- ('θ', 's'),
- ('ʒ', 'ʑ'),
- ('ʤ', 'dʑ'),
- ('ˈ', '↓'),
-]]
-
-# List of (ipa, ipa2) pairs
-_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('ʤ', 'dʒ'),
- ('ʧ', 'tʃ')
-]]
-
-
-def expand_abbreviations(text):
- for regex, replacement in _abbreviations:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def collapse_whitespace(text):
- return re.sub(r'\s+', ' ', text)
-
-
-def _remove_commas(m):
- return m.group(1).replace(',', '')
-
-
-def _expand_decimal_point(m):
- return m.group(1).replace('.', ' point ')
-
-
-def _expand_dollars(m):
- match = m.group(1)
- parts = match.split('.')
- if len(parts) > 2:
- return match + ' dollars' # Unexpected format
- dollars = int(parts[0]) if parts[0] else 0
- cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
- if dollars and cents:
- dollar_unit = 'dollar' if dollars == 1 else 'dollars'
- cent_unit = 'cent' if cents == 1 else 'cents'
- return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
- elif dollars:
- dollar_unit = 'dollar' if dollars == 1 else 'dollars'
- return '%s %s' % (dollars, dollar_unit)
- elif cents:
- cent_unit = 'cent' if cents == 1 else 'cents'
- return '%s %s' % (cents, cent_unit)
- else:
- return 'zero dollars'
-
-
-def _expand_ordinal(m):
- return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
- num = int(m.group(0))
- if num > 1000 and num < 3000:
- if num == 2000:
- return 'two thousand'
- elif num > 2000 and num < 2010:
- return 'two thousand ' + _inflect.number_to_words(num % 100)
- elif num % 100 == 0:
- return _inflect.number_to_words(num // 100) + ' hundred'
- else:
- return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
- else:
- return _inflect.number_to_words(num, andword='')
-
-
-def normalize_numbers(text):
- text = re.sub(_comma_number_re, _remove_commas, text)
- text = re.sub(_pounds_re, r'\1 pounds', text)
- text = re.sub(_dollars_re, _expand_dollars, text)
- text = re.sub(_decimal_number_re, _expand_decimal_point, text)
- text = re.sub(_ordinal_re, _expand_ordinal, text)
- text = re.sub(_number_re, _expand_number, text)
- return text
-
-
-def mark_dark_l(text):
- return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
-
-
-def english_to_ipa(text):
- text = unidecode(text).lower()
- text = expand_abbreviations(text)
- text = normalize_numbers(text)
- phonemes = ipa.convert(text)
- phonemes = collapse_whitespace(phonemes)
- return phonemes
-
-
-def english_to_lazy_ipa(text):
- text = english_to_ipa(text)
- for regex, replacement in _lazy_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def english_to_ipa2(text):
- text = english_to_ipa(text)
- text = mark_dark_l(text)
- for regex, replacement in _ipa_to_ipa2:
- text = re.sub(regex, replacement, text)
- return text.replace('...', '…')
-
-
-def english_to_lazy_ipa2(text):
- text = english_to_ipa(text)
- for regex, replacement in _lazy_ipa2:
- text = re.sub(regex, replacement, text)
- return text
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
deleted file mode 100644
index a02a814fe2f08b464454e8eb6e1c88004ab804f6..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
+++ /dev/null
@@ -1,27 +0,0 @@
-_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
-model = dict(
- pretrained='torchvision://resnet101',
- backbone=dict(depth=101),
- bbox_head=dict(
- with_deform=True,
- norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='Resize',
- img_scale=[(1333, 640), (1333, 800)],
- multiscale_mode='value',
- keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
deleted file mode 100644
index 4874121fd01e4024bfde445f451b7368c6834511..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './deeplabv3_r50-d8_480x480_40k_pascal_context_59.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
deleted file mode 100644
index 89d5e1ae0f3ef44626f3b5534c504cbce7389a32..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
- '../_base_/models/gcnet_r50-d8.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
- decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py
deleted file mode 100644
index 12f6d402a3c4a113d4c37be062790fa435b72104..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Evaluation with objective metrics for the pretrained AudioGen models.
-This grid takes signature from the training grid and runs evaluation-only stage.
-
-When running the grid for the first time, please use:
-REGEN=1 dora grid audiogen.audiogen_pretrained_16khz_eval
-and re-use the REGEN=1 option when the grid is changed to force regenerating it.
-
-Note that you need the proper metrics external libraries setup to use all
-the objective metrics activated in this grid. Refer to the README for more information.
-"""
-
-import os
-
-from ..musicgen._explorers import GenerationEvalExplorer
-from ...environment import AudioCraftEnvironment
-from ... import train
-
-
-def eval(launcher, batch_size: int = 32):
- opts = {
- 'dset': 'audio/audiocaps_16khz',
- 'solver/audiogen/evaluation': 'objective_eval',
- 'execute_only': 'evaluate',
- '+dataset.evaluate.batch_size': batch_size,
- '+metrics.fad.tf.batch_size': 32,
- }
- # binary for FAD computation: replace this path with your own path
- metrics_opts = {
- 'metrics.fad.tf.bin': '/data/home/jadecopet/local/usr/opt/google-research'
- }
- opt1 = {'generate.lm.use_sampling': True, 'generate.lm.top_k': 250, 'generate.lm.top_p': 0.}
- opt2 = {'transformer_lm.two_step_cfg': True}
-
- sub = launcher.bind(opts)
- sub.bind_(metrics_opts)
-
- # base objective metrics
- sub(opt1, opt2)
-
-
-@GenerationEvalExplorer
-def explorer(launcher):
- partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
- launcher.slurm_(gpus=4, partition=partitions)
-
- if 'REGEN' not in os.environ:
- folder = train.main.dora.dir / 'grids' / __name__.split('.', 2)[-1]
- with launcher.job_array():
- for sig in folder.iterdir():
- if not sig.is_symlink():
- continue
- xp = train.main.get_xp_from_sig(sig.name)
- launcher(xp.argv)
- return
-
- audiogen_base = launcher.bind(solver="audiogen/audiogen_base_16khz")
- audiogen_base.bind_({'autocast': False, 'fsdp.use': True})
-
- audiogen_base_medium = audiogen_base.bind({'continue_from': '//pretrained/facebook/audiogen-medium'})
- audiogen_base_medium.bind_({'model/lm/model_scale': 'medium'})
- eval(audiogen_base_medium, batch_size=128)
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py
deleted file mode 100644
index 5612ff5688d85fede0e605b244919e8081cb1da9..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Grid search file, simply list all the exp you want in `explorer`.
-Any new exp added there will be scheduled.
-You can cancel and experiment by commenting its line.
-
-This grid is a minimal example for debugging compression task
-and how to override parameters directly in a grid.
-Learn more about dora grids: https://github.com/facebookresearch/dora
-"""
-
-from ._explorers import CompressionExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@CompressionExplorer
-def explorer(launcher):
- partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
- launcher.slurm_(gpus=2, partition=partitions)
- launcher.bind_(solver='compression/debug')
-
- with launcher.job_array():
- # base debug task using config from solver=compression/debug
- launcher()
- # we can override parameters in the grid to launch additional xps
- launcher({'rvq.bins': 2048, 'rvq.n_q': 4})
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py
deleted file mode 100644
index 44f4b0a2c3c6c726857db8386491823dd85dde51..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import csv
-import json
-import logging
-from pathlib import Path
-import tempfile
-import typing as tp
-import subprocess
-import shutil
-
-import torch
-import torchaudio
-
-logger = logging.getLogger(__name__)
-
-
-class ViSQOL:
- """ViSQOL wrapper to run ViSQOL from Python using a pre-installed binary.
-
- To learn more about ViSQOL and how to build ViSQOL binary using bazel, please refer to the
- instructions available in the open source repository: https://github.com/google/visqol
-
- ViSQOL is capable of running in two modes:
-
- Audio Mode:
- When running in audio mode, input signals must have a 48kHz sample rate. Input should be resampled to 48kHz.
- Input signals can be multi-channel, but they will be down-mixed to mono for performing the comparison.
- Audio mode uses support vector regression, with the maximum range at ~4.75.
-
- Speech Mode:
- When running in speech mode, ViSQOL uses a wideband model. It therefore expects input sample rates of 16kHz.
- Input should be resampled to 16kHz.
- As part of the speech mode processing, a root mean square implementation for voice activity detection
- is performed on the reference signal to determine what parts of the signal have voice activity and
- should therefore be included in the comparison. The signal is normalized before performing the voice
- activity detection.
- Input signals can be multi-channel, but they will be down-mixed to mono for performing the comparison.
- Speech mode is scaled to have a maximum MOS of 5.0 to match previous version behavior.
-
- For more details, check the guidelines: https://github.com/google/visqol#general-guidelines-for-input
-
- Args:
- visqol_bin (str): Path to the ViSQOL binary.
- mode (str): ViSQOL computation mode, expecting "audio" or "speech".
- model (str): Name of the model to use for similarity to quality model.
- debug (bool): Whether to also get debug metrics from ViSQOL or not.
- """
- SAMPLE_RATES_MODES = {"audio": 48_000, "speech": 16_000}
- ALLOWED_SAMPLE_RATES = frozenset(SAMPLE_RATES_MODES.values())
-
- def __init__(self, bin: tp.Union[Path, str], mode: str = "audio",
- model: str = "libsvm_nu_svr_model.txt", debug: bool = False):
- assert bin is not None and Path(bin).exists(), f"Could not find ViSQOL binary in specified path: {bin}"
- self.visqol_bin = str(bin)
- self.visqol_mode = mode
- self.target_sr = self._get_target_sr(self.visqol_mode)
- self.model = model
- self.debug = debug
- assert Path(self.visqol_model).exists(), \
- f"Could not find the specified model in ViSQOL install: {self.visqol_model}"
-
- def _get_target_sr(self, mode: str) -> int:
- # returns target sampling rate for the corresponding ViSQOL mode.
- if mode not in ViSQOL.SAMPLE_RATES_MODES:
- raise ValueError(
- f"Unsupported mode! Allowed are: {', '.join(ViSQOL.SAMPLE_RATES_MODES.keys())}"
- )
- return ViSQOL.SAMPLE_RATES_MODES[mode]
-
- def _prepare_files(
- self, ref_sig: torch.Tensor, deg_sig: torch.Tensor, sr: int, target_sr: int, pad_with_silence: bool = False
- ):
- # prepare files for ViSQOL evaluation.
- assert target_sr in ViSQOL.ALLOWED_SAMPLE_RATES
- assert len(ref_sig) == len(deg_sig), (
- "Expects same number of ref and degraded inputs",
- f" but ref len {len(ref_sig)} != deg len {len(deg_sig)}"
- )
- # resample audio if needed
- if sr != target_sr:
- transform = torchaudio.transforms.Resample(sr, target_sr)
- pad = int(0.5 * target_sr)
- rs_ref = []
- rs_deg = []
- for i in range(len(ref_sig)):
- rs_ref_i = transform(ref_sig[i])
- rs_deg_i = transform(deg_sig[i])
- if pad_with_silence:
- rs_ref_i = torch.nn.functional.pad(rs_ref_i, (pad, pad), mode='constant', value=0)
- rs_deg_i = torch.nn.functional.pad(rs_deg_i, (pad, pad), mode='constant', value=0)
- rs_ref.append(rs_ref_i)
- rs_deg.append(rs_deg_i)
- ref_sig = torch.stack(rs_ref)
- deg_sig = torch.stack(rs_deg)
- # save audio chunks to tmp dir and create csv
- tmp_dir = Path(tempfile.mkdtemp())
- try:
- tmp_input_csv_path = tmp_dir / "input.csv"
- tmp_results_csv_path = tmp_dir / "results.csv"
- tmp_debug_json_path = tmp_dir / "debug.json"
- with open(tmp_input_csv_path, "w") as csv_file:
- csv_writer = csv.writer(csv_file)
- csv_writer.writerow(["reference", "degraded"])
- for i in range(len(ref_sig)):
- tmp_ref_filename = tmp_dir / f"ref_{i}.wav"
- tmp_deg_filename = tmp_dir / f"deg_{i}.wav"
- torchaudio.save(
- tmp_ref_filename,
- torch.clamp(ref_sig[i], min=-0.99, max=0.99),
- sample_rate=target_sr,
- bits_per_sample=16,
- encoding="PCM_S"
- )
- torchaudio.save(
- tmp_deg_filename,
- torch.clamp(deg_sig[i], min=-0.99, max=0.99),
- sample_rate=target_sr,
- bits_per_sample=16,
- encoding="PCM_S"
- )
- csv_writer.writerow([str(tmp_ref_filename), str(tmp_deg_filename)])
- return tmp_dir, tmp_input_csv_path, tmp_results_csv_path, tmp_debug_json_path
- except Exception as e:
- logger.error("Exception occurred when preparing files for ViSQOL: %s", e)
- return tmp_dir, None, None, None
-
- def _flush_files(self, tmp_dir: tp.Union[Path, str]):
- # flush tmp files used to compute ViSQOL.
- shutil.rmtree(str(tmp_dir))
-
- def _collect_moslqo_score(self, results_csv_path: tp.Union[Path, str]) -> float:
- # collect results for each evaluated pair and return averaged moslqo score.
- with open(results_csv_path, "r") as csv_file:
- reader = csv.DictReader(csv_file)
- moslqo_scores = [float(row["moslqo"]) for row in reader]
- if len(moslqo_scores) > 0:
- return sum(moslqo_scores) / len(moslqo_scores)
- else:
- return 0.0
-
- def _collect_debug_data(self, debug_json_path: tp.Union[Path, str]) -> dict:
- # collect debug data for the visqol inference.
- with open(debug_json_path, "r") as f:
- data = json.load(f)
- return data
-
- @property
- def visqol_model(self):
- return f'{self.visqol_bin}/model/{self.model}'
-
- def _run_visqol(
- self,
- input_csv_path: tp.Union[Path, str],
- results_csv_path: tp.Union[Path, str],
- debug_csv_path: tp.Optional[tp.Union[Path, str]],
- ):
- input_csv_path = str(input_csv_path)
- results_csv_path = str(results_csv_path)
- debug_csv_path = str(debug_csv_path)
- cmd = [
- f'{self.visqol_bin}/bazel-bin/visqol',
- '--batch_input_csv', f'{input_csv_path}',
- '--results_csv', f'{results_csv_path}'
- ]
- if debug_csv_path is not None:
- cmd += ['--output_debug', f'{debug_csv_path}']
- if self.visqol_mode == "speech":
- cmd += ['--use_speech_mode']
- cmd += ['--similarity_to_quality_model', f'{self.visqol_model}']
- result = subprocess.run(cmd, capture_output=True)
- if result.returncode:
- logger.error("Error with visqol: \n %s \n %s", result.stdout.decode(), result.stderr.decode())
- raise RuntimeError("Error while executing visqol")
- result.check_returncode()
-
- def __call__(
- self,
- ref_sig: torch.Tensor,
- deg_sig: torch.Tensor,
- sr: int,
- pad_with_silence: bool = False,
- ):
- """Calculate the ViSQOL metric for a pair of audio signals at a given sample rate.
- Args:
- ref_sig (torch.Tensor): Reference signals as [B, C, T].
- deg_sig (torch.Tensor): Degraded signals as [B, C, T].
- sr (int): Sample rate of the two audio signals.
- pad_with_silence (bool): Whether to pad the file with silences as recommended
- in visqol guidelines (see: https://github.com/google/visqol#general-guidelines-for-input).
- Returns:
- float: The ViSQOL score or mean score for the batch.
- """
- logger.debug(f"Calculating visqol with mode={self.visqol_mode} on {len(ref_sig)} samples")
- tmp_dir, input_csv, results_csv, debug_json = self._prepare_files(
- ref_sig, deg_sig, sr, self.target_sr, pad_with_silence
- )
- try:
- if input_csv and results_csv:
- self._run_visqol(
- input_csv,
- results_csv,
- debug_json if self.debug else None,
- )
- mosqol = self._collect_moslqo_score(results_csv)
- return mosqol
- else:
- raise RuntimeError("Something unexpected happened when running VISQOL!")
- except Exception as e:
- logger.error("Exception occurred when running ViSQOL: %s", e)
- finally:
- self._flush_files(tmp_dir)
diff --git a/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile b/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile
deleted file mode 100644
index 31b5f817b41d7d95055d9aa5d5e4f973abee0b45..0000000000000000000000000000000000000000
--- a/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile
+++ /dev/null
@@ -1,26 +0,0 @@
-IMG:=`cat Name`
-MOUNT:=
-PORT_MAP:=
-DOTENV:=.env
-CONTAINER_NAME:=sample-chatgpt-on-wechat
-
-echo:
- echo $(IMG)
-
-run_d:
- docker rm $(CONTAINER_NAME) || echo
- docker run -dt --name $(CONTAINER_NAME) $(PORT_MAP) \
- --env-file=$(DOTENV) \
- $(MOUNT) $(IMG)
-
-run_i:
- docker rm $(CONTAINER_NAME) || echo
- docker run -it --name $(CONTAINER_NAME) $(PORT_MAP) \
- --env-file=$(DOTENV) \
- $(MOUNT) $(IMG)
-
-stop:
- docker stop $(CONTAINER_NAME)
-
-rm: stop
- docker rm $(CONTAINER_NAME)
diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py
deleted file mode 100644
index 84e51fd71fca78131a3be3d0d35d808c07bb3a95..0000000000000000000000000000000000000000
--- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import torch.nn as nn
-import math
-
-
-def conv3x3(in_planes, out_planes, stride=1):
- """3x3 convolution with padding"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
- expansion = 1
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(BasicBlock, self).__init__()
- self.conv1 = conv3x3(inplanes, planes, stride)
- self.bn1 = nn.BatchNorm2d(planes)
- self.relu = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(planes, planes)
- self.bn2 = nn.BatchNorm2d(planes)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(Bottleneck, self).__init__()
- self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
- self.bn1 = nn.BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
- self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * 4)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
- out = self.relu(out)
-
- out = self.conv3(out)
- out = self.bn3(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class ResNet50(nn.Module):
- def __init__(self,mode='rgb'):
- self.inplanes = 64
- super(ResNet50, self).__init__()
- if(mode=='rgb'):
- self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- elif(mode=='rgbd'):
- self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- elif(mode=="share"):
- self.conv1=nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- self.conv1_d=nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- else:
- raise
- self.bn1 = nn.BatchNorm2d(64)
- self.relu = nn.ReLU(inplace=True)
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
- self.layer1 = self._make_layer(Bottleneck, 64, 3)
- self.layer2 = self._make_layer(Bottleneck, 128, 4, stride=2)
- self.layer3_1 = self._make_layer(Bottleneck, 256, 6, stride=2)
- self.layer4_1 = self._make_layer(Bottleneck, 512, 3, stride=2)
-
- self.inplanes = 512
-
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
- m.weight.data.normal_(0, math.sqrt(2. / n))
- elif isinstance(m, nn.BatchNorm2d):
- m.weight.data.fill_(1)
- m.bias.data.zero_()
-
- def _make_layer(self, block, planes, blocks, stride=1):
- downsample = None
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- nn.Conv2d(self.inplanes, planes * block.expansion,
- kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(planes * block.expansion),
- )
-
- layers = []
- layers.append(block(self.inplanes, planes, stride, downsample))
- self.inplanes = planes * block.expansion
- for i in range(1, blocks):
- layers.append(block(self.inplanes, planes))
-
- return nn.Sequential(*layers)
-
- def forward(self, x):
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
- x = self.maxpool(x)
-
- x = self.layer1(x)
- x = self.layer2(x)
- x1 = self.layer3_1(x)
- x1 = self.layer4_1(x1)
-
- return x1, x1
diff --git a/spaces/Hallucinate/demo/ldm/models/autoencoder.py b/spaces/Hallucinate/demo/ldm/models/autoencoder.py
deleted file mode 100644
index 6a9c4f45498561953b8085981609b2a3298a5473..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/ldm/models/autoencoder.py
+++ /dev/null
@@ -1,443 +0,0 @@
-import torch
-import pytorch_lightning as pl
-import torch.nn.functional as F
-from contextlib import contextmanager
-
-from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
-
-from ldm.modules.diffusionmodules.model import Encoder, Decoder
-from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
-
-from ldm.util import instantiate_from_config
-
-
-class VQModel(pl.LightningModule):
- def __init__(self,
- ddconfig,
- lossconfig,
- n_embed,
- embed_dim,
- ckpt_path=None,
- ignore_keys=[],
- image_key="image",
- colorize_nlabels=None,
- monitor=None,
- batch_resize_range=None,
- scheduler_config=None,
- lr_g_factor=1.0,
- remap=None,
- sane_index_shape=False, # tell vector quantizer to return indices as bhw
- use_ema=False
- ):
- super().__init__()
- self.embed_dim = embed_dim
- self.n_embed = n_embed
- self.image_key = image_key
- self.encoder = Encoder(**ddconfig)
- self.decoder = Decoder(**ddconfig)
- self.loss = instantiate_from_config(lossconfig)
- self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
- remap=remap,
- sane_index_shape=sane_index_shape)
- self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
- self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
- if colorize_nlabels is not None:
- assert type(colorize_nlabels)==int
- self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
- if monitor is not None:
- self.monitor = monitor
- self.batch_resize_range = batch_resize_range
- if self.batch_resize_range is not None:
- print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
-
- self.use_ema = use_ema
- if self.use_ema:
- self.model_ema = LitEma(self)
- print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
- if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
- self.scheduler_config = scheduler_config
- self.lr_g_factor = lr_g_factor
-
- @contextmanager
- def ema_scope(self, context=None):
- if self.use_ema:
- self.model_ema.store(self.parameters())
- self.model_ema.copy_to(self)
- if context is not None:
- print(f"{context}: Switched to EMA weights")
- try:
- yield None
- finally:
- if self.use_ema:
- self.model_ema.restore(self.parameters())
- if context is not None:
- print(f"{context}: Restored training weights")
-
- def init_from_ckpt(self, path, ignore_keys=list()):
- sd = torch.load(path, map_location="cpu")["state_dict"]
- keys = list(sd.keys())
- for k in keys:
- for ik in ignore_keys:
- if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
- del sd[k]
- missing, unexpected = self.load_state_dict(sd, strict=False)
- print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
- if len(missing) > 0:
- print(f"Missing Keys: {missing}")
- print(f"Unexpected Keys: {unexpected}")
-
- def on_train_batch_end(self, *args, **kwargs):
- if self.use_ema:
- self.model_ema(self)
-
- def encode(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- quant, emb_loss, info = self.quantize(h)
- return quant, emb_loss, info
-
- def encode_to_prequant(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- return h
-
- def decode(self, quant):
- quant = self.post_quant_conv(quant)
- dec = self.decoder(quant)
- return dec
-
- def decode_code(self, code_b):
- quant_b = self.quantize.embed_code(code_b)
- dec = self.decode(quant_b)
- return dec
-
- def forward(self, input, return_pred_indices=False):
- quant, diff, (_,_,ind) = self.encode(input)
- dec = self.decode(quant)
- if return_pred_indices:
- return dec, diff, ind
- return dec, diff
-
- def get_input(self, batch, k):
- x = batch[k]
- if len(x.shape) == 3:
- x = x[..., None]
- x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
- if self.batch_resize_range is not None:
- lower_size = self.batch_resize_range[0]
- upper_size = self.batch_resize_range[1]
- if self.global_step <= 4:
- # do the first few batches with max size to avoid later oom
- new_resize = upper_size
- else:
- new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
- if new_resize != x.shape[2]:
- x = F.interpolate(x, size=new_resize, mode="bicubic")
- x = x.detach()
- return x
-
- def training_step(self, batch, batch_idx, optimizer_idx):
- # https://github.com/pytorch/pytorch/issues/37142
- # try not to fool the heuristics
- x = self.get_input(batch, self.image_key)
- xrec, qloss, ind = self(x, return_pred_indices=True)
-
- if optimizer_idx == 0:
- # autoencode
- aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train",
- predicted_indices=ind)
-
- self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
- return aeloss
-
- if optimizer_idx == 1:
- # discriminator
- discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
- self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
- return discloss
-
- def validation_step(self, batch, batch_idx):
- log_dict = self._validation_step(batch, batch_idx)
- with self.ema_scope():
- log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
- return log_dict
-
- def _validation_step(self, batch, batch_idx, suffix=""):
- x = self.get_input(batch, self.image_key)
- xrec, qloss, ind = self(x, return_pred_indices=True)
- aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
- self.global_step,
- last_layer=self.get_last_layer(),
- split="val"+suffix,
- predicted_indices=ind
- )
-
- discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
- self.global_step,
- last_layer=self.get_last_layer(),
- split="val"+suffix,
- predicted_indices=ind
- )
- rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
- self.log(f"val{suffix}/rec_loss", rec_loss,
- prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
- self.log(f"val{suffix}/aeloss", aeloss,
- prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
- if version.parse(pl.__version__) >= version.parse('1.4.0'):
- del log_dict_ae[f"val{suffix}/rec_loss"]
- self.log_dict(log_dict_ae)
- self.log_dict(log_dict_disc)
- return self.log_dict
-
- def configure_optimizers(self):
- lr_d = self.learning_rate
- lr_g = self.lr_g_factor*self.learning_rate
- print("lr_d", lr_d)
- print("lr_g", lr_g)
- opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
- list(self.decoder.parameters())+
- list(self.quantize.parameters())+
- list(self.quant_conv.parameters())+
- list(self.post_quant_conv.parameters()),
- lr=lr_g, betas=(0.5, 0.9))
- opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
- lr=lr_d, betas=(0.5, 0.9))
-
- if self.scheduler_config is not None:
- scheduler = instantiate_from_config(self.scheduler_config)
-
- print("Setting up LambdaLR scheduler...")
- scheduler = [
- {
- 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
- 'interval': 'step',
- 'frequency': 1
- },
- {
- 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
- 'interval': 'step',
- 'frequency': 1
- },
- ]
- return [opt_ae, opt_disc], scheduler
- return [opt_ae, opt_disc], []
-
- def get_last_layer(self):
- return self.decoder.conv_out.weight
-
- def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
- log = dict()
- x = self.get_input(batch, self.image_key)
- x = x.to(self.device)
- if only_inputs:
- log["inputs"] = x
- return log
- xrec, _ = self(x)
- if x.shape[1] > 3:
- # colorize with random projection
- assert xrec.shape[1] > 3
- x = self.to_rgb(x)
- xrec = self.to_rgb(xrec)
- log["inputs"] = x
- log["reconstructions"] = xrec
- if plot_ema:
- with self.ema_scope():
- xrec_ema, _ = self(x)
- if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
- log["reconstructions_ema"] = xrec_ema
- return log
-
- def to_rgb(self, x):
- assert self.image_key == "segmentation"
- if not hasattr(self, "colorize"):
- self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
- x = F.conv2d(x, weight=self.colorize)
- x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
- return x
-
-
-class VQModelInterface(VQModel):
- def __init__(self, embed_dim, *args, **kwargs):
- super().__init__(embed_dim=embed_dim, *args, **kwargs)
- self.embed_dim = embed_dim
-
- def encode(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- return h
-
- def decode(self, h, force_not_quantize=False):
- # also go through quantization layer
- if not force_not_quantize:
- quant, emb_loss, info = self.quantize(h)
- else:
- quant = h
- quant = self.post_quant_conv(quant)
- dec = self.decoder(quant)
- return dec
-
-
-class AutoencoderKL(pl.LightningModule):
- def __init__(self,
- ddconfig,
- lossconfig,
- embed_dim,
- ckpt_path=None,
- ignore_keys=[],
- image_key="image",
- colorize_nlabels=None,
- monitor=None,
- ):
- super().__init__()
- self.image_key = image_key
- self.encoder = Encoder(**ddconfig)
- self.decoder = Decoder(**ddconfig)
- self.loss = instantiate_from_config(lossconfig)
- assert ddconfig["double_z"]
- self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
- self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
- self.embed_dim = embed_dim
- if colorize_nlabels is not None:
- assert type(colorize_nlabels)==int
- self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
- if monitor is not None:
- self.monitor = monitor
- if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-
- def init_from_ckpt(self, path, ignore_keys=list()):
- sd = torch.load(path, map_location="cpu")["state_dict"]
- keys = list(sd.keys())
- for k in keys:
- for ik in ignore_keys:
- if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
- del sd[k]
- self.load_state_dict(sd, strict=False)
- print(f"Restored from {path}")
-
- def encode(self, x):
- h = self.encoder(x)
- moments = self.quant_conv(h)
- posterior = DiagonalGaussianDistribution(moments)
- return posterior
-
- def decode(self, z):
- z = self.post_quant_conv(z)
- dec = self.decoder(z)
- return dec
-
- def forward(self, input, sample_posterior=True):
- posterior = self.encode(input)
- if sample_posterior:
- z = posterior.sample()
- else:
- z = posterior.mode()
- dec = self.decode(z)
- return dec, posterior
-
- def get_input(self, batch, k):
- x = batch[k]
- if len(x.shape) == 3:
- x = x[..., None]
- x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
- return x
-
- def training_step(self, batch, batch_idx, optimizer_idx):
- inputs = self.get_input(batch, self.image_key)
- reconstructions, posterior = self(inputs)
-
- if optimizer_idx == 0:
- # train encoder+decoder+logvar
- aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
- self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
- self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
- return aeloss
-
- if optimizer_idx == 1:
- # train the discriminator
- discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
-
- self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
- self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
- return discloss
-
- def validation_step(self, batch, batch_idx):
- inputs = self.get_input(batch, self.image_key)
- reconstructions, posterior = self(inputs)
- aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
- last_layer=self.get_last_layer(), split="val")
-
- discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
- last_layer=self.get_last_layer(), split="val")
-
- self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
- self.log_dict(log_dict_ae)
- self.log_dict(log_dict_disc)
- return self.log_dict
-
- def configure_optimizers(self):
- lr = self.learning_rate
- opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
- list(self.decoder.parameters())+
- list(self.quant_conv.parameters())+
- list(self.post_quant_conv.parameters()),
- lr=lr, betas=(0.5, 0.9))
- opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
- lr=lr, betas=(0.5, 0.9))
- return [opt_ae, opt_disc], []
-
- def get_last_layer(self):
- return self.decoder.conv_out.weight
-
- @torch.no_grad()
- def log_images(self, batch, only_inputs=False, **kwargs):
- log = dict()
- x = self.get_input(batch, self.image_key)
- x = x.to(self.device)
- if not only_inputs:
- xrec, posterior = self(x)
- if x.shape[1] > 3:
- # colorize with random projection
- assert xrec.shape[1] > 3
- x = self.to_rgb(x)
- xrec = self.to_rgb(xrec)
- log["samples"] = self.decode(torch.randn_like(posterior.sample()))
- log["reconstructions"] = xrec
- log["inputs"] = x
- return log
-
- def to_rgb(self, x):
- assert self.image_key == "segmentation"
- if not hasattr(self, "colorize"):
- self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
- x = F.conv2d(x, weight=self.colorize)
- x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
- return x
-
-
-class IdentityFirstStage(torch.nn.Module):
- def __init__(self, *args, vq_interface=False, **kwargs):
- self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff
- super().__init__()
-
- def encode(self, x, *args, **kwargs):
- return x
-
- def decode(self, x, *args, **kwargs):
- return x
-
- def quantize(self, x, *args, **kwargs):
- if self.vq_interface:
- return x, None, [None, None, None]
- return x
-
- def forward(self, x, *args, **kwargs):
- return x
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py b/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py
deleted file mode 100644
index 81676226e57ca519273b98328a1afe6961c37ce3..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py
+++ /dev/null
@@ -1,403 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA Authors. All rights reserved.
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-# http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Auto Config class."""
-import importlib
-import re
-import warnings
-from collections import OrderedDict
-from typing import List, Union
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.file_utils import CONFIG_NAME
-from transformers.utils import logging
-from .dynamic import get_class_from_dynamic_module
-
-
-logger = logging.get_logger(__name__)
-
-CONFIG_MAPPING_NAMES = OrderedDict(
- [
- # Add configs here
- ("roformer", "RoFormerConfig"),
- ("longformer", "LongformerConfig"),
- ]
-)
-
-CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
- [
- # Add archive maps here
- ("roformer", "ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
- ("longformer", "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
- ]
-)
-
-MODEL_NAMES_MAPPING = OrderedDict(
- [
- # Add full (and cased) model names here
- ("roformer", "Roformer"),
- ("longformer", "Longformer"),
- ]
-)
-
-SPECIAL_MODEL_TYPE_TO_MODULE_NAME = OrderedDict([("openai-gpt", "openai")])
-
-
-def model_type_to_module_name(key):
- """Converts a config key to the corresponding module."""
- # Special treatment
- if key in SPECIAL_MODEL_TYPE_TO_MODULE_NAME:
- return SPECIAL_MODEL_TYPE_TO_MODULE_NAME[key]
-
- return key.replace("-", "_")
-
-
-def config_class_to_model_type(config):
- """Converts a config class name to the corresponding model type"""
- for key, cls in CONFIG_MAPPING_NAMES.items():
- if cls == config:
- return key
- return None
-
-
-class _LazyConfigMapping(OrderedDict):
- """
- A dictionary that lazily load its values when they are requested.
- """
-
- def __init__(self, mapping):
- self._mapping = mapping
- self._extra_content = {}
- self._modules = {}
-
- def __getitem__(self, key):
- if key in self._extra_content:
- return self._extra_content[key]
- if key not in self._mapping:
- raise KeyError(key)
- value = self._mapping[key]
- module_name = model_type_to_module_name(key)
- if module_name not in self._modules:
- self._modules[module_name] = importlib.import_module(f".{module_name}", "fengshen.models")
-
- return getattr(self._modules[module_name], value)
-
- def keys(self):
- return list(self._mapping.keys()) + list(self._extra_content.keys())
-
- def values(self):
- return [self[k] for k in self._mapping.keys()] + list(self._extra_content.values())
-
- def items(self):
- return [(k, self[k]) for k in self._mapping.keys()] + list(self._extra_content.items())
-
- def __iter__(self):
- return iter(list(self._mapping.keys()) + list(self._extra_content.keys()))
-
- def __contains__(self, item):
- return item in self._mapping or item in self._extra_content
-
- def register(self, key, value):
- """
- Register a new configuration in this mapping.
- """
- if key in self._mapping.keys():
- raise ValueError(f"'{key}' is already used by a Transformers config, pick another name.")
- self._extra_content[key] = value
-
-
-CONFIG_MAPPING = _LazyConfigMapping(CONFIG_MAPPING_NAMES)
-
-
-class _LazyLoadAllMappings(OrderedDict):
- """
- A mapping that will load all pairs of key values at the first access (either by indexing, requestions keys, values,
- etc.)
-
- Args:
- mapping: The mapping to load.
- """
-
- def __init__(self, mapping):
- self._mapping = mapping
- self._initialized = False
- self._data = {}
-
- def _initialize(self):
- if self._initialized:
- return
- warnings.warn(
- "ALL_PRETRAINED_CONFIG_ARCHIVE_MAP is deprecated and will be removed in v5 of Transformers. "
- "It does not contain all available model checkpoints, far from it. Checkout hf.co/models for that.",
- FutureWarning,
- )
-
- for model_type, map_name in self._mapping.items():
- module_name = model_type_to_module_name(model_type)
- module = importlib.import_module(f".{module_name}", "transformers.models")
- mapping = getattr(module, map_name)
- self._data.update(mapping)
-
- self._initialized = True
-
- def __getitem__(self, key):
- self._initialize()
- return self._data[key]
-
- def keys(self):
- self._initialize()
- return self._data.keys()
-
- def values(self):
- self._initialize()
- return self._data.values()
-
- def items(self):
- self._initialize()
- return self._data.keys()
-
- def __iter__(self):
- self._initialize()
- return iter(self._data)
-
- def __contains__(self, item):
- self._initialize()
- return item in self._data
-
-
-ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = _LazyLoadAllMappings(CONFIG_ARCHIVE_MAP_MAPPING_NAMES)
-
-
-def _get_class_name(model_class: Union[str, List[str]]):
- if isinstance(model_class, (list, tuple)):
- return " or ".join([f"[`{c}`]" for c in model_class if c is not None])
- return f"[`{model_class}`]"
-
-
-def _list_model_options(indent, config_to_class=None, use_model_types=True):
- if config_to_class is None and not use_model_types:
- raise ValueError("Using `use_model_types=False` requires a `config_to_class` dictionary.")
- if use_model_types:
- if config_to_class is None:
- model_type_to_name = {model_type: f"[`{config}`]" for model_type, config in CONFIG_MAPPING_NAMES.items()}
- else:
- model_type_to_name = {
- model_type: _get_class_name(model_class)
- for model_type, model_class in config_to_class.items()
- if model_type in MODEL_NAMES_MAPPING
- }
- lines = [
- f"{indent}- **{model_type}** -- {model_type_to_name[model_type]} ({MODEL_NAMES_MAPPING[model_type]} model)"
- for model_type in sorted(model_type_to_name.keys())
- ]
- else:
- config_to_name = {
- CONFIG_MAPPING_NAMES[config]: _get_class_name(clas)
- for config, clas in config_to_class.items()
- if config in CONFIG_MAPPING_NAMES
- }
- config_to_model_name = {
- config: MODEL_NAMES_MAPPING[model_type] for model_type, config in CONFIG_MAPPING_NAMES.items()
- }
- lines = [
- f"{indent}- [`{config_name}`] configuration class: {config_to_name[config_name]} ({config_to_model_name[config_name]} model)"
- for config_name in sorted(config_to_name.keys())
- ]
- return "\n".join(lines)
-
-
-def replace_list_option_in_docstrings(config_to_class=None, use_model_types=True):
- def docstring_decorator(fn):
- docstrings = fn.__doc__
- lines = docstrings.split("\n")
- i = 0
- while i < len(lines) and re.search(r"^(\s*)List options\s*$", lines[i]) is None:
- i += 1
- if i < len(lines):
- indent = re.search(r"^(\s*)List options\s*$", lines[i]).groups()[0]
- if use_model_types:
- indent = f"{indent} "
- lines[i] = _list_model_options(indent, config_to_class=config_to_class, use_model_types=use_model_types)
- docstrings = "\n".join(lines)
- else:
- raise ValueError(
- f"The function {fn} should have an empty 'List options' in its docstring as placeholder, current docstring is:\n{docstrings}"
- )
- fn.__doc__ = docstrings
- return fn
-
- return docstring_decorator
-
-
-class AutoConfig:
- r"""
- This is a generic configuration class that will be instantiated as one of the configuration classes of the library
- when created with the [`~AutoConfig.from_pretrained`] class method.
-
- This class cannot be instantiated directly using `__init__()` (throws an error).
- """
-
- def __init__(self):
- raise EnvironmentError(
- "AutoConfig is designed to be instantiated "
- "using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method."
- )
-
- @classmethod
- def for_model(cls, model_type: str, *args, **kwargs):
- if model_type in CONFIG_MAPPING:
- config_class = CONFIG_MAPPING[model_type]
- return config_class(*args, **kwargs)
- raise ValueError(
- f"Unrecognized model identifier: {model_type}. Should contain one of {', '.join(CONFIG_MAPPING.keys())}"
- )
-
- @classmethod
- @replace_list_option_in_docstrings()
- def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
- r"""
- Instantiate one of the configuration classes of the library from a pretrained model configuration.
-
- The configuration class to instantiate is selected based on the `model_type` property of the config object that
- is loaded, or when it's missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:
-
- List options
-
- Args:
- pretrained_model_name_or_path (`str` or `os.PathLike`):
- Can be either:
-
- - A string, the *model id* of a pretrained model configuration hosted inside a model repo on
- huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or
- namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
- - A path to a *directory* containing a configuration file saved using the
- [`~PretrainedConfig.save_pretrained`] method, or the [`~PreTrainedModel.save_pretrained`] method,
- e.g., `./my_model_directory/`.
- - A path or url to a saved configuration JSON *file*, e.g.,
- `./my_model_directory/configuration.json`.
- cache_dir (`str` or `os.PathLike`, *optional*):
- Path to a directory in which a downloaded pretrained model configuration should be cached if the
- standard cache should not be used.
- force_download (`bool`, *optional*, defaults to `False`):
- Whether or not to force the (re-)download the model weights and configuration files and override the
- cached versions if they exist.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether or not to delete incompletely received files. Will attempt to resume the download if such a
- file exists.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
- 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- revision(`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
- git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
- identifier allowed by git.
- return_unused_kwargs (`bool`, *optional*, defaults to `False`):
- If `False`, then this function returns just the final configuration object.
-
- If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a
- dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
- part of `kwargs` which has not been used to update `config` and is otherwise ignored.
- trust_remote_code (`bool`, *optional*, defaults to `False`):
- Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
- should only be set to `True` for repositories you trust and in which you have read the code, as it will
- execute code present on the Hub on your local machine.
- kwargs(additional keyword arguments, *optional*):
- The values in kwargs of any keys which are configuration attributes will be used to override the loaded
- values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
- by the `return_unused_kwargs` keyword parameter.
-
- Examples:
-
- ```python
- >>> from transformers import AutoConfig
-
- >>> # Download configuration from huggingface.co and cache.
- >>> config = AutoConfig.from_pretrained("bert-base-uncased")
-
- >>> # Download configuration from huggingface.co (user-uploaded) and cache.
- >>> config = AutoConfig.from_pretrained("dbmdz/bert-base-german-cased")
-
- >>> # If configuration file is in a directory (e.g., was saved using *save_pretrained('./test/saved_model/')*).
- >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/")
-
- >>> # Load a specific configuration file.
- >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/my_configuration.json")
-
- >>> # Change some config attributes when loading a pretrained config.
- >>> config = AutoConfig.from_pretrained("bert-base-uncased", output_attentions=True, foo=False)
- >>> config.output_attentions
- True
-
- >>> config, unused_kwargs = AutoConfig.from_pretrained(
- ... "bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
- ... )
- >>> config.output_attentions
- True
-
- >>> config.unused_kwargs
- {'foo': False}
- ```"""
- kwargs["_from_auto"] = True
- kwargs["name_or_path"] = pretrained_model_name_or_path
- trust_remote_code = kwargs.pop("trust_remote_code", False)
- config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
- if "auto_map" in config_dict and "AutoConfig" in config_dict["auto_map"]:
- if not trust_remote_code:
- raise ValueError(
- f"Loading {pretrained_model_name_or_path} requires you to execute the configuration file in that repo "
- "on your local machine. Make sure you have read the code there to avoid malicious use, then set "
- "the option `trust_remote_code=True` to remove this error."
- )
- if kwargs.get("revision", None) is None:
- logger.warn(
- "Explicitly passing a `revision` is encouraged when loading a configuration with custom code to "
- "ensure no malicious code has been contributed in a newer revision."
- )
- class_ref = config_dict["auto_map"]["AutoConfig"]
- module_file, class_name = class_ref.split(".")
- config_class = get_class_from_dynamic_module(
- pretrained_model_name_or_path, module_file + ".py", class_name, **kwargs
- )
- return config_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
- elif "model_type" in config_dict:
- config_class = CONFIG_MAPPING[config_dict["model_type"]]
- return config_class.from_dict(config_dict, **kwargs)
- else:
- # Fallback: use pattern matching on the string.
- for pattern, config_class in CONFIG_MAPPING.items():
- if pattern in str(pretrained_model_name_or_path):
- return config_class.from_dict(config_dict, **kwargs)
-
- raise ValueError(
- f"Unrecognized model in {pretrained_model_name_or_path}. "
- f"Should have a `model_type` key in its {CONFIG_NAME}, or contain one of the following strings "
- f"in its name: {', '.join(CONFIG_MAPPING.keys())}"
- )
-
- @staticmethod
- def register(model_type, config):
- """
- Register a new configuration for this class.
-
- Args:
- model_type (`str`): The model type like "bert" or "gpt".
- config ([`PretrainedConfig`]): The config to register.
- """
- if issubclass(config, PretrainedConfig) and config.model_type != model_type:
- raise ValueError(
- "The config you are passing has a `model_type` attribute that is not consistent with the model type "
- f"you passed (config has {config.model_type} and you passed {model_type}. Fix one of those so they "
- "match!"
- )
- CONFIG_MAPPING.register(model_type, config)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py
deleted file mode 100644
index b6fb6e751cdedb2af4b1f6c0950557e187cd9519..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .scst_loss import ScstRewardCriterion
-from .label_smoothed_cross_entropy import AjustLabelSmoothedCrossEntropyCriterion
\ No newline at end of file
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py
deleted file mode 100644
index 56d63e3e1b5a036e0adf32480e2b66f371738013..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from dataclasses import dataclass, field
-
-import torch
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-from omegaconf import II
-
-
-@dataclass
-class LabelSmoothedCrossEntropyCriterionConfig(FairseqDataclass):
- label_smoothing: float = field(
- default=0.0,
- metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"},
- )
- report_accuracy: bool = field(
- default=False,
- metadata={"help": "report accuracy metric"},
- )
- ignore_prefix_size: int = field(
- default=0,
- metadata={"help": "Ignore first N tokens"},
- )
- sentence_avg: bool = II("optimization.sentence_avg")
-
-
-def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=None, reduce=True):
- if target.dim() == lprobs.dim() - 1:
- target = target.unsqueeze(-1)
- nll_loss = -lprobs.gather(dim=-1, index=target)
- smooth_loss = -lprobs.sum(dim=-1, keepdim=True)
- if ignore_index is not None:
- pad_mask = target.eq(ignore_index)
- nll_loss.masked_fill_(pad_mask, 0.0)
- smooth_loss.masked_fill_(pad_mask, 0.0)
- else:
- nll_loss = nll_loss.squeeze(-1)
- smooth_loss = smooth_loss.squeeze(-1)
- if reduce:
- nll_loss = nll_loss.sum()
- smooth_loss = smooth_loss.sum()
- eps_i = epsilon / (lprobs.size(-1) - 1)
- loss = (1.0 - epsilon - eps_i) * nll_loss + eps_i * smooth_loss
- return loss, nll_loss
-
-
-@register_criterion(
- "label_smoothed_cross_entropy", dataclass=LabelSmoothedCrossEntropyCriterionConfig
-)
-class LabelSmoothedCrossEntropyCriterion(FairseqCriterion):
- def __init__(
- self,
- task,
- sentence_avg,
- label_smoothing,
- ignore_prefix_size=0,
- report_accuracy=False,
- ):
- super().__init__(task)
- self.sentence_avg = sentence_avg
- self.eps = label_smoothing
- self.ignore_prefix_size = ignore_prefix_size
- self.report_accuracy = report_accuracy
-
- def forward(self, model, sample, reduce=True):
- """Compute the loss for the given sample.
-
- Returns a tuple with three elements:
- 1) the loss
- 2) the sample size, which is used as the denominator for the gradient
- 3) logging outputs to display while training
- """
- net_output = model(**sample["net_input"])
- loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce)
- sample_size = (
- sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
- )
- logging_output = {
- "loss": loss.data,
- "nll_loss": nll_loss.data,
- "ntokens": sample["ntokens"],
- "nsentences": sample["target"].size(0),
- "sample_size": sample_size,
- }
- if self.report_accuracy:
- n_correct, total = self.compute_accuracy(model, net_output, sample)
- logging_output["n_correct"] = utils.item(n_correct.data)
- logging_output["total"] = utils.item(total.data)
- return loss, sample_size, logging_output
-
- def get_lprobs_and_target(self, model, net_output, sample):
- lprobs = model.get_normalized_probs(net_output, log_probs=True)
- target = model.get_targets(sample, net_output)
- if self.ignore_prefix_size > 0:
- if getattr(lprobs, "batch_first", False):
- lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
- target = target[:, self.ignore_prefix_size :].contiguous()
- else:
- lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous()
- target = target[self.ignore_prefix_size :, :].contiguous()
- return lprobs.view(-1, lprobs.size(-1)), target.view(-1)
-
- def compute_loss(self, model, net_output, sample, reduce=True):
- lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
- loss, nll_loss = label_smoothed_nll_loss(
- lprobs,
- target,
- self.eps,
- ignore_index=self.padding_idx,
- reduce=reduce,
- )
- return loss, nll_loss
-
- def compute_accuracy(self, model, net_output, sample):
- lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
- mask = target.ne(self.padding_idx)
- n_correct = torch.sum(
- lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))
- )
- total = torch.sum(mask)
- return n_correct, total
-
- @classmethod
- def reduce_metrics(cls, logging_outputs) -> None:
- """Aggregate logging outputs from data parallel training."""
- loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
- nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs)
- ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
- sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
-
- metrics.log_scalar(
- "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
- )
- metrics.log_scalar(
- "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3
- )
- metrics.log_derived(
- "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
- )
-
- total = utils.item(sum(log.get("total", 0) for log in logging_outputs))
- if total > 0:
- metrics.log_scalar("total", total)
- n_correct = utils.item(
- sum(log.get("n_correct", 0) for log in logging_outputs)
- )
- metrics.log_scalar("n_correct", n_correct)
- metrics.log_derived(
- "accuracy",
- lambda meters: round(
- meters["n_correct"].sum * 100.0 / meters["total"].sum, 3
- )
- if meters["total"].sum > 0
- else float("nan"),
- )
-
- @staticmethod
- def logging_outputs_can_be_summed() -> bool:
- """
- Whether the logging outputs returned by `forward` can be summed
- across workers prior to calling `reduce_metrics`. Setting this
- to True will improves distributed training speed.
- """
- return True
diff --git a/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md b/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md
deleted file mode 100644
index d4654cdd019b1496d4c953944c9aced8a0dcc67a..0000000000000000000000000000000000000000
--- a/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Sail-rvc-Ai Hoshino From Oshi No Ko RVC V2 300 Epoch
-emoji: 📈
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Hina4867/bingo/src/components/ui/select.tsx b/spaces/Hina4867/bingo/src/components/ui/select.tsx
deleted file mode 100644
index 77f12c2996f541b97663de4c9e20ab34d4ec2fac..0000000000000000000000000000000000000000
--- a/spaces/Hina4867/bingo/src/components/ui/select.tsx
+++ /dev/null
@@ -1,123 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SelectPrimitive from '@radix-ui/react-select'
-
-import { cn } from '@/lib/utils'
-import {
- IconArrowDown,
- IconCheck,
- IconChevronUpDown
-} from '@/components/ui/icons'
-
-const Select = SelectPrimitive.Root
-
-const SelectGroup = SelectPrimitive.Group
-
-const SelectValue = SelectPrimitive.Value
-
-const SelectTrigger = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-
- {children}
-
-
-
-))
-SelectTrigger.displayName = SelectPrimitive.Trigger.displayName
-
-const SelectContent = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, position = 'popper', ...props }, ref) => (
-
-
-
- {children}
-
-
-
-))
-SelectContent.displayName = SelectPrimitive.Content.displayName
-
-const SelectLabel = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-
-
-
-
-
- {children}
-
-))
-SelectItem.displayName = SelectPrimitive.Item.displayName
-
-const SelectSeparator = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- {o.delete(i),o.size===0&&n&&(n(),n=null)}}return{set:a,update:l,subscribe:r}}var g;const E=((g=globalThis.__sveltekit_1d38e1f)==null?void 0:g.base)??"";var k;const S=((k=globalThis.__sveltekit_1d38e1f)==null?void 0:k.assets)??E,w="1698134506027",y="sveltekit:snapshot",I="sveltekit:scroll",x="sveltekit:index",_={tap:1,hover:2,viewport:3,eager:4,off:-1};function O(e){let t=e.baseURI;if(!t){const n=e.getElementsByTagName("base");t=n.length?n[0].href:e.URL}return t}function U(){return{x:pageXOffset,y:pageYOffset}}function f(e,t){return e.getAttribute(`data-sveltekit-${t}`)}const b={..._,"":_.hover};function v(e){let t=e.assignedSlot??e.parentNode;return(t==null?void 0:t.nodeType)===11&&(t=t.host),t}function L(e,t){for(;e&&e!==t;){if(e.nodeName.toUpperCase()==="A"&&e.hasAttribute("href"))return e;e=v(e)}}function N(e,t){let n;try{n=new URL(e instanceof SVGAElement?e.href.baseVal:e.href,document.baseURI)}catch{}const o=e instanceof SVGAElement?e.target.baseVal:e.target,a=!n||!!o||R(n,t)||(e.getAttribute("rel")||"").split(/\s+/).includes("external"),l=(n==null?void 0:n.origin)===location.origin&&e.hasAttribute("download");return{url:n,external:a,target:o,download:l}}function P(e){let t=null,n=null,o=null,a=null,l=null,r=null,s=e;for(;s&&s!==document.documentElement;)o===null&&(o=f(s,"preload-code")),a===null&&(a=f(s,"preload-data")),t===null&&(t=f(s,"keepfocus")),n===null&&(n=f(s,"noscroll")),l===null&&(l=f(s,"reload")),r===null&&(r=f(s,"replacestate")),s=v(s);return{preload_code:b[o??"off"],preload_data:b[a??"off"],keep_focus:t==="off"?!1:t===""?!0:null,noscroll:n==="off"?!1:n===""?!0:null,reload:l==="off"?!1:l===""?!0:null,replace_state:r==="off"?!1:r===""?!0:null}}function h(e){const t=p(e);let n=!0;function o(){n=!0,t.update(r=>r)}function a(r){n=!1,t.set(r)}function l(r){let s;return t.subscribe(u=>{(s===void 0||n&&u!==s)&&r(s=u)})}return{notify:o,set:a,subscribe:l}}function A(){const{set:e,subscribe:t}=p(!1);let n;async function o(){clearTimeout(n);try{const a=await fetch(`${S}/_app/version.json`,{headers:{pragma:"no-cache","cache-control":"no-cache"}});if(!a.ok)return!1;const r=(await a.json()).version!==w;return r&&(e(!0),clearTimeout(n)),r}catch{return!1}}return{subscribe:t,check:o}}function R(e,t){return e.origin!==location.origin||!e.pathname.startsWith(t)}function V(e){e.client}const Y={url:h({}),page:h({}),navigating:p(null),updated:A()};export{x as I,_ as P,I as S,y as a,N as b,P as c,U as d,E as e,L as f,O as g,V as h,R as i,Y as s};
diff --git a/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py b/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py
deleted file mode 100644
index f8e2eb0f15699f1b458a8445d0c1dd6229a21f77..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-import os, sys
-import subprocess
-import re
-from subprocess import check_call, check_output
-
-WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None)
-
-if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip():
- print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."')
- sys.exit(-1)
-
-
-BLEU_REGEX = re.compile("^BLEU\\S* = (\\S+) ")
-def run_eval_bleu(cmd):
- output = check_output(cmd, shell=True, stderr=subprocess.STDOUT).decode("utf-8").strip()
- print(output)
- bleu = -1.0
- for line in output.strip().split('\n'):
- m = BLEU_REGEX.search(line)
- if m is not None:
- bleu = m.groups()[0]
- bleu = float(bleu)
- break
- return bleu
-
-def check_data_test_bleu(raw_folder, data_lang_pairs):
- not_matchings = []
- for sacrebleu_set, src_tgts in data_lang_pairs:
- for src_tgt in src_tgts:
- print(f'checking test bleus for: {src_tgt} at {sacrebleu_set}')
- src, tgt = src_tgt.split('-')
- ssrc, stgt = src[:2], tgt[:2]
- if os.path.exists(f'{raw_folder}/test.{tgt}-{src}.{src}'):
- # reversed direction may have different test set
- test_src = f'{raw_folder}/test.{tgt}-{src}.{src}'
- else:
- test_src = f'{raw_folder}/test.{src}-{tgt}.{src}'
- cmd1 = f'cat {test_src} | sacrebleu -t "{sacrebleu_set}" -l {stgt}-{ssrc}; [ $? -eq 0 ] || echo ""'
- test_tgt = f'{raw_folder}/test.{src}-{tgt}.{tgt}'
- cmd2 = f'cat {test_tgt} | sacrebleu -t "{sacrebleu_set}" -l {ssrc}-{stgt}; [ $? -eq 0 ] || echo ""'
- bleu1 = run_eval_bleu(cmd1)
- if bleu1 != 100.0:
- not_matchings.append(f'{sacrebleu_set}:{src_tgt} source side not matching: {test_src}')
- bleu2 = run_eval_bleu(cmd2)
- if bleu2 != 100.0:
- not_matchings.append(f'{sacrebleu_set}:{src_tgt} target side not matching: {test_tgt}')
- return not_matchings
-
-if __name__ == "__main__":
- to_data_path = f'{WORKDIR_ROOT}/iwsltv2'
- not_matching = check_data_test_bleu(
- f'{to_data_path}/raw',
- [
- ('iwslt17', ['en_XX-ar_AR', 'en_XX-ko_KR', 'ar_AR-en_XX', 'ko_KR-en_XX']),
- ('iwslt17', ['en_XX-it_IT', 'en_XX-nl_XX', 'it_IT-en_XX', 'nl_XX-en_XX']),
- ('iwslt17/tst2015', ['en_XX-vi_VN', "vi_VN-en_XX"]),
- ]
- )
- if len(not_matching) > 0:
- print('the following datasets do not have matching test datasets:\n\t', '\n\t'.join(not_matching))
-
diff --git a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py b/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py
deleted file mode 100644
index 88597d62a505715091f9ba62d38bf0a85a31b95a..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import math
-import os
-os.environ["LRU_CACHE_CAPACITY"] = "3"
-import random
-import torch
-import torch.utils.data
-import numpy as np
-import librosa
-from librosa.util import normalize
-from librosa.filters import mel as librosa_mel_fn
-from scipy.io.wavfile import read
-import soundfile as sf
-
-def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
- sampling_rate = None
- try:
- data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
- except Exception as ex:
- print(f"'{full_path}' failed to load.\nException:")
- print(ex)
- if return_empty_on_exception:
- return [], sampling_rate or target_sr or 32000
- else:
- raise Exception(ex)
-
- if len(data.shape) > 1:
- data = data[:, 0]
- assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
-
- if np.issubdtype(data.dtype, np.integer): # if audio data is type int
- max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
- else: # if audio data is type fp32
- max_mag = max(np.amax(data), -np.amin(data))
- max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
-
- data = torch.FloatTensor(data.astype(np.float32))/max_mag
-
- if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
- return [], sampling_rate or target_sr or 32000
- if target_sr is not None and sampling_rate != target_sr:
- data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
- sampling_rate = target_sr
-
- return data, sampling_rate
-
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
- return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
-
-def dynamic_range_decompression(x, C=1):
- return np.exp(x) / C
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-def dynamic_range_decompression_torch(x, C=1):
- return torch.exp(x) / C
-
-class STFT():
- def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
- self.target_sr = sr
-
- self.n_mels = n_mels
- self.n_fft = n_fft
- self.win_size = win_size
- self.hop_length = hop_length
- self.fmin = fmin
- self.fmax = fmax
- self.clip_val = clip_val
- self.mel_basis = {}
- self.hann_window = {}
-
- def get_mel(self, y, center=False):
- sampling_rate = self.target_sr
- n_mels = self.n_mels
- n_fft = self.n_fft
- win_size = self.win_size
- hop_length = self.hop_length
- fmin = self.fmin
- fmax = self.fmax
- clip_val = self.clip_val
-
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- if fmax not in self.mel_basis:
- mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
- self.mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
- self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_length)/2), int((n_fft-hop_length)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)],
- center=center, pad_mode='reflect', normalized=False, onesided=True)
- # print(111,spec)
- spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
- # print(222,spec)
- spec = torch.matmul(self.mel_basis[str(fmax)+'_'+str(y.device)], spec)
- # print(333,spec)
- spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
- # print(444,spec)
- return spec
-
- def __call__(self, audiopath):
- audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
- spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
- return spect
-
-stft = STFT()
diff --git a/spaces/JUNGU/emotion-ko-state/README.md b/spaces/JUNGU/emotion-ko-state/README.md
deleted file mode 100644
index 48eb244083a0ac6a51b0298cd043231b60b3fde8..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/emotion-ko-state/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Emotion Ko State
-emoji: 🦀
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py b/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py
deleted file mode 100644
index 27fcd62482d0cf44e02fa1c339195be58cb745b0..0000000000000000000000000000000000000000
--- a/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import pinecone
-from colorama import Fore, Style
-
-from autogpt.llm_utils import create_embedding_with_ada
-from autogpt.logs import logger
-from autogpt.memory.base import MemoryProviderSingleton
-
-
-class PineconeMemory(MemoryProviderSingleton):
- def __init__(self, cfg):
- pinecone_api_key = cfg.pinecone_api_key
- pinecone_region = cfg.pinecone_region
- pinecone.init(api_key=pinecone_api_key, environment=pinecone_region)
- dimension = 1536
- metric = "cosine"
- pod_type = "p1"
- table_name = "auto-gpt"
- # this assumes we don't start with memory.
- # for now this works.
- # we'll need a more complicated and robust system if we want to start with
- # memory.
- self.vec_num = 0
-
- try:
- pinecone.whoami()
- except Exception as e:
- logger.typewriter_log(
- "FAILED TO CONNECT TO PINECONE",
- Fore.RED,
- Style.BRIGHT + str(e) + Style.RESET_ALL,
- )
- logger.double_check(
- "Please ensure you have setup and configured Pinecone properly for use."
- + f"You can check out {Fore.CYAN + Style.BRIGHT}"
- "https://github.com/Torantulino/Auto-GPT#-pinecone-api-key-setup"
- f"{Style.RESET_ALL} to ensure you've set up everything correctly."
- )
- exit(1)
-
- if table_name not in pinecone.list_indexes():
- pinecone.create_index(
- table_name, dimension=dimension, metric=metric, pod_type=pod_type
- )
- self.index = pinecone.Index(table_name)
-
- def add(self, data):
- vector = create_embedding_with_ada(data)
- # no metadata here. We may wish to change that long term.
- self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})])
- _text = f"Inserting data into memory at index: {self.vec_num}:\n data: {data}"
- self.vec_num += 1
- return _text
-
- def get(self, data):
- return self.get_relevant(data, 1)
-
- def clear(self):
- self.index.delete(deleteAll=True)
- return "Obliviated"
-
- def get_relevant(self, data, num_relevant=5):
- """
- Returns all the data in the memory that is relevant to the given data.
- :param data: The data to compare to.
- :param num_relevant: The number of relevant data to return. Defaults to 5
- """
- query_embedding = create_embedding_with_ada(data)
- results = self.index.query(
- query_embedding, top_k=num_relevant, include_metadata=True
- )
- sorted_results = sorted(results.matches, key=lambda x: x.score)
- return [str(item["metadata"]["raw_text"]) for item in sorted_results]
-
- def get_stats(self):
- return self.index.describe_index_stats()
diff --git a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py b/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py
deleted file mode 100644
index 1cc3b96da1275f1d01afb4c999f9c404d09b247a..0000000000000000000000000000000000000000
--- a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import cv2
-import numpy as np
-from PIL import Image
-import pickle
-import tensorflow as tf
-import os
-
-class rabbitsLogReg:
- def __init__(self,url) -> None:
- self.image = url
-
- def predict_image(self):
- # Load the model
- load_extractor = tf.keras.models.load_model("././Model/Rabbit/resnetLogreg/resnet_EXTRACTOR.h5")
-
- modelpath = "././Model/Rabbit/resnetLogreg/dataSaved.pkl"
-
- with open(modelpath, 'rb') as file:
- saved_data = pickle.load(file)
- animal_breed = saved_data['class_name']
- model = saved_data['logreg_model']
-
- im = Image.open(self.image)
- img = im.convert("RGB")
- img= np.asarray(img)
- image_resized= cv2.resize(img, (224,224))
- features = load_extractor.predict(np.expand_dims(image_resized, axis=0))
-
- reshaped_features = features.reshape(features.shape[0],-1)
- predicted_class = model.predict(reshaped_features)
- pred_prob = model.predict_proba(reshaped_features)[:2]
- prediction_probability = pred_prob[0][predicted_class[0]]
- predicted_class
-
- output_class= animal_breed[predicted_class[0]]
-
- return [output_class, prediction_probability]
diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md b/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md
deleted file mode 100644
index 80af4fbbfba5d15e1cb6d1f4b67808ca76fa37d7..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md
+++ /dev/null
@@ -1,140 +0,0 @@
-
-
-川虎 Chat 🐯 Chuanhu Chat
-
-
- 
-
-
-
-
Lightweight and User-friendly Web-UI for LLMs including ChatGPT/ChatGLM/LLaMA
-
- Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search
- LaTeX rendering / Table rendering / Code highlighting
- Auto dark mode / Adaptive web interface / WeChat-like theme
- Multi-parameters tuning / Multi-API-Key support / Multi-user support
- Compatible with GPT-4 / Local deployment for LLMs
-
- Video Tutorial
- ·
- 2.0 Introduction
- ·
- 3.0 Introduction & Tutorial
- ||
- Online trial
- ·
- One-Click deployment
-
-
- 
-
-
-
-
-## Supported LLM Models
-
-**LLM models via API**:
-
-- [ChatGPT](https://chat.openai.com) ([GPT-4](https://openai.com/product/gpt-4))
-- [Google PaLM](https://developers.generativeai.google/products/palm)
-- [Inspur Yuan 1.0](https://air.inspur.com/home)
-- [MiniMax](https://api.minimax.chat/)
-- [XMChat](https://github.com/MILVLG/xmchat)
-
-**LLM models via local deployment**:
-
-- [ChatGLM](https://github.com/THUDM/ChatGLM-6B) ([ChatGLM2](https://github.com/THUDM/ChatGLM2-6B))
-- [LLaMA](https://github.com/facebookresearch/llama)
-- [StableLM](https://github.com/Stability-AI/StableLM)
-- [MOSS](https://github.com/OpenLMLab/MOSS)
-
-## Usage Tips
-
-- To better control the ChatGPT, use System Prompt.
-- To use a Prompt Template, select the Prompt Template Collection file first, and then choose certain prompt from the drop-down menu.
-- To try again if the response is unsatisfactory, use `🔄 Regenerate` button.
-- To start a new line in the input box, press Shift + Enter keys.
-- To quickly switch between input history, press ↑ and ↓ key in the input box.
-- To deploy the program onto a server, set `"server_name": "0.0.0.0", "server_port" ,` in `config.json`.
-- To get a public shared link, set `"share": true,` in `config.json`. Please be noted that the program must be running in order to be accessed via a public link.
-- To use it in Hugging Face Spaces: It is recommended to **Duplicate Space** and run the program in your own Space for a faster and more secure experience.
-
-## Quickstart
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-Then make a copy of `config_example.json`, rename it to `config.json`, and then fill in your API-Key and other settings in the file.
-
-```shell
-python ChuanhuChatbot.py
-```
-
-A browser window will open and you will be able to chat with ChatGPT.
-
-> **Note**
->
-> Please check our [wiki page](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程) for detailed instructions.
-
-## Troubleshooting
-
-When you encounter problems, you should try manually pulling the latest changes of this project first. The steps are as follows:
-
-1. Download the latest code archive by clicking on `Download ZIP` on the webpage, or
- ```shell
- git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
- ```
-2. Try installing the dependencies again (as this project may have introduced new dependencies)
- ```
- pip install -r requirements.txt
- ```
-
-Generally, you can solve most problems by following these steps.
-
-If the problem still exists, please refer to this page: [Frequently Asked Questions (FAQ)](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-This page lists almost all the possible problems and solutions. Please read it carefully.
-
-## More Information
-
-More information could be found in our [wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki):
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-
- 
-
-
-## Sponsor
-
-🐯 If you find this project helpful, feel free to buy me a coke or a cup of coffee~
-
-
-
-
diff --git a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py b/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py
deleted file mode 100644
index 59a73cc04025762d6490fcd2945a747d963def32..0000000000000000000000000000000000000000
--- a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from os import environ
-
-
-def assert_in(file, files_to_check):
- if file not in files_to_check:
- raise AssertionError("{} does not exist in the list".format(str(file)))
- return True
-
-
-def assert_in_env(check_list: list):
- for item in check_list:
- assert_in(item, environ.keys())
- return True
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py
deleted file mode 100644
index beb6e65c61f8a16b4376494123f31178cdb88bde..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py
+++ /dev/null
@@ -1,13 +0,0 @@
-CUSTOM_STREAMLIT_CSS = """
-div[data-testid="stBlock"] button {
- width: 100% !important;
- margin-bottom: 20px !important;
- border-color: #bfbfbf !important;
-}
-section[data-testid="stSidebar"] div {
- max-width: 10rem;
-}
-pre code {
- white-space: pre-wrap;
-}
-"""
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py
deleted file mode 100644
index 34bcffd6c0975377a54ae1ce89002be1dae8151d..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import matplotlib
-matplotlib.use('Agg')
-import time
-
-class Timer():
- ''' Timer for recording training time distribution. '''
- def __init__(self):
- self.prev_t = time.time()
- self.clear()
-
- def set(self):
- self.prev_t = time.time()
-
- def cnt(self, mode):
- self.time_table[mode] += time.time()-self.prev_t
- self.set()
- if mode == 'bw':
- self.click += 1
-
- def show(self):
- total_time = sum(self.time_table.values())
- self.time_table['avg'] = total_time/self.click
- self.time_table['rd'] = 100*self.time_table['rd']/total_time
- self.time_table['fw'] = 100*self.time_table['fw']/total_time
- self.time_table['bw'] = 100*self.time_table['bw']/total_time
- msg = '{avg:.3f} sec/step (rd {rd:.1f}% | fw {fw:.1f}% | bw {bw:.1f}%)'.format(
- **self.time_table)
- self.clear()
- return msg
-
- def clear(self):
- self.time_table = {'rd': 0, 'fw': 0, 'bw': 0}
- self.click = 0
-
-# Reference : https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/e2e_asr.py#L168
-
-def human_format(num):
- magnitude = 0
- while num >= 1000:
- magnitude += 1
- num /= 1000.0
- # add more suffixes if you need them
- return '{:3.1f}{}'.format(num, [' ', 'K', 'M', 'G', 'T', 'P'][magnitude])
-
-
-# provide easy access of attribute from dict, such abc.key
-class AttrDict(dict):
- def __init__(self, *args, **kwargs):
- super(AttrDict, self).__init__(*args, **kwargs)
- self.__dict__ = self
diff --git a/spaces/KevinQHLin/UniVTG/main/train_mr.py b/spaces/KevinQHLin/UniVTG/main/train_mr.py
deleted file mode 100644
index 1a10d029f81f86733d6dab71a3aee575917b092b..0000000000000000000000000000000000000000
--- a/spaces/KevinQHLin/UniVTG/main/train_mr.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import os
-import pdb
-import sys
-import time
-import json
-import pprint
-import random
-import numpy as np
-from tqdm import tqdm, trange
-from collections import defaultdict
-
-import torch
-import torch.nn as nn
-import torch.backends.cudnn as cudnn
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-
-sys.path.append('/data/home/qinghonglin/univtg')
-from main.config import BaseOptions, setup_model
-from main.dataset import \
- DatasetMR, start_end_collate_mr, prepare_batch_inputs_mr
-from main.inference_mr import eval_epoch, start_inference
-from utils.basic_utils import set_seed, AverageMeter, dict_to_markdown
-from utils.model_utils import count_parameters
-
-import logging
-logger = logging.getLogger(__name__)
-logging.basicConfig(format="%(asctime)s.%(msecs)03d:%(levelname)s:%(name)s - %(message)s",
- datefmt="%Y-%m-%d %H:%M:%S",
- level=logging.INFO)
-
-def train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer):
- logger.info(f"[Epoch {epoch_i+1}]")
- model.train()
- criterion.train()
-
- # init meters
- time_meters = defaultdict(AverageMeter)
- loss_meters = defaultdict(AverageMeter)
-
- num_training_examples = len(train_loader)
- timer_dataloading = time.time()
- for batch_idx, batch in tqdm(enumerate(train_loader),
- desc="Training Iteration",
- total=num_training_examples):
- time_meters["dataloading_time"].update(time.time() - timer_dataloading)
-
- timer_start = time.time()
- model_inputs, targets = prepare_batch_inputs_mr(batch[1], opt.device, non_blocking=opt.pin_memory)
- time_meters["prepare_inputs_time"].update(time.time() - timer_start)
-
- timer_start = time.time()
-
- # try:
- outputs = model(**model_inputs)
- loss_dict = criterion(outputs, targets)
- weight_dict = criterion.weight_dict
- losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
- time_meters["model_forward_time"].update(time.time() - timer_start)
-
- timer_start = time.time()
- optimizer.zero_grad()
- losses.backward()
-
- if opt.grad_clip > 0:
- nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
- optimizer.step()
- time_meters["model_backward_time"].update(time.time() - timer_start)
-
- loss_dict["loss_overall"] = float(losses) # for logging only
- for k, v in loss_dict.items():
- loss_meters[k].update(float(v) * weight_dict[k] if k in weight_dict else float(v))
-
- timer_dataloading = time.time()
-
- # print/add logs
- tb_writer.add_scalar("Train/lr", float(optimizer.param_groups[0]["lr"]), epoch_i+1)
- for k, v in loss_meters.items():
- tb_writer.add_scalar("Train/{}".format(k), v.avg, epoch_i+1)
-
- to_write = opt.train_log_txt_formatter.format(
- time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
- epoch=epoch_i+1,
- loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in loss_meters.items()]))
- with open(opt.train_log_filepath, "a") as f:
- f.write(to_write)
-
- logger.info("Epoch time stats:")
- for name, meter in time_meters.items():
- d = {k: f"{getattr(meter, k):.4f}" for k in ["max", "min", "avg"]}
- logger.info(f"{name} ==> {d}")
-
-
-def train(model, criterion, optimizer, lr_scheduler, train_dataset, val_dataset, opt):
- tb_writer = SummaryWriter(opt.tensorboard_log_dir)
- tb_writer.add_text("hyperparameters", dict_to_markdown(vars(opt), max_str_len=None))
- opt.train_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str}\n"
- opt.eval_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str} [Metrics] {eval_metrics_str}\n"
-
- train_loader = DataLoader(
- train_dataset,
- collate_fn=start_end_collate_mr,
- batch_size=opt.bsz,
- num_workers=opt.num_workers,
- shuffle=True,
- pin_memory=opt.pin_memory
- )
-
- prev_best_score = 0.
- es_cnt = 0
- if opt.start_epoch is None:
- start_epoch = -1 if opt.eval_init else 0
- else:
- start_epoch = opt.start_epoch
- save_submission_filename = "latest_{}_{}_preds.jsonl".format(opt.dset_name, opt.eval_split_name)
- for epoch_i in trange(start_epoch, opt.n_epoch, desc="Epoch"):
- if epoch_i > -1:
- train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer)
- lr_scheduler.step()
- eval_epoch_interval = opt.eval_epoch
- if opt.eval_path is not None and (epoch_i + 1) % eval_epoch_interval == 0:
- with torch.no_grad():
- metrics_no_nms, metrics_nms, eval_loss_meters, latest_file_paths = \
- eval_epoch(model, val_dataset, opt, save_submission_filename, epoch_i, criterion, tb_writer)
-
- # log
- to_write = opt.eval_log_txt_formatter.format(
- time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
- epoch=epoch_i,
- loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in eval_loss_meters.items()]),
- eval_metrics_str=json.dumps(metrics_no_nms))
-
- with open(opt.eval_log_filepath, "a") as f:
- f.write(to_write)
- logger.info("metrics_no_nms {}".format(pprint.pformat(metrics_no_nms["brief"], indent=4)))
- if metrics_nms is not None:
- logger.info("metrics_nms {}".format(pprint.pformat(metrics_nms["brief"], indent=4)))
-
- metrics = metrics_nms if metrics_nms is not None else metrics_no_nms
- for k, v in metrics["brief"].items():
- tb_writer.add_scalar(f"Eval/{k}", float(v), epoch_i+1)
-
- # stop_score = metrics["brief"]["MR-full-mAP"]
- # pdb.set_trace()
- stop_score = metrics["brief"][opt.main_metric]
- if stop_score > prev_best_score:
- es_cnt = 0
- prev_best_score = stop_score
-
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "lr_scheduler": lr_scheduler.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"))
-
- best_file_paths = [e.replace("latest", "best") for e in latest_file_paths]
- for src, tgt in zip(latest_file_paths, best_file_paths):
- os.renames(src, tgt)
- logger.info("The checkpoint file has been updated.")
- else:
- es_cnt += 1
- if opt.max_es_cnt != -1 and es_cnt > opt.max_es_cnt: # early stop
- with open(opt.train_log_filepath, "a") as f:
- f.write(f"Early Stop at epoch {epoch_i}")
- logger.info(f"\n>>>>> Early stop at epoch {epoch_i} {prev_best_score}\n")
- break
-
- # save ckpt
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "lr_scheduler": lr_scheduler.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_latest.ckpt"))
-
- if (epoch_i + 1) % opt.save_interval == 0 or (epoch_i + 1) % opt.lr_drop == 0: # additional copies
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", f"_e{epoch_i:04d}.ckpt"))
-
- if opt.debug:
- break
-
- tb_writer.close()
-
-
-def start_training():
- logger.info("Setup config, data and model...")
- opt = BaseOptions().parse()
- set_seed(opt.seed)
- if opt.debug: # keep the model run deterministically
- # 'cudnn.benchmark = True' enabled auto finding the best algorithm for a specific input/net config.
- # Enable this only when input size is fixed.
- cudnn.benchmark = False
- cudnn.deterministic = True
-
- dataset_config = dict(
- dset_name=opt.dset_name,
- data_path=opt.train_path,
- v_feat_dirs=opt.v_feat_dirs,
- q_feat_dir=opt.t_feat_dir,
- v_feat_dim=opt.v_feat_dim,
- q_feat_dim=opt.t_feat_dim,
- q_feat_type="last_hidden_state",
- max_q_l=opt.max_q_l,
- max_v_l=opt.max_v_l,
- ctx_mode=opt.ctx_mode,
- data_ratio=opt.data_ratio,
- normalize_v=not opt.no_norm_vfeat,
- normalize_t=not opt.no_norm_tfeat,
- clip_len=opt.clip_length,
- max_windows=opt.max_windows,
- span_loss_type=opt.span_loss_type,
- txt_drop_ratio=opt.txt_drop_ratio,
- use_cache=opt.use_cache,
- add_easy_negative=opt.add_easy_negative,
- easy_negative_only=opt.easy_negative_only
- )
-
- dataset_config["data_path"] = opt.train_path
- train_dataset = DatasetMR(**dataset_config)
-
- if opt.eval_path is not None:
- dataset_config["data_path"] = opt.eval_path
- dataset_config["txt_drop_ratio"] = 0
- dataset_config["q_feat_dir"] = opt.t_feat_dir.replace("txt_clip_asr", "txt_clip").replace("txt_clip_cap", "txt_clip") # for pretraining
- # dataset_config["load_labels"] = False # uncomment to calculate eval loss
- eval_dataset = DatasetMR(**dataset_config)
- else:
- eval_dataset = None
-
- if opt.lr_warmup > 0:
- # total_steps = opt.n_epoch * len(train_dataset) // opt.bsz
- total_steps = opt.n_epoch
- warmup_steps = opt.lr_warmup if opt.lr_warmup > 1 else int(opt.lr_warmup * total_steps)
- opt.lr_warmup = [warmup_steps, total_steps]
- model, criterion, optimizer, lr_scheduler = setup_model(opt)
- logger.info(f"Model {model}")
- count_parameters(model)
- logger.info("Start Training...")
- train(model, criterion, optimizer, lr_scheduler, train_dataset, eval_dataset, opt)
- return opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"), opt.eval_split_name, opt.eval_path, opt.debug
-
-
-if __name__ == '__main__':
- best_ckpt_path, eval_split_name, eval_path, debug = start_training()
- if not debug:
- input_args = ["--resume", best_ckpt_path,
- "--eval_split_name", eval_split_name,
- "--eval_path", eval_path]
-
- import sys
- sys.argv[1:] = input_args
- logger.info("\n\n\nFINISHED TRAINING!!!")
- logger.info("Evaluating model at {}".format(best_ckpt_path))
- logger.info("Input args {}".format(sys.argv[1:]))
- start_inference()
\ No newline at end of file
diff --git a/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md b/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md
deleted file mode 100644
index 1f55e11e80f6fc5ebbf42dade0266e3d4ee06ce4..0000000000000000000000000000000000000000
--- a/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
-# Transliteration Models for Indian languages
-These are models for transliteration involving Indian languages.
-The models are essentially Statistical Machine Translation systems trained using Moses over a
-character-level parallel corpora of transliterations. Hence, you will need Moses to use these transliteration models.
-The transliteration corpus has itself been mined in an unsupervised fashion from a translation corpus.
-
-Currently we have trained transliteration models for five language pairs: bn-hi, ta-hi, te-hi, en-hi and mr-hi
-
-Support for transliteration has been introduced in Moses from version 2.1
-So please ensure that you have minimum 2.1 version setup for Moses
-
-Commands to run the transliteration module using moses
-
-$moseshome/mosesdecoder/scripts/Transliteration/post-decoding-transliteration.pl \
---moses-src-dir $moseshome/mosesdecoder --external-bin-dir $moseshome/tools \
---transliteration-model-dir {path to transliteration model folder} --oov-file {path to file containing oov words, oovs are space separated with each line containing all oovs for the input line}\
- --input-file {input file to transliterated} --output-file {output file location} \
- --input-extension {input language code for eg. en} --output-extension {output language code for eg. hi} --language-model {path to language model} \
- --decoder $moseshome/mosesdecoder/bin/moses
-
-A sample execution of the model will be as follows:
-
-export moseshome={path to moses installation}
-$moseshome/mosesdecoder/scripts/Transliteration/post-decoding-transliteration.pl \
---moses-src-dir $moseshome/mosesdecoder --external-bin-dir $moseshome/tools \
---transliteration-model-dir /home/ratish/project/nlp_resources/indic_nlp_resources/transliterate/en-hi \
---oov-file /home/ratish/project/translit/input.oov \
- --input-file /home/ratish/project/translit/input.en \
- --output-file /home/ratish/project/translit/output.hi \
- --input-extension en --output-extension hi --language-model /home/ratish/project/translit/lm/nc.binlm.1 \
- --decoder $moseshome/mosesdecoder/bin/moses
-
-So far, we have seen the use of transliteration in a post-editing task for machine translation task.
-In case, the models are needed for purely transliteration purpose, the input file and OOV file are the same.
-Sample input file:
-New Delhi is capital of India
-India is worlds seventh largest nation in the World
-
-OOV file
-New Delhi is capital of India
-India is worlds seventh largest nation in the World
-
-On running the transliteration module, the output is:
-न्यू डेल्ही इस कैपिटल आफ इंडिया
-इंडिया इस वर्ल्ड सेवंथ लारगेस्ट नेशन इन थे वर्ल्ड
diff --git a/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py b/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py
deleted file mode 100644
index 980440eb3434e48b7cad90f0577c838a05d787b4..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Sequence
-
-from torch.utils.data import BatchSampler, Sampler
-
-from mmdet.registry import DATA_SAMPLERS
-
-
-# TODO: maybe replace with a data_loader wrapper
-@DATA_SAMPLERS.register_module()
-class AspectRatioBatchSampler(BatchSampler):
- """A sampler wrapper for grouping images with similar aspect ratio (< 1 or.
-
- >= 1) into a same batch.
-
- Args:
- sampler (Sampler): Base sampler.
- batch_size (int): Size of mini-batch.
- drop_last (bool): If ``True``, the sampler will drop the last batch if
- its size would be less than ``batch_size``.
- """
-
- def __init__(self,
- sampler: Sampler,
- batch_size: int,
- drop_last: bool = False) -> None:
- if not isinstance(sampler, Sampler):
- raise TypeError('sampler should be an instance of ``Sampler``, '
- f'but got {sampler}')
- if not isinstance(batch_size, int) or batch_size <= 0:
- raise ValueError('batch_size should be a positive integer value, '
- f'but got batch_size={batch_size}')
- self.sampler = sampler
- self.batch_size = batch_size
- self.drop_last = drop_last
- # two groups for w < h and w >= h
- self._aspect_ratio_buckets = [[] for _ in range(2)]
-
- def __iter__(self) -> Sequence[int]:
- for idx in self.sampler:
- data_info = self.sampler.dataset.get_data_info(idx)
- width, height = data_info['width'], data_info['height']
- bucket_id = 0 if width < height else 1
- bucket = self._aspect_ratio_buckets[bucket_id]
- bucket.append(idx)
- # yield a batch of indices in the same aspect ratio group
- if len(bucket) == self.batch_size:
- yield bucket[:]
- del bucket[:]
-
- # yield the rest data and reset the bucket
- left_data = self._aspect_ratio_buckets[0] + self._aspect_ratio_buckets[
- 1]
- self._aspect_ratio_buckets = [[] for _ in range(2)]
- while len(left_data) > 0:
- if len(left_data) <= self.batch_size:
- if not self.drop_last:
- yield left_data[:]
- left_data = []
- else:
- yield left_data[:self.batch_size]
- left_data = left_data[self.batch_size:]
-
- def __len__(self) -> int:
- if self.drop_last:
- return len(self.sampler) // self.batch_size
- else:
- return (len(self.sampler) + self.batch_size - 1) // self.batch_size
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py
deleted file mode 100644
index 430b06d085d94760d56a7ea083eaf23bd32b1f53..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py
+++ /dev/null
@@ -1,722 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, Scale
-from mmcv.ops import DeformConv2d
-from torch import Tensor
-
-from mmdet.registry import MODELS, TASK_UTILS
-from mmdet.structures.bbox import bbox_overlaps
-from mmdet.utils import (ConfigType, InstanceList, MultiConfig,
- OptInstanceList, RangeType, reduce_mean)
-from ..task_modules.prior_generators import MlvlPointGenerator
-from ..task_modules.samplers import PseudoSampler
-from ..utils import multi_apply
-from .atss_head import ATSSHead
-from .fcos_head import FCOSHead
-
-INF = 1e8
-
-
-@MODELS.register_module()
-class VFNetHead(ATSSHead, FCOSHead):
- """Head of `VarifocalNet (VFNet): An IoU-aware Dense Object
- Detector.`_.
-
- The VFNet predicts IoU-aware classification scores which mix the
- object presence confidence and object localization accuracy as the
- detection score. It is built on the FCOS architecture and uses ATSS
- for defining positive/negative training examples. The VFNet is trained
- with Varifocal Loss and empolys star-shaped deformable convolution to
- extract features for a bbox.
-
- Args:
- num_classes (int): Number of categories excluding the background
- category.
- in_channels (int): Number of channels in the input feature map.
- regress_ranges (Sequence[Tuple[int, int]]): Regress range of multiple
- level points.
- center_sampling (bool): If true, use center sampling. Defaults to False.
- center_sample_radius (float): Radius of center sampling. Defaults to 1.5.
- sync_num_pos (bool): If true, synchronize the number of positive
- examples across GPUs. Defaults to True
- gradient_mul (float): The multiplier to gradients from bbox refinement
- and recognition. Defaults to 0.1.
- bbox_norm_type (str): The bbox normalization type, 'reg_denom' or
- 'stride'. Defaults to reg_denom
- loss_cls_fl (:obj:`ConfigDict` or dict): Config of focal loss.
- use_vfl (bool): If true, use varifocal loss for training.
- Defaults to True.
- loss_cls (:obj:`ConfigDict` or dict): Config of varifocal loss.
- loss_bbox (:obj:`ConfigDict` or dict): Config of localization loss,
- GIoU Loss.
- loss_bbox (:obj:`ConfigDict` or dict): Config of localization
- refinement loss, GIoU Loss.
- norm_cfg (:obj:`ConfigDict` or dict): dictionary to construct and
- config norm layer. Defaults to norm_cfg=dict(type='GN',
- num_groups=32, requires_grad=True).
- use_atss (bool): If true, use ATSS to define positive/negative
- examples. Defaults to True.
- anchor_generator (:obj:`ConfigDict` or dict): Config of anchor
- generator for ATSS.
- init_cfg (:obj:`ConfigDict` or dict or list[dict] or
- list[:obj:`ConfigDict`]): Initialization config dict.
-
- Example:
- >>> self = VFNetHead(11, 7)
- >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
- >>> cls_score, bbox_pred, bbox_pred_refine= self.forward(feats)
- >>> assert len(cls_score) == len(self.scales)
- """ # noqa: E501
-
- def __init__(self,
- num_classes: int,
- in_channels: int,
- regress_ranges: RangeType = ((-1, 64), (64, 128), (128, 256),
- (256, 512), (512, INF)),
- center_sampling: bool = False,
- center_sample_radius: float = 1.5,
- sync_num_pos: bool = True,
- gradient_mul: float = 0.1,
- bbox_norm_type: str = 'reg_denom',
- loss_cls_fl: ConfigType = dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- use_vfl: bool = True,
- loss_cls: ConfigType = dict(
- type='VarifocalLoss',
- use_sigmoid=True,
- alpha=0.75,
- gamma=2.0,
- iou_weighted=True,
- loss_weight=1.0),
- loss_bbox: ConfigType = dict(
- type='GIoULoss', loss_weight=1.5),
- loss_bbox_refine: ConfigType = dict(
- type='GIoULoss', loss_weight=2.0),
- norm_cfg: ConfigType = dict(
- type='GN', num_groups=32, requires_grad=True),
- use_atss: bool = True,
- reg_decoded_bbox: bool = True,
- anchor_generator: ConfigType = dict(
- type='AnchorGenerator',
- ratios=[1.0],
- octave_base_scale=8,
- scales_per_octave=1,
- center_offset=0.0,
- strides=[8, 16, 32, 64, 128]),
- init_cfg: MultiConfig = dict(
- type='Normal',
- layer='Conv2d',
- std=0.01,
- override=dict(
- type='Normal',
- name='vfnet_cls',
- std=0.01,
- bias_prob=0.01)),
- **kwargs) -> None:
- # dcn base offsets, adapted from reppoints_head.py
- self.num_dconv_points = 9
- self.dcn_kernel = int(np.sqrt(self.num_dconv_points))
- self.dcn_pad = int((self.dcn_kernel - 1) / 2)
- dcn_base = np.arange(-self.dcn_pad,
- self.dcn_pad + 1).astype(np.float64)
- dcn_base_y = np.repeat(dcn_base, self.dcn_kernel)
- dcn_base_x = np.tile(dcn_base, self.dcn_kernel)
- dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape(
- (-1))
- self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1)
-
- super(FCOSHead, self).__init__(
- num_classes=num_classes,
- in_channels=in_channels,
- norm_cfg=norm_cfg,
- init_cfg=init_cfg,
- **kwargs)
- self.regress_ranges = regress_ranges
- self.reg_denoms = [
- regress_range[-1] for regress_range in regress_ranges
- ]
- self.reg_denoms[-1] = self.reg_denoms[-2] * 2
- self.center_sampling = center_sampling
- self.center_sample_radius = center_sample_radius
- self.sync_num_pos = sync_num_pos
- self.bbox_norm_type = bbox_norm_type
- self.gradient_mul = gradient_mul
- self.use_vfl = use_vfl
- if self.use_vfl:
- self.loss_cls = MODELS.build(loss_cls)
- else:
- self.loss_cls = MODELS.build(loss_cls_fl)
- self.loss_bbox = MODELS.build(loss_bbox)
- self.loss_bbox_refine = MODELS.build(loss_bbox_refine)
-
- # for getting ATSS targets
- self.use_atss = use_atss
- self.reg_decoded_bbox = reg_decoded_bbox
- self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
-
- self.anchor_center_offset = anchor_generator['center_offset']
-
- self.num_base_priors = self.prior_generator.num_base_priors[0]
-
- if self.train_cfg:
- self.assigner = TASK_UTILS.build(self.train_cfg['assigner'])
- if self.train_cfg.get('sampler', None) is not None:
- self.sampler = TASK_UTILS.build(
- self.train_cfg['sampler'], default_args=dict(context=self))
- else:
- self.sampler = PseudoSampler()
- # only be used in `get_atss_targets` when `use_atss` is True
- self.atss_prior_generator = TASK_UTILS.build(anchor_generator)
-
- self.fcos_prior_generator = MlvlPointGenerator(
- anchor_generator['strides'],
- self.anchor_center_offset if self.use_atss else 0.5)
-
- # In order to reuse the `get_bboxes` in `BaseDenseHead.
- # Only be used in testing phase.
- self.prior_generator = self.fcos_prior_generator
-
- def _init_layers(self) -> None:
- """Initialize layers of the head."""
- super(FCOSHead, self)._init_cls_convs()
- super(FCOSHead, self)._init_reg_convs()
- self.relu = nn.ReLU()
- self.vfnet_reg_conv = ConvModule(
- self.feat_channels,
- self.feat_channels,
- 3,
- stride=1,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- bias=self.conv_bias)
- self.vfnet_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
- self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
- self.vfnet_reg_refine_dconv = DeformConv2d(
- self.feat_channels,
- self.feat_channels,
- self.dcn_kernel,
- 1,
- padding=self.dcn_pad)
- self.vfnet_reg_refine = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
- self.scales_refine = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
- self.vfnet_cls_dconv = DeformConv2d(
- self.feat_channels,
- self.feat_channels,
- self.dcn_kernel,
- 1,
- padding=self.dcn_pad)
- self.vfnet_cls = nn.Conv2d(
- self.feat_channels, self.cls_out_channels, 3, padding=1)
-
- def forward(self, x: Tuple[Tensor]) -> Tuple[List[Tensor]]:
- """Forward features from the upstream network.
-
- Args:
- x (tuple[Tensor]): Features from the upstream network, each is
- a 4D-tensor.
-
- Returns:
- tuple:
-
- - cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level, each is a 4D-tensor, the channel number is
- num_points * num_classes.
- - bbox_preds (list[Tensor]): Box offsets for each
- scale level, each is a 4D-tensor, the channel number is
- num_points * 4.
- - bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level, each is a 4D-tensor, the channel
- number is num_points * 4.
- """
- return multi_apply(self.forward_single, x, self.scales,
- self.scales_refine, self.strides, self.reg_denoms)
-
- def forward_single(self, x: Tensor, scale: Scale, scale_refine: Scale,
- stride: int, reg_denom: int) -> tuple:
- """Forward features of a single scale level.
-
- Args:
- x (Tensor): FPN feature maps of the specified stride.
- scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
- the bbox prediction.
- scale_refine (:obj: `mmcv.cnn.Scale`): Learnable scale module to
- resize the refined bbox prediction.
- stride (int): The corresponding stride for feature maps,
- used to normalize the bbox prediction when
- bbox_norm_type = 'stride'.
- reg_denom (int): The corresponding regression range for feature
- maps, only used to normalize the bbox prediction when
- bbox_norm_type = 'reg_denom'.
-
- Returns:
- tuple: iou-aware cls scores for each box, bbox predictions and
- refined bbox predictions of input feature maps.
- """
- cls_feat = x
- reg_feat = x
-
- for cls_layer in self.cls_convs:
- cls_feat = cls_layer(cls_feat)
-
- for reg_layer in self.reg_convs:
- reg_feat = reg_layer(reg_feat)
-
- # predict the bbox_pred of different level
- reg_feat_init = self.vfnet_reg_conv(reg_feat)
- if self.bbox_norm_type == 'reg_denom':
- bbox_pred = scale(
- self.vfnet_reg(reg_feat_init)).float().exp() * reg_denom
- elif self.bbox_norm_type == 'stride':
- bbox_pred = scale(
- self.vfnet_reg(reg_feat_init)).float().exp() * stride
- else:
- raise NotImplementedError
-
- # compute star deformable convolution offsets
- # converting dcn_offset to reg_feat.dtype thus VFNet can be
- # trained with FP16
- dcn_offset = self.star_dcn_offset(bbox_pred, self.gradient_mul,
- stride).to(reg_feat.dtype)
-
- # refine the bbox_pred
- reg_feat = self.relu(self.vfnet_reg_refine_dconv(reg_feat, dcn_offset))
- bbox_pred_refine = scale_refine(
- self.vfnet_reg_refine(reg_feat)).float().exp()
- bbox_pred_refine = bbox_pred_refine * bbox_pred.detach()
-
- # predict the iou-aware cls score
- cls_feat = self.relu(self.vfnet_cls_dconv(cls_feat, dcn_offset))
- cls_score = self.vfnet_cls(cls_feat)
-
- if self.training:
- return cls_score, bbox_pred, bbox_pred_refine
- else:
- return cls_score, bbox_pred_refine
-
- def star_dcn_offset(self, bbox_pred: Tensor, gradient_mul: float,
- stride: int) -> Tensor:
- """Compute the star deformable conv offsets.
-
- Args:
- bbox_pred (Tensor): Predicted bbox distance offsets (l, r, t, b).
- gradient_mul (float): Gradient multiplier.
- stride (int): The corresponding stride for feature maps,
- used to project the bbox onto the feature map.
-
- Returns:
- Tensor: The offsets for deformable convolution.
- """
- dcn_base_offset = self.dcn_base_offset.type_as(bbox_pred)
- bbox_pred_grad_mul = (1 - gradient_mul) * bbox_pred.detach() + \
- gradient_mul * bbox_pred
- # map to the feature map scale
- bbox_pred_grad_mul = bbox_pred_grad_mul / stride
- N, C, H, W = bbox_pred.size()
-
- x1 = bbox_pred_grad_mul[:, 0, :, :]
- y1 = bbox_pred_grad_mul[:, 1, :, :]
- x2 = bbox_pred_grad_mul[:, 2, :, :]
- y2 = bbox_pred_grad_mul[:, 3, :, :]
- bbox_pred_grad_mul_offset = bbox_pred.new_zeros(
- N, 2 * self.num_dconv_points, H, W)
- bbox_pred_grad_mul_offset[:, 0, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 1, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 2, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 4, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 5, :, :] = x2 # x2
- bbox_pred_grad_mul_offset[:, 7, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 11, :, :] = x2 # x2
- bbox_pred_grad_mul_offset[:, 12, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 13, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 14, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 16, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 17, :, :] = x2 # x2
- dcn_offset = bbox_pred_grad_mul_offset - dcn_base_offset
-
- return dcn_offset
-
- def loss_by_feat(
- self,
- cls_scores: List[Tensor],
- bbox_preds: List[Tensor],
- bbox_preds_refine: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> dict:
- """Compute loss of the head.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level, each is a 4D-tensor, the channel number is
- num_points * num_classes.
- bbox_preds (list[Tensor]): Box offsets for each
- scale level, each is a 4D-tensor, the channel number is
- num_points * 4.
- bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level, each is a 4D-tensor, the channel
- number is num_points * 4.
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine)
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- all_level_points = self.fcos_prior_generator.grid_priors(
- featmap_sizes, bbox_preds[0].dtype, bbox_preds[0].device)
- labels, label_weights, bbox_targets, bbox_weights = self.get_targets(
- cls_scores,
- all_level_points,
- batch_gt_instances,
- batch_img_metas,
- batch_gt_instances_ignore=batch_gt_instances_ignore)
-
- num_imgs = cls_scores[0].size(0)
- # flatten cls_scores, bbox_preds and bbox_preds_refine
- flatten_cls_scores = [
- cls_score.permute(0, 2, 3,
- 1).reshape(-1,
- self.cls_out_channels).contiguous()
- for cls_score in cls_scores
- ]
- flatten_bbox_preds = [
- bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
- for bbox_pred in bbox_preds
- ]
- flatten_bbox_preds_refine = [
- bbox_pred_refine.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
- for bbox_pred_refine in bbox_preds_refine
- ]
- flatten_cls_scores = torch.cat(flatten_cls_scores)
- flatten_bbox_preds = torch.cat(flatten_bbox_preds)
- flatten_bbox_preds_refine = torch.cat(flatten_bbox_preds_refine)
- flatten_labels = torch.cat(labels)
- flatten_bbox_targets = torch.cat(bbox_targets)
- # repeat points to align with bbox_preds
- flatten_points = torch.cat(
- [points.repeat(num_imgs, 1) for points in all_level_points])
-
- # FG cat_id: [0, num_classes - 1], BG cat_id: num_classes
- bg_class_ind = self.num_classes
- pos_inds = torch.where(
- ((flatten_labels >= 0) & (flatten_labels < bg_class_ind)) > 0)[0]
- num_pos = len(pos_inds)
-
- pos_bbox_preds = flatten_bbox_preds[pos_inds]
- pos_bbox_preds_refine = flatten_bbox_preds_refine[pos_inds]
- pos_labels = flatten_labels[pos_inds]
-
- # sync num_pos across all gpus
- if self.sync_num_pos:
- num_pos_avg_per_gpu = reduce_mean(
- pos_inds.new_tensor(num_pos).float()).item()
- num_pos_avg_per_gpu = max(num_pos_avg_per_gpu, 1.0)
- else:
- num_pos_avg_per_gpu = num_pos
-
- pos_bbox_targets = flatten_bbox_targets[pos_inds]
- pos_points = flatten_points[pos_inds]
-
- pos_decoded_bbox_preds = self.bbox_coder.decode(
- pos_points, pos_bbox_preds)
- pos_decoded_target_preds = self.bbox_coder.decode(
- pos_points, pos_bbox_targets)
- iou_targets_ini = bbox_overlaps(
- pos_decoded_bbox_preds,
- pos_decoded_target_preds.detach(),
- is_aligned=True).clamp(min=1e-6)
- bbox_weights_ini = iou_targets_ini.clone().detach()
- bbox_avg_factor_ini = reduce_mean(
- bbox_weights_ini.sum()).clamp_(min=1).item()
-
- pos_decoded_bbox_preds_refine = \
- self.bbox_coder.decode(pos_points, pos_bbox_preds_refine)
- iou_targets_rf = bbox_overlaps(
- pos_decoded_bbox_preds_refine,
- pos_decoded_target_preds.detach(),
- is_aligned=True).clamp(min=1e-6)
- bbox_weights_rf = iou_targets_rf.clone().detach()
- bbox_avg_factor_rf = reduce_mean(
- bbox_weights_rf.sum()).clamp_(min=1).item()
-
- if num_pos > 0:
- loss_bbox = self.loss_bbox(
- pos_decoded_bbox_preds,
- pos_decoded_target_preds.detach(),
- weight=bbox_weights_ini,
- avg_factor=bbox_avg_factor_ini)
-
- loss_bbox_refine = self.loss_bbox_refine(
- pos_decoded_bbox_preds_refine,
- pos_decoded_target_preds.detach(),
- weight=bbox_weights_rf,
- avg_factor=bbox_avg_factor_rf)
-
- # build IoU-aware cls_score targets
- if self.use_vfl:
- pos_ious = iou_targets_rf.clone().detach()
- cls_iou_targets = torch.zeros_like(flatten_cls_scores)
- cls_iou_targets[pos_inds, pos_labels] = pos_ious
- else:
- loss_bbox = pos_bbox_preds.sum() * 0
- loss_bbox_refine = pos_bbox_preds_refine.sum() * 0
- if self.use_vfl:
- cls_iou_targets = torch.zeros_like(flatten_cls_scores)
-
- if self.use_vfl:
- loss_cls = self.loss_cls(
- flatten_cls_scores,
- cls_iou_targets,
- avg_factor=num_pos_avg_per_gpu)
- else:
- loss_cls = self.loss_cls(
- flatten_cls_scores,
- flatten_labels,
- weight=label_weights,
- avg_factor=num_pos_avg_per_gpu)
-
- return dict(
- loss_cls=loss_cls,
- loss_bbox=loss_bbox,
- loss_bbox_rf=loss_bbox_refine)
-
- def get_targets(
- self,
- cls_scores: List[Tensor],
- mlvl_points: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> tuple:
- """A wrapper for computing ATSS and FCOS targets for points in multiple
- images.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- tuple:
-
- - labels_list (list[Tensor]): Labels of each level.
- - label_weights (Tensor/None): Label weights of all levels.
- - bbox_targets_list (list[Tensor]): Regression targets of each
- level, (l, t, r, b).
- - bbox_weights (Tensor/None): Bbox weights of all levels.
- """
- if self.use_atss:
- return self.get_atss_targets(cls_scores, mlvl_points,
- batch_gt_instances, batch_img_metas,
- batch_gt_instances_ignore)
- else:
- self.norm_on_bbox = False
- return self.get_fcos_targets(mlvl_points, batch_gt_instances)
-
- def _get_targets_single(self, *args, **kwargs):
- """Avoid ambiguity in multiple inheritance."""
- if self.use_atss:
- return ATSSHead._get_targets_single(self, *args, **kwargs)
- else:
- return FCOSHead._get_targets_single(self, *args, **kwargs)
-
- def get_fcos_targets(self, points: List[Tensor],
- batch_gt_instances: InstanceList) -> tuple:
- """Compute FCOS regression and classification targets for points in
- multiple images.
-
- Args:
- points (list[Tensor]): Points of each fpn level, each has shape
- (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
-
- Returns:
- tuple:
-
- - labels (list[Tensor]): Labels of each level.
- - label_weights: None, to be compatible with ATSS targets.
- - bbox_targets (list[Tensor]): BBox targets of each level.
- - bbox_weights: None, to be compatible with ATSS targets.
- """
- labels, bbox_targets = FCOSHead.get_targets(self, points,
- batch_gt_instances)
- label_weights = None
- bbox_weights = None
- return labels, label_weights, bbox_targets, bbox_weights
-
- def get_anchors(self,
- featmap_sizes: List[Tuple],
- batch_img_metas: List[dict],
- device: str = 'cuda') -> tuple:
- """Get anchors according to feature map sizes.
-
- Args:
- featmap_sizes (list[tuple]): Multi-level feature map sizes.
- batch_img_metas (list[dict]): Image meta info.
- device (str): Device for returned tensors
-
- Returns:
- tuple:
-
- - anchor_list (list[Tensor]): Anchors of each image.
- - valid_flag_list (list[Tensor]): Valid flags of each image.
- """
- num_imgs = len(batch_img_metas)
-
- # since feature map sizes of all images are the same, we only compute
- # anchors for one time
- multi_level_anchors = self.atss_prior_generator.grid_priors(
- featmap_sizes, device=device)
- anchor_list = [multi_level_anchors for _ in range(num_imgs)]
-
- # for each image, we compute valid flags of multi level anchors
- valid_flag_list = []
- for img_id, img_meta in enumerate(batch_img_metas):
- multi_level_flags = self.atss_prior_generator.valid_flags(
- featmap_sizes, img_meta['pad_shape'], device=device)
- valid_flag_list.append(multi_level_flags)
-
- return anchor_list, valid_flag_list
-
- def get_atss_targets(
- self,
- cls_scores: List[Tensor],
- mlvl_points: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> tuple:
- """A wrapper for computing ATSS targets for points in multiple images.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- tuple:
-
- - labels_list (list[Tensor]): Labels of each level.
- - label_weights (Tensor): Label weights of all levels.
- - bbox_targets_list (list[Tensor]): Regression targets of each
- level, (l, t, r, b).
- - bbox_weights (Tensor): Bbox weights of all levels.
- """
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- assert len(
- featmap_sizes
- ) == self.atss_prior_generator.num_levels == \
- self.fcos_prior_generator.num_levels
-
- device = cls_scores[0].device
-
- anchor_list, valid_flag_list = self.get_anchors(
- featmap_sizes, batch_img_metas, device=device)
-
- cls_reg_targets = ATSSHead.get_targets(
- self,
- anchor_list,
- valid_flag_list,
- batch_gt_instances,
- batch_img_metas,
- batch_gt_instances_ignore,
- unmap_outputs=True)
-
- (anchor_list, labels_list, label_weights_list, bbox_targets_list,
- bbox_weights_list, avg_factor) = cls_reg_targets
-
- bbox_targets_list = [
- bbox_targets.reshape(-1, 4) for bbox_targets in bbox_targets_list
- ]
-
- num_imgs = len(batch_img_metas)
- # transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format
- bbox_targets_list = self.transform_bbox_targets(
- bbox_targets_list, mlvl_points, num_imgs)
-
- labels_list = [labels.reshape(-1) for labels in labels_list]
- label_weights_list = [
- label_weights.reshape(-1) for label_weights in label_weights_list
- ]
- bbox_weights_list = [
- bbox_weights.reshape(-1) for bbox_weights in bbox_weights_list
- ]
- label_weights = torch.cat(label_weights_list)
- bbox_weights = torch.cat(bbox_weights_list)
- return labels_list, label_weights, bbox_targets_list, bbox_weights
-
- def transform_bbox_targets(self, decoded_bboxes: List[Tensor],
- mlvl_points: List[Tensor],
- num_imgs: int) -> List[Tensor]:
- """Transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format.
-
- Args:
- decoded_bboxes (list[Tensor]): Regression targets of each level,
- in the form of (x1, y1, x2, y2).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- num_imgs (int): the number of images in a batch.
-
- Returns:
- bbox_targets (list[Tensor]): Regression targets of each level in
- the form of (l, t, r, b).
- """
- # TODO: Re-implemented in Class PointCoder
- assert len(decoded_bboxes) == len(mlvl_points)
- num_levels = len(decoded_bboxes)
- mlvl_points = [points.repeat(num_imgs, 1) for points in mlvl_points]
- bbox_targets = []
- for i in range(num_levels):
- bbox_target = self.bbox_coder.encode(mlvl_points[i],
- decoded_bboxes[i])
- bbox_targets.append(bbox_target)
-
- return bbox_targets
-
- def _load_from_state_dict(self, state_dict: dict, prefix: str,
- local_metadata: dict, strict: bool,
- missing_keys: Union[List[str], str],
- unexpected_keys: Union[List[str], str],
- error_msgs: Union[List[str], str]) -> None:
- """Override the method in the parent class to avoid changing para's
- name."""
- pass
diff --git a/spaces/Larvuz/instruct-pix2pix/edit_app.py b/spaces/Larvuz/instruct-pix2pix/edit_app.py
deleted file mode 100644
index 0359e815ad51b1a2291dd8943555568e452981ad..0000000000000000000000000000000000000000
--- a/spaces/Larvuz/instruct-pix2pix/edit_app.py
+++ /dev/null
@@ -1,192 +0,0 @@
-from __future__ import annotations
-
-import math
-import random
-
-import gradio as gr
-import torch
-from PIL import Image, ImageOps
-from diffusers import StableDiffusionInstructPix2PixPipeline
-
-
-help_text = """
-If you're not getting what you want, there may be a few reasons:
-1. Is the image not changing enough? Your Image CFG weight may be too high. This value dictates how similar the output should be to the input. It's possible your edit requires larger changes from the original image, and your Image CFG weight isn't allowing that. Alternatively, your Text CFG weight may be too low. This value dictates how much to listen to the text instruction. The default Image CFG of 1.5 and Text CFG of 7.5 are a good starting point, but aren't necessarily optimal for each edit. Try:
- * Decreasing the Image CFG weight, or
- * Increasing the Text CFG weight, or
-2. Conversely, is the image changing too much, such that the details in the original image aren't preserved? Try:
- * Increasing the Image CFG weight, or
- * Decreasing the Text CFG weight
-3. Try generating results with different random seeds by setting "Randomize Seed" and running generation multiple times. You can also try setting "Randomize CFG" to sample new Text CFG and Image CFG values each time.
-4. Rephrasing the instruction sometimes improves results (e.g., "turn him into a dog" vs. "make him a dog" vs. "as a dog").
-5. Increasing the number of steps sometimes improves results.
-6. Do faces look weird? The Stable Diffusion autoencoder has a hard time with faces that are small in the image. Try:
- * Cropping the image so the face takes up a larger portion of the frame.
-"""
-
-
-example_instructions = [
- "Make it a picasso painting",
- "as if it were by modigliani",
- "convert to a bronze statue",
- "Turn it into an anime.",
- "have it look like a graphic novel",
- "make him gain weight",
- "what would he look like bald?",
- "Have him smile",
- "Put him in a cocktail party.",
- "move him at the beach.",
- "add dramatic lighting",
- "Convert to black and white",
- "What if it were snowing?",
- "Give him a leather jacket",
- "Turn him into a cyborg!",
- "make him wear a beanie",
-]
-
-model_id = "timbrooks/instruct-pix2pix"
-
-def main():
- pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None).to("cuda")
- example_image = Image.open("imgs/example.jpg").convert("RGB")
-
- def load_example(
- steps: int,
- randomize_seed: bool,
- seed: int,
- randomize_cfg: bool,
- text_cfg_scale: float,
- image_cfg_scale: float,
- ):
- example_instruction = random.choice(example_instructions)
- return [example_image, example_instruction] + generate(
- example_image,
- example_instruction,
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- )
-
- def generate(
- input_image: Image.Image,
- instruction: str,
- steps: int,
- randomize_seed: bool,
- seed: int,
- randomize_cfg: bool,
- text_cfg_scale: float,
- image_cfg_scale: float,
- ):
- seed = random.randint(0, 100000) if randomize_seed else seed
- text_cfg_scale = round(random.uniform(6.0, 9.0), ndigits=2) if randomize_cfg else text_cfg_scale
- image_cfg_scale = round(random.uniform(1.2, 1.8), ndigits=2) if randomize_cfg else image_cfg_scale
-
- width, height = input_image.size
- factor = 512 / max(width, height)
- factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
- width = int((width * factor) // 64) * 64
- height = int((height * factor) // 64) * 64
- input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS)
-
- if instruction == "":
- return [input_image, seed]
-
- generator = torch.manual_seed(seed)
- edited_image = pipe(
- instruction, image=input_image,
- guidance_scale=text_cfg_scale, image_guidance_scale=image_cfg_scale,
- num_inference_steps=steps, generator=generator,
- ).images[0]
- return [seed, text_cfg_scale, image_cfg_scale, edited_image]
-
- def reset():
- return [0, "Randomize Seed", 1371, "Fix CFG", 7.5, 1.5, None]
-
- with gr.Blocks() as demo:
- gr.HTML("""
- InstructPix2Pix: Learning to Follow Image Editing Instructions
-
-For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
-
-
-
-
""")
- with gr.Row():
- with gr.Column(scale=1, min_width=100):
- generate_button = gr.Button("Generate")
- with gr.Column(scale=1, min_width=100):
- load_button = gr.Button("Load Example")
- with gr.Column(scale=1, min_width=100):
- reset_button = gr.Button("Reset")
- with gr.Column(scale=3):
- instruction = gr.Textbox(lines=1, label="Edit Instruction", interactive=True)
-
- with gr.Row():
- input_image = gr.Image(label="Input Image", type="pil", interactive=True)
- edited_image = gr.Image(label=f"Edited Image", type="pil", interactive=False)
- input_image.style(height=512, width=512)
- edited_image.style(height=512, width=512)
-
- with gr.Row():
- steps = gr.Number(value=50, precision=0, label="Steps", interactive=True)
- randomize_seed = gr.Radio(
- ["Fix Seed", "Randomize Seed"],
- value="Randomize Seed",
- type="index",
- show_label=False,
- interactive=True,
- )
- seed = gr.Number(value=1371, precision=0, label="Seed", interactive=True)
- randomize_cfg = gr.Radio(
- ["Fix CFG", "Randomize CFG"],
- value="Fix CFG",
- type="index",
- show_label=False,
- interactive=True,
- )
- text_cfg_scale = gr.Number(value=7.5, label=f"Text CFG", interactive=True)
- image_cfg_scale = gr.Number(value=1.5, label=f"Image CFG", interactive=True)
-
- gr.Markdown(help_text)
-
- load_button.click(
- fn=load_example,
- inputs=[
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- ],
- outputs=[input_image, instruction, seed, text_cfg_scale, image_cfg_scale, edited_image],
- )
- generate_button.click(
- fn=generate,
- inputs=[
- input_image,
- instruction,
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- ],
- outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image],
- )
- reset_button.click(
- fn=reset,
- inputs=[],
- outputs=[steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale, edited_image],
- )
-
- demo.queue(concurrency_count=1)
- demo.launch(share=False)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/Loren/Streamlit_OCR_comparator/README.md b/spaces/Loren/Streamlit_OCR_comparator/README.md
deleted file mode 100644
index 2894464cad0f3f4d163e1f8e72750b10259159da..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Streamlit OCR Comparator
-emoji: 📰🔍🔤
-colorFrom: indigo
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: Home.py
-tags: [streamlit, ocr]
-pinned: true
----
\ No newline at end of file
diff --git a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py b/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py
deleted file mode 100644
index 6d9b36c74b1808b56ded68cf080a689db7e0ee4e..0000000000000000000000000000000000000000
--- a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# -*- coding: utf-8 -*-
-# File : __init__.py
-# Author : Jiayuan Mao
-# Email : maojiayuan@gmail.com
-# Date : 27/01/2018
-#
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-from .batchnorm import set_sbn_eps_mode
-from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d
-from .batchnorm import patch_sync_batchnorm, convert_model
-from .replicate import DataParallelWithCallback, patch_replication_callback
diff --git a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py b/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py
deleted file mode 100644
index 9794e0fc3463a5e8fad05c037cce64683059a6d3..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.ERROR)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})".format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10, 2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r", encoding="utf-8") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
\ No newline at end of file
diff --git a/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py b/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py
deleted file mode 100644
index 3a5999ddc80faa0ce6a54389562c9f092b40bb03..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# encoding: utf-8
-"""
-This files contains pytest fixtures and other test related stuff.
-
-"""
-from __future__ import absolute_import, division, print_function
-
-import numpy as np
-import pytest
-
-# save numpy's current print options
-_NP_PRINT_OPTIONS = np.get_printoptions()
-
-
-@pytest.fixture(autouse=True)
-def setup_doctest(request):
- """Set up the environment for doctests (when run through pytest)."""
- np.set_printoptions(precision=5, edgeitems=2, suppress=True)
-
- def fin():
- """Restore the environment after doctests (when run through pytest)."""
- np.set_printoptions(**_NP_PRINT_OPTIONS)
-
- request.addfinalizer(fin)
diff --git a/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py b/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py
deleted file mode 100644
index 19e34a11def040b971b534b9ed3fda71d0a35c85..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py
+++ /dev/null
@@ -1,882 +0,0 @@
-# encoding: utf-8
-# pylint: disable=no-member
-# pylint: disable=invalid-name
-# pylint: disable=too-many-arguments
-# pylint: disable=wrong-import-position
-"""
-Evaluation package.
-
-"""
-
-from __future__ import absolute_import, division, print_function
-
-import numpy as np
-
-
-# evaluation helper functions
-def find_closest_matches(detections, annotations):
- """
- Find the closest annotation for each detection.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
-
- Returns
- -------
- indices : numpy array
- Indices of the closest matches.
-
- Notes
- -----
- The sequences must be ordered.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.int)
- # if only a single annotation is given
- if len(annotations) == 1:
- # return an array as long as the detections with indices 0
- return np.zeros(len(detections), dtype=np.int)
- # solution found at: http://stackoverflow.com/questions/8914491/
- indices = annotations.searchsorted(detections)
- indices = np.clip(indices, 1, len(annotations) - 1)
- left = annotations[indices - 1]
- right = annotations[indices]
- indices -= detections - left < right - detections
- # return the indices of the closest matches
- return indices
-
-
-def calc_errors(detections, annotations, matches=None):
- """
- Errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.float)
- # determine the closest annotations
- if matches is None:
- matches = find_closest_matches(detections, annotations)
- # calc error relative to those annotations
- errors = detections - annotations[matches]
- # return the errors
- return errors
-
-
-def calc_absolute_errors(detections, annotations, matches=None):
- """
- Absolute errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Absolute errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # return the errors
- return np.abs(calc_errors(detections, annotations, matches))
-
-
-def calc_relative_errors(detections, annotations, matches=None):
- """
- Relative errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Relative errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.float)
- # determine the closest annotations
- if matches is None:
- matches = find_closest_matches(detections, annotations)
- # calculate the absolute errors
- errors = calc_errors(detections, annotations, matches)
- # return the relative errors
- return np.abs(1 - (errors / annotations[matches]))
-
-
-# abstract evaluation base class
-class EvaluationMixin(object):
- """
- Evaluation mixin class.
-
- This class has a `name` attribute which is used for display purposes and
- defaults to 'None'.
-
- `METRIC_NAMES` is a list of tuples, containing the attribute's name and the
- corresponding label, e.g.:
-
- The attributes defined in `METRIC_NAMES` will be provided as an ordered
- dictionary as the `metrics` property unless the subclass overwrites the
- property.
-
- `FLOAT_FORMAT` is used to format floats.
-
- """
-
- # Example:
- # METRIC_NAMES = [
- # ('precision', 'Precision'),
- # ('recall', 'Recall'),
- # ('fmeasure', 'F-measure'),
- # ]
- name = None
- METRIC_NAMES = []
- FLOAT_FORMAT = '{:.3f}'
-
- @property
- def metrics(self):
- """Metrics as a dictionary."""
- from collections import OrderedDict
- metrics = OrderedDict()
- # metrics = {}
- for metric in [m[0] for m in self.METRIC_NAMES]:
- metrics[metric] = getattr(self, metric)
- return metrics
-
- def __len__(self):
- """Length of the evaluation object."""
- raise NotImplementedError('must be implemented by subclass.')
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- Notes
- -----
- This is a fallback method formatting the `metrics` dictionary in a
- human readable way. Classes inheriting from this mixin class should
- provide a method better suitable.
-
- """
- # pylint: disable=unused-argument
- import pprint
- return pprint.pformat(dict(self.metrics), indent=4)
-
-
-# evaluation classes
-class SimpleEvaluation(EvaluationMixin):
- """
- Simple Precision, Recall, F-measure and Accuracy evaluation based on the
- numbers of true/false positive/negative detections.
-
- Parameters
- ----------
- num_tp : int
- Number of true positive detections.
- num_fp : int
- Number of false positive detections.
- num_tn : int
- Number of true negative detections.
- num_fn : int
- Number of false negative detections.
- name : str
- Name to be displayed.
-
- Notes
- -----
- This class is only suitable for a 1-class evaluation problem.
-
- """
-
- METRIC_NAMES = [
- ('num_tp', 'No. of true positives'),
- ('num_fp', 'No. of false positives'),
- ('num_tn', 'No. of true negatives'),
- ('num_fn', 'No. of false negatives'),
- ('num_annotations', 'No. Annotations'),
- ('precision', 'Precision'),
- ('recall', 'Recall'),
- ('fmeasure', 'F-measure'),
- ('accuracy', 'Accuracy'),
- ]
-
- def __init__(self, num_tp=0, num_fp=0, num_tn=0, num_fn=0, name=None,
- **kwargs):
- # pylint: disable=unused-argument
- # hidden variables, to be able to overwrite them in subclasses
- self._num_tp = int(num_tp)
- self._num_fp = int(num_fp)
- self._num_tn = int(num_tn)
- self._num_fn = int(num_fn)
- # name of the evaluation
- self.name = name
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return self._num_tp
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return self._num_fp
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return self._num_tn
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return self._num_fn
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- return self.num_tp + self.num_fn
-
- def __len__(self):
- # the length equals the number of annotations
- return self.num_annotations
-
- @property
- def precision(self):
- """Precision."""
- # correct / retrieved
- retrieved = float(self.num_tp + self.num_fp)
- # if there are no positive predictions, none of them are wrong
- if retrieved == 0:
- return 1.
- return self.num_tp / retrieved
-
- @property
- def recall(self):
- """Recall."""
- # correct / relevant
- relevant = float(self.num_tp + self.num_fn)
- # if there are no positive annotations, we recalled all of them
- if relevant == 0:
- return 1.
- return self.num_tp / relevant
-
- @property
- def fmeasure(self):
- """F-measure."""
- # 2pr / (p+r)
- numerator = 2. * self.precision * self.recall
- if numerator == 0:
- return 0.
- return numerator / (self.precision + self.recall)
-
- @property
- def accuracy(self):
- """Accuracy."""
- # acc: (TP + TN) / (TP + FP + TN + FN)
- denominator = self.num_fp + self.num_fn + self.num_tp + self.num_tn
- if denominator == 0:
- return 1.
- numerator = float(self.num_tp + self.num_tn)
- if numerator == 0:
- return 0.
- return numerator / denominator
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
-
- """
- ret = ''
- if self.name is not None:
- ret += '%s\n ' % self.name
- ret += 'Annotations: %5d TP: %5d FP: %5d FN: %5d ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- return ret
-
- def __str__(self):
- return self.tostring()
-
-
-# evaluate Precision, Recall, F-measure and Accuracy with lists or numpy arrays
-class Evaluation(SimpleEvaluation):
- """
- Evaluation class for measuring Precision, Recall and F-measure based on
- numpy arrays or lists with true/false positive/negative detections.
-
- Parameters
- ----------
- tp : list or numpy array
- True positive detections.
- fp : list or numpy array
- False positive detections.
- tn : list or numpy array
- True negative detections.
- fn : list or numpy array
- False negative detections.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, tp=None, fp=None, tn=None, fn=None, **kwargs):
- # set default values
- if tp is None:
- tp = []
- if fp is None:
- fp = []
- if tn is None:
- tn = []
- if fn is None:
- fn = []
- # instantiate a SimpleEvaluation object
- super(Evaluation, self).__init__(**kwargs)
- # convert everything to numpy arrays and save them
- self.tp = np.asarray(list(tp), dtype=np.float)
- self.fp = np.asarray(list(fp), dtype=np.float)
- self.tn = np.asarray(list(tn), dtype=np.float)
- self.fn = np.asarray(list(fn), dtype=np.float)
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return len(self.tp)
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return len(self.fp)
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return len(self.tn)
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return len(self.fn)
-
-
-# class for evaluation of Precision, Recall, F-measure with 2D arrays
-class MultiClassEvaluation(Evaluation):
- """
- Evaluation class for measuring Precision, Recall and F-measure based on
- 2D numpy arrays with true/false positive/negative detections.
-
- Parameters
- ----------
- tp : list of tuples or numpy array, shape (num_tp, 2)
- True positive detections.
- fp : list of tuples or numpy array, shape (num_fp, 2)
- False positive detections.
- tn : list of tuples or numpy array, shape (num_tn, 2)
- True negative detections.
- fn : list of tuples or numpy array, shape (num_fn, 2)
- False negative detections.
- name : str
- Name to be displayed.
-
- Notes
- -----
- The second item of the tuples or the second column of the arrays denote
- the class the detection belongs to.
-
- """
- def __init__(self, tp=None, fp=None, tn=None, fn=None, **kwargs):
- # set default values
- if tp is None:
- tp = np.zeros((0, 2))
- if fp is None:
- fp = np.zeros((0, 2))
- if tn is None:
- tn = np.zeros((0, 2))
- if fn is None:
- fn = np.zeros((0, 2))
- super(MultiClassEvaluation, self).__init__(**kwargs)
- self.tp = np.asarray(tp, dtype=np.float)
- self.fp = np.asarray(fp, dtype=np.float)
- self.tn = np.asarray(tn, dtype=np.float)
- self.fn = np.asarray(fn, dtype=np.float)
-
- def tostring(self, verbose=False, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Parameters
- ----------
- verbose : bool
- Add evaluation for individual classes.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- ret = ''
-
- if verbose:
- # extract all classes
- classes = []
- if self.tp.any():
- classes = np.append(classes, np.unique(self.tp[:, 1]))
- if self.fp.any():
- classes = np.append(classes, np.unique(self.fp[:, 1]))
- if self.tn.any():
- classes = np.append(classes, np.unique(self.tn[:, 1]))
- if self.fn.any():
- classes = np.append(classes, np.unique(self.fn[:, 1]))
- for cls in sorted(np.unique(classes)):
- # extract the TP, FP, TN and FN of this class
- tp = self.tp[self.tp[:, 1] == cls]
- fp = self.fp[self.fp[:, 1] == cls]
- tn = self.tn[self.tn[:, 1] == cls]
- fn = self.fn[self.fn[:, 1] == cls]
- # evaluate them
- e = Evaluation(tp, fp, tn, fn, name='Class %s' % cls)
- # append to the output string
- ret += ' %s\n' % e.tostring(verbose=False)
- # normal formatting
- ret += 'Annotations: %5d TP: %5d FP: %4d FN: %4d ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- # return
- return ret
-
-
-# class for summing Evaluations
-class SumEvaluation(SimpleEvaluation):
- """
- Simple class for summing evaluations.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, eval_objects, name=None):
- # pylint: disable=super-init-not-called
- # Note: we want to inherit the evaluation functions/properties, no need
- # to call __super__, but we need to take care of 'name'
- if not isinstance(eval_objects, list):
- # wrap the given eval_object in a list
- eval_objects = [eval_objects]
- self.eval_objects = eval_objects
- self.name = name or 'sum for %d files' % len(self)
-
- def __len__(self):
- # just use the length of the evaluation objects
- return len(self.eval_objects)
-
- # redefine the counters (number of TP, FP, TN, FN & number of annotations)
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return sum(e.num_tp for e in self.eval_objects)
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return sum(e.num_fp for e in self.eval_objects)
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return sum(e.num_tn for e in self.eval_objects)
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return sum(e.num_fn for e in self.eval_objects)
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- return sum(e.num_annotations for e in self.eval_objects)
-
-
-# class for averaging Evaluations
-class MeanEvaluation(SumEvaluation):
- """
- Simple class for averaging evaluation.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, eval_objects, name=None, **kwargs):
- super(MeanEvaluation, self).__init__(eval_objects, **kwargs)
- # handle the 'name' here to be able to set a different default value
- self.name = name or 'mean for %d files' % len(self)
-
- # overwrite the properties to calculate the mean instead of the sum
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_tp for e in self.eval_objects])
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_fp for e in self.eval_objects])
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_tn for e in self.eval_objects])
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_fn for e in self.eval_objects])
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_annotations for e in self.eval_objects])
-
- @property
- def precision(self):
- """Precision."""
- return np.nanmean([e.precision for e in self.eval_objects])
-
- @property
- def recall(self):
- """Recall."""
- return np.nanmean([e.recall for e in self.eval_objects])
-
- @property
- def fmeasure(self):
- """F-measure."""
- return np.nanmean([e.fmeasure for e in self.eval_objects])
-
- @property
- def accuracy(self):
- """Accuracy."""
- return np.nanmean([e.accuracy for e in self.eval_objects])
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- ret = ''
- if self.name is not None:
- ret += '%s\n ' % self.name
- # TODO: unify this with SimpleEvaluation but
- # add option to provide field formatters (e.g. 3d or 5.2f)
- # format with floats instead of integers
- ret += 'Annotations: %5.2f TP: %5.2f FP: %5.2f FN: %5.2f ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- return ret
-
-
-def tostring(eval_objects, **kwargs):
- """
- Format the given evaluation objects as human readable strings.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- # pylint: disable=unused-argument
- return '\n'.join([e.tostring() for e in eval_objects])
-
-
-def tocsv(eval_objects, metric_names=None, float_format='{:.3f}', **kwargs):
- """
- Format the given evaluation objects as a CSV table.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- metric_names : list of tuples, optional
- List of tuples defining the name of the property corresponding to the
- metric, and the metric label e.g. ('fp', 'False Positives').
- float_format : str, optional
- How to format the metrics.
-
- Returns
- -------
- str
- CSV table representation of the evaluation objects.
-
- Notes
- -----
- If no `metric_names` are given, they will be extracted from the first
- evaluation object.
-
- """
- # pylint: disable=unused-argument
-
- if metric_names is None:
- # get the evaluation metrics from the first evaluation object
- metric_names = eval_objects[0].METRIC_NAMES
- metric_names, metric_labels = list(zip(*metric_names))
- # add header
- lines = ['Name,' + ','.join(metric_labels)]
- # TODO: use e.metrics dict?
- # add the evaluation objects
- for e in eval_objects:
- values = [float_format.format(getattr(e, mn)) for mn in metric_names]
- lines.append(e.name + ',' + ','.join(values))
- # return everything
- return '\n'.join(lines)
-
-
-def totex(eval_objects, metric_names=None, float_format='{:.3f}', **kwargs):
- """
- Format the given evaluation objects as a LaTeX table.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- metric_names : list of tuples, optional
- List of tuples defining the name of the property corresponding to the
- metric, and the metric label e.g. ('fp', 'False Positives').
- float_format : str, optional
- How to format the metrics.
-
- Returns
- -------
- str
- LaTeX table representation of the evaluation objects.
-
- Notes
- -----
- If no `metric_names` are given, they will be extracted from the first
- evaluation object.
-
- """
- # pylint: disable=unused-argument
-
- if metric_names is None:
- # get the evaluation metrics from the first evaluation object
- metric_names = eval_objects[0].METRIC_NAMES
- metric_names, metric_labels = list(zip(*metric_names))
- # add header
- lines = ['Name & ' + ' & '.join(metric_labels) + '\\\\']
- # TODO: use e.metrics dict
- # TODO: add a generic totable() function which accepts columns separator,
- # newline stuff (e.g. tex \\\\) and others
- # add the evaluation objects
- for e in eval_objects:
- values = [float_format.format(getattr(e, mn)) for mn in metric_names]
- lines.append(e.name + ' & ' + ' & '.join(values) + '\\\\')
- # return everything
- return '\n'.join(lines)
-
-
-def evaluation_io(parser, ann_suffix, det_suffix, ann_dir=None, det_dir=None):
- """
- Add evaluation input/output and formatting related arguments to an existing
- parser object.
-
- Parameters
- ----------
- parser : argparse parser instance
- Existing argparse parser object.
- ann_suffix : str
- Suffix of the annotation files.
- det_suffix : str
- Suffix of the detection files.
- ann_dir : str, optional
- Use only annotations from this folder (and sub-folders).
- det_dir : str, optional
- Use only detections from this folder (and sub-folders).
-
- Returns
- -------
- io_group : argparse argument group
- Evaluation input / output argument group.
- formatter_group : argparse argument group
- Evaluation formatter argument group.
-
- """
- import sys
- import argparse
- # general input output file handling
- parser.add_argument('files', nargs='*',
- help='files (or folders) to be evaluated')
- parser.add_argument('-o', dest='outfile', type=argparse.FileType('w'),
- default=sys.stdout,
- help='output file [default: STDOUT]')
- # file suffixes used for evaluation
- g = parser.add_argument_group('file/folder/suffix arguments')
- g.add_argument('-a', dest='ann_suffix', action='store', default=ann_suffix,
- help='suffix of the annotation files '
- '[default: %(default)s]')
- g.add_argument('--ann_dir', action='store', default=ann_dir,
- help='search only this directory (recursively) for '
- 'annotation files [default: %(default)s]')
- g.add_argument('-d', dest='det_suffix', action='store', default=det_suffix,
- help='suffix of the detection files [default: %(default)s]')
- g.add_argument('--det_dir', action='store', default=det_dir,
- help='search only this directory (recursively) for '
- 'detection files [default: %(default)s]')
- # option to ignore non-existing detections
- g.add_argument('-i', '--ignore_non_existing', action='store_true',
- help='ignore non-existing detections [default: raise a '
- 'warning and assume empty detections]')
- # verbose
- parser.add_argument('-v', '--verbose', action='count', default=0,
- help='increase verbosity level')
- # option to suppress warnings
- parser.add_argument('-q', '--quiet', action='store_true',
- help='suppress any warnings')
- # output format options
- parser.set_defaults(output_formatter=tostring)
- f = parser.add_argument_group('formatting arguments')
- formats = f.add_mutually_exclusive_group()
- formats.add_argument('--tex', dest='output_formatter',
- action='store_const', const=totex,
- help='format output to be used in .tex files')
- formats.add_argument('--csv', dest='output_formatter',
- action='store_const', const=tocsv,
- help='format output to be used in .csv files')
- # return the output formatting group so the caller can add more options
- return g, f
-
-
-# finally import the submodules
-from . import chords, beats, notes, onsets, tempo
-
-# import often used classes
-from .beats import BeatEvaluation, BeatMeanEvaluation
-from .chords import ChordEvaluation, ChordMeanEvaluation, ChordSumEvaluation
-from .key import KeyEvaluation, KeyMeanEvaluation
-from .notes import NoteEvaluation, NoteMeanEvaluation, NoteSumEvaluation
-from .onsets import OnsetEvaluation, OnsetMeanEvaluation, OnsetSumEvaluation
-from .tempo import TempoEvaluation, TempoMeanEvaluation
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py
deleted file mode 100644
index 6fc71979190323f44c09f8b7e1761cf49cd2d76b..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
- 'iou3d_boxes_iou_bev_forward', 'iou3d_nms_forward',
- 'iou3d_nms_normal_forward'
-])
-
-
-def boxes_iou_bev(boxes_a, boxes_b):
- """Calculate boxes IoU in the Bird's Eye View.
-
- Args:
- boxes_a (torch.Tensor): Input boxes a with shape (M, 5).
- boxes_b (torch.Tensor): Input boxes b with shape (N, 5).
-
- Returns:
- ans_iou (torch.Tensor): IoU result with shape (M, N).
- """
- ans_iou = boxes_a.new_zeros(
- torch.Size((boxes_a.shape[0], boxes_b.shape[0])))
-
- ext_module.iou3d_boxes_iou_bev_forward(boxes_a.contiguous(),
- boxes_b.contiguous(), ans_iou)
-
- return ans_iou
-
-
-def nms_bev(boxes, scores, thresh, pre_max_size=None, post_max_size=None):
- """NMS function GPU implementation (for BEV boxes). The overlap of two
- boxes for IoU calculation is defined as the exact overlapping area of the
- two boxes. In this function, one can also set ``pre_max_size`` and
- ``post_max_size``.
-
- Args:
- boxes (torch.Tensor): Input boxes with the shape of [N, 5]
- ([x1, y1, x2, y2, ry]).
- scores (torch.Tensor): Scores of boxes with the shape of [N].
- thresh (float): Overlap threshold of NMS.
- pre_max_size (int, optional): Max size of boxes before NMS.
- Default: None.
- post_max_size (int, optional): Max size of boxes after NMS.
- Default: None.
-
- Returns:
- torch.Tensor: Indexes after NMS.
- """
- assert boxes.size(1) == 5, 'Input boxes shape should be [N, 5]'
- order = scores.sort(0, descending=True)[1]
-
- if pre_max_size is not None:
- order = order[:pre_max_size]
- boxes = boxes[order].contiguous()
-
- keep = torch.zeros(boxes.size(0), dtype=torch.long)
- num_out = ext_module.iou3d_nms_forward(boxes, keep, thresh)
- keep = order[keep[:num_out].cuda(boxes.device)].contiguous()
- if post_max_size is not None:
- keep = keep[:post_max_size]
- return keep
-
-
-def nms_normal_bev(boxes, scores, thresh):
- """Normal NMS function GPU implementation (for BEV boxes). The overlap of
- two boxes for IoU calculation is defined as the exact overlapping area of
- the two boxes WITH their yaw angle set to 0.
-
- Args:
- boxes (torch.Tensor): Input boxes with shape (N, 5).
- scores (torch.Tensor): Scores of predicted boxes with shape (N).
- thresh (float): Overlap threshold of NMS.
-
- Returns:
- torch.Tensor: Remaining indices with scores in descending order.
- """
- assert boxes.shape[1] == 5, 'Input boxes shape should be [N, 5]'
- order = scores.sort(0, descending=True)[1]
-
- boxes = boxes[order].contiguous()
-
- keep = torch.zeros(boxes.size(0), dtype=torch.long)
- num_out = ext_module.iou3d_nms_normal_forward(boxes, keep, thresh)
- return order[keep[:num_out].cuda(boxes.device)].contiguous()
diff --git a/spaces/MetaWabbit/Auto-GPT/run.sh b/spaces/MetaWabbit/Auto-GPT/run.sh
deleted file mode 100644
index edcbc44155b9ca9df83e283fdf976472c13e6492..0000000000000000000000000000000000000000
--- a/spaces/MetaWabbit/Auto-GPT/run.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-python scripts/check_requirements.py requirements.txt
-if [ $? -eq 1 ]
-then
- echo Installing missing packages...
- pip install -r requirements.txt
-fi
-python -m autogpt $@
-read -p "Press any key to continue..."
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py b/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py
deleted file mode 100644
index 244425feef76bf89d4de939cb8a1914a6f0f47c6..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py
+++ /dev/null
@@ -1,387 +0,0 @@
-# Lint as: python3
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Unit tests for the classifier trainer models."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import copy
-import functools
-import json
-
-import os
-import sys
-
-from typing import Any, Callable, Iterable, Mapping, MutableMapping, Optional, Tuple
-
-from absl import flags
-from absl.testing import parameterized
-import tensorflow as tf
-
-from tensorflow.python.distribute import combinations
-from tensorflow.python.distribute import strategy_combinations
-from official.utils.flags import core as flags_core
-from official.vision.image_classification import classifier_trainer
-from official.vision.image_classification import dataset_factory
-from official.vision.image_classification import test_utils
-from official.vision.image_classification.configs import base_configs
-
-classifier_trainer.define_classifier_flags()
-
-
-def distribution_strategy_combinations() -> Iterable[Tuple[Any, ...]]:
- """Returns the combinations of end-to-end tests to run."""
- return combinations.combine(
- distribution=[
- strategy_combinations.default_strategy,
- strategy_combinations.tpu_strategy,
- strategy_combinations.one_device_strategy_gpu,
- strategy_combinations.mirrored_strategy_with_two_gpus,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset=[
- 'imagenet',
- ],
- )
-
-
-def get_params_override(params_override: Mapping[str, Any]) -> str:
- """Converts params_override dict to string command."""
- return '--params_override=' + json.dumps(params_override)
-
-
-def basic_params_override(dtype: str = 'float32') -> MutableMapping[str, Any]:
- """Returns a basic parameter configuration for testing."""
- return {
- 'train_dataset': {
- 'builder': 'synthetic',
- 'use_per_replica_batch_size': True,
- 'batch_size': 1,
- 'image_size': 224,
- 'dtype': dtype,
- },
- 'validation_dataset': {
- 'builder': 'synthetic',
- 'batch_size': 1,
- 'use_per_replica_batch_size': True,
- 'image_size': 224,
- 'dtype': dtype,
- },
- 'train': {
- 'steps': 1,
- 'epochs': 1,
- 'callbacks': {
- 'enable_checkpoint_and_export': True,
- 'enable_tensorboard': False,
- },
- },
- 'evaluation': {
- 'steps': 1,
- },
- }
-
-
-def get_trivial_model(num_classes: int) -> tf.keras.Model:
- """Creates and compiles trivial model for ImageNet dataset."""
- model = test_utils.trivial_model(num_classes=num_classes)
- lr = 0.01
- optimizer = tf.keras.optimizers.SGD(learning_rate=lr)
- loss_obj = tf.keras.losses.SparseCategoricalCrossentropy()
- model.compile(optimizer=optimizer,
- loss=loss_obj,
- run_eagerly=True)
- return model
-
-
-def get_trivial_data() -> tf.data.Dataset:
- """Gets trivial data in the ImageNet size."""
- def generate_data(_) -> tf.data.Dataset:
- image = tf.zeros(shape=(224, 224, 3), dtype=tf.float32)
- label = tf.zeros([1], dtype=tf.int32)
- return image, label
-
- dataset = tf.data.Dataset.range(1)
- dataset = dataset.repeat()
- dataset = dataset.map(generate_data,
- num_parallel_calls=tf.data.experimental.AUTOTUNE)
- dataset = dataset.prefetch(buffer_size=1).batch(1)
- return dataset
-
-
-def run_end_to_end(main: Callable[[Any], None],
- extra_flags: Optional[Iterable[str]] = None,
- model_dir: Optional[str] = None):
- """Runs the classifier trainer end-to-end."""
- extra_flags = [] if extra_flags is None else extra_flags
- args = [sys.argv[0], '--model_dir', model_dir] + extra_flags
- flags_core.parse_flags(argv=args)
- main(flags.FLAGS)
-
-
-class ClassifierTest(tf.test.TestCase, parameterized.TestCase):
- """Unit tests for Keras models."""
- _tempdir = None
-
- @classmethod
- def setUpClass(cls): # pylint: disable=invalid-name
- super(ClassifierTest, cls).setUpClass()
-
- def tearDown(self):
- super(ClassifierTest, self).tearDown()
- tf.io.gfile.rmtree(self.get_temp_dir())
-
- @combinations.generate(distribution_strategy_combinations())
- def test_end_to_end_train_and_eval(self, distribution, model, dataset):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override()),
- '--mode=train_and_eval',
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
-
- @combinations.generate(
- combinations.combine(
- distribution=[
- strategy_combinations.one_device_strategy_gpu,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset='imagenet',
- dtype='float16',
- ))
- def test_gpu_train(self, distribution, model, dataset, dtype):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override(dtype)),
- '--mode=train_and_eval',
- ]
-
- export_params = basic_params_override()
- export_path = os.path.join(model_dir, 'export')
- export_params['export'] = {}
- export_params['export']['destination'] = export_path
- export_flags = base_flags + [
- '--mode=export_only',
- get_params_override(export_params)
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
- run_end_to_end(main=run,
- extra_flags=export_flags,
- model_dir=model_dir)
- self.assertTrue(os.path.exists(export_path))
-
- @combinations.generate(
- combinations.combine(
- distribution=[
- strategy_combinations.tpu_strategy,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset='imagenet',
- dtype='bfloat16',
- ))
- def test_tpu_train(self, distribution, model, dataset, dtype):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override(dtype)),
- '--mode=train_and_eval',
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
-
- @combinations.generate(distribution_strategy_combinations())
- def test_end_to_end_invalid_mode(self, distribution, model, dataset):
- """Test the Keras EfficientNet model with `strategy`."""
- model_dir = self.get_temp_dir()
- extra_flags = [
- '--data_dir=not_used',
- '--mode=invalid_mode',
- '--model_type=' + model,
- '--dataset=' + dataset,
- get_params_override(basic_params_override()),
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- with self.assertRaises(ValueError):
- run_end_to_end(main=run, extra_flags=extra_flags, model_dir=model_dir)
-
-
-class UtilTests(parameterized.TestCase, tf.test.TestCase):
- """Tests for individual utility functions within classifier_trainer.py."""
-
- @parameterized.named_parameters(
- ('efficientnet-b0', 'efficientnet', 'efficientnet-b0', 224),
- ('efficientnet-b1', 'efficientnet', 'efficientnet-b1', 240),
- ('efficientnet-b2', 'efficientnet', 'efficientnet-b2', 260),
- ('efficientnet-b3', 'efficientnet', 'efficientnet-b3', 300),
- ('efficientnet-b4', 'efficientnet', 'efficientnet-b4', 380),
- ('efficientnet-b5', 'efficientnet', 'efficientnet-b5', 456),
- ('efficientnet-b6', 'efficientnet', 'efficientnet-b6', 528),
- ('efficientnet-b7', 'efficientnet', 'efficientnet-b7', 600),
- ('resnet', 'resnet', '', None),
- )
- def test_get_model_size(self, model, model_name, expected):
- config = base_configs.ExperimentConfig(
- model_name=model,
- model=base_configs.ModelConfig(
- model_params={
- 'model_name': model_name,
- },
- )
- )
- size = classifier_trainer.get_image_size_from_model(config)
- self.assertEqual(size, expected)
-
- @parameterized.named_parameters(
- ('dynamic', 'dynamic', None, 'dynamic'),
- ('scalar', 128., None, 128.),
- ('float32', None, 'float32', 1),
- ('float16', None, 'float16', 128),
- )
- def test_get_loss_scale(self, loss_scale, dtype, expected):
- config = base_configs.ExperimentConfig(
- runtime=base_configs.RuntimeConfig(
- loss_scale=loss_scale),
- train_dataset=dataset_factory.DatasetConfig(dtype=dtype))
- ls = classifier_trainer.get_loss_scale(config, fp16_default=128)
- self.assertEqual(ls, expected)
-
- @parameterized.named_parameters(
- ('float16', 'float16'),
- ('bfloat16', 'bfloat16')
- )
- def test_initialize(self, dtype):
- config = base_configs.ExperimentConfig(
- runtime=base_configs.RuntimeConfig(
- run_eagerly=False,
- enable_xla=False,
- per_gpu_thread_count=1,
- gpu_thread_mode='gpu_private',
- num_gpus=1,
- dataset_num_private_threads=1,
- ),
- train_dataset=dataset_factory.DatasetConfig(dtype=dtype),
- model=base_configs.ModelConfig(),
- )
-
- class EmptyClass:
- pass
- fake_ds_builder = EmptyClass()
- fake_ds_builder.dtype = dtype
- fake_ds_builder.config = EmptyClass()
- classifier_trainer.initialize(config, fake_ds_builder)
-
- def test_resume_from_checkpoint(self):
- """Tests functionality for resuming from checkpoint."""
- # Set the keras policy
- policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16')
- tf.keras.mixed_precision.experimental.set_policy(policy)
-
- # Get the model, datasets, and compile it.
- model = get_trivial_model(10)
-
- # Create the checkpoint
- model_dir = self.get_temp_dir()
- train_epochs = 1
- train_steps = 10
- ds = get_trivial_data()
- callbacks = [
- tf.keras.callbacks.ModelCheckpoint(
- os.path.join(model_dir, 'model.ckpt-{epoch:04d}'),
- save_weights_only=True)
- ]
- model.fit(
- ds,
- callbacks=callbacks,
- epochs=train_epochs,
- steps_per_epoch=train_steps)
-
- # Test load from checkpoint
- clean_model = get_trivial_model(10)
- weights_before_load = copy.deepcopy(clean_model.get_weights())
- initial_epoch = classifier_trainer.resume_from_checkpoint(
- model=clean_model,
- model_dir=model_dir,
- train_steps=train_steps)
- self.assertEqual(initial_epoch, 1)
- self.assertNotAllClose(weights_before_load, clean_model.get_weights())
-
- tf.io.gfile.rmtree(model_dir)
-
- def test_serialize_config(self):
- """Tests functionality for serializing data."""
- config = base_configs.ExperimentConfig()
- model_dir = self.get_temp_dir()
- classifier_trainer.serialize_config(params=config, model_dir=model_dir)
- saved_params_path = os.path.join(model_dir, 'params.yaml')
- self.assertTrue(os.path.exists(saved_params_path))
- tf.io.gfile.rmtree(model_dir)
-
-if __name__ == '__main__':
- tf.test.main()
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py b/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py
deleted file mode 100644
index 11fcb5305660ec71153ebfc12631f455a3464115..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py
+++ /dev/null
@@ -1,231 +0,0 @@
-# Lint as: python3
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Definitions for high level configuration groups.."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-
-from typing import Any, List, Mapping, Optional
-
-import dataclasses
-
-from official.modeling import hyperparams
-from official.modeling.hyperparams import config_definitions
-
-CallbacksConfig = config_definitions.CallbacksConfig
-TensorboardConfig = config_definitions.TensorboardConfig
-RuntimeConfig = config_definitions.RuntimeConfig
-
-
-@dataclasses.dataclass
-class ExportConfig(hyperparams.Config):
- """Configuration for exports.
-
- Attributes:
- checkpoint: the path to the checkpoint to export.
- destination: the path to where the checkpoint should be exported.
- """
- checkpoint: str = None
- destination: str = None
-
-
-@dataclasses.dataclass
-class MetricsConfig(hyperparams.Config):
- """Configuration for Metrics.
-
- Attributes:
- accuracy: Whether or not to track accuracy as a Callback. Defaults to None.
- top_5: Whether or not to track top_5_accuracy as a Callback. Defaults to
- None.
- """
- accuracy: bool = None
- top_5: bool = None
-
-
-@dataclasses.dataclass
-class TimeHistoryConfig(hyperparams.Config):
- """Configuration for the TimeHistory callback.
-
- Attributes:
- log_steps: Interval of steps between logging of batch level stats.
- """
- log_steps: int = None
-
-
-@dataclasses.dataclass
-class TrainConfig(hyperparams.Config):
- """Configuration for training.
-
- Attributes:
- resume_checkpoint: Whether or not to enable load checkpoint loading.
- Defaults to None.
- epochs: The number of training epochs to run. Defaults to None.
- steps: The number of steps to run per epoch. If None, then this will be
- inferred based on the number of images and batch size. Defaults to None.
- callbacks: An instance of CallbacksConfig.
- metrics: An instance of MetricsConfig.
- tensorboard: An instance of TensorboardConfig.
- set_epoch_loop: Whether or not to set `experimental_steps_per_execution` to
- equal the number of training steps in `model.compile`. This reduces the
- number of callbacks run per epoch which significantly improves end-to-end
- TPU training time.
- """
- resume_checkpoint: bool = None
- epochs: int = None
- steps: int = None
- callbacks: CallbacksConfig = CallbacksConfig()
- metrics: MetricsConfig = None
- tensorboard: TensorboardConfig = TensorboardConfig()
- time_history: TimeHistoryConfig = TimeHistoryConfig()
- set_epoch_loop: bool = False
-
-
-@dataclasses.dataclass
-class EvalConfig(hyperparams.Config):
- """Configuration for evaluation.
-
- Attributes:
- epochs_between_evals: The number of train epochs to run between evaluations.
- Defaults to None.
- steps: The number of eval steps to run during evaluation. If None, this will
- be inferred based on the number of images and batch size. Defaults to
- None.
- skip_eval: Whether or not to skip evaluation.
- """
- epochs_between_evals: int = None
- steps: int = None
- skip_eval: bool = False
-
-
-@dataclasses.dataclass
-class LossConfig(hyperparams.Config):
- """Configuration for Loss.
-
- Attributes:
- name: The name of the loss. Defaults to None.
- label_smoothing: Whether or not to apply label smoothing to the loss. This
- only applies to 'categorical_cross_entropy'.
- """
- name: str = None
- label_smoothing: float = None
-
-
-@dataclasses.dataclass
-class OptimizerConfig(hyperparams.Config):
- """Configuration for Optimizers.
-
- Attributes:
- name: The name of the optimizer. Defaults to None.
- decay: Decay or rho, discounting factor for gradient. Defaults to None.
- epsilon: Small value used to avoid 0 denominator. Defaults to None.
- momentum: Plain momentum constant. Defaults to None.
- nesterov: Whether or not to apply Nesterov momentum. Defaults to None.
- moving_average_decay: The amount of decay to apply. If 0 or None, then
- exponential moving average is not used. Defaults to None.
- lookahead: Whether or not to apply the lookahead optimizer. Defaults to
- None.
- beta_1: The exponential decay rate for the 1st moment estimates. Used in the
- Adam optimizers. Defaults to None.
- beta_2: The exponential decay rate for the 2nd moment estimates. Used in the
- Adam optimizers. Defaults to None.
- epsilon: Small value used to avoid 0 denominator. Defaults to 1e-7.
- """
- name: str = None
- decay: float = None
- epsilon: float = None
- momentum: float = None
- nesterov: bool = None
- moving_average_decay: Optional[float] = None
- lookahead: Optional[bool] = None
- beta_1: float = None
- beta_2: float = None
- epsilon: float = None
-
-
-@dataclasses.dataclass
-class LearningRateConfig(hyperparams.Config):
- """Configuration for learning rates.
-
- Attributes:
- name: The name of the learning rate. Defaults to None.
- initial_lr: The initial learning rate. Defaults to None.
- decay_epochs: The number of decay epochs. Defaults to None.
- decay_rate: The rate of decay. Defaults to None.
- warmup_epochs: The number of warmup epochs. Defaults to None.
- batch_lr_multiplier: The multiplier to apply to the base learning rate, if
- necessary. Defaults to None.
- examples_per_epoch: the number of examples in a single epoch. Defaults to
- None.
- boundaries: boundaries used in piecewise constant decay with warmup.
- multipliers: multipliers used in piecewise constant decay with warmup.
- scale_by_batch_size: Scale the learning rate by a fraction of the batch
- size. Set to 0 for no scaling (default).
- staircase: Apply exponential decay at discrete values instead of continuous.
- """
- name: str = None
- initial_lr: float = None
- decay_epochs: float = None
- decay_rate: float = None
- warmup_epochs: int = None
- examples_per_epoch: int = None
- boundaries: List[int] = None
- multipliers: List[float] = None
- scale_by_batch_size: float = 0.
- staircase: bool = None
-
-
-@dataclasses.dataclass
-class ModelConfig(hyperparams.Config):
- """Configuration for Models.
-
- Attributes:
- name: The name of the model. Defaults to None.
- model_params: The parameters used to create the model. Defaults to None.
- num_classes: The number of classes in the model. Defaults to None.
- loss: A `LossConfig` instance. Defaults to None.
- optimizer: An `OptimizerConfig` instance. Defaults to None.
- """
- name: str = None
- model_params: hyperparams.Config = None
- num_classes: int = None
- loss: LossConfig = None
- optimizer: OptimizerConfig = None
-
-
-@dataclasses.dataclass
-class ExperimentConfig(hyperparams.Config):
- """Base configuration for an image classification experiment.
-
- Attributes:
- model_dir: The directory to use when running an experiment.
- mode: e.g. 'train_and_eval', 'export'
- runtime: A `RuntimeConfig` instance.
- train: A `TrainConfig` instance.
- evaluation: An `EvalConfig` instance.
- model: A `ModelConfig` instance.
- export: An `ExportConfig` instance.
- """
- model_dir: str = None
- model_name: str = None
- mode: str = None
- runtime: RuntimeConfig = None
- train_dataset: Any = None
- validation_dataset: Any = None
- train: TrainConfig = None
- evaluation: EvalConfig = None
- model: ModelConfig = None
- export: ExportConfig = None
diff --git a/spaces/Navneet574/Heart_Disease_Prediciton/app.py b/spaces/Navneet574/Heart_Disease_Prediciton/app.py
deleted file mode 100644
index 30f66c36eeab6633487a32671d615f07ab544f93..0000000000000000000000000000000000000000
--- a/spaces/Navneet574/Heart_Disease_Prediciton/app.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import gradio as gr
-from joblib import load
-import numpy as np
-import pandas as pd
-
-AgeCategory = ['18-24', '25-29', '35-39', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80 or older']
-Binary_Input = ['Yes', 'No']
-Sex = ['Male', 'Female']
-Race = ['White', 'Black', 'Asian', 'American Indian/Alaskan Native', 'Hispanic', 'Other']
-Diabetic = ['Yes', 'No', 'No borderline diabetes', 'Yes (during pregnancy)']
-GeneralHealth = ['Excellent', 'Very good', 'Good', 'Fair', 'Poor']
-
-dic_ageCat = {'18-24':0, '25-29':1, '35-39':2, '35-39':3, '40-44':4, '45-49':5, '50-54':6, '55-59':7, '60-64':8, '65-69':9, '70-74':10, '75-79':11, '80 or older':12}
-dic_binary = {'Yes':1, 'No':0}
-dic_sex = {'Male':0, 'Female':1}
-dic_race = {'White':5, 'Black':2, 'Asian':0, 'American Indian/Alaskan Native':3, 'Hispanic':1, 'Other':4}
-dic_diabetic = {'Yes':2, 'No':0, 'No borderline diabetes':1, 'Yes (during pregnancy)':3}
-dic_generalHealth = {'Excellent':0, 'Very good':4, 'Good':2, 'Fair':1, 'Poor':3}
-
-
-def predict_price(BMI, Smoking, AlcoholDrinking, Stroke, PhysicalHealth, MentalHealth, DiffWalking, Sex, AgeCategory, Race, Diabetic, PhysicalActivity, GenHealth, SleepTime, Asthma, KidneyDisease, SkinCancer):
- model = load('Heart_Disease_Classification.jb')
-
- data = {
- 'BMI': [BMI],
- 'Smoking': dic_binary[Smoking],
- 'AlcoholDrinking': dic_binary[AlcoholDrinking],
- 'Stroke': dic_binary[Stroke],
- 'PhysicalHealth': [PhysicalHealth],
- 'MentalHealth': [MentalHealth],
- 'DiffWalking': dic_binary[DiffWalking],
- 'Sex': dic_sex[Sex],
- 'AgeCategory': dic_ageCat[AgeCategory],
- 'Race': dic_race[Race],
- 'Diabetic': dic_diabetic[Diabetic],
- 'PhysicalActivity': dic_binary[PhysicalActivity],
- 'GenHealth': dic_generalHealth[GenHealth],
- 'SleepTime': [SleepTime],
- 'Asthma': dic_binary[Asthma],
- 'KidneyDisease': dic_binary[KidneyDisease],
- 'SkinCancer': dic_binary[SkinCancer]
- }
-
- Xinp = pd.DataFrame(data)
- print(Xinp)
-
- stone = model.predict(Xinp)
-
- if stone == 1:
- return stone["YES"]
- else:
- return stone["NO"]
-
-ui = gr.Interface(
- fn=predict_price,inputs=[
- gr.inputs.Textbox(placeholder='BMI', default=0,
- numeric=True, label='Body Mass Index (BMI) (normal is between 18.5 and 24.9)'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Smoking', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Alcohol Drinking', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Stroke', default="No"),
- gr.inputs.Textbox(placeholder='Physical Health',
- default=0, numeric=True, label='Physical Health Score'),
- gr.inputs.Textbox(placeholder='MentalHealth', default=0,
- numeric=True, label='Mental Health Score'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Diff Walking', default="No"),
- gr.inputs.Dropdown(choices=Sex, label='Sex', default=""),
- gr.inputs.Dropdown(choices=AgeCategory, label='Age Category', default="18-24"),
- gr.inputs.Dropdown(choices=Race, label='Race', default="Asian"),
- gr.inputs.Dropdown(choices=Diabetic, label='Diabetic', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Physical Activity', default="Yes"),
- gr.inputs.Dropdown(choices=GeneralHealth, label='General Health', default="Excellent"),
- gr.inputs.Textbox(placeholder='Sleep Time',
- default='7', numeric=True, label='Sleep Time (normal is between 6-8 hrs)'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Asthma', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Kidney Disease', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Skin Cancer', default="No")
- ], outputs=[
- "text"
- ]
-)
-if __name__ == "__main__":
- ui.launch()
\ No newline at end of file
diff --git a/spaces/Nikhil0987/omm/convo.py b/spaces/Nikhil0987/omm/convo.py
deleted file mode 100644
index f5c87b19d82d3f1e1869101faf1205475b5ed3b9..0000000000000000000000000000000000000000
--- a/spaces/Nikhil0987/omm/convo.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from transformers import pipeline, Conversation
-import streamlit as st
-from streamlit_option_menu import option_menu
-
-chatbot = pipeline(task="conversational", model="microsoft/DialoGPT-medium")
-
-def Convo():
- if convo := st.chat_input("Enter your message"):
-
- conversation = Conversation(convo)
-
-
-
- # candidate_labels = ["HELP", "PROBLEM SOLVE", "GENERAL TALK"]
-
-
- ans = chatbot(conversation)
- # add_user_input = st.button("Add User Input")
-
- # conversation.add_user_input("{}".format(convo))
- # conversation = chatbot(conversation)
- with st.chat_message("assistant"):
- # ans
- st.write(ans.generated_responses[-1])
-
-
-# convo()
diff --git a/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py b/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py
deleted file mode 100644
index d9d372dcbb1bed2fffbfd8e81d6da749ceab730b..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py
+++ /dev/null
@@ -1,772 +0,0 @@
-"""
-This scripts plots examples of the images that get best and worse metrics
-"""
-print("Imports...", end="")
-import os
-import sys
-from argparse import ArgumentParser
-from pathlib import Path
-
-import matplotlib.patches as mpatches
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import seaborn as sns
-import yaml
-from imageio import imread
-from matplotlib.gridspec import GridSpec
-from skimage.color import rgba2rgb
-from sklearn.metrics.pairwise import euclidean_distances
-
-sys.path.append("../")
-
-from climategan.data import encode_mask_label
-from climategan.eval_metrics import edges_coherence_std_min
-from eval_masker import crop_and_resize
-
-# -----------------------
-# ----- Constants -----
-# -----------------------
-
-# Metrics
-metrics = ["error", "f05", "edge_coherence"]
-
-dict_metrics = {
- "names": {
- "tpr": "TPR, Recall, Sensitivity",
- "tnr": "TNR, Specificity, Selectivity",
- "fpr": "FPR",
- "fpt": "False positives relative to image size",
- "fnr": "FNR, Miss rate",
- "fnt": "False negatives relative to image size",
- "mpr": "May positive rate (MPR)",
- "mnr": "May negative rate (MNR)",
- "accuracy": "Accuracy (ignoring may)",
- "error": "Error",
- "f05": "F05 score",
- "precision": "Precision",
- "edge_coherence": "Edge coherence",
- "accuracy_must_may": "Accuracy (ignoring cannot)",
- },
- "key_metrics": ["error", "f05", "edge_coherence"],
-}
-
-
-# Colors
-colorblind_palette = sns.color_palette("colorblind")
-color_cannot = colorblind_palette[1]
-color_must = colorblind_palette[2]
-color_may = colorblind_palette[7]
-color_pred = colorblind_palette[4]
-
-icefire = sns.color_palette("icefire", as_cmap=False, n_colors=5)
-color_tp = icefire[0]
-color_tn = icefire[1]
-color_fp = icefire[4]
-color_fn = icefire[3]
-
-
-def parsed_args():
- """
- Parse and returns command-line args
-
- Returns:
- argparse.Namespace: the parsed arguments
- """
- parser = ArgumentParser()
- parser.add_argument(
- "--input_csv",
- default="ablations_metrics_20210311.csv",
- type=str,
- help="CSV containing the results of the ablation study",
- )
- parser.add_argument(
- "--output_dir",
- default=None,
- type=str,
- help="Output directory",
- )
- parser.add_argument(
- "--models_log_path",
- default=None,
- type=str,
- help="Path containing the log files of the models",
- )
- parser.add_argument(
- "--masker_test_set_dir",
- default=None,
- type=str,
- help="Directory containing the test images",
- )
- parser.add_argument(
- "--best_model",
- default="dada, msd_spade, pseudo",
- type=str,
- help="The string identifier of the best model",
- )
- parser.add_argument(
- "--dpi",
- default=200,
- type=int,
- help="DPI for the output images",
- )
- parser.add_argument(
- "--alpha",
- default=0.5,
- type=float,
- help="Transparency of labels shade",
- )
- parser.add_argument(
- "--percentile",
- default=0.05,
- type=float,
- help="Transparency of labels shade",
- )
- parser.add_argument(
- "--seed",
- default=None,
- type=int,
- help="Bootstrap random seed, for reproducibility",
- )
- parser.add_argument(
- "--no_images",
- action="store_true",
- default=False,
- help="Do not generate images",
- )
-
- return parser.parse_args()
-
-
-def map_color(arr, input_color, output_color, rtol=1e-09):
- """
- Maps one color to another
- """
- input_color_arr = np.tile(input_color, (arr.shape[:2] + (1,)))
- output = arr.copy()
- output[np.all(np.isclose(arr, input_color_arr, rtol=rtol), axis=2)] = output_color
- return output
-
-
-def plot_labels(ax, img, label, img_id, n_, add_title, do_legend):
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (255, 0, 0), color_cannot)
- label_colmap = map_color(label_colmap, (0, 0, 255), color_must)
- label_colmap = map_color(label_colmap, (0, 0, 0), color_may)
-
- ax.imshow(img)
- ax.imshow(label_colmap, alpha=0.5)
- ax.axis("off")
-
- if n_ in [1, 3, 5]:
- color_ = "green"
- else:
- color_ = "red"
-
- ax.text(
- -0.15,
- 0.5,
- img_id,
- color=color_,
- fontweight="roman",
- fontsize="x-large",
- horizontalalignment="left",
- verticalalignment="center",
- transform=ax.transAxes,
- )
-
- if add_title:
- ax.set_title("Labels", rotation=0, fontsize="x-large")
-
-
-def plot_pred(ax, img, pred, img_id, add_title, do_legend):
- pred = np.tile(np.expand_dims(pred, axis=2), reps=(1, 1, 3))
-
- pred_colmap = pred.astype(float)
- pred_colmap = map_color(pred_colmap, (1, 1, 1), color_pred)
- pred_colmap_ma = np.ma.masked_not_equal(pred_colmap, color_pred)
- pred_colmap_ma = pred_colmap_ma.mask * img + pred_colmap_ma
-
- ax.imshow(img)
- ax.imshow(pred_colmap_ma, alpha=0.5)
- ax.axis("off")
-
- if add_title:
- ax.set_title("Prediction", rotation=0, fontsize="x-large")
-
-
-def plot_correct_incorrect(
- ax, img_filename, img, metric, label, img_id, n_, add_title, do_legend
-):
- # FP
- fp_map = imread(
- model_path / "eval-metrics/fp" / "{}_fp.png".format(Path(img_filename).stem)
- )
- fp_map = np.tile(np.expand_dims(fp_map, axis=2), reps=(1, 1, 3))
-
- fp_map_colmap = fp_map.astype(float)
- fp_map_colmap = map_color(fp_map_colmap, (1, 1, 1), color_fp)
-
- # FN
- fn_map = imread(
- model_path / "eval-metrics/fn" / "{}_fn.png".format(Path(img_filename).stem)
- )
- fn_map = np.tile(np.expand_dims(fn_map, axis=2), reps=(1, 1, 3))
-
- fn_map_colmap = fn_map.astype(float)
- fn_map_colmap = map_color(fn_map_colmap, (1, 1, 1), color_fn)
-
- # TP
- tp_map = imread(
- model_path / "eval-metrics/tp" / "{}_tp.png".format(Path(img_filename).stem)
- )
- tp_map = np.tile(np.expand_dims(tp_map, axis=2), reps=(1, 1, 3))
-
- tp_map_colmap = tp_map.astype(float)
- tp_map_colmap = map_color(tp_map_colmap, (1, 1, 1), color_tp)
-
- # TN
- tn_map = imread(
- model_path / "eval-metrics/tn" / "{}_tn.png".format(Path(img_filename).stem)
- )
- tn_map = np.tile(np.expand_dims(tn_map, axis=2), reps=(1, 1, 3))
-
- tn_map_colmap = tn_map.astype(float)
- tn_map_colmap = map_color(tn_map_colmap, (1, 1, 1), color_tn)
-
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (0, 0, 0), color_may)
- label_colmap_ma = np.ma.masked_not_equal(label_colmap, color_may)
- label_colmap_ma = label_colmap_ma.mask * img + label_colmap_ma
-
- # Combine masks
- maps = fp_map_colmap + fn_map_colmap + tp_map_colmap + tn_map_colmap
- maps_ma = np.ma.masked_equal(maps, (0, 0, 0))
- maps_ma = maps_ma.mask * img + maps_ma
-
- ax.imshow(img)
- ax.imshow(label_colmap_ma, alpha=0.5)
- ax.imshow(maps_ma, alpha=0.5)
- ax.axis("off")
-
- if add_title:
- ax.set_title("Metric", rotation=0, fontsize="x-large")
-
-
-def plot_edge_coherence(ax, img, metric, label, pred, img_id, n_, add_title, do_legend):
- pred = np.tile(np.expand_dims(pred, axis=2), reps=(1, 1, 3))
-
- ec, pred_ec, label_ec = edges_coherence_std_min(
- np.squeeze(pred[:, :, 0]), np.squeeze(encode_mask_label(label, "flood"))
- )
-
- ##################
- # Edge distances #
- ##################
-
- # Location of edges
- pred_ec_coord = np.argwhere(pred_ec > 0)
- label_ec_coord = np.argwhere(label_ec > 0)
-
- # Normalized pairwise distances between pred and label
- dist_mat = np.divide(
- euclidean_distances(pred_ec_coord, label_ec_coord), pred_ec.shape[0]
- )
-
- # Standard deviation of the minimum distance from pred to label
- min_dist = np.min(dist_mat, axis=1) # noqa: F841
-
- #############
- # Make plot #
- #############
-
- pred_ec = np.tile(
- np.expand_dims(np.asarray(pred_ec > 0, dtype=float), axis=2), reps=(1, 1, 3)
- )
- pred_ec_colmap = map_color(pred_ec, (1, 1, 1), color_pred)
- pred_ec_colmap_ma = np.ma.masked_not_equal(pred_ec_colmap, color_pred) # noqa: F841
-
- label_ec = np.tile(
- np.expand_dims(np.asarray(label_ec > 0, dtype=float), axis=2), reps=(1, 1, 3)
- )
- label_ec_colmap = map_color(label_ec, (1, 1, 1), color_must)
- label_ec_colmap_ma = np.ma.masked_not_equal( # noqa: F841
- label_ec_colmap, color_must
- )
-
- # Combined pred and label edges
- combined_ec = pred_ec_colmap + label_ec_colmap
- combined_ec_ma = np.ma.masked_equal(combined_ec, (0, 0, 0))
- combined_ec_img = combined_ec_ma.mask * img + combined_ec
-
- # Pred
- pred_colmap = pred.astype(float)
- pred_colmap = map_color(pred_colmap, (1, 1, 1), color_pred)
- pred_colmap_ma = np.ma.masked_not_equal(pred_colmap, color_pred)
-
- # Must
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (0, 0, 255), color_must)
- label_colmap_ma = np.ma.masked_not_equal(label_colmap, color_must)
-
- # TP
- tp_map = imread(
- model_path / "eval-metrics/tp" / "{}_tp.png".format(Path(srs_sel.filename).stem)
- )
- tp_map = np.tile(np.expand_dims(tp_map, axis=2), reps=(1, 1, 3))
- tp_map_colmap = tp_map.astype(float)
- tp_map_colmap = map_color(tp_map_colmap, (1, 1, 1), color_tp)
- tp_map_colmap_ma = np.ma.masked_not_equal(tp_map_colmap, color_tp)
-
- # Combination
- comb_pred = (
- (pred_colmap_ma.mask ^ tp_map_colmap_ma.mask)
- & tp_map_colmap_ma.mask
- & combined_ec_ma.mask
- ) * pred_colmap
- comb_label = (
- (label_colmap_ma.mask ^ pred_colmap_ma.mask)
- & pred_colmap_ma.mask
- & combined_ec_ma.mask
- ) * label_colmap
- comb_tp = combined_ec_ma.mask * tp_map_colmap.copy()
- combined = comb_tp + comb_label + comb_pred
- combined_ma = np.ma.masked_equal(combined, (0, 0, 0))
- combined_ma = combined_ma.mask * combined_ec_img + combined_ma
-
- ax.imshow(combined_ec_img, alpha=1)
- ax.imshow(combined_ma, alpha=0.5)
- ax.axis("off")
-
- # Plot lines
- idx_sort_x = np.argsort(pred_ec_coord[:, 1])
- offset = 100
- for idx in range(offset, pred_ec_coord.shape[0], offset):
- y0, x0 = pred_ec_coord[idx_sort_x[idx], :]
- argmin = np.argmin(dist_mat[idx_sort_x[idx]])
- y1, x1 = label_ec_coord[argmin, :]
- ax.plot([x0, x1], [y0, y1], color="white", linewidth=0.5)
-
- if add_title:
- ax.set_title("Metric", rotation=0, fontsize="x-large")
-
-
-def plot_images_metric(
- axes, metric, img_filename, img_id, n_, srs_sel, add_title, do_legend
-):
-
- # Read images
- img_path = imgs_orig_path / img_filename
- label_path = labels_path / "{}_labeled.png".format(Path(img_filename).stem)
- img, label = crop_and_resize(img_path, label_path)
- img = rgba2rgb(img) if img.shape[-1] == 4 else img / 255.0
-
- pred = imread(
- model_path / "eval-metrics/pred" / "{}_pred.png".format(Path(img_filename).stem)
- )
-
- # Label
- plot_labels(axes[0], img, label, img_id, n_, add_title, do_legend)
-
- # Prediction
- plot_pred(axes[1], img, pred, img_id, add_title, do_legend)
-
- # Correct / incorrect
- if metric in ["error", "f05"]:
- plot_correct_incorrect(
- axes[2],
- img_filename,
- img,
- metric,
- label,
- img_id,
- n_,
- add_title,
- do_legend=False,
- )
- handles = []
- lw = 1.0
- handles.append(
- mpatches.Patch(facecolor=color_tp, label="TP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_tn, label="TN", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_fp, label="FP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_fn, label="FN", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(
- facecolor=color_may,
- label="May-be-flooded",
- linewidth=lw,
- alpha=0.66,
- )
- )
- labels = ["TP", "TN", "FP", "FN", "May-be-flooded"]
- if metric == "error":
- if n_ in [1, 3, 5]:
- title = "Low error rate"
- else:
- title = "High error rate"
- else:
- if n_ in [1, 3, 5]:
- title = "High F05 score"
- else:
- title = "Low F05 score"
- # Edge coherence
- elif metric == "edge_coherence":
- plot_edge_coherence(
- axes[2], img, metric, label, pred, img_id, n_, add_title, do_legend=False
- )
- handles = []
- lw = 1.0
- handles.append(
- mpatches.Patch(facecolor=color_tp, label="TP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_pred, label="pred", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(
- facecolor=color_must,
- label="Must-be-flooded",
- linewidth=lw,
- alpha=0.66,
- )
- )
- labels = ["TP", "Prediction", "Must-be-flooded"]
- if n_ in [1, 3, 5]:
- title = "High edge coherence"
- else:
- title = "Low edge coherence"
-
- else:
- raise ValueError
-
- labels_values_title = "Error: {:.4f} \nFO5: {:.4f} \nEdge coherence: {:.4f}".format(
- srs_sel.error, srs_sel.f05, srs_sel.edge_coherence
- )
-
- plot_legend(axes[3], img, handles, labels, labels_values_title, title)
-
-
-def plot_legend(ax, img, handles, labels, labels_values_title, title):
- img_ = np.zeros_like(img, dtype=np.uint8)
- img_.fill(255)
- ax.imshow(img_)
- ax.axis("off")
-
- leg1 = ax.legend(
- handles=handles,
- labels=labels,
- title=title,
- title_fontsize="medium",
- labelspacing=0.6,
- loc="upper left",
- fontsize="x-small",
- frameon=False,
- )
- leg1._legend_box.align = "left"
-
- leg2 = ax.legend(
- title=labels_values_title,
- title_fontsize="small",
- loc="lower left",
- frameon=False,
- )
- leg2._legend_box.align = "left"
-
- ax.add_artist(leg1)
-
-
-def scatterplot_metrics_pair(ax, df, x_metric, y_metric, dict_images):
-
- sns.scatterplot(data=df, x=x_metric, y=y_metric, ax=ax)
-
- # Set X-label
- ax.set_xlabel(dict_metrics["names"][x_metric], rotation=0, fontsize="medium")
-
- # Set Y-label
- ax.set_ylabel(dict_metrics["names"][y_metric], rotation=90, fontsize="medium")
-
- # Change spines
- sns.despine(ax=ax, left=True, bottom=True)
-
- annotate_scatterplot(ax, dict_images, x_metric, y_metric)
-
-
-def scatterplot_metrics(ax, df, df_all, dict_images, plot_all=False):
-
- # Other
- if plot_all:
- sns.scatterplot(
- data=df_all.loc[df_all.ground == True],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='+', alpha=0.25)
- sns.scatterplot(
- data=df_all.loc[df_all.instagan == True],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='x', alpha=0.25)
- sns.scatterplot(
- data=df_all.loc[(df_all.instagan == False) & (df_all.instagan == False) &
- (df_all.model_feats != args.best_model)],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='s', alpha=0.25)
-
- # Best model
- cmap_ = sns.cubehelix_palette(as_cmap=True)
- sns.scatterplot(
- data=df, x="error", y="f05", hue="edge_coherence", ax=ax, palette=cmap_
- )
-
- norm = plt.Normalize(df["edge_coherence"].min(), df["edge_coherence"].max())
- sm = plt.cm.ScalarMappable(cmap=cmap_, norm=norm)
- sm.set_array([])
-
- # Remove the legend and add a colorbar
- ax.get_legend().remove()
- ax_cbar = ax.figure.colorbar(sm)
- ax_cbar.set_label("Edge coherence", labelpad=8)
-
- # Set X-label
- ax.set_xlabel(dict_metrics["names"]["error"], rotation=0, fontsize="medium")
-
- # Set Y-label
- ax.set_ylabel(dict_metrics["names"]["f05"], rotation=90, fontsize="medium")
-
- annotate_scatterplot(ax, dict_images, "error", "f05")
-
- # Change spines
- sns.despine(ax=ax, left=True, bottom=True)
-
- # Set XY limits
- xlim = ax.get_xlim()
- ylim = ax.get_ylim()
- ax.set_xlim([0.0, xlim[1]])
- ax.set_ylim([ylim[0], 1.0])
-
-
-def annotate_scatterplot(ax, dict_images, x_metric, y_metric, offset=0.1):
- xlim = ax.get_xlim()
- ylim = ax.get_ylim()
- x_len = xlim[1] - xlim[0]
- y_len = ylim[1] - ylim[0]
- x_th = xlim[1] - x_len / 2.0
- y_th = ylim[1] - y_len / 2.0
- for text, d in dict_images.items():
- if text in ["B", "D", "F"]:
- x = d[x_metric]
- y = d[y_metric]
-
- x_text = x + x_len * offset if x < x_th else x - x_len * offset
- y_text = y + y_len * offset if y < y_th else y - y_len * offset
-
- ax.annotate(
- xy=(x, y),
- xycoords="data",
- xytext=(x_text, y_text),
- textcoords="data",
- text=text,
- arrowprops=dict(facecolor="black", shrink=0.05),
- fontsize="medium",
- color="black",
- )
- elif text == "A":
- x = (
- dict_images["A"][x_metric]
- + dict_images["C"][x_metric]
- + dict_images["E"][x_metric]
- ) / 3
- y = (
- dict_images["A"][y_metric]
- + dict_images["C"][y_metric]
- + dict_images["E"][y_metric]
- ) / 3
-
- x_text = x + x_len * 2 * offset if x < x_th else x - x_len * 2 * offset
- y_text = (
- y + y_len * 0.45 * offset if y < y_th else y - y_len * 0.45 * offset
- )
-
- ax.annotate(
- xy=(x, y),
- xycoords="data",
- xytext=(x_text, y_text),
- textcoords="data",
- text="A, C, E",
- arrowprops=dict(facecolor="black", shrink=0.05),
- fontsize="medium",
- color="black",
- )
-
-
-if __name__ == "__main__":
- # -----------------------------
- # ----- Parse arguments -----
- # -----------------------------
- args = parsed_args()
- print("Args:\n" + "\n".join([f" {k:20}: {v}" for k, v in vars(args).items()]))
-
- # Determine output dir
- if args.output_dir is None:
- output_dir = Path(os.environ["SLURM_TMPDIR"])
- else:
- output_dir = Path(args.output_dir)
- if not output_dir.exists():
- output_dir.mkdir(parents=True, exist_ok=False)
-
- # Store args
- output_yml = output_dir / "labels.yml"
- with open(output_yml, "w") as f:
- yaml.dump(vars(args), f)
-
- # Data dirs
- imgs_orig_path = Path(args.masker_test_set_dir) / "imgs"
- labels_path = Path(args.masker_test_set_dir) / "labels"
-
- # Read CSV
- df_all = pd.read_csv(args.input_csv, index_col="model_img_idx")
-
- # Select best model
- df = df_all.loc[df_all.model_feats == args.best_model]
- v_key, model_dir = df.model.unique()[0].split("/")
- model_path = Path(args.models_log_path) / "ablation-{}".format(v_key) / model_dir
-
- # Set up plot
- sns.reset_orig()
- sns.set(style="whitegrid")
- plt.rcParams.update({"font.family": "serif"})
- plt.rcParams.update(
- {
- "font.serif": [
- "Computer Modern Roman",
- "Times New Roman",
- "Utopia",
- "New Century Schoolbook",
- "Century Schoolbook L",
- "ITC Bookman",
- "Bookman",
- "Times",
- "Palatino",
- "Charter",
- "serif" "Bitstream Vera Serif",
- "DejaVu Serif",
- ]
- }
- )
-
- if args.seed:
- np.random.seed(args.seed)
- img_ids = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
- dict_images = {}
- idx = 0
-
- # Define grid of subplots
- grid_vmargin = 0.03 # Extent of the vertical margin between metric grids
- ax_hspace = 0.04 # Extent of the vertical space between axes of same grid
- ax_wspace = 0.05 # Extent of the horizontal space between axes of same grid
- n_grids = len(metrics)
- n_cols = 4
- n_rows = 2
- h_grid = (1.0 / n_grids) - ((n_grids - 1) * grid_vmargin) / n_grids
-
- fig1 = plt.figure(dpi=200, figsize=(11, 13))
-
- n_ = 0
- add_title = False
- for metric_id, metric in enumerate(metrics):
-
- # Create grid
- top_grid = 1.0 - metric_id * h_grid - metric_id * grid_vmargin
- bottom_grid = top_grid - h_grid
- gridspec = GridSpec(
- n_rows,
- n_cols,
- wspace=ax_wspace,
- hspace=ax_hspace,
- bottom=bottom_grid,
- top=top_grid,
- )
-
- # Select best
- if metric == "error":
- ascending = True
- else:
- ascending = False
- idx_rand = np.random.permutation(int(args.percentile * len(df)))[0]
- srs_sel = df.sort_values(by=metric, ascending=ascending).iloc[idx_rand]
- img_id = img_ids[idx]
- dict_images.update({img_id: srs_sel})
- # Read images
- img_filename = srs_sel.filename
-
- axes_row = [fig1.add_subplot(gridspec[0, c]) for c in range(n_cols)]
- if not args.no_images:
- n_ += 1
- if metric_id == 0:
- add_title = True
- plot_images_metric(
- axes_row,
- metric,
- img_filename,
- img_id,
- n_,
- srs_sel,
- add_title=add_title,
- do_legend=False,
- )
- add_title = False
-
- idx += 1
- print("1 more row done.")
- # Select worst
- if metric == "error":
- ascending = False
- else:
- ascending = True
- idx_rand = np.random.permutation(int(args.percentile * len(df)))[0]
- srs_sel = df.sort_values(by=metric, ascending=ascending).iloc[idx_rand]
- img_id = img_ids[idx]
- dict_images.update({img_id: srs_sel})
- # Read images
- img_filename = srs_sel.filename
-
- axes_row = [fig1.add_subplot(gridspec[1, c]) for c in range(n_cols)]
- if not args.no_images:
- n_ += 1
- plot_images_metric(
- axes_row,
- metric,
- img_filename,
- img_id,
- n_,
- srs_sel,
- add_title=add_title,
- do_legend=False,
- )
-
- idx += 1
- print("1 more row done.")
-
- output_fig = output_dir / "all_metrics.png"
-
- fig1.tight_layout() # (pad=1.5) #
- fig1.savefig(output_fig, dpi=fig1.dpi, bbox_inches="tight")
-
- # Scatter plot
- fig2 = plt.figure(dpi=200)
-
- scatterplot_metrics(fig2.gca(), df, df_all, dict_images)
-
- # fig2, axes = plt.subplots(nrows=1, ncols=3, dpi=200, figsize=(18, 5))
- #
- # scatterplot_metrics_pair(axes[0], df, "error", "f05", dict_images)
- # scatterplot_metrics_pair(axes[1], df, "error", "edge_coherence", dict_images)
- # scatterplot_metrics_pair(axes[2], df, "f05", "edge_coherence", dict_images)
-
- output_fig = output_dir / "scatterplots.png"
- fig2.savefig(output_fig, dpi=fig2.dpi, bbox_inches="tight")
diff --git a/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh b/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh
deleted file mode 100644
index d649238546b996ae1ad00f7753c04aeebc7aaa97..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-echo "Dowloading Script" && python download_labelbox.py
-
-echo "Merging Script" && python merge_labelbox_masks.py
-
-echo "Cleaning labeled"
-rm /Users/victor/Downloads/metrics-v2/labels/*
-cp /Users/victor/Downloads/labelbox_test_flood-v2/__labeled/* /Users/victor/Downloads/metrics-v2/labels
-
-echo "Create labeled images Script" && python create_labeled.py
\ No newline at end of file
diff --git a/spaces/Nunchakuka/FrenchAnonymizer/models.py b/spaces/Nunchakuka/FrenchAnonymizer/models.py
deleted file mode 100644
index 46b8aacb1bef18f6fad4c20c968b19125626799c..0000000000000000000000000000000000000000
--- a/spaces/Nunchakuka/FrenchAnonymizer/models.py
+++ /dev/null
@@ -1,351 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
-
-class Encoder(nn.Module):
- def __init__(self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
- def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
- resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(weight_norm(
- ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
- k, u, padding=(k-u)//2)))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel//(2**(i+1))
- for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i*self.num_kernels+j](x)
- else:
- xs += self.resblocks[i*self.num_kernels+j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- print('Removing weight norm...')
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
- ])
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ])
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2,3,5,7,11]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class SpeakerEncoder(torch.nn.Module):
- def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
- super(SpeakerEncoder, self).__init__()
- self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
- self.linear = nn.Linear(model_hidden_size, model_embedding_size)
- self.relu = nn.ReLU()
-
- def forward(self, mels):
- self.lstm.flatten_parameters()
- _, (hidden, _) = self.lstm(mels)
- embeds_raw = self.relu(self.linear(hidden[-1]))
- return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
-
- def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
- mel_slices = []
- for i in range(0, total_frames-partial_frames, partial_hop):
- mel_range = torch.arange(i, i+partial_frames)
- mel_slices.append(mel_range)
-
- return mel_slices
-
- def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
- mel_len = mel.size(1)
- last_mel = mel[:,-partial_frames:]
-
- if mel_len > partial_frames:
- mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
- mels = list(mel[:,s] for s in mel_slices)
- mels.append(last_mel)
- mels = torch.stack(tuple(mels), 0).squeeze(1)
-
- with torch.no_grad():
- partial_embeds = self(mels)
- embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
- #embed = embed / torch.linalg.norm(embed, 2)
- else:
- with torch.no_grad():
- embed = self(last_mel)
-
- return embed
-
-
-class SynthesizerTrn(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels,
- ssl_dim,
- use_spk,
- **kwargs):
-
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- self.ssl_dim = ssl_dim
- self.use_spk = use_spk
-
- self.enc_p = Encoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16)
- self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
- self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
- self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
- if not self.use_spk:
- self.enc_spk = SpeakerEncoder(model_hidden_size=gin_channels, model_embedding_size=gin_channels)
-
- def forward(self, c, spec, g=None, mel=None, c_lengths=None, spec_lengths=None):
- if c_lengths == None:
- c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
- if spec_lengths == None:
- spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
-
- if not self.use_spk:
- g = self.enc_spk(mel.transpose(1,2))
- g = g.unsqueeze(-1)
-
- _, m_p, logs_p, _ = self.enc_p(c, c_lengths)
- z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g)
- z_p = self.flow(z, spec_mask, g=g)
-
- z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size)
- o = self.dec(z_slice, g=g)
-
- return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, c, g=None, mel=None, c_lengths=None):
- if c_lengths == None:
- c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
- if not self.use_spk:
- g = self.enc_spk.embed_utterance(mel.transpose(1,2))
- g = g.unsqueeze(-1)
-
- z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths)
- z = self.flow(z_p, c_mask, g=g, reverse=True)
- o = self.dec(z * c_mask, g=g)
-
- return o
diff --git a/spaces/Nunchakuka/FrenchAnonymizer/utils.py b/spaces/Nunchakuka/FrenchAnonymizer/utils.py
deleted file mode 100644
index 1bd5b6185af6c9f1c270b8ba345bfc36d059e081..0000000000000000000000000000000000000000
--- a/spaces/Nunchakuka/FrenchAnonymizer/utils.py
+++ /dev/null
@@ -1,305 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-from torch.nn import functional as F
-from commons import sequence_mask
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def get_cmodel(rank):
- checkpoint = torch.load('wavlm/WavLM-Large.pt')
- cfg = WavLMConfig(checkpoint['cfg'])
- cmodel = WavLM(cfg).cuda(rank)
- cmodel.load_state_dict(checkpoint['model'])
- cmodel.eval()
- return cmodel
-
-
-def get_content(cmodel, y):
- with torch.no_grad():
- c = cmodel.extract_features(y.squeeze(1))[0]
- c = c.transpose(1, 2)
- return c
-
-
-def get_vocoder(rank):
- with open("hifigan/config.json", "r") as f:
- config = json.load(f)
- config = hifigan.AttrDict(config)
- vocoder = hifigan.Generator(config)
- ckpt = torch.load("hifigan/generator_v1")
- vocoder.load_state_dict(ckpt["generator"])
- vocoder.eval()
- vocoder.remove_weight_norm()
- vocoder.cuda(rank)
- return vocoder
-
-
-def transform(mel, height): # 68-92
- #r = np.random.random()
- #rate = r * 0.3 + 0.85 # 0.85-1.15
- #height = int(mel.size(-2) * rate)
- tgt = torchvision.transforms.functional.resize(mel, (height, mel.size(-1)))
- if height >= mel.size(-2):
- return tgt[:, :mel.size(-2), :]
- else:
- silence = tgt[:,-1:,:].repeat(1,mel.size(-2)-height,1)
- silence += torch.randn_like(silence) / 10
- return torch.cat((tgt, silence), 1)
-
-
-def stretch(mel, width): # 0.5-2
- return torchvision.transforms.functional.resize(mel, (mel.size(-2), width))
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict= {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})" .format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info("Saving model and optimizer state at iteration {} to {}".format(
- iteration, checkpoint_path))
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- torch.save({'model': state_dict,
- 'iteration': iteration,
- 'optimizer': optimizer.state_dict(),
- 'learning_rate': learning_rate}, checkpoint_path)
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
- for k, v in scalars.items():
- writer.add_scalar(k, v, global_step)
- for k, v in histograms.items():
- writer.add_histogram(k, v, global_step)
- for k, v in images.items():
- writer.add_image(k, v, global_step, dataformats='HWC')
- for k, v in audios.items():
- writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
- x = f_list[-1]
- print(x)
- return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10,2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py
deleted file mode 100644
index bb80d11a67cd75764a89f6f41915b0348ae96e92..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py
+++ /dev/null
@@ -1,428 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from multiprocessing import Pool
-
-import numpy as np
-from fairseq import options
-from fairseq.data import dictionary
-from fairseq.scoring import bleu
-
-from examples.noisychannel import (
- rerank_generate,
- rerank_options,
- rerank_score_bw,
- rerank_score_lm,
- rerank_utils,
-)
-
-
-def score_target_hypo(
- args, a, b, c, lenpen, target_outfile, hypo_outfile, write_hypos, normalize
-):
-
- print("lenpen", lenpen, "weight1", a, "weight2", b, "weight3", c)
- gen_output_lst, bitext1_lst, bitext2_lst, lm_res_lst = load_score_files(args)
- dict = dictionary.Dictionary()
- scorer = scorer = bleu.Scorer(
- bleu.BleuConfig(
- pad=dict.pad(),
- eos=dict.eos(),
- unk=dict.unk(),
- )
- )
-
- ordered_hypos = {}
- ordered_targets = {}
-
- for shard_id in range(len(bitext1_lst)):
- bitext1 = bitext1_lst[shard_id]
- bitext2 = bitext2_lst[shard_id]
- gen_output = gen_output_lst[shard_id]
- lm_res = lm_res_lst[shard_id]
-
- total = len(bitext1.rescore_source.keys())
- source_lst = []
- hypo_lst = []
- score_lst = []
- reference_lst = []
- j = 1
- best_score = -math.inf
-
- for i in range(total):
- # length is measured in terms of words, not bpe tokens, since models may not share the same bpe
- target_len = len(bitext1.rescore_hypo[i].split())
-
- if lm_res is not None:
- lm_score = lm_res.score[i]
- else:
- lm_score = 0
-
- if bitext2 is not None:
- bitext2_score = bitext2.rescore_score[i]
- bitext2_backwards = bitext2.backwards
- else:
- bitext2_score = None
- bitext2_backwards = None
-
- score = rerank_utils.get_score(
- a,
- b,
- c,
- target_len,
- bitext1.rescore_score[i],
- bitext2_score,
- lm_score=lm_score,
- lenpen=lenpen,
- src_len=bitext1.source_lengths[i],
- tgt_len=bitext1.target_lengths[i],
- bitext1_backwards=bitext1.backwards,
- bitext2_backwards=bitext2_backwards,
- normalize=normalize,
- )
-
- if score > best_score:
- best_score = score
- best_hypo = bitext1.rescore_hypo[i]
-
- if j == gen_output.num_hypos[i] or j == args.num_rescore:
- j = 1
- hypo_lst.append(best_hypo)
- score_lst.append(best_score)
- source_lst.append(bitext1.rescore_source[i])
- reference_lst.append(bitext1.rescore_target[i])
-
- best_score = -math.inf
- best_hypo = ""
- else:
- j += 1
-
- gen_keys = list(sorted(gen_output.no_bpe_target.keys()))
-
- for key in range(len(gen_keys)):
- if args.prefix_len is None:
- assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], (
- "pred and rescore hypo mismatch: i: "
- + str(key)
- + ", "
- + str(hypo_lst[key])
- + str(gen_keys[key])
- + str(gen_output.no_bpe_hypo[key])
- )
- sys_tok = dict.encode_line(hypo_lst[key])
- ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]])
- scorer.add(ref_tok, sys_tok)
-
- else:
- full_hypo = rerank_utils.get_full_from_prefix(
- hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]]
- )
- sys_tok = dict.encode_line(full_hypo)
- ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]])
- scorer.add(ref_tok, sys_tok)
-
- # if only one set of hyper parameters is provided, write the predictions to a file
- if write_hypos:
- # recover the orinal ids from n best list generation
- for key in range(len(gen_output.no_bpe_target)):
- if args.prefix_len is None:
- assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], (
- "pred and rescore hypo mismatch:"
- + "i:"
- + str(key)
- + str(hypo_lst[key])
- + str(gen_output.no_bpe_hypo[key])
- )
- ordered_hypos[gen_keys[key]] = hypo_lst[key]
- ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[
- gen_keys[key]
- ]
-
- else:
- full_hypo = rerank_utils.get_full_from_prefix(
- hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]]
- )
- ordered_hypos[gen_keys[key]] = full_hypo
- ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[
- gen_keys[key]
- ]
-
- # write the hypos in the original order from nbest list generation
- if args.num_shards == (len(bitext1_lst)):
- with open(target_outfile, "w") as t:
- with open(hypo_outfile, "w") as h:
- for key in range(len(ordered_hypos)):
- t.write(ordered_targets[key])
- h.write(ordered_hypos[key])
-
- res = scorer.result_string(4)
- if write_hypos:
- print(res)
- score = rerank_utils.parse_bleu_scoring(res)
- return score
-
-
-def match_target_hypo(args, target_outfile, hypo_outfile):
- """combine scores from the LM and bitext models, and write the top scoring hypothesis to a file"""
- if len(args.weight1) == 1:
- res = score_target_hypo(
- args,
- args.weight1[0],
- args.weight2[0],
- args.weight3[0],
- args.lenpen[0],
- target_outfile,
- hypo_outfile,
- True,
- args.normalize,
- )
- rerank_scores = [res]
- else:
- print("launching pool")
- with Pool(32) as p:
- rerank_scores = p.starmap(
- score_target_hypo,
- [
- (
- args,
- args.weight1[i],
- args.weight2[i],
- args.weight3[i],
- args.lenpen[i],
- target_outfile,
- hypo_outfile,
- False,
- args.normalize,
- )
- for i in range(len(args.weight1))
- ],
- )
-
- if len(rerank_scores) > 1:
- best_index = np.argmax(rerank_scores)
- best_score = rerank_scores[best_index]
- print("best score", best_score)
- print("best lenpen", args.lenpen[best_index])
- print("best weight1", args.weight1[best_index])
- print("best weight2", args.weight2[best_index])
- print("best weight3", args.weight3[best_index])
- return (
- args.lenpen[best_index],
- args.weight1[best_index],
- args.weight2[best_index],
- args.weight3[best_index],
- best_score,
- )
-
- else:
- return (
- args.lenpen[0],
- args.weight1[0],
- args.weight2[0],
- args.weight3[0],
- rerank_scores[0],
- )
-
-
-def load_score_files(args):
- if args.all_shards:
- shard_ids = list(range(args.num_shards))
- else:
- shard_ids = [args.shard_id]
-
- gen_output_lst = []
- bitext1_lst = []
- bitext2_lst = []
- lm_res1_lst = []
-
- for shard_id in shard_ids:
- using_nbest = args.nbest_list is not None
- (
- pre_gen,
- left_to_right_preprocessed_dir,
- right_to_left_preprocessed_dir,
- backwards_preprocessed_dir,
- lm_preprocessed_dir,
- ) = rerank_utils.get_directories(
- args.data_dir_name,
- args.num_rescore,
- args.gen_subset,
- args.gen_model_name,
- shard_id,
- args.num_shards,
- args.sampling,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- rerank1_is_gen = (
- args.gen_model == args.score_model1 and args.source_prefix_frac is None
- )
- rerank2_is_gen = (
- args.gen_model == args.score_model2 and args.source_prefix_frac is None
- )
-
- score1_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model1_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards1,
- )
- if args.score_model2 is not None:
- score2_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model2_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards2,
- )
- if args.language_model is not None:
- lm_score_file = rerank_utils.rescore_file_name(
- pre_gen, args.prefix_len, args.lm_name, lm_file=True
- )
-
- # get gen output
- predictions_bpe_file = pre_gen + "/generate_output_bpe.txt"
- if using_nbest:
- print("Using predefined n-best list from interactive.py")
- predictions_bpe_file = args.nbest_list
- gen_output = rerank_utils.BitextOutputFromGen(
- predictions_bpe_file,
- bpe_symbol=args.post_process,
- nbest=using_nbest,
- prefix_len=args.prefix_len,
- target_prefix_frac=args.target_prefix_frac,
- )
-
- if rerank1_is_gen:
- bitext1 = gen_output
- else:
- bitext1 = rerank_utils.BitextOutput(
- score1_file,
- args.backwards1,
- args.right_to_left1,
- args.post_process,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- if args.score_model2 is not None or args.nbest_list is not None:
- if rerank2_is_gen:
- bitext2 = gen_output
- else:
- bitext2 = rerank_utils.BitextOutput(
- score2_file,
- args.backwards2,
- args.right_to_left2,
- args.post_process,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- assert (
- bitext2.source_lengths == bitext1.source_lengths
- ), "source lengths for rescoring models do not match"
- assert (
- bitext2.target_lengths == bitext1.target_lengths
- ), "target lengths for rescoring models do not match"
- else:
- if args.diff_bpe:
- assert args.score_model2 is None
- bitext2 = gen_output
- else:
- bitext2 = None
-
- if args.language_model is not None:
- lm_res1 = rerank_utils.LMOutput(
- lm_score_file,
- args.lm_dict,
- args.prefix_len,
- args.post_process,
- args.target_prefix_frac,
- )
- else:
- lm_res1 = None
-
- gen_output_lst.append(gen_output)
- bitext1_lst.append(bitext1)
- bitext2_lst.append(bitext2)
- lm_res1_lst.append(lm_res1)
- return gen_output_lst, bitext1_lst, bitext2_lst, lm_res1_lst
-
-
-def rerank(args):
- if type(args.lenpen) is not list:
- args.lenpen = [args.lenpen]
- if type(args.weight1) is not list:
- args.weight1 = [args.weight1]
- if type(args.weight2) is not list:
- args.weight2 = [args.weight2]
- if type(args.weight3) is not list:
- args.weight3 = [args.weight3]
- if args.all_shards:
- shard_ids = list(range(args.num_shards))
- else:
- shard_ids = [args.shard_id]
-
- for shard_id in shard_ids:
- (
- pre_gen,
- left_to_right_preprocessed_dir,
- right_to_left_preprocessed_dir,
- backwards_preprocessed_dir,
- lm_preprocessed_dir,
- ) = rerank_utils.get_directories(
- args.data_dir_name,
- args.num_rescore,
- args.gen_subset,
- args.gen_model_name,
- shard_id,
- args.num_shards,
- args.sampling,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
- rerank_generate.gen_and_reprocess_nbest(args)
- rerank_score_bw.score_bw(args)
- rerank_score_lm.score_lm(args)
-
- if args.write_hypos is None:
- write_targets = pre_gen + "/matched_targets"
- write_hypos = pre_gen + "/matched_hypos"
- else:
- write_targets = args.write_hypos + "_targets" + args.gen_subset
- write_hypos = args.write_hypos + "_hypos" + args.gen_subset
-
- if args.all_shards:
- write_targets += "_all_shards"
- write_hypos += "_all_shards"
-
- (
- best_lenpen,
- best_weight1,
- best_weight2,
- best_weight3,
- best_score,
- ) = match_target_hypo(args, write_targets, write_hypos)
-
- return best_lenpen, best_weight1, best_weight2, best_weight3, best_score
-
-
-def cli_main():
- parser = rerank_options.get_reranking_parser()
- args = options.parse_args_and_arch(parser)
- rerank(args)
-
-
-if __name__ == "__main__":
- cli_main()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md
deleted file mode 100644
index 9d3d7b67dc08440b5f4d1c5a7ffcd4bd6e76c14f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md
+++ /dev/null
@@ -1,179 +0,0 @@
-# Examples of Training scripts for Non-autoregressive Machine Translation models
-
-### Non-autoregressive Transformer (NAT, Gu et al., 2017)
-Note that we need to have an additional module to perform "length prediction" (`--length-loss-factor`) before generating the whole sequence.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch nonautoregressive_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-### Fast Structured Decoding for Sequence Models (NAT-CRF, Sun et al., 2019)
-Note that we implemented a low-rank appromixated CRF model by setting `--crf-lowrank-approx=32` and `--crf-beam-approx=64` as discribed in the original paper. All other settings are the same as the vanilla NAT model.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch nacrf_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --word-ins-loss-factor 0.5 \
- --crf-lowrank-approx 32 \
- --crf-beam-approx 64 \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-### Non-autoregressive Transformer with Iterative Refinement (iNAT, Lee et al., 2018)
-Note that `--train-step` means how many iterations of refinement we used during training, and `--dae-ratio` controls the ratio of denoising auto-encoder training described in the original paper.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch iterative_nonautoregressive_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --train-step 4 \
- --dae-ratio 0.5 \
- --stochastic-approx \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-### Insertion Transformer (InsT, Stern et al., 2019)
-Note that we need to specify the "slot-loss" (uniform or balanced tree) described in the original paper. Here we use `--label-tau` to control the temperature.
-
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch insertion_transformer \
- --noise random_delete \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-### Mask Predict (CMLM, Ghazvininejad et al., 2019)
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch cmlm_transformer \
- --noise random_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-
-
-### Levenshtein Transformer (LevT, Gu et al., 2019)
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch levenshtein_transformer \
- --noise random_delete \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py
deleted file mode 100644
index 8b7eb2ec4fc5190c4dcdfe34b0259e6f448e18a9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""isort:skip_file"""
-
-from .dictionary import Dictionary, TruncatedDictionary
-
-from .fairseq_dataset import FairseqDataset, FairseqIterableDataset
-
-from .base_wrapper_dataset import BaseWrapperDataset
-
-from .add_target_dataset import AddTargetDataset
-from .append_token_dataset import AppendTokenDataset
-from .audio.raw_audio_dataset import BinarizedAudioDataset, FileAudioDataset
-from .audio.hubert_dataset import HubertDataset
-from .backtranslation_dataset import BacktranslationDataset
-from .bucket_pad_length_dataset import BucketPadLengthDataset
-from .colorize_dataset import ColorizeDataset
-from .concat_dataset import ConcatDataset
-from .concat_sentences_dataset import ConcatSentencesDataset
-from .denoising_dataset import DenoisingDataset
-from .id_dataset import IdDataset
-from .indexed_dataset import (
- IndexedCachedDataset,
- IndexedDataset,
- IndexedRawTextDataset,
- MMapIndexedDataset,
-)
-from .language_pair_dataset import LanguagePairDataset
-from .list_dataset import ListDataset
-from .lm_context_window_dataset import LMContextWindowDataset
-from .lru_cache_dataset import LRUCacheDataset
-from .mask_tokens_dataset import MaskTokensDataset
-from .monolingual_dataset import MonolingualDataset
-from .multi_corpus_sampled_dataset import MultiCorpusSampledDataset
-from .nested_dictionary_dataset import NestedDictionaryDataset
-from .noising import NoisingDataset
-from .numel_dataset import NumelDataset
-from .num_samples_dataset import NumSamplesDataset
-from .offset_tokens_dataset import OffsetTokensDataset
-from .pad_dataset import LeftPadDataset, PadDataset, RightPadDataset
-from .prepend_dataset import PrependDataset
-from .prepend_token_dataset import PrependTokenDataset
-from .raw_label_dataset import RawLabelDataset
-from .replace_dataset import ReplaceDataset
-from .resampling_dataset import ResamplingDataset
-from .roll_dataset import RollDataset
-from .round_robin_zip_datasets import RoundRobinZipDatasets
-from .sort_dataset import SortDataset
-from .strip_token_dataset import StripTokenDataset
-from .subsample_dataset import SubsampleDataset
-from .token_block_dataset import TokenBlockDataset
-from .transform_eos_dataset import TransformEosDataset
-from .transform_eos_lang_pair_dataset import TransformEosLangPairDataset
-from .shorten_dataset import TruncateDataset, RandomCropDataset
-from .multilingual.sampled_multi_dataset import SampledMultiDataset
-from .multilingual.sampled_multi_epoch_dataset import SampledMultiEpochDataset
-from .fasta_dataset import FastaDataset, EncodedFastaDataset
-
-from .iterators import (
- CountingIterator,
- EpochBatchIterator,
- GroupedIterator,
- ShardedIterator,
-)
-
-__all__ = [
- "AddTargetDataset",
- "AppendTokenDataset",
- "BacktranslationDataset",
- "BaseWrapperDataset",
- "BinarizedAudioDataset",
- "BucketPadLengthDataset",
- "ColorizeDataset",
- "ConcatDataset",
- "ConcatSentencesDataset",
- "CountingIterator",
- "DenoisingDataset",
- "Dictionary",
- "EncodedFastaDataset",
- "EpochBatchIterator",
- "FairseqDataset",
- "FairseqIterableDataset",
- "FastaDataset",
- "FileAudioDataset",
- "GroupedIterator",
- "HubertDataset",
- "IdDataset",
- "IndexedCachedDataset",
- "IndexedDataset",
- "IndexedRawTextDataset",
- "LanguagePairDataset",
- "LeftPadDataset",
- "ListDataset",
- "LMContextWindowDataset",
- "LRUCacheDataset",
- "MaskTokensDataset",
- "MMapIndexedDataset",
- "MonolingualDataset",
- "MultiCorpusSampledDataset",
- "NestedDictionaryDataset",
- "NoisingDataset",
- "NumelDataset",
- "NumSamplesDataset",
- "OffsetTokensDataset",
- "PadDataset",
- "PrependDataset",
- "PrependTokenDataset",
- "RandomCropDataset",
- "RawLabelDataset",
- "ResamplingDataset",
- "ReplaceDataset",
- "RightPadDataset",
- "RollDataset",
- "RoundRobinZipDatasets",
- "SampledMultiDataset",
- "SampledMultiEpochDataset",
- "ShardedIterator",
- "SortDataset",
- "StripTokenDataset",
- "SubsampleDataset",
- "TokenBlockDataset",
- "TransformEosDataset",
- "TransformEosLangPairDataset",
- "TruncateDataset",
- "TruncatedDictionary",
-]
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py
deleted file mode 100644
index 11c7dc1ea966a54f8915ef164377e40f90e851a1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-from fairseq.tasks import register_task
-from fairseq.tasks.speech_to_text import SpeechToTextTask
-from fairseq.tasks.translation import (
- TranslationTask, TranslationConfig
-)
-
-try:
- import examples.simultaneous_translation # noqa
- import_successful = True
-except BaseException:
- import_successful = False
-
-
-logger = logging.getLogger(__name__)
-
-
-def check_import(flag):
- if not flag:
- raise ImportError(
- "'examples.simultaneous_translation' is not correctly imported. "
- "Please considering `pip install -e $FAIRSEQ_DIR`."
- )
-
-
-@register_task("simul_speech_to_text")
-class SimulSpeechToTextTask(SpeechToTextTask):
- def __init__(self, args, tgt_dict):
- check_import(import_successful)
- super().__init__(args, tgt_dict)
-
-
-@register_task("simul_text_to_text", dataclass=TranslationConfig)
-class SimulTextToTextTask(TranslationTask):
- def __init__(self, cfg, src_dict, tgt_dict):
- check_import(import_successful)
- super().__init__(cfg, src_dict, tgt_dict)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md
deleted file mode 100644
index 57104230655c7c517d25904e634c53b6159ee60f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md
+++ /dev/null
@@ -1,42 +0,0 @@
-# Unit to Speech Model (unit2speech)
-
-Unit to speech model is modified Tacotron2 model that learns to synthesize speech from discrete speech units. All models are trained on quantized [LJSpeech](https://keithito.com/LJ-Speech-Dataset/).
-
-Upstream Units | Download Link
-|-|-
-Log Mel Filterbank + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km50/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km100/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km200/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km500/tts_checkpoint_best.pt)
-Modified CPC + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km50/tts_checkpoint_best.pt)
-Modified CPC + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km100/tts_checkpoint_best.pt)
-Modified CPC + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km200/tts_checkpoint_best.pt)
-Modified CPC + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km500/tts_checkpoint_best.pt)
-HuBERT Base + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km50/tts_checkpoint_best.pt)
-HuBERT Base + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km100/tts_checkpoint_best.pt)
-HuBERT Base + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km200/tts_checkpoint_best.pt)
-HuBERT Base + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km500/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km50/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km100/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km200/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km500/tts_checkpoint_best.pt)
-
-## Run inference using a unit2speech model
-* Install librosa, unidecode and inflect using `pip install librosa, unidecode, inflect`
-* Download [Waveglow checkpoint](https://dl.fbaipublicfiles.com/textless_nlp/gslm/waveglow_256channels_new.pt). This is the vocoder.
-
-Sample commnd to run inference using trained unit2speech models. Please note that the quantized audio to synthesized should be using the same units as the unit2speech model was trained with.
-```
-FAIRSEQ_ROOT=
-TTS_MODEL_PATH=
-QUANTIZED_UNIT_PATH=
-OUT_DIR=
-WAVEGLOW_PATH=
-
-PYTHONPATH=${FAIRSEQ_ROOT}:${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech python ${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py \
- --tts_model_path $TTS_MODEL_PATH \
- --quantized_unit_path $QUANTIZED_UNIT_PATH \
- --out_audio_dir $OUT_DIR \
- --waveglow_path $WAVEGLOW_PATH \
- --max_decoder_steps 2000
-```
\ No newline at end of file
diff --git a/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py b/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py
deleted file mode 100644
index 375e4d50872d5c68ee57ca17470a2ca425425eba..0000000000000000000000000000000000000000
--- a/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import re
-from unidecode import unidecode
-import pyopenjtalk
-
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
- r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
- r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ts', 'ʦ'),
- ('u', 'ɯ'),
- ('j', 'ʥ'),
- ('y', 'j'),
- ('ni', 'n^i'),
- ('nj', 'n^'),
- ('hi', 'çi'),
- ('hj', 'ç'),
- ('f', 'ɸ'),
- ('I', 'i*'),
- ('U', 'ɯ*'),
- ('r', 'ɾ')
-]]
-
-# List of (romaji, ipa2) pairs for marks:
-_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('u', 'ɯ'),
- ('ʧ', 'tʃ'),
- ('j', 'dʑ'),
- ('y', 'j'),
- ('ni', 'n^i'),
- ('nj', 'n^'),
- ('hi', 'çi'),
- ('hj', 'ç'),
- ('f', 'ɸ'),
- ('I', 'i*'),
- ('U', 'ɯ*'),
- ('r', 'ɾ')
-]]
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'Q([↑↓]*[kg])', r'k#\1'),
- (r'Q([↑↓]*[tdjʧ])', r't#\1'),
- (r'Q([↑↓]*[sʃ])', r's\1'),
- (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'N([↑↓]*[pbm])', r'm\1'),
- (r'N([↑↓]*[ʧʥj])', r'n^\1'),
- (r'N([↑↓]*[tdn])', r'n\1'),
- (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-def symbols_to_japanese(text):
- for regex, replacement in _symbols_to_japanese:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_romaji_with_accent(text):
- '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
- text = symbols_to_japanese(text)
- sentences = re.split(_japanese_marks, text)
- marks = re.findall(_japanese_marks, text)
- text = ''
- for i, sentence in enumerate(sentences):
- if re.match(_japanese_characters, sentence):
- if text != '':
- text += ' '
- labels = pyopenjtalk.extract_fullcontext(sentence)
- for n, label in enumerate(labels):
- phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
- if phoneme not in ['sil', 'pau']:
- text += phoneme.replace('ch', 'ʧ').replace('sh',
- 'ʃ').replace('cl', 'Q')
- else:
- continue
- # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
- a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
- a2 = int(re.search(r"\+(\d+)\+", label).group(1))
- a3 = int(re.search(r"\+(\d+)/", label).group(1))
- if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
- a2_next = -1
- else:
- a2_next = int(
- re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
- # Accent phrase boundary
- if a3 == 1 and a2_next == 1:
- text += ' '
- # Falling
- elif a1 == 0 and a2_next == a2 + 1:
- text += '↓'
- # Rising
- elif a2 == 1 and a2_next == 2:
- text += '↑'
- if i < len(marks):
- text += unidecode(marks[i]).replace(' ', '')
- return text
-
-
-def get_real_sokuon(text):
- for regex, replacement in _real_sokuon:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def get_real_hatsuon(text):
- for regex, replacement in _real_hatsuon:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa(text):
- text = japanese_to_romaji_with_accent(text).replace('...', '…')
- text = re.sub(
- r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
- text = get_real_sokuon(text)
- text = get_real_hatsuon(text)
- for regex, replacement in _romaji_to_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa2(text):
- text = japanese_to_romaji_with_accent(text).replace('...', '…')
- text = get_real_sokuon(text)
- text = get_real_hatsuon(text)
- for regex, replacement in _romaji_to_ipa2:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa3(text):
- text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
- 'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
- text = re.sub(
- r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
- text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
- return text
diff --git a/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py b/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py
deleted file mode 100644
index c215099fedacae35c6798fdd9b8420a447aa16bb..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-from audiocraft.quantization.vq import ResidualVectorQuantizer
-
-
-class TestResidualVectorQuantizer:
-
- def test_rvq(self):
- x = torch.randn(1, 16, 2048)
- vq = ResidualVectorQuantizer(n_q=8, dimension=16, bins=8)
- res = vq(x, 1.)
- assert res.x.shape == torch.Size([1, 16, 2048])
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py
deleted file mode 100644
index feb7a8222487756d38482da95183bbbcbbe96ed9..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py
+++ /dev/null
@@ -1,864 +0,0 @@
-
-import math
-import json
-import copy
-from typing import List, Dict
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.modeling.proposal_generator.build import PROPOSAL_GENERATOR_REGISTRY
-from detectron2.layers import ShapeSpec, cat
-from detectron2.structures import Instances, Boxes
-from detectron2.modeling import detector_postprocess
-from detectron2.utils.comm import get_world_size
-from detectron2.config import configurable
-
-from ..layers.heatmap_focal_loss import heatmap_focal_loss_jit
-from ..layers.heatmap_focal_loss import binary_heatmap_focal_loss
-from ..layers.iou_loss import IOULoss
-from ..layers.ml_nms import ml_nms
-from ..debug import debug_train, debug_test
-from .utils import reduce_sum, _transpose
-from .centernet_head import CenterNetHead
-
-__all__ = ["CenterNet"]
-
-INF = 100000000
-
-@PROPOSAL_GENERATOR_REGISTRY.register()
-class CenterNet(nn.Module):
- @configurable
- def __init__(self,
- # input_shape: Dict[str, ShapeSpec],
- in_channels=256,
- *,
- num_classes=80,
- in_features=("p3", "p4", "p5", "p6", "p7"),
- strides=(8, 16, 32, 64, 128),
- score_thresh=0.05,
- hm_min_overlap=0.8,
- loc_loss_type='giou',
- min_radius=4,
- hm_focal_alpha=0.25,
- hm_focal_beta=4,
- loss_gamma=2.0,
- reg_weight=2.0,
- not_norm_reg=True,
- with_agn_hm=False,
- only_proposal=False,
- as_proposal=False,
- not_nms=False,
- pos_weight=1.,
- neg_weight=1.,
- sigmoid_clamp=1e-4,
- ignore_high_fp=-1.,
- center_nms=False,
- sizes_of_interest=[[0,80],[64,160],[128,320],[256,640],[512,10000000]],
- more_pos=False,
- more_pos_thresh=0.2,
- more_pos_topk=9,
- pre_nms_topk_train=1000,
- pre_nms_topk_test=1000,
- post_nms_topk_train=100,
- post_nms_topk_test=100,
- nms_thresh_train=0.6,
- nms_thresh_test=0.6,
- no_reduce=False,
- debug=False,
- vis_thresh=0.5,
- pixel_mean=[103.530,116.280,123.675],
- pixel_std=[1.0,1.0,1.0],
- device='cuda',
- centernet_head=None,
- ):
- super().__init__()
- self.num_classes = num_classes
- self.in_features = in_features
- self.strides = strides
- self.score_thresh = score_thresh
- self.min_radius = min_radius
- self.hm_focal_alpha = hm_focal_alpha
- self.hm_focal_beta = hm_focal_beta
- self.loss_gamma = loss_gamma
- self.reg_weight = reg_weight
- self.not_norm_reg = not_norm_reg
- self.with_agn_hm = with_agn_hm
- self.only_proposal = only_proposal
- self.as_proposal = as_proposal
- self.not_nms = not_nms
- self.pos_weight = pos_weight
- self.neg_weight = neg_weight
- self.sigmoid_clamp = sigmoid_clamp
- self.ignore_high_fp = ignore_high_fp
- self.center_nms = center_nms
- self.sizes_of_interest = sizes_of_interest
- self.more_pos = more_pos
- self.more_pos_thresh = more_pos_thresh
- self.more_pos_topk = more_pos_topk
- self.pre_nms_topk_train = pre_nms_topk_train
- self.pre_nms_topk_test = pre_nms_topk_test
- self.post_nms_topk_train = post_nms_topk_train
- self.post_nms_topk_test = post_nms_topk_test
- self.nms_thresh_train = nms_thresh_train
- self.nms_thresh_test = nms_thresh_test
- self.no_reduce = no_reduce
- self.debug = debug
- self.vis_thresh = vis_thresh
- if self.center_nms:
- self.not_nms = True
- self.iou_loss = IOULoss(loc_loss_type)
- assert (not self.only_proposal) or self.with_agn_hm
- # delta for rendering heatmap
- self.delta = (1 - hm_min_overlap) / (1 + hm_min_overlap)
- if centernet_head is None:
- self.centernet_head = CenterNetHead(
- in_channels=in_channels,
- num_levels=len(in_features),
- with_agn_hm=with_agn_hm,
- only_proposal=only_proposal)
- else:
- self.centernet_head = centernet_head
- if self.debug:
- pixel_mean = torch.Tensor(pixel_mean).to(
- torch.device(device)).view(3, 1, 1)
- pixel_std = torch.Tensor(pixel_std).to(
- torch.device(device)).view(3, 1, 1)
- self.denormalizer = lambda x: x * pixel_std + pixel_mean
-
- @classmethod
- def from_config(cls, cfg, input_shape):
- ret = {
- # 'input_shape': input_shape,
- 'in_channels': input_shape[
- cfg.MODEL.CENTERNET.IN_FEATURES[0]].channels,
- 'num_classes': cfg.MODEL.CENTERNET.NUM_CLASSES,
- 'in_features': cfg.MODEL.CENTERNET.IN_FEATURES,
- 'strides': cfg.MODEL.CENTERNET.FPN_STRIDES,
- 'score_thresh': cfg.MODEL.CENTERNET.INFERENCE_TH,
- 'loc_loss_type': cfg.MODEL.CENTERNET.LOC_LOSS_TYPE,
- 'hm_min_overlap': cfg.MODEL.CENTERNET.HM_MIN_OVERLAP,
- 'min_radius': cfg.MODEL.CENTERNET.MIN_RADIUS,
- 'hm_focal_alpha': cfg.MODEL.CENTERNET.HM_FOCAL_ALPHA,
- 'hm_focal_beta': cfg.MODEL.CENTERNET.HM_FOCAL_BETA,
- 'loss_gamma': cfg.MODEL.CENTERNET.LOSS_GAMMA,
- 'reg_weight': cfg.MODEL.CENTERNET.REG_WEIGHT,
- 'not_norm_reg': cfg.MODEL.CENTERNET.NOT_NORM_REG,
- 'with_agn_hm': cfg.MODEL.CENTERNET.WITH_AGN_HM,
- 'only_proposal': cfg.MODEL.CENTERNET.ONLY_PROPOSAL,
- 'as_proposal': cfg.MODEL.CENTERNET.AS_PROPOSAL,
- 'not_nms': cfg.MODEL.CENTERNET.NOT_NMS,
- 'pos_weight': cfg.MODEL.CENTERNET.POS_WEIGHT,
- 'neg_weight': cfg.MODEL.CENTERNET.NEG_WEIGHT,
- 'sigmoid_clamp': cfg.MODEL.CENTERNET.SIGMOID_CLAMP,
- 'ignore_high_fp': cfg.MODEL.CENTERNET.IGNORE_HIGH_FP,
- 'center_nms': cfg.MODEL.CENTERNET.CENTER_NMS,
- 'sizes_of_interest': cfg.MODEL.CENTERNET.SOI,
- 'more_pos': cfg.MODEL.CENTERNET.MORE_POS,
- 'more_pos_thresh': cfg.MODEL.CENTERNET.MORE_POS_THRESH,
- 'more_pos_topk': cfg.MODEL.CENTERNET.MORE_POS_TOPK,
- 'pre_nms_topk_train': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TRAIN,
- 'pre_nms_topk_test': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TEST,
- 'post_nms_topk_train': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TRAIN,
- 'post_nms_topk_test': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TEST,
- 'nms_thresh_train': cfg.MODEL.CENTERNET.NMS_TH_TRAIN,
- 'nms_thresh_test': cfg.MODEL.CENTERNET.NMS_TH_TEST,
- 'no_reduce': cfg.MODEL.CENTERNET.NO_REDUCE,
- 'debug': cfg.DEBUG,
- 'vis_thresh': cfg.VIS_THRESH,
- 'pixel_mean': cfg.MODEL.PIXEL_MEAN,
- 'pixel_std': cfg.MODEL.PIXEL_STD,
- 'device': cfg.MODEL.DEVICE,
- 'centernet_head': CenterNetHead(
- cfg, [input_shape[f] for f in cfg.MODEL.CENTERNET.IN_FEATURES]),
- }
- return ret
-
-
- def forward(self, images, features_dict, gt_instances):
- features = [features_dict[f] for f in self.in_features]
- clss_per_level, reg_pred_per_level, agn_hm_pred_per_level = \
- self.centernet_head(features)
- grids = self.compute_grids(features)
- shapes_per_level = grids[0].new_tensor(
- [(x.shape[2], x.shape[3]) for x in reg_pred_per_level])
-
- if not self.training:
- return self.inference(
- images, clss_per_level, reg_pred_per_level,
- agn_hm_pred_per_level, grids)
- else:
- pos_inds, labels, reg_targets, flattened_hms = \
- self._get_ground_truth(
- grids, shapes_per_level, gt_instances)
- # logits_pred: M x F, reg_pred: M x 4, agn_hm_pred: M
- logits_pred, reg_pred, agn_hm_pred = self._flatten_outputs(
- clss_per_level, reg_pred_per_level, agn_hm_pred_per_level)
-
- if self.more_pos:
- # add more pixels as positive if \
- # 1. they are within the center3x3 region of an object
- # 2. their regression losses are small (= 0).squeeze(1)
- reg_pred = reg_pred[reg_inds]
- reg_targets_pos = reg_targets[reg_inds]
- reg_weight_map = flattened_hms.max(dim=1)[0]
- reg_weight_map = reg_weight_map[reg_inds]
- reg_weight_map = reg_weight_map * 0 + 1 \
- if self.not_norm_reg else reg_weight_map
- if self.no_reduce:
- reg_norm = max(reg_weight_map.sum(), 1)
- else:
- reg_norm = max(reduce_sum(reg_weight_map.sum()).item() / num_gpus, 1)
-
- reg_loss = self.reg_weight * self.iou_loss(
- reg_pred, reg_targets_pos, reg_weight_map,
- reduction='sum') / reg_norm
- losses['loss_centernet_loc'] = reg_loss
-
- if self.with_agn_hm:
- cat_agn_heatmap = flattened_hms.max(dim=1)[0] # M
- agn_pos_loss, agn_neg_loss = binary_heatmap_focal_loss(
- agn_hm_pred, cat_agn_heatmap, pos_inds,
- alpha=self.hm_focal_alpha,
- beta=self.hm_focal_beta,
- gamma=self.loss_gamma,
- sigmoid_clamp=self.sigmoid_clamp,
- ignore_high_fp=self.ignore_high_fp,
- )
- agn_pos_loss = self.pos_weight * agn_pos_loss / num_pos_avg
- agn_neg_loss = self.neg_weight * agn_neg_loss / num_pos_avg
- losses['loss_centernet_agn_pos'] = agn_pos_loss
- losses['loss_centernet_agn_neg'] = agn_neg_loss
-
- if self.debug:
- print('losses', losses)
- print('total_num_pos', total_num_pos)
- return losses
-
-
- def compute_grids(self, features):
- grids = []
- for level, feature in enumerate(features):
- h, w = feature.size()[-2:]
- shifts_x = torch.arange(
- 0, w * self.strides[level],
- step=self.strides[level],
- dtype=torch.float32, device=feature.device)
- shifts_y = torch.arange(
- 0, h * self.strides[level],
- step=self.strides[level],
- dtype=torch.float32, device=feature.device)
- shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
- shift_x = shift_x.reshape(-1)
- shift_y = shift_y.reshape(-1)
- grids_per_level = torch.stack((shift_x, shift_y), dim=1) + \
- self.strides[level] // 2
- grids.append(grids_per_level)
- return grids
-
-
- def _get_ground_truth(self, grids, shapes_per_level, gt_instances):
- '''
- Input:
- grids: list of tensors [(hl x wl, 2)]_l
- shapes_per_level: list of tuples L x 2:
- gt_instances: gt instances
- Retuen:
- pos_inds: N
- labels: N
- reg_targets: M x 4
- flattened_hms: M x C or M x 1
- N: number of objects in all images
- M: number of pixels from all FPN levels
- '''
-
- # get positive pixel index
- if not self.more_pos:
- pos_inds, labels = self._get_label_inds(
- gt_instances, shapes_per_level)
- else:
- pos_inds, labels = None, None
- heatmap_channels = self.num_classes
- L = len(grids)
- num_loc_list = [len(loc) for loc in grids]
- strides = torch.cat([
- shapes_per_level.new_ones(num_loc_list[l]) * self.strides[l] \
- for l in range(L)]).float() # M
- reg_size_ranges = torch.cat([
- shapes_per_level.new_tensor(self.sizes_of_interest[l]).float().view(
- 1, 2).expand(num_loc_list[l], 2) for l in range(L)]) # M x 2
- grids = torch.cat(grids, dim=0) # M x 2
- M = grids.shape[0]
-
- reg_targets = []
- flattened_hms = []
- for i in range(len(gt_instances)): # images
- boxes = gt_instances[i].gt_boxes.tensor # N x 4
- area = gt_instances[i].gt_boxes.area() # N
- gt_classes = gt_instances[i].gt_classes # N in [0, self.num_classes]
-
- N = boxes.shape[0]
- if N == 0:
- reg_targets.append(grids.new_zeros((M, 4)) - INF)
- flattened_hms.append(
- grids.new_zeros((
- M, 1 if self.only_proposal else heatmap_channels)))
- continue
-
- l = grids[:, 0].view(M, 1) - boxes[:, 0].view(1, N) # M x N
- t = grids[:, 1].view(M, 1) - boxes[:, 1].view(1, N) # M x N
- r = boxes[:, 2].view(1, N) - grids[:, 0].view(M, 1) # M x N
- b = boxes[:, 3].view(1, N) - grids[:, 1].view(M, 1) # M x N
- reg_target = torch.stack([l, t, r, b], dim=2) # M x N x 4
-
- centers = ((boxes[:, [0, 1]] + boxes[:, [2, 3]]) / 2) # N x 2
- centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2
- strides_expanded = strides.view(M, 1, 1).expand(M, N, 2)
- centers_discret = ((centers_expanded / strides_expanded).int() * \
- strides_expanded).float() + strides_expanded / 2 # M x N x 2
-
- is_peak = (((grids.view(M, 1, 2).expand(M, N, 2) - \
- centers_discret) ** 2).sum(dim=2) == 0) # M x N
- is_in_boxes = reg_target.min(dim=2)[0] > 0 # M x N
- is_center3x3 = self.get_center3x3(
- grids, centers, strides) & is_in_boxes # M x N
- is_cared_in_the_level = self.assign_reg_fpn(
- reg_target, reg_size_ranges) # M x N
- reg_mask = is_center3x3 & is_cared_in_the_level # M x N
-
- dist2 = ((grids.view(M, 1, 2).expand(M, N, 2) - \
- centers_expanded) ** 2).sum(dim=2) # M x N
- dist2[is_peak] = 0
- radius2 = self.delta ** 2 * 2 * area # N
- radius2 = torch.clamp(
- radius2, min=self.min_radius ** 2)
- weighted_dist2 = dist2 / radius2.view(1, N).expand(M, N) # M x N
- reg_target = self._get_reg_targets(
- reg_target, weighted_dist2.clone(), reg_mask, area) # M x 4
-
- if self.only_proposal:
- flattened_hm = self._create_agn_heatmaps_from_dist(
- weighted_dist2.clone()) # M x 1
- else:
- flattened_hm = self._create_heatmaps_from_dist(
- weighted_dist2.clone(), gt_classes,
- channels=heatmap_channels) # M x C
-
- reg_targets.append(reg_target)
- flattened_hms.append(flattened_hm)
-
- # transpose im first training_targets to level first ones
- reg_targets = _transpose(reg_targets, num_loc_list)
- flattened_hms = _transpose(flattened_hms, num_loc_list)
- for l in range(len(reg_targets)):
- reg_targets[l] = reg_targets[l] / float(self.strides[l])
- reg_targets = cat([x for x in reg_targets], dim=0) # MB x 4
- flattened_hms = cat([x for x in flattened_hms], dim=0) # MB x C
-
- return pos_inds, labels, reg_targets, flattened_hms
-
-
- def _get_label_inds(self, gt_instances, shapes_per_level):
- '''
- Inputs:
- gt_instances: [n_i], sum n_i = N
- shapes_per_level: L x 2 [(h_l, w_l)]_L
- Returns:
- pos_inds: N'
- labels: N'
- '''
- pos_inds = []
- labels = []
- L = len(self.strides)
- B = len(gt_instances)
- shapes_per_level = shapes_per_level.long()
- loc_per_level = (shapes_per_level[:, 0] * shapes_per_level[:, 1]).long() # L
- level_bases = []
- s = 0
- for l in range(L):
- level_bases.append(s)
- s = s + B * loc_per_level[l]
- level_bases = shapes_per_level.new_tensor(level_bases).long() # L
- strides_default = shapes_per_level.new_tensor(self.strides).float() # L
- for im_i in range(B):
- targets_per_im = gt_instances[im_i]
- bboxes = targets_per_im.gt_boxes.tensor # n x 4
- n = bboxes.shape[0]
- centers = ((bboxes[:, [0, 1]] + bboxes[:, [2, 3]]) / 2) # n x 2
- centers = centers.view(n, 1, 2).expand(n, L, 2)
- strides = strides_default.view(1, L, 1).expand(n, L, 2)
- centers_inds = (centers / strides).long() # n x L x 2
- Ws = shapes_per_level[:, 1].view(1, L).expand(n, L)
- pos_ind = level_bases.view(1, L).expand(n, L) + \
- im_i * loc_per_level.view(1, L).expand(n, L) + \
- centers_inds[:, :, 1] * Ws + \
- centers_inds[:, :, 0] # n x L
- is_cared_in_the_level = self.assign_fpn_level(bboxes)
- pos_ind = pos_ind[is_cared_in_the_level].view(-1)
- label = targets_per_im.gt_classes.view(
- n, 1).expand(n, L)[is_cared_in_the_level].view(-1)
-
- pos_inds.append(pos_ind) # n'
- labels.append(label) # n'
- pos_inds = torch.cat(pos_inds, dim=0).long()
- labels = torch.cat(labels, dim=0)
- return pos_inds, labels # N, N
-
-
- def assign_fpn_level(self, boxes):
- '''
- Inputs:
- boxes: n x 4
- size_ranges: L x 2
- Return:
- is_cared_in_the_level: n x L
- '''
- size_ranges = boxes.new_tensor(
- self.sizes_of_interest).view(len(self.sizes_of_interest), 2) # L x 2
- crit = ((boxes[:, 2:] - boxes[:, :2]) **2).sum(dim=1) ** 0.5 / 2 # n
- n, L = crit.shape[0], size_ranges.shape[0]
- crit = crit.view(n, 1).expand(n, L)
- size_ranges_expand = size_ranges.view(1, L, 2).expand(n, L, 2)
- is_cared_in_the_level = (crit >= size_ranges_expand[:, :, 0]) & \
- (crit <= size_ranges_expand[:, :, 1])
- return is_cared_in_the_level
-
-
- def assign_reg_fpn(self, reg_targets_per_im, size_ranges):
- '''
- TODO (Xingyi): merge it with assign_fpn_level
- Inputs:
- reg_targets_per_im: M x N x 4
- size_ranges: M x 2
- '''
- crit = ((reg_targets_per_im[:, :, :2] + \
- reg_targets_per_im[:, :, 2:])**2).sum(dim=2) ** 0.5 / 2 # M x N
- is_cared_in_the_level = (crit >= size_ranges[:, [0]]) & \
- (crit <= size_ranges[:, [1]])
- return is_cared_in_the_level
-
-
- def _get_reg_targets(self, reg_targets, dist, mask, area):
- '''
- reg_targets (M x N x 4): long tensor
- dist (M x N)
- is_*: M x N
- '''
- dist[mask == 0] = INF * 1.0
- min_dist, min_inds = dist.min(dim=1) # M
- reg_targets_per_im = reg_targets[
- range(len(reg_targets)), min_inds] # M x N x 4 --> M x 4
- reg_targets_per_im[min_dist == INF] = - INF
- return reg_targets_per_im
-
-
- def _create_heatmaps_from_dist(self, dist, labels, channels):
- '''
- dist: M x N
- labels: N
- return:
- heatmaps: M x C
- '''
- heatmaps = dist.new_zeros((dist.shape[0], channels))
- for c in range(channels):
- inds = (labels == c) # N
- if inds.int().sum() == 0:
- continue
- heatmaps[:, c] = torch.exp(-dist[:, inds].min(dim=1)[0])
- zeros = heatmaps[:, c] < 1e-4
- heatmaps[zeros, c] = 0
- return heatmaps
-
-
- def _create_agn_heatmaps_from_dist(self, dist):
- '''
- TODO (Xingyi): merge it with _create_heatmaps_from_dist
- dist: M x N
- return:
- heatmaps: M x 1
- '''
- heatmaps = dist.new_zeros((dist.shape[0], 1))
- heatmaps[:, 0] = torch.exp(-dist.min(dim=1)[0])
- zeros = heatmaps < 1e-4
- heatmaps[zeros] = 0
- return heatmaps
-
-
- def _flatten_outputs(self, clss, reg_pred, agn_hm_pred):
- # Reshape: (N, F, Hl, Wl) -> (N, Hl, Wl, F) -> (sum_l N*Hl*Wl, F)
- clss = cat([x.permute(0, 2, 3, 1).reshape(-1, x.shape[1]) \
- for x in clss], dim=0) if clss[0] is not None else None
- reg_pred = cat(
- [x.permute(0, 2, 3, 1).reshape(-1, 4) for x in reg_pred], dim=0)
- agn_hm_pred = cat([x.permute(0, 2, 3, 1).reshape(-1) \
- for x in agn_hm_pred], dim=0) if self.with_agn_hm else None
- return clss, reg_pred, agn_hm_pred
-
-
- def get_center3x3(self, locations, centers, strides):
- '''
- Inputs:
- locations: M x 2
- centers: N x 2
- strides: M
- '''
- M, N = locations.shape[0], centers.shape[0]
- locations_expanded = locations.view(M, 1, 2).expand(M, N, 2) # M x N x 2
- centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2
- strides_expanded = strides.view(M, 1, 1).expand(M, N, 2) # M x N
- centers_discret = ((centers_expanded / strides_expanded).int() * \
- strides_expanded).float() + strides_expanded / 2 # M x N x 2
- dist_x = (locations_expanded[:, :, 0] - centers_discret[:, :, 0]).abs()
- dist_y = (locations_expanded[:, :, 1] - centers_discret[:, :, 1]).abs()
- return (dist_x <= strides_expanded[:, :, 0]) & \
- (dist_y <= strides_expanded[:, :, 0])
-
-
- def inference(self, images, clss_per_level, reg_pred_per_level,
- agn_hm_pred_per_level, grids):
- logits_pred = [x.sigmoid() if x is not None else None \
- for x in clss_per_level]
- agn_hm_pred_per_level = [x.sigmoid() if x is not None else None \
- for x in agn_hm_pred_per_level]
-
- if self.only_proposal:
- proposals = self.predict_instances(
- grids, agn_hm_pred_per_level, reg_pred_per_level,
- images.image_sizes, [None for _ in agn_hm_pred_per_level])
- else:
- proposals = self.predict_instances(
- grids, logits_pred, reg_pred_per_level,
- images.image_sizes, agn_hm_pred_per_level)
- if self.as_proposal or self.only_proposal:
- for p in range(len(proposals)):
- proposals[p].proposal_boxes = proposals[p].get('pred_boxes')
- proposals[p].objectness_logits = proposals[p].get('scores')
- proposals[p].remove('pred_boxes')
-
- if self.debug:
- debug_test(
- [self.denormalizer(x) for x in images],
- logits_pred, reg_pred_per_level,
- agn_hm_pred_per_level, preds=proposals,
- vis_thresh=self.vis_thresh,
- debug_show_name=False)
- return proposals, {}
-
-
- def predict_instances(
- self, grids, logits_pred, reg_pred, image_sizes, agn_hm_pred,
- is_proposal=False):
- sampled_boxes = []
- for l in range(len(grids)):
- sampled_boxes.append(self.predict_single_level(
- grids[l], logits_pred[l], reg_pred[l] * self.strides[l],
- image_sizes, agn_hm_pred[l], l, is_proposal=is_proposal))
- boxlists = list(zip(*sampled_boxes))
- boxlists = [Instances.cat(boxlist) for boxlist in boxlists]
- boxlists = self.nms_and_topK(
- boxlists, nms=not self.not_nms)
- return boxlists
-
-
- def predict_single_level(
- self, grids, heatmap, reg_pred, image_sizes, agn_hm, level,
- is_proposal=False):
- N, C, H, W = heatmap.shape
- # put in the same format as grids
- if self.center_nms:
- heatmap_nms = nn.functional.max_pool2d(
- heatmap, (3, 3), stride=1, padding=1)
- heatmap = heatmap * (heatmap_nms == heatmap).float()
- heatmap = heatmap.permute(0, 2, 3, 1) # N x H x W x C
- heatmap = heatmap.reshape(N, -1, C) # N x HW x C
- box_regression = reg_pred.view(N, 4, H, W).permute(0, 2, 3, 1) # N x H x W x 4
- box_regression = box_regression.reshape(N, -1, 4)
-
- candidate_inds = heatmap > self.score_thresh # 0.05
- pre_nms_top_n = candidate_inds.view(N, -1).sum(1) # N
- pre_nms_topk = self.pre_nms_topk_train if self.training else self.pre_nms_topk_test
- pre_nms_top_n = pre_nms_top_n.clamp(max=pre_nms_topk) # N
-
- if agn_hm is not None:
- agn_hm = agn_hm.view(N, 1, H, W).permute(0, 2, 3, 1)
- agn_hm = agn_hm.reshape(N, -1)
- heatmap = heatmap * agn_hm[:, :, None]
-
- results = []
- for i in range(N):
- per_box_cls = heatmap[i] # HW x C
- per_candidate_inds = candidate_inds[i] # n
- per_box_cls = per_box_cls[per_candidate_inds] # n
-
- per_candidate_nonzeros = per_candidate_inds.nonzero() # n
- per_box_loc = per_candidate_nonzeros[:, 0] # n
- per_class = per_candidate_nonzeros[:, 1] # n
-
- per_box_regression = box_regression[i] # HW x 4
- per_box_regression = per_box_regression[per_box_loc] # n x 4
- per_grids = grids[per_box_loc] # n x 2
-
- per_pre_nms_top_n = pre_nms_top_n[i] # 1
-
- if per_candidate_inds.sum().item() > per_pre_nms_top_n.item():
- per_box_cls, top_k_indices = \
- per_box_cls.topk(per_pre_nms_top_n, sorted=False)
- per_class = per_class[top_k_indices]
- per_box_regression = per_box_regression[top_k_indices]
- per_grids = per_grids[top_k_indices]
-
- detections = torch.stack([
- per_grids[:, 0] - per_box_regression[:, 0],
- per_grids[:, 1] - per_box_regression[:, 1],
- per_grids[:, 0] + per_box_regression[:, 2],
- per_grids[:, 1] + per_box_regression[:, 3],
- ], dim=1) # n x 4
-
- # avoid invalid boxes in RoI heads
- detections[:, 2] = torch.max(detections[:, 2], detections[:, 0] + 0.01)
- detections[:, 3] = torch.max(detections[:, 3], detections[:, 1] + 0.01)
- boxlist = Instances(image_sizes[i])
- boxlist.scores = torch.sqrt(per_box_cls) \
- if self.with_agn_hm else per_box_cls # n
- # import pdb; pdb.set_trace()
- boxlist.pred_boxes = Boxes(detections)
- boxlist.pred_classes = per_class
- results.append(boxlist)
- return results
-
-
- def nms_and_topK(self, boxlists, nms=True):
- num_images = len(boxlists)
- results = []
- for i in range(num_images):
- nms_thresh = self.nms_thresh_train if self.training else \
- self.nms_thresh_test
- result = ml_nms(boxlists[i], nms_thresh) if nms else boxlists[i]
- if self.debug:
- print('#proposals before nms', len(boxlists[i]))
- print('#proposals after nms', len(result))
- num_dets = len(result)
- post_nms_topk = self.post_nms_topk_train if self.training else \
- self.post_nms_topk_test
- if num_dets > post_nms_topk:
- cls_scores = result.scores
- image_thresh, _ = torch.kthvalue(
- cls_scores.float().cpu(),
- num_dets - post_nms_topk + 1
- )
- keep = cls_scores >= image_thresh.item()
- keep = torch.nonzero(keep).squeeze(1)
- result = result[keep]
- if self.debug:
- print('#proposals after filter', len(result))
- results.append(result)
- return results
-
-
- def _add_more_pos(self, reg_pred, gt_instances, shapes_per_level):
- labels, level_masks, c33_inds, c33_masks, c33_regs = \
- self._get_c33_inds(gt_instances, shapes_per_level)
- N, L, K = labels.shape[0], len(self.strides), 9
- c33_inds[c33_masks == 0] = 0
- reg_pred_c33 = reg_pred[c33_inds].detach() # N x L x K
- invalid_reg = c33_masks == 0
- c33_regs_expand = c33_regs.view(N * L * K, 4).clamp(min=0)
- if N > 0:
- with torch.no_grad():
- c33_reg_loss = self.iou_loss(
- reg_pred_c33.view(N * L * K, 4),
- c33_regs_expand, None,
- reduction='none').view(N, L, K).detach() # N x L x K
- else:
- c33_reg_loss = reg_pred_c33.new_zeros((N, L, K)).detach()
- c33_reg_loss[invalid_reg] = INF # N x L x K
- c33_reg_loss.view(N * L, K)[level_masks.view(N * L), 4] = 0 # real center
- c33_reg_loss = c33_reg_loss.view(N, L * K)
- if N == 0:
- loss_thresh = c33_reg_loss.new_ones((N)).float()
- else:
- loss_thresh = torch.kthvalue(
- c33_reg_loss, self.more_pos_topk, dim=1)[0] # N
- loss_thresh[loss_thresh > self.more_pos_thresh] = self.more_pos_thresh # N
- new_pos = c33_reg_loss.view(N, L, K) < \
- loss_thresh.view(N, 1, 1).expand(N, L, K)
- pos_inds = c33_inds[new_pos].view(-1) # P
- labels = labels.view(N, 1, 1).expand(N, L, K)[new_pos].view(-1)
- return pos_inds, labels
-
-
- def _get_c33_inds(self, gt_instances, shapes_per_level):
- '''
- TODO (Xingyi): The current implementation is ugly. Refactor.
- Get the center (and the 3x3 region near center) locations of each objects
- Inputs:
- gt_instances: [n_i], sum n_i = N
- shapes_per_level: L x 2 [(h_l, w_l)]_L
- '''
- labels = []
- level_masks = []
- c33_inds = []
- c33_masks = []
- c33_regs = []
- L = len(self.strides)
- B = len(gt_instances)
- shapes_per_level = shapes_per_level.long()
- loc_per_level = (shapes_per_level[:, 0] * shapes_per_level[:, 1]).long() # L
- level_bases = []
- s = 0
- for l in range(L):
- level_bases.append(s)
- s = s + B * loc_per_level[l]
- level_bases = shapes_per_level.new_tensor(level_bases).long() # L
- strides_default = shapes_per_level.new_tensor(self.strides).float() # L
- K = 9
- dx = shapes_per_level.new_tensor([-1, 0, 1, -1, 0, 1, -1, 0, 1]).long()
- dy = shapes_per_level.new_tensor([-1, -1, -1, 0, 0, 0, 1, 1, 1]).long()
- for im_i in range(B):
- targets_per_im = gt_instances[im_i]
- bboxes = targets_per_im.gt_boxes.tensor # n x 4
- n = bboxes.shape[0]
- if n == 0:
- continue
- centers = ((bboxes[:, [0, 1]] + bboxes[:, [2, 3]]) / 2) # n x 2
- centers = centers.view(n, 1, 2).expand(n, L, 2)
-
- strides = strides_default.view(1, L, 1).expand(n, L, 2) #
- centers_inds = (centers / strides).long() # n x L x 2
- center_grids = centers_inds * strides + strides // 2# n x L x 2
- l = center_grids[:, :, 0] - bboxes[:, 0].view(n, 1).expand(n, L)
- t = center_grids[:, :, 1] - bboxes[:, 1].view(n, 1).expand(n, L)
- r = bboxes[:, 2].view(n, 1).expand(n, L) - center_grids[:, :, 0]
- b = bboxes[:, 3].view(n, 1).expand(n, L) - center_grids[:, :, 1] # n x L
- reg = torch.stack([l, t, r, b], dim=2) # n x L x 4
- reg = reg / strides_default.view(1, L, 1).expand(n, L, 4).float()
-
- Ws = shapes_per_level[:, 1].view(1, L).expand(n, L)
- Hs = shapes_per_level[:, 0].view(1, L).expand(n, L)
- expand_Ws = Ws.view(n, L, 1).expand(n, L, K)
- expand_Hs = Hs.view(n, L, 1).expand(n, L, K)
- label = targets_per_im.gt_classes.view(n).clone()
- mask = reg.min(dim=2)[0] >= 0 # n x L
- mask = mask & self.assign_fpn_level(bboxes)
- labels.append(label) # n
- level_masks.append(mask) # n x L
-
- Dy = dy.view(1, 1, K).expand(n, L, K)
- Dx = dx.view(1, 1, K).expand(n, L, K)
- c33_ind = level_bases.view(1, L, 1).expand(n, L, K) + \
- im_i * loc_per_level.view(1, L, 1).expand(n, L, K) + \
- (centers_inds[:, :, 1:2].expand(n, L, K) + Dy) * expand_Ws + \
- (centers_inds[:, :, 0:1].expand(n, L, K) + Dx) # n x L x K
-
- c33_mask = \
- ((centers_inds[:, :, 1:2].expand(n, L, K) + dy) < expand_Hs) & \
- ((centers_inds[:, :, 1:2].expand(n, L, K) + dy) >= 0) & \
- ((centers_inds[:, :, 0:1].expand(n, L, K) + dx) < expand_Ws) & \
- ((centers_inds[:, :, 0:1].expand(n, L, K) + dx) >= 0)
- # TODO (Xingyi): think about better way to implement this
- # Currently it hard codes the 3x3 region
- c33_reg = reg.view(n, L, 1, 4).expand(n, L, K, 4).clone()
- c33_reg[:, :, [0, 3, 6], 0] -= 1
- c33_reg[:, :, [0, 3, 6], 2] += 1
- c33_reg[:, :, [2, 5, 8], 0] += 1
- c33_reg[:, :, [2, 5, 8], 2] -= 1
- c33_reg[:, :, [0, 1, 2], 1] -= 1
- c33_reg[:, :, [0, 1, 2], 3] += 1
- c33_reg[:, :, [6, 7, 8], 1] += 1
- c33_reg[:, :, [6, 7, 8], 3] -= 1
- c33_mask = c33_mask & (c33_reg.min(dim=3)[0] >= 0) # n x L x K
- c33_inds.append(c33_ind)
- c33_masks.append(c33_mask)
- c33_regs.append(c33_reg)
-
- if len(level_masks) > 0:
- labels = torch.cat(labels, dim=0)
- level_masks = torch.cat(level_masks, dim=0)
- c33_inds = torch.cat(c33_inds, dim=0).long()
- c33_regs = torch.cat(c33_regs, dim=0)
- c33_masks = torch.cat(c33_masks, dim=0)
- else:
- labels = shapes_per_level.new_zeros((0)).long()
- level_masks = shapes_per_level.new_zeros((0, L)).bool()
- c33_inds = shapes_per_level.new_zeros((0, L, K)).long()
- c33_regs = shapes_per_level.new_zeros((0, L, K, 4)).float()
- c33_masks = shapes_per_level.new_zeros((0, L, K)).bool()
- return labels, level_masks, c33_inds, c33_masks, c33_regs # N x L, N x L x K
\ No newline at end of file
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh b/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh
deleted file mode 100644
index 6d2ba9a6265c0d5fa580035952a1f568dd8d9e44..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh
+++ /dev/null
@@ -1,37 +0,0 @@
-mkdir celeba-hq-dataset
-
-unzip data256x256.zip -d celeba-hq-dataset/
-
-# Reindex
-for i in `echo {00001..30000}`
-do
- mv 'celeba-hq-dataset/data256x256/'$i'.jpg' 'celeba-hq-dataset/data256x256/'$[10#$i - 1]'.jpg'
-done
-
-
-# Split: split train -> train & val
-cat fetch_data/train_shuffled.flist | shuf > celeba-hq-dataset/temp_train_shuffled.flist
-cat celeba-hq-dataset/temp_train_shuffled.flist | head -n 2000 > celeba-hq-dataset/val_shuffled.flist
-cat celeba-hq-dataset/temp_train_shuffled.flist | tail -n +2001 > celeba-hq-dataset/train_shuffled.flist
-cat fetch_data/val_shuffled.flist > celeba-hq-dataset/visual_test_shuffled.flist
-
-mkdir celeba-hq-dataset/train_256/
-mkdir celeba-hq-dataset/val_source_256/
-mkdir celeba-hq-dataset/visual_test_source_256/
-
-cat celeba-hq-dataset/train_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/train_256/
-cat celeba-hq-dataset/val_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/val_source_256/
-cat celeba-hq-dataset/visual_test_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/visual_test_source_256/
-
-
-# create location config celeba.yaml
-PWD=$(pwd)
-DATASET=${PWD}/celeba-hq-dataset
-CELEBA=${PWD}/configs/training/location/celeba.yaml
-
-touch $CELEBA
-echo "# @package _group_" >> $CELEBA
-echo "data_root_dir: ${DATASET}/" >> $CELEBA
-echo "out_root_dir: ${PWD}/experiments/" >> $CELEBA
-echo "tb_dir: ${PWD}/tb_logs/" >> $CELEBA
-echo "pretrained_models: ${PWD}/" >> $CELEBA
diff --git a/spaces/OpenMotionLab/MotionGPT/pyrender/tests/conftest.py b/spaces/OpenMotionLab/MotionGPT/pyrender/tests/conftest.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py b/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py
deleted file mode 100644
index 4e06ab8cbe6b43a355c0a9cfb3f2d688438d2c64..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py
+++ /dev/null
@@ -1,201 +0,0 @@
-# ------------------------------------------------------------------------------
-# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/cityscapes_evaluation.py
-# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
-# ------------------------------------------------------------------------------
-
-import glob
-import logging
-import numpy as np
-import os
-import tempfile
-from collections import OrderedDict
-import torch
-from PIL import Image
-
-from detectron2.data import MetadataCatalog
-from detectron2.utils import comm
-from detectron2.utils.file_io import PathManager
-
-from .evaluator import DatasetEvaluator
-
-
-class CityscapesEvaluator(DatasetEvaluator):
- """
- Base class for evaluation using cityscapes API.
- """
-
- def __init__(self, dataset_name):
- """
- Args:
- dataset_name (str): the name of the dataset.
- It must have the following metadata associated with it:
- "thing_classes", "gt_dir".
- """
- self._metadata = MetadataCatalog.get(dataset_name)
- self._cpu_device = torch.device("cpu")
- self._logger = logging.getLogger(__name__)
-
- def reset(self):
- self._working_dir = tempfile.TemporaryDirectory(prefix="cityscapes_eval_")
- self._temp_dir = self._working_dir.name
- # All workers will write to the same results directory
- # TODO this does not work in distributed training
- assert (
- comm.get_local_size() == comm.get_world_size()
- ), "CityscapesEvaluator currently do not work with multiple machines."
- self._temp_dir = comm.all_gather(self._temp_dir)[0]
- if self._temp_dir != self._working_dir.name:
- self._working_dir.cleanup()
- self._logger.info(
- "Writing cityscapes results to temporary directory {} ...".format(self._temp_dir)
- )
-
-
-class CityscapesInstanceEvaluator(CityscapesEvaluator):
- """
- Evaluate instance segmentation results on cityscapes dataset using cityscapes API.
-
- Note:
- * It does not work in multi-machine distributed training.
- * It contains a synchronization, therefore has to be used on all ranks.
- * Only the main process runs evaluation.
- """
-
- def process(self, inputs, outputs):
- from cityscapesscripts.helpers.labels import name2label
-
- for input, output in zip(inputs, outputs):
- file_name = input["file_name"]
- basename = os.path.splitext(os.path.basename(file_name))[0]
- pred_txt = os.path.join(self._temp_dir, basename + "_pred.txt")
-
- if "instances" in output:
- output = output["instances"].to(self._cpu_device)
- num_instances = len(output)
- with open(pred_txt, "w") as fout:
- for i in range(num_instances):
- pred_class = output.pred_classes[i]
- classes = self._metadata.stuff_classes[pred_class]
- class_id = name2label[classes].id
- score = output.scores[i]
- mask = output.pred_masks[i].numpy().astype("uint8")
- png_filename = os.path.join(
- self._temp_dir, basename + "_{}_{}.png".format(i, classes)
- )
-
- Image.fromarray(mask * 255).save(png_filename)
- fout.write(
- "{} {} {}\n".format(os.path.basename(png_filename), class_id, score)
- )
- else:
- # Cityscapes requires a prediction file for every ground truth image.
- with open(pred_txt, "w") as fout:
- pass
-
- def evaluate(self):
- """
- Returns:
- dict: has a key "segm", whose value is a dict of "AP" and "AP50".
- """
- comm.synchronize()
- if comm.get_rank() > 0:
- return
- import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval
-
- self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
-
- # set some global states in cityscapes evaluation API, before evaluating
- cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
- cityscapes_eval.args.predictionWalk = None
- cityscapes_eval.args.JSONOutput = False
- cityscapes_eval.args.colorized = False
- cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json")
-
- # These lines are adopted from
- # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
- gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
- groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_instanceIds.png"))
- assert len(
- groundTruthImgList
- ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
- cityscapes_eval.args.groundTruthSearch
- )
- predictionImgList = []
- for gt in groundTruthImgList:
- predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args))
- results = cityscapes_eval.evaluateImgLists(
- predictionImgList, groundTruthImgList, cityscapes_eval.args
- )["averages"]
-
- ret = OrderedDict()
- ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100}
- self._working_dir.cleanup()
- return ret
-
-
-class CityscapesSemSegEvaluator(CityscapesEvaluator):
- """
- Evaluate semantic segmentation results on cityscapes dataset using cityscapes API.
-
- Note:
- * It does not work in multi-machine distributed training.
- * It contains a synchronization, therefore has to be used on all ranks.
- * Only the main process runs evaluation.
- """
-
- def process(self, inputs, outputs):
- from cityscapesscripts.helpers.labels import trainId2label
-
- for input, output in zip(inputs, outputs):
- file_name = input["file_name"]
- basename = os.path.splitext(os.path.basename(file_name))[0]
- pred_filename = os.path.join(self._temp_dir, basename + "_pred.png")
-
- output = output["sem_seg"].argmax(dim=0).to(self._cpu_device).numpy()
- pred = 255 * np.ones(output.shape, dtype=np.uint8)
- for train_id, label in trainId2label.items():
- if label.ignoreInEval:
- continue
- pred[output == train_id] = label.id
- Image.fromarray(pred).save(pred_filename)
-
- def evaluate(self):
- comm.synchronize()
- if comm.get_rank() > 0:
- return
- # Load the Cityscapes eval script *after* setting the required env var,
- # since the script reads CITYSCAPES_DATASET into global variables at load time.
- import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval
-
- self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
-
- # set some global states in cityscapes evaluation API, before evaluating
- cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
- cityscapes_eval.args.predictionWalk = None
- cityscapes_eval.args.JSONOutput = False
- cityscapes_eval.args.colorized = False
-
- # These lines are adopted from
- # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa
- gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
- groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_labelIds.png"))
- assert len(
- groundTruthImgList
- ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
- cityscapes_eval.args.groundTruthSearch
- )
- predictionImgList = []
- for gt in groundTruthImgList:
- predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt))
- results = cityscapes_eval.evaluateImgLists(
- predictionImgList, groundTruthImgList, cityscapes_eval.args
- )
- ret = OrderedDict()
- ret["sem_seg"] = {
- "IoU": 100.0 * results["averageScoreClasses"],
- "iIoU": 100.0 * results["averageScoreInstClasses"],
- "IoU_sup": 100.0 * results["averageScoreCategories"],
- "iIoU_sup": 100.0 * results["averageScoreInstCategories"],
- }
- self._working_dir.cleanup()
- return ret
diff --git a/spaces/PeepDaSlan9/AutoGPT/benchmark/__init__.py b/spaces/PeepDaSlan9/AutoGPT/benchmark/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py b/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py
deleted file mode 100644
index ba0443e0c22481af2b451d8ccc5ab6e6f79cffb8..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py
+++ /dev/null
@@ -1,573 +0,0 @@
-import os
-import random
-from typing import Optional, Callable
-import json
-import glob
-import csv
-
-import numpy as np
-import torch
-import librosa
-import pyloudnorm as pyln
-from pedalboard import Pedalboard, Limiter, Gain, Compressor, Clipping
-
-from .dataset import (
- MusdbTrainDataset,
- MusdbValidDataset,
- apply_limitaug,
-)
-from utils import (
- load_wav_arbitrary_position_stereo,
- load_wav_specific_position_stereo,
- db2linear,
-)
-
-
-class DelimitTrainDataset(MusdbTrainDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- seq_duration: Optional[float] = 6.0,
- samples_per_track: int = 64,
- source_augmentations: Optional[Callable] = lambda audio: audio,
- sample_rate: int = 44100,
- seed: int = 42,
- limitaug_method: str = "limitaug",
- limitaug_mode: str = "normal_L",
- limitaug_custom_target_lufs: float = None,
- limitaug_custom_target_lufs_std: float = None,
- target_loudnorm_lufs: float = -14.0,
- target_limitaug_mode: str = None,
- target_limitaug_custom_target_lufs: float = None,
- target_limitaug_custom_target_lufs_std: float = None,
- custom_limiter_attack_range: list = [2.0, 2.0],
- custom_limiter_release_range: list = [200.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(
- target=target,
- root=root,
- seq_duration=seq_duration,
- samples_per_track=samples_per_track,
- source_augmentations=source_augmentations,
- sample_rate=sample_rate,
- seed=seed,
- limitaug_method=limitaug_method,
- limitaug_mode=limitaug_mode,
- limitaug_custom_target_lufs=limitaug_custom_target_lufs,
- limitaug_custom_target_lufs_std=limitaug_custom_target_lufs_std,
- target_loudnorm_lufs=target_loudnorm_lufs,
- custom_limiter_attack_range=custom_limiter_attack_range,
- custom_limiter_release_range=custom_limiter_release_range,
- *args,
- **kwargs,
- )
-
- self.target_limitaug_mode = target_limitaug_mode
-
- self.target_limitaug_custom_target_lufs = (target_limitaug_custom_target_lufs,)
- self.target_limitaug_custom_target_lufs_std = (
- target_limitaug_custom_target_lufs_std,
- )
- self.limitaug_mode_statistics["target_custom"] = [
- target_limitaug_custom_target_lufs,
- target_limitaug_custom_target_lufs_std,
- ]
-
- """
- Parameters
- ----------
- limitaug_method : str
- choose from ["linear_gain_increase", "limitaug", "limitaug_then_loudnorm", "only_loudnorm"]
- limitaug_mode : str
- choose from ["uniform", "normal", "normal_L", "normal_XL", "normal_short_term", "normal_L_short_term", "normal_XL_short_term", "custom"]
- limitaug_custom_target_lufs : float
- valid only when
- limitaug_mode == "custom"
- target_loudnorm_lufs : float
- valid only when
- limitaug_method == 'limitaug_then_loudnorm' or 'only_loudnorm'
- default is -14.
- To the best of my knowledge, Spotify and Youtube music is using -14 as a reference loudness normalization level.
- No special reason for the choice of -14 as target_loudnorm_lufs.
- target : str
- target name of the source to be separated, defaults to ``vocals``.
- root : str
- root path of MUSDB
- seq_duration : float
- training is performed in chunks of ``seq_duration`` (in seconds,
- defaults to ``None`` which loads the full audio track
- samples_per_track : int
- sets the number of samples, yielded from each track per epoch.
- Defaults to 64
- source_augmentations : list[callables]
- provide list of augmentation function that take a multi-channel
- audio file of shape (src, samples) as input and output. Defaults to
- no-augmentations (input = output)
- seed : int
- control randomness of dataset iterations
- args, kwargs : additional keyword arguments
- used to add further control for the musdb dataset
- initialization function.
- """
-
- # Get a limitaug result without target (individual stem source)
- def get_limitaug_mixture(self, mixture):
- if self.limitaug_method == "limitaug":
- self.board[1].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- )
-
- elif self.limitaug_method == "limitaug_then_loudnorm":
- self.board[1].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = (
- apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- target_loudnorm_lufs=self.target_loudnorm_lufs,
- ),
- )
-
- # Apply LimitAug using Custom Limiter
- elif self.limitaug_method == "custom_limiter_limitaug":
- # Change attack time of First compressor of the Limiter
- self.board[1].attack_ms = random.uniform(
- self.custom_limiter_attack_range[0], self.custom_limiter_attack_range[1]
- )
- # Change release time of First compressor of the Limiter
- self.board[1].release_ms = random.uniform(
- self.custom_limiter_release_range[0],
- self.custom_limiter_release_range[1],
- )
- # Change release time of Second compressor of the Limiter
- self.board[2].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- target_loudnorm_lufs=self.target_loudnorm_lufs,
- )
-
- # When we want to force NN to output an appropriately compressed target output
- if self.target_limitaug_mode:
- mixture_target_lufs = random.gauss(
- self.limitaug_mode_statistics[self.target_limitaug_mode][0],
- self.limitaug_mode_statistics[self.target_limitaug_mode][1],
- )
- mixture, target_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=mixture_target_lufs,
- loudness=mixture_lufs,
- )
-
- if np.isinf(mixture_lufs):
- mixture_loudnorm = mixture
- else:
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain, eps=0.0)
-
- return mixture_limited, mixture_loudnorm
-
- def __getitem__(self, index):
- audio_sources = []
-
- for k, source in enumerate(self.sources):
- # memorize index of target source
- if source == self.target: # if source is 'vocals'
- track_path = self.train_list[
- index // self.samples_per_track
- ] # we want to use # training samples per each track.
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration
- )
- else:
- track_path = random.choice(self.train_list)
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration
- )
- audio = self.source_augmentations(audio)
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- # apply linear mix over source index=0
- # and here, linear mixture is a target unlike in MusdbTrainDataset
- mixture = stems.sum(0)
- mixture_limited, mixture_loudnorm = self.get_limitaug_mixture(mixture)
- # We will give mixture_limited as an input and mixture_loudnorm as a target to the model.
-
- mixture_limited = np.clip(mixture_limited, -1.0, 1.0)
- mixture_limited = torch.as_tensor(mixture_limited, dtype=torch.float32)
- mixture_loudnorm = torch.as_tensor(mixture_loudnorm, dtype=torch.float32)
-
- return mixture_limited, mixture_loudnorm
-
-
-class OzoneTrainDataset(DelimitTrainDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- ozone_root: str = None,
- use_fixed: float = 0.1, # ratio of fixed samples
- seq_duration: Optional[float] = 6.0,
- samples_per_track: int = 64,
- source_augmentations: Optional[Callable] = lambda audio: audio,
- sample_rate: int = 44100,
- seed: int = 42,
- limitaug_method: str = "limitaug",
- limitaug_mode: str = "normal_L",
- limitaug_custom_target_lufs: float = None,
- limitaug_custom_target_lufs_std: float = None,
- target_loudnorm_lufs: float = -14.0,
- target_limitaug_mode: str = None,
- target_limitaug_custom_target_lufs: float = None,
- target_limitaug_custom_target_lufs_std: float = None,
- custom_limiter_attack_range: list = [2.0, 2.0],
- custom_limiter_release_range: list = [200.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(
- target,
- root,
- seq_duration,
- samples_per_track,
- source_augmentations,
- sample_rate,
- seed,
- limitaug_method,
- limitaug_mode,
- limitaug_custom_target_lufs,
- limitaug_custom_target_lufs_std,
- target_loudnorm_lufs,
- target_limitaug_mode,
- target_limitaug_custom_target_lufs,
- target_limitaug_custom_target_lufs_std,
- custom_limiter_attack_range,
- custom_limiter_release_range,
- *args,
- **kwargs,
- )
-
- self.ozone_root = ozone_root
- self.use_fixed = use_fixed
- self.list_train_fixed = glob.glob(f"{self.ozone_root}/ozone_train_fixed/*.wav")
- self.list_train_random = glob.glob(
- f"{self.ozone_root}/ozone_train_random/*.wav"
- )
- self.dict_train_random = {}
-
- # Load information of pre-generated random training examples
- list_csv_files = glob.glob(f"{self.ozone_root}/ozone_train_random_*.csv")
- list_csv_files.sort()
- for csv_file in list_csv_files:
- with open(csv_file, "r") as f:
- reader = csv.reader(f)
- next(reader)
- for row in reader:
- self.dict_train_random[row[0]] = {
- "max_threshold": float(row[1]),
- "max_character": float(row[2]),
- "vocals": {
- "name": row[3],
- "start_sec": float(row[4]),
- "gain": float(row[5]),
- "channelswap": bool(row[6]),
- },
- "bass": {
- "name": row[7],
- "start_sec": float(row[8]),
- "gain": float(row[9]),
- "channelswap": bool(row[10]),
- },
- "drums": {
- "name": row[11],
- "start_sec": float(row[12]),
- "gain": float(row[13]),
- "channelswap": bool(row[14]),
- },
- "other": {
- "name": row[15],
- "start_sec": float(row[16]),
- "gain": float(row[17]),
- "channelswap": bool(row[18]),
- },
- }
-
- def __getitem__(self, idx):
- use_fixed_prob = random.random()
-
- if use_fixed_prob <= self.use_fixed:
- # Fixed examples
- audio_path = random.choice(self.list_train_fixed)
- song_name = os.path.basename(audio_path).replace(".wav", "")
- mixture_limited, start_pos_sec = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration, return_pos=True
- )
-
- audio_sources = []
- track_path = f"{self.root}/train/{song_name}"
- for source in self.sources:
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_specific_position_stereo(
- audio_path,
- self.sample_rate,
- self.seq_duration,
- start_position=start_pos_sec,
- )
- audio_sources.append(audio)
-
- else:
- # Random examples
- # Load mixture_limited (pre-generated)
- audio_path = random.choice(self.list_train_random)
- seg_name = os.path.basename(audio_path).replace(".wav", "")
- mixture_limited, sr = librosa.load(
- audio_path, sr=self.sample_rate, mono=False
- )
-
- # Load mixture_unlimited (from the original musdb18, using metadata)
- audio_sources = []
- for source in self.sources:
- dict_seg_info = self.dict_train_random[seg_name]
- dict_seg_source_info = dict_seg_info[source]
- audio_path = (
- f"{self.root}/train/{dict_seg_source_info['name']}/{source}.wav"
- )
- audio = load_wav_specific_position_stereo(
- audio_path,
- self.sample_rate,
- self.seq_duration,
- start_position=dict_seg_source_info["start_sec"],
- )
-
- # apply augmentations
- audio = audio * dict_seg_source_info["gain"]
- if dict_seg_source_info["channelswap"]:
- audio = np.flip(audio, axis=0)
-
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
- mixture = stems.sum(axis=0)
- mixture_lufs = self.meter.integrated_loudness(mixture.T)
- if np.isinf(mixture_lufs):
- mixture_loudnorm = mixture
- else:
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain, eps=0.0)
-
- return mixture_limited, mixture_loudnorm
-
-
-class DelimitValidDataset(MusdbValidDataset):
- def __init__(
- self,
- target: str = "vocals",
- root: str = None,
- delimit_valid_root: str = None,
- valid_target_lufs: float = -8.05, # From the Table 1 of the "Towards robust music source separation on loud commercial music" paper, the average loudness of commerical music.
- target_loudnorm_lufs: float = -14.0,
- delimit_valid_L_root: str = None, # This will be used when using the target as compressed (normal_L) mixture.
- use_custom_limiter: bool = False,
- custom_limiter_attack_range: list = [0.1, 10.0],
- custom_limiter_release_range: list = [30.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(target=target, root=root, *args, **kwargs)
- self.delimit_valid_root = delimit_valid_root
- if self.delimit_valid_root:
- with open(f"{self.delimit_valid_root}/valid_loudness.json", "r") as f:
- self.dict_valid_loudness = json.load(f)
- self.delimit_valid_L_root = delimit_valid_L_root
- if self.delimit_valid_L_root:
- with open(f"{self.delimit_valid_L_root}/valid_loudness.json", "r") as f:
- self.dict_valid_L_loudness = json.load(f)
-
- self.valid_target_lufs = valid_target_lufs
- self.target_loudnorm_lufs = target_loudnorm_lufs
- self.meter = pyln.Meter(self.sample_rate)
- self.use_custom_limiter = use_custom_limiter
-
- if self.use_custom_limiter:
- print("using Custom limiter limitaug for validation!!")
- self.custom_limiter_attack_range = custom_limiter_attack_range
- self.custom_limiter_release_range = custom_limiter_release_range
- self.board = Pedalboard(
- [
- Gain(gain_db=0.0),
- Compressor(
- threshold_db=-10.0, ratio=4.0, attack_ms=2.0, release_ms=200.0
- ), # attack_ms and release_ms will be changed later.
- Compressor(
- threshold_db=0.0,
- ratio=1000.0,
- attack_ms=0.001,
- release_ms=100.0,
- ),
- Gain(gain_db=3.75),
- Clipping(threshold_db=0.0),
- ]
- ) # This implementation is the same as JUCE Limiter.
- # However, we want the first compressor to have a variable attack and release time.
- # Therefore, we use the Custom Limiter instead of the JUCE Limiter.
- else:
- self.board = Pedalboard(
- [Gain(gain_db=0.0), Limiter(threshold_db=0.0, release_ms=100.0)]
- ) # Currently, we are using a limiter with a release time of 100ms.
-
- def __getitem__(self, index):
- audio_sources = []
- target_ind = None
-
- for k, source in enumerate(self.sources):
- # memorize index of target source
- if source == self.target: # if source is 'vocals'
- target_ind = k
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- audio_path = f"{track_path}/{source}.wav"
- # audio = utils.load_wav_stereo(audio_path, self.sample_rate)
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
- else:
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- audio_path = f"{track_path}/{source}.wav"
- # audio = utils.load_wav_stereo(audio_path, self.sample_rate)
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
-
- audio = torch.as_tensor(audio, dtype=torch.float32)
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- # apply linear mix over source index=0
- # and here, linear mixture is a target unlike in MusdbTrainDataset
- mixture = stems.sum(0)
- if (
- self.delimit_valid_root
- ): # If there exists a pre-processed delimit valid dataset
- audio_path = f"{self.delimit_valid_root}/valid/{song_name}.wav"
- mixture_limited = librosa.load(audio_path, mono=False, sr=self.sample_rate)[
- 0
- ]
- mixture_lufs = self.dict_valid_loudness[song_name]
-
- else:
- if self.use_custom_limiter:
- custom_limiter_attack = random.uniform(
- self.custom_limiter_attack_range[0],
- self.custom_limiter_attack_range[1],
- )
- self.board[1].attack_ms = custom_limiter_attack
-
- custom_limiter_release = random.uniform(
- self.custom_limiter_release_range[0],
- self.custom_limiter_release_range[1],
- )
- self.board[1].release_ms = custom_limiter_release
-
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=self.valid_target_lufs,
- )
- else:
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=self.valid_target_lufs,
- # target_loudnorm_lufs=self.target_loudnorm_lufs,
- ) # mixture_limited is a limiter applied mixture
- # We will give mixture_limited as an input and mixture_loudnorm as a target to the model.
-
- if self.delimit_valid_L_root:
- audio_L_path = f"{self.delimit_valid_L_root}/valid/{song_name}.wav"
- mixture_loudnorm = librosa.load(
- audio_L_path, mono=False, sr=self.sample_rate
- )[0]
- mixture_lufs = self.dict_valid_L_loudness[song_name]
- mixture = mixture_loudnorm
-
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain)
-
- if self.use_custom_limiter:
- return (
- mixture_limited,
- mixture_loudnorm,
- song_name,
- mixture_lufs,
- custom_limiter_attack,
- custom_limiter_release,
- )
- else:
- return mixture_limited, mixture_loudnorm, song_name, mixture_lufs
-
-
-class OzoneValidDataset(MusdbValidDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- ozone_root: str = None,
- target_loudnorm_lufs: float = -14.0,
- *args,
- **kwargs,
- ) -> None:
- super().__init__(target=target, root=root, *args, **kwargs)
-
- self.ozone_root = ozone_root
- self.target_loudnorm_lufs = target_loudnorm_lufs
-
- with open(f"{self.ozone_root}/valid_loudness.json", "r") as f:
- self.dict_valid_loudness = json.load(f)
-
- def __getitem__(self, index):
- audio_sources = []
-
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- for k, source in enumerate(self.sources):
- audio_path = f"{track_path}/{source}.wav"
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- mixture = stems.sum(0)
-
- audio_path = f"{self.ozone_root}/ozone_train_fixed/{song_name}.wav"
- mixture_limited = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
-
- mixture_lufs = self.dict_valid_loudness[song_name]
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain)
-
- return mixture_limited, mixture_loudnorm, song_name, mixture_lufs
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py
deleted file mode 100644
index 75559579cf053abcc99538606cbb88c723faf783..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py
+++ /dev/null
@@ -1,282 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-
-import torch
-import torch.nn as nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair, _single
-
-from annotator.uniformer.mmcv.utils import deprecated_api_warning
-from ..cnn import CONV_LAYERS
-from ..utils import ext_loader, print_log
-
-ext_module = ext_loader.load_ext(
- '_ext',
- ['modulated_deform_conv_forward', 'modulated_deform_conv_backward'])
-
-
-class ModulatedDeformConv2dFunction(Function):
-
- @staticmethod
- def symbolic(g, input, offset, mask, weight, bias, stride, padding,
- dilation, groups, deform_groups):
- input_tensors = [input, offset, mask, weight]
- if bias is not None:
- input_tensors.append(bias)
- return g.op(
- 'mmcv::MMCVModulatedDeformConv2d',
- *input_tensors,
- stride_i=stride,
- padding_i=padding,
- dilation_i=dilation,
- groups_i=groups,
- deform_groups_i=deform_groups)
-
- @staticmethod
- def forward(ctx,
- input,
- offset,
- mask,
- weight,
- bias=None,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- deform_groups=1):
- if input is not None and input.dim() != 4:
- raise ValueError(
- f'Expected 4D tensor as input, got {input.dim()}D tensor \
- instead.')
- ctx.stride = _pair(stride)
- ctx.padding = _pair(padding)
- ctx.dilation = _pair(dilation)
- ctx.groups = groups
- ctx.deform_groups = deform_groups
- ctx.with_bias = bias is not None
- if not ctx.with_bias:
- bias = input.new_empty(0) # fake tensor
- # When pytorch version >= 1.6.0, amp is adopted for fp16 mode;
- # amp won't cast the type of model (float32), but "offset" is cast
- # to float16 by nn.Conv2d automatically, leading to the type
- # mismatch with input (when it is float32) or weight.
- # The flag for whether to use fp16 or amp is the type of "offset",
- # we cast weight and input to temporarily support fp16 and amp
- # whatever the pytorch version is.
- input = input.type_as(offset)
- weight = weight.type_as(input)
- ctx.save_for_backward(input, offset, mask, weight, bias)
- output = input.new_empty(
- ModulatedDeformConv2dFunction._output_size(ctx, input, weight))
- ctx._bufs = [input.new_empty(0), input.new_empty(0)]
- ext_module.modulated_deform_conv_forward(
- input,
- weight,
- bias,
- ctx._bufs[0],
- offset,
- mask,
- output,
- ctx._bufs[1],
- kernel_h=weight.size(2),
- kernel_w=weight.size(3),
- stride_h=ctx.stride[0],
- stride_w=ctx.stride[1],
- pad_h=ctx.padding[0],
- pad_w=ctx.padding[1],
- dilation_h=ctx.dilation[0],
- dilation_w=ctx.dilation[1],
- group=ctx.groups,
- deformable_group=ctx.deform_groups,
- with_bias=ctx.with_bias)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- input, offset, mask, weight, bias = ctx.saved_tensors
- grad_input = torch.zeros_like(input)
- grad_offset = torch.zeros_like(offset)
- grad_mask = torch.zeros_like(mask)
- grad_weight = torch.zeros_like(weight)
- grad_bias = torch.zeros_like(bias)
- grad_output = grad_output.contiguous()
- ext_module.modulated_deform_conv_backward(
- input,
- weight,
- bias,
- ctx._bufs[0],
- offset,
- mask,
- ctx._bufs[1],
- grad_input,
- grad_weight,
- grad_bias,
- grad_offset,
- grad_mask,
- grad_output,
- kernel_h=weight.size(2),
- kernel_w=weight.size(3),
- stride_h=ctx.stride[0],
- stride_w=ctx.stride[1],
- pad_h=ctx.padding[0],
- pad_w=ctx.padding[1],
- dilation_h=ctx.dilation[0],
- dilation_w=ctx.dilation[1],
- group=ctx.groups,
- deformable_group=ctx.deform_groups,
- with_bias=ctx.with_bias)
- if not ctx.with_bias:
- grad_bias = None
-
- return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias,
- None, None, None, None, None)
-
- @staticmethod
- def _output_size(ctx, input, weight):
- channels = weight.size(0)
- output_size = (input.size(0), channels)
- for d in range(input.dim() - 2):
- in_size = input.size(d + 2)
- pad = ctx.padding[d]
- kernel = ctx.dilation[d] * (weight.size(d + 2) - 1) + 1
- stride_ = ctx.stride[d]
- output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
- if not all(map(lambda s: s > 0, output_size)):
- raise ValueError(
- 'convolution input is too small (output would be ' +
- 'x'.join(map(str, output_size)) + ')')
- return output_size
-
-
-modulated_deform_conv2d = ModulatedDeformConv2dFunction.apply
-
-
-class ModulatedDeformConv2d(nn.Module):
-
- @deprecated_api_warning({'deformable_groups': 'deform_groups'},
- cls_name='ModulatedDeformConv2d')
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- deform_groups=1,
- bias=True):
- super(ModulatedDeformConv2d, self).__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.kernel_size = _pair(kernel_size)
- self.stride = _pair(stride)
- self.padding = _pair(padding)
- self.dilation = _pair(dilation)
- self.groups = groups
- self.deform_groups = deform_groups
- # enable compatibility with nn.Conv2d
- self.transposed = False
- self.output_padding = _single(0)
-
- self.weight = nn.Parameter(
- torch.Tensor(out_channels, in_channels // groups,
- *self.kernel_size))
- if bias:
- self.bias = nn.Parameter(torch.Tensor(out_channels))
- else:
- self.register_parameter('bias', None)
- self.init_weights()
-
- def init_weights(self):
- n = self.in_channels
- for k in self.kernel_size:
- n *= k
- stdv = 1. / math.sqrt(n)
- self.weight.data.uniform_(-stdv, stdv)
- if self.bias is not None:
- self.bias.data.zero_()
-
- def forward(self, x, offset, mask):
- return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,
- self.stride, self.padding,
- self.dilation, self.groups,
- self.deform_groups)
-
-
-@CONV_LAYERS.register_module('DCNv2')
-class ModulatedDeformConv2dPack(ModulatedDeformConv2d):
- """A ModulatedDeformable Conv Encapsulation that acts as normal Conv
- layers.
-
- Args:
- in_channels (int): Same as nn.Conv2d.
- out_channels (int): Same as nn.Conv2d.
- kernel_size (int or tuple[int]): Same as nn.Conv2d.
- stride (int): Same as nn.Conv2d, while tuple is not supported.
- padding (int): Same as nn.Conv2d, while tuple is not supported.
- dilation (int): Same as nn.Conv2d, while tuple is not supported.
- groups (int): Same as nn.Conv2d.
- bias (bool or str): If specified as `auto`, it will be decided by the
- norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
- False.
- """
-
- _version = 2
-
- def __init__(self, *args, **kwargs):
- super(ModulatedDeformConv2dPack, self).__init__(*args, **kwargs)
- self.conv_offset = nn.Conv2d(
- self.in_channels,
- self.deform_groups * 3 * self.kernel_size[0] * self.kernel_size[1],
- kernel_size=self.kernel_size,
- stride=self.stride,
- padding=self.padding,
- dilation=self.dilation,
- bias=True)
- self.init_weights()
-
- def init_weights(self):
- super(ModulatedDeformConv2dPack, self).init_weights()
- if hasattr(self, 'conv_offset'):
- self.conv_offset.weight.data.zero_()
- self.conv_offset.bias.data.zero_()
-
- def forward(self, x):
- out = self.conv_offset(x)
- o1, o2, mask = torch.chunk(out, 3, dim=1)
- offset = torch.cat((o1, o2), dim=1)
- mask = torch.sigmoid(mask)
- return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,
- self.stride, self.padding,
- self.dilation, self.groups,
- self.deform_groups)
-
- def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
- missing_keys, unexpected_keys, error_msgs):
- version = local_metadata.get('version', None)
-
- if version is None or version < 2:
- # the key is different in early versions
- # In version < 2, ModulatedDeformConvPack
- # loads previous benchmark models.
- if (prefix + 'conv_offset.weight' not in state_dict
- and prefix[:-1] + '_offset.weight' in state_dict):
- state_dict[prefix + 'conv_offset.weight'] = state_dict.pop(
- prefix[:-1] + '_offset.weight')
- if (prefix + 'conv_offset.bias' not in state_dict
- and prefix[:-1] + '_offset.bias' in state_dict):
- state_dict[prefix +
- 'conv_offset.bias'] = state_dict.pop(prefix[:-1] +
- '_offset.bias')
-
- if version is not None and version > 1:
- print_log(
- f'ModulatedDeformConvPack {prefix.rstrip(".")} is upgraded to '
- 'version 2.',
- logger='root')
-
- super()._load_from_state_dict(state_dict, prefix, local_metadata,
- strict, missing_keys, unexpected_keys,
- error_msgs)
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py
deleted file mode 100644
index a802a74fd6c3610d9ae178e6201f47423eca7ad1..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py
+++ /dev/null
@@ -1,177 +0,0 @@
-from typing import List
-
-import torch
-from torch import nn as nn
-
-from annotator.uniformer.mmcv.runner import force_fp32
-from .furthest_point_sample import (furthest_point_sample,
- furthest_point_sample_with_dist)
-
-
-def calc_square_dist(point_feat_a, point_feat_b, norm=True):
- """Calculating square distance between a and b.
-
- Args:
- point_feat_a (Tensor): (B, N, C) Feature vector of each point.
- point_feat_b (Tensor): (B, M, C) Feature vector of each point.
- norm (Bool, optional): Whether to normalize the distance.
- Default: True.
-
- Returns:
- Tensor: (B, N, M) Distance between each pair points.
- """
- num_channel = point_feat_a.shape[-1]
- # [bs, n, 1]
- a_square = torch.sum(point_feat_a.unsqueeze(dim=2).pow(2), dim=-1)
- # [bs, 1, m]
- b_square = torch.sum(point_feat_b.unsqueeze(dim=1).pow(2), dim=-1)
-
- corr_matrix = torch.matmul(point_feat_a, point_feat_b.transpose(1, 2))
-
- dist = a_square + b_square - 2 * corr_matrix
- if norm:
- dist = torch.sqrt(dist) / num_channel
- return dist
-
-
-def get_sampler_cls(sampler_type):
- """Get the type and mode of points sampler.
-
- Args:
- sampler_type (str): The type of points sampler.
- The valid value are "D-FPS", "F-FPS", or "FS".
-
- Returns:
- class: Points sampler type.
- """
- sampler_mappings = {
- 'D-FPS': DFPSSampler,
- 'F-FPS': FFPSSampler,
- 'FS': FSSampler,
- }
- try:
- return sampler_mappings[sampler_type]
- except KeyError:
- raise KeyError(
- f'Supported `sampler_type` are {sampler_mappings.keys()}, but got \
- {sampler_type}')
-
-
-class PointsSampler(nn.Module):
- """Points sampling.
-
- Args:
- num_point (list[int]): Number of sample points.
- fps_mod_list (list[str], optional): Type of FPS method, valid mod
- ['F-FPS', 'D-FPS', 'FS'], Default: ['D-FPS'].
- F-FPS: using feature distances for FPS.
- D-FPS: using Euclidean distances of points for FPS.
- FS: using F-FPS and D-FPS simultaneously.
- fps_sample_range_list (list[int], optional):
- Range of points to apply FPS. Default: [-1].
- """
-
- def __init__(self,
- num_point: List[int],
- fps_mod_list: List[str] = ['D-FPS'],
- fps_sample_range_list: List[int] = [-1]):
- super().__init__()
- # FPS would be applied to different fps_mod in the list,
- # so the length of the num_point should be equal to
- # fps_mod_list and fps_sample_range_list.
- assert len(num_point) == len(fps_mod_list) == len(
- fps_sample_range_list)
- self.num_point = num_point
- self.fps_sample_range_list = fps_sample_range_list
- self.samplers = nn.ModuleList()
- for fps_mod in fps_mod_list:
- self.samplers.append(get_sampler_cls(fps_mod)())
- self.fp16_enabled = False
-
- @force_fp32()
- def forward(self, points_xyz, features):
- """
- Args:
- points_xyz (Tensor): (B, N, 3) xyz coordinates of the features.
- features (Tensor): (B, C, N) Descriptors of the features.
-
- Returns:
- Tensor: (B, npoint, sample_num) Indices of sampled points.
- """
- indices = []
- last_fps_end_index = 0
-
- for fps_sample_range, sampler, npoint in zip(
- self.fps_sample_range_list, self.samplers, self.num_point):
- assert fps_sample_range < points_xyz.shape[1]
-
- if fps_sample_range == -1:
- sample_points_xyz = points_xyz[:, last_fps_end_index:]
- if features is not None:
- sample_features = features[:, :, last_fps_end_index:]
- else:
- sample_features = None
- else:
- sample_points_xyz = \
- points_xyz[:, last_fps_end_index:fps_sample_range]
- if features is not None:
- sample_features = features[:, :, last_fps_end_index:
- fps_sample_range]
- else:
- sample_features = None
-
- fps_idx = sampler(sample_points_xyz.contiguous(), sample_features,
- npoint)
-
- indices.append(fps_idx + last_fps_end_index)
- last_fps_end_index += fps_sample_range
- indices = torch.cat(indices, dim=1)
-
- return indices
-
-
-class DFPSSampler(nn.Module):
- """Using Euclidean distances of points for FPS."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with D-FPS."""
- fps_idx = furthest_point_sample(points.contiguous(), npoint)
- return fps_idx
-
-
-class FFPSSampler(nn.Module):
- """Using feature distances for FPS."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with F-FPS."""
- assert features is not None, \
- 'feature input to FFPS_Sampler should not be None'
- features_for_fps = torch.cat([points, features.transpose(1, 2)], dim=2)
- features_dist = calc_square_dist(
- features_for_fps, features_for_fps, norm=False)
- fps_idx = furthest_point_sample_with_dist(features_dist, npoint)
- return fps_idx
-
-
-class FSSampler(nn.Module):
- """Using F-FPS and D-FPS simultaneously."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with FS_Sampling."""
- assert features is not None, \
- 'feature input to FS_Sampler should not be None'
- ffps_sampler = FFPSSampler()
- dfps_sampler = DFPSSampler()
- fps_idx_ffps = ffps_sampler(points, features, npoint)
- fps_idx_dfps = dfps_sampler(points, features, npoint)
- fps_idx = torch.cat([fps_idx_ffps, fps_idx_dfps], dim=1)
- return fps_idx
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py
deleted file mode 100644
index 332b242c03d1c5e80d4577df442a9a037b1816e1..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .base_pixel_sampler import BasePixelSampler
-from .ohem_pixel_sampler import OHEMPixelSampler
-
-__all__ = ['BasePixelSampler', 'OHEMPixelSampler']
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py
deleted file mode 100644
index c0e42756ee6fcd779387255391a30079a28f5e60..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import torch
-from torch import nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-from maskrcnn_benchmark import _C
-
-
-class _ROIPool(Function):
- @staticmethod
- def forward(ctx, input, roi, output_size, spatial_scale):
- ctx.output_size = _pair(output_size)
- ctx.spatial_scale = spatial_scale
- ctx.input_shape = input.size()
- output, argmax = _C.roi_pool_forward(
- input, roi, spatial_scale, output_size[0], output_size[1]
- )
- ctx.save_for_backward(input, roi, argmax)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- input, rois, argmax = ctx.saved_tensors
- output_size = ctx.output_size
- spatial_scale = ctx.spatial_scale
- bs, ch, h, w = ctx.input_shape
- grad_input = _C.roi_pool_backward(
- grad_output,
- input,
- rois,
- argmax,
- spatial_scale,
- output_size[0],
- output_size[1],
- bs,
- ch,
- h,
- w,
- )
- return grad_input, None, None, None
-
-
-roi_pool = _ROIPool.apply
-
-
-class ROIPool(nn.Module):
- def __init__(self, output_size, spatial_scale):
- super(ROIPool, self).__init__()
- self.output_size = output_size
- self.spatial_scale = spatial_scale
-
- def forward(self, input, rois):
- return roi_pool(input, rois, self.output_size, self.spatial_scale)
-
- def __repr__(self):
- tmpstr = self.__class__.__name__ + "("
- tmpstr += "output_size=" + str(self.output_size)
- tmpstr += ", spatial_scale=" + str(self.spatial_scale)
- tmpstr += ")"
- return tmpstr
diff --git a/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py b/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py
deleted file mode 100644
index e90ef2130a47ec52160709877972716352e04c9c..0000000000000000000000000000000000000000
--- a/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
- if bias is not None:
- rest_dim = [1] * (input.ndim - bias.ndim - 1)
- return (
- F.leaky_relu(
- input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope
- )
- * scale
- )
- else:
- return F.leaky_relu(input, negative_slope=0.2) * scale
-
-
-class EqualLinear(nn.Module):
- def __init__(
- self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1
- ):
- super().__init__()
- self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
- if bias:
- self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
- else:
- self.bias = None
- self.scale = (1 / math.sqrt(in_dim)) * lr_mul
- self.lr_mul = lr_mul
-
- def forward(self, input):
- out = F.linear(input, self.weight * self.scale)
- out = fused_leaky_relu(out, self.bias * self.lr_mul)
- return out
-
-
-class RandomLatentConverter(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.layers = nn.Sequential(*[EqualLinear(channels, channels, lr_mul=.1) for _ in range(5)],
- nn.Linear(channels, channels))
- self.channels = channels
-
- def forward(self, ref):
- r = torch.randn(ref.shape[0], self.channels, device=ref.device)
- y = self.layers(r)
- return y
-
-
-if __name__ == '__main__':
- model = RandomLatentConverter(512)
- model(torch.randn(5,512))
\ No newline at end of file
diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py
deleted file mode 100644
index 8d760f8a1a769bba657d4aea47857f48f7cd0d7a..0000000000000000000000000000000000000000
--- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py
+++ /dev/null
@@ -1,1832 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# Updated to account for UI changes from https://github.com/rkfg/audiocraft/blob/long/app.py
-# also released under the MIT license.
-
-import argparse
-from concurrent.futures import ProcessPoolExecutor
-import os
-from pathlib import Path
-import subprocess as sp
-from tempfile import NamedTemporaryFile
-import time
-import warnings
-import glob
-import re
-from PIL import Image
-from pydub import AudioSegment
-from datetime import datetime
-
-import json
-import shutil
-import taglib
-import torch
-import torchaudio
-import gradio as gr
-import numpy as np
-import typing as tp
-
-from audiocraft.data.audio_utils import convert_audio
-from audiocraft.data.audio import audio_write
-from audiocraft.models import AudioGen, MusicGen, MultiBandDiffusion
-from audiocraft.utils import ui
-import random, string
-
-version = "2.0.0a"
-
-theme = gr.themes.Base(
- primary_hue="lime",
- secondary_hue="lime",
- neutral_hue="neutral",
-).set(
- button_primary_background_fill_hover='*primary_500',
- button_primary_background_fill_hover_dark='*primary_500',
- button_secondary_background_fill_hover='*primary_500',
- button_secondary_background_fill_hover_dark='*primary_500'
-)
-
-MODEL = None # Last used model
-MODELS = None
-UNLOAD_MODEL = False
-MOVE_TO_CPU = False
-IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
-print(IS_BATCHED)
-MAX_BATCH_SIZE = 12
-BATCHED_DURATION = 15
-INTERRUPTING = False
-MBD = None
-# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
-_old_call = sp.call
-
-
-def generate_random_string(length):
- characters = string.ascii_letters + string.digits
- return ''.join(random.choice(characters) for _ in range(length))
-
-
-def resize_video(input_path, output_path, target_width, target_height):
- ffmpeg_cmd = [
- 'ffmpeg',
- '-y',
- '-i', input_path,
- '-vf', f'scale={target_width}:{target_height}',
- '-c:a', 'copy',
- output_path
- ]
- sp.run(ffmpeg_cmd)
-
-
-def _call_nostderr(*args, **kwargs):
- # Avoid ffmpeg vomiting on the logs.
- kwargs['stderr'] = sp.DEVNULL
- kwargs['stdout'] = sp.DEVNULL
- _old_call(*args, **kwargs)
-
-
-sp.call = _call_nostderr
-# Preallocating the pool of processes.
-pool = ProcessPoolExecutor(4)
-pool.__enter__()
-
-
-def interrupt():
- global INTERRUPTING
- INTERRUPTING = True
-
-
-class FileCleaner:
- def __init__(self, file_lifetime: float = 3600):
- self.file_lifetime = file_lifetime
- self.files = []
-
- def add(self, path: tp.Union[str, Path]):
- self._cleanup()
- self.files.append((time.time(), Path(path)))
-
- def _cleanup(self):
- now = time.time()
- for time_added, path in list(self.files):
- if now - time_added > self.file_lifetime:
- if path.exists():
- path.unlink()
- self.files.pop(0)
- else:
- break
-
-
-file_cleaner = FileCleaner()
-
-
-def make_waveform(*args, **kwargs):
- # Further remove some warnings.
- be = time.time()
- with warnings.catch_warnings():
- warnings.simplefilter('ignore')
- height = kwargs.pop('height')
- width = kwargs.pop('width')
- if height < 256:
- height = 256
- if width < 256:
- width = 256
- waveform_video = gr.make_waveform(*args, **kwargs)
- out = f"{generate_random_string(12)}.mp4"
- image = kwargs.get('bg_image', None)
- if image is None:
- resize_video(waveform_video, out, 900, 300)
- else:
- resize_video(waveform_video, out, width, height)
- print("Make a video took", time.time() - be)
- return out
-
-
-def load_model(version='GrandaddyShmax/musicgen-melody', custom_model=None, base_model='GrandaddyShmax/musicgen-medium', gen_type="music"):
- global MODEL, MODELS
- print("Loading model", version)
- if MODELS is None:
- if version == 'GrandaddyShmax/musicgen-custom':
- MODEL = MusicGen.get_pretrained(base_model)
- file_path = os.path.abspath("models/" + str(custom_model) + ".pt")
- MODEL.lm.load_state_dict(torch.load(file_path))
- else:
- if gen_type == "music":
- MODEL = MusicGen.get_pretrained(version)
- elif gen_type == "audio":
- MODEL = AudioGen.get_pretrained(version)
-
- return
-
- else:
- t1 = time.monotonic()
- if MODEL is not None:
- MODEL.to('cpu') # move to cache
- print("Previous model moved to CPU in %.2fs" % (time.monotonic() - t1))
- t1 = time.monotonic()
- if version != 'GrandaddyShmax/musicgen-custom' and MODELS.get(version) is None:
- print("Loading model %s from disk" % version)
- if gen_type == "music":
- result = MusicGen.get_pretrained(version)
- elif gen_type == "audio":
- result = AudioGen.get_pretrained(version)
- MODELS[version] = result
- print("Model loaded in %.2fs" % (time.monotonic() - t1))
- MODEL = result
- return
- result = MODELS[version].to('cuda')
- print("Cached model loaded in %.2fs" % (time.monotonic() - t1))
- MODEL = result
-
-def get_audio_info(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted. (Discord removes metadata from mp4 and wav files, so you can't use them)"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- global_prompt = str("\nGlobal Prompt: " + (data['global_prompt'] if data['global_prompt'] != "" else "none")) if 'global_prompt' in data else ""
- bpm = str("\nBPM: " + data['bpm']) if 'bpm' in data else ""
- key = str("\nKey: " + data['key']) if 'key' in data else ""
- scale = str("\nScale: " + data['scale']) if 'scale' in data else ""
- prompts = str("\nPrompts: " + (data['texts'] if data['texts'] != "['']" else "none")) if 'texts' in data else ""
- duration = str("\nDuration: " + data['duration']) if 'duration' in data else ""
- overlap = str("\nOverlap: " + data['overlap']) if 'overlap' in data else ""
- seed = str("\nSeed: " + data['seed']) if 'seed' in data else ""
- audio_mode = str("\nAudio Mode: " + data['audio_mode']) if 'audio_mode' in data else ""
- input_length = str("\nInput Length: " + data['input_length']) if 'input_length' in data else ""
- channel = str("\nChannel: " + data['channel']) if 'channel' in data else ""
- sr_select = str("\nSample Rate: " + data['sr_select']) if 'sr_select' in data else ""
- gen_type = str(data['generator'] + "gen-") if 'generator' in data else ""
- model = str("\nModel: " + gen_type + data['model']) if 'model' in data else ""
- custom_model = str("\nCustom Model: " + data['custom_model']) if 'custom_model' in data else ""
- base_model = str("\nBase Model: " + data['base_model']) if 'base_model' in data else ""
- decoder = str("\nDecoder: " + data['decoder']) if 'decoder' in data else ""
- topk = str("\nTopk: " + data['topk']) if 'topk' in data else ""
- topp = str("\nTopp: " + data['topp']) if 'topp' in data else ""
- temperature = str("\nTemperature: " + data['temperature']) if 'temperature' in data else ""
- cfg_coef = str("\nClassifier Free Guidance: " + data['cfg_coef']) if 'cfg_coef' in data else ""
- version = str("Version: " + data['version']) if 'version' in data else "Version: Unknown"
- info = str(version + global_prompt + bpm + key + scale + prompts + duration + overlap + seed + audio_mode + input_length + channel + sr_select + model + custom_model + base_model + decoder + topk + topp + temperature + cfg_coef)
- if info == "":
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted. (Discord removes metadata from mp4 and wav files, so you can't use them)"
- return info
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- #if 'global_prompt' not in data:
- #return "No tags found. Either the file is not generated by MusicGen+ V1.2.8a and higher or the tags are corrupted."
- global_prompt = str("\nGlobal Prompt: " + (data['global_prompt'] if data['global_prompt'] != "" else "none")) if 'global_prompt' in data else ""
- bpm = str("\nBPM: " + data['bpm']) if 'bpm' in data else ""
- key = str("\nKey: " + data['key']) if 'key' in data else ""
- scale = str("\nScale: " + data['scale']) if 'scale' in data else ""
- prompts = str("\nPrompts: " + (data['texts'] if data['texts'] != "['']" else "none")) if 'texts' in data else ""
- duration = str("\nDuration: " + data['duration']) if 'duration' in data else ""
- overlap = str("\nOverlap: " + data['overlap']) if 'overlap' in data else ""
- seed = str("\nSeed: " + data['seed']) if 'seed' in data else ""
- audio_mode = str("\nAudio Mode: " + data['audio_mode']) if 'audio_mode' in data else ""
- input_length = str("\nInput Length: " + data['input_length']) if 'input_length' in data else ""
- channel = str("\nChannel: " + data['channel']) if 'channel' in data else ""
- sr_select = str("\nSample Rate: " + data['sr_select']) if 'sr_select' in data else ""
- gen_type = str(data['generator'] + "gen-") if 'generator' in data else ""
- model = str("\nModel: " + gen_type + data['model']) if 'model' in data else ""
- custom_model = str("\nCustom Model: " + data['custom_model']) if 'custom_model' in data else ""
- base_model = str("\nBase Model: " + data['base_model']) if 'base_model' in data else ""
- decoder = str("\nDecoder: " + data['decoder']) if 'decoder' in data else ""
- topk = str("\nTopk: " + data['topk']) if 'topk' in data else ""
- topp = str("\nTopp: " + data['topp']) if 'topp' in data else ""
- temperature = str("\nTemperature: " + data['temperature']) if 'temperature' in data else ""
- cfg_coef = str("\nClassifier Free Guidance: " + data['cfg_coef']) if 'cfg_coef' in data else ""
- version = str("Version: " + data['version']) if 'version' in data else "Version: Unknown"
- info = str(version + global_prompt + bpm + key + scale + prompts + duration + overlap + seed + audio_mode + input_length + channel + sr_select + model + custom_model + base_model + decoder + topk + topp + temperature + cfg_coef)
- if info == "":
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted."
- return info
- else:
- return "Only .wav ,.mp4 and .json files are supported"
- else:
- return None
-
-
-def info_to_params(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- struc_prompt = (False if data['bpm'] == "none" else True) if 'bpm' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- bpm = (120 if data['bpm'] == "none" else int(data['bpm'])) if 'bpm' in data else 120
- key = ("C" if data['key'] == "none" else data['key']) if 'key' in data else "C"
- scale = ("Major" if data['scale'] == "none" else data['scale']) if 'scale' in data else "Major"
- model = data['model'] if 'model' in data else "large"
- custom_model = (data['custom_model'] if data['custom_model'] in get_available_models() else None) if 'custom_model' in data else None
- base_model = data['base_model'] if 'base_model' in data else "medium"
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- audio_mode = ("sample" if data['audio_mode'] == "none" else data['audio_mode']) if 'audio_mode' in data else "sample"
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, bpm, key, scale, model, custom_model, base_model, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], audio_mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- struc_prompt = (False if data['bpm'] == "none" else True) if 'bpm' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- bpm = (120 if data['bpm'] == "none" else int(data['bpm'])) if 'bpm' in data else 120
- key = ("C" if data['key'] == "none" else data['key']) if 'key' in data else "C"
- scale = ("Major" if data['scale'] == "none" else data['scale']) if 'scale' in data else "Major"
- model = data['model'] if 'model' in data else "large"
- custom_model = (data['custom_model'] if data['custom_model'] in get_available_models() else None) if 'custom_model' in data else None
- base_model = data['base_model'] if 'base_model' in data else "medium"
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- audio_mode = ("sample" if data['audio_mode'] == "none" else data['audio_mode']) if 'audio_mode' in data else "sample"
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, bpm, key, scale, model, custom_model, base_model, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], audio_mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- else:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-
-
-def info_to_params_a(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- struc_prompt = (False if data['global_prompt'] == "" else True) if 'global_prompt' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- struc_prompt = (False if data['global_prompt'] == "" else True) if 'global_prompt' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
-
- else:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- else:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-
-
-def make_pseudo_stereo (filename, sr_select, pan, delay):
- if pan:
- temp = AudioSegment.from_wav(filename)
- if sr_select != "32000":
- temp = temp.set_frame_rate(int(sr_select))
- left = temp.pan(-0.5) - 5
- right = temp.pan(0.6) - 5
- temp = left.overlay(right, position=5)
- temp.export(filename, format="wav")
- if delay:
- waveform, sample_rate = torchaudio.load(filename) # load mono WAV file
- delay_seconds = 0.01 # set delay 10ms
- delay_samples = int(delay_seconds * sample_rate) # Calculating delay value in number of samples
- stereo_waveform = torch.stack([waveform[0], torch.cat((torch.zeros(delay_samples), waveform[0][:-delay_samples]))]) # Generate a stereo file with original mono audio and delayed version
- torchaudio.save(filename, stereo_waveform, sample_rate)
- return
-
-
-def normalize_audio(audio_data):
- audio_data = audio_data.astype(np.float32)
- max_value = np.max(np.abs(audio_data))
- audio_data /= max_value
- return audio_data
-
-
-def load_diffusion():
- global MBD
- if MBD is None:
- print("loading MBD")
- MBD = MultiBandDiffusion.get_mbd_musicgen()
-
-
-def unload_diffusion():
- global MBD
- if MBD is not None:
- print("unloading MBD")
- MBD = None
-
-
-def _do_predictions(gen_type, texts, melodies, sample, trim_start, trim_end, duration, image, height, width, background, bar1, bar2, channel, sr_select, progress=False, **gen_kwargs):
- if gen_type == "music":
- maximum_size = 29.5
- elif gen_type == "audio":
- maximum_size = 9.5
- cut_size = 0
- input_length = 0
- sampleP = None
- if sample is not None:
- globalSR, sampleM = sample[0], sample[1]
- sampleM = normalize_audio(sampleM)
- sampleM = torch.from_numpy(sampleM).t()
- if sampleM.dim() == 1:
- sampleM = sampleM.unsqueeze(0)
- sample_length = sampleM.shape[sampleM.dim() - 1] / globalSR
- if trim_start >= sample_length:
- trim_start = sample_length - 0.5
- if trim_end >= sample_length:
- trim_end = sample_length - 0.5
- if trim_start + trim_end >= sample_length:
- tmp = sample_length - 0.5
- trim_start = tmp / 2
- trim_end = tmp / 2
- sampleM = sampleM[..., int(globalSR * trim_start):int(globalSR * (sample_length - trim_end))]
- sample_length = sample_length - (trim_start + trim_end)
- if sample_length > maximum_size:
- cut_size = sample_length - maximum_size
- sampleP = sampleM[..., :int(globalSR * cut_size)]
- sampleM = sampleM[..., int(globalSR * cut_size):]
- if sample_length >= duration:
- duration = sample_length + 0.5
- input_length = sample_length
- global MODEL
- MODEL.set_generation_params(duration=(duration - cut_size), **gen_kwargs)
- print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies], [None if sample is None else (sample[0], sample[1].shape)])
- be = time.time()
- processed_melodies = []
- if gen_type == "music":
- target_sr = 32000
- elif gen_type == "audio":
- target_sr = 16000
- target_ac = 1
-
- for melody in melodies:
- if melody is None:
- processed_melodies.append(None)
- else:
- sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
- if melody.dim() == 1:
- melody = melody[None]
- melody = melody[..., :int(sr * duration)]
- melody = convert_audio(melody, sr, target_sr, target_ac)
- processed_melodies.append(melody)
-
- if sample is not None:
- if sampleP is None:
- if gen_type == "music":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress
- )
- else:
- if sampleP.dim() > 1:
- sampleP = convert_audio(sampleP, globalSR, target_sr, target_ac)
- sampleP = sampleP.to(MODEL.device).float().unsqueeze(0)
- if gen_type == "music":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress
- )
- outputs = torch.cat([sampleP, outputs], 2)
-
- elif any(m is not None for m in processed_melodies):
- if gen_type == "music":
- outputs = MODEL.generate_with_chroma(
- descriptions=texts,
- melody_wavs=processed_melodies,
- melody_sample_rate=target_sr,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_with_chroma(
- descriptions=texts,
- melody_wavs=processed_melodies,
- melody_sample_rate=target_sr,
- progress=progress
- )
- else:
- if gen_type == "music":
- outputs = MODEL.generate(texts, progress=progress, return_tokens=USE_DIFFUSION)
- elif gen_type == "audio":
- outputs = MODEL.generate(texts, progress=progress)
-
- if USE_DIFFUSION:
- print("outputs: " + str(outputs))
- outputs_diffusion = MBD.tokens_to_wav(outputs[1])
- outputs = torch.cat([outputs[0], outputs_diffusion], dim=0)
- outputs = outputs.detach().cpu().float()
- backups = outputs
- if channel == "stereo":
- outputs = convert_audio(outputs, target_sr, int(sr_select), 2)
- elif channel == "mono" and sr_select != "32000":
- outputs = convert_audio(outputs, target_sr, int(sr_select), 1)
- out_files = []
- out_audios = []
- out_backup = []
- for output in outputs:
- with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
- audio_write(
- file.name, output, (MODEL.sample_rate if channel == "stereo effect" else int(sr_select)), strategy="loudness",
- loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
-
- if channel == "stereo effect":
- make_pseudo_stereo(file.name, sr_select, pan=True, delay=True);
-
- out_files.append(pool.submit(make_waveform, file.name, bg_image=image, bg_color=background, bars_color=(bar1, bar2), fg_alpha=1.0, bar_count=75, height=height, width=width))
- out_audios.append(file.name)
- file_cleaner.add(file.name)
- print(f'wav: {file.name}')
- for backup in backups:
- with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
- audio_write(
- file.name, backup, MODEL.sample_rate, strategy="loudness",
- loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
- out_backup.append(file.name)
- file_cleaner.add(file.name)
- res = [out_file.result() for out_file in out_files]
- res_audio = out_audios
- res_backup = out_backup
- for file in res:
- file_cleaner.add(file)
- print(f'video: {file}')
- print("batch finished", len(texts), time.time() - be)
- print("Tempfiles currently stored: ", len(file_cleaner.files))
- if MOVE_TO_CPU:
- MODEL.to('cpu')
- if UNLOAD_MODEL:
- MODEL = None
- torch.cuda.empty_cache()
- torch.cuda.ipc_collect()
- return res, res_audio, res_backup, input_length
-
-
-def predict_batched(texts, melodies):
- max_text_length = 512
- texts = [text[:max_text_length] for text in texts]
- load_model('melody')
- res = _do_predictions(texts, melodies, BATCHED_DURATION)
- return res
-
-
-def add_tags(filename, tags):
- json_string = None
-
- data = {
- "global_prompt": tags[0],
- "bpm": tags[1],
- "key": tags[2],
- "scale": tags[3],
- "texts": tags[4],
- "duration": tags[5],
- "overlap": tags[6],
- "seed": tags[7],
- "audio_mode": tags[8],
- "input_length": tags[9],
- "channel": tags[10],
- "sr_select": tags[11],
- "model": tags[12],
- "custom_model": tags[13],
- "base_model": tags[14],
- "decoder": tags[15],
- "topk": tags[16],
- "topp": tags[17],
- "temperature": tags[18],
- "cfg_coef": tags[19],
- "generator": tags[20],
- "version": version
- }
-
- json_string = json.dumps(data)
-
- if os.path.exists(filename):
- with taglib.File(filename, save_on_exit=True) as song:
- song.tags = {'COMMENT': json_string }
-
- json_file = open(tags[7] + '.json', 'w')
- json_file.write(json_string)
- json_file.close()
-
- return json_file.name;
-
-
-def save_outputs(mp4, wav_tmp, tags, gen_type):
- # mp4: .mp4 file name in root running folder of app.py
- # wav_tmp: temporary wav file located in %TEMP% folder
- # seed - used seed
- # exanple BgnJtr4Pn1AJ.mp4, C:\Users\Alex\AppData\Local\Temp\tmp4ermrebs.wav, 195123182343465
- # procedure read generated .mp4 and wav files, rename it by using seed as name,
- # and will store it to ./output/today_date/wav and ./output/today_date/mp4 folders.
- # if file with same seed number already exist its make postfix in name like seed(n)
- # where is n - consiqunce number 1-2-3-4 and so on
- # then we store generated mp4 and wav into destination folders.
-
- current_date = datetime.now().strftime("%Y%m%d")
- wav_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'wav')
- mp4_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'mp4')
- json_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'json')
- os.makedirs(wav_directory, exist_ok=True)
- os.makedirs(mp4_directory, exist_ok=True)
- os.makedirs(json_directory, exist_ok=True)
-
- filename = str(tags[7]) + '.wav'
- target = os.path.join(wav_directory, filename)
- counter = 1
- while os.path.exists(target):
- filename = str(tags[7]) + f'({counter})' + '.wav'
- target = os.path.join(wav_directory, filename)
- counter += 1
-
- shutil.copyfile(wav_tmp, target); # make copy of original file
- json_file = add_tags(target, tags);
-
- wav_target=target;
- target=target.replace('wav', 'mp4');
- mp4_target=target;
-
- mp4=r'./' +mp4;
- shutil.copyfile(mp4, target); # make copy of original file
- _ = add_tags(target, tags);
-
- target=target.replace('mp4', 'json'); # change the extension to json
- json_target=target; # store the json target
-
- with open(target, 'w') as f: # open a writable file object
- shutil.copyfile(json_file, target); # make copy of original file
-
- os.remove(json_file)
-
- return wav_target, mp4_target, json_target;
-
-
-def clear_cash():
- # delete all temporary files genegated my system
- current_date = datetime.now().date()
- current_directory = os.getcwd()
- files = glob.glob(os.path.join(current_directory, '*.mp4'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- temp_directory = os.environ.get('TEMP')
- files = glob.glob(os.path.join(temp_directory, 'tmp*.mp4'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- files = glob.glob(os.path.join(temp_directory, 'tmp*.wav'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- files = glob.glob(os.path.join(temp_directory, 'tmp*.png'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
- return
-
-
-def s2t(seconds, seconds2):
- # convert seconds to time format
- # seconds - time in seconds
- # return time in format 00:00
- m, s = divmod(seconds, 60)
- m2, s2 = divmod(seconds2, 60)
- if seconds != 0 and seconds < seconds2:
- s = s + 1
- return ("%02d:%02d - %02d:%02d" % (m, s, m2, s2))
-
-
-def calc_time(gen_type, s, duration, overlap, d0, d1, d2, d3, d4, d5, d6, d7, d8, d9):
- # calculate the time of generation
- # overlap - overlap in seconds
- # d0-d9 - drag
- # return time in seconds
- d_amount = [int(d0), int(d1), int(d2), int(d3), int(d4), int(d5), int(d6), int(d7), int(d8), int(d9)]
- calc = []
- tracks = []
- time = 0
- s = s - 1
- max_time = duration
- max_limit = 0
- if gen_type == "music":
- max_limit = 30
- elif gen_type == "audio":
- max_limit = 10
- track_add = max_limit - overlap
- tracks.append(max_limit + ((d_amount[0] - 1) * track_add))
- for i in range(1, 10):
- tracks.append(d_amount[i] * track_add)
-
- if tracks[0] >= max_time or s == 0:
- calc.append(s2t(time, max_time))
- time = max_time
- else:
- calc.append(s2t(time, tracks[0]))
- time = tracks[0]
-
- for i in range(1, 10):
- if time + tracks[i] >= max_time or i == s:
- calc.append(s2t(time, max_time))
- time = max_time
- else:
- calc.append(s2t(time, time + tracks[i]))
- time = time + tracks[i]
-
- return calc[0], calc[1], calc[2], calc[3], calc[4], calc[5], calc[6], calc[7], calc[8], calc[9]
-
-
-def predict_full(gen_type, model, decoder, custom_model, base_model, prompt_amount, struc_prompt, bpm, key, scale, global_prompt, p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, d0, d1, d2, d3, d4, d5, d6, d7, d8, d9, audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select, progress=gr.Progress()):
- global INTERRUPTING
- global USE_DIFFUSION
- INTERRUPTING = False
-
- if gen_type == "audio":
- custom_model = None
- base_model = "medium"
-
- if temperature < 0:
- raise gr.Error("Temperature must be >= 0.")
- if topk < 0:
- raise gr.Error("Topk must be non-negative.")
- if topp < 0:
- raise gr.Error("Topp must be non-negative.")
-
- if trim_start < 0:
- trim_start = 0
- if trim_end < 0:
- trim_end = 0
-
- topk = int(topk)
-
- if decoder == "MultiBand_Diffusion":
- USE_DIFFUSION = True
- load_diffusion()
- else:
- USE_DIFFUSION = False
- unload_diffusion()
-
- if gen_type == "music":
- model_shrt = model
- model = "GrandaddyShmax/musicgen-" + model
- elif gen_type == "audio":
- model_shrt = model
- model = "GrandaddyShmax/audiogen-" + model
- base_model_shrt = base_model
- base_model = "GrandaddyShmax/musicgen-" + base_model
-
- if MODEL is None or MODEL.name != (model):
- load_model(model, custom_model, base_model, gen_type)
- else:
- if MOVE_TO_CPU:
- MODEL.to('cuda')
-
- if seed < 0:
- seed = random.randint(0, 0xffff_ffff_ffff)
- torch.manual_seed(seed)
-
- def _progress(generated, to_generate):
- progress((min(generated, to_generate), to_generate))
- if INTERRUPTING:
- raise gr.Error("Interrupted.")
- MODEL.set_custom_progress_callback(_progress)
-
- audio_mode = "none"
- melody = None
- sample = None
- if audio:
- audio_mode = mode
- if mode == "sample":
- sample = audio
- elif mode == "melody":
- melody = audio
-
- base_model = "none" if model != "custom" else base_model
- custom_model = "none" if model != "custom" else custom_model
-
- text_cat = [p0, p1, p2, p3, p4, p5, p6, p7, p8, p9]
- drag_cat = [d0, d1, d2, d3, d4, d5, d6, d7, d8, d9]
- texts = []
- raw_texts = []
- ind = 0
- ind2 = 0
- while ind < prompt_amount:
- for ind2 in range(int(drag_cat[ind])):
- if not struc_prompt:
- texts.append(text_cat[ind])
- global_prompt = "none"
- bpm = "none"
- key = "none"
- scale = "none"
- raw_texts.append(text_cat[ind])
- else:
- if gen_type == "music":
- bpm_str = str(bpm) + " bpm"
- key_str = ", " + str(key) + " " + str(scale)
- global_str = (", " + str(global_prompt)) if str(global_prompt) != "" else ""
- elif gen_type == "audio":
- bpm_str = ""
- key_str = ""
- global_str = (str(global_prompt)) if str(global_prompt) != "" else ""
- texts_str = (", " + str(text_cat[ind])) if str(text_cat[ind]) != "" else ""
- texts.append(bpm_str + key_str + global_str + texts_str)
- raw_texts.append(text_cat[ind])
- ind2 = 0
- ind = ind + 1
-
- outs, outs_audio, outs_backup, input_length = _do_predictions(
- gen_type, [texts], [melody], sample, trim_start, trim_end, duration, image, height, width, background, bar1, bar2, channel, sr_select, progress=True,
- top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef, extend_stride=MODEL.max_duration-overlap)
- tags = [str(global_prompt), str(bpm), str(key), str(scale), str(raw_texts), str(duration), str(overlap), str(seed), str(audio_mode), str(input_length), str(channel), str(sr_select), str(model_shrt), str(custom_model), str(base_model_shrt), str(decoder), str(topk), str(topp), str(temperature), str(cfg_coef), str(gen_type)]
- wav_target, mp4_target, json_target = save_outputs(outs[0], outs_audio[0], tags, gen_type);
- # Removes the temporary files.
- for out in outs:
- os.remove(out)
- for out in outs_audio:
- os.remove(out)
-
- return mp4_target, wav_target, outs_backup[0], [mp4_target, wav_target, json_target], seed
-
-
-max_textboxes = 10
-
-
-def get_available_models():
- return sorted([re.sub('.pt$', '', item.name) for item in list(Path('models/').glob('*')) if item.name.endswith('.pt')])
-
-
-def toggle_audio_src(choice):
- if choice == "mic":
- return gr.update(source="microphone", value=None, label="Microphone")
- else:
- return gr.update(source="upload", value=None, label="File")
-
-
-def ui_full(launch_kwargs):
- with gr.Blocks(title='AudioCraft Plus', theme=theme) as interface:
- gr.Markdown(
- """
- # AudioCraft Plus - v2.0.0a
-
- ### An All-in-One AudioCraft WebUI
-
- Thanks to: facebookresearch, Camenduru, rkfg, oobabooga, AlexHK and GrandaddyShmax
- """
- )
- with gr.Tab("MusicGen"):
- gr.Markdown(
- """
- ### MusicGen
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Tab("Generation"):
- with gr.Accordion("Structure Prompts", open=False):
- with gr.Column():
- with gr.Row():
- struc_prompts = gr.Checkbox(label="Enable", value=False, interactive=True, container=False)
- bpm = gr.Number(label="BPM", value=120, interactive=True, scale=1, precision=0)
- key = gr.Dropdown(["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "Bb", "B"], label="Key", value="C", interactive=True)
- scale = gr.Dropdown(["Major", "Minor"], label="Scale", value="Major", interactive=True)
- with gr.Row():
- global_prompt = gr.Text(label="Global Prompt", interactive=True, scale=3)
- with gr.Row():
- s = gr.Slider(1, max_textboxes, value=1, step=1, label="Prompts:", interactive=True, scale=2)
- #s_mode = gr.Radio(["segmentation", "batch"], value="segmentation", interactive=True, scale=1, label="Generation Mode")
- with gr.Column():
- textboxes = []
- prompts = []
- repeats = []
- calcs = []
- with gr.Row():
- text0 = gr.Text(label="Input Text", interactive=True, scale=4)
- prompts.append(text0)
- drag0 = gr.Number(label="Repeat", value=1, interactive=True, scale=1)
- repeats.append(drag0)
- calc0 = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- calcs.append(calc0)
- for i in range(max_textboxes):
- with gr.Row(visible=False) as t:
- text = gr.Text(label="Input Text", interactive=True, scale=3)
- repeat = gr.Number(label="Repeat", minimum=1, value=1, interactive=True, scale=1)
- calc = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- textboxes.append(t)
- prompts.append(text)
- repeats.append(repeat)
- calcs.append(calc)
- to_calc = gr.Button("Calculate Timings", variant="secondary")
- with gr.Row():
- duration = gr.Slider(minimum=1, maximum=300, value=10, step=1, label="Duration", interactive=True)
- with gr.Row():
- overlap = gr.Slider(minimum=1, maximum=29, value=12, step=1, label="Overlap", interactive=True)
- with gr.Row():
- seed = gr.Number(label="Seed", value=-1, scale=4, precision=0, interactive=True)
- gr.Button('\U0001f3b2\ufe0f', scale=1).click(fn=lambda: -1, outputs=[seed], queue=False)
- reuse_seed = gr.Button('\u267b\ufe0f', scale=1)
-
- with gr.Tab("Audio"):
- with gr.Row():
- with gr.Column():
- input_type = gr.Radio(["file", "mic"], value="file", label="Input Type (optional)", interactive=True)
- mode = gr.Radio(["melody", "sample"], label="Input Audio Mode (optional)", value="sample", interactive=True)
- with gr.Row():
- trim_start = gr.Number(label="Trim Start", value=0, interactive=True)
- trim_end = gr.Number(label="Trim End", value=0, interactive=True)
- audio = gr.Audio(source="upload", type="numpy", label="Input Audio (optional)", interactive=True)
-
- with gr.Tab("Customization"):
- with gr.Row():
- with gr.Column():
- background = gr.ColorPicker(value="#0f0f0f", label="background color", interactive=True, scale=0)
- bar1 = gr.ColorPicker(value="#84cc16", label="bar color start", interactive=True, scale=0)
- bar2 = gr.ColorPicker(value="#10b981", label="bar color end", interactive=True, scale=0)
- with gr.Column():
- image = gr.Image(label="Background Image", type="filepath", interactive=True, scale=4)
- with gr.Row():
- height = gr.Number(label="Height", value=512, interactive=True)
- width = gr.Number(label="Width", value=768, interactive=True)
-
- with gr.Tab("Settings"):
- with gr.Row():
- channel = gr.Radio(["mono", "stereo", "stereo effect"], label="Output Audio Channels", value="stereo", interactive=True, scale=1)
- sr_select = gr.Dropdown(["11025", "16000", "22050", "24000", "32000", "44100", "48000"], label="Output Audio Sample Rate", value="48000", interactive=True)
- with gr.Row():
- model = gr.Radio(["melody", "small", "medium", "large", "custom"], label="Model", value="large", interactive=True, scale=1)
- with gr.Column():
- dropdown = gr.Dropdown(choices=get_available_models(), value=("No models found" if len(get_available_models()) < 1 else get_available_models()[0]), label='Custom Model (models folder)', elem_classes='slim-dropdown', interactive=True)
- ui.create_refresh_button(dropdown, lambda: None, lambda: {'choices': get_available_models()}, 'refresh-button')
- basemodel = gr.Radio(["small", "medium", "melody", "large"], label="Base Model", value="medium", interactive=True, scale=1)
- with gr.Row():
- decoder = gr.Radio(["Default", "MultiBand_Diffusion"], label="Decoder", value="Default", interactive=True)
- with gr.Row():
- topk = gr.Number(label="Top-k", value=250, interactive=True)
- topp = gr.Number(label="Top-p", value=0, interactive=True)
- temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
- cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
- with gr.Row():
- submit = gr.Button("Generate", variant="primary")
- # Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
- _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
- with gr.Column() as c:
- with gr.Tab("Output"):
- output = gr.Video(label="Generated Music", scale=0)
- with gr.Row():
- audio_only = gr.Audio(type="numpy", label="Audio Only", interactive=False)
- backup_only = gr.Audio(type="numpy", label="Backup Audio", interactive=False, visible=False)
- send_audio = gr.Button("Send to Input Audio")
- seed_used = gr.Number(label='Seed used', value=-1, interactive=False)
- download = gr.File(label="Generated Files", interactive=False)
- with gr.Tab("Wiki"):
- gr.Markdown(
- """
- - **[Generate (button)]:**
- Generates the music with the given settings and prompts.
-
- - **[Interrupt (button)]:**
- Stops the music generation as soon as it can, providing an incomplete output.
-
- ---
-
- ### Generation Tab:
-
- #### Structure Prompts:
-
- This feature helps reduce repetetive prompts by allowing you to set global prompts
- that will be used for all prompt segments.
-
- - **[Structure Prompts (checkbox)]:**
- Enable/Disable the structure prompts feature.
-
- - **[BPM (number)]:**
- Beats per minute of the generated music.
-
- - **[Key (dropdown)]:**
- The key of the generated music.
-
- - **[Scale (dropdown)]:**
- The scale of the generated music.
-
- - **[Global Prompt (text)]:**
- Here write the prompt that you wish to be used for all prompt segments.
-
- #### Multi-Prompt:
-
- This feature allows you to control the music, adding variation to different time segments.
- You have up to 10 prompt segments. the first prompt will always be 30s long
- the other prompts will be [30s - overlap].
- for example if the overlap is 10s, each prompt segment will be 20s.
-
- - **[Prompt Segments (number)]:**
- Amount of unique prompt to generate throughout the music generation.
-
- - **[Prompt/Input Text (prompt)]:**
- Here describe the music you wish the model to generate.
-
- - **[Repeat (number)]:**
- Write how many times this prompt will repeat (instead of wasting another prompt segment on the same prompt).
-
- - **[Time (text)]:**
- The time of the prompt segment.
-
- - **[Calculate Timings (button)]:**
- Calculates the timings of the prompt segments.
-
- - **[Duration (number)]:**
- How long you want the generated music to be (in seconds).
-
- - **[Overlap (number)]:**
- How much each new segment will reference the previous segment (in seconds).
- For example, if you choose 20s: Each new segment after the first one will reference the previous segment 20s
- and will generate only 10s of new music. The model can only process 30s of music.
-
- - **[Seed (number)]:**
- Your generated music id. If you wish to generate the exact same music,
- place the exact seed with the exact prompts
- (This way you can also extend specific song that was generated short).
-
- - **[Random Seed (button)]:**
- Gives "-1" as a seed, which counts as a random seed.
-
- - **[Copy Previous Seed (button)]:**
- Copies the seed from the output seed (if you don't feel like doing it manualy).
-
- ---
-
- ### Audio Tab:
-
- - **[Input Type (selection)]:**
- `File` mode allows you to upload an audio file to use as input
- `Mic` mode allows you to use your microphone as input
-
- - **[Input Audio Mode (selection)]:**
- `Melody` mode only works with the melody model: it conditions the music generation to reference the melody
- `Sample` mode works with any model: it gives a music sample to the model to generate its continuation.
-
- - **[Trim Start and Trim End (numbers)]:**
- `Trim Start` set how much you'd like to trim the input audio from the start
- `Trim End` same as the above but from the end
-
- - **[Input Audio (audio file)]:**
- Input here the audio you wish to use with "melody" or "sample" mode.
-
- ---
-
- ### Customization Tab:
-
- - **[Background Color (color)]:**
- Works only if you don't upload image. Color of the background of the waveform.
-
- - **[Bar Color Start (color)]:**
- First color of the waveform bars.
-
- - **[Bar Color End (color)]:**
- Second color of the waveform bars.
-
- - **[Background Image (image)]:**
- Background image that you wish to be attached to the generated video along with the waveform.
-
- - **[Height and Width (numbers)]:**
- Output video resolution, only works with image.
- (minimum height and width is 256).
-
- ---
-
- ### Settings Tab:
-
- - **[Output Audio Channels (selection)]:**
- With this you can select the amount of channels that you wish for your output audio.
- `mono` is a straightforward single channel audio
- `stereo` is a dual channel audio but it will sound more or less like mono
- `stereo effect` this one is also dual channel but uses tricks to simulate a stereo audio.
-
- - **[Output Audio Sample Rate (dropdown)]:**
- The output audio sample rate, the model default is 32000.
-
- - **[Model (selection)]:**
- Here you can choose which model you wish to use:
- `melody` model is based on the medium model with a unique feature that lets you use melody conditioning
- `small` model is trained on 300M parameters
- `medium` model is trained on 1.5B parameters
- `large` model is trained on 3.3B parameters
- `custom` model runs the custom model that you provided.
-
- - **[Custom Model (selection)]:**
- This dropdown will show you models that are placed in the `models` folder
- you must select `custom` in the model options in order to use it.
-
- - **[Refresh (button)]:**
- Refreshes the dropdown list for custom model.
-
- - **[Base Model (selection)]:**
- Choose here the model that your custom model is based on.
-
- - **[Decoder (selection)]:**
- Choose here the decoder that you wish to use:
- `Default` is the default decoder
- `MultiBand_Diffusion` is a decoder that uses diffusion to generate the audio.
-
- - **[Top-k (number)]:**
- is a parameter used in text generation models, including music generation models. It determines the number of most likely next tokens to consider at each step of the generation process. The model ranks all possible tokens based on their predicted probabilities, and then selects the top-k tokens from the ranked list. The model then samples from this reduced set of tokens to determine the next token in the generated sequence. A smaller value of k results in a more focused and deterministic output, while a larger value of k allows for more diversity in the generated music.
-
- - **[Top-p (number)]:**
- also known as nucleus sampling or probabilistic sampling, is another method used for token selection during text generation. Instead of specifying a fixed number like top-k, top-p considers the cumulative probability distribution of the ranked tokens. It selects the smallest possible set of tokens whose cumulative probability exceeds a certain threshold (usually denoted as p). The model then samples from this set to choose the next token. This approach ensures that the generated output maintains a balance between diversity and coherence, as it allows for a varying number of tokens to be considered based on their probabilities.
-
- - **[Temperature (number)]:**
- is a parameter that controls the randomness of the generated output. It is applied during the sampling process, where a higher temperature value results in more random and diverse outputs, while a lower temperature value leads to more deterministic and focused outputs. In the context of music generation, a higher temperature can introduce more variability and creativity into the generated music, but it may also lead to less coherent or structured compositions. On the other hand, a lower temperature can produce more repetitive and predictable music.
-
- - **[Classifier Free Guidance (number)]:**
- refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
- """
- )
- with gr.Tab("AudioGen"):
- gr.Markdown(
- """
- ### AudioGen
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Tab("Generation"):
- with gr.Accordion("Structure Prompts", open=False):
- with gr.Row():
- struc_prompts_a = gr.Checkbox(label="Enable", value=False, interactive=True, container=False)
- global_prompt_a = gr.Text(label="Global Prompt", interactive=True, scale=3)
- with gr.Row():
- s_a = gr.Slider(1, max_textboxes, value=1, step=1, label="Prompts:", interactive=True, scale=2)
- with gr.Column():
- textboxes_a = []
- prompts_a = []
- repeats_a = []
- calcs_a = []
- with gr.Row():
- text0_a = gr.Text(label="Input Text", interactive=True, scale=4)
- prompts_a.append(text0_a)
- drag0_a = gr.Number(label="Repeat", value=1, interactive=True, scale=1)
- repeats_a.append(drag0_a)
- calc0_a = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- calcs_a.append(calc0_a)
- for i in range(max_textboxes):
- with gr.Row(visible=False) as t_a:
- text_a = gr.Text(label="Input Text", interactive=True, scale=3)
- repeat_a = gr.Number(label="Repeat", minimum=1, value=1, interactive=True, scale=1)
- calc_a = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- textboxes_a.append(t_a)
- prompts_a.append(text_a)
- repeats_a.append(repeat_a)
- calcs_a.append(calc_a)
- to_calc_a = gr.Button("Calculate Timings", variant="secondary")
- with gr.Row():
- duration_a = gr.Slider(minimum=1, maximum=300, value=10, step=1, label="Duration", interactive=True)
- with gr.Row():
- overlap_a = gr.Slider(minimum=1, maximum=9, value=2, step=1, label="Overlap", interactive=True)
- with gr.Row():
- seed_a = gr.Number(label="Seed", value=-1, scale=4, precision=0, interactive=True)
- gr.Button('\U0001f3b2\ufe0f', scale=1).click(fn=lambda: -1, outputs=[seed_a], queue=False)
- reuse_seed_a = gr.Button('\u267b\ufe0f', scale=1)
-
- with gr.Tab("Audio"):
- with gr.Row():
- with gr.Column():
- input_type_a = gr.Radio(["file", "mic"], value="file", label="Input Type (optional)", interactive=True)
- mode_a = gr.Radio(["sample"], label="Input Audio Mode (optional)", value="sample", interactive=False, visible=False)
- with gr.Row():
- trim_start_a = gr.Number(label="Trim Start", value=0, interactive=True)
- trim_end_a = gr.Number(label="Trim End", value=0, interactive=True)
- audio_a = gr.Audio(source="upload", type="numpy", label="Input Audio (optional)", interactive=True)
-
- with gr.Tab("Customization"):
- with gr.Row():
- with gr.Column():
- background_a = gr.ColorPicker(value="#0f0f0f", label="background color", interactive=True, scale=0)
- bar1_a = gr.ColorPicker(value="#84cc16", label="bar color start", interactive=True, scale=0)
- bar2_a = gr.ColorPicker(value="#10b981", label="bar color end", interactive=True, scale=0)
- with gr.Column():
- image_a = gr.Image(label="Background Image", type="filepath", interactive=True, scale=4)
- with gr.Row():
- height_a = gr.Number(label="Height", value=512, interactive=True)
- width_a = gr.Number(label="Width", value=768, interactive=True)
-
- with gr.Tab("Settings"):
- with gr.Row():
- channel_a = gr.Radio(["mono", "stereo", "stereo effect"], label="Output Audio Channels", value="stereo", interactive=True, scale=1)
- sr_select_a = gr.Dropdown(["11025", "16000", "22050", "24000", "32000", "44100", "48000"], label="Output Audio Sample Rate", value="48000", interactive=True)
- with gr.Row():
- model_a = gr.Radio(["medium"], label="Model", value="medium", interactive=False, visible=False)
- decoder_a = gr.Radio(["Default"], label="Decoder", value="Default", interactive=False, visible=False)
- with gr.Row():
- topk_a = gr.Number(label="Top-k", value=250, interactive=True)
- topp_a = gr.Number(label="Top-p", value=0, interactive=True)
- temperature_a = gr.Number(label="Temperature", value=1.0, interactive=True)
- cfg_coef_a = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
- with gr.Row():
- submit_a = gr.Button("Generate", variant="primary")
- _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
- with gr.Column():
- with gr.Tab("Output"):
- output_a = gr.Video(label="Generated Audio", scale=0)
- with gr.Row():
- audio_only_a = gr.Audio(type="numpy", label="Audio Only", interactive=False)
- backup_only_a = gr.Audio(type="numpy", label="Backup Audio", interactive=False, visible=False)
- send_audio_a = gr.Button("Send to Input Audio")
- seed_used_a = gr.Number(label='Seed used', value=-1, interactive=False)
- download_a = gr.File(label="Generated Files", interactive=False)
- with gr.Tab("Wiki"):
- gr.Markdown(
- """
- - **[Generate (button)]:**
- Generates the audio with the given settings and prompts.
-
- - **[Interrupt (button)]:**
- Stops the audio generation as soon as it can, providing an incomplete output.
-
- ---
-
- ### Generation Tab:
-
- #### Structure Prompts:
-
- This feature helps reduce repetetive prompts by allowing you to set global prompts
- that will be used for all prompt segments.
-
- - **[Structure Prompts (checkbox)]:**
- Enable/Disable the structure prompts feature.
-
- - **[Global Prompt (text)]:**
- Here write the prompt that you wish to be used for all prompt segments.
-
- #### Multi-Prompt:
-
- This feature allows you to control the audio, adding variation to different time segments.
- You have up to 10 prompt segments. the first prompt will always be 10s long
- the other prompts will be [10s - overlap].
- for example if the overlap is 2s, each prompt segment will be 8s.
-
- - **[Prompt Segments (number)]:**
- Amount of unique prompt to generate throughout the audio generation.
-
- - **[Prompt/Input Text (prompt)]:**
- Here describe the audio you wish the model to generate.
-
- - **[Repeat (number)]:**
- Write how many times this prompt will repeat (instead of wasting another prompt segment on the same prompt).
-
- - **[Time (text)]:**
- The time of the prompt segment.
-
- - **[Calculate Timings (button)]:**
- Calculates the timings of the prompt segments.
-
- - **[Duration (number)]:**
- How long you want the generated audio to be (in seconds).
-
- - **[Overlap (number)]:**
- How much each new segment will reference the previous segment (in seconds).
- For example, if you choose 2s: Each new segment after the first one will reference the previous segment 2s
- and will generate only 8s of new audio. The model can only process 10s of music.
-
- - **[Seed (number)]:**
- Your generated audio id. If you wish to generate the exact same audio,
- place the exact seed with the exact prompts
- (This way you can also extend specific song that was generated short).
-
- - **[Random Seed (button)]:**
- Gives "-1" as a seed, which counts as a random seed.
-
- - **[Copy Previous Seed (button)]:**
- Copies the seed from the output seed (if you don't feel like doing it manualy).
-
- ---
-
- ### Audio Tab:
-
- - **[Input Type (selection)]:**
- `File` mode allows you to upload an audio file to use as input
- `Mic` mode allows you to use your microphone as input
-
- - **[Trim Start and Trim End (numbers)]:**
- `Trim Start` set how much you'd like to trim the input audio from the start
- `Trim End` same as the above but from the end
-
- - **[Input Audio (audio file)]:**
- Input here the audio you wish to use.
-
- ---
-
- ### Customization Tab:
-
- - **[Background Color (color)]:**
- Works only if you don't upload image. Color of the background of the waveform.
-
- - **[Bar Color Start (color)]:**
- First color of the waveform bars.
-
- - **[Bar Color End (color)]:**
- Second color of the waveform bars.
-
- - **[Background Image (image)]:**
- Background image that you wish to be attached to the generated video along with the waveform.
-
- - **[Height and Width (numbers)]:**
- Output video resolution, only works with image.
- (minimum height and width is 256).
-
- ---
-
- ### Settings Tab:
-
- - **[Output Audio Channels (selection)]:**
- With this you can select the amount of channels that you wish for your output audio.
- `mono` is a straightforward single channel audio
- `stereo` is a dual channel audio but it will sound more or less like mono
- `stereo effect` this one is also dual channel but uses tricks to simulate a stereo audio.
-
- - **[Output Audio Sample Rate (dropdown)]:**
- The output audio sample rate, the model default is 32000.
-
- - **[Top-k (number)]:**
- is a parameter used in text generation models, including music generation models. It determines the number of most likely next tokens to consider at each step of the generation process. The model ranks all possible tokens based on their predicted probabilities, and then selects the top-k tokens from the ranked list. The model then samples from this reduced set of tokens to determine the next token in the generated sequence. A smaller value of k results in a more focused and deterministic output, while a larger value of k allows for more diversity in the generated music.
-
- - **[Top-p (number)]:**
- also known as nucleus sampling or probabilistic sampling, is another method used for token selection during text generation. Instead of specifying a fixed number like top-k, top-p considers the cumulative probability distribution of the ranked tokens. It selects the smallest possible set of tokens whose cumulative probability exceeds a certain threshold (usually denoted as p). The model then samples from this set to choose the next token. This approach ensures that the generated output maintains a balance between diversity and coherence, as it allows for a varying number of tokens to be considered based on their probabilities.
-
- - **[Temperature (number)]:**
- is a parameter that controls the randomness of the generated output. It is applied during the sampling process, where a higher temperature value results in more random and diverse outputs, while a lower temperature value leads to more deterministic and focused outputs. In the context of music generation, a higher temperature can introduce more variability and creativity into the generated music, but it may also lead to less coherent or structured compositions. On the other hand, a lower temperature can produce more repetitive and predictable music.
-
- - **[Classifier Free Guidance (number)]:**
- refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
- """
- )
- with gr.Tab("Audio Info"):
- gr.Markdown(
- """
- ### Audio Info
- """
- )
- with gr.Row():
- with gr.Column():
- in_audio = gr.File(type="file", label="Input Any Audio", interactive=True)
- with gr.Row():
- send_gen = gr.Button("Send to MusicGen", variant="primary")
- send_gen_a = gr.Button("Send to AudioGen", variant="primary")
- with gr.Column():
- info = gr.Textbox(label="Audio Info", lines=10, interactive=False)
- with gr.Tab("Changelog"):
- gr.Markdown(
- """
- ## Changelog:
-
- ### v2.0.0a
-
- - Forgot to move all the update to app.py from temp2.py... oops
-
-
-
- ### v2.0.0
-
- - Changed name from MusicGen+ to AudioCraft Plus
-
- - Complete overhaul of the repo "backend" with the latest changes from the main facebookresearch repo
-
- - Added a new decoder: MultiBand_Diffusion
-
- - Added AudioGen: a new tab for generating audio
-
-
-
- ### v1.2.8c
-
- - Implemented Reverse compatibility for audio info tab with previous versions
-
-
-
- ### v1.2.8b
-
- - Fixed the error when loading default models
-
-
-
- ### v1.2.8a
-
- - Adapted Audio info tab to work with the new structure prompts feature
-
- - Now custom models actually work, make sure you select the correct base model
-
-
-
- ### v1.2.8
-
- - Now you will also recieve json file with metadata of generated audio
-
- - Added error messages in Audio Info tab
-
- - Added structure prompts: you can select bpm, key and global prompt for all prompts
-
- - Added time display next to each prompt, can be calculated with "Calculate Timings" button
-
-
-
- ### v1.2.7
-
- - When sending generated audio to Input Audio, it will send a backup audio with default settings
- (best for continuos generation)
-
- - Added Metadata to generated audio (Thanks to AlexHK ♥)
-
- - Added Audio Info tab that will display the metadata of the input audio
-
- - Added "send to Text2Audio" button in Audio Info tab
-
- - Generated audio is now stored in the "output" folder (Thanks to AlexHK ♥)
-
- - Added an output area with generated files and download buttons
-
- - Enhanced Stereo effect (Thanks to AlexHK ♥)
-
-
-
- ### v1.2.6
-
- - Added option to generate in stereo (instead of only mono)
-
- - Added dropdown for selecting output sample rate (model default is 32000)
-
-
-
- ### v1.2.5a
-
- - Added file cleaner (This comes from the main facebookresearch repo)
-
- - Reorganized a little, moved audio to a seperate tab
-
-
-
- ### v1.2.5
-
- - Gave a unique lime theme to the webui
-
- - Added additional output for audio only
-
- - Added button to send generated audio to Input Audio
-
- - Added option to trim Input Audio
-
-
-
- ### v1.2.4
-
- - Added mic input (This comes from the main facebookresearch repo)
-
-
-
- ### v1.2.3
-
- - Added option to change video size to fit the image you upload
-
-
-
- ### v1.2.2
-
- - Added Wiki, Changelog and About tabs
-
-
-
- ### v1.2.1
-
- - Added tabs and organized the entire interface
-
- - Added option to attach image to the output video
-
- - Added option to load fine-tuned models (Yet to be tested)
-
-
-
- ### v1.2.0
-
- - Added Multi-Prompt
-
-
-
- ### v1.1.3
-
- - Added customization options for generated waveform
-
-
-
- ### v1.1.2
-
- - Removed sample length limit: now you can input audio of any length as music sample
-
-
-
- ### v1.1.1
-
- - Improved music sample audio quality when using music continuation
-
-
-
- ### v1.1.0
-
- - Rebuilt the repo on top of the latest structure of the main MusicGen repo
-
- - Improved Music continuation feature
-
-
-
- ### v1.0.0 - Stable Version
-
- - Added Music continuation
- """
- )
- with gr.Tab("About"):
- gen_type = gr.Text(value="music", interactive=False, visible=False)
- gen_type_a = gr.Text(value="audio", interactive=False, visible=False)
- gr.Markdown(
- """
- This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
- presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
-
- ## MusicGen+ is an extended version of the original MusicGen by facebookresearch.
-
- ### Repo: https://github.com/GrandaddyShmax/audiocraft_plus/tree/plus
-
- ---
-
- ### This project was possible thanks to:
-
- #### GrandaddyShmax - https://github.com/GrandaddyShmax
-
- #### Camenduru - https://github.com/camenduru
-
- #### rkfg - https://github.com/rkfg
-
- #### oobabooga - https://github.com/oobabooga
-
- #### AlexHK - https://github.com/alanhk147
- """
- )
-
- send_gen.click(info_to_params, inputs=[in_audio], outputs=[decoder, struc_prompts, global_prompt, bpm, key, scale, model, dropdown, basemodel, s, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select], queue=False)
- reuse_seed.click(fn=lambda x: x, inputs=[seed_used], outputs=[seed], queue=False)
- send_audio.click(fn=lambda x: x, inputs=[backup_only], outputs=[audio], queue=False)
- submit.click(predict_full, inputs=[gen_type, model, decoder, dropdown, basemodel, s, struc_prompts, bpm, key, scale, global_prompt, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select], outputs=[output, audio_only, backup_only, download, seed_used])
- input_type.change(toggle_audio_src, input_type, [audio], queue=False, show_progress=False)
- to_calc.click(calc_time, inputs=[gen_type, s, duration, overlap, repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9]], outputs=[calcs[0], calcs[1], calcs[2], calcs[3], calcs[4], calcs[5], calcs[6], calcs[7], calcs[8], calcs[9]], queue=False)
-
- send_gen_a.click(info_to_params_a, inputs=[in_audio], outputs=[decoder_a, struc_prompts_a, global_prompt_a, s_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, channel_a, sr_select_a], queue=False)
- reuse_seed_a.click(fn=lambda x: x, inputs=[seed_used_a], outputs=[seed_a], queue=False)
- send_audio_a.click(fn=lambda x: x, inputs=[backup_only_a], outputs=[audio_a], queue=False)
- submit_a.click(predict_full, inputs=[gen_type_a, model_a, decoder_a, dropdown, basemodel, s_a, struc_prompts_a, bpm, key, scale, global_prompt_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], audio_a, mode_a, trim_start_a, trim_end_a, duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, image_a, height_a, width_a, background_a, bar1_a, bar2_a, channel_a, sr_select_a], outputs=[output_a, audio_only_a, backup_only_a, download_a, seed_used_a])
- input_type_a.change(toggle_audio_src, input_type_a, [audio_a], queue=False, show_progress=False)
- to_calc_a.click(calc_time, inputs=[gen_type_a, s_a, duration_a, overlap_a, repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9]], outputs=[calcs_a[0], calcs_a[1], calcs_a[2], calcs_a[3], calcs_a[4], calcs_a[5], calcs_a[6], calcs_a[7], calcs_a[8], calcs_a[9]], queue=False)
-
- in_audio.change(get_audio_info, in_audio, outputs=[info])
-
- def variable_outputs(k):
- k = int(k) - 1
- return [gr.Textbox.update(visible=True)]*k + [gr.Textbox.update(visible=False)]*(max_textboxes-k)
- def get_size(image):
- if image is not None:
- img = Image.open(image)
- img_height = img.height
- img_width = img.width
- if (img_height%2) != 0:
- img_height = img_height + 1
- if (img_width%2) != 0:
- img_width = img_width + 1
- return img_height, img_width
- else:
- return 512, 768
-
- image.change(get_size, image, outputs=[height, width])
- image_a.change(get_size, image_a, outputs=[height_a, width_a])
- s.change(variable_outputs, s, textboxes)
- s_a.change(variable_outputs, s_a, textboxes_a)
- interface.queue().launch(**launch_kwargs)
-
-
-def ui_batched(launch_kwargs):
- with gr.Blocks() as demo:
- gr.Markdown(
- """
- # MusicGen
-
- This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
- a simple and controllable model for music generation
- presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
-
-
-
- for longer sequences, more control and no queue.
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Row():
- text = gr.Text(label="Describe your music", lines=2, interactive=True)
- with gr.Column():
- radio = gr.Radio(["file", "mic"], value="file",
- label="Condition on a melody (optional) File or Mic")
- melody = gr.Audio(source="upload", type="numpy", label="File",
- interactive=True, elem_id="melody-input")
- with gr.Row():
- submit = gr.Button("Generate")
- with gr.Column():
- output = gr.Video(label="Generated Music")
- audio_output = gr.Audio(label="Generated Music (wav)", type='filepath')
- submit.click(predict_batched, inputs=[text, melody],
- outputs=[output, audio_output], batch=True, max_batch_size=MAX_BATCH_SIZE)
- radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
- gr.Examples(
- fn=predict_batched,
- examples=[
- [
- "An 80s driving pop song with heavy drums and synth pads in the background",
- "./assets/bach.mp3",
- ],
- [
- "A cheerful country song with acoustic guitars",
- "./assets/bolero_ravel.mp3",
- ],
- [
- "90s rock song with electric guitar and heavy drums",
- None,
- ],
- [
- "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
- "./assets/bach.mp3",
- ],
- [
- "lofi slow bpm electro chill with organic samples",
- None,
- ],
- ],
- inputs=[text, melody],
- outputs=[output]
- )
- gr.Markdown("""
- ### More details
-
- The model will generate 12 seconds of audio based on the description you provided.
- You can optionally provide a reference audio from which a broad melody will be extracted.
- The model will then try to follow both the description and melody provided.
- All samples are generated with the `melody` model.
-
- You can also use your own GPU or a Google Colab by following the instructions on our repo.
-
- See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
- for more details.
- """)
-
- demo.queue(max_size=8 * 4).launch(**launch_kwargs)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument(
- '--listen',
- type=str,
- default='0.0.0.0' if 'SPACE_ID' in os.environ else '127.0.0.1',
- help='IP to listen on for connections to Gradio',
- )
- parser.add_argument(
- '--username', type=str, default='', help='Username for authentication'
- )
- parser.add_argument(
- '--password', type=str, default='', help='Password for authentication'
- )
- parser.add_argument(
- '--server_port',
- type=int,
- default=0,
- help='Port to run the server listener on',
- )
- parser.add_argument(
- '--inbrowser', action='store_true', help='Open in browser'
- )
- parser.add_argument(
- '--share', action='store_true', help='Share the gradio UI'
- )
- parser.add_argument(
- '--unload_model', action='store_true', help='Unload the model after every generation to save GPU memory'
- )
-
- parser.add_argument(
- '--unload_to_cpu', action='store_true', help='Move the model to main RAM after every generation to save GPU memory but reload faster than after full unload (see above)'
- )
-
- parser.add_argument(
- '--cache', action='store_true', help='Cache models in RAM to quickly switch between them'
- )
-
- args = parser.parse_args()
- UNLOAD_MODEL = args.unload_model
- MOVE_TO_CPU = args.unload_to_cpu
- if args.cache:
- MODELS = {}
-
- launch_kwargs = {}
- launch_kwargs['server_name'] = args.listen
-
- if args.username and args.password:
- launch_kwargs['auth'] = (args.username, args.password)
- if args.server_port:
- launch_kwargs['server_port'] = args.server_port
- if args.inbrowser:
- launch_kwargs['inbrowser'] = args.inbrowser
- if args.share:
- launch_kwargs['share'] = args.share
-
- # Show the interface
- if IS_BATCHED:
- global USE_DIFFUSION
- USE_DIFFUSION = False
- ui_batched(launch_kwargs)
- else:
- ui_full(launch_kwargs)
\ No newline at end of file
diff --git a/spaces/PunPk/AI_FallingAsleepDriving/README.md b/spaces/PunPk/AI_FallingAsleepDriving/README.md
deleted file mode 100644
index 0855483dc5cdc696af1fe1d3502b584b42fde086..0000000000000000000000000000000000000000
--- a/spaces/PunPk/AI_FallingAsleepDriving/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: AI FallingAsleepDriving
-emoji: 📉
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RMXK/RVC_HFF/tensorlowest.py b/spaces/RMXK/RVC_HFF/tensorlowest.py
deleted file mode 100644
index eccd4dbf3494434e59f7defaae6ab91797263b90..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/tensorlowest.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from tensorboard.backend.event_processing import event_accumulator
-
-import os
-from shutil import copy2
-from re import search as RSearch
-import pandas as pd
-from ast import literal_eval as LEval
-
-weights_dir = 'weights/'
-
-def find_biggest_tensorboard(tensordir):
- try:
- files = [f for f in os.listdir(tensordir) if f.endswith('.0')]
- if not files:
- print("No files with the '.0' extension found!")
- return
-
- max_size = 0
- biggest_file = ""
-
- for file in files:
- file_path = os.path.join(tensordir, file)
- if os.path.isfile(file_path):
- file_size = os.path.getsize(file_path)
- if file_size > max_size:
- max_size = file_size
- biggest_file = file
-
- return biggest_file
-
- except FileNotFoundError:
- print("Couldn't find your model!")
- return
-
-def main(model_name, save_freq, lastmdls):
- global lowestval_weight_dir, scl
-
- tensordir = os.path.join('logs', model_name)
- lowestval_weight_dir = os.path.join(tensordir, "lowestvals")
-
- latest_file = find_biggest_tensorboard(tensordir)
-
- if latest_file is None:
- print("Couldn't find a valid tensorboard file!")
- return
-
- tfile = os.path.join(tensordir, latest_file)
-
- ea = event_accumulator.EventAccumulator(tfile,
- size_guidance={
- event_accumulator.COMPRESSED_HISTOGRAMS: 500,
- event_accumulator.IMAGES: 4,
- event_accumulator.AUDIO: 4,
- event_accumulator.SCALARS: 0,
- event_accumulator.HISTOGRAMS: 1,
- })
-
- ea.Reload()
- ea.Tags()
-
- scl = ea.Scalars('loss/g/total')
-
- listwstep = {}
-
- for val in scl:
- if (val.step // save_freq) * save_freq in [val.step for val in scl]:
- listwstep[float(val.value)] = (val.step // save_freq) * save_freq
-
- lowest_vals = sorted(listwstep.keys())[:lastmdls]
-
- sorted_dict = {value: step for value, step in listwstep.items() if value in lowest_vals}
-
- return sorted_dict
-
-def selectweights(model_name, file_dict, weights_dir, lowestval_weight_dir):
- os.makedirs(lowestval_weight_dir, exist_ok=True)
- logdir = []
- files = []
- lbldict = {
- 'Values': {},
- 'Names': {}
- }
- weights_dir_path = os.path.join(weights_dir, "")
- low_val_path = os.path.join(os.getcwd(), os.path.join(lowestval_weight_dir, ""))
-
- try:
- file_dict = LEval(file_dict)
- except Exception as e:
- print(f"Error! {e}")
- return f"Couldn't load tensorboard file! {e}"
-
- weights = [f for f in os.scandir(weights_dir)]
- for key, value in file_dict.items():
- pattern = fr"^{model_name}_.*_s{value}\.pth$"
- matching_weights = [f.name for f in weights if f.is_file() and RSearch(pattern, f.name)]
- for weight in matching_weights:
- source_path = weights_dir_path + weight
- destination_path = os.path.join(lowestval_weight_dir, weight)
-
- copy2(source_path, destination_path)
-
- logdir.append(f"File = {weight} Value: {key}, Step: {value}")
-
- lbldict['Names'][weight] = weight
- lbldict['Values'][weight] = key
-
- files.append(low_val_path + weight)
-
- print(f"File = {weight} Value: {key}, Step: {value}")
-
- yield ('\n'.join(logdir), files, pd.DataFrame(lbldict))
-
-
- return ''.join(logdir), files, pd.DataFrame(lbldict)
-
-
-if __name__ == "__main__":
- model = str(input("Enter the name of the model: "))
- sav_freq = int(input("Enter save frequency of the model: "))
- ds = main(model, sav_freq)
-
- if ds: selectweights(model, ds, weights_dir, lowestval_weight_dir)
-
\ No newline at end of file
diff --git a/spaces/Rekanice/hf_minimal_sushi/README.md b/spaces/Rekanice/hf_minimal_sushi/README.md
deleted file mode 100644
index 4b038ea069e1817e717615da6679be0a913f0f5d..0000000000000000000000000000000000000000
--- a/spaces/Rekanice/hf_minimal_sushi/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Hf Minimal Sushi
-emoji: 🌍
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RoCobo/WiggleGAN/main.py b/spaces/RoCobo/WiggleGAN/main.py
deleted file mode 100644
index 097c3b9aed9be52619095419859205b71110555c..0000000000000000000000000000000000000000
--- a/spaces/RoCobo/WiggleGAN/main.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import argparse
-import os
-import torch
-from WiggleGAN import WiggleGAN
-#from MyACGAN import MyACGAN
-#from MyGAN import MyGAN
-
-"""parsing and configuration"""
-
-
-def parse_args():
- desc = "Pytorch implementation of GAN collections"
- parser = argparse.ArgumentParser(description=desc)
-
- parser.add_argument('--gan_type', type=str, default='WiggleGAN',
- choices=['MyACGAN', 'MyGAN', 'WiggleGAN'],
- help='The type of GAN')
- parser.add_argument('--dataset', type=str, default='4cam',
- choices=['mnist', 'fashion-mnist', 'cifar10', 'cifar100', 'svhn', 'stl10', 'lsun-bed', '4cam'],
- help='The name of dataset')
- parser.add_argument('--split', type=str, default='', help='The split flag for svhn and stl10')
- parser.add_argument('--epoch', type=int, default=50, help='The number of epochs to run')
- parser.add_argument('--batch_size', type=int, default=16, help='The size of batch')
- parser.add_argument('--input_size', type=int, default=10, help='The size of input image')
- parser.add_argument('--save_dir', type=str, default='models',
- help='Directory name to save the model')
- parser.add_argument('--result_dir', type=str, default='results', help='Directory name to save the generated images')
- parser.add_argument('--log_dir', type=str, default='logs', help='Directory name to save training logs')
- parser.add_argument('--lrG', type=float, default=0.0002)
- parser.add_argument('--lrD', type=float, default=0.001)
- parser.add_argument('--beta1', type=float, default=0.5)
- parser.add_argument('--beta2', type=float, default=0.999)
- parser.add_argument('--gpu_mode', type=str2bool, default=True)
- parser.add_argument('--benchmark_mode', type=str2bool, default=True)
- parser.add_argument('--cameras', type=int, default=2)
- parser.add_argument('--imageDim', type=int, default=128)
- parser.add_argument('--epochV', type=int, default=0)
- parser.add_argument('--cIm', type=int, default=4)
- parser.add_argument('--seedLoad', type=str, default="-0000")
- parser.add_argument('--zGF', type=float, default=0.2)
- parser.add_argument('--zDF', type=float, default=0.2)
- parser.add_argument('--bF', type=float, default=0.2)
- parser.add_argument('--expandGen', type=int, default=3)
- parser.add_argument('--expandDis', type=int, default=3)
- parser.add_argument('--wiggleDepth', type=int, default=-1)
- parser.add_argument('--visdom', type=str2bool, default=True)
- parser.add_argument('--lambdaL1', type=int, default=100)
- parser.add_argument('--clipping', type=float, default=-1)
- parser.add_argument('--depth', type=str2bool, default=True)
- parser.add_argument('--recreate', type=str2bool, default=False)
- parser.add_argument('--name_wiggle', type=str, default='wiggle-result')
-
- return check_args(parser.parse_args())
-
-
-"""checking arguments"""
-
-def str2bool(v):
- if isinstance(v, bool):
- return v
- if v.lower() in ('yes', 'true', 't', 'y', '1'):
- return True
- elif v.lower() in ('no', 'false', 'f', 'n', '0'):
- return False
- else:
- raise argparse.ArgumentTypeError('Boolean value expected.')
-
-
-def check_args(args):
- # --save_dir
- if not os.path.exists(args.save_dir):
- os.makedirs(args.save_dir)
-
- # --result_dir
- if not os.path.exists(args.result_dir):
- os.makedirs(args.result_dir)
-
- # --result_dir
- if not os.path.exists(args.log_dir):
- os.makedirs(args.log_dir)
-
- # --epoch
- try:
- assert args.epoch >= 1
- except:
- print('number of epochs must be larger than or equal to one')
-
- # --batch_size
- try:
- assert args.batch_size >= 1
- except:
- print('batch size must be larger than or equal to one')
-
- return args
-
-
-"""main"""
-
-
-def main():
- # parse arguments
- args = parse_args()
- if args is None:
- exit()
-
- if args.benchmark_mode:
- torch.backends.cudnn.benchmark = True
-
- # declare instance for GAN
- if args.gan_type == 'WiggleGAN':
- gan = WiggleGAN(args)
- #elif args.gan_type == 'MyACGAN':
- # gan = MyACGAN(args)
- #elif args.gan_type == 'MyGAN':
- # gan = MyGAN(args)
- else:
- raise Exception("[!] There is no option for " + args.gan_type)
-
- # launch the graph in a session
- if (args.wiggleDepth < 0 and not args.recreate):
- print(" [*] Training Starting!")
- gan.train()
- print(" [*] Training finished!")
- else:
- if not args.recreate:
- print(" [*] Wiggle Started!")
- gan.wiggleEf()
- print(" [*] Wiggle finished!")
- else:
- print(" [*] Dataset recreation Started")
- gan.recreate()
- print(" [*] Dataset recreation finished")
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py
deleted file mode 100644
index f2678b321c295bcceaef945111ac3524be19d6e4..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .misc import add_prefix
-
-__all__ = ['add_prefix']
diff --git a/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py b/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py
deleted file mode 100644
index cdd69f2a24d47166425d785ef2767708b44b6593..0000000000000000000000000000000000000000
--- a/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from config import output_path_video, output_path_audio
-from logger import logger
-from resource_loader.video_loader_interface import VideoLoaderInterface
-
-
-class UploadedMediaLoader(VideoLoaderInterface):
- def __init__(self, uploaded_stream, original_name, media_type='video'):
- self.uploaded_stream = uploaded_stream
- self.original_name = original_name
- self.media_type = media_type # 'video' or 'audio'
- self.media_id = None
- self.filename = None
- self.output_path = None
- self.extract_filename()
- self.set_output_path()
-
- def extract_filename(self):
- self.filename = self.original_name.split(" - ", 1)[1]
- self.media_id = self.filename.rsplit(".", 1)[0]
-
- def set_output_path(self):
- if self.media_type == 'video':
- self.output_path = output_path_video
- elif self.media_type == 'audio':
- self.output_path = output_path_audio
- else:
- raise ValueError("Invalid media type")
-
- def download(self):
- with open(f"{self.output_path}/{self.filename}", "wb") as f:
- f.write(self.uploaded_stream.getvalue())
- logger.info(f"{self.media_type.capitalize()} processed: {self.original_name}")
diff --git a/spaces/STF-R/docker-test3/app/templates/prediction.html b/spaces/STF-R/docker-test3/app/templates/prediction.html
deleted file mode 100644
index 4366614474cbe8347297ec3d57a7b8b7bf72684d..0000000000000000000000000000000000000000
--- a/spaces/STF-R/docker-test3/app/templates/prediction.html
+++ /dev/null
@@ -1,48 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
- VIVADATA | {{iris_type}}
-
-
-
-
-
-
-
-
-
-
-
-
-
- VIVADATA - Flask Demo
-
-
-
- 🏵️🌺🌸 It's a {{iris_type|title}} 🏵️🌺🌸
-
-
- 
-
-
-
-
- Back
-
-
-
-
-
-
-
-
diff --git a/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py b/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py
deleted file mode 100644
index 3706c57541c1b7d9004957422b52cd1e2191ae68..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-# flake8: noqa
-
-from ..utils import DummyObject, requires_backends
-
-
-class LMSDiscreteScheduler(metaclass=DummyObject):
- _backends = ["scipy"]
-
- def __init__(self, *args, **kwargs):
- requires_backends(self, ["scipy"])
diff --git a/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py b/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py
deleted file mode 100644
index 2e25dd918f2714ae9a2c21d14401663c371b5f38..0000000000000000000000000000000000000000
--- a/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py
+++ /dev/null
@@ -1,704 +0,0 @@
-from typing import Callable, Optional, Tuple
-
-import flax.linen as nn
-import jax
-import jax.numpy as jnp
-from flax.core.frozen_dict import FrozenDict, unfreeze
-from jax import lax
-from jax.random import PRNGKey
-from transformers import GPT2Config, FlaxViTModel, ViTConfig
-from transformers.modeling_flax_outputs import (
- FlaxCausalLMOutputWithCrossAttentions,
- FlaxSeq2SeqLMOutput,
- FlaxSeq2SeqModelOutput,
-)
-from transformers.models.bart.modeling_flax_bart import (
- shift_tokens_right,
-)
-from .modeling_flax_gpt2 import (
- FlaxGPT2Module,
- FlaxGPT2Model,
- FlaxPreTrainedModel
-)
-from transformers.models.vit.modeling_flax_vit import FlaxViTModule
-
-from .configuration_vit_gpt2 import ViTGPT2Config
-
-
-class FlaxViTGPT2Module(nn.Module):
- config: ViTGPT2Config
- dtype: jnp.dtype = jnp.float32 # the dtype of the computation
-
- def setup(self):
-
- self.encoder = FlaxViTModule(self.config.vit_config, dtype=self.dtype)
- self.decoder = FlaxGPT2Module(self.config.gpt2_config, dtype=self.dtype)
-
- def _get_encoder_module(self):
- return self.encoder
-
- def _get_decoder_module(self):
- return self.decoder
-
- def __call__(
- self,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- output_attentions: bool = False,
- output_hidden_states: bool = False,
- return_dict: bool = True,
- deterministic: bool = True,
- ):
- encoder_outputs = self.encoder(
- pixel_values=pixel_values,
- deterministic=deterministic,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- decoder_outputs = self.decoder(
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_hidden_states=encoder_outputs[0],
- encoder_attention_mask=encoder_attention_mask,
- deterministic=deterministic,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict
- )
-
- return FlaxSeq2SeqModelOutput(
- last_hidden_state=decoder_outputs.last_hidden_state,
- decoder_hidden_states=decoder_outputs.hidden_states,
- decoder_attentions=decoder_outputs.attentions,
- cross_attentions=decoder_outputs.cross_attentions,
- encoder_last_hidden_state=encoder_outputs.last_hidden_state,
- encoder_hidden_states=encoder_outputs.hidden_states,
- encoder_attentions=encoder_outputs.attentions,
- )
-
-
-class FlaxViTGPT2ForConditionalGenerationModule(nn.Module):
- config: ViTGPT2Config
- dtype: jnp.dtype = jnp.float32
- bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
-
- def setup(self):
- self.model = FlaxViTGPT2Module(config=self.config, dtype=self.dtype)
- self.lm_head = nn.Dense(
- self.model.decoder.embed_dim,
- use_bias=False,
- dtype=self.dtype,
- kernel_init=jax.nn.initializers.normal(
- self.config.gpt2_config.initializer_range, self.dtype
- ),
- )
- self.final_logits_bias = self.param(
- "final_logits_bias", self.bias_init, (1, self.model.decoder.embed_dim)
- )
-
- def _get_encoder_module(self):
- return self.model.encoder
-
- def _get_decoder_module(self):
- return self.model.decoder
-
- def __call__(
- self,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- output_attentions: bool = False,
- output_hidden_states: bool = False,
- return_dict: bool = True,
- deterministic: bool = True,
- ):
- outputs = self.model(
- pixel_values=pixel_values,
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_attention_mask=encoder_attention_mask,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=deterministic,
- )
-
- hidden_states = outputs[0]
- lm_logits = self.lm_head(hidden_states)
- lm_logits += self.final_logits_bias
-
- if not return_dict:
- output = (lm_logits,) + outputs[1:]
- return output
-
- return FlaxSeq2SeqLMOutput(
- logits=lm_logits,
- decoder_hidden_states=outputs.decoder_hidden_states,
- decoder_attentions=outputs.decoder_attentions,
- cross_attentions=outputs.cross_attentions,
- encoder_last_hidden_state=outputs.encoder_last_hidden_state,
- encoder_hidden_states=outputs.encoder_hidden_states,
- encoder_attentions=outputs.encoder_attentions,
- )
-
-class FlaxViTGPT2PreTrainedModel(FlaxPreTrainedModel):
- config_class = ViTGPT2Config
- base_model_prefix: str = "model"
- module_class: nn.Module = None
-
- def __init__(
- self,
- config: ViTGPT2Config,
- input_shape: Tuple = None,
- seed: int = 0,
- dtype: jnp.dtype = jnp.float32,
- **kwargs,
- ):
- if input_shape is None:
- input_shape = (
- (1, config.vit_config.image_size, config.vit_config.image_size, 3),
- (1, 1),
- )
-
- module = self.module_class(config=config, dtype=dtype, **kwargs)
- super().__init__(
- config, module, input_shape=input_shape, seed=seed, dtype=dtype
- )
-
- def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
- # init input tensors
- pixel_values = jax.random.normal(rng, input_shape[0])
- # # make sure initialization pass will work for FlaxBartForSequenceClassificationModule
- # input_ids = jax.ops.index_update(input_ids, (..., -1), self.config.eos_token_id)
-
- input_ids = jnp.zeros(input_shape[1], dtype="i4")
- attention_mask = jnp.ones_like(input_ids)
-
- batch_size, sequence_length = input_ids.shape
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- params_rng, dropout_rng = jax.random.split(rng)
- rngs = {"params": params_rng, "dropout": dropout_rng}
-
- return self.module.init(
- rngs,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- )["params"]
-
- def init_cache(self, batch_size, max_length, encoder_outputs):
-
- input_ids = jnp.ones((batch_size, max_length), dtype="i4")
- attention_mask = jnp.ones_like(input_ids)
- position_ids = jnp.broadcast_to(
- jnp.arange(jnp.atleast_2d(input_ids).shape[-1]),
- input_ids.shape,
- )
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- return decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
-
- init_variables = self.module.init(
- jax.random.PRNGKey(0),
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_hidden_states=encoder_outputs[0],
- init_cache=True,
- method=_decoder_forward, # we only need to call the decoder to init the cache
- )
- return unfreeze(init_variables["cache"])
-
- def encode(
- self,
- pixel_values: jnp.ndarray,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- def _encoder_forward(module, pixel_values, **kwargs):
- encode_module = module._get_encoder_module()
- return encode_module(pixel_values, **kwargs)
-
- return self.module.apply(
- {"params": params or self.params},
- pixel_values=jnp.array(pixel_values, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- method=_encoder_forward,
- )
-
- def decode(
- self,
- input_ids,
- encoder_outputs,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- past_key_values: dict = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
-
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- encoder_hidden_states = encoder_outputs[0]
- if encoder_attention_mask is None:
- batch_size, sequence_length = encoder_hidden_states.shape[:2]
- encoder_attention_mask = jnp.ones((batch_size, sequence_length))
-
- batch_size, sequence_length = input_ids.shape
- if attention_mask is None:
- attention_mask = jnp.ones((batch_size, sequence_length))
-
- if position_ids is None:
- if past_key_values is not None:
- raise ValueError(
- "Make sure to provide `position_ids` when passing `past_key_values`."
- )
-
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- inputs = {"params": params or self.params}
-
- # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
- # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
- # it can be changed by FlaxGPT2Attention module
- if past_key_values:
- inputs["cache"] = past_key_values
- mutable = ["cache"]
- else:
- mutable = False
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- return decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
-
- outputs = self.module.apply(
- inputs,
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- mutable=mutable,
- method=_decoder_forward,
- )
-
- # add updated cache to model output
- if past_key_values is not None and return_dict:
- outputs, past = outputs
- outputs["past_key_values"] = unfreeze(past["cache"])
- return outputs
- elif past_key_values is not None and not return_dict:
- outputs, past = outputs
- outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
-
- return outputs
-
- def __call__(
- self,
- pixel_values: jnp.ndarray,
- input_ids: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
-
- # # prepare encoder inputs
- # if encoder_attention_mask is None:
- # encoder_attention_mask = jnp.ones_like(input_ids)
-
- # if position_ids is None:
- # batch_size, sequence_length = input_ids.shape
- # position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
-
- # prepare decoder inputs
- # if decoder_input_ids is None:
- # decoder_input_ids = shift_tokens_right(
- # input_ids, self.config.pad_token_id, decoder_start_token_id=self.config.decoder_start_token_id
- # ) # TODO: Check how to use this
-
- if attention_mask is None:
- attention_mask = jnp.ones_like(input_ids)
- if position_ids is None:
- batch_size, sequence_length = input_ids.shape
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
-
- return self.module.apply(
- {"params": params or self.params},
- pixel_values=jnp.array(pixel_values, dtype=jnp.float32),
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- )
-
-
-class FlaxViTGPT2ForConditionalGeneration(FlaxViTGPT2PreTrainedModel):
- module_class = FlaxViTGPT2ForConditionalGenerationModule
- dtype: jnp.dtype = jnp.float32
-
- def decode(
- self,
- input_ids,
- encoder_outputs,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- past_key_values: dict = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- deterministic: bool = True,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- encoder_hidden_states = encoder_outputs[0]
- if encoder_attention_mask is None:
- batch_size, sequence_length = encoder_hidden_states.shape[:2]
- encoder_attention_mask = jnp.ones((batch_size, sequence_length))
-
- batch_size, sequence_length = input_ids.shape
- if attention_mask is None:
- attention_mask = jnp.ones((batch_size, sequence_length))
-
- if position_ids is None:
- if past_key_values is not None:
- raise ValueError(
- "Make sure to provide `position_ids` when passing `past_key_values`."
- )
-
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- inputs = {"params": params or self.params}
-
- # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
- # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
- # it can be changed by FlaxGPT2Attention module
- if past_key_values:
- inputs["cache"] = past_key_values
- mutable = ["cache"]
- else:
- mutable = False
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- outputs = decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
- hidden_states = outputs[0]
-
- if self.config.tie_word_embeddings:
- shared_embedding = module.model.variables["params"]["shared"][
- "embedding"
- ]
- lm_logits = module.lm_head.apply(
- {"params": {"kernel": shared_embedding.T}}, hidden_states
- )
- else:
- lm_logits = module.lm_head(hidden_states)
-
- lm_logits += module.final_logits_bias
- return lm_logits, outputs
-
- outputs = self.module.apply(
- inputs,
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=deterministic,
- rngs=rngs,
- mutable=mutable,
- method=_decoder_forward,
- )
-
- if past_key_values is None:
- lm_logits, outputs = outputs
- else:
- (lm_logits, outputs), past = outputs
-
- if return_dict:
- outputs = FlaxCausalLMOutputWithCrossAttentions(
- logits=lm_logits,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- cross_attentions=outputs.cross_attentions,
- )
- else:
- outputs = (lm_logits,) + outputs[1:]
-
- # add updated cache to model output
- if past_key_values is not None and return_dict:
- outputs["past_key_values"] = unfreeze(past["cache"])
- return outputs
- elif past_key_values is not None and not return_dict:
- outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
-
- return outputs
-
- def prepare_inputs_for_generation(
- self,
- input_ids,
- max_length,
- encoder_attention_mask: Optional[jnp.DeviceArray] = None,
- attention_mask: Optional[jnp.DeviceArray] = None,
- encoder_outputs=None,
- **kwargs,
- ):
- # initializing the cache
- batch_size, seq_length = input_ids.shape
-
- past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
- # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
- # But since the decoder uses a causal mask, those positions are masked anyways.
- # Thus we can create a single static attention_mask here, which is more efficient for compilation
- extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
- if attention_mask is not None:
- position_ids = attention_mask.cumsum(axis=-1) - 1
- extended_attention_mask = lax.dynamic_update_slice(
- extended_attention_mask, attention_mask, (0, 0)
- )
- else:
- position_ids = jnp.broadcast_to(
- jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)
- )
-
- return {
- "past_key_values": past_key_values,
- "encoder_outputs": encoder_outputs,
- "encoder_attention_mask": encoder_attention_mask,
- "attention_mask": extended_attention_mask,
- "position_ids": position_ids,
- }
-
- def update_inputs_for_generation(self, model_outputs, model_kwargs):
- model_kwargs["past_key_values"] = model_outputs.past_key_values
- model_kwargs["position_ids"] = (
- model_kwargs["position_ids"][:, -1:] + 1
- )
- return model_kwargs
-
- @classmethod
- def from_vit_gpt2_pretrained(
- cls,
- vit_model_name_or_path: str = None,
- gpt2_model_name_or_path: str = None,
- *model_args,
- **kwargs,
- ) -> FlaxViTGPT2PreTrainedModel:
-
- kwargs_gpt2 = {
- argument[len("gpt2_") :]: value
- for argument, value in kwargs.items()
- if argument.startswith("gpt2_")
- }
-
- kwargs_vit = {
- argument[len("vit_") :]: value
- for argument, value in kwargs.items()
- if argument.startswith("vit_")
- }
-
- # remove gpt2, vit kwargs from kwargs
- for key in kwargs_gpt2.keys():
- del kwargs["gpt2_" + key]
- for key in kwargs_vit.keys():
- del kwargs["vit_" + key]
-
- # Load and initialize the gpt2 and vit model
- gpt2_model = kwargs_gpt2.pop("model", None)
- if gpt2_model is None:
- assert (
- gpt2_model_name_or_path is not None
- ), "If `model` is not defined as an argument, a `gpt2_model_name_or_path` has to be defined"
-
- if "config" not in kwargs_gpt2:
- gpt2_config = GPT2Config.from_pretrained(gpt2_model_name_or_path)
- kwargs_gpt2["config"] = gpt2_config
-
- kwargs_gpt2["config"].add_cross_attention = True
- gpt2_model = FlaxGPT2Model.from_pretrained(
- gpt2_model_name_or_path, *model_args, **kwargs_gpt2
- )
-
- vit_model = kwargs_vit.pop("model", None)
- if vit_model is None:
- assert (
- vit_model_name_or_path is not None
- ), "If `model` is not defined as an argument, a `vit_model_name_or_path` has to be defined"
-
- if "config" not in kwargs_vit:
- vit_config = ViTConfig.from_pretrained(vit_model_name_or_path)
- kwargs_vit["config"] = vit_config
-
- vit_model = FlaxViTModel.from_pretrained(
- vit_model_name_or_path, *model_args, **kwargs_vit
- )
-
- # instantiate config with corresponding kwargs
- dtype = kwargs.pop("dtype", jnp.float32)
- config = ViTGPT2Config.from_vit_gpt2_configs(
- vit_model.config, gpt2_model.config, **kwargs
- )
-
- # init model
- model = cls(config, *model_args, dtype=dtype, **kwargs)
- model.params["model"]["encoder"] = vit_model.params
- model.params["model"]["decoder"] = gpt2_model.params
-
- return model
-
diff --git a/spaces/Seetha/IMA-pipeline-streamlit/app.py b/spaces/Seetha/IMA-pipeline-streamlit/app.py
deleted file mode 100644
index f0db4170922ebf1b9a612db7fac952e61207154a..0000000000000000000000000000000000000000
--- a/spaces/Seetha/IMA-pipeline-streamlit/app.py
+++ /dev/null
@@ -1,581 +0,0 @@
-# import all packages
-import requests
-import streamlit as st
-from sklearn.model_selection import StratifiedKFold
-from sklearn.model_selection import train_test_split
-from sklearn.model_selection import KFold
-# tokenizer
-from transformers import AutoTokenizer, DistilBertTokenizerFast
-# sequence tagging model + training-related
-from transformers import DistilBertForTokenClassification, Trainer, TrainingArguments
-import torch
-import sys
-import os
-from sklearn.metrics import classification_report
-from pandas import read_csv
-from sklearn.linear_model import LogisticRegression
-import sklearn.model_selection
-from sklearn.feature_extraction.text import TfidfTransformer
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.pipeline import Pipeline, FeatureUnion
-import math
-# from sklearn.metrics import accuracy_score
-# from sklearn.metrics import precision_recall_fscore_support
-import json
-import re
-import numpy as np
-import pandas as pd
-import nltk
-nltk.download("punkt")
-import string
-from sklearn.model_selection import train_test_split
-from transformers import AutoTokenizer, Trainer, TrainingArguments, AutoModelForSequenceClassification, AutoConfig
-from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
-import itertools
-from transformers import TextClassificationPipeline, TFAutoModelForSequenceClassification, AutoTokenizer
-from transformers import pipeline
-import pickle
-import csv
-import pdfplumber
-import pathlib
-import shutil
-import webbrowser
-from streamlit.components.v1 import html
-import streamlit.components.v1 as components
-from PyPDF2 import PdfReader
-from huggingface_hub import HfApi
-import io
-from datasets import load_dataset
-import time
-
-import huggingface_hub
-from huggingface_hub import Repository
-from datetime import datetime
-import pathlib as Path
-from requests import get
-import urllib.request
-# import gradio as gr
-# from gradio import inputs, outputs
-from datasets import load_dataset
-from huggingface_hub import HfApi, list_models
-import os
-from huggingface_hub import HfFileSystem
-from tensorflow.keras.models import Sequential, model_from_json
-#import tensorflow_datasets as tfds
-import tensorflow as tf
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-import spacy
-from tensorflow.keras.preprocessing.text import Tokenizer
-#from spacy import en_core_web_lg
-#import en_core_web_lg
-#nlp = en_core_web_lg.load()
-nlp = spacy.load('en_core_web_sm')
-
-#tfds.disable_progress_bar()
-MAX_SEQUENCE_LENGTH = 500
-
-# dataset = load_dataset('Seetha/Visualization', streaming=True)
-# df = pd.DataFrame.from_dict(dataset['train'])
-# DATASET_REPO_URL = "https://huggingface.co/datasets/Seetha/Visualization"
-# DATA_FILENAME = "level2.json"
-#DATA_FILE = os.path.join("data", DATA_FILENAME)
-DATASET_REPO_URL = "https://huggingface.co/datasets/Seetha/visual_files"
-DATA_FILENAME = "detailedResults.json"
-DATA_FILENAME1 = "level2.json"
-
-HF_TOKEN = os.environ.get("HF_TOKEN")
-#st.write("is none?", HF_TOKEN is None)
-
-def main():
-
- st.title("Text to Causal Knowledge Graph")
- st.sidebar.title("Please upload your text documents in one file here:")
- k=2
- seed = 1
- k1= 5
- text_list = []
- causal_sents = []
-
- uploaded_file = None
- try:
- uploaded_file = st.sidebar.file_uploader("Choose a file", type = "pdf")
- except:
- uploaded_file = PdfReader('sample_anno.pdf')
- st.error("Please upload your own PDF to be analyzed")
-
- if uploaded_file is not None:
- reader = PdfReader(uploaded_file)
- for page in reader.pages:
- text = page.extract_text()
- text_list.append(text)
- else:
- st.error("Please upload your own PDF to be analyzed")
- st.stop()
-
- text_list_final = [x.replace('\n', '') for x in text_list]
- text_list_final = re.sub('"', '', str(text_list_final))
-
- sentences = nltk.sent_tokenize(text_list_final)
-
- result =[]
- for i in sentences:
- result1 = i.lower()
- result2 = re.sub(r'[^\w\s]','',result1)
- result.append(result2)
-
- #st.write("--- %s seconds ---" % (time.time() - start_time))
- tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") #bert-base-uncased
-
- model_path = "checkpoint-2850"
-
- model = AutoModelForSequenceClassification.from_pretrained(model_path,id2label={0:'non-causal',1:'causal'})
-
- #st.write('sequence classification loaded')
- pipe1 = pipeline("text-classification", model=model,tokenizer=tokenizer)
- for sent in result:
- pred = pipe1(sent)
- for lab in pred:
- if lab['label'] == 'causal': #causal
- causal_sents.append(sent)
-
- # st.write('causal sentence classification finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
-
- model_name = "distilbert-base-cased"
- tokenizer = DistilBertTokenizerFast.from_pretrained(model_name,low_cpu_mem_usage=True)
-
- model_path1 = "DistilBertforTokenclassification"
-
- model = DistilBertForTokenClassification.from_pretrained(model_path1,low_cpu_mem_usage=True) #len(unique_tags),, num_labels= 7, , id2label={0:'CT',1:'E',2:'C',3:'O'}
- pipe = pipeline('ner', model=model, tokenizer=tokenizer,aggregation_strategy='simple') #grouped_entities=True
- st.write('DistilBERT loaded')
- sentence_pred = []
- class_list = []
- entity_list = []
- for k in causal_sents:
- pred= pipe(k)
- #st.write(pred)
- #st.write('preds')
- for i in pred:
- sentence_pred.append(k)
- class_list.append(i['word'])
- entity_list.append(i['entity_group'])
-
- # st.write('causality extraction finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
-
- # filename = 'Checkpoint-classification.sav'
- # loaded_model = pickle.load(open(filename, 'rb'))
- # loaded_vectorizer = pickle.load(open('vectorizefile_classification.pickle', 'rb'))
-
- # pipeline_test_output = loaded_vectorizer.transform(class_list)
- # predicted = loaded_model.predict(pipeline_test_output)
-
- tokenizer = Tokenizer(num_words=100000)
- tokenizer.fit_on_texts(class_list)
- word_index = tokenizer.word_index
- # text_embedding = np.zeros((len(word_index) + 1, 300))
- # for word, i in word_index.items():
- # text_embedding[i] = nlp(word).vector
- json_file = open('model.json', 'r')
- loaded_model_json = json_file.read()
- json_file.close()
- loaded_model = model_from_json(loaded_model_json)
- # load weights into new model
- loaded_model.load_weights("model.h5")
-
- loss = tf.keras.losses.CategoricalCrossentropy() #from_logits=True
- loaded_model.compile(loss=loss,optimizer=tf.keras.optimizers.Adam(1e-4))
-
- predictions = loaded_model.predict(pad_sequences(tokenizer.texts_to_sequences(class_list),maxlen=MAX_SEQUENCE_LENGTH))
- predicted = np.argmax(predictions,axis=1)
-
- st.write(predictions)
- st.write(predicted)
- # st.write('stakeholder taxonomy finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
- pred1 = predicted
- level0 = []
- count =0
- for i in predicted:
- if i == 3:
- level0.append('Non-Performance')
- count +=1
- else:
- level0.append('Performance')
- count +=1
-
- list_pred = {0: 'Customers',1:'Employees',2:'Investors',3:'Non-performance',4:'Society',5:'Unclassified'}
- pred_val = [list_pred[i] for i in pred1]
-
- #print('count',count)
- for ind,(sent,preds) in enumerate(zip(class_list,pred_val)):
- if 'customers' in sent or 'client' in sent or 'consumer' in sent or 'user' in sent:
- pred_val[ind] = 'Customers'
- elif 'investor' in sent or 'finance' in sent or 'shareholder' in sent or 'stockholder' in sent or 'owners' in sent:
- pred_val[ind] = 'Investors'
- elif 'employee' in sent or 'worker' in sent or 'staff' in sent:
- pred_val[ind] = 'Employees'
- elif 'society' in sent or 'societal' in sent or 'social responsib*' in sent or 'social performance' in sent or 'community' in sent:
- pred_val[ind] = 'Society'
-
- sent_id, unique = pd.factorize(sentence_pred)
-
- final_list = pd.DataFrame(
- {'Id': sent_id,
- 'Fullsentence': sentence_pred,
- 'Component': class_list,
- 'causeOrEffect': entity_list,
- 'Labellevel1': level0,
- 'Labellevel2': pred_val
- })
- s = final_list['Component'].shift(-1)
- m = s.str.startswith('##', na=False)
- final_list.loc[m, 'Component'] += (' ' + s[m])
-
-
- final_list1 = final_list[~final_list['Component'].astype(str).str.startswith('##')]
- li = []
- uni = final_list1['Id'].unique()
- for i in uni:
- df_new = final_list1[final_list1['Id'] == i]
- uni1 = df_new['Id'].unique()
-
- # if 'E' not in df_new.values:
- # li.append(uni1)
- # out = np.concatenate(li).ravel()
- # li_pan = pd.DataFrame(out,columns=['Id'])
- # df3 = pd.merge(final_list1, li_pan[['Id']], on='Id', how='left', indicator=True) \
- # .query("_merge == 'left_only'") \
- # .drop("_merge",axis=1)
-
- df3 = final_list1
- #df = df3.groupby(['Id','Fullsentence','causeOrEffect', 'Labellevel1', 'Labellevel2'])['Component'].apply(', '.join).reset_index()
- #st.write(df)
-
- #df = df3
- df3["causeOrEffect"].replace({"C": "cause", "E": "effect"}, inplace=True)
- df_final = df3[df3['causeOrEffect'] != 'CT']
- df3['New string'] = df_final['Component'].replace(r'[##]+', ' ', regex=True)
-
- df_final = df_final.drop("Component",axis=1)
- df_final.insert(2, "Component", df3['New string'], True)
-
- df_final1 = df_final[df_final['Component'].str.split().str.len().gt(1)]
- #st.write(df_final[df_final['Component'].str.len() != 1])
- #df_final1.to_csv('predictions.csv')
-
-# buffer = io.BytesIO()
-# with pd.ExcelWriter(buffer, engine="xlsxwriter") as writer:
-# df_final.to_excel(writer, sheet_name="Sheet1", index=False)
-# writer.close()
-
- count_NP_NP = 0
- count_NP_investor = 0
- count_NP_customer = 0
- count_NP_employees = 0
- count_NP_society = 0
-
- count_inv_np = 0
- count_inv_investor = 0
- count_inv_customer = 0
- count_inv_employee = 0
- count_inv_society = 0
-
- count_cus_np = 0
- count_cus_investor = 0
- count_cus_customer = 0
- count_cus_employee = 0
- count_cus_society = 0
-
- count_emp_np = 0
- count_emp_investor = 0
- count_emp_customer = 0
- count_emp_employee = 0
- count_emp_society = 0
-
- count_soc_np = 0
- count_soc_investor = 0
- count_soc_customer = 0
- count_soc_employee = 0
- count_soc_society = 0
- for i in range(0,df_final['Id'].max()):
- j = df_final.loc[df_final['Id'] == i]
- cause_tab = j.loc[j['causeOrEffect'] == 'cause']
- effect_tab = j.loc[j['causeOrEffect'] == 'effect']
- cause_coun_NP = (cause_tab.Labellevel2 == 'Non-performance').sum()
- effect_coun_NP = (effect_tab.Labellevel2 == 'Non-performance').sum()
-
- if (cause_coun_NP > 0) and (effect_coun_NP > 0):
- count_NP = cause_coun_NP if cause_coun_NP >= effect_coun_NP else effect_coun_NP
- else:
- count_NP = 0
- effect_NP_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_NP > 0) and (effect_NP_inv > 0):
- count_NP_inv = cause_coun_NP if cause_coun_NP >= effect_NP_inv else effect_NP_inv
- else:
- count_NP_inv = 0
- effect_NP_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_NP > 0) and (effect_NP_cus > 0):
- count_NP_cus = cause_coun_NP if cause_coun_NP >= effect_NP_cus else effect_NP_cus
- else:
- count_NP_cus = 0
- effect_NP_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_NP > 0) and (effect_NP_emp > 0):
- count_NP_emp = cause_coun_NP if cause_coun_NP >= effect_NP_emp else effect_NP_emp
- else:
- count_NP_emp = 0
- effect_NP_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_NP > 0) and (effect_NP_soc > 0):
- count_NP_soc = cause_coun_NP if cause_coun_NP >= effect_NP_soc else effect_NP_soc
- else:
- count_NP_soc = 0
-
- cause_coun_inv = (cause_tab.Labellevel2 == 'Investors').sum()
- effect_coun_inv = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_inv > 0) and (effect_coun_inv > 0):
- count_NP_inv = cause_coun_inv if cause_coun_inv >= effect_coun_inv else effect_coun_inv
- else:
- count_NP_inv = 0
-
- effect_inv_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_inv > 0) and (effect_inv_inv > 0):
- count_inv_inv = cause_coun_inv if cause_coun_inv >= effect_inv_inv else effect_inv_inv
- else:
- count_inv_inv = 0
- effect_inv_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_inv > 0) and (effect_inv_cus > 0):
- count_inv_cus = cause_coun_inv if cause_coun_inv >= effect_inv_cus else effect_inv_cus
- else:
- count_inv_cus = 0
- effect_inv_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_inv > 0) and (effect_inv_emp > 0):
- count_inv_emp = cause_coun_inv if cause_coun_inv >= effect_inv_emp else effect_inv_emp
- else:
- count_inv_emp = 0
-
- effect_inv_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_inv > 0) and (effect_inv_soc > 0):
- count_inv_soc = cause_coun_inv if cause_coun_inv >= effect_inv_soc else effect_inv_soc
- else:
- count_inv_soc = 0
-
- cause_coun_cus = (cause_tab.Labellevel2 == 'Customers').sum()
- effect_coun_cus = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_cus > 0) and (effect_coun_cus > 0):
- count_NP_cus = cause_coun_cus if cause_coun_cus >= effect_coun_cus else effect_coun_cus
- else:
- count_NP_cus = 0
-
- effect_cus_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_cus > 0) and (effect_cus_inv > 0):
- count_cus_inv = cause_coun_cus if cause_coun_cus >= effect_cus_inv else effect_cus_inv
- else:
- count_cus_inv = 0
-
- effect_cus_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_cus > 0) and (effect_cus_cus > 0):
- count_cus_cus = cause_coun_cus if cause_coun_cus >= effect_cus_cus else effect_cus_cus
- else:
- count_cus_cus = 0
-
- effect_cus_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_cus > 0) and (effect_cus_emp > 0):
- count_cus_emp = cause_coun_cus if cause_coun_cus >= effect_cus_emp else effect_cus_emp
- else:
- count_cus_emp = 0
-
- effect_cus_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_cus > 0) and (effect_cus_soc > 0):
- count_cus_soc = cause_coun_cus if cause_coun_cus >= effect_cus_soc else effect_cus_soc
- else:
- count_cus_soc = 0
-
- cause_coun_emp = (cause_tab.Labellevel2 == 'Employees').sum()
- effect_coun_emp = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_emp > 0) and (effect_coun_emp > 0):
- count_NP_emp = cause_coun_emp if cause_coun_emp >= effect_coun_emp else effect_coun_emp
- else:
- count_NP_emp = 0
-
- effect_emp_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_emp > 0) and (effect_emp_inv > 0):
- count_emp_inv = cause_coun_emp if cause_coun_emp >= effect_emp_inv else effect_emp_inv
- else:
- count_emp_inv = 0
-
- effect_emp_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_emp > 0) and (effect_emp_cus > 0):
- count_emp_cus = cause_coun_emp if cause_coun_emp >= effect_emp_cus else effect_emp_cus
- else:
- count_emp_cus = 0
-
- effect_emp_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_emp > 0) and (effect_emp_emp > 0):
- count_emp_emp = cause_coun_emp if cause_coun_emp >= effect_emp_emp else effect_emp_emp
- else:
- count_emp_emp = 0
-
- effect_emp_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_emp > 0) and (effect_emp_soc > 0):
- count_emp_soc = cause_coun_emp if cause_coun_emp >= effect_emp_soc else effect_emp_soc
- else:
- count_emp_soc = 0
-
- cause_coun_soc = (cause_tab.Labellevel2 == 'Society').sum()
- effect_coun_soc = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_soc > 0) and (effect_coun_soc > 0):
- count_NP_soc = cause_coun_soc if cause_coun_soc >= effect_coun_soc else effect_coun_soc
- else:
- count_NP_soc = 0
-
- effect_soc_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_soc > 0) and (effect_soc_inv > 0):
- count_soc_inv = cause_coun_soc if cause_coun_soc >= effect_soc_inv else effect_soc_inv
- else:
- count_soc_inv = 0
-
- effect_soc_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_soc > 0) and (effect_soc_cus > 0):
- count_soc_cus = cause_coun_soc if cause_coun_soc >= effect_soc_cus else effect_soc_cus
- else:
- count_soc_cus = 0
-
- effect_soc_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_soc > 0) and (effect_soc_emp > 0):
- count_soc_emp = cause_coun_soc if cause_coun_soc >= effect_soc_emp else effect_soc_emp
- else:
- count_soc_emp = 0
-
- effect_soc_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_soc > 0) and (effect_soc_soc > 0):
- count_soc_soc = cause_coun_soc if cause_coun_soc >= effect_soc_soc else effect_soc_soc
- else:
- count_soc_soc = 0
-
- count_NP_NP = count_NP_NP + count_NP
- count_NP_investor = count_NP_investor + count_NP_inv
- count_NP_customer = count_NP_customer + count_NP_cus
- count_NP_employees = count_NP_employees + count_NP_emp
- count_NP_society = count_NP_society + count_NP_soc
-
- count_inv_np = count_inv_np + count_NP_inv
- count_inv_investor = count_inv_investor + count_inv_inv
- count_inv_customer = count_inv_customer + count_inv_cus
- count_inv_employee = count_inv_employee + count_inv_emp
- count_inv_society = count_inv_society + count_inv_soc
-
- count_cus_np = count_cus_np + count_NP_cus
- count_cus_investor = count_cus_investor + count_cus_inv
- count_cus_customer = count_cus_customer + count_cus_cus
- count_cus_employee = count_cus_employee + count_cus_emp
- count_cus_society = count_cus_society + count_cus_soc
-
- count_emp_np = count_emp_np + count_NP_emp
- count_emp_investor = count_emp_investor + count_emp_inv
- count_emp_customer = count_emp_customer + count_emp_cus
- count_emp_employee = count_emp_employee + count_emp_emp
- count_emp_society = count_emp_society + count_emp_soc
-
- count_soc_np = count_soc_np + count_NP_soc
- count_soc_investor = count_soc_investor + count_soc_inv
- count_soc_customer = count_soc_customer + count_soc_cus
- count_soc_employee = count_soc_employee + count_soc_emp
- count_soc_society = count_soc_society + count_soc_soc
-
- df_tab = pd.DataFrame(columns = ['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'],index=['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'], dtype=object)
-
- df_tab.loc['Non-performance'] = [count_NP_NP, count_NP_investor, count_NP_customer, count_NP_employees, count_NP_society]
- df_tab.loc['Investors'] = [count_inv_np, count_inv_investor, count_inv_customer, count_inv_employee, count_inv_society]
- df_tab.loc['Customers'] = [count_cus_np, count_cus_investor, count_cus_customer, count_cus_employee, count_cus_society]
- df_tab.loc['Employees'] = [count_emp_np, count_emp_investor, count_emp_customer, count_emp_employee, count_emp_society]
- df_tab.loc['Society'] = [count_soc_np, count_soc_investor, count_soc_customer, count_soc_employee, count_soc_society]
-
-
-# df_tab = pd.DataFrame({
-# 'Non-performance': [count_NP_NP, count_NP_investor, count_NP_customer, count_NP_employees, count_NP_society],
-# 'Investors': [count_inv_np, count_inv_investor, count_inv_customer, count_inv_employee, count_inv_society],
-# 'Customers': [count_cus_np, count_cus_investor, count_cus_customer, count_cus_employee, count_cus_society],
-# 'Employees': [count_emp_np, count_emp_investor, count_emp_customer, count_emp_employee, count_emp_society],
-# 'Society': [count_soc_np, count_soc_investor, count_soc_customer, count_soc_employee, count_soc_society]},
-# index=['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'])
-
- #df_tab.to_csv('final_data.csv')
-
- buffer = io.BytesIO()
- with pd.ExcelWriter(buffer, engine="xlsxwriter") as writer:
- df_tab.to_excel(writer,sheet_name="count_result",index=False)
- df_final1.to_excel(writer,sheet_name="Detailed_results",index=False)
- writer.close()
- #df = pd.read_csv('final_data.csv', index_col=0)
-#474-515
- # Convert to JSON format
- json_data = []
- for row in df_tab.index:
- for col in df_tab.columns:
- json_data.append({
- 'source': row,
- 'target': col,
- 'value': int(df_tab.loc[row, col])
- })
-
- HfApi().delete_file(path_in_repo = DATA_FILENAME1 ,repo_id = 'Seetha/visual_files',token= HF_TOKEN,repo_type='dataset')
- #st.write('file-deleted')
- fs = HfFileSystem(token=HF_TOKEN)
- with fs.open('datasets/Seetha/visual_files/level2.json', 'w') as f:
- json.dump(json_data, f)
-
- df_final1.to_csv('predictions.csv')
- csv_file = "predictions.csv"
- json_file = "detailedResults.json"
-
- # Open the CSV file and read the data
- with open(csv_file, "r") as f:
- csv_data = csv.DictReader(f)
-
- # # Convert the CSV data to a list of dictionaries
- data_list = []
- for row in csv_data:
- data_list.append(dict(row))
-
- # # Convert the list of dictionaries to JSON
- json_data = json.dumps(data_list)
-
- HfApi().delete_file(path_in_repo = DATA_FILENAME ,repo_id = 'Seetha/visual_files',token= HF_TOKEN,repo_type='dataset')
- #st.write('file2-deleted')
- with fs.open('datasets/Seetha/visual_files/detailedResults.json','w') as fi:
- #data = json.load(fi)
- fi.write(json_data)
-
- def convert_df(df):
-
- #IMPORTANT: Cache the conversion to prevent computation on every rerun
-
- return df.to_csv().encode('utf-8')
-
-
-
- csv1 = convert_df(df_final1.astype(str))
- csv2 = convert_df(df_tab.astype(str))
-
- with st.container():
-
- st.download_button(label="Download the result table",data=buffer,file_name="t2cg_outputs.xlsx",mime="application/vnd.ms-excel")
- st.markdown('Click this link in a separate tab to view knowledge graph', unsafe_allow_html=True)
- # st.download_button(label="Download the detailed result table_csv",data=csv1,file_name='results.csv',mime='text/csv')
- # st.download_button(label="Download the result table_csv",data=csv2,file_name='final_data.csv',mime='text/csv')
-
-#with st.container():
- # Execute your app
- #st.title("Visualization example")
-# components.html(source_code)
- #html(my_html)
- #webbrowser.open('https://huggingface.co/spaces/Seetha/visual-knowledgegraph')
-# # embed streamlit docs in a streamlit app
-# #components.iframe("https://webpages.charlotte.edu/ltotapal/")
-
-
-
-if __name__ == '__main__':
- start_time = time.time()
- main()
diff --git a/spaces/Shiro26/MendoBERT_RE/app.py b/spaces/Shiro26/MendoBERT_RE/app.py
deleted file mode 100644
index 1bf256334d6604b5048b7f1054c9cb1915120f15..0000000000000000000000000000000000000000
--- a/spaces/Shiro26/MendoBERT_RE/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-
-model = pipeline("text-classification", model="/home/user/app/MendoBERT/", tokenizer="indolem/indobert-base-uncased")
-basemodel = pipeline("text-classification", model="/home/user/app/IndoLEM/", tokenizer="indolem/indobert-base-uncased")
-
-st.title(':blue[MendoBERT] - Relation Extraction :family:')
-
-if 'options' not in st.session_state:
- st.session_state['options'] = ""
-
-def button1_callback():
- st.session_state['options'] = "Kami menyimpulkan bahwa polimorfisme @GENE$ dan AGT tidak berkontribusi pada kerentanan genetik terhadap @DISEASE$ dan retinopati pada populasi Kaukasia Mediterania."
-def button2_callback():
- st.session_state['options'] = "Genotipe bayi PON1 RR dan @GENE$ CC dikaitkan dengan @DISEASE$ dalam populasi penelitian kami, yang menunjukkan kemungkinan peran variabilitas paraoxonase manusia dalam etiologi kelahiran prematur."
-
-
-placeholder = st.empty()
-
-st.info("Please replace the gene and disease that you want to get their relation predicted with @GENE\$ and \@DISEASE\$", icon="ℹ️")
-
-st.caption('_Examples_')
-st.button('Kami menyimpulkan bahwa polimorfisme \@GENE\$ dan AGT tidak berkontribusi pada kerentanan genetik terhadap \@DISEASE\$ dan retinopati pada populasi Kaukasia Mediterania.', use_container_width=True, on_click = button1_callback)
-st.button('Genotipe bayi PON1 RR dan \@GENE\$ CC dikaitkan dengan \@DISEASE\$ dalam populasi penelitian kami, yang menunjukkan kemungkinan peran variabilitas paraoxonase manusia dalam etiologi kelahiran prematur.', use_container_width=True, on_click = button2_callback)
-
-with placeholder:
- text = st.text_area('Enter some text: ', key = 'options')
-
-if text:
- st.subheader('MendoBERT')
- st.write(model(text))
- st.write("\n")
- st.subheader('IndoLEM')
- st.write(basemodel(text))
\ No newline at end of file
diff --git a/spaces/Shredder/CONBERT-2/README.md b/spaces/Shredder/CONBERT-2/README.md
deleted file mode 100644
index daf10e5da8dd31671c54c18d24b0e7d1d77b62f0..0000000000000000000000000000000000000000
--- a/spaces/Shredder/CONBERT-2/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: CONBERT 2
-emoji: 👁
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Stearns/Soar/soar_io.py b/spaces/Stearns/Soar/soar_io.py
deleted file mode 100644
index c05d97417b69a804b35207fb839957a91258a5d2..0000000000000000000000000000000000000000
--- a/spaces/Stearns/Soar/soar_io.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from pysoarlib import *
-
-## DEFINE MAIN CONNECTOR CLASS
-
-class GeneralSoarIOConnector(AgentConnector):
- def __init__(self, client):
- AgentConnector.__init__(self, client)
- client.add_print_event_handler(self.agent_print_collector)
- # client.execute_command("output callbacks on")
- # client.execute_command("output console on")
-
- self.agent_printout = ""
-
- def on_input_phase(self, input_link):
- pass
-
- def on_init_soar(self):
- self.reset()
-
- def on_output_event(self, command_name, root_id):
- pass
-
-
- def agent_print_collector(self, text):
- self.agent_printout += text+"\n"
-
- def get_agent_output(self):
- return self.agent_printout
-
- def reset(self):
- self.agent_printout = ""
-
-
-
-
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py
deleted file mode 100644
index 489b7c13d0ce82b769ba50412ecde0889d0b7851..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# encoding: utf-8
-"""
-Utilities for working with external processes.
-"""
-
-# Copyright (c) IPython Development Team.
-# Distributed under the terms of the Modified BSD License.
-
-
-import os
-import shutil
-import sys
-
-if sys.platform == 'win32':
- from ._process_win32 import system, getoutput, arg_split, check_pid
-elif sys.platform == 'cli':
- from ._process_cli import system, getoutput, arg_split, check_pid
-else:
- from ._process_posix import system, getoutput, arg_split, check_pid
-
-from ._process_common import getoutputerror, get_output_error_code, process_handler
-
-
-class FindCmdError(Exception):
- pass
-
-
-def find_cmd(cmd):
- """Find absolute path to executable cmd in a cross platform manner.
-
- This function tries to determine the full path to a command line program
- using `which` on Unix/Linux/OS X and `win32api` on Windows. Most of the
- time it will use the version that is first on the users `PATH`.
-
- Warning, don't use this to find IPython command line programs as there
- is a risk you will find the wrong one. Instead find those using the
- following code and looking for the application itself::
-
- import sys
- argv = [sys.executable, '-m', 'IPython']
-
- Parameters
- ----------
- cmd : str
- The command line program to look for.
- """
- path = shutil.which(cmd)
- if path is None:
- raise FindCmdError('command could not be found: %s' % cmd)
- return path
-
-
-def abbrev_cwd():
- """ Return abbreviated version of cwd, e.g. d:mydir """
- cwd = os.getcwd().replace('\\','/')
- drivepart = ''
- tail = cwd
- if sys.platform == 'win32':
- if len(cwd) < 4:
- return cwd
- drivepart,tail = os.path.splitdrive(cwd)
-
-
- parts = tail.split('/')
- if len(parts) > 2:
- tail = '/'.join(parts[-2:])
-
- return (drivepart + (
- cwd == '/' and '/' or tail))
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py
deleted file mode 100644
index 37d3db222ef2c7920628971a92e863d9915514c6..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import sys
-from typing import Any, Dict, List, Optional, Generic, TypeVar, cast
-from types import TracebackType
-
-if sys.version_info >= (3, 8):
- from importlib.metadata import entry_points
-else:
- from importlib_metadata import entry_points
-
-from toolz import curry
-
-
-PluginType = TypeVar("PluginType")
-
-
-class NoSuchEntryPoint(Exception):
- def __init__(self, group, name):
- self.group = group
- self.name = name
-
- def __str__(self):
- return f"No {self.name!r} entry point found in group {self.group!r}"
-
-
-class PluginEnabler:
- """Context manager for enabling plugins
-
- This object lets you use enable() as a context manager to
- temporarily enable a given plugin::
-
- with plugins.enable('name'):
- do_something() # 'name' plugin temporarily enabled
- # plugins back to original state
- """
-
- def __init__(self, registry: "PluginRegistry", name: str, **options):
- self.registry = registry # type: PluginRegistry
- self.name = name # type: str
- self.options = options # type: Dict[str, Any]
- self.original_state = registry._get_state() # type: Dict[str, Any]
- self.registry._enable(name, **options)
-
- def __enter__(self) -> "PluginEnabler":
- return self
-
- def __exit__(self, typ: type, value: Exception, traceback: TracebackType) -> None:
- self.registry._set_state(self.original_state)
-
- def __repr__(self) -> str:
- return "{}.enable({!r})".format(self.registry.__class__.__name__, self.name)
-
-
-class PluginRegistry(Generic[PluginType]):
- """A registry for plugins.
-
- This is a plugin registry that allows plugins to be loaded/registered
- in two ways:
-
- 1. Through an explicit call to ``.register(name, value)``.
- 2. By looking for other Python packages that are installed and provide
- a setuptools entry point group.
-
- When you create an instance of this class, provide the name of the
- entry point group to use::
-
- reg = PluginRegister('my_entrypoint_group')
-
- """
-
- # this is a mapping of name to error message to allow custom error messages
- # in case an entrypoint is not found
- entrypoint_err_messages = {} # type: Dict[str, str]
-
- # global settings is a key-value mapping of settings that are stored globally
- # in the registry rather than passed to the plugins
- _global_settings = {} # type: Dict[str, Any]
-
- def __init__(self, entry_point_group: str = "", plugin_type: type = object):
- """Create a PluginRegistry for a named entry point group.
-
- Parameters
- ==========
- entry_point_group: str
- The name of the entry point group.
- plugin_type: object
- A type that will optionally be used for runtime type checking of
- loaded plugins using isinstance.
- """
- self.entry_point_group = entry_point_group # type: str
- self.plugin_type = plugin_type # type: Optional[type]
- self._active = None # type: Optional[PluginType]
- self._active_name = "" # type: str
- self._plugins = {} # type: Dict[str, PluginType]
- self._options = {} # type: Dict[str, Any]
- self._global_settings = self.__class__._global_settings.copy() # type: dict
-
- def register(self, name: str, value: Optional[PluginType]) -> Optional[PluginType]:
- """Register a plugin by name and value.
-
- This method is used for explicit registration of a plugin and shouldn't be
- used to manage entry point managed plugins, which are auto-loaded.
-
- Parameters
- ==========
- name: str
- The name of the plugin.
- value: PluginType or None
- The actual plugin object to register or None to unregister that plugin.
-
- Returns
- =======
- plugin: PluginType or None
- The plugin that was registered or unregistered.
- """
- if value is None:
- return self._plugins.pop(name, None)
- else:
- assert isinstance(value, self.plugin_type) # type: ignore[arg-type] # Should ideally be fixed by better annotating plugin_type
- self._plugins[name] = value
- return value
-
- def names(self) -> List[str]:
- """List the names of the registered and entry points plugins."""
- exts = list(self._plugins.keys())
- e_points = importlib_metadata_get(self.entry_point_group)
- more_exts = [ep.name for ep in e_points]
- exts.extend(more_exts)
- return sorted(set(exts))
-
- def _get_state(self) -> Dict[str, Any]:
- """Return a dictionary representing the current state of the registry"""
- return {
- "_active": self._active,
- "_active_name": self._active_name,
- "_plugins": self._plugins.copy(),
- "_options": self._options.copy(),
- "_global_settings": self._global_settings.copy(),
- }
-
- def _set_state(self, state: Dict[str, Any]) -> None:
- """Reset the state of the registry"""
- assert set(state.keys()) == {
- "_active",
- "_active_name",
- "_plugins",
- "_options",
- "_global_settings",
- }
- for key, val in state.items():
- setattr(self, key, val)
-
- def _enable(self, name: str, **options) -> None:
- if name not in self._plugins:
- try:
- (ep,) = [
- ep
- for ep in importlib_metadata_get(self.entry_point_group)
- if ep.name == name
- ]
- except ValueError as err:
- if name in self.entrypoint_err_messages:
- raise ValueError(self.entrypoint_err_messages[name]) from err
- else:
- raise NoSuchEntryPoint(self.entry_point_group, name) from err
- value = cast(PluginType, ep.load())
- self.register(name, value)
- self._active_name = name
- self._active = self._plugins[name]
- for key in set(options.keys()) & set(self._global_settings.keys()):
- self._global_settings[key] = options.pop(key)
- self._options = options
-
- def enable(self, name: Optional[str] = None, **options) -> PluginEnabler:
- """Enable a plugin by name.
-
- This can be either called directly, or used as a context manager.
-
- Parameters
- ----------
- name : string (optional)
- The name of the plugin to enable. If not specified, then use the
- current active name.
- **options :
- Any additional parameters will be passed to the plugin as keyword
- arguments
-
- Returns
- -------
- PluginEnabler:
- An object that allows enable() to be used as a context manager
- """
- if name is None:
- name = self.active
- return PluginEnabler(self, name, **options)
-
- @property
- def active(self) -> str:
- """Return the name of the currently active plugin"""
- return self._active_name
-
- @property
- def options(self) -> Dict[str, Any]:
- """Return the current options dictionary"""
- return self._options
-
- def get(self) -> Optional[PluginType]:
- """Return the currently active plugin."""
- if self._options:
- return curry(self._active, **self._options)
- else:
- return self._active
-
- def __repr__(self) -> str:
- return "{}(active={!r}, registered={!r})" "".format(
- self.__class__.__name__, self._active_name, list(self.names())
- )
-
-
-def importlib_metadata_get(group):
- ep = entry_points()
- # 'select' was introduced in Python 3.10 and 'get' got deprecated
- # We don't check for Python version here as by checking with hasattr we
- # also get compatibility with the importlib_metadata package which had a different
- # deprecation cycle for 'get'
- if hasattr(ep, "select"):
- return ep.select(group=group)
- else:
- return ep.get(group, [])
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py
deleted file mode 100644
index bba2d3f7dfffa3bdbf921bdad4ca7143be97c2fd..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py
+++ /dev/null
@@ -1,143 +0,0 @@
-from __future__ import annotations
-
-import codecs
-from dataclasses import InitVar, dataclass, field
-from typing import Any, Callable, Mapping
-
-from ..abc import (
- AnyByteReceiveStream,
- AnyByteSendStream,
- AnyByteStream,
- ObjectReceiveStream,
- ObjectSendStream,
- ObjectStream,
-)
-
-
-@dataclass(eq=False)
-class TextReceiveStream(ObjectReceiveStream[str]):
- """
- Stream wrapper that decodes bytes to strings using the given encoding.
-
- Decoding is done using :class:`~codecs.IncrementalDecoder` which returns any completely
- received unicode characters as soon as they come in.
-
- :param transport_stream: any bytes-based receive stream
- :param encoding: character encoding to use for decoding bytes to strings (defaults to
- ``utf-8``)
- :param errors: handling scheme for decoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteReceiveStream
- encoding: InitVar[str] = "utf-8"
- errors: InitVar[str] = "strict"
- _decoder: codecs.IncrementalDecoder = field(init=False)
-
- def __post_init__(self, encoding: str, errors: str) -> None:
- decoder_class = codecs.getincrementaldecoder(encoding)
- self._decoder = decoder_class(errors=errors)
-
- async def receive(self) -> str:
- while True:
- chunk = await self.transport_stream.receive()
- decoded = self._decoder.decode(chunk)
- if decoded:
- return decoded
-
- async def aclose(self) -> None:
- await self.transport_stream.aclose()
- self._decoder.reset()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return self.transport_stream.extra_attributes
-
-
-@dataclass(eq=False)
-class TextSendStream(ObjectSendStream[str]):
- """
- Sends strings to the wrapped stream as bytes using the given encoding.
-
- :param AnyByteSendStream transport_stream: any bytes-based send stream
- :param str encoding: character encoding to use for encoding strings to bytes (defaults to
- ``utf-8``)
- :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteSendStream
- encoding: InitVar[str] = "utf-8"
- errors: str = "strict"
- _encoder: Callable[..., tuple[bytes, int]] = field(init=False)
-
- def __post_init__(self, encoding: str) -> None:
- self._encoder = codecs.getencoder(encoding)
-
- async def send(self, item: str) -> None:
- encoded = self._encoder(item, self.errors)[0]
- await self.transport_stream.send(encoded)
-
- async def aclose(self) -> None:
- await self.transport_stream.aclose()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return self.transport_stream.extra_attributes
-
-
-@dataclass(eq=False)
-class TextStream(ObjectStream[str]):
- """
- A bidirectional stream that decodes bytes to strings on receive and encodes strings to bytes on
- send.
-
- Extra attributes will be provided from both streams, with the receive stream providing the
- values in case of a conflict.
-
- :param AnyByteStream transport_stream: any bytes-based stream
- :param str encoding: character encoding to use for encoding/decoding strings to/from bytes
- (defaults to ``utf-8``)
- :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteStream
- encoding: InitVar[str] = "utf-8"
- errors: InitVar[str] = "strict"
- _receive_stream: TextReceiveStream = field(init=False)
- _send_stream: TextSendStream = field(init=False)
-
- def __post_init__(self, encoding: str, errors: str) -> None:
- self._receive_stream = TextReceiveStream(
- self.transport_stream, encoding=encoding, errors=errors
- )
- self._send_stream = TextSendStream(
- self.transport_stream, encoding=encoding, errors=errors
- )
-
- async def receive(self) -> str:
- return await self._receive_stream.receive()
-
- async def send(self, item: str) -> None:
- await self._send_stream.send(item)
-
- async def send_eof(self) -> None:
- await self.transport_stream.send_eof()
-
- async def aclose(self) -> None:
- await self._send_stream.aclose()
- await self._receive_stream.aclose()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return {
- **self._send_stream.extra_attributes,
- **self._receive_stream.extra_attributes,
- }
diff --git a/spaces/TH5314/newbing/src/lib/bots/bing/types.ts b/spaces/TH5314/newbing/src/lib/bots/bing/types.ts
deleted file mode 100644
index 02cd5e8b01e3529642d28dc1539bf958f4ac420b..0000000000000000000000000000000000000000
--- a/spaces/TH5314/newbing/src/lib/bots/bing/types.ts
+++ /dev/null
@@ -1,259 +0,0 @@
-export type Author = 'user' | 'system' | 'bot'
-
-export type BotId = 'bing'
-
-export enum BingConversationStyle {
- Creative = 'Creative',
- Balanced = 'Balanced',
- Precise = 'Precise'
-}
-
-export enum ErrorCode {
- CONVERSATION_LIMIT = 'CONVERSATION_LIMIT',
- BING_UNAUTHORIZED = 'BING_UNAUTHORIZED',
- BING_FORBIDDEN = 'BING_FORBIDDEN',
- BING_CAPTCHA = 'BING_CAPTCHA',
- THROTTLE_LIMIT = 'THROTTLE_LIMIT',
- NOTFOUND_ERROR = 'NOT_FOUND_ERROR',
- UNKOWN_ERROR = 'UNKOWN_ERROR',
- NETWORK_ERROR = 'NETWORK_ERROR',
-}
-
-export class ChatError extends Error {
- code: ErrorCode
- constructor(message: string, code: ErrorCode) {
- super(message)
- this.code = code
- }
-}
-
-export type ChatMessageModel = {
- id: string
- author: Author
- text: string
- error?: ChatError
- throttling?: Throttling
- sourceAttributions?: SourceAttribution[]
- suggestedResponses?: SuggestedResponse[]
-}
-
-export interface ConversationModel {
- messages: ChatMessageModel[]
-}
-
-export type Event =
- | {
- type: 'UPDATE_ANSWER'
- data: {
- text: string
- spokenText?: string
- sourceAttributions?: SourceAttribution[]
- suggestedResponses?: SuggestedResponse[]
- throttling?: Throttling
- }
- }
- | {
- type: 'DONE'
- }
- | {
- type: 'ERROR'
- error: ChatError
- }
-
-export interface SendMessageParams {
- prompt: string
- imageUrl?: string
- options: T
- onEvent: (event: Event) => void
- signal?: AbortSignal
-}
-
-export interface ConversationResponse {
- conversationId: string
- clientId: string
- conversationSignature: string
- result: {
- value: string
- message?: string
- }
-}
-
-export interface Telemetry {
- metrics?: null
- startTime: string
-}
-
-export interface ChatUpdateArgument {
- messages?: ChatResponseMessage[]
- throttling?: Throttling
- requestId: string
- result: null
-}
-
-export type ChatUpdateCompleteResponse = {
- type: 2
- invocationId: string
- item: ChatResponseItem
-} | {
- type: 1
- target: string
- arguments: ChatUpdateArgument[]
-} | {
- type: 3
- invocationId: string
-} | {
- type: 6 | 7
-}
-
-export interface ChatRequestResult {
- value: string
- serviceVersion: string
- error?: string
-}
-
-export interface ChatResponseItem {
- messages: ChatResponseMessage[]
- firstNewMessageIndex: number
- suggestedResponses: null
- conversationId: string
- requestId: string
- conversationExpiryTime: string
- telemetry: Telemetry
- result: ChatRequestResult
- throttling: Throttling
-}
-export enum InvocationEventType {
- Invocation = 1,
- StreamItem = 2,
- Completion = 3,
- StreamInvocation = 4,
- CancelInvocation = 5,
- Ping = 6,
- Close = 7,
-}
-
-// https://github.com/bytemate/bingchat-api/blob/main/src/lib.ts
-
-export interface ConversationInfo {
- conversationId: string
- clientId: string
- conversationSignature: string
- invocationId: number
- conversationStyle: BingConversationStyle
- prompt: string
- imageUrl?: string
-}
-
-export interface BingChatResponse {
- conversationSignature: string
- conversationId: string
- clientId: string
- invocationId: number
- conversationExpiryTime: Date
- response: string
- details: ChatResponseMessage
-}
-
-export interface Throttling {
- maxNumLongDocSummaryUserMessagesInConversation: number
- maxNumUserMessagesInConversation: number
- numLongDocSummaryUserMessagesInConversation: number
- numUserMessagesInConversation: number
-}
-
-export interface ChatResponseMessage {
- text: string
- spokenText?: string
- author: string
- createdAt: Date
- timestamp: Date
- messageId: string
- requestId: string
- offense: string
- adaptiveCards: AdaptiveCard[]
- sourceAttributions: SourceAttribution[]
- feedback: Feedback
- contentOrigin: string
- messageType?: string
- contentType?: string
- privacy: null
- suggestedResponses: SuggestedResponse[]
-}
-
-export interface AdaptiveCard {
- type: string
- version: string
- body: Body[]
-}
-
-export interface Body {
- type: string
- text: string
- wrap: boolean
- size?: string
-}
-
-export interface Feedback {
- tag: null
- updatedOn: null
- type: string
-}
-
-export interface SourceAttribution {
- providerDisplayName: string
- seeMoreUrl: string
- searchQuery: string
-}
-
-export interface SuggestedResponse {
- text: string
- author?: Author
- createdAt?: Date
- timestamp?: Date
- messageId?: string
- messageType?: string
- offense?: string
- feedback?: Feedback
- contentOrigin?: string
- privacy?: null
-}
-
-export interface KBlobRequest {
- knowledgeRequest: KnowledgeRequestContext
- imageBase64?: string
-}
-
-export interface KBlobResponse {
- blobId: string
- processedBlobId?: string
-}
-
-export interface KnowledgeRequestContext {
- imageInfo: ImageInfo;
- knowledgeRequest: KnowledgeRequest;
-}
-
-export interface ImageInfo {
- url?: string;
-}
-
-export interface KnowledgeRequest {
- invokedSkills: string[];
- subscriptionId: string;
- invokedSkillsRequestData: InvokedSkillsRequestData;
- convoData: ConvoData;
-}
-
-export interface ConvoData {
- convoid: string;
- convotone: BingConversationStyle;
-}
-
-export interface InvokedSkillsRequestData {
- enableFaceBlur: boolean;
-}
-
-export interface FileItem {
- url: string;
- status?: 'loading' | 'error' | 'loaded'
-}
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py
deleted file mode 100644
index 264d564dbda676b52f446c0d25433a15939a78a3..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py
+++ /dev/null
@@ -1,519 +0,0 @@
-"""
-This module uses ctypes to bind a whole bunch of functions and constants from
-SecureTransport. The goal here is to provide the low-level API to
-SecureTransport. These are essentially the C-level functions and constants, and
-they're pretty gross to work with.
-
-This code is a bastardised version of the code found in Will Bond's oscrypto
-library. An enormous debt is owed to him for blazing this trail for us. For
-that reason, this code should be considered to be covered both by urllib3's
-license and by oscrypto's:
-
- Copyright (c) 2015-2016 Will Bond
-
- Permission is hereby granted, free of charge, to any person obtaining a
- copy of this software and associated documentation files (the "Software"),
- to deal in the Software without restriction, including without limitation
- the rights to use, copy, modify, merge, publish, distribute, sublicense,
- and/or sell copies of the Software, and to permit persons to whom the
- Software is furnished to do so, subject to the following conditions:
-
- The above copyright notice and this permission notice shall be included in
- all copies or substantial portions of the Software.
-
- THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
- FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
- DEALINGS IN THE SOFTWARE.
-"""
-from __future__ import absolute_import
-
-import platform
-from ctypes import (
- CDLL,
- CFUNCTYPE,
- POINTER,
- c_bool,
- c_byte,
- c_char_p,
- c_int32,
- c_long,
- c_size_t,
- c_uint32,
- c_ulong,
- c_void_p,
-)
-from ctypes.util import find_library
-
-from ...packages.six import raise_from
-
-if platform.system() != "Darwin":
- raise ImportError("Only macOS is supported")
-
-version = platform.mac_ver()[0]
-version_info = tuple(map(int, version.split(".")))
-if version_info < (10, 8):
- raise OSError(
- "Only OS X 10.8 and newer are supported, not %s.%s"
- % (version_info[0], version_info[1])
- )
-
-
-def load_cdll(name, macos10_16_path):
- """Loads a CDLL by name, falling back to known path on 10.16+"""
- try:
- # Big Sur is technically 11 but we use 10.16 due to the Big Sur
- # beta being labeled as 10.16.
- if version_info >= (10, 16):
- path = macos10_16_path
- else:
- path = find_library(name)
- if not path:
- raise OSError # Caught and reraised as 'ImportError'
- return CDLL(path, use_errno=True)
- except OSError:
- raise_from(ImportError("The library %s failed to load" % name), None)
-
-
-Security = load_cdll(
- "Security", "/System/Library/Frameworks/Security.framework/Security"
-)
-CoreFoundation = load_cdll(
- "CoreFoundation",
- "/System/Library/Frameworks/CoreFoundation.framework/CoreFoundation",
-)
-
-
-Boolean = c_bool
-CFIndex = c_long
-CFStringEncoding = c_uint32
-CFData = c_void_p
-CFString = c_void_p
-CFArray = c_void_p
-CFMutableArray = c_void_p
-CFDictionary = c_void_p
-CFError = c_void_p
-CFType = c_void_p
-CFTypeID = c_ulong
-
-CFTypeRef = POINTER(CFType)
-CFAllocatorRef = c_void_p
-
-OSStatus = c_int32
-
-CFDataRef = POINTER(CFData)
-CFStringRef = POINTER(CFString)
-CFArrayRef = POINTER(CFArray)
-CFMutableArrayRef = POINTER(CFMutableArray)
-CFDictionaryRef = POINTER(CFDictionary)
-CFArrayCallBacks = c_void_p
-CFDictionaryKeyCallBacks = c_void_p
-CFDictionaryValueCallBacks = c_void_p
-
-SecCertificateRef = POINTER(c_void_p)
-SecExternalFormat = c_uint32
-SecExternalItemType = c_uint32
-SecIdentityRef = POINTER(c_void_p)
-SecItemImportExportFlags = c_uint32
-SecItemImportExportKeyParameters = c_void_p
-SecKeychainRef = POINTER(c_void_p)
-SSLProtocol = c_uint32
-SSLCipherSuite = c_uint32
-SSLContextRef = POINTER(c_void_p)
-SecTrustRef = POINTER(c_void_p)
-SSLConnectionRef = c_uint32
-SecTrustResultType = c_uint32
-SecTrustOptionFlags = c_uint32
-SSLProtocolSide = c_uint32
-SSLConnectionType = c_uint32
-SSLSessionOption = c_uint32
-
-
-try:
- Security.SecItemImport.argtypes = [
- CFDataRef,
- CFStringRef,
- POINTER(SecExternalFormat),
- POINTER(SecExternalItemType),
- SecItemImportExportFlags,
- POINTER(SecItemImportExportKeyParameters),
- SecKeychainRef,
- POINTER(CFArrayRef),
- ]
- Security.SecItemImport.restype = OSStatus
-
- Security.SecCertificateGetTypeID.argtypes = []
- Security.SecCertificateGetTypeID.restype = CFTypeID
-
- Security.SecIdentityGetTypeID.argtypes = []
- Security.SecIdentityGetTypeID.restype = CFTypeID
-
- Security.SecKeyGetTypeID.argtypes = []
- Security.SecKeyGetTypeID.restype = CFTypeID
-
- Security.SecCertificateCreateWithData.argtypes = [CFAllocatorRef, CFDataRef]
- Security.SecCertificateCreateWithData.restype = SecCertificateRef
-
- Security.SecCertificateCopyData.argtypes = [SecCertificateRef]
- Security.SecCertificateCopyData.restype = CFDataRef
-
- Security.SecCopyErrorMessageString.argtypes = [OSStatus, c_void_p]
- Security.SecCopyErrorMessageString.restype = CFStringRef
-
- Security.SecIdentityCreateWithCertificate.argtypes = [
- CFTypeRef,
- SecCertificateRef,
- POINTER(SecIdentityRef),
- ]
- Security.SecIdentityCreateWithCertificate.restype = OSStatus
-
- Security.SecKeychainCreate.argtypes = [
- c_char_p,
- c_uint32,
- c_void_p,
- Boolean,
- c_void_p,
- POINTER(SecKeychainRef),
- ]
- Security.SecKeychainCreate.restype = OSStatus
-
- Security.SecKeychainDelete.argtypes = [SecKeychainRef]
- Security.SecKeychainDelete.restype = OSStatus
-
- Security.SecPKCS12Import.argtypes = [
- CFDataRef,
- CFDictionaryRef,
- POINTER(CFArrayRef),
- ]
- Security.SecPKCS12Import.restype = OSStatus
-
- SSLReadFunc = CFUNCTYPE(OSStatus, SSLConnectionRef, c_void_p, POINTER(c_size_t))
- SSLWriteFunc = CFUNCTYPE(
- OSStatus, SSLConnectionRef, POINTER(c_byte), POINTER(c_size_t)
- )
-
- Security.SSLSetIOFuncs.argtypes = [SSLContextRef, SSLReadFunc, SSLWriteFunc]
- Security.SSLSetIOFuncs.restype = OSStatus
-
- Security.SSLSetPeerID.argtypes = [SSLContextRef, c_char_p, c_size_t]
- Security.SSLSetPeerID.restype = OSStatus
-
- Security.SSLSetCertificate.argtypes = [SSLContextRef, CFArrayRef]
- Security.SSLSetCertificate.restype = OSStatus
-
- Security.SSLSetCertificateAuthorities.argtypes = [SSLContextRef, CFTypeRef, Boolean]
- Security.SSLSetCertificateAuthorities.restype = OSStatus
-
- Security.SSLSetConnection.argtypes = [SSLContextRef, SSLConnectionRef]
- Security.SSLSetConnection.restype = OSStatus
-
- Security.SSLSetPeerDomainName.argtypes = [SSLContextRef, c_char_p, c_size_t]
- Security.SSLSetPeerDomainName.restype = OSStatus
-
- Security.SSLHandshake.argtypes = [SSLContextRef]
- Security.SSLHandshake.restype = OSStatus
-
- Security.SSLRead.argtypes = [SSLContextRef, c_char_p, c_size_t, POINTER(c_size_t)]
- Security.SSLRead.restype = OSStatus
-
- Security.SSLWrite.argtypes = [SSLContextRef, c_char_p, c_size_t, POINTER(c_size_t)]
- Security.SSLWrite.restype = OSStatus
-
- Security.SSLClose.argtypes = [SSLContextRef]
- Security.SSLClose.restype = OSStatus
-
- Security.SSLGetNumberSupportedCiphers.argtypes = [SSLContextRef, POINTER(c_size_t)]
- Security.SSLGetNumberSupportedCiphers.restype = OSStatus
-
- Security.SSLGetSupportedCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- POINTER(c_size_t),
- ]
- Security.SSLGetSupportedCiphers.restype = OSStatus
-
- Security.SSLSetEnabledCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- c_size_t,
- ]
- Security.SSLSetEnabledCiphers.restype = OSStatus
-
- Security.SSLGetNumberEnabledCiphers.argtype = [SSLContextRef, POINTER(c_size_t)]
- Security.SSLGetNumberEnabledCiphers.restype = OSStatus
-
- Security.SSLGetEnabledCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- POINTER(c_size_t),
- ]
- Security.SSLGetEnabledCiphers.restype = OSStatus
-
- Security.SSLGetNegotiatedCipher.argtypes = [SSLContextRef, POINTER(SSLCipherSuite)]
- Security.SSLGetNegotiatedCipher.restype = OSStatus
-
- Security.SSLGetNegotiatedProtocolVersion.argtypes = [
- SSLContextRef,
- POINTER(SSLProtocol),
- ]
- Security.SSLGetNegotiatedProtocolVersion.restype = OSStatus
-
- Security.SSLCopyPeerTrust.argtypes = [SSLContextRef, POINTER(SecTrustRef)]
- Security.SSLCopyPeerTrust.restype = OSStatus
-
- Security.SecTrustSetAnchorCertificates.argtypes = [SecTrustRef, CFArrayRef]
- Security.SecTrustSetAnchorCertificates.restype = OSStatus
-
- Security.SecTrustSetAnchorCertificatesOnly.argstypes = [SecTrustRef, Boolean]
- Security.SecTrustSetAnchorCertificatesOnly.restype = OSStatus
-
- Security.SecTrustEvaluate.argtypes = [SecTrustRef, POINTER(SecTrustResultType)]
- Security.SecTrustEvaluate.restype = OSStatus
-
- Security.SecTrustGetCertificateCount.argtypes = [SecTrustRef]
- Security.SecTrustGetCertificateCount.restype = CFIndex
-
- Security.SecTrustGetCertificateAtIndex.argtypes = [SecTrustRef, CFIndex]
- Security.SecTrustGetCertificateAtIndex.restype = SecCertificateRef
-
- Security.SSLCreateContext.argtypes = [
- CFAllocatorRef,
- SSLProtocolSide,
- SSLConnectionType,
- ]
- Security.SSLCreateContext.restype = SSLContextRef
-
- Security.SSLSetSessionOption.argtypes = [SSLContextRef, SSLSessionOption, Boolean]
- Security.SSLSetSessionOption.restype = OSStatus
-
- Security.SSLSetProtocolVersionMin.argtypes = [SSLContextRef, SSLProtocol]
- Security.SSLSetProtocolVersionMin.restype = OSStatus
-
- Security.SSLSetProtocolVersionMax.argtypes = [SSLContextRef, SSLProtocol]
- Security.SSLSetProtocolVersionMax.restype = OSStatus
-
- try:
- Security.SSLSetALPNProtocols.argtypes = [SSLContextRef, CFArrayRef]
- Security.SSLSetALPNProtocols.restype = OSStatus
- except AttributeError:
- # Supported only in 10.12+
- pass
-
- Security.SecCopyErrorMessageString.argtypes = [OSStatus, c_void_p]
- Security.SecCopyErrorMessageString.restype = CFStringRef
-
- Security.SSLReadFunc = SSLReadFunc
- Security.SSLWriteFunc = SSLWriteFunc
- Security.SSLContextRef = SSLContextRef
- Security.SSLProtocol = SSLProtocol
- Security.SSLCipherSuite = SSLCipherSuite
- Security.SecIdentityRef = SecIdentityRef
- Security.SecKeychainRef = SecKeychainRef
- Security.SecTrustRef = SecTrustRef
- Security.SecTrustResultType = SecTrustResultType
- Security.SecExternalFormat = SecExternalFormat
- Security.OSStatus = OSStatus
-
- Security.kSecImportExportPassphrase = CFStringRef.in_dll(
- Security, "kSecImportExportPassphrase"
- )
- Security.kSecImportItemIdentity = CFStringRef.in_dll(
- Security, "kSecImportItemIdentity"
- )
-
- # CoreFoundation time!
- CoreFoundation.CFRetain.argtypes = [CFTypeRef]
- CoreFoundation.CFRetain.restype = CFTypeRef
-
- CoreFoundation.CFRelease.argtypes = [CFTypeRef]
- CoreFoundation.CFRelease.restype = None
-
- CoreFoundation.CFGetTypeID.argtypes = [CFTypeRef]
- CoreFoundation.CFGetTypeID.restype = CFTypeID
-
- CoreFoundation.CFStringCreateWithCString.argtypes = [
- CFAllocatorRef,
- c_char_p,
- CFStringEncoding,
- ]
- CoreFoundation.CFStringCreateWithCString.restype = CFStringRef
-
- CoreFoundation.CFStringGetCStringPtr.argtypes = [CFStringRef, CFStringEncoding]
- CoreFoundation.CFStringGetCStringPtr.restype = c_char_p
-
- CoreFoundation.CFStringGetCString.argtypes = [
- CFStringRef,
- c_char_p,
- CFIndex,
- CFStringEncoding,
- ]
- CoreFoundation.CFStringGetCString.restype = c_bool
-
- CoreFoundation.CFDataCreate.argtypes = [CFAllocatorRef, c_char_p, CFIndex]
- CoreFoundation.CFDataCreate.restype = CFDataRef
-
- CoreFoundation.CFDataGetLength.argtypes = [CFDataRef]
- CoreFoundation.CFDataGetLength.restype = CFIndex
-
- CoreFoundation.CFDataGetBytePtr.argtypes = [CFDataRef]
- CoreFoundation.CFDataGetBytePtr.restype = c_void_p
-
- CoreFoundation.CFDictionaryCreate.argtypes = [
- CFAllocatorRef,
- POINTER(CFTypeRef),
- POINTER(CFTypeRef),
- CFIndex,
- CFDictionaryKeyCallBacks,
- CFDictionaryValueCallBacks,
- ]
- CoreFoundation.CFDictionaryCreate.restype = CFDictionaryRef
-
- CoreFoundation.CFDictionaryGetValue.argtypes = [CFDictionaryRef, CFTypeRef]
- CoreFoundation.CFDictionaryGetValue.restype = CFTypeRef
-
- CoreFoundation.CFArrayCreate.argtypes = [
- CFAllocatorRef,
- POINTER(CFTypeRef),
- CFIndex,
- CFArrayCallBacks,
- ]
- CoreFoundation.CFArrayCreate.restype = CFArrayRef
-
- CoreFoundation.CFArrayCreateMutable.argtypes = [
- CFAllocatorRef,
- CFIndex,
- CFArrayCallBacks,
- ]
- CoreFoundation.CFArrayCreateMutable.restype = CFMutableArrayRef
-
- CoreFoundation.CFArrayAppendValue.argtypes = [CFMutableArrayRef, c_void_p]
- CoreFoundation.CFArrayAppendValue.restype = None
-
- CoreFoundation.CFArrayGetCount.argtypes = [CFArrayRef]
- CoreFoundation.CFArrayGetCount.restype = CFIndex
-
- CoreFoundation.CFArrayGetValueAtIndex.argtypes = [CFArrayRef, CFIndex]
- CoreFoundation.CFArrayGetValueAtIndex.restype = c_void_p
-
- CoreFoundation.kCFAllocatorDefault = CFAllocatorRef.in_dll(
- CoreFoundation, "kCFAllocatorDefault"
- )
- CoreFoundation.kCFTypeArrayCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeArrayCallBacks"
- )
- CoreFoundation.kCFTypeDictionaryKeyCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeDictionaryKeyCallBacks"
- )
- CoreFoundation.kCFTypeDictionaryValueCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeDictionaryValueCallBacks"
- )
-
- CoreFoundation.CFTypeRef = CFTypeRef
- CoreFoundation.CFArrayRef = CFArrayRef
- CoreFoundation.CFStringRef = CFStringRef
- CoreFoundation.CFDictionaryRef = CFDictionaryRef
-
-except (AttributeError):
- raise ImportError("Error initializing ctypes")
-
-
-class CFConst(object):
- """
- A class object that acts as essentially a namespace for CoreFoundation
- constants.
- """
-
- kCFStringEncodingUTF8 = CFStringEncoding(0x08000100)
-
-
-class SecurityConst(object):
- """
- A class object that acts as essentially a namespace for Security constants.
- """
-
- kSSLSessionOptionBreakOnServerAuth = 0
-
- kSSLProtocol2 = 1
- kSSLProtocol3 = 2
- kTLSProtocol1 = 4
- kTLSProtocol11 = 7
- kTLSProtocol12 = 8
- # SecureTransport does not support TLS 1.3 even if there's a constant for it
- kTLSProtocol13 = 10
- kTLSProtocolMaxSupported = 999
-
- kSSLClientSide = 1
- kSSLStreamType = 0
-
- kSecFormatPEMSequence = 10
-
- kSecTrustResultInvalid = 0
- kSecTrustResultProceed = 1
- # This gap is present on purpose: this was kSecTrustResultConfirm, which
- # is deprecated.
- kSecTrustResultDeny = 3
- kSecTrustResultUnspecified = 4
- kSecTrustResultRecoverableTrustFailure = 5
- kSecTrustResultFatalTrustFailure = 6
- kSecTrustResultOtherError = 7
-
- errSSLProtocol = -9800
- errSSLWouldBlock = -9803
- errSSLClosedGraceful = -9805
- errSSLClosedNoNotify = -9816
- errSSLClosedAbort = -9806
-
- errSSLXCertChainInvalid = -9807
- errSSLCrypto = -9809
- errSSLInternal = -9810
- errSSLCertExpired = -9814
- errSSLCertNotYetValid = -9815
- errSSLUnknownRootCert = -9812
- errSSLNoRootCert = -9813
- errSSLHostNameMismatch = -9843
- errSSLPeerHandshakeFail = -9824
- errSSLPeerUserCancelled = -9839
- errSSLWeakPeerEphemeralDHKey = -9850
- errSSLServerAuthCompleted = -9841
- errSSLRecordOverflow = -9847
-
- errSecVerifyFailed = -67808
- errSecNoTrustSettings = -25263
- errSecItemNotFound = -25300
- errSecInvalidTrustSettings = -25262
-
- # Cipher suites. We only pick the ones our default cipher string allows.
- # Source: https://developer.apple.com/documentation/security/1550981-ssl_cipher_suite_values
- TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384 = 0xC02C
- TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384 = 0xC030
- TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256 = 0xC02B
- TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256 = 0xC02F
- TLS_ECDHE_ECDSA_WITH_CHACHA20_POLY1305_SHA256 = 0xCCA9
- TLS_ECDHE_RSA_WITH_CHACHA20_POLY1305_SHA256 = 0xCCA8
- TLS_DHE_RSA_WITH_AES_256_GCM_SHA384 = 0x009F
- TLS_DHE_RSA_WITH_AES_128_GCM_SHA256 = 0x009E
- TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA384 = 0xC024
- TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384 = 0xC028
- TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA = 0xC00A
- TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA = 0xC014
- TLS_DHE_RSA_WITH_AES_256_CBC_SHA256 = 0x006B
- TLS_DHE_RSA_WITH_AES_256_CBC_SHA = 0x0039
- TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256 = 0xC023
- TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256 = 0xC027
- TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA = 0xC009
- TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA = 0xC013
- TLS_DHE_RSA_WITH_AES_128_CBC_SHA256 = 0x0067
- TLS_DHE_RSA_WITH_AES_128_CBC_SHA = 0x0033
- TLS_RSA_WITH_AES_256_GCM_SHA384 = 0x009D
- TLS_RSA_WITH_AES_128_GCM_SHA256 = 0x009C
- TLS_RSA_WITH_AES_256_CBC_SHA256 = 0x003D
- TLS_RSA_WITH_AES_128_CBC_SHA256 = 0x003C
- TLS_RSA_WITH_AES_256_CBC_SHA = 0x0035
- TLS_RSA_WITH_AES_128_CBC_SHA = 0x002F
- TLS_AES_128_GCM_SHA256 = 0x1301
- TLS_AES_256_GCM_SHA384 = 0x1302
- TLS_AES_128_CCM_8_SHA256 = 0x1305
- TLS_AES_128_CCM_SHA256 = 0x1304
diff --git a/spaces/TeamTonic/MultiMed/app.py b/spaces/TeamTonic/MultiMed/app.py
deleted file mode 100644
index 29b1ad119a6d38fc2fca1fa616c0f221e9751208..0000000000000000000000000000000000000000
--- a/spaces/TeamTonic/MultiMed/app.py
+++ /dev/null
@@ -1,490 +0,0 @@
-# Welcome to Team Tonic's MultiMed
-
-from gradio_client import Client
-import os
-import numpy as np
-import base64
-import gradio as gr
-import requests
-import json
-import dotenv
-from scipy.io.wavfile import write
-import PIL
-from openai import OpenAI
-import time
-dotenv.load_dotenv()
-
-seamless_client = Client("facebook/seamless_m4t")
-HuggingFace_Token = os.getenv("HuggingFace_Token")
-
-def check_hallucination(assertion,citation):
- API_URL = "https://api-inference.huggingface.co/models/vectara/hallucination_evaluation_model"
- headers = {"Authorization": f"Bearer {HuggingFace_Token}"}
- payload = {"inputs" : f"{assertion} [SEP] {citation}"}
-
- response = requests.post(API_URL, headers=headers, json=payload,timeout=120)
- output = response.json()
- output = output[0][0]["score"]
-
- return f"**hullicination score:** {output}"
-
-
-
-def process_speech(input_language, audio_input):
- """
- processing sound using seamless_m4t
- """
- if audio_input is None :
- return "no audio or audio did not save yet \nplease try again ! "
- print(f"audio : {audio_input}")
- print(f"audio type : {type(audio_input)}")
- out = seamless_client.predict(
- "S2TT",
- "file",
- None,
- audio_input, #audio_name
- "",
- input_language,# source language
- input_language,# target language
- api_name="/run",
- )
- out = out[1] # get the text
- try :
- return f"{out}"
- except Exception as e :
- return f"{e}"
-
-
-
-
-def process_image(image) :
- img_name = f"{np.random.randint(0, 100)}.jpg"
- PIL.Image.fromarray(image.astype('uint8'), 'RGB').save(img_name)
- image = open(img_name, "rb").read()
- base64_image = base64_image = base64.b64encode(image).decode('utf-8')
- openai_api_key = os.getenv('OPENAI_API_KEY')
- # oai_org = os.getenv('OAI_ORG')
-
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {openai_api_key}"
- }
-
- payload = {
- "model": "gpt-4-vision-preview",
- "messages": [
- {
- "role": "user",
- "content": [
- {
- "type": "text",
- "text": "You are clinical consultant discussion training cases with students at TonicUniversity. Assess and describe the photo in minute detail. Explain why each area or item in the photograph would be inappropriate to describe if required. Pay attention to anatomy, symptoms and remedies. Propose a course of action based on your assessment. Exclude any other commentary:"
- },
- {
- "type": "image_url",
- "image_url": {
- "url": f"data:image/jpeg;base64,{base64_image}"
- }
- }
- ]
- }
- ],
- "max_tokens": 1200
- }
-
- response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
-
- try :
- out = response.json()
- out = out["choices"][0]["message"]["content"]
-
- return out
- except Exception as e :
- return f"{e}"
-
-
-def query_vectara(text):
- user_message = text
-
- # Read authentication parameters from the .env file
- CUSTOMER_ID = os.getenv('CUSTOMER_ID')
- CORPUS_ID = os.getenv('CORPUS_ID')
- API_KEY = os.getenv('API_KEY')
-
- # Define the headers
- api_key_header = {
- "customer-id": CUSTOMER_ID,
- "x-api-key": API_KEY
- }
-
- # Define the request body in the structure provided in the example
- request_body = {
- "query": [
- {
- "query": user_message,
- "queryContext": "",
- "start": 1,
- "numResults": 50,
- "contextConfig": {
- "charsBefore": 0,
- "charsAfter": 0,
- "sentencesBefore": 2,
- "sentencesAfter": 2,
- "startTag": "%START_SNIPPET%",
- "endTag": "%END_SNIPPET%",
- },
- "rerankingConfig": {
- "rerankerId": 272725718,
- "mmrConfig": {
- "diversityBias": 0.35
- }
- },
- "corpusKey": [
- {
- "customerId": CUSTOMER_ID,
- "corpusId": CORPUS_ID,
- "semantics": 0,
- "metadataFilter": "",
- "lexicalInterpolationConfig": {
- "lambda": 0
- },
- "dim": []
- }
- ],
- "summary": [
- {
- "maxSummarizedResults": 5,
- "responseLang": "auto",
- "summarizerPromptName": "vectara-summary-ext-v1.2.0"
- }
- ]
- }
- ]
- }
-
- # Make the API request using Gradio
- response = requests.post(
- "https://api.vectara.io/v1/query",
- json=request_body, # Use json to automatically serialize the request body
- verify=True,
- headers=api_key_header
- )
-
- if response.status_code == 200:
- query_data = response.json()
- if query_data:
- sources_info = []
-
- # Extract the summary.
- summary = query_data['responseSet'][0]['summary'][0]['text']
-
- # Iterate over all response sets
- for response_set in query_data.get('responseSet', []):
- # Extract sources
- # Limit to top 5 sources.
- for source in response_set.get('response', [])[:5]:
- source_metadata = source.get('metadata', [])
- source_info = {}
-
- for metadata in source_metadata:
- metadata_name = metadata.get('name', '')
- metadata_value = metadata.get('value', '')
-
- if metadata_name == 'title':
- source_info['title'] = metadata_value
- elif metadata_name == 'author':
- source_info['author'] = metadata_value
- elif metadata_name == 'pageNumber':
- source_info['page number'] = metadata_value
-
- if source_info:
- sources_info.append(source_info)
-
- result = {"summary": summary, "sources": sources_info}
- return f"{json.dumps(result, indent=2)}"
- else:
- return "No data found in the response."
- else:
- return f"Error: {response.status_code}"
-
-
-def convert_to_markdown(vectara_response_json):
- vectara_response = json.loads(vectara_response_json)
- if vectara_response:
- summary = vectara_response.get('summary', 'No summary available')
- sources_info = vectara_response.get('sources', [])
-
- # Format the summary as Markdown
- markdown_summary = f' {summary}\n\n'
-
- # Format the sources as a numbered list
- markdown_sources = ""
- for i, source_info in enumerate(sources_info):
- author = source_info.get('author', 'Unknown author')
- title = source_info.get('title', 'Unknown title')
- page_number = source_info.get('page number', 'Unknown page number')
- markdown_sources += f"{i+1}. {title} by {author}, Page {page_number}\n"
-
- return f"{markdown_summary}**Sources:**\n{markdown_sources}"
- else:
- return "No data found in the response."
-# Main function to handle the Gradio interface logic
-
-def process_summary_with_openai(summary):
- """
- This function takes a summary text as input and processes it with OpenAI's GPT model.
- """
- try:
- # Ensure that the OpenAI client is properly initialized
- client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
-
- # Create the prompt for OpenAI's completion
- prompt = "You are clinical consultant discussion training cases with students at TonicUniversity. Assess and describe the proper options in minute detail. Propose a course of action based on your assessment. You will recieve a summary assessment in a language, respond ONLY in the original language. Exclude any other commentary:"
-
- # Call the OpenAI API with the prompt and the summary
- completion = client.chat.completions.create(
- model="gpt-4-1106-preview", # Make sure to use the correct model name
- messages=[
- {"role": "system", "content": prompt},
- {"role": "user", "content": summary}
- ]
- )
-
- # Extract the content from the completion
- final_summary = completion.choices[0].message.content
- return final_summary
- except Exception as e:
- return str(e)
-
-
-def process_and_query(text=None):
- try:
- # augment the prompt before feeding it to vectara
- text = "the user asks the following to his health adviser " + text
- # If an image is provided, process it with OpenAI and use the response as the text query for Vectara
- # if image is not None:
- # text = process_image(image)
- # return "**Summary:** "+text
- # if audio is not None:
- # text = process_speech(audio)
- # # augment the prompt before feeding it to vectara
- # text = "the user asks the following to his health adviser " + text
-
-
-
- # Use the text to query Vectara
- vectara_response_json = query_vectara(text)
-
- # Convert the Vectara response to Markdown
- markdown_output = convert_to_markdown(vectara_response_json)
-
- # Process the summary with OpenAI
- final_response = process_summary_with_openai(markdown_output)
-
- # Return the processed summary along with the full output
- return f"**Summary**: {final_response}\n\n**Full output**:\n{markdown_output}"
- except Exception as e:
- return str(e)
-
- completion = client.chat.completions.create(
- model="gpt-3.5-turbo",
- messages=[
- {"role": "system", "content": prompt},
- {"role": "user", "content": markdown_output_final}
- ]
- )
- final_response= completion.choices[0].message.content
- return f"**Summary**: {final_response}\n\n**Full output**:\n{markdown_output}"
- except Exception as e:
- return str(e)
-
-
-# Define the Gradio interface
-# iface = gr.Interface(
-# fn=process_and_query,
-# inputs=[
-# gr.Textbox(label="Input Text"),
-# gr.Image(label="Upload Image"),
-# gr.Audio(label="talk in french",
-# sources=["microphone"]),
-# ],
-# outputs=[gr.Markdown(label="Output Text")],
-# title="👋🏻Welcome to ⚕🗣️😷MultiMed - Access Chat ⚕🗣️😷",
-# description='''
-# ### How To Use ⚕🗣️😷MultiMed⚕:
-# #### 🗣️📝Interact with ⚕🗣️😷MultiMed⚕ in any language using audio or text!
-# #### 🗣️📝 This is an educational and accessible conversational tool to improve wellness and sanitation in support of public health.
-# #### 📚🌟💼 The knowledge base is composed of publicly available medical and health sources in multiple languages. We also used [Kelvalya/MedAware](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset) that we processed and converted to HTML. The quality of the answers depends on the quality of the dataset, so if you want to see some data represented here, do [get in touch](https://discord.gg/GWpVpekp). You can also use 😷MultiMed⚕️ on your own data & in your own way by cloning this space. 🧬🔬🔍 Simply click here: 
-# #### Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community on 👻Discord: [Discord](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)"
-# ''',
-# theme='ParityError/Anime',
-# examples=[
-# ["What is the proper treatment for buccal herpes?"],
-# ["Male, 40 presenting with swollen glands and a rash"],
-# ["How does cellular metabolism work TCA cycle"],
-# ["What special care must be provided to children with chicken pox?"],
-# ["When and how often should I wash my hands ?"],
-# ["بکل ہرپس کا صحیح علاج کیا ہے؟"],
-# ["구강 헤르페스의 적절한 치료법은 무엇입니까?"],
-# ["Je, ni matibabu gani sahihi kwa herpes ya buccal?"],
-# ],
-# )
-
-welcome_message = """
-# 👋🏻Welcome to ⚕🗣️😷MultiMed - Access Chat ⚕🗣️😷
-### How To Use ⚕🗣️😷MultiMed⚕:
-#### 🗣️📝Interact with ⚕🗣️😷MultiMed⚕ in any language using audio or text!
-#### 🗣️📝 This is an educational and accessible conversational tool to improve wellness and sanitation in support of public health.
-#### 📚🌟💼 The knowledge base is composed of publicly available medical and health sources in multiple languages. We also used [Kelvalya/MedAware](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset) that we processed and converted to HTML. The quality of the answers depends on the quality of the dataset, so if you want to see some data represented here, do [get in touch](https://discord.gg/GWpVpekp). You can also use 😷MultiMed⚕️ on your own data & in your own way by cloning this space. 🧬🔬🔍 Simply click here:
-#### Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community on 👻Discord: [Discord](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)"
-"""
-
-
-languages = [
- "Afrikaans",
- "Amharic",
- "Modern Standard Arabic",
- "Moroccan Arabic",
- "Egyptian Arabic",
- "Assamese",
- "Asturian",
- "North Azerbaijani",
- "Belarusian",
- "Bengali",
- "Bosnian",
- "Bulgarian",
- "Catalan",
- "Cebuano",
- "Czech",
- "Central Kurdish",
- "Mandarin Chinese",
- "Welsh",
- "Danish",
- "German",
- "Greek",
- "English",
- "Estonian",
- "Basque",
- "Finnish",
- "French",
- "West Central Oromo",
- "Irish",
- "Galician",
- "Gujarati",
- "Hebrew",
- "Hindi",
- "Croatian",
- "Hungarian",
- "Armenian",
- "Igbo",
- "Indonesian",
- "Icelandic",
- "Italian",
- "Javanese",
- "Japanese",
- "Kamba",
- "Kannada",
- "Georgian",
- "Kazakh",
- "Kabuverdianu",
- "Halh Mongolian",
- "Khmer",
- "Kyrgyz",
- "Korean",
- "Lao",
- "Lithuanian",
- "Luxembourgish",
- "Ganda",
- "Luo",
- "Standard Latvian",
- "Maithili",
- "Malayalam",
- "Marathi",
- "Macedonian",
- "Maltese",
- "Meitei",
- "Burmese",
- "Dutch",
- "Norwegian Nynorsk",
- "Norwegian Bokmål",
- "Nepali",
- "Nyanja",
- "Occitan",
- "Odia",
- "Punjabi",
- "Southern Pashto",
- "Western Persian",
- "Polish",
- "Portuguese",
- "Romanian",
- "Russian",
- "Slovak",
- "Slovenian",
- "Shona",
- "Sindhi",
- "Somali",
- "Spanish",
- "Serbian",
- "Swedish",
- "Swahili",
- "Tamil",
- "Telugu",
- "Tajik",
- "Tagalog",
- "Thai",
- "Turkish",
- "Ukrainian",
- "Urdu",
- "Northern Uzbek",
- "Vietnamese",
- "Xhosa",
- "Yoruba",
- "Cantonese",
- "Colloquial Malay",
- "Standard Malay",
- "Zulu"
-]
-
-
-with gr.Blocks(theme='ParityError/Anime') as iface :
- gr.Markdown(welcome_message)
- with gr.Tab("text summarization"):
- text_input = gr.Textbox(label="input text",lines=5)
- text_output = gr.Markdown(label="output text")
- text_button = gr.Button("process text")
- gr.Examples([
- ["What is the proper treatment for buccal herpes?"],
- ["Male, 40 presenting with swollen glands and a rash"],
- ["How does cellular metabolism work TCA cycle"],
- ["What special care must be provided to children with chicken pox?"],
- ["When and how often should I wash my hands?"],
- ["بکل ہرپس کا صحیح علاج کیا ہے؟"],
- ["구강 헤르페스의 적절한 치료법은 무엇입니까?"],
- ["Je, ni matibabu gani sahihi kwa herpes ya buccal?"],
- ],inputs=[text_input])
- with gr.Tab("image identification"):
- image_input = gr.Image(label="upload image")
- image_output = gr.Markdown(label="output text")
- image_button = gr.Button("process image")
- image_button.click(process_image, inputs=image_input, outputs=image_output)
- gr.Examples(["sick person.jpeg"],inputs=[image_input])
- with gr.Tab("speech to text"):
- input_language = gr.Dropdown(languages, label="select the language",value="English",interactive=True)
- audio_input = gr.Audio(label="speak",type="filepath",sources="microphone")
- audio_output = gr.Markdown(label="output text")
- audio_button = gr.Button("process audio")
- audio_button.click(process_speech, inputs=[input_language,audio_input], outputs=audio_output)
- gr.Examples([["English","sample_input.mp3"]],inputs=[input_language,audio_input])
- with gr.Tab("hallucination check"):
- assertion = gr.Textbox(label="assertion")
- citation = gr.Textbox(label="citation text")
- hullucination_output = gr.Markdown(label="output text")
- audio_button = gr.Button("check hallucination")
- gr.Examples([["i am drunk","sarah is pregnant"]],inputs=[assertion,citation])
- text_button.click(process_and_query, inputs=text_input, outputs=text_output)
- audio_button.click(check_hallucination,inputs=[assertion,citation],outputs=hullucination_output)
-
-
-
-
-iface.queue().launch(show_error=True,debug=True)
diff --git a/spaces/Thanarit/GPT-Detection-Demo/app.py b/spaces/Thanarit/GPT-Detection-Demo/app.py
deleted file mode 100644
index b0de7e28071553c6b86ec9a73b9881b4d540c0bc..0000000000000000000000000000000000000000
--- a/spaces/Thanarit/GPT-Detection-Demo/app.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-from ModelDriver import *
-import numpy as np
-
-# Add a title
-st.title('GPT Detection Demo')
-st.write("This is a demo for GPT detection. You can use this demo to test the model. The model is trained on two datasets: OpenGPT and CSAbstract. You can choose the model and dataset in the sidebar.")
-st.write("Reference on how we built Roberta Sentinel: https://arxiv.org/abs/2305.07969")
-
-# Add 4 options for 4 models
-ModelOption = st.sidebar.selectbox(
- 'Which Model do you want to use?',
- ('RobertaSentinel', 'RobertaClassifier'),
-)
-
-DatasetOption = st.sidebar.selectbox(
- 'Which Dataset the model was trained on?',
- ('OpenGPT', 'CSAbstract'),
-)
-
-
-text = st.text_area('Enter text here (max 500 words)', '')
-
-if st.button('Generate'):
- if ModelOption == 'RobertaSentinel':
- if DatasetOption == 'OpenGPT':
- result = RobertaSentinelOpenGPTInference(text)
- st.write("Model: RobertaSentinelOpenGPT")
- elif DatasetOption == 'CSAbstract':
- result = RobertaSentinelCSAbstractInference(text)
- st.write("Model: RobertaSentinelCSAbstract")
-
- elif ModelOption == 'RobertaClassifier':
- if DatasetOption == 'OpenGPT':
- result = RobertaClassifierOpenGPTInference(text)
- st.write("Model: RobertaClassifierOpenGPT")
- elif DatasetOption == 'CSAbstract':
- result = RobertaClassifierCSAbstractInference(text)
- st.write("Model: RobertaClassifierCSAbstract")
-
- Prediction = "Human Written" if not np.argmax(result) else "Machine Generated"
-
- st.write(f"Prediction: {Prediction} ")
- st.write(f"Probabilty:", max(result))
-
-
-
-
-
-
-
-
-
diff --git a/spaces/ThomasSimonini/Unity-MLAgents-Pyramids/index.html b/spaces/ThomasSimonini/Unity-MLAgents-Pyramids/index.html
deleted file mode 100644
index 37f4404c8056925b6523479300dec8d2406d5c96..0000000000000000000000000000000000000000
--- a/spaces/ThomasSimonini/Unity-MLAgents-Pyramids/index.html
+++ /dev/null
@@ -1,92 +0,0 @@
-
-
-
-
-
-
- Unity-MLAgents-Pyramids
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/Tirendaz/Text-Classification/app.py b/spaces/Tirendaz/Text-Classification/app.py
deleted file mode 100644
index 125b46d586913e6a24ccb8585102a076baf95d9d..0000000000000000000000000000000000000000
--- a/spaces/Tirendaz/Text-Classification/app.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-classifier = pipeline("sentiment-analysis", model="Tirendaz/my_distilbert_model")
-
-def text_classification(text):
- result= classifier(text)
- sentiment_label = result[0]['label']
- sentiment_score = result[0]['score']
- formatted_output = f"This sentiment is {sentiment_label} with the probability {sentiment_score*100:.2f}%"
- return formatted_output
-
-examples=["This is wonderful movie!", "The movie was really bad; I didn't like it."]
-
-io = gr.Interface(fn=text_classification,
- inputs= gr.Textbox(lines=2, label="Text", placeholder="Enter title here..."),
- outputs=gr.Textbox(lines=2, label="Text Classification Result"),
- title="Text Classification",
- description="Enter a text and see the text classification result!",
- examples=examples)
-
-io.launch()
-
diff --git a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Vercel.py b/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Vercel.py
deleted file mode 100644
index e5df9cf017e4c1a265f5c9d5e48eb5c10a56e60a..0000000000000000000000000000000000000000
--- a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Vercel.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import os
-import json
-import base64
-import execjs
-import queue
-import threading
-
-from curl_cffi import requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://play.vercel.ai'
-supports_stream = True
-needs_auth = False
-
-models = {
- 'claude-instant-v1': 'anthropic:claude-instant-v1',
- 'claude-v1': 'anthropic:claude-v1',
- 'alpaca-7b': 'replicate:replicate/alpaca-7b',
- 'stablelm-tuned-alpha-7b': 'replicate:stability-ai/stablelm-tuned-alpha-7b',
- 'bloom': 'huggingface:bigscience/bloom',
- 'bloomz': 'huggingface:bigscience/bloomz',
- 'flan-t5-xxl': 'huggingface:google/flan-t5-xxl',
- 'flan-ul2': 'huggingface:google/flan-ul2',
- 'gpt-neox-20b': 'huggingface:EleutherAI/gpt-neox-20b',
- 'oasst-sft-4-pythia-12b-epoch-3.5': 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5',
- 'santacoder': 'huggingface:bigcode/santacoder',
- 'command-medium-nightly': 'cohere:command-medium-nightly',
- 'command-xlarge-nightly': 'cohere:command-xlarge-nightly',
- 'code-cushman-001': 'openai:code-cushman-001',
- 'code-davinci-002': 'openai:code-davinci-002',
- 'gpt-3.5-turbo': 'openai:gpt-3.5-turbo',
- 'text-ada-001': 'openai:text-ada-001',
- 'text-babbage-001': 'openai:text-babbage-001',
- 'text-curie-001': 'openai:text-curie-001',
- 'text-davinci-002': 'openai:text-davinci-002',
- 'text-davinci-003': 'openai:text-davinci-003'
-}
-model = models.keys()
-
-vercel_models = {'anthropic:claude-instant-v1': {'id': 'anthropic:claude-instant-v1', 'provider': 'anthropic', 'providerHumanName': 'Anthropic', 'makerHumanName': 'Anthropic', 'minBillingTier': 'hobby', 'parameters': {'temperature': {'value': 1, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': ['\n\nHuman:'], 'range': []}}, 'name': 'claude-instant-v1'}, 'anthropic:claude-v1': {'id': 'anthropic:claude-v1', 'provider': 'anthropic', 'providerHumanName': 'Anthropic', 'makerHumanName': 'Anthropic', 'minBillingTier': 'hobby', 'parameters': {'temperature': {'value': 1, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': ['\n\nHuman:'], 'range': []}}, 'name': 'claude-v1'}, 'replicate:replicate/alpaca-7b': {'id': 'replicate:replicate/alpaca-7b', 'provider': 'replicate', 'providerHumanName': 'Replicate', 'makerHumanName': 'Stanford', 'parameters': {'temperature': {'value': 0.75, 'range': [0.01, 5]}, 'maximumLength': {'value': 200, 'range': [50, 512]}, 'topP': {'value': 0.95, 'range': [0.01, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'repetitionPenalty': {'value': 1.1765, 'range': [0.01, 5]}, 'stopSequences': {'value': [], 'range': []}}, 'version': '2014ee1247354f2e81c0b3650d71ca715bc1e610189855f134c30ecb841fae21', 'name': 'alpaca-7b'}, 'replicate:stability-ai/stablelm-tuned-alpha-7b': {'id': 'replicate:stability-ai/stablelm-tuned-alpha-7b', 'provider': 'replicate', 'makerHumanName': 'StabilityAI', 'providerHumanName': 'Replicate', 'parameters': {'temperature': {'value': 0.75, 'range': [0.01, 5]}, 'maximumLength': {'value': 200, 'range': [50, 512]}, 'topP': {'value': 0.95, 'range': [0.01, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'repetitionPenalty': {'value': 1.1765, 'range': [0.01, 5]}, 'stopSequences': {'value': [], 'range': []}}, 'version': '4a9a32b4fd86c2d047f1d271fa93972683ec6ef1cf82f402bd021f267330b50b', 'name': 'stablelm-tuned-alpha-7b'}, 'huggingface:bigscience/bloom': {'id': 'huggingface:bigscience/bloom', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigScience', 'instructions': "Do NOT talk to Bloom as an entity, it's not a chatbot but a webpage/blog/article completion model. For the best results: mimic a few words of a webpage similar to the content you want to generate. Start a sentence as if YOU were writing a blog, webpage, math post, coding article and Bloom will generate a coherent follow-up.", 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'bloom'}, 'huggingface:bigscience/bloomz': {'id': 'huggingface:bigscience/bloomz', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigScience', 'instructions': 'We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "Translate to English: Je t\'aime.", the model will most likely answer "I love you.".', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'bloomz'}, 'huggingface:google/flan-t5-xxl': {'id': 'huggingface:google/flan-t5-xxl', 'provider': 'huggingface', 'makerHumanName': 'Google', 'providerHumanName': 'HuggingFace', 'name': 'flan-t5-xxl', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}}, 'huggingface:google/flan-ul2': {'id': 'huggingface:google/flan-ul2', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'Google', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'flan-ul2'}, 'huggingface:EleutherAI/gpt-neox-20b': {'id': 'huggingface:EleutherAI/gpt-neox-20b', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'EleutherAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'gpt-neox-20b'}, 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5': {'id': 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'OpenAssistant', 'parameters': {'maximumLength': {'value': 200, 'range': [50, 1024]}, 'typicalP': {'value': 0.2, 'range': [0.1, 0.99]}, 'repetitionPenalty': {'value': 1, 'range': [0.1, 2]}}, 'name': 'oasst-sft-4-pythia-12b-epoch-3.5'}, 'huggingface:bigcode/santacoder': {
- 'id': 'huggingface:bigcode/santacoder', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigCode', 'instructions': 'The model was trained on GitHub code. As such it is not an instruction model and commands like "Write a function that computes the square root." do not work well. You should phrase commands like they occur in source code such as comments (e.g. # the following function computes the sqrt) or write a function signature and docstring and let the model complete the function body.', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'santacoder'}, 'cohere:command-medium-nightly': {'id': 'cohere:command-medium-nightly', 'provider': 'cohere', 'providerHumanName': 'Cohere', 'makerHumanName': 'Cohere', 'name': 'command-medium-nightly', 'parameters': {'temperature': {'value': 0.9, 'range': [0, 2]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0, 1]}, 'topK': {'value': 0, 'range': [0, 500]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'cohere:command-xlarge-nightly': {'id': 'cohere:command-xlarge-nightly', 'provider': 'cohere', 'providerHumanName': 'Cohere', 'makerHumanName': 'Cohere', 'name': 'command-xlarge-nightly', 'parameters': {'temperature': {'value': 0.9, 'range': [0, 2]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0, 1]}, 'topK': {'value': 0, 'range': [0, 500]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:gpt-4': {'id': 'openai:gpt-4', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'gpt-4', 'minBillingTier': 'pro', 'parameters': {'temperature': {'value': 0.7, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:code-cushman-001': {'id': 'openai:code-cushman-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'code-cushman-001'}, 'openai:code-davinci-002': {'id': 'openai:code-davinci-002', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'code-davinci-002'}, 'openai:gpt-3.5-turbo': {'id': 'openai:gpt-3.5-turbo', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.7, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'gpt-3.5-turbo'}, 'openai:text-ada-001': {'id': 'openai:text-ada-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-ada-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-babbage-001': {'id': 'openai:text-babbage-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-babbage-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-curie-001': {'id': 'openai:text-curie-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-curie-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-davinci-002': {'id': 'openai:text-davinci-002', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-davinci-002', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-davinci-003': {'id': 'openai:text-davinci-003', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-davinci-003', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}}
-
-
-# based on https://github.com/ading2210/vercel-llm-api // modified
-class Client:
- def __init__(self):
- self.session = requests.Session()
- self.headers = {
- 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110 Safari/537.36',
- 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8',
- 'Accept-Encoding': 'gzip, deflate, br',
- 'Accept-Language': 'en-US,en;q=0.5',
- 'Te': 'trailers',
- 'Upgrade-Insecure-Requests': '1'
- }
- self.session.headers.update(self.headers)
-
- def get_token(self):
- b64 = self.session.get('https://sdk.vercel.ai/openai.jpeg').text
- data = json.loads(base64.b64decode(b64))
-
- code = 'const globalThis = {data: `sentinel`}; function token() {return (%s)(%s)}' % (
- data['c'], data['a'])
-
- token_string = json.dumps(separators=(',', ':'),
- obj={'r': execjs.compile(code).call('token'), 't': data['t']})
-
- return base64.b64encode(token_string.encode()).decode()
-
- def get_default_params(self, model_id):
- return {key: param['value'] for key, param in vercel_models[model_id]['parameters'].items()}
-
- def generate(self, model_id: str, prompt: str, params: dict = {}):
- if not ':' in model_id:
- model_id = models[model_id]
-
- defaults = self.get_default_params(model_id)
-
- payload = defaults | params | {
- 'prompt': prompt,
- 'model': model_id,
- }
-
- headers = self.headers | {
- 'Accept-Encoding': 'gzip, deflate, br',
- 'Custom-Encoding': self.get_token(),
- 'Host': 'sdk.vercel.ai',
- 'Origin': 'https://sdk.vercel.ai',
- 'Referrer': 'https://sdk.vercel.ai',
- 'Sec-Fetch-Dest': 'empty',
- 'Sec-Fetch-Mode': 'cors',
- 'Sec-Fetch-Site': 'same-origin',
- }
-
- chunks_queue = queue.Queue()
- error = None
- response = None
-
- def callback(data):
- chunks_queue.put(data.decode())
-
- def request_thread():
- nonlocal response, error
- for _ in range(3):
- try:
- response = self.session.post('https://sdk.vercel.ai/api/generate',
- json=payload, headers=headers, content_callback=callback)
- response.raise_for_status()
-
- except Exception as e:
- if _ == 2:
- error = e
-
- else:
- continue
-
- thread = threading.Thread(target=request_thread, daemon=True)
- thread.start()
-
- text = ''
- index = 0
- while True:
- try:
- chunk = chunks_queue.get(block=True, timeout=0.1)
-
- except queue.Empty:
- if error:
- raise error
-
- elif response:
- break
-
- else:
- continue
-
- text += chunk
- lines = text.split('\n')
-
- if len(lines) - 1 > index:
- new = lines[index:-1]
- for word in new:
- yield json.loads(word)
- index = len(lines) - 1
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
- yield 'Vercel is currently not working.'
- return
-
- conversation = 'This is a conversation between a human and a language model, respond to the last message accordingly, referring to the past history of messages if needed.\n'
-
- for message in messages:
- conversation += '%s: %s\n' % (message['role'], message['content'])
-
- conversation += 'assistant: '
-
- completion = Client().generate(model, conversation)
-
- for token in completion:
- yield token
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/Vijish/Crop-CLIP/README.md b/spaces/Vijish/Crop-CLIP/README.md
deleted file mode 100644
index bdcae9f45efba75036d8cd67008634852f99dcfc..0000000000000000000000000000000000000000
--- a/spaces/Vijish/Crop-CLIP/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Crop CLIP
-emoji: 🦀
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Violette/Protogen_x3.4_Official_Release/README.md b/spaces/Violette/Protogen_x3.4_Official_Release/README.md
deleted file mode 100644
index f956b53e422114f032bad59a721dd2d563081a37..0000000000000000000000000000000000000000
--- a/spaces/Violette/Protogen_x3.4_Official_Release/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Protogen X3.4 Official Release
-emoji: 📈
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/tasks/__init__.py b/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/tasks/__init__.py
deleted file mode 100644
index ab1fb1c8289535cf9397bb9805c0cba3666ad26f..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/tasks/__init__.py
+++ /dev/null
@@ -1,26 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-from minigpt4.common.registry import registry
-from minigpt4.tasks.base_task import BaseTask
-from minigpt4.tasks.image_text_pretrain import ImageTextPretrainTask
-
-
-def setup_task(cfg):
- assert "task" in cfg.run_cfg, "Task name must be provided."
-
- task_name = cfg.run_cfg.task
- task = registry.get_task_class(task_name).setup_task(cfg=cfg)
- assert task is not None, "Task {} not properly registered.".format(task_name)
-
- return task
-
-
-__all__ = [
- "BaseTask",
- "ImageTextPretrainTask",
-]
diff --git a/spaces/Voicelab/vlT5-keywords-generation/README.md b/spaces/Voicelab/vlT5-keywords-generation/README.md
deleted file mode 100644
index bb0ec20c0d1e6457305ec098f8f8437c1d529252..0000000000000000000000000000000000000000
--- a/spaces/Voicelab/vlT5-keywords-generation/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: VlT5 Keywords Generation
-emoji: 🌍
-colorFrom: pink
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/utils.py b/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/utils.py
deleted file mode 100644
index ee4b01ddfbe8173965371b29f770f3e87615fe71..0000000000000000000000000000000000000000
--- a/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/utils.py
+++ /dev/null
@@ -1,225 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-import librosa
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict= {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})" .format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10,2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_audio_to_torch(full_path, target_sampling_rate):
- audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
- return torch.FloatTensor(audio.astype(np.float32))
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/XPMaster/KSA_Weather_Prediction/README.md b/spaces/XPMaster/KSA_Weather_Prediction/README.md
deleted file mode 100644
index eb0dad165265158f4093d00781e64bb2dc852b4f..0000000000000000000000000000000000000000
--- a/spaces/XPMaster/KSA_Weather_Prediction/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: KSA Weather Prediction
-emoji: ⚡
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/metrics.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/metrics.py
deleted file mode 100644
index 46fdddf3de2cf8d987ecb4c7d7cb3503afa995ad..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/metrics.py
+++ /dev/null
@@ -1,361 +0,0 @@
-"Implements various metrics to measure training accuracy"
-from .torch_core import *
-from .callback import *
-from .layers import *
-from .basic_train import LearnerCallback
-
-__all__ = ['error_rate', 'accuracy', 'accuracy_thresh', 'dice', 'exp_rmspe', 'fbeta','FBeta', 'mse', 'mean_squared_error',
- 'mae', 'mean_absolute_error', 'rmse', 'root_mean_squared_error', 'msle', 'mean_squared_logarithmic_error',
- 'explained_variance', 'r2_score', 'top_k_accuracy', 'KappaScore', 'ConfusionMatrix', 'MatthewsCorreff',
- 'Precision', 'Recall', 'R2Score', 'ExplainedVariance', 'ExpRMSPE', 'RMSE', 'Perplexity', 'AUROC', 'auc_roc_score',
- 'roc_curve', 'MultiLabelFbeta', 'foreground_acc']
-
-def fbeta(y_pred:Tensor, y_true:Tensor, thresh:float=0.2, beta:float=2, eps:float=1e-9, sigmoid:bool=True)->Rank0Tensor:
- "Computes the f_beta between `preds` and `targets`"
- beta2 = beta ** 2
- if sigmoid: y_pred = y_pred.sigmoid()
- y_pred = (y_pred>thresh).float()
- y_true = y_true.float()
- TP = (y_pred*y_true).sum(dim=1)
- prec = TP/(y_pred.sum(dim=1)+eps)
- rec = TP/(y_true.sum(dim=1)+eps)
- res = (prec*rec)/(prec*beta2+rec+eps)*(1+beta2)
- return res.mean()
-
-def accuracy(input:Tensor, targs:Tensor)->Rank0Tensor:
- "Computes accuracy with `targs` when `input` is bs * n_classes."
- n = targs.shape[0]
- input = input.argmax(dim=-1).view(n,-1)
- targs = targs.view(n,-1)
- return (input==targs).float().mean()
-
-def accuracy_thresh(y_pred:Tensor, y_true:Tensor, thresh:float=0.5, sigmoid:bool=True)->Rank0Tensor:
- "Computes accuracy when `y_pred` and `y_true` are the same size."
- if sigmoid: y_pred = y_pred.sigmoid()
- return ((y_pred>thresh)==y_true.byte()).float().mean()
-
-def top_k_accuracy(input:Tensor, targs:Tensor, k:int=5)->Rank0Tensor:
- "Computes the Top-k accuracy (target is in the top k predictions)."
- input = input.topk(k=k, dim=-1)[1]
- targs = targs.unsqueeze(dim=-1).expand_as(input)
- return (input == targs).max(dim=-1)[0].float().mean()
-
-def foreground_acc(input, target, void_code):
- "Computes non-background accuracy, e.g. camvid for multiclass segmentation"
- target = target.squeeze(1)
- mask = target != void_code
- return (input.argmax(dim=1)[mask]==target[mask]).float().mean()
-
-def error_rate(input:Tensor, targs:Tensor)->Rank0Tensor:
- "1 - `accuracy`"
- return 1 - accuracy(input, targs)
-
-def dice(input:Tensor, targs:Tensor, iou:bool=False, eps:float=1e-8)->Rank0Tensor:
- "Dice coefficient metric for binary target. If iou=True, returns iou metric, classic for segmentation problems."
- n = targs.shape[0]
- input = input.argmax(dim=1).view(n,-1)
- targs = targs.view(n,-1)
- intersect = (input * targs).sum().float()
- union = (input+targs).sum().float()
- if not iou: return (2. * intersect / union if union > 0 else union.new([1.]).squeeze())
- else: return (intersect / (union-intersect+eps) if union > 0 else union.new([1.]).squeeze())
-
-def psnr(input:Tensor, targs:Tensor)->Rank0Tensor:
- return 10 * (1. / mean_squared_error(input, targs)).log10()
-
-def exp_rmspe(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Exp RMSE between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- pred, targ = torch.exp(pred), torch.exp(targ)
- pct_var = (targ - pred)/targ
- return torch.sqrt((pct_var**2).mean())
-
-def mean_absolute_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean absolute error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return torch.abs(targ - pred).mean()
-
-def mean_squared_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean squared error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return F.mse_loss(pred, targ)
-
-def root_mean_squared_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Root mean squared error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return torch.sqrt(F.mse_loss(pred, targ))
-
-def mean_squared_logarithmic_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean squared logarithmic error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return F.mse_loss(torch.log(1 + pred), torch.log(1 + targ))
-
-def explained_variance(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Explained variance between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- var_pct = torch.var(targ - pred) / torch.var(targ)
- return 1 - var_pct
-
-def r2_score(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "R2 score (coefficient of determination) between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- u = torch.sum((targ - pred) ** 2)
- d = torch.sum((targ - targ.mean()) ** 2)
- return 1 - u / d
-
-class RegMetrics(Callback):
- "Stores predictions and targets to perform calculations on epoch end."
- def on_epoch_begin(self, **kwargs):
- self.targs, self.preds = Tensor([]), Tensor([])
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- assert last_output.numel() == last_target.numel(), "Expected same numbers of elements in pred & targ"
- self.preds = torch.cat((self.preds, last_output.cpu()))
- self.targs = torch.cat((self.targs, last_target.cpu()))
-
-class R2Score(RegMetrics):
- "Computes the R2 score (coefficient of determination)."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, r2_score(self.preds, self.targs))
-
-class ExplainedVariance(RegMetrics):
- "Computes the explained variance."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, explained_variance(self.preds, self.targs))
-
-class RMSE(RegMetrics):
- "Computes the root mean squared error."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, root_mean_squared_error(self.preds, self.targs))
-
-class ExpRMSPE(RegMetrics):
- "Computes the exponential of the root mean square error."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, exp_rmspe(self.preds, self.targs))
-
-# Aliases
-mse = mean_squared_error
-mae = mean_absolute_error
-msle = mean_squared_logarithmic_error
-rmse = root_mean_squared_error
-
-class ConfusionMatrix(Callback):
- "Computes the confusion matrix."
-
- def on_train_begin(self, **kwargs):
- self.n_classes = 0
-
- def on_epoch_begin(self, **kwargs):
- self.cm = None
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- preds = last_output.argmax(-1).view(-1).cpu()
- targs = last_target.cpu()
- if self.n_classes == 0:
- self.n_classes = last_output.shape[-1]
- self.x = torch.arange(0, self.n_classes)
- cm = ((preds==self.x[:, None]) & (targs==self.x[:, None, None])).sum(dim=2, dtype=torch.float32)
- if self.cm is None: self.cm = cm
- else: self.cm += cm
-
- def on_epoch_end(self, **kwargs):
- self.metric = self.cm
-
-@dataclass
-class CMScores(ConfusionMatrix):
- "Base class for metrics which rely on the calculation of the precision and/or recall score."
- average:Optional[str]="binary" # `binary`, `micro`, `macro`, `weigthed` or None
- pos_label:int=1 # 0 or 1
- eps:float=1e-9
-
- def _recall(self):
- rec = torch.diag(self.cm) / self.cm.sum(dim=1)
- if self.average is None: return rec
- else:
- if self.average == "micro": weights = self._weights(avg="weighted")
- else: weights = self._weights(avg=self.average)
- return (rec * weights).sum()
-
- def _precision(self):
- prec = torch.diag(self.cm) / self.cm.sum(dim=0)
- if self.average is None: return prec
- else:
- weights = self._weights(avg=self.average)
- return (prec * weights).sum()
-
- def _weights(self, avg:str):
- if self.n_classes != 2 and avg == "binary":
- avg = self.average = "macro"
- warn("average=`binary` was selected for a non binary case. Value for average has now been set to `macro` instead.")
- if avg == "binary":
- if self.pos_label not in (0, 1):
- self.pos_label = 1
- warn("Invalid value for pos_label. It has now been set to 1.")
- if self.pos_label == 1: return Tensor([0,1])
- else: return Tensor([1,0])
- elif avg == "micro": return self.cm.sum(dim=0) / self.cm.sum()
- elif avg == "macro": return torch.ones((self.n_classes,)) / self.n_classes
- elif avg == "weighted": return self.cm.sum(dim=1) / self.cm.sum()
-
-
-class Recall(CMScores):
- "Computes the Recall."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, self._recall())
-
-class Precision(CMScores):
- "Computes the Precision."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, self._precision())
-
-@dataclass
-class FBeta(CMScores):
- "Computes the F`beta` score."
- beta:float=2
-
- def on_train_begin(self, **kwargs):
- self.n_classes = 0
- self.beta2 = self.beta ** 2
- self.avg = self.average
- if self.average != "micro": self.average = None
-
- def on_epoch_end(self, last_metrics, **kwargs):
- prec = self._precision()
- rec = self._recall()
- metric = (1 + self.beta2) * prec * rec / (prec * self.beta2 + rec + self.eps)
- metric[metric != metric] = 0 # removing potential "nan"s
- if self.avg: metric = (self._weights(avg=self.avg) * metric).sum()
- return add_metrics(last_metrics, metric)
-
- def on_train_end(self, **kwargs): self.average = self.avg
-
-@dataclass
-class KappaScore(ConfusionMatrix):
- "Computes the rate of agreement (Cohens Kappa)."
- weights:Optional[str]=None # None, `linear`, or `quadratic`
-
- def on_epoch_end(self, last_metrics, **kwargs):
- sum0 = self.cm.sum(dim=0)
- sum1 = self.cm.sum(dim=1)
- expected = torch.einsum('i,j->ij', (sum0, sum1)) / sum0.sum()
- if self.weights is None:
- w = torch.ones((self.n_classes, self.n_classes))
- w[self.x, self.x] = 0
- elif self.weights == "linear" or self.weights == "quadratic":
- w = torch.zeros((self.n_classes, self.n_classes))
- w += torch.arange(self.n_classes, dtype=torch.float)
- w = torch.abs(w - torch.t(w)) if self.weights == "linear" else (w - torch.t(w)) ** 2
- else: raise ValueError('Unknown weights. Expected None, "linear", or "quadratic".')
- k = torch.sum(w * self.cm) / torch.sum(w * expected)
- return add_metrics(last_metrics, 1-k)
-
-@dataclass
-class MatthewsCorreff(ConfusionMatrix):
- "Computes the Matthews correlation coefficient."
- def on_epoch_end(self, last_metrics, **kwargs):
- t_sum = self.cm.sum(dim=1)
- p_sum = self.cm.sum(dim=0)
- n_correct = torch.trace(self.cm)
- n_samples = p_sum.sum()
- cov_ytyp = n_correct * n_samples - torch.dot(t_sum, p_sum)
- cov_ypyp = n_samples ** 2 - torch.dot(p_sum, p_sum)
- cov_ytyt = n_samples ** 2 - torch.dot(t_sum, t_sum)
- return add_metrics(last_metrics, cov_ytyp / torch.sqrt(cov_ytyt * cov_ypyp))
-
-class Perplexity(Callback):
- "Perplexity metric for language models."
- def on_epoch_begin(self, **kwargs): self.loss,self.len = 0.,0
-
- def on_batch_end(self, last_output, last_target, **kwargs):
- self.loss += last_target.size(1) * CrossEntropyFlat()(last_output, last_target)
- self.len += last_target.size(1)
-
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, torch.exp(self.loss / self.len))
-
-def auc_roc_score(input:Tensor, targ:Tensor):
- "Computes the area under the receiver operator characteristic (ROC) curve using the trapezoid method. Restricted binary classification tasks."
- fpr, tpr = roc_curve(input, targ)
- d = fpr[1:] - fpr[:-1]
- sl1, sl2 = [slice(None)], [slice(None)]
- sl1[-1], sl2[-1] = slice(1, None), slice(None, -1)
- return (d * (tpr[tuple(sl1)] + tpr[tuple(sl2)]) / 2.).sum(-1)
-
-def roc_curve(input:Tensor, targ:Tensor):
- "Computes the receiver operator characteristic (ROC) curve by determining the true positive ratio (TPR) and false positive ratio (FPR) for various classification thresholds. Restricted binary classification tasks."
- targ = (targ == 1)
- desc_score_indices = torch.flip(input.argsort(-1), [-1])
- input = input[desc_score_indices]
- targ = targ[desc_score_indices]
- d = input[1:] - input[:-1]
- distinct_value_indices = torch.nonzero(d).transpose(0,1)[0]
- threshold_idxs = torch.cat((distinct_value_indices, LongTensor([len(targ) - 1]).to(targ.device)))
- tps = torch.cumsum(targ * 1, dim=-1)[threshold_idxs]
- fps = (1 + threshold_idxs - tps)
- if tps[0] != 0 or fps[0] != 0:
- fps = torch.cat((LongTensor([0]), fps))
- tps = torch.cat((LongTensor([0]), tps))
- fpr, tpr = fps.float() / fps[-1], tps.float() / tps[-1]
- return fpr, tpr
-
-@dataclass
-class AUROC(Callback):
- "Computes the area under the curve (AUC) score based on the receiver operator characteristic (ROC) curve. Restricted to binary classification tasks."
- def on_epoch_begin(self, **kwargs):
- self.targs, self.preds = LongTensor([]), Tensor([])
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- last_output = F.softmax(last_output, dim=1)[:,-1]
- self.preds = torch.cat((self.preds, last_output.cpu()))
- self.targs = torch.cat((self.targs, last_target.cpu().long()))
-
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, auc_roc_score(self.preds, self.targs))
-
-class MultiLabelFbeta(LearnerCallback):
- "Computes the fbeta score for multilabel classification"
- # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html
- _order = -20
- def __init__(self, learn, beta=2, eps=1e-15, thresh=0.3, sigmoid=True, average="micro"):
- super().__init__(learn)
- self.eps, self.thresh, self.sigmoid, self.average, self.beta2 = \
- eps, thresh, sigmoid, average, beta**2
-
- def on_train_begin(self, **kwargs):
- self.c = self.learn.data.c
- if self.average != "none": self.learn.recorder.add_metric_names([f'{self.average}_fbeta'])
- else: self.learn.recorder.add_metric_names([f"fbeta_{c}" for c in self.learn.data.classes])
-
- def on_epoch_begin(self, **kwargs):
- dvc = self.learn.data.device
- self.tp = torch.zeros(self.c).to(dvc)
- self.total_pred = torch.zeros(self.c).to(dvc)
- self.total_targ = torch.zeros(self.c).to(dvc)
-
- def on_batch_end(self, last_output, last_target, **kwargs):
- pred, targ = (last_output.sigmoid() if self.sigmoid else last_output) > self.thresh, last_target.byte()
- m = pred*targ
- self.tp += m.sum(0).float()
- self.total_pred += pred.sum(0).float()
- self.total_targ += targ.sum(0).float()
-
- def fbeta_score(self, precision, recall):
- return (1 + self.beta2)*(precision*recall)/((self.beta2*precision + recall) + self.eps)
-
- def on_epoch_end(self, last_metrics, **kwargs):
- self.total_pred += self.eps
- self.total_targ += self.eps
- if self.average == "micro":
- precision, recall = self.tp.sum() / self.total_pred.sum(), self.tp.sum() / self.total_targ.sum()
- res = self.fbeta_score(precision, recall)
- elif self.average == "macro":
- res = self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ)).mean()
- elif self.average == "weighted":
- scores = self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ))
- res = (scores*self.total_targ).sum() / self.total_targ.sum()
- elif self.average == "none":
- res = listify(self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ)))
- else:
- raise Exception("Choose one of the average types: [micro, macro, weighted, none]")
-
- return add_metrics(last_metrics, res)
diff --git a/spaces/XzJosh/Taffy-Bert-VITS2/transforms.py b/spaces/XzJosh/Taffy-Bert-VITS2/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Taffy-Bert-VITS2/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
-
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {
- 'tails': tails,
- 'tail_bound': tail_bound
- }
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(
- inputs[..., None] >= bin_locations,
- dim=-1
- ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails='linear',
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == 'linear':
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError('{} tails are not implemented.'.format(tails))
-
- outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative
- )
-
- return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0., right=1., bottom=0., top=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError('Input to a transform is not within its domain')
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError('Minimal bin width too large for the number of bins')
- if min_bin_height * num_bins > 1.0:
- raise ValueError('Minimal bin height too large for the number of bins')
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (((inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta)
- + input_heights * (input_delta - input_derivatives)))
- b = (input_heights * input_derivatives
- - (inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta))
- c = - input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (input_delta * theta.pow(2)
- + input_derivatives * theta_one_minus_theta)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/XzJosh/Wenjing-Bert-VITS2/server.py b/spaces/XzJosh/Wenjing-Bert-VITS2/server.py
deleted file mode 100644
index c736ca4f95fec853950eef6654ef79856beffc0a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Wenjing-Bert-VITS2/server.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from flask import Flask, request, Response
-from io import BytesIO
-import torch
-from av import open as avopen
-
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-from scipy.io import wavfile
-
-# Flask Init
-app = Flask(__name__)
-app.config['JSON_AS_ASCII'] = False
-def get_text(text, language_str, hps):
- norm_text, phone, tone, word2ph = clean_text(text, language_str)
- print([f"{p}{t}" for p, t in zip(phone, tone)])
- phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
- if hps.data.add_blank:
- phone = commons.intersperse(phone, 0)
- tone = commons.intersperse(tone, 0)
- language = commons.intersperse(language, 0)
- for i in range(len(word2ph)):
- word2ph[i] = word2ph[i] * 2
- word2ph[0] += 1
- bert = get_bert(norm_text, word2ph, language_str)
-
- assert bert.shape[-1] == len(phone)
-
- phone = torch.LongTensor(phone)
- tone = torch.LongTensor(tone)
- language = torch.LongTensor(language)
-
- return bert, phone, tone, language
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w,length_scale,sid):
- bert, phones, tones, lang_ids = get_text(text,"ZH", hps,)
- with torch.no_grad():
- x_tst=phones.to(dev).unsqueeze(0)
- tones=tones.to(dev).unsqueeze(0)
- lang_ids=lang_ids.to(dev).unsqueeze(0)
- bert = bert.to(dev).unsqueeze(0)
- x_tst_lengths = torch.LongTensor([phones.size(0)]).to(dev)
- speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(dev)
- audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids,bert, sdp_ratio=sdp_ratio
- , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
- return audio
-
-def replace_punctuation(text, i=2):
- punctuation = "，。？！"
- for char in punctuation:
- text = text.replace(char, char * i)
- return text
-
-def wav2(i, o, format):
- inp = avopen(i, 'rb')
- out = avopen(o, 'wb', format=format)
- if format == "ogg": format = "libvorbis"
-
- ostream = out.add_stream(format)
-
- for frame in inp.decode(audio=0):
- for p in ostream.encode(frame): out.mux(p)
-
- for p in ostream.encode(None): out.mux(p)
-
- out.close()
- inp.close()
-
-# Load Generator
-hps = utils.get_hparams_from_file("./configs/config.json")
-
-dev='cuda'
-net_g = SynthesizerTrn(
- len(symbols),
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- n_speakers=hps.data.n_speakers,
- **hps.model).to(dev)
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("logs/G_649000.pth", net_g, None,skip_optimizer=True)
-
-@app.route("/",methods=['GET','POST'])
-def main():
- if request.method == 'GET':
- try:
- speaker = request.args.get('speaker')
- text = request.args.get('text').replace("/n","")
- sdp_ratio = float(request.args.get("sdp_ratio", 0.2))
- noise = float(request.args.get("noise", 0.5))
- noisew = float(request.args.get("noisew", 0.6))
- length = float(request.args.get("length", 1.2))
- if length >= 2:
- return "Too big length"
- if len(text) >=200:
- return "Too long text"
- fmt = request.args.get("format", "wav")
- if None in (speaker, text):
- return "Missing Parameter"
- if fmt not in ("mp3", "wav", "ogg"):
- return "Invalid Format"
- except:
- return "Invalid Parameter"
-
- with torch.no_grad():
- audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise, noise_scale_w=noisew, length_scale=length, sid=speaker)
-
- with BytesIO() as wav:
- wavfile.write(wav, hps.data.sampling_rate, audio)
- torch.cuda.empty_cache()
- if fmt == "wav":
- return Response(wav.getvalue(), mimetype="audio/wav")
- wav.seek(0, 0)
- with BytesIO() as ofp:
- wav2(wav, ofp, fmt)
- return Response(
- ofp.getvalue(),
- mimetype="audio/mpeg" if fmt == "mp3" else "audio/ogg"
- )
diff --git a/spaces/YlcldKlns/bing/src/components/settings.tsx b/spaces/YlcldKlns/bing/src/components/settings.tsx
deleted file mode 100644
index 80b8a2d3b252b875f5b6f7dfc2f6e3ad9cdfb22a..0000000000000000000000000000000000000000
--- a/spaces/YlcldKlns/bing/src/components/settings.tsx
+++ /dev/null
@@ -1,157 +0,0 @@
-import { useEffect, useState } from 'react'
-import { useAtom } from 'jotai'
-import { Switch } from '@headlessui/react'
-import { toast } from 'react-hot-toast'
-import { hashAtom, voiceAtom } from '@/state'
-import {
- Dialog,
- DialogContent,
- DialogDescription,
- DialogFooter,
- DialogHeader,
- DialogTitle
-} from '@/components/ui/dialog'
-import { Button } from './ui/button'
-import { Input } from './ui/input'
-import { ChunkKeys, parseCookies, extraCurlFromCookie, encodeHeadersToCookie, getCookie, setCookie } from '@/lib/utils'
-import { ExternalLink } from './external-link'
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-
-
-export function Settings() {
- const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
- const [loc, setLoc] = useAtom(hashAtom)
- const [curlValue, setCurlValue] = useState(extraCurlFromCookie(parseCookies(document.cookie, ChunkKeys)))
- const [imageOnly, setImageOnly] = useState(getCookie('IMAGE_ONLY') !== '0')
- const [enableTTS, setEnableTTS] = useAtom(voiceAtom)
-
- useEffect(() => {
- if (isCopied) {
- toast.success('复制成功')
- }
- }, [isCopied])
-
- if (loc === 'settings') {
- return (
-
- )
- } else if (loc === 'voice') {
- return (
-
- )
- }
- return null
-}
diff --git a/spaces/Yudha515/Rvc-Models/audiocraft/models/lm.py b/spaces/Yudha515/Rvc-Models/audiocraft/models/lm.py
deleted file mode 100644
index c8aad8f06797eef3293605056e1de14d07c56c2a..0000000000000000000000000000000000000000
--- a/spaces/Yudha515/Rvc-Models/audiocraft/models/lm.py
+++ /dev/null
@@ -1,527 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from dataclasses import dataclass
-from functools import partial
-import logging
-import math
-import typing as tp
-
-import torch
-from torch import nn
-
-from ..utils import utils
-from ..modules.streaming import StreamingModule, State
-from ..modules.transformer import StreamingTransformer, create_norm_fn
-from ..modules.conditioners import (
- ConditionFuser,
- ClassifierFreeGuidanceDropout,
- AttributeDropout,
- ConditioningProvider,
- ConditioningAttributes,
- ConditionType,
-)
-from ..modules.codebooks_patterns import CodebooksPatternProvider
-from ..modules.activations import get_activation_fn
-
-
-logger = logging.getLogger(__name__)
-ConditionTensors = tp.Dict[str, ConditionType]
-CFGConditions = tp.Union[ConditionTensors, tp.Tuple[ConditionTensors, ConditionTensors]]
-
-
-def get_init_fn(method: str, input_dim: int, init_depth: tp.Optional[int] = None):
- """LM layer initialization.
- Inspired from xlformers: https://github.com/fairinternal/xlformers
-
- Args:
- method (str): Method name for init function. Valid options are:
- 'gaussian', 'uniform'.
- input_dim (int): Input dimension of the initialized module.
- init_depth (Optional[int]): Optional init depth value used to rescale
- the standard deviation if defined.
- """
- # Compute std
- std = 1 / math.sqrt(input_dim)
- # Rescale with depth
- if init_depth is not None:
- std = std / math.sqrt(2 * init_depth)
-
- if method == 'gaussian':
- return partial(
- torch.nn.init.trunc_normal_, mean=0.0, std=std, a=-3 * std, b=3 * std
- )
- elif method == 'uniform':
- bound = math.sqrt(3) * std # ensure the standard deviation is `std`
- return partial(torch.nn.init.uniform_, a=-bound, b=bound)
- else:
- raise ValueError("Unsupported layer initialization method")
-
-
-def init_layer(m: nn.Module,
- method: str,
- init_depth: tp.Optional[int] = None,
- zero_bias_init: bool = False):
- """Wrapper around ``get_init_fn`` for proper initialization of LM modules.
-
- Args:
- m (nn.Module): Module to initialize.
- method (str): Method name for the init function.
- init_depth (Optional[int]): Optional init depth value used to rescale
- the standard deviation if defined.
- zero_bias_init (bool): Whether to initialize the bias to 0 or not.
- """
- if isinstance(m, nn.Linear):
- init_fn = get_init_fn(method, m.in_features, init_depth=init_depth)
- if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
- weight = m.weight.float()
- init_fn(weight)
- m.weight.data[:] = weight.half()
- else:
- init_fn(m.weight)
- if zero_bias_init and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.Embedding):
- init_fn = get_init_fn(method, m.embedding_dim, init_depth=None)
- if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
- weight = m.weight.float()
- init_fn(weight)
- m.weight.data[:] = weight.half()
- else:
- init_fn(m.weight)
-
-
-class ScaledEmbedding(nn.Embedding):
- """Boost learning rate for embeddings (with `scale`).
- """
- def __init__(self, *args, lr=None, **kwargs):
- super().__init__(*args, **kwargs)
- self.lr = lr
-
- def make_optim_group(self):
- group = {"params": list(self.parameters())}
- if self.lr is not None:
- group["lr"] = self.lr
- return group
-
-
-@dataclass
-class LMOutput:
- # The logits are already re-aligned with the input codes
- # hence no extra shift is required, e.g. when computing CE
- logits: torch.Tensor # [B, K, T, card]
- mask: torch.Tensor # [B, K, T]
-
-
-class LMModel(StreamingModule):
- """Transformer-based language model on multiple streams of codes.
-
- Args:
- pattern_provider (CodebooksPatternProvider): Pattern provider for codebook interleaving.
- condition_provider (MusicConditioningProvider): Conditioning provider from metadata.
- fuser (ConditionFuser): Fuser handling the fusing of conditions with language model input.
- n_q (int): Number of parallel streams to model.
- card (int): Cardinality, vocabulary size.
- dim (int): Dimension of the transformer encoder.
- num_heads (int): Number of heads for the transformer encoder.
- hidden_scale (int): Scale for hidden feed forward dimension of the transformer encoder.
- norm (str): Normalization method.
- norm_first (bool): Use pre-norm instead of post-norm.
- emb_lr (Optional[float]): Embedding-specific learning rate.
- bias_proj (bool): Use bias for output projections.
- weight_init (Optional[str]): Method for weight initialization.
- depthwise_init (Optional[str]): Method for depthwise weight initialization.
- zero_bias_init (bool): If true and bias in Linears, initialize bias to zeros.
- cfg_dropout (float): Classifier-free guidance dropout.
- cfg_coef (float): Classifier-free guidance coefficient.
- attribute_dropout (dict): Attribute dropout probabilities.
- two_step_cfg (bool): Whether to run classifier free-guidance with 2 distinct steps.
- **kwargs: Additional parameters for the transformer encoder.
- """
- def __init__(self, pattern_provider: CodebooksPatternProvider, condition_provider: ConditioningProvider,
- fuser: ConditionFuser, n_q: int = 8, card: int = 1024, dim: int = 128, num_heads: int = 8,
- hidden_scale: int = 4, norm: str = 'layer_norm', norm_first: bool = False,
- emb_lr: tp.Optional[float] = None, bias_proj: bool = True,
- weight_init: tp.Optional[str] = None, depthwise_init: tp.Optional[str] = None,
- zero_bias_init: bool = False, cfg_dropout: float = 0, cfg_coef: float = 1.0,
- attribute_dropout: tp.Dict[str, tp.Dict[str, float]] = {}, two_step_cfg: bool = False,
- **kwargs):
- super().__init__()
- self.cfg_coef = cfg_coef
- self.cfg_dropout = ClassifierFreeGuidanceDropout(p=cfg_dropout)
- self.att_dropout = AttributeDropout(p=attribute_dropout)
- self.condition_provider = condition_provider
- self.fuser = fuser
- self.card = card
- embed_dim = self.card + 1
- self.n_q = n_q
- self.dim = dim
- self.pattern_provider = pattern_provider
- self.two_step_cfg = two_step_cfg
- self.emb = nn.ModuleList([ScaledEmbedding(embed_dim, dim, lr=emb_lr) for _ in range(n_q)])
- if 'activation' in kwargs:
- kwargs['activation'] = get_activation_fn(kwargs['activation'])
- self.transformer = StreamingTransformer(
- d_model=dim, num_heads=num_heads, dim_feedforward=int(hidden_scale * dim),
- norm=norm, norm_first=norm_first, **kwargs)
- self.out_norm: tp.Optional[nn.Module] = None
- if norm_first:
- self.out_norm = create_norm_fn(norm, dim)
- self.linears = nn.ModuleList([nn.Linear(dim, self.card, bias=bias_proj) for _ in range(n_q)])
- self._init_weights(weight_init, depthwise_init, zero_bias_init)
- self._fsdp: tp.Optional[nn.Module]
- self.__dict__['_fsdp'] = None
-
- def _init_weights(self, weight_init: tp.Optional[str], depthwise_init: tp.Optional[str], zero_bias_init: bool):
- """Initialization of the transformer module weights.
-
- Args:
- weight_init (Optional[str]): Weight initialization strategy. See ``get_init_fn`` for valid options.
- depthwise_init (Optional[str]): Depwthwise initialization strategy. The following options are valid:
- 'current' where the depth corresponds to the current layer index or 'global' where the total number
- of layer is used as depth. If not set, no depthwise initialization strategy is used.
- zero_bias_init (bool): Whether to initalize bias to zero or not.
- """
- assert depthwise_init is None or depthwise_init in ['current', 'global']
- assert depthwise_init is None or weight_init is not None, \
- "If 'depthwise_init' is defined, a 'weight_init' method should be provided."
- assert not zero_bias_init or weight_init is not None, \
- "If 'zero_bias_init', a 'weight_init' method should be provided"
-
- if weight_init is None:
- return
-
- for emb_layer in self.emb:
- init_layer(emb_layer, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
- for layer_idx, tr_layer in enumerate(self.transformer.layers):
- depth = None
- if depthwise_init == 'current':
- depth = layer_idx + 1
- elif depthwise_init == 'global':
- depth = len(self.transformer.layers)
- init_fn = partial(init_layer, method=weight_init, init_depth=depth, zero_bias_init=zero_bias_init)
- tr_layer.apply(init_fn)
-
- for linear in self.linears:
- init_layer(linear, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
- @property
- def special_token_id(self) -> int:
- return self.card
-
- @property
- def num_codebooks(self) -> int:
- return self.n_q
-
- def forward(self, sequence: torch.Tensor,
- conditions: tp.List[ConditioningAttributes],
- condition_tensors: tp.Optional[ConditionTensors] = None) -> torch.Tensor:
- """Apply language model on sequence and conditions.
- Given a tensor of sequence of shape [B, K, S] with K the number of codebooks and
- S the sequence steps, return the logits with shape [B, card, K, S].
-
- Args:
- indices (torch.Tensor): indices of the codes to model.
- conditions (list[ConditioningAttributes]): conditionings to use when modeling
- the given codes. Note that when evaluating multiple time with the same conditioning
- you should pre-compute those and pass them as `condition_tensors`.
- condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning
- tensors, see `conditions`.
- Returns:
- torch.Tensor: Logits.
- """
- B, K, S = sequence.shape
- assert K == self.num_codebooks, 'Sequence shape must match the specified number of codebooks'
- input_ = sum([self.emb[k](sequence[:, k]) for k in range(K)])
- if condition_tensors is None:
- assert not self._is_streaming, "Conditions tensors should be precomputed when streaming."
- # apply dropout modules
- conditions = self.cfg_dropout(conditions)
- conditions = self.att_dropout(conditions)
- tokenized = self.condition_provider.tokenize(conditions)
- # encode conditions and fuse, both have a streaming cache to not recompute when generating.
- condition_tensors = self.condition_provider(tokenized)
- else:
- assert not conditions, "Shouldn't pass both conditions and condition_tensors."
-
- input_, cross_attention_input = self.fuser(input_, condition_tensors)
-
- out = self.transformer(input_, cross_attention_src=cross_attention_input)
- if self.out_norm:
- out = self.out_norm(out)
- logits = torch.stack([self.linears[k](out) for k in range(K)], dim=1) # [B, K, S, card]
-
- # remove the prefix from the model outputs
- if len(self.fuser.fuse2cond['prepend']) > 0:
- logits = logits[:, :, -S:]
-
- return logits # [B, K, S, card]
-
- def compute_predictions(
- self, codes: torch.Tensor,
- conditions: tp.List[ConditioningAttributes],
- condition_tensors: tp.Optional[ConditionTensors] = None) -> LMOutput:
- """Given an input tensor of codes [B, K, T] and list of conditions, runs the model
- forward using the specified codes interleaving pattern.
-
- Args:
- codes (torch.Tensor): Input codes of shape [B, K, T] with B the batch size,
- K the number of codebooks and T the number of timesteps.
- conditions (list[ConditioningAttributes]): conditionings to use when modeling
- the given codes. Note that when evaluating multiple time with the same conditioning
- you should pre-compute those and pass them as `condition_tensors`.
- condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning
- tensors, see `conditions`.
- Returns:
- LMOutput: Language model outputs
- logits (torch.Tensor) of shape [B, K, T, card] corresponding to the provided codes,
- i.e. the first item corresponds to logits to predict the first code, meaning that
- no additional shifting of codes and logits is required.
- mask (torch.Tensor) of shape [B, K, T], mask over valid and invalid positions.
- Given the specified interleaving strategies, parts of the logits and codes should
- not be considered as valid predictions because of invalid context.
- """
- B, K, T = codes.shape
- codes = codes.contiguous()
- # map codes [B, K, T] into pattern sequence [B, K, S] using special_token_id for masked tokens
- pattern = self.pattern_provider.get_pattern(T)
- sequence_codes, sequence_indexes, sequence_mask = pattern.build_pattern_sequence(
- codes, self.special_token_id, keep_only_valid_steps=True
- )
- # apply model on pattern sequence
- model = self if self._fsdp is None else self._fsdp
- logits = model(sequence_codes, conditions, condition_tensors) # [B, K, S, card]
- # map back the logits on pattern sequence to logits on original codes: [B, K, S, card] -> [B, K, T, card]
- # and provide the corresponding mask over invalid positions of tokens
- logits = logits.permute(0, 3, 1, 2) # [B, card, K, S]
- # note: we use nans as special token to make it obvious if we feed unexpected logits
- logits, logits_indexes, logits_mask = pattern.revert_pattern_logits(
- logits, float('nan'), keep_only_valid_steps=True
- )
- logits = logits.permute(0, 2, 3, 1) # [B, K, T, card]
- logits_mask = logits_mask[None, :, :].expand(B, -1, -1) # [K, T] -> [B, K, T]
- return LMOutput(logits, logits_mask)
-
- def _sample_next_token(self,
- sequence: torch.Tensor,
- cfg_conditions: CFGConditions,
- unconditional_state: State,
- use_sampling: bool = False,
- temp: float = 1.0,
- top_k: int = 0,
- top_p: float = 0.0,
- cfg_coef: tp.Optional[float] = None) -> torch.Tensor:
- """Sample next token from the model given a sequence and a set of conditions. The model supports
- multiple sampling strategies (greedy sampling, softmax, top-k, top-p...).
-
- Args:
- sequence (torch.Tensor): Current sequence of shape [B, K, S]
- with K corresponding to the number of codebooks and S the number of sequence steps.
- S = 1 in streaming mode, except for the first step that contains a bigger prompt.
- condition_tensors (Dict[str, ConditionType): Set of conditions. If CFG is used,
- should be twice the batch size, being the concatenation of the conditions + null conditions.
- use_sampling (bool): Whether to use a sampling strategy or not.
- temp (float): Sampling temperature.
- top_k (int): K for "top-k" sampling.
- top_p (float): P for "top-p" sampling.
- cfg_coef (float): classifier free guidance coefficient
- Returns:
- next_token (torch.Tensor): Next token tensor of shape [B, K, 1].
- """
- B = sequence.shape[0]
- cfg_coef = self.cfg_coef if cfg_coef is None else cfg_coef
- model = self if self._fsdp is None else self._fsdp
- if self.two_step_cfg and cfg_conditions != {}:
- assert isinstance(cfg_conditions, tuple)
- condition_tensors, null_condition_tensors = cfg_conditions
- cond_logits = model(sequence, conditions=[], condition_tensors=condition_tensors)
- state = self.get_streaming_state()
- self.set_streaming_state(unconditional_state)
- uncond_logits = model(sequence, conditions=[], condition_tensors=null_condition_tensors)
- unconditional_state.update(self.get_streaming_state())
- self.set_streaming_state(state)
- logits = uncond_logits + (cond_logits - uncond_logits) * self.cfg_coef
- else:
- assert isinstance(cfg_conditions, dict)
- condition_tensors = cfg_conditions
- if condition_tensors:
- # Preparing for CFG, predicting both conditional and unconditional logits.
- sequence = torch.cat([sequence, sequence], dim=0)
- all_logits = model(
- sequence,
- conditions=[], condition_tensors=condition_tensors)
- if condition_tensors:
- cond_logits, uncond_logits = all_logits.split(B, dim=0) # [B, K, T, card]
- logits = uncond_logits + (cond_logits - uncond_logits) * cfg_coef
- else:
- logits = all_logits
-
- logits = logits.permute(0, 1, 3, 2) # [B, K, card, T]
- logits = logits[..., -1] # [B x K x card]
-
- # Apply softmax for sampling if temp > 0. Else, do greedy sampling to avoid zero division error.
- if use_sampling and temp > 0.0:
- probs = torch.softmax(logits / temp, dim=-1)
- if top_p > 0.0:
- next_token = utils.sample_top_p(probs, p=top_p)
- elif top_k > 0:
- next_token = utils.sample_top_k(probs, k=top_k)
- else:
- next_token = utils.multinomial(probs, num_samples=1)
- else:
- next_token = torch.argmax(logits, dim=-1, keepdim=True)
-
- return next_token
-
- @torch.no_grad()
- def generate(self,
- prompt: tp.Optional[torch.Tensor] = None,
- conditions: tp.List[ConditioningAttributes] = [],
- num_samples: tp.Optional[int] = None,
- max_gen_len: int = 256,
- use_sampling: bool = True,
- temp: float = 1.0,
- top_k: int = 250,
- top_p: float = 0.0,
- cfg_coef: tp.Optional[float] = None,
- two_step_cfg: bool = False,
- remove_prompts: bool = False,
- check: bool = False,
- callback: tp.Optional[tp.Callable[[int, int], None]] = None) -> torch.Tensor:
- """Generate tokens sampling from the model given a prompt or unconditionally. Generation can
- be perform in a greedy fashion or using sampling with top K and top P strategies.
-
- Args:
- prompt (Optional[torch.Tensor]): Prompt tokens of shape [B, K, T].
- conditions_tensors (Dict[str, torch.Tensor]): Set of conditions or None.
- num_samples (int or None): Number of samples to generate when no prompt and no conditions are given.
- max_gen_len (int): Maximum generation length.
- use_sampling (bool): Whether to use a sampling strategy or not.
- temp (float): Sampling temperature.
- top_k (int): K for "top-k" sampling.
- top_p (float): P for "top-p" sampling.
- remove_prompts (bool): Whether to remove prompts from generation or not.
- Returns:
- torch.Tensor: Generated tokens.
- """
- assert not self.training, "generation shouldn't be used in training mode."
- first_param = next(iter(self.parameters()))
- device = first_param.device
-
- # Checking all input shapes are consistents.
- possible_num_samples = []
- if num_samples is not None:
- possible_num_samples.append(num_samples)
- elif prompt is not None:
- possible_num_samples.append(prompt.shape[0])
- elif conditions:
- possible_num_samples.append(len(conditions))
- else:
- possible_num_samples.append(1)
- assert [x == possible_num_samples[0] for x in possible_num_samples], "Inconsitent inputs shapes"
- num_samples = possible_num_samples[0]
-
- # below we create set of conditions: one conditional and one unconditional
- # to do that we merge the regular condition together with the null condition
- # we then do 1 forward pass instead of 2.
- # the reason for that is two-fold:
- # 1. it is about x2 faster than doing 2 forward passes
- # 2. avoid the streaming API treating the 2 passes as part of different time steps
- # We also support doing two different passes, in particular to ensure that
- # the padding structure is exactly the same between train anf test.
- # With a batch size of 1, this can be slower though.
- cfg_conditions: CFGConditions
- two_step_cfg = self.two_step_cfg if two_step_cfg is None else two_step_cfg
- if conditions:
- null_conditions = ClassifierFreeGuidanceDropout(p=1.0)(conditions)
- if two_step_cfg:
- cfg_conditions = (
- self.condition_provider(self.condition_provider.tokenize(conditions)),
- self.condition_provider(self.condition_provider.tokenize(null_conditions)),
- )
- else:
- conditions = conditions + null_conditions
- tokenized = self.condition_provider.tokenize(conditions)
- cfg_conditions = self.condition_provider(tokenized)
- else:
- cfg_conditions = {}
-
- if prompt is None:
- assert num_samples > 0
- prompt = torch.zeros((num_samples, self.num_codebooks, 0), dtype=torch.long, device=device)
-
- B, K, T = prompt.shape
- start_offset = T
- assert start_offset < max_gen_len
-
- pattern = self.pattern_provider.get_pattern(max_gen_len)
- # this token is used as default value for codes that are not generated yet
- unknown_token = -1
-
- # we generate codes up to the max_gen_len that will be mapped to the pattern sequence
- gen_codes = torch.full((B, K, max_gen_len), unknown_token, dtype=torch.long, device=device)
- # filling the gen_codes with the prompt if needed
- gen_codes[..., :start_offset] = prompt
- # create the gen_sequence with proper interleaving from the pattern: [B, K, S]
- gen_sequence, indexes, mask = pattern.build_pattern_sequence(gen_codes, self.special_token_id)
- # retrieve the start_offset in the sequence:
- # it is the first sequence step that contains the `start_offset` timestep
- start_offset_sequence = pattern.get_first_step_with_timesteps(start_offset)
- assert start_offset_sequence is not None
-
- with self.streaming():
- unconditional_state = self.get_streaming_state()
- prev_offset = 0
- gen_sequence_len = gen_sequence.shape[-1] # gen_sequence shape is [B, K, S]
- for offset in range(start_offset_sequence, gen_sequence_len):
- # get current sequence (note that the streaming API is providing the caching over previous offsets)
- curr_sequence = gen_sequence[..., prev_offset:offset]
- curr_mask = mask[None, ..., prev_offset:offset].expand(B, -1, -1)
- if check:
- # check coherence between mask and sequence
- assert (curr_sequence == torch.where(curr_mask, curr_sequence, self.special_token_id)).all()
- # should never happen as gen_sequence is filled progressively
- assert not (curr_sequence == unknown_token).any()
- # sample next token from the model, next token shape is [B, K, 1]
- next_token = self._sample_next_token(
- curr_sequence, cfg_conditions, unconditional_state, use_sampling, temp, top_k, top_p,
- cfg_coef=cfg_coef)
- # ensure the tokens that should be masked are properly set to special_token_id
- # as the model never output special_token_id
- valid_mask = mask[..., offset:offset+1].expand(B, -1, -1)
- next_token[~valid_mask] = self.special_token_id
- # ensure we don't overwrite prompt tokens, we only write over unknown tokens
- # (then mask tokens should be left as is as well, which is correct)
- gen_sequence[..., offset:offset+1] = torch.where(
- gen_sequence[..., offset:offset+1] == unknown_token,
- next_token, gen_sequence[..., offset:offset+1]
- )
- prev_offset = offset
- if callback is not None:
- callback(1 + offset - start_offset_sequence, gen_sequence_len - start_offset_sequence)
- unconditional_state.clear()
-
- # ensure sequence has been entirely filled
- assert not (gen_sequence == unknown_token).any()
- # ensure gen_sequence pattern and mask are matching
- # which means the gen_sequence is valid according to the pattern
- assert (
- gen_sequence == torch.where(mask[None, ...].expand(B, -1, -1), gen_sequence, self.special_token_id)
- ).all()
- # get back the codes, trimming the prompt if needed and cutting potentially incomplete timesteps
- out_codes, out_indexes, out_mask = pattern.revert_pattern_sequence(gen_sequence, special_token=unknown_token)
-
- # sanity checks over the returned codes and corresponding masks
- assert (out_codes[..., :max_gen_len] != unknown_token).all()
- assert (out_mask[..., :max_gen_len] == 1).all()
-
- out_start_offset = start_offset if remove_prompts else 0
- out_codes = out_codes[..., out_start_offset:max_gen_len]
-
- # ensure the returned codes are all valid
- assert (out_codes >= 0).all() and (out_codes <= self.card).all()
- return out_codes
diff --git a/spaces/YueMafighting/FollowYourPose/Dockerfile b/spaces/YueMafighting/FollowYourPose/Dockerfile
deleted file mode 100644
index 845d631472e9eca8e3bc2da734bb1b17eb72cc02..0000000000000000000000000000000000000000
--- a/spaces/YueMafighting/FollowYourPose/Dockerfile
+++ /dev/null
@@ -1,64 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update && \
- apt-get upgrade -y && \
- apt-get install -y --no-install-recommends \
- git \
- zip \
- unzip \
- git-lfs \
- wget \
- curl \
- # ffmpeg \
- ffmpeg \
- x264 \
- # python build dependencies \
- build-essential \
- libssl-dev \
- zlib1g-dev \
- libbz2-dev \
- libreadline-dev \
- libsqlite3-dev \
- libncursesw5-dev \
- xz-utils \
- tk-dev \
- libxml2-dev \
- libxmlsec1-dev \
- libffi-dev \
- liblzma-dev && \
- apt-get clean && \
- rm -rf /var/lib/apt/lists/*
-# RUN apt-get update && \
-# apt-get install zip
-# RUN wget https://github.com/ChenyangQiQi/FateZero/releases/download/v0.0.1/style.zip && unzip style.zip
-RUN useradd -m -u 1000 user
-RUN sudo apt-get install unzip
-USER user
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:${PATH}
-WORKDIR ${HOME}/app
-
-RUN curl https://pyenv.run | bash
-ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ENV PYTHON_VERSION=3.10.9
-RUN pyenv install ${PYTHON_VERSION} && \
- pyenv global ${PYTHON_VERSION} && \
- pyenv rehash && \
- pip install --no-cache-dir -U pip setuptools wheel
-
-RUN pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1
-COPY --chown=1000 requirements.txt /tmp/requirements.txt
-RUN pip install --no-cache-dir -U -r /tmp/requirements.txt
-
-COPY --chown=1000 . ${HOME}/app
-RUN ls -a
-# RUN cd ./FateZero/ckpt && bash download.sh
-RUN cd {WORKDIR}/data && bash download.sh
-ENV PYTHONPATH=${HOME}/app \
- PYTHONUNBUFFERED=1 \
- GRADIO_ALLOW_FLAGGING=never \
- GRADIO_NUM_PORTS=1 \
- GRADIO_SERVER_NAME=0.0.0.0 \
- GRADIO_THEME=huggingface \
- SYSTEM=spaces
-CMD ["python", "app.py"]
diff --git a/spaces/Yuliang/ECON/lib/common/render_utils.py b/spaces/Yuliang/ECON/lib/common/render_utils.py
deleted file mode 100644
index 389eab740fc341b9395e1e008b4fa91e9ef3cf83..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/common/render_utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: ps-license@tuebingen.mpg.de
-
-import math
-from typing import NewType
-
-import numpy as np
-import torch
-import trimesh
-from pytorch3d.renderer.mesh import rasterize_meshes
-from pytorch3d.structures import Meshes
-from torch import nn
-
-Tensor = NewType("Tensor", torch.Tensor)
-
-
-def solid_angles(points: Tensor, triangles: Tensor, thresh: float = 1e-8) -> Tensor:
- """Compute solid angle between the input points and triangles
- Follows the method described in:
- The Solid Angle of a Plane Triangle
- A. VAN OOSTEROM AND J. STRACKEE
- IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,
- VOL. BME-30, NO. 2, FEBRUARY 1983
- Parameters
- -----------
- points: BxQx3
- Tensor of input query points
- triangles: BxFx3x3
- Target triangles
- thresh: float
- float threshold
- Returns
- -------
- solid_angles: BxQxF
- A tensor containing the solid angle between all query points
- and input triangles
- """
- # Center the triangles on the query points. Size should be BxQxFx3x3
- centered_tris = triangles[:, None] - points[:, :, None, None]
-
- # BxQxFx3
- norms = torch.norm(centered_tris, dim=-1)
-
- # Should be BxQxFx3
- cross_prod = torch.cross(centered_tris[:, :, :, 1], centered_tris[:, :, :, 2], dim=-1)
- # Should be BxQxF
- numerator = (centered_tris[:, :, :, 0] * cross_prod).sum(dim=-1)
- del cross_prod
-
- dot01 = (centered_tris[:, :, :, 0] * centered_tris[:, :, :, 1]).sum(dim=-1)
- dot12 = (centered_tris[:, :, :, 1] * centered_tris[:, :, :, 2]).sum(dim=-1)
- dot02 = (centered_tris[:, :, :, 0] * centered_tris[:, :, :, 2]).sum(dim=-1)
- del centered_tris
-
- denominator = (
- norms.prod(dim=-1) + dot01 * norms[:, :, :, 2] + dot02 * norms[:, :, :, 1] +
- dot12 * norms[:, :, :, 0]
- )
- del dot01, dot12, dot02, norms
-
- # Should be BxQ
- solid_angle = torch.atan2(numerator, denominator)
- del numerator, denominator
-
- torch.cuda.empty_cache()
-
- return 2 * solid_angle
-
-
-def winding_numbers(points: Tensor, triangles: Tensor, thresh: float = 1e-8) -> Tensor:
- """Uses winding_numbers to compute inside/outside
- Robust inside-outside segmentation using generalized winding numbers
- Alec Jacobson,
- Ladislav Kavan,
- Olga Sorkine-Hornung
- Fast Winding Numbers for Soups and Clouds SIGGRAPH 2018
- Gavin Barill
- NEIL G. Dickson
- Ryan Schmidt
- David I.W. Levin
- and Alec Jacobson
- Parameters
- -----------
- points: BxQx3
- Tensor of input query points
- triangles: BxFx3x3
- Target triangles
- thresh: float
- float threshold
- Returns
- -------
- winding_numbers: BxQ
- A tensor containing the Generalized winding numbers
- """
- # The generalized winding number is the sum of solid angles of the point
- # with respect to all triangles.
- return (1 / (4 * math.pi) * solid_angles(points, triangles, thresh=thresh).sum(dim=-1))
-
-
-def batch_contains(verts, faces, points):
-
- B = verts.shape[0]
- N = points.shape[1]
-
- verts = verts.detach().cpu()
- faces = faces.detach().cpu()
- points = points.detach().cpu()
- contains = torch.zeros(B, N)
-
- for i in range(B):
- contains[i] = torch.as_tensor(trimesh.Trimesh(verts[i], faces[i]).contains(points[i]))
-
- return 2.0 * (contains - 0.5)
-
-
-def dict2obj(d):
- if not isinstance(d, dict):
- return d
-
- class C(object):
- pass
-
- o = C()
- for k in d:
- o.__dict__[k] = dict2obj(d[k])
- return o
-
-
-def face_vertices(vertices, faces):
- """
- :param vertices: [batch size, number of vertices, 3]
- :param faces: [batch size, number of faces, 3]
- :return: [batch size, number of faces, 3, 3]
- """
-
- bs, nv = vertices.shape[:2]
- bs, nf = faces.shape[:2]
- device = vertices.device
- faces = faces + (torch.arange(bs, dtype=torch.int32).to(device) * nv)[:, None, None]
- vertices = vertices.reshape((bs * nv, vertices.shape[-1]))
-
- return vertices[faces.long()]
-
-
-class Pytorch3dRasterizer(nn.Module):
- """Borrowed from https://github.com/facebookresearch/pytorch3d
- Notice:
- x,y,z are in image space, normalized
- can only render squared image now
- """
- def __init__(
- self, image_size=224, blur_radius=0.0, faces_per_pixel=1, device=torch.device("cuda:0")
- ):
- """
- use fixed raster_settings for rendering faces
- """
- super().__init__()
- raster_settings = {
- "image_size": image_size,
- "blur_radius": blur_radius,
- "faces_per_pixel": faces_per_pixel,
- "bin_size": -1,
- "max_faces_per_bin": None,
- "perspective_correct": False,
- "cull_backfaces": True,
- }
- raster_settings = dict2obj(raster_settings)
- self.raster_settings = raster_settings
- self.device = device
-
- def forward(self, vertices, faces, attributes=None):
- fixed_vertices = vertices.clone()
- fixed_vertices[..., :2] = -fixed_vertices[..., :2]
- meshes_screen = Meshes(verts=fixed_vertices.float(), faces=faces.long())
- raster_settings = self.raster_settings
- pix_to_face, zbuf, bary_coords, dists = rasterize_meshes(
- meshes_screen,
- image_size=raster_settings.image_size,
- blur_radius=raster_settings.blur_radius,
- faces_per_pixel=raster_settings.faces_per_pixel,
- bin_size=raster_settings.bin_size,
- max_faces_per_bin=raster_settings.max_faces_per_bin,
- perspective_correct=raster_settings.perspective_correct,
- )
- vismask = (pix_to_face > -1).float()
- D = attributes.shape[-1]
- attributes = attributes.clone()
- attributes = attributes.view(
- attributes.shape[0] * attributes.shape[1], 3, attributes.shape[-1]
- )
- N, H, W, K, _ = bary_coords.shape
- mask = pix_to_face == -1
- pix_to_face = pix_to_face.clone()
- pix_to_face[mask] = 0
- idx = pix_to_face.view(N * H * W * K, 1, 1).expand(N * H * W * K, 3, D)
- pixel_face_vals = attributes.gather(0, idx).view(N, H, W, K, 3, D)
- pixel_vals = (bary_coords[..., None] * pixel_face_vals).sum(dim=-2)
- pixel_vals[mask] = 0 # Replace masked values in output.
- pixel_vals = pixel_vals[:, :, :, 0].permute(0, 3, 1, 2)
- pixel_vals = torch.cat([pixel_vals, vismask[:, :, :, 0][:, None, :, :]], dim=1)
- return pixel_vals
-
- def get_texture(self, uvcoords, uvfaces, verts, faces, verts_color):
-
- batch_size = verts.shape[0]
- uv_verts_color = face_vertices(verts_color, faces.expand(batch_size, -1,
- -1)).to(self.device)
- uv_map = self.forward(
- uvcoords.expand(batch_size, -1, -1), uvfaces.expand(batch_size, -1, -1), uv_verts_color
- )[:, :3]
- uv_map_npy = np.flip(uv_map.squeeze(0).permute(1, 2, 0).cpu().numpy(), 0)
-
- return uv_map_npy
diff --git a/spaces/Yunshansongbai/SVC-Nahida/app.py b/spaces/Yunshansongbai/SVC-Nahida/app.py
deleted file mode 100644
index 7321a5fc776b31c97fe5729b6c4fb88a0e389ee1..0000000000000000000000000000000000000000
--- a/spaces/Yunshansongbai/SVC-Nahida/app.py
+++ /dev/null
@@ -1,194 +0,0 @@
-import io
-import os
-
-import gradio as gr
-import librosa
-import numpy as np
-import soundfile
-from inference.infer_tool import Svc
-import logging
-import os
-import paddle
-import requests
-import utils
-from spleeter import Separator
-import time
-from datetime import datetime, timedelta
-
-build_dir=os.getcwd()
-if build_dir == "/home/aistudio":
- build_dir += "/build"
-
-model_dir=build_dir+'/trained_models'
-
-model_list_path = model_dir + "/model_list.txt"
-
-# 筛选出文件夹
-models = []
-for filename in os.listdir(model_dir):
- # 判断文件名是否以 '.pdparams' 结尾，并且不包含后缀部分
- if filename.endswith('.pdparams') and os.path.splitext(filename)[0].isalpha():
- models.append(os.path.splitext(filename)[0])
-cache_model = {}
-
-def callback(text):
- if text == "reboot":
- os._exit(0)
- one_hour_later = datetime.now() + timedelta(hours=1)
- else:
- global start_time
- if time.time() - start_time >= 3600:
- os._exit(0)
- one_hour_later = datetime.now() + timedelta(hours=1)
- else:
- return text
-
-def separate_fn(song_input):
- try:
- if song_input is None:
- return "请上传歌曲",None,None,None,None
- params_2stems = {
- 'sample_rate': 44100,
- 'frame_length': 4096,
- 'frame_step': 1024,
- 'T': 512,
- 'F': 1024,
- 'num_instruments': ['vocals', 'instrumental'],
- 'output_dir': build_dir+'/output_2stems',
- 'checkpoint_path': build_dir+'/spleeter',
- 'use_elu': False}
- sampling_rate, song = song_input
- soundfile.write("temp.wav", song, sampling_rate, format="wav")
- # 初始化分离器
- sep = Separator(params_2stems)
- sep.separate('temp.wav')
- vocal_path = params_2stems["output_dir"]+"/temp-vocals.wav"
- instrumental_path = params_2stems["output_dir"]+"/temp-instrumental.wav"
- return "分离成功，请继续前往体验【转换】和【混音】",vocal_path,instrumental_path,vocal_path,instrumental_path
- except Exception as e:
- import traceback
- return traceback.format_exc() , None,None,None,None
-
-
-def convert_fn(model_name, input_audio,input_audio_micro, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale):
- try:
- if model_name in cache_model:
- model = cache_model[model_name]
- else:
- if paddle.device.is_compiled_with_cuda()==False and len(cache_model)!=0:
- return f"目前运行环境为CPU，受制于平台算力，每次启动本项目只允许加载1个模型，当前已加载{next(iter(cache_model))}",None,None
- config_path = f"{build_dir}/trained_models/config.json"
- model = Svc(f"{build_dir}/trained_models/{model_name}.pdparams", config_path,mode="test")
- cache_model[model_name] = model
- if input_audio is None and input_audio_micro is None:
- return "请上传音频", None,None
- if input_audio_micro is not None:
- input_audio = input_audio_micro
- sampling_rate, audio = input_audio
- duration = audio.shape[0] / sampling_rate
- audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio.transpose(1, 0))
- if sampling_rate != 16000:
- audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
- print(audio.shape)
- out_wav_path = "temp.wav"
- soundfile.write(out_wav_path, audio, 16000, format="wav")
- print(cluster_ratio, auto_f0, noise_scale)
- _audio = model.slice_inference(out_wav_path, model_name, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale)
- del model
- return "转换成功，请继续前往体验【混音】", (44100, _audio),(44100, _audio)
- except Exception as e:
- import traceback
- return traceback.format_exc() , None,None
-
-def compose_fn(input_vocal,input_instrumental,mixing_ratio=0.5):
- try:
- outlog = "混音成功"
- if input_vocal is None:
- return "请上传人声", None
- if input_instrumental is None:
- return "请上传伴奏", None
- vocal_sampling_rate, vocal = input_vocal
- vocal_duration = vocal.shape[0] / vocal_sampling_rate
- vocal = (vocal / np.iinfo(vocal.dtype).max).astype(np.float32)
- if len(vocal.shape) > 1:
- vocal = librosa.to_mono(vocal.transpose(1, 0))
- if vocal_sampling_rate != 44100:
- vocal = librosa.resample(vocal, orig_sr=vocal_sampling_rate, target_sr=44100)
-
- instrumental_sampling_rate, instrumental = input_instrumental
- instrumental_duration = instrumental.shape[0] / instrumental_sampling_rate
- instrumental = (instrumental / np.iinfo(instrumental.dtype).max).astype(np.float32)
- if len(instrumental.shape) > 1:
- instrumental = librosa.to_mono(instrumental.transpose(1, 0))
- if instrumental_sampling_rate != 44100:
- instrumental = librosa.resample(instrumental, orig_sr=instrumental_sampling_rate, target_sr=44100)
- if len(vocal)!=len(instrumental):
- min_length = min(len(vocal),len(instrumental))
- instrumental = instrumental[:min_length]
- vocal = vocal[:min_length]
- outlog = "人声伴奏长度不一致，已自动截断较长的音频"
-
- mixed_audio = (1 - mixing_ratio) * vocal + mixing_ratio * instrumental
- mixed_audio_data = mixed_audio.astype(np.float32)
- return outlog,(44100,mixed_audio_data)
- except Exception as e:
- import traceback
- return traceback.format_exc() , None
-
-
-app = gr.Blocks()
-
-with app:
- start_time = time.time()
- one_hour_later = datetime.now() + timedelta(hours=1)
- gr.Markdown('SVC歌声转换全流程体验（伴奏分离，转换，混音）
')
- with gr.Tabs() as tabs:
- with gr.TabItem("人声伴奏分离"):
- gr.Markdown('该项目人声分离的效果弱于UVR5，如自备分离好的伴奏和人声可跳过该步骤
')
- song_input = gr.Audio(label="上传歌曲（tips:上传后点击右上角✏可以进行歌曲剪辑）",interactive=True)
- gr.Examples(examples=[build_dir+"/examples/song/blue.wav",build_dir+"/examples/song/Counter_clockwise_Clock.wav",build_dir+"/examples/song/one_last_kiss.wav"],inputs=song_input,label="歌曲样例")
-
- btn_separate = gr.Button("人声伴奏分离", variant="primary")
- text_output1 = gr.Textbox(label="输出信息")
- vocal_output1 = gr.Audio(label="输出人声",interactive=False)
- instrumental_output1 = gr.Audio(label="输出伴奏",interactive=False)
- with gr.TabItem("转换"):
- model_name = gr.Dropdown(label="模型", choices=models, value="纳西妲")
- vocal_input1 = gr.Audio(label="上传人声",interactive=True)
- gr.Examples(examples=[build_dir+"/examples/vocals/blue_vocal.wav",build_dir+"/examples/vocals/Counter_clockwise_Clock_vocal.wav",build_dir+"/examples/vocals/one_last_kiss_vocal.wav"],inputs=vocal_input1,label="人声样例")
- btn_use_separate = gr.Button("使用【人声伴奏分离】分离的人声")
- micro_input = gr.Audio(label="麦克风输入（优先于上传的人声）",source="microphone",interactive=True)
- vc_transform = gr.Number(label="变调（半音数量,升八度12降八度-12）", value=0)
- cluster_ratio = gr.Number(label="聚类模型混合比例", value=0,visible=False)
- auto_f0 = gr.Checkbox(label="自动预测音高（转换歌声时不要打开，会严重跑调）", value=False)
- slice_db = gr.Number(label="静音分贝阈值（嘈杂的音频可以-30，干声保留呼吸可以-50）", value=-50)
- noise_scale = gr.Number(label="noise_scale", value=0.2)
- btn_convert = gr.Button("转换", variant="primary")
- text_output2 = gr.Textbox(label="输出信息")
- vc_output2 = gr.Audio(label="输出音频",interactive=False)
-
- with gr.TabItem("混音"):
- vocal_input2 = gr.Audio(label="上传人声",interactive=True)
- btn_use_convert = gr.Button("使用【转换】输出的人声")
- instrumental_input1 = gr.Audio(label="上传伴奏")
- gr.Examples(examples=[build_dir+"/examples/instrumental/blue_instrumental.wav",build_dir+"/examples/instrumental/Counter_clockwise_Clock_instrumental.wav",build_dir+"/examples/instrumental/one_last_kiss_instrumental.wav"],inputs=instrumental_input1,label="伴奏样例")
- btn_use_separate2 = gr.Button("使用【人声伴奏分离】分离的伴奏")
- mixing_ratio = gr.Slider(0, 1, value=0.75,step=0.01,label="混音比例（人声:伴奏）", info="人声:伴奏")
- btn_compose = gr.Button("混音", variant="primary")
- text_output3 = gr.Textbox(label="输出信息")
- song_output = gr.Audio(label="输出歌曲",interactive=False)
-
- with gr.TabItem("设置"):
- output = gr.Textbox(label="输出",placeholder=f"距离下一次允许重启时间为{one_hour_later}")
- btn_reboot = gr.Button("重启",variant="primary")
- btn_separate.click(separate_fn, song_input, [text_output1, vocal_output1,instrumental_output1,vocal_input1,instrumental_input1])
- btn_convert.click(convert_fn, [model_name, vocal_input1,micro_input,vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale], [text_output2, vc_output2,vocal_input2])
- btn_compose.click(compose_fn,[vocal_input2,instrumental_input1,mixing_ratio],[text_output3,song_output])
- btn_reboot.click(callback,output)
- btn_use_convert.click(lambda x:x,vc_output2,vocal_input2)
- btn_use_separate.click(lambda x:x,vocal_output1,vocal_input1)
- btn_use_separate2.click(lambda x:x,instrumental_output1,instrumental_input1)
-
-app.launch()
\ No newline at end of file
diff --git a/spaces/Ziqi/ReVersion/README.md b/spaces/Ziqi/ReVersion/README.md
deleted file mode 100644
index 1823df2c77ed395faf47aed9a4e04093a151848e..0000000000000000000000000000000000000000
--- a/spaces/Ziqi/ReVersion/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ReVersion
-emoji: 🐠
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/abcde1234www/tts/app.py b/spaces/abcde1234www/tts/app.py
deleted file mode 100644
index a97450c289843433835ebc4da6d5d885ea42167c..0000000000000000000000000000000000000000
--- a/spaces/abcde1234www/tts/app.py
+++ /dev/null
@@ -1,103 +0,0 @@
-"""
-Copyright 2022 Balacoon
-
-TTS interactive demo
-"""
-
-import logging
-from typing import cast
-
-import gradio as gr
-from balacoon_tts import TTS
-from huggingface_hub import hf_hub_download, list_repo_files
-
-# global tts module, initialized from a model selected
-tts = None
-
-
-def main():
- logging.basicConfig(level=logging.INFO)
-
- with gr.Blocks() as demo:
- gr.Markdown(
- """
- Text-to-Speech
-
- 1. Write an utterance to generate,
- 2. Select the model to synthesize with
- 3. Select speaker (only for multi-speaker models)
- 4. Hit "Generate" and listen to the result!
-
- When you select model for the first time,
- it will take a little time to download it.
- You can learn more about models available
- [here](https://huggingface.co/balacoon/tts),
- visit [Balacoon website](https://balacoon.com/) for more info.
- """
- )
- with gr.Row(variant="panel"):
- text = gr.Textbox(label="Text", placeholder="Type something here...")
-
- with gr.Row():
- with gr.Column(variant="panel"):
- repo_files = list_repo_files(repo_id="balacoon/tts")
- model_files = [x for x in repo_files if x.endswith(".addon")]
- model_name = gr.Dropdown(
- label="Model",
- choices=model_files,
- )
- with gr.Column(variant="panel"):
- speaker = gr.Dropdown(label="Speaker", choices=[])
-
- def set_model(model_name_str: str):
- """
- gets value from `model_name`, loads model,
- re-initializes tts object, gets list of
- speakers that model supports and set them to `speaker`
- """
- model_path = hf_hub_download(
- repo_id="balacoon/tts", filename=model_name_str
- )
- global tts
- tts = TTS(model_path)
- speakers = tts.get_speakers()
- if speakers:
- visible = True
- value = speakers[-1]
- else:
- visible = False
- value = ""
- return gr.Dropdown.update(
- choices=speakers, value=value, visible=visible
- )
-
- model_name.change(set_model, inputs=model_name, outputs=speaker)
-
- with gr.Row(variant="panel"):
- generate = gr.Button("Generate")
- with gr.Row(variant="panel"):
- audio = gr.Audio()
-
- def synthesize_audio(text_str: str, speaker_str: str = ""):
- """
- gets utterance to synthesize from `text` Textbox
- and speaker name from `speaker` dropdown list.
- speaker name might be empty for single-speaker models.
- Synthesizes the waveform and updates `audio` with it.
- """
- if not text_str:
- logging.info("text or speaker are not provided")
- return None
- global tts
- if len(text_str) > 1024:
- text_str = text_str[:1024]
- samples = cast(TTS, tts).synthesize(text_str, speaker_str)
- return gr.Audio.update(value=(24000, samples))
-
- generate.click(synthesize_audio, inputs=[text, speaker], outputs=audio)
-
- demo.launch()
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/tableau/setup.md b/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/tableau/setup.md
deleted file mode 100644
index 73dac977c474116928b47b28dbab6cc0705660c0..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/tableau/setup.md
+++ /dev/null
@@ -1,60 +0,0 @@
----
-title: Setup
----
-# Tableau Ingestion Guide: Setup & Prerequisites
-
-In order to configure ingestion from Tableau, you'll first have to enable Tableau Metadata API and you should have a user with Site Administrator Explorer permissions.
-
-## Tableau Prerequisites
-
-1. Grant `Site Administrator Explorer permissions` to a user
-
- A. Log in to Tableau Cloud https://sso.online.tableau.com/public/idp/SSO.
-
- B. Navigate to `Users`.
-
-
- 
-
-
-
- C. **For New User**: Follow below steps to grant permission for new user.
-
- - Click `Add Users` -> `Add Users by Email`
-
-
- 
-
-
- - Fill `Enter email addresses`, set `Site role` to `Site Administrator Explorer` and Click `Add Users`
-
-
- 
-
-
-
- D. **For Existing User:** Follow below steps to grant permission for existing user.
-
- - Select a user and click `Actions` -> `Site Role`
-
-
- 
-
-
- - Change user role to `Site Administrator Explorer`
-
-
- 
-
-
-2. **Enable Tableau Metadata API:** This step is required only for Tableau Server. The Metadata API is installed with Tableau Server but disabled by default.
-
- - Open a command prompt as an admin on the initial node (*where TSM is installed*) in the cluster
- - Run the command: `tsm maintenance metadata-services enable`
-
-
-## Next Steps
-
-Once you've done all of the above in Tableau, it's time to [move on](configuration.md) to configuring the actual ingestion source within DataHub.
-
-*Need more help? Join the conversation in [Slack](http://slack.datahubproject.io)!*
\ No newline at end of file
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/multi_scale_deform_attn.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/multi_scale_deform_attn.py
deleted file mode 100644
index c52dda18b41705705b47dd0e995b124048c16fba..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/multi_scale_deform_attn.py
+++ /dev/null
@@ -1,358 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-import warnings
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.autograd.function import Function, once_differentiable
-
-from annotator.uniformer.mmcv import deprecated_api_warning
-from annotator.uniformer.mmcv.cnn import constant_init, xavier_init
-from annotator.uniformer.mmcv.cnn.bricks.registry import ATTENTION
-from annotator.uniformer.mmcv.runner import BaseModule
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
- '_ext', ['ms_deform_attn_backward', 'ms_deform_attn_forward'])
-
-
-class MultiScaleDeformableAttnFunction(Function):
-
- @staticmethod
- def forward(ctx, value, value_spatial_shapes, value_level_start_index,
- sampling_locations, attention_weights, im2col_step):
- """GPU version of multi-scale deformable attention.
-
- Args:
- value (Tensor): The value has shape
- (bs, num_keys, mum_heads, embed_dims//num_heads)
- value_spatial_shapes (Tensor): Spatial shape of
- each feature map, has shape (num_levels, 2),
- last dimension 2 represent (h, w)
- sampling_locations (Tensor): The location of sampling points,
- has shape
- (bs ,num_queries, num_heads, num_levels, num_points, 2),
- the last dimension 2 represent (x, y).
- attention_weights (Tensor): The weight of sampling points used
- when calculate the attention, has shape
- (bs ,num_queries, num_heads, num_levels, num_points),
- im2col_step (Tensor): The step used in image to column.
-
- Returns:
- Tensor: has shape (bs, num_queries, embed_dims)
- """
-
- ctx.im2col_step = im2col_step
- output = ext_module.ms_deform_attn_forward(
- value,
- value_spatial_shapes,
- value_level_start_index,
- sampling_locations,
- attention_weights,
- im2col_step=ctx.im2col_step)
- ctx.save_for_backward(value, value_spatial_shapes,
- value_level_start_index, sampling_locations,
- attention_weights)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- """GPU version of backward function.
-
- Args:
- grad_output (Tensor): Gradient
- of output tensor of forward.
-
- Returns:
- Tuple[Tensor]: Gradient
- of input tensors in forward.
- """
- value, value_spatial_shapes, value_level_start_index,\
- sampling_locations, attention_weights = ctx.saved_tensors
- grad_value = torch.zeros_like(value)
- grad_sampling_loc = torch.zeros_like(sampling_locations)
- grad_attn_weight = torch.zeros_like(attention_weights)
-
- ext_module.ms_deform_attn_backward(
- value,
- value_spatial_shapes,
- value_level_start_index,
- sampling_locations,
- attention_weights,
- grad_output.contiguous(),
- grad_value,
- grad_sampling_loc,
- grad_attn_weight,
- im2col_step=ctx.im2col_step)
-
- return grad_value, None, None, \
- grad_sampling_loc, grad_attn_weight, None
-
-
-def multi_scale_deformable_attn_pytorch(value, value_spatial_shapes,
- sampling_locations, attention_weights):
- """CPU version of multi-scale deformable attention.
-
- Args:
- value (Tensor): The value has shape
- (bs, num_keys, mum_heads, embed_dims//num_heads)
- value_spatial_shapes (Tensor): Spatial shape of
- each feature map, has shape (num_levels, 2),
- last dimension 2 represent (h, w)
- sampling_locations (Tensor): The location of sampling points,
- has shape
- (bs ,num_queries, num_heads, num_levels, num_points, 2),
- the last dimension 2 represent (x, y).
- attention_weights (Tensor): The weight of sampling points used
- when calculate the attention, has shape
- (bs ,num_queries, num_heads, num_levels, num_points),
-
- Returns:
- Tensor: has shape (bs, num_queries, embed_dims)
- """
-
- bs, _, num_heads, embed_dims = value.shape
- _, num_queries, num_heads, num_levels, num_points, _ =\
- sampling_locations.shape
- value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes],
- dim=1)
- sampling_grids = 2 * sampling_locations - 1
- sampling_value_list = []
- for level, (H_, W_) in enumerate(value_spatial_shapes):
- # bs, H_*W_, num_heads, embed_dims ->
- # bs, H_*W_, num_heads*embed_dims ->
- # bs, num_heads*embed_dims, H_*W_ ->
- # bs*num_heads, embed_dims, H_, W_
- value_l_ = value_list[level].flatten(2).transpose(1, 2).reshape(
- bs * num_heads, embed_dims, H_, W_)
- # bs, num_queries, num_heads, num_points, 2 ->
- # bs, num_heads, num_queries, num_points, 2 ->
- # bs*num_heads, num_queries, num_points, 2
- sampling_grid_l_ = sampling_grids[:, :, :,
- level].transpose(1, 2).flatten(0, 1)
- # bs*num_heads, embed_dims, num_queries, num_points
- sampling_value_l_ = F.grid_sample(
- value_l_,
- sampling_grid_l_,
- mode='bilinear',
- padding_mode='zeros',
- align_corners=False)
- sampling_value_list.append(sampling_value_l_)
- # (bs, num_queries, num_heads, num_levels, num_points) ->
- # (bs, num_heads, num_queries, num_levels, num_points) ->
- # (bs, num_heads, 1, num_queries, num_levels*num_points)
- attention_weights = attention_weights.transpose(1, 2).reshape(
- bs * num_heads, 1, num_queries, num_levels * num_points)
- output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) *
- attention_weights).sum(-1).view(bs, num_heads * embed_dims,
- num_queries)
- return output.transpose(1, 2).contiguous()
-
-
-@ATTENTION.register_module()
-class MultiScaleDeformableAttention(BaseModule):
- """An attention module used in Deformable-Detr.
-
- `Deformable DETR: Deformable Transformers for End-to-End Object Detection.
- `_.
-
- Args:
- embed_dims (int): The embedding dimension of Attention.
- Default: 256.
- num_heads (int): Parallel attention heads. Default: 64.
- num_levels (int): The number of feature map used in
- Attention. Default: 4.
- num_points (int): The number of sampling points for
- each query in each head. Default: 4.
- im2col_step (int): The step used in image_to_column.
- Default: 64.
- dropout (float): A Dropout layer on `inp_identity`.
- Default: 0.1.
- batch_first (bool): Key, Query and Value are shape of
- (batch, n, embed_dim)
- or (n, batch, embed_dim). Default to False.
- norm_cfg (dict): Config dict for normalization layer.
- Default: None.
- init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
- Default: None.
- """
-
- def __init__(self,
- embed_dims=256,
- num_heads=8,
- num_levels=4,
- num_points=4,
- im2col_step=64,
- dropout=0.1,
- batch_first=False,
- norm_cfg=None,
- init_cfg=None):
- super().__init__(init_cfg)
- if embed_dims % num_heads != 0:
- raise ValueError(f'embed_dims must be divisible by num_heads, '
- f'but got {embed_dims} and {num_heads}')
- dim_per_head = embed_dims // num_heads
- self.norm_cfg = norm_cfg
- self.dropout = nn.Dropout(dropout)
- self.batch_first = batch_first
-
- # you'd better set dim_per_head to a power of 2
- # which is more efficient in the CUDA implementation
- def _is_power_of_2(n):
- if (not isinstance(n, int)) or (n < 0):
- raise ValueError(
- 'invalid input for _is_power_of_2: {} (type: {})'.format(
- n, type(n)))
- return (n & (n - 1) == 0) and n != 0
-
- if not _is_power_of_2(dim_per_head):
- warnings.warn(
- "You'd better set embed_dims in "
- 'MultiScaleDeformAttention to make '
- 'the dimension of each attention head a power of 2 '
- 'which is more efficient in our CUDA implementation.')
-
- self.im2col_step = im2col_step
- self.embed_dims = embed_dims
- self.num_levels = num_levels
- self.num_heads = num_heads
- self.num_points = num_points
- self.sampling_offsets = nn.Linear(
- embed_dims, num_heads * num_levels * num_points * 2)
- self.attention_weights = nn.Linear(embed_dims,
- num_heads * num_levels * num_points)
- self.value_proj = nn.Linear(embed_dims, embed_dims)
- self.output_proj = nn.Linear(embed_dims, embed_dims)
- self.init_weights()
-
- def init_weights(self):
- """Default initialization for Parameters of Module."""
- constant_init(self.sampling_offsets, 0.)
- thetas = torch.arange(
- self.num_heads,
- dtype=torch.float32) * (2.0 * math.pi / self.num_heads)
- grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
- grid_init = (grid_init /
- grid_init.abs().max(-1, keepdim=True)[0]).view(
- self.num_heads, 1, 1,
- 2).repeat(1, self.num_levels, self.num_points, 1)
- for i in range(self.num_points):
- grid_init[:, :, i, :] *= i + 1
-
- self.sampling_offsets.bias.data = grid_init.view(-1)
- constant_init(self.attention_weights, val=0., bias=0.)
- xavier_init(self.value_proj, distribution='uniform', bias=0.)
- xavier_init(self.output_proj, distribution='uniform', bias=0.)
- self._is_init = True
-
- @deprecated_api_warning({'residual': 'identity'},
- cls_name='MultiScaleDeformableAttention')
- def forward(self,
- query,
- key=None,
- value=None,
- identity=None,
- query_pos=None,
- key_padding_mask=None,
- reference_points=None,
- spatial_shapes=None,
- level_start_index=None,
- **kwargs):
- """Forward Function of MultiScaleDeformAttention.
-
- Args:
- query (Tensor): Query of Transformer with shape
- (num_query, bs, embed_dims).
- key (Tensor): The key tensor with shape
- `(num_key, bs, embed_dims)`.
- value (Tensor): The value tensor with shape
- `(num_key, bs, embed_dims)`.
- identity (Tensor): The tensor used for addition, with the
- same shape as `query`. Default None. If None,
- `query` will be used.
- query_pos (Tensor): The positional encoding for `query`.
- Default: None.
- key_pos (Tensor): The positional encoding for `key`. Default
- None.
- reference_points (Tensor): The normalized reference
- points with shape (bs, num_query, num_levels, 2),
- all elements is range in [0, 1], top-left (0,0),
- bottom-right (1, 1), including padding area.
- or (N, Length_{query}, num_levels, 4), add
- additional two dimensions is (w, h) to
- form reference boxes.
- key_padding_mask (Tensor): ByteTensor for `query`, with
- shape [bs, num_key].
- spatial_shapes (Tensor): Spatial shape of features in
- different levels. With shape (num_levels, 2),
- last dimension represents (h, w).
- level_start_index (Tensor): The start index of each level.
- A tensor has shape ``(num_levels, )`` and can be represented
- as [0, h_0*w_0, h_0*w_0+h_1*w_1, ...].
-
- Returns:
- Tensor: forwarded results with shape [num_query, bs, embed_dims].
- """
-
- if value is None:
- value = query
-
- if identity is None:
- identity = query
- if query_pos is not None:
- query = query + query_pos
- if not self.batch_first:
- # change to (bs, num_query ,embed_dims)
- query = query.permute(1, 0, 2)
- value = value.permute(1, 0, 2)
-
- bs, num_query, _ = query.shape
- bs, num_value, _ = value.shape
- assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value
-
- value = self.value_proj(value)
- if key_padding_mask is not None:
- value = value.masked_fill(key_padding_mask[..., None], 0.0)
- value = value.view(bs, num_value, self.num_heads, -1)
- sampling_offsets = self.sampling_offsets(query).view(
- bs, num_query, self.num_heads, self.num_levels, self.num_points, 2)
- attention_weights = self.attention_weights(query).view(
- bs, num_query, self.num_heads, self.num_levels * self.num_points)
- attention_weights = attention_weights.softmax(-1)
-
- attention_weights = attention_weights.view(bs, num_query,
- self.num_heads,
- self.num_levels,
- self.num_points)
- if reference_points.shape[-1] == 2:
- offset_normalizer = torch.stack(
- [spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
- sampling_locations = reference_points[:, :, None, :, None, :] \
- + sampling_offsets \
- / offset_normalizer[None, None, None, :, None, :]
- elif reference_points.shape[-1] == 4:
- sampling_locations = reference_points[:, :, None, :, None, :2] \
- + sampling_offsets / self.num_points \
- * reference_points[:, :, None, :, None, 2:] \
- * 0.5
- else:
- raise ValueError(
- f'Last dim of reference_points must be'
- f' 2 or 4, but get {reference_points.shape[-1]} instead.')
- if torch.cuda.is_available() and value.is_cuda:
- output = MultiScaleDeformableAttnFunction.apply(
- value, spatial_shapes, level_start_index, sampling_locations,
- attention_weights, self.im2col_step)
- else:
- output = multi_scale_deformable_attn_pytorch(
- value, spatial_shapes, sampling_locations, attention_weights)
-
- output = self.output_proj(output)
-
- if not self.batch_first:
- # (num_query, bs ,embed_dims)
- output = output.permute(1, 0, 2)
-
- return self.dropout(output) + identity
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/misc.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/misc.py
deleted file mode 100644
index 2c58d0d7fee9fe3d4519270ad8c1e998d0d8a18c..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/misc.py
+++ /dev/null
@@ -1,377 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import collections.abc
-import functools
-import itertools
-import subprocess
-import warnings
-from collections import abc
-from importlib import import_module
-from inspect import getfullargspec
-from itertools import repeat
-
-
-# From PyTorch internals
-def _ntuple(n):
-
- def parse(x):
- if isinstance(x, collections.abc.Iterable):
- return x
- return tuple(repeat(x, n))
-
- return parse
-
-
-to_1tuple = _ntuple(1)
-to_2tuple = _ntuple(2)
-to_3tuple = _ntuple(3)
-to_4tuple = _ntuple(4)
-to_ntuple = _ntuple
-
-
-def is_str(x):
- """Whether the input is an string instance.
-
- Note: This method is deprecated since python 2 is no longer supported.
- """
- return isinstance(x, str)
-
-
-def import_modules_from_strings(imports, allow_failed_imports=False):
- """Import modules from the given list of strings.
-
- Args:
- imports (list | str | None): The given module names to be imported.
- allow_failed_imports (bool): If True, the failed imports will return
- None. Otherwise, an ImportError is raise. Default: False.
-
- Returns:
- list[module] | module | None: The imported modules.
-
- Examples:
- >>> osp, sys = import_modules_from_strings(
- ... ['os.path', 'sys'])
- >>> import os.path as osp_
- >>> import sys as sys_
- >>> assert osp == osp_
- >>> assert sys == sys_
- """
- if not imports:
- return
- single_import = False
- if isinstance(imports, str):
- single_import = True
- imports = [imports]
- if not isinstance(imports, list):
- raise TypeError(
- f'custom_imports must be a list but got type {type(imports)}')
- imported = []
- for imp in imports:
- if not isinstance(imp, str):
- raise TypeError(
- f'{imp} is of type {type(imp)} and cannot be imported.')
- try:
- imported_tmp = import_module(imp)
- except ImportError:
- if allow_failed_imports:
- warnings.warn(f'{imp} failed to import and is ignored.',
- UserWarning)
- imported_tmp = None
- else:
- raise ImportError
- imported.append(imported_tmp)
- if single_import:
- imported = imported[0]
- return imported
-
-
-def iter_cast(inputs, dst_type, return_type=None):
- """Cast elements of an iterable object into some type.
-
- Args:
- inputs (Iterable): The input object.
- dst_type (type): Destination type.
- return_type (type, optional): If specified, the output object will be
- converted to this type, otherwise an iterator.
-
- Returns:
- iterator or specified type: The converted object.
- """
- if not isinstance(inputs, abc.Iterable):
- raise TypeError('inputs must be an iterable object')
- if not isinstance(dst_type, type):
- raise TypeError('"dst_type" must be a valid type')
-
- out_iterable = map(dst_type, inputs)
-
- if return_type is None:
- return out_iterable
- else:
- return return_type(out_iterable)
-
-
-def list_cast(inputs, dst_type):
- """Cast elements of an iterable object into a list of some type.
-
- A partial method of :func:`iter_cast`.
- """
- return iter_cast(inputs, dst_type, return_type=list)
-
-
-def tuple_cast(inputs, dst_type):
- """Cast elements of an iterable object into a tuple of some type.
-
- A partial method of :func:`iter_cast`.
- """
- return iter_cast(inputs, dst_type, return_type=tuple)
-
-
-def is_seq_of(seq, expected_type, seq_type=None):
- """Check whether it is a sequence of some type.
-
- Args:
- seq (Sequence): The sequence to be checked.
- expected_type (type): Expected type of sequence items.
- seq_type (type, optional): Expected sequence type.
-
- Returns:
- bool: Whether the sequence is valid.
- """
- if seq_type is None:
- exp_seq_type = abc.Sequence
- else:
- assert isinstance(seq_type, type)
- exp_seq_type = seq_type
- if not isinstance(seq, exp_seq_type):
- return False
- for item in seq:
- if not isinstance(item, expected_type):
- return False
- return True
-
-
-def is_list_of(seq, expected_type):
- """Check whether it is a list of some type.
-
- A partial method of :func:`is_seq_of`.
- """
- return is_seq_of(seq, expected_type, seq_type=list)
-
-
-def is_tuple_of(seq, expected_type):
- """Check whether it is a tuple of some type.
-
- A partial method of :func:`is_seq_of`.
- """
- return is_seq_of(seq, expected_type, seq_type=tuple)
-
-
-def slice_list(in_list, lens):
- """Slice a list into several sub lists by a list of given length.
-
- Args:
- in_list (list): The list to be sliced.
- lens(int or list): The expected length of each out list.
-
- Returns:
- list: A list of sliced list.
- """
- if isinstance(lens, int):
- assert len(in_list) % lens == 0
- lens = [lens] * int(len(in_list) / lens)
- if not isinstance(lens, list):
- raise TypeError('"indices" must be an integer or a list of integers')
- elif sum(lens) != len(in_list):
- raise ValueError('sum of lens and list length does not '
- f'match: {sum(lens)} != {len(in_list)}')
- out_list = []
- idx = 0
- for i in range(len(lens)):
- out_list.append(in_list[idx:idx + lens[i]])
- idx += lens[i]
- return out_list
-
-
-def concat_list(in_list):
- """Concatenate a list of list into a single list.
-
- Args:
- in_list (list): The list of list to be merged.
-
- Returns:
- list: The concatenated flat list.
- """
- return list(itertools.chain(*in_list))
-
-
-def check_prerequisites(
- prerequisites,
- checker,
- msg_tmpl='Prerequisites "{}" are required in method "{}" but not '
- 'found, please install them first.'): # yapf: disable
- """A decorator factory to check if prerequisites are satisfied.
-
- Args:
- prerequisites (str of list[str]): Prerequisites to be checked.
- checker (callable): The checker method that returns True if a
- prerequisite is meet, False otherwise.
- msg_tmpl (str): The message template with two variables.
-
- Returns:
- decorator: A specific decorator.
- """
-
- def wrap(func):
-
- @functools.wraps(func)
- def wrapped_func(*args, **kwargs):
- requirements = [prerequisites] if isinstance(
- prerequisites, str) else prerequisites
- missing = []
- for item in requirements:
- if not checker(item):
- missing.append(item)
- if missing:
- print(msg_tmpl.format(', '.join(missing), func.__name__))
- raise RuntimeError('Prerequisites not meet.')
- else:
- return func(*args, **kwargs)
-
- return wrapped_func
-
- return wrap
-
-
-def _check_py_package(package):
- try:
- import_module(package)
- except ImportError:
- return False
- else:
- return True
-
-
-def _check_executable(cmd):
- if subprocess.call(f'which {cmd}', shell=True) != 0:
- return False
- else:
- return True
-
-
-def requires_package(prerequisites):
- """A decorator to check if some python packages are installed.
-
- Example:
- >>> @requires_package('numpy')
- >>> func(arg1, args):
- >>> return numpy.zeros(1)
- array([0.])
- >>> @requires_package(['numpy', 'non_package'])
- >>> func(arg1, args):
- >>> return numpy.zeros(1)
- ImportError
- """
- return check_prerequisites(prerequisites, checker=_check_py_package)
-
-
-def requires_executable(prerequisites):
- """A decorator to check if some executable files are installed.
-
- Example:
- >>> @requires_executable('ffmpeg')
- >>> func(arg1, args):
- >>> print(1)
- 1
- """
- return check_prerequisites(prerequisites, checker=_check_executable)
-
-
-def deprecated_api_warning(name_dict, cls_name=None):
- """A decorator to check if some arguments are deprecate and try to replace
- deprecate src_arg_name to dst_arg_name.
-
- Args:
- name_dict(dict):
- key (str): Deprecate argument names.
- val (str): Expected argument names.
-
- Returns:
- func: New function.
- """
-
- def api_warning_wrapper(old_func):
-
- @functools.wraps(old_func)
- def new_func(*args, **kwargs):
- # get the arg spec of the decorated method
- args_info = getfullargspec(old_func)
- # get name of the function
- func_name = old_func.__name__
- if cls_name is not None:
- func_name = f'{cls_name}.{func_name}'
- if args:
- arg_names = args_info.args[:len(args)]
- for src_arg_name, dst_arg_name in name_dict.items():
- if src_arg_name in arg_names:
- warnings.warn(
- f'"{src_arg_name}" is deprecated in '
- f'`{func_name}`, please use "{dst_arg_name}" '
- 'instead')
- arg_names[arg_names.index(src_arg_name)] = dst_arg_name
- if kwargs:
- for src_arg_name, dst_arg_name in name_dict.items():
- if src_arg_name in kwargs:
-
- assert dst_arg_name not in kwargs, (
- f'The expected behavior is to replace '
- f'the deprecated key `{src_arg_name}` to '
- f'new key `{dst_arg_name}`, but got them '
- f'in the arguments at the same time, which '
- f'is confusing. `{src_arg_name} will be '
- f'deprecated in the future, please '
- f'use `{dst_arg_name}` instead.')
-
- warnings.warn(
- f'"{src_arg_name}" is deprecated in '
- f'`{func_name}`, please use "{dst_arg_name}" '
- 'instead')
- kwargs[dst_arg_name] = kwargs.pop(src_arg_name)
-
- # apply converted arguments to the decorated method
- output = old_func(*args, **kwargs)
- return output
-
- return new_func
-
- return api_warning_wrapper
-
-
-def is_method_overridden(method, base_class, derived_class):
- """Check if a method of base class is overridden in derived class.
-
- Args:
- method (str): the method name to check.
- base_class (type): the class of the base class.
- derived_class (type | Any): the class or instance of the derived class.
- """
- assert isinstance(base_class, type), \
- "base_class doesn't accept instance, Please pass class instead."
-
- if not isinstance(derived_class, type):
- derived_class = derived_class.__class__
-
- base_method = getattr(base_class, method)
- derived_method = getattr(derived_class, method)
- return derived_method != base_method
-
-
-def has_method(obj: object, method: str) -> bool:
- """Check whether the object has a method.
-
- Args:
- method (str): The method name to check.
- obj (object): The object to check.
-
- Returns:
- bool: True if the object has the method else False.
- """
- return hasattr(obj, method) and callable(getattr(obj, method))
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/momentum_updater.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/momentum_updater.py
deleted file mode 100644
index 60437756ceedf06055ec349df69a25465738d3f0..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/momentum_updater.py
+++ /dev/null
@@ -1,493 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import annotator.uniformer.mmcv as mmcv
-from .hook import HOOKS, Hook
-from .lr_updater import annealing_cos, annealing_linear, format_param
-
-
-class MomentumUpdaterHook(Hook):
-
- def __init__(self,
- by_epoch=True,
- warmup=None,
- warmup_iters=0,
- warmup_ratio=0.9):
- # validate the "warmup" argument
- if warmup is not None:
- if warmup not in ['constant', 'linear', 'exp']:
- raise ValueError(
- f'"{warmup}" is not a supported type for warming up, valid'
- ' types are "constant" and "linear"')
- if warmup is not None:
- assert warmup_iters > 0, \
- '"warmup_iters" must be a positive integer'
- assert 0 < warmup_ratio <= 1.0, \
- '"warmup_momentum" must be in range (0,1]'
-
- self.by_epoch = by_epoch
- self.warmup = warmup
- self.warmup_iters = warmup_iters
- self.warmup_ratio = warmup_ratio
-
- self.base_momentum = [] # initial momentum for all param groups
- self.regular_momentum = [
- ] # expected momentum if no warming up is performed
-
- def _set_momentum(self, runner, momentum_groups):
- if isinstance(runner.optimizer, dict):
- for k, optim in runner.optimizer.items():
- for param_group, mom in zip(optim.param_groups,
- momentum_groups[k]):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
- else:
- for param_group, mom in zip(runner.optimizer.param_groups,
- momentum_groups):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
-
- def get_momentum(self, runner, base_momentum):
- raise NotImplementedError
-
- def get_regular_momentum(self, runner):
- if isinstance(runner.optimizer, dict):
- momentum_groups = {}
- for k in runner.optimizer.keys():
- _momentum_group = [
- self.get_momentum(runner, _base_momentum)
- for _base_momentum in self.base_momentum[k]
- ]
- momentum_groups.update({k: _momentum_group})
- return momentum_groups
- else:
- return [
- self.get_momentum(runner, _base_momentum)
- for _base_momentum in self.base_momentum
- ]
-
- def get_warmup_momentum(self, cur_iters):
-
- def _get_warmup_momentum(cur_iters, regular_momentum):
- if self.warmup == 'constant':
- warmup_momentum = [
- _momentum / self.warmup_ratio
- for _momentum in self.regular_momentum
- ]
- elif self.warmup == 'linear':
- k = (1 - cur_iters / self.warmup_iters) * (1 -
- self.warmup_ratio)
- warmup_momentum = [
- _momentum / (1 - k) for _momentum in self.regular_mom
- ]
- elif self.warmup == 'exp':
- k = self.warmup_ratio**(1 - cur_iters / self.warmup_iters)
- warmup_momentum = [
- _momentum / k for _momentum in self.regular_mom
- ]
- return warmup_momentum
-
- if isinstance(self.regular_momentum, dict):
- momentum_groups = {}
- for key, regular_momentum in self.regular_momentum.items():
- momentum_groups[key] = _get_warmup_momentum(
- cur_iters, regular_momentum)
- return momentum_groups
- else:
- return _get_warmup_momentum(cur_iters, self.regular_momentum)
-
- def before_run(self, runner):
- # NOTE: when resuming from a checkpoint,
- # if 'initial_momentum' is not saved,
- # it will be set according to the optimizer params
- if isinstance(runner.optimizer, dict):
- self.base_momentum = {}
- for k, optim in runner.optimizer.items():
- for group in optim.param_groups:
- if 'momentum' in group.keys():
- group.setdefault('initial_momentum', group['momentum'])
- else:
- group.setdefault('initial_momentum', group['betas'][0])
- _base_momentum = [
- group['initial_momentum'] for group in optim.param_groups
- ]
- self.base_momentum.update({k: _base_momentum})
- else:
- for group in runner.optimizer.param_groups:
- if 'momentum' in group.keys():
- group.setdefault('initial_momentum', group['momentum'])
- else:
- group.setdefault('initial_momentum', group['betas'][0])
- self.base_momentum = [
- group['initial_momentum']
- for group in runner.optimizer.param_groups
- ]
-
- def before_train_epoch(self, runner):
- if not self.by_epoch:
- return
- self.regular_mom = self.get_regular_momentum(runner)
- self._set_momentum(runner, self.regular_mom)
-
- def before_train_iter(self, runner):
- cur_iter = runner.iter
- if not self.by_epoch:
- self.regular_mom = self.get_regular_momentum(runner)
- if self.warmup is None or cur_iter >= self.warmup_iters:
- self._set_momentum(runner, self.regular_mom)
- else:
- warmup_momentum = self.get_warmup_momentum(cur_iter)
- self._set_momentum(runner, warmup_momentum)
- elif self.by_epoch:
- if self.warmup is None or cur_iter > self.warmup_iters:
- return
- elif cur_iter == self.warmup_iters:
- self._set_momentum(runner, self.regular_mom)
- else:
- warmup_momentum = self.get_warmup_momentum(cur_iter)
- self._set_momentum(runner, warmup_momentum)
-
-
-@HOOKS.register_module()
-class StepMomentumUpdaterHook(MomentumUpdaterHook):
- """Step momentum scheduler with min value clipping.
-
- Args:
- step (int | list[int]): Step to decay the momentum. If an int value is
- given, regard it as the decay interval. If a list is given, decay
- momentum at these steps.
- gamma (float, optional): Decay momentum ratio. Default: 0.5.
- min_momentum (float, optional): Minimum momentum value to keep. If
- momentum after decay is lower than this value, it will be clipped
- accordingly. If None is given, we don't perform lr clipping.
- Default: None.
- """
-
- def __init__(self, step, gamma=0.5, min_momentum=None, **kwargs):
- if isinstance(step, list):
- assert mmcv.is_list_of(step, int)
- assert all([s > 0 for s in step])
- elif isinstance(step, int):
- assert step > 0
- else:
- raise TypeError('"step" must be a list or integer')
- self.step = step
- self.gamma = gamma
- self.min_momentum = min_momentum
- super(StepMomentumUpdaterHook, self).__init__(**kwargs)
-
- def get_momentum(self, runner, base_momentum):
- progress = runner.epoch if self.by_epoch else runner.iter
-
- # calculate exponential term
- if isinstance(self.step, int):
- exp = progress // self.step
- else:
- exp = len(self.step)
- for i, s in enumerate(self.step):
- if progress < s:
- exp = i
- break
-
- momentum = base_momentum * (self.gamma**exp)
- if self.min_momentum is not None:
- # clip to a minimum value
- momentum = max(momentum, self.min_momentum)
- return momentum
-
-
-@HOOKS.register_module()
-class CosineAnnealingMomentumUpdaterHook(MomentumUpdaterHook):
-
- def __init__(self, min_momentum=None, min_momentum_ratio=None, **kwargs):
- assert (min_momentum is None) ^ (min_momentum_ratio is None)
- self.min_momentum = min_momentum
- self.min_momentum_ratio = min_momentum_ratio
- super(CosineAnnealingMomentumUpdaterHook, self).__init__(**kwargs)
-
- def get_momentum(self, runner, base_momentum):
- if self.by_epoch:
- progress = runner.epoch
- max_progress = runner.max_epochs
- else:
- progress = runner.iter
- max_progress = runner.max_iters
- if self.min_momentum_ratio is not None:
- target_momentum = base_momentum * self.min_momentum_ratio
- else:
- target_momentum = self.min_momentum
- return annealing_cos(base_momentum, target_momentum,
- progress / max_progress)
-
-
-@HOOKS.register_module()
-class CyclicMomentumUpdaterHook(MomentumUpdaterHook):
- """Cyclic momentum Scheduler.
-
- Implement the cyclical momentum scheduler policy described in
- https://arxiv.org/pdf/1708.07120.pdf
-
- This momentum scheduler usually used together with the CyclicLRUpdater
- to improve the performance in the 3D detection area.
-
- Attributes:
- target_ratio (tuple[float]): Relative ratio of the lowest momentum and
- the highest momentum to the initial momentum.
- cyclic_times (int): Number of cycles during training
- step_ratio_up (float): The ratio of the increasing process of momentum
- in the total cycle.
- by_epoch (bool): Whether to update momentum by epoch.
- """
-
- def __init__(self,
- by_epoch=False,
- target_ratio=(0.85 / 0.95, 1),
- cyclic_times=1,
- step_ratio_up=0.4,
- **kwargs):
- if isinstance(target_ratio, float):
- target_ratio = (target_ratio, target_ratio / 1e5)
- elif isinstance(target_ratio, tuple):
- target_ratio = (target_ratio[0], target_ratio[0] / 1e5) \
- if len(target_ratio) == 1 else target_ratio
- else:
- raise ValueError('target_ratio should be either float '
- f'or tuple, got {type(target_ratio)}')
-
- assert len(target_ratio) == 2, \
- '"target_ratio" must be list or tuple of two floats'
- assert 0 <= step_ratio_up < 1.0, \
- '"step_ratio_up" must be in range [0,1)'
-
- self.target_ratio = target_ratio
- self.cyclic_times = cyclic_times
- self.step_ratio_up = step_ratio_up
- self.momentum_phases = [] # init momentum_phases
- # currently only support by_epoch=False
- assert not by_epoch, \
- 'currently only support "by_epoch" = False'
- super(CyclicMomentumUpdaterHook, self).__init__(by_epoch, **kwargs)
-
- def before_run(self, runner):
- super(CyclicMomentumUpdaterHook, self).before_run(runner)
- # initiate momentum_phases
- # total momentum_phases are separated as up and down
- max_iter_per_phase = runner.max_iters // self.cyclic_times
- iter_up_phase = int(self.step_ratio_up * max_iter_per_phase)
- self.momentum_phases.append(
- [0, iter_up_phase, max_iter_per_phase, 1, self.target_ratio[0]])
- self.momentum_phases.append([
- iter_up_phase, max_iter_per_phase, max_iter_per_phase,
- self.target_ratio[0], self.target_ratio[1]
- ])
-
- def get_momentum(self, runner, base_momentum):
- curr_iter = runner.iter
- for (start_iter, end_iter, max_iter_per_phase, start_ratio,
- end_ratio) in self.momentum_phases:
- curr_iter %= max_iter_per_phase
- if start_iter <= curr_iter < end_iter:
- progress = curr_iter - start_iter
- return annealing_cos(base_momentum * start_ratio,
- base_momentum * end_ratio,
- progress / (end_iter - start_iter))
-
-
-@HOOKS.register_module()
-class OneCycleMomentumUpdaterHook(MomentumUpdaterHook):
- """OneCycle momentum Scheduler.
-
- This momentum scheduler usually used together with the OneCycleLrUpdater
- to improve the performance.
-
- Args:
- base_momentum (float or list): Lower momentum boundaries in the cycle
- for each parameter group. Note that momentum is cycled inversely
- to learning rate; at the peak of a cycle, momentum is
- 'base_momentum' and learning rate is 'max_lr'.
- Default: 0.85
- max_momentum (float or list): Upper momentum boundaries in the cycle
- for each parameter group. Functionally,
- it defines the cycle amplitude (max_momentum - base_momentum).
- Note that momentum is cycled inversely
- to learning rate; at the start of a cycle, momentum is
- 'max_momentum' and learning rate is 'base_lr'
- Default: 0.95
- pct_start (float): The percentage of the cycle (in number of steps)
- spent increasing the learning rate.
- Default: 0.3
- anneal_strategy (str): {'cos', 'linear'}
- Specifies the annealing strategy: 'cos' for cosine annealing,
- 'linear' for linear annealing.
- Default: 'cos'
- three_phase (bool): If three_phase is True, use a third phase of the
- schedule to annihilate the learning rate according to
- final_div_factor instead of modifying the second phase (the first
- two phases will be symmetrical about the step indicated by
- pct_start).
- Default: False
- """
-
- def __init__(self,
- base_momentum=0.85,
- max_momentum=0.95,
- pct_start=0.3,
- anneal_strategy='cos',
- three_phase=False,
- **kwargs):
- # validate by_epoch, currently only support by_epoch=False
- if 'by_epoch' not in kwargs:
- kwargs['by_epoch'] = False
- else:
- assert not kwargs['by_epoch'], \
- 'currently only support "by_epoch" = False'
- if not isinstance(base_momentum, (float, list, dict)):
- raise ValueError('base_momentum must be the type among of float,'
- 'list or dict.')
- self._base_momentum = base_momentum
- if not isinstance(max_momentum, (float, list, dict)):
- raise ValueError('max_momentum must be the type among of float,'
- 'list or dict.')
- self._max_momentum = max_momentum
- # validate pct_start
- if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float):
- raise ValueError('Expected float between 0 and 1 pct_start, but '
- f'got {pct_start}')
- self.pct_start = pct_start
- # validate anneal_strategy
- if anneal_strategy not in ['cos', 'linear']:
- raise ValueError('anneal_strategy must by one of "cos" or '
- f'"linear", instead got {anneal_strategy}')
- elif anneal_strategy == 'cos':
- self.anneal_func = annealing_cos
- elif anneal_strategy == 'linear':
- self.anneal_func = annealing_linear
- self.three_phase = three_phase
- self.momentum_phases = [] # init momentum_phases
- super(OneCycleMomentumUpdaterHook, self).__init__(**kwargs)
-
- def before_run(self, runner):
- if isinstance(runner.optimizer, dict):
- for k, optim in runner.optimizer.items():
- if ('momentum' not in optim.defaults
- and 'betas' not in optim.defaults):
- raise ValueError('optimizer must support momentum with'
- 'option enabled')
- self.use_beta1 = 'betas' in optim.defaults
- _base_momentum = format_param(k, optim, self._base_momentum)
- _max_momentum = format_param(k, optim, self._max_momentum)
- for group, b_momentum, m_momentum in zip(
- optim.param_groups, _base_momentum, _max_momentum):
- if self.use_beta1:
- _, beta2 = group['betas']
- group['betas'] = (m_momentum, beta2)
- else:
- group['momentum'] = m_momentum
- group['base_momentum'] = b_momentum
- group['max_momentum'] = m_momentum
- else:
- optim = runner.optimizer
- if ('momentum' not in optim.defaults
- and 'betas' not in optim.defaults):
- raise ValueError('optimizer must support momentum with'
- 'option enabled')
- self.use_beta1 = 'betas' in optim.defaults
- k = type(optim).__name__
- _base_momentum = format_param(k, optim, self._base_momentum)
- _max_momentum = format_param(k, optim, self._max_momentum)
- for group, b_momentum, m_momentum in zip(optim.param_groups,
- _base_momentum,
- _max_momentum):
- if self.use_beta1:
- _, beta2 = group['betas']
- group['betas'] = (m_momentum, beta2)
- else:
- group['momentum'] = m_momentum
- group['base_momentum'] = b_momentum
- group['max_momentum'] = m_momentum
-
- if self.three_phase:
- self.momentum_phases.append({
- 'end_iter':
- float(self.pct_start * runner.max_iters) - 1,
- 'start_momentum':
- 'max_momentum',
- 'end_momentum':
- 'base_momentum'
- })
- self.momentum_phases.append({
- 'end_iter':
- float(2 * self.pct_start * runner.max_iters) - 2,
- 'start_momentum':
- 'base_momentum',
- 'end_momentum':
- 'max_momentum'
- })
- self.momentum_phases.append({
- 'end_iter': runner.max_iters - 1,
- 'start_momentum': 'max_momentum',
- 'end_momentum': 'max_momentum'
- })
- else:
- self.momentum_phases.append({
- 'end_iter':
- float(self.pct_start * runner.max_iters) - 1,
- 'start_momentum':
- 'max_momentum',
- 'end_momentum':
- 'base_momentum'
- })
- self.momentum_phases.append({
- 'end_iter': runner.max_iters - 1,
- 'start_momentum': 'base_momentum',
- 'end_momentum': 'max_momentum'
- })
-
- def _set_momentum(self, runner, momentum_groups):
- if isinstance(runner.optimizer, dict):
- for k, optim in runner.optimizer.items():
- for param_group, mom in zip(optim.param_groups,
- momentum_groups[k]):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
- else:
- for param_group, mom in zip(runner.optimizer.param_groups,
- momentum_groups):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
-
- def get_momentum(self, runner, param_group):
- curr_iter = runner.iter
- start_iter = 0
- for i, phase in enumerate(self.momentum_phases):
- end_iter = phase['end_iter']
- if curr_iter <= end_iter or i == len(self.momentum_phases) - 1:
- pct = (curr_iter - start_iter) / (end_iter - start_iter)
- momentum = self.anneal_func(
- param_group[phase['start_momentum']],
- param_group[phase['end_momentum']], pct)
- break
- start_iter = end_iter
- return momentum
-
- def get_regular_momentum(self, runner):
- if isinstance(runner.optimizer, dict):
- momentum_groups = {}
- for k, optim in runner.optimizer.items():
- _momentum_group = [
- self.get_momentum(runner, param_group)
- for param_group in optim.param_groups
- ]
- momentum_groups.update({k: _momentum_group})
- return momentum_groups
- else:
- momentum_groups = []
- for param_group in runner.optimizer.param_groups:
- momentum_groups.append(self.get_momentum(runner, param_group))
- return momentum_groups
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/core/evaluation/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/core/evaluation/__init__.py
deleted file mode 100644
index f7cc4b23413a0639e9de00eeb0bf600632d2c6cd..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/core/evaluation/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from .class_names import get_classes, get_palette
-from .eval_hooks import DistEvalHook, EvalHook
-from .metrics import eval_metrics, mean_dice, mean_fscore, mean_iou
-
-__all__ = [
- 'EvalHook', 'DistEvalHook', 'mean_dice', 'mean_iou', 'mean_fscore',
- 'eval_metrics', 'get_classes', 'get_palette'
-]
diff --git a/spaces/abnerh/video-to-subs/README.md b/spaces/abnerh/video-to-subs/README.md
deleted file mode 100644
index ed32bb5f36f156684d9ebd5458b8a93e085d7e12..0000000000000000000000000000000000000000
--- a/spaces/abnerh/video-to-subs/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Video To Subs
-emoji: 🎙️
-colorFrom: white
-colorTo: blue
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/ai-guru/composer/static/_app/pages/index.svelte-9646dd5a.js b/spaces/ai-guru/composer/static/_app/pages/index.svelte-9646dd5a.js
deleted file mode 100644
index 510a133267536503ed780d792110a1ff8173e49a..0000000000000000000000000000000000000000
--- a/spaces/ai-guru/composer/static/_app/pages/index.svelte-9646dd5a.js
+++ /dev/null
@@ -1,5 +0,0 @@
-import{S as Z,i as x,s as ee,e as b,k as I,c as w,a as E,d as g,m as C,K as se,b as d,g as P,J as f,L as U,t as N,h as H,j as Ee,E as re,M as Re,N as R,O as fe,P as Ge,w as Y,x as J,y as K,Q as Ve,q as L,o as V,B as Q,R as ue,T as Fe,f as X,U as _e,V as ie,W as qe,n as $e,p as Te,X as We,v as Xe,u as Ye,Y as Je,l as Me}from"../chunks/index-7c452e28.js";import{w as le}from"../chunks/index-d282aaf8.js";const he=le("synth"),be=le("medium"),we=le("medium"),ve=le(!1),pe=le(""),ye=le(""),ke=le(""),ne={piano:"Piano",chamber:"Chamber Music",rock_and_metal:"Rock and Metal",synth:"Synthesizer",church:"Church",timpani_strings_harp:"Timpani, Contrabass, Harp",country:"Country",reggae:"Reggae-esque"},Ke={low:"Low",medium:"Medium",high:"High"},Qe={low:"Low",medium:"Medium",high:"High",very_high:"Very High"};function Ne(l,e,s){const t=l.slice();return t[4]=e[s],t[6]=s,t}function He(l){let e,s,t,o,n,a,i,c,u,p,r,_;return{c(){e=b("label"),s=b("div"),t=b("img"),a=I(),i=b("input"),u=I(),this.h()},l(m){e=w(m,"LABEL",{"data-selected":!0,class:!0});var T=E(e);s=w(T,"DIV",{class:!0});var v=E(s);t=w(v,"IMG",{src:!0,alt:!0,class:!0}),v.forEach(g),a=C(T),i=w(T,"INPUT",{type:!0,class:!0}),u=C(T),T.forEach(g),this.h()},h(){se(t.src,o=`static/${l[4]}.svg`)||d(t,"src",o),d(t,"alt",n=ne[l[4]]),d(t,"class","svelte-1r9pswz"),d(s,"class","svelte-1r9pswz"),d(i,"type","radio"),i.__value=c=l[4],i.value=i.__value,d(i,"class","svelte-1r9pswz"),l[3][0].push(i),d(e,"data-selected",p=l[0]===l[4]),d(e,"class","svelte-1r9pswz")},m(m,T){P(m,e,T),f(e,s),f(s,t),f(e,a),f(e,i),i.checked=i.__value===l[0],f(e,u),r||(_=U(i,"change",l[2]),r=!0)},p(m,T){T&1&&(i.checked=i.__value===m[0]),T&1&&p!==(p=m[0]===m[4])&&d(e,"data-selected",p)},d(m){m&&g(e),l[3][0].splice(l[3][0].indexOf(i),1),r=!1,_()}}}function Ze(l){let e,s,t=(ne[l[0]]||"Synthesizer")+"",o,n,a,i=l[1],c=[];for(let u=0;us(0,t=i));const o=Object.keys(ne),n=[[]];function a(){t=this.__value,he.set(t)}return[t,o,a,n]}class et extends Z{constructor(e){super(),x(this,e,xe,Ze,ee,{})}}function je(l,e,s){const t=l.slice();return t[5]=e[s],t}function Be(l){let e,s=l[1][l[5]]+"",t,o,n,a,i,c,u,p;return{c(){e=b("label"),t=N(s),o=I(),n=b("input"),i=I(),this.h()},l(r){e=w(r,"LABEL",{"data-selected":!0,class:!0});var _=E(e);t=H(_,s),o=C(_),n=w(_,"INPUT",{type:!0,class:!0}),i=C(_),_.forEach(g),this.h()},h(){d(n,"type","radio"),n.__value=a=l[5],n.value=n.__value,d(n,"class","svelte-1m848u0"),l[4][0].push(n),d(e,"data-selected",c=l[5]===l[0]),d(e,"class","svelte-1m848u0")},m(r,_){P(r,e,_),f(e,t),f(e,o),f(e,n),n.checked=n.__value===l[0],f(e,i),u||(p=U(n,"change",l[3]),u=!0)},p(r,_){_&2&&s!==(s=r[1][r[5]]+"")&&Ee(t,s),_&1&&(n.checked=n.__value===r[0]),_&1&&c!==(c=r[5]===r[0])&&d(e,"data-selected",c)},d(r){r&&g(e),l[4][0].splice(l[4][0].indexOf(n),1),u=!1,p()}}}function tt(l){let e,s=l[2],t=[];for(let o=0;o{"options"in c&&s(1,t=c.options),"selection"in c&&s(0,n=c.selection)},[n,t,o,i,a]}class Ue extends Z{constructor(e){super(),x(this,e,st,tt,ee,{options:1,selection:0})}}function nt(l){let e,s,t,o,n,a,i,c;function u(r){l[1](r)}let p={options:Ke};return l[0]!==void 0&&(p.selection=l[0]),a=new Ue({props:p}),fe.push(()=>Ge(a,"selection",u)),{c(){e=b("div"),s=b("fieldset"),t=b("legend"),o=N("Note density"),n=I(),Y(a.$$.fragment),this.h()},l(r){e=w(r,"DIV",{});var _=E(e);s=w(_,"FIELDSET",{class:!0});var m=E(s);t=w(m,"LEGEND",{class:!0});var T=E(t);o=H(T,"Note density"),T.forEach(g),n=C(m),J(a.$$.fragment,m),m.forEach(g),_.forEach(g),this.h()},h(){d(t,"class","svelte-1ikh8be"),d(s,"class","svelte-1ikh8be")},m(r,_){P(r,e,_),f(e,s),f(s,t),f(t,o),f(s,n),K(a,s,null),c=!0},p(r,[_]){const m={};!i&&_&1&&(i=!0,m.selection=r[0],Ve(()=>i=!1)),a.$set(m)},i(r){c||(L(a.$$.fragment,r),c=!0)},o(r){V(a.$$.fragment,r),c=!1},d(r){r&&g(e),Q(a)}}}function lt(l,e,s){let t;R(l,be,n=>s(0,t=n));function o(n){t=n,be.set(t)}return[t,o]}class at extends Z{constructor(e){super(),x(this,e,lt,nt,ee,{})}}function ot(l){let e,s,t,o,n,a,i,c;function u(r){l[1](r)}let p={options:Qe};return l[0]!==void 0&&(p.selection=l[0]),a=new Ue({props:p}),fe.push(()=>Ge(a,"selection",u)),{c(){e=b("div"),s=b("fieldset"),t=b("legend"),o=N("Temperature"),n=I(),Y(a.$$.fragment),this.h()},l(r){e=w(r,"DIV",{});var _=E(e);s=w(_,"FIELDSET",{class:!0});var m=E(s);t=w(m,"LEGEND",{class:!0});var T=E(t);o=H(T,"Temperature"),T.forEach(g),n=C(m),J(a.$$.fragment,m),m.forEach(g),_.forEach(g),this.h()},h(){d(t,"class","svelte-1ikh8be"),d(s,"class","svelte-1ikh8be")},m(r,_){P(r,e,_),f(e,s),f(s,t),f(t,o),f(s,n),K(a,s,null),c=!0},p(r,[_]){const m={};!i&&_&1&&(i=!0,m.selection=r[0],Ve(()=>i=!1)),a.$set(m)},i(r){c||(L(a.$$.fragment,r),c=!0)},o(r){V(a.$$.fragment,r),c=!1},d(r){r&&g(e),Q(a)}}}function rt(l,e,s){let t;R(l,we,n=>s(0,t=n));function o(n){t=n,we.set(t)}return[t,o]}class it extends Z{constructor(e){super(),x(this,e,rt,ot,ee,{})}}function ct(l){let e,s,t;return{c(){e=N("Compose "),s=b("img"),this.h()},l(o){e=H(o,"Compose "),s=w(o,"IMG",{src:!0,alt:!0,class:!0}),this.h()},h(){se(s.src,t="static/wand.svg")||d(s,"src",t),d(s,"alt","Magic wand"),d(s,"class","svelte-18w38ow")},m(o,n){P(o,e,n),P(o,s,n)},d(o){o&&g(e),o&&g(s)}}}function ut(l){let e;return{c(){e=N("Composing...")},l(s){e=H(s,"Composing...")},m(s,t){P(s,e,t)},d(s){s&&g(e)}}}function ft(l){let e,s,t;function o(i,c){return i[0]?ut:ct}let n=o(l),a=n(l);return{c(){e=b("button"),a.c(),this.h()},l(i){e=w(i,"BUTTON",{class:!0});var c=E(e);a.l(c),c.forEach(g),this.h()},h(){e.disabled=l[0],d(e,"class","svelte-18w38ow")},m(i,c){P(i,e,c),a.m(e,null),s||(t=U(e,"click",l[1]),s=!0)},p(i,[c]){n!==(n=o(i))&&(a.d(1),a=n(i),a&&(a.c(),a.m(e,null))),c&1&&(e.disabled=i[0])},i:re,o:re,d(i){i&&g(e),a.d(),s=!1,t()}}}function dt(l,e,s){let t,o,n,a,i,c,u;R(l,ve,v=>s(0,t=v)),R(l,ke,v=>s(2,o=v)),R(l,ye,v=>s(3,n=v)),R(l,pe,v=>s(4,a=v)),R(l,we,v=>s(5,i=v)),R(l,be,v=>s(6,c=v)),R(l,he,v=>s(7,u=v));const p=()=>{"mediaSession"in navigator&&(navigator.mediaSession.metadata=new MediaMetadata({title:`${ne[u]} Composition`,artist:"AI Guru Composer",album:"Hugging Face",artwork:[{src:"static/hugging-face-headphones.png",sizes:"512x512",type:"image/png"}]}))},r=async({music_style:v,density:y,temperature:D})=>{var A;const S=await fetch("task/create",{method:"POST",headers:{"Content-Type":"application/json"},body:JSON.stringify({music_style:v,density:y,temperature:D})});if(!S.ok||!((A=S.headers.get("content-type"))!=null&&A.includes("application/json")))throw new Error(`Unable to create composition: [${S.status}] ${await S.text()}`);return await S.json()},_=async v=>{var D;const y=await fetch(`task/poll?task_id=${v.task_id}`);if(!y.ok||!((D=y.headers.get("content-type"))!=null&&D.includes("application/json")))throw new Error(`Unable to create composition: [${y.status}] ${await y.text()}`);return await y.json()},m=async(v,y=1e3,D=100)=>(v=await _(v),v.status==="completed"||v.status==="failed"||D&&v.poll_count>D?v:(await new Promise(S=>setTimeout(S,y)),await m(v,y,D)));return[t,async()=>{ue(ve,t=!0,t);try{const v=await r({music_style:u,density:c,temperature:i}),y=await m(v),{audio:D,image:S,tokens:j}=y.output;ue(pe,a=D,a),ue(ye,n=S,n),ue(ke,o=j,o),p()}catch(v){console.error(v)}finally{ue(ve,t=!1,t)}}]}class _t extends Z{constructor(e){super(),x(this,e,dt,ft,ee,{})}}function ce(l,{delay:e=0,duration:s=400,easing:t=Fe}={}){const o=+getComputedStyle(l).opacity;return{delay:e,duration:s,easing:t,css:n=>`opacity: ${n*o}`}}function Le(l){let e,s,t,o;return{c(){e=b("img"),this.h()},l(n){e=w(n,"IMG",{class:!0,src:!0,alt:!0,draggable:!0}),this.h()},h(){d(e,"class","play-button svelte-1536b0c"),se(e.src,s="static/play.svg")||d(e,"src",s),d(e,"alt","Play button"),d(e,"draggable","false"),X(e,"width",l[7]>100?"20%":"7.5%",!1)},m(n,a){P(n,e,a),o=!0},p(n,a){a&128&&X(e,"width",n[7]>100?"20%":"7.5%",!1)},i(n){o||(_e(()=>{t||(t=ie(e,ce,{},!0)),t.run(1)}),o=!0)},o(n){t||(t=ie(e,ce,{},!1)),t.run(0),o=!1},d(n){n&&g(e),n&&t&&t.end()}}}function ht(l){let e,s,t,o,n,a,i,c=!1,u,p=!0,r,_,m=`translate(${Math.min(l[6]*(l[1]/(l[2]-.9)),l[6])}px, -2%)`,T,v,y,D,S,j,A,q,W;function z(){cancelAnimationFrame(u),a.paused||(u=Je(z),c=!0),l[15].call(a)}let $=l[3]&&Le(l);return{c(){e=b("section"),s=b("img"),o=I(),n=b("div"),a=b("audio"),r=I(),_=b("div"),T=I(),$&&$.c(),v=I(),y=b("a"),D=N("Download"),this.h()},l(h){e=w(h,"SECTION",{class:!0});var k=E(e);s=w(k,"IMG",{class:!0,src:!0,alt:!0}),o=C(k),n=w(k,"DIV",{class:!0,tabindex:!0});var G=E(n);a=w(G,"AUDIO",{src:!0,class:!0}),E(a).forEach(g),r=C(G),_=w(G,"DIV",{class:!0}),E(_).forEach(g),T=C(G),$&&$.l(G),G.forEach(g),v=C(k),y=w(k,"A",{href:!0,download:!0,class:!0});var F=E(y);D=H(F,"Download"),F.forEach(g),k.forEach(g),this.h()},h(){d(s,"class","notes svelte-1536b0c"),se(s.src,t=l[8])||d(s,"src",t),d(s,"alt",""),se(a.src,i=l[9])||d(a,"src",i),d(a,"class","svelte-1536b0c"),l[2]===void 0&&_e(()=>l[16].call(a)),d(_,"class","handle svelte-1536b0c"),X(_,"transform",m,!1),d(n,"class","player svelte-1536b0c"),d(n,"tabindex","0"),X(n,"width",l[6]+"px",!1),X(n,"height",l[7]+"px",!1),d(y,"href",l[9]),d(y,"download",S=`${ne[l[10]]} Composition - AI Guru ft. Hugging Face.wav`),d(y,"class","download svelte-1536b0c"),d(e,"class","svelte-1536b0c")},m(h,k){P(h,e,k),f(e,s),l[14](s),f(e,o),f(e,n),f(n,a),f(n,r),f(n,_),f(n,T),$&&$.m(n,null),l[18](n),f(e,v),f(e,y),f(y,D),l[20](e),A=!0,q||(W=[U(a,"timeupdate",z),U(a,"durationchange",l[16]),U(a,"play",l[17]),U(a,"pause",l[17]),U(n,"mousemove",l[11]),U(n,"touchmove",qe(l[12])),U(n,"keydown",l[13]),U(n,"click",l[19])],q=!0)},p(h,[k]){(!A||k&256&&!se(s.src,t=h[8]))&&d(s,"src",t),(!A||k&512&&!se(a.src,i=h[9]))&&d(a,"src",i),!c&&k&2&&!isNaN(h[1])&&(a.currentTime=h[1]),c=!1,k&8&&p!==(p=h[3])&&a[p?"pause":"play"](),k&70&&m!==(m=`translate(${Math.min(h[6]*(h[1]/(h[2]-.9)),h[6])}px, -2%)`)&&X(_,"transform",m,!1),h[3]?$?($.p(h,k),k&8&&L($,1)):($=Le(h),$.c(),L($,1),$.m(n,null)):$&&($e(),V($,1,1,()=>{$=null}),Te()),k&64&&X(n,"width",h[6]+"px",!1),k&128&&X(n,"height",h[7]+"px",!1),(!A||k&512)&&d(y,"href",h[9]),(!A||k&1024&&S!==(S=`${ne[h[10]]} Composition - AI Guru ft. Hugging Face.wav`))&&d(y,"download",S)},i(h){A||(L($),_e(()=>{j||(j=ie(e,ce,{},!0)),j.run(1)}),A=!0)},o(h){V($),j||(j=ie(e,ce,{},!1)),j.run(0),A=!1},d(h){h&&g(e),l[14](null),$&&$.d(),l[18](null),l[20](null),h&&j&&j.end(),q=!1,We(W)}}}function pt(l,e,s){let t,o,n;R(l,ye,h=>s(8,t=h)),R(l,pe,h=>s(9,o=h)),R(l,he,h=>s(10,n=h));let a,i,c,u=!0,p,r,_,m;const T=()=>{s(6,_=r&&r.clientWidth),s(7,m=r&&r.clientHeight)};Xe(()=>{T(),"mediaSession"in navigator&&(navigator.mediaSession.setActionHandler("play",()=>s(3,u=!1)),navigator.mediaSession.setActionHandler("pause",()=>s(3,u=!0)),navigator.mediaSession.setActionHandler("stop",()=>{s(3,u=!0),s(1,i=0)})),window.scrollTo({top:a.offsetTop,behavior:"smooth"})}),Ye(()=>{T()});const v=h=>{if(!c||!h.buttons)return;const{left:k,right:G}=p.getBoundingClientRect();s(1,i=c*(h.clientX-k)/(G-k))},y=h=>{if(!c)return;const{left:k,right:G}=p.getBoundingClientRect();s(1,i=c*(h.touches[0].clientX-k)/(G-k))},D=h=>{h.preventDefault(),h.code==="Space"&&s(3,u=!u),h.code==="ArrowLeft"&&s(1,i=i>=1?i-1:0),h.code==="ArrowRight"&&s(1,i=i<=c-1?i+1:c)};function S(h){fe[h?"unshift":"push"](()=>{r=h,s(5,r)})}function j(){i=this.currentTime,s(1,i)}function A(){c=this.duration,s(2,c)}function q(){u=this.paused,s(3,u)}function W(h){fe[h?"unshift":"push"](()=>{p=h,s(4,p)})}const z=()=>s(3,u=!u);function $(h){fe[h?"unshift":"push"](()=>{a=h,s(0,a)})}return[a,i,c,u,p,r,_,m,t,o,n,v,y,D,S,j,A,q,W,z,$]}class mt extends Z{constructor(e){super(),x(this,e,pt,ht,ee,{})}}function ze(l){let e,s,t,o,n,a,i,c;return{c(){e=b("section"),s=b("h2"),t=N("Tokenized notes"),o=I(),n=b("p"),a=N(l[0]),this.h()},l(u){e=w(u,"SECTION",{class:!0});var p=E(e);s=w(p,"H2",{});var r=E(s);t=H(r,"Tokenized notes"),r.forEach(g),o=C(p),n=w(p,"P",{class:!0});var _=E(n);a=H(_,l[0]),_.forEach(g),p.forEach(g),this.h()},h(){d(n,"class","svelte-4un5mw"),d(e,"class","svelte-4un5mw")},m(u,p){P(u,e,p),f(e,s),f(s,t),f(e,o),f(e,n),f(n,a),c=!0},p(u,p){(!c||p&1)&&Ee(a,u[0])},i(u){c||(_e(()=>{i||(i=ie(e,ce,{},!0)),i.run(1)}),c=!0)},o(u){i||(i=ie(e,ce,{},!1)),i.run(0),c=!1},d(u){u&&g(e),u&&i&&i.end()}}}function gt(l){let e,s,t=l[0]&&ze(l);return{c(){t&&t.c(),e=Me()},l(o){t&&t.l(o),e=Me()},m(o,n){t&&t.m(o,n),P(o,e,n),s=!0},p(o,[n]){o[0]?t?(t.p(o,n),n&1&&L(t,1)):(t=ze(o),t.c(),L(t,1),t.m(e.parentNode,e)):t&&($e(),V(t,1,1,()=>{t=null}),Te())},i(o){s||(L(t),s=!0)},o(o){V(t),s=!1},d(o){t&&t.d(o),o&&g(e)}}}function vt(l,e,s){let t;return R(l,ke,o=>s(0,t=o)),[t]}class bt extends Z{constructor(e){super(),x(this,e,vt,gt,ee,{})}}function Pe(l){let e,s,t,o;return e=new mt({}),t=new bt({}),{c(){Y(e.$$.fragment),s=I(),Y(t.$$.fragment)},l(n){J(e.$$.fragment,n),s=C(n),J(t.$$.fragment,n)},m(n,a){K(e,n,a),P(n,s,a),K(t,n,a),o=!0},i(n){o||(L(e.$$.fragment,n),L(t.$$.fragment,n),o=!0)},o(n){V(e.$$.fragment,n),V(t.$$.fragment,n),o=!1},d(n){Q(e,n),n&&g(s),Q(t,n)}}}function wt(l){let e,s,t,o,n,a,i,c,u,p,r,_,m,T,v,y,D,S,j,A,q,W,z,$,h,k,G,F,me,te,ge,de;$=new et({}),k=new at({}),F=new it({}),te=new _t({});let M=l[0]&&Pe();return{c(){e=b("main"),s=b("h1"),t=N("Composer"),o=I(),n=b("p"),a=N("Trained on fifteen thousand songs. One AI model. Infinite compositions."),i=I(),c=b("p"),u=N(`This space contains a deep neural network model that can compose music. You can use it to generate audio in
- different styles, 4 bars at a time.`),p=I(),r=b("p"),_=N("Developed by "),m=b("a"),T=N("Ron Au"),v=N(` and
- `),y=b("a"),D=N("Tristan Behrens"),S=N("."),j=I(),A=b("p"),q=N("Have fun! And always feel free to send us some feedback and share your compositions!"),W=I(),z=b("section"),Y($.$$.fragment),h=I(),Y(k.$$.fragment),G=I(),Y(F.$$.fragment),me=I(),Y(te.$$.fragment),ge=I(),M&&M.c(),this.h()},l(B){e=w(B,"MAIN",{class:!0});var O=E(e);s=w(O,"H1",{class:!0});var Ie=E(s);t=H(Ie,"Composer"),Ie.forEach(g),o=C(O),n=w(O,"P",{class:!0});var Ce=E(n);a=H(Ce,"Trained on fifteen thousand songs. One AI model. Infinite compositions."),Ce.forEach(g),i=C(O),c=w(O,"P",{class:!0});var De=E(c);u=H(De,`This space contains a deep neural network model that can compose music. You can use it to generate audio in
- different styles, 4 bars at a time.`),De.forEach(g),p=C(O),r=w(O,"P",{class:!0});var ae=E(r);_=H(ae,"Developed by "),m=w(ae,"A",{href:!0,rel:!0,target:!0});var Se=E(m);T=H(Se,"Ron Au"),Se.forEach(g),v=H(ae,` and
- `),y=w(ae,"A",{href:!0,rel:!0,target:!0});var Ae=E(y);D=H(Ae,"Tristan Behrens"),Ae.forEach(g),S=H(ae,"."),ae.forEach(g),j=C(O),A=w(O,"P",{class:!0});var Oe=E(A);q=H(Oe,"Have fun! And always feel free to send us some feedback and share your compositions!"),Oe.forEach(g),W=C(O),z=w(O,"SECTION",{id:!0,class:!0});var oe=E(z);J($.$$.fragment,oe),h=C(oe),J(k.$$.fragment,oe),G=C(oe),J(F.$$.fragment,oe),oe.forEach(g),me=C(O),J(te.$$.fragment,O),ge=C(O),M&&M.l(O),O.forEach(g),this.h()},h(){d(s,"class","svelte-1rfjlkw"),d(n,"class","heading svelte-1rfjlkw"),d(c,"class","svelte-1rfjlkw"),d(m,"href","https://twitter.com/ronvoluted"),d(m,"rel","noopener"),d(m,"target","_blank"),d(y,"href","https://twitter.com/DrTBehrens"),d(y,"rel","noopener"),d(y,"target","_blank"),d(r,"class","svelte-1rfjlkw"),d(A,"class","svelte-1rfjlkw"),d(z,"id","options"),d(z,"class","svelte-1rfjlkw"),d(e,"class","svelte-1rfjlkw")},m(B,O){P(B,e,O),f(e,s),f(s,t),f(e,o),f(e,n),f(n,a),f(e,i),f(e,c),f(c,u),f(e,p),f(e,r),f(r,_),f(r,m),f(m,T),f(r,v),f(r,y),f(y,D),f(r,S),f(e,j),f(e,A),f(A,q),f(e,W),f(e,z),K($,z,null),f(z,h),K(k,z,null),f(z,G),K(F,z,null),f(e,me),K(te,e,null),f(e,ge),M&&M.m(e,null),de=!0},p(B,[O]){B[0]?M?O&1&&L(M,1):(M=Pe(),M.c(),L(M,1),M.m(e,null)):M&&($e(),V(M,1,1,()=>{M=null}),Te())},i(B){de||(L($.$$.fragment,B),L(k.$$.fragment,B),L(F.$$.fragment,B),L(te.$$.fragment,B),L(M),de=!0)},o(B){V($.$$.fragment,B),V(k.$$.fragment,B),V(F.$$.fragment,B),V(te.$$.fragment,B),V(M),de=!1},d(B){B&&g(e),Q($),Q(k),Q(F),Q(te),M&&M.d()}}}function yt(l,e,s){let t;return R(l,pe,o=>s(0,t=o)),[t]}class $t extends Z{constructor(e){super(),x(this,e,yt,wt,ee,{})}}export{$t as default};
diff --git a/spaces/aimstack/bloom/Dockerfile b/spaces/aimstack/bloom/Dockerfile
deleted file mode 100644
index 1b5313643bce150166d4135b441cd315634a68b9..0000000000000000000000000000000000000000
--- a/spaces/aimstack/bloom/Dockerfile
+++ /dev/null
@@ -1,30 +0,0 @@
-FROM python:3.9
-
-
-RUN useradd -m -u 1000 aim_user
-
-# Switch to the "aim_user" user
-USER aim_user
-
-# Set home to the user's home directory
-ENV HOME=/home/aim_user \
- PATH=/home/aim_user/.local/bin:$PATH
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME
-
-# install the `aim` package on the latest version
-RUN pip install aim==4.0.0.dev6
-
-RUN aim telemetry off
-
-ENTRYPOINT ["/bin/sh", "-c"]
-
-COPY aim_repo.tar.gz .
-RUN tar xvzf aim_repo.tar.gz
-# have to run `aim init` in the directory that stores aim data for
-# otherwise `aim up` will prompt for confirmation to create the directory itself.
-# We run aim listening on 0.0.0.0 to expose all ports. Also, we run
-# using `--dev` to print verbose logs. Port 43800 is the default port of
-# `aim up` but explicit is better than implicit.
-CMD ["aim up --host 0.0.0.0 --port 7860 --workers 2"]
\ No newline at end of file
diff --git a/spaces/airus/img-to-music/constants.py b/spaces/airus/img-to-music/constants.py
deleted file mode 100644
index 86863d1b778d4c66f0d8e1e0b699f1bb937c1d50..0000000000000000000000000000000000000000
--- a/spaces/airus/img-to-music/constants.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import numpy as np
-import os
-
-MUBERT_LICENSE = os.environ.get('MUBERT_LICENSE')
-MUBERT_TOKEN = os.environ.get('MUBERT_TOKEN')
-
-MUBERT_MODE = "loop"
-MUBERT_TAGS_STRING = 'tribal,action,kids,neo-classic,run 130,pumped,jazz / funk,ethnic,dubtechno,reggae,acid jazz,liquidfunk,funk,witch house,tech house,underground,artists,mystical,disco,sensorium,r&b,agender,psychedelic trance / psytrance,peaceful,run 140,piano,run 160,setting,meditation,christmas,ambient,horror,cinematic,electro house,idm,bass,minimal,underscore,drums,glitchy,beautiful,technology,tribal house,country pop,jazz & funk,documentary,space,classical,valentines,chillstep,experimental,trap,new jack swing,drama,post-rock,tense,corporate,neutral,happy,analog,funky,spiritual,sberzvuk special,chill hop,dramatic,catchy,holidays,fitness 90,optimistic,orchestra,acid techno,energizing,romantic,minimal house,breaks,hyper pop,warm up,dreamy,dark,urban,microfunk,dub,nu disco,vogue,keys,hardcore,aggressive,indie,electro funk,beauty,relaxing,trance,pop,hiphop,soft,acoustic,chillrave / ethno-house,deep techno,angry,dance,fun,dubstep,tropical,latin pop,heroic,world music,inspirational,uplifting,atmosphere,art,epic,advertising,chillout,scary,spooky,slow ballad,saxophone,summer,erotic,jazzy,energy 100,kara mar,xmas,atmospheric,indie pop,hip-hop,yoga,reggaeton,lounge,travel,running,folk,chillrave & ethno-house,detective,darkambient,chill,fantasy,minimal techno,special,night,tropical house,downtempo,lullaby,meditative,upbeat,glitch hop,fitness,neurofunk,sexual,indie rock,future pop,jazz,cyberpunk,melancholic,happy hardcore,family / kids,synths,electric guitar,comedy,psychedelic trance & psytrance,edm,psychedelic rock,calm,zen,bells,podcast,melodic house,ethnic percussion,nature,heavy,bassline,indie dance,techno,drumnbass,synth pop,vaporwave,sad,8-bit,chillgressive,deep,orchestral,futuristic,hardtechno,nostalgic,big room,sci-fi,tutorial,joyful,pads,minimal 170,drill,ethnic 108,amusing,sleepy ambient,psychill,italo disco,lofi,house,acoustic guitar,bassline house,rock,k-pop,synthwave,deep house,electronica,gabber,nightlife,sport & fitness,road trip,celebration,electro,disco house,electronic'
-MUBERT_TAGS = np.array(MUBERT_TAGS_STRING.split(','))
\ No newline at end of file
diff --git a/spaces/akhaliq/Real-ESRGAN/realesrgan/data/realesrgan_paired_dataset.py b/spaces/akhaliq/Real-ESRGAN/realesrgan/data/realesrgan_paired_dataset.py
deleted file mode 100644
index 386c8d72496245dae8df033c2ebbd76b41ff45f1..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Real-ESRGAN/realesrgan/data/realesrgan_paired_dataset.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import os
-from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb
-from basicsr.data.transforms import augment, paired_random_crop
-from basicsr.utils import FileClient, imfrombytes, img2tensor
-from basicsr.utils.registry import DATASET_REGISTRY
-from torch.utils import data as data
-from torchvision.transforms.functional import normalize
-
-
-@DATASET_REGISTRY.register()
-class RealESRGANPairedDataset(data.Dataset):
- """Paired image dataset for image restoration.
-
- Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs.
-
- There are three modes:
- 1. 'lmdb': Use lmdb files.
- If opt['io_backend'] == lmdb.
- 2. 'meta_info': Use meta information file to generate paths.
- If opt['io_backend'] != lmdb and opt['meta_info'] is not None.
- 3. 'folder': Scan folders to generate paths.
- The rest.
-
- Args:
- opt (dict): Config for train datasets. It contains the following keys:
- dataroot_gt (str): Data root path for gt.
- dataroot_lq (str): Data root path for lq.
- meta_info (str): Path for meta information file.
- io_backend (dict): IO backend type and other kwarg.
- filename_tmpl (str): Template for each filename. Note that the template excludes the file extension.
- Default: '{}'.
- gt_size (int): Cropped patched size for gt patches.
- use_hflip (bool): Use horizontal flips.
- use_rot (bool): Use rotation (use vertical flip and transposing h
- and w for implementation).
-
- scale (bool): Scale, which will be added automatically.
- phase (str): 'train' or 'val'.
- """
-
- def __init__(self, opt):
- super(RealESRGANPairedDataset, self).__init__()
- self.opt = opt
- self.file_client = None
- self.io_backend_opt = opt['io_backend']
- # mean and std for normalizing the input images
- self.mean = opt['mean'] if 'mean' in opt else None
- self.std = opt['std'] if 'std' in opt else None
-
- self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
- self.filename_tmpl = opt['filename_tmpl'] if 'filename_tmpl' in opt else '{}'
-
- # file client (lmdb io backend)
- if self.io_backend_opt['type'] == 'lmdb':
- self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
- self.io_backend_opt['client_keys'] = ['lq', 'gt']
- self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt'])
- elif 'meta_info' in self.opt and self.opt['meta_info'] is not None:
- # disk backend with meta_info
- # Each line in the meta_info describes the relative path to an image
- with open(self.opt['meta_info']) as fin:
- paths = [line.strip() for line in fin]
- self.paths = []
- for path in paths:
- gt_path, lq_path = path.split(', ')
- gt_path = os.path.join(self.gt_folder, gt_path)
- lq_path = os.path.join(self.lq_folder, lq_path)
- self.paths.append(dict([('gt_path', gt_path), ('lq_path', lq_path)]))
- else:
- # disk backend
- # it will scan the whole folder to get meta info
- # it will be time-consuming for folders with too many files. It is recommended using an extra meta txt file
- self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl)
-
- def __getitem__(self, index):
- if self.file_client is None:
- self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
-
- scale = self.opt['scale']
-
- # Load gt and lq images. Dimension order: HWC; channel order: BGR;
- # image range: [0, 1], float32.
- gt_path = self.paths[index]['gt_path']
- img_bytes = self.file_client.get(gt_path, 'gt')
- img_gt = imfrombytes(img_bytes, float32=True)
- lq_path = self.paths[index]['lq_path']
- img_bytes = self.file_client.get(lq_path, 'lq')
- img_lq = imfrombytes(img_bytes, float32=True)
-
- # augmentation for training
- if self.opt['phase'] == 'train':
- gt_size = self.opt['gt_size']
- # random crop
- img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path)
- # flip, rotation
- img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot'])
-
- # BGR to RGB, HWC to CHW, numpy to tensor
- img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True)
- # normalize
- if self.mean is not None or self.std is not None:
- normalize(img_lq, self.mean, self.std, inplace=True)
- normalize(img_gt, self.mean, self.std, inplace=True)
-
- return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path}
-
- def __len__(self):
- return len(self.paths)
diff --git a/spaces/akhaliq/SummerTime/evaluation/rougewe_metric.py b/spaces/akhaliq/SummerTime/evaluation/rougewe_metric.py
deleted file mode 100644
index b27aa0ce2266903a3aa898e6e1e4ea095ecbf1cf..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SummerTime/evaluation/rougewe_metric.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from evaluation.summeval_metric import SummEvalMetric
-from typing import List, Dict
-
-import nltk
-
-
-class RougeWe(SummEvalMetric):
- metric_name = "rougeWE"
- range = (0, 1)
- higher_is_better = True
- requires_heavy_compute = True
-
- def __init__(self):
- from summ_eval.rouge_we_metric import RougeWeMetric
-
- nltk.download("stopwords")
- se_metric = RougeWeMetric()
- super(RougeWe, self).__init__(se_metric)
-
- def evaluate(
- self, inputs: List[str], targets: List[str], keys: List[str] = ["rouge_we_3_f"]
- ) -> Dict[str, float]:
- # TODO zhangir: update when dataset api is merged.
- return super(RougeWe, self).evaluate(inputs, targets, keys)
diff --git a/spaces/akhaliq/runwayml-stable-diffusion-v1-5/README.md b/spaces/akhaliq/runwayml-stable-diffusion-v1-5/README.md
deleted file mode 100644
index 26a6a20988103807b5f70ec8d8d68cb530640a10..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/runwayml-stable-diffusion-v1-5/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Runwayml Stable Diffusion V1 5
-emoji: 📉
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/alexiserodriguez/whisper-transcription-app/README.md b/spaces/alexiserodriguez/whisper-transcription-app/README.md
deleted file mode 100644
index e35a01a8bf10a8e6c8e19acf836343dc6a32e40e..0000000000000000000000000000000000000000
--- a/spaces/alexiserodriguez/whisper-transcription-app/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Whisper Transcription App
-emoji: 🐢
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/glibc.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/glibc.py
deleted file mode 100644
index 7bd3c20681d865cb4fa42617cf939b5512c7663f..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/glibc.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# The following comment should be removed at some point in the future.
-# mypy: strict-optional=False
-
-import os
-import sys
-from typing import Optional, Tuple
-
-
-def glibc_version_string() -> Optional[str]:
- "Returns glibc version string, or None if not using glibc."
- return glibc_version_string_confstr() or glibc_version_string_ctypes()
-
-
-def glibc_version_string_confstr() -> Optional[str]:
- "Primary implementation of glibc_version_string using os.confstr."
- # os.confstr is quite a bit faster than ctypes.DLL. It's also less likely
- # to be broken or missing. This strategy is used in the standard library
- # platform module:
- # https://github.com/python/cpython/blob/fcf1d003bf4f0100c9d0921ff3d70e1127ca1b71/Lib/platform.py#L175-L183
- if sys.platform == "win32":
- return None
- try:
- # os.confstr("CS_GNU_LIBC_VERSION") returns a string like "glibc 2.17":
- _, version = os.confstr("CS_GNU_LIBC_VERSION").split()
- except (AttributeError, OSError, ValueError):
- # os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)...
- return None
- return version
-
-
-def glibc_version_string_ctypes() -> Optional[str]:
- "Fallback implementation of glibc_version_string using ctypes."
-
- try:
- import ctypes
- except ImportError:
- return None
-
- # ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen
- # manpage says, "If filename is NULL, then the returned handle is for the
- # main program". This way we can let the linker do the work to figure out
- # which libc our process is actually using.
- process_namespace = ctypes.CDLL(None)
- try:
- gnu_get_libc_version = process_namespace.gnu_get_libc_version
- except AttributeError:
- # Symbol doesn't exist -> therefore, we are not linked to
- # glibc.
- return None
-
- # Call gnu_get_libc_version, which returns a string like "2.5"
- gnu_get_libc_version.restype = ctypes.c_char_p
- version_str = gnu_get_libc_version()
- # py2 / py3 compatibility:
- if not isinstance(version_str, str):
- version_str = version_str.decode("ascii")
-
- return version_str
-
-
-# platform.libc_ver regularly returns completely nonsensical glibc
-# versions. E.g. on my computer, platform says:
-#
-# ~$ python2.7 -c 'import platform; print(platform.libc_ver())'
-# ('glibc', '2.7')
-# ~$ python3.5 -c 'import platform; print(platform.libc_ver())'
-# ('glibc', '2.9')
-#
-# But the truth is:
-#
-# ~$ ldd --version
-# ldd (Debian GLIBC 2.22-11) 2.22
-#
-# This is unfortunate, because it means that the linehaul data on libc
-# versions that was generated by pip 8.1.2 and earlier is useless and
-# misleading. Solution: instead of using platform, use our code that actually
-# works.
-def libc_ver() -> Tuple[str, str]:
- """Try to determine the glibc version
-
- Returns a tuple of strings (lib, version) which default to empty strings
- in case the lookup fails.
- """
- glibc_version = glibc_version_string()
- if glibc_version is None:
- return ("", "")
- else:
- return ("glibc", glibc_version)
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/temp_dir.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/temp_dir.py
deleted file mode 100644
index 442679a758b7b4b965ce3e615a243a7a6d318b4e..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/temp_dir.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import errno
-import itertools
-import logging
-import os.path
-import tempfile
-from contextlib import ExitStack, contextmanager
-from typing import Any, Dict, Iterator, Optional, TypeVar, Union
-
-from pip._internal.utils.misc import enum, rmtree
-
-logger = logging.getLogger(__name__)
-
-_T = TypeVar("_T", bound="TempDirectory")
-
-
-# Kinds of temporary directories. Only needed for ones that are
-# globally-managed.
-tempdir_kinds = enum(
- BUILD_ENV="build-env",
- EPHEM_WHEEL_CACHE="ephem-wheel-cache",
- REQ_BUILD="req-build",
-)
-
-
-_tempdir_manager: Optional[ExitStack] = None
-
-
-@contextmanager
-def global_tempdir_manager() -> Iterator[None]:
- global _tempdir_manager
- with ExitStack() as stack:
- old_tempdir_manager, _tempdir_manager = _tempdir_manager, stack
- try:
- yield
- finally:
- _tempdir_manager = old_tempdir_manager
-
-
-class TempDirectoryTypeRegistry:
- """Manages temp directory behavior"""
-
- def __init__(self) -> None:
- self._should_delete: Dict[str, bool] = {}
-
- def set_delete(self, kind: str, value: bool) -> None:
- """Indicate whether a TempDirectory of the given kind should be
- auto-deleted.
- """
- self._should_delete[kind] = value
-
- def get_delete(self, kind: str) -> bool:
- """Get configured auto-delete flag for a given TempDirectory type,
- default True.
- """
- return self._should_delete.get(kind, True)
-
-
-_tempdir_registry: Optional[TempDirectoryTypeRegistry] = None
-
-
-@contextmanager
-def tempdir_registry() -> Iterator[TempDirectoryTypeRegistry]:
- """Provides a scoped global tempdir registry that can be used to dictate
- whether directories should be deleted.
- """
- global _tempdir_registry
- old_tempdir_registry = _tempdir_registry
- _tempdir_registry = TempDirectoryTypeRegistry()
- try:
- yield _tempdir_registry
- finally:
- _tempdir_registry = old_tempdir_registry
-
-
-class _Default:
- pass
-
-
-_default = _Default()
-
-
-class TempDirectory:
- """Helper class that owns and cleans up a temporary directory.
-
- This class can be used as a context manager or as an OO representation of a
- temporary directory.
-
- Attributes:
- path
- Location to the created temporary directory
- delete
- Whether the directory should be deleted when exiting
- (when used as a contextmanager)
-
- Methods:
- cleanup()
- Deletes the temporary directory
-
- When used as a context manager, if the delete attribute is True, on
- exiting the context the temporary directory is deleted.
- """
-
- def __init__(
- self,
- path: Optional[str] = None,
- delete: Union[bool, None, _Default] = _default,
- kind: str = "temp",
- globally_managed: bool = False,
- ):
- super().__init__()
-
- if delete is _default:
- if path is not None:
- # If we were given an explicit directory, resolve delete option
- # now.
- delete = False
- else:
- # Otherwise, we wait until cleanup and see what
- # tempdir_registry says.
- delete = None
-
- # The only time we specify path is in for editables where it
- # is the value of the --src option.
- if path is None:
- path = self._create(kind)
-
- self._path = path
- self._deleted = False
- self.delete = delete
- self.kind = kind
-
- if globally_managed:
- assert _tempdir_manager is not None
- _tempdir_manager.enter_context(self)
-
- @property
- def path(self) -> str:
- assert not self._deleted, f"Attempted to access deleted path: {self._path}"
- return self._path
-
- def __repr__(self) -> str:
- return f"<{self.__class__.__name__} {self.path!r}>"
-
- def __enter__(self: _T) -> _T:
- return self
-
- def __exit__(self, exc: Any, value: Any, tb: Any) -> None:
- if self.delete is not None:
- delete = self.delete
- elif _tempdir_registry:
- delete = _tempdir_registry.get_delete(self.kind)
- else:
- delete = True
-
- if delete:
- self.cleanup()
-
- def _create(self, kind: str) -> str:
- """Create a temporary directory and store its path in self.path"""
- # We realpath here because some systems have their default tmpdir
- # symlinked to another directory. This tends to confuse build
- # scripts, so we canonicalize the path by traversing potential
- # symlinks here.
- path = os.path.realpath(tempfile.mkdtemp(prefix=f"pip-{kind}-"))
- logger.debug("Created temporary directory: %s", path)
- return path
-
- def cleanup(self) -> None:
- """Remove the temporary directory created and reset state"""
- self._deleted = True
- if not os.path.exists(self._path):
- return
- rmtree(self._path)
-
-
-class AdjacentTempDirectory(TempDirectory):
- """Helper class that creates a temporary directory adjacent to a real one.
-
- Attributes:
- original
- The original directory to create a temp directory for.
- path
- After calling create() or entering, contains the full
- path to the temporary directory.
- delete
- Whether the directory should be deleted when exiting
- (when used as a contextmanager)
-
- """
-
- # The characters that may be used to name the temp directory
- # We always prepend a ~ and then rotate through these until
- # a usable name is found.
- # pkg_resources raises a different error for .dist-info folder
- # with leading '-' and invalid metadata
- LEADING_CHARS = "-~.=%0123456789"
-
- def __init__(self, original: str, delete: Optional[bool] = None) -> None:
- self.original = original.rstrip("/\\")
- super().__init__(delete=delete)
-
- @classmethod
- def _generate_names(cls, name: str) -> Iterator[str]:
- """Generates a series of temporary names.
-
- The algorithm replaces the leading characters in the name
- with ones that are valid filesystem characters, but are not
- valid package names (for both Python and pip definitions of
- package).
- """
- for i in range(1, len(name)):
- for candidate in itertools.combinations_with_replacement(
- cls.LEADING_CHARS, i - 1
- ):
- new_name = "~" + "".join(candidate) + name[i:]
- if new_name != name:
- yield new_name
-
- # If we make it this far, we will have to make a longer name
- for i in range(len(cls.LEADING_CHARS)):
- for candidate in itertools.combinations_with_replacement(
- cls.LEADING_CHARS, i
- ):
- new_name = "~" + "".join(candidate) + name
- if new_name != name:
- yield new_name
-
- def _create(self, kind: str) -> str:
- root, name = os.path.split(self.original)
- for candidate in self._generate_names(name):
- path = os.path.join(root, candidate)
- try:
- os.mkdir(path)
- except OSError as ex:
- # Continue if the name exists already
- if ex.errno != errno.EEXIST:
- raise
- else:
- path = os.path.realpath(path)
- break
- else:
- # Final fallback on the default behavior.
- path = os.path.realpath(tempfile.mkdtemp(prefix=f"pip-{kind}-"))
-
- logger.debug("Created temporary directory: %s", path)
- return path
diff --git a/spaces/anaclaudia13ct/insect_detection/utils/segment/augmentations.py b/spaces/anaclaudia13ct/insect_detection/utils/segment/augmentations.py
deleted file mode 100644
index 169addedf0f58cf37e774e6a85eddff6eebc30be..0000000000000000000000000000000000000000
--- a/spaces/anaclaudia13ct/insect_detection/utils/segment/augmentations.py
+++ /dev/null
@@ -1,104 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Image augmentation functions
-"""
-
-import math
-import random
-
-import cv2
-import numpy as np
-
-from ..augmentations import box_candidates
-from ..general import resample_segments, segment2box
-
-
-def mixup(im, labels, segments, im2, labels2, segments2):
- # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
- r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
- im = (im * r + im2 * (1 - r)).astype(np.uint8)
- labels = np.concatenate((labels, labels2), 0)
- segments = np.concatenate((segments, segments2), 0)
- return im, labels, segments
-
-
-def random_perspective(im,
- targets=(),
- segments=(),
- degrees=10,
- translate=.1,
- scale=.1,
- shear=10,
- perspective=0.0,
- border=(0, 0)):
- # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
- # targets = [cls, xyxy]
-
- height = im.shape[0] + border[0] * 2 # shape(h,w,c)
- width = im.shape[1] + border[1] * 2
-
- # Center
- C = np.eye(3)
- C[0, 2] = -im.shape[1] / 2 # x translation (pixels)
- C[1, 2] = -im.shape[0] / 2 # y translation (pixels)
-
- # Perspective
- P = np.eye(3)
- P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
- P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
-
- # Rotation and Scale
- R = np.eye(3)
- a = random.uniform(-degrees, degrees)
- # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
- s = random.uniform(1 - scale, 1 + scale)
- # s = 2 ** random.uniform(-scale, scale)
- R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
-
- # Shear
- S = np.eye(3)
- S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
- S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
-
- # Translation
- T = np.eye(3)
- T[0, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * width) # x translation (pixels)
- T[1, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * height) # y translation (pixels)
-
- # Combined rotation matrix
- M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
- if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
- if perspective:
- im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
- else: # affine
- im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
-
- # Visualize
- # import matplotlib.pyplot as plt
- # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
- # ax[0].imshow(im[:, :, ::-1]) # base
- # ax[1].imshow(im2[:, :, ::-1]) # warped
-
- # Transform label coordinates
- n = len(targets)
- new_segments = []
- if n:
- new = np.zeros((n, 4))
- segments = resample_segments(segments) # upsample
- for i, segment in enumerate(segments):
- xy = np.ones((len(segment), 3))
- xy[:, :2] = segment
- xy = xy @ M.T # transform
- xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]) # perspective rescale or affine
-
- # clip
- new[i] = segment2box(xy, width, height)
- new_segments.append(xy)
-
- # filter candidates
- i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01)
- targets = targets[i]
- targets[:, 1:5] = new[i]
- new_segments = np.array(new_segments)[i]
-
- return im, targets, new_segments
diff --git a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/models/vitseg.py b/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/models/vitseg.py
deleted file mode 100644
index ed621431ddf930fcfa27b5929999776b96fede63..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/models/vitseg.py
+++ /dev/null
@@ -1,286 +0,0 @@
-import math
-from posixpath import basename, dirname, join
-# import clip
-from clip.model import convert_weights
-import torch
-import json
-from torch import nn
-from torch.nn import functional as nnf
-from torch.nn.modules import activation
-from torch.nn.modules.activation import ReLU
-from torchvision import transforms
-
-normalize = transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
-
-from torchvision.models import ResNet
-
-
-def process_prompts(conditional, prompt_list, conditional_map):
- # DEPRECATED
-
- # randomly sample a synonym
- words = [conditional_map[int(i)] for i in conditional]
- words = [syns[torch.multinomial(torch.ones(len(syns)), 1, replacement=True).item()] for syns in words]
- words = [w.replace('_', ' ') for w in words]
-
- if prompt_list is not None:
- prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
- prompts = [prompt_list[i] for i in prompt_indices]
- else:
- prompts = ['a photo of {}'] * (len(words))
-
- return [promt.format(w) for promt, w in zip(prompts, words)]
-
-
-class VITDenseBase(nn.Module):
-
- def rescaled_pos_emb(self, new_size):
- assert len(new_size) == 2
-
- a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape)
- b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T
- return torch.cat([self.model.positional_embedding[:1], b])
-
- def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None):
-
- with torch.no_grad():
-
- x_inp = nnf.interpolate(x_inp, (384, 384))
-
- x = self.model.patch_embed(x_inp)
- cls_token = self.model.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks
- if self.model.dist_token is None:
- x = torch.cat((cls_token, x), dim=1)
- else:
- x = torch.cat((cls_token, self.model.dist_token.expand(x.shape[0], -1, -1), x), dim=1)
- x = self.model.pos_drop(x + self.model.pos_embed)
-
- activations = []
- for i, block in enumerate(self.model.blocks):
- x = block(x)
-
- if i in extract_layers:
- # permute to be compatible with CLIP
- activations += [x.permute(1,0,2)]
-
- x = self.model.norm(x)
- x = self.model.head(self.model.pre_logits(x[:, 0]))
-
- # again for CLIP compatibility
- # x = x.permute(1, 0, 2)
-
- return x, activations, None
-
- def sample_prompts(self, words, prompt_list=None):
-
- prompt_list = prompt_list if prompt_list is not None else self.prompt_list
-
- prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
- prompts = [prompt_list[i] for i in prompt_indices]
- return [promt.format(w) for promt, w in zip(prompts, words)]
-
- def get_cond_vec(self, conditional, batch_size):
- # compute conditional from a single string
- if conditional is not None and type(conditional) == str:
- cond = self.compute_conditional(conditional)
- cond = cond.repeat(batch_size, 1)
-
- # compute conditional from string list/tuple
- elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str:
- assert len(conditional) == batch_size
- cond = self.compute_conditional(conditional)
-
- # use conditional directly
- elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2:
- cond = conditional
-
- # compute conditional from image
- elif conditional is not None and type(conditional) == torch.Tensor:
- with torch.no_grad():
- cond, _, _ = self.visual_forward(conditional)
- else:
- raise ValueError('invalid conditional')
- return cond
-
- def compute_conditional(self, conditional):
- import clip
-
- dev = next(self.parameters()).device
-
- if type(conditional) in {list, tuple}:
- text_tokens = clip.tokenize(conditional).to(dev)
- cond = self.clip_model.encode_text(text_tokens)
- else:
- if conditional in self.precomputed_prompts:
- cond = self.precomputed_prompts[conditional].float().to(dev)
- else:
- text_tokens = clip.tokenize([conditional]).to(dev)
- cond = self.clip_model.encode_text(text_tokens)[0]
-
- return cond
-
-
-class VITDensePredT(VITDenseBase):
-
- def __init__(self, extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed',
- depth=3, extra_blocks=0, reduce_cond=None, fix_shift=False,
- learn_trans_conv_only=False, refine=None, limit_to_clip_only=False, upsample=False,
- add_calibration=False, process_cond=None, not_pretrained=False):
- super().__init__()
- # device = 'cpu'
-
- self.extract_layers = extract_layers
- self.cond_layer = cond_layer
- self.limit_to_clip_only = limit_to_clip_only
- self.process_cond = None
-
- if add_calibration:
- self.calibration_conds = 1
-
- self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None
-
- self.add_activation1 = True
-
- import timm
- self.model = timm.create_model('vit_base_patch16_384', pretrained=True)
- self.model.head = nn.Linear(768, 512 if reduce_cond is None else reduce_cond)
-
- for p in self.model.parameters():
- p.requires_grad_(False)
-
- import clip
- self.clip_model, _ = clip.load('ViT-B/16', device='cpu', jit=False)
- # del self.clip_model.visual
-
-
- self.token_shape = (14, 14)
-
- # conditional
- if reduce_cond is not None:
- self.reduce_cond = nn.Linear(512, reduce_cond)
- for p in self.reduce_cond.parameters():
- p.requires_grad_(False)
- else:
- self.reduce_cond = None
-
- # self.film = AVAILABLE_BLOCKS['film'](512, 128)
- self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
- self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
-
- # DEPRECATED
- # self.conditional_map = {c['id']: c['synonyms'] for c in json.load(open(cond_map))}
-
- assert len(self.extract_layers) == depth
-
- self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)])
- self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))])
- self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)])
-
- trans_conv_ks = (16, 16)
- self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
-
- # refinement and trans conv
-
- if learn_trans_conv_only:
- for p in self.parameters():
- p.requires_grad_(False)
-
- for p in self.trans_conv.parameters():
- p.requires_grad_(True)
-
- if prompt == 'fixed':
- self.prompt_list = ['a photo of a {}.']
- elif prompt == 'shuffle':
- self.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
- elif prompt == 'shuffle+':
- self.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.',
- 'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.',
- 'a bad photo of a {}.', 'a photo of the {}.']
- elif prompt == 'shuffle_clip':
- from models.clip_prompts import imagenet_templates
- self.prompt_list = imagenet_templates
-
- if process_cond is not None:
- if process_cond == 'clamp' or process_cond[0] == 'clamp':
-
- val = process_cond[1] if type(process_cond) in {list, tuple} else 0.2
-
- def clamp_vec(x):
- return torch.clamp(x, -val, val)
-
- self.process_cond = clamp_vec
-
- elif process_cond.endswith('.pth'):
-
- shift = torch.load(process_cond)
- def add_shift(x):
- return x + shift.to(x.device)
-
- self.process_cond = add_shift
-
- import pickle
- precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
- self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}
-
-
- def forward(self, inp_image, conditional=None, return_features=False, mask=None):
-
- assert type(return_features) == bool
-
- # inp_image = inp_image.to(self.model.positional_embedding.device)
-
- if mask is not None:
- raise ValueError('mask not supported')
-
- # x_inp = normalize(inp_image)
- x_inp = inp_image
-
- bs, dev = inp_image.shape[0], x_inp.device
-
- inp_image_size = inp_image.shape[2:]
-
- cond = self.get_cond_vec(conditional, bs)
-
- visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers))
-
- activation1 = activations[0]
- activations = activations[1:]
-
- a = None
- for i, (activation, block, reduce) in enumerate(zip(activations[::-1], self.blocks, self.reduces)):
-
- if a is not None:
- a = reduce(activation) + a
- else:
- a = reduce(activation)
-
- if i == self.cond_layer:
- if self.reduce_cond is not None:
- cond = self.reduce_cond(cond)
-
- a = self.film_mul(cond) * a + self.film_add(cond)
-
- a = block(a)
-
- for block in self.extra_blocks:
- a = a + block(a)
-
- a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
-
- size = int(math.sqrt(a.shape[2]))
-
- a = a.view(bs, a.shape[1], size, size)
-
- if self.trans_conv is not None:
- a = self.trans_conv(a)
-
- if self.upsample_proj is not None:
- a = self.upsample_proj(a)
- a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear')
-
- a = nnf.interpolate(a, inp_image_size)
-
- if return_features:
- return a, visual_q, cond, [activation1] + activations
- else:
- return a,
diff --git a/spaces/aodianyun/stable-diffusion-webui/modules/script_callbacks.py b/spaces/aodianyun/stable-diffusion-webui/modules/script_callbacks.py
deleted file mode 100644
index c98c2395b6fe46ddec2a10cc6a54ee0f3ba248f5..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/modules/script_callbacks.py
+++ /dev/null
@@ -1,359 +0,0 @@
-import sys
-import traceback
-from collections import namedtuple
-import inspect
-from typing import Optional, Dict, Any
-
-from fastapi import FastAPI
-from gradio import Blocks
-
-
-def report_exception(c, job):
- print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
-
-class ImageSaveParams:
- def __init__(self, image, p, filename, pnginfo):
- self.image = image
- """the PIL image itself"""
-
- self.p = p
- """p object with processing parameters; either StableDiffusionProcessing or an object with same fields"""
-
- self.filename = filename
- """name of file that the image would be saved to"""
-
- self.pnginfo = pnginfo
- """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""
-
-
-class CFGDenoiserParams:
- def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps):
- self.x = x
- """Latent image representation in the process of being denoised"""
-
- self.image_cond = image_cond
- """Conditioning image"""
-
- self.sigma = sigma
- """Current sigma noise step value"""
-
- self.sampling_step = sampling_step
- """Current Sampling step number"""
-
- self.total_sampling_steps = total_sampling_steps
- """Total number of sampling steps planned"""
-
-
-class CFGDenoisedParams:
- def __init__(self, x, sampling_step, total_sampling_steps):
- self.x = x
- """Latent image representation in the process of being denoised"""
-
- self.sampling_step = sampling_step
- """Current Sampling step number"""
-
- self.total_sampling_steps = total_sampling_steps
- """Total number of sampling steps planned"""
-
-
-class UiTrainTabParams:
- def __init__(self, txt2img_preview_params):
- self.txt2img_preview_params = txt2img_preview_params
-
-
-class ImageGridLoopParams:
- def __init__(self, imgs, cols, rows):
- self.imgs = imgs
- self.cols = cols
- self.rows = rows
-
-
-ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
-callback_map = dict(
- callbacks_app_started=[],
- callbacks_model_loaded=[],
- callbacks_ui_tabs=[],
- callbacks_ui_train_tabs=[],
- callbacks_ui_settings=[],
- callbacks_before_image_saved=[],
- callbacks_image_saved=[],
- callbacks_cfg_denoiser=[],
- callbacks_cfg_denoised=[],
- callbacks_before_component=[],
- callbacks_after_component=[],
- callbacks_image_grid=[],
- callbacks_infotext_pasted=[],
- callbacks_script_unloaded=[],
- callbacks_before_ui=[],
-)
-
-
-def clear_callbacks():
- for callback_list in callback_map.values():
- callback_list.clear()
-
-
-def app_started_callback(demo: Optional[Blocks], app: FastAPI):
- for c in callback_map['callbacks_app_started']:
- try:
- c.callback(demo, app)
- except Exception:
- report_exception(c, 'app_started_callback')
-
-
-def model_loaded_callback(sd_model):
- for c in callback_map['callbacks_model_loaded']:
- try:
- c.callback(sd_model)
- except Exception:
- report_exception(c, 'model_loaded_callback')
-
-
-def ui_tabs_callback():
- res = []
-
- for c in callback_map['callbacks_ui_tabs']:
- try:
- res += c.callback() or []
- except Exception:
- report_exception(c, 'ui_tabs_callback')
-
- return res
-
-
-def ui_train_tabs_callback(params: UiTrainTabParams):
- for c in callback_map['callbacks_ui_train_tabs']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'callbacks_ui_train_tabs')
-
-
-def ui_settings_callback():
- for c in callback_map['callbacks_ui_settings']:
- try:
- c.callback()
- except Exception:
- report_exception(c, 'ui_settings_callback')
-
-
-def before_image_saved_callback(params: ImageSaveParams):
- for c in callback_map['callbacks_before_image_saved']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'before_image_saved_callback')
-
-
-def image_saved_callback(params: ImageSaveParams):
- for c in callback_map['callbacks_image_saved']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'image_saved_callback')
-
-
-def cfg_denoiser_callback(params: CFGDenoiserParams):
- for c in callback_map['callbacks_cfg_denoiser']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'cfg_denoiser_callback')
-
-
-def cfg_denoised_callback(params: CFGDenoisedParams):
- for c in callback_map['callbacks_cfg_denoised']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'cfg_denoised_callback')
-
-
-def before_component_callback(component, **kwargs):
- for c in callback_map['callbacks_before_component']:
- try:
- c.callback(component, **kwargs)
- except Exception:
- report_exception(c, 'before_component_callback')
-
-
-def after_component_callback(component, **kwargs):
- for c in callback_map['callbacks_after_component']:
- try:
- c.callback(component, **kwargs)
- except Exception:
- report_exception(c, 'after_component_callback')
-
-
-def image_grid_callback(params: ImageGridLoopParams):
- for c in callback_map['callbacks_image_grid']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'image_grid')
-
-
-def infotext_pasted_callback(infotext: str, params: Dict[str, Any]):
- for c in callback_map['callbacks_infotext_pasted']:
- try:
- c.callback(infotext, params)
- except Exception:
- report_exception(c, 'infotext_pasted')
-
-
-def script_unloaded_callback():
- for c in reversed(callback_map['callbacks_script_unloaded']):
- try:
- c.callback()
- except Exception:
- report_exception(c, 'script_unloaded')
-
-
-def before_ui_callback():
- for c in reversed(callback_map['callbacks_before_ui']):
- try:
- c.callback()
- except Exception:
- report_exception(c, 'before_ui')
-
-
-def add_callback(callbacks, fun):
- stack = [x for x in inspect.stack() if x.filename != __file__]
- filename = stack[0].filename if len(stack) > 0 else 'unknown file'
-
- callbacks.append(ScriptCallback(filename, fun))
-
-
-def remove_current_script_callbacks():
- stack = [x for x in inspect.stack() if x.filename != __file__]
- filename = stack[0].filename if len(stack) > 0 else 'unknown file'
- if filename == 'unknown file':
- return
- for callback_list in callback_map.values():
- for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:
- callback_list.remove(callback_to_remove)
-
-
-def remove_callbacks_for_function(callback_func):
- for callback_list in callback_map.values():
- for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:
- callback_list.remove(callback_to_remove)
-
-
-def on_app_started(callback):
- """register a function to be called when the webui started, the gradio `Block` component and
- fastapi `FastAPI` object are passed as the arguments"""
- add_callback(callback_map['callbacks_app_started'], callback)
-
-
-def on_model_loaded(callback):
- """register a function to be called when the stable diffusion model is created; the model is
- passed as an argument; this function is also called when the script is reloaded. """
- add_callback(callback_map['callbacks_model_loaded'], callback)
-
-
-def on_ui_tabs(callback):
- """register a function to be called when the UI is creating new tabs.
- The function must either return a None, which means no new tabs to be added, or a list, where
- each element is a tuple:
- (gradio_component, title, elem_id)
-
- gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks)
- title is tab text displayed to user in the UI
- elem_id is HTML id for the tab
- """
- add_callback(callback_map['callbacks_ui_tabs'], callback)
-
-
-def on_ui_train_tabs(callback):
- """register a function to be called when the UI is creating new tabs for the train tab.
- Create your new tabs with gr.Tab.
- """
- add_callback(callback_map['callbacks_ui_train_tabs'], callback)
-
-
-def on_ui_settings(callback):
- """register a function to be called before UI settings are populated; add your settings
- by using shared.opts.add_option(shared.OptionInfo(...)) """
- add_callback(callback_map['callbacks_ui_settings'], callback)
-
-
-def on_before_image_saved(callback):
- """register a function to be called before an image is saved to a file.
- The callback is called with one argument:
- - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.
- """
- add_callback(callback_map['callbacks_before_image_saved'], callback)
-
-
-def on_image_saved(callback):
- """register a function to be called after an image is saved to a file.
- The callback is called with one argument:
- - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
- """
- add_callback(callback_map['callbacks_image_saved'], callback)
-
-
-def on_cfg_denoiser(callback):
- """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
- The callback is called with one argument:
- - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
- """
- add_callback(callback_map['callbacks_cfg_denoiser'], callback)
-
-
-def on_cfg_denoised(callback):
- """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
- The callback is called with one argument:
- - params: CFGDenoisedParams - parameters to be passed to the inner model and sampling state details.
- """
- add_callback(callback_map['callbacks_cfg_denoised'], callback)
-
-
-def on_before_component(callback):
- """register a function to be called before a component is created.
- The callback is called with arguments:
- - component - gradio component that is about to be created.
- - **kwargs - args to gradio.components.IOComponent.__init__ function
-
- Use elem_id/label fields of kwargs to figure out which component it is.
- This can be useful to inject your own components somewhere in the middle of vanilla UI.
- """
- add_callback(callback_map['callbacks_before_component'], callback)
-
-
-def on_after_component(callback):
- """register a function to be called after a component is created. See on_before_component for more."""
- add_callback(callback_map['callbacks_after_component'], callback)
-
-
-def on_image_grid(callback):
- """register a function to be called before making an image grid.
- The callback is called with one argument:
- - params: ImageGridLoopParams - parameters to be used for grid creation. Can be modified.
- """
- add_callback(callback_map['callbacks_image_grid'], callback)
-
-
-def on_infotext_pasted(callback):
- """register a function to be called before applying an infotext.
- The callback is called with two arguments:
- - infotext: str - raw infotext.
- - result: Dict[str, any] - parsed infotext parameters.
- """
- add_callback(callback_map['callbacks_infotext_pasted'], callback)
-
-
-def on_script_unloaded(callback):
- """register a function to be called before the script is unloaded. Any hooks/hijacks/monkeying about that
- the script did should be reverted here"""
-
- add_callback(callback_map['callbacks_script_unloaded'], callback)
-
-
-def on_before_ui(callback):
- """register a function to be called before the UI is created."""
-
- add_callback(callback_map['callbacks_before_ui'], callback)
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/fast_pitch_config.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/fast_pitch_config.py
deleted file mode 100644
index d086d26564450c60fa04a7f3a068506f4147d3be..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/fast_pitch_config.py
+++ /dev/null
@@ -1,183 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.forward_tts import ForwardTTSArgs
-
-
-@dataclass
-class FastPitchConfig(BaseTTSConfig):
- """Configure `ForwardTTS` as FastPitch model.
-
- Example:
-
- >>> from TTS.tts.configs.fast_pitch_config import FastPitchConfig
- >>> config = FastPitchConfig()
-
- Args:
- model (str):
- Model name used for selecting the right model at initialization. Defaults to `fast_pitch`.
-
- base_model (str):
- Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
- the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
-
- model_args (Coqpit):
- Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
-
- data_dep_init_steps (int):
- Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
- Activation Normalization that pre-computes normalization stats at the beginning and use the same values
- for the rest. Defaults to 10.
-
- speakers_file (str):
- Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
- speaker names. Defaults to `None`.
-
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
-
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
-
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
-
- d_vector_dim (int):
- Dimension of the external speaker embeddings. Defaults to 0.
-
- optimizer (str):
- Name of the model optimizer. Defaults to `Adam`.
-
- optimizer_params (dict):
- Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
-
- lr_scheduler (str):
- Name of the learning rate scheduler. Defaults to `Noam`.
-
- lr_scheduler_params (dict):
- Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
-
- lr (float):
- Initial learning rate. Defaults to `1e-3`.
-
- grad_clip (float):
- Gradient norm clipping value. Defaults to `5.0`.
-
- spec_loss_type (str):
- Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- duration_loss_type (str):
- Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- use_ssim_loss (bool):
- Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
-
- wd (float):
- Weight decay coefficient. Defaults to `1e-7`.
-
- ssim_loss_alpha (float):
- Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
-
- dur_loss_alpha (float):
- Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
-
- spec_loss_alpha (float):
- Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
-
- pitch_loss_alpha (float):
- Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0.
-
- binary_align_loss_alpha (float):
- Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
-
- binary_loss_warmup_epochs (float):
- Number of epochs to gradually increase the binary loss impact. Defaults to 150.
-
- min_seq_len (int):
- Minimum input sequence length to be used at training.
-
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
-
- # dataset configs
- compute_f0(bool):
- Compute pitch. defaults to True
-
- f0_cache_path(str):
- pith cache path. defaults to None
- """
-
- model: str = "fast_pitch"
- base_model: str = "forward_tts"
-
- # model specific params
- model_args: ForwardTTSArgs = field(default_factory=ForwardTTSArgs)
-
- # multi-speaker settings
- num_speakers: int = 0
- speakers_file: str = None
- use_speaker_embedding: bool = False
- use_d_vector_file: bool = False
- d_vector_file: str = False
- d_vector_dim: int = 0
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = "NoamLR"
- lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
- lr: float = 1e-4
- grad_clip: float = 5.0
-
- # loss params
- spec_loss_type: str = "mse"
- duration_loss_type: str = "mse"
- use_ssim_loss: bool = True
- ssim_loss_alpha: float = 1.0
- spec_loss_alpha: float = 1.0
- aligner_loss_alpha: float = 1.0
- pitch_loss_alpha: float = 0.1
- dur_loss_alpha: float = 0.1
- binary_align_loss_alpha: float = 0.1
- binary_loss_warmup_epochs: int = 150
-
- # overrides
- min_seq_len: int = 13
- max_seq_len: int = 200
- r: int = 1 # DO NOT CHANGE
-
- # dataset configs
- compute_f0: bool = True
- f0_cache_path: str = None
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
-
- def __post_init__(self):
- # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
- if self.num_speakers > 0:
- self.model_args.num_speakers = self.num_speakers
-
- # speaker embedding settings
- if self.use_speaker_embedding:
- self.model_args.use_speaker_embedding = True
- if self.speakers_file:
- self.model_args.speakers_file = self.speakers_file
-
- # d-vector settings
- if self.use_d_vector_file:
- self.model_args.use_d_vector_file = True
- if self.d_vector_dim is not None and self.d_vector_dim > 0:
- self.model_args.d_vector_dim = self.d_vector_dim
- if self.d_vector_file:
- self.model_args.d_vector_file = self.d_vector_file
diff --git a/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/__init__.py b/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/__init__.py
deleted file mode 100644
index 4bae29fd5f85b41e4669302bd2603bc6924eddc7..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# -*- coding: utf-8 -*-
-
-__author__ = """Adrian Bulat"""
-__email__ = 'adrian.bulat@nottingham.ac.uk'
-__version__ = '1.0.1'
-
-from .api import FaceAlignment, LandmarksType, NetworkSize
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/scatter_with_minimap.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/scatter_with_minimap.py
deleted file mode 100644
index 7de894c43ae56bf135411e9912ca3d396aa71dec..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/scatter_with_minimap.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""
-Scatter Plot with Minimap
--------------------------
-This example shows how to create a miniature version of a plot
-such that creating a selection in the miniature version
-adjusts the axis limits in another, more detailed view.
-"""
-# category: scatter plots
-
-import altair as alt
-from vega_datasets import data
-
-source = data.seattle_weather()
-
-zoom = alt.selection_interval(encodings=["x", "y"])
-
-minimap = (
- alt.Chart(source)
- .mark_point()
- .add_selection(zoom)
- .encode(
- x="date:T",
- y="temp_max:Q",
- color=alt.condition(zoom, "weather", alt.value("lightgray")),
- )
- .properties(
- width=200,
- height=200,
- title="Minimap -- click and drag to zoom in the detail view",
- )
-)
-
-detail = (
- alt.Chart(source)
- .mark_point()
- .encode(
- x=alt.X(
- "date:T", scale=alt.Scale(domain={"selection": zoom.name, "encoding": "x"})
- ),
- y=alt.Y(
- "temp_max:Q",
- scale=alt.Scale(domain={"selection": zoom.name, "encoding": "y"}),
- ),
- color="weather",
- )
- .properties(width=600, height=400, title="Seattle weather -- detail view")
-)
-
-detail | minimap
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/distributed/legacy_distributed_data_parallel.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/distributed/legacy_distributed_data_parallel.py
deleted file mode 100644
index cd434c7372ba30ea0e6f87e084230448f53480e9..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/distributed/legacy_distributed_data_parallel.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-A modified version of the legacy DistributedDataParallel module that uses c10d
-communication primitives. This version is simpler than the latest PyTorch
-version and is useful for debugging. Notably it does not overlap gradient
-communication with the backward pass, which makes it slower but more robust
-than the PyTorch version.
-
-This version also supports the *no_sync* context manager, which allows faster
-training with `--update-freq`.
-"""
-
-from collections import OrderedDict
-from contextlib import contextmanager
-
-import torch
-from torch import nn
-
-from fairseq.distributed import utils
-
-
-class LegacyDistributedDataParallel(nn.Module):
- """Implements distributed data parallelism at the module level.
-
- A simplified version of :class:`torch.nn.parallel.DistributedDataParallel`.
- This version uses a c10d process group for communication and does not
- broadcast buffers.
-
- Args:
- module (~torch.nn.Module): module to be parallelized
- process_group: the c10d process group to be used for distributed data
- parallel all-reduction.
- buffer_size (int, optional): number of elements to buffer before
- performing all-reduce (default: 256M).
- """
-
- def __init__(self, module, process_group, buffer_size=2**28):
- super().__init__()
-
- self.module = module
- self.process_group = process_group
- self.world_size = utils.get_world_size(self.process_group)
-
- # Never use a bigger buffer than the number of model params
- self.buffer_size = min(buffer_size, sum(p.numel() for p in module.parameters()))
- self.buffer = None
-
- # We can also forcibly accumulate grads locally and only do the
- # all-reduce at some later time
- self.accumulate_grads = False
-
- # make per-device lists of parameters
- paramlists = OrderedDict()
- for param in self.module.parameters():
- device = param.device
- if paramlists.get(device) is None:
- paramlists[device] = []
- paramlists[device] += [param]
- self.per_device_params = list(paramlists.values())
-
- @contextmanager
- def no_sync(self):
- """A context manager to disable gradient synchronization."""
- old_accumulate_grads = self.accumulate_grads
- self.accumulate_grads = True
- yield
- self.accumulate_grads = old_accumulate_grads
-
- def forward(self, *inputs, **kwargs):
- return self.module(*inputs, **kwargs)
-
- def all_reduce_grads(self):
- """
- This function must be called explicitly after backward to reduce
- gradients. There is no automatic hook like c10d.
- """
-
- def all_reduce_params(params):
- buffer = self.buffer
- nonzero_buffer = False
- if len(params) > 1:
- offset = 0
- for p in params:
- sz = p.numel()
- if p.grad is not None:
- buffer[offset : offset + sz].copy_(p.grad.data.view(-1))
- nonzero_buffer = True
- else:
- buffer[offset : offset + sz].zero_()
- offset += sz
- else:
- # we only have a single grad to all-reduce
- p = params[0]
- if p.grad is not None:
- buffer = p.grad.data
- nonzero_buffer = True
- elif p.numel() <= self.buffer.numel():
- buffer = buffer[: p.numel()]
- buffer.zero_()
- else:
- buffer = torch.zeros_like(p)
-
- if nonzero_buffer:
- buffer.div_(self.world_size)
-
- utils.all_reduce(buffer, self.process_group)
-
- # copy all-reduced grads back into their original place
- offset = 0
- for p in params:
- sz = p.numel()
- if p.grad is not None:
- p.grad.data.copy_(buffer[offset : offset + sz].view_as(p))
- else:
- p.grad = buffer[offset : offset + sz].view_as(p).clone()
- offset += sz
-
- def reduction_fn():
- # This function only needs to be called once
- if self.accumulate_grads:
- return
-
- if self.buffer is None:
- self.buffer = next(self.module.parameters()).new(self.buffer_size)
-
- for params in self.per_device_params:
- # All-reduce the gradients in buckets
- offset = 0
- buffered_params = []
- for param in params:
- if not param.requires_grad:
- continue
- if param.grad is None:
- param.grad = torch.zeros_like(param)
-
- if hasattr(param, "expert"):
- # Skip gradient sync for unshared parameters
- continue
-
- if param.grad.requires_grad:
- raise RuntimeError(
- "DistributedDataParallel only works "
- "with gradients that don't require "
- "grad"
- )
- sz = param.numel()
- if sz > self.buffer.numel():
- # all-reduce big params directly
- all_reduce_params([param])
- else:
- if offset + sz > self.buffer.numel():
- all_reduce_params(buffered_params)
- offset = 0
- buffered_params.clear()
- buffered_params.append(param)
- offset += sz
-
- if len(buffered_params) > 0:
- all_reduce_params(buffered_params)
-
- reduction_fn()
diff --git a/spaces/ashutosh1919/quantum-perceptron/quantum_perceptron/__init__.py b/spaces/ashutosh1919/quantum-perceptron/quantum_perceptron/__init__.py
deleted file mode 100644
index ed4e767fa8c5310969615aa941e5b41ce6ea5099..0000000000000000000000000000000000000000
--- a/spaces/ashutosh1919/quantum-perceptron/quantum_perceptron/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from quantum_perceptron.perceptron import Perceptron
diff --git a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Saim Mehmood.html b/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Saim Mehmood.html
deleted file mode 100644
index 74fbd5bb33874558aed6731c8c07572671215e80..0000000000000000000000000000000000000000
--- a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Saim Mehmood.html
+++ /dev/null
@@ -1,134 +0,0 @@
-
-
-
- Saim Mehmood
-
-
-
-
-
- Saim Mehmood
-
-
- Interview
Saim was a menteeCareer- Moved to Canada back in Dec 2017
- grad school Jan 2018
- MSc in CS - research-oriented work
- graduated June 2020
- Sep 2019 got in touch with Omar
- Omar: "You already have a good project with your thesis", finish that up
- Covid hit, kept applying
- kept applying
- took me almost 2 years to find job
- worked as MLOps
- learned a lot throughout my journey, particularly about academia and industry
And how can you add value as a mentor?- selling yourself as a previous academic
- it's a long journey - expectation setting - you will hit a lot rejections!
- "I was getting rejection emails every day, and was happy to see them"
- market the project that you've done
- make sure they are technically sound
- Build a project if needed, post on medium
Mentorship exp- Been a TA during school
Ideal mentee- Recent CS grads would be easier, but open to other disciplines
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/drawingboard.min.css b/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/drawingboard.min.css
deleted file mode 100644
index 7d80478705de8fdceacbad40e5a85c19307be665..0000000000000000000000000000000000000000
--- a/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/drawingboard.min.css
+++ /dev/null
@@ -1,5 +0,0 @@
-/* drawingboard.js v0.4.6 - https://github.com/Leimi/drawingboard.js
-* Copyright (c) 2015 Emmanuel Pelletier
-* Licensed MIT */
-
-.drawing-board,.drawing-board *{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}.drawing-board-controls-hidden,.drawing-board-utils-hidden{display:none!important}.drawing-board{position:relative;display:block}.drawing-board-canvas-wrapper{position:relative;margin:0;border:1px solid #ddd}.drawing-board-canvas{position:absolute;top:0;left:0;width:auto;cursor:crosshair;z-index:20}.drawing-board-cursor{position:absolute;top:0;left:0;pointer-events:none;border-radius:50%;background:#ccc;background:rgba(0,0,0,.2);z-index:30}.drawing-board-control-colors-rainbows,.drawing-board-control-size .drawing-board-control-inner,.drawing-board-control-size-dropdown,.drawing-board-control>button{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;overflow:hidden;background-color:#eee;padding:2px 4px;border:1px solid #ccc;box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);-webkit-box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);height:28px}.drawing-board-control>button{cursor:pointer;min-width:28px;line-height:14px}.drawing-board-control>button:focus,.drawing-board-control>button:hover{background-color:#ddd}.drawing-board-control>button.active,.drawing-board-control>button:active{box-shadow:inset 0 1px 2px 0 rgba(0,0,0,.2);-webkit-box-shadow:inset 0 1px 2px 0 rgba(0,0,0,.2);background-color:#ddd}.drawing-board-control>button[disabled]{color:gray}.drawing-board-control>button[disabled].active,.drawing-board-control>button[disabled]:active,.drawing-board-control>button[disabled]:focus,.drawing-board-control>button[disabled]:hover{background-color:#eee;box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);-webkit-box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);cursor:default}.drawing-board-controls{margin:0 auto;text-align:center;font-size:0;display:table;border-spacing:9.33px 0;position:relative;min-height:28px}.drawing-board-controls[data-align=left]{margin:0;left:-9.33px}.drawing-board-controls[data-align=right]{margin:0 0 0 auto;right:-9.33px}.drawing-board-canvas-wrapper+.drawing-board-controls,.drawing-board-controls+.drawing-board-canvas-wrapper{margin-top:5px}.drawing-board-controls-hidden{height:0;min-height:0;padding:0;margin:0;border:0}.drawing-board-control{display:table-cell;border-collapse:separate;vertical-align:middle;font-size:16px;height:100%}.drawing-board-control-inner{position:relative;height:100%;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.drawing-board-control>button{margin:0;vertical-align:middle}.drawing-board-control-colors{font-size:0;line-height:0}.drawing-board-control-colors-current{border:1px solid #ccc;cursor:pointer;display:inline-block;width:26px;height:26px}.drawing-board-control-colors-rainbows{display:inline-block;position:absolute;left:0;top:33px;margin-left:0;z-index:100;width:250px;height:auto;padding:4px}.drawing-board-control-colors-rainbow{height:18px}.drawing-board-control-colors-picker:first-child{margin-right:5px}.drawing-board-control-colors-picker{display:inline-block;width:18px;height:18px;cursor:pointer}.drawing-board-control-colors-picker[data-color="rgba(255, 255, 255, 1)"]{width:16px;height:17px;border:1px solid #ccc;border-bottom:none}.drawing-board-control-colors-picker:hover{width:16px;height:16px;border:1px solid #555}.drawing-board-control-drawingmode>button{margin-right:2px}.drawing-board-control-drawingmode>button:last-child{margin-right:0}.drawing-board-control-drawingmode-pencil-button{overflow:hidden;background-image:url(data:image/png;base64,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);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-drawingmode-pencil-button:before{content:"";display:block;width:0;height:100%}.drawing-board-control-drawingmode-eraser-button{overflow:hidden;background-image:url(data:image/png;base64,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);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-drawingmode-eraser-button:before{content:"";display:block;width:0;height:100%}.drawing-board-control-drawingmode-filler-button{overflow:hidden;background-image:url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAnNJREFUeNp0k0trE1EUx89MJpNJooYYXBgDNtCKdRPwlbqoCKUtaNVNA0Uo7UbMxoVPEARTXEi+QWfnwn6DEAlEkrSLttTGRiULEQlJ8yChmbzI++E50yTUJA78uMy953/u/557LmOz2WDEZ2m1WrckSRJSqdR2tVrdHQyYebwHtVoNuFHqTqczhQnWKaBYLDoKhcIuzgHDMKBSqeD20qd+LNdsNocSoFhRr9ctpVLJigl4xIIJQizLAmG4cAPa7bYcy9Iug5TL5UYikbD6/X7Rbre/IUcYe3WUW5ZsnQQzW9LpNOPz+UQc5aBM5mgdh7vI9FCCAesW2tnr9YqZTAby+bw8f3AQRP6853n+Ph5hemSCntjj8YjZbFYWx2IxeS2RSEMwuA87O79eqdXquVolK+GxnP0EPbHb7RZJSGABIR6PA11zJHKIR2MhHA5DIPDj7eH3j95KpfK60Wg8Yntil8slkqgnpioLghacTidoNDpEC3q9HnheCc3s1jZeLcW943pirPw/4lKpBkqlDubnl/riycnLsLy88EKj0fhzuRyZv8RFo1E6wpBYkiqy7Z54YmIcVlYeyOKC4mYwJ0nHRaQuM5vNT6hB/iceG7sIq6sPnwmC4MerDkby40AOCCoiddie1Wp92W7zQ2KTyQSLizNP8T0EsPLBbxEDnCj0GkM2qIEwyZRCobizsfH5A1ZXFhuN52F29vpz3HkL574mk8lj24Y5wsHkvjjoX0BOIWc5jruHzbK2ufmzEwpFO3jnDhQv4JoROYdoERVyGjEgZ8iBDlF3FzXo4go6utZ9lftY4N/dXisjR0i1G0ublv8KMAA0ZoUlicxrhwAAAABJRU5ErkJggg==);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-drawingmode-filler-button:before{content:"";display:block;width:0;height:100%}.drawing-board-control-navigation>button{font-family:Helvetica,Arial,sans-serif;font-size:14px;font-weight:700;margin-right:2px}.drawing-board-control-navigation>button:last-child{margin-right:0}.drawing-board-control-size[data-drawing-board-type=range] .drawing-board-control-inner{width:75px}.drawing-board-control-size[data-drawing-board-type=dropdown] .drawing-board-control-inner{overflow:visible}.drawing-board-control-size-range-input{position:relative;width:100%;z-index:100;margin:0;padding:0;border:0}.drawing-board-control-size-dropdown span,.drawing-board-control-size-dropdown-current span,.drawing-board-control-size-range-current{display:block;background:#333;opacity:.8}.drawing-board-control-size-range-current{display:inline-block;opacity:.15;position:absolute;pointer-events:none;left:50%;top:50%;z-index:50}.drawing-board-control-size-dropdown-current{display:block;height:100%;width:40px;overflow:hidden;position:relative}.drawing-board-control-size-dropdown-current span{position:absolute;left:50%;top:50%}.drawing-board-control-size-dropdown{position:absolute;left:-6px;top:33px;height:auto;list-style-type:none;margin:0;padding:0;z-index:100}.drawing-board-control-size-dropdown li{display:block;padding:4px;margin:3px 0;min-height:16px}.drawing-board-control-size-dropdown li:hover{background:#ccc}.drawing-board-control-size-dropdown span{margin:0 auto}.drawing-board-control-download-button{overflow:hidden;background-image:url(data:image/png;base64,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);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-download-button:before{content:"";display:block;width:0;height:100%}
\ No newline at end of file
diff --git a/spaces/awacke1/Bloom.Generative.Writer/generators/topic_to_abstract.py b/spaces/awacke1/Bloom.Generative.Writer/generators/topic_to_abstract.py
deleted file mode 100644
index a91131db41007e46ce1d9a9772bdd7e9645b9d2b..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Bloom.Generative.Writer/generators/topic_to_abstract.py
+++ /dev/null
@@ -1,6 +0,0 @@
-
-from .model import model
-
-
-def topic_to_abstract_generator(template):
- return model('topic', template)
diff --git a/spaces/awacke1/Streamlit-Data-Synthesis-Example/app.py b/spaces/awacke1/Streamlit-Data-Synthesis-Example/app.py
deleted file mode 100644
index 636e3a14af4ceae48968e372e88f840f3425c791..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Streamlit-Data-Synthesis-Example/app.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import streamlit as st
-import pandas as pd
-
-
-def generate_hospital_data():
- # Generate hospital data
- hospitals = {
- "city": ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"],
- "state": ["NY", "CA", "IL", "TX", "AZ"],
- "bed_count": [1200, 1500, 1100, 1300, 1400],
- }
- df = pd.DataFrame(hospitals)
- return df
-
-
-def generate_state_data():
- # Generate state data
- states = {
- "state": ["NY", "CA", "IL", "TX", "AZ"],
- "population": [20000000, 40000000, 13000000, 29000000, 7000000],
- "square_miles": [54556, 163696, 57914, 268596, 113990],
- }
- df = pd.DataFrame(states)
- return df
-
-
-def merge_datasets(hospitals_df, states_df):
- # Merge hospital and state data
- merged_df = pd.merge(hospitals_df, states_df, on="state")
- return merged_df
-
-
-def calculate_beds_per_capita(merged_df):
- # Calculate beds per capita
- merged_df["beds_per_capita"] = merged_df["bed_count"] / merged_df["population"]
- return merged_df
-
-
-def main():
- # Generate data
- hospitals_df = generate_hospital_data()
- states_df = generate_state_data()
-
- # Merge datasets
- merged_df = merge_datasets(hospitals_df, states_df)
-
- # Calculate beds per capita
- merged_df = calculate_beds_per_capita(merged_df)
-
- # Show merged and calculated data
- st.write(merged_df)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/class_names/README.md b/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/class_names/README.md
deleted file mode 100644
index 30d74d258442c7c65512eafab474568dd706c430..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/class_names/README.md
+++ /dev/null
@@ -1 +0,0 @@
-test
\ No newline at end of file
diff --git a/spaces/badayvedat/AudioSep/models/CLAP/open_clip/bert.py b/spaces/badayvedat/AudioSep/models/CLAP/open_clip/bert.py
deleted file mode 100644
index a83d96d2a77ed05198efc05837522bc88d2499cc..0000000000000000000000000000000000000000
--- a/spaces/badayvedat/AudioSep/models/CLAP/open_clip/bert.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from transformers import BertTokenizer, BertModel
-
-tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-model = BertModel.from_pretrained("bert-base-uncased")
-text = "Replace me by any text you'd like."
-
-
-def bert_embeddings(text):
- # text = "Replace me by any text you'd like."
- encoded_input = tokenizer(text, return_tensors="pt")
- output = model(**encoded_input)
- return output
-
-
-from transformers import RobertaTokenizer, RobertaModel
-
-tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
-model = RobertaModel.from_pretrained("roberta-base")
-text = "Replace me by any text you'd like."
-
-
-def Roberta_embeddings(text):
- # text = "Replace me by any text you'd like."
- encoded_input = tokenizer(text, return_tensors="pt")
- output = model(**encoded_input)
- return output
-
-
-from transformers import BartTokenizer, BartModel
-
-tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
-model = BartModel.from_pretrained("facebook/bart-base")
-text = "Replace me by any text you'd like."
-
-
-def bart_embeddings(text):
- # text = "Replace me by any text you'd like."
- encoded_input = tokenizer(text, return_tensors="pt")
- output = model(**encoded_input)
- return output
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DAmmoLoader.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DAmmoLoader.js
deleted file mode 100644
index 9239e109afef47ff228e1fa2005fd9a6643da7ae..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DAmmoLoader.js
+++ /dev/null
@@ -1,590 +0,0 @@
-/**
- * SEA3D+AMMO for Three.JS
- * @author Sunag / http://www.sunag.com.br/
- */
-
-'use strict';
-
-THREE.SEA3D.prototype.toAmmoVec3 = function ( v ) {
-
- return new Ammo.btVector3( v.x, v.y, v.z );
-
-};
-
-//
-// Sphere
-//
-
-THREE.SEA3D.prototype.readSphere = function ( sea ) {
-
- var shape = new Ammo.btSphereShape( sea.radius );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Box
-//
-
-THREE.SEA3D.prototype.readBox = function ( sea ) {
-
- var shape = new Ammo.btBoxShape( new Ammo.btVector3( sea.width * .5, sea.height * .5, sea.depth * .5 ) );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Cone
-//
-
-THREE.SEA3D.prototype.readCone = function ( sea ) {
-
- var shape = new Ammo.btConeShape( sea.radius, sea.height );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Cylinder
-//
-
-THREE.SEA3D.prototype.readCylinder = function ( sea ) {
-
- var shape = new Ammo.btCylinderShape( new Ammo.btVector3( sea.height, sea.radius, sea.radius ) );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Capsule
-//
-
-THREE.SEA3D.prototype.readCapsule = function ( sea ) {
-
- var shape = new Ammo.btCapsuleShape( sea.radius, sea.height );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Convex Geometry
-//
-
-THREE.SEA3D.prototype.readConvexGeometry = function ( sea ) {
-
- if ( this.config.convexHull ) {
-
- var shape = SEA3D.AMMO.createConvexHull( sea.geometry.tag, sea.subGeometryIndex );
-
- } else {
-
- var triMesh = SEA3D.AMMO.createTriangleMesh( sea.geometry.tag, sea.subGeometryIndex );
-
- var shape = new Ammo.btConvexTriangleMeshShape( triMesh, true );
-
- }
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Triangle Geometry
-//
-
-THREE.SEA3D.prototype.readTriangleGeometry = function ( sea ) {
-
- var triMesh = SEA3D.AMMO.createTriangleMesh( sea.geometry.tag, sea.subGeometryIndex );
-
- var shape = new Ammo.btBvhTriangleMeshShape( triMesh, true, true );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Compound
-//
-
-THREE.SEA3D.prototype.readCompound = function ( sea ) {
-
- var shape = new Ammo.btCompoundShape();
-
- for ( var i = 0; i < sea.compounds.length; i ++ ) {
-
- var compound = sea.compounds[ i ];
-
- THREE.SEA3D.MTXBUF.elements = compound.transform;
-
- var transform = SEA3D.AMMO.getTransformFromMatrix( THREE.SEA3D.MTXBUF );
-
- shape.addChildShape( transform, compound.shape.tag );
-
- }
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Rigid Body Base
-//
-
-THREE.SEA3D.prototype.readRigidBodyBase = function ( sea ) {
-
- var shape = sea.shape.tag,
- transform, target;
-
- if ( sea.target ) {
-
- target = sea.target.tag;
-
- target.physics = { enabled: true };
- target.updateMatrix();
-
- transform = SEA3D.AMMO.getTransformFromMatrix( sea.target.tag.matrix );
-
- } else {
-
- THREE.SEA3D.MTXBUF.fromArray( sea.transform );
-
- transform = SEA3D.AMMO.getTransformFromMatrix( THREE.SEA3D.MTXBUF );
-
- }
-
- var motionState = new Ammo.btDefaultMotionState( transform );
- var localInertia = new Ammo.btVector3( 0, 0, 0 );
-
- shape.calculateLocalInertia( sea.mass, localInertia );
-
- var info = new Ammo.btRigidBodyConstructionInfo( sea.mass, motionState, shape, localInertia );
- info.set_m_friction( sea.friction );
- info.set_m_restitution( sea.restitution );
- info.set_m_linearDamping( sea.linearDamping );
- info.set_m_angularDamping( sea.angularDamping );
-
- var rb = new Ammo.btRigidBody( info );
-
- if ( target ) {
-
- target.physics.rigidBody = rb;
-
- if ( sea.offset ) {
-
- var offset = new THREE.Matrix4();
- offset.fromArray( sea.offset );
-
- target.physics.offset = offset;
-
- }
-
- }
-
- Ammo.destroy( info );
-
- this.domain.rigidBodies = this.rigidBodies = this.rigidBodies || [];
- this.rigidBodies.push( this.objects[ "rb/" + sea.name ] = sea.tag = rb );
-
- return rb;
-
-};
-
-//
-// Rigid Body
-//
-
-THREE.SEA3D.prototype.readRigidBody = function ( sea ) {
-
- var rb = this.readRigidBodyBase( sea );
-
- SEA3D.AMMO.addRigidBody( rb, sea.target ? sea.target.tag : undefined, this.config.enabledPhysics );
-
-};
-
-//
-// Car Controller
-//
-
-THREE.SEA3D.prototype.readCarController = function ( sea ) {
-
- var body = this.readRigidBodyBase( sea );
-
- body.setActivationState( SEA3D.AMMO.DISABLE_DEACTIVATION );
-
- // Car
-
- var vehicleRayCaster = new Ammo.btDefaultVehicleRaycaster( SEA3D.AMMO.world );
-
- var tuning = new Ammo.btVehicleTuning();
-
- tuning.set_m_suspensionStiffness( sea.suspensionStiffness );
- tuning.set_m_suspensionDamping( sea.suspensionDamping );
- tuning.set_m_suspensionCompression( sea.suspensionCompression );
- tuning.set_m_maxSuspensionTravelCm( sea.maxSuspensionTravelCm );
- tuning.set_m_maxSuspensionForce( sea.maxSuspensionForce );
- tuning.set_m_frictionSlip( sea.frictionSlip );
-
- var vehicle = new Ammo.btRaycastVehicle( tuning, body, vehicleRayCaster ),
- wheels = [];
-
- vehicle.setCoordinateSystem( 0, 1, 2 );
-
- for ( var i = 0; i < sea.wheel.length; i ++ ) {
-
- var wheel = sea.wheel[ i ];
-
- var wheelInfo = vehicle.addWheel(
- this.toAmmoVec3( wheel.pos ),
- this.toAmmoVec3( wheel.dir ),
- this.toAmmoVec3( wheel.axle ),
- wheel.suspensionRestLength,
- wheel.radius,
- tuning,
- wheel.isFront
- );
-
- var target = wheels[ i ] = wheel.target ? wheel.target.tag : undefined;
-
- if ( target ) {
-
- target.physics = { enabled: true, rigidBody: wheelInfo };
-
- if ( wheel.offset ) {
-
- var offset = new THREE.Matrix4();
- offset.fromArray( wheel.offset );
-
- target.physics.offset = offset;
-
- }
-
- if ( target.parent ) {
-
- target.parent.remove( target );
-
- }
-
- if ( this.container ) {
-
- this.container.add( target );
-
- }
-
- }
-
- wheelInfo.set_m_suspensionStiffness( sea.suspensionStiffness );
- wheelInfo.set_m_wheelsDampingRelaxation( sea.dampingRelaxation );
- wheelInfo.set_m_wheelsDampingCompression( sea.dampingCompression );
- wheelInfo.set_m_frictionSlip( sea.frictionSlip );
-
- }
-
- SEA3D.AMMO.addVehicle( vehicle, wheels );
- SEA3D.AMMO.addRigidBody( body, sea.target ? sea.target.tag : undefined, this.config.enabledPhysics );
-
- this.domain.vehicles = this.vehicles = this.vehicles || [];
- this.vehicles.push( this.objects[ "vhc/" + sea.name ] = sea.tag = vehicle );
-
-};
-
-//
-// P2P Constraint
-//
-
-THREE.SEA3D.prototype.readP2PConstraint = function ( sea ) {
-
- var ctrt;
-
- if ( sea.targetB ) {
-
- ctrt = new Ammo.btPoint2PointConstraint(
- sea.targetA.tag,
- sea.targetB.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.pointB )
- );
-
- } else {
-
- ctrt = new Ammo.btPoint2PointConstraint(
- sea.targetA.tag,
- this.toAmmoVec3( sea.pointA )
- );
-
- }
-
- SEA3D.AMMO.addConstraint( ctrt );
-
- this.domain.constraints = this.constraints = this.constraints || [];
- this.constraints.push( this.objects[ "ctnt/" + sea.name ] = sea.tag = ctrt );
-
-};
-
-//
-// Hinge Constraint
-//
-
-THREE.SEA3D.prototype.readHingeConstraint = function ( sea ) {
-
- var ctrt;
-
- if ( sea.targetB ) {
-
- ctrt = new Ammo.btHingeConstraint(
- sea.targetA.tag,
- sea.targetB.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.pointB ),
- this.toAmmoVec3( sea.axisA ),
- this.toAmmoVec3( sea.axisB ),
- false
- );
-
- } else {
-
- ctrt = new Ammo.btHingeConstraint(
- sea.targetA.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.axisA ),
- false
- );
-
- }
-
- if ( sea.limit ) {
-
- ctrt.setLimit( sea.limit.low, sea.limit.high, sea.limit.softness, sea.limit.biasFactor, sea.limit.relaxationFactor );
-
- }
-
- if ( sea.angularMotor ) {
-
- ctrt.enableAngularMotor( true, sea.angularMotor.velocity, sea.angularMotor.impulse );
-
- }
-
- SEA3D.AMMO.addConstraint( ctrt );
-
- this.domain.constraints = this.constraints = this.constraints || [];
- this.constraints.push( this.objects[ "ctnt/" + sea.name ] = sea.tag = ctrt );
-
-};
-
-//
-// Cone Twist Constraint
-//
-
-THREE.SEA3D.prototype.readConeTwistConstraint = function ( sea ) {
-
- var ctrt;
-
- if ( sea.targetB ) {
-
- ctrt = new Ammo.btConeTwistConstraint(
- sea.targetA.tag,
- sea.targetB.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.pointB ),
- false
- );
-
- } else {
-
- ctrt = new Ammo.btConeTwistConstraint(
- sea.targetA.tag,
- this.toAmmoVec3( sea.pointA ),
- false
- );
-
- }
-
- SEA3D.AMMO.addConstraint( ctrt );
-
- this.domain.constraints = this.constraints = this.constraints || [];
- this.constraints.push( this.objects[ "ctnt/" + sea.name ] = sea.tag = ctrt );
-
-};
-
-//
-// Domain
-//
-
-THREE.SEA3D.Domain.prototype.enabledPhysics = function ( enabled ) {
-
- var i = this.rigidBodies ? this.rigidBodies.length : 0;
-
- while ( i -- ) {
-
- SEA3D.AMMO.setEnabledRigidBody( this.rigidBodies[ i ], enabled );
-
- }
-
-};
-
-THREE.SEA3D.Domain.prototype.applyContainerTransform = function () {
-
- this.container.updateMatrix();
-
- var matrix = this.container.matrix.clone();
-
- this.container.position.set( 0, 0, 0 );
- this.container.quaternion.set( 0, 0, 0, 1 );
- this.container.scale.set( 1, 1, 1 );
-
- this.applyTransform( matrix );
-
-};
-
-THREE.SEA3D.Domain.prototype.applyTransform = function ( matrix ) {
-
- var mtx = THREE.SEA3D.MTXBUF, vec = THREE.SEA3D.VECBUF;
-
- var i = this.rigidBodies ? this.rigidBodies.length : 0,
- childs = this.container ? this.container.children : [],
- targets = [];
-
- while ( i -- ) {
-
- var rb = this.rigidBodies[ i ],
- target = SEA3D.AMMO.getTargetByRigidBody( rb ),
- transform = rb.getWorldTransform(),
- transformMatrix = SEA3D.AMMO.getMatrixFromTransform( transform );
-
- transformMatrix.multiplyMatrices( transformMatrix, matrix );
-
- transform = SEA3D.AMMO.getTransformFromMatrix( transformMatrix );
-
- rb.setWorldTransform( transform );
-
- if ( target ) targets.push( target );
-
- }
-
- for ( i = 0; i < childs.length; i ++ ) {
-
- var obj3d = childs[ i ];
-
- if ( targets.indexOf( obj3d ) > - 1 ) continue;
-
- obj3d.updateMatrix();
-
- mtx.copy( obj3d.matrix );
-
- mtx.multiplyMatrices( matrix, mtx );
-
- obj3d.position.setFromMatrixPosition( mtx );
- obj3d.scale.setFromMatrixScale( mtx );
-
- // ignore rotation scale
-
- mtx.scale( vec.set( 1 / obj3d.scale.x, 1 / obj3d.scale.y, 1 / obj3d.scale.z ) );
- obj3d.quaternion.setFromRotationMatrix( mtx );
-
- }
-
-};
-
-//
-// Extension
-//
-
-THREE.SEA3D.Domain.prototype.getShape = THREE.SEA3D.prototype.getShape = function ( name ) {
-
- return this.objects[ "shpe/" + name ];
-
-};
-
-THREE.SEA3D.Domain.prototype.getRigidBody = THREE.SEA3D.prototype.getRigidBody = function ( name ) {
-
- return this.objects[ "rb/" + name ];
-
-};
-
-THREE.SEA3D.Domain.prototype.getConstraint = THREE.SEA3D.prototype.getConstraint = function ( name ) {
-
- return this.objects[ "ctnt/" + name ];
-
-};
-
-THREE.SEA3D.EXTENSIONS_LOADER.push( {
-
- parse: function () {
-
- delete this.shapes;
- delete this.rigidBodies;
- delete this.vehicles;
- delete this.constraints;
-
- },
-
- setTypeRead: function () {
-
- // CONFIG
-
- this.config.physics = this.config.physics !== undefined ? this.config.physics : true;
- this.config.convexHull = this.config.convexHull !== undefined ? this.config.convexHull : true;
- this.config.enabledPhysics = this.config.enabledPhysics !== undefined ? this.config.enabledPhysics : true;
-
- if ( this.config.physics ) {
-
- // SHAPES
-
- this.file.typeRead[ SEA3D.Sphere.prototype.type ] = this.readSphere;
- this.file.typeRead[ SEA3D.Box.prototype.type ] = this.readBox;
- this.file.typeRead[ SEA3D.Capsule.prototype.type ] = this.readCapsule;
- this.file.typeRead[ SEA3D.Cone.prototype.type ] = this.readCone;
- this.file.typeRead[ SEA3D.Cylinder.prototype.type ] = this.readCylinder;
- this.file.typeRead[ SEA3D.ConvexGeometry.prototype.type ] = this.readConvexGeometry;
- this.file.typeRead[ SEA3D.TriangleGeometry.prototype.type ] = this.readTriangleGeometry;
- this.file.typeRead[ SEA3D.Compound.prototype.type ] = this.readCompound;
-
- // CONSTRAINTS
-
- this.file.typeRead[ SEA3D.P2PConstraint.prototype.type ] = this.readP2PConstraint;
- this.file.typeRead[ SEA3D.HingeConstraint.prototype.type ] = this.readHingeConstraint;
- this.file.typeRead[ SEA3D.ConeTwistConstraint.prototype.type ] = this.readConeTwistConstraint;
-
- // PHYSICS
-
- this.file.typeRead[ SEA3D.RigidBody.prototype.type ] = this.readRigidBody;
- this.file.typeRead[ SEA3D.CarController.prototype.type ] = this.readCarController;
-
- }
-
- }
-} );
-
-THREE.SEA3D.EXTENSIONS_DOMAIN.push( {
-
- dispose: function () {
-
- var i;
-
- i = this.rigidBodies ? this.rigidBodies.length : 0;
- while ( i -- ) SEA3D.AMMO.removeRigidBody( this.rigidBodies[ i ], true );
-
- i = this.vehicles ? this.vehicles.length : 0;
- while ( i -- ) SEA3D.AMMO.removeVehicle( this.vehicles[ i ], true );
-
- i = this.constraints ? this.constraints.length : 0;
- while ( i -- ) SEA3D.AMMO.removeConstraint( this.constraints[ i ], true );
-
- i = this.shapes ? this.shapes.length : 0;
- while ( i -- ) Ammo.destroy( this.shapes[ i ] );
-
- }
-
-} );
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/ShadowMapViewer.js b/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/ShadowMapViewer.js
deleted file mode 100644
index 340efd3f339f776062b01678a0b75adaa67e5623..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/ShadowMapViewer.js
+++ /dev/null
@@ -1,208 +0,0 @@
-/**
- * @author arya-s / https://github.com/arya-s
- *
- * This is a helper for visualising a given light's shadow map.
- * It works for shadow casting lights: THREE.DirectionalLight and THREE.SpotLight.
- * It renders out the shadow map and displays it on a HUD.
- *
- * Example usage:
- * 1) Include \n\t\t" + head + "\n\t\n\t\n\t\t" + body + "\n\t\t\n
")
- gr.Markdown("Open-World Detection with Grounding DINO")
- gr.Markdown("Note the model runs on CPU, so it may take a while to run the model.")
-
- with gr.Row():
- with gr.Column():
- input_image = gr.Image(source='upload', type="pil")
- grounding_caption = gr.Textbox(label="Detection Prompt")
- run_button = gr.Button(label="Run")
- with gr.Accordion("Advanced options", open=False):
- box_threshold = gr.Slider(
- label="Box Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
- )
- text_threshold = gr.Slider(
- label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
- )
-
- with gr.Column():
- gallery = gr.outputs.Image(
- type="pil",
- # label="grounding results"
- ).style(full_width=True, full_height=True)
- # gallery = gr.Gallery(label="Generated images", show_label=False).style(
- # grid=[1], height="auto", container=True, full_width=True, full_height=True)
-
- run_button.click(fn=run_grounding, inputs=[
- input_image, grounding_caption, box_threshold, text_threshold], outputs=[gallery])
- gr.Examples(
- [["watermelon.jpg", "watermelon", 0.25, 0.25]],
- inputs = [input_image, grounding_caption, box_threshold, text_threshold],
- outputs = [gallery],
- fn=run_grounding,
- cache_examples=True,
- label='Try this example input!'
- )
- block.launch(share=True, show_api=False, show_error=True)
-
diff --git a/spaces/Audio-AGI/WavJourney/README.md b/spaces/Audio-AGI/WavJourney/README.md
deleted file mode 100644
index 6514202f6a3a92fe09c96516f15d4e7848d85484..0000000000000000000000000000000000000000
--- a/spaces/Audio-AGI/WavJourney/README.md
+++ /dev/null
@@ -1,111 +0,0 @@
----
-title: WavJourney
-emoji: 🔥
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
-license: cc-by-nc-4.0
----
-# 🎵 WavJourney: Compositional Audio Creation with LLMs
-[](https://arxiv.org/abs/2307.14335) [](https://github.com/Audio-AGI/WavJourney/) [](https://audio-agi.github.io/WavJourney_demopage/) [](https://huggingface.co/spaces/Audio-AGI/WavJourney)
-
-
-This repository contains the official implementation of ["WavJourney: Compositional Audio Creation with Large Language Models"](https://audio-agi.github.io/WavJourney_demopage/WavJourney_arXiv.pdf).
-
-Starting with a text prompt, WavJourney can create audio content with engaging storylines encompassing personalized speakers, lifelike speech in context, emotionally resonant music compositions, and impactful sound effects that enhance the auditory experience. Check the audio examples in the [Project Page](https://audio-agi.github.io/WavJourney_demopage/)!
-
-
-
-
-
-
-## Preliminaries
-1. Install the environment:
-```bash
-bash ./scripts/EnvsSetup.sh
-```
-2. Activate the conda environment:
-```bash
-conda activate WavJourney
-```
-
-3. (Optional) You can modify the default configuration in `config.yaml`, check the details described in the configuration file.
-4. Pre-download the models (might take some time):
-```bash
-python scripts/download_models.py
-```
-
-5. Set the WAVJOURNEY_OPENAI_KEY in the environment variable for accessing [GPT-4 API](https://platform.openai.com/account/api-keys) [[Guidance](https://help.openai.com/en/articles/7102672-how-can-i-access-gpt-4)]
-```bash
-export WAVJOURNEY_OPENAI_KEY=your_openai_key_here
-```
-
-6. Set environment variables for using API services
-```bash
-# Set the port for the WAVJOURNEY service to 8021
-export WAVJOURNEY_SERVICE_PORT=8021
-
-# Set the URL for the WAVJOURNEY service to 127.0.0.1
-export WAVJOURNEY_SERVICE_URL=127.0.0.1
-
-# Limit the maximum script lines for WAVJOURNEY to 999
-export WAVJOURNEY_MAX_SCRIPT_LINES=999
-```
-
-
-7. Start Python API services (e.g., Text-to-Speech, Text-to-Audio)
-```bash
-bash scripts/start_services.sh
-```
-
-## Web APP
- ```bash
-bash scripts/start_ui.sh
- ```
-
-## Commandline Usage
- ```bash
- python wavjourney_cli.py -f --input-text "Generate a one-minute introduction to quantum mechanics"
- ```
-
-
-## Kill the services
-You can kill the running services via this command:
- ```bash
-python scripts/kill_services.py
- ```
-
-## (Advanced features) Speaker customization
-You can add voice presets to WavJourney to customize the voice actors. Simply provide the voice id, the description and a sample wav file, and WavJourney will pick the voice automatically based on the audio script. Predefined system voice presets are in `data/voice_presets`.
-
-You can manage voice presets via UI. Specifically, if you want to add voice to voice presets. Run the script via command line below:
-```bash
-python add_voice_preset.py --id "id" --desc "description" --wav-path path/to/wav --session-id ''
-```
-What makes for good voice prompt? See detailed instructions here.
-## Hardware requirement
-- The VRAM of the GPU in the default configuration should be greater than 16 GB.
-- Operation system: Linux.
-
-## Citation
-If you find this work useful, you can cite the paper below:
-
- @article{liu2023wavjourney,
- title = {WavJourney: Compositional Audio Creation with Large Language Models},
- author = {Liu, Xubo and Zhu, Zhongkai and Liu, Haohe and Yuan, Yi and Huang, Qiushi and Liang, Jinhua and Cao, Yin and Kong, Qiuqiang and Plumbley, Mark D and Wang, Wenwu},
- journal = {arXiv preprint arXiv:2307.14335},
- year = {2023}
- }
-
-[](https://www.buymeacoffee.com/liuxubo)
-
-## Appreciation
-- [Bark](https://github.com/suno-ai/bark) for a zero-shot text-to-speech synthesis model.
-- [AudioCraft](https://github.com/facebookresearch/audiocraft) for state-of-the-art audio generation models.
-
-## Disclaimer
-We are not responsible for audio generated using semantics created by this model. Just don't use it for illegal purposes.
-
diff --git a/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py b/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py
deleted file mode 100644
index be58431ed446bf6283c17274a41e37ec3eb24409..0000000000000000000000000000000000000000
--- a/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import os
-import asyncio
-import random
-from datetime import date, datetime, timezone, timedelta
-from ast import literal_eval
-
-import streamlit as st
-import openai
-
-from autoagents.utils.constants import MAIN_HEADER, MAIN_CAPTION, SAMPLE_QUESTIONS
-from autoagents.agents.search import ActionRunner
-
-from langchain.chat_models import ChatOpenAI
-
-
-async def run():
- output_acc = ""
- st.session_state["random"] = random.randint(0, 99)
- if "task" not in st.session_state:
- st.session_state.task = None
- if "model_name" not in st.session_state:
- st.session_state.model_name = "gpt-3.5-turbo"
-
- st.set_page_config(
- page_title="Search Agent",
- page_icon="🤖",
- layout="wide",
- initial_sidebar_state="expanded",
- )
-
- st.title(MAIN_HEADER)
- st.caption(MAIN_CAPTION)
-
- with st.form("my_form", clear_on_submit=False):
- st.markdown("", unsafe_allow_html=True)
- user_input = st.text_input(
- "You: ",
- key="input",
- placeholder="Ask me anything ...",
- label_visibility="hidden",
- )
-
- submitted = st.form_submit_button(
- "Search", help="Hit to submit the search query."
- )
-
- # Ask the user to enter their OpenAI API key
- if (api_key := st.sidebar.text_input("OpenAI api-key", type="password")):
- api_org = None
- else:
- api_key, api_org = os.getenv("OPENAI_API_KEY"), os.getenv("OPENAI_API_ORG")
- with st.sidebar:
- model_dict = {
- "gpt-3.5-turbo": "GPT-3.5-turbo",
- "gpt-4": "GPT-4 (Better but slower)",
- }
- st.radio(
- "OpenAI model",
- model_dict.keys(),
- key="model_name",
- format_func=lambda x: model_dict[x],
- )
-
- time_zone = str(datetime.now(timezone(timedelta(0))).astimezone().tzinfo)
- st.markdown(f"**The system time zone is {time_zone} and the date is {date.today()}**")
-
- st.markdown("**Example Queries:**")
- for q in SAMPLE_QUESTIONS:
- st.markdown(f"*{q}*")
-
- if not api_key:
- st.warning(
- "API key required to try this app. The API key is not stored in any form. [This](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key) might help."
- )
- elif api_org and st.session_state.model_name == "gpt-4":
- st.warning(
- "The free API key does not support GPT-4. Please switch to GPT-3.5-turbo or input your own API key."
- )
- else:
- outputq = asyncio.Queue()
- runner = ActionRunner(outputq,
- ChatOpenAI(openai_api_key=api_key,
- openai_organization=api_org,
- temperature=0,
- model_name=st.session_state.model_name),
- persist_logs=True) # log to HF-dataset
-
- async def cleanup(e):
- st.error(e)
- await st.session_state.task
- st.session_state.task = None
- st.stop()
-
- placeholder = st.empty()
-
- if user_input and submitted:
- if st.session_state.task is not None:
- with placeholder.container():
- st.session_state.task.cancel()
- st.warning("Previous search aborted", icon="⚠️")
-
- st.session_state.task = asyncio.create_task(
- runner.run(user_input, outputq)
- )
- iterations = 0
- with st.expander("Search Results", expanded=True):
- while True:
- with st.spinner("Wait for it..."):
- output = await outputq.get()
- placeholder.empty()
- if isinstance(output, Exception):
- if isinstance(output, openai.error.AuthenticationError):
- await cleanup(f"AuthenticationError: Invalid OpenAI API key.")
- elif isinstance(output, openai.error.InvalidRequestError) \
- and output._message == "The model: `gpt-4` does not exist":
- await cleanup(f"The free API key does not support GPT-4. Please switch to GPT-3.5-turbo or input your own API key.")
- elif isinstance(output, openai.error.OpenAIError):
- await cleanup(output)
- elif isinstance(output, RuntimeWarning):
- st.warning(output)
- continue
- else:
- await cleanup("Something went wrong. Please try searching again.")
- return
- try:
- output_fmt = literal_eval(output)
- st.json(output_fmt, expanded=False)
- st.write("---")
- iterations += 1
- except:
- output_acc += "\n" + output
- st.markdown(f" {output} ",
- unsafe_allow_html=True)
- if iterations >= runner.agent_executor.max_iterations:
- await cleanup(
- f"Maximum iterations ({iterations}) exceeded. You can try running the search again or try a variation of the query."
- )
- return
- if "Final Answer:" in output:
- break
- # Found the answer
- final_answer = await st.session_state.task
- final_answer = final_answer.replace("$", "\$")
- # st.success accepts md
- st.success(final_answer, icon="✅")
- st.balloons()
- st.session_state.task = None
- st.stop()
-
-if __name__ == "__main__":
- loop = asyncio.new_event_loop()
- loop.set_debug(enabled=False)
- loop.run_until_complete(run())
diff --git a/spaces/BAAI/vid2vid-zero/Dockerfile b/spaces/BAAI/vid2vid-zero/Dockerfile
deleted file mode 100644
index 9eaa78f8facc5066f018183f108824842f11b7e8..0000000000000000000000000000000000000000
--- a/spaces/BAAI/vid2vid-zero/Dockerfile
+++ /dev/null
@@ -1,57 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update && \
- apt-get upgrade -y && \
- apt-get install -y --no-install-recommends \
- git \
- git-lfs \
- wget \
- curl \
- # ffmpeg \
- ffmpeg \
- x264 \
- # python build dependencies \
- build-essential \
- libssl-dev \
- zlib1g-dev \
- libbz2-dev \
- libreadline-dev \
- libsqlite3-dev \
- libncursesw5-dev \
- xz-utils \
- tk-dev \
- libxml2-dev \
- libxmlsec1-dev \
- libffi-dev \
- liblzma-dev && \
- apt-get clean && \
- rm -rf /var/lib/apt/lists/*
-
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:${PATH}
-WORKDIR ${HOME}/app
-
-RUN curl https://pyenv.run | bash
-ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ENV PYTHON_VERSION=3.10.9
-RUN pyenv install ${PYTHON_VERSION} && \
- pyenv global ${PYTHON_VERSION} && \
- pyenv rehash && \
- pip install --no-cache-dir -U pip setuptools wheel
-
-RUN pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1
-COPY --chown=1000 requirements.txt /tmp/requirements.txt
-RUN pip install --no-cache-dir -U -r /tmp/requirements.txt
-
-COPY --chown=1000 . ${HOME}/app
-# RUN cd Tune-A-Video && patch -p1 < ../patch
-ENV PYTHONPATH=${HOME}/app \
- PYTHONUNBUFFERED=1 \
- GRADIO_ALLOW_FLAGGING=never \
- GRADIO_NUM_PORTS=1 \
- GRADIO_SERVER_NAME=0.0.0.0 \
- GRADIO_THEME=huggingface \
- SYSTEM=spaces
-CMD ["python", "app.py"]
diff --git a/spaces/Bala2-03-2003/MygenvioceAI/app.py b/spaces/Bala2-03-2003/MygenvioceAI/app.py
deleted file mode 100644
index ca8b6d40b4ab898c70da92f4a4298de2baf703dc..0000000000000000000000000000000000000000
--- a/spaces/Bala2-03-2003/MygenvioceAI/app.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import os
-import re
-import requests
-import json
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-PLAY_HT_API_KEY=os.getenv('PLAY_HT_API_KEY')
-PLAY_HT_USER_ID=os.getenv('PLAY_HT_USER_ID')
-
-PLAY_HT_VOICE_ID=os.getenv('PLAY_HT_VOICE_ID')
-play_ht_api_get_audio_url = "https://play.ht/api/v2/tts"
-
-
-template = """You are a helpful assistant to answer user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
- input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
- llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
- prompt=prompt,
- verbose=True,
- memory=memory,
-)
-
-headers = {
- "accept": "text/event-stream",
- "content-type": "application/json",
- "AUTHORIZATION": "Bearer "+ PLAY_HT_API_KEY,
- "X-USER-ID": PLAY_HT_USER_ID
-}
-
-
-def get_payload(text):
- return {
- "text": text,
- "voice": PLAY_HT_VOICE_ID,
- "quality": "medium",
- "output_format": "mp3",
- "speed": 1,
- "sample_rate": 24000,
- "seed": None,
- "temperature": None
- }
-
-def get_generated_audio(text):
- payload = get_payload(text)
- generated_response = {}
- try:
- response = requests.post(play_ht_api_get_audio_url, json=payload, headers=headers)
- response.raise_for_status()
- generated_response["type"]= 'SUCCESS'
- generated_response["response"] = response.text
- except requests.exceptions.RequestException as e:
- generated_response["type"]= 'ERROR'
- try:
- response_text = json.loads(response.text)
- if response_text['error_message']:
- generated_response["response"] = response_text['error_message']
- else:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["type"]= 'ERROR'
- generated_response["response"] = response.text
- return generated_response
-
-def extract_urls(text):
- # Define the regex pattern for URLs
- url_pattern = r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+[/\w\.-]*'
-
- # Find all occurrences of URLs in the text
- urls = re.findall(url_pattern, text)
-
- return urls
-
-def get_audio_reply_for_question(text):
- generated_audio_event = get_generated_audio(text)
- #From get_generated_audio, you will get events in a string format, from that we need to extract the url
- final_response = {
- "audio_url": '',
- "message": ''
- }
- if generated_audio_event["type"] == 'SUCCESS':
- audio_urls = extract_urls(generated_audio_event["response"])
- if len(audio_urls) == 0:
- final_response['message'] = "No audio file link found in generated event"
- else:
- final_response['audio_url'] = audio_urls[-1]
- else:
- final_response['message'] = generated_audio_event['response']
- return final_response
-
-def download_url(url):
- try:
- # Send a GET request to the URL to fetch the content
- final_response = {
- 'content':'',
- 'error':''
- }
- response = requests.get(url)
- # Check if the request was successful (status code 200)
- if response.status_code == 200:
- final_response['content'] = response.content
- else:
- final_response['error'] = f"Failed to download the URL. Status code: {response.status_code}"
- except Exception as e:
- final_response['error'] = f"Failed to download the URL. Error: {e}"
- return final_response
-
-def get_filename_from_url(url):
- # Use os.path.basename() to extract the file name from the URL
- file_name = os.path.basename(url)
- return file_name
-
-def get_text_response(user_message):
- response = llm_chain.predict(user_message = user_message)
- return response
-
-def get_text_response_and_audio_response(user_message):
- response = get_text_response(user_message) # Getting the reply from Open AI
- audio_reply_for_question_response = get_audio_reply_for_question(response)
- final_response = {
- 'output_file_path': '',
- 'message':''
- }
- audio_url = audio_reply_for_question_response['audio_url']
- if audio_url:
- output_file_path=get_filename_from_url(audio_url)
- download_url_response = download_url(audio_url)
- audio_content = download_url_response['content']
- if audio_content:
- with open(output_file_path, "wb") as audio_file:
- audio_file.write(audio_content)
- final_response['output_file_path'] = output_file_path
- else:
- final_response['message'] = download_url_response['error']
- else:
- final_response['message'] = audio_reply_for_question_response['message']
- return final_response
-
-def chat_bot_response(message, history):
- text_and_audio_response = get_text_response_and_audio_response(message)
- output_file_path = text_and_audio_response['output_file_path']
- if output_file_path:
- return (text_and_audio_response['output_file_path'],)
- else:
- return text_and_audio_response['message']
-
-demo = gr.ChatInterface(chat_bot_response,examples=["How are you doing?","What are your interests?","Which places do you like to visit?"])
-
-if __name__ == "__main__":
- demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/BartPoint/VoiceChange/app_multi.py b/spaces/BartPoint/VoiceChange/app_multi.py
deleted file mode 100644
index 67890355f12e2d2d5420ce8e31a722020f349b24..0000000000000000000000000000000000000000
--- a/spaces/BartPoint/VoiceChange/app_multi.py
+++ /dev/null
@@ -1,469 +0,0 @@
-from typing import Union
-
-from argparse import ArgumentParser
-
-import asyncio
-import json
-import hashlib
-from os import path, getenv
-
-import gradio as gr
-
-import torch
-
-import numpy as np
-import librosa
-
-import edge_tts
-
-import config
-import util
-from infer_pack.models import (
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono
-)
-from vc_infer_pipeline import VC
-
-# Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21 # noqa
-in_hf_space = getenv('SYSTEM') == 'spaces'
-
-# Argument parsing
-arg_parser = ArgumentParser()
-arg_parser.add_argument(
- '--hubert',
- default=getenv('RVC_HUBERT', 'hubert_base.pt'),
- help='path to hubert base model (default: hubert_base.pt)'
-)
-arg_parser.add_argument(
- '--config',
- default=getenv('RVC_MULTI_CFG', 'multi_config.json'),
- help='path to config file (default: multi_config.json)'
-)
-arg_parser.add_argument(
- '--api',
- action='store_true',
- help='enable api endpoint'
-)
-arg_parser.add_argument(
- '--cache-examples',
- action='store_true',
- help='enable example caching, please remember delete gradio_cached_examples folder when example config has been modified' # noqa
-)
-args = arg_parser.parse_args()
-
-app_css = '''
-#model_info img {
- max-width: 100px;
- max-height: 100px;
- float: right;
-}
-
-#model_info p {
- margin: unset;
-}
-'''
-
-app = gr.Blocks(
- theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate"),
- css=app_css,
- analytics_enabled=False
-)
-
-# Load hubert model
-hubert_model = util.load_hubert_model(config.device, args.hubert)
-hubert_model.eval()
-
-# Load models
-multi_cfg = json.load(open(args.config, 'r'))
-loaded_models = []
-
-for model_name in multi_cfg.get('models'):
- print(f'Loading model: {model_name}')
-
- # Load model info
- model_info = json.load(
- open(path.join('model', model_name, 'config.json'), 'r')
- )
-
- # Load RVC checkpoint
- cpt = torch.load(
- path.join('model', model_name, model_info['model']),
- map_location='cpu'
- )
- tgt_sr = cpt['config'][-1]
- cpt['config'][-3] = cpt['weight']['emb_g.weight'].shape[0] # n_spk
-
- if_f0 = cpt.get('f0', 1)
- net_g: Union[SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono]
- if if_f0 == 1:
- net_g = SynthesizerTrnMs768NSFsid(
- *cpt['config'],
- is_half=util.is_half(config.device)
- )
- else:
- net_g = SynthesizerTrnMs768NSFsid_nono(*cpt['config'])
-
- del net_g.enc_q
-
- # According to original code, this thing seems necessary.
- print(net_g.load_state_dict(cpt['weight'], strict=False))
-
- net_g.eval().to(config.device)
- net_g = net_g.half() if util.is_half(config.device) else net_g.float()
-
- vc = VC(tgt_sr, config)
-
- loaded_models.append(dict(
- name=model_name,
- metadata=model_info,
- vc=vc,
- net_g=net_g,
- if_f0=if_f0,
- target_sr=tgt_sr
- ))
-
-print(f'Models loaded: {len(loaded_models)}')
-
-# Edge TTS speakers
-tts_speakers_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) # noqa
-
-
-# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer-web.py#L118 # noqa
-def vc_func(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- if input_audio is None:
- return (None, 'Please provide input audio.')
-
- if model_index is None:
- return (None, 'Please select a model.')
-
- model = loaded_models[model_index]
-
- # Reference: so-vits
- (audio_samp, audio_npy) = input_audio
-
- # https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49
- # Can be change well, we will see
- if (audio_npy.shape[0] / audio_samp) > 320 and in_hf_space:
- return (None, 'Input audio is longer than 60 secs.')
-
- # Bloody hell: https://stackoverflow.com/questions/26921836/
- if audio_npy.dtype != np.float32: # :thonk:
- audio_npy = (
- audio_npy / np.iinfo(audio_npy.dtype).max
- ).astype(np.float32)
-
- if len(audio_npy.shape) > 1:
- audio_npy = librosa.to_mono(audio_npy.transpose(1, 0))
-
- if audio_samp != 16000:
- audio_npy = librosa.resample(
- audio_npy,
- orig_sr=audio_samp,
- target_sr=16000
- )
-
- pitch_int = int(pitch_adjust)
-
- resample = (
- 0 if resample_option == 'Disable resampling'
- else int(resample_option)
- )
-
- times = [0, 0, 0]
-
- checksum = hashlib.sha512()
- checksum.update(audio_npy.tobytes())
-
- output_audio = model['vc'].pipeline(
- hubert_model,
- model['net_g'],
- model['metadata'].get('speaker_id', 0),
- audio_npy,
- checksum.hexdigest(),
- times,
- pitch_int,
- f0_method,
- path.join('model', model['name'], model['metadata']['feat_index']),
- feat_ratio,
- model['if_f0'],
- filter_radius,
- model['target_sr'],
- resample,
- rms_mix_rate,
- 'v2'
- )
-
- out_sr = (
- resample if resample >= 16000 and model['target_sr'] != resample
- else model['target_sr']
- )
-
- print(f'npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s')
- return ((out_sr, output_audio), 'Success')
-
-
-async def edge_tts_vc_func(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- if input_text is None:
- return (None, 'Please provide TTS text.')
-
- if tts_speaker is None:
- return (None, 'Please select TTS speaker.')
-
- if model_index is None:
- return (None, 'Please select a model.')
-
- speaker = tts_speakers_list[tts_speaker]['ShortName']
- (tts_np, tts_sr) = await util.call_edge_tts(speaker, input_text)
- return vc_func(
- (tts_sr, tts_np),
- model_index,
- pitch_adjust,
- f0_method,
- feat_ratio,
- filter_radius,
- rms_mix_rate,
- resample_option
- )
-
-
-def update_model_info(model_index):
- if model_index is None:
- return str(
- '### Model info\n'
- 'Please select a model from dropdown above.'
- )
-
- model = loaded_models[model_index]
- model_icon = model['metadata'].get('icon', '')
-
- return str(
- '### Model info\n'
- ''
- '**{name}**\n\n'
- 'Author: {author}\n\n'
- 'Source: {source}\n\n'
- '{note}'
- ).format(
- name=model['metadata'].get('name'),
- author=model['metadata'].get('author', 'Anonymous'),
- source=model['metadata'].get('source', 'Unknown'),
- note=model['metadata'].get('note', ''),
- icon=(
- model_icon
- if model_icon.startswith(('http://', 'https://'))
- else '/file/model/%s/%s' % (model['name'], model_icon)
- )
- )
-
-
-def _example_vc(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- (audio, message) = vc_func(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
- )
- return (
- audio,
- message,
- update_model_info(model_index)
- )
-
-
-async def _example_edge_tts(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- (audio, message) = await edge_tts_vc_func(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method,
- feat_ratio, filter_radius, rms_mix_rate, resample_option
- )
- return (
- audio,
- message,
- update_model_info(model_index)
- )
-
-
-with app:
- gr.Markdown(
- '## A simplistic Web interface\n'
- 'RVC interface, project based on [RVC-WebUI](https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI)' # thx noqa
- 'A lot of inspiration from what\'s already out there, including [zomehwh/rvc-models](https://huggingface.co/spaces/zomehwh/rvc-models) & [DJQmUKV/rvc-inference](https://huggingface.co/spaces/DJQmUKV/rvc-inference).\n ' # thx noqa
- )
-
- with gr.Row():
- with gr.Column():
- with gr.Tab('Audio conversion'):
- input_audio = gr.Audio(label='Input audio')
-
- vc_convert_btn = gr.Button('Convert', variant='primary')
-
- with gr.Tab('TTS conversion'):
- tts_input = gr.TextArea(
- label='TTS input text'
- )
- tts_speaker = gr.Dropdown(
- [
- '%s (%s)' % (
- s['FriendlyName'],
- s['Gender']
- )
- for s in tts_speakers_list
- ],
- label='TTS speaker',
- type='index'
- )
-
- tts_convert_btn = gr.Button('Convert', variant='primary')
-
- pitch_adjust = gr.Slider(
- label='Pitch',
- minimum=-24,
- maximum=24,
- step=1,
- value=0
- )
- f0_method = gr.Radio(
- label='f0 methods',
- choices=['pm', 'harvest', 'crepe'],
- value='pm',
- interactive=True
- )
-
- with gr.Accordion('Advanced options', open=False):
- feat_ratio = gr.Slider(
- label='Feature ratio',
- minimum=0,
- maximum=1,
- step=0.1,
- value=0.6
- )
- filter_radius = gr.Slider(
- label='Filter radius',
- minimum=0,
- maximum=7,
- step=1,
- value=3
- )
- rms_mix_rate = gr.Slider(
- label='Volume envelope mix rate',
- minimum=0,
- maximum=1,
- step=0.1,
- value=1
- )
- resample_rate = gr.Dropdown(
- [
- 'Disable resampling',
- '16000',
- '22050',
- '44100',
- '48000'
- ],
- label='Resample rate',
- value='Disable resampling'
- )
-
- with gr.Column():
- # Model select
- model_index = gr.Dropdown(
- [
- '%s - %s' % (
- m['metadata'].get('source', 'Unknown'),
- m['metadata'].get('name')
- )
- for m in loaded_models
- ],
- label='Model',
- type='index'
- )
-
- # Model info
- with gr.Box():
- model_info = gr.Markdown(
- '### Model info\n'
- 'Please select a model from dropdown above.',
- elem_id='model_info'
- )
-
- output_audio = gr.Audio(label='Output audio')
- output_msg = gr.Textbox(label='Output message')
-
- multi_examples = multi_cfg.get('examples')
- if (
- multi_examples and
- multi_examples.get('vc') and multi_examples.get('tts_vc')
- ):
- with gr.Accordion('Sweet sweet examples', open=False):
- with gr.Row():
- # VC Example
- if multi_examples.get('vc'):
- gr.Examples(
- label='Audio conversion examples',
- examples=multi_examples.get('vc'),
- inputs=[
- input_audio, model_index, pitch_adjust, f0_method,
- feat_ratio
- ],
- outputs=[output_audio, output_msg, model_info],
- fn=_example_vc,
- cache_examples=args.cache_examples,
- run_on_click=args.cache_examples
- )
-
- # Edge TTS Example
- if multi_examples.get('tts_vc'):
- gr.Examples(
- label='TTS conversion examples',
- examples=multi_examples.get('tts_vc'),
- inputs=[
- tts_input, model_index, tts_speaker, pitch_adjust,
- f0_method, feat_ratio
- ],
- outputs=[output_audio, output_msg, model_info],
- fn=_example_edge_tts,
- cache_examples=args.cache_examples,
- run_on_click=args.cache_examples
- )
-
- vc_convert_btn.click(
- vc_func,
- [
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_rate
- ],
- [output_audio, output_msg],
- api_name='audio_conversion'
- )
-
- tts_convert_btn.click(
- edge_tts_vc_func,
- [
- tts_input, model_index, tts_speaker, pitch_adjust, f0_method,
- feat_ratio, filter_radius, rms_mix_rate, resample_rate
- ],
- [output_audio, output_msg],
- api_name='tts_conversion'
- )
-
- model_index.change(
- update_model_info,
- inputs=[model_index],
- outputs=[model_info],
- show_progress=False,
- queue=False
- )
-
-app.queue(
- concurrency_count=1,
- max_size=20,
- api_open=args.api
-).launch()
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md b/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md
deleted file mode 100644
index 231c36d39005d9275c61ccc88067d084ca775635..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md
+++ /dev/null
@@ -1,134 +0,0 @@
-
-Aparcamiento de coches multijugador Sudáfrica Edición APK Descargar: Una guía para los usuarios de Android
- Si usted está buscando un juego de estacionamiento de coches realista y divertido que ofrece más que solo estacionamiento, es posible que desee echa un vistazo a Parking Multijugador. Y si usted está en Sudáfrica o desea experimentar la cultura del automóvil de Sudáfrica, es posible que desee probar el Aparcamiento Multijugador South áfrica Edition APK. En este artículo, te diremos qué es el Car Parking Multiplayer, qué hace que South áfrica Edition sea diferente, cómo descargarlo e instalarlo en tu dispositivo Android, y algunos consejos y trucos para jugarlo.
- ¿Qué es el Aparcamiento Multijugador?
- Car Parking Multiplayer es un juego que puede engañarte con su nombre bastante engañoso. Pero, es mucho más que solo estar aparcando tu coche. Es una experiencia de mundo abierto donde se puede conducir gratis y sí, todavía trabajar en ese aparcamiento si lo desea. Incluso puedes saltar de tu coche y caminar. Hay diferentes áreas que se pueden explorar en el juego. Cada una es como su propio mundo abierto. Puedes optar por jugar en modo de un solo jugador o en modo online si quieres una escena más caótica (de forma divertida) .
-aparcamiento de coches multijugador sudáfrica edición apk descargar
DOWNLOAD === https://bltlly.com/2v6IE9
- Características de Aparcamiento Multijugador
- Aparcamiento de coches multijugador tiene las siguientes características :
-
-- Modo multijugador de mundo abierto
- - Caminar libremente
- - Mundo abierto libre con gasolineras reales y servicios de automóviles
- - Compite contra jugadores reales en las carreras multijugador
- - Intercambiar coches con jugadores reales
- - Miles de jugadores reales cada día
- - Lista de amigos
- - Chat de voz
- - Modo de policía
-
-
-- Personalización del coche
- - Suspensión ajustable, ángulo de rueda y más
- - Ajuste del motor: motor de intercambio, turbo, caja de cambios y escape
- - Visual auto tungs: Vynils dinámico, partes del cuerpo del coche
-
-
-- Mundo abierto de alta calidad
-
- - 100 coches con el interior real
- - 16 pieles de jugador
- - Edificios con interior
-
-
-- Juego interesante
- - 82 desafíos de estacionamiento y conducción en la vida real
- - Diferentes vehículos: Grúa, camioneta, camiones, coches deportivos y clásicos
-
-
-
- Opiniones de Aparcamiento multijugador
- Aparcamiento de coches multijugador ha recibido en su mayoría críticas positivas de los usuarios en Google Play Store y App Store. Tiene una calificación de 4.4 de 5 estrellas en Google Play Store y una calificación de 4.3 de 5 estrellas en App Store . Estos son algunos de los comentarios de los usuarios:
- "Increíble juego! No hay errores en este juego o retrasos ect. (base en el dispositivo que utiliza) Me encantan los gráficos y los coches. Los coches son tan realistas y los sonidos son increíbles. Me encanta cómo puedes personalizar tu coche y hacer que se vea genial. El modo multijugador es increíble. Puedes chatear con otros jugadores y competir con ellos. También puedes unirte a un clan o crear tu propio clan. Este juego es muy divertido y adictivo. Recomiendo este juego a todos los que aman los coches y los juegos de estacionamiento."
- "Este juego es muy bueno pero necesita algunas mejoras como agregar más coches, más mapas, más personalizaciones, más modos de juego, más desafíos, etc. Además, el juego se bloquea a veces y los controles no son muy suaves. Los gráficos son agradables, pero pueden ser mejores. El juego es divertido de jugar con amigos, pero se vuelve aburrido después de un tiempo. Espero que los desarrolladores actualicen el juego pronto y lo hagan más agradable."
-
- ¿Qué es el aparcamiento multijugador South áfrica Edition?
- Car Parking Multijugador South áfrica Edition es una versión modificada de Car Parking Multijugador que está especialmente diseñado para los usuarios sudafricanos o los fans de la cultura del coche de Sudáfrica. No es una versión oficial del juego, sino un archivo APK hecho por fans que se puede descargar e instalar en dispositivos Android .
- Diferencias entre Aparcamiento de coches multijugador y Aparcamiento de coches multijugador Sudáfrica Edición
- Car Parking Multijugador South áfrica Edition tiene algunas diferencias con el juego original Car Parking Multijugador, como :
-
-- Más coches que son populares en Sudáfrica, como BMW E30, VW Golf Mk1, Toyota Corolla, Nissan 1400, etc.
-- Más personalizaciones que reflejan la cultura del automóvil sudafricano, como llantas giratorias, escapes fuertes, pegatinas, banderas, etc.
-- Más mapas que se basan en ubicaciones reales en Sudáfrica, como Ciudad del Cabo, Johannesburgo, Durban, etc.
-- Más música inspirada en la escena musical sudafricana, como kwaito, gqom, amapiano, etc.
-- Más idiomas que se hablan en Sudáfrica, como afrikaans, zulú, xhosa, etc.
-
- Beneficios de Aparcamiento Multijugador South áfrica Edition
- Car Parking Multijugador South áfrica Edition tiene algunos beneficios para los usuarios que quieren disfrutar del juego con un toque sudafricano, como :
-
-- Más variedad y diversidad en términos de coches, personalizaciones, mapas, música e idiomas
-- Más diversión y emoción en términos de jugabilidad, gráficos, efectos de sonido e interacciones
-- Más conexión y comunidad con otros jugadores que comparten el mismo interés y pasión por la cultura del automóvil de Sudáfrica
-- Más soporte y actualizaciones de los desarrolladores que se dedican a mejorar el juego y agregar nuevas características
-
- Cómo descargar e instalar el estacionamiento de coches multijugador South áfrica Edition APK en dispositivos Android?
-
- Requisitos para descargar e instalar el estacionamiento de coches multijugador South áfrica Edition APK
- Para descargar e instalar Aparcamiento Multijugador South áfrica Edition APK en su dispositivo Android, es necesario tener :
-
-
-- Un dispositivo Android que se ejecuta en Android 4.1 o superior
-- Una conexión a Internet estable
-- Una aplicación de administrador de archivos que puede acceder al archivo APK
-- Una cantidad suficiente de espacio de almacenamiento en su dispositivo
-- Un permiso para instalar aplicaciones de fuentes desconocidas en la configuración del dispositivo
-
- Pasos para Descargar e Instalar Aparcamiento Multijugador South áfrica Edition APK
- Para descargar e instalar Aparcamiento Multijugador South áfrica Edition APK en su dispositivo Android, es necesario seguir estos pasos :
-
-- Ir a un sitio web de confianza que proporciona el enlace para descargar Car Parking Multi jugador South áfrica Edition APK. Por ejemplo, puede visitar [este sitio web] para obtener la última versión del archivo APK.
-- Haga clic en el botón de descarga y espere a que el archivo APK se descargue en su dispositivo.
-- Una vez que se complete la descarga, localizar el archivo APK en su dispositivo utilizando una aplicación de administrador de archivos. Puede encontrarlo en la carpeta Descargas o en cualquier otra carpeta donde lo haya guardado.
-- Toque en el archivo APK y seleccione Instalar. Es posible que vea un mensaje de advertencia que dice "Para su seguridad, el teléfono no está permitido instalar aplicaciones desconocidas de esta fuente". Si ves este mensaje, ve a la configuración del dispositivo y habilita la opción de instalar aplicaciones de fuentes desconocidas. Esta opción puede estar en Seguridad, Privacidad o Aplicaciones dependiendo del modelo de dispositivo y la versión de Android.
-
-- Espere a que termine el proceso de instalación. Puede tardar unos minutos dependiendo del rendimiento del dispositivo y la velocidad de Internet.
-- Una vez que se hace la instalación, se puede abrir el juego y disfrutar de aparcamiento multijugador South áfrica Edition en su dispositivo Android.
-
- Consejos y Trucos para Jugar Aparcamiento Multijugador South áfrica Edition
- Ahora que ha descargado e instalado Car Parking Multiplayer South áfrica Edition en su dispositivo Android, es posible que desee conocer algunos consejos y trucos para jugarlo. Estos son algunos de ellos:
- Cómo seleccionar un coche y un jugador
- Para seleccionar un coche y un jugador en Car Parking Multiplayer South áfrica Edition, debe hacer lo siguiente:
-
-- Toque en el icono de menú en la esquina superior izquierda de la pantalla.
-- Toque en Garaje para ver la lista de coches que usted posee o puede comprar.
-- Toque en el coche que desea utilizar y luego toque en Select.
-- Toque en Volver para volver al menú.
-- Toque en Perfil para ver la lista de jugadores entre los que puede elegir.
-- Toque en el reproductor que desea utilizar y luego toque en Select.
-- Toque en Volver para volver al menú.
-- Toque en Jugar para comenzar a jugar el juego con el coche y el jugador seleccionado.
-
- Cómo ajustar su coche y ajustar su relación de engranajes
- Para afinar su coche y ajustar su relación de engranajes en Car Parking Multiplayer South áfrica Edition, debe hacer lo siguiente:
-
-- Toque en el icono de menú en la esquina superior izquierda de la pantalla.
-- Toque en Garaje para ver la lista de coches que usted posee o puede comprar.
-- Toque en el coche que desea sintonizar y luego toque en Tune.
-- Verá cuatro pestañas: Motor, Suspensión, Ruedas y Cuerpo. Puede deslizar hacia la izquierda o hacia la derecha para cambiar entre ellas.
-
-- Para ajustar su relación de cambios, toque en Caja de cambios en la pestaña Motor. Verá un gráfico que muestra cómo cambia su velocidad con diferentes marchas. Puede arrastrar los puntos en el gráfico para cambiar la relación de transmisión para cada marcha. También puede pulsar en Auto o Manual para cambiar entre modos de transmisión automática o manual.
-- Una vez que haya terminado de ajustar su coche, toque en Guardar para aplicar los cambios.
-
- Cómo Deriva, Donut, y Burnout
- Para la deriva, donut, y burnout en el estacionamiento de coches multijugador South áfrica Edition, es necesario hacer lo siguiente:
-
-- Para la deriva, debe usar una combinación de dirección, acelerador, freno y freno de mano. Puede usar los controles de inclinación o táctiles para la dirección. Para iniciar la deriva, es necesario acelerar y girar bruscamente en una esquina. Luego, debe aplicar el freno de mano brevemente para hacer que sus ruedas traseras pierdan tracción. Después de eso, debe equilibrar el acelerador y la dirección para mantener el ángulo de deriva y la dirección. También puede utilizar el freno de mano o freno de mano de nuevo si necesita ajustar su deriva. Para terminar con la deriva, es necesario soltar el acelerador y la dirección y enderezar el coche.
-- Para la rosquilla, debe usar una combinación de dirección, acelerador y freno de mano. Puede usar controles de inclinación o táctiles para la dirección. Para comenzar a donar, debe acelerar y girar bruscamente en una dirección. Entonces, es necesario aplicar el freno de mano para hacer que su coche gire alrededor de su centro. Después de eso, es necesario mantener el acelerador y la dirección constante para mantener el círculo de la dona. También puede cambiar la dirección de su dona cambiando la dirección. Para terminar donuting, es necesario soltar el acelerador y el freno de mano y enderezar su coche.
-
-
- Conclusión
- Car Parking Multijugador South áfrica Edition es un juego que ofrece una experiencia de aparcamiento realista y divertido con un toque sudafricano. Tiene más coches, personalizaciones, mapas, música e idiomas que reflejan la cultura del automóvil sudafricano. También tiene un modo de mundo abierto, un modo de ajuste de coche, y un modo multijugador que hacen el juego más agradable y emocionante. Puede descargar e instalar Parking Multijugador South áfrica Edition APK en su dispositivo Android siguiendo los pasos en este artículo. También puedes usar los consejos y trucos de este artículo para mejorar tu juego y divertirte más.
- Preguntas frecuentes
- Aquí están algunas de las preguntas más frecuentes sobre Car Parking Multiplayer South áfrica Edition:
- ¿Es gratis Car Parking Multijugador South áfrica Edition?
- Sí, Parking Multijugador South áfrica Edition es gratis para descargar y jugar. Sin embargo, puede contener algunas compras en la aplicación que requieren dinero real.
- ¿Es seguro el aparcamiento multijugador South áfrica Edition?
- Sí, Parking Multijugador South áfrica Edition es seguro, siempre y cuando se descarga desde un sitio web de confianza. Sin embargo, siempre debe tener cuidado al descargar e instalar aplicaciones de fuentes desconocidas, ya que pueden contener virus o malware que pueden dañar su dispositivo.
- ¿Es compatible Car Parking Multiplayer South áfrica Edition con mi dispositivo?
- Car Parking Multijugador South áfrica Edition es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 4.1 o superior. Sin embargo, algunos dispositivos pueden tener diferentes especificaciones o problemas de rendimiento que pueden afectar la calidad o funcionalidad del juego.
- ¿Cómo puedo actualizar Car Parking Multijugador South áfrica Edition?
-
- ¿Cómo puedo contactar a los desarrolladores de Car Parking Multiplayer South áfrica Edition?
- Para contactar a los desarrolladores de Car Parking Multiplayer South áfrica Edition, puedes visitar su página de Facebook o su canal de YouTube y dejar un comentario o un mensaje.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md b/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md
deleted file mode 100644
index 2001fb4da4e341236f8b4ef59c584c7ee860ae80..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md
+++ /dev/null
@@ -1,76 +0,0 @@
-
-Nitro asfalto 9 leyendas Mod Apk: Una guía para los aficionados a las carreras
-Si eres un fan de los juegos de carreras, es posible que hayas oído hablar de Asphalt Nitro 9 Legends, uno de los juegos más populares y emocionantes del género. Este juego le permite tomar el volante de los coches reales de gama alta de renombrados fabricantes de automóviles legendarios, como Ferrari, Porsche, Lamborghini, y W Motors, entre muchas otras marcas internacionales. Puedes conducir, impulsar y realizar acrobacias en ubicaciones dinámicas de la vida real en modo individual o multijugador.
-Pero ¿y si quieres disfrutar del juego sin limitaciones ni restricciones? ¿Qué pasa si quieres tener dinero ilimitado y fichas, desbloquear todos los coches y pistas, personalizar sus vehículos y actualizarlos, y competir con otros jugadores en línea o fuera de línea? Bueno, hay una manera de hacerlo. Puede descargar e instalar Asphalt Nitro 9 Leyendas Mod Apk, una versión modificada del juego que le da acceso a todas estas características y más.
-asfalto nitro 9 leyendas mod apk
Download File ===== https://bltlly.com/2v6LmQ
-En este artículo, le guiará a través de las características, proceso de descarga, pasos de instalación, consejos y trucos, pros y contras, y la conclusión de Asphalt Nitro 9 Leyendas Mod Apk. Sigue leyendo para saber más.
- Características de Asphalt Nitro 9 Leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es una versión modificada del juego original que le da recursos ilimitados y características que mejoran su experiencia de juego. Estas son algunas de las características que puedes disfrutar con este mod apk:
-
-- Dinero ilimitado y fichas: Con este apk mod, nunca se quedará sin dinero o fichas para comprar nuevos coches, pistas, mejoras, o artículos. Puedes gastar todo lo que quieras sin preocuparte por tu presupuesto.
-
-- Personalizar sus vehículos y actualizarlos: Con este apk mod, puede personalizar sus vehículos de acuerdo a su preferencia. Puedes cambiar su color, pintura, calcomanías, ruedas, etc. También puedes mejorar su rendimiento mejorando su motor, transmisión, suspensión, frenos, nitro boost, etc.
-- Disfrutar de gráficos realistas y efectos de sonido: Con este apk mod, se puede disfrutar de los impresionantes gráficos y efectos de sonido del juego. El juego utiliza un motor de física realista que simula el movimiento y el comportamiento de los coches. El juego también cuenta con efectos de sonido de alta calidad que te hacen sentir como si estuvieras en una carrera real.
-- Compite con otros jugadores en línea o fuera de línea: Con este apk mod, puede competir con otros jugadores en línea o fuera de línea. Puedes unirte a carreras multijugador online y desafiar a tus amigos u otros jugadores de todo el mundo. También puedes jugar offline en modo carrera y completar varias misiones y desafíos.
-
- Cómo descargar e instalar Asphalt Nitro 9 leyendas Mod Apk
-Si desea descargar e instalar Asphalt Nitro 9 leyendas Mod Apk, es necesario seguir estos sencillos pasos:
-
-- Paso 1: Descargar el archivo apk mod de una fuente de confianza. Usted puede encontrar muchos sitios web que ofrecen el archivo apk mod para Asphalt Nitro 9 Leyendas, pero no todos ellos son seguros y fiables. Debes asegurarte de que el archivo que descargas esté libre de virus, malware o cualquier otro contenido dañino. Puede utilizar este enlace para descargar el archivo apk mod de forma segura.
-- Paso 2: Habilitar fuentes desconocidas en la configuración del dispositivo. Antes de poder instalar el archivo apk mod, debe habilitar fuentes desconocidas en la configuración de su dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a la configuración del dispositivo, luego a la seguridad, luego a fuentes desconocidas y conéctela.
-
-- Paso 4: Disfruta del juego con recursos y funciones ilimitadas. Ahora que ha instalado Asphalt Nitro 9 Leyendas Mod Apk, se puede disfrutar del juego con dinero ilimitado, fichas, coches, pistas, opciones de personalización, y más. También puedes competir con otros jugadores online o offline y divertirte.
-
- Consejos y trucos para jugar Asphalt Nitro 9 leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es un juego divertido y emocionante que le mantendrá entretenido durante horas. Sin embargo, si quieres mejorar tus habilidades y rendimiento en el juego, puedes seguir estos consejos y trucos:
-
-- Elija el coche adecuado para cada pista y modo. El juego ofrece una variedad de coches de diferentes marcas y categorías, pero no todos ellos son adecuados para cada pista o modo. Es necesario tener en cuenta la velocidad, aceleración, manejo, nitro, y otros factores de cada coche antes de elegir uno. Por ejemplo, si usted está jugando en una pista con curvas, es posible que desee elegir un coche con buen manejo y nitro. Si usted está jugando en una pista recta, es posible que desee elegir un coche con alta velocidad y aceleración.
-- Utilice nitro boost sabiamente y estratégicamente. Nitro boost es una de las características más importantes del juego, ya que puede darte una ventaja sobre tus oponentes. Sin embargo, necesitas usarlo sabiamente y estratégicamente. No debes usarlo todo de una vez ni desperdiciarlo en momentos innecesarios. Debes guardarlo para momentos cruciales, como adelantar a tus rivales, escapar de obstáculos o colisiones, o llegar a la meta. También debe usarlo en combinación con acrobacias y derivas para ganar más puntos y recompensas.
-
-- Evitar obstáculos y colisiones con otros coches. El juego cuenta con muchos obstáculos y desafíos que pueden obstaculizar su progreso o dañar su coche. Estos incluyen coches de tráfico, coches de policía, helicópteros, barricadas, picos, etc. Usted debe evitar estos obstáculos tanto como sea posible, ya que pueden ralentizar o dañar su coche. También debes evitar colisiones con otros coches, especialmente tus rivales, ya que pueden sacarte de la pista o hacerte perder tu posición. Usted debe tratar de esquivar o escapar de ellos, o utilizar su impulso nitro para alejarse de ellos.
-- Utilice diferentes ángulos de cámara y controles para adaptarse a sus preferencias. El juego ofrece diferentes ángulos de cámara y controles que puedes usar para jugar el juego. Puede elegir entre vista en primera persona, en tercera persona o de arriba hacia abajo, según su preferencia. También puede elegir entre los controles de inclinación, toque o toque, dependiendo de su comodidad. Puede cambiar estos ajustes en el menú de opciones del juego.
-
- Pros y contras de Asphalt Nitro 9 Leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es un gran juego que ofrece muchos beneficios y ventajas para los aficionados a las carreras. Sin embargo, también tiene algunos inconvenientes y desventajas que usted debe tener en cuenta. Estos son algunos de los pros y los contras de Asphalt Nitro 9 Leyendas Mod Apk:
-
-
-Pros
-Contras
-
-
-
-- Divertido, adictivo y desafiante juego
-- Dinero ilimitado y tokens
-- Desbloquear todos los coches y pistas
-- Personalizar sus vehículos y actualizarlos
-- Disfruta de gráficos realistas y efectos de sonido
-- Compite con otros jugadores online o offline
-
-- Puede causar retraso o estrellarse en algunos dispositivos
-- Puede no ser compatible con algunos dispositivos o versiones
-- No se puede actualizar regularmente o con frecuencia
-- No puede ser soportado por los desarrolladores o editores oficiales
-
-- Puede exponer su dispositivo a riesgos o amenazas de seguridad
-
-
- Conclusión
-En conclusión, Asfalto Nitro 9 Leyendas Mod Apk es un juego fantástico que le dará horas de diversión y emoción. Es una versión modificada del juego original que le da recursos ilimitados y características que mejoran su experiencia de juego. Puede descargarlo e instalarlo de forma fácil y segura siguiendo los pasos que proporcionamos en este artículo. También puedes seguir los consejos y trucos que compartimos para mejorar tus habilidades y rendimiento en el juego. Sin embargo, también debe ser consciente de los pros y los contras de Asphalt Nitro 9 Leyendas Mod Apk, y decidir si vale la pena jugar o no.
-Si usted es un fanático de las carreras, le recomendamos que pruebe Asphalt Nitro 9 Leyendas Mod Apk y ver por sí mismo lo increíble que es. No te arrepentirás.
- Preguntas frecuentes (preguntas frecuentes)
-Aquí están algunas de las preguntas y respuestas más comunes sobre Asphalt Nitro 9 Legends Mod Apk:
-
- Q: ¿Es libre de Asphalt Nitro 9 Leyendas Mod Apk?
-A: Sí, Asphalt Nitro 9 Leyendas Mod Apk es gratis para descargar y jugar. Usted no necesita pagar nada para disfrutar del juego.
- Q: ¿Es seguro Asphalt Nitro 9 Legends Mod Apk?
-A: Sí, Asfalto Nitro 9 Leyendas Mod Apk es seguro para descargar e instalar. Sin embargo, siempre debe descargarlo de una fuente de confianza y habilitar fuentes desconocidas en la configuración del dispositivo antes de instalarlo.
- Q: ¿Es legal Asphalt Nitro 9 Legends Mod Apk?
-A: No, Asphalt Nitro 9 Leyendas Mod Apk no es legal. Es una versión modificada del juego original que viola los términos y condiciones de los desarrolladores y editores oficiales. También puede infringir los derechos de propiedad intelectual del juego original.
- Q: Es Asphalt Nitro 9 Leyendas Mod Apk en línea o fuera de línea?
-
- Q: ¿Cómo puedo actualizar Asphalt Nitro 9 Legends Mod Apk?
-A: Para actualizar Asphalt Nitro 9 leyendas Mod Apk, es necesario descargar e instalar la última versión del archivo apk mod de una fuente de confianza. Es posible que tenga que desinstalar la versión anterior del archivo apk mod antes de instalar el nuevo.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md b/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md
deleted file mode 100644
index d872bc888664198a5fb77f20f85945d9c05ae2ef..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md
+++ /dev/null
@@ -1,76 +0,0 @@
-
-Camioneros de Europa 3 APK 36.6: Un juego de simulador de camiones realista y divertido
-Si eres un fan de los juegos de simuladores de camiones, es posible que hayas oído hablar de Truckers of Europe 3, un juego popular que te permite experimentar la vida de un conductor de camiones en Europa. En este artículo, te contaremos todo lo que necesitas saber sobre este juego, incluyendo qué es, cuáles son sus características, cómo descargarlo e instalarlo, cómo jugarlo y por qué deberías jugarlo. ¡Así que abróchate el cinturón y prepárate para un emocionante viaje!
-Introducción
-¿Qué es Camioneros de Europa 3?
-Truckers of Europe 3 es un juego de simulador de camioneros desarrollado por Jerryisgaming, un canal de YouTube que crea videos de juegos. El juego fue lanzado en 2020 y ha sido actualizado regularmente con nuevas características y mejoras. La última versión del juego es 0.36.6, que fue lanzado el 30 de septiembre de 2021.
-camioneros de europa 3 apk 36.6
Download Zip ❤❤❤ https://bltlly.com/2v6LwC
-¿Cuáles son las características de Camioneros de Europa 3?
-Truckers of Europe 3 tiene muchas características que lo convierten en uno de los mejores juegos de simuladores de camiones en el mercado. Algunas de estas características son:
-
-- Un gran mapa de Europa con más de 50 ciudades y países para explorar.
-- Una variedad de camiones y remolques para elegir, cada uno con diferentes especificaciones y rendimiento.
-- Un sistema de conducción realista con transmisión manual, volante, pedales, indicadores, espejos, luces, bocina, limpiaparabrisas, etc.
-- Un sistema de tráfico realista con coches, autobuses, camiones, motocicletas, policía, ambulancia, etc.
-- Un sistema meteorológico realista con ciclo de día y noche, lluvia, nieve, niebla, etc.
-- Un sistema de daños realista con desgaste de neumáticos, consumo de combustible, sobrecalentamiento del motor, etc.
-- Un modo de carrera con misiones, contratos, entrega de carga, ingresos, gastos, etc.
-- Un sistema de habilidades con niveles, puntos, ventajas, etc.
-- Un sistema de personalización con trabajos de pintura, accesorios, calcomanías, etc.
-
-
-Cómo descargar e instalar camioneros de Europa 3 APK 36.6?
-Requisitos
-Para descargar e instalar Camioneros de Europa 3 APK 36.6 en su dispositivo Android, es necesario cumplir con los siguientes requisitos:
-
-- Tu dispositivo debe tener la versión de Android 4.4 o superior.
-- Su dispositivo debe tener al menos 1 GB de RAM y 500 MB de espacio de almacenamiento libre.
-- Su dispositivo debe tener una conexión a Internet estable.
-- Su dispositivo debe permitir la instalación desde fuentes desconocidas. Puede habilitar esta opción yendo a Configuración > Seguridad > Fuentes desconocidas.
-
-Pasos
-Para descargar e instalar Camioneros de Europa 3 APK 36.6 en su dispositivo Android, debe seguir estos pasos:
-
-- Ir a este enlace y descargar el archivo APK.
-- Localice el archivo descargado en el administrador de archivos de su dispositivo y toque en él para iniciar el proceso de instalación.
-- Siga las instrucciones en la pantalla y conceda los permisos necesarios a la aplicación.
-- Espere a que la instalación se complete y luego inicie la aplicación desde el cajón de aplicaciones o la pantalla de inicio.
-- Disfruta jugando Camioneros de Europa 3 en tu dispositivo Android!
-
-Cómo jugar Camioneros de Europa 3?
-Elige tu camión y remolque
-Lo primero que tienes que hacer cuando empiezas a jugar Truckers of Europe 3 es elegir tu camión y remolque. Puede hacer esto yendo al menú del garaje y navegando a través de las opciones disponibles. También puede comprar nuevos camiones y remolques con el dinero que gana de sus misiones. Cada camión y remolque tiene diferentes atributos, como velocidad, potencia, capacidad de combustible, peso de carga, etc. Debe elegir el que se adapte a sus preferencias y necesidades.
-Conduce por toda Europa y entrega carga
-
-Mientras conduce, tendrá que seguir las reglas de tráfico y regulaciones, tales como límites de velocidad, semáforos, señales, etc. También tendrá que lidiar con condiciones de tráfico realistas, como automóviles, autobuses, camiones, motocicletas, policía, ambulancia, etc. También tendrá que enfrentarse a condiciones meteorológicas realistas, como lluvia, nieve, niebla, etc. También tendrá que controlar el estado de su camión, como el nivel de combustible, la temperatura del motor, la presión de los neumáticos, etc. También tendrá que prestar atención al estado de su conductor, como el nivel de fatiga, el nivel de hambre, etc. Tendrá que parar en gasolineras, áreas de descanso, restaurantes, etc. para llenar su tanque de combustible, descansar a su conductor, comer algo de comida, etc.
-Cuando llegue a su destino, tendrá que aparcar su camión y remolque en el lugar designado y descargar la carga. A continuación, recibirá su recompensa y comentarios sobre su rendimiento. También ganarás puntos de experiencia que te ayudarán a subir de nivel y desbloquear nuevas habilidades y beneficios.
-Personaliza tu camión y mejora tus habilidades
-Otro aspecto divertido de Truckers of Europe 3 es que puedes personalizar tu camión y mejorar tus habilidades. Puedes hacer esto yendo al menú del taller y gastando algo de dinero en varios artículos y servicios. Puede cambiar el color de su camión, agregar algunos accesorios, aplicar algunas calcomanías, etc. También puede reparar su camión si está dañado o afinarlo si no funciona bien.
-También puedes ir al menú de habilidades y gastar algunos puntos en varias habilidades y beneficios que mejorarán tus habilidades y beneficios como conductor de camión. Puede mejorar su eficiencia de combustible, manejo de carga, seguridad de conducción, habilidades de negociación, etc. También puede desbloquear nuevos tipos de carga, contratos, camiones, remolques, etc.
-
-¿Por qué deberías jugar Camioneros de Europa 3?
-Gráficos realistas y física
-
-Tráfico y clima diversos y dinámicos
-Otra razón por la que deberías jugar a Truckers of Europe 3 es porque tiene un tráfico diverso y dinámico y el clima hará que tu experiencia de conducción sea más desafiante y agradable. El juego tiene un gran mapa de Europa con más de 50 ciudades y países para explorar. Cada ciudad y país tiene sus propias características únicas, tales como monumentos, edificios, carreteras, señales, etc. El juego también tiene un sistema de tráfico diverso que incluye automóviles, autobuses, camiones, motocicletas, policía, ambulancia, etc. Cada vehículo tiene su propio comportamiento, velocidad, dirección, etc. El juego también tiene un sistema de tiempo dinámico que incluye ciclo de día y noche, lluvia, nieve, niebla, etc. Cada condición climática tiene su propio efecto en la visibilidad, tracción, manejo, etc. del camión y el remolque.
-Misiones y logros desafiantes y gratificantes
-Otra razón por la que deberías jugar a Truckers of Europe 3 es porque tiene misiones desafiantes y gratificantes y logros que te mantendrán motivado y entretenido. El juego tiene un modo de carrera que le permite comenzar como un conductor de camión novato y trabajar su camino hasta convertirse en un camionero profesional. Puede hacerlo completando varias misiones y contratos que implican la entrega de carga a diferentes destinos en toda Europa. También puede ganar dinero de sus entregas y gastarlo en la compra de nuevos camiones y remolques o la personalización de los existentes. También puede ganar puntos de experiencia de sus entregas y gastarlos en mejorar sus habilidades y beneficios. También puedes desbloquear nuevos tipos de carga, contratos, camiones, remolques, etc. a medida que avanzas en el juego.
-
-Conclusión
-Truckers of Europe 3 es un juego de simulador de camiones realista y divertido que te permite experimentar la vida de un conductor de camiones en Europa. Puede elegir su camión y remolque, conducir por toda Europa y entregar carga, personalizar su camión y mejorar sus habilidades, y ganar dinero y logros. El juego tiene gráficos y física realistas, tráfico y clima diversos y dinámicos, y misiones y logros desafiantes y gratificantes. Si usted está buscando un juego de simulador de camiones que le mantendrá enganchado durante horas, definitivamente debe probar Camioneros de Europa 3 APK 36.6.
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre los camioneros de Europa 3:
-
-- Q: ¿Los camioneros de Europa 3 son libres de jugar?
-- A: Sí, Truckers of Europe 3 es gratis. Puedes descargarlo e instalarlo desde el enlace proporcionado en este artículo. Sin embargo, el juego puede contener algunas compras en la aplicación que pueden mejorar su experiencia de juego.
-- Q: ¿Es seguro descargar e instalar Truckers of Europe 3?
-- A: Sí, Truckers of Europe 3 es seguro para descargar e instalar. El archivo APK proporcionado en este artículo es verificado y probado por nuestro equipo. Sin embargo, siempre debe descargar e instalar archivos APK de fuentes confiables solamente.
-- Q: ¿Es Truckers of Europe 3 compatible con mi dispositivo?
-- A: Camioneros de Europa 3 es compatible con la mayoría de los dispositivos Android que tienen la versión Android 4.4 o superior. Sin embargo, algunos dispositivos pueden no ser compatibles con el juego debido a limitaciones de hardware o software.
-- P: ¿Cómo puedo contactar al desarrollador de Truckers of Europe 3?
-- A: Puedes contactar al desarrollador de Truckers of Europe 3 visitando su canal de YouTube Jerryisgaming o su página de Facebook Jerryisgaming. También puedes dejar tus comentarios, sugerencias o consultas en la sección de comentarios de sus videos o publicaciones.
-- P: ¿Cómo puedo apoyar el desarrollo de Camioneros de Europa 3?
-
-
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md b/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md
deleted file mode 100644
index 516d398df61b62811ac5d43e8205d838c0714cbc..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md
+++ /dev/null
@@ -1,80 +0,0 @@
-
-Cómo descargar Genshin Impact Cloud y disfrutar del juego en cualquier lugar
-Genshin Impact es uno de los juegos más populares y exitosos de 2020, con millones de jugadores en todo el mundo. El juego es un RPG de acción de mundo abierto que te permite explorar un mundo vasto y hermoso llamado Teyvat, donde puedes conocer a varios personajes, luchar contra enemigos, reunir recursos y completar misiones. El juego está disponible en múltiples plataformas, como PC, PlayStation 4, PlayStation 5, iOS y Android. Sin embargo, si quieres jugar el juego en tu dispositivo móvil, puedes enfrentarte a algunos desafíos, como espacio de almacenamiento limitado, bajo rendimiento o dispositivos incompatibles. Ahí es donde entra Genshin Impact Cloud.
-¿Qué es la nube de impacto Genshin y por qué debe probarlo
-Genshin Impact Cloud es un servicio que te permite jugar el juego en tu dispositivo móvil sin descargar el juego entero
-Genshin Impact Cloud es una nueva característica que fue introducida por miHoYo, el desarrollador de Genshin Impact, en abril de 2021. Es un servicio de juegos en la nube que le permite transmitir el juego desde un servidor a su dispositivo móvil a través de Internet. Esto significa que no necesitas descargar o instalar el juego en tu dispositivo, lo que puede ahorrarte mucho espacio de almacenamiento. El tamaño del juego de Genshin Impact en dispositivos móviles es de alrededor de 5,2 GB, pero con Genshin Impact Cloud, solo necesitas descargar una aplicación de 56 MB de tamaño. Esto también puede ayudarle a evitar largos tiempos de carga o actualizaciones.
-descargar estrellas pelea hack ios
Download ===> https://bltlly.com/2v6KEj
-Genshin Impact Cloud tiene muchos beneficios, como ahorrar espacio de almacenamiento, mejorar el rendimiento y admitir cross-save
-Además de ahorrar espacio de almacenamiento, Genshin Impact Cloud también tiene otras ventajas que pueden mejorar su experiencia de juego. Por ejemplo:
-
-
-- Genshin Impact Cloud soporta cross-save y cross-play. Esto significa que puedes acceder a tu progreso y datos existentes desde otras plataformas iniciando sesión con tu cuenta miHoYo. También puedes jugar con tus amigos que usan diferentes dispositivos, como PC o PlayStation.
-- Genshin Impact Cloud es de uso gratuito. No es necesario pagar tarifas o suscripciones adicionales para utilizar el servicio. También puede hacer compras en la aplicación como de costumbre, y disfrutar de todas las actualizaciones y eventos del juego.
-
-Con Genshin Impact Cloud, puede disfrutar del juego en cualquier lugar y en cualquier momento, siempre y cuando tenga una conexión a Internet estable y un dispositivo compatible.
-Cómo descargar Genshin Impact Cloud en dispositivos Android
-Genshin Impact Cloud está disponible actualmente solo para usuarios de Android en Malasia y Singapur como prueba beta
-Genshin Impact Cloud todavía está en sus primeras etapas de desarrollo, y todavía no está disponible para todas las regiones y plataformas. Actualmente, el servicio solo está abierto para los usuarios de Android en Malasia y Singapur como una prueba beta. Esto significa que solo un número limitado de jugadores puede acceder al servicio, y puede haber algunos errores o errores durante el juego. miHoYo no ha anunciado cuándo se ampliará el servicio a otras regiones y plataformas, pero es probable que lo hagan en el futuro.
-Para descargar Genshin Impact Cloud, necesita tener una cuenta miHoYo y registrarse para la prueba beta en el sitio web oficial
-Si eres un usuario de Android en Malasia o Singapur, y quieres probar Genshin Impact Cloud, debes seguir estos pasos:
-
-- Crea una cuenta miHoYo si aún no tienes una. Puede hacer esto visitando https://account.mihoyo.com/#/register y llenando la información requerida.
-- Visite None:
- """
- Raise an option parsing error using parser.error().
-
- Args:
- parser: an OptionParser instance.
- option: an Option instance.
- msg: the error text.
- """
- msg = f"{option} error: {msg}"
- msg = textwrap.fill(" ".join(msg.split()))
- parser.error(msg)
-
-
-def make_option_group(group: Dict[str, Any], parser: ConfigOptionParser) -> OptionGroup:
- """
- Return an OptionGroup object
- group -- assumed to be dict with 'name' and 'options' keys
- parser -- an optparse Parser
- """
- option_group = OptionGroup(parser, group["name"])
- for option in group["options"]:
- option_group.add_option(option())
- return option_group
-
-
-def check_dist_restriction(options: Values, check_target: bool = False) -> None:
- """Function for determining if custom platform options are allowed.
-
- :param options: The OptionParser options.
- :param check_target: Whether or not to check if --target is being used.
- """
- dist_restriction_set = any(
- [
- options.python_version,
- options.platforms,
- options.abis,
- options.implementation,
- ]
- )
-
- binary_only = FormatControl(set(), {":all:"})
- sdist_dependencies_allowed = (
- options.format_control != binary_only and not options.ignore_dependencies
- )
-
- # Installations or downloads using dist restrictions must not combine
- # source distributions and dist-specific wheels, as they are not
- # guaranteed to be locally compatible.
- if dist_restriction_set and sdist_dependencies_allowed:
- raise CommandError(
- "When restricting platform and interpreter constraints using "
- "--python-version, --platform, --abi, or --implementation, "
- "either --no-deps must be set, or --only-binary=:all: must be "
- "set and --no-binary must not be set (or must be set to "
- ":none:)."
- )
-
- if check_target:
- if dist_restriction_set and not options.target_dir:
- raise CommandError(
- "Can not use any platform or abi specific options unless "
- "installing via '--target'"
- )
-
-
-def _path_option_check(option: Option, opt: str, value: str) -> str:
- return os.path.expanduser(value)
-
-
-def _package_name_option_check(option: Option, opt: str, value: str) -> str:
- return canonicalize_name(value)
-
-
-class PipOption(Option):
- TYPES = Option.TYPES + ("path", "package_name")
- TYPE_CHECKER = Option.TYPE_CHECKER.copy()
- TYPE_CHECKER["package_name"] = _package_name_option_check
- TYPE_CHECKER["path"] = _path_option_check
-
-
-###########
-# options #
-###########
-
-help_: Callable[..., Option] = partial(
- Option,
- "-h",
- "--help",
- dest="help",
- action="help",
- help="Show help.",
-)
-
-debug_mode: Callable[..., Option] = partial(
- Option,
- "--debug",
- dest="debug_mode",
- action="store_true",
- default=False,
- help=(
- "Let unhandled exceptions propagate outside the main subroutine, "
- "instead of logging them to stderr."
- ),
-)
-
-isolated_mode: Callable[..., Option] = partial(
- Option,
- "--isolated",
- dest="isolated_mode",
- action="store_true",
- default=False,
- help=(
- "Run pip in an isolated mode, ignoring environment variables and user "
- "configuration."
- ),
-)
-
-require_virtualenv: Callable[..., Option] = partial(
- Option,
- "--require-virtualenv",
- "--require-venv",
- dest="require_venv",
- action="store_true",
- default=False,
- help=(
- "Allow pip to only run in a virtual environment; "
- "exit with an error otherwise."
- ),
-)
-
-override_externally_managed: Callable[..., Option] = partial(
- Option,
- "--break-system-packages",
- dest="override_externally_managed",
- action="store_true",
- help="Allow pip to modify an EXTERNALLY-MANAGED Python installation",
-)
-
-python: Callable[..., Option] = partial(
- Option,
- "--python",
- dest="python",
- help="Run pip with the specified Python interpreter.",
-)
-
-verbose: Callable[..., Option] = partial(
- Option,
- "-v",
- "--verbose",
- dest="verbose",
- action="count",
- default=0,
- help="Give more output. Option is additive, and can be used up to 3 times.",
-)
-
-no_color: Callable[..., Option] = partial(
- Option,
- "--no-color",
- dest="no_color",
- action="store_true",
- default=False,
- help="Suppress colored output.",
-)
-
-version: Callable[..., Option] = partial(
- Option,
- "-V",
- "--version",
- dest="version",
- action="store_true",
- help="Show version and exit.",
-)
-
-quiet: Callable[..., Option] = partial(
- Option,
- "-q",
- "--quiet",
- dest="quiet",
- action="count",
- default=0,
- help=(
- "Give less output. Option is additive, and can be used up to 3"
- " times (corresponding to WARNING, ERROR, and CRITICAL logging"
- " levels)."
- ),
-)
-
-progress_bar: Callable[..., Option] = partial(
- Option,
- "--progress-bar",
- dest="progress_bar",
- type="choice",
- choices=["on", "off"],
- default="on",
- help="Specify whether the progress bar should be used [on, off] (default: on)",
-)
-
-log: Callable[..., Option] = partial(
- PipOption,
- "--log",
- "--log-file",
- "--local-log",
- dest="log",
- metavar="path",
- type="path",
- help="Path to a verbose appending log.",
-)
-
-no_input: Callable[..., Option] = partial(
- Option,
- # Don't ask for input
- "--no-input",
- dest="no_input",
- action="store_true",
- default=False,
- help="Disable prompting for input.",
-)
-
-keyring_provider: Callable[..., Option] = partial(
- Option,
- "--keyring-provider",
- dest="keyring_provider",
- choices=["auto", "disabled", "import", "subprocess"],
- default="auto",
- help=(
- "Enable the credential lookup via the keyring library if user input is allowed."
- " Specify which mechanism to use [disabled, import, subprocess]."
- " (default: disabled)"
- ),
-)
-
-proxy: Callable[..., Option] = partial(
- Option,
- "--proxy",
- dest="proxy",
- type="str",
- default="",
- help="Specify a proxy in the form scheme://[user:passwd@]proxy.server:port.",
-)
-
-retries: Callable[..., Option] = partial(
- Option,
- "--retries",
- dest="retries",
- type="int",
- default=5,
- help="Maximum number of retries each connection should attempt "
- "(default %default times).",
-)
-
-timeout: Callable[..., Option] = partial(
- Option,
- "--timeout",
- "--default-timeout",
- metavar="sec",
- dest="timeout",
- type="float",
- default=15,
- help="Set the socket timeout (default %default seconds).",
-)
-
-
-def exists_action() -> Option:
- return Option(
- # Option when path already exist
- "--exists-action",
- dest="exists_action",
- type="choice",
- choices=["s", "i", "w", "b", "a"],
- default=[],
- action="append",
- metavar="action",
- help="Default action when a path already exists: "
- "(s)witch, (i)gnore, (w)ipe, (b)ackup, (a)bort.",
- )
-
-
-cert: Callable[..., Option] = partial(
- PipOption,
- "--cert",
- dest="cert",
- type="path",
- metavar="path",
- help=(
- "Path to PEM-encoded CA certificate bundle. "
- "If provided, overrides the default. "
- "See 'SSL Certificate Verification' in pip documentation "
- "for more information."
- ),
-)
-
-client_cert: Callable[..., Option] = partial(
- PipOption,
- "--client-cert",
- dest="client_cert",
- type="path",
- default=None,
- metavar="path",
- help="Path to SSL client certificate, a single file containing the "
- "private key and the certificate in PEM format.",
-)
-
-index_url: Callable[..., Option] = partial(
- Option,
- "-i",
- "--index-url",
- "--pypi-url",
- dest="index_url",
- metavar="URL",
- default=PyPI.simple_url,
- help="Base URL of the Python Package Index (default %default). "
- "This should point to a repository compliant with PEP 503 "
- "(the simple repository API) or a local directory laid out "
- "in the same format.",
-)
-
-
-def extra_index_url() -> Option:
- return Option(
- "--extra-index-url",
- dest="extra_index_urls",
- metavar="URL",
- action="append",
- default=[],
- help="Extra URLs of package indexes to use in addition to "
- "--index-url. Should follow the same rules as "
- "--index-url.",
- )
-
-
-no_index: Callable[..., Option] = partial(
- Option,
- "--no-index",
- dest="no_index",
- action="store_true",
- default=False,
- help="Ignore package index (only looking at --find-links URLs instead).",
-)
-
-
-def find_links() -> Option:
- return Option(
- "-f",
- "--find-links",
- dest="find_links",
- action="append",
- default=[],
- metavar="url",
- help="If a URL or path to an html file, then parse for links to "
- "archives such as sdist (.tar.gz) or wheel (.whl) files. "
- "If a local path or file:// URL that's a directory, "
- "then look for archives in the directory listing. "
- "Links to VCS project URLs are not supported.",
- )
-
-
-def trusted_host() -> Option:
- return Option(
- "--trusted-host",
- dest="trusted_hosts",
- action="append",
- metavar="HOSTNAME",
- default=[],
- help="Mark this host or host:port pair as trusted, even though it "
- "does not have valid or any HTTPS.",
- )
-
-
-def constraints() -> Option:
- return Option(
- "-c",
- "--constraint",
- dest="constraints",
- action="append",
- default=[],
- metavar="file",
- help="Constrain versions using the given constraints file. "
- "This option can be used multiple times.",
- )
-
-
-def requirements() -> Option:
- return Option(
- "-r",
- "--requirement",
- dest="requirements",
- action="append",
- default=[],
- metavar="file",
- help="Install from the given requirements file. "
- "This option can be used multiple times.",
- )
-
-
-def editable() -> Option:
- return Option(
- "-e",
- "--editable",
- dest="editables",
- action="append",
- default=[],
- metavar="path/url",
- help=(
- "Install a project in editable mode (i.e. setuptools "
- '"develop mode") from a local project path or a VCS url.'
- ),
- )
-
-
-def _handle_src(option: Option, opt_str: str, value: str, parser: OptionParser) -> None:
- value = os.path.abspath(value)
- setattr(parser.values, option.dest, value)
-
-
-src: Callable[..., Option] = partial(
- PipOption,
- "--src",
- "--source",
- "--source-dir",
- "--source-directory",
- dest="src_dir",
- type="path",
- metavar="dir",
- default=get_src_prefix(),
- action="callback",
- callback=_handle_src,
- help="Directory to check out editable projects into. "
- 'The default in a virtualenv is "
/src". '
- 'The default for global installs is "/src".',
-)
-
-
-def _get_format_control(values: Values, option: Option) -> Any:
- """Get a format_control object."""
- return getattr(values, option.dest)
-
-
-def _handle_no_binary(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- existing = _get_format_control(parser.values, option)
- FormatControl.handle_mutual_excludes(
- value,
- existing.no_binary,
- existing.only_binary,
- )
-
-
-def _handle_only_binary(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- existing = _get_format_control(parser.values, option)
- FormatControl.handle_mutual_excludes(
- value,
- existing.only_binary,
- existing.no_binary,
- )
-
-
-def no_binary() -> Option:
- format_control = FormatControl(set(), set())
- return Option(
- "--no-binary",
- dest="format_control",
- action="callback",
- callback=_handle_no_binary,
- type="str",
- default=format_control,
- help="Do not use binary packages. Can be supplied multiple times, and "
- 'each time adds to the existing value. Accepts either ":all:" to '
- 'disable all binary packages, ":none:" to empty the set (notice '
- "the colons), or one or more package names with commas between "
- "them (no colons). Note that some packages are tricky to compile "
- "and may fail to install when this option is used on them.",
- )
-
-
-def only_binary() -> Option:
- format_control = FormatControl(set(), set())
- return Option(
- "--only-binary",
- dest="format_control",
- action="callback",
- callback=_handle_only_binary,
- type="str",
- default=format_control,
- help="Do not use source packages. Can be supplied multiple times, and "
- 'each time adds to the existing value. Accepts either ":all:" to '
- 'disable all source packages, ":none:" to empty the set, or one '
- "or more package names with commas between them. Packages "
- "without binary distributions will fail to install when this "
- "option is used on them.",
- )
-
-
-platforms: Callable[..., Option] = partial(
- Option,
- "--platform",
- dest="platforms",
- metavar="platform",
- action="append",
- default=None,
- help=(
- "Only use wheels compatible with . Defaults to the "
- "platform of the running system. Use this option multiple times to "
- "specify multiple platforms supported by the target interpreter."
- ),
-)
-
-
-# This was made a separate function for unit-testing purposes.
-def _convert_python_version(value: str) -> Tuple[Tuple[int, ...], Optional[str]]:
- """
- Convert a version string like "3", "37", or "3.7.3" into a tuple of ints.
-
- :return: A 2-tuple (version_info, error_msg), where `error_msg` is
- non-None if and only if there was a parsing error.
- """
- if not value:
- # The empty string is the same as not providing a value.
- return (None, None)
-
- parts = value.split(".")
- if len(parts) > 3:
- return ((), "at most three version parts are allowed")
-
- if len(parts) == 1:
- # Then we are in the case of "3" or "37".
- value = parts[0]
- if len(value) > 1:
- parts = [value[0], value[1:]]
-
- try:
- version_info = tuple(int(part) for part in parts)
- except ValueError:
- return ((), "each version part must be an integer")
-
- return (version_info, None)
-
-
-def _handle_python_version(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- """
- Handle a provided --python-version value.
- """
- version_info, error_msg = _convert_python_version(value)
- if error_msg is not None:
- msg = "invalid --python-version value: {!r}: {}".format(
- value,
- error_msg,
- )
- raise_option_error(parser, option=option, msg=msg)
-
- parser.values.python_version = version_info
-
-
-python_version: Callable[..., Option] = partial(
- Option,
- "--python-version",
- dest="python_version",
- metavar="python_version",
- action="callback",
- callback=_handle_python_version,
- type="str",
- default=None,
- help=dedent(
- """\
- The Python interpreter version to use for wheel and "Requires-Python"
- compatibility checks. Defaults to a version derived from the running
- interpreter. The version can be specified using up to three dot-separated
- integers (e.g. "3" for 3.0.0, "3.7" for 3.7.0, or "3.7.3"). A major-minor
- version can also be given as a string without dots (e.g. "37" for 3.7.0).
- """
- ),
-)
-
-
-implementation: Callable[..., Option] = partial(
- Option,
- "--implementation",
- dest="implementation",
- metavar="implementation",
- default=None,
- help=(
- "Only use wheels compatible with Python "
- "implementation , e.g. 'pp', 'jy', 'cp', "
- " or 'ip'. If not specified, then the current "
- "interpreter implementation is used. Use 'py' to force "
- "implementation-agnostic wheels."
- ),
-)
-
-
-abis: Callable[..., Option] = partial(
- Option,
- "--abi",
- dest="abis",
- metavar="abi",
- action="append",
- default=None,
- help=(
- "Only use wheels compatible with Python abi , e.g. 'pypy_41'. "
- "If not specified, then the current interpreter abi tag is used. "
- "Use this option multiple times to specify multiple abis supported "
- "by the target interpreter. Generally you will need to specify "
- "--implementation, --platform, and --python-version when using this "
- "option."
- ),
-)
-
-
-def add_target_python_options(cmd_opts: OptionGroup) -> None:
- cmd_opts.add_option(platforms())
- cmd_opts.add_option(python_version())
- cmd_opts.add_option(implementation())
- cmd_opts.add_option(abis())
-
-
-def make_target_python(options: Values) -> TargetPython:
- target_python = TargetPython(
- platforms=options.platforms,
- py_version_info=options.python_version,
- abis=options.abis,
- implementation=options.implementation,
- )
-
- return target_python
-
-
-def prefer_binary() -> Option:
- return Option(
- "--prefer-binary",
- dest="prefer_binary",
- action="store_true",
- default=False,
- help="Prefer older binary packages over newer source packages.",
- )
-
-
-cache_dir: Callable[..., Option] = partial(
- PipOption,
- "--cache-dir",
- dest="cache_dir",
- default=USER_CACHE_DIR,
- metavar="dir",
- type="path",
- help="Store the cache data in .",
-)
-
-
-def _handle_no_cache_dir(
- option: Option, opt: str, value: str, parser: OptionParser
-) -> None:
- """
- Process a value provided for the --no-cache-dir option.
-
- This is an optparse.Option callback for the --no-cache-dir option.
- """
- # The value argument will be None if --no-cache-dir is passed via the
- # command-line, since the option doesn't accept arguments. However,
- # the value can be non-None if the option is triggered e.g. by an
- # environment variable, like PIP_NO_CACHE_DIR=true.
- if value is not None:
- # Then parse the string value to get argument error-checking.
- try:
- strtobool(value)
- except ValueError as exc:
- raise_option_error(parser, option=option, msg=str(exc))
-
- # Originally, setting PIP_NO_CACHE_DIR to a value that strtobool()
- # converted to 0 (like "false" or "no") caused cache_dir to be disabled
- # rather than enabled (logic would say the latter). Thus, we disable
- # the cache directory not just on values that parse to True, but (for
- # backwards compatibility reasons) also on values that parse to False.
- # In other words, always set it to False if the option is provided in
- # some (valid) form.
- parser.values.cache_dir = False
-
-
-no_cache: Callable[..., Option] = partial(
- Option,
- "--no-cache-dir",
- dest="cache_dir",
- action="callback",
- callback=_handle_no_cache_dir,
- help="Disable the cache.",
-)
-
-no_deps: Callable[..., Option] = partial(
- Option,
- "--no-deps",
- "--no-dependencies",
- dest="ignore_dependencies",
- action="store_true",
- default=False,
- help="Don't install package dependencies.",
-)
-
-ignore_requires_python: Callable[..., Option] = partial(
- Option,
- "--ignore-requires-python",
- dest="ignore_requires_python",
- action="store_true",
- help="Ignore the Requires-Python information.",
-)
-
-no_build_isolation: Callable[..., Option] = partial(
- Option,
- "--no-build-isolation",
- dest="build_isolation",
- action="store_false",
- default=True,
- help="Disable isolation when building a modern source distribution. "
- "Build dependencies specified by PEP 518 must be already installed "
- "if this option is used.",
-)
-
-check_build_deps: Callable[..., Option] = partial(
- Option,
- "--check-build-dependencies",
- dest="check_build_deps",
- action="store_true",
- default=False,
- help="Check the build dependencies when PEP517 is used.",
-)
-
-
-def _handle_no_use_pep517(
- option: Option, opt: str, value: str, parser: OptionParser
-) -> None:
- """
- Process a value provided for the --no-use-pep517 option.
-
- This is an optparse.Option callback for the no_use_pep517 option.
- """
- # Since --no-use-pep517 doesn't accept arguments, the value argument
- # will be None if --no-use-pep517 is passed via the command-line.
- # However, the value can be non-None if the option is triggered e.g.
- # by an environment variable, for example "PIP_NO_USE_PEP517=true".
- if value is not None:
- msg = """A value was passed for --no-use-pep517,
- probably using either the PIP_NO_USE_PEP517 environment variable
- or the "no-use-pep517" config file option. Use an appropriate value
- of the PIP_USE_PEP517 environment variable or the "use-pep517"
- config file option instead.
- """
- raise_option_error(parser, option=option, msg=msg)
-
- # If user doesn't wish to use pep517, we check if setuptools and wheel are installed
- # and raise error if it is not.
- packages = ("setuptools", "wheel")
- if not all(importlib.util.find_spec(package) for package in packages):
- msg = (
- f"It is not possible to use --no-use-pep517 "
- f"without {' and '.join(packages)} installed."
- )
- raise_option_error(parser, option=option, msg=msg)
-
- # Otherwise, --no-use-pep517 was passed via the command-line.
- parser.values.use_pep517 = False
-
-
-use_pep517: Any = partial(
- Option,
- "--use-pep517",
- dest="use_pep517",
- action="store_true",
- default=None,
- help="Use PEP 517 for building source distributions "
- "(use --no-use-pep517 to force legacy behaviour).",
-)
-
-no_use_pep517: Any = partial(
- Option,
- "--no-use-pep517",
- dest="use_pep517",
- action="callback",
- callback=_handle_no_use_pep517,
- default=None,
- help=SUPPRESS_HELP,
-)
-
-
-def _handle_config_settings(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- key, sep, val = value.partition("=")
- if sep != "=":
- parser.error(f"Arguments to {opt_str} must be of the form KEY=VAL") # noqa
- dest = getattr(parser.values, option.dest)
- if dest is None:
- dest = {}
- setattr(parser.values, option.dest, dest)
- if key in dest:
- if isinstance(dest[key], list):
- dest[key].append(val)
- else:
- dest[key] = [dest[key], val]
- else:
- dest[key] = val
-
-
-config_settings: Callable[..., Option] = partial(
- Option,
- "-C",
- "--config-settings",
- dest="config_settings",
- type=str,
- action="callback",
- callback=_handle_config_settings,
- metavar="settings",
- help="Configuration settings to be passed to the PEP 517 build backend. "
- "Settings take the form KEY=VALUE. Use multiple --config-settings options "
- "to pass multiple keys to the backend.",
-)
-
-build_options: Callable[..., Option] = partial(
- Option,
- "--build-option",
- dest="build_options",
- metavar="options",
- action="append",
- help="Extra arguments to be supplied to 'setup.py bdist_wheel'.",
-)
-
-global_options: Callable[..., Option] = partial(
- Option,
- "--global-option",
- dest="global_options",
- action="append",
- metavar="options",
- help="Extra global options to be supplied to the setup.py "
- "call before the install or bdist_wheel command.",
-)
-
-no_clean: Callable[..., Option] = partial(
- Option,
- "--no-clean",
- action="store_true",
- default=False,
- help="Don't clean up build directories.",
-)
-
-pre: Callable[..., Option] = partial(
- Option,
- "--pre",
- action="store_true",
- default=False,
- help="Include pre-release and development versions. By default, "
- "pip only finds stable versions.",
-)
-
-disable_pip_version_check: Callable[..., Option] = partial(
- Option,
- "--disable-pip-version-check",
- dest="disable_pip_version_check",
- action="store_true",
- default=False,
- help="Don't periodically check PyPI to determine whether a new version "
- "of pip is available for download. Implied with --no-index.",
-)
-
-root_user_action: Callable[..., Option] = partial(
- Option,
- "--root-user-action",
- dest="root_user_action",
- default="warn",
- choices=["warn", "ignore"],
- help="Action if pip is run as a root user. By default, a warning message is shown.",
-)
-
-
-def _handle_merge_hash(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- """Given a value spelled "algo:digest", append the digest to a list
- pointed to in a dict by the algo name."""
- if not parser.values.hashes:
- parser.values.hashes = {}
- try:
- algo, digest = value.split(":", 1)
- except ValueError:
- parser.error(
- "Arguments to {} must be a hash name " # noqa
- "followed by a value, like --hash=sha256:"
- "abcde...".format(opt_str)
- )
- if algo not in STRONG_HASHES:
- parser.error(
- "Allowed hash algorithms for {} are {}.".format( # noqa
- opt_str, ", ".join(STRONG_HASHES)
- )
- )
- parser.values.hashes.setdefault(algo, []).append(digest)
-
-
-hash: Callable[..., Option] = partial(
- Option,
- "--hash",
- # Hash values eventually end up in InstallRequirement.hashes due to
- # __dict__ copying in process_line().
- dest="hashes",
- action="callback",
- callback=_handle_merge_hash,
- type="string",
- help="Verify that the package's archive matches this "
- "hash before installing. Example: --hash=sha256:abcdef...",
-)
-
-
-require_hashes: Callable[..., Option] = partial(
- Option,
- "--require-hashes",
- dest="require_hashes",
- action="store_true",
- default=False,
- help="Require a hash to check each requirement against, for "
- "repeatable installs. This option is implied when any package in a "
- "requirements file has a --hash option.",
-)
-
-
-list_path: Callable[..., Option] = partial(
- PipOption,
- "--path",
- dest="path",
- type="path",
- action="append",
- help="Restrict to the specified installation path for listing "
- "packages (can be used multiple times).",
-)
-
-
-def check_list_path_option(options: Values) -> None:
- if options.path and (options.user or options.local):
- raise CommandError("Cannot combine '--path' with '--user' or '--local'")
-
-
-list_exclude: Callable[..., Option] = partial(
- PipOption,
- "--exclude",
- dest="excludes",
- action="append",
- metavar="package",
- type="package_name",
- help="Exclude specified package from the output",
-)
-
-
-no_python_version_warning: Callable[..., Option] = partial(
- Option,
- "--no-python-version-warning",
- dest="no_python_version_warning",
- action="store_true",
- default=False,
- help="Silence deprecation warnings for upcoming unsupported Pythons.",
-)
-
-
-# Features that are now always on. A warning is printed if they are used.
-ALWAYS_ENABLED_FEATURES = [
- "no-binary-enable-wheel-cache", # always on since 23.1
-]
-
-use_new_feature: Callable[..., Option] = partial(
- Option,
- "--use-feature",
- dest="features_enabled",
- metavar="feature",
- action="append",
- default=[],
- choices=[
- "fast-deps",
- "truststore",
- ]
- + ALWAYS_ENABLED_FEATURES,
- help="Enable new functionality, that may be backward incompatible.",
-)
-
-use_deprecated_feature: Callable[..., Option] = partial(
- Option,
- "--use-deprecated",
- dest="deprecated_features_enabled",
- metavar="feature",
- action="append",
- default=[],
- choices=[
- "legacy-resolver",
- ],
- help=("Enable deprecated functionality, that will be removed in the future."),
-)
-
-
-##########
-# groups #
-##########
-
-general_group: Dict[str, Any] = {
- "name": "General Options",
- "options": [
- help_,
- debug_mode,
- isolated_mode,
- require_virtualenv,
- python,
- verbose,
- version,
- quiet,
- log,
- no_input,
- keyring_provider,
- proxy,
- retries,
- timeout,
- exists_action,
- trusted_host,
- cert,
- client_cert,
- cache_dir,
- no_cache,
- disable_pip_version_check,
- no_color,
- no_python_version_warning,
- use_new_feature,
- use_deprecated_feature,
- ],
-}
-
-index_group: Dict[str, Any] = {
- "name": "Package Index Options",
- "options": [
- index_url,
- extra_index_url,
- no_index,
- find_links,
- ],
-}
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py
deleted file mode 100644
index fd80d8c1129722b84771bd6a0f6ccfd57f5cf78e..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import re
-from ast import literal_eval
-from operator import attrgetter
-from typing import Callable, Iterable, List, Match, NamedTuple, Optional, Tuple, Union
-
-from ._emoji_replace import _emoji_replace
-from .emoji import EmojiVariant
-from .errors import MarkupError
-from .style import Style
-from .text import Span, Text
-
-RE_TAGS = re.compile(
- r"""((\\*)\[([a-z#/@][^[]*?)])""",
- re.VERBOSE,
-)
-
-RE_HANDLER = re.compile(r"^([\w.]*?)(\(.*?\))?$")
-
-
-class Tag(NamedTuple):
- """A tag in console markup."""
-
- name: str
- """The tag name. e.g. 'bold'."""
- parameters: Optional[str]
- """Any additional parameters after the name."""
-
- def __str__(self) -> str:
- return (
- self.name if self.parameters is None else f"{self.name} {self.parameters}"
- )
-
- @property
- def markup(self) -> str:
- """Get the string representation of this tag."""
- return (
- f"[{self.name}]"
- if self.parameters is None
- else f"[{self.name}={self.parameters}]"
- )
-
-
-_ReStringMatch = Match[str] # regex match object
-_ReSubCallable = Callable[[_ReStringMatch], str] # Callable invoked by re.sub
-_EscapeSubMethod = Callable[[_ReSubCallable, str], str] # Sub method of a compiled re
-
-
-def escape(
- markup: str,
- _escape: _EscapeSubMethod = re.compile(r"(\\*)(\[[a-z#/@][^[]*?])").sub,
-) -> str:
- """Escapes text so that it won't be interpreted as markup.
-
- Args:
- markup (str): Content to be inserted in to markup.
-
- Returns:
- str: Markup with square brackets escaped.
- """
-
- def escape_backslashes(match: Match[str]) -> str:
- """Called by re.sub replace matches."""
- backslashes, text = match.groups()
- return f"{backslashes}{backslashes}\\{text}"
-
- markup = _escape(escape_backslashes, markup)
- return markup
-
-
-def _parse(markup: str) -> Iterable[Tuple[int, Optional[str], Optional[Tag]]]:
- """Parse markup in to an iterable of tuples of (position, text, tag).
-
- Args:
- markup (str): A string containing console markup
-
- """
- position = 0
- _divmod = divmod
- _Tag = Tag
- for match in RE_TAGS.finditer(markup):
- full_text, escapes, tag_text = match.groups()
- start, end = match.span()
- if start > position:
- yield start, markup[position:start], None
- if escapes:
- backslashes, escaped = _divmod(len(escapes), 2)
- if backslashes:
- # Literal backslashes
- yield start, "\\" * backslashes, None
- start += backslashes * 2
- if escaped:
- # Escape of tag
- yield start, full_text[len(escapes) :], None
- position = end
- continue
- text, equals, parameters = tag_text.partition("=")
- yield start, None, _Tag(text, parameters if equals else None)
- position = end
- if position < len(markup):
- yield position, markup[position:], None
-
-
-def render(
- markup: str,
- style: Union[str, Style] = "",
- emoji: bool = True,
- emoji_variant: Optional[EmojiVariant] = None,
-) -> Text:
- """Render console markup in to a Text instance.
-
- Args:
- markup (str): A string containing console markup.
- emoji (bool, optional): Also render emoji code. Defaults to True.
-
- Raises:
- MarkupError: If there is a syntax error in the markup.
-
- Returns:
- Text: A test instance.
- """
- emoji_replace = _emoji_replace
- if "[" not in markup:
- return Text(
- emoji_replace(markup, default_variant=emoji_variant) if emoji else markup,
- style=style,
- )
- text = Text(style=style)
- append = text.append
- normalize = Style.normalize
-
- style_stack: List[Tuple[int, Tag]] = []
- pop = style_stack.pop
-
- spans: List[Span] = []
- append_span = spans.append
-
- _Span = Span
- _Tag = Tag
-
- def pop_style(style_name: str) -> Tuple[int, Tag]:
- """Pop tag matching given style name."""
- for index, (_, tag) in enumerate(reversed(style_stack), 1):
- if tag.name == style_name:
- return pop(-index)
- raise KeyError(style_name)
-
- for position, plain_text, tag in _parse(markup):
- if plain_text is not None:
- # Handle open brace escapes, where the brace is not part of a tag.
- plain_text = plain_text.replace("\\[", "[")
- append(emoji_replace(plain_text) if emoji else plain_text)
- elif tag is not None:
- if tag.name.startswith("/"): # Closing tag
- style_name = tag.name[1:].strip()
-
- if style_name: # explicit close
- style_name = normalize(style_name)
- try:
- start, open_tag = pop_style(style_name)
- except KeyError:
- raise MarkupError(
- f"closing tag '{tag.markup}' at position {position} doesn't match any open tag"
- ) from None
- else: # implicit close
- try:
- start, open_tag = pop()
- except IndexError:
- raise MarkupError(
- f"closing tag '[/]' at position {position} has nothing to close"
- ) from None
-
- if open_tag.name.startswith("@"):
- if open_tag.parameters:
- handler_name = ""
- parameters = open_tag.parameters.strip()
- handler_match = RE_HANDLER.match(parameters)
- if handler_match is not None:
- handler_name, match_parameters = handler_match.groups()
- parameters = (
- "()" if match_parameters is None else match_parameters
- )
-
- try:
- meta_params = literal_eval(parameters)
- except SyntaxError as error:
- raise MarkupError(
- f"error parsing {parameters!r} in {open_tag.parameters!r}; {error.msg}"
- )
- except Exception as error:
- raise MarkupError(
- f"error parsing {open_tag.parameters!r}; {error}"
- ) from None
-
- if handler_name:
- meta_params = (
- handler_name,
- meta_params
- if isinstance(meta_params, tuple)
- else (meta_params,),
- )
-
- else:
- meta_params = ()
-
- append_span(
- _Span(
- start, len(text), Style(meta={open_tag.name: meta_params})
- )
- )
- else:
- append_span(_Span(start, len(text), str(open_tag)))
-
- else: # Opening tag
- normalized_tag = _Tag(normalize(tag.name), tag.parameters)
- style_stack.append((len(text), normalized_tag))
-
- text_length = len(text)
- while style_stack:
- start, tag = style_stack.pop()
- style = str(tag)
- if style:
- append_span(_Span(start, text_length, style))
-
- text.spans = sorted(spans[::-1], key=attrgetter("start"))
- return text
-
-
-if __name__ == "__main__": # pragma: no cover
-
- MARKUP = [
- "[red]Hello World[/red]",
- "[magenta]Hello [b]World[/b]",
- "[bold]Bold[italic] bold and italic [/bold]italic[/italic]",
- "Click [link=https://www.willmcgugan.com]here[/link] to visit my Blog",
- ":warning-emoji: [bold red blink] DANGER![/]",
- ]
-
- from pip._vendor.rich import print
- from pip._vendor.rich.table import Table
-
- grid = Table("Markup", "Result", padding=(0, 1))
-
- for markup in MARKUP:
- grid.add_row(Text(markup), markup)
-
- print(grid)
diff --git a/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat b/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat
deleted file mode 100644
index 62a39a7584f4d7c5fbc31758e3e9e7eff700276d..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat
+++ /dev/null
@@ -1,22 +0,0 @@
-@echo off
-
-if defined _OLD_VIRTUAL_PROMPT (
- set "PROMPT=%_OLD_VIRTUAL_PROMPT%"
-)
-set _OLD_VIRTUAL_PROMPT=
-
-if defined _OLD_VIRTUAL_PYTHONHOME (
- set "PYTHONHOME=%_OLD_VIRTUAL_PYTHONHOME%"
- set _OLD_VIRTUAL_PYTHONHOME=
-)
-
-if defined _OLD_VIRTUAL_PATH (
- set "PATH=%_OLD_VIRTUAL_PATH%"
-)
-
-set _OLD_VIRTUAL_PATH=
-
-set VIRTUAL_ENV=
-set VIRTUAL_ENV_PROMPT=
-
-:END
diff --git a/spaces/CAMP-ViL/Xplainer/inference.py b/spaces/CAMP-ViL/Xplainer/inference.py
deleted file mode 100644
index 2ab6b61c2b1fa8039af58a5f5c4823b6c2f56cab..0000000000000000000000000000000000000000
--- a/spaces/CAMP-ViL/Xplainer/inference.py
+++ /dev/null
@@ -1,116 +0,0 @@
-import argparse
-import gc
-from pathlib import Path
-
-import torch
-from torch.utils.data import DataLoader
-from tqdm import tqdm
-
-from chestxray14 import ChestXray14Dataset
-from chexpert import CheXpertDataset
-from descriptors import disease_descriptors_chexpert, disease_descriptors_chestxray14
-from model import InferenceModel
-from utils import calculate_auroc
-
-torch.multiprocessing.set_sharing_strategy('file_system')
-
-
-def inference_chexpert():
- split = 'test'
- dataset = CheXpertDataset(f'data/chexpert/{split}_labels.csv') # also do test
- dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=lambda x: x, num_workers=0)
- inference_model = InferenceModel()
- all_descriptors = inference_model.get_all_descriptors(disease_descriptors_chexpert)
-
- all_labels = []
- all_probs_neg = []
-
- for batch in tqdm(dataloader):
- batch = batch[0]
- image_paths, labels, keys = batch
- image_paths = [Path(image_path) for image_path in image_paths]
- agg_probs = []
- agg_negative_probs = []
- for image_path in image_paths:
- probs, negative_probs = inference_model.get_descriptor_probs(image_path, descriptors=all_descriptors)
- agg_probs.append(probs)
- agg_negative_probs.append(negative_probs)
- probs = {} # Aggregated
- negative_probs = {} # Aggregated
- for key in agg_probs[0].keys():
- probs[key] = sum([p[key] for p in agg_probs]) / len(agg_probs) # Mean Aggregation
-
- for key in agg_negative_probs[0].keys():
- negative_probs[key] = sum([p[key] for p in agg_negative_probs]) / len(agg_negative_probs) # Mean Aggregation
-
- disease_probs, negative_disease_probs = inference_model.get_diseases_probs(disease_descriptors_chexpert, pos_probs=probs,
- negative_probs=negative_probs)
- predicted_diseases, prob_vector_neg_prompt = inference_model.get_predictions_bin_prompting(disease_descriptors_chexpert,
- disease_probs=disease_probs,
- negative_disease_probs=negative_disease_probs,
- keys=keys)
- all_labels.append(labels)
- all_probs_neg.append(prob_vector_neg_prompt)
-
- all_labels = torch.stack(all_labels)
- all_probs_neg = torch.stack(all_probs_neg)
-
- # evaluation
- existing_mask = sum(all_labels, 0) > 0
- all_labels_clean = all_labels[:, existing_mask]
- all_probs_neg_clean = all_probs_neg[:, existing_mask]
- all_keys_clean = [key for idx, key in enumerate(keys) if existing_mask[idx]]
-
- overall_auroc, per_disease_auroc = calculate_auroc(all_probs_neg_clean, all_labels_clean)
- print(f"AUROC: {overall_auroc:.5f}\n")
- for idx, key in enumerate(all_keys_clean):
- print(f'{key}: {per_disease_auroc[idx]:.5f}')
-
-
-def inference_chestxray14():
- dataset = ChestXray14Dataset(f'data/chestxray14/Data_Entry_2017_v2020_modified.csv')
- dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=lambda x: x, num_workers=1)
- inference_model = InferenceModel()
- all_descriptors = inference_model.get_all_descriptors(disease_descriptors_chestxray14)
-
- all_labels = []
- all_probs_neg = []
- for batch in tqdm(dataloader):
- batch = batch[0]
- image_path, labels, keys = batch
- image_path = Path(image_path)
- probs, negative_probs = inference_model.get_descriptor_probs(image_path, descriptors=all_descriptors)
- disease_probs, negative_disease_probs = inference_model.get_diseases_probs(disease_descriptors_chestxray14, pos_probs=probs,
- negative_probs=negative_probs)
- predicted_diseases, prob_vector_neg_prompt = inference_model.get_predictions_bin_prompting(disease_descriptors_chestxray14,
- disease_probs=disease_probs,
- negative_disease_probs=negative_disease_probs,
- keys=keys)
- all_labels.append(labels)
- all_probs_neg.append(prob_vector_neg_prompt)
- gc.collect()
-
- all_labels = torch.stack(all_labels)
- all_probs_neg = torch.stack(all_probs_neg)
-
- existing_mask = sum(all_labels, 0) > 0
- all_labels_clean = all_labels[:, existing_mask]
- all_probs_neg_clean = all_probs_neg[:, existing_mask]
- all_keys_clean = [key for idx, key in enumerate(keys) if existing_mask[idx]]
-
- overall_auroc, per_disease_auroc = calculate_auroc(all_probs_neg_clean[:, 1:], all_labels_clean[:, 1:])
- print(f"AUROC: {overall_auroc:.5f}\n")
- for idx, key in enumerate(all_keys_clean[1:]):
- print(f'{key}: {per_disease_auroc[idx]:.5f}')
-
-
-if __name__ == '__main__':
- # add argument parser
- parser = argparse.ArgumentParser()
- parser.add_argument('--dataset', type=str, default='chexpert', help='chexpert or chestxray14')
- args = parser.parse_args()
-
- if args.dataset == 'chexpert':
- inference_chexpert()
- elif args.dataset == 'chestxray14':
- inference_chestxray14()
diff --git a/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h b/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h
deleted file mode 100644
index e00d3ef054bd740b801e47cc1344e38621d8c055..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h
+++ /dev/null
@@ -1,253 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-
-namespace thrust
-{
-
-namespace detail
-{
-
-
-// adapted from http://www.boost.org/doc/libs/1_37_0/libs/iterator/doc/iterator_facade.html#iterator-category
-//
-// in our implementation, R need not be a reference type to result in a category
-// derived from forward_XXX_iterator_tag
-//
-// iterator-category(T,V,R) :=
-// if(T is convertible to input_host_iterator_tag
-// || T is convertible to output_host_iterator_tag
-// || T is convertible to input_device_iterator_tag
-// || T is convertible to output_device_iterator_tag
-// )
-// return T
-//
-// else if (T is not convertible to incrementable_traversal_tag)
-// the program is ill-formed
-//
-// else return a type X satisfying the following two constraints:
-//
-// 1. X is convertible to X1, and not to any more-derived
-// type, where X1 is defined by:
-//
-// if (T is convertible to forward_traversal_tag)
-// {
-// if (T is convertible to random_access_traversal_tag)
-// X1 = random_access_host_iterator_tag
-// else if (T is convertible to bidirectional_traversal_tag)
-// X1 = bidirectional_host_iterator_tag
-// else
-// X1 = forward_host_iterator_tag
-// }
-// else
-// {
-// if (T is convertible to single_pass_traversal_tag
-// && R is convertible to V)
-// X1 = input_host_iterator_tag
-// else
-// X1 = T
-// }
-//
-// 2. category-to-traversal(X) is convertible to the most
-// derived traversal tag type to which X is also convertible,
-// and not to any more-derived traversal tag type.
-
-
-template
- struct iterator_facade_default_category;
-
-
-// Thrust's implementation of iterator_facade_default_category is slightly
-// different from Boost's equivalent.
-// Thrust does not check is_convertible because Reference
-// may not be a complete type at this point, and implementations of is_convertible
-// typically require that both types be complete.
-// Instead, it simply assumes that if is_convertible,
-// then the category is input_iterator_tag
-
-
-// this is the function for standard system iterators
-template
- struct iterator_facade_default_category_std :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if< // XXX note we differ from Boost here
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_std
-
-
-// this is the function for host system iterators
-template
- struct iterator_facade_default_category_host :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if< // XXX note we differ from Boost here
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_host
-
-
-// this is the function for device system iterators
-template
- struct iterator_facade_default_category_device :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value, // XXX note we differ from Boost here
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_device
-
-
-// this is the function for any system iterators
-template
- struct iterator_facade_default_category_any
-{
- typedef thrust::detail::iterator_category_with_system_and_traversal<
- typename iterator_facade_default_category_std::type,
- thrust::any_system_tag,
- Traversal
- > type;
-}; // end iterator_facade_default_category_any
-
-
-template
- struct iterator_facade_default_category
- // check for any system
- : thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_any,
-
- // check for host system
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_host,
-
- // check for device system
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_device,
-
- // if we don't recognize the system, get a standard iterator category
- // and combine it with System & Traversal
- thrust::detail::identity_<
- thrust::detail::iterator_category_with_system_and_traversal<
- typename iterator_facade_default_category_std::type,
- System,
- Traversal
- >
- >
- >
- >
- >
-{};
-
-
-template
- struct iterator_facade_category_impl
-{
- typedef typename iterator_facade_default_category<
- System,Traversal,ValueParam,Reference
- >::type category;
-
- // we must be able to deduce both Traversal & System from category
- // otherwise, munge them all together
- typedef typename thrust::detail::eval_if<
- thrust::detail::and_<
- thrust::detail::is_same<
- Traversal,
- typename thrust::detail::iterator_category_to_traversal::type
- >,
- thrust::detail::is_same<
- System,
- typename thrust::detail::iterator_category_to_system::type
- >
- >::value,
- thrust::detail::identity_,
- thrust::detail::identity_ >
- >::type type;
-}; // end iterator_facade_category_impl
-
-
-template
- struct iterator_facade_category
-{
- typedef typename
- thrust::detail::eval_if<
- thrust::detail::is_iterator_category::value,
- thrust::detail::identity_, // categories are fine as-is
- iterator_facade_category_impl
- >::type type;
-}; // end iterator_facade_category
-
-
-} // end detail
-} // end thrust
-
diff --git a/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py b/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py
deleted file mode 100644
index a4f5ed44caefb1612df67785b1f4f0d9ec46ee93..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import torch.nn as nn
-from mmcv.cnn import ConvModule, xavier_init
-
-from ..builder import NECKS
-
-
-@NECKS.register_module()
-class ChannelMapper(nn.Module):
- r"""Channel Mapper to reduce/increase channels of backbone features.
-
- This is used to reduce/increase channels of backbone features.
-
- Args:
- in_channels (List[int]): Number of input channels per scale.
- out_channels (int): Number of output channels (used at each scale).
- kernel_size (int, optional): kernel_size for reducing channels (used
- at each scale). Default: 3.
- conv_cfg (dict, optional): Config dict for convolution layer.
- Default: None.
- norm_cfg (dict, optional): Config dict for normalization layer.
- Default: None.
- act_cfg (dict, optional): Config dict for activation layer in
- ConvModule. Default: dict(type='ReLU').
-
- Example:
- >>> import torch
- >>> in_channels = [2, 3, 5, 7]
- >>> scales = [340, 170, 84, 43]
- >>> inputs = [torch.rand(1, c, s, s)
- ... for c, s in zip(in_channels, scales)]
- >>> self = ChannelMapper(in_channels, 11, 3).eval()
- >>> outputs = self.forward(inputs)
- >>> for i in range(len(outputs)):
- ... print(f'outputs[{i}].shape = {outputs[i].shape}')
- outputs[0].shape = torch.Size([1, 11, 340, 340])
- outputs[1].shape = torch.Size([1, 11, 170, 170])
- outputs[2].shape = torch.Size([1, 11, 84, 84])
- outputs[3].shape = torch.Size([1, 11, 43, 43])
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size=3,
- conv_cfg=None,
- norm_cfg=None,
- act_cfg=dict(type='ReLU')):
- super(ChannelMapper, self).__init__()
- assert isinstance(in_channels, list)
-
- self.convs = nn.ModuleList()
- for in_channel in in_channels:
- self.convs.append(
- ConvModule(
- in_channel,
- out_channels,
- kernel_size,
- padding=(kernel_size - 1) // 2,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg))
-
- # default init_weights for conv(msra) and norm in ConvModule
- def init_weights(self):
- """Initialize the weights of ChannelMapper module."""
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- xavier_init(m, distribution='uniform')
-
- def forward(self, inputs):
- """Forward function."""
- assert len(inputs) == len(self.convs)
- outs = [self.convs[i](inputs[i]) for i in range(len(inputs))]
- return tuple(outs)
diff --git a/spaces/CVPR/regionclip-demo/datasets/README.md b/spaces/CVPR/regionclip-demo/datasets/README.md
deleted file mode 100644
index 0eb44cc3b23beeb1755ab8d12002d26f13434235..0000000000000000000000000000000000000000
--- a/spaces/CVPR/regionclip-demo/datasets/README.md
+++ /dev/null
@@ -1,140 +0,0 @@
-# Use Builtin Datasets
-
-A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog)
-for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc).
-This document explains how to setup the builtin datasets so they can be used by the above APIs.
-[Use Custom Datasets](https://detectron2.readthedocs.io/tutorials/datasets.html) gives a deeper dive on how to use `DatasetCatalog` and `MetadataCatalog`,
-and how to add new datasets to them.
-
-Detectron2 has builtin support for a few datasets.
-The datasets are assumed to exist in a directory specified by the environment variable
-`DETECTRON2_DATASETS`.
-Under this directory, detectron2 will look for datasets in the structure described below, if needed.
-```
-$DETECTRON2_DATASETS/
- coco/
- lvis/
- cityscapes/
- VOC20{07,12}/
-```
-
-You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`.
-If left unset, the default is `./datasets` relative to your current working directory.
-
-The [model zoo](https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md)
-contains configs and models that use these builtin datasets.
-
-## Expected dataset structure for [COCO instance/keypoint detection](https://cocodataset.org/#download):
-
-```
-coco/
- annotations/
- instances_{train,val}2017.json
- person_keypoints_{train,val}2017.json
- {train,val}2017/
- # image files that are mentioned in the corresponding json
-```
-
-You can use the 2014 version of the dataset as well.
-
-Some of the builtin tests (`dev/run_*_tests.sh`) uses a tiny version of the COCO dataset,
-which you can download with `./datasets/prepare_for_tests.sh`.
-
-## Expected dataset structure for PanopticFPN:
-
-Extract panoptic annotations from [COCO website](https://cocodataset.org/#download)
-into the following structure:
-```
-coco/
- annotations/
- panoptic_{train,val}2017.json
- panoptic_{train,val}2017/ # png annotations
- panoptic_stuff_{train,val}2017/ # generated by the script mentioned below
-```
-
-Install panopticapi by:
-```
-pip install git+https://github.com/cocodataset/panopticapi.git
-```
-Then, run `python datasets/prepare_panoptic_fpn.py`, to extract semantic annotations from panoptic annotations.
-
-## Expected dataset structure for [LVIS instance segmentation](https://www.lvisdataset.org/dataset):
-```
-coco/
- {train,val,test}2017/
-lvis/
- lvis_v0.5_{train,val}.json
- lvis_v0.5_image_info_test.json
- lvis_v1_{train,val}.json
- lvis_v1_image_info_test{,_challenge}.json
-```
-
-Install lvis-api by:
-```
-pip install git+https://github.com/lvis-dataset/lvis-api.git
-```
-
-To evaluate models trained on the COCO dataset using LVIS annotations,
-run `python datasets/prepare_cocofied_lvis.py` to prepare "cocofied" LVIS annotations.
-
-## Expected dataset structure for [cityscapes](https://www.cityscapes-dataset.com/downloads/):
-```
-cityscapes/
- gtFine/
- train/
- aachen/
- color.png, instanceIds.png, labelIds.png, polygons.json,
- labelTrainIds.png
- ...
- val/
- test/
- # below are generated Cityscapes panoptic annotation
- cityscapes_panoptic_train.json
- cityscapes_panoptic_train/
- cityscapes_panoptic_val.json
- cityscapes_panoptic_val/
- cityscapes_panoptic_test.json
- cityscapes_panoptic_test/
- leftImg8bit/
- train/
- val/
- test/
-```
-Install cityscapes scripts by:
-```
-pip install git+https://github.com/mcordts/cityscapesScripts.git
-```
-
-Note: to create labelTrainIds.png, first prepare the above structure, then run cityscapesescript with:
-```
-CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createTrainIdLabelImgs.py
-```
-These files are not needed for instance segmentation.
-
-Note: to generate Cityscapes panoptic dataset, run cityscapesescript with:
-```
-CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createPanopticImgs.py
-```
-These files are not needed for semantic and instance segmentation.
-
-## Expected dataset structure for [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/index.html):
-```
-VOC20{07,12}/
- Annotations/
- ImageSets/
- Main/
- trainval.txt
- test.txt
- # train.txt or val.txt, if you use these splits
- JPEGImages/
-```
-
-## Expected dataset structure for [ADE20k Scene Parsing](http://sceneparsing.csail.mit.edu/):
-```
-ADEChallengeData2016/
- annotations/
- annotations_detectron2/
- images/
- objectInfo150.txt
-```
-The directory `annotations_detectron2` is generated by running `python datasets/prepare_ade20k_sem_seg.py`.
diff --git a/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py b/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py
deleted file mode 100644
index ee1f6f1bcfa2fc3437fb6d5f579640482f1ad5f4..0000000000000000000000000000000000000000
--- a/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py
+++ /dev/null
@@ -1,397 +0,0 @@
-# -*- coding: utf-8 -*-
-import re
-from os.path import join, abspath, dirname
-from collections import defaultdict
-import epitran
-
-epi = epitran.Epitran("deu-Latn-nar")
-
-
-def mode_type(mode_in):
- """In the case of "sql", this will return an sqlite cursor."""
- if mode_in.lower() == "sql":
- import sqlite3
- conn = sqlite3.connect(join(abspath(dirname(__file__)), "./Resources/de.db"))
- return conn.cursor()
-
-
-#TESTS
-#NUMBERS ARE TOO HARD!
-
-
-
-def preprocess(words):
- """Returns a string of words stripped of punctuation"""
- punct_str = '!"#$%&\'()*+,-./:;<=>/?@[\\]^_`{|}~«» '
- return ' '.join([w.strip(punct_str).lower() for w in words.split()])
-
-
-def preserve_punc(words):
- """converts words to IPA and finds punctuation before and after the word."""
- words_preserved = []
- for w in words.split():
- punct_list = ["", preprocess(w), ""]
- before = re.search("^([^A-Za-z0-9]+)[A-Za-z]", w)
- after = re.search("[A-Za-z]([^A-Za-z0-9]+)$", w)
- if before:
- punct_list[0] = str(before.group(1))
- if after:
- punct_list[2] = str(after.group(1))
- words_preserved.append(punct_list)
- return words_preserved
-
-
-
-def apply_punct(triple, as_str=False):
- """places surrounding punctuation back on center on a list of preserve_punc triples"""
- if type(triple[0]) == list:
- for i, t in enumerate(triple):
- triple[i] = str(''.join(triple[i]))
- if as_str:
- return ' '.join(triple)
- return triple
- if as_str:
- return str(''.join(t for t in triple))
- return [''.join(t for t in triple)]
-
-
-def _punct_replace_word(original, transcription):
- """Get the IPA transcription of word with the original punctuation marks"""
- for i, trans_list in enumerate(transcription):
- for j, item in enumerate(trans_list):
- triple = [original[i][0]] + [item] + [original[i][2]]
- transcription[i][j] = apply_punct(triple, as_str=True)
- return transcription
-
-
-def fetch_words(words_in, db_type="sql"):
- """fetches a list of words from the database"""
- asset = mode_type(db_type)
- f_result = []
- if db_type.lower() == "sql":
- for word in words_in:
- asset.execute("SELECT Words, phonemes FROM De_words WHERE Words IN (?)", (word,))
- result = asset.fetchall()
- flag = True
- try:
- f_result.append(result.pop())
- flag = False
- except IndexError:
- pass
- if result == [] and flag is True:
- result = epi.transliterate(word)
- f_result.append((word, result))
- f_result = list(filter(None,f_result))
- f_set = set(f_result)
- d = defaultdict(list)
- for k, v in f_set:
- d[k].append(v)
- return list(d.items())
-
-def get_deu(tokens_in, db_type="sql"):
- """query the SQL database for the words and return the phonemes in the order of user_in"""
- result = fetch_words(tokens_in, db_type)
- ordered = []
- for word in tokens_in:
- this_word = [[i[1] for i in result if i[0] == word]][0]
- if this_word:
- ordered.append(this_word[0])
- else:
- ordered.append(["__IGNORE__" + word])
- return ordered
-
-
-def deu_to_ipa(deu_list, mark=True):
- """converts the deu word lists into IPA transcriptions"""
- symbols = {}
- ipa_list = [] # the final list of IPA tokens to be returned
- for word_list in deu_list:
- ipa_word_list = [] # the word list for each word
- for word in word_list:
- if re.sub("\d*", "", word.replace("__IGNORE__", "")) == "":
- pass # do not delete token if it's all numbers
- else:
- word = re.sub("[0-9]", "", word)
- ipa_form = ''
- if word.startswith("__IGNORE__"):
- ipa_form = word.replace("__IGNORE__", "")
- # mark words we couldn't transliterate with an asterisk:
-
- if mark:
- if not re.sub("\d*", "", ipa_form) == "":
- ipa_form += "*"
- else:
- for piece in word.split(" "):
- marked = False
- unmarked = piece
- if piece[0] in ["ˈ", "ˌ"] or piece[0] is None:
- marked = True
- mark = piece
- unmarked = piece[1:]
-
- if unmarked in symbols:
- if marked:
- ipa_form += mark + symbols[unmarked]
- else:
- ipa_form += symbols[unmarked]
-
- else:
- ipa_form += piece
- swap_list = [["ˈər", "əˈr"], ["ˈie", "iˈe"]]
- for sym in swap_list:
- if not ipa_form.startswith(sym[0]):
- ipa_form = ipa_form.replace(sym[0], sym[1])
- ipa_word_list.append(ipa_form)
- ipa_list.append(sorted(list(set(ipa_word_list))))
- return ipa_list
-
-
-def get_top(ipa_list):
- """Returns only the one result for a query. If multiple entries for words are found, only the first is used."""
- return ' '.join([word_list[-1] for word_list in ipa_list])
-
-
-def get_all(ipa_list):
- """utilizes an algorithm to discover and return all possible combinations of IPA transcriptions"""
- final_size = 1
- for word_list in ipa_list:
- final_size *= len(word_list)
- list_all = ["" for s in range(final_size)]
- for i in range(len(ipa_list)):
- if i == 0:
- swtich_rate = final_size / len(ipa_list[i])
- else:
- swtich_rate /= len(ipa_list[i])
- k = 0
- for j in range(final_size):
- if (j+1) % int(swtich_rate) == 0:
- k += 1
- if k == len(ipa_list[i]):
- k = 0
- list_all[j] = list_all[j] + ipa_list[i][k] + " "
- return sorted([sent[:-1] for sent in list_all])
-
-
-def ipa_list(words_in, keep_punct=True, db_type="sql"):
- """Returns a list of all the discovered IPA transcriptions for each word."""
- if type(words_in) == str:
- words = [preserve_punc(w.lower())[0] for w in words_in.split()]
- else:
- words = [preserve_punc(w.lower())[0] for w in words_in]
- deu = get_deu([w[1] for w in words], db_type=db_type)
- ipa = deu_to_ipa(deu)
- if keep_punct:
- ipa = _punct_replace_word(words, ipa)
- return ipa
-
-
-def isin_deu(word, db_type="sql"):
- """checks if a word is in the deu dictionary. Doesn't strip punctuation.
- If given more than one word, returns True only if all words are present."""
- if type(word) == str:
- word = [preprocess(w) for w in word.split()]
- results = fetch_words(word, db_type)
- as_set = list(set(t[0] for t in results))
- return len(as_set) == len(set(word))
-
-def replace_number(text):
- text = text.replace("1","eins ")
- text = text.replace("2","zwei ")
- text = text.replace("3","drei ")
- text = text.replace("4","vier ")
- text = text.replace("5","fünf ")
- text = text.replace("6","sechs ")
- text = text.replace("7","sieben ")
- text = text.replace("8","acht ")
- text = text.replace("9","neun ")
- text = text.replace("0","null ")
- return text
-
-
-
-def convert(text, retrieve_all=False, keep_punct=True, mode="sql"):
- """takes either a string or list of German words and converts them to IPA"""
- text = replace_number(text)
- ipa = ipa_list(
- words_in=text,
- keep_punct=keep_punct,
- db_type=mode)
- if retrieve_all:
- return get_all(ipa)
- return get_top(ipa)
-
-
-
-_decimal_number_re = re.compile(r'\d+\,\d+')
-_euros_pre = re.compile(r'€([0-9\,]*[0-9]+)')
-_euros_re = re.compile(r'([0-9\,]*[0-9]+)€')
-_ordinal_re = re.compile(r'(der |die |das )([0-9]+)\.')
-_clock_re=re.compile(r'\d{1,2}\:\d{2}')
-_number_re = re.compile(r'[0-9]+')
-
-def base(text):
- text = text.replace("1", "eins ")
- text = text.replace("2", "zwei ")
- text = text.replace("3", "drei ")
- text = text.replace("4", "vier ")
- text = text.replace("5", "fünf ")
- text = text.replace("6", "sechs ")
- text = text.replace("7", "sieben ")
- text = text.replace("8", "acht ")
- text = text.replace("9", "neun ")
- text = text.replace("0", "null ")
- return text
-
-def tens_to_word(num):
- tens = num[0]
- ones = num[1]
- ones_word = base(ones)
-
- if num =="10":
- return "zehn"
- elif num=="11":
- return "elf"
- elif num=="12":
- return "zwölf"
-
- if tens == "1":
- if ones == "6":
- ones_word = ones_word[:-1]
- elif ones == "7":
- ones_word = ones_word[:-2]
- return ones_word + "zehn"
- else:
- tens_word = base(tens)
- if ones == "1":
- ones_word = ones_word[:-1]
- if tens == "2":
- tens_word = "zwan"
- elif tens == "6":
- tens_word = tens_word[:-1]
- elif tens == "7":
- tens_word = tens_word[:-2]
- if tens == "3":
- tens_word += "ßig"
- else:
- tens_word += "zig"
- if ones == "0":
- return tens_word
- else:
- return ones_word + " und " + tens_word
-
-def huns_to_word(num):
- huns = num[0]
- tens = num[1]
-
- if huns == "1":
- huns_word= "hundert"
- else:
- huns_word = base(huns)+" hundert"
-
- remain = num_to_word(num[1:])
- if remain != "":
- remain = " " + remain
- return huns_word + remain
-
-def thos_to_word(num):
- thos = num[0]
- if thos == "1":
- thos_word= "tausend"
- else:
- thos_word = base(thos)+" tausend"
- remain=num_to_word(num[1:])
- if remain!="":
- remain=" "+remain
- return thos_word+remain
-
-def num_to_word(num):
- num=num.lstrip("0")
- if num=="":
- return("")
- digit=len(num)
- if digit==1:
- return base(num)
- elif digit==2:
- return tens_to_word(num)
- elif digit == 3:
- return huns_to_word(num)
- elif digit == 4:
- return thos_to_word(num)
- else:
- return base(num)
-
-def number_to_words(m):
- m=m.group(0).lstrip("0")
- if m=="":
- return"null"
- return num_to_word(m)
-
-def _expand_ordinal(m):
-
- pre=m.group(1)
- m = m.group(2).lstrip("0")
-
- if m=="":
- return"NULL"
- num=int(m)
- if num<=19 & num>=1:
- if num ==1:
- return "erste"
- elif num==3:
- return "dritte"
- elif num==7:
- return "siebte"
- elif num==8:
- return "achte"
- else:
- return pre + num_to_word(m) + "te"
- else:
- return pre + num_to_word(m) + "ste"
-
-def _expand_decimal(m):
- match=m.group(0)
- parts = match.split(',')
- if int(parts[0])==0:
- return '%s komma %s' % ("null", base(parts[1]))
- return '%s komma %s' % (num_to_word(parts[0]),base(parts[1]))
-
-def _expand_euros(m):
- match = m.group(1)
- parts = match.split(',')
- if len(parts) > 2:
- return match + ' euro' # Unexpected format
- euros = int(parts[0]) if parts[0] else 0
- cents = int(parts[1])*10 if len(parts) > 1 and parts[1] else 0
- if euros and cents:
- return '%s euro %s' % (euros, cents)
- elif euros:
- return '%s euro' % (euros)
- elif cents:
- return '%s cent' % (cents)
- else:
- return 'null euro'
-
-def _expand_clock(m):
- match = m.group(0)
- parts = match.split(':')
- if int(parts[0]) == 0:
- return '%s Uhr %s' % ("null",num_to_word(parts[1]))
- elif int(parts[0]) == 1:
- return '%s Uhr %s' % ("ein", num_to_word(parts[1]))
- return '%s Uhr %s' % (num_to_word(parts[0]),num_to_word(parts[1]))
-
-def normalize_numbers(text):
- text = re.sub(_euros_pre, _expand_euros, text)
- text = re.sub(_euros_re, _expand_euros, text)
- text = re.sub(_clock_re, _expand_clock, text)
- text = re.sub(_decimal_number_re, _expand_decimal, text)
- text = re.sub(_ordinal_re, _expand_ordinal, text)
- text = re.sub(_number_re, number_to_words, text)
- text=text.replace(" "," ")
- return text
-
-def collapse_whitespace(text):
- return re.sub(r'\s+', ' ', text)
-
-def mark_dark_l(text):
- return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
diff --git a/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py b/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py
deleted file mode 100644
index 1519b3095bc5250016066754ebe6c0328773f24a..0000000000000000000000000000000000000000
--- a/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from utils import randomize_seed_fn
-
-
-def create_demo(process, max_images=12, default_num_images=3):
- with gr.Blocks() as demo:
- with gr.Row():
- with gr.Column():
- image = gr.Image()
- prompt = gr.Textbox(label='Prompt')
- run_button = gr.Button('Run')
- with gr.Accordion('Advanced options', open=False):
- preprocessor_name = gr.Radio(label='Preprocessor',
- choices=['NormalBae', 'None'],
- type='value',
- value='NormalBae')
- num_samples = gr.Slider(label='Images',
- minimum=1,
- maximum=max_images,
- value=default_num_images,
- step=1)
- image_resolution = gr.Slider(label='Image resolution',
- minimum=256,
- maximum=512,
- value=512,
- step=256)
- preprocess_resolution = gr.Slider(
- label='Preprocess resolution',
- minimum=128,
- maximum=512,
- value=384,
- step=1)
- num_steps = gr.Slider(label='Number of steps',
- minimum=1,
- maximum=100,
- value=20,
- step=1)
- guidance_scale = gr.Slider(label='Guidance scale',
- minimum=0.1,
- maximum=30.0,
- value=9.0,
- step=0.1)
- seed = gr.Slider(label='Seed',
- minimum=0,
- maximum=1000000,
- step=1,
- value=0,
- randomize=True)
- randomize_seed = gr.Checkbox(label='Randomize seed',
- value=True)
- a_prompt = gr.Textbox(
- label='Additional prompt',
- value='best quality, extremely detailed')
- n_prompt = gr.Textbox(
- label='Negative prompt',
- value=
- 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
- )
- with gr.Column():
- result = gr.Gallery(label='Output', show_label=False).style(
- columns=2, object_fit='scale-down')
- inputs = [
- image,
- prompt,
- a_prompt,
- n_prompt,
- num_samples,
- image_resolution,
- preprocess_resolution,
- num_steps,
- guidance_scale,
- seed,
- preprocessor_name,
- ]
- prompt.submit(
- fn=randomize_seed_fn,
- inputs=[seed, randomize_seed],
- outputs=seed,
- ).then(
- fn=process,
- inputs=inputs,
- outputs=result,
- )
- run_button.click(
- fn=randomize_seed_fn,
- inputs=[seed, randomize_seed],
- outputs=seed,
- ).then(
- fn=process,
- inputs=inputs,
- outputs=result,
- api_name='normal',
- )
- return demo
-
-
-if __name__ == '__main__':
- from model import Model
- model = Model(task_name='NormalBae')
- demo = create_demo(model.process_normal)
- demo.queue().launch()
diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py
deleted file mode 100644
index 0a44b37fc5563deea8406c6ddf109860a56e9485..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from PIL.Image import Image as IMG
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.utils import save_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def knock(images: List[BuildImage], texts, args):
- img = images[0].convert("RGBA").square()
- # fmt: off
- locs = [(60, 308, 210, 195), (60, 308, 210, 198), (45, 330, 250, 172), (58, 320, 218, 180),
- (60, 310, 215, 193), (40, 320, 250, 285), (48, 308, 226, 192), (51, 301, 223, 200)]
- # fmt: on
- frames: List[IMG] = []
- for i in range(8):
- frame = BuildImage.open(img_dir / f"{i}.png")
- x, y, w, h = locs[i]
- frame.paste(img.resize((w, h)), (x, y), below=True)
- frames.append(frame.image)
- return save_gif(frames, 0.04)
-
-
-add_meme("knock", knock, min_images=1, max_images=1, keywords=["敲"])
diff --git a/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py b/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py
deleted file mode 100644
index 37f310fadd0b48b2f364877158fb2105d645fc03..0000000000000000000000000000000000000000
--- a/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import locale
-import json
-import os
-
-
-def load_language_list(language):
- with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f:
- language_list = json.load(f)
- return language_list
-
-
-class I18nAuto:
- def __init__(self, language=None):
- if language in ["Auto", None]:
- language = locale.getdefaultlocale()[
- 0
- ] # getlocale can't identify the system's language ((None, None))
- if not os.path.exists(f"./i18n/{language}.json"):
- language = "en_US"
- self.language = language
- # print("Use Language:", language)
- self.language_map = load_language_list(language)
-
- def __call__(self, key):
- return self.language_map.get(key, key)
-
- def print(self):
- print("Use Language:", self.language)
diff --git a/spaces/Codecooker/rvcapi/src/mdx.py b/spaces/Codecooker/rvcapi/src/mdx.py
deleted file mode 100644
index 575c456fbdf9e5b5955401f41a3a58ddc27267b3..0000000000000000000000000000000000000000
--- a/spaces/Codecooker/rvcapi/src/mdx.py
+++ /dev/null
@@ -1,287 +0,0 @@
-import gc
-import hashlib
-import os
-import queue
-import threading
-import warnings
-
-import librosa
-import numpy as np
-import onnxruntime as ort
-import soundfile as sf
-import torch
-from tqdm import tqdm
-
-warnings.filterwarnings("ignore")
-stem_naming = {'Vocals': 'Instrumental', 'Other': 'Instruments', 'Instrumental': 'Vocals', 'Drums': 'Drumless', 'Bass': 'Bassless'}
-
-
-class MDXModel:
- def __init__(self, device, dim_f, dim_t, n_fft, hop=1024, stem_name=None, compensation=1.000):
- self.dim_f = dim_f
- self.dim_t = dim_t
- self.dim_c = 4
- self.n_fft = n_fft
- self.hop = hop
- self.stem_name = stem_name
- self.compensation = compensation
-
- self.n_bins = self.n_fft // 2 + 1
- self.chunk_size = hop * (self.dim_t - 1)
- self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(device)
-
- out_c = self.dim_c
-
- self.freq_pad = torch.zeros([1, out_c, self.n_bins - self.dim_f, self.dim_t]).to(device)
-
- def stft(self, x):
- x = x.reshape([-1, self.chunk_size])
- x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True, return_complex=True)
- x = torch.view_as_real(x)
- x = x.permute([0, 3, 1, 2])
- x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 4, self.n_bins, self.dim_t])
- return x[:, :, :self.dim_f]
-
- def istft(self, x, freq_pad=None):
- freq_pad = self.freq_pad.repeat([x.shape[0], 1, 1, 1]) if freq_pad is None else freq_pad
- x = torch.cat([x, freq_pad], -2)
- # c = 4*2 if self.target_name=='*' else 2
- x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 2, self.n_bins, self.dim_t])
- x = x.permute([0, 2, 3, 1])
- x = x.contiguous()
- x = torch.view_as_complex(x)
- x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True)
- return x.reshape([-1, 2, self.chunk_size])
-
-
-class MDX:
- DEFAULT_SR = 44100
- # Unit: seconds
- DEFAULT_CHUNK_SIZE = 0 * DEFAULT_SR
- DEFAULT_MARGIN_SIZE = 1 * DEFAULT_SR
-
- DEFAULT_PROCESSOR = 0
-
- def __init__(self, model_path: str, params: MDXModel, processor=DEFAULT_PROCESSOR):
-
- # Set the device and the provider (CPU or CUDA)
- self.device = torch.device(f'cuda:{processor}') if processor >= 0 else torch.device('cpu')
- self.provider = ['CUDAExecutionProvider'] if processor >= 0 else ['CPUExecutionProvider']
-
- self.model = params
-
- # Load the ONNX model using ONNX Runtime
- self.ort = ort.InferenceSession(model_path, providers=self.provider)
- # Preload the model for faster performance
- self.ort.run(None, {'input': torch.rand(1, 4, params.dim_f, params.dim_t).numpy()})
- self.process = lambda spec: self.ort.run(None, {'input': spec.cpu().numpy()})[0]
-
- self.prog = None
-
- @staticmethod
- def get_hash(model_path):
- try:
- with open(model_path, 'rb') as f:
- f.seek(- 10000 * 1024, 2)
- model_hash = hashlib.md5(f.read()).hexdigest()
- except:
- model_hash = hashlib.md5(open(model_path, 'rb').read()).hexdigest()
-
- return model_hash
-
- @staticmethod
- def segment(wave, combine=True, chunk_size=DEFAULT_CHUNK_SIZE, margin_size=DEFAULT_MARGIN_SIZE):
- """
- Segment or join segmented wave array
-
- Args:
- wave: (np.array) Wave array to be segmented or joined
- combine: (bool) If True, combines segmented wave array. If False, segments wave array.
- chunk_size: (int) Size of each segment (in samples)
- margin_size: (int) Size of margin between segments (in samples)
-
- Returns:
- numpy array: Segmented or joined wave array
- """
-
- if combine:
- processed_wave = None # Initializing as None instead of [] for later numpy array concatenation
- for segment_count, segment in enumerate(wave):
- start = 0 if segment_count == 0 else margin_size
- end = None if segment_count == len(wave) - 1 else -margin_size
- if margin_size == 0:
- end = None
- if processed_wave is None: # Create array for first segment
- processed_wave = segment[:, start:end]
- else: # Concatenate to existing array for subsequent segments
- processed_wave = np.concatenate((processed_wave, segment[:, start:end]), axis=-1)
-
- else:
- processed_wave = []
- sample_count = wave.shape[-1]
-
- if chunk_size <= 0 or chunk_size > sample_count:
- chunk_size = sample_count
-
- if margin_size > chunk_size:
- margin_size = chunk_size
-
- for segment_count, skip in enumerate(range(0, sample_count, chunk_size)):
-
- margin = 0 if segment_count == 0 else margin_size
- end = min(skip + chunk_size + margin_size, sample_count)
- start = skip - margin
-
- cut = wave[:, start:end].copy()
- processed_wave.append(cut)
-
- if end == sample_count:
- break
-
- return processed_wave
-
- def pad_wave(self, wave):
- """
- Pad the wave array to match the required chunk size
-
- Args:
- wave: (np.array) Wave array to be padded
-
- Returns:
- tuple: (padded_wave, pad, trim)
- - padded_wave: Padded wave array
- - pad: Number of samples that were padded
- - trim: Number of samples that were trimmed
- """
- n_sample = wave.shape[1]
- trim = self.model.n_fft // 2
- gen_size = self.model.chunk_size - 2 * trim
- pad = gen_size - n_sample % gen_size
-
- # Padded wave
- wave_p = np.concatenate((np.zeros((2, trim)), wave, np.zeros((2, pad)), np.zeros((2, trim))), 1)
-
- mix_waves = []
- for i in range(0, n_sample + pad, gen_size):
- waves = np.array(wave_p[:, i:i + self.model.chunk_size])
- mix_waves.append(waves)
-
- mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(self.device)
-
- return mix_waves, pad, trim
-
- def _process_wave(self, mix_waves, trim, pad, q: queue.Queue, _id: int):
- """
- Process each wave segment in a multi-threaded environment
-
- Args:
- mix_waves: (torch.Tensor) Wave segments to be processed
- trim: (int) Number of samples trimmed during padding
- pad: (int) Number of samples padded during padding
- q: (queue.Queue) Queue to hold the processed wave segments
- _id: (int) Identifier of the processed wave segment
-
- Returns:
- numpy array: Processed wave segment
- """
- mix_waves = mix_waves.split(1)
- with torch.no_grad():
- pw = []
- for mix_wave in mix_waves:
- self.prog.update()
- spec = self.model.stft(mix_wave)
- processed_spec = torch.tensor(self.process(spec))
- processed_wav = self.model.istft(processed_spec.to(self.device))
- processed_wav = processed_wav[:, :, trim:-trim].transpose(0, 1).reshape(2, -1).cpu().numpy()
- pw.append(processed_wav)
- processed_signal = np.concatenate(pw, axis=-1)[:, :-pad]
- q.put({_id: processed_signal})
- return processed_signal
-
- def process_wave(self, wave: np.array, mt_threads=1):
- """
- Process the wave array in a multi-threaded environment
-
- Args:
- wave: (np.array) Wave array to be processed
- mt_threads: (int) Number of threads to be used for processing
-
- Returns:
- numpy array: Processed wave array
- """
- self.prog = tqdm(total=0)
- chunk = wave.shape[-1] // mt_threads
- waves = self.segment(wave, False, chunk)
-
- # Create a queue to hold the processed wave segments
- q = queue.Queue()
- threads = []
- for c, batch in enumerate(waves):
- mix_waves, pad, trim = self.pad_wave(batch)
- self.prog.total = len(mix_waves) * mt_threads
- thread = threading.Thread(target=self._process_wave, args=(mix_waves, trim, pad, q, c))
- thread.start()
- threads.append(thread)
- for thread in threads:
- thread.join()
- self.prog.close()
-
- processed_batches = []
- while not q.empty():
- processed_batches.append(q.get())
- processed_batches = [list(wave.values())[0] for wave in
- sorted(processed_batches, key=lambda d: list(d.keys())[0])]
- assert len(processed_batches) == len(waves), 'Incomplete processed batches, please reduce batch size!'
- return self.segment(processed_batches, True, chunk)
-
-
-def run_mdx(model_params, output_dir, model_path, filename, exclude_main=False, exclude_inversion=False, suffix=None, invert_suffix=None, denoise=False, keep_orig=True, m_threads=2):
- device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
-
- device_properties = torch.cuda.get_device_properties(device)
- vram_gb = device_properties.total_memory / 1024**3
- m_threads = 1 if vram_gb < 8 else 2
-
- model_hash = MDX.get_hash(model_path)
- mp = model_params.get(model_hash)
- model = MDXModel(
- device,
- dim_f=mp["mdx_dim_f_set"],
- dim_t=2 ** mp["mdx_dim_t_set"],
- n_fft=mp["mdx_n_fft_scale_set"],
- stem_name=mp["primary_stem"],
- compensation=mp["compensate"]
- )
-
- mdx_sess = MDX(model_path, model)
- wave, sr = librosa.load(filename, mono=False, sr=44100)
- # normalizing input wave gives better output
- peak = max(np.max(wave), abs(np.min(wave)))
- wave /= peak
- if denoise:
- wave_processed = -(mdx_sess.process_wave(-wave, m_threads)) + (mdx_sess.process_wave(wave, m_threads))
- wave_processed *= 0.5
- else:
- wave_processed = mdx_sess.process_wave(wave, m_threads)
- # return to previous peak
- wave_processed *= peak
- stem_name = model.stem_name if suffix is None else suffix
-
- main_filepath = None
- if not exclude_main:
- main_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav")
- sf.write(main_filepath, wave_processed.T, sr)
-
- invert_filepath = None
- if not exclude_inversion:
- diff_stem_name = stem_naming.get(stem_name) if invert_suffix is None else invert_suffix
- stem_name = f"{stem_name}_diff" if diff_stem_name is None else diff_stem_name
- invert_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav")
- sf.write(invert_filepath, (-wave_processed.T * model.compensation) + wave.T, sr)
-
- if not keep_orig:
- os.remove(filename)
-
- del mdx_sess, wave_processed, wave
- gc.collect()
- return main_filepath, invert_filepath
diff --git a/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py b/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py
deleted file mode 100644
index 76dce7e41c803a8055f3627cccb98deb51419b09..0000000000000000000000000000000000000000
--- a/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import argparse
-import glob
-import os
-
-
-def main(args):
- txt_file = open(args.meta_info, 'w')
- # sca images
- img_paths_gt = sorted(glob.glob(os.path.join(args.input[0], '*')))
- img_paths_lq = sorted(glob.glob(os.path.join(args.input[1], '*')))
-
- assert len(img_paths_gt) == len(img_paths_lq), ('GT folder and LQ folder should have the same length, but got '
- f'{len(img_paths_gt)} and {len(img_paths_lq)}.')
-
- for img_path_gt, img_path_lq in zip(img_paths_gt, img_paths_lq):
- # get the relative paths
- img_name_gt = os.path.relpath(img_path_gt, args.root[0])
- img_name_lq = os.path.relpath(img_path_lq, args.root[1])
- print(f'{img_name_gt}, {img_name_lq}')
- txt_file.write(f'{img_name_gt}, {img_name_lq}\n')
-
-
-if __name__ == '__main__':
- """This script is used to generate meta info (txt file) for paired images.
- """
- parser = argparse.ArgumentParser()
- parser.add_argument(
- '--input',
- nargs='+',
- default=['datasets/DF2K/DIV2K_train_HR_sub', 'datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub'],
- help='Input folder, should be [gt_folder, lq_folder]')
- parser.add_argument('--root', nargs='+', default=[None, None], help='Folder root, will use the ')
- parser.add_argument(
- '--meta_info',
- type=str,
- default='datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt',
- help='txt path for meta info')
- args = parser.parse_args()
-
- assert len(args.input) == 2, 'Input folder should have two elements: gt folder and lq folder'
- assert len(args.root) == 2, 'Root path should have two elements: root for gt folder and lq folder'
- os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)
- for i in range(2):
- if args.input[i].endswith('/'):
- args.input[i] = args.input[i][:-1]
- if args.root[i] is None:
- args.root[i] = os.path.dirname(args.input[i])
-
- main(args)
diff --git a/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py b/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py
deleted file mode 100644
index eb453a809d17e6dee04e158d1c68dc807478edef..0000000000000000000000000000000000000000
--- a/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Onodofthenorth/SD_PixelArt_SpriteSheet_Generator").launch()
\ No newline at end of file
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py
deleted file mode 100644
index bafc43a19d432290867a5c08b9820f2e4f79aea3..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py
+++ /dev/null
@@ -1,97 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# GD file handling
-#
-# History:
-# 1996-04-12 fl Created
-#
-# Copyright (c) 1997 by Secret Labs AB.
-# Copyright (c) 1996 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-"""
-.. note::
- This format cannot be automatically recognized, so the
- class is not registered for use with :py:func:`PIL.Image.open()`. To open a
- gd file, use the :py:func:`PIL.GdImageFile.open()` function instead.
-
-.. warning::
- THE GD FORMAT IS NOT DESIGNED FOR DATA INTERCHANGE. This
- implementation is provided for convenience and demonstrational
- purposes only.
-"""
-
-
-from . import ImageFile, ImagePalette, UnidentifiedImageError
-from ._binary import i16be as i16
-from ._binary import i32be as i32
-
-
-class GdImageFile(ImageFile.ImageFile):
- """
- Image plugin for the GD uncompressed format. Note that this format
- is not supported by the standard :py:func:`PIL.Image.open()` function. To use
- this plugin, you have to import the :py:mod:`PIL.GdImageFile` module and
- use the :py:func:`PIL.GdImageFile.open()` function.
- """
-
- format = "GD"
- format_description = "GD uncompressed images"
-
- def _open(self):
- # Header
- s = self.fp.read(1037)
-
- if i16(s) not in [65534, 65535]:
- msg = "Not a valid GD 2.x .gd file"
- raise SyntaxError(msg)
-
- self.mode = "L" # FIXME: "P"
- self._size = i16(s, 2), i16(s, 4)
-
- true_color = s[6]
- true_color_offset = 2 if true_color else 0
-
- # transparency index
- tindex = i32(s, 7 + true_color_offset)
- if tindex < 256:
- self.info["transparency"] = tindex
-
- self.palette = ImagePalette.raw(
- "XBGR", s[7 + true_color_offset + 4 : 7 + true_color_offset + 4 + 256 * 4]
- )
-
- self.tile = [
- (
- "raw",
- (0, 0) + self.size,
- 7 + true_color_offset + 4 + 256 * 4,
- ("L", 0, 1),
- )
- ]
-
-
-def open(fp, mode="r"):
- """
- Load texture from a GD image file.
-
- :param fp: GD file name, or an opened file handle.
- :param mode: Optional mode. In this version, if the mode argument
- is given, it must be "r".
- :returns: An image instance.
- :raises OSError: If the image could not be read.
- """
- if mode != "r":
- msg = "bad mode"
- raise ValueError(msg)
-
- try:
- return GdImageFile(fp)
- except SyntaxError as e:
- msg = "cannot identify this image file"
- raise UnidentifiedImageError(msg) from e
diff --git a/spaces/Dana19/ImageRecognition_FaceCount/README.md b/spaces/Dana19/ImageRecognition_FaceCount/README.md
deleted file mode 100644
index 8afdea6aa817a1ec3b056820a882b63deedc6772..0000000000000000000000000000000000000000
--- a/spaces/Dana19/ImageRecognition_FaceCount/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Headcount
-emoji: 💻
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.3
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/DemoLou/moe-tts/text/thai.py b/spaces/DemoLou/moe-tts/text/thai.py
deleted file mode 100644
index 998207c01a85c710a46db1ec8b62c39c2d94bc84..0000000000000000000000000000000000000000
--- a/spaces/DemoLou/moe-tts/text/thai.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import re
-from num_thai.thainumbers import NumThai
-
-
-num = NumThai()
-
-# List of (Latin alphabet, Thai) pairs:
-_latin_to_thai = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', 'เอ'),
- ('b','บี'),
- ('c','ซี'),
- ('d','ดี'),
- ('e','อี'),
- ('f','เอฟ'),
- ('g','จี'),
- ('h','เอช'),
- ('i','ไอ'),
- ('j','เจ'),
- ('k','เค'),
- ('l','แอล'),
- ('m','เอ็ม'),
- ('n','เอ็น'),
- ('o','โอ'),
- ('p','พี'),
- ('q','คิว'),
- ('r','แอร์'),
- ('s','เอส'),
- ('t','ที'),
- ('u','ยู'),
- ('v','วี'),
- ('w','ดับเบิลยู'),
- ('x','เอ็กซ์'),
- ('y','วาย'),
- ('z','ซี')
-]]
-
-
-def num_to_thai(text):
- return re.sub(r'(?:\d+(?:,?\d+)?)+(?:\.\d+(?:,?\d+)?)?', lambda x: ''.join(num.NumberToTextThai(float(x.group(0).replace(',', '')))), text)
-
-def latin_to_thai(text):
- for regex, replacement in _latin_to_thai:
- text = re.sub(regex, replacement, text)
- return text
diff --git a/spaces/Detomo/AI-Galary/README.md b/spaces/Detomo/AI-Galary/README.md
deleted file mode 100644
index 5772a1eb261aa38b6407c07b0633d4a12566eccf..0000000000000000000000000000000000000000
--- a/spaces/Detomo/AI-Galary/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AI Galary
-emoji: ⚡
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Detomo/generate_wifi_qrcode/app.py b/spaces/Detomo/generate_wifi_qrcode/app.py
deleted file mode 100644
index e6681ad55d07851290ef2f0f04f1e026a95b917a..0000000000000000000000000000000000000000
--- a/spaces/Detomo/generate_wifi_qrcode/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import wifi_qrcode_generator
-import gradio as gr
-
-examples = [['Home wifi', 'very complicated password', 'WPA']]
-
-def infer(wifi_name, wifi_pw, authentication_type):
- image = wifi_qrcode_generator.wifi_qrcode(
- wifi_name, False, authentication_type, wifi_pw
- )
- image.save('qrcode_wifi.png')
- return 'qrcode_wifi.png'
-
-description= "Generate code for accessing WIFI. Fill your wifi name, wifi password and select authentication type. Then click submit and enjoy 😎"
-
-iface = gr.Interface(
- fn=infer,
- title="Generate QR code for accessing WIFI 🥃",
- description=description,
- inputs=[
- gr.Textbox(
- label="Wifi name",
- lines=1,
- ),
- gr.Textbox(
- label="Wifi password",
- lines=1,
- ),
- gr.Dropdown(
- label='authentication_type',
- choices=["WPA", "WEP", "nopass"],
- value='WPA'
- ),
- ],
- outputs="image",
- examples=examples,
- article="Author: Vu Minh Chien", cache_examples=True).launch(enable_queue=True)
diff --git a/spaces/DiffusionArtco/scifi-art-creator/app.py b/spaces/DiffusionArtco/scifi-art-creator/app.py
deleted file mode 100644
index 236417b745e1681f1a9e1ae644d75254da2de370..0000000000000000000000000000000000000000
--- a/spaces/DiffusionArtco/scifi-art-creator/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import gradio as gr
-import requests
-from PIL import Image
-from io import BytesIO
-import base64
-
-api_url = "https://5cb20b40-572c-426f-9466-995256f9b6eb.id.repl.co/generate_image"
-
-def generate_image(model="Sci-Fi Diffusion", prompt="", seed=0, negative_prompt="", sampler="k_dpmpp_2s_a", steps=50):
- data = "?model=" + model + "&prompt=" + prompt + "&seed=" + str(seed) + "&negative_prompt=" + negative_prompt + "&sampler=" + sampler + "&steps=" + str(steps)
- response = requests.post(api_url + data, timeout=400)
- if response.status_code == 200:
- img_base64 = response.json()["url"]
- img_bytes = base64.b64decode(img_base64)
- img = Image.open(BytesIO(img_bytes))
- return img
- else:
- return None
-
-inputs = [
- gr.inputs.Dropdown([ 'Experience', 'FKing SciFi', 'Future Diffusion', 'JWST Deep Space Diffusion', 'Protogen Infinity', 'RCNZ Dumb Monkey', 'RealBiter', 'Robo-Diffusion', 'Sci-Fi Diffusion'], label="Model", default="Sci-Fi Diffusion"),
- gr.inputs.Textbox(label="Prompt"),
- gr.inputs.Number(label="Seed", default=0),
- gr.inputs.Textbox(label="Negative Prompt", default=""),
- gr.inputs.Dropdown(["k_lms", "k_heun", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "DDIM", "k_dpm_fast", "k_dpm_adaptive", "k_dpmpp_2m", "k_dpmpp_2s_a", "k_dpmpp_sde"], label="Sampler", default="k_dpmpp_2s_a"),
- gr.inputs.Number(label="Steps", default=50)
-]
-
-outputs = gr.outputs.Image(label="Generated Image", type="pil")
-
-interface = gr.Interface(generate_image, inputs, outputs, title="",
- description="
-#include
-#include
-#include "filtered_lrelu.h"
-
-//------------------------------------------------------------------------
-
-static std::tuple filtered_lrelu(
- torch::Tensor x, torch::Tensor fu, torch::Tensor fd, torch::Tensor b, torch::Tensor si,
- int up, int down, int px0, int px1, int py0, int py1, int sx, int sy, float gain, float slope, float clamp, bool flip_filters, bool writeSigns)
-{
- // Set CUDA device.
- TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
- const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-
- // Validate arguments.
- TORCH_CHECK(fu.device() == x.device() && fd.device() == x.device() && b.device() == x.device(), "all input tensors must reside on the same device");
- TORCH_CHECK(fu.dtype() == torch::kFloat && fd.dtype() == torch::kFloat, "fu and fd must be float32");
- TORCH_CHECK(b.dtype() == x.dtype(), "x and b must have the same dtype");
- TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat, "x and b must be float16 or float32");
- TORCH_CHECK(x.dim() == 4, "x must be rank 4");
- TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
- TORCH_CHECK(x.numel() > 0, "x is empty");
- TORCH_CHECK((fu.dim() == 1 || fu.dim() == 2) && (fd.dim() == 1 || fd.dim() == 2), "fu and fd must be rank 1 or 2");
- TORCH_CHECK(fu.size(0) <= INT_MAX && fu.size(-1) <= INT_MAX, "fu is too large");
- TORCH_CHECK(fd.size(0) <= INT_MAX && fd.size(-1) <= INT_MAX, "fd is too large");
- TORCH_CHECK(fu.numel() > 0, "fu is empty");
- TORCH_CHECK(fd.numel() > 0, "fd is empty");
- TORCH_CHECK(b.dim() == 1 && b.size(0) == x.size(1), "b must be a vector with the same number of channels as x");
- TORCH_CHECK(up >= 1 && down >= 1, "up and down must be at least 1");
-
- // Figure out how much shared memory is available on the device.
- int maxSharedBytes = 0;
- AT_CUDA_CHECK(cudaDeviceGetAttribute(&maxSharedBytes, cudaDevAttrMaxSharedMemoryPerBlockOptin, x.device().index()));
- int sharedKB = maxSharedBytes >> 10;
-
- // Populate enough launch parameters to check if a CUDA kernel exists.
- filtered_lrelu_kernel_params p;
- p.up = up;
- p.down = down;
- p.fuShape = make_int2((int)fu.size(-1), fu.dim() == 2 ? (int)fu.size(0) : 0); // shape [n, 0] indicates separable filter.
- p.fdShape = make_int2((int)fd.size(-1), fd.dim() == 2 ? (int)fd.size(0) : 0);
- filtered_lrelu_kernel_spec test_spec = choose_filtered_lrelu_kernel(p, sharedKB);
- if (!test_spec.exec)
- {
- // No kernel found - return empty tensors and indicate missing kernel with return code of -1.
- return std::make_tuple(torch::Tensor(), torch::Tensor(), -1);
- }
-
- // Input/output element size.
- int64_t sz = (x.dtype() == torch::kHalf) ? 2 : 4;
-
- // Input sizes.
- int64_t xw = (int)x.size(3);
- int64_t xh = (int)x.size(2);
- int64_t fut_w = (int)fu.size(-1) - 1;
- int64_t fut_h = (int)fu.size(0) - 1;
- int64_t fdt_w = (int)fd.size(-1) - 1;
- int64_t fdt_h = (int)fd.size(0) - 1;
-
- // Logical size of upsampled buffer.
- int64_t cw = xw * up + (px0 + px1) - fut_w;
- int64_t ch = xh * up + (py0 + py1) - fut_h;
- TORCH_CHECK(cw > fdt_w && ch > fdt_h, "upsampled buffer must be at least the size of downsampling filter");
- TORCH_CHECK(cw <= INT_MAX && ch <= INT_MAX, "upsampled buffer is too large");
-
- // Compute output size and allocate.
- int64_t yw = (cw - fdt_w + (down - 1)) / down;
- int64_t yh = (ch - fdt_h + (down - 1)) / down;
- TORCH_CHECK(yw > 0 && yh > 0, "output must be at least 1x1");
- TORCH_CHECK(yw <= INT_MAX && yh <= INT_MAX, "output is too large");
- torch::Tensor y = torch::empty({x.size(0), x.size(1), yh, yw}, x.options(), x.suggest_memory_format());
-
- // Allocate sign tensor.
- torch::Tensor so;
- torch::Tensor s = si;
- bool readSigns = !!s.numel();
- int64_t sw_active = 0; // Active width of sign tensor.
- if (writeSigns)
- {
- sw_active = yw * down - (down - 1) + fdt_w; // Active width in elements.
- int64_t sh = yh * down - (down - 1) + fdt_h; // Height = active height.
- int64_t sw = (sw_active + 15) & ~15; // Width = active width in elements, rounded up to multiple of 16.
- TORCH_CHECK(sh <= INT_MAX && (sw >> 2) <= INT_MAX, "signs is too large");
- s = so = torch::empty({x.size(0), x.size(1), sh, sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
- }
- else if (readSigns)
- sw_active = s.size(3) << 2;
-
- // Validate sign tensor if in use.
- if (readSigns || writeSigns)
- {
- TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
- TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
- TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
- TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
- TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
- TORCH_CHECK(s.size(2) <= INT_MAX && s.size(3) <= INT_MAX, "signs is too large");
- }
-
- // Populate rest of CUDA kernel parameters.
- p.x = x.data_ptr();
- p.y = y.data_ptr();
- p.b = b.data_ptr();
- p.s = (readSigns || writeSigns) ? s.data_ptr() : 0;
- p.fu = fu.data_ptr();
- p.fd = fd.data_ptr();
- p.pad0 = make_int2(px0, py0);
- p.gain = gain;
- p.slope = slope;
- p.clamp = clamp;
- p.flip = (flip_filters) ? 1 : 0;
- p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
- p.yShape = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
- p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3), (int)s.size(2)) : make_int2(0, 0); // Width is in bytes. Contiguous.
- p.sOfs = make_int2(sx, sy);
- p.swLimit = (sw_active + 3) >> 2; // Rounded up to bytes.
-
- // x, y, b strides are in bytes.
- p.xStride = make_longlong4(sz * x.stride(3), sz * x.stride(2), sz * x.stride(1), sz * x.stride(0));
- p.yStride = make_longlong4(sz * y.stride(3), sz * y.stride(2), sz * y.stride(1), sz * y.stride(0));
- p.bStride = sz * b.stride(0);
-
- // fu, fd strides are in elements.
- p.fuStride = make_longlong3(fu.stride(-1), fu.dim() == 2 ? fu.stride(0) : 0, 0);
- p.fdStride = make_longlong3(fd.stride(-1), fd.dim() == 2 ? fd.stride(0) : 0, 0);
-
- // Determine if indices don't fit in int32. Support negative strides although Torch currently never produces those.
- bool index64b = false;
- if (std::abs(p.bStride * x.size(1)) > INT_MAX) index64b = true;
- if (std::min(x.size(0) * p.xStride.w, 0ll) + std::min(x.size(1) * p.xStride.z, 0ll) + std::min(x.size(2) * p.xStride.y, 0ll) + std::min(x.size(3) * p.xStride.x, 0ll) < -INT_MAX) index64b = true;
- if (std::max(x.size(0) * p.xStride.w, 0ll) + std::max(x.size(1) * p.xStride.z, 0ll) + std::max(x.size(2) * p.xStride.y, 0ll) + std::max(x.size(3) * p.xStride.x, 0ll) > INT_MAX) index64b = true;
- if (std::min(y.size(0) * p.yStride.w, 0ll) + std::min(y.size(1) * p.yStride.z, 0ll) + std::min(y.size(2) * p.yStride.y, 0ll) + std::min(y.size(3) * p.yStride.x, 0ll) < -INT_MAX) index64b = true;
- if (std::max(y.size(0) * p.yStride.w, 0ll) + std::max(y.size(1) * p.yStride.z, 0ll) + std::max(y.size(2) * p.yStride.y, 0ll) + std::max(y.size(3) * p.yStride.x, 0ll) > INT_MAX) index64b = true;
- if (s.numel() > INT_MAX) index64b = true;
-
- // Choose CUDA kernel.
- filtered_lrelu_kernel_spec spec = { 0 };
- AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_cuda", [&]
- {
- if constexpr (sizeof(scalar_t) <= 4) // Exclude doubles. constexpr prevents template instantiation.
- {
- // Choose kernel based on index type, datatype and sign read/write modes.
- if (!index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if (!index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if (!index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- }
- });
- TORCH_CHECK(spec.exec, "internal error - CUDA kernel not found") // This should not happen because we tested earlier that kernel exists.
-
- // Launch CUDA kernel.
- void* args[] = {&p};
- int bx = spec.numWarps * 32;
- int gx = (p.yShape.x - 1) / spec.tileOut.x + 1;
- int gy = (p.yShape.y - 1) / spec.tileOut.y + 1;
- int gz = p.yShape.z * p.yShape.w;
-
- // Repeat multiple horizontal tiles in a CTA?
- if (spec.xrep)
- {
- p.tilesXrep = spec.xrep;
- p.tilesXdim = gx;
-
- gx = (gx + p.tilesXrep - 1) / p.tilesXrep;
- std::swap(gx, gy);
- }
- else
- {
- p.tilesXrep = 0;
- p.tilesXdim = 0;
- }
-
- // Launch filter setup kernel.
- AT_CUDA_CHECK(cudaLaunchKernel(spec.setup, 1, 1024, args, 0, at::cuda::getCurrentCUDAStream()));
-
- // Copy kernels to constant memory.
- if ( writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
- else if (!writeSigns && readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
- else if (!writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
-
- // Set cache and shared memory configurations for main kernel.
- AT_CUDA_CHECK(cudaFuncSetCacheConfig(spec.exec, cudaFuncCachePreferShared));
- if (spec.dynamicSharedKB) // Need dynamically allocated shared memory?
- AT_CUDA_CHECK(cudaFuncSetAttribute(spec.exec, cudaFuncAttributeMaxDynamicSharedMemorySize, spec.dynamicSharedKB << 10));
- AT_CUDA_CHECK(cudaFuncSetSharedMemConfig(spec.exec, cudaSharedMemBankSizeFourByte));
-
- // Launch main kernel.
- const int maxSubGz = 65535; // CUDA maximum for block z dimension.
- for (int zofs=0; zofs < gz; zofs += maxSubGz) // Do multiple launches if gz is too big.
- {
- p.blockZofs = zofs;
- int subGz = std::min(maxSubGz, gz - zofs);
- AT_CUDA_CHECK(cudaLaunchKernel(spec.exec, dim3(gx, gy, subGz), bx, args, spec.dynamicSharedKB << 10, at::cuda::getCurrentCUDAStream()));
- }
-
- // Done.
- return std::make_tuple(y, so, 0);
-}
-
-//------------------------------------------------------------------------
-
-static torch::Tensor filtered_lrelu_act(torch::Tensor x, torch::Tensor si, int sx, int sy, float gain, float slope, float clamp, bool writeSigns)
-{
- // Set CUDA device.
- TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
- const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-
- // Validate arguments.
- TORCH_CHECK(x.dim() == 4, "x must be rank 4");
- TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
- TORCH_CHECK(x.numel() > 0, "x is empty");
- TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat || x.dtype() == torch::kDouble, "x must be float16, float32 or float64");
-
- // Output signs if we don't have sign input.
- torch::Tensor so;
- torch::Tensor s = si;
- bool readSigns = !!s.numel();
- if (writeSigns)
- {
- int64_t sw = x.size(3);
- sw = (sw + 15) & ~15; // Round to a multiple of 16 for coalescing.
- s = so = torch::empty({x.size(0), x.size(1), x.size(2), sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
- }
-
- // Validate sign tensor if in use.
- if (readSigns || writeSigns)
- {
- TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
- TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
- TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
- TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
- TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
- TORCH_CHECK(s.size(2) <= INT_MAX && (s.size(3) << 2) <= INT_MAX, "signs tensor is too large");
- }
-
- // Initialize CUDA kernel parameters.
- filtered_lrelu_act_kernel_params p;
- p.x = x.data_ptr();
- p.s = (readSigns || writeSigns) ? s.data_ptr() : 0;
- p.gain = gain;
- p.slope = slope;
- p.clamp = clamp;
- p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
- p.xStride = make_longlong4(x.stride(3), x.stride(2), x.stride(1), x.stride(0));
- p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3) << 2, (int)s.size(2)) : make_int2(0, 0); // Width is in elements. Contiguous.
- p.sOfs = make_int2(sx, sy);
-
- // Choose CUDA kernel.
- void* func = 0;
- AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_act_cuda", [&]
- {
- if (writeSigns)
- func = choose_filtered_lrelu_act_kernel();
- else if (readSigns)
- func = choose_filtered_lrelu_act_kernel();
- else
- func = choose_filtered_lrelu_act_kernel();
- });
- TORCH_CHECK(func, "internal error - CUDA kernel not found");
-
- // Launch CUDA kernel.
- void* args[] = {&p};
- int bx = 128; // 4 warps per block.
-
- // Logical size of launch = writeSigns ? p.s : p.x
- uint32_t gx = writeSigns ? p.sShape.x : p.xShape.x;
- uint32_t gy = writeSigns ? p.sShape.y : p.xShape.y;
- uint32_t gz = p.xShape.z * p.xShape.w; // Same as in p.sShape if signs are in use.
- gx = (gx - 1) / bx + 1;
-
- // Make sure grid y and z dimensions are within CUDA launch limits. Kernel loops internally to do the rest.
- const uint32_t gmax = 65535;
- gy = std::min(gy, gmax);
- gz = std::min(gz, gmax);
-
- // Launch.
- AT_CUDA_CHECK(cudaLaunchKernel(func, dim3(gx, gy, gz), bx, args, 0, at::cuda::getCurrentCUDAStream()));
- return so;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
- m.def("filtered_lrelu", &filtered_lrelu); // The whole thing.
- m.def("filtered_lrelu_act_", &filtered_lrelu_act); // Activation and sign tensor handling only. Modifies data tensor in-place.
-}
-
-//------------------------------------------------------------------------
diff --git a/spaces/EronSamez/RVC_HFmeu/gui_v0.py b/spaces/EronSamez/RVC_HFmeu/gui_v0.py
deleted file mode 100644
index 88c3cf9eb1eaa0fa812b32ae4d3750b4ce0a8699..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/gui_v0.py
+++ /dev/null
@@ -1,786 +0,0 @@
-import os, sys, traceback, re
-
-import json
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from configs.config import Config
-
-Config = Config()
-import PySimpleGUI as sg
-import sounddevice as sd
-import noisereduce as nr
-import numpy as np
-from fairseq import checkpoint_utils
-import librosa, torch, pyworld, faiss, time, threading
-import torch.nn.functional as F
-import torchaudio.transforms as tat
-import scipy.signal as signal
-import torchcrepe
-
-# import matplotlib.pyplot as plt
-from lib.infer_pack.models import (
- SynthesizerTrnMs256NSFsid,
- SynthesizerTrnMs256NSFsid_nono,
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono,
-)
-from i18n import I18nAuto
-
-i18n = I18nAuto()
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-current_dir = os.getcwd()
-
-
-class RVC:
- def __init__(
- self, key, f0_method, hubert_path, pth_path, index_path, npy_path, index_rate
- ) -> None:
- """
- 初始化
- """
- try:
- self.f0_up_key = key
- self.time_step = 160 / 16000 * 1000
- self.f0_min = 50
- self.f0_max = 1100
- self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
- self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
- self.f0_method = f0_method
- self.sr = 16000
- self.window = 160
-
- # Get Torch Device
- if torch.cuda.is_available():
- self.torch_device = torch.device(
- f"cuda:{0 % torch.cuda.device_count()}"
- )
- elif torch.backends.mps.is_available():
- self.torch_device = torch.device("mps")
- else:
- self.torch_device = torch.device("cpu")
-
- if index_rate != 0:
- self.index = faiss.read_index(index_path)
- # self.big_npy = np.load(npy_path)
- self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
- print("index search enabled")
- self.index_rate = index_rate
- model_path = hubert_path
- print("load model(s) from {}".format(model_path))
- models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
- [model_path],
- suffix="",
- )
- self.model = models[0]
- self.model = self.model.to(device)
- if Config.is_half:
- self.model = self.model.half()
- else:
- self.model = self.model.float()
- self.model.eval()
- cpt = torch.load(pth_path, map_location="cpu")
- self.tgt_sr = cpt["config"][-1]
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- self.if_f0 = cpt.get("f0", 1)
- self.version = cpt.get("version", "v1")
- if self.version == "v1":
- if self.if_f0 == 1:
- self.net_g = SynthesizerTrnMs256NSFsid(
- *cpt["config"], is_half=Config.is_half
- )
- else:
- self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
- elif self.version == "v2":
- if self.if_f0 == 1:
- self.net_g = SynthesizerTrnMs768NSFsid(
- *cpt["config"], is_half=Config.is_half
- )
- else:
- self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
- del self.net_g.enc_q
- print(self.net_g.load_state_dict(cpt["weight"], strict=False))
- self.net_g.eval().to(device)
- if Config.is_half:
- self.net_g = self.net_g.half()
- else:
- self.net_g = self.net_g.float()
- except:
- print(traceback.format_exc())
-
- def get_regular_crepe_computation(self, x, f0_min, f0_max, model="full"):
- batch_size = 512
- # Compute pitch using first gpu
- audio = torch.tensor(np.copy(x))[None].float()
- f0, pd = torchcrepe.predict(
- audio,
- self.sr,
- self.window,
- f0_min,
- f0_max,
- model,
- batch_size=batch_size,
- device=self.torch_device,
- return_periodicity=True,
- )
- pd = torchcrepe.filter.median(pd, 3)
- f0 = torchcrepe.filter.mean(f0, 3)
- f0[pd < 0.1] = 0
- f0 = f0[0].cpu().numpy()
- return f0
-
- def get_harvest_computation(self, x, f0_min, f0_max):
- f0, t = pyworld.harvest(
- x.astype(np.double),
- fs=self.sr,
- f0_ceil=f0_max,
- f0_floor=f0_min,
- frame_period=10,
- )
- f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
- f0 = signal.medfilt(f0, 3)
- return f0
-
- def get_f0(self, x, f0_up_key, inp_f0=None):
- # Calculate Padding and f0 details here
- p_len = x.shape[0] // 512 # For Now This probs doesn't work
- x_pad = 1
- f0_min = 50
- f0_max = 1100
- f0_mel_min = 1127 * np.log(1 + f0_min / 700)
- f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
- f0 = 0
- # Here, check f0_methods and get their computations
- if self.f0_method == "harvest":
- f0 = self.get_harvest_computation(x, f0_min, f0_max)
- elif self.f0_method == "reg-crepe":
- f0 = self.get_regular_crepe_computation(x, f0_min, f0_max)
- elif self.f0_method == "reg-crepe-tiny":
- f0 = self.get_regular_crepe_computation(x, f0_min, f0_max, "tiny")
-
- # Calculate f0_course and f0_bak here
- f0 *= pow(2, f0_up_key / 12)
- # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- tf0 = self.sr // self.window # 每秒f0点数
- if inp_f0 is not None:
- delta_t = np.round(
- (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
- ).astype("int16")
- replace_f0 = np.interp(
- list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
- )
- shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
- f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
- # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- f0bak = f0.copy()
- f0_mel = 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
- f0_mel_max - f0_mel_min
- ) + 1
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > 255] = 255
- f0_coarse = np.rint(f0_mel).astype(np.int)
- return f0_coarse, f0bak # 1-0
-
- def infer(self, feats: torch.Tensor) -> np.ndarray:
- """
- 推理函数
- """
- audio = feats.clone().cpu().numpy()
- assert feats.dim() == 1, feats.dim()
- feats = feats.view(1, -1)
- padding_mask = torch.BoolTensor(feats.shape).fill_(False)
- if Config.is_half:
- feats = feats.half()
- else:
- feats = feats.float()
- inputs = {
- "source": feats.to(device),
- "padding_mask": padding_mask.to(device),
- "output_layer": 9 if self.version == "v1" else 12,
- }
- torch.cuda.synchronize()
- with torch.no_grad():
- logits = self.model.extract_features(**inputs)
- feats = (
- self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
- )
-
- ####索引优化
- try:
- if (
- hasattr(self, "index")
- and hasattr(self, "big_npy")
- and self.index_rate != 0
- ):
- npy = feats[0].cpu().numpy().astype("float32")
- score, ix = self.index.search(npy, k=8)
- weight = np.square(1 / score)
- weight /= weight.sum(axis=1, keepdims=True)
- npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
- if Config.is_half:
- npy = npy.astype("float16")
- feats = (
- torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
- + (1 - self.index_rate) * feats
- )
- else:
- print("index search FAIL or disabled")
- except:
- traceback.print_exc()
- print("index search FAIL")
- feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
- torch.cuda.synchronize()
- print(feats.shape)
- if self.if_f0 == 1:
- pitch, pitchf = self.get_f0(audio, self.f0_up_key)
- p_len = min(feats.shape[1], 13000, pitch.shape[0]) # 太大了爆显存
- else:
- pitch, pitchf = None, None
- p_len = min(feats.shape[1], 13000) # 太大了爆显存
- torch.cuda.synchronize()
- # print(feats.shape,pitch.shape)
- feats = feats[:, :p_len, :]
- if self.if_f0 == 1:
- pitch = pitch[:p_len]
- pitchf = pitchf[:p_len]
- pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
- pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
- p_len = torch.LongTensor([p_len]).to(device)
- ii = 0 # sid
- sid = torch.LongTensor([ii]).to(device)
- with torch.no_grad():
- if self.if_f0 == 1:
- infered_audio = (
- self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
- .data.cpu()
- .float()
- )
- else:
- infered_audio = (
- self.net_g.infer(feats, p_len, sid)[0][0, 0].data.cpu().float()
- )
- torch.cuda.synchronize()
- return infered_audio
-
-
-class GUIConfig:
- def __init__(self) -> None:
- self.hubert_path: str = ""
- self.pth_path: str = ""
- self.index_path: str = ""
- self.npy_path: str = ""
- self.f0_method: str = ""
- self.pitch: int = 12
- self.samplerate: int = 44100
- self.block_time: float = 1.0 # s
- self.buffer_num: int = 1
- self.threhold: int = -30
- self.crossfade_time: float = 0.08
- self.extra_time: float = 0.04
- self.I_noise_reduce = False
- self.O_noise_reduce = False
- self.index_rate = 0.3
-
-
-class GUI:
- def __init__(self) -> None:
- self.config = GUIConfig()
- self.flag_vc = False
-
- self.launcher()
-
- def load(self):
- (
- input_devices,
- output_devices,
- input_devices_indices,
- output_devices_indices,
- ) = self.get_devices()
- try:
- with open("values1.json", "r") as j:
- data = json.load(j)
- except:
- # Injecting f0_method into the json data
- with open("values1.json", "w") as j:
- data = {
- "pth_path": "",
- "index_path": "",
- "sg_input_device": input_devices[
- input_devices_indices.index(sd.default.device[0])
- ],
- "sg_output_device": output_devices[
- output_devices_indices.index(sd.default.device[1])
- ],
- "threhold": "-45",
- "pitch": "0",
- "index_rate": "0",
- "block_time": "1",
- "crossfade_length": "0.04",
- "extra_time": "1",
- }
- return data
-
- def launcher(self):
- data = self.load()
- sg.theme("DarkTeal12")
- input_devices, output_devices, _, _ = self.get_devices()
- layout = [
- [
- sg.Frame(
- title="Proudly forked by Mangio621",
- ),
- sg.Frame(
- title=i18n("Load model"),
- layout=[
- [
- sg.Input(
- default_text="hubert_base.pt",
- key="hubert_path",
- disabled=True,
- ),
- sg.FileBrowse(
- i18n("Hubert Model"),
- initial_folder=os.path.join(os.getcwd()),
- file_types=(("pt files", "*.pt"),),
- ),
- ],
- [
- sg.Input(
- default_text=data.get("pth_path", ""),
- key="pth_path",
- ),
- sg.FileBrowse(
- i18n("Select the .pth file"),
- initial_folder=os.path.join(os.getcwd(), "weights"),
- file_types=(("weight files", "*.pth"),),
- ),
- ],
- [
- sg.Input(
- default_text=data.get("index_path", ""),
- key="index_path",
- ),
- sg.FileBrowse(
- i18n("Select the .index file"),
- initial_folder=os.path.join(os.getcwd(), "logs"),
- file_types=(("index files", "*.index"),),
- ),
- ],
- [
- sg.Input(
- default_text="你不需要填写这个You don't need write this.",
- key="npy_path",
- disabled=True,
- ),
- sg.FileBrowse(
- i18n("Select the .npy file"),
- initial_folder=os.path.join(os.getcwd(), "logs"),
- file_types=(("feature files", "*.npy"),),
- ),
- ],
- ],
- ),
- ],
- [
- # Mangio f0 Selection frame Here
- sg.Frame(
- layout=[
- [
- sg.Radio(
- "Harvest", "f0_method", key="harvest", default=True
- ),
- sg.Radio("Crepe", "f0_method", key="reg-crepe"),
- sg.Radio("Crepe Tiny", "f0_method", key="reg-crepe-tiny"),
- ]
- ],
- title="Select an f0 Method",
- )
- ],
- [
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Input device")),
- sg.Combo(
- input_devices,
- key="sg_input_device",
- default_value=data.get("sg_input_device", ""),
- ),
- ],
- [
- sg.Text(i18n("Output device")),
- sg.Combo(
- output_devices,
- key="sg_output_device",
- default_value=data.get("sg_output_device", ""),
- ),
- ],
- ],
- title=i18n("Audio device (please use the same type of driver)"),
- )
- ],
- [
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Response threshold")),
- sg.Slider(
- range=(-60, 0),
- key="threhold",
- resolution=1,
- orientation="h",
- default_value=data.get("threhold", ""),
- ),
- ],
- [
- sg.Text(i18n("Pitch settings")),
- sg.Slider(
- range=(-24, 24),
- key="pitch",
- resolution=1,
- orientation="h",
- default_value=data.get("pitch", ""),
- ),
- ],
- [
- sg.Text(i18n("Index Rate")),
- sg.Slider(
- range=(0.0, 1.0),
- key="index_rate",
- resolution=0.01,
- orientation="h",
- default_value=data.get("index_rate", ""),
- ),
- ],
- ],
- title=i18n("General settings"),
- ),
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Sample length")),
- sg.Slider(
- range=(0.1, 3.0),
- key="block_time",
- resolution=0.1,
- orientation="h",
- default_value=data.get("block_time", ""),
- ),
- ],
- [
- sg.Text(i18n("Fade length")),
- sg.Slider(
- range=(0.01, 0.15),
- key="crossfade_length",
- resolution=0.01,
- orientation="h",
- default_value=data.get("crossfade_length", ""),
- ),
- ],
- [
- sg.Text(i18n("Extra推理时长")),
- sg.Slider(
- range=(0.05, 3.00),
- key="extra_time",
- resolution=0.01,
- orientation="h",
- default_value=data.get("extra_time", ""),
- ),
- ],
- [
- sg.Checkbox(i18n("Input noise reduction"), key="I_noise_reduce"),
- sg.Checkbox(i18n("Output noise reduction"), key="O_noise_reduce"),
- ],
- ],
- title=i18n("Performance settings"),
- ),
- ],
- [
- sg.Button(i18n("开始音频Convert"), key="start_vc"),
- sg.Button(i18n("停止音频Convert"), key="stop_vc"),
- sg.Text(i18n("Inference time (ms):")),
- sg.Text("0", key="infer_time"),
- ],
- ]
- self.window = sg.Window("RVC - GUI", layout=layout)
- self.event_handler()
-
- def event_handler(self):
- while True:
- event, values = self.window.read()
- if event == sg.WINDOW_CLOSED:
- self.flag_vc = False
- exit()
- if event == "start_vc" and self.flag_vc == False:
- if self.set_values(values) == True:
- print("using_cuda:" + str(torch.cuda.is_available()))
- self.start_vc()
- settings = {
- "pth_path": values["pth_path"],
- "index_path": values["index_path"],
- "f0_method": self.get_f0_method_from_radios(values),
- "sg_input_device": values["sg_input_device"],
- "sg_output_device": values["sg_output_device"],
- "threhold": values["threhold"],
- "pitch": values["pitch"],
- "index_rate": values["index_rate"],
- "block_time": values["block_time"],
- "crossfade_length": values["crossfade_length"],
- "extra_time": values["extra_time"],
- }
- with open("values1.json", "w") as j:
- json.dump(settings, j)
- if event == "stop_vc" and self.flag_vc == True:
- self.flag_vc = False
-
- # Function that returns the used f0 method in string format "harvest"
- def get_f0_method_from_radios(self, values):
- f0_array = [
- {"name": "harvest", "val": values["harvest"]},
- {"name": "reg-crepe", "val": values["reg-crepe"]},
- {"name": "reg-crepe-tiny", "val": values["reg-crepe-tiny"]},
- ]
- # Filter through to find a true value
- used_f0 = ""
- for f0 in f0_array:
- if f0["val"] == True:
- used_f0 = f0["name"]
- break
- if used_f0 == "":
- used_f0 = "harvest" # Default Harvest if used_f0 is empty somehow
- return used_f0
-
- def set_values(self, values):
- if len(values["pth_path"].strip()) == 0:
- sg.popup(i18n("Select the pth file"))
- return False
- if len(values["index_path"].strip()) == 0:
- sg.popup(i18n("Select the index file"))
- return False
- pattern = re.compile("[^\x00-\x7F]+")
- if pattern.findall(values["hubert_path"]):
- sg.popup(i18n("The hubert model path must not contain Chinese characters"))
- return False
- if pattern.findall(values["pth_path"]):
- sg.popup(i18n("The pth file path must not contain Chinese characters."))
- return False
- if pattern.findall(values["index_path"]):
- sg.popup(i18n("The index file path must not contain Chinese characters."))
- return False
- self.set_devices(values["sg_input_device"], values["sg_output_device"])
- self.config.hubert_path = os.path.join(current_dir, "hubert_base.pt")
- self.config.pth_path = values["pth_path"]
- self.config.index_path = values["index_path"]
- self.config.npy_path = values["npy_path"]
- self.config.f0_method = self.get_f0_method_from_radios(values)
- self.config.threhold = values["threhold"]
- self.config.pitch = values["pitch"]
- self.config.block_time = values["block_time"]
- self.config.crossfade_time = values["crossfade_length"]
- self.config.extra_time = values["extra_time"]
- self.config.I_noise_reduce = values["I_noise_reduce"]
- self.config.O_noise_reduce = values["O_noise_reduce"]
- self.config.index_rate = values["index_rate"]
- return True
-
- def start_vc(self):
- torch.cuda.empty_cache()
- self.flag_vc = True
- self.block_frame = int(self.config.block_time * self.config.samplerate)
- self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate)
- self.sola_search_frame = int(0.012 * self.config.samplerate)
- self.delay_frame = int(0.01 * self.config.samplerate) # 往前预留0.02s
- self.extra_frame = int(self.config.extra_time * self.config.samplerate)
- self.rvc = None
- self.rvc = RVC(
- self.config.pitch,
- self.config.f0_method,
- self.config.hubert_path,
- self.config.pth_path,
- self.config.index_path,
- self.config.npy_path,
- self.config.index_rate,
- )
- self.input_wav: np.ndarray = np.zeros(
- self.extra_frame
- + self.crossfade_frame
- + self.sola_search_frame
- + self.block_frame,
- dtype="float32",
- )
- self.output_wav: torch.Tensor = torch.zeros(
- self.block_frame, device=device, dtype=torch.float32
- )
- self.sola_buffer: torch.Tensor = torch.zeros(
- self.crossfade_frame, device=device, dtype=torch.float32
- )
- self.fade_in_window: torch.Tensor = torch.linspace(
- 0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32
- )
- self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
- self.resampler1 = tat.Resample(
- orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
- )
- self.resampler2 = tat.Resample(
- orig_freq=self.rvc.tgt_sr,
- new_freq=self.config.samplerate,
- dtype=torch.float32,
- )
- thread_vc = threading.Thread(target=self.soundinput)
- thread_vc.start()
-
- def soundinput(self):
- """
- 接受音频输入
- """
- with sd.Stream(
- channels=2,
- callback=self.audio_callback,
- blocksize=self.block_frame,
- samplerate=self.config.samplerate,
- dtype="float32",
- ):
- while self.flag_vc:
- time.sleep(self.config.block_time)
- print("Audio block passed.")
- print("ENDing VC")
-
- def audio_callback(
- self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
- ):
- """
- 音频处理
- """
- start_time = time.perf_counter()
- indata = librosa.to_mono(indata.T)
- if self.config.I_noise_reduce:
- indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate)
-
- """noise gate"""
- frame_length = 2048
- hop_length = 1024
- rms = librosa.feature.rms(
- y=indata, frame_length=frame_length, hop_length=hop_length
- )
- db_threhold = librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold
- # print(rms.shape,db.shape,db)
- for i in range(db_threhold.shape[0]):
- if db_threhold[i]:
- indata[i * hop_length : (i + 1) * hop_length] = 0
- self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata)
-
- # infer
- print("input_wav:" + str(self.input_wav.shape))
- # print('infered_wav:'+str(infer_wav.shape))
- infer_wav: torch.Tensor = self.resampler2(
- self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav)))
- )[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to(
- device
- )
- print("infer_wav:" + str(infer_wav.shape))
-
- # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
- cor_nom = F.conv1d(
- infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
- self.sola_buffer[None, None, :],
- )
- cor_den = torch.sqrt(
- F.conv1d(
- infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
- ** 2,
- torch.ones(1, 1, self.crossfade_frame, device=device),
- )
- + 1e-8
- )
- sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
- print("sola offset: " + str(int(sola_offset)))
-
- # crossfade
- self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
- self.output_wav[: self.crossfade_frame] *= self.fade_in_window
- self.output_wav[: self.crossfade_frame] += self.sola_buffer[:]
- if sola_offset < self.sola_search_frame:
- self.sola_buffer[:] = (
- infer_wav[
- -self.sola_search_frame
- - self.crossfade_frame
- + sola_offset : -self.sola_search_frame
- + sola_offset
- ]
- * self.fade_out_window
- )
- else:
- self.sola_buffer[:] = (
- infer_wav[-self.crossfade_frame :] * self.fade_out_window
- )
-
- if self.config.O_noise_reduce:
- outdata[:] = np.tile(
- nr.reduce_noise(
- y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
- ),
- (2, 1),
- ).T
- else:
- outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
- total_time = time.perf_counter() - start_time
- self.window["infer_time"].update(int(total_time * 1000))
- print("infer time:" + str(total_time))
- print("f0_method: " + str(self.config.f0_method))
-
- def get_devices(self, update: bool = True):
- """获取设备列表"""
- if update:
- sd._terminate()
- sd._initialize()
- devices = sd.query_devices()
- hostapis = sd.query_hostapis()
- for hostapi in hostapis:
- for device_idx in hostapi["devices"]:
- devices[device_idx]["hostapi_name"] = hostapi["name"]
- input_devices = [
- f"{d['name']} ({d['hostapi_name']})"
- for d in devices
- if d["max_input_channels"] > 0
- ]
- output_devices = [
- f"{d['name']} ({d['hostapi_name']})"
- for d in devices
- if d["max_output_channels"] > 0
- ]
- input_devices_indices = [
- d["index"] if "index" in d else d["name"]
- for d in devices
- if d["max_input_channels"] > 0
- ]
- output_devices_indices = [
- d["index"] if "index" in d else d["name"]
- for d in devices
- if d["max_output_channels"] > 0
- ]
- return (
- input_devices,
- output_devices,
- input_devices_indices,
- output_devices_indices,
- )
-
- def set_devices(self, input_device, output_device):
- """设置输出设备"""
- (
- input_devices,
- output_devices,
- input_device_indices,
- output_device_indices,
- ) = self.get_devices()
- sd.default.device[0] = input_device_indices[input_devices.index(input_device)]
- sd.default.device[1] = output_device_indices[
- output_devices.index(output_device)
- ]
- print("input device:" + str(sd.default.device[0]) + ":" + str(input_device))
- print("output device:" + str(sd.default.device[1]) + ":" + str(output_device))
-
-
-gui = GUI()
diff --git a/spaces/EuroPython2022/clickbaitonator/fudge/main.py b/spaces/EuroPython2022/clickbaitonator/fudge/main.py
deleted file mode 100644
index e8c2299b2449b6dd07d26c7ae678732b1dabca88..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/clickbaitonator/fudge/main.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import os
-import random
-import time
-import pickle
-import math
-from argparse import ArgumentParser
-
-from tqdm import tqdm
-import numpy as np
-import torch
-import torch.nn as nn
-
-from data import Dataset
-from model import Model
-from util import save_checkpoint, ProgressMeter, AverageMeter, num_params, pad_mask
-from constants import *
-
-
-def train(model, dataset, optimizer, criterion, epoch, args, data_start_index):
- model.train()
- if data_start_index == 0:
- dataset.shuffle('train', seed=epoch + args.seed)
- if args.epoch_max_len is not None:
- data_end_index = min(data_start_index + args.epoch_max_len, len(dataset.splits['train']))
- loader = dataset.loader('train', num_workers=args.num_workers, indices=list(range(data_start_index, data_end_index)))
- data_start_index = data_end_index if data_end_index < len(dataset.splits['train']) else 0
- else:
- loader = dataset.loader('train', num_workers=args.num_workers)
- loss_meter = AverageMeter('loss', ':6.4f')
- total_length = len(loader)
- progress = ProgressMeter(total_length, [loss_meter], prefix='Training: ')
- for batch_num, batch in enumerate(tqdm(loader, total=len(loader))):
- batch = [tensor.to(args.device) for tensor in batch]
- inputs, lengths, future_words, log_probs, labels, classification_targets, syllables_to_go, future_word_num_syllables, rhyme_group_index = batch
- if args.task not in ['formality', 'iambic']:
- if not args.debug and len(inputs) != args.batch_size: # it'll screw up the bias...?
- continue
- scores = model(inputs, lengths, future_words, log_probs, syllables_to_go, future_word_num_syllables, rhyme_group_index, run_classifier=True)
- if args.task == 'formality': # we're learning for all positions at once. scores are batch x seq
- expanded_labels = classification_targets.unsqueeze(1).expand(-1, scores.shape[1]) # batch x seq
- length_mask = pad_mask(lengths).permute(1, 0) # batch x seq
- loss = criterion(scores.flatten()[length_mask.flatten()==1], expanded_labels.flatten().float()[length_mask.flatten()==1])
- elif args.task in ['iambic', 'newline']:
- use_indices = classification_targets.flatten() != -1
- loss = criterion(scores.flatten()[use_indices], classification_targets.flatten().float()[use_indices])
- else: # topic, rhyme
- loss = criterion(scores.flatten(), labels.flatten().float())
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
- loss_meter.update(loss.detach(), len(labels))
- if batch_num % args.train_print_freq == 0:
- progress.display(batch_num)
- progress.display(total_length)
- return data_start_index
-
-
-def validate(model, dataset, criterion, epoch, args):
- model.eval()
- random.seed(0)
- loader = dataset.loader('val', num_workers=args.num_workers)
- loss_meter = AverageMeter('loss', ':6.4f')
- total_length = len(loader)
- progress = ProgressMeter(total_length, [loss_meter], prefix='Validation: ')
- with torch.no_grad():
- for batch_num, batch in enumerate(tqdm(loader, total=len(loader))):
- batch = [tensor.to(args.device) for tensor in batch]
- inputs, lengths, future_words, log_probs, labels, classification_targets, syllables_to_go, future_word_num_syllables, rhyme_group_index = batch
- if args.task not in ['formality', 'iambic']: # topic predictor
- if not args.debug and len(inputs) != args.batch_size:
- continue
- scores = model(inputs, lengths, future_words, log_probs, syllables_to_go, future_word_num_syllables, rhyme_group_index, run_classifier=True)
- if args.task == 'formality': # we're learning for all positions at once. scores are batch x seq
- expanded_labels = classification_targets.unsqueeze(1).expand(-1, scores.shape[1]) # batch x seq
- length_mask = pad_mask(lengths).permute(1, 0) # batch x seq
- loss = criterion(scores.flatten()[length_mask.flatten()==1], expanded_labels.flatten().float()[length_mask.flatten()==1])
- elif args.task in ['iambic', 'newline']:
- use_indices = classification_targets.flatten() != -1
- loss = criterion(scores.flatten()[use_indices], classification_targets.flatten().float()[use_indices])
- else: # topic, rhyme
- loss = criterion(scores.flatten(), labels.flatten().float())
- loss_meter.update(loss.detach(), len(labels))
- if batch_num % args.train_print_freq == 0:
- progress.display(batch_num)
- progress.display(total_length)
- return loss_meter.avg
-
-
-def main(args):
- dataset = Dataset(args)
- os.makedirs(args.save_dir, exist_ok=True)
- with open(os.path.join(args.save_dir, 'dataset_info'), 'wb') as wf:
- pickle.dump(dataset.dataset_info, wf)
- if args.task == 'rhyme':
- with open(os.path.join(args.save_dir, 'rhyme_info'), 'wb') as wf:
- pickle.dump(dataset.rhyme_info, wf)
- if args.ckpt:
- checkpoint = torch.load(args.ckpt, map_location=args.device)
- start_epoch = checkpoint['epoch'] + 1
- best_val_metric = checkpoint['best_metric']
- model_args = checkpoint['args']
- model = Model(model_args, dataset.gpt_pad_id, len(dataset.index2word), rhyme_group_size=len(dataset.index2rhyme_group) if args.task == 'rhyme' else None) # no need to get the glove embeddings when reloading since they're saved in model ckpt anyway
- model.load_state_dict(checkpoint['state_dict'])
- model = model.to(args.device)
- optimizer = torch.optim.Adam(model.parameters(), lr=model_args.lr)
- optimizer.load_state_dict(checkpoint['optimizer'])
- data_start_index = checkpoint['data_start_index']
- print("=> loaded checkpoint '{}' (epoch {})"
- .format(args.ckpt, checkpoint['epoch']))
- # NOTE: just import pdb after loading the model here if you want to play with it, it's easy
- # model.eval()
- # import pdb; pdb.set_trace()
- else:
- model = Model(args, dataset.gpt_pad_id, len(dataset.index2word), rhyme_group_size=len(dataset.index2rhyme_group) if args.task == 'rhyme' else None, glove_embeddings=dataset.glove_embeddings)
- model = model.to(args.device)
- optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
- best_val_metric = 1e8 # lower is better for BCE
- data_start_index = 0
- print('num params', num_params(model))
- criterion = nn.BCEWithLogitsLoss().to(args.device)
-
- if args.evaluate:
- epoch = 0
- validate(model, dataset, criterion, epoch, args)
- return
- for epoch in range(args.epochs):
- print("TRAINING: Epoch {} at {}".format(epoch, time.ctime()))
- data_start_index = train(model, dataset, optimizer, criterion, epoch, args, data_start_index)
- if epoch % args.validation_freq == 0:
- print("VALIDATION: Epoch {} at {}".format(epoch, time.ctime()))
- metric = validate(model, dataset, criterion, epoch, args)
-
- if not args.debug:
- if metric < best_val_metric:
- print('new best val metric', metric)
- best_val_metric = metric
- save_checkpoint({
- 'epoch': epoch,
- 'state_dict': model.state_dict(),
- 'best_metric': best_val_metric,
- 'optimizer': optimizer.state_dict(),
- 'data_start_index': data_start_index,
- 'args': args
- }, os.path.join(args.save_dir, 'model_best.pth.tar'))
- save_checkpoint({
- 'epoch': epoch,
- 'state_dict': model.state_dict(),
- 'best_metric': metric,
- 'optimizer': optimizer.state_dict(),
- 'data_start_index': data_start_index,
- 'args': args
- }, os.path.join(args.save_dir, 'model_epoch' + str(epoch) + '.pth.tar'))
-
-
-if __name__=='__main__':
- parser = ArgumentParser()
-
- # DATA
- parser.add_argument('--task', type=str, required=True, choices=['iambic', 'rhyme', 'newline', 'topic', 'formality', 'clickbait'])
- parser.add_argument('--data_dir', type=str, required=True)
- parser.add_argument('--glove_file', type=str, help='glove embedding init, for topic task')
-
- # SAVE/LOAD
- parser.add_argument('--save_dir', type=str, required=True, help='where to save ckpts')
- parser.add_argument('--ckpt', type=str, default=None, help='load ckpt from file if given')
- parser.add_argument('--dataset_info', type=str, help='saved dataset info')
- parser.add_argument('--rhyme_info', type=str, help='saved dataset rhyme info, for a ckpt with task==rhyme')
-
- # TRAINING
- parser.add_argument('--batch_size', type=int, default=128)
- parser.add_argument('--epochs', type=int, default=100)
- parser.add_argument('--epoch_max_len', type=int, default=None, help='max batches per epoch if set, for more frequent validation')
- parser.add_argument('--validation_freq', type=int, default=1, help='validate every X epochs')
- parser.add_argument('--lr', type=float, default=1e-3, help='Adam learning rate')
- parser.add_argument('--seed', type=int, default=1, help='random seed')
- parser.add_argument('--device', type=str, default='cuda', choices=['cpu', 'cuda'])
- parser.add_argument('--num_workers', type=int, default=20, help='num workers for data loader')
- parser.add_argument('--evaluate', action='store_true', default=False)
- parser.add_argument('--debug', action='store_true', default=False)
-
- # PRINTING
- parser.add_argument('--train_print_freq', type=int, default=100, help='how often to print metrics (every X batches)')
-
- args = parser.parse_args()
-
- random.seed(args.seed)
- np.random.seed(args.seed)
- torch.manual_seed(args.seed)
- if args.evaluate:
- assert args.ckpt is not None
-
- main(args)
\ No newline at end of file
diff --git a/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts b/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts
deleted file mode 100644
index 9ba0fe30a092c73c8321a6bb2502750471b133f2..0000000000000000000000000000000000000000
--- a/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts
+++ /dev/null
@@ -1,279 +0,0 @@
-import {
- ChatInterface,
- ChatModule,
- ChatRestModule,
- ChatWorkerClient,
-} from "@mlc-ai/web-llm";
-
-function getElementAndCheck(id: string): HTMLElement {
- const element = document.getElementById(id);
- if (element == null) {
- throw Error("Cannot find element " + id);
- }
- return element;
-}
-
-const appConfig = {
- model_list: [
- {
- model_url:
- "https://huggingface.co/Felladrin/mlc-chat-Mistral-7B-OpenOrca-q4f32_1/resolve/main/params/",
- local_id: "Mistral-7B-OpenOrca-q4f32_1",
- },
- ],
- model_lib_map: {
- "Mistral-7B-OpenOrca-q4f32_1":
- "https://huggingface.co/Felladrin/mlc-chat-Mistral-7B-OpenOrca-q4f32_1/resolve/main/Mistral-7B-OpenOrca-q4f32_1-webgpu.wasm",
- },
- use_web_worker: true,
-};
-
-class ChatUI {
- private uiChat: HTMLElement;
- private uiChatInput: HTMLInputElement;
- private uiChatInfoLabel: HTMLLabelElement;
- private chat: ChatInterface;
- private localChat: ChatInterface;
- private config = appConfig;
- private selectedModel: string;
- private chatLoaded = false;
- private requestInProgress = false;
- // We use a request chain to ensure that
- // all requests send to chat are sequentialized
- private chatRequestChain: Promise = Promise.resolve();
-
- constructor(chat: ChatInterface, localChat: ChatInterface) {
- // use web worker to run chat generation in background
- this.chat = chat;
- this.localChat = localChat;
- // get the elements
- this.uiChat = getElementAndCheck("chatui-chat");
- this.uiChatInput = getElementAndCheck("chatui-input") as HTMLInputElement;
- this.uiChatInfoLabel = getElementAndCheck(
- "chatui-info-label"
- ) as HTMLLabelElement;
- // register event handlers
- getElementAndCheck("chatui-reset-btn").onclick = () => {
- this.onReset();
- };
- getElementAndCheck("chatui-send-btn").onclick = () => {
- this.onGenerate();
- };
- // TODO: find other alternative triggers
- getElementAndCheck("chatui-input").onkeypress = (event) => {
- if (event.keyCode === 13) {
- this.onGenerate();
- }
- };
-
- const modelSelector = getElementAndCheck(
- "chatui-select"
- ) as HTMLSelectElement;
- for (let i = 0; i < this.config.model_list.length; ++i) {
- const item = this.config.model_list[i];
- const opt = document.createElement("option");
- opt.value = item.local_id;
- opt.innerHTML = item.local_id;
- opt.selected = i == 0;
- modelSelector.appendChild(opt);
- }
- // Append local server option to the model selector
- const localServerOpt = document.createElement("option");
- localServerOpt.value = "Local Server";
- localServerOpt.innerHTML = "Local Server";
- modelSelector.append(localServerOpt);
- this.selectedModel = modelSelector.value;
- modelSelector.onchange = () => {
- this.onSelectChange(modelSelector);
- };
- }
- /**
- * Push a task to the execution queue.
- *
- * @param task The task to be executed;
- */
- private pushTask(task: () => Promise) {
- const lastEvent = this.chatRequestChain;
- this.chatRequestChain = lastEvent.then(task);
- }
- // Event handlers
- // all event handler pushes the tasks to a queue
- // that get executed sequentially
- // the tasks previous tasks, which causes them to early stop
- // can be interrupted by chat.interruptGenerate
- private async onGenerate() {
- if (this.requestInProgress) {
- return;
- }
- this.pushTask(async () => {
- await this.asyncGenerate();
- });
- }
-
- private async onSelectChange(modelSelector: HTMLSelectElement) {
- if (this.requestInProgress) {
- // interrupt previous generation if any
- this.chat.interruptGenerate();
- }
- // try reset after previous requests finishes
- this.pushTask(async () => {
- await this.chat.resetChat();
- this.resetChatHistory();
- await this.unloadChat();
- this.selectedModel = modelSelector.value;
- await this.asyncInitChat();
- });
- }
-
- private async onReset() {
- if (this.requestInProgress) {
- // interrupt previous generation if any
- this.chat.interruptGenerate();
- }
- // try reset after previous requests finishes
- this.pushTask(async () => {
- await this.chat.resetChat();
- this.resetChatHistory();
- });
- }
-
- // Internal helper functions
- private appendMessage(kind, text) {
- if (kind == "init") {
- text = "[System Initalize] " + text;
- }
- if (this.uiChat === undefined) {
- throw Error("cannot find ui chat");
- }
- const msg = `
-
-
- ${text}
-
-
- `;
- this.uiChat.insertAdjacentHTML("beforeend", msg);
- this.uiChat.scrollTo(0, this.uiChat.scrollHeight);
- }
-
- private updateLastMessage(kind, text) {
- if (kind == "init") {
- text = "[System Initalize] " + text;
- }
- if (this.uiChat === undefined) {
- throw Error("cannot find ui chat");
- }
- const matches = this.uiChat.getElementsByClassName(`msg ${kind}-msg`);
- if (matches.length == 0) throw Error(`${kind} message do not exist`);
- const msg = matches[matches.length - 1];
- const msgText = msg.getElementsByClassName("msg-text");
- if (msgText.length != 1) throw Error("Expect msg-text");
- if (msgText[0].innerHTML == text) return;
- const list = text.split("\n").map((t) => {
- const item = document.createElement("div");
- item.textContent = t;
- return item;
- });
- msgText[0].innerHTML = "";
- list.forEach((item) => msgText[0].append(item));
- this.uiChat.scrollTo(0, this.uiChat.scrollHeight);
- }
-
- private resetChatHistory() {
- const clearTags = ["left", "right", "init", "error"];
- for (const tag of clearTags) {
- // need to unpack to list so the iterator don't get affected by mutation
- const matches = [...this.uiChat.getElementsByClassName(`msg ${tag}-msg`)];
- for (const item of matches) {
- this.uiChat.removeChild(item);
- }
- }
- if (this.uiChatInfoLabel !== undefined) {
- this.uiChatInfoLabel.innerHTML = "";
- }
- }
-
- private async asyncInitChat() {
- if (this.chatLoaded) return;
- this.requestInProgress = true;
- this.appendMessage("init", "");
- const initProgressCallback = (report) => {
- this.updateLastMessage("init", report.text);
- };
- this.chat.setInitProgressCallback(initProgressCallback);
-
- try {
- if (this.selectedModel != "Local Server") {
- await this.chat.reload(this.selectedModel, undefined, this.config);
- }
- } catch (err) {
- this.appendMessage("error", "Init error, " + err.toString());
- console.log(err.stack);
- this.unloadChat();
- this.requestInProgress = false;
- return;
- }
- this.requestInProgress = false;
- this.chatLoaded = true;
- }
-
- private async unloadChat() {
- await this.chat.unload();
- this.chatLoaded = false;
- }
-
- /**
- * Run generate
- */
- private async asyncGenerate() {
- await this.asyncInitChat();
- this.requestInProgress = true;
- const prompt = this.uiChatInput.value;
- if (prompt == "") {
- this.requestInProgress = false;
- return;
- }
-
- this.appendMessage("right", prompt);
- this.uiChatInput.value = "";
- this.uiChatInput.setAttribute("placeholder", "Generating...");
-
- this.appendMessage("left", "");
- const callbackUpdateResponse = (step, msg) => {
- if (msg.length === 0) return this.chat.interruptGenerate();
- this.updateLastMessage("left", msg);
- };
-
- try {
- if (this.selectedModel == "Local Server") {
- await this.localChat.generate(prompt, callbackUpdateResponse);
- this.uiChatInfoLabel.innerHTML =
- await this.localChat.runtimeStatsText();
- } else {
- await this.chat.generate(prompt, callbackUpdateResponse);
- this.uiChatInfoLabel.innerHTML = await this.chat.runtimeStatsText();
- }
- } catch (err) {
- this.appendMessage("error", "Generate error, " + err.toString());
- console.log(err.stack);
- await this.unloadChat();
- }
- this.uiChatInput.setAttribute("placeholder", "Enter your message...");
- this.requestInProgress = false;
- }
-}
-
-const useWebWorker = appConfig.use_web_worker;
-let chat: ChatInterface;
-let localChat: ChatInterface;
-
-if (useWebWorker) {
- chat = new ChatWorkerClient(
- new Worker(new URL("./worker.ts", import.meta.url), { type: "module" })
- );
- localChat = new ChatRestModule();
-} else {
- chat = new ChatModule();
- localChat = new ChatRestModule();
-}
-new ChatUI(chat, localChat);
diff --git a/spaces/Francesco/torch-cam-transformers/app.py b/spaces/Francesco/torch-cam-transformers/app.py
deleted file mode 100644
index 6c5bf7ea8344430235dfcc553c112be801348a50..0000000000000000000000000000000000000000
--- a/spaces/Francesco/torch-cam-transformers/app.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import streamlit as st
-
-import sys
-
-
-def main():
- # Wide mode
- st.set_page_config(layout="wide")
-
- # Designing the interface
- st.title(sys.version)
-
-
-main()
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py
deleted file mode 100644
index bf18572329019d7a8f1df01799eda207c16dd7ff..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py
+++ /dev/null
@@ -1,284 +0,0 @@
-import os
-import random
-import re
-import numpy as np
-import librosa
-import torch
-import random
-from utils import repeat_expand_2d
-from tqdm import tqdm
-from torch.utils.data import Dataset
-
-def traverse_dir(
- root_dir,
- extensions,
- amount=None,
- str_include=None,
- str_exclude=None,
- is_pure=False,
- is_sort=False,
- is_ext=True):
-
- file_list = []
- cnt = 0
- for root, _, files in os.walk(root_dir):
- for file in files:
- if any([file.endswith(f".{ext}") for ext in extensions]):
- # path
- mix_path = os.path.join(root, file)
- pure_path = mix_path[len(root_dir)+1:] if is_pure else mix_path
-
- # amount
- if (amount is not None) and (cnt == amount):
- if is_sort:
- file_list.sort()
- return file_list
-
- # check string
- if (str_include is not None) and (str_include not in pure_path):
- continue
- if (str_exclude is not None) and (str_exclude in pure_path):
- continue
-
- if not is_ext:
- ext = pure_path.split('.')[-1]
- pure_path = pure_path[:-(len(ext)+1)]
- file_list.append(pure_path)
- cnt += 1
- if is_sort:
- file_list.sort()
- return file_list
-
-
-def get_data_loaders(args, whole_audio=False):
- data_train = AudioDataset(
- filelists = args.data.training_files,
- waveform_sec=args.data.duration,
- hop_size=args.data.block_size,
- sample_rate=args.data.sampling_rate,
- load_all_data=args.train.cache_all_data,
- whole_audio=whole_audio,
- extensions=args.data.extensions,
- n_spk=args.model.n_spk,
- spk=args.spk,
- device=args.train.cache_device,
- fp16=args.train.cache_fp16,
- use_aug=True)
- loader_train = torch.utils.data.DataLoader(
- data_train ,
- batch_size=args.train.batch_size if not whole_audio else 1,
- shuffle=True,
- num_workers=args.train.num_workers if args.train.cache_device=='cpu' else 0,
- persistent_workers=(args.train.num_workers > 0) if args.train.cache_device=='cpu' else False,
- pin_memory=True if args.train.cache_device=='cpu' else False
- )
- data_valid = AudioDataset(
- filelists = args.data.validation_files,
- waveform_sec=args.data.duration,
- hop_size=args.data.block_size,
- sample_rate=args.data.sampling_rate,
- load_all_data=args.train.cache_all_data,
- whole_audio=True,
- spk=args.spk,
- extensions=args.data.extensions,
- n_spk=args.model.n_spk)
- loader_valid = torch.utils.data.DataLoader(
- data_valid,
- batch_size=1,
- shuffle=False,
- num_workers=0,
- pin_memory=True
- )
- return loader_train, loader_valid
-
-
-class AudioDataset(Dataset):
- def __init__(
- self,
- filelists,
- waveform_sec,
- hop_size,
- sample_rate,
- spk,
- load_all_data=True,
- whole_audio=False,
- extensions=['wav'],
- n_spk=1,
- device='cpu',
- fp16=False,
- use_aug=False,
- ):
- super().__init__()
-
- self.waveform_sec = waveform_sec
- self.sample_rate = sample_rate
- self.hop_size = hop_size
- self.filelists = filelists
- self.whole_audio = whole_audio
- self.use_aug = use_aug
- self.data_buffer={}
- self.pitch_aug_dict = {}
- # np.load(os.path.join(self.path_root, 'pitch_aug_dict.npy'), allow_pickle=True).item()
- if load_all_data:
- print('Load all the data filelists:', filelists)
- else:
- print('Load the f0, volume data filelists:', filelists)
- with open(filelists,"r") as f:
- self.paths = f.read().splitlines()
- for name_ext in tqdm(self.paths, total=len(self.paths)):
- name = os.path.splitext(name_ext)[0]
- path_audio = name_ext
- duration = librosa.get_duration(filename = path_audio, sr = self.sample_rate)
-
- path_f0 = name_ext + ".f0.npy"
- f0,_ = np.load(path_f0,allow_pickle=True)
- f0 = torch.from_numpy(np.array(f0,dtype=float)).float().unsqueeze(-1).to(device)
-
- path_volume = name_ext + ".vol.npy"
- volume = np.load(path_volume)
- volume = torch.from_numpy(volume).float().unsqueeze(-1).to(device)
-
- path_augvol = name_ext + ".aug_vol.npy"
- aug_vol = np.load(path_augvol)
- aug_vol = torch.from_numpy(aug_vol).float().unsqueeze(-1).to(device)
-
- if n_spk is not None and n_spk > 1:
- spk_name = name_ext.split("/")[-2]
- spk_id = spk[spk_name] if spk_name in spk else 0
- if spk_id < 0 or spk_id >= n_spk:
- raise ValueError(' [x] Muiti-speaker traing error : spk_id must be a positive integer from 0 to n_spk-1 ')
- else:
- spk_id = 0
- spk_id = torch.LongTensor(np.array([spk_id])).to(device)
-
- if load_all_data:
- '''
- audio, sr = librosa.load(path_audio, sr=self.sample_rate)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio)
- audio = torch.from_numpy(audio).to(device)
- '''
- path_mel = name_ext + ".mel.npy"
- mel = np.load(path_mel)
- mel = torch.from_numpy(mel).to(device)
-
- path_augmel = name_ext + ".aug_mel.npy"
- aug_mel,keyshift = np.load(path_augmel, allow_pickle=True)
- aug_mel = np.array(aug_mel,dtype=float)
- aug_mel = torch.from_numpy(aug_mel).to(device)
- self.pitch_aug_dict[name_ext] = keyshift
-
- path_units = name_ext + ".soft.pt"
- units = torch.load(path_units).to(device)
- units = units[0]
- units = repeat_expand_2d(units,f0.size(0)).transpose(0,1)
-
- if fp16:
- mel = mel.half()
- aug_mel = aug_mel.half()
- units = units.half()
-
- self.data_buffer[name_ext] = {
- 'duration': duration,
- 'mel': mel,
- 'aug_mel': aug_mel,
- 'units': units,
- 'f0': f0,
- 'volume': volume,
- 'aug_vol': aug_vol,
- 'spk_id': spk_id
- }
- else:
- path_augmel = name_ext + ".aug_mel.npy"
- aug_mel,keyshift = np.load(path_augmel, allow_pickle=True)
- self.pitch_aug_dict[name_ext] = keyshift
- self.data_buffer[name_ext] = {
- 'duration': duration,
- 'f0': f0,
- 'volume': volume,
- 'aug_vol': aug_vol,
- 'spk_id': spk_id
- }
-
-
- def __getitem__(self, file_idx):
- name_ext = self.paths[file_idx]
- data_buffer = self.data_buffer[name_ext]
- # check duration. if too short, then skip
- if data_buffer['duration'] < (self.waveform_sec + 0.1):
- return self.__getitem__( (file_idx + 1) % len(self.paths))
-
- # get item
- return self.get_data(name_ext, data_buffer)
-
- def get_data(self, name_ext, data_buffer):
- name = os.path.splitext(name_ext)[0]
- frame_resolution = self.hop_size / self.sample_rate
- duration = data_buffer['duration']
- waveform_sec = duration if self.whole_audio else self.waveform_sec
-
- # load audio
- idx_from = 0 if self.whole_audio else random.uniform(0, duration - waveform_sec - 0.1)
- start_frame = int(idx_from / frame_resolution)
- units_frame_len = int(waveform_sec / frame_resolution)
- aug_flag = random.choice([True, False]) and self.use_aug
- '''
- audio = data_buffer.get('audio')
- if audio is None:
- path_audio = os.path.join(self.path_root, 'audio', name) + '.wav'
- audio, sr = librosa.load(
- path_audio,
- sr = self.sample_rate,
- offset = start_frame * frame_resolution,
- duration = waveform_sec)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio)
- # clip audio into N seconds
- audio = audio[ : audio.shape[-1] // self.hop_size * self.hop_size]
- audio = torch.from_numpy(audio).float()
- else:
- audio = audio[start_frame * self.hop_size : (start_frame + units_frame_len) * self.hop_size]
- '''
- # load mel
- mel_key = 'aug_mel' if aug_flag else 'mel'
- mel = data_buffer.get(mel_key)
- if mel is None:
- mel = name_ext + ".mel.npy"
- mel = np.load(mel)
- mel = mel[start_frame : start_frame + units_frame_len]
- mel = torch.from_numpy(mel).float()
- else:
- mel = mel[start_frame : start_frame + units_frame_len]
-
- # load f0
- f0 = data_buffer.get('f0')
- aug_shift = 0
- if aug_flag:
- aug_shift = self.pitch_aug_dict[name_ext]
- f0_frames = 2 ** (aug_shift / 12) * f0[start_frame : start_frame + units_frame_len]
-
- # load units
- units = data_buffer.get('units')
- if units is None:
- path_units = name_ext + ".soft.pt"
- units = torch.load(path_units)
- units = units[0]
- units = repeat_expand_2d(units,f0.size(0)).transpose(0,1)
-
- units = units[start_frame : start_frame + units_frame_len]
-
- # load volume
- vol_key = 'aug_vol' if aug_flag else 'volume'
- volume = data_buffer.get(vol_key)
- volume_frames = volume[start_frame : start_frame + units_frame_len]
-
- # load spk_id
- spk_id = data_buffer.get('spk_id')
-
- # load shift
- aug_shift = torch.from_numpy(np.array([[aug_shift]])).float()
-
- return dict(mel=mel, f0=f0_frames, volume=volume_frames, units=units, spk_id=spk_id, aug_shift=aug_shift, name=name, name_ext=name_ext)
-
- def __len__(self):
- return len(self.paths)
\ No newline at end of file
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py
deleted file mode 100644
index 3bdb70ba9357e95ff05853dcc06437c3401ef3be..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import os
-from functools import lru_cache
-from typing import Union
-
-import ffmpeg
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-from .utils import exact_div
-
-from librosa.filters import mel as librosa_mel_fn
-
-# hard-coded audio hyperparameters
-SAMPLE_RATE = 16000
-N_FFT = 400
-N_MELS = 80
-HOP_LENGTH = 160
-CHUNK_LENGTH = 30
-N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk
-N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH) # 3000: number of frames in a mel spectrogram input
-
-
-def load_audio(file: str, sr: int = SAMPLE_RATE):
- """
- Open an audio file and read as mono waveform, resampling as necessary
-
- Parameters
- ----------
- file: str
- The audio file to open
-
- sr: int
- The sample rate to resample the audio if necessary
-
- Returns
- -------
- A NumPy array containing the audio waveform, in float32 dtype.
- """
- try:
- # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
- # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
- out, _ = (
- ffmpeg.input(file, threads=0)
- .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
- .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
- )
- except ffmpeg.Error as e:
- raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
-
- return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
-
-
-def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
- """
- Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
- """
- if torch.is_tensor(array):
- if array.shape[axis] > length:
- array = array.index_select(dim=axis, index=torch.arange(length, device=array.device))
-
- if array.shape[axis] < length:
- pad_widths = [(0, 0)] * array.ndim
- pad_widths[axis] = (0, length - array.shape[axis])
- array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
- else:
- if array.shape[axis] > length:
- array = array.take(indices=range(length), axis=axis)
-
- if array.shape[axis] < length:
- pad_widths = [(0, 0)] * array.ndim
- pad_widths[axis] = (0, length - array.shape[axis])
- array = np.pad(array, pad_widths)
-
- return array
-
-
-@lru_cache(maxsize=None)
-def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
- """
- load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
- Allows decoupling librosa dependency; saved using:
-
- np.savez_compressed(
- "mel_filters.npz",
- mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
- )
- """
- assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
- return torch.from_numpy(librosa_mel_fn(sr=SAMPLE_RATE,n_fft=N_FFT,n_mels=n_mels)).to(device)
-
-
-def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS):
- """
- Compute the log-Mel spectrogram of
-
- Parameters
- ----------
- audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
- The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
-
- n_mels: int
- The number of Mel-frequency filters, only 80 is supported
-
- Returns
- -------
- torch.Tensor, shape = (80, n_frames)
- A Tensor that contains the Mel spectrogram
- """
- if not torch.is_tensor(audio):
- if isinstance(audio, str):
- audio = load_audio(audio)
- audio = torch.from_numpy(audio)
-
- window = torch.hann_window(N_FFT).to(audio.device)
- stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
- magnitudes = stft[..., :-1].abs() ** 2
-
- filters = mel_filters(audio.device, n_mels)
- mel_spec = filters @ magnitudes
-
- log_spec = torch.clamp(mel_spec, min=1e-10).log10()
- log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
- log_spec = (log_spec + 4.0) / 4.0
- return log_spec
diff --git a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py b/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py
deleted file mode 100644
index 6817392ba8a9eb830351de89fb7afc5ad72f5e42..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py
+++ /dev/null
@@ -1,188 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
- 1. "english_cleaners" for English text
- 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
- the Unidecode library (https://pypi.python.org/pypi/Unidecode)
- 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
- the symbols in symbols.py to match your data).
-'''
-
-
-# Regular expression matching whitespace:
-
-
-import re
-import inflect
-from unidecode import unidecode
-import eng_to_ipa as ipa
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
-_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
-_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
-_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
-_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
-_number_re = re.compile(r'[0-9]+')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
- ('mrs', 'misess'),
- ('mr', 'mister'),
- ('dr', 'doctor'),
- ('st', 'saint'),
- ('co', 'company'),
- ('jr', 'junior'),
- ('maj', 'major'),
- ('gen', 'general'),
- ('drs', 'doctors'),
- ('rev', 'reverend'),
- ('lt', 'lieutenant'),
- ('hon', 'honorable'),
- ('sgt', 'sergeant'),
- ('capt', 'captain'),
- ('esq', 'esquire'),
- ('ltd', 'limited'),
- ('col', 'colonel'),
- ('ft', 'fort'),
-]]
-
-
-# List of (ipa, lazy ipa) pairs:
-_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('æ', 'e'),
- ('ɑ', 'a'),
- ('ɔ', 'o'),
- ('ð', 'z'),
- ('θ', 's'),
- ('ɛ', 'e'),
- ('ɪ', 'i'),
- ('ʊ', 'u'),
- ('ʒ', 'ʥ'),
- ('ʤ', 'ʥ'),
- ('ˈ', '↓'),
-]]
-
-# List of (ipa, lazy ipa2) pairs:
-_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('ð', 'z'),
- ('θ', 's'),
- ('ʒ', 'ʑ'),
- ('ʤ', 'dʑ'),
- ('ˈ', '↓'),
-]]
-
-# List of (ipa, ipa2) pairs
-_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('ʤ', 'dʒ'),
- ('ʧ', 'tʃ')
-]]
-
-
-def expand_abbreviations(text):
- for regex, replacement in _abbreviations:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def collapse_whitespace(text):
- return re.sub(r'\s+', ' ', text)
-
-
-def _remove_commas(m):
- return m.group(1).replace(',', '')
-
-
-def _expand_decimal_point(m):
- return m.group(1).replace('.', ' point ')
-
-
-def _expand_dollars(m):
- match = m.group(1)
- parts = match.split('.')
- if len(parts) > 2:
- return match + ' dollars' # Unexpected format
- dollars = int(parts[0]) if parts[0] else 0
- cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
- if dollars and cents:
- dollar_unit = 'dollar' if dollars == 1 else 'dollars'
- cent_unit = 'cent' if cents == 1 else 'cents'
- return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
- elif dollars:
- dollar_unit = 'dollar' if dollars == 1 else 'dollars'
- return '%s %s' % (dollars, dollar_unit)
- elif cents:
- cent_unit = 'cent' if cents == 1 else 'cents'
- return '%s %s' % (cents, cent_unit)
- else:
- return 'zero dollars'
-
-
-def _expand_ordinal(m):
- return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
- num = int(m.group(0))
- if num > 1000 and num < 3000:
- if num == 2000:
- return 'two thousand'
- elif num > 2000 and num < 2010:
- return 'two thousand ' + _inflect.number_to_words(num % 100)
- elif num % 100 == 0:
- return _inflect.number_to_words(num // 100) + ' hundred'
- else:
- return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
- else:
- return _inflect.number_to_words(num, andword='')
-
-
-def normalize_numbers(text):
- text = re.sub(_comma_number_re, _remove_commas, text)
- text = re.sub(_pounds_re, r'\1 pounds', text)
- text = re.sub(_dollars_re, _expand_dollars, text)
- text = re.sub(_decimal_number_re, _expand_decimal_point, text)
- text = re.sub(_ordinal_re, _expand_ordinal, text)
- text = re.sub(_number_re, _expand_number, text)
- return text
-
-
-def mark_dark_l(text):
- return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
-
-
-def english_to_ipa(text):
- text = unidecode(text).lower()
- text = expand_abbreviations(text)
- text = normalize_numbers(text)
- phonemes = ipa.convert(text)
- phonemes = collapse_whitespace(phonemes)
- return phonemes
-
-
-def english_to_lazy_ipa(text):
- text = english_to_ipa(text)
- for regex, replacement in _lazy_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def english_to_ipa2(text):
- text = english_to_ipa(text)
- text = mark_dark_l(text)
- for regex, replacement in _ipa_to_ipa2:
- text = re.sub(regex, replacement, text)
- return text.replace('...', '…')
-
-
-def english_to_lazy_ipa2(text):
- text = english_to_ipa(text)
- for regex, replacement in _lazy_ipa2:
- text = re.sub(regex, replacement, text)
- return text
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
deleted file mode 100644
index a02a814fe2f08b464454e8eb6e1c88004ab804f6..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
+++ /dev/null
@@ -1,27 +0,0 @@
-_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
-model = dict(
- pretrained='torchvision://resnet101',
- backbone=dict(depth=101),
- bbox_head=dict(
- with_deform=True,
- norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='Resize',
- img_scale=[(1333, 640), (1333, 800)],
- multiscale_mode='value',
- keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
deleted file mode 100644
index 4874121fd01e4024bfde445f451b7368c6834511..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './deeplabv3_r50-d8_480x480_40k_pascal_context_59.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
deleted file mode 100644
index 89d5e1ae0f3ef44626f3b5534c504cbce7389a32..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
- '../_base_/models/gcnet_r50-d8.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
- decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py
deleted file mode 100644
index 12f6d402a3c4a113d4c37be062790fa435b72104..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Evaluation with objective metrics for the pretrained AudioGen models.
-This grid takes signature from the training grid and runs evaluation-only stage.
-
-When running the grid for the first time, please use:
-REGEN=1 dora grid audiogen.audiogen_pretrained_16khz_eval
-and re-use the REGEN=1 option when the grid is changed to force regenerating it.
-
-Note that you need the proper metrics external libraries setup to use all
-the objective metrics activated in this grid. Refer to the README for more information.
-"""
-
-import os
-
-from ..musicgen._explorers import GenerationEvalExplorer
-from ...environment import AudioCraftEnvironment
-from ... import train
-
-
-def eval(launcher, batch_size: int = 32):
- opts = {
- 'dset': 'audio/audiocaps_16khz',
- 'solver/audiogen/evaluation': 'objective_eval',
- 'execute_only': 'evaluate',
- '+dataset.evaluate.batch_size': batch_size,
- '+metrics.fad.tf.batch_size': 32,
- }
- # binary for FAD computation: replace this path with your own path
- metrics_opts = {
- 'metrics.fad.tf.bin': '/data/home/jadecopet/local/usr/opt/google-research'
- }
- opt1 = {'generate.lm.use_sampling': True, 'generate.lm.top_k': 250, 'generate.lm.top_p': 0.}
- opt2 = {'transformer_lm.two_step_cfg': True}
-
- sub = launcher.bind(opts)
- sub.bind_(metrics_opts)
-
- # base objective metrics
- sub(opt1, opt2)
-
-
-@GenerationEvalExplorer
-def explorer(launcher):
- partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
- launcher.slurm_(gpus=4, partition=partitions)
-
- if 'REGEN' not in os.environ:
- folder = train.main.dora.dir / 'grids' / __name__.split('.', 2)[-1]
- with launcher.job_array():
- for sig in folder.iterdir():
- if not sig.is_symlink():
- continue
- xp = train.main.get_xp_from_sig(sig.name)
- launcher(xp.argv)
- return
-
- audiogen_base = launcher.bind(solver="audiogen/audiogen_base_16khz")
- audiogen_base.bind_({'autocast': False, 'fsdp.use': True})
-
- audiogen_base_medium = audiogen_base.bind({'continue_from': '//pretrained/facebook/audiogen-medium'})
- audiogen_base_medium.bind_({'model/lm/model_scale': 'medium'})
- eval(audiogen_base_medium, batch_size=128)
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py
deleted file mode 100644
index 5612ff5688d85fede0e605b244919e8081cb1da9..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Grid search file, simply list all the exp you want in `explorer`.
-Any new exp added there will be scheduled.
-You can cancel and experiment by commenting its line.
-
-This grid is a minimal example for debugging compression task
-and how to override parameters directly in a grid.
-Learn more about dora grids: https://github.com/facebookresearch/dora
-"""
-
-from ._explorers import CompressionExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@CompressionExplorer
-def explorer(launcher):
- partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
- launcher.slurm_(gpus=2, partition=partitions)
- launcher.bind_(solver='compression/debug')
-
- with launcher.job_array():
- # base debug task using config from solver=compression/debug
- launcher()
- # we can override parameters in the grid to launch additional xps
- launcher({'rvq.bins': 2048, 'rvq.n_q': 4})
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py
deleted file mode 100644
index 44f4b0a2c3c6c726857db8386491823dd85dde51..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import csv
-import json
-import logging
-from pathlib import Path
-import tempfile
-import typing as tp
-import subprocess
-import shutil
-
-import torch
-import torchaudio
-
-logger = logging.getLogger(__name__)
-
-
-class ViSQOL:
- """ViSQOL wrapper to run ViSQOL from Python using a pre-installed binary.
-
- To learn more about ViSQOL and how to build ViSQOL binary using bazel, please refer to the
- instructions available in the open source repository: https://github.com/google/visqol
-
- ViSQOL is capable of running in two modes:
-
- Audio Mode:
- When running in audio mode, input signals must have a 48kHz sample rate. Input should be resampled to 48kHz.
- Input signals can be multi-channel, but they will be down-mixed to mono for performing the comparison.
- Audio mode uses support vector regression, with the maximum range at ~4.75.
-
- Speech Mode:
- When running in speech mode, ViSQOL uses a wideband model. It therefore expects input sample rates of 16kHz.
- Input should be resampled to 16kHz.
- As part of the speech mode processing, a root mean square implementation for voice activity detection
- is performed on the reference signal to determine what parts of the signal have voice activity and
- should therefore be included in the comparison. The signal is normalized before performing the voice
- activity detection.
- Input signals can be multi-channel, but they will be down-mixed to mono for performing the comparison.
- Speech mode is scaled to have a maximum MOS of 5.0 to match previous version behavior.
-
- For more details, check the guidelines: https://github.com/google/visqol#general-guidelines-for-input
-
- Args:
- visqol_bin (str): Path to the ViSQOL binary.
- mode (str): ViSQOL computation mode, expecting "audio" or "speech".
- model (str): Name of the model to use for similarity to quality model.
- debug (bool): Whether to also get debug metrics from ViSQOL or not.
- """
- SAMPLE_RATES_MODES = {"audio": 48_000, "speech": 16_000}
- ALLOWED_SAMPLE_RATES = frozenset(SAMPLE_RATES_MODES.values())
-
- def __init__(self, bin: tp.Union[Path, str], mode: str = "audio",
- model: str = "libsvm_nu_svr_model.txt", debug: bool = False):
- assert bin is not None and Path(bin).exists(), f"Could not find ViSQOL binary in specified path: {bin}"
- self.visqol_bin = str(bin)
- self.visqol_mode = mode
- self.target_sr = self._get_target_sr(self.visqol_mode)
- self.model = model
- self.debug = debug
- assert Path(self.visqol_model).exists(), \
- f"Could not find the specified model in ViSQOL install: {self.visqol_model}"
-
- def _get_target_sr(self, mode: str) -> int:
- # returns target sampling rate for the corresponding ViSQOL mode.
- if mode not in ViSQOL.SAMPLE_RATES_MODES:
- raise ValueError(
- f"Unsupported mode! Allowed are: {', '.join(ViSQOL.SAMPLE_RATES_MODES.keys())}"
- )
- return ViSQOL.SAMPLE_RATES_MODES[mode]
-
- def _prepare_files(
- self, ref_sig: torch.Tensor, deg_sig: torch.Tensor, sr: int, target_sr: int, pad_with_silence: bool = False
- ):
- # prepare files for ViSQOL evaluation.
- assert target_sr in ViSQOL.ALLOWED_SAMPLE_RATES
- assert len(ref_sig) == len(deg_sig), (
- "Expects same number of ref and degraded inputs",
- f" but ref len {len(ref_sig)} != deg len {len(deg_sig)}"
- )
- # resample audio if needed
- if sr != target_sr:
- transform = torchaudio.transforms.Resample(sr, target_sr)
- pad = int(0.5 * target_sr)
- rs_ref = []
- rs_deg = []
- for i in range(len(ref_sig)):
- rs_ref_i = transform(ref_sig[i])
- rs_deg_i = transform(deg_sig[i])
- if pad_with_silence:
- rs_ref_i = torch.nn.functional.pad(rs_ref_i, (pad, pad), mode='constant', value=0)
- rs_deg_i = torch.nn.functional.pad(rs_deg_i, (pad, pad), mode='constant', value=0)
- rs_ref.append(rs_ref_i)
- rs_deg.append(rs_deg_i)
- ref_sig = torch.stack(rs_ref)
- deg_sig = torch.stack(rs_deg)
- # save audio chunks to tmp dir and create csv
- tmp_dir = Path(tempfile.mkdtemp())
- try:
- tmp_input_csv_path = tmp_dir / "input.csv"
- tmp_results_csv_path = tmp_dir / "results.csv"
- tmp_debug_json_path = tmp_dir / "debug.json"
- with open(tmp_input_csv_path, "w") as csv_file:
- csv_writer = csv.writer(csv_file)
- csv_writer.writerow(["reference", "degraded"])
- for i in range(len(ref_sig)):
- tmp_ref_filename = tmp_dir / f"ref_{i}.wav"
- tmp_deg_filename = tmp_dir / f"deg_{i}.wav"
- torchaudio.save(
- tmp_ref_filename,
- torch.clamp(ref_sig[i], min=-0.99, max=0.99),
- sample_rate=target_sr,
- bits_per_sample=16,
- encoding="PCM_S"
- )
- torchaudio.save(
- tmp_deg_filename,
- torch.clamp(deg_sig[i], min=-0.99, max=0.99),
- sample_rate=target_sr,
- bits_per_sample=16,
- encoding="PCM_S"
- )
- csv_writer.writerow([str(tmp_ref_filename), str(tmp_deg_filename)])
- return tmp_dir, tmp_input_csv_path, tmp_results_csv_path, tmp_debug_json_path
- except Exception as e:
- logger.error("Exception occurred when preparing files for ViSQOL: %s", e)
- return tmp_dir, None, None, None
-
- def _flush_files(self, tmp_dir: tp.Union[Path, str]):
- # flush tmp files used to compute ViSQOL.
- shutil.rmtree(str(tmp_dir))
-
- def _collect_moslqo_score(self, results_csv_path: tp.Union[Path, str]) -> float:
- # collect results for each evaluated pair and return averaged moslqo score.
- with open(results_csv_path, "r") as csv_file:
- reader = csv.DictReader(csv_file)
- moslqo_scores = [float(row["moslqo"]) for row in reader]
- if len(moslqo_scores) > 0:
- return sum(moslqo_scores) / len(moslqo_scores)
- else:
- return 0.0
-
- def _collect_debug_data(self, debug_json_path: tp.Union[Path, str]) -> dict:
- # collect debug data for the visqol inference.
- with open(debug_json_path, "r") as f:
- data = json.load(f)
- return data
-
- @property
- def visqol_model(self):
- return f'{self.visqol_bin}/model/{self.model}'
-
- def _run_visqol(
- self,
- input_csv_path: tp.Union[Path, str],
- results_csv_path: tp.Union[Path, str],
- debug_csv_path: tp.Optional[tp.Union[Path, str]],
- ):
- input_csv_path = str(input_csv_path)
- results_csv_path = str(results_csv_path)
- debug_csv_path = str(debug_csv_path)
- cmd = [
- f'{self.visqol_bin}/bazel-bin/visqol',
- '--batch_input_csv', f'{input_csv_path}',
- '--results_csv', f'{results_csv_path}'
- ]
- if debug_csv_path is not None:
- cmd += ['--output_debug', f'{debug_csv_path}']
- if self.visqol_mode == "speech":
- cmd += ['--use_speech_mode']
- cmd += ['--similarity_to_quality_model', f'{self.visqol_model}']
- result = subprocess.run(cmd, capture_output=True)
- if result.returncode:
- logger.error("Error with visqol: \n %s \n %s", result.stdout.decode(), result.stderr.decode())
- raise RuntimeError("Error while executing visqol")
- result.check_returncode()
-
- def __call__(
- self,
- ref_sig: torch.Tensor,
- deg_sig: torch.Tensor,
- sr: int,
- pad_with_silence: bool = False,
- ):
- """Calculate the ViSQOL metric for a pair of audio signals at a given sample rate.
- Args:
- ref_sig (torch.Tensor): Reference signals as [B, C, T].
- deg_sig (torch.Tensor): Degraded signals as [B, C, T].
- sr (int): Sample rate of the two audio signals.
- pad_with_silence (bool): Whether to pad the file with silences as recommended
- in visqol guidelines (see: https://github.com/google/visqol#general-guidelines-for-input).
- Returns:
- float: The ViSQOL score or mean score for the batch.
- """
- logger.debug(f"Calculating visqol with mode={self.visqol_mode} on {len(ref_sig)} samples")
- tmp_dir, input_csv, results_csv, debug_json = self._prepare_files(
- ref_sig, deg_sig, sr, self.target_sr, pad_with_silence
- )
- try:
- if input_csv and results_csv:
- self._run_visqol(
- input_csv,
- results_csv,
- debug_json if self.debug else None,
- )
- mosqol = self._collect_moslqo_score(results_csv)
- return mosqol
- else:
- raise RuntimeError("Something unexpected happened when running VISQOL!")
- except Exception as e:
- logger.error("Exception occurred when running ViSQOL: %s", e)
- finally:
- self._flush_files(tmp_dir)
diff --git a/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile b/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile
deleted file mode 100644
index 31b5f817b41d7d95055d9aa5d5e4f973abee0b45..0000000000000000000000000000000000000000
--- a/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile
+++ /dev/null
@@ -1,26 +0,0 @@
-IMG:=`cat Name`
-MOUNT:=
-PORT_MAP:=
-DOTENV:=.env
-CONTAINER_NAME:=sample-chatgpt-on-wechat
-
-echo:
- echo $(IMG)
-
-run_d:
- docker rm $(CONTAINER_NAME) || echo
- docker run -dt --name $(CONTAINER_NAME) $(PORT_MAP) \
- --env-file=$(DOTENV) \
- $(MOUNT) $(IMG)
-
-run_i:
- docker rm $(CONTAINER_NAME) || echo
- docker run -it --name $(CONTAINER_NAME) $(PORT_MAP) \
- --env-file=$(DOTENV) \
- $(MOUNT) $(IMG)
-
-stop:
- docker stop $(CONTAINER_NAME)
-
-rm: stop
- docker rm $(CONTAINER_NAME)
diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py
deleted file mode 100644
index 84e51fd71fca78131a3be3d0d35d808c07bb3a95..0000000000000000000000000000000000000000
--- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import torch.nn as nn
-import math
-
-
-def conv3x3(in_planes, out_planes, stride=1):
- """3x3 convolution with padding"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
- expansion = 1
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(BasicBlock, self).__init__()
- self.conv1 = conv3x3(inplanes, planes, stride)
- self.bn1 = nn.BatchNorm2d(planes)
- self.relu = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(planes, planes)
- self.bn2 = nn.BatchNorm2d(planes)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(Bottleneck, self).__init__()
- self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
- self.bn1 = nn.BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
- self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * 4)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
- out = self.relu(out)
-
- out = self.conv3(out)
- out = self.bn3(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class ResNet50(nn.Module):
- def __init__(self,mode='rgb'):
- self.inplanes = 64
- super(ResNet50, self).__init__()
- if(mode=='rgb'):
- self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- elif(mode=='rgbd'):
- self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- elif(mode=="share"):
- self.conv1=nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- self.conv1_d=nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- else:
- raise
- self.bn1 = nn.BatchNorm2d(64)
- self.relu = nn.ReLU(inplace=True)
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
- self.layer1 = self._make_layer(Bottleneck, 64, 3)
- self.layer2 = self._make_layer(Bottleneck, 128, 4, stride=2)
- self.layer3_1 = self._make_layer(Bottleneck, 256, 6, stride=2)
- self.layer4_1 = self._make_layer(Bottleneck, 512, 3, stride=2)
-
- self.inplanes = 512
-
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
- m.weight.data.normal_(0, math.sqrt(2. / n))
- elif isinstance(m, nn.BatchNorm2d):
- m.weight.data.fill_(1)
- m.bias.data.zero_()
-
- def _make_layer(self, block, planes, blocks, stride=1):
- downsample = None
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- nn.Conv2d(self.inplanes, planes * block.expansion,
- kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(planes * block.expansion),
- )
-
- layers = []
- layers.append(block(self.inplanes, planes, stride, downsample))
- self.inplanes = planes * block.expansion
- for i in range(1, blocks):
- layers.append(block(self.inplanes, planes))
-
- return nn.Sequential(*layers)
-
- def forward(self, x):
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
- x = self.maxpool(x)
-
- x = self.layer1(x)
- x = self.layer2(x)
- x1 = self.layer3_1(x)
- x1 = self.layer4_1(x1)
-
- return x1, x1
diff --git a/spaces/Hallucinate/demo/ldm/models/autoencoder.py b/spaces/Hallucinate/demo/ldm/models/autoencoder.py
deleted file mode 100644
index 6a9c4f45498561953b8085981609b2a3298a5473..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/ldm/models/autoencoder.py
+++ /dev/null
@@ -1,443 +0,0 @@
-import torch
-import pytorch_lightning as pl
-import torch.nn.functional as F
-from contextlib import contextmanager
-
-from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
-
-from ldm.modules.diffusionmodules.model import Encoder, Decoder
-from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
-
-from ldm.util import instantiate_from_config
-
-
-class VQModel(pl.LightningModule):
- def __init__(self,
- ddconfig,
- lossconfig,
- n_embed,
- embed_dim,
- ckpt_path=None,
- ignore_keys=[],
- image_key="image",
- colorize_nlabels=None,
- monitor=None,
- batch_resize_range=None,
- scheduler_config=None,
- lr_g_factor=1.0,
- remap=None,
- sane_index_shape=False, # tell vector quantizer to return indices as bhw
- use_ema=False
- ):
- super().__init__()
- self.embed_dim = embed_dim
- self.n_embed = n_embed
- self.image_key = image_key
- self.encoder = Encoder(**ddconfig)
- self.decoder = Decoder(**ddconfig)
- self.loss = instantiate_from_config(lossconfig)
- self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
- remap=remap,
- sane_index_shape=sane_index_shape)
- self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
- self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
- if colorize_nlabels is not None:
- assert type(colorize_nlabels)==int
- self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
- if monitor is not None:
- self.monitor = monitor
- self.batch_resize_range = batch_resize_range
- if self.batch_resize_range is not None:
- print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
-
- self.use_ema = use_ema
- if self.use_ema:
- self.model_ema = LitEma(self)
- print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
- if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
- self.scheduler_config = scheduler_config
- self.lr_g_factor = lr_g_factor
-
- @contextmanager
- def ema_scope(self, context=None):
- if self.use_ema:
- self.model_ema.store(self.parameters())
- self.model_ema.copy_to(self)
- if context is not None:
- print(f"{context}: Switched to EMA weights")
- try:
- yield None
- finally:
- if self.use_ema:
- self.model_ema.restore(self.parameters())
- if context is not None:
- print(f"{context}: Restored training weights")
-
- def init_from_ckpt(self, path, ignore_keys=list()):
- sd = torch.load(path, map_location="cpu")["state_dict"]
- keys = list(sd.keys())
- for k in keys:
- for ik in ignore_keys:
- if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
- del sd[k]
- missing, unexpected = self.load_state_dict(sd, strict=False)
- print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
- if len(missing) > 0:
- print(f"Missing Keys: {missing}")
- print(f"Unexpected Keys: {unexpected}")
-
- def on_train_batch_end(self, *args, **kwargs):
- if self.use_ema:
- self.model_ema(self)
-
- def encode(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- quant, emb_loss, info = self.quantize(h)
- return quant, emb_loss, info
-
- def encode_to_prequant(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- return h
-
- def decode(self, quant):
- quant = self.post_quant_conv(quant)
- dec = self.decoder(quant)
- return dec
-
- def decode_code(self, code_b):
- quant_b = self.quantize.embed_code(code_b)
- dec = self.decode(quant_b)
- return dec
-
- def forward(self, input, return_pred_indices=False):
- quant, diff, (_,_,ind) = self.encode(input)
- dec = self.decode(quant)
- if return_pred_indices:
- return dec, diff, ind
- return dec, diff
-
- def get_input(self, batch, k):
- x = batch[k]
- if len(x.shape) == 3:
- x = x[..., None]
- x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
- if self.batch_resize_range is not None:
- lower_size = self.batch_resize_range[0]
- upper_size = self.batch_resize_range[1]
- if self.global_step <= 4:
- # do the first few batches with max size to avoid later oom
- new_resize = upper_size
- else:
- new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
- if new_resize != x.shape[2]:
- x = F.interpolate(x, size=new_resize, mode="bicubic")
- x = x.detach()
- return x
-
- def training_step(self, batch, batch_idx, optimizer_idx):
- # https://github.com/pytorch/pytorch/issues/37142
- # try not to fool the heuristics
- x = self.get_input(batch, self.image_key)
- xrec, qloss, ind = self(x, return_pred_indices=True)
-
- if optimizer_idx == 0:
- # autoencode
- aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train",
- predicted_indices=ind)
-
- self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
- return aeloss
-
- if optimizer_idx == 1:
- # discriminator
- discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
- self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
- return discloss
-
- def validation_step(self, batch, batch_idx):
- log_dict = self._validation_step(batch, batch_idx)
- with self.ema_scope():
- log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
- return log_dict
-
- def _validation_step(self, batch, batch_idx, suffix=""):
- x = self.get_input(batch, self.image_key)
- xrec, qloss, ind = self(x, return_pred_indices=True)
- aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
- self.global_step,
- last_layer=self.get_last_layer(),
- split="val"+suffix,
- predicted_indices=ind
- )
-
- discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
- self.global_step,
- last_layer=self.get_last_layer(),
- split="val"+suffix,
- predicted_indices=ind
- )
- rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
- self.log(f"val{suffix}/rec_loss", rec_loss,
- prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
- self.log(f"val{suffix}/aeloss", aeloss,
- prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
- if version.parse(pl.__version__) >= version.parse('1.4.0'):
- del log_dict_ae[f"val{suffix}/rec_loss"]
- self.log_dict(log_dict_ae)
- self.log_dict(log_dict_disc)
- return self.log_dict
-
- def configure_optimizers(self):
- lr_d = self.learning_rate
- lr_g = self.lr_g_factor*self.learning_rate
- print("lr_d", lr_d)
- print("lr_g", lr_g)
- opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
- list(self.decoder.parameters())+
- list(self.quantize.parameters())+
- list(self.quant_conv.parameters())+
- list(self.post_quant_conv.parameters()),
- lr=lr_g, betas=(0.5, 0.9))
- opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
- lr=lr_d, betas=(0.5, 0.9))
-
- if self.scheduler_config is not None:
- scheduler = instantiate_from_config(self.scheduler_config)
-
- print("Setting up LambdaLR scheduler...")
- scheduler = [
- {
- 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
- 'interval': 'step',
- 'frequency': 1
- },
- {
- 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
- 'interval': 'step',
- 'frequency': 1
- },
- ]
- return [opt_ae, opt_disc], scheduler
- return [opt_ae, opt_disc], []
-
- def get_last_layer(self):
- return self.decoder.conv_out.weight
-
- def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
- log = dict()
- x = self.get_input(batch, self.image_key)
- x = x.to(self.device)
- if only_inputs:
- log["inputs"] = x
- return log
- xrec, _ = self(x)
- if x.shape[1] > 3:
- # colorize with random projection
- assert xrec.shape[1] > 3
- x = self.to_rgb(x)
- xrec = self.to_rgb(xrec)
- log["inputs"] = x
- log["reconstructions"] = xrec
- if plot_ema:
- with self.ema_scope():
- xrec_ema, _ = self(x)
- if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
- log["reconstructions_ema"] = xrec_ema
- return log
-
- def to_rgb(self, x):
- assert self.image_key == "segmentation"
- if not hasattr(self, "colorize"):
- self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
- x = F.conv2d(x, weight=self.colorize)
- x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
- return x
-
-
-class VQModelInterface(VQModel):
- def __init__(self, embed_dim, *args, **kwargs):
- super().__init__(embed_dim=embed_dim, *args, **kwargs)
- self.embed_dim = embed_dim
-
- def encode(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- return h
-
- def decode(self, h, force_not_quantize=False):
- # also go through quantization layer
- if not force_not_quantize:
- quant, emb_loss, info = self.quantize(h)
- else:
- quant = h
- quant = self.post_quant_conv(quant)
- dec = self.decoder(quant)
- return dec
-
-
-class AutoencoderKL(pl.LightningModule):
- def __init__(self,
- ddconfig,
- lossconfig,
- embed_dim,
- ckpt_path=None,
- ignore_keys=[],
- image_key="image",
- colorize_nlabels=None,
- monitor=None,
- ):
- super().__init__()
- self.image_key = image_key
- self.encoder = Encoder(**ddconfig)
- self.decoder = Decoder(**ddconfig)
- self.loss = instantiate_from_config(lossconfig)
- assert ddconfig["double_z"]
- self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
- self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
- self.embed_dim = embed_dim
- if colorize_nlabels is not None:
- assert type(colorize_nlabels)==int
- self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
- if monitor is not None:
- self.monitor = monitor
- if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-
- def init_from_ckpt(self, path, ignore_keys=list()):
- sd = torch.load(path, map_location="cpu")["state_dict"]
- keys = list(sd.keys())
- for k in keys:
- for ik in ignore_keys:
- if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
- del sd[k]
- self.load_state_dict(sd, strict=False)
- print(f"Restored from {path}")
-
- def encode(self, x):
- h = self.encoder(x)
- moments = self.quant_conv(h)
- posterior = DiagonalGaussianDistribution(moments)
- return posterior
-
- def decode(self, z):
- z = self.post_quant_conv(z)
- dec = self.decoder(z)
- return dec
-
- def forward(self, input, sample_posterior=True):
- posterior = self.encode(input)
- if sample_posterior:
- z = posterior.sample()
- else:
- z = posterior.mode()
- dec = self.decode(z)
- return dec, posterior
-
- def get_input(self, batch, k):
- x = batch[k]
- if len(x.shape) == 3:
- x = x[..., None]
- x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
- return x
-
- def training_step(self, batch, batch_idx, optimizer_idx):
- inputs = self.get_input(batch, self.image_key)
- reconstructions, posterior = self(inputs)
-
- if optimizer_idx == 0:
- # train encoder+decoder+logvar
- aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
- self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
- self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
- return aeloss
-
- if optimizer_idx == 1:
- # train the discriminator
- discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
-
- self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
- self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
- return discloss
-
- def validation_step(self, batch, batch_idx):
- inputs = self.get_input(batch, self.image_key)
- reconstructions, posterior = self(inputs)
- aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
- last_layer=self.get_last_layer(), split="val")
-
- discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
- last_layer=self.get_last_layer(), split="val")
-
- self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
- self.log_dict(log_dict_ae)
- self.log_dict(log_dict_disc)
- return self.log_dict
-
- def configure_optimizers(self):
- lr = self.learning_rate
- opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
- list(self.decoder.parameters())+
- list(self.quant_conv.parameters())+
- list(self.post_quant_conv.parameters()),
- lr=lr, betas=(0.5, 0.9))
- opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
- lr=lr, betas=(0.5, 0.9))
- return [opt_ae, opt_disc], []
-
- def get_last_layer(self):
- return self.decoder.conv_out.weight
-
- @torch.no_grad()
- def log_images(self, batch, only_inputs=False, **kwargs):
- log = dict()
- x = self.get_input(batch, self.image_key)
- x = x.to(self.device)
- if not only_inputs:
- xrec, posterior = self(x)
- if x.shape[1] > 3:
- # colorize with random projection
- assert xrec.shape[1] > 3
- x = self.to_rgb(x)
- xrec = self.to_rgb(xrec)
- log["samples"] = self.decode(torch.randn_like(posterior.sample()))
- log["reconstructions"] = xrec
- log["inputs"] = x
- return log
-
- def to_rgb(self, x):
- assert self.image_key == "segmentation"
- if not hasattr(self, "colorize"):
- self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
- x = F.conv2d(x, weight=self.colorize)
- x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
- return x
-
-
-class IdentityFirstStage(torch.nn.Module):
- def __init__(self, *args, vq_interface=False, **kwargs):
- self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff
- super().__init__()
-
- def encode(self, x, *args, **kwargs):
- return x
-
- def decode(self, x, *args, **kwargs):
- return x
-
- def quantize(self, x, *args, **kwargs):
- if self.vq_interface:
- return x, None, [None, None, None]
- return x
-
- def forward(self, x, *args, **kwargs):
- return x
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py b/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py
deleted file mode 100644
index 81676226e57ca519273b98328a1afe6961c37ce3..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py
+++ /dev/null
@@ -1,403 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA Authors. All rights reserved.
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-# http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Auto Config class."""
-import importlib
-import re
-import warnings
-from collections import OrderedDict
-from typing import List, Union
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.file_utils import CONFIG_NAME
-from transformers.utils import logging
-from .dynamic import get_class_from_dynamic_module
-
-
-logger = logging.get_logger(__name__)
-
-CONFIG_MAPPING_NAMES = OrderedDict(
- [
- # Add configs here
- ("roformer", "RoFormerConfig"),
- ("longformer", "LongformerConfig"),
- ]
-)
-
-CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
- [
- # Add archive maps here
- ("roformer", "ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
- ("longformer", "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
- ]
-)
-
-MODEL_NAMES_MAPPING = OrderedDict(
- [
- # Add full (and cased) model names here
- ("roformer", "Roformer"),
- ("longformer", "Longformer"),
- ]
-)
-
-SPECIAL_MODEL_TYPE_TO_MODULE_NAME = OrderedDict([("openai-gpt", "openai")])
-
-
-def model_type_to_module_name(key):
- """Converts a config key to the corresponding module."""
- # Special treatment
- if key in SPECIAL_MODEL_TYPE_TO_MODULE_NAME:
- return SPECIAL_MODEL_TYPE_TO_MODULE_NAME[key]
-
- return key.replace("-", "_")
-
-
-def config_class_to_model_type(config):
- """Converts a config class name to the corresponding model type"""
- for key, cls in CONFIG_MAPPING_NAMES.items():
- if cls == config:
- return key
- return None
-
-
-class _LazyConfigMapping(OrderedDict):
- """
- A dictionary that lazily load its values when they are requested.
- """
-
- def __init__(self, mapping):
- self._mapping = mapping
- self._extra_content = {}
- self._modules = {}
-
- def __getitem__(self, key):
- if key in self._extra_content:
- return self._extra_content[key]
- if key not in self._mapping:
- raise KeyError(key)
- value = self._mapping[key]
- module_name = model_type_to_module_name(key)
- if module_name not in self._modules:
- self._modules[module_name] = importlib.import_module(f".{module_name}", "fengshen.models")
-
- return getattr(self._modules[module_name], value)
-
- def keys(self):
- return list(self._mapping.keys()) + list(self._extra_content.keys())
-
- def values(self):
- return [self[k] for k in self._mapping.keys()] + list(self._extra_content.values())
-
- def items(self):
- return [(k, self[k]) for k in self._mapping.keys()] + list(self._extra_content.items())
-
- def __iter__(self):
- return iter(list(self._mapping.keys()) + list(self._extra_content.keys()))
-
- def __contains__(self, item):
- return item in self._mapping or item in self._extra_content
-
- def register(self, key, value):
- """
- Register a new configuration in this mapping.
- """
- if key in self._mapping.keys():
- raise ValueError(f"'{key}' is already used by a Transformers config, pick another name.")
- self._extra_content[key] = value
-
-
-CONFIG_MAPPING = _LazyConfigMapping(CONFIG_MAPPING_NAMES)
-
-
-class _LazyLoadAllMappings(OrderedDict):
- """
- A mapping that will load all pairs of key values at the first access (either by indexing, requestions keys, values,
- etc.)
-
- Args:
- mapping: The mapping to load.
- """
-
- def __init__(self, mapping):
- self._mapping = mapping
- self._initialized = False
- self._data = {}
-
- def _initialize(self):
- if self._initialized:
- return
- warnings.warn(
- "ALL_PRETRAINED_CONFIG_ARCHIVE_MAP is deprecated and will be removed in v5 of Transformers. "
- "It does not contain all available model checkpoints, far from it. Checkout hf.co/models for that.",
- FutureWarning,
- )
-
- for model_type, map_name in self._mapping.items():
- module_name = model_type_to_module_name(model_type)
- module = importlib.import_module(f".{module_name}", "transformers.models")
- mapping = getattr(module, map_name)
- self._data.update(mapping)
-
- self._initialized = True
-
- def __getitem__(self, key):
- self._initialize()
- return self._data[key]
-
- def keys(self):
- self._initialize()
- return self._data.keys()
-
- def values(self):
- self._initialize()
- return self._data.values()
-
- def items(self):
- self._initialize()
- return self._data.keys()
-
- def __iter__(self):
- self._initialize()
- return iter(self._data)
-
- def __contains__(self, item):
- self._initialize()
- return item in self._data
-
-
-ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = _LazyLoadAllMappings(CONFIG_ARCHIVE_MAP_MAPPING_NAMES)
-
-
-def _get_class_name(model_class: Union[str, List[str]]):
- if isinstance(model_class, (list, tuple)):
- return " or ".join([f"[`{c}`]" for c in model_class if c is not None])
- return f"[`{model_class}`]"
-
-
-def _list_model_options(indent, config_to_class=None, use_model_types=True):
- if config_to_class is None and not use_model_types:
- raise ValueError("Using `use_model_types=False` requires a `config_to_class` dictionary.")
- if use_model_types:
- if config_to_class is None:
- model_type_to_name = {model_type: f"[`{config}`]" for model_type, config in CONFIG_MAPPING_NAMES.items()}
- else:
- model_type_to_name = {
- model_type: _get_class_name(model_class)
- for model_type, model_class in config_to_class.items()
- if model_type in MODEL_NAMES_MAPPING
- }
- lines = [
- f"{indent}- **{model_type}** -- {model_type_to_name[model_type]} ({MODEL_NAMES_MAPPING[model_type]} model)"
- for model_type in sorted(model_type_to_name.keys())
- ]
- else:
- config_to_name = {
- CONFIG_MAPPING_NAMES[config]: _get_class_name(clas)
- for config, clas in config_to_class.items()
- if config in CONFIG_MAPPING_NAMES
- }
- config_to_model_name = {
- config: MODEL_NAMES_MAPPING[model_type] for model_type, config in CONFIG_MAPPING_NAMES.items()
- }
- lines = [
- f"{indent}- [`{config_name}`] configuration class: {config_to_name[config_name]} ({config_to_model_name[config_name]} model)"
- for config_name in sorted(config_to_name.keys())
- ]
- return "\n".join(lines)
-
-
-def replace_list_option_in_docstrings(config_to_class=None, use_model_types=True):
- def docstring_decorator(fn):
- docstrings = fn.__doc__
- lines = docstrings.split("\n")
- i = 0
- while i < len(lines) and re.search(r"^(\s*)List options\s*$", lines[i]) is None:
- i += 1
- if i < len(lines):
- indent = re.search(r"^(\s*)List options\s*$", lines[i]).groups()[0]
- if use_model_types:
- indent = f"{indent} "
- lines[i] = _list_model_options(indent, config_to_class=config_to_class, use_model_types=use_model_types)
- docstrings = "\n".join(lines)
- else:
- raise ValueError(
- f"The function {fn} should have an empty 'List options' in its docstring as placeholder, current docstring is:\n{docstrings}"
- )
- fn.__doc__ = docstrings
- return fn
-
- return docstring_decorator
-
-
-class AutoConfig:
- r"""
- This is a generic configuration class that will be instantiated as one of the configuration classes of the library
- when created with the [`~AutoConfig.from_pretrained`] class method.
-
- This class cannot be instantiated directly using `__init__()` (throws an error).
- """
-
- def __init__(self):
- raise EnvironmentError(
- "AutoConfig is designed to be instantiated "
- "using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method."
- )
-
- @classmethod
- def for_model(cls, model_type: str, *args, **kwargs):
- if model_type in CONFIG_MAPPING:
- config_class = CONFIG_MAPPING[model_type]
- return config_class(*args, **kwargs)
- raise ValueError(
- f"Unrecognized model identifier: {model_type}. Should contain one of {', '.join(CONFIG_MAPPING.keys())}"
- )
-
- @classmethod
- @replace_list_option_in_docstrings()
- def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
- r"""
- Instantiate one of the configuration classes of the library from a pretrained model configuration.
-
- The configuration class to instantiate is selected based on the `model_type` property of the config object that
- is loaded, or when it's missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:
-
- List options
-
- Args:
- pretrained_model_name_or_path (`str` or `os.PathLike`):
- Can be either:
-
- - A string, the *model id* of a pretrained model configuration hosted inside a model repo on
- huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or
- namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
- - A path to a *directory* containing a configuration file saved using the
- [`~PretrainedConfig.save_pretrained`] method, or the [`~PreTrainedModel.save_pretrained`] method,
- e.g., `./my_model_directory/`.
- - A path or url to a saved configuration JSON *file*, e.g.,
- `./my_model_directory/configuration.json`.
- cache_dir (`str` or `os.PathLike`, *optional*):
- Path to a directory in which a downloaded pretrained model configuration should be cached if the
- standard cache should not be used.
- force_download (`bool`, *optional*, defaults to `False`):
- Whether or not to force the (re-)download the model weights and configuration files and override the
- cached versions if they exist.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether or not to delete incompletely received files. Will attempt to resume the download if such a
- file exists.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
- 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- revision(`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
- git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
- identifier allowed by git.
- return_unused_kwargs (`bool`, *optional*, defaults to `False`):
- If `False`, then this function returns just the final configuration object.
-
- If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a
- dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
- part of `kwargs` which has not been used to update `config` and is otherwise ignored.
- trust_remote_code (`bool`, *optional*, defaults to `False`):
- Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
- should only be set to `True` for repositories you trust and in which you have read the code, as it will
- execute code present on the Hub on your local machine.
- kwargs(additional keyword arguments, *optional*):
- The values in kwargs of any keys which are configuration attributes will be used to override the loaded
- values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
- by the `return_unused_kwargs` keyword parameter.
-
- Examples:
-
- ```python
- >>> from transformers import AutoConfig
-
- >>> # Download configuration from huggingface.co and cache.
- >>> config = AutoConfig.from_pretrained("bert-base-uncased")
-
- >>> # Download configuration from huggingface.co (user-uploaded) and cache.
- >>> config = AutoConfig.from_pretrained("dbmdz/bert-base-german-cased")
-
- >>> # If configuration file is in a directory (e.g., was saved using *save_pretrained('./test/saved_model/')*).
- >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/")
-
- >>> # Load a specific configuration file.
- >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/my_configuration.json")
-
- >>> # Change some config attributes when loading a pretrained config.
- >>> config = AutoConfig.from_pretrained("bert-base-uncased", output_attentions=True, foo=False)
- >>> config.output_attentions
- True
-
- >>> config, unused_kwargs = AutoConfig.from_pretrained(
- ... "bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
- ... )
- >>> config.output_attentions
- True
-
- >>> config.unused_kwargs
- {'foo': False}
- ```"""
- kwargs["_from_auto"] = True
- kwargs["name_or_path"] = pretrained_model_name_or_path
- trust_remote_code = kwargs.pop("trust_remote_code", False)
- config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
- if "auto_map" in config_dict and "AutoConfig" in config_dict["auto_map"]:
- if not trust_remote_code:
- raise ValueError(
- f"Loading {pretrained_model_name_or_path} requires you to execute the configuration file in that repo "
- "on your local machine. Make sure you have read the code there to avoid malicious use, then set "
- "the option `trust_remote_code=True` to remove this error."
- )
- if kwargs.get("revision", None) is None:
- logger.warn(
- "Explicitly passing a `revision` is encouraged when loading a configuration with custom code to "
- "ensure no malicious code has been contributed in a newer revision."
- )
- class_ref = config_dict["auto_map"]["AutoConfig"]
- module_file, class_name = class_ref.split(".")
- config_class = get_class_from_dynamic_module(
- pretrained_model_name_or_path, module_file + ".py", class_name, **kwargs
- )
- return config_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
- elif "model_type" in config_dict:
- config_class = CONFIG_MAPPING[config_dict["model_type"]]
- return config_class.from_dict(config_dict, **kwargs)
- else:
- # Fallback: use pattern matching on the string.
- for pattern, config_class in CONFIG_MAPPING.items():
- if pattern in str(pretrained_model_name_or_path):
- return config_class.from_dict(config_dict, **kwargs)
-
- raise ValueError(
- f"Unrecognized model in {pretrained_model_name_or_path}. "
- f"Should have a `model_type` key in its {CONFIG_NAME}, or contain one of the following strings "
- f"in its name: {', '.join(CONFIG_MAPPING.keys())}"
- )
-
- @staticmethod
- def register(model_type, config):
- """
- Register a new configuration for this class.
-
- Args:
- model_type (`str`): The model type like "bert" or "gpt".
- config ([`PretrainedConfig`]): The config to register.
- """
- if issubclass(config, PretrainedConfig) and config.model_type != model_type:
- raise ValueError(
- "The config you are passing has a `model_type` attribute that is not consistent with the model type "
- f"you passed (config has {config.model_type} and you passed {model_type}. Fix one of those so they "
- "match!"
- )
- CONFIG_MAPPING.register(model_type, config)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py
deleted file mode 100644
index b6fb6e751cdedb2af4b1f6c0950557e187cd9519..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .scst_loss import ScstRewardCriterion
-from .label_smoothed_cross_entropy import AjustLabelSmoothedCrossEntropyCriterion
\ No newline at end of file
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py
deleted file mode 100644
index 56d63e3e1b5a036e0adf32480e2b66f371738013..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from dataclasses import dataclass, field
-
-import torch
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-from omegaconf import II
-
-
-@dataclass
-class LabelSmoothedCrossEntropyCriterionConfig(FairseqDataclass):
- label_smoothing: float = field(
- default=0.0,
- metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"},
- )
- report_accuracy: bool = field(
- default=False,
- metadata={"help": "report accuracy metric"},
- )
- ignore_prefix_size: int = field(
- default=0,
- metadata={"help": "Ignore first N tokens"},
- )
- sentence_avg: bool = II("optimization.sentence_avg")
-
-
-def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=None, reduce=True):
- if target.dim() == lprobs.dim() - 1:
- target = target.unsqueeze(-1)
- nll_loss = -lprobs.gather(dim=-1, index=target)
- smooth_loss = -lprobs.sum(dim=-1, keepdim=True)
- if ignore_index is not None:
- pad_mask = target.eq(ignore_index)
- nll_loss.masked_fill_(pad_mask, 0.0)
- smooth_loss.masked_fill_(pad_mask, 0.0)
- else:
- nll_loss = nll_loss.squeeze(-1)
- smooth_loss = smooth_loss.squeeze(-1)
- if reduce:
- nll_loss = nll_loss.sum()
- smooth_loss = smooth_loss.sum()
- eps_i = epsilon / (lprobs.size(-1) - 1)
- loss = (1.0 - epsilon - eps_i) * nll_loss + eps_i * smooth_loss
- return loss, nll_loss
-
-
-@register_criterion(
- "label_smoothed_cross_entropy", dataclass=LabelSmoothedCrossEntropyCriterionConfig
-)
-class LabelSmoothedCrossEntropyCriterion(FairseqCriterion):
- def __init__(
- self,
- task,
- sentence_avg,
- label_smoothing,
- ignore_prefix_size=0,
- report_accuracy=False,
- ):
- super().__init__(task)
- self.sentence_avg = sentence_avg
- self.eps = label_smoothing
- self.ignore_prefix_size = ignore_prefix_size
- self.report_accuracy = report_accuracy
-
- def forward(self, model, sample, reduce=True):
- """Compute the loss for the given sample.
-
- Returns a tuple with three elements:
- 1) the loss
- 2) the sample size, which is used as the denominator for the gradient
- 3) logging outputs to display while training
- """
- net_output = model(**sample["net_input"])
- loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce)
- sample_size = (
- sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
- )
- logging_output = {
- "loss": loss.data,
- "nll_loss": nll_loss.data,
- "ntokens": sample["ntokens"],
- "nsentences": sample["target"].size(0),
- "sample_size": sample_size,
- }
- if self.report_accuracy:
- n_correct, total = self.compute_accuracy(model, net_output, sample)
- logging_output["n_correct"] = utils.item(n_correct.data)
- logging_output["total"] = utils.item(total.data)
- return loss, sample_size, logging_output
-
- def get_lprobs_and_target(self, model, net_output, sample):
- lprobs = model.get_normalized_probs(net_output, log_probs=True)
- target = model.get_targets(sample, net_output)
- if self.ignore_prefix_size > 0:
- if getattr(lprobs, "batch_first", False):
- lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
- target = target[:, self.ignore_prefix_size :].contiguous()
- else:
- lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous()
- target = target[self.ignore_prefix_size :, :].contiguous()
- return lprobs.view(-1, lprobs.size(-1)), target.view(-1)
-
- def compute_loss(self, model, net_output, sample, reduce=True):
- lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
- loss, nll_loss = label_smoothed_nll_loss(
- lprobs,
- target,
- self.eps,
- ignore_index=self.padding_idx,
- reduce=reduce,
- )
- return loss, nll_loss
-
- def compute_accuracy(self, model, net_output, sample):
- lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
- mask = target.ne(self.padding_idx)
- n_correct = torch.sum(
- lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))
- )
- total = torch.sum(mask)
- return n_correct, total
-
- @classmethod
- def reduce_metrics(cls, logging_outputs) -> None:
- """Aggregate logging outputs from data parallel training."""
- loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
- nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs)
- ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
- sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
-
- metrics.log_scalar(
- "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
- )
- metrics.log_scalar(
- "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3
- )
- metrics.log_derived(
- "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
- )
-
- total = utils.item(sum(log.get("total", 0) for log in logging_outputs))
- if total > 0:
- metrics.log_scalar("total", total)
- n_correct = utils.item(
- sum(log.get("n_correct", 0) for log in logging_outputs)
- )
- metrics.log_scalar("n_correct", n_correct)
- metrics.log_derived(
- "accuracy",
- lambda meters: round(
- meters["n_correct"].sum * 100.0 / meters["total"].sum, 3
- )
- if meters["total"].sum > 0
- else float("nan"),
- )
-
- @staticmethod
- def logging_outputs_can_be_summed() -> bool:
- """
- Whether the logging outputs returned by `forward` can be summed
- across workers prior to calling `reduce_metrics`. Setting this
- to True will improves distributed training speed.
- """
- return True
diff --git a/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md b/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md
deleted file mode 100644
index d4654cdd019b1496d4c953944c9aced8a0dcc67a..0000000000000000000000000000000000000000
--- a/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Sail-rvc-Ai Hoshino From Oshi No Ko RVC V2 300 Epoch
-emoji: 📈
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Hina4867/bingo/src/components/ui/select.tsx b/spaces/Hina4867/bingo/src/components/ui/select.tsx
deleted file mode 100644
index 77f12c2996f541b97663de4c9e20ab34d4ec2fac..0000000000000000000000000000000000000000
--- a/spaces/Hina4867/bingo/src/components/ui/select.tsx
+++ /dev/null
@@ -1,123 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SelectPrimitive from '@radix-ui/react-select'
-
-import { cn } from '@/lib/utils'
-import {
- IconArrowDown,
- IconCheck,
- IconChevronUpDown
-} from '@/components/ui/icons'
-
-const Select = SelectPrimitive.Root
-
-const SelectGroup = SelectPrimitive.Group
-
-const SelectValue = SelectPrimitive.Value
-
-const SelectTrigger = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-
- {children}
-
-
-
-))
-SelectTrigger.displayName = SelectPrimitive.Trigger.displayName
-
-const SelectContent = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, position = 'popper', ...props }, ref) => (
-
-
-
- {children}
-
-
-
-))
-SelectContent.displayName = SelectPrimitive.Content.displayName
-
-const SelectLabel = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-
-
-
-
-
- {children}
-
-))
-SelectItem.displayName = SelectPrimitive.Item.displayName
-
-const SelectSeparator = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- {o.delete(i),o.size===0&&n&&(n(),n=null)}}return{set:a,update:l,subscribe:r}}var g;const E=((g=globalThis.__sveltekit_1d38e1f)==null?void 0:g.base)??"";var k;const S=((k=globalThis.__sveltekit_1d38e1f)==null?void 0:k.assets)??E,w="1698134506027",y="sveltekit:snapshot",I="sveltekit:scroll",x="sveltekit:index",_={tap:1,hover:2,viewport:3,eager:4,off:-1};function O(e){let t=e.baseURI;if(!t){const n=e.getElementsByTagName("base");t=n.length?n[0].href:e.URL}return t}function U(){return{x:pageXOffset,y:pageYOffset}}function f(e,t){return e.getAttribute(`data-sveltekit-${t}`)}const b={..._,"":_.hover};function v(e){let t=e.assignedSlot??e.parentNode;return(t==null?void 0:t.nodeType)===11&&(t=t.host),t}function L(e,t){for(;e&&e!==t;){if(e.nodeName.toUpperCase()==="A"&&e.hasAttribute("href"))return e;e=v(e)}}function N(e,t){let n;try{n=new URL(e instanceof SVGAElement?e.href.baseVal:e.href,document.baseURI)}catch{}const o=e instanceof SVGAElement?e.target.baseVal:e.target,a=!n||!!o||R(n,t)||(e.getAttribute("rel")||"").split(/\s+/).includes("external"),l=(n==null?void 0:n.origin)===location.origin&&e.hasAttribute("download");return{url:n,external:a,target:o,download:l}}function P(e){let t=null,n=null,o=null,a=null,l=null,r=null,s=e;for(;s&&s!==document.documentElement;)o===null&&(o=f(s,"preload-code")),a===null&&(a=f(s,"preload-data")),t===null&&(t=f(s,"keepfocus")),n===null&&(n=f(s,"noscroll")),l===null&&(l=f(s,"reload")),r===null&&(r=f(s,"replacestate")),s=v(s);return{preload_code:b[o??"off"],preload_data:b[a??"off"],keep_focus:t==="off"?!1:t===""?!0:null,noscroll:n==="off"?!1:n===""?!0:null,reload:l==="off"?!1:l===""?!0:null,replace_state:r==="off"?!1:r===""?!0:null}}function h(e){const t=p(e);let n=!0;function o(){n=!0,t.update(r=>r)}function a(r){n=!1,t.set(r)}function l(r){let s;return t.subscribe(u=>{(s===void 0||n&&u!==s)&&r(s=u)})}return{notify:o,set:a,subscribe:l}}function A(){const{set:e,subscribe:t}=p(!1);let n;async function o(){clearTimeout(n);try{const a=await fetch(`${S}/_app/version.json`,{headers:{pragma:"no-cache","cache-control":"no-cache"}});if(!a.ok)return!1;const r=(await a.json()).version!==w;return r&&(e(!0),clearTimeout(n)),r}catch{return!1}}return{subscribe:t,check:o}}function R(e,t){return e.origin!==location.origin||!e.pathname.startsWith(t)}function V(e){e.client}const Y={url:h({}),page:h({}),navigating:p(null),updated:A()};export{x as I,_ as P,I as S,y as a,N as b,P as c,U as d,E as e,L as f,O as g,V as h,R as i,Y as s};
diff --git a/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py b/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py
deleted file mode 100644
index f8e2eb0f15699f1b458a8445d0c1dd6229a21f77..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-import os, sys
-import subprocess
-import re
-from subprocess import check_call, check_output
-
-WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None)
-
-if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip():
- print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."')
- sys.exit(-1)
-
-
-BLEU_REGEX = re.compile("^BLEU\\S* = (\\S+) ")
-def run_eval_bleu(cmd):
- output = check_output(cmd, shell=True, stderr=subprocess.STDOUT).decode("utf-8").strip()
- print(output)
- bleu = -1.0
- for line in output.strip().split('\n'):
- m = BLEU_REGEX.search(line)
- if m is not None:
- bleu = m.groups()[0]
- bleu = float(bleu)
- break
- return bleu
-
-def check_data_test_bleu(raw_folder, data_lang_pairs):
- not_matchings = []
- for sacrebleu_set, src_tgts in data_lang_pairs:
- for src_tgt in src_tgts:
- print(f'checking test bleus for: {src_tgt} at {sacrebleu_set}')
- src, tgt = src_tgt.split('-')
- ssrc, stgt = src[:2], tgt[:2]
- if os.path.exists(f'{raw_folder}/test.{tgt}-{src}.{src}'):
- # reversed direction may have different test set
- test_src = f'{raw_folder}/test.{tgt}-{src}.{src}'
- else:
- test_src = f'{raw_folder}/test.{src}-{tgt}.{src}'
- cmd1 = f'cat {test_src} | sacrebleu -t "{sacrebleu_set}" -l {stgt}-{ssrc}; [ $? -eq 0 ] || echo ""'
- test_tgt = f'{raw_folder}/test.{src}-{tgt}.{tgt}'
- cmd2 = f'cat {test_tgt} | sacrebleu -t "{sacrebleu_set}" -l {ssrc}-{stgt}; [ $? -eq 0 ] || echo ""'
- bleu1 = run_eval_bleu(cmd1)
- if bleu1 != 100.0:
- not_matchings.append(f'{sacrebleu_set}:{src_tgt} source side not matching: {test_src}')
- bleu2 = run_eval_bleu(cmd2)
- if bleu2 != 100.0:
- not_matchings.append(f'{sacrebleu_set}:{src_tgt} target side not matching: {test_tgt}')
- return not_matchings
-
-if __name__ == "__main__":
- to_data_path = f'{WORKDIR_ROOT}/iwsltv2'
- not_matching = check_data_test_bleu(
- f'{to_data_path}/raw',
- [
- ('iwslt17', ['en_XX-ar_AR', 'en_XX-ko_KR', 'ar_AR-en_XX', 'ko_KR-en_XX']),
- ('iwslt17', ['en_XX-it_IT', 'en_XX-nl_XX', 'it_IT-en_XX', 'nl_XX-en_XX']),
- ('iwslt17/tst2015', ['en_XX-vi_VN', "vi_VN-en_XX"]),
- ]
- )
- if len(not_matching) > 0:
- print('the following datasets do not have matching test datasets:\n\t', '\n\t'.join(not_matching))
-
diff --git a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py b/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py
deleted file mode 100644
index 88597d62a505715091f9ba62d38bf0a85a31b95a..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import math
-import os
-os.environ["LRU_CACHE_CAPACITY"] = "3"
-import random
-import torch
-import torch.utils.data
-import numpy as np
-import librosa
-from librosa.util import normalize
-from librosa.filters import mel as librosa_mel_fn
-from scipy.io.wavfile import read
-import soundfile as sf
-
-def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
- sampling_rate = None
- try:
- data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
- except Exception as ex:
- print(f"'{full_path}' failed to load.\nException:")
- print(ex)
- if return_empty_on_exception:
- return [], sampling_rate or target_sr or 32000
- else:
- raise Exception(ex)
-
- if len(data.shape) > 1:
- data = data[:, 0]
- assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
-
- if np.issubdtype(data.dtype, np.integer): # if audio data is type int
- max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
- else: # if audio data is type fp32
- max_mag = max(np.amax(data), -np.amin(data))
- max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
-
- data = torch.FloatTensor(data.astype(np.float32))/max_mag
-
- if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
- return [], sampling_rate or target_sr or 32000
- if target_sr is not None and sampling_rate != target_sr:
- data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
- sampling_rate = target_sr
-
- return data, sampling_rate
-
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
- return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
-
-def dynamic_range_decompression(x, C=1):
- return np.exp(x) / C
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-def dynamic_range_decompression_torch(x, C=1):
- return torch.exp(x) / C
-
-class STFT():
- def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
- self.target_sr = sr
-
- self.n_mels = n_mels
- self.n_fft = n_fft
- self.win_size = win_size
- self.hop_length = hop_length
- self.fmin = fmin
- self.fmax = fmax
- self.clip_val = clip_val
- self.mel_basis = {}
- self.hann_window = {}
-
- def get_mel(self, y, center=False):
- sampling_rate = self.target_sr
- n_mels = self.n_mels
- n_fft = self.n_fft
- win_size = self.win_size
- hop_length = self.hop_length
- fmin = self.fmin
- fmax = self.fmax
- clip_val = self.clip_val
-
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- if fmax not in self.mel_basis:
- mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
- self.mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
- self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_length)/2), int((n_fft-hop_length)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)],
- center=center, pad_mode='reflect', normalized=False, onesided=True)
- # print(111,spec)
- spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
- # print(222,spec)
- spec = torch.matmul(self.mel_basis[str(fmax)+'_'+str(y.device)], spec)
- # print(333,spec)
- spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
- # print(444,spec)
- return spec
-
- def __call__(self, audiopath):
- audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
- spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
- return spect
-
-stft = STFT()
diff --git a/spaces/JUNGU/emotion-ko-state/README.md b/spaces/JUNGU/emotion-ko-state/README.md
deleted file mode 100644
index 48eb244083a0ac6a51b0298cd043231b60b3fde8..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/emotion-ko-state/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Emotion Ko State
-emoji: 🦀
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py b/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py
deleted file mode 100644
index 27fcd62482d0cf44e02fa1c339195be58cb745b0..0000000000000000000000000000000000000000
--- a/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import pinecone
-from colorama import Fore, Style
-
-from autogpt.llm_utils import create_embedding_with_ada
-from autogpt.logs import logger
-from autogpt.memory.base import MemoryProviderSingleton
-
-
-class PineconeMemory(MemoryProviderSingleton):
- def __init__(self, cfg):
- pinecone_api_key = cfg.pinecone_api_key
- pinecone_region = cfg.pinecone_region
- pinecone.init(api_key=pinecone_api_key, environment=pinecone_region)
- dimension = 1536
- metric = "cosine"
- pod_type = "p1"
- table_name = "auto-gpt"
- # this assumes we don't start with memory.
- # for now this works.
- # we'll need a more complicated and robust system if we want to start with
- # memory.
- self.vec_num = 0
-
- try:
- pinecone.whoami()
- except Exception as e:
- logger.typewriter_log(
- "FAILED TO CONNECT TO PINECONE",
- Fore.RED,
- Style.BRIGHT + str(e) + Style.RESET_ALL,
- )
- logger.double_check(
- "Please ensure you have setup and configured Pinecone properly for use."
- + f"You can check out {Fore.CYAN + Style.BRIGHT}"
- "https://github.com/Torantulino/Auto-GPT#-pinecone-api-key-setup"
- f"{Style.RESET_ALL} to ensure you've set up everything correctly."
- )
- exit(1)
-
- if table_name not in pinecone.list_indexes():
- pinecone.create_index(
- table_name, dimension=dimension, metric=metric, pod_type=pod_type
- )
- self.index = pinecone.Index(table_name)
-
- def add(self, data):
- vector = create_embedding_with_ada(data)
- # no metadata here. We may wish to change that long term.
- self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})])
- _text = f"Inserting data into memory at index: {self.vec_num}:\n data: {data}"
- self.vec_num += 1
- return _text
-
- def get(self, data):
- return self.get_relevant(data, 1)
-
- def clear(self):
- self.index.delete(deleteAll=True)
- return "Obliviated"
-
- def get_relevant(self, data, num_relevant=5):
- """
- Returns all the data in the memory that is relevant to the given data.
- :param data: The data to compare to.
- :param num_relevant: The number of relevant data to return. Defaults to 5
- """
- query_embedding = create_embedding_with_ada(data)
- results = self.index.query(
- query_embedding, top_k=num_relevant, include_metadata=True
- )
- sorted_results = sorted(results.matches, key=lambda x: x.score)
- return [str(item["metadata"]["raw_text"]) for item in sorted_results]
-
- def get_stats(self):
- return self.index.describe_index_stats()
diff --git a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py b/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py
deleted file mode 100644
index 1cc3b96da1275f1d01afb4c999f9c404d09b247a..0000000000000000000000000000000000000000
--- a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import cv2
-import numpy as np
-from PIL import Image
-import pickle
-import tensorflow as tf
-import os
-
-class rabbitsLogReg:
- def __init__(self,url) -> None:
- self.image = url
-
- def predict_image(self):
- # Load the model
- load_extractor = tf.keras.models.load_model("././Model/Rabbit/resnetLogreg/resnet_EXTRACTOR.h5")
-
- modelpath = "././Model/Rabbit/resnetLogreg/dataSaved.pkl"
-
- with open(modelpath, 'rb') as file:
- saved_data = pickle.load(file)
- animal_breed = saved_data['class_name']
- model = saved_data['logreg_model']
-
- im = Image.open(self.image)
- img = im.convert("RGB")
- img= np.asarray(img)
- image_resized= cv2.resize(img, (224,224))
- features = load_extractor.predict(np.expand_dims(image_resized, axis=0))
-
- reshaped_features = features.reshape(features.shape[0],-1)
- predicted_class = model.predict(reshaped_features)
- pred_prob = model.predict_proba(reshaped_features)[:2]
- prediction_probability = pred_prob[0][predicted_class[0]]
- predicted_class
-
- output_class= animal_breed[predicted_class[0]]
-
- return [output_class, prediction_probability]
diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md b/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md
deleted file mode 100644
index 80af4fbbfba5d15e1cb6d1f4b67808ca76fa37d7..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md
+++ /dev/null
@@ -1,140 +0,0 @@
-
-
-川虎 Chat 🐯 Chuanhu Chat
-
-
-
-
-
-
-
Lightweight and User-friendly Web-UI for LLMs including ChatGPT/ChatGLM/LLaMA
-
- Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search
- LaTeX rendering / Table rendering / Code highlighting
- Auto dark mode / Adaptive web interface / WeChat-like theme
- Multi-parameters tuning / Multi-API-Key support / Multi-user support
- Compatible with GPT-4 / Local deployment for LLMs
-
- Video Tutorial
- ·
- 2.0 Introduction
- ·
- 3.0 Introduction & Tutorial
- ||
- Online trial
- ·
- One-Click deployment
-
-
-
-
-
-
-
-## Supported LLM Models
-
-**LLM models via API**:
-
-- [ChatGPT](https://chat.openai.com) ([GPT-4](https://openai.com/product/gpt-4))
-- [Google PaLM](https://developers.generativeai.google/products/palm)
-- [Inspur Yuan 1.0](https://air.inspur.com/home)
-- [MiniMax](https://api.minimax.chat/)
-- [XMChat](https://github.com/MILVLG/xmchat)
-
-**LLM models via local deployment**:
-
-- [ChatGLM](https://github.com/THUDM/ChatGLM-6B) ([ChatGLM2](https://github.com/THUDM/ChatGLM2-6B))
-- [LLaMA](https://github.com/facebookresearch/llama)
-- [StableLM](https://github.com/Stability-AI/StableLM)
-- [MOSS](https://github.com/OpenLMLab/MOSS)
-
-## Usage Tips
-
-- To better control the ChatGPT, use System Prompt.
-- To use a Prompt Template, select the Prompt Template Collection file first, and then choose certain prompt from the drop-down menu.
-- To try again if the response is unsatisfactory, use `🔄 Regenerate` button.
-- To start a new line in the input box, press Shift + Enter keys.
-- To quickly switch between input history, press ↑ and ↓ key in the input box.
-- To deploy the program onto a server, set `"server_name": "0.0.0.0", "server_port" ,` in `config.json`.
-- To get a public shared link, set `"share": true,` in `config.json`. Please be noted that the program must be running in order to be accessed via a public link.
-- To use it in Hugging Face Spaces: It is recommended to **Duplicate Space** and run the program in your own Space for a faster and more secure experience.
-
-## Quickstart
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-Then make a copy of `config_example.json`, rename it to `config.json`, and then fill in your API-Key and other settings in the file.
-
-```shell
-python ChuanhuChatbot.py
-```
-
-A browser window will open and you will be able to chat with ChatGPT.
-
-> **Note**
->
-> Please check our [wiki page](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程) for detailed instructions.
-
-## Troubleshooting
-
-When you encounter problems, you should try manually pulling the latest changes of this project first. The steps are as follows:
-
-1. Download the latest code archive by clicking on `Download ZIP` on the webpage, or
- ```shell
- git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
- ```
-2. Try installing the dependencies again (as this project may have introduced new dependencies)
- ```
- pip install -r requirements.txt
- ```
-
-Generally, you can solve most problems by following these steps.
-
-If the problem still exists, please refer to this page: [Frequently Asked Questions (FAQ)](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-This page lists almost all the possible problems and solutions. Please read it carefully.
-
-## More Information
-
-More information could be found in our [wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki):
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-
-
-
-
-## Sponsor
-
-🐯 If you find this project helpful, feel free to buy me a coke or a cup of coffee~
-
-
-
-
diff --git a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py b/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py
deleted file mode 100644
index 59a73cc04025762d6490fcd2945a747d963def32..0000000000000000000000000000000000000000
--- a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from os import environ
-
-
-def assert_in(file, files_to_check):
- if file not in files_to_check:
- raise AssertionError("{} does not exist in the list".format(str(file)))
- return True
-
-
-def assert_in_env(check_list: list):
- for item in check_list:
- assert_in(item, environ.keys())
- return True
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py
deleted file mode 100644
index beb6e65c61f8a16b4376494123f31178cdb88bde..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py
+++ /dev/null
@@ -1,13 +0,0 @@
-CUSTOM_STREAMLIT_CSS = """
-div[data-testid="stBlock"] button {
- width: 100% !important;
- margin-bottom: 20px !important;
- border-color: #bfbfbf !important;
-}
-section[data-testid="stSidebar"] div {
- max-width: 10rem;
-}
-pre code {
- white-space: pre-wrap;
-}
-"""
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py
deleted file mode 100644
index 34bcffd6c0975377a54ae1ce89002be1dae8151d..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import matplotlib
-matplotlib.use('Agg')
-import time
-
-class Timer():
- ''' Timer for recording training time distribution. '''
- def __init__(self):
- self.prev_t = time.time()
- self.clear()
-
- def set(self):
- self.prev_t = time.time()
-
- def cnt(self, mode):
- self.time_table[mode] += time.time()-self.prev_t
- self.set()
- if mode == 'bw':
- self.click += 1
-
- def show(self):
- total_time = sum(self.time_table.values())
- self.time_table['avg'] = total_time/self.click
- self.time_table['rd'] = 100*self.time_table['rd']/total_time
- self.time_table['fw'] = 100*self.time_table['fw']/total_time
- self.time_table['bw'] = 100*self.time_table['bw']/total_time
- msg = '{avg:.3f} sec/step (rd {rd:.1f}% | fw {fw:.1f}% | bw {bw:.1f}%)'.format(
- **self.time_table)
- self.clear()
- return msg
-
- def clear(self):
- self.time_table = {'rd': 0, 'fw': 0, 'bw': 0}
- self.click = 0
-
-# Reference : https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/e2e_asr.py#L168
-
-def human_format(num):
- magnitude = 0
- while num >= 1000:
- magnitude += 1
- num /= 1000.0
- # add more suffixes if you need them
- return '{:3.1f}{}'.format(num, [' ', 'K', 'M', 'G', 'T', 'P'][magnitude])
-
-
-# provide easy access of attribute from dict, such abc.key
-class AttrDict(dict):
- def __init__(self, *args, **kwargs):
- super(AttrDict, self).__init__(*args, **kwargs)
- self.__dict__ = self
diff --git a/spaces/KevinQHLin/UniVTG/main/train_mr.py b/spaces/KevinQHLin/UniVTG/main/train_mr.py
deleted file mode 100644
index 1a10d029f81f86733d6dab71a3aee575917b092b..0000000000000000000000000000000000000000
--- a/spaces/KevinQHLin/UniVTG/main/train_mr.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import os
-import pdb
-import sys
-import time
-import json
-import pprint
-import random
-import numpy as np
-from tqdm import tqdm, trange
-from collections import defaultdict
-
-import torch
-import torch.nn as nn
-import torch.backends.cudnn as cudnn
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-
-sys.path.append('/data/home/qinghonglin/univtg')
-from main.config import BaseOptions, setup_model
-from main.dataset import \
- DatasetMR, start_end_collate_mr, prepare_batch_inputs_mr
-from main.inference_mr import eval_epoch, start_inference
-from utils.basic_utils import set_seed, AverageMeter, dict_to_markdown
-from utils.model_utils import count_parameters
-
-import logging
-logger = logging.getLogger(__name__)
-logging.basicConfig(format="%(asctime)s.%(msecs)03d:%(levelname)s:%(name)s - %(message)s",
- datefmt="%Y-%m-%d %H:%M:%S",
- level=logging.INFO)
-
-def train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer):
- logger.info(f"[Epoch {epoch_i+1}]")
- model.train()
- criterion.train()
-
- # init meters
- time_meters = defaultdict(AverageMeter)
- loss_meters = defaultdict(AverageMeter)
-
- num_training_examples = len(train_loader)
- timer_dataloading = time.time()
- for batch_idx, batch in tqdm(enumerate(train_loader),
- desc="Training Iteration",
- total=num_training_examples):
- time_meters["dataloading_time"].update(time.time() - timer_dataloading)
-
- timer_start = time.time()
- model_inputs, targets = prepare_batch_inputs_mr(batch[1], opt.device, non_blocking=opt.pin_memory)
- time_meters["prepare_inputs_time"].update(time.time() - timer_start)
-
- timer_start = time.time()
-
- # try:
- outputs = model(**model_inputs)
- loss_dict = criterion(outputs, targets)
- weight_dict = criterion.weight_dict
- losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
- time_meters["model_forward_time"].update(time.time() - timer_start)
-
- timer_start = time.time()
- optimizer.zero_grad()
- losses.backward()
-
- if opt.grad_clip > 0:
- nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
- optimizer.step()
- time_meters["model_backward_time"].update(time.time() - timer_start)
-
- loss_dict["loss_overall"] = float(losses) # for logging only
- for k, v in loss_dict.items():
- loss_meters[k].update(float(v) * weight_dict[k] if k in weight_dict else float(v))
-
- timer_dataloading = time.time()
-
- # print/add logs
- tb_writer.add_scalar("Train/lr", float(optimizer.param_groups[0]["lr"]), epoch_i+1)
- for k, v in loss_meters.items():
- tb_writer.add_scalar("Train/{}".format(k), v.avg, epoch_i+1)
-
- to_write = opt.train_log_txt_formatter.format(
- time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
- epoch=epoch_i+1,
- loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in loss_meters.items()]))
- with open(opt.train_log_filepath, "a") as f:
- f.write(to_write)
-
- logger.info("Epoch time stats:")
- for name, meter in time_meters.items():
- d = {k: f"{getattr(meter, k):.4f}" for k in ["max", "min", "avg"]}
- logger.info(f"{name} ==> {d}")
-
-
-def train(model, criterion, optimizer, lr_scheduler, train_dataset, val_dataset, opt):
- tb_writer = SummaryWriter(opt.tensorboard_log_dir)
- tb_writer.add_text("hyperparameters", dict_to_markdown(vars(opt), max_str_len=None))
- opt.train_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str}\n"
- opt.eval_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str} [Metrics] {eval_metrics_str}\n"
-
- train_loader = DataLoader(
- train_dataset,
- collate_fn=start_end_collate_mr,
- batch_size=opt.bsz,
- num_workers=opt.num_workers,
- shuffle=True,
- pin_memory=opt.pin_memory
- )
-
- prev_best_score = 0.
- es_cnt = 0
- if opt.start_epoch is None:
- start_epoch = -1 if opt.eval_init else 0
- else:
- start_epoch = opt.start_epoch
- save_submission_filename = "latest_{}_{}_preds.jsonl".format(opt.dset_name, opt.eval_split_name)
- for epoch_i in trange(start_epoch, opt.n_epoch, desc="Epoch"):
- if epoch_i > -1:
- train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer)
- lr_scheduler.step()
- eval_epoch_interval = opt.eval_epoch
- if opt.eval_path is not None and (epoch_i + 1) % eval_epoch_interval == 0:
- with torch.no_grad():
- metrics_no_nms, metrics_nms, eval_loss_meters, latest_file_paths = \
- eval_epoch(model, val_dataset, opt, save_submission_filename, epoch_i, criterion, tb_writer)
-
- # log
- to_write = opt.eval_log_txt_formatter.format(
- time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
- epoch=epoch_i,
- loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in eval_loss_meters.items()]),
- eval_metrics_str=json.dumps(metrics_no_nms))
-
- with open(opt.eval_log_filepath, "a") as f:
- f.write(to_write)
- logger.info("metrics_no_nms {}".format(pprint.pformat(metrics_no_nms["brief"], indent=4)))
- if metrics_nms is not None:
- logger.info("metrics_nms {}".format(pprint.pformat(metrics_nms["brief"], indent=4)))
-
- metrics = metrics_nms if metrics_nms is not None else metrics_no_nms
- for k, v in metrics["brief"].items():
- tb_writer.add_scalar(f"Eval/{k}", float(v), epoch_i+1)
-
- # stop_score = metrics["brief"]["MR-full-mAP"]
- # pdb.set_trace()
- stop_score = metrics["brief"][opt.main_metric]
- if stop_score > prev_best_score:
- es_cnt = 0
- prev_best_score = stop_score
-
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "lr_scheduler": lr_scheduler.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"))
-
- best_file_paths = [e.replace("latest", "best") for e in latest_file_paths]
- for src, tgt in zip(latest_file_paths, best_file_paths):
- os.renames(src, tgt)
- logger.info("The checkpoint file has been updated.")
- else:
- es_cnt += 1
- if opt.max_es_cnt != -1 and es_cnt > opt.max_es_cnt: # early stop
- with open(opt.train_log_filepath, "a") as f:
- f.write(f"Early Stop at epoch {epoch_i}")
- logger.info(f"\n>>>>> Early stop at epoch {epoch_i} {prev_best_score}\n")
- break
-
- # save ckpt
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "lr_scheduler": lr_scheduler.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_latest.ckpt"))
-
- if (epoch_i + 1) % opt.save_interval == 0 or (epoch_i + 1) % opt.lr_drop == 0: # additional copies
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", f"_e{epoch_i:04d}.ckpt"))
-
- if opt.debug:
- break
-
- tb_writer.close()
-
-
-def start_training():
- logger.info("Setup config, data and model...")
- opt = BaseOptions().parse()
- set_seed(opt.seed)
- if opt.debug: # keep the model run deterministically
- # 'cudnn.benchmark = True' enabled auto finding the best algorithm for a specific input/net config.
- # Enable this only when input size is fixed.
- cudnn.benchmark = False
- cudnn.deterministic = True
-
- dataset_config = dict(
- dset_name=opt.dset_name,
- data_path=opt.train_path,
- v_feat_dirs=opt.v_feat_dirs,
- q_feat_dir=opt.t_feat_dir,
- v_feat_dim=opt.v_feat_dim,
- q_feat_dim=opt.t_feat_dim,
- q_feat_type="last_hidden_state",
- max_q_l=opt.max_q_l,
- max_v_l=opt.max_v_l,
- ctx_mode=opt.ctx_mode,
- data_ratio=opt.data_ratio,
- normalize_v=not opt.no_norm_vfeat,
- normalize_t=not opt.no_norm_tfeat,
- clip_len=opt.clip_length,
- max_windows=opt.max_windows,
- span_loss_type=opt.span_loss_type,
- txt_drop_ratio=opt.txt_drop_ratio,
- use_cache=opt.use_cache,
- add_easy_negative=opt.add_easy_negative,
- easy_negative_only=opt.easy_negative_only
- )
-
- dataset_config["data_path"] = opt.train_path
- train_dataset = DatasetMR(**dataset_config)
-
- if opt.eval_path is not None:
- dataset_config["data_path"] = opt.eval_path
- dataset_config["txt_drop_ratio"] = 0
- dataset_config["q_feat_dir"] = opt.t_feat_dir.replace("txt_clip_asr", "txt_clip").replace("txt_clip_cap", "txt_clip") # for pretraining
- # dataset_config["load_labels"] = False # uncomment to calculate eval loss
- eval_dataset = DatasetMR(**dataset_config)
- else:
- eval_dataset = None
-
- if opt.lr_warmup > 0:
- # total_steps = opt.n_epoch * len(train_dataset) // opt.bsz
- total_steps = opt.n_epoch
- warmup_steps = opt.lr_warmup if opt.lr_warmup > 1 else int(opt.lr_warmup * total_steps)
- opt.lr_warmup = [warmup_steps, total_steps]
- model, criterion, optimizer, lr_scheduler = setup_model(opt)
- logger.info(f"Model {model}")
- count_parameters(model)
- logger.info("Start Training...")
- train(model, criterion, optimizer, lr_scheduler, train_dataset, eval_dataset, opt)
- return opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"), opt.eval_split_name, opt.eval_path, opt.debug
-
-
-if __name__ == '__main__':
- best_ckpt_path, eval_split_name, eval_path, debug = start_training()
- if not debug:
- input_args = ["--resume", best_ckpt_path,
- "--eval_split_name", eval_split_name,
- "--eval_path", eval_path]
-
- import sys
- sys.argv[1:] = input_args
- logger.info("\n\n\nFINISHED TRAINING!!!")
- logger.info("Evaluating model at {}".format(best_ckpt_path))
- logger.info("Input args {}".format(sys.argv[1:]))
- start_inference()
\ No newline at end of file
diff --git a/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md b/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md
deleted file mode 100644
index 1f55e11e80f6fc5ebbf42dade0266e3d4ee06ce4..0000000000000000000000000000000000000000
--- a/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
-# Transliteration Models for Indian languages
-These are models for transliteration involving Indian languages.
-The models are essentially Statistical Machine Translation systems trained using Moses over a
-character-level parallel corpora of transliterations. Hence, you will need Moses to use these transliteration models.
-The transliteration corpus has itself been mined in an unsupervised fashion from a translation corpus.
-
-Currently we have trained transliteration models for five language pairs: bn-hi, ta-hi, te-hi, en-hi and mr-hi
-
-Support for transliteration has been introduced in Moses from version 2.1
-So please ensure that you have minimum 2.1 version setup for Moses
-
-Commands to run the transliteration module using moses
-
-$moseshome/mosesdecoder/scripts/Transliteration/post-decoding-transliteration.pl \
---moses-src-dir $moseshome/mosesdecoder --external-bin-dir $moseshome/tools \
---transliteration-model-dir {path to transliteration model folder} --oov-file {path to file containing oov words, oovs are space separated with each line containing all oovs for the input line}\
- --input-file {input file to transliterated} --output-file {output file location} \
- --input-extension {input language code for eg. en} --output-extension {output language code for eg. hi} --language-model {path to language model} \
- --decoder $moseshome/mosesdecoder/bin/moses
-
-A sample execution of the model will be as follows:
-
-export moseshome={path to moses installation}
-$moseshome/mosesdecoder/scripts/Transliteration/post-decoding-transliteration.pl \
---moses-src-dir $moseshome/mosesdecoder --external-bin-dir $moseshome/tools \
---transliteration-model-dir /home/ratish/project/nlp_resources/indic_nlp_resources/transliterate/en-hi \
---oov-file /home/ratish/project/translit/input.oov \
- --input-file /home/ratish/project/translit/input.en \
- --output-file /home/ratish/project/translit/output.hi \
- --input-extension en --output-extension hi --language-model /home/ratish/project/translit/lm/nc.binlm.1 \
- --decoder $moseshome/mosesdecoder/bin/moses
-
-So far, we have seen the use of transliteration in a post-editing task for machine translation task.
-In case, the models are needed for purely transliteration purpose, the input file and OOV file are the same.
-Sample input file:
-New Delhi is capital of India
-India is worlds seventh largest nation in the World
-
-OOV file
-New Delhi is capital of India
-India is worlds seventh largest nation in the World
-
-On running the transliteration module, the output is:
-न्यू डेल्ही इस कैपिटल आफ इंडिया
-इंडिया इस वर्ल्ड सेवंथ लारगेस्ट नेशन इन थे वर्ल्ड
diff --git a/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py b/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py
deleted file mode 100644
index 980440eb3434e48b7cad90f0577c838a05d787b4..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Sequence
-
-from torch.utils.data import BatchSampler, Sampler
-
-from mmdet.registry import DATA_SAMPLERS
-
-
-# TODO: maybe replace with a data_loader wrapper
-@DATA_SAMPLERS.register_module()
-class AspectRatioBatchSampler(BatchSampler):
- """A sampler wrapper for grouping images with similar aspect ratio (< 1 or.
-
- >= 1) into a same batch.
-
- Args:
- sampler (Sampler): Base sampler.
- batch_size (int): Size of mini-batch.
- drop_last (bool): If ``True``, the sampler will drop the last batch if
- its size would be less than ``batch_size``.
- """
-
- def __init__(self,
- sampler: Sampler,
- batch_size: int,
- drop_last: bool = False) -> None:
- if not isinstance(sampler, Sampler):
- raise TypeError('sampler should be an instance of ``Sampler``, '
- f'but got {sampler}')
- if not isinstance(batch_size, int) or batch_size <= 0:
- raise ValueError('batch_size should be a positive integer value, '
- f'but got batch_size={batch_size}')
- self.sampler = sampler
- self.batch_size = batch_size
- self.drop_last = drop_last
- # two groups for w < h and w >= h
- self._aspect_ratio_buckets = [[] for _ in range(2)]
-
- def __iter__(self) -> Sequence[int]:
- for idx in self.sampler:
- data_info = self.sampler.dataset.get_data_info(idx)
- width, height = data_info['width'], data_info['height']
- bucket_id = 0 if width < height else 1
- bucket = self._aspect_ratio_buckets[bucket_id]
- bucket.append(idx)
- # yield a batch of indices in the same aspect ratio group
- if len(bucket) == self.batch_size:
- yield bucket[:]
- del bucket[:]
-
- # yield the rest data and reset the bucket
- left_data = self._aspect_ratio_buckets[0] + self._aspect_ratio_buckets[
- 1]
- self._aspect_ratio_buckets = [[] for _ in range(2)]
- while len(left_data) > 0:
- if len(left_data) <= self.batch_size:
- if not self.drop_last:
- yield left_data[:]
- left_data = []
- else:
- yield left_data[:self.batch_size]
- left_data = left_data[self.batch_size:]
-
- def __len__(self) -> int:
- if self.drop_last:
- return len(self.sampler) // self.batch_size
- else:
- return (len(self.sampler) + self.batch_size - 1) // self.batch_size
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py
deleted file mode 100644
index 430b06d085d94760d56a7ea083eaf23bd32b1f53..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py
+++ /dev/null
@@ -1,722 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, Scale
-from mmcv.ops import DeformConv2d
-from torch import Tensor
-
-from mmdet.registry import MODELS, TASK_UTILS
-from mmdet.structures.bbox import bbox_overlaps
-from mmdet.utils import (ConfigType, InstanceList, MultiConfig,
- OptInstanceList, RangeType, reduce_mean)
-from ..task_modules.prior_generators import MlvlPointGenerator
-from ..task_modules.samplers import PseudoSampler
-from ..utils import multi_apply
-from .atss_head import ATSSHead
-from .fcos_head import FCOSHead
-
-INF = 1e8
-
-
-@MODELS.register_module()
-class VFNetHead(ATSSHead, FCOSHead):
- """Head of `VarifocalNet (VFNet): An IoU-aware Dense Object
- Detector.`_.
-
- The VFNet predicts IoU-aware classification scores which mix the
- object presence confidence and object localization accuracy as the
- detection score. It is built on the FCOS architecture and uses ATSS
- for defining positive/negative training examples. The VFNet is trained
- with Varifocal Loss and empolys star-shaped deformable convolution to
- extract features for a bbox.
-
- Args:
- num_classes (int): Number of categories excluding the background
- category.
- in_channels (int): Number of channels in the input feature map.
- regress_ranges (Sequence[Tuple[int, int]]): Regress range of multiple
- level points.
- center_sampling (bool): If true, use center sampling. Defaults to False.
- center_sample_radius (float): Radius of center sampling. Defaults to 1.5.
- sync_num_pos (bool): If true, synchronize the number of positive
- examples across GPUs. Defaults to True
- gradient_mul (float): The multiplier to gradients from bbox refinement
- and recognition. Defaults to 0.1.
- bbox_norm_type (str): The bbox normalization type, 'reg_denom' or
- 'stride'. Defaults to reg_denom
- loss_cls_fl (:obj:`ConfigDict` or dict): Config of focal loss.
- use_vfl (bool): If true, use varifocal loss for training.
- Defaults to True.
- loss_cls (:obj:`ConfigDict` or dict): Config of varifocal loss.
- loss_bbox (:obj:`ConfigDict` or dict): Config of localization loss,
- GIoU Loss.
- loss_bbox (:obj:`ConfigDict` or dict): Config of localization
- refinement loss, GIoU Loss.
- norm_cfg (:obj:`ConfigDict` or dict): dictionary to construct and
- config norm layer. Defaults to norm_cfg=dict(type='GN',
- num_groups=32, requires_grad=True).
- use_atss (bool): If true, use ATSS to define positive/negative
- examples. Defaults to True.
- anchor_generator (:obj:`ConfigDict` or dict): Config of anchor
- generator for ATSS.
- init_cfg (:obj:`ConfigDict` or dict or list[dict] or
- list[:obj:`ConfigDict`]): Initialization config dict.
-
- Example:
- >>> self = VFNetHead(11, 7)
- >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
- >>> cls_score, bbox_pred, bbox_pred_refine= self.forward(feats)
- >>> assert len(cls_score) == len(self.scales)
- """ # noqa: E501
-
- def __init__(self,
- num_classes: int,
- in_channels: int,
- regress_ranges: RangeType = ((-1, 64), (64, 128), (128, 256),
- (256, 512), (512, INF)),
- center_sampling: bool = False,
- center_sample_radius: float = 1.5,
- sync_num_pos: bool = True,
- gradient_mul: float = 0.1,
- bbox_norm_type: str = 'reg_denom',
- loss_cls_fl: ConfigType = dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- use_vfl: bool = True,
- loss_cls: ConfigType = dict(
- type='VarifocalLoss',
- use_sigmoid=True,
- alpha=0.75,
- gamma=2.0,
- iou_weighted=True,
- loss_weight=1.0),
- loss_bbox: ConfigType = dict(
- type='GIoULoss', loss_weight=1.5),
- loss_bbox_refine: ConfigType = dict(
- type='GIoULoss', loss_weight=2.0),
- norm_cfg: ConfigType = dict(
- type='GN', num_groups=32, requires_grad=True),
- use_atss: bool = True,
- reg_decoded_bbox: bool = True,
- anchor_generator: ConfigType = dict(
- type='AnchorGenerator',
- ratios=[1.0],
- octave_base_scale=8,
- scales_per_octave=1,
- center_offset=0.0,
- strides=[8, 16, 32, 64, 128]),
- init_cfg: MultiConfig = dict(
- type='Normal',
- layer='Conv2d',
- std=0.01,
- override=dict(
- type='Normal',
- name='vfnet_cls',
- std=0.01,
- bias_prob=0.01)),
- **kwargs) -> None:
- # dcn base offsets, adapted from reppoints_head.py
- self.num_dconv_points = 9
- self.dcn_kernel = int(np.sqrt(self.num_dconv_points))
- self.dcn_pad = int((self.dcn_kernel - 1) / 2)
- dcn_base = np.arange(-self.dcn_pad,
- self.dcn_pad + 1).astype(np.float64)
- dcn_base_y = np.repeat(dcn_base, self.dcn_kernel)
- dcn_base_x = np.tile(dcn_base, self.dcn_kernel)
- dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape(
- (-1))
- self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1)
-
- super(FCOSHead, self).__init__(
- num_classes=num_classes,
- in_channels=in_channels,
- norm_cfg=norm_cfg,
- init_cfg=init_cfg,
- **kwargs)
- self.regress_ranges = regress_ranges
- self.reg_denoms = [
- regress_range[-1] for regress_range in regress_ranges
- ]
- self.reg_denoms[-1] = self.reg_denoms[-2] * 2
- self.center_sampling = center_sampling
- self.center_sample_radius = center_sample_radius
- self.sync_num_pos = sync_num_pos
- self.bbox_norm_type = bbox_norm_type
- self.gradient_mul = gradient_mul
- self.use_vfl = use_vfl
- if self.use_vfl:
- self.loss_cls = MODELS.build(loss_cls)
- else:
- self.loss_cls = MODELS.build(loss_cls_fl)
- self.loss_bbox = MODELS.build(loss_bbox)
- self.loss_bbox_refine = MODELS.build(loss_bbox_refine)
-
- # for getting ATSS targets
- self.use_atss = use_atss
- self.reg_decoded_bbox = reg_decoded_bbox
- self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
-
- self.anchor_center_offset = anchor_generator['center_offset']
-
- self.num_base_priors = self.prior_generator.num_base_priors[0]
-
- if self.train_cfg:
- self.assigner = TASK_UTILS.build(self.train_cfg['assigner'])
- if self.train_cfg.get('sampler', None) is not None:
- self.sampler = TASK_UTILS.build(
- self.train_cfg['sampler'], default_args=dict(context=self))
- else:
- self.sampler = PseudoSampler()
- # only be used in `get_atss_targets` when `use_atss` is True
- self.atss_prior_generator = TASK_UTILS.build(anchor_generator)
-
- self.fcos_prior_generator = MlvlPointGenerator(
- anchor_generator['strides'],
- self.anchor_center_offset if self.use_atss else 0.5)
-
- # In order to reuse the `get_bboxes` in `BaseDenseHead.
- # Only be used in testing phase.
- self.prior_generator = self.fcos_prior_generator
-
- def _init_layers(self) -> None:
- """Initialize layers of the head."""
- super(FCOSHead, self)._init_cls_convs()
- super(FCOSHead, self)._init_reg_convs()
- self.relu = nn.ReLU()
- self.vfnet_reg_conv = ConvModule(
- self.feat_channels,
- self.feat_channels,
- 3,
- stride=1,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- bias=self.conv_bias)
- self.vfnet_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
- self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
- self.vfnet_reg_refine_dconv = DeformConv2d(
- self.feat_channels,
- self.feat_channels,
- self.dcn_kernel,
- 1,
- padding=self.dcn_pad)
- self.vfnet_reg_refine = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
- self.scales_refine = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
- self.vfnet_cls_dconv = DeformConv2d(
- self.feat_channels,
- self.feat_channels,
- self.dcn_kernel,
- 1,
- padding=self.dcn_pad)
- self.vfnet_cls = nn.Conv2d(
- self.feat_channels, self.cls_out_channels, 3, padding=1)
-
- def forward(self, x: Tuple[Tensor]) -> Tuple[List[Tensor]]:
- """Forward features from the upstream network.
-
- Args:
- x (tuple[Tensor]): Features from the upstream network, each is
- a 4D-tensor.
-
- Returns:
- tuple:
-
- - cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level, each is a 4D-tensor, the channel number is
- num_points * num_classes.
- - bbox_preds (list[Tensor]): Box offsets for each
- scale level, each is a 4D-tensor, the channel number is
- num_points * 4.
- - bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level, each is a 4D-tensor, the channel
- number is num_points * 4.
- """
- return multi_apply(self.forward_single, x, self.scales,
- self.scales_refine, self.strides, self.reg_denoms)
-
- def forward_single(self, x: Tensor, scale: Scale, scale_refine: Scale,
- stride: int, reg_denom: int) -> tuple:
- """Forward features of a single scale level.
-
- Args:
- x (Tensor): FPN feature maps of the specified stride.
- scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
- the bbox prediction.
- scale_refine (:obj: `mmcv.cnn.Scale`): Learnable scale module to
- resize the refined bbox prediction.
- stride (int): The corresponding stride for feature maps,
- used to normalize the bbox prediction when
- bbox_norm_type = 'stride'.
- reg_denom (int): The corresponding regression range for feature
- maps, only used to normalize the bbox prediction when
- bbox_norm_type = 'reg_denom'.
-
- Returns:
- tuple: iou-aware cls scores for each box, bbox predictions and
- refined bbox predictions of input feature maps.
- """
- cls_feat = x
- reg_feat = x
-
- for cls_layer in self.cls_convs:
- cls_feat = cls_layer(cls_feat)
-
- for reg_layer in self.reg_convs:
- reg_feat = reg_layer(reg_feat)
-
- # predict the bbox_pred of different level
- reg_feat_init = self.vfnet_reg_conv(reg_feat)
- if self.bbox_norm_type == 'reg_denom':
- bbox_pred = scale(
- self.vfnet_reg(reg_feat_init)).float().exp() * reg_denom
- elif self.bbox_norm_type == 'stride':
- bbox_pred = scale(
- self.vfnet_reg(reg_feat_init)).float().exp() * stride
- else:
- raise NotImplementedError
-
- # compute star deformable convolution offsets
- # converting dcn_offset to reg_feat.dtype thus VFNet can be
- # trained with FP16
- dcn_offset = self.star_dcn_offset(bbox_pred, self.gradient_mul,
- stride).to(reg_feat.dtype)
-
- # refine the bbox_pred
- reg_feat = self.relu(self.vfnet_reg_refine_dconv(reg_feat, dcn_offset))
- bbox_pred_refine = scale_refine(
- self.vfnet_reg_refine(reg_feat)).float().exp()
- bbox_pred_refine = bbox_pred_refine * bbox_pred.detach()
-
- # predict the iou-aware cls score
- cls_feat = self.relu(self.vfnet_cls_dconv(cls_feat, dcn_offset))
- cls_score = self.vfnet_cls(cls_feat)
-
- if self.training:
- return cls_score, bbox_pred, bbox_pred_refine
- else:
- return cls_score, bbox_pred_refine
-
- def star_dcn_offset(self, bbox_pred: Tensor, gradient_mul: float,
- stride: int) -> Tensor:
- """Compute the star deformable conv offsets.
-
- Args:
- bbox_pred (Tensor): Predicted bbox distance offsets (l, r, t, b).
- gradient_mul (float): Gradient multiplier.
- stride (int): The corresponding stride for feature maps,
- used to project the bbox onto the feature map.
-
- Returns:
- Tensor: The offsets for deformable convolution.
- """
- dcn_base_offset = self.dcn_base_offset.type_as(bbox_pred)
- bbox_pred_grad_mul = (1 - gradient_mul) * bbox_pred.detach() + \
- gradient_mul * bbox_pred
- # map to the feature map scale
- bbox_pred_grad_mul = bbox_pred_grad_mul / stride
- N, C, H, W = bbox_pred.size()
-
- x1 = bbox_pred_grad_mul[:, 0, :, :]
- y1 = bbox_pred_grad_mul[:, 1, :, :]
- x2 = bbox_pred_grad_mul[:, 2, :, :]
- y2 = bbox_pred_grad_mul[:, 3, :, :]
- bbox_pred_grad_mul_offset = bbox_pred.new_zeros(
- N, 2 * self.num_dconv_points, H, W)
- bbox_pred_grad_mul_offset[:, 0, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 1, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 2, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 4, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 5, :, :] = x2 # x2
- bbox_pred_grad_mul_offset[:, 7, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 11, :, :] = x2 # x2
- bbox_pred_grad_mul_offset[:, 12, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 13, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 14, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 16, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 17, :, :] = x2 # x2
- dcn_offset = bbox_pred_grad_mul_offset - dcn_base_offset
-
- return dcn_offset
-
- def loss_by_feat(
- self,
- cls_scores: List[Tensor],
- bbox_preds: List[Tensor],
- bbox_preds_refine: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> dict:
- """Compute loss of the head.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level, each is a 4D-tensor, the channel number is
- num_points * num_classes.
- bbox_preds (list[Tensor]): Box offsets for each
- scale level, each is a 4D-tensor, the channel number is
- num_points * 4.
- bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level, each is a 4D-tensor, the channel
- number is num_points * 4.
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine)
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- all_level_points = self.fcos_prior_generator.grid_priors(
- featmap_sizes, bbox_preds[0].dtype, bbox_preds[0].device)
- labels, label_weights, bbox_targets, bbox_weights = self.get_targets(
- cls_scores,
- all_level_points,
- batch_gt_instances,
- batch_img_metas,
- batch_gt_instances_ignore=batch_gt_instances_ignore)
-
- num_imgs = cls_scores[0].size(0)
- # flatten cls_scores, bbox_preds and bbox_preds_refine
- flatten_cls_scores = [
- cls_score.permute(0, 2, 3,
- 1).reshape(-1,
- self.cls_out_channels).contiguous()
- for cls_score in cls_scores
- ]
- flatten_bbox_preds = [
- bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
- for bbox_pred in bbox_preds
- ]
- flatten_bbox_preds_refine = [
- bbox_pred_refine.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
- for bbox_pred_refine in bbox_preds_refine
- ]
- flatten_cls_scores = torch.cat(flatten_cls_scores)
- flatten_bbox_preds = torch.cat(flatten_bbox_preds)
- flatten_bbox_preds_refine = torch.cat(flatten_bbox_preds_refine)
- flatten_labels = torch.cat(labels)
- flatten_bbox_targets = torch.cat(bbox_targets)
- # repeat points to align with bbox_preds
- flatten_points = torch.cat(
- [points.repeat(num_imgs, 1) for points in all_level_points])
-
- # FG cat_id: [0, num_classes - 1], BG cat_id: num_classes
- bg_class_ind = self.num_classes
- pos_inds = torch.where(
- ((flatten_labels >= 0) & (flatten_labels < bg_class_ind)) > 0)[0]
- num_pos = len(pos_inds)
-
- pos_bbox_preds = flatten_bbox_preds[pos_inds]
- pos_bbox_preds_refine = flatten_bbox_preds_refine[pos_inds]
- pos_labels = flatten_labels[pos_inds]
-
- # sync num_pos across all gpus
- if self.sync_num_pos:
- num_pos_avg_per_gpu = reduce_mean(
- pos_inds.new_tensor(num_pos).float()).item()
- num_pos_avg_per_gpu = max(num_pos_avg_per_gpu, 1.0)
- else:
- num_pos_avg_per_gpu = num_pos
-
- pos_bbox_targets = flatten_bbox_targets[pos_inds]
- pos_points = flatten_points[pos_inds]
-
- pos_decoded_bbox_preds = self.bbox_coder.decode(
- pos_points, pos_bbox_preds)
- pos_decoded_target_preds = self.bbox_coder.decode(
- pos_points, pos_bbox_targets)
- iou_targets_ini = bbox_overlaps(
- pos_decoded_bbox_preds,
- pos_decoded_target_preds.detach(),
- is_aligned=True).clamp(min=1e-6)
- bbox_weights_ini = iou_targets_ini.clone().detach()
- bbox_avg_factor_ini = reduce_mean(
- bbox_weights_ini.sum()).clamp_(min=1).item()
-
- pos_decoded_bbox_preds_refine = \
- self.bbox_coder.decode(pos_points, pos_bbox_preds_refine)
- iou_targets_rf = bbox_overlaps(
- pos_decoded_bbox_preds_refine,
- pos_decoded_target_preds.detach(),
- is_aligned=True).clamp(min=1e-6)
- bbox_weights_rf = iou_targets_rf.clone().detach()
- bbox_avg_factor_rf = reduce_mean(
- bbox_weights_rf.sum()).clamp_(min=1).item()
-
- if num_pos > 0:
- loss_bbox = self.loss_bbox(
- pos_decoded_bbox_preds,
- pos_decoded_target_preds.detach(),
- weight=bbox_weights_ini,
- avg_factor=bbox_avg_factor_ini)
-
- loss_bbox_refine = self.loss_bbox_refine(
- pos_decoded_bbox_preds_refine,
- pos_decoded_target_preds.detach(),
- weight=bbox_weights_rf,
- avg_factor=bbox_avg_factor_rf)
-
- # build IoU-aware cls_score targets
- if self.use_vfl:
- pos_ious = iou_targets_rf.clone().detach()
- cls_iou_targets = torch.zeros_like(flatten_cls_scores)
- cls_iou_targets[pos_inds, pos_labels] = pos_ious
- else:
- loss_bbox = pos_bbox_preds.sum() * 0
- loss_bbox_refine = pos_bbox_preds_refine.sum() * 0
- if self.use_vfl:
- cls_iou_targets = torch.zeros_like(flatten_cls_scores)
-
- if self.use_vfl:
- loss_cls = self.loss_cls(
- flatten_cls_scores,
- cls_iou_targets,
- avg_factor=num_pos_avg_per_gpu)
- else:
- loss_cls = self.loss_cls(
- flatten_cls_scores,
- flatten_labels,
- weight=label_weights,
- avg_factor=num_pos_avg_per_gpu)
-
- return dict(
- loss_cls=loss_cls,
- loss_bbox=loss_bbox,
- loss_bbox_rf=loss_bbox_refine)
-
- def get_targets(
- self,
- cls_scores: List[Tensor],
- mlvl_points: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> tuple:
- """A wrapper for computing ATSS and FCOS targets for points in multiple
- images.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- tuple:
-
- - labels_list (list[Tensor]): Labels of each level.
- - label_weights (Tensor/None): Label weights of all levels.
- - bbox_targets_list (list[Tensor]): Regression targets of each
- level, (l, t, r, b).
- - bbox_weights (Tensor/None): Bbox weights of all levels.
- """
- if self.use_atss:
- return self.get_atss_targets(cls_scores, mlvl_points,
- batch_gt_instances, batch_img_metas,
- batch_gt_instances_ignore)
- else:
- self.norm_on_bbox = False
- return self.get_fcos_targets(mlvl_points, batch_gt_instances)
-
- def _get_targets_single(self, *args, **kwargs):
- """Avoid ambiguity in multiple inheritance."""
- if self.use_atss:
- return ATSSHead._get_targets_single(self, *args, **kwargs)
- else:
- return FCOSHead._get_targets_single(self, *args, **kwargs)
-
- def get_fcos_targets(self, points: List[Tensor],
- batch_gt_instances: InstanceList) -> tuple:
- """Compute FCOS regression and classification targets for points in
- multiple images.
-
- Args:
- points (list[Tensor]): Points of each fpn level, each has shape
- (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
-
- Returns:
- tuple:
-
- - labels (list[Tensor]): Labels of each level.
- - label_weights: None, to be compatible with ATSS targets.
- - bbox_targets (list[Tensor]): BBox targets of each level.
- - bbox_weights: None, to be compatible with ATSS targets.
- """
- labels, bbox_targets = FCOSHead.get_targets(self, points,
- batch_gt_instances)
- label_weights = None
- bbox_weights = None
- return labels, label_weights, bbox_targets, bbox_weights
-
- def get_anchors(self,
- featmap_sizes: List[Tuple],
- batch_img_metas: List[dict],
- device: str = 'cuda') -> tuple:
- """Get anchors according to feature map sizes.
-
- Args:
- featmap_sizes (list[tuple]): Multi-level feature map sizes.
- batch_img_metas (list[dict]): Image meta info.
- device (str): Device for returned tensors
-
- Returns:
- tuple:
-
- - anchor_list (list[Tensor]): Anchors of each image.
- - valid_flag_list (list[Tensor]): Valid flags of each image.
- """
- num_imgs = len(batch_img_metas)
-
- # since feature map sizes of all images are the same, we only compute
- # anchors for one time
- multi_level_anchors = self.atss_prior_generator.grid_priors(
- featmap_sizes, device=device)
- anchor_list = [multi_level_anchors for _ in range(num_imgs)]
-
- # for each image, we compute valid flags of multi level anchors
- valid_flag_list = []
- for img_id, img_meta in enumerate(batch_img_metas):
- multi_level_flags = self.atss_prior_generator.valid_flags(
- featmap_sizes, img_meta['pad_shape'], device=device)
- valid_flag_list.append(multi_level_flags)
-
- return anchor_list, valid_flag_list
-
- def get_atss_targets(
- self,
- cls_scores: List[Tensor],
- mlvl_points: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> tuple:
- """A wrapper for computing ATSS targets for points in multiple images.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- tuple:
-
- - labels_list (list[Tensor]): Labels of each level.
- - label_weights (Tensor): Label weights of all levels.
- - bbox_targets_list (list[Tensor]): Regression targets of each
- level, (l, t, r, b).
- - bbox_weights (Tensor): Bbox weights of all levels.
- """
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- assert len(
- featmap_sizes
- ) == self.atss_prior_generator.num_levels == \
- self.fcos_prior_generator.num_levels
-
- device = cls_scores[0].device
-
- anchor_list, valid_flag_list = self.get_anchors(
- featmap_sizes, batch_img_metas, device=device)
-
- cls_reg_targets = ATSSHead.get_targets(
- self,
- anchor_list,
- valid_flag_list,
- batch_gt_instances,
- batch_img_metas,
- batch_gt_instances_ignore,
- unmap_outputs=True)
-
- (anchor_list, labels_list, label_weights_list, bbox_targets_list,
- bbox_weights_list, avg_factor) = cls_reg_targets
-
- bbox_targets_list = [
- bbox_targets.reshape(-1, 4) for bbox_targets in bbox_targets_list
- ]
-
- num_imgs = len(batch_img_metas)
- # transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format
- bbox_targets_list = self.transform_bbox_targets(
- bbox_targets_list, mlvl_points, num_imgs)
-
- labels_list = [labels.reshape(-1) for labels in labels_list]
- label_weights_list = [
- label_weights.reshape(-1) for label_weights in label_weights_list
- ]
- bbox_weights_list = [
- bbox_weights.reshape(-1) for bbox_weights in bbox_weights_list
- ]
- label_weights = torch.cat(label_weights_list)
- bbox_weights = torch.cat(bbox_weights_list)
- return labels_list, label_weights, bbox_targets_list, bbox_weights
-
- def transform_bbox_targets(self, decoded_bboxes: List[Tensor],
- mlvl_points: List[Tensor],
- num_imgs: int) -> List[Tensor]:
- """Transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format.
-
- Args:
- decoded_bboxes (list[Tensor]): Regression targets of each level,
- in the form of (x1, y1, x2, y2).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- num_imgs (int): the number of images in a batch.
-
- Returns:
- bbox_targets (list[Tensor]): Regression targets of each level in
- the form of (l, t, r, b).
- """
- # TODO: Re-implemented in Class PointCoder
- assert len(decoded_bboxes) == len(mlvl_points)
- num_levels = len(decoded_bboxes)
- mlvl_points = [points.repeat(num_imgs, 1) for points in mlvl_points]
- bbox_targets = []
- for i in range(num_levels):
- bbox_target = self.bbox_coder.encode(mlvl_points[i],
- decoded_bboxes[i])
- bbox_targets.append(bbox_target)
-
- return bbox_targets
-
- def _load_from_state_dict(self, state_dict: dict, prefix: str,
- local_metadata: dict, strict: bool,
- missing_keys: Union[List[str], str],
- unexpected_keys: Union[List[str], str],
- error_msgs: Union[List[str], str]) -> None:
- """Override the method in the parent class to avoid changing para's
- name."""
- pass
diff --git a/spaces/Larvuz/instruct-pix2pix/edit_app.py b/spaces/Larvuz/instruct-pix2pix/edit_app.py
deleted file mode 100644
index 0359e815ad51b1a2291dd8943555568e452981ad..0000000000000000000000000000000000000000
--- a/spaces/Larvuz/instruct-pix2pix/edit_app.py
+++ /dev/null
@@ -1,192 +0,0 @@
-from __future__ import annotations
-
-import math
-import random
-
-import gradio as gr
-import torch
-from PIL import Image, ImageOps
-from diffusers import StableDiffusionInstructPix2PixPipeline
-
-
-help_text = """
-If you're not getting what you want, there may be a few reasons:
-1. Is the image not changing enough? Your Image CFG weight may be too high. This value dictates how similar the output should be to the input. It's possible your edit requires larger changes from the original image, and your Image CFG weight isn't allowing that. Alternatively, your Text CFG weight may be too low. This value dictates how much to listen to the text instruction. The default Image CFG of 1.5 and Text CFG of 7.5 are a good starting point, but aren't necessarily optimal for each edit. Try:
- * Decreasing the Image CFG weight, or
- * Increasing the Text CFG weight, or
-2. Conversely, is the image changing too much, such that the details in the original image aren't preserved? Try:
- * Increasing the Image CFG weight, or
- * Decreasing the Text CFG weight
-3. Try generating results with different random seeds by setting "Randomize Seed" and running generation multiple times. You can also try setting "Randomize CFG" to sample new Text CFG and Image CFG values each time.
-4. Rephrasing the instruction sometimes improves results (e.g., "turn him into a dog" vs. "make him a dog" vs. "as a dog").
-5. Increasing the number of steps sometimes improves results.
-6. Do faces look weird? The Stable Diffusion autoencoder has a hard time with faces that are small in the image. Try:
- * Cropping the image so the face takes up a larger portion of the frame.
-"""
-
-
-example_instructions = [
- "Make it a picasso painting",
- "as if it were by modigliani",
- "convert to a bronze statue",
- "Turn it into an anime.",
- "have it look like a graphic novel",
- "make him gain weight",
- "what would he look like bald?",
- "Have him smile",
- "Put him in a cocktail party.",
- "move him at the beach.",
- "add dramatic lighting",
- "Convert to black and white",
- "What if it were snowing?",
- "Give him a leather jacket",
- "Turn him into a cyborg!",
- "make him wear a beanie",
-]
-
-model_id = "timbrooks/instruct-pix2pix"
-
-def main():
- pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None).to("cuda")
- example_image = Image.open("imgs/example.jpg").convert("RGB")
-
- def load_example(
- steps: int,
- randomize_seed: bool,
- seed: int,
- randomize_cfg: bool,
- text_cfg_scale: float,
- image_cfg_scale: float,
- ):
- example_instruction = random.choice(example_instructions)
- return [example_image, example_instruction] + generate(
- example_image,
- example_instruction,
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- )
-
- def generate(
- input_image: Image.Image,
- instruction: str,
- steps: int,
- randomize_seed: bool,
- seed: int,
- randomize_cfg: bool,
- text_cfg_scale: float,
- image_cfg_scale: float,
- ):
- seed = random.randint(0, 100000) if randomize_seed else seed
- text_cfg_scale = round(random.uniform(6.0, 9.0), ndigits=2) if randomize_cfg else text_cfg_scale
- image_cfg_scale = round(random.uniform(1.2, 1.8), ndigits=2) if randomize_cfg else image_cfg_scale
-
- width, height = input_image.size
- factor = 512 / max(width, height)
- factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
- width = int((width * factor) // 64) * 64
- height = int((height * factor) // 64) * 64
- input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS)
-
- if instruction == "":
- return [input_image, seed]
-
- generator = torch.manual_seed(seed)
- edited_image = pipe(
- instruction, image=input_image,
- guidance_scale=text_cfg_scale, image_guidance_scale=image_cfg_scale,
- num_inference_steps=steps, generator=generator,
- ).images[0]
- return [seed, text_cfg_scale, image_cfg_scale, edited_image]
-
- def reset():
- return [0, "Randomize Seed", 1371, "Fix CFG", 7.5, 1.5, None]
-
- with gr.Blocks() as demo:
- gr.HTML("""
- InstructPix2Pix: Learning to Follow Image Editing Instructions
-
-For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
-
-
-
-
""")
- with gr.Row():
- with gr.Column(scale=1, min_width=100):
- generate_button = gr.Button("Generate")
- with gr.Column(scale=1, min_width=100):
- load_button = gr.Button("Load Example")
- with gr.Column(scale=1, min_width=100):
- reset_button = gr.Button("Reset")
- with gr.Column(scale=3):
- instruction = gr.Textbox(lines=1, label="Edit Instruction", interactive=True)
-
- with gr.Row():
- input_image = gr.Image(label="Input Image", type="pil", interactive=True)
- edited_image = gr.Image(label=f"Edited Image", type="pil", interactive=False)
- input_image.style(height=512, width=512)
- edited_image.style(height=512, width=512)
-
- with gr.Row():
- steps = gr.Number(value=50, precision=0, label="Steps", interactive=True)
- randomize_seed = gr.Radio(
- ["Fix Seed", "Randomize Seed"],
- value="Randomize Seed",
- type="index",
- show_label=False,
- interactive=True,
- )
- seed = gr.Number(value=1371, precision=0, label="Seed", interactive=True)
- randomize_cfg = gr.Radio(
- ["Fix CFG", "Randomize CFG"],
- value="Fix CFG",
- type="index",
- show_label=False,
- interactive=True,
- )
- text_cfg_scale = gr.Number(value=7.5, label=f"Text CFG", interactive=True)
- image_cfg_scale = gr.Number(value=1.5, label=f"Image CFG", interactive=True)
-
- gr.Markdown(help_text)
-
- load_button.click(
- fn=load_example,
- inputs=[
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- ],
- outputs=[input_image, instruction, seed, text_cfg_scale, image_cfg_scale, edited_image],
- )
- generate_button.click(
- fn=generate,
- inputs=[
- input_image,
- instruction,
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- ],
- outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image],
- )
- reset_button.click(
- fn=reset,
- inputs=[],
- outputs=[steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale, edited_image],
- )
-
- demo.queue(concurrency_count=1)
- demo.launch(share=False)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/Loren/Streamlit_OCR_comparator/README.md b/spaces/Loren/Streamlit_OCR_comparator/README.md
deleted file mode 100644
index 2894464cad0f3f4d163e1f8e72750b10259159da..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Streamlit OCR Comparator
-emoji: 📰🔍🔤
-colorFrom: indigo
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: Home.py
-tags: [streamlit, ocr]
-pinned: true
----
\ No newline at end of file
diff --git a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py b/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py
deleted file mode 100644
index 6d9b36c74b1808b56ded68cf080a689db7e0ee4e..0000000000000000000000000000000000000000
--- a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# -*- coding: utf-8 -*-
-# File : __init__.py
-# Author : Jiayuan Mao
-# Email : maojiayuan@gmail.com
-# Date : 27/01/2018
-#
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-from .batchnorm import set_sbn_eps_mode
-from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d
-from .batchnorm import patch_sync_batchnorm, convert_model
-from .replicate import DataParallelWithCallback, patch_replication_callback
diff --git a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py b/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py
deleted file mode 100644
index 9794e0fc3463a5e8fad05c037cce64683059a6d3..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.ERROR)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})".format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10, 2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r", encoding="utf-8") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
\ No newline at end of file
diff --git a/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py b/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py
deleted file mode 100644
index 3a5999ddc80faa0ce6a54389562c9f092b40bb03..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# encoding: utf-8
-"""
-This files contains pytest fixtures and other test related stuff.
-
-"""
-from __future__ import absolute_import, division, print_function
-
-import numpy as np
-import pytest
-
-# save numpy's current print options
-_NP_PRINT_OPTIONS = np.get_printoptions()
-
-
-@pytest.fixture(autouse=True)
-def setup_doctest(request):
- """Set up the environment for doctests (when run through pytest)."""
- np.set_printoptions(precision=5, edgeitems=2, suppress=True)
-
- def fin():
- """Restore the environment after doctests (when run through pytest)."""
- np.set_printoptions(**_NP_PRINT_OPTIONS)
-
- request.addfinalizer(fin)
diff --git a/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py b/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py
deleted file mode 100644
index 19e34a11def040b971b534b9ed3fda71d0a35c85..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py
+++ /dev/null
@@ -1,882 +0,0 @@
-# encoding: utf-8
-# pylint: disable=no-member
-# pylint: disable=invalid-name
-# pylint: disable=too-many-arguments
-# pylint: disable=wrong-import-position
-"""
-Evaluation package.
-
-"""
-
-from __future__ import absolute_import, division, print_function
-
-import numpy as np
-
-
-# evaluation helper functions
-def find_closest_matches(detections, annotations):
- """
- Find the closest annotation for each detection.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
-
- Returns
- -------
- indices : numpy array
- Indices of the closest matches.
-
- Notes
- -----
- The sequences must be ordered.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.int)
- # if only a single annotation is given
- if len(annotations) == 1:
- # return an array as long as the detections with indices 0
- return np.zeros(len(detections), dtype=np.int)
- # solution found at: http://stackoverflow.com/questions/8914491/
- indices = annotations.searchsorted(detections)
- indices = np.clip(indices, 1, len(annotations) - 1)
- left = annotations[indices - 1]
- right = annotations[indices]
- indices -= detections - left < right - detections
- # return the indices of the closest matches
- return indices
-
-
-def calc_errors(detections, annotations, matches=None):
- """
- Errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.float)
- # determine the closest annotations
- if matches is None:
- matches = find_closest_matches(detections, annotations)
- # calc error relative to those annotations
- errors = detections - annotations[matches]
- # return the errors
- return errors
-
-
-def calc_absolute_errors(detections, annotations, matches=None):
- """
- Absolute errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Absolute errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # return the errors
- return np.abs(calc_errors(detections, annotations, matches))
-
-
-def calc_relative_errors(detections, annotations, matches=None):
- """
- Relative errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Relative errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.float)
- # determine the closest annotations
- if matches is None:
- matches = find_closest_matches(detections, annotations)
- # calculate the absolute errors
- errors = calc_errors(detections, annotations, matches)
- # return the relative errors
- return np.abs(1 - (errors / annotations[matches]))
-
-
-# abstract evaluation base class
-class EvaluationMixin(object):
- """
- Evaluation mixin class.
-
- This class has a `name` attribute which is used for display purposes and
- defaults to 'None'.
-
- `METRIC_NAMES` is a list of tuples, containing the attribute's name and the
- corresponding label, e.g.:
-
- The attributes defined in `METRIC_NAMES` will be provided as an ordered
- dictionary as the `metrics` property unless the subclass overwrites the
- property.
-
- `FLOAT_FORMAT` is used to format floats.
-
- """
-
- # Example:
- # METRIC_NAMES = [
- # ('precision', 'Precision'),
- # ('recall', 'Recall'),
- # ('fmeasure', 'F-measure'),
- # ]
- name = None
- METRIC_NAMES = []
- FLOAT_FORMAT = '{:.3f}'
-
- @property
- def metrics(self):
- """Metrics as a dictionary."""
- from collections import OrderedDict
- metrics = OrderedDict()
- # metrics = {}
- for metric in [m[0] for m in self.METRIC_NAMES]:
- metrics[metric] = getattr(self, metric)
- return metrics
-
- def __len__(self):
- """Length of the evaluation object."""
- raise NotImplementedError('must be implemented by subclass.')
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- Notes
- -----
- This is a fallback method formatting the `metrics` dictionary in a
- human readable way. Classes inheriting from this mixin class should
- provide a method better suitable.
-
- """
- # pylint: disable=unused-argument
- import pprint
- return pprint.pformat(dict(self.metrics), indent=4)
-
-
-# evaluation classes
-class SimpleEvaluation(EvaluationMixin):
- """
- Simple Precision, Recall, F-measure and Accuracy evaluation based on the
- numbers of true/false positive/negative detections.
-
- Parameters
- ----------
- num_tp : int
- Number of true positive detections.
- num_fp : int
- Number of false positive detections.
- num_tn : int
- Number of true negative detections.
- num_fn : int
- Number of false negative detections.
- name : str
- Name to be displayed.
-
- Notes
- -----
- This class is only suitable for a 1-class evaluation problem.
-
- """
-
- METRIC_NAMES = [
- ('num_tp', 'No. of true positives'),
- ('num_fp', 'No. of false positives'),
- ('num_tn', 'No. of true negatives'),
- ('num_fn', 'No. of false negatives'),
- ('num_annotations', 'No. Annotations'),
- ('precision', 'Precision'),
- ('recall', 'Recall'),
- ('fmeasure', 'F-measure'),
- ('accuracy', 'Accuracy'),
- ]
-
- def __init__(self, num_tp=0, num_fp=0, num_tn=0, num_fn=0, name=None,
- **kwargs):
- # pylint: disable=unused-argument
- # hidden variables, to be able to overwrite them in subclasses
- self._num_tp = int(num_tp)
- self._num_fp = int(num_fp)
- self._num_tn = int(num_tn)
- self._num_fn = int(num_fn)
- # name of the evaluation
- self.name = name
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return self._num_tp
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return self._num_fp
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return self._num_tn
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return self._num_fn
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- return self.num_tp + self.num_fn
-
- def __len__(self):
- # the length equals the number of annotations
- return self.num_annotations
-
- @property
- def precision(self):
- """Precision."""
- # correct / retrieved
- retrieved = float(self.num_tp + self.num_fp)
- # if there are no positive predictions, none of them are wrong
- if retrieved == 0:
- return 1.
- return self.num_tp / retrieved
-
- @property
- def recall(self):
- """Recall."""
- # correct / relevant
- relevant = float(self.num_tp + self.num_fn)
- # if there are no positive annotations, we recalled all of them
- if relevant == 0:
- return 1.
- return self.num_tp / relevant
-
- @property
- def fmeasure(self):
- """F-measure."""
- # 2pr / (p+r)
- numerator = 2. * self.precision * self.recall
- if numerator == 0:
- return 0.
- return numerator / (self.precision + self.recall)
-
- @property
- def accuracy(self):
- """Accuracy."""
- # acc: (TP + TN) / (TP + FP + TN + FN)
- denominator = self.num_fp + self.num_fn + self.num_tp + self.num_tn
- if denominator == 0:
- return 1.
- numerator = float(self.num_tp + self.num_tn)
- if numerator == 0:
- return 0.
- return numerator / denominator
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
-
- """
- ret = ''
- if self.name is not None:
- ret += '%s\n ' % self.name
- ret += 'Annotations: %5d TP: %5d FP: %5d FN: %5d ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- return ret
-
- def __str__(self):
- return self.tostring()
-
-
-# evaluate Precision, Recall, F-measure and Accuracy with lists or numpy arrays
-class Evaluation(SimpleEvaluation):
- """
- Evaluation class for measuring Precision, Recall and F-measure based on
- numpy arrays or lists with true/false positive/negative detections.
-
- Parameters
- ----------
- tp : list or numpy array
- True positive detections.
- fp : list or numpy array
- False positive detections.
- tn : list or numpy array
- True negative detections.
- fn : list or numpy array
- False negative detections.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, tp=None, fp=None, tn=None, fn=None, **kwargs):
- # set default values
- if tp is None:
- tp = []
- if fp is None:
- fp = []
- if tn is None:
- tn = []
- if fn is None:
- fn = []
- # instantiate a SimpleEvaluation object
- super(Evaluation, self).__init__(**kwargs)
- # convert everything to numpy arrays and save them
- self.tp = np.asarray(list(tp), dtype=np.float)
- self.fp = np.asarray(list(fp), dtype=np.float)
- self.tn = np.asarray(list(tn), dtype=np.float)
- self.fn = np.asarray(list(fn), dtype=np.float)
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return len(self.tp)
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return len(self.fp)
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return len(self.tn)
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return len(self.fn)
-
-
-# class for evaluation of Precision, Recall, F-measure with 2D arrays
-class MultiClassEvaluation(Evaluation):
- """
- Evaluation class for measuring Precision, Recall and F-measure based on
- 2D numpy arrays with true/false positive/negative detections.
-
- Parameters
- ----------
- tp : list of tuples or numpy array, shape (num_tp, 2)
- True positive detections.
- fp : list of tuples or numpy array, shape (num_fp, 2)
- False positive detections.
- tn : list of tuples or numpy array, shape (num_tn, 2)
- True negative detections.
- fn : list of tuples or numpy array, shape (num_fn, 2)
- False negative detections.
- name : str
- Name to be displayed.
-
- Notes
- -----
- The second item of the tuples or the second column of the arrays denote
- the class the detection belongs to.
-
- """
- def __init__(self, tp=None, fp=None, tn=None, fn=None, **kwargs):
- # set default values
- if tp is None:
- tp = np.zeros((0, 2))
- if fp is None:
- fp = np.zeros((0, 2))
- if tn is None:
- tn = np.zeros((0, 2))
- if fn is None:
- fn = np.zeros((0, 2))
- super(MultiClassEvaluation, self).__init__(**kwargs)
- self.tp = np.asarray(tp, dtype=np.float)
- self.fp = np.asarray(fp, dtype=np.float)
- self.tn = np.asarray(tn, dtype=np.float)
- self.fn = np.asarray(fn, dtype=np.float)
-
- def tostring(self, verbose=False, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Parameters
- ----------
- verbose : bool
- Add evaluation for individual classes.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- ret = ''
-
- if verbose:
- # extract all classes
- classes = []
- if self.tp.any():
- classes = np.append(classes, np.unique(self.tp[:, 1]))
- if self.fp.any():
- classes = np.append(classes, np.unique(self.fp[:, 1]))
- if self.tn.any():
- classes = np.append(classes, np.unique(self.tn[:, 1]))
- if self.fn.any():
- classes = np.append(classes, np.unique(self.fn[:, 1]))
- for cls in sorted(np.unique(classes)):
- # extract the TP, FP, TN and FN of this class
- tp = self.tp[self.tp[:, 1] == cls]
- fp = self.fp[self.fp[:, 1] == cls]
- tn = self.tn[self.tn[:, 1] == cls]
- fn = self.fn[self.fn[:, 1] == cls]
- # evaluate them
- e = Evaluation(tp, fp, tn, fn, name='Class %s' % cls)
- # append to the output string
- ret += ' %s\n' % e.tostring(verbose=False)
- # normal formatting
- ret += 'Annotations: %5d TP: %5d FP: %4d FN: %4d ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- # return
- return ret
-
-
-# class for summing Evaluations
-class SumEvaluation(SimpleEvaluation):
- """
- Simple class for summing evaluations.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, eval_objects, name=None):
- # pylint: disable=super-init-not-called
- # Note: we want to inherit the evaluation functions/properties, no need
- # to call __super__, but we need to take care of 'name'
- if not isinstance(eval_objects, list):
- # wrap the given eval_object in a list
- eval_objects = [eval_objects]
- self.eval_objects = eval_objects
- self.name = name or 'sum for %d files' % len(self)
-
- def __len__(self):
- # just use the length of the evaluation objects
- return len(self.eval_objects)
-
- # redefine the counters (number of TP, FP, TN, FN & number of annotations)
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return sum(e.num_tp for e in self.eval_objects)
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return sum(e.num_fp for e in self.eval_objects)
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return sum(e.num_tn for e in self.eval_objects)
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return sum(e.num_fn for e in self.eval_objects)
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- return sum(e.num_annotations for e in self.eval_objects)
-
-
-# class for averaging Evaluations
-class MeanEvaluation(SumEvaluation):
- """
- Simple class for averaging evaluation.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, eval_objects, name=None, **kwargs):
- super(MeanEvaluation, self).__init__(eval_objects, **kwargs)
- # handle the 'name' here to be able to set a different default value
- self.name = name or 'mean for %d files' % len(self)
-
- # overwrite the properties to calculate the mean instead of the sum
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_tp for e in self.eval_objects])
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_fp for e in self.eval_objects])
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_tn for e in self.eval_objects])
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_fn for e in self.eval_objects])
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_annotations for e in self.eval_objects])
-
- @property
- def precision(self):
- """Precision."""
- return np.nanmean([e.precision for e in self.eval_objects])
-
- @property
- def recall(self):
- """Recall."""
- return np.nanmean([e.recall for e in self.eval_objects])
-
- @property
- def fmeasure(self):
- """F-measure."""
- return np.nanmean([e.fmeasure for e in self.eval_objects])
-
- @property
- def accuracy(self):
- """Accuracy."""
- return np.nanmean([e.accuracy for e in self.eval_objects])
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- ret = ''
- if self.name is not None:
- ret += '%s\n ' % self.name
- # TODO: unify this with SimpleEvaluation but
- # add option to provide field formatters (e.g. 3d or 5.2f)
- # format with floats instead of integers
- ret += 'Annotations: %5.2f TP: %5.2f FP: %5.2f FN: %5.2f ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- return ret
-
-
-def tostring(eval_objects, **kwargs):
- """
- Format the given evaluation objects as human readable strings.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- # pylint: disable=unused-argument
- return '\n'.join([e.tostring() for e in eval_objects])
-
-
-def tocsv(eval_objects, metric_names=None, float_format='{:.3f}', **kwargs):
- """
- Format the given evaluation objects as a CSV table.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- metric_names : list of tuples, optional
- List of tuples defining the name of the property corresponding to the
- metric, and the metric label e.g. ('fp', 'False Positives').
- float_format : str, optional
- How to format the metrics.
-
- Returns
- -------
- str
- CSV table representation of the evaluation objects.
-
- Notes
- -----
- If no `metric_names` are given, they will be extracted from the first
- evaluation object.
-
- """
- # pylint: disable=unused-argument
-
- if metric_names is None:
- # get the evaluation metrics from the first evaluation object
- metric_names = eval_objects[0].METRIC_NAMES
- metric_names, metric_labels = list(zip(*metric_names))
- # add header
- lines = ['Name,' + ','.join(metric_labels)]
- # TODO: use e.metrics dict?
- # add the evaluation objects
- for e in eval_objects:
- values = [float_format.format(getattr(e, mn)) for mn in metric_names]
- lines.append(e.name + ',' + ','.join(values))
- # return everything
- return '\n'.join(lines)
-
-
-def totex(eval_objects, metric_names=None, float_format='{:.3f}', **kwargs):
- """
- Format the given evaluation objects as a LaTeX table.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- metric_names : list of tuples, optional
- List of tuples defining the name of the property corresponding to the
- metric, and the metric label e.g. ('fp', 'False Positives').
- float_format : str, optional
- How to format the metrics.
-
- Returns
- -------
- str
- LaTeX table representation of the evaluation objects.
-
- Notes
- -----
- If no `metric_names` are given, they will be extracted from the first
- evaluation object.
-
- """
- # pylint: disable=unused-argument
-
- if metric_names is None:
- # get the evaluation metrics from the first evaluation object
- metric_names = eval_objects[0].METRIC_NAMES
- metric_names, metric_labels = list(zip(*metric_names))
- # add header
- lines = ['Name & ' + ' & '.join(metric_labels) + '\\\\']
- # TODO: use e.metrics dict
- # TODO: add a generic totable() function which accepts columns separator,
- # newline stuff (e.g. tex \\\\) and others
- # add the evaluation objects
- for e in eval_objects:
- values = [float_format.format(getattr(e, mn)) for mn in metric_names]
- lines.append(e.name + ' & ' + ' & '.join(values) + '\\\\')
- # return everything
- return '\n'.join(lines)
-
-
-def evaluation_io(parser, ann_suffix, det_suffix, ann_dir=None, det_dir=None):
- """
- Add evaluation input/output and formatting related arguments to an existing
- parser object.
-
- Parameters
- ----------
- parser : argparse parser instance
- Existing argparse parser object.
- ann_suffix : str
- Suffix of the annotation files.
- det_suffix : str
- Suffix of the detection files.
- ann_dir : str, optional
- Use only annotations from this folder (and sub-folders).
- det_dir : str, optional
- Use only detections from this folder (and sub-folders).
-
- Returns
- -------
- io_group : argparse argument group
- Evaluation input / output argument group.
- formatter_group : argparse argument group
- Evaluation formatter argument group.
-
- """
- import sys
- import argparse
- # general input output file handling
- parser.add_argument('files', nargs='*',
- help='files (or folders) to be evaluated')
- parser.add_argument('-o', dest='outfile', type=argparse.FileType('w'),
- default=sys.stdout,
- help='output file [default: STDOUT]')
- # file suffixes used for evaluation
- g = parser.add_argument_group('file/folder/suffix arguments')
- g.add_argument('-a', dest='ann_suffix', action='store', default=ann_suffix,
- help='suffix of the annotation files '
- '[default: %(default)s]')
- g.add_argument('--ann_dir', action='store', default=ann_dir,
- help='search only this directory (recursively) for '
- 'annotation files [default: %(default)s]')
- g.add_argument('-d', dest='det_suffix', action='store', default=det_suffix,
- help='suffix of the detection files [default: %(default)s]')
- g.add_argument('--det_dir', action='store', default=det_dir,
- help='search only this directory (recursively) for '
- 'detection files [default: %(default)s]')
- # option to ignore non-existing detections
- g.add_argument('-i', '--ignore_non_existing', action='store_true',
- help='ignore non-existing detections [default: raise a '
- 'warning and assume empty detections]')
- # verbose
- parser.add_argument('-v', '--verbose', action='count', default=0,
- help='increase verbosity level')
- # option to suppress warnings
- parser.add_argument('-q', '--quiet', action='store_true',
- help='suppress any warnings')
- # output format options
- parser.set_defaults(output_formatter=tostring)
- f = parser.add_argument_group('formatting arguments')
- formats = f.add_mutually_exclusive_group()
- formats.add_argument('--tex', dest='output_formatter',
- action='store_const', const=totex,
- help='format output to be used in .tex files')
- formats.add_argument('--csv', dest='output_formatter',
- action='store_const', const=tocsv,
- help='format output to be used in .csv files')
- # return the output formatting group so the caller can add more options
- return g, f
-
-
-# finally import the submodules
-from . import chords, beats, notes, onsets, tempo
-
-# import often used classes
-from .beats import BeatEvaluation, BeatMeanEvaluation
-from .chords import ChordEvaluation, ChordMeanEvaluation, ChordSumEvaluation
-from .key import KeyEvaluation, KeyMeanEvaluation
-from .notes import NoteEvaluation, NoteMeanEvaluation, NoteSumEvaluation
-from .onsets import OnsetEvaluation, OnsetMeanEvaluation, OnsetSumEvaluation
-from .tempo import TempoEvaluation, TempoMeanEvaluation
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py
deleted file mode 100644
index 6fc71979190323f44c09f8b7e1761cf49cd2d76b..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
- 'iou3d_boxes_iou_bev_forward', 'iou3d_nms_forward',
- 'iou3d_nms_normal_forward'
-])
-
-
-def boxes_iou_bev(boxes_a, boxes_b):
- """Calculate boxes IoU in the Bird's Eye View.
-
- Args:
- boxes_a (torch.Tensor): Input boxes a with shape (M, 5).
- boxes_b (torch.Tensor): Input boxes b with shape (N, 5).
-
- Returns:
- ans_iou (torch.Tensor): IoU result with shape (M, N).
- """
- ans_iou = boxes_a.new_zeros(
- torch.Size((boxes_a.shape[0], boxes_b.shape[0])))
-
- ext_module.iou3d_boxes_iou_bev_forward(boxes_a.contiguous(),
- boxes_b.contiguous(), ans_iou)
-
- return ans_iou
-
-
-def nms_bev(boxes, scores, thresh, pre_max_size=None, post_max_size=None):
- """NMS function GPU implementation (for BEV boxes). The overlap of two
- boxes for IoU calculation is defined as the exact overlapping area of the
- two boxes. In this function, one can also set ``pre_max_size`` and
- ``post_max_size``.
-
- Args:
- boxes (torch.Tensor): Input boxes with the shape of [N, 5]
- ([x1, y1, x2, y2, ry]).
- scores (torch.Tensor): Scores of boxes with the shape of [N].
- thresh (float): Overlap threshold of NMS.
- pre_max_size (int, optional): Max size of boxes before NMS.
- Default: None.
- post_max_size (int, optional): Max size of boxes after NMS.
- Default: None.
-
- Returns:
- torch.Tensor: Indexes after NMS.
- """
- assert boxes.size(1) == 5, 'Input boxes shape should be [N, 5]'
- order = scores.sort(0, descending=True)[1]
-
- if pre_max_size is not None:
- order = order[:pre_max_size]
- boxes = boxes[order].contiguous()
-
- keep = torch.zeros(boxes.size(0), dtype=torch.long)
- num_out = ext_module.iou3d_nms_forward(boxes, keep, thresh)
- keep = order[keep[:num_out].cuda(boxes.device)].contiguous()
- if post_max_size is not None:
- keep = keep[:post_max_size]
- return keep
-
-
-def nms_normal_bev(boxes, scores, thresh):
- """Normal NMS function GPU implementation (for BEV boxes). The overlap of
- two boxes for IoU calculation is defined as the exact overlapping area of
- the two boxes WITH their yaw angle set to 0.
-
- Args:
- boxes (torch.Tensor): Input boxes with shape (N, 5).
- scores (torch.Tensor): Scores of predicted boxes with shape (N).
- thresh (float): Overlap threshold of NMS.
-
- Returns:
- torch.Tensor: Remaining indices with scores in descending order.
- """
- assert boxes.shape[1] == 5, 'Input boxes shape should be [N, 5]'
- order = scores.sort(0, descending=True)[1]
-
- boxes = boxes[order].contiguous()
-
- keep = torch.zeros(boxes.size(0), dtype=torch.long)
- num_out = ext_module.iou3d_nms_normal_forward(boxes, keep, thresh)
- return order[keep[:num_out].cuda(boxes.device)].contiguous()
diff --git a/spaces/MetaWabbit/Auto-GPT/run.sh b/spaces/MetaWabbit/Auto-GPT/run.sh
deleted file mode 100644
index edcbc44155b9ca9df83e283fdf976472c13e6492..0000000000000000000000000000000000000000
--- a/spaces/MetaWabbit/Auto-GPT/run.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-python scripts/check_requirements.py requirements.txt
-if [ $? -eq 1 ]
-then
- echo Installing missing packages...
- pip install -r requirements.txt
-fi
-python -m autogpt $@
-read -p "Press any key to continue..."
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py b/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py
deleted file mode 100644
index 244425feef76bf89d4de939cb8a1914a6f0f47c6..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py
+++ /dev/null
@@ -1,387 +0,0 @@
-# Lint as: python3
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Unit tests for the classifier trainer models."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import copy
-import functools
-import json
-
-import os
-import sys
-
-from typing import Any, Callable, Iterable, Mapping, MutableMapping, Optional, Tuple
-
-from absl import flags
-from absl.testing import parameterized
-import tensorflow as tf
-
-from tensorflow.python.distribute import combinations
-from tensorflow.python.distribute import strategy_combinations
-from official.utils.flags import core as flags_core
-from official.vision.image_classification import classifier_trainer
-from official.vision.image_classification import dataset_factory
-from official.vision.image_classification import test_utils
-from official.vision.image_classification.configs import base_configs
-
-classifier_trainer.define_classifier_flags()
-
-
-def distribution_strategy_combinations() -> Iterable[Tuple[Any, ...]]:
- """Returns the combinations of end-to-end tests to run."""
- return combinations.combine(
- distribution=[
- strategy_combinations.default_strategy,
- strategy_combinations.tpu_strategy,
- strategy_combinations.one_device_strategy_gpu,
- strategy_combinations.mirrored_strategy_with_two_gpus,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset=[
- 'imagenet',
- ],
- )
-
-
-def get_params_override(params_override: Mapping[str, Any]) -> str:
- """Converts params_override dict to string command."""
- return '--params_override=' + json.dumps(params_override)
-
-
-def basic_params_override(dtype: str = 'float32') -> MutableMapping[str, Any]:
- """Returns a basic parameter configuration for testing."""
- return {
- 'train_dataset': {
- 'builder': 'synthetic',
- 'use_per_replica_batch_size': True,
- 'batch_size': 1,
- 'image_size': 224,
- 'dtype': dtype,
- },
- 'validation_dataset': {
- 'builder': 'synthetic',
- 'batch_size': 1,
- 'use_per_replica_batch_size': True,
- 'image_size': 224,
- 'dtype': dtype,
- },
- 'train': {
- 'steps': 1,
- 'epochs': 1,
- 'callbacks': {
- 'enable_checkpoint_and_export': True,
- 'enable_tensorboard': False,
- },
- },
- 'evaluation': {
- 'steps': 1,
- },
- }
-
-
-def get_trivial_model(num_classes: int) -> tf.keras.Model:
- """Creates and compiles trivial model for ImageNet dataset."""
- model = test_utils.trivial_model(num_classes=num_classes)
- lr = 0.01
- optimizer = tf.keras.optimizers.SGD(learning_rate=lr)
- loss_obj = tf.keras.losses.SparseCategoricalCrossentropy()
- model.compile(optimizer=optimizer,
- loss=loss_obj,
- run_eagerly=True)
- return model
-
-
-def get_trivial_data() -> tf.data.Dataset:
- """Gets trivial data in the ImageNet size."""
- def generate_data(_) -> tf.data.Dataset:
- image = tf.zeros(shape=(224, 224, 3), dtype=tf.float32)
- label = tf.zeros([1], dtype=tf.int32)
- return image, label
-
- dataset = tf.data.Dataset.range(1)
- dataset = dataset.repeat()
- dataset = dataset.map(generate_data,
- num_parallel_calls=tf.data.experimental.AUTOTUNE)
- dataset = dataset.prefetch(buffer_size=1).batch(1)
- return dataset
-
-
-def run_end_to_end(main: Callable[[Any], None],
- extra_flags: Optional[Iterable[str]] = None,
- model_dir: Optional[str] = None):
- """Runs the classifier trainer end-to-end."""
- extra_flags = [] if extra_flags is None else extra_flags
- args = [sys.argv[0], '--model_dir', model_dir] + extra_flags
- flags_core.parse_flags(argv=args)
- main(flags.FLAGS)
-
-
-class ClassifierTest(tf.test.TestCase, parameterized.TestCase):
- """Unit tests for Keras models."""
- _tempdir = None
-
- @classmethod
- def setUpClass(cls): # pylint: disable=invalid-name
- super(ClassifierTest, cls).setUpClass()
-
- def tearDown(self):
- super(ClassifierTest, self).tearDown()
- tf.io.gfile.rmtree(self.get_temp_dir())
-
- @combinations.generate(distribution_strategy_combinations())
- def test_end_to_end_train_and_eval(self, distribution, model, dataset):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override()),
- '--mode=train_and_eval',
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
-
- @combinations.generate(
- combinations.combine(
- distribution=[
- strategy_combinations.one_device_strategy_gpu,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset='imagenet',
- dtype='float16',
- ))
- def test_gpu_train(self, distribution, model, dataset, dtype):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override(dtype)),
- '--mode=train_and_eval',
- ]
-
- export_params = basic_params_override()
- export_path = os.path.join(model_dir, 'export')
- export_params['export'] = {}
- export_params['export']['destination'] = export_path
- export_flags = base_flags + [
- '--mode=export_only',
- get_params_override(export_params)
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
- run_end_to_end(main=run,
- extra_flags=export_flags,
- model_dir=model_dir)
- self.assertTrue(os.path.exists(export_path))
-
- @combinations.generate(
- combinations.combine(
- distribution=[
- strategy_combinations.tpu_strategy,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset='imagenet',
- dtype='bfloat16',
- ))
- def test_tpu_train(self, distribution, model, dataset, dtype):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override(dtype)),
- '--mode=train_and_eval',
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
-
- @combinations.generate(distribution_strategy_combinations())
- def test_end_to_end_invalid_mode(self, distribution, model, dataset):
- """Test the Keras EfficientNet model with `strategy`."""
- model_dir = self.get_temp_dir()
- extra_flags = [
- '--data_dir=not_used',
- '--mode=invalid_mode',
- '--model_type=' + model,
- '--dataset=' + dataset,
- get_params_override(basic_params_override()),
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- with self.assertRaises(ValueError):
- run_end_to_end(main=run, extra_flags=extra_flags, model_dir=model_dir)
-
-
-class UtilTests(parameterized.TestCase, tf.test.TestCase):
- """Tests for individual utility functions within classifier_trainer.py."""
-
- @parameterized.named_parameters(
- ('efficientnet-b0', 'efficientnet', 'efficientnet-b0', 224),
- ('efficientnet-b1', 'efficientnet', 'efficientnet-b1', 240),
- ('efficientnet-b2', 'efficientnet', 'efficientnet-b2', 260),
- ('efficientnet-b3', 'efficientnet', 'efficientnet-b3', 300),
- ('efficientnet-b4', 'efficientnet', 'efficientnet-b4', 380),
- ('efficientnet-b5', 'efficientnet', 'efficientnet-b5', 456),
- ('efficientnet-b6', 'efficientnet', 'efficientnet-b6', 528),
- ('efficientnet-b7', 'efficientnet', 'efficientnet-b7', 600),
- ('resnet', 'resnet', '', None),
- )
- def test_get_model_size(self, model, model_name, expected):
- config = base_configs.ExperimentConfig(
- model_name=model,
- model=base_configs.ModelConfig(
- model_params={
- 'model_name': model_name,
- },
- )
- )
- size = classifier_trainer.get_image_size_from_model(config)
- self.assertEqual(size, expected)
-
- @parameterized.named_parameters(
- ('dynamic', 'dynamic', None, 'dynamic'),
- ('scalar', 128., None, 128.),
- ('float32', None, 'float32', 1),
- ('float16', None, 'float16', 128),
- )
- def test_get_loss_scale(self, loss_scale, dtype, expected):
- config = base_configs.ExperimentConfig(
- runtime=base_configs.RuntimeConfig(
- loss_scale=loss_scale),
- train_dataset=dataset_factory.DatasetConfig(dtype=dtype))
- ls = classifier_trainer.get_loss_scale(config, fp16_default=128)
- self.assertEqual(ls, expected)
-
- @parameterized.named_parameters(
- ('float16', 'float16'),
- ('bfloat16', 'bfloat16')
- )
- def test_initialize(self, dtype):
- config = base_configs.ExperimentConfig(
- runtime=base_configs.RuntimeConfig(
- run_eagerly=False,
- enable_xla=False,
- per_gpu_thread_count=1,
- gpu_thread_mode='gpu_private',
- num_gpus=1,
- dataset_num_private_threads=1,
- ),
- train_dataset=dataset_factory.DatasetConfig(dtype=dtype),
- model=base_configs.ModelConfig(),
- )
-
- class EmptyClass:
- pass
- fake_ds_builder = EmptyClass()
- fake_ds_builder.dtype = dtype
- fake_ds_builder.config = EmptyClass()
- classifier_trainer.initialize(config, fake_ds_builder)
-
- def test_resume_from_checkpoint(self):
- """Tests functionality for resuming from checkpoint."""
- # Set the keras policy
- policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16')
- tf.keras.mixed_precision.experimental.set_policy(policy)
-
- # Get the model, datasets, and compile it.
- model = get_trivial_model(10)
-
- # Create the checkpoint
- model_dir = self.get_temp_dir()
- train_epochs = 1
- train_steps = 10
- ds = get_trivial_data()
- callbacks = [
- tf.keras.callbacks.ModelCheckpoint(
- os.path.join(model_dir, 'model.ckpt-{epoch:04d}'),
- save_weights_only=True)
- ]
- model.fit(
- ds,
- callbacks=callbacks,
- epochs=train_epochs,
- steps_per_epoch=train_steps)
-
- # Test load from checkpoint
- clean_model = get_trivial_model(10)
- weights_before_load = copy.deepcopy(clean_model.get_weights())
- initial_epoch = classifier_trainer.resume_from_checkpoint(
- model=clean_model,
- model_dir=model_dir,
- train_steps=train_steps)
- self.assertEqual(initial_epoch, 1)
- self.assertNotAllClose(weights_before_load, clean_model.get_weights())
-
- tf.io.gfile.rmtree(model_dir)
-
- def test_serialize_config(self):
- """Tests functionality for serializing data."""
- config = base_configs.ExperimentConfig()
- model_dir = self.get_temp_dir()
- classifier_trainer.serialize_config(params=config, model_dir=model_dir)
- saved_params_path = os.path.join(model_dir, 'params.yaml')
- self.assertTrue(os.path.exists(saved_params_path))
- tf.io.gfile.rmtree(model_dir)
-
-if __name__ == '__main__':
- tf.test.main()
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py b/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py
deleted file mode 100644
index 11fcb5305660ec71153ebfc12631f455a3464115..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py
+++ /dev/null
@@ -1,231 +0,0 @@
-# Lint as: python3
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Definitions for high level configuration groups.."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-
-from typing import Any, List, Mapping, Optional
-
-import dataclasses
-
-from official.modeling import hyperparams
-from official.modeling.hyperparams import config_definitions
-
-CallbacksConfig = config_definitions.CallbacksConfig
-TensorboardConfig = config_definitions.TensorboardConfig
-RuntimeConfig = config_definitions.RuntimeConfig
-
-
-@dataclasses.dataclass
-class ExportConfig(hyperparams.Config):
- """Configuration for exports.
-
- Attributes:
- checkpoint: the path to the checkpoint to export.
- destination: the path to where the checkpoint should be exported.
- """
- checkpoint: str = None
- destination: str = None
-
-
-@dataclasses.dataclass
-class MetricsConfig(hyperparams.Config):
- """Configuration for Metrics.
-
- Attributes:
- accuracy: Whether or not to track accuracy as a Callback. Defaults to None.
- top_5: Whether or not to track top_5_accuracy as a Callback. Defaults to
- None.
- """
- accuracy: bool = None
- top_5: bool = None
-
-
-@dataclasses.dataclass
-class TimeHistoryConfig(hyperparams.Config):
- """Configuration for the TimeHistory callback.
-
- Attributes:
- log_steps: Interval of steps between logging of batch level stats.
- """
- log_steps: int = None
-
-
-@dataclasses.dataclass
-class TrainConfig(hyperparams.Config):
- """Configuration for training.
-
- Attributes:
- resume_checkpoint: Whether or not to enable load checkpoint loading.
- Defaults to None.
- epochs: The number of training epochs to run. Defaults to None.
- steps: The number of steps to run per epoch. If None, then this will be
- inferred based on the number of images and batch size. Defaults to None.
- callbacks: An instance of CallbacksConfig.
- metrics: An instance of MetricsConfig.
- tensorboard: An instance of TensorboardConfig.
- set_epoch_loop: Whether or not to set `experimental_steps_per_execution` to
- equal the number of training steps in `model.compile`. This reduces the
- number of callbacks run per epoch which significantly improves end-to-end
- TPU training time.
- """
- resume_checkpoint: bool = None
- epochs: int = None
- steps: int = None
- callbacks: CallbacksConfig = CallbacksConfig()
- metrics: MetricsConfig = None
- tensorboard: TensorboardConfig = TensorboardConfig()
- time_history: TimeHistoryConfig = TimeHistoryConfig()
- set_epoch_loop: bool = False
-
-
-@dataclasses.dataclass
-class EvalConfig(hyperparams.Config):
- """Configuration for evaluation.
-
- Attributes:
- epochs_between_evals: The number of train epochs to run between evaluations.
- Defaults to None.
- steps: The number of eval steps to run during evaluation. If None, this will
- be inferred based on the number of images and batch size. Defaults to
- None.
- skip_eval: Whether or not to skip evaluation.
- """
- epochs_between_evals: int = None
- steps: int = None
- skip_eval: bool = False
-
-
-@dataclasses.dataclass
-class LossConfig(hyperparams.Config):
- """Configuration for Loss.
-
- Attributes:
- name: The name of the loss. Defaults to None.
- label_smoothing: Whether or not to apply label smoothing to the loss. This
- only applies to 'categorical_cross_entropy'.
- """
- name: str = None
- label_smoothing: float = None
-
-
-@dataclasses.dataclass
-class OptimizerConfig(hyperparams.Config):
- """Configuration for Optimizers.
-
- Attributes:
- name: The name of the optimizer. Defaults to None.
- decay: Decay or rho, discounting factor for gradient. Defaults to None.
- epsilon: Small value used to avoid 0 denominator. Defaults to None.
- momentum: Plain momentum constant. Defaults to None.
- nesterov: Whether or not to apply Nesterov momentum. Defaults to None.
- moving_average_decay: The amount of decay to apply. If 0 or None, then
- exponential moving average is not used. Defaults to None.
- lookahead: Whether or not to apply the lookahead optimizer. Defaults to
- None.
- beta_1: The exponential decay rate for the 1st moment estimates. Used in the
- Adam optimizers. Defaults to None.
- beta_2: The exponential decay rate for the 2nd moment estimates. Used in the
- Adam optimizers. Defaults to None.
- epsilon: Small value used to avoid 0 denominator. Defaults to 1e-7.
- """
- name: str = None
- decay: float = None
- epsilon: float = None
- momentum: float = None
- nesterov: bool = None
- moving_average_decay: Optional[float] = None
- lookahead: Optional[bool] = None
- beta_1: float = None
- beta_2: float = None
- epsilon: float = None
-
-
-@dataclasses.dataclass
-class LearningRateConfig(hyperparams.Config):
- """Configuration for learning rates.
-
- Attributes:
- name: The name of the learning rate. Defaults to None.
- initial_lr: The initial learning rate. Defaults to None.
- decay_epochs: The number of decay epochs. Defaults to None.
- decay_rate: The rate of decay. Defaults to None.
- warmup_epochs: The number of warmup epochs. Defaults to None.
- batch_lr_multiplier: The multiplier to apply to the base learning rate, if
- necessary. Defaults to None.
- examples_per_epoch: the number of examples in a single epoch. Defaults to
- None.
- boundaries: boundaries used in piecewise constant decay with warmup.
- multipliers: multipliers used in piecewise constant decay with warmup.
- scale_by_batch_size: Scale the learning rate by a fraction of the batch
- size. Set to 0 for no scaling (default).
- staircase: Apply exponential decay at discrete values instead of continuous.
- """
- name: str = None
- initial_lr: float = None
- decay_epochs: float = None
- decay_rate: float = None
- warmup_epochs: int = None
- examples_per_epoch: int = None
- boundaries: List[int] = None
- multipliers: List[float] = None
- scale_by_batch_size: float = 0.
- staircase: bool = None
-
-
-@dataclasses.dataclass
-class ModelConfig(hyperparams.Config):
- """Configuration for Models.
-
- Attributes:
- name: The name of the model. Defaults to None.
- model_params: The parameters used to create the model. Defaults to None.
- num_classes: The number of classes in the model. Defaults to None.
- loss: A `LossConfig` instance. Defaults to None.
- optimizer: An `OptimizerConfig` instance. Defaults to None.
- """
- name: str = None
- model_params: hyperparams.Config = None
- num_classes: int = None
- loss: LossConfig = None
- optimizer: OptimizerConfig = None
-
-
-@dataclasses.dataclass
-class ExperimentConfig(hyperparams.Config):
- """Base configuration for an image classification experiment.
-
- Attributes:
- model_dir: The directory to use when running an experiment.
- mode: e.g. 'train_and_eval', 'export'
- runtime: A `RuntimeConfig` instance.
- train: A `TrainConfig` instance.
- evaluation: An `EvalConfig` instance.
- model: A `ModelConfig` instance.
- export: An `ExportConfig` instance.
- """
- model_dir: str = None
- model_name: str = None
- mode: str = None
- runtime: RuntimeConfig = None
- train_dataset: Any = None
- validation_dataset: Any = None
- train: TrainConfig = None
- evaluation: EvalConfig = None
- model: ModelConfig = None
- export: ExportConfig = None
diff --git a/spaces/Navneet574/Heart_Disease_Prediciton/app.py b/spaces/Navneet574/Heart_Disease_Prediciton/app.py
deleted file mode 100644
index 30f66c36eeab6633487a32671d615f07ab544f93..0000000000000000000000000000000000000000
--- a/spaces/Navneet574/Heart_Disease_Prediciton/app.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import gradio as gr
-from joblib import load
-import numpy as np
-import pandas as pd
-
-AgeCategory = ['18-24', '25-29', '35-39', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80 or older']
-Binary_Input = ['Yes', 'No']
-Sex = ['Male', 'Female']
-Race = ['White', 'Black', 'Asian', 'American Indian/Alaskan Native', 'Hispanic', 'Other']
-Diabetic = ['Yes', 'No', 'No borderline diabetes', 'Yes (during pregnancy)']
-GeneralHealth = ['Excellent', 'Very good', 'Good', 'Fair', 'Poor']
-
-dic_ageCat = {'18-24':0, '25-29':1, '35-39':2, '35-39':3, '40-44':4, '45-49':5, '50-54':6, '55-59':7, '60-64':8, '65-69':9, '70-74':10, '75-79':11, '80 or older':12}
-dic_binary = {'Yes':1, 'No':0}
-dic_sex = {'Male':0, 'Female':1}
-dic_race = {'White':5, 'Black':2, 'Asian':0, 'American Indian/Alaskan Native':3, 'Hispanic':1, 'Other':4}
-dic_diabetic = {'Yes':2, 'No':0, 'No borderline diabetes':1, 'Yes (during pregnancy)':3}
-dic_generalHealth = {'Excellent':0, 'Very good':4, 'Good':2, 'Fair':1, 'Poor':3}
-
-
-def predict_price(BMI, Smoking, AlcoholDrinking, Stroke, PhysicalHealth, MentalHealth, DiffWalking, Sex, AgeCategory, Race, Diabetic, PhysicalActivity, GenHealth, SleepTime, Asthma, KidneyDisease, SkinCancer):
- model = load('Heart_Disease_Classification.jb')
-
- data = {
- 'BMI': [BMI],
- 'Smoking': dic_binary[Smoking],
- 'AlcoholDrinking': dic_binary[AlcoholDrinking],
- 'Stroke': dic_binary[Stroke],
- 'PhysicalHealth': [PhysicalHealth],
- 'MentalHealth': [MentalHealth],
- 'DiffWalking': dic_binary[DiffWalking],
- 'Sex': dic_sex[Sex],
- 'AgeCategory': dic_ageCat[AgeCategory],
- 'Race': dic_race[Race],
- 'Diabetic': dic_diabetic[Diabetic],
- 'PhysicalActivity': dic_binary[PhysicalActivity],
- 'GenHealth': dic_generalHealth[GenHealth],
- 'SleepTime': [SleepTime],
- 'Asthma': dic_binary[Asthma],
- 'KidneyDisease': dic_binary[KidneyDisease],
- 'SkinCancer': dic_binary[SkinCancer]
- }
-
- Xinp = pd.DataFrame(data)
- print(Xinp)
-
- stone = model.predict(Xinp)
-
- if stone == 1:
- return stone["YES"]
- else:
- return stone["NO"]
-
-ui = gr.Interface(
- fn=predict_price,inputs=[
- gr.inputs.Textbox(placeholder='BMI', default=0,
- numeric=True, label='Body Mass Index (BMI) (normal is between 18.5 and 24.9)'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Smoking', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Alcohol Drinking', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Stroke', default="No"),
- gr.inputs.Textbox(placeholder='Physical Health',
- default=0, numeric=True, label='Physical Health Score'),
- gr.inputs.Textbox(placeholder='MentalHealth', default=0,
- numeric=True, label='Mental Health Score'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Diff Walking', default="No"),
- gr.inputs.Dropdown(choices=Sex, label='Sex', default=""),
- gr.inputs.Dropdown(choices=AgeCategory, label='Age Category', default="18-24"),
- gr.inputs.Dropdown(choices=Race, label='Race', default="Asian"),
- gr.inputs.Dropdown(choices=Diabetic, label='Diabetic', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Physical Activity', default="Yes"),
- gr.inputs.Dropdown(choices=GeneralHealth, label='General Health', default="Excellent"),
- gr.inputs.Textbox(placeholder='Sleep Time',
- default='7', numeric=True, label='Sleep Time (normal is between 6-8 hrs)'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Asthma', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Kidney Disease', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Skin Cancer', default="No")
- ], outputs=[
- "text"
- ]
-)
-if __name__ == "__main__":
- ui.launch()
\ No newline at end of file
diff --git a/spaces/Nikhil0987/omm/convo.py b/spaces/Nikhil0987/omm/convo.py
deleted file mode 100644
index f5c87b19d82d3f1e1869101faf1205475b5ed3b9..0000000000000000000000000000000000000000
--- a/spaces/Nikhil0987/omm/convo.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from transformers import pipeline, Conversation
-import streamlit as st
-from streamlit_option_menu import option_menu
-
-chatbot = pipeline(task="conversational", model="microsoft/DialoGPT-medium")
-
-def Convo():
- if convo := st.chat_input("Enter your message"):
-
- conversation = Conversation(convo)
-
-
-
- # candidate_labels = ["HELP", "PROBLEM SOLVE", "GENERAL TALK"]
-
-
- ans = chatbot(conversation)
- # add_user_input = st.button("Add User Input")
-
- # conversation.add_user_input("{}".format(convo))
- # conversation = chatbot(conversation)
- with st.chat_message("assistant"):
- # ans
- st.write(ans.generated_responses[-1])
-
-
-# convo()
diff --git a/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py b/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py
deleted file mode 100644
index d9d372dcbb1bed2fffbfd8e81d6da749ceab730b..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py
+++ /dev/null
@@ -1,772 +0,0 @@
-"""
-This scripts plots examples of the images that get best and worse metrics
-"""
-print("Imports...", end="")
-import os
-import sys
-from argparse import ArgumentParser
-from pathlib import Path
-
-import matplotlib.patches as mpatches
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import seaborn as sns
-import yaml
-from imageio import imread
-from matplotlib.gridspec import GridSpec
-from skimage.color import rgba2rgb
-from sklearn.metrics.pairwise import euclidean_distances
-
-sys.path.append("../")
-
-from climategan.data import encode_mask_label
-from climategan.eval_metrics import edges_coherence_std_min
-from eval_masker import crop_and_resize
-
-# -----------------------
-# ----- Constants -----
-# -----------------------
-
-# Metrics
-metrics = ["error", "f05", "edge_coherence"]
-
-dict_metrics = {
- "names": {
- "tpr": "TPR, Recall, Sensitivity",
- "tnr": "TNR, Specificity, Selectivity",
- "fpr": "FPR",
- "fpt": "False positives relative to image size",
- "fnr": "FNR, Miss rate",
- "fnt": "False negatives relative to image size",
- "mpr": "May positive rate (MPR)",
- "mnr": "May negative rate (MNR)",
- "accuracy": "Accuracy (ignoring may)",
- "error": "Error",
- "f05": "F05 score",
- "precision": "Precision",
- "edge_coherence": "Edge coherence",
- "accuracy_must_may": "Accuracy (ignoring cannot)",
- },
- "key_metrics": ["error", "f05", "edge_coherence"],
-}
-
-
-# Colors
-colorblind_palette = sns.color_palette("colorblind")
-color_cannot = colorblind_palette[1]
-color_must = colorblind_palette[2]
-color_may = colorblind_palette[7]
-color_pred = colorblind_palette[4]
-
-icefire = sns.color_palette("icefire", as_cmap=False, n_colors=5)
-color_tp = icefire[0]
-color_tn = icefire[1]
-color_fp = icefire[4]
-color_fn = icefire[3]
-
-
-def parsed_args():
- """
- Parse and returns command-line args
-
- Returns:
- argparse.Namespace: the parsed arguments
- """
- parser = ArgumentParser()
- parser.add_argument(
- "--input_csv",
- default="ablations_metrics_20210311.csv",
- type=str,
- help="CSV containing the results of the ablation study",
- )
- parser.add_argument(
- "--output_dir",
- default=None,
- type=str,
- help="Output directory",
- )
- parser.add_argument(
- "--models_log_path",
- default=None,
- type=str,
- help="Path containing the log files of the models",
- )
- parser.add_argument(
- "--masker_test_set_dir",
- default=None,
- type=str,
- help="Directory containing the test images",
- )
- parser.add_argument(
- "--best_model",
- default="dada, msd_spade, pseudo",
- type=str,
- help="The string identifier of the best model",
- )
- parser.add_argument(
- "--dpi",
- default=200,
- type=int,
- help="DPI for the output images",
- )
- parser.add_argument(
- "--alpha",
- default=0.5,
- type=float,
- help="Transparency of labels shade",
- )
- parser.add_argument(
- "--percentile",
- default=0.05,
- type=float,
- help="Transparency of labels shade",
- )
- parser.add_argument(
- "--seed",
- default=None,
- type=int,
- help="Bootstrap random seed, for reproducibility",
- )
- parser.add_argument(
- "--no_images",
- action="store_true",
- default=False,
- help="Do not generate images",
- )
-
- return parser.parse_args()
-
-
-def map_color(arr, input_color, output_color, rtol=1e-09):
- """
- Maps one color to another
- """
- input_color_arr = np.tile(input_color, (arr.shape[:2] + (1,)))
- output = arr.copy()
- output[np.all(np.isclose(arr, input_color_arr, rtol=rtol), axis=2)] = output_color
- return output
-
-
-def plot_labels(ax, img, label, img_id, n_, add_title, do_legend):
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (255, 0, 0), color_cannot)
- label_colmap = map_color(label_colmap, (0, 0, 255), color_must)
- label_colmap = map_color(label_colmap, (0, 0, 0), color_may)
-
- ax.imshow(img)
- ax.imshow(label_colmap, alpha=0.5)
- ax.axis("off")
-
- if n_ in [1, 3, 5]:
- color_ = "green"
- else:
- color_ = "red"
-
- ax.text(
- -0.15,
- 0.5,
- img_id,
- color=color_,
- fontweight="roman",
- fontsize="x-large",
- horizontalalignment="left",
- verticalalignment="center",
- transform=ax.transAxes,
- )
-
- if add_title:
- ax.set_title("Labels", rotation=0, fontsize="x-large")
-
-
-def plot_pred(ax, img, pred, img_id, add_title, do_legend):
- pred = np.tile(np.expand_dims(pred, axis=2), reps=(1, 1, 3))
-
- pred_colmap = pred.astype(float)
- pred_colmap = map_color(pred_colmap, (1, 1, 1), color_pred)
- pred_colmap_ma = np.ma.masked_not_equal(pred_colmap, color_pred)
- pred_colmap_ma = pred_colmap_ma.mask * img + pred_colmap_ma
-
- ax.imshow(img)
- ax.imshow(pred_colmap_ma, alpha=0.5)
- ax.axis("off")
-
- if add_title:
- ax.set_title("Prediction", rotation=0, fontsize="x-large")
-
-
-def plot_correct_incorrect(
- ax, img_filename, img, metric, label, img_id, n_, add_title, do_legend
-):
- # FP
- fp_map = imread(
- model_path / "eval-metrics/fp" / "{}_fp.png".format(Path(img_filename).stem)
- )
- fp_map = np.tile(np.expand_dims(fp_map, axis=2), reps=(1, 1, 3))
-
- fp_map_colmap = fp_map.astype(float)
- fp_map_colmap = map_color(fp_map_colmap, (1, 1, 1), color_fp)
-
- # FN
- fn_map = imread(
- model_path / "eval-metrics/fn" / "{}_fn.png".format(Path(img_filename).stem)
- )
- fn_map = np.tile(np.expand_dims(fn_map, axis=2), reps=(1, 1, 3))
-
- fn_map_colmap = fn_map.astype(float)
- fn_map_colmap = map_color(fn_map_colmap, (1, 1, 1), color_fn)
-
- # TP
- tp_map = imread(
- model_path / "eval-metrics/tp" / "{}_tp.png".format(Path(img_filename).stem)
- )
- tp_map = np.tile(np.expand_dims(tp_map, axis=2), reps=(1, 1, 3))
-
- tp_map_colmap = tp_map.astype(float)
- tp_map_colmap = map_color(tp_map_colmap, (1, 1, 1), color_tp)
-
- # TN
- tn_map = imread(
- model_path / "eval-metrics/tn" / "{}_tn.png".format(Path(img_filename).stem)
- )
- tn_map = np.tile(np.expand_dims(tn_map, axis=2), reps=(1, 1, 3))
-
- tn_map_colmap = tn_map.astype(float)
- tn_map_colmap = map_color(tn_map_colmap, (1, 1, 1), color_tn)
-
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (0, 0, 0), color_may)
- label_colmap_ma = np.ma.masked_not_equal(label_colmap, color_may)
- label_colmap_ma = label_colmap_ma.mask * img + label_colmap_ma
-
- # Combine masks
- maps = fp_map_colmap + fn_map_colmap + tp_map_colmap + tn_map_colmap
- maps_ma = np.ma.masked_equal(maps, (0, 0, 0))
- maps_ma = maps_ma.mask * img + maps_ma
-
- ax.imshow(img)
- ax.imshow(label_colmap_ma, alpha=0.5)
- ax.imshow(maps_ma, alpha=0.5)
- ax.axis("off")
-
- if add_title:
- ax.set_title("Metric", rotation=0, fontsize="x-large")
-
-
-def plot_edge_coherence(ax, img, metric, label, pred, img_id, n_, add_title, do_legend):
- pred = np.tile(np.expand_dims(pred, axis=2), reps=(1, 1, 3))
-
- ec, pred_ec, label_ec = edges_coherence_std_min(
- np.squeeze(pred[:, :, 0]), np.squeeze(encode_mask_label(label, "flood"))
- )
-
- ##################
- # Edge distances #
- ##################
-
- # Location of edges
- pred_ec_coord = np.argwhere(pred_ec > 0)
- label_ec_coord = np.argwhere(label_ec > 0)
-
- # Normalized pairwise distances between pred and label
- dist_mat = np.divide(
- euclidean_distances(pred_ec_coord, label_ec_coord), pred_ec.shape[0]
- )
-
- # Standard deviation of the minimum distance from pred to label
- min_dist = np.min(dist_mat, axis=1) # noqa: F841
-
- #############
- # Make plot #
- #############
-
- pred_ec = np.tile(
- np.expand_dims(np.asarray(pred_ec > 0, dtype=float), axis=2), reps=(1, 1, 3)
- )
- pred_ec_colmap = map_color(pred_ec, (1, 1, 1), color_pred)
- pred_ec_colmap_ma = np.ma.masked_not_equal(pred_ec_colmap, color_pred) # noqa: F841
-
- label_ec = np.tile(
- np.expand_dims(np.asarray(label_ec > 0, dtype=float), axis=2), reps=(1, 1, 3)
- )
- label_ec_colmap = map_color(label_ec, (1, 1, 1), color_must)
- label_ec_colmap_ma = np.ma.masked_not_equal( # noqa: F841
- label_ec_colmap, color_must
- )
-
- # Combined pred and label edges
- combined_ec = pred_ec_colmap + label_ec_colmap
- combined_ec_ma = np.ma.masked_equal(combined_ec, (0, 0, 0))
- combined_ec_img = combined_ec_ma.mask * img + combined_ec
-
- # Pred
- pred_colmap = pred.astype(float)
- pred_colmap = map_color(pred_colmap, (1, 1, 1), color_pred)
- pred_colmap_ma = np.ma.masked_not_equal(pred_colmap, color_pred)
-
- # Must
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (0, 0, 255), color_must)
- label_colmap_ma = np.ma.masked_not_equal(label_colmap, color_must)
-
- # TP
- tp_map = imread(
- model_path / "eval-metrics/tp" / "{}_tp.png".format(Path(srs_sel.filename).stem)
- )
- tp_map = np.tile(np.expand_dims(tp_map, axis=2), reps=(1, 1, 3))
- tp_map_colmap = tp_map.astype(float)
- tp_map_colmap = map_color(tp_map_colmap, (1, 1, 1), color_tp)
- tp_map_colmap_ma = np.ma.masked_not_equal(tp_map_colmap, color_tp)
-
- # Combination
- comb_pred = (
- (pred_colmap_ma.mask ^ tp_map_colmap_ma.mask)
- & tp_map_colmap_ma.mask
- & combined_ec_ma.mask
- ) * pred_colmap
- comb_label = (
- (label_colmap_ma.mask ^ pred_colmap_ma.mask)
- & pred_colmap_ma.mask
- & combined_ec_ma.mask
- ) * label_colmap
- comb_tp = combined_ec_ma.mask * tp_map_colmap.copy()
- combined = comb_tp + comb_label + comb_pred
- combined_ma = np.ma.masked_equal(combined, (0, 0, 0))
- combined_ma = combined_ma.mask * combined_ec_img + combined_ma
-
- ax.imshow(combined_ec_img, alpha=1)
- ax.imshow(combined_ma, alpha=0.5)
- ax.axis("off")
-
- # Plot lines
- idx_sort_x = np.argsort(pred_ec_coord[:, 1])
- offset = 100
- for idx in range(offset, pred_ec_coord.shape[0], offset):
- y0, x0 = pred_ec_coord[idx_sort_x[idx], :]
- argmin = np.argmin(dist_mat[idx_sort_x[idx]])
- y1, x1 = label_ec_coord[argmin, :]
- ax.plot([x0, x1], [y0, y1], color="white", linewidth=0.5)
-
- if add_title:
- ax.set_title("Metric", rotation=0, fontsize="x-large")
-
-
-def plot_images_metric(
- axes, metric, img_filename, img_id, n_, srs_sel, add_title, do_legend
-):
-
- # Read images
- img_path = imgs_orig_path / img_filename
- label_path = labels_path / "{}_labeled.png".format(Path(img_filename).stem)
- img, label = crop_and_resize(img_path, label_path)
- img = rgba2rgb(img) if img.shape[-1] == 4 else img / 255.0
-
- pred = imread(
- model_path / "eval-metrics/pred" / "{}_pred.png".format(Path(img_filename).stem)
- )
-
- # Label
- plot_labels(axes[0], img, label, img_id, n_, add_title, do_legend)
-
- # Prediction
- plot_pred(axes[1], img, pred, img_id, add_title, do_legend)
-
- # Correct / incorrect
- if metric in ["error", "f05"]:
- plot_correct_incorrect(
- axes[2],
- img_filename,
- img,
- metric,
- label,
- img_id,
- n_,
- add_title,
- do_legend=False,
- )
- handles = []
- lw = 1.0
- handles.append(
- mpatches.Patch(facecolor=color_tp, label="TP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_tn, label="TN", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_fp, label="FP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_fn, label="FN", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(
- facecolor=color_may,
- label="May-be-flooded",
- linewidth=lw,
- alpha=0.66,
- )
- )
- labels = ["TP", "TN", "FP", "FN", "May-be-flooded"]
- if metric == "error":
- if n_ in [1, 3, 5]:
- title = "Low error rate"
- else:
- title = "High error rate"
- else:
- if n_ in [1, 3, 5]:
- title = "High F05 score"
- else:
- title = "Low F05 score"
- # Edge coherence
- elif metric == "edge_coherence":
- plot_edge_coherence(
- axes[2], img, metric, label, pred, img_id, n_, add_title, do_legend=False
- )
- handles = []
- lw = 1.0
- handles.append(
- mpatches.Patch(facecolor=color_tp, label="TP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_pred, label="pred", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(
- facecolor=color_must,
- label="Must-be-flooded",
- linewidth=lw,
- alpha=0.66,
- )
- )
- labels = ["TP", "Prediction", "Must-be-flooded"]
- if n_ in [1, 3, 5]:
- title = "High edge coherence"
- else:
- title = "Low edge coherence"
-
- else:
- raise ValueError
-
- labels_values_title = "Error: {:.4f} \nFO5: {:.4f} \nEdge coherence: {:.4f}".format(
- srs_sel.error, srs_sel.f05, srs_sel.edge_coherence
- )
-
- plot_legend(axes[3], img, handles, labels, labels_values_title, title)
-
-
-def plot_legend(ax, img, handles, labels, labels_values_title, title):
- img_ = np.zeros_like(img, dtype=np.uint8)
- img_.fill(255)
- ax.imshow(img_)
- ax.axis("off")
-
- leg1 = ax.legend(
- handles=handles,
- labels=labels,
- title=title,
- title_fontsize="medium",
- labelspacing=0.6,
- loc="upper left",
- fontsize="x-small",
- frameon=False,
- )
- leg1._legend_box.align = "left"
-
- leg2 = ax.legend(
- title=labels_values_title,
- title_fontsize="small",
- loc="lower left",
- frameon=False,
- )
- leg2._legend_box.align = "left"
-
- ax.add_artist(leg1)
-
-
-def scatterplot_metrics_pair(ax, df, x_metric, y_metric, dict_images):
-
- sns.scatterplot(data=df, x=x_metric, y=y_metric, ax=ax)
-
- # Set X-label
- ax.set_xlabel(dict_metrics["names"][x_metric], rotation=0, fontsize="medium")
-
- # Set Y-label
- ax.set_ylabel(dict_metrics["names"][y_metric], rotation=90, fontsize="medium")
-
- # Change spines
- sns.despine(ax=ax, left=True, bottom=True)
-
- annotate_scatterplot(ax, dict_images, x_metric, y_metric)
-
-
-def scatterplot_metrics(ax, df, df_all, dict_images, plot_all=False):
-
- # Other
- if plot_all:
- sns.scatterplot(
- data=df_all.loc[df_all.ground == True],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='+', alpha=0.25)
- sns.scatterplot(
- data=df_all.loc[df_all.instagan == True],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='x', alpha=0.25)
- sns.scatterplot(
- data=df_all.loc[(df_all.instagan == False) & (df_all.instagan == False) &
- (df_all.model_feats != args.best_model)],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='s', alpha=0.25)
-
- # Best model
- cmap_ = sns.cubehelix_palette(as_cmap=True)
- sns.scatterplot(
- data=df, x="error", y="f05", hue="edge_coherence", ax=ax, palette=cmap_
- )
-
- norm = plt.Normalize(df["edge_coherence"].min(), df["edge_coherence"].max())
- sm = plt.cm.ScalarMappable(cmap=cmap_, norm=norm)
- sm.set_array([])
-
- # Remove the legend and add a colorbar
- ax.get_legend().remove()
- ax_cbar = ax.figure.colorbar(sm)
- ax_cbar.set_label("Edge coherence", labelpad=8)
-
- # Set X-label
- ax.set_xlabel(dict_metrics["names"]["error"], rotation=0, fontsize="medium")
-
- # Set Y-label
- ax.set_ylabel(dict_metrics["names"]["f05"], rotation=90, fontsize="medium")
-
- annotate_scatterplot(ax, dict_images, "error", "f05")
-
- # Change spines
- sns.despine(ax=ax, left=True, bottom=True)
-
- # Set XY limits
- xlim = ax.get_xlim()
- ylim = ax.get_ylim()
- ax.set_xlim([0.0, xlim[1]])
- ax.set_ylim([ylim[0], 1.0])
-
-
-def annotate_scatterplot(ax, dict_images, x_metric, y_metric, offset=0.1):
- xlim = ax.get_xlim()
- ylim = ax.get_ylim()
- x_len = xlim[1] - xlim[0]
- y_len = ylim[1] - ylim[0]
- x_th = xlim[1] - x_len / 2.0
- y_th = ylim[1] - y_len / 2.0
- for text, d in dict_images.items():
- if text in ["B", "D", "F"]:
- x = d[x_metric]
- y = d[y_metric]
-
- x_text = x + x_len * offset if x < x_th else x - x_len * offset
- y_text = y + y_len * offset if y < y_th else y - y_len * offset
-
- ax.annotate(
- xy=(x, y),
- xycoords="data",
- xytext=(x_text, y_text),
- textcoords="data",
- text=text,
- arrowprops=dict(facecolor="black", shrink=0.05),
- fontsize="medium",
- color="black",
- )
- elif text == "A":
- x = (
- dict_images["A"][x_metric]
- + dict_images["C"][x_metric]
- + dict_images["E"][x_metric]
- ) / 3
- y = (
- dict_images["A"][y_metric]
- + dict_images["C"][y_metric]
- + dict_images["E"][y_metric]
- ) / 3
-
- x_text = x + x_len * 2 * offset if x < x_th else x - x_len * 2 * offset
- y_text = (
- y + y_len * 0.45 * offset if y < y_th else y - y_len * 0.45 * offset
- )
-
- ax.annotate(
- xy=(x, y),
- xycoords="data",
- xytext=(x_text, y_text),
- textcoords="data",
- text="A, C, E",
- arrowprops=dict(facecolor="black", shrink=0.05),
- fontsize="medium",
- color="black",
- )
-
-
-if __name__ == "__main__":
- # -----------------------------
- # ----- Parse arguments -----
- # -----------------------------
- args = parsed_args()
- print("Args:\n" + "\n".join([f" {k:20}: {v}" for k, v in vars(args).items()]))
-
- # Determine output dir
- if args.output_dir is None:
- output_dir = Path(os.environ["SLURM_TMPDIR"])
- else:
- output_dir = Path(args.output_dir)
- if not output_dir.exists():
- output_dir.mkdir(parents=True, exist_ok=False)
-
- # Store args
- output_yml = output_dir / "labels.yml"
- with open(output_yml, "w") as f:
- yaml.dump(vars(args), f)
-
- # Data dirs
- imgs_orig_path = Path(args.masker_test_set_dir) / "imgs"
- labels_path = Path(args.masker_test_set_dir) / "labels"
-
- # Read CSV
- df_all = pd.read_csv(args.input_csv, index_col="model_img_idx")
-
- # Select best model
- df = df_all.loc[df_all.model_feats == args.best_model]
- v_key, model_dir = df.model.unique()[0].split("/")
- model_path = Path(args.models_log_path) / "ablation-{}".format(v_key) / model_dir
-
- # Set up plot
- sns.reset_orig()
- sns.set(style="whitegrid")
- plt.rcParams.update({"font.family": "serif"})
- plt.rcParams.update(
- {
- "font.serif": [
- "Computer Modern Roman",
- "Times New Roman",
- "Utopia",
- "New Century Schoolbook",
- "Century Schoolbook L",
- "ITC Bookman",
- "Bookman",
- "Times",
- "Palatino",
- "Charter",
- "serif" "Bitstream Vera Serif",
- "DejaVu Serif",
- ]
- }
- )
-
- if args.seed:
- np.random.seed(args.seed)
- img_ids = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
- dict_images = {}
- idx = 0
-
- # Define grid of subplots
- grid_vmargin = 0.03 # Extent of the vertical margin between metric grids
- ax_hspace = 0.04 # Extent of the vertical space between axes of same grid
- ax_wspace = 0.05 # Extent of the horizontal space between axes of same grid
- n_grids = len(metrics)
- n_cols = 4
- n_rows = 2
- h_grid = (1.0 / n_grids) - ((n_grids - 1) * grid_vmargin) / n_grids
-
- fig1 = plt.figure(dpi=200, figsize=(11, 13))
-
- n_ = 0
- add_title = False
- for metric_id, metric in enumerate(metrics):
-
- # Create grid
- top_grid = 1.0 - metric_id * h_grid - metric_id * grid_vmargin
- bottom_grid = top_grid - h_grid
- gridspec = GridSpec(
- n_rows,
- n_cols,
- wspace=ax_wspace,
- hspace=ax_hspace,
- bottom=bottom_grid,
- top=top_grid,
- )
-
- # Select best
- if metric == "error":
- ascending = True
- else:
- ascending = False
- idx_rand = np.random.permutation(int(args.percentile * len(df)))[0]
- srs_sel = df.sort_values(by=metric, ascending=ascending).iloc[idx_rand]
- img_id = img_ids[idx]
- dict_images.update({img_id: srs_sel})
- # Read images
- img_filename = srs_sel.filename
-
- axes_row = [fig1.add_subplot(gridspec[0, c]) for c in range(n_cols)]
- if not args.no_images:
- n_ += 1
- if metric_id == 0:
- add_title = True
- plot_images_metric(
- axes_row,
- metric,
- img_filename,
- img_id,
- n_,
- srs_sel,
- add_title=add_title,
- do_legend=False,
- )
- add_title = False
-
- idx += 1
- print("1 more row done.")
- # Select worst
- if metric == "error":
- ascending = False
- else:
- ascending = True
- idx_rand = np.random.permutation(int(args.percentile * len(df)))[0]
- srs_sel = df.sort_values(by=metric, ascending=ascending).iloc[idx_rand]
- img_id = img_ids[idx]
- dict_images.update({img_id: srs_sel})
- # Read images
- img_filename = srs_sel.filename
-
- axes_row = [fig1.add_subplot(gridspec[1, c]) for c in range(n_cols)]
- if not args.no_images:
- n_ += 1
- plot_images_metric(
- axes_row,
- metric,
- img_filename,
- img_id,
- n_,
- srs_sel,
- add_title=add_title,
- do_legend=False,
- )
-
- idx += 1
- print("1 more row done.")
-
- output_fig = output_dir / "all_metrics.png"
-
- fig1.tight_layout() # (pad=1.5) #
- fig1.savefig(output_fig, dpi=fig1.dpi, bbox_inches="tight")
-
- # Scatter plot
- fig2 = plt.figure(dpi=200)
-
- scatterplot_metrics(fig2.gca(), df, df_all, dict_images)
-
- # fig2, axes = plt.subplots(nrows=1, ncols=3, dpi=200, figsize=(18, 5))
- #
- # scatterplot_metrics_pair(axes[0], df, "error", "f05", dict_images)
- # scatterplot_metrics_pair(axes[1], df, "error", "edge_coherence", dict_images)
- # scatterplot_metrics_pair(axes[2], df, "f05", "edge_coherence", dict_images)
-
- output_fig = output_dir / "scatterplots.png"
- fig2.savefig(output_fig, dpi=fig2.dpi, bbox_inches="tight")
diff --git a/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh b/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh
deleted file mode 100644
index d649238546b996ae1ad00f7753c04aeebc7aaa97..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-echo "Dowloading Script" && python download_labelbox.py
-
-echo "Merging Script" && python merge_labelbox_masks.py
-
-echo "Cleaning labeled"
-rm /Users/victor/Downloads/metrics-v2/labels/*
-cp /Users/victor/Downloads/labelbox_test_flood-v2/__labeled/* /Users/victor/Downloads/metrics-v2/labels
-
-echo "Create labeled images Script" && python create_labeled.py
\ No newline at end of file
diff --git a/spaces/Nunchakuka/FrenchAnonymizer/models.py b/spaces/Nunchakuka/FrenchAnonymizer/models.py
deleted file mode 100644
index 46b8aacb1bef18f6fad4c20c968b19125626799c..0000000000000000000000000000000000000000
--- a/spaces/Nunchakuka/FrenchAnonymizer/models.py
+++ /dev/null
@@ -1,351 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
-
-class Encoder(nn.Module):
- def __init__(self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
- def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
- resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(weight_norm(
- ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
- k, u, padding=(k-u)//2)))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel//(2**(i+1))
- for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i*self.num_kernels+j](x)
- else:
- xs += self.resblocks[i*self.num_kernels+j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- print('Removing weight norm...')
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
- ])
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ])
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2,3,5,7,11]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class SpeakerEncoder(torch.nn.Module):
- def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
- super(SpeakerEncoder, self).__init__()
- self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
- self.linear = nn.Linear(model_hidden_size, model_embedding_size)
- self.relu = nn.ReLU()
-
- def forward(self, mels):
- self.lstm.flatten_parameters()
- _, (hidden, _) = self.lstm(mels)
- embeds_raw = self.relu(self.linear(hidden[-1]))
- return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
-
- def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
- mel_slices = []
- for i in range(0, total_frames-partial_frames, partial_hop):
- mel_range = torch.arange(i, i+partial_frames)
- mel_slices.append(mel_range)
-
- return mel_slices
-
- def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
- mel_len = mel.size(1)
- last_mel = mel[:,-partial_frames:]
-
- if mel_len > partial_frames:
- mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
- mels = list(mel[:,s] for s in mel_slices)
- mels.append(last_mel)
- mels = torch.stack(tuple(mels), 0).squeeze(1)
-
- with torch.no_grad():
- partial_embeds = self(mels)
- embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
- #embed = embed / torch.linalg.norm(embed, 2)
- else:
- with torch.no_grad():
- embed = self(last_mel)
-
- return embed
-
-
-class SynthesizerTrn(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels,
- ssl_dim,
- use_spk,
- **kwargs):
-
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- self.ssl_dim = ssl_dim
- self.use_spk = use_spk
-
- self.enc_p = Encoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16)
- self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
- self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
- self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
- if not self.use_spk:
- self.enc_spk = SpeakerEncoder(model_hidden_size=gin_channels, model_embedding_size=gin_channels)
-
- def forward(self, c, spec, g=None, mel=None, c_lengths=None, spec_lengths=None):
- if c_lengths == None:
- c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
- if spec_lengths == None:
- spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
-
- if not self.use_spk:
- g = self.enc_spk(mel.transpose(1,2))
- g = g.unsqueeze(-1)
-
- _, m_p, logs_p, _ = self.enc_p(c, c_lengths)
- z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g)
- z_p = self.flow(z, spec_mask, g=g)
-
- z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size)
- o = self.dec(z_slice, g=g)
-
- return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, c, g=None, mel=None, c_lengths=None):
- if c_lengths == None:
- c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
- if not self.use_spk:
- g = self.enc_spk.embed_utterance(mel.transpose(1,2))
- g = g.unsqueeze(-1)
-
- z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths)
- z = self.flow(z_p, c_mask, g=g, reverse=True)
- o = self.dec(z * c_mask, g=g)
-
- return o
diff --git a/spaces/Nunchakuka/FrenchAnonymizer/utils.py b/spaces/Nunchakuka/FrenchAnonymizer/utils.py
deleted file mode 100644
index 1bd5b6185af6c9f1c270b8ba345bfc36d059e081..0000000000000000000000000000000000000000
--- a/spaces/Nunchakuka/FrenchAnonymizer/utils.py
+++ /dev/null
@@ -1,305 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-from torch.nn import functional as F
-from commons import sequence_mask
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def get_cmodel(rank):
- checkpoint = torch.load('wavlm/WavLM-Large.pt')
- cfg = WavLMConfig(checkpoint['cfg'])
- cmodel = WavLM(cfg).cuda(rank)
- cmodel.load_state_dict(checkpoint['model'])
- cmodel.eval()
- return cmodel
-
-
-def get_content(cmodel, y):
- with torch.no_grad():
- c = cmodel.extract_features(y.squeeze(1))[0]
- c = c.transpose(1, 2)
- return c
-
-
-def get_vocoder(rank):
- with open("hifigan/config.json", "r") as f:
- config = json.load(f)
- config = hifigan.AttrDict(config)
- vocoder = hifigan.Generator(config)
- ckpt = torch.load("hifigan/generator_v1")
- vocoder.load_state_dict(ckpt["generator"])
- vocoder.eval()
- vocoder.remove_weight_norm()
- vocoder.cuda(rank)
- return vocoder
-
-
-def transform(mel, height): # 68-92
- #r = np.random.random()
- #rate = r * 0.3 + 0.85 # 0.85-1.15
- #height = int(mel.size(-2) * rate)
- tgt = torchvision.transforms.functional.resize(mel, (height, mel.size(-1)))
- if height >= mel.size(-2):
- return tgt[:, :mel.size(-2), :]
- else:
- silence = tgt[:,-1:,:].repeat(1,mel.size(-2)-height,1)
- silence += torch.randn_like(silence) / 10
- return torch.cat((tgt, silence), 1)
-
-
-def stretch(mel, width): # 0.5-2
- return torchvision.transforms.functional.resize(mel, (mel.size(-2), width))
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict= {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})" .format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info("Saving model and optimizer state at iteration {} to {}".format(
- iteration, checkpoint_path))
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- torch.save({'model': state_dict,
- 'iteration': iteration,
- 'optimizer': optimizer.state_dict(),
- 'learning_rate': learning_rate}, checkpoint_path)
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
- for k, v in scalars.items():
- writer.add_scalar(k, v, global_step)
- for k, v in histograms.items():
- writer.add_histogram(k, v, global_step)
- for k, v in images.items():
- writer.add_image(k, v, global_step, dataformats='HWC')
- for k, v in audios.items():
- writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
- x = f_list[-1]
- print(x)
- return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10,2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py
deleted file mode 100644
index bb80d11a67cd75764a89f6f41915b0348ae96e92..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py
+++ /dev/null
@@ -1,428 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from multiprocessing import Pool
-
-import numpy as np
-from fairseq import options
-from fairseq.data import dictionary
-from fairseq.scoring import bleu
-
-from examples.noisychannel import (
- rerank_generate,
- rerank_options,
- rerank_score_bw,
- rerank_score_lm,
- rerank_utils,
-)
-
-
-def score_target_hypo(
- args, a, b, c, lenpen, target_outfile, hypo_outfile, write_hypos, normalize
-):
-
- print("lenpen", lenpen, "weight1", a, "weight2", b, "weight3", c)
- gen_output_lst, bitext1_lst, bitext2_lst, lm_res_lst = load_score_files(args)
- dict = dictionary.Dictionary()
- scorer = scorer = bleu.Scorer(
- bleu.BleuConfig(
- pad=dict.pad(),
- eos=dict.eos(),
- unk=dict.unk(),
- )
- )
-
- ordered_hypos = {}
- ordered_targets = {}
-
- for shard_id in range(len(bitext1_lst)):
- bitext1 = bitext1_lst[shard_id]
- bitext2 = bitext2_lst[shard_id]
- gen_output = gen_output_lst[shard_id]
- lm_res = lm_res_lst[shard_id]
-
- total = len(bitext1.rescore_source.keys())
- source_lst = []
- hypo_lst = []
- score_lst = []
- reference_lst = []
- j = 1
- best_score = -math.inf
-
- for i in range(total):
- # length is measured in terms of words, not bpe tokens, since models may not share the same bpe
- target_len = len(bitext1.rescore_hypo[i].split())
-
- if lm_res is not None:
- lm_score = lm_res.score[i]
- else:
- lm_score = 0
-
- if bitext2 is not None:
- bitext2_score = bitext2.rescore_score[i]
- bitext2_backwards = bitext2.backwards
- else:
- bitext2_score = None
- bitext2_backwards = None
-
- score = rerank_utils.get_score(
- a,
- b,
- c,
- target_len,
- bitext1.rescore_score[i],
- bitext2_score,
- lm_score=lm_score,
- lenpen=lenpen,
- src_len=bitext1.source_lengths[i],
- tgt_len=bitext1.target_lengths[i],
- bitext1_backwards=bitext1.backwards,
- bitext2_backwards=bitext2_backwards,
- normalize=normalize,
- )
-
- if score > best_score:
- best_score = score
- best_hypo = bitext1.rescore_hypo[i]
-
- if j == gen_output.num_hypos[i] or j == args.num_rescore:
- j = 1
- hypo_lst.append(best_hypo)
- score_lst.append(best_score)
- source_lst.append(bitext1.rescore_source[i])
- reference_lst.append(bitext1.rescore_target[i])
-
- best_score = -math.inf
- best_hypo = ""
- else:
- j += 1
-
- gen_keys = list(sorted(gen_output.no_bpe_target.keys()))
-
- for key in range(len(gen_keys)):
- if args.prefix_len is None:
- assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], (
- "pred and rescore hypo mismatch: i: "
- + str(key)
- + ", "
- + str(hypo_lst[key])
- + str(gen_keys[key])
- + str(gen_output.no_bpe_hypo[key])
- )
- sys_tok = dict.encode_line(hypo_lst[key])
- ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]])
- scorer.add(ref_tok, sys_tok)
-
- else:
- full_hypo = rerank_utils.get_full_from_prefix(
- hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]]
- )
- sys_tok = dict.encode_line(full_hypo)
- ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]])
- scorer.add(ref_tok, sys_tok)
-
- # if only one set of hyper parameters is provided, write the predictions to a file
- if write_hypos:
- # recover the orinal ids from n best list generation
- for key in range(len(gen_output.no_bpe_target)):
- if args.prefix_len is None:
- assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], (
- "pred and rescore hypo mismatch:"
- + "i:"
- + str(key)
- + str(hypo_lst[key])
- + str(gen_output.no_bpe_hypo[key])
- )
- ordered_hypos[gen_keys[key]] = hypo_lst[key]
- ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[
- gen_keys[key]
- ]
-
- else:
- full_hypo = rerank_utils.get_full_from_prefix(
- hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]]
- )
- ordered_hypos[gen_keys[key]] = full_hypo
- ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[
- gen_keys[key]
- ]
-
- # write the hypos in the original order from nbest list generation
- if args.num_shards == (len(bitext1_lst)):
- with open(target_outfile, "w") as t:
- with open(hypo_outfile, "w") as h:
- for key in range(len(ordered_hypos)):
- t.write(ordered_targets[key])
- h.write(ordered_hypos[key])
-
- res = scorer.result_string(4)
- if write_hypos:
- print(res)
- score = rerank_utils.parse_bleu_scoring(res)
- return score
-
-
-def match_target_hypo(args, target_outfile, hypo_outfile):
- """combine scores from the LM and bitext models, and write the top scoring hypothesis to a file"""
- if len(args.weight1) == 1:
- res = score_target_hypo(
- args,
- args.weight1[0],
- args.weight2[0],
- args.weight3[0],
- args.lenpen[0],
- target_outfile,
- hypo_outfile,
- True,
- args.normalize,
- )
- rerank_scores = [res]
- else:
- print("launching pool")
- with Pool(32) as p:
- rerank_scores = p.starmap(
- score_target_hypo,
- [
- (
- args,
- args.weight1[i],
- args.weight2[i],
- args.weight3[i],
- args.lenpen[i],
- target_outfile,
- hypo_outfile,
- False,
- args.normalize,
- )
- for i in range(len(args.weight1))
- ],
- )
-
- if len(rerank_scores) > 1:
- best_index = np.argmax(rerank_scores)
- best_score = rerank_scores[best_index]
- print("best score", best_score)
- print("best lenpen", args.lenpen[best_index])
- print("best weight1", args.weight1[best_index])
- print("best weight2", args.weight2[best_index])
- print("best weight3", args.weight3[best_index])
- return (
- args.lenpen[best_index],
- args.weight1[best_index],
- args.weight2[best_index],
- args.weight3[best_index],
- best_score,
- )
-
- else:
- return (
- args.lenpen[0],
- args.weight1[0],
- args.weight2[0],
- args.weight3[0],
- rerank_scores[0],
- )
-
-
-def load_score_files(args):
- if args.all_shards:
- shard_ids = list(range(args.num_shards))
- else:
- shard_ids = [args.shard_id]
-
- gen_output_lst = []
- bitext1_lst = []
- bitext2_lst = []
- lm_res1_lst = []
-
- for shard_id in shard_ids:
- using_nbest = args.nbest_list is not None
- (
- pre_gen,
- left_to_right_preprocessed_dir,
- right_to_left_preprocessed_dir,
- backwards_preprocessed_dir,
- lm_preprocessed_dir,
- ) = rerank_utils.get_directories(
- args.data_dir_name,
- args.num_rescore,
- args.gen_subset,
- args.gen_model_name,
- shard_id,
- args.num_shards,
- args.sampling,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- rerank1_is_gen = (
- args.gen_model == args.score_model1 and args.source_prefix_frac is None
- )
- rerank2_is_gen = (
- args.gen_model == args.score_model2 and args.source_prefix_frac is None
- )
-
- score1_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model1_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards1,
- )
- if args.score_model2 is not None:
- score2_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model2_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards2,
- )
- if args.language_model is not None:
- lm_score_file = rerank_utils.rescore_file_name(
- pre_gen, args.prefix_len, args.lm_name, lm_file=True
- )
-
- # get gen output
- predictions_bpe_file = pre_gen + "/generate_output_bpe.txt"
- if using_nbest:
- print("Using predefined n-best list from interactive.py")
- predictions_bpe_file = args.nbest_list
- gen_output = rerank_utils.BitextOutputFromGen(
- predictions_bpe_file,
- bpe_symbol=args.post_process,
- nbest=using_nbest,
- prefix_len=args.prefix_len,
- target_prefix_frac=args.target_prefix_frac,
- )
-
- if rerank1_is_gen:
- bitext1 = gen_output
- else:
- bitext1 = rerank_utils.BitextOutput(
- score1_file,
- args.backwards1,
- args.right_to_left1,
- args.post_process,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- if args.score_model2 is not None or args.nbest_list is not None:
- if rerank2_is_gen:
- bitext2 = gen_output
- else:
- bitext2 = rerank_utils.BitextOutput(
- score2_file,
- args.backwards2,
- args.right_to_left2,
- args.post_process,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- assert (
- bitext2.source_lengths == bitext1.source_lengths
- ), "source lengths for rescoring models do not match"
- assert (
- bitext2.target_lengths == bitext1.target_lengths
- ), "target lengths for rescoring models do not match"
- else:
- if args.diff_bpe:
- assert args.score_model2 is None
- bitext2 = gen_output
- else:
- bitext2 = None
-
- if args.language_model is not None:
- lm_res1 = rerank_utils.LMOutput(
- lm_score_file,
- args.lm_dict,
- args.prefix_len,
- args.post_process,
- args.target_prefix_frac,
- )
- else:
- lm_res1 = None
-
- gen_output_lst.append(gen_output)
- bitext1_lst.append(bitext1)
- bitext2_lst.append(bitext2)
- lm_res1_lst.append(lm_res1)
- return gen_output_lst, bitext1_lst, bitext2_lst, lm_res1_lst
-
-
-def rerank(args):
- if type(args.lenpen) is not list:
- args.lenpen = [args.lenpen]
- if type(args.weight1) is not list:
- args.weight1 = [args.weight1]
- if type(args.weight2) is not list:
- args.weight2 = [args.weight2]
- if type(args.weight3) is not list:
- args.weight3 = [args.weight3]
- if args.all_shards:
- shard_ids = list(range(args.num_shards))
- else:
- shard_ids = [args.shard_id]
-
- for shard_id in shard_ids:
- (
- pre_gen,
- left_to_right_preprocessed_dir,
- right_to_left_preprocessed_dir,
- backwards_preprocessed_dir,
- lm_preprocessed_dir,
- ) = rerank_utils.get_directories(
- args.data_dir_name,
- args.num_rescore,
- args.gen_subset,
- args.gen_model_name,
- shard_id,
- args.num_shards,
- args.sampling,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
- rerank_generate.gen_and_reprocess_nbest(args)
- rerank_score_bw.score_bw(args)
- rerank_score_lm.score_lm(args)
-
- if args.write_hypos is None:
- write_targets = pre_gen + "/matched_targets"
- write_hypos = pre_gen + "/matched_hypos"
- else:
- write_targets = args.write_hypos + "_targets" + args.gen_subset
- write_hypos = args.write_hypos + "_hypos" + args.gen_subset
-
- if args.all_shards:
- write_targets += "_all_shards"
- write_hypos += "_all_shards"
-
- (
- best_lenpen,
- best_weight1,
- best_weight2,
- best_weight3,
- best_score,
- ) = match_target_hypo(args, write_targets, write_hypos)
-
- return best_lenpen, best_weight1, best_weight2, best_weight3, best_score
-
-
-def cli_main():
- parser = rerank_options.get_reranking_parser()
- args = options.parse_args_and_arch(parser)
- rerank(args)
-
-
-if __name__ == "__main__":
- cli_main()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md
deleted file mode 100644
index 9d3d7b67dc08440b5f4d1c5a7ffcd4bd6e76c14f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md
+++ /dev/null
@@ -1,179 +0,0 @@
-# Examples of Training scripts for Non-autoregressive Machine Translation models
-
-### Non-autoregressive Transformer (NAT, Gu et al., 2017)
-Note that we need to have an additional module to perform "length prediction" (`--length-loss-factor`) before generating the whole sequence.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch nonautoregressive_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-### Fast Structured Decoding for Sequence Models (NAT-CRF, Sun et al., 2019)
-Note that we implemented a low-rank appromixated CRF model by setting `--crf-lowrank-approx=32` and `--crf-beam-approx=64` as discribed in the original paper. All other settings are the same as the vanilla NAT model.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch nacrf_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --word-ins-loss-factor 0.5 \
- --crf-lowrank-approx 32 \
- --crf-beam-approx 64 \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-### Non-autoregressive Transformer with Iterative Refinement (iNAT, Lee et al., 2018)
-Note that `--train-step` means how many iterations of refinement we used during training, and `--dae-ratio` controls the ratio of denoising auto-encoder training described in the original paper.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch iterative_nonautoregressive_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --train-step 4 \
- --dae-ratio 0.5 \
- --stochastic-approx \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-### Insertion Transformer (InsT, Stern et al., 2019)
-Note that we need to specify the "slot-loss" (uniform or balanced tree) described in the original paper. Here we use `--label-tau` to control the temperature.
-
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch insertion_transformer \
- --noise random_delete \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-### Mask Predict (CMLM, Ghazvininejad et al., 2019)
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch cmlm_transformer \
- --noise random_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-
-
-### Levenshtein Transformer (LevT, Gu et al., 2019)
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch levenshtein_transformer \
- --noise random_delete \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py
deleted file mode 100644
index 8b7eb2ec4fc5190c4dcdfe34b0259e6f448e18a9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""isort:skip_file"""
-
-from .dictionary import Dictionary, TruncatedDictionary
-
-from .fairseq_dataset import FairseqDataset, FairseqIterableDataset
-
-from .base_wrapper_dataset import BaseWrapperDataset
-
-from .add_target_dataset import AddTargetDataset
-from .append_token_dataset import AppendTokenDataset
-from .audio.raw_audio_dataset import BinarizedAudioDataset, FileAudioDataset
-from .audio.hubert_dataset import HubertDataset
-from .backtranslation_dataset import BacktranslationDataset
-from .bucket_pad_length_dataset import BucketPadLengthDataset
-from .colorize_dataset import ColorizeDataset
-from .concat_dataset import ConcatDataset
-from .concat_sentences_dataset import ConcatSentencesDataset
-from .denoising_dataset import DenoisingDataset
-from .id_dataset import IdDataset
-from .indexed_dataset import (
- IndexedCachedDataset,
- IndexedDataset,
- IndexedRawTextDataset,
- MMapIndexedDataset,
-)
-from .language_pair_dataset import LanguagePairDataset
-from .list_dataset import ListDataset
-from .lm_context_window_dataset import LMContextWindowDataset
-from .lru_cache_dataset import LRUCacheDataset
-from .mask_tokens_dataset import MaskTokensDataset
-from .monolingual_dataset import MonolingualDataset
-from .multi_corpus_sampled_dataset import MultiCorpusSampledDataset
-from .nested_dictionary_dataset import NestedDictionaryDataset
-from .noising import NoisingDataset
-from .numel_dataset import NumelDataset
-from .num_samples_dataset import NumSamplesDataset
-from .offset_tokens_dataset import OffsetTokensDataset
-from .pad_dataset import LeftPadDataset, PadDataset, RightPadDataset
-from .prepend_dataset import PrependDataset
-from .prepend_token_dataset import PrependTokenDataset
-from .raw_label_dataset import RawLabelDataset
-from .replace_dataset import ReplaceDataset
-from .resampling_dataset import ResamplingDataset
-from .roll_dataset import RollDataset
-from .round_robin_zip_datasets import RoundRobinZipDatasets
-from .sort_dataset import SortDataset
-from .strip_token_dataset import StripTokenDataset
-from .subsample_dataset import SubsampleDataset
-from .token_block_dataset import TokenBlockDataset
-from .transform_eos_dataset import TransformEosDataset
-from .transform_eos_lang_pair_dataset import TransformEosLangPairDataset
-from .shorten_dataset import TruncateDataset, RandomCropDataset
-from .multilingual.sampled_multi_dataset import SampledMultiDataset
-from .multilingual.sampled_multi_epoch_dataset import SampledMultiEpochDataset
-from .fasta_dataset import FastaDataset, EncodedFastaDataset
-
-from .iterators import (
- CountingIterator,
- EpochBatchIterator,
- GroupedIterator,
- ShardedIterator,
-)
-
-__all__ = [
- "AddTargetDataset",
- "AppendTokenDataset",
- "BacktranslationDataset",
- "BaseWrapperDataset",
- "BinarizedAudioDataset",
- "BucketPadLengthDataset",
- "ColorizeDataset",
- "ConcatDataset",
- "ConcatSentencesDataset",
- "CountingIterator",
- "DenoisingDataset",
- "Dictionary",
- "EncodedFastaDataset",
- "EpochBatchIterator",
- "FairseqDataset",
- "FairseqIterableDataset",
- "FastaDataset",
- "FileAudioDataset",
- "GroupedIterator",
- "HubertDataset",
- "IdDataset",
- "IndexedCachedDataset",
- "IndexedDataset",
- "IndexedRawTextDataset",
- "LanguagePairDataset",
- "LeftPadDataset",
- "ListDataset",
- "LMContextWindowDataset",
- "LRUCacheDataset",
- "MaskTokensDataset",
- "MMapIndexedDataset",
- "MonolingualDataset",
- "MultiCorpusSampledDataset",
- "NestedDictionaryDataset",
- "NoisingDataset",
- "NumelDataset",
- "NumSamplesDataset",
- "OffsetTokensDataset",
- "PadDataset",
- "PrependDataset",
- "PrependTokenDataset",
- "RandomCropDataset",
- "RawLabelDataset",
- "ResamplingDataset",
- "ReplaceDataset",
- "RightPadDataset",
- "RollDataset",
- "RoundRobinZipDatasets",
- "SampledMultiDataset",
- "SampledMultiEpochDataset",
- "ShardedIterator",
- "SortDataset",
- "StripTokenDataset",
- "SubsampleDataset",
- "TokenBlockDataset",
- "TransformEosDataset",
- "TransformEosLangPairDataset",
- "TruncateDataset",
- "TruncatedDictionary",
-]
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py
deleted file mode 100644
index 11c7dc1ea966a54f8915ef164377e40f90e851a1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-from fairseq.tasks import register_task
-from fairseq.tasks.speech_to_text import SpeechToTextTask
-from fairseq.tasks.translation import (
- TranslationTask, TranslationConfig
-)
-
-try:
- import examples.simultaneous_translation # noqa
- import_successful = True
-except BaseException:
- import_successful = False
-
-
-logger = logging.getLogger(__name__)
-
-
-def check_import(flag):
- if not flag:
- raise ImportError(
- "'examples.simultaneous_translation' is not correctly imported. "
- "Please considering `pip install -e $FAIRSEQ_DIR`."
- )
-
-
-@register_task("simul_speech_to_text")
-class SimulSpeechToTextTask(SpeechToTextTask):
- def __init__(self, args, tgt_dict):
- check_import(import_successful)
- super().__init__(args, tgt_dict)
-
-
-@register_task("simul_text_to_text", dataclass=TranslationConfig)
-class SimulTextToTextTask(TranslationTask):
- def __init__(self, cfg, src_dict, tgt_dict):
- check_import(import_successful)
- super().__init__(cfg, src_dict, tgt_dict)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md
deleted file mode 100644
index 57104230655c7c517d25904e634c53b6159ee60f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md
+++ /dev/null
@@ -1,42 +0,0 @@
-# Unit to Speech Model (unit2speech)
-
-Unit to speech model is modified Tacotron2 model that learns to synthesize speech from discrete speech units. All models are trained on quantized [LJSpeech](https://keithito.com/LJ-Speech-Dataset/).
-
-Upstream Units | Download Link
-|-|-
-Log Mel Filterbank + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km50/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km100/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km200/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km500/tts_checkpoint_best.pt)
-Modified CPC + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km50/tts_checkpoint_best.pt)
-Modified CPC + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km100/tts_checkpoint_best.pt)
-Modified CPC + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km200/tts_checkpoint_best.pt)
-Modified CPC + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km500/tts_checkpoint_best.pt)
-HuBERT Base + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km50/tts_checkpoint_best.pt)
-HuBERT Base + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km100/tts_checkpoint_best.pt)
-HuBERT Base + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km200/tts_checkpoint_best.pt)
-HuBERT Base + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km500/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km50/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km100/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km200/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km500/tts_checkpoint_best.pt)
-
-## Run inference using a unit2speech model
-* Install librosa, unidecode and inflect using `pip install librosa, unidecode, inflect`
-* Download [Waveglow checkpoint](https://dl.fbaipublicfiles.com/textless_nlp/gslm/waveglow_256channels_new.pt). This is the vocoder.
-
-Sample commnd to run inference using trained unit2speech models. Please note that the quantized audio to synthesized should be using the same units as the unit2speech model was trained with.
-```
-FAIRSEQ_ROOT=
-TTS_MODEL_PATH=
-QUANTIZED_UNIT_PATH=
-OUT_DIR=
-WAVEGLOW_PATH=
-
-PYTHONPATH=${FAIRSEQ_ROOT}:${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech python ${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py \
- --tts_model_path $TTS_MODEL_PATH \
- --quantized_unit_path $QUANTIZED_UNIT_PATH \
- --out_audio_dir $OUT_DIR \
- --waveglow_path $WAVEGLOW_PATH \
- --max_decoder_steps 2000
-```
\ No newline at end of file
diff --git a/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py b/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py
deleted file mode 100644
index 375e4d50872d5c68ee57ca17470a2ca425425eba..0000000000000000000000000000000000000000
--- a/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import re
-from unidecode import unidecode
-import pyopenjtalk
-
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
- r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
- r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ts', 'ʦ'),
- ('u', 'ɯ'),
- ('j', 'ʥ'),
- ('y', 'j'),
- ('ni', 'n^i'),
- ('nj', 'n^'),
- ('hi', 'çi'),
- ('hj', 'ç'),
- ('f', 'ɸ'),
- ('I', 'i*'),
- ('U', 'ɯ*'),
- ('r', 'ɾ')
-]]
-
-# List of (romaji, ipa2) pairs for marks:
-_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('u', 'ɯ'),
- ('ʧ', 'tʃ'),
- ('j', 'dʑ'),
- ('y', 'j'),
- ('ni', 'n^i'),
- ('nj', 'n^'),
- ('hi', 'çi'),
- ('hj', 'ç'),
- ('f', 'ɸ'),
- ('I', 'i*'),
- ('U', 'ɯ*'),
- ('r', 'ɾ')
-]]
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'Q([↑↓]*[kg])', r'k#\1'),
- (r'Q([↑↓]*[tdjʧ])', r't#\1'),
- (r'Q([↑↓]*[sʃ])', r's\1'),
- (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'N([↑↓]*[pbm])', r'm\1'),
- (r'N([↑↓]*[ʧʥj])', r'n^\1'),
- (r'N([↑↓]*[tdn])', r'n\1'),
- (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-def symbols_to_japanese(text):
- for regex, replacement in _symbols_to_japanese:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_romaji_with_accent(text):
- '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
- text = symbols_to_japanese(text)
- sentences = re.split(_japanese_marks, text)
- marks = re.findall(_japanese_marks, text)
- text = ''
- for i, sentence in enumerate(sentences):
- if re.match(_japanese_characters, sentence):
- if text != '':
- text += ' '
- labels = pyopenjtalk.extract_fullcontext(sentence)
- for n, label in enumerate(labels):
- phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
- if phoneme not in ['sil', 'pau']:
- text += phoneme.replace('ch', 'ʧ').replace('sh',
- 'ʃ').replace('cl', 'Q')
- else:
- continue
- # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
- a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
- a2 = int(re.search(r"\+(\d+)\+", label).group(1))
- a3 = int(re.search(r"\+(\d+)/", label).group(1))
- if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
- a2_next = -1
- else:
- a2_next = int(
- re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
- # Accent phrase boundary
- if a3 == 1 and a2_next == 1:
- text += ' '
- # Falling
- elif a1 == 0 and a2_next == a2 + 1:
- text += '↓'
- # Rising
- elif a2 == 1 and a2_next == 2:
- text += '↑'
- if i < len(marks):
- text += unidecode(marks[i]).replace(' ', '')
- return text
-
-
-def get_real_sokuon(text):
- for regex, replacement in _real_sokuon:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def get_real_hatsuon(text):
- for regex, replacement in _real_hatsuon:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa(text):
- text = japanese_to_romaji_with_accent(text).replace('...', '…')
- text = re.sub(
- r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
- text = get_real_sokuon(text)
- text = get_real_hatsuon(text)
- for regex, replacement in _romaji_to_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa2(text):
- text = japanese_to_romaji_with_accent(text).replace('...', '…')
- text = get_real_sokuon(text)
- text = get_real_hatsuon(text)
- for regex, replacement in _romaji_to_ipa2:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa3(text):
- text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
- 'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
- text = re.sub(
- r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
- text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
- return text
diff --git a/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py b/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py
deleted file mode 100644
index c215099fedacae35c6798fdd9b8420a447aa16bb..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-from audiocraft.quantization.vq import ResidualVectorQuantizer
-
-
-class TestResidualVectorQuantizer:
-
- def test_rvq(self):
- x = torch.randn(1, 16, 2048)
- vq = ResidualVectorQuantizer(n_q=8, dimension=16, bins=8)
- res = vq(x, 1.)
- assert res.x.shape == torch.Size([1, 16, 2048])
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py
deleted file mode 100644
index feb7a8222487756d38482da95183bbbcbbe96ed9..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py
+++ /dev/null
@@ -1,864 +0,0 @@
-
-import math
-import json
-import copy
-from typing import List, Dict
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.modeling.proposal_generator.build import PROPOSAL_GENERATOR_REGISTRY
-from detectron2.layers import ShapeSpec, cat
-from detectron2.structures import Instances, Boxes
-from detectron2.modeling import detector_postprocess
-from detectron2.utils.comm import get_world_size
-from detectron2.config import configurable
-
-from ..layers.heatmap_focal_loss import heatmap_focal_loss_jit
-from ..layers.heatmap_focal_loss import binary_heatmap_focal_loss
-from ..layers.iou_loss import IOULoss
-from ..layers.ml_nms import ml_nms
-from ..debug import debug_train, debug_test
-from .utils import reduce_sum, _transpose
-from .centernet_head import CenterNetHead
-
-__all__ = ["CenterNet"]
-
-INF = 100000000
-
-@PROPOSAL_GENERATOR_REGISTRY.register()
-class CenterNet(nn.Module):
- @configurable
- def __init__(self,
- # input_shape: Dict[str, ShapeSpec],
- in_channels=256,
- *,
- num_classes=80,
- in_features=("p3", "p4", "p5", "p6", "p7"),
- strides=(8, 16, 32, 64, 128),
- score_thresh=0.05,
- hm_min_overlap=0.8,
- loc_loss_type='giou',
- min_radius=4,
- hm_focal_alpha=0.25,
- hm_focal_beta=4,
- loss_gamma=2.0,
- reg_weight=2.0,
- not_norm_reg=True,
- with_agn_hm=False,
- only_proposal=False,
- as_proposal=False,
- not_nms=False,
- pos_weight=1.,
- neg_weight=1.,
- sigmoid_clamp=1e-4,
- ignore_high_fp=-1.,
- center_nms=False,
- sizes_of_interest=[[0,80],[64,160],[128,320],[256,640],[512,10000000]],
- more_pos=False,
- more_pos_thresh=0.2,
- more_pos_topk=9,
- pre_nms_topk_train=1000,
- pre_nms_topk_test=1000,
- post_nms_topk_train=100,
- post_nms_topk_test=100,
- nms_thresh_train=0.6,
- nms_thresh_test=0.6,
- no_reduce=False,
- debug=False,
- vis_thresh=0.5,
- pixel_mean=[103.530,116.280,123.675],
- pixel_std=[1.0,1.0,1.0],
- device='cuda',
- centernet_head=None,
- ):
- super().__init__()
- self.num_classes = num_classes
- self.in_features = in_features
- self.strides = strides
- self.score_thresh = score_thresh
- self.min_radius = min_radius
- self.hm_focal_alpha = hm_focal_alpha
- self.hm_focal_beta = hm_focal_beta
- self.loss_gamma = loss_gamma
- self.reg_weight = reg_weight
- self.not_norm_reg = not_norm_reg
- self.with_agn_hm = with_agn_hm
- self.only_proposal = only_proposal
- self.as_proposal = as_proposal
- self.not_nms = not_nms
- self.pos_weight = pos_weight
- self.neg_weight = neg_weight
- self.sigmoid_clamp = sigmoid_clamp
- self.ignore_high_fp = ignore_high_fp
- self.center_nms = center_nms
- self.sizes_of_interest = sizes_of_interest
- self.more_pos = more_pos
- self.more_pos_thresh = more_pos_thresh
- self.more_pos_topk = more_pos_topk
- self.pre_nms_topk_train = pre_nms_topk_train
- self.pre_nms_topk_test = pre_nms_topk_test
- self.post_nms_topk_train = post_nms_topk_train
- self.post_nms_topk_test = post_nms_topk_test
- self.nms_thresh_train = nms_thresh_train
- self.nms_thresh_test = nms_thresh_test
- self.no_reduce = no_reduce
- self.debug = debug
- self.vis_thresh = vis_thresh
- if self.center_nms:
- self.not_nms = True
- self.iou_loss = IOULoss(loc_loss_type)
- assert (not self.only_proposal) or self.with_agn_hm
- # delta for rendering heatmap
- self.delta = (1 - hm_min_overlap) / (1 + hm_min_overlap)
- if centernet_head is None:
- self.centernet_head = CenterNetHead(
- in_channels=in_channels,
- num_levels=len(in_features),
- with_agn_hm=with_agn_hm,
- only_proposal=only_proposal)
- else:
- self.centernet_head = centernet_head
- if self.debug:
- pixel_mean = torch.Tensor(pixel_mean).to(
- torch.device(device)).view(3, 1, 1)
- pixel_std = torch.Tensor(pixel_std).to(
- torch.device(device)).view(3, 1, 1)
- self.denormalizer = lambda x: x * pixel_std + pixel_mean
-
- @classmethod
- def from_config(cls, cfg, input_shape):
- ret = {
- # 'input_shape': input_shape,
- 'in_channels': input_shape[
- cfg.MODEL.CENTERNET.IN_FEATURES[0]].channels,
- 'num_classes': cfg.MODEL.CENTERNET.NUM_CLASSES,
- 'in_features': cfg.MODEL.CENTERNET.IN_FEATURES,
- 'strides': cfg.MODEL.CENTERNET.FPN_STRIDES,
- 'score_thresh': cfg.MODEL.CENTERNET.INFERENCE_TH,
- 'loc_loss_type': cfg.MODEL.CENTERNET.LOC_LOSS_TYPE,
- 'hm_min_overlap': cfg.MODEL.CENTERNET.HM_MIN_OVERLAP,
- 'min_radius': cfg.MODEL.CENTERNET.MIN_RADIUS,
- 'hm_focal_alpha': cfg.MODEL.CENTERNET.HM_FOCAL_ALPHA,
- 'hm_focal_beta': cfg.MODEL.CENTERNET.HM_FOCAL_BETA,
- 'loss_gamma': cfg.MODEL.CENTERNET.LOSS_GAMMA,
- 'reg_weight': cfg.MODEL.CENTERNET.REG_WEIGHT,
- 'not_norm_reg': cfg.MODEL.CENTERNET.NOT_NORM_REG,
- 'with_agn_hm': cfg.MODEL.CENTERNET.WITH_AGN_HM,
- 'only_proposal': cfg.MODEL.CENTERNET.ONLY_PROPOSAL,
- 'as_proposal': cfg.MODEL.CENTERNET.AS_PROPOSAL,
- 'not_nms': cfg.MODEL.CENTERNET.NOT_NMS,
- 'pos_weight': cfg.MODEL.CENTERNET.POS_WEIGHT,
- 'neg_weight': cfg.MODEL.CENTERNET.NEG_WEIGHT,
- 'sigmoid_clamp': cfg.MODEL.CENTERNET.SIGMOID_CLAMP,
- 'ignore_high_fp': cfg.MODEL.CENTERNET.IGNORE_HIGH_FP,
- 'center_nms': cfg.MODEL.CENTERNET.CENTER_NMS,
- 'sizes_of_interest': cfg.MODEL.CENTERNET.SOI,
- 'more_pos': cfg.MODEL.CENTERNET.MORE_POS,
- 'more_pos_thresh': cfg.MODEL.CENTERNET.MORE_POS_THRESH,
- 'more_pos_topk': cfg.MODEL.CENTERNET.MORE_POS_TOPK,
- 'pre_nms_topk_train': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TRAIN,
- 'pre_nms_topk_test': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TEST,
- 'post_nms_topk_train': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TRAIN,
- 'post_nms_topk_test': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TEST,
- 'nms_thresh_train': cfg.MODEL.CENTERNET.NMS_TH_TRAIN,
- 'nms_thresh_test': cfg.MODEL.CENTERNET.NMS_TH_TEST,
- 'no_reduce': cfg.MODEL.CENTERNET.NO_REDUCE,
- 'debug': cfg.DEBUG,
- 'vis_thresh': cfg.VIS_THRESH,
- 'pixel_mean': cfg.MODEL.PIXEL_MEAN,
- 'pixel_std': cfg.MODEL.PIXEL_STD,
- 'device': cfg.MODEL.DEVICE,
- 'centernet_head': CenterNetHead(
- cfg, [input_shape[f] for f in cfg.MODEL.CENTERNET.IN_FEATURES]),
- }
- return ret
-
-
- def forward(self, images, features_dict, gt_instances):
- features = [features_dict[f] for f in self.in_features]
- clss_per_level, reg_pred_per_level, agn_hm_pred_per_level = \
- self.centernet_head(features)
- grids = self.compute_grids(features)
- shapes_per_level = grids[0].new_tensor(
- [(x.shape[2], x.shape[3]) for x in reg_pred_per_level])
-
- if not self.training:
- return self.inference(
- images, clss_per_level, reg_pred_per_level,
- agn_hm_pred_per_level, grids)
- else:
- pos_inds, labels, reg_targets, flattened_hms = \
- self._get_ground_truth(
- grids, shapes_per_level, gt_instances)
- # logits_pred: M x F, reg_pred: M x 4, agn_hm_pred: M
- logits_pred, reg_pred, agn_hm_pred = self._flatten_outputs(
- clss_per_level, reg_pred_per_level, agn_hm_pred_per_level)
-
- if self.more_pos:
- # add more pixels as positive if \
- # 1. they are within the center3x3 region of an object
- # 2. their regression losses are small (= 0).squeeze(1)
- reg_pred = reg_pred[reg_inds]
- reg_targets_pos = reg_targets[reg_inds]
- reg_weight_map = flattened_hms.max(dim=1)[0]
- reg_weight_map = reg_weight_map[reg_inds]
- reg_weight_map = reg_weight_map * 0 + 1 \
- if self.not_norm_reg else reg_weight_map
- if self.no_reduce:
- reg_norm = max(reg_weight_map.sum(), 1)
- else:
- reg_norm = max(reduce_sum(reg_weight_map.sum()).item() / num_gpus, 1)
-
- reg_loss = self.reg_weight * self.iou_loss(
- reg_pred, reg_targets_pos, reg_weight_map,
- reduction='sum') / reg_norm
- losses['loss_centernet_loc'] = reg_loss
-
- if self.with_agn_hm:
- cat_agn_heatmap = flattened_hms.max(dim=1)[0] # M
- agn_pos_loss, agn_neg_loss = binary_heatmap_focal_loss(
- agn_hm_pred, cat_agn_heatmap, pos_inds,
- alpha=self.hm_focal_alpha,
- beta=self.hm_focal_beta,
- gamma=self.loss_gamma,
- sigmoid_clamp=self.sigmoid_clamp,
- ignore_high_fp=self.ignore_high_fp,
- )
- agn_pos_loss = self.pos_weight * agn_pos_loss / num_pos_avg
- agn_neg_loss = self.neg_weight * agn_neg_loss / num_pos_avg
- losses['loss_centernet_agn_pos'] = agn_pos_loss
- losses['loss_centernet_agn_neg'] = agn_neg_loss
-
- if self.debug:
- print('losses', losses)
- print('total_num_pos', total_num_pos)
- return losses
-
-
- def compute_grids(self, features):
- grids = []
- for level, feature in enumerate(features):
- h, w = feature.size()[-2:]
- shifts_x = torch.arange(
- 0, w * self.strides[level],
- step=self.strides[level],
- dtype=torch.float32, device=feature.device)
- shifts_y = torch.arange(
- 0, h * self.strides[level],
- step=self.strides[level],
- dtype=torch.float32, device=feature.device)
- shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
- shift_x = shift_x.reshape(-1)
- shift_y = shift_y.reshape(-1)
- grids_per_level = torch.stack((shift_x, shift_y), dim=1) + \
- self.strides[level] // 2
- grids.append(grids_per_level)
- return grids
-
-
- def _get_ground_truth(self, grids, shapes_per_level, gt_instances):
- '''
- Input:
- grids: list of tensors [(hl x wl, 2)]_l
- shapes_per_level: list of tuples L x 2:
- gt_instances: gt instances
- Retuen:
- pos_inds: N
- labels: N
- reg_targets: M x 4
- flattened_hms: M x C or M x 1
- N: number of objects in all images
- M: number of pixels from all FPN levels
- '''
-
- # get positive pixel index
- if not self.more_pos:
- pos_inds, labels = self._get_label_inds(
- gt_instances, shapes_per_level)
- else:
- pos_inds, labels = None, None
- heatmap_channels = self.num_classes
- L = len(grids)
- num_loc_list = [len(loc) for loc in grids]
- strides = torch.cat([
- shapes_per_level.new_ones(num_loc_list[l]) * self.strides[l] \
- for l in range(L)]).float() # M
- reg_size_ranges = torch.cat([
- shapes_per_level.new_tensor(self.sizes_of_interest[l]).float().view(
- 1, 2).expand(num_loc_list[l], 2) for l in range(L)]) # M x 2
- grids = torch.cat(grids, dim=0) # M x 2
- M = grids.shape[0]
-
- reg_targets = []
- flattened_hms = []
- for i in range(len(gt_instances)): # images
- boxes = gt_instances[i].gt_boxes.tensor # N x 4
- area = gt_instances[i].gt_boxes.area() # N
- gt_classes = gt_instances[i].gt_classes # N in [0, self.num_classes]
-
- N = boxes.shape[0]
- if N == 0:
- reg_targets.append(grids.new_zeros((M, 4)) - INF)
- flattened_hms.append(
- grids.new_zeros((
- M, 1 if self.only_proposal else heatmap_channels)))
- continue
-
- l = grids[:, 0].view(M, 1) - boxes[:, 0].view(1, N) # M x N
- t = grids[:, 1].view(M, 1) - boxes[:, 1].view(1, N) # M x N
- r = boxes[:, 2].view(1, N) - grids[:, 0].view(M, 1) # M x N
- b = boxes[:, 3].view(1, N) - grids[:, 1].view(M, 1) # M x N
- reg_target = torch.stack([l, t, r, b], dim=2) # M x N x 4
-
- centers = ((boxes[:, [0, 1]] + boxes[:, [2, 3]]) / 2) # N x 2
- centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2
- strides_expanded = strides.view(M, 1, 1).expand(M, N, 2)
- centers_discret = ((centers_expanded / strides_expanded).int() * \
- strides_expanded).float() + strides_expanded / 2 # M x N x 2
-
- is_peak = (((grids.view(M, 1, 2).expand(M, N, 2) - \
- centers_discret) ** 2).sum(dim=2) == 0) # M x N
- is_in_boxes = reg_target.min(dim=2)[0] > 0 # M x N
- is_center3x3 = self.get_center3x3(
- grids, centers, strides) & is_in_boxes # M x N
- is_cared_in_the_level = self.assign_reg_fpn(
- reg_target, reg_size_ranges) # M x N
- reg_mask = is_center3x3 & is_cared_in_the_level # M x N
-
- dist2 = ((grids.view(M, 1, 2).expand(M, N, 2) - \
- centers_expanded) ** 2).sum(dim=2) # M x N
- dist2[is_peak] = 0
- radius2 = self.delta ** 2 * 2 * area # N
- radius2 = torch.clamp(
- radius2, min=self.min_radius ** 2)
- weighted_dist2 = dist2 / radius2.view(1, N).expand(M, N) # M x N
- reg_target = self._get_reg_targets(
- reg_target, weighted_dist2.clone(), reg_mask, area) # M x 4
-
- if self.only_proposal:
- flattened_hm = self._create_agn_heatmaps_from_dist(
- weighted_dist2.clone()) # M x 1
- else:
- flattened_hm = self._create_heatmaps_from_dist(
- weighted_dist2.clone(), gt_classes,
- channels=heatmap_channels) # M x C
-
- reg_targets.append(reg_target)
- flattened_hms.append(flattened_hm)
-
- # transpose im first training_targets to level first ones
- reg_targets = _transpose(reg_targets, num_loc_list)
- flattened_hms = _transpose(flattened_hms, num_loc_list)
- for l in range(len(reg_targets)):
- reg_targets[l] = reg_targets[l] / float(self.strides[l])
- reg_targets = cat([x for x in reg_targets], dim=0) # MB x 4
- flattened_hms = cat([x for x in flattened_hms], dim=0) # MB x C
-
- return pos_inds, labels, reg_targets, flattened_hms
-
-
- def _get_label_inds(self, gt_instances, shapes_per_level):
- '''
- Inputs:
- gt_instances: [n_i], sum n_i = N
- shapes_per_level: L x 2 [(h_l, w_l)]_L
- Returns:
- pos_inds: N'
- labels: N'
- '''
- pos_inds = []
- labels = []
- L = len(self.strides)
- B = len(gt_instances)
- shapes_per_level = shapes_per_level.long()
- loc_per_level = (shapes_per_level[:, 0] * shapes_per_level[:, 1]).long() # L
- level_bases = []
- s = 0
- for l in range(L):
- level_bases.append(s)
- s = s + B * loc_per_level[l]
- level_bases = shapes_per_level.new_tensor(level_bases).long() # L
- strides_default = shapes_per_level.new_tensor(self.strides).float() # L
- for im_i in range(B):
- targets_per_im = gt_instances[im_i]
- bboxes = targets_per_im.gt_boxes.tensor # n x 4
- n = bboxes.shape[0]
- centers = ((bboxes[:, [0, 1]] + bboxes[:, [2, 3]]) / 2) # n x 2
- centers = centers.view(n, 1, 2).expand(n, L, 2)
- strides = strides_default.view(1, L, 1).expand(n, L, 2)
- centers_inds = (centers / strides).long() # n x L x 2
- Ws = shapes_per_level[:, 1].view(1, L).expand(n, L)
- pos_ind = level_bases.view(1, L).expand(n, L) + \
- im_i * loc_per_level.view(1, L).expand(n, L) + \
- centers_inds[:, :, 1] * Ws + \
- centers_inds[:, :, 0] # n x L
- is_cared_in_the_level = self.assign_fpn_level(bboxes)
- pos_ind = pos_ind[is_cared_in_the_level].view(-1)
- label = targets_per_im.gt_classes.view(
- n, 1).expand(n, L)[is_cared_in_the_level].view(-1)
-
- pos_inds.append(pos_ind) # n'
- labels.append(label) # n'
- pos_inds = torch.cat(pos_inds, dim=0).long()
- labels = torch.cat(labels, dim=0)
- return pos_inds, labels # N, N
-
-
- def assign_fpn_level(self, boxes):
- '''
- Inputs:
- boxes: n x 4
- size_ranges: L x 2
- Return:
- is_cared_in_the_level: n x L
- '''
- size_ranges = boxes.new_tensor(
- self.sizes_of_interest).view(len(self.sizes_of_interest), 2) # L x 2
- crit = ((boxes[:, 2:] - boxes[:, :2]) **2).sum(dim=1) ** 0.5 / 2 # n
- n, L = crit.shape[0], size_ranges.shape[0]
- crit = crit.view(n, 1).expand(n, L)
- size_ranges_expand = size_ranges.view(1, L, 2).expand(n, L, 2)
- is_cared_in_the_level = (crit >= size_ranges_expand[:, :, 0]) & \
- (crit <= size_ranges_expand[:, :, 1])
- return is_cared_in_the_level
-
-
- def assign_reg_fpn(self, reg_targets_per_im, size_ranges):
- '''
- TODO (Xingyi): merge it with assign_fpn_level
- Inputs:
- reg_targets_per_im: M x N x 4
- size_ranges: M x 2
- '''
- crit = ((reg_targets_per_im[:, :, :2] + \
- reg_targets_per_im[:, :, 2:])**2).sum(dim=2) ** 0.5 / 2 # M x N
- is_cared_in_the_level = (crit >= size_ranges[:, [0]]) & \
- (crit <= size_ranges[:, [1]])
- return is_cared_in_the_level
-
-
- def _get_reg_targets(self, reg_targets, dist, mask, area):
- '''
- reg_targets (M x N x 4): long tensor
- dist (M x N)
- is_*: M x N
- '''
- dist[mask == 0] = INF * 1.0
- min_dist, min_inds = dist.min(dim=1) # M
- reg_targets_per_im = reg_targets[
- range(len(reg_targets)), min_inds] # M x N x 4 --> M x 4
- reg_targets_per_im[min_dist == INF] = - INF
- return reg_targets_per_im
-
-
- def _create_heatmaps_from_dist(self, dist, labels, channels):
- '''
- dist: M x N
- labels: N
- return:
- heatmaps: M x C
- '''
- heatmaps = dist.new_zeros((dist.shape[0], channels))
- for c in range(channels):
- inds = (labels == c) # N
- if inds.int().sum() == 0:
- continue
- heatmaps[:, c] = torch.exp(-dist[:, inds].min(dim=1)[0])
- zeros = heatmaps[:, c] < 1e-4
- heatmaps[zeros, c] = 0
- return heatmaps
-
-
- def _create_agn_heatmaps_from_dist(self, dist):
- '''
- TODO (Xingyi): merge it with _create_heatmaps_from_dist
- dist: M x N
- return:
- heatmaps: M x 1
- '''
- heatmaps = dist.new_zeros((dist.shape[0], 1))
- heatmaps[:, 0] = torch.exp(-dist.min(dim=1)[0])
- zeros = heatmaps < 1e-4
- heatmaps[zeros] = 0
- return heatmaps
-
-
- def _flatten_outputs(self, clss, reg_pred, agn_hm_pred):
- # Reshape: (N, F, Hl, Wl) -> (N, Hl, Wl, F) -> (sum_l N*Hl*Wl, F)
- clss = cat([x.permute(0, 2, 3, 1).reshape(-1, x.shape[1]) \
- for x in clss], dim=0) if clss[0] is not None else None
- reg_pred = cat(
- [x.permute(0, 2, 3, 1).reshape(-1, 4) for x in reg_pred], dim=0)
- agn_hm_pred = cat([x.permute(0, 2, 3, 1).reshape(-1) \
- for x in agn_hm_pred], dim=0) if self.with_agn_hm else None
- return clss, reg_pred, agn_hm_pred
-
-
- def get_center3x3(self, locations, centers, strides):
- '''
- Inputs:
- locations: M x 2
- centers: N x 2
- strides: M
- '''
- M, N = locations.shape[0], centers.shape[0]
- locations_expanded = locations.view(M, 1, 2).expand(M, N, 2) # M x N x 2
- centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2
- strides_expanded = strides.view(M, 1, 1).expand(M, N, 2) # M x N
- centers_discret = ((centers_expanded / strides_expanded).int() * \
- strides_expanded).float() + strides_expanded / 2 # M x N x 2
- dist_x = (locations_expanded[:, :, 0] - centers_discret[:, :, 0]).abs()
- dist_y = (locations_expanded[:, :, 1] - centers_discret[:, :, 1]).abs()
- return (dist_x <= strides_expanded[:, :, 0]) & \
- (dist_y <= strides_expanded[:, :, 0])
-
-
- def inference(self, images, clss_per_level, reg_pred_per_level,
- agn_hm_pred_per_level, grids):
- logits_pred = [x.sigmoid() if x is not None else None \
- for x in clss_per_level]
- agn_hm_pred_per_level = [x.sigmoid() if x is not None else None \
- for x in agn_hm_pred_per_level]
-
- if self.only_proposal:
- proposals = self.predict_instances(
- grids, agn_hm_pred_per_level, reg_pred_per_level,
- images.image_sizes, [None for _ in agn_hm_pred_per_level])
- else:
- proposals = self.predict_instances(
- grids, logits_pred, reg_pred_per_level,
- images.image_sizes, agn_hm_pred_per_level)
- if self.as_proposal or self.only_proposal:
- for p in range(len(proposals)):
- proposals[p].proposal_boxes = proposals[p].get('pred_boxes')
- proposals[p].objectness_logits = proposals[p].get('scores')
- proposals[p].remove('pred_boxes')
-
- if self.debug:
- debug_test(
- [self.denormalizer(x) for x in images],
- logits_pred, reg_pred_per_level,
- agn_hm_pred_per_level, preds=proposals,
- vis_thresh=self.vis_thresh,
- debug_show_name=False)
- return proposals, {}
-
-
- def predict_instances(
- self, grids, logits_pred, reg_pred, image_sizes, agn_hm_pred,
- is_proposal=False):
- sampled_boxes = []
- for l in range(len(grids)):
- sampled_boxes.append(self.predict_single_level(
- grids[l], logits_pred[l], reg_pred[l] * self.strides[l],
- image_sizes, agn_hm_pred[l], l, is_proposal=is_proposal))
- boxlists = list(zip(*sampled_boxes))
- boxlists = [Instances.cat(boxlist) for boxlist in boxlists]
- boxlists = self.nms_and_topK(
- boxlists, nms=not self.not_nms)
- return boxlists
-
-
- def predict_single_level(
- self, grids, heatmap, reg_pred, image_sizes, agn_hm, level,
- is_proposal=False):
- N, C, H, W = heatmap.shape
- # put in the same format as grids
- if self.center_nms:
- heatmap_nms = nn.functional.max_pool2d(
- heatmap, (3, 3), stride=1, padding=1)
- heatmap = heatmap * (heatmap_nms == heatmap).float()
- heatmap = heatmap.permute(0, 2, 3, 1) # N x H x W x C
- heatmap = heatmap.reshape(N, -1, C) # N x HW x C
- box_regression = reg_pred.view(N, 4, H, W).permute(0, 2, 3, 1) # N x H x W x 4
- box_regression = box_regression.reshape(N, -1, 4)
-
- candidate_inds = heatmap > self.score_thresh # 0.05
- pre_nms_top_n = candidate_inds.view(N, -1).sum(1) # N
- pre_nms_topk = self.pre_nms_topk_train if self.training else self.pre_nms_topk_test
- pre_nms_top_n = pre_nms_top_n.clamp(max=pre_nms_topk) # N
-
- if agn_hm is not None:
- agn_hm = agn_hm.view(N, 1, H, W).permute(0, 2, 3, 1)
- agn_hm = agn_hm.reshape(N, -1)
- heatmap = heatmap * agn_hm[:, :, None]
-
- results = []
- for i in range(N):
- per_box_cls = heatmap[i] # HW x C
- per_candidate_inds = candidate_inds[i] # n
- per_box_cls = per_box_cls[per_candidate_inds] # n
-
- per_candidate_nonzeros = per_candidate_inds.nonzero() # n
- per_box_loc = per_candidate_nonzeros[:, 0] # n
- per_class = per_candidate_nonzeros[:, 1] # n
-
- per_box_regression = box_regression[i] # HW x 4
- per_box_regression = per_box_regression[per_box_loc] # n x 4
- per_grids = grids[per_box_loc] # n x 2
-
- per_pre_nms_top_n = pre_nms_top_n[i] # 1
-
- if per_candidate_inds.sum().item() > per_pre_nms_top_n.item():
- per_box_cls, top_k_indices = \
- per_box_cls.topk(per_pre_nms_top_n, sorted=False)
- per_class = per_class[top_k_indices]
- per_box_regression = per_box_regression[top_k_indices]
- per_grids = per_grids[top_k_indices]
-
- detections = torch.stack([
- per_grids[:, 0] - per_box_regression[:, 0],
- per_grids[:, 1] - per_box_regression[:, 1],
- per_grids[:, 0] + per_box_regression[:, 2],
- per_grids[:, 1] + per_box_regression[:, 3],
- ], dim=1) # n x 4
-
- # avoid invalid boxes in RoI heads
- detections[:, 2] = torch.max(detections[:, 2], detections[:, 0] + 0.01)
- detections[:, 3] = torch.max(detections[:, 3], detections[:, 1] + 0.01)
- boxlist = Instances(image_sizes[i])
- boxlist.scores = torch.sqrt(per_box_cls) \
- if self.with_agn_hm else per_box_cls # n
- # import pdb; pdb.set_trace()
- boxlist.pred_boxes = Boxes(detections)
- boxlist.pred_classes = per_class
- results.append(boxlist)
- return results
-
-
- def nms_and_topK(self, boxlists, nms=True):
- num_images = len(boxlists)
- results = []
- for i in range(num_images):
- nms_thresh = self.nms_thresh_train if self.training else \
- self.nms_thresh_test
- result = ml_nms(boxlists[i], nms_thresh) if nms else boxlists[i]
- if self.debug:
- print('#proposals before nms', len(boxlists[i]))
- print('#proposals after nms', len(result))
- num_dets = len(result)
- post_nms_topk = self.post_nms_topk_train if self.training else \
- self.post_nms_topk_test
- if num_dets > post_nms_topk:
- cls_scores = result.scores
- image_thresh, _ = torch.kthvalue(
- cls_scores.float().cpu(),
- num_dets - post_nms_topk + 1
- )
- keep = cls_scores >= image_thresh.item()
- keep = torch.nonzero(keep).squeeze(1)
- result = result[keep]
- if self.debug:
- print('#proposals after filter', len(result))
- results.append(result)
- return results
-
-
- def _add_more_pos(self, reg_pred, gt_instances, shapes_per_level):
- labels, level_masks, c33_inds, c33_masks, c33_regs = \
- self._get_c33_inds(gt_instances, shapes_per_level)
- N, L, K = labels.shape[0], len(self.strides), 9
- c33_inds[c33_masks == 0] = 0
- reg_pred_c33 = reg_pred[c33_inds].detach() # N x L x K
- invalid_reg = c33_masks == 0
- c33_regs_expand = c33_regs.view(N * L * K, 4).clamp(min=0)
- if N > 0:
- with torch.no_grad():
- c33_reg_loss = self.iou_loss(
- reg_pred_c33.view(N * L * K, 4),
- c33_regs_expand, None,
- reduction='none').view(N, L, K).detach() # N x L x K
- else:
- c33_reg_loss = reg_pred_c33.new_zeros((N, L, K)).detach()
- c33_reg_loss[invalid_reg] = INF # N x L x K
- c33_reg_loss.view(N * L, K)[level_masks.view(N * L), 4] = 0 # real center
- c33_reg_loss = c33_reg_loss.view(N, L * K)
- if N == 0:
- loss_thresh = c33_reg_loss.new_ones((N)).float()
- else:
- loss_thresh = torch.kthvalue(
- c33_reg_loss, self.more_pos_topk, dim=1)[0] # N
- loss_thresh[loss_thresh > self.more_pos_thresh] = self.more_pos_thresh # N
- new_pos = c33_reg_loss.view(N, L, K) < \
- loss_thresh.view(N, 1, 1).expand(N, L, K)
- pos_inds = c33_inds[new_pos].view(-1) # P
- labels = labels.view(N, 1, 1).expand(N, L, K)[new_pos].view(-1)
- return pos_inds, labels
-
-
- def _get_c33_inds(self, gt_instances, shapes_per_level):
- '''
- TODO (Xingyi): The current implementation is ugly. Refactor.
- Get the center (and the 3x3 region near center) locations of each objects
- Inputs:
- gt_instances: [n_i], sum n_i = N
- shapes_per_level: L x 2 [(h_l, w_l)]_L
- '''
- labels = []
- level_masks = []
- c33_inds = []
- c33_masks = []
- c33_regs = []
- L = len(self.strides)
- B = len(gt_instances)
- shapes_per_level = shapes_per_level.long()
- loc_per_level = (shapes_per_level[:, 0] * shapes_per_level[:, 1]).long() # L
- level_bases = []
- s = 0
- for l in range(L):
- level_bases.append(s)
- s = s + B * loc_per_level[l]
- level_bases = shapes_per_level.new_tensor(level_bases).long() # L
- strides_default = shapes_per_level.new_tensor(self.strides).float() # L
- K = 9
- dx = shapes_per_level.new_tensor([-1, 0, 1, -1, 0, 1, -1, 0, 1]).long()
- dy = shapes_per_level.new_tensor([-1, -1, -1, 0, 0, 0, 1, 1, 1]).long()
- for im_i in range(B):
- targets_per_im = gt_instances[im_i]
- bboxes = targets_per_im.gt_boxes.tensor # n x 4
- n = bboxes.shape[0]
- if n == 0:
- continue
- centers = ((bboxes[:, [0, 1]] + bboxes[:, [2, 3]]) / 2) # n x 2
- centers = centers.view(n, 1, 2).expand(n, L, 2)
-
- strides = strides_default.view(1, L, 1).expand(n, L, 2) #
- centers_inds = (centers / strides).long() # n x L x 2
- center_grids = centers_inds * strides + strides // 2# n x L x 2
- l = center_grids[:, :, 0] - bboxes[:, 0].view(n, 1).expand(n, L)
- t = center_grids[:, :, 1] - bboxes[:, 1].view(n, 1).expand(n, L)
- r = bboxes[:, 2].view(n, 1).expand(n, L) - center_grids[:, :, 0]
- b = bboxes[:, 3].view(n, 1).expand(n, L) - center_grids[:, :, 1] # n x L
- reg = torch.stack([l, t, r, b], dim=2) # n x L x 4
- reg = reg / strides_default.view(1, L, 1).expand(n, L, 4).float()
-
- Ws = shapes_per_level[:, 1].view(1, L).expand(n, L)
- Hs = shapes_per_level[:, 0].view(1, L).expand(n, L)
- expand_Ws = Ws.view(n, L, 1).expand(n, L, K)
- expand_Hs = Hs.view(n, L, 1).expand(n, L, K)
- label = targets_per_im.gt_classes.view(n).clone()
- mask = reg.min(dim=2)[0] >= 0 # n x L
- mask = mask & self.assign_fpn_level(bboxes)
- labels.append(label) # n
- level_masks.append(mask) # n x L
-
- Dy = dy.view(1, 1, K).expand(n, L, K)
- Dx = dx.view(1, 1, K).expand(n, L, K)
- c33_ind = level_bases.view(1, L, 1).expand(n, L, K) + \
- im_i * loc_per_level.view(1, L, 1).expand(n, L, K) + \
- (centers_inds[:, :, 1:2].expand(n, L, K) + Dy) * expand_Ws + \
- (centers_inds[:, :, 0:1].expand(n, L, K) + Dx) # n x L x K
-
- c33_mask = \
- ((centers_inds[:, :, 1:2].expand(n, L, K) + dy) < expand_Hs) & \
- ((centers_inds[:, :, 1:2].expand(n, L, K) + dy) >= 0) & \
- ((centers_inds[:, :, 0:1].expand(n, L, K) + dx) < expand_Ws) & \
- ((centers_inds[:, :, 0:1].expand(n, L, K) + dx) >= 0)
- # TODO (Xingyi): think about better way to implement this
- # Currently it hard codes the 3x3 region
- c33_reg = reg.view(n, L, 1, 4).expand(n, L, K, 4).clone()
- c33_reg[:, :, [0, 3, 6], 0] -= 1
- c33_reg[:, :, [0, 3, 6], 2] += 1
- c33_reg[:, :, [2, 5, 8], 0] += 1
- c33_reg[:, :, [2, 5, 8], 2] -= 1
- c33_reg[:, :, [0, 1, 2], 1] -= 1
- c33_reg[:, :, [0, 1, 2], 3] += 1
- c33_reg[:, :, [6, 7, 8], 1] += 1
- c33_reg[:, :, [6, 7, 8], 3] -= 1
- c33_mask = c33_mask & (c33_reg.min(dim=3)[0] >= 0) # n x L x K
- c33_inds.append(c33_ind)
- c33_masks.append(c33_mask)
- c33_regs.append(c33_reg)
-
- if len(level_masks) > 0:
- labels = torch.cat(labels, dim=0)
- level_masks = torch.cat(level_masks, dim=0)
- c33_inds = torch.cat(c33_inds, dim=0).long()
- c33_regs = torch.cat(c33_regs, dim=0)
- c33_masks = torch.cat(c33_masks, dim=0)
- else:
- labels = shapes_per_level.new_zeros((0)).long()
- level_masks = shapes_per_level.new_zeros((0, L)).bool()
- c33_inds = shapes_per_level.new_zeros((0, L, K)).long()
- c33_regs = shapes_per_level.new_zeros((0, L, K, 4)).float()
- c33_masks = shapes_per_level.new_zeros((0, L, K)).bool()
- return labels, level_masks, c33_inds, c33_masks, c33_regs # N x L, N x L x K
\ No newline at end of file
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh b/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh
deleted file mode 100644
index 6d2ba9a6265c0d5fa580035952a1f568dd8d9e44..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh
+++ /dev/null
@@ -1,37 +0,0 @@
-mkdir celeba-hq-dataset
-
-unzip data256x256.zip -d celeba-hq-dataset/
-
-# Reindex
-for i in `echo {00001..30000}`
-do
- mv 'celeba-hq-dataset/data256x256/'$i'.jpg' 'celeba-hq-dataset/data256x256/'$[10#$i - 1]'.jpg'
-done
-
-
-# Split: split train -> train & val
-cat fetch_data/train_shuffled.flist | shuf > celeba-hq-dataset/temp_train_shuffled.flist
-cat celeba-hq-dataset/temp_train_shuffled.flist | head -n 2000 > celeba-hq-dataset/val_shuffled.flist
-cat celeba-hq-dataset/temp_train_shuffled.flist | tail -n +2001 > celeba-hq-dataset/train_shuffled.flist
-cat fetch_data/val_shuffled.flist > celeba-hq-dataset/visual_test_shuffled.flist
-
-mkdir celeba-hq-dataset/train_256/
-mkdir celeba-hq-dataset/val_source_256/
-mkdir celeba-hq-dataset/visual_test_source_256/
-
-cat celeba-hq-dataset/train_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/train_256/
-cat celeba-hq-dataset/val_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/val_source_256/
-cat celeba-hq-dataset/visual_test_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/visual_test_source_256/
-
-
-# create location config celeba.yaml
-PWD=$(pwd)
-DATASET=${PWD}/celeba-hq-dataset
-CELEBA=${PWD}/configs/training/location/celeba.yaml
-
-touch $CELEBA
-echo "# @package _group_" >> $CELEBA
-echo "data_root_dir: ${DATASET}/" >> $CELEBA
-echo "out_root_dir: ${PWD}/experiments/" >> $CELEBA
-echo "tb_dir: ${PWD}/tb_logs/" >> $CELEBA
-echo "pretrained_models: ${PWD}/" >> $CELEBA
diff --git a/spaces/OpenMotionLab/MotionGPT/pyrender/tests/conftest.py b/spaces/OpenMotionLab/MotionGPT/pyrender/tests/conftest.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py b/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py
deleted file mode 100644
index 4e06ab8cbe6b43a355c0a9cfb3f2d688438d2c64..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py
+++ /dev/null
@@ -1,201 +0,0 @@
-# ------------------------------------------------------------------------------
-# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/cityscapes_evaluation.py
-# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
-# ------------------------------------------------------------------------------
-
-import glob
-import logging
-import numpy as np
-import os
-import tempfile
-from collections import OrderedDict
-import torch
-from PIL import Image
-
-from detectron2.data import MetadataCatalog
-from detectron2.utils import comm
-from detectron2.utils.file_io import PathManager
-
-from .evaluator import DatasetEvaluator
-
-
-class CityscapesEvaluator(DatasetEvaluator):
- """
- Base class for evaluation using cityscapes API.
- """
-
- def __init__(self, dataset_name):
- """
- Args:
- dataset_name (str): the name of the dataset.
- It must have the following metadata associated with it:
- "thing_classes", "gt_dir".
- """
- self._metadata = MetadataCatalog.get(dataset_name)
- self._cpu_device = torch.device("cpu")
- self._logger = logging.getLogger(__name__)
-
- def reset(self):
- self._working_dir = tempfile.TemporaryDirectory(prefix="cityscapes_eval_")
- self._temp_dir = self._working_dir.name
- # All workers will write to the same results directory
- # TODO this does not work in distributed training
- assert (
- comm.get_local_size() == comm.get_world_size()
- ), "CityscapesEvaluator currently do not work with multiple machines."
- self._temp_dir = comm.all_gather(self._temp_dir)[0]
- if self._temp_dir != self._working_dir.name:
- self._working_dir.cleanup()
- self._logger.info(
- "Writing cityscapes results to temporary directory {} ...".format(self._temp_dir)
- )
-
-
-class CityscapesInstanceEvaluator(CityscapesEvaluator):
- """
- Evaluate instance segmentation results on cityscapes dataset using cityscapes API.
-
- Note:
- * It does not work in multi-machine distributed training.
- * It contains a synchronization, therefore has to be used on all ranks.
- * Only the main process runs evaluation.
- """
-
- def process(self, inputs, outputs):
- from cityscapesscripts.helpers.labels import name2label
-
- for input, output in zip(inputs, outputs):
- file_name = input["file_name"]
- basename = os.path.splitext(os.path.basename(file_name))[0]
- pred_txt = os.path.join(self._temp_dir, basename + "_pred.txt")
-
- if "instances" in output:
- output = output["instances"].to(self._cpu_device)
- num_instances = len(output)
- with open(pred_txt, "w") as fout:
- for i in range(num_instances):
- pred_class = output.pred_classes[i]
- classes = self._metadata.stuff_classes[pred_class]
- class_id = name2label[classes].id
- score = output.scores[i]
- mask = output.pred_masks[i].numpy().astype("uint8")
- png_filename = os.path.join(
- self._temp_dir, basename + "_{}_{}.png".format(i, classes)
- )
-
- Image.fromarray(mask * 255).save(png_filename)
- fout.write(
- "{} {} {}\n".format(os.path.basename(png_filename), class_id, score)
- )
- else:
- # Cityscapes requires a prediction file for every ground truth image.
- with open(pred_txt, "w") as fout:
- pass
-
- def evaluate(self):
- """
- Returns:
- dict: has a key "segm", whose value is a dict of "AP" and "AP50".
- """
- comm.synchronize()
- if comm.get_rank() > 0:
- return
- import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval
-
- self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
-
- # set some global states in cityscapes evaluation API, before evaluating
- cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
- cityscapes_eval.args.predictionWalk = None
- cityscapes_eval.args.JSONOutput = False
- cityscapes_eval.args.colorized = False
- cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json")
-
- # These lines are adopted from
- # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
- gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
- groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_instanceIds.png"))
- assert len(
- groundTruthImgList
- ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
- cityscapes_eval.args.groundTruthSearch
- )
- predictionImgList = []
- for gt in groundTruthImgList:
- predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args))
- results = cityscapes_eval.evaluateImgLists(
- predictionImgList, groundTruthImgList, cityscapes_eval.args
- )["averages"]
-
- ret = OrderedDict()
- ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100}
- self._working_dir.cleanup()
- return ret
-
-
-class CityscapesSemSegEvaluator(CityscapesEvaluator):
- """
- Evaluate semantic segmentation results on cityscapes dataset using cityscapes API.
-
- Note:
- * It does not work in multi-machine distributed training.
- * It contains a synchronization, therefore has to be used on all ranks.
- * Only the main process runs evaluation.
- """
-
- def process(self, inputs, outputs):
- from cityscapesscripts.helpers.labels import trainId2label
-
- for input, output in zip(inputs, outputs):
- file_name = input["file_name"]
- basename = os.path.splitext(os.path.basename(file_name))[0]
- pred_filename = os.path.join(self._temp_dir, basename + "_pred.png")
-
- output = output["sem_seg"].argmax(dim=0).to(self._cpu_device).numpy()
- pred = 255 * np.ones(output.shape, dtype=np.uint8)
- for train_id, label in trainId2label.items():
- if label.ignoreInEval:
- continue
- pred[output == train_id] = label.id
- Image.fromarray(pred).save(pred_filename)
-
- def evaluate(self):
- comm.synchronize()
- if comm.get_rank() > 0:
- return
- # Load the Cityscapes eval script *after* setting the required env var,
- # since the script reads CITYSCAPES_DATASET into global variables at load time.
- import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval
-
- self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
-
- # set some global states in cityscapes evaluation API, before evaluating
- cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
- cityscapes_eval.args.predictionWalk = None
- cityscapes_eval.args.JSONOutput = False
- cityscapes_eval.args.colorized = False
-
- # These lines are adopted from
- # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa
- gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
- groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_labelIds.png"))
- assert len(
- groundTruthImgList
- ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
- cityscapes_eval.args.groundTruthSearch
- )
- predictionImgList = []
- for gt in groundTruthImgList:
- predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt))
- results = cityscapes_eval.evaluateImgLists(
- predictionImgList, groundTruthImgList, cityscapes_eval.args
- )
- ret = OrderedDict()
- ret["sem_seg"] = {
- "IoU": 100.0 * results["averageScoreClasses"],
- "iIoU": 100.0 * results["averageScoreInstClasses"],
- "IoU_sup": 100.0 * results["averageScoreCategories"],
- "iIoU_sup": 100.0 * results["averageScoreInstCategories"],
- }
- self._working_dir.cleanup()
- return ret
diff --git a/spaces/PeepDaSlan9/AutoGPT/benchmark/__init__.py b/spaces/PeepDaSlan9/AutoGPT/benchmark/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py b/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py
deleted file mode 100644
index ba0443e0c22481af2b451d8ccc5ab6e6f79cffb8..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py
+++ /dev/null
@@ -1,573 +0,0 @@
-import os
-import random
-from typing import Optional, Callable
-import json
-import glob
-import csv
-
-import numpy as np
-import torch
-import librosa
-import pyloudnorm as pyln
-from pedalboard import Pedalboard, Limiter, Gain, Compressor, Clipping
-
-from .dataset import (
- MusdbTrainDataset,
- MusdbValidDataset,
- apply_limitaug,
-)
-from utils import (
- load_wav_arbitrary_position_stereo,
- load_wav_specific_position_stereo,
- db2linear,
-)
-
-
-class DelimitTrainDataset(MusdbTrainDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- seq_duration: Optional[float] = 6.0,
- samples_per_track: int = 64,
- source_augmentations: Optional[Callable] = lambda audio: audio,
- sample_rate: int = 44100,
- seed: int = 42,
- limitaug_method: str = "limitaug",
- limitaug_mode: str = "normal_L",
- limitaug_custom_target_lufs: float = None,
- limitaug_custom_target_lufs_std: float = None,
- target_loudnorm_lufs: float = -14.0,
- target_limitaug_mode: str = None,
- target_limitaug_custom_target_lufs: float = None,
- target_limitaug_custom_target_lufs_std: float = None,
- custom_limiter_attack_range: list = [2.0, 2.0],
- custom_limiter_release_range: list = [200.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(
- target=target,
- root=root,
- seq_duration=seq_duration,
- samples_per_track=samples_per_track,
- source_augmentations=source_augmentations,
- sample_rate=sample_rate,
- seed=seed,
- limitaug_method=limitaug_method,
- limitaug_mode=limitaug_mode,
- limitaug_custom_target_lufs=limitaug_custom_target_lufs,
- limitaug_custom_target_lufs_std=limitaug_custom_target_lufs_std,
- target_loudnorm_lufs=target_loudnorm_lufs,
- custom_limiter_attack_range=custom_limiter_attack_range,
- custom_limiter_release_range=custom_limiter_release_range,
- *args,
- **kwargs,
- )
-
- self.target_limitaug_mode = target_limitaug_mode
-
- self.target_limitaug_custom_target_lufs = (target_limitaug_custom_target_lufs,)
- self.target_limitaug_custom_target_lufs_std = (
- target_limitaug_custom_target_lufs_std,
- )
- self.limitaug_mode_statistics["target_custom"] = [
- target_limitaug_custom_target_lufs,
- target_limitaug_custom_target_lufs_std,
- ]
-
- """
- Parameters
- ----------
- limitaug_method : str
- choose from ["linear_gain_increase", "limitaug", "limitaug_then_loudnorm", "only_loudnorm"]
- limitaug_mode : str
- choose from ["uniform", "normal", "normal_L", "normal_XL", "normal_short_term", "normal_L_short_term", "normal_XL_short_term", "custom"]
- limitaug_custom_target_lufs : float
- valid only when
- limitaug_mode == "custom"
- target_loudnorm_lufs : float
- valid only when
- limitaug_method == 'limitaug_then_loudnorm' or 'only_loudnorm'
- default is -14.
- To the best of my knowledge, Spotify and Youtube music is using -14 as a reference loudness normalization level.
- No special reason for the choice of -14 as target_loudnorm_lufs.
- target : str
- target name of the source to be separated, defaults to ``vocals``.
- root : str
- root path of MUSDB
- seq_duration : float
- training is performed in chunks of ``seq_duration`` (in seconds,
- defaults to ``None`` which loads the full audio track
- samples_per_track : int
- sets the number of samples, yielded from each track per epoch.
- Defaults to 64
- source_augmentations : list[callables]
- provide list of augmentation function that take a multi-channel
- audio file of shape (src, samples) as input and output. Defaults to
- no-augmentations (input = output)
- seed : int
- control randomness of dataset iterations
- args, kwargs : additional keyword arguments
- used to add further control for the musdb dataset
- initialization function.
- """
-
- # Get a limitaug result without target (individual stem source)
- def get_limitaug_mixture(self, mixture):
- if self.limitaug_method == "limitaug":
- self.board[1].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- )
-
- elif self.limitaug_method == "limitaug_then_loudnorm":
- self.board[1].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = (
- apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- target_loudnorm_lufs=self.target_loudnorm_lufs,
- ),
- )
-
- # Apply LimitAug using Custom Limiter
- elif self.limitaug_method == "custom_limiter_limitaug":
- # Change attack time of First compressor of the Limiter
- self.board[1].attack_ms = random.uniform(
- self.custom_limiter_attack_range[0], self.custom_limiter_attack_range[1]
- )
- # Change release time of First compressor of the Limiter
- self.board[1].release_ms = random.uniform(
- self.custom_limiter_release_range[0],
- self.custom_limiter_release_range[1],
- )
- # Change release time of Second compressor of the Limiter
- self.board[2].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- target_loudnorm_lufs=self.target_loudnorm_lufs,
- )
-
- # When we want to force NN to output an appropriately compressed target output
- if self.target_limitaug_mode:
- mixture_target_lufs = random.gauss(
- self.limitaug_mode_statistics[self.target_limitaug_mode][0],
- self.limitaug_mode_statistics[self.target_limitaug_mode][1],
- )
- mixture, target_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=mixture_target_lufs,
- loudness=mixture_lufs,
- )
-
- if np.isinf(mixture_lufs):
- mixture_loudnorm = mixture
- else:
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain, eps=0.0)
-
- return mixture_limited, mixture_loudnorm
-
- def __getitem__(self, index):
- audio_sources = []
-
- for k, source in enumerate(self.sources):
- # memorize index of target source
- if source == self.target: # if source is 'vocals'
- track_path = self.train_list[
- index // self.samples_per_track
- ] # we want to use # training samples per each track.
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration
- )
- else:
- track_path = random.choice(self.train_list)
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration
- )
- audio = self.source_augmentations(audio)
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- # apply linear mix over source index=0
- # and here, linear mixture is a target unlike in MusdbTrainDataset
- mixture = stems.sum(0)
- mixture_limited, mixture_loudnorm = self.get_limitaug_mixture(mixture)
- # We will give mixture_limited as an input and mixture_loudnorm as a target to the model.
-
- mixture_limited = np.clip(mixture_limited, -1.0, 1.0)
- mixture_limited = torch.as_tensor(mixture_limited, dtype=torch.float32)
- mixture_loudnorm = torch.as_tensor(mixture_loudnorm, dtype=torch.float32)
-
- return mixture_limited, mixture_loudnorm
-
-
-class OzoneTrainDataset(DelimitTrainDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- ozone_root: str = None,
- use_fixed: float = 0.1, # ratio of fixed samples
- seq_duration: Optional[float] = 6.0,
- samples_per_track: int = 64,
- source_augmentations: Optional[Callable] = lambda audio: audio,
- sample_rate: int = 44100,
- seed: int = 42,
- limitaug_method: str = "limitaug",
- limitaug_mode: str = "normal_L",
- limitaug_custom_target_lufs: float = None,
- limitaug_custom_target_lufs_std: float = None,
- target_loudnorm_lufs: float = -14.0,
- target_limitaug_mode: str = None,
- target_limitaug_custom_target_lufs: float = None,
- target_limitaug_custom_target_lufs_std: float = None,
- custom_limiter_attack_range: list = [2.0, 2.0],
- custom_limiter_release_range: list = [200.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(
- target,
- root,
- seq_duration,
- samples_per_track,
- source_augmentations,
- sample_rate,
- seed,
- limitaug_method,
- limitaug_mode,
- limitaug_custom_target_lufs,
- limitaug_custom_target_lufs_std,
- target_loudnorm_lufs,
- target_limitaug_mode,
- target_limitaug_custom_target_lufs,
- target_limitaug_custom_target_lufs_std,
- custom_limiter_attack_range,
- custom_limiter_release_range,
- *args,
- **kwargs,
- )
-
- self.ozone_root = ozone_root
- self.use_fixed = use_fixed
- self.list_train_fixed = glob.glob(f"{self.ozone_root}/ozone_train_fixed/*.wav")
- self.list_train_random = glob.glob(
- f"{self.ozone_root}/ozone_train_random/*.wav"
- )
- self.dict_train_random = {}
-
- # Load information of pre-generated random training examples
- list_csv_files = glob.glob(f"{self.ozone_root}/ozone_train_random_*.csv")
- list_csv_files.sort()
- for csv_file in list_csv_files:
- with open(csv_file, "r") as f:
- reader = csv.reader(f)
- next(reader)
- for row in reader:
- self.dict_train_random[row[0]] = {
- "max_threshold": float(row[1]),
- "max_character": float(row[2]),
- "vocals": {
- "name": row[3],
- "start_sec": float(row[4]),
- "gain": float(row[5]),
- "channelswap": bool(row[6]),
- },
- "bass": {
- "name": row[7],
- "start_sec": float(row[8]),
- "gain": float(row[9]),
- "channelswap": bool(row[10]),
- },
- "drums": {
- "name": row[11],
- "start_sec": float(row[12]),
- "gain": float(row[13]),
- "channelswap": bool(row[14]),
- },
- "other": {
- "name": row[15],
- "start_sec": float(row[16]),
- "gain": float(row[17]),
- "channelswap": bool(row[18]),
- },
- }
-
- def __getitem__(self, idx):
- use_fixed_prob = random.random()
-
- if use_fixed_prob <= self.use_fixed:
- # Fixed examples
- audio_path = random.choice(self.list_train_fixed)
- song_name = os.path.basename(audio_path).replace(".wav", "")
- mixture_limited, start_pos_sec = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration, return_pos=True
- )
-
- audio_sources = []
- track_path = f"{self.root}/train/{song_name}"
- for source in self.sources:
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_specific_position_stereo(
- audio_path,
- self.sample_rate,
- self.seq_duration,
- start_position=start_pos_sec,
- )
- audio_sources.append(audio)
-
- else:
- # Random examples
- # Load mixture_limited (pre-generated)
- audio_path = random.choice(self.list_train_random)
- seg_name = os.path.basename(audio_path).replace(".wav", "")
- mixture_limited, sr = librosa.load(
- audio_path, sr=self.sample_rate, mono=False
- )
-
- # Load mixture_unlimited (from the original musdb18, using metadata)
- audio_sources = []
- for source in self.sources:
- dict_seg_info = self.dict_train_random[seg_name]
- dict_seg_source_info = dict_seg_info[source]
- audio_path = (
- f"{self.root}/train/{dict_seg_source_info['name']}/{source}.wav"
- )
- audio = load_wav_specific_position_stereo(
- audio_path,
- self.sample_rate,
- self.seq_duration,
- start_position=dict_seg_source_info["start_sec"],
- )
-
- # apply augmentations
- audio = audio * dict_seg_source_info["gain"]
- if dict_seg_source_info["channelswap"]:
- audio = np.flip(audio, axis=0)
-
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
- mixture = stems.sum(axis=0)
- mixture_lufs = self.meter.integrated_loudness(mixture.T)
- if np.isinf(mixture_lufs):
- mixture_loudnorm = mixture
- else:
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain, eps=0.0)
-
- return mixture_limited, mixture_loudnorm
-
-
-class DelimitValidDataset(MusdbValidDataset):
- def __init__(
- self,
- target: str = "vocals",
- root: str = None,
- delimit_valid_root: str = None,
- valid_target_lufs: float = -8.05, # From the Table 1 of the "Towards robust music source separation on loud commercial music" paper, the average loudness of commerical music.
- target_loudnorm_lufs: float = -14.0,
- delimit_valid_L_root: str = None, # This will be used when using the target as compressed (normal_L) mixture.
- use_custom_limiter: bool = False,
- custom_limiter_attack_range: list = [0.1, 10.0],
- custom_limiter_release_range: list = [30.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(target=target, root=root, *args, **kwargs)
- self.delimit_valid_root = delimit_valid_root
- if self.delimit_valid_root:
- with open(f"{self.delimit_valid_root}/valid_loudness.json", "r") as f:
- self.dict_valid_loudness = json.load(f)
- self.delimit_valid_L_root = delimit_valid_L_root
- if self.delimit_valid_L_root:
- with open(f"{self.delimit_valid_L_root}/valid_loudness.json", "r") as f:
- self.dict_valid_L_loudness = json.load(f)
-
- self.valid_target_lufs = valid_target_lufs
- self.target_loudnorm_lufs = target_loudnorm_lufs
- self.meter = pyln.Meter(self.sample_rate)
- self.use_custom_limiter = use_custom_limiter
-
- if self.use_custom_limiter:
- print("using Custom limiter limitaug for validation!!")
- self.custom_limiter_attack_range = custom_limiter_attack_range
- self.custom_limiter_release_range = custom_limiter_release_range
- self.board = Pedalboard(
- [
- Gain(gain_db=0.0),
- Compressor(
- threshold_db=-10.0, ratio=4.0, attack_ms=2.0, release_ms=200.0
- ), # attack_ms and release_ms will be changed later.
- Compressor(
- threshold_db=0.0,
- ratio=1000.0,
- attack_ms=0.001,
- release_ms=100.0,
- ),
- Gain(gain_db=3.75),
- Clipping(threshold_db=0.0),
- ]
- ) # This implementation is the same as JUCE Limiter.
- # However, we want the first compressor to have a variable attack and release time.
- # Therefore, we use the Custom Limiter instead of the JUCE Limiter.
- else:
- self.board = Pedalboard(
- [Gain(gain_db=0.0), Limiter(threshold_db=0.0, release_ms=100.0)]
- ) # Currently, we are using a limiter with a release time of 100ms.
-
- def __getitem__(self, index):
- audio_sources = []
- target_ind = None
-
- for k, source in enumerate(self.sources):
- # memorize index of target source
- if source == self.target: # if source is 'vocals'
- target_ind = k
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- audio_path = f"{track_path}/{source}.wav"
- # audio = utils.load_wav_stereo(audio_path, self.sample_rate)
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
- else:
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- audio_path = f"{track_path}/{source}.wav"
- # audio = utils.load_wav_stereo(audio_path, self.sample_rate)
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
-
- audio = torch.as_tensor(audio, dtype=torch.float32)
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- # apply linear mix over source index=0
- # and here, linear mixture is a target unlike in MusdbTrainDataset
- mixture = stems.sum(0)
- if (
- self.delimit_valid_root
- ): # If there exists a pre-processed delimit valid dataset
- audio_path = f"{self.delimit_valid_root}/valid/{song_name}.wav"
- mixture_limited = librosa.load(audio_path, mono=False, sr=self.sample_rate)[
- 0
- ]
- mixture_lufs = self.dict_valid_loudness[song_name]
-
- else:
- if self.use_custom_limiter:
- custom_limiter_attack = random.uniform(
- self.custom_limiter_attack_range[0],
- self.custom_limiter_attack_range[1],
- )
- self.board[1].attack_ms = custom_limiter_attack
-
- custom_limiter_release = random.uniform(
- self.custom_limiter_release_range[0],
- self.custom_limiter_release_range[1],
- )
- self.board[1].release_ms = custom_limiter_release
-
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=self.valid_target_lufs,
- )
- else:
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=self.valid_target_lufs,
- # target_loudnorm_lufs=self.target_loudnorm_lufs,
- ) # mixture_limited is a limiter applied mixture
- # We will give mixture_limited as an input and mixture_loudnorm as a target to the model.
-
- if self.delimit_valid_L_root:
- audio_L_path = f"{self.delimit_valid_L_root}/valid/{song_name}.wav"
- mixture_loudnorm = librosa.load(
- audio_L_path, mono=False, sr=self.sample_rate
- )[0]
- mixture_lufs = self.dict_valid_L_loudness[song_name]
- mixture = mixture_loudnorm
-
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain)
-
- if self.use_custom_limiter:
- return (
- mixture_limited,
- mixture_loudnorm,
- song_name,
- mixture_lufs,
- custom_limiter_attack,
- custom_limiter_release,
- )
- else:
- return mixture_limited, mixture_loudnorm, song_name, mixture_lufs
-
-
-class OzoneValidDataset(MusdbValidDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- ozone_root: str = None,
- target_loudnorm_lufs: float = -14.0,
- *args,
- **kwargs,
- ) -> None:
- super().__init__(target=target, root=root, *args, **kwargs)
-
- self.ozone_root = ozone_root
- self.target_loudnorm_lufs = target_loudnorm_lufs
-
- with open(f"{self.ozone_root}/valid_loudness.json", "r") as f:
- self.dict_valid_loudness = json.load(f)
-
- def __getitem__(self, index):
- audio_sources = []
-
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- for k, source in enumerate(self.sources):
- audio_path = f"{track_path}/{source}.wav"
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- mixture = stems.sum(0)
-
- audio_path = f"{self.ozone_root}/ozone_train_fixed/{song_name}.wav"
- mixture_limited = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
-
- mixture_lufs = self.dict_valid_loudness[song_name]
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain)
-
- return mixture_limited, mixture_loudnorm, song_name, mixture_lufs
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py
deleted file mode 100644
index 75559579cf053abcc99538606cbb88c723faf783..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py
+++ /dev/null
@@ -1,282 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-
-import torch
-import torch.nn as nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair, _single
-
-from annotator.uniformer.mmcv.utils import deprecated_api_warning
-from ..cnn import CONV_LAYERS
-from ..utils import ext_loader, print_log
-
-ext_module = ext_loader.load_ext(
- '_ext',
- ['modulated_deform_conv_forward', 'modulated_deform_conv_backward'])
-
-
-class ModulatedDeformConv2dFunction(Function):
-
- @staticmethod
- def symbolic(g, input, offset, mask, weight, bias, stride, padding,
- dilation, groups, deform_groups):
- input_tensors = [input, offset, mask, weight]
- if bias is not None:
- input_tensors.append(bias)
- return g.op(
- 'mmcv::MMCVModulatedDeformConv2d',
- *input_tensors,
- stride_i=stride,
- padding_i=padding,
- dilation_i=dilation,
- groups_i=groups,
- deform_groups_i=deform_groups)
-
- @staticmethod
- def forward(ctx,
- input,
- offset,
- mask,
- weight,
- bias=None,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- deform_groups=1):
- if input is not None and input.dim() != 4:
- raise ValueError(
- f'Expected 4D tensor as input, got {input.dim()}D tensor \
- instead.')
- ctx.stride = _pair(stride)
- ctx.padding = _pair(padding)
- ctx.dilation = _pair(dilation)
- ctx.groups = groups
- ctx.deform_groups = deform_groups
- ctx.with_bias = bias is not None
- if not ctx.with_bias:
- bias = input.new_empty(0) # fake tensor
- # When pytorch version >= 1.6.0, amp is adopted for fp16 mode;
- # amp won't cast the type of model (float32), but "offset" is cast
- # to float16 by nn.Conv2d automatically, leading to the type
- # mismatch with input (when it is float32) or weight.
- # The flag for whether to use fp16 or amp is the type of "offset",
- # we cast weight and input to temporarily support fp16 and amp
- # whatever the pytorch version is.
- input = input.type_as(offset)
- weight = weight.type_as(input)
- ctx.save_for_backward(input, offset, mask, weight, bias)
- output = input.new_empty(
- ModulatedDeformConv2dFunction._output_size(ctx, input, weight))
- ctx._bufs = [input.new_empty(0), input.new_empty(0)]
- ext_module.modulated_deform_conv_forward(
- input,
- weight,
- bias,
- ctx._bufs[0],
- offset,
- mask,
- output,
- ctx._bufs[1],
- kernel_h=weight.size(2),
- kernel_w=weight.size(3),
- stride_h=ctx.stride[0],
- stride_w=ctx.stride[1],
- pad_h=ctx.padding[0],
- pad_w=ctx.padding[1],
- dilation_h=ctx.dilation[0],
- dilation_w=ctx.dilation[1],
- group=ctx.groups,
- deformable_group=ctx.deform_groups,
- with_bias=ctx.with_bias)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- input, offset, mask, weight, bias = ctx.saved_tensors
- grad_input = torch.zeros_like(input)
- grad_offset = torch.zeros_like(offset)
- grad_mask = torch.zeros_like(mask)
- grad_weight = torch.zeros_like(weight)
- grad_bias = torch.zeros_like(bias)
- grad_output = grad_output.contiguous()
- ext_module.modulated_deform_conv_backward(
- input,
- weight,
- bias,
- ctx._bufs[0],
- offset,
- mask,
- ctx._bufs[1],
- grad_input,
- grad_weight,
- grad_bias,
- grad_offset,
- grad_mask,
- grad_output,
- kernel_h=weight.size(2),
- kernel_w=weight.size(3),
- stride_h=ctx.stride[0],
- stride_w=ctx.stride[1],
- pad_h=ctx.padding[0],
- pad_w=ctx.padding[1],
- dilation_h=ctx.dilation[0],
- dilation_w=ctx.dilation[1],
- group=ctx.groups,
- deformable_group=ctx.deform_groups,
- with_bias=ctx.with_bias)
- if not ctx.with_bias:
- grad_bias = None
-
- return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias,
- None, None, None, None, None)
-
- @staticmethod
- def _output_size(ctx, input, weight):
- channels = weight.size(0)
- output_size = (input.size(0), channels)
- for d in range(input.dim() - 2):
- in_size = input.size(d + 2)
- pad = ctx.padding[d]
- kernel = ctx.dilation[d] * (weight.size(d + 2) - 1) + 1
- stride_ = ctx.stride[d]
- output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
- if not all(map(lambda s: s > 0, output_size)):
- raise ValueError(
- 'convolution input is too small (output would be ' +
- 'x'.join(map(str, output_size)) + ')')
- return output_size
-
-
-modulated_deform_conv2d = ModulatedDeformConv2dFunction.apply
-
-
-class ModulatedDeformConv2d(nn.Module):
-
- @deprecated_api_warning({'deformable_groups': 'deform_groups'},
- cls_name='ModulatedDeformConv2d')
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- deform_groups=1,
- bias=True):
- super(ModulatedDeformConv2d, self).__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.kernel_size = _pair(kernel_size)
- self.stride = _pair(stride)
- self.padding = _pair(padding)
- self.dilation = _pair(dilation)
- self.groups = groups
- self.deform_groups = deform_groups
- # enable compatibility with nn.Conv2d
- self.transposed = False
- self.output_padding = _single(0)
-
- self.weight = nn.Parameter(
- torch.Tensor(out_channels, in_channels // groups,
- *self.kernel_size))
- if bias:
- self.bias = nn.Parameter(torch.Tensor(out_channels))
- else:
- self.register_parameter('bias', None)
- self.init_weights()
-
- def init_weights(self):
- n = self.in_channels
- for k in self.kernel_size:
- n *= k
- stdv = 1. / math.sqrt(n)
- self.weight.data.uniform_(-stdv, stdv)
- if self.bias is not None:
- self.bias.data.zero_()
-
- def forward(self, x, offset, mask):
- return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,
- self.stride, self.padding,
- self.dilation, self.groups,
- self.deform_groups)
-
-
-@CONV_LAYERS.register_module('DCNv2')
-class ModulatedDeformConv2dPack(ModulatedDeformConv2d):
- """A ModulatedDeformable Conv Encapsulation that acts as normal Conv
- layers.
-
- Args:
- in_channels (int): Same as nn.Conv2d.
- out_channels (int): Same as nn.Conv2d.
- kernel_size (int or tuple[int]): Same as nn.Conv2d.
- stride (int): Same as nn.Conv2d, while tuple is not supported.
- padding (int): Same as nn.Conv2d, while tuple is not supported.
- dilation (int): Same as nn.Conv2d, while tuple is not supported.
- groups (int): Same as nn.Conv2d.
- bias (bool or str): If specified as `auto`, it will be decided by the
- norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
- False.
- """
-
- _version = 2
-
- def __init__(self, *args, **kwargs):
- super(ModulatedDeformConv2dPack, self).__init__(*args, **kwargs)
- self.conv_offset = nn.Conv2d(
- self.in_channels,
- self.deform_groups * 3 * self.kernel_size[0] * self.kernel_size[1],
- kernel_size=self.kernel_size,
- stride=self.stride,
- padding=self.padding,
- dilation=self.dilation,
- bias=True)
- self.init_weights()
-
- def init_weights(self):
- super(ModulatedDeformConv2dPack, self).init_weights()
- if hasattr(self, 'conv_offset'):
- self.conv_offset.weight.data.zero_()
- self.conv_offset.bias.data.zero_()
-
- def forward(self, x):
- out = self.conv_offset(x)
- o1, o2, mask = torch.chunk(out, 3, dim=1)
- offset = torch.cat((o1, o2), dim=1)
- mask = torch.sigmoid(mask)
- return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,
- self.stride, self.padding,
- self.dilation, self.groups,
- self.deform_groups)
-
- def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
- missing_keys, unexpected_keys, error_msgs):
- version = local_metadata.get('version', None)
-
- if version is None or version < 2:
- # the key is different in early versions
- # In version < 2, ModulatedDeformConvPack
- # loads previous benchmark models.
- if (prefix + 'conv_offset.weight' not in state_dict
- and prefix[:-1] + '_offset.weight' in state_dict):
- state_dict[prefix + 'conv_offset.weight'] = state_dict.pop(
- prefix[:-1] + '_offset.weight')
- if (prefix + 'conv_offset.bias' not in state_dict
- and prefix[:-1] + '_offset.bias' in state_dict):
- state_dict[prefix +
- 'conv_offset.bias'] = state_dict.pop(prefix[:-1] +
- '_offset.bias')
-
- if version is not None and version > 1:
- print_log(
- f'ModulatedDeformConvPack {prefix.rstrip(".")} is upgraded to '
- 'version 2.',
- logger='root')
-
- super()._load_from_state_dict(state_dict, prefix, local_metadata,
- strict, missing_keys, unexpected_keys,
- error_msgs)
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py
deleted file mode 100644
index a802a74fd6c3610d9ae178e6201f47423eca7ad1..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py
+++ /dev/null
@@ -1,177 +0,0 @@
-from typing import List
-
-import torch
-from torch import nn as nn
-
-from annotator.uniformer.mmcv.runner import force_fp32
-from .furthest_point_sample import (furthest_point_sample,
- furthest_point_sample_with_dist)
-
-
-def calc_square_dist(point_feat_a, point_feat_b, norm=True):
- """Calculating square distance between a and b.
-
- Args:
- point_feat_a (Tensor): (B, N, C) Feature vector of each point.
- point_feat_b (Tensor): (B, M, C) Feature vector of each point.
- norm (Bool, optional): Whether to normalize the distance.
- Default: True.
-
- Returns:
- Tensor: (B, N, M) Distance between each pair points.
- """
- num_channel = point_feat_a.shape[-1]
- # [bs, n, 1]
- a_square = torch.sum(point_feat_a.unsqueeze(dim=2).pow(2), dim=-1)
- # [bs, 1, m]
- b_square = torch.sum(point_feat_b.unsqueeze(dim=1).pow(2), dim=-1)
-
- corr_matrix = torch.matmul(point_feat_a, point_feat_b.transpose(1, 2))
-
- dist = a_square + b_square - 2 * corr_matrix
- if norm:
- dist = torch.sqrt(dist) / num_channel
- return dist
-
-
-def get_sampler_cls(sampler_type):
- """Get the type and mode of points sampler.
-
- Args:
- sampler_type (str): The type of points sampler.
- The valid value are "D-FPS", "F-FPS", or "FS".
-
- Returns:
- class: Points sampler type.
- """
- sampler_mappings = {
- 'D-FPS': DFPSSampler,
- 'F-FPS': FFPSSampler,
- 'FS': FSSampler,
- }
- try:
- return sampler_mappings[sampler_type]
- except KeyError:
- raise KeyError(
- f'Supported `sampler_type` are {sampler_mappings.keys()}, but got \
- {sampler_type}')
-
-
-class PointsSampler(nn.Module):
- """Points sampling.
-
- Args:
- num_point (list[int]): Number of sample points.
- fps_mod_list (list[str], optional): Type of FPS method, valid mod
- ['F-FPS', 'D-FPS', 'FS'], Default: ['D-FPS'].
- F-FPS: using feature distances for FPS.
- D-FPS: using Euclidean distances of points for FPS.
- FS: using F-FPS and D-FPS simultaneously.
- fps_sample_range_list (list[int], optional):
- Range of points to apply FPS. Default: [-1].
- """
-
- def __init__(self,
- num_point: List[int],
- fps_mod_list: List[str] = ['D-FPS'],
- fps_sample_range_list: List[int] = [-1]):
- super().__init__()
- # FPS would be applied to different fps_mod in the list,
- # so the length of the num_point should be equal to
- # fps_mod_list and fps_sample_range_list.
- assert len(num_point) == len(fps_mod_list) == len(
- fps_sample_range_list)
- self.num_point = num_point
- self.fps_sample_range_list = fps_sample_range_list
- self.samplers = nn.ModuleList()
- for fps_mod in fps_mod_list:
- self.samplers.append(get_sampler_cls(fps_mod)())
- self.fp16_enabled = False
-
- @force_fp32()
- def forward(self, points_xyz, features):
- """
- Args:
- points_xyz (Tensor): (B, N, 3) xyz coordinates of the features.
- features (Tensor): (B, C, N) Descriptors of the features.
-
- Returns:
- Tensor: (B, npoint, sample_num) Indices of sampled points.
- """
- indices = []
- last_fps_end_index = 0
-
- for fps_sample_range, sampler, npoint in zip(
- self.fps_sample_range_list, self.samplers, self.num_point):
- assert fps_sample_range < points_xyz.shape[1]
-
- if fps_sample_range == -1:
- sample_points_xyz = points_xyz[:, last_fps_end_index:]
- if features is not None:
- sample_features = features[:, :, last_fps_end_index:]
- else:
- sample_features = None
- else:
- sample_points_xyz = \
- points_xyz[:, last_fps_end_index:fps_sample_range]
- if features is not None:
- sample_features = features[:, :, last_fps_end_index:
- fps_sample_range]
- else:
- sample_features = None
-
- fps_idx = sampler(sample_points_xyz.contiguous(), sample_features,
- npoint)
-
- indices.append(fps_idx + last_fps_end_index)
- last_fps_end_index += fps_sample_range
- indices = torch.cat(indices, dim=1)
-
- return indices
-
-
-class DFPSSampler(nn.Module):
- """Using Euclidean distances of points for FPS."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with D-FPS."""
- fps_idx = furthest_point_sample(points.contiguous(), npoint)
- return fps_idx
-
-
-class FFPSSampler(nn.Module):
- """Using feature distances for FPS."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with F-FPS."""
- assert features is not None, \
- 'feature input to FFPS_Sampler should not be None'
- features_for_fps = torch.cat([points, features.transpose(1, 2)], dim=2)
- features_dist = calc_square_dist(
- features_for_fps, features_for_fps, norm=False)
- fps_idx = furthest_point_sample_with_dist(features_dist, npoint)
- return fps_idx
-
-
-class FSSampler(nn.Module):
- """Using F-FPS and D-FPS simultaneously."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with FS_Sampling."""
- assert features is not None, \
- 'feature input to FS_Sampler should not be None'
- ffps_sampler = FFPSSampler()
- dfps_sampler = DFPSSampler()
- fps_idx_ffps = ffps_sampler(points, features, npoint)
- fps_idx_dfps = dfps_sampler(points, features, npoint)
- fps_idx = torch.cat([fps_idx_ffps, fps_idx_dfps], dim=1)
- return fps_idx
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py
deleted file mode 100644
index 332b242c03d1c5e80d4577df442a9a037b1816e1..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .base_pixel_sampler import BasePixelSampler
-from .ohem_pixel_sampler import OHEMPixelSampler
-
-__all__ = ['BasePixelSampler', 'OHEMPixelSampler']
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py
deleted file mode 100644
index c0e42756ee6fcd779387255391a30079a28f5e60..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import torch
-from torch import nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-from maskrcnn_benchmark import _C
-
-
-class _ROIPool(Function):
- @staticmethod
- def forward(ctx, input, roi, output_size, spatial_scale):
- ctx.output_size = _pair(output_size)
- ctx.spatial_scale = spatial_scale
- ctx.input_shape = input.size()
- output, argmax = _C.roi_pool_forward(
- input, roi, spatial_scale, output_size[0], output_size[1]
- )
- ctx.save_for_backward(input, roi, argmax)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- input, rois, argmax = ctx.saved_tensors
- output_size = ctx.output_size
- spatial_scale = ctx.spatial_scale
- bs, ch, h, w = ctx.input_shape
- grad_input = _C.roi_pool_backward(
- grad_output,
- input,
- rois,
- argmax,
- spatial_scale,
- output_size[0],
- output_size[1],
- bs,
- ch,
- h,
- w,
- )
- return grad_input, None, None, None
-
-
-roi_pool = _ROIPool.apply
-
-
-class ROIPool(nn.Module):
- def __init__(self, output_size, spatial_scale):
- super(ROIPool, self).__init__()
- self.output_size = output_size
- self.spatial_scale = spatial_scale
-
- def forward(self, input, rois):
- return roi_pool(input, rois, self.output_size, self.spatial_scale)
-
- def __repr__(self):
- tmpstr = self.__class__.__name__ + "("
- tmpstr += "output_size=" + str(self.output_size)
- tmpstr += ", spatial_scale=" + str(self.spatial_scale)
- tmpstr += ")"
- return tmpstr
diff --git a/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py b/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py
deleted file mode 100644
index e90ef2130a47ec52160709877972716352e04c9c..0000000000000000000000000000000000000000
--- a/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
- if bias is not None:
- rest_dim = [1] * (input.ndim - bias.ndim - 1)
- return (
- F.leaky_relu(
- input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope
- )
- * scale
- )
- else:
- return F.leaky_relu(input, negative_slope=0.2) * scale
-
-
-class EqualLinear(nn.Module):
- def __init__(
- self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1
- ):
- super().__init__()
- self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
- if bias:
- self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
- else:
- self.bias = None
- self.scale = (1 / math.sqrt(in_dim)) * lr_mul
- self.lr_mul = lr_mul
-
- def forward(self, input):
- out = F.linear(input, self.weight * self.scale)
- out = fused_leaky_relu(out, self.bias * self.lr_mul)
- return out
-
-
-class RandomLatentConverter(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.layers = nn.Sequential(*[EqualLinear(channels, channels, lr_mul=.1) for _ in range(5)],
- nn.Linear(channels, channels))
- self.channels = channels
-
- def forward(self, ref):
- r = torch.randn(ref.shape[0], self.channels, device=ref.device)
- y = self.layers(r)
- return y
-
-
-if __name__ == '__main__':
- model = RandomLatentConverter(512)
- model(torch.randn(5,512))
\ No newline at end of file
diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py
deleted file mode 100644
index 8d760f8a1a769bba657d4aea47857f48f7cd0d7a..0000000000000000000000000000000000000000
--- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py
+++ /dev/null
@@ -1,1832 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# Updated to account for UI changes from https://github.com/rkfg/audiocraft/blob/long/app.py
-# also released under the MIT license.
-
-import argparse
-from concurrent.futures import ProcessPoolExecutor
-import os
-from pathlib import Path
-import subprocess as sp
-from tempfile import NamedTemporaryFile
-import time
-import warnings
-import glob
-import re
-from PIL import Image
-from pydub import AudioSegment
-from datetime import datetime
-
-import json
-import shutil
-import taglib
-import torch
-import torchaudio
-import gradio as gr
-import numpy as np
-import typing as tp
-
-from audiocraft.data.audio_utils import convert_audio
-from audiocraft.data.audio import audio_write
-from audiocraft.models import AudioGen, MusicGen, MultiBandDiffusion
-from audiocraft.utils import ui
-import random, string
-
-version = "2.0.0a"
-
-theme = gr.themes.Base(
- primary_hue="lime",
- secondary_hue="lime",
- neutral_hue="neutral",
-).set(
- button_primary_background_fill_hover='*primary_500',
- button_primary_background_fill_hover_dark='*primary_500',
- button_secondary_background_fill_hover='*primary_500',
- button_secondary_background_fill_hover_dark='*primary_500'
-)
-
-MODEL = None # Last used model
-MODELS = None
-UNLOAD_MODEL = False
-MOVE_TO_CPU = False
-IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
-print(IS_BATCHED)
-MAX_BATCH_SIZE = 12
-BATCHED_DURATION = 15
-INTERRUPTING = False
-MBD = None
-# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
-_old_call = sp.call
-
-
-def generate_random_string(length):
- characters = string.ascii_letters + string.digits
- return ''.join(random.choice(characters) for _ in range(length))
-
-
-def resize_video(input_path, output_path, target_width, target_height):
- ffmpeg_cmd = [
- 'ffmpeg',
- '-y',
- '-i', input_path,
- '-vf', f'scale={target_width}:{target_height}',
- '-c:a', 'copy',
- output_path
- ]
- sp.run(ffmpeg_cmd)
-
-
-def _call_nostderr(*args, **kwargs):
- # Avoid ffmpeg vomiting on the logs.
- kwargs['stderr'] = sp.DEVNULL
- kwargs['stdout'] = sp.DEVNULL
- _old_call(*args, **kwargs)
-
-
-sp.call = _call_nostderr
-# Preallocating the pool of processes.
-pool = ProcessPoolExecutor(4)
-pool.__enter__()
-
-
-def interrupt():
- global INTERRUPTING
- INTERRUPTING = True
-
-
-class FileCleaner:
- def __init__(self, file_lifetime: float = 3600):
- self.file_lifetime = file_lifetime
- self.files = []
-
- def add(self, path: tp.Union[str, Path]):
- self._cleanup()
- self.files.append((time.time(), Path(path)))
-
- def _cleanup(self):
- now = time.time()
- for time_added, path in list(self.files):
- if now - time_added > self.file_lifetime:
- if path.exists():
- path.unlink()
- self.files.pop(0)
- else:
- break
-
-
-file_cleaner = FileCleaner()
-
-
-def make_waveform(*args, **kwargs):
- # Further remove some warnings.
- be = time.time()
- with warnings.catch_warnings():
- warnings.simplefilter('ignore')
- height = kwargs.pop('height')
- width = kwargs.pop('width')
- if height < 256:
- height = 256
- if width < 256:
- width = 256
- waveform_video = gr.make_waveform(*args, **kwargs)
- out = f"{generate_random_string(12)}.mp4"
- image = kwargs.get('bg_image', None)
- if image is None:
- resize_video(waveform_video, out, 900, 300)
- else:
- resize_video(waveform_video, out, width, height)
- print("Make a video took", time.time() - be)
- return out
-
-
-def load_model(version='GrandaddyShmax/musicgen-melody', custom_model=None, base_model='GrandaddyShmax/musicgen-medium', gen_type="music"):
- global MODEL, MODELS
- print("Loading model", version)
- if MODELS is None:
- if version == 'GrandaddyShmax/musicgen-custom':
- MODEL = MusicGen.get_pretrained(base_model)
- file_path = os.path.abspath("models/" + str(custom_model) + ".pt")
- MODEL.lm.load_state_dict(torch.load(file_path))
- else:
- if gen_type == "music":
- MODEL = MusicGen.get_pretrained(version)
- elif gen_type == "audio":
- MODEL = AudioGen.get_pretrained(version)
-
- return
-
- else:
- t1 = time.monotonic()
- if MODEL is not None:
- MODEL.to('cpu') # move to cache
- print("Previous model moved to CPU in %.2fs" % (time.monotonic() - t1))
- t1 = time.monotonic()
- if version != 'GrandaddyShmax/musicgen-custom' and MODELS.get(version) is None:
- print("Loading model %s from disk" % version)
- if gen_type == "music":
- result = MusicGen.get_pretrained(version)
- elif gen_type == "audio":
- result = AudioGen.get_pretrained(version)
- MODELS[version] = result
- print("Model loaded in %.2fs" % (time.monotonic() - t1))
- MODEL = result
- return
- result = MODELS[version].to('cuda')
- print("Cached model loaded in %.2fs" % (time.monotonic() - t1))
- MODEL = result
-
-def get_audio_info(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted. (Discord removes metadata from mp4 and wav files, so you can't use them)"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- global_prompt = str("\nGlobal Prompt: " + (data['global_prompt'] if data['global_prompt'] != "" else "none")) if 'global_prompt' in data else ""
- bpm = str("\nBPM: " + data['bpm']) if 'bpm' in data else ""
- key = str("\nKey: " + data['key']) if 'key' in data else ""
- scale = str("\nScale: " + data['scale']) if 'scale' in data else ""
- prompts = str("\nPrompts: " + (data['texts'] if data['texts'] != "['']" else "none")) if 'texts' in data else ""
- duration = str("\nDuration: " + data['duration']) if 'duration' in data else ""
- overlap = str("\nOverlap: " + data['overlap']) if 'overlap' in data else ""
- seed = str("\nSeed: " + data['seed']) if 'seed' in data else ""
- audio_mode = str("\nAudio Mode: " + data['audio_mode']) if 'audio_mode' in data else ""
- input_length = str("\nInput Length: " + data['input_length']) if 'input_length' in data else ""
- channel = str("\nChannel: " + data['channel']) if 'channel' in data else ""
- sr_select = str("\nSample Rate: " + data['sr_select']) if 'sr_select' in data else ""
- gen_type = str(data['generator'] + "gen-") if 'generator' in data else ""
- model = str("\nModel: " + gen_type + data['model']) if 'model' in data else ""
- custom_model = str("\nCustom Model: " + data['custom_model']) if 'custom_model' in data else ""
- base_model = str("\nBase Model: " + data['base_model']) if 'base_model' in data else ""
- decoder = str("\nDecoder: " + data['decoder']) if 'decoder' in data else ""
- topk = str("\nTopk: " + data['topk']) if 'topk' in data else ""
- topp = str("\nTopp: " + data['topp']) if 'topp' in data else ""
- temperature = str("\nTemperature: " + data['temperature']) if 'temperature' in data else ""
- cfg_coef = str("\nClassifier Free Guidance: " + data['cfg_coef']) if 'cfg_coef' in data else ""
- version = str("Version: " + data['version']) if 'version' in data else "Version: Unknown"
- info = str(version + global_prompt + bpm + key + scale + prompts + duration + overlap + seed + audio_mode + input_length + channel + sr_select + model + custom_model + base_model + decoder + topk + topp + temperature + cfg_coef)
- if info == "":
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted. (Discord removes metadata from mp4 and wav files, so you can't use them)"
- return info
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- #if 'global_prompt' not in data:
- #return "No tags found. Either the file is not generated by MusicGen+ V1.2.8a and higher or the tags are corrupted."
- global_prompt = str("\nGlobal Prompt: " + (data['global_prompt'] if data['global_prompt'] != "" else "none")) if 'global_prompt' in data else ""
- bpm = str("\nBPM: " + data['bpm']) if 'bpm' in data else ""
- key = str("\nKey: " + data['key']) if 'key' in data else ""
- scale = str("\nScale: " + data['scale']) if 'scale' in data else ""
- prompts = str("\nPrompts: " + (data['texts'] if data['texts'] != "['']" else "none")) if 'texts' in data else ""
- duration = str("\nDuration: " + data['duration']) if 'duration' in data else ""
- overlap = str("\nOverlap: " + data['overlap']) if 'overlap' in data else ""
- seed = str("\nSeed: " + data['seed']) if 'seed' in data else ""
- audio_mode = str("\nAudio Mode: " + data['audio_mode']) if 'audio_mode' in data else ""
- input_length = str("\nInput Length: " + data['input_length']) if 'input_length' in data else ""
- channel = str("\nChannel: " + data['channel']) if 'channel' in data else ""
- sr_select = str("\nSample Rate: " + data['sr_select']) if 'sr_select' in data else ""
- gen_type = str(data['generator'] + "gen-") if 'generator' in data else ""
- model = str("\nModel: " + gen_type + data['model']) if 'model' in data else ""
- custom_model = str("\nCustom Model: " + data['custom_model']) if 'custom_model' in data else ""
- base_model = str("\nBase Model: " + data['base_model']) if 'base_model' in data else ""
- decoder = str("\nDecoder: " + data['decoder']) if 'decoder' in data else ""
- topk = str("\nTopk: " + data['topk']) if 'topk' in data else ""
- topp = str("\nTopp: " + data['topp']) if 'topp' in data else ""
- temperature = str("\nTemperature: " + data['temperature']) if 'temperature' in data else ""
- cfg_coef = str("\nClassifier Free Guidance: " + data['cfg_coef']) if 'cfg_coef' in data else ""
- version = str("Version: " + data['version']) if 'version' in data else "Version: Unknown"
- info = str(version + global_prompt + bpm + key + scale + prompts + duration + overlap + seed + audio_mode + input_length + channel + sr_select + model + custom_model + base_model + decoder + topk + topp + temperature + cfg_coef)
- if info == "":
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted."
- return info
- else:
- return "Only .wav ,.mp4 and .json files are supported"
- else:
- return None
-
-
-def info_to_params(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- struc_prompt = (False if data['bpm'] == "none" else True) if 'bpm' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- bpm = (120 if data['bpm'] == "none" else int(data['bpm'])) if 'bpm' in data else 120
- key = ("C" if data['key'] == "none" else data['key']) if 'key' in data else "C"
- scale = ("Major" if data['scale'] == "none" else data['scale']) if 'scale' in data else "Major"
- model = data['model'] if 'model' in data else "large"
- custom_model = (data['custom_model'] if data['custom_model'] in get_available_models() else None) if 'custom_model' in data else None
- base_model = data['base_model'] if 'base_model' in data else "medium"
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- audio_mode = ("sample" if data['audio_mode'] == "none" else data['audio_mode']) if 'audio_mode' in data else "sample"
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, bpm, key, scale, model, custom_model, base_model, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], audio_mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- struc_prompt = (False if data['bpm'] == "none" else True) if 'bpm' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- bpm = (120 if data['bpm'] == "none" else int(data['bpm'])) if 'bpm' in data else 120
- key = ("C" if data['key'] == "none" else data['key']) if 'key' in data else "C"
- scale = ("Major" if data['scale'] == "none" else data['scale']) if 'scale' in data else "Major"
- model = data['model'] if 'model' in data else "large"
- custom_model = (data['custom_model'] if data['custom_model'] in get_available_models() else None) if 'custom_model' in data else None
- base_model = data['base_model'] if 'base_model' in data else "medium"
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- audio_mode = ("sample" if data['audio_mode'] == "none" else data['audio_mode']) if 'audio_mode' in data else "sample"
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, bpm, key, scale, model, custom_model, base_model, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], audio_mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- else:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-
-
-def info_to_params_a(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- struc_prompt = (False if data['global_prompt'] == "" else True) if 'global_prompt' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- struc_prompt = (False if data['global_prompt'] == "" else True) if 'global_prompt' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
-
- else:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- else:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-
-
-def make_pseudo_stereo (filename, sr_select, pan, delay):
- if pan:
- temp = AudioSegment.from_wav(filename)
- if sr_select != "32000":
- temp = temp.set_frame_rate(int(sr_select))
- left = temp.pan(-0.5) - 5
- right = temp.pan(0.6) - 5
- temp = left.overlay(right, position=5)
- temp.export(filename, format="wav")
- if delay:
- waveform, sample_rate = torchaudio.load(filename) # load mono WAV file
- delay_seconds = 0.01 # set delay 10ms
- delay_samples = int(delay_seconds * sample_rate) # Calculating delay value in number of samples
- stereo_waveform = torch.stack([waveform[0], torch.cat((torch.zeros(delay_samples), waveform[0][:-delay_samples]))]) # Generate a stereo file with original mono audio and delayed version
- torchaudio.save(filename, stereo_waveform, sample_rate)
- return
-
-
-def normalize_audio(audio_data):
- audio_data = audio_data.astype(np.float32)
- max_value = np.max(np.abs(audio_data))
- audio_data /= max_value
- return audio_data
-
-
-def load_diffusion():
- global MBD
- if MBD is None:
- print("loading MBD")
- MBD = MultiBandDiffusion.get_mbd_musicgen()
-
-
-def unload_diffusion():
- global MBD
- if MBD is not None:
- print("unloading MBD")
- MBD = None
-
-
-def _do_predictions(gen_type, texts, melodies, sample, trim_start, trim_end, duration, image, height, width, background, bar1, bar2, channel, sr_select, progress=False, **gen_kwargs):
- if gen_type == "music":
- maximum_size = 29.5
- elif gen_type == "audio":
- maximum_size = 9.5
- cut_size = 0
- input_length = 0
- sampleP = None
- if sample is not None:
- globalSR, sampleM = sample[0], sample[1]
- sampleM = normalize_audio(sampleM)
- sampleM = torch.from_numpy(sampleM).t()
- if sampleM.dim() == 1:
- sampleM = sampleM.unsqueeze(0)
- sample_length = sampleM.shape[sampleM.dim() - 1] / globalSR
- if trim_start >= sample_length:
- trim_start = sample_length - 0.5
- if trim_end >= sample_length:
- trim_end = sample_length - 0.5
- if trim_start + trim_end >= sample_length:
- tmp = sample_length - 0.5
- trim_start = tmp / 2
- trim_end = tmp / 2
- sampleM = sampleM[..., int(globalSR * trim_start):int(globalSR * (sample_length - trim_end))]
- sample_length = sample_length - (trim_start + trim_end)
- if sample_length > maximum_size:
- cut_size = sample_length - maximum_size
- sampleP = sampleM[..., :int(globalSR * cut_size)]
- sampleM = sampleM[..., int(globalSR * cut_size):]
- if sample_length >= duration:
- duration = sample_length + 0.5
- input_length = sample_length
- global MODEL
- MODEL.set_generation_params(duration=(duration - cut_size), **gen_kwargs)
- print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies], [None if sample is None else (sample[0], sample[1].shape)])
- be = time.time()
- processed_melodies = []
- if gen_type == "music":
- target_sr = 32000
- elif gen_type == "audio":
- target_sr = 16000
- target_ac = 1
-
- for melody in melodies:
- if melody is None:
- processed_melodies.append(None)
- else:
- sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
- if melody.dim() == 1:
- melody = melody[None]
- melody = melody[..., :int(sr * duration)]
- melody = convert_audio(melody, sr, target_sr, target_ac)
- processed_melodies.append(melody)
-
- if sample is not None:
- if sampleP is None:
- if gen_type == "music":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress
- )
- else:
- if sampleP.dim() > 1:
- sampleP = convert_audio(sampleP, globalSR, target_sr, target_ac)
- sampleP = sampleP.to(MODEL.device).float().unsqueeze(0)
- if gen_type == "music":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress
- )
- outputs = torch.cat([sampleP, outputs], 2)
-
- elif any(m is not None for m in processed_melodies):
- if gen_type == "music":
- outputs = MODEL.generate_with_chroma(
- descriptions=texts,
- melody_wavs=processed_melodies,
- melody_sample_rate=target_sr,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_with_chroma(
- descriptions=texts,
- melody_wavs=processed_melodies,
- melody_sample_rate=target_sr,
- progress=progress
- )
- else:
- if gen_type == "music":
- outputs = MODEL.generate(texts, progress=progress, return_tokens=USE_DIFFUSION)
- elif gen_type == "audio":
- outputs = MODEL.generate(texts, progress=progress)
-
- if USE_DIFFUSION:
- print("outputs: " + str(outputs))
- outputs_diffusion = MBD.tokens_to_wav(outputs[1])
- outputs = torch.cat([outputs[0], outputs_diffusion], dim=0)
- outputs = outputs.detach().cpu().float()
- backups = outputs
- if channel == "stereo":
- outputs = convert_audio(outputs, target_sr, int(sr_select), 2)
- elif channel == "mono" and sr_select != "32000":
- outputs = convert_audio(outputs, target_sr, int(sr_select), 1)
- out_files = []
- out_audios = []
- out_backup = []
- for output in outputs:
- with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
- audio_write(
- file.name, output, (MODEL.sample_rate if channel == "stereo effect" else int(sr_select)), strategy="loudness",
- loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
-
- if channel == "stereo effect":
- make_pseudo_stereo(file.name, sr_select, pan=True, delay=True);
-
- out_files.append(pool.submit(make_waveform, file.name, bg_image=image, bg_color=background, bars_color=(bar1, bar2), fg_alpha=1.0, bar_count=75, height=height, width=width))
- out_audios.append(file.name)
- file_cleaner.add(file.name)
- print(f'wav: {file.name}')
- for backup in backups:
- with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
- audio_write(
- file.name, backup, MODEL.sample_rate, strategy="loudness",
- loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
- out_backup.append(file.name)
- file_cleaner.add(file.name)
- res = [out_file.result() for out_file in out_files]
- res_audio = out_audios
- res_backup = out_backup
- for file in res:
- file_cleaner.add(file)
- print(f'video: {file}')
- print("batch finished", len(texts), time.time() - be)
- print("Tempfiles currently stored: ", len(file_cleaner.files))
- if MOVE_TO_CPU:
- MODEL.to('cpu')
- if UNLOAD_MODEL:
- MODEL = None
- torch.cuda.empty_cache()
- torch.cuda.ipc_collect()
- return res, res_audio, res_backup, input_length
-
-
-def predict_batched(texts, melodies):
- max_text_length = 512
- texts = [text[:max_text_length] for text in texts]
- load_model('melody')
- res = _do_predictions(texts, melodies, BATCHED_DURATION)
- return res
-
-
-def add_tags(filename, tags):
- json_string = None
-
- data = {
- "global_prompt": tags[0],
- "bpm": tags[1],
- "key": tags[2],
- "scale": tags[3],
- "texts": tags[4],
- "duration": tags[5],
- "overlap": tags[6],
- "seed": tags[7],
- "audio_mode": tags[8],
- "input_length": tags[9],
- "channel": tags[10],
- "sr_select": tags[11],
- "model": tags[12],
- "custom_model": tags[13],
- "base_model": tags[14],
- "decoder": tags[15],
- "topk": tags[16],
- "topp": tags[17],
- "temperature": tags[18],
- "cfg_coef": tags[19],
- "generator": tags[20],
- "version": version
- }
-
- json_string = json.dumps(data)
-
- if os.path.exists(filename):
- with taglib.File(filename, save_on_exit=True) as song:
- song.tags = {'COMMENT': json_string }
-
- json_file = open(tags[7] + '.json', 'w')
- json_file.write(json_string)
- json_file.close()
-
- return json_file.name;
-
-
-def save_outputs(mp4, wav_tmp, tags, gen_type):
- # mp4: .mp4 file name in root running folder of app.py
- # wav_tmp: temporary wav file located in %TEMP% folder
- # seed - used seed
- # exanple BgnJtr4Pn1AJ.mp4, C:\Users\Alex\AppData\Local\Temp\tmp4ermrebs.wav, 195123182343465
- # procedure read generated .mp4 and wav files, rename it by using seed as name,
- # and will store it to ./output/today_date/wav and ./output/today_date/mp4 folders.
- # if file with same seed number already exist its make postfix in name like seed(n)
- # where is n - consiqunce number 1-2-3-4 and so on
- # then we store generated mp4 and wav into destination folders.
-
- current_date = datetime.now().strftime("%Y%m%d")
- wav_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'wav')
- mp4_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'mp4')
- json_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'json')
- os.makedirs(wav_directory, exist_ok=True)
- os.makedirs(mp4_directory, exist_ok=True)
- os.makedirs(json_directory, exist_ok=True)
-
- filename = str(tags[7]) + '.wav'
- target = os.path.join(wav_directory, filename)
- counter = 1
- while os.path.exists(target):
- filename = str(tags[7]) + f'({counter})' + '.wav'
- target = os.path.join(wav_directory, filename)
- counter += 1
-
- shutil.copyfile(wav_tmp, target); # make copy of original file
- json_file = add_tags(target, tags);
-
- wav_target=target;
- target=target.replace('wav', 'mp4');
- mp4_target=target;
-
- mp4=r'./' +mp4;
- shutil.copyfile(mp4, target); # make copy of original file
- _ = add_tags(target, tags);
-
- target=target.replace('mp4', 'json'); # change the extension to json
- json_target=target; # store the json target
-
- with open(target, 'w') as f: # open a writable file object
- shutil.copyfile(json_file, target); # make copy of original file
-
- os.remove(json_file)
-
- return wav_target, mp4_target, json_target;
-
-
-def clear_cash():
- # delete all temporary files genegated my system
- current_date = datetime.now().date()
- current_directory = os.getcwd()
- files = glob.glob(os.path.join(current_directory, '*.mp4'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- temp_directory = os.environ.get('TEMP')
- files = glob.glob(os.path.join(temp_directory, 'tmp*.mp4'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- files = glob.glob(os.path.join(temp_directory, 'tmp*.wav'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- files = glob.glob(os.path.join(temp_directory, 'tmp*.png'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
- return
-
-
-def s2t(seconds, seconds2):
- # convert seconds to time format
- # seconds - time in seconds
- # return time in format 00:00
- m, s = divmod(seconds, 60)
- m2, s2 = divmod(seconds2, 60)
- if seconds != 0 and seconds < seconds2:
- s = s + 1
- return ("%02d:%02d - %02d:%02d" % (m, s, m2, s2))
-
-
-def calc_time(gen_type, s, duration, overlap, d0, d1, d2, d3, d4, d5, d6, d7, d8, d9):
- # calculate the time of generation
- # overlap - overlap in seconds
- # d0-d9 - drag
- # return time in seconds
- d_amount = [int(d0), int(d1), int(d2), int(d3), int(d4), int(d5), int(d6), int(d7), int(d8), int(d9)]
- calc = []
- tracks = []
- time = 0
- s = s - 1
- max_time = duration
- max_limit = 0
- if gen_type == "music":
- max_limit = 30
- elif gen_type == "audio":
- max_limit = 10
- track_add = max_limit - overlap
- tracks.append(max_limit + ((d_amount[0] - 1) * track_add))
- for i in range(1, 10):
- tracks.append(d_amount[i] * track_add)
-
- if tracks[0] >= max_time or s == 0:
- calc.append(s2t(time, max_time))
- time = max_time
- else:
- calc.append(s2t(time, tracks[0]))
- time = tracks[0]
-
- for i in range(1, 10):
- if time + tracks[i] >= max_time or i == s:
- calc.append(s2t(time, max_time))
- time = max_time
- else:
- calc.append(s2t(time, time + tracks[i]))
- time = time + tracks[i]
-
- return calc[0], calc[1], calc[2], calc[3], calc[4], calc[5], calc[6], calc[7], calc[8], calc[9]
-
-
-def predict_full(gen_type, model, decoder, custom_model, base_model, prompt_amount, struc_prompt, bpm, key, scale, global_prompt, p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, d0, d1, d2, d3, d4, d5, d6, d7, d8, d9, audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select, progress=gr.Progress()):
- global INTERRUPTING
- global USE_DIFFUSION
- INTERRUPTING = False
-
- if gen_type == "audio":
- custom_model = None
- base_model = "medium"
-
- if temperature < 0:
- raise gr.Error("Temperature must be >= 0.")
- if topk < 0:
- raise gr.Error("Topk must be non-negative.")
- if topp < 0:
- raise gr.Error("Topp must be non-negative.")
-
- if trim_start < 0:
- trim_start = 0
- if trim_end < 0:
- trim_end = 0
-
- topk = int(topk)
-
- if decoder == "MultiBand_Diffusion":
- USE_DIFFUSION = True
- load_diffusion()
- else:
- USE_DIFFUSION = False
- unload_diffusion()
-
- if gen_type == "music":
- model_shrt = model
- model = "GrandaddyShmax/musicgen-" + model
- elif gen_type == "audio":
- model_shrt = model
- model = "GrandaddyShmax/audiogen-" + model
- base_model_shrt = base_model
- base_model = "GrandaddyShmax/musicgen-" + base_model
-
- if MODEL is None or MODEL.name != (model):
- load_model(model, custom_model, base_model, gen_type)
- else:
- if MOVE_TO_CPU:
- MODEL.to('cuda')
-
- if seed < 0:
- seed = random.randint(0, 0xffff_ffff_ffff)
- torch.manual_seed(seed)
-
- def _progress(generated, to_generate):
- progress((min(generated, to_generate), to_generate))
- if INTERRUPTING:
- raise gr.Error("Interrupted.")
- MODEL.set_custom_progress_callback(_progress)
-
- audio_mode = "none"
- melody = None
- sample = None
- if audio:
- audio_mode = mode
- if mode == "sample":
- sample = audio
- elif mode == "melody":
- melody = audio
-
- base_model = "none" if model != "custom" else base_model
- custom_model = "none" if model != "custom" else custom_model
-
- text_cat = [p0, p1, p2, p3, p4, p5, p6, p7, p8, p9]
- drag_cat = [d0, d1, d2, d3, d4, d5, d6, d7, d8, d9]
- texts = []
- raw_texts = []
- ind = 0
- ind2 = 0
- while ind < prompt_amount:
- for ind2 in range(int(drag_cat[ind])):
- if not struc_prompt:
- texts.append(text_cat[ind])
- global_prompt = "none"
- bpm = "none"
- key = "none"
- scale = "none"
- raw_texts.append(text_cat[ind])
- else:
- if gen_type == "music":
- bpm_str = str(bpm) + " bpm"
- key_str = ", " + str(key) + " " + str(scale)
- global_str = (", " + str(global_prompt)) if str(global_prompt) != "" else ""
- elif gen_type == "audio":
- bpm_str = ""
- key_str = ""
- global_str = (str(global_prompt)) if str(global_prompt) != "" else ""
- texts_str = (", " + str(text_cat[ind])) if str(text_cat[ind]) != "" else ""
- texts.append(bpm_str + key_str + global_str + texts_str)
- raw_texts.append(text_cat[ind])
- ind2 = 0
- ind = ind + 1
-
- outs, outs_audio, outs_backup, input_length = _do_predictions(
- gen_type, [texts], [melody], sample, trim_start, trim_end, duration, image, height, width, background, bar1, bar2, channel, sr_select, progress=True,
- top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef, extend_stride=MODEL.max_duration-overlap)
- tags = [str(global_prompt), str(bpm), str(key), str(scale), str(raw_texts), str(duration), str(overlap), str(seed), str(audio_mode), str(input_length), str(channel), str(sr_select), str(model_shrt), str(custom_model), str(base_model_shrt), str(decoder), str(topk), str(topp), str(temperature), str(cfg_coef), str(gen_type)]
- wav_target, mp4_target, json_target = save_outputs(outs[0], outs_audio[0], tags, gen_type);
- # Removes the temporary files.
- for out in outs:
- os.remove(out)
- for out in outs_audio:
- os.remove(out)
-
- return mp4_target, wav_target, outs_backup[0], [mp4_target, wav_target, json_target], seed
-
-
-max_textboxes = 10
-
-
-def get_available_models():
- return sorted([re.sub('.pt$', '', item.name) for item in list(Path('models/').glob('*')) if item.name.endswith('.pt')])
-
-
-def toggle_audio_src(choice):
- if choice == "mic":
- return gr.update(source="microphone", value=None, label="Microphone")
- else:
- return gr.update(source="upload", value=None, label="File")
-
-
-def ui_full(launch_kwargs):
- with gr.Blocks(title='AudioCraft Plus', theme=theme) as interface:
- gr.Markdown(
- """
- # AudioCraft Plus - v2.0.0a
-
- ### An All-in-One AudioCraft WebUI
-
- Thanks to: facebookresearch, Camenduru, rkfg, oobabooga, AlexHK and GrandaddyShmax
- """
- )
- with gr.Tab("MusicGen"):
- gr.Markdown(
- """
- ### MusicGen
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Tab("Generation"):
- with gr.Accordion("Structure Prompts", open=False):
- with gr.Column():
- with gr.Row():
- struc_prompts = gr.Checkbox(label="Enable", value=False, interactive=True, container=False)
- bpm = gr.Number(label="BPM", value=120, interactive=True, scale=1, precision=0)
- key = gr.Dropdown(["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "Bb", "B"], label="Key", value="C", interactive=True)
- scale = gr.Dropdown(["Major", "Minor"], label="Scale", value="Major", interactive=True)
- with gr.Row():
- global_prompt = gr.Text(label="Global Prompt", interactive=True, scale=3)
- with gr.Row():
- s = gr.Slider(1, max_textboxes, value=1, step=1, label="Prompts:", interactive=True, scale=2)
- #s_mode = gr.Radio(["segmentation", "batch"], value="segmentation", interactive=True, scale=1, label="Generation Mode")
- with gr.Column():
- textboxes = []
- prompts = []
- repeats = []
- calcs = []
- with gr.Row():
- text0 = gr.Text(label="Input Text", interactive=True, scale=4)
- prompts.append(text0)
- drag0 = gr.Number(label="Repeat", value=1, interactive=True, scale=1)
- repeats.append(drag0)
- calc0 = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- calcs.append(calc0)
- for i in range(max_textboxes):
- with gr.Row(visible=False) as t:
- text = gr.Text(label="Input Text", interactive=True, scale=3)
- repeat = gr.Number(label="Repeat", minimum=1, value=1, interactive=True, scale=1)
- calc = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- textboxes.append(t)
- prompts.append(text)
- repeats.append(repeat)
- calcs.append(calc)
- to_calc = gr.Button("Calculate Timings", variant="secondary")
- with gr.Row():
- duration = gr.Slider(minimum=1, maximum=300, value=10, step=1, label="Duration", interactive=True)
- with gr.Row():
- overlap = gr.Slider(minimum=1, maximum=29, value=12, step=1, label="Overlap", interactive=True)
- with gr.Row():
- seed = gr.Number(label="Seed", value=-1, scale=4, precision=0, interactive=True)
- gr.Button('\U0001f3b2\ufe0f', scale=1).click(fn=lambda: -1, outputs=[seed], queue=False)
- reuse_seed = gr.Button('\u267b\ufe0f', scale=1)
-
- with gr.Tab("Audio"):
- with gr.Row():
- with gr.Column():
- input_type = gr.Radio(["file", "mic"], value="file", label="Input Type (optional)", interactive=True)
- mode = gr.Radio(["melody", "sample"], label="Input Audio Mode (optional)", value="sample", interactive=True)
- with gr.Row():
- trim_start = gr.Number(label="Trim Start", value=0, interactive=True)
- trim_end = gr.Number(label="Trim End", value=0, interactive=True)
- audio = gr.Audio(source="upload", type="numpy", label="Input Audio (optional)", interactive=True)
-
- with gr.Tab("Customization"):
- with gr.Row():
- with gr.Column():
- background = gr.ColorPicker(value="#0f0f0f", label="background color", interactive=True, scale=0)
- bar1 = gr.ColorPicker(value="#84cc16", label="bar color start", interactive=True, scale=0)
- bar2 = gr.ColorPicker(value="#10b981", label="bar color end", interactive=True, scale=0)
- with gr.Column():
- image = gr.Image(label="Background Image", type="filepath", interactive=True, scale=4)
- with gr.Row():
- height = gr.Number(label="Height", value=512, interactive=True)
- width = gr.Number(label="Width", value=768, interactive=True)
-
- with gr.Tab("Settings"):
- with gr.Row():
- channel = gr.Radio(["mono", "stereo", "stereo effect"], label="Output Audio Channels", value="stereo", interactive=True, scale=1)
- sr_select = gr.Dropdown(["11025", "16000", "22050", "24000", "32000", "44100", "48000"], label="Output Audio Sample Rate", value="48000", interactive=True)
- with gr.Row():
- model = gr.Radio(["melody", "small", "medium", "large", "custom"], label="Model", value="large", interactive=True, scale=1)
- with gr.Column():
- dropdown = gr.Dropdown(choices=get_available_models(), value=("No models found" if len(get_available_models()) < 1 else get_available_models()[0]), label='Custom Model (models folder)', elem_classes='slim-dropdown', interactive=True)
- ui.create_refresh_button(dropdown, lambda: None, lambda: {'choices': get_available_models()}, 'refresh-button')
- basemodel = gr.Radio(["small", "medium", "melody", "large"], label="Base Model", value="medium", interactive=True, scale=1)
- with gr.Row():
- decoder = gr.Radio(["Default", "MultiBand_Diffusion"], label="Decoder", value="Default", interactive=True)
- with gr.Row():
- topk = gr.Number(label="Top-k", value=250, interactive=True)
- topp = gr.Number(label="Top-p", value=0, interactive=True)
- temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
- cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
- with gr.Row():
- submit = gr.Button("Generate", variant="primary")
- # Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
- _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
- with gr.Column() as c:
- with gr.Tab("Output"):
- output = gr.Video(label="Generated Music", scale=0)
- with gr.Row():
- audio_only = gr.Audio(type="numpy", label="Audio Only", interactive=False)
- backup_only = gr.Audio(type="numpy", label="Backup Audio", interactive=False, visible=False)
- send_audio = gr.Button("Send to Input Audio")
- seed_used = gr.Number(label='Seed used', value=-1, interactive=False)
- download = gr.File(label="Generated Files", interactive=False)
- with gr.Tab("Wiki"):
- gr.Markdown(
- """
- - **[Generate (button)]:**
- Generates the music with the given settings and prompts.
-
- - **[Interrupt (button)]:**
- Stops the music generation as soon as it can, providing an incomplete output.
-
- ---
-
- ### Generation Tab:
-
- #### Structure Prompts:
-
- This feature helps reduce repetetive prompts by allowing you to set global prompts
- that will be used for all prompt segments.
-
- - **[Structure Prompts (checkbox)]:**
- Enable/Disable the structure prompts feature.
-
- - **[BPM (number)]:**
- Beats per minute of the generated music.
-
- - **[Key (dropdown)]:**
- The key of the generated music.
-
- - **[Scale (dropdown)]:**
- The scale of the generated music.
-
- - **[Global Prompt (text)]:**
- Here write the prompt that you wish to be used for all prompt segments.
-
- #### Multi-Prompt:
-
- This feature allows you to control the music, adding variation to different time segments.
- You have up to 10 prompt segments. the first prompt will always be 30s long
- the other prompts will be [30s - overlap].
- for example if the overlap is 10s, each prompt segment will be 20s.
-
- - **[Prompt Segments (number)]:**
- Amount of unique prompt to generate throughout the music generation.
-
- - **[Prompt/Input Text (prompt)]:**
- Here describe the music you wish the model to generate.
-
- - **[Repeat (number)]:**
- Write how many times this prompt will repeat (instead of wasting another prompt segment on the same prompt).
-
- - **[Time (text)]:**
- The time of the prompt segment.
-
- - **[Calculate Timings (button)]:**
- Calculates the timings of the prompt segments.
-
- - **[Duration (number)]:**
- How long you want the generated music to be (in seconds).
-
- - **[Overlap (number)]:**
- How much each new segment will reference the previous segment (in seconds).
- For example, if you choose 20s: Each new segment after the first one will reference the previous segment 20s
- and will generate only 10s of new music. The model can only process 30s of music.
-
- - **[Seed (number)]:**
- Your generated music id. If you wish to generate the exact same music,
- place the exact seed with the exact prompts
- (This way you can also extend specific song that was generated short).
-
- - **[Random Seed (button)]:**
- Gives "-1" as a seed, which counts as a random seed.
-
- - **[Copy Previous Seed (button)]:**
- Copies the seed from the output seed (if you don't feel like doing it manualy).
-
- ---
-
- ### Audio Tab:
-
- - **[Input Type (selection)]:**
- `File` mode allows you to upload an audio file to use as input
- `Mic` mode allows you to use your microphone as input
-
- - **[Input Audio Mode (selection)]:**
- `Melody` mode only works with the melody model: it conditions the music generation to reference the melody
- `Sample` mode works with any model: it gives a music sample to the model to generate its continuation.
-
- - **[Trim Start and Trim End (numbers)]:**
- `Trim Start` set how much you'd like to trim the input audio from the start
- `Trim End` same as the above but from the end
-
- - **[Input Audio (audio file)]:**
- Input here the audio you wish to use with "melody" or "sample" mode.
-
- ---
-
- ### Customization Tab:
-
- - **[Background Color (color)]:**
- Works only if you don't upload image. Color of the background of the waveform.
-
- - **[Bar Color Start (color)]:**
- First color of the waveform bars.
-
- - **[Bar Color End (color)]:**
- Second color of the waveform bars.
-
- - **[Background Image (image)]:**
- Background image that you wish to be attached to the generated video along with the waveform.
-
- - **[Height and Width (numbers)]:**
- Output video resolution, only works with image.
- (minimum height and width is 256).
-
- ---
-
- ### Settings Tab:
-
- - **[Output Audio Channels (selection)]:**
- With this you can select the amount of channels that you wish for your output audio.
- `mono` is a straightforward single channel audio
- `stereo` is a dual channel audio but it will sound more or less like mono
- `stereo effect` this one is also dual channel but uses tricks to simulate a stereo audio.
-
- - **[Output Audio Sample Rate (dropdown)]:**
- The output audio sample rate, the model default is 32000.
-
- - **[Model (selection)]:**
- Here you can choose which model you wish to use:
- `melody` model is based on the medium model with a unique feature that lets you use melody conditioning
- `small` model is trained on 300M parameters
- `medium` model is trained on 1.5B parameters
- `large` model is trained on 3.3B parameters
- `custom` model runs the custom model that you provided.
-
- - **[Custom Model (selection)]:**
- This dropdown will show you models that are placed in the `models` folder
- you must select `custom` in the model options in order to use it.
-
- - **[Refresh (button)]:**
- Refreshes the dropdown list for custom model.
-
- - **[Base Model (selection)]:**
- Choose here the model that your custom model is based on.
-
- - **[Decoder (selection)]:**
- Choose here the decoder that you wish to use:
- `Default` is the default decoder
- `MultiBand_Diffusion` is a decoder that uses diffusion to generate the audio.
-
- - **[Top-k (number)]:**
- is a parameter used in text generation models, including music generation models. It determines the number of most likely next tokens to consider at each step of the generation process. The model ranks all possible tokens based on their predicted probabilities, and then selects the top-k tokens from the ranked list. The model then samples from this reduced set of tokens to determine the next token in the generated sequence. A smaller value of k results in a more focused and deterministic output, while a larger value of k allows for more diversity in the generated music.
-
- - **[Top-p (number)]:**
- also known as nucleus sampling or probabilistic sampling, is another method used for token selection during text generation. Instead of specifying a fixed number like top-k, top-p considers the cumulative probability distribution of the ranked tokens. It selects the smallest possible set of tokens whose cumulative probability exceeds a certain threshold (usually denoted as p). The model then samples from this set to choose the next token. This approach ensures that the generated output maintains a balance between diversity and coherence, as it allows for a varying number of tokens to be considered based on their probabilities.
-
- - **[Temperature (number)]:**
- is a parameter that controls the randomness of the generated output. It is applied during the sampling process, where a higher temperature value results in more random and diverse outputs, while a lower temperature value leads to more deterministic and focused outputs. In the context of music generation, a higher temperature can introduce more variability and creativity into the generated music, but it may also lead to less coherent or structured compositions. On the other hand, a lower temperature can produce more repetitive and predictable music.
-
- - **[Classifier Free Guidance (number)]:**
- refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
- """
- )
- with gr.Tab("AudioGen"):
- gr.Markdown(
- """
- ### AudioGen
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Tab("Generation"):
- with gr.Accordion("Structure Prompts", open=False):
- with gr.Row():
- struc_prompts_a = gr.Checkbox(label="Enable", value=False, interactive=True, container=False)
- global_prompt_a = gr.Text(label="Global Prompt", interactive=True, scale=3)
- with gr.Row():
- s_a = gr.Slider(1, max_textboxes, value=1, step=1, label="Prompts:", interactive=True, scale=2)
- with gr.Column():
- textboxes_a = []
- prompts_a = []
- repeats_a = []
- calcs_a = []
- with gr.Row():
- text0_a = gr.Text(label="Input Text", interactive=True, scale=4)
- prompts_a.append(text0_a)
- drag0_a = gr.Number(label="Repeat", value=1, interactive=True, scale=1)
- repeats_a.append(drag0_a)
- calc0_a = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- calcs_a.append(calc0_a)
- for i in range(max_textboxes):
- with gr.Row(visible=False) as t_a:
- text_a = gr.Text(label="Input Text", interactive=True, scale=3)
- repeat_a = gr.Number(label="Repeat", minimum=1, value=1, interactive=True, scale=1)
- calc_a = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- textboxes_a.append(t_a)
- prompts_a.append(text_a)
- repeats_a.append(repeat_a)
- calcs_a.append(calc_a)
- to_calc_a = gr.Button("Calculate Timings", variant="secondary")
- with gr.Row():
- duration_a = gr.Slider(minimum=1, maximum=300, value=10, step=1, label="Duration", interactive=True)
- with gr.Row():
- overlap_a = gr.Slider(minimum=1, maximum=9, value=2, step=1, label="Overlap", interactive=True)
- with gr.Row():
- seed_a = gr.Number(label="Seed", value=-1, scale=4, precision=0, interactive=True)
- gr.Button('\U0001f3b2\ufe0f', scale=1).click(fn=lambda: -1, outputs=[seed_a], queue=False)
- reuse_seed_a = gr.Button('\u267b\ufe0f', scale=1)
-
- with gr.Tab("Audio"):
- with gr.Row():
- with gr.Column():
- input_type_a = gr.Radio(["file", "mic"], value="file", label="Input Type (optional)", interactive=True)
- mode_a = gr.Radio(["sample"], label="Input Audio Mode (optional)", value="sample", interactive=False, visible=False)
- with gr.Row():
- trim_start_a = gr.Number(label="Trim Start", value=0, interactive=True)
- trim_end_a = gr.Number(label="Trim End", value=0, interactive=True)
- audio_a = gr.Audio(source="upload", type="numpy", label="Input Audio (optional)", interactive=True)
-
- with gr.Tab("Customization"):
- with gr.Row():
- with gr.Column():
- background_a = gr.ColorPicker(value="#0f0f0f", label="background color", interactive=True, scale=0)
- bar1_a = gr.ColorPicker(value="#84cc16", label="bar color start", interactive=True, scale=0)
- bar2_a = gr.ColorPicker(value="#10b981", label="bar color end", interactive=True, scale=0)
- with gr.Column():
- image_a = gr.Image(label="Background Image", type="filepath", interactive=True, scale=4)
- with gr.Row():
- height_a = gr.Number(label="Height", value=512, interactive=True)
- width_a = gr.Number(label="Width", value=768, interactive=True)
-
- with gr.Tab("Settings"):
- with gr.Row():
- channel_a = gr.Radio(["mono", "stereo", "stereo effect"], label="Output Audio Channels", value="stereo", interactive=True, scale=1)
- sr_select_a = gr.Dropdown(["11025", "16000", "22050", "24000", "32000", "44100", "48000"], label="Output Audio Sample Rate", value="48000", interactive=True)
- with gr.Row():
- model_a = gr.Radio(["medium"], label="Model", value="medium", interactive=False, visible=False)
- decoder_a = gr.Radio(["Default"], label="Decoder", value="Default", interactive=False, visible=False)
- with gr.Row():
- topk_a = gr.Number(label="Top-k", value=250, interactive=True)
- topp_a = gr.Number(label="Top-p", value=0, interactive=True)
- temperature_a = gr.Number(label="Temperature", value=1.0, interactive=True)
- cfg_coef_a = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
- with gr.Row():
- submit_a = gr.Button("Generate", variant="primary")
- _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
- with gr.Column():
- with gr.Tab("Output"):
- output_a = gr.Video(label="Generated Audio", scale=0)
- with gr.Row():
- audio_only_a = gr.Audio(type="numpy", label="Audio Only", interactive=False)
- backup_only_a = gr.Audio(type="numpy", label="Backup Audio", interactive=False, visible=False)
- send_audio_a = gr.Button("Send to Input Audio")
- seed_used_a = gr.Number(label='Seed used', value=-1, interactive=False)
- download_a = gr.File(label="Generated Files", interactive=False)
- with gr.Tab("Wiki"):
- gr.Markdown(
- """
- - **[Generate (button)]:**
- Generates the audio with the given settings and prompts.
-
- - **[Interrupt (button)]:**
- Stops the audio generation as soon as it can, providing an incomplete output.
-
- ---
-
- ### Generation Tab:
-
- #### Structure Prompts:
-
- This feature helps reduce repetetive prompts by allowing you to set global prompts
- that will be used for all prompt segments.
-
- - **[Structure Prompts (checkbox)]:**
- Enable/Disable the structure prompts feature.
-
- - **[Global Prompt (text)]:**
- Here write the prompt that you wish to be used for all prompt segments.
-
- #### Multi-Prompt:
-
- This feature allows you to control the audio, adding variation to different time segments.
- You have up to 10 prompt segments. the first prompt will always be 10s long
- the other prompts will be [10s - overlap].
- for example if the overlap is 2s, each prompt segment will be 8s.
-
- - **[Prompt Segments (number)]:**
- Amount of unique prompt to generate throughout the audio generation.
-
- - **[Prompt/Input Text (prompt)]:**
- Here describe the audio you wish the model to generate.
-
- - **[Repeat (number)]:**
- Write how many times this prompt will repeat (instead of wasting another prompt segment on the same prompt).
-
- - **[Time (text)]:**
- The time of the prompt segment.
-
- - **[Calculate Timings (button)]:**
- Calculates the timings of the prompt segments.
-
- - **[Duration (number)]:**
- How long you want the generated audio to be (in seconds).
-
- - **[Overlap (number)]:**
- How much each new segment will reference the previous segment (in seconds).
- For example, if you choose 2s: Each new segment after the first one will reference the previous segment 2s
- and will generate only 8s of new audio. The model can only process 10s of music.
-
- - **[Seed (number)]:**
- Your generated audio id. If you wish to generate the exact same audio,
- place the exact seed with the exact prompts
- (This way you can also extend specific song that was generated short).
-
- - **[Random Seed (button)]:**
- Gives "-1" as a seed, which counts as a random seed.
-
- - **[Copy Previous Seed (button)]:**
- Copies the seed from the output seed (if you don't feel like doing it manualy).
-
- ---
-
- ### Audio Tab:
-
- - **[Input Type (selection)]:**
- `File` mode allows you to upload an audio file to use as input
- `Mic` mode allows you to use your microphone as input
-
- - **[Trim Start and Trim End (numbers)]:**
- `Trim Start` set how much you'd like to trim the input audio from the start
- `Trim End` same as the above but from the end
-
- - **[Input Audio (audio file)]:**
- Input here the audio you wish to use.
-
- ---
-
- ### Customization Tab:
-
- - **[Background Color (color)]:**
- Works only if you don't upload image. Color of the background of the waveform.
-
- - **[Bar Color Start (color)]:**
- First color of the waveform bars.
-
- - **[Bar Color End (color)]:**
- Second color of the waveform bars.
-
- - **[Background Image (image)]:**
- Background image that you wish to be attached to the generated video along with the waveform.
-
- - **[Height and Width (numbers)]:**
- Output video resolution, only works with image.
- (minimum height and width is 256).
-
- ---
-
- ### Settings Tab:
-
- - **[Output Audio Channels (selection)]:**
- With this you can select the amount of channels that you wish for your output audio.
- `mono` is a straightforward single channel audio
- `stereo` is a dual channel audio but it will sound more or less like mono
- `stereo effect` this one is also dual channel but uses tricks to simulate a stereo audio.
-
- - **[Output Audio Sample Rate (dropdown)]:**
- The output audio sample rate, the model default is 32000.
-
- - **[Top-k (number)]:**
- is a parameter used in text generation models, including music generation models. It determines the number of most likely next tokens to consider at each step of the generation process. The model ranks all possible tokens based on their predicted probabilities, and then selects the top-k tokens from the ranked list. The model then samples from this reduced set of tokens to determine the next token in the generated sequence. A smaller value of k results in a more focused and deterministic output, while a larger value of k allows for more diversity in the generated music.
-
- - **[Top-p (number)]:**
- also known as nucleus sampling or probabilistic sampling, is another method used for token selection during text generation. Instead of specifying a fixed number like top-k, top-p considers the cumulative probability distribution of the ranked tokens. It selects the smallest possible set of tokens whose cumulative probability exceeds a certain threshold (usually denoted as p). The model then samples from this set to choose the next token. This approach ensures that the generated output maintains a balance between diversity and coherence, as it allows for a varying number of tokens to be considered based on their probabilities.
-
- - **[Temperature (number)]:**
- is a parameter that controls the randomness of the generated output. It is applied during the sampling process, where a higher temperature value results in more random and diverse outputs, while a lower temperature value leads to more deterministic and focused outputs. In the context of music generation, a higher temperature can introduce more variability and creativity into the generated music, but it may also lead to less coherent or structured compositions. On the other hand, a lower temperature can produce more repetitive and predictable music.
-
- - **[Classifier Free Guidance (number)]:**
- refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
- """
- )
- with gr.Tab("Audio Info"):
- gr.Markdown(
- """
- ### Audio Info
- """
- )
- with gr.Row():
- with gr.Column():
- in_audio = gr.File(type="file", label="Input Any Audio", interactive=True)
- with gr.Row():
- send_gen = gr.Button("Send to MusicGen", variant="primary")
- send_gen_a = gr.Button("Send to AudioGen", variant="primary")
- with gr.Column():
- info = gr.Textbox(label="Audio Info", lines=10, interactive=False)
- with gr.Tab("Changelog"):
- gr.Markdown(
- """
- ## Changelog:
-
- ### v2.0.0a
-
- - Forgot to move all the update to app.py from temp2.py... oops
-
-
-
- ### v2.0.0
-
- - Changed name from MusicGen+ to AudioCraft Plus
-
- - Complete overhaul of the repo "backend" with the latest changes from the main facebookresearch repo
-
- - Added a new decoder: MultiBand_Diffusion
-
- - Added AudioGen: a new tab for generating audio
-
-
-
- ### v1.2.8c
-
- - Implemented Reverse compatibility for audio info tab with previous versions
-
-
-
- ### v1.2.8b
-
- - Fixed the error when loading default models
-
-
-
- ### v1.2.8a
-
- - Adapted Audio info tab to work with the new structure prompts feature
-
- - Now custom models actually work, make sure you select the correct base model
-
-
-
- ### v1.2.8
-
- - Now you will also recieve json file with metadata of generated audio
-
- - Added error messages in Audio Info tab
-
- - Added structure prompts: you can select bpm, key and global prompt for all prompts
-
- - Added time display next to each prompt, can be calculated with "Calculate Timings" button
-
-
-
- ### v1.2.7
-
- - When sending generated audio to Input Audio, it will send a backup audio with default settings
- (best for continuos generation)
-
- - Added Metadata to generated audio (Thanks to AlexHK ♥)
-
- - Added Audio Info tab that will display the metadata of the input audio
-
- - Added "send to Text2Audio" button in Audio Info tab
-
- - Generated audio is now stored in the "output" folder (Thanks to AlexHK ♥)
-
- - Added an output area with generated files and download buttons
-
- - Enhanced Stereo effect (Thanks to AlexHK ♥)
-
-
-
- ### v1.2.6
-
- - Added option to generate in stereo (instead of only mono)
-
- - Added dropdown for selecting output sample rate (model default is 32000)
-
-
-
- ### v1.2.5a
-
- - Added file cleaner (This comes from the main facebookresearch repo)
-
- - Reorganized a little, moved audio to a seperate tab
-
-
-
- ### v1.2.5
-
- - Gave a unique lime theme to the webui
-
- - Added additional output for audio only
-
- - Added button to send generated audio to Input Audio
-
- - Added option to trim Input Audio
-
-
-
- ### v1.2.4
-
- - Added mic input (This comes from the main facebookresearch repo)
-
-
-
- ### v1.2.3
-
- - Added option to change video size to fit the image you upload
-
-
-
- ### v1.2.2
-
- - Added Wiki, Changelog and About tabs
-
-
-
- ### v1.2.1
-
- - Added tabs and organized the entire interface
-
- - Added option to attach image to the output video
-
- - Added option to load fine-tuned models (Yet to be tested)
-
-
-
- ### v1.2.0
-
- - Added Multi-Prompt
-
-
-
- ### v1.1.3
-
- - Added customization options for generated waveform
-
-
-
- ### v1.1.2
-
- - Removed sample length limit: now you can input audio of any length as music sample
-
-
-
- ### v1.1.1
-
- - Improved music sample audio quality when using music continuation
-
-
-
- ### v1.1.0
-
- - Rebuilt the repo on top of the latest structure of the main MusicGen repo
-
- - Improved Music continuation feature
-
-
-
- ### v1.0.0 - Stable Version
-
- - Added Music continuation
- """
- )
- with gr.Tab("About"):
- gen_type = gr.Text(value="music", interactive=False, visible=False)
- gen_type_a = gr.Text(value="audio", interactive=False, visible=False)
- gr.Markdown(
- """
- This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
- presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
-
- ## MusicGen+ is an extended version of the original MusicGen by facebookresearch.
-
- ### Repo: https://github.com/GrandaddyShmax/audiocraft_plus/tree/plus
-
- ---
-
- ### This project was possible thanks to:
-
- #### GrandaddyShmax - https://github.com/GrandaddyShmax
-
- #### Camenduru - https://github.com/camenduru
-
- #### rkfg - https://github.com/rkfg
-
- #### oobabooga - https://github.com/oobabooga
-
- #### AlexHK - https://github.com/alanhk147
- """
- )
-
- send_gen.click(info_to_params, inputs=[in_audio], outputs=[decoder, struc_prompts, global_prompt, bpm, key, scale, model, dropdown, basemodel, s, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select], queue=False)
- reuse_seed.click(fn=lambda x: x, inputs=[seed_used], outputs=[seed], queue=False)
- send_audio.click(fn=lambda x: x, inputs=[backup_only], outputs=[audio], queue=False)
- submit.click(predict_full, inputs=[gen_type, model, decoder, dropdown, basemodel, s, struc_prompts, bpm, key, scale, global_prompt, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select], outputs=[output, audio_only, backup_only, download, seed_used])
- input_type.change(toggle_audio_src, input_type, [audio], queue=False, show_progress=False)
- to_calc.click(calc_time, inputs=[gen_type, s, duration, overlap, repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9]], outputs=[calcs[0], calcs[1], calcs[2], calcs[3], calcs[4], calcs[5], calcs[6], calcs[7], calcs[8], calcs[9]], queue=False)
-
- send_gen_a.click(info_to_params_a, inputs=[in_audio], outputs=[decoder_a, struc_prompts_a, global_prompt_a, s_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, channel_a, sr_select_a], queue=False)
- reuse_seed_a.click(fn=lambda x: x, inputs=[seed_used_a], outputs=[seed_a], queue=False)
- send_audio_a.click(fn=lambda x: x, inputs=[backup_only_a], outputs=[audio_a], queue=False)
- submit_a.click(predict_full, inputs=[gen_type_a, model_a, decoder_a, dropdown, basemodel, s_a, struc_prompts_a, bpm, key, scale, global_prompt_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], audio_a, mode_a, trim_start_a, trim_end_a, duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, image_a, height_a, width_a, background_a, bar1_a, bar2_a, channel_a, sr_select_a], outputs=[output_a, audio_only_a, backup_only_a, download_a, seed_used_a])
- input_type_a.change(toggle_audio_src, input_type_a, [audio_a], queue=False, show_progress=False)
- to_calc_a.click(calc_time, inputs=[gen_type_a, s_a, duration_a, overlap_a, repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9]], outputs=[calcs_a[0], calcs_a[1], calcs_a[2], calcs_a[3], calcs_a[4], calcs_a[5], calcs_a[6], calcs_a[7], calcs_a[8], calcs_a[9]], queue=False)
-
- in_audio.change(get_audio_info, in_audio, outputs=[info])
-
- def variable_outputs(k):
- k = int(k) - 1
- return [gr.Textbox.update(visible=True)]*k + [gr.Textbox.update(visible=False)]*(max_textboxes-k)
- def get_size(image):
- if image is not None:
- img = Image.open(image)
- img_height = img.height
- img_width = img.width
- if (img_height%2) != 0:
- img_height = img_height + 1
- if (img_width%2) != 0:
- img_width = img_width + 1
- return img_height, img_width
- else:
- return 512, 768
-
- image.change(get_size, image, outputs=[height, width])
- image_a.change(get_size, image_a, outputs=[height_a, width_a])
- s.change(variable_outputs, s, textboxes)
- s_a.change(variable_outputs, s_a, textboxes_a)
- interface.queue().launch(**launch_kwargs)
-
-
-def ui_batched(launch_kwargs):
- with gr.Blocks() as demo:
- gr.Markdown(
- """
- # MusicGen
-
- This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
- a simple and controllable model for music generation
- presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
-
-
-
- for longer sequences, more control and no queue.
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Row():
- text = gr.Text(label="Describe your music", lines=2, interactive=True)
- with gr.Column():
- radio = gr.Radio(["file", "mic"], value="file",
- label="Condition on a melody (optional) File or Mic")
- melody = gr.Audio(source="upload", type="numpy", label="File",
- interactive=True, elem_id="melody-input")
- with gr.Row():
- submit = gr.Button("Generate")
- with gr.Column():
- output = gr.Video(label="Generated Music")
- audio_output = gr.Audio(label="Generated Music (wav)", type='filepath')
- submit.click(predict_batched, inputs=[text, melody],
- outputs=[output, audio_output], batch=True, max_batch_size=MAX_BATCH_SIZE)
- radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
- gr.Examples(
- fn=predict_batched,
- examples=[
- [
- "An 80s driving pop song with heavy drums and synth pads in the background",
- "./assets/bach.mp3",
- ],
- [
- "A cheerful country song with acoustic guitars",
- "./assets/bolero_ravel.mp3",
- ],
- [
- "90s rock song with electric guitar and heavy drums",
- None,
- ],
- [
- "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
- "./assets/bach.mp3",
- ],
- [
- "lofi slow bpm electro chill with organic samples",
- None,
- ],
- ],
- inputs=[text, melody],
- outputs=[output]
- )
- gr.Markdown("""
- ### More details
-
- The model will generate 12 seconds of audio based on the description you provided.
- You can optionally provide a reference audio from which a broad melody will be extracted.
- The model will then try to follow both the description and melody provided.
- All samples are generated with the `melody` model.
-
- You can also use your own GPU or a Google Colab by following the instructions on our repo.
-
- See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
- for more details.
- """)
-
- demo.queue(max_size=8 * 4).launch(**launch_kwargs)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument(
- '--listen',
- type=str,
- default='0.0.0.0' if 'SPACE_ID' in os.environ else '127.0.0.1',
- help='IP to listen on for connections to Gradio',
- )
- parser.add_argument(
- '--username', type=str, default='', help='Username for authentication'
- )
- parser.add_argument(
- '--password', type=str, default='', help='Password for authentication'
- )
- parser.add_argument(
- '--server_port',
- type=int,
- default=0,
- help='Port to run the server listener on',
- )
- parser.add_argument(
- '--inbrowser', action='store_true', help='Open in browser'
- )
- parser.add_argument(
- '--share', action='store_true', help='Share the gradio UI'
- )
- parser.add_argument(
- '--unload_model', action='store_true', help='Unload the model after every generation to save GPU memory'
- )
-
- parser.add_argument(
- '--unload_to_cpu', action='store_true', help='Move the model to main RAM after every generation to save GPU memory but reload faster than after full unload (see above)'
- )
-
- parser.add_argument(
- '--cache', action='store_true', help='Cache models in RAM to quickly switch between them'
- )
-
- args = parser.parse_args()
- UNLOAD_MODEL = args.unload_model
- MOVE_TO_CPU = args.unload_to_cpu
- if args.cache:
- MODELS = {}
-
- launch_kwargs = {}
- launch_kwargs['server_name'] = args.listen
-
- if args.username and args.password:
- launch_kwargs['auth'] = (args.username, args.password)
- if args.server_port:
- launch_kwargs['server_port'] = args.server_port
- if args.inbrowser:
- launch_kwargs['inbrowser'] = args.inbrowser
- if args.share:
- launch_kwargs['share'] = args.share
-
- # Show the interface
- if IS_BATCHED:
- global USE_DIFFUSION
- USE_DIFFUSION = False
- ui_batched(launch_kwargs)
- else:
- ui_full(launch_kwargs)
\ No newline at end of file
diff --git a/spaces/PunPk/AI_FallingAsleepDriving/README.md b/spaces/PunPk/AI_FallingAsleepDriving/README.md
deleted file mode 100644
index 0855483dc5cdc696af1fe1d3502b584b42fde086..0000000000000000000000000000000000000000
--- a/spaces/PunPk/AI_FallingAsleepDriving/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: AI FallingAsleepDriving
-emoji: 📉
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RMXK/RVC_HFF/tensorlowest.py b/spaces/RMXK/RVC_HFF/tensorlowest.py
deleted file mode 100644
index eccd4dbf3494434e59f7defaae6ab91797263b90..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/tensorlowest.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from tensorboard.backend.event_processing import event_accumulator
-
-import os
-from shutil import copy2
-from re import search as RSearch
-import pandas as pd
-from ast import literal_eval as LEval
-
-weights_dir = 'weights/'
-
-def find_biggest_tensorboard(tensordir):
- try:
- files = [f for f in os.listdir(tensordir) if f.endswith('.0')]
- if not files:
- print("No files with the '.0' extension found!")
- return
-
- max_size = 0
- biggest_file = ""
-
- for file in files:
- file_path = os.path.join(tensordir, file)
- if os.path.isfile(file_path):
- file_size = os.path.getsize(file_path)
- if file_size > max_size:
- max_size = file_size
- biggest_file = file
-
- return biggest_file
-
- except FileNotFoundError:
- print("Couldn't find your model!")
- return
-
-def main(model_name, save_freq, lastmdls):
- global lowestval_weight_dir, scl
-
- tensordir = os.path.join('logs', model_name)
- lowestval_weight_dir = os.path.join(tensordir, "lowestvals")
-
- latest_file = find_biggest_tensorboard(tensordir)
-
- if latest_file is None:
- print("Couldn't find a valid tensorboard file!")
- return
-
- tfile = os.path.join(tensordir, latest_file)
-
- ea = event_accumulator.EventAccumulator(tfile,
- size_guidance={
- event_accumulator.COMPRESSED_HISTOGRAMS: 500,
- event_accumulator.IMAGES: 4,
- event_accumulator.AUDIO: 4,
- event_accumulator.SCALARS: 0,
- event_accumulator.HISTOGRAMS: 1,
- })
-
- ea.Reload()
- ea.Tags()
-
- scl = ea.Scalars('loss/g/total')
-
- listwstep = {}
-
- for val in scl:
- if (val.step // save_freq) * save_freq in [val.step for val in scl]:
- listwstep[float(val.value)] = (val.step // save_freq) * save_freq
-
- lowest_vals = sorted(listwstep.keys())[:lastmdls]
-
- sorted_dict = {value: step for value, step in listwstep.items() if value in lowest_vals}
-
- return sorted_dict
-
-def selectweights(model_name, file_dict, weights_dir, lowestval_weight_dir):
- os.makedirs(lowestval_weight_dir, exist_ok=True)
- logdir = []
- files = []
- lbldict = {
- 'Values': {},
- 'Names': {}
- }
- weights_dir_path = os.path.join(weights_dir, "")
- low_val_path = os.path.join(os.getcwd(), os.path.join(lowestval_weight_dir, ""))
-
- try:
- file_dict = LEval(file_dict)
- except Exception as e:
- print(f"Error! {e}")
- return f"Couldn't load tensorboard file! {e}"
-
- weights = [f for f in os.scandir(weights_dir)]
- for key, value in file_dict.items():
- pattern = fr"^{model_name}_.*_s{value}\.pth$"
- matching_weights = [f.name for f in weights if f.is_file() and RSearch(pattern, f.name)]
- for weight in matching_weights:
- source_path = weights_dir_path + weight
- destination_path = os.path.join(lowestval_weight_dir, weight)
-
- copy2(source_path, destination_path)
-
- logdir.append(f"File = {weight} Value: {key}, Step: {value}")
-
- lbldict['Names'][weight] = weight
- lbldict['Values'][weight] = key
-
- files.append(low_val_path + weight)
-
- print(f"File = {weight} Value: {key}, Step: {value}")
-
- yield ('\n'.join(logdir), files, pd.DataFrame(lbldict))
-
-
- return ''.join(logdir), files, pd.DataFrame(lbldict)
-
-
-if __name__ == "__main__":
- model = str(input("Enter the name of the model: "))
- sav_freq = int(input("Enter save frequency of the model: "))
- ds = main(model, sav_freq)
-
- if ds: selectweights(model, ds, weights_dir, lowestval_weight_dir)
-
\ No newline at end of file
diff --git a/spaces/Rekanice/hf_minimal_sushi/README.md b/spaces/Rekanice/hf_minimal_sushi/README.md
deleted file mode 100644
index 4b038ea069e1817e717615da6679be0a913f0f5d..0000000000000000000000000000000000000000
--- a/spaces/Rekanice/hf_minimal_sushi/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Hf Minimal Sushi
-emoji: 🌍
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RoCobo/WiggleGAN/main.py b/spaces/RoCobo/WiggleGAN/main.py
deleted file mode 100644
index 097c3b9aed9be52619095419859205b71110555c..0000000000000000000000000000000000000000
--- a/spaces/RoCobo/WiggleGAN/main.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import argparse
-import os
-import torch
-from WiggleGAN import WiggleGAN
-#from MyACGAN import MyACGAN
-#from MyGAN import MyGAN
-
-"""parsing and configuration"""
-
-
-def parse_args():
- desc = "Pytorch implementation of GAN collections"
- parser = argparse.ArgumentParser(description=desc)
-
- parser.add_argument('--gan_type', type=str, default='WiggleGAN',
- choices=['MyACGAN', 'MyGAN', 'WiggleGAN'],
- help='The type of GAN')
- parser.add_argument('--dataset', type=str, default='4cam',
- choices=['mnist', 'fashion-mnist', 'cifar10', 'cifar100', 'svhn', 'stl10', 'lsun-bed', '4cam'],
- help='The name of dataset')
- parser.add_argument('--split', type=str, default='', help='The split flag for svhn and stl10')
- parser.add_argument('--epoch', type=int, default=50, help='The number of epochs to run')
- parser.add_argument('--batch_size', type=int, default=16, help='The size of batch')
- parser.add_argument('--input_size', type=int, default=10, help='The size of input image')
- parser.add_argument('--save_dir', type=str, default='models',
- help='Directory name to save the model')
- parser.add_argument('--result_dir', type=str, default='results', help='Directory name to save the generated images')
- parser.add_argument('--log_dir', type=str, default='logs', help='Directory name to save training logs')
- parser.add_argument('--lrG', type=float, default=0.0002)
- parser.add_argument('--lrD', type=float, default=0.001)
- parser.add_argument('--beta1', type=float, default=0.5)
- parser.add_argument('--beta2', type=float, default=0.999)
- parser.add_argument('--gpu_mode', type=str2bool, default=True)
- parser.add_argument('--benchmark_mode', type=str2bool, default=True)
- parser.add_argument('--cameras', type=int, default=2)
- parser.add_argument('--imageDim', type=int, default=128)
- parser.add_argument('--epochV', type=int, default=0)
- parser.add_argument('--cIm', type=int, default=4)
- parser.add_argument('--seedLoad', type=str, default="-0000")
- parser.add_argument('--zGF', type=float, default=0.2)
- parser.add_argument('--zDF', type=float, default=0.2)
- parser.add_argument('--bF', type=float, default=0.2)
- parser.add_argument('--expandGen', type=int, default=3)
- parser.add_argument('--expandDis', type=int, default=3)
- parser.add_argument('--wiggleDepth', type=int, default=-1)
- parser.add_argument('--visdom', type=str2bool, default=True)
- parser.add_argument('--lambdaL1', type=int, default=100)
- parser.add_argument('--clipping', type=float, default=-1)
- parser.add_argument('--depth', type=str2bool, default=True)
- parser.add_argument('--recreate', type=str2bool, default=False)
- parser.add_argument('--name_wiggle', type=str, default='wiggle-result')
-
- return check_args(parser.parse_args())
-
-
-"""checking arguments"""
-
-def str2bool(v):
- if isinstance(v, bool):
- return v
- if v.lower() in ('yes', 'true', 't', 'y', '1'):
- return True
- elif v.lower() in ('no', 'false', 'f', 'n', '0'):
- return False
- else:
- raise argparse.ArgumentTypeError('Boolean value expected.')
-
-
-def check_args(args):
- # --save_dir
- if not os.path.exists(args.save_dir):
- os.makedirs(args.save_dir)
-
- # --result_dir
- if not os.path.exists(args.result_dir):
- os.makedirs(args.result_dir)
-
- # --result_dir
- if not os.path.exists(args.log_dir):
- os.makedirs(args.log_dir)
-
- # --epoch
- try:
- assert args.epoch >= 1
- except:
- print('number of epochs must be larger than or equal to one')
-
- # --batch_size
- try:
- assert args.batch_size >= 1
- except:
- print('batch size must be larger than or equal to one')
-
- return args
-
-
-"""main"""
-
-
-def main():
- # parse arguments
- args = parse_args()
- if args is None:
- exit()
-
- if args.benchmark_mode:
- torch.backends.cudnn.benchmark = True
-
- # declare instance for GAN
- if args.gan_type == 'WiggleGAN':
- gan = WiggleGAN(args)
- #elif args.gan_type == 'MyACGAN':
- # gan = MyACGAN(args)
- #elif args.gan_type == 'MyGAN':
- # gan = MyGAN(args)
- else:
- raise Exception("[!] There is no option for " + args.gan_type)
-
- # launch the graph in a session
- if (args.wiggleDepth < 0 and not args.recreate):
- print(" [*] Training Starting!")
- gan.train()
- print(" [*] Training finished!")
- else:
- if not args.recreate:
- print(" [*] Wiggle Started!")
- gan.wiggleEf()
- print(" [*] Wiggle finished!")
- else:
- print(" [*] Dataset recreation Started")
- gan.recreate()
- print(" [*] Dataset recreation finished")
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py
deleted file mode 100644
index f2678b321c295bcceaef945111ac3524be19d6e4..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .misc import add_prefix
-
-__all__ = ['add_prefix']
diff --git a/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py b/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py
deleted file mode 100644
index cdd69f2a24d47166425d785ef2767708b44b6593..0000000000000000000000000000000000000000
--- a/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from config import output_path_video, output_path_audio
-from logger import logger
-from resource_loader.video_loader_interface import VideoLoaderInterface
-
-
-class UploadedMediaLoader(VideoLoaderInterface):
- def __init__(self, uploaded_stream, original_name, media_type='video'):
- self.uploaded_stream = uploaded_stream
- self.original_name = original_name
- self.media_type = media_type # 'video' or 'audio'
- self.media_id = None
- self.filename = None
- self.output_path = None
- self.extract_filename()
- self.set_output_path()
-
- def extract_filename(self):
- self.filename = self.original_name.split(" - ", 1)[1]
- self.media_id = self.filename.rsplit(".", 1)[0]
-
- def set_output_path(self):
- if self.media_type == 'video':
- self.output_path = output_path_video
- elif self.media_type == 'audio':
- self.output_path = output_path_audio
- else:
- raise ValueError("Invalid media type")
-
- def download(self):
- with open(f"{self.output_path}/{self.filename}", "wb") as f:
- f.write(self.uploaded_stream.getvalue())
- logger.info(f"{self.media_type.capitalize()} processed: {self.original_name}")
diff --git a/spaces/STF-R/docker-test3/app/templates/prediction.html b/spaces/STF-R/docker-test3/app/templates/prediction.html
deleted file mode 100644
index 4366614474cbe8347297ec3d57a7b8b7bf72684d..0000000000000000000000000000000000000000
--- a/spaces/STF-R/docker-test3/app/templates/prediction.html
+++ /dev/null
@@ -1,48 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
- VIVADATA | {{iris_type}}
-
-
-
-
-
-
-
-
-
-
-
-
-
- VIVADATA - Flask Demo
-
-
-
- 🏵️🌺🌸 It's a {{iris_type|title}} 🏵️🌺🌸
-
-
-
-
-
-
-
- Back
-
-
-
-
-
-
-
-
diff --git a/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py b/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py
deleted file mode 100644
index 3706c57541c1b7d9004957422b52cd1e2191ae68..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-# flake8: noqa
-
-from ..utils import DummyObject, requires_backends
-
-
-class LMSDiscreteScheduler(metaclass=DummyObject):
- _backends = ["scipy"]
-
- def __init__(self, *args, **kwargs):
- requires_backends(self, ["scipy"])
diff --git a/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py b/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py
deleted file mode 100644
index 2e25dd918f2714ae9a2c21d14401663c371b5f38..0000000000000000000000000000000000000000
--- a/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py
+++ /dev/null
@@ -1,704 +0,0 @@
-from typing import Callable, Optional, Tuple
-
-import flax.linen as nn
-import jax
-import jax.numpy as jnp
-from flax.core.frozen_dict import FrozenDict, unfreeze
-from jax import lax
-from jax.random import PRNGKey
-from transformers import GPT2Config, FlaxViTModel, ViTConfig
-from transformers.modeling_flax_outputs import (
- FlaxCausalLMOutputWithCrossAttentions,
- FlaxSeq2SeqLMOutput,
- FlaxSeq2SeqModelOutput,
-)
-from transformers.models.bart.modeling_flax_bart import (
- shift_tokens_right,
-)
-from .modeling_flax_gpt2 import (
- FlaxGPT2Module,
- FlaxGPT2Model,
- FlaxPreTrainedModel
-)
-from transformers.models.vit.modeling_flax_vit import FlaxViTModule
-
-from .configuration_vit_gpt2 import ViTGPT2Config
-
-
-class FlaxViTGPT2Module(nn.Module):
- config: ViTGPT2Config
- dtype: jnp.dtype = jnp.float32 # the dtype of the computation
-
- def setup(self):
-
- self.encoder = FlaxViTModule(self.config.vit_config, dtype=self.dtype)
- self.decoder = FlaxGPT2Module(self.config.gpt2_config, dtype=self.dtype)
-
- def _get_encoder_module(self):
- return self.encoder
-
- def _get_decoder_module(self):
- return self.decoder
-
- def __call__(
- self,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- output_attentions: bool = False,
- output_hidden_states: bool = False,
- return_dict: bool = True,
- deterministic: bool = True,
- ):
- encoder_outputs = self.encoder(
- pixel_values=pixel_values,
- deterministic=deterministic,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- decoder_outputs = self.decoder(
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_hidden_states=encoder_outputs[0],
- encoder_attention_mask=encoder_attention_mask,
- deterministic=deterministic,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict
- )
-
- return FlaxSeq2SeqModelOutput(
- last_hidden_state=decoder_outputs.last_hidden_state,
- decoder_hidden_states=decoder_outputs.hidden_states,
- decoder_attentions=decoder_outputs.attentions,
- cross_attentions=decoder_outputs.cross_attentions,
- encoder_last_hidden_state=encoder_outputs.last_hidden_state,
- encoder_hidden_states=encoder_outputs.hidden_states,
- encoder_attentions=encoder_outputs.attentions,
- )
-
-
-class FlaxViTGPT2ForConditionalGenerationModule(nn.Module):
- config: ViTGPT2Config
- dtype: jnp.dtype = jnp.float32
- bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
-
- def setup(self):
- self.model = FlaxViTGPT2Module(config=self.config, dtype=self.dtype)
- self.lm_head = nn.Dense(
- self.model.decoder.embed_dim,
- use_bias=False,
- dtype=self.dtype,
- kernel_init=jax.nn.initializers.normal(
- self.config.gpt2_config.initializer_range, self.dtype
- ),
- )
- self.final_logits_bias = self.param(
- "final_logits_bias", self.bias_init, (1, self.model.decoder.embed_dim)
- )
-
- def _get_encoder_module(self):
- return self.model.encoder
-
- def _get_decoder_module(self):
- return self.model.decoder
-
- def __call__(
- self,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- output_attentions: bool = False,
- output_hidden_states: bool = False,
- return_dict: bool = True,
- deterministic: bool = True,
- ):
- outputs = self.model(
- pixel_values=pixel_values,
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_attention_mask=encoder_attention_mask,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=deterministic,
- )
-
- hidden_states = outputs[0]
- lm_logits = self.lm_head(hidden_states)
- lm_logits += self.final_logits_bias
-
- if not return_dict:
- output = (lm_logits,) + outputs[1:]
- return output
-
- return FlaxSeq2SeqLMOutput(
- logits=lm_logits,
- decoder_hidden_states=outputs.decoder_hidden_states,
- decoder_attentions=outputs.decoder_attentions,
- cross_attentions=outputs.cross_attentions,
- encoder_last_hidden_state=outputs.encoder_last_hidden_state,
- encoder_hidden_states=outputs.encoder_hidden_states,
- encoder_attentions=outputs.encoder_attentions,
- )
-
-class FlaxViTGPT2PreTrainedModel(FlaxPreTrainedModel):
- config_class = ViTGPT2Config
- base_model_prefix: str = "model"
- module_class: nn.Module = None
-
- def __init__(
- self,
- config: ViTGPT2Config,
- input_shape: Tuple = None,
- seed: int = 0,
- dtype: jnp.dtype = jnp.float32,
- **kwargs,
- ):
- if input_shape is None:
- input_shape = (
- (1, config.vit_config.image_size, config.vit_config.image_size, 3),
- (1, 1),
- )
-
- module = self.module_class(config=config, dtype=dtype, **kwargs)
- super().__init__(
- config, module, input_shape=input_shape, seed=seed, dtype=dtype
- )
-
- def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
- # init input tensors
- pixel_values = jax.random.normal(rng, input_shape[0])
- # # make sure initialization pass will work for FlaxBartForSequenceClassificationModule
- # input_ids = jax.ops.index_update(input_ids, (..., -1), self.config.eos_token_id)
-
- input_ids = jnp.zeros(input_shape[1], dtype="i4")
- attention_mask = jnp.ones_like(input_ids)
-
- batch_size, sequence_length = input_ids.shape
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- params_rng, dropout_rng = jax.random.split(rng)
- rngs = {"params": params_rng, "dropout": dropout_rng}
-
- return self.module.init(
- rngs,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- )["params"]
-
- def init_cache(self, batch_size, max_length, encoder_outputs):
-
- input_ids = jnp.ones((batch_size, max_length), dtype="i4")
- attention_mask = jnp.ones_like(input_ids)
- position_ids = jnp.broadcast_to(
- jnp.arange(jnp.atleast_2d(input_ids).shape[-1]),
- input_ids.shape,
- )
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- return decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
-
- init_variables = self.module.init(
- jax.random.PRNGKey(0),
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_hidden_states=encoder_outputs[0],
- init_cache=True,
- method=_decoder_forward, # we only need to call the decoder to init the cache
- )
- return unfreeze(init_variables["cache"])
-
- def encode(
- self,
- pixel_values: jnp.ndarray,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- def _encoder_forward(module, pixel_values, **kwargs):
- encode_module = module._get_encoder_module()
- return encode_module(pixel_values, **kwargs)
-
- return self.module.apply(
- {"params": params or self.params},
- pixel_values=jnp.array(pixel_values, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- method=_encoder_forward,
- )
-
- def decode(
- self,
- input_ids,
- encoder_outputs,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- past_key_values: dict = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
-
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- encoder_hidden_states = encoder_outputs[0]
- if encoder_attention_mask is None:
- batch_size, sequence_length = encoder_hidden_states.shape[:2]
- encoder_attention_mask = jnp.ones((batch_size, sequence_length))
-
- batch_size, sequence_length = input_ids.shape
- if attention_mask is None:
- attention_mask = jnp.ones((batch_size, sequence_length))
-
- if position_ids is None:
- if past_key_values is not None:
- raise ValueError(
- "Make sure to provide `position_ids` when passing `past_key_values`."
- )
-
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- inputs = {"params": params or self.params}
-
- # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
- # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
- # it can be changed by FlaxGPT2Attention module
- if past_key_values:
- inputs["cache"] = past_key_values
- mutable = ["cache"]
- else:
- mutable = False
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- return decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
-
- outputs = self.module.apply(
- inputs,
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- mutable=mutable,
- method=_decoder_forward,
- )
-
- # add updated cache to model output
- if past_key_values is not None and return_dict:
- outputs, past = outputs
- outputs["past_key_values"] = unfreeze(past["cache"])
- return outputs
- elif past_key_values is not None and not return_dict:
- outputs, past = outputs
- outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
-
- return outputs
-
- def __call__(
- self,
- pixel_values: jnp.ndarray,
- input_ids: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
-
- # # prepare encoder inputs
- # if encoder_attention_mask is None:
- # encoder_attention_mask = jnp.ones_like(input_ids)
-
- # if position_ids is None:
- # batch_size, sequence_length = input_ids.shape
- # position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
-
- # prepare decoder inputs
- # if decoder_input_ids is None:
- # decoder_input_ids = shift_tokens_right(
- # input_ids, self.config.pad_token_id, decoder_start_token_id=self.config.decoder_start_token_id
- # ) # TODO: Check how to use this
-
- if attention_mask is None:
- attention_mask = jnp.ones_like(input_ids)
- if position_ids is None:
- batch_size, sequence_length = input_ids.shape
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
-
- return self.module.apply(
- {"params": params or self.params},
- pixel_values=jnp.array(pixel_values, dtype=jnp.float32),
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- )
-
-
-class FlaxViTGPT2ForConditionalGeneration(FlaxViTGPT2PreTrainedModel):
- module_class = FlaxViTGPT2ForConditionalGenerationModule
- dtype: jnp.dtype = jnp.float32
-
- def decode(
- self,
- input_ids,
- encoder_outputs,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- past_key_values: dict = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- deterministic: bool = True,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- encoder_hidden_states = encoder_outputs[0]
- if encoder_attention_mask is None:
- batch_size, sequence_length = encoder_hidden_states.shape[:2]
- encoder_attention_mask = jnp.ones((batch_size, sequence_length))
-
- batch_size, sequence_length = input_ids.shape
- if attention_mask is None:
- attention_mask = jnp.ones((batch_size, sequence_length))
-
- if position_ids is None:
- if past_key_values is not None:
- raise ValueError(
- "Make sure to provide `position_ids` when passing `past_key_values`."
- )
-
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- inputs = {"params": params or self.params}
-
- # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
- # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
- # it can be changed by FlaxGPT2Attention module
- if past_key_values:
- inputs["cache"] = past_key_values
- mutable = ["cache"]
- else:
- mutable = False
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- outputs = decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
- hidden_states = outputs[0]
-
- if self.config.tie_word_embeddings:
- shared_embedding = module.model.variables["params"]["shared"][
- "embedding"
- ]
- lm_logits = module.lm_head.apply(
- {"params": {"kernel": shared_embedding.T}}, hidden_states
- )
- else:
- lm_logits = module.lm_head(hidden_states)
-
- lm_logits += module.final_logits_bias
- return lm_logits, outputs
-
- outputs = self.module.apply(
- inputs,
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=deterministic,
- rngs=rngs,
- mutable=mutable,
- method=_decoder_forward,
- )
-
- if past_key_values is None:
- lm_logits, outputs = outputs
- else:
- (lm_logits, outputs), past = outputs
-
- if return_dict:
- outputs = FlaxCausalLMOutputWithCrossAttentions(
- logits=lm_logits,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- cross_attentions=outputs.cross_attentions,
- )
- else:
- outputs = (lm_logits,) + outputs[1:]
-
- # add updated cache to model output
- if past_key_values is not None and return_dict:
- outputs["past_key_values"] = unfreeze(past["cache"])
- return outputs
- elif past_key_values is not None and not return_dict:
- outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
-
- return outputs
-
- def prepare_inputs_for_generation(
- self,
- input_ids,
- max_length,
- encoder_attention_mask: Optional[jnp.DeviceArray] = None,
- attention_mask: Optional[jnp.DeviceArray] = None,
- encoder_outputs=None,
- **kwargs,
- ):
- # initializing the cache
- batch_size, seq_length = input_ids.shape
-
- past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
- # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
- # But since the decoder uses a causal mask, those positions are masked anyways.
- # Thus we can create a single static attention_mask here, which is more efficient for compilation
- extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
- if attention_mask is not None:
- position_ids = attention_mask.cumsum(axis=-1) - 1
- extended_attention_mask = lax.dynamic_update_slice(
- extended_attention_mask, attention_mask, (0, 0)
- )
- else:
- position_ids = jnp.broadcast_to(
- jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)
- )
-
- return {
- "past_key_values": past_key_values,
- "encoder_outputs": encoder_outputs,
- "encoder_attention_mask": encoder_attention_mask,
- "attention_mask": extended_attention_mask,
- "position_ids": position_ids,
- }
-
- def update_inputs_for_generation(self, model_outputs, model_kwargs):
- model_kwargs["past_key_values"] = model_outputs.past_key_values
- model_kwargs["position_ids"] = (
- model_kwargs["position_ids"][:, -1:] + 1
- )
- return model_kwargs
-
- @classmethod
- def from_vit_gpt2_pretrained(
- cls,
- vit_model_name_or_path: str = None,
- gpt2_model_name_or_path: str = None,
- *model_args,
- **kwargs,
- ) -> FlaxViTGPT2PreTrainedModel:
-
- kwargs_gpt2 = {
- argument[len("gpt2_") :]: value
- for argument, value in kwargs.items()
- if argument.startswith("gpt2_")
- }
-
- kwargs_vit = {
- argument[len("vit_") :]: value
- for argument, value in kwargs.items()
- if argument.startswith("vit_")
- }
-
- # remove gpt2, vit kwargs from kwargs
- for key in kwargs_gpt2.keys():
- del kwargs["gpt2_" + key]
- for key in kwargs_vit.keys():
- del kwargs["vit_" + key]
-
- # Load and initialize the gpt2 and vit model
- gpt2_model = kwargs_gpt2.pop("model", None)
- if gpt2_model is None:
- assert (
- gpt2_model_name_or_path is not None
- ), "If `model` is not defined as an argument, a `gpt2_model_name_or_path` has to be defined"
-
- if "config" not in kwargs_gpt2:
- gpt2_config = GPT2Config.from_pretrained(gpt2_model_name_or_path)
- kwargs_gpt2["config"] = gpt2_config
-
- kwargs_gpt2["config"].add_cross_attention = True
- gpt2_model = FlaxGPT2Model.from_pretrained(
- gpt2_model_name_or_path, *model_args, **kwargs_gpt2
- )
-
- vit_model = kwargs_vit.pop("model", None)
- if vit_model is None:
- assert (
- vit_model_name_or_path is not None
- ), "If `model` is not defined as an argument, a `vit_model_name_or_path` has to be defined"
-
- if "config" not in kwargs_vit:
- vit_config = ViTConfig.from_pretrained(vit_model_name_or_path)
- kwargs_vit["config"] = vit_config
-
- vit_model = FlaxViTModel.from_pretrained(
- vit_model_name_or_path, *model_args, **kwargs_vit
- )
-
- # instantiate config with corresponding kwargs
- dtype = kwargs.pop("dtype", jnp.float32)
- config = ViTGPT2Config.from_vit_gpt2_configs(
- vit_model.config, gpt2_model.config, **kwargs
- )
-
- # init model
- model = cls(config, *model_args, dtype=dtype, **kwargs)
- model.params["model"]["encoder"] = vit_model.params
- model.params["model"]["decoder"] = gpt2_model.params
-
- return model
-
diff --git a/spaces/Seetha/IMA-pipeline-streamlit/app.py b/spaces/Seetha/IMA-pipeline-streamlit/app.py
deleted file mode 100644
index f0db4170922ebf1b9a612db7fac952e61207154a..0000000000000000000000000000000000000000
--- a/spaces/Seetha/IMA-pipeline-streamlit/app.py
+++ /dev/null
@@ -1,581 +0,0 @@
-# import all packages
-import requests
-import streamlit as st
-from sklearn.model_selection import StratifiedKFold
-from sklearn.model_selection import train_test_split
-from sklearn.model_selection import KFold
-# tokenizer
-from transformers import AutoTokenizer, DistilBertTokenizerFast
-# sequence tagging model + training-related
-from transformers import DistilBertForTokenClassification, Trainer, TrainingArguments
-import torch
-import sys
-import os
-from sklearn.metrics import classification_report
-from pandas import read_csv
-from sklearn.linear_model import LogisticRegression
-import sklearn.model_selection
-from sklearn.feature_extraction.text import TfidfTransformer
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.pipeline import Pipeline, FeatureUnion
-import math
-# from sklearn.metrics import accuracy_score
-# from sklearn.metrics import precision_recall_fscore_support
-import json
-import re
-import numpy as np
-import pandas as pd
-import nltk
-nltk.download("punkt")
-import string
-from sklearn.model_selection import train_test_split
-from transformers import AutoTokenizer, Trainer, TrainingArguments, AutoModelForSequenceClassification, AutoConfig
-from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
-import itertools
-from transformers import TextClassificationPipeline, TFAutoModelForSequenceClassification, AutoTokenizer
-from transformers import pipeline
-import pickle
-import csv
-import pdfplumber
-import pathlib
-import shutil
-import webbrowser
-from streamlit.components.v1 import html
-import streamlit.components.v1 as components
-from PyPDF2 import PdfReader
-from huggingface_hub import HfApi
-import io
-from datasets import load_dataset
-import time
-
-import huggingface_hub
-from huggingface_hub import Repository
-from datetime import datetime
-import pathlib as Path
-from requests import get
-import urllib.request
-# import gradio as gr
-# from gradio import inputs, outputs
-from datasets import load_dataset
-from huggingface_hub import HfApi, list_models
-import os
-from huggingface_hub import HfFileSystem
-from tensorflow.keras.models import Sequential, model_from_json
-#import tensorflow_datasets as tfds
-import tensorflow as tf
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-import spacy
-from tensorflow.keras.preprocessing.text import Tokenizer
-#from spacy import en_core_web_lg
-#import en_core_web_lg
-#nlp = en_core_web_lg.load()
-nlp = spacy.load('en_core_web_sm')
-
-#tfds.disable_progress_bar()
-MAX_SEQUENCE_LENGTH = 500
-
-# dataset = load_dataset('Seetha/Visualization', streaming=True)
-# df = pd.DataFrame.from_dict(dataset['train'])
-# DATASET_REPO_URL = "https://huggingface.co/datasets/Seetha/Visualization"
-# DATA_FILENAME = "level2.json"
-#DATA_FILE = os.path.join("data", DATA_FILENAME)
-DATASET_REPO_URL = "https://huggingface.co/datasets/Seetha/visual_files"
-DATA_FILENAME = "detailedResults.json"
-DATA_FILENAME1 = "level2.json"
-
-HF_TOKEN = os.environ.get("HF_TOKEN")
-#st.write("is none?", HF_TOKEN is None)
-
-def main():
-
- st.title("Text to Causal Knowledge Graph")
- st.sidebar.title("Please upload your text documents in one file here:")
- k=2
- seed = 1
- k1= 5
- text_list = []
- causal_sents = []
-
- uploaded_file = None
- try:
- uploaded_file = st.sidebar.file_uploader("Choose a file", type = "pdf")
- except:
- uploaded_file = PdfReader('sample_anno.pdf')
- st.error("Please upload your own PDF to be analyzed")
-
- if uploaded_file is not None:
- reader = PdfReader(uploaded_file)
- for page in reader.pages:
- text = page.extract_text()
- text_list.append(text)
- else:
- st.error("Please upload your own PDF to be analyzed")
- st.stop()
-
- text_list_final = [x.replace('\n', '') for x in text_list]
- text_list_final = re.sub('"', '', str(text_list_final))
-
- sentences = nltk.sent_tokenize(text_list_final)
-
- result =[]
- for i in sentences:
- result1 = i.lower()
- result2 = re.sub(r'[^\w\s]','',result1)
- result.append(result2)
-
- #st.write("--- %s seconds ---" % (time.time() - start_time))
- tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") #bert-base-uncased
-
- model_path = "checkpoint-2850"
-
- model = AutoModelForSequenceClassification.from_pretrained(model_path,id2label={0:'non-causal',1:'causal'})
-
- #st.write('sequence classification loaded')
- pipe1 = pipeline("text-classification", model=model,tokenizer=tokenizer)
- for sent in result:
- pred = pipe1(sent)
- for lab in pred:
- if lab['label'] == 'causal': #causal
- causal_sents.append(sent)
-
- # st.write('causal sentence classification finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
-
- model_name = "distilbert-base-cased"
- tokenizer = DistilBertTokenizerFast.from_pretrained(model_name,low_cpu_mem_usage=True)
-
- model_path1 = "DistilBertforTokenclassification"
-
- model = DistilBertForTokenClassification.from_pretrained(model_path1,low_cpu_mem_usage=True) #len(unique_tags),, num_labels= 7, , id2label={0:'CT',1:'E',2:'C',3:'O'}
- pipe = pipeline('ner', model=model, tokenizer=tokenizer,aggregation_strategy='simple') #grouped_entities=True
- st.write('DistilBERT loaded')
- sentence_pred = []
- class_list = []
- entity_list = []
- for k in causal_sents:
- pred= pipe(k)
- #st.write(pred)
- #st.write('preds')
- for i in pred:
- sentence_pred.append(k)
- class_list.append(i['word'])
- entity_list.append(i['entity_group'])
-
- # st.write('causality extraction finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
-
- # filename = 'Checkpoint-classification.sav'
- # loaded_model = pickle.load(open(filename, 'rb'))
- # loaded_vectorizer = pickle.load(open('vectorizefile_classification.pickle', 'rb'))
-
- # pipeline_test_output = loaded_vectorizer.transform(class_list)
- # predicted = loaded_model.predict(pipeline_test_output)
-
- tokenizer = Tokenizer(num_words=100000)
- tokenizer.fit_on_texts(class_list)
- word_index = tokenizer.word_index
- # text_embedding = np.zeros((len(word_index) + 1, 300))
- # for word, i in word_index.items():
- # text_embedding[i] = nlp(word).vector
- json_file = open('model.json', 'r')
- loaded_model_json = json_file.read()
- json_file.close()
- loaded_model = model_from_json(loaded_model_json)
- # load weights into new model
- loaded_model.load_weights("model.h5")
-
- loss = tf.keras.losses.CategoricalCrossentropy() #from_logits=True
- loaded_model.compile(loss=loss,optimizer=tf.keras.optimizers.Adam(1e-4))
-
- predictions = loaded_model.predict(pad_sequences(tokenizer.texts_to_sequences(class_list),maxlen=MAX_SEQUENCE_LENGTH))
- predicted = np.argmax(predictions,axis=1)
-
- st.write(predictions)
- st.write(predicted)
- # st.write('stakeholder taxonomy finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
- pred1 = predicted
- level0 = []
- count =0
- for i in predicted:
- if i == 3:
- level0.append('Non-Performance')
- count +=1
- else:
- level0.append('Performance')
- count +=1
-
- list_pred = {0: 'Customers',1:'Employees',2:'Investors',3:'Non-performance',4:'Society',5:'Unclassified'}
- pred_val = [list_pred[i] for i in pred1]
-
- #print('count',count)
- for ind,(sent,preds) in enumerate(zip(class_list,pred_val)):
- if 'customers' in sent or 'client' in sent or 'consumer' in sent or 'user' in sent:
- pred_val[ind] = 'Customers'
- elif 'investor' in sent or 'finance' in sent or 'shareholder' in sent or 'stockholder' in sent or 'owners' in sent:
- pred_val[ind] = 'Investors'
- elif 'employee' in sent or 'worker' in sent or 'staff' in sent:
- pred_val[ind] = 'Employees'
- elif 'society' in sent or 'societal' in sent or 'social responsib*' in sent or 'social performance' in sent or 'community' in sent:
- pred_val[ind] = 'Society'
-
- sent_id, unique = pd.factorize(sentence_pred)
-
- final_list = pd.DataFrame(
- {'Id': sent_id,
- 'Fullsentence': sentence_pred,
- 'Component': class_list,
- 'causeOrEffect': entity_list,
- 'Labellevel1': level0,
- 'Labellevel2': pred_val
- })
- s = final_list['Component'].shift(-1)
- m = s.str.startswith('##', na=False)
- final_list.loc[m, 'Component'] += (' ' + s[m])
-
-
- final_list1 = final_list[~final_list['Component'].astype(str).str.startswith('##')]
- li = []
- uni = final_list1['Id'].unique()
- for i in uni:
- df_new = final_list1[final_list1['Id'] == i]
- uni1 = df_new['Id'].unique()
-
- # if 'E' not in df_new.values:
- # li.append(uni1)
- # out = np.concatenate(li).ravel()
- # li_pan = pd.DataFrame(out,columns=['Id'])
- # df3 = pd.merge(final_list1, li_pan[['Id']], on='Id', how='left', indicator=True) \
- # .query("_merge == 'left_only'") \
- # .drop("_merge",axis=1)
-
- df3 = final_list1
- #df = df3.groupby(['Id','Fullsentence','causeOrEffect', 'Labellevel1', 'Labellevel2'])['Component'].apply(', '.join).reset_index()
- #st.write(df)
-
- #df = df3
- df3["causeOrEffect"].replace({"C": "cause", "E": "effect"}, inplace=True)
- df_final = df3[df3['causeOrEffect'] != 'CT']
- df3['New string'] = df_final['Component'].replace(r'[##]+', ' ', regex=True)
-
- df_final = df_final.drop("Component",axis=1)
- df_final.insert(2, "Component", df3['New string'], True)
-
- df_final1 = df_final[df_final['Component'].str.split().str.len().gt(1)]
- #st.write(df_final[df_final['Component'].str.len() != 1])
- #df_final1.to_csv('predictions.csv')
-
-# buffer = io.BytesIO()
-# with pd.ExcelWriter(buffer, engine="xlsxwriter") as writer:
-# df_final.to_excel(writer, sheet_name="Sheet1", index=False)
-# writer.close()
-
- count_NP_NP = 0
- count_NP_investor = 0
- count_NP_customer = 0
- count_NP_employees = 0
- count_NP_society = 0
-
- count_inv_np = 0
- count_inv_investor = 0
- count_inv_customer = 0
- count_inv_employee = 0
- count_inv_society = 0
-
- count_cus_np = 0
- count_cus_investor = 0
- count_cus_customer = 0
- count_cus_employee = 0
- count_cus_society = 0
-
- count_emp_np = 0
- count_emp_investor = 0
- count_emp_customer = 0
- count_emp_employee = 0
- count_emp_society = 0
-
- count_soc_np = 0
- count_soc_investor = 0
- count_soc_customer = 0
- count_soc_employee = 0
- count_soc_society = 0
- for i in range(0,df_final['Id'].max()):
- j = df_final.loc[df_final['Id'] == i]
- cause_tab = j.loc[j['causeOrEffect'] == 'cause']
- effect_tab = j.loc[j['causeOrEffect'] == 'effect']
- cause_coun_NP = (cause_tab.Labellevel2 == 'Non-performance').sum()
- effect_coun_NP = (effect_tab.Labellevel2 == 'Non-performance').sum()
-
- if (cause_coun_NP > 0) and (effect_coun_NP > 0):
- count_NP = cause_coun_NP if cause_coun_NP >= effect_coun_NP else effect_coun_NP
- else:
- count_NP = 0
- effect_NP_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_NP > 0) and (effect_NP_inv > 0):
- count_NP_inv = cause_coun_NP if cause_coun_NP >= effect_NP_inv else effect_NP_inv
- else:
- count_NP_inv = 0
- effect_NP_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_NP > 0) and (effect_NP_cus > 0):
- count_NP_cus = cause_coun_NP if cause_coun_NP >= effect_NP_cus else effect_NP_cus
- else:
- count_NP_cus = 0
- effect_NP_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_NP > 0) and (effect_NP_emp > 0):
- count_NP_emp = cause_coun_NP if cause_coun_NP >= effect_NP_emp else effect_NP_emp
- else:
- count_NP_emp = 0
- effect_NP_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_NP > 0) and (effect_NP_soc > 0):
- count_NP_soc = cause_coun_NP if cause_coun_NP >= effect_NP_soc else effect_NP_soc
- else:
- count_NP_soc = 0
-
- cause_coun_inv = (cause_tab.Labellevel2 == 'Investors').sum()
- effect_coun_inv = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_inv > 0) and (effect_coun_inv > 0):
- count_NP_inv = cause_coun_inv if cause_coun_inv >= effect_coun_inv else effect_coun_inv
- else:
- count_NP_inv = 0
-
- effect_inv_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_inv > 0) and (effect_inv_inv > 0):
- count_inv_inv = cause_coun_inv if cause_coun_inv >= effect_inv_inv else effect_inv_inv
- else:
- count_inv_inv = 0
- effect_inv_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_inv > 0) and (effect_inv_cus > 0):
- count_inv_cus = cause_coun_inv if cause_coun_inv >= effect_inv_cus else effect_inv_cus
- else:
- count_inv_cus = 0
- effect_inv_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_inv > 0) and (effect_inv_emp > 0):
- count_inv_emp = cause_coun_inv if cause_coun_inv >= effect_inv_emp else effect_inv_emp
- else:
- count_inv_emp = 0
-
- effect_inv_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_inv > 0) and (effect_inv_soc > 0):
- count_inv_soc = cause_coun_inv if cause_coun_inv >= effect_inv_soc else effect_inv_soc
- else:
- count_inv_soc = 0
-
- cause_coun_cus = (cause_tab.Labellevel2 == 'Customers').sum()
- effect_coun_cus = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_cus > 0) and (effect_coun_cus > 0):
- count_NP_cus = cause_coun_cus if cause_coun_cus >= effect_coun_cus else effect_coun_cus
- else:
- count_NP_cus = 0
-
- effect_cus_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_cus > 0) and (effect_cus_inv > 0):
- count_cus_inv = cause_coun_cus if cause_coun_cus >= effect_cus_inv else effect_cus_inv
- else:
- count_cus_inv = 0
-
- effect_cus_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_cus > 0) and (effect_cus_cus > 0):
- count_cus_cus = cause_coun_cus if cause_coun_cus >= effect_cus_cus else effect_cus_cus
- else:
- count_cus_cus = 0
-
- effect_cus_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_cus > 0) and (effect_cus_emp > 0):
- count_cus_emp = cause_coun_cus if cause_coun_cus >= effect_cus_emp else effect_cus_emp
- else:
- count_cus_emp = 0
-
- effect_cus_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_cus > 0) and (effect_cus_soc > 0):
- count_cus_soc = cause_coun_cus if cause_coun_cus >= effect_cus_soc else effect_cus_soc
- else:
- count_cus_soc = 0
-
- cause_coun_emp = (cause_tab.Labellevel2 == 'Employees').sum()
- effect_coun_emp = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_emp > 0) and (effect_coun_emp > 0):
- count_NP_emp = cause_coun_emp if cause_coun_emp >= effect_coun_emp else effect_coun_emp
- else:
- count_NP_emp = 0
-
- effect_emp_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_emp > 0) and (effect_emp_inv > 0):
- count_emp_inv = cause_coun_emp if cause_coun_emp >= effect_emp_inv else effect_emp_inv
- else:
- count_emp_inv = 0
-
- effect_emp_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_emp > 0) and (effect_emp_cus > 0):
- count_emp_cus = cause_coun_emp if cause_coun_emp >= effect_emp_cus else effect_emp_cus
- else:
- count_emp_cus = 0
-
- effect_emp_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_emp > 0) and (effect_emp_emp > 0):
- count_emp_emp = cause_coun_emp if cause_coun_emp >= effect_emp_emp else effect_emp_emp
- else:
- count_emp_emp = 0
-
- effect_emp_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_emp > 0) and (effect_emp_soc > 0):
- count_emp_soc = cause_coun_emp if cause_coun_emp >= effect_emp_soc else effect_emp_soc
- else:
- count_emp_soc = 0
-
- cause_coun_soc = (cause_tab.Labellevel2 == 'Society').sum()
- effect_coun_soc = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_soc > 0) and (effect_coun_soc > 0):
- count_NP_soc = cause_coun_soc if cause_coun_soc >= effect_coun_soc else effect_coun_soc
- else:
- count_NP_soc = 0
-
- effect_soc_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_soc > 0) and (effect_soc_inv > 0):
- count_soc_inv = cause_coun_soc if cause_coun_soc >= effect_soc_inv else effect_soc_inv
- else:
- count_soc_inv = 0
-
- effect_soc_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_soc > 0) and (effect_soc_cus > 0):
- count_soc_cus = cause_coun_soc if cause_coun_soc >= effect_soc_cus else effect_soc_cus
- else:
- count_soc_cus = 0
-
- effect_soc_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_soc > 0) and (effect_soc_emp > 0):
- count_soc_emp = cause_coun_soc if cause_coun_soc >= effect_soc_emp else effect_soc_emp
- else:
- count_soc_emp = 0
-
- effect_soc_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_soc > 0) and (effect_soc_soc > 0):
- count_soc_soc = cause_coun_soc if cause_coun_soc >= effect_soc_soc else effect_soc_soc
- else:
- count_soc_soc = 0
-
- count_NP_NP = count_NP_NP + count_NP
- count_NP_investor = count_NP_investor + count_NP_inv
- count_NP_customer = count_NP_customer + count_NP_cus
- count_NP_employees = count_NP_employees + count_NP_emp
- count_NP_society = count_NP_society + count_NP_soc
-
- count_inv_np = count_inv_np + count_NP_inv
- count_inv_investor = count_inv_investor + count_inv_inv
- count_inv_customer = count_inv_customer + count_inv_cus
- count_inv_employee = count_inv_employee + count_inv_emp
- count_inv_society = count_inv_society + count_inv_soc
-
- count_cus_np = count_cus_np + count_NP_cus
- count_cus_investor = count_cus_investor + count_cus_inv
- count_cus_customer = count_cus_customer + count_cus_cus
- count_cus_employee = count_cus_employee + count_cus_emp
- count_cus_society = count_cus_society + count_cus_soc
-
- count_emp_np = count_emp_np + count_NP_emp
- count_emp_investor = count_emp_investor + count_emp_inv
- count_emp_customer = count_emp_customer + count_emp_cus
- count_emp_employee = count_emp_employee + count_emp_emp
- count_emp_society = count_emp_society + count_emp_soc
-
- count_soc_np = count_soc_np + count_NP_soc
- count_soc_investor = count_soc_investor + count_soc_inv
- count_soc_customer = count_soc_customer + count_soc_cus
- count_soc_employee = count_soc_employee + count_soc_emp
- count_soc_society = count_soc_society + count_soc_soc
-
- df_tab = pd.DataFrame(columns = ['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'],index=['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'], dtype=object)
-
- df_tab.loc['Non-performance'] = [count_NP_NP, count_NP_investor, count_NP_customer, count_NP_employees, count_NP_society]
- df_tab.loc['Investors'] = [count_inv_np, count_inv_investor, count_inv_customer, count_inv_employee, count_inv_society]
- df_tab.loc['Customers'] = [count_cus_np, count_cus_investor, count_cus_customer, count_cus_employee, count_cus_society]
- df_tab.loc['Employees'] = [count_emp_np, count_emp_investor, count_emp_customer, count_emp_employee, count_emp_society]
- df_tab.loc['Society'] = [count_soc_np, count_soc_investor, count_soc_customer, count_soc_employee, count_soc_society]
-
-
-# df_tab = pd.DataFrame({
-# 'Non-performance': [count_NP_NP, count_NP_investor, count_NP_customer, count_NP_employees, count_NP_society],
-# 'Investors': [count_inv_np, count_inv_investor, count_inv_customer, count_inv_employee, count_inv_society],
-# 'Customers': [count_cus_np, count_cus_investor, count_cus_customer, count_cus_employee, count_cus_society],
-# 'Employees': [count_emp_np, count_emp_investor, count_emp_customer, count_emp_employee, count_emp_society],
-# 'Society': [count_soc_np, count_soc_investor, count_soc_customer, count_soc_employee, count_soc_society]},
-# index=['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'])
-
- #df_tab.to_csv('final_data.csv')
-
- buffer = io.BytesIO()
- with pd.ExcelWriter(buffer, engine="xlsxwriter") as writer:
- df_tab.to_excel(writer,sheet_name="count_result",index=False)
- df_final1.to_excel(writer,sheet_name="Detailed_results",index=False)
- writer.close()
- #df = pd.read_csv('final_data.csv', index_col=0)
-#474-515
- # Convert to JSON format
- json_data = []
- for row in df_tab.index:
- for col in df_tab.columns:
- json_data.append({
- 'source': row,
- 'target': col,
- 'value': int(df_tab.loc[row, col])
- })
-
- HfApi().delete_file(path_in_repo = DATA_FILENAME1 ,repo_id = 'Seetha/visual_files',token= HF_TOKEN,repo_type='dataset')
- #st.write('file-deleted')
- fs = HfFileSystem(token=HF_TOKEN)
- with fs.open('datasets/Seetha/visual_files/level2.json', 'w') as f:
- json.dump(json_data, f)
-
- df_final1.to_csv('predictions.csv')
- csv_file = "predictions.csv"
- json_file = "detailedResults.json"
-
- # Open the CSV file and read the data
- with open(csv_file, "r") as f:
- csv_data = csv.DictReader(f)
-
- # # Convert the CSV data to a list of dictionaries
- data_list = []
- for row in csv_data:
- data_list.append(dict(row))
-
- # # Convert the list of dictionaries to JSON
- json_data = json.dumps(data_list)
-
- HfApi().delete_file(path_in_repo = DATA_FILENAME ,repo_id = 'Seetha/visual_files',token= HF_TOKEN,repo_type='dataset')
- #st.write('file2-deleted')
- with fs.open('datasets/Seetha/visual_files/detailedResults.json','w') as fi:
- #data = json.load(fi)
- fi.write(json_data)
-
- def convert_df(df):
-
- #IMPORTANT: Cache the conversion to prevent computation on every rerun
-
- return df.to_csv().encode('utf-8')
-
-
-
- csv1 = convert_df(df_final1.astype(str))
- csv2 = convert_df(df_tab.astype(str))
-
- with st.container():
-
- st.download_button(label="Download the result table",data=buffer,file_name="t2cg_outputs.xlsx",mime="application/vnd.ms-excel")
- st.markdown('Click this link in a separate tab to view knowledge graph', unsafe_allow_html=True)
- # st.download_button(label="Download the detailed result table_csv",data=csv1,file_name='results.csv',mime='text/csv')
- # st.download_button(label="Download the result table_csv",data=csv2,file_name='final_data.csv',mime='text/csv')
-
-#with st.container():
- # Execute your app
- #st.title("Visualization example")
-# components.html(source_code)
- #html(my_html)
- #webbrowser.open('https://huggingface.co/spaces/Seetha/visual-knowledgegraph')
-# # embed streamlit docs in a streamlit app
-# #components.iframe("https://webpages.charlotte.edu/ltotapal/")
-
-
-
-if __name__ == '__main__':
- start_time = time.time()
- main()
diff --git a/spaces/Shiro26/MendoBERT_RE/app.py b/spaces/Shiro26/MendoBERT_RE/app.py
deleted file mode 100644
index 1bf256334d6604b5048b7f1054c9cb1915120f15..0000000000000000000000000000000000000000
--- a/spaces/Shiro26/MendoBERT_RE/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-
-model = pipeline("text-classification", model="/home/user/app/MendoBERT/", tokenizer="indolem/indobert-base-uncased")
-basemodel = pipeline("text-classification", model="/home/user/app/IndoLEM/", tokenizer="indolem/indobert-base-uncased")
-
-st.title(':blue[MendoBERT] - Relation Extraction :family:')
-
-if 'options' not in st.session_state:
- st.session_state['options'] = ""
-
-def button1_callback():
- st.session_state['options'] = "Kami menyimpulkan bahwa polimorfisme @GENE$ dan AGT tidak berkontribusi pada kerentanan genetik terhadap @DISEASE$ dan retinopati pada populasi Kaukasia Mediterania."
-def button2_callback():
- st.session_state['options'] = "Genotipe bayi PON1 RR dan @GENE$ CC dikaitkan dengan @DISEASE$ dalam populasi penelitian kami, yang menunjukkan kemungkinan peran variabilitas paraoxonase manusia dalam etiologi kelahiran prematur."
-
-
-placeholder = st.empty()
-
-st.info("Please replace the gene and disease that you want to get their relation predicted with @GENE\$ and \@DISEASE\$", icon="ℹ️")
-
-st.caption('_Examples_')
-st.button('Kami menyimpulkan bahwa polimorfisme \@GENE\$ dan AGT tidak berkontribusi pada kerentanan genetik terhadap \@DISEASE\$ dan retinopati pada populasi Kaukasia Mediterania.', use_container_width=True, on_click = button1_callback)
-st.button('Genotipe bayi PON1 RR dan \@GENE\$ CC dikaitkan dengan \@DISEASE\$ dalam populasi penelitian kami, yang menunjukkan kemungkinan peran variabilitas paraoxonase manusia dalam etiologi kelahiran prematur.', use_container_width=True, on_click = button2_callback)
-
-with placeholder:
- text = st.text_area('Enter some text: ', key = 'options')
-
-if text:
- st.subheader('MendoBERT')
- st.write(model(text))
- st.write("\n")
- st.subheader('IndoLEM')
- st.write(basemodel(text))
\ No newline at end of file
diff --git a/spaces/Shredder/CONBERT-2/README.md b/spaces/Shredder/CONBERT-2/README.md
deleted file mode 100644
index daf10e5da8dd31671c54c18d24b0e7d1d77b62f0..0000000000000000000000000000000000000000
--- a/spaces/Shredder/CONBERT-2/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: CONBERT 2
-emoji: 👁
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Stearns/Soar/soar_io.py b/spaces/Stearns/Soar/soar_io.py
deleted file mode 100644
index c05d97417b69a804b35207fb839957a91258a5d2..0000000000000000000000000000000000000000
--- a/spaces/Stearns/Soar/soar_io.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from pysoarlib import *
-
-## DEFINE MAIN CONNECTOR CLASS
-
-class GeneralSoarIOConnector(AgentConnector):
- def __init__(self, client):
- AgentConnector.__init__(self, client)
- client.add_print_event_handler(self.agent_print_collector)
- # client.execute_command("output callbacks on")
- # client.execute_command("output console on")
-
- self.agent_printout = ""
-
- def on_input_phase(self, input_link):
- pass
-
- def on_init_soar(self):
- self.reset()
-
- def on_output_event(self, command_name, root_id):
- pass
-
-
- def agent_print_collector(self, text):
- self.agent_printout += text+"\n"
-
- def get_agent_output(self):
- return self.agent_printout
-
- def reset(self):
- self.agent_printout = ""
-
-
-
-
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py
deleted file mode 100644
index 489b7c13d0ce82b769ba50412ecde0889d0b7851..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# encoding: utf-8
-"""
-Utilities for working with external processes.
-"""
-
-# Copyright (c) IPython Development Team.
-# Distributed under the terms of the Modified BSD License.
-
-
-import os
-import shutil
-import sys
-
-if sys.platform == 'win32':
- from ._process_win32 import system, getoutput, arg_split, check_pid
-elif sys.platform == 'cli':
- from ._process_cli import system, getoutput, arg_split, check_pid
-else:
- from ._process_posix import system, getoutput, arg_split, check_pid
-
-from ._process_common import getoutputerror, get_output_error_code, process_handler
-
-
-class FindCmdError(Exception):
- pass
-
-
-def find_cmd(cmd):
- """Find absolute path to executable cmd in a cross platform manner.
-
- This function tries to determine the full path to a command line program
- using `which` on Unix/Linux/OS X and `win32api` on Windows. Most of the
- time it will use the version that is first on the users `PATH`.
-
- Warning, don't use this to find IPython command line programs as there
- is a risk you will find the wrong one. Instead find those using the
- following code and looking for the application itself::
-
- import sys
- argv = [sys.executable, '-m', 'IPython']
-
- Parameters
- ----------
- cmd : str
- The command line program to look for.
- """
- path = shutil.which(cmd)
- if path is None:
- raise FindCmdError('command could not be found: %s' % cmd)
- return path
-
-
-def abbrev_cwd():
- """ Return abbreviated version of cwd, e.g. d:mydir """
- cwd = os.getcwd().replace('\\','/')
- drivepart = ''
- tail = cwd
- if sys.platform == 'win32':
- if len(cwd) < 4:
- return cwd
- drivepart,tail = os.path.splitdrive(cwd)
-
-
- parts = tail.split('/')
- if len(parts) > 2:
- tail = '/'.join(parts[-2:])
-
- return (drivepart + (
- cwd == '/' and '/' or tail))
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py
deleted file mode 100644
index 37d3db222ef2c7920628971a92e863d9915514c6..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import sys
-from typing import Any, Dict, List, Optional, Generic, TypeVar, cast
-from types import TracebackType
-
-if sys.version_info >= (3, 8):
- from importlib.metadata import entry_points
-else:
- from importlib_metadata import entry_points
-
-from toolz import curry
-
-
-PluginType = TypeVar("PluginType")
-
-
-class NoSuchEntryPoint(Exception):
- def __init__(self, group, name):
- self.group = group
- self.name = name
-
- def __str__(self):
- return f"No {self.name!r} entry point found in group {self.group!r}"
-
-
-class PluginEnabler:
- """Context manager for enabling plugins
-
- This object lets you use enable() as a context manager to
- temporarily enable a given plugin::
-
- with plugins.enable('name'):
- do_something() # 'name' plugin temporarily enabled
- # plugins back to original state
- """
-
- def __init__(self, registry: "PluginRegistry", name: str, **options):
- self.registry = registry # type: PluginRegistry
- self.name = name # type: str
- self.options = options # type: Dict[str, Any]
- self.original_state = registry._get_state() # type: Dict[str, Any]
- self.registry._enable(name, **options)
-
- def __enter__(self) -> "PluginEnabler":
- return self
-
- def __exit__(self, typ: type, value: Exception, traceback: TracebackType) -> None:
- self.registry._set_state(self.original_state)
-
- def __repr__(self) -> str:
- return "{}.enable({!r})".format(self.registry.__class__.__name__, self.name)
-
-
-class PluginRegistry(Generic[PluginType]):
- """A registry for plugins.
-
- This is a plugin registry that allows plugins to be loaded/registered
- in two ways:
-
- 1. Through an explicit call to ``.register(name, value)``.
- 2. By looking for other Python packages that are installed and provide
- a setuptools entry point group.
-
- When you create an instance of this class, provide the name of the
- entry point group to use::
-
- reg = PluginRegister('my_entrypoint_group')
-
- """
-
- # this is a mapping of name to error message to allow custom error messages
- # in case an entrypoint is not found
- entrypoint_err_messages = {} # type: Dict[str, str]
-
- # global settings is a key-value mapping of settings that are stored globally
- # in the registry rather than passed to the plugins
- _global_settings = {} # type: Dict[str, Any]
-
- def __init__(self, entry_point_group: str = "", plugin_type: type = object):
- """Create a PluginRegistry for a named entry point group.
-
- Parameters
- ==========
- entry_point_group: str
- The name of the entry point group.
- plugin_type: object
- A type that will optionally be used for runtime type checking of
- loaded plugins using isinstance.
- """
- self.entry_point_group = entry_point_group # type: str
- self.plugin_type = plugin_type # type: Optional[type]
- self._active = None # type: Optional[PluginType]
- self._active_name = "" # type: str
- self._plugins = {} # type: Dict[str, PluginType]
- self._options = {} # type: Dict[str, Any]
- self._global_settings = self.__class__._global_settings.copy() # type: dict
-
- def register(self, name: str, value: Optional[PluginType]) -> Optional[PluginType]:
- """Register a plugin by name and value.
-
- This method is used for explicit registration of a plugin and shouldn't be
- used to manage entry point managed plugins, which are auto-loaded.
-
- Parameters
- ==========
- name: str
- The name of the plugin.
- value: PluginType or None
- The actual plugin object to register or None to unregister that plugin.
-
- Returns
- =======
- plugin: PluginType or None
- The plugin that was registered or unregistered.
- """
- if value is None:
- return self._plugins.pop(name, None)
- else:
- assert isinstance(value, self.plugin_type) # type: ignore[arg-type] # Should ideally be fixed by better annotating plugin_type
- self._plugins[name] = value
- return value
-
- def names(self) -> List[str]:
- """List the names of the registered and entry points plugins."""
- exts = list(self._plugins.keys())
- e_points = importlib_metadata_get(self.entry_point_group)
- more_exts = [ep.name for ep in e_points]
- exts.extend(more_exts)
- return sorted(set(exts))
-
- def _get_state(self) -> Dict[str, Any]:
- """Return a dictionary representing the current state of the registry"""
- return {
- "_active": self._active,
- "_active_name": self._active_name,
- "_plugins": self._plugins.copy(),
- "_options": self._options.copy(),
- "_global_settings": self._global_settings.copy(),
- }
-
- def _set_state(self, state: Dict[str, Any]) -> None:
- """Reset the state of the registry"""
- assert set(state.keys()) == {
- "_active",
- "_active_name",
- "_plugins",
- "_options",
- "_global_settings",
- }
- for key, val in state.items():
- setattr(self, key, val)
-
- def _enable(self, name: str, **options) -> None:
- if name not in self._plugins:
- try:
- (ep,) = [
- ep
- for ep in importlib_metadata_get(self.entry_point_group)
- if ep.name == name
- ]
- except ValueError as err:
- if name in self.entrypoint_err_messages:
- raise ValueError(self.entrypoint_err_messages[name]) from err
- else:
- raise NoSuchEntryPoint(self.entry_point_group, name) from err
- value = cast(PluginType, ep.load())
- self.register(name, value)
- self._active_name = name
- self._active = self._plugins[name]
- for key in set(options.keys()) & set(self._global_settings.keys()):
- self._global_settings[key] = options.pop(key)
- self._options = options
-
- def enable(self, name: Optional[str] = None, **options) -> PluginEnabler:
- """Enable a plugin by name.
-
- This can be either called directly, or used as a context manager.
-
- Parameters
- ----------
- name : string (optional)
- The name of the plugin to enable. If not specified, then use the
- current active name.
- **options :
- Any additional parameters will be passed to the plugin as keyword
- arguments
-
- Returns
- -------
- PluginEnabler:
- An object that allows enable() to be used as a context manager
- """
- if name is None:
- name = self.active
- return PluginEnabler(self, name, **options)
-
- @property
- def active(self) -> str:
- """Return the name of the currently active plugin"""
- return self._active_name
-
- @property
- def options(self) -> Dict[str, Any]:
- """Return the current options dictionary"""
- return self._options
-
- def get(self) -> Optional[PluginType]:
- """Return the currently active plugin."""
- if self._options:
- return curry(self._active, **self._options)
- else:
- return self._active
-
- def __repr__(self) -> str:
- return "{}(active={!r}, registered={!r})" "".format(
- self.__class__.__name__, self._active_name, list(self.names())
- )
-
-
-def importlib_metadata_get(group):
- ep = entry_points()
- # 'select' was introduced in Python 3.10 and 'get' got deprecated
- # We don't check for Python version here as by checking with hasattr we
- # also get compatibility with the importlib_metadata package which had a different
- # deprecation cycle for 'get'
- if hasattr(ep, "select"):
- return ep.select(group=group)
- else:
- return ep.get(group, [])
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py
deleted file mode 100644
index bba2d3f7dfffa3bdbf921bdad4ca7143be97c2fd..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py
+++ /dev/null
@@ -1,143 +0,0 @@
-from __future__ import annotations
-
-import codecs
-from dataclasses import InitVar, dataclass, field
-from typing import Any, Callable, Mapping
-
-from ..abc import (
- AnyByteReceiveStream,
- AnyByteSendStream,
- AnyByteStream,
- ObjectReceiveStream,
- ObjectSendStream,
- ObjectStream,
-)
-
-
-@dataclass(eq=False)
-class TextReceiveStream(ObjectReceiveStream[str]):
- """
- Stream wrapper that decodes bytes to strings using the given encoding.
-
- Decoding is done using :class:`~codecs.IncrementalDecoder` which returns any completely
- received unicode characters as soon as they come in.
-
- :param transport_stream: any bytes-based receive stream
- :param encoding: character encoding to use for decoding bytes to strings (defaults to
- ``utf-8``)
- :param errors: handling scheme for decoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteReceiveStream
- encoding: InitVar[str] = "utf-8"
- errors: InitVar[str] = "strict"
- _decoder: codecs.IncrementalDecoder = field(init=False)
-
- def __post_init__(self, encoding: str, errors: str) -> None:
- decoder_class = codecs.getincrementaldecoder(encoding)
- self._decoder = decoder_class(errors=errors)
-
- async def receive(self) -> str:
- while True:
- chunk = await self.transport_stream.receive()
- decoded = self._decoder.decode(chunk)
- if decoded:
- return decoded
-
- async def aclose(self) -> None:
- await self.transport_stream.aclose()
- self._decoder.reset()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return self.transport_stream.extra_attributes
-
-
-@dataclass(eq=False)
-class TextSendStream(ObjectSendStream[str]):
- """
- Sends strings to the wrapped stream as bytes using the given encoding.
-
- :param AnyByteSendStream transport_stream: any bytes-based send stream
- :param str encoding: character encoding to use for encoding strings to bytes (defaults to
- ``utf-8``)
- :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteSendStream
- encoding: InitVar[str] = "utf-8"
- errors: str = "strict"
- _encoder: Callable[..., tuple[bytes, int]] = field(init=False)
-
- def __post_init__(self, encoding: str) -> None:
- self._encoder = codecs.getencoder(encoding)
-
- async def send(self, item: str) -> None:
- encoded = self._encoder(item, self.errors)[0]
- await self.transport_stream.send(encoded)
-
- async def aclose(self) -> None:
- await self.transport_stream.aclose()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return self.transport_stream.extra_attributes
-
-
-@dataclass(eq=False)
-class TextStream(ObjectStream[str]):
- """
- A bidirectional stream that decodes bytes to strings on receive and encodes strings to bytes on
- send.
-
- Extra attributes will be provided from both streams, with the receive stream providing the
- values in case of a conflict.
-
- :param AnyByteStream transport_stream: any bytes-based stream
- :param str encoding: character encoding to use for encoding/decoding strings to/from bytes
- (defaults to ``utf-8``)
- :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteStream
- encoding: InitVar[str] = "utf-8"
- errors: InitVar[str] = "strict"
- _receive_stream: TextReceiveStream = field(init=False)
- _send_stream: TextSendStream = field(init=False)
-
- def __post_init__(self, encoding: str, errors: str) -> None:
- self._receive_stream = TextReceiveStream(
- self.transport_stream, encoding=encoding, errors=errors
- )
- self._send_stream = TextSendStream(
- self.transport_stream, encoding=encoding, errors=errors
- )
-
- async def receive(self) -> str:
- return await self._receive_stream.receive()
-
- async def send(self, item: str) -> None:
- await self._send_stream.send(item)
-
- async def send_eof(self) -> None:
- await self.transport_stream.send_eof()
-
- async def aclose(self) -> None:
- await self._send_stream.aclose()
- await self._receive_stream.aclose()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return {
- **self._send_stream.extra_attributes,
- **self._receive_stream.extra_attributes,
- }
diff --git a/spaces/TH5314/newbing/src/lib/bots/bing/types.ts b/spaces/TH5314/newbing/src/lib/bots/bing/types.ts
deleted file mode 100644
index 02cd5e8b01e3529642d28dc1539bf958f4ac420b..0000000000000000000000000000000000000000
--- a/spaces/TH5314/newbing/src/lib/bots/bing/types.ts
+++ /dev/null
@@ -1,259 +0,0 @@
-export type Author = 'user' | 'system' | 'bot'
-
-export type BotId = 'bing'
-
-export enum BingConversationStyle {
- Creative = 'Creative',
- Balanced = 'Balanced',
- Precise = 'Precise'
-}
-
-export enum ErrorCode {
- CONVERSATION_LIMIT = 'CONVERSATION_LIMIT',
- BING_UNAUTHORIZED = 'BING_UNAUTHORIZED',
- BING_FORBIDDEN = 'BING_FORBIDDEN',
- BING_CAPTCHA = 'BING_CAPTCHA',
- THROTTLE_LIMIT = 'THROTTLE_LIMIT',
- NOTFOUND_ERROR = 'NOT_FOUND_ERROR',
- UNKOWN_ERROR = 'UNKOWN_ERROR',
- NETWORK_ERROR = 'NETWORK_ERROR',
-}
-
-export class ChatError extends Error {
- code: ErrorCode
- constructor(message: string, code: ErrorCode) {
- super(message)
- this.code = code
- }
-}
-
-export type ChatMessageModel = {
- id: string
- author: Author
- text: string
- error?: ChatError
- throttling?: Throttling
- sourceAttributions?: SourceAttribution[]
- suggestedResponses?: SuggestedResponse[]
-}
-
-export interface ConversationModel {
- messages: ChatMessageModel[]
-}
-
-export type Event =
- | {
- type: 'UPDATE_ANSWER'
- data: {
- text: string
- spokenText?: string
- sourceAttributions?: SourceAttribution[]
- suggestedResponses?: SuggestedResponse[]
- throttling?: Throttling
- }
- }
- | {
- type: 'DONE'
- }
- | {
- type: 'ERROR'
- error: ChatError
- }
-
-export interface SendMessageParams {
- prompt: string
- imageUrl?: string
- options: T
- onEvent: (event: Event) => void
- signal?: AbortSignal
-}
-
-export interface ConversationResponse {
- conversationId: string
- clientId: string
- conversationSignature: string
- result: {
- value: string
- message?: string
- }
-}
-
-export interface Telemetry {
- metrics?: null
- startTime: string
-}
-
-export interface ChatUpdateArgument {
- messages?: ChatResponseMessage[]
- throttling?: Throttling
- requestId: string
- result: null
-}
-
-export type ChatUpdateCompleteResponse = {
- type: 2
- invocationId: string
- item: ChatResponseItem
-} | {
- type: 1
- target: string
- arguments: ChatUpdateArgument[]
-} | {
- type: 3
- invocationId: string
-} | {
- type: 6 | 7
-}
-
-export interface ChatRequestResult {
- value: string
- serviceVersion: string
- error?: string
-}
-
-export interface ChatResponseItem {
- messages: ChatResponseMessage[]
- firstNewMessageIndex: number
- suggestedResponses: null
- conversationId: string
- requestId: string
- conversationExpiryTime: string
- telemetry: Telemetry
- result: ChatRequestResult
- throttling: Throttling
-}
-export enum InvocationEventType {
- Invocation = 1,
- StreamItem = 2,
- Completion = 3,
- StreamInvocation = 4,
- CancelInvocation = 5,
- Ping = 6,
- Close = 7,
-}
-
-// https://github.com/bytemate/bingchat-api/blob/main/src/lib.ts
-
-export interface ConversationInfo {
- conversationId: string
- clientId: string
- conversationSignature: string
- invocationId: number
- conversationStyle: BingConversationStyle
- prompt: string
- imageUrl?: string
-}
-
-export interface BingChatResponse {
- conversationSignature: string
- conversationId: string
- clientId: string
- invocationId: number
- conversationExpiryTime: Date
- response: string
- details: ChatResponseMessage
-}
-
-export interface Throttling {
- maxNumLongDocSummaryUserMessagesInConversation: number
- maxNumUserMessagesInConversation: number
- numLongDocSummaryUserMessagesInConversation: number
- numUserMessagesInConversation: number
-}
-
-export interface ChatResponseMessage {
- text: string
- spokenText?: string
- author: string
- createdAt: Date
- timestamp: Date
- messageId: string
- requestId: string
- offense: string
- adaptiveCards: AdaptiveCard[]
- sourceAttributions: SourceAttribution[]
- feedback: Feedback
- contentOrigin: string
- messageType?: string
- contentType?: string
- privacy: null
- suggestedResponses: SuggestedResponse[]
-}
-
-export interface AdaptiveCard {
- type: string
- version: string
- body: Body[]
-}
-
-export interface Body {
- type: string
- text: string
- wrap: boolean
- size?: string
-}
-
-export interface Feedback {
- tag: null
- updatedOn: null
- type: string
-}
-
-export interface SourceAttribution {
- providerDisplayName: string
- seeMoreUrl: string
- searchQuery: string
-}
-
-export interface SuggestedResponse {
- text: string
- author?: Author
- createdAt?: Date
- timestamp?: Date
- messageId?: string
- messageType?: string
- offense?: string
- feedback?: Feedback
- contentOrigin?: string
- privacy?: null
-}
-
-export interface KBlobRequest {
- knowledgeRequest: KnowledgeRequestContext
- imageBase64?: string
-}
-
-export interface KBlobResponse {
- blobId: string
- processedBlobId?: string
-}
-
-export interface KnowledgeRequestContext {
- imageInfo: ImageInfo;
- knowledgeRequest: KnowledgeRequest;
-}
-
-export interface ImageInfo {
- url?: string;
-}
-
-export interface KnowledgeRequest {
- invokedSkills: string[];
- subscriptionId: string;
- invokedSkillsRequestData: InvokedSkillsRequestData;
- convoData: ConvoData;
-}
-
-export interface ConvoData {
- convoid: string;
- convotone: BingConversationStyle;
-}
-
-export interface InvokedSkillsRequestData {
- enableFaceBlur: boolean;
-}
-
-export interface FileItem {
- url: string;
- status?: 'loading' | 'error' | 'loaded'
-}
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py
deleted file mode 100644
index 264d564dbda676b52f446c0d25433a15939a78a3..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py
+++ /dev/null
@@ -1,519 +0,0 @@
-"""
-This module uses ctypes to bind a whole bunch of functions and constants from
-SecureTransport. The goal here is to provide the low-level API to
-SecureTransport. These are essentially the C-level functions and constants, and
-they're pretty gross to work with.
-
-This code is a bastardised version of the code found in Will Bond's oscrypto
-library. An enormous debt is owed to him for blazing this trail for us. For
-that reason, this code should be considered to be covered both by urllib3's
-license and by oscrypto's:
-
- Copyright (c) 2015-2016 Will Bond
-
- Permission is hereby granted, free of charge, to any person obtaining a
- copy of this software and associated documentation files (the "Software"),
- to deal in the Software without restriction, including without limitation
- the rights to use, copy, modify, merge, publish, distribute, sublicense,
- and/or sell copies of the Software, and to permit persons to whom the
- Software is furnished to do so, subject to the following conditions:
-
- The above copyright notice and this permission notice shall be included in
- all copies or substantial portions of the Software.
-
- THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
- FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
- DEALINGS IN THE SOFTWARE.
-"""
-from __future__ import absolute_import
-
-import platform
-from ctypes import (
- CDLL,
- CFUNCTYPE,
- POINTER,
- c_bool,
- c_byte,
- c_char_p,
- c_int32,
- c_long,
- c_size_t,
- c_uint32,
- c_ulong,
- c_void_p,
-)
-from ctypes.util import find_library
-
-from ...packages.six import raise_from
-
-if platform.system() != "Darwin":
- raise ImportError("Only macOS is supported")
-
-version = platform.mac_ver()[0]
-version_info = tuple(map(int, version.split(".")))
-if version_info < (10, 8):
- raise OSError(
- "Only OS X 10.8 and newer are supported, not %s.%s"
- % (version_info[0], version_info[1])
- )
-
-
-def load_cdll(name, macos10_16_path):
- """Loads a CDLL by name, falling back to known path on 10.16+"""
- try:
- # Big Sur is technically 11 but we use 10.16 due to the Big Sur
- # beta being labeled as 10.16.
- if version_info >= (10, 16):
- path = macos10_16_path
- else:
- path = find_library(name)
- if not path:
- raise OSError # Caught and reraised as 'ImportError'
- return CDLL(path, use_errno=True)
- except OSError:
- raise_from(ImportError("The library %s failed to load" % name), None)
-
-
-Security = load_cdll(
- "Security", "/System/Library/Frameworks/Security.framework/Security"
-)
-CoreFoundation = load_cdll(
- "CoreFoundation",
- "/System/Library/Frameworks/CoreFoundation.framework/CoreFoundation",
-)
-
-
-Boolean = c_bool
-CFIndex = c_long
-CFStringEncoding = c_uint32
-CFData = c_void_p
-CFString = c_void_p
-CFArray = c_void_p
-CFMutableArray = c_void_p
-CFDictionary = c_void_p
-CFError = c_void_p
-CFType = c_void_p
-CFTypeID = c_ulong
-
-CFTypeRef = POINTER(CFType)
-CFAllocatorRef = c_void_p
-
-OSStatus = c_int32
-
-CFDataRef = POINTER(CFData)
-CFStringRef = POINTER(CFString)
-CFArrayRef = POINTER(CFArray)
-CFMutableArrayRef = POINTER(CFMutableArray)
-CFDictionaryRef = POINTER(CFDictionary)
-CFArrayCallBacks = c_void_p
-CFDictionaryKeyCallBacks = c_void_p
-CFDictionaryValueCallBacks = c_void_p
-
-SecCertificateRef = POINTER(c_void_p)
-SecExternalFormat = c_uint32
-SecExternalItemType = c_uint32
-SecIdentityRef = POINTER(c_void_p)
-SecItemImportExportFlags = c_uint32
-SecItemImportExportKeyParameters = c_void_p
-SecKeychainRef = POINTER(c_void_p)
-SSLProtocol = c_uint32
-SSLCipherSuite = c_uint32
-SSLContextRef = POINTER(c_void_p)
-SecTrustRef = POINTER(c_void_p)
-SSLConnectionRef = c_uint32
-SecTrustResultType = c_uint32
-SecTrustOptionFlags = c_uint32
-SSLProtocolSide = c_uint32
-SSLConnectionType = c_uint32
-SSLSessionOption = c_uint32
-
-
-try:
- Security.SecItemImport.argtypes = [
- CFDataRef,
- CFStringRef,
- POINTER(SecExternalFormat),
- POINTER(SecExternalItemType),
- SecItemImportExportFlags,
- POINTER(SecItemImportExportKeyParameters),
- SecKeychainRef,
- POINTER(CFArrayRef),
- ]
- Security.SecItemImport.restype = OSStatus
-
- Security.SecCertificateGetTypeID.argtypes = []
- Security.SecCertificateGetTypeID.restype = CFTypeID
-
- Security.SecIdentityGetTypeID.argtypes = []
- Security.SecIdentityGetTypeID.restype = CFTypeID
-
- Security.SecKeyGetTypeID.argtypes = []
- Security.SecKeyGetTypeID.restype = CFTypeID
-
- Security.SecCertificateCreateWithData.argtypes = [CFAllocatorRef, CFDataRef]
- Security.SecCertificateCreateWithData.restype = SecCertificateRef
-
- Security.SecCertificateCopyData.argtypes = [SecCertificateRef]
- Security.SecCertificateCopyData.restype = CFDataRef
-
- Security.SecCopyErrorMessageString.argtypes = [OSStatus, c_void_p]
- Security.SecCopyErrorMessageString.restype = CFStringRef
-
- Security.SecIdentityCreateWithCertificate.argtypes = [
- CFTypeRef,
- SecCertificateRef,
- POINTER(SecIdentityRef),
- ]
- Security.SecIdentityCreateWithCertificate.restype = OSStatus
-
- Security.SecKeychainCreate.argtypes = [
- c_char_p,
- c_uint32,
- c_void_p,
- Boolean,
- c_void_p,
- POINTER(SecKeychainRef),
- ]
- Security.SecKeychainCreate.restype = OSStatus
-
- Security.SecKeychainDelete.argtypes = [SecKeychainRef]
- Security.SecKeychainDelete.restype = OSStatus
-
- Security.SecPKCS12Import.argtypes = [
- CFDataRef,
- CFDictionaryRef,
- POINTER(CFArrayRef),
- ]
- Security.SecPKCS12Import.restype = OSStatus
-
- SSLReadFunc = CFUNCTYPE(OSStatus, SSLConnectionRef, c_void_p, POINTER(c_size_t))
- SSLWriteFunc = CFUNCTYPE(
- OSStatus, SSLConnectionRef, POINTER(c_byte), POINTER(c_size_t)
- )
-
- Security.SSLSetIOFuncs.argtypes = [SSLContextRef, SSLReadFunc, SSLWriteFunc]
- Security.SSLSetIOFuncs.restype = OSStatus
-
- Security.SSLSetPeerID.argtypes = [SSLContextRef, c_char_p, c_size_t]
- Security.SSLSetPeerID.restype = OSStatus
-
- Security.SSLSetCertificate.argtypes = [SSLContextRef, CFArrayRef]
- Security.SSLSetCertificate.restype = OSStatus
-
- Security.SSLSetCertificateAuthorities.argtypes = [SSLContextRef, CFTypeRef, Boolean]
- Security.SSLSetCertificateAuthorities.restype = OSStatus
-
- Security.SSLSetConnection.argtypes = [SSLContextRef, SSLConnectionRef]
- Security.SSLSetConnection.restype = OSStatus
-
- Security.SSLSetPeerDomainName.argtypes = [SSLContextRef, c_char_p, c_size_t]
- Security.SSLSetPeerDomainName.restype = OSStatus
-
- Security.SSLHandshake.argtypes = [SSLContextRef]
- Security.SSLHandshake.restype = OSStatus
-
- Security.SSLRead.argtypes = [SSLContextRef, c_char_p, c_size_t, POINTER(c_size_t)]
- Security.SSLRead.restype = OSStatus
-
- Security.SSLWrite.argtypes = [SSLContextRef, c_char_p, c_size_t, POINTER(c_size_t)]
- Security.SSLWrite.restype = OSStatus
-
- Security.SSLClose.argtypes = [SSLContextRef]
- Security.SSLClose.restype = OSStatus
-
- Security.SSLGetNumberSupportedCiphers.argtypes = [SSLContextRef, POINTER(c_size_t)]
- Security.SSLGetNumberSupportedCiphers.restype = OSStatus
-
- Security.SSLGetSupportedCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- POINTER(c_size_t),
- ]
- Security.SSLGetSupportedCiphers.restype = OSStatus
-
- Security.SSLSetEnabledCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- c_size_t,
- ]
- Security.SSLSetEnabledCiphers.restype = OSStatus
-
- Security.SSLGetNumberEnabledCiphers.argtype = [SSLContextRef, POINTER(c_size_t)]
- Security.SSLGetNumberEnabledCiphers.restype = OSStatus
-
- Security.SSLGetEnabledCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- POINTER(c_size_t),
- ]
- Security.SSLGetEnabledCiphers.restype = OSStatus
-
- Security.SSLGetNegotiatedCipher.argtypes = [SSLContextRef, POINTER(SSLCipherSuite)]
- Security.SSLGetNegotiatedCipher.restype = OSStatus
-
- Security.SSLGetNegotiatedProtocolVersion.argtypes = [
- SSLContextRef,
- POINTER(SSLProtocol),
- ]
- Security.SSLGetNegotiatedProtocolVersion.restype = OSStatus
-
- Security.SSLCopyPeerTrust.argtypes = [SSLContextRef, POINTER(SecTrustRef)]
- Security.SSLCopyPeerTrust.restype = OSStatus
-
- Security.SecTrustSetAnchorCertificates.argtypes = [SecTrustRef, CFArrayRef]
- Security.SecTrustSetAnchorCertificates.restype = OSStatus
-
- Security.SecTrustSetAnchorCertificatesOnly.argstypes = [SecTrustRef, Boolean]
- Security.SecTrustSetAnchorCertificatesOnly.restype = OSStatus
-
- Security.SecTrustEvaluate.argtypes = [SecTrustRef, POINTER(SecTrustResultType)]
- Security.SecTrustEvaluate.restype = OSStatus
-
- Security.SecTrustGetCertificateCount.argtypes = [SecTrustRef]
- Security.SecTrustGetCertificateCount.restype = CFIndex
-
- Security.SecTrustGetCertificateAtIndex.argtypes = [SecTrustRef, CFIndex]
- Security.SecTrustGetCertificateAtIndex.restype = SecCertificateRef
-
- Security.SSLCreateContext.argtypes = [
- CFAllocatorRef,
- SSLProtocolSide,
- SSLConnectionType,
- ]
- Security.SSLCreateContext.restype = SSLContextRef
-
- Security.SSLSetSessionOption.argtypes = [SSLContextRef, SSLSessionOption, Boolean]
- Security.SSLSetSessionOption.restype = OSStatus
-
- Security.SSLSetProtocolVersionMin.argtypes = [SSLContextRef, SSLProtocol]
- Security.SSLSetProtocolVersionMin.restype = OSStatus
-
- Security.SSLSetProtocolVersionMax.argtypes = [SSLContextRef, SSLProtocol]
- Security.SSLSetProtocolVersionMax.restype = OSStatus
-
- try:
- Security.SSLSetALPNProtocols.argtypes = [SSLContextRef, CFArrayRef]
- Security.SSLSetALPNProtocols.restype = OSStatus
- except AttributeError:
- # Supported only in 10.12+
- pass
-
- Security.SecCopyErrorMessageString.argtypes = [OSStatus, c_void_p]
- Security.SecCopyErrorMessageString.restype = CFStringRef
-
- Security.SSLReadFunc = SSLReadFunc
- Security.SSLWriteFunc = SSLWriteFunc
- Security.SSLContextRef = SSLContextRef
- Security.SSLProtocol = SSLProtocol
- Security.SSLCipherSuite = SSLCipherSuite
- Security.SecIdentityRef = SecIdentityRef
- Security.SecKeychainRef = SecKeychainRef
- Security.SecTrustRef = SecTrustRef
- Security.SecTrustResultType = SecTrustResultType
- Security.SecExternalFormat = SecExternalFormat
- Security.OSStatus = OSStatus
-
- Security.kSecImportExportPassphrase = CFStringRef.in_dll(
- Security, "kSecImportExportPassphrase"
- )
- Security.kSecImportItemIdentity = CFStringRef.in_dll(
- Security, "kSecImportItemIdentity"
- )
-
- # CoreFoundation time!
- CoreFoundation.CFRetain.argtypes = [CFTypeRef]
- CoreFoundation.CFRetain.restype = CFTypeRef
-
- CoreFoundation.CFRelease.argtypes = [CFTypeRef]
- CoreFoundation.CFRelease.restype = None
-
- CoreFoundation.CFGetTypeID.argtypes = [CFTypeRef]
- CoreFoundation.CFGetTypeID.restype = CFTypeID
-
- CoreFoundation.CFStringCreateWithCString.argtypes = [
- CFAllocatorRef,
- c_char_p,
- CFStringEncoding,
- ]
- CoreFoundation.CFStringCreateWithCString.restype = CFStringRef
-
- CoreFoundation.CFStringGetCStringPtr.argtypes = [CFStringRef, CFStringEncoding]
- CoreFoundation.CFStringGetCStringPtr.restype = c_char_p
-
- CoreFoundation.CFStringGetCString.argtypes = [
- CFStringRef,
- c_char_p,
- CFIndex,
- CFStringEncoding,
- ]
- CoreFoundation.CFStringGetCString.restype = c_bool
-
- CoreFoundation.CFDataCreate.argtypes = [CFAllocatorRef, c_char_p, CFIndex]
- CoreFoundation.CFDataCreate.restype = CFDataRef
-
- CoreFoundation.CFDataGetLength.argtypes = [CFDataRef]
- CoreFoundation.CFDataGetLength.restype = CFIndex
-
- CoreFoundation.CFDataGetBytePtr.argtypes = [CFDataRef]
- CoreFoundation.CFDataGetBytePtr.restype = c_void_p
-
- CoreFoundation.CFDictionaryCreate.argtypes = [
- CFAllocatorRef,
- POINTER(CFTypeRef),
- POINTER(CFTypeRef),
- CFIndex,
- CFDictionaryKeyCallBacks,
- CFDictionaryValueCallBacks,
- ]
- CoreFoundation.CFDictionaryCreate.restype = CFDictionaryRef
-
- CoreFoundation.CFDictionaryGetValue.argtypes = [CFDictionaryRef, CFTypeRef]
- CoreFoundation.CFDictionaryGetValue.restype = CFTypeRef
-
- CoreFoundation.CFArrayCreate.argtypes = [
- CFAllocatorRef,
- POINTER(CFTypeRef),
- CFIndex,
- CFArrayCallBacks,
- ]
- CoreFoundation.CFArrayCreate.restype = CFArrayRef
-
- CoreFoundation.CFArrayCreateMutable.argtypes = [
- CFAllocatorRef,
- CFIndex,
- CFArrayCallBacks,
- ]
- CoreFoundation.CFArrayCreateMutable.restype = CFMutableArrayRef
-
- CoreFoundation.CFArrayAppendValue.argtypes = [CFMutableArrayRef, c_void_p]
- CoreFoundation.CFArrayAppendValue.restype = None
-
- CoreFoundation.CFArrayGetCount.argtypes = [CFArrayRef]
- CoreFoundation.CFArrayGetCount.restype = CFIndex
-
- CoreFoundation.CFArrayGetValueAtIndex.argtypes = [CFArrayRef, CFIndex]
- CoreFoundation.CFArrayGetValueAtIndex.restype = c_void_p
-
- CoreFoundation.kCFAllocatorDefault = CFAllocatorRef.in_dll(
- CoreFoundation, "kCFAllocatorDefault"
- )
- CoreFoundation.kCFTypeArrayCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeArrayCallBacks"
- )
- CoreFoundation.kCFTypeDictionaryKeyCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeDictionaryKeyCallBacks"
- )
- CoreFoundation.kCFTypeDictionaryValueCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeDictionaryValueCallBacks"
- )
-
- CoreFoundation.CFTypeRef = CFTypeRef
- CoreFoundation.CFArrayRef = CFArrayRef
- CoreFoundation.CFStringRef = CFStringRef
- CoreFoundation.CFDictionaryRef = CFDictionaryRef
-
-except (AttributeError):
- raise ImportError("Error initializing ctypes")
-
-
-class CFConst(object):
- """
- A class object that acts as essentially a namespace for CoreFoundation
- constants.
- """
-
- kCFStringEncodingUTF8 = CFStringEncoding(0x08000100)
-
-
-class SecurityConst(object):
- """
- A class object that acts as essentially a namespace for Security constants.
- """
-
- kSSLSessionOptionBreakOnServerAuth = 0
-
- kSSLProtocol2 = 1
- kSSLProtocol3 = 2
- kTLSProtocol1 = 4
- kTLSProtocol11 = 7
- kTLSProtocol12 = 8
- # SecureTransport does not support TLS 1.3 even if there's a constant for it
- kTLSProtocol13 = 10
- kTLSProtocolMaxSupported = 999
-
- kSSLClientSide = 1
- kSSLStreamType = 0
-
- kSecFormatPEMSequence = 10
-
- kSecTrustResultInvalid = 0
- kSecTrustResultProceed = 1
- # This gap is present on purpose: this was kSecTrustResultConfirm, which
- # is deprecated.
- kSecTrustResultDeny = 3
- kSecTrustResultUnspecified = 4
- kSecTrustResultRecoverableTrustFailure = 5
- kSecTrustResultFatalTrustFailure = 6
- kSecTrustResultOtherError = 7
-
- errSSLProtocol = -9800
- errSSLWouldBlock = -9803
- errSSLClosedGraceful = -9805
- errSSLClosedNoNotify = -9816
- errSSLClosedAbort = -9806
-
- errSSLXCertChainInvalid = -9807
- errSSLCrypto = -9809
- errSSLInternal = -9810
- errSSLCertExpired = -9814
- errSSLCertNotYetValid = -9815
- errSSLUnknownRootCert = -9812
- errSSLNoRootCert = -9813
- errSSLHostNameMismatch = -9843
- errSSLPeerHandshakeFail = -9824
- errSSLPeerUserCancelled = -9839
- errSSLWeakPeerEphemeralDHKey = -9850
- errSSLServerAuthCompleted = -9841
- errSSLRecordOverflow = -9847
-
- errSecVerifyFailed = -67808
- errSecNoTrustSettings = -25263
- errSecItemNotFound = -25300
- errSecInvalidTrustSettings = -25262
-
- # Cipher suites. We only pick the ones our default cipher string allows.
- # Source: https://developer.apple.com/documentation/security/1550981-ssl_cipher_suite_values
- TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384 = 0xC02C
- TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384 = 0xC030
- TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256 = 0xC02B
- TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256 = 0xC02F
- TLS_ECDHE_ECDSA_WITH_CHACHA20_POLY1305_SHA256 = 0xCCA9
- TLS_ECDHE_RSA_WITH_CHACHA20_POLY1305_SHA256 = 0xCCA8
- TLS_DHE_RSA_WITH_AES_256_GCM_SHA384 = 0x009F
- TLS_DHE_RSA_WITH_AES_128_GCM_SHA256 = 0x009E
- TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA384 = 0xC024
- TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384 = 0xC028
- TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA = 0xC00A
- TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA = 0xC014
- TLS_DHE_RSA_WITH_AES_256_CBC_SHA256 = 0x006B
- TLS_DHE_RSA_WITH_AES_256_CBC_SHA = 0x0039
- TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256 = 0xC023
- TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256 = 0xC027
- TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA = 0xC009
- TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA = 0xC013
- TLS_DHE_RSA_WITH_AES_128_CBC_SHA256 = 0x0067
- TLS_DHE_RSA_WITH_AES_128_CBC_SHA = 0x0033
- TLS_RSA_WITH_AES_256_GCM_SHA384 = 0x009D
- TLS_RSA_WITH_AES_128_GCM_SHA256 = 0x009C
- TLS_RSA_WITH_AES_256_CBC_SHA256 = 0x003D
- TLS_RSA_WITH_AES_128_CBC_SHA256 = 0x003C
- TLS_RSA_WITH_AES_256_CBC_SHA = 0x0035
- TLS_RSA_WITH_AES_128_CBC_SHA = 0x002F
- TLS_AES_128_GCM_SHA256 = 0x1301
- TLS_AES_256_GCM_SHA384 = 0x1302
- TLS_AES_128_CCM_8_SHA256 = 0x1305
- TLS_AES_128_CCM_SHA256 = 0x1304
diff --git a/spaces/TeamTonic/MultiMed/app.py b/spaces/TeamTonic/MultiMed/app.py
deleted file mode 100644
index 29b1ad119a6d38fc2fca1fa616c0f221e9751208..0000000000000000000000000000000000000000
--- a/spaces/TeamTonic/MultiMed/app.py
+++ /dev/null
@@ -1,490 +0,0 @@
-# Welcome to Team Tonic's MultiMed
-
-from gradio_client import Client
-import os
-import numpy as np
-import base64
-import gradio as gr
-import requests
-import json
-import dotenv
-from scipy.io.wavfile import write
-import PIL
-from openai import OpenAI
-import time
-dotenv.load_dotenv()
-
-seamless_client = Client("facebook/seamless_m4t")
-HuggingFace_Token = os.getenv("HuggingFace_Token")
-
-def check_hallucination(assertion,citation):
- API_URL = "https://api-inference.huggingface.co/models/vectara/hallucination_evaluation_model"
- headers = {"Authorization": f"Bearer {HuggingFace_Token}"}
- payload = {"inputs" : f"{assertion} [SEP] {citation}"}
-
- response = requests.post(API_URL, headers=headers, json=payload,timeout=120)
- output = response.json()
- output = output[0][0]["score"]
-
- return f"**hullicination score:** {output}"
-
-
-
-def process_speech(input_language, audio_input):
- """
- processing sound using seamless_m4t
- """
- if audio_input is None :
- return "no audio or audio did not save yet \nplease try again ! "
- print(f"audio : {audio_input}")
- print(f"audio type : {type(audio_input)}")
- out = seamless_client.predict(
- "S2TT",
- "file",
- None,
- audio_input, #audio_name
- "",
- input_language,# source language
- input_language,# target language
- api_name="/run",
- )
- out = out[1] # get the text
- try :
- return f"{out}"
- except Exception as e :
- return f"{e}"
-
-
-
-
-def process_image(image) :
- img_name = f"{np.random.randint(0, 100)}.jpg"
- PIL.Image.fromarray(image.astype('uint8'), 'RGB').save(img_name)
- image = open(img_name, "rb").read()
- base64_image = base64_image = base64.b64encode(image).decode('utf-8')
- openai_api_key = os.getenv('OPENAI_API_KEY')
- # oai_org = os.getenv('OAI_ORG')
-
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {openai_api_key}"
- }
-
- payload = {
- "model": "gpt-4-vision-preview",
- "messages": [
- {
- "role": "user",
- "content": [
- {
- "type": "text",
- "text": "You are clinical consultant discussion training cases with students at TonicUniversity. Assess and describe the photo in minute detail. Explain why each area or item in the photograph would be inappropriate to describe if required. Pay attention to anatomy, symptoms and remedies. Propose a course of action based on your assessment. Exclude any other commentary:"
- },
- {
- "type": "image_url",
- "image_url": {
- "url": f"data:image/jpeg;base64,{base64_image}"
- }
- }
- ]
- }
- ],
- "max_tokens": 1200
- }
-
- response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
-
- try :
- out = response.json()
- out = out["choices"][0]["message"]["content"]
-
- return out
- except Exception as e :
- return f"{e}"
-
-
-def query_vectara(text):
- user_message = text
-
- # Read authentication parameters from the .env file
- CUSTOMER_ID = os.getenv('CUSTOMER_ID')
- CORPUS_ID = os.getenv('CORPUS_ID')
- API_KEY = os.getenv('API_KEY')
-
- # Define the headers
- api_key_header = {
- "customer-id": CUSTOMER_ID,
- "x-api-key": API_KEY
- }
-
- # Define the request body in the structure provided in the example
- request_body = {
- "query": [
- {
- "query": user_message,
- "queryContext": "",
- "start": 1,
- "numResults": 50,
- "contextConfig": {
- "charsBefore": 0,
- "charsAfter": 0,
- "sentencesBefore": 2,
- "sentencesAfter": 2,
- "startTag": "%START_SNIPPET%",
- "endTag": "%END_SNIPPET%",
- },
- "rerankingConfig": {
- "rerankerId": 272725718,
- "mmrConfig": {
- "diversityBias": 0.35
- }
- },
- "corpusKey": [
- {
- "customerId": CUSTOMER_ID,
- "corpusId": CORPUS_ID,
- "semantics": 0,
- "metadataFilter": "",
- "lexicalInterpolationConfig": {
- "lambda": 0
- },
- "dim": []
- }
- ],
- "summary": [
- {
- "maxSummarizedResults": 5,
- "responseLang": "auto",
- "summarizerPromptName": "vectara-summary-ext-v1.2.0"
- }
- ]
- }
- ]
- }
-
- # Make the API request using Gradio
- response = requests.post(
- "https://api.vectara.io/v1/query",
- json=request_body, # Use json to automatically serialize the request body
- verify=True,
- headers=api_key_header
- )
-
- if response.status_code == 200:
- query_data = response.json()
- if query_data:
- sources_info = []
-
- # Extract the summary.
- summary = query_data['responseSet'][0]['summary'][0]['text']
-
- # Iterate over all response sets
- for response_set in query_data.get('responseSet', []):
- # Extract sources
- # Limit to top 5 sources.
- for source in response_set.get('response', [])[:5]:
- source_metadata = source.get('metadata', [])
- source_info = {}
-
- for metadata in source_metadata:
- metadata_name = metadata.get('name', '')
- metadata_value = metadata.get('value', '')
-
- if metadata_name == 'title':
- source_info['title'] = metadata_value
- elif metadata_name == 'author':
- source_info['author'] = metadata_value
- elif metadata_name == 'pageNumber':
- source_info['page number'] = metadata_value
-
- if source_info:
- sources_info.append(source_info)
-
- result = {"summary": summary, "sources": sources_info}
- return f"{json.dumps(result, indent=2)}"
- else:
- return "No data found in the response."
- else:
- return f"Error: {response.status_code}"
-
-
-def convert_to_markdown(vectara_response_json):
- vectara_response = json.loads(vectara_response_json)
- if vectara_response:
- summary = vectara_response.get('summary', 'No summary available')
- sources_info = vectara_response.get('sources', [])
-
- # Format the summary as Markdown
- markdown_summary = f' {summary}\n\n'
-
- # Format the sources as a numbered list
- markdown_sources = ""
- for i, source_info in enumerate(sources_info):
- author = source_info.get('author', 'Unknown author')
- title = source_info.get('title', 'Unknown title')
- page_number = source_info.get('page number', 'Unknown page number')
- markdown_sources += f"{i+1}. {title} by {author}, Page {page_number}\n"
-
- return f"{markdown_summary}**Sources:**\n{markdown_sources}"
- else:
- return "No data found in the response."
-# Main function to handle the Gradio interface logic
-
-def process_summary_with_openai(summary):
- """
- This function takes a summary text as input and processes it with OpenAI's GPT model.
- """
- try:
- # Ensure that the OpenAI client is properly initialized
- client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
-
- # Create the prompt for OpenAI's completion
- prompt = "You are clinical consultant discussion training cases with students at TonicUniversity. Assess and describe the proper options in minute detail. Propose a course of action based on your assessment. You will recieve a summary assessment in a language, respond ONLY in the original language. Exclude any other commentary:"
-
- # Call the OpenAI API with the prompt and the summary
- completion = client.chat.completions.create(
- model="gpt-4-1106-preview", # Make sure to use the correct model name
- messages=[
- {"role": "system", "content": prompt},
- {"role": "user", "content": summary}
- ]
- )
-
- # Extract the content from the completion
- final_summary = completion.choices[0].message.content
- return final_summary
- except Exception as e:
- return str(e)
-
-
-def process_and_query(text=None):
- try:
- # augment the prompt before feeding it to vectara
- text = "the user asks the following to his health adviser " + text
- # If an image is provided, process it with OpenAI and use the response as the text query for Vectara
- # if image is not None:
- # text = process_image(image)
- # return "**Summary:** "+text
- # if audio is not None:
- # text = process_speech(audio)
- # # augment the prompt before feeding it to vectara
- # text = "the user asks the following to his health adviser " + text
-
-
-
- # Use the text to query Vectara
- vectara_response_json = query_vectara(text)
-
- # Convert the Vectara response to Markdown
- markdown_output = convert_to_markdown(vectara_response_json)
-
- # Process the summary with OpenAI
- final_response = process_summary_with_openai(markdown_output)
-
- # Return the processed summary along with the full output
- return f"**Summary**: {final_response}\n\n**Full output**:\n{markdown_output}"
- except Exception as e:
- return str(e)
-
- completion = client.chat.completions.create(
- model="gpt-3.5-turbo",
- messages=[
- {"role": "system", "content": prompt},
- {"role": "user", "content": markdown_output_final}
- ]
- )
- final_response= completion.choices[0].message.content
- return f"**Summary**: {final_response}\n\n**Full output**:\n{markdown_output}"
- except Exception as e:
- return str(e)
-
-
-# Define the Gradio interface
-# iface = gr.Interface(
-# fn=process_and_query,
-# inputs=[
-# gr.Textbox(label="Input Text"),
-# gr.Image(label="Upload Image"),
-# gr.Audio(label="talk in french",
-# sources=["microphone"]),
-# ],
-# outputs=[gr.Markdown(label="Output Text")],
-# title="👋🏻Welcome to ⚕🗣️😷MultiMed - Access Chat ⚕🗣️😷",
-# description='''
-# ### How To Use ⚕🗣️😷MultiMed⚕:
-# #### 🗣️📝Interact with ⚕🗣️😷MultiMed⚕ in any language using audio or text!
-# #### 🗣️📝 This is an educational and accessible conversational tool to improve wellness and sanitation in support of public health.
-# #### 📚🌟💼 The knowledge base is composed of publicly available medical and health sources in multiple languages. We also used [Kelvalya/MedAware](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset) that we processed and converted to HTML. The quality of the answers depends on the quality of the dataset, so if you want to see some data represented here, do [get in touch](https://discord.gg/GWpVpekp). You can also use 😷MultiMed⚕️ on your own data & in your own way by cloning this space. 🧬🔬🔍 Simply click here:
-# #### Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community on 👻Discord: [Discord](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)"
-# ''',
-# theme='ParityError/Anime',
-# examples=[
-# ["What is the proper treatment for buccal herpes?"],
-# ["Male, 40 presenting with swollen glands and a rash"],
-# ["How does cellular metabolism work TCA cycle"],
-# ["What special care must be provided to children with chicken pox?"],
-# ["When and how often should I wash my hands ?"],
-# ["بکل ہرپس کا صحیح علاج کیا ہے؟"],
-# ["구강 헤르페스의 적절한 치료법은 무엇입니까?"],
-# ["Je, ni matibabu gani sahihi kwa herpes ya buccal?"],
-# ],
-# )
-
-welcome_message = """
-# 👋🏻Welcome to ⚕🗣️😷MultiMed - Access Chat ⚕🗣️😷
-### How To Use ⚕🗣️😷MultiMed⚕:
-#### 🗣️📝Interact with ⚕🗣️😷MultiMed⚕ in any language using audio or text!
-#### 🗣️📝 This is an educational and accessible conversational tool to improve wellness and sanitation in support of public health.
-#### 📚🌟💼 The knowledge base is composed of publicly available medical and health sources in multiple languages. We also used [Kelvalya/MedAware](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset) that we processed and converted to HTML. The quality of the answers depends on the quality of the dataset, so if you want to see some data represented here, do [get in touch](https://discord.gg/GWpVpekp). You can also use 😷MultiMed⚕️ on your own data & in your own way by cloning this space. 🧬🔬🔍 Simply click here:
-#### Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community on 👻Discord: [Discord](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)"
-"""
-
-
-languages = [
- "Afrikaans",
- "Amharic",
- "Modern Standard Arabic",
- "Moroccan Arabic",
- "Egyptian Arabic",
- "Assamese",
- "Asturian",
- "North Azerbaijani",
- "Belarusian",
- "Bengali",
- "Bosnian",
- "Bulgarian",
- "Catalan",
- "Cebuano",
- "Czech",
- "Central Kurdish",
- "Mandarin Chinese",
- "Welsh",
- "Danish",
- "German",
- "Greek",
- "English",
- "Estonian",
- "Basque",
- "Finnish",
- "French",
- "West Central Oromo",
- "Irish",
- "Galician",
- "Gujarati",
- "Hebrew",
- "Hindi",
- "Croatian",
- "Hungarian",
- "Armenian",
- "Igbo",
- "Indonesian",
- "Icelandic",
- "Italian",
- "Javanese",
- "Japanese",
- "Kamba",
- "Kannada",
- "Georgian",
- "Kazakh",
- "Kabuverdianu",
- "Halh Mongolian",
- "Khmer",
- "Kyrgyz",
- "Korean",
- "Lao",
- "Lithuanian",
- "Luxembourgish",
- "Ganda",
- "Luo",
- "Standard Latvian",
- "Maithili",
- "Malayalam",
- "Marathi",
- "Macedonian",
- "Maltese",
- "Meitei",
- "Burmese",
- "Dutch",
- "Norwegian Nynorsk",
- "Norwegian Bokmål",
- "Nepali",
- "Nyanja",
- "Occitan",
- "Odia",
- "Punjabi",
- "Southern Pashto",
- "Western Persian",
- "Polish",
- "Portuguese",
- "Romanian",
- "Russian",
- "Slovak",
- "Slovenian",
- "Shona",
- "Sindhi",
- "Somali",
- "Spanish",
- "Serbian",
- "Swedish",
- "Swahili",
- "Tamil",
- "Telugu",
- "Tajik",
- "Tagalog",
- "Thai",
- "Turkish",
- "Ukrainian",
- "Urdu",
- "Northern Uzbek",
- "Vietnamese",
- "Xhosa",
- "Yoruba",
- "Cantonese",
- "Colloquial Malay",
- "Standard Malay",
- "Zulu"
-]
-
-
-with gr.Blocks(theme='ParityError/Anime') as iface :
- gr.Markdown(welcome_message)
- with gr.Tab("text summarization"):
- text_input = gr.Textbox(label="input text",lines=5)
- text_output = gr.Markdown(label="output text")
- text_button = gr.Button("process text")
- gr.Examples([
- ["What is the proper treatment for buccal herpes?"],
- ["Male, 40 presenting with swollen glands and a rash"],
- ["How does cellular metabolism work TCA cycle"],
- ["What special care must be provided to children with chicken pox?"],
- ["When and how often should I wash my hands?"],
- ["بکل ہرپس کا صحیح علاج کیا ہے؟"],
- ["구강 헤르페스의 적절한 치료법은 무엇입니까?"],
- ["Je, ni matibabu gani sahihi kwa herpes ya buccal?"],
- ],inputs=[text_input])
- with gr.Tab("image identification"):
- image_input = gr.Image(label="upload image")
- image_output = gr.Markdown(label="output text")
- image_button = gr.Button("process image")
- image_button.click(process_image, inputs=image_input, outputs=image_output)
- gr.Examples(["sick person.jpeg"],inputs=[image_input])
- with gr.Tab("speech to text"):
- input_language = gr.Dropdown(languages, label="select the language",value="English",interactive=True)
- audio_input = gr.Audio(label="speak",type="filepath",sources="microphone")
- audio_output = gr.Markdown(label="output text")
- audio_button = gr.Button("process audio")
- audio_button.click(process_speech, inputs=[input_language,audio_input], outputs=audio_output)
- gr.Examples([["English","sample_input.mp3"]],inputs=[input_language,audio_input])
- with gr.Tab("hallucination check"):
- assertion = gr.Textbox(label="assertion")
- citation = gr.Textbox(label="citation text")
- hullucination_output = gr.Markdown(label="output text")
- audio_button = gr.Button("check hallucination")
- gr.Examples([["i am drunk","sarah is pregnant"]],inputs=[assertion,citation])
- text_button.click(process_and_query, inputs=text_input, outputs=text_output)
- audio_button.click(check_hallucination,inputs=[assertion,citation],outputs=hullucination_output)
-
-
-
-
-iface.queue().launch(show_error=True,debug=True)
diff --git a/spaces/Thanarit/GPT-Detection-Demo/app.py b/spaces/Thanarit/GPT-Detection-Demo/app.py
deleted file mode 100644
index b0de7e28071553c6b86ec9a73b9881b4d540c0bc..0000000000000000000000000000000000000000
--- a/spaces/Thanarit/GPT-Detection-Demo/app.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-from ModelDriver import *
-import numpy as np
-
-# Add a title
-st.title('GPT Detection Demo')
-st.write("This is a demo for GPT detection. You can use this demo to test the model. The model is trained on two datasets: OpenGPT and CSAbstract. You can choose the model and dataset in the sidebar.")
-st.write("Reference on how we built Roberta Sentinel: https://arxiv.org/abs/2305.07969")
-
-# Add 4 options for 4 models
-ModelOption = st.sidebar.selectbox(
- 'Which Model do you want to use?',
- ('RobertaSentinel', 'RobertaClassifier'),
-)
-
-DatasetOption = st.sidebar.selectbox(
- 'Which Dataset the model was trained on?',
- ('OpenGPT', 'CSAbstract'),
-)
-
-
-text = st.text_area('Enter text here (max 500 words)', '')
-
-if st.button('Generate'):
- if ModelOption == 'RobertaSentinel':
- if DatasetOption == 'OpenGPT':
- result = RobertaSentinelOpenGPTInference(text)
- st.write("Model: RobertaSentinelOpenGPT")
- elif DatasetOption == 'CSAbstract':
- result = RobertaSentinelCSAbstractInference(text)
- st.write("Model: RobertaSentinelCSAbstract")
-
- elif ModelOption == 'RobertaClassifier':
- if DatasetOption == 'OpenGPT':
- result = RobertaClassifierOpenGPTInference(text)
- st.write("Model: RobertaClassifierOpenGPT")
- elif DatasetOption == 'CSAbstract':
- result = RobertaClassifierCSAbstractInference(text)
- st.write("Model: RobertaClassifierCSAbstract")
-
- Prediction = "Human Written" if not np.argmax(result) else "Machine Generated"
-
- st.write(f"Prediction: {Prediction} ")
- st.write(f"Probabilty:", max(result))
-
-
-
-
-
-
-
-
-
diff --git a/spaces/ThomasSimonini/Unity-MLAgents-Pyramids/index.html b/spaces/ThomasSimonini/Unity-MLAgents-Pyramids/index.html
deleted file mode 100644
index 37f4404c8056925b6523479300dec8d2406d5c96..0000000000000000000000000000000000000000
--- a/spaces/ThomasSimonini/Unity-MLAgents-Pyramids/index.html
+++ /dev/null
@@ -1,92 +0,0 @@
-
-
-
-
-
-
- Unity-MLAgents-Pyramids
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/Tirendaz/Text-Classification/app.py b/spaces/Tirendaz/Text-Classification/app.py
deleted file mode 100644
index 125b46d586913e6a24ccb8585102a076baf95d9d..0000000000000000000000000000000000000000
--- a/spaces/Tirendaz/Text-Classification/app.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-classifier = pipeline("sentiment-analysis", model="Tirendaz/my_distilbert_model")
-
-def text_classification(text):
- result= classifier(text)
- sentiment_label = result[0]['label']
- sentiment_score = result[0]['score']
- formatted_output = f"This sentiment is {sentiment_label} with the probability {sentiment_score*100:.2f}%"
- return formatted_output
-
-examples=["This is wonderful movie!", "The movie was really bad; I didn't like it."]
-
-io = gr.Interface(fn=text_classification,
- inputs= gr.Textbox(lines=2, label="Text", placeholder="Enter title here..."),
- outputs=gr.Textbox(lines=2, label="Text Classification Result"),
- title="Text Classification",
- description="Enter a text and see the text classification result!",
- examples=examples)
-
-io.launch()
-
diff --git a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Vercel.py b/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Vercel.py
deleted file mode 100644
index e5df9cf017e4c1a265f5c9d5e48eb5c10a56e60a..0000000000000000000000000000000000000000
--- a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Vercel.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import os
-import json
-import base64
-import execjs
-import queue
-import threading
-
-from curl_cffi import requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://play.vercel.ai'
-supports_stream = True
-needs_auth = False
-
-models = {
- 'claude-instant-v1': 'anthropic:claude-instant-v1',
- 'claude-v1': 'anthropic:claude-v1',
- 'alpaca-7b': 'replicate:replicate/alpaca-7b',
- 'stablelm-tuned-alpha-7b': 'replicate:stability-ai/stablelm-tuned-alpha-7b',
- 'bloom': 'huggingface:bigscience/bloom',
- 'bloomz': 'huggingface:bigscience/bloomz',
- 'flan-t5-xxl': 'huggingface:google/flan-t5-xxl',
- 'flan-ul2': 'huggingface:google/flan-ul2',
- 'gpt-neox-20b': 'huggingface:EleutherAI/gpt-neox-20b',
- 'oasst-sft-4-pythia-12b-epoch-3.5': 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5',
- 'santacoder': 'huggingface:bigcode/santacoder',
- 'command-medium-nightly': 'cohere:command-medium-nightly',
- 'command-xlarge-nightly': 'cohere:command-xlarge-nightly',
- 'code-cushman-001': 'openai:code-cushman-001',
- 'code-davinci-002': 'openai:code-davinci-002',
- 'gpt-3.5-turbo': 'openai:gpt-3.5-turbo',
- 'text-ada-001': 'openai:text-ada-001',
- 'text-babbage-001': 'openai:text-babbage-001',
- 'text-curie-001': 'openai:text-curie-001',
- 'text-davinci-002': 'openai:text-davinci-002',
- 'text-davinci-003': 'openai:text-davinci-003'
-}
-model = models.keys()
-
-vercel_models = {'anthropic:claude-instant-v1': {'id': 'anthropic:claude-instant-v1', 'provider': 'anthropic', 'providerHumanName': 'Anthropic', 'makerHumanName': 'Anthropic', 'minBillingTier': 'hobby', 'parameters': {'temperature': {'value': 1, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': ['\n\nHuman:'], 'range': []}}, 'name': 'claude-instant-v1'}, 'anthropic:claude-v1': {'id': 'anthropic:claude-v1', 'provider': 'anthropic', 'providerHumanName': 'Anthropic', 'makerHumanName': 'Anthropic', 'minBillingTier': 'hobby', 'parameters': {'temperature': {'value': 1, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': ['\n\nHuman:'], 'range': []}}, 'name': 'claude-v1'}, 'replicate:replicate/alpaca-7b': {'id': 'replicate:replicate/alpaca-7b', 'provider': 'replicate', 'providerHumanName': 'Replicate', 'makerHumanName': 'Stanford', 'parameters': {'temperature': {'value': 0.75, 'range': [0.01, 5]}, 'maximumLength': {'value': 200, 'range': [50, 512]}, 'topP': {'value': 0.95, 'range': [0.01, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'repetitionPenalty': {'value': 1.1765, 'range': [0.01, 5]}, 'stopSequences': {'value': [], 'range': []}}, 'version': '2014ee1247354f2e81c0b3650d71ca715bc1e610189855f134c30ecb841fae21', 'name': 'alpaca-7b'}, 'replicate:stability-ai/stablelm-tuned-alpha-7b': {'id': 'replicate:stability-ai/stablelm-tuned-alpha-7b', 'provider': 'replicate', 'makerHumanName': 'StabilityAI', 'providerHumanName': 'Replicate', 'parameters': {'temperature': {'value': 0.75, 'range': [0.01, 5]}, 'maximumLength': {'value': 200, 'range': [50, 512]}, 'topP': {'value': 0.95, 'range': [0.01, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'repetitionPenalty': {'value': 1.1765, 'range': [0.01, 5]}, 'stopSequences': {'value': [], 'range': []}}, 'version': '4a9a32b4fd86c2d047f1d271fa93972683ec6ef1cf82f402bd021f267330b50b', 'name': 'stablelm-tuned-alpha-7b'}, 'huggingface:bigscience/bloom': {'id': 'huggingface:bigscience/bloom', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigScience', 'instructions': "Do NOT talk to Bloom as an entity, it's not a chatbot but a webpage/blog/article completion model. For the best results: mimic a few words of a webpage similar to the content you want to generate. Start a sentence as if YOU were writing a blog, webpage, math post, coding article and Bloom will generate a coherent follow-up.", 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'bloom'}, 'huggingface:bigscience/bloomz': {'id': 'huggingface:bigscience/bloomz', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigScience', 'instructions': 'We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "Translate to English: Je t\'aime.", the model will most likely answer "I love you.".', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'bloomz'}, 'huggingface:google/flan-t5-xxl': {'id': 'huggingface:google/flan-t5-xxl', 'provider': 'huggingface', 'makerHumanName': 'Google', 'providerHumanName': 'HuggingFace', 'name': 'flan-t5-xxl', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}}, 'huggingface:google/flan-ul2': {'id': 'huggingface:google/flan-ul2', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'Google', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'flan-ul2'}, 'huggingface:EleutherAI/gpt-neox-20b': {'id': 'huggingface:EleutherAI/gpt-neox-20b', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'EleutherAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'gpt-neox-20b'}, 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5': {'id': 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'OpenAssistant', 'parameters': {'maximumLength': {'value': 200, 'range': [50, 1024]}, 'typicalP': {'value': 0.2, 'range': [0.1, 0.99]}, 'repetitionPenalty': {'value': 1, 'range': [0.1, 2]}}, 'name': 'oasst-sft-4-pythia-12b-epoch-3.5'}, 'huggingface:bigcode/santacoder': {
- 'id': 'huggingface:bigcode/santacoder', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigCode', 'instructions': 'The model was trained on GitHub code. As such it is not an instruction model and commands like "Write a function that computes the square root." do not work well. You should phrase commands like they occur in source code such as comments (e.g. # the following function computes the sqrt) or write a function signature and docstring and let the model complete the function body.', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'santacoder'}, 'cohere:command-medium-nightly': {'id': 'cohere:command-medium-nightly', 'provider': 'cohere', 'providerHumanName': 'Cohere', 'makerHumanName': 'Cohere', 'name': 'command-medium-nightly', 'parameters': {'temperature': {'value': 0.9, 'range': [0, 2]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0, 1]}, 'topK': {'value': 0, 'range': [0, 500]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'cohere:command-xlarge-nightly': {'id': 'cohere:command-xlarge-nightly', 'provider': 'cohere', 'providerHumanName': 'Cohere', 'makerHumanName': 'Cohere', 'name': 'command-xlarge-nightly', 'parameters': {'temperature': {'value': 0.9, 'range': [0, 2]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0, 1]}, 'topK': {'value': 0, 'range': [0, 500]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:gpt-4': {'id': 'openai:gpt-4', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'gpt-4', 'minBillingTier': 'pro', 'parameters': {'temperature': {'value': 0.7, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:code-cushman-001': {'id': 'openai:code-cushman-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'code-cushman-001'}, 'openai:code-davinci-002': {'id': 'openai:code-davinci-002', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'code-davinci-002'}, 'openai:gpt-3.5-turbo': {'id': 'openai:gpt-3.5-turbo', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.7, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'gpt-3.5-turbo'}, 'openai:text-ada-001': {'id': 'openai:text-ada-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-ada-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-babbage-001': {'id': 'openai:text-babbage-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-babbage-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-curie-001': {'id': 'openai:text-curie-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-curie-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-davinci-002': {'id': 'openai:text-davinci-002', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-davinci-002', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-davinci-003': {'id': 'openai:text-davinci-003', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-davinci-003', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}}
-
-
-# based on https://github.com/ading2210/vercel-llm-api // modified
-class Client:
- def __init__(self):
- self.session = requests.Session()
- self.headers = {
- 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110 Safari/537.36',
- 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8',
- 'Accept-Encoding': 'gzip, deflate, br',
- 'Accept-Language': 'en-US,en;q=0.5',
- 'Te': 'trailers',
- 'Upgrade-Insecure-Requests': '1'
- }
- self.session.headers.update(self.headers)
-
- def get_token(self):
- b64 = self.session.get('https://sdk.vercel.ai/openai.jpeg').text
- data = json.loads(base64.b64decode(b64))
-
- code = 'const globalThis = {data: `sentinel`}; function token() {return (%s)(%s)}' % (
- data['c'], data['a'])
-
- token_string = json.dumps(separators=(',', ':'),
- obj={'r': execjs.compile(code).call('token'), 't': data['t']})
-
- return base64.b64encode(token_string.encode()).decode()
-
- def get_default_params(self, model_id):
- return {key: param['value'] for key, param in vercel_models[model_id]['parameters'].items()}
-
- def generate(self, model_id: str, prompt: str, params: dict = {}):
- if not ':' in model_id:
- model_id = models[model_id]
-
- defaults = self.get_default_params(model_id)
-
- payload = defaults | params | {
- 'prompt': prompt,
- 'model': model_id,
- }
-
- headers = self.headers | {
- 'Accept-Encoding': 'gzip, deflate, br',
- 'Custom-Encoding': self.get_token(),
- 'Host': 'sdk.vercel.ai',
- 'Origin': 'https://sdk.vercel.ai',
- 'Referrer': 'https://sdk.vercel.ai',
- 'Sec-Fetch-Dest': 'empty',
- 'Sec-Fetch-Mode': 'cors',
- 'Sec-Fetch-Site': 'same-origin',
- }
-
- chunks_queue = queue.Queue()
- error = None
- response = None
-
- def callback(data):
- chunks_queue.put(data.decode())
-
- def request_thread():
- nonlocal response, error
- for _ in range(3):
- try:
- response = self.session.post('https://sdk.vercel.ai/api/generate',
- json=payload, headers=headers, content_callback=callback)
- response.raise_for_status()
-
- except Exception as e:
- if _ == 2:
- error = e
-
- else:
- continue
-
- thread = threading.Thread(target=request_thread, daemon=True)
- thread.start()
-
- text = ''
- index = 0
- while True:
- try:
- chunk = chunks_queue.get(block=True, timeout=0.1)
-
- except queue.Empty:
- if error:
- raise error
-
- elif response:
- break
-
- else:
- continue
-
- text += chunk
- lines = text.split('\n')
-
- if len(lines) - 1 > index:
- new = lines[index:-1]
- for word in new:
- yield json.loads(word)
- index = len(lines) - 1
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
- yield 'Vercel is currently not working.'
- return
-
- conversation = 'This is a conversation between a human and a language model, respond to the last message accordingly, referring to the past history of messages if needed.\n'
-
- for message in messages:
- conversation += '%s: %s\n' % (message['role'], message['content'])
-
- conversation += 'assistant: '
-
- completion = Client().generate(model, conversation)
-
- for token in completion:
- yield token
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/Vijish/Crop-CLIP/README.md b/spaces/Vijish/Crop-CLIP/README.md
deleted file mode 100644
index bdcae9f45efba75036d8cd67008634852f99dcfc..0000000000000000000000000000000000000000
--- a/spaces/Vijish/Crop-CLIP/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Crop CLIP
-emoji: 🦀
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Violette/Protogen_x3.4_Official_Release/README.md b/spaces/Violette/Protogen_x3.4_Official_Release/README.md
deleted file mode 100644
index f956b53e422114f032bad59a721dd2d563081a37..0000000000000000000000000000000000000000
--- a/spaces/Violette/Protogen_x3.4_Official_Release/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Protogen X3.4 Official Release
-emoji: 📈
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/tasks/__init__.py b/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/tasks/__init__.py
deleted file mode 100644
index ab1fb1c8289535cf9397bb9805c0cba3666ad26f..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/tasks/__init__.py
+++ /dev/null
@@ -1,26 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-from minigpt4.common.registry import registry
-from minigpt4.tasks.base_task import BaseTask
-from minigpt4.tasks.image_text_pretrain import ImageTextPretrainTask
-
-
-def setup_task(cfg):
- assert "task" in cfg.run_cfg, "Task name must be provided."
-
- task_name = cfg.run_cfg.task
- task = registry.get_task_class(task_name).setup_task(cfg=cfg)
- assert task is not None, "Task {} not properly registered.".format(task_name)
-
- return task
-
-
-__all__ = [
- "BaseTask",
- "ImageTextPretrainTask",
-]
diff --git a/spaces/Voicelab/vlT5-keywords-generation/README.md b/spaces/Voicelab/vlT5-keywords-generation/README.md
deleted file mode 100644
index bb0ec20c0d1e6457305ec098f8f8437c1d529252..0000000000000000000000000000000000000000
--- a/spaces/Voicelab/vlT5-keywords-generation/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: VlT5 Keywords Generation
-emoji: 🌍
-colorFrom: pink
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/utils.py b/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/utils.py
deleted file mode 100644
index ee4b01ddfbe8173965371b29f770f3e87615fe71..0000000000000000000000000000000000000000
--- a/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/utils.py
+++ /dev/null
@@ -1,225 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-import librosa
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict= {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})" .format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10,2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_audio_to_torch(full_path, target_sampling_rate):
- audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
- return torch.FloatTensor(audio.astype(np.float32))
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/XPMaster/KSA_Weather_Prediction/README.md b/spaces/XPMaster/KSA_Weather_Prediction/README.md
deleted file mode 100644
index eb0dad165265158f4093d00781e64bb2dc852b4f..0000000000000000000000000000000000000000
--- a/spaces/XPMaster/KSA_Weather_Prediction/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: KSA Weather Prediction
-emoji: ⚡
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/metrics.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/metrics.py
deleted file mode 100644
index 46fdddf3de2cf8d987ecb4c7d7cb3503afa995ad..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/metrics.py
+++ /dev/null
@@ -1,361 +0,0 @@
-"Implements various metrics to measure training accuracy"
-from .torch_core import *
-from .callback import *
-from .layers import *
-from .basic_train import LearnerCallback
-
-__all__ = ['error_rate', 'accuracy', 'accuracy_thresh', 'dice', 'exp_rmspe', 'fbeta','FBeta', 'mse', 'mean_squared_error',
- 'mae', 'mean_absolute_error', 'rmse', 'root_mean_squared_error', 'msle', 'mean_squared_logarithmic_error',
- 'explained_variance', 'r2_score', 'top_k_accuracy', 'KappaScore', 'ConfusionMatrix', 'MatthewsCorreff',
- 'Precision', 'Recall', 'R2Score', 'ExplainedVariance', 'ExpRMSPE', 'RMSE', 'Perplexity', 'AUROC', 'auc_roc_score',
- 'roc_curve', 'MultiLabelFbeta', 'foreground_acc']
-
-def fbeta(y_pred:Tensor, y_true:Tensor, thresh:float=0.2, beta:float=2, eps:float=1e-9, sigmoid:bool=True)->Rank0Tensor:
- "Computes the f_beta between `preds` and `targets`"
- beta2 = beta ** 2
- if sigmoid: y_pred = y_pred.sigmoid()
- y_pred = (y_pred>thresh).float()
- y_true = y_true.float()
- TP = (y_pred*y_true).sum(dim=1)
- prec = TP/(y_pred.sum(dim=1)+eps)
- rec = TP/(y_true.sum(dim=1)+eps)
- res = (prec*rec)/(prec*beta2+rec+eps)*(1+beta2)
- return res.mean()
-
-def accuracy(input:Tensor, targs:Tensor)->Rank0Tensor:
- "Computes accuracy with `targs` when `input` is bs * n_classes."
- n = targs.shape[0]
- input = input.argmax(dim=-1).view(n,-1)
- targs = targs.view(n,-1)
- return (input==targs).float().mean()
-
-def accuracy_thresh(y_pred:Tensor, y_true:Tensor, thresh:float=0.5, sigmoid:bool=True)->Rank0Tensor:
- "Computes accuracy when `y_pred` and `y_true` are the same size."
- if sigmoid: y_pred = y_pred.sigmoid()
- return ((y_pred>thresh)==y_true.byte()).float().mean()
-
-def top_k_accuracy(input:Tensor, targs:Tensor, k:int=5)->Rank0Tensor:
- "Computes the Top-k accuracy (target is in the top k predictions)."
- input = input.topk(k=k, dim=-1)[1]
- targs = targs.unsqueeze(dim=-1).expand_as(input)
- return (input == targs).max(dim=-1)[0].float().mean()
-
-def foreground_acc(input, target, void_code):
- "Computes non-background accuracy, e.g. camvid for multiclass segmentation"
- target = target.squeeze(1)
- mask = target != void_code
- return (input.argmax(dim=1)[mask]==target[mask]).float().mean()
-
-def error_rate(input:Tensor, targs:Tensor)->Rank0Tensor:
- "1 - `accuracy`"
- return 1 - accuracy(input, targs)
-
-def dice(input:Tensor, targs:Tensor, iou:bool=False, eps:float=1e-8)->Rank0Tensor:
- "Dice coefficient metric for binary target. If iou=True, returns iou metric, classic for segmentation problems."
- n = targs.shape[0]
- input = input.argmax(dim=1).view(n,-1)
- targs = targs.view(n,-1)
- intersect = (input * targs).sum().float()
- union = (input+targs).sum().float()
- if not iou: return (2. * intersect / union if union > 0 else union.new([1.]).squeeze())
- else: return (intersect / (union-intersect+eps) if union > 0 else union.new([1.]).squeeze())
-
-def psnr(input:Tensor, targs:Tensor)->Rank0Tensor:
- return 10 * (1. / mean_squared_error(input, targs)).log10()
-
-def exp_rmspe(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Exp RMSE between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- pred, targ = torch.exp(pred), torch.exp(targ)
- pct_var = (targ - pred)/targ
- return torch.sqrt((pct_var**2).mean())
-
-def mean_absolute_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean absolute error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return torch.abs(targ - pred).mean()
-
-def mean_squared_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean squared error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return F.mse_loss(pred, targ)
-
-def root_mean_squared_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Root mean squared error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return torch.sqrt(F.mse_loss(pred, targ))
-
-def mean_squared_logarithmic_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean squared logarithmic error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return F.mse_loss(torch.log(1 + pred), torch.log(1 + targ))
-
-def explained_variance(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Explained variance between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- var_pct = torch.var(targ - pred) / torch.var(targ)
- return 1 - var_pct
-
-def r2_score(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "R2 score (coefficient of determination) between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- u = torch.sum((targ - pred) ** 2)
- d = torch.sum((targ - targ.mean()) ** 2)
- return 1 - u / d
-
-class RegMetrics(Callback):
- "Stores predictions and targets to perform calculations on epoch end."
- def on_epoch_begin(self, **kwargs):
- self.targs, self.preds = Tensor([]), Tensor([])
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- assert last_output.numel() == last_target.numel(), "Expected same numbers of elements in pred & targ"
- self.preds = torch.cat((self.preds, last_output.cpu()))
- self.targs = torch.cat((self.targs, last_target.cpu()))
-
-class R2Score(RegMetrics):
- "Computes the R2 score (coefficient of determination)."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, r2_score(self.preds, self.targs))
-
-class ExplainedVariance(RegMetrics):
- "Computes the explained variance."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, explained_variance(self.preds, self.targs))
-
-class RMSE(RegMetrics):
- "Computes the root mean squared error."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, root_mean_squared_error(self.preds, self.targs))
-
-class ExpRMSPE(RegMetrics):
- "Computes the exponential of the root mean square error."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, exp_rmspe(self.preds, self.targs))
-
-# Aliases
-mse = mean_squared_error
-mae = mean_absolute_error
-msle = mean_squared_logarithmic_error
-rmse = root_mean_squared_error
-
-class ConfusionMatrix(Callback):
- "Computes the confusion matrix."
-
- def on_train_begin(self, **kwargs):
- self.n_classes = 0
-
- def on_epoch_begin(self, **kwargs):
- self.cm = None
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- preds = last_output.argmax(-1).view(-1).cpu()
- targs = last_target.cpu()
- if self.n_classes == 0:
- self.n_classes = last_output.shape[-1]
- self.x = torch.arange(0, self.n_classes)
- cm = ((preds==self.x[:, None]) & (targs==self.x[:, None, None])).sum(dim=2, dtype=torch.float32)
- if self.cm is None: self.cm = cm
- else: self.cm += cm
-
- def on_epoch_end(self, **kwargs):
- self.metric = self.cm
-
-@dataclass
-class CMScores(ConfusionMatrix):
- "Base class for metrics which rely on the calculation of the precision and/or recall score."
- average:Optional[str]="binary" # `binary`, `micro`, `macro`, `weigthed` or None
- pos_label:int=1 # 0 or 1
- eps:float=1e-9
-
- def _recall(self):
- rec = torch.diag(self.cm) / self.cm.sum(dim=1)
- if self.average is None: return rec
- else:
- if self.average == "micro": weights = self._weights(avg="weighted")
- else: weights = self._weights(avg=self.average)
- return (rec * weights).sum()
-
- def _precision(self):
- prec = torch.diag(self.cm) / self.cm.sum(dim=0)
- if self.average is None: return prec
- else:
- weights = self._weights(avg=self.average)
- return (prec * weights).sum()
-
- def _weights(self, avg:str):
- if self.n_classes != 2 and avg == "binary":
- avg = self.average = "macro"
- warn("average=`binary` was selected for a non binary case. Value for average has now been set to `macro` instead.")
- if avg == "binary":
- if self.pos_label not in (0, 1):
- self.pos_label = 1
- warn("Invalid value for pos_label. It has now been set to 1.")
- if self.pos_label == 1: return Tensor([0,1])
- else: return Tensor([1,0])
- elif avg == "micro": return self.cm.sum(dim=0) / self.cm.sum()
- elif avg == "macro": return torch.ones((self.n_classes,)) / self.n_classes
- elif avg == "weighted": return self.cm.sum(dim=1) / self.cm.sum()
-
-
-class Recall(CMScores):
- "Computes the Recall."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, self._recall())
-
-class Precision(CMScores):
- "Computes the Precision."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, self._precision())
-
-@dataclass
-class FBeta(CMScores):
- "Computes the F`beta` score."
- beta:float=2
-
- def on_train_begin(self, **kwargs):
- self.n_classes = 0
- self.beta2 = self.beta ** 2
- self.avg = self.average
- if self.average != "micro": self.average = None
-
- def on_epoch_end(self, last_metrics, **kwargs):
- prec = self._precision()
- rec = self._recall()
- metric = (1 + self.beta2) * prec * rec / (prec * self.beta2 + rec + self.eps)
- metric[metric != metric] = 0 # removing potential "nan"s
- if self.avg: metric = (self._weights(avg=self.avg) * metric).sum()
- return add_metrics(last_metrics, metric)
-
- def on_train_end(self, **kwargs): self.average = self.avg
-
-@dataclass
-class KappaScore(ConfusionMatrix):
- "Computes the rate of agreement (Cohens Kappa)."
- weights:Optional[str]=None # None, `linear`, or `quadratic`
-
- def on_epoch_end(self, last_metrics, **kwargs):
- sum0 = self.cm.sum(dim=0)
- sum1 = self.cm.sum(dim=1)
- expected = torch.einsum('i,j->ij', (sum0, sum1)) / sum0.sum()
- if self.weights is None:
- w = torch.ones((self.n_classes, self.n_classes))
- w[self.x, self.x] = 0
- elif self.weights == "linear" or self.weights == "quadratic":
- w = torch.zeros((self.n_classes, self.n_classes))
- w += torch.arange(self.n_classes, dtype=torch.float)
- w = torch.abs(w - torch.t(w)) if self.weights == "linear" else (w - torch.t(w)) ** 2
- else: raise ValueError('Unknown weights. Expected None, "linear", or "quadratic".')
- k = torch.sum(w * self.cm) / torch.sum(w * expected)
- return add_metrics(last_metrics, 1-k)
-
-@dataclass
-class MatthewsCorreff(ConfusionMatrix):
- "Computes the Matthews correlation coefficient."
- def on_epoch_end(self, last_metrics, **kwargs):
- t_sum = self.cm.sum(dim=1)
- p_sum = self.cm.sum(dim=0)
- n_correct = torch.trace(self.cm)
- n_samples = p_sum.sum()
- cov_ytyp = n_correct * n_samples - torch.dot(t_sum, p_sum)
- cov_ypyp = n_samples ** 2 - torch.dot(p_sum, p_sum)
- cov_ytyt = n_samples ** 2 - torch.dot(t_sum, t_sum)
- return add_metrics(last_metrics, cov_ytyp / torch.sqrt(cov_ytyt * cov_ypyp))
-
-class Perplexity(Callback):
- "Perplexity metric for language models."
- def on_epoch_begin(self, **kwargs): self.loss,self.len = 0.,0
-
- def on_batch_end(self, last_output, last_target, **kwargs):
- self.loss += last_target.size(1) * CrossEntropyFlat()(last_output, last_target)
- self.len += last_target.size(1)
-
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, torch.exp(self.loss / self.len))
-
-def auc_roc_score(input:Tensor, targ:Tensor):
- "Computes the area under the receiver operator characteristic (ROC) curve using the trapezoid method. Restricted binary classification tasks."
- fpr, tpr = roc_curve(input, targ)
- d = fpr[1:] - fpr[:-1]
- sl1, sl2 = [slice(None)], [slice(None)]
- sl1[-1], sl2[-1] = slice(1, None), slice(None, -1)
- return (d * (tpr[tuple(sl1)] + tpr[tuple(sl2)]) / 2.).sum(-1)
-
-def roc_curve(input:Tensor, targ:Tensor):
- "Computes the receiver operator characteristic (ROC) curve by determining the true positive ratio (TPR) and false positive ratio (FPR) for various classification thresholds. Restricted binary classification tasks."
- targ = (targ == 1)
- desc_score_indices = torch.flip(input.argsort(-1), [-1])
- input = input[desc_score_indices]
- targ = targ[desc_score_indices]
- d = input[1:] - input[:-1]
- distinct_value_indices = torch.nonzero(d).transpose(0,1)[0]
- threshold_idxs = torch.cat((distinct_value_indices, LongTensor([len(targ) - 1]).to(targ.device)))
- tps = torch.cumsum(targ * 1, dim=-1)[threshold_idxs]
- fps = (1 + threshold_idxs - tps)
- if tps[0] != 0 or fps[0] != 0:
- fps = torch.cat((LongTensor([0]), fps))
- tps = torch.cat((LongTensor([0]), tps))
- fpr, tpr = fps.float() / fps[-1], tps.float() / tps[-1]
- return fpr, tpr
-
-@dataclass
-class AUROC(Callback):
- "Computes the area under the curve (AUC) score based on the receiver operator characteristic (ROC) curve. Restricted to binary classification tasks."
- def on_epoch_begin(self, **kwargs):
- self.targs, self.preds = LongTensor([]), Tensor([])
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- last_output = F.softmax(last_output, dim=1)[:,-1]
- self.preds = torch.cat((self.preds, last_output.cpu()))
- self.targs = torch.cat((self.targs, last_target.cpu().long()))
-
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, auc_roc_score(self.preds, self.targs))
-
-class MultiLabelFbeta(LearnerCallback):
- "Computes the fbeta score for multilabel classification"
- # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html
- _order = -20
- def __init__(self, learn, beta=2, eps=1e-15, thresh=0.3, sigmoid=True, average="micro"):
- super().__init__(learn)
- self.eps, self.thresh, self.sigmoid, self.average, self.beta2 = \
- eps, thresh, sigmoid, average, beta**2
-
- def on_train_begin(self, **kwargs):
- self.c = self.learn.data.c
- if self.average != "none": self.learn.recorder.add_metric_names([f'{self.average}_fbeta'])
- else: self.learn.recorder.add_metric_names([f"fbeta_{c}" for c in self.learn.data.classes])
-
- def on_epoch_begin(self, **kwargs):
- dvc = self.learn.data.device
- self.tp = torch.zeros(self.c).to(dvc)
- self.total_pred = torch.zeros(self.c).to(dvc)
- self.total_targ = torch.zeros(self.c).to(dvc)
-
- def on_batch_end(self, last_output, last_target, **kwargs):
- pred, targ = (last_output.sigmoid() if self.sigmoid else last_output) > self.thresh, last_target.byte()
- m = pred*targ
- self.tp += m.sum(0).float()
- self.total_pred += pred.sum(0).float()
- self.total_targ += targ.sum(0).float()
-
- def fbeta_score(self, precision, recall):
- return (1 + self.beta2)*(precision*recall)/((self.beta2*precision + recall) + self.eps)
-
- def on_epoch_end(self, last_metrics, **kwargs):
- self.total_pred += self.eps
- self.total_targ += self.eps
- if self.average == "micro":
- precision, recall = self.tp.sum() / self.total_pred.sum(), self.tp.sum() / self.total_targ.sum()
- res = self.fbeta_score(precision, recall)
- elif self.average == "macro":
- res = self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ)).mean()
- elif self.average == "weighted":
- scores = self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ))
- res = (scores*self.total_targ).sum() / self.total_targ.sum()
- elif self.average == "none":
- res = listify(self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ)))
- else:
- raise Exception("Choose one of the average types: [micro, macro, weighted, none]")
-
- return add_metrics(last_metrics, res)
diff --git a/spaces/XzJosh/Taffy-Bert-VITS2/transforms.py b/spaces/XzJosh/Taffy-Bert-VITS2/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Taffy-Bert-VITS2/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
-
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {
- 'tails': tails,
- 'tail_bound': tail_bound
- }
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(
- inputs[..., None] >= bin_locations,
- dim=-1
- ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails='linear',
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == 'linear':
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError('{} tails are not implemented.'.format(tails))
-
- outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative
- )
-
- return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0., right=1., bottom=0., top=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError('Input to a transform is not within its domain')
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError('Minimal bin width too large for the number of bins')
- if min_bin_height * num_bins > 1.0:
- raise ValueError('Minimal bin height too large for the number of bins')
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (((inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta)
- + input_heights * (input_delta - input_derivatives)))
- b = (input_heights * input_derivatives
- - (inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta))
- c = - input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (input_delta * theta.pow(2)
- + input_derivatives * theta_one_minus_theta)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/XzJosh/Wenjing-Bert-VITS2/server.py b/spaces/XzJosh/Wenjing-Bert-VITS2/server.py
deleted file mode 100644
index c736ca4f95fec853950eef6654ef79856beffc0a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Wenjing-Bert-VITS2/server.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from flask import Flask, request, Response
-from io import BytesIO
-import torch
-from av import open as avopen
-
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-from scipy.io import wavfile
-
-# Flask Init
-app = Flask(__name__)
-app.config['JSON_AS_ASCII'] = False
-def get_text(text, language_str, hps):
- norm_text, phone, tone, word2ph = clean_text(text, language_str)
- print([f"{p}{t}" for p, t in zip(phone, tone)])
- phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
- if hps.data.add_blank:
- phone = commons.intersperse(phone, 0)
- tone = commons.intersperse(tone, 0)
- language = commons.intersperse(language, 0)
- for i in range(len(word2ph)):
- word2ph[i] = word2ph[i] * 2
- word2ph[0] += 1
- bert = get_bert(norm_text, word2ph, language_str)
-
- assert bert.shape[-1] == len(phone)
-
- phone = torch.LongTensor(phone)
- tone = torch.LongTensor(tone)
- language = torch.LongTensor(language)
-
- return bert, phone, tone, language
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w,length_scale,sid):
- bert, phones, tones, lang_ids = get_text(text,"ZH", hps,)
- with torch.no_grad():
- x_tst=phones.to(dev).unsqueeze(0)
- tones=tones.to(dev).unsqueeze(0)
- lang_ids=lang_ids.to(dev).unsqueeze(0)
- bert = bert.to(dev).unsqueeze(0)
- x_tst_lengths = torch.LongTensor([phones.size(0)]).to(dev)
- speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(dev)
- audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids,bert, sdp_ratio=sdp_ratio
- , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
- return audio
-
-def replace_punctuation(text, i=2):
- punctuation = "，。？！"
- for char in punctuation:
- text = text.replace(char, char * i)
- return text
-
-def wav2(i, o, format):
- inp = avopen(i, 'rb')
- out = avopen(o, 'wb', format=format)
- if format == "ogg": format = "libvorbis"
-
- ostream = out.add_stream(format)
-
- for frame in inp.decode(audio=0):
- for p in ostream.encode(frame): out.mux(p)
-
- for p in ostream.encode(None): out.mux(p)
-
- out.close()
- inp.close()
-
-# Load Generator
-hps = utils.get_hparams_from_file("./configs/config.json")
-
-dev='cuda'
-net_g = SynthesizerTrn(
- len(symbols),
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- n_speakers=hps.data.n_speakers,
- **hps.model).to(dev)
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("logs/G_649000.pth", net_g, None,skip_optimizer=True)
-
-@app.route("/",methods=['GET','POST'])
-def main():
- if request.method == 'GET':
- try:
- speaker = request.args.get('speaker')
- text = request.args.get('text').replace("/n","")
- sdp_ratio = float(request.args.get("sdp_ratio", 0.2))
- noise = float(request.args.get("noise", 0.5))
- noisew = float(request.args.get("noisew", 0.6))
- length = float(request.args.get("length", 1.2))
- if length >= 2:
- return "Too big length"
- if len(text) >=200:
- return "Too long text"
- fmt = request.args.get("format", "wav")
- if None in (speaker, text):
- return "Missing Parameter"
- if fmt not in ("mp3", "wav", "ogg"):
- return "Invalid Format"
- except:
- return "Invalid Parameter"
-
- with torch.no_grad():
- audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise, noise_scale_w=noisew, length_scale=length, sid=speaker)
-
- with BytesIO() as wav:
- wavfile.write(wav, hps.data.sampling_rate, audio)
- torch.cuda.empty_cache()
- if fmt == "wav":
- return Response(wav.getvalue(), mimetype="audio/wav")
- wav.seek(0, 0)
- with BytesIO() as ofp:
- wav2(wav, ofp, fmt)
- return Response(
- ofp.getvalue(),
- mimetype="audio/mpeg" if fmt == "mp3" else "audio/ogg"
- )
diff --git a/spaces/YlcldKlns/bing/src/components/settings.tsx b/spaces/YlcldKlns/bing/src/components/settings.tsx
deleted file mode 100644
index 80b8a2d3b252b875f5b6f7dfc2f6e3ad9cdfb22a..0000000000000000000000000000000000000000
--- a/spaces/YlcldKlns/bing/src/components/settings.tsx
+++ /dev/null
@@ -1,157 +0,0 @@
-import { useEffect, useState } from 'react'
-import { useAtom } from 'jotai'
-import { Switch } from '@headlessui/react'
-import { toast } from 'react-hot-toast'
-import { hashAtom, voiceAtom } from '@/state'
-import {
- Dialog,
- DialogContent,
- DialogDescription,
- DialogFooter,
- DialogHeader,
- DialogTitle
-} from '@/components/ui/dialog'
-import { Button } from './ui/button'
-import { Input } from './ui/input'
-import { ChunkKeys, parseCookies, extraCurlFromCookie, encodeHeadersToCookie, getCookie, setCookie } from '@/lib/utils'
-import { ExternalLink } from './external-link'
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-
-
-export function Settings() {
- const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
- const [loc, setLoc] = useAtom(hashAtom)
- const [curlValue, setCurlValue] = useState(extraCurlFromCookie(parseCookies(document.cookie, ChunkKeys)))
- const [imageOnly, setImageOnly] = useState(getCookie('IMAGE_ONLY') !== '0')
- const [enableTTS, setEnableTTS] = useAtom(voiceAtom)
-
- useEffect(() => {
- if (isCopied) {
- toast.success('复制成功')
- }
- }, [isCopied])
-
- if (loc === 'settings') {
- return (
-
- )
- } else if (loc === 'voice') {
- return (
-
- )
- }
- return null
-}
diff --git a/spaces/Yudha515/Rvc-Models/audiocraft/models/lm.py b/spaces/Yudha515/Rvc-Models/audiocraft/models/lm.py
deleted file mode 100644
index c8aad8f06797eef3293605056e1de14d07c56c2a..0000000000000000000000000000000000000000
--- a/spaces/Yudha515/Rvc-Models/audiocraft/models/lm.py
+++ /dev/null
@@ -1,527 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from dataclasses import dataclass
-from functools import partial
-import logging
-import math
-import typing as tp
-
-import torch
-from torch import nn
-
-from ..utils import utils
-from ..modules.streaming import StreamingModule, State
-from ..modules.transformer import StreamingTransformer, create_norm_fn
-from ..modules.conditioners import (
- ConditionFuser,
- ClassifierFreeGuidanceDropout,
- AttributeDropout,
- ConditioningProvider,
- ConditioningAttributes,
- ConditionType,
-)
-from ..modules.codebooks_patterns import CodebooksPatternProvider
-from ..modules.activations import get_activation_fn
-
-
-logger = logging.getLogger(__name__)
-ConditionTensors = tp.Dict[str, ConditionType]
-CFGConditions = tp.Union[ConditionTensors, tp.Tuple[ConditionTensors, ConditionTensors]]
-
-
-def get_init_fn(method: str, input_dim: int, init_depth: tp.Optional[int] = None):
- """LM layer initialization.
- Inspired from xlformers: https://github.com/fairinternal/xlformers
-
- Args:
- method (str): Method name for init function. Valid options are:
- 'gaussian', 'uniform'.
- input_dim (int): Input dimension of the initialized module.
- init_depth (Optional[int]): Optional init depth value used to rescale
- the standard deviation if defined.
- """
- # Compute std
- std = 1 / math.sqrt(input_dim)
- # Rescale with depth
- if init_depth is not None:
- std = std / math.sqrt(2 * init_depth)
-
- if method == 'gaussian':
- return partial(
- torch.nn.init.trunc_normal_, mean=0.0, std=std, a=-3 * std, b=3 * std
- )
- elif method == 'uniform':
- bound = math.sqrt(3) * std # ensure the standard deviation is `std`
- return partial(torch.nn.init.uniform_, a=-bound, b=bound)
- else:
- raise ValueError("Unsupported layer initialization method")
-
-
-def init_layer(m: nn.Module,
- method: str,
- init_depth: tp.Optional[int] = None,
- zero_bias_init: bool = False):
- """Wrapper around ``get_init_fn`` for proper initialization of LM modules.
-
- Args:
- m (nn.Module): Module to initialize.
- method (str): Method name for the init function.
- init_depth (Optional[int]): Optional init depth value used to rescale
- the standard deviation if defined.
- zero_bias_init (bool): Whether to initialize the bias to 0 or not.
- """
- if isinstance(m, nn.Linear):
- init_fn = get_init_fn(method, m.in_features, init_depth=init_depth)
- if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
- weight = m.weight.float()
- init_fn(weight)
- m.weight.data[:] = weight.half()
- else:
- init_fn(m.weight)
- if zero_bias_init and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.Embedding):
- init_fn = get_init_fn(method, m.embedding_dim, init_depth=None)
- if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
- weight = m.weight.float()
- init_fn(weight)
- m.weight.data[:] = weight.half()
- else:
- init_fn(m.weight)
-
-
-class ScaledEmbedding(nn.Embedding):
- """Boost learning rate for embeddings (with `scale`).
- """
- def __init__(self, *args, lr=None, **kwargs):
- super().__init__(*args, **kwargs)
- self.lr = lr
-
- def make_optim_group(self):
- group = {"params": list(self.parameters())}
- if self.lr is not None:
- group["lr"] = self.lr
- return group
-
-
-@dataclass
-class LMOutput:
- # The logits are already re-aligned with the input codes
- # hence no extra shift is required, e.g. when computing CE
- logits: torch.Tensor # [B, K, T, card]
- mask: torch.Tensor # [B, K, T]
-
-
-class LMModel(StreamingModule):
- """Transformer-based language model on multiple streams of codes.
-
- Args:
- pattern_provider (CodebooksPatternProvider): Pattern provider for codebook interleaving.
- condition_provider (MusicConditioningProvider): Conditioning provider from metadata.
- fuser (ConditionFuser): Fuser handling the fusing of conditions with language model input.
- n_q (int): Number of parallel streams to model.
- card (int): Cardinality, vocabulary size.
- dim (int): Dimension of the transformer encoder.
- num_heads (int): Number of heads for the transformer encoder.
- hidden_scale (int): Scale for hidden feed forward dimension of the transformer encoder.
- norm (str): Normalization method.
- norm_first (bool): Use pre-norm instead of post-norm.
- emb_lr (Optional[float]): Embedding-specific learning rate.
- bias_proj (bool): Use bias for output projections.
- weight_init (Optional[str]): Method for weight initialization.
- depthwise_init (Optional[str]): Method for depthwise weight initialization.
- zero_bias_init (bool): If true and bias in Linears, initialize bias to zeros.
- cfg_dropout (float): Classifier-free guidance dropout.
- cfg_coef (float): Classifier-free guidance coefficient.
- attribute_dropout (dict): Attribute dropout probabilities.
- two_step_cfg (bool): Whether to run classifier free-guidance with 2 distinct steps.
- **kwargs: Additional parameters for the transformer encoder.
- """
- def __init__(self, pattern_provider: CodebooksPatternProvider, condition_provider: ConditioningProvider,
- fuser: ConditionFuser, n_q: int = 8, card: int = 1024, dim: int = 128, num_heads: int = 8,
- hidden_scale: int = 4, norm: str = 'layer_norm', norm_first: bool = False,
- emb_lr: tp.Optional[float] = None, bias_proj: bool = True,
- weight_init: tp.Optional[str] = None, depthwise_init: tp.Optional[str] = None,
- zero_bias_init: bool = False, cfg_dropout: float = 0, cfg_coef: float = 1.0,
- attribute_dropout: tp.Dict[str, tp.Dict[str, float]] = {}, two_step_cfg: bool = False,
- **kwargs):
- super().__init__()
- self.cfg_coef = cfg_coef
- self.cfg_dropout = ClassifierFreeGuidanceDropout(p=cfg_dropout)
- self.att_dropout = AttributeDropout(p=attribute_dropout)
- self.condition_provider = condition_provider
- self.fuser = fuser
- self.card = card
- embed_dim = self.card + 1
- self.n_q = n_q
- self.dim = dim
- self.pattern_provider = pattern_provider
- self.two_step_cfg = two_step_cfg
- self.emb = nn.ModuleList([ScaledEmbedding(embed_dim, dim, lr=emb_lr) for _ in range(n_q)])
- if 'activation' in kwargs:
- kwargs['activation'] = get_activation_fn(kwargs['activation'])
- self.transformer = StreamingTransformer(
- d_model=dim, num_heads=num_heads, dim_feedforward=int(hidden_scale * dim),
- norm=norm, norm_first=norm_first, **kwargs)
- self.out_norm: tp.Optional[nn.Module] = None
- if norm_first:
- self.out_norm = create_norm_fn(norm, dim)
- self.linears = nn.ModuleList([nn.Linear(dim, self.card, bias=bias_proj) for _ in range(n_q)])
- self._init_weights(weight_init, depthwise_init, zero_bias_init)
- self._fsdp: tp.Optional[nn.Module]
- self.__dict__['_fsdp'] = None
-
- def _init_weights(self, weight_init: tp.Optional[str], depthwise_init: tp.Optional[str], zero_bias_init: bool):
- """Initialization of the transformer module weights.
-
- Args:
- weight_init (Optional[str]): Weight initialization strategy. See ``get_init_fn`` for valid options.
- depthwise_init (Optional[str]): Depwthwise initialization strategy. The following options are valid:
- 'current' where the depth corresponds to the current layer index or 'global' where the total number
- of layer is used as depth. If not set, no depthwise initialization strategy is used.
- zero_bias_init (bool): Whether to initalize bias to zero or not.
- """
- assert depthwise_init is None or depthwise_init in ['current', 'global']
- assert depthwise_init is None or weight_init is not None, \
- "If 'depthwise_init' is defined, a 'weight_init' method should be provided."
- assert not zero_bias_init or weight_init is not None, \
- "If 'zero_bias_init', a 'weight_init' method should be provided"
-
- if weight_init is None:
- return
-
- for emb_layer in self.emb:
- init_layer(emb_layer, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
- for layer_idx, tr_layer in enumerate(self.transformer.layers):
- depth = None
- if depthwise_init == 'current':
- depth = layer_idx + 1
- elif depthwise_init == 'global':
- depth = len(self.transformer.layers)
- init_fn = partial(init_layer, method=weight_init, init_depth=depth, zero_bias_init=zero_bias_init)
- tr_layer.apply(init_fn)
-
- for linear in self.linears:
- init_layer(linear, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
- @property
- def special_token_id(self) -> int:
- return self.card
-
- @property
- def num_codebooks(self) -> int:
- return self.n_q
-
- def forward(self, sequence: torch.Tensor,
- conditions: tp.List[ConditioningAttributes],
- condition_tensors: tp.Optional[ConditionTensors] = None) -> torch.Tensor:
- """Apply language model on sequence and conditions.
- Given a tensor of sequence of shape [B, K, S] with K the number of codebooks and
- S the sequence steps, return the logits with shape [B, card, K, S].
-
- Args:
- indices (torch.Tensor): indices of the codes to model.
- conditions (list[ConditioningAttributes]): conditionings to use when modeling
- the given codes. Note that when evaluating multiple time with the same conditioning
- you should pre-compute those and pass them as `condition_tensors`.
- condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning
- tensors, see `conditions`.
- Returns:
- torch.Tensor: Logits.
- """
- B, K, S = sequence.shape
- assert K == self.num_codebooks, 'Sequence shape must match the specified number of codebooks'
- input_ = sum([self.emb[k](sequence[:, k]) for k in range(K)])
- if condition_tensors is None:
- assert not self._is_streaming, "Conditions tensors should be precomputed when streaming."
- # apply dropout modules
- conditions = self.cfg_dropout(conditions)
- conditions = self.att_dropout(conditions)
- tokenized = self.condition_provider.tokenize(conditions)
- # encode conditions and fuse, both have a streaming cache to not recompute when generating.
- condition_tensors = self.condition_provider(tokenized)
- else:
- assert not conditions, "Shouldn't pass both conditions and condition_tensors."
-
- input_, cross_attention_input = self.fuser(input_, condition_tensors)
-
- out = self.transformer(input_, cross_attention_src=cross_attention_input)
- if self.out_norm:
- out = self.out_norm(out)
- logits = torch.stack([self.linears[k](out) for k in range(K)], dim=1) # [B, K, S, card]
-
- # remove the prefix from the model outputs
- if len(self.fuser.fuse2cond['prepend']) > 0:
- logits = logits[:, :, -S:]
-
- return logits # [B, K, S, card]
-
- def compute_predictions(
- self, codes: torch.Tensor,
- conditions: tp.List[ConditioningAttributes],
- condition_tensors: tp.Optional[ConditionTensors] = None) -> LMOutput:
- """Given an input tensor of codes [B, K, T] and list of conditions, runs the model
- forward using the specified codes interleaving pattern.
-
- Args:
- codes (torch.Tensor): Input codes of shape [B, K, T] with B the batch size,
- K the number of codebooks and T the number of timesteps.
- conditions (list[ConditioningAttributes]): conditionings to use when modeling
- the given codes. Note that when evaluating multiple time with the same conditioning
- you should pre-compute those and pass them as `condition_tensors`.
- condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning
- tensors, see `conditions`.
- Returns:
- LMOutput: Language model outputs
- logits (torch.Tensor) of shape [B, K, T, card] corresponding to the provided codes,
- i.e. the first item corresponds to logits to predict the first code, meaning that
- no additional shifting of codes and logits is required.
- mask (torch.Tensor) of shape [B, K, T], mask over valid and invalid positions.
- Given the specified interleaving strategies, parts of the logits and codes should
- not be considered as valid predictions because of invalid context.
- """
- B, K, T = codes.shape
- codes = codes.contiguous()
- # map codes [B, K, T] into pattern sequence [B, K, S] using special_token_id for masked tokens
- pattern = self.pattern_provider.get_pattern(T)
- sequence_codes, sequence_indexes, sequence_mask = pattern.build_pattern_sequence(
- codes, self.special_token_id, keep_only_valid_steps=True
- )
- # apply model on pattern sequence
- model = self if self._fsdp is None else self._fsdp
- logits = model(sequence_codes, conditions, condition_tensors) # [B, K, S, card]
- # map back the logits on pattern sequence to logits on original codes: [B, K, S, card] -> [B, K, T, card]
- # and provide the corresponding mask over invalid positions of tokens
- logits = logits.permute(0, 3, 1, 2) # [B, card, K, S]
- # note: we use nans as special token to make it obvious if we feed unexpected logits
- logits, logits_indexes, logits_mask = pattern.revert_pattern_logits(
- logits, float('nan'), keep_only_valid_steps=True
- )
- logits = logits.permute(0, 2, 3, 1) # [B, K, T, card]
- logits_mask = logits_mask[None, :, :].expand(B, -1, -1) # [K, T] -> [B, K, T]
- return LMOutput(logits, logits_mask)
-
- def _sample_next_token(self,
- sequence: torch.Tensor,
- cfg_conditions: CFGConditions,
- unconditional_state: State,
- use_sampling: bool = False,
- temp: float = 1.0,
- top_k: int = 0,
- top_p: float = 0.0,
- cfg_coef: tp.Optional[float] = None) -> torch.Tensor:
- """Sample next token from the model given a sequence and a set of conditions. The model supports
- multiple sampling strategies (greedy sampling, softmax, top-k, top-p...).
-
- Args:
- sequence (torch.Tensor): Current sequence of shape [B, K, S]
- with K corresponding to the number of codebooks and S the number of sequence steps.
- S = 1 in streaming mode, except for the first step that contains a bigger prompt.
- condition_tensors (Dict[str, ConditionType): Set of conditions. If CFG is used,
- should be twice the batch size, being the concatenation of the conditions + null conditions.
- use_sampling (bool): Whether to use a sampling strategy or not.
- temp (float): Sampling temperature.
- top_k (int): K for "top-k" sampling.
- top_p (float): P for "top-p" sampling.
- cfg_coef (float): classifier free guidance coefficient
- Returns:
- next_token (torch.Tensor): Next token tensor of shape [B, K, 1].
- """
- B = sequence.shape[0]
- cfg_coef = self.cfg_coef if cfg_coef is None else cfg_coef
- model = self if self._fsdp is None else self._fsdp
- if self.two_step_cfg and cfg_conditions != {}:
- assert isinstance(cfg_conditions, tuple)
- condition_tensors, null_condition_tensors = cfg_conditions
- cond_logits = model(sequence, conditions=[], condition_tensors=condition_tensors)
- state = self.get_streaming_state()
- self.set_streaming_state(unconditional_state)
- uncond_logits = model(sequence, conditions=[], condition_tensors=null_condition_tensors)
- unconditional_state.update(self.get_streaming_state())
- self.set_streaming_state(state)
- logits = uncond_logits + (cond_logits - uncond_logits) * self.cfg_coef
- else:
- assert isinstance(cfg_conditions, dict)
- condition_tensors = cfg_conditions
- if condition_tensors:
- # Preparing for CFG, predicting both conditional and unconditional logits.
- sequence = torch.cat([sequence, sequence], dim=0)
- all_logits = model(
- sequence,
- conditions=[], condition_tensors=condition_tensors)
- if condition_tensors:
- cond_logits, uncond_logits = all_logits.split(B, dim=0) # [B, K, T, card]
- logits = uncond_logits + (cond_logits - uncond_logits) * cfg_coef
- else:
- logits = all_logits
-
- logits = logits.permute(0, 1, 3, 2) # [B, K, card, T]
- logits = logits[..., -1] # [B x K x card]
-
- # Apply softmax for sampling if temp > 0. Else, do greedy sampling to avoid zero division error.
- if use_sampling and temp > 0.0:
- probs = torch.softmax(logits / temp, dim=-1)
- if top_p > 0.0:
- next_token = utils.sample_top_p(probs, p=top_p)
- elif top_k > 0:
- next_token = utils.sample_top_k(probs, k=top_k)
- else:
- next_token = utils.multinomial(probs, num_samples=1)
- else:
- next_token = torch.argmax(logits, dim=-1, keepdim=True)
-
- return next_token
-
- @torch.no_grad()
- def generate(self,
- prompt: tp.Optional[torch.Tensor] = None,
- conditions: tp.List[ConditioningAttributes] = [],
- num_samples: tp.Optional[int] = None,
- max_gen_len: int = 256,
- use_sampling: bool = True,
- temp: float = 1.0,
- top_k: int = 250,
- top_p: float = 0.0,
- cfg_coef: tp.Optional[float] = None,
- two_step_cfg: bool = False,
- remove_prompts: bool = False,
- check: bool = False,
- callback: tp.Optional[tp.Callable[[int, int], None]] = None) -> torch.Tensor:
- """Generate tokens sampling from the model given a prompt or unconditionally. Generation can
- be perform in a greedy fashion or using sampling with top K and top P strategies.
-
- Args:
- prompt (Optional[torch.Tensor]): Prompt tokens of shape [B, K, T].
- conditions_tensors (Dict[str, torch.Tensor]): Set of conditions or None.
- num_samples (int or None): Number of samples to generate when no prompt and no conditions are given.
- max_gen_len (int): Maximum generation length.
- use_sampling (bool): Whether to use a sampling strategy or not.
- temp (float): Sampling temperature.
- top_k (int): K for "top-k" sampling.
- top_p (float): P for "top-p" sampling.
- remove_prompts (bool): Whether to remove prompts from generation or not.
- Returns:
- torch.Tensor: Generated tokens.
- """
- assert not self.training, "generation shouldn't be used in training mode."
- first_param = next(iter(self.parameters()))
- device = first_param.device
-
- # Checking all input shapes are consistents.
- possible_num_samples = []
- if num_samples is not None:
- possible_num_samples.append(num_samples)
- elif prompt is not None:
- possible_num_samples.append(prompt.shape[0])
- elif conditions:
- possible_num_samples.append(len(conditions))
- else:
- possible_num_samples.append(1)
- assert [x == possible_num_samples[0] for x in possible_num_samples], "Inconsitent inputs shapes"
- num_samples = possible_num_samples[0]
-
- # below we create set of conditions: one conditional and one unconditional
- # to do that we merge the regular condition together with the null condition
- # we then do 1 forward pass instead of 2.
- # the reason for that is two-fold:
- # 1. it is about x2 faster than doing 2 forward passes
- # 2. avoid the streaming API treating the 2 passes as part of different time steps
- # We also support doing two different passes, in particular to ensure that
- # the padding structure is exactly the same between train anf test.
- # With a batch size of 1, this can be slower though.
- cfg_conditions: CFGConditions
- two_step_cfg = self.two_step_cfg if two_step_cfg is None else two_step_cfg
- if conditions:
- null_conditions = ClassifierFreeGuidanceDropout(p=1.0)(conditions)
- if two_step_cfg:
- cfg_conditions = (
- self.condition_provider(self.condition_provider.tokenize(conditions)),
- self.condition_provider(self.condition_provider.tokenize(null_conditions)),
- )
- else:
- conditions = conditions + null_conditions
- tokenized = self.condition_provider.tokenize(conditions)
- cfg_conditions = self.condition_provider(tokenized)
- else:
- cfg_conditions = {}
-
- if prompt is None:
- assert num_samples > 0
- prompt = torch.zeros((num_samples, self.num_codebooks, 0), dtype=torch.long, device=device)
-
- B, K, T = prompt.shape
- start_offset = T
- assert start_offset < max_gen_len
-
- pattern = self.pattern_provider.get_pattern(max_gen_len)
- # this token is used as default value for codes that are not generated yet
- unknown_token = -1
-
- # we generate codes up to the max_gen_len that will be mapped to the pattern sequence
- gen_codes = torch.full((B, K, max_gen_len), unknown_token, dtype=torch.long, device=device)
- # filling the gen_codes with the prompt if needed
- gen_codes[..., :start_offset] = prompt
- # create the gen_sequence with proper interleaving from the pattern: [B, K, S]
- gen_sequence, indexes, mask = pattern.build_pattern_sequence(gen_codes, self.special_token_id)
- # retrieve the start_offset in the sequence:
- # it is the first sequence step that contains the `start_offset` timestep
- start_offset_sequence = pattern.get_first_step_with_timesteps(start_offset)
- assert start_offset_sequence is not None
-
- with self.streaming():
- unconditional_state = self.get_streaming_state()
- prev_offset = 0
- gen_sequence_len = gen_sequence.shape[-1] # gen_sequence shape is [B, K, S]
- for offset in range(start_offset_sequence, gen_sequence_len):
- # get current sequence (note that the streaming API is providing the caching over previous offsets)
- curr_sequence = gen_sequence[..., prev_offset:offset]
- curr_mask = mask[None, ..., prev_offset:offset].expand(B, -1, -1)
- if check:
- # check coherence between mask and sequence
- assert (curr_sequence == torch.where(curr_mask, curr_sequence, self.special_token_id)).all()
- # should never happen as gen_sequence is filled progressively
- assert not (curr_sequence == unknown_token).any()
- # sample next token from the model, next token shape is [B, K, 1]
- next_token = self._sample_next_token(
- curr_sequence, cfg_conditions, unconditional_state, use_sampling, temp, top_k, top_p,
- cfg_coef=cfg_coef)
- # ensure the tokens that should be masked are properly set to special_token_id
- # as the model never output special_token_id
- valid_mask = mask[..., offset:offset+1].expand(B, -1, -1)
- next_token[~valid_mask] = self.special_token_id
- # ensure we don't overwrite prompt tokens, we only write over unknown tokens
- # (then mask tokens should be left as is as well, which is correct)
- gen_sequence[..., offset:offset+1] = torch.where(
- gen_sequence[..., offset:offset+1] == unknown_token,
- next_token, gen_sequence[..., offset:offset+1]
- )
- prev_offset = offset
- if callback is not None:
- callback(1 + offset - start_offset_sequence, gen_sequence_len - start_offset_sequence)
- unconditional_state.clear()
-
- # ensure sequence has been entirely filled
- assert not (gen_sequence == unknown_token).any()
- # ensure gen_sequence pattern and mask are matching
- # which means the gen_sequence is valid according to the pattern
- assert (
- gen_sequence == torch.where(mask[None, ...].expand(B, -1, -1), gen_sequence, self.special_token_id)
- ).all()
- # get back the codes, trimming the prompt if needed and cutting potentially incomplete timesteps
- out_codes, out_indexes, out_mask = pattern.revert_pattern_sequence(gen_sequence, special_token=unknown_token)
-
- # sanity checks over the returned codes and corresponding masks
- assert (out_codes[..., :max_gen_len] != unknown_token).all()
- assert (out_mask[..., :max_gen_len] == 1).all()
-
- out_start_offset = start_offset if remove_prompts else 0
- out_codes = out_codes[..., out_start_offset:max_gen_len]
-
- # ensure the returned codes are all valid
- assert (out_codes >= 0).all() and (out_codes <= self.card).all()
- return out_codes
diff --git a/spaces/YueMafighting/FollowYourPose/Dockerfile b/spaces/YueMafighting/FollowYourPose/Dockerfile
deleted file mode 100644
index 845d631472e9eca8e3bc2da734bb1b17eb72cc02..0000000000000000000000000000000000000000
--- a/spaces/YueMafighting/FollowYourPose/Dockerfile
+++ /dev/null
@@ -1,64 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update && \
- apt-get upgrade -y && \
- apt-get install -y --no-install-recommends \
- git \
- zip \
- unzip \
- git-lfs \
- wget \
- curl \
- # ffmpeg \
- ffmpeg \
- x264 \
- # python build dependencies \
- build-essential \
- libssl-dev \
- zlib1g-dev \
- libbz2-dev \
- libreadline-dev \
- libsqlite3-dev \
- libncursesw5-dev \
- xz-utils \
- tk-dev \
- libxml2-dev \
- libxmlsec1-dev \
- libffi-dev \
- liblzma-dev && \
- apt-get clean && \
- rm -rf /var/lib/apt/lists/*
-# RUN apt-get update && \
-# apt-get install zip
-# RUN wget https://github.com/ChenyangQiQi/FateZero/releases/download/v0.0.1/style.zip && unzip style.zip
-RUN useradd -m -u 1000 user
-RUN sudo apt-get install unzip
-USER user
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:${PATH}
-WORKDIR ${HOME}/app
-
-RUN curl https://pyenv.run | bash
-ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ENV PYTHON_VERSION=3.10.9
-RUN pyenv install ${PYTHON_VERSION} && \
- pyenv global ${PYTHON_VERSION} && \
- pyenv rehash && \
- pip install --no-cache-dir -U pip setuptools wheel
-
-RUN pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1
-COPY --chown=1000 requirements.txt /tmp/requirements.txt
-RUN pip install --no-cache-dir -U -r /tmp/requirements.txt
-
-COPY --chown=1000 . ${HOME}/app
-RUN ls -a
-# RUN cd ./FateZero/ckpt && bash download.sh
-RUN cd {WORKDIR}/data && bash download.sh
-ENV PYTHONPATH=${HOME}/app \
- PYTHONUNBUFFERED=1 \
- GRADIO_ALLOW_FLAGGING=never \
- GRADIO_NUM_PORTS=1 \
- GRADIO_SERVER_NAME=0.0.0.0 \
- GRADIO_THEME=huggingface \
- SYSTEM=spaces
-CMD ["python", "app.py"]
diff --git a/spaces/Yuliang/ECON/lib/common/render_utils.py b/spaces/Yuliang/ECON/lib/common/render_utils.py
deleted file mode 100644
index 389eab740fc341b9395e1e008b4fa91e9ef3cf83..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/common/render_utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: ps-license@tuebingen.mpg.de
-
-import math
-from typing import NewType
-
-import numpy as np
-import torch
-import trimesh
-from pytorch3d.renderer.mesh import rasterize_meshes
-from pytorch3d.structures import Meshes
-from torch import nn
-
-Tensor = NewType("Tensor", torch.Tensor)
-
-
-def solid_angles(points: Tensor, triangles: Tensor, thresh: float = 1e-8) -> Tensor:
- """Compute solid angle between the input points and triangles
- Follows the method described in:
- The Solid Angle of a Plane Triangle
- A. VAN OOSTEROM AND J. STRACKEE
- IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,
- VOL. BME-30, NO. 2, FEBRUARY 1983
- Parameters
- -----------
- points: BxQx3
- Tensor of input query points
- triangles: BxFx3x3
- Target triangles
- thresh: float
- float threshold
- Returns
- -------
- solid_angles: BxQxF
- A tensor containing the solid angle between all query points
- and input triangles
- """
- # Center the triangles on the query points. Size should be BxQxFx3x3
- centered_tris = triangles[:, None] - points[:, :, None, None]
-
- # BxQxFx3
- norms = torch.norm(centered_tris, dim=-1)
-
- # Should be BxQxFx3
- cross_prod = torch.cross(centered_tris[:, :, :, 1], centered_tris[:, :, :, 2], dim=-1)
- # Should be BxQxF
- numerator = (centered_tris[:, :, :, 0] * cross_prod).sum(dim=-1)
- del cross_prod
-
- dot01 = (centered_tris[:, :, :, 0] * centered_tris[:, :, :, 1]).sum(dim=-1)
- dot12 = (centered_tris[:, :, :, 1] * centered_tris[:, :, :, 2]).sum(dim=-1)
- dot02 = (centered_tris[:, :, :, 0] * centered_tris[:, :, :, 2]).sum(dim=-1)
- del centered_tris
-
- denominator = (
- norms.prod(dim=-1) + dot01 * norms[:, :, :, 2] + dot02 * norms[:, :, :, 1] +
- dot12 * norms[:, :, :, 0]
- )
- del dot01, dot12, dot02, norms
-
- # Should be BxQ
- solid_angle = torch.atan2(numerator, denominator)
- del numerator, denominator
-
- torch.cuda.empty_cache()
-
- return 2 * solid_angle
-
-
-def winding_numbers(points: Tensor, triangles: Tensor, thresh: float = 1e-8) -> Tensor:
- """Uses winding_numbers to compute inside/outside
- Robust inside-outside segmentation using generalized winding numbers
- Alec Jacobson,
- Ladislav Kavan,
- Olga Sorkine-Hornung
- Fast Winding Numbers for Soups and Clouds SIGGRAPH 2018
- Gavin Barill
- NEIL G. Dickson
- Ryan Schmidt
- David I.W. Levin
- and Alec Jacobson
- Parameters
- -----------
- points: BxQx3
- Tensor of input query points
- triangles: BxFx3x3
- Target triangles
- thresh: float
- float threshold
- Returns
- -------
- winding_numbers: BxQ
- A tensor containing the Generalized winding numbers
- """
- # The generalized winding number is the sum of solid angles of the point
- # with respect to all triangles.
- return (1 / (4 * math.pi) * solid_angles(points, triangles, thresh=thresh).sum(dim=-1))
-
-
-def batch_contains(verts, faces, points):
-
- B = verts.shape[0]
- N = points.shape[1]
-
- verts = verts.detach().cpu()
- faces = faces.detach().cpu()
- points = points.detach().cpu()
- contains = torch.zeros(B, N)
-
- for i in range(B):
- contains[i] = torch.as_tensor(trimesh.Trimesh(verts[i], faces[i]).contains(points[i]))
-
- return 2.0 * (contains - 0.5)
-
-
-def dict2obj(d):
- if not isinstance(d, dict):
- return d
-
- class C(object):
- pass
-
- o = C()
- for k in d:
- o.__dict__[k] = dict2obj(d[k])
- return o
-
-
-def face_vertices(vertices, faces):
- """
- :param vertices: [batch size, number of vertices, 3]
- :param faces: [batch size, number of faces, 3]
- :return: [batch size, number of faces, 3, 3]
- """
-
- bs, nv = vertices.shape[:2]
- bs, nf = faces.shape[:2]
- device = vertices.device
- faces = faces + (torch.arange(bs, dtype=torch.int32).to(device) * nv)[:, None, None]
- vertices = vertices.reshape((bs * nv, vertices.shape[-1]))
-
- return vertices[faces.long()]
-
-
-class Pytorch3dRasterizer(nn.Module):
- """Borrowed from https://github.com/facebookresearch/pytorch3d
- Notice:
- x,y,z are in image space, normalized
- can only render squared image now
- """
- def __init__(
- self, image_size=224, blur_radius=0.0, faces_per_pixel=1, device=torch.device("cuda:0")
- ):
- """
- use fixed raster_settings for rendering faces
- """
- super().__init__()
- raster_settings = {
- "image_size": image_size,
- "blur_radius": blur_radius,
- "faces_per_pixel": faces_per_pixel,
- "bin_size": -1,
- "max_faces_per_bin": None,
- "perspective_correct": False,
- "cull_backfaces": True,
- }
- raster_settings = dict2obj(raster_settings)
- self.raster_settings = raster_settings
- self.device = device
-
- def forward(self, vertices, faces, attributes=None):
- fixed_vertices = vertices.clone()
- fixed_vertices[..., :2] = -fixed_vertices[..., :2]
- meshes_screen = Meshes(verts=fixed_vertices.float(), faces=faces.long())
- raster_settings = self.raster_settings
- pix_to_face, zbuf, bary_coords, dists = rasterize_meshes(
- meshes_screen,
- image_size=raster_settings.image_size,
- blur_radius=raster_settings.blur_radius,
- faces_per_pixel=raster_settings.faces_per_pixel,
- bin_size=raster_settings.bin_size,
- max_faces_per_bin=raster_settings.max_faces_per_bin,
- perspective_correct=raster_settings.perspective_correct,
- )
- vismask = (pix_to_face > -1).float()
- D = attributes.shape[-1]
- attributes = attributes.clone()
- attributes = attributes.view(
- attributes.shape[0] * attributes.shape[1], 3, attributes.shape[-1]
- )
- N, H, W, K, _ = bary_coords.shape
- mask = pix_to_face == -1
- pix_to_face = pix_to_face.clone()
- pix_to_face[mask] = 0
- idx = pix_to_face.view(N * H * W * K, 1, 1).expand(N * H * W * K, 3, D)
- pixel_face_vals = attributes.gather(0, idx).view(N, H, W, K, 3, D)
- pixel_vals = (bary_coords[..., None] * pixel_face_vals).sum(dim=-2)
- pixel_vals[mask] = 0 # Replace masked values in output.
- pixel_vals = pixel_vals[:, :, :, 0].permute(0, 3, 1, 2)
- pixel_vals = torch.cat([pixel_vals, vismask[:, :, :, 0][:, None, :, :]], dim=1)
- return pixel_vals
-
- def get_texture(self, uvcoords, uvfaces, verts, faces, verts_color):
-
- batch_size = verts.shape[0]
- uv_verts_color = face_vertices(verts_color, faces.expand(batch_size, -1,
- -1)).to(self.device)
- uv_map = self.forward(
- uvcoords.expand(batch_size, -1, -1), uvfaces.expand(batch_size, -1, -1), uv_verts_color
- )[:, :3]
- uv_map_npy = np.flip(uv_map.squeeze(0).permute(1, 2, 0).cpu().numpy(), 0)
-
- return uv_map_npy
diff --git a/spaces/Yunshansongbai/SVC-Nahida/app.py b/spaces/Yunshansongbai/SVC-Nahida/app.py
deleted file mode 100644
index 7321a5fc776b31c97fe5729b6c4fb88a0e389ee1..0000000000000000000000000000000000000000
--- a/spaces/Yunshansongbai/SVC-Nahida/app.py
+++ /dev/null
@@ -1,194 +0,0 @@
-import io
-import os
-
-import gradio as gr
-import librosa
-import numpy as np
-import soundfile
-from inference.infer_tool import Svc
-import logging
-import os
-import paddle
-import requests
-import utils
-from spleeter import Separator
-import time
-from datetime import datetime, timedelta
-
-build_dir=os.getcwd()
-if build_dir == "/home/aistudio":
- build_dir += "/build"
-
-model_dir=build_dir+'/trained_models'
-
-model_list_path = model_dir + "/model_list.txt"
-
-# 筛选出文件夹
-models = []
-for filename in os.listdir(model_dir):
- # 判断文件名是否以 '.pdparams' 结尾，并且不包含后缀部分
- if filename.endswith('.pdparams') and os.path.splitext(filename)[0].isalpha():
- models.append(os.path.splitext(filename)[0])
-cache_model = {}
-
-def callback(text):
- if text == "reboot":
- os._exit(0)
- one_hour_later = datetime.now() + timedelta(hours=1)
- else:
- global start_time
- if time.time() - start_time >= 3600:
- os._exit(0)
- one_hour_later = datetime.now() + timedelta(hours=1)
- else:
- return text
-
-def separate_fn(song_input):
- try:
- if song_input is None:
- return "请上传歌曲",None,None,None,None
- params_2stems = {
- 'sample_rate': 44100,
- 'frame_length': 4096,
- 'frame_step': 1024,
- 'T': 512,
- 'F': 1024,
- 'num_instruments': ['vocals', 'instrumental'],
- 'output_dir': build_dir+'/output_2stems',
- 'checkpoint_path': build_dir+'/spleeter',
- 'use_elu': False}
- sampling_rate, song = song_input
- soundfile.write("temp.wav", song, sampling_rate, format="wav")
- # 初始化分离器
- sep = Separator(params_2stems)
- sep.separate('temp.wav')
- vocal_path = params_2stems["output_dir"]+"/temp-vocals.wav"
- instrumental_path = params_2stems["output_dir"]+"/temp-instrumental.wav"
- return "分离成功，请继续前往体验【转换】和【混音】",vocal_path,instrumental_path,vocal_path,instrumental_path
- except Exception as e:
- import traceback
- return traceback.format_exc() , None,None,None,None
-
-
-def convert_fn(model_name, input_audio,input_audio_micro, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale):
- try:
- if model_name in cache_model:
- model = cache_model[model_name]
- else:
- if paddle.device.is_compiled_with_cuda()==False and len(cache_model)!=0:
- return f"目前运行环境为CPU，受制于平台算力，每次启动本项目只允许加载1个模型，当前已加载{next(iter(cache_model))}",None,None
- config_path = f"{build_dir}/trained_models/config.json"
- model = Svc(f"{build_dir}/trained_models/{model_name}.pdparams", config_path,mode="test")
- cache_model[model_name] = model
- if input_audio is None and input_audio_micro is None:
- return "请上传音频", None,None
- if input_audio_micro is not None:
- input_audio = input_audio_micro
- sampling_rate, audio = input_audio
- duration = audio.shape[0] / sampling_rate
- audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio.transpose(1, 0))
- if sampling_rate != 16000:
- audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
- print(audio.shape)
- out_wav_path = "temp.wav"
- soundfile.write(out_wav_path, audio, 16000, format="wav")
- print(cluster_ratio, auto_f0, noise_scale)
- _audio = model.slice_inference(out_wav_path, model_name, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale)
- del model
- return "转换成功，请继续前往体验【混音】", (44100, _audio),(44100, _audio)
- except Exception as e:
- import traceback
- return traceback.format_exc() , None,None
-
-def compose_fn(input_vocal,input_instrumental,mixing_ratio=0.5):
- try:
- outlog = "混音成功"
- if input_vocal is None:
- return "请上传人声", None
- if input_instrumental is None:
- return "请上传伴奏", None
- vocal_sampling_rate, vocal = input_vocal
- vocal_duration = vocal.shape[0] / vocal_sampling_rate
- vocal = (vocal / np.iinfo(vocal.dtype).max).astype(np.float32)
- if len(vocal.shape) > 1:
- vocal = librosa.to_mono(vocal.transpose(1, 0))
- if vocal_sampling_rate != 44100:
- vocal = librosa.resample(vocal, orig_sr=vocal_sampling_rate, target_sr=44100)
-
- instrumental_sampling_rate, instrumental = input_instrumental
- instrumental_duration = instrumental.shape[0] / instrumental_sampling_rate
- instrumental = (instrumental / np.iinfo(instrumental.dtype).max).astype(np.float32)
- if len(instrumental.shape) > 1:
- instrumental = librosa.to_mono(instrumental.transpose(1, 0))
- if instrumental_sampling_rate != 44100:
- instrumental = librosa.resample(instrumental, orig_sr=instrumental_sampling_rate, target_sr=44100)
- if len(vocal)!=len(instrumental):
- min_length = min(len(vocal),len(instrumental))
- instrumental = instrumental[:min_length]
- vocal = vocal[:min_length]
- outlog = "人声伴奏长度不一致，已自动截断较长的音频"
-
- mixed_audio = (1 - mixing_ratio) * vocal + mixing_ratio * instrumental
- mixed_audio_data = mixed_audio.astype(np.float32)
- return outlog,(44100,mixed_audio_data)
- except Exception as e:
- import traceback
- return traceback.format_exc() , None
-
-
-app = gr.Blocks()
-
-with app:
- start_time = time.time()
- one_hour_later = datetime.now() + timedelta(hours=1)
- gr.Markdown('SVC歌声转换全流程体验（伴奏分离，转换，混音）
')
- with gr.Tabs() as tabs:
- with gr.TabItem("人声伴奏分离"):
- gr.Markdown('该项目人声分离的效果弱于UVR5，如自备分离好的伴奏和人声可跳过该步骤
')
- song_input = gr.Audio(label="上传歌曲（tips:上传后点击右上角✏可以进行歌曲剪辑）",interactive=True)
- gr.Examples(examples=[build_dir+"/examples/song/blue.wav",build_dir+"/examples/song/Counter_clockwise_Clock.wav",build_dir+"/examples/song/one_last_kiss.wav"],inputs=song_input,label="歌曲样例")
-
- btn_separate = gr.Button("人声伴奏分离", variant="primary")
- text_output1 = gr.Textbox(label="输出信息")
- vocal_output1 = gr.Audio(label="输出人声",interactive=False)
- instrumental_output1 = gr.Audio(label="输出伴奏",interactive=False)
- with gr.TabItem("转换"):
- model_name = gr.Dropdown(label="模型", choices=models, value="纳西妲")
- vocal_input1 = gr.Audio(label="上传人声",interactive=True)
- gr.Examples(examples=[build_dir+"/examples/vocals/blue_vocal.wav",build_dir+"/examples/vocals/Counter_clockwise_Clock_vocal.wav",build_dir+"/examples/vocals/one_last_kiss_vocal.wav"],inputs=vocal_input1,label="人声样例")
- btn_use_separate = gr.Button("使用【人声伴奏分离】分离的人声")
- micro_input = gr.Audio(label="麦克风输入（优先于上传的人声）",source="microphone",interactive=True)
- vc_transform = gr.Number(label="变调（半音数量,升八度12降八度-12）", value=0)
- cluster_ratio = gr.Number(label="聚类模型混合比例", value=0,visible=False)
- auto_f0 = gr.Checkbox(label="自动预测音高（转换歌声时不要打开，会严重跑调）", value=False)
- slice_db = gr.Number(label="静音分贝阈值（嘈杂的音频可以-30，干声保留呼吸可以-50）", value=-50)
- noise_scale = gr.Number(label="noise_scale", value=0.2)
- btn_convert = gr.Button("转换", variant="primary")
- text_output2 = gr.Textbox(label="输出信息")
- vc_output2 = gr.Audio(label="输出音频",interactive=False)
-
- with gr.TabItem("混音"):
- vocal_input2 = gr.Audio(label="上传人声",interactive=True)
- btn_use_convert = gr.Button("使用【转换】输出的人声")
- instrumental_input1 = gr.Audio(label="上传伴奏")
- gr.Examples(examples=[build_dir+"/examples/instrumental/blue_instrumental.wav",build_dir+"/examples/instrumental/Counter_clockwise_Clock_instrumental.wav",build_dir+"/examples/instrumental/one_last_kiss_instrumental.wav"],inputs=instrumental_input1,label="伴奏样例")
- btn_use_separate2 = gr.Button("使用【人声伴奏分离】分离的伴奏")
- mixing_ratio = gr.Slider(0, 1, value=0.75,step=0.01,label="混音比例（人声:伴奏）", info="人声:伴奏")
- btn_compose = gr.Button("混音", variant="primary")
- text_output3 = gr.Textbox(label="输出信息")
- song_output = gr.Audio(label="输出歌曲",interactive=False)
-
- with gr.TabItem("设置"):
- output = gr.Textbox(label="输出",placeholder=f"距离下一次允许重启时间为{one_hour_later}")
- btn_reboot = gr.Button("重启",variant="primary")
- btn_separate.click(separate_fn, song_input, [text_output1, vocal_output1,instrumental_output1,vocal_input1,instrumental_input1])
- btn_convert.click(convert_fn, [model_name, vocal_input1,micro_input,vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale], [text_output2, vc_output2,vocal_input2])
- btn_compose.click(compose_fn,[vocal_input2,instrumental_input1,mixing_ratio],[text_output3,song_output])
- btn_reboot.click(callback,output)
- btn_use_convert.click(lambda x:x,vc_output2,vocal_input2)
- btn_use_separate.click(lambda x:x,vocal_output1,vocal_input1)
- btn_use_separate2.click(lambda x:x,instrumental_output1,instrumental_input1)
-
-app.launch()
\ No newline at end of file
diff --git a/spaces/Ziqi/ReVersion/README.md b/spaces/Ziqi/ReVersion/README.md
deleted file mode 100644
index 1823df2c77ed395faf47aed9a4e04093a151848e..0000000000000000000000000000000000000000
--- a/spaces/Ziqi/ReVersion/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ReVersion
-emoji: 🐠
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/abcde1234www/tts/app.py b/spaces/abcde1234www/tts/app.py
deleted file mode 100644
index a97450c289843433835ebc4da6d5d885ea42167c..0000000000000000000000000000000000000000
--- a/spaces/abcde1234www/tts/app.py
+++ /dev/null
@@ -1,103 +0,0 @@
-"""
-Copyright 2022 Balacoon
-
-TTS interactive demo
-"""
-
-import logging
-from typing import cast
-
-import gradio as gr
-from balacoon_tts import TTS
-from huggingface_hub import hf_hub_download, list_repo_files
-
-# global tts module, initialized from a model selected
-tts = None
-
-
-def main():
- logging.basicConfig(level=logging.INFO)
-
- with gr.Blocks() as demo:
- gr.Markdown(
- """
- Text-to-Speech
-
- 1. Write an utterance to generate,
- 2. Select the model to synthesize with
- 3. Select speaker (only for multi-speaker models)
- 4. Hit "Generate" and listen to the result!
-
- When you select model for the first time,
- it will take a little time to download it.
- You can learn more about models available
- [here](https://huggingface.co/balacoon/tts),
- visit [Balacoon website](https://balacoon.com/) for more info.
- """
- )
- with gr.Row(variant="panel"):
- text = gr.Textbox(label="Text", placeholder="Type something here...")
-
- with gr.Row():
- with gr.Column(variant="panel"):
- repo_files = list_repo_files(repo_id="balacoon/tts")
- model_files = [x for x in repo_files if x.endswith(".addon")]
- model_name = gr.Dropdown(
- label="Model",
- choices=model_files,
- )
- with gr.Column(variant="panel"):
- speaker = gr.Dropdown(label="Speaker", choices=[])
-
- def set_model(model_name_str: str):
- """
- gets value from `model_name`, loads model,
- re-initializes tts object, gets list of
- speakers that model supports and set them to `speaker`
- """
- model_path = hf_hub_download(
- repo_id="balacoon/tts", filename=model_name_str
- )
- global tts
- tts = TTS(model_path)
- speakers = tts.get_speakers()
- if speakers:
- visible = True
- value = speakers[-1]
- else:
- visible = False
- value = ""
- return gr.Dropdown.update(
- choices=speakers, value=value, visible=visible
- )
-
- model_name.change(set_model, inputs=model_name, outputs=speaker)
-
- with gr.Row(variant="panel"):
- generate = gr.Button("Generate")
- with gr.Row(variant="panel"):
- audio = gr.Audio()
-
- def synthesize_audio(text_str: str, speaker_str: str = ""):
- """
- gets utterance to synthesize from `text` Textbox
- and speaker name from `speaker` dropdown list.
- speaker name might be empty for single-speaker models.
- Synthesizes the waveform and updates `audio` with it.
- """
- if not text_str:
- logging.info("text or speaker are not provided")
- return None
- global tts
- if len(text_str) > 1024:
- text_str = text_str[:1024]
- samples = cast(TTS, tts).synthesize(text_str, speaker_str)
- return gr.Audio.update(value=(24000, samples))
-
- generate.click(synthesize_audio, inputs=[text, speaker], outputs=audio)
-
- demo.launch()
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/tableau/setup.md b/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/tableau/setup.md
deleted file mode 100644
index 73dac977c474116928b47b28dbab6cc0705660c0..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/tableau/setup.md
+++ /dev/null
@@ -1,60 +0,0 @@
----
-title: Setup
----
-# Tableau Ingestion Guide: Setup & Prerequisites
-
-In order to configure ingestion from Tableau, you'll first have to enable Tableau Metadata API and you should have a user with Site Administrator Explorer permissions.
-
-## Tableau Prerequisites
-
-1. Grant `Site Administrator Explorer permissions` to a user
-
- A. Log in to Tableau Cloud https://sso.online.tableau.com/public/idp/SSO.
-
- B. Navigate to `Users`.
-
-
-
-
-
-
- C. **For New User**: Follow below steps to grant permission for new user.
-
- - Click `Add Users` -> `Add Users by Email`
-
-
-
-
-
- - Fill `Enter email addresses`, set `Site role` to `Site Administrator Explorer` and Click `Add Users`
-
-
-
-
-
-
- D. **For Existing User:** Follow below steps to grant permission for existing user.
-
- - Select a user and click `Actions` -> `Site Role`
-
-
-
-
-
- - Change user role to `Site Administrator Explorer`
-
-
-
-
-
-2. **Enable Tableau Metadata API:** This step is required only for Tableau Server. The Metadata API is installed with Tableau Server but disabled by default.
-
- - Open a command prompt as an admin on the initial node (*where TSM is installed*) in the cluster
- - Run the command: `tsm maintenance metadata-services enable`
-
-
-## Next Steps
-
-Once you've done all of the above in Tableau, it's time to [move on](configuration.md) to configuring the actual ingestion source within DataHub.
-
-*Need more help? Join the conversation in [Slack](http://slack.datahubproject.io)!*
\ No newline at end of file
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/multi_scale_deform_attn.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/multi_scale_deform_attn.py
deleted file mode 100644
index c52dda18b41705705b47dd0e995b124048c16fba..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/multi_scale_deform_attn.py
+++ /dev/null
@@ -1,358 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-import warnings
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.autograd.function import Function, once_differentiable
-
-from annotator.uniformer.mmcv import deprecated_api_warning
-from annotator.uniformer.mmcv.cnn import constant_init, xavier_init
-from annotator.uniformer.mmcv.cnn.bricks.registry import ATTENTION
-from annotator.uniformer.mmcv.runner import BaseModule
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
- '_ext', ['ms_deform_attn_backward', 'ms_deform_attn_forward'])
-
-
-class MultiScaleDeformableAttnFunction(Function):
-
- @staticmethod
- def forward(ctx, value, value_spatial_shapes, value_level_start_index,
- sampling_locations, attention_weights, im2col_step):
- """GPU version of multi-scale deformable attention.
-
- Args:
- value (Tensor): The value has shape
- (bs, num_keys, mum_heads, embed_dims//num_heads)
- value_spatial_shapes (Tensor): Spatial shape of
- each feature map, has shape (num_levels, 2),
- last dimension 2 represent (h, w)
- sampling_locations (Tensor): The location of sampling points,
- has shape
- (bs ,num_queries, num_heads, num_levels, num_points, 2),
- the last dimension 2 represent (x, y).
- attention_weights (Tensor): The weight of sampling points used
- when calculate the attention, has shape
- (bs ,num_queries, num_heads, num_levels, num_points),
- im2col_step (Tensor): The step used in image to column.
-
- Returns:
- Tensor: has shape (bs, num_queries, embed_dims)
- """
-
- ctx.im2col_step = im2col_step
- output = ext_module.ms_deform_attn_forward(
- value,
- value_spatial_shapes,
- value_level_start_index,
- sampling_locations,
- attention_weights,
- im2col_step=ctx.im2col_step)
- ctx.save_for_backward(value, value_spatial_shapes,
- value_level_start_index, sampling_locations,
- attention_weights)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- """GPU version of backward function.
-
- Args:
- grad_output (Tensor): Gradient
- of output tensor of forward.
-
- Returns:
- Tuple[Tensor]: Gradient
- of input tensors in forward.
- """
- value, value_spatial_shapes, value_level_start_index,\
- sampling_locations, attention_weights = ctx.saved_tensors
- grad_value = torch.zeros_like(value)
- grad_sampling_loc = torch.zeros_like(sampling_locations)
- grad_attn_weight = torch.zeros_like(attention_weights)
-
- ext_module.ms_deform_attn_backward(
- value,
- value_spatial_shapes,
- value_level_start_index,
- sampling_locations,
- attention_weights,
- grad_output.contiguous(),
- grad_value,
- grad_sampling_loc,
- grad_attn_weight,
- im2col_step=ctx.im2col_step)
-
- return grad_value, None, None, \
- grad_sampling_loc, grad_attn_weight, None
-
-
-def multi_scale_deformable_attn_pytorch(value, value_spatial_shapes,
- sampling_locations, attention_weights):
- """CPU version of multi-scale deformable attention.
-
- Args:
- value (Tensor): The value has shape
- (bs, num_keys, mum_heads, embed_dims//num_heads)
- value_spatial_shapes (Tensor): Spatial shape of
- each feature map, has shape (num_levels, 2),
- last dimension 2 represent (h, w)
- sampling_locations (Tensor): The location of sampling points,
- has shape
- (bs ,num_queries, num_heads, num_levels, num_points, 2),
- the last dimension 2 represent (x, y).
- attention_weights (Tensor): The weight of sampling points used
- when calculate the attention, has shape
- (bs ,num_queries, num_heads, num_levels, num_points),
-
- Returns:
- Tensor: has shape (bs, num_queries, embed_dims)
- """
-
- bs, _, num_heads, embed_dims = value.shape
- _, num_queries, num_heads, num_levels, num_points, _ =\
- sampling_locations.shape
- value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes],
- dim=1)
- sampling_grids = 2 * sampling_locations - 1
- sampling_value_list = []
- for level, (H_, W_) in enumerate(value_spatial_shapes):
- # bs, H_*W_, num_heads, embed_dims ->
- # bs, H_*W_, num_heads*embed_dims ->
- # bs, num_heads*embed_dims, H_*W_ ->
- # bs*num_heads, embed_dims, H_, W_
- value_l_ = value_list[level].flatten(2).transpose(1, 2).reshape(
- bs * num_heads, embed_dims, H_, W_)
- # bs, num_queries, num_heads, num_points, 2 ->
- # bs, num_heads, num_queries, num_points, 2 ->
- # bs*num_heads, num_queries, num_points, 2
- sampling_grid_l_ = sampling_grids[:, :, :,
- level].transpose(1, 2).flatten(0, 1)
- # bs*num_heads, embed_dims, num_queries, num_points
- sampling_value_l_ = F.grid_sample(
- value_l_,
- sampling_grid_l_,
- mode='bilinear',
- padding_mode='zeros',
- align_corners=False)
- sampling_value_list.append(sampling_value_l_)
- # (bs, num_queries, num_heads, num_levels, num_points) ->
- # (bs, num_heads, num_queries, num_levels, num_points) ->
- # (bs, num_heads, 1, num_queries, num_levels*num_points)
- attention_weights = attention_weights.transpose(1, 2).reshape(
- bs * num_heads, 1, num_queries, num_levels * num_points)
- output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) *
- attention_weights).sum(-1).view(bs, num_heads * embed_dims,
- num_queries)
- return output.transpose(1, 2).contiguous()
-
-
-@ATTENTION.register_module()
-class MultiScaleDeformableAttention(BaseModule):
- """An attention module used in Deformable-Detr.
-
- `Deformable DETR: Deformable Transformers for End-to-End Object Detection.
- `_.
-
- Args:
- embed_dims (int): The embedding dimension of Attention.
- Default: 256.
- num_heads (int): Parallel attention heads. Default: 64.
- num_levels (int): The number of feature map used in
- Attention. Default: 4.
- num_points (int): The number of sampling points for
- each query in each head. Default: 4.
- im2col_step (int): The step used in image_to_column.
- Default: 64.
- dropout (float): A Dropout layer on `inp_identity`.
- Default: 0.1.
- batch_first (bool): Key, Query and Value are shape of
- (batch, n, embed_dim)
- or (n, batch, embed_dim). Default to False.
- norm_cfg (dict): Config dict for normalization layer.
- Default: None.
- init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
- Default: None.
- """
-
- def __init__(self,
- embed_dims=256,
- num_heads=8,
- num_levels=4,
- num_points=4,
- im2col_step=64,
- dropout=0.1,
- batch_first=False,
- norm_cfg=None,
- init_cfg=None):
- super().__init__(init_cfg)
- if embed_dims % num_heads != 0:
- raise ValueError(f'embed_dims must be divisible by num_heads, '
- f'but got {embed_dims} and {num_heads}')
- dim_per_head = embed_dims // num_heads
- self.norm_cfg = norm_cfg
- self.dropout = nn.Dropout(dropout)
- self.batch_first = batch_first
-
- # you'd better set dim_per_head to a power of 2
- # which is more efficient in the CUDA implementation
- def _is_power_of_2(n):
- if (not isinstance(n, int)) or (n < 0):
- raise ValueError(
- 'invalid input for _is_power_of_2: {} (type: {})'.format(
- n, type(n)))
- return (n & (n - 1) == 0) and n != 0
-
- if not _is_power_of_2(dim_per_head):
- warnings.warn(
- "You'd better set embed_dims in "
- 'MultiScaleDeformAttention to make '
- 'the dimension of each attention head a power of 2 '
- 'which is more efficient in our CUDA implementation.')
-
- self.im2col_step = im2col_step
- self.embed_dims = embed_dims
- self.num_levels = num_levels
- self.num_heads = num_heads
- self.num_points = num_points
- self.sampling_offsets = nn.Linear(
- embed_dims, num_heads * num_levels * num_points * 2)
- self.attention_weights = nn.Linear(embed_dims,
- num_heads * num_levels * num_points)
- self.value_proj = nn.Linear(embed_dims, embed_dims)
- self.output_proj = nn.Linear(embed_dims, embed_dims)
- self.init_weights()
-
- def init_weights(self):
- """Default initialization for Parameters of Module."""
- constant_init(self.sampling_offsets, 0.)
- thetas = torch.arange(
- self.num_heads,
- dtype=torch.float32) * (2.0 * math.pi / self.num_heads)
- grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
- grid_init = (grid_init /
- grid_init.abs().max(-1, keepdim=True)[0]).view(
- self.num_heads, 1, 1,
- 2).repeat(1, self.num_levels, self.num_points, 1)
- for i in range(self.num_points):
- grid_init[:, :, i, :] *= i + 1
-
- self.sampling_offsets.bias.data = grid_init.view(-1)
- constant_init(self.attention_weights, val=0., bias=0.)
- xavier_init(self.value_proj, distribution='uniform', bias=0.)
- xavier_init(self.output_proj, distribution='uniform', bias=0.)
- self._is_init = True
-
- @deprecated_api_warning({'residual': 'identity'},
- cls_name='MultiScaleDeformableAttention')
- def forward(self,
- query,
- key=None,
- value=None,
- identity=None,
- query_pos=None,
- key_padding_mask=None,
- reference_points=None,
- spatial_shapes=None,
- level_start_index=None,
- **kwargs):
- """Forward Function of MultiScaleDeformAttention.
-
- Args:
- query (Tensor): Query of Transformer with shape
- (num_query, bs, embed_dims).
- key (Tensor): The key tensor with shape
- `(num_key, bs, embed_dims)`.
- value (Tensor): The value tensor with shape
- `(num_key, bs, embed_dims)`.
- identity (Tensor): The tensor used for addition, with the
- same shape as `query`. Default None. If None,
- `query` will be used.
- query_pos (Tensor): The positional encoding for `query`.
- Default: None.
- key_pos (Tensor): The positional encoding for `key`. Default
- None.
- reference_points (Tensor): The normalized reference
- points with shape (bs, num_query, num_levels, 2),
- all elements is range in [0, 1], top-left (0,0),
- bottom-right (1, 1), including padding area.
- or (N, Length_{query}, num_levels, 4), add
- additional two dimensions is (w, h) to
- form reference boxes.
- key_padding_mask (Tensor): ByteTensor for `query`, with
- shape [bs, num_key].
- spatial_shapes (Tensor): Spatial shape of features in
- different levels. With shape (num_levels, 2),
- last dimension represents (h, w).
- level_start_index (Tensor): The start index of each level.
- A tensor has shape ``(num_levels, )`` and can be represented
- as [0, h_0*w_0, h_0*w_0+h_1*w_1, ...].
-
- Returns:
- Tensor: forwarded results with shape [num_query, bs, embed_dims].
- """
-
- if value is None:
- value = query
-
- if identity is None:
- identity = query
- if query_pos is not None:
- query = query + query_pos
- if not self.batch_first:
- # change to (bs, num_query ,embed_dims)
- query = query.permute(1, 0, 2)
- value = value.permute(1, 0, 2)
-
- bs, num_query, _ = query.shape
- bs, num_value, _ = value.shape
- assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value
-
- value = self.value_proj(value)
- if key_padding_mask is not None:
- value = value.masked_fill(key_padding_mask[..., None], 0.0)
- value = value.view(bs, num_value, self.num_heads, -1)
- sampling_offsets = self.sampling_offsets(query).view(
- bs, num_query, self.num_heads, self.num_levels, self.num_points, 2)
- attention_weights = self.attention_weights(query).view(
- bs, num_query, self.num_heads, self.num_levels * self.num_points)
- attention_weights = attention_weights.softmax(-1)
-
- attention_weights = attention_weights.view(bs, num_query,
- self.num_heads,
- self.num_levels,
- self.num_points)
- if reference_points.shape[-1] == 2:
- offset_normalizer = torch.stack(
- [spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
- sampling_locations = reference_points[:, :, None, :, None, :] \
- + sampling_offsets \
- / offset_normalizer[None, None, None, :, None, :]
- elif reference_points.shape[-1] == 4:
- sampling_locations = reference_points[:, :, None, :, None, :2] \
- + sampling_offsets / self.num_points \
- * reference_points[:, :, None, :, None, 2:] \
- * 0.5
- else:
- raise ValueError(
- f'Last dim of reference_points must be'
- f' 2 or 4, but get {reference_points.shape[-1]} instead.')
- if torch.cuda.is_available() and value.is_cuda:
- output = MultiScaleDeformableAttnFunction.apply(
- value, spatial_shapes, level_start_index, sampling_locations,
- attention_weights, self.im2col_step)
- else:
- output = multi_scale_deformable_attn_pytorch(
- value, spatial_shapes, sampling_locations, attention_weights)
-
- output = self.output_proj(output)
-
- if not self.batch_first:
- # (num_query, bs ,embed_dims)
- output = output.permute(1, 0, 2)
-
- return self.dropout(output) + identity
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/misc.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/misc.py
deleted file mode 100644
index 2c58d0d7fee9fe3d4519270ad8c1e998d0d8a18c..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/misc.py
+++ /dev/null
@@ -1,377 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import collections.abc
-import functools
-import itertools
-import subprocess
-import warnings
-from collections import abc
-from importlib import import_module
-from inspect import getfullargspec
-from itertools import repeat
-
-
-# From PyTorch internals
-def _ntuple(n):
-
- def parse(x):
- if isinstance(x, collections.abc.Iterable):
- return x
- return tuple(repeat(x, n))
-
- return parse
-
-
-to_1tuple = _ntuple(1)
-to_2tuple = _ntuple(2)
-to_3tuple = _ntuple(3)
-to_4tuple = _ntuple(4)
-to_ntuple = _ntuple
-
-
-def is_str(x):
- """Whether the input is an string instance.
-
- Note: This method is deprecated since python 2 is no longer supported.
- """
- return isinstance(x, str)
-
-
-def import_modules_from_strings(imports, allow_failed_imports=False):
- """Import modules from the given list of strings.
-
- Args:
- imports (list | str | None): The given module names to be imported.
- allow_failed_imports (bool): If True, the failed imports will return
- None. Otherwise, an ImportError is raise. Default: False.
-
- Returns:
- list[module] | module | None: The imported modules.
-
- Examples:
- >>> osp, sys = import_modules_from_strings(
- ... ['os.path', 'sys'])
- >>> import os.path as osp_
- >>> import sys as sys_
- >>> assert osp == osp_
- >>> assert sys == sys_
- """
- if not imports:
- return
- single_import = False
- if isinstance(imports, str):
- single_import = True
- imports = [imports]
- if not isinstance(imports, list):
- raise TypeError(
- f'custom_imports must be a list but got type {type(imports)}')
- imported = []
- for imp in imports:
- if not isinstance(imp, str):
- raise TypeError(
- f'{imp} is of type {type(imp)} and cannot be imported.')
- try:
- imported_tmp = import_module(imp)
- except ImportError:
- if allow_failed_imports:
- warnings.warn(f'{imp} failed to import and is ignored.',
- UserWarning)
- imported_tmp = None
- else:
- raise ImportError
- imported.append(imported_tmp)
- if single_import:
- imported = imported[0]
- return imported
-
-
-def iter_cast(inputs, dst_type, return_type=None):
- """Cast elements of an iterable object into some type.
-
- Args:
- inputs (Iterable): The input object.
- dst_type (type): Destination type.
- return_type (type, optional): If specified, the output object will be
- converted to this type, otherwise an iterator.
-
- Returns:
- iterator or specified type: The converted object.
- """
- if not isinstance(inputs, abc.Iterable):
- raise TypeError('inputs must be an iterable object')
- if not isinstance(dst_type, type):
- raise TypeError('"dst_type" must be a valid type')
-
- out_iterable = map(dst_type, inputs)
-
- if return_type is None:
- return out_iterable
- else:
- return return_type(out_iterable)
-
-
-def list_cast(inputs, dst_type):
- """Cast elements of an iterable object into a list of some type.
-
- A partial method of :func:`iter_cast`.
- """
- return iter_cast(inputs, dst_type, return_type=list)
-
-
-def tuple_cast(inputs, dst_type):
- """Cast elements of an iterable object into a tuple of some type.
-
- A partial method of :func:`iter_cast`.
- """
- return iter_cast(inputs, dst_type, return_type=tuple)
-
-
-def is_seq_of(seq, expected_type, seq_type=None):
- """Check whether it is a sequence of some type.
-
- Args:
- seq (Sequence): The sequence to be checked.
- expected_type (type): Expected type of sequence items.
- seq_type (type, optional): Expected sequence type.
-
- Returns:
- bool: Whether the sequence is valid.
- """
- if seq_type is None:
- exp_seq_type = abc.Sequence
- else:
- assert isinstance(seq_type, type)
- exp_seq_type = seq_type
- if not isinstance(seq, exp_seq_type):
- return False
- for item in seq:
- if not isinstance(item, expected_type):
- return False
- return True
-
-
-def is_list_of(seq, expected_type):
- """Check whether it is a list of some type.
-
- A partial method of :func:`is_seq_of`.
- """
- return is_seq_of(seq, expected_type, seq_type=list)
-
-
-def is_tuple_of(seq, expected_type):
- """Check whether it is a tuple of some type.
-
- A partial method of :func:`is_seq_of`.
- """
- return is_seq_of(seq, expected_type, seq_type=tuple)
-
-
-def slice_list(in_list, lens):
- """Slice a list into several sub lists by a list of given length.
-
- Args:
- in_list (list): The list to be sliced.
- lens(int or list): The expected length of each out list.
-
- Returns:
- list: A list of sliced list.
- """
- if isinstance(lens, int):
- assert len(in_list) % lens == 0
- lens = [lens] * int(len(in_list) / lens)
- if not isinstance(lens, list):
- raise TypeError('"indices" must be an integer or a list of integers')
- elif sum(lens) != len(in_list):
- raise ValueError('sum of lens and list length does not '
- f'match: {sum(lens)} != {len(in_list)}')
- out_list = []
- idx = 0
- for i in range(len(lens)):
- out_list.append(in_list[idx:idx + lens[i]])
- idx += lens[i]
- return out_list
-
-
-def concat_list(in_list):
- """Concatenate a list of list into a single list.
-
- Args:
- in_list (list): The list of list to be merged.
-
- Returns:
- list: The concatenated flat list.
- """
- return list(itertools.chain(*in_list))
-
-
-def check_prerequisites(
- prerequisites,
- checker,
- msg_tmpl='Prerequisites "{}" are required in method "{}" but not '
- 'found, please install them first.'): # yapf: disable
- """A decorator factory to check if prerequisites are satisfied.
-
- Args:
- prerequisites (str of list[str]): Prerequisites to be checked.
- checker (callable): The checker method that returns True if a
- prerequisite is meet, False otherwise.
- msg_tmpl (str): The message template with two variables.
-
- Returns:
- decorator: A specific decorator.
- """
-
- def wrap(func):
-
- @functools.wraps(func)
- def wrapped_func(*args, **kwargs):
- requirements = [prerequisites] if isinstance(
- prerequisites, str) else prerequisites
- missing = []
- for item in requirements:
- if not checker(item):
- missing.append(item)
- if missing:
- print(msg_tmpl.format(', '.join(missing), func.__name__))
- raise RuntimeError('Prerequisites not meet.')
- else:
- return func(*args, **kwargs)
-
- return wrapped_func
-
- return wrap
-
-
-def _check_py_package(package):
- try:
- import_module(package)
- except ImportError:
- return False
- else:
- return True
-
-
-def _check_executable(cmd):
- if subprocess.call(f'which {cmd}', shell=True) != 0:
- return False
- else:
- return True
-
-
-def requires_package(prerequisites):
- """A decorator to check if some python packages are installed.
-
- Example:
- >>> @requires_package('numpy')
- >>> func(arg1, args):
- >>> return numpy.zeros(1)
- array([0.])
- >>> @requires_package(['numpy', 'non_package'])
- >>> func(arg1, args):
- >>> return numpy.zeros(1)
- ImportError
- """
- return check_prerequisites(prerequisites, checker=_check_py_package)
-
-
-def requires_executable(prerequisites):
- """A decorator to check if some executable files are installed.
-
- Example:
- >>> @requires_executable('ffmpeg')
- >>> func(arg1, args):
- >>> print(1)
- 1
- """
- return check_prerequisites(prerequisites, checker=_check_executable)
-
-
-def deprecated_api_warning(name_dict, cls_name=None):
- """A decorator to check if some arguments are deprecate and try to replace
- deprecate src_arg_name to dst_arg_name.
-
- Args:
- name_dict(dict):
- key (str): Deprecate argument names.
- val (str): Expected argument names.
-
- Returns:
- func: New function.
- """
-
- def api_warning_wrapper(old_func):
-
- @functools.wraps(old_func)
- def new_func(*args, **kwargs):
- # get the arg spec of the decorated method
- args_info = getfullargspec(old_func)
- # get name of the function
- func_name = old_func.__name__
- if cls_name is not None:
- func_name = f'{cls_name}.{func_name}'
- if args:
- arg_names = args_info.args[:len(args)]
- for src_arg_name, dst_arg_name in name_dict.items():
- if src_arg_name in arg_names:
- warnings.warn(
- f'"{src_arg_name}" is deprecated in '
- f'`{func_name}`, please use "{dst_arg_name}" '
- 'instead')
- arg_names[arg_names.index(src_arg_name)] = dst_arg_name
- if kwargs:
- for src_arg_name, dst_arg_name in name_dict.items():
- if src_arg_name in kwargs:
-
- assert dst_arg_name not in kwargs, (
- f'The expected behavior is to replace '
- f'the deprecated key `{src_arg_name}` to '
- f'new key `{dst_arg_name}`, but got them '
- f'in the arguments at the same time, which '
- f'is confusing. `{src_arg_name} will be '
- f'deprecated in the future, please '
- f'use `{dst_arg_name}` instead.')
-
- warnings.warn(
- f'"{src_arg_name}" is deprecated in '
- f'`{func_name}`, please use "{dst_arg_name}" '
- 'instead')
- kwargs[dst_arg_name] = kwargs.pop(src_arg_name)
-
- # apply converted arguments to the decorated method
- output = old_func(*args, **kwargs)
- return output
-
- return new_func
-
- return api_warning_wrapper
-
-
-def is_method_overridden(method, base_class, derived_class):
- """Check if a method of base class is overridden in derived class.
-
- Args:
- method (str): the method name to check.
- base_class (type): the class of the base class.
- derived_class (type | Any): the class or instance of the derived class.
- """
- assert isinstance(base_class, type), \
- "base_class doesn't accept instance, Please pass class instead."
-
- if not isinstance(derived_class, type):
- derived_class = derived_class.__class__
-
- base_method = getattr(base_class, method)
- derived_method = getattr(derived_class, method)
- return derived_method != base_method
-
-
-def has_method(obj: object, method: str) -> bool:
- """Check whether the object has a method.
-
- Args:
- method (str): The method name to check.
- obj (object): The object to check.
-
- Returns:
- bool: True if the object has the method else False.
- """
- return hasattr(obj, method) and callable(getattr(obj, method))
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/momentum_updater.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/momentum_updater.py
deleted file mode 100644
index 60437756ceedf06055ec349df69a25465738d3f0..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/momentum_updater.py
+++ /dev/null
@@ -1,493 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import annotator.uniformer.mmcv as mmcv
-from .hook import HOOKS, Hook
-from .lr_updater import annealing_cos, annealing_linear, format_param
-
-
-class MomentumUpdaterHook(Hook):
-
- def __init__(self,
- by_epoch=True,
- warmup=None,
- warmup_iters=0,
- warmup_ratio=0.9):
- # validate the "warmup" argument
- if warmup is not None:
- if warmup not in ['constant', 'linear', 'exp']:
- raise ValueError(
- f'"{warmup}" is not a supported type for warming up, valid'
- ' types are "constant" and "linear"')
- if warmup is not None:
- assert warmup_iters > 0, \
- '"warmup_iters" must be a positive integer'
- assert 0 < warmup_ratio <= 1.0, \
- '"warmup_momentum" must be in range (0,1]'
-
- self.by_epoch = by_epoch
- self.warmup = warmup
- self.warmup_iters = warmup_iters
- self.warmup_ratio = warmup_ratio
-
- self.base_momentum = [] # initial momentum for all param groups
- self.regular_momentum = [
- ] # expected momentum if no warming up is performed
-
- def _set_momentum(self, runner, momentum_groups):
- if isinstance(runner.optimizer, dict):
- for k, optim in runner.optimizer.items():
- for param_group, mom in zip(optim.param_groups,
- momentum_groups[k]):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
- else:
- for param_group, mom in zip(runner.optimizer.param_groups,
- momentum_groups):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
-
- def get_momentum(self, runner, base_momentum):
- raise NotImplementedError
-
- def get_regular_momentum(self, runner):
- if isinstance(runner.optimizer, dict):
- momentum_groups = {}
- for k in runner.optimizer.keys():
- _momentum_group = [
- self.get_momentum(runner, _base_momentum)
- for _base_momentum in self.base_momentum[k]
- ]
- momentum_groups.update({k: _momentum_group})
- return momentum_groups
- else:
- return [
- self.get_momentum(runner, _base_momentum)
- for _base_momentum in self.base_momentum
- ]
-
- def get_warmup_momentum(self, cur_iters):
-
- def _get_warmup_momentum(cur_iters, regular_momentum):
- if self.warmup == 'constant':
- warmup_momentum = [
- _momentum / self.warmup_ratio
- for _momentum in self.regular_momentum
- ]
- elif self.warmup == 'linear':
- k = (1 - cur_iters / self.warmup_iters) * (1 -
- self.warmup_ratio)
- warmup_momentum = [
- _momentum / (1 - k) for _momentum in self.regular_mom
- ]
- elif self.warmup == 'exp':
- k = self.warmup_ratio**(1 - cur_iters / self.warmup_iters)
- warmup_momentum = [
- _momentum / k for _momentum in self.regular_mom
- ]
- return warmup_momentum
-
- if isinstance(self.regular_momentum, dict):
- momentum_groups = {}
- for key, regular_momentum in self.regular_momentum.items():
- momentum_groups[key] = _get_warmup_momentum(
- cur_iters, regular_momentum)
- return momentum_groups
- else:
- return _get_warmup_momentum(cur_iters, self.regular_momentum)
-
- def before_run(self, runner):
- # NOTE: when resuming from a checkpoint,
- # if 'initial_momentum' is not saved,
- # it will be set according to the optimizer params
- if isinstance(runner.optimizer, dict):
- self.base_momentum = {}
- for k, optim in runner.optimizer.items():
- for group in optim.param_groups:
- if 'momentum' in group.keys():
- group.setdefault('initial_momentum', group['momentum'])
- else:
- group.setdefault('initial_momentum', group['betas'][0])
- _base_momentum = [
- group['initial_momentum'] for group in optim.param_groups
- ]
- self.base_momentum.update({k: _base_momentum})
- else:
- for group in runner.optimizer.param_groups:
- if 'momentum' in group.keys():
- group.setdefault('initial_momentum', group['momentum'])
- else:
- group.setdefault('initial_momentum', group['betas'][0])
- self.base_momentum = [
- group['initial_momentum']
- for group in runner.optimizer.param_groups
- ]
-
- def before_train_epoch(self, runner):
- if not self.by_epoch:
- return
- self.regular_mom = self.get_regular_momentum(runner)
- self._set_momentum(runner, self.regular_mom)
-
- def before_train_iter(self, runner):
- cur_iter = runner.iter
- if not self.by_epoch:
- self.regular_mom = self.get_regular_momentum(runner)
- if self.warmup is None or cur_iter >= self.warmup_iters:
- self._set_momentum(runner, self.regular_mom)
- else:
- warmup_momentum = self.get_warmup_momentum(cur_iter)
- self._set_momentum(runner, warmup_momentum)
- elif self.by_epoch:
- if self.warmup is None or cur_iter > self.warmup_iters:
- return
- elif cur_iter == self.warmup_iters:
- self._set_momentum(runner, self.regular_mom)
- else:
- warmup_momentum = self.get_warmup_momentum(cur_iter)
- self._set_momentum(runner, warmup_momentum)
-
-
-@HOOKS.register_module()
-class StepMomentumUpdaterHook(MomentumUpdaterHook):
- """Step momentum scheduler with min value clipping.
-
- Args:
- step (int | list[int]): Step to decay the momentum. If an int value is
- given, regard it as the decay interval. If a list is given, decay
- momentum at these steps.
- gamma (float, optional): Decay momentum ratio. Default: 0.5.
- min_momentum (float, optional): Minimum momentum value to keep. If
- momentum after decay is lower than this value, it will be clipped
- accordingly. If None is given, we don't perform lr clipping.
- Default: None.
- """
-
- def __init__(self, step, gamma=0.5, min_momentum=None, **kwargs):
- if isinstance(step, list):
- assert mmcv.is_list_of(step, int)
- assert all([s > 0 for s in step])
- elif isinstance(step, int):
- assert step > 0
- else:
- raise TypeError('"step" must be a list or integer')
- self.step = step
- self.gamma = gamma
- self.min_momentum = min_momentum
- super(StepMomentumUpdaterHook, self).__init__(**kwargs)
-
- def get_momentum(self, runner, base_momentum):
- progress = runner.epoch if self.by_epoch else runner.iter
-
- # calculate exponential term
- if isinstance(self.step, int):
- exp = progress // self.step
- else:
- exp = len(self.step)
- for i, s in enumerate(self.step):
- if progress < s:
- exp = i
- break
-
- momentum = base_momentum * (self.gamma**exp)
- if self.min_momentum is not None:
- # clip to a minimum value
- momentum = max(momentum, self.min_momentum)
- return momentum
-
-
-@HOOKS.register_module()
-class CosineAnnealingMomentumUpdaterHook(MomentumUpdaterHook):
-
- def __init__(self, min_momentum=None, min_momentum_ratio=None, **kwargs):
- assert (min_momentum is None) ^ (min_momentum_ratio is None)
- self.min_momentum = min_momentum
- self.min_momentum_ratio = min_momentum_ratio
- super(CosineAnnealingMomentumUpdaterHook, self).__init__(**kwargs)
-
- def get_momentum(self, runner, base_momentum):
- if self.by_epoch:
- progress = runner.epoch
- max_progress = runner.max_epochs
- else:
- progress = runner.iter
- max_progress = runner.max_iters
- if self.min_momentum_ratio is not None:
- target_momentum = base_momentum * self.min_momentum_ratio
- else:
- target_momentum = self.min_momentum
- return annealing_cos(base_momentum, target_momentum,
- progress / max_progress)
-
-
-@HOOKS.register_module()
-class CyclicMomentumUpdaterHook(MomentumUpdaterHook):
- """Cyclic momentum Scheduler.
-
- Implement the cyclical momentum scheduler policy described in
- https://arxiv.org/pdf/1708.07120.pdf
-
- This momentum scheduler usually used together with the CyclicLRUpdater
- to improve the performance in the 3D detection area.
-
- Attributes:
- target_ratio (tuple[float]): Relative ratio of the lowest momentum and
- the highest momentum to the initial momentum.
- cyclic_times (int): Number of cycles during training
- step_ratio_up (float): The ratio of the increasing process of momentum
- in the total cycle.
- by_epoch (bool): Whether to update momentum by epoch.
- """
-
- def __init__(self,
- by_epoch=False,
- target_ratio=(0.85 / 0.95, 1),
- cyclic_times=1,
- step_ratio_up=0.4,
- **kwargs):
- if isinstance(target_ratio, float):
- target_ratio = (target_ratio, target_ratio / 1e5)
- elif isinstance(target_ratio, tuple):
- target_ratio = (target_ratio[0], target_ratio[0] / 1e5) \
- if len(target_ratio) == 1 else target_ratio
- else:
- raise ValueError('target_ratio should be either float '
- f'or tuple, got {type(target_ratio)}')
-
- assert len(target_ratio) == 2, \
- '"target_ratio" must be list or tuple of two floats'
- assert 0 <= step_ratio_up < 1.0, \
- '"step_ratio_up" must be in range [0,1)'
-
- self.target_ratio = target_ratio
- self.cyclic_times = cyclic_times
- self.step_ratio_up = step_ratio_up
- self.momentum_phases = [] # init momentum_phases
- # currently only support by_epoch=False
- assert not by_epoch, \
- 'currently only support "by_epoch" = False'
- super(CyclicMomentumUpdaterHook, self).__init__(by_epoch, **kwargs)
-
- def before_run(self, runner):
- super(CyclicMomentumUpdaterHook, self).before_run(runner)
- # initiate momentum_phases
- # total momentum_phases are separated as up and down
- max_iter_per_phase = runner.max_iters // self.cyclic_times
- iter_up_phase = int(self.step_ratio_up * max_iter_per_phase)
- self.momentum_phases.append(
- [0, iter_up_phase, max_iter_per_phase, 1, self.target_ratio[0]])
- self.momentum_phases.append([
- iter_up_phase, max_iter_per_phase, max_iter_per_phase,
- self.target_ratio[0], self.target_ratio[1]
- ])
-
- def get_momentum(self, runner, base_momentum):
- curr_iter = runner.iter
- for (start_iter, end_iter, max_iter_per_phase, start_ratio,
- end_ratio) in self.momentum_phases:
- curr_iter %= max_iter_per_phase
- if start_iter <= curr_iter < end_iter:
- progress = curr_iter - start_iter
- return annealing_cos(base_momentum * start_ratio,
- base_momentum * end_ratio,
- progress / (end_iter - start_iter))
-
-
-@HOOKS.register_module()
-class OneCycleMomentumUpdaterHook(MomentumUpdaterHook):
- """OneCycle momentum Scheduler.
-
- This momentum scheduler usually used together with the OneCycleLrUpdater
- to improve the performance.
-
- Args:
- base_momentum (float or list): Lower momentum boundaries in the cycle
- for each parameter group. Note that momentum is cycled inversely
- to learning rate; at the peak of a cycle, momentum is
- 'base_momentum' and learning rate is 'max_lr'.
- Default: 0.85
- max_momentum (float or list): Upper momentum boundaries in the cycle
- for each parameter group. Functionally,
- it defines the cycle amplitude (max_momentum - base_momentum).
- Note that momentum is cycled inversely
- to learning rate; at the start of a cycle, momentum is
- 'max_momentum' and learning rate is 'base_lr'
- Default: 0.95
- pct_start (float): The percentage of the cycle (in number of steps)
- spent increasing the learning rate.
- Default: 0.3
- anneal_strategy (str): {'cos', 'linear'}
- Specifies the annealing strategy: 'cos' for cosine annealing,
- 'linear' for linear annealing.
- Default: 'cos'
- three_phase (bool): If three_phase is True, use a third phase of the
- schedule to annihilate the learning rate according to
- final_div_factor instead of modifying the second phase (the first
- two phases will be symmetrical about the step indicated by
- pct_start).
- Default: False
- """
-
- def __init__(self,
- base_momentum=0.85,
- max_momentum=0.95,
- pct_start=0.3,
- anneal_strategy='cos',
- three_phase=False,
- **kwargs):
- # validate by_epoch, currently only support by_epoch=False
- if 'by_epoch' not in kwargs:
- kwargs['by_epoch'] = False
- else:
- assert not kwargs['by_epoch'], \
- 'currently only support "by_epoch" = False'
- if not isinstance(base_momentum, (float, list, dict)):
- raise ValueError('base_momentum must be the type among of float,'
- 'list or dict.')
- self._base_momentum = base_momentum
- if not isinstance(max_momentum, (float, list, dict)):
- raise ValueError('max_momentum must be the type among of float,'
- 'list or dict.')
- self._max_momentum = max_momentum
- # validate pct_start
- if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float):
- raise ValueError('Expected float between 0 and 1 pct_start, but '
- f'got {pct_start}')
- self.pct_start = pct_start
- # validate anneal_strategy
- if anneal_strategy not in ['cos', 'linear']:
- raise ValueError('anneal_strategy must by one of "cos" or '
- f'"linear", instead got {anneal_strategy}')
- elif anneal_strategy == 'cos':
- self.anneal_func = annealing_cos
- elif anneal_strategy == 'linear':
- self.anneal_func = annealing_linear
- self.three_phase = three_phase
- self.momentum_phases = [] # init momentum_phases
- super(OneCycleMomentumUpdaterHook, self).__init__(**kwargs)
-
- def before_run(self, runner):
- if isinstance(runner.optimizer, dict):
- for k, optim in runner.optimizer.items():
- if ('momentum' not in optim.defaults
- and 'betas' not in optim.defaults):
- raise ValueError('optimizer must support momentum with'
- 'option enabled')
- self.use_beta1 = 'betas' in optim.defaults
- _base_momentum = format_param(k, optim, self._base_momentum)
- _max_momentum = format_param(k, optim, self._max_momentum)
- for group, b_momentum, m_momentum in zip(
- optim.param_groups, _base_momentum, _max_momentum):
- if self.use_beta1:
- _, beta2 = group['betas']
- group['betas'] = (m_momentum, beta2)
- else:
- group['momentum'] = m_momentum
- group['base_momentum'] = b_momentum
- group['max_momentum'] = m_momentum
- else:
- optim = runner.optimizer
- if ('momentum' not in optim.defaults
- and 'betas' not in optim.defaults):
- raise ValueError('optimizer must support momentum with'
- 'option enabled')
- self.use_beta1 = 'betas' in optim.defaults
- k = type(optim).__name__
- _base_momentum = format_param(k, optim, self._base_momentum)
- _max_momentum = format_param(k, optim, self._max_momentum)
- for group, b_momentum, m_momentum in zip(optim.param_groups,
- _base_momentum,
- _max_momentum):
- if self.use_beta1:
- _, beta2 = group['betas']
- group['betas'] = (m_momentum, beta2)
- else:
- group['momentum'] = m_momentum
- group['base_momentum'] = b_momentum
- group['max_momentum'] = m_momentum
-
- if self.three_phase:
- self.momentum_phases.append({
- 'end_iter':
- float(self.pct_start * runner.max_iters) - 1,
- 'start_momentum':
- 'max_momentum',
- 'end_momentum':
- 'base_momentum'
- })
- self.momentum_phases.append({
- 'end_iter':
- float(2 * self.pct_start * runner.max_iters) - 2,
- 'start_momentum':
- 'base_momentum',
- 'end_momentum':
- 'max_momentum'
- })
- self.momentum_phases.append({
- 'end_iter': runner.max_iters - 1,
- 'start_momentum': 'max_momentum',
- 'end_momentum': 'max_momentum'
- })
- else:
- self.momentum_phases.append({
- 'end_iter':
- float(self.pct_start * runner.max_iters) - 1,
- 'start_momentum':
- 'max_momentum',
- 'end_momentum':
- 'base_momentum'
- })
- self.momentum_phases.append({
- 'end_iter': runner.max_iters - 1,
- 'start_momentum': 'base_momentum',
- 'end_momentum': 'max_momentum'
- })
-
- def _set_momentum(self, runner, momentum_groups):
- if isinstance(runner.optimizer, dict):
- for k, optim in runner.optimizer.items():
- for param_group, mom in zip(optim.param_groups,
- momentum_groups[k]):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
- else:
- for param_group, mom in zip(runner.optimizer.param_groups,
- momentum_groups):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
-
- def get_momentum(self, runner, param_group):
- curr_iter = runner.iter
- start_iter = 0
- for i, phase in enumerate(self.momentum_phases):
- end_iter = phase['end_iter']
- if curr_iter <= end_iter or i == len(self.momentum_phases) - 1:
- pct = (curr_iter - start_iter) / (end_iter - start_iter)
- momentum = self.anneal_func(
- param_group[phase['start_momentum']],
- param_group[phase['end_momentum']], pct)
- break
- start_iter = end_iter
- return momentum
-
- def get_regular_momentum(self, runner):
- if isinstance(runner.optimizer, dict):
- momentum_groups = {}
- for k, optim in runner.optimizer.items():
- _momentum_group = [
- self.get_momentum(runner, param_group)
- for param_group in optim.param_groups
- ]
- momentum_groups.update({k: _momentum_group})
- return momentum_groups
- else:
- momentum_groups = []
- for param_group in runner.optimizer.param_groups:
- momentum_groups.append(self.get_momentum(runner, param_group))
- return momentum_groups
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/core/evaluation/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/core/evaluation/__init__.py
deleted file mode 100644
index f7cc4b23413a0639e9de00eeb0bf600632d2c6cd..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/core/evaluation/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from .class_names import get_classes, get_palette
-from .eval_hooks import DistEvalHook, EvalHook
-from .metrics import eval_metrics, mean_dice, mean_fscore, mean_iou
-
-__all__ = [
- 'EvalHook', 'DistEvalHook', 'mean_dice', 'mean_iou', 'mean_fscore',
- 'eval_metrics', 'get_classes', 'get_palette'
-]
diff --git a/spaces/abnerh/video-to-subs/README.md b/spaces/abnerh/video-to-subs/README.md
deleted file mode 100644
index ed32bb5f36f156684d9ebd5458b8a93e085d7e12..0000000000000000000000000000000000000000
--- a/spaces/abnerh/video-to-subs/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Video To Subs
-emoji: 🎙️
-colorFrom: white
-colorTo: blue
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/ai-guru/composer/static/_app/pages/index.svelte-9646dd5a.js b/spaces/ai-guru/composer/static/_app/pages/index.svelte-9646dd5a.js
deleted file mode 100644
index 510a133267536503ed780d792110a1ff8173e49a..0000000000000000000000000000000000000000
--- a/spaces/ai-guru/composer/static/_app/pages/index.svelte-9646dd5a.js
+++ /dev/null
@@ -1,5 +0,0 @@
-import{S as Z,i as x,s as ee,e as b,k as I,c as w,a as E,d as g,m as C,K as se,b as d,g as P,J as f,L as U,t as N,h as H,j as Ee,E as re,M as Re,N as R,O as fe,P as Ge,w as Y,x as J,y as K,Q as Ve,q as L,o as V,B as Q,R as ue,T as Fe,f as X,U as _e,V as ie,W as qe,n as $e,p as Te,X as We,v as Xe,u as Ye,Y as Je,l as Me}from"../chunks/index-7c452e28.js";import{w as le}from"../chunks/index-d282aaf8.js";const he=le("synth"),be=le("medium"),we=le("medium"),ve=le(!1),pe=le(""),ye=le(""),ke=le(""),ne={piano:"Piano",chamber:"Chamber Music",rock_and_metal:"Rock and Metal",synth:"Synthesizer",church:"Church",timpani_strings_harp:"Timpani, Contrabass, Harp",country:"Country",reggae:"Reggae-esque"},Ke={low:"Low",medium:"Medium",high:"High"},Qe={low:"Low",medium:"Medium",high:"High",very_high:"Very High"};function Ne(l,e,s){const t=l.slice();return t[4]=e[s],t[6]=s,t}function He(l){let e,s,t,o,n,a,i,c,u,p,r,_;return{c(){e=b("label"),s=b("div"),t=b("img"),a=I(),i=b("input"),u=I(),this.h()},l(m){e=w(m,"LABEL",{"data-selected":!0,class:!0});var T=E(e);s=w(T,"DIV",{class:!0});var v=E(s);t=w(v,"IMG",{src:!0,alt:!0,class:!0}),v.forEach(g),a=C(T),i=w(T,"INPUT",{type:!0,class:!0}),u=C(T),T.forEach(g),this.h()},h(){se(t.src,o=`static/${l[4]}.svg`)||d(t,"src",o),d(t,"alt",n=ne[l[4]]),d(t,"class","svelte-1r9pswz"),d(s,"class","svelte-1r9pswz"),d(i,"type","radio"),i.__value=c=l[4],i.value=i.__value,d(i,"class","svelte-1r9pswz"),l[3][0].push(i),d(e,"data-selected",p=l[0]===l[4]),d(e,"class","svelte-1r9pswz")},m(m,T){P(m,e,T),f(e,s),f(s,t),f(e,a),f(e,i),i.checked=i.__value===l[0],f(e,u),r||(_=U(i,"change",l[2]),r=!0)},p(m,T){T&1&&(i.checked=i.__value===m[0]),T&1&&p!==(p=m[0]===m[4])&&d(e,"data-selected",p)},d(m){m&&g(e),l[3][0].splice(l[3][0].indexOf(i),1),r=!1,_()}}}function Ze(l){let e,s,t=(ne[l[0]]||"Synthesizer")+"",o,n,a,i=l[1],c=[];for(let u=0;us(0,t=i));const o=Object.keys(ne),n=[[]];function a(){t=this.__value,he.set(t)}return[t,o,a,n]}class et extends Z{constructor(e){super(),x(this,e,xe,Ze,ee,{})}}function je(l,e,s){const t=l.slice();return t[5]=e[s],t}function Be(l){let e,s=l[1][l[5]]+"",t,o,n,a,i,c,u,p;return{c(){e=b("label"),t=N(s),o=I(),n=b("input"),i=I(),this.h()},l(r){e=w(r,"LABEL",{"data-selected":!0,class:!0});var _=E(e);t=H(_,s),o=C(_),n=w(_,"INPUT",{type:!0,class:!0}),i=C(_),_.forEach(g),this.h()},h(){d(n,"type","radio"),n.__value=a=l[5],n.value=n.__value,d(n,"class","svelte-1m848u0"),l[4][0].push(n),d(e,"data-selected",c=l[5]===l[0]),d(e,"class","svelte-1m848u0")},m(r,_){P(r,e,_),f(e,t),f(e,o),f(e,n),n.checked=n.__value===l[0],f(e,i),u||(p=U(n,"change",l[3]),u=!0)},p(r,_){_&2&&s!==(s=r[1][r[5]]+"")&&Ee(t,s),_&1&&(n.checked=n.__value===r[0]),_&1&&c!==(c=r[5]===r[0])&&d(e,"data-selected",c)},d(r){r&&g(e),l[4][0].splice(l[4][0].indexOf(n),1),u=!1,p()}}}function tt(l){let e,s=l[2],t=[];for(let o=0;o{"options"in c&&s(1,t=c.options),"selection"in c&&s(0,n=c.selection)},[n,t,o,i,a]}class Ue extends Z{constructor(e){super(),x(this,e,st,tt,ee,{options:1,selection:0})}}function nt(l){let e,s,t,o,n,a,i,c;function u(r){l[1](r)}let p={options:Ke};return l[0]!==void 0&&(p.selection=l[0]),a=new Ue({props:p}),fe.push(()=>Ge(a,"selection",u)),{c(){e=b("div"),s=b("fieldset"),t=b("legend"),o=N("Note density"),n=I(),Y(a.$$.fragment),this.h()},l(r){e=w(r,"DIV",{});var _=E(e);s=w(_,"FIELDSET",{class:!0});var m=E(s);t=w(m,"LEGEND",{class:!0});var T=E(t);o=H(T,"Note density"),T.forEach(g),n=C(m),J(a.$$.fragment,m),m.forEach(g),_.forEach(g),this.h()},h(){d(t,"class","svelte-1ikh8be"),d(s,"class","svelte-1ikh8be")},m(r,_){P(r,e,_),f(e,s),f(s,t),f(t,o),f(s,n),K(a,s,null),c=!0},p(r,[_]){const m={};!i&&_&1&&(i=!0,m.selection=r[0],Ve(()=>i=!1)),a.$set(m)},i(r){c||(L(a.$$.fragment,r),c=!0)},o(r){V(a.$$.fragment,r),c=!1},d(r){r&&g(e),Q(a)}}}function lt(l,e,s){let t;R(l,be,n=>s(0,t=n));function o(n){t=n,be.set(t)}return[t,o]}class at extends Z{constructor(e){super(),x(this,e,lt,nt,ee,{})}}function ot(l){let e,s,t,o,n,a,i,c;function u(r){l[1](r)}let p={options:Qe};return l[0]!==void 0&&(p.selection=l[0]),a=new Ue({props:p}),fe.push(()=>Ge(a,"selection",u)),{c(){e=b("div"),s=b("fieldset"),t=b("legend"),o=N("Temperature"),n=I(),Y(a.$$.fragment),this.h()},l(r){e=w(r,"DIV",{});var _=E(e);s=w(_,"FIELDSET",{class:!0});var m=E(s);t=w(m,"LEGEND",{class:!0});var T=E(t);o=H(T,"Temperature"),T.forEach(g),n=C(m),J(a.$$.fragment,m),m.forEach(g),_.forEach(g),this.h()},h(){d(t,"class","svelte-1ikh8be"),d(s,"class","svelte-1ikh8be")},m(r,_){P(r,e,_),f(e,s),f(s,t),f(t,o),f(s,n),K(a,s,null),c=!0},p(r,[_]){const m={};!i&&_&1&&(i=!0,m.selection=r[0],Ve(()=>i=!1)),a.$set(m)},i(r){c||(L(a.$$.fragment,r),c=!0)},o(r){V(a.$$.fragment,r),c=!1},d(r){r&&g(e),Q(a)}}}function rt(l,e,s){let t;R(l,we,n=>s(0,t=n));function o(n){t=n,we.set(t)}return[t,o]}class it extends Z{constructor(e){super(),x(this,e,rt,ot,ee,{})}}function ct(l){let e,s,t;return{c(){e=N("Compose "),s=b("img"),this.h()},l(o){e=H(o,"Compose "),s=w(o,"IMG",{src:!0,alt:!0,class:!0}),this.h()},h(){se(s.src,t="static/wand.svg")||d(s,"src",t),d(s,"alt","Magic wand"),d(s,"class","svelte-18w38ow")},m(o,n){P(o,e,n),P(o,s,n)},d(o){o&&g(e),o&&g(s)}}}function ut(l){let e;return{c(){e=N("Composing...")},l(s){e=H(s,"Composing...")},m(s,t){P(s,e,t)},d(s){s&&g(e)}}}function ft(l){let e,s,t;function o(i,c){return i[0]?ut:ct}let n=o(l),a=n(l);return{c(){e=b("button"),a.c(),this.h()},l(i){e=w(i,"BUTTON",{class:!0});var c=E(e);a.l(c),c.forEach(g),this.h()},h(){e.disabled=l[0],d(e,"class","svelte-18w38ow")},m(i,c){P(i,e,c),a.m(e,null),s||(t=U(e,"click",l[1]),s=!0)},p(i,[c]){n!==(n=o(i))&&(a.d(1),a=n(i),a&&(a.c(),a.m(e,null))),c&1&&(e.disabled=i[0])},i:re,o:re,d(i){i&&g(e),a.d(),s=!1,t()}}}function dt(l,e,s){let t,o,n,a,i,c,u;R(l,ve,v=>s(0,t=v)),R(l,ke,v=>s(2,o=v)),R(l,ye,v=>s(3,n=v)),R(l,pe,v=>s(4,a=v)),R(l,we,v=>s(5,i=v)),R(l,be,v=>s(6,c=v)),R(l,he,v=>s(7,u=v));const p=()=>{"mediaSession"in navigator&&(navigator.mediaSession.metadata=new MediaMetadata({title:`${ne[u]} Composition`,artist:"AI Guru Composer",album:"Hugging Face",artwork:[{src:"static/hugging-face-headphones.png",sizes:"512x512",type:"image/png"}]}))},r=async({music_style:v,density:y,temperature:D})=>{var A;const S=await fetch("task/create",{method:"POST",headers:{"Content-Type":"application/json"},body:JSON.stringify({music_style:v,density:y,temperature:D})});if(!S.ok||!((A=S.headers.get("content-type"))!=null&&A.includes("application/json")))throw new Error(`Unable to create composition: [${S.status}] ${await S.text()}`);return await S.json()},_=async v=>{var D;const y=await fetch(`task/poll?task_id=${v.task_id}`);if(!y.ok||!((D=y.headers.get("content-type"))!=null&&D.includes("application/json")))throw new Error(`Unable to create composition: [${y.status}] ${await y.text()}`);return await y.json()},m=async(v,y=1e3,D=100)=>(v=await _(v),v.status==="completed"||v.status==="failed"||D&&v.poll_count>D?v:(await new Promise(S=>setTimeout(S,y)),await m(v,y,D)));return[t,async()=>{ue(ve,t=!0,t);try{const v=await r({music_style:u,density:c,temperature:i}),y=await m(v),{audio:D,image:S,tokens:j}=y.output;ue(pe,a=D,a),ue(ye,n=S,n),ue(ke,o=j,o),p()}catch(v){console.error(v)}finally{ue(ve,t=!1,t)}}]}class _t extends Z{constructor(e){super(),x(this,e,dt,ft,ee,{})}}function ce(l,{delay:e=0,duration:s=400,easing:t=Fe}={}){const o=+getComputedStyle(l).opacity;return{delay:e,duration:s,easing:t,css:n=>`opacity: ${n*o}`}}function Le(l){let e,s,t,o;return{c(){e=b("img"),this.h()},l(n){e=w(n,"IMG",{class:!0,src:!0,alt:!0,draggable:!0}),this.h()},h(){d(e,"class","play-button svelte-1536b0c"),se(e.src,s="static/play.svg")||d(e,"src",s),d(e,"alt","Play button"),d(e,"draggable","false"),X(e,"width",l[7]>100?"20%":"7.5%",!1)},m(n,a){P(n,e,a),o=!0},p(n,a){a&128&&X(e,"width",n[7]>100?"20%":"7.5%",!1)},i(n){o||(_e(()=>{t||(t=ie(e,ce,{},!0)),t.run(1)}),o=!0)},o(n){t||(t=ie(e,ce,{},!1)),t.run(0),o=!1},d(n){n&&g(e),n&&t&&t.end()}}}function ht(l){let e,s,t,o,n,a,i,c=!1,u,p=!0,r,_,m=`translate(${Math.min(l[6]*(l[1]/(l[2]-.9)),l[6])}px, -2%)`,T,v,y,D,S,j,A,q,W;function z(){cancelAnimationFrame(u),a.paused||(u=Je(z),c=!0),l[15].call(a)}let $=l[3]&&Le(l);return{c(){e=b("section"),s=b("img"),o=I(),n=b("div"),a=b("audio"),r=I(),_=b("div"),T=I(),$&&$.c(),v=I(),y=b("a"),D=N("Download"),this.h()},l(h){e=w(h,"SECTION",{class:!0});var k=E(e);s=w(k,"IMG",{class:!0,src:!0,alt:!0}),o=C(k),n=w(k,"DIV",{class:!0,tabindex:!0});var G=E(n);a=w(G,"AUDIO",{src:!0,class:!0}),E(a).forEach(g),r=C(G),_=w(G,"DIV",{class:!0}),E(_).forEach(g),T=C(G),$&&$.l(G),G.forEach(g),v=C(k),y=w(k,"A",{href:!0,download:!0,class:!0});var F=E(y);D=H(F,"Download"),F.forEach(g),k.forEach(g),this.h()},h(){d(s,"class","notes svelte-1536b0c"),se(s.src,t=l[8])||d(s,"src",t),d(s,"alt",""),se(a.src,i=l[9])||d(a,"src",i),d(a,"class","svelte-1536b0c"),l[2]===void 0&&_e(()=>l[16].call(a)),d(_,"class","handle svelte-1536b0c"),X(_,"transform",m,!1),d(n,"class","player svelte-1536b0c"),d(n,"tabindex","0"),X(n,"width",l[6]+"px",!1),X(n,"height",l[7]+"px",!1),d(y,"href",l[9]),d(y,"download",S=`${ne[l[10]]} Composition - AI Guru ft. Hugging Face.wav`),d(y,"class","download svelte-1536b0c"),d(e,"class","svelte-1536b0c")},m(h,k){P(h,e,k),f(e,s),l[14](s),f(e,o),f(e,n),f(n,a),f(n,r),f(n,_),f(n,T),$&&$.m(n,null),l[18](n),f(e,v),f(e,y),f(y,D),l[20](e),A=!0,q||(W=[U(a,"timeupdate",z),U(a,"durationchange",l[16]),U(a,"play",l[17]),U(a,"pause",l[17]),U(n,"mousemove",l[11]),U(n,"touchmove",qe(l[12])),U(n,"keydown",l[13]),U(n,"click",l[19])],q=!0)},p(h,[k]){(!A||k&256&&!se(s.src,t=h[8]))&&d(s,"src",t),(!A||k&512&&!se(a.src,i=h[9]))&&d(a,"src",i),!c&&k&2&&!isNaN(h[1])&&(a.currentTime=h[1]),c=!1,k&8&&p!==(p=h[3])&&a[p?"pause":"play"](),k&70&&m!==(m=`translate(${Math.min(h[6]*(h[1]/(h[2]-.9)),h[6])}px, -2%)`)&&X(_,"transform",m,!1),h[3]?$?($.p(h,k),k&8&&L($,1)):($=Le(h),$.c(),L($,1),$.m(n,null)):$&&($e(),V($,1,1,()=>{$=null}),Te()),k&64&&X(n,"width",h[6]+"px",!1),k&128&&X(n,"height",h[7]+"px",!1),(!A||k&512)&&d(y,"href",h[9]),(!A||k&1024&&S!==(S=`${ne[h[10]]} Composition - AI Guru ft. Hugging Face.wav`))&&d(y,"download",S)},i(h){A||(L($),_e(()=>{j||(j=ie(e,ce,{},!0)),j.run(1)}),A=!0)},o(h){V($),j||(j=ie(e,ce,{},!1)),j.run(0),A=!1},d(h){h&&g(e),l[14](null),$&&$.d(),l[18](null),l[20](null),h&&j&&j.end(),q=!1,We(W)}}}function pt(l,e,s){let t,o,n;R(l,ye,h=>s(8,t=h)),R(l,pe,h=>s(9,o=h)),R(l,he,h=>s(10,n=h));let a,i,c,u=!0,p,r,_,m;const T=()=>{s(6,_=r&&r.clientWidth),s(7,m=r&&r.clientHeight)};Xe(()=>{T(),"mediaSession"in navigator&&(navigator.mediaSession.setActionHandler("play",()=>s(3,u=!1)),navigator.mediaSession.setActionHandler("pause",()=>s(3,u=!0)),navigator.mediaSession.setActionHandler("stop",()=>{s(3,u=!0),s(1,i=0)})),window.scrollTo({top:a.offsetTop,behavior:"smooth"})}),Ye(()=>{T()});const v=h=>{if(!c||!h.buttons)return;const{left:k,right:G}=p.getBoundingClientRect();s(1,i=c*(h.clientX-k)/(G-k))},y=h=>{if(!c)return;const{left:k,right:G}=p.getBoundingClientRect();s(1,i=c*(h.touches[0].clientX-k)/(G-k))},D=h=>{h.preventDefault(),h.code==="Space"&&s(3,u=!u),h.code==="ArrowLeft"&&s(1,i=i>=1?i-1:0),h.code==="ArrowRight"&&s(1,i=i<=c-1?i+1:c)};function S(h){fe[h?"unshift":"push"](()=>{r=h,s(5,r)})}function j(){i=this.currentTime,s(1,i)}function A(){c=this.duration,s(2,c)}function q(){u=this.paused,s(3,u)}function W(h){fe[h?"unshift":"push"](()=>{p=h,s(4,p)})}const z=()=>s(3,u=!u);function $(h){fe[h?"unshift":"push"](()=>{a=h,s(0,a)})}return[a,i,c,u,p,r,_,m,t,o,n,v,y,D,S,j,A,q,W,z,$]}class mt extends Z{constructor(e){super(),x(this,e,pt,ht,ee,{})}}function ze(l){let e,s,t,o,n,a,i,c;return{c(){e=b("section"),s=b("h2"),t=N("Tokenized notes"),o=I(),n=b("p"),a=N(l[0]),this.h()},l(u){e=w(u,"SECTION",{class:!0});var p=E(e);s=w(p,"H2",{});var r=E(s);t=H(r,"Tokenized notes"),r.forEach(g),o=C(p),n=w(p,"P",{class:!0});var _=E(n);a=H(_,l[0]),_.forEach(g),p.forEach(g),this.h()},h(){d(n,"class","svelte-4un5mw"),d(e,"class","svelte-4un5mw")},m(u,p){P(u,e,p),f(e,s),f(s,t),f(e,o),f(e,n),f(n,a),c=!0},p(u,p){(!c||p&1)&&Ee(a,u[0])},i(u){c||(_e(()=>{i||(i=ie(e,ce,{},!0)),i.run(1)}),c=!0)},o(u){i||(i=ie(e,ce,{},!1)),i.run(0),c=!1},d(u){u&&g(e),u&&i&&i.end()}}}function gt(l){let e,s,t=l[0]&&ze(l);return{c(){t&&t.c(),e=Me()},l(o){t&&t.l(o),e=Me()},m(o,n){t&&t.m(o,n),P(o,e,n),s=!0},p(o,[n]){o[0]?t?(t.p(o,n),n&1&&L(t,1)):(t=ze(o),t.c(),L(t,1),t.m(e.parentNode,e)):t&&($e(),V(t,1,1,()=>{t=null}),Te())},i(o){s||(L(t),s=!0)},o(o){V(t),s=!1},d(o){t&&t.d(o),o&&g(e)}}}function vt(l,e,s){let t;return R(l,ke,o=>s(0,t=o)),[t]}class bt extends Z{constructor(e){super(),x(this,e,vt,gt,ee,{})}}function Pe(l){let e,s,t,o;return e=new mt({}),t=new bt({}),{c(){Y(e.$$.fragment),s=I(),Y(t.$$.fragment)},l(n){J(e.$$.fragment,n),s=C(n),J(t.$$.fragment,n)},m(n,a){K(e,n,a),P(n,s,a),K(t,n,a),o=!0},i(n){o||(L(e.$$.fragment,n),L(t.$$.fragment,n),o=!0)},o(n){V(e.$$.fragment,n),V(t.$$.fragment,n),o=!1},d(n){Q(e,n),n&&g(s),Q(t,n)}}}function wt(l){let e,s,t,o,n,a,i,c,u,p,r,_,m,T,v,y,D,S,j,A,q,W,z,$,h,k,G,F,me,te,ge,de;$=new et({}),k=new at({}),F=new it({}),te=new _t({});let M=l[0]&&Pe();return{c(){e=b("main"),s=b("h1"),t=N("Composer"),o=I(),n=b("p"),a=N("Trained on fifteen thousand songs. One AI model. Infinite compositions."),i=I(),c=b("p"),u=N(`This space contains a deep neural network model that can compose music. You can use it to generate audio in
- different styles, 4 bars at a time.`),p=I(),r=b("p"),_=N("Developed by "),m=b("a"),T=N("Ron Au"),v=N(` and
- `),y=b("a"),D=N("Tristan Behrens"),S=N("."),j=I(),A=b("p"),q=N("Have fun! And always feel free to send us some feedback and share your compositions!"),W=I(),z=b("section"),Y($.$$.fragment),h=I(),Y(k.$$.fragment),G=I(),Y(F.$$.fragment),me=I(),Y(te.$$.fragment),ge=I(),M&&M.c(),this.h()},l(B){e=w(B,"MAIN",{class:!0});var O=E(e);s=w(O,"H1",{class:!0});var Ie=E(s);t=H(Ie,"Composer"),Ie.forEach(g),o=C(O),n=w(O,"P",{class:!0});var Ce=E(n);a=H(Ce,"Trained on fifteen thousand songs. One AI model. Infinite compositions."),Ce.forEach(g),i=C(O),c=w(O,"P",{class:!0});var De=E(c);u=H(De,`This space contains a deep neural network model that can compose music. You can use it to generate audio in
- different styles, 4 bars at a time.`),De.forEach(g),p=C(O),r=w(O,"P",{class:!0});var ae=E(r);_=H(ae,"Developed by "),m=w(ae,"A",{href:!0,rel:!0,target:!0});var Se=E(m);T=H(Se,"Ron Au"),Se.forEach(g),v=H(ae,` and
- `),y=w(ae,"A",{href:!0,rel:!0,target:!0});var Ae=E(y);D=H(Ae,"Tristan Behrens"),Ae.forEach(g),S=H(ae,"."),ae.forEach(g),j=C(O),A=w(O,"P",{class:!0});var Oe=E(A);q=H(Oe,"Have fun! And always feel free to send us some feedback and share your compositions!"),Oe.forEach(g),W=C(O),z=w(O,"SECTION",{id:!0,class:!0});var oe=E(z);J($.$$.fragment,oe),h=C(oe),J(k.$$.fragment,oe),G=C(oe),J(F.$$.fragment,oe),oe.forEach(g),me=C(O),J(te.$$.fragment,O),ge=C(O),M&&M.l(O),O.forEach(g),this.h()},h(){d(s,"class","svelte-1rfjlkw"),d(n,"class","heading svelte-1rfjlkw"),d(c,"class","svelte-1rfjlkw"),d(m,"href","https://twitter.com/ronvoluted"),d(m,"rel","noopener"),d(m,"target","_blank"),d(y,"href","https://twitter.com/DrTBehrens"),d(y,"rel","noopener"),d(y,"target","_blank"),d(r,"class","svelte-1rfjlkw"),d(A,"class","svelte-1rfjlkw"),d(z,"id","options"),d(z,"class","svelte-1rfjlkw"),d(e,"class","svelte-1rfjlkw")},m(B,O){P(B,e,O),f(e,s),f(s,t),f(e,o),f(e,n),f(n,a),f(e,i),f(e,c),f(c,u),f(e,p),f(e,r),f(r,_),f(r,m),f(m,T),f(r,v),f(r,y),f(y,D),f(r,S),f(e,j),f(e,A),f(A,q),f(e,W),f(e,z),K($,z,null),f(z,h),K(k,z,null),f(z,G),K(F,z,null),f(e,me),K(te,e,null),f(e,ge),M&&M.m(e,null),de=!0},p(B,[O]){B[0]?M?O&1&&L(M,1):(M=Pe(),M.c(),L(M,1),M.m(e,null)):M&&($e(),V(M,1,1,()=>{M=null}),Te())},i(B){de||(L($.$$.fragment,B),L(k.$$.fragment,B),L(F.$$.fragment,B),L(te.$$.fragment,B),L(M),de=!0)},o(B){V($.$$.fragment,B),V(k.$$.fragment,B),V(F.$$.fragment,B),V(te.$$.fragment,B),V(M),de=!1},d(B){B&&g(e),Q($),Q(k),Q(F),Q(te),M&&M.d()}}}function yt(l,e,s){let t;return R(l,pe,o=>s(0,t=o)),[t]}class $t extends Z{constructor(e){super(),x(this,e,yt,wt,ee,{})}}export{$t as default};
diff --git a/spaces/aimstack/bloom/Dockerfile b/spaces/aimstack/bloom/Dockerfile
deleted file mode 100644
index 1b5313643bce150166d4135b441cd315634a68b9..0000000000000000000000000000000000000000
--- a/spaces/aimstack/bloom/Dockerfile
+++ /dev/null
@@ -1,30 +0,0 @@
-FROM python:3.9
-
-
-RUN useradd -m -u 1000 aim_user
-
-# Switch to the "aim_user" user
-USER aim_user
-
-# Set home to the user's home directory
-ENV HOME=/home/aim_user \
- PATH=/home/aim_user/.local/bin:$PATH
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME
-
-# install the `aim` package on the latest version
-RUN pip install aim==4.0.0.dev6
-
-RUN aim telemetry off
-
-ENTRYPOINT ["/bin/sh", "-c"]
-
-COPY aim_repo.tar.gz .
-RUN tar xvzf aim_repo.tar.gz
-# have to run `aim init` in the directory that stores aim data for
-# otherwise `aim up` will prompt for confirmation to create the directory itself.
-# We run aim listening on 0.0.0.0 to expose all ports. Also, we run
-# using `--dev` to print verbose logs. Port 43800 is the default port of
-# `aim up` but explicit is better than implicit.
-CMD ["aim up --host 0.0.0.0 --port 7860 --workers 2"]
\ No newline at end of file
diff --git a/spaces/airus/img-to-music/constants.py b/spaces/airus/img-to-music/constants.py
deleted file mode 100644
index 86863d1b778d4c66f0d8e1e0b699f1bb937c1d50..0000000000000000000000000000000000000000
--- a/spaces/airus/img-to-music/constants.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import numpy as np
-import os
-
-MUBERT_LICENSE = os.environ.get('MUBERT_LICENSE')
-MUBERT_TOKEN = os.environ.get('MUBERT_TOKEN')
-
-MUBERT_MODE = "loop"
-MUBERT_TAGS_STRING = 'tribal,action,kids,neo-classic,run 130,pumped,jazz / funk,ethnic,dubtechno,reggae,acid jazz,liquidfunk,funk,witch house,tech house,underground,artists,mystical,disco,sensorium,r&b,agender,psychedelic trance / psytrance,peaceful,run 140,piano,run 160,setting,meditation,christmas,ambient,horror,cinematic,electro house,idm,bass,minimal,underscore,drums,glitchy,beautiful,technology,tribal house,country pop,jazz & funk,documentary,space,classical,valentines,chillstep,experimental,trap,new jack swing,drama,post-rock,tense,corporate,neutral,happy,analog,funky,spiritual,sberzvuk special,chill hop,dramatic,catchy,holidays,fitness 90,optimistic,orchestra,acid techno,energizing,romantic,minimal house,breaks,hyper pop,warm up,dreamy,dark,urban,microfunk,dub,nu disco,vogue,keys,hardcore,aggressive,indie,electro funk,beauty,relaxing,trance,pop,hiphop,soft,acoustic,chillrave / ethno-house,deep techno,angry,dance,fun,dubstep,tropical,latin pop,heroic,world music,inspirational,uplifting,atmosphere,art,epic,advertising,chillout,scary,spooky,slow ballad,saxophone,summer,erotic,jazzy,energy 100,kara mar,xmas,atmospheric,indie pop,hip-hop,yoga,reggaeton,lounge,travel,running,folk,chillrave & ethno-house,detective,darkambient,chill,fantasy,minimal techno,special,night,tropical house,downtempo,lullaby,meditative,upbeat,glitch hop,fitness,neurofunk,sexual,indie rock,future pop,jazz,cyberpunk,melancholic,happy hardcore,family / kids,synths,electric guitar,comedy,psychedelic trance & psytrance,edm,psychedelic rock,calm,zen,bells,podcast,melodic house,ethnic percussion,nature,heavy,bassline,indie dance,techno,drumnbass,synth pop,vaporwave,sad,8-bit,chillgressive,deep,orchestral,futuristic,hardtechno,nostalgic,big room,sci-fi,tutorial,joyful,pads,minimal 170,drill,ethnic 108,amusing,sleepy ambient,psychill,italo disco,lofi,house,acoustic guitar,bassline house,rock,k-pop,synthwave,deep house,electronica,gabber,nightlife,sport & fitness,road trip,celebration,electro,disco house,electronic'
-MUBERT_TAGS = np.array(MUBERT_TAGS_STRING.split(','))
\ No newline at end of file
diff --git a/spaces/akhaliq/Real-ESRGAN/realesrgan/data/realesrgan_paired_dataset.py b/spaces/akhaliq/Real-ESRGAN/realesrgan/data/realesrgan_paired_dataset.py
deleted file mode 100644
index 386c8d72496245dae8df033c2ebbd76b41ff45f1..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Real-ESRGAN/realesrgan/data/realesrgan_paired_dataset.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import os
-from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb
-from basicsr.data.transforms import augment, paired_random_crop
-from basicsr.utils import FileClient, imfrombytes, img2tensor
-from basicsr.utils.registry import DATASET_REGISTRY
-from torch.utils import data as data
-from torchvision.transforms.functional import normalize
-
-
-@DATASET_REGISTRY.register()
-class RealESRGANPairedDataset(data.Dataset):
- """Paired image dataset for image restoration.
-
- Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs.
-
- There are three modes:
- 1. 'lmdb': Use lmdb files.
- If opt['io_backend'] == lmdb.
- 2. 'meta_info': Use meta information file to generate paths.
- If opt['io_backend'] != lmdb and opt['meta_info'] is not None.
- 3. 'folder': Scan folders to generate paths.
- The rest.
-
- Args:
- opt (dict): Config for train datasets. It contains the following keys:
- dataroot_gt (str): Data root path for gt.
- dataroot_lq (str): Data root path for lq.
- meta_info (str): Path for meta information file.
- io_backend (dict): IO backend type and other kwarg.
- filename_tmpl (str): Template for each filename. Note that the template excludes the file extension.
- Default: '{}'.
- gt_size (int): Cropped patched size for gt patches.
- use_hflip (bool): Use horizontal flips.
- use_rot (bool): Use rotation (use vertical flip and transposing h
- and w for implementation).
-
- scale (bool): Scale, which will be added automatically.
- phase (str): 'train' or 'val'.
- """
-
- def __init__(self, opt):
- super(RealESRGANPairedDataset, self).__init__()
- self.opt = opt
- self.file_client = None
- self.io_backend_opt = opt['io_backend']
- # mean and std for normalizing the input images
- self.mean = opt['mean'] if 'mean' in opt else None
- self.std = opt['std'] if 'std' in opt else None
-
- self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
- self.filename_tmpl = opt['filename_tmpl'] if 'filename_tmpl' in opt else '{}'
-
- # file client (lmdb io backend)
- if self.io_backend_opt['type'] == 'lmdb':
- self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
- self.io_backend_opt['client_keys'] = ['lq', 'gt']
- self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt'])
- elif 'meta_info' in self.opt and self.opt['meta_info'] is not None:
- # disk backend with meta_info
- # Each line in the meta_info describes the relative path to an image
- with open(self.opt['meta_info']) as fin:
- paths = [line.strip() for line in fin]
- self.paths = []
- for path in paths:
- gt_path, lq_path = path.split(', ')
- gt_path = os.path.join(self.gt_folder, gt_path)
- lq_path = os.path.join(self.lq_folder, lq_path)
- self.paths.append(dict([('gt_path', gt_path), ('lq_path', lq_path)]))
- else:
- # disk backend
- # it will scan the whole folder to get meta info
- # it will be time-consuming for folders with too many files. It is recommended using an extra meta txt file
- self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl)
-
- def __getitem__(self, index):
- if self.file_client is None:
- self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
-
- scale = self.opt['scale']
-
- # Load gt and lq images. Dimension order: HWC; channel order: BGR;
- # image range: [0, 1], float32.
- gt_path = self.paths[index]['gt_path']
- img_bytes = self.file_client.get(gt_path, 'gt')
- img_gt = imfrombytes(img_bytes, float32=True)
- lq_path = self.paths[index]['lq_path']
- img_bytes = self.file_client.get(lq_path, 'lq')
- img_lq = imfrombytes(img_bytes, float32=True)
-
- # augmentation for training
- if self.opt['phase'] == 'train':
- gt_size = self.opt['gt_size']
- # random crop
- img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path)
- # flip, rotation
- img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot'])
-
- # BGR to RGB, HWC to CHW, numpy to tensor
- img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True)
- # normalize
- if self.mean is not None or self.std is not None:
- normalize(img_lq, self.mean, self.std, inplace=True)
- normalize(img_gt, self.mean, self.std, inplace=True)
-
- return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path}
-
- def __len__(self):
- return len(self.paths)
diff --git a/spaces/akhaliq/SummerTime/evaluation/rougewe_metric.py b/spaces/akhaliq/SummerTime/evaluation/rougewe_metric.py
deleted file mode 100644
index b27aa0ce2266903a3aa898e6e1e4ea095ecbf1cf..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SummerTime/evaluation/rougewe_metric.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from evaluation.summeval_metric import SummEvalMetric
-from typing import List, Dict
-
-import nltk
-
-
-class RougeWe(SummEvalMetric):
- metric_name = "rougeWE"
- range = (0, 1)
- higher_is_better = True
- requires_heavy_compute = True
-
- def __init__(self):
- from summ_eval.rouge_we_metric import RougeWeMetric
-
- nltk.download("stopwords")
- se_metric = RougeWeMetric()
- super(RougeWe, self).__init__(se_metric)
-
- def evaluate(
- self, inputs: List[str], targets: List[str], keys: List[str] = ["rouge_we_3_f"]
- ) -> Dict[str, float]:
- # TODO zhangir: update when dataset api is merged.
- return super(RougeWe, self).evaluate(inputs, targets, keys)
diff --git a/spaces/akhaliq/runwayml-stable-diffusion-v1-5/README.md b/spaces/akhaliq/runwayml-stable-diffusion-v1-5/README.md
deleted file mode 100644
index 26a6a20988103807b5f70ec8d8d68cb530640a10..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/runwayml-stable-diffusion-v1-5/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Runwayml Stable Diffusion V1 5
-emoji: 📉
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/alexiserodriguez/whisper-transcription-app/README.md b/spaces/alexiserodriguez/whisper-transcription-app/README.md
deleted file mode 100644
index e35a01a8bf10a8e6c8e19acf836343dc6a32e40e..0000000000000000000000000000000000000000
--- a/spaces/alexiserodriguez/whisper-transcription-app/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Whisper Transcription App
-emoji: 🐢
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/glibc.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/glibc.py
deleted file mode 100644
index 7bd3c20681d865cb4fa42617cf939b5512c7663f..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/glibc.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# The following comment should be removed at some point in the future.
-# mypy: strict-optional=False
-
-import os
-import sys
-from typing import Optional, Tuple
-
-
-def glibc_version_string() -> Optional[str]:
- "Returns glibc version string, or None if not using glibc."
- return glibc_version_string_confstr() or glibc_version_string_ctypes()
-
-
-def glibc_version_string_confstr() -> Optional[str]:
- "Primary implementation of glibc_version_string using os.confstr."
- # os.confstr is quite a bit faster than ctypes.DLL. It's also less likely
- # to be broken or missing. This strategy is used in the standard library
- # platform module:
- # https://github.com/python/cpython/blob/fcf1d003bf4f0100c9d0921ff3d70e1127ca1b71/Lib/platform.py#L175-L183
- if sys.platform == "win32":
- return None
- try:
- # os.confstr("CS_GNU_LIBC_VERSION") returns a string like "glibc 2.17":
- _, version = os.confstr("CS_GNU_LIBC_VERSION").split()
- except (AttributeError, OSError, ValueError):
- # os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)...
- return None
- return version
-
-
-def glibc_version_string_ctypes() -> Optional[str]:
- "Fallback implementation of glibc_version_string using ctypes."
-
- try:
- import ctypes
- except ImportError:
- return None
-
- # ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen
- # manpage says, "If filename is NULL, then the returned handle is for the
- # main program". This way we can let the linker do the work to figure out
- # which libc our process is actually using.
- process_namespace = ctypes.CDLL(None)
- try:
- gnu_get_libc_version = process_namespace.gnu_get_libc_version
- except AttributeError:
- # Symbol doesn't exist -> therefore, we are not linked to
- # glibc.
- return None
-
- # Call gnu_get_libc_version, which returns a string like "2.5"
- gnu_get_libc_version.restype = ctypes.c_char_p
- version_str = gnu_get_libc_version()
- # py2 / py3 compatibility:
- if not isinstance(version_str, str):
- version_str = version_str.decode("ascii")
-
- return version_str
-
-
-# platform.libc_ver regularly returns completely nonsensical glibc
-# versions. E.g. on my computer, platform says:
-#
-# ~$ python2.7 -c 'import platform; print(platform.libc_ver())'
-# ('glibc', '2.7')
-# ~$ python3.5 -c 'import platform; print(platform.libc_ver())'
-# ('glibc', '2.9')
-#
-# But the truth is:
-#
-# ~$ ldd --version
-# ldd (Debian GLIBC 2.22-11) 2.22
-#
-# This is unfortunate, because it means that the linehaul data on libc
-# versions that was generated by pip 8.1.2 and earlier is useless and
-# misleading. Solution: instead of using platform, use our code that actually
-# works.
-def libc_ver() -> Tuple[str, str]:
- """Try to determine the glibc version
-
- Returns a tuple of strings (lib, version) which default to empty strings
- in case the lookup fails.
- """
- glibc_version = glibc_version_string()
- if glibc_version is None:
- return ("", "")
- else:
- return ("glibc", glibc_version)
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/temp_dir.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/temp_dir.py
deleted file mode 100644
index 442679a758b7b4b965ce3e615a243a7a6d318b4e..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/temp_dir.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import errno
-import itertools
-import logging
-import os.path
-import tempfile
-from contextlib import ExitStack, contextmanager
-from typing import Any, Dict, Iterator, Optional, TypeVar, Union
-
-from pip._internal.utils.misc import enum, rmtree
-
-logger = logging.getLogger(__name__)
-
-_T = TypeVar("_T", bound="TempDirectory")
-
-
-# Kinds of temporary directories. Only needed for ones that are
-# globally-managed.
-tempdir_kinds = enum(
- BUILD_ENV="build-env",
- EPHEM_WHEEL_CACHE="ephem-wheel-cache",
- REQ_BUILD="req-build",
-)
-
-
-_tempdir_manager: Optional[ExitStack] = None
-
-
-@contextmanager
-def global_tempdir_manager() -> Iterator[None]:
- global _tempdir_manager
- with ExitStack() as stack:
- old_tempdir_manager, _tempdir_manager = _tempdir_manager, stack
- try:
- yield
- finally:
- _tempdir_manager = old_tempdir_manager
-
-
-class TempDirectoryTypeRegistry:
- """Manages temp directory behavior"""
-
- def __init__(self) -> None:
- self._should_delete: Dict[str, bool] = {}
-
- def set_delete(self, kind: str, value: bool) -> None:
- """Indicate whether a TempDirectory of the given kind should be
- auto-deleted.
- """
- self._should_delete[kind] = value
-
- def get_delete(self, kind: str) -> bool:
- """Get configured auto-delete flag for a given TempDirectory type,
- default True.
- """
- return self._should_delete.get(kind, True)
-
-
-_tempdir_registry: Optional[TempDirectoryTypeRegistry] = None
-
-
-@contextmanager
-def tempdir_registry() -> Iterator[TempDirectoryTypeRegistry]:
- """Provides a scoped global tempdir registry that can be used to dictate
- whether directories should be deleted.
- """
- global _tempdir_registry
- old_tempdir_registry = _tempdir_registry
- _tempdir_registry = TempDirectoryTypeRegistry()
- try:
- yield _tempdir_registry
- finally:
- _tempdir_registry = old_tempdir_registry
-
-
-class _Default:
- pass
-
-
-_default = _Default()
-
-
-class TempDirectory:
- """Helper class that owns and cleans up a temporary directory.
-
- This class can be used as a context manager or as an OO representation of a
- temporary directory.
-
- Attributes:
- path
- Location to the created temporary directory
- delete
- Whether the directory should be deleted when exiting
- (when used as a contextmanager)
-
- Methods:
- cleanup()
- Deletes the temporary directory
-
- When used as a context manager, if the delete attribute is True, on
- exiting the context the temporary directory is deleted.
- """
-
- def __init__(
- self,
- path: Optional[str] = None,
- delete: Union[bool, None, _Default] = _default,
- kind: str = "temp",
- globally_managed: bool = False,
- ):
- super().__init__()
-
- if delete is _default:
- if path is not None:
- # If we were given an explicit directory, resolve delete option
- # now.
- delete = False
- else:
- # Otherwise, we wait until cleanup and see what
- # tempdir_registry says.
- delete = None
-
- # The only time we specify path is in for editables where it
- # is the value of the --src option.
- if path is None:
- path = self._create(kind)
-
- self._path = path
- self._deleted = False
- self.delete = delete
- self.kind = kind
-
- if globally_managed:
- assert _tempdir_manager is not None
- _tempdir_manager.enter_context(self)
-
- @property
- def path(self) -> str:
- assert not self._deleted, f"Attempted to access deleted path: {self._path}"
- return self._path
-
- def __repr__(self) -> str:
- return f"<{self.__class__.__name__} {self.path!r}>"
-
- def __enter__(self: _T) -> _T:
- return self
-
- def __exit__(self, exc: Any, value: Any, tb: Any) -> None:
- if self.delete is not None:
- delete = self.delete
- elif _tempdir_registry:
- delete = _tempdir_registry.get_delete(self.kind)
- else:
- delete = True
-
- if delete:
- self.cleanup()
-
- def _create(self, kind: str) -> str:
- """Create a temporary directory and store its path in self.path"""
- # We realpath here because some systems have their default tmpdir
- # symlinked to another directory. This tends to confuse build
- # scripts, so we canonicalize the path by traversing potential
- # symlinks here.
- path = os.path.realpath(tempfile.mkdtemp(prefix=f"pip-{kind}-"))
- logger.debug("Created temporary directory: %s", path)
- return path
-
- def cleanup(self) -> None:
- """Remove the temporary directory created and reset state"""
- self._deleted = True
- if not os.path.exists(self._path):
- return
- rmtree(self._path)
-
-
-class AdjacentTempDirectory(TempDirectory):
- """Helper class that creates a temporary directory adjacent to a real one.
-
- Attributes:
- original
- The original directory to create a temp directory for.
- path
- After calling create() or entering, contains the full
- path to the temporary directory.
- delete
- Whether the directory should be deleted when exiting
- (when used as a contextmanager)
-
- """
-
- # The characters that may be used to name the temp directory
- # We always prepend a ~ and then rotate through these until
- # a usable name is found.
- # pkg_resources raises a different error for .dist-info folder
- # with leading '-' and invalid metadata
- LEADING_CHARS = "-~.=%0123456789"
-
- def __init__(self, original: str, delete: Optional[bool] = None) -> None:
- self.original = original.rstrip("/\\")
- super().__init__(delete=delete)
-
- @classmethod
- def _generate_names(cls, name: str) -> Iterator[str]:
- """Generates a series of temporary names.
-
- The algorithm replaces the leading characters in the name
- with ones that are valid filesystem characters, but are not
- valid package names (for both Python and pip definitions of
- package).
- """
- for i in range(1, len(name)):
- for candidate in itertools.combinations_with_replacement(
- cls.LEADING_CHARS, i - 1
- ):
- new_name = "~" + "".join(candidate) + name[i:]
- if new_name != name:
- yield new_name
-
- # If we make it this far, we will have to make a longer name
- for i in range(len(cls.LEADING_CHARS)):
- for candidate in itertools.combinations_with_replacement(
- cls.LEADING_CHARS, i
- ):
- new_name = "~" + "".join(candidate) + name
- if new_name != name:
- yield new_name
-
- def _create(self, kind: str) -> str:
- root, name = os.path.split(self.original)
- for candidate in self._generate_names(name):
- path = os.path.join(root, candidate)
- try:
- os.mkdir(path)
- except OSError as ex:
- # Continue if the name exists already
- if ex.errno != errno.EEXIST:
- raise
- else:
- path = os.path.realpath(path)
- break
- else:
- # Final fallback on the default behavior.
- path = os.path.realpath(tempfile.mkdtemp(prefix=f"pip-{kind}-"))
-
- logger.debug("Created temporary directory: %s", path)
- return path
diff --git a/spaces/anaclaudia13ct/insect_detection/utils/segment/augmentations.py b/spaces/anaclaudia13ct/insect_detection/utils/segment/augmentations.py
deleted file mode 100644
index 169addedf0f58cf37e774e6a85eddff6eebc30be..0000000000000000000000000000000000000000
--- a/spaces/anaclaudia13ct/insect_detection/utils/segment/augmentations.py
+++ /dev/null
@@ -1,104 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Image augmentation functions
-"""
-
-import math
-import random
-
-import cv2
-import numpy as np
-
-from ..augmentations import box_candidates
-from ..general import resample_segments, segment2box
-
-
-def mixup(im, labels, segments, im2, labels2, segments2):
- # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
- r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
- im = (im * r + im2 * (1 - r)).astype(np.uint8)
- labels = np.concatenate((labels, labels2), 0)
- segments = np.concatenate((segments, segments2), 0)
- return im, labels, segments
-
-
-def random_perspective(im,
- targets=(),
- segments=(),
- degrees=10,
- translate=.1,
- scale=.1,
- shear=10,
- perspective=0.0,
- border=(0, 0)):
- # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
- # targets = [cls, xyxy]
-
- height = im.shape[0] + border[0] * 2 # shape(h,w,c)
- width = im.shape[1] + border[1] * 2
-
- # Center
- C = np.eye(3)
- C[0, 2] = -im.shape[1] / 2 # x translation (pixels)
- C[1, 2] = -im.shape[0] / 2 # y translation (pixels)
-
- # Perspective
- P = np.eye(3)
- P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
- P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
-
- # Rotation and Scale
- R = np.eye(3)
- a = random.uniform(-degrees, degrees)
- # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
- s = random.uniform(1 - scale, 1 + scale)
- # s = 2 ** random.uniform(-scale, scale)
- R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
-
- # Shear
- S = np.eye(3)
- S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
- S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
-
- # Translation
- T = np.eye(3)
- T[0, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * width) # x translation (pixels)
- T[1, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * height) # y translation (pixels)
-
- # Combined rotation matrix
- M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
- if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
- if perspective:
- im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
- else: # affine
- im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
-
- # Visualize
- # import matplotlib.pyplot as plt
- # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
- # ax[0].imshow(im[:, :, ::-1]) # base
- # ax[1].imshow(im2[:, :, ::-1]) # warped
-
- # Transform label coordinates
- n = len(targets)
- new_segments = []
- if n:
- new = np.zeros((n, 4))
- segments = resample_segments(segments) # upsample
- for i, segment in enumerate(segments):
- xy = np.ones((len(segment), 3))
- xy[:, :2] = segment
- xy = xy @ M.T # transform
- xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]) # perspective rescale or affine
-
- # clip
- new[i] = segment2box(xy, width, height)
- new_segments.append(xy)
-
- # filter candidates
- i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01)
- targets = targets[i]
- targets[:, 1:5] = new[i]
- new_segments = np.array(new_segments)[i]
-
- return im, targets, new_segments
diff --git a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/models/vitseg.py b/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/models/vitseg.py
deleted file mode 100644
index ed621431ddf930fcfa27b5929999776b96fede63..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/models/vitseg.py
+++ /dev/null
@@ -1,286 +0,0 @@
-import math
-from posixpath import basename, dirname, join
-# import clip
-from clip.model import convert_weights
-import torch
-import json
-from torch import nn
-from torch.nn import functional as nnf
-from torch.nn.modules import activation
-from torch.nn.modules.activation import ReLU
-from torchvision import transforms
-
-normalize = transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
-
-from torchvision.models import ResNet
-
-
-def process_prompts(conditional, prompt_list, conditional_map):
- # DEPRECATED
-
- # randomly sample a synonym
- words = [conditional_map[int(i)] for i in conditional]
- words = [syns[torch.multinomial(torch.ones(len(syns)), 1, replacement=True).item()] for syns in words]
- words = [w.replace('_', ' ') for w in words]
-
- if prompt_list is not None:
- prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
- prompts = [prompt_list[i] for i in prompt_indices]
- else:
- prompts = ['a photo of {}'] * (len(words))
-
- return [promt.format(w) for promt, w in zip(prompts, words)]
-
-
-class VITDenseBase(nn.Module):
-
- def rescaled_pos_emb(self, new_size):
- assert len(new_size) == 2
-
- a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape)
- b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T
- return torch.cat([self.model.positional_embedding[:1], b])
-
- def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None):
-
- with torch.no_grad():
-
- x_inp = nnf.interpolate(x_inp, (384, 384))
-
- x = self.model.patch_embed(x_inp)
- cls_token = self.model.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks
- if self.model.dist_token is None:
- x = torch.cat((cls_token, x), dim=1)
- else:
- x = torch.cat((cls_token, self.model.dist_token.expand(x.shape[0], -1, -1), x), dim=1)
- x = self.model.pos_drop(x + self.model.pos_embed)
-
- activations = []
- for i, block in enumerate(self.model.blocks):
- x = block(x)
-
- if i in extract_layers:
- # permute to be compatible with CLIP
- activations += [x.permute(1,0,2)]
-
- x = self.model.norm(x)
- x = self.model.head(self.model.pre_logits(x[:, 0]))
-
- # again for CLIP compatibility
- # x = x.permute(1, 0, 2)
-
- return x, activations, None
-
- def sample_prompts(self, words, prompt_list=None):
-
- prompt_list = prompt_list if prompt_list is not None else self.prompt_list
-
- prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
- prompts = [prompt_list[i] for i in prompt_indices]
- return [promt.format(w) for promt, w in zip(prompts, words)]
-
- def get_cond_vec(self, conditional, batch_size):
- # compute conditional from a single string
- if conditional is not None and type(conditional) == str:
- cond = self.compute_conditional(conditional)
- cond = cond.repeat(batch_size, 1)
-
- # compute conditional from string list/tuple
- elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str:
- assert len(conditional) == batch_size
- cond = self.compute_conditional(conditional)
-
- # use conditional directly
- elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2:
- cond = conditional
-
- # compute conditional from image
- elif conditional is not None and type(conditional) == torch.Tensor:
- with torch.no_grad():
- cond, _, _ = self.visual_forward(conditional)
- else:
- raise ValueError('invalid conditional')
- return cond
-
- def compute_conditional(self, conditional):
- import clip
-
- dev = next(self.parameters()).device
-
- if type(conditional) in {list, tuple}:
- text_tokens = clip.tokenize(conditional).to(dev)
- cond = self.clip_model.encode_text(text_tokens)
- else:
- if conditional in self.precomputed_prompts:
- cond = self.precomputed_prompts[conditional].float().to(dev)
- else:
- text_tokens = clip.tokenize([conditional]).to(dev)
- cond = self.clip_model.encode_text(text_tokens)[0]
-
- return cond
-
-
-class VITDensePredT(VITDenseBase):
-
- def __init__(self, extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed',
- depth=3, extra_blocks=0, reduce_cond=None, fix_shift=False,
- learn_trans_conv_only=False, refine=None, limit_to_clip_only=False, upsample=False,
- add_calibration=False, process_cond=None, not_pretrained=False):
- super().__init__()
- # device = 'cpu'
-
- self.extract_layers = extract_layers
- self.cond_layer = cond_layer
- self.limit_to_clip_only = limit_to_clip_only
- self.process_cond = None
-
- if add_calibration:
- self.calibration_conds = 1
-
- self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None
-
- self.add_activation1 = True
-
- import timm
- self.model = timm.create_model('vit_base_patch16_384', pretrained=True)
- self.model.head = nn.Linear(768, 512 if reduce_cond is None else reduce_cond)
-
- for p in self.model.parameters():
- p.requires_grad_(False)
-
- import clip
- self.clip_model, _ = clip.load('ViT-B/16', device='cpu', jit=False)
- # del self.clip_model.visual
-
-
- self.token_shape = (14, 14)
-
- # conditional
- if reduce_cond is not None:
- self.reduce_cond = nn.Linear(512, reduce_cond)
- for p in self.reduce_cond.parameters():
- p.requires_grad_(False)
- else:
- self.reduce_cond = None
-
- # self.film = AVAILABLE_BLOCKS['film'](512, 128)
- self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
- self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
-
- # DEPRECATED
- # self.conditional_map = {c['id']: c['synonyms'] for c in json.load(open(cond_map))}
-
- assert len(self.extract_layers) == depth
-
- self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)])
- self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))])
- self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)])
-
- trans_conv_ks = (16, 16)
- self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
-
- # refinement and trans conv
-
- if learn_trans_conv_only:
- for p in self.parameters():
- p.requires_grad_(False)
-
- for p in self.trans_conv.parameters():
- p.requires_grad_(True)
-
- if prompt == 'fixed':
- self.prompt_list = ['a photo of a {}.']
- elif prompt == 'shuffle':
- self.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
- elif prompt == 'shuffle+':
- self.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.',
- 'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.',
- 'a bad photo of a {}.', 'a photo of the {}.']
- elif prompt == 'shuffle_clip':
- from models.clip_prompts import imagenet_templates
- self.prompt_list = imagenet_templates
-
- if process_cond is not None:
- if process_cond == 'clamp' or process_cond[0] == 'clamp':
-
- val = process_cond[1] if type(process_cond) in {list, tuple} else 0.2
-
- def clamp_vec(x):
- return torch.clamp(x, -val, val)
-
- self.process_cond = clamp_vec
-
- elif process_cond.endswith('.pth'):
-
- shift = torch.load(process_cond)
- def add_shift(x):
- return x + shift.to(x.device)
-
- self.process_cond = add_shift
-
- import pickle
- precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
- self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}
-
-
- def forward(self, inp_image, conditional=None, return_features=False, mask=None):
-
- assert type(return_features) == bool
-
- # inp_image = inp_image.to(self.model.positional_embedding.device)
-
- if mask is not None:
- raise ValueError('mask not supported')
-
- # x_inp = normalize(inp_image)
- x_inp = inp_image
-
- bs, dev = inp_image.shape[0], x_inp.device
-
- inp_image_size = inp_image.shape[2:]
-
- cond = self.get_cond_vec(conditional, bs)
-
- visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers))
-
- activation1 = activations[0]
- activations = activations[1:]
-
- a = None
- for i, (activation, block, reduce) in enumerate(zip(activations[::-1], self.blocks, self.reduces)):
-
- if a is not None:
- a = reduce(activation) + a
- else:
- a = reduce(activation)
-
- if i == self.cond_layer:
- if self.reduce_cond is not None:
- cond = self.reduce_cond(cond)
-
- a = self.film_mul(cond) * a + self.film_add(cond)
-
- a = block(a)
-
- for block in self.extra_blocks:
- a = a + block(a)
-
- a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
-
- size = int(math.sqrt(a.shape[2]))
-
- a = a.view(bs, a.shape[1], size, size)
-
- if self.trans_conv is not None:
- a = self.trans_conv(a)
-
- if self.upsample_proj is not None:
- a = self.upsample_proj(a)
- a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear')
-
- a = nnf.interpolate(a, inp_image_size)
-
- if return_features:
- return a, visual_q, cond, [activation1] + activations
- else:
- return a,
diff --git a/spaces/aodianyun/stable-diffusion-webui/modules/script_callbacks.py b/spaces/aodianyun/stable-diffusion-webui/modules/script_callbacks.py
deleted file mode 100644
index c98c2395b6fe46ddec2a10cc6a54ee0f3ba248f5..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/modules/script_callbacks.py
+++ /dev/null
@@ -1,359 +0,0 @@
-import sys
-import traceback
-from collections import namedtuple
-import inspect
-from typing import Optional, Dict, Any
-
-from fastapi import FastAPI
-from gradio import Blocks
-
-
-def report_exception(c, job):
- print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
-
-class ImageSaveParams:
- def __init__(self, image, p, filename, pnginfo):
- self.image = image
- """the PIL image itself"""
-
- self.p = p
- """p object with processing parameters; either StableDiffusionProcessing or an object with same fields"""
-
- self.filename = filename
- """name of file that the image would be saved to"""
-
- self.pnginfo = pnginfo
- """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""
-
-
-class CFGDenoiserParams:
- def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps):
- self.x = x
- """Latent image representation in the process of being denoised"""
-
- self.image_cond = image_cond
- """Conditioning image"""
-
- self.sigma = sigma
- """Current sigma noise step value"""
-
- self.sampling_step = sampling_step
- """Current Sampling step number"""
-
- self.total_sampling_steps = total_sampling_steps
- """Total number of sampling steps planned"""
-
-
-class CFGDenoisedParams:
- def __init__(self, x, sampling_step, total_sampling_steps):
- self.x = x
- """Latent image representation in the process of being denoised"""
-
- self.sampling_step = sampling_step
- """Current Sampling step number"""
-
- self.total_sampling_steps = total_sampling_steps
- """Total number of sampling steps planned"""
-
-
-class UiTrainTabParams:
- def __init__(self, txt2img_preview_params):
- self.txt2img_preview_params = txt2img_preview_params
-
-
-class ImageGridLoopParams:
- def __init__(self, imgs, cols, rows):
- self.imgs = imgs
- self.cols = cols
- self.rows = rows
-
-
-ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
-callback_map = dict(
- callbacks_app_started=[],
- callbacks_model_loaded=[],
- callbacks_ui_tabs=[],
- callbacks_ui_train_tabs=[],
- callbacks_ui_settings=[],
- callbacks_before_image_saved=[],
- callbacks_image_saved=[],
- callbacks_cfg_denoiser=[],
- callbacks_cfg_denoised=[],
- callbacks_before_component=[],
- callbacks_after_component=[],
- callbacks_image_grid=[],
- callbacks_infotext_pasted=[],
- callbacks_script_unloaded=[],
- callbacks_before_ui=[],
-)
-
-
-def clear_callbacks():
- for callback_list in callback_map.values():
- callback_list.clear()
-
-
-def app_started_callback(demo: Optional[Blocks], app: FastAPI):
- for c in callback_map['callbacks_app_started']:
- try:
- c.callback(demo, app)
- except Exception:
- report_exception(c, 'app_started_callback')
-
-
-def model_loaded_callback(sd_model):
- for c in callback_map['callbacks_model_loaded']:
- try:
- c.callback(sd_model)
- except Exception:
- report_exception(c, 'model_loaded_callback')
-
-
-def ui_tabs_callback():
- res = []
-
- for c in callback_map['callbacks_ui_tabs']:
- try:
- res += c.callback() or []
- except Exception:
- report_exception(c, 'ui_tabs_callback')
-
- return res
-
-
-def ui_train_tabs_callback(params: UiTrainTabParams):
- for c in callback_map['callbacks_ui_train_tabs']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'callbacks_ui_train_tabs')
-
-
-def ui_settings_callback():
- for c in callback_map['callbacks_ui_settings']:
- try:
- c.callback()
- except Exception:
- report_exception(c, 'ui_settings_callback')
-
-
-def before_image_saved_callback(params: ImageSaveParams):
- for c in callback_map['callbacks_before_image_saved']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'before_image_saved_callback')
-
-
-def image_saved_callback(params: ImageSaveParams):
- for c in callback_map['callbacks_image_saved']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'image_saved_callback')
-
-
-def cfg_denoiser_callback(params: CFGDenoiserParams):
- for c in callback_map['callbacks_cfg_denoiser']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'cfg_denoiser_callback')
-
-
-def cfg_denoised_callback(params: CFGDenoisedParams):
- for c in callback_map['callbacks_cfg_denoised']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'cfg_denoised_callback')
-
-
-def before_component_callback(component, **kwargs):
- for c in callback_map['callbacks_before_component']:
- try:
- c.callback(component, **kwargs)
- except Exception:
- report_exception(c, 'before_component_callback')
-
-
-def after_component_callback(component, **kwargs):
- for c in callback_map['callbacks_after_component']:
- try:
- c.callback(component, **kwargs)
- except Exception:
- report_exception(c, 'after_component_callback')
-
-
-def image_grid_callback(params: ImageGridLoopParams):
- for c in callback_map['callbacks_image_grid']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'image_grid')
-
-
-def infotext_pasted_callback(infotext: str, params: Dict[str, Any]):
- for c in callback_map['callbacks_infotext_pasted']:
- try:
- c.callback(infotext, params)
- except Exception:
- report_exception(c, 'infotext_pasted')
-
-
-def script_unloaded_callback():
- for c in reversed(callback_map['callbacks_script_unloaded']):
- try:
- c.callback()
- except Exception:
- report_exception(c, 'script_unloaded')
-
-
-def before_ui_callback():
- for c in reversed(callback_map['callbacks_before_ui']):
- try:
- c.callback()
- except Exception:
- report_exception(c, 'before_ui')
-
-
-def add_callback(callbacks, fun):
- stack = [x for x in inspect.stack() if x.filename != __file__]
- filename = stack[0].filename if len(stack) > 0 else 'unknown file'
-
- callbacks.append(ScriptCallback(filename, fun))
-
-
-def remove_current_script_callbacks():
- stack = [x for x in inspect.stack() if x.filename != __file__]
- filename = stack[0].filename if len(stack) > 0 else 'unknown file'
- if filename == 'unknown file':
- return
- for callback_list in callback_map.values():
- for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:
- callback_list.remove(callback_to_remove)
-
-
-def remove_callbacks_for_function(callback_func):
- for callback_list in callback_map.values():
- for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:
- callback_list.remove(callback_to_remove)
-
-
-def on_app_started(callback):
- """register a function to be called when the webui started, the gradio `Block` component and
- fastapi `FastAPI` object are passed as the arguments"""
- add_callback(callback_map['callbacks_app_started'], callback)
-
-
-def on_model_loaded(callback):
- """register a function to be called when the stable diffusion model is created; the model is
- passed as an argument; this function is also called when the script is reloaded. """
- add_callback(callback_map['callbacks_model_loaded'], callback)
-
-
-def on_ui_tabs(callback):
- """register a function to be called when the UI is creating new tabs.
- The function must either return a None, which means no new tabs to be added, or a list, where
- each element is a tuple:
- (gradio_component, title, elem_id)
-
- gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks)
- title is tab text displayed to user in the UI
- elem_id is HTML id for the tab
- """
- add_callback(callback_map['callbacks_ui_tabs'], callback)
-
-
-def on_ui_train_tabs(callback):
- """register a function to be called when the UI is creating new tabs for the train tab.
- Create your new tabs with gr.Tab.
- """
- add_callback(callback_map['callbacks_ui_train_tabs'], callback)
-
-
-def on_ui_settings(callback):
- """register a function to be called before UI settings are populated; add your settings
- by using shared.opts.add_option(shared.OptionInfo(...)) """
- add_callback(callback_map['callbacks_ui_settings'], callback)
-
-
-def on_before_image_saved(callback):
- """register a function to be called before an image is saved to a file.
- The callback is called with one argument:
- - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.
- """
- add_callback(callback_map['callbacks_before_image_saved'], callback)
-
-
-def on_image_saved(callback):
- """register a function to be called after an image is saved to a file.
- The callback is called with one argument:
- - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
- """
- add_callback(callback_map['callbacks_image_saved'], callback)
-
-
-def on_cfg_denoiser(callback):
- """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
- The callback is called with one argument:
- - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
- """
- add_callback(callback_map['callbacks_cfg_denoiser'], callback)
-
-
-def on_cfg_denoised(callback):
- """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
- The callback is called with one argument:
- - params: CFGDenoisedParams - parameters to be passed to the inner model and sampling state details.
- """
- add_callback(callback_map['callbacks_cfg_denoised'], callback)
-
-
-def on_before_component(callback):
- """register a function to be called before a component is created.
- The callback is called with arguments:
- - component - gradio component that is about to be created.
- - **kwargs - args to gradio.components.IOComponent.__init__ function
-
- Use elem_id/label fields of kwargs to figure out which component it is.
- This can be useful to inject your own components somewhere in the middle of vanilla UI.
- """
- add_callback(callback_map['callbacks_before_component'], callback)
-
-
-def on_after_component(callback):
- """register a function to be called after a component is created. See on_before_component for more."""
- add_callback(callback_map['callbacks_after_component'], callback)
-
-
-def on_image_grid(callback):
- """register a function to be called before making an image grid.
- The callback is called with one argument:
- - params: ImageGridLoopParams - parameters to be used for grid creation. Can be modified.
- """
- add_callback(callback_map['callbacks_image_grid'], callback)
-
-
-def on_infotext_pasted(callback):
- """register a function to be called before applying an infotext.
- The callback is called with two arguments:
- - infotext: str - raw infotext.
- - result: Dict[str, any] - parsed infotext parameters.
- """
- add_callback(callback_map['callbacks_infotext_pasted'], callback)
-
-
-def on_script_unloaded(callback):
- """register a function to be called before the script is unloaded. Any hooks/hijacks/monkeying about that
- the script did should be reverted here"""
-
- add_callback(callback_map['callbacks_script_unloaded'], callback)
-
-
-def on_before_ui(callback):
- """register a function to be called before the UI is created."""
-
- add_callback(callback_map['callbacks_before_ui'], callback)
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/fast_pitch_config.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/fast_pitch_config.py
deleted file mode 100644
index d086d26564450c60fa04a7f3a068506f4147d3be..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/fast_pitch_config.py
+++ /dev/null
@@ -1,183 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.forward_tts import ForwardTTSArgs
-
-
-@dataclass
-class FastPitchConfig(BaseTTSConfig):
- """Configure `ForwardTTS` as FastPitch model.
-
- Example:
-
- >>> from TTS.tts.configs.fast_pitch_config import FastPitchConfig
- >>> config = FastPitchConfig()
-
- Args:
- model (str):
- Model name used for selecting the right model at initialization. Defaults to `fast_pitch`.
-
- base_model (str):
- Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
- the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
-
- model_args (Coqpit):
- Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
-
- data_dep_init_steps (int):
- Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
- Activation Normalization that pre-computes normalization stats at the beginning and use the same values
- for the rest. Defaults to 10.
-
- speakers_file (str):
- Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
- speaker names. Defaults to `None`.
-
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
-
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
-
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
-
- d_vector_dim (int):
- Dimension of the external speaker embeddings. Defaults to 0.
-
- optimizer (str):
- Name of the model optimizer. Defaults to `Adam`.
-
- optimizer_params (dict):
- Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
-
- lr_scheduler (str):
- Name of the learning rate scheduler. Defaults to `Noam`.
-
- lr_scheduler_params (dict):
- Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
-
- lr (float):
- Initial learning rate. Defaults to `1e-3`.
-
- grad_clip (float):
- Gradient norm clipping value. Defaults to `5.0`.
-
- spec_loss_type (str):
- Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- duration_loss_type (str):
- Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- use_ssim_loss (bool):
- Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
-
- wd (float):
- Weight decay coefficient. Defaults to `1e-7`.
-
- ssim_loss_alpha (float):
- Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
-
- dur_loss_alpha (float):
- Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
-
- spec_loss_alpha (float):
- Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
-
- pitch_loss_alpha (float):
- Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0.
-
- binary_align_loss_alpha (float):
- Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
-
- binary_loss_warmup_epochs (float):
- Number of epochs to gradually increase the binary loss impact. Defaults to 150.
-
- min_seq_len (int):
- Minimum input sequence length to be used at training.
-
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
-
- # dataset configs
- compute_f0(bool):
- Compute pitch. defaults to True
-
- f0_cache_path(str):
- pith cache path. defaults to None
- """
-
- model: str = "fast_pitch"
- base_model: str = "forward_tts"
-
- # model specific params
- model_args: ForwardTTSArgs = field(default_factory=ForwardTTSArgs)
-
- # multi-speaker settings
- num_speakers: int = 0
- speakers_file: str = None
- use_speaker_embedding: bool = False
- use_d_vector_file: bool = False
- d_vector_file: str = False
- d_vector_dim: int = 0
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = "NoamLR"
- lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
- lr: float = 1e-4
- grad_clip: float = 5.0
-
- # loss params
- spec_loss_type: str = "mse"
- duration_loss_type: str = "mse"
- use_ssim_loss: bool = True
- ssim_loss_alpha: float = 1.0
- spec_loss_alpha: float = 1.0
- aligner_loss_alpha: float = 1.0
- pitch_loss_alpha: float = 0.1
- dur_loss_alpha: float = 0.1
- binary_align_loss_alpha: float = 0.1
- binary_loss_warmup_epochs: int = 150
-
- # overrides
- min_seq_len: int = 13
- max_seq_len: int = 200
- r: int = 1 # DO NOT CHANGE
-
- # dataset configs
- compute_f0: bool = True
- f0_cache_path: str = None
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
-
- def __post_init__(self):
- # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
- if self.num_speakers > 0:
- self.model_args.num_speakers = self.num_speakers
-
- # speaker embedding settings
- if self.use_speaker_embedding:
- self.model_args.use_speaker_embedding = True
- if self.speakers_file:
- self.model_args.speakers_file = self.speakers_file
-
- # d-vector settings
- if self.use_d_vector_file:
- self.model_args.use_d_vector_file = True
- if self.d_vector_dim is not None and self.d_vector_dim > 0:
- self.model_args.d_vector_dim = self.d_vector_dim
- if self.d_vector_file:
- self.model_args.d_vector_file = self.d_vector_file
diff --git a/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/__init__.py b/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/__init__.py
deleted file mode 100644
index 4bae29fd5f85b41e4669302bd2603bc6924eddc7..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# -*- coding: utf-8 -*-
-
-__author__ = """Adrian Bulat"""
-__email__ = 'adrian.bulat@nottingham.ac.uk'
-__version__ = '1.0.1'
-
-from .api import FaceAlignment, LandmarksType, NetworkSize
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/scatter_with_minimap.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/scatter_with_minimap.py
deleted file mode 100644
index 7de894c43ae56bf135411e9912ca3d396aa71dec..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/scatter_with_minimap.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""
-Scatter Plot with Minimap
--------------------------
-This example shows how to create a miniature version of a plot
-such that creating a selection in the miniature version
-adjusts the axis limits in another, more detailed view.
-"""
-# category: scatter plots
-
-import altair as alt
-from vega_datasets import data
-
-source = data.seattle_weather()
-
-zoom = alt.selection_interval(encodings=["x", "y"])
-
-minimap = (
- alt.Chart(source)
- .mark_point()
- .add_selection(zoom)
- .encode(
- x="date:T",
- y="temp_max:Q",
- color=alt.condition(zoom, "weather", alt.value("lightgray")),
- )
- .properties(
- width=200,
- height=200,
- title="Minimap -- click and drag to zoom in the detail view",
- )
-)
-
-detail = (
- alt.Chart(source)
- .mark_point()
- .encode(
- x=alt.X(
- "date:T", scale=alt.Scale(domain={"selection": zoom.name, "encoding": "x"})
- ),
- y=alt.Y(
- "temp_max:Q",
- scale=alt.Scale(domain={"selection": zoom.name, "encoding": "y"}),
- ),
- color="weather",
- )
- .properties(width=600, height=400, title="Seattle weather -- detail view")
-)
-
-detail | minimap
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/distributed/legacy_distributed_data_parallel.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/distributed/legacy_distributed_data_parallel.py
deleted file mode 100644
index cd434c7372ba30ea0e6f87e084230448f53480e9..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/distributed/legacy_distributed_data_parallel.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-A modified version of the legacy DistributedDataParallel module that uses c10d
-communication primitives. This version is simpler than the latest PyTorch
-version and is useful for debugging. Notably it does not overlap gradient
-communication with the backward pass, which makes it slower but more robust
-than the PyTorch version.
-
-This version also supports the *no_sync* context manager, which allows faster
-training with `--update-freq`.
-"""
-
-from collections import OrderedDict
-from contextlib import contextmanager
-
-import torch
-from torch import nn
-
-from fairseq.distributed import utils
-
-
-class LegacyDistributedDataParallel(nn.Module):
- """Implements distributed data parallelism at the module level.
-
- A simplified version of :class:`torch.nn.parallel.DistributedDataParallel`.
- This version uses a c10d process group for communication and does not
- broadcast buffers.
-
- Args:
- module (~torch.nn.Module): module to be parallelized
- process_group: the c10d process group to be used for distributed data
- parallel all-reduction.
- buffer_size (int, optional): number of elements to buffer before
- performing all-reduce (default: 256M).
- """
-
- def __init__(self, module, process_group, buffer_size=2**28):
- super().__init__()
-
- self.module = module
- self.process_group = process_group
- self.world_size = utils.get_world_size(self.process_group)
-
- # Never use a bigger buffer than the number of model params
- self.buffer_size = min(buffer_size, sum(p.numel() for p in module.parameters()))
- self.buffer = None
-
- # We can also forcibly accumulate grads locally and only do the
- # all-reduce at some later time
- self.accumulate_grads = False
-
- # make per-device lists of parameters
- paramlists = OrderedDict()
- for param in self.module.parameters():
- device = param.device
- if paramlists.get(device) is None:
- paramlists[device] = []
- paramlists[device] += [param]
- self.per_device_params = list(paramlists.values())
-
- @contextmanager
- def no_sync(self):
- """A context manager to disable gradient synchronization."""
- old_accumulate_grads = self.accumulate_grads
- self.accumulate_grads = True
- yield
- self.accumulate_grads = old_accumulate_grads
-
- def forward(self, *inputs, **kwargs):
- return self.module(*inputs, **kwargs)
-
- def all_reduce_grads(self):
- """
- This function must be called explicitly after backward to reduce
- gradients. There is no automatic hook like c10d.
- """
-
- def all_reduce_params(params):
- buffer = self.buffer
- nonzero_buffer = False
- if len(params) > 1:
- offset = 0
- for p in params:
- sz = p.numel()
- if p.grad is not None:
- buffer[offset : offset + sz].copy_(p.grad.data.view(-1))
- nonzero_buffer = True
- else:
- buffer[offset : offset + sz].zero_()
- offset += sz
- else:
- # we only have a single grad to all-reduce
- p = params[0]
- if p.grad is not None:
- buffer = p.grad.data
- nonzero_buffer = True
- elif p.numel() <= self.buffer.numel():
- buffer = buffer[: p.numel()]
- buffer.zero_()
- else:
- buffer = torch.zeros_like(p)
-
- if nonzero_buffer:
- buffer.div_(self.world_size)
-
- utils.all_reduce(buffer, self.process_group)
-
- # copy all-reduced grads back into their original place
- offset = 0
- for p in params:
- sz = p.numel()
- if p.grad is not None:
- p.grad.data.copy_(buffer[offset : offset + sz].view_as(p))
- else:
- p.grad = buffer[offset : offset + sz].view_as(p).clone()
- offset += sz
-
- def reduction_fn():
- # This function only needs to be called once
- if self.accumulate_grads:
- return
-
- if self.buffer is None:
- self.buffer = next(self.module.parameters()).new(self.buffer_size)
-
- for params in self.per_device_params:
- # All-reduce the gradients in buckets
- offset = 0
- buffered_params = []
- for param in params:
- if not param.requires_grad:
- continue
- if param.grad is None:
- param.grad = torch.zeros_like(param)
-
- if hasattr(param, "expert"):
- # Skip gradient sync for unshared parameters
- continue
-
- if param.grad.requires_grad:
- raise RuntimeError(
- "DistributedDataParallel only works "
- "with gradients that don't require "
- "grad"
- )
- sz = param.numel()
- if sz > self.buffer.numel():
- # all-reduce big params directly
- all_reduce_params([param])
- else:
- if offset + sz > self.buffer.numel():
- all_reduce_params(buffered_params)
- offset = 0
- buffered_params.clear()
- buffered_params.append(param)
- offset += sz
-
- if len(buffered_params) > 0:
- all_reduce_params(buffered_params)
-
- reduction_fn()
diff --git a/spaces/ashutosh1919/quantum-perceptron/quantum_perceptron/__init__.py b/spaces/ashutosh1919/quantum-perceptron/quantum_perceptron/__init__.py
deleted file mode 100644
index ed4e767fa8c5310969615aa941e5b41ce6ea5099..0000000000000000000000000000000000000000
--- a/spaces/ashutosh1919/quantum-perceptron/quantum_perceptron/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from quantum_perceptron.perceptron import Perceptron
diff --git a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Saim Mehmood.html b/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Saim Mehmood.html
deleted file mode 100644
index 74fbd5bb33874558aed6731c8c07572671215e80..0000000000000000000000000000000000000000
--- a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Saim Mehmood.html
+++ /dev/null
@@ -1,134 +0,0 @@
-
-
-
- Saim Mehmood
-
-
-
-
-
- Saim Mehmood
-
-
- Interview
Saim was a menteeCareer- Moved to Canada back in Dec 2017
- grad school Jan 2018
- MSc in CS - research-oriented work
- graduated June 2020
- Sep 2019 got in touch with Omar
- Omar: "You already have a good project with your thesis", finish that up
- Covid hit, kept applying
- kept applying
- took me almost 2 years to find job
- worked as MLOps
- learned a lot throughout my journey, particularly about academia and industry
And how can you add value as a mentor?- selling yourself as a previous academic
- it's a long journey - expectation setting - you will hit a lot rejections!
- "I was getting rejection emails every day, and was happy to see them"
- market the project that you've done
- make sure they are technically sound
- Build a project if needed, post on medium
Mentorship exp- Been a TA during school
Ideal mentee- Recent CS grads would be easier, but open to other disciplines
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/drawingboard.min.css b/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/drawingboard.min.css
deleted file mode 100644
index 7d80478705de8fdceacbad40e5a85c19307be665..0000000000000000000000000000000000000000
--- a/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/drawingboard.min.css
+++ /dev/null
@@ -1,5 +0,0 @@
-/* drawingboard.js v0.4.6 - https://github.com/Leimi/drawingboard.js
-* Copyright (c) 2015 Emmanuel Pelletier
-* Licensed MIT */
-
-.drawing-board,.drawing-board *{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}.drawing-board-controls-hidden,.drawing-board-utils-hidden{display:none!important}.drawing-board{position:relative;display:block}.drawing-board-canvas-wrapper{position:relative;margin:0;border:1px solid #ddd}.drawing-board-canvas{position:absolute;top:0;left:0;width:auto;cursor:crosshair;z-index:20}.drawing-board-cursor{position:absolute;top:0;left:0;pointer-events:none;border-radius:50%;background:#ccc;background:rgba(0,0,0,.2);z-index:30}.drawing-board-control-colors-rainbows,.drawing-board-control-size .drawing-board-control-inner,.drawing-board-control-size-dropdown,.drawing-board-control>button{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;overflow:hidden;background-color:#eee;padding:2px 4px;border:1px solid #ccc;box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);-webkit-box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);height:28px}.drawing-board-control>button{cursor:pointer;min-width:28px;line-height:14px}.drawing-board-control>button:focus,.drawing-board-control>button:hover{background-color:#ddd}.drawing-board-control>button.active,.drawing-board-control>button:active{box-shadow:inset 0 1px 2px 0 rgba(0,0,0,.2);-webkit-box-shadow:inset 0 1px 2px 0 rgba(0,0,0,.2);background-color:#ddd}.drawing-board-control>button[disabled]{color:gray}.drawing-board-control>button[disabled].active,.drawing-board-control>button[disabled]:active,.drawing-board-control>button[disabled]:focus,.drawing-board-control>button[disabled]:hover{background-color:#eee;box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);-webkit-box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);cursor:default}.drawing-board-controls{margin:0 auto;text-align:center;font-size:0;display:table;border-spacing:9.33px 0;position:relative;min-height:28px}.drawing-board-controls[data-align=left]{margin:0;left:-9.33px}.drawing-board-controls[data-align=right]{margin:0 0 0 auto;right:-9.33px}.drawing-board-canvas-wrapper+.drawing-board-controls,.drawing-board-controls+.drawing-board-canvas-wrapper{margin-top:5px}.drawing-board-controls-hidden{height:0;min-height:0;padding:0;margin:0;border:0}.drawing-board-control{display:table-cell;border-collapse:separate;vertical-align:middle;font-size:16px;height:100%}.drawing-board-control-inner{position:relative;height:100%;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.drawing-board-control>button{margin:0;vertical-align:middle}.drawing-board-control-colors{font-size:0;line-height:0}.drawing-board-control-colors-current{border:1px solid #ccc;cursor:pointer;display:inline-block;width:26px;height:26px}.drawing-board-control-colors-rainbows{display:inline-block;position:absolute;left:0;top:33px;margin-left:0;z-index:100;width:250px;height:auto;padding:4px}.drawing-board-control-colors-rainbow{height:18px}.drawing-board-control-colors-picker:first-child{margin-right:5px}.drawing-board-control-colors-picker{display:inline-block;width:18px;height:18px;cursor:pointer}.drawing-board-control-colors-picker[data-color="rgba(255, 255, 255, 1)"]{width:16px;height:17px;border:1px solid #ccc;border-bottom:none}.drawing-board-control-colors-picker:hover{width:16px;height:16px;border:1px solid #555}.drawing-board-control-drawingmode>button{margin-right:2px}.drawing-board-control-drawingmode>button:last-child{margin-right:0}.drawing-board-control-drawingmode-pencil-button{overflow:hidden;background-image:url(data:image/png;base64,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);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-drawingmode-pencil-button:before{content:"";display:block;width:0;height:100%}.drawing-board-control-drawingmode-eraser-button{overflow:hidden;background-image:url(data:image/png;base64,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);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-drawingmode-eraser-button:before{content:"";display:block;width:0;height:100%}.drawing-board-control-drawingmode-filler-button{overflow:hidden;background-image:url(data:image/png;base64,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);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-drawingmode-filler-button:before{content:"";display:block;width:0;height:100%}.drawing-board-control-navigation>button{font-family:Helvetica,Arial,sans-serif;font-size:14px;font-weight:700;margin-right:2px}.drawing-board-control-navigation>button:last-child{margin-right:0}.drawing-board-control-size[data-drawing-board-type=range] .drawing-board-control-inner{width:75px}.drawing-board-control-size[data-drawing-board-type=dropdown] .drawing-board-control-inner{overflow:visible}.drawing-board-control-size-range-input{position:relative;width:100%;z-index:100;margin:0;padding:0;border:0}.drawing-board-control-size-dropdown span,.drawing-board-control-size-dropdown-current span,.drawing-board-control-size-range-current{display:block;background:#333;opacity:.8}.drawing-board-control-size-range-current{display:inline-block;opacity:.15;position:absolute;pointer-events:none;left:50%;top:50%;z-index:50}.drawing-board-control-size-dropdown-current{display:block;height:100%;width:40px;overflow:hidden;position:relative}.drawing-board-control-size-dropdown-current span{position:absolute;left:50%;top:50%}.drawing-board-control-size-dropdown{position:absolute;left:-6px;top:33px;height:auto;list-style-type:none;margin:0;padding:0;z-index:100}.drawing-board-control-size-dropdown li{display:block;padding:4px;margin:3px 0;min-height:16px}.drawing-board-control-size-dropdown li:hover{background:#ccc}.drawing-board-control-size-dropdown span{margin:0 auto}.drawing-board-control-download-button{overflow:hidden;background-image:url(data:image/png;base64,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);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-download-button:before{content:"";display:block;width:0;height:100%}
\ No newline at end of file
diff --git a/spaces/awacke1/Bloom.Generative.Writer/generators/topic_to_abstract.py b/spaces/awacke1/Bloom.Generative.Writer/generators/topic_to_abstract.py
deleted file mode 100644
index a91131db41007e46ce1d9a9772bdd7e9645b9d2b..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Bloom.Generative.Writer/generators/topic_to_abstract.py
+++ /dev/null
@@ -1,6 +0,0 @@
-
-from .model import model
-
-
-def topic_to_abstract_generator(template):
- return model('topic', template)
diff --git a/spaces/awacke1/Streamlit-Data-Synthesis-Example/app.py b/spaces/awacke1/Streamlit-Data-Synthesis-Example/app.py
deleted file mode 100644
index 636e3a14af4ceae48968e372e88f840f3425c791..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Streamlit-Data-Synthesis-Example/app.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import streamlit as st
-import pandas as pd
-
-
-def generate_hospital_data():
- # Generate hospital data
- hospitals = {
- "city": ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"],
- "state": ["NY", "CA", "IL", "TX", "AZ"],
- "bed_count": [1200, 1500, 1100, 1300, 1400],
- }
- df = pd.DataFrame(hospitals)
- return df
-
-
-def generate_state_data():
- # Generate state data
- states = {
- "state": ["NY", "CA", "IL", "TX", "AZ"],
- "population": [20000000, 40000000, 13000000, 29000000, 7000000],
- "square_miles": [54556, 163696, 57914, 268596, 113990],
- }
- df = pd.DataFrame(states)
- return df
-
-
-def merge_datasets(hospitals_df, states_df):
- # Merge hospital and state data
- merged_df = pd.merge(hospitals_df, states_df, on="state")
- return merged_df
-
-
-def calculate_beds_per_capita(merged_df):
- # Calculate beds per capita
- merged_df["beds_per_capita"] = merged_df["bed_count"] / merged_df["population"]
- return merged_df
-
-
-def main():
- # Generate data
- hospitals_df = generate_hospital_data()
- states_df = generate_state_data()
-
- # Merge datasets
- merged_df = merge_datasets(hospitals_df, states_df)
-
- # Calculate beds per capita
- merged_df = calculate_beds_per_capita(merged_df)
-
- # Show merged and calculated data
- st.write(merged_df)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/class_names/README.md b/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/class_names/README.md
deleted file mode 100644
index 30d74d258442c7c65512eafab474568dd706c430..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/class_names/README.md
+++ /dev/null
@@ -1 +0,0 @@
-test
\ No newline at end of file
diff --git a/spaces/badayvedat/AudioSep/models/CLAP/open_clip/bert.py b/spaces/badayvedat/AudioSep/models/CLAP/open_clip/bert.py
deleted file mode 100644
index a83d96d2a77ed05198efc05837522bc88d2499cc..0000000000000000000000000000000000000000
--- a/spaces/badayvedat/AudioSep/models/CLAP/open_clip/bert.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from transformers import BertTokenizer, BertModel
-
-tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-model = BertModel.from_pretrained("bert-base-uncased")
-text = "Replace me by any text you'd like."
-
-
-def bert_embeddings(text):
- # text = "Replace me by any text you'd like."
- encoded_input = tokenizer(text, return_tensors="pt")
- output = model(**encoded_input)
- return output
-
-
-from transformers import RobertaTokenizer, RobertaModel
-
-tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
-model = RobertaModel.from_pretrained("roberta-base")
-text = "Replace me by any text you'd like."
-
-
-def Roberta_embeddings(text):
- # text = "Replace me by any text you'd like."
- encoded_input = tokenizer(text, return_tensors="pt")
- output = model(**encoded_input)
- return output
-
-
-from transformers import BartTokenizer, BartModel
-
-tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
-model = BartModel.from_pretrained("facebook/bart-base")
-text = "Replace me by any text you'd like."
-
-
-def bart_embeddings(text):
- # text = "Replace me by any text you'd like."
- encoded_input = tokenizer(text, return_tensors="pt")
- output = model(**encoded_input)
- return output
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DAmmoLoader.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DAmmoLoader.js
deleted file mode 100644
index 9239e109afef47ff228e1fa2005fd9a6643da7ae..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DAmmoLoader.js
+++ /dev/null
@@ -1,590 +0,0 @@
-/**
- * SEA3D+AMMO for Three.JS
- * @author Sunag / http://www.sunag.com.br/
- */
-
-'use strict';
-
-THREE.SEA3D.prototype.toAmmoVec3 = function ( v ) {
-
- return new Ammo.btVector3( v.x, v.y, v.z );
-
-};
-
-//
-// Sphere
-//
-
-THREE.SEA3D.prototype.readSphere = function ( sea ) {
-
- var shape = new Ammo.btSphereShape( sea.radius );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Box
-//
-
-THREE.SEA3D.prototype.readBox = function ( sea ) {
-
- var shape = new Ammo.btBoxShape( new Ammo.btVector3( sea.width * .5, sea.height * .5, sea.depth * .5 ) );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Cone
-//
-
-THREE.SEA3D.prototype.readCone = function ( sea ) {
-
- var shape = new Ammo.btConeShape( sea.radius, sea.height );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Cylinder
-//
-
-THREE.SEA3D.prototype.readCylinder = function ( sea ) {
-
- var shape = new Ammo.btCylinderShape( new Ammo.btVector3( sea.height, sea.radius, sea.radius ) );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Capsule
-//
-
-THREE.SEA3D.prototype.readCapsule = function ( sea ) {
-
- var shape = new Ammo.btCapsuleShape( sea.radius, sea.height );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Convex Geometry
-//
-
-THREE.SEA3D.prototype.readConvexGeometry = function ( sea ) {
-
- if ( this.config.convexHull ) {
-
- var shape = SEA3D.AMMO.createConvexHull( sea.geometry.tag, sea.subGeometryIndex );
-
- } else {
-
- var triMesh = SEA3D.AMMO.createTriangleMesh( sea.geometry.tag, sea.subGeometryIndex );
-
- var shape = new Ammo.btConvexTriangleMeshShape( triMesh, true );
-
- }
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Triangle Geometry
-//
-
-THREE.SEA3D.prototype.readTriangleGeometry = function ( sea ) {
-
- var triMesh = SEA3D.AMMO.createTriangleMesh( sea.geometry.tag, sea.subGeometryIndex );
-
- var shape = new Ammo.btBvhTriangleMeshShape( triMesh, true, true );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Compound
-//
-
-THREE.SEA3D.prototype.readCompound = function ( sea ) {
-
- var shape = new Ammo.btCompoundShape();
-
- for ( var i = 0; i < sea.compounds.length; i ++ ) {
-
- var compound = sea.compounds[ i ];
-
- THREE.SEA3D.MTXBUF.elements = compound.transform;
-
- var transform = SEA3D.AMMO.getTransformFromMatrix( THREE.SEA3D.MTXBUF );
-
- shape.addChildShape( transform, compound.shape.tag );
-
- }
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Rigid Body Base
-//
-
-THREE.SEA3D.prototype.readRigidBodyBase = function ( sea ) {
-
- var shape = sea.shape.tag,
- transform, target;
-
- if ( sea.target ) {
-
- target = sea.target.tag;
-
- target.physics = { enabled: true };
- target.updateMatrix();
-
- transform = SEA3D.AMMO.getTransformFromMatrix( sea.target.tag.matrix );
-
- } else {
-
- THREE.SEA3D.MTXBUF.fromArray( sea.transform );
-
- transform = SEA3D.AMMO.getTransformFromMatrix( THREE.SEA3D.MTXBUF );
-
- }
-
- var motionState = new Ammo.btDefaultMotionState( transform );
- var localInertia = new Ammo.btVector3( 0, 0, 0 );
-
- shape.calculateLocalInertia( sea.mass, localInertia );
-
- var info = new Ammo.btRigidBodyConstructionInfo( sea.mass, motionState, shape, localInertia );
- info.set_m_friction( sea.friction );
- info.set_m_restitution( sea.restitution );
- info.set_m_linearDamping( sea.linearDamping );
- info.set_m_angularDamping( sea.angularDamping );
-
- var rb = new Ammo.btRigidBody( info );
-
- if ( target ) {
-
- target.physics.rigidBody = rb;
-
- if ( sea.offset ) {
-
- var offset = new THREE.Matrix4();
- offset.fromArray( sea.offset );
-
- target.physics.offset = offset;
-
- }
-
- }
-
- Ammo.destroy( info );
-
- this.domain.rigidBodies = this.rigidBodies = this.rigidBodies || [];
- this.rigidBodies.push( this.objects[ "rb/" + sea.name ] = sea.tag = rb );
-
- return rb;
-
-};
-
-//
-// Rigid Body
-//
-
-THREE.SEA3D.prototype.readRigidBody = function ( sea ) {
-
- var rb = this.readRigidBodyBase( sea );
-
- SEA3D.AMMO.addRigidBody( rb, sea.target ? sea.target.tag : undefined, this.config.enabledPhysics );
-
-};
-
-//
-// Car Controller
-//
-
-THREE.SEA3D.prototype.readCarController = function ( sea ) {
-
- var body = this.readRigidBodyBase( sea );
-
- body.setActivationState( SEA3D.AMMO.DISABLE_DEACTIVATION );
-
- // Car
-
- var vehicleRayCaster = new Ammo.btDefaultVehicleRaycaster( SEA3D.AMMO.world );
-
- var tuning = new Ammo.btVehicleTuning();
-
- tuning.set_m_suspensionStiffness( sea.suspensionStiffness );
- tuning.set_m_suspensionDamping( sea.suspensionDamping );
- tuning.set_m_suspensionCompression( sea.suspensionCompression );
- tuning.set_m_maxSuspensionTravelCm( sea.maxSuspensionTravelCm );
- tuning.set_m_maxSuspensionForce( sea.maxSuspensionForce );
- tuning.set_m_frictionSlip( sea.frictionSlip );
-
- var vehicle = new Ammo.btRaycastVehicle( tuning, body, vehicleRayCaster ),
- wheels = [];
-
- vehicle.setCoordinateSystem( 0, 1, 2 );
-
- for ( var i = 0; i < sea.wheel.length; i ++ ) {
-
- var wheel = sea.wheel[ i ];
-
- var wheelInfo = vehicle.addWheel(
- this.toAmmoVec3( wheel.pos ),
- this.toAmmoVec3( wheel.dir ),
- this.toAmmoVec3( wheel.axle ),
- wheel.suspensionRestLength,
- wheel.radius,
- tuning,
- wheel.isFront
- );
-
- var target = wheels[ i ] = wheel.target ? wheel.target.tag : undefined;
-
- if ( target ) {
-
- target.physics = { enabled: true, rigidBody: wheelInfo };
-
- if ( wheel.offset ) {
-
- var offset = new THREE.Matrix4();
- offset.fromArray( wheel.offset );
-
- target.physics.offset = offset;
-
- }
-
- if ( target.parent ) {
-
- target.parent.remove( target );
-
- }
-
- if ( this.container ) {
-
- this.container.add( target );
-
- }
-
- }
-
- wheelInfo.set_m_suspensionStiffness( sea.suspensionStiffness );
- wheelInfo.set_m_wheelsDampingRelaxation( sea.dampingRelaxation );
- wheelInfo.set_m_wheelsDampingCompression( sea.dampingCompression );
- wheelInfo.set_m_frictionSlip( sea.frictionSlip );
-
- }
-
- SEA3D.AMMO.addVehicle( vehicle, wheels );
- SEA3D.AMMO.addRigidBody( body, sea.target ? sea.target.tag : undefined, this.config.enabledPhysics );
-
- this.domain.vehicles = this.vehicles = this.vehicles || [];
- this.vehicles.push( this.objects[ "vhc/" + sea.name ] = sea.tag = vehicle );
-
-};
-
-//
-// P2P Constraint
-//
-
-THREE.SEA3D.prototype.readP2PConstraint = function ( sea ) {
-
- var ctrt;
-
- if ( sea.targetB ) {
-
- ctrt = new Ammo.btPoint2PointConstraint(
- sea.targetA.tag,
- sea.targetB.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.pointB )
- );
-
- } else {
-
- ctrt = new Ammo.btPoint2PointConstraint(
- sea.targetA.tag,
- this.toAmmoVec3( sea.pointA )
- );
-
- }
-
- SEA3D.AMMO.addConstraint( ctrt );
-
- this.domain.constraints = this.constraints = this.constraints || [];
- this.constraints.push( this.objects[ "ctnt/" + sea.name ] = sea.tag = ctrt );
-
-};
-
-//
-// Hinge Constraint
-//
-
-THREE.SEA3D.prototype.readHingeConstraint = function ( sea ) {
-
- var ctrt;
-
- if ( sea.targetB ) {
-
- ctrt = new Ammo.btHingeConstraint(
- sea.targetA.tag,
- sea.targetB.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.pointB ),
- this.toAmmoVec3( sea.axisA ),
- this.toAmmoVec3( sea.axisB ),
- false
- );
-
- } else {
-
- ctrt = new Ammo.btHingeConstraint(
- sea.targetA.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.axisA ),
- false
- );
-
- }
-
- if ( sea.limit ) {
-
- ctrt.setLimit( sea.limit.low, sea.limit.high, sea.limit.softness, sea.limit.biasFactor, sea.limit.relaxationFactor );
-
- }
-
- if ( sea.angularMotor ) {
-
- ctrt.enableAngularMotor( true, sea.angularMotor.velocity, sea.angularMotor.impulse );
-
- }
-
- SEA3D.AMMO.addConstraint( ctrt );
-
- this.domain.constraints = this.constraints = this.constraints || [];
- this.constraints.push( this.objects[ "ctnt/" + sea.name ] = sea.tag = ctrt );
-
-};
-
-//
-// Cone Twist Constraint
-//
-
-THREE.SEA3D.prototype.readConeTwistConstraint = function ( sea ) {
-
- var ctrt;
-
- if ( sea.targetB ) {
-
- ctrt = new Ammo.btConeTwistConstraint(
- sea.targetA.tag,
- sea.targetB.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.pointB ),
- false
- );
-
- } else {
-
- ctrt = new Ammo.btConeTwistConstraint(
- sea.targetA.tag,
- this.toAmmoVec3( sea.pointA ),
- false
- );
-
- }
-
- SEA3D.AMMO.addConstraint( ctrt );
-
- this.domain.constraints = this.constraints = this.constraints || [];
- this.constraints.push( this.objects[ "ctnt/" + sea.name ] = sea.tag = ctrt );
-
-};
-
-//
-// Domain
-//
-
-THREE.SEA3D.Domain.prototype.enabledPhysics = function ( enabled ) {
-
- var i = this.rigidBodies ? this.rigidBodies.length : 0;
-
- while ( i -- ) {
-
- SEA3D.AMMO.setEnabledRigidBody( this.rigidBodies[ i ], enabled );
-
- }
-
-};
-
-THREE.SEA3D.Domain.prototype.applyContainerTransform = function () {
-
- this.container.updateMatrix();
-
- var matrix = this.container.matrix.clone();
-
- this.container.position.set( 0, 0, 0 );
- this.container.quaternion.set( 0, 0, 0, 1 );
- this.container.scale.set( 1, 1, 1 );
-
- this.applyTransform( matrix );
-
-};
-
-THREE.SEA3D.Domain.prototype.applyTransform = function ( matrix ) {
-
- var mtx = THREE.SEA3D.MTXBUF, vec = THREE.SEA3D.VECBUF;
-
- var i = this.rigidBodies ? this.rigidBodies.length : 0,
- childs = this.container ? this.container.children : [],
- targets = [];
-
- while ( i -- ) {
-
- var rb = this.rigidBodies[ i ],
- target = SEA3D.AMMO.getTargetByRigidBody( rb ),
- transform = rb.getWorldTransform(),
- transformMatrix = SEA3D.AMMO.getMatrixFromTransform( transform );
-
- transformMatrix.multiplyMatrices( transformMatrix, matrix );
-
- transform = SEA3D.AMMO.getTransformFromMatrix( transformMatrix );
-
- rb.setWorldTransform( transform );
-
- if ( target ) targets.push( target );
-
- }
-
- for ( i = 0; i < childs.length; i ++ ) {
-
- var obj3d = childs[ i ];
-
- if ( targets.indexOf( obj3d ) > - 1 ) continue;
-
- obj3d.updateMatrix();
-
- mtx.copy( obj3d.matrix );
-
- mtx.multiplyMatrices( matrix, mtx );
-
- obj3d.position.setFromMatrixPosition( mtx );
- obj3d.scale.setFromMatrixScale( mtx );
-
- // ignore rotation scale
-
- mtx.scale( vec.set( 1 / obj3d.scale.x, 1 / obj3d.scale.y, 1 / obj3d.scale.z ) );
- obj3d.quaternion.setFromRotationMatrix( mtx );
-
- }
-
-};
-
-//
-// Extension
-//
-
-THREE.SEA3D.Domain.prototype.getShape = THREE.SEA3D.prototype.getShape = function ( name ) {
-
- return this.objects[ "shpe/" + name ];
-
-};
-
-THREE.SEA3D.Domain.prototype.getRigidBody = THREE.SEA3D.prototype.getRigidBody = function ( name ) {
-
- return this.objects[ "rb/" + name ];
-
-};
-
-THREE.SEA3D.Domain.prototype.getConstraint = THREE.SEA3D.prototype.getConstraint = function ( name ) {
-
- return this.objects[ "ctnt/" + name ];
-
-};
-
-THREE.SEA3D.EXTENSIONS_LOADER.push( {
-
- parse: function () {
-
- delete this.shapes;
- delete this.rigidBodies;
- delete this.vehicles;
- delete this.constraints;
-
- },
-
- setTypeRead: function () {
-
- // CONFIG
-
- this.config.physics = this.config.physics !== undefined ? this.config.physics : true;
- this.config.convexHull = this.config.convexHull !== undefined ? this.config.convexHull : true;
- this.config.enabledPhysics = this.config.enabledPhysics !== undefined ? this.config.enabledPhysics : true;
-
- if ( this.config.physics ) {
-
- // SHAPES
-
- this.file.typeRead[ SEA3D.Sphere.prototype.type ] = this.readSphere;
- this.file.typeRead[ SEA3D.Box.prototype.type ] = this.readBox;
- this.file.typeRead[ SEA3D.Capsule.prototype.type ] = this.readCapsule;
- this.file.typeRead[ SEA3D.Cone.prototype.type ] = this.readCone;
- this.file.typeRead[ SEA3D.Cylinder.prototype.type ] = this.readCylinder;
- this.file.typeRead[ SEA3D.ConvexGeometry.prototype.type ] = this.readConvexGeometry;
- this.file.typeRead[ SEA3D.TriangleGeometry.prototype.type ] = this.readTriangleGeometry;
- this.file.typeRead[ SEA3D.Compound.prototype.type ] = this.readCompound;
-
- // CONSTRAINTS
-
- this.file.typeRead[ SEA3D.P2PConstraint.prototype.type ] = this.readP2PConstraint;
- this.file.typeRead[ SEA3D.HingeConstraint.prototype.type ] = this.readHingeConstraint;
- this.file.typeRead[ SEA3D.ConeTwistConstraint.prototype.type ] = this.readConeTwistConstraint;
-
- // PHYSICS
-
- this.file.typeRead[ SEA3D.RigidBody.prototype.type ] = this.readRigidBody;
- this.file.typeRead[ SEA3D.CarController.prototype.type ] = this.readCarController;
-
- }
-
- }
-} );
-
-THREE.SEA3D.EXTENSIONS_DOMAIN.push( {
-
- dispose: function () {
-
- var i;
-
- i = this.rigidBodies ? this.rigidBodies.length : 0;
- while ( i -- ) SEA3D.AMMO.removeRigidBody( this.rigidBodies[ i ], true );
-
- i = this.vehicles ? this.vehicles.length : 0;
- while ( i -- ) SEA3D.AMMO.removeVehicle( this.vehicles[ i ], true );
-
- i = this.constraints ? this.constraints.length : 0;
- while ( i -- ) SEA3D.AMMO.removeConstraint( this.constraints[ i ], true );
-
- i = this.shapes ? this.shapes.length : 0;
- while ( i -- ) Ammo.destroy( this.shapes[ i ] );
-
- }
-
-} );
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/ShadowMapViewer.js b/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/ShadowMapViewer.js
deleted file mode 100644
index 340efd3f339f776062b01678a0b75adaa67e5623..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/ShadowMapViewer.js
+++ /dev/null
@@ -1,208 +0,0 @@
-/**
- * @author arya-s / https://github.com/arya-s
- *
- * This is a helper for visualising a given light's shadow map.
- * It works for shadow casting lights: THREE.DirectionalLight and THREE.SpotLight.
- * It renders out the shadow map and displays it on a HUD.
- *
- * Example usage:
- * 1) Include \n\t\t" + head + "\n\t\n\t\n\t\t" + body + "\n\t\t\n
Open-World Detection with Grounding DINO")
- gr.Markdown("Note the model runs on CPU, so it may take a while to run the model.")
-
- with gr.Row():
- with gr.Column():
- input_image = gr.Image(source='upload', type="pil")
- grounding_caption = gr.Textbox(label="Detection Prompt")
- run_button = gr.Button(label="Run")
- with gr.Accordion("Advanced options", open=False):
- box_threshold = gr.Slider(
- label="Box Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
- )
- text_threshold = gr.Slider(
- label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
- )
-
- with gr.Column():
- gallery = gr.outputs.Image(
- type="pil",
- # label="grounding results"
- ).style(full_width=True, full_height=True)
- # gallery = gr.Gallery(label="Generated images", show_label=False).style(
- # grid=[1], height="auto", container=True, full_width=True, full_height=True)
-
- run_button.click(fn=run_grounding, inputs=[
- input_image, grounding_caption, box_threshold, text_threshold], outputs=[gallery])
- gr.Examples(
- [["watermelon.jpg", "watermelon", 0.25, 0.25]],
- inputs = [input_image, grounding_caption, box_threshold, text_threshold],
- outputs = [gallery],
- fn=run_grounding,
- cache_examples=True,
- label='Try this example input!'
- )
- block.launch(share=True, show_api=False, show_error=True)
-
diff --git a/spaces/Audio-AGI/WavJourney/README.md b/spaces/Audio-AGI/WavJourney/README.md
deleted file mode 100644
index 6514202f6a3a92fe09c96516f15d4e7848d85484..0000000000000000000000000000000000000000
--- a/spaces/Audio-AGI/WavJourney/README.md
+++ /dev/null
@@ -1,111 +0,0 @@
----
-title: WavJourney
-emoji: 🔥
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
-license: cc-by-nc-4.0
----
-# 🎵 WavJourney: Compositional Audio Creation with LLMs
-[](https://arxiv.org/abs/2307.14335) [](https://github.com/Audio-AGI/WavJourney/) [](https://audio-agi.github.io/WavJourney_demopage/) [](https://huggingface.co/spaces/Audio-AGI/WavJourney)
-
-
-This repository contains the official implementation of ["WavJourney: Compositional Audio Creation with Large Language Models"](https://audio-agi.github.io/WavJourney_demopage/WavJourney_arXiv.pdf).
-
-Starting with a text prompt, WavJourney can create audio content with engaging storylines encompassing personalized speakers, lifelike speech in context, emotionally resonant music compositions, and impactful sound effects that enhance the auditory experience. Check the audio examples in the [Project Page](https://audio-agi.github.io/WavJourney_demopage/)!
-
-
-
-
-
-
-## Preliminaries
-1. Install the environment:
-```bash
-bash ./scripts/EnvsSetup.sh
-```
-2. Activate the conda environment:
-```bash
-conda activate WavJourney
-```
-
-3. (Optional) You can modify the default configuration in `config.yaml`, check the details described in the configuration file.
-4. Pre-download the models (might take some time):
-```bash
-python scripts/download_models.py
-```
-
-5. Set the WAVJOURNEY_OPENAI_KEY in the environment variable for accessing [GPT-4 API](https://platform.openai.com/account/api-keys) [[Guidance](https://help.openai.com/en/articles/7102672-how-can-i-access-gpt-4)]
-```bash
-export WAVJOURNEY_OPENAI_KEY=your_openai_key_here
-```
-
-6. Set environment variables for using API services
-```bash
-# Set the port for the WAVJOURNEY service to 8021
-export WAVJOURNEY_SERVICE_PORT=8021
-
-# Set the URL for the WAVJOURNEY service to 127.0.0.1
-export WAVJOURNEY_SERVICE_URL=127.0.0.1
-
-# Limit the maximum script lines for WAVJOURNEY to 999
-export WAVJOURNEY_MAX_SCRIPT_LINES=999
-```
-
-
-7. Start Python API services (e.g., Text-to-Speech, Text-to-Audio)
-```bash
-bash scripts/start_services.sh
-```
-
-## Web APP
- ```bash
-bash scripts/start_ui.sh
- ```
-
-## Commandline Usage
- ```bash
- python wavjourney_cli.py -f --input-text "Generate a one-minute introduction to quantum mechanics"
- ```
-
-
-## Kill the services
-You can kill the running services via this command:
- ```bash
-python scripts/kill_services.py
- ```
-
-## (Advanced features) Speaker customization
-You can add voice presets to WavJourney to customize the voice actors. Simply provide the voice id, the description and a sample wav file, and WavJourney will pick the voice automatically based on the audio script. Predefined system voice presets are in `data/voice_presets`.
-
-You can manage voice presets via UI. Specifically, if you want to add voice to voice presets. Run the script via command line below:
-```bash
-python add_voice_preset.py --id "id" --desc "description" --wav-path path/to/wav --session-id ''
-```
-What makes for good voice prompt? See detailed instructions here.
-## Hardware requirement
-- The VRAM of the GPU in the default configuration should be greater than 16 GB.
-- Operation system: Linux.
-
-## Citation
-If you find this work useful, you can cite the paper below:
-
- @article{liu2023wavjourney,
- title = {WavJourney: Compositional Audio Creation with Large Language Models},
- author = {Liu, Xubo and Zhu, Zhongkai and Liu, Haohe and Yuan, Yi and Huang, Qiushi and Liang, Jinhua and Cao, Yin and Kong, Qiuqiang and Plumbley, Mark D and Wang, Wenwu},
- journal = {arXiv preprint arXiv:2307.14335},
- year = {2023}
- }
-
-[](https://www.buymeacoffee.com/liuxubo)
-
-## Appreciation
-- [Bark](https://github.com/suno-ai/bark) for a zero-shot text-to-speech synthesis model.
-- [AudioCraft](https://github.com/facebookresearch/audiocraft) for state-of-the-art audio generation models.
-
-## Disclaimer
-We are not responsible for audio generated using semantics created by this model. Just don't use it for illegal purposes.
-
diff --git a/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py b/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py
deleted file mode 100644
index be58431ed446bf6283c17274a41e37ec3eb24409..0000000000000000000000000000000000000000
--- a/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import os
-import asyncio
-import random
-from datetime import date, datetime, timezone, timedelta
-from ast import literal_eval
-
-import streamlit as st
-import openai
-
-from autoagents.utils.constants import MAIN_HEADER, MAIN_CAPTION, SAMPLE_QUESTIONS
-from autoagents.agents.search import ActionRunner
-
-from langchain.chat_models import ChatOpenAI
-
-
-async def run():
- output_acc = ""
- st.session_state["random"] = random.randint(0, 99)
- if "task" not in st.session_state:
- st.session_state.task = None
- if "model_name" not in st.session_state:
- st.session_state.model_name = "gpt-3.5-turbo"
-
- st.set_page_config(
- page_title="Search Agent",
- page_icon="🤖",
- layout="wide",
- initial_sidebar_state="expanded",
- )
-
- st.title(MAIN_HEADER)
- st.caption(MAIN_CAPTION)
-
- with st.form("my_form", clear_on_submit=False):
- st.markdown("", unsafe_allow_html=True)
- user_input = st.text_input(
- "You: ",
- key="input",
- placeholder="Ask me anything ...",
- label_visibility="hidden",
- )
-
- submitted = st.form_submit_button(
- "Search", help="Hit to submit the search query."
- )
-
- # Ask the user to enter their OpenAI API key
- if (api_key := st.sidebar.text_input("OpenAI api-key", type="password")):
- api_org = None
- else:
- api_key, api_org = os.getenv("OPENAI_API_KEY"), os.getenv("OPENAI_API_ORG")
- with st.sidebar:
- model_dict = {
- "gpt-3.5-turbo": "GPT-3.5-turbo",
- "gpt-4": "GPT-4 (Better but slower)",
- }
- st.radio(
- "OpenAI model",
- model_dict.keys(),
- key="model_name",
- format_func=lambda x: model_dict[x],
- )
-
- time_zone = str(datetime.now(timezone(timedelta(0))).astimezone().tzinfo)
- st.markdown(f"**The system time zone is {time_zone} and the date is {date.today()}**")
-
- st.markdown("**Example Queries:**")
- for q in SAMPLE_QUESTIONS:
- st.markdown(f"*{q}*")
-
- if not api_key:
- st.warning(
- "API key required to try this app. The API key is not stored in any form. [This](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key) might help."
- )
- elif api_org and st.session_state.model_name == "gpt-4":
- st.warning(
- "The free API key does not support GPT-4. Please switch to GPT-3.5-turbo or input your own API key."
- )
- else:
- outputq = asyncio.Queue()
- runner = ActionRunner(outputq,
- ChatOpenAI(openai_api_key=api_key,
- openai_organization=api_org,
- temperature=0,
- model_name=st.session_state.model_name),
- persist_logs=True) # log to HF-dataset
-
- async def cleanup(e):
- st.error(e)
- await st.session_state.task
- st.session_state.task = None
- st.stop()
-
- placeholder = st.empty()
-
- if user_input and submitted:
- if st.session_state.task is not None:
- with placeholder.container():
- st.session_state.task.cancel()
- st.warning("Previous search aborted", icon="⚠️")
-
- st.session_state.task = asyncio.create_task(
- runner.run(user_input, outputq)
- )
- iterations = 0
- with st.expander("Search Results", expanded=True):
- while True:
- with st.spinner("Wait for it..."):
- output = await outputq.get()
- placeholder.empty()
- if isinstance(output, Exception):
- if isinstance(output, openai.error.AuthenticationError):
- await cleanup(f"AuthenticationError: Invalid OpenAI API key.")
- elif isinstance(output, openai.error.InvalidRequestError) \
- and output._message == "The model: `gpt-4` does not exist":
- await cleanup(f"The free API key does not support GPT-4. Please switch to GPT-3.5-turbo or input your own API key.")
- elif isinstance(output, openai.error.OpenAIError):
- await cleanup(output)
- elif isinstance(output, RuntimeWarning):
- st.warning(output)
- continue
- else:
- await cleanup("Something went wrong. Please try searching again.")
- return
- try:
- output_fmt = literal_eval(output)
- st.json(output_fmt, expanded=False)
- st.write("---")
- iterations += 1
- except:
- output_acc += "\n" + output
- st.markdown(f" {output} ",
- unsafe_allow_html=True)
- if iterations >= runner.agent_executor.max_iterations:
- await cleanup(
- f"Maximum iterations ({iterations}) exceeded. You can try running the search again or try a variation of the query."
- )
- return
- if "Final Answer:" in output:
- break
- # Found the answer
- final_answer = await st.session_state.task
- final_answer = final_answer.replace("$", "\$")
- # st.success accepts md
- st.success(final_answer, icon="✅")
- st.balloons()
- st.session_state.task = None
- st.stop()
-
-if __name__ == "__main__":
- loop = asyncio.new_event_loop()
- loop.set_debug(enabled=False)
- loop.run_until_complete(run())
diff --git a/spaces/BAAI/vid2vid-zero/Dockerfile b/spaces/BAAI/vid2vid-zero/Dockerfile
deleted file mode 100644
index 9eaa78f8facc5066f018183f108824842f11b7e8..0000000000000000000000000000000000000000
--- a/spaces/BAAI/vid2vid-zero/Dockerfile
+++ /dev/null
@@ -1,57 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update && \
- apt-get upgrade -y && \
- apt-get install -y --no-install-recommends \
- git \
- git-lfs \
- wget \
- curl \
- # ffmpeg \
- ffmpeg \
- x264 \
- # python build dependencies \
- build-essential \
- libssl-dev \
- zlib1g-dev \
- libbz2-dev \
- libreadline-dev \
- libsqlite3-dev \
- libncursesw5-dev \
- xz-utils \
- tk-dev \
- libxml2-dev \
- libxmlsec1-dev \
- libffi-dev \
- liblzma-dev && \
- apt-get clean && \
- rm -rf /var/lib/apt/lists/*
-
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:${PATH}
-WORKDIR ${HOME}/app
-
-RUN curl https://pyenv.run | bash
-ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ENV PYTHON_VERSION=3.10.9
-RUN pyenv install ${PYTHON_VERSION} && \
- pyenv global ${PYTHON_VERSION} && \
- pyenv rehash && \
- pip install --no-cache-dir -U pip setuptools wheel
-
-RUN pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1
-COPY --chown=1000 requirements.txt /tmp/requirements.txt
-RUN pip install --no-cache-dir -U -r /tmp/requirements.txt
-
-COPY --chown=1000 . ${HOME}/app
-# RUN cd Tune-A-Video && patch -p1 < ../patch
-ENV PYTHONPATH=${HOME}/app \
- PYTHONUNBUFFERED=1 \
- GRADIO_ALLOW_FLAGGING=never \
- GRADIO_NUM_PORTS=1 \
- GRADIO_SERVER_NAME=0.0.0.0 \
- GRADIO_THEME=huggingface \
- SYSTEM=spaces
-CMD ["python", "app.py"]
diff --git a/spaces/Bala2-03-2003/MygenvioceAI/app.py b/spaces/Bala2-03-2003/MygenvioceAI/app.py
deleted file mode 100644
index ca8b6d40b4ab898c70da92f4a4298de2baf703dc..0000000000000000000000000000000000000000
--- a/spaces/Bala2-03-2003/MygenvioceAI/app.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import os
-import re
-import requests
-import json
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-PLAY_HT_API_KEY=os.getenv('PLAY_HT_API_KEY')
-PLAY_HT_USER_ID=os.getenv('PLAY_HT_USER_ID')
-
-PLAY_HT_VOICE_ID=os.getenv('PLAY_HT_VOICE_ID')
-play_ht_api_get_audio_url = "https://play.ht/api/v2/tts"
-
-
-template = """You are a helpful assistant to answer user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
- input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
- llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
- prompt=prompt,
- verbose=True,
- memory=memory,
-)
-
-headers = {
- "accept": "text/event-stream",
- "content-type": "application/json",
- "AUTHORIZATION": "Bearer "+ PLAY_HT_API_KEY,
- "X-USER-ID": PLAY_HT_USER_ID
-}
-
-
-def get_payload(text):
- return {
- "text": text,
- "voice": PLAY_HT_VOICE_ID,
- "quality": "medium",
- "output_format": "mp3",
- "speed": 1,
- "sample_rate": 24000,
- "seed": None,
- "temperature": None
- }
-
-def get_generated_audio(text):
- payload = get_payload(text)
- generated_response = {}
- try:
- response = requests.post(play_ht_api_get_audio_url, json=payload, headers=headers)
- response.raise_for_status()
- generated_response["type"]= 'SUCCESS'
- generated_response["response"] = response.text
- except requests.exceptions.RequestException as e:
- generated_response["type"]= 'ERROR'
- try:
- response_text = json.loads(response.text)
- if response_text['error_message']:
- generated_response["response"] = response_text['error_message']
- else:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["type"]= 'ERROR'
- generated_response["response"] = response.text
- return generated_response
-
-def extract_urls(text):
- # Define the regex pattern for URLs
- url_pattern = r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+[/\w\.-]*'
-
- # Find all occurrences of URLs in the text
- urls = re.findall(url_pattern, text)
-
- return urls
-
-def get_audio_reply_for_question(text):
- generated_audio_event = get_generated_audio(text)
- #From get_generated_audio, you will get events in a string format, from that we need to extract the url
- final_response = {
- "audio_url": '',
- "message": ''
- }
- if generated_audio_event["type"] == 'SUCCESS':
- audio_urls = extract_urls(generated_audio_event["response"])
- if len(audio_urls) == 0:
- final_response['message'] = "No audio file link found in generated event"
- else:
- final_response['audio_url'] = audio_urls[-1]
- else:
- final_response['message'] = generated_audio_event['response']
- return final_response
-
-def download_url(url):
- try:
- # Send a GET request to the URL to fetch the content
- final_response = {
- 'content':'',
- 'error':''
- }
- response = requests.get(url)
- # Check if the request was successful (status code 200)
- if response.status_code == 200:
- final_response['content'] = response.content
- else:
- final_response['error'] = f"Failed to download the URL. Status code: {response.status_code}"
- except Exception as e:
- final_response['error'] = f"Failed to download the URL. Error: {e}"
- return final_response
-
-def get_filename_from_url(url):
- # Use os.path.basename() to extract the file name from the URL
- file_name = os.path.basename(url)
- return file_name
-
-def get_text_response(user_message):
- response = llm_chain.predict(user_message = user_message)
- return response
-
-def get_text_response_and_audio_response(user_message):
- response = get_text_response(user_message) # Getting the reply from Open AI
- audio_reply_for_question_response = get_audio_reply_for_question(response)
- final_response = {
- 'output_file_path': '',
- 'message':''
- }
- audio_url = audio_reply_for_question_response['audio_url']
- if audio_url:
- output_file_path=get_filename_from_url(audio_url)
- download_url_response = download_url(audio_url)
- audio_content = download_url_response['content']
- if audio_content:
- with open(output_file_path, "wb") as audio_file:
- audio_file.write(audio_content)
- final_response['output_file_path'] = output_file_path
- else:
- final_response['message'] = download_url_response['error']
- else:
- final_response['message'] = audio_reply_for_question_response['message']
- return final_response
-
-def chat_bot_response(message, history):
- text_and_audio_response = get_text_response_and_audio_response(message)
- output_file_path = text_and_audio_response['output_file_path']
- if output_file_path:
- return (text_and_audio_response['output_file_path'],)
- else:
- return text_and_audio_response['message']
-
-demo = gr.ChatInterface(chat_bot_response,examples=["How are you doing?","What are your interests?","Which places do you like to visit?"])
-
-if __name__ == "__main__":
- demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/BartPoint/VoiceChange/app_multi.py b/spaces/BartPoint/VoiceChange/app_multi.py
deleted file mode 100644
index 67890355f12e2d2d5420ce8e31a722020f349b24..0000000000000000000000000000000000000000
--- a/spaces/BartPoint/VoiceChange/app_multi.py
+++ /dev/null
@@ -1,469 +0,0 @@
-from typing import Union
-
-from argparse import ArgumentParser
-
-import asyncio
-import json
-import hashlib
-from os import path, getenv
-
-import gradio as gr
-
-import torch
-
-import numpy as np
-import librosa
-
-import edge_tts
-
-import config
-import util
-from infer_pack.models import (
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono
-)
-from vc_infer_pipeline import VC
-
-# Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21 # noqa
-in_hf_space = getenv('SYSTEM') == 'spaces'
-
-# Argument parsing
-arg_parser = ArgumentParser()
-arg_parser.add_argument(
- '--hubert',
- default=getenv('RVC_HUBERT', 'hubert_base.pt'),
- help='path to hubert base model (default: hubert_base.pt)'
-)
-arg_parser.add_argument(
- '--config',
- default=getenv('RVC_MULTI_CFG', 'multi_config.json'),
- help='path to config file (default: multi_config.json)'
-)
-arg_parser.add_argument(
- '--api',
- action='store_true',
- help='enable api endpoint'
-)
-arg_parser.add_argument(
- '--cache-examples',
- action='store_true',
- help='enable example caching, please remember delete gradio_cached_examples folder when example config has been modified' # noqa
-)
-args = arg_parser.parse_args()
-
-app_css = '''
-#model_info img {
- max-width: 100px;
- max-height: 100px;
- float: right;
-}
-
-#model_info p {
- margin: unset;
-}
-'''
-
-app = gr.Blocks(
- theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate"),
- css=app_css,
- analytics_enabled=False
-)
-
-# Load hubert model
-hubert_model = util.load_hubert_model(config.device, args.hubert)
-hubert_model.eval()
-
-# Load models
-multi_cfg = json.load(open(args.config, 'r'))
-loaded_models = []
-
-for model_name in multi_cfg.get('models'):
- print(f'Loading model: {model_name}')
-
- # Load model info
- model_info = json.load(
- open(path.join('model', model_name, 'config.json'), 'r')
- )
-
- # Load RVC checkpoint
- cpt = torch.load(
- path.join('model', model_name, model_info['model']),
- map_location='cpu'
- )
- tgt_sr = cpt['config'][-1]
- cpt['config'][-3] = cpt['weight']['emb_g.weight'].shape[0] # n_spk
-
- if_f0 = cpt.get('f0', 1)
- net_g: Union[SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono]
- if if_f0 == 1:
- net_g = SynthesizerTrnMs768NSFsid(
- *cpt['config'],
- is_half=util.is_half(config.device)
- )
- else:
- net_g = SynthesizerTrnMs768NSFsid_nono(*cpt['config'])
-
- del net_g.enc_q
-
- # According to original code, this thing seems necessary.
- print(net_g.load_state_dict(cpt['weight'], strict=False))
-
- net_g.eval().to(config.device)
- net_g = net_g.half() if util.is_half(config.device) else net_g.float()
-
- vc = VC(tgt_sr, config)
-
- loaded_models.append(dict(
- name=model_name,
- metadata=model_info,
- vc=vc,
- net_g=net_g,
- if_f0=if_f0,
- target_sr=tgt_sr
- ))
-
-print(f'Models loaded: {len(loaded_models)}')
-
-# Edge TTS speakers
-tts_speakers_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) # noqa
-
-
-# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer-web.py#L118 # noqa
-def vc_func(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- if input_audio is None:
- return (None, 'Please provide input audio.')
-
- if model_index is None:
- return (None, 'Please select a model.')
-
- model = loaded_models[model_index]
-
- # Reference: so-vits
- (audio_samp, audio_npy) = input_audio
-
- # https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49
- # Can be change well, we will see
- if (audio_npy.shape[0] / audio_samp) > 320 and in_hf_space:
- return (None, 'Input audio is longer than 60 secs.')
-
- # Bloody hell: https://stackoverflow.com/questions/26921836/
- if audio_npy.dtype != np.float32: # :thonk:
- audio_npy = (
- audio_npy / np.iinfo(audio_npy.dtype).max
- ).astype(np.float32)
-
- if len(audio_npy.shape) > 1:
- audio_npy = librosa.to_mono(audio_npy.transpose(1, 0))
-
- if audio_samp != 16000:
- audio_npy = librosa.resample(
- audio_npy,
- orig_sr=audio_samp,
- target_sr=16000
- )
-
- pitch_int = int(pitch_adjust)
-
- resample = (
- 0 if resample_option == 'Disable resampling'
- else int(resample_option)
- )
-
- times = [0, 0, 0]
-
- checksum = hashlib.sha512()
- checksum.update(audio_npy.tobytes())
-
- output_audio = model['vc'].pipeline(
- hubert_model,
- model['net_g'],
- model['metadata'].get('speaker_id', 0),
- audio_npy,
- checksum.hexdigest(),
- times,
- pitch_int,
- f0_method,
- path.join('model', model['name'], model['metadata']['feat_index']),
- feat_ratio,
- model['if_f0'],
- filter_radius,
- model['target_sr'],
- resample,
- rms_mix_rate,
- 'v2'
- )
-
- out_sr = (
- resample if resample >= 16000 and model['target_sr'] != resample
- else model['target_sr']
- )
-
- print(f'npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s')
- return ((out_sr, output_audio), 'Success')
-
-
-async def edge_tts_vc_func(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- if input_text is None:
- return (None, 'Please provide TTS text.')
-
- if tts_speaker is None:
- return (None, 'Please select TTS speaker.')
-
- if model_index is None:
- return (None, 'Please select a model.')
-
- speaker = tts_speakers_list[tts_speaker]['ShortName']
- (tts_np, tts_sr) = await util.call_edge_tts(speaker, input_text)
- return vc_func(
- (tts_sr, tts_np),
- model_index,
- pitch_adjust,
- f0_method,
- feat_ratio,
- filter_radius,
- rms_mix_rate,
- resample_option
- )
-
-
-def update_model_info(model_index):
- if model_index is None:
- return str(
- '### Model info\n'
- 'Please select a model from dropdown above.'
- )
-
- model = loaded_models[model_index]
- model_icon = model['metadata'].get('icon', '')
-
- return str(
- '### Model info\n'
- ''
- '**{name}**\n\n'
- 'Author: {author}\n\n'
- 'Source: {source}\n\n'
- '{note}'
- ).format(
- name=model['metadata'].get('name'),
- author=model['metadata'].get('author', 'Anonymous'),
- source=model['metadata'].get('source', 'Unknown'),
- note=model['metadata'].get('note', ''),
- icon=(
- model_icon
- if model_icon.startswith(('http://', 'https://'))
- else '/file/model/%s/%s' % (model['name'], model_icon)
- )
- )
-
-
-def _example_vc(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- (audio, message) = vc_func(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
- )
- return (
- audio,
- message,
- update_model_info(model_index)
- )
-
-
-async def _example_edge_tts(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- (audio, message) = await edge_tts_vc_func(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method,
- feat_ratio, filter_radius, rms_mix_rate, resample_option
- )
- return (
- audio,
- message,
- update_model_info(model_index)
- )
-
-
-with app:
- gr.Markdown(
- '## A simplistic Web interface\n'
- 'RVC interface, project based on [RVC-WebUI](https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI)' # thx noqa
- 'A lot of inspiration from what\'s already out there, including [zomehwh/rvc-models](https://huggingface.co/spaces/zomehwh/rvc-models) & [DJQmUKV/rvc-inference](https://huggingface.co/spaces/DJQmUKV/rvc-inference).\n ' # thx noqa
- )
-
- with gr.Row():
- with gr.Column():
- with gr.Tab('Audio conversion'):
- input_audio = gr.Audio(label='Input audio')
-
- vc_convert_btn = gr.Button('Convert', variant='primary')
-
- with gr.Tab('TTS conversion'):
- tts_input = gr.TextArea(
- label='TTS input text'
- )
- tts_speaker = gr.Dropdown(
- [
- '%s (%s)' % (
- s['FriendlyName'],
- s['Gender']
- )
- for s in tts_speakers_list
- ],
- label='TTS speaker',
- type='index'
- )
-
- tts_convert_btn = gr.Button('Convert', variant='primary')
-
- pitch_adjust = gr.Slider(
- label='Pitch',
- minimum=-24,
- maximum=24,
- step=1,
- value=0
- )
- f0_method = gr.Radio(
- label='f0 methods',
- choices=['pm', 'harvest', 'crepe'],
- value='pm',
- interactive=True
- )
-
- with gr.Accordion('Advanced options', open=False):
- feat_ratio = gr.Slider(
- label='Feature ratio',
- minimum=0,
- maximum=1,
- step=0.1,
- value=0.6
- )
- filter_radius = gr.Slider(
- label='Filter radius',
- minimum=0,
- maximum=7,
- step=1,
- value=3
- )
- rms_mix_rate = gr.Slider(
- label='Volume envelope mix rate',
- minimum=0,
- maximum=1,
- step=0.1,
- value=1
- )
- resample_rate = gr.Dropdown(
- [
- 'Disable resampling',
- '16000',
- '22050',
- '44100',
- '48000'
- ],
- label='Resample rate',
- value='Disable resampling'
- )
-
- with gr.Column():
- # Model select
- model_index = gr.Dropdown(
- [
- '%s - %s' % (
- m['metadata'].get('source', 'Unknown'),
- m['metadata'].get('name')
- )
- for m in loaded_models
- ],
- label='Model',
- type='index'
- )
-
- # Model info
- with gr.Box():
- model_info = gr.Markdown(
- '### Model info\n'
- 'Please select a model from dropdown above.',
- elem_id='model_info'
- )
-
- output_audio = gr.Audio(label='Output audio')
- output_msg = gr.Textbox(label='Output message')
-
- multi_examples = multi_cfg.get('examples')
- if (
- multi_examples and
- multi_examples.get('vc') and multi_examples.get('tts_vc')
- ):
- with gr.Accordion('Sweet sweet examples', open=False):
- with gr.Row():
- # VC Example
- if multi_examples.get('vc'):
- gr.Examples(
- label='Audio conversion examples',
- examples=multi_examples.get('vc'),
- inputs=[
- input_audio, model_index, pitch_adjust, f0_method,
- feat_ratio
- ],
- outputs=[output_audio, output_msg, model_info],
- fn=_example_vc,
- cache_examples=args.cache_examples,
- run_on_click=args.cache_examples
- )
-
- # Edge TTS Example
- if multi_examples.get('tts_vc'):
- gr.Examples(
- label='TTS conversion examples',
- examples=multi_examples.get('tts_vc'),
- inputs=[
- tts_input, model_index, tts_speaker, pitch_adjust,
- f0_method, feat_ratio
- ],
- outputs=[output_audio, output_msg, model_info],
- fn=_example_edge_tts,
- cache_examples=args.cache_examples,
- run_on_click=args.cache_examples
- )
-
- vc_convert_btn.click(
- vc_func,
- [
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_rate
- ],
- [output_audio, output_msg],
- api_name='audio_conversion'
- )
-
- tts_convert_btn.click(
- edge_tts_vc_func,
- [
- tts_input, model_index, tts_speaker, pitch_adjust, f0_method,
- feat_ratio, filter_radius, rms_mix_rate, resample_rate
- ],
- [output_audio, output_msg],
- api_name='tts_conversion'
- )
-
- model_index.change(
- update_model_info,
- inputs=[model_index],
- outputs=[model_info],
- show_progress=False,
- queue=False
- )
-
-app.queue(
- concurrency_count=1,
- max_size=20,
- api_open=args.api
-).launch()
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md b/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md
deleted file mode 100644
index 231c36d39005d9275c61ccc88067d084ca775635..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md
+++ /dev/null
@@ -1,134 +0,0 @@
-
-Aparcamiento de coches multijugador Sudáfrica Edición APK Descargar: Una guía para los usuarios de Android
- Si usted está buscando un juego de estacionamiento de coches realista y divertido que ofrece más que solo estacionamiento, es posible que desee echa un vistazo a Parking Multijugador. Y si usted está en Sudáfrica o desea experimentar la cultura del automóvil de Sudáfrica, es posible que desee probar el Aparcamiento Multijugador South áfrica Edition APK. En este artículo, te diremos qué es el Car Parking Multiplayer, qué hace que South áfrica Edition sea diferente, cómo descargarlo e instalarlo en tu dispositivo Android, y algunos consejos y trucos para jugarlo.
- ¿Qué es el Aparcamiento Multijugador?
- Car Parking Multiplayer es un juego que puede engañarte con su nombre bastante engañoso. Pero, es mucho más que solo estar aparcando tu coche. Es una experiencia de mundo abierto donde se puede conducir gratis y sí, todavía trabajar en ese aparcamiento si lo desea. Incluso puedes saltar de tu coche y caminar. Hay diferentes áreas que se pueden explorar en el juego. Cada una es como su propio mundo abierto. Puedes optar por jugar en modo de un solo jugador o en modo online si quieres una escena más caótica (de forma divertida) .
-aparcamiento de coches multijugador sudáfrica edición apk descargar
DOWNLOAD === https://bltlly.com/2v6IE9
- Características de Aparcamiento Multijugador
- Aparcamiento de coches multijugador tiene las siguientes características :
-
-- Modo multijugador de mundo abierto
- - Caminar libremente
- - Mundo abierto libre con gasolineras reales y servicios de automóviles
- - Compite contra jugadores reales en las carreras multijugador
- - Intercambiar coches con jugadores reales
- - Miles de jugadores reales cada día
- - Lista de amigos
- - Chat de voz
- - Modo de policía
-
-
-- Personalización del coche
- - Suspensión ajustable, ángulo de rueda y más
- - Ajuste del motor: motor de intercambio, turbo, caja de cambios y escape
- - Visual auto tungs: Vynils dinámico, partes del cuerpo del coche
-
-
-- Mundo abierto de alta calidad
-
- - 100 coches con el interior real
- - 16 pieles de jugador
- - Edificios con interior
-
-
-- Juego interesante
- - 82 desafíos de estacionamiento y conducción en la vida real
- - Diferentes vehículos: Grúa, camioneta, camiones, coches deportivos y clásicos
-
-
-
- Opiniones de Aparcamiento multijugador
- Aparcamiento de coches multijugador ha recibido en su mayoría críticas positivas de los usuarios en Google Play Store y App Store. Tiene una calificación de 4.4 de 5 estrellas en Google Play Store y una calificación de 4.3 de 5 estrellas en App Store . Estos son algunos de los comentarios de los usuarios:
- "Increíble juego! No hay errores en este juego o retrasos ect. (base en el dispositivo que utiliza) Me encantan los gráficos y los coches. Los coches son tan realistas y los sonidos son increíbles. Me encanta cómo puedes personalizar tu coche y hacer que se vea genial. El modo multijugador es increíble. Puedes chatear con otros jugadores y competir con ellos. También puedes unirte a un clan o crear tu propio clan. Este juego es muy divertido y adictivo. Recomiendo este juego a todos los que aman los coches y los juegos de estacionamiento."
- "Este juego es muy bueno pero necesita algunas mejoras como agregar más coches, más mapas, más personalizaciones, más modos de juego, más desafíos, etc. Además, el juego se bloquea a veces y los controles no son muy suaves. Los gráficos son agradables, pero pueden ser mejores. El juego es divertido de jugar con amigos, pero se vuelve aburrido después de un tiempo. Espero que los desarrolladores actualicen el juego pronto y lo hagan más agradable."
-
- ¿Qué es el aparcamiento multijugador South áfrica Edition?
- Car Parking Multijugador South áfrica Edition es una versión modificada de Car Parking Multijugador que está especialmente diseñado para los usuarios sudafricanos o los fans de la cultura del coche de Sudáfrica. No es una versión oficial del juego, sino un archivo APK hecho por fans que se puede descargar e instalar en dispositivos Android .
- Diferencias entre Aparcamiento de coches multijugador y Aparcamiento de coches multijugador Sudáfrica Edición
- Car Parking Multijugador South áfrica Edition tiene algunas diferencias con el juego original Car Parking Multijugador, como :
-
-- Más coches que son populares en Sudáfrica, como BMW E30, VW Golf Mk1, Toyota Corolla, Nissan 1400, etc.
-- Más personalizaciones que reflejan la cultura del automóvil sudafricano, como llantas giratorias, escapes fuertes, pegatinas, banderas, etc.
-- Más mapas que se basan en ubicaciones reales en Sudáfrica, como Ciudad del Cabo, Johannesburgo, Durban, etc.
-- Más música inspirada en la escena musical sudafricana, como kwaito, gqom, amapiano, etc.
-- Más idiomas que se hablan en Sudáfrica, como afrikaans, zulú, xhosa, etc.
-
- Beneficios de Aparcamiento Multijugador South áfrica Edition
- Car Parking Multijugador South áfrica Edition tiene algunos beneficios para los usuarios que quieren disfrutar del juego con un toque sudafricano, como :
-
-- Más variedad y diversidad en términos de coches, personalizaciones, mapas, música e idiomas
-- Más diversión y emoción en términos de jugabilidad, gráficos, efectos de sonido e interacciones
-- Más conexión y comunidad con otros jugadores que comparten el mismo interés y pasión por la cultura del automóvil de Sudáfrica
-- Más soporte y actualizaciones de los desarrolladores que se dedican a mejorar el juego y agregar nuevas características
-
- Cómo descargar e instalar el estacionamiento de coches multijugador South áfrica Edition APK en dispositivos Android?
-
- Requisitos para descargar e instalar el estacionamiento de coches multijugador South áfrica Edition APK
- Para descargar e instalar Aparcamiento Multijugador South áfrica Edition APK en su dispositivo Android, es necesario tener :
-
-
-- Un dispositivo Android que se ejecuta en Android 4.1 o superior
-- Una conexión a Internet estable
-- Una aplicación de administrador de archivos que puede acceder al archivo APK
-- Una cantidad suficiente de espacio de almacenamiento en su dispositivo
-- Un permiso para instalar aplicaciones de fuentes desconocidas en la configuración del dispositivo
-
- Pasos para Descargar e Instalar Aparcamiento Multijugador South áfrica Edition APK
- Para descargar e instalar Aparcamiento Multijugador South áfrica Edition APK en su dispositivo Android, es necesario seguir estos pasos :
-
-- Ir a un sitio web de confianza que proporciona el enlace para descargar Car Parking Multi jugador South áfrica Edition APK. Por ejemplo, puede visitar [este sitio web] para obtener la última versión del archivo APK.
-- Haga clic en el botón de descarga y espere a que el archivo APK se descargue en su dispositivo.
-- Una vez que se complete la descarga, localizar el archivo APK en su dispositivo utilizando una aplicación de administrador de archivos. Puede encontrarlo en la carpeta Descargas o en cualquier otra carpeta donde lo haya guardado.
-- Toque en el archivo APK y seleccione Instalar. Es posible que vea un mensaje de advertencia que dice "Para su seguridad, el teléfono no está permitido instalar aplicaciones desconocidas de esta fuente". Si ves este mensaje, ve a la configuración del dispositivo y habilita la opción de instalar aplicaciones de fuentes desconocidas. Esta opción puede estar en Seguridad, Privacidad o Aplicaciones dependiendo del modelo de dispositivo y la versión de Android.
-
-- Espere a que termine el proceso de instalación. Puede tardar unos minutos dependiendo del rendimiento del dispositivo y la velocidad de Internet.
-- Una vez que se hace la instalación, se puede abrir el juego y disfrutar de aparcamiento multijugador South áfrica Edition en su dispositivo Android.
-
- Consejos y Trucos para Jugar Aparcamiento Multijugador South áfrica Edition
- Ahora que ha descargado e instalado Car Parking Multiplayer South áfrica Edition en su dispositivo Android, es posible que desee conocer algunos consejos y trucos para jugarlo. Estos son algunos de ellos:
- Cómo seleccionar un coche y un jugador
- Para seleccionar un coche y un jugador en Car Parking Multiplayer South áfrica Edition, debe hacer lo siguiente:
-
-- Toque en el icono de menú en la esquina superior izquierda de la pantalla.
-- Toque en Garaje para ver la lista de coches que usted posee o puede comprar.
-- Toque en el coche que desea utilizar y luego toque en Select.
-- Toque en Volver para volver al menú.
-- Toque en Perfil para ver la lista de jugadores entre los que puede elegir.
-- Toque en el reproductor que desea utilizar y luego toque en Select.
-- Toque en Volver para volver al menú.
-- Toque en Jugar para comenzar a jugar el juego con el coche y el jugador seleccionado.
-
- Cómo ajustar su coche y ajustar su relación de engranajes
- Para afinar su coche y ajustar su relación de engranajes en Car Parking Multiplayer South áfrica Edition, debe hacer lo siguiente:
-
-- Toque en el icono de menú en la esquina superior izquierda de la pantalla.
-- Toque en Garaje para ver la lista de coches que usted posee o puede comprar.
-- Toque en el coche que desea sintonizar y luego toque en Tune.
-- Verá cuatro pestañas: Motor, Suspensión, Ruedas y Cuerpo. Puede deslizar hacia la izquierda o hacia la derecha para cambiar entre ellas.
-
-- Para ajustar su relación de cambios, toque en Caja de cambios en la pestaña Motor. Verá un gráfico que muestra cómo cambia su velocidad con diferentes marchas. Puede arrastrar los puntos en el gráfico para cambiar la relación de transmisión para cada marcha. También puede pulsar en Auto o Manual para cambiar entre modos de transmisión automática o manual.
-- Una vez que haya terminado de ajustar su coche, toque en Guardar para aplicar los cambios.
-
- Cómo Deriva, Donut, y Burnout
- Para la deriva, donut, y burnout en el estacionamiento de coches multijugador South áfrica Edition, es necesario hacer lo siguiente:
-
-- Para la deriva, debe usar una combinación de dirección, acelerador, freno y freno de mano. Puede usar los controles de inclinación o táctiles para la dirección. Para iniciar la deriva, es necesario acelerar y girar bruscamente en una esquina. Luego, debe aplicar el freno de mano brevemente para hacer que sus ruedas traseras pierdan tracción. Después de eso, debe equilibrar el acelerador y la dirección para mantener el ángulo de deriva y la dirección. También puede utilizar el freno de mano o freno de mano de nuevo si necesita ajustar su deriva. Para terminar con la deriva, es necesario soltar el acelerador y la dirección y enderezar el coche.
-- Para la rosquilla, debe usar una combinación de dirección, acelerador y freno de mano. Puede usar controles de inclinación o táctiles para la dirección. Para comenzar a donar, debe acelerar y girar bruscamente en una dirección. Entonces, es necesario aplicar el freno de mano para hacer que su coche gire alrededor de su centro. Después de eso, es necesario mantener el acelerador y la dirección constante para mantener el círculo de la dona. También puede cambiar la dirección de su dona cambiando la dirección. Para terminar donuting, es necesario soltar el acelerador y el freno de mano y enderezar su coche.
-
-
- Conclusión
- Car Parking Multijugador South áfrica Edition es un juego que ofrece una experiencia de aparcamiento realista y divertido con un toque sudafricano. Tiene más coches, personalizaciones, mapas, música e idiomas que reflejan la cultura del automóvil sudafricano. También tiene un modo de mundo abierto, un modo de ajuste de coche, y un modo multijugador que hacen el juego más agradable y emocionante. Puede descargar e instalar Parking Multijugador South áfrica Edition APK en su dispositivo Android siguiendo los pasos en este artículo. También puedes usar los consejos y trucos de este artículo para mejorar tu juego y divertirte más.
- Preguntas frecuentes
- Aquí están algunas de las preguntas más frecuentes sobre Car Parking Multiplayer South áfrica Edition:
- ¿Es gratis Car Parking Multijugador South áfrica Edition?
- Sí, Parking Multijugador South áfrica Edition es gratis para descargar y jugar. Sin embargo, puede contener algunas compras en la aplicación que requieren dinero real.
- ¿Es seguro el aparcamiento multijugador South áfrica Edition?
- Sí, Parking Multijugador South áfrica Edition es seguro, siempre y cuando se descarga desde un sitio web de confianza. Sin embargo, siempre debe tener cuidado al descargar e instalar aplicaciones de fuentes desconocidas, ya que pueden contener virus o malware que pueden dañar su dispositivo.
- ¿Es compatible Car Parking Multiplayer South áfrica Edition con mi dispositivo?
- Car Parking Multijugador South áfrica Edition es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 4.1 o superior. Sin embargo, algunos dispositivos pueden tener diferentes especificaciones o problemas de rendimiento que pueden afectar la calidad o funcionalidad del juego.
- ¿Cómo puedo actualizar Car Parking Multijugador South áfrica Edition?
-
- ¿Cómo puedo contactar a los desarrolladores de Car Parking Multiplayer South áfrica Edition?
- Para contactar a los desarrolladores de Car Parking Multiplayer South áfrica Edition, puedes visitar su página de Facebook o su canal de YouTube y dejar un comentario o un mensaje.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md b/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md
deleted file mode 100644
index 2001fb4da4e341236f8b4ef59c584c7ee860ae80..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md
+++ /dev/null
@@ -1,76 +0,0 @@
-
-Nitro asfalto 9 leyendas Mod Apk: Una guía para los aficionados a las carreras
-Si eres un fan de los juegos de carreras, es posible que hayas oído hablar de Asphalt Nitro 9 Legends, uno de los juegos más populares y emocionantes del género. Este juego le permite tomar el volante de los coches reales de gama alta de renombrados fabricantes de automóviles legendarios, como Ferrari, Porsche, Lamborghini, y W Motors, entre muchas otras marcas internacionales. Puedes conducir, impulsar y realizar acrobacias en ubicaciones dinámicas de la vida real en modo individual o multijugador.
-Pero ¿y si quieres disfrutar del juego sin limitaciones ni restricciones? ¿Qué pasa si quieres tener dinero ilimitado y fichas, desbloquear todos los coches y pistas, personalizar sus vehículos y actualizarlos, y competir con otros jugadores en línea o fuera de línea? Bueno, hay una manera de hacerlo. Puede descargar e instalar Asphalt Nitro 9 Leyendas Mod Apk, una versión modificada del juego que le da acceso a todas estas características y más.
-asfalto nitro 9 leyendas mod apk
Download File ===== https://bltlly.com/2v6LmQ
-En este artículo, le guiará a través de las características, proceso de descarga, pasos de instalación, consejos y trucos, pros y contras, y la conclusión de Asphalt Nitro 9 Leyendas Mod Apk. Sigue leyendo para saber más.
- Características de Asphalt Nitro 9 Leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es una versión modificada del juego original que le da recursos ilimitados y características que mejoran su experiencia de juego. Estas son algunas de las características que puedes disfrutar con este mod apk:
-
-- Dinero ilimitado y fichas: Con este apk mod, nunca se quedará sin dinero o fichas para comprar nuevos coches, pistas, mejoras, o artículos. Puedes gastar todo lo que quieras sin preocuparte por tu presupuesto.
-
-- Personalizar sus vehículos y actualizarlos: Con este apk mod, puede personalizar sus vehículos de acuerdo a su preferencia. Puedes cambiar su color, pintura, calcomanías, ruedas, etc. También puedes mejorar su rendimiento mejorando su motor, transmisión, suspensión, frenos, nitro boost, etc.
-- Disfrutar de gráficos realistas y efectos de sonido: Con este apk mod, se puede disfrutar de los impresionantes gráficos y efectos de sonido del juego. El juego utiliza un motor de física realista que simula el movimiento y el comportamiento de los coches. El juego también cuenta con efectos de sonido de alta calidad que te hacen sentir como si estuvieras en una carrera real.
-- Compite con otros jugadores en línea o fuera de línea: Con este apk mod, puede competir con otros jugadores en línea o fuera de línea. Puedes unirte a carreras multijugador online y desafiar a tus amigos u otros jugadores de todo el mundo. También puedes jugar offline en modo carrera y completar varias misiones y desafíos.
-
- Cómo descargar e instalar Asphalt Nitro 9 leyendas Mod Apk
-Si desea descargar e instalar Asphalt Nitro 9 leyendas Mod Apk, es necesario seguir estos sencillos pasos:
-
-- Paso 1: Descargar el archivo apk mod de una fuente de confianza. Usted puede encontrar muchos sitios web que ofrecen el archivo apk mod para Asphalt Nitro 9 Leyendas, pero no todos ellos son seguros y fiables. Debes asegurarte de que el archivo que descargas esté libre de virus, malware o cualquier otro contenido dañino. Puede utilizar este enlace para descargar el archivo apk mod de forma segura.
-- Paso 2: Habilitar fuentes desconocidas en la configuración del dispositivo. Antes de poder instalar el archivo apk mod, debe habilitar fuentes desconocidas en la configuración de su dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a la configuración del dispositivo, luego a la seguridad, luego a fuentes desconocidas y conéctela.
-
-- Paso 4: Disfruta del juego con recursos y funciones ilimitadas. Ahora que ha instalado Asphalt Nitro 9 Leyendas Mod Apk, se puede disfrutar del juego con dinero ilimitado, fichas, coches, pistas, opciones de personalización, y más. También puedes competir con otros jugadores online o offline y divertirte.
-
- Consejos y trucos para jugar Asphalt Nitro 9 leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es un juego divertido y emocionante que le mantendrá entretenido durante horas. Sin embargo, si quieres mejorar tus habilidades y rendimiento en el juego, puedes seguir estos consejos y trucos:
-
-- Elija el coche adecuado para cada pista y modo. El juego ofrece una variedad de coches de diferentes marcas y categorías, pero no todos ellos son adecuados para cada pista o modo. Es necesario tener en cuenta la velocidad, aceleración, manejo, nitro, y otros factores de cada coche antes de elegir uno. Por ejemplo, si usted está jugando en una pista con curvas, es posible que desee elegir un coche con buen manejo y nitro. Si usted está jugando en una pista recta, es posible que desee elegir un coche con alta velocidad y aceleración.
-- Utilice nitro boost sabiamente y estratégicamente. Nitro boost es una de las características más importantes del juego, ya que puede darte una ventaja sobre tus oponentes. Sin embargo, necesitas usarlo sabiamente y estratégicamente. No debes usarlo todo de una vez ni desperdiciarlo en momentos innecesarios. Debes guardarlo para momentos cruciales, como adelantar a tus rivales, escapar de obstáculos o colisiones, o llegar a la meta. También debe usarlo en combinación con acrobacias y derivas para ganar más puntos y recompensas.
-
-- Evitar obstáculos y colisiones con otros coches. El juego cuenta con muchos obstáculos y desafíos que pueden obstaculizar su progreso o dañar su coche. Estos incluyen coches de tráfico, coches de policía, helicópteros, barricadas, picos, etc. Usted debe evitar estos obstáculos tanto como sea posible, ya que pueden ralentizar o dañar su coche. También debes evitar colisiones con otros coches, especialmente tus rivales, ya que pueden sacarte de la pista o hacerte perder tu posición. Usted debe tratar de esquivar o escapar de ellos, o utilizar su impulso nitro para alejarse de ellos.
-- Utilice diferentes ángulos de cámara y controles para adaptarse a sus preferencias. El juego ofrece diferentes ángulos de cámara y controles que puedes usar para jugar el juego. Puede elegir entre vista en primera persona, en tercera persona o de arriba hacia abajo, según su preferencia. También puede elegir entre los controles de inclinación, toque o toque, dependiendo de su comodidad. Puede cambiar estos ajustes en el menú de opciones del juego.
-
- Pros y contras de Asphalt Nitro 9 Leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es un gran juego que ofrece muchos beneficios y ventajas para los aficionados a las carreras. Sin embargo, también tiene algunos inconvenientes y desventajas que usted debe tener en cuenta. Estos son algunos de los pros y los contras de Asphalt Nitro 9 Leyendas Mod Apk:
-
-
-Pros
-Contras
-
-
-
-- Divertido, adictivo y desafiante juego
-- Dinero ilimitado y tokens
-- Desbloquear todos los coches y pistas
-- Personalizar sus vehículos y actualizarlos
-- Disfruta de gráficos realistas y efectos de sonido
-- Compite con otros jugadores online o offline
-
-- Puede causar retraso o estrellarse en algunos dispositivos
-- Puede no ser compatible con algunos dispositivos o versiones
-- No se puede actualizar regularmente o con frecuencia
-- No puede ser soportado por los desarrolladores o editores oficiales
-
-- Puede exponer su dispositivo a riesgos o amenazas de seguridad
-
-
- Conclusión
-En conclusión, Asfalto Nitro 9 Leyendas Mod Apk es un juego fantástico que le dará horas de diversión y emoción. Es una versión modificada del juego original que le da recursos ilimitados y características que mejoran su experiencia de juego. Puede descargarlo e instalarlo de forma fácil y segura siguiendo los pasos que proporcionamos en este artículo. También puedes seguir los consejos y trucos que compartimos para mejorar tus habilidades y rendimiento en el juego. Sin embargo, también debe ser consciente de los pros y los contras de Asphalt Nitro 9 Leyendas Mod Apk, y decidir si vale la pena jugar o no.
-Si usted es un fanático de las carreras, le recomendamos que pruebe Asphalt Nitro 9 Leyendas Mod Apk y ver por sí mismo lo increíble que es. No te arrepentirás.
- Preguntas frecuentes (preguntas frecuentes)
-Aquí están algunas de las preguntas y respuestas más comunes sobre Asphalt Nitro 9 Legends Mod Apk:
-
- Q: ¿Es libre de Asphalt Nitro 9 Leyendas Mod Apk?
-A: Sí, Asphalt Nitro 9 Leyendas Mod Apk es gratis para descargar y jugar. Usted no necesita pagar nada para disfrutar del juego.
- Q: ¿Es seguro Asphalt Nitro 9 Legends Mod Apk?
-A: Sí, Asfalto Nitro 9 Leyendas Mod Apk es seguro para descargar e instalar. Sin embargo, siempre debe descargarlo de una fuente de confianza y habilitar fuentes desconocidas en la configuración del dispositivo antes de instalarlo.
- Q: ¿Es legal Asphalt Nitro 9 Legends Mod Apk?
-A: No, Asphalt Nitro 9 Leyendas Mod Apk no es legal. Es una versión modificada del juego original que viola los términos y condiciones de los desarrolladores y editores oficiales. También puede infringir los derechos de propiedad intelectual del juego original.
- Q: Es Asphalt Nitro 9 Leyendas Mod Apk en línea o fuera de línea?
-
- Q: ¿Cómo puedo actualizar Asphalt Nitro 9 Legends Mod Apk?
-A: Para actualizar Asphalt Nitro 9 leyendas Mod Apk, es necesario descargar e instalar la última versión del archivo apk mod de una fuente de confianza. Es posible que tenga que desinstalar la versión anterior del archivo apk mod antes de instalar el nuevo.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md b/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md
deleted file mode 100644
index d872bc888664198a5fb77f20f85945d9c05ae2ef..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md
+++ /dev/null
@@ -1,76 +0,0 @@
-
-Camioneros de Europa 3 APK 36.6: Un juego de simulador de camiones realista y divertido
-Si eres un fan de los juegos de simuladores de camiones, es posible que hayas oído hablar de Truckers of Europe 3, un juego popular que te permite experimentar la vida de un conductor de camiones en Europa. En este artículo, te contaremos todo lo que necesitas saber sobre este juego, incluyendo qué es, cuáles son sus características, cómo descargarlo e instalarlo, cómo jugarlo y por qué deberías jugarlo. ¡Así que abróchate el cinturón y prepárate para un emocionante viaje!
-Introducción
-¿Qué es Camioneros de Europa 3?
-Truckers of Europe 3 es un juego de simulador de camioneros desarrollado por Jerryisgaming, un canal de YouTube que crea videos de juegos. El juego fue lanzado en 2020 y ha sido actualizado regularmente con nuevas características y mejoras. La última versión del juego es 0.36.6, que fue lanzado el 30 de septiembre de 2021.
-camioneros de europa 3 apk 36.6
Download Zip ❤❤❤ https://bltlly.com/2v6LwC
-¿Cuáles son las características de Camioneros de Europa 3?
-Truckers of Europe 3 tiene muchas características que lo convierten en uno de los mejores juegos de simuladores de camiones en el mercado. Algunas de estas características son:
-
-- Un gran mapa de Europa con más de 50 ciudades y países para explorar.
-- Una variedad de camiones y remolques para elegir, cada uno con diferentes especificaciones y rendimiento.
-- Un sistema de conducción realista con transmisión manual, volante, pedales, indicadores, espejos, luces, bocina, limpiaparabrisas, etc.
-- Un sistema de tráfico realista con coches, autobuses, camiones, motocicletas, policía, ambulancia, etc.
-- Un sistema meteorológico realista con ciclo de día y noche, lluvia, nieve, niebla, etc.
-- Un sistema de daños realista con desgaste de neumáticos, consumo de combustible, sobrecalentamiento del motor, etc.
-- Un modo de carrera con misiones, contratos, entrega de carga, ingresos, gastos, etc.
-- Un sistema de habilidades con niveles, puntos, ventajas, etc.
-- Un sistema de personalización con trabajos de pintura, accesorios, calcomanías, etc.
-
-
-Cómo descargar e instalar camioneros de Europa 3 APK 36.6?
-Requisitos
-Para descargar e instalar Camioneros de Europa 3 APK 36.6 en su dispositivo Android, es necesario cumplir con los siguientes requisitos:
-
-- Tu dispositivo debe tener la versión de Android 4.4 o superior.
-- Su dispositivo debe tener al menos 1 GB de RAM y 500 MB de espacio de almacenamiento libre.
-- Su dispositivo debe tener una conexión a Internet estable.
-- Su dispositivo debe permitir la instalación desde fuentes desconocidas. Puede habilitar esta opción yendo a Configuración > Seguridad > Fuentes desconocidas.
-
-Pasos
-Para descargar e instalar Camioneros de Europa 3 APK 36.6 en su dispositivo Android, debe seguir estos pasos:
-
-- Ir a este enlace y descargar el archivo APK.
-- Localice el archivo descargado en el administrador de archivos de su dispositivo y toque en él para iniciar el proceso de instalación.
-- Siga las instrucciones en la pantalla y conceda los permisos necesarios a la aplicación.
-- Espere a que la instalación se complete y luego inicie la aplicación desde el cajón de aplicaciones o la pantalla de inicio.
-- Disfruta jugando Camioneros de Europa 3 en tu dispositivo Android!
-
-Cómo jugar Camioneros de Europa 3?
-Elige tu camión y remolque
-Lo primero que tienes que hacer cuando empiezas a jugar Truckers of Europe 3 es elegir tu camión y remolque. Puede hacer esto yendo al menú del garaje y navegando a través de las opciones disponibles. También puede comprar nuevos camiones y remolques con el dinero que gana de sus misiones. Cada camión y remolque tiene diferentes atributos, como velocidad, potencia, capacidad de combustible, peso de carga, etc. Debe elegir el que se adapte a sus preferencias y necesidades.
-Conduce por toda Europa y entrega carga
-
-Mientras conduce, tendrá que seguir las reglas de tráfico y regulaciones, tales como límites de velocidad, semáforos, señales, etc. También tendrá que lidiar con condiciones de tráfico realistas, como automóviles, autobuses, camiones, motocicletas, policía, ambulancia, etc. También tendrá que enfrentarse a condiciones meteorológicas realistas, como lluvia, nieve, niebla, etc. También tendrá que controlar el estado de su camión, como el nivel de combustible, la temperatura del motor, la presión de los neumáticos, etc. También tendrá que prestar atención al estado de su conductor, como el nivel de fatiga, el nivel de hambre, etc. Tendrá que parar en gasolineras, áreas de descanso, restaurantes, etc. para llenar su tanque de combustible, descansar a su conductor, comer algo de comida, etc.
-Cuando llegue a su destino, tendrá que aparcar su camión y remolque en el lugar designado y descargar la carga. A continuación, recibirá su recompensa y comentarios sobre su rendimiento. También ganarás puntos de experiencia que te ayudarán a subir de nivel y desbloquear nuevas habilidades y beneficios.
-Personaliza tu camión y mejora tus habilidades
-Otro aspecto divertido de Truckers of Europe 3 es que puedes personalizar tu camión y mejorar tus habilidades. Puedes hacer esto yendo al menú del taller y gastando algo de dinero en varios artículos y servicios. Puede cambiar el color de su camión, agregar algunos accesorios, aplicar algunas calcomanías, etc. También puede reparar su camión si está dañado o afinarlo si no funciona bien.
-También puedes ir al menú de habilidades y gastar algunos puntos en varias habilidades y beneficios que mejorarán tus habilidades y beneficios como conductor de camión. Puede mejorar su eficiencia de combustible, manejo de carga, seguridad de conducción, habilidades de negociación, etc. También puede desbloquear nuevos tipos de carga, contratos, camiones, remolques, etc.
-
-¿Por qué deberías jugar Camioneros de Europa 3?
-Gráficos realistas y física
-
-Tráfico y clima diversos y dinámicos
-Otra razón por la que deberías jugar a Truckers of Europe 3 es porque tiene un tráfico diverso y dinámico y el clima hará que tu experiencia de conducción sea más desafiante y agradable. El juego tiene un gran mapa de Europa con más de 50 ciudades y países para explorar. Cada ciudad y país tiene sus propias características únicas, tales como monumentos, edificios, carreteras, señales, etc. El juego también tiene un sistema de tráfico diverso que incluye automóviles, autobuses, camiones, motocicletas, policía, ambulancia, etc. Cada vehículo tiene su propio comportamiento, velocidad, dirección, etc. El juego también tiene un sistema de tiempo dinámico que incluye ciclo de día y noche, lluvia, nieve, niebla, etc. Cada condición climática tiene su propio efecto en la visibilidad, tracción, manejo, etc. del camión y el remolque.
-Misiones y logros desafiantes y gratificantes
-Otra razón por la que deberías jugar a Truckers of Europe 3 es porque tiene misiones desafiantes y gratificantes y logros que te mantendrán motivado y entretenido. El juego tiene un modo de carrera que le permite comenzar como un conductor de camión novato y trabajar su camino hasta convertirse en un camionero profesional. Puede hacerlo completando varias misiones y contratos que implican la entrega de carga a diferentes destinos en toda Europa. También puede ganar dinero de sus entregas y gastarlo en la compra de nuevos camiones y remolques o la personalización de los existentes. También puede ganar puntos de experiencia de sus entregas y gastarlos en mejorar sus habilidades y beneficios. También puedes desbloquear nuevos tipos de carga, contratos, camiones, remolques, etc. a medida que avanzas en el juego.
-
-Conclusión
-Truckers of Europe 3 es un juego de simulador de camiones realista y divertido que te permite experimentar la vida de un conductor de camiones en Europa. Puede elegir su camión y remolque, conducir por toda Europa y entregar carga, personalizar su camión y mejorar sus habilidades, y ganar dinero y logros. El juego tiene gráficos y física realistas, tráfico y clima diversos y dinámicos, y misiones y logros desafiantes y gratificantes. Si usted está buscando un juego de simulador de camiones que le mantendrá enganchado durante horas, definitivamente debe probar Camioneros de Europa 3 APK 36.6.
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre los camioneros de Europa 3:
-
-- Q: ¿Los camioneros de Europa 3 son libres de jugar?
-- A: Sí, Truckers of Europe 3 es gratis. Puedes descargarlo e instalarlo desde el enlace proporcionado en este artículo. Sin embargo, el juego puede contener algunas compras en la aplicación que pueden mejorar su experiencia de juego.
-- Q: ¿Es seguro descargar e instalar Truckers of Europe 3?
-- A: Sí, Truckers of Europe 3 es seguro para descargar e instalar. El archivo APK proporcionado en este artículo es verificado y probado por nuestro equipo. Sin embargo, siempre debe descargar e instalar archivos APK de fuentes confiables solamente.
-- Q: ¿Es Truckers of Europe 3 compatible con mi dispositivo?
-- A: Camioneros de Europa 3 es compatible con la mayoría de los dispositivos Android que tienen la versión Android 4.4 o superior. Sin embargo, algunos dispositivos pueden no ser compatibles con el juego debido a limitaciones de hardware o software.
-- P: ¿Cómo puedo contactar al desarrollador de Truckers of Europe 3?
-- A: Puedes contactar al desarrollador de Truckers of Europe 3 visitando su canal de YouTube Jerryisgaming o su página de Facebook Jerryisgaming. También puedes dejar tus comentarios, sugerencias o consultas en la sección de comentarios de sus videos o publicaciones.
-- P: ¿Cómo puedo apoyar el desarrollo de Camioneros de Europa 3?
-
-
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md b/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md
deleted file mode 100644
index 516d398df61b62811ac5d43e8205d838c0714cbc..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md
+++ /dev/null
@@ -1,80 +0,0 @@
-
-Cómo descargar Genshin Impact Cloud y disfrutar del juego en cualquier lugar
-Genshin Impact es uno de los juegos más populares y exitosos de 2020, con millones de jugadores en todo el mundo. El juego es un RPG de acción de mundo abierto que te permite explorar un mundo vasto y hermoso llamado Teyvat, donde puedes conocer a varios personajes, luchar contra enemigos, reunir recursos y completar misiones. El juego está disponible en múltiples plataformas, como PC, PlayStation 4, PlayStation 5, iOS y Android. Sin embargo, si quieres jugar el juego en tu dispositivo móvil, puedes enfrentarte a algunos desafíos, como espacio de almacenamiento limitado, bajo rendimiento o dispositivos incompatibles. Ahí es donde entra Genshin Impact Cloud.
-¿Qué es la nube de impacto Genshin y por qué debe probarlo
-Genshin Impact Cloud es un servicio que te permite jugar el juego en tu dispositivo móvil sin descargar el juego entero
-Genshin Impact Cloud es una nueva característica que fue introducida por miHoYo, el desarrollador de Genshin Impact, en abril de 2021. Es un servicio de juegos en la nube que le permite transmitir el juego desde un servidor a su dispositivo móvil a través de Internet. Esto significa que no necesitas descargar o instalar el juego en tu dispositivo, lo que puede ahorrarte mucho espacio de almacenamiento. El tamaño del juego de Genshin Impact en dispositivos móviles es de alrededor de 5,2 GB, pero con Genshin Impact Cloud, solo necesitas descargar una aplicación de 56 MB de tamaño. Esto también puede ayudarle a evitar largos tiempos de carga o actualizaciones.
-descargar estrellas pelea hack ios
Download ===> https://bltlly.com/2v6KEj
-Genshin Impact Cloud tiene muchos beneficios, como ahorrar espacio de almacenamiento, mejorar el rendimiento y admitir cross-save
-Además de ahorrar espacio de almacenamiento, Genshin Impact Cloud también tiene otras ventajas que pueden mejorar su experiencia de juego. Por ejemplo:
-
-
-- Genshin Impact Cloud soporta cross-save y cross-play. Esto significa que puedes acceder a tu progreso y datos existentes desde otras plataformas iniciando sesión con tu cuenta miHoYo. También puedes jugar con tus amigos que usan diferentes dispositivos, como PC o PlayStation.
-- Genshin Impact Cloud es de uso gratuito. No es necesario pagar tarifas o suscripciones adicionales para utilizar el servicio. También puede hacer compras en la aplicación como de costumbre, y disfrutar de todas las actualizaciones y eventos del juego.
-
-Con Genshin Impact Cloud, puede disfrutar del juego en cualquier lugar y en cualquier momento, siempre y cuando tenga una conexión a Internet estable y un dispositivo compatible.
-Cómo descargar Genshin Impact Cloud en dispositivos Android
-Genshin Impact Cloud está disponible actualmente solo para usuarios de Android en Malasia y Singapur como prueba beta
-Genshin Impact Cloud todavía está en sus primeras etapas de desarrollo, y todavía no está disponible para todas las regiones y plataformas. Actualmente, el servicio solo está abierto para los usuarios de Android en Malasia y Singapur como una prueba beta. Esto significa que solo un número limitado de jugadores puede acceder al servicio, y puede haber algunos errores o errores durante el juego. miHoYo no ha anunciado cuándo se ampliará el servicio a otras regiones y plataformas, pero es probable que lo hagan en el futuro.
-Para descargar Genshin Impact Cloud, necesita tener una cuenta miHoYo y registrarse para la prueba beta en el sitio web oficial
-Si eres un usuario de Android en Malasia o Singapur, y quieres probar Genshin Impact Cloud, debes seguir estos pasos:
-
-- Crea una cuenta miHoYo si aún no tienes una. Puede hacer esto visitando https://account.mihoyo.com/#/register y llenando la información requerida.
-- Visite None:
- """
- Raise an option parsing error using parser.error().
-
- Args:
- parser: an OptionParser instance.
- option: an Option instance.
- msg: the error text.
- """
- msg = f"{option} error: {msg}"
- msg = textwrap.fill(" ".join(msg.split()))
- parser.error(msg)
-
-
-def make_option_group(group: Dict[str, Any], parser: ConfigOptionParser) -> OptionGroup:
- """
- Return an OptionGroup object
- group -- assumed to be dict with 'name' and 'options' keys
- parser -- an optparse Parser
- """
- option_group = OptionGroup(parser, group["name"])
- for option in group["options"]:
- option_group.add_option(option())
- return option_group
-
-
-def check_dist_restriction(options: Values, check_target: bool = False) -> None:
- """Function for determining if custom platform options are allowed.
-
- :param options: The OptionParser options.
- :param check_target: Whether or not to check if --target is being used.
- """
- dist_restriction_set = any(
- [
- options.python_version,
- options.platforms,
- options.abis,
- options.implementation,
- ]
- )
-
- binary_only = FormatControl(set(), {":all:"})
- sdist_dependencies_allowed = (
- options.format_control != binary_only and not options.ignore_dependencies
- )
-
- # Installations or downloads using dist restrictions must not combine
- # source distributions and dist-specific wheels, as they are not
- # guaranteed to be locally compatible.
- if dist_restriction_set and sdist_dependencies_allowed:
- raise CommandError(
- "When restricting platform and interpreter constraints using "
- "--python-version, --platform, --abi, or --implementation, "
- "either --no-deps must be set, or --only-binary=:all: must be "
- "set and --no-binary must not be set (or must be set to "
- ":none:)."
- )
-
- if check_target:
- if dist_restriction_set and not options.target_dir:
- raise CommandError(
- "Can not use any platform or abi specific options unless "
- "installing via '--target'"
- )
-
-
-def _path_option_check(option: Option, opt: str, value: str) -> str:
- return os.path.expanduser(value)
-
-
-def _package_name_option_check(option: Option, opt: str, value: str) -> str:
- return canonicalize_name(value)
-
-
-class PipOption(Option):
- TYPES = Option.TYPES + ("path", "package_name")
- TYPE_CHECKER = Option.TYPE_CHECKER.copy()
- TYPE_CHECKER["package_name"] = _package_name_option_check
- TYPE_CHECKER["path"] = _path_option_check
-
-
-###########
-# options #
-###########
-
-help_: Callable[..., Option] = partial(
- Option,
- "-h",
- "--help",
- dest="help",
- action="help",
- help="Show help.",
-)
-
-debug_mode: Callable[..., Option] = partial(
- Option,
- "--debug",
- dest="debug_mode",
- action="store_true",
- default=False,
- help=(
- "Let unhandled exceptions propagate outside the main subroutine, "
- "instead of logging them to stderr."
- ),
-)
-
-isolated_mode: Callable[..., Option] = partial(
- Option,
- "--isolated",
- dest="isolated_mode",
- action="store_true",
- default=False,
- help=(
- "Run pip in an isolated mode, ignoring environment variables and user "
- "configuration."
- ),
-)
-
-require_virtualenv: Callable[..., Option] = partial(
- Option,
- "--require-virtualenv",
- "--require-venv",
- dest="require_venv",
- action="store_true",
- default=False,
- help=(
- "Allow pip to only run in a virtual environment; "
- "exit with an error otherwise."
- ),
-)
-
-override_externally_managed: Callable[..., Option] = partial(
- Option,
- "--break-system-packages",
- dest="override_externally_managed",
- action="store_true",
- help="Allow pip to modify an EXTERNALLY-MANAGED Python installation",
-)
-
-python: Callable[..., Option] = partial(
- Option,
- "--python",
- dest="python",
- help="Run pip with the specified Python interpreter.",
-)
-
-verbose: Callable[..., Option] = partial(
- Option,
- "-v",
- "--verbose",
- dest="verbose",
- action="count",
- default=0,
- help="Give more output. Option is additive, and can be used up to 3 times.",
-)
-
-no_color: Callable[..., Option] = partial(
- Option,
- "--no-color",
- dest="no_color",
- action="store_true",
- default=False,
- help="Suppress colored output.",
-)
-
-version: Callable[..., Option] = partial(
- Option,
- "-V",
- "--version",
- dest="version",
- action="store_true",
- help="Show version and exit.",
-)
-
-quiet: Callable[..., Option] = partial(
- Option,
- "-q",
- "--quiet",
- dest="quiet",
- action="count",
- default=0,
- help=(
- "Give less output. Option is additive, and can be used up to 3"
- " times (corresponding to WARNING, ERROR, and CRITICAL logging"
- " levels)."
- ),
-)
-
-progress_bar: Callable[..., Option] = partial(
- Option,
- "--progress-bar",
- dest="progress_bar",
- type="choice",
- choices=["on", "off"],
- default="on",
- help="Specify whether the progress bar should be used [on, off] (default: on)",
-)
-
-log: Callable[..., Option] = partial(
- PipOption,
- "--log",
- "--log-file",
- "--local-log",
- dest="log",
- metavar="path",
- type="path",
- help="Path to a verbose appending log.",
-)
-
-no_input: Callable[..., Option] = partial(
- Option,
- # Don't ask for input
- "--no-input",
- dest="no_input",
- action="store_true",
- default=False,
- help="Disable prompting for input.",
-)
-
-keyring_provider: Callable[..., Option] = partial(
- Option,
- "--keyring-provider",
- dest="keyring_provider",
- choices=["auto", "disabled", "import", "subprocess"],
- default="auto",
- help=(
- "Enable the credential lookup via the keyring library if user input is allowed."
- " Specify which mechanism to use [disabled, import, subprocess]."
- " (default: disabled)"
- ),
-)
-
-proxy: Callable[..., Option] = partial(
- Option,
- "--proxy",
- dest="proxy",
- type="str",
- default="",
- help="Specify a proxy in the form scheme://[user:passwd@]proxy.server:port.",
-)
-
-retries: Callable[..., Option] = partial(
- Option,
- "--retries",
- dest="retries",
- type="int",
- default=5,
- help="Maximum number of retries each connection should attempt "
- "(default %default times).",
-)
-
-timeout: Callable[..., Option] = partial(
- Option,
- "--timeout",
- "--default-timeout",
- metavar="sec",
- dest="timeout",
- type="float",
- default=15,
- help="Set the socket timeout (default %default seconds).",
-)
-
-
-def exists_action() -> Option:
- return Option(
- # Option when path already exist
- "--exists-action",
- dest="exists_action",
- type="choice",
- choices=["s", "i", "w", "b", "a"],
- default=[],
- action="append",
- metavar="action",
- help="Default action when a path already exists: "
- "(s)witch, (i)gnore, (w)ipe, (b)ackup, (a)bort.",
- )
-
-
-cert: Callable[..., Option] = partial(
- PipOption,
- "--cert",
- dest="cert",
- type="path",
- metavar="path",
- help=(
- "Path to PEM-encoded CA certificate bundle. "
- "If provided, overrides the default. "
- "See 'SSL Certificate Verification' in pip documentation "
- "for more information."
- ),
-)
-
-client_cert: Callable[..., Option] = partial(
- PipOption,
- "--client-cert",
- dest="client_cert",
- type="path",
- default=None,
- metavar="path",
- help="Path to SSL client certificate, a single file containing the "
- "private key and the certificate in PEM format.",
-)
-
-index_url: Callable[..., Option] = partial(
- Option,
- "-i",
- "--index-url",
- "--pypi-url",
- dest="index_url",
- metavar="URL",
- default=PyPI.simple_url,
- help="Base URL of the Python Package Index (default %default). "
- "This should point to a repository compliant with PEP 503 "
- "(the simple repository API) or a local directory laid out "
- "in the same format.",
-)
-
-
-def extra_index_url() -> Option:
- return Option(
- "--extra-index-url",
- dest="extra_index_urls",
- metavar="URL",
- action="append",
- default=[],
- help="Extra URLs of package indexes to use in addition to "
- "--index-url. Should follow the same rules as "
- "--index-url.",
- )
-
-
-no_index: Callable[..., Option] = partial(
- Option,
- "--no-index",
- dest="no_index",
- action="store_true",
- default=False,
- help="Ignore package index (only looking at --find-links URLs instead).",
-)
-
-
-def find_links() -> Option:
- return Option(
- "-f",
- "--find-links",
- dest="find_links",
- action="append",
- default=[],
- metavar="url",
- help="If a URL or path to an html file, then parse for links to "
- "archives such as sdist (.tar.gz) or wheel (.whl) files. "
- "If a local path or file:// URL that's a directory, "
- "then look for archives in the directory listing. "
- "Links to VCS project URLs are not supported.",
- )
-
-
-def trusted_host() -> Option:
- return Option(
- "--trusted-host",
- dest="trusted_hosts",
- action="append",
- metavar="HOSTNAME",
- default=[],
- help="Mark this host or host:port pair as trusted, even though it "
- "does not have valid or any HTTPS.",
- )
-
-
-def constraints() -> Option:
- return Option(
- "-c",
- "--constraint",
- dest="constraints",
- action="append",
- default=[],
- metavar="file",
- help="Constrain versions using the given constraints file. "
- "This option can be used multiple times.",
- )
-
-
-def requirements() -> Option:
- return Option(
- "-r",
- "--requirement",
- dest="requirements",
- action="append",
- default=[],
- metavar="file",
- help="Install from the given requirements file. "
- "This option can be used multiple times.",
- )
-
-
-def editable() -> Option:
- return Option(
- "-e",
- "--editable",
- dest="editables",
- action="append",
- default=[],
- metavar="path/url",
- help=(
- "Install a project in editable mode (i.e. setuptools "
- '"develop mode") from a local project path or a VCS url.'
- ),
- )
-
-
-def _handle_src(option: Option, opt_str: str, value: str, parser: OptionParser) -> None:
- value = os.path.abspath(value)
- setattr(parser.values, option.dest, value)
-
-
-src: Callable[..., Option] = partial(
- PipOption,
- "--src",
- "--source",
- "--source-dir",
- "--source-directory",
- dest="src_dir",
- type="path",
- metavar="dir",
- default=get_src_prefix(),
- action="callback",
- callback=_handle_src,
- help="Directory to check out editable projects into. "
- 'The default in a virtualenv is "
/src". '
- 'The default for global installs is "/src".',
-)
-
-
-def _get_format_control(values: Values, option: Option) -> Any:
- """Get a format_control object."""
- return getattr(values, option.dest)
-
-
-def _handle_no_binary(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- existing = _get_format_control(parser.values, option)
- FormatControl.handle_mutual_excludes(
- value,
- existing.no_binary,
- existing.only_binary,
- )
-
-
-def _handle_only_binary(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- existing = _get_format_control(parser.values, option)
- FormatControl.handle_mutual_excludes(
- value,
- existing.only_binary,
- existing.no_binary,
- )
-
-
-def no_binary() -> Option:
- format_control = FormatControl(set(), set())
- return Option(
- "--no-binary",
- dest="format_control",
- action="callback",
- callback=_handle_no_binary,
- type="str",
- default=format_control,
- help="Do not use binary packages. Can be supplied multiple times, and "
- 'each time adds to the existing value. Accepts either ":all:" to '
- 'disable all binary packages, ":none:" to empty the set (notice '
- "the colons), or one or more package names with commas between "
- "them (no colons). Note that some packages are tricky to compile "
- "and may fail to install when this option is used on them.",
- )
-
-
-def only_binary() -> Option:
- format_control = FormatControl(set(), set())
- return Option(
- "--only-binary",
- dest="format_control",
- action="callback",
- callback=_handle_only_binary,
- type="str",
- default=format_control,
- help="Do not use source packages. Can be supplied multiple times, and "
- 'each time adds to the existing value. Accepts either ":all:" to '
- 'disable all source packages, ":none:" to empty the set, or one '
- "or more package names with commas between them. Packages "
- "without binary distributions will fail to install when this "
- "option is used on them.",
- )
-
-
-platforms: Callable[..., Option] = partial(
- Option,
- "--platform",
- dest="platforms",
- metavar="platform",
- action="append",
- default=None,
- help=(
- "Only use wheels compatible with . Defaults to the "
- "platform of the running system. Use this option multiple times to "
- "specify multiple platforms supported by the target interpreter."
- ),
-)
-
-
-# This was made a separate function for unit-testing purposes.
-def _convert_python_version(value: str) -> Tuple[Tuple[int, ...], Optional[str]]:
- """
- Convert a version string like "3", "37", or "3.7.3" into a tuple of ints.
-
- :return: A 2-tuple (version_info, error_msg), where `error_msg` is
- non-None if and only if there was a parsing error.
- """
- if not value:
- # The empty string is the same as not providing a value.
- return (None, None)
-
- parts = value.split(".")
- if len(parts) > 3:
- return ((), "at most three version parts are allowed")
-
- if len(parts) == 1:
- # Then we are in the case of "3" or "37".
- value = parts[0]
- if len(value) > 1:
- parts = [value[0], value[1:]]
-
- try:
- version_info = tuple(int(part) for part in parts)
- except ValueError:
- return ((), "each version part must be an integer")
-
- return (version_info, None)
-
-
-def _handle_python_version(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- """
- Handle a provided --python-version value.
- """
- version_info, error_msg = _convert_python_version(value)
- if error_msg is not None:
- msg = "invalid --python-version value: {!r}: {}".format(
- value,
- error_msg,
- )
- raise_option_error(parser, option=option, msg=msg)
-
- parser.values.python_version = version_info
-
-
-python_version: Callable[..., Option] = partial(
- Option,
- "--python-version",
- dest="python_version",
- metavar="python_version",
- action="callback",
- callback=_handle_python_version,
- type="str",
- default=None,
- help=dedent(
- """\
- The Python interpreter version to use for wheel and "Requires-Python"
- compatibility checks. Defaults to a version derived from the running
- interpreter. The version can be specified using up to three dot-separated
- integers (e.g. "3" for 3.0.0, "3.7" for 3.7.0, or "3.7.3"). A major-minor
- version can also be given as a string without dots (e.g. "37" for 3.7.0).
- """
- ),
-)
-
-
-implementation: Callable[..., Option] = partial(
- Option,
- "--implementation",
- dest="implementation",
- metavar="implementation",
- default=None,
- help=(
- "Only use wheels compatible with Python "
- "implementation , e.g. 'pp', 'jy', 'cp', "
- " or 'ip'. If not specified, then the current "
- "interpreter implementation is used. Use 'py' to force "
- "implementation-agnostic wheels."
- ),
-)
-
-
-abis: Callable[..., Option] = partial(
- Option,
- "--abi",
- dest="abis",
- metavar="abi",
- action="append",
- default=None,
- help=(
- "Only use wheels compatible with Python abi , e.g. 'pypy_41'. "
- "If not specified, then the current interpreter abi tag is used. "
- "Use this option multiple times to specify multiple abis supported "
- "by the target interpreter. Generally you will need to specify "
- "--implementation, --platform, and --python-version when using this "
- "option."
- ),
-)
-
-
-def add_target_python_options(cmd_opts: OptionGroup) -> None:
- cmd_opts.add_option(platforms())
- cmd_opts.add_option(python_version())
- cmd_opts.add_option(implementation())
- cmd_opts.add_option(abis())
-
-
-def make_target_python(options: Values) -> TargetPython:
- target_python = TargetPython(
- platforms=options.platforms,
- py_version_info=options.python_version,
- abis=options.abis,
- implementation=options.implementation,
- )
-
- return target_python
-
-
-def prefer_binary() -> Option:
- return Option(
- "--prefer-binary",
- dest="prefer_binary",
- action="store_true",
- default=False,
- help="Prefer older binary packages over newer source packages.",
- )
-
-
-cache_dir: Callable[..., Option] = partial(
- PipOption,
- "--cache-dir",
- dest="cache_dir",
- default=USER_CACHE_DIR,
- metavar="dir",
- type="path",
- help="Store the cache data in .",
-)
-
-
-def _handle_no_cache_dir(
- option: Option, opt: str, value: str, parser: OptionParser
-) -> None:
- """
- Process a value provided for the --no-cache-dir option.
-
- This is an optparse.Option callback for the --no-cache-dir option.
- """
- # The value argument will be None if --no-cache-dir is passed via the
- # command-line, since the option doesn't accept arguments. However,
- # the value can be non-None if the option is triggered e.g. by an
- # environment variable, like PIP_NO_CACHE_DIR=true.
- if value is not None:
- # Then parse the string value to get argument error-checking.
- try:
- strtobool(value)
- except ValueError as exc:
- raise_option_error(parser, option=option, msg=str(exc))
-
- # Originally, setting PIP_NO_CACHE_DIR to a value that strtobool()
- # converted to 0 (like "false" or "no") caused cache_dir to be disabled
- # rather than enabled (logic would say the latter). Thus, we disable
- # the cache directory not just on values that parse to True, but (for
- # backwards compatibility reasons) also on values that parse to False.
- # In other words, always set it to False if the option is provided in
- # some (valid) form.
- parser.values.cache_dir = False
-
-
-no_cache: Callable[..., Option] = partial(
- Option,
- "--no-cache-dir",
- dest="cache_dir",
- action="callback",
- callback=_handle_no_cache_dir,
- help="Disable the cache.",
-)
-
-no_deps: Callable[..., Option] = partial(
- Option,
- "--no-deps",
- "--no-dependencies",
- dest="ignore_dependencies",
- action="store_true",
- default=False,
- help="Don't install package dependencies.",
-)
-
-ignore_requires_python: Callable[..., Option] = partial(
- Option,
- "--ignore-requires-python",
- dest="ignore_requires_python",
- action="store_true",
- help="Ignore the Requires-Python information.",
-)
-
-no_build_isolation: Callable[..., Option] = partial(
- Option,
- "--no-build-isolation",
- dest="build_isolation",
- action="store_false",
- default=True,
- help="Disable isolation when building a modern source distribution. "
- "Build dependencies specified by PEP 518 must be already installed "
- "if this option is used.",
-)
-
-check_build_deps: Callable[..., Option] = partial(
- Option,
- "--check-build-dependencies",
- dest="check_build_deps",
- action="store_true",
- default=False,
- help="Check the build dependencies when PEP517 is used.",
-)
-
-
-def _handle_no_use_pep517(
- option: Option, opt: str, value: str, parser: OptionParser
-) -> None:
- """
- Process a value provided for the --no-use-pep517 option.
-
- This is an optparse.Option callback for the no_use_pep517 option.
- """
- # Since --no-use-pep517 doesn't accept arguments, the value argument
- # will be None if --no-use-pep517 is passed via the command-line.
- # However, the value can be non-None if the option is triggered e.g.
- # by an environment variable, for example "PIP_NO_USE_PEP517=true".
- if value is not None:
- msg = """A value was passed for --no-use-pep517,
- probably using either the PIP_NO_USE_PEP517 environment variable
- or the "no-use-pep517" config file option. Use an appropriate value
- of the PIP_USE_PEP517 environment variable or the "use-pep517"
- config file option instead.
- """
- raise_option_error(parser, option=option, msg=msg)
-
- # If user doesn't wish to use pep517, we check if setuptools and wheel are installed
- # and raise error if it is not.
- packages = ("setuptools", "wheel")
- if not all(importlib.util.find_spec(package) for package in packages):
- msg = (
- f"It is not possible to use --no-use-pep517 "
- f"without {' and '.join(packages)} installed."
- )
- raise_option_error(parser, option=option, msg=msg)
-
- # Otherwise, --no-use-pep517 was passed via the command-line.
- parser.values.use_pep517 = False
-
-
-use_pep517: Any = partial(
- Option,
- "--use-pep517",
- dest="use_pep517",
- action="store_true",
- default=None,
- help="Use PEP 517 for building source distributions "
- "(use --no-use-pep517 to force legacy behaviour).",
-)
-
-no_use_pep517: Any = partial(
- Option,
- "--no-use-pep517",
- dest="use_pep517",
- action="callback",
- callback=_handle_no_use_pep517,
- default=None,
- help=SUPPRESS_HELP,
-)
-
-
-def _handle_config_settings(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- key, sep, val = value.partition("=")
- if sep != "=":
- parser.error(f"Arguments to {opt_str} must be of the form KEY=VAL") # noqa
- dest = getattr(parser.values, option.dest)
- if dest is None:
- dest = {}
- setattr(parser.values, option.dest, dest)
- if key in dest:
- if isinstance(dest[key], list):
- dest[key].append(val)
- else:
- dest[key] = [dest[key], val]
- else:
- dest[key] = val
-
-
-config_settings: Callable[..., Option] = partial(
- Option,
- "-C",
- "--config-settings",
- dest="config_settings",
- type=str,
- action="callback",
- callback=_handle_config_settings,
- metavar="settings",
- help="Configuration settings to be passed to the PEP 517 build backend. "
- "Settings take the form KEY=VALUE. Use multiple --config-settings options "
- "to pass multiple keys to the backend.",
-)
-
-build_options: Callable[..., Option] = partial(
- Option,
- "--build-option",
- dest="build_options",
- metavar="options",
- action="append",
- help="Extra arguments to be supplied to 'setup.py bdist_wheel'.",
-)
-
-global_options: Callable[..., Option] = partial(
- Option,
- "--global-option",
- dest="global_options",
- action="append",
- metavar="options",
- help="Extra global options to be supplied to the setup.py "
- "call before the install or bdist_wheel command.",
-)
-
-no_clean: Callable[..., Option] = partial(
- Option,
- "--no-clean",
- action="store_true",
- default=False,
- help="Don't clean up build directories.",
-)
-
-pre: Callable[..., Option] = partial(
- Option,
- "--pre",
- action="store_true",
- default=False,
- help="Include pre-release and development versions. By default, "
- "pip only finds stable versions.",
-)
-
-disable_pip_version_check: Callable[..., Option] = partial(
- Option,
- "--disable-pip-version-check",
- dest="disable_pip_version_check",
- action="store_true",
- default=False,
- help="Don't periodically check PyPI to determine whether a new version "
- "of pip is available for download. Implied with --no-index.",
-)
-
-root_user_action: Callable[..., Option] = partial(
- Option,
- "--root-user-action",
- dest="root_user_action",
- default="warn",
- choices=["warn", "ignore"],
- help="Action if pip is run as a root user. By default, a warning message is shown.",
-)
-
-
-def _handle_merge_hash(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- """Given a value spelled "algo:digest", append the digest to a list
- pointed to in a dict by the algo name."""
- if not parser.values.hashes:
- parser.values.hashes = {}
- try:
- algo, digest = value.split(":", 1)
- except ValueError:
- parser.error(
- "Arguments to {} must be a hash name " # noqa
- "followed by a value, like --hash=sha256:"
- "abcde...".format(opt_str)
- )
- if algo not in STRONG_HASHES:
- parser.error(
- "Allowed hash algorithms for {} are {}.".format( # noqa
- opt_str, ", ".join(STRONG_HASHES)
- )
- )
- parser.values.hashes.setdefault(algo, []).append(digest)
-
-
-hash: Callable[..., Option] = partial(
- Option,
- "--hash",
- # Hash values eventually end up in InstallRequirement.hashes due to
- # __dict__ copying in process_line().
- dest="hashes",
- action="callback",
- callback=_handle_merge_hash,
- type="string",
- help="Verify that the package's archive matches this "
- "hash before installing. Example: --hash=sha256:abcdef...",
-)
-
-
-require_hashes: Callable[..., Option] = partial(
- Option,
- "--require-hashes",
- dest="require_hashes",
- action="store_true",
- default=False,
- help="Require a hash to check each requirement against, for "
- "repeatable installs. This option is implied when any package in a "
- "requirements file has a --hash option.",
-)
-
-
-list_path: Callable[..., Option] = partial(
- PipOption,
- "--path",
- dest="path",
- type="path",
- action="append",
- help="Restrict to the specified installation path for listing "
- "packages (can be used multiple times).",
-)
-
-
-def check_list_path_option(options: Values) -> None:
- if options.path and (options.user or options.local):
- raise CommandError("Cannot combine '--path' with '--user' or '--local'")
-
-
-list_exclude: Callable[..., Option] = partial(
- PipOption,
- "--exclude",
- dest="excludes",
- action="append",
- metavar="package",
- type="package_name",
- help="Exclude specified package from the output",
-)
-
-
-no_python_version_warning: Callable[..., Option] = partial(
- Option,
- "--no-python-version-warning",
- dest="no_python_version_warning",
- action="store_true",
- default=False,
- help="Silence deprecation warnings for upcoming unsupported Pythons.",
-)
-
-
-# Features that are now always on. A warning is printed if they are used.
-ALWAYS_ENABLED_FEATURES = [
- "no-binary-enable-wheel-cache", # always on since 23.1
-]
-
-use_new_feature: Callable[..., Option] = partial(
- Option,
- "--use-feature",
- dest="features_enabled",
- metavar="feature",
- action="append",
- default=[],
- choices=[
- "fast-deps",
- "truststore",
- ]
- + ALWAYS_ENABLED_FEATURES,
- help="Enable new functionality, that may be backward incompatible.",
-)
-
-use_deprecated_feature: Callable[..., Option] = partial(
- Option,
- "--use-deprecated",
- dest="deprecated_features_enabled",
- metavar="feature",
- action="append",
- default=[],
- choices=[
- "legacy-resolver",
- ],
- help=("Enable deprecated functionality, that will be removed in the future."),
-)
-
-
-##########
-# groups #
-##########
-
-general_group: Dict[str, Any] = {
- "name": "General Options",
- "options": [
- help_,
- debug_mode,
- isolated_mode,
- require_virtualenv,
- python,
- verbose,
- version,
- quiet,
- log,
- no_input,
- keyring_provider,
- proxy,
- retries,
- timeout,
- exists_action,
- trusted_host,
- cert,
- client_cert,
- cache_dir,
- no_cache,
- disable_pip_version_check,
- no_color,
- no_python_version_warning,
- use_new_feature,
- use_deprecated_feature,
- ],
-}
-
-index_group: Dict[str, Any] = {
- "name": "Package Index Options",
- "options": [
- index_url,
- extra_index_url,
- no_index,
- find_links,
- ],
-}
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py
deleted file mode 100644
index fd80d8c1129722b84771bd6a0f6ccfd57f5cf78e..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import re
-from ast import literal_eval
-from operator import attrgetter
-from typing import Callable, Iterable, List, Match, NamedTuple, Optional, Tuple, Union
-
-from ._emoji_replace import _emoji_replace
-from .emoji import EmojiVariant
-from .errors import MarkupError
-from .style import Style
-from .text import Span, Text
-
-RE_TAGS = re.compile(
- r"""((\\*)\[([a-z#/@][^[]*?)])""",
- re.VERBOSE,
-)
-
-RE_HANDLER = re.compile(r"^([\w.]*?)(\(.*?\))?$")
-
-
-class Tag(NamedTuple):
- """A tag in console markup."""
-
- name: str
- """The tag name. e.g. 'bold'."""
- parameters: Optional[str]
- """Any additional parameters after the name."""
-
- def __str__(self) -> str:
- return (
- self.name if self.parameters is None else f"{self.name} {self.parameters}"
- )
-
- @property
- def markup(self) -> str:
- """Get the string representation of this tag."""
- return (
- f"[{self.name}]"
- if self.parameters is None
- else f"[{self.name}={self.parameters}]"
- )
-
-
-_ReStringMatch = Match[str] # regex match object
-_ReSubCallable = Callable[[_ReStringMatch], str] # Callable invoked by re.sub
-_EscapeSubMethod = Callable[[_ReSubCallable, str], str] # Sub method of a compiled re
-
-
-def escape(
- markup: str,
- _escape: _EscapeSubMethod = re.compile(r"(\\*)(\[[a-z#/@][^[]*?])").sub,
-) -> str:
- """Escapes text so that it won't be interpreted as markup.
-
- Args:
- markup (str): Content to be inserted in to markup.
-
- Returns:
- str: Markup with square brackets escaped.
- """
-
- def escape_backslashes(match: Match[str]) -> str:
- """Called by re.sub replace matches."""
- backslashes, text = match.groups()
- return f"{backslashes}{backslashes}\\{text}"
-
- markup = _escape(escape_backslashes, markup)
- return markup
-
-
-def _parse(markup: str) -> Iterable[Tuple[int, Optional[str], Optional[Tag]]]:
- """Parse markup in to an iterable of tuples of (position, text, tag).
-
- Args:
- markup (str): A string containing console markup
-
- """
- position = 0
- _divmod = divmod
- _Tag = Tag
- for match in RE_TAGS.finditer(markup):
- full_text, escapes, tag_text = match.groups()
- start, end = match.span()
- if start > position:
- yield start, markup[position:start], None
- if escapes:
- backslashes, escaped = _divmod(len(escapes), 2)
- if backslashes:
- # Literal backslashes
- yield start, "\\" * backslashes, None
- start += backslashes * 2
- if escaped:
- # Escape of tag
- yield start, full_text[len(escapes) :], None
- position = end
- continue
- text, equals, parameters = tag_text.partition("=")
- yield start, None, _Tag(text, parameters if equals else None)
- position = end
- if position < len(markup):
- yield position, markup[position:], None
-
-
-def render(
- markup: str,
- style: Union[str, Style] = "",
- emoji: bool = True,
- emoji_variant: Optional[EmojiVariant] = None,
-) -> Text:
- """Render console markup in to a Text instance.
-
- Args:
- markup (str): A string containing console markup.
- emoji (bool, optional): Also render emoji code. Defaults to True.
-
- Raises:
- MarkupError: If there is a syntax error in the markup.
-
- Returns:
- Text: A test instance.
- """
- emoji_replace = _emoji_replace
- if "[" not in markup:
- return Text(
- emoji_replace(markup, default_variant=emoji_variant) if emoji else markup,
- style=style,
- )
- text = Text(style=style)
- append = text.append
- normalize = Style.normalize
-
- style_stack: List[Tuple[int, Tag]] = []
- pop = style_stack.pop
-
- spans: List[Span] = []
- append_span = spans.append
-
- _Span = Span
- _Tag = Tag
-
- def pop_style(style_name: str) -> Tuple[int, Tag]:
- """Pop tag matching given style name."""
- for index, (_, tag) in enumerate(reversed(style_stack), 1):
- if tag.name == style_name:
- return pop(-index)
- raise KeyError(style_name)
-
- for position, plain_text, tag in _parse(markup):
- if plain_text is not None:
- # Handle open brace escapes, where the brace is not part of a tag.
- plain_text = plain_text.replace("\\[", "[")
- append(emoji_replace(plain_text) if emoji else plain_text)
- elif tag is not None:
- if tag.name.startswith("/"): # Closing tag
- style_name = tag.name[1:].strip()
-
- if style_name: # explicit close
- style_name = normalize(style_name)
- try:
- start, open_tag = pop_style(style_name)
- except KeyError:
- raise MarkupError(
- f"closing tag '{tag.markup}' at position {position} doesn't match any open tag"
- ) from None
- else: # implicit close
- try:
- start, open_tag = pop()
- except IndexError:
- raise MarkupError(
- f"closing tag '[/]' at position {position} has nothing to close"
- ) from None
-
- if open_tag.name.startswith("@"):
- if open_tag.parameters:
- handler_name = ""
- parameters = open_tag.parameters.strip()
- handler_match = RE_HANDLER.match(parameters)
- if handler_match is not None:
- handler_name, match_parameters = handler_match.groups()
- parameters = (
- "()" if match_parameters is None else match_parameters
- )
-
- try:
- meta_params = literal_eval(parameters)
- except SyntaxError as error:
- raise MarkupError(
- f"error parsing {parameters!r} in {open_tag.parameters!r}; {error.msg}"
- )
- except Exception as error:
- raise MarkupError(
- f"error parsing {open_tag.parameters!r}; {error}"
- ) from None
-
- if handler_name:
- meta_params = (
- handler_name,
- meta_params
- if isinstance(meta_params, tuple)
- else (meta_params,),
- )
-
- else:
- meta_params = ()
-
- append_span(
- _Span(
- start, len(text), Style(meta={open_tag.name: meta_params})
- )
- )
- else:
- append_span(_Span(start, len(text), str(open_tag)))
-
- else: # Opening tag
- normalized_tag = _Tag(normalize(tag.name), tag.parameters)
- style_stack.append((len(text), normalized_tag))
-
- text_length = len(text)
- while style_stack:
- start, tag = style_stack.pop()
- style = str(tag)
- if style:
- append_span(_Span(start, text_length, style))
-
- text.spans = sorted(spans[::-1], key=attrgetter("start"))
- return text
-
-
-if __name__ == "__main__": # pragma: no cover
-
- MARKUP = [
- "[red]Hello World[/red]",
- "[magenta]Hello [b]World[/b]",
- "[bold]Bold[italic] bold and italic [/bold]italic[/italic]",
- "Click [link=https://www.willmcgugan.com]here[/link] to visit my Blog",
- ":warning-emoji: [bold red blink] DANGER![/]",
- ]
-
- from pip._vendor.rich import print
- from pip._vendor.rich.table import Table
-
- grid = Table("Markup", "Result", padding=(0, 1))
-
- for markup in MARKUP:
- grid.add_row(Text(markup), markup)
-
- print(grid)
diff --git a/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat b/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat
deleted file mode 100644
index 62a39a7584f4d7c5fbc31758e3e9e7eff700276d..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat
+++ /dev/null
@@ -1,22 +0,0 @@
-@echo off
-
-if defined _OLD_VIRTUAL_PROMPT (
- set "PROMPT=%_OLD_VIRTUAL_PROMPT%"
-)
-set _OLD_VIRTUAL_PROMPT=
-
-if defined _OLD_VIRTUAL_PYTHONHOME (
- set "PYTHONHOME=%_OLD_VIRTUAL_PYTHONHOME%"
- set _OLD_VIRTUAL_PYTHONHOME=
-)
-
-if defined _OLD_VIRTUAL_PATH (
- set "PATH=%_OLD_VIRTUAL_PATH%"
-)
-
-set _OLD_VIRTUAL_PATH=
-
-set VIRTUAL_ENV=
-set VIRTUAL_ENV_PROMPT=
-
-:END
diff --git a/spaces/CAMP-ViL/Xplainer/inference.py b/spaces/CAMP-ViL/Xplainer/inference.py
deleted file mode 100644
index 2ab6b61c2b1fa8039af58a5f5c4823b6c2f56cab..0000000000000000000000000000000000000000
--- a/spaces/CAMP-ViL/Xplainer/inference.py
+++ /dev/null
@@ -1,116 +0,0 @@
-import argparse
-import gc
-from pathlib import Path
-
-import torch
-from torch.utils.data import DataLoader
-from tqdm import tqdm
-
-from chestxray14 import ChestXray14Dataset
-from chexpert import CheXpertDataset
-from descriptors import disease_descriptors_chexpert, disease_descriptors_chestxray14
-from model import InferenceModel
-from utils import calculate_auroc
-
-torch.multiprocessing.set_sharing_strategy('file_system')
-
-
-def inference_chexpert():
- split = 'test'
- dataset = CheXpertDataset(f'data/chexpert/{split}_labels.csv') # also do test
- dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=lambda x: x, num_workers=0)
- inference_model = InferenceModel()
- all_descriptors = inference_model.get_all_descriptors(disease_descriptors_chexpert)
-
- all_labels = []
- all_probs_neg = []
-
- for batch in tqdm(dataloader):
- batch = batch[0]
- image_paths, labels, keys = batch
- image_paths = [Path(image_path) for image_path in image_paths]
- agg_probs = []
- agg_negative_probs = []
- for image_path in image_paths:
- probs, negative_probs = inference_model.get_descriptor_probs(image_path, descriptors=all_descriptors)
- agg_probs.append(probs)
- agg_negative_probs.append(negative_probs)
- probs = {} # Aggregated
- negative_probs = {} # Aggregated
- for key in agg_probs[0].keys():
- probs[key] = sum([p[key] for p in agg_probs]) / len(agg_probs) # Mean Aggregation
-
- for key in agg_negative_probs[0].keys():
- negative_probs[key] = sum([p[key] for p in agg_negative_probs]) / len(agg_negative_probs) # Mean Aggregation
-
- disease_probs, negative_disease_probs = inference_model.get_diseases_probs(disease_descriptors_chexpert, pos_probs=probs,
- negative_probs=negative_probs)
- predicted_diseases, prob_vector_neg_prompt = inference_model.get_predictions_bin_prompting(disease_descriptors_chexpert,
- disease_probs=disease_probs,
- negative_disease_probs=negative_disease_probs,
- keys=keys)
- all_labels.append(labels)
- all_probs_neg.append(prob_vector_neg_prompt)
-
- all_labels = torch.stack(all_labels)
- all_probs_neg = torch.stack(all_probs_neg)
-
- # evaluation
- existing_mask = sum(all_labels, 0) > 0
- all_labels_clean = all_labels[:, existing_mask]
- all_probs_neg_clean = all_probs_neg[:, existing_mask]
- all_keys_clean = [key for idx, key in enumerate(keys) if existing_mask[idx]]
-
- overall_auroc, per_disease_auroc = calculate_auroc(all_probs_neg_clean, all_labels_clean)
- print(f"AUROC: {overall_auroc:.5f}\n")
- for idx, key in enumerate(all_keys_clean):
- print(f'{key}: {per_disease_auroc[idx]:.5f}')
-
-
-def inference_chestxray14():
- dataset = ChestXray14Dataset(f'data/chestxray14/Data_Entry_2017_v2020_modified.csv')
- dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=lambda x: x, num_workers=1)
- inference_model = InferenceModel()
- all_descriptors = inference_model.get_all_descriptors(disease_descriptors_chestxray14)
-
- all_labels = []
- all_probs_neg = []
- for batch in tqdm(dataloader):
- batch = batch[0]
- image_path, labels, keys = batch
- image_path = Path(image_path)
- probs, negative_probs = inference_model.get_descriptor_probs(image_path, descriptors=all_descriptors)
- disease_probs, negative_disease_probs = inference_model.get_diseases_probs(disease_descriptors_chestxray14, pos_probs=probs,
- negative_probs=negative_probs)
- predicted_diseases, prob_vector_neg_prompt = inference_model.get_predictions_bin_prompting(disease_descriptors_chestxray14,
- disease_probs=disease_probs,
- negative_disease_probs=negative_disease_probs,
- keys=keys)
- all_labels.append(labels)
- all_probs_neg.append(prob_vector_neg_prompt)
- gc.collect()
-
- all_labels = torch.stack(all_labels)
- all_probs_neg = torch.stack(all_probs_neg)
-
- existing_mask = sum(all_labels, 0) > 0
- all_labels_clean = all_labels[:, existing_mask]
- all_probs_neg_clean = all_probs_neg[:, existing_mask]
- all_keys_clean = [key for idx, key in enumerate(keys) if existing_mask[idx]]
-
- overall_auroc, per_disease_auroc = calculate_auroc(all_probs_neg_clean[:, 1:], all_labels_clean[:, 1:])
- print(f"AUROC: {overall_auroc:.5f}\n")
- for idx, key in enumerate(all_keys_clean[1:]):
- print(f'{key}: {per_disease_auroc[idx]:.5f}')
-
-
-if __name__ == '__main__':
- # add argument parser
- parser = argparse.ArgumentParser()
- parser.add_argument('--dataset', type=str, default='chexpert', help='chexpert or chestxray14')
- args = parser.parse_args()
-
- if args.dataset == 'chexpert':
- inference_chexpert()
- elif args.dataset == 'chestxray14':
- inference_chestxray14()
diff --git a/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h b/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h
deleted file mode 100644
index e00d3ef054bd740b801e47cc1344e38621d8c055..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h
+++ /dev/null
@@ -1,253 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-
-namespace thrust
-{
-
-namespace detail
-{
-
-
-// adapted from http://www.boost.org/doc/libs/1_37_0/libs/iterator/doc/iterator_facade.html#iterator-category
-//
-// in our implementation, R need not be a reference type to result in a category
-// derived from forward_XXX_iterator_tag
-//
-// iterator-category(T,V,R) :=
-// if(T is convertible to input_host_iterator_tag
-// || T is convertible to output_host_iterator_tag
-// || T is convertible to input_device_iterator_tag
-// || T is convertible to output_device_iterator_tag
-// )
-// return T
-//
-// else if (T is not convertible to incrementable_traversal_tag)
-// the program is ill-formed
-//
-// else return a type X satisfying the following two constraints:
-//
-// 1. X is convertible to X1, and not to any more-derived
-// type, where X1 is defined by:
-//
-// if (T is convertible to forward_traversal_tag)
-// {
-// if (T is convertible to random_access_traversal_tag)
-// X1 = random_access_host_iterator_tag
-// else if (T is convertible to bidirectional_traversal_tag)
-// X1 = bidirectional_host_iterator_tag
-// else
-// X1 = forward_host_iterator_tag
-// }
-// else
-// {
-// if (T is convertible to single_pass_traversal_tag
-// && R is convertible to V)
-// X1 = input_host_iterator_tag
-// else
-// X1 = T
-// }
-//
-// 2. category-to-traversal(X) is convertible to the most
-// derived traversal tag type to which X is also convertible,
-// and not to any more-derived traversal tag type.
-
-
-template
- struct iterator_facade_default_category;
-
-
-// Thrust's implementation of iterator_facade_default_category is slightly
-// different from Boost's equivalent.
-// Thrust does not check is_convertible because Reference
-// may not be a complete type at this point, and implementations of is_convertible
-// typically require that both types be complete.
-// Instead, it simply assumes that if is_convertible,
-// then the category is input_iterator_tag
-
-
-// this is the function for standard system iterators
-template
- struct iterator_facade_default_category_std :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if< // XXX note we differ from Boost here
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_std
-
-
-// this is the function for host system iterators
-template
- struct iterator_facade_default_category_host :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if< // XXX note we differ from Boost here
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_host
-
-
-// this is the function for device system iterators
-template
- struct iterator_facade_default_category_device :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value, // XXX note we differ from Boost here
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_device
-
-
-// this is the function for any system iterators
-template
- struct iterator_facade_default_category_any
-{
- typedef thrust::detail::iterator_category_with_system_and_traversal<
- typename iterator_facade_default_category_std::type,
- thrust::any_system_tag,
- Traversal
- > type;
-}; // end iterator_facade_default_category_any
-
-
-template
- struct iterator_facade_default_category
- // check for any system
- : thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_any,
-
- // check for host system
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_host,
-
- // check for device system
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_device,
-
- // if we don't recognize the system, get a standard iterator category
- // and combine it with System & Traversal
- thrust::detail::identity_<
- thrust::detail::iterator_category_with_system_and_traversal<
- typename iterator_facade_default_category_std::type,
- System,
- Traversal
- >
- >
- >
- >
- >
-{};
-
-
-template
- struct iterator_facade_category_impl
-{
- typedef typename iterator_facade_default_category<
- System,Traversal,ValueParam,Reference
- >::type category;
-
- // we must be able to deduce both Traversal & System from category
- // otherwise, munge them all together
- typedef typename thrust::detail::eval_if<
- thrust::detail::and_<
- thrust::detail::is_same<
- Traversal,
- typename thrust::detail::iterator_category_to_traversal::type
- >,
- thrust::detail::is_same<
- System,
- typename thrust::detail::iterator_category_to_system::type
- >
- >::value,
- thrust::detail::identity_,
- thrust::detail::identity_ >
- >::type type;
-}; // end iterator_facade_category_impl
-
-
-template
- struct iterator_facade_category
-{
- typedef typename
- thrust::detail::eval_if<
- thrust::detail::is_iterator_category::value,
- thrust::detail::identity_, // categories are fine as-is
- iterator_facade_category_impl
- >::type type;
-}; // end iterator_facade_category
-
-
-} // end detail
-} // end thrust
-
diff --git a/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py b/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py
deleted file mode 100644
index a4f5ed44caefb1612df67785b1f4f0d9ec46ee93..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import torch.nn as nn
-from mmcv.cnn import ConvModule, xavier_init
-
-from ..builder import NECKS
-
-
-@NECKS.register_module()
-class ChannelMapper(nn.Module):
- r"""Channel Mapper to reduce/increase channels of backbone features.
-
- This is used to reduce/increase channels of backbone features.
-
- Args:
- in_channels (List[int]): Number of input channels per scale.
- out_channels (int): Number of output channels (used at each scale).
- kernel_size (int, optional): kernel_size for reducing channels (used
- at each scale). Default: 3.
- conv_cfg (dict, optional): Config dict for convolution layer.
- Default: None.
- norm_cfg (dict, optional): Config dict for normalization layer.
- Default: None.
- act_cfg (dict, optional): Config dict for activation layer in
- ConvModule. Default: dict(type='ReLU').
-
- Example:
- >>> import torch
- >>> in_channels = [2, 3, 5, 7]
- >>> scales = [340, 170, 84, 43]
- >>> inputs = [torch.rand(1, c, s, s)
- ... for c, s in zip(in_channels, scales)]
- >>> self = ChannelMapper(in_channels, 11, 3).eval()
- >>> outputs = self.forward(inputs)
- >>> for i in range(len(outputs)):
- ... print(f'outputs[{i}].shape = {outputs[i].shape}')
- outputs[0].shape = torch.Size([1, 11, 340, 340])
- outputs[1].shape = torch.Size([1, 11, 170, 170])
- outputs[2].shape = torch.Size([1, 11, 84, 84])
- outputs[3].shape = torch.Size([1, 11, 43, 43])
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size=3,
- conv_cfg=None,
- norm_cfg=None,
- act_cfg=dict(type='ReLU')):
- super(ChannelMapper, self).__init__()
- assert isinstance(in_channels, list)
-
- self.convs = nn.ModuleList()
- for in_channel in in_channels:
- self.convs.append(
- ConvModule(
- in_channel,
- out_channels,
- kernel_size,
- padding=(kernel_size - 1) // 2,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg))
-
- # default init_weights for conv(msra) and norm in ConvModule
- def init_weights(self):
- """Initialize the weights of ChannelMapper module."""
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- xavier_init(m, distribution='uniform')
-
- def forward(self, inputs):
- """Forward function."""
- assert len(inputs) == len(self.convs)
- outs = [self.convs[i](inputs[i]) for i in range(len(inputs))]
- return tuple(outs)
diff --git a/spaces/CVPR/regionclip-demo/datasets/README.md b/spaces/CVPR/regionclip-demo/datasets/README.md
deleted file mode 100644
index 0eb44cc3b23beeb1755ab8d12002d26f13434235..0000000000000000000000000000000000000000
--- a/spaces/CVPR/regionclip-demo/datasets/README.md
+++ /dev/null
@@ -1,140 +0,0 @@
-# Use Builtin Datasets
-
-A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog)
-for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc).
-This document explains how to setup the builtin datasets so they can be used by the above APIs.
-[Use Custom Datasets](https://detectron2.readthedocs.io/tutorials/datasets.html) gives a deeper dive on how to use `DatasetCatalog` and `MetadataCatalog`,
-and how to add new datasets to them.
-
-Detectron2 has builtin support for a few datasets.
-The datasets are assumed to exist in a directory specified by the environment variable
-`DETECTRON2_DATASETS`.
-Under this directory, detectron2 will look for datasets in the structure described below, if needed.
-```
-$DETECTRON2_DATASETS/
- coco/
- lvis/
- cityscapes/
- VOC20{07,12}/
-```
-
-You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`.
-If left unset, the default is `./datasets` relative to your current working directory.
-
-The [model zoo](https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md)
-contains configs and models that use these builtin datasets.
-
-## Expected dataset structure for [COCO instance/keypoint detection](https://cocodataset.org/#download):
-
-```
-coco/
- annotations/
- instances_{train,val}2017.json
- person_keypoints_{train,val}2017.json
- {train,val}2017/
- # image files that are mentioned in the corresponding json
-```
-
-You can use the 2014 version of the dataset as well.
-
-Some of the builtin tests (`dev/run_*_tests.sh`) uses a tiny version of the COCO dataset,
-which you can download with `./datasets/prepare_for_tests.sh`.
-
-## Expected dataset structure for PanopticFPN:
-
-Extract panoptic annotations from [COCO website](https://cocodataset.org/#download)
-into the following structure:
-```
-coco/
- annotations/
- panoptic_{train,val}2017.json
- panoptic_{train,val}2017/ # png annotations
- panoptic_stuff_{train,val}2017/ # generated by the script mentioned below
-```
-
-Install panopticapi by:
-```
-pip install git+https://github.com/cocodataset/panopticapi.git
-```
-Then, run `python datasets/prepare_panoptic_fpn.py`, to extract semantic annotations from panoptic annotations.
-
-## Expected dataset structure for [LVIS instance segmentation](https://www.lvisdataset.org/dataset):
-```
-coco/
- {train,val,test}2017/
-lvis/
- lvis_v0.5_{train,val}.json
- lvis_v0.5_image_info_test.json
- lvis_v1_{train,val}.json
- lvis_v1_image_info_test{,_challenge}.json
-```
-
-Install lvis-api by:
-```
-pip install git+https://github.com/lvis-dataset/lvis-api.git
-```
-
-To evaluate models trained on the COCO dataset using LVIS annotations,
-run `python datasets/prepare_cocofied_lvis.py` to prepare "cocofied" LVIS annotations.
-
-## Expected dataset structure for [cityscapes](https://www.cityscapes-dataset.com/downloads/):
-```
-cityscapes/
- gtFine/
- train/
- aachen/
- color.png, instanceIds.png, labelIds.png, polygons.json,
- labelTrainIds.png
- ...
- val/
- test/
- # below are generated Cityscapes panoptic annotation
- cityscapes_panoptic_train.json
- cityscapes_panoptic_train/
- cityscapes_panoptic_val.json
- cityscapes_panoptic_val/
- cityscapes_panoptic_test.json
- cityscapes_panoptic_test/
- leftImg8bit/
- train/
- val/
- test/
-```
-Install cityscapes scripts by:
-```
-pip install git+https://github.com/mcordts/cityscapesScripts.git
-```
-
-Note: to create labelTrainIds.png, first prepare the above structure, then run cityscapesescript with:
-```
-CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createTrainIdLabelImgs.py
-```
-These files are not needed for instance segmentation.
-
-Note: to generate Cityscapes panoptic dataset, run cityscapesescript with:
-```
-CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createPanopticImgs.py
-```
-These files are not needed for semantic and instance segmentation.
-
-## Expected dataset structure for [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/index.html):
-```
-VOC20{07,12}/
- Annotations/
- ImageSets/
- Main/
- trainval.txt
- test.txt
- # train.txt or val.txt, if you use these splits
- JPEGImages/
-```
-
-## Expected dataset structure for [ADE20k Scene Parsing](http://sceneparsing.csail.mit.edu/):
-```
-ADEChallengeData2016/
- annotations/
- annotations_detectron2/
- images/
- objectInfo150.txt
-```
-The directory `annotations_detectron2` is generated by running `python datasets/prepare_ade20k_sem_seg.py`.
diff --git a/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py b/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py
deleted file mode 100644
index ee1f6f1bcfa2fc3437fb6d5f579640482f1ad5f4..0000000000000000000000000000000000000000
--- a/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py
+++ /dev/null
@@ -1,397 +0,0 @@
-# -*- coding: utf-8 -*-
-import re
-from os.path import join, abspath, dirname
-from collections import defaultdict
-import epitran
-
-epi = epitran.Epitran("deu-Latn-nar")
-
-
-def mode_type(mode_in):
- """In the case of "sql", this will return an sqlite cursor."""
- if mode_in.lower() == "sql":
- import sqlite3
- conn = sqlite3.connect(join(abspath(dirname(__file__)), "./Resources/de.db"))
- return conn.cursor()
-
-
-#TESTS
-#NUMBERS ARE TOO HARD!
-
-
-
-def preprocess(words):
- """Returns a string of words stripped of punctuation"""
- punct_str = '!"#$%&\'()*+,-./:;<=>/?@[\\]^_`{|}~«» '
- return ' '.join([w.strip(punct_str).lower() for w in words.split()])
-
-
-def preserve_punc(words):
- """converts words to IPA and finds punctuation before and after the word."""
- words_preserved = []
- for w in words.split():
- punct_list = ["", preprocess(w), ""]
- before = re.search("^([^A-Za-z0-9]+)[A-Za-z]", w)
- after = re.search("[A-Za-z]([^A-Za-z0-9]+)$", w)
- if before:
- punct_list[0] = str(before.group(1))
- if after:
- punct_list[2] = str(after.group(1))
- words_preserved.append(punct_list)
- return words_preserved
-
-
-
-def apply_punct(triple, as_str=False):
- """places surrounding punctuation back on center on a list of preserve_punc triples"""
- if type(triple[0]) == list:
- for i, t in enumerate(triple):
- triple[i] = str(''.join(triple[i]))
- if as_str:
- return ' '.join(triple)
- return triple
- if as_str:
- return str(''.join(t for t in triple))
- return [''.join(t for t in triple)]
-
-
-def _punct_replace_word(original, transcription):
- """Get the IPA transcription of word with the original punctuation marks"""
- for i, trans_list in enumerate(transcription):
- for j, item in enumerate(trans_list):
- triple = [original[i][0]] + [item] + [original[i][2]]
- transcription[i][j] = apply_punct(triple, as_str=True)
- return transcription
-
-
-def fetch_words(words_in, db_type="sql"):
- """fetches a list of words from the database"""
- asset = mode_type(db_type)
- f_result = []
- if db_type.lower() == "sql":
- for word in words_in:
- asset.execute("SELECT Words, phonemes FROM De_words WHERE Words IN (?)", (word,))
- result = asset.fetchall()
- flag = True
- try:
- f_result.append(result.pop())
- flag = False
- except IndexError:
- pass
- if result == [] and flag is True:
- result = epi.transliterate(word)
- f_result.append((word, result))
- f_result = list(filter(None,f_result))
- f_set = set(f_result)
- d = defaultdict(list)
- for k, v in f_set:
- d[k].append(v)
- return list(d.items())
-
-def get_deu(tokens_in, db_type="sql"):
- """query the SQL database for the words and return the phonemes in the order of user_in"""
- result = fetch_words(tokens_in, db_type)
- ordered = []
- for word in tokens_in:
- this_word = [[i[1] for i in result if i[0] == word]][0]
- if this_word:
- ordered.append(this_word[0])
- else:
- ordered.append(["__IGNORE__" + word])
- return ordered
-
-
-def deu_to_ipa(deu_list, mark=True):
- """converts the deu word lists into IPA transcriptions"""
- symbols = {}
- ipa_list = [] # the final list of IPA tokens to be returned
- for word_list in deu_list:
- ipa_word_list = [] # the word list for each word
- for word in word_list:
- if re.sub("\d*", "", word.replace("__IGNORE__", "")) == "":
- pass # do not delete token if it's all numbers
- else:
- word = re.sub("[0-9]", "", word)
- ipa_form = ''
- if word.startswith("__IGNORE__"):
- ipa_form = word.replace("__IGNORE__", "")
- # mark words we couldn't transliterate with an asterisk:
-
- if mark:
- if not re.sub("\d*", "", ipa_form) == "":
- ipa_form += "*"
- else:
- for piece in word.split(" "):
- marked = False
- unmarked = piece
- if piece[0] in ["ˈ", "ˌ"] or piece[0] is None:
- marked = True
- mark = piece
- unmarked = piece[1:]
-
- if unmarked in symbols:
- if marked:
- ipa_form += mark + symbols[unmarked]
- else:
- ipa_form += symbols[unmarked]
-
- else:
- ipa_form += piece
- swap_list = [["ˈər", "əˈr"], ["ˈie", "iˈe"]]
- for sym in swap_list:
- if not ipa_form.startswith(sym[0]):
- ipa_form = ipa_form.replace(sym[0], sym[1])
- ipa_word_list.append(ipa_form)
- ipa_list.append(sorted(list(set(ipa_word_list))))
- return ipa_list
-
-
-def get_top(ipa_list):
- """Returns only the one result for a query. If multiple entries for words are found, only the first is used."""
- return ' '.join([word_list[-1] for word_list in ipa_list])
-
-
-def get_all(ipa_list):
- """utilizes an algorithm to discover and return all possible combinations of IPA transcriptions"""
- final_size = 1
- for word_list in ipa_list:
- final_size *= len(word_list)
- list_all = ["" for s in range(final_size)]
- for i in range(len(ipa_list)):
- if i == 0:
- swtich_rate = final_size / len(ipa_list[i])
- else:
- swtich_rate /= len(ipa_list[i])
- k = 0
- for j in range(final_size):
- if (j+1) % int(swtich_rate) == 0:
- k += 1
- if k == len(ipa_list[i]):
- k = 0
- list_all[j] = list_all[j] + ipa_list[i][k] + " "
- return sorted([sent[:-1] for sent in list_all])
-
-
-def ipa_list(words_in, keep_punct=True, db_type="sql"):
- """Returns a list of all the discovered IPA transcriptions for each word."""
- if type(words_in) == str:
- words = [preserve_punc(w.lower())[0] for w in words_in.split()]
- else:
- words = [preserve_punc(w.lower())[0] for w in words_in]
- deu = get_deu([w[1] for w in words], db_type=db_type)
- ipa = deu_to_ipa(deu)
- if keep_punct:
- ipa = _punct_replace_word(words, ipa)
- return ipa
-
-
-def isin_deu(word, db_type="sql"):
- """checks if a word is in the deu dictionary. Doesn't strip punctuation.
- If given more than one word, returns True only if all words are present."""
- if type(word) == str:
- word = [preprocess(w) for w in word.split()]
- results = fetch_words(word, db_type)
- as_set = list(set(t[0] for t in results))
- return len(as_set) == len(set(word))
-
-def replace_number(text):
- text = text.replace("1","eins ")
- text = text.replace("2","zwei ")
- text = text.replace("3","drei ")
- text = text.replace("4","vier ")
- text = text.replace("5","fünf ")
- text = text.replace("6","sechs ")
- text = text.replace("7","sieben ")
- text = text.replace("8","acht ")
- text = text.replace("9","neun ")
- text = text.replace("0","null ")
- return text
-
-
-
-def convert(text, retrieve_all=False, keep_punct=True, mode="sql"):
- """takes either a string or list of German words and converts them to IPA"""
- text = replace_number(text)
- ipa = ipa_list(
- words_in=text,
- keep_punct=keep_punct,
- db_type=mode)
- if retrieve_all:
- return get_all(ipa)
- return get_top(ipa)
-
-
-
-_decimal_number_re = re.compile(r'\d+\,\d+')
-_euros_pre = re.compile(r'€([0-9\,]*[0-9]+)')
-_euros_re = re.compile(r'([0-9\,]*[0-9]+)€')
-_ordinal_re = re.compile(r'(der |die |das )([0-9]+)\.')
-_clock_re=re.compile(r'\d{1,2}\:\d{2}')
-_number_re = re.compile(r'[0-9]+')
-
-def base(text):
- text = text.replace("1", "eins ")
- text = text.replace("2", "zwei ")
- text = text.replace("3", "drei ")
- text = text.replace("4", "vier ")
- text = text.replace("5", "fünf ")
- text = text.replace("6", "sechs ")
- text = text.replace("7", "sieben ")
- text = text.replace("8", "acht ")
- text = text.replace("9", "neun ")
- text = text.replace("0", "null ")
- return text
-
-def tens_to_word(num):
- tens = num[0]
- ones = num[1]
- ones_word = base(ones)
-
- if num =="10":
- return "zehn"
- elif num=="11":
- return "elf"
- elif num=="12":
- return "zwölf"
-
- if tens == "1":
- if ones == "6":
- ones_word = ones_word[:-1]
- elif ones == "7":
- ones_word = ones_word[:-2]
- return ones_word + "zehn"
- else:
- tens_word = base(tens)
- if ones == "1":
- ones_word = ones_word[:-1]
- if tens == "2":
- tens_word = "zwan"
- elif tens == "6":
- tens_word = tens_word[:-1]
- elif tens == "7":
- tens_word = tens_word[:-2]
- if tens == "3":
- tens_word += "ßig"
- else:
- tens_word += "zig"
- if ones == "0":
- return tens_word
- else:
- return ones_word + " und " + tens_word
-
-def huns_to_word(num):
- huns = num[0]
- tens = num[1]
-
- if huns == "1":
- huns_word= "hundert"
- else:
- huns_word = base(huns)+" hundert"
-
- remain = num_to_word(num[1:])
- if remain != "":
- remain = " " + remain
- return huns_word + remain
-
-def thos_to_word(num):
- thos = num[0]
- if thos == "1":
- thos_word= "tausend"
- else:
- thos_word = base(thos)+" tausend"
- remain=num_to_word(num[1:])
- if remain!="":
- remain=" "+remain
- return thos_word+remain
-
-def num_to_word(num):
- num=num.lstrip("0")
- if num=="":
- return("")
- digit=len(num)
- if digit==1:
- return base(num)
- elif digit==2:
- return tens_to_word(num)
- elif digit == 3:
- return huns_to_word(num)
- elif digit == 4:
- return thos_to_word(num)
- else:
- return base(num)
-
-def number_to_words(m):
- m=m.group(0).lstrip("0")
- if m=="":
- return"null"
- return num_to_word(m)
-
-def _expand_ordinal(m):
-
- pre=m.group(1)
- m = m.group(2).lstrip("0")
-
- if m=="":
- return"NULL"
- num=int(m)
- if num<=19 & num>=1:
- if num ==1:
- return "erste"
- elif num==3:
- return "dritte"
- elif num==7:
- return "siebte"
- elif num==8:
- return "achte"
- else:
- return pre + num_to_word(m) + "te"
- else:
- return pre + num_to_word(m) + "ste"
-
-def _expand_decimal(m):
- match=m.group(0)
- parts = match.split(',')
- if int(parts[0])==0:
- return '%s komma %s' % ("null", base(parts[1]))
- return '%s komma %s' % (num_to_word(parts[0]),base(parts[1]))
-
-def _expand_euros(m):
- match = m.group(1)
- parts = match.split(',')
- if len(parts) > 2:
- return match + ' euro' # Unexpected format
- euros = int(parts[0]) if parts[0] else 0
- cents = int(parts[1])*10 if len(parts) > 1 and parts[1] else 0
- if euros and cents:
- return '%s euro %s' % (euros, cents)
- elif euros:
- return '%s euro' % (euros)
- elif cents:
- return '%s cent' % (cents)
- else:
- return 'null euro'
-
-def _expand_clock(m):
- match = m.group(0)
- parts = match.split(':')
- if int(parts[0]) == 0:
- return '%s Uhr %s' % ("null",num_to_word(parts[1]))
- elif int(parts[0]) == 1:
- return '%s Uhr %s' % ("ein", num_to_word(parts[1]))
- return '%s Uhr %s' % (num_to_word(parts[0]),num_to_word(parts[1]))
-
-def normalize_numbers(text):
- text = re.sub(_euros_pre, _expand_euros, text)
- text = re.sub(_euros_re, _expand_euros, text)
- text = re.sub(_clock_re, _expand_clock, text)
- text = re.sub(_decimal_number_re, _expand_decimal, text)
- text = re.sub(_ordinal_re, _expand_ordinal, text)
- text = re.sub(_number_re, number_to_words, text)
- text=text.replace(" "," ")
- return text
-
-def collapse_whitespace(text):
- return re.sub(r'\s+', ' ', text)
-
-def mark_dark_l(text):
- return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
diff --git a/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py b/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py
deleted file mode 100644
index 1519b3095bc5250016066754ebe6c0328773f24a..0000000000000000000000000000000000000000
--- a/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from utils import randomize_seed_fn
-
-
-def create_demo(process, max_images=12, default_num_images=3):
- with gr.Blocks() as demo:
- with gr.Row():
- with gr.Column():
- image = gr.Image()
- prompt = gr.Textbox(label='Prompt')
- run_button = gr.Button('Run')
- with gr.Accordion('Advanced options', open=False):
- preprocessor_name = gr.Radio(label='Preprocessor',
- choices=['NormalBae', 'None'],
- type='value',
- value='NormalBae')
- num_samples = gr.Slider(label='Images',
- minimum=1,
- maximum=max_images,
- value=default_num_images,
- step=1)
- image_resolution = gr.Slider(label='Image resolution',
- minimum=256,
- maximum=512,
- value=512,
- step=256)
- preprocess_resolution = gr.Slider(
- label='Preprocess resolution',
- minimum=128,
- maximum=512,
- value=384,
- step=1)
- num_steps = gr.Slider(label='Number of steps',
- minimum=1,
- maximum=100,
- value=20,
- step=1)
- guidance_scale = gr.Slider(label='Guidance scale',
- minimum=0.1,
- maximum=30.0,
- value=9.0,
- step=0.1)
- seed = gr.Slider(label='Seed',
- minimum=0,
- maximum=1000000,
- step=1,
- value=0,
- randomize=True)
- randomize_seed = gr.Checkbox(label='Randomize seed',
- value=True)
- a_prompt = gr.Textbox(
- label='Additional prompt',
- value='best quality, extremely detailed')
- n_prompt = gr.Textbox(
- label='Negative prompt',
- value=
- 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
- )
- with gr.Column():
- result = gr.Gallery(label='Output', show_label=False).style(
- columns=2, object_fit='scale-down')
- inputs = [
- image,
- prompt,
- a_prompt,
- n_prompt,
- num_samples,
- image_resolution,
- preprocess_resolution,
- num_steps,
- guidance_scale,
- seed,
- preprocessor_name,
- ]
- prompt.submit(
- fn=randomize_seed_fn,
- inputs=[seed, randomize_seed],
- outputs=seed,
- ).then(
- fn=process,
- inputs=inputs,
- outputs=result,
- )
- run_button.click(
- fn=randomize_seed_fn,
- inputs=[seed, randomize_seed],
- outputs=seed,
- ).then(
- fn=process,
- inputs=inputs,
- outputs=result,
- api_name='normal',
- )
- return demo
-
-
-if __name__ == '__main__':
- from model import Model
- model = Model(task_name='NormalBae')
- demo = create_demo(model.process_normal)
- demo.queue().launch()
diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py
deleted file mode 100644
index 0a44b37fc5563deea8406c6ddf109860a56e9485..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from PIL.Image import Image as IMG
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.utils import save_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def knock(images: List[BuildImage], texts, args):
- img = images[0].convert("RGBA").square()
- # fmt: off
- locs = [(60, 308, 210, 195), (60, 308, 210, 198), (45, 330, 250, 172), (58, 320, 218, 180),
- (60, 310, 215, 193), (40, 320, 250, 285), (48, 308, 226, 192), (51, 301, 223, 200)]
- # fmt: on
- frames: List[IMG] = []
- for i in range(8):
- frame = BuildImage.open(img_dir / f"{i}.png")
- x, y, w, h = locs[i]
- frame.paste(img.resize((w, h)), (x, y), below=True)
- frames.append(frame.image)
- return save_gif(frames, 0.04)
-
-
-add_meme("knock", knock, min_images=1, max_images=1, keywords=["敲"])
diff --git a/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py b/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py
deleted file mode 100644
index 37f310fadd0b48b2f364877158fb2105d645fc03..0000000000000000000000000000000000000000
--- a/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import locale
-import json
-import os
-
-
-def load_language_list(language):
- with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f:
- language_list = json.load(f)
- return language_list
-
-
-class I18nAuto:
- def __init__(self, language=None):
- if language in ["Auto", None]:
- language = locale.getdefaultlocale()[
- 0
- ] # getlocale can't identify the system's language ((None, None))
- if not os.path.exists(f"./i18n/{language}.json"):
- language = "en_US"
- self.language = language
- # print("Use Language:", language)
- self.language_map = load_language_list(language)
-
- def __call__(self, key):
- return self.language_map.get(key, key)
-
- def print(self):
- print("Use Language:", self.language)
diff --git a/spaces/Codecooker/rvcapi/src/mdx.py b/spaces/Codecooker/rvcapi/src/mdx.py
deleted file mode 100644
index 575c456fbdf9e5b5955401f41a3a58ddc27267b3..0000000000000000000000000000000000000000
--- a/spaces/Codecooker/rvcapi/src/mdx.py
+++ /dev/null
@@ -1,287 +0,0 @@
-import gc
-import hashlib
-import os
-import queue
-import threading
-import warnings
-
-import librosa
-import numpy as np
-import onnxruntime as ort
-import soundfile as sf
-import torch
-from tqdm import tqdm
-
-warnings.filterwarnings("ignore")
-stem_naming = {'Vocals': 'Instrumental', 'Other': 'Instruments', 'Instrumental': 'Vocals', 'Drums': 'Drumless', 'Bass': 'Bassless'}
-
-
-class MDXModel:
- def __init__(self, device, dim_f, dim_t, n_fft, hop=1024, stem_name=None, compensation=1.000):
- self.dim_f = dim_f
- self.dim_t = dim_t
- self.dim_c = 4
- self.n_fft = n_fft
- self.hop = hop
- self.stem_name = stem_name
- self.compensation = compensation
-
- self.n_bins = self.n_fft // 2 + 1
- self.chunk_size = hop * (self.dim_t - 1)
- self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(device)
-
- out_c = self.dim_c
-
- self.freq_pad = torch.zeros([1, out_c, self.n_bins - self.dim_f, self.dim_t]).to(device)
-
- def stft(self, x):
- x = x.reshape([-1, self.chunk_size])
- x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True, return_complex=True)
- x = torch.view_as_real(x)
- x = x.permute([0, 3, 1, 2])
- x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 4, self.n_bins, self.dim_t])
- return x[:, :, :self.dim_f]
-
- def istft(self, x, freq_pad=None):
- freq_pad = self.freq_pad.repeat([x.shape[0], 1, 1, 1]) if freq_pad is None else freq_pad
- x = torch.cat([x, freq_pad], -2)
- # c = 4*2 if self.target_name=='*' else 2
- x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 2, self.n_bins, self.dim_t])
- x = x.permute([0, 2, 3, 1])
- x = x.contiguous()
- x = torch.view_as_complex(x)
- x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True)
- return x.reshape([-1, 2, self.chunk_size])
-
-
-class MDX:
- DEFAULT_SR = 44100
- # Unit: seconds
- DEFAULT_CHUNK_SIZE = 0 * DEFAULT_SR
- DEFAULT_MARGIN_SIZE = 1 * DEFAULT_SR
-
- DEFAULT_PROCESSOR = 0
-
- def __init__(self, model_path: str, params: MDXModel, processor=DEFAULT_PROCESSOR):
-
- # Set the device and the provider (CPU or CUDA)
- self.device = torch.device(f'cuda:{processor}') if processor >= 0 else torch.device('cpu')
- self.provider = ['CUDAExecutionProvider'] if processor >= 0 else ['CPUExecutionProvider']
-
- self.model = params
-
- # Load the ONNX model using ONNX Runtime
- self.ort = ort.InferenceSession(model_path, providers=self.provider)
- # Preload the model for faster performance
- self.ort.run(None, {'input': torch.rand(1, 4, params.dim_f, params.dim_t).numpy()})
- self.process = lambda spec: self.ort.run(None, {'input': spec.cpu().numpy()})[0]
-
- self.prog = None
-
- @staticmethod
- def get_hash(model_path):
- try:
- with open(model_path, 'rb') as f:
- f.seek(- 10000 * 1024, 2)
- model_hash = hashlib.md5(f.read()).hexdigest()
- except:
- model_hash = hashlib.md5(open(model_path, 'rb').read()).hexdigest()
-
- return model_hash
-
- @staticmethod
- def segment(wave, combine=True, chunk_size=DEFAULT_CHUNK_SIZE, margin_size=DEFAULT_MARGIN_SIZE):
- """
- Segment or join segmented wave array
-
- Args:
- wave: (np.array) Wave array to be segmented or joined
- combine: (bool) If True, combines segmented wave array. If False, segments wave array.
- chunk_size: (int) Size of each segment (in samples)
- margin_size: (int) Size of margin between segments (in samples)
-
- Returns:
- numpy array: Segmented or joined wave array
- """
-
- if combine:
- processed_wave = None # Initializing as None instead of [] for later numpy array concatenation
- for segment_count, segment in enumerate(wave):
- start = 0 if segment_count == 0 else margin_size
- end = None if segment_count == len(wave) - 1 else -margin_size
- if margin_size == 0:
- end = None
- if processed_wave is None: # Create array for first segment
- processed_wave = segment[:, start:end]
- else: # Concatenate to existing array for subsequent segments
- processed_wave = np.concatenate((processed_wave, segment[:, start:end]), axis=-1)
-
- else:
- processed_wave = []
- sample_count = wave.shape[-1]
-
- if chunk_size <= 0 or chunk_size > sample_count:
- chunk_size = sample_count
-
- if margin_size > chunk_size:
- margin_size = chunk_size
-
- for segment_count, skip in enumerate(range(0, sample_count, chunk_size)):
-
- margin = 0 if segment_count == 0 else margin_size
- end = min(skip + chunk_size + margin_size, sample_count)
- start = skip - margin
-
- cut = wave[:, start:end].copy()
- processed_wave.append(cut)
-
- if end == sample_count:
- break
-
- return processed_wave
-
- def pad_wave(self, wave):
- """
- Pad the wave array to match the required chunk size
-
- Args:
- wave: (np.array) Wave array to be padded
-
- Returns:
- tuple: (padded_wave, pad, trim)
- - padded_wave: Padded wave array
- - pad: Number of samples that were padded
- - trim: Number of samples that were trimmed
- """
- n_sample = wave.shape[1]
- trim = self.model.n_fft // 2
- gen_size = self.model.chunk_size - 2 * trim
- pad = gen_size - n_sample % gen_size
-
- # Padded wave
- wave_p = np.concatenate((np.zeros((2, trim)), wave, np.zeros((2, pad)), np.zeros((2, trim))), 1)
-
- mix_waves = []
- for i in range(0, n_sample + pad, gen_size):
- waves = np.array(wave_p[:, i:i + self.model.chunk_size])
- mix_waves.append(waves)
-
- mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(self.device)
-
- return mix_waves, pad, trim
-
- def _process_wave(self, mix_waves, trim, pad, q: queue.Queue, _id: int):
- """
- Process each wave segment in a multi-threaded environment
-
- Args:
- mix_waves: (torch.Tensor) Wave segments to be processed
- trim: (int) Number of samples trimmed during padding
- pad: (int) Number of samples padded during padding
- q: (queue.Queue) Queue to hold the processed wave segments
- _id: (int) Identifier of the processed wave segment
-
- Returns:
- numpy array: Processed wave segment
- """
- mix_waves = mix_waves.split(1)
- with torch.no_grad():
- pw = []
- for mix_wave in mix_waves:
- self.prog.update()
- spec = self.model.stft(mix_wave)
- processed_spec = torch.tensor(self.process(spec))
- processed_wav = self.model.istft(processed_spec.to(self.device))
- processed_wav = processed_wav[:, :, trim:-trim].transpose(0, 1).reshape(2, -1).cpu().numpy()
- pw.append(processed_wav)
- processed_signal = np.concatenate(pw, axis=-1)[:, :-pad]
- q.put({_id: processed_signal})
- return processed_signal
-
- def process_wave(self, wave: np.array, mt_threads=1):
- """
- Process the wave array in a multi-threaded environment
-
- Args:
- wave: (np.array) Wave array to be processed
- mt_threads: (int) Number of threads to be used for processing
-
- Returns:
- numpy array: Processed wave array
- """
- self.prog = tqdm(total=0)
- chunk = wave.shape[-1] // mt_threads
- waves = self.segment(wave, False, chunk)
-
- # Create a queue to hold the processed wave segments
- q = queue.Queue()
- threads = []
- for c, batch in enumerate(waves):
- mix_waves, pad, trim = self.pad_wave(batch)
- self.prog.total = len(mix_waves) * mt_threads
- thread = threading.Thread(target=self._process_wave, args=(mix_waves, trim, pad, q, c))
- thread.start()
- threads.append(thread)
- for thread in threads:
- thread.join()
- self.prog.close()
-
- processed_batches = []
- while not q.empty():
- processed_batches.append(q.get())
- processed_batches = [list(wave.values())[0] for wave in
- sorted(processed_batches, key=lambda d: list(d.keys())[0])]
- assert len(processed_batches) == len(waves), 'Incomplete processed batches, please reduce batch size!'
- return self.segment(processed_batches, True, chunk)
-
-
-def run_mdx(model_params, output_dir, model_path, filename, exclude_main=False, exclude_inversion=False, suffix=None, invert_suffix=None, denoise=False, keep_orig=True, m_threads=2):
- device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
-
- device_properties = torch.cuda.get_device_properties(device)
- vram_gb = device_properties.total_memory / 1024**3
- m_threads = 1 if vram_gb < 8 else 2
-
- model_hash = MDX.get_hash(model_path)
- mp = model_params.get(model_hash)
- model = MDXModel(
- device,
- dim_f=mp["mdx_dim_f_set"],
- dim_t=2 ** mp["mdx_dim_t_set"],
- n_fft=mp["mdx_n_fft_scale_set"],
- stem_name=mp["primary_stem"],
- compensation=mp["compensate"]
- )
-
- mdx_sess = MDX(model_path, model)
- wave, sr = librosa.load(filename, mono=False, sr=44100)
- # normalizing input wave gives better output
- peak = max(np.max(wave), abs(np.min(wave)))
- wave /= peak
- if denoise:
- wave_processed = -(mdx_sess.process_wave(-wave, m_threads)) + (mdx_sess.process_wave(wave, m_threads))
- wave_processed *= 0.5
- else:
- wave_processed = mdx_sess.process_wave(wave, m_threads)
- # return to previous peak
- wave_processed *= peak
- stem_name = model.stem_name if suffix is None else suffix
-
- main_filepath = None
- if not exclude_main:
- main_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav")
- sf.write(main_filepath, wave_processed.T, sr)
-
- invert_filepath = None
- if not exclude_inversion:
- diff_stem_name = stem_naming.get(stem_name) if invert_suffix is None else invert_suffix
- stem_name = f"{stem_name}_diff" if diff_stem_name is None else diff_stem_name
- invert_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav")
- sf.write(invert_filepath, (-wave_processed.T * model.compensation) + wave.T, sr)
-
- if not keep_orig:
- os.remove(filename)
-
- del mdx_sess, wave_processed, wave
- gc.collect()
- return main_filepath, invert_filepath
diff --git a/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py b/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py
deleted file mode 100644
index 76dce7e41c803a8055f3627cccb98deb51419b09..0000000000000000000000000000000000000000
--- a/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import argparse
-import glob
-import os
-
-
-def main(args):
- txt_file = open(args.meta_info, 'w')
- # sca images
- img_paths_gt = sorted(glob.glob(os.path.join(args.input[0], '*')))
- img_paths_lq = sorted(glob.glob(os.path.join(args.input[1], '*')))
-
- assert len(img_paths_gt) == len(img_paths_lq), ('GT folder and LQ folder should have the same length, but got '
- f'{len(img_paths_gt)} and {len(img_paths_lq)}.')
-
- for img_path_gt, img_path_lq in zip(img_paths_gt, img_paths_lq):
- # get the relative paths
- img_name_gt = os.path.relpath(img_path_gt, args.root[0])
- img_name_lq = os.path.relpath(img_path_lq, args.root[1])
- print(f'{img_name_gt}, {img_name_lq}')
- txt_file.write(f'{img_name_gt}, {img_name_lq}\n')
-
-
-if __name__ == '__main__':
- """This script is used to generate meta info (txt file) for paired images.
- """
- parser = argparse.ArgumentParser()
- parser.add_argument(
- '--input',
- nargs='+',
- default=['datasets/DF2K/DIV2K_train_HR_sub', 'datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub'],
- help='Input folder, should be [gt_folder, lq_folder]')
- parser.add_argument('--root', nargs='+', default=[None, None], help='Folder root, will use the ')
- parser.add_argument(
- '--meta_info',
- type=str,
- default='datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt',
- help='txt path for meta info')
- args = parser.parse_args()
-
- assert len(args.input) == 2, 'Input folder should have two elements: gt folder and lq folder'
- assert len(args.root) == 2, 'Root path should have two elements: root for gt folder and lq folder'
- os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)
- for i in range(2):
- if args.input[i].endswith('/'):
- args.input[i] = args.input[i][:-1]
- if args.root[i] is None:
- args.root[i] = os.path.dirname(args.input[i])
-
- main(args)
diff --git a/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py b/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py
deleted file mode 100644
index eb453a809d17e6dee04e158d1c68dc807478edef..0000000000000000000000000000000000000000
--- a/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Onodofthenorth/SD_PixelArt_SpriteSheet_Generator").launch()
\ No newline at end of file
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py
deleted file mode 100644
index bafc43a19d432290867a5c08b9820f2e4f79aea3..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py
+++ /dev/null
@@ -1,97 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# GD file handling
-#
-# History:
-# 1996-04-12 fl Created
-#
-# Copyright (c) 1997 by Secret Labs AB.
-# Copyright (c) 1996 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-"""
-.. note::
- This format cannot be automatically recognized, so the
- class is not registered for use with :py:func:`PIL.Image.open()`. To open a
- gd file, use the :py:func:`PIL.GdImageFile.open()` function instead.
-
-.. warning::
- THE GD FORMAT IS NOT DESIGNED FOR DATA INTERCHANGE. This
- implementation is provided for convenience and demonstrational
- purposes only.
-"""
-
-
-from . import ImageFile, ImagePalette, UnidentifiedImageError
-from ._binary import i16be as i16
-from ._binary import i32be as i32
-
-
-class GdImageFile(ImageFile.ImageFile):
- """
- Image plugin for the GD uncompressed format. Note that this format
- is not supported by the standard :py:func:`PIL.Image.open()` function. To use
- this plugin, you have to import the :py:mod:`PIL.GdImageFile` module and
- use the :py:func:`PIL.GdImageFile.open()` function.
- """
-
- format = "GD"
- format_description = "GD uncompressed images"
-
- def _open(self):
- # Header
- s = self.fp.read(1037)
-
- if i16(s) not in [65534, 65535]:
- msg = "Not a valid GD 2.x .gd file"
- raise SyntaxError(msg)
-
- self.mode = "L" # FIXME: "P"
- self._size = i16(s, 2), i16(s, 4)
-
- true_color = s[6]
- true_color_offset = 2 if true_color else 0
-
- # transparency index
- tindex = i32(s, 7 + true_color_offset)
- if tindex < 256:
- self.info["transparency"] = tindex
-
- self.palette = ImagePalette.raw(
- "XBGR", s[7 + true_color_offset + 4 : 7 + true_color_offset + 4 + 256 * 4]
- )
-
- self.tile = [
- (
- "raw",
- (0, 0) + self.size,
- 7 + true_color_offset + 4 + 256 * 4,
- ("L", 0, 1),
- )
- ]
-
-
-def open(fp, mode="r"):
- """
- Load texture from a GD image file.
-
- :param fp: GD file name, or an opened file handle.
- :param mode: Optional mode. In this version, if the mode argument
- is given, it must be "r".
- :returns: An image instance.
- :raises OSError: If the image could not be read.
- """
- if mode != "r":
- msg = "bad mode"
- raise ValueError(msg)
-
- try:
- return GdImageFile(fp)
- except SyntaxError as e:
- msg = "cannot identify this image file"
- raise UnidentifiedImageError(msg) from e
diff --git a/spaces/Dana19/ImageRecognition_FaceCount/README.md b/spaces/Dana19/ImageRecognition_FaceCount/README.md
deleted file mode 100644
index 8afdea6aa817a1ec3b056820a882b63deedc6772..0000000000000000000000000000000000000000
--- a/spaces/Dana19/ImageRecognition_FaceCount/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Headcount
-emoji: 💻
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.3
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/DemoLou/moe-tts/text/thai.py b/spaces/DemoLou/moe-tts/text/thai.py
deleted file mode 100644
index 998207c01a85c710a46db1ec8b62c39c2d94bc84..0000000000000000000000000000000000000000
--- a/spaces/DemoLou/moe-tts/text/thai.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import re
-from num_thai.thainumbers import NumThai
-
-
-num = NumThai()
-
-# List of (Latin alphabet, Thai) pairs:
-_latin_to_thai = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', 'เอ'),
- ('b','บี'),
- ('c','ซี'),
- ('d','ดี'),
- ('e','อี'),
- ('f','เอฟ'),
- ('g','จี'),
- ('h','เอช'),
- ('i','ไอ'),
- ('j','เจ'),
- ('k','เค'),
- ('l','แอล'),
- ('m','เอ็ม'),
- ('n','เอ็น'),
- ('o','โอ'),
- ('p','พี'),
- ('q','คิว'),
- ('r','แอร์'),
- ('s','เอส'),
- ('t','ที'),
- ('u','ยู'),
- ('v','วี'),
- ('w','ดับเบิลยู'),
- ('x','เอ็กซ์'),
- ('y','วาย'),
- ('z','ซี')
-]]
-
-
-def num_to_thai(text):
- return re.sub(r'(?:\d+(?:,?\d+)?)+(?:\.\d+(?:,?\d+)?)?', lambda x: ''.join(num.NumberToTextThai(float(x.group(0).replace(',', '')))), text)
-
-def latin_to_thai(text):
- for regex, replacement in _latin_to_thai:
- text = re.sub(regex, replacement, text)
- return text
diff --git a/spaces/Detomo/AI-Galary/README.md b/spaces/Detomo/AI-Galary/README.md
deleted file mode 100644
index 5772a1eb261aa38b6407c07b0633d4a12566eccf..0000000000000000000000000000000000000000
--- a/spaces/Detomo/AI-Galary/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AI Galary
-emoji: ⚡
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Detomo/generate_wifi_qrcode/app.py b/spaces/Detomo/generate_wifi_qrcode/app.py
deleted file mode 100644
index e6681ad55d07851290ef2f0f04f1e026a95b917a..0000000000000000000000000000000000000000
--- a/spaces/Detomo/generate_wifi_qrcode/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import wifi_qrcode_generator
-import gradio as gr
-
-examples = [['Home wifi', 'very complicated password', 'WPA']]
-
-def infer(wifi_name, wifi_pw, authentication_type):
- image = wifi_qrcode_generator.wifi_qrcode(
- wifi_name, False, authentication_type, wifi_pw
- )
- image.save('qrcode_wifi.png')
- return 'qrcode_wifi.png'
-
-description= "Generate code for accessing WIFI. Fill your wifi name, wifi password and select authentication type. Then click submit and enjoy 😎"
-
-iface = gr.Interface(
- fn=infer,
- title="Generate QR code for accessing WIFI 🥃",
- description=description,
- inputs=[
- gr.Textbox(
- label="Wifi name",
- lines=1,
- ),
- gr.Textbox(
- label="Wifi password",
- lines=1,
- ),
- gr.Dropdown(
- label='authentication_type',
- choices=["WPA", "WEP", "nopass"],
- value='WPA'
- ),
- ],
- outputs="image",
- examples=examples,
- article="Author: Vu Minh Chien", cache_examples=True).launch(enable_queue=True)
diff --git a/spaces/DiffusionArtco/scifi-art-creator/app.py b/spaces/DiffusionArtco/scifi-art-creator/app.py
deleted file mode 100644
index 236417b745e1681f1a9e1ae644d75254da2de370..0000000000000000000000000000000000000000
--- a/spaces/DiffusionArtco/scifi-art-creator/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import gradio as gr
-import requests
-from PIL import Image
-from io import BytesIO
-import base64
-
-api_url = "https://5cb20b40-572c-426f-9466-995256f9b6eb.id.repl.co/generate_image"
-
-def generate_image(model="Sci-Fi Diffusion", prompt="", seed=0, negative_prompt="", sampler="k_dpmpp_2s_a", steps=50):
- data = "?model=" + model + "&prompt=" + prompt + "&seed=" + str(seed) + "&negative_prompt=" + negative_prompt + "&sampler=" + sampler + "&steps=" + str(steps)
- response = requests.post(api_url + data, timeout=400)
- if response.status_code == 200:
- img_base64 = response.json()["url"]
- img_bytes = base64.b64decode(img_base64)
- img = Image.open(BytesIO(img_bytes))
- return img
- else:
- return None
-
-inputs = [
- gr.inputs.Dropdown([ 'Experience', 'FKing SciFi', 'Future Diffusion', 'JWST Deep Space Diffusion', 'Protogen Infinity', 'RCNZ Dumb Monkey', 'RealBiter', 'Robo-Diffusion', 'Sci-Fi Diffusion'], label="Model", default="Sci-Fi Diffusion"),
- gr.inputs.Textbox(label="Prompt"),
- gr.inputs.Number(label="Seed", default=0),
- gr.inputs.Textbox(label="Negative Prompt", default=""),
- gr.inputs.Dropdown(["k_lms", "k_heun", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "DDIM", "k_dpm_fast", "k_dpm_adaptive", "k_dpmpp_2m", "k_dpmpp_2s_a", "k_dpmpp_sde"], label="Sampler", default="k_dpmpp_2s_a"),
- gr.inputs.Number(label="Steps", default=50)
-]
-
-outputs = gr.outputs.Image(label="Generated Image", type="pil")
-
-interface = gr.Interface(generate_image, inputs, outputs, title="",
- description="
-#include
-#include
-#include "filtered_lrelu.h"
-
-//------------------------------------------------------------------------
-
-static std::tuple filtered_lrelu(
- torch::Tensor x, torch::Tensor fu, torch::Tensor fd, torch::Tensor b, torch::Tensor si,
- int up, int down, int px0, int px1, int py0, int py1, int sx, int sy, float gain, float slope, float clamp, bool flip_filters, bool writeSigns)
-{
- // Set CUDA device.
- TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
- const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-
- // Validate arguments.
- TORCH_CHECK(fu.device() == x.device() && fd.device() == x.device() && b.device() == x.device(), "all input tensors must reside on the same device");
- TORCH_CHECK(fu.dtype() == torch::kFloat && fd.dtype() == torch::kFloat, "fu and fd must be float32");
- TORCH_CHECK(b.dtype() == x.dtype(), "x and b must have the same dtype");
- TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat, "x and b must be float16 or float32");
- TORCH_CHECK(x.dim() == 4, "x must be rank 4");
- TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
- TORCH_CHECK(x.numel() > 0, "x is empty");
- TORCH_CHECK((fu.dim() == 1 || fu.dim() == 2) && (fd.dim() == 1 || fd.dim() == 2), "fu and fd must be rank 1 or 2");
- TORCH_CHECK(fu.size(0) <= INT_MAX && fu.size(-1) <= INT_MAX, "fu is too large");
- TORCH_CHECK(fd.size(0) <= INT_MAX && fd.size(-1) <= INT_MAX, "fd is too large");
- TORCH_CHECK(fu.numel() > 0, "fu is empty");
- TORCH_CHECK(fd.numel() > 0, "fd is empty");
- TORCH_CHECK(b.dim() == 1 && b.size(0) == x.size(1), "b must be a vector with the same number of channels as x");
- TORCH_CHECK(up >= 1 && down >= 1, "up and down must be at least 1");
-
- // Figure out how much shared memory is available on the device.
- int maxSharedBytes = 0;
- AT_CUDA_CHECK(cudaDeviceGetAttribute(&maxSharedBytes, cudaDevAttrMaxSharedMemoryPerBlockOptin, x.device().index()));
- int sharedKB = maxSharedBytes >> 10;
-
- // Populate enough launch parameters to check if a CUDA kernel exists.
- filtered_lrelu_kernel_params p;
- p.up = up;
- p.down = down;
- p.fuShape = make_int2((int)fu.size(-1), fu.dim() == 2 ? (int)fu.size(0) : 0); // shape [n, 0] indicates separable filter.
- p.fdShape = make_int2((int)fd.size(-1), fd.dim() == 2 ? (int)fd.size(0) : 0);
- filtered_lrelu_kernel_spec test_spec = choose_filtered_lrelu_kernel(p, sharedKB);
- if (!test_spec.exec)
- {
- // No kernel found - return empty tensors and indicate missing kernel with return code of -1.
- return std::make_tuple(torch::Tensor(), torch::Tensor(), -1);
- }
-
- // Input/output element size.
- int64_t sz = (x.dtype() == torch::kHalf) ? 2 : 4;
-
- // Input sizes.
- int64_t xw = (int)x.size(3);
- int64_t xh = (int)x.size(2);
- int64_t fut_w = (int)fu.size(-1) - 1;
- int64_t fut_h = (int)fu.size(0) - 1;
- int64_t fdt_w = (int)fd.size(-1) - 1;
- int64_t fdt_h = (int)fd.size(0) - 1;
-
- // Logical size of upsampled buffer.
- int64_t cw = xw * up + (px0 + px1) - fut_w;
- int64_t ch = xh * up + (py0 + py1) - fut_h;
- TORCH_CHECK(cw > fdt_w && ch > fdt_h, "upsampled buffer must be at least the size of downsampling filter");
- TORCH_CHECK(cw <= INT_MAX && ch <= INT_MAX, "upsampled buffer is too large");
-
- // Compute output size and allocate.
- int64_t yw = (cw - fdt_w + (down - 1)) / down;
- int64_t yh = (ch - fdt_h + (down - 1)) / down;
- TORCH_CHECK(yw > 0 && yh > 0, "output must be at least 1x1");
- TORCH_CHECK(yw <= INT_MAX && yh <= INT_MAX, "output is too large");
- torch::Tensor y = torch::empty({x.size(0), x.size(1), yh, yw}, x.options(), x.suggest_memory_format());
-
- // Allocate sign tensor.
- torch::Tensor so;
- torch::Tensor s = si;
- bool readSigns = !!s.numel();
- int64_t sw_active = 0; // Active width of sign tensor.
- if (writeSigns)
- {
- sw_active = yw * down - (down - 1) + fdt_w; // Active width in elements.
- int64_t sh = yh * down - (down - 1) + fdt_h; // Height = active height.
- int64_t sw = (sw_active + 15) & ~15; // Width = active width in elements, rounded up to multiple of 16.
- TORCH_CHECK(sh <= INT_MAX && (sw >> 2) <= INT_MAX, "signs is too large");
- s = so = torch::empty({x.size(0), x.size(1), sh, sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
- }
- else if (readSigns)
- sw_active = s.size(3) << 2;
-
- // Validate sign tensor if in use.
- if (readSigns || writeSigns)
- {
- TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
- TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
- TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
- TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
- TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
- TORCH_CHECK(s.size(2) <= INT_MAX && s.size(3) <= INT_MAX, "signs is too large");
- }
-
- // Populate rest of CUDA kernel parameters.
- p.x = x.data_ptr();
- p.y = y.data_ptr();
- p.b = b.data_ptr();
- p.s = (readSigns || writeSigns) ? s.data_ptr() : 0;
- p.fu = fu.data_ptr();
- p.fd = fd.data_ptr();
- p.pad0 = make_int2(px0, py0);
- p.gain = gain;
- p.slope = slope;
- p.clamp = clamp;
- p.flip = (flip_filters) ? 1 : 0;
- p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
- p.yShape = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
- p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3), (int)s.size(2)) : make_int2(0, 0); // Width is in bytes. Contiguous.
- p.sOfs = make_int2(sx, sy);
- p.swLimit = (sw_active + 3) >> 2; // Rounded up to bytes.
-
- // x, y, b strides are in bytes.
- p.xStride = make_longlong4(sz * x.stride(3), sz * x.stride(2), sz * x.stride(1), sz * x.stride(0));
- p.yStride = make_longlong4(sz * y.stride(3), sz * y.stride(2), sz * y.stride(1), sz * y.stride(0));
- p.bStride = sz * b.stride(0);
-
- // fu, fd strides are in elements.
- p.fuStride = make_longlong3(fu.stride(-1), fu.dim() == 2 ? fu.stride(0) : 0, 0);
- p.fdStride = make_longlong3(fd.stride(-1), fd.dim() == 2 ? fd.stride(0) : 0, 0);
-
- // Determine if indices don't fit in int32. Support negative strides although Torch currently never produces those.
- bool index64b = false;
- if (std::abs(p.bStride * x.size(1)) > INT_MAX) index64b = true;
- if (std::min(x.size(0) * p.xStride.w, 0ll) + std::min(x.size(1) * p.xStride.z, 0ll) + std::min(x.size(2) * p.xStride.y, 0ll) + std::min(x.size(3) * p.xStride.x, 0ll) < -INT_MAX) index64b = true;
- if (std::max(x.size(0) * p.xStride.w, 0ll) + std::max(x.size(1) * p.xStride.z, 0ll) + std::max(x.size(2) * p.xStride.y, 0ll) + std::max(x.size(3) * p.xStride.x, 0ll) > INT_MAX) index64b = true;
- if (std::min(y.size(0) * p.yStride.w, 0ll) + std::min(y.size(1) * p.yStride.z, 0ll) + std::min(y.size(2) * p.yStride.y, 0ll) + std::min(y.size(3) * p.yStride.x, 0ll) < -INT_MAX) index64b = true;
- if (std::max(y.size(0) * p.yStride.w, 0ll) + std::max(y.size(1) * p.yStride.z, 0ll) + std::max(y.size(2) * p.yStride.y, 0ll) + std::max(y.size(3) * p.yStride.x, 0ll) > INT_MAX) index64b = true;
- if (s.numel() > INT_MAX) index64b = true;
-
- // Choose CUDA kernel.
- filtered_lrelu_kernel_spec spec = { 0 };
- AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_cuda", [&]
- {
- if constexpr (sizeof(scalar_t) <= 4) // Exclude doubles. constexpr prevents template instantiation.
- {
- // Choose kernel based on index type, datatype and sign read/write modes.
- if (!index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if (!index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if (!index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- }
- });
- TORCH_CHECK(spec.exec, "internal error - CUDA kernel not found") // This should not happen because we tested earlier that kernel exists.
-
- // Launch CUDA kernel.
- void* args[] = {&p};
- int bx = spec.numWarps * 32;
- int gx = (p.yShape.x - 1) / spec.tileOut.x + 1;
- int gy = (p.yShape.y - 1) / spec.tileOut.y + 1;
- int gz = p.yShape.z * p.yShape.w;
-
- // Repeat multiple horizontal tiles in a CTA?
- if (spec.xrep)
- {
- p.tilesXrep = spec.xrep;
- p.tilesXdim = gx;
-
- gx = (gx + p.tilesXrep - 1) / p.tilesXrep;
- std::swap(gx, gy);
- }
- else
- {
- p.tilesXrep = 0;
- p.tilesXdim = 0;
- }
-
- // Launch filter setup kernel.
- AT_CUDA_CHECK(cudaLaunchKernel(spec.setup, 1, 1024, args, 0, at::cuda::getCurrentCUDAStream()));
-
- // Copy kernels to constant memory.
- if ( writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
- else if (!writeSigns && readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
- else if (!writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
-
- // Set cache and shared memory configurations for main kernel.
- AT_CUDA_CHECK(cudaFuncSetCacheConfig(spec.exec, cudaFuncCachePreferShared));
- if (spec.dynamicSharedKB) // Need dynamically allocated shared memory?
- AT_CUDA_CHECK(cudaFuncSetAttribute(spec.exec, cudaFuncAttributeMaxDynamicSharedMemorySize, spec.dynamicSharedKB << 10));
- AT_CUDA_CHECK(cudaFuncSetSharedMemConfig(spec.exec, cudaSharedMemBankSizeFourByte));
-
- // Launch main kernel.
- const int maxSubGz = 65535; // CUDA maximum for block z dimension.
- for (int zofs=0; zofs < gz; zofs += maxSubGz) // Do multiple launches if gz is too big.
- {
- p.blockZofs = zofs;
- int subGz = std::min(maxSubGz, gz - zofs);
- AT_CUDA_CHECK(cudaLaunchKernel(spec.exec, dim3(gx, gy, subGz), bx, args, spec.dynamicSharedKB << 10, at::cuda::getCurrentCUDAStream()));
- }
-
- // Done.
- return std::make_tuple(y, so, 0);
-}
-
-//------------------------------------------------------------------------
-
-static torch::Tensor filtered_lrelu_act(torch::Tensor x, torch::Tensor si, int sx, int sy, float gain, float slope, float clamp, bool writeSigns)
-{
- // Set CUDA device.
- TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
- const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-
- // Validate arguments.
- TORCH_CHECK(x.dim() == 4, "x must be rank 4");
- TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
- TORCH_CHECK(x.numel() > 0, "x is empty");
- TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat || x.dtype() == torch::kDouble, "x must be float16, float32 or float64");
-
- // Output signs if we don't have sign input.
- torch::Tensor so;
- torch::Tensor s = si;
- bool readSigns = !!s.numel();
- if (writeSigns)
- {
- int64_t sw = x.size(3);
- sw = (sw + 15) & ~15; // Round to a multiple of 16 for coalescing.
- s = so = torch::empty({x.size(0), x.size(1), x.size(2), sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
- }
-
- // Validate sign tensor if in use.
- if (readSigns || writeSigns)
- {
- TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
- TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
- TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
- TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
- TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
- TORCH_CHECK(s.size(2) <= INT_MAX && (s.size(3) << 2) <= INT_MAX, "signs tensor is too large");
- }
-
- // Initialize CUDA kernel parameters.
- filtered_lrelu_act_kernel_params p;
- p.x = x.data_ptr();
- p.s = (readSigns || writeSigns) ? s.data_ptr() : 0;
- p.gain = gain;
- p.slope = slope;
- p.clamp = clamp;
- p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
- p.xStride = make_longlong4(x.stride(3), x.stride(2), x.stride(1), x.stride(0));
- p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3) << 2, (int)s.size(2)) : make_int2(0, 0); // Width is in elements. Contiguous.
- p.sOfs = make_int2(sx, sy);
-
- // Choose CUDA kernel.
- void* func = 0;
- AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_act_cuda", [&]
- {
- if (writeSigns)
- func = choose_filtered_lrelu_act_kernel();
- else if (readSigns)
- func = choose_filtered_lrelu_act_kernel();
- else
- func = choose_filtered_lrelu_act_kernel();
- });
- TORCH_CHECK(func, "internal error - CUDA kernel not found");
-
- // Launch CUDA kernel.
- void* args[] = {&p};
- int bx = 128; // 4 warps per block.
-
- // Logical size of launch = writeSigns ? p.s : p.x
- uint32_t gx = writeSigns ? p.sShape.x : p.xShape.x;
- uint32_t gy = writeSigns ? p.sShape.y : p.xShape.y;
- uint32_t gz = p.xShape.z * p.xShape.w; // Same as in p.sShape if signs are in use.
- gx = (gx - 1) / bx + 1;
-
- // Make sure grid y and z dimensions are within CUDA launch limits. Kernel loops internally to do the rest.
- const uint32_t gmax = 65535;
- gy = std::min(gy, gmax);
- gz = std::min(gz, gmax);
-
- // Launch.
- AT_CUDA_CHECK(cudaLaunchKernel(func, dim3(gx, gy, gz), bx, args, 0, at::cuda::getCurrentCUDAStream()));
- return so;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
- m.def("filtered_lrelu", &filtered_lrelu); // The whole thing.
- m.def("filtered_lrelu_act_", &filtered_lrelu_act); // Activation and sign tensor handling only. Modifies data tensor in-place.
-}
-
-//------------------------------------------------------------------------
diff --git a/spaces/EronSamez/RVC_HFmeu/gui_v0.py b/spaces/EronSamez/RVC_HFmeu/gui_v0.py
deleted file mode 100644
index 88c3cf9eb1eaa0fa812b32ae4d3750b4ce0a8699..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/gui_v0.py
+++ /dev/null
@@ -1,786 +0,0 @@
-import os, sys, traceback, re
-
-import json
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from configs.config import Config
-
-Config = Config()
-import PySimpleGUI as sg
-import sounddevice as sd
-import noisereduce as nr
-import numpy as np
-from fairseq import checkpoint_utils
-import librosa, torch, pyworld, faiss, time, threading
-import torch.nn.functional as F
-import torchaudio.transforms as tat
-import scipy.signal as signal
-import torchcrepe
-
-# import matplotlib.pyplot as plt
-from lib.infer_pack.models import (
- SynthesizerTrnMs256NSFsid,
- SynthesizerTrnMs256NSFsid_nono,
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono,
-)
-from i18n import I18nAuto
-
-i18n = I18nAuto()
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-current_dir = os.getcwd()
-
-
-class RVC:
- def __init__(
- self, key, f0_method, hubert_path, pth_path, index_path, npy_path, index_rate
- ) -> None:
- """
- 初始化
- """
- try:
- self.f0_up_key = key
- self.time_step = 160 / 16000 * 1000
- self.f0_min = 50
- self.f0_max = 1100
- self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
- self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
- self.f0_method = f0_method
- self.sr = 16000
- self.window = 160
-
- # Get Torch Device
- if torch.cuda.is_available():
- self.torch_device = torch.device(
- f"cuda:{0 % torch.cuda.device_count()}"
- )
- elif torch.backends.mps.is_available():
- self.torch_device = torch.device("mps")
- else:
- self.torch_device = torch.device("cpu")
-
- if index_rate != 0:
- self.index = faiss.read_index(index_path)
- # self.big_npy = np.load(npy_path)
- self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
- print("index search enabled")
- self.index_rate = index_rate
- model_path = hubert_path
- print("load model(s) from {}".format(model_path))
- models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
- [model_path],
- suffix="",
- )
- self.model = models[0]
- self.model = self.model.to(device)
- if Config.is_half:
- self.model = self.model.half()
- else:
- self.model = self.model.float()
- self.model.eval()
- cpt = torch.load(pth_path, map_location="cpu")
- self.tgt_sr = cpt["config"][-1]
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- self.if_f0 = cpt.get("f0", 1)
- self.version = cpt.get("version", "v1")
- if self.version == "v1":
- if self.if_f0 == 1:
- self.net_g = SynthesizerTrnMs256NSFsid(
- *cpt["config"], is_half=Config.is_half
- )
- else:
- self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
- elif self.version == "v2":
- if self.if_f0 == 1:
- self.net_g = SynthesizerTrnMs768NSFsid(
- *cpt["config"], is_half=Config.is_half
- )
- else:
- self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
- del self.net_g.enc_q
- print(self.net_g.load_state_dict(cpt["weight"], strict=False))
- self.net_g.eval().to(device)
- if Config.is_half:
- self.net_g = self.net_g.half()
- else:
- self.net_g = self.net_g.float()
- except:
- print(traceback.format_exc())
-
- def get_regular_crepe_computation(self, x, f0_min, f0_max, model="full"):
- batch_size = 512
- # Compute pitch using first gpu
- audio = torch.tensor(np.copy(x))[None].float()
- f0, pd = torchcrepe.predict(
- audio,
- self.sr,
- self.window,
- f0_min,
- f0_max,
- model,
- batch_size=batch_size,
- device=self.torch_device,
- return_periodicity=True,
- )
- pd = torchcrepe.filter.median(pd, 3)
- f0 = torchcrepe.filter.mean(f0, 3)
- f0[pd < 0.1] = 0
- f0 = f0[0].cpu().numpy()
- return f0
-
- def get_harvest_computation(self, x, f0_min, f0_max):
- f0, t = pyworld.harvest(
- x.astype(np.double),
- fs=self.sr,
- f0_ceil=f0_max,
- f0_floor=f0_min,
- frame_period=10,
- )
- f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
- f0 = signal.medfilt(f0, 3)
- return f0
-
- def get_f0(self, x, f0_up_key, inp_f0=None):
- # Calculate Padding and f0 details here
- p_len = x.shape[0] // 512 # For Now This probs doesn't work
- x_pad = 1
- f0_min = 50
- f0_max = 1100
- f0_mel_min = 1127 * np.log(1 + f0_min / 700)
- f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
- f0 = 0
- # Here, check f0_methods and get their computations
- if self.f0_method == "harvest":
- f0 = self.get_harvest_computation(x, f0_min, f0_max)
- elif self.f0_method == "reg-crepe":
- f0 = self.get_regular_crepe_computation(x, f0_min, f0_max)
- elif self.f0_method == "reg-crepe-tiny":
- f0 = self.get_regular_crepe_computation(x, f0_min, f0_max, "tiny")
-
- # Calculate f0_course and f0_bak here
- f0 *= pow(2, f0_up_key / 12)
- # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- tf0 = self.sr // self.window # 每秒f0点数
- if inp_f0 is not None:
- delta_t = np.round(
- (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
- ).astype("int16")
- replace_f0 = np.interp(
- list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
- )
- shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
- f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
- # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- f0bak = f0.copy()
- f0_mel = 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
- f0_mel_max - f0_mel_min
- ) + 1
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > 255] = 255
- f0_coarse = np.rint(f0_mel).astype(np.int)
- return f0_coarse, f0bak # 1-0
-
- def infer(self, feats: torch.Tensor) -> np.ndarray:
- """
- 推理函数
- """
- audio = feats.clone().cpu().numpy()
- assert feats.dim() == 1, feats.dim()
- feats = feats.view(1, -1)
- padding_mask = torch.BoolTensor(feats.shape).fill_(False)
- if Config.is_half:
- feats = feats.half()
- else:
- feats = feats.float()
- inputs = {
- "source": feats.to(device),
- "padding_mask": padding_mask.to(device),
- "output_layer": 9 if self.version == "v1" else 12,
- }
- torch.cuda.synchronize()
- with torch.no_grad():
- logits = self.model.extract_features(**inputs)
- feats = (
- self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
- )
-
- ####索引优化
- try:
- if (
- hasattr(self, "index")
- and hasattr(self, "big_npy")
- and self.index_rate != 0
- ):
- npy = feats[0].cpu().numpy().astype("float32")
- score, ix = self.index.search(npy, k=8)
- weight = np.square(1 / score)
- weight /= weight.sum(axis=1, keepdims=True)
- npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
- if Config.is_half:
- npy = npy.astype("float16")
- feats = (
- torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
- + (1 - self.index_rate) * feats
- )
- else:
- print("index search FAIL or disabled")
- except:
- traceback.print_exc()
- print("index search FAIL")
- feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
- torch.cuda.synchronize()
- print(feats.shape)
- if self.if_f0 == 1:
- pitch, pitchf = self.get_f0(audio, self.f0_up_key)
- p_len = min(feats.shape[1], 13000, pitch.shape[0]) # 太大了爆显存
- else:
- pitch, pitchf = None, None
- p_len = min(feats.shape[1], 13000) # 太大了爆显存
- torch.cuda.synchronize()
- # print(feats.shape,pitch.shape)
- feats = feats[:, :p_len, :]
- if self.if_f0 == 1:
- pitch = pitch[:p_len]
- pitchf = pitchf[:p_len]
- pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
- pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
- p_len = torch.LongTensor([p_len]).to(device)
- ii = 0 # sid
- sid = torch.LongTensor([ii]).to(device)
- with torch.no_grad():
- if self.if_f0 == 1:
- infered_audio = (
- self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
- .data.cpu()
- .float()
- )
- else:
- infered_audio = (
- self.net_g.infer(feats, p_len, sid)[0][0, 0].data.cpu().float()
- )
- torch.cuda.synchronize()
- return infered_audio
-
-
-class GUIConfig:
- def __init__(self) -> None:
- self.hubert_path: str = ""
- self.pth_path: str = ""
- self.index_path: str = ""
- self.npy_path: str = ""
- self.f0_method: str = ""
- self.pitch: int = 12
- self.samplerate: int = 44100
- self.block_time: float = 1.0 # s
- self.buffer_num: int = 1
- self.threhold: int = -30
- self.crossfade_time: float = 0.08
- self.extra_time: float = 0.04
- self.I_noise_reduce = False
- self.O_noise_reduce = False
- self.index_rate = 0.3
-
-
-class GUI:
- def __init__(self) -> None:
- self.config = GUIConfig()
- self.flag_vc = False
-
- self.launcher()
-
- def load(self):
- (
- input_devices,
- output_devices,
- input_devices_indices,
- output_devices_indices,
- ) = self.get_devices()
- try:
- with open("values1.json", "r") as j:
- data = json.load(j)
- except:
- # Injecting f0_method into the json data
- with open("values1.json", "w") as j:
- data = {
- "pth_path": "",
- "index_path": "",
- "sg_input_device": input_devices[
- input_devices_indices.index(sd.default.device[0])
- ],
- "sg_output_device": output_devices[
- output_devices_indices.index(sd.default.device[1])
- ],
- "threhold": "-45",
- "pitch": "0",
- "index_rate": "0",
- "block_time": "1",
- "crossfade_length": "0.04",
- "extra_time": "1",
- }
- return data
-
- def launcher(self):
- data = self.load()
- sg.theme("DarkTeal12")
- input_devices, output_devices, _, _ = self.get_devices()
- layout = [
- [
- sg.Frame(
- title="Proudly forked by Mangio621",
- ),
- sg.Frame(
- title=i18n("Load model"),
- layout=[
- [
- sg.Input(
- default_text="hubert_base.pt",
- key="hubert_path",
- disabled=True,
- ),
- sg.FileBrowse(
- i18n("Hubert Model"),
- initial_folder=os.path.join(os.getcwd()),
- file_types=(("pt files", "*.pt"),),
- ),
- ],
- [
- sg.Input(
- default_text=data.get("pth_path", ""),
- key="pth_path",
- ),
- sg.FileBrowse(
- i18n("Select the .pth file"),
- initial_folder=os.path.join(os.getcwd(), "weights"),
- file_types=(("weight files", "*.pth"),),
- ),
- ],
- [
- sg.Input(
- default_text=data.get("index_path", ""),
- key="index_path",
- ),
- sg.FileBrowse(
- i18n("Select the .index file"),
- initial_folder=os.path.join(os.getcwd(), "logs"),
- file_types=(("index files", "*.index"),),
- ),
- ],
- [
- sg.Input(
- default_text="你不需要填写这个You don't need write this.",
- key="npy_path",
- disabled=True,
- ),
- sg.FileBrowse(
- i18n("Select the .npy file"),
- initial_folder=os.path.join(os.getcwd(), "logs"),
- file_types=(("feature files", "*.npy"),),
- ),
- ],
- ],
- ),
- ],
- [
- # Mangio f0 Selection frame Here
- sg.Frame(
- layout=[
- [
- sg.Radio(
- "Harvest", "f0_method", key="harvest", default=True
- ),
- sg.Radio("Crepe", "f0_method", key="reg-crepe"),
- sg.Radio("Crepe Tiny", "f0_method", key="reg-crepe-tiny"),
- ]
- ],
- title="Select an f0 Method",
- )
- ],
- [
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Input device")),
- sg.Combo(
- input_devices,
- key="sg_input_device",
- default_value=data.get("sg_input_device", ""),
- ),
- ],
- [
- sg.Text(i18n("Output device")),
- sg.Combo(
- output_devices,
- key="sg_output_device",
- default_value=data.get("sg_output_device", ""),
- ),
- ],
- ],
- title=i18n("Audio device (please use the same type of driver)"),
- )
- ],
- [
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Response threshold")),
- sg.Slider(
- range=(-60, 0),
- key="threhold",
- resolution=1,
- orientation="h",
- default_value=data.get("threhold", ""),
- ),
- ],
- [
- sg.Text(i18n("Pitch settings")),
- sg.Slider(
- range=(-24, 24),
- key="pitch",
- resolution=1,
- orientation="h",
- default_value=data.get("pitch", ""),
- ),
- ],
- [
- sg.Text(i18n("Index Rate")),
- sg.Slider(
- range=(0.0, 1.0),
- key="index_rate",
- resolution=0.01,
- orientation="h",
- default_value=data.get("index_rate", ""),
- ),
- ],
- ],
- title=i18n("General settings"),
- ),
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Sample length")),
- sg.Slider(
- range=(0.1, 3.0),
- key="block_time",
- resolution=0.1,
- orientation="h",
- default_value=data.get("block_time", ""),
- ),
- ],
- [
- sg.Text(i18n("Fade length")),
- sg.Slider(
- range=(0.01, 0.15),
- key="crossfade_length",
- resolution=0.01,
- orientation="h",
- default_value=data.get("crossfade_length", ""),
- ),
- ],
- [
- sg.Text(i18n("Extra推理时长")),
- sg.Slider(
- range=(0.05, 3.00),
- key="extra_time",
- resolution=0.01,
- orientation="h",
- default_value=data.get("extra_time", ""),
- ),
- ],
- [
- sg.Checkbox(i18n("Input noise reduction"), key="I_noise_reduce"),
- sg.Checkbox(i18n("Output noise reduction"), key="O_noise_reduce"),
- ],
- ],
- title=i18n("Performance settings"),
- ),
- ],
- [
- sg.Button(i18n("开始音频Convert"), key="start_vc"),
- sg.Button(i18n("停止音频Convert"), key="stop_vc"),
- sg.Text(i18n("Inference time (ms):")),
- sg.Text("0", key="infer_time"),
- ],
- ]
- self.window = sg.Window("RVC - GUI", layout=layout)
- self.event_handler()
-
- def event_handler(self):
- while True:
- event, values = self.window.read()
- if event == sg.WINDOW_CLOSED:
- self.flag_vc = False
- exit()
- if event == "start_vc" and self.flag_vc == False:
- if self.set_values(values) == True:
- print("using_cuda:" + str(torch.cuda.is_available()))
- self.start_vc()
- settings = {
- "pth_path": values["pth_path"],
- "index_path": values["index_path"],
- "f0_method": self.get_f0_method_from_radios(values),
- "sg_input_device": values["sg_input_device"],
- "sg_output_device": values["sg_output_device"],
- "threhold": values["threhold"],
- "pitch": values["pitch"],
- "index_rate": values["index_rate"],
- "block_time": values["block_time"],
- "crossfade_length": values["crossfade_length"],
- "extra_time": values["extra_time"],
- }
- with open("values1.json", "w") as j:
- json.dump(settings, j)
- if event == "stop_vc" and self.flag_vc == True:
- self.flag_vc = False
-
- # Function that returns the used f0 method in string format "harvest"
- def get_f0_method_from_radios(self, values):
- f0_array = [
- {"name": "harvest", "val": values["harvest"]},
- {"name": "reg-crepe", "val": values["reg-crepe"]},
- {"name": "reg-crepe-tiny", "val": values["reg-crepe-tiny"]},
- ]
- # Filter through to find a true value
- used_f0 = ""
- for f0 in f0_array:
- if f0["val"] == True:
- used_f0 = f0["name"]
- break
- if used_f0 == "":
- used_f0 = "harvest" # Default Harvest if used_f0 is empty somehow
- return used_f0
-
- def set_values(self, values):
- if len(values["pth_path"].strip()) == 0:
- sg.popup(i18n("Select the pth file"))
- return False
- if len(values["index_path"].strip()) == 0:
- sg.popup(i18n("Select the index file"))
- return False
- pattern = re.compile("[^\x00-\x7F]+")
- if pattern.findall(values["hubert_path"]):
- sg.popup(i18n("The hubert model path must not contain Chinese characters"))
- return False
- if pattern.findall(values["pth_path"]):
- sg.popup(i18n("The pth file path must not contain Chinese characters."))
- return False
- if pattern.findall(values["index_path"]):
- sg.popup(i18n("The index file path must not contain Chinese characters."))
- return False
- self.set_devices(values["sg_input_device"], values["sg_output_device"])
- self.config.hubert_path = os.path.join(current_dir, "hubert_base.pt")
- self.config.pth_path = values["pth_path"]
- self.config.index_path = values["index_path"]
- self.config.npy_path = values["npy_path"]
- self.config.f0_method = self.get_f0_method_from_radios(values)
- self.config.threhold = values["threhold"]
- self.config.pitch = values["pitch"]
- self.config.block_time = values["block_time"]
- self.config.crossfade_time = values["crossfade_length"]
- self.config.extra_time = values["extra_time"]
- self.config.I_noise_reduce = values["I_noise_reduce"]
- self.config.O_noise_reduce = values["O_noise_reduce"]
- self.config.index_rate = values["index_rate"]
- return True
-
- def start_vc(self):
- torch.cuda.empty_cache()
- self.flag_vc = True
- self.block_frame = int(self.config.block_time * self.config.samplerate)
- self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate)
- self.sola_search_frame = int(0.012 * self.config.samplerate)
- self.delay_frame = int(0.01 * self.config.samplerate) # 往前预留0.02s
- self.extra_frame = int(self.config.extra_time * self.config.samplerate)
- self.rvc = None
- self.rvc = RVC(
- self.config.pitch,
- self.config.f0_method,
- self.config.hubert_path,
- self.config.pth_path,
- self.config.index_path,
- self.config.npy_path,
- self.config.index_rate,
- )
- self.input_wav: np.ndarray = np.zeros(
- self.extra_frame
- + self.crossfade_frame
- + self.sola_search_frame
- + self.block_frame,
- dtype="float32",
- )
- self.output_wav: torch.Tensor = torch.zeros(
- self.block_frame, device=device, dtype=torch.float32
- )
- self.sola_buffer: torch.Tensor = torch.zeros(
- self.crossfade_frame, device=device, dtype=torch.float32
- )
- self.fade_in_window: torch.Tensor = torch.linspace(
- 0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32
- )
- self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
- self.resampler1 = tat.Resample(
- orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
- )
- self.resampler2 = tat.Resample(
- orig_freq=self.rvc.tgt_sr,
- new_freq=self.config.samplerate,
- dtype=torch.float32,
- )
- thread_vc = threading.Thread(target=self.soundinput)
- thread_vc.start()
-
- def soundinput(self):
- """
- 接受音频输入
- """
- with sd.Stream(
- channels=2,
- callback=self.audio_callback,
- blocksize=self.block_frame,
- samplerate=self.config.samplerate,
- dtype="float32",
- ):
- while self.flag_vc:
- time.sleep(self.config.block_time)
- print("Audio block passed.")
- print("ENDing VC")
-
- def audio_callback(
- self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
- ):
- """
- 音频处理
- """
- start_time = time.perf_counter()
- indata = librosa.to_mono(indata.T)
- if self.config.I_noise_reduce:
- indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate)
-
- """noise gate"""
- frame_length = 2048
- hop_length = 1024
- rms = librosa.feature.rms(
- y=indata, frame_length=frame_length, hop_length=hop_length
- )
- db_threhold = librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold
- # print(rms.shape,db.shape,db)
- for i in range(db_threhold.shape[0]):
- if db_threhold[i]:
- indata[i * hop_length : (i + 1) * hop_length] = 0
- self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata)
-
- # infer
- print("input_wav:" + str(self.input_wav.shape))
- # print('infered_wav:'+str(infer_wav.shape))
- infer_wav: torch.Tensor = self.resampler2(
- self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav)))
- )[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to(
- device
- )
- print("infer_wav:" + str(infer_wav.shape))
-
- # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
- cor_nom = F.conv1d(
- infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
- self.sola_buffer[None, None, :],
- )
- cor_den = torch.sqrt(
- F.conv1d(
- infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
- ** 2,
- torch.ones(1, 1, self.crossfade_frame, device=device),
- )
- + 1e-8
- )
- sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
- print("sola offset: " + str(int(sola_offset)))
-
- # crossfade
- self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
- self.output_wav[: self.crossfade_frame] *= self.fade_in_window
- self.output_wav[: self.crossfade_frame] += self.sola_buffer[:]
- if sola_offset < self.sola_search_frame:
- self.sola_buffer[:] = (
- infer_wav[
- -self.sola_search_frame
- - self.crossfade_frame
- + sola_offset : -self.sola_search_frame
- + sola_offset
- ]
- * self.fade_out_window
- )
- else:
- self.sola_buffer[:] = (
- infer_wav[-self.crossfade_frame :] * self.fade_out_window
- )
-
- if self.config.O_noise_reduce:
- outdata[:] = np.tile(
- nr.reduce_noise(
- y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
- ),
- (2, 1),
- ).T
- else:
- outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
- total_time = time.perf_counter() - start_time
- self.window["infer_time"].update(int(total_time * 1000))
- print("infer time:" + str(total_time))
- print("f0_method: " + str(self.config.f0_method))
-
- def get_devices(self, update: bool = True):
- """获取设备列表"""
- if update:
- sd._terminate()
- sd._initialize()
- devices = sd.query_devices()
- hostapis = sd.query_hostapis()
- for hostapi in hostapis:
- for device_idx in hostapi["devices"]:
- devices[device_idx]["hostapi_name"] = hostapi["name"]
- input_devices = [
- f"{d['name']} ({d['hostapi_name']})"
- for d in devices
- if d["max_input_channels"] > 0
- ]
- output_devices = [
- f"{d['name']} ({d['hostapi_name']})"
- for d in devices
- if d["max_output_channels"] > 0
- ]
- input_devices_indices = [
- d["index"] if "index" in d else d["name"]
- for d in devices
- if d["max_input_channels"] > 0
- ]
- output_devices_indices = [
- d["index"] if "index" in d else d["name"]
- for d in devices
- if d["max_output_channels"] > 0
- ]
- return (
- input_devices,
- output_devices,
- input_devices_indices,
- output_devices_indices,
- )
-
- def set_devices(self, input_device, output_device):
- """设置输出设备"""
- (
- input_devices,
- output_devices,
- input_device_indices,
- output_device_indices,
- ) = self.get_devices()
- sd.default.device[0] = input_device_indices[input_devices.index(input_device)]
- sd.default.device[1] = output_device_indices[
- output_devices.index(output_device)
- ]
- print("input device:" + str(sd.default.device[0]) + ":" + str(input_device))
- print("output device:" + str(sd.default.device[1]) + ":" + str(output_device))
-
-
-gui = GUI()
diff --git a/spaces/EuroPython2022/clickbaitonator/fudge/main.py b/spaces/EuroPython2022/clickbaitonator/fudge/main.py
deleted file mode 100644
index e8c2299b2449b6dd07d26c7ae678732b1dabca88..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/clickbaitonator/fudge/main.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import os
-import random
-import time
-import pickle
-import math
-from argparse import ArgumentParser
-
-from tqdm import tqdm
-import numpy as np
-import torch
-import torch.nn as nn
-
-from data import Dataset
-from model import Model
-from util import save_checkpoint, ProgressMeter, AverageMeter, num_params, pad_mask
-from constants import *
-
-
-def train(model, dataset, optimizer, criterion, epoch, args, data_start_index):
- model.train()
- if data_start_index == 0:
- dataset.shuffle('train', seed=epoch + args.seed)
- if args.epoch_max_len is not None:
- data_end_index = min(data_start_index + args.epoch_max_len, len(dataset.splits['train']))
- loader = dataset.loader('train', num_workers=args.num_workers, indices=list(range(data_start_index, data_end_index)))
- data_start_index = data_end_index if data_end_index < len(dataset.splits['train']) else 0
- else:
- loader = dataset.loader('train', num_workers=args.num_workers)
- loss_meter = AverageMeter('loss', ':6.4f')
- total_length = len(loader)
- progress = ProgressMeter(total_length, [loss_meter], prefix='Training: ')
- for batch_num, batch in enumerate(tqdm(loader, total=len(loader))):
- batch = [tensor.to(args.device) for tensor in batch]
- inputs, lengths, future_words, log_probs, labels, classification_targets, syllables_to_go, future_word_num_syllables, rhyme_group_index = batch
- if args.task not in ['formality', 'iambic']:
- if not args.debug and len(inputs) != args.batch_size: # it'll screw up the bias...?
- continue
- scores = model(inputs, lengths, future_words, log_probs, syllables_to_go, future_word_num_syllables, rhyme_group_index, run_classifier=True)
- if args.task == 'formality': # we're learning for all positions at once. scores are batch x seq
- expanded_labels = classification_targets.unsqueeze(1).expand(-1, scores.shape[1]) # batch x seq
- length_mask = pad_mask(lengths).permute(1, 0) # batch x seq
- loss = criterion(scores.flatten()[length_mask.flatten()==1], expanded_labels.flatten().float()[length_mask.flatten()==1])
- elif args.task in ['iambic', 'newline']:
- use_indices = classification_targets.flatten() != -1
- loss = criterion(scores.flatten()[use_indices], classification_targets.flatten().float()[use_indices])
- else: # topic, rhyme
- loss = criterion(scores.flatten(), labels.flatten().float())
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
- loss_meter.update(loss.detach(), len(labels))
- if batch_num % args.train_print_freq == 0:
- progress.display(batch_num)
- progress.display(total_length)
- return data_start_index
-
-
-def validate(model, dataset, criterion, epoch, args):
- model.eval()
- random.seed(0)
- loader = dataset.loader('val', num_workers=args.num_workers)
- loss_meter = AverageMeter('loss', ':6.4f')
- total_length = len(loader)
- progress = ProgressMeter(total_length, [loss_meter], prefix='Validation: ')
- with torch.no_grad():
- for batch_num, batch in enumerate(tqdm(loader, total=len(loader))):
- batch = [tensor.to(args.device) for tensor in batch]
- inputs, lengths, future_words, log_probs, labels, classification_targets, syllables_to_go, future_word_num_syllables, rhyme_group_index = batch
- if args.task not in ['formality', 'iambic']: # topic predictor
- if not args.debug and len(inputs) != args.batch_size:
- continue
- scores = model(inputs, lengths, future_words, log_probs, syllables_to_go, future_word_num_syllables, rhyme_group_index, run_classifier=True)
- if args.task == 'formality': # we're learning for all positions at once. scores are batch x seq
- expanded_labels = classification_targets.unsqueeze(1).expand(-1, scores.shape[1]) # batch x seq
- length_mask = pad_mask(lengths).permute(1, 0) # batch x seq
- loss = criterion(scores.flatten()[length_mask.flatten()==1], expanded_labels.flatten().float()[length_mask.flatten()==1])
- elif args.task in ['iambic', 'newline']:
- use_indices = classification_targets.flatten() != -1
- loss = criterion(scores.flatten()[use_indices], classification_targets.flatten().float()[use_indices])
- else: # topic, rhyme
- loss = criterion(scores.flatten(), labels.flatten().float())
- loss_meter.update(loss.detach(), len(labels))
- if batch_num % args.train_print_freq == 0:
- progress.display(batch_num)
- progress.display(total_length)
- return loss_meter.avg
-
-
-def main(args):
- dataset = Dataset(args)
- os.makedirs(args.save_dir, exist_ok=True)
- with open(os.path.join(args.save_dir, 'dataset_info'), 'wb') as wf:
- pickle.dump(dataset.dataset_info, wf)
- if args.task == 'rhyme':
- with open(os.path.join(args.save_dir, 'rhyme_info'), 'wb') as wf:
- pickle.dump(dataset.rhyme_info, wf)
- if args.ckpt:
- checkpoint = torch.load(args.ckpt, map_location=args.device)
- start_epoch = checkpoint['epoch'] + 1
- best_val_metric = checkpoint['best_metric']
- model_args = checkpoint['args']
- model = Model(model_args, dataset.gpt_pad_id, len(dataset.index2word), rhyme_group_size=len(dataset.index2rhyme_group) if args.task == 'rhyme' else None) # no need to get the glove embeddings when reloading since they're saved in model ckpt anyway
- model.load_state_dict(checkpoint['state_dict'])
- model = model.to(args.device)
- optimizer = torch.optim.Adam(model.parameters(), lr=model_args.lr)
- optimizer.load_state_dict(checkpoint['optimizer'])
- data_start_index = checkpoint['data_start_index']
- print("=> loaded checkpoint '{}' (epoch {})"
- .format(args.ckpt, checkpoint['epoch']))
- # NOTE: just import pdb after loading the model here if you want to play with it, it's easy
- # model.eval()
- # import pdb; pdb.set_trace()
- else:
- model = Model(args, dataset.gpt_pad_id, len(dataset.index2word), rhyme_group_size=len(dataset.index2rhyme_group) if args.task == 'rhyme' else None, glove_embeddings=dataset.glove_embeddings)
- model = model.to(args.device)
- optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
- best_val_metric = 1e8 # lower is better for BCE
- data_start_index = 0
- print('num params', num_params(model))
- criterion = nn.BCEWithLogitsLoss().to(args.device)
-
- if args.evaluate:
- epoch = 0
- validate(model, dataset, criterion, epoch, args)
- return
- for epoch in range(args.epochs):
- print("TRAINING: Epoch {} at {}".format(epoch, time.ctime()))
- data_start_index = train(model, dataset, optimizer, criterion, epoch, args, data_start_index)
- if epoch % args.validation_freq == 0:
- print("VALIDATION: Epoch {} at {}".format(epoch, time.ctime()))
- metric = validate(model, dataset, criterion, epoch, args)
-
- if not args.debug:
- if metric < best_val_metric:
- print('new best val metric', metric)
- best_val_metric = metric
- save_checkpoint({
- 'epoch': epoch,
- 'state_dict': model.state_dict(),
- 'best_metric': best_val_metric,
- 'optimizer': optimizer.state_dict(),
- 'data_start_index': data_start_index,
- 'args': args
- }, os.path.join(args.save_dir, 'model_best.pth.tar'))
- save_checkpoint({
- 'epoch': epoch,
- 'state_dict': model.state_dict(),
- 'best_metric': metric,
- 'optimizer': optimizer.state_dict(),
- 'data_start_index': data_start_index,
- 'args': args
- }, os.path.join(args.save_dir, 'model_epoch' + str(epoch) + '.pth.tar'))
-
-
-if __name__=='__main__':
- parser = ArgumentParser()
-
- # DATA
- parser.add_argument('--task', type=str, required=True, choices=['iambic', 'rhyme', 'newline', 'topic', 'formality', 'clickbait'])
- parser.add_argument('--data_dir', type=str, required=True)
- parser.add_argument('--glove_file', type=str, help='glove embedding init, for topic task')
-
- # SAVE/LOAD
- parser.add_argument('--save_dir', type=str, required=True, help='where to save ckpts')
- parser.add_argument('--ckpt', type=str, default=None, help='load ckpt from file if given')
- parser.add_argument('--dataset_info', type=str, help='saved dataset info')
- parser.add_argument('--rhyme_info', type=str, help='saved dataset rhyme info, for a ckpt with task==rhyme')
-
- # TRAINING
- parser.add_argument('--batch_size', type=int, default=128)
- parser.add_argument('--epochs', type=int, default=100)
- parser.add_argument('--epoch_max_len', type=int, default=None, help='max batches per epoch if set, for more frequent validation')
- parser.add_argument('--validation_freq', type=int, default=1, help='validate every X epochs')
- parser.add_argument('--lr', type=float, default=1e-3, help='Adam learning rate')
- parser.add_argument('--seed', type=int, default=1, help='random seed')
- parser.add_argument('--device', type=str, default='cuda', choices=['cpu', 'cuda'])
- parser.add_argument('--num_workers', type=int, default=20, help='num workers for data loader')
- parser.add_argument('--evaluate', action='store_true', default=False)
- parser.add_argument('--debug', action='store_true', default=False)
-
- # PRINTING
- parser.add_argument('--train_print_freq', type=int, default=100, help='how often to print metrics (every X batches)')
-
- args = parser.parse_args()
-
- random.seed(args.seed)
- np.random.seed(args.seed)
- torch.manual_seed(args.seed)
- if args.evaluate:
- assert args.ckpt is not None
-
- main(args)
\ No newline at end of file
diff --git a/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts b/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts
deleted file mode 100644
index 9ba0fe30a092c73c8321a6bb2502750471b133f2..0000000000000000000000000000000000000000
--- a/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts
+++ /dev/null
@@ -1,279 +0,0 @@
-import {
- ChatInterface,
- ChatModule,
- ChatRestModule,
- ChatWorkerClient,
-} from "@mlc-ai/web-llm";
-
-function getElementAndCheck(id: string): HTMLElement {
- const element = document.getElementById(id);
- if (element == null) {
- throw Error("Cannot find element " + id);
- }
- return element;
-}
-
-const appConfig = {
- model_list: [
- {
- model_url:
- "https://huggingface.co/Felladrin/mlc-chat-Mistral-7B-OpenOrca-q4f32_1/resolve/main/params/",
- local_id: "Mistral-7B-OpenOrca-q4f32_1",
- },
- ],
- model_lib_map: {
- "Mistral-7B-OpenOrca-q4f32_1":
- "https://huggingface.co/Felladrin/mlc-chat-Mistral-7B-OpenOrca-q4f32_1/resolve/main/Mistral-7B-OpenOrca-q4f32_1-webgpu.wasm",
- },
- use_web_worker: true,
-};
-
-class ChatUI {
- private uiChat: HTMLElement;
- private uiChatInput: HTMLInputElement;
- private uiChatInfoLabel: HTMLLabelElement;
- private chat: ChatInterface;
- private localChat: ChatInterface;
- private config = appConfig;
- private selectedModel: string;
- private chatLoaded = false;
- private requestInProgress = false;
- // We use a request chain to ensure that
- // all requests send to chat are sequentialized
- private chatRequestChain: Promise = Promise.resolve();
-
- constructor(chat: ChatInterface, localChat: ChatInterface) {
- // use web worker to run chat generation in background
- this.chat = chat;
- this.localChat = localChat;
- // get the elements
- this.uiChat = getElementAndCheck("chatui-chat");
- this.uiChatInput = getElementAndCheck("chatui-input") as HTMLInputElement;
- this.uiChatInfoLabel = getElementAndCheck(
- "chatui-info-label"
- ) as HTMLLabelElement;
- // register event handlers
- getElementAndCheck("chatui-reset-btn").onclick = () => {
- this.onReset();
- };
- getElementAndCheck("chatui-send-btn").onclick = () => {
- this.onGenerate();
- };
- // TODO: find other alternative triggers
- getElementAndCheck("chatui-input").onkeypress = (event) => {
- if (event.keyCode === 13) {
- this.onGenerate();
- }
- };
-
- const modelSelector = getElementAndCheck(
- "chatui-select"
- ) as HTMLSelectElement;
- for (let i = 0; i < this.config.model_list.length; ++i) {
- const item = this.config.model_list[i];
- const opt = document.createElement("option");
- opt.value = item.local_id;
- opt.innerHTML = item.local_id;
- opt.selected = i == 0;
- modelSelector.appendChild(opt);
- }
- // Append local server option to the model selector
- const localServerOpt = document.createElement("option");
- localServerOpt.value = "Local Server";
- localServerOpt.innerHTML = "Local Server";
- modelSelector.append(localServerOpt);
- this.selectedModel = modelSelector.value;
- modelSelector.onchange = () => {
- this.onSelectChange(modelSelector);
- };
- }
- /**
- * Push a task to the execution queue.
- *
- * @param task The task to be executed;
- */
- private pushTask(task: () => Promise) {
- const lastEvent = this.chatRequestChain;
- this.chatRequestChain = lastEvent.then(task);
- }
- // Event handlers
- // all event handler pushes the tasks to a queue
- // that get executed sequentially
- // the tasks previous tasks, which causes them to early stop
- // can be interrupted by chat.interruptGenerate
- private async onGenerate() {
- if (this.requestInProgress) {
- return;
- }
- this.pushTask(async () => {
- await this.asyncGenerate();
- });
- }
-
- private async onSelectChange(modelSelector: HTMLSelectElement) {
- if (this.requestInProgress) {
- // interrupt previous generation if any
- this.chat.interruptGenerate();
- }
- // try reset after previous requests finishes
- this.pushTask(async () => {
- await this.chat.resetChat();
- this.resetChatHistory();
- await this.unloadChat();
- this.selectedModel = modelSelector.value;
- await this.asyncInitChat();
- });
- }
-
- private async onReset() {
- if (this.requestInProgress) {
- // interrupt previous generation if any
- this.chat.interruptGenerate();
- }
- // try reset after previous requests finishes
- this.pushTask(async () => {
- await this.chat.resetChat();
- this.resetChatHistory();
- });
- }
-
- // Internal helper functions
- private appendMessage(kind, text) {
- if (kind == "init") {
- text = "[System Initalize] " + text;
- }
- if (this.uiChat === undefined) {
- throw Error("cannot find ui chat");
- }
- const msg = `
-
-
- ${text}
-
-
- `;
- this.uiChat.insertAdjacentHTML("beforeend", msg);
- this.uiChat.scrollTo(0, this.uiChat.scrollHeight);
- }
-
- private updateLastMessage(kind, text) {
- if (kind == "init") {
- text = "[System Initalize] " + text;
- }
- if (this.uiChat === undefined) {
- throw Error("cannot find ui chat");
- }
- const matches = this.uiChat.getElementsByClassName(`msg ${kind}-msg`);
- if (matches.length == 0) throw Error(`${kind} message do not exist`);
- const msg = matches[matches.length - 1];
- const msgText = msg.getElementsByClassName("msg-text");
- if (msgText.length != 1) throw Error("Expect msg-text");
- if (msgText[0].innerHTML == text) return;
- const list = text.split("\n").map((t) => {
- const item = document.createElement("div");
- item.textContent = t;
- return item;
- });
- msgText[0].innerHTML = "";
- list.forEach((item) => msgText[0].append(item));
- this.uiChat.scrollTo(0, this.uiChat.scrollHeight);
- }
-
- private resetChatHistory() {
- const clearTags = ["left", "right", "init", "error"];
- for (const tag of clearTags) {
- // need to unpack to list so the iterator don't get affected by mutation
- const matches = [...this.uiChat.getElementsByClassName(`msg ${tag}-msg`)];
- for (const item of matches) {
- this.uiChat.removeChild(item);
- }
- }
- if (this.uiChatInfoLabel !== undefined) {
- this.uiChatInfoLabel.innerHTML = "";
- }
- }
-
- private async asyncInitChat() {
- if (this.chatLoaded) return;
- this.requestInProgress = true;
- this.appendMessage("init", "");
- const initProgressCallback = (report) => {
- this.updateLastMessage("init", report.text);
- };
- this.chat.setInitProgressCallback(initProgressCallback);
-
- try {
- if (this.selectedModel != "Local Server") {
- await this.chat.reload(this.selectedModel, undefined, this.config);
- }
- } catch (err) {
- this.appendMessage("error", "Init error, " + err.toString());
- console.log(err.stack);
- this.unloadChat();
- this.requestInProgress = false;
- return;
- }
- this.requestInProgress = false;
- this.chatLoaded = true;
- }
-
- private async unloadChat() {
- await this.chat.unload();
- this.chatLoaded = false;
- }
-
- /**
- * Run generate
- */
- private async asyncGenerate() {
- await this.asyncInitChat();
- this.requestInProgress = true;
- const prompt = this.uiChatInput.value;
- if (prompt == "") {
- this.requestInProgress = false;
- return;
- }
-
- this.appendMessage("right", prompt);
- this.uiChatInput.value = "";
- this.uiChatInput.setAttribute("placeholder", "Generating...");
-
- this.appendMessage("left", "");
- const callbackUpdateResponse = (step, msg) => {
- if (msg.length === 0) return this.chat.interruptGenerate();
- this.updateLastMessage("left", msg);
- };
-
- try {
- if (this.selectedModel == "Local Server") {
- await this.localChat.generate(prompt, callbackUpdateResponse);
- this.uiChatInfoLabel.innerHTML =
- await this.localChat.runtimeStatsText();
- } else {
- await this.chat.generate(prompt, callbackUpdateResponse);
- this.uiChatInfoLabel.innerHTML = await this.chat.runtimeStatsText();
- }
- } catch (err) {
- this.appendMessage("error", "Generate error, " + err.toString());
- console.log(err.stack);
- await this.unloadChat();
- }
- this.uiChatInput.setAttribute("placeholder", "Enter your message...");
- this.requestInProgress = false;
- }
-}
-
-const useWebWorker = appConfig.use_web_worker;
-let chat: ChatInterface;
-let localChat: ChatInterface;
-
-if (useWebWorker) {
- chat = new ChatWorkerClient(
- new Worker(new URL("./worker.ts", import.meta.url), { type: "module" })
- );
- localChat = new ChatRestModule();
-} else {
- chat = new ChatModule();
- localChat = new ChatRestModule();
-}
-new ChatUI(chat, localChat);
diff --git a/spaces/Francesco/torch-cam-transformers/app.py b/spaces/Francesco/torch-cam-transformers/app.py
deleted file mode 100644
index 6c5bf7ea8344430235dfcc553c112be801348a50..0000000000000000000000000000000000000000
--- a/spaces/Francesco/torch-cam-transformers/app.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import streamlit as st
-
-import sys
-
-
-def main():
- # Wide mode
- st.set_page_config(layout="wide")
-
- # Designing the interface
- st.title(sys.version)
-
-
-main()
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py
deleted file mode 100644
index bf18572329019d7a8f1df01799eda207c16dd7ff..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py
+++ /dev/null
@@ -1,284 +0,0 @@
-import os
-import random
-import re
-import numpy as np
-import librosa
-import torch
-import random
-from utils import repeat_expand_2d
-from tqdm import tqdm
-from torch.utils.data import Dataset
-
-def traverse_dir(
- root_dir,
- extensions,
- amount=None,
- str_include=None,
- str_exclude=None,
- is_pure=False,
- is_sort=False,
- is_ext=True):
-
- file_list = []
- cnt = 0
- for root, _, files in os.walk(root_dir):
- for file in files:
- if any([file.endswith(f".{ext}") for ext in extensions]):
- # path
- mix_path = os.path.join(root, file)
- pure_path = mix_path[len(root_dir)+1:] if is_pure else mix_path
-
- # amount
- if (amount is not None) and (cnt == amount):
- if is_sort:
- file_list.sort()
- return file_list
-
- # check string
- if (str_include is not None) and (str_include not in pure_path):
- continue
- if (str_exclude is not None) and (str_exclude in pure_path):
- continue
-
- if not is_ext:
- ext = pure_path.split('.')[-1]
- pure_path = pure_path[:-(len(ext)+1)]
- file_list.append(pure_path)
- cnt += 1
- if is_sort:
- file_list.sort()
- return file_list
-
-
-def get_data_loaders(args, whole_audio=False):
- data_train = AudioDataset(
- filelists = args.data.training_files,
- waveform_sec=args.data.duration,
- hop_size=args.data.block_size,
- sample_rate=args.data.sampling_rate,
- load_all_data=args.train.cache_all_data,
- whole_audio=whole_audio,
- extensions=args.data.extensions,
- n_spk=args.model.n_spk,
- spk=args.spk,
- device=args.train.cache_device,
- fp16=args.train.cache_fp16,
- use_aug=True)
- loader_train = torch.utils.data.DataLoader(
- data_train ,
- batch_size=args.train.batch_size if not whole_audio else 1,
- shuffle=True,
- num_workers=args.train.num_workers if args.train.cache_device=='cpu' else 0,
- persistent_workers=(args.train.num_workers > 0) if args.train.cache_device=='cpu' else False,
- pin_memory=True if args.train.cache_device=='cpu' else False
- )
- data_valid = AudioDataset(
- filelists = args.data.validation_files,
- waveform_sec=args.data.duration,
- hop_size=args.data.block_size,
- sample_rate=args.data.sampling_rate,
- load_all_data=args.train.cache_all_data,
- whole_audio=True,
- spk=args.spk,
- extensions=args.data.extensions,
- n_spk=args.model.n_spk)
- loader_valid = torch.utils.data.DataLoader(
- data_valid,
- batch_size=1,
- shuffle=False,
- num_workers=0,
- pin_memory=True
- )
- return loader_train, loader_valid
-
-
-class AudioDataset(Dataset):
- def __init__(
- self,
- filelists,
- waveform_sec,
- hop_size,
- sample_rate,
- spk,
- load_all_data=True,
- whole_audio=False,
- extensions=['wav'],
- n_spk=1,
- device='cpu',
- fp16=False,
- use_aug=False,
- ):
- super().__init__()
-
- self.waveform_sec = waveform_sec
- self.sample_rate = sample_rate
- self.hop_size = hop_size
- self.filelists = filelists
- self.whole_audio = whole_audio
- self.use_aug = use_aug
- self.data_buffer={}
- self.pitch_aug_dict = {}
- # np.load(os.path.join(self.path_root, 'pitch_aug_dict.npy'), allow_pickle=True).item()
- if load_all_data:
- print('Load all the data filelists:', filelists)
- else:
- print('Load the f0, volume data filelists:', filelists)
- with open(filelists,"r") as f:
- self.paths = f.read().splitlines()
- for name_ext in tqdm(self.paths, total=len(self.paths)):
- name = os.path.splitext(name_ext)[0]
- path_audio = name_ext
- duration = librosa.get_duration(filename = path_audio, sr = self.sample_rate)
-
- path_f0 = name_ext + ".f0.npy"
- f0,_ = np.load(path_f0,allow_pickle=True)
- f0 = torch.from_numpy(np.array(f0,dtype=float)).float().unsqueeze(-1).to(device)
-
- path_volume = name_ext + ".vol.npy"
- volume = np.load(path_volume)
- volume = torch.from_numpy(volume).float().unsqueeze(-1).to(device)
-
- path_augvol = name_ext + ".aug_vol.npy"
- aug_vol = np.load(path_augvol)
- aug_vol = torch.from_numpy(aug_vol).float().unsqueeze(-1).to(device)
-
- if n_spk is not None and n_spk > 1:
- spk_name = name_ext.split("/")[-2]
- spk_id = spk[spk_name] if spk_name in spk else 0
- if spk_id < 0 or spk_id >= n_spk:
- raise ValueError(' [x] Muiti-speaker traing error : spk_id must be a positive integer from 0 to n_spk-1 ')
- else:
- spk_id = 0
- spk_id = torch.LongTensor(np.array([spk_id])).to(device)
-
- if load_all_data:
- '''
- audio, sr = librosa.load(path_audio, sr=self.sample_rate)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio)
- audio = torch.from_numpy(audio).to(device)
- '''
- path_mel = name_ext + ".mel.npy"
- mel = np.load(path_mel)
- mel = torch.from_numpy(mel).to(device)
-
- path_augmel = name_ext + ".aug_mel.npy"
- aug_mel,keyshift = np.load(path_augmel, allow_pickle=True)
- aug_mel = np.array(aug_mel,dtype=float)
- aug_mel = torch.from_numpy(aug_mel).to(device)
- self.pitch_aug_dict[name_ext] = keyshift
-
- path_units = name_ext + ".soft.pt"
- units = torch.load(path_units).to(device)
- units = units[0]
- units = repeat_expand_2d(units,f0.size(0)).transpose(0,1)
-
- if fp16:
- mel = mel.half()
- aug_mel = aug_mel.half()
- units = units.half()
-
- self.data_buffer[name_ext] = {
- 'duration': duration,
- 'mel': mel,
- 'aug_mel': aug_mel,
- 'units': units,
- 'f0': f0,
- 'volume': volume,
- 'aug_vol': aug_vol,
- 'spk_id': spk_id
- }
- else:
- path_augmel = name_ext + ".aug_mel.npy"
- aug_mel,keyshift = np.load(path_augmel, allow_pickle=True)
- self.pitch_aug_dict[name_ext] = keyshift
- self.data_buffer[name_ext] = {
- 'duration': duration,
- 'f0': f0,
- 'volume': volume,
- 'aug_vol': aug_vol,
- 'spk_id': spk_id
- }
-
-
- def __getitem__(self, file_idx):
- name_ext = self.paths[file_idx]
- data_buffer = self.data_buffer[name_ext]
- # check duration. if too short, then skip
- if data_buffer['duration'] < (self.waveform_sec + 0.1):
- return self.__getitem__( (file_idx + 1) % len(self.paths))
-
- # get item
- return self.get_data(name_ext, data_buffer)
-
- def get_data(self, name_ext, data_buffer):
- name = os.path.splitext(name_ext)[0]
- frame_resolution = self.hop_size / self.sample_rate
- duration = data_buffer['duration']
- waveform_sec = duration if self.whole_audio else self.waveform_sec
-
- # load audio
- idx_from = 0 if self.whole_audio else random.uniform(0, duration - waveform_sec - 0.1)
- start_frame = int(idx_from / frame_resolution)
- units_frame_len = int(waveform_sec / frame_resolution)
- aug_flag = random.choice([True, False]) and self.use_aug
- '''
- audio = data_buffer.get('audio')
- if audio is None:
- path_audio = os.path.join(self.path_root, 'audio', name) + '.wav'
- audio, sr = librosa.load(
- path_audio,
- sr = self.sample_rate,
- offset = start_frame * frame_resolution,
- duration = waveform_sec)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio)
- # clip audio into N seconds
- audio = audio[ : audio.shape[-1] // self.hop_size * self.hop_size]
- audio = torch.from_numpy(audio).float()
- else:
- audio = audio[start_frame * self.hop_size : (start_frame + units_frame_len) * self.hop_size]
- '''
- # load mel
- mel_key = 'aug_mel' if aug_flag else 'mel'
- mel = data_buffer.get(mel_key)
- if mel is None:
- mel = name_ext + ".mel.npy"
- mel = np.load(mel)
- mel = mel[start_frame : start_frame + units_frame_len]
- mel = torch.from_numpy(mel).float()
- else:
- mel = mel[start_frame : start_frame + units_frame_len]
-
- # load f0
- f0 = data_buffer.get('f0')
- aug_shift = 0
- if aug_flag:
- aug_shift = self.pitch_aug_dict[name_ext]
- f0_frames = 2 ** (aug_shift / 12) * f0[start_frame : start_frame + units_frame_len]
-
- # load units
- units = data_buffer.get('units')
- if units is None:
- path_units = name_ext + ".soft.pt"
- units = torch.load(path_units)
- units = units[0]
- units = repeat_expand_2d(units,f0.size(0)).transpose(0,1)
-
- units = units[start_frame : start_frame + units_frame_len]
-
- # load volume
- vol_key = 'aug_vol' if aug_flag else 'volume'
- volume = data_buffer.get(vol_key)
- volume_frames = volume[start_frame : start_frame + units_frame_len]
-
- # load spk_id
- spk_id = data_buffer.get('spk_id')
-
- # load shift
- aug_shift = torch.from_numpy(np.array([[aug_shift]])).float()
-
- return dict(mel=mel, f0=f0_frames, volume=volume_frames, units=units, spk_id=spk_id, aug_shift=aug_shift, name=name, name_ext=name_ext)
-
- def __len__(self):
- return len(self.paths)
\ No newline at end of file
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py
deleted file mode 100644
index 3bdb70ba9357e95ff05853dcc06437c3401ef3be..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import os
-from functools import lru_cache
-from typing import Union
-
-import ffmpeg
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-from .utils import exact_div
-
-from librosa.filters import mel as librosa_mel_fn
-
-# hard-coded audio hyperparameters
-SAMPLE_RATE = 16000
-N_FFT = 400
-N_MELS = 80
-HOP_LENGTH = 160
-CHUNK_LENGTH = 30
-N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk
-N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH) # 3000: number of frames in a mel spectrogram input
-
-
-def load_audio(file: str, sr: int = SAMPLE_RATE):
- """
- Open an audio file and read as mono waveform, resampling as necessary
-
- Parameters
- ----------
- file: str
- The audio file to open
-
- sr: int
- The sample rate to resample the audio if necessary
-
- Returns
- -------
- A NumPy array containing the audio waveform, in float32 dtype.
- """
- try:
- # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
- # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
- out, _ = (
- ffmpeg.input(file, threads=0)
- .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
- .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
- )
- except ffmpeg.Error as e:
- raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
-
- return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
-
-
-def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
- """
- Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
- """
- if torch.is_tensor(array):
- if array.shape[axis] > length:
- array = array.index_select(dim=axis, index=torch.arange(length, device=array.device))
-
- if array.shape[axis] < length:
- pad_widths = [(0, 0)] * array.ndim
- pad_widths[axis] = (0, length - array.shape[axis])
- array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
- else:
- if array.shape[axis] > length:
- array = array.take(indices=range(length), axis=axis)
-
- if array.shape[axis] < length:
- pad_widths = [(0, 0)] * array.ndim
- pad_widths[axis] = (0, length - array.shape[axis])
- array = np.pad(array, pad_widths)
-
- return array
-
-
-@lru_cache(maxsize=None)
-def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
- """
- load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
- Allows decoupling librosa dependency; saved using:
-
- np.savez_compressed(
- "mel_filters.npz",
- mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
- )
- """
- assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
- return torch.from_numpy(librosa_mel_fn(sr=SAMPLE_RATE,n_fft=N_FFT,n_mels=n_mels)).to(device)
-
-
-def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS):
- """
- Compute the log-Mel spectrogram of
-
- Parameters
- ----------
- audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
- The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
-
- n_mels: int
- The number of Mel-frequency filters, only 80 is supported
-
- Returns
- -------
- torch.Tensor, shape = (80, n_frames)
- A Tensor that contains the Mel spectrogram
- """
- if not torch.is_tensor(audio):
- if isinstance(audio, str):
- audio = load_audio(audio)
- audio = torch.from_numpy(audio)
-
- window = torch.hann_window(N_FFT).to(audio.device)
- stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
- magnitudes = stft[..., :-1].abs() ** 2
-
- filters = mel_filters(audio.device, n_mels)
- mel_spec = filters @ magnitudes
-
- log_spec = torch.clamp(mel_spec, min=1e-10).log10()
- log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
- log_spec = (log_spec + 4.0) / 4.0
- return log_spec
diff --git a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py b/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py
deleted file mode 100644
index 6817392ba8a9eb830351de89fb7afc5ad72f5e42..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py
+++ /dev/null
@@ -1,188 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
- 1. "english_cleaners" for English text
- 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
- the Unidecode library (https://pypi.python.org/pypi/Unidecode)
- 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
- the symbols in symbols.py to match your data).
-'''
-
-
-# Regular expression matching whitespace:
-
-
-import re
-import inflect
-from unidecode import unidecode
-import eng_to_ipa as ipa
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
-_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
-_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
-_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
-_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
-_number_re = re.compile(r'[0-9]+')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
- ('mrs', 'misess'),
- ('mr', 'mister'),
- ('dr', 'doctor'),
- ('st', 'saint'),
- ('co', 'company'),
- ('jr', 'junior'),
- ('maj', 'major'),
- ('gen', 'general'),
- ('drs', 'doctors'),
- ('rev', 'reverend'),
- ('lt', 'lieutenant'),
- ('hon', 'honorable'),
- ('sgt', 'sergeant'),
- ('capt', 'captain'),
- ('esq', 'esquire'),
- ('ltd', 'limited'),
- ('col', 'colonel'),
- ('ft', 'fort'),
-]]
-
-
-# List of (ipa, lazy ipa) pairs:
-_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('æ', 'e'),
- ('ɑ', 'a'),
- ('ɔ', 'o'),
- ('ð', 'z'),
- ('θ', 's'),
- ('ɛ', 'e'),
- ('ɪ', 'i'),
- ('ʊ', 'u'),
- ('ʒ', 'ʥ'),
- ('ʤ', 'ʥ'),
- ('ˈ', '↓'),
-]]
-
-# List of (ipa, lazy ipa2) pairs:
-_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('ð', 'z'),
- ('θ', 's'),
- ('ʒ', 'ʑ'),
- ('ʤ', 'dʑ'),
- ('ˈ', '↓'),
-]]
-
-# List of (ipa, ipa2) pairs
-_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('ʤ', 'dʒ'),
- ('ʧ', 'tʃ')
-]]
-
-
-def expand_abbreviations(text):
- for regex, replacement in _abbreviations:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def collapse_whitespace(text):
- return re.sub(r'\s+', ' ', text)
-
-
-def _remove_commas(m):
- return m.group(1).replace(',', '')
-
-
-def _expand_decimal_point(m):
- return m.group(1).replace('.', ' point ')
-
-
-def _expand_dollars(m):
- match = m.group(1)
- parts = match.split('.')
- if len(parts) > 2:
- return match + ' dollars' # Unexpected format
- dollars = int(parts[0]) if parts[0] else 0
- cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
- if dollars and cents:
- dollar_unit = 'dollar' if dollars == 1 else 'dollars'
- cent_unit = 'cent' if cents == 1 else 'cents'
- return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
- elif dollars:
- dollar_unit = 'dollar' if dollars == 1 else 'dollars'
- return '%s %s' % (dollars, dollar_unit)
- elif cents:
- cent_unit = 'cent' if cents == 1 else 'cents'
- return '%s %s' % (cents, cent_unit)
- else:
- return 'zero dollars'
-
-
-def _expand_ordinal(m):
- return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
- num = int(m.group(0))
- if num > 1000 and num < 3000:
- if num == 2000:
- return 'two thousand'
- elif num > 2000 and num < 2010:
- return 'two thousand ' + _inflect.number_to_words(num % 100)
- elif num % 100 == 0:
- return _inflect.number_to_words(num // 100) + ' hundred'
- else:
- return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
- else:
- return _inflect.number_to_words(num, andword='')
-
-
-def normalize_numbers(text):
- text = re.sub(_comma_number_re, _remove_commas, text)
- text = re.sub(_pounds_re, r'\1 pounds', text)
- text = re.sub(_dollars_re, _expand_dollars, text)
- text = re.sub(_decimal_number_re, _expand_decimal_point, text)
- text = re.sub(_ordinal_re, _expand_ordinal, text)
- text = re.sub(_number_re, _expand_number, text)
- return text
-
-
-def mark_dark_l(text):
- return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
-
-
-def english_to_ipa(text):
- text = unidecode(text).lower()
- text = expand_abbreviations(text)
- text = normalize_numbers(text)
- phonemes = ipa.convert(text)
- phonemes = collapse_whitespace(phonemes)
- return phonemes
-
-
-def english_to_lazy_ipa(text):
- text = english_to_ipa(text)
- for regex, replacement in _lazy_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def english_to_ipa2(text):
- text = english_to_ipa(text)
- text = mark_dark_l(text)
- for regex, replacement in _ipa_to_ipa2:
- text = re.sub(regex, replacement, text)
- return text.replace('...', '…')
-
-
-def english_to_lazy_ipa2(text):
- text = english_to_ipa(text)
- for regex, replacement in _lazy_ipa2:
- text = re.sub(regex, replacement, text)
- return text
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
deleted file mode 100644
index a02a814fe2f08b464454e8eb6e1c88004ab804f6..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
+++ /dev/null
@@ -1,27 +0,0 @@
-_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
-model = dict(
- pretrained='torchvision://resnet101',
- backbone=dict(depth=101),
- bbox_head=dict(
- with_deform=True,
- norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='Resize',
- img_scale=[(1333, 640), (1333, 800)],
- multiscale_mode='value',
- keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
deleted file mode 100644
index 4874121fd01e4024bfde445f451b7368c6834511..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './deeplabv3_r50-d8_480x480_40k_pascal_context_59.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
deleted file mode 100644
index 89d5e1ae0f3ef44626f3b5534c504cbce7389a32..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
- '../_base_/models/gcnet_r50-d8.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
- decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py
deleted file mode 100644
index 12f6d402a3c4a113d4c37be062790fa435b72104..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Evaluation with objective metrics for the pretrained AudioGen models.
-This grid takes signature from the training grid and runs evaluation-only stage.
-
-When running the grid for the first time, please use:
-REGEN=1 dora grid audiogen.audiogen_pretrained_16khz_eval
-and re-use the REGEN=1 option when the grid is changed to force regenerating it.
-
-Note that you need the proper metrics external libraries setup to use all
-the objective metrics activated in this grid. Refer to the README for more information.
-"""
-
-import os
-
-from ..musicgen._explorers import GenerationEvalExplorer
-from ...environment import AudioCraftEnvironment
-from ... import train
-
-
-def eval(launcher, batch_size: int = 32):
- opts = {
- 'dset': 'audio/audiocaps_16khz',
- 'solver/audiogen/evaluation': 'objective_eval',
- 'execute_only': 'evaluate',
- '+dataset.evaluate.batch_size': batch_size,
- '+metrics.fad.tf.batch_size': 32,
- }
- # binary for FAD computation: replace this path with your own path
- metrics_opts = {
- 'metrics.fad.tf.bin': '/data/home/jadecopet/local/usr/opt/google-research'
- }
- opt1 = {'generate.lm.use_sampling': True, 'generate.lm.top_k': 250, 'generate.lm.top_p': 0.}
- opt2 = {'transformer_lm.two_step_cfg': True}
-
- sub = launcher.bind(opts)
- sub.bind_(metrics_opts)
-
- # base objective metrics
- sub(opt1, opt2)
-
-
-@GenerationEvalExplorer
-def explorer(launcher):
- partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
- launcher.slurm_(gpus=4, partition=partitions)
-
- if 'REGEN' not in os.environ:
- folder = train.main.dora.dir / 'grids' / __name__.split('.', 2)[-1]
- with launcher.job_array():
- for sig in folder.iterdir():
- if not sig.is_symlink():
- continue
- xp = train.main.get_xp_from_sig(sig.name)
- launcher(xp.argv)
- return
-
- audiogen_base = launcher.bind(solver="audiogen/audiogen_base_16khz")
- audiogen_base.bind_({'autocast': False, 'fsdp.use': True})
-
- audiogen_base_medium = audiogen_base.bind({'continue_from': '//pretrained/facebook/audiogen-medium'})
- audiogen_base_medium.bind_({'model/lm/model_scale': 'medium'})
- eval(audiogen_base_medium, batch_size=128)
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py
deleted file mode 100644
index 5612ff5688d85fede0e605b244919e8081cb1da9..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Grid search file, simply list all the exp you want in `explorer`.
-Any new exp added there will be scheduled.
-You can cancel and experiment by commenting its line.
-
-This grid is a minimal example for debugging compression task
-and how to override parameters directly in a grid.
-Learn more about dora grids: https://github.com/facebookresearch/dora
-"""
-
-from ._explorers import CompressionExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@CompressionExplorer
-def explorer(launcher):
- partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
- launcher.slurm_(gpus=2, partition=partitions)
- launcher.bind_(solver='compression/debug')
-
- with launcher.job_array():
- # base debug task using config from solver=compression/debug
- launcher()
- # we can override parameters in the grid to launch additional xps
- launcher({'rvq.bins': 2048, 'rvq.n_q': 4})
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py
deleted file mode 100644
index 44f4b0a2c3c6c726857db8386491823dd85dde51..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import csv
-import json
-import logging
-from pathlib import Path
-import tempfile
-import typing as tp
-import subprocess
-import shutil
-
-import torch
-import torchaudio
-
-logger = logging.getLogger(__name__)
-
-
-class ViSQOL:
- """ViSQOL wrapper to run ViSQOL from Python using a pre-installed binary.
-
- To learn more about ViSQOL and how to build ViSQOL binary using bazel, please refer to the
- instructions available in the open source repository: https://github.com/google/visqol
-
- ViSQOL is capable of running in two modes:
-
- Audio Mode:
- When running in audio mode, input signals must have a 48kHz sample rate. Input should be resampled to 48kHz.
- Input signals can be multi-channel, but they will be down-mixed to mono for performing the comparison.
- Audio mode uses support vector regression, with the maximum range at ~4.75.
-
- Speech Mode:
- When running in speech mode, ViSQOL uses a wideband model. It therefore expects input sample rates of 16kHz.
- Input should be resampled to 16kHz.
- As part of the speech mode processing, a root mean square implementation for voice activity detection
- is performed on the reference signal to determine what parts of the signal have voice activity and
- should therefore be included in the comparison. The signal is normalized before performing the voice
- activity detection.
- Input signals can be multi-channel, but they will be down-mixed to mono for performing the comparison.
- Speech mode is scaled to have a maximum MOS of 5.0 to match previous version behavior.
-
- For more details, check the guidelines: https://github.com/google/visqol#general-guidelines-for-input
-
- Args:
- visqol_bin (str): Path to the ViSQOL binary.
- mode (str): ViSQOL computation mode, expecting "audio" or "speech".
- model (str): Name of the model to use for similarity to quality model.
- debug (bool): Whether to also get debug metrics from ViSQOL or not.
- """
- SAMPLE_RATES_MODES = {"audio": 48_000, "speech": 16_000}
- ALLOWED_SAMPLE_RATES = frozenset(SAMPLE_RATES_MODES.values())
-
- def __init__(self, bin: tp.Union[Path, str], mode: str = "audio",
- model: str = "libsvm_nu_svr_model.txt", debug: bool = False):
- assert bin is not None and Path(bin).exists(), f"Could not find ViSQOL binary in specified path: {bin}"
- self.visqol_bin = str(bin)
- self.visqol_mode = mode
- self.target_sr = self._get_target_sr(self.visqol_mode)
- self.model = model
- self.debug = debug
- assert Path(self.visqol_model).exists(), \
- f"Could not find the specified model in ViSQOL install: {self.visqol_model}"
-
- def _get_target_sr(self, mode: str) -> int:
- # returns target sampling rate for the corresponding ViSQOL mode.
- if mode not in ViSQOL.SAMPLE_RATES_MODES:
- raise ValueError(
- f"Unsupported mode! Allowed are: {', '.join(ViSQOL.SAMPLE_RATES_MODES.keys())}"
- )
- return ViSQOL.SAMPLE_RATES_MODES[mode]
-
- def _prepare_files(
- self, ref_sig: torch.Tensor, deg_sig: torch.Tensor, sr: int, target_sr: int, pad_with_silence: bool = False
- ):
- # prepare files for ViSQOL evaluation.
- assert target_sr in ViSQOL.ALLOWED_SAMPLE_RATES
- assert len(ref_sig) == len(deg_sig), (
- "Expects same number of ref and degraded inputs",
- f" but ref len {len(ref_sig)} != deg len {len(deg_sig)}"
- )
- # resample audio if needed
- if sr != target_sr:
- transform = torchaudio.transforms.Resample(sr, target_sr)
- pad = int(0.5 * target_sr)
- rs_ref = []
- rs_deg = []
- for i in range(len(ref_sig)):
- rs_ref_i = transform(ref_sig[i])
- rs_deg_i = transform(deg_sig[i])
- if pad_with_silence:
- rs_ref_i = torch.nn.functional.pad(rs_ref_i, (pad, pad), mode='constant', value=0)
- rs_deg_i = torch.nn.functional.pad(rs_deg_i, (pad, pad), mode='constant', value=0)
- rs_ref.append(rs_ref_i)
- rs_deg.append(rs_deg_i)
- ref_sig = torch.stack(rs_ref)
- deg_sig = torch.stack(rs_deg)
- # save audio chunks to tmp dir and create csv
- tmp_dir = Path(tempfile.mkdtemp())
- try:
- tmp_input_csv_path = tmp_dir / "input.csv"
- tmp_results_csv_path = tmp_dir / "results.csv"
- tmp_debug_json_path = tmp_dir / "debug.json"
- with open(tmp_input_csv_path, "w") as csv_file:
- csv_writer = csv.writer(csv_file)
- csv_writer.writerow(["reference", "degraded"])
- for i in range(len(ref_sig)):
- tmp_ref_filename = tmp_dir / f"ref_{i}.wav"
- tmp_deg_filename = tmp_dir / f"deg_{i}.wav"
- torchaudio.save(
- tmp_ref_filename,
- torch.clamp(ref_sig[i], min=-0.99, max=0.99),
- sample_rate=target_sr,
- bits_per_sample=16,
- encoding="PCM_S"
- )
- torchaudio.save(
- tmp_deg_filename,
- torch.clamp(deg_sig[i], min=-0.99, max=0.99),
- sample_rate=target_sr,
- bits_per_sample=16,
- encoding="PCM_S"
- )
- csv_writer.writerow([str(tmp_ref_filename), str(tmp_deg_filename)])
- return tmp_dir, tmp_input_csv_path, tmp_results_csv_path, tmp_debug_json_path
- except Exception as e:
- logger.error("Exception occurred when preparing files for ViSQOL: %s", e)
- return tmp_dir, None, None, None
-
- def _flush_files(self, tmp_dir: tp.Union[Path, str]):
- # flush tmp files used to compute ViSQOL.
- shutil.rmtree(str(tmp_dir))
-
- def _collect_moslqo_score(self, results_csv_path: tp.Union[Path, str]) -> float:
- # collect results for each evaluated pair and return averaged moslqo score.
- with open(results_csv_path, "r") as csv_file:
- reader = csv.DictReader(csv_file)
- moslqo_scores = [float(row["moslqo"]) for row in reader]
- if len(moslqo_scores) > 0:
- return sum(moslqo_scores) / len(moslqo_scores)
- else:
- return 0.0
-
- def _collect_debug_data(self, debug_json_path: tp.Union[Path, str]) -> dict:
- # collect debug data for the visqol inference.
- with open(debug_json_path, "r") as f:
- data = json.load(f)
- return data
-
- @property
- def visqol_model(self):
- return f'{self.visqol_bin}/model/{self.model}'
-
- def _run_visqol(
- self,
- input_csv_path: tp.Union[Path, str],
- results_csv_path: tp.Union[Path, str],
- debug_csv_path: tp.Optional[tp.Union[Path, str]],
- ):
- input_csv_path = str(input_csv_path)
- results_csv_path = str(results_csv_path)
- debug_csv_path = str(debug_csv_path)
- cmd = [
- f'{self.visqol_bin}/bazel-bin/visqol',
- '--batch_input_csv', f'{input_csv_path}',
- '--results_csv', f'{results_csv_path}'
- ]
- if debug_csv_path is not None:
- cmd += ['--output_debug', f'{debug_csv_path}']
- if self.visqol_mode == "speech":
- cmd += ['--use_speech_mode']
- cmd += ['--similarity_to_quality_model', f'{self.visqol_model}']
- result = subprocess.run(cmd, capture_output=True)
- if result.returncode:
- logger.error("Error with visqol: \n %s \n %s", result.stdout.decode(), result.stderr.decode())
- raise RuntimeError("Error while executing visqol")
- result.check_returncode()
-
- def __call__(
- self,
- ref_sig: torch.Tensor,
- deg_sig: torch.Tensor,
- sr: int,
- pad_with_silence: bool = False,
- ):
- """Calculate the ViSQOL metric for a pair of audio signals at a given sample rate.
- Args:
- ref_sig (torch.Tensor): Reference signals as [B, C, T].
- deg_sig (torch.Tensor): Degraded signals as [B, C, T].
- sr (int): Sample rate of the two audio signals.
- pad_with_silence (bool): Whether to pad the file with silences as recommended
- in visqol guidelines (see: https://github.com/google/visqol#general-guidelines-for-input).
- Returns:
- float: The ViSQOL score or mean score for the batch.
- """
- logger.debug(f"Calculating visqol with mode={self.visqol_mode} on {len(ref_sig)} samples")
- tmp_dir, input_csv, results_csv, debug_json = self._prepare_files(
- ref_sig, deg_sig, sr, self.target_sr, pad_with_silence
- )
- try:
- if input_csv and results_csv:
- self._run_visqol(
- input_csv,
- results_csv,
- debug_json if self.debug else None,
- )
- mosqol = self._collect_moslqo_score(results_csv)
- return mosqol
- else:
- raise RuntimeError("Something unexpected happened when running VISQOL!")
- except Exception as e:
- logger.error("Exception occurred when running ViSQOL: %s", e)
- finally:
- self._flush_files(tmp_dir)
diff --git a/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile b/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile
deleted file mode 100644
index 31b5f817b41d7d95055d9aa5d5e4f973abee0b45..0000000000000000000000000000000000000000
--- a/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile
+++ /dev/null
@@ -1,26 +0,0 @@
-IMG:=`cat Name`
-MOUNT:=
-PORT_MAP:=
-DOTENV:=.env
-CONTAINER_NAME:=sample-chatgpt-on-wechat
-
-echo:
- echo $(IMG)
-
-run_d:
- docker rm $(CONTAINER_NAME) || echo
- docker run -dt --name $(CONTAINER_NAME) $(PORT_MAP) \
- --env-file=$(DOTENV) \
- $(MOUNT) $(IMG)
-
-run_i:
- docker rm $(CONTAINER_NAME) || echo
- docker run -it --name $(CONTAINER_NAME) $(PORT_MAP) \
- --env-file=$(DOTENV) \
- $(MOUNT) $(IMG)
-
-stop:
- docker stop $(CONTAINER_NAME)
-
-rm: stop
- docker rm $(CONTAINER_NAME)
diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py
deleted file mode 100644
index 84e51fd71fca78131a3be3d0d35d808c07bb3a95..0000000000000000000000000000000000000000
--- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import torch.nn as nn
-import math
-
-
-def conv3x3(in_planes, out_planes, stride=1):
- """3x3 convolution with padding"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
- expansion = 1
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(BasicBlock, self).__init__()
- self.conv1 = conv3x3(inplanes, planes, stride)
- self.bn1 = nn.BatchNorm2d(planes)
- self.relu = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(planes, planes)
- self.bn2 = nn.BatchNorm2d(planes)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(Bottleneck, self).__init__()
- self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
- self.bn1 = nn.BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
- self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * 4)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
- out = self.relu(out)
-
- out = self.conv3(out)
- out = self.bn3(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class ResNet50(nn.Module):
- def __init__(self,mode='rgb'):
- self.inplanes = 64
- super(ResNet50, self).__init__()
- if(mode=='rgb'):
- self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- elif(mode=='rgbd'):
- self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- elif(mode=="share"):
- self.conv1=nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- self.conv1_d=nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- else:
- raise
- self.bn1 = nn.BatchNorm2d(64)
- self.relu = nn.ReLU(inplace=True)
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
- self.layer1 = self._make_layer(Bottleneck, 64, 3)
- self.layer2 = self._make_layer(Bottleneck, 128, 4, stride=2)
- self.layer3_1 = self._make_layer(Bottleneck, 256, 6, stride=2)
- self.layer4_1 = self._make_layer(Bottleneck, 512, 3, stride=2)
-
- self.inplanes = 512
-
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
- m.weight.data.normal_(0, math.sqrt(2. / n))
- elif isinstance(m, nn.BatchNorm2d):
- m.weight.data.fill_(1)
- m.bias.data.zero_()
-
- def _make_layer(self, block, planes, blocks, stride=1):
- downsample = None
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- nn.Conv2d(self.inplanes, planes * block.expansion,
- kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(planes * block.expansion),
- )
-
- layers = []
- layers.append(block(self.inplanes, planes, stride, downsample))
- self.inplanes = planes * block.expansion
- for i in range(1, blocks):
- layers.append(block(self.inplanes, planes))
-
- return nn.Sequential(*layers)
-
- def forward(self, x):
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
- x = self.maxpool(x)
-
- x = self.layer1(x)
- x = self.layer2(x)
- x1 = self.layer3_1(x)
- x1 = self.layer4_1(x1)
-
- return x1, x1
diff --git a/spaces/Hallucinate/demo/ldm/models/autoencoder.py b/spaces/Hallucinate/demo/ldm/models/autoencoder.py
deleted file mode 100644
index 6a9c4f45498561953b8085981609b2a3298a5473..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/ldm/models/autoencoder.py
+++ /dev/null
@@ -1,443 +0,0 @@
-import torch
-import pytorch_lightning as pl
-import torch.nn.functional as F
-from contextlib import contextmanager
-
-from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
-
-from ldm.modules.diffusionmodules.model import Encoder, Decoder
-from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
-
-from ldm.util import instantiate_from_config
-
-
-class VQModel(pl.LightningModule):
- def __init__(self,
- ddconfig,
- lossconfig,
- n_embed,
- embed_dim,
- ckpt_path=None,
- ignore_keys=[],
- image_key="image",
- colorize_nlabels=None,
- monitor=None,
- batch_resize_range=None,
- scheduler_config=None,
- lr_g_factor=1.0,
- remap=None,
- sane_index_shape=False, # tell vector quantizer to return indices as bhw
- use_ema=False
- ):
- super().__init__()
- self.embed_dim = embed_dim
- self.n_embed = n_embed
- self.image_key = image_key
- self.encoder = Encoder(**ddconfig)
- self.decoder = Decoder(**ddconfig)
- self.loss = instantiate_from_config(lossconfig)
- self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
- remap=remap,
- sane_index_shape=sane_index_shape)
- self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
- self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
- if colorize_nlabels is not None:
- assert type(colorize_nlabels)==int
- self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
- if monitor is not None:
- self.monitor = monitor
- self.batch_resize_range = batch_resize_range
- if self.batch_resize_range is not None:
- print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
-
- self.use_ema = use_ema
- if self.use_ema:
- self.model_ema = LitEma(self)
- print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
- if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
- self.scheduler_config = scheduler_config
- self.lr_g_factor = lr_g_factor
-
- @contextmanager
- def ema_scope(self, context=None):
- if self.use_ema:
- self.model_ema.store(self.parameters())
- self.model_ema.copy_to(self)
- if context is not None:
- print(f"{context}: Switched to EMA weights")
- try:
- yield None
- finally:
- if self.use_ema:
- self.model_ema.restore(self.parameters())
- if context is not None:
- print(f"{context}: Restored training weights")
-
- def init_from_ckpt(self, path, ignore_keys=list()):
- sd = torch.load(path, map_location="cpu")["state_dict"]
- keys = list(sd.keys())
- for k in keys:
- for ik in ignore_keys:
- if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
- del sd[k]
- missing, unexpected = self.load_state_dict(sd, strict=False)
- print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
- if len(missing) > 0:
- print(f"Missing Keys: {missing}")
- print(f"Unexpected Keys: {unexpected}")
-
- def on_train_batch_end(self, *args, **kwargs):
- if self.use_ema:
- self.model_ema(self)
-
- def encode(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- quant, emb_loss, info = self.quantize(h)
- return quant, emb_loss, info
-
- def encode_to_prequant(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- return h
-
- def decode(self, quant):
- quant = self.post_quant_conv(quant)
- dec = self.decoder(quant)
- return dec
-
- def decode_code(self, code_b):
- quant_b = self.quantize.embed_code(code_b)
- dec = self.decode(quant_b)
- return dec
-
- def forward(self, input, return_pred_indices=False):
- quant, diff, (_,_,ind) = self.encode(input)
- dec = self.decode(quant)
- if return_pred_indices:
- return dec, diff, ind
- return dec, diff
-
- def get_input(self, batch, k):
- x = batch[k]
- if len(x.shape) == 3:
- x = x[..., None]
- x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
- if self.batch_resize_range is not None:
- lower_size = self.batch_resize_range[0]
- upper_size = self.batch_resize_range[1]
- if self.global_step <= 4:
- # do the first few batches with max size to avoid later oom
- new_resize = upper_size
- else:
- new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
- if new_resize != x.shape[2]:
- x = F.interpolate(x, size=new_resize, mode="bicubic")
- x = x.detach()
- return x
-
- def training_step(self, batch, batch_idx, optimizer_idx):
- # https://github.com/pytorch/pytorch/issues/37142
- # try not to fool the heuristics
- x = self.get_input(batch, self.image_key)
- xrec, qloss, ind = self(x, return_pred_indices=True)
-
- if optimizer_idx == 0:
- # autoencode
- aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train",
- predicted_indices=ind)
-
- self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
- return aeloss
-
- if optimizer_idx == 1:
- # discriminator
- discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
- self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
- return discloss
-
- def validation_step(self, batch, batch_idx):
- log_dict = self._validation_step(batch, batch_idx)
- with self.ema_scope():
- log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
- return log_dict
-
- def _validation_step(self, batch, batch_idx, suffix=""):
- x = self.get_input(batch, self.image_key)
- xrec, qloss, ind = self(x, return_pred_indices=True)
- aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
- self.global_step,
- last_layer=self.get_last_layer(),
- split="val"+suffix,
- predicted_indices=ind
- )
-
- discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
- self.global_step,
- last_layer=self.get_last_layer(),
- split="val"+suffix,
- predicted_indices=ind
- )
- rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
- self.log(f"val{suffix}/rec_loss", rec_loss,
- prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
- self.log(f"val{suffix}/aeloss", aeloss,
- prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
- if version.parse(pl.__version__) >= version.parse('1.4.0'):
- del log_dict_ae[f"val{suffix}/rec_loss"]
- self.log_dict(log_dict_ae)
- self.log_dict(log_dict_disc)
- return self.log_dict
-
- def configure_optimizers(self):
- lr_d = self.learning_rate
- lr_g = self.lr_g_factor*self.learning_rate
- print("lr_d", lr_d)
- print("lr_g", lr_g)
- opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
- list(self.decoder.parameters())+
- list(self.quantize.parameters())+
- list(self.quant_conv.parameters())+
- list(self.post_quant_conv.parameters()),
- lr=lr_g, betas=(0.5, 0.9))
- opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
- lr=lr_d, betas=(0.5, 0.9))
-
- if self.scheduler_config is not None:
- scheduler = instantiate_from_config(self.scheduler_config)
-
- print("Setting up LambdaLR scheduler...")
- scheduler = [
- {
- 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
- 'interval': 'step',
- 'frequency': 1
- },
- {
- 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
- 'interval': 'step',
- 'frequency': 1
- },
- ]
- return [opt_ae, opt_disc], scheduler
- return [opt_ae, opt_disc], []
-
- def get_last_layer(self):
- return self.decoder.conv_out.weight
-
- def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
- log = dict()
- x = self.get_input(batch, self.image_key)
- x = x.to(self.device)
- if only_inputs:
- log["inputs"] = x
- return log
- xrec, _ = self(x)
- if x.shape[1] > 3:
- # colorize with random projection
- assert xrec.shape[1] > 3
- x = self.to_rgb(x)
- xrec = self.to_rgb(xrec)
- log["inputs"] = x
- log["reconstructions"] = xrec
- if plot_ema:
- with self.ema_scope():
- xrec_ema, _ = self(x)
- if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
- log["reconstructions_ema"] = xrec_ema
- return log
-
- def to_rgb(self, x):
- assert self.image_key == "segmentation"
- if not hasattr(self, "colorize"):
- self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
- x = F.conv2d(x, weight=self.colorize)
- x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
- return x
-
-
-class VQModelInterface(VQModel):
- def __init__(self, embed_dim, *args, **kwargs):
- super().__init__(embed_dim=embed_dim, *args, **kwargs)
- self.embed_dim = embed_dim
-
- def encode(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- return h
-
- def decode(self, h, force_not_quantize=False):
- # also go through quantization layer
- if not force_not_quantize:
- quant, emb_loss, info = self.quantize(h)
- else:
- quant = h
- quant = self.post_quant_conv(quant)
- dec = self.decoder(quant)
- return dec
-
-
-class AutoencoderKL(pl.LightningModule):
- def __init__(self,
- ddconfig,
- lossconfig,
- embed_dim,
- ckpt_path=None,
- ignore_keys=[],
- image_key="image",
- colorize_nlabels=None,
- monitor=None,
- ):
- super().__init__()
- self.image_key = image_key
- self.encoder = Encoder(**ddconfig)
- self.decoder = Decoder(**ddconfig)
- self.loss = instantiate_from_config(lossconfig)
- assert ddconfig["double_z"]
- self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
- self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
- self.embed_dim = embed_dim
- if colorize_nlabels is not None:
- assert type(colorize_nlabels)==int
- self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
- if monitor is not None:
- self.monitor = monitor
- if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-
- def init_from_ckpt(self, path, ignore_keys=list()):
- sd = torch.load(path, map_location="cpu")["state_dict"]
- keys = list(sd.keys())
- for k in keys:
- for ik in ignore_keys:
- if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
- del sd[k]
- self.load_state_dict(sd, strict=False)
- print(f"Restored from {path}")
-
- def encode(self, x):
- h = self.encoder(x)
- moments = self.quant_conv(h)
- posterior = DiagonalGaussianDistribution(moments)
- return posterior
-
- def decode(self, z):
- z = self.post_quant_conv(z)
- dec = self.decoder(z)
- return dec
-
- def forward(self, input, sample_posterior=True):
- posterior = self.encode(input)
- if sample_posterior:
- z = posterior.sample()
- else:
- z = posterior.mode()
- dec = self.decode(z)
- return dec, posterior
-
- def get_input(self, batch, k):
- x = batch[k]
- if len(x.shape) == 3:
- x = x[..., None]
- x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
- return x
-
- def training_step(self, batch, batch_idx, optimizer_idx):
- inputs = self.get_input(batch, self.image_key)
- reconstructions, posterior = self(inputs)
-
- if optimizer_idx == 0:
- # train encoder+decoder+logvar
- aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
- self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
- self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
- return aeloss
-
- if optimizer_idx == 1:
- # train the discriminator
- discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
-
- self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
- self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
- return discloss
-
- def validation_step(self, batch, batch_idx):
- inputs = self.get_input(batch, self.image_key)
- reconstructions, posterior = self(inputs)
- aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
- last_layer=self.get_last_layer(), split="val")
-
- discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
- last_layer=self.get_last_layer(), split="val")
-
- self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
- self.log_dict(log_dict_ae)
- self.log_dict(log_dict_disc)
- return self.log_dict
-
- def configure_optimizers(self):
- lr = self.learning_rate
- opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
- list(self.decoder.parameters())+
- list(self.quant_conv.parameters())+
- list(self.post_quant_conv.parameters()),
- lr=lr, betas=(0.5, 0.9))
- opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
- lr=lr, betas=(0.5, 0.9))
- return [opt_ae, opt_disc], []
-
- def get_last_layer(self):
- return self.decoder.conv_out.weight
-
- @torch.no_grad()
- def log_images(self, batch, only_inputs=False, **kwargs):
- log = dict()
- x = self.get_input(batch, self.image_key)
- x = x.to(self.device)
- if not only_inputs:
- xrec, posterior = self(x)
- if x.shape[1] > 3:
- # colorize with random projection
- assert xrec.shape[1] > 3
- x = self.to_rgb(x)
- xrec = self.to_rgb(xrec)
- log["samples"] = self.decode(torch.randn_like(posterior.sample()))
- log["reconstructions"] = xrec
- log["inputs"] = x
- return log
-
- def to_rgb(self, x):
- assert self.image_key == "segmentation"
- if not hasattr(self, "colorize"):
- self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
- x = F.conv2d(x, weight=self.colorize)
- x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
- return x
-
-
-class IdentityFirstStage(torch.nn.Module):
- def __init__(self, *args, vq_interface=False, **kwargs):
- self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff
- super().__init__()
-
- def encode(self, x, *args, **kwargs):
- return x
-
- def decode(self, x, *args, **kwargs):
- return x
-
- def quantize(self, x, *args, **kwargs):
- if self.vq_interface:
- return x, None, [None, None, None]
- return x
-
- def forward(self, x, *args, **kwargs):
- return x
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py b/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py
deleted file mode 100644
index 81676226e57ca519273b98328a1afe6961c37ce3..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py
+++ /dev/null
@@ -1,403 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA Authors. All rights reserved.
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-# http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Auto Config class."""
-import importlib
-import re
-import warnings
-from collections import OrderedDict
-from typing import List, Union
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.file_utils import CONFIG_NAME
-from transformers.utils import logging
-from .dynamic import get_class_from_dynamic_module
-
-
-logger = logging.get_logger(__name__)
-
-CONFIG_MAPPING_NAMES = OrderedDict(
- [
- # Add configs here
- ("roformer", "RoFormerConfig"),
- ("longformer", "LongformerConfig"),
- ]
-)
-
-CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
- [
- # Add archive maps here
- ("roformer", "ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
- ("longformer", "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
- ]
-)
-
-MODEL_NAMES_MAPPING = OrderedDict(
- [
- # Add full (and cased) model names here
- ("roformer", "Roformer"),
- ("longformer", "Longformer"),
- ]
-)
-
-SPECIAL_MODEL_TYPE_TO_MODULE_NAME = OrderedDict([("openai-gpt", "openai")])
-
-
-def model_type_to_module_name(key):
- """Converts a config key to the corresponding module."""
- # Special treatment
- if key in SPECIAL_MODEL_TYPE_TO_MODULE_NAME:
- return SPECIAL_MODEL_TYPE_TO_MODULE_NAME[key]
-
- return key.replace("-", "_")
-
-
-def config_class_to_model_type(config):
- """Converts a config class name to the corresponding model type"""
- for key, cls in CONFIG_MAPPING_NAMES.items():
- if cls == config:
- return key
- return None
-
-
-class _LazyConfigMapping(OrderedDict):
- """
- A dictionary that lazily load its values when they are requested.
- """
-
- def __init__(self, mapping):
- self._mapping = mapping
- self._extra_content = {}
- self._modules = {}
-
- def __getitem__(self, key):
- if key in self._extra_content:
- return self._extra_content[key]
- if key not in self._mapping:
- raise KeyError(key)
- value = self._mapping[key]
- module_name = model_type_to_module_name(key)
- if module_name not in self._modules:
- self._modules[module_name] = importlib.import_module(f".{module_name}", "fengshen.models")
-
- return getattr(self._modules[module_name], value)
-
- def keys(self):
- return list(self._mapping.keys()) + list(self._extra_content.keys())
-
- def values(self):
- return [self[k] for k in self._mapping.keys()] + list(self._extra_content.values())
-
- def items(self):
- return [(k, self[k]) for k in self._mapping.keys()] + list(self._extra_content.items())
-
- def __iter__(self):
- return iter(list(self._mapping.keys()) + list(self._extra_content.keys()))
-
- def __contains__(self, item):
- return item in self._mapping or item in self._extra_content
-
- def register(self, key, value):
- """
- Register a new configuration in this mapping.
- """
- if key in self._mapping.keys():
- raise ValueError(f"'{key}' is already used by a Transformers config, pick another name.")
- self._extra_content[key] = value
-
-
-CONFIG_MAPPING = _LazyConfigMapping(CONFIG_MAPPING_NAMES)
-
-
-class _LazyLoadAllMappings(OrderedDict):
- """
- A mapping that will load all pairs of key values at the first access (either by indexing, requestions keys, values,
- etc.)
-
- Args:
- mapping: The mapping to load.
- """
-
- def __init__(self, mapping):
- self._mapping = mapping
- self._initialized = False
- self._data = {}
-
- def _initialize(self):
- if self._initialized:
- return
- warnings.warn(
- "ALL_PRETRAINED_CONFIG_ARCHIVE_MAP is deprecated and will be removed in v5 of Transformers. "
- "It does not contain all available model checkpoints, far from it. Checkout hf.co/models for that.",
- FutureWarning,
- )
-
- for model_type, map_name in self._mapping.items():
- module_name = model_type_to_module_name(model_type)
- module = importlib.import_module(f".{module_name}", "transformers.models")
- mapping = getattr(module, map_name)
- self._data.update(mapping)
-
- self._initialized = True
-
- def __getitem__(self, key):
- self._initialize()
- return self._data[key]
-
- def keys(self):
- self._initialize()
- return self._data.keys()
-
- def values(self):
- self._initialize()
- return self._data.values()
-
- def items(self):
- self._initialize()
- return self._data.keys()
-
- def __iter__(self):
- self._initialize()
- return iter(self._data)
-
- def __contains__(self, item):
- self._initialize()
- return item in self._data
-
-
-ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = _LazyLoadAllMappings(CONFIG_ARCHIVE_MAP_MAPPING_NAMES)
-
-
-def _get_class_name(model_class: Union[str, List[str]]):
- if isinstance(model_class, (list, tuple)):
- return " or ".join([f"[`{c}`]" for c in model_class if c is not None])
- return f"[`{model_class}`]"
-
-
-def _list_model_options(indent, config_to_class=None, use_model_types=True):
- if config_to_class is None and not use_model_types:
- raise ValueError("Using `use_model_types=False` requires a `config_to_class` dictionary.")
- if use_model_types:
- if config_to_class is None:
- model_type_to_name = {model_type: f"[`{config}`]" for model_type, config in CONFIG_MAPPING_NAMES.items()}
- else:
- model_type_to_name = {
- model_type: _get_class_name(model_class)
- for model_type, model_class in config_to_class.items()
- if model_type in MODEL_NAMES_MAPPING
- }
- lines = [
- f"{indent}- **{model_type}** -- {model_type_to_name[model_type]} ({MODEL_NAMES_MAPPING[model_type]} model)"
- for model_type in sorted(model_type_to_name.keys())
- ]
- else:
- config_to_name = {
- CONFIG_MAPPING_NAMES[config]: _get_class_name(clas)
- for config, clas in config_to_class.items()
- if config in CONFIG_MAPPING_NAMES
- }
- config_to_model_name = {
- config: MODEL_NAMES_MAPPING[model_type] for model_type, config in CONFIG_MAPPING_NAMES.items()
- }
- lines = [
- f"{indent}- [`{config_name}`] configuration class: {config_to_name[config_name]} ({config_to_model_name[config_name]} model)"
- for config_name in sorted(config_to_name.keys())
- ]
- return "\n".join(lines)
-
-
-def replace_list_option_in_docstrings(config_to_class=None, use_model_types=True):
- def docstring_decorator(fn):
- docstrings = fn.__doc__
- lines = docstrings.split("\n")
- i = 0
- while i < len(lines) and re.search(r"^(\s*)List options\s*$", lines[i]) is None:
- i += 1
- if i < len(lines):
- indent = re.search(r"^(\s*)List options\s*$", lines[i]).groups()[0]
- if use_model_types:
- indent = f"{indent} "
- lines[i] = _list_model_options(indent, config_to_class=config_to_class, use_model_types=use_model_types)
- docstrings = "\n".join(lines)
- else:
- raise ValueError(
- f"The function {fn} should have an empty 'List options' in its docstring as placeholder, current docstring is:\n{docstrings}"
- )
- fn.__doc__ = docstrings
- return fn
-
- return docstring_decorator
-
-
-class AutoConfig:
- r"""
- This is a generic configuration class that will be instantiated as one of the configuration classes of the library
- when created with the [`~AutoConfig.from_pretrained`] class method.
-
- This class cannot be instantiated directly using `__init__()` (throws an error).
- """
-
- def __init__(self):
- raise EnvironmentError(
- "AutoConfig is designed to be instantiated "
- "using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method."
- )
-
- @classmethod
- def for_model(cls, model_type: str, *args, **kwargs):
- if model_type in CONFIG_MAPPING:
- config_class = CONFIG_MAPPING[model_type]
- return config_class(*args, **kwargs)
- raise ValueError(
- f"Unrecognized model identifier: {model_type}. Should contain one of {', '.join(CONFIG_MAPPING.keys())}"
- )
-
- @classmethod
- @replace_list_option_in_docstrings()
- def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
- r"""
- Instantiate one of the configuration classes of the library from a pretrained model configuration.
-
- The configuration class to instantiate is selected based on the `model_type` property of the config object that
- is loaded, or when it's missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:
-
- List options
-
- Args:
- pretrained_model_name_or_path (`str` or `os.PathLike`):
- Can be either:
-
- - A string, the *model id* of a pretrained model configuration hosted inside a model repo on
- huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or
- namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
- - A path to a *directory* containing a configuration file saved using the
- [`~PretrainedConfig.save_pretrained`] method, or the [`~PreTrainedModel.save_pretrained`] method,
- e.g., `./my_model_directory/`.
- - A path or url to a saved configuration JSON *file*, e.g.,
- `./my_model_directory/configuration.json`.
- cache_dir (`str` or `os.PathLike`, *optional*):
- Path to a directory in which a downloaded pretrained model configuration should be cached if the
- standard cache should not be used.
- force_download (`bool`, *optional*, defaults to `False`):
- Whether or not to force the (re-)download the model weights and configuration files and override the
- cached versions if they exist.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether or not to delete incompletely received files. Will attempt to resume the download if such a
- file exists.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
- 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- revision(`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
- git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
- identifier allowed by git.
- return_unused_kwargs (`bool`, *optional*, defaults to `False`):
- If `False`, then this function returns just the final configuration object.
-
- If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a
- dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
- part of `kwargs` which has not been used to update `config` and is otherwise ignored.
- trust_remote_code (`bool`, *optional*, defaults to `False`):
- Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
- should only be set to `True` for repositories you trust and in which you have read the code, as it will
- execute code present on the Hub on your local machine.
- kwargs(additional keyword arguments, *optional*):
- The values in kwargs of any keys which are configuration attributes will be used to override the loaded
- values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
- by the `return_unused_kwargs` keyword parameter.
-
- Examples:
-
- ```python
- >>> from transformers import AutoConfig
-
- >>> # Download configuration from huggingface.co and cache.
- >>> config = AutoConfig.from_pretrained("bert-base-uncased")
-
- >>> # Download configuration from huggingface.co (user-uploaded) and cache.
- >>> config = AutoConfig.from_pretrained("dbmdz/bert-base-german-cased")
-
- >>> # If configuration file is in a directory (e.g., was saved using *save_pretrained('./test/saved_model/')*).
- >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/")
-
- >>> # Load a specific configuration file.
- >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/my_configuration.json")
-
- >>> # Change some config attributes when loading a pretrained config.
- >>> config = AutoConfig.from_pretrained("bert-base-uncased", output_attentions=True, foo=False)
- >>> config.output_attentions
- True
-
- >>> config, unused_kwargs = AutoConfig.from_pretrained(
- ... "bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
- ... )
- >>> config.output_attentions
- True
-
- >>> config.unused_kwargs
- {'foo': False}
- ```"""
- kwargs["_from_auto"] = True
- kwargs["name_or_path"] = pretrained_model_name_or_path
- trust_remote_code = kwargs.pop("trust_remote_code", False)
- config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
- if "auto_map" in config_dict and "AutoConfig" in config_dict["auto_map"]:
- if not trust_remote_code:
- raise ValueError(
- f"Loading {pretrained_model_name_or_path} requires you to execute the configuration file in that repo "
- "on your local machine. Make sure you have read the code there to avoid malicious use, then set "
- "the option `trust_remote_code=True` to remove this error."
- )
- if kwargs.get("revision", None) is None:
- logger.warn(
- "Explicitly passing a `revision` is encouraged when loading a configuration with custom code to "
- "ensure no malicious code has been contributed in a newer revision."
- )
- class_ref = config_dict["auto_map"]["AutoConfig"]
- module_file, class_name = class_ref.split(".")
- config_class = get_class_from_dynamic_module(
- pretrained_model_name_or_path, module_file + ".py", class_name, **kwargs
- )
- return config_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
- elif "model_type" in config_dict:
- config_class = CONFIG_MAPPING[config_dict["model_type"]]
- return config_class.from_dict(config_dict, **kwargs)
- else:
- # Fallback: use pattern matching on the string.
- for pattern, config_class in CONFIG_MAPPING.items():
- if pattern in str(pretrained_model_name_or_path):
- return config_class.from_dict(config_dict, **kwargs)
-
- raise ValueError(
- f"Unrecognized model in {pretrained_model_name_or_path}. "
- f"Should have a `model_type` key in its {CONFIG_NAME}, or contain one of the following strings "
- f"in its name: {', '.join(CONFIG_MAPPING.keys())}"
- )
-
- @staticmethod
- def register(model_type, config):
- """
- Register a new configuration for this class.
-
- Args:
- model_type (`str`): The model type like "bert" or "gpt".
- config ([`PretrainedConfig`]): The config to register.
- """
- if issubclass(config, PretrainedConfig) and config.model_type != model_type:
- raise ValueError(
- "The config you are passing has a `model_type` attribute that is not consistent with the model type "
- f"you passed (config has {config.model_type} and you passed {model_type}. Fix one of those so they "
- "match!"
- )
- CONFIG_MAPPING.register(model_type, config)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py
deleted file mode 100644
index b6fb6e751cdedb2af4b1f6c0950557e187cd9519..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .scst_loss import ScstRewardCriterion
-from .label_smoothed_cross_entropy import AjustLabelSmoothedCrossEntropyCriterion
\ No newline at end of file
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py
deleted file mode 100644
index 56d63e3e1b5a036e0adf32480e2b66f371738013..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from dataclasses import dataclass, field
-
-import torch
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-from omegaconf import II
-
-
-@dataclass
-class LabelSmoothedCrossEntropyCriterionConfig(FairseqDataclass):
- label_smoothing: float = field(
- default=0.0,
- metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"},
- )
- report_accuracy: bool = field(
- default=False,
- metadata={"help": "report accuracy metric"},
- )
- ignore_prefix_size: int = field(
- default=0,
- metadata={"help": "Ignore first N tokens"},
- )
- sentence_avg: bool = II("optimization.sentence_avg")
-
-
-def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=None, reduce=True):
- if target.dim() == lprobs.dim() - 1:
- target = target.unsqueeze(-1)
- nll_loss = -lprobs.gather(dim=-1, index=target)
- smooth_loss = -lprobs.sum(dim=-1, keepdim=True)
- if ignore_index is not None:
- pad_mask = target.eq(ignore_index)
- nll_loss.masked_fill_(pad_mask, 0.0)
- smooth_loss.masked_fill_(pad_mask, 0.0)
- else:
- nll_loss = nll_loss.squeeze(-1)
- smooth_loss = smooth_loss.squeeze(-1)
- if reduce:
- nll_loss = nll_loss.sum()
- smooth_loss = smooth_loss.sum()
- eps_i = epsilon / (lprobs.size(-1) - 1)
- loss = (1.0 - epsilon - eps_i) * nll_loss + eps_i * smooth_loss
- return loss, nll_loss
-
-
-@register_criterion(
- "label_smoothed_cross_entropy", dataclass=LabelSmoothedCrossEntropyCriterionConfig
-)
-class LabelSmoothedCrossEntropyCriterion(FairseqCriterion):
- def __init__(
- self,
- task,
- sentence_avg,
- label_smoothing,
- ignore_prefix_size=0,
- report_accuracy=False,
- ):
- super().__init__(task)
- self.sentence_avg = sentence_avg
- self.eps = label_smoothing
- self.ignore_prefix_size = ignore_prefix_size
- self.report_accuracy = report_accuracy
-
- def forward(self, model, sample, reduce=True):
- """Compute the loss for the given sample.
-
- Returns a tuple with three elements:
- 1) the loss
- 2) the sample size, which is used as the denominator for the gradient
- 3) logging outputs to display while training
- """
- net_output = model(**sample["net_input"])
- loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce)
- sample_size = (
- sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
- )
- logging_output = {
- "loss": loss.data,
- "nll_loss": nll_loss.data,
- "ntokens": sample["ntokens"],
- "nsentences": sample["target"].size(0),
- "sample_size": sample_size,
- }
- if self.report_accuracy:
- n_correct, total = self.compute_accuracy(model, net_output, sample)
- logging_output["n_correct"] = utils.item(n_correct.data)
- logging_output["total"] = utils.item(total.data)
- return loss, sample_size, logging_output
-
- def get_lprobs_and_target(self, model, net_output, sample):
- lprobs = model.get_normalized_probs(net_output, log_probs=True)
- target = model.get_targets(sample, net_output)
- if self.ignore_prefix_size > 0:
- if getattr(lprobs, "batch_first", False):
- lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
- target = target[:, self.ignore_prefix_size :].contiguous()
- else:
- lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous()
- target = target[self.ignore_prefix_size :, :].contiguous()
- return lprobs.view(-1, lprobs.size(-1)), target.view(-1)
-
- def compute_loss(self, model, net_output, sample, reduce=True):
- lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
- loss, nll_loss = label_smoothed_nll_loss(
- lprobs,
- target,
- self.eps,
- ignore_index=self.padding_idx,
- reduce=reduce,
- )
- return loss, nll_loss
-
- def compute_accuracy(self, model, net_output, sample):
- lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
- mask = target.ne(self.padding_idx)
- n_correct = torch.sum(
- lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))
- )
- total = torch.sum(mask)
- return n_correct, total
-
- @classmethod
- def reduce_metrics(cls, logging_outputs) -> None:
- """Aggregate logging outputs from data parallel training."""
- loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
- nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs)
- ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
- sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
-
- metrics.log_scalar(
- "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
- )
- metrics.log_scalar(
- "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3
- )
- metrics.log_derived(
- "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
- )
-
- total = utils.item(sum(log.get("total", 0) for log in logging_outputs))
- if total > 0:
- metrics.log_scalar("total", total)
- n_correct = utils.item(
- sum(log.get("n_correct", 0) for log in logging_outputs)
- )
- metrics.log_scalar("n_correct", n_correct)
- metrics.log_derived(
- "accuracy",
- lambda meters: round(
- meters["n_correct"].sum * 100.0 / meters["total"].sum, 3
- )
- if meters["total"].sum > 0
- else float("nan"),
- )
-
- @staticmethod
- def logging_outputs_can_be_summed() -> bool:
- """
- Whether the logging outputs returned by `forward` can be summed
- across workers prior to calling `reduce_metrics`. Setting this
- to True will improves distributed training speed.
- """
- return True
diff --git a/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md b/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md
deleted file mode 100644
index d4654cdd019b1496d4c953944c9aced8a0dcc67a..0000000000000000000000000000000000000000
--- a/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Sail-rvc-Ai Hoshino From Oshi No Ko RVC V2 300 Epoch
-emoji: 📈
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Hina4867/bingo/src/components/ui/select.tsx b/spaces/Hina4867/bingo/src/components/ui/select.tsx
deleted file mode 100644
index 77f12c2996f541b97663de4c9e20ab34d4ec2fac..0000000000000000000000000000000000000000
--- a/spaces/Hina4867/bingo/src/components/ui/select.tsx
+++ /dev/null
@@ -1,123 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SelectPrimitive from '@radix-ui/react-select'
-
-import { cn } from '@/lib/utils'
-import {
- IconArrowDown,
- IconCheck,
- IconChevronUpDown
-} from '@/components/ui/icons'
-
-const Select = SelectPrimitive.Root
-
-const SelectGroup = SelectPrimitive.Group
-
-const SelectValue = SelectPrimitive.Value
-
-const SelectTrigger = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-
- {children}
-
-
-
-))
-SelectTrigger.displayName = SelectPrimitive.Trigger.displayName
-
-const SelectContent = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, position = 'popper', ...props }, ref) => (
-
-
-
- {children}
-
-
-
-))
-SelectContent.displayName = SelectPrimitive.Content.displayName
-
-const SelectLabel = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-
-
-
-
-
- {children}
-
-))
-SelectItem.displayName = SelectPrimitive.Item.displayName
-
-const SelectSeparator = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- {o.delete(i),o.size===0&&n&&(n(),n=null)}}return{set:a,update:l,subscribe:r}}var g;const E=((g=globalThis.__sveltekit_1d38e1f)==null?void 0:g.base)??"";var k;const S=((k=globalThis.__sveltekit_1d38e1f)==null?void 0:k.assets)??E,w="1698134506027",y="sveltekit:snapshot",I="sveltekit:scroll",x="sveltekit:index",_={tap:1,hover:2,viewport:3,eager:4,off:-1};function O(e){let t=e.baseURI;if(!t){const n=e.getElementsByTagName("base");t=n.length?n[0].href:e.URL}return t}function U(){return{x:pageXOffset,y:pageYOffset}}function f(e,t){return e.getAttribute(`data-sveltekit-${t}`)}const b={..._,"":_.hover};function v(e){let t=e.assignedSlot??e.parentNode;return(t==null?void 0:t.nodeType)===11&&(t=t.host),t}function L(e,t){for(;e&&e!==t;){if(e.nodeName.toUpperCase()==="A"&&e.hasAttribute("href"))return e;e=v(e)}}function N(e,t){let n;try{n=new URL(e instanceof SVGAElement?e.href.baseVal:e.href,document.baseURI)}catch{}const o=e instanceof SVGAElement?e.target.baseVal:e.target,a=!n||!!o||R(n,t)||(e.getAttribute("rel")||"").split(/\s+/).includes("external"),l=(n==null?void 0:n.origin)===location.origin&&e.hasAttribute("download");return{url:n,external:a,target:o,download:l}}function P(e){let t=null,n=null,o=null,a=null,l=null,r=null,s=e;for(;s&&s!==document.documentElement;)o===null&&(o=f(s,"preload-code")),a===null&&(a=f(s,"preload-data")),t===null&&(t=f(s,"keepfocus")),n===null&&(n=f(s,"noscroll")),l===null&&(l=f(s,"reload")),r===null&&(r=f(s,"replacestate")),s=v(s);return{preload_code:b[o??"off"],preload_data:b[a??"off"],keep_focus:t==="off"?!1:t===""?!0:null,noscroll:n==="off"?!1:n===""?!0:null,reload:l==="off"?!1:l===""?!0:null,replace_state:r==="off"?!1:r===""?!0:null}}function h(e){const t=p(e);let n=!0;function o(){n=!0,t.update(r=>r)}function a(r){n=!1,t.set(r)}function l(r){let s;return t.subscribe(u=>{(s===void 0||n&&u!==s)&&r(s=u)})}return{notify:o,set:a,subscribe:l}}function A(){const{set:e,subscribe:t}=p(!1);let n;async function o(){clearTimeout(n);try{const a=await fetch(`${S}/_app/version.json`,{headers:{pragma:"no-cache","cache-control":"no-cache"}});if(!a.ok)return!1;const r=(await a.json()).version!==w;return r&&(e(!0),clearTimeout(n)),r}catch{return!1}}return{subscribe:t,check:o}}function R(e,t){return e.origin!==location.origin||!e.pathname.startsWith(t)}function V(e){e.client}const Y={url:h({}),page:h({}),navigating:p(null),updated:A()};export{x as I,_ as P,I as S,y as a,N as b,P as c,U as d,E as e,L as f,O as g,V as h,R as i,Y as s};
diff --git a/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py b/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py
deleted file mode 100644
index f8e2eb0f15699f1b458a8445d0c1dd6229a21f77..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-import os, sys
-import subprocess
-import re
-from subprocess import check_call, check_output
-
-WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None)
-
-if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip():
- print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."')
- sys.exit(-1)
-
-
-BLEU_REGEX = re.compile("^BLEU\\S* = (\\S+) ")
-def run_eval_bleu(cmd):
- output = check_output(cmd, shell=True, stderr=subprocess.STDOUT).decode("utf-8").strip()
- print(output)
- bleu = -1.0
- for line in output.strip().split('\n'):
- m = BLEU_REGEX.search(line)
- if m is not None:
- bleu = m.groups()[0]
- bleu = float(bleu)
- break
- return bleu
-
-def check_data_test_bleu(raw_folder, data_lang_pairs):
- not_matchings = []
- for sacrebleu_set, src_tgts in data_lang_pairs:
- for src_tgt in src_tgts:
- print(f'checking test bleus for: {src_tgt} at {sacrebleu_set}')
- src, tgt = src_tgt.split('-')
- ssrc, stgt = src[:2], tgt[:2]
- if os.path.exists(f'{raw_folder}/test.{tgt}-{src}.{src}'):
- # reversed direction may have different test set
- test_src = f'{raw_folder}/test.{tgt}-{src}.{src}'
- else:
- test_src = f'{raw_folder}/test.{src}-{tgt}.{src}'
- cmd1 = f'cat {test_src} | sacrebleu -t "{sacrebleu_set}" -l {stgt}-{ssrc}; [ $? -eq 0 ] || echo ""'
- test_tgt = f'{raw_folder}/test.{src}-{tgt}.{tgt}'
- cmd2 = f'cat {test_tgt} | sacrebleu -t "{sacrebleu_set}" -l {ssrc}-{stgt}; [ $? -eq 0 ] || echo ""'
- bleu1 = run_eval_bleu(cmd1)
- if bleu1 != 100.0:
- not_matchings.append(f'{sacrebleu_set}:{src_tgt} source side not matching: {test_src}')
- bleu2 = run_eval_bleu(cmd2)
- if bleu2 != 100.0:
- not_matchings.append(f'{sacrebleu_set}:{src_tgt} target side not matching: {test_tgt}')
- return not_matchings
-
-if __name__ == "__main__":
- to_data_path = f'{WORKDIR_ROOT}/iwsltv2'
- not_matching = check_data_test_bleu(
- f'{to_data_path}/raw',
- [
- ('iwslt17', ['en_XX-ar_AR', 'en_XX-ko_KR', 'ar_AR-en_XX', 'ko_KR-en_XX']),
- ('iwslt17', ['en_XX-it_IT', 'en_XX-nl_XX', 'it_IT-en_XX', 'nl_XX-en_XX']),
- ('iwslt17/tst2015', ['en_XX-vi_VN', "vi_VN-en_XX"]),
- ]
- )
- if len(not_matching) > 0:
- print('the following datasets do not have matching test datasets:\n\t', '\n\t'.join(not_matching))
-
diff --git a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py b/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py
deleted file mode 100644
index 88597d62a505715091f9ba62d38bf0a85a31b95a..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import math
-import os
-os.environ["LRU_CACHE_CAPACITY"] = "3"
-import random
-import torch
-import torch.utils.data
-import numpy as np
-import librosa
-from librosa.util import normalize
-from librosa.filters import mel as librosa_mel_fn
-from scipy.io.wavfile import read
-import soundfile as sf
-
-def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
- sampling_rate = None
- try:
- data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
- except Exception as ex:
- print(f"'{full_path}' failed to load.\nException:")
- print(ex)
- if return_empty_on_exception:
- return [], sampling_rate or target_sr or 32000
- else:
- raise Exception(ex)
-
- if len(data.shape) > 1:
- data = data[:, 0]
- assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
-
- if np.issubdtype(data.dtype, np.integer): # if audio data is type int
- max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
- else: # if audio data is type fp32
- max_mag = max(np.amax(data), -np.amin(data))
- max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
-
- data = torch.FloatTensor(data.astype(np.float32))/max_mag
-
- if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
- return [], sampling_rate or target_sr or 32000
- if target_sr is not None and sampling_rate != target_sr:
- data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
- sampling_rate = target_sr
-
- return data, sampling_rate
-
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
- return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
-
-def dynamic_range_decompression(x, C=1):
- return np.exp(x) / C
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-def dynamic_range_decompression_torch(x, C=1):
- return torch.exp(x) / C
-
-class STFT():
- def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
- self.target_sr = sr
-
- self.n_mels = n_mels
- self.n_fft = n_fft
- self.win_size = win_size
- self.hop_length = hop_length
- self.fmin = fmin
- self.fmax = fmax
- self.clip_val = clip_val
- self.mel_basis = {}
- self.hann_window = {}
-
- def get_mel(self, y, center=False):
- sampling_rate = self.target_sr
- n_mels = self.n_mels
- n_fft = self.n_fft
- win_size = self.win_size
- hop_length = self.hop_length
- fmin = self.fmin
- fmax = self.fmax
- clip_val = self.clip_val
-
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- if fmax not in self.mel_basis:
- mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
- self.mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
- self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_length)/2), int((n_fft-hop_length)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)],
- center=center, pad_mode='reflect', normalized=False, onesided=True)
- # print(111,spec)
- spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
- # print(222,spec)
- spec = torch.matmul(self.mel_basis[str(fmax)+'_'+str(y.device)], spec)
- # print(333,spec)
- spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
- # print(444,spec)
- return spec
-
- def __call__(self, audiopath):
- audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
- spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
- return spect
-
-stft = STFT()
diff --git a/spaces/JUNGU/emotion-ko-state/README.md b/spaces/JUNGU/emotion-ko-state/README.md
deleted file mode 100644
index 48eb244083a0ac6a51b0298cd043231b60b3fde8..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/emotion-ko-state/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Emotion Ko State
-emoji: 🦀
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py b/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py
deleted file mode 100644
index 27fcd62482d0cf44e02fa1c339195be58cb745b0..0000000000000000000000000000000000000000
--- a/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import pinecone
-from colorama import Fore, Style
-
-from autogpt.llm_utils import create_embedding_with_ada
-from autogpt.logs import logger
-from autogpt.memory.base import MemoryProviderSingleton
-
-
-class PineconeMemory(MemoryProviderSingleton):
- def __init__(self, cfg):
- pinecone_api_key = cfg.pinecone_api_key
- pinecone_region = cfg.pinecone_region
- pinecone.init(api_key=pinecone_api_key, environment=pinecone_region)
- dimension = 1536
- metric = "cosine"
- pod_type = "p1"
- table_name = "auto-gpt"
- # this assumes we don't start with memory.
- # for now this works.
- # we'll need a more complicated and robust system if we want to start with
- # memory.
- self.vec_num = 0
-
- try:
- pinecone.whoami()
- except Exception as e:
- logger.typewriter_log(
- "FAILED TO CONNECT TO PINECONE",
- Fore.RED,
- Style.BRIGHT + str(e) + Style.RESET_ALL,
- )
- logger.double_check(
- "Please ensure you have setup and configured Pinecone properly for use."
- + f"You can check out {Fore.CYAN + Style.BRIGHT}"
- "https://github.com/Torantulino/Auto-GPT#-pinecone-api-key-setup"
- f"{Style.RESET_ALL} to ensure you've set up everything correctly."
- )
- exit(1)
-
- if table_name not in pinecone.list_indexes():
- pinecone.create_index(
- table_name, dimension=dimension, metric=metric, pod_type=pod_type
- )
- self.index = pinecone.Index(table_name)
-
- def add(self, data):
- vector = create_embedding_with_ada(data)
- # no metadata here. We may wish to change that long term.
- self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})])
- _text = f"Inserting data into memory at index: {self.vec_num}:\n data: {data}"
- self.vec_num += 1
- return _text
-
- def get(self, data):
- return self.get_relevant(data, 1)
-
- def clear(self):
- self.index.delete(deleteAll=True)
- return "Obliviated"
-
- def get_relevant(self, data, num_relevant=5):
- """
- Returns all the data in the memory that is relevant to the given data.
- :param data: The data to compare to.
- :param num_relevant: The number of relevant data to return. Defaults to 5
- """
- query_embedding = create_embedding_with_ada(data)
- results = self.index.query(
- query_embedding, top_k=num_relevant, include_metadata=True
- )
- sorted_results = sorted(results.matches, key=lambda x: x.score)
- return [str(item["metadata"]["raw_text"]) for item in sorted_results]
-
- def get_stats(self):
- return self.index.describe_index_stats()
diff --git a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py b/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py
deleted file mode 100644
index 1cc3b96da1275f1d01afb4c999f9c404d09b247a..0000000000000000000000000000000000000000
--- a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import cv2
-import numpy as np
-from PIL import Image
-import pickle
-import tensorflow as tf
-import os
-
-class rabbitsLogReg:
- def __init__(self,url) -> None:
- self.image = url
-
- def predict_image(self):
- # Load the model
- load_extractor = tf.keras.models.load_model("././Model/Rabbit/resnetLogreg/resnet_EXTRACTOR.h5")
-
- modelpath = "././Model/Rabbit/resnetLogreg/dataSaved.pkl"
-
- with open(modelpath, 'rb') as file:
- saved_data = pickle.load(file)
- animal_breed = saved_data['class_name']
- model = saved_data['logreg_model']
-
- im = Image.open(self.image)
- img = im.convert("RGB")
- img= np.asarray(img)
- image_resized= cv2.resize(img, (224,224))
- features = load_extractor.predict(np.expand_dims(image_resized, axis=0))
-
- reshaped_features = features.reshape(features.shape[0],-1)
- predicted_class = model.predict(reshaped_features)
- pred_prob = model.predict_proba(reshaped_features)[:2]
- prediction_probability = pred_prob[0][predicted_class[0]]
- predicted_class
-
- output_class= animal_breed[predicted_class[0]]
-
- return [output_class, prediction_probability]
diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md b/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md
deleted file mode 100644
index 80af4fbbfba5d15e1cb6d1f4b67808ca76fa37d7..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md
+++ /dev/null
@@ -1,140 +0,0 @@
-
-
-川虎 Chat 🐯 Chuanhu Chat
-
-
-
-
-
-
-
Lightweight and User-friendly Web-UI for LLMs including ChatGPT/ChatGLM/LLaMA
-
- Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search
- LaTeX rendering / Table rendering / Code highlighting
- Auto dark mode / Adaptive web interface / WeChat-like theme
- Multi-parameters tuning / Multi-API-Key support / Multi-user support
- Compatible with GPT-4 / Local deployment for LLMs
-
- Video Tutorial
- ·
- 2.0 Introduction
- ·
- 3.0 Introduction & Tutorial
- ||
- Online trial
- ·
- One-Click deployment
-
-
-
-
-
-
-
-## Supported LLM Models
-
-**LLM models via API**:
-
-- [ChatGPT](https://chat.openai.com) ([GPT-4](https://openai.com/product/gpt-4))
-- [Google PaLM](https://developers.generativeai.google/products/palm)
-- [Inspur Yuan 1.0](https://air.inspur.com/home)
-- [MiniMax](https://api.minimax.chat/)
-- [XMChat](https://github.com/MILVLG/xmchat)
-
-**LLM models via local deployment**:
-
-- [ChatGLM](https://github.com/THUDM/ChatGLM-6B) ([ChatGLM2](https://github.com/THUDM/ChatGLM2-6B))
-- [LLaMA](https://github.com/facebookresearch/llama)
-- [StableLM](https://github.com/Stability-AI/StableLM)
-- [MOSS](https://github.com/OpenLMLab/MOSS)
-
-## Usage Tips
-
-- To better control the ChatGPT, use System Prompt.
-- To use a Prompt Template, select the Prompt Template Collection file first, and then choose certain prompt from the drop-down menu.
-- To try again if the response is unsatisfactory, use `🔄 Regenerate` button.
-- To start a new line in the input box, press Shift + Enter keys.
-- To quickly switch between input history, press ↑ and ↓ key in the input box.
-- To deploy the program onto a server, set `"server_name": "0.0.0.0", "server_port" ,` in `config.json`.
-- To get a public shared link, set `"share": true,` in `config.json`. Please be noted that the program must be running in order to be accessed via a public link.
-- To use it in Hugging Face Spaces: It is recommended to **Duplicate Space** and run the program in your own Space for a faster and more secure experience.
-
-## Quickstart
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-Then make a copy of `config_example.json`, rename it to `config.json`, and then fill in your API-Key and other settings in the file.
-
-```shell
-python ChuanhuChatbot.py
-```
-
-A browser window will open and you will be able to chat with ChatGPT.
-
-> **Note**
->
-> Please check our [wiki page](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程) for detailed instructions.
-
-## Troubleshooting
-
-When you encounter problems, you should try manually pulling the latest changes of this project first. The steps are as follows:
-
-1. Download the latest code archive by clicking on `Download ZIP` on the webpage, or
- ```shell
- git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
- ```
-2. Try installing the dependencies again (as this project may have introduced new dependencies)
- ```
- pip install -r requirements.txt
- ```
-
-Generally, you can solve most problems by following these steps.
-
-If the problem still exists, please refer to this page: [Frequently Asked Questions (FAQ)](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-This page lists almost all the possible problems and solutions. Please read it carefully.
-
-## More Information
-
-More information could be found in our [wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki):
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-
-
-
-
-## Sponsor
-
-🐯 If you find this project helpful, feel free to buy me a coke or a cup of coffee~
-
-
-
-
diff --git a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py b/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py
deleted file mode 100644
index 59a73cc04025762d6490fcd2945a747d963def32..0000000000000000000000000000000000000000
--- a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from os import environ
-
-
-def assert_in(file, files_to_check):
- if file not in files_to_check:
- raise AssertionError("{} does not exist in the list".format(str(file)))
- return True
-
-
-def assert_in_env(check_list: list):
- for item in check_list:
- assert_in(item, environ.keys())
- return True
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py
deleted file mode 100644
index beb6e65c61f8a16b4376494123f31178cdb88bde..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py
+++ /dev/null
@@ -1,13 +0,0 @@
-CUSTOM_STREAMLIT_CSS = """
-div[data-testid="stBlock"] button {
- width: 100% !important;
- margin-bottom: 20px !important;
- border-color: #bfbfbf !important;
-}
-section[data-testid="stSidebar"] div {
- max-width: 10rem;
-}
-pre code {
- white-space: pre-wrap;
-}
-"""
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py
deleted file mode 100644
index 34bcffd6c0975377a54ae1ce89002be1dae8151d..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import matplotlib
-matplotlib.use('Agg')
-import time
-
-class Timer():
- ''' Timer for recording training time distribution. '''
- def __init__(self):
- self.prev_t = time.time()
- self.clear()
-
- def set(self):
- self.prev_t = time.time()
-
- def cnt(self, mode):
- self.time_table[mode] += time.time()-self.prev_t
- self.set()
- if mode == 'bw':
- self.click += 1
-
- def show(self):
- total_time = sum(self.time_table.values())
- self.time_table['avg'] = total_time/self.click
- self.time_table['rd'] = 100*self.time_table['rd']/total_time
- self.time_table['fw'] = 100*self.time_table['fw']/total_time
- self.time_table['bw'] = 100*self.time_table['bw']/total_time
- msg = '{avg:.3f} sec/step (rd {rd:.1f}% | fw {fw:.1f}% | bw {bw:.1f}%)'.format(
- **self.time_table)
- self.clear()
- return msg
-
- def clear(self):
- self.time_table = {'rd': 0, 'fw': 0, 'bw': 0}
- self.click = 0
-
-# Reference : https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/e2e_asr.py#L168
-
-def human_format(num):
- magnitude = 0
- while num >= 1000:
- magnitude += 1
- num /= 1000.0
- # add more suffixes if you need them
- return '{:3.1f}{}'.format(num, [' ', 'K', 'M', 'G', 'T', 'P'][magnitude])
-
-
-# provide easy access of attribute from dict, such abc.key
-class AttrDict(dict):
- def __init__(self, *args, **kwargs):
- super(AttrDict, self).__init__(*args, **kwargs)
- self.__dict__ = self
diff --git a/spaces/KevinQHLin/UniVTG/main/train_mr.py b/spaces/KevinQHLin/UniVTG/main/train_mr.py
deleted file mode 100644
index 1a10d029f81f86733d6dab71a3aee575917b092b..0000000000000000000000000000000000000000
--- a/spaces/KevinQHLin/UniVTG/main/train_mr.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import os
-import pdb
-import sys
-import time
-import json
-import pprint
-import random
-import numpy as np
-from tqdm import tqdm, trange
-from collections import defaultdict
-
-import torch
-import torch.nn as nn
-import torch.backends.cudnn as cudnn
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-
-sys.path.append('/data/home/qinghonglin/univtg')
-from main.config import BaseOptions, setup_model
-from main.dataset import \
- DatasetMR, start_end_collate_mr, prepare_batch_inputs_mr
-from main.inference_mr import eval_epoch, start_inference
-from utils.basic_utils import set_seed, AverageMeter, dict_to_markdown
-from utils.model_utils import count_parameters
-
-import logging
-logger = logging.getLogger(__name__)
-logging.basicConfig(format="%(asctime)s.%(msecs)03d:%(levelname)s:%(name)s - %(message)s",
- datefmt="%Y-%m-%d %H:%M:%S",
- level=logging.INFO)
-
-def train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer):
- logger.info(f"[Epoch {epoch_i+1}]")
- model.train()
- criterion.train()
-
- # init meters
- time_meters = defaultdict(AverageMeter)
- loss_meters = defaultdict(AverageMeter)
-
- num_training_examples = len(train_loader)
- timer_dataloading = time.time()
- for batch_idx, batch in tqdm(enumerate(train_loader),
- desc="Training Iteration",
- total=num_training_examples):
- time_meters["dataloading_time"].update(time.time() - timer_dataloading)
-
- timer_start = time.time()
- model_inputs, targets = prepare_batch_inputs_mr(batch[1], opt.device, non_blocking=opt.pin_memory)
- time_meters["prepare_inputs_time"].update(time.time() - timer_start)
-
- timer_start = time.time()
-
- # try:
- outputs = model(**model_inputs)
- loss_dict = criterion(outputs, targets)
- weight_dict = criterion.weight_dict
- losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
- time_meters["model_forward_time"].update(time.time() - timer_start)
-
- timer_start = time.time()
- optimizer.zero_grad()
- losses.backward()
-
- if opt.grad_clip > 0:
- nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
- optimizer.step()
- time_meters["model_backward_time"].update(time.time() - timer_start)
-
- loss_dict["loss_overall"] = float(losses) # for logging only
- for k, v in loss_dict.items():
- loss_meters[k].update(float(v) * weight_dict[k] if k in weight_dict else float(v))
-
- timer_dataloading = time.time()
-
- # print/add logs
- tb_writer.add_scalar("Train/lr", float(optimizer.param_groups[0]["lr"]), epoch_i+1)
- for k, v in loss_meters.items():
- tb_writer.add_scalar("Train/{}".format(k), v.avg, epoch_i+1)
-
- to_write = opt.train_log_txt_formatter.format(
- time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
- epoch=epoch_i+1,
- loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in loss_meters.items()]))
- with open(opt.train_log_filepath, "a") as f:
- f.write(to_write)
-
- logger.info("Epoch time stats:")
- for name, meter in time_meters.items():
- d = {k: f"{getattr(meter, k):.4f}" for k in ["max", "min", "avg"]}
- logger.info(f"{name} ==> {d}")
-
-
-def train(model, criterion, optimizer, lr_scheduler, train_dataset, val_dataset, opt):
- tb_writer = SummaryWriter(opt.tensorboard_log_dir)
- tb_writer.add_text("hyperparameters", dict_to_markdown(vars(opt), max_str_len=None))
- opt.train_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str}\n"
- opt.eval_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str} [Metrics] {eval_metrics_str}\n"
-
- train_loader = DataLoader(
- train_dataset,
- collate_fn=start_end_collate_mr,
- batch_size=opt.bsz,
- num_workers=opt.num_workers,
- shuffle=True,
- pin_memory=opt.pin_memory
- )
-
- prev_best_score = 0.
- es_cnt = 0
- if opt.start_epoch is None:
- start_epoch = -1 if opt.eval_init else 0
- else:
- start_epoch = opt.start_epoch
- save_submission_filename = "latest_{}_{}_preds.jsonl".format(opt.dset_name, opt.eval_split_name)
- for epoch_i in trange(start_epoch, opt.n_epoch, desc="Epoch"):
- if epoch_i > -1:
- train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer)
- lr_scheduler.step()
- eval_epoch_interval = opt.eval_epoch
- if opt.eval_path is not None and (epoch_i + 1) % eval_epoch_interval == 0:
- with torch.no_grad():
- metrics_no_nms, metrics_nms, eval_loss_meters, latest_file_paths = \
- eval_epoch(model, val_dataset, opt, save_submission_filename, epoch_i, criterion, tb_writer)
-
- # log
- to_write = opt.eval_log_txt_formatter.format(
- time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
- epoch=epoch_i,
- loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in eval_loss_meters.items()]),
- eval_metrics_str=json.dumps(metrics_no_nms))
-
- with open(opt.eval_log_filepath, "a") as f:
- f.write(to_write)
- logger.info("metrics_no_nms {}".format(pprint.pformat(metrics_no_nms["brief"], indent=4)))
- if metrics_nms is not None:
- logger.info("metrics_nms {}".format(pprint.pformat(metrics_nms["brief"], indent=4)))
-
- metrics = metrics_nms if metrics_nms is not None else metrics_no_nms
- for k, v in metrics["brief"].items():
- tb_writer.add_scalar(f"Eval/{k}", float(v), epoch_i+1)
-
- # stop_score = metrics["brief"]["MR-full-mAP"]
- # pdb.set_trace()
- stop_score = metrics["brief"][opt.main_metric]
- if stop_score > prev_best_score:
- es_cnt = 0
- prev_best_score = stop_score
-
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "lr_scheduler": lr_scheduler.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"))
-
- best_file_paths = [e.replace("latest", "best") for e in latest_file_paths]
- for src, tgt in zip(latest_file_paths, best_file_paths):
- os.renames(src, tgt)
- logger.info("The checkpoint file has been updated.")
- else:
- es_cnt += 1
- if opt.max_es_cnt != -1 and es_cnt > opt.max_es_cnt: # early stop
- with open(opt.train_log_filepath, "a") as f:
- f.write(f"Early Stop at epoch {epoch_i}")
- logger.info(f"\n>>>>> Early stop at epoch {epoch_i} {prev_best_score}\n")
- break
-
- # save ckpt
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "lr_scheduler": lr_scheduler.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_latest.ckpt"))
-
- if (epoch_i + 1) % opt.save_interval == 0 or (epoch_i + 1) % opt.lr_drop == 0: # additional copies
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", f"_e{epoch_i:04d}.ckpt"))
-
- if opt.debug:
- break
-
- tb_writer.close()
-
-
-def start_training():
- logger.info("Setup config, data and model...")
- opt = BaseOptions().parse()
- set_seed(opt.seed)
- if opt.debug: # keep the model run deterministically
- # 'cudnn.benchmark = True' enabled auto finding the best algorithm for a specific input/net config.
- # Enable this only when input size is fixed.
- cudnn.benchmark = False
- cudnn.deterministic = True
-
- dataset_config = dict(
- dset_name=opt.dset_name,
- data_path=opt.train_path,
- v_feat_dirs=opt.v_feat_dirs,
- q_feat_dir=opt.t_feat_dir,
- v_feat_dim=opt.v_feat_dim,
- q_feat_dim=opt.t_feat_dim,
- q_feat_type="last_hidden_state",
- max_q_l=opt.max_q_l,
- max_v_l=opt.max_v_l,
- ctx_mode=opt.ctx_mode,
- data_ratio=opt.data_ratio,
- normalize_v=not opt.no_norm_vfeat,
- normalize_t=not opt.no_norm_tfeat,
- clip_len=opt.clip_length,
- max_windows=opt.max_windows,
- span_loss_type=opt.span_loss_type,
- txt_drop_ratio=opt.txt_drop_ratio,
- use_cache=opt.use_cache,
- add_easy_negative=opt.add_easy_negative,
- easy_negative_only=opt.easy_negative_only
- )
-
- dataset_config["data_path"] = opt.train_path
- train_dataset = DatasetMR(**dataset_config)
-
- if opt.eval_path is not None:
- dataset_config["data_path"] = opt.eval_path
- dataset_config["txt_drop_ratio"] = 0
- dataset_config["q_feat_dir"] = opt.t_feat_dir.replace("txt_clip_asr", "txt_clip").replace("txt_clip_cap", "txt_clip") # for pretraining
- # dataset_config["load_labels"] = False # uncomment to calculate eval loss
- eval_dataset = DatasetMR(**dataset_config)
- else:
- eval_dataset = None
-
- if opt.lr_warmup > 0:
- # total_steps = opt.n_epoch * len(train_dataset) // opt.bsz
- total_steps = opt.n_epoch
- warmup_steps = opt.lr_warmup if opt.lr_warmup > 1 else int(opt.lr_warmup * total_steps)
- opt.lr_warmup = [warmup_steps, total_steps]
- model, criterion, optimizer, lr_scheduler = setup_model(opt)
- logger.info(f"Model {model}")
- count_parameters(model)
- logger.info("Start Training...")
- train(model, criterion, optimizer, lr_scheduler, train_dataset, eval_dataset, opt)
- return opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"), opt.eval_split_name, opt.eval_path, opt.debug
-
-
-if __name__ == '__main__':
- best_ckpt_path, eval_split_name, eval_path, debug = start_training()
- if not debug:
- input_args = ["--resume", best_ckpt_path,
- "--eval_split_name", eval_split_name,
- "--eval_path", eval_path]
-
- import sys
- sys.argv[1:] = input_args
- logger.info("\n\n\nFINISHED TRAINING!!!")
- logger.info("Evaluating model at {}".format(best_ckpt_path))
- logger.info("Input args {}".format(sys.argv[1:]))
- start_inference()
\ No newline at end of file
diff --git a/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md b/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md
deleted file mode 100644
index 1f55e11e80f6fc5ebbf42dade0266e3d4ee06ce4..0000000000000000000000000000000000000000
--- a/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
-# Transliteration Models for Indian languages
-These are models for transliteration involving Indian languages.
-The models are essentially Statistical Machine Translation systems trained using Moses over a
-character-level parallel corpora of transliterations. Hence, you will need Moses to use these transliteration models.
-The transliteration corpus has itself been mined in an unsupervised fashion from a translation corpus.
-
-Currently we have trained transliteration models for five language pairs: bn-hi, ta-hi, te-hi, en-hi and mr-hi
-
-Support for transliteration has been introduced in Moses from version 2.1
-So please ensure that you have minimum 2.1 version setup for Moses
-
-Commands to run the transliteration module using moses
-
-$moseshome/mosesdecoder/scripts/Transliteration/post-decoding-transliteration.pl \
---moses-src-dir $moseshome/mosesdecoder --external-bin-dir $moseshome/tools \
---transliteration-model-dir {path to transliteration model folder} --oov-file {path to file containing oov words, oovs are space separated with each line containing all oovs for the input line}\
- --input-file {input file to transliterated} --output-file {output file location} \
- --input-extension {input language code for eg. en} --output-extension {output language code for eg. hi} --language-model {path to language model} \
- --decoder $moseshome/mosesdecoder/bin/moses
-
-A sample execution of the model will be as follows:
-
-export moseshome={path to moses installation}
-$moseshome/mosesdecoder/scripts/Transliteration/post-decoding-transliteration.pl \
---moses-src-dir $moseshome/mosesdecoder --external-bin-dir $moseshome/tools \
---transliteration-model-dir /home/ratish/project/nlp_resources/indic_nlp_resources/transliterate/en-hi \
---oov-file /home/ratish/project/translit/input.oov \
- --input-file /home/ratish/project/translit/input.en \
- --output-file /home/ratish/project/translit/output.hi \
- --input-extension en --output-extension hi --language-model /home/ratish/project/translit/lm/nc.binlm.1 \
- --decoder $moseshome/mosesdecoder/bin/moses
-
-So far, we have seen the use of transliteration in a post-editing task for machine translation task.
-In case, the models are needed for purely transliteration purpose, the input file and OOV file are the same.
-Sample input file:
-New Delhi is capital of India
-India is worlds seventh largest nation in the World
-
-OOV file
-New Delhi is capital of India
-India is worlds seventh largest nation in the World
-
-On running the transliteration module, the output is:
-न्यू डेल्ही इस कैपिटल आफ इंडिया
-इंडिया इस वर्ल्ड सेवंथ लारगेस्ट नेशन इन थे वर्ल्ड
diff --git a/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py b/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py
deleted file mode 100644
index 980440eb3434e48b7cad90f0577c838a05d787b4..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Sequence
-
-from torch.utils.data import BatchSampler, Sampler
-
-from mmdet.registry import DATA_SAMPLERS
-
-
-# TODO: maybe replace with a data_loader wrapper
-@DATA_SAMPLERS.register_module()
-class AspectRatioBatchSampler(BatchSampler):
- """A sampler wrapper for grouping images with similar aspect ratio (< 1 or.
-
- >= 1) into a same batch.
-
- Args:
- sampler (Sampler): Base sampler.
- batch_size (int): Size of mini-batch.
- drop_last (bool): If ``True``, the sampler will drop the last batch if
- its size would be less than ``batch_size``.
- """
-
- def __init__(self,
- sampler: Sampler,
- batch_size: int,
- drop_last: bool = False) -> None:
- if not isinstance(sampler, Sampler):
- raise TypeError('sampler should be an instance of ``Sampler``, '
- f'but got {sampler}')
- if not isinstance(batch_size, int) or batch_size <= 0:
- raise ValueError('batch_size should be a positive integer value, '
- f'but got batch_size={batch_size}')
- self.sampler = sampler
- self.batch_size = batch_size
- self.drop_last = drop_last
- # two groups for w < h and w >= h
- self._aspect_ratio_buckets = [[] for _ in range(2)]
-
- def __iter__(self) -> Sequence[int]:
- for idx in self.sampler:
- data_info = self.sampler.dataset.get_data_info(idx)
- width, height = data_info['width'], data_info['height']
- bucket_id = 0 if width < height else 1
- bucket = self._aspect_ratio_buckets[bucket_id]
- bucket.append(idx)
- # yield a batch of indices in the same aspect ratio group
- if len(bucket) == self.batch_size:
- yield bucket[:]
- del bucket[:]
-
- # yield the rest data and reset the bucket
- left_data = self._aspect_ratio_buckets[0] + self._aspect_ratio_buckets[
- 1]
- self._aspect_ratio_buckets = [[] for _ in range(2)]
- while len(left_data) > 0:
- if len(left_data) <= self.batch_size:
- if not self.drop_last:
- yield left_data[:]
- left_data = []
- else:
- yield left_data[:self.batch_size]
- left_data = left_data[self.batch_size:]
-
- def __len__(self) -> int:
- if self.drop_last:
- return len(self.sampler) // self.batch_size
- else:
- return (len(self.sampler) + self.batch_size - 1) // self.batch_size
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py
deleted file mode 100644
index 430b06d085d94760d56a7ea083eaf23bd32b1f53..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py
+++ /dev/null
@@ -1,722 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, Scale
-from mmcv.ops import DeformConv2d
-from torch import Tensor
-
-from mmdet.registry import MODELS, TASK_UTILS
-from mmdet.structures.bbox import bbox_overlaps
-from mmdet.utils import (ConfigType, InstanceList, MultiConfig,
- OptInstanceList, RangeType, reduce_mean)
-from ..task_modules.prior_generators import MlvlPointGenerator
-from ..task_modules.samplers import PseudoSampler
-from ..utils import multi_apply
-from .atss_head import ATSSHead
-from .fcos_head import FCOSHead
-
-INF = 1e8
-
-
-@MODELS.register_module()
-class VFNetHead(ATSSHead, FCOSHead):
- """Head of `VarifocalNet (VFNet): An IoU-aware Dense Object
- Detector.`_.
-
- The VFNet predicts IoU-aware classification scores which mix the
- object presence confidence and object localization accuracy as the
- detection score. It is built on the FCOS architecture and uses ATSS
- for defining positive/negative training examples. The VFNet is trained
- with Varifocal Loss and empolys star-shaped deformable convolution to
- extract features for a bbox.
-
- Args:
- num_classes (int): Number of categories excluding the background
- category.
- in_channels (int): Number of channels in the input feature map.
- regress_ranges (Sequence[Tuple[int, int]]): Regress range of multiple
- level points.
- center_sampling (bool): If true, use center sampling. Defaults to False.
- center_sample_radius (float): Radius of center sampling. Defaults to 1.5.
- sync_num_pos (bool): If true, synchronize the number of positive
- examples across GPUs. Defaults to True
- gradient_mul (float): The multiplier to gradients from bbox refinement
- and recognition. Defaults to 0.1.
- bbox_norm_type (str): The bbox normalization type, 'reg_denom' or
- 'stride'. Defaults to reg_denom
- loss_cls_fl (:obj:`ConfigDict` or dict): Config of focal loss.
- use_vfl (bool): If true, use varifocal loss for training.
- Defaults to True.
- loss_cls (:obj:`ConfigDict` or dict): Config of varifocal loss.
- loss_bbox (:obj:`ConfigDict` or dict): Config of localization loss,
- GIoU Loss.
- loss_bbox (:obj:`ConfigDict` or dict): Config of localization
- refinement loss, GIoU Loss.
- norm_cfg (:obj:`ConfigDict` or dict): dictionary to construct and
- config norm layer. Defaults to norm_cfg=dict(type='GN',
- num_groups=32, requires_grad=True).
- use_atss (bool): If true, use ATSS to define positive/negative
- examples. Defaults to True.
- anchor_generator (:obj:`ConfigDict` or dict): Config of anchor
- generator for ATSS.
- init_cfg (:obj:`ConfigDict` or dict or list[dict] or
- list[:obj:`ConfigDict`]): Initialization config dict.
-
- Example:
- >>> self = VFNetHead(11, 7)
- >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
- >>> cls_score, bbox_pred, bbox_pred_refine= self.forward(feats)
- >>> assert len(cls_score) == len(self.scales)
- """ # noqa: E501
-
- def __init__(self,
- num_classes: int,
- in_channels: int,
- regress_ranges: RangeType = ((-1, 64), (64, 128), (128, 256),
- (256, 512), (512, INF)),
- center_sampling: bool = False,
- center_sample_radius: float = 1.5,
- sync_num_pos: bool = True,
- gradient_mul: float = 0.1,
- bbox_norm_type: str = 'reg_denom',
- loss_cls_fl: ConfigType = dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- use_vfl: bool = True,
- loss_cls: ConfigType = dict(
- type='VarifocalLoss',
- use_sigmoid=True,
- alpha=0.75,
- gamma=2.0,
- iou_weighted=True,
- loss_weight=1.0),
- loss_bbox: ConfigType = dict(
- type='GIoULoss', loss_weight=1.5),
- loss_bbox_refine: ConfigType = dict(
- type='GIoULoss', loss_weight=2.0),
- norm_cfg: ConfigType = dict(
- type='GN', num_groups=32, requires_grad=True),
- use_atss: bool = True,
- reg_decoded_bbox: bool = True,
- anchor_generator: ConfigType = dict(
- type='AnchorGenerator',
- ratios=[1.0],
- octave_base_scale=8,
- scales_per_octave=1,
- center_offset=0.0,
- strides=[8, 16, 32, 64, 128]),
- init_cfg: MultiConfig = dict(
- type='Normal',
- layer='Conv2d',
- std=0.01,
- override=dict(
- type='Normal',
- name='vfnet_cls',
- std=0.01,
- bias_prob=0.01)),
- **kwargs) -> None:
- # dcn base offsets, adapted from reppoints_head.py
- self.num_dconv_points = 9
- self.dcn_kernel = int(np.sqrt(self.num_dconv_points))
- self.dcn_pad = int((self.dcn_kernel - 1) / 2)
- dcn_base = np.arange(-self.dcn_pad,
- self.dcn_pad + 1).astype(np.float64)
- dcn_base_y = np.repeat(dcn_base, self.dcn_kernel)
- dcn_base_x = np.tile(dcn_base, self.dcn_kernel)
- dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape(
- (-1))
- self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1)
-
- super(FCOSHead, self).__init__(
- num_classes=num_classes,
- in_channels=in_channels,
- norm_cfg=norm_cfg,
- init_cfg=init_cfg,
- **kwargs)
- self.regress_ranges = regress_ranges
- self.reg_denoms = [
- regress_range[-1] for regress_range in regress_ranges
- ]
- self.reg_denoms[-1] = self.reg_denoms[-2] * 2
- self.center_sampling = center_sampling
- self.center_sample_radius = center_sample_radius
- self.sync_num_pos = sync_num_pos
- self.bbox_norm_type = bbox_norm_type
- self.gradient_mul = gradient_mul
- self.use_vfl = use_vfl
- if self.use_vfl:
- self.loss_cls = MODELS.build(loss_cls)
- else:
- self.loss_cls = MODELS.build(loss_cls_fl)
- self.loss_bbox = MODELS.build(loss_bbox)
- self.loss_bbox_refine = MODELS.build(loss_bbox_refine)
-
- # for getting ATSS targets
- self.use_atss = use_atss
- self.reg_decoded_bbox = reg_decoded_bbox
- self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
-
- self.anchor_center_offset = anchor_generator['center_offset']
-
- self.num_base_priors = self.prior_generator.num_base_priors[0]
-
- if self.train_cfg:
- self.assigner = TASK_UTILS.build(self.train_cfg['assigner'])
- if self.train_cfg.get('sampler', None) is not None:
- self.sampler = TASK_UTILS.build(
- self.train_cfg['sampler'], default_args=dict(context=self))
- else:
- self.sampler = PseudoSampler()
- # only be used in `get_atss_targets` when `use_atss` is True
- self.atss_prior_generator = TASK_UTILS.build(anchor_generator)
-
- self.fcos_prior_generator = MlvlPointGenerator(
- anchor_generator['strides'],
- self.anchor_center_offset if self.use_atss else 0.5)
-
- # In order to reuse the `get_bboxes` in `BaseDenseHead.
- # Only be used in testing phase.
- self.prior_generator = self.fcos_prior_generator
-
- def _init_layers(self) -> None:
- """Initialize layers of the head."""
- super(FCOSHead, self)._init_cls_convs()
- super(FCOSHead, self)._init_reg_convs()
- self.relu = nn.ReLU()
- self.vfnet_reg_conv = ConvModule(
- self.feat_channels,
- self.feat_channels,
- 3,
- stride=1,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- bias=self.conv_bias)
- self.vfnet_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
- self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
- self.vfnet_reg_refine_dconv = DeformConv2d(
- self.feat_channels,
- self.feat_channels,
- self.dcn_kernel,
- 1,
- padding=self.dcn_pad)
- self.vfnet_reg_refine = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
- self.scales_refine = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
- self.vfnet_cls_dconv = DeformConv2d(
- self.feat_channels,
- self.feat_channels,
- self.dcn_kernel,
- 1,
- padding=self.dcn_pad)
- self.vfnet_cls = nn.Conv2d(
- self.feat_channels, self.cls_out_channels, 3, padding=1)
-
- def forward(self, x: Tuple[Tensor]) -> Tuple[List[Tensor]]:
- """Forward features from the upstream network.
-
- Args:
- x (tuple[Tensor]): Features from the upstream network, each is
- a 4D-tensor.
-
- Returns:
- tuple:
-
- - cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level, each is a 4D-tensor, the channel number is
- num_points * num_classes.
- - bbox_preds (list[Tensor]): Box offsets for each
- scale level, each is a 4D-tensor, the channel number is
- num_points * 4.
- - bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level, each is a 4D-tensor, the channel
- number is num_points * 4.
- """
- return multi_apply(self.forward_single, x, self.scales,
- self.scales_refine, self.strides, self.reg_denoms)
-
- def forward_single(self, x: Tensor, scale: Scale, scale_refine: Scale,
- stride: int, reg_denom: int) -> tuple:
- """Forward features of a single scale level.
-
- Args:
- x (Tensor): FPN feature maps of the specified stride.
- scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
- the bbox prediction.
- scale_refine (:obj: `mmcv.cnn.Scale`): Learnable scale module to
- resize the refined bbox prediction.
- stride (int): The corresponding stride for feature maps,
- used to normalize the bbox prediction when
- bbox_norm_type = 'stride'.
- reg_denom (int): The corresponding regression range for feature
- maps, only used to normalize the bbox prediction when
- bbox_norm_type = 'reg_denom'.
-
- Returns:
- tuple: iou-aware cls scores for each box, bbox predictions and
- refined bbox predictions of input feature maps.
- """
- cls_feat = x
- reg_feat = x
-
- for cls_layer in self.cls_convs:
- cls_feat = cls_layer(cls_feat)
-
- for reg_layer in self.reg_convs:
- reg_feat = reg_layer(reg_feat)
-
- # predict the bbox_pred of different level
- reg_feat_init = self.vfnet_reg_conv(reg_feat)
- if self.bbox_norm_type == 'reg_denom':
- bbox_pred = scale(
- self.vfnet_reg(reg_feat_init)).float().exp() * reg_denom
- elif self.bbox_norm_type == 'stride':
- bbox_pred = scale(
- self.vfnet_reg(reg_feat_init)).float().exp() * stride
- else:
- raise NotImplementedError
-
- # compute star deformable convolution offsets
- # converting dcn_offset to reg_feat.dtype thus VFNet can be
- # trained with FP16
- dcn_offset = self.star_dcn_offset(bbox_pred, self.gradient_mul,
- stride).to(reg_feat.dtype)
-
- # refine the bbox_pred
- reg_feat = self.relu(self.vfnet_reg_refine_dconv(reg_feat, dcn_offset))
- bbox_pred_refine = scale_refine(
- self.vfnet_reg_refine(reg_feat)).float().exp()
- bbox_pred_refine = bbox_pred_refine * bbox_pred.detach()
-
- # predict the iou-aware cls score
- cls_feat = self.relu(self.vfnet_cls_dconv(cls_feat, dcn_offset))
- cls_score = self.vfnet_cls(cls_feat)
-
- if self.training:
- return cls_score, bbox_pred, bbox_pred_refine
- else:
- return cls_score, bbox_pred_refine
-
- def star_dcn_offset(self, bbox_pred: Tensor, gradient_mul: float,
- stride: int) -> Tensor:
- """Compute the star deformable conv offsets.
-
- Args:
- bbox_pred (Tensor): Predicted bbox distance offsets (l, r, t, b).
- gradient_mul (float): Gradient multiplier.
- stride (int): The corresponding stride for feature maps,
- used to project the bbox onto the feature map.
-
- Returns:
- Tensor: The offsets for deformable convolution.
- """
- dcn_base_offset = self.dcn_base_offset.type_as(bbox_pred)
- bbox_pred_grad_mul = (1 - gradient_mul) * bbox_pred.detach() + \
- gradient_mul * bbox_pred
- # map to the feature map scale
- bbox_pred_grad_mul = bbox_pred_grad_mul / stride
- N, C, H, W = bbox_pred.size()
-
- x1 = bbox_pred_grad_mul[:, 0, :, :]
- y1 = bbox_pred_grad_mul[:, 1, :, :]
- x2 = bbox_pred_grad_mul[:, 2, :, :]
- y2 = bbox_pred_grad_mul[:, 3, :, :]
- bbox_pred_grad_mul_offset = bbox_pred.new_zeros(
- N, 2 * self.num_dconv_points, H, W)
- bbox_pred_grad_mul_offset[:, 0, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 1, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 2, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 4, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 5, :, :] = x2 # x2
- bbox_pred_grad_mul_offset[:, 7, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 11, :, :] = x2 # x2
- bbox_pred_grad_mul_offset[:, 12, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 13, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 14, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 16, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 17, :, :] = x2 # x2
- dcn_offset = bbox_pred_grad_mul_offset - dcn_base_offset
-
- return dcn_offset
-
- def loss_by_feat(
- self,
- cls_scores: List[Tensor],
- bbox_preds: List[Tensor],
- bbox_preds_refine: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> dict:
- """Compute loss of the head.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level, each is a 4D-tensor, the channel number is
- num_points * num_classes.
- bbox_preds (list[Tensor]): Box offsets for each
- scale level, each is a 4D-tensor, the channel number is
- num_points * 4.
- bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level, each is a 4D-tensor, the channel
- number is num_points * 4.
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine)
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- all_level_points = self.fcos_prior_generator.grid_priors(
- featmap_sizes, bbox_preds[0].dtype, bbox_preds[0].device)
- labels, label_weights, bbox_targets, bbox_weights = self.get_targets(
- cls_scores,
- all_level_points,
- batch_gt_instances,
- batch_img_metas,
- batch_gt_instances_ignore=batch_gt_instances_ignore)
-
- num_imgs = cls_scores[0].size(0)
- # flatten cls_scores, bbox_preds and bbox_preds_refine
- flatten_cls_scores = [
- cls_score.permute(0, 2, 3,
- 1).reshape(-1,
- self.cls_out_channels).contiguous()
- for cls_score in cls_scores
- ]
- flatten_bbox_preds = [
- bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
- for bbox_pred in bbox_preds
- ]
- flatten_bbox_preds_refine = [
- bbox_pred_refine.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
- for bbox_pred_refine in bbox_preds_refine
- ]
- flatten_cls_scores = torch.cat(flatten_cls_scores)
- flatten_bbox_preds = torch.cat(flatten_bbox_preds)
- flatten_bbox_preds_refine = torch.cat(flatten_bbox_preds_refine)
- flatten_labels = torch.cat(labels)
- flatten_bbox_targets = torch.cat(bbox_targets)
- # repeat points to align with bbox_preds
- flatten_points = torch.cat(
- [points.repeat(num_imgs, 1) for points in all_level_points])
-
- # FG cat_id: [0, num_classes - 1], BG cat_id: num_classes
- bg_class_ind = self.num_classes
- pos_inds = torch.where(
- ((flatten_labels >= 0) & (flatten_labels < bg_class_ind)) > 0)[0]
- num_pos = len(pos_inds)
-
- pos_bbox_preds = flatten_bbox_preds[pos_inds]
- pos_bbox_preds_refine = flatten_bbox_preds_refine[pos_inds]
- pos_labels = flatten_labels[pos_inds]
-
- # sync num_pos across all gpus
- if self.sync_num_pos:
- num_pos_avg_per_gpu = reduce_mean(
- pos_inds.new_tensor(num_pos).float()).item()
- num_pos_avg_per_gpu = max(num_pos_avg_per_gpu, 1.0)
- else:
- num_pos_avg_per_gpu = num_pos
-
- pos_bbox_targets = flatten_bbox_targets[pos_inds]
- pos_points = flatten_points[pos_inds]
-
- pos_decoded_bbox_preds = self.bbox_coder.decode(
- pos_points, pos_bbox_preds)
- pos_decoded_target_preds = self.bbox_coder.decode(
- pos_points, pos_bbox_targets)
- iou_targets_ini = bbox_overlaps(
- pos_decoded_bbox_preds,
- pos_decoded_target_preds.detach(),
- is_aligned=True).clamp(min=1e-6)
- bbox_weights_ini = iou_targets_ini.clone().detach()
- bbox_avg_factor_ini = reduce_mean(
- bbox_weights_ini.sum()).clamp_(min=1).item()
-
- pos_decoded_bbox_preds_refine = \
- self.bbox_coder.decode(pos_points, pos_bbox_preds_refine)
- iou_targets_rf = bbox_overlaps(
- pos_decoded_bbox_preds_refine,
- pos_decoded_target_preds.detach(),
- is_aligned=True).clamp(min=1e-6)
- bbox_weights_rf = iou_targets_rf.clone().detach()
- bbox_avg_factor_rf = reduce_mean(
- bbox_weights_rf.sum()).clamp_(min=1).item()
-
- if num_pos > 0:
- loss_bbox = self.loss_bbox(
- pos_decoded_bbox_preds,
- pos_decoded_target_preds.detach(),
- weight=bbox_weights_ini,
- avg_factor=bbox_avg_factor_ini)
-
- loss_bbox_refine = self.loss_bbox_refine(
- pos_decoded_bbox_preds_refine,
- pos_decoded_target_preds.detach(),
- weight=bbox_weights_rf,
- avg_factor=bbox_avg_factor_rf)
-
- # build IoU-aware cls_score targets
- if self.use_vfl:
- pos_ious = iou_targets_rf.clone().detach()
- cls_iou_targets = torch.zeros_like(flatten_cls_scores)
- cls_iou_targets[pos_inds, pos_labels] = pos_ious
- else:
- loss_bbox = pos_bbox_preds.sum() * 0
- loss_bbox_refine = pos_bbox_preds_refine.sum() * 0
- if self.use_vfl:
- cls_iou_targets = torch.zeros_like(flatten_cls_scores)
-
- if self.use_vfl:
- loss_cls = self.loss_cls(
- flatten_cls_scores,
- cls_iou_targets,
- avg_factor=num_pos_avg_per_gpu)
- else:
- loss_cls = self.loss_cls(
- flatten_cls_scores,
- flatten_labels,
- weight=label_weights,
- avg_factor=num_pos_avg_per_gpu)
-
- return dict(
- loss_cls=loss_cls,
- loss_bbox=loss_bbox,
- loss_bbox_rf=loss_bbox_refine)
-
- def get_targets(
- self,
- cls_scores: List[Tensor],
- mlvl_points: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> tuple:
- """A wrapper for computing ATSS and FCOS targets for points in multiple
- images.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- tuple:
-
- - labels_list (list[Tensor]): Labels of each level.
- - label_weights (Tensor/None): Label weights of all levels.
- - bbox_targets_list (list[Tensor]): Regression targets of each
- level, (l, t, r, b).
- - bbox_weights (Tensor/None): Bbox weights of all levels.
- """
- if self.use_atss:
- return self.get_atss_targets(cls_scores, mlvl_points,
- batch_gt_instances, batch_img_metas,
- batch_gt_instances_ignore)
- else:
- self.norm_on_bbox = False
- return self.get_fcos_targets(mlvl_points, batch_gt_instances)
-
- def _get_targets_single(self, *args, **kwargs):
- """Avoid ambiguity in multiple inheritance."""
- if self.use_atss:
- return ATSSHead._get_targets_single(self, *args, **kwargs)
- else:
- return FCOSHead._get_targets_single(self, *args, **kwargs)
-
- def get_fcos_targets(self, points: List[Tensor],
- batch_gt_instances: InstanceList) -> tuple:
- """Compute FCOS regression and classification targets for points in
- multiple images.
-
- Args:
- points (list[Tensor]): Points of each fpn level, each has shape
- (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
-
- Returns:
- tuple:
-
- - labels (list[Tensor]): Labels of each level.
- - label_weights: None, to be compatible with ATSS targets.
- - bbox_targets (list[Tensor]): BBox targets of each level.
- - bbox_weights: None, to be compatible with ATSS targets.
- """
- labels, bbox_targets = FCOSHead.get_targets(self, points,
- batch_gt_instances)
- label_weights = None
- bbox_weights = None
- return labels, label_weights, bbox_targets, bbox_weights
-
- def get_anchors(self,
- featmap_sizes: List[Tuple],
- batch_img_metas: List[dict],
- device: str = 'cuda') -> tuple:
- """Get anchors according to feature map sizes.
-
- Args:
- featmap_sizes (list[tuple]): Multi-level feature map sizes.
- batch_img_metas (list[dict]): Image meta info.
- device (str): Device for returned tensors
-
- Returns:
- tuple:
-
- - anchor_list (list[Tensor]): Anchors of each image.
- - valid_flag_list (list[Tensor]): Valid flags of each image.
- """
- num_imgs = len(batch_img_metas)
-
- # since feature map sizes of all images are the same, we only compute
- # anchors for one time
- multi_level_anchors = self.atss_prior_generator.grid_priors(
- featmap_sizes, device=device)
- anchor_list = [multi_level_anchors for _ in range(num_imgs)]
-
- # for each image, we compute valid flags of multi level anchors
- valid_flag_list = []
- for img_id, img_meta in enumerate(batch_img_metas):
- multi_level_flags = self.atss_prior_generator.valid_flags(
- featmap_sizes, img_meta['pad_shape'], device=device)
- valid_flag_list.append(multi_level_flags)
-
- return anchor_list, valid_flag_list
-
- def get_atss_targets(
- self,
- cls_scores: List[Tensor],
- mlvl_points: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> tuple:
- """A wrapper for computing ATSS targets for points in multiple images.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- tuple:
-
- - labels_list (list[Tensor]): Labels of each level.
- - label_weights (Tensor): Label weights of all levels.
- - bbox_targets_list (list[Tensor]): Regression targets of each
- level, (l, t, r, b).
- - bbox_weights (Tensor): Bbox weights of all levels.
- """
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- assert len(
- featmap_sizes
- ) == self.atss_prior_generator.num_levels == \
- self.fcos_prior_generator.num_levels
-
- device = cls_scores[0].device
-
- anchor_list, valid_flag_list = self.get_anchors(
- featmap_sizes, batch_img_metas, device=device)
-
- cls_reg_targets = ATSSHead.get_targets(
- self,
- anchor_list,
- valid_flag_list,
- batch_gt_instances,
- batch_img_metas,
- batch_gt_instances_ignore,
- unmap_outputs=True)
-
- (anchor_list, labels_list, label_weights_list, bbox_targets_list,
- bbox_weights_list, avg_factor) = cls_reg_targets
-
- bbox_targets_list = [
- bbox_targets.reshape(-1, 4) for bbox_targets in bbox_targets_list
- ]
-
- num_imgs = len(batch_img_metas)
- # transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format
- bbox_targets_list = self.transform_bbox_targets(
- bbox_targets_list, mlvl_points, num_imgs)
-
- labels_list = [labels.reshape(-1) for labels in labels_list]
- label_weights_list = [
- label_weights.reshape(-1) for label_weights in label_weights_list
- ]
- bbox_weights_list = [
- bbox_weights.reshape(-1) for bbox_weights in bbox_weights_list
- ]
- label_weights = torch.cat(label_weights_list)
- bbox_weights = torch.cat(bbox_weights_list)
- return labels_list, label_weights, bbox_targets_list, bbox_weights
-
- def transform_bbox_targets(self, decoded_bboxes: List[Tensor],
- mlvl_points: List[Tensor],
- num_imgs: int) -> List[Tensor]:
- """Transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format.
-
- Args:
- decoded_bboxes (list[Tensor]): Regression targets of each level,
- in the form of (x1, y1, x2, y2).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- num_imgs (int): the number of images in a batch.
-
- Returns:
- bbox_targets (list[Tensor]): Regression targets of each level in
- the form of (l, t, r, b).
- """
- # TODO: Re-implemented in Class PointCoder
- assert len(decoded_bboxes) == len(mlvl_points)
- num_levels = len(decoded_bboxes)
- mlvl_points = [points.repeat(num_imgs, 1) for points in mlvl_points]
- bbox_targets = []
- for i in range(num_levels):
- bbox_target = self.bbox_coder.encode(mlvl_points[i],
- decoded_bboxes[i])
- bbox_targets.append(bbox_target)
-
- return bbox_targets
-
- def _load_from_state_dict(self, state_dict: dict, prefix: str,
- local_metadata: dict, strict: bool,
- missing_keys: Union[List[str], str],
- unexpected_keys: Union[List[str], str],
- error_msgs: Union[List[str], str]) -> None:
- """Override the method in the parent class to avoid changing para's
- name."""
- pass
diff --git a/spaces/Larvuz/instruct-pix2pix/edit_app.py b/spaces/Larvuz/instruct-pix2pix/edit_app.py
deleted file mode 100644
index 0359e815ad51b1a2291dd8943555568e452981ad..0000000000000000000000000000000000000000
--- a/spaces/Larvuz/instruct-pix2pix/edit_app.py
+++ /dev/null
@@ -1,192 +0,0 @@
-from __future__ import annotations
-
-import math
-import random
-
-import gradio as gr
-import torch
-from PIL import Image, ImageOps
-from diffusers import StableDiffusionInstructPix2PixPipeline
-
-
-help_text = """
-If you're not getting what you want, there may be a few reasons:
-1. Is the image not changing enough? Your Image CFG weight may be too high. This value dictates how similar the output should be to the input. It's possible your edit requires larger changes from the original image, and your Image CFG weight isn't allowing that. Alternatively, your Text CFG weight may be too low. This value dictates how much to listen to the text instruction. The default Image CFG of 1.5 and Text CFG of 7.5 are a good starting point, but aren't necessarily optimal for each edit. Try:
- * Decreasing the Image CFG weight, or
- * Increasing the Text CFG weight, or
-2. Conversely, is the image changing too much, such that the details in the original image aren't preserved? Try:
- * Increasing the Image CFG weight, or
- * Decreasing the Text CFG weight
-3. Try generating results with different random seeds by setting "Randomize Seed" and running generation multiple times. You can also try setting "Randomize CFG" to sample new Text CFG and Image CFG values each time.
-4. Rephrasing the instruction sometimes improves results (e.g., "turn him into a dog" vs. "make him a dog" vs. "as a dog").
-5. Increasing the number of steps sometimes improves results.
-6. Do faces look weird? The Stable Diffusion autoencoder has a hard time with faces that are small in the image. Try:
- * Cropping the image so the face takes up a larger portion of the frame.
-"""
-
-
-example_instructions = [
- "Make it a picasso painting",
- "as if it were by modigliani",
- "convert to a bronze statue",
- "Turn it into an anime.",
- "have it look like a graphic novel",
- "make him gain weight",
- "what would he look like bald?",
- "Have him smile",
- "Put him in a cocktail party.",
- "move him at the beach.",
- "add dramatic lighting",
- "Convert to black and white",
- "What if it were snowing?",
- "Give him a leather jacket",
- "Turn him into a cyborg!",
- "make him wear a beanie",
-]
-
-model_id = "timbrooks/instruct-pix2pix"
-
-def main():
- pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None).to("cuda")
- example_image = Image.open("imgs/example.jpg").convert("RGB")
-
- def load_example(
- steps: int,
- randomize_seed: bool,
- seed: int,
- randomize_cfg: bool,
- text_cfg_scale: float,
- image_cfg_scale: float,
- ):
- example_instruction = random.choice(example_instructions)
- return [example_image, example_instruction] + generate(
- example_image,
- example_instruction,
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- )
-
- def generate(
- input_image: Image.Image,
- instruction: str,
- steps: int,
- randomize_seed: bool,
- seed: int,
- randomize_cfg: bool,
- text_cfg_scale: float,
- image_cfg_scale: float,
- ):
- seed = random.randint(0, 100000) if randomize_seed else seed
- text_cfg_scale = round(random.uniform(6.0, 9.0), ndigits=2) if randomize_cfg else text_cfg_scale
- image_cfg_scale = round(random.uniform(1.2, 1.8), ndigits=2) if randomize_cfg else image_cfg_scale
-
- width, height = input_image.size
- factor = 512 / max(width, height)
- factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
- width = int((width * factor) // 64) * 64
- height = int((height * factor) // 64) * 64
- input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS)
-
- if instruction == "":
- return [input_image, seed]
-
- generator = torch.manual_seed(seed)
- edited_image = pipe(
- instruction, image=input_image,
- guidance_scale=text_cfg_scale, image_guidance_scale=image_cfg_scale,
- num_inference_steps=steps, generator=generator,
- ).images[0]
- return [seed, text_cfg_scale, image_cfg_scale, edited_image]
-
- def reset():
- return [0, "Randomize Seed", 1371, "Fix CFG", 7.5, 1.5, None]
-
- with gr.Blocks() as demo:
- gr.HTML("""
- InstructPix2Pix: Learning to Follow Image Editing Instructions
-
-For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
-
-
-
-
""")
- with gr.Row():
- with gr.Column(scale=1, min_width=100):
- generate_button = gr.Button("Generate")
- with gr.Column(scale=1, min_width=100):
- load_button = gr.Button("Load Example")
- with gr.Column(scale=1, min_width=100):
- reset_button = gr.Button("Reset")
- with gr.Column(scale=3):
- instruction = gr.Textbox(lines=1, label="Edit Instruction", interactive=True)
-
- with gr.Row():
- input_image = gr.Image(label="Input Image", type="pil", interactive=True)
- edited_image = gr.Image(label=f"Edited Image", type="pil", interactive=False)
- input_image.style(height=512, width=512)
- edited_image.style(height=512, width=512)
-
- with gr.Row():
- steps = gr.Number(value=50, precision=0, label="Steps", interactive=True)
- randomize_seed = gr.Radio(
- ["Fix Seed", "Randomize Seed"],
- value="Randomize Seed",
- type="index",
- show_label=False,
- interactive=True,
- )
- seed = gr.Number(value=1371, precision=0, label="Seed", interactive=True)
- randomize_cfg = gr.Radio(
- ["Fix CFG", "Randomize CFG"],
- value="Fix CFG",
- type="index",
- show_label=False,
- interactive=True,
- )
- text_cfg_scale = gr.Number(value=7.5, label=f"Text CFG", interactive=True)
- image_cfg_scale = gr.Number(value=1.5, label=f"Image CFG", interactive=True)
-
- gr.Markdown(help_text)
-
- load_button.click(
- fn=load_example,
- inputs=[
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- ],
- outputs=[input_image, instruction, seed, text_cfg_scale, image_cfg_scale, edited_image],
- )
- generate_button.click(
- fn=generate,
- inputs=[
- input_image,
- instruction,
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- ],
- outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image],
- )
- reset_button.click(
- fn=reset,
- inputs=[],
- outputs=[steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale, edited_image],
- )
-
- demo.queue(concurrency_count=1)
- demo.launch(share=False)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/Loren/Streamlit_OCR_comparator/README.md b/spaces/Loren/Streamlit_OCR_comparator/README.md
deleted file mode 100644
index 2894464cad0f3f4d163e1f8e72750b10259159da..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Streamlit OCR Comparator
-emoji: 📰🔍🔤
-colorFrom: indigo
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: Home.py
-tags: [streamlit, ocr]
-pinned: true
----
\ No newline at end of file
diff --git a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py b/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py
deleted file mode 100644
index 6d9b36c74b1808b56ded68cf080a689db7e0ee4e..0000000000000000000000000000000000000000
--- a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# -*- coding: utf-8 -*-
-# File : __init__.py
-# Author : Jiayuan Mao
-# Email : maojiayuan@gmail.com
-# Date : 27/01/2018
-#
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-from .batchnorm import set_sbn_eps_mode
-from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d
-from .batchnorm import patch_sync_batchnorm, convert_model
-from .replicate import DataParallelWithCallback, patch_replication_callback
diff --git a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py b/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py
deleted file mode 100644
index 9794e0fc3463a5e8fad05c037cce64683059a6d3..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.ERROR)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})".format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10, 2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r", encoding="utf-8") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
\ No newline at end of file
diff --git a/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py b/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py
deleted file mode 100644
index 3a5999ddc80faa0ce6a54389562c9f092b40bb03..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# encoding: utf-8
-"""
-This files contains pytest fixtures and other test related stuff.
-
-"""
-from __future__ import absolute_import, division, print_function
-
-import numpy as np
-import pytest
-
-# save numpy's current print options
-_NP_PRINT_OPTIONS = np.get_printoptions()
-
-
-@pytest.fixture(autouse=True)
-def setup_doctest(request):
- """Set up the environment for doctests (when run through pytest)."""
- np.set_printoptions(precision=5, edgeitems=2, suppress=True)
-
- def fin():
- """Restore the environment after doctests (when run through pytest)."""
- np.set_printoptions(**_NP_PRINT_OPTIONS)
-
- request.addfinalizer(fin)
diff --git a/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py b/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py
deleted file mode 100644
index 19e34a11def040b971b534b9ed3fda71d0a35c85..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py
+++ /dev/null
@@ -1,882 +0,0 @@
-# encoding: utf-8
-# pylint: disable=no-member
-# pylint: disable=invalid-name
-# pylint: disable=too-many-arguments
-# pylint: disable=wrong-import-position
-"""
-Evaluation package.
-
-"""
-
-from __future__ import absolute_import, division, print_function
-
-import numpy as np
-
-
-# evaluation helper functions
-def find_closest_matches(detections, annotations):
- """
- Find the closest annotation for each detection.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
-
- Returns
- -------
- indices : numpy array
- Indices of the closest matches.
-
- Notes
- -----
- The sequences must be ordered.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.int)
- # if only a single annotation is given
- if len(annotations) == 1:
- # return an array as long as the detections with indices 0
- return np.zeros(len(detections), dtype=np.int)
- # solution found at: http://stackoverflow.com/questions/8914491/
- indices = annotations.searchsorted(detections)
- indices = np.clip(indices, 1, len(annotations) - 1)
- left = annotations[indices - 1]
- right = annotations[indices]
- indices -= detections - left < right - detections
- # return the indices of the closest matches
- return indices
-
-
-def calc_errors(detections, annotations, matches=None):
- """
- Errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.float)
- # determine the closest annotations
- if matches is None:
- matches = find_closest_matches(detections, annotations)
- # calc error relative to those annotations
- errors = detections - annotations[matches]
- # return the errors
- return errors
-
-
-def calc_absolute_errors(detections, annotations, matches=None):
- """
- Absolute errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Absolute errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # return the errors
- return np.abs(calc_errors(detections, annotations, matches))
-
-
-def calc_relative_errors(detections, annotations, matches=None):
- """
- Relative errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Relative errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.float)
- # determine the closest annotations
- if matches is None:
- matches = find_closest_matches(detections, annotations)
- # calculate the absolute errors
- errors = calc_errors(detections, annotations, matches)
- # return the relative errors
- return np.abs(1 - (errors / annotations[matches]))
-
-
-# abstract evaluation base class
-class EvaluationMixin(object):
- """
- Evaluation mixin class.
-
- This class has a `name` attribute which is used for display purposes and
- defaults to 'None'.
-
- `METRIC_NAMES` is a list of tuples, containing the attribute's name and the
- corresponding label, e.g.:
-
- The attributes defined in `METRIC_NAMES` will be provided as an ordered
- dictionary as the `metrics` property unless the subclass overwrites the
- property.
-
- `FLOAT_FORMAT` is used to format floats.
-
- """
-
- # Example:
- # METRIC_NAMES = [
- # ('precision', 'Precision'),
- # ('recall', 'Recall'),
- # ('fmeasure', 'F-measure'),
- # ]
- name = None
- METRIC_NAMES = []
- FLOAT_FORMAT = '{:.3f}'
-
- @property
- def metrics(self):
- """Metrics as a dictionary."""
- from collections import OrderedDict
- metrics = OrderedDict()
- # metrics = {}
- for metric in [m[0] for m in self.METRIC_NAMES]:
- metrics[metric] = getattr(self, metric)
- return metrics
-
- def __len__(self):
- """Length of the evaluation object."""
- raise NotImplementedError('must be implemented by subclass.')
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- Notes
- -----
- This is a fallback method formatting the `metrics` dictionary in a
- human readable way. Classes inheriting from this mixin class should
- provide a method better suitable.
-
- """
- # pylint: disable=unused-argument
- import pprint
- return pprint.pformat(dict(self.metrics), indent=4)
-
-
-# evaluation classes
-class SimpleEvaluation(EvaluationMixin):
- """
- Simple Precision, Recall, F-measure and Accuracy evaluation based on the
- numbers of true/false positive/negative detections.
-
- Parameters
- ----------
- num_tp : int
- Number of true positive detections.
- num_fp : int
- Number of false positive detections.
- num_tn : int
- Number of true negative detections.
- num_fn : int
- Number of false negative detections.
- name : str
- Name to be displayed.
-
- Notes
- -----
- This class is only suitable for a 1-class evaluation problem.
-
- """
-
- METRIC_NAMES = [
- ('num_tp', 'No. of true positives'),
- ('num_fp', 'No. of false positives'),
- ('num_tn', 'No. of true negatives'),
- ('num_fn', 'No. of false negatives'),
- ('num_annotations', 'No. Annotations'),
- ('precision', 'Precision'),
- ('recall', 'Recall'),
- ('fmeasure', 'F-measure'),
- ('accuracy', 'Accuracy'),
- ]
-
- def __init__(self, num_tp=0, num_fp=0, num_tn=0, num_fn=0, name=None,
- **kwargs):
- # pylint: disable=unused-argument
- # hidden variables, to be able to overwrite them in subclasses
- self._num_tp = int(num_tp)
- self._num_fp = int(num_fp)
- self._num_tn = int(num_tn)
- self._num_fn = int(num_fn)
- # name of the evaluation
- self.name = name
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return self._num_tp
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return self._num_fp
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return self._num_tn
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return self._num_fn
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- return self.num_tp + self.num_fn
-
- def __len__(self):
- # the length equals the number of annotations
- return self.num_annotations
-
- @property
- def precision(self):
- """Precision."""
- # correct / retrieved
- retrieved = float(self.num_tp + self.num_fp)
- # if there are no positive predictions, none of them are wrong
- if retrieved == 0:
- return 1.
- return self.num_tp / retrieved
-
- @property
- def recall(self):
- """Recall."""
- # correct / relevant
- relevant = float(self.num_tp + self.num_fn)
- # if there are no positive annotations, we recalled all of them
- if relevant == 0:
- return 1.
- return self.num_tp / relevant
-
- @property
- def fmeasure(self):
- """F-measure."""
- # 2pr / (p+r)
- numerator = 2. * self.precision * self.recall
- if numerator == 0:
- return 0.
- return numerator / (self.precision + self.recall)
-
- @property
- def accuracy(self):
- """Accuracy."""
- # acc: (TP + TN) / (TP + FP + TN + FN)
- denominator = self.num_fp + self.num_fn + self.num_tp + self.num_tn
- if denominator == 0:
- return 1.
- numerator = float(self.num_tp + self.num_tn)
- if numerator == 0:
- return 0.
- return numerator / denominator
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
-
- """
- ret = ''
- if self.name is not None:
- ret += '%s\n ' % self.name
- ret += 'Annotations: %5d TP: %5d FP: %5d FN: %5d ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- return ret
-
- def __str__(self):
- return self.tostring()
-
-
-# evaluate Precision, Recall, F-measure and Accuracy with lists or numpy arrays
-class Evaluation(SimpleEvaluation):
- """
- Evaluation class for measuring Precision, Recall and F-measure based on
- numpy arrays or lists with true/false positive/negative detections.
-
- Parameters
- ----------
- tp : list or numpy array
- True positive detections.
- fp : list or numpy array
- False positive detections.
- tn : list or numpy array
- True negative detections.
- fn : list or numpy array
- False negative detections.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, tp=None, fp=None, tn=None, fn=None, **kwargs):
- # set default values
- if tp is None:
- tp = []
- if fp is None:
- fp = []
- if tn is None:
- tn = []
- if fn is None:
- fn = []
- # instantiate a SimpleEvaluation object
- super(Evaluation, self).__init__(**kwargs)
- # convert everything to numpy arrays and save them
- self.tp = np.asarray(list(tp), dtype=np.float)
- self.fp = np.asarray(list(fp), dtype=np.float)
- self.tn = np.asarray(list(tn), dtype=np.float)
- self.fn = np.asarray(list(fn), dtype=np.float)
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return len(self.tp)
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return len(self.fp)
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return len(self.tn)
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return len(self.fn)
-
-
-# class for evaluation of Precision, Recall, F-measure with 2D arrays
-class MultiClassEvaluation(Evaluation):
- """
- Evaluation class for measuring Precision, Recall and F-measure based on
- 2D numpy arrays with true/false positive/negative detections.
-
- Parameters
- ----------
- tp : list of tuples or numpy array, shape (num_tp, 2)
- True positive detections.
- fp : list of tuples or numpy array, shape (num_fp, 2)
- False positive detections.
- tn : list of tuples or numpy array, shape (num_tn, 2)
- True negative detections.
- fn : list of tuples or numpy array, shape (num_fn, 2)
- False negative detections.
- name : str
- Name to be displayed.
-
- Notes
- -----
- The second item of the tuples or the second column of the arrays denote
- the class the detection belongs to.
-
- """
- def __init__(self, tp=None, fp=None, tn=None, fn=None, **kwargs):
- # set default values
- if tp is None:
- tp = np.zeros((0, 2))
- if fp is None:
- fp = np.zeros((0, 2))
- if tn is None:
- tn = np.zeros((0, 2))
- if fn is None:
- fn = np.zeros((0, 2))
- super(MultiClassEvaluation, self).__init__(**kwargs)
- self.tp = np.asarray(tp, dtype=np.float)
- self.fp = np.asarray(fp, dtype=np.float)
- self.tn = np.asarray(tn, dtype=np.float)
- self.fn = np.asarray(fn, dtype=np.float)
-
- def tostring(self, verbose=False, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Parameters
- ----------
- verbose : bool
- Add evaluation for individual classes.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- ret = ''
-
- if verbose:
- # extract all classes
- classes = []
- if self.tp.any():
- classes = np.append(classes, np.unique(self.tp[:, 1]))
- if self.fp.any():
- classes = np.append(classes, np.unique(self.fp[:, 1]))
- if self.tn.any():
- classes = np.append(classes, np.unique(self.tn[:, 1]))
- if self.fn.any():
- classes = np.append(classes, np.unique(self.fn[:, 1]))
- for cls in sorted(np.unique(classes)):
- # extract the TP, FP, TN and FN of this class
- tp = self.tp[self.tp[:, 1] == cls]
- fp = self.fp[self.fp[:, 1] == cls]
- tn = self.tn[self.tn[:, 1] == cls]
- fn = self.fn[self.fn[:, 1] == cls]
- # evaluate them
- e = Evaluation(tp, fp, tn, fn, name='Class %s' % cls)
- # append to the output string
- ret += ' %s\n' % e.tostring(verbose=False)
- # normal formatting
- ret += 'Annotations: %5d TP: %5d FP: %4d FN: %4d ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- # return
- return ret
-
-
-# class for summing Evaluations
-class SumEvaluation(SimpleEvaluation):
- """
- Simple class for summing evaluations.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, eval_objects, name=None):
- # pylint: disable=super-init-not-called
- # Note: we want to inherit the evaluation functions/properties, no need
- # to call __super__, but we need to take care of 'name'
- if not isinstance(eval_objects, list):
- # wrap the given eval_object in a list
- eval_objects = [eval_objects]
- self.eval_objects = eval_objects
- self.name = name or 'sum for %d files' % len(self)
-
- def __len__(self):
- # just use the length of the evaluation objects
- return len(self.eval_objects)
-
- # redefine the counters (number of TP, FP, TN, FN & number of annotations)
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return sum(e.num_tp for e in self.eval_objects)
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return sum(e.num_fp for e in self.eval_objects)
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return sum(e.num_tn for e in self.eval_objects)
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return sum(e.num_fn for e in self.eval_objects)
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- return sum(e.num_annotations for e in self.eval_objects)
-
-
-# class for averaging Evaluations
-class MeanEvaluation(SumEvaluation):
- """
- Simple class for averaging evaluation.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, eval_objects, name=None, **kwargs):
- super(MeanEvaluation, self).__init__(eval_objects, **kwargs)
- # handle the 'name' here to be able to set a different default value
- self.name = name or 'mean for %d files' % len(self)
-
- # overwrite the properties to calculate the mean instead of the sum
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_tp for e in self.eval_objects])
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_fp for e in self.eval_objects])
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_tn for e in self.eval_objects])
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_fn for e in self.eval_objects])
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_annotations for e in self.eval_objects])
-
- @property
- def precision(self):
- """Precision."""
- return np.nanmean([e.precision for e in self.eval_objects])
-
- @property
- def recall(self):
- """Recall."""
- return np.nanmean([e.recall for e in self.eval_objects])
-
- @property
- def fmeasure(self):
- """F-measure."""
- return np.nanmean([e.fmeasure for e in self.eval_objects])
-
- @property
- def accuracy(self):
- """Accuracy."""
- return np.nanmean([e.accuracy for e in self.eval_objects])
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- ret = ''
- if self.name is not None:
- ret += '%s\n ' % self.name
- # TODO: unify this with SimpleEvaluation but
- # add option to provide field formatters (e.g. 3d or 5.2f)
- # format with floats instead of integers
- ret += 'Annotations: %5.2f TP: %5.2f FP: %5.2f FN: %5.2f ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- return ret
-
-
-def tostring(eval_objects, **kwargs):
- """
- Format the given evaluation objects as human readable strings.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- # pylint: disable=unused-argument
- return '\n'.join([e.tostring() for e in eval_objects])
-
-
-def tocsv(eval_objects, metric_names=None, float_format='{:.3f}', **kwargs):
- """
- Format the given evaluation objects as a CSV table.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- metric_names : list of tuples, optional
- List of tuples defining the name of the property corresponding to the
- metric, and the metric label e.g. ('fp', 'False Positives').
- float_format : str, optional
- How to format the metrics.
-
- Returns
- -------
- str
- CSV table representation of the evaluation objects.
-
- Notes
- -----
- If no `metric_names` are given, they will be extracted from the first
- evaluation object.
-
- """
- # pylint: disable=unused-argument
-
- if metric_names is None:
- # get the evaluation metrics from the first evaluation object
- metric_names = eval_objects[0].METRIC_NAMES
- metric_names, metric_labels = list(zip(*metric_names))
- # add header
- lines = ['Name,' + ','.join(metric_labels)]
- # TODO: use e.metrics dict?
- # add the evaluation objects
- for e in eval_objects:
- values = [float_format.format(getattr(e, mn)) for mn in metric_names]
- lines.append(e.name + ',' + ','.join(values))
- # return everything
- return '\n'.join(lines)
-
-
-def totex(eval_objects, metric_names=None, float_format='{:.3f}', **kwargs):
- """
- Format the given evaluation objects as a LaTeX table.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- metric_names : list of tuples, optional
- List of tuples defining the name of the property corresponding to the
- metric, and the metric label e.g. ('fp', 'False Positives').
- float_format : str, optional
- How to format the metrics.
-
- Returns
- -------
- str
- LaTeX table representation of the evaluation objects.
-
- Notes
- -----
- If no `metric_names` are given, they will be extracted from the first
- evaluation object.
-
- """
- # pylint: disable=unused-argument
-
- if metric_names is None:
- # get the evaluation metrics from the first evaluation object
- metric_names = eval_objects[0].METRIC_NAMES
- metric_names, metric_labels = list(zip(*metric_names))
- # add header
- lines = ['Name & ' + ' & '.join(metric_labels) + '\\\\']
- # TODO: use e.metrics dict
- # TODO: add a generic totable() function which accepts columns separator,
- # newline stuff (e.g. tex \\\\) and others
- # add the evaluation objects
- for e in eval_objects:
- values = [float_format.format(getattr(e, mn)) for mn in metric_names]
- lines.append(e.name + ' & ' + ' & '.join(values) + '\\\\')
- # return everything
- return '\n'.join(lines)
-
-
-def evaluation_io(parser, ann_suffix, det_suffix, ann_dir=None, det_dir=None):
- """
- Add evaluation input/output and formatting related arguments to an existing
- parser object.
-
- Parameters
- ----------
- parser : argparse parser instance
- Existing argparse parser object.
- ann_suffix : str
- Suffix of the annotation files.
- det_suffix : str
- Suffix of the detection files.
- ann_dir : str, optional
- Use only annotations from this folder (and sub-folders).
- det_dir : str, optional
- Use only detections from this folder (and sub-folders).
-
- Returns
- -------
- io_group : argparse argument group
- Evaluation input / output argument group.
- formatter_group : argparse argument group
- Evaluation formatter argument group.
-
- """
- import sys
- import argparse
- # general input output file handling
- parser.add_argument('files', nargs='*',
- help='files (or folders) to be evaluated')
- parser.add_argument('-o', dest='outfile', type=argparse.FileType('w'),
- default=sys.stdout,
- help='output file [default: STDOUT]')
- # file suffixes used for evaluation
- g = parser.add_argument_group('file/folder/suffix arguments')
- g.add_argument('-a', dest='ann_suffix', action='store', default=ann_suffix,
- help='suffix of the annotation files '
- '[default: %(default)s]')
- g.add_argument('--ann_dir', action='store', default=ann_dir,
- help='search only this directory (recursively) for '
- 'annotation files [default: %(default)s]')
- g.add_argument('-d', dest='det_suffix', action='store', default=det_suffix,
- help='suffix of the detection files [default: %(default)s]')
- g.add_argument('--det_dir', action='store', default=det_dir,
- help='search only this directory (recursively) for '
- 'detection files [default: %(default)s]')
- # option to ignore non-existing detections
- g.add_argument('-i', '--ignore_non_existing', action='store_true',
- help='ignore non-existing detections [default: raise a '
- 'warning and assume empty detections]')
- # verbose
- parser.add_argument('-v', '--verbose', action='count', default=0,
- help='increase verbosity level')
- # option to suppress warnings
- parser.add_argument('-q', '--quiet', action='store_true',
- help='suppress any warnings')
- # output format options
- parser.set_defaults(output_formatter=tostring)
- f = parser.add_argument_group('formatting arguments')
- formats = f.add_mutually_exclusive_group()
- formats.add_argument('--tex', dest='output_formatter',
- action='store_const', const=totex,
- help='format output to be used in .tex files')
- formats.add_argument('--csv', dest='output_formatter',
- action='store_const', const=tocsv,
- help='format output to be used in .csv files')
- # return the output formatting group so the caller can add more options
- return g, f
-
-
-# finally import the submodules
-from . import chords, beats, notes, onsets, tempo
-
-# import often used classes
-from .beats import BeatEvaluation, BeatMeanEvaluation
-from .chords import ChordEvaluation, ChordMeanEvaluation, ChordSumEvaluation
-from .key import KeyEvaluation, KeyMeanEvaluation
-from .notes import NoteEvaluation, NoteMeanEvaluation, NoteSumEvaluation
-from .onsets import OnsetEvaluation, OnsetMeanEvaluation, OnsetSumEvaluation
-from .tempo import TempoEvaluation, TempoMeanEvaluation
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py
deleted file mode 100644
index 6fc71979190323f44c09f8b7e1761cf49cd2d76b..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
- 'iou3d_boxes_iou_bev_forward', 'iou3d_nms_forward',
- 'iou3d_nms_normal_forward'
-])
-
-
-def boxes_iou_bev(boxes_a, boxes_b):
- """Calculate boxes IoU in the Bird's Eye View.
-
- Args:
- boxes_a (torch.Tensor): Input boxes a with shape (M, 5).
- boxes_b (torch.Tensor): Input boxes b with shape (N, 5).
-
- Returns:
- ans_iou (torch.Tensor): IoU result with shape (M, N).
- """
- ans_iou = boxes_a.new_zeros(
- torch.Size((boxes_a.shape[0], boxes_b.shape[0])))
-
- ext_module.iou3d_boxes_iou_bev_forward(boxes_a.contiguous(),
- boxes_b.contiguous(), ans_iou)
-
- return ans_iou
-
-
-def nms_bev(boxes, scores, thresh, pre_max_size=None, post_max_size=None):
- """NMS function GPU implementation (for BEV boxes). The overlap of two
- boxes for IoU calculation is defined as the exact overlapping area of the
- two boxes. In this function, one can also set ``pre_max_size`` and
- ``post_max_size``.
-
- Args:
- boxes (torch.Tensor): Input boxes with the shape of [N, 5]
- ([x1, y1, x2, y2, ry]).
- scores (torch.Tensor): Scores of boxes with the shape of [N].
- thresh (float): Overlap threshold of NMS.
- pre_max_size (int, optional): Max size of boxes before NMS.
- Default: None.
- post_max_size (int, optional): Max size of boxes after NMS.
- Default: None.
-
- Returns:
- torch.Tensor: Indexes after NMS.
- """
- assert boxes.size(1) == 5, 'Input boxes shape should be [N, 5]'
- order = scores.sort(0, descending=True)[1]
-
- if pre_max_size is not None:
- order = order[:pre_max_size]
- boxes = boxes[order].contiguous()
-
- keep = torch.zeros(boxes.size(0), dtype=torch.long)
- num_out = ext_module.iou3d_nms_forward(boxes, keep, thresh)
- keep = order[keep[:num_out].cuda(boxes.device)].contiguous()
- if post_max_size is not None:
- keep = keep[:post_max_size]
- return keep
-
-
-def nms_normal_bev(boxes, scores, thresh):
- """Normal NMS function GPU implementation (for BEV boxes). The overlap of
- two boxes for IoU calculation is defined as the exact overlapping area of
- the two boxes WITH their yaw angle set to 0.
-
- Args:
- boxes (torch.Tensor): Input boxes with shape (N, 5).
- scores (torch.Tensor): Scores of predicted boxes with shape (N).
- thresh (float): Overlap threshold of NMS.
-
- Returns:
- torch.Tensor: Remaining indices with scores in descending order.
- """
- assert boxes.shape[1] == 5, 'Input boxes shape should be [N, 5]'
- order = scores.sort(0, descending=True)[1]
-
- boxes = boxes[order].contiguous()
-
- keep = torch.zeros(boxes.size(0), dtype=torch.long)
- num_out = ext_module.iou3d_nms_normal_forward(boxes, keep, thresh)
- return order[keep[:num_out].cuda(boxes.device)].contiguous()
diff --git a/spaces/MetaWabbit/Auto-GPT/run.sh b/spaces/MetaWabbit/Auto-GPT/run.sh
deleted file mode 100644
index edcbc44155b9ca9df83e283fdf976472c13e6492..0000000000000000000000000000000000000000
--- a/spaces/MetaWabbit/Auto-GPT/run.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-python scripts/check_requirements.py requirements.txt
-if [ $? -eq 1 ]
-then
- echo Installing missing packages...
- pip install -r requirements.txt
-fi
-python -m autogpt $@
-read -p "Press any key to continue..."
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py b/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py
deleted file mode 100644
index 244425feef76bf89d4de939cb8a1914a6f0f47c6..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py
+++ /dev/null
@@ -1,387 +0,0 @@
-# Lint as: python3
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Unit tests for the classifier trainer models."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import copy
-import functools
-import json
-
-import os
-import sys
-
-from typing import Any, Callable, Iterable, Mapping, MutableMapping, Optional, Tuple
-
-from absl import flags
-from absl.testing import parameterized
-import tensorflow as tf
-
-from tensorflow.python.distribute import combinations
-from tensorflow.python.distribute import strategy_combinations
-from official.utils.flags import core as flags_core
-from official.vision.image_classification import classifier_trainer
-from official.vision.image_classification import dataset_factory
-from official.vision.image_classification import test_utils
-from official.vision.image_classification.configs import base_configs
-
-classifier_trainer.define_classifier_flags()
-
-
-def distribution_strategy_combinations() -> Iterable[Tuple[Any, ...]]:
- """Returns the combinations of end-to-end tests to run."""
- return combinations.combine(
- distribution=[
- strategy_combinations.default_strategy,
- strategy_combinations.tpu_strategy,
- strategy_combinations.one_device_strategy_gpu,
- strategy_combinations.mirrored_strategy_with_two_gpus,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset=[
- 'imagenet',
- ],
- )
-
-
-def get_params_override(params_override: Mapping[str, Any]) -> str:
- """Converts params_override dict to string command."""
- return '--params_override=' + json.dumps(params_override)
-
-
-def basic_params_override(dtype: str = 'float32') -> MutableMapping[str, Any]:
- """Returns a basic parameter configuration for testing."""
- return {
- 'train_dataset': {
- 'builder': 'synthetic',
- 'use_per_replica_batch_size': True,
- 'batch_size': 1,
- 'image_size': 224,
- 'dtype': dtype,
- },
- 'validation_dataset': {
- 'builder': 'synthetic',
- 'batch_size': 1,
- 'use_per_replica_batch_size': True,
- 'image_size': 224,
- 'dtype': dtype,
- },
- 'train': {
- 'steps': 1,
- 'epochs': 1,
- 'callbacks': {
- 'enable_checkpoint_and_export': True,
- 'enable_tensorboard': False,
- },
- },
- 'evaluation': {
- 'steps': 1,
- },
- }
-
-
-def get_trivial_model(num_classes: int) -> tf.keras.Model:
- """Creates and compiles trivial model for ImageNet dataset."""
- model = test_utils.trivial_model(num_classes=num_classes)
- lr = 0.01
- optimizer = tf.keras.optimizers.SGD(learning_rate=lr)
- loss_obj = tf.keras.losses.SparseCategoricalCrossentropy()
- model.compile(optimizer=optimizer,
- loss=loss_obj,
- run_eagerly=True)
- return model
-
-
-def get_trivial_data() -> tf.data.Dataset:
- """Gets trivial data in the ImageNet size."""
- def generate_data(_) -> tf.data.Dataset:
- image = tf.zeros(shape=(224, 224, 3), dtype=tf.float32)
- label = tf.zeros([1], dtype=tf.int32)
- return image, label
-
- dataset = tf.data.Dataset.range(1)
- dataset = dataset.repeat()
- dataset = dataset.map(generate_data,
- num_parallel_calls=tf.data.experimental.AUTOTUNE)
- dataset = dataset.prefetch(buffer_size=1).batch(1)
- return dataset
-
-
-def run_end_to_end(main: Callable[[Any], None],
- extra_flags: Optional[Iterable[str]] = None,
- model_dir: Optional[str] = None):
- """Runs the classifier trainer end-to-end."""
- extra_flags = [] if extra_flags is None else extra_flags
- args = [sys.argv[0], '--model_dir', model_dir] + extra_flags
- flags_core.parse_flags(argv=args)
- main(flags.FLAGS)
-
-
-class ClassifierTest(tf.test.TestCase, parameterized.TestCase):
- """Unit tests for Keras models."""
- _tempdir = None
-
- @classmethod
- def setUpClass(cls): # pylint: disable=invalid-name
- super(ClassifierTest, cls).setUpClass()
-
- def tearDown(self):
- super(ClassifierTest, self).tearDown()
- tf.io.gfile.rmtree(self.get_temp_dir())
-
- @combinations.generate(distribution_strategy_combinations())
- def test_end_to_end_train_and_eval(self, distribution, model, dataset):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override()),
- '--mode=train_and_eval',
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
-
- @combinations.generate(
- combinations.combine(
- distribution=[
- strategy_combinations.one_device_strategy_gpu,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset='imagenet',
- dtype='float16',
- ))
- def test_gpu_train(self, distribution, model, dataset, dtype):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override(dtype)),
- '--mode=train_and_eval',
- ]
-
- export_params = basic_params_override()
- export_path = os.path.join(model_dir, 'export')
- export_params['export'] = {}
- export_params['export']['destination'] = export_path
- export_flags = base_flags + [
- '--mode=export_only',
- get_params_override(export_params)
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
- run_end_to_end(main=run,
- extra_flags=export_flags,
- model_dir=model_dir)
- self.assertTrue(os.path.exists(export_path))
-
- @combinations.generate(
- combinations.combine(
- distribution=[
- strategy_combinations.tpu_strategy,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset='imagenet',
- dtype='bfloat16',
- ))
- def test_tpu_train(self, distribution, model, dataset, dtype):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override(dtype)),
- '--mode=train_and_eval',
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
-
- @combinations.generate(distribution_strategy_combinations())
- def test_end_to_end_invalid_mode(self, distribution, model, dataset):
- """Test the Keras EfficientNet model with `strategy`."""
- model_dir = self.get_temp_dir()
- extra_flags = [
- '--data_dir=not_used',
- '--mode=invalid_mode',
- '--model_type=' + model,
- '--dataset=' + dataset,
- get_params_override(basic_params_override()),
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- with self.assertRaises(ValueError):
- run_end_to_end(main=run, extra_flags=extra_flags, model_dir=model_dir)
-
-
-class UtilTests(parameterized.TestCase, tf.test.TestCase):
- """Tests for individual utility functions within classifier_trainer.py."""
-
- @parameterized.named_parameters(
- ('efficientnet-b0', 'efficientnet', 'efficientnet-b0', 224),
- ('efficientnet-b1', 'efficientnet', 'efficientnet-b1', 240),
- ('efficientnet-b2', 'efficientnet', 'efficientnet-b2', 260),
- ('efficientnet-b3', 'efficientnet', 'efficientnet-b3', 300),
- ('efficientnet-b4', 'efficientnet', 'efficientnet-b4', 380),
- ('efficientnet-b5', 'efficientnet', 'efficientnet-b5', 456),
- ('efficientnet-b6', 'efficientnet', 'efficientnet-b6', 528),
- ('efficientnet-b7', 'efficientnet', 'efficientnet-b7', 600),
- ('resnet', 'resnet', '', None),
- )
- def test_get_model_size(self, model, model_name, expected):
- config = base_configs.ExperimentConfig(
- model_name=model,
- model=base_configs.ModelConfig(
- model_params={
- 'model_name': model_name,
- },
- )
- )
- size = classifier_trainer.get_image_size_from_model(config)
- self.assertEqual(size, expected)
-
- @parameterized.named_parameters(
- ('dynamic', 'dynamic', None, 'dynamic'),
- ('scalar', 128., None, 128.),
- ('float32', None, 'float32', 1),
- ('float16', None, 'float16', 128),
- )
- def test_get_loss_scale(self, loss_scale, dtype, expected):
- config = base_configs.ExperimentConfig(
- runtime=base_configs.RuntimeConfig(
- loss_scale=loss_scale),
- train_dataset=dataset_factory.DatasetConfig(dtype=dtype))
- ls = classifier_trainer.get_loss_scale(config, fp16_default=128)
- self.assertEqual(ls, expected)
-
- @parameterized.named_parameters(
- ('float16', 'float16'),
- ('bfloat16', 'bfloat16')
- )
- def test_initialize(self, dtype):
- config = base_configs.ExperimentConfig(
- runtime=base_configs.RuntimeConfig(
- run_eagerly=False,
- enable_xla=False,
- per_gpu_thread_count=1,
- gpu_thread_mode='gpu_private',
- num_gpus=1,
- dataset_num_private_threads=1,
- ),
- train_dataset=dataset_factory.DatasetConfig(dtype=dtype),
- model=base_configs.ModelConfig(),
- )
-
- class EmptyClass:
- pass
- fake_ds_builder = EmptyClass()
- fake_ds_builder.dtype = dtype
- fake_ds_builder.config = EmptyClass()
- classifier_trainer.initialize(config, fake_ds_builder)
-
- def test_resume_from_checkpoint(self):
- """Tests functionality for resuming from checkpoint."""
- # Set the keras policy
- policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16')
- tf.keras.mixed_precision.experimental.set_policy(policy)
-
- # Get the model, datasets, and compile it.
- model = get_trivial_model(10)
-
- # Create the checkpoint
- model_dir = self.get_temp_dir()
- train_epochs = 1
- train_steps = 10
- ds = get_trivial_data()
- callbacks = [
- tf.keras.callbacks.ModelCheckpoint(
- os.path.join(model_dir, 'model.ckpt-{epoch:04d}'),
- save_weights_only=True)
- ]
- model.fit(
- ds,
- callbacks=callbacks,
- epochs=train_epochs,
- steps_per_epoch=train_steps)
-
- # Test load from checkpoint
- clean_model = get_trivial_model(10)
- weights_before_load = copy.deepcopy(clean_model.get_weights())
- initial_epoch = classifier_trainer.resume_from_checkpoint(
- model=clean_model,
- model_dir=model_dir,
- train_steps=train_steps)
- self.assertEqual(initial_epoch, 1)
- self.assertNotAllClose(weights_before_load, clean_model.get_weights())
-
- tf.io.gfile.rmtree(model_dir)
-
- def test_serialize_config(self):
- """Tests functionality for serializing data."""
- config = base_configs.ExperimentConfig()
- model_dir = self.get_temp_dir()
- classifier_trainer.serialize_config(params=config, model_dir=model_dir)
- saved_params_path = os.path.join(model_dir, 'params.yaml')
- self.assertTrue(os.path.exists(saved_params_path))
- tf.io.gfile.rmtree(model_dir)
-
-if __name__ == '__main__':
- tf.test.main()
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py b/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py
deleted file mode 100644
index 11fcb5305660ec71153ebfc12631f455a3464115..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py
+++ /dev/null
@@ -1,231 +0,0 @@
-# Lint as: python3
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Definitions for high level configuration groups.."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-
-from typing import Any, List, Mapping, Optional
-
-import dataclasses
-
-from official.modeling import hyperparams
-from official.modeling.hyperparams import config_definitions
-
-CallbacksConfig = config_definitions.CallbacksConfig
-TensorboardConfig = config_definitions.TensorboardConfig
-RuntimeConfig = config_definitions.RuntimeConfig
-
-
-@dataclasses.dataclass
-class ExportConfig(hyperparams.Config):
- """Configuration for exports.
-
- Attributes:
- checkpoint: the path to the checkpoint to export.
- destination: the path to where the checkpoint should be exported.
- """
- checkpoint: str = None
- destination: str = None
-
-
-@dataclasses.dataclass
-class MetricsConfig(hyperparams.Config):
- """Configuration for Metrics.
-
- Attributes:
- accuracy: Whether or not to track accuracy as a Callback. Defaults to None.
- top_5: Whether or not to track top_5_accuracy as a Callback. Defaults to
- None.
- """
- accuracy: bool = None
- top_5: bool = None
-
-
-@dataclasses.dataclass
-class TimeHistoryConfig(hyperparams.Config):
- """Configuration for the TimeHistory callback.
-
- Attributes:
- log_steps: Interval of steps between logging of batch level stats.
- """
- log_steps: int = None
-
-
-@dataclasses.dataclass
-class TrainConfig(hyperparams.Config):
- """Configuration for training.
-
- Attributes:
- resume_checkpoint: Whether or not to enable load checkpoint loading.
- Defaults to None.
- epochs: The number of training epochs to run. Defaults to None.
- steps: The number of steps to run per epoch. If None, then this will be
- inferred based on the number of images and batch size. Defaults to None.
- callbacks: An instance of CallbacksConfig.
- metrics: An instance of MetricsConfig.
- tensorboard: An instance of TensorboardConfig.
- set_epoch_loop: Whether or not to set `experimental_steps_per_execution` to
- equal the number of training steps in `model.compile`. This reduces the
- number of callbacks run per epoch which significantly improves end-to-end
- TPU training time.
- """
- resume_checkpoint: bool = None
- epochs: int = None
- steps: int = None
- callbacks: CallbacksConfig = CallbacksConfig()
- metrics: MetricsConfig = None
- tensorboard: TensorboardConfig = TensorboardConfig()
- time_history: TimeHistoryConfig = TimeHistoryConfig()
- set_epoch_loop: bool = False
-
-
-@dataclasses.dataclass
-class EvalConfig(hyperparams.Config):
- """Configuration for evaluation.
-
- Attributes:
- epochs_between_evals: The number of train epochs to run between evaluations.
- Defaults to None.
- steps: The number of eval steps to run during evaluation. If None, this will
- be inferred based on the number of images and batch size. Defaults to
- None.
- skip_eval: Whether or not to skip evaluation.
- """
- epochs_between_evals: int = None
- steps: int = None
- skip_eval: bool = False
-
-
-@dataclasses.dataclass
-class LossConfig(hyperparams.Config):
- """Configuration for Loss.
-
- Attributes:
- name: The name of the loss. Defaults to None.
- label_smoothing: Whether or not to apply label smoothing to the loss. This
- only applies to 'categorical_cross_entropy'.
- """
- name: str = None
- label_smoothing: float = None
-
-
-@dataclasses.dataclass
-class OptimizerConfig(hyperparams.Config):
- """Configuration for Optimizers.
-
- Attributes:
- name: The name of the optimizer. Defaults to None.
- decay: Decay or rho, discounting factor for gradient. Defaults to None.
- epsilon: Small value used to avoid 0 denominator. Defaults to None.
- momentum: Plain momentum constant. Defaults to None.
- nesterov: Whether or not to apply Nesterov momentum. Defaults to None.
- moving_average_decay: The amount of decay to apply. If 0 or None, then
- exponential moving average is not used. Defaults to None.
- lookahead: Whether or not to apply the lookahead optimizer. Defaults to
- None.
- beta_1: The exponential decay rate for the 1st moment estimates. Used in the
- Adam optimizers. Defaults to None.
- beta_2: The exponential decay rate for the 2nd moment estimates. Used in the
- Adam optimizers. Defaults to None.
- epsilon: Small value used to avoid 0 denominator. Defaults to 1e-7.
- """
- name: str = None
- decay: float = None
- epsilon: float = None
- momentum: float = None
- nesterov: bool = None
- moving_average_decay: Optional[float] = None
- lookahead: Optional[bool] = None
- beta_1: float = None
- beta_2: float = None
- epsilon: float = None
-
-
-@dataclasses.dataclass
-class LearningRateConfig(hyperparams.Config):
- """Configuration for learning rates.
-
- Attributes:
- name: The name of the learning rate. Defaults to None.
- initial_lr: The initial learning rate. Defaults to None.
- decay_epochs: The number of decay epochs. Defaults to None.
- decay_rate: The rate of decay. Defaults to None.
- warmup_epochs: The number of warmup epochs. Defaults to None.
- batch_lr_multiplier: The multiplier to apply to the base learning rate, if
- necessary. Defaults to None.
- examples_per_epoch: the number of examples in a single epoch. Defaults to
- None.
- boundaries: boundaries used in piecewise constant decay with warmup.
- multipliers: multipliers used in piecewise constant decay with warmup.
- scale_by_batch_size: Scale the learning rate by a fraction of the batch
- size. Set to 0 for no scaling (default).
- staircase: Apply exponential decay at discrete values instead of continuous.
- """
- name: str = None
- initial_lr: float = None
- decay_epochs: float = None
- decay_rate: float = None
- warmup_epochs: int = None
- examples_per_epoch: int = None
- boundaries: List[int] = None
- multipliers: List[float] = None
- scale_by_batch_size: float = 0.
- staircase: bool = None
-
-
-@dataclasses.dataclass
-class ModelConfig(hyperparams.Config):
- """Configuration for Models.
-
- Attributes:
- name: The name of the model. Defaults to None.
- model_params: The parameters used to create the model. Defaults to None.
- num_classes: The number of classes in the model. Defaults to None.
- loss: A `LossConfig` instance. Defaults to None.
- optimizer: An `OptimizerConfig` instance. Defaults to None.
- """
- name: str = None
- model_params: hyperparams.Config = None
- num_classes: int = None
- loss: LossConfig = None
- optimizer: OptimizerConfig = None
-
-
-@dataclasses.dataclass
-class ExperimentConfig(hyperparams.Config):
- """Base configuration for an image classification experiment.
-
- Attributes:
- model_dir: The directory to use when running an experiment.
- mode: e.g. 'train_and_eval', 'export'
- runtime: A `RuntimeConfig` instance.
- train: A `TrainConfig` instance.
- evaluation: An `EvalConfig` instance.
- model: A `ModelConfig` instance.
- export: An `ExportConfig` instance.
- """
- model_dir: str = None
- model_name: str = None
- mode: str = None
- runtime: RuntimeConfig = None
- train_dataset: Any = None
- validation_dataset: Any = None
- train: TrainConfig = None
- evaluation: EvalConfig = None
- model: ModelConfig = None
- export: ExportConfig = None
diff --git a/spaces/Navneet574/Heart_Disease_Prediciton/app.py b/spaces/Navneet574/Heart_Disease_Prediciton/app.py
deleted file mode 100644
index 30f66c36eeab6633487a32671d615f07ab544f93..0000000000000000000000000000000000000000
--- a/spaces/Navneet574/Heart_Disease_Prediciton/app.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import gradio as gr
-from joblib import load
-import numpy as np
-import pandas as pd
-
-AgeCategory = ['18-24', '25-29', '35-39', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80 or older']
-Binary_Input = ['Yes', 'No']
-Sex = ['Male', 'Female']
-Race = ['White', 'Black', 'Asian', 'American Indian/Alaskan Native', 'Hispanic', 'Other']
-Diabetic = ['Yes', 'No', 'No borderline diabetes', 'Yes (during pregnancy)']
-GeneralHealth = ['Excellent', 'Very good', 'Good', 'Fair', 'Poor']
-
-dic_ageCat = {'18-24':0, '25-29':1, '35-39':2, '35-39':3, '40-44':4, '45-49':5, '50-54':6, '55-59':7, '60-64':8, '65-69':9, '70-74':10, '75-79':11, '80 or older':12}
-dic_binary = {'Yes':1, 'No':0}
-dic_sex = {'Male':0, 'Female':1}
-dic_race = {'White':5, 'Black':2, 'Asian':0, 'American Indian/Alaskan Native':3, 'Hispanic':1, 'Other':4}
-dic_diabetic = {'Yes':2, 'No':0, 'No borderline diabetes':1, 'Yes (during pregnancy)':3}
-dic_generalHealth = {'Excellent':0, 'Very good':4, 'Good':2, 'Fair':1, 'Poor':3}
-
-
-def predict_price(BMI, Smoking, AlcoholDrinking, Stroke, PhysicalHealth, MentalHealth, DiffWalking, Sex, AgeCategory, Race, Diabetic, PhysicalActivity, GenHealth, SleepTime, Asthma, KidneyDisease, SkinCancer):
- model = load('Heart_Disease_Classification.jb')
-
- data = {
- 'BMI': [BMI],
- 'Smoking': dic_binary[Smoking],
- 'AlcoholDrinking': dic_binary[AlcoholDrinking],
- 'Stroke': dic_binary[Stroke],
- 'PhysicalHealth': [PhysicalHealth],
- 'MentalHealth': [MentalHealth],
- 'DiffWalking': dic_binary[DiffWalking],
- 'Sex': dic_sex[Sex],
- 'AgeCategory': dic_ageCat[AgeCategory],
- 'Race': dic_race[Race],
- 'Diabetic': dic_diabetic[Diabetic],
- 'PhysicalActivity': dic_binary[PhysicalActivity],
- 'GenHealth': dic_generalHealth[GenHealth],
- 'SleepTime': [SleepTime],
- 'Asthma': dic_binary[Asthma],
- 'KidneyDisease': dic_binary[KidneyDisease],
- 'SkinCancer': dic_binary[SkinCancer]
- }
-
- Xinp = pd.DataFrame(data)
- print(Xinp)
-
- stone = model.predict(Xinp)
-
- if stone == 1:
- return stone["YES"]
- else:
- return stone["NO"]
-
-ui = gr.Interface(
- fn=predict_price,inputs=[
- gr.inputs.Textbox(placeholder='BMI', default=0,
- numeric=True, label='Body Mass Index (BMI) (normal is between 18.5 and 24.9)'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Smoking', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Alcohol Drinking', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Stroke', default="No"),
- gr.inputs.Textbox(placeholder='Physical Health',
- default=0, numeric=True, label='Physical Health Score'),
- gr.inputs.Textbox(placeholder='MentalHealth', default=0,
- numeric=True, label='Mental Health Score'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Diff Walking', default="No"),
- gr.inputs.Dropdown(choices=Sex, label='Sex', default=""),
- gr.inputs.Dropdown(choices=AgeCategory, label='Age Category', default="18-24"),
- gr.inputs.Dropdown(choices=Race, label='Race', default="Asian"),
- gr.inputs.Dropdown(choices=Diabetic, label='Diabetic', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Physical Activity', default="Yes"),
- gr.inputs.Dropdown(choices=GeneralHealth, label='General Health', default="Excellent"),
- gr.inputs.Textbox(placeholder='Sleep Time',
- default='7', numeric=True, label='Sleep Time (normal is between 6-8 hrs)'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Asthma', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Kidney Disease', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Skin Cancer', default="No")
- ], outputs=[
- "text"
- ]
-)
-if __name__ == "__main__":
- ui.launch()
\ No newline at end of file
diff --git a/spaces/Nikhil0987/omm/convo.py b/spaces/Nikhil0987/omm/convo.py
deleted file mode 100644
index f5c87b19d82d3f1e1869101faf1205475b5ed3b9..0000000000000000000000000000000000000000
--- a/spaces/Nikhil0987/omm/convo.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from transformers import pipeline, Conversation
-import streamlit as st
-from streamlit_option_menu import option_menu
-
-chatbot = pipeline(task="conversational", model="microsoft/DialoGPT-medium")
-
-def Convo():
- if convo := st.chat_input("Enter your message"):
-
- conversation = Conversation(convo)
-
-
-
- # candidate_labels = ["HELP", "PROBLEM SOLVE", "GENERAL TALK"]
-
-
- ans = chatbot(conversation)
- # add_user_input = st.button("Add User Input")
-
- # conversation.add_user_input("{}".format(convo))
- # conversation = chatbot(conversation)
- with st.chat_message("assistant"):
- # ans
- st.write(ans.generated_responses[-1])
-
-
-# convo()
diff --git a/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py b/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py
deleted file mode 100644
index d9d372dcbb1bed2fffbfd8e81d6da749ceab730b..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py
+++ /dev/null
@@ -1,772 +0,0 @@
-"""
-This scripts plots examples of the images that get best and worse metrics
-"""
-print("Imports...", end="")
-import os
-import sys
-from argparse import ArgumentParser
-from pathlib import Path
-
-import matplotlib.patches as mpatches
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import seaborn as sns
-import yaml
-from imageio import imread
-from matplotlib.gridspec import GridSpec
-from skimage.color import rgba2rgb
-from sklearn.metrics.pairwise import euclidean_distances
-
-sys.path.append("../")
-
-from climategan.data import encode_mask_label
-from climategan.eval_metrics import edges_coherence_std_min
-from eval_masker import crop_and_resize
-
-# -----------------------
-# ----- Constants -----
-# -----------------------
-
-# Metrics
-metrics = ["error", "f05", "edge_coherence"]
-
-dict_metrics = {
- "names": {
- "tpr": "TPR, Recall, Sensitivity",
- "tnr": "TNR, Specificity, Selectivity",
- "fpr": "FPR",
- "fpt": "False positives relative to image size",
- "fnr": "FNR, Miss rate",
- "fnt": "False negatives relative to image size",
- "mpr": "May positive rate (MPR)",
- "mnr": "May negative rate (MNR)",
- "accuracy": "Accuracy (ignoring may)",
- "error": "Error",
- "f05": "F05 score",
- "precision": "Precision",
- "edge_coherence": "Edge coherence",
- "accuracy_must_may": "Accuracy (ignoring cannot)",
- },
- "key_metrics": ["error", "f05", "edge_coherence"],
-}
-
-
-# Colors
-colorblind_palette = sns.color_palette("colorblind")
-color_cannot = colorblind_palette[1]
-color_must = colorblind_palette[2]
-color_may = colorblind_palette[7]
-color_pred = colorblind_palette[4]
-
-icefire = sns.color_palette("icefire", as_cmap=False, n_colors=5)
-color_tp = icefire[0]
-color_tn = icefire[1]
-color_fp = icefire[4]
-color_fn = icefire[3]
-
-
-def parsed_args():
- """
- Parse and returns command-line args
-
- Returns:
- argparse.Namespace: the parsed arguments
- """
- parser = ArgumentParser()
- parser.add_argument(
- "--input_csv",
- default="ablations_metrics_20210311.csv",
- type=str,
- help="CSV containing the results of the ablation study",
- )
- parser.add_argument(
- "--output_dir",
- default=None,
- type=str,
- help="Output directory",
- )
- parser.add_argument(
- "--models_log_path",
- default=None,
- type=str,
- help="Path containing the log files of the models",
- )
- parser.add_argument(
- "--masker_test_set_dir",
- default=None,
- type=str,
- help="Directory containing the test images",
- )
- parser.add_argument(
- "--best_model",
- default="dada, msd_spade, pseudo",
- type=str,
- help="The string identifier of the best model",
- )
- parser.add_argument(
- "--dpi",
- default=200,
- type=int,
- help="DPI for the output images",
- )
- parser.add_argument(
- "--alpha",
- default=0.5,
- type=float,
- help="Transparency of labels shade",
- )
- parser.add_argument(
- "--percentile",
- default=0.05,
- type=float,
- help="Transparency of labels shade",
- )
- parser.add_argument(
- "--seed",
- default=None,
- type=int,
- help="Bootstrap random seed, for reproducibility",
- )
- parser.add_argument(
- "--no_images",
- action="store_true",
- default=False,
- help="Do not generate images",
- )
-
- return parser.parse_args()
-
-
-def map_color(arr, input_color, output_color, rtol=1e-09):
- """
- Maps one color to another
- """
- input_color_arr = np.tile(input_color, (arr.shape[:2] + (1,)))
- output = arr.copy()
- output[np.all(np.isclose(arr, input_color_arr, rtol=rtol), axis=2)] = output_color
- return output
-
-
-def plot_labels(ax, img, label, img_id, n_, add_title, do_legend):
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (255, 0, 0), color_cannot)
- label_colmap = map_color(label_colmap, (0, 0, 255), color_must)
- label_colmap = map_color(label_colmap, (0, 0, 0), color_may)
-
- ax.imshow(img)
- ax.imshow(label_colmap, alpha=0.5)
- ax.axis("off")
-
- if n_ in [1, 3, 5]:
- color_ = "green"
- else:
- color_ = "red"
-
- ax.text(
- -0.15,
- 0.5,
- img_id,
- color=color_,
- fontweight="roman",
- fontsize="x-large",
- horizontalalignment="left",
- verticalalignment="center",
- transform=ax.transAxes,
- )
-
- if add_title:
- ax.set_title("Labels", rotation=0, fontsize="x-large")
-
-
-def plot_pred(ax, img, pred, img_id, add_title, do_legend):
- pred = np.tile(np.expand_dims(pred, axis=2), reps=(1, 1, 3))
-
- pred_colmap = pred.astype(float)
- pred_colmap = map_color(pred_colmap, (1, 1, 1), color_pred)
- pred_colmap_ma = np.ma.masked_not_equal(pred_colmap, color_pred)
- pred_colmap_ma = pred_colmap_ma.mask * img + pred_colmap_ma
-
- ax.imshow(img)
- ax.imshow(pred_colmap_ma, alpha=0.5)
- ax.axis("off")
-
- if add_title:
- ax.set_title("Prediction", rotation=0, fontsize="x-large")
-
-
-def plot_correct_incorrect(
- ax, img_filename, img, metric, label, img_id, n_, add_title, do_legend
-):
- # FP
- fp_map = imread(
- model_path / "eval-metrics/fp" / "{}_fp.png".format(Path(img_filename).stem)
- )
- fp_map = np.tile(np.expand_dims(fp_map, axis=2), reps=(1, 1, 3))
-
- fp_map_colmap = fp_map.astype(float)
- fp_map_colmap = map_color(fp_map_colmap, (1, 1, 1), color_fp)
-
- # FN
- fn_map = imread(
- model_path / "eval-metrics/fn" / "{}_fn.png".format(Path(img_filename).stem)
- )
- fn_map = np.tile(np.expand_dims(fn_map, axis=2), reps=(1, 1, 3))
-
- fn_map_colmap = fn_map.astype(float)
- fn_map_colmap = map_color(fn_map_colmap, (1, 1, 1), color_fn)
-
- # TP
- tp_map = imread(
- model_path / "eval-metrics/tp" / "{}_tp.png".format(Path(img_filename).stem)
- )
- tp_map = np.tile(np.expand_dims(tp_map, axis=2), reps=(1, 1, 3))
-
- tp_map_colmap = tp_map.astype(float)
- tp_map_colmap = map_color(tp_map_colmap, (1, 1, 1), color_tp)
-
- # TN
- tn_map = imread(
- model_path / "eval-metrics/tn" / "{}_tn.png".format(Path(img_filename).stem)
- )
- tn_map = np.tile(np.expand_dims(tn_map, axis=2), reps=(1, 1, 3))
-
- tn_map_colmap = tn_map.astype(float)
- tn_map_colmap = map_color(tn_map_colmap, (1, 1, 1), color_tn)
-
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (0, 0, 0), color_may)
- label_colmap_ma = np.ma.masked_not_equal(label_colmap, color_may)
- label_colmap_ma = label_colmap_ma.mask * img + label_colmap_ma
-
- # Combine masks
- maps = fp_map_colmap + fn_map_colmap + tp_map_colmap + tn_map_colmap
- maps_ma = np.ma.masked_equal(maps, (0, 0, 0))
- maps_ma = maps_ma.mask * img + maps_ma
-
- ax.imshow(img)
- ax.imshow(label_colmap_ma, alpha=0.5)
- ax.imshow(maps_ma, alpha=0.5)
- ax.axis("off")
-
- if add_title:
- ax.set_title("Metric", rotation=0, fontsize="x-large")
-
-
-def plot_edge_coherence(ax, img, metric, label, pred, img_id, n_, add_title, do_legend):
- pred = np.tile(np.expand_dims(pred, axis=2), reps=(1, 1, 3))
-
- ec, pred_ec, label_ec = edges_coherence_std_min(
- np.squeeze(pred[:, :, 0]), np.squeeze(encode_mask_label(label, "flood"))
- )
-
- ##################
- # Edge distances #
- ##################
-
- # Location of edges
- pred_ec_coord = np.argwhere(pred_ec > 0)
- label_ec_coord = np.argwhere(label_ec > 0)
-
- # Normalized pairwise distances between pred and label
- dist_mat = np.divide(
- euclidean_distances(pred_ec_coord, label_ec_coord), pred_ec.shape[0]
- )
-
- # Standard deviation of the minimum distance from pred to label
- min_dist = np.min(dist_mat, axis=1) # noqa: F841
-
- #############
- # Make plot #
- #############
-
- pred_ec = np.tile(
- np.expand_dims(np.asarray(pred_ec > 0, dtype=float), axis=2), reps=(1, 1, 3)
- )
- pred_ec_colmap = map_color(pred_ec, (1, 1, 1), color_pred)
- pred_ec_colmap_ma = np.ma.masked_not_equal(pred_ec_colmap, color_pred) # noqa: F841
-
- label_ec = np.tile(
- np.expand_dims(np.asarray(label_ec > 0, dtype=float), axis=2), reps=(1, 1, 3)
- )
- label_ec_colmap = map_color(label_ec, (1, 1, 1), color_must)
- label_ec_colmap_ma = np.ma.masked_not_equal( # noqa: F841
- label_ec_colmap, color_must
- )
-
- # Combined pred and label edges
- combined_ec = pred_ec_colmap + label_ec_colmap
- combined_ec_ma = np.ma.masked_equal(combined_ec, (0, 0, 0))
- combined_ec_img = combined_ec_ma.mask * img + combined_ec
-
- # Pred
- pred_colmap = pred.astype(float)
- pred_colmap = map_color(pred_colmap, (1, 1, 1), color_pred)
- pred_colmap_ma = np.ma.masked_not_equal(pred_colmap, color_pred)
-
- # Must
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (0, 0, 255), color_must)
- label_colmap_ma = np.ma.masked_not_equal(label_colmap, color_must)
-
- # TP
- tp_map = imread(
- model_path / "eval-metrics/tp" / "{}_tp.png".format(Path(srs_sel.filename).stem)
- )
- tp_map = np.tile(np.expand_dims(tp_map, axis=2), reps=(1, 1, 3))
- tp_map_colmap = tp_map.astype(float)
- tp_map_colmap = map_color(tp_map_colmap, (1, 1, 1), color_tp)
- tp_map_colmap_ma = np.ma.masked_not_equal(tp_map_colmap, color_tp)
-
- # Combination
- comb_pred = (
- (pred_colmap_ma.mask ^ tp_map_colmap_ma.mask)
- & tp_map_colmap_ma.mask
- & combined_ec_ma.mask
- ) * pred_colmap
- comb_label = (
- (label_colmap_ma.mask ^ pred_colmap_ma.mask)
- & pred_colmap_ma.mask
- & combined_ec_ma.mask
- ) * label_colmap
- comb_tp = combined_ec_ma.mask * tp_map_colmap.copy()
- combined = comb_tp + comb_label + comb_pred
- combined_ma = np.ma.masked_equal(combined, (0, 0, 0))
- combined_ma = combined_ma.mask * combined_ec_img + combined_ma
-
- ax.imshow(combined_ec_img, alpha=1)
- ax.imshow(combined_ma, alpha=0.5)
- ax.axis("off")
-
- # Plot lines
- idx_sort_x = np.argsort(pred_ec_coord[:, 1])
- offset = 100
- for idx in range(offset, pred_ec_coord.shape[0], offset):
- y0, x0 = pred_ec_coord[idx_sort_x[idx], :]
- argmin = np.argmin(dist_mat[idx_sort_x[idx]])
- y1, x1 = label_ec_coord[argmin, :]
- ax.plot([x0, x1], [y0, y1], color="white", linewidth=0.5)
-
- if add_title:
- ax.set_title("Metric", rotation=0, fontsize="x-large")
-
-
-def plot_images_metric(
- axes, metric, img_filename, img_id, n_, srs_sel, add_title, do_legend
-):
-
- # Read images
- img_path = imgs_orig_path / img_filename
- label_path = labels_path / "{}_labeled.png".format(Path(img_filename).stem)
- img, label = crop_and_resize(img_path, label_path)
- img = rgba2rgb(img) if img.shape[-1] == 4 else img / 255.0
-
- pred = imread(
- model_path / "eval-metrics/pred" / "{}_pred.png".format(Path(img_filename).stem)
- )
-
- # Label
- plot_labels(axes[0], img, label, img_id, n_, add_title, do_legend)
-
- # Prediction
- plot_pred(axes[1], img, pred, img_id, add_title, do_legend)
-
- # Correct / incorrect
- if metric in ["error", "f05"]:
- plot_correct_incorrect(
- axes[2],
- img_filename,
- img,
- metric,
- label,
- img_id,
- n_,
- add_title,
- do_legend=False,
- )
- handles = []
- lw = 1.0
- handles.append(
- mpatches.Patch(facecolor=color_tp, label="TP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_tn, label="TN", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_fp, label="FP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_fn, label="FN", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(
- facecolor=color_may,
- label="May-be-flooded",
- linewidth=lw,
- alpha=0.66,
- )
- )
- labels = ["TP", "TN", "FP", "FN", "May-be-flooded"]
- if metric == "error":
- if n_ in [1, 3, 5]:
- title = "Low error rate"
- else:
- title = "High error rate"
- else:
- if n_ in [1, 3, 5]:
- title = "High F05 score"
- else:
- title = "Low F05 score"
- # Edge coherence
- elif metric == "edge_coherence":
- plot_edge_coherence(
- axes[2], img, metric, label, pred, img_id, n_, add_title, do_legend=False
- )
- handles = []
- lw = 1.0
- handles.append(
- mpatches.Patch(facecolor=color_tp, label="TP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_pred, label="pred", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(
- facecolor=color_must,
- label="Must-be-flooded",
- linewidth=lw,
- alpha=0.66,
- )
- )
- labels = ["TP", "Prediction", "Must-be-flooded"]
- if n_ in [1, 3, 5]:
- title = "High edge coherence"
- else:
- title = "Low edge coherence"
-
- else:
- raise ValueError
-
- labels_values_title = "Error: {:.4f} \nFO5: {:.4f} \nEdge coherence: {:.4f}".format(
- srs_sel.error, srs_sel.f05, srs_sel.edge_coherence
- )
-
- plot_legend(axes[3], img, handles, labels, labels_values_title, title)
-
-
-def plot_legend(ax, img, handles, labels, labels_values_title, title):
- img_ = np.zeros_like(img, dtype=np.uint8)
- img_.fill(255)
- ax.imshow(img_)
- ax.axis("off")
-
- leg1 = ax.legend(
- handles=handles,
- labels=labels,
- title=title,
- title_fontsize="medium",
- labelspacing=0.6,
- loc="upper left",
- fontsize="x-small",
- frameon=False,
- )
- leg1._legend_box.align = "left"
-
- leg2 = ax.legend(
- title=labels_values_title,
- title_fontsize="small",
- loc="lower left",
- frameon=False,
- )
- leg2._legend_box.align = "left"
-
- ax.add_artist(leg1)
-
-
-def scatterplot_metrics_pair(ax, df, x_metric, y_metric, dict_images):
-
- sns.scatterplot(data=df, x=x_metric, y=y_metric, ax=ax)
-
- # Set X-label
- ax.set_xlabel(dict_metrics["names"][x_metric], rotation=0, fontsize="medium")
-
- # Set Y-label
- ax.set_ylabel(dict_metrics["names"][y_metric], rotation=90, fontsize="medium")
-
- # Change spines
- sns.despine(ax=ax, left=True, bottom=True)
-
- annotate_scatterplot(ax, dict_images, x_metric, y_metric)
-
-
-def scatterplot_metrics(ax, df, df_all, dict_images, plot_all=False):
-
- # Other
- if plot_all:
- sns.scatterplot(
- data=df_all.loc[df_all.ground == True],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='+', alpha=0.25)
- sns.scatterplot(
- data=df_all.loc[df_all.instagan == True],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='x', alpha=0.25)
- sns.scatterplot(
- data=df_all.loc[(df_all.instagan == False) & (df_all.instagan == False) &
- (df_all.model_feats != args.best_model)],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='s', alpha=0.25)
-
- # Best model
- cmap_ = sns.cubehelix_palette(as_cmap=True)
- sns.scatterplot(
- data=df, x="error", y="f05", hue="edge_coherence", ax=ax, palette=cmap_
- )
-
- norm = plt.Normalize(df["edge_coherence"].min(), df["edge_coherence"].max())
- sm = plt.cm.ScalarMappable(cmap=cmap_, norm=norm)
- sm.set_array([])
-
- # Remove the legend and add a colorbar
- ax.get_legend().remove()
- ax_cbar = ax.figure.colorbar(sm)
- ax_cbar.set_label("Edge coherence", labelpad=8)
-
- # Set X-label
- ax.set_xlabel(dict_metrics["names"]["error"], rotation=0, fontsize="medium")
-
- # Set Y-label
- ax.set_ylabel(dict_metrics["names"]["f05"], rotation=90, fontsize="medium")
-
- annotate_scatterplot(ax, dict_images, "error", "f05")
-
- # Change spines
- sns.despine(ax=ax, left=True, bottom=True)
-
- # Set XY limits
- xlim = ax.get_xlim()
- ylim = ax.get_ylim()
- ax.set_xlim([0.0, xlim[1]])
- ax.set_ylim([ylim[0], 1.0])
-
-
-def annotate_scatterplot(ax, dict_images, x_metric, y_metric, offset=0.1):
- xlim = ax.get_xlim()
- ylim = ax.get_ylim()
- x_len = xlim[1] - xlim[0]
- y_len = ylim[1] - ylim[0]
- x_th = xlim[1] - x_len / 2.0
- y_th = ylim[1] - y_len / 2.0
- for text, d in dict_images.items():
- if text in ["B", "D", "F"]:
- x = d[x_metric]
- y = d[y_metric]
-
- x_text = x + x_len * offset if x < x_th else x - x_len * offset
- y_text = y + y_len * offset if y < y_th else y - y_len * offset
-
- ax.annotate(
- xy=(x, y),
- xycoords="data",
- xytext=(x_text, y_text),
- textcoords="data",
- text=text,
- arrowprops=dict(facecolor="black", shrink=0.05),
- fontsize="medium",
- color="black",
- )
- elif text == "A":
- x = (
- dict_images["A"][x_metric]
- + dict_images["C"][x_metric]
- + dict_images["E"][x_metric]
- ) / 3
- y = (
- dict_images["A"][y_metric]
- + dict_images["C"][y_metric]
- + dict_images["E"][y_metric]
- ) / 3
-
- x_text = x + x_len * 2 * offset if x < x_th else x - x_len * 2 * offset
- y_text = (
- y + y_len * 0.45 * offset if y < y_th else y - y_len * 0.45 * offset
- )
-
- ax.annotate(
- xy=(x, y),
- xycoords="data",
- xytext=(x_text, y_text),
- textcoords="data",
- text="A, C, E",
- arrowprops=dict(facecolor="black", shrink=0.05),
- fontsize="medium",
- color="black",
- )
-
-
-if __name__ == "__main__":
- # -----------------------------
- # ----- Parse arguments -----
- # -----------------------------
- args = parsed_args()
- print("Args:\n" + "\n".join([f" {k:20}: {v}" for k, v in vars(args).items()]))
-
- # Determine output dir
- if args.output_dir is None:
- output_dir = Path(os.environ["SLURM_TMPDIR"])
- else:
- output_dir = Path(args.output_dir)
- if not output_dir.exists():
- output_dir.mkdir(parents=True, exist_ok=False)
-
- # Store args
- output_yml = output_dir / "labels.yml"
- with open(output_yml, "w") as f:
- yaml.dump(vars(args), f)
-
- # Data dirs
- imgs_orig_path = Path(args.masker_test_set_dir) / "imgs"
- labels_path = Path(args.masker_test_set_dir) / "labels"
-
- # Read CSV
- df_all = pd.read_csv(args.input_csv, index_col="model_img_idx")
-
- # Select best model
- df = df_all.loc[df_all.model_feats == args.best_model]
- v_key, model_dir = df.model.unique()[0].split("/")
- model_path = Path(args.models_log_path) / "ablation-{}".format(v_key) / model_dir
-
- # Set up plot
- sns.reset_orig()
- sns.set(style="whitegrid")
- plt.rcParams.update({"font.family": "serif"})
- plt.rcParams.update(
- {
- "font.serif": [
- "Computer Modern Roman",
- "Times New Roman",
- "Utopia",
- "New Century Schoolbook",
- "Century Schoolbook L",
- "ITC Bookman",
- "Bookman",
- "Times",
- "Palatino",
- "Charter",
- "serif" "Bitstream Vera Serif",
- "DejaVu Serif",
- ]
- }
- )
-
- if args.seed:
- np.random.seed(args.seed)
- img_ids = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
- dict_images = {}
- idx = 0
-
- # Define grid of subplots
- grid_vmargin = 0.03 # Extent of the vertical margin between metric grids
- ax_hspace = 0.04 # Extent of the vertical space between axes of same grid
- ax_wspace = 0.05 # Extent of the horizontal space between axes of same grid
- n_grids = len(metrics)
- n_cols = 4
- n_rows = 2
- h_grid = (1.0 / n_grids) - ((n_grids - 1) * grid_vmargin) / n_grids
-
- fig1 = plt.figure(dpi=200, figsize=(11, 13))
-
- n_ = 0
- add_title = False
- for metric_id, metric in enumerate(metrics):
-
- # Create grid
- top_grid = 1.0 - metric_id * h_grid - metric_id * grid_vmargin
- bottom_grid = top_grid - h_grid
- gridspec = GridSpec(
- n_rows,
- n_cols,
- wspace=ax_wspace,
- hspace=ax_hspace,
- bottom=bottom_grid,
- top=top_grid,
- )
-
- # Select best
- if metric == "error":
- ascending = True
- else:
- ascending = False
- idx_rand = np.random.permutation(int(args.percentile * len(df)))[0]
- srs_sel = df.sort_values(by=metric, ascending=ascending).iloc[idx_rand]
- img_id = img_ids[idx]
- dict_images.update({img_id: srs_sel})
- # Read images
- img_filename = srs_sel.filename
-
- axes_row = [fig1.add_subplot(gridspec[0, c]) for c in range(n_cols)]
- if not args.no_images:
- n_ += 1
- if metric_id == 0:
- add_title = True
- plot_images_metric(
- axes_row,
- metric,
- img_filename,
- img_id,
- n_,
- srs_sel,
- add_title=add_title,
- do_legend=False,
- )
- add_title = False
-
- idx += 1
- print("1 more row done.")
- # Select worst
- if metric == "error":
- ascending = False
- else:
- ascending = True
- idx_rand = np.random.permutation(int(args.percentile * len(df)))[0]
- srs_sel = df.sort_values(by=metric, ascending=ascending).iloc[idx_rand]
- img_id = img_ids[idx]
- dict_images.update({img_id: srs_sel})
- # Read images
- img_filename = srs_sel.filename
-
- axes_row = [fig1.add_subplot(gridspec[1, c]) for c in range(n_cols)]
- if not args.no_images:
- n_ += 1
- plot_images_metric(
- axes_row,
- metric,
- img_filename,
- img_id,
- n_,
- srs_sel,
- add_title=add_title,
- do_legend=False,
- )
-
- idx += 1
- print("1 more row done.")
-
- output_fig = output_dir / "all_metrics.png"
-
- fig1.tight_layout() # (pad=1.5) #
- fig1.savefig(output_fig, dpi=fig1.dpi, bbox_inches="tight")
-
- # Scatter plot
- fig2 = plt.figure(dpi=200)
-
- scatterplot_metrics(fig2.gca(), df, df_all, dict_images)
-
- # fig2, axes = plt.subplots(nrows=1, ncols=3, dpi=200, figsize=(18, 5))
- #
- # scatterplot_metrics_pair(axes[0], df, "error", "f05", dict_images)
- # scatterplot_metrics_pair(axes[1], df, "error", "edge_coherence", dict_images)
- # scatterplot_metrics_pair(axes[2], df, "f05", "edge_coherence", dict_images)
-
- output_fig = output_dir / "scatterplots.png"
- fig2.savefig(output_fig, dpi=fig2.dpi, bbox_inches="tight")
diff --git a/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh b/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh
deleted file mode 100644
index d649238546b996ae1ad00f7753c04aeebc7aaa97..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-echo "Dowloading Script" && python download_labelbox.py
-
-echo "Merging Script" && python merge_labelbox_masks.py
-
-echo "Cleaning labeled"
-rm /Users/victor/Downloads/metrics-v2/labels/*
-cp /Users/victor/Downloads/labelbox_test_flood-v2/__labeled/* /Users/victor/Downloads/metrics-v2/labels
-
-echo "Create labeled images Script" && python create_labeled.py
\ No newline at end of file
diff --git a/spaces/Nunchakuka/FrenchAnonymizer/models.py b/spaces/Nunchakuka/FrenchAnonymizer/models.py
deleted file mode 100644
index 46b8aacb1bef18f6fad4c20c968b19125626799c..0000000000000000000000000000000000000000
--- a/spaces/Nunchakuka/FrenchAnonymizer/models.py
+++ /dev/null
@@ -1,351 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
-
-class Encoder(nn.Module):
- def __init__(self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
- def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
- resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(weight_norm(
- ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
- k, u, padding=(k-u)//2)))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel//(2**(i+1))
- for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i*self.num_kernels+j](x)
- else:
- xs += self.resblocks[i*self.num_kernels+j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- print('Removing weight norm...')
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
- ])
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ])
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2,3,5,7,11]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class SpeakerEncoder(torch.nn.Module):
- def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
- super(SpeakerEncoder, self).__init__()
- self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
- self.linear = nn.Linear(model_hidden_size, model_embedding_size)
- self.relu = nn.ReLU()
-
- def forward(self, mels):
- self.lstm.flatten_parameters()
- _, (hidden, _) = self.lstm(mels)
- embeds_raw = self.relu(self.linear(hidden[-1]))
- return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
-
- def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
- mel_slices = []
- for i in range(0, total_frames-partial_frames, partial_hop):
- mel_range = torch.arange(i, i+partial_frames)
- mel_slices.append(mel_range)
-
- return mel_slices
-
- def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
- mel_len = mel.size(1)
- last_mel = mel[:,-partial_frames:]
-
- if mel_len > partial_frames:
- mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
- mels = list(mel[:,s] for s in mel_slices)
- mels.append(last_mel)
- mels = torch.stack(tuple(mels), 0).squeeze(1)
-
- with torch.no_grad():
- partial_embeds = self(mels)
- embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
- #embed = embed / torch.linalg.norm(embed, 2)
- else:
- with torch.no_grad():
- embed = self(last_mel)
-
- return embed
-
-
-class SynthesizerTrn(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels,
- ssl_dim,
- use_spk,
- **kwargs):
-
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- self.ssl_dim = ssl_dim
- self.use_spk = use_spk
-
- self.enc_p = Encoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16)
- self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
- self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
- self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
- if not self.use_spk:
- self.enc_spk = SpeakerEncoder(model_hidden_size=gin_channels, model_embedding_size=gin_channels)
-
- def forward(self, c, spec, g=None, mel=None, c_lengths=None, spec_lengths=None):
- if c_lengths == None:
- c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
- if spec_lengths == None:
- spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
-
- if not self.use_spk:
- g = self.enc_spk(mel.transpose(1,2))
- g = g.unsqueeze(-1)
-
- _, m_p, logs_p, _ = self.enc_p(c, c_lengths)
- z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g)
- z_p = self.flow(z, spec_mask, g=g)
-
- z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size)
- o = self.dec(z_slice, g=g)
-
- return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, c, g=None, mel=None, c_lengths=None):
- if c_lengths == None:
- c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
- if not self.use_spk:
- g = self.enc_spk.embed_utterance(mel.transpose(1,2))
- g = g.unsqueeze(-1)
-
- z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths)
- z = self.flow(z_p, c_mask, g=g, reverse=True)
- o = self.dec(z * c_mask, g=g)
-
- return o
diff --git a/spaces/Nunchakuka/FrenchAnonymizer/utils.py b/spaces/Nunchakuka/FrenchAnonymizer/utils.py
deleted file mode 100644
index 1bd5b6185af6c9f1c270b8ba345bfc36d059e081..0000000000000000000000000000000000000000
--- a/spaces/Nunchakuka/FrenchAnonymizer/utils.py
+++ /dev/null
@@ -1,305 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-from torch.nn import functional as F
-from commons import sequence_mask
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def get_cmodel(rank):
- checkpoint = torch.load('wavlm/WavLM-Large.pt')
- cfg = WavLMConfig(checkpoint['cfg'])
- cmodel = WavLM(cfg).cuda(rank)
- cmodel.load_state_dict(checkpoint['model'])
- cmodel.eval()
- return cmodel
-
-
-def get_content(cmodel, y):
- with torch.no_grad():
- c = cmodel.extract_features(y.squeeze(1))[0]
- c = c.transpose(1, 2)
- return c
-
-
-def get_vocoder(rank):
- with open("hifigan/config.json", "r") as f:
- config = json.load(f)
- config = hifigan.AttrDict(config)
- vocoder = hifigan.Generator(config)
- ckpt = torch.load("hifigan/generator_v1")
- vocoder.load_state_dict(ckpt["generator"])
- vocoder.eval()
- vocoder.remove_weight_norm()
- vocoder.cuda(rank)
- return vocoder
-
-
-def transform(mel, height): # 68-92
- #r = np.random.random()
- #rate = r * 0.3 + 0.85 # 0.85-1.15
- #height = int(mel.size(-2) * rate)
- tgt = torchvision.transforms.functional.resize(mel, (height, mel.size(-1)))
- if height >= mel.size(-2):
- return tgt[:, :mel.size(-2), :]
- else:
- silence = tgt[:,-1:,:].repeat(1,mel.size(-2)-height,1)
- silence += torch.randn_like(silence) / 10
- return torch.cat((tgt, silence), 1)
-
-
-def stretch(mel, width): # 0.5-2
- return torchvision.transforms.functional.resize(mel, (mel.size(-2), width))
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict= {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})" .format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info("Saving model and optimizer state at iteration {} to {}".format(
- iteration, checkpoint_path))
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- torch.save({'model': state_dict,
- 'iteration': iteration,
- 'optimizer': optimizer.state_dict(),
- 'learning_rate': learning_rate}, checkpoint_path)
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
- for k, v in scalars.items():
- writer.add_scalar(k, v, global_step)
- for k, v in histograms.items():
- writer.add_histogram(k, v, global_step)
- for k, v in images.items():
- writer.add_image(k, v, global_step, dataformats='HWC')
- for k, v in audios.items():
- writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
- x = f_list[-1]
- print(x)
- return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10,2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py
deleted file mode 100644
index bb80d11a67cd75764a89f6f41915b0348ae96e92..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py
+++ /dev/null
@@ -1,428 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from multiprocessing import Pool
-
-import numpy as np
-from fairseq import options
-from fairseq.data import dictionary
-from fairseq.scoring import bleu
-
-from examples.noisychannel import (
- rerank_generate,
- rerank_options,
- rerank_score_bw,
- rerank_score_lm,
- rerank_utils,
-)
-
-
-def score_target_hypo(
- args, a, b, c, lenpen, target_outfile, hypo_outfile, write_hypos, normalize
-):
-
- print("lenpen", lenpen, "weight1", a, "weight2", b, "weight3", c)
- gen_output_lst, bitext1_lst, bitext2_lst, lm_res_lst = load_score_files(args)
- dict = dictionary.Dictionary()
- scorer = scorer = bleu.Scorer(
- bleu.BleuConfig(
- pad=dict.pad(),
- eos=dict.eos(),
- unk=dict.unk(),
- )
- )
-
- ordered_hypos = {}
- ordered_targets = {}
-
- for shard_id in range(len(bitext1_lst)):
- bitext1 = bitext1_lst[shard_id]
- bitext2 = bitext2_lst[shard_id]
- gen_output = gen_output_lst[shard_id]
- lm_res = lm_res_lst[shard_id]
-
- total = len(bitext1.rescore_source.keys())
- source_lst = []
- hypo_lst = []
- score_lst = []
- reference_lst = []
- j = 1
- best_score = -math.inf
-
- for i in range(total):
- # length is measured in terms of words, not bpe tokens, since models may not share the same bpe
- target_len = len(bitext1.rescore_hypo[i].split())
-
- if lm_res is not None:
- lm_score = lm_res.score[i]
- else:
- lm_score = 0
-
- if bitext2 is not None:
- bitext2_score = bitext2.rescore_score[i]
- bitext2_backwards = bitext2.backwards
- else:
- bitext2_score = None
- bitext2_backwards = None
-
- score = rerank_utils.get_score(
- a,
- b,
- c,
- target_len,
- bitext1.rescore_score[i],
- bitext2_score,
- lm_score=lm_score,
- lenpen=lenpen,
- src_len=bitext1.source_lengths[i],
- tgt_len=bitext1.target_lengths[i],
- bitext1_backwards=bitext1.backwards,
- bitext2_backwards=bitext2_backwards,
- normalize=normalize,
- )
-
- if score > best_score:
- best_score = score
- best_hypo = bitext1.rescore_hypo[i]
-
- if j == gen_output.num_hypos[i] or j == args.num_rescore:
- j = 1
- hypo_lst.append(best_hypo)
- score_lst.append(best_score)
- source_lst.append(bitext1.rescore_source[i])
- reference_lst.append(bitext1.rescore_target[i])
-
- best_score = -math.inf
- best_hypo = ""
- else:
- j += 1
-
- gen_keys = list(sorted(gen_output.no_bpe_target.keys()))
-
- for key in range(len(gen_keys)):
- if args.prefix_len is None:
- assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], (
- "pred and rescore hypo mismatch: i: "
- + str(key)
- + ", "
- + str(hypo_lst[key])
- + str(gen_keys[key])
- + str(gen_output.no_bpe_hypo[key])
- )
- sys_tok = dict.encode_line(hypo_lst[key])
- ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]])
- scorer.add(ref_tok, sys_tok)
-
- else:
- full_hypo = rerank_utils.get_full_from_prefix(
- hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]]
- )
- sys_tok = dict.encode_line(full_hypo)
- ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]])
- scorer.add(ref_tok, sys_tok)
-
- # if only one set of hyper parameters is provided, write the predictions to a file
- if write_hypos:
- # recover the orinal ids from n best list generation
- for key in range(len(gen_output.no_bpe_target)):
- if args.prefix_len is None:
- assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], (
- "pred and rescore hypo mismatch:"
- + "i:"
- + str(key)
- + str(hypo_lst[key])
- + str(gen_output.no_bpe_hypo[key])
- )
- ordered_hypos[gen_keys[key]] = hypo_lst[key]
- ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[
- gen_keys[key]
- ]
-
- else:
- full_hypo = rerank_utils.get_full_from_prefix(
- hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]]
- )
- ordered_hypos[gen_keys[key]] = full_hypo
- ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[
- gen_keys[key]
- ]
-
- # write the hypos in the original order from nbest list generation
- if args.num_shards == (len(bitext1_lst)):
- with open(target_outfile, "w") as t:
- with open(hypo_outfile, "w") as h:
- for key in range(len(ordered_hypos)):
- t.write(ordered_targets[key])
- h.write(ordered_hypos[key])
-
- res = scorer.result_string(4)
- if write_hypos:
- print(res)
- score = rerank_utils.parse_bleu_scoring(res)
- return score
-
-
-def match_target_hypo(args, target_outfile, hypo_outfile):
- """combine scores from the LM and bitext models, and write the top scoring hypothesis to a file"""
- if len(args.weight1) == 1:
- res = score_target_hypo(
- args,
- args.weight1[0],
- args.weight2[0],
- args.weight3[0],
- args.lenpen[0],
- target_outfile,
- hypo_outfile,
- True,
- args.normalize,
- )
- rerank_scores = [res]
- else:
- print("launching pool")
- with Pool(32) as p:
- rerank_scores = p.starmap(
- score_target_hypo,
- [
- (
- args,
- args.weight1[i],
- args.weight2[i],
- args.weight3[i],
- args.lenpen[i],
- target_outfile,
- hypo_outfile,
- False,
- args.normalize,
- )
- for i in range(len(args.weight1))
- ],
- )
-
- if len(rerank_scores) > 1:
- best_index = np.argmax(rerank_scores)
- best_score = rerank_scores[best_index]
- print("best score", best_score)
- print("best lenpen", args.lenpen[best_index])
- print("best weight1", args.weight1[best_index])
- print("best weight2", args.weight2[best_index])
- print("best weight3", args.weight3[best_index])
- return (
- args.lenpen[best_index],
- args.weight1[best_index],
- args.weight2[best_index],
- args.weight3[best_index],
- best_score,
- )
-
- else:
- return (
- args.lenpen[0],
- args.weight1[0],
- args.weight2[0],
- args.weight3[0],
- rerank_scores[0],
- )
-
-
-def load_score_files(args):
- if args.all_shards:
- shard_ids = list(range(args.num_shards))
- else:
- shard_ids = [args.shard_id]
-
- gen_output_lst = []
- bitext1_lst = []
- bitext2_lst = []
- lm_res1_lst = []
-
- for shard_id in shard_ids:
- using_nbest = args.nbest_list is not None
- (
- pre_gen,
- left_to_right_preprocessed_dir,
- right_to_left_preprocessed_dir,
- backwards_preprocessed_dir,
- lm_preprocessed_dir,
- ) = rerank_utils.get_directories(
- args.data_dir_name,
- args.num_rescore,
- args.gen_subset,
- args.gen_model_name,
- shard_id,
- args.num_shards,
- args.sampling,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- rerank1_is_gen = (
- args.gen_model == args.score_model1 and args.source_prefix_frac is None
- )
- rerank2_is_gen = (
- args.gen_model == args.score_model2 and args.source_prefix_frac is None
- )
-
- score1_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model1_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards1,
- )
- if args.score_model2 is not None:
- score2_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model2_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards2,
- )
- if args.language_model is not None:
- lm_score_file = rerank_utils.rescore_file_name(
- pre_gen, args.prefix_len, args.lm_name, lm_file=True
- )
-
- # get gen output
- predictions_bpe_file = pre_gen + "/generate_output_bpe.txt"
- if using_nbest:
- print("Using predefined n-best list from interactive.py")
- predictions_bpe_file = args.nbest_list
- gen_output = rerank_utils.BitextOutputFromGen(
- predictions_bpe_file,
- bpe_symbol=args.post_process,
- nbest=using_nbest,
- prefix_len=args.prefix_len,
- target_prefix_frac=args.target_prefix_frac,
- )
-
- if rerank1_is_gen:
- bitext1 = gen_output
- else:
- bitext1 = rerank_utils.BitextOutput(
- score1_file,
- args.backwards1,
- args.right_to_left1,
- args.post_process,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- if args.score_model2 is not None or args.nbest_list is not None:
- if rerank2_is_gen:
- bitext2 = gen_output
- else:
- bitext2 = rerank_utils.BitextOutput(
- score2_file,
- args.backwards2,
- args.right_to_left2,
- args.post_process,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- assert (
- bitext2.source_lengths == bitext1.source_lengths
- ), "source lengths for rescoring models do not match"
- assert (
- bitext2.target_lengths == bitext1.target_lengths
- ), "target lengths for rescoring models do not match"
- else:
- if args.diff_bpe:
- assert args.score_model2 is None
- bitext2 = gen_output
- else:
- bitext2 = None
-
- if args.language_model is not None:
- lm_res1 = rerank_utils.LMOutput(
- lm_score_file,
- args.lm_dict,
- args.prefix_len,
- args.post_process,
- args.target_prefix_frac,
- )
- else:
- lm_res1 = None
-
- gen_output_lst.append(gen_output)
- bitext1_lst.append(bitext1)
- bitext2_lst.append(bitext2)
- lm_res1_lst.append(lm_res1)
- return gen_output_lst, bitext1_lst, bitext2_lst, lm_res1_lst
-
-
-def rerank(args):
- if type(args.lenpen) is not list:
- args.lenpen = [args.lenpen]
- if type(args.weight1) is not list:
- args.weight1 = [args.weight1]
- if type(args.weight2) is not list:
- args.weight2 = [args.weight2]
- if type(args.weight3) is not list:
- args.weight3 = [args.weight3]
- if args.all_shards:
- shard_ids = list(range(args.num_shards))
- else:
- shard_ids = [args.shard_id]
-
- for shard_id in shard_ids:
- (
- pre_gen,
- left_to_right_preprocessed_dir,
- right_to_left_preprocessed_dir,
- backwards_preprocessed_dir,
- lm_preprocessed_dir,
- ) = rerank_utils.get_directories(
- args.data_dir_name,
- args.num_rescore,
- args.gen_subset,
- args.gen_model_name,
- shard_id,
- args.num_shards,
- args.sampling,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
- rerank_generate.gen_and_reprocess_nbest(args)
- rerank_score_bw.score_bw(args)
- rerank_score_lm.score_lm(args)
-
- if args.write_hypos is None:
- write_targets = pre_gen + "/matched_targets"
- write_hypos = pre_gen + "/matched_hypos"
- else:
- write_targets = args.write_hypos + "_targets" + args.gen_subset
- write_hypos = args.write_hypos + "_hypos" + args.gen_subset
-
- if args.all_shards:
- write_targets += "_all_shards"
- write_hypos += "_all_shards"
-
- (
- best_lenpen,
- best_weight1,
- best_weight2,
- best_weight3,
- best_score,
- ) = match_target_hypo(args, write_targets, write_hypos)
-
- return best_lenpen, best_weight1, best_weight2, best_weight3, best_score
-
-
-def cli_main():
- parser = rerank_options.get_reranking_parser()
- args = options.parse_args_and_arch(parser)
- rerank(args)
-
-
-if __name__ == "__main__":
- cli_main()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md
deleted file mode 100644
index 9d3d7b67dc08440b5f4d1c5a7ffcd4bd6e76c14f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md
+++ /dev/null
@@ -1,179 +0,0 @@
-# Examples of Training scripts for Non-autoregressive Machine Translation models
-
-### Non-autoregressive Transformer (NAT, Gu et al., 2017)
-Note that we need to have an additional module to perform "length prediction" (`--length-loss-factor`) before generating the whole sequence.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch nonautoregressive_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-### Fast Structured Decoding for Sequence Models (NAT-CRF, Sun et al., 2019)
-Note that we implemented a low-rank appromixated CRF model by setting `--crf-lowrank-approx=32` and `--crf-beam-approx=64` as discribed in the original paper. All other settings are the same as the vanilla NAT model.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch nacrf_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --word-ins-loss-factor 0.5 \
- --crf-lowrank-approx 32 \
- --crf-beam-approx 64 \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-### Non-autoregressive Transformer with Iterative Refinement (iNAT, Lee et al., 2018)
-Note that `--train-step` means how many iterations of refinement we used during training, and `--dae-ratio` controls the ratio of denoising auto-encoder training described in the original paper.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch iterative_nonautoregressive_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --train-step 4 \
- --dae-ratio 0.5 \
- --stochastic-approx \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-### Insertion Transformer (InsT, Stern et al., 2019)
-Note that we need to specify the "slot-loss" (uniform or balanced tree) described in the original paper. Here we use `--label-tau` to control the temperature.
-
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch insertion_transformer \
- --noise random_delete \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-### Mask Predict (CMLM, Ghazvininejad et al., 2019)
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch cmlm_transformer \
- --noise random_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-
-
-### Levenshtein Transformer (LevT, Gu et al., 2019)
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch levenshtein_transformer \
- --noise random_delete \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py
deleted file mode 100644
index 8b7eb2ec4fc5190c4dcdfe34b0259e6f448e18a9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""isort:skip_file"""
-
-from .dictionary import Dictionary, TruncatedDictionary
-
-from .fairseq_dataset import FairseqDataset, FairseqIterableDataset
-
-from .base_wrapper_dataset import BaseWrapperDataset
-
-from .add_target_dataset import AddTargetDataset
-from .append_token_dataset import AppendTokenDataset
-from .audio.raw_audio_dataset import BinarizedAudioDataset, FileAudioDataset
-from .audio.hubert_dataset import HubertDataset
-from .backtranslation_dataset import BacktranslationDataset
-from .bucket_pad_length_dataset import BucketPadLengthDataset
-from .colorize_dataset import ColorizeDataset
-from .concat_dataset import ConcatDataset
-from .concat_sentences_dataset import ConcatSentencesDataset
-from .denoising_dataset import DenoisingDataset
-from .id_dataset import IdDataset
-from .indexed_dataset import (
- IndexedCachedDataset,
- IndexedDataset,
- IndexedRawTextDataset,
- MMapIndexedDataset,
-)
-from .language_pair_dataset import LanguagePairDataset
-from .list_dataset import ListDataset
-from .lm_context_window_dataset import LMContextWindowDataset
-from .lru_cache_dataset import LRUCacheDataset
-from .mask_tokens_dataset import MaskTokensDataset
-from .monolingual_dataset import MonolingualDataset
-from .multi_corpus_sampled_dataset import MultiCorpusSampledDataset
-from .nested_dictionary_dataset import NestedDictionaryDataset
-from .noising import NoisingDataset
-from .numel_dataset import NumelDataset
-from .num_samples_dataset import NumSamplesDataset
-from .offset_tokens_dataset import OffsetTokensDataset
-from .pad_dataset import LeftPadDataset, PadDataset, RightPadDataset
-from .prepend_dataset import PrependDataset
-from .prepend_token_dataset import PrependTokenDataset
-from .raw_label_dataset import RawLabelDataset
-from .replace_dataset import ReplaceDataset
-from .resampling_dataset import ResamplingDataset
-from .roll_dataset import RollDataset
-from .round_robin_zip_datasets import RoundRobinZipDatasets
-from .sort_dataset import SortDataset
-from .strip_token_dataset import StripTokenDataset
-from .subsample_dataset import SubsampleDataset
-from .token_block_dataset import TokenBlockDataset
-from .transform_eos_dataset import TransformEosDataset
-from .transform_eos_lang_pair_dataset import TransformEosLangPairDataset
-from .shorten_dataset import TruncateDataset, RandomCropDataset
-from .multilingual.sampled_multi_dataset import SampledMultiDataset
-from .multilingual.sampled_multi_epoch_dataset import SampledMultiEpochDataset
-from .fasta_dataset import FastaDataset, EncodedFastaDataset
-
-from .iterators import (
- CountingIterator,
- EpochBatchIterator,
- GroupedIterator,
- ShardedIterator,
-)
-
-__all__ = [
- "AddTargetDataset",
- "AppendTokenDataset",
- "BacktranslationDataset",
- "BaseWrapperDataset",
- "BinarizedAudioDataset",
- "BucketPadLengthDataset",
- "ColorizeDataset",
- "ConcatDataset",
- "ConcatSentencesDataset",
- "CountingIterator",
- "DenoisingDataset",
- "Dictionary",
- "EncodedFastaDataset",
- "EpochBatchIterator",
- "FairseqDataset",
- "FairseqIterableDataset",
- "FastaDataset",
- "FileAudioDataset",
- "GroupedIterator",
- "HubertDataset",
- "IdDataset",
- "IndexedCachedDataset",
- "IndexedDataset",
- "IndexedRawTextDataset",
- "LanguagePairDataset",
- "LeftPadDataset",
- "ListDataset",
- "LMContextWindowDataset",
- "LRUCacheDataset",
- "MaskTokensDataset",
- "MMapIndexedDataset",
- "MonolingualDataset",
- "MultiCorpusSampledDataset",
- "NestedDictionaryDataset",
- "NoisingDataset",
- "NumelDataset",
- "NumSamplesDataset",
- "OffsetTokensDataset",
- "PadDataset",
- "PrependDataset",
- "PrependTokenDataset",
- "RandomCropDataset",
- "RawLabelDataset",
- "ResamplingDataset",
- "ReplaceDataset",
- "RightPadDataset",
- "RollDataset",
- "RoundRobinZipDatasets",
- "SampledMultiDataset",
- "SampledMultiEpochDataset",
- "ShardedIterator",
- "SortDataset",
- "StripTokenDataset",
- "SubsampleDataset",
- "TokenBlockDataset",
- "TransformEosDataset",
- "TransformEosLangPairDataset",
- "TruncateDataset",
- "TruncatedDictionary",
-]
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py
deleted file mode 100644
index 11c7dc1ea966a54f8915ef164377e40f90e851a1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-from fairseq.tasks import register_task
-from fairseq.tasks.speech_to_text import SpeechToTextTask
-from fairseq.tasks.translation import (
- TranslationTask, TranslationConfig
-)
-
-try:
- import examples.simultaneous_translation # noqa
- import_successful = True
-except BaseException:
- import_successful = False
-
-
-logger = logging.getLogger(__name__)
-
-
-def check_import(flag):
- if not flag:
- raise ImportError(
- "'examples.simultaneous_translation' is not correctly imported. "
- "Please considering `pip install -e $FAIRSEQ_DIR`."
- )
-
-
-@register_task("simul_speech_to_text")
-class SimulSpeechToTextTask(SpeechToTextTask):
- def __init__(self, args, tgt_dict):
- check_import(import_successful)
- super().__init__(args, tgt_dict)
-
-
-@register_task("simul_text_to_text", dataclass=TranslationConfig)
-class SimulTextToTextTask(TranslationTask):
- def __init__(self, cfg, src_dict, tgt_dict):
- check_import(import_successful)
- super().__init__(cfg, src_dict, tgt_dict)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md
deleted file mode 100644
index 57104230655c7c517d25904e634c53b6159ee60f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md
+++ /dev/null
@@ -1,42 +0,0 @@
-# Unit to Speech Model (unit2speech)
-
-Unit to speech model is modified Tacotron2 model that learns to synthesize speech from discrete speech units. All models are trained on quantized [LJSpeech](https://keithito.com/LJ-Speech-Dataset/).
-
-Upstream Units | Download Link
-|-|-
-Log Mel Filterbank + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km50/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km100/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km200/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km500/tts_checkpoint_best.pt)
-Modified CPC + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km50/tts_checkpoint_best.pt)
-Modified CPC + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km100/tts_checkpoint_best.pt)
-Modified CPC + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km200/tts_checkpoint_best.pt)
-Modified CPC + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km500/tts_checkpoint_best.pt)
-HuBERT Base + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km50/tts_checkpoint_best.pt)
-HuBERT Base + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km100/tts_checkpoint_best.pt)
-HuBERT Base + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km200/tts_checkpoint_best.pt)
-HuBERT Base + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km500/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km50/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km100/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km200/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km500/tts_checkpoint_best.pt)
-
-## Run inference using a unit2speech model
-* Install librosa, unidecode and inflect using `pip install librosa, unidecode, inflect`
-* Download [Waveglow checkpoint](https://dl.fbaipublicfiles.com/textless_nlp/gslm/waveglow_256channels_new.pt). This is the vocoder.
-
-Sample commnd to run inference using trained unit2speech models. Please note that the quantized audio to synthesized should be using the same units as the unit2speech model was trained with.
-```
-FAIRSEQ_ROOT=
-TTS_MODEL_PATH=
-QUANTIZED_UNIT_PATH=
-OUT_DIR=
-WAVEGLOW_PATH=
-
-PYTHONPATH=${FAIRSEQ_ROOT}:${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech python ${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py \
- --tts_model_path $TTS_MODEL_PATH \
- --quantized_unit_path $QUANTIZED_UNIT_PATH \
- --out_audio_dir $OUT_DIR \
- --waveglow_path $WAVEGLOW_PATH \
- --max_decoder_steps 2000
-```
\ No newline at end of file
diff --git a/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py b/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py
deleted file mode 100644
index 375e4d50872d5c68ee57ca17470a2ca425425eba..0000000000000000000000000000000000000000
--- a/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import re
-from unidecode import unidecode
-import pyopenjtalk
-
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
- r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
- r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ts', 'ʦ'),
- ('u', 'ɯ'),
- ('j', 'ʥ'),
- ('y', 'j'),
- ('ni', 'n^i'),
- ('nj', 'n^'),
- ('hi', 'çi'),
- ('hj', 'ç'),
- ('f', 'ɸ'),
- ('I', 'i*'),
- ('U', 'ɯ*'),
- ('r', 'ɾ')
-]]
-
-# List of (romaji, ipa2) pairs for marks:
-_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('u', 'ɯ'),
- ('ʧ', 'tʃ'),
- ('j', 'dʑ'),
- ('y', 'j'),
- ('ni', 'n^i'),
- ('nj', 'n^'),
- ('hi', 'çi'),
- ('hj', 'ç'),
- ('f', 'ɸ'),
- ('I', 'i*'),
- ('U', 'ɯ*'),
- ('r', 'ɾ')
-]]
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'Q([↑↓]*[kg])', r'k#\1'),
- (r'Q([↑↓]*[tdjʧ])', r't#\1'),
- (r'Q([↑↓]*[sʃ])', r's\1'),
- (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'N([↑↓]*[pbm])', r'm\1'),
- (r'N([↑↓]*[ʧʥj])', r'n^\1'),
- (r'N([↑↓]*[tdn])', r'n\1'),
- (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-def symbols_to_japanese(text):
- for regex, replacement in _symbols_to_japanese:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_romaji_with_accent(text):
- '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
- text = symbols_to_japanese(text)
- sentences = re.split(_japanese_marks, text)
- marks = re.findall(_japanese_marks, text)
- text = ''
- for i, sentence in enumerate(sentences):
- if re.match(_japanese_characters, sentence):
- if text != '':
- text += ' '
- labels = pyopenjtalk.extract_fullcontext(sentence)
- for n, label in enumerate(labels):
- phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
- if phoneme not in ['sil', 'pau']:
- text += phoneme.replace('ch', 'ʧ').replace('sh',
- 'ʃ').replace('cl', 'Q')
- else:
- continue
- # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
- a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
- a2 = int(re.search(r"\+(\d+)\+", label).group(1))
- a3 = int(re.search(r"\+(\d+)/", label).group(1))
- if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
- a2_next = -1
- else:
- a2_next = int(
- re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
- # Accent phrase boundary
- if a3 == 1 and a2_next == 1:
- text += ' '
- # Falling
- elif a1 == 0 and a2_next == a2 + 1:
- text += '↓'
- # Rising
- elif a2 == 1 and a2_next == 2:
- text += '↑'
- if i < len(marks):
- text += unidecode(marks[i]).replace(' ', '')
- return text
-
-
-def get_real_sokuon(text):
- for regex, replacement in _real_sokuon:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def get_real_hatsuon(text):
- for regex, replacement in _real_hatsuon:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa(text):
- text = japanese_to_romaji_with_accent(text).replace('...', '…')
- text = re.sub(
- r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
- text = get_real_sokuon(text)
- text = get_real_hatsuon(text)
- for regex, replacement in _romaji_to_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa2(text):
- text = japanese_to_romaji_with_accent(text).replace('...', '…')
- text = get_real_sokuon(text)
- text = get_real_hatsuon(text)
- for regex, replacement in _romaji_to_ipa2:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa3(text):
- text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
- 'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
- text = re.sub(
- r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
- text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
- return text
diff --git a/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py b/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py
deleted file mode 100644
index c215099fedacae35c6798fdd9b8420a447aa16bb..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-from audiocraft.quantization.vq import ResidualVectorQuantizer
-
-
-class TestResidualVectorQuantizer:
-
- def test_rvq(self):
- x = torch.randn(1, 16, 2048)
- vq = ResidualVectorQuantizer(n_q=8, dimension=16, bins=8)
- res = vq(x, 1.)
- assert res.x.shape == torch.Size([1, 16, 2048])
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py
deleted file mode 100644
index feb7a8222487756d38482da95183bbbcbbe96ed9..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py
+++ /dev/null
@@ -1,864 +0,0 @@
-
-import math
-import json
-import copy
-from typing import List, Dict
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.modeling.proposal_generator.build import PROPOSAL_GENERATOR_REGISTRY
-from detectron2.layers import ShapeSpec, cat
-from detectron2.structures import Instances, Boxes
-from detectron2.modeling import detector_postprocess
-from detectron2.utils.comm import get_world_size
-from detectron2.config import configurable
-
-from ..layers.heatmap_focal_loss import heatmap_focal_loss_jit
-from ..layers.heatmap_focal_loss import binary_heatmap_focal_loss
-from ..layers.iou_loss import IOULoss
-from ..layers.ml_nms import ml_nms
-from ..debug import debug_train, debug_test
-from .utils import reduce_sum, _transpose
-from .centernet_head import CenterNetHead
-
-__all__ = ["CenterNet"]
-
-INF = 100000000
-
-@PROPOSAL_GENERATOR_REGISTRY.register()
-class CenterNet(nn.Module):
- @configurable
- def __init__(self,
- # input_shape: Dict[str, ShapeSpec],
- in_channels=256,
- *,
- num_classes=80,
- in_features=("p3", "p4", "p5", "p6", "p7"),
- strides=(8, 16, 32, 64, 128),
- score_thresh=0.05,
- hm_min_overlap=0.8,
- loc_loss_type='giou',
- min_radius=4,
- hm_focal_alpha=0.25,
- hm_focal_beta=4,
- loss_gamma=2.0,
- reg_weight=2.0,
- not_norm_reg=True,
- with_agn_hm=False,
- only_proposal=False,
- as_proposal=False,
- not_nms=False,
- pos_weight=1.,
- neg_weight=1.,
- sigmoid_clamp=1e-4,
- ignore_high_fp=-1.,
- center_nms=False,
- sizes_of_interest=[[0,80],[64,160],[128,320],[256,640],[512,10000000]],
- more_pos=False,
- more_pos_thresh=0.2,
- more_pos_topk=9,
- pre_nms_topk_train=1000,
- pre_nms_topk_test=1000,
- post_nms_topk_train=100,
- post_nms_topk_test=100,
- nms_thresh_train=0.6,
- nms_thresh_test=0.6,
- no_reduce=False,
- debug=False,
- vis_thresh=0.5,
- pixel_mean=[103.530,116.280,123.675],
- pixel_std=[1.0,1.0,1.0],
- device='cuda',
- centernet_head=None,
- ):
- super().__init__()
- self.num_classes = num_classes
- self.in_features = in_features
- self.strides = strides
- self.score_thresh = score_thresh
- self.min_radius = min_radius
- self.hm_focal_alpha = hm_focal_alpha
- self.hm_focal_beta = hm_focal_beta
- self.loss_gamma = loss_gamma
- self.reg_weight = reg_weight
- self.not_norm_reg = not_norm_reg
- self.with_agn_hm = with_agn_hm
- self.only_proposal = only_proposal
- self.as_proposal = as_proposal
- self.not_nms = not_nms
- self.pos_weight = pos_weight
- self.neg_weight = neg_weight
- self.sigmoid_clamp = sigmoid_clamp
- self.ignore_high_fp = ignore_high_fp
- self.center_nms = center_nms
- self.sizes_of_interest = sizes_of_interest
- self.more_pos = more_pos
- self.more_pos_thresh = more_pos_thresh
- self.more_pos_topk = more_pos_topk
- self.pre_nms_topk_train = pre_nms_topk_train
- self.pre_nms_topk_test = pre_nms_topk_test
- self.post_nms_topk_train = post_nms_topk_train
- self.post_nms_topk_test = post_nms_topk_test
- self.nms_thresh_train = nms_thresh_train
- self.nms_thresh_test = nms_thresh_test
- self.no_reduce = no_reduce
- self.debug = debug
- self.vis_thresh = vis_thresh
- if self.center_nms:
- self.not_nms = True
- self.iou_loss = IOULoss(loc_loss_type)
- assert (not self.only_proposal) or self.with_agn_hm
- # delta for rendering heatmap
- self.delta = (1 - hm_min_overlap) / (1 + hm_min_overlap)
- if centernet_head is None:
- self.centernet_head = CenterNetHead(
- in_channels=in_channels,
- num_levels=len(in_features),
- with_agn_hm=with_agn_hm,
- only_proposal=only_proposal)
- else:
- self.centernet_head = centernet_head
- if self.debug:
- pixel_mean = torch.Tensor(pixel_mean).to(
- torch.device(device)).view(3, 1, 1)
- pixel_std = torch.Tensor(pixel_std).to(
- torch.device(device)).view(3, 1, 1)
- self.denormalizer = lambda x: x * pixel_std + pixel_mean
-
- @classmethod
- def from_config(cls, cfg, input_shape):
- ret = {
- # 'input_shape': input_shape,
- 'in_channels': input_shape[
- cfg.MODEL.CENTERNET.IN_FEATURES[0]].channels,
- 'num_classes': cfg.MODEL.CENTERNET.NUM_CLASSES,
- 'in_features': cfg.MODEL.CENTERNET.IN_FEATURES,
- 'strides': cfg.MODEL.CENTERNET.FPN_STRIDES,
- 'score_thresh': cfg.MODEL.CENTERNET.INFERENCE_TH,
- 'loc_loss_type': cfg.MODEL.CENTERNET.LOC_LOSS_TYPE,
- 'hm_min_overlap': cfg.MODEL.CENTERNET.HM_MIN_OVERLAP,
- 'min_radius': cfg.MODEL.CENTERNET.MIN_RADIUS,
- 'hm_focal_alpha': cfg.MODEL.CENTERNET.HM_FOCAL_ALPHA,
- 'hm_focal_beta': cfg.MODEL.CENTERNET.HM_FOCAL_BETA,
- 'loss_gamma': cfg.MODEL.CENTERNET.LOSS_GAMMA,
- 'reg_weight': cfg.MODEL.CENTERNET.REG_WEIGHT,
- 'not_norm_reg': cfg.MODEL.CENTERNET.NOT_NORM_REG,
- 'with_agn_hm': cfg.MODEL.CENTERNET.WITH_AGN_HM,
- 'only_proposal': cfg.MODEL.CENTERNET.ONLY_PROPOSAL,
- 'as_proposal': cfg.MODEL.CENTERNET.AS_PROPOSAL,
- 'not_nms': cfg.MODEL.CENTERNET.NOT_NMS,
- 'pos_weight': cfg.MODEL.CENTERNET.POS_WEIGHT,
- 'neg_weight': cfg.MODEL.CENTERNET.NEG_WEIGHT,
- 'sigmoid_clamp': cfg.MODEL.CENTERNET.SIGMOID_CLAMP,
- 'ignore_high_fp': cfg.MODEL.CENTERNET.IGNORE_HIGH_FP,
- 'center_nms': cfg.MODEL.CENTERNET.CENTER_NMS,
- 'sizes_of_interest': cfg.MODEL.CENTERNET.SOI,
- 'more_pos': cfg.MODEL.CENTERNET.MORE_POS,
- 'more_pos_thresh': cfg.MODEL.CENTERNET.MORE_POS_THRESH,
- 'more_pos_topk': cfg.MODEL.CENTERNET.MORE_POS_TOPK,
- 'pre_nms_topk_train': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TRAIN,
- 'pre_nms_topk_test': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TEST,
- 'post_nms_topk_train': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TRAIN,
- 'post_nms_topk_test': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TEST,
- 'nms_thresh_train': cfg.MODEL.CENTERNET.NMS_TH_TRAIN,
- 'nms_thresh_test': cfg.MODEL.CENTERNET.NMS_TH_TEST,
- 'no_reduce': cfg.MODEL.CENTERNET.NO_REDUCE,
- 'debug': cfg.DEBUG,
- 'vis_thresh': cfg.VIS_THRESH,
- 'pixel_mean': cfg.MODEL.PIXEL_MEAN,
- 'pixel_std': cfg.MODEL.PIXEL_STD,
- 'device': cfg.MODEL.DEVICE,
- 'centernet_head': CenterNetHead(
- cfg, [input_shape[f] for f in cfg.MODEL.CENTERNET.IN_FEATURES]),
- }
- return ret
-
-
- def forward(self, images, features_dict, gt_instances):
- features = [features_dict[f] for f in self.in_features]
- clss_per_level, reg_pred_per_level, agn_hm_pred_per_level = \
- self.centernet_head(features)
- grids = self.compute_grids(features)
- shapes_per_level = grids[0].new_tensor(
- [(x.shape[2], x.shape[3]) for x in reg_pred_per_level])
-
- if not self.training:
- return self.inference(
- images, clss_per_level, reg_pred_per_level,
- agn_hm_pred_per_level, grids)
- else:
- pos_inds, labels, reg_targets, flattened_hms = \
- self._get_ground_truth(
- grids, shapes_per_level, gt_instances)
- # logits_pred: M x F, reg_pred: M x 4, agn_hm_pred: M
- logits_pred, reg_pred, agn_hm_pred = self._flatten_outputs(
- clss_per_level, reg_pred_per_level, agn_hm_pred_per_level)
-
- if self.more_pos:
- # add more pixels as positive if \
- # 1. they are within the center3x3 region of an object
- # 2. their regression losses are small (= 0).squeeze(1)
- reg_pred = reg_pred[reg_inds]
- reg_targets_pos = reg_targets[reg_inds]
- reg_weight_map = flattened_hms.max(dim=1)[0]
- reg_weight_map = reg_weight_map[reg_inds]
- reg_weight_map = reg_weight_map * 0 + 1 \
- if self.not_norm_reg else reg_weight_map
- if self.no_reduce:
- reg_norm = max(reg_weight_map.sum(), 1)
- else:
- reg_norm = max(reduce_sum(reg_weight_map.sum()).item() / num_gpus, 1)
-
- reg_loss = self.reg_weight * self.iou_loss(
- reg_pred, reg_targets_pos, reg_weight_map,
- reduction='sum') / reg_norm
- losses['loss_centernet_loc'] = reg_loss
-
- if self.with_agn_hm:
- cat_agn_heatmap = flattened_hms.max(dim=1)[0] # M
- agn_pos_loss, agn_neg_loss = binary_heatmap_focal_loss(
- agn_hm_pred, cat_agn_heatmap, pos_inds,
- alpha=self.hm_focal_alpha,
- beta=self.hm_focal_beta,
- gamma=self.loss_gamma,
- sigmoid_clamp=self.sigmoid_clamp,
- ignore_high_fp=self.ignore_high_fp,
- )
- agn_pos_loss = self.pos_weight * agn_pos_loss / num_pos_avg
- agn_neg_loss = self.neg_weight * agn_neg_loss / num_pos_avg
- losses['loss_centernet_agn_pos'] = agn_pos_loss
- losses['loss_centernet_agn_neg'] = agn_neg_loss
-
- if self.debug:
- print('losses', losses)
- print('total_num_pos', total_num_pos)
- return losses
-
-
- def compute_grids(self, features):
- grids = []
- for level, feature in enumerate(features):
- h, w = feature.size()[-2:]
- shifts_x = torch.arange(
- 0, w * self.strides[level],
- step=self.strides[level],
- dtype=torch.float32, device=feature.device)
- shifts_y = torch.arange(
- 0, h * self.strides[level],
- step=self.strides[level],
- dtype=torch.float32, device=feature.device)
- shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
- shift_x = shift_x.reshape(-1)
- shift_y = shift_y.reshape(-1)
- grids_per_level = torch.stack((shift_x, shift_y), dim=1) + \
- self.strides[level] // 2
- grids.append(grids_per_level)
- return grids
-
-
- def _get_ground_truth(self, grids, shapes_per_level, gt_instances):
- '''
- Input:
- grids: list of tensors [(hl x wl, 2)]_l
- shapes_per_level: list of tuples L x 2:
- gt_instances: gt instances
- Retuen:
- pos_inds: N
- labels: N
- reg_targets: M x 4
- flattened_hms: M x C or M x 1
- N: number of objects in all images
- M: number of pixels from all FPN levels
- '''
-
- # get positive pixel index
- if not self.more_pos:
- pos_inds, labels = self._get_label_inds(
- gt_instances, shapes_per_level)
- else:
- pos_inds, labels = None, None
- heatmap_channels = self.num_classes
- L = len(grids)
- num_loc_list = [len(loc) for loc in grids]
- strides = torch.cat([
- shapes_per_level.new_ones(num_loc_list[l]) * self.strides[l] \
- for l in range(L)]).float() # M
- reg_size_ranges = torch.cat([
- shapes_per_level.new_tensor(self.sizes_of_interest[l]).float().view(
- 1, 2).expand(num_loc_list[l], 2) for l in range(L)]) # M x 2
- grids = torch.cat(grids, dim=0) # M x 2
- M = grids.shape[0]
-
- reg_targets = []
- flattened_hms = []
- for i in range(len(gt_instances)): # images
- boxes = gt_instances[i].gt_boxes.tensor # N x 4
- area = gt_instances[i].gt_boxes.area() # N
- gt_classes = gt_instances[i].gt_classes # N in [0, self.num_classes]
-
- N = boxes.shape[0]
- if N == 0:
- reg_targets.append(grids.new_zeros((M, 4)) - INF)
- flattened_hms.append(
- grids.new_zeros((
- M, 1 if self.only_proposal else heatmap_channels)))
- continue
-
- l = grids[:, 0].view(M, 1) - boxes[:, 0].view(1, N) # M x N
- t = grids[:, 1].view(M, 1) - boxes[:, 1].view(1, N) # M x N
- r = boxes[:, 2].view(1, N) - grids[:, 0].view(M, 1) # M x N
- b = boxes[:, 3].view(1, N) - grids[:, 1].view(M, 1) # M x N
- reg_target = torch.stack([l, t, r, b], dim=2) # M x N x 4
-
- centers = ((boxes[:, [0, 1]] + boxes[:, [2, 3]]) / 2) # N x 2
- centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2
- strides_expanded = strides.view(M, 1, 1).expand(M, N, 2)
- centers_discret = ((centers_expanded / strides_expanded).int() * \
- strides_expanded).float() + strides_expanded / 2 # M x N x 2
-
- is_peak = (((grids.view(M, 1, 2).expand(M, N, 2) - \
- centers_discret) ** 2).sum(dim=2) == 0) # M x N
- is_in_boxes = reg_target.min(dim=2)[0] > 0 # M x N
- is_center3x3 = self.get_center3x3(
- grids, centers, strides) & is_in_boxes # M x N
- is_cared_in_the_level = self.assign_reg_fpn(
- reg_target, reg_size_ranges) # M x N
- reg_mask = is_center3x3 & is_cared_in_the_level # M x N
-
- dist2 = ((grids.view(M, 1, 2).expand(M, N, 2) - \
- centers_expanded) ** 2).sum(dim=2) # M x N
- dist2[is_peak] = 0
- radius2 = self.delta ** 2 * 2 * area # N
- radius2 = torch.clamp(
- radius2, min=self.min_radius ** 2)
- weighted_dist2 = dist2 / radius2.view(1, N).expand(M, N) # M x N
- reg_target = self._get_reg_targets(
- reg_target, weighted_dist2.clone(), reg_mask, area) # M x 4
-
- if self.only_proposal:
- flattened_hm = self._create_agn_heatmaps_from_dist(
- weighted_dist2.clone()) # M x 1
- else:
- flattened_hm = self._create_heatmaps_from_dist(
- weighted_dist2.clone(), gt_classes,
- channels=heatmap_channels) # M x C
-
- reg_targets.append(reg_target)
- flattened_hms.append(flattened_hm)
-
- # transpose im first training_targets to level first ones
- reg_targets = _transpose(reg_targets, num_loc_list)
- flattened_hms = _transpose(flattened_hms, num_loc_list)
- for l in range(len(reg_targets)):
- reg_targets[l] = reg_targets[l] / float(self.strides[l])
- reg_targets = cat([x for x in reg_targets], dim=0) # MB x 4
- flattened_hms = cat([x for x in flattened_hms], dim=0) # MB x C
-
- return pos_inds, labels, reg_targets, flattened_hms
-
-
- def _get_label_inds(self, gt_instances, shapes_per_level):
- '''
- Inputs:
- gt_instances: [n_i], sum n_i = N
- shapes_per_level: L x 2 [(h_l, w_l)]_L
- Returns:
- pos_inds: N'
- labels: N'
- '''
- pos_inds = []
- labels = []
- L = len(self.strides)
- B = len(gt_instances)
- shapes_per_level = shapes_per_level.long()
- loc_per_level = (shapes_per_level[:, 0] * shapes_per_level[:, 1]).long() # L
- level_bases = []
- s = 0
- for l in range(L):
- level_bases.append(s)
- s = s + B * loc_per_level[l]
- level_bases = shapes_per_level.new_tensor(level_bases).long() # L
- strides_default = shapes_per_level.new_tensor(self.strides).float() # L
- for im_i in range(B):
- targets_per_im = gt_instances[im_i]
- bboxes = targets_per_im.gt_boxes.tensor # n x 4
- n = bboxes.shape[0]
- centers = ((bboxes[:, [0, 1]] + bboxes[:, [2, 3]]) / 2) # n x 2
- centers = centers.view(n, 1, 2).expand(n, L, 2)
- strides = strides_default.view(1, L, 1).expand(n, L, 2)
- centers_inds = (centers / strides).long() # n x L x 2
- Ws = shapes_per_level[:, 1].view(1, L).expand(n, L)
- pos_ind = level_bases.view(1, L).expand(n, L) + \
- im_i * loc_per_level.view(1, L).expand(n, L) + \
- centers_inds[:, :, 1] * Ws + \
- centers_inds[:, :, 0] # n x L
- is_cared_in_the_level = self.assign_fpn_level(bboxes)
- pos_ind = pos_ind[is_cared_in_the_level].view(-1)
- label = targets_per_im.gt_classes.view(
- n, 1).expand(n, L)[is_cared_in_the_level].view(-1)
-
- pos_inds.append(pos_ind) # n'
- labels.append(label) # n'
- pos_inds = torch.cat(pos_inds, dim=0).long()
- labels = torch.cat(labels, dim=0)
- return pos_inds, labels # N, N
-
-
- def assign_fpn_level(self, boxes):
- '''
- Inputs:
- boxes: n x 4
- size_ranges: L x 2
- Return:
- is_cared_in_the_level: n x L
- '''
- size_ranges = boxes.new_tensor(
- self.sizes_of_interest).view(len(self.sizes_of_interest), 2) # L x 2
- crit = ((boxes[:, 2:] - boxes[:, :2]) **2).sum(dim=1) ** 0.5 / 2 # n
- n, L = crit.shape[0], size_ranges.shape[0]
- crit = crit.view(n, 1).expand(n, L)
- size_ranges_expand = size_ranges.view(1, L, 2).expand(n, L, 2)
- is_cared_in_the_level = (crit >= size_ranges_expand[:, :, 0]) & \
- (crit <= size_ranges_expand[:, :, 1])
- return is_cared_in_the_level
-
-
- def assign_reg_fpn(self, reg_targets_per_im, size_ranges):
- '''
- TODO (Xingyi): merge it with assign_fpn_level
- Inputs:
- reg_targets_per_im: M x N x 4
- size_ranges: M x 2
- '''
- crit = ((reg_targets_per_im[:, :, :2] + \
- reg_targets_per_im[:, :, 2:])**2).sum(dim=2) ** 0.5 / 2 # M x N
- is_cared_in_the_level = (crit >= size_ranges[:, [0]]) & \
- (crit <= size_ranges[:, [1]])
- return is_cared_in_the_level
-
-
- def _get_reg_targets(self, reg_targets, dist, mask, area):
- '''
- reg_targets (M x N x 4): long tensor
- dist (M x N)
- is_*: M x N
- '''
- dist[mask == 0] = INF * 1.0
- min_dist, min_inds = dist.min(dim=1) # M
- reg_targets_per_im = reg_targets[
- range(len(reg_targets)), min_inds] # M x N x 4 --> M x 4
- reg_targets_per_im[min_dist == INF] = - INF
- return reg_targets_per_im
-
-
- def _create_heatmaps_from_dist(self, dist, labels, channels):
- '''
- dist: M x N
- labels: N
- return:
- heatmaps: M x C
- '''
- heatmaps = dist.new_zeros((dist.shape[0], channels))
- for c in range(channels):
- inds = (labels == c) # N
- if inds.int().sum() == 0:
- continue
- heatmaps[:, c] = torch.exp(-dist[:, inds].min(dim=1)[0])
- zeros = heatmaps[:, c] < 1e-4
- heatmaps[zeros, c] = 0
- return heatmaps
-
-
- def _create_agn_heatmaps_from_dist(self, dist):
- '''
- TODO (Xingyi): merge it with _create_heatmaps_from_dist
- dist: M x N
- return:
- heatmaps: M x 1
- '''
- heatmaps = dist.new_zeros((dist.shape[0], 1))
- heatmaps[:, 0] = torch.exp(-dist.min(dim=1)[0])
- zeros = heatmaps < 1e-4
- heatmaps[zeros] = 0
- return heatmaps
-
-
- def _flatten_outputs(self, clss, reg_pred, agn_hm_pred):
- # Reshape: (N, F, Hl, Wl) -> (N, Hl, Wl, F) -> (sum_l N*Hl*Wl, F)
- clss = cat([x.permute(0, 2, 3, 1).reshape(-1, x.shape[1]) \
- for x in clss], dim=0) if clss[0] is not None else None
- reg_pred = cat(
- [x.permute(0, 2, 3, 1).reshape(-1, 4) for x in reg_pred], dim=0)
- agn_hm_pred = cat([x.permute(0, 2, 3, 1).reshape(-1) \
- for x in agn_hm_pred], dim=0) if self.with_agn_hm else None
- return clss, reg_pred, agn_hm_pred
-
-
- def get_center3x3(self, locations, centers, strides):
- '''
- Inputs:
- locations: M x 2
- centers: N x 2
- strides: M
- '''
- M, N = locations.shape[0], centers.shape[0]
- locations_expanded = locations.view(M, 1, 2).expand(M, N, 2) # M x N x 2
- centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2
- strides_expanded = strides.view(M, 1, 1).expand(M, N, 2) # M x N
- centers_discret = ((centers_expanded / strides_expanded).int() * \
- strides_expanded).float() + strides_expanded / 2 # M x N x 2
- dist_x = (locations_expanded[:, :, 0] - centers_discret[:, :, 0]).abs()
- dist_y = (locations_expanded[:, :, 1] - centers_discret[:, :, 1]).abs()
- return (dist_x <= strides_expanded[:, :, 0]) & \
- (dist_y <= strides_expanded[:, :, 0])
-
-
- def inference(self, images, clss_per_level, reg_pred_per_level,
- agn_hm_pred_per_level, grids):
- logits_pred = [x.sigmoid() if x is not None else None \
- for x in clss_per_level]
- agn_hm_pred_per_level = [x.sigmoid() if x is not None else None \
- for x in agn_hm_pred_per_level]
-
- if self.only_proposal:
- proposals = self.predict_instances(
- grids, agn_hm_pred_per_level, reg_pred_per_level,
- images.image_sizes, [None for _ in agn_hm_pred_per_level])
- else:
- proposals = self.predict_instances(
- grids, logits_pred, reg_pred_per_level,
- images.image_sizes, agn_hm_pred_per_level)
- if self.as_proposal or self.only_proposal:
- for p in range(len(proposals)):
- proposals[p].proposal_boxes = proposals[p].get('pred_boxes')
- proposals[p].objectness_logits = proposals[p].get('scores')
- proposals[p].remove('pred_boxes')
-
- if self.debug:
- debug_test(
- [self.denormalizer(x) for x in images],
- logits_pred, reg_pred_per_level,
- agn_hm_pred_per_level, preds=proposals,
- vis_thresh=self.vis_thresh,
- debug_show_name=False)
- return proposals, {}
-
-
- def predict_instances(
- self, grids, logits_pred, reg_pred, image_sizes, agn_hm_pred,
- is_proposal=False):
- sampled_boxes = []
- for l in range(len(grids)):
- sampled_boxes.append(self.predict_single_level(
- grids[l], logits_pred[l], reg_pred[l] * self.strides[l],
- image_sizes, agn_hm_pred[l], l, is_proposal=is_proposal))
- boxlists = list(zip(*sampled_boxes))
- boxlists = [Instances.cat(boxlist) for boxlist in boxlists]
- boxlists = self.nms_and_topK(
- boxlists, nms=not self.not_nms)
- return boxlists
-
-
- def predict_single_level(
- self, grids, heatmap, reg_pred, image_sizes, agn_hm, level,
- is_proposal=False):
- N, C, H, W = heatmap.shape
- # put in the same format as grids
- if self.center_nms:
- heatmap_nms = nn.functional.max_pool2d(
- heatmap, (3, 3), stride=1, padding=1)
- heatmap = heatmap * (heatmap_nms == heatmap).float()
- heatmap = heatmap.permute(0, 2, 3, 1) # N x H x W x C
- heatmap = heatmap.reshape(N, -1, C) # N x HW x C
- box_regression = reg_pred.view(N, 4, H, W).permute(0, 2, 3, 1) # N x H x W x 4
- box_regression = box_regression.reshape(N, -1, 4)
-
- candidate_inds = heatmap > self.score_thresh # 0.05
- pre_nms_top_n = candidate_inds.view(N, -1).sum(1) # N
- pre_nms_topk = self.pre_nms_topk_train if self.training else self.pre_nms_topk_test
- pre_nms_top_n = pre_nms_top_n.clamp(max=pre_nms_topk) # N
-
- if agn_hm is not None:
- agn_hm = agn_hm.view(N, 1, H, W).permute(0, 2, 3, 1)
- agn_hm = agn_hm.reshape(N, -1)
- heatmap = heatmap * agn_hm[:, :, None]
-
- results = []
- for i in range(N):
- per_box_cls = heatmap[i] # HW x C
- per_candidate_inds = candidate_inds[i] # n
- per_box_cls = per_box_cls[per_candidate_inds] # n
-
- per_candidate_nonzeros = per_candidate_inds.nonzero() # n
- per_box_loc = per_candidate_nonzeros[:, 0] # n
- per_class = per_candidate_nonzeros[:, 1] # n
-
- per_box_regression = box_regression[i] # HW x 4
- per_box_regression = per_box_regression[per_box_loc] # n x 4
- per_grids = grids[per_box_loc] # n x 2
-
- per_pre_nms_top_n = pre_nms_top_n[i] # 1
-
- if per_candidate_inds.sum().item() > per_pre_nms_top_n.item():
- per_box_cls, top_k_indices = \
- per_box_cls.topk(per_pre_nms_top_n, sorted=False)
- per_class = per_class[top_k_indices]
- per_box_regression = per_box_regression[top_k_indices]
- per_grids = per_grids[top_k_indices]
-
- detections = torch.stack([
- per_grids[:, 0] - per_box_regression[:, 0],
- per_grids[:, 1] - per_box_regression[:, 1],
- per_grids[:, 0] + per_box_regression[:, 2],
- per_grids[:, 1] + per_box_regression[:, 3],
- ], dim=1) # n x 4
-
- # avoid invalid boxes in RoI heads
- detections[:, 2] = torch.max(detections[:, 2], detections[:, 0] + 0.01)
- detections[:, 3] = torch.max(detections[:, 3], detections[:, 1] + 0.01)
- boxlist = Instances(image_sizes[i])
- boxlist.scores = torch.sqrt(per_box_cls) \
- if self.with_agn_hm else per_box_cls # n
- # import pdb; pdb.set_trace()
- boxlist.pred_boxes = Boxes(detections)
- boxlist.pred_classes = per_class
- results.append(boxlist)
- return results
-
-
- def nms_and_topK(self, boxlists, nms=True):
- num_images = len(boxlists)
- results = []
- for i in range(num_images):
- nms_thresh = self.nms_thresh_train if self.training else \
- self.nms_thresh_test
- result = ml_nms(boxlists[i], nms_thresh) if nms else boxlists[i]
- if self.debug:
- print('#proposals before nms', len(boxlists[i]))
- print('#proposals after nms', len(result))
- num_dets = len(result)
- post_nms_topk = self.post_nms_topk_train if self.training else \
- self.post_nms_topk_test
- if num_dets > post_nms_topk:
- cls_scores = result.scores
- image_thresh, _ = torch.kthvalue(
- cls_scores.float().cpu(),
- num_dets - post_nms_topk + 1
- )
- keep = cls_scores >= image_thresh.item()
- keep = torch.nonzero(keep).squeeze(1)
- result = result[keep]
- if self.debug:
- print('#proposals after filter', len(result))
- results.append(result)
- return results
-
-
- def _add_more_pos(self, reg_pred, gt_instances, shapes_per_level):
- labels, level_masks, c33_inds, c33_masks, c33_regs = \
- self._get_c33_inds(gt_instances, shapes_per_level)
- N, L, K = labels.shape[0], len(self.strides), 9
- c33_inds[c33_masks == 0] = 0
- reg_pred_c33 = reg_pred[c33_inds].detach() # N x L x K
- invalid_reg = c33_masks == 0
- c33_regs_expand = c33_regs.view(N * L * K, 4).clamp(min=0)
- if N > 0:
- with torch.no_grad():
- c33_reg_loss = self.iou_loss(
- reg_pred_c33.view(N * L * K, 4),
- c33_regs_expand, None,
- reduction='none').view(N, L, K).detach() # N x L x K
- else:
- c33_reg_loss = reg_pred_c33.new_zeros((N, L, K)).detach()
- c33_reg_loss[invalid_reg] = INF # N x L x K
- c33_reg_loss.view(N * L, K)[level_masks.view(N * L), 4] = 0 # real center
- c33_reg_loss = c33_reg_loss.view(N, L * K)
- if N == 0:
- loss_thresh = c33_reg_loss.new_ones((N)).float()
- else:
- loss_thresh = torch.kthvalue(
- c33_reg_loss, self.more_pos_topk, dim=1)[0] # N
- loss_thresh[loss_thresh > self.more_pos_thresh] = self.more_pos_thresh # N
- new_pos = c33_reg_loss.view(N, L, K) < \
- loss_thresh.view(N, 1, 1).expand(N, L, K)
- pos_inds = c33_inds[new_pos].view(-1) # P
- labels = labels.view(N, 1, 1).expand(N, L, K)[new_pos].view(-1)
- return pos_inds, labels
-
-
- def _get_c33_inds(self, gt_instances, shapes_per_level):
- '''
- TODO (Xingyi): The current implementation is ugly. Refactor.
- Get the center (and the 3x3 region near center) locations of each objects
- Inputs:
- gt_instances: [n_i], sum n_i = N
- shapes_per_level: L x 2 [(h_l, w_l)]_L
- '''
- labels = []
- level_masks = []
- c33_inds = []
- c33_masks = []
- c33_regs = []
- L = len(self.strides)
- B = len(gt_instances)
- shapes_per_level = shapes_per_level.long()
- loc_per_level = (shapes_per_level[:, 0] * shapes_per_level[:, 1]).long() # L
- level_bases = []
- s = 0
- for l in range(L):
- level_bases.append(s)
- s = s + B * loc_per_level[l]
- level_bases = shapes_per_level.new_tensor(level_bases).long() # L
- strides_default = shapes_per_level.new_tensor(self.strides).float() # L
- K = 9
- dx = shapes_per_level.new_tensor([-1, 0, 1, -1, 0, 1, -1, 0, 1]).long()
- dy = shapes_per_level.new_tensor([-1, -1, -1, 0, 0, 0, 1, 1, 1]).long()
- for im_i in range(B):
- targets_per_im = gt_instances[im_i]
- bboxes = targets_per_im.gt_boxes.tensor # n x 4
- n = bboxes.shape[0]
- if n == 0:
- continue
- centers = ((bboxes[:, [0, 1]] + bboxes[:, [2, 3]]) / 2) # n x 2
- centers = centers.view(n, 1, 2).expand(n, L, 2)
-
- strides = strides_default.view(1, L, 1).expand(n, L, 2) #
- centers_inds = (centers / strides).long() # n x L x 2
- center_grids = centers_inds * strides + strides // 2# n x L x 2
- l = center_grids[:, :, 0] - bboxes[:, 0].view(n, 1).expand(n, L)
- t = center_grids[:, :, 1] - bboxes[:, 1].view(n, 1).expand(n, L)
- r = bboxes[:, 2].view(n, 1).expand(n, L) - center_grids[:, :, 0]
- b = bboxes[:, 3].view(n, 1).expand(n, L) - center_grids[:, :, 1] # n x L
- reg = torch.stack([l, t, r, b], dim=2) # n x L x 4
- reg = reg / strides_default.view(1, L, 1).expand(n, L, 4).float()
-
- Ws = shapes_per_level[:, 1].view(1, L).expand(n, L)
- Hs = shapes_per_level[:, 0].view(1, L).expand(n, L)
- expand_Ws = Ws.view(n, L, 1).expand(n, L, K)
- expand_Hs = Hs.view(n, L, 1).expand(n, L, K)
- label = targets_per_im.gt_classes.view(n).clone()
- mask = reg.min(dim=2)[0] >= 0 # n x L
- mask = mask & self.assign_fpn_level(bboxes)
- labels.append(label) # n
- level_masks.append(mask) # n x L
-
- Dy = dy.view(1, 1, K).expand(n, L, K)
- Dx = dx.view(1, 1, K).expand(n, L, K)
- c33_ind = level_bases.view(1, L, 1).expand(n, L, K) + \
- im_i * loc_per_level.view(1, L, 1).expand(n, L, K) + \
- (centers_inds[:, :, 1:2].expand(n, L, K) + Dy) * expand_Ws + \
- (centers_inds[:, :, 0:1].expand(n, L, K) + Dx) # n x L x K
-
- c33_mask = \
- ((centers_inds[:, :, 1:2].expand(n, L, K) + dy) < expand_Hs) & \
- ((centers_inds[:, :, 1:2].expand(n, L, K) + dy) >= 0) & \
- ((centers_inds[:, :, 0:1].expand(n, L, K) + dx) < expand_Ws) & \
- ((centers_inds[:, :, 0:1].expand(n, L, K) + dx) >= 0)
- # TODO (Xingyi): think about better way to implement this
- # Currently it hard codes the 3x3 region
- c33_reg = reg.view(n, L, 1, 4).expand(n, L, K, 4).clone()
- c33_reg[:, :, [0, 3, 6], 0] -= 1
- c33_reg[:, :, [0, 3, 6], 2] += 1
- c33_reg[:, :, [2, 5, 8], 0] += 1
- c33_reg[:, :, [2, 5, 8], 2] -= 1
- c33_reg[:, :, [0, 1, 2], 1] -= 1
- c33_reg[:, :, [0, 1, 2], 3] += 1
- c33_reg[:, :, [6, 7, 8], 1] += 1
- c33_reg[:, :, [6, 7, 8], 3] -= 1
- c33_mask = c33_mask & (c33_reg.min(dim=3)[0] >= 0) # n x L x K
- c33_inds.append(c33_ind)
- c33_masks.append(c33_mask)
- c33_regs.append(c33_reg)
-
- if len(level_masks) > 0:
- labels = torch.cat(labels, dim=0)
- level_masks = torch.cat(level_masks, dim=0)
- c33_inds = torch.cat(c33_inds, dim=0).long()
- c33_regs = torch.cat(c33_regs, dim=0)
- c33_masks = torch.cat(c33_masks, dim=0)
- else:
- labels = shapes_per_level.new_zeros((0)).long()
- level_masks = shapes_per_level.new_zeros((0, L)).bool()
- c33_inds = shapes_per_level.new_zeros((0, L, K)).long()
- c33_regs = shapes_per_level.new_zeros((0, L, K, 4)).float()
- c33_masks = shapes_per_level.new_zeros((0, L, K)).bool()
- return labels, level_masks, c33_inds, c33_masks, c33_regs # N x L, N x L x K
\ No newline at end of file
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh b/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh
deleted file mode 100644
index 6d2ba9a6265c0d5fa580035952a1f568dd8d9e44..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh
+++ /dev/null
@@ -1,37 +0,0 @@
-mkdir celeba-hq-dataset
-
-unzip data256x256.zip -d celeba-hq-dataset/
-
-# Reindex
-for i in `echo {00001..30000}`
-do
- mv 'celeba-hq-dataset/data256x256/'$i'.jpg' 'celeba-hq-dataset/data256x256/'$[10#$i - 1]'.jpg'
-done
-
-
-# Split: split train -> train & val
-cat fetch_data/train_shuffled.flist | shuf > celeba-hq-dataset/temp_train_shuffled.flist
-cat celeba-hq-dataset/temp_train_shuffled.flist | head -n 2000 > celeba-hq-dataset/val_shuffled.flist
-cat celeba-hq-dataset/temp_train_shuffled.flist | tail -n +2001 > celeba-hq-dataset/train_shuffled.flist
-cat fetch_data/val_shuffled.flist > celeba-hq-dataset/visual_test_shuffled.flist
-
-mkdir celeba-hq-dataset/train_256/
-mkdir celeba-hq-dataset/val_source_256/
-mkdir celeba-hq-dataset/visual_test_source_256/
-
-cat celeba-hq-dataset/train_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/train_256/
-cat celeba-hq-dataset/val_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/val_source_256/
-cat celeba-hq-dataset/visual_test_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/visual_test_source_256/
-
-
-# create location config celeba.yaml
-PWD=$(pwd)
-DATASET=${PWD}/celeba-hq-dataset
-CELEBA=${PWD}/configs/training/location/celeba.yaml
-
-touch $CELEBA
-echo "# @package _group_" >> $CELEBA
-echo "data_root_dir: ${DATASET}/" >> $CELEBA
-echo "out_root_dir: ${PWD}/experiments/" >> $CELEBA
-echo "tb_dir: ${PWD}/tb_logs/" >> $CELEBA
-echo "pretrained_models: ${PWD}/" >> $CELEBA
diff --git a/spaces/OpenMotionLab/MotionGPT/pyrender/tests/conftest.py b/spaces/OpenMotionLab/MotionGPT/pyrender/tests/conftest.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py b/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py
deleted file mode 100644
index 4e06ab8cbe6b43a355c0a9cfb3f2d688438d2c64..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py
+++ /dev/null
@@ -1,201 +0,0 @@
-# ------------------------------------------------------------------------------
-# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/cityscapes_evaluation.py
-# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
-# ------------------------------------------------------------------------------
-
-import glob
-import logging
-import numpy as np
-import os
-import tempfile
-from collections import OrderedDict
-import torch
-from PIL import Image
-
-from detectron2.data import MetadataCatalog
-from detectron2.utils import comm
-from detectron2.utils.file_io import PathManager
-
-from .evaluator import DatasetEvaluator
-
-
-class CityscapesEvaluator(DatasetEvaluator):
- """
- Base class for evaluation using cityscapes API.
- """
-
- def __init__(self, dataset_name):
- """
- Args:
- dataset_name (str): the name of the dataset.
- It must have the following metadata associated with it:
- "thing_classes", "gt_dir".
- """
- self._metadata = MetadataCatalog.get(dataset_name)
- self._cpu_device = torch.device("cpu")
- self._logger = logging.getLogger(__name__)
-
- def reset(self):
- self._working_dir = tempfile.TemporaryDirectory(prefix="cityscapes_eval_")
- self._temp_dir = self._working_dir.name
- # All workers will write to the same results directory
- # TODO this does not work in distributed training
- assert (
- comm.get_local_size() == comm.get_world_size()
- ), "CityscapesEvaluator currently do not work with multiple machines."
- self._temp_dir = comm.all_gather(self._temp_dir)[0]
- if self._temp_dir != self._working_dir.name:
- self._working_dir.cleanup()
- self._logger.info(
- "Writing cityscapes results to temporary directory {} ...".format(self._temp_dir)
- )
-
-
-class CityscapesInstanceEvaluator(CityscapesEvaluator):
- """
- Evaluate instance segmentation results on cityscapes dataset using cityscapes API.
-
- Note:
- * It does not work in multi-machine distributed training.
- * It contains a synchronization, therefore has to be used on all ranks.
- * Only the main process runs evaluation.
- """
-
- def process(self, inputs, outputs):
- from cityscapesscripts.helpers.labels import name2label
-
- for input, output in zip(inputs, outputs):
- file_name = input["file_name"]
- basename = os.path.splitext(os.path.basename(file_name))[0]
- pred_txt = os.path.join(self._temp_dir, basename + "_pred.txt")
-
- if "instances" in output:
- output = output["instances"].to(self._cpu_device)
- num_instances = len(output)
- with open(pred_txt, "w") as fout:
- for i in range(num_instances):
- pred_class = output.pred_classes[i]
- classes = self._metadata.stuff_classes[pred_class]
- class_id = name2label[classes].id
- score = output.scores[i]
- mask = output.pred_masks[i].numpy().astype("uint8")
- png_filename = os.path.join(
- self._temp_dir, basename + "_{}_{}.png".format(i, classes)
- )
-
- Image.fromarray(mask * 255).save(png_filename)
- fout.write(
- "{} {} {}\n".format(os.path.basename(png_filename), class_id, score)
- )
- else:
- # Cityscapes requires a prediction file for every ground truth image.
- with open(pred_txt, "w") as fout:
- pass
-
- def evaluate(self):
- """
- Returns:
- dict: has a key "segm", whose value is a dict of "AP" and "AP50".
- """
- comm.synchronize()
- if comm.get_rank() > 0:
- return
- import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval
-
- self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
-
- # set some global states in cityscapes evaluation API, before evaluating
- cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
- cityscapes_eval.args.predictionWalk = None
- cityscapes_eval.args.JSONOutput = False
- cityscapes_eval.args.colorized = False
- cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json")
-
- # These lines are adopted from
- # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
- gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
- groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_instanceIds.png"))
- assert len(
- groundTruthImgList
- ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
- cityscapes_eval.args.groundTruthSearch
- )
- predictionImgList = []
- for gt in groundTruthImgList:
- predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args))
- results = cityscapes_eval.evaluateImgLists(
- predictionImgList, groundTruthImgList, cityscapes_eval.args
- )["averages"]
-
- ret = OrderedDict()
- ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100}
- self._working_dir.cleanup()
- return ret
-
-
-class CityscapesSemSegEvaluator(CityscapesEvaluator):
- """
- Evaluate semantic segmentation results on cityscapes dataset using cityscapes API.
-
- Note:
- * It does not work in multi-machine distributed training.
- * It contains a synchronization, therefore has to be used on all ranks.
- * Only the main process runs evaluation.
- """
-
- def process(self, inputs, outputs):
- from cityscapesscripts.helpers.labels import trainId2label
-
- for input, output in zip(inputs, outputs):
- file_name = input["file_name"]
- basename = os.path.splitext(os.path.basename(file_name))[0]
- pred_filename = os.path.join(self._temp_dir, basename + "_pred.png")
-
- output = output["sem_seg"].argmax(dim=0).to(self._cpu_device).numpy()
- pred = 255 * np.ones(output.shape, dtype=np.uint8)
- for train_id, label in trainId2label.items():
- if label.ignoreInEval:
- continue
- pred[output == train_id] = label.id
- Image.fromarray(pred).save(pred_filename)
-
- def evaluate(self):
- comm.synchronize()
- if comm.get_rank() > 0:
- return
- # Load the Cityscapes eval script *after* setting the required env var,
- # since the script reads CITYSCAPES_DATASET into global variables at load time.
- import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval
-
- self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
-
- # set some global states in cityscapes evaluation API, before evaluating
- cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
- cityscapes_eval.args.predictionWalk = None
- cityscapes_eval.args.JSONOutput = False
- cityscapes_eval.args.colorized = False
-
- # These lines are adopted from
- # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa
- gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
- groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_labelIds.png"))
- assert len(
- groundTruthImgList
- ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
- cityscapes_eval.args.groundTruthSearch
- )
- predictionImgList = []
- for gt in groundTruthImgList:
- predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt))
- results = cityscapes_eval.evaluateImgLists(
- predictionImgList, groundTruthImgList, cityscapes_eval.args
- )
- ret = OrderedDict()
- ret["sem_seg"] = {
- "IoU": 100.0 * results["averageScoreClasses"],
- "iIoU": 100.0 * results["averageScoreInstClasses"],
- "IoU_sup": 100.0 * results["averageScoreCategories"],
- "iIoU_sup": 100.0 * results["averageScoreInstCategories"],
- }
- self._working_dir.cleanup()
- return ret
diff --git a/spaces/PeepDaSlan9/AutoGPT/benchmark/__init__.py b/spaces/PeepDaSlan9/AutoGPT/benchmark/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py b/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py
deleted file mode 100644
index ba0443e0c22481af2b451d8ccc5ab6e6f79cffb8..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py
+++ /dev/null
@@ -1,573 +0,0 @@
-import os
-import random
-from typing import Optional, Callable
-import json
-import glob
-import csv
-
-import numpy as np
-import torch
-import librosa
-import pyloudnorm as pyln
-from pedalboard import Pedalboard, Limiter, Gain, Compressor, Clipping
-
-from .dataset import (
- MusdbTrainDataset,
- MusdbValidDataset,
- apply_limitaug,
-)
-from utils import (
- load_wav_arbitrary_position_stereo,
- load_wav_specific_position_stereo,
- db2linear,
-)
-
-
-class DelimitTrainDataset(MusdbTrainDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- seq_duration: Optional[float] = 6.0,
- samples_per_track: int = 64,
- source_augmentations: Optional[Callable] = lambda audio: audio,
- sample_rate: int = 44100,
- seed: int = 42,
- limitaug_method: str = "limitaug",
- limitaug_mode: str = "normal_L",
- limitaug_custom_target_lufs: float = None,
- limitaug_custom_target_lufs_std: float = None,
- target_loudnorm_lufs: float = -14.0,
- target_limitaug_mode: str = None,
- target_limitaug_custom_target_lufs: float = None,
- target_limitaug_custom_target_lufs_std: float = None,
- custom_limiter_attack_range: list = [2.0, 2.0],
- custom_limiter_release_range: list = [200.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(
- target=target,
- root=root,
- seq_duration=seq_duration,
- samples_per_track=samples_per_track,
- source_augmentations=source_augmentations,
- sample_rate=sample_rate,
- seed=seed,
- limitaug_method=limitaug_method,
- limitaug_mode=limitaug_mode,
- limitaug_custom_target_lufs=limitaug_custom_target_lufs,
- limitaug_custom_target_lufs_std=limitaug_custom_target_lufs_std,
- target_loudnorm_lufs=target_loudnorm_lufs,
- custom_limiter_attack_range=custom_limiter_attack_range,
- custom_limiter_release_range=custom_limiter_release_range,
- *args,
- **kwargs,
- )
-
- self.target_limitaug_mode = target_limitaug_mode
-
- self.target_limitaug_custom_target_lufs = (target_limitaug_custom_target_lufs,)
- self.target_limitaug_custom_target_lufs_std = (
- target_limitaug_custom_target_lufs_std,
- )
- self.limitaug_mode_statistics["target_custom"] = [
- target_limitaug_custom_target_lufs,
- target_limitaug_custom_target_lufs_std,
- ]
-
- """
- Parameters
- ----------
- limitaug_method : str
- choose from ["linear_gain_increase", "limitaug", "limitaug_then_loudnorm", "only_loudnorm"]
- limitaug_mode : str
- choose from ["uniform", "normal", "normal_L", "normal_XL", "normal_short_term", "normal_L_short_term", "normal_XL_short_term", "custom"]
- limitaug_custom_target_lufs : float
- valid only when
- limitaug_mode == "custom"
- target_loudnorm_lufs : float
- valid only when
- limitaug_method == 'limitaug_then_loudnorm' or 'only_loudnorm'
- default is -14.
- To the best of my knowledge, Spotify and Youtube music is using -14 as a reference loudness normalization level.
- No special reason for the choice of -14 as target_loudnorm_lufs.
- target : str
- target name of the source to be separated, defaults to ``vocals``.
- root : str
- root path of MUSDB
- seq_duration : float
- training is performed in chunks of ``seq_duration`` (in seconds,
- defaults to ``None`` which loads the full audio track
- samples_per_track : int
- sets the number of samples, yielded from each track per epoch.
- Defaults to 64
- source_augmentations : list[callables]
- provide list of augmentation function that take a multi-channel
- audio file of shape (src, samples) as input and output. Defaults to
- no-augmentations (input = output)
- seed : int
- control randomness of dataset iterations
- args, kwargs : additional keyword arguments
- used to add further control for the musdb dataset
- initialization function.
- """
-
- # Get a limitaug result without target (individual stem source)
- def get_limitaug_mixture(self, mixture):
- if self.limitaug_method == "limitaug":
- self.board[1].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- )
-
- elif self.limitaug_method == "limitaug_then_loudnorm":
- self.board[1].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = (
- apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- target_loudnorm_lufs=self.target_loudnorm_lufs,
- ),
- )
-
- # Apply LimitAug using Custom Limiter
- elif self.limitaug_method == "custom_limiter_limitaug":
- # Change attack time of First compressor of the Limiter
- self.board[1].attack_ms = random.uniform(
- self.custom_limiter_attack_range[0], self.custom_limiter_attack_range[1]
- )
- # Change release time of First compressor of the Limiter
- self.board[1].release_ms = random.uniform(
- self.custom_limiter_release_range[0],
- self.custom_limiter_release_range[1],
- )
- # Change release time of Second compressor of the Limiter
- self.board[2].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- target_loudnorm_lufs=self.target_loudnorm_lufs,
- )
-
- # When we want to force NN to output an appropriately compressed target output
- if self.target_limitaug_mode:
- mixture_target_lufs = random.gauss(
- self.limitaug_mode_statistics[self.target_limitaug_mode][0],
- self.limitaug_mode_statistics[self.target_limitaug_mode][1],
- )
- mixture, target_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=mixture_target_lufs,
- loudness=mixture_lufs,
- )
-
- if np.isinf(mixture_lufs):
- mixture_loudnorm = mixture
- else:
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain, eps=0.0)
-
- return mixture_limited, mixture_loudnorm
-
- def __getitem__(self, index):
- audio_sources = []
-
- for k, source in enumerate(self.sources):
- # memorize index of target source
- if source == self.target: # if source is 'vocals'
- track_path = self.train_list[
- index // self.samples_per_track
- ] # we want to use # training samples per each track.
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration
- )
- else:
- track_path = random.choice(self.train_list)
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration
- )
- audio = self.source_augmentations(audio)
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- # apply linear mix over source index=0
- # and here, linear mixture is a target unlike in MusdbTrainDataset
- mixture = stems.sum(0)
- mixture_limited, mixture_loudnorm = self.get_limitaug_mixture(mixture)
- # We will give mixture_limited as an input and mixture_loudnorm as a target to the model.
-
- mixture_limited = np.clip(mixture_limited, -1.0, 1.0)
- mixture_limited = torch.as_tensor(mixture_limited, dtype=torch.float32)
- mixture_loudnorm = torch.as_tensor(mixture_loudnorm, dtype=torch.float32)
-
- return mixture_limited, mixture_loudnorm
-
-
-class OzoneTrainDataset(DelimitTrainDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- ozone_root: str = None,
- use_fixed: float = 0.1, # ratio of fixed samples
- seq_duration: Optional[float] = 6.0,
- samples_per_track: int = 64,
- source_augmentations: Optional[Callable] = lambda audio: audio,
- sample_rate: int = 44100,
- seed: int = 42,
- limitaug_method: str = "limitaug",
- limitaug_mode: str = "normal_L",
- limitaug_custom_target_lufs: float = None,
- limitaug_custom_target_lufs_std: float = None,
- target_loudnorm_lufs: float = -14.0,
- target_limitaug_mode: str = None,
- target_limitaug_custom_target_lufs: float = None,
- target_limitaug_custom_target_lufs_std: float = None,
- custom_limiter_attack_range: list = [2.0, 2.0],
- custom_limiter_release_range: list = [200.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(
- target,
- root,
- seq_duration,
- samples_per_track,
- source_augmentations,
- sample_rate,
- seed,
- limitaug_method,
- limitaug_mode,
- limitaug_custom_target_lufs,
- limitaug_custom_target_lufs_std,
- target_loudnorm_lufs,
- target_limitaug_mode,
- target_limitaug_custom_target_lufs,
- target_limitaug_custom_target_lufs_std,
- custom_limiter_attack_range,
- custom_limiter_release_range,
- *args,
- **kwargs,
- )
-
- self.ozone_root = ozone_root
- self.use_fixed = use_fixed
- self.list_train_fixed = glob.glob(f"{self.ozone_root}/ozone_train_fixed/*.wav")
- self.list_train_random = glob.glob(
- f"{self.ozone_root}/ozone_train_random/*.wav"
- )
- self.dict_train_random = {}
-
- # Load information of pre-generated random training examples
- list_csv_files = glob.glob(f"{self.ozone_root}/ozone_train_random_*.csv")
- list_csv_files.sort()
- for csv_file in list_csv_files:
- with open(csv_file, "r") as f:
- reader = csv.reader(f)
- next(reader)
- for row in reader:
- self.dict_train_random[row[0]] = {
- "max_threshold": float(row[1]),
- "max_character": float(row[2]),
- "vocals": {
- "name": row[3],
- "start_sec": float(row[4]),
- "gain": float(row[5]),
- "channelswap": bool(row[6]),
- },
- "bass": {
- "name": row[7],
- "start_sec": float(row[8]),
- "gain": float(row[9]),
- "channelswap": bool(row[10]),
- },
- "drums": {
- "name": row[11],
- "start_sec": float(row[12]),
- "gain": float(row[13]),
- "channelswap": bool(row[14]),
- },
- "other": {
- "name": row[15],
- "start_sec": float(row[16]),
- "gain": float(row[17]),
- "channelswap": bool(row[18]),
- },
- }
-
- def __getitem__(self, idx):
- use_fixed_prob = random.random()
-
- if use_fixed_prob <= self.use_fixed:
- # Fixed examples
- audio_path = random.choice(self.list_train_fixed)
- song_name = os.path.basename(audio_path).replace(".wav", "")
- mixture_limited, start_pos_sec = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration, return_pos=True
- )
-
- audio_sources = []
- track_path = f"{self.root}/train/{song_name}"
- for source in self.sources:
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_specific_position_stereo(
- audio_path,
- self.sample_rate,
- self.seq_duration,
- start_position=start_pos_sec,
- )
- audio_sources.append(audio)
-
- else:
- # Random examples
- # Load mixture_limited (pre-generated)
- audio_path = random.choice(self.list_train_random)
- seg_name = os.path.basename(audio_path).replace(".wav", "")
- mixture_limited, sr = librosa.load(
- audio_path, sr=self.sample_rate, mono=False
- )
-
- # Load mixture_unlimited (from the original musdb18, using metadata)
- audio_sources = []
- for source in self.sources:
- dict_seg_info = self.dict_train_random[seg_name]
- dict_seg_source_info = dict_seg_info[source]
- audio_path = (
- f"{self.root}/train/{dict_seg_source_info['name']}/{source}.wav"
- )
- audio = load_wav_specific_position_stereo(
- audio_path,
- self.sample_rate,
- self.seq_duration,
- start_position=dict_seg_source_info["start_sec"],
- )
-
- # apply augmentations
- audio = audio * dict_seg_source_info["gain"]
- if dict_seg_source_info["channelswap"]:
- audio = np.flip(audio, axis=0)
-
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
- mixture = stems.sum(axis=0)
- mixture_lufs = self.meter.integrated_loudness(mixture.T)
- if np.isinf(mixture_lufs):
- mixture_loudnorm = mixture
- else:
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain, eps=0.0)
-
- return mixture_limited, mixture_loudnorm
-
-
-class DelimitValidDataset(MusdbValidDataset):
- def __init__(
- self,
- target: str = "vocals",
- root: str = None,
- delimit_valid_root: str = None,
- valid_target_lufs: float = -8.05, # From the Table 1 of the "Towards robust music source separation on loud commercial music" paper, the average loudness of commerical music.
- target_loudnorm_lufs: float = -14.0,
- delimit_valid_L_root: str = None, # This will be used when using the target as compressed (normal_L) mixture.
- use_custom_limiter: bool = False,
- custom_limiter_attack_range: list = [0.1, 10.0],
- custom_limiter_release_range: list = [30.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(target=target, root=root, *args, **kwargs)
- self.delimit_valid_root = delimit_valid_root
- if self.delimit_valid_root:
- with open(f"{self.delimit_valid_root}/valid_loudness.json", "r") as f:
- self.dict_valid_loudness = json.load(f)
- self.delimit_valid_L_root = delimit_valid_L_root
- if self.delimit_valid_L_root:
- with open(f"{self.delimit_valid_L_root}/valid_loudness.json", "r") as f:
- self.dict_valid_L_loudness = json.load(f)
-
- self.valid_target_lufs = valid_target_lufs
- self.target_loudnorm_lufs = target_loudnorm_lufs
- self.meter = pyln.Meter(self.sample_rate)
- self.use_custom_limiter = use_custom_limiter
-
- if self.use_custom_limiter:
- print("using Custom limiter limitaug for validation!!")
- self.custom_limiter_attack_range = custom_limiter_attack_range
- self.custom_limiter_release_range = custom_limiter_release_range
- self.board = Pedalboard(
- [
- Gain(gain_db=0.0),
- Compressor(
- threshold_db=-10.0, ratio=4.0, attack_ms=2.0, release_ms=200.0
- ), # attack_ms and release_ms will be changed later.
- Compressor(
- threshold_db=0.0,
- ratio=1000.0,
- attack_ms=0.001,
- release_ms=100.0,
- ),
- Gain(gain_db=3.75),
- Clipping(threshold_db=0.0),
- ]
- ) # This implementation is the same as JUCE Limiter.
- # However, we want the first compressor to have a variable attack and release time.
- # Therefore, we use the Custom Limiter instead of the JUCE Limiter.
- else:
- self.board = Pedalboard(
- [Gain(gain_db=0.0), Limiter(threshold_db=0.0, release_ms=100.0)]
- ) # Currently, we are using a limiter with a release time of 100ms.
-
- def __getitem__(self, index):
- audio_sources = []
- target_ind = None
-
- for k, source in enumerate(self.sources):
- # memorize index of target source
- if source == self.target: # if source is 'vocals'
- target_ind = k
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- audio_path = f"{track_path}/{source}.wav"
- # audio = utils.load_wav_stereo(audio_path, self.sample_rate)
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
- else:
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- audio_path = f"{track_path}/{source}.wav"
- # audio = utils.load_wav_stereo(audio_path, self.sample_rate)
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
-
- audio = torch.as_tensor(audio, dtype=torch.float32)
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- # apply linear mix over source index=0
- # and here, linear mixture is a target unlike in MusdbTrainDataset
- mixture = stems.sum(0)
- if (
- self.delimit_valid_root
- ): # If there exists a pre-processed delimit valid dataset
- audio_path = f"{self.delimit_valid_root}/valid/{song_name}.wav"
- mixture_limited = librosa.load(audio_path, mono=False, sr=self.sample_rate)[
- 0
- ]
- mixture_lufs = self.dict_valid_loudness[song_name]
-
- else:
- if self.use_custom_limiter:
- custom_limiter_attack = random.uniform(
- self.custom_limiter_attack_range[0],
- self.custom_limiter_attack_range[1],
- )
- self.board[1].attack_ms = custom_limiter_attack
-
- custom_limiter_release = random.uniform(
- self.custom_limiter_release_range[0],
- self.custom_limiter_release_range[1],
- )
- self.board[1].release_ms = custom_limiter_release
-
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=self.valid_target_lufs,
- )
- else:
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=self.valid_target_lufs,
- # target_loudnorm_lufs=self.target_loudnorm_lufs,
- ) # mixture_limited is a limiter applied mixture
- # We will give mixture_limited as an input and mixture_loudnorm as a target to the model.
-
- if self.delimit_valid_L_root:
- audio_L_path = f"{self.delimit_valid_L_root}/valid/{song_name}.wav"
- mixture_loudnorm = librosa.load(
- audio_L_path, mono=False, sr=self.sample_rate
- )[0]
- mixture_lufs = self.dict_valid_L_loudness[song_name]
- mixture = mixture_loudnorm
-
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain)
-
- if self.use_custom_limiter:
- return (
- mixture_limited,
- mixture_loudnorm,
- song_name,
- mixture_lufs,
- custom_limiter_attack,
- custom_limiter_release,
- )
- else:
- return mixture_limited, mixture_loudnorm, song_name, mixture_lufs
-
-
-class OzoneValidDataset(MusdbValidDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- ozone_root: str = None,
- target_loudnorm_lufs: float = -14.0,
- *args,
- **kwargs,
- ) -> None:
- super().__init__(target=target, root=root, *args, **kwargs)
-
- self.ozone_root = ozone_root
- self.target_loudnorm_lufs = target_loudnorm_lufs
-
- with open(f"{self.ozone_root}/valid_loudness.json", "r") as f:
- self.dict_valid_loudness = json.load(f)
-
- def __getitem__(self, index):
- audio_sources = []
-
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- for k, source in enumerate(self.sources):
- audio_path = f"{track_path}/{source}.wav"
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- mixture = stems.sum(0)
-
- audio_path = f"{self.ozone_root}/ozone_train_fixed/{song_name}.wav"
- mixture_limited = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
-
- mixture_lufs = self.dict_valid_loudness[song_name]
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain)
-
- return mixture_limited, mixture_loudnorm, song_name, mixture_lufs
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py
deleted file mode 100644
index 75559579cf053abcc99538606cbb88c723faf783..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py
+++ /dev/null
@@ -1,282 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-
-import torch
-import torch.nn as nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair, _single
-
-from annotator.uniformer.mmcv.utils import deprecated_api_warning
-from ..cnn import CONV_LAYERS
-from ..utils import ext_loader, print_log
-
-ext_module = ext_loader.load_ext(
- '_ext',
- ['modulated_deform_conv_forward', 'modulated_deform_conv_backward'])
-
-
-class ModulatedDeformConv2dFunction(Function):
-
- @staticmethod
- def symbolic(g, input, offset, mask, weight, bias, stride, padding,
- dilation, groups, deform_groups):
- input_tensors = [input, offset, mask, weight]
- if bias is not None:
- input_tensors.append(bias)
- return g.op(
- 'mmcv::MMCVModulatedDeformConv2d',
- *input_tensors,
- stride_i=stride,
- padding_i=padding,
- dilation_i=dilation,
- groups_i=groups,
- deform_groups_i=deform_groups)
-
- @staticmethod
- def forward(ctx,
- input,
- offset,
- mask,
- weight,
- bias=None,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- deform_groups=1):
- if input is not None and input.dim() != 4:
- raise ValueError(
- f'Expected 4D tensor as input, got {input.dim()}D tensor \
- instead.')
- ctx.stride = _pair(stride)
- ctx.padding = _pair(padding)
- ctx.dilation = _pair(dilation)
- ctx.groups = groups
- ctx.deform_groups = deform_groups
- ctx.with_bias = bias is not None
- if not ctx.with_bias:
- bias = input.new_empty(0) # fake tensor
- # When pytorch version >= 1.6.0, amp is adopted for fp16 mode;
- # amp won't cast the type of model (float32), but "offset" is cast
- # to float16 by nn.Conv2d automatically, leading to the type
- # mismatch with input (when it is float32) or weight.
- # The flag for whether to use fp16 or amp is the type of "offset",
- # we cast weight and input to temporarily support fp16 and amp
- # whatever the pytorch version is.
- input = input.type_as(offset)
- weight = weight.type_as(input)
- ctx.save_for_backward(input, offset, mask, weight, bias)
- output = input.new_empty(
- ModulatedDeformConv2dFunction._output_size(ctx, input, weight))
- ctx._bufs = [input.new_empty(0), input.new_empty(0)]
- ext_module.modulated_deform_conv_forward(
- input,
- weight,
- bias,
- ctx._bufs[0],
- offset,
- mask,
- output,
- ctx._bufs[1],
- kernel_h=weight.size(2),
- kernel_w=weight.size(3),
- stride_h=ctx.stride[0],
- stride_w=ctx.stride[1],
- pad_h=ctx.padding[0],
- pad_w=ctx.padding[1],
- dilation_h=ctx.dilation[0],
- dilation_w=ctx.dilation[1],
- group=ctx.groups,
- deformable_group=ctx.deform_groups,
- with_bias=ctx.with_bias)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- input, offset, mask, weight, bias = ctx.saved_tensors
- grad_input = torch.zeros_like(input)
- grad_offset = torch.zeros_like(offset)
- grad_mask = torch.zeros_like(mask)
- grad_weight = torch.zeros_like(weight)
- grad_bias = torch.zeros_like(bias)
- grad_output = grad_output.contiguous()
- ext_module.modulated_deform_conv_backward(
- input,
- weight,
- bias,
- ctx._bufs[0],
- offset,
- mask,
- ctx._bufs[1],
- grad_input,
- grad_weight,
- grad_bias,
- grad_offset,
- grad_mask,
- grad_output,
- kernel_h=weight.size(2),
- kernel_w=weight.size(3),
- stride_h=ctx.stride[0],
- stride_w=ctx.stride[1],
- pad_h=ctx.padding[0],
- pad_w=ctx.padding[1],
- dilation_h=ctx.dilation[0],
- dilation_w=ctx.dilation[1],
- group=ctx.groups,
- deformable_group=ctx.deform_groups,
- with_bias=ctx.with_bias)
- if not ctx.with_bias:
- grad_bias = None
-
- return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias,
- None, None, None, None, None)
-
- @staticmethod
- def _output_size(ctx, input, weight):
- channels = weight.size(0)
- output_size = (input.size(0), channels)
- for d in range(input.dim() - 2):
- in_size = input.size(d + 2)
- pad = ctx.padding[d]
- kernel = ctx.dilation[d] * (weight.size(d + 2) - 1) + 1
- stride_ = ctx.stride[d]
- output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
- if not all(map(lambda s: s > 0, output_size)):
- raise ValueError(
- 'convolution input is too small (output would be ' +
- 'x'.join(map(str, output_size)) + ')')
- return output_size
-
-
-modulated_deform_conv2d = ModulatedDeformConv2dFunction.apply
-
-
-class ModulatedDeformConv2d(nn.Module):
-
- @deprecated_api_warning({'deformable_groups': 'deform_groups'},
- cls_name='ModulatedDeformConv2d')
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- deform_groups=1,
- bias=True):
- super(ModulatedDeformConv2d, self).__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.kernel_size = _pair(kernel_size)
- self.stride = _pair(stride)
- self.padding = _pair(padding)
- self.dilation = _pair(dilation)
- self.groups = groups
- self.deform_groups = deform_groups
- # enable compatibility with nn.Conv2d
- self.transposed = False
- self.output_padding = _single(0)
-
- self.weight = nn.Parameter(
- torch.Tensor(out_channels, in_channels // groups,
- *self.kernel_size))
- if bias:
- self.bias = nn.Parameter(torch.Tensor(out_channels))
- else:
- self.register_parameter('bias', None)
- self.init_weights()
-
- def init_weights(self):
- n = self.in_channels
- for k in self.kernel_size:
- n *= k
- stdv = 1. / math.sqrt(n)
- self.weight.data.uniform_(-stdv, stdv)
- if self.bias is not None:
- self.bias.data.zero_()
-
- def forward(self, x, offset, mask):
- return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,
- self.stride, self.padding,
- self.dilation, self.groups,
- self.deform_groups)
-
-
-@CONV_LAYERS.register_module('DCNv2')
-class ModulatedDeformConv2dPack(ModulatedDeformConv2d):
- """A ModulatedDeformable Conv Encapsulation that acts as normal Conv
- layers.
-
- Args:
- in_channels (int): Same as nn.Conv2d.
- out_channels (int): Same as nn.Conv2d.
- kernel_size (int or tuple[int]): Same as nn.Conv2d.
- stride (int): Same as nn.Conv2d, while tuple is not supported.
- padding (int): Same as nn.Conv2d, while tuple is not supported.
- dilation (int): Same as nn.Conv2d, while tuple is not supported.
- groups (int): Same as nn.Conv2d.
- bias (bool or str): If specified as `auto`, it will be decided by the
- norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
- False.
- """
-
- _version = 2
-
- def __init__(self, *args, **kwargs):
- super(ModulatedDeformConv2dPack, self).__init__(*args, **kwargs)
- self.conv_offset = nn.Conv2d(
- self.in_channels,
- self.deform_groups * 3 * self.kernel_size[0] * self.kernel_size[1],
- kernel_size=self.kernel_size,
- stride=self.stride,
- padding=self.padding,
- dilation=self.dilation,
- bias=True)
- self.init_weights()
-
- def init_weights(self):
- super(ModulatedDeformConv2dPack, self).init_weights()
- if hasattr(self, 'conv_offset'):
- self.conv_offset.weight.data.zero_()
- self.conv_offset.bias.data.zero_()
-
- def forward(self, x):
- out = self.conv_offset(x)
- o1, o2, mask = torch.chunk(out, 3, dim=1)
- offset = torch.cat((o1, o2), dim=1)
- mask = torch.sigmoid(mask)
- return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,
- self.stride, self.padding,
- self.dilation, self.groups,
- self.deform_groups)
-
- def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
- missing_keys, unexpected_keys, error_msgs):
- version = local_metadata.get('version', None)
-
- if version is None or version < 2:
- # the key is different in early versions
- # In version < 2, ModulatedDeformConvPack
- # loads previous benchmark models.
- if (prefix + 'conv_offset.weight' not in state_dict
- and prefix[:-1] + '_offset.weight' in state_dict):
- state_dict[prefix + 'conv_offset.weight'] = state_dict.pop(
- prefix[:-1] + '_offset.weight')
- if (prefix + 'conv_offset.bias' not in state_dict
- and prefix[:-1] + '_offset.bias' in state_dict):
- state_dict[prefix +
- 'conv_offset.bias'] = state_dict.pop(prefix[:-1] +
- '_offset.bias')
-
- if version is not None and version > 1:
- print_log(
- f'ModulatedDeformConvPack {prefix.rstrip(".")} is upgraded to '
- 'version 2.',
- logger='root')
-
- super()._load_from_state_dict(state_dict, prefix, local_metadata,
- strict, missing_keys, unexpected_keys,
- error_msgs)
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py
deleted file mode 100644
index a802a74fd6c3610d9ae178e6201f47423eca7ad1..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py
+++ /dev/null
@@ -1,177 +0,0 @@
-from typing import List
-
-import torch
-from torch import nn as nn
-
-from annotator.uniformer.mmcv.runner import force_fp32
-from .furthest_point_sample import (furthest_point_sample,
- furthest_point_sample_with_dist)
-
-
-def calc_square_dist(point_feat_a, point_feat_b, norm=True):
- """Calculating square distance between a and b.
-
- Args:
- point_feat_a (Tensor): (B, N, C) Feature vector of each point.
- point_feat_b (Tensor): (B, M, C) Feature vector of each point.
- norm (Bool, optional): Whether to normalize the distance.
- Default: True.
-
- Returns:
- Tensor: (B, N, M) Distance between each pair points.
- """
- num_channel = point_feat_a.shape[-1]
- # [bs, n, 1]
- a_square = torch.sum(point_feat_a.unsqueeze(dim=2).pow(2), dim=-1)
- # [bs, 1, m]
- b_square = torch.sum(point_feat_b.unsqueeze(dim=1).pow(2), dim=-1)
-
- corr_matrix = torch.matmul(point_feat_a, point_feat_b.transpose(1, 2))
-
- dist = a_square + b_square - 2 * corr_matrix
- if norm:
- dist = torch.sqrt(dist) / num_channel
- return dist
-
-
-def get_sampler_cls(sampler_type):
- """Get the type and mode of points sampler.
-
- Args:
- sampler_type (str): The type of points sampler.
- The valid value are "D-FPS", "F-FPS", or "FS".
-
- Returns:
- class: Points sampler type.
- """
- sampler_mappings = {
- 'D-FPS': DFPSSampler,
- 'F-FPS': FFPSSampler,
- 'FS': FSSampler,
- }
- try:
- return sampler_mappings[sampler_type]
- except KeyError:
- raise KeyError(
- f'Supported `sampler_type` are {sampler_mappings.keys()}, but got \
- {sampler_type}')
-
-
-class PointsSampler(nn.Module):
- """Points sampling.
-
- Args:
- num_point (list[int]): Number of sample points.
- fps_mod_list (list[str], optional): Type of FPS method, valid mod
- ['F-FPS', 'D-FPS', 'FS'], Default: ['D-FPS'].
- F-FPS: using feature distances for FPS.
- D-FPS: using Euclidean distances of points for FPS.
- FS: using F-FPS and D-FPS simultaneously.
- fps_sample_range_list (list[int], optional):
- Range of points to apply FPS. Default: [-1].
- """
-
- def __init__(self,
- num_point: List[int],
- fps_mod_list: List[str] = ['D-FPS'],
- fps_sample_range_list: List[int] = [-1]):
- super().__init__()
- # FPS would be applied to different fps_mod in the list,
- # so the length of the num_point should be equal to
- # fps_mod_list and fps_sample_range_list.
- assert len(num_point) == len(fps_mod_list) == len(
- fps_sample_range_list)
- self.num_point = num_point
- self.fps_sample_range_list = fps_sample_range_list
- self.samplers = nn.ModuleList()
- for fps_mod in fps_mod_list:
- self.samplers.append(get_sampler_cls(fps_mod)())
- self.fp16_enabled = False
-
- @force_fp32()
- def forward(self, points_xyz, features):
- """
- Args:
- points_xyz (Tensor): (B, N, 3) xyz coordinates of the features.
- features (Tensor): (B, C, N) Descriptors of the features.
-
- Returns:
- Tensor: (B, npoint, sample_num) Indices of sampled points.
- """
- indices = []
- last_fps_end_index = 0
-
- for fps_sample_range, sampler, npoint in zip(
- self.fps_sample_range_list, self.samplers, self.num_point):
- assert fps_sample_range < points_xyz.shape[1]
-
- if fps_sample_range == -1:
- sample_points_xyz = points_xyz[:, last_fps_end_index:]
- if features is not None:
- sample_features = features[:, :, last_fps_end_index:]
- else:
- sample_features = None
- else:
- sample_points_xyz = \
- points_xyz[:, last_fps_end_index:fps_sample_range]
- if features is not None:
- sample_features = features[:, :, last_fps_end_index:
- fps_sample_range]
- else:
- sample_features = None
-
- fps_idx = sampler(sample_points_xyz.contiguous(), sample_features,
- npoint)
-
- indices.append(fps_idx + last_fps_end_index)
- last_fps_end_index += fps_sample_range
- indices = torch.cat(indices, dim=1)
-
- return indices
-
-
-class DFPSSampler(nn.Module):
- """Using Euclidean distances of points for FPS."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with D-FPS."""
- fps_idx = furthest_point_sample(points.contiguous(), npoint)
- return fps_idx
-
-
-class FFPSSampler(nn.Module):
- """Using feature distances for FPS."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with F-FPS."""
- assert features is not None, \
- 'feature input to FFPS_Sampler should not be None'
- features_for_fps = torch.cat([points, features.transpose(1, 2)], dim=2)
- features_dist = calc_square_dist(
- features_for_fps, features_for_fps, norm=False)
- fps_idx = furthest_point_sample_with_dist(features_dist, npoint)
- return fps_idx
-
-
-class FSSampler(nn.Module):
- """Using F-FPS and D-FPS simultaneously."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with FS_Sampling."""
- assert features is not None, \
- 'feature input to FS_Sampler should not be None'
- ffps_sampler = FFPSSampler()
- dfps_sampler = DFPSSampler()
- fps_idx_ffps = ffps_sampler(points, features, npoint)
- fps_idx_dfps = dfps_sampler(points, features, npoint)
- fps_idx = torch.cat([fps_idx_ffps, fps_idx_dfps], dim=1)
- return fps_idx
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py
deleted file mode 100644
index 332b242c03d1c5e80d4577df442a9a037b1816e1..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .base_pixel_sampler import BasePixelSampler
-from .ohem_pixel_sampler import OHEMPixelSampler
-
-__all__ = ['BasePixelSampler', 'OHEMPixelSampler']
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py
deleted file mode 100644
index c0e42756ee6fcd779387255391a30079a28f5e60..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import torch
-from torch import nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-from maskrcnn_benchmark import _C
-
-
-class _ROIPool(Function):
- @staticmethod
- def forward(ctx, input, roi, output_size, spatial_scale):
- ctx.output_size = _pair(output_size)
- ctx.spatial_scale = spatial_scale
- ctx.input_shape = input.size()
- output, argmax = _C.roi_pool_forward(
- input, roi, spatial_scale, output_size[0], output_size[1]
- )
- ctx.save_for_backward(input, roi, argmax)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- input, rois, argmax = ctx.saved_tensors
- output_size = ctx.output_size
- spatial_scale = ctx.spatial_scale
- bs, ch, h, w = ctx.input_shape
- grad_input = _C.roi_pool_backward(
- grad_output,
- input,
- rois,
- argmax,
- spatial_scale,
- output_size[0],
- output_size[1],
- bs,
- ch,
- h,
- w,
- )
- return grad_input, None, None, None
-
-
-roi_pool = _ROIPool.apply
-
-
-class ROIPool(nn.Module):
- def __init__(self, output_size, spatial_scale):
- super(ROIPool, self).__init__()
- self.output_size = output_size
- self.spatial_scale = spatial_scale
-
- def forward(self, input, rois):
- return roi_pool(input, rois, self.output_size, self.spatial_scale)
-
- def __repr__(self):
- tmpstr = self.__class__.__name__ + "("
- tmpstr += "output_size=" + str(self.output_size)
- tmpstr += ", spatial_scale=" + str(self.spatial_scale)
- tmpstr += ")"
- return tmpstr
diff --git a/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py b/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py
deleted file mode 100644
index e90ef2130a47ec52160709877972716352e04c9c..0000000000000000000000000000000000000000
--- a/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
- if bias is not None:
- rest_dim = [1] * (input.ndim - bias.ndim - 1)
- return (
- F.leaky_relu(
- input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope
- )
- * scale
- )
- else:
- return F.leaky_relu(input, negative_slope=0.2) * scale
-
-
-class EqualLinear(nn.Module):
- def __init__(
- self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1
- ):
- super().__init__()
- self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
- if bias:
- self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
- else:
- self.bias = None
- self.scale = (1 / math.sqrt(in_dim)) * lr_mul
- self.lr_mul = lr_mul
-
- def forward(self, input):
- out = F.linear(input, self.weight * self.scale)
- out = fused_leaky_relu(out, self.bias * self.lr_mul)
- return out
-
-
-class RandomLatentConverter(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.layers = nn.Sequential(*[EqualLinear(channels, channels, lr_mul=.1) for _ in range(5)],
- nn.Linear(channels, channels))
- self.channels = channels
-
- def forward(self, ref):
- r = torch.randn(ref.shape[0], self.channels, device=ref.device)
- y = self.layers(r)
- return y
-
-
-if __name__ == '__main__':
- model = RandomLatentConverter(512)
- model(torch.randn(5,512))
\ No newline at end of file
diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py
deleted file mode 100644
index 8d760f8a1a769bba657d4aea47857f48f7cd0d7a..0000000000000000000000000000000000000000
--- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py
+++ /dev/null
@@ -1,1832 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# Updated to account for UI changes from https://github.com/rkfg/audiocraft/blob/long/app.py
-# also released under the MIT license.
-
-import argparse
-from concurrent.futures import ProcessPoolExecutor
-import os
-from pathlib import Path
-import subprocess as sp
-from tempfile import NamedTemporaryFile
-import time
-import warnings
-import glob
-import re
-from PIL import Image
-from pydub import AudioSegment
-from datetime import datetime
-
-import json
-import shutil
-import taglib
-import torch
-import torchaudio
-import gradio as gr
-import numpy as np
-import typing as tp
-
-from audiocraft.data.audio_utils import convert_audio
-from audiocraft.data.audio import audio_write
-from audiocraft.models import AudioGen, MusicGen, MultiBandDiffusion
-from audiocraft.utils import ui
-import random, string
-
-version = "2.0.0a"
-
-theme = gr.themes.Base(
- primary_hue="lime",
- secondary_hue="lime",
- neutral_hue="neutral",
-).set(
- button_primary_background_fill_hover='*primary_500',
- button_primary_background_fill_hover_dark='*primary_500',
- button_secondary_background_fill_hover='*primary_500',
- button_secondary_background_fill_hover_dark='*primary_500'
-)
-
-MODEL = None # Last used model
-MODELS = None
-UNLOAD_MODEL = False
-MOVE_TO_CPU = False
-IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
-print(IS_BATCHED)
-MAX_BATCH_SIZE = 12
-BATCHED_DURATION = 15
-INTERRUPTING = False
-MBD = None
-# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
-_old_call = sp.call
-
-
-def generate_random_string(length):
- characters = string.ascii_letters + string.digits
- return ''.join(random.choice(characters) for _ in range(length))
-
-
-def resize_video(input_path, output_path, target_width, target_height):
- ffmpeg_cmd = [
- 'ffmpeg',
- '-y',
- '-i', input_path,
- '-vf', f'scale={target_width}:{target_height}',
- '-c:a', 'copy',
- output_path
- ]
- sp.run(ffmpeg_cmd)
-
-
-def _call_nostderr(*args, **kwargs):
- # Avoid ffmpeg vomiting on the logs.
- kwargs['stderr'] = sp.DEVNULL
- kwargs['stdout'] = sp.DEVNULL
- _old_call(*args, **kwargs)
-
-
-sp.call = _call_nostderr
-# Preallocating the pool of processes.
-pool = ProcessPoolExecutor(4)
-pool.__enter__()
-
-
-def interrupt():
- global INTERRUPTING
- INTERRUPTING = True
-
-
-class FileCleaner:
- def __init__(self, file_lifetime: float = 3600):
- self.file_lifetime = file_lifetime
- self.files = []
-
- def add(self, path: tp.Union[str, Path]):
- self._cleanup()
- self.files.append((time.time(), Path(path)))
-
- def _cleanup(self):
- now = time.time()
- for time_added, path in list(self.files):
- if now - time_added > self.file_lifetime:
- if path.exists():
- path.unlink()
- self.files.pop(0)
- else:
- break
-
-
-file_cleaner = FileCleaner()
-
-
-def make_waveform(*args, **kwargs):
- # Further remove some warnings.
- be = time.time()
- with warnings.catch_warnings():
- warnings.simplefilter('ignore')
- height = kwargs.pop('height')
- width = kwargs.pop('width')
- if height < 256:
- height = 256
- if width < 256:
- width = 256
- waveform_video = gr.make_waveform(*args, **kwargs)
- out = f"{generate_random_string(12)}.mp4"
- image = kwargs.get('bg_image', None)
- if image is None:
- resize_video(waveform_video, out, 900, 300)
- else:
- resize_video(waveform_video, out, width, height)
- print("Make a video took", time.time() - be)
- return out
-
-
-def load_model(version='GrandaddyShmax/musicgen-melody', custom_model=None, base_model='GrandaddyShmax/musicgen-medium', gen_type="music"):
- global MODEL, MODELS
- print("Loading model", version)
- if MODELS is None:
- if version == 'GrandaddyShmax/musicgen-custom':
- MODEL = MusicGen.get_pretrained(base_model)
- file_path = os.path.abspath("models/" + str(custom_model) + ".pt")
- MODEL.lm.load_state_dict(torch.load(file_path))
- else:
- if gen_type == "music":
- MODEL = MusicGen.get_pretrained(version)
- elif gen_type == "audio":
- MODEL = AudioGen.get_pretrained(version)
-
- return
-
- else:
- t1 = time.monotonic()
- if MODEL is not None:
- MODEL.to('cpu') # move to cache
- print("Previous model moved to CPU in %.2fs" % (time.monotonic() - t1))
- t1 = time.monotonic()
- if version != 'GrandaddyShmax/musicgen-custom' and MODELS.get(version) is None:
- print("Loading model %s from disk" % version)
- if gen_type == "music":
- result = MusicGen.get_pretrained(version)
- elif gen_type == "audio":
- result = AudioGen.get_pretrained(version)
- MODELS[version] = result
- print("Model loaded in %.2fs" % (time.monotonic() - t1))
- MODEL = result
- return
- result = MODELS[version].to('cuda')
- print("Cached model loaded in %.2fs" % (time.monotonic() - t1))
- MODEL = result
-
-def get_audio_info(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted. (Discord removes metadata from mp4 and wav files, so you can't use them)"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- global_prompt = str("\nGlobal Prompt: " + (data['global_prompt'] if data['global_prompt'] != "" else "none")) if 'global_prompt' in data else ""
- bpm = str("\nBPM: " + data['bpm']) if 'bpm' in data else ""
- key = str("\nKey: " + data['key']) if 'key' in data else ""
- scale = str("\nScale: " + data['scale']) if 'scale' in data else ""
- prompts = str("\nPrompts: " + (data['texts'] if data['texts'] != "['']" else "none")) if 'texts' in data else ""
- duration = str("\nDuration: " + data['duration']) if 'duration' in data else ""
- overlap = str("\nOverlap: " + data['overlap']) if 'overlap' in data else ""
- seed = str("\nSeed: " + data['seed']) if 'seed' in data else ""
- audio_mode = str("\nAudio Mode: " + data['audio_mode']) if 'audio_mode' in data else ""
- input_length = str("\nInput Length: " + data['input_length']) if 'input_length' in data else ""
- channel = str("\nChannel: " + data['channel']) if 'channel' in data else ""
- sr_select = str("\nSample Rate: " + data['sr_select']) if 'sr_select' in data else ""
- gen_type = str(data['generator'] + "gen-") if 'generator' in data else ""
- model = str("\nModel: " + gen_type + data['model']) if 'model' in data else ""
- custom_model = str("\nCustom Model: " + data['custom_model']) if 'custom_model' in data else ""
- base_model = str("\nBase Model: " + data['base_model']) if 'base_model' in data else ""
- decoder = str("\nDecoder: " + data['decoder']) if 'decoder' in data else ""
- topk = str("\nTopk: " + data['topk']) if 'topk' in data else ""
- topp = str("\nTopp: " + data['topp']) if 'topp' in data else ""
- temperature = str("\nTemperature: " + data['temperature']) if 'temperature' in data else ""
- cfg_coef = str("\nClassifier Free Guidance: " + data['cfg_coef']) if 'cfg_coef' in data else ""
- version = str("Version: " + data['version']) if 'version' in data else "Version: Unknown"
- info = str(version + global_prompt + bpm + key + scale + prompts + duration + overlap + seed + audio_mode + input_length + channel + sr_select + model + custom_model + base_model + decoder + topk + topp + temperature + cfg_coef)
- if info == "":
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted. (Discord removes metadata from mp4 and wav files, so you can't use them)"
- return info
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- #if 'global_prompt' not in data:
- #return "No tags found. Either the file is not generated by MusicGen+ V1.2.8a and higher or the tags are corrupted."
- global_prompt = str("\nGlobal Prompt: " + (data['global_prompt'] if data['global_prompt'] != "" else "none")) if 'global_prompt' in data else ""
- bpm = str("\nBPM: " + data['bpm']) if 'bpm' in data else ""
- key = str("\nKey: " + data['key']) if 'key' in data else ""
- scale = str("\nScale: " + data['scale']) if 'scale' in data else ""
- prompts = str("\nPrompts: " + (data['texts'] if data['texts'] != "['']" else "none")) if 'texts' in data else ""
- duration = str("\nDuration: " + data['duration']) if 'duration' in data else ""
- overlap = str("\nOverlap: " + data['overlap']) if 'overlap' in data else ""
- seed = str("\nSeed: " + data['seed']) if 'seed' in data else ""
- audio_mode = str("\nAudio Mode: " + data['audio_mode']) if 'audio_mode' in data else ""
- input_length = str("\nInput Length: " + data['input_length']) if 'input_length' in data else ""
- channel = str("\nChannel: " + data['channel']) if 'channel' in data else ""
- sr_select = str("\nSample Rate: " + data['sr_select']) if 'sr_select' in data else ""
- gen_type = str(data['generator'] + "gen-") if 'generator' in data else ""
- model = str("\nModel: " + gen_type + data['model']) if 'model' in data else ""
- custom_model = str("\nCustom Model: " + data['custom_model']) if 'custom_model' in data else ""
- base_model = str("\nBase Model: " + data['base_model']) if 'base_model' in data else ""
- decoder = str("\nDecoder: " + data['decoder']) if 'decoder' in data else ""
- topk = str("\nTopk: " + data['topk']) if 'topk' in data else ""
- topp = str("\nTopp: " + data['topp']) if 'topp' in data else ""
- temperature = str("\nTemperature: " + data['temperature']) if 'temperature' in data else ""
- cfg_coef = str("\nClassifier Free Guidance: " + data['cfg_coef']) if 'cfg_coef' in data else ""
- version = str("Version: " + data['version']) if 'version' in data else "Version: Unknown"
- info = str(version + global_prompt + bpm + key + scale + prompts + duration + overlap + seed + audio_mode + input_length + channel + sr_select + model + custom_model + base_model + decoder + topk + topp + temperature + cfg_coef)
- if info == "":
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted."
- return info
- else:
- return "Only .wav ,.mp4 and .json files are supported"
- else:
- return None
-
-
-def info_to_params(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- struc_prompt = (False if data['bpm'] == "none" else True) if 'bpm' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- bpm = (120 if data['bpm'] == "none" else int(data['bpm'])) if 'bpm' in data else 120
- key = ("C" if data['key'] == "none" else data['key']) if 'key' in data else "C"
- scale = ("Major" if data['scale'] == "none" else data['scale']) if 'scale' in data else "Major"
- model = data['model'] if 'model' in data else "large"
- custom_model = (data['custom_model'] if data['custom_model'] in get_available_models() else None) if 'custom_model' in data else None
- base_model = data['base_model'] if 'base_model' in data else "medium"
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- audio_mode = ("sample" if data['audio_mode'] == "none" else data['audio_mode']) if 'audio_mode' in data else "sample"
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, bpm, key, scale, model, custom_model, base_model, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], audio_mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- struc_prompt = (False if data['bpm'] == "none" else True) if 'bpm' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- bpm = (120 if data['bpm'] == "none" else int(data['bpm'])) if 'bpm' in data else 120
- key = ("C" if data['key'] == "none" else data['key']) if 'key' in data else "C"
- scale = ("Major" if data['scale'] == "none" else data['scale']) if 'scale' in data else "Major"
- model = data['model'] if 'model' in data else "large"
- custom_model = (data['custom_model'] if data['custom_model'] in get_available_models() else None) if 'custom_model' in data else None
- base_model = data['base_model'] if 'base_model' in data else "medium"
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- audio_mode = ("sample" if data['audio_mode'] == "none" else data['audio_mode']) if 'audio_mode' in data else "sample"
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, bpm, key, scale, model, custom_model, base_model, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], audio_mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- else:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-
-
-def info_to_params_a(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- struc_prompt = (False if data['global_prompt'] == "" else True) if 'global_prompt' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- struc_prompt = (False if data['global_prompt'] == "" else True) if 'global_prompt' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
-
- else:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- else:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-
-
-def make_pseudo_stereo (filename, sr_select, pan, delay):
- if pan:
- temp = AudioSegment.from_wav(filename)
- if sr_select != "32000":
- temp = temp.set_frame_rate(int(sr_select))
- left = temp.pan(-0.5) - 5
- right = temp.pan(0.6) - 5
- temp = left.overlay(right, position=5)
- temp.export(filename, format="wav")
- if delay:
- waveform, sample_rate = torchaudio.load(filename) # load mono WAV file
- delay_seconds = 0.01 # set delay 10ms
- delay_samples = int(delay_seconds * sample_rate) # Calculating delay value in number of samples
- stereo_waveform = torch.stack([waveform[0], torch.cat((torch.zeros(delay_samples), waveform[0][:-delay_samples]))]) # Generate a stereo file with original mono audio and delayed version
- torchaudio.save(filename, stereo_waveform, sample_rate)
- return
-
-
-def normalize_audio(audio_data):
- audio_data = audio_data.astype(np.float32)
- max_value = np.max(np.abs(audio_data))
- audio_data /= max_value
- return audio_data
-
-
-def load_diffusion():
- global MBD
- if MBD is None:
- print("loading MBD")
- MBD = MultiBandDiffusion.get_mbd_musicgen()
-
-
-def unload_diffusion():
- global MBD
- if MBD is not None:
- print("unloading MBD")
- MBD = None
-
-
-def _do_predictions(gen_type, texts, melodies, sample, trim_start, trim_end, duration, image, height, width, background, bar1, bar2, channel, sr_select, progress=False, **gen_kwargs):
- if gen_type == "music":
- maximum_size = 29.5
- elif gen_type == "audio":
- maximum_size = 9.5
- cut_size = 0
- input_length = 0
- sampleP = None
- if sample is not None:
- globalSR, sampleM = sample[0], sample[1]
- sampleM = normalize_audio(sampleM)
- sampleM = torch.from_numpy(sampleM).t()
- if sampleM.dim() == 1:
- sampleM = sampleM.unsqueeze(0)
- sample_length = sampleM.shape[sampleM.dim() - 1] / globalSR
- if trim_start >= sample_length:
- trim_start = sample_length - 0.5
- if trim_end >= sample_length:
- trim_end = sample_length - 0.5
- if trim_start + trim_end >= sample_length:
- tmp = sample_length - 0.5
- trim_start = tmp / 2
- trim_end = tmp / 2
- sampleM = sampleM[..., int(globalSR * trim_start):int(globalSR * (sample_length - trim_end))]
- sample_length = sample_length - (trim_start + trim_end)
- if sample_length > maximum_size:
- cut_size = sample_length - maximum_size
- sampleP = sampleM[..., :int(globalSR * cut_size)]
- sampleM = sampleM[..., int(globalSR * cut_size):]
- if sample_length >= duration:
- duration = sample_length + 0.5
- input_length = sample_length
- global MODEL
- MODEL.set_generation_params(duration=(duration - cut_size), **gen_kwargs)
- print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies], [None if sample is None else (sample[0], sample[1].shape)])
- be = time.time()
- processed_melodies = []
- if gen_type == "music":
- target_sr = 32000
- elif gen_type == "audio":
- target_sr = 16000
- target_ac = 1
-
- for melody in melodies:
- if melody is None:
- processed_melodies.append(None)
- else:
- sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
- if melody.dim() == 1:
- melody = melody[None]
- melody = melody[..., :int(sr * duration)]
- melody = convert_audio(melody, sr, target_sr, target_ac)
- processed_melodies.append(melody)
-
- if sample is not None:
- if sampleP is None:
- if gen_type == "music":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress
- )
- else:
- if sampleP.dim() > 1:
- sampleP = convert_audio(sampleP, globalSR, target_sr, target_ac)
- sampleP = sampleP.to(MODEL.device).float().unsqueeze(0)
- if gen_type == "music":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress
- )
- outputs = torch.cat([sampleP, outputs], 2)
-
- elif any(m is not None for m in processed_melodies):
- if gen_type == "music":
- outputs = MODEL.generate_with_chroma(
- descriptions=texts,
- melody_wavs=processed_melodies,
- melody_sample_rate=target_sr,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_with_chroma(
- descriptions=texts,
- melody_wavs=processed_melodies,
- melody_sample_rate=target_sr,
- progress=progress
- )
- else:
- if gen_type == "music":
- outputs = MODEL.generate(texts, progress=progress, return_tokens=USE_DIFFUSION)
- elif gen_type == "audio":
- outputs = MODEL.generate(texts, progress=progress)
-
- if USE_DIFFUSION:
- print("outputs: " + str(outputs))
- outputs_diffusion = MBD.tokens_to_wav(outputs[1])
- outputs = torch.cat([outputs[0], outputs_diffusion], dim=0)
- outputs = outputs.detach().cpu().float()
- backups = outputs
- if channel == "stereo":
- outputs = convert_audio(outputs, target_sr, int(sr_select), 2)
- elif channel == "mono" and sr_select != "32000":
- outputs = convert_audio(outputs, target_sr, int(sr_select), 1)
- out_files = []
- out_audios = []
- out_backup = []
- for output in outputs:
- with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
- audio_write(
- file.name, output, (MODEL.sample_rate if channel == "stereo effect" else int(sr_select)), strategy="loudness",
- loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
-
- if channel == "stereo effect":
- make_pseudo_stereo(file.name, sr_select, pan=True, delay=True);
-
- out_files.append(pool.submit(make_waveform, file.name, bg_image=image, bg_color=background, bars_color=(bar1, bar2), fg_alpha=1.0, bar_count=75, height=height, width=width))
- out_audios.append(file.name)
- file_cleaner.add(file.name)
- print(f'wav: {file.name}')
- for backup in backups:
- with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
- audio_write(
- file.name, backup, MODEL.sample_rate, strategy="loudness",
- loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
- out_backup.append(file.name)
- file_cleaner.add(file.name)
- res = [out_file.result() for out_file in out_files]
- res_audio = out_audios
- res_backup = out_backup
- for file in res:
- file_cleaner.add(file)
- print(f'video: {file}')
- print("batch finished", len(texts), time.time() - be)
- print("Tempfiles currently stored: ", len(file_cleaner.files))
- if MOVE_TO_CPU:
- MODEL.to('cpu')
- if UNLOAD_MODEL:
- MODEL = None
- torch.cuda.empty_cache()
- torch.cuda.ipc_collect()
- return res, res_audio, res_backup, input_length
-
-
-def predict_batched(texts, melodies):
- max_text_length = 512
- texts = [text[:max_text_length] for text in texts]
- load_model('melody')
- res = _do_predictions(texts, melodies, BATCHED_DURATION)
- return res
-
-
-def add_tags(filename, tags):
- json_string = None
-
- data = {
- "global_prompt": tags[0],
- "bpm": tags[1],
- "key": tags[2],
- "scale": tags[3],
- "texts": tags[4],
- "duration": tags[5],
- "overlap": tags[6],
- "seed": tags[7],
- "audio_mode": tags[8],
- "input_length": tags[9],
- "channel": tags[10],
- "sr_select": tags[11],
- "model": tags[12],
- "custom_model": tags[13],
- "base_model": tags[14],
- "decoder": tags[15],
- "topk": tags[16],
- "topp": tags[17],
- "temperature": tags[18],
- "cfg_coef": tags[19],
- "generator": tags[20],
- "version": version
- }
-
- json_string = json.dumps(data)
-
- if os.path.exists(filename):
- with taglib.File(filename, save_on_exit=True) as song:
- song.tags = {'COMMENT': json_string }
-
- json_file = open(tags[7] + '.json', 'w')
- json_file.write(json_string)
- json_file.close()
-
- return json_file.name;
-
-
-def save_outputs(mp4, wav_tmp, tags, gen_type):
- # mp4: .mp4 file name in root running folder of app.py
- # wav_tmp: temporary wav file located in %TEMP% folder
- # seed - used seed
- # exanple BgnJtr4Pn1AJ.mp4, C:\Users\Alex\AppData\Local\Temp\tmp4ermrebs.wav, 195123182343465
- # procedure read generated .mp4 and wav files, rename it by using seed as name,
- # and will store it to ./output/today_date/wav and ./output/today_date/mp4 folders.
- # if file with same seed number already exist its make postfix in name like seed(n)
- # where is n - consiqunce number 1-2-3-4 and so on
- # then we store generated mp4 and wav into destination folders.
-
- current_date = datetime.now().strftime("%Y%m%d")
- wav_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'wav')
- mp4_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'mp4')
- json_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'json')
- os.makedirs(wav_directory, exist_ok=True)
- os.makedirs(mp4_directory, exist_ok=True)
- os.makedirs(json_directory, exist_ok=True)
-
- filename = str(tags[7]) + '.wav'
- target = os.path.join(wav_directory, filename)
- counter = 1
- while os.path.exists(target):
- filename = str(tags[7]) + f'({counter})' + '.wav'
- target = os.path.join(wav_directory, filename)
- counter += 1
-
- shutil.copyfile(wav_tmp, target); # make copy of original file
- json_file = add_tags(target, tags);
-
- wav_target=target;
- target=target.replace('wav', 'mp4');
- mp4_target=target;
-
- mp4=r'./' +mp4;
- shutil.copyfile(mp4, target); # make copy of original file
- _ = add_tags(target, tags);
-
- target=target.replace('mp4', 'json'); # change the extension to json
- json_target=target; # store the json target
-
- with open(target, 'w') as f: # open a writable file object
- shutil.copyfile(json_file, target); # make copy of original file
-
- os.remove(json_file)
-
- return wav_target, mp4_target, json_target;
-
-
-def clear_cash():
- # delete all temporary files genegated my system
- current_date = datetime.now().date()
- current_directory = os.getcwd()
- files = glob.glob(os.path.join(current_directory, '*.mp4'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- temp_directory = os.environ.get('TEMP')
- files = glob.glob(os.path.join(temp_directory, 'tmp*.mp4'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- files = glob.glob(os.path.join(temp_directory, 'tmp*.wav'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- files = glob.glob(os.path.join(temp_directory, 'tmp*.png'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
- return
-
-
-def s2t(seconds, seconds2):
- # convert seconds to time format
- # seconds - time in seconds
- # return time in format 00:00
- m, s = divmod(seconds, 60)
- m2, s2 = divmod(seconds2, 60)
- if seconds != 0 and seconds < seconds2:
- s = s + 1
- return ("%02d:%02d - %02d:%02d" % (m, s, m2, s2))
-
-
-def calc_time(gen_type, s, duration, overlap, d0, d1, d2, d3, d4, d5, d6, d7, d8, d9):
- # calculate the time of generation
- # overlap - overlap in seconds
- # d0-d9 - drag
- # return time in seconds
- d_amount = [int(d0), int(d1), int(d2), int(d3), int(d4), int(d5), int(d6), int(d7), int(d8), int(d9)]
- calc = []
- tracks = []
- time = 0
- s = s - 1
- max_time = duration
- max_limit = 0
- if gen_type == "music":
- max_limit = 30
- elif gen_type == "audio":
- max_limit = 10
- track_add = max_limit - overlap
- tracks.append(max_limit + ((d_amount[0] - 1) * track_add))
- for i in range(1, 10):
- tracks.append(d_amount[i] * track_add)
-
- if tracks[0] >= max_time or s == 0:
- calc.append(s2t(time, max_time))
- time = max_time
- else:
- calc.append(s2t(time, tracks[0]))
- time = tracks[0]
-
- for i in range(1, 10):
- if time + tracks[i] >= max_time or i == s:
- calc.append(s2t(time, max_time))
- time = max_time
- else:
- calc.append(s2t(time, time + tracks[i]))
- time = time + tracks[i]
-
- return calc[0], calc[1], calc[2], calc[3], calc[4], calc[5], calc[6], calc[7], calc[8], calc[9]
-
-
-def predict_full(gen_type, model, decoder, custom_model, base_model, prompt_amount, struc_prompt, bpm, key, scale, global_prompt, p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, d0, d1, d2, d3, d4, d5, d6, d7, d8, d9, audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select, progress=gr.Progress()):
- global INTERRUPTING
- global USE_DIFFUSION
- INTERRUPTING = False
-
- if gen_type == "audio":
- custom_model = None
- base_model = "medium"
-
- if temperature < 0:
- raise gr.Error("Temperature must be >= 0.")
- if topk < 0:
- raise gr.Error("Topk must be non-negative.")
- if topp < 0:
- raise gr.Error("Topp must be non-negative.")
-
- if trim_start < 0:
- trim_start = 0
- if trim_end < 0:
- trim_end = 0
-
- topk = int(topk)
-
- if decoder == "MultiBand_Diffusion":
- USE_DIFFUSION = True
- load_diffusion()
- else:
- USE_DIFFUSION = False
- unload_diffusion()
-
- if gen_type == "music":
- model_shrt = model
- model = "GrandaddyShmax/musicgen-" + model
- elif gen_type == "audio":
- model_shrt = model
- model = "GrandaddyShmax/audiogen-" + model
- base_model_shrt = base_model
- base_model = "GrandaddyShmax/musicgen-" + base_model
-
- if MODEL is None or MODEL.name != (model):
- load_model(model, custom_model, base_model, gen_type)
- else:
- if MOVE_TO_CPU:
- MODEL.to('cuda')
-
- if seed < 0:
- seed = random.randint(0, 0xffff_ffff_ffff)
- torch.manual_seed(seed)
-
- def _progress(generated, to_generate):
- progress((min(generated, to_generate), to_generate))
- if INTERRUPTING:
- raise gr.Error("Interrupted.")
- MODEL.set_custom_progress_callback(_progress)
-
- audio_mode = "none"
- melody = None
- sample = None
- if audio:
- audio_mode = mode
- if mode == "sample":
- sample = audio
- elif mode == "melody":
- melody = audio
-
- base_model = "none" if model != "custom" else base_model
- custom_model = "none" if model != "custom" else custom_model
-
- text_cat = [p0, p1, p2, p3, p4, p5, p6, p7, p8, p9]
- drag_cat = [d0, d1, d2, d3, d4, d5, d6, d7, d8, d9]
- texts = []
- raw_texts = []
- ind = 0
- ind2 = 0
- while ind < prompt_amount:
- for ind2 in range(int(drag_cat[ind])):
- if not struc_prompt:
- texts.append(text_cat[ind])
- global_prompt = "none"
- bpm = "none"
- key = "none"
- scale = "none"
- raw_texts.append(text_cat[ind])
- else:
- if gen_type == "music":
- bpm_str = str(bpm) + " bpm"
- key_str = ", " + str(key) + " " + str(scale)
- global_str = (", " + str(global_prompt)) if str(global_prompt) != "" else ""
- elif gen_type == "audio":
- bpm_str = ""
- key_str = ""
- global_str = (str(global_prompt)) if str(global_prompt) != "" else ""
- texts_str = (", " + str(text_cat[ind])) if str(text_cat[ind]) != "" else ""
- texts.append(bpm_str + key_str + global_str + texts_str)
- raw_texts.append(text_cat[ind])
- ind2 = 0
- ind = ind + 1
-
- outs, outs_audio, outs_backup, input_length = _do_predictions(
- gen_type, [texts], [melody], sample, trim_start, trim_end, duration, image, height, width, background, bar1, bar2, channel, sr_select, progress=True,
- top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef, extend_stride=MODEL.max_duration-overlap)
- tags = [str(global_prompt), str(bpm), str(key), str(scale), str(raw_texts), str(duration), str(overlap), str(seed), str(audio_mode), str(input_length), str(channel), str(sr_select), str(model_shrt), str(custom_model), str(base_model_shrt), str(decoder), str(topk), str(topp), str(temperature), str(cfg_coef), str(gen_type)]
- wav_target, mp4_target, json_target = save_outputs(outs[0], outs_audio[0], tags, gen_type);
- # Removes the temporary files.
- for out in outs:
- os.remove(out)
- for out in outs_audio:
- os.remove(out)
-
- return mp4_target, wav_target, outs_backup[0], [mp4_target, wav_target, json_target], seed
-
-
-max_textboxes = 10
-
-
-def get_available_models():
- return sorted([re.sub('.pt$', '', item.name) for item in list(Path('models/').glob('*')) if item.name.endswith('.pt')])
-
-
-def toggle_audio_src(choice):
- if choice == "mic":
- return gr.update(source="microphone", value=None, label="Microphone")
- else:
- return gr.update(source="upload", value=None, label="File")
-
-
-def ui_full(launch_kwargs):
- with gr.Blocks(title='AudioCraft Plus', theme=theme) as interface:
- gr.Markdown(
- """
- # AudioCraft Plus - v2.0.0a
-
- ### An All-in-One AudioCraft WebUI
-
- Thanks to: facebookresearch, Camenduru, rkfg, oobabooga, AlexHK and GrandaddyShmax
- """
- )
- with gr.Tab("MusicGen"):
- gr.Markdown(
- """
- ### MusicGen
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Tab("Generation"):
- with gr.Accordion("Structure Prompts", open=False):
- with gr.Column():
- with gr.Row():
- struc_prompts = gr.Checkbox(label="Enable", value=False, interactive=True, container=False)
- bpm = gr.Number(label="BPM", value=120, interactive=True, scale=1, precision=0)
- key = gr.Dropdown(["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "Bb", "B"], label="Key", value="C", interactive=True)
- scale = gr.Dropdown(["Major", "Minor"], label="Scale", value="Major", interactive=True)
- with gr.Row():
- global_prompt = gr.Text(label="Global Prompt", interactive=True, scale=3)
- with gr.Row():
- s = gr.Slider(1, max_textboxes, value=1, step=1, label="Prompts:", interactive=True, scale=2)
- #s_mode = gr.Radio(["segmentation", "batch"], value="segmentation", interactive=True, scale=1, label="Generation Mode")
- with gr.Column():
- textboxes = []
- prompts = []
- repeats = []
- calcs = []
- with gr.Row():
- text0 = gr.Text(label="Input Text", interactive=True, scale=4)
- prompts.append(text0)
- drag0 = gr.Number(label="Repeat", value=1, interactive=True, scale=1)
- repeats.append(drag0)
- calc0 = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- calcs.append(calc0)
- for i in range(max_textboxes):
- with gr.Row(visible=False) as t:
- text = gr.Text(label="Input Text", interactive=True, scale=3)
- repeat = gr.Number(label="Repeat", minimum=1, value=1, interactive=True, scale=1)
- calc = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- textboxes.append(t)
- prompts.append(text)
- repeats.append(repeat)
- calcs.append(calc)
- to_calc = gr.Button("Calculate Timings", variant="secondary")
- with gr.Row():
- duration = gr.Slider(minimum=1, maximum=300, value=10, step=1, label="Duration", interactive=True)
- with gr.Row():
- overlap = gr.Slider(minimum=1, maximum=29, value=12, step=1, label="Overlap", interactive=True)
- with gr.Row():
- seed = gr.Number(label="Seed", value=-1, scale=4, precision=0, interactive=True)
- gr.Button('\U0001f3b2\ufe0f', scale=1).click(fn=lambda: -1, outputs=[seed], queue=False)
- reuse_seed = gr.Button('\u267b\ufe0f', scale=1)
-
- with gr.Tab("Audio"):
- with gr.Row():
- with gr.Column():
- input_type = gr.Radio(["file", "mic"], value="file", label="Input Type (optional)", interactive=True)
- mode = gr.Radio(["melody", "sample"], label="Input Audio Mode (optional)", value="sample", interactive=True)
- with gr.Row():
- trim_start = gr.Number(label="Trim Start", value=0, interactive=True)
- trim_end = gr.Number(label="Trim End", value=0, interactive=True)
- audio = gr.Audio(source="upload", type="numpy", label="Input Audio (optional)", interactive=True)
-
- with gr.Tab("Customization"):
- with gr.Row():
- with gr.Column():
- background = gr.ColorPicker(value="#0f0f0f", label="background color", interactive=True, scale=0)
- bar1 = gr.ColorPicker(value="#84cc16", label="bar color start", interactive=True, scale=0)
- bar2 = gr.ColorPicker(value="#10b981", label="bar color end", interactive=True, scale=0)
- with gr.Column():
- image = gr.Image(label="Background Image", type="filepath", interactive=True, scale=4)
- with gr.Row():
- height = gr.Number(label="Height", value=512, interactive=True)
- width = gr.Number(label="Width", value=768, interactive=True)
-
- with gr.Tab("Settings"):
- with gr.Row():
- channel = gr.Radio(["mono", "stereo", "stereo effect"], label="Output Audio Channels", value="stereo", interactive=True, scale=1)
- sr_select = gr.Dropdown(["11025", "16000", "22050", "24000", "32000", "44100", "48000"], label="Output Audio Sample Rate", value="48000", interactive=True)
- with gr.Row():
- model = gr.Radio(["melody", "small", "medium", "large", "custom"], label="Model", value="large", interactive=True, scale=1)
- with gr.Column():
- dropdown = gr.Dropdown(choices=get_available_models(), value=("No models found" if len(get_available_models()) < 1 else get_available_models()[0]), label='Custom Model (models folder)', elem_classes='slim-dropdown', interactive=True)
- ui.create_refresh_button(dropdown, lambda: None, lambda: {'choices': get_available_models()}, 'refresh-button')
- basemodel = gr.Radio(["small", "medium", "melody", "large"], label="Base Model", value="medium", interactive=True, scale=1)
- with gr.Row():
- decoder = gr.Radio(["Default", "MultiBand_Diffusion"], label="Decoder", value="Default", interactive=True)
- with gr.Row():
- topk = gr.Number(label="Top-k", value=250, interactive=True)
- topp = gr.Number(label="Top-p", value=0, interactive=True)
- temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
- cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
- with gr.Row():
- submit = gr.Button("Generate", variant="primary")
- # Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
- _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
- with gr.Column() as c:
- with gr.Tab("Output"):
- output = gr.Video(label="Generated Music", scale=0)
- with gr.Row():
- audio_only = gr.Audio(type="numpy", label="Audio Only", interactive=False)
- backup_only = gr.Audio(type="numpy", label="Backup Audio", interactive=False, visible=False)
- send_audio = gr.Button("Send to Input Audio")
- seed_used = gr.Number(label='Seed used', value=-1, interactive=False)
- download = gr.File(label="Generated Files", interactive=False)
- with gr.Tab("Wiki"):
- gr.Markdown(
- """
- - **[Generate (button)]:**
- Generates the music with the given settings and prompts.
-
- - **[Interrupt (button)]:**
- Stops the music generation as soon as it can, providing an incomplete output.
-
- ---
-
- ### Generation Tab:
-
- #### Structure Prompts:
-
- This feature helps reduce repetetive prompts by allowing you to set global prompts
- that will be used for all prompt segments.
-
- - **[Structure Prompts (checkbox)]:**
- Enable/Disable the structure prompts feature.
-
- - **[BPM (number)]:**
- Beats per minute of the generated music.
-
- - **[Key (dropdown)]:**
- The key of the generated music.
-
- - **[Scale (dropdown)]:**
- The scale of the generated music.
-
- - **[Global Prompt (text)]:**
- Here write the prompt that you wish to be used for all prompt segments.
-
- #### Multi-Prompt:
-
- This feature allows you to control the music, adding variation to different time segments.
- You have up to 10 prompt segments. the first prompt will always be 30s long
- the other prompts will be [30s - overlap].
- for example if the overlap is 10s, each prompt segment will be 20s.
-
- - **[Prompt Segments (number)]:**
- Amount of unique prompt to generate throughout the music generation.
-
- - **[Prompt/Input Text (prompt)]:**
- Here describe the music you wish the model to generate.
-
- - **[Repeat (number)]:**
- Write how many times this prompt will repeat (instead of wasting another prompt segment on the same prompt).
-
- - **[Time (text)]:**
- The time of the prompt segment.
-
- - **[Calculate Timings (button)]:**
- Calculates the timings of the prompt segments.
-
- - **[Duration (number)]:**
- How long you want the generated music to be (in seconds).
-
- - **[Overlap (number)]:**
- How much each new segment will reference the previous segment (in seconds).
- For example, if you choose 20s: Each new segment after the first one will reference the previous segment 20s
- and will generate only 10s of new music. The model can only process 30s of music.
-
- - **[Seed (number)]:**
- Your generated music id. If you wish to generate the exact same music,
- place the exact seed with the exact prompts
- (This way you can also extend specific song that was generated short).
-
- - **[Random Seed (button)]:**
- Gives "-1" as a seed, which counts as a random seed.
-
- - **[Copy Previous Seed (button)]:**
- Copies the seed from the output seed (if you don't feel like doing it manualy).
-
- ---
-
- ### Audio Tab:
-
- - **[Input Type (selection)]:**
- `File` mode allows you to upload an audio file to use as input
- `Mic` mode allows you to use your microphone as input
-
- - **[Input Audio Mode (selection)]:**
- `Melody` mode only works with the melody model: it conditions the music generation to reference the melody
- `Sample` mode works with any model: it gives a music sample to the model to generate its continuation.
-
- - **[Trim Start and Trim End (numbers)]:**
- `Trim Start` set how much you'd like to trim the input audio from the start
- `Trim End` same as the above but from the end
-
- - **[Input Audio (audio file)]:**
- Input here the audio you wish to use with "melody" or "sample" mode.
-
- ---
-
- ### Customization Tab:
-
- - **[Background Color (color)]:**
- Works only if you don't upload image. Color of the background of the waveform.
-
- - **[Bar Color Start (color)]:**
- First color of the waveform bars.
-
- - **[Bar Color End (color)]:**
- Second color of the waveform bars.
-
- - **[Background Image (image)]:**
- Background image that you wish to be attached to the generated video along with the waveform.
-
- - **[Height and Width (numbers)]:**
- Output video resolution, only works with image.
- (minimum height and width is 256).
-
- ---
-
- ### Settings Tab:
-
- - **[Output Audio Channels (selection)]:**
- With this you can select the amount of channels that you wish for your output audio.
- `mono` is a straightforward single channel audio
- `stereo` is a dual channel audio but it will sound more or less like mono
- `stereo effect` this one is also dual channel but uses tricks to simulate a stereo audio.
-
- - **[Output Audio Sample Rate (dropdown)]:**
- The output audio sample rate, the model default is 32000.
-
- - **[Model (selection)]:**
- Here you can choose which model you wish to use:
- `melody` model is based on the medium model with a unique feature that lets you use melody conditioning
- `small` model is trained on 300M parameters
- `medium` model is trained on 1.5B parameters
- `large` model is trained on 3.3B parameters
- `custom` model runs the custom model that you provided.
-
- - **[Custom Model (selection)]:**
- This dropdown will show you models that are placed in the `models` folder
- you must select `custom` in the model options in order to use it.
-
- - **[Refresh (button)]:**
- Refreshes the dropdown list for custom model.
-
- - **[Base Model (selection)]:**
- Choose here the model that your custom model is based on.
-
- - **[Decoder (selection)]:**
- Choose here the decoder that you wish to use:
- `Default` is the default decoder
- `MultiBand_Diffusion` is a decoder that uses diffusion to generate the audio.
-
- - **[Top-k (number)]:**
- is a parameter used in text generation models, including music generation models. It determines the number of most likely next tokens to consider at each step of the generation process. The model ranks all possible tokens based on their predicted probabilities, and then selects the top-k tokens from the ranked list. The model then samples from this reduced set of tokens to determine the next token in the generated sequence. A smaller value of k results in a more focused and deterministic output, while a larger value of k allows for more diversity in the generated music.
-
- - **[Top-p (number)]:**
- also known as nucleus sampling or probabilistic sampling, is another method used for token selection during text generation. Instead of specifying a fixed number like top-k, top-p considers the cumulative probability distribution of the ranked tokens. It selects the smallest possible set of tokens whose cumulative probability exceeds a certain threshold (usually denoted as p). The model then samples from this set to choose the next token. This approach ensures that the generated output maintains a balance between diversity and coherence, as it allows for a varying number of tokens to be considered based on their probabilities.
-
- - **[Temperature (number)]:**
- is a parameter that controls the randomness of the generated output. It is applied during the sampling process, where a higher temperature value results in more random and diverse outputs, while a lower temperature value leads to more deterministic and focused outputs. In the context of music generation, a higher temperature can introduce more variability and creativity into the generated music, but it may also lead to less coherent or structured compositions. On the other hand, a lower temperature can produce more repetitive and predictable music.
-
- - **[Classifier Free Guidance (number)]:**
- refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
- """
- )
- with gr.Tab("AudioGen"):
- gr.Markdown(
- """
- ### AudioGen
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Tab("Generation"):
- with gr.Accordion("Structure Prompts", open=False):
- with gr.Row():
- struc_prompts_a = gr.Checkbox(label="Enable", value=False, interactive=True, container=False)
- global_prompt_a = gr.Text(label="Global Prompt", interactive=True, scale=3)
- with gr.Row():
- s_a = gr.Slider(1, max_textboxes, value=1, step=1, label="Prompts:", interactive=True, scale=2)
- with gr.Column():
- textboxes_a = []
- prompts_a = []
- repeats_a = []
- calcs_a = []
- with gr.Row():
- text0_a = gr.Text(label="Input Text", interactive=True, scale=4)
- prompts_a.append(text0_a)
- drag0_a = gr.Number(label="Repeat", value=1, interactive=True, scale=1)
- repeats_a.append(drag0_a)
- calc0_a = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- calcs_a.append(calc0_a)
- for i in range(max_textboxes):
- with gr.Row(visible=False) as t_a:
- text_a = gr.Text(label="Input Text", interactive=True, scale=3)
- repeat_a = gr.Number(label="Repeat", minimum=1, value=1, interactive=True, scale=1)
- calc_a = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- textboxes_a.append(t_a)
- prompts_a.append(text_a)
- repeats_a.append(repeat_a)
- calcs_a.append(calc_a)
- to_calc_a = gr.Button("Calculate Timings", variant="secondary")
- with gr.Row():
- duration_a = gr.Slider(minimum=1, maximum=300, value=10, step=1, label="Duration", interactive=True)
- with gr.Row():
- overlap_a = gr.Slider(minimum=1, maximum=9, value=2, step=1, label="Overlap", interactive=True)
- with gr.Row():
- seed_a = gr.Number(label="Seed", value=-1, scale=4, precision=0, interactive=True)
- gr.Button('\U0001f3b2\ufe0f', scale=1).click(fn=lambda: -1, outputs=[seed_a], queue=False)
- reuse_seed_a = gr.Button('\u267b\ufe0f', scale=1)
-
- with gr.Tab("Audio"):
- with gr.Row():
- with gr.Column():
- input_type_a = gr.Radio(["file", "mic"], value="file", label="Input Type (optional)", interactive=True)
- mode_a = gr.Radio(["sample"], label="Input Audio Mode (optional)", value="sample", interactive=False, visible=False)
- with gr.Row():
- trim_start_a = gr.Number(label="Trim Start", value=0, interactive=True)
- trim_end_a = gr.Number(label="Trim End", value=0, interactive=True)
- audio_a = gr.Audio(source="upload", type="numpy", label="Input Audio (optional)", interactive=True)
-
- with gr.Tab("Customization"):
- with gr.Row():
- with gr.Column():
- background_a = gr.ColorPicker(value="#0f0f0f", label="background color", interactive=True, scale=0)
- bar1_a = gr.ColorPicker(value="#84cc16", label="bar color start", interactive=True, scale=0)
- bar2_a = gr.ColorPicker(value="#10b981", label="bar color end", interactive=True, scale=0)
- with gr.Column():
- image_a = gr.Image(label="Background Image", type="filepath", interactive=True, scale=4)
- with gr.Row():
- height_a = gr.Number(label="Height", value=512, interactive=True)
- width_a = gr.Number(label="Width", value=768, interactive=True)
-
- with gr.Tab("Settings"):
- with gr.Row():
- channel_a = gr.Radio(["mono", "stereo", "stereo effect"], label="Output Audio Channels", value="stereo", interactive=True, scale=1)
- sr_select_a = gr.Dropdown(["11025", "16000", "22050", "24000", "32000", "44100", "48000"], label="Output Audio Sample Rate", value="48000", interactive=True)
- with gr.Row():
- model_a = gr.Radio(["medium"], label="Model", value="medium", interactive=False, visible=False)
- decoder_a = gr.Radio(["Default"], label="Decoder", value="Default", interactive=False, visible=False)
- with gr.Row():
- topk_a = gr.Number(label="Top-k", value=250, interactive=True)
- topp_a = gr.Number(label="Top-p", value=0, interactive=True)
- temperature_a = gr.Number(label="Temperature", value=1.0, interactive=True)
- cfg_coef_a = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
- with gr.Row():
- submit_a = gr.Button("Generate", variant="primary")
- _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
- with gr.Column():
- with gr.Tab("Output"):
- output_a = gr.Video(label="Generated Audio", scale=0)
- with gr.Row():
- audio_only_a = gr.Audio(type="numpy", label="Audio Only", interactive=False)
- backup_only_a = gr.Audio(type="numpy", label="Backup Audio", interactive=False, visible=False)
- send_audio_a = gr.Button("Send to Input Audio")
- seed_used_a = gr.Number(label='Seed used', value=-1, interactive=False)
- download_a = gr.File(label="Generated Files", interactive=False)
- with gr.Tab("Wiki"):
- gr.Markdown(
- """
- - **[Generate (button)]:**
- Generates the audio with the given settings and prompts.
-
- - **[Interrupt (button)]:**
- Stops the audio generation as soon as it can, providing an incomplete output.
-
- ---
-
- ### Generation Tab:
-
- #### Structure Prompts:
-
- This feature helps reduce repetetive prompts by allowing you to set global prompts
- that will be used for all prompt segments.
-
- - **[Structure Prompts (checkbox)]:**
- Enable/Disable the structure prompts feature.
-
- - **[Global Prompt (text)]:**
- Here write the prompt that you wish to be used for all prompt segments.
-
- #### Multi-Prompt:
-
- This feature allows you to control the audio, adding variation to different time segments.
- You have up to 10 prompt segments. the first prompt will always be 10s long
- the other prompts will be [10s - overlap].
- for example if the overlap is 2s, each prompt segment will be 8s.
-
- - **[Prompt Segments (number)]:**
- Amount of unique prompt to generate throughout the audio generation.
-
- - **[Prompt/Input Text (prompt)]:**
- Here describe the audio you wish the model to generate.
-
- - **[Repeat (number)]:**
- Write how many times this prompt will repeat (instead of wasting another prompt segment on the same prompt).
-
- - **[Time (text)]:**
- The time of the prompt segment.
-
- - **[Calculate Timings (button)]:**
- Calculates the timings of the prompt segments.
-
- - **[Duration (number)]:**
- How long you want the generated audio to be (in seconds).
-
- - **[Overlap (number)]:**
- How much each new segment will reference the previous segment (in seconds).
- For example, if you choose 2s: Each new segment after the first one will reference the previous segment 2s
- and will generate only 8s of new audio. The model can only process 10s of music.
-
- - **[Seed (number)]:**
- Your generated audio id. If you wish to generate the exact same audio,
- place the exact seed with the exact prompts
- (This way you can also extend specific song that was generated short).
-
- - **[Random Seed (button)]:**
- Gives "-1" as a seed, which counts as a random seed.
-
- - **[Copy Previous Seed (button)]:**
- Copies the seed from the output seed (if you don't feel like doing it manualy).
-
- ---
-
- ### Audio Tab:
-
- - **[Input Type (selection)]:**
- `File` mode allows you to upload an audio file to use as input
- `Mic` mode allows you to use your microphone as input
-
- - **[Trim Start and Trim End (numbers)]:**
- `Trim Start` set how much you'd like to trim the input audio from the start
- `Trim End` same as the above but from the end
-
- - **[Input Audio (audio file)]:**
- Input here the audio you wish to use.
-
- ---
-
- ### Customization Tab:
-
- - **[Background Color (color)]:**
- Works only if you don't upload image. Color of the background of the waveform.
-
- - **[Bar Color Start (color)]:**
- First color of the waveform bars.
-
- - **[Bar Color End (color)]:**
- Second color of the waveform bars.
-
- - **[Background Image (image)]:**
- Background image that you wish to be attached to the generated video along with the waveform.
-
- - **[Height and Width (numbers)]:**
- Output video resolution, only works with image.
- (minimum height and width is 256).
-
- ---
-
- ### Settings Tab:
-
- - **[Output Audio Channels (selection)]:**
- With this you can select the amount of channels that you wish for your output audio.
- `mono` is a straightforward single channel audio
- `stereo` is a dual channel audio but it will sound more or less like mono
- `stereo effect` this one is also dual channel but uses tricks to simulate a stereo audio.
-
- - **[Output Audio Sample Rate (dropdown)]:**
- The output audio sample rate, the model default is 32000.
-
- - **[Top-k (number)]:**
- is a parameter used in text generation models, including music generation models. It determines the number of most likely next tokens to consider at each step of the generation process. The model ranks all possible tokens based on their predicted probabilities, and then selects the top-k tokens from the ranked list. The model then samples from this reduced set of tokens to determine the next token in the generated sequence. A smaller value of k results in a more focused and deterministic output, while a larger value of k allows for more diversity in the generated music.
-
- - **[Top-p (number)]:**
- also known as nucleus sampling or probabilistic sampling, is another method used for token selection during text generation. Instead of specifying a fixed number like top-k, top-p considers the cumulative probability distribution of the ranked tokens. It selects the smallest possible set of tokens whose cumulative probability exceeds a certain threshold (usually denoted as p). The model then samples from this set to choose the next token. This approach ensures that the generated output maintains a balance between diversity and coherence, as it allows for a varying number of tokens to be considered based on their probabilities.
-
- - **[Temperature (number)]:**
- is a parameter that controls the randomness of the generated output. It is applied during the sampling process, where a higher temperature value results in more random and diverse outputs, while a lower temperature value leads to more deterministic and focused outputs. In the context of music generation, a higher temperature can introduce more variability and creativity into the generated music, but it may also lead to less coherent or structured compositions. On the other hand, a lower temperature can produce more repetitive and predictable music.
-
- - **[Classifier Free Guidance (number)]:**
- refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
- """
- )
- with gr.Tab("Audio Info"):
- gr.Markdown(
- """
- ### Audio Info
- """
- )
- with gr.Row():
- with gr.Column():
- in_audio = gr.File(type="file", label="Input Any Audio", interactive=True)
- with gr.Row():
- send_gen = gr.Button("Send to MusicGen", variant="primary")
- send_gen_a = gr.Button("Send to AudioGen", variant="primary")
- with gr.Column():
- info = gr.Textbox(label="Audio Info", lines=10, interactive=False)
- with gr.Tab("Changelog"):
- gr.Markdown(
- """
- ## Changelog:
-
- ### v2.0.0a
-
- - Forgot to move all the update to app.py from temp2.py... oops
-
-
-
- ### v2.0.0
-
- - Changed name from MusicGen+ to AudioCraft Plus
-
- - Complete overhaul of the repo "backend" with the latest changes from the main facebookresearch repo
-
- - Added a new decoder: MultiBand_Diffusion
-
- - Added AudioGen: a new tab for generating audio
-
-
-
- ### v1.2.8c
-
- - Implemented Reverse compatibility for audio info tab with previous versions
-
-
-
- ### v1.2.8b
-
- - Fixed the error when loading default models
-
-
-
- ### v1.2.8a
-
- - Adapted Audio info tab to work with the new structure prompts feature
-
- - Now custom models actually work, make sure you select the correct base model
-
-
-
- ### v1.2.8
-
- - Now you will also recieve json file with metadata of generated audio
-
- - Added error messages in Audio Info tab
-
- - Added structure prompts: you can select bpm, key and global prompt for all prompts
-
- - Added time display next to each prompt, can be calculated with "Calculate Timings" button
-
-
-
- ### v1.2.7
-
- - When sending generated audio to Input Audio, it will send a backup audio with default settings
- (best for continuos generation)
-
- - Added Metadata to generated audio (Thanks to AlexHK ♥)
-
- - Added Audio Info tab that will display the metadata of the input audio
-
- - Added "send to Text2Audio" button in Audio Info tab
-
- - Generated audio is now stored in the "output" folder (Thanks to AlexHK ♥)
-
- - Added an output area with generated files and download buttons
-
- - Enhanced Stereo effect (Thanks to AlexHK ♥)
-
-
-
- ### v1.2.6
-
- - Added option to generate in stereo (instead of only mono)
-
- - Added dropdown for selecting output sample rate (model default is 32000)
-
-
-
- ### v1.2.5a
-
- - Added file cleaner (This comes from the main facebookresearch repo)
-
- - Reorganized a little, moved audio to a seperate tab
-
-
-
- ### v1.2.5
-
- - Gave a unique lime theme to the webui
-
- - Added additional output for audio only
-
- - Added button to send generated audio to Input Audio
-
- - Added option to trim Input Audio
-
-
-
- ### v1.2.4
-
- - Added mic input (This comes from the main facebookresearch repo)
-
-
-
- ### v1.2.3
-
- - Added option to change video size to fit the image you upload
-
-
-
- ### v1.2.2
-
- - Added Wiki, Changelog and About tabs
-
-
-
- ### v1.2.1
-
- - Added tabs and organized the entire interface
-
- - Added option to attach image to the output video
-
- - Added option to load fine-tuned models (Yet to be tested)
-
-
-
- ### v1.2.0
-
- - Added Multi-Prompt
-
-
-
- ### v1.1.3
-
- - Added customization options for generated waveform
-
-
-
- ### v1.1.2
-
- - Removed sample length limit: now you can input audio of any length as music sample
-
-
-
- ### v1.1.1
-
- - Improved music sample audio quality when using music continuation
-
-
-
- ### v1.1.0
-
- - Rebuilt the repo on top of the latest structure of the main MusicGen repo
-
- - Improved Music continuation feature
-
-
-
- ### v1.0.0 - Stable Version
-
- - Added Music continuation
- """
- )
- with gr.Tab("About"):
- gen_type = gr.Text(value="music", interactive=False, visible=False)
- gen_type_a = gr.Text(value="audio", interactive=False, visible=False)
- gr.Markdown(
- """
- This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
- presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
-
- ## MusicGen+ is an extended version of the original MusicGen by facebookresearch.
-
- ### Repo: https://github.com/GrandaddyShmax/audiocraft_plus/tree/plus
-
- ---
-
- ### This project was possible thanks to:
-
- #### GrandaddyShmax - https://github.com/GrandaddyShmax
-
- #### Camenduru - https://github.com/camenduru
-
- #### rkfg - https://github.com/rkfg
-
- #### oobabooga - https://github.com/oobabooga
-
- #### AlexHK - https://github.com/alanhk147
- """
- )
-
- send_gen.click(info_to_params, inputs=[in_audio], outputs=[decoder, struc_prompts, global_prompt, bpm, key, scale, model, dropdown, basemodel, s, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select], queue=False)
- reuse_seed.click(fn=lambda x: x, inputs=[seed_used], outputs=[seed], queue=False)
- send_audio.click(fn=lambda x: x, inputs=[backup_only], outputs=[audio], queue=False)
- submit.click(predict_full, inputs=[gen_type, model, decoder, dropdown, basemodel, s, struc_prompts, bpm, key, scale, global_prompt, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select], outputs=[output, audio_only, backup_only, download, seed_used])
- input_type.change(toggle_audio_src, input_type, [audio], queue=False, show_progress=False)
- to_calc.click(calc_time, inputs=[gen_type, s, duration, overlap, repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9]], outputs=[calcs[0], calcs[1], calcs[2], calcs[3], calcs[4], calcs[5], calcs[6], calcs[7], calcs[8], calcs[9]], queue=False)
-
- send_gen_a.click(info_to_params_a, inputs=[in_audio], outputs=[decoder_a, struc_prompts_a, global_prompt_a, s_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, channel_a, sr_select_a], queue=False)
- reuse_seed_a.click(fn=lambda x: x, inputs=[seed_used_a], outputs=[seed_a], queue=False)
- send_audio_a.click(fn=lambda x: x, inputs=[backup_only_a], outputs=[audio_a], queue=False)
- submit_a.click(predict_full, inputs=[gen_type_a, model_a, decoder_a, dropdown, basemodel, s_a, struc_prompts_a, bpm, key, scale, global_prompt_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], audio_a, mode_a, trim_start_a, trim_end_a, duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, image_a, height_a, width_a, background_a, bar1_a, bar2_a, channel_a, sr_select_a], outputs=[output_a, audio_only_a, backup_only_a, download_a, seed_used_a])
- input_type_a.change(toggle_audio_src, input_type_a, [audio_a], queue=False, show_progress=False)
- to_calc_a.click(calc_time, inputs=[gen_type_a, s_a, duration_a, overlap_a, repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9]], outputs=[calcs_a[0], calcs_a[1], calcs_a[2], calcs_a[3], calcs_a[4], calcs_a[5], calcs_a[6], calcs_a[7], calcs_a[8], calcs_a[9]], queue=False)
-
- in_audio.change(get_audio_info, in_audio, outputs=[info])
-
- def variable_outputs(k):
- k = int(k) - 1
- return [gr.Textbox.update(visible=True)]*k + [gr.Textbox.update(visible=False)]*(max_textboxes-k)
- def get_size(image):
- if image is not None:
- img = Image.open(image)
- img_height = img.height
- img_width = img.width
- if (img_height%2) != 0:
- img_height = img_height + 1
- if (img_width%2) != 0:
- img_width = img_width + 1
- return img_height, img_width
- else:
- return 512, 768
-
- image.change(get_size, image, outputs=[height, width])
- image_a.change(get_size, image_a, outputs=[height_a, width_a])
- s.change(variable_outputs, s, textboxes)
- s_a.change(variable_outputs, s_a, textboxes_a)
- interface.queue().launch(**launch_kwargs)
-
-
-def ui_batched(launch_kwargs):
- with gr.Blocks() as demo:
- gr.Markdown(
- """
- # MusicGen
-
- This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
- a simple and controllable model for music generation
- presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
-
-
-
- for longer sequences, more control and no queue.
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Row():
- text = gr.Text(label="Describe your music", lines=2, interactive=True)
- with gr.Column():
- radio = gr.Radio(["file", "mic"], value="file",
- label="Condition on a melody (optional) File or Mic")
- melody = gr.Audio(source="upload", type="numpy", label="File",
- interactive=True, elem_id="melody-input")
- with gr.Row():
- submit = gr.Button("Generate")
- with gr.Column():
- output = gr.Video(label="Generated Music")
- audio_output = gr.Audio(label="Generated Music (wav)", type='filepath')
- submit.click(predict_batched, inputs=[text, melody],
- outputs=[output, audio_output], batch=True, max_batch_size=MAX_BATCH_SIZE)
- radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
- gr.Examples(
- fn=predict_batched,
- examples=[
- [
- "An 80s driving pop song with heavy drums and synth pads in the background",
- "./assets/bach.mp3",
- ],
- [
- "A cheerful country song with acoustic guitars",
- "./assets/bolero_ravel.mp3",
- ],
- [
- "90s rock song with electric guitar and heavy drums",
- None,
- ],
- [
- "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
- "./assets/bach.mp3",
- ],
- [
- "lofi slow bpm electro chill with organic samples",
- None,
- ],
- ],
- inputs=[text, melody],
- outputs=[output]
- )
- gr.Markdown("""
- ### More details
-
- The model will generate 12 seconds of audio based on the description you provided.
- You can optionally provide a reference audio from which a broad melody will be extracted.
- The model will then try to follow both the description and melody provided.
- All samples are generated with the `melody` model.
-
- You can also use your own GPU or a Google Colab by following the instructions on our repo.
-
- See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
- for more details.
- """)
-
- demo.queue(max_size=8 * 4).launch(**launch_kwargs)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument(
- '--listen',
- type=str,
- default='0.0.0.0' if 'SPACE_ID' in os.environ else '127.0.0.1',
- help='IP to listen on for connections to Gradio',
- )
- parser.add_argument(
- '--username', type=str, default='', help='Username for authentication'
- )
- parser.add_argument(
- '--password', type=str, default='', help='Password for authentication'
- )
- parser.add_argument(
- '--server_port',
- type=int,
- default=0,
- help='Port to run the server listener on',
- )
- parser.add_argument(
- '--inbrowser', action='store_true', help='Open in browser'
- )
- parser.add_argument(
- '--share', action='store_true', help='Share the gradio UI'
- )
- parser.add_argument(
- '--unload_model', action='store_true', help='Unload the model after every generation to save GPU memory'
- )
-
- parser.add_argument(
- '--unload_to_cpu', action='store_true', help='Move the model to main RAM after every generation to save GPU memory but reload faster than after full unload (see above)'
- )
-
- parser.add_argument(
- '--cache', action='store_true', help='Cache models in RAM to quickly switch between them'
- )
-
- args = parser.parse_args()
- UNLOAD_MODEL = args.unload_model
- MOVE_TO_CPU = args.unload_to_cpu
- if args.cache:
- MODELS = {}
-
- launch_kwargs = {}
- launch_kwargs['server_name'] = args.listen
-
- if args.username and args.password:
- launch_kwargs['auth'] = (args.username, args.password)
- if args.server_port:
- launch_kwargs['server_port'] = args.server_port
- if args.inbrowser:
- launch_kwargs['inbrowser'] = args.inbrowser
- if args.share:
- launch_kwargs['share'] = args.share
-
- # Show the interface
- if IS_BATCHED:
- global USE_DIFFUSION
- USE_DIFFUSION = False
- ui_batched(launch_kwargs)
- else:
- ui_full(launch_kwargs)
\ No newline at end of file
diff --git a/spaces/PunPk/AI_FallingAsleepDriving/README.md b/spaces/PunPk/AI_FallingAsleepDriving/README.md
deleted file mode 100644
index 0855483dc5cdc696af1fe1d3502b584b42fde086..0000000000000000000000000000000000000000
--- a/spaces/PunPk/AI_FallingAsleepDriving/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: AI FallingAsleepDriving
-emoji: 📉
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RMXK/RVC_HFF/tensorlowest.py b/spaces/RMXK/RVC_HFF/tensorlowest.py
deleted file mode 100644
index eccd4dbf3494434e59f7defaae6ab91797263b90..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/tensorlowest.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from tensorboard.backend.event_processing import event_accumulator
-
-import os
-from shutil import copy2
-from re import search as RSearch
-import pandas as pd
-from ast import literal_eval as LEval
-
-weights_dir = 'weights/'
-
-def find_biggest_tensorboard(tensordir):
- try:
- files = [f for f in os.listdir(tensordir) if f.endswith('.0')]
- if not files:
- print("No files with the '.0' extension found!")
- return
-
- max_size = 0
- biggest_file = ""
-
- for file in files:
- file_path = os.path.join(tensordir, file)
- if os.path.isfile(file_path):
- file_size = os.path.getsize(file_path)
- if file_size > max_size:
- max_size = file_size
- biggest_file = file
-
- return biggest_file
-
- except FileNotFoundError:
- print("Couldn't find your model!")
- return
-
-def main(model_name, save_freq, lastmdls):
- global lowestval_weight_dir, scl
-
- tensordir = os.path.join('logs', model_name)
- lowestval_weight_dir = os.path.join(tensordir, "lowestvals")
-
- latest_file = find_biggest_tensorboard(tensordir)
-
- if latest_file is None:
- print("Couldn't find a valid tensorboard file!")
- return
-
- tfile = os.path.join(tensordir, latest_file)
-
- ea = event_accumulator.EventAccumulator(tfile,
- size_guidance={
- event_accumulator.COMPRESSED_HISTOGRAMS: 500,
- event_accumulator.IMAGES: 4,
- event_accumulator.AUDIO: 4,
- event_accumulator.SCALARS: 0,
- event_accumulator.HISTOGRAMS: 1,
- })
-
- ea.Reload()
- ea.Tags()
-
- scl = ea.Scalars('loss/g/total')
-
- listwstep = {}
-
- for val in scl:
- if (val.step // save_freq) * save_freq in [val.step for val in scl]:
- listwstep[float(val.value)] = (val.step // save_freq) * save_freq
-
- lowest_vals = sorted(listwstep.keys())[:lastmdls]
-
- sorted_dict = {value: step for value, step in listwstep.items() if value in lowest_vals}
-
- return sorted_dict
-
-def selectweights(model_name, file_dict, weights_dir, lowestval_weight_dir):
- os.makedirs(lowestval_weight_dir, exist_ok=True)
- logdir = []
- files = []
- lbldict = {
- 'Values': {},
- 'Names': {}
- }
- weights_dir_path = os.path.join(weights_dir, "")
- low_val_path = os.path.join(os.getcwd(), os.path.join(lowestval_weight_dir, ""))
-
- try:
- file_dict = LEval(file_dict)
- except Exception as e:
- print(f"Error! {e}")
- return f"Couldn't load tensorboard file! {e}"
-
- weights = [f for f in os.scandir(weights_dir)]
- for key, value in file_dict.items():
- pattern = fr"^{model_name}_.*_s{value}\.pth$"
- matching_weights = [f.name for f in weights if f.is_file() and RSearch(pattern, f.name)]
- for weight in matching_weights:
- source_path = weights_dir_path + weight
- destination_path = os.path.join(lowestval_weight_dir, weight)
-
- copy2(source_path, destination_path)
-
- logdir.append(f"File = {weight} Value: {key}, Step: {value}")
-
- lbldict['Names'][weight] = weight
- lbldict['Values'][weight] = key
-
- files.append(low_val_path + weight)
-
- print(f"File = {weight} Value: {key}, Step: {value}")
-
- yield ('\n'.join(logdir), files, pd.DataFrame(lbldict))
-
-
- return ''.join(logdir), files, pd.DataFrame(lbldict)
-
-
-if __name__ == "__main__":
- model = str(input("Enter the name of the model: "))
- sav_freq = int(input("Enter save frequency of the model: "))
- ds = main(model, sav_freq)
-
- if ds: selectweights(model, ds, weights_dir, lowestval_weight_dir)
-
\ No newline at end of file
diff --git a/spaces/Rekanice/hf_minimal_sushi/README.md b/spaces/Rekanice/hf_minimal_sushi/README.md
deleted file mode 100644
index 4b038ea069e1817e717615da6679be0a913f0f5d..0000000000000000000000000000000000000000
--- a/spaces/Rekanice/hf_minimal_sushi/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Hf Minimal Sushi
-emoji: 🌍
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RoCobo/WiggleGAN/main.py b/spaces/RoCobo/WiggleGAN/main.py
deleted file mode 100644
index 097c3b9aed9be52619095419859205b71110555c..0000000000000000000000000000000000000000
--- a/spaces/RoCobo/WiggleGAN/main.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import argparse
-import os
-import torch
-from WiggleGAN import WiggleGAN
-#from MyACGAN import MyACGAN
-#from MyGAN import MyGAN
-
-"""parsing and configuration"""
-
-
-def parse_args():
- desc = "Pytorch implementation of GAN collections"
- parser = argparse.ArgumentParser(description=desc)
-
- parser.add_argument('--gan_type', type=str, default='WiggleGAN',
- choices=['MyACGAN', 'MyGAN', 'WiggleGAN'],
- help='The type of GAN')
- parser.add_argument('--dataset', type=str, default='4cam',
- choices=['mnist', 'fashion-mnist', 'cifar10', 'cifar100', 'svhn', 'stl10', 'lsun-bed', '4cam'],
- help='The name of dataset')
- parser.add_argument('--split', type=str, default='', help='The split flag for svhn and stl10')
- parser.add_argument('--epoch', type=int, default=50, help='The number of epochs to run')
- parser.add_argument('--batch_size', type=int, default=16, help='The size of batch')
- parser.add_argument('--input_size', type=int, default=10, help='The size of input image')
- parser.add_argument('--save_dir', type=str, default='models',
- help='Directory name to save the model')
- parser.add_argument('--result_dir', type=str, default='results', help='Directory name to save the generated images')
- parser.add_argument('--log_dir', type=str, default='logs', help='Directory name to save training logs')
- parser.add_argument('--lrG', type=float, default=0.0002)
- parser.add_argument('--lrD', type=float, default=0.001)
- parser.add_argument('--beta1', type=float, default=0.5)
- parser.add_argument('--beta2', type=float, default=0.999)
- parser.add_argument('--gpu_mode', type=str2bool, default=True)
- parser.add_argument('--benchmark_mode', type=str2bool, default=True)
- parser.add_argument('--cameras', type=int, default=2)
- parser.add_argument('--imageDim', type=int, default=128)
- parser.add_argument('--epochV', type=int, default=0)
- parser.add_argument('--cIm', type=int, default=4)
- parser.add_argument('--seedLoad', type=str, default="-0000")
- parser.add_argument('--zGF', type=float, default=0.2)
- parser.add_argument('--zDF', type=float, default=0.2)
- parser.add_argument('--bF', type=float, default=0.2)
- parser.add_argument('--expandGen', type=int, default=3)
- parser.add_argument('--expandDis', type=int, default=3)
- parser.add_argument('--wiggleDepth', type=int, default=-1)
- parser.add_argument('--visdom', type=str2bool, default=True)
- parser.add_argument('--lambdaL1', type=int, default=100)
- parser.add_argument('--clipping', type=float, default=-1)
- parser.add_argument('--depth', type=str2bool, default=True)
- parser.add_argument('--recreate', type=str2bool, default=False)
- parser.add_argument('--name_wiggle', type=str, default='wiggle-result')
-
- return check_args(parser.parse_args())
-
-
-"""checking arguments"""
-
-def str2bool(v):
- if isinstance(v, bool):
- return v
- if v.lower() in ('yes', 'true', 't', 'y', '1'):
- return True
- elif v.lower() in ('no', 'false', 'f', 'n', '0'):
- return False
- else:
- raise argparse.ArgumentTypeError('Boolean value expected.')
-
-
-def check_args(args):
- # --save_dir
- if not os.path.exists(args.save_dir):
- os.makedirs(args.save_dir)
-
- # --result_dir
- if not os.path.exists(args.result_dir):
- os.makedirs(args.result_dir)
-
- # --result_dir
- if not os.path.exists(args.log_dir):
- os.makedirs(args.log_dir)
-
- # --epoch
- try:
- assert args.epoch >= 1
- except:
- print('number of epochs must be larger than or equal to one')
-
- # --batch_size
- try:
- assert args.batch_size >= 1
- except:
- print('batch size must be larger than or equal to one')
-
- return args
-
-
-"""main"""
-
-
-def main():
- # parse arguments
- args = parse_args()
- if args is None:
- exit()
-
- if args.benchmark_mode:
- torch.backends.cudnn.benchmark = True
-
- # declare instance for GAN
- if args.gan_type == 'WiggleGAN':
- gan = WiggleGAN(args)
- #elif args.gan_type == 'MyACGAN':
- # gan = MyACGAN(args)
- #elif args.gan_type == 'MyGAN':
- # gan = MyGAN(args)
- else:
- raise Exception("[!] There is no option for " + args.gan_type)
-
- # launch the graph in a session
- if (args.wiggleDepth < 0 and not args.recreate):
- print(" [*] Training Starting!")
- gan.train()
- print(" [*] Training finished!")
- else:
- if not args.recreate:
- print(" [*] Wiggle Started!")
- gan.wiggleEf()
- print(" [*] Wiggle finished!")
- else:
- print(" [*] Dataset recreation Started")
- gan.recreate()
- print(" [*] Dataset recreation finished")
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py
deleted file mode 100644
index f2678b321c295bcceaef945111ac3524be19d6e4..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .misc import add_prefix
-
-__all__ = ['add_prefix']
diff --git a/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py b/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py
deleted file mode 100644
index cdd69f2a24d47166425d785ef2767708b44b6593..0000000000000000000000000000000000000000
--- a/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from config import output_path_video, output_path_audio
-from logger import logger
-from resource_loader.video_loader_interface import VideoLoaderInterface
-
-
-class UploadedMediaLoader(VideoLoaderInterface):
- def __init__(self, uploaded_stream, original_name, media_type='video'):
- self.uploaded_stream = uploaded_stream
- self.original_name = original_name
- self.media_type = media_type # 'video' or 'audio'
- self.media_id = None
- self.filename = None
- self.output_path = None
- self.extract_filename()
- self.set_output_path()
-
- def extract_filename(self):
- self.filename = self.original_name.split(" - ", 1)[1]
- self.media_id = self.filename.rsplit(".", 1)[0]
-
- def set_output_path(self):
- if self.media_type == 'video':
- self.output_path = output_path_video
- elif self.media_type == 'audio':
- self.output_path = output_path_audio
- else:
- raise ValueError("Invalid media type")
-
- def download(self):
- with open(f"{self.output_path}/{self.filename}", "wb") as f:
- f.write(self.uploaded_stream.getvalue())
- logger.info(f"{self.media_type.capitalize()} processed: {self.original_name}")
diff --git a/spaces/STF-R/docker-test3/app/templates/prediction.html b/spaces/STF-R/docker-test3/app/templates/prediction.html
deleted file mode 100644
index 4366614474cbe8347297ec3d57a7b8b7bf72684d..0000000000000000000000000000000000000000
--- a/spaces/STF-R/docker-test3/app/templates/prediction.html
+++ /dev/null
@@ -1,48 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
- VIVADATA | {{iris_type}}
-
-
-
-
-
-
-
-
-
-
-
-
-
- VIVADATA - Flask Demo
-
-
-
- 🏵️🌺🌸 It's a {{iris_type|title}} 🏵️🌺🌸
-
-
-
-
-
-
-
- Back
-
-
-
-
-
-
-
-
diff --git a/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py b/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py
deleted file mode 100644
index 3706c57541c1b7d9004957422b52cd1e2191ae68..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-# flake8: noqa
-
-from ..utils import DummyObject, requires_backends
-
-
-class LMSDiscreteScheduler(metaclass=DummyObject):
- _backends = ["scipy"]
-
- def __init__(self, *args, **kwargs):
- requires_backends(self, ["scipy"])
diff --git a/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py b/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py
deleted file mode 100644
index 2e25dd918f2714ae9a2c21d14401663c371b5f38..0000000000000000000000000000000000000000
--- a/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py
+++ /dev/null
@@ -1,704 +0,0 @@
-from typing import Callable, Optional, Tuple
-
-import flax.linen as nn
-import jax
-import jax.numpy as jnp
-from flax.core.frozen_dict import FrozenDict, unfreeze
-from jax import lax
-from jax.random import PRNGKey
-from transformers import GPT2Config, FlaxViTModel, ViTConfig
-from transformers.modeling_flax_outputs import (
- FlaxCausalLMOutputWithCrossAttentions,
- FlaxSeq2SeqLMOutput,
- FlaxSeq2SeqModelOutput,
-)
-from transformers.models.bart.modeling_flax_bart import (
- shift_tokens_right,
-)
-from .modeling_flax_gpt2 import (
- FlaxGPT2Module,
- FlaxGPT2Model,
- FlaxPreTrainedModel
-)
-from transformers.models.vit.modeling_flax_vit import FlaxViTModule
-
-from .configuration_vit_gpt2 import ViTGPT2Config
-
-
-class FlaxViTGPT2Module(nn.Module):
- config: ViTGPT2Config
- dtype: jnp.dtype = jnp.float32 # the dtype of the computation
-
- def setup(self):
-
- self.encoder = FlaxViTModule(self.config.vit_config, dtype=self.dtype)
- self.decoder = FlaxGPT2Module(self.config.gpt2_config, dtype=self.dtype)
-
- def _get_encoder_module(self):
- return self.encoder
-
- def _get_decoder_module(self):
- return self.decoder
-
- def __call__(
- self,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- output_attentions: bool = False,
- output_hidden_states: bool = False,
- return_dict: bool = True,
- deterministic: bool = True,
- ):
- encoder_outputs = self.encoder(
- pixel_values=pixel_values,
- deterministic=deterministic,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- decoder_outputs = self.decoder(
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_hidden_states=encoder_outputs[0],
- encoder_attention_mask=encoder_attention_mask,
- deterministic=deterministic,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict
- )
-
- return FlaxSeq2SeqModelOutput(
- last_hidden_state=decoder_outputs.last_hidden_state,
- decoder_hidden_states=decoder_outputs.hidden_states,
- decoder_attentions=decoder_outputs.attentions,
- cross_attentions=decoder_outputs.cross_attentions,
- encoder_last_hidden_state=encoder_outputs.last_hidden_state,
- encoder_hidden_states=encoder_outputs.hidden_states,
- encoder_attentions=encoder_outputs.attentions,
- )
-
-
-class FlaxViTGPT2ForConditionalGenerationModule(nn.Module):
- config: ViTGPT2Config
- dtype: jnp.dtype = jnp.float32
- bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
-
- def setup(self):
- self.model = FlaxViTGPT2Module(config=self.config, dtype=self.dtype)
- self.lm_head = nn.Dense(
- self.model.decoder.embed_dim,
- use_bias=False,
- dtype=self.dtype,
- kernel_init=jax.nn.initializers.normal(
- self.config.gpt2_config.initializer_range, self.dtype
- ),
- )
- self.final_logits_bias = self.param(
- "final_logits_bias", self.bias_init, (1, self.model.decoder.embed_dim)
- )
-
- def _get_encoder_module(self):
- return self.model.encoder
-
- def _get_decoder_module(self):
- return self.model.decoder
-
- def __call__(
- self,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- output_attentions: bool = False,
- output_hidden_states: bool = False,
- return_dict: bool = True,
- deterministic: bool = True,
- ):
- outputs = self.model(
- pixel_values=pixel_values,
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_attention_mask=encoder_attention_mask,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=deterministic,
- )
-
- hidden_states = outputs[0]
- lm_logits = self.lm_head(hidden_states)
- lm_logits += self.final_logits_bias
-
- if not return_dict:
- output = (lm_logits,) + outputs[1:]
- return output
-
- return FlaxSeq2SeqLMOutput(
- logits=lm_logits,
- decoder_hidden_states=outputs.decoder_hidden_states,
- decoder_attentions=outputs.decoder_attentions,
- cross_attentions=outputs.cross_attentions,
- encoder_last_hidden_state=outputs.encoder_last_hidden_state,
- encoder_hidden_states=outputs.encoder_hidden_states,
- encoder_attentions=outputs.encoder_attentions,
- )
-
-class FlaxViTGPT2PreTrainedModel(FlaxPreTrainedModel):
- config_class = ViTGPT2Config
- base_model_prefix: str = "model"
- module_class: nn.Module = None
-
- def __init__(
- self,
- config: ViTGPT2Config,
- input_shape: Tuple = None,
- seed: int = 0,
- dtype: jnp.dtype = jnp.float32,
- **kwargs,
- ):
- if input_shape is None:
- input_shape = (
- (1, config.vit_config.image_size, config.vit_config.image_size, 3),
- (1, 1),
- )
-
- module = self.module_class(config=config, dtype=dtype, **kwargs)
- super().__init__(
- config, module, input_shape=input_shape, seed=seed, dtype=dtype
- )
-
- def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
- # init input tensors
- pixel_values = jax.random.normal(rng, input_shape[0])
- # # make sure initialization pass will work for FlaxBartForSequenceClassificationModule
- # input_ids = jax.ops.index_update(input_ids, (..., -1), self.config.eos_token_id)
-
- input_ids = jnp.zeros(input_shape[1], dtype="i4")
- attention_mask = jnp.ones_like(input_ids)
-
- batch_size, sequence_length = input_ids.shape
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- params_rng, dropout_rng = jax.random.split(rng)
- rngs = {"params": params_rng, "dropout": dropout_rng}
-
- return self.module.init(
- rngs,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- )["params"]
-
- def init_cache(self, batch_size, max_length, encoder_outputs):
-
- input_ids = jnp.ones((batch_size, max_length), dtype="i4")
- attention_mask = jnp.ones_like(input_ids)
- position_ids = jnp.broadcast_to(
- jnp.arange(jnp.atleast_2d(input_ids).shape[-1]),
- input_ids.shape,
- )
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- return decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
-
- init_variables = self.module.init(
- jax.random.PRNGKey(0),
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_hidden_states=encoder_outputs[0],
- init_cache=True,
- method=_decoder_forward, # we only need to call the decoder to init the cache
- )
- return unfreeze(init_variables["cache"])
-
- def encode(
- self,
- pixel_values: jnp.ndarray,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- def _encoder_forward(module, pixel_values, **kwargs):
- encode_module = module._get_encoder_module()
- return encode_module(pixel_values, **kwargs)
-
- return self.module.apply(
- {"params": params or self.params},
- pixel_values=jnp.array(pixel_values, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- method=_encoder_forward,
- )
-
- def decode(
- self,
- input_ids,
- encoder_outputs,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- past_key_values: dict = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
-
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- encoder_hidden_states = encoder_outputs[0]
- if encoder_attention_mask is None:
- batch_size, sequence_length = encoder_hidden_states.shape[:2]
- encoder_attention_mask = jnp.ones((batch_size, sequence_length))
-
- batch_size, sequence_length = input_ids.shape
- if attention_mask is None:
- attention_mask = jnp.ones((batch_size, sequence_length))
-
- if position_ids is None:
- if past_key_values is not None:
- raise ValueError(
- "Make sure to provide `position_ids` when passing `past_key_values`."
- )
-
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- inputs = {"params": params or self.params}
-
- # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
- # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
- # it can be changed by FlaxGPT2Attention module
- if past_key_values:
- inputs["cache"] = past_key_values
- mutable = ["cache"]
- else:
- mutable = False
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- return decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
-
- outputs = self.module.apply(
- inputs,
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- mutable=mutable,
- method=_decoder_forward,
- )
-
- # add updated cache to model output
- if past_key_values is not None and return_dict:
- outputs, past = outputs
- outputs["past_key_values"] = unfreeze(past["cache"])
- return outputs
- elif past_key_values is not None and not return_dict:
- outputs, past = outputs
- outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
-
- return outputs
-
- def __call__(
- self,
- pixel_values: jnp.ndarray,
- input_ids: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
-
- # # prepare encoder inputs
- # if encoder_attention_mask is None:
- # encoder_attention_mask = jnp.ones_like(input_ids)
-
- # if position_ids is None:
- # batch_size, sequence_length = input_ids.shape
- # position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
-
- # prepare decoder inputs
- # if decoder_input_ids is None:
- # decoder_input_ids = shift_tokens_right(
- # input_ids, self.config.pad_token_id, decoder_start_token_id=self.config.decoder_start_token_id
- # ) # TODO: Check how to use this
-
- if attention_mask is None:
- attention_mask = jnp.ones_like(input_ids)
- if position_ids is None:
- batch_size, sequence_length = input_ids.shape
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
-
- return self.module.apply(
- {"params": params or self.params},
- pixel_values=jnp.array(pixel_values, dtype=jnp.float32),
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- )
-
-
-class FlaxViTGPT2ForConditionalGeneration(FlaxViTGPT2PreTrainedModel):
- module_class = FlaxViTGPT2ForConditionalGenerationModule
- dtype: jnp.dtype = jnp.float32
-
- def decode(
- self,
- input_ids,
- encoder_outputs,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- past_key_values: dict = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- deterministic: bool = True,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- encoder_hidden_states = encoder_outputs[0]
- if encoder_attention_mask is None:
- batch_size, sequence_length = encoder_hidden_states.shape[:2]
- encoder_attention_mask = jnp.ones((batch_size, sequence_length))
-
- batch_size, sequence_length = input_ids.shape
- if attention_mask is None:
- attention_mask = jnp.ones((batch_size, sequence_length))
-
- if position_ids is None:
- if past_key_values is not None:
- raise ValueError(
- "Make sure to provide `position_ids` when passing `past_key_values`."
- )
-
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- inputs = {"params": params or self.params}
-
- # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
- # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
- # it can be changed by FlaxGPT2Attention module
- if past_key_values:
- inputs["cache"] = past_key_values
- mutable = ["cache"]
- else:
- mutable = False
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- outputs = decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
- hidden_states = outputs[0]
-
- if self.config.tie_word_embeddings:
- shared_embedding = module.model.variables["params"]["shared"][
- "embedding"
- ]
- lm_logits = module.lm_head.apply(
- {"params": {"kernel": shared_embedding.T}}, hidden_states
- )
- else:
- lm_logits = module.lm_head(hidden_states)
-
- lm_logits += module.final_logits_bias
- return lm_logits, outputs
-
- outputs = self.module.apply(
- inputs,
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=deterministic,
- rngs=rngs,
- mutable=mutable,
- method=_decoder_forward,
- )
-
- if past_key_values is None:
- lm_logits, outputs = outputs
- else:
- (lm_logits, outputs), past = outputs
-
- if return_dict:
- outputs = FlaxCausalLMOutputWithCrossAttentions(
- logits=lm_logits,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- cross_attentions=outputs.cross_attentions,
- )
- else:
- outputs = (lm_logits,) + outputs[1:]
-
- # add updated cache to model output
- if past_key_values is not None and return_dict:
- outputs["past_key_values"] = unfreeze(past["cache"])
- return outputs
- elif past_key_values is not None and not return_dict:
- outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
-
- return outputs
-
- def prepare_inputs_for_generation(
- self,
- input_ids,
- max_length,
- encoder_attention_mask: Optional[jnp.DeviceArray] = None,
- attention_mask: Optional[jnp.DeviceArray] = None,
- encoder_outputs=None,
- **kwargs,
- ):
- # initializing the cache
- batch_size, seq_length = input_ids.shape
-
- past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
- # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
- # But since the decoder uses a causal mask, those positions are masked anyways.
- # Thus we can create a single static attention_mask here, which is more efficient for compilation
- extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
- if attention_mask is not None:
- position_ids = attention_mask.cumsum(axis=-1) - 1
- extended_attention_mask = lax.dynamic_update_slice(
- extended_attention_mask, attention_mask, (0, 0)
- )
- else:
- position_ids = jnp.broadcast_to(
- jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)
- )
-
- return {
- "past_key_values": past_key_values,
- "encoder_outputs": encoder_outputs,
- "encoder_attention_mask": encoder_attention_mask,
- "attention_mask": extended_attention_mask,
- "position_ids": position_ids,
- }
-
- def update_inputs_for_generation(self, model_outputs, model_kwargs):
- model_kwargs["past_key_values"] = model_outputs.past_key_values
- model_kwargs["position_ids"] = (
- model_kwargs["position_ids"][:, -1:] + 1
- )
- return model_kwargs
-
- @classmethod
- def from_vit_gpt2_pretrained(
- cls,
- vit_model_name_or_path: str = None,
- gpt2_model_name_or_path: str = None,
- *model_args,
- **kwargs,
- ) -> FlaxViTGPT2PreTrainedModel:
-
- kwargs_gpt2 = {
- argument[len("gpt2_") :]: value
- for argument, value in kwargs.items()
- if argument.startswith("gpt2_")
- }
-
- kwargs_vit = {
- argument[len("vit_") :]: value
- for argument, value in kwargs.items()
- if argument.startswith("vit_")
- }
-
- # remove gpt2, vit kwargs from kwargs
- for key in kwargs_gpt2.keys():
- del kwargs["gpt2_" + key]
- for key in kwargs_vit.keys():
- del kwargs["vit_" + key]
-
- # Load and initialize the gpt2 and vit model
- gpt2_model = kwargs_gpt2.pop("model", None)
- if gpt2_model is None:
- assert (
- gpt2_model_name_or_path is not None
- ), "If `model` is not defined as an argument, a `gpt2_model_name_or_path` has to be defined"
-
- if "config" not in kwargs_gpt2:
- gpt2_config = GPT2Config.from_pretrained(gpt2_model_name_or_path)
- kwargs_gpt2["config"] = gpt2_config
-
- kwargs_gpt2["config"].add_cross_attention = True
- gpt2_model = FlaxGPT2Model.from_pretrained(
- gpt2_model_name_or_path, *model_args, **kwargs_gpt2
- )
-
- vit_model = kwargs_vit.pop("model", None)
- if vit_model is None:
- assert (
- vit_model_name_or_path is not None
- ), "If `model` is not defined as an argument, a `vit_model_name_or_path` has to be defined"
-
- if "config" not in kwargs_vit:
- vit_config = ViTConfig.from_pretrained(vit_model_name_or_path)
- kwargs_vit["config"] = vit_config
-
- vit_model = FlaxViTModel.from_pretrained(
- vit_model_name_or_path, *model_args, **kwargs_vit
- )
-
- # instantiate config with corresponding kwargs
- dtype = kwargs.pop("dtype", jnp.float32)
- config = ViTGPT2Config.from_vit_gpt2_configs(
- vit_model.config, gpt2_model.config, **kwargs
- )
-
- # init model
- model = cls(config, *model_args, dtype=dtype, **kwargs)
- model.params["model"]["encoder"] = vit_model.params
- model.params["model"]["decoder"] = gpt2_model.params
-
- return model
-
diff --git a/spaces/Seetha/IMA-pipeline-streamlit/app.py b/spaces/Seetha/IMA-pipeline-streamlit/app.py
deleted file mode 100644
index f0db4170922ebf1b9a612db7fac952e61207154a..0000000000000000000000000000000000000000
--- a/spaces/Seetha/IMA-pipeline-streamlit/app.py
+++ /dev/null
@@ -1,581 +0,0 @@
-# import all packages
-import requests
-import streamlit as st
-from sklearn.model_selection import StratifiedKFold
-from sklearn.model_selection import train_test_split
-from sklearn.model_selection import KFold
-# tokenizer
-from transformers import AutoTokenizer, DistilBertTokenizerFast
-# sequence tagging model + training-related
-from transformers import DistilBertForTokenClassification, Trainer, TrainingArguments
-import torch
-import sys
-import os
-from sklearn.metrics import classification_report
-from pandas import read_csv
-from sklearn.linear_model import LogisticRegression
-import sklearn.model_selection
-from sklearn.feature_extraction.text import TfidfTransformer
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.pipeline import Pipeline, FeatureUnion
-import math
-# from sklearn.metrics import accuracy_score
-# from sklearn.metrics import precision_recall_fscore_support
-import json
-import re
-import numpy as np
-import pandas as pd
-import nltk
-nltk.download("punkt")
-import string
-from sklearn.model_selection import train_test_split
-from transformers import AutoTokenizer, Trainer, TrainingArguments, AutoModelForSequenceClassification, AutoConfig
-from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
-import itertools
-from transformers import TextClassificationPipeline, TFAutoModelForSequenceClassification, AutoTokenizer
-from transformers import pipeline
-import pickle
-import csv
-import pdfplumber
-import pathlib
-import shutil
-import webbrowser
-from streamlit.components.v1 import html
-import streamlit.components.v1 as components
-from PyPDF2 import PdfReader
-from huggingface_hub import HfApi
-import io
-from datasets import load_dataset
-import time
-
-import huggingface_hub
-from huggingface_hub import Repository
-from datetime import datetime
-import pathlib as Path
-from requests import get
-import urllib.request
-# import gradio as gr
-# from gradio import inputs, outputs
-from datasets import load_dataset
-from huggingface_hub import HfApi, list_models
-import os
-from huggingface_hub import HfFileSystem
-from tensorflow.keras.models import Sequential, model_from_json
-#import tensorflow_datasets as tfds
-import tensorflow as tf
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-import spacy
-from tensorflow.keras.preprocessing.text import Tokenizer
-#from spacy import en_core_web_lg
-#import en_core_web_lg
-#nlp = en_core_web_lg.load()
-nlp = spacy.load('en_core_web_sm')
-
-#tfds.disable_progress_bar()
-MAX_SEQUENCE_LENGTH = 500
-
-# dataset = load_dataset('Seetha/Visualization', streaming=True)
-# df = pd.DataFrame.from_dict(dataset['train'])
-# DATASET_REPO_URL = "https://huggingface.co/datasets/Seetha/Visualization"
-# DATA_FILENAME = "level2.json"
-#DATA_FILE = os.path.join("data", DATA_FILENAME)
-DATASET_REPO_URL = "https://huggingface.co/datasets/Seetha/visual_files"
-DATA_FILENAME = "detailedResults.json"
-DATA_FILENAME1 = "level2.json"
-
-HF_TOKEN = os.environ.get("HF_TOKEN")
-#st.write("is none?", HF_TOKEN is None)
-
-def main():
-
- st.title("Text to Causal Knowledge Graph")
- st.sidebar.title("Please upload your text documents in one file here:")
- k=2
- seed = 1
- k1= 5
- text_list = []
- causal_sents = []
-
- uploaded_file = None
- try:
- uploaded_file = st.sidebar.file_uploader("Choose a file", type = "pdf")
- except:
- uploaded_file = PdfReader('sample_anno.pdf')
- st.error("Please upload your own PDF to be analyzed")
-
- if uploaded_file is not None:
- reader = PdfReader(uploaded_file)
- for page in reader.pages:
- text = page.extract_text()
- text_list.append(text)
- else:
- st.error("Please upload your own PDF to be analyzed")
- st.stop()
-
- text_list_final = [x.replace('\n', '') for x in text_list]
- text_list_final = re.sub('"', '', str(text_list_final))
-
- sentences = nltk.sent_tokenize(text_list_final)
-
- result =[]
- for i in sentences:
- result1 = i.lower()
- result2 = re.sub(r'[^\w\s]','',result1)
- result.append(result2)
-
- #st.write("--- %s seconds ---" % (time.time() - start_time))
- tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") #bert-base-uncased
-
- model_path = "checkpoint-2850"
-
- model = AutoModelForSequenceClassification.from_pretrained(model_path,id2label={0:'non-causal',1:'causal'})
-
- #st.write('sequence classification loaded')
- pipe1 = pipeline("text-classification", model=model,tokenizer=tokenizer)
- for sent in result:
- pred = pipe1(sent)
- for lab in pred:
- if lab['label'] == 'causal': #causal
- causal_sents.append(sent)
-
- # st.write('causal sentence classification finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
-
- model_name = "distilbert-base-cased"
- tokenizer = DistilBertTokenizerFast.from_pretrained(model_name,low_cpu_mem_usage=True)
-
- model_path1 = "DistilBertforTokenclassification"
-
- model = DistilBertForTokenClassification.from_pretrained(model_path1,low_cpu_mem_usage=True) #len(unique_tags),, num_labels= 7, , id2label={0:'CT',1:'E',2:'C',3:'O'}
- pipe = pipeline('ner', model=model, tokenizer=tokenizer,aggregation_strategy='simple') #grouped_entities=True
- st.write('DistilBERT loaded')
- sentence_pred = []
- class_list = []
- entity_list = []
- for k in causal_sents:
- pred= pipe(k)
- #st.write(pred)
- #st.write('preds')
- for i in pred:
- sentence_pred.append(k)
- class_list.append(i['word'])
- entity_list.append(i['entity_group'])
-
- # st.write('causality extraction finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
-
- # filename = 'Checkpoint-classification.sav'
- # loaded_model = pickle.load(open(filename, 'rb'))
- # loaded_vectorizer = pickle.load(open('vectorizefile_classification.pickle', 'rb'))
-
- # pipeline_test_output = loaded_vectorizer.transform(class_list)
- # predicted = loaded_model.predict(pipeline_test_output)
-
- tokenizer = Tokenizer(num_words=100000)
- tokenizer.fit_on_texts(class_list)
- word_index = tokenizer.word_index
- # text_embedding = np.zeros((len(word_index) + 1, 300))
- # for word, i in word_index.items():
- # text_embedding[i] = nlp(word).vector
- json_file = open('model.json', 'r')
- loaded_model_json = json_file.read()
- json_file.close()
- loaded_model = model_from_json(loaded_model_json)
- # load weights into new model
- loaded_model.load_weights("model.h5")
-
- loss = tf.keras.losses.CategoricalCrossentropy() #from_logits=True
- loaded_model.compile(loss=loss,optimizer=tf.keras.optimizers.Adam(1e-4))
-
- predictions = loaded_model.predict(pad_sequences(tokenizer.texts_to_sequences(class_list),maxlen=MAX_SEQUENCE_LENGTH))
- predicted = np.argmax(predictions,axis=1)
-
- st.write(predictions)
- st.write(predicted)
- # st.write('stakeholder taxonomy finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
- pred1 = predicted
- level0 = []
- count =0
- for i in predicted:
- if i == 3:
- level0.append('Non-Performance')
- count +=1
- else:
- level0.append('Performance')
- count +=1
-
- list_pred = {0: 'Customers',1:'Employees',2:'Investors',3:'Non-performance',4:'Society',5:'Unclassified'}
- pred_val = [list_pred[i] for i in pred1]
-
- #print('count',count)
- for ind,(sent,preds) in enumerate(zip(class_list,pred_val)):
- if 'customers' in sent or 'client' in sent or 'consumer' in sent or 'user' in sent:
- pred_val[ind] = 'Customers'
- elif 'investor' in sent or 'finance' in sent or 'shareholder' in sent or 'stockholder' in sent or 'owners' in sent:
- pred_val[ind] = 'Investors'
- elif 'employee' in sent or 'worker' in sent or 'staff' in sent:
- pred_val[ind] = 'Employees'
- elif 'society' in sent or 'societal' in sent or 'social responsib*' in sent or 'social performance' in sent or 'community' in sent:
- pred_val[ind] = 'Society'
-
- sent_id, unique = pd.factorize(sentence_pred)
-
- final_list = pd.DataFrame(
- {'Id': sent_id,
- 'Fullsentence': sentence_pred,
- 'Component': class_list,
- 'causeOrEffect': entity_list,
- 'Labellevel1': level0,
- 'Labellevel2': pred_val
- })
- s = final_list['Component'].shift(-1)
- m = s.str.startswith('##', na=False)
- final_list.loc[m, 'Component'] += (' ' + s[m])
-
-
- final_list1 = final_list[~final_list['Component'].astype(str).str.startswith('##')]
- li = []
- uni = final_list1['Id'].unique()
- for i in uni:
- df_new = final_list1[final_list1['Id'] == i]
- uni1 = df_new['Id'].unique()
-
- # if 'E' not in df_new.values:
- # li.append(uni1)
- # out = np.concatenate(li).ravel()
- # li_pan = pd.DataFrame(out,columns=['Id'])
- # df3 = pd.merge(final_list1, li_pan[['Id']], on='Id', how='left', indicator=True) \
- # .query("_merge == 'left_only'") \
- # .drop("_merge",axis=1)
-
- df3 = final_list1
- #df = df3.groupby(['Id','Fullsentence','causeOrEffect', 'Labellevel1', 'Labellevel2'])['Component'].apply(', '.join).reset_index()
- #st.write(df)
-
- #df = df3
- df3["causeOrEffect"].replace({"C": "cause", "E": "effect"}, inplace=True)
- df_final = df3[df3['causeOrEffect'] != 'CT']
- df3['New string'] = df_final['Component'].replace(r'[##]+', ' ', regex=True)
-
- df_final = df_final.drop("Component",axis=1)
- df_final.insert(2, "Component", df3['New string'], True)
-
- df_final1 = df_final[df_final['Component'].str.split().str.len().gt(1)]
- #st.write(df_final[df_final['Component'].str.len() != 1])
- #df_final1.to_csv('predictions.csv')
-
-# buffer = io.BytesIO()
-# with pd.ExcelWriter(buffer, engine="xlsxwriter") as writer:
-# df_final.to_excel(writer, sheet_name="Sheet1", index=False)
-# writer.close()
-
- count_NP_NP = 0
- count_NP_investor = 0
- count_NP_customer = 0
- count_NP_employees = 0
- count_NP_society = 0
-
- count_inv_np = 0
- count_inv_investor = 0
- count_inv_customer = 0
- count_inv_employee = 0
- count_inv_society = 0
-
- count_cus_np = 0
- count_cus_investor = 0
- count_cus_customer = 0
- count_cus_employee = 0
- count_cus_society = 0
-
- count_emp_np = 0
- count_emp_investor = 0
- count_emp_customer = 0
- count_emp_employee = 0
- count_emp_society = 0
-
- count_soc_np = 0
- count_soc_investor = 0
- count_soc_customer = 0
- count_soc_employee = 0
- count_soc_society = 0
- for i in range(0,df_final['Id'].max()):
- j = df_final.loc[df_final['Id'] == i]
- cause_tab = j.loc[j['causeOrEffect'] == 'cause']
- effect_tab = j.loc[j['causeOrEffect'] == 'effect']
- cause_coun_NP = (cause_tab.Labellevel2 == 'Non-performance').sum()
- effect_coun_NP = (effect_tab.Labellevel2 == 'Non-performance').sum()
-
- if (cause_coun_NP > 0) and (effect_coun_NP > 0):
- count_NP = cause_coun_NP if cause_coun_NP >= effect_coun_NP else effect_coun_NP
- else:
- count_NP = 0
- effect_NP_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_NP > 0) and (effect_NP_inv > 0):
- count_NP_inv = cause_coun_NP if cause_coun_NP >= effect_NP_inv else effect_NP_inv
- else:
- count_NP_inv = 0
- effect_NP_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_NP > 0) and (effect_NP_cus > 0):
- count_NP_cus = cause_coun_NP if cause_coun_NP >= effect_NP_cus else effect_NP_cus
- else:
- count_NP_cus = 0
- effect_NP_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_NP > 0) and (effect_NP_emp > 0):
- count_NP_emp = cause_coun_NP if cause_coun_NP >= effect_NP_emp else effect_NP_emp
- else:
- count_NP_emp = 0
- effect_NP_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_NP > 0) and (effect_NP_soc > 0):
- count_NP_soc = cause_coun_NP if cause_coun_NP >= effect_NP_soc else effect_NP_soc
- else:
- count_NP_soc = 0
-
- cause_coun_inv = (cause_tab.Labellevel2 == 'Investors').sum()
- effect_coun_inv = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_inv > 0) and (effect_coun_inv > 0):
- count_NP_inv = cause_coun_inv if cause_coun_inv >= effect_coun_inv else effect_coun_inv
- else:
- count_NP_inv = 0
-
- effect_inv_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_inv > 0) and (effect_inv_inv > 0):
- count_inv_inv = cause_coun_inv if cause_coun_inv >= effect_inv_inv else effect_inv_inv
- else:
- count_inv_inv = 0
- effect_inv_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_inv > 0) and (effect_inv_cus > 0):
- count_inv_cus = cause_coun_inv if cause_coun_inv >= effect_inv_cus else effect_inv_cus
- else:
- count_inv_cus = 0
- effect_inv_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_inv > 0) and (effect_inv_emp > 0):
- count_inv_emp = cause_coun_inv if cause_coun_inv >= effect_inv_emp else effect_inv_emp
- else:
- count_inv_emp = 0
-
- effect_inv_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_inv > 0) and (effect_inv_soc > 0):
- count_inv_soc = cause_coun_inv if cause_coun_inv >= effect_inv_soc else effect_inv_soc
- else:
- count_inv_soc = 0
-
- cause_coun_cus = (cause_tab.Labellevel2 == 'Customers').sum()
- effect_coun_cus = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_cus > 0) and (effect_coun_cus > 0):
- count_NP_cus = cause_coun_cus if cause_coun_cus >= effect_coun_cus else effect_coun_cus
- else:
- count_NP_cus = 0
-
- effect_cus_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_cus > 0) and (effect_cus_inv > 0):
- count_cus_inv = cause_coun_cus if cause_coun_cus >= effect_cus_inv else effect_cus_inv
- else:
- count_cus_inv = 0
-
- effect_cus_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_cus > 0) and (effect_cus_cus > 0):
- count_cus_cus = cause_coun_cus if cause_coun_cus >= effect_cus_cus else effect_cus_cus
- else:
- count_cus_cus = 0
-
- effect_cus_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_cus > 0) and (effect_cus_emp > 0):
- count_cus_emp = cause_coun_cus if cause_coun_cus >= effect_cus_emp else effect_cus_emp
- else:
- count_cus_emp = 0
-
- effect_cus_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_cus > 0) and (effect_cus_soc > 0):
- count_cus_soc = cause_coun_cus if cause_coun_cus >= effect_cus_soc else effect_cus_soc
- else:
- count_cus_soc = 0
-
- cause_coun_emp = (cause_tab.Labellevel2 == 'Employees').sum()
- effect_coun_emp = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_emp > 0) and (effect_coun_emp > 0):
- count_NP_emp = cause_coun_emp if cause_coun_emp >= effect_coun_emp else effect_coun_emp
- else:
- count_NP_emp = 0
-
- effect_emp_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_emp > 0) and (effect_emp_inv > 0):
- count_emp_inv = cause_coun_emp if cause_coun_emp >= effect_emp_inv else effect_emp_inv
- else:
- count_emp_inv = 0
-
- effect_emp_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_emp > 0) and (effect_emp_cus > 0):
- count_emp_cus = cause_coun_emp if cause_coun_emp >= effect_emp_cus else effect_emp_cus
- else:
- count_emp_cus = 0
-
- effect_emp_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_emp > 0) and (effect_emp_emp > 0):
- count_emp_emp = cause_coun_emp if cause_coun_emp >= effect_emp_emp else effect_emp_emp
- else:
- count_emp_emp = 0
-
- effect_emp_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_emp > 0) and (effect_emp_soc > 0):
- count_emp_soc = cause_coun_emp if cause_coun_emp >= effect_emp_soc else effect_emp_soc
- else:
- count_emp_soc = 0
-
- cause_coun_soc = (cause_tab.Labellevel2 == 'Society').sum()
- effect_coun_soc = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_soc > 0) and (effect_coun_soc > 0):
- count_NP_soc = cause_coun_soc if cause_coun_soc >= effect_coun_soc else effect_coun_soc
- else:
- count_NP_soc = 0
-
- effect_soc_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_soc > 0) and (effect_soc_inv > 0):
- count_soc_inv = cause_coun_soc if cause_coun_soc >= effect_soc_inv else effect_soc_inv
- else:
- count_soc_inv = 0
-
- effect_soc_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_soc > 0) and (effect_soc_cus > 0):
- count_soc_cus = cause_coun_soc if cause_coun_soc >= effect_soc_cus else effect_soc_cus
- else:
- count_soc_cus = 0
-
- effect_soc_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_soc > 0) and (effect_soc_emp > 0):
- count_soc_emp = cause_coun_soc if cause_coun_soc >= effect_soc_emp else effect_soc_emp
- else:
- count_soc_emp = 0
-
- effect_soc_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_soc > 0) and (effect_soc_soc > 0):
- count_soc_soc = cause_coun_soc if cause_coun_soc >= effect_soc_soc else effect_soc_soc
- else:
- count_soc_soc = 0
-
- count_NP_NP = count_NP_NP + count_NP
- count_NP_investor = count_NP_investor + count_NP_inv
- count_NP_customer = count_NP_customer + count_NP_cus
- count_NP_employees = count_NP_employees + count_NP_emp
- count_NP_society = count_NP_society + count_NP_soc
-
- count_inv_np = count_inv_np + count_NP_inv
- count_inv_investor = count_inv_investor + count_inv_inv
- count_inv_customer = count_inv_customer + count_inv_cus
- count_inv_employee = count_inv_employee + count_inv_emp
- count_inv_society = count_inv_society + count_inv_soc
-
- count_cus_np = count_cus_np + count_NP_cus
- count_cus_investor = count_cus_investor + count_cus_inv
- count_cus_customer = count_cus_customer + count_cus_cus
- count_cus_employee = count_cus_employee + count_cus_emp
- count_cus_society = count_cus_society + count_cus_soc
-
- count_emp_np = count_emp_np + count_NP_emp
- count_emp_investor = count_emp_investor + count_emp_inv
- count_emp_customer = count_emp_customer + count_emp_cus
- count_emp_employee = count_emp_employee + count_emp_emp
- count_emp_society = count_emp_society + count_emp_soc
-
- count_soc_np = count_soc_np + count_NP_soc
- count_soc_investor = count_soc_investor + count_soc_inv
- count_soc_customer = count_soc_customer + count_soc_cus
- count_soc_employee = count_soc_employee + count_soc_emp
- count_soc_society = count_soc_society + count_soc_soc
-
- df_tab = pd.DataFrame(columns = ['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'],index=['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'], dtype=object)
-
- df_tab.loc['Non-performance'] = [count_NP_NP, count_NP_investor, count_NP_customer, count_NP_employees, count_NP_society]
- df_tab.loc['Investors'] = [count_inv_np, count_inv_investor, count_inv_customer, count_inv_employee, count_inv_society]
- df_tab.loc['Customers'] = [count_cus_np, count_cus_investor, count_cus_customer, count_cus_employee, count_cus_society]
- df_tab.loc['Employees'] = [count_emp_np, count_emp_investor, count_emp_customer, count_emp_employee, count_emp_society]
- df_tab.loc['Society'] = [count_soc_np, count_soc_investor, count_soc_customer, count_soc_employee, count_soc_society]
-
-
-# df_tab = pd.DataFrame({
-# 'Non-performance': [count_NP_NP, count_NP_investor, count_NP_customer, count_NP_employees, count_NP_society],
-# 'Investors': [count_inv_np, count_inv_investor, count_inv_customer, count_inv_employee, count_inv_society],
-# 'Customers': [count_cus_np, count_cus_investor, count_cus_customer, count_cus_employee, count_cus_society],
-# 'Employees': [count_emp_np, count_emp_investor, count_emp_customer, count_emp_employee, count_emp_society],
-# 'Society': [count_soc_np, count_soc_investor, count_soc_customer, count_soc_employee, count_soc_society]},
-# index=['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'])
-
- #df_tab.to_csv('final_data.csv')
-
- buffer = io.BytesIO()
- with pd.ExcelWriter(buffer, engine="xlsxwriter") as writer:
- df_tab.to_excel(writer,sheet_name="count_result",index=False)
- df_final1.to_excel(writer,sheet_name="Detailed_results",index=False)
- writer.close()
- #df = pd.read_csv('final_data.csv', index_col=0)
-#474-515
- # Convert to JSON format
- json_data = []
- for row in df_tab.index:
- for col in df_tab.columns:
- json_data.append({
- 'source': row,
- 'target': col,
- 'value': int(df_tab.loc[row, col])
- })
-
- HfApi().delete_file(path_in_repo = DATA_FILENAME1 ,repo_id = 'Seetha/visual_files',token= HF_TOKEN,repo_type='dataset')
- #st.write('file-deleted')
- fs = HfFileSystem(token=HF_TOKEN)
- with fs.open('datasets/Seetha/visual_files/level2.json', 'w') as f:
- json.dump(json_data, f)
-
- df_final1.to_csv('predictions.csv')
- csv_file = "predictions.csv"
- json_file = "detailedResults.json"
-
- # Open the CSV file and read the data
- with open(csv_file, "r") as f:
- csv_data = csv.DictReader(f)
-
- # # Convert the CSV data to a list of dictionaries
- data_list = []
- for row in csv_data:
- data_list.append(dict(row))
-
- # # Convert the list of dictionaries to JSON
- json_data = json.dumps(data_list)
-
- HfApi().delete_file(path_in_repo = DATA_FILENAME ,repo_id = 'Seetha/visual_files',token= HF_TOKEN,repo_type='dataset')
- #st.write('file2-deleted')
- with fs.open('datasets/Seetha/visual_files/detailedResults.json','w') as fi:
- #data = json.load(fi)
- fi.write(json_data)
-
- def convert_df(df):
-
- #IMPORTANT: Cache the conversion to prevent computation on every rerun
-
- return df.to_csv().encode('utf-8')
-
-
-
- csv1 = convert_df(df_final1.astype(str))
- csv2 = convert_df(df_tab.astype(str))
-
- with st.container():
-
- st.download_button(label="Download the result table",data=buffer,file_name="t2cg_outputs.xlsx",mime="application/vnd.ms-excel")
- st.markdown('Click this link in a separate tab to view knowledge graph', unsafe_allow_html=True)
- # st.download_button(label="Download the detailed result table_csv",data=csv1,file_name='results.csv',mime='text/csv')
- # st.download_button(label="Download the result table_csv",data=csv2,file_name='final_data.csv',mime='text/csv')
-
-#with st.container():
- # Execute your app
- #st.title("Visualization example")
-# components.html(source_code)
- #html(my_html)
- #webbrowser.open('https://huggingface.co/spaces/Seetha/visual-knowledgegraph')
-# # embed streamlit docs in a streamlit app
-# #components.iframe("https://webpages.charlotte.edu/ltotapal/")
-
-
-
-if __name__ == '__main__':
- start_time = time.time()
- main()
diff --git a/spaces/Shiro26/MendoBERT_RE/app.py b/spaces/Shiro26/MendoBERT_RE/app.py
deleted file mode 100644
index 1bf256334d6604b5048b7f1054c9cb1915120f15..0000000000000000000000000000000000000000
--- a/spaces/Shiro26/MendoBERT_RE/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-
-model = pipeline("text-classification", model="/home/user/app/MendoBERT/", tokenizer="indolem/indobert-base-uncased")
-basemodel = pipeline("text-classification", model="/home/user/app/IndoLEM/", tokenizer="indolem/indobert-base-uncased")
-
-st.title(':blue[MendoBERT] - Relation Extraction :family:')
-
-if 'options' not in st.session_state:
- st.session_state['options'] = ""
-
-def button1_callback():
- st.session_state['options'] = "Kami menyimpulkan bahwa polimorfisme @GENE$ dan AGT tidak berkontribusi pada kerentanan genetik terhadap @DISEASE$ dan retinopati pada populasi Kaukasia Mediterania."
-def button2_callback():
- st.session_state['options'] = "Genotipe bayi PON1 RR dan @GENE$ CC dikaitkan dengan @DISEASE$ dalam populasi penelitian kami, yang menunjukkan kemungkinan peran variabilitas paraoxonase manusia dalam etiologi kelahiran prematur."
-
-
-placeholder = st.empty()
-
-st.info("Please replace the gene and disease that you want to get their relation predicted with @GENE\$ and \@DISEASE\$", icon="ℹ️")
-
-st.caption('_Examples_')
-st.button('Kami menyimpulkan bahwa polimorfisme \@GENE\$ dan AGT tidak berkontribusi pada kerentanan genetik terhadap \@DISEASE\$ dan retinopati pada populasi Kaukasia Mediterania.', use_container_width=True, on_click = button1_callback)
-st.button('Genotipe bayi PON1 RR dan \@GENE\$ CC dikaitkan dengan \@DISEASE\$ dalam populasi penelitian kami, yang menunjukkan kemungkinan peran variabilitas paraoxonase manusia dalam etiologi kelahiran prematur.', use_container_width=True, on_click = button2_callback)
-
-with placeholder:
- text = st.text_area('Enter some text: ', key = 'options')
-
-if text:
- st.subheader('MendoBERT')
- st.write(model(text))
- st.write("\n")
- st.subheader('IndoLEM')
- st.write(basemodel(text))
\ No newline at end of file
diff --git a/spaces/Shredder/CONBERT-2/README.md b/spaces/Shredder/CONBERT-2/README.md
deleted file mode 100644
index daf10e5da8dd31671c54c18d24b0e7d1d77b62f0..0000000000000000000000000000000000000000
--- a/spaces/Shredder/CONBERT-2/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: CONBERT 2
-emoji: 👁
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Stearns/Soar/soar_io.py b/spaces/Stearns/Soar/soar_io.py
deleted file mode 100644
index c05d97417b69a804b35207fb839957a91258a5d2..0000000000000000000000000000000000000000
--- a/spaces/Stearns/Soar/soar_io.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from pysoarlib import *
-
-## DEFINE MAIN CONNECTOR CLASS
-
-class GeneralSoarIOConnector(AgentConnector):
- def __init__(self, client):
- AgentConnector.__init__(self, client)
- client.add_print_event_handler(self.agent_print_collector)
- # client.execute_command("output callbacks on")
- # client.execute_command("output console on")
-
- self.agent_printout = ""
-
- def on_input_phase(self, input_link):
- pass
-
- def on_init_soar(self):
- self.reset()
-
- def on_output_event(self, command_name, root_id):
- pass
-
-
- def agent_print_collector(self, text):
- self.agent_printout += text+"\n"
-
- def get_agent_output(self):
- return self.agent_printout
-
- def reset(self):
- self.agent_printout = ""
-
-
-
-
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py
deleted file mode 100644
index 489b7c13d0ce82b769ba50412ecde0889d0b7851..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# encoding: utf-8
-"""
-Utilities for working with external processes.
-"""
-
-# Copyright (c) IPython Development Team.
-# Distributed under the terms of the Modified BSD License.
-
-
-import os
-import shutil
-import sys
-
-if sys.platform == 'win32':
- from ._process_win32 import system, getoutput, arg_split, check_pid
-elif sys.platform == 'cli':
- from ._process_cli import system, getoutput, arg_split, check_pid
-else:
- from ._process_posix import system, getoutput, arg_split, check_pid
-
-from ._process_common import getoutputerror, get_output_error_code, process_handler
-
-
-class FindCmdError(Exception):
- pass
-
-
-def find_cmd(cmd):
- """Find absolute path to executable cmd in a cross platform manner.
-
- This function tries to determine the full path to a command line program
- using `which` on Unix/Linux/OS X and `win32api` on Windows. Most of the
- time it will use the version that is first on the users `PATH`.
-
- Warning, don't use this to find IPython command line programs as there
- is a risk you will find the wrong one. Instead find those using the
- following code and looking for the application itself::
-
- import sys
- argv = [sys.executable, '-m', 'IPython']
-
- Parameters
- ----------
- cmd : str
- The command line program to look for.
- """
- path = shutil.which(cmd)
- if path is None:
- raise FindCmdError('command could not be found: %s' % cmd)
- return path
-
-
-def abbrev_cwd():
- """ Return abbreviated version of cwd, e.g. d:mydir """
- cwd = os.getcwd().replace('\\','/')
- drivepart = ''
- tail = cwd
- if sys.platform == 'win32':
- if len(cwd) < 4:
- return cwd
- drivepart,tail = os.path.splitdrive(cwd)
-
-
- parts = tail.split('/')
- if len(parts) > 2:
- tail = '/'.join(parts[-2:])
-
- return (drivepart + (
- cwd == '/' and '/' or tail))
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py
deleted file mode 100644
index 37d3db222ef2c7920628971a92e863d9915514c6..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import sys
-from typing import Any, Dict, List, Optional, Generic, TypeVar, cast
-from types import TracebackType
-
-if sys.version_info >= (3, 8):
- from importlib.metadata import entry_points
-else:
- from importlib_metadata import entry_points
-
-from toolz import curry
-
-
-PluginType = TypeVar("PluginType")
-
-
-class NoSuchEntryPoint(Exception):
- def __init__(self, group, name):
- self.group = group
- self.name = name
-
- def __str__(self):
- return f"No {self.name!r} entry point found in group {self.group!r}"
-
-
-class PluginEnabler:
- """Context manager for enabling plugins
-
- This object lets you use enable() as a context manager to
- temporarily enable a given plugin::
-
- with plugins.enable('name'):
- do_something() # 'name' plugin temporarily enabled
- # plugins back to original state
- """
-
- def __init__(self, registry: "PluginRegistry", name: str, **options):
- self.registry = registry # type: PluginRegistry
- self.name = name # type: str
- self.options = options # type: Dict[str, Any]
- self.original_state = registry._get_state() # type: Dict[str, Any]
- self.registry._enable(name, **options)
-
- def __enter__(self) -> "PluginEnabler":
- return self
-
- def __exit__(self, typ: type, value: Exception, traceback: TracebackType) -> None:
- self.registry._set_state(self.original_state)
-
- def __repr__(self) -> str:
- return "{}.enable({!r})".format(self.registry.__class__.__name__, self.name)
-
-
-class PluginRegistry(Generic[PluginType]):
- """A registry for plugins.
-
- This is a plugin registry that allows plugins to be loaded/registered
- in two ways:
-
- 1. Through an explicit call to ``.register(name, value)``.
- 2. By looking for other Python packages that are installed and provide
- a setuptools entry point group.
-
- When you create an instance of this class, provide the name of the
- entry point group to use::
-
- reg = PluginRegister('my_entrypoint_group')
-
- """
-
- # this is a mapping of name to error message to allow custom error messages
- # in case an entrypoint is not found
- entrypoint_err_messages = {} # type: Dict[str, str]
-
- # global settings is a key-value mapping of settings that are stored globally
- # in the registry rather than passed to the plugins
- _global_settings = {} # type: Dict[str, Any]
-
- def __init__(self, entry_point_group: str = "", plugin_type: type = object):
- """Create a PluginRegistry for a named entry point group.
-
- Parameters
- ==========
- entry_point_group: str
- The name of the entry point group.
- plugin_type: object
- A type that will optionally be used for runtime type checking of
- loaded plugins using isinstance.
- """
- self.entry_point_group = entry_point_group # type: str
- self.plugin_type = plugin_type # type: Optional[type]
- self._active = None # type: Optional[PluginType]
- self._active_name = "" # type: str
- self._plugins = {} # type: Dict[str, PluginType]
- self._options = {} # type: Dict[str, Any]
- self._global_settings = self.__class__._global_settings.copy() # type: dict
-
- def register(self, name: str, value: Optional[PluginType]) -> Optional[PluginType]:
- """Register a plugin by name and value.
-
- This method is used for explicit registration of a plugin and shouldn't be
- used to manage entry point managed plugins, which are auto-loaded.
-
- Parameters
- ==========
- name: str
- The name of the plugin.
- value: PluginType or None
- The actual plugin object to register or None to unregister that plugin.
-
- Returns
- =======
- plugin: PluginType or None
- The plugin that was registered or unregistered.
- """
- if value is None:
- return self._plugins.pop(name, None)
- else:
- assert isinstance(value, self.plugin_type) # type: ignore[arg-type] # Should ideally be fixed by better annotating plugin_type
- self._plugins[name] = value
- return value
-
- def names(self) -> List[str]:
- """List the names of the registered and entry points plugins."""
- exts = list(self._plugins.keys())
- e_points = importlib_metadata_get(self.entry_point_group)
- more_exts = [ep.name for ep in e_points]
- exts.extend(more_exts)
- return sorted(set(exts))
-
- def _get_state(self) -> Dict[str, Any]:
- """Return a dictionary representing the current state of the registry"""
- return {
- "_active": self._active,
- "_active_name": self._active_name,
- "_plugins": self._plugins.copy(),
- "_options": self._options.copy(),
- "_global_settings": self._global_settings.copy(),
- }
-
- def _set_state(self, state: Dict[str, Any]) -> None:
- """Reset the state of the registry"""
- assert set(state.keys()) == {
- "_active",
- "_active_name",
- "_plugins",
- "_options",
- "_global_settings",
- }
- for key, val in state.items():
- setattr(self, key, val)
-
- def _enable(self, name: str, **options) -> None:
- if name not in self._plugins:
- try:
- (ep,) = [
- ep
- for ep in importlib_metadata_get(self.entry_point_group)
- if ep.name == name
- ]
- except ValueError as err:
- if name in self.entrypoint_err_messages:
- raise ValueError(self.entrypoint_err_messages[name]) from err
- else:
- raise NoSuchEntryPoint(self.entry_point_group, name) from err
- value = cast(PluginType, ep.load())
- self.register(name, value)
- self._active_name = name
- self._active = self._plugins[name]
- for key in set(options.keys()) & set(self._global_settings.keys()):
- self._global_settings[key] = options.pop(key)
- self._options = options
-
- def enable(self, name: Optional[str] = None, **options) -> PluginEnabler:
- """Enable a plugin by name.
-
- This can be either called directly, or used as a context manager.
-
- Parameters
- ----------
- name : string (optional)
- The name of the plugin to enable. If not specified, then use the
- current active name.
- **options :
- Any additional parameters will be passed to the plugin as keyword
- arguments
-
- Returns
- -------
- PluginEnabler:
- An object that allows enable() to be used as a context manager
- """
- if name is None:
- name = self.active
- return PluginEnabler(self, name, **options)
-
- @property
- def active(self) -> str:
- """Return the name of the currently active plugin"""
- return self._active_name
-
- @property
- def options(self) -> Dict[str, Any]:
- """Return the current options dictionary"""
- return self._options
-
- def get(self) -> Optional[PluginType]:
- """Return the currently active plugin."""
- if self._options:
- return curry(self._active, **self._options)
- else:
- return self._active
-
- def __repr__(self) -> str:
- return "{}(active={!r}, registered={!r})" "".format(
- self.__class__.__name__, self._active_name, list(self.names())
- )
-
-
-def importlib_metadata_get(group):
- ep = entry_points()
- # 'select' was introduced in Python 3.10 and 'get' got deprecated
- # We don't check for Python version here as by checking with hasattr we
- # also get compatibility with the importlib_metadata package which had a different
- # deprecation cycle for 'get'
- if hasattr(ep, "select"):
- return ep.select(group=group)
- else:
- return ep.get(group, [])
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py
deleted file mode 100644
index bba2d3f7dfffa3bdbf921bdad4ca7143be97c2fd..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py
+++ /dev/null
@@ -1,143 +0,0 @@
-from __future__ import annotations
-
-import codecs
-from dataclasses import InitVar, dataclass, field
-from typing import Any, Callable, Mapping
-
-from ..abc import (
- AnyByteReceiveStream,
- AnyByteSendStream,
- AnyByteStream,
- ObjectReceiveStream,
- ObjectSendStream,
- ObjectStream,
-)
-
-
-@dataclass(eq=False)
-class TextReceiveStream(ObjectReceiveStream[str]):
- """
- Stream wrapper that decodes bytes to strings using the given encoding.
-
- Decoding is done using :class:`~codecs.IncrementalDecoder` which returns any completely
- received unicode characters as soon as they come in.
-
- :param transport_stream: any bytes-based receive stream
- :param encoding: character encoding to use for decoding bytes to strings (defaults to
- ``utf-8``)
- :param errors: handling scheme for decoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteReceiveStream
- encoding: InitVar[str] = "utf-8"
- errors: InitVar[str] = "strict"
- _decoder: codecs.IncrementalDecoder = field(init=False)
-
- def __post_init__(self, encoding: str, errors: str) -> None:
- decoder_class = codecs.getincrementaldecoder(encoding)
- self._decoder = decoder_class(errors=errors)
-
- async def receive(self) -> str:
- while True:
- chunk = await self.transport_stream.receive()
- decoded = self._decoder.decode(chunk)
- if decoded:
- return decoded
-
- async def aclose(self) -> None:
- await self.transport_stream.aclose()
- self._decoder.reset()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return self.transport_stream.extra_attributes
-
-
-@dataclass(eq=False)
-class TextSendStream(ObjectSendStream[str]):
- """
- Sends strings to the wrapped stream as bytes using the given encoding.
-
- :param AnyByteSendStream transport_stream: any bytes-based send stream
- :param str encoding: character encoding to use for encoding strings to bytes (defaults to
- ``utf-8``)
- :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteSendStream
- encoding: InitVar[str] = "utf-8"
- errors: str = "strict"
- _encoder: Callable[..., tuple[bytes, int]] = field(init=False)
-
- def __post_init__(self, encoding: str) -> None:
- self._encoder = codecs.getencoder(encoding)
-
- async def send(self, item: str) -> None:
- encoded = self._encoder(item, self.errors)[0]
- await self.transport_stream.send(encoded)
-
- async def aclose(self) -> None:
- await self.transport_stream.aclose()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return self.transport_stream.extra_attributes
-
-
-@dataclass(eq=False)
-class TextStream(ObjectStream[str]):
- """
- A bidirectional stream that decodes bytes to strings on receive and encodes strings to bytes on
- send.
-
- Extra attributes will be provided from both streams, with the receive stream providing the
- values in case of a conflict.
-
- :param AnyByteStream transport_stream: any bytes-based stream
- :param str encoding: character encoding to use for encoding/decoding strings to/from bytes
- (defaults to ``utf-8``)
- :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteStream
- encoding: InitVar[str] = "utf-8"
- errors: InitVar[str] = "strict"
- _receive_stream: TextReceiveStream = field(init=False)
- _send_stream: TextSendStream = field(init=False)
-
- def __post_init__(self, encoding: str, errors: str) -> None:
- self._receive_stream = TextReceiveStream(
- self.transport_stream, encoding=encoding, errors=errors
- )
- self._send_stream = TextSendStream(
- self.transport_stream, encoding=encoding, errors=errors
- )
-
- async def receive(self) -> str:
- return await self._receive_stream.receive()
-
- async def send(self, item: str) -> None:
- await self._send_stream.send(item)
-
- async def send_eof(self) -> None:
- await self.transport_stream.send_eof()
-
- async def aclose(self) -> None:
- await self._send_stream.aclose()
- await self._receive_stream.aclose()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return {
- **self._send_stream.extra_attributes,
- **self._receive_stream.extra_attributes,
- }
diff --git a/spaces/TH5314/newbing/src/lib/bots/bing/types.ts b/spaces/TH5314/newbing/src/lib/bots/bing/types.ts
deleted file mode 100644
index 02cd5e8b01e3529642d28dc1539bf958f4ac420b..0000000000000000000000000000000000000000
--- a/spaces/TH5314/newbing/src/lib/bots/bing/types.ts
+++ /dev/null
@@ -1,259 +0,0 @@
-export type Author = 'user' | 'system' | 'bot'
-
-export type BotId = 'bing'
-
-export enum BingConversationStyle {
- Creative = 'Creative',
- Balanced = 'Balanced',
- Precise = 'Precise'
-}
-
-export enum ErrorCode {
- CONVERSATION_LIMIT = 'CONVERSATION_LIMIT',
- BING_UNAUTHORIZED = 'BING_UNAUTHORIZED',
- BING_FORBIDDEN = 'BING_FORBIDDEN',
- BING_CAPTCHA = 'BING_CAPTCHA',
- THROTTLE_LIMIT = 'THROTTLE_LIMIT',
- NOTFOUND_ERROR = 'NOT_FOUND_ERROR',
- UNKOWN_ERROR = 'UNKOWN_ERROR',
- NETWORK_ERROR = 'NETWORK_ERROR',
-}
-
-export class ChatError extends Error {
- code: ErrorCode
- constructor(message: string, code: ErrorCode) {
- super(message)
- this.code = code
- }
-}
-
-export type ChatMessageModel = {
- id: string
- author: Author
- text: string
- error?: ChatError
- throttling?: Throttling
- sourceAttributions?: SourceAttribution[]
- suggestedResponses?: SuggestedResponse[]
-}
-
-export interface ConversationModel {
- messages: ChatMessageModel[]
-}
-
-export type Event =
- | {
- type: 'UPDATE_ANSWER'
- data: {
- text: string
- spokenText?: string
- sourceAttributions?: SourceAttribution[]
- suggestedResponses?: SuggestedResponse[]
- throttling?: Throttling
- }
- }
- | {
- type: 'DONE'
- }
- | {
- type: 'ERROR'
- error: ChatError
- }
-
-export interface SendMessageParams {
- prompt: string
- imageUrl?: string
- options: T
- onEvent: (event: Event) => void
- signal?: AbortSignal
-}
-
-export interface ConversationResponse {
- conversationId: string
- clientId: string
- conversationSignature: string
- result: {
- value: string
- message?: string
- }
-}
-
-export interface Telemetry {
- metrics?: null
- startTime: string
-}
-
-export interface ChatUpdateArgument {
- messages?: ChatResponseMessage[]
- throttling?: Throttling
- requestId: string
- result: null
-}
-
-export type ChatUpdateCompleteResponse = {
- type: 2
- invocationId: string
- item: ChatResponseItem
-} | {
- type: 1
- target: string
- arguments: ChatUpdateArgument[]
-} | {
- type: 3
- invocationId: string
-} | {
- type: 6 | 7
-}
-
-export interface ChatRequestResult {
- value: string
- serviceVersion: string
- error?: string
-}
-
-export interface ChatResponseItem {
- messages: ChatResponseMessage[]
- firstNewMessageIndex: number
- suggestedResponses: null
- conversationId: string
- requestId: string
- conversationExpiryTime: string
- telemetry: Telemetry
- result: ChatRequestResult
- throttling: Throttling
-}
-export enum InvocationEventType {
- Invocation = 1,
- StreamItem = 2,
- Completion = 3,
- StreamInvocation = 4,
- CancelInvocation = 5,
- Ping = 6,
- Close = 7,
-}
-
-// https://github.com/bytemate/bingchat-api/blob/main/src/lib.ts
-
-export interface ConversationInfo {
- conversationId: string
- clientId: string
- conversationSignature: string
- invocationId: number
- conversationStyle: BingConversationStyle
- prompt: string
- imageUrl?: string
-}
-
-export interface BingChatResponse {
- conversationSignature: string
- conversationId: string
- clientId: string
- invocationId: number
- conversationExpiryTime: Date
- response: string
- details: ChatResponseMessage
-}
-
-export interface Throttling {
- maxNumLongDocSummaryUserMessagesInConversation: number
- maxNumUserMessagesInConversation: number
- numLongDocSummaryUserMessagesInConversation: number
- numUserMessagesInConversation: number
-}
-
-export interface ChatResponseMessage {
- text: string
- spokenText?: string
- author: string
- createdAt: Date
- timestamp: Date
- messageId: string
- requestId: string
- offense: string
- adaptiveCards: AdaptiveCard[]
- sourceAttributions: SourceAttribution[]
- feedback: Feedback
- contentOrigin: string
- messageType?: string
- contentType?: string
- privacy: null
- suggestedResponses: SuggestedResponse[]
-}
-
-export interface AdaptiveCard {
- type: string
- version: string
- body: Body[]
-}
-
-export interface Body {
- type: string
- text: string
- wrap: boolean
- size?: string
-}
-
-export interface Feedback {
- tag: null
- updatedOn: null
- type: string
-}
-
-export interface SourceAttribution {
- providerDisplayName: string
- seeMoreUrl: string
- searchQuery: string
-}
-
-export interface SuggestedResponse {
- text: string
- author?: Author
- createdAt?: Date
- timestamp?: Date
- messageId?: string
- messageType?: string
- offense?: string
- feedback?: Feedback
- contentOrigin?: string
- privacy?: null
-}
-
-export interface KBlobRequest {
- knowledgeRequest: KnowledgeRequestContext
- imageBase64?: string
-}
-
-export interface KBlobResponse {
- blobId: string
- processedBlobId?: string
-}
-
-export interface KnowledgeRequestContext {
- imageInfo: ImageInfo;
- knowledgeRequest: KnowledgeRequest;
-}
-
-export interface ImageInfo {
- url?: string;
-}
-
-export interface KnowledgeRequest {
- invokedSkills: string[];
- subscriptionId: string;
- invokedSkillsRequestData: InvokedSkillsRequestData;
- convoData: ConvoData;
-}
-
-export interface ConvoData {
- convoid: string;
- convotone: BingConversationStyle;
-}
-
-export interface InvokedSkillsRequestData {
- enableFaceBlur: boolean;
-}
-
-export interface FileItem {
- url: string;
- status?: 'loading' | 'error' | 'loaded'
-}
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py
deleted file mode 100644
index 264d564dbda676b52f446c0d25433a15939a78a3..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py
+++ /dev/null
@@ -1,519 +0,0 @@
-"""
-This module uses ctypes to bind a whole bunch of functions and constants from
-SecureTransport. The goal here is to provide the low-level API to
-SecureTransport. These are essentially the C-level functions and constants, and
-they're pretty gross to work with.
-
-This code is a bastardised version of the code found in Will Bond's oscrypto
-library. An enormous debt is owed to him for blazing this trail for us. For
-that reason, this code should be considered to be covered both by urllib3's
-license and by oscrypto's:
-
- Copyright (c) 2015-2016 Will Bond
-
- Permission is hereby granted, free of charge, to any person obtaining a
- copy of this software and associated documentation files (the "Software"),
- to deal in the Software without restriction, including without limitation
- the rights to use, copy, modify, merge, publish, distribute, sublicense,
- and/or sell copies of the Software, and to permit persons to whom the
- Software is furnished to do so, subject to the following conditions:
-
- The above copyright notice and this permission notice shall be included in
- all copies or substantial portions of the Software.
-
- THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
- FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
- DEALINGS IN THE SOFTWARE.
-"""
-from __future__ import absolute_import
-
-import platform
-from ctypes import (
- CDLL,
- CFUNCTYPE,
- POINTER,
- c_bool,
- c_byte,
- c_char_p,
- c_int32,
- c_long,
- c_size_t,
- c_uint32,
- c_ulong,
- c_void_p,
-)
-from ctypes.util import find_library
-
-from ...packages.six import raise_from
-
-if platform.system() != "Darwin":
- raise ImportError("Only macOS is supported")
-
-version = platform.mac_ver()[0]
-version_info = tuple(map(int, version.split(".")))
-if version_info < (10, 8):
- raise OSError(
- "Only OS X 10.8 and newer are supported, not %s.%s"
- % (version_info[0], version_info[1])
- )
-
-
-def load_cdll(name, macos10_16_path):
- """Loads a CDLL by name, falling back to known path on 10.16+"""
- try:
- # Big Sur is technically 11 but we use 10.16 due to the Big Sur
- # beta being labeled as 10.16.
- if version_info >= (10, 16):
- path = macos10_16_path
- else:
- path = find_library(name)
- if not path:
- raise OSError # Caught and reraised as 'ImportError'
- return CDLL(path, use_errno=True)
- except OSError:
- raise_from(ImportError("The library %s failed to load" % name), None)
-
-
-Security = load_cdll(
- "Security", "/System/Library/Frameworks/Security.framework/Security"
-)
-CoreFoundation = load_cdll(
- "CoreFoundation",
- "/System/Library/Frameworks/CoreFoundation.framework/CoreFoundation",
-)
-
-
-Boolean = c_bool
-CFIndex = c_long
-CFStringEncoding = c_uint32
-CFData = c_void_p
-CFString = c_void_p
-CFArray = c_void_p
-CFMutableArray = c_void_p
-CFDictionary = c_void_p
-CFError = c_void_p
-CFType = c_void_p
-CFTypeID = c_ulong
-
-CFTypeRef = POINTER(CFType)
-CFAllocatorRef = c_void_p
-
-OSStatus = c_int32
-
-CFDataRef = POINTER(CFData)
-CFStringRef = POINTER(CFString)
-CFArrayRef = POINTER(CFArray)
-CFMutableArrayRef = POINTER(CFMutableArray)
-CFDictionaryRef = POINTER(CFDictionary)
-CFArrayCallBacks = c_void_p
-CFDictionaryKeyCallBacks = c_void_p
-CFDictionaryValueCallBacks = c_void_p
-
-SecCertificateRef = POINTER(c_void_p)
-SecExternalFormat = c_uint32
-SecExternalItemType = c_uint32
-SecIdentityRef = POINTER(c_void_p)
-SecItemImportExportFlags = c_uint32
-SecItemImportExportKeyParameters = c_void_p
-SecKeychainRef = POINTER(c_void_p)
-SSLProtocol = c_uint32
-SSLCipherSuite = c_uint32
-SSLContextRef = POINTER(c_void_p)
-SecTrustRef = POINTER(c_void_p)
-SSLConnectionRef = c_uint32
-SecTrustResultType = c_uint32
-SecTrustOptionFlags = c_uint32
-SSLProtocolSide = c_uint32
-SSLConnectionType = c_uint32
-SSLSessionOption = c_uint32
-
-
-try:
- Security.SecItemImport.argtypes = [
- CFDataRef,
- CFStringRef,
- POINTER(SecExternalFormat),
- POINTER(SecExternalItemType),
- SecItemImportExportFlags,
- POINTER(SecItemImportExportKeyParameters),
- SecKeychainRef,
- POINTER(CFArrayRef),
- ]
- Security.SecItemImport.restype = OSStatus
-
- Security.SecCertificateGetTypeID.argtypes = []
- Security.SecCertificateGetTypeID.restype = CFTypeID
-
- Security.SecIdentityGetTypeID.argtypes = []
- Security.SecIdentityGetTypeID.restype = CFTypeID
-
- Security.SecKeyGetTypeID.argtypes = []
- Security.SecKeyGetTypeID.restype = CFTypeID
-
- Security.SecCertificateCreateWithData.argtypes = [CFAllocatorRef, CFDataRef]
- Security.SecCertificateCreateWithData.restype = SecCertificateRef
-
- Security.SecCertificateCopyData.argtypes = [SecCertificateRef]
- Security.SecCertificateCopyData.restype = CFDataRef
-
- Security.SecCopyErrorMessageString.argtypes = [OSStatus, c_void_p]
- Security.SecCopyErrorMessageString.restype = CFStringRef
-
- Security.SecIdentityCreateWithCertificate.argtypes = [
- CFTypeRef,
- SecCertificateRef,
- POINTER(SecIdentityRef),
- ]
- Security.SecIdentityCreateWithCertificate.restype = OSStatus
-
- Security.SecKeychainCreate.argtypes = [
- c_char_p,
- c_uint32,
- c_void_p,
- Boolean,
- c_void_p,
- POINTER(SecKeychainRef),
- ]
- Security.SecKeychainCreate.restype = OSStatus
-
- Security.SecKeychainDelete.argtypes = [SecKeychainRef]
- Security.SecKeychainDelete.restype = OSStatus
-
- Security.SecPKCS12Import.argtypes = [
- CFDataRef,
- CFDictionaryRef,
- POINTER(CFArrayRef),
- ]
- Security.SecPKCS12Import.restype = OSStatus
-
- SSLReadFunc = CFUNCTYPE(OSStatus, SSLConnectionRef, c_void_p, POINTER(c_size_t))
- SSLWriteFunc = CFUNCTYPE(
- OSStatus, SSLConnectionRef, POINTER(c_byte), POINTER(c_size_t)
- )
-
- Security.SSLSetIOFuncs.argtypes = [SSLContextRef, SSLReadFunc, SSLWriteFunc]
- Security.SSLSetIOFuncs.restype = OSStatus
-
- Security.SSLSetPeerID.argtypes = [SSLContextRef, c_char_p, c_size_t]
- Security.SSLSetPeerID.restype = OSStatus
-
- Security.SSLSetCertificate.argtypes = [SSLContextRef, CFArrayRef]
- Security.SSLSetCertificate.restype = OSStatus
-
- Security.SSLSetCertificateAuthorities.argtypes = [SSLContextRef, CFTypeRef, Boolean]
- Security.SSLSetCertificateAuthorities.restype = OSStatus
-
- Security.SSLSetConnection.argtypes = [SSLContextRef, SSLConnectionRef]
- Security.SSLSetConnection.restype = OSStatus
-
- Security.SSLSetPeerDomainName.argtypes = [SSLContextRef, c_char_p, c_size_t]
- Security.SSLSetPeerDomainName.restype = OSStatus
-
- Security.SSLHandshake.argtypes = [SSLContextRef]
- Security.SSLHandshake.restype = OSStatus
-
- Security.SSLRead.argtypes = [SSLContextRef, c_char_p, c_size_t, POINTER(c_size_t)]
- Security.SSLRead.restype = OSStatus
-
- Security.SSLWrite.argtypes = [SSLContextRef, c_char_p, c_size_t, POINTER(c_size_t)]
- Security.SSLWrite.restype = OSStatus
-
- Security.SSLClose.argtypes = [SSLContextRef]
- Security.SSLClose.restype = OSStatus
-
- Security.SSLGetNumberSupportedCiphers.argtypes = [SSLContextRef, POINTER(c_size_t)]
- Security.SSLGetNumberSupportedCiphers.restype = OSStatus
-
- Security.SSLGetSupportedCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- POINTER(c_size_t),
- ]
- Security.SSLGetSupportedCiphers.restype = OSStatus
-
- Security.SSLSetEnabledCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- c_size_t,
- ]
- Security.SSLSetEnabledCiphers.restype = OSStatus
-
- Security.SSLGetNumberEnabledCiphers.argtype = [SSLContextRef, POINTER(c_size_t)]
- Security.SSLGetNumberEnabledCiphers.restype = OSStatus
-
- Security.SSLGetEnabledCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- POINTER(c_size_t),
- ]
- Security.SSLGetEnabledCiphers.restype = OSStatus
-
- Security.SSLGetNegotiatedCipher.argtypes = [SSLContextRef, POINTER(SSLCipherSuite)]
- Security.SSLGetNegotiatedCipher.restype = OSStatus
-
- Security.SSLGetNegotiatedProtocolVersion.argtypes = [
- SSLContextRef,
- POINTER(SSLProtocol),
- ]
- Security.SSLGetNegotiatedProtocolVersion.restype = OSStatus
-
- Security.SSLCopyPeerTrust.argtypes = [SSLContextRef, POINTER(SecTrustRef)]
- Security.SSLCopyPeerTrust.restype = OSStatus
-
- Security.SecTrustSetAnchorCertificates.argtypes = [SecTrustRef, CFArrayRef]
- Security.SecTrustSetAnchorCertificates.restype = OSStatus
-
- Security.SecTrustSetAnchorCertificatesOnly.argstypes = [SecTrustRef, Boolean]
- Security.SecTrustSetAnchorCertificatesOnly.restype = OSStatus
-
- Security.SecTrustEvaluate.argtypes = [SecTrustRef, POINTER(SecTrustResultType)]
- Security.SecTrustEvaluate.restype = OSStatus
-
- Security.SecTrustGetCertificateCount.argtypes = [SecTrustRef]
- Security.SecTrustGetCertificateCount.restype = CFIndex
-
- Security.SecTrustGetCertificateAtIndex.argtypes = [SecTrustRef, CFIndex]
- Security.SecTrustGetCertificateAtIndex.restype = SecCertificateRef
-
- Security.SSLCreateContext.argtypes = [
- CFAllocatorRef,
- SSLProtocolSide,
- SSLConnectionType,
- ]
- Security.SSLCreateContext.restype = SSLContextRef
-
- Security.SSLSetSessionOption.argtypes = [SSLContextRef, SSLSessionOption, Boolean]
- Security.SSLSetSessionOption.restype = OSStatus
-
- Security.SSLSetProtocolVersionMin.argtypes = [SSLContextRef, SSLProtocol]
- Security.SSLSetProtocolVersionMin.restype = OSStatus
-
- Security.SSLSetProtocolVersionMax.argtypes = [SSLContextRef, SSLProtocol]
- Security.SSLSetProtocolVersionMax.restype = OSStatus
-
- try:
- Security.SSLSetALPNProtocols.argtypes = [SSLContextRef, CFArrayRef]
- Security.SSLSetALPNProtocols.restype = OSStatus
- except AttributeError:
- # Supported only in 10.12+
- pass
-
- Security.SecCopyErrorMessageString.argtypes = [OSStatus, c_void_p]
- Security.SecCopyErrorMessageString.restype = CFStringRef
-
- Security.SSLReadFunc = SSLReadFunc
- Security.SSLWriteFunc = SSLWriteFunc
- Security.SSLContextRef = SSLContextRef
- Security.SSLProtocol = SSLProtocol
- Security.SSLCipherSuite = SSLCipherSuite
- Security.SecIdentityRef = SecIdentityRef
- Security.SecKeychainRef = SecKeychainRef
- Security.SecTrustRef = SecTrustRef
- Security.SecTrustResultType = SecTrustResultType
- Security.SecExternalFormat = SecExternalFormat
- Security.OSStatus = OSStatus
-
- Security.kSecImportExportPassphrase = CFStringRef.in_dll(
- Security, "kSecImportExportPassphrase"
- )
- Security.kSecImportItemIdentity = CFStringRef.in_dll(
- Security, "kSecImportItemIdentity"
- )
-
- # CoreFoundation time!
- CoreFoundation.CFRetain.argtypes = [CFTypeRef]
- CoreFoundation.CFRetain.restype = CFTypeRef
-
- CoreFoundation.CFRelease.argtypes = [CFTypeRef]
- CoreFoundation.CFRelease.restype = None
-
- CoreFoundation.CFGetTypeID.argtypes = [CFTypeRef]
- CoreFoundation.CFGetTypeID.restype = CFTypeID
-
- CoreFoundation.CFStringCreateWithCString.argtypes = [
- CFAllocatorRef,
- c_char_p,
- CFStringEncoding,
- ]
- CoreFoundation.CFStringCreateWithCString.restype = CFStringRef
-
- CoreFoundation.CFStringGetCStringPtr.argtypes = [CFStringRef, CFStringEncoding]
- CoreFoundation.CFStringGetCStringPtr.restype = c_char_p
-
- CoreFoundation.CFStringGetCString.argtypes = [
- CFStringRef,
- c_char_p,
- CFIndex,
- CFStringEncoding,
- ]
- CoreFoundation.CFStringGetCString.restype = c_bool
-
- CoreFoundation.CFDataCreate.argtypes = [CFAllocatorRef, c_char_p, CFIndex]
- CoreFoundation.CFDataCreate.restype = CFDataRef
-
- CoreFoundation.CFDataGetLength.argtypes = [CFDataRef]
- CoreFoundation.CFDataGetLength.restype = CFIndex
-
- CoreFoundation.CFDataGetBytePtr.argtypes = [CFDataRef]
- CoreFoundation.CFDataGetBytePtr.restype = c_void_p
-
- CoreFoundation.CFDictionaryCreate.argtypes = [
- CFAllocatorRef,
- POINTER(CFTypeRef),
- POINTER(CFTypeRef),
- CFIndex,
- CFDictionaryKeyCallBacks,
- CFDictionaryValueCallBacks,
- ]
- CoreFoundation.CFDictionaryCreate.restype = CFDictionaryRef
-
- CoreFoundation.CFDictionaryGetValue.argtypes = [CFDictionaryRef, CFTypeRef]
- CoreFoundation.CFDictionaryGetValue.restype = CFTypeRef
-
- CoreFoundation.CFArrayCreate.argtypes = [
- CFAllocatorRef,
- POINTER(CFTypeRef),
- CFIndex,
- CFArrayCallBacks,
- ]
- CoreFoundation.CFArrayCreate.restype = CFArrayRef
-
- CoreFoundation.CFArrayCreateMutable.argtypes = [
- CFAllocatorRef,
- CFIndex,
- CFArrayCallBacks,
- ]
- CoreFoundation.CFArrayCreateMutable.restype = CFMutableArrayRef
-
- CoreFoundation.CFArrayAppendValue.argtypes = [CFMutableArrayRef, c_void_p]
- CoreFoundation.CFArrayAppendValue.restype = None
-
- CoreFoundation.CFArrayGetCount.argtypes = [CFArrayRef]
- CoreFoundation.CFArrayGetCount.restype = CFIndex
-
- CoreFoundation.CFArrayGetValueAtIndex.argtypes = [CFArrayRef, CFIndex]
- CoreFoundation.CFArrayGetValueAtIndex.restype = c_void_p
-
- CoreFoundation.kCFAllocatorDefault = CFAllocatorRef.in_dll(
- CoreFoundation, "kCFAllocatorDefault"
- )
- CoreFoundation.kCFTypeArrayCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeArrayCallBacks"
- )
- CoreFoundation.kCFTypeDictionaryKeyCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeDictionaryKeyCallBacks"
- )
- CoreFoundation.kCFTypeDictionaryValueCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeDictionaryValueCallBacks"
- )
-
- CoreFoundation.CFTypeRef = CFTypeRef
- CoreFoundation.CFArrayRef = CFArrayRef
- CoreFoundation.CFStringRef = CFStringRef
- CoreFoundation.CFDictionaryRef = CFDictionaryRef
-
-except (AttributeError):
- raise ImportError("Error initializing ctypes")
-
-
-class CFConst(object):
- """
- A class object that acts as essentially a namespace for CoreFoundation
- constants.
- """
-
- kCFStringEncodingUTF8 = CFStringEncoding(0x08000100)
-
-
-class SecurityConst(object):
- """
- A class object that acts as essentially a namespace for Security constants.
- """
-
- kSSLSessionOptionBreakOnServerAuth = 0
-
- kSSLProtocol2 = 1
- kSSLProtocol3 = 2
- kTLSProtocol1 = 4
- kTLSProtocol11 = 7
- kTLSProtocol12 = 8
- # SecureTransport does not support TLS 1.3 even if there's a constant for it
- kTLSProtocol13 = 10
- kTLSProtocolMaxSupported = 999
-
- kSSLClientSide = 1
- kSSLStreamType = 0
-
- kSecFormatPEMSequence = 10
-
- kSecTrustResultInvalid = 0
- kSecTrustResultProceed = 1
- # This gap is present on purpose: this was kSecTrustResultConfirm, which
- # is deprecated.
- kSecTrustResultDeny = 3
- kSecTrustResultUnspecified = 4
- kSecTrustResultRecoverableTrustFailure = 5
- kSecTrustResultFatalTrustFailure = 6
- kSecTrustResultOtherError = 7
-
- errSSLProtocol = -9800
- errSSLWouldBlock = -9803
- errSSLClosedGraceful = -9805
- errSSLClosedNoNotify = -9816
- errSSLClosedAbort = -9806
-
- errSSLXCertChainInvalid = -9807
- errSSLCrypto = -9809
- errSSLInternal = -9810
- errSSLCertExpired = -9814
- errSSLCertNotYetValid = -9815
- errSSLUnknownRootCert = -9812
- errSSLNoRootCert = -9813
- errSSLHostNameMismatch = -9843
- errSSLPeerHandshakeFail = -9824
- errSSLPeerUserCancelled = -9839
- errSSLWeakPeerEphemeralDHKey = -9850
- errSSLServerAuthCompleted = -9841
- errSSLRecordOverflow = -9847
-
- errSecVerifyFailed = -67808
- errSecNoTrustSettings = -25263
- errSecItemNotFound = -25300
- errSecInvalidTrustSettings = -25262
-
- # Cipher suites. We only pick the ones our default cipher string allows.
- # Source: https://developer.apple.com/documentation/security/1550981-ssl_cipher_suite_values
- TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384 = 0xC02C
- TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384 = 0xC030
- TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256 = 0xC02B
- TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256 = 0xC02F
- TLS_ECDHE_ECDSA_WITH_CHACHA20_POLY1305_SHA256 = 0xCCA9
- TLS_ECDHE_RSA_WITH_CHACHA20_POLY1305_SHA256 = 0xCCA8
- TLS_DHE_RSA_WITH_AES_256_GCM_SHA384 = 0x009F
- TLS_DHE_RSA_WITH_AES_128_GCM_SHA256 = 0x009E
- TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA384 = 0xC024
- TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384 = 0xC028
- TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA = 0xC00A
- TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA = 0xC014
- TLS_DHE_RSA_WITH_AES_256_CBC_SHA256 = 0x006B
- TLS_DHE_RSA_WITH_AES_256_CBC_SHA = 0x0039
- TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256 = 0xC023
- TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256 = 0xC027
- TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA = 0xC009
- TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA = 0xC013
- TLS_DHE_RSA_WITH_AES_128_CBC_SHA256 = 0x0067
- TLS_DHE_RSA_WITH_AES_128_CBC_SHA = 0x0033
- TLS_RSA_WITH_AES_256_GCM_SHA384 = 0x009D
- TLS_RSA_WITH_AES_128_GCM_SHA256 = 0x009C
- TLS_RSA_WITH_AES_256_CBC_SHA256 = 0x003D
- TLS_RSA_WITH_AES_128_CBC_SHA256 = 0x003C
- TLS_RSA_WITH_AES_256_CBC_SHA = 0x0035
- TLS_RSA_WITH_AES_128_CBC_SHA = 0x002F
- TLS_AES_128_GCM_SHA256 = 0x1301
- TLS_AES_256_GCM_SHA384 = 0x1302
- TLS_AES_128_CCM_8_SHA256 = 0x1305
- TLS_AES_128_CCM_SHA256 = 0x1304
diff --git a/spaces/TeamTonic/MultiMed/app.py b/spaces/TeamTonic/MultiMed/app.py
deleted file mode 100644
index 29b1ad119a6d38fc2fca1fa616c0f221e9751208..0000000000000000000000000000000000000000
--- a/spaces/TeamTonic/MultiMed/app.py
+++ /dev/null
@@ -1,490 +0,0 @@
-# Welcome to Team Tonic's MultiMed
-
-from gradio_client import Client
-import os
-import numpy as np
-import base64
-import gradio as gr
-import requests
-import json
-import dotenv
-from scipy.io.wavfile import write
-import PIL
-from openai import OpenAI
-import time
-dotenv.load_dotenv()
-
-seamless_client = Client("facebook/seamless_m4t")
-HuggingFace_Token = os.getenv("HuggingFace_Token")
-
-def check_hallucination(assertion,citation):
- API_URL = "https://api-inference.huggingface.co/models/vectara/hallucination_evaluation_model"
- headers = {"Authorization": f"Bearer {HuggingFace_Token}"}
- payload = {"inputs" : f"{assertion} [SEP] {citation}"}
-
- response = requests.post(API_URL, headers=headers, json=payload,timeout=120)
- output = response.json()
- output = output[0][0]["score"]
-
- return f"**hullicination score:** {output}"
-
-
-
-def process_speech(input_language, audio_input):
- """
- processing sound using seamless_m4t
- """
- if audio_input is None :
- return "no audio or audio did not save yet \nplease try again ! "
- print(f"audio : {audio_input}")
- print(f"audio type : {type(audio_input)}")
- out = seamless_client.predict(
- "S2TT",
- "file",
- None,
- audio_input, #audio_name
- "",
- input_language,# source language
- input_language,# target language
- api_name="/run",
- )
- out = out[1] # get the text
- try :
- return f"{out}"
- except Exception as e :
- return f"{e}"
-
-
-
-
-def process_image(image) :
- img_name = f"{np.random.randint(0, 100)}.jpg"
- PIL.Image.fromarray(image.astype('uint8'), 'RGB').save(img_name)
- image = open(img_name, "rb").read()
- base64_image = base64_image = base64.b64encode(image).decode('utf-8')
- openai_api_key = os.getenv('OPENAI_API_KEY')
- # oai_org = os.getenv('OAI_ORG')
-
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {openai_api_key}"
- }
-
- payload = {
- "model": "gpt-4-vision-preview",
- "messages": [
- {
- "role": "user",
- "content": [
- {
- "type": "text",
- "text": "You are clinical consultant discussion training cases with students at TonicUniversity. Assess and describe the photo in minute detail. Explain why each area or item in the photograph would be inappropriate to describe if required. Pay attention to anatomy, symptoms and remedies. Propose a course of action based on your assessment. Exclude any other commentary:"
- },
- {
- "type": "image_url",
- "image_url": {
- "url": f"data:image/jpeg;base64,{base64_image}"
- }
- }
- ]
- }
- ],
- "max_tokens": 1200
- }
-
- response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
-
- try :
- out = response.json()
- out = out["choices"][0]["message"]["content"]
-
- return out
- except Exception as e :
- return f"{e}"
-
-
-def query_vectara(text):
- user_message = text
-
- # Read authentication parameters from the .env file
- CUSTOMER_ID = os.getenv('CUSTOMER_ID')
- CORPUS_ID = os.getenv('CORPUS_ID')
- API_KEY = os.getenv('API_KEY')
-
- # Define the headers
- api_key_header = {
- "customer-id": CUSTOMER_ID,
- "x-api-key": API_KEY
- }
-
- # Define the request body in the structure provided in the example
- request_body = {
- "query": [
- {
- "query": user_message,
- "queryContext": "",
- "start": 1,
- "numResults": 50,
- "contextConfig": {
- "charsBefore": 0,
- "charsAfter": 0,
- "sentencesBefore": 2,
- "sentencesAfter": 2,
- "startTag": "%START_SNIPPET%",
- "endTag": "%END_SNIPPET%",
- },
- "rerankingConfig": {
- "rerankerId": 272725718,
- "mmrConfig": {
- "diversityBias": 0.35
- }
- },
- "corpusKey": [
- {
- "customerId": CUSTOMER_ID,
- "corpusId": CORPUS_ID,
- "semantics": 0,
- "metadataFilter": "",
- "lexicalInterpolationConfig": {
- "lambda": 0
- },
- "dim": []
- }
- ],
- "summary": [
- {
- "maxSummarizedResults": 5,
- "responseLang": "auto",
- "summarizerPromptName": "vectara-summary-ext-v1.2.0"
- }
- ]
- }
- ]
- }
-
- # Make the API request using Gradio
- response = requests.post(
- "https://api.vectara.io/v1/query",
- json=request_body, # Use json to automatically serialize the request body
- verify=True,
- headers=api_key_header
- )
-
- if response.status_code == 200:
- query_data = response.json()
- if query_data:
- sources_info = []
-
- # Extract the summary.
- summary = query_data['responseSet'][0]['summary'][0]['text']
-
- # Iterate over all response sets
- for response_set in query_data.get('responseSet', []):
- # Extract sources
- # Limit to top 5 sources.
- for source in response_set.get('response', [])[:5]:
- source_metadata = source.get('metadata', [])
- source_info = {}
-
- for metadata in source_metadata:
- metadata_name = metadata.get('name', '')
- metadata_value = metadata.get('value', '')
-
- if metadata_name == 'title':
- source_info['title'] = metadata_value
- elif metadata_name == 'author':
- source_info['author'] = metadata_value
- elif metadata_name == 'pageNumber':
- source_info['page number'] = metadata_value
-
- if source_info:
- sources_info.append(source_info)
-
- result = {"summary": summary, "sources": sources_info}
- return f"{json.dumps(result, indent=2)}"
- else:
- return "No data found in the response."
- else:
- return f"Error: {response.status_code}"
-
-
-def convert_to_markdown(vectara_response_json):
- vectara_response = json.loads(vectara_response_json)
- if vectara_response:
- summary = vectara_response.get('summary', 'No summary available')
- sources_info = vectara_response.get('sources', [])
-
- # Format the summary as Markdown
- markdown_summary = f' {summary}\n\n'
-
- # Format the sources as a numbered list
- markdown_sources = ""
- for i, source_info in enumerate(sources_info):
- author = source_info.get('author', 'Unknown author')
- title = source_info.get('title', 'Unknown title')
- page_number = source_info.get('page number', 'Unknown page number')
- markdown_sources += f"{i+1}. {title} by {author}, Page {page_number}\n"
-
- return f"{markdown_summary}**Sources:**\n{markdown_sources}"
- else:
- return "No data found in the response."
-# Main function to handle the Gradio interface logic
-
-def process_summary_with_openai(summary):
- """
- This function takes a summary text as input and processes it with OpenAI's GPT model.
- """
- try:
- # Ensure that the OpenAI client is properly initialized
- client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
-
- # Create the prompt for OpenAI's completion
- prompt = "You are clinical consultant discussion training cases with students at TonicUniversity. Assess and describe the proper options in minute detail. Propose a course of action based on your assessment. You will recieve a summary assessment in a language, respond ONLY in the original language. Exclude any other commentary:"
-
- # Call the OpenAI API with the prompt and the summary
- completion = client.chat.completions.create(
- model="gpt-4-1106-preview", # Make sure to use the correct model name
- messages=[
- {"role": "system", "content": prompt},
- {"role": "user", "content": summary}
- ]
- )
-
- # Extract the content from the completion
- final_summary = completion.choices[0].message.content
- return final_summary
- except Exception as e:
- return str(e)
-
-
-def process_and_query(text=None):
- try:
- # augment the prompt before feeding it to vectara
- text = "the user asks the following to his health adviser " + text
- # If an image is provided, process it with OpenAI and use the response as the text query for Vectara
- # if image is not None:
- # text = process_image(image)
- # return "**Summary:** "+text
- # if audio is not None:
- # text = process_speech(audio)
- # # augment the prompt before feeding it to vectara
- # text = "the user asks the following to his health adviser " + text
-
-
-
- # Use the text to query Vectara
- vectara_response_json = query_vectara(text)
-
- # Convert the Vectara response to Markdown
- markdown_output = convert_to_markdown(vectara_response_json)
-
- # Process the summary with OpenAI
- final_response = process_summary_with_openai(markdown_output)
-
- # Return the processed summary along with the full output
- return f"**Summary**: {final_response}\n\n**Full output**:\n{markdown_output}"
- except Exception as e:
- return str(e)
-
- completion = client.chat.completions.create(
- model="gpt-3.5-turbo",
- messages=[
- {"role": "system", "content": prompt},
- {"role": "user", "content": markdown_output_final}
- ]
- )
- final_response= completion.choices[0].message.content
- return f"**Summary**: {final_response}\n\n**Full output**:\n{markdown_output}"
- except Exception as e:
- return str(e)
-
-
-# Define the Gradio interface
-# iface = gr.Interface(
-# fn=process_and_query,
-# inputs=[
-# gr.Textbox(label="Input Text"),
-# gr.Image(label="Upload Image"),
-# gr.Audio(label="talk in french",
-# sources=["microphone"]),
-# ],
-# outputs=[gr.Markdown(label="Output Text")],
-# title="👋🏻Welcome to ⚕🗣️😷MultiMed - Access Chat ⚕🗣️😷",
-# description='''
-# ### How To Use ⚕🗣️😷MultiMed⚕:
-# #### 🗣️📝Interact with ⚕🗣️😷MultiMed⚕ in any language using audio or text!
-# #### 🗣️📝 This is an educational and accessible conversational tool to improve wellness and sanitation in support of public health.
-# #### 📚🌟💼 The knowledge base is composed of publicly available medical and health sources in multiple languages. We also used [Kelvalya/MedAware](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset) that we processed and converted to HTML. The quality of the answers depends on the quality of the dataset, so if you want to see some data represented here, do [get in touch](https://discord.gg/GWpVpekp). You can also use 😷MultiMed⚕️ on your own data & in your own way by cloning this space. 🧬🔬🔍 Simply click here:
-# #### Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community on 👻Discord: [Discord](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)"
-# ''',
-# theme='ParityError/Anime',
-# examples=[
-# ["What is the proper treatment for buccal herpes?"],
-# ["Male, 40 presenting with swollen glands and a rash"],
-# ["How does cellular metabolism work TCA cycle"],
-# ["What special care must be provided to children with chicken pox?"],
-# ["When and how often should I wash my hands ?"],
-# ["بکل ہرپس کا صحیح علاج کیا ہے؟"],
-# ["구강 헤르페스의 적절한 치료법은 무엇입니까?"],
-# ["Je, ni matibabu gani sahihi kwa herpes ya buccal?"],
-# ],
-# )
-
-welcome_message = """
-# 👋🏻Welcome to ⚕🗣️😷MultiMed - Access Chat ⚕🗣️😷
-### How To Use ⚕🗣️😷MultiMed⚕:
-#### 🗣️📝Interact with ⚕🗣️😷MultiMed⚕ in any language using audio or text!
-#### 🗣️📝 This is an educational and accessible conversational tool to improve wellness and sanitation in support of public health.
-#### 📚🌟💼 The knowledge base is composed of publicly available medical and health sources in multiple languages. We also used [Kelvalya/MedAware](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset) that we processed and converted to HTML. The quality of the answers depends on the quality of the dataset, so if you want to see some data represented here, do [get in touch](https://discord.gg/GWpVpekp). You can also use 😷MultiMed⚕️ on your own data & in your own way by cloning this space. 🧬🔬🔍 Simply click here:
-#### Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community on 👻Discord: [Discord](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)"
-"""
-
-
-languages = [
- "Afrikaans",
- "Amharic",
- "Modern Standard Arabic",
- "Moroccan Arabic",
- "Egyptian Arabic",
- "Assamese",
- "Asturian",
- "North Azerbaijani",
- "Belarusian",
- "Bengali",
- "Bosnian",
- "Bulgarian",
- "Catalan",
- "Cebuano",
- "Czech",
- "Central Kurdish",
- "Mandarin Chinese",
- "Welsh",
- "Danish",
- "German",
- "Greek",
- "English",
- "Estonian",
- "Basque",
- "Finnish",
- "French",
- "West Central Oromo",
- "Irish",
- "Galician",
- "Gujarati",
- "Hebrew",
- "Hindi",
- "Croatian",
- "Hungarian",
- "Armenian",
- "Igbo",
- "Indonesian",
- "Icelandic",
- "Italian",
- "Javanese",
- "Japanese",
- "Kamba",
- "Kannada",
- "Georgian",
- "Kazakh",
- "Kabuverdianu",
- "Halh Mongolian",
- "Khmer",
- "Kyrgyz",
- "Korean",
- "Lao",
- "Lithuanian",
- "Luxembourgish",
- "Ganda",
- "Luo",
- "Standard Latvian",
- "Maithili",
- "Malayalam",
- "Marathi",
- "Macedonian",
- "Maltese",
- "Meitei",
- "Burmese",
- "Dutch",
- "Norwegian Nynorsk",
- "Norwegian Bokmål",
- "Nepali",
- "Nyanja",
- "Occitan",
- "Odia",
- "Punjabi",
- "Southern Pashto",
- "Western Persian",
- "Polish",
- "Portuguese",
- "Romanian",
- "Russian",
- "Slovak",
- "Slovenian",
- "Shona",
- "Sindhi",
- "Somali",
- "Spanish",
- "Serbian",
- "Swedish",
- "Swahili",
- "Tamil",
- "Telugu",
- "Tajik",
- "Tagalog",
- "Thai",
- "Turkish",
- "Ukrainian",
- "Urdu",
- "Northern Uzbek",
- "Vietnamese",
- "Xhosa",
- "Yoruba",
- "Cantonese",
- "Colloquial Malay",
- "Standard Malay",
- "Zulu"
-]
-
-
-with gr.Blocks(theme='ParityError/Anime') as iface :
- gr.Markdown(welcome_message)
- with gr.Tab("text summarization"):
- text_input = gr.Textbox(label="input text",lines=5)
- text_output = gr.Markdown(label="output text")
- text_button = gr.Button("process text")
- gr.Examples([
- ["What is the proper treatment for buccal herpes?"],
- ["Male, 40 presenting with swollen glands and a rash"],
- ["How does cellular metabolism work TCA cycle"],
- ["What special care must be provided to children with chicken pox?"],
- ["When and how often should I wash my hands?"],
- ["بکل ہرپس کا صحیح علاج کیا ہے؟"],
- ["구강 헤르페스의 적절한 치료법은 무엇입니까?"],
- ["Je, ni matibabu gani sahihi kwa herpes ya buccal?"],
- ],inputs=[text_input])
- with gr.Tab("image identification"):
- image_input = gr.Image(label="upload image")
- image_output = gr.Markdown(label="output text")
- image_button = gr.Button("process image")
- image_button.click(process_image, inputs=image_input, outputs=image_output)
- gr.Examples(["sick person.jpeg"],inputs=[image_input])
- with gr.Tab("speech to text"):
- input_language = gr.Dropdown(languages, label="select the language",value="English",interactive=True)
- audio_input = gr.Audio(label="speak",type="filepath",sources="microphone")
- audio_output = gr.Markdown(label="output text")
- audio_button = gr.Button("process audio")
- audio_button.click(process_speech, inputs=[input_language,audio_input], outputs=audio_output)
- gr.Examples([["English","sample_input.mp3"]],inputs=[input_language,audio_input])
- with gr.Tab("hallucination check"):
- assertion = gr.Textbox(label="assertion")
- citation = gr.Textbox(label="citation text")
- hullucination_output = gr.Markdown(label="output text")
- audio_button = gr.Button("check hallucination")
- gr.Examples([["i am drunk","sarah is pregnant"]],inputs=[assertion,citation])
- text_button.click(process_and_query, inputs=text_input, outputs=text_output)
- audio_button.click(check_hallucination,inputs=[assertion,citation],outputs=hullucination_output)
-
-
-
-
-iface.queue().launch(show_error=True,debug=True)
diff --git a/spaces/Thanarit/GPT-Detection-Demo/app.py b/spaces/Thanarit/GPT-Detection-Demo/app.py
deleted file mode 100644
index b0de7e28071553c6b86ec9a73b9881b4d540c0bc..0000000000000000000000000000000000000000
--- a/spaces/Thanarit/GPT-Detection-Demo/app.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-from ModelDriver import *
-import numpy as np
-
-# Add a title
-st.title('GPT Detection Demo')
-st.write("This is a demo for GPT detection. You can use this demo to test the model. The model is trained on two datasets: OpenGPT and CSAbstract. You can choose the model and dataset in the sidebar.")
-st.write("Reference on how we built Roberta Sentinel: https://arxiv.org/abs/2305.07969")
-
-# Add 4 options for 4 models
-ModelOption = st.sidebar.selectbox(
- 'Which Model do you want to use?',
- ('RobertaSentinel', 'RobertaClassifier'),
-)
-
-DatasetOption = st.sidebar.selectbox(
- 'Which Dataset the model was trained on?',
- ('OpenGPT', 'CSAbstract'),
-)
-
-
-text = st.text_area('Enter text here (max 500 words)', '')
-
-if st.button('Generate'):
- if ModelOption == 'RobertaSentinel':
- if DatasetOption == 'OpenGPT':
- result = RobertaSentinelOpenGPTInference(text)
- st.write("Model: RobertaSentinelOpenGPT")
- elif DatasetOption == 'CSAbstract':
- result = RobertaSentinelCSAbstractInference(text)
- st.write("Model: RobertaSentinelCSAbstract")
-
- elif ModelOption == 'RobertaClassifier':
- if DatasetOption == 'OpenGPT':
- result = RobertaClassifierOpenGPTInference(text)
- st.write("Model: RobertaClassifierOpenGPT")
- elif DatasetOption == 'CSAbstract':
- result = RobertaClassifierCSAbstractInference(text)
- st.write("Model: RobertaClassifierCSAbstract")
-
- Prediction = "Human Written" if not np.argmax(result) else "Machine Generated"
-
- st.write(f"Prediction: {Prediction} ")
- st.write(f"Probabilty:", max(result))
-
-
-
-
-
-
-
-
-
diff --git a/spaces/ThomasSimonini/Unity-MLAgents-Pyramids/index.html b/spaces/ThomasSimonini/Unity-MLAgents-Pyramids/index.html
deleted file mode 100644
index 37f4404c8056925b6523479300dec8d2406d5c96..0000000000000000000000000000000000000000
--- a/spaces/ThomasSimonini/Unity-MLAgents-Pyramids/index.html
+++ /dev/null
@@ -1,92 +0,0 @@
-
-
-
-
-
-
- Unity-MLAgents-Pyramids
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/Tirendaz/Text-Classification/app.py b/spaces/Tirendaz/Text-Classification/app.py
deleted file mode 100644
index 125b46d586913e6a24ccb8585102a076baf95d9d..0000000000000000000000000000000000000000
--- a/spaces/Tirendaz/Text-Classification/app.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-classifier = pipeline("sentiment-analysis", model="Tirendaz/my_distilbert_model")
-
-def text_classification(text):
- result= classifier(text)
- sentiment_label = result[0]['label']
- sentiment_score = result[0]['score']
- formatted_output = f"This sentiment is {sentiment_label} with the probability {sentiment_score*100:.2f}%"
- return formatted_output
-
-examples=["This is wonderful movie!", "The movie was really bad; I didn't like it."]
-
-io = gr.Interface(fn=text_classification,
- inputs= gr.Textbox(lines=2, label="Text", placeholder="Enter title here..."),
- outputs=gr.Textbox(lines=2, label="Text Classification Result"),
- title="Text Classification",
- description="Enter a text and see the text classification result!",
- examples=examples)
-
-io.launch()
-
diff --git a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Vercel.py b/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Vercel.py
deleted file mode 100644
index e5df9cf017e4c1a265f5c9d5e48eb5c10a56e60a..0000000000000000000000000000000000000000
--- a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Vercel.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import os
-import json
-import base64
-import execjs
-import queue
-import threading
-
-from curl_cffi import requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://play.vercel.ai'
-supports_stream = True
-needs_auth = False
-
-models = {
- 'claude-instant-v1': 'anthropic:claude-instant-v1',
- 'claude-v1': 'anthropic:claude-v1',
- 'alpaca-7b': 'replicate:replicate/alpaca-7b',
- 'stablelm-tuned-alpha-7b': 'replicate:stability-ai/stablelm-tuned-alpha-7b',
- 'bloom': 'huggingface:bigscience/bloom',
- 'bloomz': 'huggingface:bigscience/bloomz',
- 'flan-t5-xxl': 'huggingface:google/flan-t5-xxl',
- 'flan-ul2': 'huggingface:google/flan-ul2',
- 'gpt-neox-20b': 'huggingface:EleutherAI/gpt-neox-20b',
- 'oasst-sft-4-pythia-12b-epoch-3.5': 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5',
- 'santacoder': 'huggingface:bigcode/santacoder',
- 'command-medium-nightly': 'cohere:command-medium-nightly',
- 'command-xlarge-nightly': 'cohere:command-xlarge-nightly',
- 'code-cushman-001': 'openai:code-cushman-001',
- 'code-davinci-002': 'openai:code-davinci-002',
- 'gpt-3.5-turbo': 'openai:gpt-3.5-turbo',
- 'text-ada-001': 'openai:text-ada-001',
- 'text-babbage-001': 'openai:text-babbage-001',
- 'text-curie-001': 'openai:text-curie-001',
- 'text-davinci-002': 'openai:text-davinci-002',
- 'text-davinci-003': 'openai:text-davinci-003'
-}
-model = models.keys()
-
-vercel_models = {'anthropic:claude-instant-v1': {'id': 'anthropic:claude-instant-v1', 'provider': 'anthropic', 'providerHumanName': 'Anthropic', 'makerHumanName': 'Anthropic', 'minBillingTier': 'hobby', 'parameters': {'temperature': {'value': 1, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': ['\n\nHuman:'], 'range': []}}, 'name': 'claude-instant-v1'}, 'anthropic:claude-v1': {'id': 'anthropic:claude-v1', 'provider': 'anthropic', 'providerHumanName': 'Anthropic', 'makerHumanName': 'Anthropic', 'minBillingTier': 'hobby', 'parameters': {'temperature': {'value': 1, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': ['\n\nHuman:'], 'range': []}}, 'name': 'claude-v1'}, 'replicate:replicate/alpaca-7b': {'id': 'replicate:replicate/alpaca-7b', 'provider': 'replicate', 'providerHumanName': 'Replicate', 'makerHumanName': 'Stanford', 'parameters': {'temperature': {'value': 0.75, 'range': [0.01, 5]}, 'maximumLength': {'value': 200, 'range': [50, 512]}, 'topP': {'value': 0.95, 'range': [0.01, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'repetitionPenalty': {'value': 1.1765, 'range': [0.01, 5]}, 'stopSequences': {'value': [], 'range': []}}, 'version': '2014ee1247354f2e81c0b3650d71ca715bc1e610189855f134c30ecb841fae21', 'name': 'alpaca-7b'}, 'replicate:stability-ai/stablelm-tuned-alpha-7b': {'id': 'replicate:stability-ai/stablelm-tuned-alpha-7b', 'provider': 'replicate', 'makerHumanName': 'StabilityAI', 'providerHumanName': 'Replicate', 'parameters': {'temperature': {'value': 0.75, 'range': [0.01, 5]}, 'maximumLength': {'value': 200, 'range': [50, 512]}, 'topP': {'value': 0.95, 'range': [0.01, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'repetitionPenalty': {'value': 1.1765, 'range': [0.01, 5]}, 'stopSequences': {'value': [], 'range': []}}, 'version': '4a9a32b4fd86c2d047f1d271fa93972683ec6ef1cf82f402bd021f267330b50b', 'name': 'stablelm-tuned-alpha-7b'}, 'huggingface:bigscience/bloom': {'id': 'huggingface:bigscience/bloom', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigScience', 'instructions': "Do NOT talk to Bloom as an entity, it's not a chatbot but a webpage/blog/article completion model. For the best results: mimic a few words of a webpage similar to the content you want to generate. Start a sentence as if YOU were writing a blog, webpage, math post, coding article and Bloom will generate a coherent follow-up.", 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'bloom'}, 'huggingface:bigscience/bloomz': {'id': 'huggingface:bigscience/bloomz', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigScience', 'instructions': 'We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "Translate to English: Je t\'aime.", the model will most likely answer "I love you.".', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'bloomz'}, 'huggingface:google/flan-t5-xxl': {'id': 'huggingface:google/flan-t5-xxl', 'provider': 'huggingface', 'makerHumanName': 'Google', 'providerHumanName': 'HuggingFace', 'name': 'flan-t5-xxl', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}}, 'huggingface:google/flan-ul2': {'id': 'huggingface:google/flan-ul2', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'Google', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'flan-ul2'}, 'huggingface:EleutherAI/gpt-neox-20b': {'id': 'huggingface:EleutherAI/gpt-neox-20b', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'EleutherAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'gpt-neox-20b'}, 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5': {'id': 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'OpenAssistant', 'parameters': {'maximumLength': {'value': 200, 'range': [50, 1024]}, 'typicalP': {'value': 0.2, 'range': [0.1, 0.99]}, 'repetitionPenalty': {'value': 1, 'range': [0.1, 2]}}, 'name': 'oasst-sft-4-pythia-12b-epoch-3.5'}, 'huggingface:bigcode/santacoder': {
- 'id': 'huggingface:bigcode/santacoder', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigCode', 'instructions': 'The model was trained on GitHub code. As such it is not an instruction model and commands like "Write a function that computes the square root." do not work well. You should phrase commands like they occur in source code such as comments (e.g. # the following function computes the sqrt) or write a function signature and docstring and let the model complete the function body.', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'santacoder'}, 'cohere:command-medium-nightly': {'id': 'cohere:command-medium-nightly', 'provider': 'cohere', 'providerHumanName': 'Cohere', 'makerHumanName': 'Cohere', 'name': 'command-medium-nightly', 'parameters': {'temperature': {'value': 0.9, 'range': [0, 2]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0, 1]}, 'topK': {'value': 0, 'range': [0, 500]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'cohere:command-xlarge-nightly': {'id': 'cohere:command-xlarge-nightly', 'provider': 'cohere', 'providerHumanName': 'Cohere', 'makerHumanName': 'Cohere', 'name': 'command-xlarge-nightly', 'parameters': {'temperature': {'value': 0.9, 'range': [0, 2]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0, 1]}, 'topK': {'value': 0, 'range': [0, 500]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:gpt-4': {'id': 'openai:gpt-4', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'gpt-4', 'minBillingTier': 'pro', 'parameters': {'temperature': {'value': 0.7, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:code-cushman-001': {'id': 'openai:code-cushman-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'code-cushman-001'}, 'openai:code-davinci-002': {'id': 'openai:code-davinci-002', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'code-davinci-002'}, 'openai:gpt-3.5-turbo': {'id': 'openai:gpt-3.5-turbo', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.7, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'gpt-3.5-turbo'}, 'openai:text-ada-001': {'id': 'openai:text-ada-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-ada-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-babbage-001': {'id': 'openai:text-babbage-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-babbage-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-curie-001': {'id': 'openai:text-curie-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-curie-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-davinci-002': {'id': 'openai:text-davinci-002', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-davinci-002', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-davinci-003': {'id': 'openai:text-davinci-003', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-davinci-003', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}}
-
-
-# based on https://github.com/ading2210/vercel-llm-api // modified
-class Client:
- def __init__(self):
- self.session = requests.Session()
- self.headers = {
- 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110 Safari/537.36',
- 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8',
- 'Accept-Encoding': 'gzip, deflate, br',
- 'Accept-Language': 'en-US,en;q=0.5',
- 'Te': 'trailers',
- 'Upgrade-Insecure-Requests': '1'
- }
- self.session.headers.update(self.headers)
-
- def get_token(self):
- b64 = self.session.get('https://sdk.vercel.ai/openai.jpeg').text
- data = json.loads(base64.b64decode(b64))
-
- code = 'const globalThis = {data: `sentinel`}; function token() {return (%s)(%s)}' % (
- data['c'], data['a'])
-
- token_string = json.dumps(separators=(',', ':'),
- obj={'r': execjs.compile(code).call('token'), 't': data['t']})
-
- return base64.b64encode(token_string.encode()).decode()
-
- def get_default_params(self, model_id):
- return {key: param['value'] for key, param in vercel_models[model_id]['parameters'].items()}
-
- def generate(self, model_id: str, prompt: str, params: dict = {}):
- if not ':' in model_id:
- model_id = models[model_id]
-
- defaults = self.get_default_params(model_id)
-
- payload = defaults | params | {
- 'prompt': prompt,
- 'model': model_id,
- }
-
- headers = self.headers | {
- 'Accept-Encoding': 'gzip, deflate, br',
- 'Custom-Encoding': self.get_token(),
- 'Host': 'sdk.vercel.ai',
- 'Origin': 'https://sdk.vercel.ai',
- 'Referrer': 'https://sdk.vercel.ai',
- 'Sec-Fetch-Dest': 'empty',
- 'Sec-Fetch-Mode': 'cors',
- 'Sec-Fetch-Site': 'same-origin',
- }
-
- chunks_queue = queue.Queue()
- error = None
- response = None
-
- def callback(data):
- chunks_queue.put(data.decode())
-
- def request_thread():
- nonlocal response, error
- for _ in range(3):
- try:
- response = self.session.post('https://sdk.vercel.ai/api/generate',
- json=payload, headers=headers, content_callback=callback)
- response.raise_for_status()
-
- except Exception as e:
- if _ == 2:
- error = e
-
- else:
- continue
-
- thread = threading.Thread(target=request_thread, daemon=True)
- thread.start()
-
- text = ''
- index = 0
- while True:
- try:
- chunk = chunks_queue.get(block=True, timeout=0.1)
-
- except queue.Empty:
- if error:
- raise error
-
- elif response:
- break
-
- else:
- continue
-
- text += chunk
- lines = text.split('\n')
-
- if len(lines) - 1 > index:
- new = lines[index:-1]
- for word in new:
- yield json.loads(word)
- index = len(lines) - 1
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
- yield 'Vercel is currently not working.'
- return
-
- conversation = 'This is a conversation between a human and a language model, respond to the last message accordingly, referring to the past history of messages if needed.\n'
-
- for message in messages:
- conversation += '%s: %s\n' % (message['role'], message['content'])
-
- conversation += 'assistant: '
-
- completion = Client().generate(model, conversation)
-
- for token in completion:
- yield token
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/Vijish/Crop-CLIP/README.md b/spaces/Vijish/Crop-CLIP/README.md
deleted file mode 100644
index bdcae9f45efba75036d8cd67008634852f99dcfc..0000000000000000000000000000000000000000
--- a/spaces/Vijish/Crop-CLIP/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Crop CLIP
-emoji: 🦀
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Violette/Protogen_x3.4_Official_Release/README.md b/spaces/Violette/Protogen_x3.4_Official_Release/README.md
deleted file mode 100644
index f956b53e422114f032bad59a721dd2d563081a37..0000000000000000000000000000000000000000
--- a/spaces/Violette/Protogen_x3.4_Official_Release/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Protogen X3.4 Official Release
-emoji: 📈
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/tasks/__init__.py b/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/tasks/__init__.py
deleted file mode 100644
index ab1fb1c8289535cf9397bb9805c0cba3666ad26f..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/tasks/__init__.py
+++ /dev/null
@@ -1,26 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-from minigpt4.common.registry import registry
-from minigpt4.tasks.base_task import BaseTask
-from minigpt4.tasks.image_text_pretrain import ImageTextPretrainTask
-
-
-def setup_task(cfg):
- assert "task" in cfg.run_cfg, "Task name must be provided."
-
- task_name = cfg.run_cfg.task
- task = registry.get_task_class(task_name).setup_task(cfg=cfg)
- assert task is not None, "Task {} not properly registered.".format(task_name)
-
- return task
-
-
-__all__ = [
- "BaseTask",
- "ImageTextPretrainTask",
-]
diff --git a/spaces/Voicelab/vlT5-keywords-generation/README.md b/spaces/Voicelab/vlT5-keywords-generation/README.md
deleted file mode 100644
index bb0ec20c0d1e6457305ec098f8f8437c1d529252..0000000000000000000000000000000000000000
--- a/spaces/Voicelab/vlT5-keywords-generation/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: VlT5 Keywords Generation
-emoji: 🌍
-colorFrom: pink
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/utils.py b/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/utils.py
deleted file mode 100644
index ee4b01ddfbe8173965371b29f770f3e87615fe71..0000000000000000000000000000000000000000
--- a/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/utils.py
+++ /dev/null
@@ -1,225 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-import librosa
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict= {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})" .format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10,2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_audio_to_torch(full_path, target_sampling_rate):
- audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
- return torch.FloatTensor(audio.astype(np.float32))
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/XPMaster/KSA_Weather_Prediction/README.md b/spaces/XPMaster/KSA_Weather_Prediction/README.md
deleted file mode 100644
index eb0dad165265158f4093d00781e64bb2dc852b4f..0000000000000000000000000000000000000000
--- a/spaces/XPMaster/KSA_Weather_Prediction/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: KSA Weather Prediction
-emoji: ⚡
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/metrics.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/metrics.py
deleted file mode 100644
index 46fdddf3de2cf8d987ecb4c7d7cb3503afa995ad..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/metrics.py
+++ /dev/null
@@ -1,361 +0,0 @@
-"Implements various metrics to measure training accuracy"
-from .torch_core import *
-from .callback import *
-from .layers import *
-from .basic_train import LearnerCallback
-
-__all__ = ['error_rate', 'accuracy', 'accuracy_thresh', 'dice', 'exp_rmspe', 'fbeta','FBeta', 'mse', 'mean_squared_error',
- 'mae', 'mean_absolute_error', 'rmse', 'root_mean_squared_error', 'msle', 'mean_squared_logarithmic_error',
- 'explained_variance', 'r2_score', 'top_k_accuracy', 'KappaScore', 'ConfusionMatrix', 'MatthewsCorreff',
- 'Precision', 'Recall', 'R2Score', 'ExplainedVariance', 'ExpRMSPE', 'RMSE', 'Perplexity', 'AUROC', 'auc_roc_score',
- 'roc_curve', 'MultiLabelFbeta', 'foreground_acc']
-
-def fbeta(y_pred:Tensor, y_true:Tensor, thresh:float=0.2, beta:float=2, eps:float=1e-9, sigmoid:bool=True)->Rank0Tensor:
- "Computes the f_beta between `preds` and `targets`"
- beta2 = beta ** 2
- if sigmoid: y_pred = y_pred.sigmoid()
- y_pred = (y_pred>thresh).float()
- y_true = y_true.float()
- TP = (y_pred*y_true).sum(dim=1)
- prec = TP/(y_pred.sum(dim=1)+eps)
- rec = TP/(y_true.sum(dim=1)+eps)
- res = (prec*rec)/(prec*beta2+rec+eps)*(1+beta2)
- return res.mean()
-
-def accuracy(input:Tensor, targs:Tensor)->Rank0Tensor:
- "Computes accuracy with `targs` when `input` is bs * n_classes."
- n = targs.shape[0]
- input = input.argmax(dim=-1).view(n,-1)
- targs = targs.view(n,-1)
- return (input==targs).float().mean()
-
-def accuracy_thresh(y_pred:Tensor, y_true:Tensor, thresh:float=0.5, sigmoid:bool=True)->Rank0Tensor:
- "Computes accuracy when `y_pred` and `y_true` are the same size."
- if sigmoid: y_pred = y_pred.sigmoid()
- return ((y_pred>thresh)==y_true.byte()).float().mean()
-
-def top_k_accuracy(input:Tensor, targs:Tensor, k:int=5)->Rank0Tensor:
- "Computes the Top-k accuracy (target is in the top k predictions)."
- input = input.topk(k=k, dim=-1)[1]
- targs = targs.unsqueeze(dim=-1).expand_as(input)
- return (input == targs).max(dim=-1)[0].float().mean()
-
-def foreground_acc(input, target, void_code):
- "Computes non-background accuracy, e.g. camvid for multiclass segmentation"
- target = target.squeeze(1)
- mask = target != void_code
- return (input.argmax(dim=1)[mask]==target[mask]).float().mean()
-
-def error_rate(input:Tensor, targs:Tensor)->Rank0Tensor:
- "1 - `accuracy`"
- return 1 - accuracy(input, targs)
-
-def dice(input:Tensor, targs:Tensor, iou:bool=False, eps:float=1e-8)->Rank0Tensor:
- "Dice coefficient metric for binary target. If iou=True, returns iou metric, classic for segmentation problems."
- n = targs.shape[0]
- input = input.argmax(dim=1).view(n,-1)
- targs = targs.view(n,-1)
- intersect = (input * targs).sum().float()
- union = (input+targs).sum().float()
- if not iou: return (2. * intersect / union if union > 0 else union.new([1.]).squeeze())
- else: return (intersect / (union-intersect+eps) if union > 0 else union.new([1.]).squeeze())
-
-def psnr(input:Tensor, targs:Tensor)->Rank0Tensor:
- return 10 * (1. / mean_squared_error(input, targs)).log10()
-
-def exp_rmspe(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Exp RMSE between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- pred, targ = torch.exp(pred), torch.exp(targ)
- pct_var = (targ - pred)/targ
- return torch.sqrt((pct_var**2).mean())
-
-def mean_absolute_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean absolute error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return torch.abs(targ - pred).mean()
-
-def mean_squared_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean squared error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return F.mse_loss(pred, targ)
-
-def root_mean_squared_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Root mean squared error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return torch.sqrt(F.mse_loss(pred, targ))
-
-def mean_squared_logarithmic_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean squared logarithmic error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return F.mse_loss(torch.log(1 + pred), torch.log(1 + targ))
-
-def explained_variance(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Explained variance between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- var_pct = torch.var(targ - pred) / torch.var(targ)
- return 1 - var_pct
-
-def r2_score(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "R2 score (coefficient of determination) between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- u = torch.sum((targ - pred) ** 2)
- d = torch.sum((targ - targ.mean()) ** 2)
- return 1 - u / d
-
-class RegMetrics(Callback):
- "Stores predictions and targets to perform calculations on epoch end."
- def on_epoch_begin(self, **kwargs):
- self.targs, self.preds = Tensor([]), Tensor([])
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- assert last_output.numel() == last_target.numel(), "Expected same numbers of elements in pred & targ"
- self.preds = torch.cat((self.preds, last_output.cpu()))
- self.targs = torch.cat((self.targs, last_target.cpu()))
-
-class R2Score(RegMetrics):
- "Computes the R2 score (coefficient of determination)."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, r2_score(self.preds, self.targs))
-
-class ExplainedVariance(RegMetrics):
- "Computes the explained variance."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, explained_variance(self.preds, self.targs))
-
-class RMSE(RegMetrics):
- "Computes the root mean squared error."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, root_mean_squared_error(self.preds, self.targs))
-
-class ExpRMSPE(RegMetrics):
- "Computes the exponential of the root mean square error."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, exp_rmspe(self.preds, self.targs))
-
-# Aliases
-mse = mean_squared_error
-mae = mean_absolute_error
-msle = mean_squared_logarithmic_error
-rmse = root_mean_squared_error
-
-class ConfusionMatrix(Callback):
- "Computes the confusion matrix."
-
- def on_train_begin(self, **kwargs):
- self.n_classes = 0
-
- def on_epoch_begin(self, **kwargs):
- self.cm = None
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- preds = last_output.argmax(-1).view(-1).cpu()
- targs = last_target.cpu()
- if self.n_classes == 0:
- self.n_classes = last_output.shape[-1]
- self.x = torch.arange(0, self.n_classes)
- cm = ((preds==self.x[:, None]) & (targs==self.x[:, None, None])).sum(dim=2, dtype=torch.float32)
- if self.cm is None: self.cm = cm
- else: self.cm += cm
-
- def on_epoch_end(self, **kwargs):
- self.metric = self.cm
-
-@dataclass
-class CMScores(ConfusionMatrix):
- "Base class for metrics which rely on the calculation of the precision and/or recall score."
- average:Optional[str]="binary" # `binary`, `micro`, `macro`, `weigthed` or None
- pos_label:int=1 # 0 or 1
- eps:float=1e-9
-
- def _recall(self):
- rec = torch.diag(self.cm) / self.cm.sum(dim=1)
- if self.average is None: return rec
- else:
- if self.average == "micro": weights = self._weights(avg="weighted")
- else: weights = self._weights(avg=self.average)
- return (rec * weights).sum()
-
- def _precision(self):
- prec = torch.diag(self.cm) / self.cm.sum(dim=0)
- if self.average is None: return prec
- else:
- weights = self._weights(avg=self.average)
- return (prec * weights).sum()
-
- def _weights(self, avg:str):
- if self.n_classes != 2 and avg == "binary":
- avg = self.average = "macro"
- warn("average=`binary` was selected for a non binary case. Value for average has now been set to `macro` instead.")
- if avg == "binary":
- if self.pos_label not in (0, 1):
- self.pos_label = 1
- warn("Invalid value for pos_label. It has now been set to 1.")
- if self.pos_label == 1: return Tensor([0,1])
- else: return Tensor([1,0])
- elif avg == "micro": return self.cm.sum(dim=0) / self.cm.sum()
- elif avg == "macro": return torch.ones((self.n_classes,)) / self.n_classes
- elif avg == "weighted": return self.cm.sum(dim=1) / self.cm.sum()
-
-
-class Recall(CMScores):
- "Computes the Recall."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, self._recall())
-
-class Precision(CMScores):
- "Computes the Precision."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, self._precision())
-
-@dataclass
-class FBeta(CMScores):
- "Computes the F`beta` score."
- beta:float=2
-
- def on_train_begin(self, **kwargs):
- self.n_classes = 0
- self.beta2 = self.beta ** 2
- self.avg = self.average
- if self.average != "micro": self.average = None
-
- def on_epoch_end(self, last_metrics, **kwargs):
- prec = self._precision()
- rec = self._recall()
- metric = (1 + self.beta2) * prec * rec / (prec * self.beta2 + rec + self.eps)
- metric[metric != metric] = 0 # removing potential "nan"s
- if self.avg: metric = (self._weights(avg=self.avg) * metric).sum()
- return add_metrics(last_metrics, metric)
-
- def on_train_end(self, **kwargs): self.average = self.avg
-
-@dataclass
-class KappaScore(ConfusionMatrix):
- "Computes the rate of agreement (Cohens Kappa)."
- weights:Optional[str]=None # None, `linear`, or `quadratic`
-
- def on_epoch_end(self, last_metrics, **kwargs):
- sum0 = self.cm.sum(dim=0)
- sum1 = self.cm.sum(dim=1)
- expected = torch.einsum('i,j->ij', (sum0, sum1)) / sum0.sum()
- if self.weights is None:
- w = torch.ones((self.n_classes, self.n_classes))
- w[self.x, self.x] = 0
- elif self.weights == "linear" or self.weights == "quadratic":
- w = torch.zeros((self.n_classes, self.n_classes))
- w += torch.arange(self.n_classes, dtype=torch.float)
- w = torch.abs(w - torch.t(w)) if self.weights == "linear" else (w - torch.t(w)) ** 2
- else: raise ValueError('Unknown weights. Expected None, "linear", or "quadratic".')
- k = torch.sum(w * self.cm) / torch.sum(w * expected)
- return add_metrics(last_metrics, 1-k)
-
-@dataclass
-class MatthewsCorreff(ConfusionMatrix):
- "Computes the Matthews correlation coefficient."
- def on_epoch_end(self, last_metrics, **kwargs):
- t_sum = self.cm.sum(dim=1)
- p_sum = self.cm.sum(dim=0)
- n_correct = torch.trace(self.cm)
- n_samples = p_sum.sum()
- cov_ytyp = n_correct * n_samples - torch.dot(t_sum, p_sum)
- cov_ypyp = n_samples ** 2 - torch.dot(p_sum, p_sum)
- cov_ytyt = n_samples ** 2 - torch.dot(t_sum, t_sum)
- return add_metrics(last_metrics, cov_ytyp / torch.sqrt(cov_ytyt * cov_ypyp))
-
-class Perplexity(Callback):
- "Perplexity metric for language models."
- def on_epoch_begin(self, **kwargs): self.loss,self.len = 0.,0
-
- def on_batch_end(self, last_output, last_target, **kwargs):
- self.loss += last_target.size(1) * CrossEntropyFlat()(last_output, last_target)
- self.len += last_target.size(1)
-
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, torch.exp(self.loss / self.len))
-
-def auc_roc_score(input:Tensor, targ:Tensor):
- "Computes the area under the receiver operator characteristic (ROC) curve using the trapezoid method. Restricted binary classification tasks."
- fpr, tpr = roc_curve(input, targ)
- d = fpr[1:] - fpr[:-1]
- sl1, sl2 = [slice(None)], [slice(None)]
- sl1[-1], sl2[-1] = slice(1, None), slice(None, -1)
- return (d * (tpr[tuple(sl1)] + tpr[tuple(sl2)]) / 2.).sum(-1)
-
-def roc_curve(input:Tensor, targ:Tensor):
- "Computes the receiver operator characteristic (ROC) curve by determining the true positive ratio (TPR) and false positive ratio (FPR) for various classification thresholds. Restricted binary classification tasks."
- targ = (targ == 1)
- desc_score_indices = torch.flip(input.argsort(-1), [-1])
- input = input[desc_score_indices]
- targ = targ[desc_score_indices]
- d = input[1:] - input[:-1]
- distinct_value_indices = torch.nonzero(d).transpose(0,1)[0]
- threshold_idxs = torch.cat((distinct_value_indices, LongTensor([len(targ) - 1]).to(targ.device)))
- tps = torch.cumsum(targ * 1, dim=-1)[threshold_idxs]
- fps = (1 + threshold_idxs - tps)
- if tps[0] != 0 or fps[0] != 0:
- fps = torch.cat((LongTensor([0]), fps))
- tps = torch.cat((LongTensor([0]), tps))
- fpr, tpr = fps.float() / fps[-1], tps.float() / tps[-1]
- return fpr, tpr
-
-@dataclass
-class AUROC(Callback):
- "Computes the area under the curve (AUC) score based on the receiver operator characteristic (ROC) curve. Restricted to binary classification tasks."
- def on_epoch_begin(self, **kwargs):
- self.targs, self.preds = LongTensor([]), Tensor([])
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- last_output = F.softmax(last_output, dim=1)[:,-1]
- self.preds = torch.cat((self.preds, last_output.cpu()))
- self.targs = torch.cat((self.targs, last_target.cpu().long()))
-
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, auc_roc_score(self.preds, self.targs))
-
-class MultiLabelFbeta(LearnerCallback):
- "Computes the fbeta score for multilabel classification"
- # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html
- _order = -20
- def __init__(self, learn, beta=2, eps=1e-15, thresh=0.3, sigmoid=True, average="micro"):
- super().__init__(learn)
- self.eps, self.thresh, self.sigmoid, self.average, self.beta2 = \
- eps, thresh, sigmoid, average, beta**2
-
- def on_train_begin(self, **kwargs):
- self.c = self.learn.data.c
- if self.average != "none": self.learn.recorder.add_metric_names([f'{self.average}_fbeta'])
- else: self.learn.recorder.add_metric_names([f"fbeta_{c}" for c in self.learn.data.classes])
-
- def on_epoch_begin(self, **kwargs):
- dvc = self.learn.data.device
- self.tp = torch.zeros(self.c).to(dvc)
- self.total_pred = torch.zeros(self.c).to(dvc)
- self.total_targ = torch.zeros(self.c).to(dvc)
-
- def on_batch_end(self, last_output, last_target, **kwargs):
- pred, targ = (last_output.sigmoid() if self.sigmoid else last_output) > self.thresh, last_target.byte()
- m = pred*targ
- self.tp += m.sum(0).float()
- self.total_pred += pred.sum(0).float()
- self.total_targ += targ.sum(0).float()
-
- def fbeta_score(self, precision, recall):
- return (1 + self.beta2)*(precision*recall)/((self.beta2*precision + recall) + self.eps)
-
- def on_epoch_end(self, last_metrics, **kwargs):
- self.total_pred += self.eps
- self.total_targ += self.eps
- if self.average == "micro":
- precision, recall = self.tp.sum() / self.total_pred.sum(), self.tp.sum() / self.total_targ.sum()
- res = self.fbeta_score(precision, recall)
- elif self.average == "macro":
- res = self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ)).mean()
- elif self.average == "weighted":
- scores = self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ))
- res = (scores*self.total_targ).sum() / self.total_targ.sum()
- elif self.average == "none":
- res = listify(self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ)))
- else:
- raise Exception("Choose one of the average types: [micro, macro, weighted, none]")
-
- return add_metrics(last_metrics, res)
diff --git a/spaces/XzJosh/Taffy-Bert-VITS2/transforms.py b/spaces/XzJosh/Taffy-Bert-VITS2/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Taffy-Bert-VITS2/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
-
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {
- 'tails': tails,
- 'tail_bound': tail_bound
- }
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(
- inputs[..., None] >= bin_locations,
- dim=-1
- ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails='linear',
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == 'linear':
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError('{} tails are not implemented.'.format(tails))
-
- outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative
- )
-
- return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0., right=1., bottom=0., top=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError('Input to a transform is not within its domain')
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError('Minimal bin width too large for the number of bins')
- if min_bin_height * num_bins > 1.0:
- raise ValueError('Minimal bin height too large for the number of bins')
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (((inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta)
- + input_heights * (input_delta - input_derivatives)))
- b = (input_heights * input_derivatives
- - (inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta))
- c = - input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (input_delta * theta.pow(2)
- + input_derivatives * theta_one_minus_theta)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/XzJosh/Wenjing-Bert-VITS2/server.py b/spaces/XzJosh/Wenjing-Bert-VITS2/server.py
deleted file mode 100644
index c736ca4f95fec853950eef6654ef79856beffc0a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Wenjing-Bert-VITS2/server.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from flask import Flask, request, Response
-from io import BytesIO
-import torch
-from av import open as avopen
-
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-from scipy.io import wavfile
-
-# Flask Init
-app = Flask(__name__)
-app.config['JSON_AS_ASCII'] = False
-def get_text(text, language_str, hps):
- norm_text, phone, tone, word2ph = clean_text(text, language_str)
- print([f"{p}{t}" for p, t in zip(phone, tone)])
- phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
- if hps.data.add_blank:
- phone = commons.intersperse(phone, 0)
- tone = commons.intersperse(tone, 0)
- language = commons.intersperse(language, 0)
- for i in range(len(word2ph)):
- word2ph[i] = word2ph[i] * 2
- word2ph[0] += 1
- bert = get_bert(norm_text, word2ph, language_str)
-
- assert bert.shape[-1] == len(phone)
-
- phone = torch.LongTensor(phone)
- tone = torch.LongTensor(tone)
- language = torch.LongTensor(language)
-
- return bert, phone, tone, language
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w,length_scale,sid):
- bert, phones, tones, lang_ids = get_text(text,"ZH", hps,)
- with torch.no_grad():
- x_tst=phones.to(dev).unsqueeze(0)
- tones=tones.to(dev).unsqueeze(0)
- lang_ids=lang_ids.to(dev).unsqueeze(0)
- bert = bert.to(dev).unsqueeze(0)
- x_tst_lengths = torch.LongTensor([phones.size(0)]).to(dev)
- speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(dev)
- audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids,bert, sdp_ratio=sdp_ratio
- , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
- return audio
-
-def replace_punctuation(text, i=2):
- punctuation = "，。？！"
- for char in punctuation:
- text = text.replace(char, char * i)
- return text
-
-def wav2(i, o, format):
- inp = avopen(i, 'rb')
- out = avopen(o, 'wb', format=format)
- if format == "ogg": format = "libvorbis"
-
- ostream = out.add_stream(format)
-
- for frame in inp.decode(audio=0):
- for p in ostream.encode(frame): out.mux(p)
-
- for p in ostream.encode(None): out.mux(p)
-
- out.close()
- inp.close()
-
-# Load Generator
-hps = utils.get_hparams_from_file("./configs/config.json")
-
-dev='cuda'
-net_g = SynthesizerTrn(
- len(symbols),
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- n_speakers=hps.data.n_speakers,
- **hps.model).to(dev)
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("logs/G_649000.pth", net_g, None,skip_optimizer=True)
-
-@app.route("/",methods=['GET','POST'])
-def main():
- if request.method == 'GET':
- try:
- speaker = request.args.get('speaker')
- text = request.args.get('text').replace("/n","")
- sdp_ratio = float(request.args.get("sdp_ratio", 0.2))
- noise = float(request.args.get("noise", 0.5))
- noisew = float(request.args.get("noisew", 0.6))
- length = float(request.args.get("length", 1.2))
- if length >= 2:
- return "Too big length"
- if len(text) >=200:
- return "Too long text"
- fmt = request.args.get("format", "wav")
- if None in (speaker, text):
- return "Missing Parameter"
- if fmt not in ("mp3", "wav", "ogg"):
- return "Invalid Format"
- except:
- return "Invalid Parameter"
-
- with torch.no_grad():
- audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise, noise_scale_w=noisew, length_scale=length, sid=speaker)
-
- with BytesIO() as wav:
- wavfile.write(wav, hps.data.sampling_rate, audio)
- torch.cuda.empty_cache()
- if fmt == "wav":
- return Response(wav.getvalue(), mimetype="audio/wav")
- wav.seek(0, 0)
- with BytesIO() as ofp:
- wav2(wav, ofp, fmt)
- return Response(
- ofp.getvalue(),
- mimetype="audio/mpeg" if fmt == "mp3" else "audio/ogg"
- )
diff --git a/spaces/YlcldKlns/bing/src/components/settings.tsx b/spaces/YlcldKlns/bing/src/components/settings.tsx
deleted file mode 100644
index 80b8a2d3b252b875f5b6f7dfc2f6e3ad9cdfb22a..0000000000000000000000000000000000000000
--- a/spaces/YlcldKlns/bing/src/components/settings.tsx
+++ /dev/null
@@ -1,157 +0,0 @@
-import { useEffect, useState } from 'react'
-import { useAtom } from 'jotai'
-import { Switch } from '@headlessui/react'
-import { toast } from 'react-hot-toast'
-import { hashAtom, voiceAtom } from '@/state'
-import {
- Dialog,
- DialogContent,
- DialogDescription,
- DialogFooter,
- DialogHeader,
- DialogTitle
-} from '@/components/ui/dialog'
-import { Button } from './ui/button'
-import { Input } from './ui/input'
-import { ChunkKeys, parseCookies, extraCurlFromCookie, encodeHeadersToCookie, getCookie, setCookie } from '@/lib/utils'
-import { ExternalLink } from './external-link'
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-
-
-export function Settings() {
- const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
- const [loc, setLoc] = useAtom(hashAtom)
- const [curlValue, setCurlValue] = useState(extraCurlFromCookie(parseCookies(document.cookie, ChunkKeys)))
- const [imageOnly, setImageOnly] = useState(getCookie('IMAGE_ONLY') !== '0')
- const [enableTTS, setEnableTTS] = useAtom(voiceAtom)
-
- useEffect(() => {
- if (isCopied) {
- toast.success('复制成功')
- }
- }, [isCopied])
-
- if (loc === 'settings') {
- return (
-
- )
- } else if (loc === 'voice') {
- return (
-
- )
- }
- return null
-}
diff --git a/spaces/Yudha515/Rvc-Models/audiocraft/models/lm.py b/spaces/Yudha515/Rvc-Models/audiocraft/models/lm.py
deleted file mode 100644
index c8aad8f06797eef3293605056e1de14d07c56c2a..0000000000000000000000000000000000000000
--- a/spaces/Yudha515/Rvc-Models/audiocraft/models/lm.py
+++ /dev/null
@@ -1,527 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from dataclasses import dataclass
-from functools import partial
-import logging
-import math
-import typing as tp
-
-import torch
-from torch import nn
-
-from ..utils import utils
-from ..modules.streaming import StreamingModule, State
-from ..modules.transformer import StreamingTransformer, create_norm_fn
-from ..modules.conditioners import (
- ConditionFuser,
- ClassifierFreeGuidanceDropout,
- AttributeDropout,
- ConditioningProvider,
- ConditioningAttributes,
- ConditionType,
-)
-from ..modules.codebooks_patterns import CodebooksPatternProvider
-from ..modules.activations import get_activation_fn
-
-
-logger = logging.getLogger(__name__)
-ConditionTensors = tp.Dict[str, ConditionType]
-CFGConditions = tp.Union[ConditionTensors, tp.Tuple[ConditionTensors, ConditionTensors]]
-
-
-def get_init_fn(method: str, input_dim: int, init_depth: tp.Optional[int] = None):
- """LM layer initialization.
- Inspired from xlformers: https://github.com/fairinternal/xlformers
-
- Args:
- method (str): Method name for init function. Valid options are:
- 'gaussian', 'uniform'.
- input_dim (int): Input dimension of the initialized module.
- init_depth (Optional[int]): Optional init depth value used to rescale
- the standard deviation if defined.
- """
- # Compute std
- std = 1 / math.sqrt(input_dim)
- # Rescale with depth
- if init_depth is not None:
- std = std / math.sqrt(2 * init_depth)
-
- if method == 'gaussian':
- return partial(
- torch.nn.init.trunc_normal_, mean=0.0, std=std, a=-3 * std, b=3 * std
- )
- elif method == 'uniform':
- bound = math.sqrt(3) * std # ensure the standard deviation is `std`
- return partial(torch.nn.init.uniform_, a=-bound, b=bound)
- else:
- raise ValueError("Unsupported layer initialization method")
-
-
-def init_layer(m: nn.Module,
- method: str,
- init_depth: tp.Optional[int] = None,
- zero_bias_init: bool = False):
- """Wrapper around ``get_init_fn`` for proper initialization of LM modules.
-
- Args:
- m (nn.Module): Module to initialize.
- method (str): Method name for the init function.
- init_depth (Optional[int]): Optional init depth value used to rescale
- the standard deviation if defined.
- zero_bias_init (bool): Whether to initialize the bias to 0 or not.
- """
- if isinstance(m, nn.Linear):
- init_fn = get_init_fn(method, m.in_features, init_depth=init_depth)
- if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
- weight = m.weight.float()
- init_fn(weight)
- m.weight.data[:] = weight.half()
- else:
- init_fn(m.weight)
- if zero_bias_init and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.Embedding):
- init_fn = get_init_fn(method, m.embedding_dim, init_depth=None)
- if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
- weight = m.weight.float()
- init_fn(weight)
- m.weight.data[:] = weight.half()
- else:
- init_fn(m.weight)
-
-
-class ScaledEmbedding(nn.Embedding):
- """Boost learning rate for embeddings (with `scale`).
- """
- def __init__(self, *args, lr=None, **kwargs):
- super().__init__(*args, **kwargs)
- self.lr = lr
-
- def make_optim_group(self):
- group = {"params": list(self.parameters())}
- if self.lr is not None:
- group["lr"] = self.lr
- return group
-
-
-@dataclass
-class LMOutput:
- # The logits are already re-aligned with the input codes
- # hence no extra shift is required, e.g. when computing CE
- logits: torch.Tensor # [B, K, T, card]
- mask: torch.Tensor # [B, K, T]
-
-
-class LMModel(StreamingModule):
- """Transformer-based language model on multiple streams of codes.
-
- Args:
- pattern_provider (CodebooksPatternProvider): Pattern provider for codebook interleaving.
- condition_provider (MusicConditioningProvider): Conditioning provider from metadata.
- fuser (ConditionFuser): Fuser handling the fusing of conditions with language model input.
- n_q (int): Number of parallel streams to model.
- card (int): Cardinality, vocabulary size.
- dim (int): Dimension of the transformer encoder.
- num_heads (int): Number of heads for the transformer encoder.
- hidden_scale (int): Scale for hidden feed forward dimension of the transformer encoder.
- norm (str): Normalization method.
- norm_first (bool): Use pre-norm instead of post-norm.
- emb_lr (Optional[float]): Embedding-specific learning rate.
- bias_proj (bool): Use bias for output projections.
- weight_init (Optional[str]): Method for weight initialization.
- depthwise_init (Optional[str]): Method for depthwise weight initialization.
- zero_bias_init (bool): If true and bias in Linears, initialize bias to zeros.
- cfg_dropout (float): Classifier-free guidance dropout.
- cfg_coef (float): Classifier-free guidance coefficient.
- attribute_dropout (dict): Attribute dropout probabilities.
- two_step_cfg (bool): Whether to run classifier free-guidance with 2 distinct steps.
- **kwargs: Additional parameters for the transformer encoder.
- """
- def __init__(self, pattern_provider: CodebooksPatternProvider, condition_provider: ConditioningProvider,
- fuser: ConditionFuser, n_q: int = 8, card: int = 1024, dim: int = 128, num_heads: int = 8,
- hidden_scale: int = 4, norm: str = 'layer_norm', norm_first: bool = False,
- emb_lr: tp.Optional[float] = None, bias_proj: bool = True,
- weight_init: tp.Optional[str] = None, depthwise_init: tp.Optional[str] = None,
- zero_bias_init: bool = False, cfg_dropout: float = 0, cfg_coef: float = 1.0,
- attribute_dropout: tp.Dict[str, tp.Dict[str, float]] = {}, two_step_cfg: bool = False,
- **kwargs):
- super().__init__()
- self.cfg_coef = cfg_coef
- self.cfg_dropout = ClassifierFreeGuidanceDropout(p=cfg_dropout)
- self.att_dropout = AttributeDropout(p=attribute_dropout)
- self.condition_provider = condition_provider
- self.fuser = fuser
- self.card = card
- embed_dim = self.card + 1
- self.n_q = n_q
- self.dim = dim
- self.pattern_provider = pattern_provider
- self.two_step_cfg = two_step_cfg
- self.emb = nn.ModuleList([ScaledEmbedding(embed_dim, dim, lr=emb_lr) for _ in range(n_q)])
- if 'activation' in kwargs:
- kwargs['activation'] = get_activation_fn(kwargs['activation'])
- self.transformer = StreamingTransformer(
- d_model=dim, num_heads=num_heads, dim_feedforward=int(hidden_scale * dim),
- norm=norm, norm_first=norm_first, **kwargs)
- self.out_norm: tp.Optional[nn.Module] = None
- if norm_first:
- self.out_norm = create_norm_fn(norm, dim)
- self.linears = nn.ModuleList([nn.Linear(dim, self.card, bias=bias_proj) for _ in range(n_q)])
- self._init_weights(weight_init, depthwise_init, zero_bias_init)
- self._fsdp: tp.Optional[nn.Module]
- self.__dict__['_fsdp'] = None
-
- def _init_weights(self, weight_init: tp.Optional[str], depthwise_init: tp.Optional[str], zero_bias_init: bool):
- """Initialization of the transformer module weights.
-
- Args:
- weight_init (Optional[str]): Weight initialization strategy. See ``get_init_fn`` for valid options.
- depthwise_init (Optional[str]): Depwthwise initialization strategy. The following options are valid:
- 'current' where the depth corresponds to the current layer index or 'global' where the total number
- of layer is used as depth. If not set, no depthwise initialization strategy is used.
- zero_bias_init (bool): Whether to initalize bias to zero or not.
- """
- assert depthwise_init is None or depthwise_init in ['current', 'global']
- assert depthwise_init is None or weight_init is not None, \
- "If 'depthwise_init' is defined, a 'weight_init' method should be provided."
- assert not zero_bias_init or weight_init is not None, \
- "If 'zero_bias_init', a 'weight_init' method should be provided"
-
- if weight_init is None:
- return
-
- for emb_layer in self.emb:
- init_layer(emb_layer, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
- for layer_idx, tr_layer in enumerate(self.transformer.layers):
- depth = None
- if depthwise_init == 'current':
- depth = layer_idx + 1
- elif depthwise_init == 'global':
- depth = len(self.transformer.layers)
- init_fn = partial(init_layer, method=weight_init, init_depth=depth, zero_bias_init=zero_bias_init)
- tr_layer.apply(init_fn)
-
- for linear in self.linears:
- init_layer(linear, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
- @property
- def special_token_id(self) -> int:
- return self.card
-
- @property
- def num_codebooks(self) -> int:
- return self.n_q
-
- def forward(self, sequence: torch.Tensor,
- conditions: tp.List[ConditioningAttributes],
- condition_tensors: tp.Optional[ConditionTensors] = None) -> torch.Tensor:
- """Apply language model on sequence and conditions.
- Given a tensor of sequence of shape [B, K, S] with K the number of codebooks and
- S the sequence steps, return the logits with shape [B, card, K, S].
-
- Args:
- indices (torch.Tensor): indices of the codes to model.
- conditions (list[ConditioningAttributes]): conditionings to use when modeling
- the given codes. Note that when evaluating multiple time with the same conditioning
- you should pre-compute those and pass them as `condition_tensors`.
- condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning
- tensors, see `conditions`.
- Returns:
- torch.Tensor: Logits.
- """
- B, K, S = sequence.shape
- assert K == self.num_codebooks, 'Sequence shape must match the specified number of codebooks'
- input_ = sum([self.emb[k](sequence[:, k]) for k in range(K)])
- if condition_tensors is None:
- assert not self._is_streaming, "Conditions tensors should be precomputed when streaming."
- # apply dropout modules
- conditions = self.cfg_dropout(conditions)
- conditions = self.att_dropout(conditions)
- tokenized = self.condition_provider.tokenize(conditions)
- # encode conditions and fuse, both have a streaming cache to not recompute when generating.
- condition_tensors = self.condition_provider(tokenized)
- else:
- assert not conditions, "Shouldn't pass both conditions and condition_tensors."
-
- input_, cross_attention_input = self.fuser(input_, condition_tensors)
-
- out = self.transformer(input_, cross_attention_src=cross_attention_input)
- if self.out_norm:
- out = self.out_norm(out)
- logits = torch.stack([self.linears[k](out) for k in range(K)], dim=1) # [B, K, S, card]
-
- # remove the prefix from the model outputs
- if len(self.fuser.fuse2cond['prepend']) > 0:
- logits = logits[:, :, -S:]
-
- return logits # [B, K, S, card]
-
- def compute_predictions(
- self, codes: torch.Tensor,
- conditions: tp.List[ConditioningAttributes],
- condition_tensors: tp.Optional[ConditionTensors] = None) -> LMOutput:
- """Given an input tensor of codes [B, K, T] and list of conditions, runs the model
- forward using the specified codes interleaving pattern.
-
- Args:
- codes (torch.Tensor): Input codes of shape [B, K, T] with B the batch size,
- K the number of codebooks and T the number of timesteps.
- conditions (list[ConditioningAttributes]): conditionings to use when modeling
- the given codes. Note that when evaluating multiple time with the same conditioning
- you should pre-compute those and pass them as `condition_tensors`.
- condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning
- tensors, see `conditions`.
- Returns:
- LMOutput: Language model outputs
- logits (torch.Tensor) of shape [B, K, T, card] corresponding to the provided codes,
- i.e. the first item corresponds to logits to predict the first code, meaning that
- no additional shifting of codes and logits is required.
- mask (torch.Tensor) of shape [B, K, T], mask over valid and invalid positions.
- Given the specified interleaving strategies, parts of the logits and codes should
- not be considered as valid predictions because of invalid context.
- """
- B, K, T = codes.shape
- codes = codes.contiguous()
- # map codes [B, K, T] into pattern sequence [B, K, S] using special_token_id for masked tokens
- pattern = self.pattern_provider.get_pattern(T)
- sequence_codes, sequence_indexes, sequence_mask = pattern.build_pattern_sequence(
- codes, self.special_token_id, keep_only_valid_steps=True
- )
- # apply model on pattern sequence
- model = self if self._fsdp is None else self._fsdp
- logits = model(sequence_codes, conditions, condition_tensors) # [B, K, S, card]
- # map back the logits on pattern sequence to logits on original codes: [B, K, S, card] -> [B, K, T, card]
- # and provide the corresponding mask over invalid positions of tokens
- logits = logits.permute(0, 3, 1, 2) # [B, card, K, S]
- # note: we use nans as special token to make it obvious if we feed unexpected logits
- logits, logits_indexes, logits_mask = pattern.revert_pattern_logits(
- logits, float('nan'), keep_only_valid_steps=True
- )
- logits = logits.permute(0, 2, 3, 1) # [B, K, T, card]
- logits_mask = logits_mask[None, :, :].expand(B, -1, -1) # [K, T] -> [B, K, T]
- return LMOutput(logits, logits_mask)
-
- def _sample_next_token(self,
- sequence: torch.Tensor,
- cfg_conditions: CFGConditions,
- unconditional_state: State,
- use_sampling: bool = False,
- temp: float = 1.0,
- top_k: int = 0,
- top_p: float = 0.0,
- cfg_coef: tp.Optional[float] = None) -> torch.Tensor:
- """Sample next token from the model given a sequence and a set of conditions. The model supports
- multiple sampling strategies (greedy sampling, softmax, top-k, top-p...).
-
- Args:
- sequence (torch.Tensor): Current sequence of shape [B, K, S]
- with K corresponding to the number of codebooks and S the number of sequence steps.
- S = 1 in streaming mode, except for the first step that contains a bigger prompt.
- condition_tensors (Dict[str, ConditionType): Set of conditions. If CFG is used,
- should be twice the batch size, being the concatenation of the conditions + null conditions.
- use_sampling (bool): Whether to use a sampling strategy or not.
- temp (float): Sampling temperature.
- top_k (int): K for "top-k" sampling.
- top_p (float): P for "top-p" sampling.
- cfg_coef (float): classifier free guidance coefficient
- Returns:
- next_token (torch.Tensor): Next token tensor of shape [B, K, 1].
- """
- B = sequence.shape[0]
- cfg_coef = self.cfg_coef if cfg_coef is None else cfg_coef
- model = self if self._fsdp is None else self._fsdp
- if self.two_step_cfg and cfg_conditions != {}:
- assert isinstance(cfg_conditions, tuple)
- condition_tensors, null_condition_tensors = cfg_conditions
- cond_logits = model(sequence, conditions=[], condition_tensors=condition_tensors)
- state = self.get_streaming_state()
- self.set_streaming_state(unconditional_state)
- uncond_logits = model(sequence, conditions=[], condition_tensors=null_condition_tensors)
- unconditional_state.update(self.get_streaming_state())
- self.set_streaming_state(state)
- logits = uncond_logits + (cond_logits - uncond_logits) * self.cfg_coef
- else:
- assert isinstance(cfg_conditions, dict)
- condition_tensors = cfg_conditions
- if condition_tensors:
- # Preparing for CFG, predicting both conditional and unconditional logits.
- sequence = torch.cat([sequence, sequence], dim=0)
- all_logits = model(
- sequence,
- conditions=[], condition_tensors=condition_tensors)
- if condition_tensors:
- cond_logits, uncond_logits = all_logits.split(B, dim=0) # [B, K, T, card]
- logits = uncond_logits + (cond_logits - uncond_logits) * cfg_coef
- else:
- logits = all_logits
-
- logits = logits.permute(0, 1, 3, 2) # [B, K, card, T]
- logits = logits[..., -1] # [B x K x card]
-
- # Apply softmax for sampling if temp > 0. Else, do greedy sampling to avoid zero division error.
- if use_sampling and temp > 0.0:
- probs = torch.softmax(logits / temp, dim=-1)
- if top_p > 0.0:
- next_token = utils.sample_top_p(probs, p=top_p)
- elif top_k > 0:
- next_token = utils.sample_top_k(probs, k=top_k)
- else:
- next_token = utils.multinomial(probs, num_samples=1)
- else:
- next_token = torch.argmax(logits, dim=-1, keepdim=True)
-
- return next_token
-
- @torch.no_grad()
- def generate(self,
- prompt: tp.Optional[torch.Tensor] = None,
- conditions: tp.List[ConditioningAttributes] = [],
- num_samples: tp.Optional[int] = None,
- max_gen_len: int = 256,
- use_sampling: bool = True,
- temp: float = 1.0,
- top_k: int = 250,
- top_p: float = 0.0,
- cfg_coef: tp.Optional[float] = None,
- two_step_cfg: bool = False,
- remove_prompts: bool = False,
- check: bool = False,
- callback: tp.Optional[tp.Callable[[int, int], None]] = None) -> torch.Tensor:
- """Generate tokens sampling from the model given a prompt or unconditionally. Generation can
- be perform in a greedy fashion or using sampling with top K and top P strategies.
-
- Args:
- prompt (Optional[torch.Tensor]): Prompt tokens of shape [B, K, T].
- conditions_tensors (Dict[str, torch.Tensor]): Set of conditions or None.
- num_samples (int or None): Number of samples to generate when no prompt and no conditions are given.
- max_gen_len (int): Maximum generation length.
- use_sampling (bool): Whether to use a sampling strategy or not.
- temp (float): Sampling temperature.
- top_k (int): K for "top-k" sampling.
- top_p (float): P for "top-p" sampling.
- remove_prompts (bool): Whether to remove prompts from generation or not.
- Returns:
- torch.Tensor: Generated tokens.
- """
- assert not self.training, "generation shouldn't be used in training mode."
- first_param = next(iter(self.parameters()))
- device = first_param.device
-
- # Checking all input shapes are consistents.
- possible_num_samples = []
- if num_samples is not None:
- possible_num_samples.append(num_samples)
- elif prompt is not None:
- possible_num_samples.append(prompt.shape[0])
- elif conditions:
- possible_num_samples.append(len(conditions))
- else:
- possible_num_samples.append(1)
- assert [x == possible_num_samples[0] for x in possible_num_samples], "Inconsitent inputs shapes"
- num_samples = possible_num_samples[0]
-
- # below we create set of conditions: one conditional and one unconditional
- # to do that we merge the regular condition together with the null condition
- # we then do 1 forward pass instead of 2.
- # the reason for that is two-fold:
- # 1. it is about x2 faster than doing 2 forward passes
- # 2. avoid the streaming API treating the 2 passes as part of different time steps
- # We also support doing two different passes, in particular to ensure that
- # the padding structure is exactly the same between train anf test.
- # With a batch size of 1, this can be slower though.
- cfg_conditions: CFGConditions
- two_step_cfg = self.two_step_cfg if two_step_cfg is None else two_step_cfg
- if conditions:
- null_conditions = ClassifierFreeGuidanceDropout(p=1.0)(conditions)
- if two_step_cfg:
- cfg_conditions = (
- self.condition_provider(self.condition_provider.tokenize(conditions)),
- self.condition_provider(self.condition_provider.tokenize(null_conditions)),
- )
- else:
- conditions = conditions + null_conditions
- tokenized = self.condition_provider.tokenize(conditions)
- cfg_conditions = self.condition_provider(tokenized)
- else:
- cfg_conditions = {}
-
- if prompt is None:
- assert num_samples > 0
- prompt = torch.zeros((num_samples, self.num_codebooks, 0), dtype=torch.long, device=device)
-
- B, K, T = prompt.shape
- start_offset = T
- assert start_offset < max_gen_len
-
- pattern = self.pattern_provider.get_pattern(max_gen_len)
- # this token is used as default value for codes that are not generated yet
- unknown_token = -1
-
- # we generate codes up to the max_gen_len that will be mapped to the pattern sequence
- gen_codes = torch.full((B, K, max_gen_len), unknown_token, dtype=torch.long, device=device)
- # filling the gen_codes with the prompt if needed
- gen_codes[..., :start_offset] = prompt
- # create the gen_sequence with proper interleaving from the pattern: [B, K, S]
- gen_sequence, indexes, mask = pattern.build_pattern_sequence(gen_codes, self.special_token_id)
- # retrieve the start_offset in the sequence:
- # it is the first sequence step that contains the `start_offset` timestep
- start_offset_sequence = pattern.get_first_step_with_timesteps(start_offset)
- assert start_offset_sequence is not None
-
- with self.streaming():
- unconditional_state = self.get_streaming_state()
- prev_offset = 0
- gen_sequence_len = gen_sequence.shape[-1] # gen_sequence shape is [B, K, S]
- for offset in range(start_offset_sequence, gen_sequence_len):
- # get current sequence (note that the streaming API is providing the caching over previous offsets)
- curr_sequence = gen_sequence[..., prev_offset:offset]
- curr_mask = mask[None, ..., prev_offset:offset].expand(B, -1, -1)
- if check:
- # check coherence between mask and sequence
- assert (curr_sequence == torch.where(curr_mask, curr_sequence, self.special_token_id)).all()
- # should never happen as gen_sequence is filled progressively
- assert not (curr_sequence == unknown_token).any()
- # sample next token from the model, next token shape is [B, K, 1]
- next_token = self._sample_next_token(
- curr_sequence, cfg_conditions, unconditional_state, use_sampling, temp, top_k, top_p,
- cfg_coef=cfg_coef)
- # ensure the tokens that should be masked are properly set to special_token_id
- # as the model never output special_token_id
- valid_mask = mask[..., offset:offset+1].expand(B, -1, -1)
- next_token[~valid_mask] = self.special_token_id
- # ensure we don't overwrite prompt tokens, we only write over unknown tokens
- # (then mask tokens should be left as is as well, which is correct)
- gen_sequence[..., offset:offset+1] = torch.where(
- gen_sequence[..., offset:offset+1] == unknown_token,
- next_token, gen_sequence[..., offset:offset+1]
- )
- prev_offset = offset
- if callback is not None:
- callback(1 + offset - start_offset_sequence, gen_sequence_len - start_offset_sequence)
- unconditional_state.clear()
-
- # ensure sequence has been entirely filled
- assert not (gen_sequence == unknown_token).any()
- # ensure gen_sequence pattern and mask are matching
- # which means the gen_sequence is valid according to the pattern
- assert (
- gen_sequence == torch.where(mask[None, ...].expand(B, -1, -1), gen_sequence, self.special_token_id)
- ).all()
- # get back the codes, trimming the prompt if needed and cutting potentially incomplete timesteps
- out_codes, out_indexes, out_mask = pattern.revert_pattern_sequence(gen_sequence, special_token=unknown_token)
-
- # sanity checks over the returned codes and corresponding masks
- assert (out_codes[..., :max_gen_len] != unknown_token).all()
- assert (out_mask[..., :max_gen_len] == 1).all()
-
- out_start_offset = start_offset if remove_prompts else 0
- out_codes = out_codes[..., out_start_offset:max_gen_len]
-
- # ensure the returned codes are all valid
- assert (out_codes >= 0).all() and (out_codes <= self.card).all()
- return out_codes
diff --git a/spaces/YueMafighting/FollowYourPose/Dockerfile b/spaces/YueMafighting/FollowYourPose/Dockerfile
deleted file mode 100644
index 845d631472e9eca8e3bc2da734bb1b17eb72cc02..0000000000000000000000000000000000000000
--- a/spaces/YueMafighting/FollowYourPose/Dockerfile
+++ /dev/null
@@ -1,64 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update && \
- apt-get upgrade -y && \
- apt-get install -y --no-install-recommends \
- git \
- zip \
- unzip \
- git-lfs \
- wget \
- curl \
- # ffmpeg \
- ffmpeg \
- x264 \
- # python build dependencies \
- build-essential \
- libssl-dev \
- zlib1g-dev \
- libbz2-dev \
- libreadline-dev \
- libsqlite3-dev \
- libncursesw5-dev \
- xz-utils \
- tk-dev \
- libxml2-dev \
- libxmlsec1-dev \
- libffi-dev \
- liblzma-dev && \
- apt-get clean && \
- rm -rf /var/lib/apt/lists/*
-# RUN apt-get update && \
-# apt-get install zip
-# RUN wget https://github.com/ChenyangQiQi/FateZero/releases/download/v0.0.1/style.zip && unzip style.zip
-RUN useradd -m -u 1000 user
-RUN sudo apt-get install unzip
-USER user
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:${PATH}
-WORKDIR ${HOME}/app
-
-RUN curl https://pyenv.run | bash
-ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ENV PYTHON_VERSION=3.10.9
-RUN pyenv install ${PYTHON_VERSION} && \
- pyenv global ${PYTHON_VERSION} && \
- pyenv rehash && \
- pip install --no-cache-dir -U pip setuptools wheel
-
-RUN pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1
-COPY --chown=1000 requirements.txt /tmp/requirements.txt
-RUN pip install --no-cache-dir -U -r /tmp/requirements.txt
-
-COPY --chown=1000 . ${HOME}/app
-RUN ls -a
-# RUN cd ./FateZero/ckpt && bash download.sh
-RUN cd {WORKDIR}/data && bash download.sh
-ENV PYTHONPATH=${HOME}/app \
- PYTHONUNBUFFERED=1 \
- GRADIO_ALLOW_FLAGGING=never \
- GRADIO_NUM_PORTS=1 \
- GRADIO_SERVER_NAME=0.0.0.0 \
- GRADIO_THEME=huggingface \
- SYSTEM=spaces
-CMD ["python", "app.py"]
diff --git a/spaces/Yuliang/ECON/lib/common/render_utils.py b/spaces/Yuliang/ECON/lib/common/render_utils.py
deleted file mode 100644
index 389eab740fc341b9395e1e008b4fa91e9ef3cf83..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/common/render_utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: ps-license@tuebingen.mpg.de
-
-import math
-from typing import NewType
-
-import numpy as np
-import torch
-import trimesh
-from pytorch3d.renderer.mesh import rasterize_meshes
-from pytorch3d.structures import Meshes
-from torch import nn
-
-Tensor = NewType("Tensor", torch.Tensor)
-
-
-def solid_angles(points: Tensor, triangles: Tensor, thresh: float = 1e-8) -> Tensor:
- """Compute solid angle between the input points and triangles
- Follows the method described in:
- The Solid Angle of a Plane Triangle
- A. VAN OOSTEROM AND J. STRACKEE
- IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,
- VOL. BME-30, NO. 2, FEBRUARY 1983
- Parameters
- -----------
- points: BxQx3
- Tensor of input query points
- triangles: BxFx3x3
- Target triangles
- thresh: float
- float threshold
- Returns
- -------
- solid_angles: BxQxF
- A tensor containing the solid angle between all query points
- and input triangles
- """
- # Center the triangles on the query points. Size should be BxQxFx3x3
- centered_tris = triangles[:, None] - points[:, :, None, None]
-
- # BxQxFx3
- norms = torch.norm(centered_tris, dim=-1)
-
- # Should be BxQxFx3
- cross_prod = torch.cross(centered_tris[:, :, :, 1], centered_tris[:, :, :, 2], dim=-1)
- # Should be BxQxF
- numerator = (centered_tris[:, :, :, 0] * cross_prod).sum(dim=-1)
- del cross_prod
-
- dot01 = (centered_tris[:, :, :, 0] * centered_tris[:, :, :, 1]).sum(dim=-1)
- dot12 = (centered_tris[:, :, :, 1] * centered_tris[:, :, :, 2]).sum(dim=-1)
- dot02 = (centered_tris[:, :, :, 0] * centered_tris[:, :, :, 2]).sum(dim=-1)
- del centered_tris
-
- denominator = (
- norms.prod(dim=-1) + dot01 * norms[:, :, :, 2] + dot02 * norms[:, :, :, 1] +
- dot12 * norms[:, :, :, 0]
- )
- del dot01, dot12, dot02, norms
-
- # Should be BxQ
- solid_angle = torch.atan2(numerator, denominator)
- del numerator, denominator
-
- torch.cuda.empty_cache()
-
- return 2 * solid_angle
-
-
-def winding_numbers(points: Tensor, triangles: Tensor, thresh: float = 1e-8) -> Tensor:
- """Uses winding_numbers to compute inside/outside
- Robust inside-outside segmentation using generalized winding numbers
- Alec Jacobson,
- Ladislav Kavan,
- Olga Sorkine-Hornung
- Fast Winding Numbers for Soups and Clouds SIGGRAPH 2018
- Gavin Barill
- NEIL G. Dickson
- Ryan Schmidt
- David I.W. Levin
- and Alec Jacobson
- Parameters
- -----------
- points: BxQx3
- Tensor of input query points
- triangles: BxFx3x3
- Target triangles
- thresh: float
- float threshold
- Returns
- -------
- winding_numbers: BxQ
- A tensor containing the Generalized winding numbers
- """
- # The generalized winding number is the sum of solid angles of the point
- # with respect to all triangles.
- return (1 / (4 * math.pi) * solid_angles(points, triangles, thresh=thresh).sum(dim=-1))
-
-
-def batch_contains(verts, faces, points):
-
- B = verts.shape[0]
- N = points.shape[1]
-
- verts = verts.detach().cpu()
- faces = faces.detach().cpu()
- points = points.detach().cpu()
- contains = torch.zeros(B, N)
-
- for i in range(B):
- contains[i] = torch.as_tensor(trimesh.Trimesh(verts[i], faces[i]).contains(points[i]))
-
- return 2.0 * (contains - 0.5)
-
-
-def dict2obj(d):
- if not isinstance(d, dict):
- return d
-
- class C(object):
- pass
-
- o = C()
- for k in d:
- o.__dict__[k] = dict2obj(d[k])
- return o
-
-
-def face_vertices(vertices, faces):
- """
- :param vertices: [batch size, number of vertices, 3]
- :param faces: [batch size, number of faces, 3]
- :return: [batch size, number of faces, 3, 3]
- """
-
- bs, nv = vertices.shape[:2]
- bs, nf = faces.shape[:2]
- device = vertices.device
- faces = faces + (torch.arange(bs, dtype=torch.int32).to(device) * nv)[:, None, None]
- vertices = vertices.reshape((bs * nv, vertices.shape[-1]))
-
- return vertices[faces.long()]
-
-
-class Pytorch3dRasterizer(nn.Module):
- """Borrowed from https://github.com/facebookresearch/pytorch3d
- Notice:
- x,y,z are in image space, normalized
- can only render squared image now
- """
- def __init__(
- self, image_size=224, blur_radius=0.0, faces_per_pixel=1, device=torch.device("cuda:0")
- ):
- """
- use fixed raster_settings for rendering faces
- """
- super().__init__()
- raster_settings = {
- "image_size": image_size,
- "blur_radius": blur_radius,
- "faces_per_pixel": faces_per_pixel,
- "bin_size": -1,
- "max_faces_per_bin": None,
- "perspective_correct": False,
- "cull_backfaces": True,
- }
- raster_settings = dict2obj(raster_settings)
- self.raster_settings = raster_settings
- self.device = device
-
- def forward(self, vertices, faces, attributes=None):
- fixed_vertices = vertices.clone()
- fixed_vertices[..., :2] = -fixed_vertices[..., :2]
- meshes_screen = Meshes(verts=fixed_vertices.float(), faces=faces.long())
- raster_settings = self.raster_settings
- pix_to_face, zbuf, bary_coords, dists = rasterize_meshes(
- meshes_screen,
- image_size=raster_settings.image_size,
- blur_radius=raster_settings.blur_radius,
- faces_per_pixel=raster_settings.faces_per_pixel,
- bin_size=raster_settings.bin_size,
- max_faces_per_bin=raster_settings.max_faces_per_bin,
- perspective_correct=raster_settings.perspective_correct,
- )
- vismask = (pix_to_face > -1).float()
- D = attributes.shape[-1]
- attributes = attributes.clone()
- attributes = attributes.view(
- attributes.shape[0] * attributes.shape[1], 3, attributes.shape[-1]
- )
- N, H, W, K, _ = bary_coords.shape
- mask = pix_to_face == -1
- pix_to_face = pix_to_face.clone()
- pix_to_face[mask] = 0
- idx = pix_to_face.view(N * H * W * K, 1, 1).expand(N * H * W * K, 3, D)
- pixel_face_vals = attributes.gather(0, idx).view(N, H, W, K, 3, D)
- pixel_vals = (bary_coords[..., None] * pixel_face_vals).sum(dim=-2)
- pixel_vals[mask] = 0 # Replace masked values in output.
- pixel_vals = pixel_vals[:, :, :, 0].permute(0, 3, 1, 2)
- pixel_vals = torch.cat([pixel_vals, vismask[:, :, :, 0][:, None, :, :]], dim=1)
- return pixel_vals
-
- def get_texture(self, uvcoords, uvfaces, verts, faces, verts_color):
-
- batch_size = verts.shape[0]
- uv_verts_color = face_vertices(verts_color, faces.expand(batch_size, -1,
- -1)).to(self.device)
- uv_map = self.forward(
- uvcoords.expand(batch_size, -1, -1), uvfaces.expand(batch_size, -1, -1), uv_verts_color
- )[:, :3]
- uv_map_npy = np.flip(uv_map.squeeze(0).permute(1, 2, 0).cpu().numpy(), 0)
-
- return uv_map_npy
diff --git a/spaces/Yunshansongbai/SVC-Nahida/app.py b/spaces/Yunshansongbai/SVC-Nahida/app.py
deleted file mode 100644
index 7321a5fc776b31c97fe5729b6c4fb88a0e389ee1..0000000000000000000000000000000000000000
--- a/spaces/Yunshansongbai/SVC-Nahida/app.py
+++ /dev/null
@@ -1,194 +0,0 @@
-import io
-import os
-
-import gradio as gr
-import librosa
-import numpy as np
-import soundfile
-from inference.infer_tool import Svc
-import logging
-import os
-import paddle
-import requests
-import utils
-from spleeter import Separator
-import time
-from datetime import datetime, timedelta
-
-build_dir=os.getcwd()
-if build_dir == "/home/aistudio":
- build_dir += "/build"
-
-model_dir=build_dir+'/trained_models'
-
-model_list_path = model_dir + "/model_list.txt"
-
-# 筛选出文件夹
-models = []
-for filename in os.listdir(model_dir):
- # 判断文件名是否以 '.pdparams' 结尾，并且不包含后缀部分
- if filename.endswith('.pdparams') and os.path.splitext(filename)[0].isalpha():
- models.append(os.path.splitext(filename)[0])
-cache_model = {}
-
-def callback(text):
- if text == "reboot":
- os._exit(0)
- one_hour_later = datetime.now() + timedelta(hours=1)
- else:
- global start_time
- if time.time() - start_time >= 3600:
- os._exit(0)
- one_hour_later = datetime.now() + timedelta(hours=1)
- else:
- return text
-
-def separate_fn(song_input):
- try:
- if song_input is None:
- return "请上传歌曲",None,None,None,None
- params_2stems = {
- 'sample_rate': 44100,
- 'frame_length': 4096,
- 'frame_step': 1024,
- 'T': 512,
- 'F': 1024,
- 'num_instruments': ['vocals', 'instrumental'],
- 'output_dir': build_dir+'/output_2stems',
- 'checkpoint_path': build_dir+'/spleeter',
- 'use_elu': False}
- sampling_rate, song = song_input
- soundfile.write("temp.wav", song, sampling_rate, format="wav")
- # 初始化分离器
- sep = Separator(params_2stems)
- sep.separate('temp.wav')
- vocal_path = params_2stems["output_dir"]+"/temp-vocals.wav"
- instrumental_path = params_2stems["output_dir"]+"/temp-instrumental.wav"
- return "分离成功，请继续前往体验【转换】和【混音】",vocal_path,instrumental_path,vocal_path,instrumental_path
- except Exception as e:
- import traceback
- return traceback.format_exc() , None,None,None,None
-
-
-def convert_fn(model_name, input_audio,input_audio_micro, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale):
- try:
- if model_name in cache_model:
- model = cache_model[model_name]
- else:
- if paddle.device.is_compiled_with_cuda()==False and len(cache_model)!=0:
- return f"目前运行环境为CPU，受制于平台算力，每次启动本项目只允许加载1个模型，当前已加载{next(iter(cache_model))}",None,None
- config_path = f"{build_dir}/trained_models/config.json"
- model = Svc(f"{build_dir}/trained_models/{model_name}.pdparams", config_path,mode="test")
- cache_model[model_name] = model
- if input_audio is None and input_audio_micro is None:
- return "请上传音频", None,None
- if input_audio_micro is not None:
- input_audio = input_audio_micro
- sampling_rate, audio = input_audio
- duration = audio.shape[0] / sampling_rate
- audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio.transpose(1, 0))
- if sampling_rate != 16000:
- audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
- print(audio.shape)
- out_wav_path = "temp.wav"
- soundfile.write(out_wav_path, audio, 16000, format="wav")
- print(cluster_ratio, auto_f0, noise_scale)
- _audio = model.slice_inference(out_wav_path, model_name, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale)
- del model
- return "转换成功，请继续前往体验【混音】", (44100, _audio),(44100, _audio)
- except Exception as e:
- import traceback
- return traceback.format_exc() , None,None
-
-def compose_fn(input_vocal,input_instrumental,mixing_ratio=0.5):
- try:
- outlog = "混音成功"
- if input_vocal is None:
- return "请上传人声", None
- if input_instrumental is None:
- return "请上传伴奏", None
- vocal_sampling_rate, vocal = input_vocal
- vocal_duration = vocal.shape[0] / vocal_sampling_rate
- vocal = (vocal / np.iinfo(vocal.dtype).max).astype(np.float32)
- if len(vocal.shape) > 1:
- vocal = librosa.to_mono(vocal.transpose(1, 0))
- if vocal_sampling_rate != 44100:
- vocal = librosa.resample(vocal, orig_sr=vocal_sampling_rate, target_sr=44100)
-
- instrumental_sampling_rate, instrumental = input_instrumental
- instrumental_duration = instrumental.shape[0] / instrumental_sampling_rate
- instrumental = (instrumental / np.iinfo(instrumental.dtype).max).astype(np.float32)
- if len(instrumental.shape) > 1:
- instrumental = librosa.to_mono(instrumental.transpose(1, 0))
- if instrumental_sampling_rate != 44100:
- instrumental = librosa.resample(instrumental, orig_sr=instrumental_sampling_rate, target_sr=44100)
- if len(vocal)!=len(instrumental):
- min_length = min(len(vocal),len(instrumental))
- instrumental = instrumental[:min_length]
- vocal = vocal[:min_length]
- outlog = "人声伴奏长度不一致，已自动截断较长的音频"
-
- mixed_audio = (1 - mixing_ratio) * vocal + mixing_ratio * instrumental
- mixed_audio_data = mixed_audio.astype(np.float32)
- return outlog,(44100,mixed_audio_data)
- except Exception as e:
- import traceback
- return traceback.format_exc() , None
-
-
-app = gr.Blocks()
-
-with app:
- start_time = time.time()
- one_hour_later = datetime.now() + timedelta(hours=1)
- gr.Markdown('SVC歌声转换全流程体验（伴奏分离，转换，混音）
')
- with gr.Tabs() as tabs:
- with gr.TabItem("人声伴奏分离"):
- gr.Markdown('该项目人声分离的效果弱于UVR5，如自备分离好的伴奏和人声可跳过该步骤
')
- song_input = gr.Audio(label="上传歌曲（tips:上传后点击右上角✏可以进行歌曲剪辑）",interactive=True)
- gr.Examples(examples=[build_dir+"/examples/song/blue.wav",build_dir+"/examples/song/Counter_clockwise_Clock.wav",build_dir+"/examples/song/one_last_kiss.wav"],inputs=song_input,label="歌曲样例")
-
- btn_separate = gr.Button("人声伴奏分离", variant="primary")
- text_output1 = gr.Textbox(label="输出信息")
- vocal_output1 = gr.Audio(label="输出人声",interactive=False)
- instrumental_output1 = gr.Audio(label="输出伴奏",interactive=False)
- with gr.TabItem("转换"):
- model_name = gr.Dropdown(label="模型", choices=models, value="纳西妲")
- vocal_input1 = gr.Audio(label="上传人声",interactive=True)
- gr.Examples(examples=[build_dir+"/examples/vocals/blue_vocal.wav",build_dir+"/examples/vocals/Counter_clockwise_Clock_vocal.wav",build_dir+"/examples/vocals/one_last_kiss_vocal.wav"],inputs=vocal_input1,label="人声样例")
- btn_use_separate = gr.Button("使用【人声伴奏分离】分离的人声")
- micro_input = gr.Audio(label="麦克风输入（优先于上传的人声）",source="microphone",interactive=True)
- vc_transform = gr.Number(label="变调（半音数量,升八度12降八度-12）", value=0)
- cluster_ratio = gr.Number(label="聚类模型混合比例", value=0,visible=False)
- auto_f0 = gr.Checkbox(label="自动预测音高（转换歌声时不要打开，会严重跑调）", value=False)
- slice_db = gr.Number(label="静音分贝阈值（嘈杂的音频可以-30，干声保留呼吸可以-50）", value=-50)
- noise_scale = gr.Number(label="noise_scale", value=0.2)
- btn_convert = gr.Button("转换", variant="primary")
- text_output2 = gr.Textbox(label="输出信息")
- vc_output2 = gr.Audio(label="输出音频",interactive=False)
-
- with gr.TabItem("混音"):
- vocal_input2 = gr.Audio(label="上传人声",interactive=True)
- btn_use_convert = gr.Button("使用【转换】输出的人声")
- instrumental_input1 = gr.Audio(label="上传伴奏")
- gr.Examples(examples=[build_dir+"/examples/instrumental/blue_instrumental.wav",build_dir+"/examples/instrumental/Counter_clockwise_Clock_instrumental.wav",build_dir+"/examples/instrumental/one_last_kiss_instrumental.wav"],inputs=instrumental_input1,label="伴奏样例")
- btn_use_separate2 = gr.Button("使用【人声伴奏分离】分离的伴奏")
- mixing_ratio = gr.Slider(0, 1, value=0.75,step=0.01,label="混音比例（人声:伴奏）", info="人声:伴奏")
- btn_compose = gr.Button("混音", variant="primary")
- text_output3 = gr.Textbox(label="输出信息")
- song_output = gr.Audio(label="输出歌曲",interactive=False)
-
- with gr.TabItem("设置"):
- output = gr.Textbox(label="输出",placeholder=f"距离下一次允许重启时间为{one_hour_later}")
- btn_reboot = gr.Button("重启",variant="primary")
- btn_separate.click(separate_fn, song_input, [text_output1, vocal_output1,instrumental_output1,vocal_input1,instrumental_input1])
- btn_convert.click(convert_fn, [model_name, vocal_input1,micro_input,vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale], [text_output2, vc_output2,vocal_input2])
- btn_compose.click(compose_fn,[vocal_input2,instrumental_input1,mixing_ratio],[text_output3,song_output])
- btn_reboot.click(callback,output)
- btn_use_convert.click(lambda x:x,vc_output2,vocal_input2)
- btn_use_separate.click(lambda x:x,vocal_output1,vocal_input1)
- btn_use_separate2.click(lambda x:x,instrumental_output1,instrumental_input1)
-
-app.launch()
\ No newline at end of file
diff --git a/spaces/Ziqi/ReVersion/README.md b/spaces/Ziqi/ReVersion/README.md
deleted file mode 100644
index 1823df2c77ed395faf47aed9a4e04093a151848e..0000000000000000000000000000000000000000
--- a/spaces/Ziqi/ReVersion/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ReVersion
-emoji: 🐠
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/abcde1234www/tts/app.py b/spaces/abcde1234www/tts/app.py
deleted file mode 100644
index a97450c289843433835ebc4da6d5d885ea42167c..0000000000000000000000000000000000000000
--- a/spaces/abcde1234www/tts/app.py
+++ /dev/null
@@ -1,103 +0,0 @@
-"""
-Copyright 2022 Balacoon
-
-TTS interactive demo
-"""
-
-import logging
-from typing import cast
-
-import gradio as gr
-from balacoon_tts import TTS
-from huggingface_hub import hf_hub_download, list_repo_files
-
-# global tts module, initialized from a model selected
-tts = None
-
-
-def main():
- logging.basicConfig(level=logging.INFO)
-
- with gr.Blocks() as demo:
- gr.Markdown(
- """
- Text-to-Speech
-
- 1. Write an utterance to generate,
- 2. Select the model to synthesize with
- 3. Select speaker (only for multi-speaker models)
- 4. Hit "Generate" and listen to the result!
-
- When you select model for the first time,
- it will take a little time to download it.
- You can learn more about models available
- [here](https://huggingface.co/balacoon/tts),
- visit [Balacoon website](https://balacoon.com/) for more info.
- """
- )
- with gr.Row(variant="panel"):
- text = gr.Textbox(label="Text", placeholder="Type something here...")
-
- with gr.Row():
- with gr.Column(variant="panel"):
- repo_files = list_repo_files(repo_id="balacoon/tts")
- model_files = [x for x in repo_files if x.endswith(".addon")]
- model_name = gr.Dropdown(
- label="Model",
- choices=model_files,
- )
- with gr.Column(variant="panel"):
- speaker = gr.Dropdown(label="Speaker", choices=[])
-
- def set_model(model_name_str: str):
- """
- gets value from `model_name`, loads model,
- re-initializes tts object, gets list of
- speakers that model supports and set them to `speaker`
- """
- model_path = hf_hub_download(
- repo_id="balacoon/tts", filename=model_name_str
- )
- global tts
- tts = TTS(model_path)
- speakers = tts.get_speakers()
- if speakers:
- visible = True
- value = speakers[-1]
- else:
- visible = False
- value = ""
- return gr.Dropdown.update(
- choices=speakers, value=value, visible=visible
- )
-
- model_name.change(set_model, inputs=model_name, outputs=speaker)
-
- with gr.Row(variant="panel"):
- generate = gr.Button("Generate")
- with gr.Row(variant="panel"):
- audio = gr.Audio()
-
- def synthesize_audio(text_str: str, speaker_str: str = ""):
- """
- gets utterance to synthesize from `text` Textbox
- and speaker name from `speaker` dropdown list.
- speaker name might be empty for single-speaker models.
- Synthesizes the waveform and updates `audio` with it.
- """
- if not text_str:
- logging.info("text or speaker are not provided")
- return None
- global tts
- if len(text_str) > 1024:
- text_str = text_str[:1024]
- samples = cast(TTS, tts).synthesize(text_str, speaker_str)
- return gr.Audio.update(value=(24000, samples))
-
- generate.click(synthesize_audio, inputs=[text, speaker], outputs=audio)
-
- demo.launch()
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/tableau/setup.md b/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/tableau/setup.md
deleted file mode 100644
index 73dac977c474116928b47b28dbab6cc0705660c0..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/tableau/setup.md
+++ /dev/null
@@ -1,60 +0,0 @@
----
-title: Setup
----
-# Tableau Ingestion Guide: Setup & Prerequisites
-
-In order to configure ingestion from Tableau, you'll first have to enable Tableau Metadata API and you should have a user with Site Administrator Explorer permissions.
-
-## Tableau Prerequisites
-
-1. Grant `Site Administrator Explorer permissions` to a user
-
- A. Log in to Tableau Cloud https://sso.online.tableau.com/public/idp/SSO.
-
- B. Navigate to `Users`.
-
-
-
-
-
-
- C. **For New User**: Follow below steps to grant permission for new user.
-
- - Click `Add Users` -> `Add Users by Email`
-
-
-
-
-
- - Fill `Enter email addresses`, set `Site role` to `Site Administrator Explorer` and Click `Add Users`
-
-
-
-
-
-
- D. **For Existing User:** Follow below steps to grant permission for existing user.
-
- - Select a user and click `Actions` -> `Site Role`
-
-
-
-
-
- - Change user role to `Site Administrator Explorer`
-
-
-
-
-
-2. **Enable Tableau Metadata API:** This step is required only for Tableau Server. The Metadata API is installed with Tableau Server but disabled by default.
-
- - Open a command prompt as an admin on the initial node (*where TSM is installed*) in the cluster
- - Run the command: `tsm maintenance metadata-services enable`
-
-
-## Next Steps
-
-Once you've done all of the above in Tableau, it's time to [move on](configuration.md) to configuring the actual ingestion source within DataHub.
-
-*Need more help? Join the conversation in [Slack](http://slack.datahubproject.io)!*
\ No newline at end of file
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/multi_scale_deform_attn.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/multi_scale_deform_attn.py
deleted file mode 100644
index c52dda18b41705705b47dd0e995b124048c16fba..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/multi_scale_deform_attn.py
+++ /dev/null
@@ -1,358 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-import warnings
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.autograd.function import Function, once_differentiable
-
-from annotator.uniformer.mmcv import deprecated_api_warning
-from annotator.uniformer.mmcv.cnn import constant_init, xavier_init
-from annotator.uniformer.mmcv.cnn.bricks.registry import ATTENTION
-from annotator.uniformer.mmcv.runner import BaseModule
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
- '_ext', ['ms_deform_attn_backward', 'ms_deform_attn_forward'])
-
-
-class MultiScaleDeformableAttnFunction(Function):
-
- @staticmethod
- def forward(ctx, value, value_spatial_shapes, value_level_start_index,
- sampling_locations, attention_weights, im2col_step):
- """GPU version of multi-scale deformable attention.
-
- Args:
- value (Tensor): The value has shape
- (bs, num_keys, mum_heads, embed_dims//num_heads)
- value_spatial_shapes (Tensor): Spatial shape of
- each feature map, has shape (num_levels, 2),
- last dimension 2 represent (h, w)
- sampling_locations (Tensor): The location of sampling points,
- has shape
- (bs ,num_queries, num_heads, num_levels, num_points, 2),
- the last dimension 2 represent (x, y).
- attention_weights (Tensor): The weight of sampling points used
- when calculate the attention, has shape
- (bs ,num_queries, num_heads, num_levels, num_points),
- im2col_step (Tensor): The step used in image to column.
-
- Returns:
- Tensor: has shape (bs, num_queries, embed_dims)
- """
-
- ctx.im2col_step = im2col_step
- output = ext_module.ms_deform_attn_forward(
- value,
- value_spatial_shapes,
- value_level_start_index,
- sampling_locations,
- attention_weights,
- im2col_step=ctx.im2col_step)
- ctx.save_for_backward(value, value_spatial_shapes,
- value_level_start_index, sampling_locations,
- attention_weights)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- """GPU version of backward function.
-
- Args:
- grad_output (Tensor): Gradient
- of output tensor of forward.
-
- Returns:
- Tuple[Tensor]: Gradient
- of input tensors in forward.
- """
- value, value_spatial_shapes, value_level_start_index,\
- sampling_locations, attention_weights = ctx.saved_tensors
- grad_value = torch.zeros_like(value)
- grad_sampling_loc = torch.zeros_like(sampling_locations)
- grad_attn_weight = torch.zeros_like(attention_weights)
-
- ext_module.ms_deform_attn_backward(
- value,
- value_spatial_shapes,
- value_level_start_index,
- sampling_locations,
- attention_weights,
- grad_output.contiguous(),
- grad_value,
- grad_sampling_loc,
- grad_attn_weight,
- im2col_step=ctx.im2col_step)
-
- return grad_value, None, None, \
- grad_sampling_loc, grad_attn_weight, None
-
-
-def multi_scale_deformable_attn_pytorch(value, value_spatial_shapes,
- sampling_locations, attention_weights):
- """CPU version of multi-scale deformable attention.
-
- Args:
- value (Tensor): The value has shape
- (bs, num_keys, mum_heads, embed_dims//num_heads)
- value_spatial_shapes (Tensor): Spatial shape of
- each feature map, has shape (num_levels, 2),
- last dimension 2 represent (h, w)
- sampling_locations (Tensor): The location of sampling points,
- has shape
- (bs ,num_queries, num_heads, num_levels, num_points, 2),
- the last dimension 2 represent (x, y).
- attention_weights (Tensor): The weight of sampling points used
- when calculate the attention, has shape
- (bs ,num_queries, num_heads, num_levels, num_points),
-
- Returns:
- Tensor: has shape (bs, num_queries, embed_dims)
- """
-
- bs, _, num_heads, embed_dims = value.shape
- _, num_queries, num_heads, num_levels, num_points, _ =\
- sampling_locations.shape
- value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes],
- dim=1)
- sampling_grids = 2 * sampling_locations - 1
- sampling_value_list = []
- for level, (H_, W_) in enumerate(value_spatial_shapes):
- # bs, H_*W_, num_heads, embed_dims ->
- # bs, H_*W_, num_heads*embed_dims ->
- # bs, num_heads*embed_dims, H_*W_ ->
- # bs*num_heads, embed_dims, H_, W_
- value_l_ = value_list[level].flatten(2).transpose(1, 2).reshape(
- bs * num_heads, embed_dims, H_, W_)
- # bs, num_queries, num_heads, num_points, 2 ->
- # bs, num_heads, num_queries, num_points, 2 ->
- # bs*num_heads, num_queries, num_points, 2
- sampling_grid_l_ = sampling_grids[:, :, :,
- level].transpose(1, 2).flatten(0, 1)
- # bs*num_heads, embed_dims, num_queries, num_points
- sampling_value_l_ = F.grid_sample(
- value_l_,
- sampling_grid_l_,
- mode='bilinear',
- padding_mode='zeros',
- align_corners=False)
- sampling_value_list.append(sampling_value_l_)
- # (bs, num_queries, num_heads, num_levels, num_points) ->
- # (bs, num_heads, num_queries, num_levels, num_points) ->
- # (bs, num_heads, 1, num_queries, num_levels*num_points)
- attention_weights = attention_weights.transpose(1, 2).reshape(
- bs * num_heads, 1, num_queries, num_levels * num_points)
- output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) *
- attention_weights).sum(-1).view(bs, num_heads * embed_dims,
- num_queries)
- return output.transpose(1, 2).contiguous()
-
-
-@ATTENTION.register_module()
-class MultiScaleDeformableAttention(BaseModule):
- """An attention module used in Deformable-Detr.
-
- `Deformable DETR: Deformable Transformers for End-to-End Object Detection.
- `_.
-
- Args:
- embed_dims (int): The embedding dimension of Attention.
- Default: 256.
- num_heads (int): Parallel attention heads. Default: 64.
- num_levels (int): The number of feature map used in
- Attention. Default: 4.
- num_points (int): The number of sampling points for
- each query in each head. Default: 4.
- im2col_step (int): The step used in image_to_column.
- Default: 64.
- dropout (float): A Dropout layer on `inp_identity`.
- Default: 0.1.
- batch_first (bool): Key, Query and Value are shape of
- (batch, n, embed_dim)
- or (n, batch, embed_dim). Default to False.
- norm_cfg (dict): Config dict for normalization layer.
- Default: None.
- init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
- Default: None.
- """
-
- def __init__(self,
- embed_dims=256,
- num_heads=8,
- num_levels=4,
- num_points=4,
- im2col_step=64,
- dropout=0.1,
- batch_first=False,
- norm_cfg=None,
- init_cfg=None):
- super().__init__(init_cfg)
- if embed_dims % num_heads != 0:
- raise ValueError(f'embed_dims must be divisible by num_heads, '
- f'but got {embed_dims} and {num_heads}')
- dim_per_head = embed_dims // num_heads
- self.norm_cfg = norm_cfg
- self.dropout = nn.Dropout(dropout)
- self.batch_first = batch_first
-
- # you'd better set dim_per_head to a power of 2
- # which is more efficient in the CUDA implementation
- def _is_power_of_2(n):
- if (not isinstance(n, int)) or (n < 0):
- raise ValueError(
- 'invalid input for _is_power_of_2: {} (type: {})'.format(
- n, type(n)))
- return (n & (n - 1) == 0) and n != 0
-
- if not _is_power_of_2(dim_per_head):
- warnings.warn(
- "You'd better set embed_dims in "
- 'MultiScaleDeformAttention to make '
- 'the dimension of each attention head a power of 2 '
- 'which is more efficient in our CUDA implementation.')
-
- self.im2col_step = im2col_step
- self.embed_dims = embed_dims
- self.num_levels = num_levels
- self.num_heads = num_heads
- self.num_points = num_points
- self.sampling_offsets = nn.Linear(
- embed_dims, num_heads * num_levels * num_points * 2)
- self.attention_weights = nn.Linear(embed_dims,
- num_heads * num_levels * num_points)
- self.value_proj = nn.Linear(embed_dims, embed_dims)
- self.output_proj = nn.Linear(embed_dims, embed_dims)
- self.init_weights()
-
- def init_weights(self):
- """Default initialization for Parameters of Module."""
- constant_init(self.sampling_offsets, 0.)
- thetas = torch.arange(
- self.num_heads,
- dtype=torch.float32) * (2.0 * math.pi / self.num_heads)
- grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
- grid_init = (grid_init /
- grid_init.abs().max(-1, keepdim=True)[0]).view(
- self.num_heads, 1, 1,
- 2).repeat(1, self.num_levels, self.num_points, 1)
- for i in range(self.num_points):
- grid_init[:, :, i, :] *= i + 1
-
- self.sampling_offsets.bias.data = grid_init.view(-1)
- constant_init(self.attention_weights, val=0., bias=0.)
- xavier_init(self.value_proj, distribution='uniform', bias=0.)
- xavier_init(self.output_proj, distribution='uniform', bias=0.)
- self._is_init = True
-
- @deprecated_api_warning({'residual': 'identity'},
- cls_name='MultiScaleDeformableAttention')
- def forward(self,
- query,
- key=None,
- value=None,
- identity=None,
- query_pos=None,
- key_padding_mask=None,
- reference_points=None,
- spatial_shapes=None,
- level_start_index=None,
- **kwargs):
- """Forward Function of MultiScaleDeformAttention.
-
- Args:
- query (Tensor): Query of Transformer with shape
- (num_query, bs, embed_dims).
- key (Tensor): The key tensor with shape
- `(num_key, bs, embed_dims)`.
- value (Tensor): The value tensor with shape
- `(num_key, bs, embed_dims)`.
- identity (Tensor): The tensor used for addition, with the
- same shape as `query`. Default None. If None,
- `query` will be used.
- query_pos (Tensor): The positional encoding for `query`.
- Default: None.
- key_pos (Tensor): The positional encoding for `key`. Default
- None.
- reference_points (Tensor): The normalized reference
- points with shape (bs, num_query, num_levels, 2),
- all elements is range in [0, 1], top-left (0,0),
- bottom-right (1, 1), including padding area.
- or (N, Length_{query}, num_levels, 4), add
- additional two dimensions is (w, h) to
- form reference boxes.
- key_padding_mask (Tensor): ByteTensor for `query`, with
- shape [bs, num_key].
- spatial_shapes (Tensor): Spatial shape of features in
- different levels. With shape (num_levels, 2),
- last dimension represents (h, w).
- level_start_index (Tensor): The start index of each level.
- A tensor has shape ``(num_levels, )`` and can be represented
- as [0, h_0*w_0, h_0*w_0+h_1*w_1, ...].
-
- Returns:
- Tensor: forwarded results with shape [num_query, bs, embed_dims].
- """
-
- if value is None:
- value = query
-
- if identity is None:
- identity = query
- if query_pos is not None:
- query = query + query_pos
- if not self.batch_first:
- # change to (bs, num_query ,embed_dims)
- query = query.permute(1, 0, 2)
- value = value.permute(1, 0, 2)
-
- bs, num_query, _ = query.shape
- bs, num_value, _ = value.shape
- assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value
-
- value = self.value_proj(value)
- if key_padding_mask is not None:
- value = value.masked_fill(key_padding_mask[..., None], 0.0)
- value = value.view(bs, num_value, self.num_heads, -1)
- sampling_offsets = self.sampling_offsets(query).view(
- bs, num_query, self.num_heads, self.num_levels, self.num_points, 2)
- attention_weights = self.attention_weights(query).view(
- bs, num_query, self.num_heads, self.num_levels * self.num_points)
- attention_weights = attention_weights.softmax(-1)
-
- attention_weights = attention_weights.view(bs, num_query,
- self.num_heads,
- self.num_levels,
- self.num_points)
- if reference_points.shape[-1] == 2:
- offset_normalizer = torch.stack(
- [spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
- sampling_locations = reference_points[:, :, None, :, None, :] \
- + sampling_offsets \
- / offset_normalizer[None, None, None, :, None, :]
- elif reference_points.shape[-1] == 4:
- sampling_locations = reference_points[:, :, None, :, None, :2] \
- + sampling_offsets / self.num_points \
- * reference_points[:, :, None, :, None, 2:] \
- * 0.5
- else:
- raise ValueError(
- f'Last dim of reference_points must be'
- f' 2 or 4, but get {reference_points.shape[-1]} instead.')
- if torch.cuda.is_available() and value.is_cuda:
- output = MultiScaleDeformableAttnFunction.apply(
- value, spatial_shapes, level_start_index, sampling_locations,
- attention_weights, self.im2col_step)
- else:
- output = multi_scale_deformable_attn_pytorch(
- value, spatial_shapes, sampling_locations, attention_weights)
-
- output = self.output_proj(output)
-
- if not self.batch_first:
- # (num_query, bs ,embed_dims)
- output = output.permute(1, 0, 2)
-
- return self.dropout(output) + identity
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/misc.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/misc.py
deleted file mode 100644
index 2c58d0d7fee9fe3d4519270ad8c1e998d0d8a18c..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/misc.py
+++ /dev/null
@@ -1,377 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import collections.abc
-import functools
-import itertools
-import subprocess
-import warnings
-from collections import abc
-from importlib import import_module
-from inspect import getfullargspec
-from itertools import repeat
-
-
-# From PyTorch internals
-def _ntuple(n):
-
- def parse(x):
- if isinstance(x, collections.abc.Iterable):
- return x
- return tuple(repeat(x, n))
-
- return parse
-
-
-to_1tuple = _ntuple(1)
-to_2tuple = _ntuple(2)
-to_3tuple = _ntuple(3)
-to_4tuple = _ntuple(4)
-to_ntuple = _ntuple
-
-
-def is_str(x):
- """Whether the input is an string instance.
-
- Note: This method is deprecated since python 2 is no longer supported.
- """
- return isinstance(x, str)
-
-
-def import_modules_from_strings(imports, allow_failed_imports=False):
- """Import modules from the given list of strings.
-
- Args:
- imports (list | str | None): The given module names to be imported.
- allow_failed_imports (bool): If True, the failed imports will return
- None. Otherwise, an ImportError is raise. Default: False.
-
- Returns:
- list[module] | module | None: The imported modules.
-
- Examples:
- >>> osp, sys = import_modules_from_strings(
- ... ['os.path', 'sys'])
- >>> import os.path as osp_
- >>> import sys as sys_
- >>> assert osp == osp_
- >>> assert sys == sys_
- """
- if not imports:
- return
- single_import = False
- if isinstance(imports, str):
- single_import = True
- imports = [imports]
- if not isinstance(imports, list):
- raise TypeError(
- f'custom_imports must be a list but got type {type(imports)}')
- imported = []
- for imp in imports:
- if not isinstance(imp, str):
- raise TypeError(
- f'{imp} is of type {type(imp)} and cannot be imported.')
- try:
- imported_tmp = import_module(imp)
- except ImportError:
- if allow_failed_imports:
- warnings.warn(f'{imp} failed to import and is ignored.',
- UserWarning)
- imported_tmp = None
- else:
- raise ImportError
- imported.append(imported_tmp)
- if single_import:
- imported = imported[0]
- return imported
-
-
-def iter_cast(inputs, dst_type, return_type=None):
- """Cast elements of an iterable object into some type.
-
- Args:
- inputs (Iterable): The input object.
- dst_type (type): Destination type.
- return_type (type, optional): If specified, the output object will be
- converted to this type, otherwise an iterator.
-
- Returns:
- iterator or specified type: The converted object.
- """
- if not isinstance(inputs, abc.Iterable):
- raise TypeError('inputs must be an iterable object')
- if not isinstance(dst_type, type):
- raise TypeError('"dst_type" must be a valid type')
-
- out_iterable = map(dst_type, inputs)
-
- if return_type is None:
- return out_iterable
- else:
- return return_type(out_iterable)
-
-
-def list_cast(inputs, dst_type):
- """Cast elements of an iterable object into a list of some type.
-
- A partial method of :func:`iter_cast`.
- """
- return iter_cast(inputs, dst_type, return_type=list)
-
-
-def tuple_cast(inputs, dst_type):
- """Cast elements of an iterable object into a tuple of some type.
-
- A partial method of :func:`iter_cast`.
- """
- return iter_cast(inputs, dst_type, return_type=tuple)
-
-
-def is_seq_of(seq, expected_type, seq_type=None):
- """Check whether it is a sequence of some type.
-
- Args:
- seq (Sequence): The sequence to be checked.
- expected_type (type): Expected type of sequence items.
- seq_type (type, optional): Expected sequence type.
-
- Returns:
- bool: Whether the sequence is valid.
- """
- if seq_type is None:
- exp_seq_type = abc.Sequence
- else:
- assert isinstance(seq_type, type)
- exp_seq_type = seq_type
- if not isinstance(seq, exp_seq_type):
- return False
- for item in seq:
- if not isinstance(item, expected_type):
- return False
- return True
-
-
-def is_list_of(seq, expected_type):
- """Check whether it is a list of some type.
-
- A partial method of :func:`is_seq_of`.
- """
- return is_seq_of(seq, expected_type, seq_type=list)
-
-
-def is_tuple_of(seq, expected_type):
- """Check whether it is a tuple of some type.
-
- A partial method of :func:`is_seq_of`.
- """
- return is_seq_of(seq, expected_type, seq_type=tuple)
-
-
-def slice_list(in_list, lens):
- """Slice a list into several sub lists by a list of given length.
-
- Args:
- in_list (list): The list to be sliced.
- lens(int or list): The expected length of each out list.
-
- Returns:
- list: A list of sliced list.
- """
- if isinstance(lens, int):
- assert len(in_list) % lens == 0
- lens = [lens] * int(len(in_list) / lens)
- if not isinstance(lens, list):
- raise TypeError('"indices" must be an integer or a list of integers')
- elif sum(lens) != len(in_list):
- raise ValueError('sum of lens and list length does not '
- f'match: {sum(lens)} != {len(in_list)}')
- out_list = []
- idx = 0
- for i in range(len(lens)):
- out_list.append(in_list[idx:idx + lens[i]])
- idx += lens[i]
- return out_list
-
-
-def concat_list(in_list):
- """Concatenate a list of list into a single list.
-
- Args:
- in_list (list): The list of list to be merged.
-
- Returns:
- list: The concatenated flat list.
- """
- return list(itertools.chain(*in_list))
-
-
-def check_prerequisites(
- prerequisites,
- checker,
- msg_tmpl='Prerequisites "{}" are required in method "{}" but not '
- 'found, please install them first.'): # yapf: disable
- """A decorator factory to check if prerequisites are satisfied.
-
- Args:
- prerequisites (str of list[str]): Prerequisites to be checked.
- checker (callable): The checker method that returns True if a
- prerequisite is meet, False otherwise.
- msg_tmpl (str): The message template with two variables.
-
- Returns:
- decorator: A specific decorator.
- """
-
- def wrap(func):
-
- @functools.wraps(func)
- def wrapped_func(*args, **kwargs):
- requirements = [prerequisites] if isinstance(
- prerequisites, str) else prerequisites
- missing = []
- for item in requirements:
- if not checker(item):
- missing.append(item)
- if missing:
- print(msg_tmpl.format(', '.join(missing), func.__name__))
- raise RuntimeError('Prerequisites not meet.')
- else:
- return func(*args, **kwargs)
-
- return wrapped_func
-
- return wrap
-
-
-def _check_py_package(package):
- try:
- import_module(package)
- except ImportError:
- return False
- else:
- return True
-
-
-def _check_executable(cmd):
- if subprocess.call(f'which {cmd}', shell=True) != 0:
- return False
- else:
- return True
-
-
-def requires_package(prerequisites):
- """A decorator to check if some python packages are installed.
-
- Example:
- >>> @requires_package('numpy')
- >>> func(arg1, args):
- >>> return numpy.zeros(1)
- array([0.])
- >>> @requires_package(['numpy', 'non_package'])
- >>> func(arg1, args):
- >>> return numpy.zeros(1)
- ImportError
- """
- return check_prerequisites(prerequisites, checker=_check_py_package)
-
-
-def requires_executable(prerequisites):
- """A decorator to check if some executable files are installed.
-
- Example:
- >>> @requires_executable('ffmpeg')
- >>> func(arg1, args):
- >>> print(1)
- 1
- """
- return check_prerequisites(prerequisites, checker=_check_executable)
-
-
-def deprecated_api_warning(name_dict, cls_name=None):
- """A decorator to check if some arguments are deprecate and try to replace
- deprecate src_arg_name to dst_arg_name.
-
- Args:
- name_dict(dict):
- key (str): Deprecate argument names.
- val (str): Expected argument names.
-
- Returns:
- func: New function.
- """
-
- def api_warning_wrapper(old_func):
-
- @functools.wraps(old_func)
- def new_func(*args, **kwargs):
- # get the arg spec of the decorated method
- args_info = getfullargspec(old_func)
- # get name of the function
- func_name = old_func.__name__
- if cls_name is not None:
- func_name = f'{cls_name}.{func_name}'
- if args:
- arg_names = args_info.args[:len(args)]
- for src_arg_name, dst_arg_name in name_dict.items():
- if src_arg_name in arg_names:
- warnings.warn(
- f'"{src_arg_name}" is deprecated in '
- f'`{func_name}`, please use "{dst_arg_name}" '
- 'instead')
- arg_names[arg_names.index(src_arg_name)] = dst_arg_name
- if kwargs:
- for src_arg_name, dst_arg_name in name_dict.items():
- if src_arg_name in kwargs:
-
- assert dst_arg_name not in kwargs, (
- f'The expected behavior is to replace '
- f'the deprecated key `{src_arg_name}` to '
- f'new key `{dst_arg_name}`, but got them '
- f'in the arguments at the same time, which '
- f'is confusing. `{src_arg_name} will be '
- f'deprecated in the future, please '
- f'use `{dst_arg_name}` instead.')
-
- warnings.warn(
- f'"{src_arg_name}" is deprecated in '
- f'`{func_name}`, please use "{dst_arg_name}" '
- 'instead')
- kwargs[dst_arg_name] = kwargs.pop(src_arg_name)
-
- # apply converted arguments to the decorated method
- output = old_func(*args, **kwargs)
- return output
-
- return new_func
-
- return api_warning_wrapper
-
-
-def is_method_overridden(method, base_class, derived_class):
- """Check if a method of base class is overridden in derived class.
-
- Args:
- method (str): the method name to check.
- base_class (type): the class of the base class.
- derived_class (type | Any): the class or instance of the derived class.
- """
- assert isinstance(base_class, type), \
- "base_class doesn't accept instance, Please pass class instead."
-
- if not isinstance(derived_class, type):
- derived_class = derived_class.__class__
-
- base_method = getattr(base_class, method)
- derived_method = getattr(derived_class, method)
- return derived_method != base_method
-
-
-def has_method(obj: object, method: str) -> bool:
- """Check whether the object has a method.
-
- Args:
- method (str): The method name to check.
- obj (object): The object to check.
-
- Returns:
- bool: True if the object has the method else False.
- """
- return hasattr(obj, method) and callable(getattr(obj, method))
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/momentum_updater.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/momentum_updater.py
deleted file mode 100644
index 60437756ceedf06055ec349df69a25465738d3f0..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/momentum_updater.py
+++ /dev/null
@@ -1,493 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import annotator.uniformer.mmcv as mmcv
-from .hook import HOOKS, Hook
-from .lr_updater import annealing_cos, annealing_linear, format_param
-
-
-class MomentumUpdaterHook(Hook):
-
- def __init__(self,
- by_epoch=True,
- warmup=None,
- warmup_iters=0,
- warmup_ratio=0.9):
- # validate the "warmup" argument
- if warmup is not None:
- if warmup not in ['constant', 'linear', 'exp']:
- raise ValueError(
- f'"{warmup}" is not a supported type for warming up, valid'
- ' types are "constant" and "linear"')
- if warmup is not None:
- assert warmup_iters > 0, \
- '"warmup_iters" must be a positive integer'
- assert 0 < warmup_ratio <= 1.0, \
- '"warmup_momentum" must be in range (0,1]'
-
- self.by_epoch = by_epoch
- self.warmup = warmup
- self.warmup_iters = warmup_iters
- self.warmup_ratio = warmup_ratio
-
- self.base_momentum = [] # initial momentum for all param groups
- self.regular_momentum = [
- ] # expected momentum if no warming up is performed
-
- def _set_momentum(self, runner, momentum_groups):
- if isinstance(runner.optimizer, dict):
- for k, optim in runner.optimizer.items():
- for param_group, mom in zip(optim.param_groups,
- momentum_groups[k]):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
- else:
- for param_group, mom in zip(runner.optimizer.param_groups,
- momentum_groups):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
-
- def get_momentum(self, runner, base_momentum):
- raise NotImplementedError
-
- def get_regular_momentum(self, runner):
- if isinstance(runner.optimizer, dict):
- momentum_groups = {}
- for k in runner.optimizer.keys():
- _momentum_group = [
- self.get_momentum(runner, _base_momentum)
- for _base_momentum in self.base_momentum[k]
- ]
- momentum_groups.update({k: _momentum_group})
- return momentum_groups
- else:
- return [
- self.get_momentum(runner, _base_momentum)
- for _base_momentum in self.base_momentum
- ]
-
- def get_warmup_momentum(self, cur_iters):
-
- def _get_warmup_momentum(cur_iters, regular_momentum):
- if self.warmup == 'constant':
- warmup_momentum = [
- _momentum / self.warmup_ratio
- for _momentum in self.regular_momentum
- ]
- elif self.warmup == 'linear':
- k = (1 - cur_iters / self.warmup_iters) * (1 -
- self.warmup_ratio)
- warmup_momentum = [
- _momentum / (1 - k) for _momentum in self.regular_mom
- ]
- elif self.warmup == 'exp':
- k = self.warmup_ratio**(1 - cur_iters / self.warmup_iters)
- warmup_momentum = [
- _momentum / k for _momentum in self.regular_mom
- ]
- return warmup_momentum
-
- if isinstance(self.regular_momentum, dict):
- momentum_groups = {}
- for key, regular_momentum in self.regular_momentum.items():
- momentum_groups[key] = _get_warmup_momentum(
- cur_iters, regular_momentum)
- return momentum_groups
- else:
- return _get_warmup_momentum(cur_iters, self.regular_momentum)
-
- def before_run(self, runner):
- # NOTE: when resuming from a checkpoint,
- # if 'initial_momentum' is not saved,
- # it will be set according to the optimizer params
- if isinstance(runner.optimizer, dict):
- self.base_momentum = {}
- for k, optim in runner.optimizer.items():
- for group in optim.param_groups:
- if 'momentum' in group.keys():
- group.setdefault('initial_momentum', group['momentum'])
- else:
- group.setdefault('initial_momentum', group['betas'][0])
- _base_momentum = [
- group['initial_momentum'] for group in optim.param_groups
- ]
- self.base_momentum.update({k: _base_momentum})
- else:
- for group in runner.optimizer.param_groups:
- if 'momentum' in group.keys():
- group.setdefault('initial_momentum', group['momentum'])
- else:
- group.setdefault('initial_momentum', group['betas'][0])
- self.base_momentum = [
- group['initial_momentum']
- for group in runner.optimizer.param_groups
- ]
-
- def before_train_epoch(self, runner):
- if not self.by_epoch:
- return
- self.regular_mom = self.get_regular_momentum(runner)
- self._set_momentum(runner, self.regular_mom)
-
- def before_train_iter(self, runner):
- cur_iter = runner.iter
- if not self.by_epoch:
- self.regular_mom = self.get_regular_momentum(runner)
- if self.warmup is None or cur_iter >= self.warmup_iters:
- self._set_momentum(runner, self.regular_mom)
- else:
- warmup_momentum = self.get_warmup_momentum(cur_iter)
- self._set_momentum(runner, warmup_momentum)
- elif self.by_epoch:
- if self.warmup is None or cur_iter > self.warmup_iters:
- return
- elif cur_iter == self.warmup_iters:
- self._set_momentum(runner, self.regular_mom)
- else:
- warmup_momentum = self.get_warmup_momentum(cur_iter)
- self._set_momentum(runner, warmup_momentum)
-
-
-@HOOKS.register_module()
-class StepMomentumUpdaterHook(MomentumUpdaterHook):
- """Step momentum scheduler with min value clipping.
-
- Args:
- step (int | list[int]): Step to decay the momentum. If an int value is
- given, regard it as the decay interval. If a list is given, decay
- momentum at these steps.
- gamma (float, optional): Decay momentum ratio. Default: 0.5.
- min_momentum (float, optional): Minimum momentum value to keep. If
- momentum after decay is lower than this value, it will be clipped
- accordingly. If None is given, we don't perform lr clipping.
- Default: None.
- """
-
- def __init__(self, step, gamma=0.5, min_momentum=None, **kwargs):
- if isinstance(step, list):
- assert mmcv.is_list_of(step, int)
- assert all([s > 0 for s in step])
- elif isinstance(step, int):
- assert step > 0
- else:
- raise TypeError('"step" must be a list or integer')
- self.step = step
- self.gamma = gamma
- self.min_momentum = min_momentum
- super(StepMomentumUpdaterHook, self).__init__(**kwargs)
-
- def get_momentum(self, runner, base_momentum):
- progress = runner.epoch if self.by_epoch else runner.iter
-
- # calculate exponential term
- if isinstance(self.step, int):
- exp = progress // self.step
- else:
- exp = len(self.step)
- for i, s in enumerate(self.step):
- if progress < s:
- exp = i
- break
-
- momentum = base_momentum * (self.gamma**exp)
- if self.min_momentum is not None:
- # clip to a minimum value
- momentum = max(momentum, self.min_momentum)
- return momentum
-
-
-@HOOKS.register_module()
-class CosineAnnealingMomentumUpdaterHook(MomentumUpdaterHook):
-
- def __init__(self, min_momentum=None, min_momentum_ratio=None, **kwargs):
- assert (min_momentum is None) ^ (min_momentum_ratio is None)
- self.min_momentum = min_momentum
- self.min_momentum_ratio = min_momentum_ratio
- super(CosineAnnealingMomentumUpdaterHook, self).__init__(**kwargs)
-
- def get_momentum(self, runner, base_momentum):
- if self.by_epoch:
- progress = runner.epoch
- max_progress = runner.max_epochs
- else:
- progress = runner.iter
- max_progress = runner.max_iters
- if self.min_momentum_ratio is not None:
- target_momentum = base_momentum * self.min_momentum_ratio
- else:
- target_momentum = self.min_momentum
- return annealing_cos(base_momentum, target_momentum,
- progress / max_progress)
-
-
-@HOOKS.register_module()
-class CyclicMomentumUpdaterHook(MomentumUpdaterHook):
- """Cyclic momentum Scheduler.
-
- Implement the cyclical momentum scheduler policy described in
- https://arxiv.org/pdf/1708.07120.pdf
-
- This momentum scheduler usually used together with the CyclicLRUpdater
- to improve the performance in the 3D detection area.
-
- Attributes:
- target_ratio (tuple[float]): Relative ratio of the lowest momentum and
- the highest momentum to the initial momentum.
- cyclic_times (int): Number of cycles during training
- step_ratio_up (float): The ratio of the increasing process of momentum
- in the total cycle.
- by_epoch (bool): Whether to update momentum by epoch.
- """
-
- def __init__(self,
- by_epoch=False,
- target_ratio=(0.85 / 0.95, 1),
- cyclic_times=1,
- step_ratio_up=0.4,
- **kwargs):
- if isinstance(target_ratio, float):
- target_ratio = (target_ratio, target_ratio / 1e5)
- elif isinstance(target_ratio, tuple):
- target_ratio = (target_ratio[0], target_ratio[0] / 1e5) \
- if len(target_ratio) == 1 else target_ratio
- else:
- raise ValueError('target_ratio should be either float '
- f'or tuple, got {type(target_ratio)}')
-
- assert len(target_ratio) == 2, \
- '"target_ratio" must be list or tuple of two floats'
- assert 0 <= step_ratio_up < 1.0, \
- '"step_ratio_up" must be in range [0,1)'
-
- self.target_ratio = target_ratio
- self.cyclic_times = cyclic_times
- self.step_ratio_up = step_ratio_up
- self.momentum_phases = [] # init momentum_phases
- # currently only support by_epoch=False
- assert not by_epoch, \
- 'currently only support "by_epoch" = False'
- super(CyclicMomentumUpdaterHook, self).__init__(by_epoch, **kwargs)
-
- def before_run(self, runner):
- super(CyclicMomentumUpdaterHook, self).before_run(runner)
- # initiate momentum_phases
- # total momentum_phases are separated as up and down
- max_iter_per_phase = runner.max_iters // self.cyclic_times
- iter_up_phase = int(self.step_ratio_up * max_iter_per_phase)
- self.momentum_phases.append(
- [0, iter_up_phase, max_iter_per_phase, 1, self.target_ratio[0]])
- self.momentum_phases.append([
- iter_up_phase, max_iter_per_phase, max_iter_per_phase,
- self.target_ratio[0], self.target_ratio[1]
- ])
-
- def get_momentum(self, runner, base_momentum):
- curr_iter = runner.iter
- for (start_iter, end_iter, max_iter_per_phase, start_ratio,
- end_ratio) in self.momentum_phases:
- curr_iter %= max_iter_per_phase
- if start_iter <= curr_iter < end_iter:
- progress = curr_iter - start_iter
- return annealing_cos(base_momentum * start_ratio,
- base_momentum * end_ratio,
- progress / (end_iter - start_iter))
-
-
-@HOOKS.register_module()
-class OneCycleMomentumUpdaterHook(MomentumUpdaterHook):
- """OneCycle momentum Scheduler.
-
- This momentum scheduler usually used together with the OneCycleLrUpdater
- to improve the performance.
-
- Args:
- base_momentum (float or list): Lower momentum boundaries in the cycle
- for each parameter group. Note that momentum is cycled inversely
- to learning rate; at the peak of a cycle, momentum is
- 'base_momentum' and learning rate is 'max_lr'.
- Default: 0.85
- max_momentum (float or list): Upper momentum boundaries in the cycle
- for each parameter group. Functionally,
- it defines the cycle amplitude (max_momentum - base_momentum).
- Note that momentum is cycled inversely
- to learning rate; at the start of a cycle, momentum is
- 'max_momentum' and learning rate is 'base_lr'
- Default: 0.95
- pct_start (float): The percentage of the cycle (in number of steps)
- spent increasing the learning rate.
- Default: 0.3
- anneal_strategy (str): {'cos', 'linear'}
- Specifies the annealing strategy: 'cos' for cosine annealing,
- 'linear' for linear annealing.
- Default: 'cos'
- three_phase (bool): If three_phase is True, use a third phase of the
- schedule to annihilate the learning rate according to
- final_div_factor instead of modifying the second phase (the first
- two phases will be symmetrical about the step indicated by
- pct_start).
- Default: False
- """
-
- def __init__(self,
- base_momentum=0.85,
- max_momentum=0.95,
- pct_start=0.3,
- anneal_strategy='cos',
- three_phase=False,
- **kwargs):
- # validate by_epoch, currently only support by_epoch=False
- if 'by_epoch' not in kwargs:
- kwargs['by_epoch'] = False
- else:
- assert not kwargs['by_epoch'], \
- 'currently only support "by_epoch" = False'
- if not isinstance(base_momentum, (float, list, dict)):
- raise ValueError('base_momentum must be the type among of float,'
- 'list or dict.')
- self._base_momentum = base_momentum
- if not isinstance(max_momentum, (float, list, dict)):
- raise ValueError('max_momentum must be the type among of float,'
- 'list or dict.')
- self._max_momentum = max_momentum
- # validate pct_start
- if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float):
- raise ValueError('Expected float between 0 and 1 pct_start, but '
- f'got {pct_start}')
- self.pct_start = pct_start
- # validate anneal_strategy
- if anneal_strategy not in ['cos', 'linear']:
- raise ValueError('anneal_strategy must by one of "cos" or '
- f'"linear", instead got {anneal_strategy}')
- elif anneal_strategy == 'cos':
- self.anneal_func = annealing_cos
- elif anneal_strategy == 'linear':
- self.anneal_func = annealing_linear
- self.three_phase = three_phase
- self.momentum_phases = [] # init momentum_phases
- super(OneCycleMomentumUpdaterHook, self).__init__(**kwargs)
-
- def before_run(self, runner):
- if isinstance(runner.optimizer, dict):
- for k, optim in runner.optimizer.items():
- if ('momentum' not in optim.defaults
- and 'betas' not in optim.defaults):
- raise ValueError('optimizer must support momentum with'
- 'option enabled')
- self.use_beta1 = 'betas' in optim.defaults
- _base_momentum = format_param(k, optim, self._base_momentum)
- _max_momentum = format_param(k, optim, self._max_momentum)
- for group, b_momentum, m_momentum in zip(
- optim.param_groups, _base_momentum, _max_momentum):
- if self.use_beta1:
- _, beta2 = group['betas']
- group['betas'] = (m_momentum, beta2)
- else:
- group['momentum'] = m_momentum
- group['base_momentum'] = b_momentum
- group['max_momentum'] = m_momentum
- else:
- optim = runner.optimizer
- if ('momentum' not in optim.defaults
- and 'betas' not in optim.defaults):
- raise ValueError('optimizer must support momentum with'
- 'option enabled')
- self.use_beta1 = 'betas' in optim.defaults
- k = type(optim).__name__
- _base_momentum = format_param(k, optim, self._base_momentum)
- _max_momentum = format_param(k, optim, self._max_momentum)
- for group, b_momentum, m_momentum in zip(optim.param_groups,
- _base_momentum,
- _max_momentum):
- if self.use_beta1:
- _, beta2 = group['betas']
- group['betas'] = (m_momentum, beta2)
- else:
- group['momentum'] = m_momentum
- group['base_momentum'] = b_momentum
- group['max_momentum'] = m_momentum
-
- if self.three_phase:
- self.momentum_phases.append({
- 'end_iter':
- float(self.pct_start * runner.max_iters) - 1,
- 'start_momentum':
- 'max_momentum',
- 'end_momentum':
- 'base_momentum'
- })
- self.momentum_phases.append({
- 'end_iter':
- float(2 * self.pct_start * runner.max_iters) - 2,
- 'start_momentum':
- 'base_momentum',
- 'end_momentum':
- 'max_momentum'
- })
- self.momentum_phases.append({
- 'end_iter': runner.max_iters - 1,
- 'start_momentum': 'max_momentum',
- 'end_momentum': 'max_momentum'
- })
- else:
- self.momentum_phases.append({
- 'end_iter':
- float(self.pct_start * runner.max_iters) - 1,
- 'start_momentum':
- 'max_momentum',
- 'end_momentum':
- 'base_momentum'
- })
- self.momentum_phases.append({
- 'end_iter': runner.max_iters - 1,
- 'start_momentum': 'base_momentum',
- 'end_momentum': 'max_momentum'
- })
-
- def _set_momentum(self, runner, momentum_groups):
- if isinstance(runner.optimizer, dict):
- for k, optim in runner.optimizer.items():
- for param_group, mom in zip(optim.param_groups,
- momentum_groups[k]):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
- else:
- for param_group, mom in zip(runner.optimizer.param_groups,
- momentum_groups):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
-
- def get_momentum(self, runner, param_group):
- curr_iter = runner.iter
- start_iter = 0
- for i, phase in enumerate(self.momentum_phases):
- end_iter = phase['end_iter']
- if curr_iter <= end_iter or i == len(self.momentum_phases) - 1:
- pct = (curr_iter - start_iter) / (end_iter - start_iter)
- momentum = self.anneal_func(
- param_group[phase['start_momentum']],
- param_group[phase['end_momentum']], pct)
- break
- start_iter = end_iter
- return momentum
-
- def get_regular_momentum(self, runner):
- if isinstance(runner.optimizer, dict):
- momentum_groups = {}
- for k, optim in runner.optimizer.items():
- _momentum_group = [
- self.get_momentum(runner, param_group)
- for param_group in optim.param_groups
- ]
- momentum_groups.update({k: _momentum_group})
- return momentum_groups
- else:
- momentum_groups = []
- for param_group in runner.optimizer.param_groups:
- momentum_groups.append(self.get_momentum(runner, param_group))
- return momentum_groups
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/core/evaluation/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/core/evaluation/__init__.py
deleted file mode 100644
index f7cc4b23413a0639e9de00eeb0bf600632d2c6cd..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/core/evaluation/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from .class_names import get_classes, get_palette
-from .eval_hooks import DistEvalHook, EvalHook
-from .metrics import eval_metrics, mean_dice, mean_fscore, mean_iou
-
-__all__ = [
- 'EvalHook', 'DistEvalHook', 'mean_dice', 'mean_iou', 'mean_fscore',
- 'eval_metrics', 'get_classes', 'get_palette'
-]
diff --git a/spaces/abnerh/video-to-subs/README.md b/spaces/abnerh/video-to-subs/README.md
deleted file mode 100644
index ed32bb5f36f156684d9ebd5458b8a93e085d7e12..0000000000000000000000000000000000000000
--- a/spaces/abnerh/video-to-subs/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Video To Subs
-emoji: 🎙️
-colorFrom: white
-colorTo: blue
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/ai-guru/composer/static/_app/pages/index.svelte-9646dd5a.js b/spaces/ai-guru/composer/static/_app/pages/index.svelte-9646dd5a.js
deleted file mode 100644
index 510a133267536503ed780d792110a1ff8173e49a..0000000000000000000000000000000000000000
--- a/spaces/ai-guru/composer/static/_app/pages/index.svelte-9646dd5a.js
+++ /dev/null
@@ -1,5 +0,0 @@
-import{S as Z,i as x,s as ee,e as b,k as I,c as w,a as E,d as g,m as C,K as se,b as d,g as P,J as f,L as U,t as N,h as H,j as Ee,E as re,M as Re,N as R,O as fe,P as Ge,w as Y,x as J,y as K,Q as Ve,q as L,o as V,B as Q,R as ue,T as Fe,f as X,U as _e,V as ie,W as qe,n as $e,p as Te,X as We,v as Xe,u as Ye,Y as Je,l as Me}from"../chunks/index-7c452e28.js";import{w as le}from"../chunks/index-d282aaf8.js";const he=le("synth"),be=le("medium"),we=le("medium"),ve=le(!1),pe=le(""),ye=le(""),ke=le(""),ne={piano:"Piano",chamber:"Chamber Music",rock_and_metal:"Rock and Metal",synth:"Synthesizer",church:"Church",timpani_strings_harp:"Timpani, Contrabass, Harp",country:"Country",reggae:"Reggae-esque"},Ke={low:"Low",medium:"Medium",high:"High"},Qe={low:"Low",medium:"Medium",high:"High",very_high:"Very High"};function Ne(l,e,s){const t=l.slice();return t[4]=e[s],t[6]=s,t}function He(l){let e,s,t,o,n,a,i,c,u,p,r,_;return{c(){e=b("label"),s=b("div"),t=b("img"),a=I(),i=b("input"),u=I(),this.h()},l(m){e=w(m,"LABEL",{"data-selected":!0,class:!0});var T=E(e);s=w(T,"DIV",{class:!0});var v=E(s);t=w(v,"IMG",{src:!0,alt:!0,class:!0}),v.forEach(g),a=C(T),i=w(T,"INPUT",{type:!0,class:!0}),u=C(T),T.forEach(g),this.h()},h(){se(t.src,o=`static/${l[4]}.svg`)||d(t,"src",o),d(t,"alt",n=ne[l[4]]),d(t,"class","svelte-1r9pswz"),d(s,"class","svelte-1r9pswz"),d(i,"type","radio"),i.__value=c=l[4],i.value=i.__value,d(i,"class","svelte-1r9pswz"),l[3][0].push(i),d(e,"data-selected",p=l[0]===l[4]),d(e,"class","svelte-1r9pswz")},m(m,T){P(m,e,T),f(e,s),f(s,t),f(e,a),f(e,i),i.checked=i.__value===l[0],f(e,u),r||(_=U(i,"change",l[2]),r=!0)},p(m,T){T&1&&(i.checked=i.__value===m[0]),T&1&&p!==(p=m[0]===m[4])&&d(e,"data-selected",p)},d(m){m&&g(e),l[3][0].splice(l[3][0].indexOf(i),1),r=!1,_()}}}function Ze(l){let e,s,t=(ne[l[0]]||"Synthesizer")+"",o,n,a,i=l[1],c=[];for(let u=0;us(0,t=i));const o=Object.keys(ne),n=[[]];function a(){t=this.__value,he.set(t)}return[t,o,a,n]}class et extends Z{constructor(e){super(),x(this,e,xe,Ze,ee,{})}}function je(l,e,s){const t=l.slice();return t[5]=e[s],t}function Be(l){let e,s=l[1][l[5]]+"",t,o,n,a,i,c,u,p;return{c(){e=b("label"),t=N(s),o=I(),n=b("input"),i=I(),this.h()},l(r){e=w(r,"LABEL",{"data-selected":!0,class:!0});var _=E(e);t=H(_,s),o=C(_),n=w(_,"INPUT",{type:!0,class:!0}),i=C(_),_.forEach(g),this.h()},h(){d(n,"type","radio"),n.__value=a=l[5],n.value=n.__value,d(n,"class","svelte-1m848u0"),l[4][0].push(n),d(e,"data-selected",c=l[5]===l[0]),d(e,"class","svelte-1m848u0")},m(r,_){P(r,e,_),f(e,t),f(e,o),f(e,n),n.checked=n.__value===l[0],f(e,i),u||(p=U(n,"change",l[3]),u=!0)},p(r,_){_&2&&s!==(s=r[1][r[5]]+"")&&Ee(t,s),_&1&&(n.checked=n.__value===r[0]),_&1&&c!==(c=r[5]===r[0])&&d(e,"data-selected",c)},d(r){r&&g(e),l[4][0].splice(l[4][0].indexOf(n),1),u=!1,p()}}}function tt(l){let e,s=l[2],t=[];for(let o=0;o{"options"in c&&s(1,t=c.options),"selection"in c&&s(0,n=c.selection)},[n,t,o,i,a]}class Ue extends Z{constructor(e){super(),x(this,e,st,tt,ee,{options:1,selection:0})}}function nt(l){let e,s,t,o,n,a,i,c;function u(r){l[1](r)}let p={options:Ke};return l[0]!==void 0&&(p.selection=l[0]),a=new Ue({props:p}),fe.push(()=>Ge(a,"selection",u)),{c(){e=b("div"),s=b("fieldset"),t=b("legend"),o=N("Note density"),n=I(),Y(a.$$.fragment),this.h()},l(r){e=w(r,"DIV",{});var _=E(e);s=w(_,"FIELDSET",{class:!0});var m=E(s);t=w(m,"LEGEND",{class:!0});var T=E(t);o=H(T,"Note density"),T.forEach(g),n=C(m),J(a.$$.fragment,m),m.forEach(g),_.forEach(g),this.h()},h(){d(t,"class","svelte-1ikh8be"),d(s,"class","svelte-1ikh8be")},m(r,_){P(r,e,_),f(e,s),f(s,t),f(t,o),f(s,n),K(a,s,null),c=!0},p(r,[_]){const m={};!i&&_&1&&(i=!0,m.selection=r[0],Ve(()=>i=!1)),a.$set(m)},i(r){c||(L(a.$$.fragment,r),c=!0)},o(r){V(a.$$.fragment,r),c=!1},d(r){r&&g(e),Q(a)}}}function lt(l,e,s){let t;R(l,be,n=>s(0,t=n));function o(n){t=n,be.set(t)}return[t,o]}class at extends Z{constructor(e){super(),x(this,e,lt,nt,ee,{})}}function ot(l){let e,s,t,o,n,a,i,c;function u(r){l[1](r)}let p={options:Qe};return l[0]!==void 0&&(p.selection=l[0]),a=new Ue({props:p}),fe.push(()=>Ge(a,"selection",u)),{c(){e=b("div"),s=b("fieldset"),t=b("legend"),o=N("Temperature"),n=I(),Y(a.$$.fragment),this.h()},l(r){e=w(r,"DIV",{});var _=E(e);s=w(_,"FIELDSET",{class:!0});var m=E(s);t=w(m,"LEGEND",{class:!0});var T=E(t);o=H(T,"Temperature"),T.forEach(g),n=C(m),J(a.$$.fragment,m),m.forEach(g),_.forEach(g),this.h()},h(){d(t,"class","svelte-1ikh8be"),d(s,"class","svelte-1ikh8be")},m(r,_){P(r,e,_),f(e,s),f(s,t),f(t,o),f(s,n),K(a,s,null),c=!0},p(r,[_]){const m={};!i&&_&1&&(i=!0,m.selection=r[0],Ve(()=>i=!1)),a.$set(m)},i(r){c||(L(a.$$.fragment,r),c=!0)},o(r){V(a.$$.fragment,r),c=!1},d(r){r&&g(e),Q(a)}}}function rt(l,e,s){let t;R(l,we,n=>s(0,t=n));function o(n){t=n,we.set(t)}return[t,o]}class it extends Z{constructor(e){super(),x(this,e,rt,ot,ee,{})}}function ct(l){let e,s,t;return{c(){e=N("Compose "),s=b("img"),this.h()},l(o){e=H(o,"Compose "),s=w(o,"IMG",{src:!0,alt:!0,class:!0}),this.h()},h(){se(s.src,t="static/wand.svg")||d(s,"src",t),d(s,"alt","Magic wand"),d(s,"class","svelte-18w38ow")},m(o,n){P(o,e,n),P(o,s,n)},d(o){o&&g(e),o&&g(s)}}}function ut(l){let e;return{c(){e=N("Composing...")},l(s){e=H(s,"Composing...")},m(s,t){P(s,e,t)},d(s){s&&g(e)}}}function ft(l){let e,s,t;function o(i,c){return i[0]?ut:ct}let n=o(l),a=n(l);return{c(){e=b("button"),a.c(),this.h()},l(i){e=w(i,"BUTTON",{class:!0});var c=E(e);a.l(c),c.forEach(g),this.h()},h(){e.disabled=l[0],d(e,"class","svelte-18w38ow")},m(i,c){P(i,e,c),a.m(e,null),s||(t=U(e,"click",l[1]),s=!0)},p(i,[c]){n!==(n=o(i))&&(a.d(1),a=n(i),a&&(a.c(),a.m(e,null))),c&1&&(e.disabled=i[0])},i:re,o:re,d(i){i&&g(e),a.d(),s=!1,t()}}}function dt(l,e,s){let t,o,n,a,i,c,u;R(l,ve,v=>s(0,t=v)),R(l,ke,v=>s(2,o=v)),R(l,ye,v=>s(3,n=v)),R(l,pe,v=>s(4,a=v)),R(l,we,v=>s(5,i=v)),R(l,be,v=>s(6,c=v)),R(l,he,v=>s(7,u=v));const p=()=>{"mediaSession"in navigator&&(navigator.mediaSession.metadata=new MediaMetadata({title:`${ne[u]} Composition`,artist:"AI Guru Composer",album:"Hugging Face",artwork:[{src:"static/hugging-face-headphones.png",sizes:"512x512",type:"image/png"}]}))},r=async({music_style:v,density:y,temperature:D})=>{var A;const S=await fetch("task/create",{method:"POST",headers:{"Content-Type":"application/json"},body:JSON.stringify({music_style:v,density:y,temperature:D})});if(!S.ok||!((A=S.headers.get("content-type"))!=null&&A.includes("application/json")))throw new Error(`Unable to create composition: [${S.status}] ${await S.text()}`);return await S.json()},_=async v=>{var D;const y=await fetch(`task/poll?task_id=${v.task_id}`);if(!y.ok||!((D=y.headers.get("content-type"))!=null&&D.includes("application/json")))throw new Error(`Unable to create composition: [${y.status}] ${await y.text()}`);return await y.json()},m=async(v,y=1e3,D=100)=>(v=await _(v),v.status==="completed"||v.status==="failed"||D&&v.poll_count>D?v:(await new Promise(S=>setTimeout(S,y)),await m(v,y,D)));return[t,async()=>{ue(ve,t=!0,t);try{const v=await r({music_style:u,density:c,temperature:i}),y=await m(v),{audio:D,image:S,tokens:j}=y.output;ue(pe,a=D,a),ue(ye,n=S,n),ue(ke,o=j,o),p()}catch(v){console.error(v)}finally{ue(ve,t=!1,t)}}]}class _t extends Z{constructor(e){super(),x(this,e,dt,ft,ee,{})}}function ce(l,{delay:e=0,duration:s=400,easing:t=Fe}={}){const o=+getComputedStyle(l).opacity;return{delay:e,duration:s,easing:t,css:n=>`opacity: ${n*o}`}}function Le(l){let e,s,t,o;return{c(){e=b("img"),this.h()},l(n){e=w(n,"IMG",{class:!0,src:!0,alt:!0,draggable:!0}),this.h()},h(){d(e,"class","play-button svelte-1536b0c"),se(e.src,s="static/play.svg")||d(e,"src",s),d(e,"alt","Play button"),d(e,"draggable","false"),X(e,"width",l[7]>100?"20%":"7.5%",!1)},m(n,a){P(n,e,a),o=!0},p(n,a){a&128&&X(e,"width",n[7]>100?"20%":"7.5%",!1)},i(n){o||(_e(()=>{t||(t=ie(e,ce,{},!0)),t.run(1)}),o=!0)},o(n){t||(t=ie(e,ce,{},!1)),t.run(0),o=!1},d(n){n&&g(e),n&&t&&t.end()}}}function ht(l){let e,s,t,o,n,a,i,c=!1,u,p=!0,r,_,m=`translate(${Math.min(l[6]*(l[1]/(l[2]-.9)),l[6])}px, -2%)`,T,v,y,D,S,j,A,q,W;function z(){cancelAnimationFrame(u),a.paused||(u=Je(z),c=!0),l[15].call(a)}let $=l[3]&&Le(l);return{c(){e=b("section"),s=b("img"),o=I(),n=b("div"),a=b("audio"),r=I(),_=b("div"),T=I(),$&&$.c(),v=I(),y=b("a"),D=N("Download"),this.h()},l(h){e=w(h,"SECTION",{class:!0});var k=E(e);s=w(k,"IMG",{class:!0,src:!0,alt:!0}),o=C(k),n=w(k,"DIV",{class:!0,tabindex:!0});var G=E(n);a=w(G,"AUDIO",{src:!0,class:!0}),E(a).forEach(g),r=C(G),_=w(G,"DIV",{class:!0}),E(_).forEach(g),T=C(G),$&&$.l(G),G.forEach(g),v=C(k),y=w(k,"A",{href:!0,download:!0,class:!0});var F=E(y);D=H(F,"Download"),F.forEach(g),k.forEach(g),this.h()},h(){d(s,"class","notes svelte-1536b0c"),se(s.src,t=l[8])||d(s,"src",t),d(s,"alt",""),se(a.src,i=l[9])||d(a,"src",i),d(a,"class","svelte-1536b0c"),l[2]===void 0&&_e(()=>l[16].call(a)),d(_,"class","handle svelte-1536b0c"),X(_,"transform",m,!1),d(n,"class","player svelte-1536b0c"),d(n,"tabindex","0"),X(n,"width",l[6]+"px",!1),X(n,"height",l[7]+"px",!1),d(y,"href",l[9]),d(y,"download",S=`${ne[l[10]]} Composition - AI Guru ft. Hugging Face.wav`),d(y,"class","download svelte-1536b0c"),d(e,"class","svelte-1536b0c")},m(h,k){P(h,e,k),f(e,s),l[14](s),f(e,o),f(e,n),f(n,a),f(n,r),f(n,_),f(n,T),$&&$.m(n,null),l[18](n),f(e,v),f(e,y),f(y,D),l[20](e),A=!0,q||(W=[U(a,"timeupdate",z),U(a,"durationchange",l[16]),U(a,"play",l[17]),U(a,"pause",l[17]),U(n,"mousemove",l[11]),U(n,"touchmove",qe(l[12])),U(n,"keydown",l[13]),U(n,"click",l[19])],q=!0)},p(h,[k]){(!A||k&256&&!se(s.src,t=h[8]))&&d(s,"src",t),(!A||k&512&&!se(a.src,i=h[9]))&&d(a,"src",i),!c&&k&2&&!isNaN(h[1])&&(a.currentTime=h[1]),c=!1,k&8&&p!==(p=h[3])&&a[p?"pause":"play"](),k&70&&m!==(m=`translate(${Math.min(h[6]*(h[1]/(h[2]-.9)),h[6])}px, -2%)`)&&X(_,"transform",m,!1),h[3]?$?($.p(h,k),k&8&&L($,1)):($=Le(h),$.c(),L($,1),$.m(n,null)):$&&($e(),V($,1,1,()=>{$=null}),Te()),k&64&&X(n,"width",h[6]+"px",!1),k&128&&X(n,"height",h[7]+"px",!1),(!A||k&512)&&d(y,"href",h[9]),(!A||k&1024&&S!==(S=`${ne[h[10]]} Composition - AI Guru ft. Hugging Face.wav`))&&d(y,"download",S)},i(h){A||(L($),_e(()=>{j||(j=ie(e,ce,{},!0)),j.run(1)}),A=!0)},o(h){V($),j||(j=ie(e,ce,{},!1)),j.run(0),A=!1},d(h){h&&g(e),l[14](null),$&&$.d(),l[18](null),l[20](null),h&&j&&j.end(),q=!1,We(W)}}}function pt(l,e,s){let t,o,n;R(l,ye,h=>s(8,t=h)),R(l,pe,h=>s(9,o=h)),R(l,he,h=>s(10,n=h));let a,i,c,u=!0,p,r,_,m;const T=()=>{s(6,_=r&&r.clientWidth),s(7,m=r&&r.clientHeight)};Xe(()=>{T(),"mediaSession"in navigator&&(navigator.mediaSession.setActionHandler("play",()=>s(3,u=!1)),navigator.mediaSession.setActionHandler("pause",()=>s(3,u=!0)),navigator.mediaSession.setActionHandler("stop",()=>{s(3,u=!0),s(1,i=0)})),window.scrollTo({top:a.offsetTop,behavior:"smooth"})}),Ye(()=>{T()});const v=h=>{if(!c||!h.buttons)return;const{left:k,right:G}=p.getBoundingClientRect();s(1,i=c*(h.clientX-k)/(G-k))},y=h=>{if(!c)return;const{left:k,right:G}=p.getBoundingClientRect();s(1,i=c*(h.touches[0].clientX-k)/(G-k))},D=h=>{h.preventDefault(),h.code==="Space"&&s(3,u=!u),h.code==="ArrowLeft"&&s(1,i=i>=1?i-1:0),h.code==="ArrowRight"&&s(1,i=i<=c-1?i+1:c)};function S(h){fe[h?"unshift":"push"](()=>{r=h,s(5,r)})}function j(){i=this.currentTime,s(1,i)}function A(){c=this.duration,s(2,c)}function q(){u=this.paused,s(3,u)}function W(h){fe[h?"unshift":"push"](()=>{p=h,s(4,p)})}const z=()=>s(3,u=!u);function $(h){fe[h?"unshift":"push"](()=>{a=h,s(0,a)})}return[a,i,c,u,p,r,_,m,t,o,n,v,y,D,S,j,A,q,W,z,$]}class mt extends Z{constructor(e){super(),x(this,e,pt,ht,ee,{})}}function ze(l){let e,s,t,o,n,a,i,c;return{c(){e=b("section"),s=b("h2"),t=N("Tokenized notes"),o=I(),n=b("p"),a=N(l[0]),this.h()},l(u){e=w(u,"SECTION",{class:!0});var p=E(e);s=w(p,"H2",{});var r=E(s);t=H(r,"Tokenized notes"),r.forEach(g),o=C(p),n=w(p,"P",{class:!0});var _=E(n);a=H(_,l[0]),_.forEach(g),p.forEach(g),this.h()},h(){d(n,"class","svelte-4un5mw"),d(e,"class","svelte-4un5mw")},m(u,p){P(u,e,p),f(e,s),f(s,t),f(e,o),f(e,n),f(n,a),c=!0},p(u,p){(!c||p&1)&&Ee(a,u[0])},i(u){c||(_e(()=>{i||(i=ie(e,ce,{},!0)),i.run(1)}),c=!0)},o(u){i||(i=ie(e,ce,{},!1)),i.run(0),c=!1},d(u){u&&g(e),u&&i&&i.end()}}}function gt(l){let e,s,t=l[0]&&ze(l);return{c(){t&&t.c(),e=Me()},l(o){t&&t.l(o),e=Me()},m(o,n){t&&t.m(o,n),P(o,e,n),s=!0},p(o,[n]){o[0]?t?(t.p(o,n),n&1&&L(t,1)):(t=ze(o),t.c(),L(t,1),t.m(e.parentNode,e)):t&&($e(),V(t,1,1,()=>{t=null}),Te())},i(o){s||(L(t),s=!0)},o(o){V(t),s=!1},d(o){t&&t.d(o),o&&g(e)}}}function vt(l,e,s){let t;return R(l,ke,o=>s(0,t=o)),[t]}class bt extends Z{constructor(e){super(),x(this,e,vt,gt,ee,{})}}function Pe(l){let e,s,t,o;return e=new mt({}),t=new bt({}),{c(){Y(e.$$.fragment),s=I(),Y(t.$$.fragment)},l(n){J(e.$$.fragment,n),s=C(n),J(t.$$.fragment,n)},m(n,a){K(e,n,a),P(n,s,a),K(t,n,a),o=!0},i(n){o||(L(e.$$.fragment,n),L(t.$$.fragment,n),o=!0)},o(n){V(e.$$.fragment,n),V(t.$$.fragment,n),o=!1},d(n){Q(e,n),n&&g(s),Q(t,n)}}}function wt(l){let e,s,t,o,n,a,i,c,u,p,r,_,m,T,v,y,D,S,j,A,q,W,z,$,h,k,G,F,me,te,ge,de;$=new et({}),k=new at({}),F=new it({}),te=new _t({});let M=l[0]&&Pe();return{c(){e=b("main"),s=b("h1"),t=N("Composer"),o=I(),n=b("p"),a=N("Trained on fifteen thousand songs. One AI model. Infinite compositions."),i=I(),c=b("p"),u=N(`This space contains a deep neural network model that can compose music. You can use it to generate audio in
- different styles, 4 bars at a time.`),p=I(),r=b("p"),_=N("Developed by "),m=b("a"),T=N("Ron Au"),v=N(` and
- `),y=b("a"),D=N("Tristan Behrens"),S=N("."),j=I(),A=b("p"),q=N("Have fun! And always feel free to send us some feedback and share your compositions!"),W=I(),z=b("section"),Y($.$$.fragment),h=I(),Y(k.$$.fragment),G=I(),Y(F.$$.fragment),me=I(),Y(te.$$.fragment),ge=I(),M&&M.c(),this.h()},l(B){e=w(B,"MAIN",{class:!0});var O=E(e);s=w(O,"H1",{class:!0});var Ie=E(s);t=H(Ie,"Composer"),Ie.forEach(g),o=C(O),n=w(O,"P",{class:!0});var Ce=E(n);a=H(Ce,"Trained on fifteen thousand songs. One AI model. Infinite compositions."),Ce.forEach(g),i=C(O),c=w(O,"P",{class:!0});var De=E(c);u=H(De,`This space contains a deep neural network model that can compose music. You can use it to generate audio in
- different styles, 4 bars at a time.`),De.forEach(g),p=C(O),r=w(O,"P",{class:!0});var ae=E(r);_=H(ae,"Developed by "),m=w(ae,"A",{href:!0,rel:!0,target:!0});var Se=E(m);T=H(Se,"Ron Au"),Se.forEach(g),v=H(ae,` and
- `),y=w(ae,"A",{href:!0,rel:!0,target:!0});var Ae=E(y);D=H(Ae,"Tristan Behrens"),Ae.forEach(g),S=H(ae,"."),ae.forEach(g),j=C(O),A=w(O,"P",{class:!0});var Oe=E(A);q=H(Oe,"Have fun! And always feel free to send us some feedback and share your compositions!"),Oe.forEach(g),W=C(O),z=w(O,"SECTION",{id:!0,class:!0});var oe=E(z);J($.$$.fragment,oe),h=C(oe),J(k.$$.fragment,oe),G=C(oe),J(F.$$.fragment,oe),oe.forEach(g),me=C(O),J(te.$$.fragment,O),ge=C(O),M&&M.l(O),O.forEach(g),this.h()},h(){d(s,"class","svelte-1rfjlkw"),d(n,"class","heading svelte-1rfjlkw"),d(c,"class","svelte-1rfjlkw"),d(m,"href","https://twitter.com/ronvoluted"),d(m,"rel","noopener"),d(m,"target","_blank"),d(y,"href","https://twitter.com/DrTBehrens"),d(y,"rel","noopener"),d(y,"target","_blank"),d(r,"class","svelte-1rfjlkw"),d(A,"class","svelte-1rfjlkw"),d(z,"id","options"),d(z,"class","svelte-1rfjlkw"),d(e,"class","svelte-1rfjlkw")},m(B,O){P(B,e,O),f(e,s),f(s,t),f(e,o),f(e,n),f(n,a),f(e,i),f(e,c),f(c,u),f(e,p),f(e,r),f(r,_),f(r,m),f(m,T),f(r,v),f(r,y),f(y,D),f(r,S),f(e,j),f(e,A),f(A,q),f(e,W),f(e,z),K($,z,null),f(z,h),K(k,z,null),f(z,G),K(F,z,null),f(e,me),K(te,e,null),f(e,ge),M&&M.m(e,null),de=!0},p(B,[O]){B[0]?M?O&1&&L(M,1):(M=Pe(),M.c(),L(M,1),M.m(e,null)):M&&($e(),V(M,1,1,()=>{M=null}),Te())},i(B){de||(L($.$$.fragment,B),L(k.$$.fragment,B),L(F.$$.fragment,B),L(te.$$.fragment,B),L(M),de=!0)},o(B){V($.$$.fragment,B),V(k.$$.fragment,B),V(F.$$.fragment,B),V(te.$$.fragment,B),V(M),de=!1},d(B){B&&g(e),Q($),Q(k),Q(F),Q(te),M&&M.d()}}}function yt(l,e,s){let t;return R(l,pe,o=>s(0,t=o)),[t]}class $t extends Z{constructor(e){super(),x(this,e,yt,wt,ee,{})}}export{$t as default};
diff --git a/spaces/aimstack/bloom/Dockerfile b/spaces/aimstack/bloom/Dockerfile
deleted file mode 100644
index 1b5313643bce150166d4135b441cd315634a68b9..0000000000000000000000000000000000000000
--- a/spaces/aimstack/bloom/Dockerfile
+++ /dev/null
@@ -1,30 +0,0 @@
-FROM python:3.9
-
-
-RUN useradd -m -u 1000 aim_user
-
-# Switch to the "aim_user" user
-USER aim_user
-
-# Set home to the user's home directory
-ENV HOME=/home/aim_user \
- PATH=/home/aim_user/.local/bin:$PATH
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME
-
-# install the `aim` package on the latest version
-RUN pip install aim==4.0.0.dev6
-
-RUN aim telemetry off
-
-ENTRYPOINT ["/bin/sh", "-c"]
-
-COPY aim_repo.tar.gz .
-RUN tar xvzf aim_repo.tar.gz
-# have to run `aim init` in the directory that stores aim data for
-# otherwise `aim up` will prompt for confirmation to create the directory itself.
-# We run aim listening on 0.0.0.0 to expose all ports. Also, we run
-# using `--dev` to print verbose logs. Port 43800 is the default port of
-# `aim up` but explicit is better than implicit.
-CMD ["aim up --host 0.0.0.0 --port 7860 --workers 2"]
\ No newline at end of file
diff --git a/spaces/airus/img-to-music/constants.py b/spaces/airus/img-to-music/constants.py
deleted file mode 100644
index 86863d1b778d4c66f0d8e1e0b699f1bb937c1d50..0000000000000000000000000000000000000000
--- a/spaces/airus/img-to-music/constants.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import numpy as np
-import os
-
-MUBERT_LICENSE = os.environ.get('MUBERT_LICENSE')
-MUBERT_TOKEN = os.environ.get('MUBERT_TOKEN')
-
-MUBERT_MODE = "loop"
-MUBERT_TAGS_STRING = 'tribal,action,kids,neo-classic,run 130,pumped,jazz / funk,ethnic,dubtechno,reggae,acid jazz,liquidfunk,funk,witch house,tech house,underground,artists,mystical,disco,sensorium,r&b,agender,psychedelic trance / psytrance,peaceful,run 140,piano,run 160,setting,meditation,christmas,ambient,horror,cinematic,electro house,idm,bass,minimal,underscore,drums,glitchy,beautiful,technology,tribal house,country pop,jazz & funk,documentary,space,classical,valentines,chillstep,experimental,trap,new jack swing,drama,post-rock,tense,corporate,neutral,happy,analog,funky,spiritual,sberzvuk special,chill hop,dramatic,catchy,holidays,fitness 90,optimistic,orchestra,acid techno,energizing,romantic,minimal house,breaks,hyper pop,warm up,dreamy,dark,urban,microfunk,dub,nu disco,vogue,keys,hardcore,aggressive,indie,electro funk,beauty,relaxing,trance,pop,hiphop,soft,acoustic,chillrave / ethno-house,deep techno,angry,dance,fun,dubstep,tropical,latin pop,heroic,world music,inspirational,uplifting,atmosphere,art,epic,advertising,chillout,scary,spooky,slow ballad,saxophone,summer,erotic,jazzy,energy 100,kara mar,xmas,atmospheric,indie pop,hip-hop,yoga,reggaeton,lounge,travel,running,folk,chillrave & ethno-house,detective,darkambient,chill,fantasy,minimal techno,special,night,tropical house,downtempo,lullaby,meditative,upbeat,glitch hop,fitness,neurofunk,sexual,indie rock,future pop,jazz,cyberpunk,melancholic,happy hardcore,family / kids,synths,electric guitar,comedy,psychedelic trance & psytrance,edm,psychedelic rock,calm,zen,bells,podcast,melodic house,ethnic percussion,nature,heavy,bassline,indie dance,techno,drumnbass,synth pop,vaporwave,sad,8-bit,chillgressive,deep,orchestral,futuristic,hardtechno,nostalgic,big room,sci-fi,tutorial,joyful,pads,minimal 170,drill,ethnic 108,amusing,sleepy ambient,psychill,italo disco,lofi,house,acoustic guitar,bassline house,rock,k-pop,synthwave,deep house,electronica,gabber,nightlife,sport & fitness,road trip,celebration,electro,disco house,electronic'
-MUBERT_TAGS = np.array(MUBERT_TAGS_STRING.split(','))
\ No newline at end of file
diff --git a/spaces/akhaliq/Real-ESRGAN/realesrgan/data/realesrgan_paired_dataset.py b/spaces/akhaliq/Real-ESRGAN/realesrgan/data/realesrgan_paired_dataset.py
deleted file mode 100644
index 386c8d72496245dae8df033c2ebbd76b41ff45f1..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Real-ESRGAN/realesrgan/data/realesrgan_paired_dataset.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import os
-from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb
-from basicsr.data.transforms import augment, paired_random_crop
-from basicsr.utils import FileClient, imfrombytes, img2tensor
-from basicsr.utils.registry import DATASET_REGISTRY
-from torch.utils import data as data
-from torchvision.transforms.functional import normalize
-
-
-@DATASET_REGISTRY.register()
-class RealESRGANPairedDataset(data.Dataset):
- """Paired image dataset for image restoration.
-
- Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs.
-
- There are three modes:
- 1. 'lmdb': Use lmdb files.
- If opt['io_backend'] == lmdb.
- 2. 'meta_info': Use meta information file to generate paths.
- If opt['io_backend'] != lmdb and opt['meta_info'] is not None.
- 3. 'folder': Scan folders to generate paths.
- The rest.
-
- Args:
- opt (dict): Config for train datasets. It contains the following keys:
- dataroot_gt (str): Data root path for gt.
- dataroot_lq (str): Data root path for lq.
- meta_info (str): Path for meta information file.
- io_backend (dict): IO backend type and other kwarg.
- filename_tmpl (str): Template for each filename. Note that the template excludes the file extension.
- Default: '{}'.
- gt_size (int): Cropped patched size for gt patches.
- use_hflip (bool): Use horizontal flips.
- use_rot (bool): Use rotation (use vertical flip and transposing h
- and w for implementation).
-
- scale (bool): Scale, which will be added automatically.
- phase (str): 'train' or 'val'.
- """
-
- def __init__(self, opt):
- super(RealESRGANPairedDataset, self).__init__()
- self.opt = opt
- self.file_client = None
- self.io_backend_opt = opt['io_backend']
- # mean and std for normalizing the input images
- self.mean = opt['mean'] if 'mean' in opt else None
- self.std = opt['std'] if 'std' in opt else None
-
- self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
- self.filename_tmpl = opt['filename_tmpl'] if 'filename_tmpl' in opt else '{}'
-
- # file client (lmdb io backend)
- if self.io_backend_opt['type'] == 'lmdb':
- self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
- self.io_backend_opt['client_keys'] = ['lq', 'gt']
- self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt'])
- elif 'meta_info' in self.opt and self.opt['meta_info'] is not None:
- # disk backend with meta_info
- # Each line in the meta_info describes the relative path to an image
- with open(self.opt['meta_info']) as fin:
- paths = [line.strip() for line in fin]
- self.paths = []
- for path in paths:
- gt_path, lq_path = path.split(', ')
- gt_path = os.path.join(self.gt_folder, gt_path)
- lq_path = os.path.join(self.lq_folder, lq_path)
- self.paths.append(dict([('gt_path', gt_path), ('lq_path', lq_path)]))
- else:
- # disk backend
- # it will scan the whole folder to get meta info
- # it will be time-consuming for folders with too many files. It is recommended using an extra meta txt file
- self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl)
-
- def __getitem__(self, index):
- if self.file_client is None:
- self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
-
- scale = self.opt['scale']
-
- # Load gt and lq images. Dimension order: HWC; channel order: BGR;
- # image range: [0, 1], float32.
- gt_path = self.paths[index]['gt_path']
- img_bytes = self.file_client.get(gt_path, 'gt')
- img_gt = imfrombytes(img_bytes, float32=True)
- lq_path = self.paths[index]['lq_path']
- img_bytes = self.file_client.get(lq_path, 'lq')
- img_lq = imfrombytes(img_bytes, float32=True)
-
- # augmentation for training
- if self.opt['phase'] == 'train':
- gt_size = self.opt['gt_size']
- # random crop
- img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path)
- # flip, rotation
- img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot'])
-
- # BGR to RGB, HWC to CHW, numpy to tensor
- img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True)
- # normalize
- if self.mean is not None or self.std is not None:
- normalize(img_lq, self.mean, self.std, inplace=True)
- normalize(img_gt, self.mean, self.std, inplace=True)
-
- return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path}
-
- def __len__(self):
- return len(self.paths)
diff --git a/spaces/akhaliq/SummerTime/evaluation/rougewe_metric.py b/spaces/akhaliq/SummerTime/evaluation/rougewe_metric.py
deleted file mode 100644
index b27aa0ce2266903a3aa898e6e1e4ea095ecbf1cf..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SummerTime/evaluation/rougewe_metric.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from evaluation.summeval_metric import SummEvalMetric
-from typing import List, Dict
-
-import nltk
-
-
-class RougeWe(SummEvalMetric):
- metric_name = "rougeWE"
- range = (0, 1)
- higher_is_better = True
- requires_heavy_compute = True
-
- def __init__(self):
- from summ_eval.rouge_we_metric import RougeWeMetric
-
- nltk.download("stopwords")
- se_metric = RougeWeMetric()
- super(RougeWe, self).__init__(se_metric)
-
- def evaluate(
- self, inputs: List[str], targets: List[str], keys: List[str] = ["rouge_we_3_f"]
- ) -> Dict[str, float]:
- # TODO zhangir: update when dataset api is merged.
- return super(RougeWe, self).evaluate(inputs, targets, keys)
diff --git a/spaces/akhaliq/runwayml-stable-diffusion-v1-5/README.md b/spaces/akhaliq/runwayml-stable-diffusion-v1-5/README.md
deleted file mode 100644
index 26a6a20988103807b5f70ec8d8d68cb530640a10..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/runwayml-stable-diffusion-v1-5/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Runwayml Stable Diffusion V1 5
-emoji: 📉
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/alexiserodriguez/whisper-transcription-app/README.md b/spaces/alexiserodriguez/whisper-transcription-app/README.md
deleted file mode 100644
index e35a01a8bf10a8e6c8e19acf836343dc6a32e40e..0000000000000000000000000000000000000000
--- a/spaces/alexiserodriguez/whisper-transcription-app/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Whisper Transcription App
-emoji: 🐢
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/glibc.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/glibc.py
deleted file mode 100644
index 7bd3c20681d865cb4fa42617cf939b5512c7663f..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/glibc.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# The following comment should be removed at some point in the future.
-# mypy: strict-optional=False
-
-import os
-import sys
-from typing import Optional, Tuple
-
-
-def glibc_version_string() -> Optional[str]:
- "Returns glibc version string, or None if not using glibc."
- return glibc_version_string_confstr() or glibc_version_string_ctypes()
-
-
-def glibc_version_string_confstr() -> Optional[str]:
- "Primary implementation of glibc_version_string using os.confstr."
- # os.confstr is quite a bit faster than ctypes.DLL. It's also less likely
- # to be broken or missing. This strategy is used in the standard library
- # platform module:
- # https://github.com/python/cpython/blob/fcf1d003bf4f0100c9d0921ff3d70e1127ca1b71/Lib/platform.py#L175-L183
- if sys.platform == "win32":
- return None
- try:
- # os.confstr("CS_GNU_LIBC_VERSION") returns a string like "glibc 2.17":
- _, version = os.confstr("CS_GNU_LIBC_VERSION").split()
- except (AttributeError, OSError, ValueError):
- # os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)...
- return None
- return version
-
-
-def glibc_version_string_ctypes() -> Optional[str]:
- "Fallback implementation of glibc_version_string using ctypes."
-
- try:
- import ctypes
- except ImportError:
- return None
-
- # ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen
- # manpage says, "If filename is NULL, then the returned handle is for the
- # main program". This way we can let the linker do the work to figure out
- # which libc our process is actually using.
- process_namespace = ctypes.CDLL(None)
- try:
- gnu_get_libc_version = process_namespace.gnu_get_libc_version
- except AttributeError:
- # Symbol doesn't exist -> therefore, we are not linked to
- # glibc.
- return None
-
- # Call gnu_get_libc_version, which returns a string like "2.5"
- gnu_get_libc_version.restype = ctypes.c_char_p
- version_str = gnu_get_libc_version()
- # py2 / py3 compatibility:
- if not isinstance(version_str, str):
- version_str = version_str.decode("ascii")
-
- return version_str
-
-
-# platform.libc_ver regularly returns completely nonsensical glibc
-# versions. E.g. on my computer, platform says:
-#
-# ~$ python2.7 -c 'import platform; print(platform.libc_ver())'
-# ('glibc', '2.7')
-# ~$ python3.5 -c 'import platform; print(platform.libc_ver())'
-# ('glibc', '2.9')
-#
-# But the truth is:
-#
-# ~$ ldd --version
-# ldd (Debian GLIBC 2.22-11) 2.22
-#
-# This is unfortunate, because it means that the linehaul data on libc
-# versions that was generated by pip 8.1.2 and earlier is useless and
-# misleading. Solution: instead of using platform, use our code that actually
-# works.
-def libc_ver() -> Tuple[str, str]:
- """Try to determine the glibc version
-
- Returns a tuple of strings (lib, version) which default to empty strings
- in case the lookup fails.
- """
- glibc_version = glibc_version_string()
- if glibc_version is None:
- return ("", "")
- else:
- return ("glibc", glibc_version)
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/temp_dir.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/temp_dir.py
deleted file mode 100644
index 442679a758b7b4b965ce3e615a243a7a6d318b4e..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/temp_dir.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import errno
-import itertools
-import logging
-import os.path
-import tempfile
-from contextlib import ExitStack, contextmanager
-from typing import Any, Dict, Iterator, Optional, TypeVar, Union
-
-from pip._internal.utils.misc import enum, rmtree
-
-logger = logging.getLogger(__name__)
-
-_T = TypeVar("_T", bound="TempDirectory")
-
-
-# Kinds of temporary directories. Only needed for ones that are
-# globally-managed.
-tempdir_kinds = enum(
- BUILD_ENV="build-env",
- EPHEM_WHEEL_CACHE="ephem-wheel-cache",
- REQ_BUILD="req-build",
-)
-
-
-_tempdir_manager: Optional[ExitStack] = None
-
-
-@contextmanager
-def global_tempdir_manager() -> Iterator[None]:
- global _tempdir_manager
- with ExitStack() as stack:
- old_tempdir_manager, _tempdir_manager = _tempdir_manager, stack
- try:
- yield
- finally:
- _tempdir_manager = old_tempdir_manager
-
-
-class TempDirectoryTypeRegistry:
- """Manages temp directory behavior"""
-
- def __init__(self) -> None:
- self._should_delete: Dict[str, bool] = {}
-
- def set_delete(self, kind: str, value: bool) -> None:
- """Indicate whether a TempDirectory of the given kind should be
- auto-deleted.
- """
- self._should_delete[kind] = value
-
- def get_delete(self, kind: str) -> bool:
- """Get configured auto-delete flag for a given TempDirectory type,
- default True.
- """
- return self._should_delete.get(kind, True)
-
-
-_tempdir_registry: Optional[TempDirectoryTypeRegistry] = None
-
-
-@contextmanager
-def tempdir_registry() -> Iterator[TempDirectoryTypeRegistry]:
- """Provides a scoped global tempdir registry that can be used to dictate
- whether directories should be deleted.
- """
- global _tempdir_registry
- old_tempdir_registry = _tempdir_registry
- _tempdir_registry = TempDirectoryTypeRegistry()
- try:
- yield _tempdir_registry
- finally:
- _tempdir_registry = old_tempdir_registry
-
-
-class _Default:
- pass
-
-
-_default = _Default()
-
-
-class TempDirectory:
- """Helper class that owns and cleans up a temporary directory.
-
- This class can be used as a context manager or as an OO representation of a
- temporary directory.
-
- Attributes:
- path
- Location to the created temporary directory
- delete
- Whether the directory should be deleted when exiting
- (when used as a contextmanager)
-
- Methods:
- cleanup()
- Deletes the temporary directory
-
- When used as a context manager, if the delete attribute is True, on
- exiting the context the temporary directory is deleted.
- """
-
- def __init__(
- self,
- path: Optional[str] = None,
- delete: Union[bool, None, _Default] = _default,
- kind: str = "temp",
- globally_managed: bool = False,
- ):
- super().__init__()
-
- if delete is _default:
- if path is not None:
- # If we were given an explicit directory, resolve delete option
- # now.
- delete = False
- else:
- # Otherwise, we wait until cleanup and see what
- # tempdir_registry says.
- delete = None
-
- # The only time we specify path is in for editables where it
- # is the value of the --src option.
- if path is None:
- path = self._create(kind)
-
- self._path = path
- self._deleted = False
- self.delete = delete
- self.kind = kind
-
- if globally_managed:
- assert _tempdir_manager is not None
- _tempdir_manager.enter_context(self)
-
- @property
- def path(self) -> str:
- assert not self._deleted, f"Attempted to access deleted path: {self._path}"
- return self._path
-
- def __repr__(self) -> str:
- return f"<{self.__class__.__name__} {self.path!r}>"
-
- def __enter__(self: _T) -> _T:
- return self
-
- def __exit__(self, exc: Any, value: Any, tb: Any) -> None:
- if self.delete is not None:
- delete = self.delete
- elif _tempdir_registry:
- delete = _tempdir_registry.get_delete(self.kind)
- else:
- delete = True
-
- if delete:
- self.cleanup()
-
- def _create(self, kind: str) -> str:
- """Create a temporary directory and store its path in self.path"""
- # We realpath here because some systems have their default tmpdir
- # symlinked to another directory. This tends to confuse build
- # scripts, so we canonicalize the path by traversing potential
- # symlinks here.
- path = os.path.realpath(tempfile.mkdtemp(prefix=f"pip-{kind}-"))
- logger.debug("Created temporary directory: %s", path)
- return path
-
- def cleanup(self) -> None:
- """Remove the temporary directory created and reset state"""
- self._deleted = True
- if not os.path.exists(self._path):
- return
- rmtree(self._path)
-
-
-class AdjacentTempDirectory(TempDirectory):
- """Helper class that creates a temporary directory adjacent to a real one.
-
- Attributes:
- original
- The original directory to create a temp directory for.
- path
- After calling create() or entering, contains the full
- path to the temporary directory.
- delete
- Whether the directory should be deleted when exiting
- (when used as a contextmanager)
-
- """
-
- # The characters that may be used to name the temp directory
- # We always prepend a ~ and then rotate through these until
- # a usable name is found.
- # pkg_resources raises a different error for .dist-info folder
- # with leading '-' and invalid metadata
- LEADING_CHARS = "-~.=%0123456789"
-
- def __init__(self, original: str, delete: Optional[bool] = None) -> None:
- self.original = original.rstrip("/\\")
- super().__init__(delete=delete)
-
- @classmethod
- def _generate_names(cls, name: str) -> Iterator[str]:
- """Generates a series of temporary names.
-
- The algorithm replaces the leading characters in the name
- with ones that are valid filesystem characters, but are not
- valid package names (for both Python and pip definitions of
- package).
- """
- for i in range(1, len(name)):
- for candidate in itertools.combinations_with_replacement(
- cls.LEADING_CHARS, i - 1
- ):
- new_name = "~" + "".join(candidate) + name[i:]
- if new_name != name:
- yield new_name
-
- # If we make it this far, we will have to make a longer name
- for i in range(len(cls.LEADING_CHARS)):
- for candidate in itertools.combinations_with_replacement(
- cls.LEADING_CHARS, i
- ):
- new_name = "~" + "".join(candidate) + name
- if new_name != name:
- yield new_name
-
- def _create(self, kind: str) -> str:
- root, name = os.path.split(self.original)
- for candidate in self._generate_names(name):
- path = os.path.join(root, candidate)
- try:
- os.mkdir(path)
- except OSError as ex:
- # Continue if the name exists already
- if ex.errno != errno.EEXIST:
- raise
- else:
- path = os.path.realpath(path)
- break
- else:
- # Final fallback on the default behavior.
- path = os.path.realpath(tempfile.mkdtemp(prefix=f"pip-{kind}-"))
-
- logger.debug("Created temporary directory: %s", path)
- return path
diff --git a/spaces/anaclaudia13ct/insect_detection/utils/segment/augmentations.py b/spaces/anaclaudia13ct/insect_detection/utils/segment/augmentations.py
deleted file mode 100644
index 169addedf0f58cf37e774e6a85eddff6eebc30be..0000000000000000000000000000000000000000
--- a/spaces/anaclaudia13ct/insect_detection/utils/segment/augmentations.py
+++ /dev/null
@@ -1,104 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Image augmentation functions
-"""
-
-import math
-import random
-
-import cv2
-import numpy as np
-
-from ..augmentations import box_candidates
-from ..general import resample_segments, segment2box
-
-
-def mixup(im, labels, segments, im2, labels2, segments2):
- # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
- r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
- im = (im * r + im2 * (1 - r)).astype(np.uint8)
- labels = np.concatenate((labels, labels2), 0)
- segments = np.concatenate((segments, segments2), 0)
- return im, labels, segments
-
-
-def random_perspective(im,
- targets=(),
- segments=(),
- degrees=10,
- translate=.1,
- scale=.1,
- shear=10,
- perspective=0.0,
- border=(0, 0)):
- # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
- # targets = [cls, xyxy]
-
- height = im.shape[0] + border[0] * 2 # shape(h,w,c)
- width = im.shape[1] + border[1] * 2
-
- # Center
- C = np.eye(3)
- C[0, 2] = -im.shape[1] / 2 # x translation (pixels)
- C[1, 2] = -im.shape[0] / 2 # y translation (pixels)
-
- # Perspective
- P = np.eye(3)
- P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
- P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
-
- # Rotation and Scale
- R = np.eye(3)
- a = random.uniform(-degrees, degrees)
- # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
- s = random.uniform(1 - scale, 1 + scale)
- # s = 2 ** random.uniform(-scale, scale)
- R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
-
- # Shear
- S = np.eye(3)
- S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
- S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
-
- # Translation
- T = np.eye(3)
- T[0, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * width) # x translation (pixels)
- T[1, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * height) # y translation (pixels)
-
- # Combined rotation matrix
- M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
- if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
- if perspective:
- im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
- else: # affine
- im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
-
- # Visualize
- # import matplotlib.pyplot as plt
- # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
- # ax[0].imshow(im[:, :, ::-1]) # base
- # ax[1].imshow(im2[:, :, ::-1]) # warped
-
- # Transform label coordinates
- n = len(targets)
- new_segments = []
- if n:
- new = np.zeros((n, 4))
- segments = resample_segments(segments) # upsample
- for i, segment in enumerate(segments):
- xy = np.ones((len(segment), 3))
- xy[:, :2] = segment
- xy = xy @ M.T # transform
- xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]) # perspective rescale or affine
-
- # clip
- new[i] = segment2box(xy, width, height)
- new_segments.append(xy)
-
- # filter candidates
- i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01)
- targets = targets[i]
- targets[:, 1:5] = new[i]
- new_segments = np.array(new_segments)[i]
-
- return im, targets, new_segments
diff --git a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/models/vitseg.py b/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/models/vitseg.py
deleted file mode 100644
index ed621431ddf930fcfa27b5929999776b96fede63..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/models/vitseg.py
+++ /dev/null
@@ -1,286 +0,0 @@
-import math
-from posixpath import basename, dirname, join
-# import clip
-from clip.model import convert_weights
-import torch
-import json
-from torch import nn
-from torch.nn import functional as nnf
-from torch.nn.modules import activation
-from torch.nn.modules.activation import ReLU
-from torchvision import transforms
-
-normalize = transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
-
-from torchvision.models import ResNet
-
-
-def process_prompts(conditional, prompt_list, conditional_map):
- # DEPRECATED
-
- # randomly sample a synonym
- words = [conditional_map[int(i)] for i in conditional]
- words = [syns[torch.multinomial(torch.ones(len(syns)), 1, replacement=True).item()] for syns in words]
- words = [w.replace('_', ' ') for w in words]
-
- if prompt_list is not None:
- prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
- prompts = [prompt_list[i] for i in prompt_indices]
- else:
- prompts = ['a photo of {}'] * (len(words))
-
- return [promt.format(w) for promt, w in zip(prompts, words)]
-
-
-class VITDenseBase(nn.Module):
-
- def rescaled_pos_emb(self, new_size):
- assert len(new_size) == 2
-
- a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape)
- b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T
- return torch.cat([self.model.positional_embedding[:1], b])
-
- def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None):
-
- with torch.no_grad():
-
- x_inp = nnf.interpolate(x_inp, (384, 384))
-
- x = self.model.patch_embed(x_inp)
- cls_token = self.model.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks
- if self.model.dist_token is None:
- x = torch.cat((cls_token, x), dim=1)
- else:
- x = torch.cat((cls_token, self.model.dist_token.expand(x.shape[0], -1, -1), x), dim=1)
- x = self.model.pos_drop(x + self.model.pos_embed)
-
- activations = []
- for i, block in enumerate(self.model.blocks):
- x = block(x)
-
- if i in extract_layers:
- # permute to be compatible with CLIP
- activations += [x.permute(1,0,2)]
-
- x = self.model.norm(x)
- x = self.model.head(self.model.pre_logits(x[:, 0]))
-
- # again for CLIP compatibility
- # x = x.permute(1, 0, 2)
-
- return x, activations, None
-
- def sample_prompts(self, words, prompt_list=None):
-
- prompt_list = prompt_list if prompt_list is not None else self.prompt_list
-
- prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
- prompts = [prompt_list[i] for i in prompt_indices]
- return [promt.format(w) for promt, w in zip(prompts, words)]
-
- def get_cond_vec(self, conditional, batch_size):
- # compute conditional from a single string
- if conditional is not None and type(conditional) == str:
- cond = self.compute_conditional(conditional)
- cond = cond.repeat(batch_size, 1)
-
- # compute conditional from string list/tuple
- elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str:
- assert len(conditional) == batch_size
- cond = self.compute_conditional(conditional)
-
- # use conditional directly
- elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2:
- cond = conditional
-
- # compute conditional from image
- elif conditional is not None and type(conditional) == torch.Tensor:
- with torch.no_grad():
- cond, _, _ = self.visual_forward(conditional)
- else:
- raise ValueError('invalid conditional')
- return cond
-
- def compute_conditional(self, conditional):
- import clip
-
- dev = next(self.parameters()).device
-
- if type(conditional) in {list, tuple}:
- text_tokens = clip.tokenize(conditional).to(dev)
- cond = self.clip_model.encode_text(text_tokens)
- else:
- if conditional in self.precomputed_prompts:
- cond = self.precomputed_prompts[conditional].float().to(dev)
- else:
- text_tokens = clip.tokenize([conditional]).to(dev)
- cond = self.clip_model.encode_text(text_tokens)[0]
-
- return cond
-
-
-class VITDensePredT(VITDenseBase):
-
- def __init__(self, extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed',
- depth=3, extra_blocks=0, reduce_cond=None, fix_shift=False,
- learn_trans_conv_only=False, refine=None, limit_to_clip_only=False, upsample=False,
- add_calibration=False, process_cond=None, not_pretrained=False):
- super().__init__()
- # device = 'cpu'
-
- self.extract_layers = extract_layers
- self.cond_layer = cond_layer
- self.limit_to_clip_only = limit_to_clip_only
- self.process_cond = None
-
- if add_calibration:
- self.calibration_conds = 1
-
- self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None
-
- self.add_activation1 = True
-
- import timm
- self.model = timm.create_model('vit_base_patch16_384', pretrained=True)
- self.model.head = nn.Linear(768, 512 if reduce_cond is None else reduce_cond)
-
- for p in self.model.parameters():
- p.requires_grad_(False)
-
- import clip
- self.clip_model, _ = clip.load('ViT-B/16', device='cpu', jit=False)
- # del self.clip_model.visual
-
-
- self.token_shape = (14, 14)
-
- # conditional
- if reduce_cond is not None:
- self.reduce_cond = nn.Linear(512, reduce_cond)
- for p in self.reduce_cond.parameters():
- p.requires_grad_(False)
- else:
- self.reduce_cond = None
-
- # self.film = AVAILABLE_BLOCKS['film'](512, 128)
- self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
- self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
-
- # DEPRECATED
- # self.conditional_map = {c['id']: c['synonyms'] for c in json.load(open(cond_map))}
-
- assert len(self.extract_layers) == depth
-
- self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)])
- self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))])
- self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)])
-
- trans_conv_ks = (16, 16)
- self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
-
- # refinement and trans conv
-
- if learn_trans_conv_only:
- for p in self.parameters():
- p.requires_grad_(False)
-
- for p in self.trans_conv.parameters():
- p.requires_grad_(True)
-
- if prompt == 'fixed':
- self.prompt_list = ['a photo of a {}.']
- elif prompt == 'shuffle':
- self.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
- elif prompt == 'shuffle+':
- self.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.',
- 'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.',
- 'a bad photo of a {}.', 'a photo of the {}.']
- elif prompt == 'shuffle_clip':
- from models.clip_prompts import imagenet_templates
- self.prompt_list = imagenet_templates
-
- if process_cond is not None:
- if process_cond == 'clamp' or process_cond[0] == 'clamp':
-
- val = process_cond[1] if type(process_cond) in {list, tuple} else 0.2
-
- def clamp_vec(x):
- return torch.clamp(x, -val, val)
-
- self.process_cond = clamp_vec
-
- elif process_cond.endswith('.pth'):
-
- shift = torch.load(process_cond)
- def add_shift(x):
- return x + shift.to(x.device)
-
- self.process_cond = add_shift
-
- import pickle
- precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
- self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}
-
-
- def forward(self, inp_image, conditional=None, return_features=False, mask=None):
-
- assert type(return_features) == bool
-
- # inp_image = inp_image.to(self.model.positional_embedding.device)
-
- if mask is not None:
- raise ValueError('mask not supported')
-
- # x_inp = normalize(inp_image)
- x_inp = inp_image
-
- bs, dev = inp_image.shape[0], x_inp.device
-
- inp_image_size = inp_image.shape[2:]
-
- cond = self.get_cond_vec(conditional, bs)
-
- visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers))
-
- activation1 = activations[0]
- activations = activations[1:]
-
- a = None
- for i, (activation, block, reduce) in enumerate(zip(activations[::-1], self.blocks, self.reduces)):
-
- if a is not None:
- a = reduce(activation) + a
- else:
- a = reduce(activation)
-
- if i == self.cond_layer:
- if self.reduce_cond is not None:
- cond = self.reduce_cond(cond)
-
- a = self.film_mul(cond) * a + self.film_add(cond)
-
- a = block(a)
-
- for block in self.extra_blocks:
- a = a + block(a)
-
- a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
-
- size = int(math.sqrt(a.shape[2]))
-
- a = a.view(bs, a.shape[1], size, size)
-
- if self.trans_conv is not None:
- a = self.trans_conv(a)
-
- if self.upsample_proj is not None:
- a = self.upsample_proj(a)
- a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear')
-
- a = nnf.interpolate(a, inp_image_size)
-
- if return_features:
- return a, visual_q, cond, [activation1] + activations
- else:
- return a,
diff --git a/spaces/aodianyun/stable-diffusion-webui/modules/script_callbacks.py b/spaces/aodianyun/stable-diffusion-webui/modules/script_callbacks.py
deleted file mode 100644
index c98c2395b6fe46ddec2a10cc6a54ee0f3ba248f5..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/modules/script_callbacks.py
+++ /dev/null
@@ -1,359 +0,0 @@
-import sys
-import traceback
-from collections import namedtuple
-import inspect
-from typing import Optional, Dict, Any
-
-from fastapi import FastAPI
-from gradio import Blocks
-
-
-def report_exception(c, job):
- print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
-
-class ImageSaveParams:
- def __init__(self, image, p, filename, pnginfo):
- self.image = image
- """the PIL image itself"""
-
- self.p = p
- """p object with processing parameters; either StableDiffusionProcessing or an object with same fields"""
-
- self.filename = filename
- """name of file that the image would be saved to"""
-
- self.pnginfo = pnginfo
- """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""
-
-
-class CFGDenoiserParams:
- def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps):
- self.x = x
- """Latent image representation in the process of being denoised"""
-
- self.image_cond = image_cond
- """Conditioning image"""
-
- self.sigma = sigma
- """Current sigma noise step value"""
-
- self.sampling_step = sampling_step
- """Current Sampling step number"""
-
- self.total_sampling_steps = total_sampling_steps
- """Total number of sampling steps planned"""
-
-
-class CFGDenoisedParams:
- def __init__(self, x, sampling_step, total_sampling_steps):
- self.x = x
- """Latent image representation in the process of being denoised"""
-
- self.sampling_step = sampling_step
- """Current Sampling step number"""
-
- self.total_sampling_steps = total_sampling_steps
- """Total number of sampling steps planned"""
-
-
-class UiTrainTabParams:
- def __init__(self, txt2img_preview_params):
- self.txt2img_preview_params = txt2img_preview_params
-
-
-class ImageGridLoopParams:
- def __init__(self, imgs, cols, rows):
- self.imgs = imgs
- self.cols = cols
- self.rows = rows
-
-
-ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
-callback_map = dict(
- callbacks_app_started=[],
- callbacks_model_loaded=[],
- callbacks_ui_tabs=[],
- callbacks_ui_train_tabs=[],
- callbacks_ui_settings=[],
- callbacks_before_image_saved=[],
- callbacks_image_saved=[],
- callbacks_cfg_denoiser=[],
- callbacks_cfg_denoised=[],
- callbacks_before_component=[],
- callbacks_after_component=[],
- callbacks_image_grid=[],
- callbacks_infotext_pasted=[],
- callbacks_script_unloaded=[],
- callbacks_before_ui=[],
-)
-
-
-def clear_callbacks():
- for callback_list in callback_map.values():
- callback_list.clear()
-
-
-def app_started_callback(demo: Optional[Blocks], app: FastAPI):
- for c in callback_map['callbacks_app_started']:
- try:
- c.callback(demo, app)
- except Exception:
- report_exception(c, 'app_started_callback')
-
-
-def model_loaded_callback(sd_model):
- for c in callback_map['callbacks_model_loaded']:
- try:
- c.callback(sd_model)
- except Exception:
- report_exception(c, 'model_loaded_callback')
-
-
-def ui_tabs_callback():
- res = []
-
- for c in callback_map['callbacks_ui_tabs']:
- try:
- res += c.callback() or []
- except Exception:
- report_exception(c, 'ui_tabs_callback')
-
- return res
-
-
-def ui_train_tabs_callback(params: UiTrainTabParams):
- for c in callback_map['callbacks_ui_train_tabs']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'callbacks_ui_train_tabs')
-
-
-def ui_settings_callback():
- for c in callback_map['callbacks_ui_settings']:
- try:
- c.callback()
- except Exception:
- report_exception(c, 'ui_settings_callback')
-
-
-def before_image_saved_callback(params: ImageSaveParams):
- for c in callback_map['callbacks_before_image_saved']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'before_image_saved_callback')
-
-
-def image_saved_callback(params: ImageSaveParams):
- for c in callback_map['callbacks_image_saved']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'image_saved_callback')
-
-
-def cfg_denoiser_callback(params: CFGDenoiserParams):
- for c in callback_map['callbacks_cfg_denoiser']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'cfg_denoiser_callback')
-
-
-def cfg_denoised_callback(params: CFGDenoisedParams):
- for c in callback_map['callbacks_cfg_denoised']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'cfg_denoised_callback')
-
-
-def before_component_callback(component, **kwargs):
- for c in callback_map['callbacks_before_component']:
- try:
- c.callback(component, **kwargs)
- except Exception:
- report_exception(c, 'before_component_callback')
-
-
-def after_component_callback(component, **kwargs):
- for c in callback_map['callbacks_after_component']:
- try:
- c.callback(component, **kwargs)
- except Exception:
- report_exception(c, 'after_component_callback')
-
-
-def image_grid_callback(params: ImageGridLoopParams):
- for c in callback_map['callbacks_image_grid']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'image_grid')
-
-
-def infotext_pasted_callback(infotext: str, params: Dict[str, Any]):
- for c in callback_map['callbacks_infotext_pasted']:
- try:
- c.callback(infotext, params)
- except Exception:
- report_exception(c, 'infotext_pasted')
-
-
-def script_unloaded_callback():
- for c in reversed(callback_map['callbacks_script_unloaded']):
- try:
- c.callback()
- except Exception:
- report_exception(c, 'script_unloaded')
-
-
-def before_ui_callback():
- for c in reversed(callback_map['callbacks_before_ui']):
- try:
- c.callback()
- except Exception:
- report_exception(c, 'before_ui')
-
-
-def add_callback(callbacks, fun):
- stack = [x for x in inspect.stack() if x.filename != __file__]
- filename = stack[0].filename if len(stack) > 0 else 'unknown file'
-
- callbacks.append(ScriptCallback(filename, fun))
-
-
-def remove_current_script_callbacks():
- stack = [x for x in inspect.stack() if x.filename != __file__]
- filename = stack[0].filename if len(stack) > 0 else 'unknown file'
- if filename == 'unknown file':
- return
- for callback_list in callback_map.values():
- for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:
- callback_list.remove(callback_to_remove)
-
-
-def remove_callbacks_for_function(callback_func):
- for callback_list in callback_map.values():
- for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:
- callback_list.remove(callback_to_remove)
-
-
-def on_app_started(callback):
- """register a function to be called when the webui started, the gradio `Block` component and
- fastapi `FastAPI` object are passed as the arguments"""
- add_callback(callback_map['callbacks_app_started'], callback)
-
-
-def on_model_loaded(callback):
- """register a function to be called when the stable diffusion model is created; the model is
- passed as an argument; this function is also called when the script is reloaded. """
- add_callback(callback_map['callbacks_model_loaded'], callback)
-
-
-def on_ui_tabs(callback):
- """register a function to be called when the UI is creating new tabs.
- The function must either return a None, which means no new tabs to be added, or a list, where
- each element is a tuple:
- (gradio_component, title, elem_id)
-
- gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks)
- title is tab text displayed to user in the UI
- elem_id is HTML id for the tab
- """
- add_callback(callback_map['callbacks_ui_tabs'], callback)
-
-
-def on_ui_train_tabs(callback):
- """register a function to be called when the UI is creating new tabs for the train tab.
- Create your new tabs with gr.Tab.
- """
- add_callback(callback_map['callbacks_ui_train_tabs'], callback)
-
-
-def on_ui_settings(callback):
- """register a function to be called before UI settings are populated; add your settings
- by using shared.opts.add_option(shared.OptionInfo(...)) """
- add_callback(callback_map['callbacks_ui_settings'], callback)
-
-
-def on_before_image_saved(callback):
- """register a function to be called before an image is saved to a file.
- The callback is called with one argument:
- - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.
- """
- add_callback(callback_map['callbacks_before_image_saved'], callback)
-
-
-def on_image_saved(callback):
- """register a function to be called after an image is saved to a file.
- The callback is called with one argument:
- - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
- """
- add_callback(callback_map['callbacks_image_saved'], callback)
-
-
-def on_cfg_denoiser(callback):
- """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
- The callback is called with one argument:
- - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
- """
- add_callback(callback_map['callbacks_cfg_denoiser'], callback)
-
-
-def on_cfg_denoised(callback):
- """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
- The callback is called with one argument:
- - params: CFGDenoisedParams - parameters to be passed to the inner model and sampling state details.
- """
- add_callback(callback_map['callbacks_cfg_denoised'], callback)
-
-
-def on_before_component(callback):
- """register a function to be called before a component is created.
- The callback is called with arguments:
- - component - gradio component that is about to be created.
- - **kwargs - args to gradio.components.IOComponent.__init__ function
-
- Use elem_id/label fields of kwargs to figure out which component it is.
- This can be useful to inject your own components somewhere in the middle of vanilla UI.
- """
- add_callback(callback_map['callbacks_before_component'], callback)
-
-
-def on_after_component(callback):
- """register a function to be called after a component is created. See on_before_component for more."""
- add_callback(callback_map['callbacks_after_component'], callback)
-
-
-def on_image_grid(callback):
- """register a function to be called before making an image grid.
- The callback is called with one argument:
- - params: ImageGridLoopParams - parameters to be used for grid creation. Can be modified.
- """
- add_callback(callback_map['callbacks_image_grid'], callback)
-
-
-def on_infotext_pasted(callback):
- """register a function to be called before applying an infotext.
- The callback is called with two arguments:
- - infotext: str - raw infotext.
- - result: Dict[str, any] - parsed infotext parameters.
- """
- add_callback(callback_map['callbacks_infotext_pasted'], callback)
-
-
-def on_script_unloaded(callback):
- """register a function to be called before the script is unloaded. Any hooks/hijacks/monkeying about that
- the script did should be reverted here"""
-
- add_callback(callback_map['callbacks_script_unloaded'], callback)
-
-
-def on_before_ui(callback):
- """register a function to be called before the UI is created."""
-
- add_callback(callback_map['callbacks_before_ui'], callback)
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/fast_pitch_config.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/fast_pitch_config.py
deleted file mode 100644
index d086d26564450c60fa04a7f3a068506f4147d3be..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/fast_pitch_config.py
+++ /dev/null
@@ -1,183 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.forward_tts import ForwardTTSArgs
-
-
-@dataclass
-class FastPitchConfig(BaseTTSConfig):
- """Configure `ForwardTTS` as FastPitch model.
-
- Example:
-
- >>> from TTS.tts.configs.fast_pitch_config import FastPitchConfig
- >>> config = FastPitchConfig()
-
- Args:
- model (str):
- Model name used for selecting the right model at initialization. Defaults to `fast_pitch`.
-
- base_model (str):
- Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
- the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
-
- model_args (Coqpit):
- Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
-
- data_dep_init_steps (int):
- Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
- Activation Normalization that pre-computes normalization stats at the beginning and use the same values
- for the rest. Defaults to 10.
-
- speakers_file (str):
- Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
- speaker names. Defaults to `None`.
-
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
-
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
-
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
-
- d_vector_dim (int):
- Dimension of the external speaker embeddings. Defaults to 0.
-
- optimizer (str):
- Name of the model optimizer. Defaults to `Adam`.
-
- optimizer_params (dict):
- Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
-
- lr_scheduler (str):
- Name of the learning rate scheduler. Defaults to `Noam`.
-
- lr_scheduler_params (dict):
- Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
-
- lr (float):
- Initial learning rate. Defaults to `1e-3`.
-
- grad_clip (float):
- Gradient norm clipping value. Defaults to `5.0`.
-
- spec_loss_type (str):
- Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- duration_loss_type (str):
- Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- use_ssim_loss (bool):
- Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
-
- wd (float):
- Weight decay coefficient. Defaults to `1e-7`.
-
- ssim_loss_alpha (float):
- Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
-
- dur_loss_alpha (float):
- Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
-
- spec_loss_alpha (float):
- Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
-
- pitch_loss_alpha (float):
- Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0.
-
- binary_align_loss_alpha (float):
- Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
-
- binary_loss_warmup_epochs (float):
- Number of epochs to gradually increase the binary loss impact. Defaults to 150.
-
- min_seq_len (int):
- Minimum input sequence length to be used at training.
-
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
-
- # dataset configs
- compute_f0(bool):
- Compute pitch. defaults to True
-
- f0_cache_path(str):
- pith cache path. defaults to None
- """
-
- model: str = "fast_pitch"
- base_model: str = "forward_tts"
-
- # model specific params
- model_args: ForwardTTSArgs = field(default_factory=ForwardTTSArgs)
-
- # multi-speaker settings
- num_speakers: int = 0
- speakers_file: str = None
- use_speaker_embedding: bool = False
- use_d_vector_file: bool = False
- d_vector_file: str = False
- d_vector_dim: int = 0
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = "NoamLR"
- lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
- lr: float = 1e-4
- grad_clip: float = 5.0
-
- # loss params
- spec_loss_type: str = "mse"
- duration_loss_type: str = "mse"
- use_ssim_loss: bool = True
- ssim_loss_alpha: float = 1.0
- spec_loss_alpha: float = 1.0
- aligner_loss_alpha: float = 1.0
- pitch_loss_alpha: float = 0.1
- dur_loss_alpha: float = 0.1
- binary_align_loss_alpha: float = 0.1
- binary_loss_warmup_epochs: int = 150
-
- # overrides
- min_seq_len: int = 13
- max_seq_len: int = 200
- r: int = 1 # DO NOT CHANGE
-
- # dataset configs
- compute_f0: bool = True
- f0_cache_path: str = None
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
-
- def __post_init__(self):
- # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
- if self.num_speakers > 0:
- self.model_args.num_speakers = self.num_speakers
-
- # speaker embedding settings
- if self.use_speaker_embedding:
- self.model_args.use_speaker_embedding = True
- if self.speakers_file:
- self.model_args.speakers_file = self.speakers_file
-
- # d-vector settings
- if self.use_d_vector_file:
- self.model_args.use_d_vector_file = True
- if self.d_vector_dim is not None and self.d_vector_dim > 0:
- self.model_args.d_vector_dim = self.d_vector_dim
- if self.d_vector_file:
- self.model_args.d_vector_file = self.d_vector_file
diff --git a/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/__init__.py b/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/__init__.py
deleted file mode 100644
index 4bae29fd5f85b41e4669302bd2603bc6924eddc7..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# -*- coding: utf-8 -*-
-
-__author__ = """Adrian Bulat"""
-__email__ = 'adrian.bulat@nottingham.ac.uk'
-__version__ = '1.0.1'
-
-from .api import FaceAlignment, LandmarksType, NetworkSize
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/scatter_with_minimap.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/scatter_with_minimap.py
deleted file mode 100644
index 7de894c43ae56bf135411e9912ca3d396aa71dec..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/scatter_with_minimap.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""
-Scatter Plot with Minimap
--------------------------
-This example shows how to create a miniature version of a plot
-such that creating a selection in the miniature version
-adjusts the axis limits in another, more detailed view.
-"""
-# category: scatter plots
-
-import altair as alt
-from vega_datasets import data
-
-source = data.seattle_weather()
-
-zoom = alt.selection_interval(encodings=["x", "y"])
-
-minimap = (
- alt.Chart(source)
- .mark_point()
- .add_selection(zoom)
- .encode(
- x="date:T",
- y="temp_max:Q",
- color=alt.condition(zoom, "weather", alt.value("lightgray")),
- )
- .properties(
- width=200,
- height=200,
- title="Minimap -- click and drag to zoom in the detail view",
- )
-)
-
-detail = (
- alt.Chart(source)
- .mark_point()
- .encode(
- x=alt.X(
- "date:T", scale=alt.Scale(domain={"selection": zoom.name, "encoding": "x"})
- ),
- y=alt.Y(
- "temp_max:Q",
- scale=alt.Scale(domain={"selection": zoom.name, "encoding": "y"}),
- ),
- color="weather",
- )
- .properties(width=600, height=400, title="Seattle weather -- detail view")
-)
-
-detail | minimap
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/distributed/legacy_distributed_data_parallel.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/distributed/legacy_distributed_data_parallel.py
deleted file mode 100644
index cd434c7372ba30ea0e6f87e084230448f53480e9..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/distributed/legacy_distributed_data_parallel.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-A modified version of the legacy DistributedDataParallel module that uses c10d
-communication primitives. This version is simpler than the latest PyTorch
-version and is useful for debugging. Notably it does not overlap gradient
-communication with the backward pass, which makes it slower but more robust
-than the PyTorch version.
-
-This version also supports the *no_sync* context manager, which allows faster
-training with `--update-freq`.
-"""
-
-from collections import OrderedDict
-from contextlib import contextmanager
-
-import torch
-from torch import nn
-
-from fairseq.distributed import utils
-
-
-class LegacyDistributedDataParallel(nn.Module):
- """Implements distributed data parallelism at the module level.
-
- A simplified version of :class:`torch.nn.parallel.DistributedDataParallel`.
- This version uses a c10d process group for communication and does not
- broadcast buffers.
-
- Args:
- module (~torch.nn.Module): module to be parallelized
- process_group: the c10d process group to be used for distributed data
- parallel all-reduction.
- buffer_size (int, optional): number of elements to buffer before
- performing all-reduce (default: 256M).
- """
-
- def __init__(self, module, process_group, buffer_size=2**28):
- super().__init__()
-
- self.module = module
- self.process_group = process_group
- self.world_size = utils.get_world_size(self.process_group)
-
- # Never use a bigger buffer than the number of model params
- self.buffer_size = min(buffer_size, sum(p.numel() for p in module.parameters()))
- self.buffer = None
-
- # We can also forcibly accumulate grads locally and only do the
- # all-reduce at some later time
- self.accumulate_grads = False
-
- # make per-device lists of parameters
- paramlists = OrderedDict()
- for param in self.module.parameters():
- device = param.device
- if paramlists.get(device) is None:
- paramlists[device] = []
- paramlists[device] += [param]
- self.per_device_params = list(paramlists.values())
-
- @contextmanager
- def no_sync(self):
- """A context manager to disable gradient synchronization."""
- old_accumulate_grads = self.accumulate_grads
- self.accumulate_grads = True
- yield
- self.accumulate_grads = old_accumulate_grads
-
- def forward(self, *inputs, **kwargs):
- return self.module(*inputs, **kwargs)
-
- def all_reduce_grads(self):
- """
- This function must be called explicitly after backward to reduce
- gradients. There is no automatic hook like c10d.
- """
-
- def all_reduce_params(params):
- buffer = self.buffer
- nonzero_buffer = False
- if len(params) > 1:
- offset = 0
- for p in params:
- sz = p.numel()
- if p.grad is not None:
- buffer[offset : offset + sz].copy_(p.grad.data.view(-1))
- nonzero_buffer = True
- else:
- buffer[offset : offset + sz].zero_()
- offset += sz
- else:
- # we only have a single grad to all-reduce
- p = params[0]
- if p.grad is not None:
- buffer = p.grad.data
- nonzero_buffer = True
- elif p.numel() <= self.buffer.numel():
- buffer = buffer[: p.numel()]
- buffer.zero_()
- else:
- buffer = torch.zeros_like(p)
-
- if nonzero_buffer:
- buffer.div_(self.world_size)
-
- utils.all_reduce(buffer, self.process_group)
-
- # copy all-reduced grads back into their original place
- offset = 0
- for p in params:
- sz = p.numel()
- if p.grad is not None:
- p.grad.data.copy_(buffer[offset : offset + sz].view_as(p))
- else:
- p.grad = buffer[offset : offset + sz].view_as(p).clone()
- offset += sz
-
- def reduction_fn():
- # This function only needs to be called once
- if self.accumulate_grads:
- return
-
- if self.buffer is None:
- self.buffer = next(self.module.parameters()).new(self.buffer_size)
-
- for params in self.per_device_params:
- # All-reduce the gradients in buckets
- offset = 0
- buffered_params = []
- for param in params:
- if not param.requires_grad:
- continue
- if param.grad is None:
- param.grad = torch.zeros_like(param)
-
- if hasattr(param, "expert"):
- # Skip gradient sync for unshared parameters
- continue
-
- if param.grad.requires_grad:
- raise RuntimeError(
- "DistributedDataParallel only works "
- "with gradients that don't require "
- "grad"
- )
- sz = param.numel()
- if sz > self.buffer.numel():
- # all-reduce big params directly
- all_reduce_params([param])
- else:
- if offset + sz > self.buffer.numel():
- all_reduce_params(buffered_params)
- offset = 0
- buffered_params.clear()
- buffered_params.append(param)
- offset += sz
-
- if len(buffered_params) > 0:
- all_reduce_params(buffered_params)
-
- reduction_fn()
diff --git a/spaces/ashutosh1919/quantum-perceptron/quantum_perceptron/__init__.py b/spaces/ashutosh1919/quantum-perceptron/quantum_perceptron/__init__.py
deleted file mode 100644
index ed4e767fa8c5310969615aa941e5b41ce6ea5099..0000000000000000000000000000000000000000
--- a/spaces/ashutosh1919/quantum-perceptron/quantum_perceptron/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from quantum_perceptron.perceptron import Perceptron
diff --git a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Saim Mehmood.html b/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Saim Mehmood.html
deleted file mode 100644
index 74fbd5bb33874558aed6731c8c07572671215e80..0000000000000000000000000000000000000000
--- a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Saim Mehmood.html
+++ /dev/null
@@ -1,134 +0,0 @@
-
-
-
- Saim Mehmood
-
-
-
-
-
- Saim Mehmood
-
-
- Interview
Saim was a menteeCareer- Moved to Canada back in Dec 2017
- grad school Jan 2018
- MSc in CS - research-oriented work
- graduated June 2020
- Sep 2019 got in touch with Omar
- Omar: "You already have a good project with your thesis", finish that up
- Covid hit, kept applying
- kept applying
- took me almost 2 years to find job
- worked as MLOps
- learned a lot throughout my journey, particularly about academia and industry
And how can you add value as a mentor?- selling yourself as a previous academic
- it's a long journey - expectation setting - you will hit a lot rejections!
- "I was getting rejection emails every day, and was happy to see them"
- market the project that you've done
- make sure they are technically sound
- Build a project if needed, post on medium
Mentorship exp- Been a TA during school
Ideal mentee- Recent CS grads would be easier, but open to other disciplines
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/drawingboard.min.css b/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/drawingboard.min.css
deleted file mode 100644
index 7d80478705de8fdceacbad40e5a85c19307be665..0000000000000000000000000000000000000000
--- a/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/drawingboard.min.css
+++ /dev/null
@@ -1,5 +0,0 @@
-/* drawingboard.js v0.4.6 - https://github.com/Leimi/drawingboard.js
-* Copyright (c) 2015 Emmanuel Pelletier
-* Licensed MIT */
-
-.drawing-board,.drawing-board *{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}.drawing-board-controls-hidden,.drawing-board-utils-hidden{display:none!important}.drawing-board{position:relative;display:block}.drawing-board-canvas-wrapper{position:relative;margin:0;border:1px solid #ddd}.drawing-board-canvas{position:absolute;top:0;left:0;width:auto;cursor:crosshair;z-index:20}.drawing-board-cursor{position:absolute;top:0;left:0;pointer-events:none;border-radius:50%;background:#ccc;background:rgba(0,0,0,.2);z-index:30}.drawing-board-control-colors-rainbows,.drawing-board-control-size .drawing-board-control-inner,.drawing-board-control-size-dropdown,.drawing-board-control>button{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;overflow:hidden;background-color:#eee;padding:2px 4px;border:1px solid #ccc;box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);-webkit-box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);height:28px}.drawing-board-control>button{cursor:pointer;min-width:28px;line-height:14px}.drawing-board-control>button:focus,.drawing-board-control>button:hover{background-color:#ddd}.drawing-board-control>button.active,.drawing-board-control>button:active{box-shadow:inset 0 1px 2px 0 rgba(0,0,0,.2);-webkit-box-shadow:inset 0 1px 2px 0 rgba(0,0,0,.2);background-color:#ddd}.drawing-board-control>button[disabled]{color:gray}.drawing-board-control>button[disabled].active,.drawing-board-control>button[disabled]:active,.drawing-board-control>button[disabled]:focus,.drawing-board-control>button[disabled]:hover{background-color:#eee;box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);-webkit-box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);cursor:default}.drawing-board-controls{margin:0 auto;text-align:center;font-size:0;display:table;border-spacing:9.33px 0;position:relative;min-height:28px}.drawing-board-controls[data-align=left]{margin:0;left:-9.33px}.drawing-board-controls[data-align=right]{margin:0 0 0 auto;right:-9.33px}.drawing-board-canvas-wrapper+.drawing-board-controls,.drawing-board-controls+.drawing-board-canvas-wrapper{margin-top:5px}.drawing-board-controls-hidden{height:0;min-height:0;padding:0;margin:0;border:0}.drawing-board-control{display:table-cell;border-collapse:separate;vertical-align:middle;font-size:16px;height:100%}.drawing-board-control-inner{position:relative;height:100%;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.drawing-board-control>button{margin:0;vertical-align:middle}.drawing-board-control-colors{font-size:0;line-height:0}.drawing-board-control-colors-current{border:1px solid #ccc;cursor:pointer;display:inline-block;width:26px;height:26px}.drawing-board-control-colors-rainbows{display:inline-block;position:absolute;left:0;top:33px;margin-left:0;z-index:100;width:250px;height:auto;padding:4px}.drawing-board-control-colors-rainbow{height:18px}.drawing-board-control-colors-picker:first-child{margin-right:5px}.drawing-board-control-colors-picker{display:inline-block;width:18px;height:18px;cursor:pointer}.drawing-board-control-colors-picker[data-color="rgba(255, 255, 255, 1)"]{width:16px;height:17px;border:1px solid #ccc;border-bottom:none}.drawing-board-control-colors-picker:hover{width:16px;height:16px;border:1px solid #555}.drawing-board-control-drawingmode>button{margin-right:2px}.drawing-board-control-drawingmode>button:last-child{margin-right:0}.drawing-board-control-drawingmode-pencil-button{overflow:hidden;background-image:url(data:image/png;base64,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);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-drawingmode-pencil-button:before{content:"";display:block;width:0;height:100%}.drawing-board-control-drawingmode-eraser-button{overflow:hidden;background-image:url(data:image/png;base64,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);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-drawingmode-eraser-button:before{content:"";display:block;width:0;height:100%}.drawing-board-control-drawingmode-filler-button{overflow:hidden;background-image:url(data:image/png;base64,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);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-drawingmode-filler-button:before{content:"";display:block;width:0;height:100%}.drawing-board-control-navigation>button{font-family:Helvetica,Arial,sans-serif;font-size:14px;font-weight:700;margin-right:2px}.drawing-board-control-navigation>button:last-child{margin-right:0}.drawing-board-control-size[data-drawing-board-type=range] .drawing-board-control-inner{width:75px}.drawing-board-control-size[data-drawing-board-type=dropdown] .drawing-board-control-inner{overflow:visible}.drawing-board-control-size-range-input{position:relative;width:100%;z-index:100;margin:0;padding:0;border:0}.drawing-board-control-size-dropdown span,.drawing-board-control-size-dropdown-current span,.drawing-board-control-size-range-current{display:block;background:#333;opacity:.8}.drawing-board-control-size-range-current{display:inline-block;opacity:.15;position:absolute;pointer-events:none;left:50%;top:50%;z-index:50}.drawing-board-control-size-dropdown-current{display:block;height:100%;width:40px;overflow:hidden;position:relative}.drawing-board-control-size-dropdown-current span{position:absolute;left:50%;top:50%}.drawing-board-control-size-dropdown{position:absolute;left:-6px;top:33px;height:auto;list-style-type:none;margin:0;padding:0;z-index:100}.drawing-board-control-size-dropdown li{display:block;padding:4px;margin:3px 0;min-height:16px}.drawing-board-control-size-dropdown li:hover{background:#ccc}.drawing-board-control-size-dropdown span{margin:0 auto}.drawing-board-control-download-button{overflow:hidden;background-image:url(data:image/png;base64,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);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-download-button:before{content:"";display:block;width:0;height:100%}
\ No newline at end of file
diff --git a/spaces/awacke1/Bloom.Generative.Writer/generators/topic_to_abstract.py b/spaces/awacke1/Bloom.Generative.Writer/generators/topic_to_abstract.py
deleted file mode 100644
index a91131db41007e46ce1d9a9772bdd7e9645b9d2b..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Bloom.Generative.Writer/generators/topic_to_abstract.py
+++ /dev/null
@@ -1,6 +0,0 @@
-
-from .model import model
-
-
-def topic_to_abstract_generator(template):
- return model('topic', template)
diff --git a/spaces/awacke1/Streamlit-Data-Synthesis-Example/app.py b/spaces/awacke1/Streamlit-Data-Synthesis-Example/app.py
deleted file mode 100644
index 636e3a14af4ceae48968e372e88f840f3425c791..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Streamlit-Data-Synthesis-Example/app.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import streamlit as st
-import pandas as pd
-
-
-def generate_hospital_data():
- # Generate hospital data
- hospitals = {
- "city": ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"],
- "state": ["NY", "CA", "IL", "TX", "AZ"],
- "bed_count": [1200, 1500, 1100, 1300, 1400],
- }
- df = pd.DataFrame(hospitals)
- return df
-
-
-def generate_state_data():
- # Generate state data
- states = {
- "state": ["NY", "CA", "IL", "TX", "AZ"],
- "population": [20000000, 40000000, 13000000, 29000000, 7000000],
- "square_miles": [54556, 163696, 57914, 268596, 113990],
- }
- df = pd.DataFrame(states)
- return df
-
-
-def merge_datasets(hospitals_df, states_df):
- # Merge hospital and state data
- merged_df = pd.merge(hospitals_df, states_df, on="state")
- return merged_df
-
-
-def calculate_beds_per_capita(merged_df):
- # Calculate beds per capita
- merged_df["beds_per_capita"] = merged_df["bed_count"] / merged_df["population"]
- return merged_df
-
-
-def main():
- # Generate data
- hospitals_df = generate_hospital_data()
- states_df = generate_state_data()
-
- # Merge datasets
- merged_df = merge_datasets(hospitals_df, states_df)
-
- # Calculate beds per capita
- merged_df = calculate_beds_per_capita(merged_df)
-
- # Show merged and calculated data
- st.write(merged_df)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/class_names/README.md b/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/class_names/README.md
deleted file mode 100644
index 30d74d258442c7c65512eafab474568dd706c430..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/class_names/README.md
+++ /dev/null
@@ -1 +0,0 @@
-test
\ No newline at end of file
diff --git a/spaces/badayvedat/AudioSep/models/CLAP/open_clip/bert.py b/spaces/badayvedat/AudioSep/models/CLAP/open_clip/bert.py
deleted file mode 100644
index a83d96d2a77ed05198efc05837522bc88d2499cc..0000000000000000000000000000000000000000
--- a/spaces/badayvedat/AudioSep/models/CLAP/open_clip/bert.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from transformers import BertTokenizer, BertModel
-
-tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-model = BertModel.from_pretrained("bert-base-uncased")
-text = "Replace me by any text you'd like."
-
-
-def bert_embeddings(text):
- # text = "Replace me by any text you'd like."
- encoded_input = tokenizer(text, return_tensors="pt")
- output = model(**encoded_input)
- return output
-
-
-from transformers import RobertaTokenizer, RobertaModel
-
-tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
-model = RobertaModel.from_pretrained("roberta-base")
-text = "Replace me by any text you'd like."
-
-
-def Roberta_embeddings(text):
- # text = "Replace me by any text you'd like."
- encoded_input = tokenizer(text, return_tensors="pt")
- output = model(**encoded_input)
- return output
-
-
-from transformers import BartTokenizer, BartModel
-
-tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
-model = BartModel.from_pretrained("facebook/bart-base")
-text = "Replace me by any text you'd like."
-
-
-def bart_embeddings(text):
- # text = "Replace me by any text you'd like."
- encoded_input = tokenizer(text, return_tensors="pt")
- output = model(**encoded_input)
- return output
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DAmmoLoader.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DAmmoLoader.js
deleted file mode 100644
index 9239e109afef47ff228e1fa2005fd9a6643da7ae..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DAmmoLoader.js
+++ /dev/null
@@ -1,590 +0,0 @@
-/**
- * SEA3D+AMMO for Three.JS
- * @author Sunag / http://www.sunag.com.br/
- */
-
-'use strict';
-
-THREE.SEA3D.prototype.toAmmoVec3 = function ( v ) {
-
- return new Ammo.btVector3( v.x, v.y, v.z );
-
-};
-
-//
-// Sphere
-//
-
-THREE.SEA3D.prototype.readSphere = function ( sea ) {
-
- var shape = new Ammo.btSphereShape( sea.radius );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Box
-//
-
-THREE.SEA3D.prototype.readBox = function ( sea ) {
-
- var shape = new Ammo.btBoxShape( new Ammo.btVector3( sea.width * .5, sea.height * .5, sea.depth * .5 ) );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Cone
-//
-
-THREE.SEA3D.prototype.readCone = function ( sea ) {
-
- var shape = new Ammo.btConeShape( sea.radius, sea.height );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Cylinder
-//
-
-THREE.SEA3D.prototype.readCylinder = function ( sea ) {
-
- var shape = new Ammo.btCylinderShape( new Ammo.btVector3( sea.height, sea.radius, sea.radius ) );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Capsule
-//
-
-THREE.SEA3D.prototype.readCapsule = function ( sea ) {
-
- var shape = new Ammo.btCapsuleShape( sea.radius, sea.height );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Convex Geometry
-//
-
-THREE.SEA3D.prototype.readConvexGeometry = function ( sea ) {
-
- if ( this.config.convexHull ) {
-
- var shape = SEA3D.AMMO.createConvexHull( sea.geometry.tag, sea.subGeometryIndex );
-
- } else {
-
- var triMesh = SEA3D.AMMO.createTriangleMesh( sea.geometry.tag, sea.subGeometryIndex );
-
- var shape = new Ammo.btConvexTriangleMeshShape( triMesh, true );
-
- }
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Triangle Geometry
-//
-
-THREE.SEA3D.prototype.readTriangleGeometry = function ( sea ) {
-
- var triMesh = SEA3D.AMMO.createTriangleMesh( sea.geometry.tag, sea.subGeometryIndex );
-
- var shape = new Ammo.btBvhTriangleMeshShape( triMesh, true, true );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Compound
-//
-
-THREE.SEA3D.prototype.readCompound = function ( sea ) {
-
- var shape = new Ammo.btCompoundShape();
-
- for ( var i = 0; i < sea.compounds.length; i ++ ) {
-
- var compound = sea.compounds[ i ];
-
- THREE.SEA3D.MTXBUF.elements = compound.transform;
-
- var transform = SEA3D.AMMO.getTransformFromMatrix( THREE.SEA3D.MTXBUF );
-
- shape.addChildShape( transform, compound.shape.tag );
-
- }
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Rigid Body Base
-//
-
-THREE.SEA3D.prototype.readRigidBodyBase = function ( sea ) {
-
- var shape = sea.shape.tag,
- transform, target;
-
- if ( sea.target ) {
-
- target = sea.target.tag;
-
- target.physics = { enabled: true };
- target.updateMatrix();
-
- transform = SEA3D.AMMO.getTransformFromMatrix( sea.target.tag.matrix );
-
- } else {
-
- THREE.SEA3D.MTXBUF.fromArray( sea.transform );
-
- transform = SEA3D.AMMO.getTransformFromMatrix( THREE.SEA3D.MTXBUF );
-
- }
-
- var motionState = new Ammo.btDefaultMotionState( transform );
- var localInertia = new Ammo.btVector3( 0, 0, 0 );
-
- shape.calculateLocalInertia( sea.mass, localInertia );
-
- var info = new Ammo.btRigidBodyConstructionInfo( sea.mass, motionState, shape, localInertia );
- info.set_m_friction( sea.friction );
- info.set_m_restitution( sea.restitution );
- info.set_m_linearDamping( sea.linearDamping );
- info.set_m_angularDamping( sea.angularDamping );
-
- var rb = new Ammo.btRigidBody( info );
-
- if ( target ) {
-
- target.physics.rigidBody = rb;
-
- if ( sea.offset ) {
-
- var offset = new THREE.Matrix4();
- offset.fromArray( sea.offset );
-
- target.physics.offset = offset;
-
- }
-
- }
-
- Ammo.destroy( info );
-
- this.domain.rigidBodies = this.rigidBodies = this.rigidBodies || [];
- this.rigidBodies.push( this.objects[ "rb/" + sea.name ] = sea.tag = rb );
-
- return rb;
-
-};
-
-//
-// Rigid Body
-//
-
-THREE.SEA3D.prototype.readRigidBody = function ( sea ) {
-
- var rb = this.readRigidBodyBase( sea );
-
- SEA3D.AMMO.addRigidBody( rb, sea.target ? sea.target.tag : undefined, this.config.enabledPhysics );
-
-};
-
-//
-// Car Controller
-//
-
-THREE.SEA3D.prototype.readCarController = function ( sea ) {
-
- var body = this.readRigidBodyBase( sea );
-
- body.setActivationState( SEA3D.AMMO.DISABLE_DEACTIVATION );
-
- // Car
-
- var vehicleRayCaster = new Ammo.btDefaultVehicleRaycaster( SEA3D.AMMO.world );
-
- var tuning = new Ammo.btVehicleTuning();
-
- tuning.set_m_suspensionStiffness( sea.suspensionStiffness );
- tuning.set_m_suspensionDamping( sea.suspensionDamping );
- tuning.set_m_suspensionCompression( sea.suspensionCompression );
- tuning.set_m_maxSuspensionTravelCm( sea.maxSuspensionTravelCm );
- tuning.set_m_maxSuspensionForce( sea.maxSuspensionForce );
- tuning.set_m_frictionSlip( sea.frictionSlip );
-
- var vehicle = new Ammo.btRaycastVehicle( tuning, body, vehicleRayCaster ),
- wheels = [];
-
- vehicle.setCoordinateSystem( 0, 1, 2 );
-
- for ( var i = 0; i < sea.wheel.length; i ++ ) {
-
- var wheel = sea.wheel[ i ];
-
- var wheelInfo = vehicle.addWheel(
- this.toAmmoVec3( wheel.pos ),
- this.toAmmoVec3( wheel.dir ),
- this.toAmmoVec3( wheel.axle ),
- wheel.suspensionRestLength,
- wheel.radius,
- tuning,
- wheel.isFront
- );
-
- var target = wheels[ i ] = wheel.target ? wheel.target.tag : undefined;
-
- if ( target ) {
-
- target.physics = { enabled: true, rigidBody: wheelInfo };
-
- if ( wheel.offset ) {
-
- var offset = new THREE.Matrix4();
- offset.fromArray( wheel.offset );
-
- target.physics.offset = offset;
-
- }
-
- if ( target.parent ) {
-
- target.parent.remove( target );
-
- }
-
- if ( this.container ) {
-
- this.container.add( target );
-
- }
-
- }
-
- wheelInfo.set_m_suspensionStiffness( sea.suspensionStiffness );
- wheelInfo.set_m_wheelsDampingRelaxation( sea.dampingRelaxation );
- wheelInfo.set_m_wheelsDampingCompression( sea.dampingCompression );
- wheelInfo.set_m_frictionSlip( sea.frictionSlip );
-
- }
-
- SEA3D.AMMO.addVehicle( vehicle, wheels );
- SEA3D.AMMO.addRigidBody( body, sea.target ? sea.target.tag : undefined, this.config.enabledPhysics );
-
- this.domain.vehicles = this.vehicles = this.vehicles || [];
- this.vehicles.push( this.objects[ "vhc/" + sea.name ] = sea.tag = vehicle );
-
-};
-
-//
-// P2P Constraint
-//
-
-THREE.SEA3D.prototype.readP2PConstraint = function ( sea ) {
-
- var ctrt;
-
- if ( sea.targetB ) {
-
- ctrt = new Ammo.btPoint2PointConstraint(
- sea.targetA.tag,
- sea.targetB.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.pointB )
- );
-
- } else {
-
- ctrt = new Ammo.btPoint2PointConstraint(
- sea.targetA.tag,
- this.toAmmoVec3( sea.pointA )
- );
-
- }
-
- SEA3D.AMMO.addConstraint( ctrt );
-
- this.domain.constraints = this.constraints = this.constraints || [];
- this.constraints.push( this.objects[ "ctnt/" + sea.name ] = sea.tag = ctrt );
-
-};
-
-//
-// Hinge Constraint
-//
-
-THREE.SEA3D.prototype.readHingeConstraint = function ( sea ) {
-
- var ctrt;
-
- if ( sea.targetB ) {
-
- ctrt = new Ammo.btHingeConstraint(
- sea.targetA.tag,
- sea.targetB.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.pointB ),
- this.toAmmoVec3( sea.axisA ),
- this.toAmmoVec3( sea.axisB ),
- false
- );
-
- } else {
-
- ctrt = new Ammo.btHingeConstraint(
- sea.targetA.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.axisA ),
- false
- );
-
- }
-
- if ( sea.limit ) {
-
- ctrt.setLimit( sea.limit.low, sea.limit.high, sea.limit.softness, sea.limit.biasFactor, sea.limit.relaxationFactor );
-
- }
-
- if ( sea.angularMotor ) {
-
- ctrt.enableAngularMotor( true, sea.angularMotor.velocity, sea.angularMotor.impulse );
-
- }
-
- SEA3D.AMMO.addConstraint( ctrt );
-
- this.domain.constraints = this.constraints = this.constraints || [];
- this.constraints.push( this.objects[ "ctnt/" + sea.name ] = sea.tag = ctrt );
-
-};
-
-//
-// Cone Twist Constraint
-//
-
-THREE.SEA3D.prototype.readConeTwistConstraint = function ( sea ) {
-
- var ctrt;
-
- if ( sea.targetB ) {
-
- ctrt = new Ammo.btConeTwistConstraint(
- sea.targetA.tag,
- sea.targetB.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.pointB ),
- false
- );
-
- } else {
-
- ctrt = new Ammo.btConeTwistConstraint(
- sea.targetA.tag,
- this.toAmmoVec3( sea.pointA ),
- false
- );
-
- }
-
- SEA3D.AMMO.addConstraint( ctrt );
-
- this.domain.constraints = this.constraints = this.constraints || [];
- this.constraints.push( this.objects[ "ctnt/" + sea.name ] = sea.tag = ctrt );
-
-};
-
-//
-// Domain
-//
-
-THREE.SEA3D.Domain.prototype.enabledPhysics = function ( enabled ) {
-
- var i = this.rigidBodies ? this.rigidBodies.length : 0;
-
- while ( i -- ) {
-
- SEA3D.AMMO.setEnabledRigidBody( this.rigidBodies[ i ], enabled );
-
- }
-
-};
-
-THREE.SEA3D.Domain.prototype.applyContainerTransform = function () {
-
- this.container.updateMatrix();
-
- var matrix = this.container.matrix.clone();
-
- this.container.position.set( 0, 0, 0 );
- this.container.quaternion.set( 0, 0, 0, 1 );
- this.container.scale.set( 1, 1, 1 );
-
- this.applyTransform( matrix );
-
-};
-
-THREE.SEA3D.Domain.prototype.applyTransform = function ( matrix ) {
-
- var mtx = THREE.SEA3D.MTXBUF, vec = THREE.SEA3D.VECBUF;
-
- var i = this.rigidBodies ? this.rigidBodies.length : 0,
- childs = this.container ? this.container.children : [],
- targets = [];
-
- while ( i -- ) {
-
- var rb = this.rigidBodies[ i ],
- target = SEA3D.AMMO.getTargetByRigidBody( rb ),
- transform = rb.getWorldTransform(),
- transformMatrix = SEA3D.AMMO.getMatrixFromTransform( transform );
-
- transformMatrix.multiplyMatrices( transformMatrix, matrix );
-
- transform = SEA3D.AMMO.getTransformFromMatrix( transformMatrix );
-
- rb.setWorldTransform( transform );
-
- if ( target ) targets.push( target );
-
- }
-
- for ( i = 0; i < childs.length; i ++ ) {
-
- var obj3d = childs[ i ];
-
- if ( targets.indexOf( obj3d ) > - 1 ) continue;
-
- obj3d.updateMatrix();
-
- mtx.copy( obj3d.matrix );
-
- mtx.multiplyMatrices( matrix, mtx );
-
- obj3d.position.setFromMatrixPosition( mtx );
- obj3d.scale.setFromMatrixScale( mtx );
-
- // ignore rotation scale
-
- mtx.scale( vec.set( 1 / obj3d.scale.x, 1 / obj3d.scale.y, 1 / obj3d.scale.z ) );
- obj3d.quaternion.setFromRotationMatrix( mtx );
-
- }
-
-};
-
-//
-// Extension
-//
-
-THREE.SEA3D.Domain.prototype.getShape = THREE.SEA3D.prototype.getShape = function ( name ) {
-
- return this.objects[ "shpe/" + name ];
-
-};
-
-THREE.SEA3D.Domain.prototype.getRigidBody = THREE.SEA3D.prototype.getRigidBody = function ( name ) {
-
- return this.objects[ "rb/" + name ];
-
-};
-
-THREE.SEA3D.Domain.prototype.getConstraint = THREE.SEA3D.prototype.getConstraint = function ( name ) {
-
- return this.objects[ "ctnt/" + name ];
-
-};
-
-THREE.SEA3D.EXTENSIONS_LOADER.push( {
-
- parse: function () {
-
- delete this.shapes;
- delete this.rigidBodies;
- delete this.vehicles;
- delete this.constraints;
-
- },
-
- setTypeRead: function () {
-
- // CONFIG
-
- this.config.physics = this.config.physics !== undefined ? this.config.physics : true;
- this.config.convexHull = this.config.convexHull !== undefined ? this.config.convexHull : true;
- this.config.enabledPhysics = this.config.enabledPhysics !== undefined ? this.config.enabledPhysics : true;
-
- if ( this.config.physics ) {
-
- // SHAPES
-
- this.file.typeRead[ SEA3D.Sphere.prototype.type ] = this.readSphere;
- this.file.typeRead[ SEA3D.Box.prototype.type ] = this.readBox;
- this.file.typeRead[ SEA3D.Capsule.prototype.type ] = this.readCapsule;
- this.file.typeRead[ SEA3D.Cone.prototype.type ] = this.readCone;
- this.file.typeRead[ SEA3D.Cylinder.prototype.type ] = this.readCylinder;
- this.file.typeRead[ SEA3D.ConvexGeometry.prototype.type ] = this.readConvexGeometry;
- this.file.typeRead[ SEA3D.TriangleGeometry.prototype.type ] = this.readTriangleGeometry;
- this.file.typeRead[ SEA3D.Compound.prototype.type ] = this.readCompound;
-
- // CONSTRAINTS
-
- this.file.typeRead[ SEA3D.P2PConstraint.prototype.type ] = this.readP2PConstraint;
- this.file.typeRead[ SEA3D.HingeConstraint.prototype.type ] = this.readHingeConstraint;
- this.file.typeRead[ SEA3D.ConeTwistConstraint.prototype.type ] = this.readConeTwistConstraint;
-
- // PHYSICS
-
- this.file.typeRead[ SEA3D.RigidBody.prototype.type ] = this.readRigidBody;
- this.file.typeRead[ SEA3D.CarController.prototype.type ] = this.readCarController;
-
- }
-
- }
-} );
-
-THREE.SEA3D.EXTENSIONS_DOMAIN.push( {
-
- dispose: function () {
-
- var i;
-
- i = this.rigidBodies ? this.rigidBodies.length : 0;
- while ( i -- ) SEA3D.AMMO.removeRigidBody( this.rigidBodies[ i ], true );
-
- i = this.vehicles ? this.vehicles.length : 0;
- while ( i -- ) SEA3D.AMMO.removeVehicle( this.vehicles[ i ], true );
-
- i = this.constraints ? this.constraints.length : 0;
- while ( i -- ) SEA3D.AMMO.removeConstraint( this.constraints[ i ], true );
-
- i = this.shapes ? this.shapes.length : 0;
- while ( i -- ) Ammo.destroy( this.shapes[ i ] );
-
- }
-
-} );
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/ShadowMapViewer.js b/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/ShadowMapViewer.js
deleted file mode 100644
index 340efd3f339f776062b01678a0b75adaa67e5623..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/ShadowMapViewer.js
+++ /dev/null
@@ -1,208 +0,0 @@
-/**
- * @author arya-s / https://github.com/arya-s
- *
- * This is a helper for visualising a given light's shadow map.
- * It works for shadow casting lights: THREE.DirectionalLight and THREE.SpotLight.
- * It renders out the shadow map and displays it on a HUD.
- *
- * Example usage:
- * 1) Include \n\t\t" + head + "\n\t\n\t\n\t\t" + body + "\n\t\t\n
")
- gr.Markdown("Note the model runs on CPU, so it may take a while to run the model.")
-
- with gr.Row():
- with gr.Column():
- input_image = gr.Image(source='upload', type="pil")
- grounding_caption = gr.Textbox(label="Detection Prompt")
- run_button = gr.Button(label="Run")
- with gr.Accordion("Advanced options", open=False):
- box_threshold = gr.Slider(
- label="Box Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
- )
- text_threshold = gr.Slider(
- label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
- )
-
- with gr.Column():
- gallery = gr.outputs.Image(
- type="pil",
- # label="grounding results"
- ).style(full_width=True, full_height=True)
- # gallery = gr.Gallery(label="Generated images", show_label=False).style(
- # grid=[1], height="auto", container=True, full_width=True, full_height=True)
-
- run_button.click(fn=run_grounding, inputs=[
- input_image, grounding_caption, box_threshold, text_threshold], outputs=[gallery])
- gr.Examples(
- [["watermelon.jpg", "watermelon", 0.25, 0.25]],
- inputs = [input_image, grounding_caption, box_threshold, text_threshold],
- outputs = [gallery],
- fn=run_grounding,
- cache_examples=True,
- label='Try this example input!'
- )
- block.launch(share=True, show_api=False, show_error=True)
-
diff --git a/spaces/Audio-AGI/WavJourney/README.md b/spaces/Audio-AGI/WavJourney/README.md
deleted file mode 100644
index 6514202f6a3a92fe09c96516f15d4e7848d85484..0000000000000000000000000000000000000000
--- a/spaces/Audio-AGI/WavJourney/README.md
+++ /dev/null
@@ -1,111 +0,0 @@
----
-title: WavJourney
-emoji: 🔥
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
-license: cc-by-nc-4.0
----
-# 🎵 WavJourney: Compositional Audio Creation with LLMs
-[](https://arxiv.org/abs/2307.14335) [](https://github.com/Audio-AGI/WavJourney/) [](https://audio-agi.github.io/WavJourney_demopage/) [](https://huggingface.co/spaces/Audio-AGI/WavJourney)
-
-
-This repository contains the official implementation of ["WavJourney: Compositional Audio Creation with Large Language Models"](https://audio-agi.github.io/WavJourney_demopage/WavJourney_arXiv.pdf).
-
-Starting with a text prompt, WavJourney can create audio content with engaging storylines encompassing personalized speakers, lifelike speech in context, emotionally resonant music compositions, and impactful sound effects that enhance the auditory experience. Check the audio examples in the [Project Page](https://audio-agi.github.io/WavJourney_demopage/)!
-
-
-
-
-
-
-## Preliminaries
-1. Install the environment:
-```bash
-bash ./scripts/EnvsSetup.sh
-```
-2. Activate the conda environment:
-```bash
-conda activate WavJourney
-```
-
-3. (Optional) You can modify the default configuration in `config.yaml`, check the details described in the configuration file.
-4. Pre-download the models (might take some time):
-```bash
-python scripts/download_models.py
-```
-
-5. Set the WAVJOURNEY_OPENAI_KEY in the environment variable for accessing [GPT-4 API](https://platform.openai.com/account/api-keys) [[Guidance](https://help.openai.com/en/articles/7102672-how-can-i-access-gpt-4)]
-```bash
-export WAVJOURNEY_OPENAI_KEY=your_openai_key_here
-```
-
-6. Set environment variables for using API services
-```bash
-# Set the port for the WAVJOURNEY service to 8021
-export WAVJOURNEY_SERVICE_PORT=8021
-
-# Set the URL for the WAVJOURNEY service to 127.0.0.1
-export WAVJOURNEY_SERVICE_URL=127.0.0.1
-
-# Limit the maximum script lines for WAVJOURNEY to 999
-export WAVJOURNEY_MAX_SCRIPT_LINES=999
-```
-
-
-7. Start Python API services (e.g., Text-to-Speech, Text-to-Audio)
-```bash
-bash scripts/start_services.sh
-```
-
-## Web APP
- ```bash
-bash scripts/start_ui.sh
- ```
-
-## Commandline Usage
- ```bash
- python wavjourney_cli.py -f --input-text "Generate a one-minute introduction to quantum mechanics"
- ```
-
-
-## Kill the services
-You can kill the running services via this command:
- ```bash
-python scripts/kill_services.py
- ```
-
-## (Advanced features) Speaker customization
-You can add voice presets to WavJourney to customize the voice actors. Simply provide the voice id, the description and a sample wav file, and WavJourney will pick the voice automatically based on the audio script. Predefined system voice presets are in `data/voice_presets`.
-
-You can manage voice presets via UI. Specifically, if you want to add voice to voice presets. Run the script via command line below:
-```bash
-python add_voice_preset.py --id "id" --desc "description" --wav-path path/to/wav --session-id ''
-```
-What makes for good voice prompt? See detailed instructions here.
-## Hardware requirement
-- The VRAM of the GPU in the default configuration should be greater than 16 GB.
-- Operation system: Linux.
-
-## Citation
-If you find this work useful, you can cite the paper below:
-
- @article{liu2023wavjourney,
- title = {WavJourney: Compositional Audio Creation with Large Language Models},
- author = {Liu, Xubo and Zhu, Zhongkai and Liu, Haohe and Yuan, Yi and Huang, Qiushi and Liang, Jinhua and Cao, Yin and Kong, Qiuqiang and Plumbley, Mark D and Wang, Wenwu},
- journal = {arXiv preprint arXiv:2307.14335},
- year = {2023}
- }
-
-[](https://www.buymeacoffee.com/liuxubo)
-
-## Appreciation
-- [Bark](https://github.com/suno-ai/bark) for a zero-shot text-to-speech synthesis model.
-- [AudioCraft](https://github.com/facebookresearch/audiocraft) for state-of-the-art audio generation models.
-
-## Disclaimer
-We are not responsible for audio generated using semantics created by this model. Just don't use it for illegal purposes.
-
diff --git a/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py b/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py
deleted file mode 100644
index be58431ed446bf6283c17274a41e37ec3eb24409..0000000000000000000000000000000000000000
--- a/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import os
-import asyncio
-import random
-from datetime import date, datetime, timezone, timedelta
-from ast import literal_eval
-
-import streamlit as st
-import openai
-
-from autoagents.utils.constants import MAIN_HEADER, MAIN_CAPTION, SAMPLE_QUESTIONS
-from autoagents.agents.search import ActionRunner
-
-from langchain.chat_models import ChatOpenAI
-
-
-async def run():
- output_acc = ""
- st.session_state["random"] = random.randint(0, 99)
- if "task" not in st.session_state:
- st.session_state.task = None
- if "model_name" not in st.session_state:
- st.session_state.model_name = "gpt-3.5-turbo"
-
- st.set_page_config(
- page_title="Search Agent",
- page_icon="🤖",
- layout="wide",
- initial_sidebar_state="expanded",
- )
-
- st.title(MAIN_HEADER)
- st.caption(MAIN_CAPTION)
-
- with st.form("my_form", clear_on_submit=False):
- st.markdown("", unsafe_allow_html=True)
- user_input = st.text_input(
- "You: ",
- key="input",
- placeholder="Ask me anything ...",
- label_visibility="hidden",
- )
-
- submitted = st.form_submit_button(
- "Search", help="Hit to submit the search query."
- )
-
- # Ask the user to enter their OpenAI API key
- if (api_key := st.sidebar.text_input("OpenAI api-key", type="password")):
- api_org = None
- else:
- api_key, api_org = os.getenv("OPENAI_API_KEY"), os.getenv("OPENAI_API_ORG")
- with st.sidebar:
- model_dict = {
- "gpt-3.5-turbo": "GPT-3.5-turbo",
- "gpt-4": "GPT-4 (Better but slower)",
- }
- st.radio(
- "OpenAI model",
- model_dict.keys(),
- key="model_name",
- format_func=lambda x: model_dict[x],
- )
-
- time_zone = str(datetime.now(timezone(timedelta(0))).astimezone().tzinfo)
- st.markdown(f"**The system time zone is {time_zone} and the date is {date.today()}**")
-
- st.markdown("**Example Queries:**")
- for q in SAMPLE_QUESTIONS:
- st.markdown(f"*{q}*")
-
- if not api_key:
- st.warning(
- "API key required to try this app. The API key is not stored in any form. [This](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key) might help."
- )
- elif api_org and st.session_state.model_name == "gpt-4":
- st.warning(
- "The free API key does not support GPT-4. Please switch to GPT-3.5-turbo or input your own API key."
- )
- else:
- outputq = asyncio.Queue()
- runner = ActionRunner(outputq,
- ChatOpenAI(openai_api_key=api_key,
- openai_organization=api_org,
- temperature=0,
- model_name=st.session_state.model_name),
- persist_logs=True) # log to HF-dataset
-
- async def cleanup(e):
- st.error(e)
- await st.session_state.task
- st.session_state.task = None
- st.stop()
-
- placeholder = st.empty()
-
- if user_input and submitted:
- if st.session_state.task is not None:
- with placeholder.container():
- st.session_state.task.cancel()
- st.warning("Previous search aborted", icon="⚠️")
-
- st.session_state.task = asyncio.create_task(
- runner.run(user_input, outputq)
- )
- iterations = 0
- with st.expander("Search Results", expanded=True):
- while True:
- with st.spinner("Wait for it..."):
- output = await outputq.get()
- placeholder.empty()
- if isinstance(output, Exception):
- if isinstance(output, openai.error.AuthenticationError):
- await cleanup(f"AuthenticationError: Invalid OpenAI API key.")
- elif isinstance(output, openai.error.InvalidRequestError) \
- and output._message == "The model: `gpt-4` does not exist":
- await cleanup(f"The free API key does not support GPT-4. Please switch to GPT-3.5-turbo or input your own API key.")
- elif isinstance(output, openai.error.OpenAIError):
- await cleanup(output)
- elif isinstance(output, RuntimeWarning):
- st.warning(output)
- continue
- else:
- await cleanup("Something went wrong. Please try searching again.")
- return
- try:
- output_fmt = literal_eval(output)
- st.json(output_fmt, expanded=False)
- st.write("---")
- iterations += 1
- except:
- output_acc += "\n" + output
- st.markdown(f" {output} ",
- unsafe_allow_html=True)
- if iterations >= runner.agent_executor.max_iterations:
- await cleanup(
- f"Maximum iterations ({iterations}) exceeded. You can try running the search again or try a variation of the query."
- )
- return
- if "Final Answer:" in output:
- break
- # Found the answer
- final_answer = await st.session_state.task
- final_answer = final_answer.replace("$", "\$")
- # st.success accepts md
- st.success(final_answer, icon="✅")
- st.balloons()
- st.session_state.task = None
- st.stop()
-
-if __name__ == "__main__":
- loop = asyncio.new_event_loop()
- loop.set_debug(enabled=False)
- loop.run_until_complete(run())
diff --git a/spaces/BAAI/vid2vid-zero/Dockerfile b/spaces/BAAI/vid2vid-zero/Dockerfile
deleted file mode 100644
index 9eaa78f8facc5066f018183f108824842f11b7e8..0000000000000000000000000000000000000000
--- a/spaces/BAAI/vid2vid-zero/Dockerfile
+++ /dev/null
@@ -1,57 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update && \
- apt-get upgrade -y && \
- apt-get install -y --no-install-recommends \
- git \
- git-lfs \
- wget \
- curl \
- # ffmpeg \
- ffmpeg \
- x264 \
- # python build dependencies \
- build-essential \
- libssl-dev \
- zlib1g-dev \
- libbz2-dev \
- libreadline-dev \
- libsqlite3-dev \
- libncursesw5-dev \
- xz-utils \
- tk-dev \
- libxml2-dev \
- libxmlsec1-dev \
- libffi-dev \
- liblzma-dev && \
- apt-get clean && \
- rm -rf /var/lib/apt/lists/*
-
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:${PATH}
-WORKDIR ${HOME}/app
-
-RUN curl https://pyenv.run | bash
-ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ENV PYTHON_VERSION=3.10.9
-RUN pyenv install ${PYTHON_VERSION} && \
- pyenv global ${PYTHON_VERSION} && \
- pyenv rehash && \
- pip install --no-cache-dir -U pip setuptools wheel
-
-RUN pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1
-COPY --chown=1000 requirements.txt /tmp/requirements.txt
-RUN pip install --no-cache-dir -U -r /tmp/requirements.txt
-
-COPY --chown=1000 . ${HOME}/app
-# RUN cd Tune-A-Video && patch -p1 < ../patch
-ENV PYTHONPATH=${HOME}/app \
- PYTHONUNBUFFERED=1 \
- GRADIO_ALLOW_FLAGGING=never \
- GRADIO_NUM_PORTS=1 \
- GRADIO_SERVER_NAME=0.0.0.0 \
- GRADIO_THEME=huggingface \
- SYSTEM=spaces
-CMD ["python", "app.py"]
diff --git a/spaces/Bala2-03-2003/MygenvioceAI/app.py b/spaces/Bala2-03-2003/MygenvioceAI/app.py
deleted file mode 100644
index ca8b6d40b4ab898c70da92f4a4298de2baf703dc..0000000000000000000000000000000000000000
--- a/spaces/Bala2-03-2003/MygenvioceAI/app.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import os
-import re
-import requests
-import json
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-PLAY_HT_API_KEY=os.getenv('PLAY_HT_API_KEY')
-PLAY_HT_USER_ID=os.getenv('PLAY_HT_USER_ID')
-
-PLAY_HT_VOICE_ID=os.getenv('PLAY_HT_VOICE_ID')
-play_ht_api_get_audio_url = "https://play.ht/api/v2/tts"
-
-
-template = """You are a helpful assistant to answer user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
- input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
- llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
- prompt=prompt,
- verbose=True,
- memory=memory,
-)
-
-headers = {
- "accept": "text/event-stream",
- "content-type": "application/json",
- "AUTHORIZATION": "Bearer "+ PLAY_HT_API_KEY,
- "X-USER-ID": PLAY_HT_USER_ID
-}
-
-
-def get_payload(text):
- return {
- "text": text,
- "voice": PLAY_HT_VOICE_ID,
- "quality": "medium",
- "output_format": "mp3",
- "speed": 1,
- "sample_rate": 24000,
- "seed": None,
- "temperature": None
- }
-
-def get_generated_audio(text):
- payload = get_payload(text)
- generated_response = {}
- try:
- response = requests.post(play_ht_api_get_audio_url, json=payload, headers=headers)
- response.raise_for_status()
- generated_response["type"]= 'SUCCESS'
- generated_response["response"] = response.text
- except requests.exceptions.RequestException as e:
- generated_response["type"]= 'ERROR'
- try:
- response_text = json.loads(response.text)
- if response_text['error_message']:
- generated_response["response"] = response_text['error_message']
- else:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["type"]= 'ERROR'
- generated_response["response"] = response.text
- return generated_response
-
-def extract_urls(text):
- # Define the regex pattern for URLs
- url_pattern = r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+[/\w\.-]*'
-
- # Find all occurrences of URLs in the text
- urls = re.findall(url_pattern, text)
-
- return urls
-
-def get_audio_reply_for_question(text):
- generated_audio_event = get_generated_audio(text)
- #From get_generated_audio, you will get events in a string format, from that we need to extract the url
- final_response = {
- "audio_url": '',
- "message": ''
- }
- if generated_audio_event["type"] == 'SUCCESS':
- audio_urls = extract_urls(generated_audio_event["response"])
- if len(audio_urls) == 0:
- final_response['message'] = "No audio file link found in generated event"
- else:
- final_response['audio_url'] = audio_urls[-1]
- else:
- final_response['message'] = generated_audio_event['response']
- return final_response
-
-def download_url(url):
- try:
- # Send a GET request to the URL to fetch the content
- final_response = {
- 'content':'',
- 'error':''
- }
- response = requests.get(url)
- # Check if the request was successful (status code 200)
- if response.status_code == 200:
- final_response['content'] = response.content
- else:
- final_response['error'] = f"Failed to download the URL. Status code: {response.status_code}"
- except Exception as e:
- final_response['error'] = f"Failed to download the URL. Error: {e}"
- return final_response
-
-def get_filename_from_url(url):
- # Use os.path.basename() to extract the file name from the URL
- file_name = os.path.basename(url)
- return file_name
-
-def get_text_response(user_message):
- response = llm_chain.predict(user_message = user_message)
- return response
-
-def get_text_response_and_audio_response(user_message):
- response = get_text_response(user_message) # Getting the reply from Open AI
- audio_reply_for_question_response = get_audio_reply_for_question(response)
- final_response = {
- 'output_file_path': '',
- 'message':''
- }
- audio_url = audio_reply_for_question_response['audio_url']
- if audio_url:
- output_file_path=get_filename_from_url(audio_url)
- download_url_response = download_url(audio_url)
- audio_content = download_url_response['content']
- if audio_content:
- with open(output_file_path, "wb") as audio_file:
- audio_file.write(audio_content)
- final_response['output_file_path'] = output_file_path
- else:
- final_response['message'] = download_url_response['error']
- else:
- final_response['message'] = audio_reply_for_question_response['message']
- return final_response
-
-def chat_bot_response(message, history):
- text_and_audio_response = get_text_response_and_audio_response(message)
- output_file_path = text_and_audio_response['output_file_path']
- if output_file_path:
- return (text_and_audio_response['output_file_path'],)
- else:
- return text_and_audio_response['message']
-
-demo = gr.ChatInterface(chat_bot_response,examples=["How are you doing?","What are your interests?","Which places do you like to visit?"])
-
-if __name__ == "__main__":
- demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/BartPoint/VoiceChange/app_multi.py b/spaces/BartPoint/VoiceChange/app_multi.py
deleted file mode 100644
index 67890355f12e2d2d5420ce8e31a722020f349b24..0000000000000000000000000000000000000000
--- a/spaces/BartPoint/VoiceChange/app_multi.py
+++ /dev/null
@@ -1,469 +0,0 @@
-from typing import Union
-
-from argparse import ArgumentParser
-
-import asyncio
-import json
-import hashlib
-from os import path, getenv
-
-import gradio as gr
-
-import torch
-
-import numpy as np
-import librosa
-
-import edge_tts
-
-import config
-import util
-from infer_pack.models import (
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono
-)
-from vc_infer_pipeline import VC
-
-# Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21 # noqa
-in_hf_space = getenv('SYSTEM') == 'spaces'
-
-# Argument parsing
-arg_parser = ArgumentParser()
-arg_parser.add_argument(
- '--hubert',
- default=getenv('RVC_HUBERT', 'hubert_base.pt'),
- help='path to hubert base model (default: hubert_base.pt)'
-)
-arg_parser.add_argument(
- '--config',
- default=getenv('RVC_MULTI_CFG', 'multi_config.json'),
- help='path to config file (default: multi_config.json)'
-)
-arg_parser.add_argument(
- '--api',
- action='store_true',
- help='enable api endpoint'
-)
-arg_parser.add_argument(
- '--cache-examples',
- action='store_true',
- help='enable example caching, please remember delete gradio_cached_examples folder when example config has been modified' # noqa
-)
-args = arg_parser.parse_args()
-
-app_css = '''
-#model_info img {
- max-width: 100px;
- max-height: 100px;
- float: right;
-}
-
-#model_info p {
- margin: unset;
-}
-'''
-
-app = gr.Blocks(
- theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate"),
- css=app_css,
- analytics_enabled=False
-)
-
-# Load hubert model
-hubert_model = util.load_hubert_model(config.device, args.hubert)
-hubert_model.eval()
-
-# Load models
-multi_cfg = json.load(open(args.config, 'r'))
-loaded_models = []
-
-for model_name in multi_cfg.get('models'):
- print(f'Loading model: {model_name}')
-
- # Load model info
- model_info = json.load(
- open(path.join('model', model_name, 'config.json'), 'r')
- )
-
- # Load RVC checkpoint
- cpt = torch.load(
- path.join('model', model_name, model_info['model']),
- map_location='cpu'
- )
- tgt_sr = cpt['config'][-1]
- cpt['config'][-3] = cpt['weight']['emb_g.weight'].shape[0] # n_spk
-
- if_f0 = cpt.get('f0', 1)
- net_g: Union[SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono]
- if if_f0 == 1:
- net_g = SynthesizerTrnMs768NSFsid(
- *cpt['config'],
- is_half=util.is_half(config.device)
- )
- else:
- net_g = SynthesizerTrnMs768NSFsid_nono(*cpt['config'])
-
- del net_g.enc_q
-
- # According to original code, this thing seems necessary.
- print(net_g.load_state_dict(cpt['weight'], strict=False))
-
- net_g.eval().to(config.device)
- net_g = net_g.half() if util.is_half(config.device) else net_g.float()
-
- vc = VC(tgt_sr, config)
-
- loaded_models.append(dict(
- name=model_name,
- metadata=model_info,
- vc=vc,
- net_g=net_g,
- if_f0=if_f0,
- target_sr=tgt_sr
- ))
-
-print(f'Models loaded: {len(loaded_models)}')
-
-# Edge TTS speakers
-tts_speakers_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) # noqa
-
-
-# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer-web.py#L118 # noqa
-def vc_func(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- if input_audio is None:
- return (None, 'Please provide input audio.')
-
- if model_index is None:
- return (None, 'Please select a model.')
-
- model = loaded_models[model_index]
-
- # Reference: so-vits
- (audio_samp, audio_npy) = input_audio
-
- # https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49
- # Can be change well, we will see
- if (audio_npy.shape[0] / audio_samp) > 320 and in_hf_space:
- return (None, 'Input audio is longer than 60 secs.')
-
- # Bloody hell: https://stackoverflow.com/questions/26921836/
- if audio_npy.dtype != np.float32: # :thonk:
- audio_npy = (
- audio_npy / np.iinfo(audio_npy.dtype).max
- ).astype(np.float32)
-
- if len(audio_npy.shape) > 1:
- audio_npy = librosa.to_mono(audio_npy.transpose(1, 0))
-
- if audio_samp != 16000:
- audio_npy = librosa.resample(
- audio_npy,
- orig_sr=audio_samp,
- target_sr=16000
- )
-
- pitch_int = int(pitch_adjust)
-
- resample = (
- 0 if resample_option == 'Disable resampling'
- else int(resample_option)
- )
-
- times = [0, 0, 0]
-
- checksum = hashlib.sha512()
- checksum.update(audio_npy.tobytes())
-
- output_audio = model['vc'].pipeline(
- hubert_model,
- model['net_g'],
- model['metadata'].get('speaker_id', 0),
- audio_npy,
- checksum.hexdigest(),
- times,
- pitch_int,
- f0_method,
- path.join('model', model['name'], model['metadata']['feat_index']),
- feat_ratio,
- model['if_f0'],
- filter_radius,
- model['target_sr'],
- resample,
- rms_mix_rate,
- 'v2'
- )
-
- out_sr = (
- resample if resample >= 16000 and model['target_sr'] != resample
- else model['target_sr']
- )
-
- print(f'npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s')
- return ((out_sr, output_audio), 'Success')
-
-
-async def edge_tts_vc_func(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- if input_text is None:
- return (None, 'Please provide TTS text.')
-
- if tts_speaker is None:
- return (None, 'Please select TTS speaker.')
-
- if model_index is None:
- return (None, 'Please select a model.')
-
- speaker = tts_speakers_list[tts_speaker]['ShortName']
- (tts_np, tts_sr) = await util.call_edge_tts(speaker, input_text)
- return vc_func(
- (tts_sr, tts_np),
- model_index,
- pitch_adjust,
- f0_method,
- feat_ratio,
- filter_radius,
- rms_mix_rate,
- resample_option
- )
-
-
-def update_model_info(model_index):
- if model_index is None:
- return str(
- '### Model info\n'
- 'Please select a model from dropdown above.'
- )
-
- model = loaded_models[model_index]
- model_icon = model['metadata'].get('icon', '')
-
- return str(
- '### Model info\n'
- ''
- '**{name}**\n\n'
- 'Author: {author}\n\n'
- 'Source: {source}\n\n'
- '{note}'
- ).format(
- name=model['metadata'].get('name'),
- author=model['metadata'].get('author', 'Anonymous'),
- source=model['metadata'].get('source', 'Unknown'),
- note=model['metadata'].get('note', ''),
- icon=(
- model_icon
- if model_icon.startswith(('http://', 'https://'))
- else '/file/model/%s/%s' % (model['name'], model_icon)
- )
- )
-
-
-def _example_vc(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- (audio, message) = vc_func(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
- )
- return (
- audio,
- message,
- update_model_info(model_index)
- )
-
-
-async def _example_edge_tts(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- (audio, message) = await edge_tts_vc_func(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method,
- feat_ratio, filter_radius, rms_mix_rate, resample_option
- )
- return (
- audio,
- message,
- update_model_info(model_index)
- )
-
-
-with app:
- gr.Markdown(
- '## A simplistic Web interface\n'
- 'RVC interface, project based on [RVC-WebUI](https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI)' # thx noqa
- 'A lot of inspiration from what\'s already out there, including [zomehwh/rvc-models](https://huggingface.co/spaces/zomehwh/rvc-models) & [DJQmUKV/rvc-inference](https://huggingface.co/spaces/DJQmUKV/rvc-inference).\n ' # thx noqa
- )
-
- with gr.Row():
- with gr.Column():
- with gr.Tab('Audio conversion'):
- input_audio = gr.Audio(label='Input audio')
-
- vc_convert_btn = gr.Button('Convert', variant='primary')
-
- with gr.Tab('TTS conversion'):
- tts_input = gr.TextArea(
- label='TTS input text'
- )
- tts_speaker = gr.Dropdown(
- [
- '%s (%s)' % (
- s['FriendlyName'],
- s['Gender']
- )
- for s in tts_speakers_list
- ],
- label='TTS speaker',
- type='index'
- )
-
- tts_convert_btn = gr.Button('Convert', variant='primary')
-
- pitch_adjust = gr.Slider(
- label='Pitch',
- minimum=-24,
- maximum=24,
- step=1,
- value=0
- )
- f0_method = gr.Radio(
- label='f0 methods',
- choices=['pm', 'harvest', 'crepe'],
- value='pm',
- interactive=True
- )
-
- with gr.Accordion('Advanced options', open=False):
- feat_ratio = gr.Slider(
- label='Feature ratio',
- minimum=0,
- maximum=1,
- step=0.1,
- value=0.6
- )
- filter_radius = gr.Slider(
- label='Filter radius',
- minimum=0,
- maximum=7,
- step=1,
- value=3
- )
- rms_mix_rate = gr.Slider(
- label='Volume envelope mix rate',
- minimum=0,
- maximum=1,
- step=0.1,
- value=1
- )
- resample_rate = gr.Dropdown(
- [
- 'Disable resampling',
- '16000',
- '22050',
- '44100',
- '48000'
- ],
- label='Resample rate',
- value='Disable resampling'
- )
-
- with gr.Column():
- # Model select
- model_index = gr.Dropdown(
- [
- '%s - %s' % (
- m['metadata'].get('source', 'Unknown'),
- m['metadata'].get('name')
- )
- for m in loaded_models
- ],
- label='Model',
- type='index'
- )
-
- # Model info
- with gr.Box():
- model_info = gr.Markdown(
- '### Model info\n'
- 'Please select a model from dropdown above.',
- elem_id='model_info'
- )
-
- output_audio = gr.Audio(label='Output audio')
- output_msg = gr.Textbox(label='Output message')
-
- multi_examples = multi_cfg.get('examples')
- if (
- multi_examples and
- multi_examples.get('vc') and multi_examples.get('tts_vc')
- ):
- with gr.Accordion('Sweet sweet examples', open=False):
- with gr.Row():
- # VC Example
- if multi_examples.get('vc'):
- gr.Examples(
- label='Audio conversion examples',
- examples=multi_examples.get('vc'),
- inputs=[
- input_audio, model_index, pitch_adjust, f0_method,
- feat_ratio
- ],
- outputs=[output_audio, output_msg, model_info],
- fn=_example_vc,
- cache_examples=args.cache_examples,
- run_on_click=args.cache_examples
- )
-
- # Edge TTS Example
- if multi_examples.get('tts_vc'):
- gr.Examples(
- label='TTS conversion examples',
- examples=multi_examples.get('tts_vc'),
- inputs=[
- tts_input, model_index, tts_speaker, pitch_adjust,
- f0_method, feat_ratio
- ],
- outputs=[output_audio, output_msg, model_info],
- fn=_example_edge_tts,
- cache_examples=args.cache_examples,
- run_on_click=args.cache_examples
- )
-
- vc_convert_btn.click(
- vc_func,
- [
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_rate
- ],
- [output_audio, output_msg],
- api_name='audio_conversion'
- )
-
- tts_convert_btn.click(
- edge_tts_vc_func,
- [
- tts_input, model_index, tts_speaker, pitch_adjust, f0_method,
- feat_ratio, filter_radius, rms_mix_rate, resample_rate
- ],
- [output_audio, output_msg],
- api_name='tts_conversion'
- )
-
- model_index.change(
- update_model_info,
- inputs=[model_index],
- outputs=[model_info],
- show_progress=False,
- queue=False
- )
-
-app.queue(
- concurrency_count=1,
- max_size=20,
- api_open=args.api
-).launch()
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md b/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md
deleted file mode 100644
index 231c36d39005d9275c61ccc88067d084ca775635..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md
+++ /dev/null
@@ -1,134 +0,0 @@
-
-Aparcamiento de coches multijugador Sudáfrica Edición APK Descargar: Una guía para los usuarios de Android
- Si usted está buscando un juego de estacionamiento de coches realista y divertido que ofrece más que solo estacionamiento, es posible que desee echa un vistazo a Parking Multijugador. Y si usted está en Sudáfrica o desea experimentar la cultura del automóvil de Sudáfrica, es posible que desee probar el Aparcamiento Multijugador South áfrica Edition APK. En este artículo, te diremos qué es el Car Parking Multiplayer, qué hace que South áfrica Edition sea diferente, cómo descargarlo e instalarlo en tu dispositivo Android, y algunos consejos y trucos para jugarlo.
- ¿Qué es el Aparcamiento Multijugador?
- Car Parking Multiplayer es un juego que puede engañarte con su nombre bastante engañoso. Pero, es mucho más que solo estar aparcando tu coche. Es una experiencia de mundo abierto donde se puede conducir gratis y sí, todavía trabajar en ese aparcamiento si lo desea. Incluso puedes saltar de tu coche y caminar. Hay diferentes áreas que se pueden explorar en el juego. Cada una es como su propio mundo abierto. Puedes optar por jugar en modo de un solo jugador o en modo online si quieres una escena más caótica (de forma divertida) .
-aparcamiento de coches multijugador sudáfrica edición apk descargar
DOWNLOAD === https://bltlly.com/2v6IE9
- Características de Aparcamiento Multijugador
- Aparcamiento de coches multijugador tiene las siguientes características :
-
-- Modo multijugador de mundo abierto
- - Caminar libremente
- - Mundo abierto libre con gasolineras reales y servicios de automóviles
- - Compite contra jugadores reales en las carreras multijugador
- - Intercambiar coches con jugadores reales
- - Miles de jugadores reales cada día
- - Lista de amigos
- - Chat de voz
- - Modo de policía
-
-
-- Personalización del coche
- - Suspensión ajustable, ángulo de rueda y más
- - Ajuste del motor: motor de intercambio, turbo, caja de cambios y escape
- - Visual auto tungs: Vynils dinámico, partes del cuerpo del coche
-
-
-- Mundo abierto de alta calidad
-
- - 100 coches con el interior real
- - 16 pieles de jugador
- - Edificios con interior
-
-
-- Juego interesante
- - 82 desafíos de estacionamiento y conducción en la vida real
- - Diferentes vehículos: Grúa, camioneta, camiones, coches deportivos y clásicos
-
-
-
- Opiniones de Aparcamiento multijugador
- Aparcamiento de coches multijugador ha recibido en su mayoría críticas positivas de los usuarios en Google Play Store y App Store. Tiene una calificación de 4.4 de 5 estrellas en Google Play Store y una calificación de 4.3 de 5 estrellas en App Store . Estos son algunos de los comentarios de los usuarios:
- "Increíble juego! No hay errores en este juego o retrasos ect. (base en el dispositivo que utiliza) Me encantan los gráficos y los coches. Los coches son tan realistas y los sonidos son increíbles. Me encanta cómo puedes personalizar tu coche y hacer que se vea genial. El modo multijugador es increíble. Puedes chatear con otros jugadores y competir con ellos. También puedes unirte a un clan o crear tu propio clan. Este juego es muy divertido y adictivo. Recomiendo este juego a todos los que aman los coches y los juegos de estacionamiento."
- "Este juego es muy bueno pero necesita algunas mejoras como agregar más coches, más mapas, más personalizaciones, más modos de juego, más desafíos, etc. Además, el juego se bloquea a veces y los controles no son muy suaves. Los gráficos son agradables, pero pueden ser mejores. El juego es divertido de jugar con amigos, pero se vuelve aburrido después de un tiempo. Espero que los desarrolladores actualicen el juego pronto y lo hagan más agradable."
-
- ¿Qué es el aparcamiento multijugador South áfrica Edition?
- Car Parking Multijugador South áfrica Edition es una versión modificada de Car Parking Multijugador que está especialmente diseñado para los usuarios sudafricanos o los fans de la cultura del coche de Sudáfrica. No es una versión oficial del juego, sino un archivo APK hecho por fans que se puede descargar e instalar en dispositivos Android .
- Diferencias entre Aparcamiento de coches multijugador y Aparcamiento de coches multijugador Sudáfrica Edición
- Car Parking Multijugador South áfrica Edition tiene algunas diferencias con el juego original Car Parking Multijugador, como :
-
-- Más coches que son populares en Sudáfrica, como BMW E30, VW Golf Mk1, Toyota Corolla, Nissan 1400, etc.
-- Más personalizaciones que reflejan la cultura del automóvil sudafricano, como llantas giratorias, escapes fuertes, pegatinas, banderas, etc.
-- Más mapas que se basan en ubicaciones reales en Sudáfrica, como Ciudad del Cabo, Johannesburgo, Durban, etc.
-- Más música inspirada en la escena musical sudafricana, como kwaito, gqom, amapiano, etc.
-- Más idiomas que se hablan en Sudáfrica, como afrikaans, zulú, xhosa, etc.
-
- Beneficios de Aparcamiento Multijugador South áfrica Edition
- Car Parking Multijugador South áfrica Edition tiene algunos beneficios para los usuarios que quieren disfrutar del juego con un toque sudafricano, como :
-
-- Más variedad y diversidad en términos de coches, personalizaciones, mapas, música e idiomas
-- Más diversión y emoción en términos de jugabilidad, gráficos, efectos de sonido e interacciones
-- Más conexión y comunidad con otros jugadores que comparten el mismo interés y pasión por la cultura del automóvil de Sudáfrica
-- Más soporte y actualizaciones de los desarrolladores que se dedican a mejorar el juego y agregar nuevas características
-
- Cómo descargar e instalar el estacionamiento de coches multijugador South áfrica Edition APK en dispositivos Android?
-
- Requisitos para descargar e instalar el estacionamiento de coches multijugador South áfrica Edition APK
- Para descargar e instalar Aparcamiento Multijugador South áfrica Edition APK en su dispositivo Android, es necesario tener :
-
-
-- Un dispositivo Android que se ejecuta en Android 4.1 o superior
-- Una conexión a Internet estable
-- Una aplicación de administrador de archivos que puede acceder al archivo APK
-- Una cantidad suficiente de espacio de almacenamiento en su dispositivo
-- Un permiso para instalar aplicaciones de fuentes desconocidas en la configuración del dispositivo
-
- Pasos para Descargar e Instalar Aparcamiento Multijugador South áfrica Edition APK
- Para descargar e instalar Aparcamiento Multijugador South áfrica Edition APK en su dispositivo Android, es necesario seguir estos pasos :
-
-- Ir a un sitio web de confianza que proporciona el enlace para descargar Car Parking Multi jugador South áfrica Edition APK. Por ejemplo, puede visitar [este sitio web] para obtener la última versión del archivo APK.
-- Haga clic en el botón de descarga y espere a que el archivo APK se descargue en su dispositivo.
-- Una vez que se complete la descarga, localizar el archivo APK en su dispositivo utilizando una aplicación de administrador de archivos. Puede encontrarlo en la carpeta Descargas o en cualquier otra carpeta donde lo haya guardado.
-- Toque en el archivo APK y seleccione Instalar. Es posible que vea un mensaje de advertencia que dice "Para su seguridad, el teléfono no está permitido instalar aplicaciones desconocidas de esta fuente". Si ves este mensaje, ve a la configuración del dispositivo y habilita la opción de instalar aplicaciones de fuentes desconocidas. Esta opción puede estar en Seguridad, Privacidad o Aplicaciones dependiendo del modelo de dispositivo y la versión de Android.
-
-- Espere a que termine el proceso de instalación. Puede tardar unos minutos dependiendo del rendimiento del dispositivo y la velocidad de Internet.
-- Una vez que se hace la instalación, se puede abrir el juego y disfrutar de aparcamiento multijugador South áfrica Edition en su dispositivo Android.
-
- Consejos y Trucos para Jugar Aparcamiento Multijugador South áfrica Edition
- Ahora que ha descargado e instalado Car Parking Multiplayer South áfrica Edition en su dispositivo Android, es posible que desee conocer algunos consejos y trucos para jugarlo. Estos son algunos de ellos:
- Cómo seleccionar un coche y un jugador
- Para seleccionar un coche y un jugador en Car Parking Multiplayer South áfrica Edition, debe hacer lo siguiente:
-
-- Toque en el icono de menú en la esquina superior izquierda de la pantalla.
-- Toque en Garaje para ver la lista de coches que usted posee o puede comprar.
-- Toque en el coche que desea utilizar y luego toque en Select.
-- Toque en Volver para volver al menú.
-- Toque en Perfil para ver la lista de jugadores entre los que puede elegir.
-- Toque en el reproductor que desea utilizar y luego toque en Select.
-- Toque en Volver para volver al menú.
-- Toque en Jugar para comenzar a jugar el juego con el coche y el jugador seleccionado.
-
- Cómo ajustar su coche y ajustar su relación de engranajes
- Para afinar su coche y ajustar su relación de engranajes en Car Parking Multiplayer South áfrica Edition, debe hacer lo siguiente:
-
-- Toque en el icono de menú en la esquina superior izquierda de la pantalla.
-- Toque en Garaje para ver la lista de coches que usted posee o puede comprar.
-- Toque en el coche que desea sintonizar y luego toque en Tune.
-- Verá cuatro pestañas: Motor, Suspensión, Ruedas y Cuerpo. Puede deslizar hacia la izquierda o hacia la derecha para cambiar entre ellas.
-
-- Para ajustar su relación de cambios, toque en Caja de cambios en la pestaña Motor. Verá un gráfico que muestra cómo cambia su velocidad con diferentes marchas. Puede arrastrar los puntos en el gráfico para cambiar la relación de transmisión para cada marcha. También puede pulsar en Auto o Manual para cambiar entre modos de transmisión automática o manual.
-- Una vez que haya terminado de ajustar su coche, toque en Guardar para aplicar los cambios.
-
- Cómo Deriva, Donut, y Burnout
- Para la deriva, donut, y burnout en el estacionamiento de coches multijugador South áfrica Edition, es necesario hacer lo siguiente:
-
-- Para la deriva, debe usar una combinación de dirección, acelerador, freno y freno de mano. Puede usar los controles de inclinación o táctiles para la dirección. Para iniciar la deriva, es necesario acelerar y girar bruscamente en una esquina. Luego, debe aplicar el freno de mano brevemente para hacer que sus ruedas traseras pierdan tracción. Después de eso, debe equilibrar el acelerador y la dirección para mantener el ángulo de deriva y la dirección. También puede utilizar el freno de mano o freno de mano de nuevo si necesita ajustar su deriva. Para terminar con la deriva, es necesario soltar el acelerador y la dirección y enderezar el coche.
-- Para la rosquilla, debe usar una combinación de dirección, acelerador y freno de mano. Puede usar controles de inclinación o táctiles para la dirección. Para comenzar a donar, debe acelerar y girar bruscamente en una dirección. Entonces, es necesario aplicar el freno de mano para hacer que su coche gire alrededor de su centro. Después de eso, es necesario mantener el acelerador y la dirección constante para mantener el círculo de la dona. También puede cambiar la dirección de su dona cambiando la dirección. Para terminar donuting, es necesario soltar el acelerador y el freno de mano y enderezar su coche.
-
-
- Conclusión
- Car Parking Multijugador South áfrica Edition es un juego que ofrece una experiencia de aparcamiento realista y divertido con un toque sudafricano. Tiene más coches, personalizaciones, mapas, música e idiomas que reflejan la cultura del automóvil sudafricano. También tiene un modo de mundo abierto, un modo de ajuste de coche, y un modo multijugador que hacen el juego más agradable y emocionante. Puede descargar e instalar Parking Multijugador South áfrica Edition APK en su dispositivo Android siguiendo los pasos en este artículo. También puedes usar los consejos y trucos de este artículo para mejorar tu juego y divertirte más.
- Preguntas frecuentes
- Aquí están algunas de las preguntas más frecuentes sobre Car Parking Multiplayer South áfrica Edition:
- ¿Es gratis Car Parking Multijugador South áfrica Edition?
- Sí, Parking Multijugador South áfrica Edition es gratis para descargar y jugar. Sin embargo, puede contener algunas compras en la aplicación que requieren dinero real.
- ¿Es seguro el aparcamiento multijugador South áfrica Edition?
- Sí, Parking Multijugador South áfrica Edition es seguro, siempre y cuando se descarga desde un sitio web de confianza. Sin embargo, siempre debe tener cuidado al descargar e instalar aplicaciones de fuentes desconocidas, ya que pueden contener virus o malware que pueden dañar su dispositivo.
- ¿Es compatible Car Parking Multiplayer South áfrica Edition con mi dispositivo?
- Car Parking Multijugador South áfrica Edition es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 4.1 o superior. Sin embargo, algunos dispositivos pueden tener diferentes especificaciones o problemas de rendimiento que pueden afectar la calidad o funcionalidad del juego.
- ¿Cómo puedo actualizar Car Parking Multijugador South áfrica Edition?
-
- ¿Cómo puedo contactar a los desarrolladores de Car Parking Multiplayer South áfrica Edition?
- Para contactar a los desarrolladores de Car Parking Multiplayer South áfrica Edition, puedes visitar su página de Facebook o su canal de YouTube y dejar un comentario o un mensaje.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md b/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md
deleted file mode 100644
index 2001fb4da4e341236f8b4ef59c584c7ee860ae80..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md
+++ /dev/null
@@ -1,76 +0,0 @@
-
-Nitro asfalto 9 leyendas Mod Apk: Una guía para los aficionados a las carreras
-Si eres un fan de los juegos de carreras, es posible que hayas oído hablar de Asphalt Nitro 9 Legends, uno de los juegos más populares y emocionantes del género. Este juego le permite tomar el volante de los coches reales de gama alta de renombrados fabricantes de automóviles legendarios, como Ferrari, Porsche, Lamborghini, y W Motors, entre muchas otras marcas internacionales. Puedes conducir, impulsar y realizar acrobacias en ubicaciones dinámicas de la vida real en modo individual o multijugador.
-Pero ¿y si quieres disfrutar del juego sin limitaciones ni restricciones? ¿Qué pasa si quieres tener dinero ilimitado y fichas, desbloquear todos los coches y pistas, personalizar sus vehículos y actualizarlos, y competir con otros jugadores en línea o fuera de línea? Bueno, hay una manera de hacerlo. Puede descargar e instalar Asphalt Nitro 9 Leyendas Mod Apk, una versión modificada del juego que le da acceso a todas estas características y más.
-asfalto nitro 9 leyendas mod apk
Download File ===== https://bltlly.com/2v6LmQ
-En este artículo, le guiará a través de las características, proceso de descarga, pasos de instalación, consejos y trucos, pros y contras, y la conclusión de Asphalt Nitro 9 Leyendas Mod Apk. Sigue leyendo para saber más.
- Características de Asphalt Nitro 9 Leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es una versión modificada del juego original que le da recursos ilimitados y características que mejoran su experiencia de juego. Estas son algunas de las características que puedes disfrutar con este mod apk:
-
-- Dinero ilimitado y fichas: Con este apk mod, nunca se quedará sin dinero o fichas para comprar nuevos coches, pistas, mejoras, o artículos. Puedes gastar todo lo que quieras sin preocuparte por tu presupuesto.
-
-- Personalizar sus vehículos y actualizarlos: Con este apk mod, puede personalizar sus vehículos de acuerdo a su preferencia. Puedes cambiar su color, pintura, calcomanías, ruedas, etc. También puedes mejorar su rendimiento mejorando su motor, transmisión, suspensión, frenos, nitro boost, etc.
-- Disfrutar de gráficos realistas y efectos de sonido: Con este apk mod, se puede disfrutar de los impresionantes gráficos y efectos de sonido del juego. El juego utiliza un motor de física realista que simula el movimiento y el comportamiento de los coches. El juego también cuenta con efectos de sonido de alta calidad que te hacen sentir como si estuvieras en una carrera real.
-- Compite con otros jugadores en línea o fuera de línea: Con este apk mod, puede competir con otros jugadores en línea o fuera de línea. Puedes unirte a carreras multijugador online y desafiar a tus amigos u otros jugadores de todo el mundo. También puedes jugar offline en modo carrera y completar varias misiones y desafíos.
-
- Cómo descargar e instalar Asphalt Nitro 9 leyendas Mod Apk
-Si desea descargar e instalar Asphalt Nitro 9 leyendas Mod Apk, es necesario seguir estos sencillos pasos:
-
-- Paso 1: Descargar el archivo apk mod de una fuente de confianza. Usted puede encontrar muchos sitios web que ofrecen el archivo apk mod para Asphalt Nitro 9 Leyendas, pero no todos ellos son seguros y fiables. Debes asegurarte de que el archivo que descargas esté libre de virus, malware o cualquier otro contenido dañino. Puede utilizar este enlace para descargar el archivo apk mod de forma segura.
-- Paso 2: Habilitar fuentes desconocidas en la configuración del dispositivo. Antes de poder instalar el archivo apk mod, debe habilitar fuentes desconocidas en la configuración de su dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a la configuración del dispositivo, luego a la seguridad, luego a fuentes desconocidas y conéctela.
-
-- Paso 4: Disfruta del juego con recursos y funciones ilimitadas. Ahora que ha instalado Asphalt Nitro 9 Leyendas Mod Apk, se puede disfrutar del juego con dinero ilimitado, fichas, coches, pistas, opciones de personalización, y más. También puedes competir con otros jugadores online o offline y divertirte.
-
- Consejos y trucos para jugar Asphalt Nitro 9 leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es un juego divertido y emocionante que le mantendrá entretenido durante horas. Sin embargo, si quieres mejorar tus habilidades y rendimiento en el juego, puedes seguir estos consejos y trucos:
-
-- Elija el coche adecuado para cada pista y modo. El juego ofrece una variedad de coches de diferentes marcas y categorías, pero no todos ellos son adecuados para cada pista o modo. Es necesario tener en cuenta la velocidad, aceleración, manejo, nitro, y otros factores de cada coche antes de elegir uno. Por ejemplo, si usted está jugando en una pista con curvas, es posible que desee elegir un coche con buen manejo y nitro. Si usted está jugando en una pista recta, es posible que desee elegir un coche con alta velocidad y aceleración.
-- Utilice nitro boost sabiamente y estratégicamente. Nitro boost es una de las características más importantes del juego, ya que puede darte una ventaja sobre tus oponentes. Sin embargo, necesitas usarlo sabiamente y estratégicamente. No debes usarlo todo de una vez ni desperdiciarlo en momentos innecesarios. Debes guardarlo para momentos cruciales, como adelantar a tus rivales, escapar de obstáculos o colisiones, o llegar a la meta. También debe usarlo en combinación con acrobacias y derivas para ganar más puntos y recompensas.
-
-- Evitar obstáculos y colisiones con otros coches. El juego cuenta con muchos obstáculos y desafíos que pueden obstaculizar su progreso o dañar su coche. Estos incluyen coches de tráfico, coches de policía, helicópteros, barricadas, picos, etc. Usted debe evitar estos obstáculos tanto como sea posible, ya que pueden ralentizar o dañar su coche. También debes evitar colisiones con otros coches, especialmente tus rivales, ya que pueden sacarte de la pista o hacerte perder tu posición. Usted debe tratar de esquivar o escapar de ellos, o utilizar su impulso nitro para alejarse de ellos.
-- Utilice diferentes ángulos de cámara y controles para adaptarse a sus preferencias. El juego ofrece diferentes ángulos de cámara y controles que puedes usar para jugar el juego. Puede elegir entre vista en primera persona, en tercera persona o de arriba hacia abajo, según su preferencia. También puede elegir entre los controles de inclinación, toque o toque, dependiendo de su comodidad. Puede cambiar estos ajustes en el menú de opciones del juego.
-
- Pros y contras de Asphalt Nitro 9 Leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es un gran juego que ofrece muchos beneficios y ventajas para los aficionados a las carreras. Sin embargo, también tiene algunos inconvenientes y desventajas que usted debe tener en cuenta. Estos son algunos de los pros y los contras de Asphalt Nitro 9 Leyendas Mod Apk:
-
-
-Pros
-Contras
-
-
-
-- Divertido, adictivo y desafiante juego
-- Dinero ilimitado y tokens
-- Desbloquear todos los coches y pistas
-- Personalizar sus vehículos y actualizarlos
-- Disfruta de gráficos realistas y efectos de sonido
-- Compite con otros jugadores online o offline
-
-- Puede causar retraso o estrellarse en algunos dispositivos
-- Puede no ser compatible con algunos dispositivos o versiones
-- No se puede actualizar regularmente o con frecuencia
-- No puede ser soportado por los desarrolladores o editores oficiales
-
-- Puede exponer su dispositivo a riesgos o amenazas de seguridad
-
-
- Conclusión
-En conclusión, Asfalto Nitro 9 Leyendas Mod Apk es un juego fantástico que le dará horas de diversión y emoción. Es una versión modificada del juego original que le da recursos ilimitados y características que mejoran su experiencia de juego. Puede descargarlo e instalarlo de forma fácil y segura siguiendo los pasos que proporcionamos en este artículo. También puedes seguir los consejos y trucos que compartimos para mejorar tus habilidades y rendimiento en el juego. Sin embargo, también debe ser consciente de los pros y los contras de Asphalt Nitro 9 Leyendas Mod Apk, y decidir si vale la pena jugar o no.
-Si usted es un fanático de las carreras, le recomendamos que pruebe Asphalt Nitro 9 Leyendas Mod Apk y ver por sí mismo lo increíble que es. No te arrepentirás.
- Preguntas frecuentes (preguntas frecuentes)
-Aquí están algunas de las preguntas y respuestas más comunes sobre Asphalt Nitro 9 Legends Mod Apk:
-
- Q: ¿Es libre de Asphalt Nitro 9 Leyendas Mod Apk?
-A: Sí, Asphalt Nitro 9 Leyendas Mod Apk es gratis para descargar y jugar. Usted no necesita pagar nada para disfrutar del juego.
- Q: ¿Es seguro Asphalt Nitro 9 Legends Mod Apk?
-A: Sí, Asfalto Nitro 9 Leyendas Mod Apk es seguro para descargar e instalar. Sin embargo, siempre debe descargarlo de una fuente de confianza y habilitar fuentes desconocidas en la configuración del dispositivo antes de instalarlo.
- Q: ¿Es legal Asphalt Nitro 9 Legends Mod Apk?
-A: No, Asphalt Nitro 9 Leyendas Mod Apk no es legal. Es una versión modificada del juego original que viola los términos y condiciones de los desarrolladores y editores oficiales. También puede infringir los derechos de propiedad intelectual del juego original.
- Q: Es Asphalt Nitro 9 Leyendas Mod Apk en línea o fuera de línea?
-
- Q: ¿Cómo puedo actualizar Asphalt Nitro 9 Legends Mod Apk?
-A: Para actualizar Asphalt Nitro 9 leyendas Mod Apk, es necesario descargar e instalar la última versión del archivo apk mod de una fuente de confianza. Es posible que tenga que desinstalar la versión anterior del archivo apk mod antes de instalar el nuevo.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md b/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md
deleted file mode 100644
index d872bc888664198a5fb77f20f85945d9c05ae2ef..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md
+++ /dev/null
@@ -1,76 +0,0 @@
-
-Camioneros de Europa 3 APK 36.6: Un juego de simulador de camiones realista y divertido
-Si eres un fan de los juegos de simuladores de camiones, es posible que hayas oído hablar de Truckers of Europe 3, un juego popular que te permite experimentar la vida de un conductor de camiones en Europa. En este artículo, te contaremos todo lo que necesitas saber sobre este juego, incluyendo qué es, cuáles son sus características, cómo descargarlo e instalarlo, cómo jugarlo y por qué deberías jugarlo. ¡Así que abróchate el cinturón y prepárate para un emocionante viaje!
-Introducción
-¿Qué es Camioneros de Europa 3?
-Truckers of Europe 3 es un juego de simulador de camioneros desarrollado por Jerryisgaming, un canal de YouTube que crea videos de juegos. El juego fue lanzado en 2020 y ha sido actualizado regularmente con nuevas características y mejoras. La última versión del juego es 0.36.6, que fue lanzado el 30 de septiembre de 2021.
-camioneros de europa 3 apk 36.6
Download Zip ❤❤❤ https://bltlly.com/2v6LwC
-¿Cuáles son las características de Camioneros de Europa 3?
-Truckers of Europe 3 tiene muchas características que lo convierten en uno de los mejores juegos de simuladores de camiones en el mercado. Algunas de estas características son:
-
-- Un gran mapa de Europa con más de 50 ciudades y países para explorar.
-- Una variedad de camiones y remolques para elegir, cada uno con diferentes especificaciones y rendimiento.
-- Un sistema de conducción realista con transmisión manual, volante, pedales, indicadores, espejos, luces, bocina, limpiaparabrisas, etc.
-- Un sistema de tráfico realista con coches, autobuses, camiones, motocicletas, policía, ambulancia, etc.
-- Un sistema meteorológico realista con ciclo de día y noche, lluvia, nieve, niebla, etc.
-- Un sistema de daños realista con desgaste de neumáticos, consumo de combustible, sobrecalentamiento del motor, etc.
-- Un modo de carrera con misiones, contratos, entrega de carga, ingresos, gastos, etc.
-- Un sistema de habilidades con niveles, puntos, ventajas, etc.
-- Un sistema de personalización con trabajos de pintura, accesorios, calcomanías, etc.
-
-
-Cómo descargar e instalar camioneros de Europa 3 APK 36.6?
-Requisitos
-Para descargar e instalar Camioneros de Europa 3 APK 36.6 en su dispositivo Android, es necesario cumplir con los siguientes requisitos:
-
-- Tu dispositivo debe tener la versión de Android 4.4 o superior.
-- Su dispositivo debe tener al menos 1 GB de RAM y 500 MB de espacio de almacenamiento libre.
-- Su dispositivo debe tener una conexión a Internet estable.
-- Su dispositivo debe permitir la instalación desde fuentes desconocidas. Puede habilitar esta opción yendo a Configuración > Seguridad > Fuentes desconocidas.
-
-Pasos
-Para descargar e instalar Camioneros de Europa 3 APK 36.6 en su dispositivo Android, debe seguir estos pasos:
-
-- Ir a este enlace y descargar el archivo APK.
-- Localice el archivo descargado en el administrador de archivos de su dispositivo y toque en él para iniciar el proceso de instalación.
-- Siga las instrucciones en la pantalla y conceda los permisos necesarios a la aplicación.
-- Espere a que la instalación se complete y luego inicie la aplicación desde el cajón de aplicaciones o la pantalla de inicio.
-- Disfruta jugando Camioneros de Europa 3 en tu dispositivo Android!
-
-Cómo jugar Camioneros de Europa 3?
-Elige tu camión y remolque
-Lo primero que tienes que hacer cuando empiezas a jugar Truckers of Europe 3 es elegir tu camión y remolque. Puede hacer esto yendo al menú del garaje y navegando a través de las opciones disponibles. También puede comprar nuevos camiones y remolques con el dinero que gana de sus misiones. Cada camión y remolque tiene diferentes atributos, como velocidad, potencia, capacidad de combustible, peso de carga, etc. Debe elegir el que se adapte a sus preferencias y necesidades.
-Conduce por toda Europa y entrega carga
-
-Mientras conduce, tendrá que seguir las reglas de tráfico y regulaciones, tales como límites de velocidad, semáforos, señales, etc. También tendrá que lidiar con condiciones de tráfico realistas, como automóviles, autobuses, camiones, motocicletas, policía, ambulancia, etc. También tendrá que enfrentarse a condiciones meteorológicas realistas, como lluvia, nieve, niebla, etc. También tendrá que controlar el estado de su camión, como el nivel de combustible, la temperatura del motor, la presión de los neumáticos, etc. También tendrá que prestar atención al estado de su conductor, como el nivel de fatiga, el nivel de hambre, etc. Tendrá que parar en gasolineras, áreas de descanso, restaurantes, etc. para llenar su tanque de combustible, descansar a su conductor, comer algo de comida, etc.
-Cuando llegue a su destino, tendrá que aparcar su camión y remolque en el lugar designado y descargar la carga. A continuación, recibirá su recompensa y comentarios sobre su rendimiento. También ganarás puntos de experiencia que te ayudarán a subir de nivel y desbloquear nuevas habilidades y beneficios.
-Personaliza tu camión y mejora tus habilidades
-Otro aspecto divertido de Truckers of Europe 3 es que puedes personalizar tu camión y mejorar tus habilidades. Puedes hacer esto yendo al menú del taller y gastando algo de dinero en varios artículos y servicios. Puede cambiar el color de su camión, agregar algunos accesorios, aplicar algunas calcomanías, etc. También puede reparar su camión si está dañado o afinarlo si no funciona bien.
-También puedes ir al menú de habilidades y gastar algunos puntos en varias habilidades y beneficios que mejorarán tus habilidades y beneficios como conductor de camión. Puede mejorar su eficiencia de combustible, manejo de carga, seguridad de conducción, habilidades de negociación, etc. También puede desbloquear nuevos tipos de carga, contratos, camiones, remolques, etc.
-
-¿Por qué deberías jugar Camioneros de Europa 3?
-Gráficos realistas y física
-
-Tráfico y clima diversos y dinámicos
-Otra razón por la que deberías jugar a Truckers of Europe 3 es porque tiene un tráfico diverso y dinámico y el clima hará que tu experiencia de conducción sea más desafiante y agradable. El juego tiene un gran mapa de Europa con más de 50 ciudades y países para explorar. Cada ciudad y país tiene sus propias características únicas, tales como monumentos, edificios, carreteras, señales, etc. El juego también tiene un sistema de tráfico diverso que incluye automóviles, autobuses, camiones, motocicletas, policía, ambulancia, etc. Cada vehículo tiene su propio comportamiento, velocidad, dirección, etc. El juego también tiene un sistema de tiempo dinámico que incluye ciclo de día y noche, lluvia, nieve, niebla, etc. Cada condición climática tiene su propio efecto en la visibilidad, tracción, manejo, etc. del camión y el remolque.
-Misiones y logros desafiantes y gratificantes
-Otra razón por la que deberías jugar a Truckers of Europe 3 es porque tiene misiones desafiantes y gratificantes y logros que te mantendrán motivado y entretenido. El juego tiene un modo de carrera que le permite comenzar como un conductor de camión novato y trabajar su camino hasta convertirse en un camionero profesional. Puede hacerlo completando varias misiones y contratos que implican la entrega de carga a diferentes destinos en toda Europa. También puede ganar dinero de sus entregas y gastarlo en la compra de nuevos camiones y remolques o la personalización de los existentes. También puede ganar puntos de experiencia de sus entregas y gastarlos en mejorar sus habilidades y beneficios. También puedes desbloquear nuevos tipos de carga, contratos, camiones, remolques, etc. a medida que avanzas en el juego.
-
-Conclusión
-Truckers of Europe 3 es un juego de simulador de camiones realista y divertido que te permite experimentar la vida de un conductor de camiones en Europa. Puede elegir su camión y remolque, conducir por toda Europa y entregar carga, personalizar su camión y mejorar sus habilidades, y ganar dinero y logros. El juego tiene gráficos y física realistas, tráfico y clima diversos y dinámicos, y misiones y logros desafiantes y gratificantes. Si usted está buscando un juego de simulador de camiones que le mantendrá enganchado durante horas, definitivamente debe probar Camioneros de Europa 3 APK 36.6.
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre los camioneros de Europa 3:
-
-- Q: ¿Los camioneros de Europa 3 son libres de jugar?
-- A: Sí, Truckers of Europe 3 es gratis. Puedes descargarlo e instalarlo desde el enlace proporcionado en este artículo. Sin embargo, el juego puede contener algunas compras en la aplicación que pueden mejorar su experiencia de juego.
-- Q: ¿Es seguro descargar e instalar Truckers of Europe 3?
-- A: Sí, Truckers of Europe 3 es seguro para descargar e instalar. El archivo APK proporcionado en este artículo es verificado y probado por nuestro equipo. Sin embargo, siempre debe descargar e instalar archivos APK de fuentes confiables solamente.
-- Q: ¿Es Truckers of Europe 3 compatible con mi dispositivo?
-- A: Camioneros de Europa 3 es compatible con la mayoría de los dispositivos Android que tienen la versión Android 4.4 o superior. Sin embargo, algunos dispositivos pueden no ser compatibles con el juego debido a limitaciones de hardware o software.
-- P: ¿Cómo puedo contactar al desarrollador de Truckers of Europe 3?
-- A: Puedes contactar al desarrollador de Truckers of Europe 3 visitando su canal de YouTube Jerryisgaming o su página de Facebook Jerryisgaming. También puedes dejar tus comentarios, sugerencias o consultas en la sección de comentarios de sus videos o publicaciones.
-- P: ¿Cómo puedo apoyar el desarrollo de Camioneros de Europa 3?
-
-
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md b/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md
deleted file mode 100644
index 516d398df61b62811ac5d43e8205d838c0714cbc..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md
+++ /dev/null
@@ -1,80 +0,0 @@
-
-Cómo descargar Genshin Impact Cloud y disfrutar del juego en cualquier lugar
-Genshin Impact es uno de los juegos más populares y exitosos de 2020, con millones de jugadores en todo el mundo. El juego es un RPG de acción de mundo abierto que te permite explorar un mundo vasto y hermoso llamado Teyvat, donde puedes conocer a varios personajes, luchar contra enemigos, reunir recursos y completar misiones. El juego está disponible en múltiples plataformas, como PC, PlayStation 4, PlayStation 5, iOS y Android. Sin embargo, si quieres jugar el juego en tu dispositivo móvil, puedes enfrentarte a algunos desafíos, como espacio de almacenamiento limitado, bajo rendimiento o dispositivos incompatibles. Ahí es donde entra Genshin Impact Cloud.
-¿Qué es la nube de impacto Genshin y por qué debe probarlo
-Genshin Impact Cloud es un servicio que te permite jugar el juego en tu dispositivo móvil sin descargar el juego entero
-Genshin Impact Cloud es una nueva característica que fue introducida por miHoYo, el desarrollador de Genshin Impact, en abril de 2021. Es un servicio de juegos en la nube que le permite transmitir el juego desde un servidor a su dispositivo móvil a través de Internet. Esto significa que no necesitas descargar o instalar el juego en tu dispositivo, lo que puede ahorrarte mucho espacio de almacenamiento. El tamaño del juego de Genshin Impact en dispositivos móviles es de alrededor de 5,2 GB, pero con Genshin Impact Cloud, solo necesitas descargar una aplicación de 56 MB de tamaño. Esto también puede ayudarle a evitar largos tiempos de carga o actualizaciones.
-descargar estrellas pelea hack ios
Download ===> https://bltlly.com/2v6KEj
-Genshin Impact Cloud tiene muchos beneficios, como ahorrar espacio de almacenamiento, mejorar el rendimiento y admitir cross-save
-Además de ahorrar espacio de almacenamiento, Genshin Impact Cloud también tiene otras ventajas que pueden mejorar su experiencia de juego. Por ejemplo:
-
-
-- Genshin Impact Cloud soporta cross-save y cross-play. Esto significa que puedes acceder a tu progreso y datos existentes desde otras plataformas iniciando sesión con tu cuenta miHoYo. También puedes jugar con tus amigos que usan diferentes dispositivos, como PC o PlayStation.
-- Genshin Impact Cloud es de uso gratuito. No es necesario pagar tarifas o suscripciones adicionales para utilizar el servicio. También puede hacer compras en la aplicación como de costumbre, y disfrutar de todas las actualizaciones y eventos del juego.
-
-Con Genshin Impact Cloud, puede disfrutar del juego en cualquier lugar y en cualquier momento, siempre y cuando tenga una conexión a Internet estable y un dispositivo compatible.
-Cómo descargar Genshin Impact Cloud en dispositivos Android
-Genshin Impact Cloud está disponible actualmente solo para usuarios de Android en Malasia y Singapur como prueba beta
-Genshin Impact Cloud todavía está en sus primeras etapas de desarrollo, y todavía no está disponible para todas las regiones y plataformas. Actualmente, el servicio solo está abierto para los usuarios de Android en Malasia y Singapur como una prueba beta. Esto significa que solo un número limitado de jugadores puede acceder al servicio, y puede haber algunos errores o errores durante el juego. miHoYo no ha anunciado cuándo se ampliará el servicio a otras regiones y plataformas, pero es probable que lo hagan en el futuro.
-Para descargar Genshin Impact Cloud, necesita tener una cuenta miHoYo y registrarse para la prueba beta en el sitio web oficial
-Si eres un usuario de Android en Malasia o Singapur, y quieres probar Genshin Impact Cloud, debes seguir estos pasos:
-
-- Crea una cuenta miHoYo si aún no tienes una. Puede hacer esto visitando https://account.mihoyo.com/#/register y llenando la información requerida.
-- Visite None:
- """
- Raise an option parsing error using parser.error().
-
- Args:
- parser: an OptionParser instance.
- option: an Option instance.
- msg: the error text.
- """
- msg = f"{option} error: {msg}"
- msg = textwrap.fill(" ".join(msg.split()))
- parser.error(msg)
-
-
-def make_option_group(group: Dict[str, Any], parser: ConfigOptionParser) -> OptionGroup:
- """
- Return an OptionGroup object
- group -- assumed to be dict with 'name' and 'options' keys
- parser -- an optparse Parser
- """
- option_group = OptionGroup(parser, group["name"])
- for option in group["options"]:
- option_group.add_option(option())
- return option_group
-
-
-def check_dist_restriction(options: Values, check_target: bool = False) -> None:
- """Function for determining if custom platform options are allowed.
-
- :param options: The OptionParser options.
- :param check_target: Whether or not to check if --target is being used.
- """
- dist_restriction_set = any(
- [
- options.python_version,
- options.platforms,
- options.abis,
- options.implementation,
- ]
- )
-
- binary_only = FormatControl(set(), {":all:"})
- sdist_dependencies_allowed = (
- options.format_control != binary_only and not options.ignore_dependencies
- )
-
- # Installations or downloads using dist restrictions must not combine
- # source distributions and dist-specific wheels, as they are not
- # guaranteed to be locally compatible.
- if dist_restriction_set and sdist_dependencies_allowed:
- raise CommandError(
- "When restricting platform and interpreter constraints using "
- "--python-version, --platform, --abi, or --implementation, "
- "either --no-deps must be set, or --only-binary=:all: must be "
- "set and --no-binary must not be set (or must be set to "
- ":none:)."
- )
-
- if check_target:
- if dist_restriction_set and not options.target_dir:
- raise CommandError(
- "Can not use any platform or abi specific options unless "
- "installing via '--target'"
- )
-
-
-def _path_option_check(option: Option, opt: str, value: str) -> str:
- return os.path.expanduser(value)
-
-
-def _package_name_option_check(option: Option, opt: str, value: str) -> str:
- return canonicalize_name(value)
-
-
-class PipOption(Option):
- TYPES = Option.TYPES + ("path", "package_name")
- TYPE_CHECKER = Option.TYPE_CHECKER.copy()
- TYPE_CHECKER["package_name"] = _package_name_option_check
- TYPE_CHECKER["path"] = _path_option_check
-
-
-###########
-# options #
-###########
-
-help_: Callable[..., Option] = partial(
- Option,
- "-h",
- "--help",
- dest="help",
- action="help",
- help="Show help.",
-)
-
-debug_mode: Callable[..., Option] = partial(
- Option,
- "--debug",
- dest="debug_mode",
- action="store_true",
- default=False,
- help=(
- "Let unhandled exceptions propagate outside the main subroutine, "
- "instead of logging them to stderr."
- ),
-)
-
-isolated_mode: Callable[..., Option] = partial(
- Option,
- "--isolated",
- dest="isolated_mode",
- action="store_true",
- default=False,
- help=(
- "Run pip in an isolated mode, ignoring environment variables and user "
- "configuration."
- ),
-)
-
-require_virtualenv: Callable[..., Option] = partial(
- Option,
- "--require-virtualenv",
- "--require-venv",
- dest="require_venv",
- action="store_true",
- default=False,
- help=(
- "Allow pip to only run in a virtual environment; "
- "exit with an error otherwise."
- ),
-)
-
-override_externally_managed: Callable[..., Option] = partial(
- Option,
- "--break-system-packages",
- dest="override_externally_managed",
- action="store_true",
- help="Allow pip to modify an EXTERNALLY-MANAGED Python installation",
-)
-
-python: Callable[..., Option] = partial(
- Option,
- "--python",
- dest="python",
- help="Run pip with the specified Python interpreter.",
-)
-
-verbose: Callable[..., Option] = partial(
- Option,
- "-v",
- "--verbose",
- dest="verbose",
- action="count",
- default=0,
- help="Give more output. Option is additive, and can be used up to 3 times.",
-)
-
-no_color: Callable[..., Option] = partial(
- Option,
- "--no-color",
- dest="no_color",
- action="store_true",
- default=False,
- help="Suppress colored output.",
-)
-
-version: Callable[..., Option] = partial(
- Option,
- "-V",
- "--version",
- dest="version",
- action="store_true",
- help="Show version and exit.",
-)
-
-quiet: Callable[..., Option] = partial(
- Option,
- "-q",
- "--quiet",
- dest="quiet",
- action="count",
- default=0,
- help=(
- "Give less output. Option is additive, and can be used up to 3"
- " times (corresponding to WARNING, ERROR, and CRITICAL logging"
- " levels)."
- ),
-)
-
-progress_bar: Callable[..., Option] = partial(
- Option,
- "--progress-bar",
- dest="progress_bar",
- type="choice",
- choices=["on", "off"],
- default="on",
- help="Specify whether the progress bar should be used [on, off] (default: on)",
-)
-
-log: Callable[..., Option] = partial(
- PipOption,
- "--log",
- "--log-file",
- "--local-log",
- dest="log",
- metavar="path",
- type="path",
- help="Path to a verbose appending log.",
-)
-
-no_input: Callable[..., Option] = partial(
- Option,
- # Don't ask for input
- "--no-input",
- dest="no_input",
- action="store_true",
- default=False,
- help="Disable prompting for input.",
-)
-
-keyring_provider: Callable[..., Option] = partial(
- Option,
- "--keyring-provider",
- dest="keyring_provider",
- choices=["auto", "disabled", "import", "subprocess"],
- default="auto",
- help=(
- "Enable the credential lookup via the keyring library if user input is allowed."
- " Specify which mechanism to use [disabled, import, subprocess]."
- " (default: disabled)"
- ),
-)
-
-proxy: Callable[..., Option] = partial(
- Option,
- "--proxy",
- dest="proxy",
- type="str",
- default="",
- help="Specify a proxy in the form scheme://[user:passwd@]proxy.server:port.",
-)
-
-retries: Callable[..., Option] = partial(
- Option,
- "--retries",
- dest="retries",
- type="int",
- default=5,
- help="Maximum number of retries each connection should attempt "
- "(default %default times).",
-)
-
-timeout: Callable[..., Option] = partial(
- Option,
- "--timeout",
- "--default-timeout",
- metavar="sec",
- dest="timeout",
- type="float",
- default=15,
- help="Set the socket timeout (default %default seconds).",
-)
-
-
-def exists_action() -> Option:
- return Option(
- # Option when path already exist
- "--exists-action",
- dest="exists_action",
- type="choice",
- choices=["s", "i", "w", "b", "a"],
- default=[],
- action="append",
- metavar="action",
- help="Default action when a path already exists: "
- "(s)witch, (i)gnore, (w)ipe, (b)ackup, (a)bort.",
- )
-
-
-cert: Callable[..., Option] = partial(
- PipOption,
- "--cert",
- dest="cert",
- type="path",
- metavar="path",
- help=(
- "Path to PEM-encoded CA certificate bundle. "
- "If provided, overrides the default. "
- "See 'SSL Certificate Verification' in pip documentation "
- "for more information."
- ),
-)
-
-client_cert: Callable[..., Option] = partial(
- PipOption,
- "--client-cert",
- dest="client_cert",
- type="path",
- default=None,
- metavar="path",
- help="Path to SSL client certificate, a single file containing the "
- "private key and the certificate in PEM format.",
-)
-
-index_url: Callable[..., Option] = partial(
- Option,
- "-i",
- "--index-url",
- "--pypi-url",
- dest="index_url",
- metavar="URL",
- default=PyPI.simple_url,
- help="Base URL of the Python Package Index (default %default). "
- "This should point to a repository compliant with PEP 503 "
- "(the simple repository API) or a local directory laid out "
- "in the same format.",
-)
-
-
-def extra_index_url() -> Option:
- return Option(
- "--extra-index-url",
- dest="extra_index_urls",
- metavar="URL",
- action="append",
- default=[],
- help="Extra URLs of package indexes to use in addition to "
- "--index-url. Should follow the same rules as "
- "--index-url.",
- )
-
-
-no_index: Callable[..., Option] = partial(
- Option,
- "--no-index",
- dest="no_index",
- action="store_true",
- default=False,
- help="Ignore package index (only looking at --find-links URLs instead).",
-)
-
-
-def find_links() -> Option:
- return Option(
- "-f",
- "--find-links",
- dest="find_links",
- action="append",
- default=[],
- metavar="url",
- help="If a URL or path to an html file, then parse for links to "
- "archives such as sdist (.tar.gz) or wheel (.whl) files. "
- "If a local path or file:// URL that's a directory, "
- "then look for archives in the directory listing. "
- "Links to VCS project URLs are not supported.",
- )
-
-
-def trusted_host() -> Option:
- return Option(
- "--trusted-host",
- dest="trusted_hosts",
- action="append",
- metavar="HOSTNAME",
- default=[],
- help="Mark this host or host:port pair as trusted, even though it "
- "does not have valid or any HTTPS.",
- )
-
-
-def constraints() -> Option:
- return Option(
- "-c",
- "--constraint",
- dest="constraints",
- action="append",
- default=[],
- metavar="file",
- help="Constrain versions using the given constraints file. "
- "This option can be used multiple times.",
- )
-
-
-def requirements() -> Option:
- return Option(
- "-r",
- "--requirement",
- dest="requirements",
- action="append",
- default=[],
- metavar="file",
- help="Install from the given requirements file. "
- "This option can be used multiple times.",
- )
-
-
-def editable() -> Option:
- return Option(
- "-e",
- "--editable",
- dest="editables",
- action="append",
- default=[],
- metavar="path/url",
- help=(
- "Install a project in editable mode (i.e. setuptools "
- '"develop mode") from a local project path or a VCS url.'
- ),
- )
-
-
-def _handle_src(option: Option, opt_str: str, value: str, parser: OptionParser) -> None:
- value = os.path.abspath(value)
- setattr(parser.values, option.dest, value)
-
-
-src: Callable[..., Option] = partial(
- PipOption,
- "--src",
- "--source",
- "--source-dir",
- "--source-directory",
- dest="src_dir",
- type="path",
- metavar="dir",
- default=get_src_prefix(),
- action="callback",
- callback=_handle_src,
- help="Directory to check out editable projects into. "
- 'The default in a virtualenv is "
/src". '
- 'The default for global installs is "/src".',
-)
-
-
-def _get_format_control(values: Values, option: Option) -> Any:
- """Get a format_control object."""
- return getattr(values, option.dest)
-
-
-def _handle_no_binary(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- existing = _get_format_control(parser.values, option)
- FormatControl.handle_mutual_excludes(
- value,
- existing.no_binary,
- existing.only_binary,
- )
-
-
-def _handle_only_binary(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- existing = _get_format_control(parser.values, option)
- FormatControl.handle_mutual_excludes(
- value,
- existing.only_binary,
- existing.no_binary,
- )
-
-
-def no_binary() -> Option:
- format_control = FormatControl(set(), set())
- return Option(
- "--no-binary",
- dest="format_control",
- action="callback",
- callback=_handle_no_binary,
- type="str",
- default=format_control,
- help="Do not use binary packages. Can be supplied multiple times, and "
- 'each time adds to the existing value. Accepts either ":all:" to '
- 'disable all binary packages, ":none:" to empty the set (notice '
- "the colons), or one or more package names with commas between "
- "them (no colons). Note that some packages are tricky to compile "
- "and may fail to install when this option is used on them.",
- )
-
-
-def only_binary() -> Option:
- format_control = FormatControl(set(), set())
- return Option(
- "--only-binary",
- dest="format_control",
- action="callback",
- callback=_handle_only_binary,
- type="str",
- default=format_control,
- help="Do not use source packages. Can be supplied multiple times, and "
- 'each time adds to the existing value. Accepts either ":all:" to '
- 'disable all source packages, ":none:" to empty the set, or one '
- "or more package names with commas between them. Packages "
- "without binary distributions will fail to install when this "
- "option is used on them.",
- )
-
-
-platforms: Callable[..., Option] = partial(
- Option,
- "--platform",
- dest="platforms",
- metavar="platform",
- action="append",
- default=None,
- help=(
- "Only use wheels compatible with . Defaults to the "
- "platform of the running system. Use this option multiple times to "
- "specify multiple platforms supported by the target interpreter."
- ),
-)
-
-
-# This was made a separate function for unit-testing purposes.
-def _convert_python_version(value: str) -> Tuple[Tuple[int, ...], Optional[str]]:
- """
- Convert a version string like "3", "37", or "3.7.3" into a tuple of ints.
-
- :return: A 2-tuple (version_info, error_msg), where `error_msg` is
- non-None if and only if there was a parsing error.
- """
- if not value:
- # The empty string is the same as not providing a value.
- return (None, None)
-
- parts = value.split(".")
- if len(parts) > 3:
- return ((), "at most three version parts are allowed")
-
- if len(parts) == 1:
- # Then we are in the case of "3" or "37".
- value = parts[0]
- if len(value) > 1:
- parts = [value[0], value[1:]]
-
- try:
- version_info = tuple(int(part) for part in parts)
- except ValueError:
- return ((), "each version part must be an integer")
-
- return (version_info, None)
-
-
-def _handle_python_version(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- """
- Handle a provided --python-version value.
- """
- version_info, error_msg = _convert_python_version(value)
- if error_msg is not None:
- msg = "invalid --python-version value: {!r}: {}".format(
- value,
- error_msg,
- )
- raise_option_error(parser, option=option, msg=msg)
-
- parser.values.python_version = version_info
-
-
-python_version: Callable[..., Option] = partial(
- Option,
- "--python-version",
- dest="python_version",
- metavar="python_version",
- action="callback",
- callback=_handle_python_version,
- type="str",
- default=None,
- help=dedent(
- """\
- The Python interpreter version to use for wheel and "Requires-Python"
- compatibility checks. Defaults to a version derived from the running
- interpreter. The version can be specified using up to three dot-separated
- integers (e.g. "3" for 3.0.0, "3.7" for 3.7.0, or "3.7.3"). A major-minor
- version can also be given as a string without dots (e.g. "37" for 3.7.0).
- """
- ),
-)
-
-
-implementation: Callable[..., Option] = partial(
- Option,
- "--implementation",
- dest="implementation",
- metavar="implementation",
- default=None,
- help=(
- "Only use wheels compatible with Python "
- "implementation , e.g. 'pp', 'jy', 'cp', "
- " or 'ip'. If not specified, then the current "
- "interpreter implementation is used. Use 'py' to force "
- "implementation-agnostic wheels."
- ),
-)
-
-
-abis: Callable[..., Option] = partial(
- Option,
- "--abi",
- dest="abis",
- metavar="abi",
- action="append",
- default=None,
- help=(
- "Only use wheels compatible with Python abi , e.g. 'pypy_41'. "
- "If not specified, then the current interpreter abi tag is used. "
- "Use this option multiple times to specify multiple abis supported "
- "by the target interpreter. Generally you will need to specify "
- "--implementation, --platform, and --python-version when using this "
- "option."
- ),
-)
-
-
-def add_target_python_options(cmd_opts: OptionGroup) -> None:
- cmd_opts.add_option(platforms())
- cmd_opts.add_option(python_version())
- cmd_opts.add_option(implementation())
- cmd_opts.add_option(abis())
-
-
-def make_target_python(options: Values) -> TargetPython:
- target_python = TargetPython(
- platforms=options.platforms,
- py_version_info=options.python_version,
- abis=options.abis,
- implementation=options.implementation,
- )
-
- return target_python
-
-
-def prefer_binary() -> Option:
- return Option(
- "--prefer-binary",
- dest="prefer_binary",
- action="store_true",
- default=False,
- help="Prefer older binary packages over newer source packages.",
- )
-
-
-cache_dir: Callable[..., Option] = partial(
- PipOption,
- "--cache-dir",
- dest="cache_dir",
- default=USER_CACHE_DIR,
- metavar="dir",
- type="path",
- help="Store the cache data in .",
-)
-
-
-def _handle_no_cache_dir(
- option: Option, opt: str, value: str, parser: OptionParser
-) -> None:
- """
- Process a value provided for the --no-cache-dir option.
-
- This is an optparse.Option callback for the --no-cache-dir option.
- """
- # The value argument will be None if --no-cache-dir is passed via the
- # command-line, since the option doesn't accept arguments. However,
- # the value can be non-None if the option is triggered e.g. by an
- # environment variable, like PIP_NO_CACHE_DIR=true.
- if value is not None:
- # Then parse the string value to get argument error-checking.
- try:
- strtobool(value)
- except ValueError as exc:
- raise_option_error(parser, option=option, msg=str(exc))
-
- # Originally, setting PIP_NO_CACHE_DIR to a value that strtobool()
- # converted to 0 (like "false" or "no") caused cache_dir to be disabled
- # rather than enabled (logic would say the latter). Thus, we disable
- # the cache directory not just on values that parse to True, but (for
- # backwards compatibility reasons) also on values that parse to False.
- # In other words, always set it to False if the option is provided in
- # some (valid) form.
- parser.values.cache_dir = False
-
-
-no_cache: Callable[..., Option] = partial(
- Option,
- "--no-cache-dir",
- dest="cache_dir",
- action="callback",
- callback=_handle_no_cache_dir,
- help="Disable the cache.",
-)
-
-no_deps: Callable[..., Option] = partial(
- Option,
- "--no-deps",
- "--no-dependencies",
- dest="ignore_dependencies",
- action="store_true",
- default=False,
- help="Don't install package dependencies.",
-)
-
-ignore_requires_python: Callable[..., Option] = partial(
- Option,
- "--ignore-requires-python",
- dest="ignore_requires_python",
- action="store_true",
- help="Ignore the Requires-Python information.",
-)
-
-no_build_isolation: Callable[..., Option] = partial(
- Option,
- "--no-build-isolation",
- dest="build_isolation",
- action="store_false",
- default=True,
- help="Disable isolation when building a modern source distribution. "
- "Build dependencies specified by PEP 518 must be already installed "
- "if this option is used.",
-)
-
-check_build_deps: Callable[..., Option] = partial(
- Option,
- "--check-build-dependencies",
- dest="check_build_deps",
- action="store_true",
- default=False,
- help="Check the build dependencies when PEP517 is used.",
-)
-
-
-def _handle_no_use_pep517(
- option: Option, opt: str, value: str, parser: OptionParser
-) -> None:
- """
- Process a value provided for the --no-use-pep517 option.
-
- This is an optparse.Option callback for the no_use_pep517 option.
- """
- # Since --no-use-pep517 doesn't accept arguments, the value argument
- # will be None if --no-use-pep517 is passed via the command-line.
- # However, the value can be non-None if the option is triggered e.g.
- # by an environment variable, for example "PIP_NO_USE_PEP517=true".
- if value is not None:
- msg = """A value was passed for --no-use-pep517,
- probably using either the PIP_NO_USE_PEP517 environment variable
- or the "no-use-pep517" config file option. Use an appropriate value
- of the PIP_USE_PEP517 environment variable or the "use-pep517"
- config file option instead.
- """
- raise_option_error(parser, option=option, msg=msg)
-
- # If user doesn't wish to use pep517, we check if setuptools and wheel are installed
- # and raise error if it is not.
- packages = ("setuptools", "wheel")
- if not all(importlib.util.find_spec(package) for package in packages):
- msg = (
- f"It is not possible to use --no-use-pep517 "
- f"without {' and '.join(packages)} installed."
- )
- raise_option_error(parser, option=option, msg=msg)
-
- # Otherwise, --no-use-pep517 was passed via the command-line.
- parser.values.use_pep517 = False
-
-
-use_pep517: Any = partial(
- Option,
- "--use-pep517",
- dest="use_pep517",
- action="store_true",
- default=None,
- help="Use PEP 517 for building source distributions "
- "(use --no-use-pep517 to force legacy behaviour).",
-)
-
-no_use_pep517: Any = partial(
- Option,
- "--no-use-pep517",
- dest="use_pep517",
- action="callback",
- callback=_handle_no_use_pep517,
- default=None,
- help=SUPPRESS_HELP,
-)
-
-
-def _handle_config_settings(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- key, sep, val = value.partition("=")
- if sep != "=":
- parser.error(f"Arguments to {opt_str} must be of the form KEY=VAL") # noqa
- dest = getattr(parser.values, option.dest)
- if dest is None:
- dest = {}
- setattr(parser.values, option.dest, dest)
- if key in dest:
- if isinstance(dest[key], list):
- dest[key].append(val)
- else:
- dest[key] = [dest[key], val]
- else:
- dest[key] = val
-
-
-config_settings: Callable[..., Option] = partial(
- Option,
- "-C",
- "--config-settings",
- dest="config_settings",
- type=str,
- action="callback",
- callback=_handle_config_settings,
- metavar="settings",
- help="Configuration settings to be passed to the PEP 517 build backend. "
- "Settings take the form KEY=VALUE. Use multiple --config-settings options "
- "to pass multiple keys to the backend.",
-)
-
-build_options: Callable[..., Option] = partial(
- Option,
- "--build-option",
- dest="build_options",
- metavar="options",
- action="append",
- help="Extra arguments to be supplied to 'setup.py bdist_wheel'.",
-)
-
-global_options: Callable[..., Option] = partial(
- Option,
- "--global-option",
- dest="global_options",
- action="append",
- metavar="options",
- help="Extra global options to be supplied to the setup.py "
- "call before the install or bdist_wheel command.",
-)
-
-no_clean: Callable[..., Option] = partial(
- Option,
- "--no-clean",
- action="store_true",
- default=False,
- help="Don't clean up build directories.",
-)
-
-pre: Callable[..., Option] = partial(
- Option,
- "--pre",
- action="store_true",
- default=False,
- help="Include pre-release and development versions. By default, "
- "pip only finds stable versions.",
-)
-
-disable_pip_version_check: Callable[..., Option] = partial(
- Option,
- "--disable-pip-version-check",
- dest="disable_pip_version_check",
- action="store_true",
- default=False,
- help="Don't periodically check PyPI to determine whether a new version "
- "of pip is available for download. Implied with --no-index.",
-)
-
-root_user_action: Callable[..., Option] = partial(
- Option,
- "--root-user-action",
- dest="root_user_action",
- default="warn",
- choices=["warn", "ignore"],
- help="Action if pip is run as a root user. By default, a warning message is shown.",
-)
-
-
-def _handle_merge_hash(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- """Given a value spelled "algo:digest", append the digest to a list
- pointed to in a dict by the algo name."""
- if not parser.values.hashes:
- parser.values.hashes = {}
- try:
- algo, digest = value.split(":", 1)
- except ValueError:
- parser.error(
- "Arguments to {} must be a hash name " # noqa
- "followed by a value, like --hash=sha256:"
- "abcde...".format(opt_str)
- )
- if algo not in STRONG_HASHES:
- parser.error(
- "Allowed hash algorithms for {} are {}.".format( # noqa
- opt_str, ", ".join(STRONG_HASHES)
- )
- )
- parser.values.hashes.setdefault(algo, []).append(digest)
-
-
-hash: Callable[..., Option] = partial(
- Option,
- "--hash",
- # Hash values eventually end up in InstallRequirement.hashes due to
- # __dict__ copying in process_line().
- dest="hashes",
- action="callback",
- callback=_handle_merge_hash,
- type="string",
- help="Verify that the package's archive matches this "
- "hash before installing. Example: --hash=sha256:abcdef...",
-)
-
-
-require_hashes: Callable[..., Option] = partial(
- Option,
- "--require-hashes",
- dest="require_hashes",
- action="store_true",
- default=False,
- help="Require a hash to check each requirement against, for "
- "repeatable installs. This option is implied when any package in a "
- "requirements file has a --hash option.",
-)
-
-
-list_path: Callable[..., Option] = partial(
- PipOption,
- "--path",
- dest="path",
- type="path",
- action="append",
- help="Restrict to the specified installation path for listing "
- "packages (can be used multiple times).",
-)
-
-
-def check_list_path_option(options: Values) -> None:
- if options.path and (options.user or options.local):
- raise CommandError("Cannot combine '--path' with '--user' or '--local'")
-
-
-list_exclude: Callable[..., Option] = partial(
- PipOption,
- "--exclude",
- dest="excludes",
- action="append",
- metavar="package",
- type="package_name",
- help="Exclude specified package from the output",
-)
-
-
-no_python_version_warning: Callable[..., Option] = partial(
- Option,
- "--no-python-version-warning",
- dest="no_python_version_warning",
- action="store_true",
- default=False,
- help="Silence deprecation warnings for upcoming unsupported Pythons.",
-)
-
-
-# Features that are now always on. A warning is printed if they are used.
-ALWAYS_ENABLED_FEATURES = [
- "no-binary-enable-wheel-cache", # always on since 23.1
-]
-
-use_new_feature: Callable[..., Option] = partial(
- Option,
- "--use-feature",
- dest="features_enabled",
- metavar="feature",
- action="append",
- default=[],
- choices=[
- "fast-deps",
- "truststore",
- ]
- + ALWAYS_ENABLED_FEATURES,
- help="Enable new functionality, that may be backward incompatible.",
-)
-
-use_deprecated_feature: Callable[..., Option] = partial(
- Option,
- "--use-deprecated",
- dest="deprecated_features_enabled",
- metavar="feature",
- action="append",
- default=[],
- choices=[
- "legacy-resolver",
- ],
- help=("Enable deprecated functionality, that will be removed in the future."),
-)
-
-
-##########
-# groups #
-##########
-
-general_group: Dict[str, Any] = {
- "name": "General Options",
- "options": [
- help_,
- debug_mode,
- isolated_mode,
- require_virtualenv,
- python,
- verbose,
- version,
- quiet,
- log,
- no_input,
- keyring_provider,
- proxy,
- retries,
- timeout,
- exists_action,
- trusted_host,
- cert,
- client_cert,
- cache_dir,
- no_cache,
- disable_pip_version_check,
- no_color,
- no_python_version_warning,
- use_new_feature,
- use_deprecated_feature,
- ],
-}
-
-index_group: Dict[str, Any] = {
- "name": "Package Index Options",
- "options": [
- index_url,
- extra_index_url,
- no_index,
- find_links,
- ],
-}
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py
deleted file mode 100644
index fd80d8c1129722b84771bd6a0f6ccfd57f5cf78e..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import re
-from ast import literal_eval
-from operator import attrgetter
-from typing import Callable, Iterable, List, Match, NamedTuple, Optional, Tuple, Union
-
-from ._emoji_replace import _emoji_replace
-from .emoji import EmojiVariant
-from .errors import MarkupError
-from .style import Style
-from .text import Span, Text
-
-RE_TAGS = re.compile(
- r"""((\\*)\[([a-z#/@][^[]*?)])""",
- re.VERBOSE,
-)
-
-RE_HANDLER = re.compile(r"^([\w.]*?)(\(.*?\))?$")
-
-
-class Tag(NamedTuple):
- """A tag in console markup."""
-
- name: str
- """The tag name. e.g. 'bold'."""
- parameters: Optional[str]
- """Any additional parameters after the name."""
-
- def __str__(self) -> str:
- return (
- self.name if self.parameters is None else f"{self.name} {self.parameters}"
- )
-
- @property
- def markup(self) -> str:
- """Get the string representation of this tag."""
- return (
- f"[{self.name}]"
- if self.parameters is None
- else f"[{self.name}={self.parameters}]"
- )
-
-
-_ReStringMatch = Match[str] # regex match object
-_ReSubCallable = Callable[[_ReStringMatch], str] # Callable invoked by re.sub
-_EscapeSubMethod = Callable[[_ReSubCallable, str], str] # Sub method of a compiled re
-
-
-def escape(
- markup: str,
- _escape: _EscapeSubMethod = re.compile(r"(\\*)(\[[a-z#/@][^[]*?])").sub,
-) -> str:
- """Escapes text so that it won't be interpreted as markup.
-
- Args:
- markup (str): Content to be inserted in to markup.
-
- Returns:
- str: Markup with square brackets escaped.
- """
-
- def escape_backslashes(match: Match[str]) -> str:
- """Called by re.sub replace matches."""
- backslashes, text = match.groups()
- return f"{backslashes}{backslashes}\\{text}"
-
- markup = _escape(escape_backslashes, markup)
- return markup
-
-
-def _parse(markup: str) -> Iterable[Tuple[int, Optional[str], Optional[Tag]]]:
- """Parse markup in to an iterable of tuples of (position, text, tag).
-
- Args:
- markup (str): A string containing console markup
-
- """
- position = 0
- _divmod = divmod
- _Tag = Tag
- for match in RE_TAGS.finditer(markup):
- full_text, escapes, tag_text = match.groups()
- start, end = match.span()
- if start > position:
- yield start, markup[position:start], None
- if escapes:
- backslashes, escaped = _divmod(len(escapes), 2)
- if backslashes:
- # Literal backslashes
- yield start, "\\" * backslashes, None
- start += backslashes * 2
- if escaped:
- # Escape of tag
- yield start, full_text[len(escapes) :], None
- position = end
- continue
- text, equals, parameters = tag_text.partition("=")
- yield start, None, _Tag(text, parameters if equals else None)
- position = end
- if position < len(markup):
- yield position, markup[position:], None
-
-
-def render(
- markup: str,
- style: Union[str, Style] = "",
- emoji: bool = True,
- emoji_variant: Optional[EmojiVariant] = None,
-) -> Text:
- """Render console markup in to a Text instance.
-
- Args:
- markup (str): A string containing console markup.
- emoji (bool, optional): Also render emoji code. Defaults to True.
-
- Raises:
- MarkupError: If there is a syntax error in the markup.
-
- Returns:
- Text: A test instance.
- """
- emoji_replace = _emoji_replace
- if "[" not in markup:
- return Text(
- emoji_replace(markup, default_variant=emoji_variant) if emoji else markup,
- style=style,
- )
- text = Text(style=style)
- append = text.append
- normalize = Style.normalize
-
- style_stack: List[Tuple[int, Tag]] = []
- pop = style_stack.pop
-
- spans: List[Span] = []
- append_span = spans.append
-
- _Span = Span
- _Tag = Tag
-
- def pop_style(style_name: str) -> Tuple[int, Tag]:
- """Pop tag matching given style name."""
- for index, (_, tag) in enumerate(reversed(style_stack), 1):
- if tag.name == style_name:
- return pop(-index)
- raise KeyError(style_name)
-
- for position, plain_text, tag in _parse(markup):
- if plain_text is not None:
- # Handle open brace escapes, where the brace is not part of a tag.
- plain_text = plain_text.replace("\\[", "[")
- append(emoji_replace(plain_text) if emoji else plain_text)
- elif tag is not None:
- if tag.name.startswith("/"): # Closing tag
- style_name = tag.name[1:].strip()
-
- if style_name: # explicit close
- style_name = normalize(style_name)
- try:
- start, open_tag = pop_style(style_name)
- except KeyError:
- raise MarkupError(
- f"closing tag '{tag.markup}' at position {position} doesn't match any open tag"
- ) from None
- else: # implicit close
- try:
- start, open_tag = pop()
- except IndexError:
- raise MarkupError(
- f"closing tag '[/]' at position {position} has nothing to close"
- ) from None
-
- if open_tag.name.startswith("@"):
- if open_tag.parameters:
- handler_name = ""
- parameters = open_tag.parameters.strip()
- handler_match = RE_HANDLER.match(parameters)
- if handler_match is not None:
- handler_name, match_parameters = handler_match.groups()
- parameters = (
- "()" if match_parameters is None else match_parameters
- )
-
- try:
- meta_params = literal_eval(parameters)
- except SyntaxError as error:
- raise MarkupError(
- f"error parsing {parameters!r} in {open_tag.parameters!r}; {error.msg}"
- )
- except Exception as error:
- raise MarkupError(
- f"error parsing {open_tag.parameters!r}; {error}"
- ) from None
-
- if handler_name:
- meta_params = (
- handler_name,
- meta_params
- if isinstance(meta_params, tuple)
- else (meta_params,),
- )
-
- else:
- meta_params = ()
-
- append_span(
- _Span(
- start, len(text), Style(meta={open_tag.name: meta_params})
- )
- )
- else:
- append_span(_Span(start, len(text), str(open_tag)))
-
- else: # Opening tag
- normalized_tag = _Tag(normalize(tag.name), tag.parameters)
- style_stack.append((len(text), normalized_tag))
-
- text_length = len(text)
- while style_stack:
- start, tag = style_stack.pop()
- style = str(tag)
- if style:
- append_span(_Span(start, text_length, style))
-
- text.spans = sorted(spans[::-1], key=attrgetter("start"))
- return text
-
-
-if __name__ == "__main__": # pragma: no cover
-
- MARKUP = [
- "[red]Hello World[/red]",
- "[magenta]Hello [b]World[/b]",
- "[bold]Bold[italic] bold and italic [/bold]italic[/italic]",
- "Click [link=https://www.willmcgugan.com]here[/link] to visit my Blog",
- ":warning-emoji: [bold red blink] DANGER![/]",
- ]
-
- from pip._vendor.rich import print
- from pip._vendor.rich.table import Table
-
- grid = Table("Markup", "Result", padding=(0, 1))
-
- for markup in MARKUP:
- grid.add_row(Text(markup), markup)
-
- print(grid)
diff --git a/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat b/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat
deleted file mode 100644
index 62a39a7584f4d7c5fbc31758e3e9e7eff700276d..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat
+++ /dev/null
@@ -1,22 +0,0 @@
-@echo off
-
-if defined _OLD_VIRTUAL_PROMPT (
- set "PROMPT=%_OLD_VIRTUAL_PROMPT%"
-)
-set _OLD_VIRTUAL_PROMPT=
-
-if defined _OLD_VIRTUAL_PYTHONHOME (
- set "PYTHONHOME=%_OLD_VIRTUAL_PYTHONHOME%"
- set _OLD_VIRTUAL_PYTHONHOME=
-)
-
-if defined _OLD_VIRTUAL_PATH (
- set "PATH=%_OLD_VIRTUAL_PATH%"
-)
-
-set _OLD_VIRTUAL_PATH=
-
-set VIRTUAL_ENV=
-set VIRTUAL_ENV_PROMPT=
-
-:END
diff --git a/spaces/CAMP-ViL/Xplainer/inference.py b/spaces/CAMP-ViL/Xplainer/inference.py
deleted file mode 100644
index 2ab6b61c2b1fa8039af58a5f5c4823b6c2f56cab..0000000000000000000000000000000000000000
--- a/spaces/CAMP-ViL/Xplainer/inference.py
+++ /dev/null
@@ -1,116 +0,0 @@
-import argparse
-import gc
-from pathlib import Path
-
-import torch
-from torch.utils.data import DataLoader
-from tqdm import tqdm
-
-from chestxray14 import ChestXray14Dataset
-from chexpert import CheXpertDataset
-from descriptors import disease_descriptors_chexpert, disease_descriptors_chestxray14
-from model import InferenceModel
-from utils import calculate_auroc
-
-torch.multiprocessing.set_sharing_strategy('file_system')
-
-
-def inference_chexpert():
- split = 'test'
- dataset = CheXpertDataset(f'data/chexpert/{split}_labels.csv') # also do test
- dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=lambda x: x, num_workers=0)
- inference_model = InferenceModel()
- all_descriptors = inference_model.get_all_descriptors(disease_descriptors_chexpert)
-
- all_labels = []
- all_probs_neg = []
-
- for batch in tqdm(dataloader):
- batch = batch[0]
- image_paths, labels, keys = batch
- image_paths = [Path(image_path) for image_path in image_paths]
- agg_probs = []
- agg_negative_probs = []
- for image_path in image_paths:
- probs, negative_probs = inference_model.get_descriptor_probs(image_path, descriptors=all_descriptors)
- agg_probs.append(probs)
- agg_negative_probs.append(negative_probs)
- probs = {} # Aggregated
- negative_probs = {} # Aggregated
- for key in agg_probs[0].keys():
- probs[key] = sum([p[key] for p in agg_probs]) / len(agg_probs) # Mean Aggregation
-
- for key in agg_negative_probs[0].keys():
- negative_probs[key] = sum([p[key] for p in agg_negative_probs]) / len(agg_negative_probs) # Mean Aggregation
-
- disease_probs, negative_disease_probs = inference_model.get_diseases_probs(disease_descriptors_chexpert, pos_probs=probs,
- negative_probs=negative_probs)
- predicted_diseases, prob_vector_neg_prompt = inference_model.get_predictions_bin_prompting(disease_descriptors_chexpert,
- disease_probs=disease_probs,
- negative_disease_probs=negative_disease_probs,
- keys=keys)
- all_labels.append(labels)
- all_probs_neg.append(prob_vector_neg_prompt)
-
- all_labels = torch.stack(all_labels)
- all_probs_neg = torch.stack(all_probs_neg)
-
- # evaluation
- existing_mask = sum(all_labels, 0) > 0
- all_labels_clean = all_labels[:, existing_mask]
- all_probs_neg_clean = all_probs_neg[:, existing_mask]
- all_keys_clean = [key for idx, key in enumerate(keys) if existing_mask[idx]]
-
- overall_auroc, per_disease_auroc = calculate_auroc(all_probs_neg_clean, all_labels_clean)
- print(f"AUROC: {overall_auroc:.5f}\n")
- for idx, key in enumerate(all_keys_clean):
- print(f'{key}: {per_disease_auroc[idx]:.5f}')
-
-
-def inference_chestxray14():
- dataset = ChestXray14Dataset(f'data/chestxray14/Data_Entry_2017_v2020_modified.csv')
- dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=lambda x: x, num_workers=1)
- inference_model = InferenceModel()
- all_descriptors = inference_model.get_all_descriptors(disease_descriptors_chestxray14)
-
- all_labels = []
- all_probs_neg = []
- for batch in tqdm(dataloader):
- batch = batch[0]
- image_path, labels, keys = batch
- image_path = Path(image_path)
- probs, negative_probs = inference_model.get_descriptor_probs(image_path, descriptors=all_descriptors)
- disease_probs, negative_disease_probs = inference_model.get_diseases_probs(disease_descriptors_chestxray14, pos_probs=probs,
- negative_probs=negative_probs)
- predicted_diseases, prob_vector_neg_prompt = inference_model.get_predictions_bin_prompting(disease_descriptors_chestxray14,
- disease_probs=disease_probs,
- negative_disease_probs=negative_disease_probs,
- keys=keys)
- all_labels.append(labels)
- all_probs_neg.append(prob_vector_neg_prompt)
- gc.collect()
-
- all_labels = torch.stack(all_labels)
- all_probs_neg = torch.stack(all_probs_neg)
-
- existing_mask = sum(all_labels, 0) > 0
- all_labels_clean = all_labels[:, existing_mask]
- all_probs_neg_clean = all_probs_neg[:, existing_mask]
- all_keys_clean = [key for idx, key in enumerate(keys) if existing_mask[idx]]
-
- overall_auroc, per_disease_auroc = calculate_auroc(all_probs_neg_clean[:, 1:], all_labels_clean[:, 1:])
- print(f"AUROC: {overall_auroc:.5f}\n")
- for idx, key in enumerate(all_keys_clean[1:]):
- print(f'{key}: {per_disease_auroc[idx]:.5f}')
-
-
-if __name__ == '__main__':
- # add argument parser
- parser = argparse.ArgumentParser()
- parser.add_argument('--dataset', type=str, default='chexpert', help='chexpert or chestxray14')
- args = parser.parse_args()
-
- if args.dataset == 'chexpert':
- inference_chexpert()
- elif args.dataset == 'chestxray14':
- inference_chestxray14()
diff --git a/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h b/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h
deleted file mode 100644
index e00d3ef054bd740b801e47cc1344e38621d8c055..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h
+++ /dev/null
@@ -1,253 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-
-namespace thrust
-{
-
-namespace detail
-{
-
-
-// adapted from http://www.boost.org/doc/libs/1_37_0/libs/iterator/doc/iterator_facade.html#iterator-category
-//
-// in our implementation, R need not be a reference type to result in a category
-// derived from forward_XXX_iterator_tag
-//
-// iterator-category(T,V,R) :=
-// if(T is convertible to input_host_iterator_tag
-// || T is convertible to output_host_iterator_tag
-// || T is convertible to input_device_iterator_tag
-// || T is convertible to output_device_iterator_tag
-// )
-// return T
-//
-// else if (T is not convertible to incrementable_traversal_tag)
-// the program is ill-formed
-//
-// else return a type X satisfying the following two constraints:
-//
-// 1. X is convertible to X1, and not to any more-derived
-// type, where X1 is defined by:
-//
-// if (T is convertible to forward_traversal_tag)
-// {
-// if (T is convertible to random_access_traversal_tag)
-// X1 = random_access_host_iterator_tag
-// else if (T is convertible to bidirectional_traversal_tag)
-// X1 = bidirectional_host_iterator_tag
-// else
-// X1 = forward_host_iterator_tag
-// }
-// else
-// {
-// if (T is convertible to single_pass_traversal_tag
-// && R is convertible to V)
-// X1 = input_host_iterator_tag
-// else
-// X1 = T
-// }
-//
-// 2. category-to-traversal(X) is convertible to the most
-// derived traversal tag type to which X is also convertible,
-// and not to any more-derived traversal tag type.
-
-
-template
- struct iterator_facade_default_category;
-
-
-// Thrust's implementation of iterator_facade_default_category is slightly
-// different from Boost's equivalent.
-// Thrust does not check is_convertible because Reference
-// may not be a complete type at this point, and implementations of is_convertible
-// typically require that both types be complete.
-// Instead, it simply assumes that if is_convertible,
-// then the category is input_iterator_tag
-
-
-// this is the function for standard system iterators
-template
- struct iterator_facade_default_category_std :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if< // XXX note we differ from Boost here
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_std
-
-
-// this is the function for host system iterators
-template
- struct iterator_facade_default_category_host :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if< // XXX note we differ from Boost here
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_host
-
-
-// this is the function for device system iterators
-template
- struct iterator_facade_default_category_device :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value, // XXX note we differ from Boost here
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_device
-
-
-// this is the function for any system iterators
-template
- struct iterator_facade_default_category_any
-{
- typedef thrust::detail::iterator_category_with_system_and_traversal<
- typename iterator_facade_default_category_std::type,
- thrust::any_system_tag,
- Traversal
- > type;
-}; // end iterator_facade_default_category_any
-
-
-template
- struct iterator_facade_default_category
- // check for any system
- : thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_any,
-
- // check for host system
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_host,
-
- // check for device system
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_device,
-
- // if we don't recognize the system, get a standard iterator category
- // and combine it with System & Traversal
- thrust::detail::identity_<
- thrust::detail::iterator_category_with_system_and_traversal<
- typename iterator_facade_default_category_std::type,
- System,
- Traversal
- >
- >
- >
- >
- >
-{};
-
-
-template
- struct iterator_facade_category_impl
-{
- typedef typename iterator_facade_default_category<
- System,Traversal,ValueParam,Reference
- >::type category;
-
- // we must be able to deduce both Traversal & System from category
- // otherwise, munge them all together
- typedef typename thrust::detail::eval_if<
- thrust::detail::and_<
- thrust::detail::is_same<
- Traversal,
- typename thrust::detail::iterator_category_to_traversal::type
- >,
- thrust::detail::is_same<
- System,
- typename thrust::detail::iterator_category_to_system::type
- >
- >::value,
- thrust::detail::identity_,
- thrust::detail::identity_ >
- >::type type;
-}; // end iterator_facade_category_impl
-
-
-template
- struct iterator_facade_category
-{
- typedef typename
- thrust::detail::eval_if<
- thrust::detail::is_iterator_category::value,
- thrust::detail::identity_, // categories are fine as-is
- iterator_facade_category_impl
- >::type type;
-}; // end iterator_facade_category
-
-
-} // end detail
-} // end thrust
-
diff --git a/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py b/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py
deleted file mode 100644
index a4f5ed44caefb1612df67785b1f4f0d9ec46ee93..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import torch.nn as nn
-from mmcv.cnn import ConvModule, xavier_init
-
-from ..builder import NECKS
-
-
-@NECKS.register_module()
-class ChannelMapper(nn.Module):
- r"""Channel Mapper to reduce/increase channels of backbone features.
-
- This is used to reduce/increase channels of backbone features.
-
- Args:
- in_channels (List[int]): Number of input channels per scale.
- out_channels (int): Number of output channels (used at each scale).
- kernel_size (int, optional): kernel_size for reducing channels (used
- at each scale). Default: 3.
- conv_cfg (dict, optional): Config dict for convolution layer.
- Default: None.
- norm_cfg (dict, optional): Config dict for normalization layer.
- Default: None.
- act_cfg (dict, optional): Config dict for activation layer in
- ConvModule. Default: dict(type='ReLU').
-
- Example:
- >>> import torch
- >>> in_channels = [2, 3, 5, 7]
- >>> scales = [340, 170, 84, 43]
- >>> inputs = [torch.rand(1, c, s, s)
- ... for c, s in zip(in_channels, scales)]
- >>> self = ChannelMapper(in_channels, 11, 3).eval()
- >>> outputs = self.forward(inputs)
- >>> for i in range(len(outputs)):
- ... print(f'outputs[{i}].shape = {outputs[i].shape}')
- outputs[0].shape = torch.Size([1, 11, 340, 340])
- outputs[1].shape = torch.Size([1, 11, 170, 170])
- outputs[2].shape = torch.Size([1, 11, 84, 84])
- outputs[3].shape = torch.Size([1, 11, 43, 43])
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size=3,
- conv_cfg=None,
- norm_cfg=None,
- act_cfg=dict(type='ReLU')):
- super(ChannelMapper, self).__init__()
- assert isinstance(in_channels, list)
-
- self.convs = nn.ModuleList()
- for in_channel in in_channels:
- self.convs.append(
- ConvModule(
- in_channel,
- out_channels,
- kernel_size,
- padding=(kernel_size - 1) // 2,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg))
-
- # default init_weights for conv(msra) and norm in ConvModule
- def init_weights(self):
- """Initialize the weights of ChannelMapper module."""
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- xavier_init(m, distribution='uniform')
-
- def forward(self, inputs):
- """Forward function."""
- assert len(inputs) == len(self.convs)
- outs = [self.convs[i](inputs[i]) for i in range(len(inputs))]
- return tuple(outs)
diff --git a/spaces/CVPR/regionclip-demo/datasets/README.md b/spaces/CVPR/regionclip-demo/datasets/README.md
deleted file mode 100644
index 0eb44cc3b23beeb1755ab8d12002d26f13434235..0000000000000000000000000000000000000000
--- a/spaces/CVPR/regionclip-demo/datasets/README.md
+++ /dev/null
@@ -1,140 +0,0 @@
-# Use Builtin Datasets
-
-A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog)
-for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc).
-This document explains how to setup the builtin datasets so they can be used by the above APIs.
-[Use Custom Datasets](https://detectron2.readthedocs.io/tutorials/datasets.html) gives a deeper dive on how to use `DatasetCatalog` and `MetadataCatalog`,
-and how to add new datasets to them.
-
-Detectron2 has builtin support for a few datasets.
-The datasets are assumed to exist in a directory specified by the environment variable
-`DETECTRON2_DATASETS`.
-Under this directory, detectron2 will look for datasets in the structure described below, if needed.
-```
-$DETECTRON2_DATASETS/
- coco/
- lvis/
- cityscapes/
- VOC20{07,12}/
-```
-
-You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`.
-If left unset, the default is `./datasets` relative to your current working directory.
-
-The [model zoo](https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md)
-contains configs and models that use these builtin datasets.
-
-## Expected dataset structure for [COCO instance/keypoint detection](https://cocodataset.org/#download):
-
-```
-coco/
- annotations/
- instances_{train,val}2017.json
- person_keypoints_{train,val}2017.json
- {train,val}2017/
- # image files that are mentioned in the corresponding json
-```
-
-You can use the 2014 version of the dataset as well.
-
-Some of the builtin tests (`dev/run_*_tests.sh`) uses a tiny version of the COCO dataset,
-which you can download with `./datasets/prepare_for_tests.sh`.
-
-## Expected dataset structure for PanopticFPN:
-
-Extract panoptic annotations from [COCO website](https://cocodataset.org/#download)
-into the following structure:
-```
-coco/
- annotations/
- panoptic_{train,val}2017.json
- panoptic_{train,val}2017/ # png annotations
- panoptic_stuff_{train,val}2017/ # generated by the script mentioned below
-```
-
-Install panopticapi by:
-```
-pip install git+https://github.com/cocodataset/panopticapi.git
-```
-Then, run `python datasets/prepare_panoptic_fpn.py`, to extract semantic annotations from panoptic annotations.
-
-## Expected dataset structure for [LVIS instance segmentation](https://www.lvisdataset.org/dataset):
-```
-coco/
- {train,val,test}2017/
-lvis/
- lvis_v0.5_{train,val}.json
- lvis_v0.5_image_info_test.json
- lvis_v1_{train,val}.json
- lvis_v1_image_info_test{,_challenge}.json
-```
-
-Install lvis-api by:
-```
-pip install git+https://github.com/lvis-dataset/lvis-api.git
-```
-
-To evaluate models trained on the COCO dataset using LVIS annotations,
-run `python datasets/prepare_cocofied_lvis.py` to prepare "cocofied" LVIS annotations.
-
-## Expected dataset structure for [cityscapes](https://www.cityscapes-dataset.com/downloads/):
-```
-cityscapes/
- gtFine/
- train/
- aachen/
- color.png, instanceIds.png, labelIds.png, polygons.json,
- labelTrainIds.png
- ...
- val/
- test/
- # below are generated Cityscapes panoptic annotation
- cityscapes_panoptic_train.json
- cityscapes_panoptic_train/
- cityscapes_panoptic_val.json
- cityscapes_panoptic_val/
- cityscapes_panoptic_test.json
- cityscapes_panoptic_test/
- leftImg8bit/
- train/
- val/
- test/
-```
-Install cityscapes scripts by:
-```
-pip install git+https://github.com/mcordts/cityscapesScripts.git
-```
-
-Note: to create labelTrainIds.png, first prepare the above structure, then run cityscapesescript with:
-```
-CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createTrainIdLabelImgs.py
-```
-These files are not needed for instance segmentation.
-
-Note: to generate Cityscapes panoptic dataset, run cityscapesescript with:
-```
-CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createPanopticImgs.py
-```
-These files are not needed for semantic and instance segmentation.
-
-## Expected dataset structure for [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/index.html):
-```
-VOC20{07,12}/
- Annotations/
- ImageSets/
- Main/
- trainval.txt
- test.txt
- # train.txt or val.txt, if you use these splits
- JPEGImages/
-```
-
-## Expected dataset structure for [ADE20k Scene Parsing](http://sceneparsing.csail.mit.edu/):
-```
-ADEChallengeData2016/
- annotations/
- annotations_detectron2/
- images/
- objectInfo150.txt
-```
-The directory `annotations_detectron2` is generated by running `python datasets/prepare_ade20k_sem_seg.py`.
diff --git a/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py b/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py
deleted file mode 100644
index ee1f6f1bcfa2fc3437fb6d5f579640482f1ad5f4..0000000000000000000000000000000000000000
--- a/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py
+++ /dev/null
@@ -1,397 +0,0 @@
-# -*- coding: utf-8 -*-
-import re
-from os.path import join, abspath, dirname
-from collections import defaultdict
-import epitran
-
-epi = epitran.Epitran("deu-Latn-nar")
-
-
-def mode_type(mode_in):
- """In the case of "sql", this will return an sqlite cursor."""
- if mode_in.lower() == "sql":
- import sqlite3
- conn = sqlite3.connect(join(abspath(dirname(__file__)), "./Resources/de.db"))
- return conn.cursor()
-
-
-#TESTS
-#NUMBERS ARE TOO HARD!
-
-
-
-def preprocess(words):
- """Returns a string of words stripped of punctuation"""
- punct_str = '!"#$%&\'()*+,-./:;<=>/?@[\\]^_`{|}~«» '
- return ' '.join([w.strip(punct_str).lower() for w in words.split()])
-
-
-def preserve_punc(words):
- """converts words to IPA and finds punctuation before and after the word."""
- words_preserved = []
- for w in words.split():
- punct_list = ["", preprocess(w), ""]
- before = re.search("^([^A-Za-z0-9]+)[A-Za-z]", w)
- after = re.search("[A-Za-z]([^A-Za-z0-9]+)$", w)
- if before:
- punct_list[0] = str(before.group(1))
- if after:
- punct_list[2] = str(after.group(1))
- words_preserved.append(punct_list)
- return words_preserved
-
-
-
-def apply_punct(triple, as_str=False):
- """places surrounding punctuation back on center on a list of preserve_punc triples"""
- if type(triple[0]) == list:
- for i, t in enumerate(triple):
- triple[i] = str(''.join(triple[i]))
- if as_str:
- return ' '.join(triple)
- return triple
- if as_str:
- return str(''.join(t for t in triple))
- return [''.join(t for t in triple)]
-
-
-def _punct_replace_word(original, transcription):
- """Get the IPA transcription of word with the original punctuation marks"""
- for i, trans_list in enumerate(transcription):
- for j, item in enumerate(trans_list):
- triple = [original[i][0]] + [item] + [original[i][2]]
- transcription[i][j] = apply_punct(triple, as_str=True)
- return transcription
-
-
-def fetch_words(words_in, db_type="sql"):
- """fetches a list of words from the database"""
- asset = mode_type(db_type)
- f_result = []
- if db_type.lower() == "sql":
- for word in words_in:
- asset.execute("SELECT Words, phonemes FROM De_words WHERE Words IN (?)", (word,))
- result = asset.fetchall()
- flag = True
- try:
- f_result.append(result.pop())
- flag = False
- except IndexError:
- pass
- if result == [] and flag is True:
- result = epi.transliterate(word)
- f_result.append((word, result))
- f_result = list(filter(None,f_result))
- f_set = set(f_result)
- d = defaultdict(list)
- for k, v in f_set:
- d[k].append(v)
- return list(d.items())
-
-def get_deu(tokens_in, db_type="sql"):
- """query the SQL database for the words and return the phonemes in the order of user_in"""
- result = fetch_words(tokens_in, db_type)
- ordered = []
- for word in tokens_in:
- this_word = [[i[1] for i in result if i[0] == word]][0]
- if this_word:
- ordered.append(this_word[0])
- else:
- ordered.append(["__IGNORE__" + word])
- return ordered
-
-
-def deu_to_ipa(deu_list, mark=True):
- """converts the deu word lists into IPA transcriptions"""
- symbols = {}
- ipa_list = [] # the final list of IPA tokens to be returned
- for word_list in deu_list:
- ipa_word_list = [] # the word list for each word
- for word in word_list:
- if re.sub("\d*", "", word.replace("__IGNORE__", "")) == "":
- pass # do not delete token if it's all numbers
- else:
- word = re.sub("[0-9]", "", word)
- ipa_form = ''
- if word.startswith("__IGNORE__"):
- ipa_form = word.replace("__IGNORE__", "")
- # mark words we couldn't transliterate with an asterisk:
-
- if mark:
- if not re.sub("\d*", "", ipa_form) == "":
- ipa_form += "*"
- else:
- for piece in word.split(" "):
- marked = False
- unmarked = piece
- if piece[0] in ["ˈ", "ˌ"] or piece[0] is None:
- marked = True
- mark = piece
- unmarked = piece[1:]
-
- if unmarked in symbols:
- if marked:
- ipa_form += mark + symbols[unmarked]
- else:
- ipa_form += symbols[unmarked]
-
- else:
- ipa_form += piece
- swap_list = [["ˈər", "əˈr"], ["ˈie", "iˈe"]]
- for sym in swap_list:
- if not ipa_form.startswith(sym[0]):
- ipa_form = ipa_form.replace(sym[0], sym[1])
- ipa_word_list.append(ipa_form)
- ipa_list.append(sorted(list(set(ipa_word_list))))
- return ipa_list
-
-
-def get_top(ipa_list):
- """Returns only the one result for a query. If multiple entries for words are found, only the first is used."""
- return ' '.join([word_list[-1] for word_list in ipa_list])
-
-
-def get_all(ipa_list):
- """utilizes an algorithm to discover and return all possible combinations of IPA transcriptions"""
- final_size = 1
- for word_list in ipa_list:
- final_size *= len(word_list)
- list_all = ["" for s in range(final_size)]
- for i in range(len(ipa_list)):
- if i == 0:
- swtich_rate = final_size / len(ipa_list[i])
- else:
- swtich_rate /= len(ipa_list[i])
- k = 0
- for j in range(final_size):
- if (j+1) % int(swtich_rate) == 0:
- k += 1
- if k == len(ipa_list[i]):
- k = 0
- list_all[j] = list_all[j] + ipa_list[i][k] + " "
- return sorted([sent[:-1] for sent in list_all])
-
-
-def ipa_list(words_in, keep_punct=True, db_type="sql"):
- """Returns a list of all the discovered IPA transcriptions for each word."""
- if type(words_in) == str:
- words = [preserve_punc(w.lower())[0] for w in words_in.split()]
- else:
- words = [preserve_punc(w.lower())[0] for w in words_in]
- deu = get_deu([w[1] for w in words], db_type=db_type)
- ipa = deu_to_ipa(deu)
- if keep_punct:
- ipa = _punct_replace_word(words, ipa)
- return ipa
-
-
-def isin_deu(word, db_type="sql"):
- """checks if a word is in the deu dictionary. Doesn't strip punctuation.
- If given more than one word, returns True only if all words are present."""
- if type(word) == str:
- word = [preprocess(w) for w in word.split()]
- results = fetch_words(word, db_type)
- as_set = list(set(t[0] for t in results))
- return len(as_set) == len(set(word))
-
-def replace_number(text):
- text = text.replace("1","eins ")
- text = text.replace("2","zwei ")
- text = text.replace("3","drei ")
- text = text.replace("4","vier ")
- text = text.replace("5","fünf ")
- text = text.replace("6","sechs ")
- text = text.replace("7","sieben ")
- text = text.replace("8","acht ")
- text = text.replace("9","neun ")
- text = text.replace("0","null ")
- return text
-
-
-
-def convert(text, retrieve_all=False, keep_punct=True, mode="sql"):
- """takes either a string or list of German words and converts them to IPA"""
- text = replace_number(text)
- ipa = ipa_list(
- words_in=text,
- keep_punct=keep_punct,
- db_type=mode)
- if retrieve_all:
- return get_all(ipa)
- return get_top(ipa)
-
-
-
-_decimal_number_re = re.compile(r'\d+\,\d+')
-_euros_pre = re.compile(r'€([0-9\,]*[0-9]+)')
-_euros_re = re.compile(r'([0-9\,]*[0-9]+)€')
-_ordinal_re = re.compile(r'(der |die |das )([0-9]+)\.')
-_clock_re=re.compile(r'\d{1,2}\:\d{2}')
-_number_re = re.compile(r'[0-9]+')
-
-def base(text):
- text = text.replace("1", "eins ")
- text = text.replace("2", "zwei ")
- text = text.replace("3", "drei ")
- text = text.replace("4", "vier ")
- text = text.replace("5", "fünf ")
- text = text.replace("6", "sechs ")
- text = text.replace("7", "sieben ")
- text = text.replace("8", "acht ")
- text = text.replace("9", "neun ")
- text = text.replace("0", "null ")
- return text
-
-def tens_to_word(num):
- tens = num[0]
- ones = num[1]
- ones_word = base(ones)
-
- if num =="10":
- return "zehn"
- elif num=="11":
- return "elf"
- elif num=="12":
- return "zwölf"
-
- if tens == "1":
- if ones == "6":
- ones_word = ones_word[:-1]
- elif ones == "7":
- ones_word = ones_word[:-2]
- return ones_word + "zehn"
- else:
- tens_word = base(tens)
- if ones == "1":
- ones_word = ones_word[:-1]
- if tens == "2":
- tens_word = "zwan"
- elif tens == "6":
- tens_word = tens_word[:-1]
- elif tens == "7":
- tens_word = tens_word[:-2]
- if tens == "3":
- tens_word += "ßig"
- else:
- tens_word += "zig"
- if ones == "0":
- return tens_word
- else:
- return ones_word + " und " + tens_word
-
-def huns_to_word(num):
- huns = num[0]
- tens = num[1]
-
- if huns == "1":
- huns_word= "hundert"
- else:
- huns_word = base(huns)+" hundert"
-
- remain = num_to_word(num[1:])
- if remain != "":
- remain = " " + remain
- return huns_word + remain
-
-def thos_to_word(num):
- thos = num[0]
- if thos == "1":
- thos_word= "tausend"
- else:
- thos_word = base(thos)+" tausend"
- remain=num_to_word(num[1:])
- if remain!="":
- remain=" "+remain
- return thos_word+remain
-
-def num_to_word(num):
- num=num.lstrip("0")
- if num=="":
- return("")
- digit=len(num)
- if digit==1:
- return base(num)
- elif digit==2:
- return tens_to_word(num)
- elif digit == 3:
- return huns_to_word(num)
- elif digit == 4:
- return thos_to_word(num)
- else:
- return base(num)
-
-def number_to_words(m):
- m=m.group(0).lstrip("0")
- if m=="":
- return"null"
- return num_to_word(m)
-
-def _expand_ordinal(m):
-
- pre=m.group(1)
- m = m.group(2).lstrip("0")
-
- if m=="":
- return"NULL"
- num=int(m)
- if num<=19 & num>=1:
- if num ==1:
- return "erste"
- elif num==3:
- return "dritte"
- elif num==7:
- return "siebte"
- elif num==8:
- return "achte"
- else:
- return pre + num_to_word(m) + "te"
- else:
- return pre + num_to_word(m) + "ste"
-
-def _expand_decimal(m):
- match=m.group(0)
- parts = match.split(',')
- if int(parts[0])==0:
- return '%s komma %s' % ("null", base(parts[1]))
- return '%s komma %s' % (num_to_word(parts[0]),base(parts[1]))
-
-def _expand_euros(m):
- match = m.group(1)
- parts = match.split(',')
- if len(parts) > 2:
- return match + ' euro' # Unexpected format
- euros = int(parts[0]) if parts[0] else 0
- cents = int(parts[1])*10 if len(parts) > 1 and parts[1] else 0
- if euros and cents:
- return '%s euro %s' % (euros, cents)
- elif euros:
- return '%s euro' % (euros)
- elif cents:
- return '%s cent' % (cents)
- else:
- return 'null euro'
-
-def _expand_clock(m):
- match = m.group(0)
- parts = match.split(':')
- if int(parts[0]) == 0:
- return '%s Uhr %s' % ("null",num_to_word(parts[1]))
- elif int(parts[0]) == 1:
- return '%s Uhr %s' % ("ein", num_to_word(parts[1]))
- return '%s Uhr %s' % (num_to_word(parts[0]),num_to_word(parts[1]))
-
-def normalize_numbers(text):
- text = re.sub(_euros_pre, _expand_euros, text)
- text = re.sub(_euros_re, _expand_euros, text)
- text = re.sub(_clock_re, _expand_clock, text)
- text = re.sub(_decimal_number_re, _expand_decimal, text)
- text = re.sub(_ordinal_re, _expand_ordinal, text)
- text = re.sub(_number_re, number_to_words, text)
- text=text.replace(" "," ")
- return text
-
-def collapse_whitespace(text):
- return re.sub(r'\s+', ' ', text)
-
-def mark_dark_l(text):
- return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
diff --git a/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py b/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py
deleted file mode 100644
index 1519b3095bc5250016066754ebe6c0328773f24a..0000000000000000000000000000000000000000
--- a/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from utils import randomize_seed_fn
-
-
-def create_demo(process, max_images=12, default_num_images=3):
- with gr.Blocks() as demo:
- with gr.Row():
- with gr.Column():
- image = gr.Image()
- prompt = gr.Textbox(label='Prompt')
- run_button = gr.Button('Run')
- with gr.Accordion('Advanced options', open=False):
- preprocessor_name = gr.Radio(label='Preprocessor',
- choices=['NormalBae', 'None'],
- type='value',
- value='NormalBae')
- num_samples = gr.Slider(label='Images',
- minimum=1,
- maximum=max_images,
- value=default_num_images,
- step=1)
- image_resolution = gr.Slider(label='Image resolution',
- minimum=256,
- maximum=512,
- value=512,
- step=256)
- preprocess_resolution = gr.Slider(
- label='Preprocess resolution',
- minimum=128,
- maximum=512,
- value=384,
- step=1)
- num_steps = gr.Slider(label='Number of steps',
- minimum=1,
- maximum=100,
- value=20,
- step=1)
- guidance_scale = gr.Slider(label='Guidance scale',
- minimum=0.1,
- maximum=30.0,
- value=9.0,
- step=0.1)
- seed = gr.Slider(label='Seed',
- minimum=0,
- maximum=1000000,
- step=1,
- value=0,
- randomize=True)
- randomize_seed = gr.Checkbox(label='Randomize seed',
- value=True)
- a_prompt = gr.Textbox(
- label='Additional prompt',
- value='best quality, extremely detailed')
- n_prompt = gr.Textbox(
- label='Negative prompt',
- value=
- 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
- )
- with gr.Column():
- result = gr.Gallery(label='Output', show_label=False).style(
- columns=2, object_fit='scale-down')
- inputs = [
- image,
- prompt,
- a_prompt,
- n_prompt,
- num_samples,
- image_resolution,
- preprocess_resolution,
- num_steps,
- guidance_scale,
- seed,
- preprocessor_name,
- ]
- prompt.submit(
- fn=randomize_seed_fn,
- inputs=[seed, randomize_seed],
- outputs=seed,
- ).then(
- fn=process,
- inputs=inputs,
- outputs=result,
- )
- run_button.click(
- fn=randomize_seed_fn,
- inputs=[seed, randomize_seed],
- outputs=seed,
- ).then(
- fn=process,
- inputs=inputs,
- outputs=result,
- api_name='normal',
- )
- return demo
-
-
-if __name__ == '__main__':
- from model import Model
- model = Model(task_name='NormalBae')
- demo = create_demo(model.process_normal)
- demo.queue().launch()
diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py
deleted file mode 100644
index 0a44b37fc5563deea8406c6ddf109860a56e9485..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from PIL.Image import Image as IMG
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.utils import save_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def knock(images: List[BuildImage], texts, args):
- img = images[0].convert("RGBA").square()
- # fmt: off
- locs = [(60, 308, 210, 195), (60, 308, 210, 198), (45, 330, 250, 172), (58, 320, 218, 180),
- (60, 310, 215, 193), (40, 320, 250, 285), (48, 308, 226, 192), (51, 301, 223, 200)]
- # fmt: on
- frames: List[IMG] = []
- for i in range(8):
- frame = BuildImage.open(img_dir / f"{i}.png")
- x, y, w, h = locs[i]
- frame.paste(img.resize((w, h)), (x, y), below=True)
- frames.append(frame.image)
- return save_gif(frames, 0.04)
-
-
-add_meme("knock", knock, min_images=1, max_images=1, keywords=["敲"])
diff --git a/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py b/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py
deleted file mode 100644
index 37f310fadd0b48b2f364877158fb2105d645fc03..0000000000000000000000000000000000000000
--- a/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import locale
-import json
-import os
-
-
-def load_language_list(language):
- with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f:
- language_list = json.load(f)
- return language_list
-
-
-class I18nAuto:
- def __init__(self, language=None):
- if language in ["Auto", None]:
- language = locale.getdefaultlocale()[
- 0
- ] # getlocale can't identify the system's language ((None, None))
- if not os.path.exists(f"./i18n/{language}.json"):
- language = "en_US"
- self.language = language
- # print("Use Language:", language)
- self.language_map = load_language_list(language)
-
- def __call__(self, key):
- return self.language_map.get(key, key)
-
- def print(self):
- print("Use Language:", self.language)
diff --git a/spaces/Codecooker/rvcapi/src/mdx.py b/spaces/Codecooker/rvcapi/src/mdx.py
deleted file mode 100644
index 575c456fbdf9e5b5955401f41a3a58ddc27267b3..0000000000000000000000000000000000000000
--- a/spaces/Codecooker/rvcapi/src/mdx.py
+++ /dev/null
@@ -1,287 +0,0 @@
-import gc
-import hashlib
-import os
-import queue
-import threading
-import warnings
-
-import librosa
-import numpy as np
-import onnxruntime as ort
-import soundfile as sf
-import torch
-from tqdm import tqdm
-
-warnings.filterwarnings("ignore")
-stem_naming = {'Vocals': 'Instrumental', 'Other': 'Instruments', 'Instrumental': 'Vocals', 'Drums': 'Drumless', 'Bass': 'Bassless'}
-
-
-class MDXModel:
- def __init__(self, device, dim_f, dim_t, n_fft, hop=1024, stem_name=None, compensation=1.000):
- self.dim_f = dim_f
- self.dim_t = dim_t
- self.dim_c = 4
- self.n_fft = n_fft
- self.hop = hop
- self.stem_name = stem_name
- self.compensation = compensation
-
- self.n_bins = self.n_fft // 2 + 1
- self.chunk_size = hop * (self.dim_t - 1)
- self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(device)
-
- out_c = self.dim_c
-
- self.freq_pad = torch.zeros([1, out_c, self.n_bins - self.dim_f, self.dim_t]).to(device)
-
- def stft(self, x):
- x = x.reshape([-1, self.chunk_size])
- x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True, return_complex=True)
- x = torch.view_as_real(x)
- x = x.permute([0, 3, 1, 2])
- x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 4, self.n_bins, self.dim_t])
- return x[:, :, :self.dim_f]
-
- def istft(self, x, freq_pad=None):
- freq_pad = self.freq_pad.repeat([x.shape[0], 1, 1, 1]) if freq_pad is None else freq_pad
- x = torch.cat([x, freq_pad], -2)
- # c = 4*2 if self.target_name=='*' else 2
- x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 2, self.n_bins, self.dim_t])
- x = x.permute([0, 2, 3, 1])
- x = x.contiguous()
- x = torch.view_as_complex(x)
- x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True)
- return x.reshape([-1, 2, self.chunk_size])
-
-
-class MDX:
- DEFAULT_SR = 44100
- # Unit: seconds
- DEFAULT_CHUNK_SIZE = 0 * DEFAULT_SR
- DEFAULT_MARGIN_SIZE = 1 * DEFAULT_SR
-
- DEFAULT_PROCESSOR = 0
-
- def __init__(self, model_path: str, params: MDXModel, processor=DEFAULT_PROCESSOR):
-
- # Set the device and the provider (CPU or CUDA)
- self.device = torch.device(f'cuda:{processor}') if processor >= 0 else torch.device('cpu')
- self.provider = ['CUDAExecutionProvider'] if processor >= 0 else ['CPUExecutionProvider']
-
- self.model = params
-
- # Load the ONNX model using ONNX Runtime
- self.ort = ort.InferenceSession(model_path, providers=self.provider)
- # Preload the model for faster performance
- self.ort.run(None, {'input': torch.rand(1, 4, params.dim_f, params.dim_t).numpy()})
- self.process = lambda spec: self.ort.run(None, {'input': spec.cpu().numpy()})[0]
-
- self.prog = None
-
- @staticmethod
- def get_hash(model_path):
- try:
- with open(model_path, 'rb') as f:
- f.seek(- 10000 * 1024, 2)
- model_hash = hashlib.md5(f.read()).hexdigest()
- except:
- model_hash = hashlib.md5(open(model_path, 'rb').read()).hexdigest()
-
- return model_hash
-
- @staticmethod
- def segment(wave, combine=True, chunk_size=DEFAULT_CHUNK_SIZE, margin_size=DEFAULT_MARGIN_SIZE):
- """
- Segment or join segmented wave array
-
- Args:
- wave: (np.array) Wave array to be segmented or joined
- combine: (bool) If True, combines segmented wave array. If False, segments wave array.
- chunk_size: (int) Size of each segment (in samples)
- margin_size: (int) Size of margin between segments (in samples)
-
- Returns:
- numpy array: Segmented or joined wave array
- """
-
- if combine:
- processed_wave = None # Initializing as None instead of [] for later numpy array concatenation
- for segment_count, segment in enumerate(wave):
- start = 0 if segment_count == 0 else margin_size
- end = None if segment_count == len(wave) - 1 else -margin_size
- if margin_size == 0:
- end = None
- if processed_wave is None: # Create array for first segment
- processed_wave = segment[:, start:end]
- else: # Concatenate to existing array for subsequent segments
- processed_wave = np.concatenate((processed_wave, segment[:, start:end]), axis=-1)
-
- else:
- processed_wave = []
- sample_count = wave.shape[-1]
-
- if chunk_size <= 0 or chunk_size > sample_count:
- chunk_size = sample_count
-
- if margin_size > chunk_size:
- margin_size = chunk_size
-
- for segment_count, skip in enumerate(range(0, sample_count, chunk_size)):
-
- margin = 0 if segment_count == 0 else margin_size
- end = min(skip + chunk_size + margin_size, sample_count)
- start = skip - margin
-
- cut = wave[:, start:end].copy()
- processed_wave.append(cut)
-
- if end == sample_count:
- break
-
- return processed_wave
-
- def pad_wave(self, wave):
- """
- Pad the wave array to match the required chunk size
-
- Args:
- wave: (np.array) Wave array to be padded
-
- Returns:
- tuple: (padded_wave, pad, trim)
- - padded_wave: Padded wave array
- - pad: Number of samples that were padded
- - trim: Number of samples that were trimmed
- """
- n_sample = wave.shape[1]
- trim = self.model.n_fft // 2
- gen_size = self.model.chunk_size - 2 * trim
- pad = gen_size - n_sample % gen_size
-
- # Padded wave
- wave_p = np.concatenate((np.zeros((2, trim)), wave, np.zeros((2, pad)), np.zeros((2, trim))), 1)
-
- mix_waves = []
- for i in range(0, n_sample + pad, gen_size):
- waves = np.array(wave_p[:, i:i + self.model.chunk_size])
- mix_waves.append(waves)
-
- mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(self.device)
-
- return mix_waves, pad, trim
-
- def _process_wave(self, mix_waves, trim, pad, q: queue.Queue, _id: int):
- """
- Process each wave segment in a multi-threaded environment
-
- Args:
- mix_waves: (torch.Tensor) Wave segments to be processed
- trim: (int) Number of samples trimmed during padding
- pad: (int) Number of samples padded during padding
- q: (queue.Queue) Queue to hold the processed wave segments
- _id: (int) Identifier of the processed wave segment
-
- Returns:
- numpy array: Processed wave segment
- """
- mix_waves = mix_waves.split(1)
- with torch.no_grad():
- pw = []
- for mix_wave in mix_waves:
- self.prog.update()
- spec = self.model.stft(mix_wave)
- processed_spec = torch.tensor(self.process(spec))
- processed_wav = self.model.istft(processed_spec.to(self.device))
- processed_wav = processed_wav[:, :, trim:-trim].transpose(0, 1).reshape(2, -1).cpu().numpy()
- pw.append(processed_wav)
- processed_signal = np.concatenate(pw, axis=-1)[:, :-pad]
- q.put({_id: processed_signal})
- return processed_signal
-
- def process_wave(self, wave: np.array, mt_threads=1):
- """
- Process the wave array in a multi-threaded environment
-
- Args:
- wave: (np.array) Wave array to be processed
- mt_threads: (int) Number of threads to be used for processing
-
- Returns:
- numpy array: Processed wave array
- """
- self.prog = tqdm(total=0)
- chunk = wave.shape[-1] // mt_threads
- waves = self.segment(wave, False, chunk)
-
- # Create a queue to hold the processed wave segments
- q = queue.Queue()
- threads = []
- for c, batch in enumerate(waves):
- mix_waves, pad, trim = self.pad_wave(batch)
- self.prog.total = len(mix_waves) * mt_threads
- thread = threading.Thread(target=self._process_wave, args=(mix_waves, trim, pad, q, c))
- thread.start()
- threads.append(thread)
- for thread in threads:
- thread.join()
- self.prog.close()
-
- processed_batches = []
- while not q.empty():
- processed_batches.append(q.get())
- processed_batches = [list(wave.values())[0] for wave in
- sorted(processed_batches, key=lambda d: list(d.keys())[0])]
- assert len(processed_batches) == len(waves), 'Incomplete processed batches, please reduce batch size!'
- return self.segment(processed_batches, True, chunk)
-
-
-def run_mdx(model_params, output_dir, model_path, filename, exclude_main=False, exclude_inversion=False, suffix=None, invert_suffix=None, denoise=False, keep_orig=True, m_threads=2):
- device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
-
- device_properties = torch.cuda.get_device_properties(device)
- vram_gb = device_properties.total_memory / 1024**3
- m_threads = 1 if vram_gb < 8 else 2
-
- model_hash = MDX.get_hash(model_path)
- mp = model_params.get(model_hash)
- model = MDXModel(
- device,
- dim_f=mp["mdx_dim_f_set"],
- dim_t=2 ** mp["mdx_dim_t_set"],
- n_fft=mp["mdx_n_fft_scale_set"],
- stem_name=mp["primary_stem"],
- compensation=mp["compensate"]
- )
-
- mdx_sess = MDX(model_path, model)
- wave, sr = librosa.load(filename, mono=False, sr=44100)
- # normalizing input wave gives better output
- peak = max(np.max(wave), abs(np.min(wave)))
- wave /= peak
- if denoise:
- wave_processed = -(mdx_sess.process_wave(-wave, m_threads)) + (mdx_sess.process_wave(wave, m_threads))
- wave_processed *= 0.5
- else:
- wave_processed = mdx_sess.process_wave(wave, m_threads)
- # return to previous peak
- wave_processed *= peak
- stem_name = model.stem_name if suffix is None else suffix
-
- main_filepath = None
- if not exclude_main:
- main_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav")
- sf.write(main_filepath, wave_processed.T, sr)
-
- invert_filepath = None
- if not exclude_inversion:
- diff_stem_name = stem_naming.get(stem_name) if invert_suffix is None else invert_suffix
- stem_name = f"{stem_name}_diff" if diff_stem_name is None else diff_stem_name
- invert_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav")
- sf.write(invert_filepath, (-wave_processed.T * model.compensation) + wave.T, sr)
-
- if not keep_orig:
- os.remove(filename)
-
- del mdx_sess, wave_processed, wave
- gc.collect()
- return main_filepath, invert_filepath
diff --git a/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py b/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py
deleted file mode 100644
index 76dce7e41c803a8055f3627cccb98deb51419b09..0000000000000000000000000000000000000000
--- a/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import argparse
-import glob
-import os
-
-
-def main(args):
- txt_file = open(args.meta_info, 'w')
- # sca images
- img_paths_gt = sorted(glob.glob(os.path.join(args.input[0], '*')))
- img_paths_lq = sorted(glob.glob(os.path.join(args.input[1], '*')))
-
- assert len(img_paths_gt) == len(img_paths_lq), ('GT folder and LQ folder should have the same length, but got '
- f'{len(img_paths_gt)} and {len(img_paths_lq)}.')
-
- for img_path_gt, img_path_lq in zip(img_paths_gt, img_paths_lq):
- # get the relative paths
- img_name_gt = os.path.relpath(img_path_gt, args.root[0])
- img_name_lq = os.path.relpath(img_path_lq, args.root[1])
- print(f'{img_name_gt}, {img_name_lq}')
- txt_file.write(f'{img_name_gt}, {img_name_lq}\n')
-
-
-if __name__ == '__main__':
- """This script is used to generate meta info (txt file) for paired images.
- """
- parser = argparse.ArgumentParser()
- parser.add_argument(
- '--input',
- nargs='+',
- default=['datasets/DF2K/DIV2K_train_HR_sub', 'datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub'],
- help='Input folder, should be [gt_folder, lq_folder]')
- parser.add_argument('--root', nargs='+', default=[None, None], help='Folder root, will use the ')
- parser.add_argument(
- '--meta_info',
- type=str,
- default='datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt',
- help='txt path for meta info')
- args = parser.parse_args()
-
- assert len(args.input) == 2, 'Input folder should have two elements: gt folder and lq folder'
- assert len(args.root) == 2, 'Root path should have two elements: root for gt folder and lq folder'
- os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)
- for i in range(2):
- if args.input[i].endswith('/'):
- args.input[i] = args.input[i][:-1]
- if args.root[i] is None:
- args.root[i] = os.path.dirname(args.input[i])
-
- main(args)
diff --git a/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py b/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py
deleted file mode 100644
index eb453a809d17e6dee04e158d1c68dc807478edef..0000000000000000000000000000000000000000
--- a/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Onodofthenorth/SD_PixelArt_SpriteSheet_Generator").launch()
\ No newline at end of file
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py
deleted file mode 100644
index bafc43a19d432290867a5c08b9820f2e4f79aea3..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py
+++ /dev/null
@@ -1,97 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# GD file handling
-#
-# History:
-# 1996-04-12 fl Created
-#
-# Copyright (c) 1997 by Secret Labs AB.
-# Copyright (c) 1996 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-"""
-.. note::
- This format cannot be automatically recognized, so the
- class is not registered for use with :py:func:`PIL.Image.open()`. To open a
- gd file, use the :py:func:`PIL.GdImageFile.open()` function instead.
-
-.. warning::
- THE GD FORMAT IS NOT DESIGNED FOR DATA INTERCHANGE. This
- implementation is provided for convenience and demonstrational
- purposes only.
-"""
-
-
-from . import ImageFile, ImagePalette, UnidentifiedImageError
-from ._binary import i16be as i16
-from ._binary import i32be as i32
-
-
-class GdImageFile(ImageFile.ImageFile):
- """
- Image plugin for the GD uncompressed format. Note that this format
- is not supported by the standard :py:func:`PIL.Image.open()` function. To use
- this plugin, you have to import the :py:mod:`PIL.GdImageFile` module and
- use the :py:func:`PIL.GdImageFile.open()` function.
- """
-
- format = "GD"
- format_description = "GD uncompressed images"
-
- def _open(self):
- # Header
- s = self.fp.read(1037)
-
- if i16(s) not in [65534, 65535]:
- msg = "Not a valid GD 2.x .gd file"
- raise SyntaxError(msg)
-
- self.mode = "L" # FIXME: "P"
- self._size = i16(s, 2), i16(s, 4)
-
- true_color = s[6]
- true_color_offset = 2 if true_color else 0
-
- # transparency index
- tindex = i32(s, 7 + true_color_offset)
- if tindex < 256:
- self.info["transparency"] = tindex
-
- self.palette = ImagePalette.raw(
- "XBGR", s[7 + true_color_offset + 4 : 7 + true_color_offset + 4 + 256 * 4]
- )
-
- self.tile = [
- (
- "raw",
- (0, 0) + self.size,
- 7 + true_color_offset + 4 + 256 * 4,
- ("L", 0, 1),
- )
- ]
-
-
-def open(fp, mode="r"):
- """
- Load texture from a GD image file.
-
- :param fp: GD file name, or an opened file handle.
- :param mode: Optional mode. In this version, if the mode argument
- is given, it must be "r".
- :returns: An image instance.
- :raises OSError: If the image could not be read.
- """
- if mode != "r":
- msg = "bad mode"
- raise ValueError(msg)
-
- try:
- return GdImageFile(fp)
- except SyntaxError as e:
- msg = "cannot identify this image file"
- raise UnidentifiedImageError(msg) from e
diff --git a/spaces/Dana19/ImageRecognition_FaceCount/README.md b/spaces/Dana19/ImageRecognition_FaceCount/README.md
deleted file mode 100644
index 8afdea6aa817a1ec3b056820a882b63deedc6772..0000000000000000000000000000000000000000
--- a/spaces/Dana19/ImageRecognition_FaceCount/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Headcount
-emoji: 💻
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.3
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/DemoLou/moe-tts/text/thai.py b/spaces/DemoLou/moe-tts/text/thai.py
deleted file mode 100644
index 998207c01a85c710a46db1ec8b62c39c2d94bc84..0000000000000000000000000000000000000000
--- a/spaces/DemoLou/moe-tts/text/thai.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import re
-from num_thai.thainumbers import NumThai
-
-
-num = NumThai()
-
-# List of (Latin alphabet, Thai) pairs:
-_latin_to_thai = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', 'เอ'),
- ('b','บี'),
- ('c','ซี'),
- ('d','ดี'),
- ('e','อี'),
- ('f','เอฟ'),
- ('g','จี'),
- ('h','เอช'),
- ('i','ไอ'),
- ('j','เจ'),
- ('k','เค'),
- ('l','แอล'),
- ('m','เอ็ม'),
- ('n','เอ็น'),
- ('o','โอ'),
- ('p','พี'),
- ('q','คิว'),
- ('r','แอร์'),
- ('s','เอส'),
- ('t','ที'),
- ('u','ยู'),
- ('v','วี'),
- ('w','ดับเบิลยู'),
- ('x','เอ็กซ์'),
- ('y','วาย'),
- ('z','ซี')
-]]
-
-
-def num_to_thai(text):
- return re.sub(r'(?:\d+(?:,?\d+)?)+(?:\.\d+(?:,?\d+)?)?', lambda x: ''.join(num.NumberToTextThai(float(x.group(0).replace(',', '')))), text)
-
-def latin_to_thai(text):
- for regex, replacement in _latin_to_thai:
- text = re.sub(regex, replacement, text)
- return text
diff --git a/spaces/Detomo/AI-Galary/README.md b/spaces/Detomo/AI-Galary/README.md
deleted file mode 100644
index 5772a1eb261aa38b6407c07b0633d4a12566eccf..0000000000000000000000000000000000000000
--- a/spaces/Detomo/AI-Galary/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AI Galary
-emoji: ⚡
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Detomo/generate_wifi_qrcode/app.py b/spaces/Detomo/generate_wifi_qrcode/app.py
deleted file mode 100644
index e6681ad55d07851290ef2f0f04f1e026a95b917a..0000000000000000000000000000000000000000
--- a/spaces/Detomo/generate_wifi_qrcode/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import wifi_qrcode_generator
-import gradio as gr
-
-examples = [['Home wifi', 'very complicated password', 'WPA']]
-
-def infer(wifi_name, wifi_pw, authentication_type):
- image = wifi_qrcode_generator.wifi_qrcode(
- wifi_name, False, authentication_type, wifi_pw
- )
- image.save('qrcode_wifi.png')
- return 'qrcode_wifi.png'
-
-description= "Generate code for accessing WIFI. Fill your wifi name, wifi password and select authentication type. Then click submit and enjoy 😎"
-
-iface = gr.Interface(
- fn=infer,
- title="Generate QR code for accessing WIFI 🥃",
- description=description,
- inputs=[
- gr.Textbox(
- label="Wifi name",
- lines=1,
- ),
- gr.Textbox(
- label="Wifi password",
- lines=1,
- ),
- gr.Dropdown(
- label='authentication_type',
- choices=["WPA", "WEP", "nopass"],
- value='WPA'
- ),
- ],
- outputs="image",
- examples=examples,
- article="Author: Vu Minh Chien", cache_examples=True).launch(enable_queue=True)
diff --git a/spaces/DiffusionArtco/scifi-art-creator/app.py b/spaces/DiffusionArtco/scifi-art-creator/app.py
deleted file mode 100644
index 236417b745e1681f1a9e1ae644d75254da2de370..0000000000000000000000000000000000000000
--- a/spaces/DiffusionArtco/scifi-art-creator/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import gradio as gr
-import requests
-from PIL import Image
-from io import BytesIO
-import base64
-
-api_url = "https://5cb20b40-572c-426f-9466-995256f9b6eb.id.repl.co/generate_image"
-
-def generate_image(model="Sci-Fi Diffusion", prompt="", seed=0, negative_prompt="", sampler="k_dpmpp_2s_a", steps=50):
- data = "?model=" + model + "&prompt=" + prompt + "&seed=" + str(seed) + "&negative_prompt=" + negative_prompt + "&sampler=" + sampler + "&steps=" + str(steps)
- response = requests.post(api_url + data, timeout=400)
- if response.status_code == 200:
- img_base64 = response.json()["url"]
- img_bytes = base64.b64decode(img_base64)
- img = Image.open(BytesIO(img_bytes))
- return img
- else:
- return None
-
-inputs = [
- gr.inputs.Dropdown([ 'Experience', 'FKing SciFi', 'Future Diffusion', 'JWST Deep Space Diffusion', 'Protogen Infinity', 'RCNZ Dumb Monkey', 'RealBiter', 'Robo-Diffusion', 'Sci-Fi Diffusion'], label="Model", default="Sci-Fi Diffusion"),
- gr.inputs.Textbox(label="Prompt"),
- gr.inputs.Number(label="Seed", default=0),
- gr.inputs.Textbox(label="Negative Prompt", default=""),
- gr.inputs.Dropdown(["k_lms", "k_heun", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "DDIM", "k_dpm_fast", "k_dpm_adaptive", "k_dpmpp_2m", "k_dpmpp_2s_a", "k_dpmpp_sde"], label="Sampler", default="k_dpmpp_2s_a"),
- gr.inputs.Number(label="Steps", default=50)
-]
-
-outputs = gr.outputs.Image(label="Generated Image", type="pil")
-
-interface = gr.Interface(generate_image, inputs, outputs, title="",
- description="
-#include
-#include
-#include "filtered_lrelu.h"
-
-//------------------------------------------------------------------------
-
-static std::tuple filtered_lrelu(
- torch::Tensor x, torch::Tensor fu, torch::Tensor fd, torch::Tensor b, torch::Tensor si,
- int up, int down, int px0, int px1, int py0, int py1, int sx, int sy, float gain, float slope, float clamp, bool flip_filters, bool writeSigns)
-{
- // Set CUDA device.
- TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
- const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-
- // Validate arguments.
- TORCH_CHECK(fu.device() == x.device() && fd.device() == x.device() && b.device() == x.device(), "all input tensors must reside on the same device");
- TORCH_CHECK(fu.dtype() == torch::kFloat && fd.dtype() == torch::kFloat, "fu and fd must be float32");
- TORCH_CHECK(b.dtype() == x.dtype(), "x and b must have the same dtype");
- TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat, "x and b must be float16 or float32");
- TORCH_CHECK(x.dim() == 4, "x must be rank 4");
- TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
- TORCH_CHECK(x.numel() > 0, "x is empty");
- TORCH_CHECK((fu.dim() == 1 || fu.dim() == 2) && (fd.dim() == 1 || fd.dim() == 2), "fu and fd must be rank 1 or 2");
- TORCH_CHECK(fu.size(0) <= INT_MAX && fu.size(-1) <= INT_MAX, "fu is too large");
- TORCH_CHECK(fd.size(0) <= INT_MAX && fd.size(-1) <= INT_MAX, "fd is too large");
- TORCH_CHECK(fu.numel() > 0, "fu is empty");
- TORCH_CHECK(fd.numel() > 0, "fd is empty");
- TORCH_CHECK(b.dim() == 1 && b.size(0) == x.size(1), "b must be a vector with the same number of channels as x");
- TORCH_CHECK(up >= 1 && down >= 1, "up and down must be at least 1");
-
- // Figure out how much shared memory is available on the device.
- int maxSharedBytes = 0;
- AT_CUDA_CHECK(cudaDeviceGetAttribute(&maxSharedBytes, cudaDevAttrMaxSharedMemoryPerBlockOptin, x.device().index()));
- int sharedKB = maxSharedBytes >> 10;
-
- // Populate enough launch parameters to check if a CUDA kernel exists.
- filtered_lrelu_kernel_params p;
- p.up = up;
- p.down = down;
- p.fuShape = make_int2((int)fu.size(-1), fu.dim() == 2 ? (int)fu.size(0) : 0); // shape [n, 0] indicates separable filter.
- p.fdShape = make_int2((int)fd.size(-1), fd.dim() == 2 ? (int)fd.size(0) : 0);
- filtered_lrelu_kernel_spec test_spec = choose_filtered_lrelu_kernel(p, sharedKB);
- if (!test_spec.exec)
- {
- // No kernel found - return empty tensors and indicate missing kernel with return code of -1.
- return std::make_tuple(torch::Tensor(), torch::Tensor(), -1);
- }
-
- // Input/output element size.
- int64_t sz = (x.dtype() == torch::kHalf) ? 2 : 4;
-
- // Input sizes.
- int64_t xw = (int)x.size(3);
- int64_t xh = (int)x.size(2);
- int64_t fut_w = (int)fu.size(-1) - 1;
- int64_t fut_h = (int)fu.size(0) - 1;
- int64_t fdt_w = (int)fd.size(-1) - 1;
- int64_t fdt_h = (int)fd.size(0) - 1;
-
- // Logical size of upsampled buffer.
- int64_t cw = xw * up + (px0 + px1) - fut_w;
- int64_t ch = xh * up + (py0 + py1) - fut_h;
- TORCH_CHECK(cw > fdt_w && ch > fdt_h, "upsampled buffer must be at least the size of downsampling filter");
- TORCH_CHECK(cw <= INT_MAX && ch <= INT_MAX, "upsampled buffer is too large");
-
- // Compute output size and allocate.
- int64_t yw = (cw - fdt_w + (down - 1)) / down;
- int64_t yh = (ch - fdt_h + (down - 1)) / down;
- TORCH_CHECK(yw > 0 && yh > 0, "output must be at least 1x1");
- TORCH_CHECK(yw <= INT_MAX && yh <= INT_MAX, "output is too large");
- torch::Tensor y = torch::empty({x.size(0), x.size(1), yh, yw}, x.options(), x.suggest_memory_format());
-
- // Allocate sign tensor.
- torch::Tensor so;
- torch::Tensor s = si;
- bool readSigns = !!s.numel();
- int64_t sw_active = 0; // Active width of sign tensor.
- if (writeSigns)
- {
- sw_active = yw * down - (down - 1) + fdt_w; // Active width in elements.
- int64_t sh = yh * down - (down - 1) + fdt_h; // Height = active height.
- int64_t sw = (sw_active + 15) & ~15; // Width = active width in elements, rounded up to multiple of 16.
- TORCH_CHECK(sh <= INT_MAX && (sw >> 2) <= INT_MAX, "signs is too large");
- s = so = torch::empty({x.size(0), x.size(1), sh, sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
- }
- else if (readSigns)
- sw_active = s.size(3) << 2;
-
- // Validate sign tensor if in use.
- if (readSigns || writeSigns)
- {
- TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
- TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
- TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
- TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
- TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
- TORCH_CHECK(s.size(2) <= INT_MAX && s.size(3) <= INT_MAX, "signs is too large");
- }
-
- // Populate rest of CUDA kernel parameters.
- p.x = x.data_ptr();
- p.y = y.data_ptr();
- p.b = b.data_ptr();
- p.s = (readSigns || writeSigns) ? s.data_ptr() : 0;
- p.fu = fu.data_ptr();
- p.fd = fd.data_ptr();
- p.pad0 = make_int2(px0, py0);
- p.gain = gain;
- p.slope = slope;
- p.clamp = clamp;
- p.flip = (flip_filters) ? 1 : 0;
- p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
- p.yShape = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
- p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3), (int)s.size(2)) : make_int2(0, 0); // Width is in bytes. Contiguous.
- p.sOfs = make_int2(sx, sy);
- p.swLimit = (sw_active + 3) >> 2; // Rounded up to bytes.
-
- // x, y, b strides are in bytes.
- p.xStride = make_longlong4(sz * x.stride(3), sz * x.stride(2), sz * x.stride(1), sz * x.stride(0));
- p.yStride = make_longlong4(sz * y.stride(3), sz * y.stride(2), sz * y.stride(1), sz * y.stride(0));
- p.bStride = sz * b.stride(0);
-
- // fu, fd strides are in elements.
- p.fuStride = make_longlong3(fu.stride(-1), fu.dim() == 2 ? fu.stride(0) : 0, 0);
- p.fdStride = make_longlong3(fd.stride(-1), fd.dim() == 2 ? fd.stride(0) : 0, 0);
-
- // Determine if indices don't fit in int32. Support negative strides although Torch currently never produces those.
- bool index64b = false;
- if (std::abs(p.bStride * x.size(1)) > INT_MAX) index64b = true;
- if (std::min(x.size(0) * p.xStride.w, 0ll) + std::min(x.size(1) * p.xStride.z, 0ll) + std::min(x.size(2) * p.xStride.y, 0ll) + std::min(x.size(3) * p.xStride.x, 0ll) < -INT_MAX) index64b = true;
- if (std::max(x.size(0) * p.xStride.w, 0ll) + std::max(x.size(1) * p.xStride.z, 0ll) + std::max(x.size(2) * p.xStride.y, 0ll) + std::max(x.size(3) * p.xStride.x, 0ll) > INT_MAX) index64b = true;
- if (std::min(y.size(0) * p.yStride.w, 0ll) + std::min(y.size(1) * p.yStride.z, 0ll) + std::min(y.size(2) * p.yStride.y, 0ll) + std::min(y.size(3) * p.yStride.x, 0ll) < -INT_MAX) index64b = true;
- if (std::max(y.size(0) * p.yStride.w, 0ll) + std::max(y.size(1) * p.yStride.z, 0ll) + std::max(y.size(2) * p.yStride.y, 0ll) + std::max(y.size(3) * p.yStride.x, 0ll) > INT_MAX) index64b = true;
- if (s.numel() > INT_MAX) index64b = true;
-
- // Choose CUDA kernel.
- filtered_lrelu_kernel_spec spec = { 0 };
- AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_cuda", [&]
- {
- if constexpr (sizeof(scalar_t) <= 4) // Exclude doubles. constexpr prevents template instantiation.
- {
- // Choose kernel based on index type, datatype and sign read/write modes.
- if (!index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if (!index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if (!index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- }
- });
- TORCH_CHECK(spec.exec, "internal error - CUDA kernel not found") // This should not happen because we tested earlier that kernel exists.
-
- // Launch CUDA kernel.
- void* args[] = {&p};
- int bx = spec.numWarps * 32;
- int gx = (p.yShape.x - 1) / spec.tileOut.x + 1;
- int gy = (p.yShape.y - 1) / spec.tileOut.y + 1;
- int gz = p.yShape.z * p.yShape.w;
-
- // Repeat multiple horizontal tiles in a CTA?
- if (spec.xrep)
- {
- p.tilesXrep = spec.xrep;
- p.tilesXdim = gx;
-
- gx = (gx + p.tilesXrep - 1) / p.tilesXrep;
- std::swap(gx, gy);
- }
- else
- {
- p.tilesXrep = 0;
- p.tilesXdim = 0;
- }
-
- // Launch filter setup kernel.
- AT_CUDA_CHECK(cudaLaunchKernel(spec.setup, 1, 1024, args, 0, at::cuda::getCurrentCUDAStream()));
-
- // Copy kernels to constant memory.
- if ( writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
- else if (!writeSigns && readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
- else if (!writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
-
- // Set cache and shared memory configurations for main kernel.
- AT_CUDA_CHECK(cudaFuncSetCacheConfig(spec.exec, cudaFuncCachePreferShared));
- if (spec.dynamicSharedKB) // Need dynamically allocated shared memory?
- AT_CUDA_CHECK(cudaFuncSetAttribute(spec.exec, cudaFuncAttributeMaxDynamicSharedMemorySize, spec.dynamicSharedKB << 10));
- AT_CUDA_CHECK(cudaFuncSetSharedMemConfig(spec.exec, cudaSharedMemBankSizeFourByte));
-
- // Launch main kernel.
- const int maxSubGz = 65535; // CUDA maximum for block z dimension.
- for (int zofs=0; zofs < gz; zofs += maxSubGz) // Do multiple launches if gz is too big.
- {
- p.blockZofs = zofs;
- int subGz = std::min(maxSubGz, gz - zofs);
- AT_CUDA_CHECK(cudaLaunchKernel(spec.exec, dim3(gx, gy, subGz), bx, args, spec.dynamicSharedKB << 10, at::cuda::getCurrentCUDAStream()));
- }
-
- // Done.
- return std::make_tuple(y, so, 0);
-}
-
-//------------------------------------------------------------------------
-
-static torch::Tensor filtered_lrelu_act(torch::Tensor x, torch::Tensor si, int sx, int sy, float gain, float slope, float clamp, bool writeSigns)
-{
- // Set CUDA device.
- TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
- const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-
- // Validate arguments.
- TORCH_CHECK(x.dim() == 4, "x must be rank 4");
- TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
- TORCH_CHECK(x.numel() > 0, "x is empty");
- TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat || x.dtype() == torch::kDouble, "x must be float16, float32 or float64");
-
- // Output signs if we don't have sign input.
- torch::Tensor so;
- torch::Tensor s = si;
- bool readSigns = !!s.numel();
- if (writeSigns)
- {
- int64_t sw = x.size(3);
- sw = (sw + 15) & ~15; // Round to a multiple of 16 for coalescing.
- s = so = torch::empty({x.size(0), x.size(1), x.size(2), sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
- }
-
- // Validate sign tensor if in use.
- if (readSigns || writeSigns)
- {
- TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
- TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
- TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
- TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
- TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
- TORCH_CHECK(s.size(2) <= INT_MAX && (s.size(3) << 2) <= INT_MAX, "signs tensor is too large");
- }
-
- // Initialize CUDA kernel parameters.
- filtered_lrelu_act_kernel_params p;
- p.x = x.data_ptr();
- p.s = (readSigns || writeSigns) ? s.data_ptr() : 0;
- p.gain = gain;
- p.slope = slope;
- p.clamp = clamp;
- p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
- p.xStride = make_longlong4(x.stride(3), x.stride(2), x.stride(1), x.stride(0));
- p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3) << 2, (int)s.size(2)) : make_int2(0, 0); // Width is in elements. Contiguous.
- p.sOfs = make_int2(sx, sy);
-
- // Choose CUDA kernel.
- void* func = 0;
- AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_act_cuda", [&]
- {
- if (writeSigns)
- func = choose_filtered_lrelu_act_kernel();
- else if (readSigns)
- func = choose_filtered_lrelu_act_kernel();
- else
- func = choose_filtered_lrelu_act_kernel();
- });
- TORCH_CHECK(func, "internal error - CUDA kernel not found");
-
- // Launch CUDA kernel.
- void* args[] = {&p};
- int bx = 128; // 4 warps per block.
-
- // Logical size of launch = writeSigns ? p.s : p.x
- uint32_t gx = writeSigns ? p.sShape.x : p.xShape.x;
- uint32_t gy = writeSigns ? p.sShape.y : p.xShape.y;
- uint32_t gz = p.xShape.z * p.xShape.w; // Same as in p.sShape if signs are in use.
- gx = (gx - 1) / bx + 1;
-
- // Make sure grid y and z dimensions are within CUDA launch limits. Kernel loops internally to do the rest.
- const uint32_t gmax = 65535;
- gy = std::min(gy, gmax);
- gz = std::min(gz, gmax);
-
- // Launch.
- AT_CUDA_CHECK(cudaLaunchKernel(func, dim3(gx, gy, gz), bx, args, 0, at::cuda::getCurrentCUDAStream()));
- return so;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
- m.def("filtered_lrelu", &filtered_lrelu); // The whole thing.
- m.def("filtered_lrelu_act_", &filtered_lrelu_act); // Activation and sign tensor handling only. Modifies data tensor in-place.
-}
-
-//------------------------------------------------------------------------
diff --git a/spaces/EronSamez/RVC_HFmeu/gui_v0.py b/spaces/EronSamez/RVC_HFmeu/gui_v0.py
deleted file mode 100644
index 88c3cf9eb1eaa0fa812b32ae4d3750b4ce0a8699..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/gui_v0.py
+++ /dev/null
@@ -1,786 +0,0 @@
-import os, sys, traceback, re
-
-import json
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from configs.config import Config
-
-Config = Config()
-import PySimpleGUI as sg
-import sounddevice as sd
-import noisereduce as nr
-import numpy as np
-from fairseq import checkpoint_utils
-import librosa, torch, pyworld, faiss, time, threading
-import torch.nn.functional as F
-import torchaudio.transforms as tat
-import scipy.signal as signal
-import torchcrepe
-
-# import matplotlib.pyplot as plt
-from lib.infer_pack.models import (
- SynthesizerTrnMs256NSFsid,
- SynthesizerTrnMs256NSFsid_nono,
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono,
-)
-from i18n import I18nAuto
-
-i18n = I18nAuto()
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-current_dir = os.getcwd()
-
-
-class RVC:
- def __init__(
- self, key, f0_method, hubert_path, pth_path, index_path, npy_path, index_rate
- ) -> None:
- """
- 初始化
- """
- try:
- self.f0_up_key = key
- self.time_step = 160 / 16000 * 1000
- self.f0_min = 50
- self.f0_max = 1100
- self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
- self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
- self.f0_method = f0_method
- self.sr = 16000
- self.window = 160
-
- # Get Torch Device
- if torch.cuda.is_available():
- self.torch_device = torch.device(
- f"cuda:{0 % torch.cuda.device_count()}"
- )
- elif torch.backends.mps.is_available():
- self.torch_device = torch.device("mps")
- else:
- self.torch_device = torch.device("cpu")
-
- if index_rate != 0:
- self.index = faiss.read_index(index_path)
- # self.big_npy = np.load(npy_path)
- self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
- print("index search enabled")
- self.index_rate = index_rate
- model_path = hubert_path
- print("load model(s) from {}".format(model_path))
- models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
- [model_path],
- suffix="",
- )
- self.model = models[0]
- self.model = self.model.to(device)
- if Config.is_half:
- self.model = self.model.half()
- else:
- self.model = self.model.float()
- self.model.eval()
- cpt = torch.load(pth_path, map_location="cpu")
- self.tgt_sr = cpt["config"][-1]
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- self.if_f0 = cpt.get("f0", 1)
- self.version = cpt.get("version", "v1")
- if self.version == "v1":
- if self.if_f0 == 1:
- self.net_g = SynthesizerTrnMs256NSFsid(
- *cpt["config"], is_half=Config.is_half
- )
- else:
- self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
- elif self.version == "v2":
- if self.if_f0 == 1:
- self.net_g = SynthesizerTrnMs768NSFsid(
- *cpt["config"], is_half=Config.is_half
- )
- else:
- self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
- del self.net_g.enc_q
- print(self.net_g.load_state_dict(cpt["weight"], strict=False))
- self.net_g.eval().to(device)
- if Config.is_half:
- self.net_g = self.net_g.half()
- else:
- self.net_g = self.net_g.float()
- except:
- print(traceback.format_exc())
-
- def get_regular_crepe_computation(self, x, f0_min, f0_max, model="full"):
- batch_size = 512
- # Compute pitch using first gpu
- audio = torch.tensor(np.copy(x))[None].float()
- f0, pd = torchcrepe.predict(
- audio,
- self.sr,
- self.window,
- f0_min,
- f0_max,
- model,
- batch_size=batch_size,
- device=self.torch_device,
- return_periodicity=True,
- )
- pd = torchcrepe.filter.median(pd, 3)
- f0 = torchcrepe.filter.mean(f0, 3)
- f0[pd < 0.1] = 0
- f0 = f0[0].cpu().numpy()
- return f0
-
- def get_harvest_computation(self, x, f0_min, f0_max):
- f0, t = pyworld.harvest(
- x.astype(np.double),
- fs=self.sr,
- f0_ceil=f0_max,
- f0_floor=f0_min,
- frame_period=10,
- )
- f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
- f0 = signal.medfilt(f0, 3)
- return f0
-
- def get_f0(self, x, f0_up_key, inp_f0=None):
- # Calculate Padding and f0 details here
- p_len = x.shape[0] // 512 # For Now This probs doesn't work
- x_pad = 1
- f0_min = 50
- f0_max = 1100
- f0_mel_min = 1127 * np.log(1 + f0_min / 700)
- f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
- f0 = 0
- # Here, check f0_methods and get their computations
- if self.f0_method == "harvest":
- f0 = self.get_harvest_computation(x, f0_min, f0_max)
- elif self.f0_method == "reg-crepe":
- f0 = self.get_regular_crepe_computation(x, f0_min, f0_max)
- elif self.f0_method == "reg-crepe-tiny":
- f0 = self.get_regular_crepe_computation(x, f0_min, f0_max, "tiny")
-
- # Calculate f0_course and f0_bak here
- f0 *= pow(2, f0_up_key / 12)
- # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- tf0 = self.sr // self.window # 每秒f0点数
- if inp_f0 is not None:
- delta_t = np.round(
- (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
- ).astype("int16")
- replace_f0 = np.interp(
- list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
- )
- shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
- f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
- # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- f0bak = f0.copy()
- f0_mel = 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
- f0_mel_max - f0_mel_min
- ) + 1
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > 255] = 255
- f0_coarse = np.rint(f0_mel).astype(np.int)
- return f0_coarse, f0bak # 1-0
-
- def infer(self, feats: torch.Tensor) -> np.ndarray:
- """
- 推理函数
- """
- audio = feats.clone().cpu().numpy()
- assert feats.dim() == 1, feats.dim()
- feats = feats.view(1, -1)
- padding_mask = torch.BoolTensor(feats.shape).fill_(False)
- if Config.is_half:
- feats = feats.half()
- else:
- feats = feats.float()
- inputs = {
- "source": feats.to(device),
- "padding_mask": padding_mask.to(device),
- "output_layer": 9 if self.version == "v1" else 12,
- }
- torch.cuda.synchronize()
- with torch.no_grad():
- logits = self.model.extract_features(**inputs)
- feats = (
- self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
- )
-
- ####索引优化
- try:
- if (
- hasattr(self, "index")
- and hasattr(self, "big_npy")
- and self.index_rate != 0
- ):
- npy = feats[0].cpu().numpy().astype("float32")
- score, ix = self.index.search(npy, k=8)
- weight = np.square(1 / score)
- weight /= weight.sum(axis=1, keepdims=True)
- npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
- if Config.is_half:
- npy = npy.astype("float16")
- feats = (
- torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
- + (1 - self.index_rate) * feats
- )
- else:
- print("index search FAIL or disabled")
- except:
- traceback.print_exc()
- print("index search FAIL")
- feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
- torch.cuda.synchronize()
- print(feats.shape)
- if self.if_f0 == 1:
- pitch, pitchf = self.get_f0(audio, self.f0_up_key)
- p_len = min(feats.shape[1], 13000, pitch.shape[0]) # 太大了爆显存
- else:
- pitch, pitchf = None, None
- p_len = min(feats.shape[1], 13000) # 太大了爆显存
- torch.cuda.synchronize()
- # print(feats.shape,pitch.shape)
- feats = feats[:, :p_len, :]
- if self.if_f0 == 1:
- pitch = pitch[:p_len]
- pitchf = pitchf[:p_len]
- pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
- pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
- p_len = torch.LongTensor([p_len]).to(device)
- ii = 0 # sid
- sid = torch.LongTensor([ii]).to(device)
- with torch.no_grad():
- if self.if_f0 == 1:
- infered_audio = (
- self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
- .data.cpu()
- .float()
- )
- else:
- infered_audio = (
- self.net_g.infer(feats, p_len, sid)[0][0, 0].data.cpu().float()
- )
- torch.cuda.synchronize()
- return infered_audio
-
-
-class GUIConfig:
- def __init__(self) -> None:
- self.hubert_path: str = ""
- self.pth_path: str = ""
- self.index_path: str = ""
- self.npy_path: str = ""
- self.f0_method: str = ""
- self.pitch: int = 12
- self.samplerate: int = 44100
- self.block_time: float = 1.0 # s
- self.buffer_num: int = 1
- self.threhold: int = -30
- self.crossfade_time: float = 0.08
- self.extra_time: float = 0.04
- self.I_noise_reduce = False
- self.O_noise_reduce = False
- self.index_rate = 0.3
-
-
-class GUI:
- def __init__(self) -> None:
- self.config = GUIConfig()
- self.flag_vc = False
-
- self.launcher()
-
- def load(self):
- (
- input_devices,
- output_devices,
- input_devices_indices,
- output_devices_indices,
- ) = self.get_devices()
- try:
- with open("values1.json", "r") as j:
- data = json.load(j)
- except:
- # Injecting f0_method into the json data
- with open("values1.json", "w") as j:
- data = {
- "pth_path": "",
- "index_path": "",
- "sg_input_device": input_devices[
- input_devices_indices.index(sd.default.device[0])
- ],
- "sg_output_device": output_devices[
- output_devices_indices.index(sd.default.device[1])
- ],
- "threhold": "-45",
- "pitch": "0",
- "index_rate": "0",
- "block_time": "1",
- "crossfade_length": "0.04",
- "extra_time": "1",
- }
- return data
-
- def launcher(self):
- data = self.load()
- sg.theme("DarkTeal12")
- input_devices, output_devices, _, _ = self.get_devices()
- layout = [
- [
- sg.Frame(
- title="Proudly forked by Mangio621",
- ),
- sg.Frame(
- title=i18n("Load model"),
- layout=[
- [
- sg.Input(
- default_text="hubert_base.pt",
- key="hubert_path",
- disabled=True,
- ),
- sg.FileBrowse(
- i18n("Hubert Model"),
- initial_folder=os.path.join(os.getcwd()),
- file_types=(("pt files", "*.pt"),),
- ),
- ],
- [
- sg.Input(
- default_text=data.get("pth_path", ""),
- key="pth_path",
- ),
- sg.FileBrowse(
- i18n("Select the .pth file"),
- initial_folder=os.path.join(os.getcwd(), "weights"),
- file_types=(("weight files", "*.pth"),),
- ),
- ],
- [
- sg.Input(
- default_text=data.get("index_path", ""),
- key="index_path",
- ),
- sg.FileBrowse(
- i18n("Select the .index file"),
- initial_folder=os.path.join(os.getcwd(), "logs"),
- file_types=(("index files", "*.index"),),
- ),
- ],
- [
- sg.Input(
- default_text="你不需要填写这个You don't need write this.",
- key="npy_path",
- disabled=True,
- ),
- sg.FileBrowse(
- i18n("Select the .npy file"),
- initial_folder=os.path.join(os.getcwd(), "logs"),
- file_types=(("feature files", "*.npy"),),
- ),
- ],
- ],
- ),
- ],
- [
- # Mangio f0 Selection frame Here
- sg.Frame(
- layout=[
- [
- sg.Radio(
- "Harvest", "f0_method", key="harvest", default=True
- ),
- sg.Radio("Crepe", "f0_method", key="reg-crepe"),
- sg.Radio("Crepe Tiny", "f0_method", key="reg-crepe-tiny"),
- ]
- ],
- title="Select an f0 Method",
- )
- ],
- [
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Input device")),
- sg.Combo(
- input_devices,
- key="sg_input_device",
- default_value=data.get("sg_input_device", ""),
- ),
- ],
- [
- sg.Text(i18n("Output device")),
- sg.Combo(
- output_devices,
- key="sg_output_device",
- default_value=data.get("sg_output_device", ""),
- ),
- ],
- ],
- title=i18n("Audio device (please use the same type of driver)"),
- )
- ],
- [
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Response threshold")),
- sg.Slider(
- range=(-60, 0),
- key="threhold",
- resolution=1,
- orientation="h",
- default_value=data.get("threhold", ""),
- ),
- ],
- [
- sg.Text(i18n("Pitch settings")),
- sg.Slider(
- range=(-24, 24),
- key="pitch",
- resolution=1,
- orientation="h",
- default_value=data.get("pitch", ""),
- ),
- ],
- [
- sg.Text(i18n("Index Rate")),
- sg.Slider(
- range=(0.0, 1.0),
- key="index_rate",
- resolution=0.01,
- orientation="h",
- default_value=data.get("index_rate", ""),
- ),
- ],
- ],
- title=i18n("General settings"),
- ),
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Sample length")),
- sg.Slider(
- range=(0.1, 3.0),
- key="block_time",
- resolution=0.1,
- orientation="h",
- default_value=data.get("block_time", ""),
- ),
- ],
- [
- sg.Text(i18n("Fade length")),
- sg.Slider(
- range=(0.01, 0.15),
- key="crossfade_length",
- resolution=0.01,
- orientation="h",
- default_value=data.get("crossfade_length", ""),
- ),
- ],
- [
- sg.Text(i18n("Extra推理时长")),
- sg.Slider(
- range=(0.05, 3.00),
- key="extra_time",
- resolution=0.01,
- orientation="h",
- default_value=data.get("extra_time", ""),
- ),
- ],
- [
- sg.Checkbox(i18n("Input noise reduction"), key="I_noise_reduce"),
- sg.Checkbox(i18n("Output noise reduction"), key="O_noise_reduce"),
- ],
- ],
- title=i18n("Performance settings"),
- ),
- ],
- [
- sg.Button(i18n("开始音频Convert"), key="start_vc"),
- sg.Button(i18n("停止音频Convert"), key="stop_vc"),
- sg.Text(i18n("Inference time (ms):")),
- sg.Text("0", key="infer_time"),
- ],
- ]
- self.window = sg.Window("RVC - GUI", layout=layout)
- self.event_handler()
-
- def event_handler(self):
- while True:
- event, values = self.window.read()
- if event == sg.WINDOW_CLOSED:
- self.flag_vc = False
- exit()
- if event == "start_vc" and self.flag_vc == False:
- if self.set_values(values) == True:
- print("using_cuda:" + str(torch.cuda.is_available()))
- self.start_vc()
- settings = {
- "pth_path": values["pth_path"],
- "index_path": values["index_path"],
- "f0_method": self.get_f0_method_from_radios(values),
- "sg_input_device": values["sg_input_device"],
- "sg_output_device": values["sg_output_device"],
- "threhold": values["threhold"],
- "pitch": values["pitch"],
- "index_rate": values["index_rate"],
- "block_time": values["block_time"],
- "crossfade_length": values["crossfade_length"],
- "extra_time": values["extra_time"],
- }
- with open("values1.json", "w") as j:
- json.dump(settings, j)
- if event == "stop_vc" and self.flag_vc == True:
- self.flag_vc = False
-
- # Function that returns the used f0 method in string format "harvest"
- def get_f0_method_from_radios(self, values):
- f0_array = [
- {"name": "harvest", "val": values["harvest"]},
- {"name": "reg-crepe", "val": values["reg-crepe"]},
- {"name": "reg-crepe-tiny", "val": values["reg-crepe-tiny"]},
- ]
- # Filter through to find a true value
- used_f0 = ""
- for f0 in f0_array:
- if f0["val"] == True:
- used_f0 = f0["name"]
- break
- if used_f0 == "":
- used_f0 = "harvest" # Default Harvest if used_f0 is empty somehow
- return used_f0
-
- def set_values(self, values):
- if len(values["pth_path"].strip()) == 0:
- sg.popup(i18n("Select the pth file"))
- return False
- if len(values["index_path"].strip()) == 0:
- sg.popup(i18n("Select the index file"))
- return False
- pattern = re.compile("[^\x00-\x7F]+")
- if pattern.findall(values["hubert_path"]):
- sg.popup(i18n("The hubert model path must not contain Chinese characters"))
- return False
- if pattern.findall(values["pth_path"]):
- sg.popup(i18n("The pth file path must not contain Chinese characters."))
- return False
- if pattern.findall(values["index_path"]):
- sg.popup(i18n("The index file path must not contain Chinese characters."))
- return False
- self.set_devices(values["sg_input_device"], values["sg_output_device"])
- self.config.hubert_path = os.path.join(current_dir, "hubert_base.pt")
- self.config.pth_path = values["pth_path"]
- self.config.index_path = values["index_path"]
- self.config.npy_path = values["npy_path"]
- self.config.f0_method = self.get_f0_method_from_radios(values)
- self.config.threhold = values["threhold"]
- self.config.pitch = values["pitch"]
- self.config.block_time = values["block_time"]
- self.config.crossfade_time = values["crossfade_length"]
- self.config.extra_time = values["extra_time"]
- self.config.I_noise_reduce = values["I_noise_reduce"]
- self.config.O_noise_reduce = values["O_noise_reduce"]
- self.config.index_rate = values["index_rate"]
- return True
-
- def start_vc(self):
- torch.cuda.empty_cache()
- self.flag_vc = True
- self.block_frame = int(self.config.block_time * self.config.samplerate)
- self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate)
- self.sola_search_frame = int(0.012 * self.config.samplerate)
- self.delay_frame = int(0.01 * self.config.samplerate) # 往前预留0.02s
- self.extra_frame = int(self.config.extra_time * self.config.samplerate)
- self.rvc = None
- self.rvc = RVC(
- self.config.pitch,
- self.config.f0_method,
- self.config.hubert_path,
- self.config.pth_path,
- self.config.index_path,
- self.config.npy_path,
- self.config.index_rate,
- )
- self.input_wav: np.ndarray = np.zeros(
- self.extra_frame
- + self.crossfade_frame
- + self.sola_search_frame
- + self.block_frame,
- dtype="float32",
- )
- self.output_wav: torch.Tensor = torch.zeros(
- self.block_frame, device=device, dtype=torch.float32
- )
- self.sola_buffer: torch.Tensor = torch.zeros(
- self.crossfade_frame, device=device, dtype=torch.float32
- )
- self.fade_in_window: torch.Tensor = torch.linspace(
- 0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32
- )
- self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
- self.resampler1 = tat.Resample(
- orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
- )
- self.resampler2 = tat.Resample(
- orig_freq=self.rvc.tgt_sr,
- new_freq=self.config.samplerate,
- dtype=torch.float32,
- )
- thread_vc = threading.Thread(target=self.soundinput)
- thread_vc.start()
-
- def soundinput(self):
- """
- 接受音频输入
- """
- with sd.Stream(
- channels=2,
- callback=self.audio_callback,
- blocksize=self.block_frame,
- samplerate=self.config.samplerate,
- dtype="float32",
- ):
- while self.flag_vc:
- time.sleep(self.config.block_time)
- print("Audio block passed.")
- print("ENDing VC")
-
- def audio_callback(
- self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
- ):
- """
- 音频处理
- """
- start_time = time.perf_counter()
- indata = librosa.to_mono(indata.T)
- if self.config.I_noise_reduce:
- indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate)
-
- """noise gate"""
- frame_length = 2048
- hop_length = 1024
- rms = librosa.feature.rms(
- y=indata, frame_length=frame_length, hop_length=hop_length
- )
- db_threhold = librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold
- # print(rms.shape,db.shape,db)
- for i in range(db_threhold.shape[0]):
- if db_threhold[i]:
- indata[i * hop_length : (i + 1) * hop_length] = 0
- self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata)
-
- # infer
- print("input_wav:" + str(self.input_wav.shape))
- # print('infered_wav:'+str(infer_wav.shape))
- infer_wav: torch.Tensor = self.resampler2(
- self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav)))
- )[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to(
- device
- )
- print("infer_wav:" + str(infer_wav.shape))
-
- # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
- cor_nom = F.conv1d(
- infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
- self.sola_buffer[None, None, :],
- )
- cor_den = torch.sqrt(
- F.conv1d(
- infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
- ** 2,
- torch.ones(1, 1, self.crossfade_frame, device=device),
- )
- + 1e-8
- )
- sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
- print("sola offset: " + str(int(sola_offset)))
-
- # crossfade
- self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
- self.output_wav[: self.crossfade_frame] *= self.fade_in_window
- self.output_wav[: self.crossfade_frame] += self.sola_buffer[:]
- if sola_offset < self.sola_search_frame:
- self.sola_buffer[:] = (
- infer_wav[
- -self.sola_search_frame
- - self.crossfade_frame
- + sola_offset : -self.sola_search_frame
- + sola_offset
- ]
- * self.fade_out_window
- )
- else:
- self.sola_buffer[:] = (
- infer_wav[-self.crossfade_frame :] * self.fade_out_window
- )
-
- if self.config.O_noise_reduce:
- outdata[:] = np.tile(
- nr.reduce_noise(
- y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
- ),
- (2, 1),
- ).T
- else:
- outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
- total_time = time.perf_counter() - start_time
- self.window["infer_time"].update(int(total_time * 1000))
- print("infer time:" + str(total_time))
- print("f0_method: " + str(self.config.f0_method))
-
- def get_devices(self, update: bool = True):
- """获取设备列表"""
- if update:
- sd._terminate()
- sd._initialize()
- devices = sd.query_devices()
- hostapis = sd.query_hostapis()
- for hostapi in hostapis:
- for device_idx in hostapi["devices"]:
- devices[device_idx]["hostapi_name"] = hostapi["name"]
- input_devices = [
- f"{d['name']} ({d['hostapi_name']})"
- for d in devices
- if d["max_input_channels"] > 0
- ]
- output_devices = [
- f"{d['name']} ({d['hostapi_name']})"
- for d in devices
- if d["max_output_channels"] > 0
- ]
- input_devices_indices = [
- d["index"] if "index" in d else d["name"]
- for d in devices
- if d["max_input_channels"] > 0
- ]
- output_devices_indices = [
- d["index"] if "index" in d else d["name"]
- for d in devices
- if d["max_output_channels"] > 0
- ]
- return (
- input_devices,
- output_devices,
- input_devices_indices,
- output_devices_indices,
- )
-
- def set_devices(self, input_device, output_device):
- """设置输出设备"""
- (
- input_devices,
- output_devices,
- input_device_indices,
- output_device_indices,
- ) = self.get_devices()
- sd.default.device[0] = input_device_indices[input_devices.index(input_device)]
- sd.default.device[1] = output_device_indices[
- output_devices.index(output_device)
- ]
- print("input device:" + str(sd.default.device[0]) + ":" + str(input_device))
- print("output device:" + str(sd.default.device[1]) + ":" + str(output_device))
-
-
-gui = GUI()
diff --git a/spaces/EuroPython2022/clickbaitonator/fudge/main.py b/spaces/EuroPython2022/clickbaitonator/fudge/main.py
deleted file mode 100644
index e8c2299b2449b6dd07d26c7ae678732b1dabca88..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/clickbaitonator/fudge/main.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import os
-import random
-import time
-import pickle
-import math
-from argparse import ArgumentParser
-
-from tqdm import tqdm
-import numpy as np
-import torch
-import torch.nn as nn
-
-from data import Dataset
-from model import Model
-from util import save_checkpoint, ProgressMeter, AverageMeter, num_params, pad_mask
-from constants import *
-
-
-def train(model, dataset, optimizer, criterion, epoch, args, data_start_index):
- model.train()
- if data_start_index == 0:
- dataset.shuffle('train', seed=epoch + args.seed)
- if args.epoch_max_len is not None:
- data_end_index = min(data_start_index + args.epoch_max_len, len(dataset.splits['train']))
- loader = dataset.loader('train', num_workers=args.num_workers, indices=list(range(data_start_index, data_end_index)))
- data_start_index = data_end_index if data_end_index < len(dataset.splits['train']) else 0
- else:
- loader = dataset.loader('train', num_workers=args.num_workers)
- loss_meter = AverageMeter('loss', ':6.4f')
- total_length = len(loader)
- progress = ProgressMeter(total_length, [loss_meter], prefix='Training: ')
- for batch_num, batch in enumerate(tqdm(loader, total=len(loader))):
- batch = [tensor.to(args.device) for tensor in batch]
- inputs, lengths, future_words, log_probs, labels, classification_targets, syllables_to_go, future_word_num_syllables, rhyme_group_index = batch
- if args.task not in ['formality', 'iambic']:
- if not args.debug and len(inputs) != args.batch_size: # it'll screw up the bias...?
- continue
- scores = model(inputs, lengths, future_words, log_probs, syllables_to_go, future_word_num_syllables, rhyme_group_index, run_classifier=True)
- if args.task == 'formality': # we're learning for all positions at once. scores are batch x seq
- expanded_labels = classification_targets.unsqueeze(1).expand(-1, scores.shape[1]) # batch x seq
- length_mask = pad_mask(lengths).permute(1, 0) # batch x seq
- loss = criterion(scores.flatten()[length_mask.flatten()==1], expanded_labels.flatten().float()[length_mask.flatten()==1])
- elif args.task in ['iambic', 'newline']:
- use_indices = classification_targets.flatten() != -1
- loss = criterion(scores.flatten()[use_indices], classification_targets.flatten().float()[use_indices])
- else: # topic, rhyme
- loss = criterion(scores.flatten(), labels.flatten().float())
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
- loss_meter.update(loss.detach(), len(labels))
- if batch_num % args.train_print_freq == 0:
- progress.display(batch_num)
- progress.display(total_length)
- return data_start_index
-
-
-def validate(model, dataset, criterion, epoch, args):
- model.eval()
- random.seed(0)
- loader = dataset.loader('val', num_workers=args.num_workers)
- loss_meter = AverageMeter('loss', ':6.4f')
- total_length = len(loader)
- progress = ProgressMeter(total_length, [loss_meter], prefix='Validation: ')
- with torch.no_grad():
- for batch_num, batch in enumerate(tqdm(loader, total=len(loader))):
- batch = [tensor.to(args.device) for tensor in batch]
- inputs, lengths, future_words, log_probs, labels, classification_targets, syllables_to_go, future_word_num_syllables, rhyme_group_index = batch
- if args.task not in ['formality', 'iambic']: # topic predictor
- if not args.debug and len(inputs) != args.batch_size:
- continue
- scores = model(inputs, lengths, future_words, log_probs, syllables_to_go, future_word_num_syllables, rhyme_group_index, run_classifier=True)
- if args.task == 'formality': # we're learning for all positions at once. scores are batch x seq
- expanded_labels = classification_targets.unsqueeze(1).expand(-1, scores.shape[1]) # batch x seq
- length_mask = pad_mask(lengths).permute(1, 0) # batch x seq
- loss = criterion(scores.flatten()[length_mask.flatten()==1], expanded_labels.flatten().float()[length_mask.flatten()==1])
- elif args.task in ['iambic', 'newline']:
- use_indices = classification_targets.flatten() != -1
- loss = criterion(scores.flatten()[use_indices], classification_targets.flatten().float()[use_indices])
- else: # topic, rhyme
- loss = criterion(scores.flatten(), labels.flatten().float())
- loss_meter.update(loss.detach(), len(labels))
- if batch_num % args.train_print_freq == 0:
- progress.display(batch_num)
- progress.display(total_length)
- return loss_meter.avg
-
-
-def main(args):
- dataset = Dataset(args)
- os.makedirs(args.save_dir, exist_ok=True)
- with open(os.path.join(args.save_dir, 'dataset_info'), 'wb') as wf:
- pickle.dump(dataset.dataset_info, wf)
- if args.task == 'rhyme':
- with open(os.path.join(args.save_dir, 'rhyme_info'), 'wb') as wf:
- pickle.dump(dataset.rhyme_info, wf)
- if args.ckpt:
- checkpoint = torch.load(args.ckpt, map_location=args.device)
- start_epoch = checkpoint['epoch'] + 1
- best_val_metric = checkpoint['best_metric']
- model_args = checkpoint['args']
- model = Model(model_args, dataset.gpt_pad_id, len(dataset.index2word), rhyme_group_size=len(dataset.index2rhyme_group) if args.task == 'rhyme' else None) # no need to get the glove embeddings when reloading since they're saved in model ckpt anyway
- model.load_state_dict(checkpoint['state_dict'])
- model = model.to(args.device)
- optimizer = torch.optim.Adam(model.parameters(), lr=model_args.lr)
- optimizer.load_state_dict(checkpoint['optimizer'])
- data_start_index = checkpoint['data_start_index']
- print("=> loaded checkpoint '{}' (epoch {})"
- .format(args.ckpt, checkpoint['epoch']))
- # NOTE: just import pdb after loading the model here if you want to play with it, it's easy
- # model.eval()
- # import pdb; pdb.set_trace()
- else:
- model = Model(args, dataset.gpt_pad_id, len(dataset.index2word), rhyme_group_size=len(dataset.index2rhyme_group) if args.task == 'rhyme' else None, glove_embeddings=dataset.glove_embeddings)
- model = model.to(args.device)
- optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
- best_val_metric = 1e8 # lower is better for BCE
- data_start_index = 0
- print('num params', num_params(model))
- criterion = nn.BCEWithLogitsLoss().to(args.device)
-
- if args.evaluate:
- epoch = 0
- validate(model, dataset, criterion, epoch, args)
- return
- for epoch in range(args.epochs):
- print("TRAINING: Epoch {} at {}".format(epoch, time.ctime()))
- data_start_index = train(model, dataset, optimizer, criterion, epoch, args, data_start_index)
- if epoch % args.validation_freq == 0:
- print("VALIDATION: Epoch {} at {}".format(epoch, time.ctime()))
- metric = validate(model, dataset, criterion, epoch, args)
-
- if not args.debug:
- if metric < best_val_metric:
- print('new best val metric', metric)
- best_val_metric = metric
- save_checkpoint({
- 'epoch': epoch,
- 'state_dict': model.state_dict(),
- 'best_metric': best_val_metric,
- 'optimizer': optimizer.state_dict(),
- 'data_start_index': data_start_index,
- 'args': args
- }, os.path.join(args.save_dir, 'model_best.pth.tar'))
- save_checkpoint({
- 'epoch': epoch,
- 'state_dict': model.state_dict(),
- 'best_metric': metric,
- 'optimizer': optimizer.state_dict(),
- 'data_start_index': data_start_index,
- 'args': args
- }, os.path.join(args.save_dir, 'model_epoch' + str(epoch) + '.pth.tar'))
-
-
-if __name__=='__main__':
- parser = ArgumentParser()
-
- # DATA
- parser.add_argument('--task', type=str, required=True, choices=['iambic', 'rhyme', 'newline', 'topic', 'formality', 'clickbait'])
- parser.add_argument('--data_dir', type=str, required=True)
- parser.add_argument('--glove_file', type=str, help='glove embedding init, for topic task')
-
- # SAVE/LOAD
- parser.add_argument('--save_dir', type=str, required=True, help='where to save ckpts')
- parser.add_argument('--ckpt', type=str, default=None, help='load ckpt from file if given')
- parser.add_argument('--dataset_info', type=str, help='saved dataset info')
- parser.add_argument('--rhyme_info', type=str, help='saved dataset rhyme info, for a ckpt with task==rhyme')
-
- # TRAINING
- parser.add_argument('--batch_size', type=int, default=128)
- parser.add_argument('--epochs', type=int, default=100)
- parser.add_argument('--epoch_max_len', type=int, default=None, help='max batches per epoch if set, for more frequent validation')
- parser.add_argument('--validation_freq', type=int, default=1, help='validate every X epochs')
- parser.add_argument('--lr', type=float, default=1e-3, help='Adam learning rate')
- parser.add_argument('--seed', type=int, default=1, help='random seed')
- parser.add_argument('--device', type=str, default='cuda', choices=['cpu', 'cuda'])
- parser.add_argument('--num_workers', type=int, default=20, help='num workers for data loader')
- parser.add_argument('--evaluate', action='store_true', default=False)
- parser.add_argument('--debug', action='store_true', default=False)
-
- # PRINTING
- parser.add_argument('--train_print_freq', type=int, default=100, help='how often to print metrics (every X batches)')
-
- args = parser.parse_args()
-
- random.seed(args.seed)
- np.random.seed(args.seed)
- torch.manual_seed(args.seed)
- if args.evaluate:
- assert args.ckpt is not None
-
- main(args)
\ No newline at end of file
diff --git a/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts b/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts
deleted file mode 100644
index 9ba0fe30a092c73c8321a6bb2502750471b133f2..0000000000000000000000000000000000000000
--- a/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts
+++ /dev/null
@@ -1,279 +0,0 @@
-import {
- ChatInterface,
- ChatModule,
- ChatRestModule,
- ChatWorkerClient,
-} from "@mlc-ai/web-llm";
-
-function getElementAndCheck(id: string): HTMLElement {
- const element = document.getElementById(id);
- if (element == null) {
- throw Error("Cannot find element " + id);
- }
- return element;
-}
-
-const appConfig = {
- model_list: [
- {
- model_url:
- "https://huggingface.co/Felladrin/mlc-chat-Mistral-7B-OpenOrca-q4f32_1/resolve/main/params/",
- local_id: "Mistral-7B-OpenOrca-q4f32_1",
- },
- ],
- model_lib_map: {
- "Mistral-7B-OpenOrca-q4f32_1":
- "https://huggingface.co/Felladrin/mlc-chat-Mistral-7B-OpenOrca-q4f32_1/resolve/main/Mistral-7B-OpenOrca-q4f32_1-webgpu.wasm",
- },
- use_web_worker: true,
-};
-
-class ChatUI {
- private uiChat: HTMLElement;
- private uiChatInput: HTMLInputElement;
- private uiChatInfoLabel: HTMLLabelElement;
- private chat: ChatInterface;
- private localChat: ChatInterface;
- private config = appConfig;
- private selectedModel: string;
- private chatLoaded = false;
- private requestInProgress = false;
- // We use a request chain to ensure that
- // all requests send to chat are sequentialized
- private chatRequestChain: Promise = Promise.resolve();
-
- constructor(chat: ChatInterface, localChat: ChatInterface) {
- // use web worker to run chat generation in background
- this.chat = chat;
- this.localChat = localChat;
- // get the elements
- this.uiChat = getElementAndCheck("chatui-chat");
- this.uiChatInput = getElementAndCheck("chatui-input") as HTMLInputElement;
- this.uiChatInfoLabel = getElementAndCheck(
- "chatui-info-label"
- ) as HTMLLabelElement;
- // register event handlers
- getElementAndCheck("chatui-reset-btn").onclick = () => {
- this.onReset();
- };
- getElementAndCheck("chatui-send-btn").onclick = () => {
- this.onGenerate();
- };
- // TODO: find other alternative triggers
- getElementAndCheck("chatui-input").onkeypress = (event) => {
- if (event.keyCode === 13) {
- this.onGenerate();
- }
- };
-
- const modelSelector = getElementAndCheck(
- "chatui-select"
- ) as HTMLSelectElement;
- for (let i = 0; i < this.config.model_list.length; ++i) {
- const item = this.config.model_list[i];
- const opt = document.createElement("option");
- opt.value = item.local_id;
- opt.innerHTML = item.local_id;
- opt.selected = i == 0;
- modelSelector.appendChild(opt);
- }
- // Append local server option to the model selector
- const localServerOpt = document.createElement("option");
- localServerOpt.value = "Local Server";
- localServerOpt.innerHTML = "Local Server";
- modelSelector.append(localServerOpt);
- this.selectedModel = modelSelector.value;
- modelSelector.onchange = () => {
- this.onSelectChange(modelSelector);
- };
- }
- /**
- * Push a task to the execution queue.
- *
- * @param task The task to be executed;
- */
- private pushTask(task: () => Promise) {
- const lastEvent = this.chatRequestChain;
- this.chatRequestChain = lastEvent.then(task);
- }
- // Event handlers
- // all event handler pushes the tasks to a queue
- // that get executed sequentially
- // the tasks previous tasks, which causes them to early stop
- // can be interrupted by chat.interruptGenerate
- private async onGenerate() {
- if (this.requestInProgress) {
- return;
- }
- this.pushTask(async () => {
- await this.asyncGenerate();
- });
- }
-
- private async onSelectChange(modelSelector: HTMLSelectElement) {
- if (this.requestInProgress) {
- // interrupt previous generation if any
- this.chat.interruptGenerate();
- }
- // try reset after previous requests finishes
- this.pushTask(async () => {
- await this.chat.resetChat();
- this.resetChatHistory();
- await this.unloadChat();
- this.selectedModel = modelSelector.value;
- await this.asyncInitChat();
- });
- }
-
- private async onReset() {
- if (this.requestInProgress) {
- // interrupt previous generation if any
- this.chat.interruptGenerate();
- }
- // try reset after previous requests finishes
- this.pushTask(async () => {
- await this.chat.resetChat();
- this.resetChatHistory();
- });
- }
-
- // Internal helper functions
- private appendMessage(kind, text) {
- if (kind == "init") {
- text = "[System Initalize] " + text;
- }
- if (this.uiChat === undefined) {
- throw Error("cannot find ui chat");
- }
- const msg = `
-
-
- ${text}
-
-
- `;
- this.uiChat.insertAdjacentHTML("beforeend", msg);
- this.uiChat.scrollTo(0, this.uiChat.scrollHeight);
- }
-
- private updateLastMessage(kind, text) {
- if (kind == "init") {
- text = "[System Initalize] " + text;
- }
- if (this.uiChat === undefined) {
- throw Error("cannot find ui chat");
- }
- const matches = this.uiChat.getElementsByClassName(`msg ${kind}-msg`);
- if (matches.length == 0) throw Error(`${kind} message do not exist`);
- const msg = matches[matches.length - 1];
- const msgText = msg.getElementsByClassName("msg-text");
- if (msgText.length != 1) throw Error("Expect msg-text");
- if (msgText[0].innerHTML == text) return;
- const list = text.split("\n").map((t) => {
- const item = document.createElement("div");
- item.textContent = t;
- return item;
- });
- msgText[0].innerHTML = "";
- list.forEach((item) => msgText[0].append(item));
- this.uiChat.scrollTo(0, this.uiChat.scrollHeight);
- }
-
- private resetChatHistory() {
- const clearTags = ["left", "right", "init", "error"];
- for (const tag of clearTags) {
- // need to unpack to list so the iterator don't get affected by mutation
- const matches = [...this.uiChat.getElementsByClassName(`msg ${tag}-msg`)];
- for (const item of matches) {
- this.uiChat.removeChild(item);
- }
- }
- if (this.uiChatInfoLabel !== undefined) {
- this.uiChatInfoLabel.innerHTML = "";
- }
- }
-
- private async asyncInitChat() {
- if (this.chatLoaded) return;
- this.requestInProgress = true;
- this.appendMessage("init", "");
- const initProgressCallback = (report) => {
- this.updateLastMessage("init", report.text);
- };
- this.chat.setInitProgressCallback(initProgressCallback);
-
- try {
- if (this.selectedModel != "Local Server") {
- await this.chat.reload(this.selectedModel, undefined, this.config);
- }
- } catch (err) {
- this.appendMessage("error", "Init error, " + err.toString());
- console.log(err.stack);
- this.unloadChat();
- this.requestInProgress = false;
- return;
- }
- this.requestInProgress = false;
- this.chatLoaded = true;
- }
-
- private async unloadChat() {
- await this.chat.unload();
- this.chatLoaded = false;
- }
-
- /**
- * Run generate
- */
- private async asyncGenerate() {
- await this.asyncInitChat();
- this.requestInProgress = true;
- const prompt = this.uiChatInput.value;
- if (prompt == "") {
- this.requestInProgress = false;
- return;
- }
-
- this.appendMessage("right", prompt);
- this.uiChatInput.value = "";
- this.uiChatInput.setAttribute("placeholder", "Generating...");
-
- this.appendMessage("left", "");
- const callbackUpdateResponse = (step, msg) => {
- if (msg.length === 0) return this.chat.interruptGenerate();
- this.updateLastMessage("left", msg);
- };
-
- try {
- if (this.selectedModel == "Local Server") {
- await this.localChat.generate(prompt, callbackUpdateResponse);
- this.uiChatInfoLabel.innerHTML =
- await this.localChat.runtimeStatsText();
- } else {
- await this.chat.generate(prompt, callbackUpdateResponse);
- this.uiChatInfoLabel.innerHTML = await this.chat.runtimeStatsText();
- }
- } catch (err) {
- this.appendMessage("error", "Generate error, " + err.toString());
- console.log(err.stack);
- await this.unloadChat();
- }
- this.uiChatInput.setAttribute("placeholder", "Enter your message...");
- this.requestInProgress = false;
- }
-}
-
-const useWebWorker = appConfig.use_web_worker;
-let chat: ChatInterface;
-let localChat: ChatInterface;
-
-if (useWebWorker) {
- chat = new ChatWorkerClient(
- new Worker(new URL("./worker.ts", import.meta.url), { type: "module" })
- );
- localChat = new ChatRestModule();
-} else {
- chat = new ChatModule();
- localChat = new ChatRestModule();
-}
-new ChatUI(chat, localChat);
diff --git a/spaces/Francesco/torch-cam-transformers/app.py b/spaces/Francesco/torch-cam-transformers/app.py
deleted file mode 100644
index 6c5bf7ea8344430235dfcc553c112be801348a50..0000000000000000000000000000000000000000
--- a/spaces/Francesco/torch-cam-transformers/app.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import streamlit as st
-
-import sys
-
-
-def main():
- # Wide mode
- st.set_page_config(layout="wide")
-
- # Designing the interface
- st.title(sys.version)
-
-
-main()
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py
deleted file mode 100644
index bf18572329019d7a8f1df01799eda207c16dd7ff..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py
+++ /dev/null
@@ -1,284 +0,0 @@
-import os
-import random
-import re
-import numpy as np
-import librosa
-import torch
-import random
-from utils import repeat_expand_2d
-from tqdm import tqdm
-from torch.utils.data import Dataset
-
-def traverse_dir(
- root_dir,
- extensions,
- amount=None,
- str_include=None,
- str_exclude=None,
- is_pure=False,
- is_sort=False,
- is_ext=True):
-
- file_list = []
- cnt = 0
- for root, _, files in os.walk(root_dir):
- for file in files:
- if any([file.endswith(f".{ext}") for ext in extensions]):
- # path
- mix_path = os.path.join(root, file)
- pure_path = mix_path[len(root_dir)+1:] if is_pure else mix_path
-
- # amount
- if (amount is not None) and (cnt == amount):
- if is_sort:
- file_list.sort()
- return file_list
-
- # check string
- if (str_include is not None) and (str_include not in pure_path):
- continue
- if (str_exclude is not None) and (str_exclude in pure_path):
- continue
-
- if not is_ext:
- ext = pure_path.split('.')[-1]
- pure_path = pure_path[:-(len(ext)+1)]
- file_list.append(pure_path)
- cnt += 1
- if is_sort:
- file_list.sort()
- return file_list
-
-
-def get_data_loaders(args, whole_audio=False):
- data_train = AudioDataset(
- filelists = args.data.training_files,
- waveform_sec=args.data.duration,
- hop_size=args.data.block_size,
- sample_rate=args.data.sampling_rate,
- load_all_data=args.train.cache_all_data,
- whole_audio=whole_audio,
- extensions=args.data.extensions,
- n_spk=args.model.n_spk,
- spk=args.spk,
- device=args.train.cache_device,
- fp16=args.train.cache_fp16,
- use_aug=True)
- loader_train = torch.utils.data.DataLoader(
- data_train ,
- batch_size=args.train.batch_size if not whole_audio else 1,
- shuffle=True,
- num_workers=args.train.num_workers if args.train.cache_device=='cpu' else 0,
- persistent_workers=(args.train.num_workers > 0) if args.train.cache_device=='cpu' else False,
- pin_memory=True if args.train.cache_device=='cpu' else False
- )
- data_valid = AudioDataset(
- filelists = args.data.validation_files,
- waveform_sec=args.data.duration,
- hop_size=args.data.block_size,
- sample_rate=args.data.sampling_rate,
- load_all_data=args.train.cache_all_data,
- whole_audio=True,
- spk=args.spk,
- extensions=args.data.extensions,
- n_spk=args.model.n_spk)
- loader_valid = torch.utils.data.DataLoader(
- data_valid,
- batch_size=1,
- shuffle=False,
- num_workers=0,
- pin_memory=True
- )
- return loader_train, loader_valid
-
-
-class AudioDataset(Dataset):
- def __init__(
- self,
- filelists,
- waveform_sec,
- hop_size,
- sample_rate,
- spk,
- load_all_data=True,
- whole_audio=False,
- extensions=['wav'],
- n_spk=1,
- device='cpu',
- fp16=False,
- use_aug=False,
- ):
- super().__init__()
-
- self.waveform_sec = waveform_sec
- self.sample_rate = sample_rate
- self.hop_size = hop_size
- self.filelists = filelists
- self.whole_audio = whole_audio
- self.use_aug = use_aug
- self.data_buffer={}
- self.pitch_aug_dict = {}
- # np.load(os.path.join(self.path_root, 'pitch_aug_dict.npy'), allow_pickle=True).item()
- if load_all_data:
- print('Load all the data filelists:', filelists)
- else:
- print('Load the f0, volume data filelists:', filelists)
- with open(filelists,"r") as f:
- self.paths = f.read().splitlines()
- for name_ext in tqdm(self.paths, total=len(self.paths)):
- name = os.path.splitext(name_ext)[0]
- path_audio = name_ext
- duration = librosa.get_duration(filename = path_audio, sr = self.sample_rate)
-
- path_f0 = name_ext + ".f0.npy"
- f0,_ = np.load(path_f0,allow_pickle=True)
- f0 = torch.from_numpy(np.array(f0,dtype=float)).float().unsqueeze(-1).to(device)
-
- path_volume = name_ext + ".vol.npy"
- volume = np.load(path_volume)
- volume = torch.from_numpy(volume).float().unsqueeze(-1).to(device)
-
- path_augvol = name_ext + ".aug_vol.npy"
- aug_vol = np.load(path_augvol)
- aug_vol = torch.from_numpy(aug_vol).float().unsqueeze(-1).to(device)
-
- if n_spk is not None and n_spk > 1:
- spk_name = name_ext.split("/")[-2]
- spk_id = spk[spk_name] if spk_name in spk else 0
- if spk_id < 0 or spk_id >= n_spk:
- raise ValueError(' [x] Muiti-speaker traing error : spk_id must be a positive integer from 0 to n_spk-1 ')
- else:
- spk_id = 0
- spk_id = torch.LongTensor(np.array([spk_id])).to(device)
-
- if load_all_data:
- '''
- audio, sr = librosa.load(path_audio, sr=self.sample_rate)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio)
- audio = torch.from_numpy(audio).to(device)
- '''
- path_mel = name_ext + ".mel.npy"
- mel = np.load(path_mel)
- mel = torch.from_numpy(mel).to(device)
-
- path_augmel = name_ext + ".aug_mel.npy"
- aug_mel,keyshift = np.load(path_augmel, allow_pickle=True)
- aug_mel = np.array(aug_mel,dtype=float)
- aug_mel = torch.from_numpy(aug_mel).to(device)
- self.pitch_aug_dict[name_ext] = keyshift
-
- path_units = name_ext + ".soft.pt"
- units = torch.load(path_units).to(device)
- units = units[0]
- units = repeat_expand_2d(units,f0.size(0)).transpose(0,1)
-
- if fp16:
- mel = mel.half()
- aug_mel = aug_mel.half()
- units = units.half()
-
- self.data_buffer[name_ext] = {
- 'duration': duration,
- 'mel': mel,
- 'aug_mel': aug_mel,
- 'units': units,
- 'f0': f0,
- 'volume': volume,
- 'aug_vol': aug_vol,
- 'spk_id': spk_id
- }
- else:
- path_augmel = name_ext + ".aug_mel.npy"
- aug_mel,keyshift = np.load(path_augmel, allow_pickle=True)
- self.pitch_aug_dict[name_ext] = keyshift
- self.data_buffer[name_ext] = {
- 'duration': duration,
- 'f0': f0,
- 'volume': volume,
- 'aug_vol': aug_vol,
- 'spk_id': spk_id
- }
-
-
- def __getitem__(self, file_idx):
- name_ext = self.paths[file_idx]
- data_buffer = self.data_buffer[name_ext]
- # check duration. if too short, then skip
- if data_buffer['duration'] < (self.waveform_sec + 0.1):
- return self.__getitem__( (file_idx + 1) % len(self.paths))
-
- # get item
- return self.get_data(name_ext, data_buffer)
-
- def get_data(self, name_ext, data_buffer):
- name = os.path.splitext(name_ext)[0]
- frame_resolution = self.hop_size / self.sample_rate
- duration = data_buffer['duration']
- waveform_sec = duration if self.whole_audio else self.waveform_sec
-
- # load audio
- idx_from = 0 if self.whole_audio else random.uniform(0, duration - waveform_sec - 0.1)
- start_frame = int(idx_from / frame_resolution)
- units_frame_len = int(waveform_sec / frame_resolution)
- aug_flag = random.choice([True, False]) and self.use_aug
- '''
- audio = data_buffer.get('audio')
- if audio is None:
- path_audio = os.path.join(self.path_root, 'audio', name) + '.wav'
- audio, sr = librosa.load(
- path_audio,
- sr = self.sample_rate,
- offset = start_frame * frame_resolution,
- duration = waveform_sec)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio)
- # clip audio into N seconds
- audio = audio[ : audio.shape[-1] // self.hop_size * self.hop_size]
- audio = torch.from_numpy(audio).float()
- else:
- audio = audio[start_frame * self.hop_size : (start_frame + units_frame_len) * self.hop_size]
- '''
- # load mel
- mel_key = 'aug_mel' if aug_flag else 'mel'
- mel = data_buffer.get(mel_key)
- if mel is None:
- mel = name_ext + ".mel.npy"
- mel = np.load(mel)
- mel = mel[start_frame : start_frame + units_frame_len]
- mel = torch.from_numpy(mel).float()
- else:
- mel = mel[start_frame : start_frame + units_frame_len]
-
- # load f0
- f0 = data_buffer.get('f0')
- aug_shift = 0
- if aug_flag:
- aug_shift = self.pitch_aug_dict[name_ext]
- f0_frames = 2 ** (aug_shift / 12) * f0[start_frame : start_frame + units_frame_len]
-
- # load units
- units = data_buffer.get('units')
- if units is None:
- path_units = name_ext + ".soft.pt"
- units = torch.load(path_units)
- units = units[0]
- units = repeat_expand_2d(units,f0.size(0)).transpose(0,1)
-
- units = units[start_frame : start_frame + units_frame_len]
-
- # load volume
- vol_key = 'aug_vol' if aug_flag else 'volume'
- volume = data_buffer.get(vol_key)
- volume_frames = volume[start_frame : start_frame + units_frame_len]
-
- # load spk_id
- spk_id = data_buffer.get('spk_id')
-
- # load shift
- aug_shift = torch.from_numpy(np.array([[aug_shift]])).float()
-
- return dict(mel=mel, f0=f0_frames, volume=volume_frames, units=units, spk_id=spk_id, aug_shift=aug_shift, name=name, name_ext=name_ext)
-
- def __len__(self):
- return len(self.paths)
\ No newline at end of file
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py
deleted file mode 100644
index 3bdb70ba9357e95ff05853dcc06437c3401ef3be..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import os
-from functools import lru_cache
-from typing import Union
-
-import ffmpeg
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-from .utils import exact_div
-
-from librosa.filters import mel as librosa_mel_fn
-
-# hard-coded audio hyperparameters
-SAMPLE_RATE = 16000
-N_FFT = 400
-N_MELS = 80
-HOP_LENGTH = 160
-CHUNK_LENGTH = 30
-N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk
-N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH) # 3000: number of frames in a mel spectrogram input
-
-
-def load_audio(file: str, sr: int = SAMPLE_RATE):
- """
- Open an audio file and read as mono waveform, resampling as necessary
-
- Parameters
- ----------
- file: str
- The audio file to open
-
- sr: int
- The sample rate to resample the audio if necessary
-
- Returns
- -------
- A NumPy array containing the audio waveform, in float32 dtype.
- """
- try:
- # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
- # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
- out, _ = (
- ffmpeg.input(file, threads=0)
- .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
- .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
- )
- except ffmpeg.Error as e:
- raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
-
- return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
-
-
-def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
- """
- Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
- """
- if torch.is_tensor(array):
- if array.shape[axis] > length:
- array = array.index_select(dim=axis, index=torch.arange(length, device=array.device))
-
- if array.shape[axis] < length:
- pad_widths = [(0, 0)] * array.ndim
- pad_widths[axis] = (0, length - array.shape[axis])
- array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
- else:
- if array.shape[axis] > length:
- array = array.take(indices=range(length), axis=axis)
-
- if array.shape[axis] < length:
- pad_widths = [(0, 0)] * array.ndim
- pad_widths[axis] = (0, length - array.shape[axis])
- array = np.pad(array, pad_widths)
-
- return array
-
-
-@lru_cache(maxsize=None)
-def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
- """
- load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
- Allows decoupling librosa dependency; saved using:
-
- np.savez_compressed(
- "mel_filters.npz",
- mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
- )
- """
- assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
- return torch.from_numpy(librosa_mel_fn(sr=SAMPLE_RATE,n_fft=N_FFT,n_mels=n_mels)).to(device)
-
-
-def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS):
- """
- Compute the log-Mel spectrogram of
-
- Parameters
- ----------
- audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
- The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
-
- n_mels: int
- The number of Mel-frequency filters, only 80 is supported
-
- Returns
- -------
- torch.Tensor, shape = (80, n_frames)
- A Tensor that contains the Mel spectrogram
- """
- if not torch.is_tensor(audio):
- if isinstance(audio, str):
- audio = load_audio(audio)
- audio = torch.from_numpy(audio)
-
- window = torch.hann_window(N_FFT).to(audio.device)
- stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
- magnitudes = stft[..., :-1].abs() ** 2
-
- filters = mel_filters(audio.device, n_mels)
- mel_spec = filters @ magnitudes
-
- log_spec = torch.clamp(mel_spec, min=1e-10).log10()
- log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
- log_spec = (log_spec + 4.0) / 4.0
- return log_spec
diff --git a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py b/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py
deleted file mode 100644
index 6817392ba8a9eb830351de89fb7afc5ad72f5e42..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py
+++ /dev/null
@@ -1,188 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
- 1. "english_cleaners" for English text
- 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
- the Unidecode library (https://pypi.python.org/pypi/Unidecode)
- 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
- the symbols in symbols.py to match your data).
-'''
-
-
-# Regular expression matching whitespace:
-
-
-import re
-import inflect
-from unidecode import unidecode
-import eng_to_ipa as ipa
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
-_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
-_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
-_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
-_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
-_number_re = re.compile(r'[0-9]+')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
- ('mrs', 'misess'),
- ('mr', 'mister'),
- ('dr', 'doctor'),
- ('st', 'saint'),
- ('co', 'company'),
- ('jr', 'junior'),
- ('maj', 'major'),
- ('gen', 'general'),
- ('drs', 'doctors'),
- ('rev', 'reverend'),
- ('lt', 'lieutenant'),
- ('hon', 'honorable'),
- ('sgt', 'sergeant'),
- ('capt', 'captain'),
- ('esq', 'esquire'),
- ('ltd', 'limited'),
- ('col', 'colonel'),
- ('ft', 'fort'),
-]]
-
-
-# List of (ipa, lazy ipa) pairs:
-_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('æ', 'e'),
- ('ɑ', 'a'),
- ('ɔ', 'o'),
- ('ð', 'z'),
- ('θ', 's'),
- ('ɛ', 'e'),
- ('ɪ', 'i'),
- ('ʊ', 'u'),
- ('ʒ', 'ʥ'),
- ('ʤ', 'ʥ'),
- ('ˈ', '↓'),
-]]
-
-# List of (ipa, lazy ipa2) pairs:
-_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('ð', 'z'),
- ('θ', 's'),
- ('ʒ', 'ʑ'),
- ('ʤ', 'dʑ'),
- ('ˈ', '↓'),
-]]
-
-# List of (ipa, ipa2) pairs
-_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('ʤ', 'dʒ'),
- ('ʧ', 'tʃ')
-]]
-
-
-def expand_abbreviations(text):
- for regex, replacement in _abbreviations:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def collapse_whitespace(text):
- return re.sub(r'\s+', ' ', text)
-
-
-def _remove_commas(m):
- return m.group(1).replace(',', '')
-
-
-def _expand_decimal_point(m):
- return m.group(1).replace('.', ' point ')
-
-
-def _expand_dollars(m):
- match = m.group(1)
- parts = match.split('.')
- if len(parts) > 2:
- return match + ' dollars' # Unexpected format
- dollars = int(parts[0]) if parts[0] else 0
- cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
- if dollars and cents:
- dollar_unit = 'dollar' if dollars == 1 else 'dollars'
- cent_unit = 'cent' if cents == 1 else 'cents'
- return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
- elif dollars:
- dollar_unit = 'dollar' if dollars == 1 else 'dollars'
- return '%s %s' % (dollars, dollar_unit)
- elif cents:
- cent_unit = 'cent' if cents == 1 else 'cents'
- return '%s %s' % (cents, cent_unit)
- else:
- return 'zero dollars'
-
-
-def _expand_ordinal(m):
- return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
- num = int(m.group(0))
- if num > 1000 and num < 3000:
- if num == 2000:
- return 'two thousand'
- elif num > 2000 and num < 2010:
- return 'two thousand ' + _inflect.number_to_words(num % 100)
- elif num % 100 == 0:
- return _inflect.number_to_words(num // 100) + ' hundred'
- else:
- return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
- else:
- return _inflect.number_to_words(num, andword='')
-
-
-def normalize_numbers(text):
- text = re.sub(_comma_number_re, _remove_commas, text)
- text = re.sub(_pounds_re, r'\1 pounds', text)
- text = re.sub(_dollars_re, _expand_dollars, text)
- text = re.sub(_decimal_number_re, _expand_decimal_point, text)
- text = re.sub(_ordinal_re, _expand_ordinal, text)
- text = re.sub(_number_re, _expand_number, text)
- return text
-
-
-def mark_dark_l(text):
- return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
-
-
-def english_to_ipa(text):
- text = unidecode(text).lower()
- text = expand_abbreviations(text)
- text = normalize_numbers(text)
- phonemes = ipa.convert(text)
- phonemes = collapse_whitespace(phonemes)
- return phonemes
-
-
-def english_to_lazy_ipa(text):
- text = english_to_ipa(text)
- for regex, replacement in _lazy_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def english_to_ipa2(text):
- text = english_to_ipa(text)
- text = mark_dark_l(text)
- for regex, replacement in _ipa_to_ipa2:
- text = re.sub(regex, replacement, text)
- return text.replace('...', '…')
-
-
-def english_to_lazy_ipa2(text):
- text = english_to_ipa(text)
- for regex, replacement in _lazy_ipa2:
- text = re.sub(regex, replacement, text)
- return text
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
deleted file mode 100644
index a02a814fe2f08b464454e8eb6e1c88004ab804f6..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
+++ /dev/null
@@ -1,27 +0,0 @@
-_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
-model = dict(
- pretrained='torchvision://resnet101',
- backbone=dict(depth=101),
- bbox_head=dict(
- with_deform=True,
- norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='Resize',
- img_scale=[(1333, 640), (1333, 800)],
- multiscale_mode='value',
- keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
deleted file mode 100644
index 4874121fd01e4024bfde445f451b7368c6834511..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './deeplabv3_r50-d8_480x480_40k_pascal_context_59.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
deleted file mode 100644
index 89d5e1ae0f3ef44626f3b5534c504cbce7389a32..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
- '../_base_/models/gcnet_r50-d8.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
- decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py
deleted file mode 100644
index 12f6d402a3c4a113d4c37be062790fa435b72104..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Evaluation with objective metrics for the pretrained AudioGen models.
-This grid takes signature from the training grid and runs evaluation-only stage.
-
-When running the grid for the first time, please use:
-REGEN=1 dora grid audiogen.audiogen_pretrained_16khz_eval
-and re-use the REGEN=1 option when the grid is changed to force regenerating it.
-
-Note that you need the proper metrics external libraries setup to use all
-the objective metrics activated in this grid. Refer to the README for more information.
-"""
-
-import os
-
-from ..musicgen._explorers import GenerationEvalExplorer
-from ...environment import AudioCraftEnvironment
-from ... import train
-
-
-def eval(launcher, batch_size: int = 32):
- opts = {
- 'dset': 'audio/audiocaps_16khz',
- 'solver/audiogen/evaluation': 'objective_eval',
- 'execute_only': 'evaluate',
- '+dataset.evaluate.batch_size': batch_size,
- '+metrics.fad.tf.batch_size': 32,
- }
- # binary for FAD computation: replace this path with your own path
- metrics_opts = {
- 'metrics.fad.tf.bin': '/data/home/jadecopet/local/usr/opt/google-research'
- }
- opt1 = {'generate.lm.use_sampling': True, 'generate.lm.top_k': 250, 'generate.lm.top_p': 0.}
- opt2 = {'transformer_lm.two_step_cfg': True}
-
- sub = launcher.bind(opts)
- sub.bind_(metrics_opts)
-
- # base objective metrics
- sub(opt1, opt2)
-
-
-@GenerationEvalExplorer
-def explorer(launcher):
- partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
- launcher.slurm_(gpus=4, partition=partitions)
-
- if 'REGEN' not in os.environ:
- folder = train.main.dora.dir / 'grids' / __name__.split('.', 2)[-1]
- with launcher.job_array():
- for sig in folder.iterdir():
- if not sig.is_symlink():
- continue
- xp = train.main.get_xp_from_sig(sig.name)
- launcher(xp.argv)
- return
-
- audiogen_base = launcher.bind(solver="audiogen/audiogen_base_16khz")
- audiogen_base.bind_({'autocast': False, 'fsdp.use': True})
-
- audiogen_base_medium = audiogen_base.bind({'continue_from': '//pretrained/facebook/audiogen-medium'})
- audiogen_base_medium.bind_({'model/lm/model_scale': 'medium'})
- eval(audiogen_base_medium, batch_size=128)
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py
deleted file mode 100644
index 5612ff5688d85fede0e605b244919e8081cb1da9..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Grid search file, simply list all the exp you want in `explorer`.
-Any new exp added there will be scheduled.
-You can cancel and experiment by commenting its line.
-
-This grid is a minimal example for debugging compression task
-and how to override parameters directly in a grid.
-Learn more about dora grids: https://github.com/facebookresearch/dora
-"""
-
-from ._explorers import CompressionExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@CompressionExplorer
-def explorer(launcher):
- partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
- launcher.slurm_(gpus=2, partition=partitions)
- launcher.bind_(solver='compression/debug')
-
- with launcher.job_array():
- # base debug task using config from solver=compression/debug
- launcher()
- # we can override parameters in the grid to launch additional xps
- launcher({'rvq.bins': 2048, 'rvq.n_q': 4})
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py
deleted file mode 100644
index 44f4b0a2c3c6c726857db8386491823dd85dde51..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import csv
-import json
-import logging
-from pathlib import Path
-import tempfile
-import typing as tp
-import subprocess
-import shutil
-
-import torch
-import torchaudio
-
-logger = logging.getLogger(__name__)
-
-
-class ViSQOL:
- """ViSQOL wrapper to run ViSQOL from Python using a pre-installed binary.
-
- To learn more about ViSQOL and how to build ViSQOL binary using bazel, please refer to the
- instructions available in the open source repository: https://github.com/google/visqol
-
- ViSQOL is capable of running in two modes:
-
- Audio Mode:
- When running in audio mode, input signals must have a 48kHz sample rate. Input should be resampled to 48kHz.
- Input signals can be multi-channel, but they will be down-mixed to mono for performing the comparison.
- Audio mode uses support vector regression, with the maximum range at ~4.75.
-
- Speech Mode:
- When running in speech mode, ViSQOL uses a wideband model. It therefore expects input sample rates of 16kHz.
- Input should be resampled to 16kHz.
- As part of the speech mode processing, a root mean square implementation for voice activity detection
- is performed on the reference signal to determine what parts of the signal have voice activity and
- should therefore be included in the comparison. The signal is normalized before performing the voice
- activity detection.
- Input signals can be multi-channel, but they will be down-mixed to mono for performing the comparison.
- Speech mode is scaled to have a maximum MOS of 5.0 to match previous version behavior.
-
- For more details, check the guidelines: https://github.com/google/visqol#general-guidelines-for-input
-
- Args:
- visqol_bin (str): Path to the ViSQOL binary.
- mode (str): ViSQOL computation mode, expecting "audio" or "speech".
- model (str): Name of the model to use for similarity to quality model.
- debug (bool): Whether to also get debug metrics from ViSQOL or not.
- """
- SAMPLE_RATES_MODES = {"audio": 48_000, "speech": 16_000}
- ALLOWED_SAMPLE_RATES = frozenset(SAMPLE_RATES_MODES.values())
-
- def __init__(self, bin: tp.Union[Path, str], mode: str = "audio",
- model: str = "libsvm_nu_svr_model.txt", debug: bool = False):
- assert bin is not None and Path(bin).exists(), f"Could not find ViSQOL binary in specified path: {bin}"
- self.visqol_bin = str(bin)
- self.visqol_mode = mode
- self.target_sr = self._get_target_sr(self.visqol_mode)
- self.model = model
- self.debug = debug
- assert Path(self.visqol_model).exists(), \
- f"Could not find the specified model in ViSQOL install: {self.visqol_model}"
-
- def _get_target_sr(self, mode: str) -> int:
- # returns target sampling rate for the corresponding ViSQOL mode.
- if mode not in ViSQOL.SAMPLE_RATES_MODES:
- raise ValueError(
- f"Unsupported mode! Allowed are: {', '.join(ViSQOL.SAMPLE_RATES_MODES.keys())}"
- )
- return ViSQOL.SAMPLE_RATES_MODES[mode]
-
- def _prepare_files(
- self, ref_sig: torch.Tensor, deg_sig: torch.Tensor, sr: int, target_sr: int, pad_with_silence: bool = False
- ):
- # prepare files for ViSQOL evaluation.
- assert target_sr in ViSQOL.ALLOWED_SAMPLE_RATES
- assert len(ref_sig) == len(deg_sig), (
- "Expects same number of ref and degraded inputs",
- f" but ref len {len(ref_sig)} != deg len {len(deg_sig)}"
- )
- # resample audio if needed
- if sr != target_sr:
- transform = torchaudio.transforms.Resample(sr, target_sr)
- pad = int(0.5 * target_sr)
- rs_ref = []
- rs_deg = []
- for i in range(len(ref_sig)):
- rs_ref_i = transform(ref_sig[i])
- rs_deg_i = transform(deg_sig[i])
- if pad_with_silence:
- rs_ref_i = torch.nn.functional.pad(rs_ref_i, (pad, pad), mode='constant', value=0)
- rs_deg_i = torch.nn.functional.pad(rs_deg_i, (pad, pad), mode='constant', value=0)
- rs_ref.append(rs_ref_i)
- rs_deg.append(rs_deg_i)
- ref_sig = torch.stack(rs_ref)
- deg_sig = torch.stack(rs_deg)
- # save audio chunks to tmp dir and create csv
- tmp_dir = Path(tempfile.mkdtemp())
- try:
- tmp_input_csv_path = tmp_dir / "input.csv"
- tmp_results_csv_path = tmp_dir / "results.csv"
- tmp_debug_json_path = tmp_dir / "debug.json"
- with open(tmp_input_csv_path, "w") as csv_file:
- csv_writer = csv.writer(csv_file)
- csv_writer.writerow(["reference", "degraded"])
- for i in range(len(ref_sig)):
- tmp_ref_filename = tmp_dir / f"ref_{i}.wav"
- tmp_deg_filename = tmp_dir / f"deg_{i}.wav"
- torchaudio.save(
- tmp_ref_filename,
- torch.clamp(ref_sig[i], min=-0.99, max=0.99),
- sample_rate=target_sr,
- bits_per_sample=16,
- encoding="PCM_S"
- )
- torchaudio.save(
- tmp_deg_filename,
- torch.clamp(deg_sig[i], min=-0.99, max=0.99),
- sample_rate=target_sr,
- bits_per_sample=16,
- encoding="PCM_S"
- )
- csv_writer.writerow([str(tmp_ref_filename), str(tmp_deg_filename)])
- return tmp_dir, tmp_input_csv_path, tmp_results_csv_path, tmp_debug_json_path
- except Exception as e:
- logger.error("Exception occurred when preparing files for ViSQOL: %s", e)
- return tmp_dir, None, None, None
-
- def _flush_files(self, tmp_dir: tp.Union[Path, str]):
- # flush tmp files used to compute ViSQOL.
- shutil.rmtree(str(tmp_dir))
-
- def _collect_moslqo_score(self, results_csv_path: tp.Union[Path, str]) -> float:
- # collect results for each evaluated pair and return averaged moslqo score.
- with open(results_csv_path, "r") as csv_file:
- reader = csv.DictReader(csv_file)
- moslqo_scores = [float(row["moslqo"]) for row in reader]
- if len(moslqo_scores) > 0:
- return sum(moslqo_scores) / len(moslqo_scores)
- else:
- return 0.0
-
- def _collect_debug_data(self, debug_json_path: tp.Union[Path, str]) -> dict:
- # collect debug data for the visqol inference.
- with open(debug_json_path, "r") as f:
- data = json.load(f)
- return data
-
- @property
- def visqol_model(self):
- return f'{self.visqol_bin}/model/{self.model}'
-
- def _run_visqol(
- self,
- input_csv_path: tp.Union[Path, str],
- results_csv_path: tp.Union[Path, str],
- debug_csv_path: tp.Optional[tp.Union[Path, str]],
- ):
- input_csv_path = str(input_csv_path)
- results_csv_path = str(results_csv_path)
- debug_csv_path = str(debug_csv_path)
- cmd = [
- f'{self.visqol_bin}/bazel-bin/visqol',
- '--batch_input_csv', f'{input_csv_path}',
- '--results_csv', f'{results_csv_path}'
- ]
- if debug_csv_path is not None:
- cmd += ['--output_debug', f'{debug_csv_path}']
- if self.visqol_mode == "speech":
- cmd += ['--use_speech_mode']
- cmd += ['--similarity_to_quality_model', f'{self.visqol_model}']
- result = subprocess.run(cmd, capture_output=True)
- if result.returncode:
- logger.error("Error with visqol: \n %s \n %s", result.stdout.decode(), result.stderr.decode())
- raise RuntimeError("Error while executing visqol")
- result.check_returncode()
-
- def __call__(
- self,
- ref_sig: torch.Tensor,
- deg_sig: torch.Tensor,
- sr: int,
- pad_with_silence: bool = False,
- ):
- """Calculate the ViSQOL metric for a pair of audio signals at a given sample rate.
- Args:
- ref_sig (torch.Tensor): Reference signals as [B, C, T].
- deg_sig (torch.Tensor): Degraded signals as [B, C, T].
- sr (int): Sample rate of the two audio signals.
- pad_with_silence (bool): Whether to pad the file with silences as recommended
- in visqol guidelines (see: https://github.com/google/visqol#general-guidelines-for-input).
- Returns:
- float: The ViSQOL score or mean score for the batch.
- """
- logger.debug(f"Calculating visqol with mode={self.visqol_mode} on {len(ref_sig)} samples")
- tmp_dir, input_csv, results_csv, debug_json = self._prepare_files(
- ref_sig, deg_sig, sr, self.target_sr, pad_with_silence
- )
- try:
- if input_csv and results_csv:
- self._run_visqol(
- input_csv,
- results_csv,
- debug_json if self.debug else None,
- )
- mosqol = self._collect_moslqo_score(results_csv)
- return mosqol
- else:
- raise RuntimeError("Something unexpected happened when running VISQOL!")
- except Exception as e:
- logger.error("Exception occurred when running ViSQOL: %s", e)
- finally:
- self._flush_files(tmp_dir)
diff --git a/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile b/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile
deleted file mode 100644
index 31b5f817b41d7d95055d9aa5d5e4f973abee0b45..0000000000000000000000000000000000000000
--- a/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile
+++ /dev/null
@@ -1,26 +0,0 @@
-IMG:=`cat Name`
-MOUNT:=
-PORT_MAP:=
-DOTENV:=.env
-CONTAINER_NAME:=sample-chatgpt-on-wechat
-
-echo:
- echo $(IMG)
-
-run_d:
- docker rm $(CONTAINER_NAME) || echo
- docker run -dt --name $(CONTAINER_NAME) $(PORT_MAP) \
- --env-file=$(DOTENV) \
- $(MOUNT) $(IMG)
-
-run_i:
- docker rm $(CONTAINER_NAME) || echo
- docker run -it --name $(CONTAINER_NAME) $(PORT_MAP) \
- --env-file=$(DOTENV) \
- $(MOUNT) $(IMG)
-
-stop:
- docker stop $(CONTAINER_NAME)
-
-rm: stop
- docker rm $(CONTAINER_NAME)
diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py
deleted file mode 100644
index 84e51fd71fca78131a3be3d0d35d808c07bb3a95..0000000000000000000000000000000000000000
--- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import torch.nn as nn
-import math
-
-
-def conv3x3(in_planes, out_planes, stride=1):
- """3x3 convolution with padding"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
- expansion = 1
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(BasicBlock, self).__init__()
- self.conv1 = conv3x3(inplanes, planes, stride)
- self.bn1 = nn.BatchNorm2d(planes)
- self.relu = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(planes, planes)
- self.bn2 = nn.BatchNorm2d(planes)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(Bottleneck, self).__init__()
- self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
- self.bn1 = nn.BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
- self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * 4)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
- out = self.relu(out)
-
- out = self.conv3(out)
- out = self.bn3(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class ResNet50(nn.Module):
- def __init__(self,mode='rgb'):
- self.inplanes = 64
- super(ResNet50, self).__init__()
- if(mode=='rgb'):
- self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- elif(mode=='rgbd'):
- self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- elif(mode=="share"):
- self.conv1=nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- self.conv1_d=nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- else:
- raise
- self.bn1 = nn.BatchNorm2d(64)
- self.relu = nn.ReLU(inplace=True)
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
- self.layer1 = self._make_layer(Bottleneck, 64, 3)
- self.layer2 = self._make_layer(Bottleneck, 128, 4, stride=2)
- self.layer3_1 = self._make_layer(Bottleneck, 256, 6, stride=2)
- self.layer4_1 = self._make_layer(Bottleneck, 512, 3, stride=2)
-
- self.inplanes = 512
-
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
- m.weight.data.normal_(0, math.sqrt(2. / n))
- elif isinstance(m, nn.BatchNorm2d):
- m.weight.data.fill_(1)
- m.bias.data.zero_()
-
- def _make_layer(self, block, planes, blocks, stride=1):
- downsample = None
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- nn.Conv2d(self.inplanes, planes * block.expansion,
- kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(planes * block.expansion),
- )
-
- layers = []
- layers.append(block(self.inplanes, planes, stride, downsample))
- self.inplanes = planes * block.expansion
- for i in range(1, blocks):
- layers.append(block(self.inplanes, planes))
-
- return nn.Sequential(*layers)
-
- def forward(self, x):
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
- x = self.maxpool(x)
-
- x = self.layer1(x)
- x = self.layer2(x)
- x1 = self.layer3_1(x)
- x1 = self.layer4_1(x1)
-
- return x1, x1
diff --git a/spaces/Hallucinate/demo/ldm/models/autoencoder.py b/spaces/Hallucinate/demo/ldm/models/autoencoder.py
deleted file mode 100644
index 6a9c4f45498561953b8085981609b2a3298a5473..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/ldm/models/autoencoder.py
+++ /dev/null
@@ -1,443 +0,0 @@
-import torch
-import pytorch_lightning as pl
-import torch.nn.functional as F
-from contextlib import contextmanager
-
-from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
-
-from ldm.modules.diffusionmodules.model import Encoder, Decoder
-from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
-
-from ldm.util import instantiate_from_config
-
-
-class VQModel(pl.LightningModule):
- def __init__(self,
- ddconfig,
- lossconfig,
- n_embed,
- embed_dim,
- ckpt_path=None,
- ignore_keys=[],
- image_key="image",
- colorize_nlabels=None,
- monitor=None,
- batch_resize_range=None,
- scheduler_config=None,
- lr_g_factor=1.0,
- remap=None,
- sane_index_shape=False, # tell vector quantizer to return indices as bhw
- use_ema=False
- ):
- super().__init__()
- self.embed_dim = embed_dim
- self.n_embed = n_embed
- self.image_key = image_key
- self.encoder = Encoder(**ddconfig)
- self.decoder = Decoder(**ddconfig)
- self.loss = instantiate_from_config(lossconfig)
- self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
- remap=remap,
- sane_index_shape=sane_index_shape)
- self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
- self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
- if colorize_nlabels is not None:
- assert type(colorize_nlabels)==int
- self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
- if monitor is not None:
- self.monitor = monitor
- self.batch_resize_range = batch_resize_range
- if self.batch_resize_range is not None:
- print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
-
- self.use_ema = use_ema
- if self.use_ema:
- self.model_ema = LitEma(self)
- print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
- if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
- self.scheduler_config = scheduler_config
- self.lr_g_factor = lr_g_factor
-
- @contextmanager
- def ema_scope(self, context=None):
- if self.use_ema:
- self.model_ema.store(self.parameters())
- self.model_ema.copy_to(self)
- if context is not None:
- print(f"{context}: Switched to EMA weights")
- try:
- yield None
- finally:
- if self.use_ema:
- self.model_ema.restore(self.parameters())
- if context is not None:
- print(f"{context}: Restored training weights")
-
- def init_from_ckpt(self, path, ignore_keys=list()):
- sd = torch.load(path, map_location="cpu")["state_dict"]
- keys = list(sd.keys())
- for k in keys:
- for ik in ignore_keys:
- if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
- del sd[k]
- missing, unexpected = self.load_state_dict(sd, strict=False)
- print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
- if len(missing) > 0:
- print(f"Missing Keys: {missing}")
- print(f"Unexpected Keys: {unexpected}")
-
- def on_train_batch_end(self, *args, **kwargs):
- if self.use_ema:
- self.model_ema(self)
-
- def encode(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- quant, emb_loss, info = self.quantize(h)
- return quant, emb_loss, info
-
- def encode_to_prequant(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- return h
-
- def decode(self, quant):
- quant = self.post_quant_conv(quant)
- dec = self.decoder(quant)
- return dec
-
- def decode_code(self, code_b):
- quant_b = self.quantize.embed_code(code_b)
- dec = self.decode(quant_b)
- return dec
-
- def forward(self, input, return_pred_indices=False):
- quant, diff, (_,_,ind) = self.encode(input)
- dec = self.decode(quant)
- if return_pred_indices:
- return dec, diff, ind
- return dec, diff
-
- def get_input(self, batch, k):
- x = batch[k]
- if len(x.shape) == 3:
- x = x[..., None]
- x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
- if self.batch_resize_range is not None:
- lower_size = self.batch_resize_range[0]
- upper_size = self.batch_resize_range[1]
- if self.global_step <= 4:
- # do the first few batches with max size to avoid later oom
- new_resize = upper_size
- else:
- new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
- if new_resize != x.shape[2]:
- x = F.interpolate(x, size=new_resize, mode="bicubic")
- x = x.detach()
- return x
-
- def training_step(self, batch, batch_idx, optimizer_idx):
- # https://github.com/pytorch/pytorch/issues/37142
- # try not to fool the heuristics
- x = self.get_input(batch, self.image_key)
- xrec, qloss, ind = self(x, return_pred_indices=True)
-
- if optimizer_idx == 0:
- # autoencode
- aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train",
- predicted_indices=ind)
-
- self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
- return aeloss
-
- if optimizer_idx == 1:
- # discriminator
- discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
- self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
- return discloss
-
- def validation_step(self, batch, batch_idx):
- log_dict = self._validation_step(batch, batch_idx)
- with self.ema_scope():
- log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
- return log_dict
-
- def _validation_step(self, batch, batch_idx, suffix=""):
- x = self.get_input(batch, self.image_key)
- xrec, qloss, ind = self(x, return_pred_indices=True)
- aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
- self.global_step,
- last_layer=self.get_last_layer(),
- split="val"+suffix,
- predicted_indices=ind
- )
-
- discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
- self.global_step,
- last_layer=self.get_last_layer(),
- split="val"+suffix,
- predicted_indices=ind
- )
- rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
- self.log(f"val{suffix}/rec_loss", rec_loss,
- prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
- self.log(f"val{suffix}/aeloss", aeloss,
- prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
- if version.parse(pl.__version__) >= version.parse('1.4.0'):
- del log_dict_ae[f"val{suffix}/rec_loss"]
- self.log_dict(log_dict_ae)
- self.log_dict(log_dict_disc)
- return self.log_dict
-
- def configure_optimizers(self):
- lr_d = self.learning_rate
- lr_g = self.lr_g_factor*self.learning_rate
- print("lr_d", lr_d)
- print("lr_g", lr_g)
- opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
- list(self.decoder.parameters())+
- list(self.quantize.parameters())+
- list(self.quant_conv.parameters())+
- list(self.post_quant_conv.parameters()),
- lr=lr_g, betas=(0.5, 0.9))
- opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
- lr=lr_d, betas=(0.5, 0.9))
-
- if self.scheduler_config is not None:
- scheduler = instantiate_from_config(self.scheduler_config)
-
- print("Setting up LambdaLR scheduler...")
- scheduler = [
- {
- 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
- 'interval': 'step',
- 'frequency': 1
- },
- {
- 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
- 'interval': 'step',
- 'frequency': 1
- },
- ]
- return [opt_ae, opt_disc], scheduler
- return [opt_ae, opt_disc], []
-
- def get_last_layer(self):
- return self.decoder.conv_out.weight
-
- def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
- log = dict()
- x = self.get_input(batch, self.image_key)
- x = x.to(self.device)
- if only_inputs:
- log["inputs"] = x
- return log
- xrec, _ = self(x)
- if x.shape[1] > 3:
- # colorize with random projection
- assert xrec.shape[1] > 3
- x = self.to_rgb(x)
- xrec = self.to_rgb(xrec)
- log["inputs"] = x
- log["reconstructions"] = xrec
- if plot_ema:
- with self.ema_scope():
- xrec_ema, _ = self(x)
- if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
- log["reconstructions_ema"] = xrec_ema
- return log
-
- def to_rgb(self, x):
- assert self.image_key == "segmentation"
- if not hasattr(self, "colorize"):
- self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
- x = F.conv2d(x, weight=self.colorize)
- x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
- return x
-
-
-class VQModelInterface(VQModel):
- def __init__(self, embed_dim, *args, **kwargs):
- super().__init__(embed_dim=embed_dim, *args, **kwargs)
- self.embed_dim = embed_dim
-
- def encode(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- return h
-
- def decode(self, h, force_not_quantize=False):
- # also go through quantization layer
- if not force_not_quantize:
- quant, emb_loss, info = self.quantize(h)
- else:
- quant = h
- quant = self.post_quant_conv(quant)
- dec = self.decoder(quant)
- return dec
-
-
-class AutoencoderKL(pl.LightningModule):
- def __init__(self,
- ddconfig,
- lossconfig,
- embed_dim,
- ckpt_path=None,
- ignore_keys=[],
- image_key="image",
- colorize_nlabels=None,
- monitor=None,
- ):
- super().__init__()
- self.image_key = image_key
- self.encoder = Encoder(**ddconfig)
- self.decoder = Decoder(**ddconfig)
- self.loss = instantiate_from_config(lossconfig)
- assert ddconfig["double_z"]
- self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
- self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
- self.embed_dim = embed_dim
- if colorize_nlabels is not None:
- assert type(colorize_nlabels)==int
- self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
- if monitor is not None:
- self.monitor = monitor
- if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-
- def init_from_ckpt(self, path, ignore_keys=list()):
- sd = torch.load(path, map_location="cpu")["state_dict"]
- keys = list(sd.keys())
- for k in keys:
- for ik in ignore_keys:
- if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
- del sd[k]
- self.load_state_dict(sd, strict=False)
- print(f"Restored from {path}")
-
- def encode(self, x):
- h = self.encoder(x)
- moments = self.quant_conv(h)
- posterior = DiagonalGaussianDistribution(moments)
- return posterior
-
- def decode(self, z):
- z = self.post_quant_conv(z)
- dec = self.decoder(z)
- return dec
-
- def forward(self, input, sample_posterior=True):
- posterior = self.encode(input)
- if sample_posterior:
- z = posterior.sample()
- else:
- z = posterior.mode()
- dec = self.decode(z)
- return dec, posterior
-
- def get_input(self, batch, k):
- x = batch[k]
- if len(x.shape) == 3:
- x = x[..., None]
- x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
- return x
-
- def training_step(self, batch, batch_idx, optimizer_idx):
- inputs = self.get_input(batch, self.image_key)
- reconstructions, posterior = self(inputs)
-
- if optimizer_idx == 0:
- # train encoder+decoder+logvar
- aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
- self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
- self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
- return aeloss
-
- if optimizer_idx == 1:
- # train the discriminator
- discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
-
- self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
- self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
- return discloss
-
- def validation_step(self, batch, batch_idx):
- inputs = self.get_input(batch, self.image_key)
- reconstructions, posterior = self(inputs)
- aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
- last_layer=self.get_last_layer(), split="val")
-
- discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
- last_layer=self.get_last_layer(), split="val")
-
- self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
- self.log_dict(log_dict_ae)
- self.log_dict(log_dict_disc)
- return self.log_dict
-
- def configure_optimizers(self):
- lr = self.learning_rate
- opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
- list(self.decoder.parameters())+
- list(self.quant_conv.parameters())+
- list(self.post_quant_conv.parameters()),
- lr=lr, betas=(0.5, 0.9))
- opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
- lr=lr, betas=(0.5, 0.9))
- return [opt_ae, opt_disc], []
-
- def get_last_layer(self):
- return self.decoder.conv_out.weight
-
- @torch.no_grad()
- def log_images(self, batch, only_inputs=False, **kwargs):
- log = dict()
- x = self.get_input(batch, self.image_key)
- x = x.to(self.device)
- if not only_inputs:
- xrec, posterior = self(x)
- if x.shape[1] > 3:
- # colorize with random projection
- assert xrec.shape[1] > 3
- x = self.to_rgb(x)
- xrec = self.to_rgb(xrec)
- log["samples"] = self.decode(torch.randn_like(posterior.sample()))
- log["reconstructions"] = xrec
- log["inputs"] = x
- return log
-
- def to_rgb(self, x):
- assert self.image_key == "segmentation"
- if not hasattr(self, "colorize"):
- self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
- x = F.conv2d(x, weight=self.colorize)
- x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
- return x
-
-
-class IdentityFirstStage(torch.nn.Module):
- def __init__(self, *args, vq_interface=False, **kwargs):
- self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff
- super().__init__()
-
- def encode(self, x, *args, **kwargs):
- return x
-
- def decode(self, x, *args, **kwargs):
- return x
-
- def quantize(self, x, *args, **kwargs):
- if self.vq_interface:
- return x, None, [None, None, None]
- return x
-
- def forward(self, x, *args, **kwargs):
- return x
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py b/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py
deleted file mode 100644
index 81676226e57ca519273b98328a1afe6961c37ce3..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py
+++ /dev/null
@@ -1,403 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA Authors. All rights reserved.
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-# http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Auto Config class."""
-import importlib
-import re
-import warnings
-from collections import OrderedDict
-from typing import List, Union
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.file_utils import CONFIG_NAME
-from transformers.utils import logging
-from .dynamic import get_class_from_dynamic_module
-
-
-logger = logging.get_logger(__name__)
-
-CONFIG_MAPPING_NAMES = OrderedDict(
- [
- # Add configs here
- ("roformer", "RoFormerConfig"),
- ("longformer", "LongformerConfig"),
- ]
-)
-
-CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
- [
- # Add archive maps here
- ("roformer", "ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
- ("longformer", "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
- ]
-)
-
-MODEL_NAMES_MAPPING = OrderedDict(
- [
- # Add full (and cased) model names here
- ("roformer", "Roformer"),
- ("longformer", "Longformer"),
- ]
-)
-
-SPECIAL_MODEL_TYPE_TO_MODULE_NAME = OrderedDict([("openai-gpt", "openai")])
-
-
-def model_type_to_module_name(key):
- """Converts a config key to the corresponding module."""
- # Special treatment
- if key in SPECIAL_MODEL_TYPE_TO_MODULE_NAME:
- return SPECIAL_MODEL_TYPE_TO_MODULE_NAME[key]
-
- return key.replace("-", "_")
-
-
-def config_class_to_model_type(config):
- """Converts a config class name to the corresponding model type"""
- for key, cls in CONFIG_MAPPING_NAMES.items():
- if cls == config:
- return key
- return None
-
-
-class _LazyConfigMapping(OrderedDict):
- """
- A dictionary that lazily load its values when they are requested.
- """
-
- def __init__(self, mapping):
- self._mapping = mapping
- self._extra_content = {}
- self._modules = {}
-
- def __getitem__(self, key):
- if key in self._extra_content:
- return self._extra_content[key]
- if key not in self._mapping:
- raise KeyError(key)
- value = self._mapping[key]
- module_name = model_type_to_module_name(key)
- if module_name not in self._modules:
- self._modules[module_name] = importlib.import_module(f".{module_name}", "fengshen.models")
-
- return getattr(self._modules[module_name], value)
-
- def keys(self):
- return list(self._mapping.keys()) + list(self._extra_content.keys())
-
- def values(self):
- return [self[k] for k in self._mapping.keys()] + list(self._extra_content.values())
-
- def items(self):
- return [(k, self[k]) for k in self._mapping.keys()] + list(self._extra_content.items())
-
- def __iter__(self):
- return iter(list(self._mapping.keys()) + list(self._extra_content.keys()))
-
- def __contains__(self, item):
- return item in self._mapping or item in self._extra_content
-
- def register(self, key, value):
- """
- Register a new configuration in this mapping.
- """
- if key in self._mapping.keys():
- raise ValueError(f"'{key}' is already used by a Transformers config, pick another name.")
- self._extra_content[key] = value
-
-
-CONFIG_MAPPING = _LazyConfigMapping(CONFIG_MAPPING_NAMES)
-
-
-class _LazyLoadAllMappings(OrderedDict):
- """
- A mapping that will load all pairs of key values at the first access (either by indexing, requestions keys, values,
- etc.)
-
- Args:
- mapping: The mapping to load.
- """
-
- def __init__(self, mapping):
- self._mapping = mapping
- self._initialized = False
- self._data = {}
-
- def _initialize(self):
- if self._initialized:
- return
- warnings.warn(
- "ALL_PRETRAINED_CONFIG_ARCHIVE_MAP is deprecated and will be removed in v5 of Transformers. "
- "It does not contain all available model checkpoints, far from it. Checkout hf.co/models for that.",
- FutureWarning,
- )
-
- for model_type, map_name in self._mapping.items():
- module_name = model_type_to_module_name(model_type)
- module = importlib.import_module(f".{module_name}", "transformers.models")
- mapping = getattr(module, map_name)
- self._data.update(mapping)
-
- self._initialized = True
-
- def __getitem__(self, key):
- self._initialize()
- return self._data[key]
-
- def keys(self):
- self._initialize()
- return self._data.keys()
-
- def values(self):
- self._initialize()
- return self._data.values()
-
- def items(self):
- self._initialize()
- return self._data.keys()
-
- def __iter__(self):
- self._initialize()
- return iter(self._data)
-
- def __contains__(self, item):
- self._initialize()
- return item in self._data
-
-
-ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = _LazyLoadAllMappings(CONFIG_ARCHIVE_MAP_MAPPING_NAMES)
-
-
-def _get_class_name(model_class: Union[str, List[str]]):
- if isinstance(model_class, (list, tuple)):
- return " or ".join([f"[`{c}`]" for c in model_class if c is not None])
- return f"[`{model_class}`]"
-
-
-def _list_model_options(indent, config_to_class=None, use_model_types=True):
- if config_to_class is None and not use_model_types:
- raise ValueError("Using `use_model_types=False` requires a `config_to_class` dictionary.")
- if use_model_types:
- if config_to_class is None:
- model_type_to_name = {model_type: f"[`{config}`]" for model_type, config in CONFIG_MAPPING_NAMES.items()}
- else:
- model_type_to_name = {
- model_type: _get_class_name(model_class)
- for model_type, model_class in config_to_class.items()
- if model_type in MODEL_NAMES_MAPPING
- }
- lines = [
- f"{indent}- **{model_type}** -- {model_type_to_name[model_type]} ({MODEL_NAMES_MAPPING[model_type]} model)"
- for model_type in sorted(model_type_to_name.keys())
- ]
- else:
- config_to_name = {
- CONFIG_MAPPING_NAMES[config]: _get_class_name(clas)
- for config, clas in config_to_class.items()
- if config in CONFIG_MAPPING_NAMES
- }
- config_to_model_name = {
- config: MODEL_NAMES_MAPPING[model_type] for model_type, config in CONFIG_MAPPING_NAMES.items()
- }
- lines = [
- f"{indent}- [`{config_name}`] configuration class: {config_to_name[config_name]} ({config_to_model_name[config_name]} model)"
- for config_name in sorted(config_to_name.keys())
- ]
- return "\n".join(lines)
-
-
-def replace_list_option_in_docstrings(config_to_class=None, use_model_types=True):
- def docstring_decorator(fn):
- docstrings = fn.__doc__
- lines = docstrings.split("\n")
- i = 0
- while i < len(lines) and re.search(r"^(\s*)List options\s*$", lines[i]) is None:
- i += 1
- if i < len(lines):
- indent = re.search(r"^(\s*)List options\s*$", lines[i]).groups()[0]
- if use_model_types:
- indent = f"{indent} "
- lines[i] = _list_model_options(indent, config_to_class=config_to_class, use_model_types=use_model_types)
- docstrings = "\n".join(lines)
- else:
- raise ValueError(
- f"The function {fn} should have an empty 'List options' in its docstring as placeholder, current docstring is:\n{docstrings}"
- )
- fn.__doc__ = docstrings
- return fn
-
- return docstring_decorator
-
-
-class AutoConfig:
- r"""
- This is a generic configuration class that will be instantiated as one of the configuration classes of the library
- when created with the [`~AutoConfig.from_pretrained`] class method.
-
- This class cannot be instantiated directly using `__init__()` (throws an error).
- """
-
- def __init__(self):
- raise EnvironmentError(
- "AutoConfig is designed to be instantiated "
- "using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method."
- )
-
- @classmethod
- def for_model(cls, model_type: str, *args, **kwargs):
- if model_type in CONFIG_MAPPING:
- config_class = CONFIG_MAPPING[model_type]
- return config_class(*args, **kwargs)
- raise ValueError(
- f"Unrecognized model identifier: {model_type}. Should contain one of {', '.join(CONFIG_MAPPING.keys())}"
- )
-
- @classmethod
- @replace_list_option_in_docstrings()
- def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
- r"""
- Instantiate one of the configuration classes of the library from a pretrained model configuration.
-
- The configuration class to instantiate is selected based on the `model_type` property of the config object that
- is loaded, or when it's missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:
-
- List options
-
- Args:
- pretrained_model_name_or_path (`str` or `os.PathLike`):
- Can be either:
-
- - A string, the *model id* of a pretrained model configuration hosted inside a model repo on
- huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or
- namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
- - A path to a *directory* containing a configuration file saved using the
- [`~PretrainedConfig.save_pretrained`] method, or the [`~PreTrainedModel.save_pretrained`] method,
- e.g., `./my_model_directory/`.
- - A path or url to a saved configuration JSON *file*, e.g.,
- `./my_model_directory/configuration.json`.
- cache_dir (`str` or `os.PathLike`, *optional*):
- Path to a directory in which a downloaded pretrained model configuration should be cached if the
- standard cache should not be used.
- force_download (`bool`, *optional*, defaults to `False`):
- Whether or not to force the (re-)download the model weights and configuration files and override the
- cached versions if they exist.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether or not to delete incompletely received files. Will attempt to resume the download if such a
- file exists.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
- 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- revision(`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
- git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
- identifier allowed by git.
- return_unused_kwargs (`bool`, *optional*, defaults to `False`):
- If `False`, then this function returns just the final configuration object.
-
- If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a
- dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
- part of `kwargs` which has not been used to update `config` and is otherwise ignored.
- trust_remote_code (`bool`, *optional*, defaults to `False`):
- Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
- should only be set to `True` for repositories you trust and in which you have read the code, as it will
- execute code present on the Hub on your local machine.
- kwargs(additional keyword arguments, *optional*):
- The values in kwargs of any keys which are configuration attributes will be used to override the loaded
- values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
- by the `return_unused_kwargs` keyword parameter.
-
- Examples:
-
- ```python
- >>> from transformers import AutoConfig
-
- >>> # Download configuration from huggingface.co and cache.
- >>> config = AutoConfig.from_pretrained("bert-base-uncased")
-
- >>> # Download configuration from huggingface.co (user-uploaded) and cache.
- >>> config = AutoConfig.from_pretrained("dbmdz/bert-base-german-cased")
-
- >>> # If configuration file is in a directory (e.g., was saved using *save_pretrained('./test/saved_model/')*).
- >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/")
-
- >>> # Load a specific configuration file.
- >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/my_configuration.json")
-
- >>> # Change some config attributes when loading a pretrained config.
- >>> config = AutoConfig.from_pretrained("bert-base-uncased", output_attentions=True, foo=False)
- >>> config.output_attentions
- True
-
- >>> config, unused_kwargs = AutoConfig.from_pretrained(
- ... "bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
- ... )
- >>> config.output_attentions
- True
-
- >>> config.unused_kwargs
- {'foo': False}
- ```"""
- kwargs["_from_auto"] = True
- kwargs["name_or_path"] = pretrained_model_name_or_path
- trust_remote_code = kwargs.pop("trust_remote_code", False)
- config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
- if "auto_map" in config_dict and "AutoConfig" in config_dict["auto_map"]:
- if not trust_remote_code:
- raise ValueError(
- f"Loading {pretrained_model_name_or_path} requires you to execute the configuration file in that repo "
- "on your local machine. Make sure you have read the code there to avoid malicious use, then set "
- "the option `trust_remote_code=True` to remove this error."
- )
- if kwargs.get("revision", None) is None:
- logger.warn(
- "Explicitly passing a `revision` is encouraged when loading a configuration with custom code to "
- "ensure no malicious code has been contributed in a newer revision."
- )
- class_ref = config_dict["auto_map"]["AutoConfig"]
- module_file, class_name = class_ref.split(".")
- config_class = get_class_from_dynamic_module(
- pretrained_model_name_or_path, module_file + ".py", class_name, **kwargs
- )
- return config_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
- elif "model_type" in config_dict:
- config_class = CONFIG_MAPPING[config_dict["model_type"]]
- return config_class.from_dict(config_dict, **kwargs)
- else:
- # Fallback: use pattern matching on the string.
- for pattern, config_class in CONFIG_MAPPING.items():
- if pattern in str(pretrained_model_name_or_path):
- return config_class.from_dict(config_dict, **kwargs)
-
- raise ValueError(
- f"Unrecognized model in {pretrained_model_name_or_path}. "
- f"Should have a `model_type` key in its {CONFIG_NAME}, or contain one of the following strings "
- f"in its name: {', '.join(CONFIG_MAPPING.keys())}"
- )
-
- @staticmethod
- def register(model_type, config):
- """
- Register a new configuration for this class.
-
- Args:
- model_type (`str`): The model type like "bert" or "gpt".
- config ([`PretrainedConfig`]): The config to register.
- """
- if issubclass(config, PretrainedConfig) and config.model_type != model_type:
- raise ValueError(
- "The config you are passing has a `model_type` attribute that is not consistent with the model type "
- f"you passed (config has {config.model_type} and you passed {model_type}. Fix one of those so they "
- "match!"
- )
- CONFIG_MAPPING.register(model_type, config)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py
deleted file mode 100644
index b6fb6e751cdedb2af4b1f6c0950557e187cd9519..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .scst_loss import ScstRewardCriterion
-from .label_smoothed_cross_entropy import AjustLabelSmoothedCrossEntropyCriterion
\ No newline at end of file
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py
deleted file mode 100644
index 56d63e3e1b5a036e0adf32480e2b66f371738013..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from dataclasses import dataclass, field
-
-import torch
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-from omegaconf import II
-
-
-@dataclass
-class LabelSmoothedCrossEntropyCriterionConfig(FairseqDataclass):
- label_smoothing: float = field(
- default=0.0,
- metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"},
- )
- report_accuracy: bool = field(
- default=False,
- metadata={"help": "report accuracy metric"},
- )
- ignore_prefix_size: int = field(
- default=0,
- metadata={"help": "Ignore first N tokens"},
- )
- sentence_avg: bool = II("optimization.sentence_avg")
-
-
-def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=None, reduce=True):
- if target.dim() == lprobs.dim() - 1:
- target = target.unsqueeze(-1)
- nll_loss = -lprobs.gather(dim=-1, index=target)
- smooth_loss = -lprobs.sum(dim=-1, keepdim=True)
- if ignore_index is not None:
- pad_mask = target.eq(ignore_index)
- nll_loss.masked_fill_(pad_mask, 0.0)
- smooth_loss.masked_fill_(pad_mask, 0.0)
- else:
- nll_loss = nll_loss.squeeze(-1)
- smooth_loss = smooth_loss.squeeze(-1)
- if reduce:
- nll_loss = nll_loss.sum()
- smooth_loss = smooth_loss.sum()
- eps_i = epsilon / (lprobs.size(-1) - 1)
- loss = (1.0 - epsilon - eps_i) * nll_loss + eps_i * smooth_loss
- return loss, nll_loss
-
-
-@register_criterion(
- "label_smoothed_cross_entropy", dataclass=LabelSmoothedCrossEntropyCriterionConfig
-)
-class LabelSmoothedCrossEntropyCriterion(FairseqCriterion):
- def __init__(
- self,
- task,
- sentence_avg,
- label_smoothing,
- ignore_prefix_size=0,
- report_accuracy=False,
- ):
- super().__init__(task)
- self.sentence_avg = sentence_avg
- self.eps = label_smoothing
- self.ignore_prefix_size = ignore_prefix_size
- self.report_accuracy = report_accuracy
-
- def forward(self, model, sample, reduce=True):
- """Compute the loss for the given sample.
-
- Returns a tuple with three elements:
- 1) the loss
- 2) the sample size, which is used as the denominator for the gradient
- 3) logging outputs to display while training
- """
- net_output = model(**sample["net_input"])
- loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce)
- sample_size = (
- sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
- )
- logging_output = {
- "loss": loss.data,
- "nll_loss": nll_loss.data,
- "ntokens": sample["ntokens"],
- "nsentences": sample["target"].size(0),
- "sample_size": sample_size,
- }
- if self.report_accuracy:
- n_correct, total = self.compute_accuracy(model, net_output, sample)
- logging_output["n_correct"] = utils.item(n_correct.data)
- logging_output["total"] = utils.item(total.data)
- return loss, sample_size, logging_output
-
- def get_lprobs_and_target(self, model, net_output, sample):
- lprobs = model.get_normalized_probs(net_output, log_probs=True)
- target = model.get_targets(sample, net_output)
- if self.ignore_prefix_size > 0:
- if getattr(lprobs, "batch_first", False):
- lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
- target = target[:, self.ignore_prefix_size :].contiguous()
- else:
- lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous()
- target = target[self.ignore_prefix_size :, :].contiguous()
- return lprobs.view(-1, lprobs.size(-1)), target.view(-1)
-
- def compute_loss(self, model, net_output, sample, reduce=True):
- lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
- loss, nll_loss = label_smoothed_nll_loss(
- lprobs,
- target,
- self.eps,
- ignore_index=self.padding_idx,
- reduce=reduce,
- )
- return loss, nll_loss
-
- def compute_accuracy(self, model, net_output, sample):
- lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
- mask = target.ne(self.padding_idx)
- n_correct = torch.sum(
- lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))
- )
- total = torch.sum(mask)
- return n_correct, total
-
- @classmethod
- def reduce_metrics(cls, logging_outputs) -> None:
- """Aggregate logging outputs from data parallel training."""
- loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
- nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs)
- ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
- sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
-
- metrics.log_scalar(
- "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
- )
- metrics.log_scalar(
- "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3
- )
- metrics.log_derived(
- "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
- )
-
- total = utils.item(sum(log.get("total", 0) for log in logging_outputs))
- if total > 0:
- metrics.log_scalar("total", total)
- n_correct = utils.item(
- sum(log.get("n_correct", 0) for log in logging_outputs)
- )
- metrics.log_scalar("n_correct", n_correct)
- metrics.log_derived(
- "accuracy",
- lambda meters: round(
- meters["n_correct"].sum * 100.0 / meters["total"].sum, 3
- )
- if meters["total"].sum > 0
- else float("nan"),
- )
-
- @staticmethod
- def logging_outputs_can_be_summed() -> bool:
- """
- Whether the logging outputs returned by `forward` can be summed
- across workers prior to calling `reduce_metrics`. Setting this
- to True will improves distributed training speed.
- """
- return True
diff --git a/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md b/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md
deleted file mode 100644
index d4654cdd019b1496d4c953944c9aced8a0dcc67a..0000000000000000000000000000000000000000
--- a/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Sail-rvc-Ai Hoshino From Oshi No Ko RVC V2 300 Epoch
-emoji: 📈
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Hina4867/bingo/src/components/ui/select.tsx b/spaces/Hina4867/bingo/src/components/ui/select.tsx
deleted file mode 100644
index 77f12c2996f541b97663de4c9e20ab34d4ec2fac..0000000000000000000000000000000000000000
--- a/spaces/Hina4867/bingo/src/components/ui/select.tsx
+++ /dev/null
@@ -1,123 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SelectPrimitive from '@radix-ui/react-select'
-
-import { cn } from '@/lib/utils'
-import {
- IconArrowDown,
- IconCheck,
- IconChevronUpDown
-} from '@/components/ui/icons'
-
-const Select = SelectPrimitive.Root
-
-const SelectGroup = SelectPrimitive.Group
-
-const SelectValue = SelectPrimitive.Value
-
-const SelectTrigger = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-
- {children}
-
-
-
-))
-SelectTrigger.displayName = SelectPrimitive.Trigger.displayName
-
-const SelectContent = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, position = 'popper', ...props }, ref) => (
-
-
-
- {children}
-
-
-
-))
-SelectContent.displayName = SelectPrimitive.Content.displayName
-
-const SelectLabel = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-
-
-
-
-
- {children}
-
-))
-SelectItem.displayName = SelectPrimitive.Item.displayName
-
-const SelectSeparator = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- {o.delete(i),o.size===0&&n&&(n(),n=null)}}return{set:a,update:l,subscribe:r}}var g;const E=((g=globalThis.__sveltekit_1d38e1f)==null?void 0:g.base)??"";var k;const S=((k=globalThis.__sveltekit_1d38e1f)==null?void 0:k.assets)??E,w="1698134506027",y="sveltekit:snapshot",I="sveltekit:scroll",x="sveltekit:index",_={tap:1,hover:2,viewport:3,eager:4,off:-1};function O(e){let t=e.baseURI;if(!t){const n=e.getElementsByTagName("base");t=n.length?n[0].href:e.URL}return t}function U(){return{x:pageXOffset,y:pageYOffset}}function f(e,t){return e.getAttribute(`data-sveltekit-${t}`)}const b={..._,"":_.hover};function v(e){let t=e.assignedSlot??e.parentNode;return(t==null?void 0:t.nodeType)===11&&(t=t.host),t}function L(e,t){for(;e&&e!==t;){if(e.nodeName.toUpperCase()==="A"&&e.hasAttribute("href"))return e;e=v(e)}}function N(e,t){let n;try{n=new URL(e instanceof SVGAElement?e.href.baseVal:e.href,document.baseURI)}catch{}const o=e instanceof SVGAElement?e.target.baseVal:e.target,a=!n||!!o||R(n,t)||(e.getAttribute("rel")||"").split(/\s+/).includes("external"),l=(n==null?void 0:n.origin)===location.origin&&e.hasAttribute("download");return{url:n,external:a,target:o,download:l}}function P(e){let t=null,n=null,o=null,a=null,l=null,r=null,s=e;for(;s&&s!==document.documentElement;)o===null&&(o=f(s,"preload-code")),a===null&&(a=f(s,"preload-data")),t===null&&(t=f(s,"keepfocus")),n===null&&(n=f(s,"noscroll")),l===null&&(l=f(s,"reload")),r===null&&(r=f(s,"replacestate")),s=v(s);return{preload_code:b[o??"off"],preload_data:b[a??"off"],keep_focus:t==="off"?!1:t===""?!0:null,noscroll:n==="off"?!1:n===""?!0:null,reload:l==="off"?!1:l===""?!0:null,replace_state:r==="off"?!1:r===""?!0:null}}function h(e){const t=p(e);let n=!0;function o(){n=!0,t.update(r=>r)}function a(r){n=!1,t.set(r)}function l(r){let s;return t.subscribe(u=>{(s===void 0||n&&u!==s)&&r(s=u)})}return{notify:o,set:a,subscribe:l}}function A(){const{set:e,subscribe:t}=p(!1);let n;async function o(){clearTimeout(n);try{const a=await fetch(`${S}/_app/version.json`,{headers:{pragma:"no-cache","cache-control":"no-cache"}});if(!a.ok)return!1;const r=(await a.json()).version!==w;return r&&(e(!0),clearTimeout(n)),r}catch{return!1}}return{subscribe:t,check:o}}function R(e,t){return e.origin!==location.origin||!e.pathname.startsWith(t)}function V(e){e.client}const Y={url:h({}),page:h({}),navigating:p(null),updated:A()};export{x as I,_ as P,I as S,y as a,N as b,P as c,U as d,E as e,L as f,O as g,V as h,R as i,Y as s};
diff --git a/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py b/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py
deleted file mode 100644
index f8e2eb0f15699f1b458a8445d0c1dd6229a21f77..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-import os, sys
-import subprocess
-import re
-from subprocess import check_call, check_output
-
-WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None)
-
-if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip():
- print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."')
- sys.exit(-1)
-
-
-BLEU_REGEX = re.compile("^BLEU\\S* = (\\S+) ")
-def run_eval_bleu(cmd):
- output = check_output(cmd, shell=True, stderr=subprocess.STDOUT).decode("utf-8").strip()
- print(output)
- bleu = -1.0
- for line in output.strip().split('\n'):
- m = BLEU_REGEX.search(line)
- if m is not None:
- bleu = m.groups()[0]
- bleu = float(bleu)
- break
- return bleu
-
-def check_data_test_bleu(raw_folder, data_lang_pairs):
- not_matchings = []
- for sacrebleu_set, src_tgts in data_lang_pairs:
- for src_tgt in src_tgts:
- print(f'checking test bleus for: {src_tgt} at {sacrebleu_set}')
- src, tgt = src_tgt.split('-')
- ssrc, stgt = src[:2], tgt[:2]
- if os.path.exists(f'{raw_folder}/test.{tgt}-{src}.{src}'):
- # reversed direction may have different test set
- test_src = f'{raw_folder}/test.{tgt}-{src}.{src}'
- else:
- test_src = f'{raw_folder}/test.{src}-{tgt}.{src}'
- cmd1 = f'cat {test_src} | sacrebleu -t "{sacrebleu_set}" -l {stgt}-{ssrc}; [ $? -eq 0 ] || echo ""'
- test_tgt = f'{raw_folder}/test.{src}-{tgt}.{tgt}'
- cmd2 = f'cat {test_tgt} | sacrebleu -t "{sacrebleu_set}" -l {ssrc}-{stgt}; [ $? -eq 0 ] || echo ""'
- bleu1 = run_eval_bleu(cmd1)
- if bleu1 != 100.0:
- not_matchings.append(f'{sacrebleu_set}:{src_tgt} source side not matching: {test_src}')
- bleu2 = run_eval_bleu(cmd2)
- if bleu2 != 100.0:
- not_matchings.append(f'{sacrebleu_set}:{src_tgt} target side not matching: {test_tgt}')
- return not_matchings
-
-if __name__ == "__main__":
- to_data_path = f'{WORKDIR_ROOT}/iwsltv2'
- not_matching = check_data_test_bleu(
- f'{to_data_path}/raw',
- [
- ('iwslt17', ['en_XX-ar_AR', 'en_XX-ko_KR', 'ar_AR-en_XX', 'ko_KR-en_XX']),
- ('iwslt17', ['en_XX-it_IT', 'en_XX-nl_XX', 'it_IT-en_XX', 'nl_XX-en_XX']),
- ('iwslt17/tst2015', ['en_XX-vi_VN', "vi_VN-en_XX"]),
- ]
- )
- if len(not_matching) > 0:
- print('the following datasets do not have matching test datasets:\n\t', '\n\t'.join(not_matching))
-
diff --git a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py b/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py
deleted file mode 100644
index 88597d62a505715091f9ba62d38bf0a85a31b95a..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import math
-import os
-os.environ["LRU_CACHE_CAPACITY"] = "3"
-import random
-import torch
-import torch.utils.data
-import numpy as np
-import librosa
-from librosa.util import normalize
-from librosa.filters import mel as librosa_mel_fn
-from scipy.io.wavfile import read
-import soundfile as sf
-
-def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
- sampling_rate = None
- try:
- data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
- except Exception as ex:
- print(f"'{full_path}' failed to load.\nException:")
- print(ex)
- if return_empty_on_exception:
- return [], sampling_rate or target_sr or 32000
- else:
- raise Exception(ex)
-
- if len(data.shape) > 1:
- data = data[:, 0]
- assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
-
- if np.issubdtype(data.dtype, np.integer): # if audio data is type int
- max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
- else: # if audio data is type fp32
- max_mag = max(np.amax(data), -np.amin(data))
- max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
-
- data = torch.FloatTensor(data.astype(np.float32))/max_mag
-
- if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
- return [], sampling_rate or target_sr or 32000
- if target_sr is not None and sampling_rate != target_sr:
- data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
- sampling_rate = target_sr
-
- return data, sampling_rate
-
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
- return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
-
-def dynamic_range_decompression(x, C=1):
- return np.exp(x) / C
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-def dynamic_range_decompression_torch(x, C=1):
- return torch.exp(x) / C
-
-class STFT():
- def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
- self.target_sr = sr
-
- self.n_mels = n_mels
- self.n_fft = n_fft
- self.win_size = win_size
- self.hop_length = hop_length
- self.fmin = fmin
- self.fmax = fmax
- self.clip_val = clip_val
- self.mel_basis = {}
- self.hann_window = {}
-
- def get_mel(self, y, center=False):
- sampling_rate = self.target_sr
- n_mels = self.n_mels
- n_fft = self.n_fft
- win_size = self.win_size
- hop_length = self.hop_length
- fmin = self.fmin
- fmax = self.fmax
- clip_val = self.clip_val
-
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- if fmax not in self.mel_basis:
- mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
- self.mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
- self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_length)/2), int((n_fft-hop_length)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)],
- center=center, pad_mode='reflect', normalized=False, onesided=True)
- # print(111,spec)
- spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
- # print(222,spec)
- spec = torch.matmul(self.mel_basis[str(fmax)+'_'+str(y.device)], spec)
- # print(333,spec)
- spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
- # print(444,spec)
- return spec
-
- def __call__(self, audiopath):
- audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
- spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
- return spect
-
-stft = STFT()
diff --git a/spaces/JUNGU/emotion-ko-state/README.md b/spaces/JUNGU/emotion-ko-state/README.md
deleted file mode 100644
index 48eb244083a0ac6a51b0298cd043231b60b3fde8..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/emotion-ko-state/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Emotion Ko State
-emoji: 🦀
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py b/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py
deleted file mode 100644
index 27fcd62482d0cf44e02fa1c339195be58cb745b0..0000000000000000000000000000000000000000
--- a/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import pinecone
-from colorama import Fore, Style
-
-from autogpt.llm_utils import create_embedding_with_ada
-from autogpt.logs import logger
-from autogpt.memory.base import MemoryProviderSingleton
-
-
-class PineconeMemory(MemoryProviderSingleton):
- def __init__(self, cfg):
- pinecone_api_key = cfg.pinecone_api_key
- pinecone_region = cfg.pinecone_region
- pinecone.init(api_key=pinecone_api_key, environment=pinecone_region)
- dimension = 1536
- metric = "cosine"
- pod_type = "p1"
- table_name = "auto-gpt"
- # this assumes we don't start with memory.
- # for now this works.
- # we'll need a more complicated and robust system if we want to start with
- # memory.
- self.vec_num = 0
-
- try:
- pinecone.whoami()
- except Exception as e:
- logger.typewriter_log(
- "FAILED TO CONNECT TO PINECONE",
- Fore.RED,
- Style.BRIGHT + str(e) + Style.RESET_ALL,
- )
- logger.double_check(
- "Please ensure you have setup and configured Pinecone properly for use."
- + f"You can check out {Fore.CYAN + Style.BRIGHT}"
- "https://github.com/Torantulino/Auto-GPT#-pinecone-api-key-setup"
- f"{Style.RESET_ALL} to ensure you've set up everything correctly."
- )
- exit(1)
-
- if table_name not in pinecone.list_indexes():
- pinecone.create_index(
- table_name, dimension=dimension, metric=metric, pod_type=pod_type
- )
- self.index = pinecone.Index(table_name)
-
- def add(self, data):
- vector = create_embedding_with_ada(data)
- # no metadata here. We may wish to change that long term.
- self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})])
- _text = f"Inserting data into memory at index: {self.vec_num}:\n data: {data}"
- self.vec_num += 1
- return _text
-
- def get(self, data):
- return self.get_relevant(data, 1)
-
- def clear(self):
- self.index.delete(deleteAll=True)
- return "Obliviated"
-
- def get_relevant(self, data, num_relevant=5):
- """
- Returns all the data in the memory that is relevant to the given data.
- :param data: The data to compare to.
- :param num_relevant: The number of relevant data to return. Defaults to 5
- """
- query_embedding = create_embedding_with_ada(data)
- results = self.index.query(
- query_embedding, top_k=num_relevant, include_metadata=True
- )
- sorted_results = sorted(results.matches, key=lambda x: x.score)
- return [str(item["metadata"]["raw_text"]) for item in sorted_results]
-
- def get_stats(self):
- return self.index.describe_index_stats()
diff --git a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py b/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py
deleted file mode 100644
index 1cc3b96da1275f1d01afb4c999f9c404d09b247a..0000000000000000000000000000000000000000
--- a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import cv2
-import numpy as np
-from PIL import Image
-import pickle
-import tensorflow as tf
-import os
-
-class rabbitsLogReg:
- def __init__(self,url) -> None:
- self.image = url
-
- def predict_image(self):
- # Load the model
- load_extractor = tf.keras.models.load_model("././Model/Rabbit/resnetLogreg/resnet_EXTRACTOR.h5")
-
- modelpath = "././Model/Rabbit/resnetLogreg/dataSaved.pkl"
-
- with open(modelpath, 'rb') as file:
- saved_data = pickle.load(file)
- animal_breed = saved_data['class_name']
- model = saved_data['logreg_model']
-
- im = Image.open(self.image)
- img = im.convert("RGB")
- img= np.asarray(img)
- image_resized= cv2.resize(img, (224,224))
- features = load_extractor.predict(np.expand_dims(image_resized, axis=0))
-
- reshaped_features = features.reshape(features.shape[0],-1)
- predicted_class = model.predict(reshaped_features)
- pred_prob = model.predict_proba(reshaped_features)[:2]
- prediction_probability = pred_prob[0][predicted_class[0]]
- predicted_class
-
- output_class= animal_breed[predicted_class[0]]
-
- return [output_class, prediction_probability]
diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md b/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md
deleted file mode 100644
index 80af4fbbfba5d15e1cb6d1f4b67808ca76fa37d7..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md
+++ /dev/null
@@ -1,140 +0,0 @@
-
-
-川虎 Chat 🐯 Chuanhu Chat
-
-
-
-
-
-
-
Lightweight and User-friendly Web-UI for LLMs including ChatGPT/ChatGLM/LLaMA
-
- Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search
- LaTeX rendering / Table rendering / Code highlighting
- Auto dark mode / Adaptive web interface / WeChat-like theme
- Multi-parameters tuning / Multi-API-Key support / Multi-user support
- Compatible with GPT-4 / Local deployment for LLMs
-
- Video Tutorial
- ·
- 2.0 Introduction
- ·
- 3.0 Introduction & Tutorial
- ||
- Online trial
- ·
- One-Click deployment
-
-
-
-
-
-
-
-## Supported LLM Models
-
-**LLM models via API**:
-
-- [ChatGPT](https://chat.openai.com) ([GPT-4](https://openai.com/product/gpt-4))
-- [Google PaLM](https://developers.generativeai.google/products/palm)
-- [Inspur Yuan 1.0](https://air.inspur.com/home)
-- [MiniMax](https://api.minimax.chat/)
-- [XMChat](https://github.com/MILVLG/xmchat)
-
-**LLM models via local deployment**:
-
-- [ChatGLM](https://github.com/THUDM/ChatGLM-6B) ([ChatGLM2](https://github.com/THUDM/ChatGLM2-6B))
-- [LLaMA](https://github.com/facebookresearch/llama)
-- [StableLM](https://github.com/Stability-AI/StableLM)
-- [MOSS](https://github.com/OpenLMLab/MOSS)
-
-## Usage Tips
-
-- To better control the ChatGPT, use System Prompt.
-- To use a Prompt Template, select the Prompt Template Collection file first, and then choose certain prompt from the drop-down menu.
-- To try again if the response is unsatisfactory, use `🔄 Regenerate` button.
-- To start a new line in the input box, press Shift + Enter keys.
-- To quickly switch between input history, press ↑ and ↓ key in the input box.
-- To deploy the program onto a server, set `"server_name": "0.0.0.0", "server_port" ,` in `config.json`.
-- To get a public shared link, set `"share": true,` in `config.json`. Please be noted that the program must be running in order to be accessed via a public link.
-- To use it in Hugging Face Spaces: It is recommended to **Duplicate Space** and run the program in your own Space for a faster and more secure experience.
-
-## Quickstart
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-Then make a copy of `config_example.json`, rename it to `config.json`, and then fill in your API-Key and other settings in the file.
-
-```shell
-python ChuanhuChatbot.py
-```
-
-A browser window will open and you will be able to chat with ChatGPT.
-
-> **Note**
->
-> Please check our [wiki page](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程) for detailed instructions.
-
-## Troubleshooting
-
-When you encounter problems, you should try manually pulling the latest changes of this project first. The steps are as follows:
-
-1. Download the latest code archive by clicking on `Download ZIP` on the webpage, or
- ```shell
- git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
- ```
-2. Try installing the dependencies again (as this project may have introduced new dependencies)
- ```
- pip install -r requirements.txt
- ```
-
-Generally, you can solve most problems by following these steps.
-
-If the problem still exists, please refer to this page: [Frequently Asked Questions (FAQ)](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-This page lists almost all the possible problems and solutions. Please read it carefully.
-
-## More Information
-
-More information could be found in our [wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki):
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-
-
-
-
-## Sponsor
-
-🐯 If you find this project helpful, feel free to buy me a coke or a cup of coffee~
-
-
-
-
diff --git a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py b/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py
deleted file mode 100644
index 59a73cc04025762d6490fcd2945a747d963def32..0000000000000000000000000000000000000000
--- a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from os import environ
-
-
-def assert_in(file, files_to_check):
- if file not in files_to_check:
- raise AssertionError("{} does not exist in the list".format(str(file)))
- return True
-
-
-def assert_in_env(check_list: list):
- for item in check_list:
- assert_in(item, environ.keys())
- return True
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py
deleted file mode 100644
index beb6e65c61f8a16b4376494123f31178cdb88bde..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py
+++ /dev/null
@@ -1,13 +0,0 @@
-CUSTOM_STREAMLIT_CSS = """
-div[data-testid="stBlock"] button {
- width: 100% !important;
- margin-bottom: 20px !important;
- border-color: #bfbfbf !important;
-}
-section[data-testid="stSidebar"] div {
- max-width: 10rem;
-}
-pre code {
- white-space: pre-wrap;
-}
-"""
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py
deleted file mode 100644
index 34bcffd6c0975377a54ae1ce89002be1dae8151d..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import matplotlib
-matplotlib.use('Agg')
-import time
-
-class Timer():
- ''' Timer for recording training time distribution. '''
- def __init__(self):
- self.prev_t = time.time()
- self.clear()
-
- def set(self):
- self.prev_t = time.time()
-
- def cnt(self, mode):
- self.time_table[mode] += time.time()-self.prev_t
- self.set()
- if mode == 'bw':
- self.click += 1
-
- def show(self):
- total_time = sum(self.time_table.values())
- self.time_table['avg'] = total_time/self.click
- self.time_table['rd'] = 100*self.time_table['rd']/total_time
- self.time_table['fw'] = 100*self.time_table['fw']/total_time
- self.time_table['bw'] = 100*self.time_table['bw']/total_time
- msg = '{avg:.3f} sec/step (rd {rd:.1f}% | fw {fw:.1f}% | bw {bw:.1f}%)'.format(
- **self.time_table)
- self.clear()
- return msg
-
- def clear(self):
- self.time_table = {'rd': 0, 'fw': 0, 'bw': 0}
- self.click = 0
-
-# Reference : https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/e2e_asr.py#L168
-
-def human_format(num):
- magnitude = 0
- while num >= 1000:
- magnitude += 1
- num /= 1000.0
- # add more suffixes if you need them
- return '{:3.1f}{}'.format(num, [' ', 'K', 'M', 'G', 'T', 'P'][magnitude])
-
-
-# provide easy access of attribute from dict, such abc.key
-class AttrDict(dict):
- def __init__(self, *args, **kwargs):
- super(AttrDict, self).__init__(*args, **kwargs)
- self.__dict__ = self
diff --git a/spaces/KevinQHLin/UniVTG/main/train_mr.py b/spaces/KevinQHLin/UniVTG/main/train_mr.py
deleted file mode 100644
index 1a10d029f81f86733d6dab71a3aee575917b092b..0000000000000000000000000000000000000000
--- a/spaces/KevinQHLin/UniVTG/main/train_mr.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import os
-import pdb
-import sys
-import time
-import json
-import pprint
-import random
-import numpy as np
-from tqdm import tqdm, trange
-from collections import defaultdict
-
-import torch
-import torch.nn as nn
-import torch.backends.cudnn as cudnn
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-
-sys.path.append('/data/home/qinghonglin/univtg')
-from main.config import BaseOptions, setup_model
-from main.dataset import \
- DatasetMR, start_end_collate_mr, prepare_batch_inputs_mr
-from main.inference_mr import eval_epoch, start_inference
-from utils.basic_utils import set_seed, AverageMeter, dict_to_markdown
-from utils.model_utils import count_parameters
-
-import logging
-logger = logging.getLogger(__name__)
-logging.basicConfig(format="%(asctime)s.%(msecs)03d:%(levelname)s:%(name)s - %(message)s",
- datefmt="%Y-%m-%d %H:%M:%S",
- level=logging.INFO)
-
-def train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer):
- logger.info(f"[Epoch {epoch_i+1}]")
- model.train()
- criterion.train()
-
- # init meters
- time_meters = defaultdict(AverageMeter)
- loss_meters = defaultdict(AverageMeter)
-
- num_training_examples = len(train_loader)
- timer_dataloading = time.time()
- for batch_idx, batch in tqdm(enumerate(train_loader),
- desc="Training Iteration",
- total=num_training_examples):
- time_meters["dataloading_time"].update(time.time() - timer_dataloading)
-
- timer_start = time.time()
- model_inputs, targets = prepare_batch_inputs_mr(batch[1], opt.device, non_blocking=opt.pin_memory)
- time_meters["prepare_inputs_time"].update(time.time() - timer_start)
-
- timer_start = time.time()
-
- # try:
- outputs = model(**model_inputs)
- loss_dict = criterion(outputs, targets)
- weight_dict = criterion.weight_dict
- losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
- time_meters["model_forward_time"].update(time.time() - timer_start)
-
- timer_start = time.time()
- optimizer.zero_grad()
- losses.backward()
-
- if opt.grad_clip > 0:
- nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
- optimizer.step()
- time_meters["model_backward_time"].update(time.time() - timer_start)
-
- loss_dict["loss_overall"] = float(losses) # for logging only
- for k, v in loss_dict.items():
- loss_meters[k].update(float(v) * weight_dict[k] if k in weight_dict else float(v))
-
- timer_dataloading = time.time()
-
- # print/add logs
- tb_writer.add_scalar("Train/lr", float(optimizer.param_groups[0]["lr"]), epoch_i+1)
- for k, v in loss_meters.items():
- tb_writer.add_scalar("Train/{}".format(k), v.avg, epoch_i+1)
-
- to_write = opt.train_log_txt_formatter.format(
- time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
- epoch=epoch_i+1,
- loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in loss_meters.items()]))
- with open(opt.train_log_filepath, "a") as f:
- f.write(to_write)
-
- logger.info("Epoch time stats:")
- for name, meter in time_meters.items():
- d = {k: f"{getattr(meter, k):.4f}" for k in ["max", "min", "avg"]}
- logger.info(f"{name} ==> {d}")
-
-
-def train(model, criterion, optimizer, lr_scheduler, train_dataset, val_dataset, opt):
- tb_writer = SummaryWriter(opt.tensorboard_log_dir)
- tb_writer.add_text("hyperparameters", dict_to_markdown(vars(opt), max_str_len=None))
- opt.train_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str}\n"
- opt.eval_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str} [Metrics] {eval_metrics_str}\n"
-
- train_loader = DataLoader(
- train_dataset,
- collate_fn=start_end_collate_mr,
- batch_size=opt.bsz,
- num_workers=opt.num_workers,
- shuffle=True,
- pin_memory=opt.pin_memory
- )
-
- prev_best_score = 0.
- es_cnt = 0
- if opt.start_epoch is None:
- start_epoch = -1 if opt.eval_init else 0
- else:
- start_epoch = opt.start_epoch
- save_submission_filename = "latest_{}_{}_preds.jsonl".format(opt.dset_name, opt.eval_split_name)
- for epoch_i in trange(start_epoch, opt.n_epoch, desc="Epoch"):
- if epoch_i > -1:
- train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer)
- lr_scheduler.step()
- eval_epoch_interval = opt.eval_epoch
- if opt.eval_path is not None and (epoch_i + 1) % eval_epoch_interval == 0:
- with torch.no_grad():
- metrics_no_nms, metrics_nms, eval_loss_meters, latest_file_paths = \
- eval_epoch(model, val_dataset, opt, save_submission_filename, epoch_i, criterion, tb_writer)
-
- # log
- to_write = opt.eval_log_txt_formatter.format(
- time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
- epoch=epoch_i,
- loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in eval_loss_meters.items()]),
- eval_metrics_str=json.dumps(metrics_no_nms))
-
- with open(opt.eval_log_filepath, "a") as f:
- f.write(to_write)
- logger.info("metrics_no_nms {}".format(pprint.pformat(metrics_no_nms["brief"], indent=4)))
- if metrics_nms is not None:
- logger.info("metrics_nms {}".format(pprint.pformat(metrics_nms["brief"], indent=4)))
-
- metrics = metrics_nms if metrics_nms is not None else metrics_no_nms
- for k, v in metrics["brief"].items():
- tb_writer.add_scalar(f"Eval/{k}", float(v), epoch_i+1)
-
- # stop_score = metrics["brief"]["MR-full-mAP"]
- # pdb.set_trace()
- stop_score = metrics["brief"][opt.main_metric]
- if stop_score > prev_best_score:
- es_cnt = 0
- prev_best_score = stop_score
-
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "lr_scheduler": lr_scheduler.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"))
-
- best_file_paths = [e.replace("latest", "best") for e in latest_file_paths]
- for src, tgt in zip(latest_file_paths, best_file_paths):
- os.renames(src, tgt)
- logger.info("The checkpoint file has been updated.")
- else:
- es_cnt += 1
- if opt.max_es_cnt != -1 and es_cnt > opt.max_es_cnt: # early stop
- with open(opt.train_log_filepath, "a") as f:
- f.write(f"Early Stop at epoch {epoch_i}")
- logger.info(f"\n>>>>> Early stop at epoch {epoch_i} {prev_best_score}\n")
- break
-
- # save ckpt
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "lr_scheduler": lr_scheduler.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_latest.ckpt"))
-
- if (epoch_i + 1) % opt.save_interval == 0 or (epoch_i + 1) % opt.lr_drop == 0: # additional copies
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", f"_e{epoch_i:04d}.ckpt"))
-
- if opt.debug:
- break
-
- tb_writer.close()
-
-
-def start_training():
- logger.info("Setup config, data and model...")
- opt = BaseOptions().parse()
- set_seed(opt.seed)
- if opt.debug: # keep the model run deterministically
- # 'cudnn.benchmark = True' enabled auto finding the best algorithm for a specific input/net config.
- # Enable this only when input size is fixed.
- cudnn.benchmark = False
- cudnn.deterministic = True
-
- dataset_config = dict(
- dset_name=opt.dset_name,
- data_path=opt.train_path,
- v_feat_dirs=opt.v_feat_dirs,
- q_feat_dir=opt.t_feat_dir,
- v_feat_dim=opt.v_feat_dim,
- q_feat_dim=opt.t_feat_dim,
- q_feat_type="last_hidden_state",
- max_q_l=opt.max_q_l,
- max_v_l=opt.max_v_l,
- ctx_mode=opt.ctx_mode,
- data_ratio=opt.data_ratio,
- normalize_v=not opt.no_norm_vfeat,
- normalize_t=not opt.no_norm_tfeat,
- clip_len=opt.clip_length,
- max_windows=opt.max_windows,
- span_loss_type=opt.span_loss_type,
- txt_drop_ratio=opt.txt_drop_ratio,
- use_cache=opt.use_cache,
- add_easy_negative=opt.add_easy_negative,
- easy_negative_only=opt.easy_negative_only
- )
-
- dataset_config["data_path"] = opt.train_path
- train_dataset = DatasetMR(**dataset_config)
-
- if opt.eval_path is not None:
- dataset_config["data_path"] = opt.eval_path
- dataset_config["txt_drop_ratio"] = 0
- dataset_config["q_feat_dir"] = opt.t_feat_dir.replace("txt_clip_asr", "txt_clip").replace("txt_clip_cap", "txt_clip") # for pretraining
- # dataset_config["load_labels"] = False # uncomment to calculate eval loss
- eval_dataset = DatasetMR(**dataset_config)
- else:
- eval_dataset = None
-
- if opt.lr_warmup > 0:
- # total_steps = opt.n_epoch * len(train_dataset) // opt.bsz
- total_steps = opt.n_epoch
- warmup_steps = opt.lr_warmup if opt.lr_warmup > 1 else int(opt.lr_warmup * total_steps)
- opt.lr_warmup = [warmup_steps, total_steps]
- model, criterion, optimizer, lr_scheduler = setup_model(opt)
- logger.info(f"Model {model}")
- count_parameters(model)
- logger.info("Start Training...")
- train(model, criterion, optimizer, lr_scheduler, train_dataset, eval_dataset, opt)
- return opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"), opt.eval_split_name, opt.eval_path, opt.debug
-
-
-if __name__ == '__main__':
- best_ckpt_path, eval_split_name, eval_path, debug = start_training()
- if not debug:
- input_args = ["--resume", best_ckpt_path,
- "--eval_split_name", eval_split_name,
- "--eval_path", eval_path]
-
- import sys
- sys.argv[1:] = input_args
- logger.info("\n\n\nFINISHED TRAINING!!!")
- logger.info("Evaluating model at {}".format(best_ckpt_path))
- logger.info("Input args {}".format(sys.argv[1:]))
- start_inference()
\ No newline at end of file
diff --git a/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md b/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md
deleted file mode 100644
index 1f55e11e80f6fc5ebbf42dade0266e3d4ee06ce4..0000000000000000000000000000000000000000
--- a/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
-# Transliteration Models for Indian languages
-These are models for transliteration involving Indian languages.
-The models are essentially Statistical Machine Translation systems trained using Moses over a
-character-level parallel corpora of transliterations. Hence, you will need Moses to use these transliteration models.
-The transliteration corpus has itself been mined in an unsupervised fashion from a translation corpus.
-
-Currently we have trained transliteration models for five language pairs: bn-hi, ta-hi, te-hi, en-hi and mr-hi
-
-Support for transliteration has been introduced in Moses from version 2.1
-So please ensure that you have minimum 2.1 version setup for Moses
-
-Commands to run the transliteration module using moses
-
-$moseshome/mosesdecoder/scripts/Transliteration/post-decoding-transliteration.pl \
---moses-src-dir $moseshome/mosesdecoder --external-bin-dir $moseshome/tools \
---transliteration-model-dir {path to transliteration model folder} --oov-file {path to file containing oov words, oovs are space separated with each line containing all oovs for the input line}\
- --input-file {input file to transliterated} --output-file {output file location} \
- --input-extension {input language code for eg. en} --output-extension {output language code for eg. hi} --language-model {path to language model} \
- --decoder $moseshome/mosesdecoder/bin/moses
-
-A sample execution of the model will be as follows:
-
-export moseshome={path to moses installation}
-$moseshome/mosesdecoder/scripts/Transliteration/post-decoding-transliteration.pl \
---moses-src-dir $moseshome/mosesdecoder --external-bin-dir $moseshome/tools \
---transliteration-model-dir /home/ratish/project/nlp_resources/indic_nlp_resources/transliterate/en-hi \
---oov-file /home/ratish/project/translit/input.oov \
- --input-file /home/ratish/project/translit/input.en \
- --output-file /home/ratish/project/translit/output.hi \
- --input-extension en --output-extension hi --language-model /home/ratish/project/translit/lm/nc.binlm.1 \
- --decoder $moseshome/mosesdecoder/bin/moses
-
-So far, we have seen the use of transliteration in a post-editing task for machine translation task.
-In case, the models are needed for purely transliteration purpose, the input file and OOV file are the same.
-Sample input file:
-New Delhi is capital of India
-India is worlds seventh largest nation in the World
-
-OOV file
-New Delhi is capital of India
-India is worlds seventh largest nation in the World
-
-On running the transliteration module, the output is:
-न्यू डेल्ही इस कैपिटल आफ इंडिया
-इंडिया इस वर्ल्ड सेवंथ लारगेस्ट नेशन इन थे वर्ल्ड
diff --git a/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py b/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py
deleted file mode 100644
index 980440eb3434e48b7cad90f0577c838a05d787b4..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Sequence
-
-from torch.utils.data import BatchSampler, Sampler
-
-from mmdet.registry import DATA_SAMPLERS
-
-
-# TODO: maybe replace with a data_loader wrapper
-@DATA_SAMPLERS.register_module()
-class AspectRatioBatchSampler(BatchSampler):
- """A sampler wrapper for grouping images with similar aspect ratio (< 1 or.
-
- >= 1) into a same batch.
-
- Args:
- sampler (Sampler): Base sampler.
- batch_size (int): Size of mini-batch.
- drop_last (bool): If ``True``, the sampler will drop the last batch if
- its size would be less than ``batch_size``.
- """
-
- def __init__(self,
- sampler: Sampler,
- batch_size: int,
- drop_last: bool = False) -> None:
- if not isinstance(sampler, Sampler):
- raise TypeError('sampler should be an instance of ``Sampler``, '
- f'but got {sampler}')
- if not isinstance(batch_size, int) or batch_size <= 0:
- raise ValueError('batch_size should be a positive integer value, '
- f'but got batch_size={batch_size}')
- self.sampler = sampler
- self.batch_size = batch_size
- self.drop_last = drop_last
- # two groups for w < h and w >= h
- self._aspect_ratio_buckets = [[] for _ in range(2)]
-
- def __iter__(self) -> Sequence[int]:
- for idx in self.sampler:
- data_info = self.sampler.dataset.get_data_info(idx)
- width, height = data_info['width'], data_info['height']
- bucket_id = 0 if width < height else 1
- bucket = self._aspect_ratio_buckets[bucket_id]
- bucket.append(idx)
- # yield a batch of indices in the same aspect ratio group
- if len(bucket) == self.batch_size:
- yield bucket[:]
- del bucket[:]
-
- # yield the rest data and reset the bucket
- left_data = self._aspect_ratio_buckets[0] + self._aspect_ratio_buckets[
- 1]
- self._aspect_ratio_buckets = [[] for _ in range(2)]
- while len(left_data) > 0:
- if len(left_data) <= self.batch_size:
- if not self.drop_last:
- yield left_data[:]
- left_data = []
- else:
- yield left_data[:self.batch_size]
- left_data = left_data[self.batch_size:]
-
- def __len__(self) -> int:
- if self.drop_last:
- return len(self.sampler) // self.batch_size
- else:
- return (len(self.sampler) + self.batch_size - 1) // self.batch_size
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py
deleted file mode 100644
index 430b06d085d94760d56a7ea083eaf23bd32b1f53..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py
+++ /dev/null
@@ -1,722 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, Scale
-from mmcv.ops import DeformConv2d
-from torch import Tensor
-
-from mmdet.registry import MODELS, TASK_UTILS
-from mmdet.structures.bbox import bbox_overlaps
-from mmdet.utils import (ConfigType, InstanceList, MultiConfig,
- OptInstanceList, RangeType, reduce_mean)
-from ..task_modules.prior_generators import MlvlPointGenerator
-from ..task_modules.samplers import PseudoSampler
-from ..utils import multi_apply
-from .atss_head import ATSSHead
-from .fcos_head import FCOSHead
-
-INF = 1e8
-
-
-@MODELS.register_module()
-class VFNetHead(ATSSHead, FCOSHead):
- """Head of `VarifocalNet (VFNet): An IoU-aware Dense Object
- Detector.`_.
-
- The VFNet predicts IoU-aware classification scores which mix the
- object presence confidence and object localization accuracy as the
- detection score. It is built on the FCOS architecture and uses ATSS
- for defining positive/negative training examples. The VFNet is trained
- with Varifocal Loss and empolys star-shaped deformable convolution to
- extract features for a bbox.
-
- Args:
- num_classes (int): Number of categories excluding the background
- category.
- in_channels (int): Number of channels in the input feature map.
- regress_ranges (Sequence[Tuple[int, int]]): Regress range of multiple
- level points.
- center_sampling (bool): If true, use center sampling. Defaults to False.
- center_sample_radius (float): Radius of center sampling. Defaults to 1.5.
- sync_num_pos (bool): If true, synchronize the number of positive
- examples across GPUs. Defaults to True
- gradient_mul (float): The multiplier to gradients from bbox refinement
- and recognition. Defaults to 0.1.
- bbox_norm_type (str): The bbox normalization type, 'reg_denom' or
- 'stride'. Defaults to reg_denom
- loss_cls_fl (:obj:`ConfigDict` or dict): Config of focal loss.
- use_vfl (bool): If true, use varifocal loss for training.
- Defaults to True.
- loss_cls (:obj:`ConfigDict` or dict): Config of varifocal loss.
- loss_bbox (:obj:`ConfigDict` or dict): Config of localization loss,
- GIoU Loss.
- loss_bbox (:obj:`ConfigDict` or dict): Config of localization
- refinement loss, GIoU Loss.
- norm_cfg (:obj:`ConfigDict` or dict): dictionary to construct and
- config norm layer. Defaults to norm_cfg=dict(type='GN',
- num_groups=32, requires_grad=True).
- use_atss (bool): If true, use ATSS to define positive/negative
- examples. Defaults to True.
- anchor_generator (:obj:`ConfigDict` or dict): Config of anchor
- generator for ATSS.
- init_cfg (:obj:`ConfigDict` or dict or list[dict] or
- list[:obj:`ConfigDict`]): Initialization config dict.
-
- Example:
- >>> self = VFNetHead(11, 7)
- >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
- >>> cls_score, bbox_pred, bbox_pred_refine= self.forward(feats)
- >>> assert len(cls_score) == len(self.scales)
- """ # noqa: E501
-
- def __init__(self,
- num_classes: int,
- in_channels: int,
- regress_ranges: RangeType = ((-1, 64), (64, 128), (128, 256),
- (256, 512), (512, INF)),
- center_sampling: bool = False,
- center_sample_radius: float = 1.5,
- sync_num_pos: bool = True,
- gradient_mul: float = 0.1,
- bbox_norm_type: str = 'reg_denom',
- loss_cls_fl: ConfigType = dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- use_vfl: bool = True,
- loss_cls: ConfigType = dict(
- type='VarifocalLoss',
- use_sigmoid=True,
- alpha=0.75,
- gamma=2.0,
- iou_weighted=True,
- loss_weight=1.0),
- loss_bbox: ConfigType = dict(
- type='GIoULoss', loss_weight=1.5),
- loss_bbox_refine: ConfigType = dict(
- type='GIoULoss', loss_weight=2.0),
- norm_cfg: ConfigType = dict(
- type='GN', num_groups=32, requires_grad=True),
- use_atss: bool = True,
- reg_decoded_bbox: bool = True,
- anchor_generator: ConfigType = dict(
- type='AnchorGenerator',
- ratios=[1.0],
- octave_base_scale=8,
- scales_per_octave=1,
- center_offset=0.0,
- strides=[8, 16, 32, 64, 128]),
- init_cfg: MultiConfig = dict(
- type='Normal',
- layer='Conv2d',
- std=0.01,
- override=dict(
- type='Normal',
- name='vfnet_cls',
- std=0.01,
- bias_prob=0.01)),
- **kwargs) -> None:
- # dcn base offsets, adapted from reppoints_head.py
- self.num_dconv_points = 9
- self.dcn_kernel = int(np.sqrt(self.num_dconv_points))
- self.dcn_pad = int((self.dcn_kernel - 1) / 2)
- dcn_base = np.arange(-self.dcn_pad,
- self.dcn_pad + 1).astype(np.float64)
- dcn_base_y = np.repeat(dcn_base, self.dcn_kernel)
- dcn_base_x = np.tile(dcn_base, self.dcn_kernel)
- dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape(
- (-1))
- self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1)
-
- super(FCOSHead, self).__init__(
- num_classes=num_classes,
- in_channels=in_channels,
- norm_cfg=norm_cfg,
- init_cfg=init_cfg,
- **kwargs)
- self.regress_ranges = regress_ranges
- self.reg_denoms = [
- regress_range[-1] for regress_range in regress_ranges
- ]
- self.reg_denoms[-1] = self.reg_denoms[-2] * 2
- self.center_sampling = center_sampling
- self.center_sample_radius = center_sample_radius
- self.sync_num_pos = sync_num_pos
- self.bbox_norm_type = bbox_norm_type
- self.gradient_mul = gradient_mul
- self.use_vfl = use_vfl
- if self.use_vfl:
- self.loss_cls = MODELS.build(loss_cls)
- else:
- self.loss_cls = MODELS.build(loss_cls_fl)
- self.loss_bbox = MODELS.build(loss_bbox)
- self.loss_bbox_refine = MODELS.build(loss_bbox_refine)
-
- # for getting ATSS targets
- self.use_atss = use_atss
- self.reg_decoded_bbox = reg_decoded_bbox
- self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
-
- self.anchor_center_offset = anchor_generator['center_offset']
-
- self.num_base_priors = self.prior_generator.num_base_priors[0]
-
- if self.train_cfg:
- self.assigner = TASK_UTILS.build(self.train_cfg['assigner'])
- if self.train_cfg.get('sampler', None) is not None:
- self.sampler = TASK_UTILS.build(
- self.train_cfg['sampler'], default_args=dict(context=self))
- else:
- self.sampler = PseudoSampler()
- # only be used in `get_atss_targets` when `use_atss` is True
- self.atss_prior_generator = TASK_UTILS.build(anchor_generator)
-
- self.fcos_prior_generator = MlvlPointGenerator(
- anchor_generator['strides'],
- self.anchor_center_offset if self.use_atss else 0.5)
-
- # In order to reuse the `get_bboxes` in `BaseDenseHead.
- # Only be used in testing phase.
- self.prior_generator = self.fcos_prior_generator
-
- def _init_layers(self) -> None:
- """Initialize layers of the head."""
- super(FCOSHead, self)._init_cls_convs()
- super(FCOSHead, self)._init_reg_convs()
- self.relu = nn.ReLU()
- self.vfnet_reg_conv = ConvModule(
- self.feat_channels,
- self.feat_channels,
- 3,
- stride=1,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- bias=self.conv_bias)
- self.vfnet_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
- self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
- self.vfnet_reg_refine_dconv = DeformConv2d(
- self.feat_channels,
- self.feat_channels,
- self.dcn_kernel,
- 1,
- padding=self.dcn_pad)
- self.vfnet_reg_refine = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
- self.scales_refine = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
- self.vfnet_cls_dconv = DeformConv2d(
- self.feat_channels,
- self.feat_channels,
- self.dcn_kernel,
- 1,
- padding=self.dcn_pad)
- self.vfnet_cls = nn.Conv2d(
- self.feat_channels, self.cls_out_channels, 3, padding=1)
-
- def forward(self, x: Tuple[Tensor]) -> Tuple[List[Tensor]]:
- """Forward features from the upstream network.
-
- Args:
- x (tuple[Tensor]): Features from the upstream network, each is
- a 4D-tensor.
-
- Returns:
- tuple:
-
- - cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level, each is a 4D-tensor, the channel number is
- num_points * num_classes.
- - bbox_preds (list[Tensor]): Box offsets for each
- scale level, each is a 4D-tensor, the channel number is
- num_points * 4.
- - bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level, each is a 4D-tensor, the channel
- number is num_points * 4.
- """
- return multi_apply(self.forward_single, x, self.scales,
- self.scales_refine, self.strides, self.reg_denoms)
-
- def forward_single(self, x: Tensor, scale: Scale, scale_refine: Scale,
- stride: int, reg_denom: int) -> tuple:
- """Forward features of a single scale level.
-
- Args:
- x (Tensor): FPN feature maps of the specified stride.
- scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
- the bbox prediction.
- scale_refine (:obj: `mmcv.cnn.Scale`): Learnable scale module to
- resize the refined bbox prediction.
- stride (int): The corresponding stride for feature maps,
- used to normalize the bbox prediction when
- bbox_norm_type = 'stride'.
- reg_denom (int): The corresponding regression range for feature
- maps, only used to normalize the bbox prediction when
- bbox_norm_type = 'reg_denom'.
-
- Returns:
- tuple: iou-aware cls scores for each box, bbox predictions and
- refined bbox predictions of input feature maps.
- """
- cls_feat = x
- reg_feat = x
-
- for cls_layer in self.cls_convs:
- cls_feat = cls_layer(cls_feat)
-
- for reg_layer in self.reg_convs:
- reg_feat = reg_layer(reg_feat)
-
- # predict the bbox_pred of different level
- reg_feat_init = self.vfnet_reg_conv(reg_feat)
- if self.bbox_norm_type == 'reg_denom':
- bbox_pred = scale(
- self.vfnet_reg(reg_feat_init)).float().exp() * reg_denom
- elif self.bbox_norm_type == 'stride':
- bbox_pred = scale(
- self.vfnet_reg(reg_feat_init)).float().exp() * stride
- else:
- raise NotImplementedError
-
- # compute star deformable convolution offsets
- # converting dcn_offset to reg_feat.dtype thus VFNet can be
- # trained with FP16
- dcn_offset = self.star_dcn_offset(bbox_pred, self.gradient_mul,
- stride).to(reg_feat.dtype)
-
- # refine the bbox_pred
- reg_feat = self.relu(self.vfnet_reg_refine_dconv(reg_feat, dcn_offset))
- bbox_pred_refine = scale_refine(
- self.vfnet_reg_refine(reg_feat)).float().exp()
- bbox_pred_refine = bbox_pred_refine * bbox_pred.detach()
-
- # predict the iou-aware cls score
- cls_feat = self.relu(self.vfnet_cls_dconv(cls_feat, dcn_offset))
- cls_score = self.vfnet_cls(cls_feat)
-
- if self.training:
- return cls_score, bbox_pred, bbox_pred_refine
- else:
- return cls_score, bbox_pred_refine
-
- def star_dcn_offset(self, bbox_pred: Tensor, gradient_mul: float,
- stride: int) -> Tensor:
- """Compute the star deformable conv offsets.
-
- Args:
- bbox_pred (Tensor): Predicted bbox distance offsets (l, r, t, b).
- gradient_mul (float): Gradient multiplier.
- stride (int): The corresponding stride for feature maps,
- used to project the bbox onto the feature map.
-
- Returns:
- Tensor: The offsets for deformable convolution.
- """
- dcn_base_offset = self.dcn_base_offset.type_as(bbox_pred)
- bbox_pred_grad_mul = (1 - gradient_mul) * bbox_pred.detach() + \
- gradient_mul * bbox_pred
- # map to the feature map scale
- bbox_pred_grad_mul = bbox_pred_grad_mul / stride
- N, C, H, W = bbox_pred.size()
-
- x1 = bbox_pred_grad_mul[:, 0, :, :]
- y1 = bbox_pred_grad_mul[:, 1, :, :]
- x2 = bbox_pred_grad_mul[:, 2, :, :]
- y2 = bbox_pred_grad_mul[:, 3, :, :]
- bbox_pred_grad_mul_offset = bbox_pred.new_zeros(
- N, 2 * self.num_dconv_points, H, W)
- bbox_pred_grad_mul_offset[:, 0, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 1, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 2, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 4, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 5, :, :] = x2 # x2
- bbox_pred_grad_mul_offset[:, 7, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 11, :, :] = x2 # x2
- bbox_pred_grad_mul_offset[:, 12, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 13, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 14, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 16, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 17, :, :] = x2 # x2
- dcn_offset = bbox_pred_grad_mul_offset - dcn_base_offset
-
- return dcn_offset
-
- def loss_by_feat(
- self,
- cls_scores: List[Tensor],
- bbox_preds: List[Tensor],
- bbox_preds_refine: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> dict:
- """Compute loss of the head.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level, each is a 4D-tensor, the channel number is
- num_points * num_classes.
- bbox_preds (list[Tensor]): Box offsets for each
- scale level, each is a 4D-tensor, the channel number is
- num_points * 4.
- bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level, each is a 4D-tensor, the channel
- number is num_points * 4.
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine)
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- all_level_points = self.fcos_prior_generator.grid_priors(
- featmap_sizes, bbox_preds[0].dtype, bbox_preds[0].device)
- labels, label_weights, bbox_targets, bbox_weights = self.get_targets(
- cls_scores,
- all_level_points,
- batch_gt_instances,
- batch_img_metas,
- batch_gt_instances_ignore=batch_gt_instances_ignore)
-
- num_imgs = cls_scores[0].size(0)
- # flatten cls_scores, bbox_preds and bbox_preds_refine
- flatten_cls_scores = [
- cls_score.permute(0, 2, 3,
- 1).reshape(-1,
- self.cls_out_channels).contiguous()
- for cls_score in cls_scores
- ]
- flatten_bbox_preds = [
- bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
- for bbox_pred in bbox_preds
- ]
- flatten_bbox_preds_refine = [
- bbox_pred_refine.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
- for bbox_pred_refine in bbox_preds_refine
- ]
- flatten_cls_scores = torch.cat(flatten_cls_scores)
- flatten_bbox_preds = torch.cat(flatten_bbox_preds)
- flatten_bbox_preds_refine = torch.cat(flatten_bbox_preds_refine)
- flatten_labels = torch.cat(labels)
- flatten_bbox_targets = torch.cat(bbox_targets)
- # repeat points to align with bbox_preds
- flatten_points = torch.cat(
- [points.repeat(num_imgs, 1) for points in all_level_points])
-
- # FG cat_id: [0, num_classes - 1], BG cat_id: num_classes
- bg_class_ind = self.num_classes
- pos_inds = torch.where(
- ((flatten_labels >= 0) & (flatten_labels < bg_class_ind)) > 0)[0]
- num_pos = len(pos_inds)
-
- pos_bbox_preds = flatten_bbox_preds[pos_inds]
- pos_bbox_preds_refine = flatten_bbox_preds_refine[pos_inds]
- pos_labels = flatten_labels[pos_inds]
-
- # sync num_pos across all gpus
- if self.sync_num_pos:
- num_pos_avg_per_gpu = reduce_mean(
- pos_inds.new_tensor(num_pos).float()).item()
- num_pos_avg_per_gpu = max(num_pos_avg_per_gpu, 1.0)
- else:
- num_pos_avg_per_gpu = num_pos
-
- pos_bbox_targets = flatten_bbox_targets[pos_inds]
- pos_points = flatten_points[pos_inds]
-
- pos_decoded_bbox_preds = self.bbox_coder.decode(
- pos_points, pos_bbox_preds)
- pos_decoded_target_preds = self.bbox_coder.decode(
- pos_points, pos_bbox_targets)
- iou_targets_ini = bbox_overlaps(
- pos_decoded_bbox_preds,
- pos_decoded_target_preds.detach(),
- is_aligned=True).clamp(min=1e-6)
- bbox_weights_ini = iou_targets_ini.clone().detach()
- bbox_avg_factor_ini = reduce_mean(
- bbox_weights_ini.sum()).clamp_(min=1).item()
-
- pos_decoded_bbox_preds_refine = \
- self.bbox_coder.decode(pos_points, pos_bbox_preds_refine)
- iou_targets_rf = bbox_overlaps(
- pos_decoded_bbox_preds_refine,
- pos_decoded_target_preds.detach(),
- is_aligned=True).clamp(min=1e-6)
- bbox_weights_rf = iou_targets_rf.clone().detach()
- bbox_avg_factor_rf = reduce_mean(
- bbox_weights_rf.sum()).clamp_(min=1).item()
-
- if num_pos > 0:
- loss_bbox = self.loss_bbox(
- pos_decoded_bbox_preds,
- pos_decoded_target_preds.detach(),
- weight=bbox_weights_ini,
- avg_factor=bbox_avg_factor_ini)
-
- loss_bbox_refine = self.loss_bbox_refine(
- pos_decoded_bbox_preds_refine,
- pos_decoded_target_preds.detach(),
- weight=bbox_weights_rf,
- avg_factor=bbox_avg_factor_rf)
-
- # build IoU-aware cls_score targets
- if self.use_vfl:
- pos_ious = iou_targets_rf.clone().detach()
- cls_iou_targets = torch.zeros_like(flatten_cls_scores)
- cls_iou_targets[pos_inds, pos_labels] = pos_ious
- else:
- loss_bbox = pos_bbox_preds.sum() * 0
- loss_bbox_refine = pos_bbox_preds_refine.sum() * 0
- if self.use_vfl:
- cls_iou_targets = torch.zeros_like(flatten_cls_scores)
-
- if self.use_vfl:
- loss_cls = self.loss_cls(
- flatten_cls_scores,
- cls_iou_targets,
- avg_factor=num_pos_avg_per_gpu)
- else:
- loss_cls = self.loss_cls(
- flatten_cls_scores,
- flatten_labels,
- weight=label_weights,
- avg_factor=num_pos_avg_per_gpu)
-
- return dict(
- loss_cls=loss_cls,
- loss_bbox=loss_bbox,
- loss_bbox_rf=loss_bbox_refine)
-
- def get_targets(
- self,
- cls_scores: List[Tensor],
- mlvl_points: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> tuple:
- """A wrapper for computing ATSS and FCOS targets for points in multiple
- images.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- tuple:
-
- - labels_list (list[Tensor]): Labels of each level.
- - label_weights (Tensor/None): Label weights of all levels.
- - bbox_targets_list (list[Tensor]): Regression targets of each
- level, (l, t, r, b).
- - bbox_weights (Tensor/None): Bbox weights of all levels.
- """
- if self.use_atss:
- return self.get_atss_targets(cls_scores, mlvl_points,
- batch_gt_instances, batch_img_metas,
- batch_gt_instances_ignore)
- else:
- self.norm_on_bbox = False
- return self.get_fcos_targets(mlvl_points, batch_gt_instances)
-
- def _get_targets_single(self, *args, **kwargs):
- """Avoid ambiguity in multiple inheritance."""
- if self.use_atss:
- return ATSSHead._get_targets_single(self, *args, **kwargs)
- else:
- return FCOSHead._get_targets_single(self, *args, **kwargs)
-
- def get_fcos_targets(self, points: List[Tensor],
- batch_gt_instances: InstanceList) -> tuple:
- """Compute FCOS regression and classification targets for points in
- multiple images.
-
- Args:
- points (list[Tensor]): Points of each fpn level, each has shape
- (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
-
- Returns:
- tuple:
-
- - labels (list[Tensor]): Labels of each level.
- - label_weights: None, to be compatible with ATSS targets.
- - bbox_targets (list[Tensor]): BBox targets of each level.
- - bbox_weights: None, to be compatible with ATSS targets.
- """
- labels, bbox_targets = FCOSHead.get_targets(self, points,
- batch_gt_instances)
- label_weights = None
- bbox_weights = None
- return labels, label_weights, bbox_targets, bbox_weights
-
- def get_anchors(self,
- featmap_sizes: List[Tuple],
- batch_img_metas: List[dict],
- device: str = 'cuda') -> tuple:
- """Get anchors according to feature map sizes.
-
- Args:
- featmap_sizes (list[tuple]): Multi-level feature map sizes.
- batch_img_metas (list[dict]): Image meta info.
- device (str): Device for returned tensors
-
- Returns:
- tuple:
-
- - anchor_list (list[Tensor]): Anchors of each image.
- - valid_flag_list (list[Tensor]): Valid flags of each image.
- """
- num_imgs = len(batch_img_metas)
-
- # since feature map sizes of all images are the same, we only compute
- # anchors for one time
- multi_level_anchors = self.atss_prior_generator.grid_priors(
- featmap_sizes, device=device)
- anchor_list = [multi_level_anchors for _ in range(num_imgs)]
-
- # for each image, we compute valid flags of multi level anchors
- valid_flag_list = []
- for img_id, img_meta in enumerate(batch_img_metas):
- multi_level_flags = self.atss_prior_generator.valid_flags(
- featmap_sizes, img_meta['pad_shape'], device=device)
- valid_flag_list.append(multi_level_flags)
-
- return anchor_list, valid_flag_list
-
- def get_atss_targets(
- self,
- cls_scores: List[Tensor],
- mlvl_points: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> tuple:
- """A wrapper for computing ATSS targets for points in multiple images.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- tuple:
-
- - labels_list (list[Tensor]): Labels of each level.
- - label_weights (Tensor): Label weights of all levels.
- - bbox_targets_list (list[Tensor]): Regression targets of each
- level, (l, t, r, b).
- - bbox_weights (Tensor): Bbox weights of all levels.
- """
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- assert len(
- featmap_sizes
- ) == self.atss_prior_generator.num_levels == \
- self.fcos_prior_generator.num_levels
-
- device = cls_scores[0].device
-
- anchor_list, valid_flag_list = self.get_anchors(
- featmap_sizes, batch_img_metas, device=device)
-
- cls_reg_targets = ATSSHead.get_targets(
- self,
- anchor_list,
- valid_flag_list,
- batch_gt_instances,
- batch_img_metas,
- batch_gt_instances_ignore,
- unmap_outputs=True)
-
- (anchor_list, labels_list, label_weights_list, bbox_targets_list,
- bbox_weights_list, avg_factor) = cls_reg_targets
-
- bbox_targets_list = [
- bbox_targets.reshape(-1, 4) for bbox_targets in bbox_targets_list
- ]
-
- num_imgs = len(batch_img_metas)
- # transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format
- bbox_targets_list = self.transform_bbox_targets(
- bbox_targets_list, mlvl_points, num_imgs)
-
- labels_list = [labels.reshape(-1) for labels in labels_list]
- label_weights_list = [
- label_weights.reshape(-1) for label_weights in label_weights_list
- ]
- bbox_weights_list = [
- bbox_weights.reshape(-1) for bbox_weights in bbox_weights_list
- ]
- label_weights = torch.cat(label_weights_list)
- bbox_weights = torch.cat(bbox_weights_list)
- return labels_list, label_weights, bbox_targets_list, bbox_weights
-
- def transform_bbox_targets(self, decoded_bboxes: List[Tensor],
- mlvl_points: List[Tensor],
- num_imgs: int) -> List[Tensor]:
- """Transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format.
-
- Args:
- decoded_bboxes (list[Tensor]): Regression targets of each level,
- in the form of (x1, y1, x2, y2).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- num_imgs (int): the number of images in a batch.
-
- Returns:
- bbox_targets (list[Tensor]): Regression targets of each level in
- the form of (l, t, r, b).
- """
- # TODO: Re-implemented in Class PointCoder
- assert len(decoded_bboxes) == len(mlvl_points)
- num_levels = len(decoded_bboxes)
- mlvl_points = [points.repeat(num_imgs, 1) for points in mlvl_points]
- bbox_targets = []
- for i in range(num_levels):
- bbox_target = self.bbox_coder.encode(mlvl_points[i],
- decoded_bboxes[i])
- bbox_targets.append(bbox_target)
-
- return bbox_targets
-
- def _load_from_state_dict(self, state_dict: dict, prefix: str,
- local_metadata: dict, strict: bool,
- missing_keys: Union[List[str], str],
- unexpected_keys: Union[List[str], str],
- error_msgs: Union[List[str], str]) -> None:
- """Override the method in the parent class to avoid changing para's
- name."""
- pass
diff --git a/spaces/Larvuz/instruct-pix2pix/edit_app.py b/spaces/Larvuz/instruct-pix2pix/edit_app.py
deleted file mode 100644
index 0359e815ad51b1a2291dd8943555568e452981ad..0000000000000000000000000000000000000000
--- a/spaces/Larvuz/instruct-pix2pix/edit_app.py
+++ /dev/null
@@ -1,192 +0,0 @@
-from __future__ import annotations
-
-import math
-import random
-
-import gradio as gr
-import torch
-from PIL import Image, ImageOps
-from diffusers import StableDiffusionInstructPix2PixPipeline
-
-
-help_text = """
-If you're not getting what you want, there may be a few reasons:
-1. Is the image not changing enough? Your Image CFG weight may be too high. This value dictates how similar the output should be to the input. It's possible your edit requires larger changes from the original image, and your Image CFG weight isn't allowing that. Alternatively, your Text CFG weight may be too low. This value dictates how much to listen to the text instruction. The default Image CFG of 1.5 and Text CFG of 7.5 are a good starting point, but aren't necessarily optimal for each edit. Try:
- * Decreasing the Image CFG weight, or
- * Increasing the Text CFG weight, or
-2. Conversely, is the image changing too much, such that the details in the original image aren't preserved? Try:
- * Increasing the Image CFG weight, or
- * Decreasing the Text CFG weight
-3. Try generating results with different random seeds by setting "Randomize Seed" and running generation multiple times. You can also try setting "Randomize CFG" to sample new Text CFG and Image CFG values each time.
-4. Rephrasing the instruction sometimes improves results (e.g., "turn him into a dog" vs. "make him a dog" vs. "as a dog").
-5. Increasing the number of steps sometimes improves results.
-6. Do faces look weird? The Stable Diffusion autoencoder has a hard time with faces that are small in the image. Try:
- * Cropping the image so the face takes up a larger portion of the frame.
-"""
-
-
-example_instructions = [
- "Make it a picasso painting",
- "as if it were by modigliani",
- "convert to a bronze statue",
- "Turn it into an anime.",
- "have it look like a graphic novel",
- "make him gain weight",
- "what would he look like bald?",
- "Have him smile",
- "Put him in a cocktail party.",
- "move him at the beach.",
- "add dramatic lighting",
- "Convert to black and white",
- "What if it were snowing?",
- "Give him a leather jacket",
- "Turn him into a cyborg!",
- "make him wear a beanie",
-]
-
-model_id = "timbrooks/instruct-pix2pix"
-
-def main():
- pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None).to("cuda")
- example_image = Image.open("imgs/example.jpg").convert("RGB")
-
- def load_example(
- steps: int,
- randomize_seed: bool,
- seed: int,
- randomize_cfg: bool,
- text_cfg_scale: float,
- image_cfg_scale: float,
- ):
- example_instruction = random.choice(example_instructions)
- return [example_image, example_instruction] + generate(
- example_image,
- example_instruction,
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- )
-
- def generate(
- input_image: Image.Image,
- instruction: str,
- steps: int,
- randomize_seed: bool,
- seed: int,
- randomize_cfg: bool,
- text_cfg_scale: float,
- image_cfg_scale: float,
- ):
- seed = random.randint(0, 100000) if randomize_seed else seed
- text_cfg_scale = round(random.uniform(6.0, 9.0), ndigits=2) if randomize_cfg else text_cfg_scale
- image_cfg_scale = round(random.uniform(1.2, 1.8), ndigits=2) if randomize_cfg else image_cfg_scale
-
- width, height = input_image.size
- factor = 512 / max(width, height)
- factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
- width = int((width * factor) // 64) * 64
- height = int((height * factor) // 64) * 64
- input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS)
-
- if instruction == "":
- return [input_image, seed]
-
- generator = torch.manual_seed(seed)
- edited_image = pipe(
- instruction, image=input_image,
- guidance_scale=text_cfg_scale, image_guidance_scale=image_cfg_scale,
- num_inference_steps=steps, generator=generator,
- ).images[0]
- return [seed, text_cfg_scale, image_cfg_scale, edited_image]
-
- def reset():
- return [0, "Randomize Seed", 1371, "Fix CFG", 7.5, 1.5, None]
-
- with gr.Blocks() as demo:
- gr.HTML("""
- InstructPix2Pix: Learning to Follow Image Editing Instructions
-
-For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
-
-
-
-
""")
- with gr.Row():
- with gr.Column(scale=1, min_width=100):
- generate_button = gr.Button("Generate")
- with gr.Column(scale=1, min_width=100):
- load_button = gr.Button("Load Example")
- with gr.Column(scale=1, min_width=100):
- reset_button = gr.Button("Reset")
- with gr.Column(scale=3):
- instruction = gr.Textbox(lines=1, label="Edit Instruction", interactive=True)
-
- with gr.Row():
- input_image = gr.Image(label="Input Image", type="pil", interactive=True)
- edited_image = gr.Image(label=f"Edited Image", type="pil", interactive=False)
- input_image.style(height=512, width=512)
- edited_image.style(height=512, width=512)
-
- with gr.Row():
- steps = gr.Number(value=50, precision=0, label="Steps", interactive=True)
- randomize_seed = gr.Radio(
- ["Fix Seed", "Randomize Seed"],
- value="Randomize Seed",
- type="index",
- show_label=False,
- interactive=True,
- )
- seed = gr.Number(value=1371, precision=0, label="Seed", interactive=True)
- randomize_cfg = gr.Radio(
- ["Fix CFG", "Randomize CFG"],
- value="Fix CFG",
- type="index",
- show_label=False,
- interactive=True,
- )
- text_cfg_scale = gr.Number(value=7.5, label=f"Text CFG", interactive=True)
- image_cfg_scale = gr.Number(value=1.5, label=f"Image CFG", interactive=True)
-
- gr.Markdown(help_text)
-
- load_button.click(
- fn=load_example,
- inputs=[
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- ],
- outputs=[input_image, instruction, seed, text_cfg_scale, image_cfg_scale, edited_image],
- )
- generate_button.click(
- fn=generate,
- inputs=[
- input_image,
- instruction,
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- ],
- outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image],
- )
- reset_button.click(
- fn=reset,
- inputs=[],
- outputs=[steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale, edited_image],
- )
-
- demo.queue(concurrency_count=1)
- demo.launch(share=False)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/Loren/Streamlit_OCR_comparator/README.md b/spaces/Loren/Streamlit_OCR_comparator/README.md
deleted file mode 100644
index 2894464cad0f3f4d163e1f8e72750b10259159da..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Streamlit OCR Comparator
-emoji: 📰🔍🔤
-colorFrom: indigo
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: Home.py
-tags: [streamlit, ocr]
-pinned: true
----
\ No newline at end of file
diff --git a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py b/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py
deleted file mode 100644
index 6d9b36c74b1808b56ded68cf080a689db7e0ee4e..0000000000000000000000000000000000000000
--- a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# -*- coding: utf-8 -*-
-# File : __init__.py
-# Author : Jiayuan Mao
-# Email : maojiayuan@gmail.com
-# Date : 27/01/2018
-#
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-from .batchnorm import set_sbn_eps_mode
-from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d
-from .batchnorm import patch_sync_batchnorm, convert_model
-from .replicate import DataParallelWithCallback, patch_replication_callback
diff --git a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py b/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py
deleted file mode 100644
index 9794e0fc3463a5e8fad05c037cce64683059a6d3..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.ERROR)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})".format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10, 2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r", encoding="utf-8") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
\ No newline at end of file
diff --git a/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py b/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py
deleted file mode 100644
index 3a5999ddc80faa0ce6a54389562c9f092b40bb03..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# encoding: utf-8
-"""
-This files contains pytest fixtures and other test related stuff.
-
-"""
-from __future__ import absolute_import, division, print_function
-
-import numpy as np
-import pytest
-
-# save numpy's current print options
-_NP_PRINT_OPTIONS = np.get_printoptions()
-
-
-@pytest.fixture(autouse=True)
-def setup_doctest(request):
- """Set up the environment for doctests (when run through pytest)."""
- np.set_printoptions(precision=5, edgeitems=2, suppress=True)
-
- def fin():
- """Restore the environment after doctests (when run through pytest)."""
- np.set_printoptions(**_NP_PRINT_OPTIONS)
-
- request.addfinalizer(fin)
diff --git a/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py b/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py
deleted file mode 100644
index 19e34a11def040b971b534b9ed3fda71d0a35c85..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py
+++ /dev/null
@@ -1,882 +0,0 @@
-# encoding: utf-8
-# pylint: disable=no-member
-# pylint: disable=invalid-name
-# pylint: disable=too-many-arguments
-# pylint: disable=wrong-import-position
-"""
-Evaluation package.
-
-"""
-
-from __future__ import absolute_import, division, print_function
-
-import numpy as np
-
-
-# evaluation helper functions
-def find_closest_matches(detections, annotations):
- """
- Find the closest annotation for each detection.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
-
- Returns
- -------
- indices : numpy array
- Indices of the closest matches.
-
- Notes
- -----
- The sequences must be ordered.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.int)
- # if only a single annotation is given
- if len(annotations) == 1:
- # return an array as long as the detections with indices 0
- return np.zeros(len(detections), dtype=np.int)
- # solution found at: http://stackoverflow.com/questions/8914491/
- indices = annotations.searchsorted(detections)
- indices = np.clip(indices, 1, len(annotations) - 1)
- left = annotations[indices - 1]
- right = annotations[indices]
- indices -= detections - left < right - detections
- # return the indices of the closest matches
- return indices
-
-
-def calc_errors(detections, annotations, matches=None):
- """
- Errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.float)
- # determine the closest annotations
- if matches is None:
- matches = find_closest_matches(detections, annotations)
- # calc error relative to those annotations
- errors = detections - annotations[matches]
- # return the errors
- return errors
-
-
-def calc_absolute_errors(detections, annotations, matches=None):
- """
- Absolute errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Absolute errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # return the errors
- return np.abs(calc_errors(detections, annotations, matches))
-
-
-def calc_relative_errors(detections, annotations, matches=None):
- """
- Relative errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Relative errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.float)
- # determine the closest annotations
- if matches is None:
- matches = find_closest_matches(detections, annotations)
- # calculate the absolute errors
- errors = calc_errors(detections, annotations, matches)
- # return the relative errors
- return np.abs(1 - (errors / annotations[matches]))
-
-
-# abstract evaluation base class
-class EvaluationMixin(object):
- """
- Evaluation mixin class.
-
- This class has a `name` attribute which is used for display purposes and
- defaults to 'None'.
-
- `METRIC_NAMES` is a list of tuples, containing the attribute's name and the
- corresponding label, e.g.:
-
- The attributes defined in `METRIC_NAMES` will be provided as an ordered
- dictionary as the `metrics` property unless the subclass overwrites the
- property.
-
- `FLOAT_FORMAT` is used to format floats.
-
- """
-
- # Example:
- # METRIC_NAMES = [
- # ('precision', 'Precision'),
- # ('recall', 'Recall'),
- # ('fmeasure', 'F-measure'),
- # ]
- name = None
- METRIC_NAMES = []
- FLOAT_FORMAT = '{:.3f}'
-
- @property
- def metrics(self):
- """Metrics as a dictionary."""
- from collections import OrderedDict
- metrics = OrderedDict()
- # metrics = {}
- for metric in [m[0] for m in self.METRIC_NAMES]:
- metrics[metric] = getattr(self, metric)
- return metrics
-
- def __len__(self):
- """Length of the evaluation object."""
- raise NotImplementedError('must be implemented by subclass.')
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- Notes
- -----
- This is a fallback method formatting the `metrics` dictionary in a
- human readable way. Classes inheriting from this mixin class should
- provide a method better suitable.
-
- """
- # pylint: disable=unused-argument
- import pprint
- return pprint.pformat(dict(self.metrics), indent=4)
-
-
-# evaluation classes
-class SimpleEvaluation(EvaluationMixin):
- """
- Simple Precision, Recall, F-measure and Accuracy evaluation based on the
- numbers of true/false positive/negative detections.
-
- Parameters
- ----------
- num_tp : int
- Number of true positive detections.
- num_fp : int
- Number of false positive detections.
- num_tn : int
- Number of true negative detections.
- num_fn : int
- Number of false negative detections.
- name : str
- Name to be displayed.
-
- Notes
- -----
- This class is only suitable for a 1-class evaluation problem.
-
- """
-
- METRIC_NAMES = [
- ('num_tp', 'No. of true positives'),
- ('num_fp', 'No. of false positives'),
- ('num_tn', 'No. of true negatives'),
- ('num_fn', 'No. of false negatives'),
- ('num_annotations', 'No. Annotations'),
- ('precision', 'Precision'),
- ('recall', 'Recall'),
- ('fmeasure', 'F-measure'),
- ('accuracy', 'Accuracy'),
- ]
-
- def __init__(self, num_tp=0, num_fp=0, num_tn=0, num_fn=0, name=None,
- **kwargs):
- # pylint: disable=unused-argument
- # hidden variables, to be able to overwrite them in subclasses
- self._num_tp = int(num_tp)
- self._num_fp = int(num_fp)
- self._num_tn = int(num_tn)
- self._num_fn = int(num_fn)
- # name of the evaluation
- self.name = name
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return self._num_tp
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return self._num_fp
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return self._num_tn
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return self._num_fn
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- return self.num_tp + self.num_fn
-
- def __len__(self):
- # the length equals the number of annotations
- return self.num_annotations
-
- @property
- def precision(self):
- """Precision."""
- # correct / retrieved
- retrieved = float(self.num_tp + self.num_fp)
- # if there are no positive predictions, none of them are wrong
- if retrieved == 0:
- return 1.
- return self.num_tp / retrieved
-
- @property
- def recall(self):
- """Recall."""
- # correct / relevant
- relevant = float(self.num_tp + self.num_fn)
- # if there are no positive annotations, we recalled all of them
- if relevant == 0:
- return 1.
- return self.num_tp / relevant
-
- @property
- def fmeasure(self):
- """F-measure."""
- # 2pr / (p+r)
- numerator = 2. * self.precision * self.recall
- if numerator == 0:
- return 0.
- return numerator / (self.precision + self.recall)
-
- @property
- def accuracy(self):
- """Accuracy."""
- # acc: (TP + TN) / (TP + FP + TN + FN)
- denominator = self.num_fp + self.num_fn + self.num_tp + self.num_tn
- if denominator == 0:
- return 1.
- numerator = float(self.num_tp + self.num_tn)
- if numerator == 0:
- return 0.
- return numerator / denominator
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
-
- """
- ret = ''
- if self.name is not None:
- ret += '%s\n ' % self.name
- ret += 'Annotations: %5d TP: %5d FP: %5d FN: %5d ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- return ret
-
- def __str__(self):
- return self.tostring()
-
-
-# evaluate Precision, Recall, F-measure and Accuracy with lists or numpy arrays
-class Evaluation(SimpleEvaluation):
- """
- Evaluation class for measuring Precision, Recall and F-measure based on
- numpy arrays or lists with true/false positive/negative detections.
-
- Parameters
- ----------
- tp : list or numpy array
- True positive detections.
- fp : list or numpy array
- False positive detections.
- tn : list or numpy array
- True negative detections.
- fn : list or numpy array
- False negative detections.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, tp=None, fp=None, tn=None, fn=None, **kwargs):
- # set default values
- if tp is None:
- tp = []
- if fp is None:
- fp = []
- if tn is None:
- tn = []
- if fn is None:
- fn = []
- # instantiate a SimpleEvaluation object
- super(Evaluation, self).__init__(**kwargs)
- # convert everything to numpy arrays and save them
- self.tp = np.asarray(list(tp), dtype=np.float)
- self.fp = np.asarray(list(fp), dtype=np.float)
- self.tn = np.asarray(list(tn), dtype=np.float)
- self.fn = np.asarray(list(fn), dtype=np.float)
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return len(self.tp)
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return len(self.fp)
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return len(self.tn)
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return len(self.fn)
-
-
-# class for evaluation of Precision, Recall, F-measure with 2D arrays
-class MultiClassEvaluation(Evaluation):
- """
- Evaluation class for measuring Precision, Recall and F-measure based on
- 2D numpy arrays with true/false positive/negative detections.
-
- Parameters
- ----------
- tp : list of tuples or numpy array, shape (num_tp, 2)
- True positive detections.
- fp : list of tuples or numpy array, shape (num_fp, 2)
- False positive detections.
- tn : list of tuples or numpy array, shape (num_tn, 2)
- True negative detections.
- fn : list of tuples or numpy array, shape (num_fn, 2)
- False negative detections.
- name : str
- Name to be displayed.
-
- Notes
- -----
- The second item of the tuples or the second column of the arrays denote
- the class the detection belongs to.
-
- """
- def __init__(self, tp=None, fp=None, tn=None, fn=None, **kwargs):
- # set default values
- if tp is None:
- tp = np.zeros((0, 2))
- if fp is None:
- fp = np.zeros((0, 2))
- if tn is None:
- tn = np.zeros((0, 2))
- if fn is None:
- fn = np.zeros((0, 2))
- super(MultiClassEvaluation, self).__init__(**kwargs)
- self.tp = np.asarray(tp, dtype=np.float)
- self.fp = np.asarray(fp, dtype=np.float)
- self.tn = np.asarray(tn, dtype=np.float)
- self.fn = np.asarray(fn, dtype=np.float)
-
- def tostring(self, verbose=False, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Parameters
- ----------
- verbose : bool
- Add evaluation for individual classes.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- ret = ''
-
- if verbose:
- # extract all classes
- classes = []
- if self.tp.any():
- classes = np.append(classes, np.unique(self.tp[:, 1]))
- if self.fp.any():
- classes = np.append(classes, np.unique(self.fp[:, 1]))
- if self.tn.any():
- classes = np.append(classes, np.unique(self.tn[:, 1]))
- if self.fn.any():
- classes = np.append(classes, np.unique(self.fn[:, 1]))
- for cls in sorted(np.unique(classes)):
- # extract the TP, FP, TN and FN of this class
- tp = self.tp[self.tp[:, 1] == cls]
- fp = self.fp[self.fp[:, 1] == cls]
- tn = self.tn[self.tn[:, 1] == cls]
- fn = self.fn[self.fn[:, 1] == cls]
- # evaluate them
- e = Evaluation(tp, fp, tn, fn, name='Class %s' % cls)
- # append to the output string
- ret += ' %s\n' % e.tostring(verbose=False)
- # normal formatting
- ret += 'Annotations: %5d TP: %5d FP: %4d FN: %4d ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- # return
- return ret
-
-
-# class for summing Evaluations
-class SumEvaluation(SimpleEvaluation):
- """
- Simple class for summing evaluations.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, eval_objects, name=None):
- # pylint: disable=super-init-not-called
- # Note: we want to inherit the evaluation functions/properties, no need
- # to call __super__, but we need to take care of 'name'
- if not isinstance(eval_objects, list):
- # wrap the given eval_object in a list
- eval_objects = [eval_objects]
- self.eval_objects = eval_objects
- self.name = name or 'sum for %d files' % len(self)
-
- def __len__(self):
- # just use the length of the evaluation objects
- return len(self.eval_objects)
-
- # redefine the counters (number of TP, FP, TN, FN & number of annotations)
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return sum(e.num_tp for e in self.eval_objects)
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return sum(e.num_fp for e in self.eval_objects)
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return sum(e.num_tn for e in self.eval_objects)
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return sum(e.num_fn for e in self.eval_objects)
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- return sum(e.num_annotations for e in self.eval_objects)
-
-
-# class for averaging Evaluations
-class MeanEvaluation(SumEvaluation):
- """
- Simple class for averaging evaluation.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, eval_objects, name=None, **kwargs):
- super(MeanEvaluation, self).__init__(eval_objects, **kwargs)
- # handle the 'name' here to be able to set a different default value
- self.name = name or 'mean for %d files' % len(self)
-
- # overwrite the properties to calculate the mean instead of the sum
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_tp for e in self.eval_objects])
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_fp for e in self.eval_objects])
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_tn for e in self.eval_objects])
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_fn for e in self.eval_objects])
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_annotations for e in self.eval_objects])
-
- @property
- def precision(self):
- """Precision."""
- return np.nanmean([e.precision for e in self.eval_objects])
-
- @property
- def recall(self):
- """Recall."""
- return np.nanmean([e.recall for e in self.eval_objects])
-
- @property
- def fmeasure(self):
- """F-measure."""
- return np.nanmean([e.fmeasure for e in self.eval_objects])
-
- @property
- def accuracy(self):
- """Accuracy."""
- return np.nanmean([e.accuracy for e in self.eval_objects])
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- ret = ''
- if self.name is not None:
- ret += '%s\n ' % self.name
- # TODO: unify this with SimpleEvaluation but
- # add option to provide field formatters (e.g. 3d or 5.2f)
- # format with floats instead of integers
- ret += 'Annotations: %5.2f TP: %5.2f FP: %5.2f FN: %5.2f ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- return ret
-
-
-def tostring(eval_objects, **kwargs):
- """
- Format the given evaluation objects as human readable strings.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- # pylint: disable=unused-argument
- return '\n'.join([e.tostring() for e in eval_objects])
-
-
-def tocsv(eval_objects, metric_names=None, float_format='{:.3f}', **kwargs):
- """
- Format the given evaluation objects as a CSV table.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- metric_names : list of tuples, optional
- List of tuples defining the name of the property corresponding to the
- metric, and the metric label e.g. ('fp', 'False Positives').
- float_format : str, optional
- How to format the metrics.
-
- Returns
- -------
- str
- CSV table representation of the evaluation objects.
-
- Notes
- -----
- If no `metric_names` are given, they will be extracted from the first
- evaluation object.
-
- """
- # pylint: disable=unused-argument
-
- if metric_names is None:
- # get the evaluation metrics from the first evaluation object
- metric_names = eval_objects[0].METRIC_NAMES
- metric_names, metric_labels = list(zip(*metric_names))
- # add header
- lines = ['Name,' + ','.join(metric_labels)]
- # TODO: use e.metrics dict?
- # add the evaluation objects
- for e in eval_objects:
- values = [float_format.format(getattr(e, mn)) for mn in metric_names]
- lines.append(e.name + ',' + ','.join(values))
- # return everything
- return '\n'.join(lines)
-
-
-def totex(eval_objects, metric_names=None, float_format='{:.3f}', **kwargs):
- """
- Format the given evaluation objects as a LaTeX table.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- metric_names : list of tuples, optional
- List of tuples defining the name of the property corresponding to the
- metric, and the metric label e.g. ('fp', 'False Positives').
- float_format : str, optional
- How to format the metrics.
-
- Returns
- -------
- str
- LaTeX table representation of the evaluation objects.
-
- Notes
- -----
- If no `metric_names` are given, they will be extracted from the first
- evaluation object.
-
- """
- # pylint: disable=unused-argument
-
- if metric_names is None:
- # get the evaluation metrics from the first evaluation object
- metric_names = eval_objects[0].METRIC_NAMES
- metric_names, metric_labels = list(zip(*metric_names))
- # add header
- lines = ['Name & ' + ' & '.join(metric_labels) + '\\\\']
- # TODO: use e.metrics dict
- # TODO: add a generic totable() function which accepts columns separator,
- # newline stuff (e.g. tex \\\\) and others
- # add the evaluation objects
- for e in eval_objects:
- values = [float_format.format(getattr(e, mn)) for mn in metric_names]
- lines.append(e.name + ' & ' + ' & '.join(values) + '\\\\')
- # return everything
- return '\n'.join(lines)
-
-
-def evaluation_io(parser, ann_suffix, det_suffix, ann_dir=None, det_dir=None):
- """
- Add evaluation input/output and formatting related arguments to an existing
- parser object.
-
- Parameters
- ----------
- parser : argparse parser instance
- Existing argparse parser object.
- ann_suffix : str
- Suffix of the annotation files.
- det_suffix : str
- Suffix of the detection files.
- ann_dir : str, optional
- Use only annotations from this folder (and sub-folders).
- det_dir : str, optional
- Use only detections from this folder (and sub-folders).
-
- Returns
- -------
- io_group : argparse argument group
- Evaluation input / output argument group.
- formatter_group : argparse argument group
- Evaluation formatter argument group.
-
- """
- import sys
- import argparse
- # general input output file handling
- parser.add_argument('files', nargs='*',
- help='files (or folders) to be evaluated')
- parser.add_argument('-o', dest='outfile', type=argparse.FileType('w'),
- default=sys.stdout,
- help='output file [default: STDOUT]')
- # file suffixes used for evaluation
- g = parser.add_argument_group('file/folder/suffix arguments')
- g.add_argument('-a', dest='ann_suffix', action='store', default=ann_suffix,
- help='suffix of the annotation files '
- '[default: %(default)s]')
- g.add_argument('--ann_dir', action='store', default=ann_dir,
- help='search only this directory (recursively) for '
- 'annotation files [default: %(default)s]')
- g.add_argument('-d', dest='det_suffix', action='store', default=det_suffix,
- help='suffix of the detection files [default: %(default)s]')
- g.add_argument('--det_dir', action='store', default=det_dir,
- help='search only this directory (recursively) for '
- 'detection files [default: %(default)s]')
- # option to ignore non-existing detections
- g.add_argument('-i', '--ignore_non_existing', action='store_true',
- help='ignore non-existing detections [default: raise a '
- 'warning and assume empty detections]')
- # verbose
- parser.add_argument('-v', '--verbose', action='count', default=0,
- help='increase verbosity level')
- # option to suppress warnings
- parser.add_argument('-q', '--quiet', action='store_true',
- help='suppress any warnings')
- # output format options
- parser.set_defaults(output_formatter=tostring)
- f = parser.add_argument_group('formatting arguments')
- formats = f.add_mutually_exclusive_group()
- formats.add_argument('--tex', dest='output_formatter',
- action='store_const', const=totex,
- help='format output to be used in .tex files')
- formats.add_argument('--csv', dest='output_formatter',
- action='store_const', const=tocsv,
- help='format output to be used in .csv files')
- # return the output formatting group so the caller can add more options
- return g, f
-
-
-# finally import the submodules
-from . import chords, beats, notes, onsets, tempo
-
-# import often used classes
-from .beats import BeatEvaluation, BeatMeanEvaluation
-from .chords import ChordEvaluation, ChordMeanEvaluation, ChordSumEvaluation
-from .key import KeyEvaluation, KeyMeanEvaluation
-from .notes import NoteEvaluation, NoteMeanEvaluation, NoteSumEvaluation
-from .onsets import OnsetEvaluation, OnsetMeanEvaluation, OnsetSumEvaluation
-from .tempo import TempoEvaluation, TempoMeanEvaluation
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py
deleted file mode 100644
index 6fc71979190323f44c09f8b7e1761cf49cd2d76b..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
- 'iou3d_boxes_iou_bev_forward', 'iou3d_nms_forward',
- 'iou3d_nms_normal_forward'
-])
-
-
-def boxes_iou_bev(boxes_a, boxes_b):
- """Calculate boxes IoU in the Bird's Eye View.
-
- Args:
- boxes_a (torch.Tensor): Input boxes a with shape (M, 5).
- boxes_b (torch.Tensor): Input boxes b with shape (N, 5).
-
- Returns:
- ans_iou (torch.Tensor): IoU result with shape (M, N).
- """
- ans_iou = boxes_a.new_zeros(
- torch.Size((boxes_a.shape[0], boxes_b.shape[0])))
-
- ext_module.iou3d_boxes_iou_bev_forward(boxes_a.contiguous(),
- boxes_b.contiguous(), ans_iou)
-
- return ans_iou
-
-
-def nms_bev(boxes, scores, thresh, pre_max_size=None, post_max_size=None):
- """NMS function GPU implementation (for BEV boxes). The overlap of two
- boxes for IoU calculation is defined as the exact overlapping area of the
- two boxes. In this function, one can also set ``pre_max_size`` and
- ``post_max_size``.
-
- Args:
- boxes (torch.Tensor): Input boxes with the shape of [N, 5]
- ([x1, y1, x2, y2, ry]).
- scores (torch.Tensor): Scores of boxes with the shape of [N].
- thresh (float): Overlap threshold of NMS.
- pre_max_size (int, optional): Max size of boxes before NMS.
- Default: None.
- post_max_size (int, optional): Max size of boxes after NMS.
- Default: None.
-
- Returns:
- torch.Tensor: Indexes after NMS.
- """
- assert boxes.size(1) == 5, 'Input boxes shape should be [N, 5]'
- order = scores.sort(0, descending=True)[1]
-
- if pre_max_size is not None:
- order = order[:pre_max_size]
- boxes = boxes[order].contiguous()
-
- keep = torch.zeros(boxes.size(0), dtype=torch.long)
- num_out = ext_module.iou3d_nms_forward(boxes, keep, thresh)
- keep = order[keep[:num_out].cuda(boxes.device)].contiguous()
- if post_max_size is not None:
- keep = keep[:post_max_size]
- return keep
-
-
-def nms_normal_bev(boxes, scores, thresh):
- """Normal NMS function GPU implementation (for BEV boxes). The overlap of
- two boxes for IoU calculation is defined as the exact overlapping area of
- the two boxes WITH their yaw angle set to 0.
-
- Args:
- boxes (torch.Tensor): Input boxes with shape (N, 5).
- scores (torch.Tensor): Scores of predicted boxes with shape (N).
- thresh (float): Overlap threshold of NMS.
-
- Returns:
- torch.Tensor: Remaining indices with scores in descending order.
- """
- assert boxes.shape[1] == 5, 'Input boxes shape should be [N, 5]'
- order = scores.sort(0, descending=True)[1]
-
- boxes = boxes[order].contiguous()
-
- keep = torch.zeros(boxes.size(0), dtype=torch.long)
- num_out = ext_module.iou3d_nms_normal_forward(boxes, keep, thresh)
- return order[keep[:num_out].cuda(boxes.device)].contiguous()
diff --git a/spaces/MetaWabbit/Auto-GPT/run.sh b/spaces/MetaWabbit/Auto-GPT/run.sh
deleted file mode 100644
index edcbc44155b9ca9df83e283fdf976472c13e6492..0000000000000000000000000000000000000000
--- a/spaces/MetaWabbit/Auto-GPT/run.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-python scripts/check_requirements.py requirements.txt
-if [ $? -eq 1 ]
-then
- echo Installing missing packages...
- pip install -r requirements.txt
-fi
-python -m autogpt $@
-read -p "Press any key to continue..."
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py b/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py
deleted file mode 100644
index 244425feef76bf89d4de939cb8a1914a6f0f47c6..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py
+++ /dev/null
@@ -1,387 +0,0 @@
-# Lint as: python3
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Unit tests for the classifier trainer models."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import copy
-import functools
-import json
-
-import os
-import sys
-
-from typing import Any, Callable, Iterable, Mapping, MutableMapping, Optional, Tuple
-
-from absl import flags
-from absl.testing import parameterized
-import tensorflow as tf
-
-from tensorflow.python.distribute import combinations
-from tensorflow.python.distribute import strategy_combinations
-from official.utils.flags import core as flags_core
-from official.vision.image_classification import classifier_trainer
-from official.vision.image_classification import dataset_factory
-from official.vision.image_classification import test_utils
-from official.vision.image_classification.configs import base_configs
-
-classifier_trainer.define_classifier_flags()
-
-
-def distribution_strategy_combinations() -> Iterable[Tuple[Any, ...]]:
- """Returns the combinations of end-to-end tests to run."""
- return combinations.combine(
- distribution=[
- strategy_combinations.default_strategy,
- strategy_combinations.tpu_strategy,
- strategy_combinations.one_device_strategy_gpu,
- strategy_combinations.mirrored_strategy_with_two_gpus,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset=[
- 'imagenet',
- ],
- )
-
-
-def get_params_override(params_override: Mapping[str, Any]) -> str:
- """Converts params_override dict to string command."""
- return '--params_override=' + json.dumps(params_override)
-
-
-def basic_params_override(dtype: str = 'float32') -> MutableMapping[str, Any]:
- """Returns a basic parameter configuration for testing."""
- return {
- 'train_dataset': {
- 'builder': 'synthetic',
- 'use_per_replica_batch_size': True,
- 'batch_size': 1,
- 'image_size': 224,
- 'dtype': dtype,
- },
- 'validation_dataset': {
- 'builder': 'synthetic',
- 'batch_size': 1,
- 'use_per_replica_batch_size': True,
- 'image_size': 224,
- 'dtype': dtype,
- },
- 'train': {
- 'steps': 1,
- 'epochs': 1,
- 'callbacks': {
- 'enable_checkpoint_and_export': True,
- 'enable_tensorboard': False,
- },
- },
- 'evaluation': {
- 'steps': 1,
- },
- }
-
-
-def get_trivial_model(num_classes: int) -> tf.keras.Model:
- """Creates and compiles trivial model for ImageNet dataset."""
- model = test_utils.trivial_model(num_classes=num_classes)
- lr = 0.01
- optimizer = tf.keras.optimizers.SGD(learning_rate=lr)
- loss_obj = tf.keras.losses.SparseCategoricalCrossentropy()
- model.compile(optimizer=optimizer,
- loss=loss_obj,
- run_eagerly=True)
- return model
-
-
-def get_trivial_data() -> tf.data.Dataset:
- """Gets trivial data in the ImageNet size."""
- def generate_data(_) -> tf.data.Dataset:
- image = tf.zeros(shape=(224, 224, 3), dtype=tf.float32)
- label = tf.zeros([1], dtype=tf.int32)
- return image, label
-
- dataset = tf.data.Dataset.range(1)
- dataset = dataset.repeat()
- dataset = dataset.map(generate_data,
- num_parallel_calls=tf.data.experimental.AUTOTUNE)
- dataset = dataset.prefetch(buffer_size=1).batch(1)
- return dataset
-
-
-def run_end_to_end(main: Callable[[Any], None],
- extra_flags: Optional[Iterable[str]] = None,
- model_dir: Optional[str] = None):
- """Runs the classifier trainer end-to-end."""
- extra_flags = [] if extra_flags is None else extra_flags
- args = [sys.argv[0], '--model_dir', model_dir] + extra_flags
- flags_core.parse_flags(argv=args)
- main(flags.FLAGS)
-
-
-class ClassifierTest(tf.test.TestCase, parameterized.TestCase):
- """Unit tests for Keras models."""
- _tempdir = None
-
- @classmethod
- def setUpClass(cls): # pylint: disable=invalid-name
- super(ClassifierTest, cls).setUpClass()
-
- def tearDown(self):
- super(ClassifierTest, self).tearDown()
- tf.io.gfile.rmtree(self.get_temp_dir())
-
- @combinations.generate(distribution_strategy_combinations())
- def test_end_to_end_train_and_eval(self, distribution, model, dataset):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override()),
- '--mode=train_and_eval',
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
-
- @combinations.generate(
- combinations.combine(
- distribution=[
- strategy_combinations.one_device_strategy_gpu,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset='imagenet',
- dtype='float16',
- ))
- def test_gpu_train(self, distribution, model, dataset, dtype):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override(dtype)),
- '--mode=train_and_eval',
- ]
-
- export_params = basic_params_override()
- export_path = os.path.join(model_dir, 'export')
- export_params['export'] = {}
- export_params['export']['destination'] = export_path
- export_flags = base_flags + [
- '--mode=export_only',
- get_params_override(export_params)
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
- run_end_to_end(main=run,
- extra_flags=export_flags,
- model_dir=model_dir)
- self.assertTrue(os.path.exists(export_path))
-
- @combinations.generate(
- combinations.combine(
- distribution=[
- strategy_combinations.tpu_strategy,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset='imagenet',
- dtype='bfloat16',
- ))
- def test_tpu_train(self, distribution, model, dataset, dtype):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override(dtype)),
- '--mode=train_and_eval',
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
-
- @combinations.generate(distribution_strategy_combinations())
- def test_end_to_end_invalid_mode(self, distribution, model, dataset):
- """Test the Keras EfficientNet model with `strategy`."""
- model_dir = self.get_temp_dir()
- extra_flags = [
- '--data_dir=not_used',
- '--mode=invalid_mode',
- '--model_type=' + model,
- '--dataset=' + dataset,
- get_params_override(basic_params_override()),
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- with self.assertRaises(ValueError):
- run_end_to_end(main=run, extra_flags=extra_flags, model_dir=model_dir)
-
-
-class UtilTests(parameterized.TestCase, tf.test.TestCase):
- """Tests for individual utility functions within classifier_trainer.py."""
-
- @parameterized.named_parameters(
- ('efficientnet-b0', 'efficientnet', 'efficientnet-b0', 224),
- ('efficientnet-b1', 'efficientnet', 'efficientnet-b1', 240),
- ('efficientnet-b2', 'efficientnet', 'efficientnet-b2', 260),
- ('efficientnet-b3', 'efficientnet', 'efficientnet-b3', 300),
- ('efficientnet-b4', 'efficientnet', 'efficientnet-b4', 380),
- ('efficientnet-b5', 'efficientnet', 'efficientnet-b5', 456),
- ('efficientnet-b6', 'efficientnet', 'efficientnet-b6', 528),
- ('efficientnet-b7', 'efficientnet', 'efficientnet-b7', 600),
- ('resnet', 'resnet', '', None),
- )
- def test_get_model_size(self, model, model_name, expected):
- config = base_configs.ExperimentConfig(
- model_name=model,
- model=base_configs.ModelConfig(
- model_params={
- 'model_name': model_name,
- },
- )
- )
- size = classifier_trainer.get_image_size_from_model(config)
- self.assertEqual(size, expected)
-
- @parameterized.named_parameters(
- ('dynamic', 'dynamic', None, 'dynamic'),
- ('scalar', 128., None, 128.),
- ('float32', None, 'float32', 1),
- ('float16', None, 'float16', 128),
- )
- def test_get_loss_scale(self, loss_scale, dtype, expected):
- config = base_configs.ExperimentConfig(
- runtime=base_configs.RuntimeConfig(
- loss_scale=loss_scale),
- train_dataset=dataset_factory.DatasetConfig(dtype=dtype))
- ls = classifier_trainer.get_loss_scale(config, fp16_default=128)
- self.assertEqual(ls, expected)
-
- @parameterized.named_parameters(
- ('float16', 'float16'),
- ('bfloat16', 'bfloat16')
- )
- def test_initialize(self, dtype):
- config = base_configs.ExperimentConfig(
- runtime=base_configs.RuntimeConfig(
- run_eagerly=False,
- enable_xla=False,
- per_gpu_thread_count=1,
- gpu_thread_mode='gpu_private',
- num_gpus=1,
- dataset_num_private_threads=1,
- ),
- train_dataset=dataset_factory.DatasetConfig(dtype=dtype),
- model=base_configs.ModelConfig(),
- )
-
- class EmptyClass:
- pass
- fake_ds_builder = EmptyClass()
- fake_ds_builder.dtype = dtype
- fake_ds_builder.config = EmptyClass()
- classifier_trainer.initialize(config, fake_ds_builder)
-
- def test_resume_from_checkpoint(self):
- """Tests functionality for resuming from checkpoint."""
- # Set the keras policy
- policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16')
- tf.keras.mixed_precision.experimental.set_policy(policy)
-
- # Get the model, datasets, and compile it.
- model = get_trivial_model(10)
-
- # Create the checkpoint
- model_dir = self.get_temp_dir()
- train_epochs = 1
- train_steps = 10
- ds = get_trivial_data()
- callbacks = [
- tf.keras.callbacks.ModelCheckpoint(
- os.path.join(model_dir, 'model.ckpt-{epoch:04d}'),
- save_weights_only=True)
- ]
- model.fit(
- ds,
- callbacks=callbacks,
- epochs=train_epochs,
- steps_per_epoch=train_steps)
-
- # Test load from checkpoint
- clean_model = get_trivial_model(10)
- weights_before_load = copy.deepcopy(clean_model.get_weights())
- initial_epoch = classifier_trainer.resume_from_checkpoint(
- model=clean_model,
- model_dir=model_dir,
- train_steps=train_steps)
- self.assertEqual(initial_epoch, 1)
- self.assertNotAllClose(weights_before_load, clean_model.get_weights())
-
- tf.io.gfile.rmtree(model_dir)
-
- def test_serialize_config(self):
- """Tests functionality for serializing data."""
- config = base_configs.ExperimentConfig()
- model_dir = self.get_temp_dir()
- classifier_trainer.serialize_config(params=config, model_dir=model_dir)
- saved_params_path = os.path.join(model_dir, 'params.yaml')
- self.assertTrue(os.path.exists(saved_params_path))
- tf.io.gfile.rmtree(model_dir)
-
-if __name__ == '__main__':
- tf.test.main()
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py b/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py
deleted file mode 100644
index 11fcb5305660ec71153ebfc12631f455a3464115..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py
+++ /dev/null
@@ -1,231 +0,0 @@
-# Lint as: python3
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Definitions for high level configuration groups.."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-
-from typing import Any, List, Mapping, Optional
-
-import dataclasses
-
-from official.modeling import hyperparams
-from official.modeling.hyperparams import config_definitions
-
-CallbacksConfig = config_definitions.CallbacksConfig
-TensorboardConfig = config_definitions.TensorboardConfig
-RuntimeConfig = config_definitions.RuntimeConfig
-
-
-@dataclasses.dataclass
-class ExportConfig(hyperparams.Config):
- """Configuration for exports.
-
- Attributes:
- checkpoint: the path to the checkpoint to export.
- destination: the path to where the checkpoint should be exported.
- """
- checkpoint: str = None
- destination: str = None
-
-
-@dataclasses.dataclass
-class MetricsConfig(hyperparams.Config):
- """Configuration for Metrics.
-
- Attributes:
- accuracy: Whether or not to track accuracy as a Callback. Defaults to None.
- top_5: Whether or not to track top_5_accuracy as a Callback. Defaults to
- None.
- """
- accuracy: bool = None
- top_5: bool = None
-
-
-@dataclasses.dataclass
-class TimeHistoryConfig(hyperparams.Config):
- """Configuration for the TimeHistory callback.
-
- Attributes:
- log_steps: Interval of steps between logging of batch level stats.
- """
- log_steps: int = None
-
-
-@dataclasses.dataclass
-class TrainConfig(hyperparams.Config):
- """Configuration for training.
-
- Attributes:
- resume_checkpoint: Whether or not to enable load checkpoint loading.
- Defaults to None.
- epochs: The number of training epochs to run. Defaults to None.
- steps: The number of steps to run per epoch. If None, then this will be
- inferred based on the number of images and batch size. Defaults to None.
- callbacks: An instance of CallbacksConfig.
- metrics: An instance of MetricsConfig.
- tensorboard: An instance of TensorboardConfig.
- set_epoch_loop: Whether or not to set `experimental_steps_per_execution` to
- equal the number of training steps in `model.compile`. This reduces the
- number of callbacks run per epoch which significantly improves end-to-end
- TPU training time.
- """
- resume_checkpoint: bool = None
- epochs: int = None
- steps: int = None
- callbacks: CallbacksConfig = CallbacksConfig()
- metrics: MetricsConfig = None
- tensorboard: TensorboardConfig = TensorboardConfig()
- time_history: TimeHistoryConfig = TimeHistoryConfig()
- set_epoch_loop: bool = False
-
-
-@dataclasses.dataclass
-class EvalConfig(hyperparams.Config):
- """Configuration for evaluation.
-
- Attributes:
- epochs_between_evals: The number of train epochs to run between evaluations.
- Defaults to None.
- steps: The number of eval steps to run during evaluation. If None, this will
- be inferred based on the number of images and batch size. Defaults to
- None.
- skip_eval: Whether or not to skip evaluation.
- """
- epochs_between_evals: int = None
- steps: int = None
- skip_eval: bool = False
-
-
-@dataclasses.dataclass
-class LossConfig(hyperparams.Config):
- """Configuration for Loss.
-
- Attributes:
- name: The name of the loss. Defaults to None.
- label_smoothing: Whether or not to apply label smoothing to the loss. This
- only applies to 'categorical_cross_entropy'.
- """
- name: str = None
- label_smoothing: float = None
-
-
-@dataclasses.dataclass
-class OptimizerConfig(hyperparams.Config):
- """Configuration for Optimizers.
-
- Attributes:
- name: The name of the optimizer. Defaults to None.
- decay: Decay or rho, discounting factor for gradient. Defaults to None.
- epsilon: Small value used to avoid 0 denominator. Defaults to None.
- momentum: Plain momentum constant. Defaults to None.
- nesterov: Whether or not to apply Nesterov momentum. Defaults to None.
- moving_average_decay: The amount of decay to apply. If 0 or None, then
- exponential moving average is not used. Defaults to None.
- lookahead: Whether or not to apply the lookahead optimizer. Defaults to
- None.
- beta_1: The exponential decay rate for the 1st moment estimates. Used in the
- Adam optimizers. Defaults to None.
- beta_2: The exponential decay rate for the 2nd moment estimates. Used in the
- Adam optimizers. Defaults to None.
- epsilon: Small value used to avoid 0 denominator. Defaults to 1e-7.
- """
- name: str = None
- decay: float = None
- epsilon: float = None
- momentum: float = None
- nesterov: bool = None
- moving_average_decay: Optional[float] = None
- lookahead: Optional[bool] = None
- beta_1: float = None
- beta_2: float = None
- epsilon: float = None
-
-
-@dataclasses.dataclass
-class LearningRateConfig(hyperparams.Config):
- """Configuration for learning rates.
-
- Attributes:
- name: The name of the learning rate. Defaults to None.
- initial_lr: The initial learning rate. Defaults to None.
- decay_epochs: The number of decay epochs. Defaults to None.
- decay_rate: The rate of decay. Defaults to None.
- warmup_epochs: The number of warmup epochs. Defaults to None.
- batch_lr_multiplier: The multiplier to apply to the base learning rate, if
- necessary. Defaults to None.
- examples_per_epoch: the number of examples in a single epoch. Defaults to
- None.
- boundaries: boundaries used in piecewise constant decay with warmup.
- multipliers: multipliers used in piecewise constant decay with warmup.
- scale_by_batch_size: Scale the learning rate by a fraction of the batch
- size. Set to 0 for no scaling (default).
- staircase: Apply exponential decay at discrete values instead of continuous.
- """
- name: str = None
- initial_lr: float = None
- decay_epochs: float = None
- decay_rate: float = None
- warmup_epochs: int = None
- examples_per_epoch: int = None
- boundaries: List[int] = None
- multipliers: List[float] = None
- scale_by_batch_size: float = 0.
- staircase: bool = None
-
-
-@dataclasses.dataclass
-class ModelConfig(hyperparams.Config):
- """Configuration for Models.
-
- Attributes:
- name: The name of the model. Defaults to None.
- model_params: The parameters used to create the model. Defaults to None.
- num_classes: The number of classes in the model. Defaults to None.
- loss: A `LossConfig` instance. Defaults to None.
- optimizer: An `OptimizerConfig` instance. Defaults to None.
- """
- name: str = None
- model_params: hyperparams.Config = None
- num_classes: int = None
- loss: LossConfig = None
- optimizer: OptimizerConfig = None
-
-
-@dataclasses.dataclass
-class ExperimentConfig(hyperparams.Config):
- """Base configuration for an image classification experiment.
-
- Attributes:
- model_dir: The directory to use when running an experiment.
- mode: e.g. 'train_and_eval', 'export'
- runtime: A `RuntimeConfig` instance.
- train: A `TrainConfig` instance.
- evaluation: An `EvalConfig` instance.
- model: A `ModelConfig` instance.
- export: An `ExportConfig` instance.
- """
- model_dir: str = None
- model_name: str = None
- mode: str = None
- runtime: RuntimeConfig = None
- train_dataset: Any = None
- validation_dataset: Any = None
- train: TrainConfig = None
- evaluation: EvalConfig = None
- model: ModelConfig = None
- export: ExportConfig = None
diff --git a/spaces/Navneet574/Heart_Disease_Prediciton/app.py b/spaces/Navneet574/Heart_Disease_Prediciton/app.py
deleted file mode 100644
index 30f66c36eeab6633487a32671d615f07ab544f93..0000000000000000000000000000000000000000
--- a/spaces/Navneet574/Heart_Disease_Prediciton/app.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import gradio as gr
-from joblib import load
-import numpy as np
-import pandas as pd
-
-AgeCategory = ['18-24', '25-29', '35-39', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80 or older']
-Binary_Input = ['Yes', 'No']
-Sex = ['Male', 'Female']
-Race = ['White', 'Black', 'Asian', 'American Indian/Alaskan Native', 'Hispanic', 'Other']
-Diabetic = ['Yes', 'No', 'No borderline diabetes', 'Yes (during pregnancy)']
-GeneralHealth = ['Excellent', 'Very good', 'Good', 'Fair', 'Poor']
-
-dic_ageCat = {'18-24':0, '25-29':1, '35-39':2, '35-39':3, '40-44':4, '45-49':5, '50-54':6, '55-59':7, '60-64':8, '65-69':9, '70-74':10, '75-79':11, '80 or older':12}
-dic_binary = {'Yes':1, 'No':0}
-dic_sex = {'Male':0, 'Female':1}
-dic_race = {'White':5, 'Black':2, 'Asian':0, 'American Indian/Alaskan Native':3, 'Hispanic':1, 'Other':4}
-dic_diabetic = {'Yes':2, 'No':0, 'No borderline diabetes':1, 'Yes (during pregnancy)':3}
-dic_generalHealth = {'Excellent':0, 'Very good':4, 'Good':2, 'Fair':1, 'Poor':3}
-
-
-def predict_price(BMI, Smoking, AlcoholDrinking, Stroke, PhysicalHealth, MentalHealth, DiffWalking, Sex, AgeCategory, Race, Diabetic, PhysicalActivity, GenHealth, SleepTime, Asthma, KidneyDisease, SkinCancer):
- model = load('Heart_Disease_Classification.jb')
-
- data = {
- 'BMI': [BMI],
- 'Smoking': dic_binary[Smoking],
- 'AlcoholDrinking': dic_binary[AlcoholDrinking],
- 'Stroke': dic_binary[Stroke],
- 'PhysicalHealth': [PhysicalHealth],
- 'MentalHealth': [MentalHealth],
- 'DiffWalking': dic_binary[DiffWalking],
- 'Sex': dic_sex[Sex],
- 'AgeCategory': dic_ageCat[AgeCategory],
- 'Race': dic_race[Race],
- 'Diabetic': dic_diabetic[Diabetic],
- 'PhysicalActivity': dic_binary[PhysicalActivity],
- 'GenHealth': dic_generalHealth[GenHealth],
- 'SleepTime': [SleepTime],
- 'Asthma': dic_binary[Asthma],
- 'KidneyDisease': dic_binary[KidneyDisease],
- 'SkinCancer': dic_binary[SkinCancer]
- }
-
- Xinp = pd.DataFrame(data)
- print(Xinp)
-
- stone = model.predict(Xinp)
-
- if stone == 1:
- return stone["YES"]
- else:
- return stone["NO"]
-
-ui = gr.Interface(
- fn=predict_price,inputs=[
- gr.inputs.Textbox(placeholder='BMI', default=0,
- numeric=True, label='Body Mass Index (BMI) (normal is between 18.5 and 24.9)'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Smoking', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Alcohol Drinking', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Stroke', default="No"),
- gr.inputs.Textbox(placeholder='Physical Health',
- default=0, numeric=True, label='Physical Health Score'),
- gr.inputs.Textbox(placeholder='MentalHealth', default=0,
- numeric=True, label='Mental Health Score'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Diff Walking', default="No"),
- gr.inputs.Dropdown(choices=Sex, label='Sex', default=""),
- gr.inputs.Dropdown(choices=AgeCategory, label='Age Category', default="18-24"),
- gr.inputs.Dropdown(choices=Race, label='Race', default="Asian"),
- gr.inputs.Dropdown(choices=Diabetic, label='Diabetic', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Physical Activity', default="Yes"),
- gr.inputs.Dropdown(choices=GeneralHealth, label='General Health', default="Excellent"),
- gr.inputs.Textbox(placeholder='Sleep Time',
- default='7', numeric=True, label='Sleep Time (normal is between 6-8 hrs)'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Asthma', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Kidney Disease', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Skin Cancer', default="No")
- ], outputs=[
- "text"
- ]
-)
-if __name__ == "__main__":
- ui.launch()
\ No newline at end of file
diff --git a/spaces/Nikhil0987/omm/convo.py b/spaces/Nikhil0987/omm/convo.py
deleted file mode 100644
index f5c87b19d82d3f1e1869101faf1205475b5ed3b9..0000000000000000000000000000000000000000
--- a/spaces/Nikhil0987/omm/convo.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from transformers import pipeline, Conversation
-import streamlit as st
-from streamlit_option_menu import option_menu
-
-chatbot = pipeline(task="conversational", model="microsoft/DialoGPT-medium")
-
-def Convo():
- if convo := st.chat_input("Enter your message"):
-
- conversation = Conversation(convo)
-
-
-
- # candidate_labels = ["HELP", "PROBLEM SOLVE", "GENERAL TALK"]
-
-
- ans = chatbot(conversation)
- # add_user_input = st.button("Add User Input")
-
- # conversation.add_user_input("{}".format(convo))
- # conversation = chatbot(conversation)
- with st.chat_message("assistant"):
- # ans
- st.write(ans.generated_responses[-1])
-
-
-# convo()
diff --git a/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py b/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py
deleted file mode 100644
index d9d372dcbb1bed2fffbfd8e81d6da749ceab730b..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py
+++ /dev/null
@@ -1,772 +0,0 @@
-"""
-This scripts plots examples of the images that get best and worse metrics
-"""
-print("Imports...", end="")
-import os
-import sys
-from argparse import ArgumentParser
-from pathlib import Path
-
-import matplotlib.patches as mpatches
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import seaborn as sns
-import yaml
-from imageio import imread
-from matplotlib.gridspec import GridSpec
-from skimage.color import rgba2rgb
-from sklearn.metrics.pairwise import euclidean_distances
-
-sys.path.append("../")
-
-from climategan.data import encode_mask_label
-from climategan.eval_metrics import edges_coherence_std_min
-from eval_masker import crop_and_resize
-
-# -----------------------
-# ----- Constants -----
-# -----------------------
-
-# Metrics
-metrics = ["error", "f05", "edge_coherence"]
-
-dict_metrics = {
- "names": {
- "tpr": "TPR, Recall, Sensitivity",
- "tnr": "TNR, Specificity, Selectivity",
- "fpr": "FPR",
- "fpt": "False positives relative to image size",
- "fnr": "FNR, Miss rate",
- "fnt": "False negatives relative to image size",
- "mpr": "May positive rate (MPR)",
- "mnr": "May negative rate (MNR)",
- "accuracy": "Accuracy (ignoring may)",
- "error": "Error",
- "f05": "F05 score",
- "precision": "Precision",
- "edge_coherence": "Edge coherence",
- "accuracy_must_may": "Accuracy (ignoring cannot)",
- },
- "key_metrics": ["error", "f05", "edge_coherence"],
-}
-
-
-# Colors
-colorblind_palette = sns.color_palette("colorblind")
-color_cannot = colorblind_palette[1]
-color_must = colorblind_palette[2]
-color_may = colorblind_palette[7]
-color_pred = colorblind_palette[4]
-
-icefire = sns.color_palette("icefire", as_cmap=False, n_colors=5)
-color_tp = icefire[0]
-color_tn = icefire[1]
-color_fp = icefire[4]
-color_fn = icefire[3]
-
-
-def parsed_args():
- """
- Parse and returns command-line args
-
- Returns:
- argparse.Namespace: the parsed arguments
- """
- parser = ArgumentParser()
- parser.add_argument(
- "--input_csv",
- default="ablations_metrics_20210311.csv",
- type=str,
- help="CSV containing the results of the ablation study",
- )
- parser.add_argument(
- "--output_dir",
- default=None,
- type=str,
- help="Output directory",
- )
- parser.add_argument(
- "--models_log_path",
- default=None,
- type=str,
- help="Path containing the log files of the models",
- )
- parser.add_argument(
- "--masker_test_set_dir",
- default=None,
- type=str,
- help="Directory containing the test images",
- )
- parser.add_argument(
- "--best_model",
- default="dada, msd_spade, pseudo",
- type=str,
- help="The string identifier of the best model",
- )
- parser.add_argument(
- "--dpi",
- default=200,
- type=int,
- help="DPI for the output images",
- )
- parser.add_argument(
- "--alpha",
- default=0.5,
- type=float,
- help="Transparency of labels shade",
- )
- parser.add_argument(
- "--percentile",
- default=0.05,
- type=float,
- help="Transparency of labels shade",
- )
- parser.add_argument(
- "--seed",
- default=None,
- type=int,
- help="Bootstrap random seed, for reproducibility",
- )
- parser.add_argument(
- "--no_images",
- action="store_true",
- default=False,
- help="Do not generate images",
- )
-
- return parser.parse_args()
-
-
-def map_color(arr, input_color, output_color, rtol=1e-09):
- """
- Maps one color to another
- """
- input_color_arr = np.tile(input_color, (arr.shape[:2] + (1,)))
- output = arr.copy()
- output[np.all(np.isclose(arr, input_color_arr, rtol=rtol), axis=2)] = output_color
- return output
-
-
-def plot_labels(ax, img, label, img_id, n_, add_title, do_legend):
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (255, 0, 0), color_cannot)
- label_colmap = map_color(label_colmap, (0, 0, 255), color_must)
- label_colmap = map_color(label_colmap, (0, 0, 0), color_may)
-
- ax.imshow(img)
- ax.imshow(label_colmap, alpha=0.5)
- ax.axis("off")
-
- if n_ in [1, 3, 5]:
- color_ = "green"
- else:
- color_ = "red"
-
- ax.text(
- -0.15,
- 0.5,
- img_id,
- color=color_,
- fontweight="roman",
- fontsize="x-large",
- horizontalalignment="left",
- verticalalignment="center",
- transform=ax.transAxes,
- )
-
- if add_title:
- ax.set_title("Labels", rotation=0, fontsize="x-large")
-
-
-def plot_pred(ax, img, pred, img_id, add_title, do_legend):
- pred = np.tile(np.expand_dims(pred, axis=2), reps=(1, 1, 3))
-
- pred_colmap = pred.astype(float)
- pred_colmap = map_color(pred_colmap, (1, 1, 1), color_pred)
- pred_colmap_ma = np.ma.masked_not_equal(pred_colmap, color_pred)
- pred_colmap_ma = pred_colmap_ma.mask * img + pred_colmap_ma
-
- ax.imshow(img)
- ax.imshow(pred_colmap_ma, alpha=0.5)
- ax.axis("off")
-
- if add_title:
- ax.set_title("Prediction", rotation=0, fontsize="x-large")
-
-
-def plot_correct_incorrect(
- ax, img_filename, img, metric, label, img_id, n_, add_title, do_legend
-):
- # FP
- fp_map = imread(
- model_path / "eval-metrics/fp" / "{}_fp.png".format(Path(img_filename).stem)
- )
- fp_map = np.tile(np.expand_dims(fp_map, axis=2), reps=(1, 1, 3))
-
- fp_map_colmap = fp_map.astype(float)
- fp_map_colmap = map_color(fp_map_colmap, (1, 1, 1), color_fp)
-
- # FN
- fn_map = imread(
- model_path / "eval-metrics/fn" / "{}_fn.png".format(Path(img_filename).stem)
- )
- fn_map = np.tile(np.expand_dims(fn_map, axis=2), reps=(1, 1, 3))
-
- fn_map_colmap = fn_map.astype(float)
- fn_map_colmap = map_color(fn_map_colmap, (1, 1, 1), color_fn)
-
- # TP
- tp_map = imread(
- model_path / "eval-metrics/tp" / "{}_tp.png".format(Path(img_filename).stem)
- )
- tp_map = np.tile(np.expand_dims(tp_map, axis=2), reps=(1, 1, 3))
-
- tp_map_colmap = tp_map.astype(float)
- tp_map_colmap = map_color(tp_map_colmap, (1, 1, 1), color_tp)
-
- # TN
- tn_map = imread(
- model_path / "eval-metrics/tn" / "{}_tn.png".format(Path(img_filename).stem)
- )
- tn_map = np.tile(np.expand_dims(tn_map, axis=2), reps=(1, 1, 3))
-
- tn_map_colmap = tn_map.astype(float)
- tn_map_colmap = map_color(tn_map_colmap, (1, 1, 1), color_tn)
-
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (0, 0, 0), color_may)
- label_colmap_ma = np.ma.masked_not_equal(label_colmap, color_may)
- label_colmap_ma = label_colmap_ma.mask * img + label_colmap_ma
-
- # Combine masks
- maps = fp_map_colmap + fn_map_colmap + tp_map_colmap + tn_map_colmap
- maps_ma = np.ma.masked_equal(maps, (0, 0, 0))
- maps_ma = maps_ma.mask * img + maps_ma
-
- ax.imshow(img)
- ax.imshow(label_colmap_ma, alpha=0.5)
- ax.imshow(maps_ma, alpha=0.5)
- ax.axis("off")
-
- if add_title:
- ax.set_title("Metric", rotation=0, fontsize="x-large")
-
-
-def plot_edge_coherence(ax, img, metric, label, pred, img_id, n_, add_title, do_legend):
- pred = np.tile(np.expand_dims(pred, axis=2), reps=(1, 1, 3))
-
- ec, pred_ec, label_ec = edges_coherence_std_min(
- np.squeeze(pred[:, :, 0]), np.squeeze(encode_mask_label(label, "flood"))
- )
-
- ##################
- # Edge distances #
- ##################
-
- # Location of edges
- pred_ec_coord = np.argwhere(pred_ec > 0)
- label_ec_coord = np.argwhere(label_ec > 0)
-
- # Normalized pairwise distances between pred and label
- dist_mat = np.divide(
- euclidean_distances(pred_ec_coord, label_ec_coord), pred_ec.shape[0]
- )
-
- # Standard deviation of the minimum distance from pred to label
- min_dist = np.min(dist_mat, axis=1) # noqa: F841
-
- #############
- # Make plot #
- #############
-
- pred_ec = np.tile(
- np.expand_dims(np.asarray(pred_ec > 0, dtype=float), axis=2), reps=(1, 1, 3)
- )
- pred_ec_colmap = map_color(pred_ec, (1, 1, 1), color_pred)
- pred_ec_colmap_ma = np.ma.masked_not_equal(pred_ec_colmap, color_pred) # noqa: F841
-
- label_ec = np.tile(
- np.expand_dims(np.asarray(label_ec > 0, dtype=float), axis=2), reps=(1, 1, 3)
- )
- label_ec_colmap = map_color(label_ec, (1, 1, 1), color_must)
- label_ec_colmap_ma = np.ma.masked_not_equal( # noqa: F841
- label_ec_colmap, color_must
- )
-
- # Combined pred and label edges
- combined_ec = pred_ec_colmap + label_ec_colmap
- combined_ec_ma = np.ma.masked_equal(combined_ec, (0, 0, 0))
- combined_ec_img = combined_ec_ma.mask * img + combined_ec
-
- # Pred
- pred_colmap = pred.astype(float)
- pred_colmap = map_color(pred_colmap, (1, 1, 1), color_pred)
- pred_colmap_ma = np.ma.masked_not_equal(pred_colmap, color_pred)
-
- # Must
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (0, 0, 255), color_must)
- label_colmap_ma = np.ma.masked_not_equal(label_colmap, color_must)
-
- # TP
- tp_map = imread(
- model_path / "eval-metrics/tp" / "{}_tp.png".format(Path(srs_sel.filename).stem)
- )
- tp_map = np.tile(np.expand_dims(tp_map, axis=2), reps=(1, 1, 3))
- tp_map_colmap = tp_map.astype(float)
- tp_map_colmap = map_color(tp_map_colmap, (1, 1, 1), color_tp)
- tp_map_colmap_ma = np.ma.masked_not_equal(tp_map_colmap, color_tp)
-
- # Combination
- comb_pred = (
- (pred_colmap_ma.mask ^ tp_map_colmap_ma.mask)
- & tp_map_colmap_ma.mask
- & combined_ec_ma.mask
- ) * pred_colmap
- comb_label = (
- (label_colmap_ma.mask ^ pred_colmap_ma.mask)
- & pred_colmap_ma.mask
- & combined_ec_ma.mask
- ) * label_colmap
- comb_tp = combined_ec_ma.mask * tp_map_colmap.copy()
- combined = comb_tp + comb_label + comb_pred
- combined_ma = np.ma.masked_equal(combined, (0, 0, 0))
- combined_ma = combined_ma.mask * combined_ec_img + combined_ma
-
- ax.imshow(combined_ec_img, alpha=1)
- ax.imshow(combined_ma, alpha=0.5)
- ax.axis("off")
-
- # Plot lines
- idx_sort_x = np.argsort(pred_ec_coord[:, 1])
- offset = 100
- for idx in range(offset, pred_ec_coord.shape[0], offset):
- y0, x0 = pred_ec_coord[idx_sort_x[idx], :]
- argmin = np.argmin(dist_mat[idx_sort_x[idx]])
- y1, x1 = label_ec_coord[argmin, :]
- ax.plot([x0, x1], [y0, y1], color="white", linewidth=0.5)
-
- if add_title:
- ax.set_title("Metric", rotation=0, fontsize="x-large")
-
-
-def plot_images_metric(
- axes, metric, img_filename, img_id, n_, srs_sel, add_title, do_legend
-):
-
- # Read images
- img_path = imgs_orig_path / img_filename
- label_path = labels_path / "{}_labeled.png".format(Path(img_filename).stem)
- img, label = crop_and_resize(img_path, label_path)
- img = rgba2rgb(img) if img.shape[-1] == 4 else img / 255.0
-
- pred = imread(
- model_path / "eval-metrics/pred" / "{}_pred.png".format(Path(img_filename).stem)
- )
-
- # Label
- plot_labels(axes[0], img, label, img_id, n_, add_title, do_legend)
-
- # Prediction
- plot_pred(axes[1], img, pred, img_id, add_title, do_legend)
-
- # Correct / incorrect
- if metric in ["error", "f05"]:
- plot_correct_incorrect(
- axes[2],
- img_filename,
- img,
- metric,
- label,
- img_id,
- n_,
- add_title,
- do_legend=False,
- )
- handles = []
- lw = 1.0
- handles.append(
- mpatches.Patch(facecolor=color_tp, label="TP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_tn, label="TN", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_fp, label="FP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_fn, label="FN", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(
- facecolor=color_may,
- label="May-be-flooded",
- linewidth=lw,
- alpha=0.66,
- )
- )
- labels = ["TP", "TN", "FP", "FN", "May-be-flooded"]
- if metric == "error":
- if n_ in [1, 3, 5]:
- title = "Low error rate"
- else:
- title = "High error rate"
- else:
- if n_ in [1, 3, 5]:
- title = "High F05 score"
- else:
- title = "Low F05 score"
- # Edge coherence
- elif metric == "edge_coherence":
- plot_edge_coherence(
- axes[2], img, metric, label, pred, img_id, n_, add_title, do_legend=False
- )
- handles = []
- lw = 1.0
- handles.append(
- mpatches.Patch(facecolor=color_tp, label="TP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_pred, label="pred", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(
- facecolor=color_must,
- label="Must-be-flooded",
- linewidth=lw,
- alpha=0.66,
- )
- )
- labels = ["TP", "Prediction", "Must-be-flooded"]
- if n_ in [1, 3, 5]:
- title = "High edge coherence"
- else:
- title = "Low edge coherence"
-
- else:
- raise ValueError
-
- labels_values_title = "Error: {:.4f} \nFO5: {:.4f} \nEdge coherence: {:.4f}".format(
- srs_sel.error, srs_sel.f05, srs_sel.edge_coherence
- )
-
- plot_legend(axes[3], img, handles, labels, labels_values_title, title)
-
-
-def plot_legend(ax, img, handles, labels, labels_values_title, title):
- img_ = np.zeros_like(img, dtype=np.uint8)
- img_.fill(255)
- ax.imshow(img_)
- ax.axis("off")
-
- leg1 = ax.legend(
- handles=handles,
- labels=labels,
- title=title,
- title_fontsize="medium",
- labelspacing=0.6,
- loc="upper left",
- fontsize="x-small",
- frameon=False,
- )
- leg1._legend_box.align = "left"
-
- leg2 = ax.legend(
- title=labels_values_title,
- title_fontsize="small",
- loc="lower left",
- frameon=False,
- )
- leg2._legend_box.align = "left"
-
- ax.add_artist(leg1)
-
-
-def scatterplot_metrics_pair(ax, df, x_metric, y_metric, dict_images):
-
- sns.scatterplot(data=df, x=x_metric, y=y_metric, ax=ax)
-
- # Set X-label
- ax.set_xlabel(dict_metrics["names"][x_metric], rotation=0, fontsize="medium")
-
- # Set Y-label
- ax.set_ylabel(dict_metrics["names"][y_metric], rotation=90, fontsize="medium")
-
- # Change spines
- sns.despine(ax=ax, left=True, bottom=True)
-
- annotate_scatterplot(ax, dict_images, x_metric, y_metric)
-
-
-def scatterplot_metrics(ax, df, df_all, dict_images, plot_all=False):
-
- # Other
- if plot_all:
- sns.scatterplot(
- data=df_all.loc[df_all.ground == True],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='+', alpha=0.25)
- sns.scatterplot(
- data=df_all.loc[df_all.instagan == True],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='x', alpha=0.25)
- sns.scatterplot(
- data=df_all.loc[(df_all.instagan == False) & (df_all.instagan == False) &
- (df_all.model_feats != args.best_model)],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='s', alpha=0.25)
-
- # Best model
- cmap_ = sns.cubehelix_palette(as_cmap=True)
- sns.scatterplot(
- data=df, x="error", y="f05", hue="edge_coherence", ax=ax, palette=cmap_
- )
-
- norm = plt.Normalize(df["edge_coherence"].min(), df["edge_coherence"].max())
- sm = plt.cm.ScalarMappable(cmap=cmap_, norm=norm)
- sm.set_array([])
-
- # Remove the legend and add a colorbar
- ax.get_legend().remove()
- ax_cbar = ax.figure.colorbar(sm)
- ax_cbar.set_label("Edge coherence", labelpad=8)
-
- # Set X-label
- ax.set_xlabel(dict_metrics["names"]["error"], rotation=0, fontsize="medium")
-
- # Set Y-label
- ax.set_ylabel(dict_metrics["names"]["f05"], rotation=90, fontsize="medium")
-
- annotate_scatterplot(ax, dict_images, "error", "f05")
-
- # Change spines
- sns.despine(ax=ax, left=True, bottom=True)
-
- # Set XY limits
- xlim = ax.get_xlim()
- ylim = ax.get_ylim()
- ax.set_xlim([0.0, xlim[1]])
- ax.set_ylim([ylim[0], 1.0])
-
-
-def annotate_scatterplot(ax, dict_images, x_metric, y_metric, offset=0.1):
- xlim = ax.get_xlim()
- ylim = ax.get_ylim()
- x_len = xlim[1] - xlim[0]
- y_len = ylim[1] - ylim[0]
- x_th = xlim[1] - x_len / 2.0
- y_th = ylim[1] - y_len / 2.0
- for text, d in dict_images.items():
- if text in ["B", "D", "F"]:
- x = d[x_metric]
- y = d[y_metric]
-
- x_text = x + x_len * offset if x < x_th else x - x_len * offset
- y_text = y + y_len * offset if y < y_th else y - y_len * offset
-
- ax.annotate(
- xy=(x, y),
- xycoords="data",
- xytext=(x_text, y_text),
- textcoords="data",
- text=text,
- arrowprops=dict(facecolor="black", shrink=0.05),
- fontsize="medium",
- color="black",
- )
- elif text == "A":
- x = (
- dict_images["A"][x_metric]
- + dict_images["C"][x_metric]
- + dict_images["E"][x_metric]
- ) / 3
- y = (
- dict_images["A"][y_metric]
- + dict_images["C"][y_metric]
- + dict_images["E"][y_metric]
- ) / 3
-
- x_text = x + x_len * 2 * offset if x < x_th else x - x_len * 2 * offset
- y_text = (
- y + y_len * 0.45 * offset if y < y_th else y - y_len * 0.45 * offset
- )
-
- ax.annotate(
- xy=(x, y),
- xycoords="data",
- xytext=(x_text, y_text),
- textcoords="data",
- text="A, C, E",
- arrowprops=dict(facecolor="black", shrink=0.05),
- fontsize="medium",
- color="black",
- )
-
-
-if __name__ == "__main__":
- # -----------------------------
- # ----- Parse arguments -----
- # -----------------------------
- args = parsed_args()
- print("Args:\n" + "\n".join([f" {k:20}: {v}" for k, v in vars(args).items()]))
-
- # Determine output dir
- if args.output_dir is None:
- output_dir = Path(os.environ["SLURM_TMPDIR"])
- else:
- output_dir = Path(args.output_dir)
- if not output_dir.exists():
- output_dir.mkdir(parents=True, exist_ok=False)
-
- # Store args
- output_yml = output_dir / "labels.yml"
- with open(output_yml, "w") as f:
- yaml.dump(vars(args), f)
-
- # Data dirs
- imgs_orig_path = Path(args.masker_test_set_dir) / "imgs"
- labels_path = Path(args.masker_test_set_dir) / "labels"
-
- # Read CSV
- df_all = pd.read_csv(args.input_csv, index_col="model_img_idx")
-
- # Select best model
- df = df_all.loc[df_all.model_feats == args.best_model]
- v_key, model_dir = df.model.unique()[0].split("/")
- model_path = Path(args.models_log_path) / "ablation-{}".format(v_key) / model_dir
-
- # Set up plot
- sns.reset_orig()
- sns.set(style="whitegrid")
- plt.rcParams.update({"font.family": "serif"})
- plt.rcParams.update(
- {
- "font.serif": [
- "Computer Modern Roman",
- "Times New Roman",
- "Utopia",
- "New Century Schoolbook",
- "Century Schoolbook L",
- "ITC Bookman",
- "Bookman",
- "Times",
- "Palatino",
- "Charter",
- "serif" "Bitstream Vera Serif",
- "DejaVu Serif",
- ]
- }
- )
-
- if args.seed:
- np.random.seed(args.seed)
- img_ids = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
- dict_images = {}
- idx = 0
-
- # Define grid of subplots
- grid_vmargin = 0.03 # Extent of the vertical margin between metric grids
- ax_hspace = 0.04 # Extent of the vertical space between axes of same grid
- ax_wspace = 0.05 # Extent of the horizontal space between axes of same grid
- n_grids = len(metrics)
- n_cols = 4
- n_rows = 2
- h_grid = (1.0 / n_grids) - ((n_grids - 1) * grid_vmargin) / n_grids
-
- fig1 = plt.figure(dpi=200, figsize=(11, 13))
-
- n_ = 0
- add_title = False
- for metric_id, metric in enumerate(metrics):
-
- # Create grid
- top_grid = 1.0 - metric_id * h_grid - metric_id * grid_vmargin
- bottom_grid = top_grid - h_grid
- gridspec = GridSpec(
- n_rows,
- n_cols,
- wspace=ax_wspace,
- hspace=ax_hspace,
- bottom=bottom_grid,
- top=top_grid,
- )
-
- # Select best
- if metric == "error":
- ascending = True
- else:
- ascending = False
- idx_rand = np.random.permutation(int(args.percentile * len(df)))[0]
- srs_sel = df.sort_values(by=metric, ascending=ascending).iloc[idx_rand]
- img_id = img_ids[idx]
- dict_images.update({img_id: srs_sel})
- # Read images
- img_filename = srs_sel.filename
-
- axes_row = [fig1.add_subplot(gridspec[0, c]) for c in range(n_cols)]
- if not args.no_images:
- n_ += 1
- if metric_id == 0:
- add_title = True
- plot_images_metric(
- axes_row,
- metric,
- img_filename,
- img_id,
- n_,
- srs_sel,
- add_title=add_title,
- do_legend=False,
- )
- add_title = False
-
- idx += 1
- print("1 more row done.")
- # Select worst
- if metric == "error":
- ascending = False
- else:
- ascending = True
- idx_rand = np.random.permutation(int(args.percentile * len(df)))[0]
- srs_sel = df.sort_values(by=metric, ascending=ascending).iloc[idx_rand]
- img_id = img_ids[idx]
- dict_images.update({img_id: srs_sel})
- # Read images
- img_filename = srs_sel.filename
-
- axes_row = [fig1.add_subplot(gridspec[1, c]) for c in range(n_cols)]
- if not args.no_images:
- n_ += 1
- plot_images_metric(
- axes_row,
- metric,
- img_filename,
- img_id,
- n_,
- srs_sel,
- add_title=add_title,
- do_legend=False,
- )
-
- idx += 1
- print("1 more row done.")
-
- output_fig = output_dir / "all_metrics.png"
-
- fig1.tight_layout() # (pad=1.5) #
- fig1.savefig(output_fig, dpi=fig1.dpi, bbox_inches="tight")
-
- # Scatter plot
- fig2 = plt.figure(dpi=200)
-
- scatterplot_metrics(fig2.gca(), df, df_all, dict_images)
-
- # fig2, axes = plt.subplots(nrows=1, ncols=3, dpi=200, figsize=(18, 5))
- #
- # scatterplot_metrics_pair(axes[0], df, "error", "f05", dict_images)
- # scatterplot_metrics_pair(axes[1], df, "error", "edge_coherence", dict_images)
- # scatterplot_metrics_pair(axes[2], df, "f05", "edge_coherence", dict_images)
-
- output_fig = output_dir / "scatterplots.png"
- fig2.savefig(output_fig, dpi=fig2.dpi, bbox_inches="tight")
diff --git a/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh b/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh
deleted file mode 100644
index d649238546b996ae1ad00f7753c04aeebc7aaa97..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-echo "Dowloading Script" && python download_labelbox.py
-
-echo "Merging Script" && python merge_labelbox_masks.py
-
-echo "Cleaning labeled"
-rm /Users/victor/Downloads/metrics-v2/labels/*
-cp /Users/victor/Downloads/labelbox_test_flood-v2/__labeled/* /Users/victor/Downloads/metrics-v2/labels
-
-echo "Create labeled images Script" && python create_labeled.py
\ No newline at end of file
diff --git a/spaces/Nunchakuka/FrenchAnonymizer/models.py b/spaces/Nunchakuka/FrenchAnonymizer/models.py
deleted file mode 100644
index 46b8aacb1bef18f6fad4c20c968b19125626799c..0000000000000000000000000000000000000000
--- a/spaces/Nunchakuka/FrenchAnonymizer/models.py
+++ /dev/null
@@ -1,351 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
-
-class Encoder(nn.Module):
- def __init__(self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
- def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
- resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(weight_norm(
- ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
- k, u, padding=(k-u)//2)))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel//(2**(i+1))
- for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i*self.num_kernels+j](x)
- else:
- xs += self.resblocks[i*self.num_kernels+j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- print('Removing weight norm...')
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
- ])
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ])
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2,3,5,7,11]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class SpeakerEncoder(torch.nn.Module):
- def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
- super(SpeakerEncoder, self).__init__()
- self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
- self.linear = nn.Linear(model_hidden_size, model_embedding_size)
- self.relu = nn.ReLU()
-
- def forward(self, mels):
- self.lstm.flatten_parameters()
- _, (hidden, _) = self.lstm(mels)
- embeds_raw = self.relu(self.linear(hidden[-1]))
- return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
-
- def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
- mel_slices = []
- for i in range(0, total_frames-partial_frames, partial_hop):
- mel_range = torch.arange(i, i+partial_frames)
- mel_slices.append(mel_range)
-
- return mel_slices
-
- def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
- mel_len = mel.size(1)
- last_mel = mel[:,-partial_frames:]
-
- if mel_len > partial_frames:
- mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
- mels = list(mel[:,s] for s in mel_slices)
- mels.append(last_mel)
- mels = torch.stack(tuple(mels), 0).squeeze(1)
-
- with torch.no_grad():
- partial_embeds = self(mels)
- embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
- #embed = embed / torch.linalg.norm(embed, 2)
- else:
- with torch.no_grad():
- embed = self(last_mel)
-
- return embed
-
-
-class SynthesizerTrn(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels,
- ssl_dim,
- use_spk,
- **kwargs):
-
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- self.ssl_dim = ssl_dim
- self.use_spk = use_spk
-
- self.enc_p = Encoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16)
- self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
- self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
- self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
- if not self.use_spk:
- self.enc_spk = SpeakerEncoder(model_hidden_size=gin_channels, model_embedding_size=gin_channels)
-
- def forward(self, c, spec, g=None, mel=None, c_lengths=None, spec_lengths=None):
- if c_lengths == None:
- c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
- if spec_lengths == None:
- spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
-
- if not self.use_spk:
- g = self.enc_spk(mel.transpose(1,2))
- g = g.unsqueeze(-1)
-
- _, m_p, logs_p, _ = self.enc_p(c, c_lengths)
- z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g)
- z_p = self.flow(z, spec_mask, g=g)
-
- z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size)
- o = self.dec(z_slice, g=g)
-
- return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, c, g=None, mel=None, c_lengths=None):
- if c_lengths == None:
- c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
- if not self.use_spk:
- g = self.enc_spk.embed_utterance(mel.transpose(1,2))
- g = g.unsqueeze(-1)
-
- z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths)
- z = self.flow(z_p, c_mask, g=g, reverse=True)
- o = self.dec(z * c_mask, g=g)
-
- return o
diff --git a/spaces/Nunchakuka/FrenchAnonymizer/utils.py b/spaces/Nunchakuka/FrenchAnonymizer/utils.py
deleted file mode 100644
index 1bd5b6185af6c9f1c270b8ba345bfc36d059e081..0000000000000000000000000000000000000000
--- a/spaces/Nunchakuka/FrenchAnonymizer/utils.py
+++ /dev/null
@@ -1,305 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-from torch.nn import functional as F
-from commons import sequence_mask
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def get_cmodel(rank):
- checkpoint = torch.load('wavlm/WavLM-Large.pt')
- cfg = WavLMConfig(checkpoint['cfg'])
- cmodel = WavLM(cfg).cuda(rank)
- cmodel.load_state_dict(checkpoint['model'])
- cmodel.eval()
- return cmodel
-
-
-def get_content(cmodel, y):
- with torch.no_grad():
- c = cmodel.extract_features(y.squeeze(1))[0]
- c = c.transpose(1, 2)
- return c
-
-
-def get_vocoder(rank):
- with open("hifigan/config.json", "r") as f:
- config = json.load(f)
- config = hifigan.AttrDict(config)
- vocoder = hifigan.Generator(config)
- ckpt = torch.load("hifigan/generator_v1")
- vocoder.load_state_dict(ckpt["generator"])
- vocoder.eval()
- vocoder.remove_weight_norm()
- vocoder.cuda(rank)
- return vocoder
-
-
-def transform(mel, height): # 68-92
- #r = np.random.random()
- #rate = r * 0.3 + 0.85 # 0.85-1.15
- #height = int(mel.size(-2) * rate)
- tgt = torchvision.transforms.functional.resize(mel, (height, mel.size(-1)))
- if height >= mel.size(-2):
- return tgt[:, :mel.size(-2), :]
- else:
- silence = tgt[:,-1:,:].repeat(1,mel.size(-2)-height,1)
- silence += torch.randn_like(silence) / 10
- return torch.cat((tgt, silence), 1)
-
-
-def stretch(mel, width): # 0.5-2
- return torchvision.transforms.functional.resize(mel, (mel.size(-2), width))
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict= {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})" .format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info("Saving model and optimizer state at iteration {} to {}".format(
- iteration, checkpoint_path))
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- torch.save({'model': state_dict,
- 'iteration': iteration,
- 'optimizer': optimizer.state_dict(),
- 'learning_rate': learning_rate}, checkpoint_path)
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
- for k, v in scalars.items():
- writer.add_scalar(k, v, global_step)
- for k, v in histograms.items():
- writer.add_histogram(k, v, global_step)
- for k, v in images.items():
- writer.add_image(k, v, global_step, dataformats='HWC')
- for k, v in audios.items():
- writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
- x = f_list[-1]
- print(x)
- return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10,2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py
deleted file mode 100644
index bb80d11a67cd75764a89f6f41915b0348ae96e92..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py
+++ /dev/null
@@ -1,428 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from multiprocessing import Pool
-
-import numpy as np
-from fairseq import options
-from fairseq.data import dictionary
-from fairseq.scoring import bleu
-
-from examples.noisychannel import (
- rerank_generate,
- rerank_options,
- rerank_score_bw,
- rerank_score_lm,
- rerank_utils,
-)
-
-
-def score_target_hypo(
- args, a, b, c, lenpen, target_outfile, hypo_outfile, write_hypos, normalize
-):
-
- print("lenpen", lenpen, "weight1", a, "weight2", b, "weight3", c)
- gen_output_lst, bitext1_lst, bitext2_lst, lm_res_lst = load_score_files(args)
- dict = dictionary.Dictionary()
- scorer = scorer = bleu.Scorer(
- bleu.BleuConfig(
- pad=dict.pad(),
- eos=dict.eos(),
- unk=dict.unk(),
- )
- )
-
- ordered_hypos = {}
- ordered_targets = {}
-
- for shard_id in range(len(bitext1_lst)):
- bitext1 = bitext1_lst[shard_id]
- bitext2 = bitext2_lst[shard_id]
- gen_output = gen_output_lst[shard_id]
- lm_res = lm_res_lst[shard_id]
-
- total = len(bitext1.rescore_source.keys())
- source_lst = []
- hypo_lst = []
- score_lst = []
- reference_lst = []
- j = 1
- best_score = -math.inf
-
- for i in range(total):
- # length is measured in terms of words, not bpe tokens, since models may not share the same bpe
- target_len = len(bitext1.rescore_hypo[i].split())
-
- if lm_res is not None:
- lm_score = lm_res.score[i]
- else:
- lm_score = 0
-
- if bitext2 is not None:
- bitext2_score = bitext2.rescore_score[i]
- bitext2_backwards = bitext2.backwards
- else:
- bitext2_score = None
- bitext2_backwards = None
-
- score = rerank_utils.get_score(
- a,
- b,
- c,
- target_len,
- bitext1.rescore_score[i],
- bitext2_score,
- lm_score=lm_score,
- lenpen=lenpen,
- src_len=bitext1.source_lengths[i],
- tgt_len=bitext1.target_lengths[i],
- bitext1_backwards=bitext1.backwards,
- bitext2_backwards=bitext2_backwards,
- normalize=normalize,
- )
-
- if score > best_score:
- best_score = score
- best_hypo = bitext1.rescore_hypo[i]
-
- if j == gen_output.num_hypos[i] or j == args.num_rescore:
- j = 1
- hypo_lst.append(best_hypo)
- score_lst.append(best_score)
- source_lst.append(bitext1.rescore_source[i])
- reference_lst.append(bitext1.rescore_target[i])
-
- best_score = -math.inf
- best_hypo = ""
- else:
- j += 1
-
- gen_keys = list(sorted(gen_output.no_bpe_target.keys()))
-
- for key in range(len(gen_keys)):
- if args.prefix_len is None:
- assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], (
- "pred and rescore hypo mismatch: i: "
- + str(key)
- + ", "
- + str(hypo_lst[key])
- + str(gen_keys[key])
- + str(gen_output.no_bpe_hypo[key])
- )
- sys_tok = dict.encode_line(hypo_lst[key])
- ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]])
- scorer.add(ref_tok, sys_tok)
-
- else:
- full_hypo = rerank_utils.get_full_from_prefix(
- hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]]
- )
- sys_tok = dict.encode_line(full_hypo)
- ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]])
- scorer.add(ref_tok, sys_tok)
-
- # if only one set of hyper parameters is provided, write the predictions to a file
- if write_hypos:
- # recover the orinal ids from n best list generation
- for key in range(len(gen_output.no_bpe_target)):
- if args.prefix_len is None:
- assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], (
- "pred and rescore hypo mismatch:"
- + "i:"
- + str(key)
- + str(hypo_lst[key])
- + str(gen_output.no_bpe_hypo[key])
- )
- ordered_hypos[gen_keys[key]] = hypo_lst[key]
- ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[
- gen_keys[key]
- ]
-
- else:
- full_hypo = rerank_utils.get_full_from_prefix(
- hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]]
- )
- ordered_hypos[gen_keys[key]] = full_hypo
- ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[
- gen_keys[key]
- ]
-
- # write the hypos in the original order from nbest list generation
- if args.num_shards == (len(bitext1_lst)):
- with open(target_outfile, "w") as t:
- with open(hypo_outfile, "w") as h:
- for key in range(len(ordered_hypos)):
- t.write(ordered_targets[key])
- h.write(ordered_hypos[key])
-
- res = scorer.result_string(4)
- if write_hypos:
- print(res)
- score = rerank_utils.parse_bleu_scoring(res)
- return score
-
-
-def match_target_hypo(args, target_outfile, hypo_outfile):
- """combine scores from the LM and bitext models, and write the top scoring hypothesis to a file"""
- if len(args.weight1) == 1:
- res = score_target_hypo(
- args,
- args.weight1[0],
- args.weight2[0],
- args.weight3[0],
- args.lenpen[0],
- target_outfile,
- hypo_outfile,
- True,
- args.normalize,
- )
- rerank_scores = [res]
- else:
- print("launching pool")
- with Pool(32) as p:
- rerank_scores = p.starmap(
- score_target_hypo,
- [
- (
- args,
- args.weight1[i],
- args.weight2[i],
- args.weight3[i],
- args.lenpen[i],
- target_outfile,
- hypo_outfile,
- False,
- args.normalize,
- )
- for i in range(len(args.weight1))
- ],
- )
-
- if len(rerank_scores) > 1:
- best_index = np.argmax(rerank_scores)
- best_score = rerank_scores[best_index]
- print("best score", best_score)
- print("best lenpen", args.lenpen[best_index])
- print("best weight1", args.weight1[best_index])
- print("best weight2", args.weight2[best_index])
- print("best weight3", args.weight3[best_index])
- return (
- args.lenpen[best_index],
- args.weight1[best_index],
- args.weight2[best_index],
- args.weight3[best_index],
- best_score,
- )
-
- else:
- return (
- args.lenpen[0],
- args.weight1[0],
- args.weight2[0],
- args.weight3[0],
- rerank_scores[0],
- )
-
-
-def load_score_files(args):
- if args.all_shards:
- shard_ids = list(range(args.num_shards))
- else:
- shard_ids = [args.shard_id]
-
- gen_output_lst = []
- bitext1_lst = []
- bitext2_lst = []
- lm_res1_lst = []
-
- for shard_id in shard_ids:
- using_nbest = args.nbest_list is not None
- (
- pre_gen,
- left_to_right_preprocessed_dir,
- right_to_left_preprocessed_dir,
- backwards_preprocessed_dir,
- lm_preprocessed_dir,
- ) = rerank_utils.get_directories(
- args.data_dir_name,
- args.num_rescore,
- args.gen_subset,
- args.gen_model_name,
- shard_id,
- args.num_shards,
- args.sampling,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- rerank1_is_gen = (
- args.gen_model == args.score_model1 and args.source_prefix_frac is None
- )
- rerank2_is_gen = (
- args.gen_model == args.score_model2 and args.source_prefix_frac is None
- )
-
- score1_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model1_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards1,
- )
- if args.score_model2 is not None:
- score2_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model2_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards2,
- )
- if args.language_model is not None:
- lm_score_file = rerank_utils.rescore_file_name(
- pre_gen, args.prefix_len, args.lm_name, lm_file=True
- )
-
- # get gen output
- predictions_bpe_file = pre_gen + "/generate_output_bpe.txt"
- if using_nbest:
- print("Using predefined n-best list from interactive.py")
- predictions_bpe_file = args.nbest_list
- gen_output = rerank_utils.BitextOutputFromGen(
- predictions_bpe_file,
- bpe_symbol=args.post_process,
- nbest=using_nbest,
- prefix_len=args.prefix_len,
- target_prefix_frac=args.target_prefix_frac,
- )
-
- if rerank1_is_gen:
- bitext1 = gen_output
- else:
- bitext1 = rerank_utils.BitextOutput(
- score1_file,
- args.backwards1,
- args.right_to_left1,
- args.post_process,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- if args.score_model2 is not None or args.nbest_list is not None:
- if rerank2_is_gen:
- bitext2 = gen_output
- else:
- bitext2 = rerank_utils.BitextOutput(
- score2_file,
- args.backwards2,
- args.right_to_left2,
- args.post_process,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- assert (
- bitext2.source_lengths == bitext1.source_lengths
- ), "source lengths for rescoring models do not match"
- assert (
- bitext2.target_lengths == bitext1.target_lengths
- ), "target lengths for rescoring models do not match"
- else:
- if args.diff_bpe:
- assert args.score_model2 is None
- bitext2 = gen_output
- else:
- bitext2 = None
-
- if args.language_model is not None:
- lm_res1 = rerank_utils.LMOutput(
- lm_score_file,
- args.lm_dict,
- args.prefix_len,
- args.post_process,
- args.target_prefix_frac,
- )
- else:
- lm_res1 = None
-
- gen_output_lst.append(gen_output)
- bitext1_lst.append(bitext1)
- bitext2_lst.append(bitext2)
- lm_res1_lst.append(lm_res1)
- return gen_output_lst, bitext1_lst, bitext2_lst, lm_res1_lst
-
-
-def rerank(args):
- if type(args.lenpen) is not list:
- args.lenpen = [args.lenpen]
- if type(args.weight1) is not list:
- args.weight1 = [args.weight1]
- if type(args.weight2) is not list:
- args.weight2 = [args.weight2]
- if type(args.weight3) is not list:
- args.weight3 = [args.weight3]
- if args.all_shards:
- shard_ids = list(range(args.num_shards))
- else:
- shard_ids = [args.shard_id]
-
- for shard_id in shard_ids:
- (
- pre_gen,
- left_to_right_preprocessed_dir,
- right_to_left_preprocessed_dir,
- backwards_preprocessed_dir,
- lm_preprocessed_dir,
- ) = rerank_utils.get_directories(
- args.data_dir_name,
- args.num_rescore,
- args.gen_subset,
- args.gen_model_name,
- shard_id,
- args.num_shards,
- args.sampling,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
- rerank_generate.gen_and_reprocess_nbest(args)
- rerank_score_bw.score_bw(args)
- rerank_score_lm.score_lm(args)
-
- if args.write_hypos is None:
- write_targets = pre_gen + "/matched_targets"
- write_hypos = pre_gen + "/matched_hypos"
- else:
- write_targets = args.write_hypos + "_targets" + args.gen_subset
- write_hypos = args.write_hypos + "_hypos" + args.gen_subset
-
- if args.all_shards:
- write_targets += "_all_shards"
- write_hypos += "_all_shards"
-
- (
- best_lenpen,
- best_weight1,
- best_weight2,
- best_weight3,
- best_score,
- ) = match_target_hypo(args, write_targets, write_hypos)
-
- return best_lenpen, best_weight1, best_weight2, best_weight3, best_score
-
-
-def cli_main():
- parser = rerank_options.get_reranking_parser()
- args = options.parse_args_and_arch(parser)
- rerank(args)
-
-
-if __name__ == "__main__":
- cli_main()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md
deleted file mode 100644
index 9d3d7b67dc08440b5f4d1c5a7ffcd4bd6e76c14f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md
+++ /dev/null
@@ -1,179 +0,0 @@
-# Examples of Training scripts for Non-autoregressive Machine Translation models
-
-### Non-autoregressive Transformer (NAT, Gu et al., 2017)
-Note that we need to have an additional module to perform "length prediction" (`--length-loss-factor`) before generating the whole sequence.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch nonautoregressive_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-### Fast Structured Decoding for Sequence Models (NAT-CRF, Sun et al., 2019)
-Note that we implemented a low-rank appromixated CRF model by setting `--crf-lowrank-approx=32` and `--crf-beam-approx=64` as discribed in the original paper. All other settings are the same as the vanilla NAT model.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch nacrf_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --word-ins-loss-factor 0.5 \
- --crf-lowrank-approx 32 \
- --crf-beam-approx 64 \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-### Non-autoregressive Transformer with Iterative Refinement (iNAT, Lee et al., 2018)
-Note that `--train-step` means how many iterations of refinement we used during training, and `--dae-ratio` controls the ratio of denoising auto-encoder training described in the original paper.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch iterative_nonautoregressive_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --train-step 4 \
- --dae-ratio 0.5 \
- --stochastic-approx \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-### Insertion Transformer (InsT, Stern et al., 2019)
-Note that we need to specify the "slot-loss" (uniform or balanced tree) described in the original paper. Here we use `--label-tau` to control the temperature.
-
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch insertion_transformer \
- --noise random_delete \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-### Mask Predict (CMLM, Ghazvininejad et al., 2019)
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch cmlm_transformer \
- --noise random_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-
-
-### Levenshtein Transformer (LevT, Gu et al., 2019)
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch levenshtein_transformer \
- --noise random_delete \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py
deleted file mode 100644
index 8b7eb2ec4fc5190c4dcdfe34b0259e6f448e18a9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""isort:skip_file"""
-
-from .dictionary import Dictionary, TruncatedDictionary
-
-from .fairseq_dataset import FairseqDataset, FairseqIterableDataset
-
-from .base_wrapper_dataset import BaseWrapperDataset
-
-from .add_target_dataset import AddTargetDataset
-from .append_token_dataset import AppendTokenDataset
-from .audio.raw_audio_dataset import BinarizedAudioDataset, FileAudioDataset
-from .audio.hubert_dataset import HubertDataset
-from .backtranslation_dataset import BacktranslationDataset
-from .bucket_pad_length_dataset import BucketPadLengthDataset
-from .colorize_dataset import ColorizeDataset
-from .concat_dataset import ConcatDataset
-from .concat_sentences_dataset import ConcatSentencesDataset
-from .denoising_dataset import DenoisingDataset
-from .id_dataset import IdDataset
-from .indexed_dataset import (
- IndexedCachedDataset,
- IndexedDataset,
- IndexedRawTextDataset,
- MMapIndexedDataset,
-)
-from .language_pair_dataset import LanguagePairDataset
-from .list_dataset import ListDataset
-from .lm_context_window_dataset import LMContextWindowDataset
-from .lru_cache_dataset import LRUCacheDataset
-from .mask_tokens_dataset import MaskTokensDataset
-from .monolingual_dataset import MonolingualDataset
-from .multi_corpus_sampled_dataset import MultiCorpusSampledDataset
-from .nested_dictionary_dataset import NestedDictionaryDataset
-from .noising import NoisingDataset
-from .numel_dataset import NumelDataset
-from .num_samples_dataset import NumSamplesDataset
-from .offset_tokens_dataset import OffsetTokensDataset
-from .pad_dataset import LeftPadDataset, PadDataset, RightPadDataset
-from .prepend_dataset import PrependDataset
-from .prepend_token_dataset import PrependTokenDataset
-from .raw_label_dataset import RawLabelDataset
-from .replace_dataset import ReplaceDataset
-from .resampling_dataset import ResamplingDataset
-from .roll_dataset import RollDataset
-from .round_robin_zip_datasets import RoundRobinZipDatasets
-from .sort_dataset import SortDataset
-from .strip_token_dataset import StripTokenDataset
-from .subsample_dataset import SubsampleDataset
-from .token_block_dataset import TokenBlockDataset
-from .transform_eos_dataset import TransformEosDataset
-from .transform_eos_lang_pair_dataset import TransformEosLangPairDataset
-from .shorten_dataset import TruncateDataset, RandomCropDataset
-from .multilingual.sampled_multi_dataset import SampledMultiDataset
-from .multilingual.sampled_multi_epoch_dataset import SampledMultiEpochDataset
-from .fasta_dataset import FastaDataset, EncodedFastaDataset
-
-from .iterators import (
- CountingIterator,
- EpochBatchIterator,
- GroupedIterator,
- ShardedIterator,
-)
-
-__all__ = [
- "AddTargetDataset",
- "AppendTokenDataset",
- "BacktranslationDataset",
- "BaseWrapperDataset",
- "BinarizedAudioDataset",
- "BucketPadLengthDataset",
- "ColorizeDataset",
- "ConcatDataset",
- "ConcatSentencesDataset",
- "CountingIterator",
- "DenoisingDataset",
- "Dictionary",
- "EncodedFastaDataset",
- "EpochBatchIterator",
- "FairseqDataset",
- "FairseqIterableDataset",
- "FastaDataset",
- "FileAudioDataset",
- "GroupedIterator",
- "HubertDataset",
- "IdDataset",
- "IndexedCachedDataset",
- "IndexedDataset",
- "IndexedRawTextDataset",
- "LanguagePairDataset",
- "LeftPadDataset",
- "ListDataset",
- "LMContextWindowDataset",
- "LRUCacheDataset",
- "MaskTokensDataset",
- "MMapIndexedDataset",
- "MonolingualDataset",
- "MultiCorpusSampledDataset",
- "NestedDictionaryDataset",
- "NoisingDataset",
- "NumelDataset",
- "NumSamplesDataset",
- "OffsetTokensDataset",
- "PadDataset",
- "PrependDataset",
- "PrependTokenDataset",
- "RandomCropDataset",
- "RawLabelDataset",
- "ResamplingDataset",
- "ReplaceDataset",
- "RightPadDataset",
- "RollDataset",
- "RoundRobinZipDatasets",
- "SampledMultiDataset",
- "SampledMultiEpochDataset",
- "ShardedIterator",
- "SortDataset",
- "StripTokenDataset",
- "SubsampleDataset",
- "TokenBlockDataset",
- "TransformEosDataset",
- "TransformEosLangPairDataset",
- "TruncateDataset",
- "TruncatedDictionary",
-]
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py
deleted file mode 100644
index 11c7dc1ea966a54f8915ef164377e40f90e851a1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-from fairseq.tasks import register_task
-from fairseq.tasks.speech_to_text import SpeechToTextTask
-from fairseq.tasks.translation import (
- TranslationTask, TranslationConfig
-)
-
-try:
- import examples.simultaneous_translation # noqa
- import_successful = True
-except BaseException:
- import_successful = False
-
-
-logger = logging.getLogger(__name__)
-
-
-def check_import(flag):
- if not flag:
- raise ImportError(
- "'examples.simultaneous_translation' is not correctly imported. "
- "Please considering `pip install -e $FAIRSEQ_DIR`."
- )
-
-
-@register_task("simul_speech_to_text")
-class SimulSpeechToTextTask(SpeechToTextTask):
- def __init__(self, args, tgt_dict):
- check_import(import_successful)
- super().__init__(args, tgt_dict)
-
-
-@register_task("simul_text_to_text", dataclass=TranslationConfig)
-class SimulTextToTextTask(TranslationTask):
- def __init__(self, cfg, src_dict, tgt_dict):
- check_import(import_successful)
- super().__init__(cfg, src_dict, tgt_dict)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md
deleted file mode 100644
index 57104230655c7c517d25904e634c53b6159ee60f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md
+++ /dev/null
@@ -1,42 +0,0 @@
-# Unit to Speech Model (unit2speech)
-
-Unit to speech model is modified Tacotron2 model that learns to synthesize speech from discrete speech units. All models are trained on quantized [LJSpeech](https://keithito.com/LJ-Speech-Dataset/).
-
-Upstream Units | Download Link
-|-|-
-Log Mel Filterbank + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km50/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km100/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km200/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km500/tts_checkpoint_best.pt)
-Modified CPC + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km50/tts_checkpoint_best.pt)
-Modified CPC + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km100/tts_checkpoint_best.pt)
-Modified CPC + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km200/tts_checkpoint_best.pt)
-Modified CPC + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km500/tts_checkpoint_best.pt)
-HuBERT Base + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km50/tts_checkpoint_best.pt)
-HuBERT Base + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km100/tts_checkpoint_best.pt)
-HuBERT Base + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km200/tts_checkpoint_best.pt)
-HuBERT Base + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km500/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km50/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km100/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km200/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km500/tts_checkpoint_best.pt)
-
-## Run inference using a unit2speech model
-* Install librosa, unidecode and inflect using `pip install librosa, unidecode, inflect`
-* Download [Waveglow checkpoint](https://dl.fbaipublicfiles.com/textless_nlp/gslm/waveglow_256channels_new.pt). This is the vocoder.
-
-Sample commnd to run inference using trained unit2speech models. Please note that the quantized audio to synthesized should be using the same units as the unit2speech model was trained with.
-```
-FAIRSEQ_ROOT=
-TTS_MODEL_PATH=
-QUANTIZED_UNIT_PATH=
-OUT_DIR=
-WAVEGLOW_PATH=
-
-PYTHONPATH=${FAIRSEQ_ROOT}:${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech python ${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py \
- --tts_model_path $TTS_MODEL_PATH \
- --quantized_unit_path $QUANTIZED_UNIT_PATH \
- --out_audio_dir $OUT_DIR \
- --waveglow_path $WAVEGLOW_PATH \
- --max_decoder_steps 2000
-```
\ No newline at end of file
diff --git a/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py b/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py
deleted file mode 100644
index 375e4d50872d5c68ee57ca17470a2ca425425eba..0000000000000000000000000000000000000000
--- a/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import re
-from unidecode import unidecode
-import pyopenjtalk
-
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
- r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
- r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ts', 'ʦ'),
- ('u', 'ɯ'),
- ('j', 'ʥ'),
- ('y', 'j'),
- ('ni', 'n^i'),
- ('nj', 'n^'),
- ('hi', 'çi'),
- ('hj', 'ç'),
- ('f', 'ɸ'),
- ('I', 'i*'),
- ('U', 'ɯ*'),
- ('r', 'ɾ')
-]]
-
-# List of (romaji, ipa2) pairs for marks:
-_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('u', 'ɯ'),
- ('ʧ', 'tʃ'),
- ('j', 'dʑ'),
- ('y', 'j'),
- ('ni', 'n^i'),
- ('nj', 'n^'),
- ('hi', 'çi'),
- ('hj', 'ç'),
- ('f', 'ɸ'),
- ('I', 'i*'),
- ('U', 'ɯ*'),
- ('r', 'ɾ')
-]]
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'Q([↑↓]*[kg])', r'k#\1'),
- (r'Q([↑↓]*[tdjʧ])', r't#\1'),
- (r'Q([↑↓]*[sʃ])', r's\1'),
- (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'N([↑↓]*[pbm])', r'm\1'),
- (r'N([↑↓]*[ʧʥj])', r'n^\1'),
- (r'N([↑↓]*[tdn])', r'n\1'),
- (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-def symbols_to_japanese(text):
- for regex, replacement in _symbols_to_japanese:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_romaji_with_accent(text):
- '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
- text = symbols_to_japanese(text)
- sentences = re.split(_japanese_marks, text)
- marks = re.findall(_japanese_marks, text)
- text = ''
- for i, sentence in enumerate(sentences):
- if re.match(_japanese_characters, sentence):
- if text != '':
- text += ' '
- labels = pyopenjtalk.extract_fullcontext(sentence)
- for n, label in enumerate(labels):
- phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
- if phoneme not in ['sil', 'pau']:
- text += phoneme.replace('ch', 'ʧ').replace('sh',
- 'ʃ').replace('cl', 'Q')
- else:
- continue
- # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
- a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
- a2 = int(re.search(r"\+(\d+)\+", label).group(1))
- a3 = int(re.search(r"\+(\d+)/", label).group(1))
- if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
- a2_next = -1
- else:
- a2_next = int(
- re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
- # Accent phrase boundary
- if a3 == 1 and a2_next == 1:
- text += ' '
- # Falling
- elif a1 == 0 and a2_next == a2 + 1:
- text += '↓'
- # Rising
- elif a2 == 1 and a2_next == 2:
- text += '↑'
- if i < len(marks):
- text += unidecode(marks[i]).replace(' ', '')
- return text
-
-
-def get_real_sokuon(text):
- for regex, replacement in _real_sokuon:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def get_real_hatsuon(text):
- for regex, replacement in _real_hatsuon:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa(text):
- text = japanese_to_romaji_with_accent(text).replace('...', '…')
- text = re.sub(
- r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
- text = get_real_sokuon(text)
- text = get_real_hatsuon(text)
- for regex, replacement in _romaji_to_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa2(text):
- text = japanese_to_romaji_with_accent(text).replace('...', '…')
- text = get_real_sokuon(text)
- text = get_real_hatsuon(text)
- for regex, replacement in _romaji_to_ipa2:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa3(text):
- text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
- 'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
- text = re.sub(
- r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
- text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
- return text
diff --git a/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py b/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py
deleted file mode 100644
index c215099fedacae35c6798fdd9b8420a447aa16bb..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-from audiocraft.quantization.vq import ResidualVectorQuantizer
-
-
-class TestResidualVectorQuantizer:
-
- def test_rvq(self):
- x = torch.randn(1, 16, 2048)
- vq = ResidualVectorQuantizer(n_q=8, dimension=16, bins=8)
- res = vq(x, 1.)
- assert res.x.shape == torch.Size([1, 16, 2048])
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py
deleted file mode 100644
index feb7a8222487756d38482da95183bbbcbbe96ed9..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py
+++ /dev/null
@@ -1,864 +0,0 @@
-
-import math
-import json
-import copy
-from typing import List, Dict
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.modeling.proposal_generator.build import PROPOSAL_GENERATOR_REGISTRY
-from detectron2.layers import ShapeSpec, cat
-from detectron2.structures import Instances, Boxes
-from detectron2.modeling import detector_postprocess
-from detectron2.utils.comm import get_world_size
-from detectron2.config import configurable
-
-from ..layers.heatmap_focal_loss import heatmap_focal_loss_jit
-from ..layers.heatmap_focal_loss import binary_heatmap_focal_loss
-from ..layers.iou_loss import IOULoss
-from ..layers.ml_nms import ml_nms
-from ..debug import debug_train, debug_test
-from .utils import reduce_sum, _transpose
-from .centernet_head import CenterNetHead
-
-__all__ = ["CenterNet"]
-
-INF = 100000000
-
-@PROPOSAL_GENERATOR_REGISTRY.register()
-class CenterNet(nn.Module):
- @configurable
- def __init__(self,
- # input_shape: Dict[str, ShapeSpec],
- in_channels=256,
- *,
- num_classes=80,
- in_features=("p3", "p4", "p5", "p6", "p7"),
- strides=(8, 16, 32, 64, 128),
- score_thresh=0.05,
- hm_min_overlap=0.8,
- loc_loss_type='giou',
- min_radius=4,
- hm_focal_alpha=0.25,
- hm_focal_beta=4,
- loss_gamma=2.0,
- reg_weight=2.0,
- not_norm_reg=True,
- with_agn_hm=False,
- only_proposal=False,
- as_proposal=False,
- not_nms=False,
- pos_weight=1.,
- neg_weight=1.,
- sigmoid_clamp=1e-4,
- ignore_high_fp=-1.,
- center_nms=False,
- sizes_of_interest=[[0,80],[64,160],[128,320],[256,640],[512,10000000]],
- more_pos=False,
- more_pos_thresh=0.2,
- more_pos_topk=9,
- pre_nms_topk_train=1000,
- pre_nms_topk_test=1000,
- post_nms_topk_train=100,
- post_nms_topk_test=100,
- nms_thresh_train=0.6,
- nms_thresh_test=0.6,
- no_reduce=False,
- debug=False,
- vis_thresh=0.5,
- pixel_mean=[103.530,116.280,123.675],
- pixel_std=[1.0,1.0,1.0],
- device='cuda',
- centernet_head=None,
- ):
- super().__init__()
- self.num_classes = num_classes
- self.in_features = in_features
- self.strides = strides
- self.score_thresh = score_thresh
- self.min_radius = min_radius
- self.hm_focal_alpha = hm_focal_alpha
- self.hm_focal_beta = hm_focal_beta
- self.loss_gamma = loss_gamma
- self.reg_weight = reg_weight
- self.not_norm_reg = not_norm_reg
- self.with_agn_hm = with_agn_hm
- self.only_proposal = only_proposal
- self.as_proposal = as_proposal
- self.not_nms = not_nms
- self.pos_weight = pos_weight
- self.neg_weight = neg_weight
- self.sigmoid_clamp = sigmoid_clamp
- self.ignore_high_fp = ignore_high_fp
- self.center_nms = center_nms
- self.sizes_of_interest = sizes_of_interest
- self.more_pos = more_pos
- self.more_pos_thresh = more_pos_thresh
- self.more_pos_topk = more_pos_topk
- self.pre_nms_topk_train = pre_nms_topk_train
- self.pre_nms_topk_test = pre_nms_topk_test
- self.post_nms_topk_train = post_nms_topk_train
- self.post_nms_topk_test = post_nms_topk_test
- self.nms_thresh_train = nms_thresh_train
- self.nms_thresh_test = nms_thresh_test
- self.no_reduce = no_reduce
- self.debug = debug
- self.vis_thresh = vis_thresh
- if self.center_nms:
- self.not_nms = True
- self.iou_loss = IOULoss(loc_loss_type)
- assert (not self.only_proposal) or self.with_agn_hm
- # delta for rendering heatmap
- self.delta = (1 - hm_min_overlap) / (1 + hm_min_overlap)
- if centernet_head is None:
- self.centernet_head = CenterNetHead(
- in_channels=in_channels,
- num_levels=len(in_features),
- with_agn_hm=with_agn_hm,
- only_proposal=only_proposal)
- else:
- self.centernet_head = centernet_head
- if self.debug:
- pixel_mean = torch.Tensor(pixel_mean).to(
- torch.device(device)).view(3, 1, 1)
- pixel_std = torch.Tensor(pixel_std).to(
- torch.device(device)).view(3, 1, 1)
- self.denormalizer = lambda x: x * pixel_std + pixel_mean
-
- @classmethod
- def from_config(cls, cfg, input_shape):
- ret = {
- # 'input_shape': input_shape,
- 'in_channels': input_shape[
- cfg.MODEL.CENTERNET.IN_FEATURES[0]].channels,
- 'num_classes': cfg.MODEL.CENTERNET.NUM_CLASSES,
- 'in_features': cfg.MODEL.CENTERNET.IN_FEATURES,
- 'strides': cfg.MODEL.CENTERNET.FPN_STRIDES,
- 'score_thresh': cfg.MODEL.CENTERNET.INFERENCE_TH,
- 'loc_loss_type': cfg.MODEL.CENTERNET.LOC_LOSS_TYPE,
- 'hm_min_overlap': cfg.MODEL.CENTERNET.HM_MIN_OVERLAP,
- 'min_radius': cfg.MODEL.CENTERNET.MIN_RADIUS,
- 'hm_focal_alpha': cfg.MODEL.CENTERNET.HM_FOCAL_ALPHA,
- 'hm_focal_beta': cfg.MODEL.CENTERNET.HM_FOCAL_BETA,
- 'loss_gamma': cfg.MODEL.CENTERNET.LOSS_GAMMA,
- 'reg_weight': cfg.MODEL.CENTERNET.REG_WEIGHT,
- 'not_norm_reg': cfg.MODEL.CENTERNET.NOT_NORM_REG,
- 'with_agn_hm': cfg.MODEL.CENTERNET.WITH_AGN_HM,
- 'only_proposal': cfg.MODEL.CENTERNET.ONLY_PROPOSAL,
- 'as_proposal': cfg.MODEL.CENTERNET.AS_PROPOSAL,
- 'not_nms': cfg.MODEL.CENTERNET.NOT_NMS,
- 'pos_weight': cfg.MODEL.CENTERNET.POS_WEIGHT,
- 'neg_weight': cfg.MODEL.CENTERNET.NEG_WEIGHT,
- 'sigmoid_clamp': cfg.MODEL.CENTERNET.SIGMOID_CLAMP,
- 'ignore_high_fp': cfg.MODEL.CENTERNET.IGNORE_HIGH_FP,
- 'center_nms': cfg.MODEL.CENTERNET.CENTER_NMS,
- 'sizes_of_interest': cfg.MODEL.CENTERNET.SOI,
- 'more_pos': cfg.MODEL.CENTERNET.MORE_POS,
- 'more_pos_thresh': cfg.MODEL.CENTERNET.MORE_POS_THRESH,
- 'more_pos_topk': cfg.MODEL.CENTERNET.MORE_POS_TOPK,
- 'pre_nms_topk_train': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TRAIN,
- 'pre_nms_topk_test': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TEST,
- 'post_nms_topk_train': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TRAIN,
- 'post_nms_topk_test': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TEST,
- 'nms_thresh_train': cfg.MODEL.CENTERNET.NMS_TH_TRAIN,
- 'nms_thresh_test': cfg.MODEL.CENTERNET.NMS_TH_TEST,
- 'no_reduce': cfg.MODEL.CENTERNET.NO_REDUCE,
- 'debug': cfg.DEBUG,
- 'vis_thresh': cfg.VIS_THRESH,
- 'pixel_mean': cfg.MODEL.PIXEL_MEAN,
- 'pixel_std': cfg.MODEL.PIXEL_STD,
- 'device': cfg.MODEL.DEVICE,
- 'centernet_head': CenterNetHead(
- cfg, [input_shape[f] for f in cfg.MODEL.CENTERNET.IN_FEATURES]),
- }
- return ret
-
-
- def forward(self, images, features_dict, gt_instances):
- features = [features_dict[f] for f in self.in_features]
- clss_per_level, reg_pred_per_level, agn_hm_pred_per_level = \
- self.centernet_head(features)
- grids = self.compute_grids(features)
- shapes_per_level = grids[0].new_tensor(
- [(x.shape[2], x.shape[3]) for x in reg_pred_per_level])
-
- if not self.training:
- return self.inference(
- images, clss_per_level, reg_pred_per_level,
- agn_hm_pred_per_level, grids)
- else:
- pos_inds, labels, reg_targets, flattened_hms = \
- self._get_ground_truth(
- grids, shapes_per_level, gt_instances)
- # logits_pred: M x F, reg_pred: M x 4, agn_hm_pred: M
- logits_pred, reg_pred, agn_hm_pred = self._flatten_outputs(
- clss_per_level, reg_pred_per_level, agn_hm_pred_per_level)
-
- if self.more_pos:
- # add more pixels as positive if \
- # 1. they are within the center3x3 region of an object
- # 2. their regression losses are small (= 0).squeeze(1)
- reg_pred = reg_pred[reg_inds]
- reg_targets_pos = reg_targets[reg_inds]
- reg_weight_map = flattened_hms.max(dim=1)[0]
- reg_weight_map = reg_weight_map[reg_inds]
- reg_weight_map = reg_weight_map * 0 + 1 \
- if self.not_norm_reg else reg_weight_map
- if self.no_reduce:
- reg_norm = max(reg_weight_map.sum(), 1)
- else:
- reg_norm = max(reduce_sum(reg_weight_map.sum()).item() / num_gpus, 1)
-
- reg_loss = self.reg_weight * self.iou_loss(
- reg_pred, reg_targets_pos, reg_weight_map,
- reduction='sum') / reg_norm
- losses['loss_centernet_loc'] = reg_loss
-
- if self.with_agn_hm:
- cat_agn_heatmap = flattened_hms.max(dim=1)[0] # M
- agn_pos_loss, agn_neg_loss = binary_heatmap_focal_loss(
- agn_hm_pred, cat_agn_heatmap, pos_inds,
- alpha=self.hm_focal_alpha,
- beta=self.hm_focal_beta,
- gamma=self.loss_gamma,
- sigmoid_clamp=self.sigmoid_clamp,
- ignore_high_fp=self.ignore_high_fp,
- )
- agn_pos_loss = self.pos_weight * agn_pos_loss / num_pos_avg
- agn_neg_loss = self.neg_weight * agn_neg_loss / num_pos_avg
- losses['loss_centernet_agn_pos'] = agn_pos_loss
- losses['loss_centernet_agn_neg'] = agn_neg_loss
-
- if self.debug:
- print('losses', losses)
- print('total_num_pos', total_num_pos)
- return losses
-
-
- def compute_grids(self, features):
- grids = []
- for level, feature in enumerate(features):
- h, w = feature.size()[-2:]
- shifts_x = torch.arange(
- 0, w * self.strides[level],
- step=self.strides[level],
- dtype=torch.float32, device=feature.device)
- shifts_y = torch.arange(
- 0, h * self.strides[level],
- step=self.strides[level],
- dtype=torch.float32, device=feature.device)
- shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
- shift_x = shift_x.reshape(-1)
- shift_y = shift_y.reshape(-1)
- grids_per_level = torch.stack((shift_x, shift_y), dim=1) + \
- self.strides[level] // 2
- grids.append(grids_per_level)
- return grids
-
-
- def _get_ground_truth(self, grids, shapes_per_level, gt_instances):
- '''
- Input:
- grids: list of tensors [(hl x wl, 2)]_l
- shapes_per_level: list of tuples L x 2:
- gt_instances: gt instances
- Retuen:
- pos_inds: N
- labels: N
- reg_targets: M x 4
- flattened_hms: M x C or M x 1
- N: number of objects in all images
- M: number of pixels from all FPN levels
- '''
-
- # get positive pixel index
- if not self.more_pos:
- pos_inds, labels = self._get_label_inds(
- gt_instances, shapes_per_level)
- else:
- pos_inds, labels = None, None
- heatmap_channels = self.num_classes
- L = len(grids)
- num_loc_list = [len(loc) for loc in grids]
- strides = torch.cat([
- shapes_per_level.new_ones(num_loc_list[l]) * self.strides[l] \
- for l in range(L)]).float() # M
- reg_size_ranges = torch.cat([
- shapes_per_level.new_tensor(self.sizes_of_interest[l]).float().view(
- 1, 2).expand(num_loc_list[l], 2) for l in range(L)]) # M x 2
- grids = torch.cat(grids, dim=0) # M x 2
- M = grids.shape[0]
-
- reg_targets = []
- flattened_hms = []
- for i in range(len(gt_instances)): # images
- boxes = gt_instances[i].gt_boxes.tensor # N x 4
- area = gt_instances[i].gt_boxes.area() # N
- gt_classes = gt_instances[i].gt_classes # N in [0, self.num_classes]
-
- N = boxes.shape[0]
- if N == 0:
- reg_targets.append(grids.new_zeros((M, 4)) - INF)
- flattened_hms.append(
- grids.new_zeros((
- M, 1 if self.only_proposal else heatmap_channels)))
- continue
-
- l = grids[:, 0].view(M, 1) - boxes[:, 0].view(1, N) # M x N
- t = grids[:, 1].view(M, 1) - boxes[:, 1].view(1, N) # M x N
- r = boxes[:, 2].view(1, N) - grids[:, 0].view(M, 1) # M x N
- b = boxes[:, 3].view(1, N) - grids[:, 1].view(M, 1) # M x N
- reg_target = torch.stack([l, t, r, b], dim=2) # M x N x 4
-
- centers = ((boxes[:, [0, 1]] + boxes[:, [2, 3]]) / 2) # N x 2
- centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2
- strides_expanded = strides.view(M, 1, 1).expand(M, N, 2)
- centers_discret = ((centers_expanded / strides_expanded).int() * \
- strides_expanded).float() + strides_expanded / 2 # M x N x 2
-
- is_peak = (((grids.view(M, 1, 2).expand(M, N, 2) - \
- centers_discret) ** 2).sum(dim=2) == 0) # M x N
- is_in_boxes = reg_target.min(dim=2)[0] > 0 # M x N
- is_center3x3 = self.get_center3x3(
- grids, centers, strides) & is_in_boxes # M x N
- is_cared_in_the_level = self.assign_reg_fpn(
- reg_target, reg_size_ranges) # M x N
- reg_mask = is_center3x3 & is_cared_in_the_level # M x N
-
- dist2 = ((grids.view(M, 1, 2).expand(M, N, 2) - \
- centers_expanded) ** 2).sum(dim=2) # M x N
- dist2[is_peak] = 0
- radius2 = self.delta ** 2 * 2 * area # N
- radius2 = torch.clamp(
- radius2, min=self.min_radius ** 2)
- weighted_dist2 = dist2 / radius2.view(1, N).expand(M, N) # M x N
- reg_target = self._get_reg_targets(
- reg_target, weighted_dist2.clone(), reg_mask, area) # M x 4
-
- if self.only_proposal:
- flattened_hm = self._create_agn_heatmaps_from_dist(
- weighted_dist2.clone()) # M x 1
- else:
- flattened_hm = self._create_heatmaps_from_dist(
- weighted_dist2.clone(), gt_classes,
- channels=heatmap_channels) # M x C
-
- reg_targets.append(reg_target)
- flattened_hms.append(flattened_hm)
-
- # transpose im first training_targets to level first ones
- reg_targets = _transpose(reg_targets, num_loc_list)
- flattened_hms = _transpose(flattened_hms, num_loc_list)
- for l in range(len(reg_targets)):
- reg_targets[l] = reg_targets[l] / float(self.strides[l])
- reg_targets = cat([x for x in reg_targets], dim=0) # MB x 4
- flattened_hms = cat([x for x in flattened_hms], dim=0) # MB x C
-
- return pos_inds, labels, reg_targets, flattened_hms
-
-
- def _get_label_inds(self, gt_instances, shapes_per_level):
- '''
- Inputs:
- gt_instances: [n_i], sum n_i = N
- shapes_per_level: L x 2 [(h_l, w_l)]_L
- Returns:
- pos_inds: N'
- labels: N'
- '''
- pos_inds = []
- labels = []
- L = len(self.strides)
- B = len(gt_instances)
- shapes_per_level = shapes_per_level.long()
- loc_per_level = (shapes_per_level[:, 0] * shapes_per_level[:, 1]).long() # L
- level_bases = []
- s = 0
- for l in range(L):
- level_bases.append(s)
- s = s + B * loc_per_level[l]
- level_bases = shapes_per_level.new_tensor(level_bases).long() # L
- strides_default = shapes_per_level.new_tensor(self.strides).float() # L
- for im_i in range(B):
- targets_per_im = gt_instances[im_i]
- bboxes = targets_per_im.gt_boxes.tensor # n x 4
- n = bboxes.shape[0]
- centers = ((bboxes[:, [0, 1]] + bboxes[:, [2, 3]]) / 2) # n x 2
- centers = centers.view(n, 1, 2).expand(n, L, 2)
- strides = strides_default.view(1, L, 1).expand(n, L, 2)
- centers_inds = (centers / strides).long() # n x L x 2
- Ws = shapes_per_level[:, 1].view(1, L).expand(n, L)
- pos_ind = level_bases.view(1, L).expand(n, L) + \
- im_i * loc_per_level.view(1, L).expand(n, L) + \
- centers_inds[:, :, 1] * Ws + \
- centers_inds[:, :, 0] # n x L
- is_cared_in_the_level = self.assign_fpn_level(bboxes)
- pos_ind = pos_ind[is_cared_in_the_level].view(-1)
- label = targets_per_im.gt_classes.view(
- n, 1).expand(n, L)[is_cared_in_the_level].view(-1)
-
- pos_inds.append(pos_ind) # n'
- labels.append(label) # n'
- pos_inds = torch.cat(pos_inds, dim=0).long()
- labels = torch.cat(labels, dim=0)
- return pos_inds, labels # N, N
-
-
- def assign_fpn_level(self, boxes):
- '''
- Inputs:
- boxes: n x 4
- size_ranges: L x 2
- Return:
- is_cared_in_the_level: n x L
- '''
- size_ranges = boxes.new_tensor(
- self.sizes_of_interest).view(len(self.sizes_of_interest), 2) # L x 2
- crit = ((boxes[:, 2:] - boxes[:, :2]) **2).sum(dim=1) ** 0.5 / 2 # n
- n, L = crit.shape[0], size_ranges.shape[0]
- crit = crit.view(n, 1).expand(n, L)
- size_ranges_expand = size_ranges.view(1, L, 2).expand(n, L, 2)
- is_cared_in_the_level = (crit >= size_ranges_expand[:, :, 0]) & \
- (crit <= size_ranges_expand[:, :, 1])
- return is_cared_in_the_level
-
-
- def assign_reg_fpn(self, reg_targets_per_im, size_ranges):
- '''
- TODO (Xingyi): merge it with assign_fpn_level
- Inputs:
- reg_targets_per_im: M x N x 4
- size_ranges: M x 2
- '''
- crit = ((reg_targets_per_im[:, :, :2] + \
- reg_targets_per_im[:, :, 2:])**2).sum(dim=2) ** 0.5 / 2 # M x N
- is_cared_in_the_level = (crit >= size_ranges[:, [0]]) & \
- (crit <= size_ranges[:, [1]])
- return is_cared_in_the_level
-
-
- def _get_reg_targets(self, reg_targets, dist, mask, area):
- '''
- reg_targets (M x N x 4): long tensor
- dist (M x N)
- is_*: M x N
- '''
- dist[mask == 0] = INF * 1.0
- min_dist, min_inds = dist.min(dim=1) # M
- reg_targets_per_im = reg_targets[
- range(len(reg_targets)), min_inds] # M x N x 4 --> M x 4
- reg_targets_per_im[min_dist == INF] = - INF
- return reg_targets_per_im
-
-
- def _create_heatmaps_from_dist(self, dist, labels, channels):
- '''
- dist: M x N
- labels: N
- return:
- heatmaps: M x C
- '''
- heatmaps = dist.new_zeros((dist.shape[0], channels))
- for c in range(channels):
- inds = (labels == c) # N
- if inds.int().sum() == 0:
- continue
- heatmaps[:, c] = torch.exp(-dist[:, inds].min(dim=1)[0])
- zeros = heatmaps[:, c] < 1e-4
- heatmaps[zeros, c] = 0
- return heatmaps
-
-
- def _create_agn_heatmaps_from_dist(self, dist):
- '''
- TODO (Xingyi): merge it with _create_heatmaps_from_dist
- dist: M x N
- return:
- heatmaps: M x 1
- '''
- heatmaps = dist.new_zeros((dist.shape[0], 1))
- heatmaps[:, 0] = torch.exp(-dist.min(dim=1)[0])
- zeros = heatmaps < 1e-4
- heatmaps[zeros] = 0
- return heatmaps
-
-
- def _flatten_outputs(self, clss, reg_pred, agn_hm_pred):
- # Reshape: (N, F, Hl, Wl) -> (N, Hl, Wl, F) -> (sum_l N*Hl*Wl, F)
- clss = cat([x.permute(0, 2, 3, 1).reshape(-1, x.shape[1]) \
- for x in clss], dim=0) if clss[0] is not None else None
- reg_pred = cat(
- [x.permute(0, 2, 3, 1).reshape(-1, 4) for x in reg_pred], dim=0)
- agn_hm_pred = cat([x.permute(0, 2, 3, 1).reshape(-1) \
- for x in agn_hm_pred], dim=0) if self.with_agn_hm else None
- return clss, reg_pred, agn_hm_pred
-
-
- def get_center3x3(self, locations, centers, strides):
- '''
- Inputs:
- locations: M x 2
- centers: N x 2
- strides: M
- '''
- M, N = locations.shape[0], centers.shape[0]
- locations_expanded = locations.view(M, 1, 2).expand(M, N, 2) # M x N x 2
- centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2
- strides_expanded = strides.view(M, 1, 1).expand(M, N, 2) # M x N
- centers_discret = ((centers_expanded / strides_expanded).int() * \
- strides_expanded).float() + strides_expanded / 2 # M x N x 2
- dist_x = (locations_expanded[:, :, 0] - centers_discret[:, :, 0]).abs()
- dist_y = (locations_expanded[:, :, 1] - centers_discret[:, :, 1]).abs()
- return (dist_x <= strides_expanded[:, :, 0]) & \
- (dist_y <= strides_expanded[:, :, 0])
-
-
- def inference(self, images, clss_per_level, reg_pred_per_level,
- agn_hm_pred_per_level, grids):
- logits_pred = [x.sigmoid() if x is not None else None \
- for x in clss_per_level]
- agn_hm_pred_per_level = [x.sigmoid() if x is not None else None \
- for x in agn_hm_pred_per_level]
-
- if self.only_proposal:
- proposals = self.predict_instances(
- grids, agn_hm_pred_per_level, reg_pred_per_level,
- images.image_sizes, [None for _ in agn_hm_pred_per_level])
- else:
- proposals = self.predict_instances(
- grids, logits_pred, reg_pred_per_level,
- images.image_sizes, agn_hm_pred_per_level)
- if self.as_proposal or self.only_proposal:
- for p in range(len(proposals)):
- proposals[p].proposal_boxes = proposals[p].get('pred_boxes')
- proposals[p].objectness_logits = proposals[p].get('scores')
- proposals[p].remove('pred_boxes')
-
- if self.debug:
- debug_test(
- [self.denormalizer(x) for x in images],
- logits_pred, reg_pred_per_level,
- agn_hm_pred_per_level, preds=proposals,
- vis_thresh=self.vis_thresh,
- debug_show_name=False)
- return proposals, {}
-
-
- def predict_instances(
- self, grids, logits_pred, reg_pred, image_sizes, agn_hm_pred,
- is_proposal=False):
- sampled_boxes = []
- for l in range(len(grids)):
- sampled_boxes.append(self.predict_single_level(
- grids[l], logits_pred[l], reg_pred[l] * self.strides[l],
- image_sizes, agn_hm_pred[l], l, is_proposal=is_proposal))
- boxlists = list(zip(*sampled_boxes))
- boxlists = [Instances.cat(boxlist) for boxlist in boxlists]
- boxlists = self.nms_and_topK(
- boxlists, nms=not self.not_nms)
- return boxlists
-
-
- def predict_single_level(
- self, grids, heatmap, reg_pred, image_sizes, agn_hm, level,
- is_proposal=False):
- N, C, H, W = heatmap.shape
- # put in the same format as grids
- if self.center_nms:
- heatmap_nms = nn.functional.max_pool2d(
- heatmap, (3, 3), stride=1, padding=1)
- heatmap = heatmap * (heatmap_nms == heatmap).float()
- heatmap = heatmap.permute(0, 2, 3, 1) # N x H x W x C
- heatmap = heatmap.reshape(N, -1, C) # N x HW x C
- box_regression = reg_pred.view(N, 4, H, W).permute(0, 2, 3, 1) # N x H x W x 4
- box_regression = box_regression.reshape(N, -1, 4)
-
- candidate_inds = heatmap > self.score_thresh # 0.05
- pre_nms_top_n = candidate_inds.view(N, -1).sum(1) # N
- pre_nms_topk = self.pre_nms_topk_train if self.training else self.pre_nms_topk_test
- pre_nms_top_n = pre_nms_top_n.clamp(max=pre_nms_topk) # N
-
- if agn_hm is not None:
- agn_hm = agn_hm.view(N, 1, H, W).permute(0, 2, 3, 1)
- agn_hm = agn_hm.reshape(N, -1)
- heatmap = heatmap * agn_hm[:, :, None]
-
- results = []
- for i in range(N):
- per_box_cls = heatmap[i] # HW x C
- per_candidate_inds = candidate_inds[i] # n
- per_box_cls = per_box_cls[per_candidate_inds] # n
-
- per_candidate_nonzeros = per_candidate_inds.nonzero() # n
- per_box_loc = per_candidate_nonzeros[:, 0] # n
- per_class = per_candidate_nonzeros[:, 1] # n
-
- per_box_regression = box_regression[i] # HW x 4
- per_box_regression = per_box_regression[per_box_loc] # n x 4
- per_grids = grids[per_box_loc] # n x 2
-
- per_pre_nms_top_n = pre_nms_top_n[i] # 1
-
- if per_candidate_inds.sum().item() > per_pre_nms_top_n.item():
- per_box_cls, top_k_indices = \
- per_box_cls.topk(per_pre_nms_top_n, sorted=False)
- per_class = per_class[top_k_indices]
- per_box_regression = per_box_regression[top_k_indices]
- per_grids = per_grids[top_k_indices]
-
- detections = torch.stack([
- per_grids[:, 0] - per_box_regression[:, 0],
- per_grids[:, 1] - per_box_regression[:, 1],
- per_grids[:, 0] + per_box_regression[:, 2],
- per_grids[:, 1] + per_box_regression[:, 3],
- ], dim=1) # n x 4
-
- # avoid invalid boxes in RoI heads
- detections[:, 2] = torch.max(detections[:, 2], detections[:, 0] + 0.01)
- detections[:, 3] = torch.max(detections[:, 3], detections[:, 1] + 0.01)
- boxlist = Instances(image_sizes[i])
- boxlist.scores = torch.sqrt(per_box_cls) \
- if self.with_agn_hm else per_box_cls # n
- # import pdb; pdb.set_trace()
- boxlist.pred_boxes = Boxes(detections)
- boxlist.pred_classes = per_class
- results.append(boxlist)
- return results
-
-
- def nms_and_topK(self, boxlists, nms=True):
- num_images = len(boxlists)
- results = []
- for i in range(num_images):
- nms_thresh = self.nms_thresh_train if self.training else \
- self.nms_thresh_test
- result = ml_nms(boxlists[i], nms_thresh) if nms else boxlists[i]
- if self.debug:
- print('#proposals before nms', len(boxlists[i]))
- print('#proposals after nms', len(result))
- num_dets = len(result)
- post_nms_topk = self.post_nms_topk_train if self.training else \
- self.post_nms_topk_test
- if num_dets > post_nms_topk:
- cls_scores = result.scores
- image_thresh, _ = torch.kthvalue(
- cls_scores.float().cpu(),
- num_dets - post_nms_topk + 1
- )
- keep = cls_scores >= image_thresh.item()
- keep = torch.nonzero(keep).squeeze(1)
- result = result[keep]
- if self.debug:
- print('#proposals after filter', len(result))
- results.append(result)
- return results
-
-
- def _add_more_pos(self, reg_pred, gt_instances, shapes_per_level):
- labels, level_masks, c33_inds, c33_masks, c33_regs = \
- self._get_c33_inds(gt_instances, shapes_per_level)
- N, L, K = labels.shape[0], len(self.strides), 9
- c33_inds[c33_masks == 0] = 0
- reg_pred_c33 = reg_pred[c33_inds].detach() # N x L x K
- invalid_reg = c33_masks == 0
- c33_regs_expand = c33_regs.view(N * L * K, 4).clamp(min=0)
- if N > 0:
- with torch.no_grad():
- c33_reg_loss = self.iou_loss(
- reg_pred_c33.view(N * L * K, 4),
- c33_regs_expand, None,
- reduction='none').view(N, L, K).detach() # N x L x K
- else:
- c33_reg_loss = reg_pred_c33.new_zeros((N, L, K)).detach()
- c33_reg_loss[invalid_reg] = INF # N x L x K
- c33_reg_loss.view(N * L, K)[level_masks.view(N * L), 4] = 0 # real center
- c33_reg_loss = c33_reg_loss.view(N, L * K)
- if N == 0:
- loss_thresh = c33_reg_loss.new_ones((N)).float()
- else:
- loss_thresh = torch.kthvalue(
- c33_reg_loss, self.more_pos_topk, dim=1)[0] # N
- loss_thresh[loss_thresh > self.more_pos_thresh] = self.more_pos_thresh # N
- new_pos = c33_reg_loss.view(N, L, K) < \
- loss_thresh.view(N, 1, 1).expand(N, L, K)
- pos_inds = c33_inds[new_pos].view(-1) # P
- labels = labels.view(N, 1, 1).expand(N, L, K)[new_pos].view(-1)
- return pos_inds, labels
-
-
- def _get_c33_inds(self, gt_instances, shapes_per_level):
- '''
- TODO (Xingyi): The current implementation is ugly. Refactor.
- Get the center (and the 3x3 region near center) locations of each objects
- Inputs:
- gt_instances: [n_i], sum n_i = N
- shapes_per_level: L x 2 [(h_l, w_l)]_L
- '''
- labels = []
- level_masks = []
- c33_inds = []
- c33_masks = []
- c33_regs = []
- L = len(self.strides)
- B = len(gt_instances)
- shapes_per_level = shapes_per_level.long()
- loc_per_level = (shapes_per_level[:, 0] * shapes_per_level[:, 1]).long() # L
- level_bases = []
- s = 0
- for l in range(L):
- level_bases.append(s)
- s = s + B * loc_per_level[l]
- level_bases = shapes_per_level.new_tensor(level_bases).long() # L
- strides_default = shapes_per_level.new_tensor(self.strides).float() # L
- K = 9
- dx = shapes_per_level.new_tensor([-1, 0, 1, -1, 0, 1, -1, 0, 1]).long()
- dy = shapes_per_level.new_tensor([-1, -1, -1, 0, 0, 0, 1, 1, 1]).long()
- for im_i in range(B):
- targets_per_im = gt_instances[im_i]
- bboxes = targets_per_im.gt_boxes.tensor # n x 4
- n = bboxes.shape[0]
- if n == 0:
- continue
- centers = ((bboxes[:, [0, 1]] + bboxes[:, [2, 3]]) / 2) # n x 2
- centers = centers.view(n, 1, 2).expand(n, L, 2)
-
- strides = strides_default.view(1, L, 1).expand(n, L, 2) #
- centers_inds = (centers / strides).long() # n x L x 2
- center_grids = centers_inds * strides + strides // 2# n x L x 2
- l = center_grids[:, :, 0] - bboxes[:, 0].view(n, 1).expand(n, L)
- t = center_grids[:, :, 1] - bboxes[:, 1].view(n, 1).expand(n, L)
- r = bboxes[:, 2].view(n, 1).expand(n, L) - center_grids[:, :, 0]
- b = bboxes[:, 3].view(n, 1).expand(n, L) - center_grids[:, :, 1] # n x L
- reg = torch.stack([l, t, r, b], dim=2) # n x L x 4
- reg = reg / strides_default.view(1, L, 1).expand(n, L, 4).float()
-
- Ws = shapes_per_level[:, 1].view(1, L).expand(n, L)
- Hs = shapes_per_level[:, 0].view(1, L).expand(n, L)
- expand_Ws = Ws.view(n, L, 1).expand(n, L, K)
- expand_Hs = Hs.view(n, L, 1).expand(n, L, K)
- label = targets_per_im.gt_classes.view(n).clone()
- mask = reg.min(dim=2)[0] >= 0 # n x L
- mask = mask & self.assign_fpn_level(bboxes)
- labels.append(label) # n
- level_masks.append(mask) # n x L
-
- Dy = dy.view(1, 1, K).expand(n, L, K)
- Dx = dx.view(1, 1, K).expand(n, L, K)
- c33_ind = level_bases.view(1, L, 1).expand(n, L, K) + \
- im_i * loc_per_level.view(1, L, 1).expand(n, L, K) + \
- (centers_inds[:, :, 1:2].expand(n, L, K) + Dy) * expand_Ws + \
- (centers_inds[:, :, 0:1].expand(n, L, K) + Dx) # n x L x K
-
- c33_mask = \
- ((centers_inds[:, :, 1:2].expand(n, L, K) + dy) < expand_Hs) & \
- ((centers_inds[:, :, 1:2].expand(n, L, K) + dy) >= 0) & \
- ((centers_inds[:, :, 0:1].expand(n, L, K) + dx) < expand_Ws) & \
- ((centers_inds[:, :, 0:1].expand(n, L, K) + dx) >= 0)
- # TODO (Xingyi): think about better way to implement this
- # Currently it hard codes the 3x3 region
- c33_reg = reg.view(n, L, 1, 4).expand(n, L, K, 4).clone()
- c33_reg[:, :, [0, 3, 6], 0] -= 1
- c33_reg[:, :, [0, 3, 6], 2] += 1
- c33_reg[:, :, [2, 5, 8], 0] += 1
- c33_reg[:, :, [2, 5, 8], 2] -= 1
- c33_reg[:, :, [0, 1, 2], 1] -= 1
- c33_reg[:, :, [0, 1, 2], 3] += 1
- c33_reg[:, :, [6, 7, 8], 1] += 1
- c33_reg[:, :, [6, 7, 8], 3] -= 1
- c33_mask = c33_mask & (c33_reg.min(dim=3)[0] >= 0) # n x L x K
- c33_inds.append(c33_ind)
- c33_masks.append(c33_mask)
- c33_regs.append(c33_reg)
-
- if len(level_masks) > 0:
- labels = torch.cat(labels, dim=0)
- level_masks = torch.cat(level_masks, dim=0)
- c33_inds = torch.cat(c33_inds, dim=0).long()
- c33_regs = torch.cat(c33_regs, dim=0)
- c33_masks = torch.cat(c33_masks, dim=0)
- else:
- labels = shapes_per_level.new_zeros((0)).long()
- level_masks = shapes_per_level.new_zeros((0, L)).bool()
- c33_inds = shapes_per_level.new_zeros((0, L, K)).long()
- c33_regs = shapes_per_level.new_zeros((0, L, K, 4)).float()
- c33_masks = shapes_per_level.new_zeros((0, L, K)).bool()
- return labels, level_masks, c33_inds, c33_masks, c33_regs # N x L, N x L x K
\ No newline at end of file
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh b/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh
deleted file mode 100644
index 6d2ba9a6265c0d5fa580035952a1f568dd8d9e44..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh
+++ /dev/null
@@ -1,37 +0,0 @@
-mkdir celeba-hq-dataset
-
-unzip data256x256.zip -d celeba-hq-dataset/
-
-# Reindex
-for i in `echo {00001..30000}`
-do
- mv 'celeba-hq-dataset/data256x256/'$i'.jpg' 'celeba-hq-dataset/data256x256/'$[10#$i - 1]'.jpg'
-done
-
-
-# Split: split train -> train & val
-cat fetch_data/train_shuffled.flist | shuf > celeba-hq-dataset/temp_train_shuffled.flist
-cat celeba-hq-dataset/temp_train_shuffled.flist | head -n 2000 > celeba-hq-dataset/val_shuffled.flist
-cat celeba-hq-dataset/temp_train_shuffled.flist | tail -n +2001 > celeba-hq-dataset/train_shuffled.flist
-cat fetch_data/val_shuffled.flist > celeba-hq-dataset/visual_test_shuffled.flist
-
-mkdir celeba-hq-dataset/train_256/
-mkdir celeba-hq-dataset/val_source_256/
-mkdir celeba-hq-dataset/visual_test_source_256/
-
-cat celeba-hq-dataset/train_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/train_256/
-cat celeba-hq-dataset/val_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/val_source_256/
-cat celeba-hq-dataset/visual_test_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/visual_test_source_256/
-
-
-# create location config celeba.yaml
-PWD=$(pwd)
-DATASET=${PWD}/celeba-hq-dataset
-CELEBA=${PWD}/configs/training/location/celeba.yaml
-
-touch $CELEBA
-echo "# @package _group_" >> $CELEBA
-echo "data_root_dir: ${DATASET}/" >> $CELEBA
-echo "out_root_dir: ${PWD}/experiments/" >> $CELEBA
-echo "tb_dir: ${PWD}/tb_logs/" >> $CELEBA
-echo "pretrained_models: ${PWD}/" >> $CELEBA
diff --git a/spaces/OpenMotionLab/MotionGPT/pyrender/tests/conftest.py b/spaces/OpenMotionLab/MotionGPT/pyrender/tests/conftest.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py b/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py
deleted file mode 100644
index 4e06ab8cbe6b43a355c0a9cfb3f2d688438d2c64..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py
+++ /dev/null
@@ -1,201 +0,0 @@
-# ------------------------------------------------------------------------------
-# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/cityscapes_evaluation.py
-# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
-# ------------------------------------------------------------------------------
-
-import glob
-import logging
-import numpy as np
-import os
-import tempfile
-from collections import OrderedDict
-import torch
-from PIL import Image
-
-from detectron2.data import MetadataCatalog
-from detectron2.utils import comm
-from detectron2.utils.file_io import PathManager
-
-from .evaluator import DatasetEvaluator
-
-
-class CityscapesEvaluator(DatasetEvaluator):
- """
- Base class for evaluation using cityscapes API.
- """
-
- def __init__(self, dataset_name):
- """
- Args:
- dataset_name (str): the name of the dataset.
- It must have the following metadata associated with it:
- "thing_classes", "gt_dir".
- """
- self._metadata = MetadataCatalog.get(dataset_name)
- self._cpu_device = torch.device("cpu")
- self._logger = logging.getLogger(__name__)
-
- def reset(self):
- self._working_dir = tempfile.TemporaryDirectory(prefix="cityscapes_eval_")
- self._temp_dir = self._working_dir.name
- # All workers will write to the same results directory
- # TODO this does not work in distributed training
- assert (
- comm.get_local_size() == comm.get_world_size()
- ), "CityscapesEvaluator currently do not work with multiple machines."
- self._temp_dir = comm.all_gather(self._temp_dir)[0]
- if self._temp_dir != self._working_dir.name:
- self._working_dir.cleanup()
- self._logger.info(
- "Writing cityscapes results to temporary directory {} ...".format(self._temp_dir)
- )
-
-
-class CityscapesInstanceEvaluator(CityscapesEvaluator):
- """
- Evaluate instance segmentation results on cityscapes dataset using cityscapes API.
-
- Note:
- * It does not work in multi-machine distributed training.
- * It contains a synchronization, therefore has to be used on all ranks.
- * Only the main process runs evaluation.
- """
-
- def process(self, inputs, outputs):
- from cityscapesscripts.helpers.labels import name2label
-
- for input, output in zip(inputs, outputs):
- file_name = input["file_name"]
- basename = os.path.splitext(os.path.basename(file_name))[0]
- pred_txt = os.path.join(self._temp_dir, basename + "_pred.txt")
-
- if "instances" in output:
- output = output["instances"].to(self._cpu_device)
- num_instances = len(output)
- with open(pred_txt, "w") as fout:
- for i in range(num_instances):
- pred_class = output.pred_classes[i]
- classes = self._metadata.stuff_classes[pred_class]
- class_id = name2label[classes].id
- score = output.scores[i]
- mask = output.pred_masks[i].numpy().astype("uint8")
- png_filename = os.path.join(
- self._temp_dir, basename + "_{}_{}.png".format(i, classes)
- )
-
- Image.fromarray(mask * 255).save(png_filename)
- fout.write(
- "{} {} {}\n".format(os.path.basename(png_filename), class_id, score)
- )
- else:
- # Cityscapes requires a prediction file for every ground truth image.
- with open(pred_txt, "w") as fout:
- pass
-
- def evaluate(self):
- """
- Returns:
- dict: has a key "segm", whose value is a dict of "AP" and "AP50".
- """
- comm.synchronize()
- if comm.get_rank() > 0:
- return
- import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval
-
- self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
-
- # set some global states in cityscapes evaluation API, before evaluating
- cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
- cityscapes_eval.args.predictionWalk = None
- cityscapes_eval.args.JSONOutput = False
- cityscapes_eval.args.colorized = False
- cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json")
-
- # These lines are adopted from
- # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
- gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
- groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_instanceIds.png"))
- assert len(
- groundTruthImgList
- ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
- cityscapes_eval.args.groundTruthSearch
- )
- predictionImgList = []
- for gt in groundTruthImgList:
- predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args))
- results = cityscapes_eval.evaluateImgLists(
- predictionImgList, groundTruthImgList, cityscapes_eval.args
- )["averages"]
-
- ret = OrderedDict()
- ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100}
- self._working_dir.cleanup()
- return ret
-
-
-class CityscapesSemSegEvaluator(CityscapesEvaluator):
- """
- Evaluate semantic segmentation results on cityscapes dataset using cityscapes API.
-
- Note:
- * It does not work in multi-machine distributed training.
- * It contains a synchronization, therefore has to be used on all ranks.
- * Only the main process runs evaluation.
- """
-
- def process(self, inputs, outputs):
- from cityscapesscripts.helpers.labels import trainId2label
-
- for input, output in zip(inputs, outputs):
- file_name = input["file_name"]
- basename = os.path.splitext(os.path.basename(file_name))[0]
- pred_filename = os.path.join(self._temp_dir, basename + "_pred.png")
-
- output = output["sem_seg"].argmax(dim=0).to(self._cpu_device).numpy()
- pred = 255 * np.ones(output.shape, dtype=np.uint8)
- for train_id, label in trainId2label.items():
- if label.ignoreInEval:
- continue
- pred[output == train_id] = label.id
- Image.fromarray(pred).save(pred_filename)
-
- def evaluate(self):
- comm.synchronize()
- if comm.get_rank() > 0:
- return
- # Load the Cityscapes eval script *after* setting the required env var,
- # since the script reads CITYSCAPES_DATASET into global variables at load time.
- import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval
-
- self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
-
- # set some global states in cityscapes evaluation API, before evaluating
- cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
- cityscapes_eval.args.predictionWalk = None
- cityscapes_eval.args.JSONOutput = False
- cityscapes_eval.args.colorized = False
-
- # These lines are adopted from
- # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa
- gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
- groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_labelIds.png"))
- assert len(
- groundTruthImgList
- ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
- cityscapes_eval.args.groundTruthSearch
- )
- predictionImgList = []
- for gt in groundTruthImgList:
- predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt))
- results = cityscapes_eval.evaluateImgLists(
- predictionImgList, groundTruthImgList, cityscapes_eval.args
- )
- ret = OrderedDict()
- ret["sem_seg"] = {
- "IoU": 100.0 * results["averageScoreClasses"],
- "iIoU": 100.0 * results["averageScoreInstClasses"],
- "IoU_sup": 100.0 * results["averageScoreCategories"],
- "iIoU_sup": 100.0 * results["averageScoreInstCategories"],
- }
- self._working_dir.cleanup()
- return ret
diff --git a/spaces/PeepDaSlan9/AutoGPT/benchmark/__init__.py b/spaces/PeepDaSlan9/AutoGPT/benchmark/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py b/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py
deleted file mode 100644
index ba0443e0c22481af2b451d8ccc5ab6e6f79cffb8..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py
+++ /dev/null
@@ -1,573 +0,0 @@
-import os
-import random
-from typing import Optional, Callable
-import json
-import glob
-import csv
-
-import numpy as np
-import torch
-import librosa
-import pyloudnorm as pyln
-from pedalboard import Pedalboard, Limiter, Gain, Compressor, Clipping
-
-from .dataset import (
- MusdbTrainDataset,
- MusdbValidDataset,
- apply_limitaug,
-)
-from utils import (
- load_wav_arbitrary_position_stereo,
- load_wav_specific_position_stereo,
- db2linear,
-)
-
-
-class DelimitTrainDataset(MusdbTrainDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- seq_duration: Optional[float] = 6.0,
- samples_per_track: int = 64,
- source_augmentations: Optional[Callable] = lambda audio: audio,
- sample_rate: int = 44100,
- seed: int = 42,
- limitaug_method: str = "limitaug",
- limitaug_mode: str = "normal_L",
- limitaug_custom_target_lufs: float = None,
- limitaug_custom_target_lufs_std: float = None,
- target_loudnorm_lufs: float = -14.0,
- target_limitaug_mode: str = None,
- target_limitaug_custom_target_lufs: float = None,
- target_limitaug_custom_target_lufs_std: float = None,
- custom_limiter_attack_range: list = [2.0, 2.0],
- custom_limiter_release_range: list = [200.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(
- target=target,
- root=root,
- seq_duration=seq_duration,
- samples_per_track=samples_per_track,
- source_augmentations=source_augmentations,
- sample_rate=sample_rate,
- seed=seed,
- limitaug_method=limitaug_method,
- limitaug_mode=limitaug_mode,
- limitaug_custom_target_lufs=limitaug_custom_target_lufs,
- limitaug_custom_target_lufs_std=limitaug_custom_target_lufs_std,
- target_loudnorm_lufs=target_loudnorm_lufs,
- custom_limiter_attack_range=custom_limiter_attack_range,
- custom_limiter_release_range=custom_limiter_release_range,
- *args,
- **kwargs,
- )
-
- self.target_limitaug_mode = target_limitaug_mode
-
- self.target_limitaug_custom_target_lufs = (target_limitaug_custom_target_lufs,)
- self.target_limitaug_custom_target_lufs_std = (
- target_limitaug_custom_target_lufs_std,
- )
- self.limitaug_mode_statistics["target_custom"] = [
- target_limitaug_custom_target_lufs,
- target_limitaug_custom_target_lufs_std,
- ]
-
- """
- Parameters
- ----------
- limitaug_method : str
- choose from ["linear_gain_increase", "limitaug", "limitaug_then_loudnorm", "only_loudnorm"]
- limitaug_mode : str
- choose from ["uniform", "normal", "normal_L", "normal_XL", "normal_short_term", "normal_L_short_term", "normal_XL_short_term", "custom"]
- limitaug_custom_target_lufs : float
- valid only when
- limitaug_mode == "custom"
- target_loudnorm_lufs : float
- valid only when
- limitaug_method == 'limitaug_then_loudnorm' or 'only_loudnorm'
- default is -14.
- To the best of my knowledge, Spotify and Youtube music is using -14 as a reference loudness normalization level.
- No special reason for the choice of -14 as target_loudnorm_lufs.
- target : str
- target name of the source to be separated, defaults to ``vocals``.
- root : str
- root path of MUSDB
- seq_duration : float
- training is performed in chunks of ``seq_duration`` (in seconds,
- defaults to ``None`` which loads the full audio track
- samples_per_track : int
- sets the number of samples, yielded from each track per epoch.
- Defaults to 64
- source_augmentations : list[callables]
- provide list of augmentation function that take a multi-channel
- audio file of shape (src, samples) as input and output. Defaults to
- no-augmentations (input = output)
- seed : int
- control randomness of dataset iterations
- args, kwargs : additional keyword arguments
- used to add further control for the musdb dataset
- initialization function.
- """
-
- # Get a limitaug result without target (individual stem source)
- def get_limitaug_mixture(self, mixture):
- if self.limitaug_method == "limitaug":
- self.board[1].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- )
-
- elif self.limitaug_method == "limitaug_then_loudnorm":
- self.board[1].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = (
- apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- target_loudnorm_lufs=self.target_loudnorm_lufs,
- ),
- )
-
- # Apply LimitAug using Custom Limiter
- elif self.limitaug_method == "custom_limiter_limitaug":
- # Change attack time of First compressor of the Limiter
- self.board[1].attack_ms = random.uniform(
- self.custom_limiter_attack_range[0], self.custom_limiter_attack_range[1]
- )
- # Change release time of First compressor of the Limiter
- self.board[1].release_ms = random.uniform(
- self.custom_limiter_release_range[0],
- self.custom_limiter_release_range[1],
- )
- # Change release time of Second compressor of the Limiter
- self.board[2].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- target_loudnorm_lufs=self.target_loudnorm_lufs,
- )
-
- # When we want to force NN to output an appropriately compressed target output
- if self.target_limitaug_mode:
- mixture_target_lufs = random.gauss(
- self.limitaug_mode_statistics[self.target_limitaug_mode][0],
- self.limitaug_mode_statistics[self.target_limitaug_mode][1],
- )
- mixture, target_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=mixture_target_lufs,
- loudness=mixture_lufs,
- )
-
- if np.isinf(mixture_lufs):
- mixture_loudnorm = mixture
- else:
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain, eps=0.0)
-
- return mixture_limited, mixture_loudnorm
-
- def __getitem__(self, index):
- audio_sources = []
-
- for k, source in enumerate(self.sources):
- # memorize index of target source
- if source == self.target: # if source is 'vocals'
- track_path = self.train_list[
- index // self.samples_per_track
- ] # we want to use # training samples per each track.
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration
- )
- else:
- track_path = random.choice(self.train_list)
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration
- )
- audio = self.source_augmentations(audio)
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- # apply linear mix over source index=0
- # and here, linear mixture is a target unlike in MusdbTrainDataset
- mixture = stems.sum(0)
- mixture_limited, mixture_loudnorm = self.get_limitaug_mixture(mixture)
- # We will give mixture_limited as an input and mixture_loudnorm as a target to the model.
-
- mixture_limited = np.clip(mixture_limited, -1.0, 1.0)
- mixture_limited = torch.as_tensor(mixture_limited, dtype=torch.float32)
- mixture_loudnorm = torch.as_tensor(mixture_loudnorm, dtype=torch.float32)
-
- return mixture_limited, mixture_loudnorm
-
-
-class OzoneTrainDataset(DelimitTrainDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- ozone_root: str = None,
- use_fixed: float = 0.1, # ratio of fixed samples
- seq_duration: Optional[float] = 6.0,
- samples_per_track: int = 64,
- source_augmentations: Optional[Callable] = lambda audio: audio,
- sample_rate: int = 44100,
- seed: int = 42,
- limitaug_method: str = "limitaug",
- limitaug_mode: str = "normal_L",
- limitaug_custom_target_lufs: float = None,
- limitaug_custom_target_lufs_std: float = None,
- target_loudnorm_lufs: float = -14.0,
- target_limitaug_mode: str = None,
- target_limitaug_custom_target_lufs: float = None,
- target_limitaug_custom_target_lufs_std: float = None,
- custom_limiter_attack_range: list = [2.0, 2.0],
- custom_limiter_release_range: list = [200.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(
- target,
- root,
- seq_duration,
- samples_per_track,
- source_augmentations,
- sample_rate,
- seed,
- limitaug_method,
- limitaug_mode,
- limitaug_custom_target_lufs,
- limitaug_custom_target_lufs_std,
- target_loudnorm_lufs,
- target_limitaug_mode,
- target_limitaug_custom_target_lufs,
- target_limitaug_custom_target_lufs_std,
- custom_limiter_attack_range,
- custom_limiter_release_range,
- *args,
- **kwargs,
- )
-
- self.ozone_root = ozone_root
- self.use_fixed = use_fixed
- self.list_train_fixed = glob.glob(f"{self.ozone_root}/ozone_train_fixed/*.wav")
- self.list_train_random = glob.glob(
- f"{self.ozone_root}/ozone_train_random/*.wav"
- )
- self.dict_train_random = {}
-
- # Load information of pre-generated random training examples
- list_csv_files = glob.glob(f"{self.ozone_root}/ozone_train_random_*.csv")
- list_csv_files.sort()
- for csv_file in list_csv_files:
- with open(csv_file, "r") as f:
- reader = csv.reader(f)
- next(reader)
- for row in reader:
- self.dict_train_random[row[0]] = {
- "max_threshold": float(row[1]),
- "max_character": float(row[2]),
- "vocals": {
- "name": row[3],
- "start_sec": float(row[4]),
- "gain": float(row[5]),
- "channelswap": bool(row[6]),
- },
- "bass": {
- "name": row[7],
- "start_sec": float(row[8]),
- "gain": float(row[9]),
- "channelswap": bool(row[10]),
- },
- "drums": {
- "name": row[11],
- "start_sec": float(row[12]),
- "gain": float(row[13]),
- "channelswap": bool(row[14]),
- },
- "other": {
- "name": row[15],
- "start_sec": float(row[16]),
- "gain": float(row[17]),
- "channelswap": bool(row[18]),
- },
- }
-
- def __getitem__(self, idx):
- use_fixed_prob = random.random()
-
- if use_fixed_prob <= self.use_fixed:
- # Fixed examples
- audio_path = random.choice(self.list_train_fixed)
- song_name = os.path.basename(audio_path).replace(".wav", "")
- mixture_limited, start_pos_sec = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration, return_pos=True
- )
-
- audio_sources = []
- track_path = f"{self.root}/train/{song_name}"
- for source in self.sources:
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_specific_position_stereo(
- audio_path,
- self.sample_rate,
- self.seq_duration,
- start_position=start_pos_sec,
- )
- audio_sources.append(audio)
-
- else:
- # Random examples
- # Load mixture_limited (pre-generated)
- audio_path = random.choice(self.list_train_random)
- seg_name = os.path.basename(audio_path).replace(".wav", "")
- mixture_limited, sr = librosa.load(
- audio_path, sr=self.sample_rate, mono=False
- )
-
- # Load mixture_unlimited (from the original musdb18, using metadata)
- audio_sources = []
- for source in self.sources:
- dict_seg_info = self.dict_train_random[seg_name]
- dict_seg_source_info = dict_seg_info[source]
- audio_path = (
- f"{self.root}/train/{dict_seg_source_info['name']}/{source}.wav"
- )
- audio = load_wav_specific_position_stereo(
- audio_path,
- self.sample_rate,
- self.seq_duration,
- start_position=dict_seg_source_info["start_sec"],
- )
-
- # apply augmentations
- audio = audio * dict_seg_source_info["gain"]
- if dict_seg_source_info["channelswap"]:
- audio = np.flip(audio, axis=0)
-
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
- mixture = stems.sum(axis=0)
- mixture_lufs = self.meter.integrated_loudness(mixture.T)
- if np.isinf(mixture_lufs):
- mixture_loudnorm = mixture
- else:
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain, eps=0.0)
-
- return mixture_limited, mixture_loudnorm
-
-
-class DelimitValidDataset(MusdbValidDataset):
- def __init__(
- self,
- target: str = "vocals",
- root: str = None,
- delimit_valid_root: str = None,
- valid_target_lufs: float = -8.05, # From the Table 1 of the "Towards robust music source separation on loud commercial music" paper, the average loudness of commerical music.
- target_loudnorm_lufs: float = -14.0,
- delimit_valid_L_root: str = None, # This will be used when using the target as compressed (normal_L) mixture.
- use_custom_limiter: bool = False,
- custom_limiter_attack_range: list = [0.1, 10.0],
- custom_limiter_release_range: list = [30.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(target=target, root=root, *args, **kwargs)
- self.delimit_valid_root = delimit_valid_root
- if self.delimit_valid_root:
- with open(f"{self.delimit_valid_root}/valid_loudness.json", "r") as f:
- self.dict_valid_loudness = json.load(f)
- self.delimit_valid_L_root = delimit_valid_L_root
- if self.delimit_valid_L_root:
- with open(f"{self.delimit_valid_L_root}/valid_loudness.json", "r") as f:
- self.dict_valid_L_loudness = json.load(f)
-
- self.valid_target_lufs = valid_target_lufs
- self.target_loudnorm_lufs = target_loudnorm_lufs
- self.meter = pyln.Meter(self.sample_rate)
- self.use_custom_limiter = use_custom_limiter
-
- if self.use_custom_limiter:
- print("using Custom limiter limitaug for validation!!")
- self.custom_limiter_attack_range = custom_limiter_attack_range
- self.custom_limiter_release_range = custom_limiter_release_range
- self.board = Pedalboard(
- [
- Gain(gain_db=0.0),
- Compressor(
- threshold_db=-10.0, ratio=4.0, attack_ms=2.0, release_ms=200.0
- ), # attack_ms and release_ms will be changed later.
- Compressor(
- threshold_db=0.0,
- ratio=1000.0,
- attack_ms=0.001,
- release_ms=100.0,
- ),
- Gain(gain_db=3.75),
- Clipping(threshold_db=0.0),
- ]
- ) # This implementation is the same as JUCE Limiter.
- # However, we want the first compressor to have a variable attack and release time.
- # Therefore, we use the Custom Limiter instead of the JUCE Limiter.
- else:
- self.board = Pedalboard(
- [Gain(gain_db=0.0), Limiter(threshold_db=0.0, release_ms=100.0)]
- ) # Currently, we are using a limiter with a release time of 100ms.
-
- def __getitem__(self, index):
- audio_sources = []
- target_ind = None
-
- for k, source in enumerate(self.sources):
- # memorize index of target source
- if source == self.target: # if source is 'vocals'
- target_ind = k
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- audio_path = f"{track_path}/{source}.wav"
- # audio = utils.load_wav_stereo(audio_path, self.sample_rate)
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
- else:
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- audio_path = f"{track_path}/{source}.wav"
- # audio = utils.load_wav_stereo(audio_path, self.sample_rate)
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
-
- audio = torch.as_tensor(audio, dtype=torch.float32)
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- # apply linear mix over source index=0
- # and here, linear mixture is a target unlike in MusdbTrainDataset
- mixture = stems.sum(0)
- if (
- self.delimit_valid_root
- ): # If there exists a pre-processed delimit valid dataset
- audio_path = f"{self.delimit_valid_root}/valid/{song_name}.wav"
- mixture_limited = librosa.load(audio_path, mono=False, sr=self.sample_rate)[
- 0
- ]
- mixture_lufs = self.dict_valid_loudness[song_name]
-
- else:
- if self.use_custom_limiter:
- custom_limiter_attack = random.uniform(
- self.custom_limiter_attack_range[0],
- self.custom_limiter_attack_range[1],
- )
- self.board[1].attack_ms = custom_limiter_attack
-
- custom_limiter_release = random.uniform(
- self.custom_limiter_release_range[0],
- self.custom_limiter_release_range[1],
- )
- self.board[1].release_ms = custom_limiter_release
-
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=self.valid_target_lufs,
- )
- else:
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=self.valid_target_lufs,
- # target_loudnorm_lufs=self.target_loudnorm_lufs,
- ) # mixture_limited is a limiter applied mixture
- # We will give mixture_limited as an input and mixture_loudnorm as a target to the model.
-
- if self.delimit_valid_L_root:
- audio_L_path = f"{self.delimit_valid_L_root}/valid/{song_name}.wav"
- mixture_loudnorm = librosa.load(
- audio_L_path, mono=False, sr=self.sample_rate
- )[0]
- mixture_lufs = self.dict_valid_L_loudness[song_name]
- mixture = mixture_loudnorm
-
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain)
-
- if self.use_custom_limiter:
- return (
- mixture_limited,
- mixture_loudnorm,
- song_name,
- mixture_lufs,
- custom_limiter_attack,
- custom_limiter_release,
- )
- else:
- return mixture_limited, mixture_loudnorm, song_name, mixture_lufs
-
-
-class OzoneValidDataset(MusdbValidDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- ozone_root: str = None,
- target_loudnorm_lufs: float = -14.0,
- *args,
- **kwargs,
- ) -> None:
- super().__init__(target=target, root=root, *args, **kwargs)
-
- self.ozone_root = ozone_root
- self.target_loudnorm_lufs = target_loudnorm_lufs
-
- with open(f"{self.ozone_root}/valid_loudness.json", "r") as f:
- self.dict_valid_loudness = json.load(f)
-
- def __getitem__(self, index):
- audio_sources = []
-
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- for k, source in enumerate(self.sources):
- audio_path = f"{track_path}/{source}.wav"
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- mixture = stems.sum(0)
-
- audio_path = f"{self.ozone_root}/ozone_train_fixed/{song_name}.wav"
- mixture_limited = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
-
- mixture_lufs = self.dict_valid_loudness[song_name]
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain)
-
- return mixture_limited, mixture_loudnorm, song_name, mixture_lufs
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py
deleted file mode 100644
index 75559579cf053abcc99538606cbb88c723faf783..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py
+++ /dev/null
@@ -1,282 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-
-import torch
-import torch.nn as nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair, _single
-
-from annotator.uniformer.mmcv.utils import deprecated_api_warning
-from ..cnn import CONV_LAYERS
-from ..utils import ext_loader, print_log
-
-ext_module = ext_loader.load_ext(
- '_ext',
- ['modulated_deform_conv_forward', 'modulated_deform_conv_backward'])
-
-
-class ModulatedDeformConv2dFunction(Function):
-
- @staticmethod
- def symbolic(g, input, offset, mask, weight, bias, stride, padding,
- dilation, groups, deform_groups):
- input_tensors = [input, offset, mask, weight]
- if bias is not None:
- input_tensors.append(bias)
- return g.op(
- 'mmcv::MMCVModulatedDeformConv2d',
- *input_tensors,
- stride_i=stride,
- padding_i=padding,
- dilation_i=dilation,
- groups_i=groups,
- deform_groups_i=deform_groups)
-
- @staticmethod
- def forward(ctx,
- input,
- offset,
- mask,
- weight,
- bias=None,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- deform_groups=1):
- if input is not None and input.dim() != 4:
- raise ValueError(
- f'Expected 4D tensor as input, got {input.dim()}D tensor \
- instead.')
- ctx.stride = _pair(stride)
- ctx.padding = _pair(padding)
- ctx.dilation = _pair(dilation)
- ctx.groups = groups
- ctx.deform_groups = deform_groups
- ctx.with_bias = bias is not None
- if not ctx.with_bias:
- bias = input.new_empty(0) # fake tensor
- # When pytorch version >= 1.6.0, amp is adopted for fp16 mode;
- # amp won't cast the type of model (float32), but "offset" is cast
- # to float16 by nn.Conv2d automatically, leading to the type
- # mismatch with input (when it is float32) or weight.
- # The flag for whether to use fp16 or amp is the type of "offset",
- # we cast weight and input to temporarily support fp16 and amp
- # whatever the pytorch version is.
- input = input.type_as(offset)
- weight = weight.type_as(input)
- ctx.save_for_backward(input, offset, mask, weight, bias)
- output = input.new_empty(
- ModulatedDeformConv2dFunction._output_size(ctx, input, weight))
- ctx._bufs = [input.new_empty(0), input.new_empty(0)]
- ext_module.modulated_deform_conv_forward(
- input,
- weight,
- bias,
- ctx._bufs[0],
- offset,
- mask,
- output,
- ctx._bufs[1],
- kernel_h=weight.size(2),
- kernel_w=weight.size(3),
- stride_h=ctx.stride[0],
- stride_w=ctx.stride[1],
- pad_h=ctx.padding[0],
- pad_w=ctx.padding[1],
- dilation_h=ctx.dilation[0],
- dilation_w=ctx.dilation[1],
- group=ctx.groups,
- deformable_group=ctx.deform_groups,
- with_bias=ctx.with_bias)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- input, offset, mask, weight, bias = ctx.saved_tensors
- grad_input = torch.zeros_like(input)
- grad_offset = torch.zeros_like(offset)
- grad_mask = torch.zeros_like(mask)
- grad_weight = torch.zeros_like(weight)
- grad_bias = torch.zeros_like(bias)
- grad_output = grad_output.contiguous()
- ext_module.modulated_deform_conv_backward(
- input,
- weight,
- bias,
- ctx._bufs[0],
- offset,
- mask,
- ctx._bufs[1],
- grad_input,
- grad_weight,
- grad_bias,
- grad_offset,
- grad_mask,
- grad_output,
- kernel_h=weight.size(2),
- kernel_w=weight.size(3),
- stride_h=ctx.stride[0],
- stride_w=ctx.stride[1],
- pad_h=ctx.padding[0],
- pad_w=ctx.padding[1],
- dilation_h=ctx.dilation[0],
- dilation_w=ctx.dilation[1],
- group=ctx.groups,
- deformable_group=ctx.deform_groups,
- with_bias=ctx.with_bias)
- if not ctx.with_bias:
- grad_bias = None
-
- return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias,
- None, None, None, None, None)
-
- @staticmethod
- def _output_size(ctx, input, weight):
- channels = weight.size(0)
- output_size = (input.size(0), channels)
- for d in range(input.dim() - 2):
- in_size = input.size(d + 2)
- pad = ctx.padding[d]
- kernel = ctx.dilation[d] * (weight.size(d + 2) - 1) + 1
- stride_ = ctx.stride[d]
- output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
- if not all(map(lambda s: s > 0, output_size)):
- raise ValueError(
- 'convolution input is too small (output would be ' +
- 'x'.join(map(str, output_size)) + ')')
- return output_size
-
-
-modulated_deform_conv2d = ModulatedDeformConv2dFunction.apply
-
-
-class ModulatedDeformConv2d(nn.Module):
-
- @deprecated_api_warning({'deformable_groups': 'deform_groups'},
- cls_name='ModulatedDeformConv2d')
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- deform_groups=1,
- bias=True):
- super(ModulatedDeformConv2d, self).__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.kernel_size = _pair(kernel_size)
- self.stride = _pair(stride)
- self.padding = _pair(padding)
- self.dilation = _pair(dilation)
- self.groups = groups
- self.deform_groups = deform_groups
- # enable compatibility with nn.Conv2d
- self.transposed = False
- self.output_padding = _single(0)
-
- self.weight = nn.Parameter(
- torch.Tensor(out_channels, in_channels // groups,
- *self.kernel_size))
- if bias:
- self.bias = nn.Parameter(torch.Tensor(out_channels))
- else:
- self.register_parameter('bias', None)
- self.init_weights()
-
- def init_weights(self):
- n = self.in_channels
- for k in self.kernel_size:
- n *= k
- stdv = 1. / math.sqrt(n)
- self.weight.data.uniform_(-stdv, stdv)
- if self.bias is not None:
- self.bias.data.zero_()
-
- def forward(self, x, offset, mask):
- return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,
- self.stride, self.padding,
- self.dilation, self.groups,
- self.deform_groups)
-
-
-@CONV_LAYERS.register_module('DCNv2')
-class ModulatedDeformConv2dPack(ModulatedDeformConv2d):
- """A ModulatedDeformable Conv Encapsulation that acts as normal Conv
- layers.
-
- Args:
- in_channels (int): Same as nn.Conv2d.
- out_channels (int): Same as nn.Conv2d.
- kernel_size (int or tuple[int]): Same as nn.Conv2d.
- stride (int): Same as nn.Conv2d, while tuple is not supported.
- padding (int): Same as nn.Conv2d, while tuple is not supported.
- dilation (int): Same as nn.Conv2d, while tuple is not supported.
- groups (int): Same as nn.Conv2d.
- bias (bool or str): If specified as `auto`, it will be decided by the
- norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
- False.
- """
-
- _version = 2
-
- def __init__(self, *args, **kwargs):
- super(ModulatedDeformConv2dPack, self).__init__(*args, **kwargs)
- self.conv_offset = nn.Conv2d(
- self.in_channels,
- self.deform_groups * 3 * self.kernel_size[0] * self.kernel_size[1],
- kernel_size=self.kernel_size,
- stride=self.stride,
- padding=self.padding,
- dilation=self.dilation,
- bias=True)
- self.init_weights()
-
- def init_weights(self):
- super(ModulatedDeformConv2dPack, self).init_weights()
- if hasattr(self, 'conv_offset'):
- self.conv_offset.weight.data.zero_()
- self.conv_offset.bias.data.zero_()
-
- def forward(self, x):
- out = self.conv_offset(x)
- o1, o2, mask = torch.chunk(out, 3, dim=1)
- offset = torch.cat((o1, o2), dim=1)
- mask = torch.sigmoid(mask)
- return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,
- self.stride, self.padding,
- self.dilation, self.groups,
- self.deform_groups)
-
- def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
- missing_keys, unexpected_keys, error_msgs):
- version = local_metadata.get('version', None)
-
- if version is None or version < 2:
- # the key is different in early versions
- # In version < 2, ModulatedDeformConvPack
- # loads previous benchmark models.
- if (prefix + 'conv_offset.weight' not in state_dict
- and prefix[:-1] + '_offset.weight' in state_dict):
- state_dict[prefix + 'conv_offset.weight'] = state_dict.pop(
- prefix[:-1] + '_offset.weight')
- if (prefix + 'conv_offset.bias' not in state_dict
- and prefix[:-1] + '_offset.bias' in state_dict):
- state_dict[prefix +
- 'conv_offset.bias'] = state_dict.pop(prefix[:-1] +
- '_offset.bias')
-
- if version is not None and version > 1:
- print_log(
- f'ModulatedDeformConvPack {prefix.rstrip(".")} is upgraded to '
- 'version 2.',
- logger='root')
-
- super()._load_from_state_dict(state_dict, prefix, local_metadata,
- strict, missing_keys, unexpected_keys,
- error_msgs)
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py
deleted file mode 100644
index a802a74fd6c3610d9ae178e6201f47423eca7ad1..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py
+++ /dev/null
@@ -1,177 +0,0 @@
-from typing import List
-
-import torch
-from torch import nn as nn
-
-from annotator.uniformer.mmcv.runner import force_fp32
-from .furthest_point_sample import (furthest_point_sample,
- furthest_point_sample_with_dist)
-
-
-def calc_square_dist(point_feat_a, point_feat_b, norm=True):
- """Calculating square distance between a and b.
-
- Args:
- point_feat_a (Tensor): (B, N, C) Feature vector of each point.
- point_feat_b (Tensor): (B, M, C) Feature vector of each point.
- norm (Bool, optional): Whether to normalize the distance.
- Default: True.
-
- Returns:
- Tensor: (B, N, M) Distance between each pair points.
- """
- num_channel = point_feat_a.shape[-1]
- # [bs, n, 1]
- a_square = torch.sum(point_feat_a.unsqueeze(dim=2).pow(2), dim=-1)
- # [bs, 1, m]
- b_square = torch.sum(point_feat_b.unsqueeze(dim=1).pow(2), dim=-1)
-
- corr_matrix = torch.matmul(point_feat_a, point_feat_b.transpose(1, 2))
-
- dist = a_square + b_square - 2 * corr_matrix
- if norm:
- dist = torch.sqrt(dist) / num_channel
- return dist
-
-
-def get_sampler_cls(sampler_type):
- """Get the type and mode of points sampler.
-
- Args:
- sampler_type (str): The type of points sampler.
- The valid value are "D-FPS", "F-FPS", or "FS".
-
- Returns:
- class: Points sampler type.
- """
- sampler_mappings = {
- 'D-FPS': DFPSSampler,
- 'F-FPS': FFPSSampler,
- 'FS': FSSampler,
- }
- try:
- return sampler_mappings[sampler_type]
- except KeyError:
- raise KeyError(
- f'Supported `sampler_type` are {sampler_mappings.keys()}, but got \
- {sampler_type}')
-
-
-class PointsSampler(nn.Module):
- """Points sampling.
-
- Args:
- num_point (list[int]): Number of sample points.
- fps_mod_list (list[str], optional): Type of FPS method, valid mod
- ['F-FPS', 'D-FPS', 'FS'], Default: ['D-FPS'].
- F-FPS: using feature distances for FPS.
- D-FPS: using Euclidean distances of points for FPS.
- FS: using F-FPS and D-FPS simultaneously.
- fps_sample_range_list (list[int], optional):
- Range of points to apply FPS. Default: [-1].
- """
-
- def __init__(self,
- num_point: List[int],
- fps_mod_list: List[str] = ['D-FPS'],
- fps_sample_range_list: List[int] = [-1]):
- super().__init__()
- # FPS would be applied to different fps_mod in the list,
- # so the length of the num_point should be equal to
- # fps_mod_list and fps_sample_range_list.
- assert len(num_point) == len(fps_mod_list) == len(
- fps_sample_range_list)
- self.num_point = num_point
- self.fps_sample_range_list = fps_sample_range_list
- self.samplers = nn.ModuleList()
- for fps_mod in fps_mod_list:
- self.samplers.append(get_sampler_cls(fps_mod)())
- self.fp16_enabled = False
-
- @force_fp32()
- def forward(self, points_xyz, features):
- """
- Args:
- points_xyz (Tensor): (B, N, 3) xyz coordinates of the features.
- features (Tensor): (B, C, N) Descriptors of the features.
-
- Returns:
- Tensor: (B, npoint, sample_num) Indices of sampled points.
- """
- indices = []
- last_fps_end_index = 0
-
- for fps_sample_range, sampler, npoint in zip(
- self.fps_sample_range_list, self.samplers, self.num_point):
- assert fps_sample_range < points_xyz.shape[1]
-
- if fps_sample_range == -1:
- sample_points_xyz = points_xyz[:, last_fps_end_index:]
- if features is not None:
- sample_features = features[:, :, last_fps_end_index:]
- else:
- sample_features = None
- else:
- sample_points_xyz = \
- points_xyz[:, last_fps_end_index:fps_sample_range]
- if features is not None:
- sample_features = features[:, :, last_fps_end_index:
- fps_sample_range]
- else:
- sample_features = None
-
- fps_idx = sampler(sample_points_xyz.contiguous(), sample_features,
- npoint)
-
- indices.append(fps_idx + last_fps_end_index)
- last_fps_end_index += fps_sample_range
- indices = torch.cat(indices, dim=1)
-
- return indices
-
-
-class DFPSSampler(nn.Module):
- """Using Euclidean distances of points for FPS."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with D-FPS."""
- fps_idx = furthest_point_sample(points.contiguous(), npoint)
- return fps_idx
-
-
-class FFPSSampler(nn.Module):
- """Using feature distances for FPS."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with F-FPS."""
- assert features is not None, \
- 'feature input to FFPS_Sampler should not be None'
- features_for_fps = torch.cat([points, features.transpose(1, 2)], dim=2)
- features_dist = calc_square_dist(
- features_for_fps, features_for_fps, norm=False)
- fps_idx = furthest_point_sample_with_dist(features_dist, npoint)
- return fps_idx
-
-
-class FSSampler(nn.Module):
- """Using F-FPS and D-FPS simultaneously."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with FS_Sampling."""
- assert features is not None, \
- 'feature input to FS_Sampler should not be None'
- ffps_sampler = FFPSSampler()
- dfps_sampler = DFPSSampler()
- fps_idx_ffps = ffps_sampler(points, features, npoint)
- fps_idx_dfps = dfps_sampler(points, features, npoint)
- fps_idx = torch.cat([fps_idx_ffps, fps_idx_dfps], dim=1)
- return fps_idx
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py
deleted file mode 100644
index 332b242c03d1c5e80d4577df442a9a037b1816e1..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .base_pixel_sampler import BasePixelSampler
-from .ohem_pixel_sampler import OHEMPixelSampler
-
-__all__ = ['BasePixelSampler', 'OHEMPixelSampler']
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py
deleted file mode 100644
index c0e42756ee6fcd779387255391a30079a28f5e60..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import torch
-from torch import nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-from maskrcnn_benchmark import _C
-
-
-class _ROIPool(Function):
- @staticmethod
- def forward(ctx, input, roi, output_size, spatial_scale):
- ctx.output_size = _pair(output_size)
- ctx.spatial_scale = spatial_scale
- ctx.input_shape = input.size()
- output, argmax = _C.roi_pool_forward(
- input, roi, spatial_scale, output_size[0], output_size[1]
- )
- ctx.save_for_backward(input, roi, argmax)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- input, rois, argmax = ctx.saved_tensors
- output_size = ctx.output_size
- spatial_scale = ctx.spatial_scale
- bs, ch, h, w = ctx.input_shape
- grad_input = _C.roi_pool_backward(
- grad_output,
- input,
- rois,
- argmax,
- spatial_scale,
- output_size[0],
- output_size[1],
- bs,
- ch,
- h,
- w,
- )
- return grad_input, None, None, None
-
-
-roi_pool = _ROIPool.apply
-
-
-class ROIPool(nn.Module):
- def __init__(self, output_size, spatial_scale):
- super(ROIPool, self).__init__()
- self.output_size = output_size
- self.spatial_scale = spatial_scale
-
- def forward(self, input, rois):
- return roi_pool(input, rois, self.output_size, self.spatial_scale)
-
- def __repr__(self):
- tmpstr = self.__class__.__name__ + "("
- tmpstr += "output_size=" + str(self.output_size)
- tmpstr += ", spatial_scale=" + str(self.spatial_scale)
- tmpstr += ")"
- return tmpstr
diff --git a/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py b/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py
deleted file mode 100644
index e90ef2130a47ec52160709877972716352e04c9c..0000000000000000000000000000000000000000
--- a/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
- if bias is not None:
- rest_dim = [1] * (input.ndim - bias.ndim - 1)
- return (
- F.leaky_relu(
- input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope
- )
- * scale
- )
- else:
- return F.leaky_relu(input, negative_slope=0.2) * scale
-
-
-class EqualLinear(nn.Module):
- def __init__(
- self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1
- ):
- super().__init__()
- self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
- if bias:
- self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
- else:
- self.bias = None
- self.scale = (1 / math.sqrt(in_dim)) * lr_mul
- self.lr_mul = lr_mul
-
- def forward(self, input):
- out = F.linear(input, self.weight * self.scale)
- out = fused_leaky_relu(out, self.bias * self.lr_mul)
- return out
-
-
-class RandomLatentConverter(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.layers = nn.Sequential(*[EqualLinear(channels, channels, lr_mul=.1) for _ in range(5)],
- nn.Linear(channels, channels))
- self.channels = channels
-
- def forward(self, ref):
- r = torch.randn(ref.shape[0], self.channels, device=ref.device)
- y = self.layers(r)
- return y
-
-
-if __name__ == '__main__':
- model = RandomLatentConverter(512)
- model(torch.randn(5,512))
\ No newline at end of file
diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py
deleted file mode 100644
index 8d760f8a1a769bba657d4aea47857f48f7cd0d7a..0000000000000000000000000000000000000000
--- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py
+++ /dev/null
@@ -1,1832 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# Updated to account for UI changes from https://github.com/rkfg/audiocraft/blob/long/app.py
-# also released under the MIT license.
-
-import argparse
-from concurrent.futures import ProcessPoolExecutor
-import os
-from pathlib import Path
-import subprocess as sp
-from tempfile import NamedTemporaryFile
-import time
-import warnings
-import glob
-import re
-from PIL import Image
-from pydub import AudioSegment
-from datetime import datetime
-
-import json
-import shutil
-import taglib
-import torch
-import torchaudio
-import gradio as gr
-import numpy as np
-import typing as tp
-
-from audiocraft.data.audio_utils import convert_audio
-from audiocraft.data.audio import audio_write
-from audiocraft.models import AudioGen, MusicGen, MultiBandDiffusion
-from audiocraft.utils import ui
-import random, string
-
-version = "2.0.0a"
-
-theme = gr.themes.Base(
- primary_hue="lime",
- secondary_hue="lime",
- neutral_hue="neutral",
-).set(
- button_primary_background_fill_hover='*primary_500',
- button_primary_background_fill_hover_dark='*primary_500',
- button_secondary_background_fill_hover='*primary_500',
- button_secondary_background_fill_hover_dark='*primary_500'
-)
-
-MODEL = None # Last used model
-MODELS = None
-UNLOAD_MODEL = False
-MOVE_TO_CPU = False
-IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
-print(IS_BATCHED)
-MAX_BATCH_SIZE = 12
-BATCHED_DURATION = 15
-INTERRUPTING = False
-MBD = None
-# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
-_old_call = sp.call
-
-
-def generate_random_string(length):
- characters = string.ascii_letters + string.digits
- return ''.join(random.choice(characters) for _ in range(length))
-
-
-def resize_video(input_path, output_path, target_width, target_height):
- ffmpeg_cmd = [
- 'ffmpeg',
- '-y',
- '-i', input_path,
- '-vf', f'scale={target_width}:{target_height}',
- '-c:a', 'copy',
- output_path
- ]
- sp.run(ffmpeg_cmd)
-
-
-def _call_nostderr(*args, **kwargs):
- # Avoid ffmpeg vomiting on the logs.
- kwargs['stderr'] = sp.DEVNULL
- kwargs['stdout'] = sp.DEVNULL
- _old_call(*args, **kwargs)
-
-
-sp.call = _call_nostderr
-# Preallocating the pool of processes.
-pool = ProcessPoolExecutor(4)
-pool.__enter__()
-
-
-def interrupt():
- global INTERRUPTING
- INTERRUPTING = True
-
-
-class FileCleaner:
- def __init__(self, file_lifetime: float = 3600):
- self.file_lifetime = file_lifetime
- self.files = []
-
- def add(self, path: tp.Union[str, Path]):
- self._cleanup()
- self.files.append((time.time(), Path(path)))
-
- def _cleanup(self):
- now = time.time()
- for time_added, path in list(self.files):
- if now - time_added > self.file_lifetime:
- if path.exists():
- path.unlink()
- self.files.pop(0)
- else:
- break
-
-
-file_cleaner = FileCleaner()
-
-
-def make_waveform(*args, **kwargs):
- # Further remove some warnings.
- be = time.time()
- with warnings.catch_warnings():
- warnings.simplefilter('ignore')
- height = kwargs.pop('height')
- width = kwargs.pop('width')
- if height < 256:
- height = 256
- if width < 256:
- width = 256
- waveform_video = gr.make_waveform(*args, **kwargs)
- out = f"{generate_random_string(12)}.mp4"
- image = kwargs.get('bg_image', None)
- if image is None:
- resize_video(waveform_video, out, 900, 300)
- else:
- resize_video(waveform_video, out, width, height)
- print("Make a video took", time.time() - be)
- return out
-
-
-def load_model(version='GrandaddyShmax/musicgen-melody', custom_model=None, base_model='GrandaddyShmax/musicgen-medium', gen_type="music"):
- global MODEL, MODELS
- print("Loading model", version)
- if MODELS is None:
- if version == 'GrandaddyShmax/musicgen-custom':
- MODEL = MusicGen.get_pretrained(base_model)
- file_path = os.path.abspath("models/" + str(custom_model) + ".pt")
- MODEL.lm.load_state_dict(torch.load(file_path))
- else:
- if gen_type == "music":
- MODEL = MusicGen.get_pretrained(version)
- elif gen_type == "audio":
- MODEL = AudioGen.get_pretrained(version)
-
- return
-
- else:
- t1 = time.monotonic()
- if MODEL is not None:
- MODEL.to('cpu') # move to cache
- print("Previous model moved to CPU in %.2fs" % (time.monotonic() - t1))
- t1 = time.monotonic()
- if version != 'GrandaddyShmax/musicgen-custom' and MODELS.get(version) is None:
- print("Loading model %s from disk" % version)
- if gen_type == "music":
- result = MusicGen.get_pretrained(version)
- elif gen_type == "audio":
- result = AudioGen.get_pretrained(version)
- MODELS[version] = result
- print("Model loaded in %.2fs" % (time.monotonic() - t1))
- MODEL = result
- return
- result = MODELS[version].to('cuda')
- print("Cached model loaded in %.2fs" % (time.monotonic() - t1))
- MODEL = result
-
-def get_audio_info(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted. (Discord removes metadata from mp4 and wav files, so you can't use them)"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- global_prompt = str("\nGlobal Prompt: " + (data['global_prompt'] if data['global_prompt'] != "" else "none")) if 'global_prompt' in data else ""
- bpm = str("\nBPM: " + data['bpm']) if 'bpm' in data else ""
- key = str("\nKey: " + data['key']) if 'key' in data else ""
- scale = str("\nScale: " + data['scale']) if 'scale' in data else ""
- prompts = str("\nPrompts: " + (data['texts'] if data['texts'] != "['']" else "none")) if 'texts' in data else ""
- duration = str("\nDuration: " + data['duration']) if 'duration' in data else ""
- overlap = str("\nOverlap: " + data['overlap']) if 'overlap' in data else ""
- seed = str("\nSeed: " + data['seed']) if 'seed' in data else ""
- audio_mode = str("\nAudio Mode: " + data['audio_mode']) if 'audio_mode' in data else ""
- input_length = str("\nInput Length: " + data['input_length']) if 'input_length' in data else ""
- channel = str("\nChannel: " + data['channel']) if 'channel' in data else ""
- sr_select = str("\nSample Rate: " + data['sr_select']) if 'sr_select' in data else ""
- gen_type = str(data['generator'] + "gen-") if 'generator' in data else ""
- model = str("\nModel: " + gen_type + data['model']) if 'model' in data else ""
- custom_model = str("\nCustom Model: " + data['custom_model']) if 'custom_model' in data else ""
- base_model = str("\nBase Model: " + data['base_model']) if 'base_model' in data else ""
- decoder = str("\nDecoder: " + data['decoder']) if 'decoder' in data else ""
- topk = str("\nTopk: " + data['topk']) if 'topk' in data else ""
- topp = str("\nTopp: " + data['topp']) if 'topp' in data else ""
- temperature = str("\nTemperature: " + data['temperature']) if 'temperature' in data else ""
- cfg_coef = str("\nClassifier Free Guidance: " + data['cfg_coef']) if 'cfg_coef' in data else ""
- version = str("Version: " + data['version']) if 'version' in data else "Version: Unknown"
- info = str(version + global_prompt + bpm + key + scale + prompts + duration + overlap + seed + audio_mode + input_length + channel + sr_select + model + custom_model + base_model + decoder + topk + topp + temperature + cfg_coef)
- if info == "":
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted. (Discord removes metadata from mp4 and wav files, so you can't use them)"
- return info
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- #if 'global_prompt' not in data:
- #return "No tags found. Either the file is not generated by MusicGen+ V1.2.8a and higher or the tags are corrupted."
- global_prompt = str("\nGlobal Prompt: " + (data['global_prompt'] if data['global_prompt'] != "" else "none")) if 'global_prompt' in data else ""
- bpm = str("\nBPM: " + data['bpm']) if 'bpm' in data else ""
- key = str("\nKey: " + data['key']) if 'key' in data else ""
- scale = str("\nScale: " + data['scale']) if 'scale' in data else ""
- prompts = str("\nPrompts: " + (data['texts'] if data['texts'] != "['']" else "none")) if 'texts' in data else ""
- duration = str("\nDuration: " + data['duration']) if 'duration' in data else ""
- overlap = str("\nOverlap: " + data['overlap']) if 'overlap' in data else ""
- seed = str("\nSeed: " + data['seed']) if 'seed' in data else ""
- audio_mode = str("\nAudio Mode: " + data['audio_mode']) if 'audio_mode' in data else ""
- input_length = str("\nInput Length: " + data['input_length']) if 'input_length' in data else ""
- channel = str("\nChannel: " + data['channel']) if 'channel' in data else ""
- sr_select = str("\nSample Rate: " + data['sr_select']) if 'sr_select' in data else ""
- gen_type = str(data['generator'] + "gen-") if 'generator' in data else ""
- model = str("\nModel: " + gen_type + data['model']) if 'model' in data else ""
- custom_model = str("\nCustom Model: " + data['custom_model']) if 'custom_model' in data else ""
- base_model = str("\nBase Model: " + data['base_model']) if 'base_model' in data else ""
- decoder = str("\nDecoder: " + data['decoder']) if 'decoder' in data else ""
- topk = str("\nTopk: " + data['topk']) if 'topk' in data else ""
- topp = str("\nTopp: " + data['topp']) if 'topp' in data else ""
- temperature = str("\nTemperature: " + data['temperature']) if 'temperature' in data else ""
- cfg_coef = str("\nClassifier Free Guidance: " + data['cfg_coef']) if 'cfg_coef' in data else ""
- version = str("Version: " + data['version']) if 'version' in data else "Version: Unknown"
- info = str(version + global_prompt + bpm + key + scale + prompts + duration + overlap + seed + audio_mode + input_length + channel + sr_select + model + custom_model + base_model + decoder + topk + topp + temperature + cfg_coef)
- if info == "":
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted."
- return info
- else:
- return "Only .wav ,.mp4 and .json files are supported"
- else:
- return None
-
-
-def info_to_params(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- struc_prompt = (False if data['bpm'] == "none" else True) if 'bpm' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- bpm = (120 if data['bpm'] == "none" else int(data['bpm'])) if 'bpm' in data else 120
- key = ("C" if data['key'] == "none" else data['key']) if 'key' in data else "C"
- scale = ("Major" if data['scale'] == "none" else data['scale']) if 'scale' in data else "Major"
- model = data['model'] if 'model' in data else "large"
- custom_model = (data['custom_model'] if data['custom_model'] in get_available_models() else None) if 'custom_model' in data else None
- base_model = data['base_model'] if 'base_model' in data else "medium"
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- audio_mode = ("sample" if data['audio_mode'] == "none" else data['audio_mode']) if 'audio_mode' in data else "sample"
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, bpm, key, scale, model, custom_model, base_model, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], audio_mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- struc_prompt = (False if data['bpm'] == "none" else True) if 'bpm' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- bpm = (120 if data['bpm'] == "none" else int(data['bpm'])) if 'bpm' in data else 120
- key = ("C" if data['key'] == "none" else data['key']) if 'key' in data else "C"
- scale = ("Major" if data['scale'] == "none" else data['scale']) if 'scale' in data else "Major"
- model = data['model'] if 'model' in data else "large"
- custom_model = (data['custom_model'] if data['custom_model'] in get_available_models() else None) if 'custom_model' in data else None
- base_model = data['base_model'] if 'base_model' in data else "medium"
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- audio_mode = ("sample" if data['audio_mode'] == "none" else data['audio_mode']) if 'audio_mode' in data else "sample"
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, bpm, key, scale, model, custom_model, base_model, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], audio_mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- else:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-
-
-def info_to_params_a(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- struc_prompt = (False if data['global_prompt'] == "" else True) if 'global_prompt' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- struc_prompt = (False if data['global_prompt'] == "" else True) if 'global_prompt' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
-
- else:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- else:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-
-
-def make_pseudo_stereo (filename, sr_select, pan, delay):
- if pan:
- temp = AudioSegment.from_wav(filename)
- if sr_select != "32000":
- temp = temp.set_frame_rate(int(sr_select))
- left = temp.pan(-0.5) - 5
- right = temp.pan(0.6) - 5
- temp = left.overlay(right, position=5)
- temp.export(filename, format="wav")
- if delay:
- waveform, sample_rate = torchaudio.load(filename) # load mono WAV file
- delay_seconds = 0.01 # set delay 10ms
- delay_samples = int(delay_seconds * sample_rate) # Calculating delay value in number of samples
- stereo_waveform = torch.stack([waveform[0], torch.cat((torch.zeros(delay_samples), waveform[0][:-delay_samples]))]) # Generate a stereo file with original mono audio and delayed version
- torchaudio.save(filename, stereo_waveform, sample_rate)
- return
-
-
-def normalize_audio(audio_data):
- audio_data = audio_data.astype(np.float32)
- max_value = np.max(np.abs(audio_data))
- audio_data /= max_value
- return audio_data
-
-
-def load_diffusion():
- global MBD
- if MBD is None:
- print("loading MBD")
- MBD = MultiBandDiffusion.get_mbd_musicgen()
-
-
-def unload_diffusion():
- global MBD
- if MBD is not None:
- print("unloading MBD")
- MBD = None
-
-
-def _do_predictions(gen_type, texts, melodies, sample, trim_start, trim_end, duration, image, height, width, background, bar1, bar2, channel, sr_select, progress=False, **gen_kwargs):
- if gen_type == "music":
- maximum_size = 29.5
- elif gen_type == "audio":
- maximum_size = 9.5
- cut_size = 0
- input_length = 0
- sampleP = None
- if sample is not None:
- globalSR, sampleM = sample[0], sample[1]
- sampleM = normalize_audio(sampleM)
- sampleM = torch.from_numpy(sampleM).t()
- if sampleM.dim() == 1:
- sampleM = sampleM.unsqueeze(0)
- sample_length = sampleM.shape[sampleM.dim() - 1] / globalSR
- if trim_start >= sample_length:
- trim_start = sample_length - 0.5
- if trim_end >= sample_length:
- trim_end = sample_length - 0.5
- if trim_start + trim_end >= sample_length:
- tmp = sample_length - 0.5
- trim_start = tmp / 2
- trim_end = tmp / 2
- sampleM = sampleM[..., int(globalSR * trim_start):int(globalSR * (sample_length - trim_end))]
- sample_length = sample_length - (trim_start + trim_end)
- if sample_length > maximum_size:
- cut_size = sample_length - maximum_size
- sampleP = sampleM[..., :int(globalSR * cut_size)]
- sampleM = sampleM[..., int(globalSR * cut_size):]
- if sample_length >= duration:
- duration = sample_length + 0.5
- input_length = sample_length
- global MODEL
- MODEL.set_generation_params(duration=(duration - cut_size), **gen_kwargs)
- print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies], [None if sample is None else (sample[0], sample[1].shape)])
- be = time.time()
- processed_melodies = []
- if gen_type == "music":
- target_sr = 32000
- elif gen_type == "audio":
- target_sr = 16000
- target_ac = 1
-
- for melody in melodies:
- if melody is None:
- processed_melodies.append(None)
- else:
- sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
- if melody.dim() == 1:
- melody = melody[None]
- melody = melody[..., :int(sr * duration)]
- melody = convert_audio(melody, sr, target_sr, target_ac)
- processed_melodies.append(melody)
-
- if sample is not None:
- if sampleP is None:
- if gen_type == "music":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress
- )
- else:
- if sampleP.dim() > 1:
- sampleP = convert_audio(sampleP, globalSR, target_sr, target_ac)
- sampleP = sampleP.to(MODEL.device).float().unsqueeze(0)
- if gen_type == "music":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress
- )
- outputs = torch.cat([sampleP, outputs], 2)
-
- elif any(m is not None for m in processed_melodies):
- if gen_type == "music":
- outputs = MODEL.generate_with_chroma(
- descriptions=texts,
- melody_wavs=processed_melodies,
- melody_sample_rate=target_sr,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_with_chroma(
- descriptions=texts,
- melody_wavs=processed_melodies,
- melody_sample_rate=target_sr,
- progress=progress
- )
- else:
- if gen_type == "music":
- outputs = MODEL.generate(texts, progress=progress, return_tokens=USE_DIFFUSION)
- elif gen_type == "audio":
- outputs = MODEL.generate(texts, progress=progress)
-
- if USE_DIFFUSION:
- print("outputs: " + str(outputs))
- outputs_diffusion = MBD.tokens_to_wav(outputs[1])
- outputs = torch.cat([outputs[0], outputs_diffusion], dim=0)
- outputs = outputs.detach().cpu().float()
- backups = outputs
- if channel == "stereo":
- outputs = convert_audio(outputs, target_sr, int(sr_select), 2)
- elif channel == "mono" and sr_select != "32000":
- outputs = convert_audio(outputs, target_sr, int(sr_select), 1)
- out_files = []
- out_audios = []
- out_backup = []
- for output in outputs:
- with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
- audio_write(
- file.name, output, (MODEL.sample_rate if channel == "stereo effect" else int(sr_select)), strategy="loudness",
- loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
-
- if channel == "stereo effect":
- make_pseudo_stereo(file.name, sr_select, pan=True, delay=True);
-
- out_files.append(pool.submit(make_waveform, file.name, bg_image=image, bg_color=background, bars_color=(bar1, bar2), fg_alpha=1.0, bar_count=75, height=height, width=width))
- out_audios.append(file.name)
- file_cleaner.add(file.name)
- print(f'wav: {file.name}')
- for backup in backups:
- with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
- audio_write(
- file.name, backup, MODEL.sample_rate, strategy="loudness",
- loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
- out_backup.append(file.name)
- file_cleaner.add(file.name)
- res = [out_file.result() for out_file in out_files]
- res_audio = out_audios
- res_backup = out_backup
- for file in res:
- file_cleaner.add(file)
- print(f'video: {file}')
- print("batch finished", len(texts), time.time() - be)
- print("Tempfiles currently stored: ", len(file_cleaner.files))
- if MOVE_TO_CPU:
- MODEL.to('cpu')
- if UNLOAD_MODEL:
- MODEL = None
- torch.cuda.empty_cache()
- torch.cuda.ipc_collect()
- return res, res_audio, res_backup, input_length
-
-
-def predict_batched(texts, melodies):
- max_text_length = 512
- texts = [text[:max_text_length] for text in texts]
- load_model('melody')
- res = _do_predictions(texts, melodies, BATCHED_DURATION)
- return res
-
-
-def add_tags(filename, tags):
- json_string = None
-
- data = {
- "global_prompt": tags[0],
- "bpm": tags[1],
- "key": tags[2],
- "scale": tags[3],
- "texts": tags[4],
- "duration": tags[5],
- "overlap": tags[6],
- "seed": tags[7],
- "audio_mode": tags[8],
- "input_length": tags[9],
- "channel": tags[10],
- "sr_select": tags[11],
- "model": tags[12],
- "custom_model": tags[13],
- "base_model": tags[14],
- "decoder": tags[15],
- "topk": tags[16],
- "topp": tags[17],
- "temperature": tags[18],
- "cfg_coef": tags[19],
- "generator": tags[20],
- "version": version
- }
-
- json_string = json.dumps(data)
-
- if os.path.exists(filename):
- with taglib.File(filename, save_on_exit=True) as song:
- song.tags = {'COMMENT': json_string }
-
- json_file = open(tags[7] + '.json', 'w')
- json_file.write(json_string)
- json_file.close()
-
- return json_file.name;
-
-
-def save_outputs(mp4, wav_tmp, tags, gen_type):
- # mp4: .mp4 file name in root running folder of app.py
- # wav_tmp: temporary wav file located in %TEMP% folder
- # seed - used seed
- # exanple BgnJtr4Pn1AJ.mp4, C:\Users\Alex\AppData\Local\Temp\tmp4ermrebs.wav, 195123182343465
- # procedure read generated .mp4 and wav files, rename it by using seed as name,
- # and will store it to ./output/today_date/wav and ./output/today_date/mp4 folders.
- # if file with same seed number already exist its make postfix in name like seed(n)
- # where is n - consiqunce number 1-2-3-4 and so on
- # then we store generated mp4 and wav into destination folders.
-
- current_date = datetime.now().strftime("%Y%m%d")
- wav_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'wav')
- mp4_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'mp4')
- json_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'json')
- os.makedirs(wav_directory, exist_ok=True)
- os.makedirs(mp4_directory, exist_ok=True)
- os.makedirs(json_directory, exist_ok=True)
-
- filename = str(tags[7]) + '.wav'
- target = os.path.join(wav_directory, filename)
- counter = 1
- while os.path.exists(target):
- filename = str(tags[7]) + f'({counter})' + '.wav'
- target = os.path.join(wav_directory, filename)
- counter += 1
-
- shutil.copyfile(wav_tmp, target); # make copy of original file
- json_file = add_tags(target, tags);
-
- wav_target=target;
- target=target.replace('wav', 'mp4');
- mp4_target=target;
-
- mp4=r'./' +mp4;
- shutil.copyfile(mp4, target); # make copy of original file
- _ = add_tags(target, tags);
-
- target=target.replace('mp4', 'json'); # change the extension to json
- json_target=target; # store the json target
-
- with open(target, 'w') as f: # open a writable file object
- shutil.copyfile(json_file, target); # make copy of original file
-
- os.remove(json_file)
-
- return wav_target, mp4_target, json_target;
-
-
-def clear_cash():
- # delete all temporary files genegated my system
- current_date = datetime.now().date()
- current_directory = os.getcwd()
- files = glob.glob(os.path.join(current_directory, '*.mp4'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- temp_directory = os.environ.get('TEMP')
- files = glob.glob(os.path.join(temp_directory, 'tmp*.mp4'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- files = glob.glob(os.path.join(temp_directory, 'tmp*.wav'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- files = glob.glob(os.path.join(temp_directory, 'tmp*.png'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
- return
-
-
-def s2t(seconds, seconds2):
- # convert seconds to time format
- # seconds - time in seconds
- # return time in format 00:00
- m, s = divmod(seconds, 60)
- m2, s2 = divmod(seconds2, 60)
- if seconds != 0 and seconds < seconds2:
- s = s + 1
- return ("%02d:%02d - %02d:%02d" % (m, s, m2, s2))
-
-
-def calc_time(gen_type, s, duration, overlap, d0, d1, d2, d3, d4, d5, d6, d7, d8, d9):
- # calculate the time of generation
- # overlap - overlap in seconds
- # d0-d9 - drag
- # return time in seconds
- d_amount = [int(d0), int(d1), int(d2), int(d3), int(d4), int(d5), int(d6), int(d7), int(d8), int(d9)]
- calc = []
- tracks = []
- time = 0
- s = s - 1
- max_time = duration
- max_limit = 0
- if gen_type == "music":
- max_limit = 30
- elif gen_type == "audio":
- max_limit = 10
- track_add = max_limit - overlap
- tracks.append(max_limit + ((d_amount[0] - 1) * track_add))
- for i in range(1, 10):
- tracks.append(d_amount[i] * track_add)
-
- if tracks[0] >= max_time or s == 0:
- calc.append(s2t(time, max_time))
- time = max_time
- else:
- calc.append(s2t(time, tracks[0]))
- time = tracks[0]
-
- for i in range(1, 10):
- if time + tracks[i] >= max_time or i == s:
- calc.append(s2t(time, max_time))
- time = max_time
- else:
- calc.append(s2t(time, time + tracks[i]))
- time = time + tracks[i]
-
- return calc[0], calc[1], calc[2], calc[3], calc[4], calc[5], calc[6], calc[7], calc[8], calc[9]
-
-
-def predict_full(gen_type, model, decoder, custom_model, base_model, prompt_amount, struc_prompt, bpm, key, scale, global_prompt, p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, d0, d1, d2, d3, d4, d5, d6, d7, d8, d9, audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select, progress=gr.Progress()):
- global INTERRUPTING
- global USE_DIFFUSION
- INTERRUPTING = False
-
- if gen_type == "audio":
- custom_model = None
- base_model = "medium"
-
- if temperature < 0:
- raise gr.Error("Temperature must be >= 0.")
- if topk < 0:
- raise gr.Error("Topk must be non-negative.")
- if topp < 0:
- raise gr.Error("Topp must be non-negative.")
-
- if trim_start < 0:
- trim_start = 0
- if trim_end < 0:
- trim_end = 0
-
- topk = int(topk)
-
- if decoder == "MultiBand_Diffusion":
- USE_DIFFUSION = True
- load_diffusion()
- else:
- USE_DIFFUSION = False
- unload_diffusion()
-
- if gen_type == "music":
- model_shrt = model
- model = "GrandaddyShmax/musicgen-" + model
- elif gen_type == "audio":
- model_shrt = model
- model = "GrandaddyShmax/audiogen-" + model
- base_model_shrt = base_model
- base_model = "GrandaddyShmax/musicgen-" + base_model
-
- if MODEL is None or MODEL.name != (model):
- load_model(model, custom_model, base_model, gen_type)
- else:
- if MOVE_TO_CPU:
- MODEL.to('cuda')
-
- if seed < 0:
- seed = random.randint(0, 0xffff_ffff_ffff)
- torch.manual_seed(seed)
-
- def _progress(generated, to_generate):
- progress((min(generated, to_generate), to_generate))
- if INTERRUPTING:
- raise gr.Error("Interrupted.")
- MODEL.set_custom_progress_callback(_progress)
-
- audio_mode = "none"
- melody = None
- sample = None
- if audio:
- audio_mode = mode
- if mode == "sample":
- sample = audio
- elif mode == "melody":
- melody = audio
-
- base_model = "none" if model != "custom" else base_model
- custom_model = "none" if model != "custom" else custom_model
-
- text_cat = [p0, p1, p2, p3, p4, p5, p6, p7, p8, p9]
- drag_cat = [d0, d1, d2, d3, d4, d5, d6, d7, d8, d9]
- texts = []
- raw_texts = []
- ind = 0
- ind2 = 0
- while ind < prompt_amount:
- for ind2 in range(int(drag_cat[ind])):
- if not struc_prompt:
- texts.append(text_cat[ind])
- global_prompt = "none"
- bpm = "none"
- key = "none"
- scale = "none"
- raw_texts.append(text_cat[ind])
- else:
- if gen_type == "music":
- bpm_str = str(bpm) + " bpm"
- key_str = ", " + str(key) + " " + str(scale)
- global_str = (", " + str(global_prompt)) if str(global_prompt) != "" else ""
- elif gen_type == "audio":
- bpm_str = ""
- key_str = ""
- global_str = (str(global_prompt)) if str(global_prompt) != "" else ""
- texts_str = (", " + str(text_cat[ind])) if str(text_cat[ind]) != "" else ""
- texts.append(bpm_str + key_str + global_str + texts_str)
- raw_texts.append(text_cat[ind])
- ind2 = 0
- ind = ind + 1
-
- outs, outs_audio, outs_backup, input_length = _do_predictions(
- gen_type, [texts], [melody], sample, trim_start, trim_end, duration, image, height, width, background, bar1, bar2, channel, sr_select, progress=True,
- top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef, extend_stride=MODEL.max_duration-overlap)
- tags = [str(global_prompt), str(bpm), str(key), str(scale), str(raw_texts), str(duration), str(overlap), str(seed), str(audio_mode), str(input_length), str(channel), str(sr_select), str(model_shrt), str(custom_model), str(base_model_shrt), str(decoder), str(topk), str(topp), str(temperature), str(cfg_coef), str(gen_type)]
- wav_target, mp4_target, json_target = save_outputs(outs[0], outs_audio[0], tags, gen_type);
- # Removes the temporary files.
- for out in outs:
- os.remove(out)
- for out in outs_audio:
- os.remove(out)
-
- return mp4_target, wav_target, outs_backup[0], [mp4_target, wav_target, json_target], seed
-
-
-max_textboxes = 10
-
-
-def get_available_models():
- return sorted([re.sub('.pt$', '', item.name) for item in list(Path('models/').glob('*')) if item.name.endswith('.pt')])
-
-
-def toggle_audio_src(choice):
- if choice == "mic":
- return gr.update(source="microphone", value=None, label="Microphone")
- else:
- return gr.update(source="upload", value=None, label="File")
-
-
-def ui_full(launch_kwargs):
- with gr.Blocks(title='AudioCraft Plus', theme=theme) as interface:
- gr.Markdown(
- """
- # AudioCraft Plus - v2.0.0a
-
- ### An All-in-One AudioCraft WebUI
-
- Thanks to: facebookresearch, Camenduru, rkfg, oobabooga, AlexHK and GrandaddyShmax
- """
- )
- with gr.Tab("MusicGen"):
- gr.Markdown(
- """
- ### MusicGen
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Tab("Generation"):
- with gr.Accordion("Structure Prompts", open=False):
- with gr.Column():
- with gr.Row():
- struc_prompts = gr.Checkbox(label="Enable", value=False, interactive=True, container=False)
- bpm = gr.Number(label="BPM", value=120, interactive=True, scale=1, precision=0)
- key = gr.Dropdown(["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "Bb", "B"], label="Key", value="C", interactive=True)
- scale = gr.Dropdown(["Major", "Minor"], label="Scale", value="Major", interactive=True)
- with gr.Row():
- global_prompt = gr.Text(label="Global Prompt", interactive=True, scale=3)
- with gr.Row():
- s = gr.Slider(1, max_textboxes, value=1, step=1, label="Prompts:", interactive=True, scale=2)
- #s_mode = gr.Radio(["segmentation", "batch"], value="segmentation", interactive=True, scale=1, label="Generation Mode")
- with gr.Column():
- textboxes = []
- prompts = []
- repeats = []
- calcs = []
- with gr.Row():
- text0 = gr.Text(label="Input Text", interactive=True, scale=4)
- prompts.append(text0)
- drag0 = gr.Number(label="Repeat", value=1, interactive=True, scale=1)
- repeats.append(drag0)
- calc0 = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- calcs.append(calc0)
- for i in range(max_textboxes):
- with gr.Row(visible=False) as t:
- text = gr.Text(label="Input Text", interactive=True, scale=3)
- repeat = gr.Number(label="Repeat", minimum=1, value=1, interactive=True, scale=1)
- calc = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- textboxes.append(t)
- prompts.append(text)
- repeats.append(repeat)
- calcs.append(calc)
- to_calc = gr.Button("Calculate Timings", variant="secondary")
- with gr.Row():
- duration = gr.Slider(minimum=1, maximum=300, value=10, step=1, label="Duration", interactive=True)
- with gr.Row():
- overlap = gr.Slider(minimum=1, maximum=29, value=12, step=1, label="Overlap", interactive=True)
- with gr.Row():
- seed = gr.Number(label="Seed", value=-1, scale=4, precision=0, interactive=True)
- gr.Button('\U0001f3b2\ufe0f', scale=1).click(fn=lambda: -1, outputs=[seed], queue=False)
- reuse_seed = gr.Button('\u267b\ufe0f', scale=1)
-
- with gr.Tab("Audio"):
- with gr.Row():
- with gr.Column():
- input_type = gr.Radio(["file", "mic"], value="file", label="Input Type (optional)", interactive=True)
- mode = gr.Radio(["melody", "sample"], label="Input Audio Mode (optional)", value="sample", interactive=True)
- with gr.Row():
- trim_start = gr.Number(label="Trim Start", value=0, interactive=True)
- trim_end = gr.Number(label="Trim End", value=0, interactive=True)
- audio = gr.Audio(source="upload", type="numpy", label="Input Audio (optional)", interactive=True)
-
- with gr.Tab("Customization"):
- with gr.Row():
- with gr.Column():
- background = gr.ColorPicker(value="#0f0f0f", label="background color", interactive=True, scale=0)
- bar1 = gr.ColorPicker(value="#84cc16", label="bar color start", interactive=True, scale=0)
- bar2 = gr.ColorPicker(value="#10b981", label="bar color end", interactive=True, scale=0)
- with gr.Column():
- image = gr.Image(label="Background Image", type="filepath", interactive=True, scale=4)
- with gr.Row():
- height = gr.Number(label="Height", value=512, interactive=True)
- width = gr.Number(label="Width", value=768, interactive=True)
-
- with gr.Tab("Settings"):
- with gr.Row():
- channel = gr.Radio(["mono", "stereo", "stereo effect"], label="Output Audio Channels", value="stereo", interactive=True, scale=1)
- sr_select = gr.Dropdown(["11025", "16000", "22050", "24000", "32000", "44100", "48000"], label="Output Audio Sample Rate", value="48000", interactive=True)
- with gr.Row():
- model = gr.Radio(["melody", "small", "medium", "large", "custom"], label="Model", value="large", interactive=True, scale=1)
- with gr.Column():
- dropdown = gr.Dropdown(choices=get_available_models(), value=("No models found" if len(get_available_models()) < 1 else get_available_models()[0]), label='Custom Model (models folder)', elem_classes='slim-dropdown', interactive=True)
- ui.create_refresh_button(dropdown, lambda: None, lambda: {'choices': get_available_models()}, 'refresh-button')
- basemodel = gr.Radio(["small", "medium", "melody", "large"], label="Base Model", value="medium", interactive=True, scale=1)
- with gr.Row():
- decoder = gr.Radio(["Default", "MultiBand_Diffusion"], label="Decoder", value="Default", interactive=True)
- with gr.Row():
- topk = gr.Number(label="Top-k", value=250, interactive=True)
- topp = gr.Number(label="Top-p", value=0, interactive=True)
- temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
- cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
- with gr.Row():
- submit = gr.Button("Generate", variant="primary")
- # Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
- _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
- with gr.Column() as c:
- with gr.Tab("Output"):
- output = gr.Video(label="Generated Music", scale=0)
- with gr.Row():
- audio_only = gr.Audio(type="numpy", label="Audio Only", interactive=False)
- backup_only = gr.Audio(type="numpy", label="Backup Audio", interactive=False, visible=False)
- send_audio = gr.Button("Send to Input Audio")
- seed_used = gr.Number(label='Seed used', value=-1, interactive=False)
- download = gr.File(label="Generated Files", interactive=False)
- with gr.Tab("Wiki"):
- gr.Markdown(
- """
- - **[Generate (button)]:**
- Generates the music with the given settings and prompts.
-
- - **[Interrupt (button)]:**
- Stops the music generation as soon as it can, providing an incomplete output.
-
- ---
-
- ### Generation Tab:
-
- #### Structure Prompts:
-
- This feature helps reduce repetetive prompts by allowing you to set global prompts
- that will be used for all prompt segments.
-
- - **[Structure Prompts (checkbox)]:**
- Enable/Disable the structure prompts feature.
-
- - **[BPM (number)]:**
- Beats per minute of the generated music.
-
- - **[Key (dropdown)]:**
- The key of the generated music.
-
- - **[Scale (dropdown)]:**
- The scale of the generated music.
-
- - **[Global Prompt (text)]:**
- Here write the prompt that you wish to be used for all prompt segments.
-
- #### Multi-Prompt:
-
- This feature allows you to control the music, adding variation to different time segments.
- You have up to 10 prompt segments. the first prompt will always be 30s long
- the other prompts will be [30s - overlap].
- for example if the overlap is 10s, each prompt segment will be 20s.
-
- - **[Prompt Segments (number)]:**
- Amount of unique prompt to generate throughout the music generation.
-
- - **[Prompt/Input Text (prompt)]:**
- Here describe the music you wish the model to generate.
-
- - **[Repeat (number)]:**
- Write how many times this prompt will repeat (instead of wasting another prompt segment on the same prompt).
-
- - **[Time (text)]:**
- The time of the prompt segment.
-
- - **[Calculate Timings (button)]:**
- Calculates the timings of the prompt segments.
-
- - **[Duration (number)]:**
- How long you want the generated music to be (in seconds).
-
- - **[Overlap (number)]:**
- How much each new segment will reference the previous segment (in seconds).
- For example, if you choose 20s: Each new segment after the first one will reference the previous segment 20s
- and will generate only 10s of new music. The model can only process 30s of music.
-
- - **[Seed (number)]:**
- Your generated music id. If you wish to generate the exact same music,
- place the exact seed with the exact prompts
- (This way you can also extend specific song that was generated short).
-
- - **[Random Seed (button)]:**
- Gives "-1" as a seed, which counts as a random seed.
-
- - **[Copy Previous Seed (button)]:**
- Copies the seed from the output seed (if you don't feel like doing it manualy).
-
- ---
-
- ### Audio Tab:
-
- - **[Input Type (selection)]:**
- `File` mode allows you to upload an audio file to use as input
- `Mic` mode allows you to use your microphone as input
-
- - **[Input Audio Mode (selection)]:**
- `Melody` mode only works with the melody model: it conditions the music generation to reference the melody
- `Sample` mode works with any model: it gives a music sample to the model to generate its continuation.
-
- - **[Trim Start and Trim End (numbers)]:**
- `Trim Start` set how much you'd like to trim the input audio from the start
- `Trim End` same as the above but from the end
-
- - **[Input Audio (audio file)]:**
- Input here the audio you wish to use with "melody" or "sample" mode.
-
- ---
-
- ### Customization Tab:
-
- - **[Background Color (color)]:**
- Works only if you don't upload image. Color of the background of the waveform.
-
- - **[Bar Color Start (color)]:**
- First color of the waveform bars.
-
- - **[Bar Color End (color)]:**
- Second color of the waveform bars.
-
- - **[Background Image (image)]:**
- Background image that you wish to be attached to the generated video along with the waveform.
-
- - **[Height and Width (numbers)]:**
- Output video resolution, only works with image.
- (minimum height and width is 256).
-
- ---
-
- ### Settings Tab:
-
- - **[Output Audio Channels (selection)]:**
- With this you can select the amount of channels that you wish for your output audio.
- `mono` is a straightforward single channel audio
- `stereo` is a dual channel audio but it will sound more or less like mono
- `stereo effect` this one is also dual channel but uses tricks to simulate a stereo audio.
-
- - **[Output Audio Sample Rate (dropdown)]:**
- The output audio sample rate, the model default is 32000.
-
- - **[Model (selection)]:**
- Here you can choose which model you wish to use:
- `melody` model is based on the medium model with a unique feature that lets you use melody conditioning
- `small` model is trained on 300M parameters
- `medium` model is trained on 1.5B parameters
- `large` model is trained on 3.3B parameters
- `custom` model runs the custom model that you provided.
-
- - **[Custom Model (selection)]:**
- This dropdown will show you models that are placed in the `models` folder
- you must select `custom` in the model options in order to use it.
-
- - **[Refresh (button)]:**
- Refreshes the dropdown list for custom model.
-
- - **[Base Model (selection)]:**
- Choose here the model that your custom model is based on.
-
- - **[Decoder (selection)]:**
- Choose here the decoder that you wish to use:
- `Default` is the default decoder
- `MultiBand_Diffusion` is a decoder that uses diffusion to generate the audio.
-
- - **[Top-k (number)]:**
- is a parameter used in text generation models, including music generation models. It determines the number of most likely next tokens to consider at each step of the generation process. The model ranks all possible tokens based on their predicted probabilities, and then selects the top-k tokens from the ranked list. The model then samples from this reduced set of tokens to determine the next token in the generated sequence. A smaller value of k results in a more focused and deterministic output, while a larger value of k allows for more diversity in the generated music.
-
- - **[Top-p (number)]:**
- also known as nucleus sampling or probabilistic sampling, is another method used for token selection during text generation. Instead of specifying a fixed number like top-k, top-p considers the cumulative probability distribution of the ranked tokens. It selects the smallest possible set of tokens whose cumulative probability exceeds a certain threshold (usually denoted as p). The model then samples from this set to choose the next token. This approach ensures that the generated output maintains a balance between diversity and coherence, as it allows for a varying number of tokens to be considered based on their probabilities.
-
- - **[Temperature (number)]:**
- is a parameter that controls the randomness of the generated output. It is applied during the sampling process, where a higher temperature value results in more random and diverse outputs, while a lower temperature value leads to more deterministic and focused outputs. In the context of music generation, a higher temperature can introduce more variability and creativity into the generated music, but it may also lead to less coherent or structured compositions. On the other hand, a lower temperature can produce more repetitive and predictable music.
-
- - **[Classifier Free Guidance (number)]:**
- refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
- """
- )
- with gr.Tab("AudioGen"):
- gr.Markdown(
- """
- ### AudioGen
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Tab("Generation"):
- with gr.Accordion("Structure Prompts", open=False):
- with gr.Row():
- struc_prompts_a = gr.Checkbox(label="Enable", value=False, interactive=True, container=False)
- global_prompt_a = gr.Text(label="Global Prompt", interactive=True, scale=3)
- with gr.Row():
- s_a = gr.Slider(1, max_textboxes, value=1, step=1, label="Prompts:", interactive=True, scale=2)
- with gr.Column():
- textboxes_a = []
- prompts_a = []
- repeats_a = []
- calcs_a = []
- with gr.Row():
- text0_a = gr.Text(label="Input Text", interactive=True, scale=4)
- prompts_a.append(text0_a)
- drag0_a = gr.Number(label="Repeat", value=1, interactive=True, scale=1)
- repeats_a.append(drag0_a)
- calc0_a = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- calcs_a.append(calc0_a)
- for i in range(max_textboxes):
- with gr.Row(visible=False) as t_a:
- text_a = gr.Text(label="Input Text", interactive=True, scale=3)
- repeat_a = gr.Number(label="Repeat", minimum=1, value=1, interactive=True, scale=1)
- calc_a = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- textboxes_a.append(t_a)
- prompts_a.append(text_a)
- repeats_a.append(repeat_a)
- calcs_a.append(calc_a)
- to_calc_a = gr.Button("Calculate Timings", variant="secondary")
- with gr.Row():
- duration_a = gr.Slider(minimum=1, maximum=300, value=10, step=1, label="Duration", interactive=True)
- with gr.Row():
- overlap_a = gr.Slider(minimum=1, maximum=9, value=2, step=1, label="Overlap", interactive=True)
- with gr.Row():
- seed_a = gr.Number(label="Seed", value=-1, scale=4, precision=0, interactive=True)
- gr.Button('\U0001f3b2\ufe0f', scale=1).click(fn=lambda: -1, outputs=[seed_a], queue=False)
- reuse_seed_a = gr.Button('\u267b\ufe0f', scale=1)
-
- with gr.Tab("Audio"):
- with gr.Row():
- with gr.Column():
- input_type_a = gr.Radio(["file", "mic"], value="file", label="Input Type (optional)", interactive=True)
- mode_a = gr.Radio(["sample"], label="Input Audio Mode (optional)", value="sample", interactive=False, visible=False)
- with gr.Row():
- trim_start_a = gr.Number(label="Trim Start", value=0, interactive=True)
- trim_end_a = gr.Number(label="Trim End", value=0, interactive=True)
- audio_a = gr.Audio(source="upload", type="numpy", label="Input Audio (optional)", interactive=True)
-
- with gr.Tab("Customization"):
- with gr.Row():
- with gr.Column():
- background_a = gr.ColorPicker(value="#0f0f0f", label="background color", interactive=True, scale=0)
- bar1_a = gr.ColorPicker(value="#84cc16", label="bar color start", interactive=True, scale=0)
- bar2_a = gr.ColorPicker(value="#10b981", label="bar color end", interactive=True, scale=0)
- with gr.Column():
- image_a = gr.Image(label="Background Image", type="filepath", interactive=True, scale=4)
- with gr.Row():
- height_a = gr.Number(label="Height", value=512, interactive=True)
- width_a = gr.Number(label="Width", value=768, interactive=True)
-
- with gr.Tab("Settings"):
- with gr.Row():
- channel_a = gr.Radio(["mono", "stereo", "stereo effect"], label="Output Audio Channels", value="stereo", interactive=True, scale=1)
- sr_select_a = gr.Dropdown(["11025", "16000", "22050", "24000", "32000", "44100", "48000"], label="Output Audio Sample Rate", value="48000", interactive=True)
- with gr.Row():
- model_a = gr.Radio(["medium"], label="Model", value="medium", interactive=False, visible=False)
- decoder_a = gr.Radio(["Default"], label="Decoder", value="Default", interactive=False, visible=False)
- with gr.Row():
- topk_a = gr.Number(label="Top-k", value=250, interactive=True)
- topp_a = gr.Number(label="Top-p", value=0, interactive=True)
- temperature_a = gr.Number(label="Temperature", value=1.0, interactive=True)
- cfg_coef_a = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
- with gr.Row():
- submit_a = gr.Button("Generate", variant="primary")
- _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
- with gr.Column():
- with gr.Tab("Output"):
- output_a = gr.Video(label="Generated Audio", scale=0)
- with gr.Row():
- audio_only_a = gr.Audio(type="numpy", label="Audio Only", interactive=False)
- backup_only_a = gr.Audio(type="numpy", label="Backup Audio", interactive=False, visible=False)
- send_audio_a = gr.Button("Send to Input Audio")
- seed_used_a = gr.Number(label='Seed used', value=-1, interactive=False)
- download_a = gr.File(label="Generated Files", interactive=False)
- with gr.Tab("Wiki"):
- gr.Markdown(
- """
- - **[Generate (button)]:**
- Generates the audio with the given settings and prompts.
-
- - **[Interrupt (button)]:**
- Stops the audio generation as soon as it can, providing an incomplete output.
-
- ---
-
- ### Generation Tab:
-
- #### Structure Prompts:
-
- This feature helps reduce repetetive prompts by allowing you to set global prompts
- that will be used for all prompt segments.
-
- - **[Structure Prompts (checkbox)]:**
- Enable/Disable the structure prompts feature.
-
- - **[Global Prompt (text)]:**
- Here write the prompt that you wish to be used for all prompt segments.
-
- #### Multi-Prompt:
-
- This feature allows you to control the audio, adding variation to different time segments.
- You have up to 10 prompt segments. the first prompt will always be 10s long
- the other prompts will be [10s - overlap].
- for example if the overlap is 2s, each prompt segment will be 8s.
-
- - **[Prompt Segments (number)]:**
- Amount of unique prompt to generate throughout the audio generation.
-
- - **[Prompt/Input Text (prompt)]:**
- Here describe the audio you wish the model to generate.
-
- - **[Repeat (number)]:**
- Write how many times this prompt will repeat (instead of wasting another prompt segment on the same prompt).
-
- - **[Time (text)]:**
- The time of the prompt segment.
-
- - **[Calculate Timings (button)]:**
- Calculates the timings of the prompt segments.
-
- - **[Duration (number)]:**
- How long you want the generated audio to be (in seconds).
-
- - **[Overlap (number)]:**
- How much each new segment will reference the previous segment (in seconds).
- For example, if you choose 2s: Each new segment after the first one will reference the previous segment 2s
- and will generate only 8s of new audio. The model can only process 10s of music.
-
- - **[Seed (number)]:**
- Your generated audio id. If you wish to generate the exact same audio,
- place the exact seed with the exact prompts
- (This way you can also extend specific song that was generated short).
-
- - **[Random Seed (button)]:**
- Gives "-1" as a seed, which counts as a random seed.
-
- - **[Copy Previous Seed (button)]:**
- Copies the seed from the output seed (if you don't feel like doing it manualy).
-
- ---
-
- ### Audio Tab:
-
- - **[Input Type (selection)]:**
- `File` mode allows you to upload an audio file to use as input
- `Mic` mode allows you to use your microphone as input
-
- - **[Trim Start and Trim End (numbers)]:**
- `Trim Start` set how much you'd like to trim the input audio from the start
- `Trim End` same as the above but from the end
-
- - **[Input Audio (audio file)]:**
- Input here the audio you wish to use.
-
- ---
-
- ### Customization Tab:
-
- - **[Background Color (color)]:**
- Works only if you don't upload image. Color of the background of the waveform.
-
- - **[Bar Color Start (color)]:**
- First color of the waveform bars.
-
- - **[Bar Color End (color)]:**
- Second color of the waveform bars.
-
- - **[Background Image (image)]:**
- Background image that you wish to be attached to the generated video along with the waveform.
-
- - **[Height and Width (numbers)]:**
- Output video resolution, only works with image.
- (minimum height and width is 256).
-
- ---
-
- ### Settings Tab:
-
- - **[Output Audio Channels (selection)]:**
- With this you can select the amount of channels that you wish for your output audio.
- `mono` is a straightforward single channel audio
- `stereo` is a dual channel audio but it will sound more or less like mono
- `stereo effect` this one is also dual channel but uses tricks to simulate a stereo audio.
-
- - **[Output Audio Sample Rate (dropdown)]:**
- The output audio sample rate, the model default is 32000.
-
- - **[Top-k (number)]:**
- is a parameter used in text generation models, including music generation models. It determines the number of most likely next tokens to consider at each step of the generation process. The model ranks all possible tokens based on their predicted probabilities, and then selects the top-k tokens from the ranked list. The model then samples from this reduced set of tokens to determine the next token in the generated sequence. A smaller value of k results in a more focused and deterministic output, while a larger value of k allows for more diversity in the generated music.
-
- - **[Top-p (number)]:**
- also known as nucleus sampling or probabilistic sampling, is another method used for token selection during text generation. Instead of specifying a fixed number like top-k, top-p considers the cumulative probability distribution of the ranked tokens. It selects the smallest possible set of tokens whose cumulative probability exceeds a certain threshold (usually denoted as p). The model then samples from this set to choose the next token. This approach ensures that the generated output maintains a balance between diversity and coherence, as it allows for a varying number of tokens to be considered based on their probabilities.
-
- - **[Temperature (number)]:**
- is a parameter that controls the randomness of the generated output. It is applied during the sampling process, where a higher temperature value results in more random and diverse outputs, while a lower temperature value leads to more deterministic and focused outputs. In the context of music generation, a higher temperature can introduce more variability and creativity into the generated music, but it may also lead to less coherent or structured compositions. On the other hand, a lower temperature can produce more repetitive and predictable music.
-
- - **[Classifier Free Guidance (number)]:**
- refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
- """
- )
- with gr.Tab("Audio Info"):
- gr.Markdown(
- """
- ### Audio Info
- """
- )
- with gr.Row():
- with gr.Column():
- in_audio = gr.File(type="file", label="Input Any Audio", interactive=True)
- with gr.Row():
- send_gen = gr.Button("Send to MusicGen", variant="primary")
- send_gen_a = gr.Button("Send to AudioGen", variant="primary")
- with gr.Column():
- info = gr.Textbox(label="Audio Info", lines=10, interactive=False)
- with gr.Tab("Changelog"):
- gr.Markdown(
- """
- ## Changelog:
-
- ### v2.0.0a
-
- - Forgot to move all the update to app.py from temp2.py... oops
-
-
-
- ### v2.0.0
-
- - Changed name from MusicGen+ to AudioCraft Plus
-
- - Complete overhaul of the repo "backend" with the latest changes from the main facebookresearch repo
-
- - Added a new decoder: MultiBand_Diffusion
-
- - Added AudioGen: a new tab for generating audio
-
-
-
- ### v1.2.8c
-
- - Implemented Reverse compatibility for audio info tab with previous versions
-
-
-
- ### v1.2.8b
-
- - Fixed the error when loading default models
-
-
-
- ### v1.2.8a
-
- - Adapted Audio info tab to work with the new structure prompts feature
-
- - Now custom models actually work, make sure you select the correct base model
-
-
-
- ### v1.2.8
-
- - Now you will also recieve json file with metadata of generated audio
-
- - Added error messages in Audio Info tab
-
- - Added structure prompts: you can select bpm, key and global prompt for all prompts
-
- - Added time display next to each prompt, can be calculated with "Calculate Timings" button
-
-
-
- ### v1.2.7
-
- - When sending generated audio to Input Audio, it will send a backup audio with default settings
- (best for continuos generation)
-
- - Added Metadata to generated audio (Thanks to AlexHK ♥)
-
- - Added Audio Info tab that will display the metadata of the input audio
-
- - Added "send to Text2Audio" button in Audio Info tab
-
- - Generated audio is now stored in the "output" folder (Thanks to AlexHK ♥)
-
- - Added an output area with generated files and download buttons
-
- - Enhanced Stereo effect (Thanks to AlexHK ♥)
-
-
-
- ### v1.2.6
-
- - Added option to generate in stereo (instead of only mono)
-
- - Added dropdown for selecting output sample rate (model default is 32000)
-
-
-
- ### v1.2.5a
-
- - Added file cleaner (This comes from the main facebookresearch repo)
-
- - Reorganized a little, moved audio to a seperate tab
-
-
-
- ### v1.2.5
-
- - Gave a unique lime theme to the webui
-
- - Added additional output for audio only
-
- - Added button to send generated audio to Input Audio
-
- - Added option to trim Input Audio
-
-
-
- ### v1.2.4
-
- - Added mic input (This comes from the main facebookresearch repo)
-
-
-
- ### v1.2.3
-
- - Added option to change video size to fit the image you upload
-
-
-
- ### v1.2.2
-
- - Added Wiki, Changelog and About tabs
-
-
-
- ### v1.2.1
-
- - Added tabs and organized the entire interface
-
- - Added option to attach image to the output video
-
- - Added option to load fine-tuned models (Yet to be tested)
-
-
-
- ### v1.2.0
-
- - Added Multi-Prompt
-
-
-
- ### v1.1.3
-
- - Added customization options for generated waveform
-
-
-
- ### v1.1.2
-
- - Removed sample length limit: now you can input audio of any length as music sample
-
-
-
- ### v1.1.1
-
- - Improved music sample audio quality when using music continuation
-
-
-
- ### v1.1.0
-
- - Rebuilt the repo on top of the latest structure of the main MusicGen repo
-
- - Improved Music continuation feature
-
-
-
- ### v1.0.0 - Stable Version
-
- - Added Music continuation
- """
- )
- with gr.Tab("About"):
- gen_type = gr.Text(value="music", interactive=False, visible=False)
- gen_type_a = gr.Text(value="audio", interactive=False, visible=False)
- gr.Markdown(
- """
- This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
- presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
-
- ## MusicGen+ is an extended version of the original MusicGen by facebookresearch.
-
- ### Repo: https://github.com/GrandaddyShmax/audiocraft_plus/tree/plus
-
- ---
-
- ### This project was possible thanks to:
-
- #### GrandaddyShmax - https://github.com/GrandaddyShmax
-
- #### Camenduru - https://github.com/camenduru
-
- #### rkfg - https://github.com/rkfg
-
- #### oobabooga - https://github.com/oobabooga
-
- #### AlexHK - https://github.com/alanhk147
- """
- )
-
- send_gen.click(info_to_params, inputs=[in_audio], outputs=[decoder, struc_prompts, global_prompt, bpm, key, scale, model, dropdown, basemodel, s, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select], queue=False)
- reuse_seed.click(fn=lambda x: x, inputs=[seed_used], outputs=[seed], queue=False)
- send_audio.click(fn=lambda x: x, inputs=[backup_only], outputs=[audio], queue=False)
- submit.click(predict_full, inputs=[gen_type, model, decoder, dropdown, basemodel, s, struc_prompts, bpm, key, scale, global_prompt, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select], outputs=[output, audio_only, backup_only, download, seed_used])
- input_type.change(toggle_audio_src, input_type, [audio], queue=False, show_progress=False)
- to_calc.click(calc_time, inputs=[gen_type, s, duration, overlap, repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9]], outputs=[calcs[0], calcs[1], calcs[2], calcs[3], calcs[4], calcs[5], calcs[6], calcs[7], calcs[8], calcs[9]], queue=False)
-
- send_gen_a.click(info_to_params_a, inputs=[in_audio], outputs=[decoder_a, struc_prompts_a, global_prompt_a, s_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, channel_a, sr_select_a], queue=False)
- reuse_seed_a.click(fn=lambda x: x, inputs=[seed_used_a], outputs=[seed_a], queue=False)
- send_audio_a.click(fn=lambda x: x, inputs=[backup_only_a], outputs=[audio_a], queue=False)
- submit_a.click(predict_full, inputs=[gen_type_a, model_a, decoder_a, dropdown, basemodel, s_a, struc_prompts_a, bpm, key, scale, global_prompt_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], audio_a, mode_a, trim_start_a, trim_end_a, duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, image_a, height_a, width_a, background_a, bar1_a, bar2_a, channel_a, sr_select_a], outputs=[output_a, audio_only_a, backup_only_a, download_a, seed_used_a])
- input_type_a.change(toggle_audio_src, input_type_a, [audio_a], queue=False, show_progress=False)
- to_calc_a.click(calc_time, inputs=[gen_type_a, s_a, duration_a, overlap_a, repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9]], outputs=[calcs_a[0], calcs_a[1], calcs_a[2], calcs_a[3], calcs_a[4], calcs_a[5], calcs_a[6], calcs_a[7], calcs_a[8], calcs_a[9]], queue=False)
-
- in_audio.change(get_audio_info, in_audio, outputs=[info])
-
- def variable_outputs(k):
- k = int(k) - 1
- return [gr.Textbox.update(visible=True)]*k + [gr.Textbox.update(visible=False)]*(max_textboxes-k)
- def get_size(image):
- if image is not None:
- img = Image.open(image)
- img_height = img.height
- img_width = img.width
- if (img_height%2) != 0:
- img_height = img_height + 1
- if (img_width%2) != 0:
- img_width = img_width + 1
- return img_height, img_width
- else:
- return 512, 768
-
- image.change(get_size, image, outputs=[height, width])
- image_a.change(get_size, image_a, outputs=[height_a, width_a])
- s.change(variable_outputs, s, textboxes)
- s_a.change(variable_outputs, s_a, textboxes_a)
- interface.queue().launch(**launch_kwargs)
-
-
-def ui_batched(launch_kwargs):
- with gr.Blocks() as demo:
- gr.Markdown(
- """
- # MusicGen
-
- This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
- a simple and controllable model for music generation
- presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
-
-
-
- for longer sequences, more control and no queue.
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Row():
- text = gr.Text(label="Describe your music", lines=2, interactive=True)
- with gr.Column():
- radio = gr.Radio(["file", "mic"], value="file",
- label="Condition on a melody (optional) File or Mic")
- melody = gr.Audio(source="upload", type="numpy", label="File",
- interactive=True, elem_id="melody-input")
- with gr.Row():
- submit = gr.Button("Generate")
- with gr.Column():
- output = gr.Video(label="Generated Music")
- audio_output = gr.Audio(label="Generated Music (wav)", type='filepath')
- submit.click(predict_batched, inputs=[text, melody],
- outputs=[output, audio_output], batch=True, max_batch_size=MAX_BATCH_SIZE)
- radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
- gr.Examples(
- fn=predict_batched,
- examples=[
- [
- "An 80s driving pop song with heavy drums and synth pads in the background",
- "./assets/bach.mp3",
- ],
- [
- "A cheerful country song with acoustic guitars",
- "./assets/bolero_ravel.mp3",
- ],
- [
- "90s rock song with electric guitar and heavy drums",
- None,
- ],
- [
- "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
- "./assets/bach.mp3",
- ],
- [
- "lofi slow bpm electro chill with organic samples",
- None,
- ],
- ],
- inputs=[text, melody],
- outputs=[output]
- )
- gr.Markdown("""
- ### More details
-
- The model will generate 12 seconds of audio based on the description you provided.
- You can optionally provide a reference audio from which a broad melody will be extracted.
- The model will then try to follow both the description and melody provided.
- All samples are generated with the `melody` model.
-
- You can also use your own GPU or a Google Colab by following the instructions on our repo.
-
- See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
- for more details.
- """)
-
- demo.queue(max_size=8 * 4).launch(**launch_kwargs)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument(
- '--listen',
- type=str,
- default='0.0.0.0' if 'SPACE_ID' in os.environ else '127.0.0.1',
- help='IP to listen on for connections to Gradio',
- )
- parser.add_argument(
- '--username', type=str, default='', help='Username for authentication'
- )
- parser.add_argument(
- '--password', type=str, default='', help='Password for authentication'
- )
- parser.add_argument(
- '--server_port',
- type=int,
- default=0,
- help='Port to run the server listener on',
- )
- parser.add_argument(
- '--inbrowser', action='store_true', help='Open in browser'
- )
- parser.add_argument(
- '--share', action='store_true', help='Share the gradio UI'
- )
- parser.add_argument(
- '--unload_model', action='store_true', help='Unload the model after every generation to save GPU memory'
- )
-
- parser.add_argument(
- '--unload_to_cpu', action='store_true', help='Move the model to main RAM after every generation to save GPU memory but reload faster than after full unload (see above)'
- )
-
- parser.add_argument(
- '--cache', action='store_true', help='Cache models in RAM to quickly switch between them'
- )
-
- args = parser.parse_args()
- UNLOAD_MODEL = args.unload_model
- MOVE_TO_CPU = args.unload_to_cpu
- if args.cache:
- MODELS = {}
-
- launch_kwargs = {}
- launch_kwargs['server_name'] = args.listen
-
- if args.username and args.password:
- launch_kwargs['auth'] = (args.username, args.password)
- if args.server_port:
- launch_kwargs['server_port'] = args.server_port
- if args.inbrowser:
- launch_kwargs['inbrowser'] = args.inbrowser
- if args.share:
- launch_kwargs['share'] = args.share
-
- # Show the interface
- if IS_BATCHED:
- global USE_DIFFUSION
- USE_DIFFUSION = False
- ui_batched(launch_kwargs)
- else:
- ui_full(launch_kwargs)
\ No newline at end of file
diff --git a/spaces/PunPk/AI_FallingAsleepDriving/README.md b/spaces/PunPk/AI_FallingAsleepDriving/README.md
deleted file mode 100644
index 0855483dc5cdc696af1fe1d3502b584b42fde086..0000000000000000000000000000000000000000
--- a/spaces/PunPk/AI_FallingAsleepDriving/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: AI FallingAsleepDriving
-emoji: 📉
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RMXK/RVC_HFF/tensorlowest.py b/spaces/RMXK/RVC_HFF/tensorlowest.py
deleted file mode 100644
index eccd4dbf3494434e59f7defaae6ab91797263b90..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/tensorlowest.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from tensorboard.backend.event_processing import event_accumulator
-
-import os
-from shutil import copy2
-from re import search as RSearch
-import pandas as pd
-from ast import literal_eval as LEval
-
-weights_dir = 'weights/'
-
-def find_biggest_tensorboard(tensordir):
- try:
- files = [f for f in os.listdir(tensordir) if f.endswith('.0')]
- if not files:
- print("No files with the '.0' extension found!")
- return
-
- max_size = 0
- biggest_file = ""
-
- for file in files:
- file_path = os.path.join(tensordir, file)
- if os.path.isfile(file_path):
- file_size = os.path.getsize(file_path)
- if file_size > max_size:
- max_size = file_size
- biggest_file = file
-
- return biggest_file
-
- except FileNotFoundError:
- print("Couldn't find your model!")
- return
-
-def main(model_name, save_freq, lastmdls):
- global lowestval_weight_dir, scl
-
- tensordir = os.path.join('logs', model_name)
- lowestval_weight_dir = os.path.join(tensordir, "lowestvals")
-
- latest_file = find_biggest_tensorboard(tensordir)
-
- if latest_file is None:
- print("Couldn't find a valid tensorboard file!")
- return
-
- tfile = os.path.join(tensordir, latest_file)
-
- ea = event_accumulator.EventAccumulator(tfile,
- size_guidance={
- event_accumulator.COMPRESSED_HISTOGRAMS: 500,
- event_accumulator.IMAGES: 4,
- event_accumulator.AUDIO: 4,
- event_accumulator.SCALARS: 0,
- event_accumulator.HISTOGRAMS: 1,
- })
-
- ea.Reload()
- ea.Tags()
-
- scl = ea.Scalars('loss/g/total')
-
- listwstep = {}
-
- for val in scl:
- if (val.step // save_freq) * save_freq in [val.step for val in scl]:
- listwstep[float(val.value)] = (val.step // save_freq) * save_freq
-
- lowest_vals = sorted(listwstep.keys())[:lastmdls]
-
- sorted_dict = {value: step for value, step in listwstep.items() if value in lowest_vals}
-
- return sorted_dict
-
-def selectweights(model_name, file_dict, weights_dir, lowestval_weight_dir):
- os.makedirs(lowestval_weight_dir, exist_ok=True)
- logdir = []
- files = []
- lbldict = {
- 'Values': {},
- 'Names': {}
- }
- weights_dir_path = os.path.join(weights_dir, "")
- low_val_path = os.path.join(os.getcwd(), os.path.join(lowestval_weight_dir, ""))
-
- try:
- file_dict = LEval(file_dict)
- except Exception as e:
- print(f"Error! {e}")
- return f"Couldn't load tensorboard file! {e}"
-
- weights = [f for f in os.scandir(weights_dir)]
- for key, value in file_dict.items():
- pattern = fr"^{model_name}_.*_s{value}\.pth$"
- matching_weights = [f.name for f in weights if f.is_file() and RSearch(pattern, f.name)]
- for weight in matching_weights:
- source_path = weights_dir_path + weight
- destination_path = os.path.join(lowestval_weight_dir, weight)
-
- copy2(source_path, destination_path)
-
- logdir.append(f"File = {weight} Value: {key}, Step: {value}")
-
- lbldict['Names'][weight] = weight
- lbldict['Values'][weight] = key
-
- files.append(low_val_path + weight)
-
- print(f"File = {weight} Value: {key}, Step: {value}")
-
- yield ('\n'.join(logdir), files, pd.DataFrame(lbldict))
-
-
- return ''.join(logdir), files, pd.DataFrame(lbldict)
-
-
-if __name__ == "__main__":
- model = str(input("Enter the name of the model: "))
- sav_freq = int(input("Enter save frequency of the model: "))
- ds = main(model, sav_freq)
-
- if ds: selectweights(model, ds, weights_dir, lowestval_weight_dir)
-
\ No newline at end of file
diff --git a/spaces/Rekanice/hf_minimal_sushi/README.md b/spaces/Rekanice/hf_minimal_sushi/README.md
deleted file mode 100644
index 4b038ea069e1817e717615da6679be0a913f0f5d..0000000000000000000000000000000000000000
--- a/spaces/Rekanice/hf_minimal_sushi/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Hf Minimal Sushi
-emoji: 🌍
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RoCobo/WiggleGAN/main.py b/spaces/RoCobo/WiggleGAN/main.py
deleted file mode 100644
index 097c3b9aed9be52619095419859205b71110555c..0000000000000000000000000000000000000000
--- a/spaces/RoCobo/WiggleGAN/main.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import argparse
-import os
-import torch
-from WiggleGAN import WiggleGAN
-#from MyACGAN import MyACGAN
-#from MyGAN import MyGAN
-
-"""parsing and configuration"""
-
-
-def parse_args():
- desc = "Pytorch implementation of GAN collections"
- parser = argparse.ArgumentParser(description=desc)
-
- parser.add_argument('--gan_type', type=str, default='WiggleGAN',
- choices=['MyACGAN', 'MyGAN', 'WiggleGAN'],
- help='The type of GAN')
- parser.add_argument('--dataset', type=str, default='4cam',
- choices=['mnist', 'fashion-mnist', 'cifar10', 'cifar100', 'svhn', 'stl10', 'lsun-bed', '4cam'],
- help='The name of dataset')
- parser.add_argument('--split', type=str, default='', help='The split flag for svhn and stl10')
- parser.add_argument('--epoch', type=int, default=50, help='The number of epochs to run')
- parser.add_argument('--batch_size', type=int, default=16, help='The size of batch')
- parser.add_argument('--input_size', type=int, default=10, help='The size of input image')
- parser.add_argument('--save_dir', type=str, default='models',
- help='Directory name to save the model')
- parser.add_argument('--result_dir', type=str, default='results', help='Directory name to save the generated images')
- parser.add_argument('--log_dir', type=str, default='logs', help='Directory name to save training logs')
- parser.add_argument('--lrG', type=float, default=0.0002)
- parser.add_argument('--lrD', type=float, default=0.001)
- parser.add_argument('--beta1', type=float, default=0.5)
- parser.add_argument('--beta2', type=float, default=0.999)
- parser.add_argument('--gpu_mode', type=str2bool, default=True)
- parser.add_argument('--benchmark_mode', type=str2bool, default=True)
- parser.add_argument('--cameras', type=int, default=2)
- parser.add_argument('--imageDim', type=int, default=128)
- parser.add_argument('--epochV', type=int, default=0)
- parser.add_argument('--cIm', type=int, default=4)
- parser.add_argument('--seedLoad', type=str, default="-0000")
- parser.add_argument('--zGF', type=float, default=0.2)
- parser.add_argument('--zDF', type=float, default=0.2)
- parser.add_argument('--bF', type=float, default=0.2)
- parser.add_argument('--expandGen', type=int, default=3)
- parser.add_argument('--expandDis', type=int, default=3)
- parser.add_argument('--wiggleDepth', type=int, default=-1)
- parser.add_argument('--visdom', type=str2bool, default=True)
- parser.add_argument('--lambdaL1', type=int, default=100)
- parser.add_argument('--clipping', type=float, default=-1)
- parser.add_argument('--depth', type=str2bool, default=True)
- parser.add_argument('--recreate', type=str2bool, default=False)
- parser.add_argument('--name_wiggle', type=str, default='wiggle-result')
-
- return check_args(parser.parse_args())
-
-
-"""checking arguments"""
-
-def str2bool(v):
- if isinstance(v, bool):
- return v
- if v.lower() in ('yes', 'true', 't', 'y', '1'):
- return True
- elif v.lower() in ('no', 'false', 'f', 'n', '0'):
- return False
- else:
- raise argparse.ArgumentTypeError('Boolean value expected.')
-
-
-def check_args(args):
- # --save_dir
- if not os.path.exists(args.save_dir):
- os.makedirs(args.save_dir)
-
- # --result_dir
- if not os.path.exists(args.result_dir):
- os.makedirs(args.result_dir)
-
- # --result_dir
- if not os.path.exists(args.log_dir):
- os.makedirs(args.log_dir)
-
- # --epoch
- try:
- assert args.epoch >= 1
- except:
- print('number of epochs must be larger than or equal to one')
-
- # --batch_size
- try:
- assert args.batch_size >= 1
- except:
- print('batch size must be larger than or equal to one')
-
- return args
-
-
-"""main"""
-
-
-def main():
- # parse arguments
- args = parse_args()
- if args is None:
- exit()
-
- if args.benchmark_mode:
- torch.backends.cudnn.benchmark = True
-
- # declare instance for GAN
- if args.gan_type == 'WiggleGAN':
- gan = WiggleGAN(args)
- #elif args.gan_type == 'MyACGAN':
- # gan = MyACGAN(args)
- #elif args.gan_type == 'MyGAN':
- # gan = MyGAN(args)
- else:
- raise Exception("[!] There is no option for " + args.gan_type)
-
- # launch the graph in a session
- if (args.wiggleDepth < 0 and not args.recreate):
- print(" [*] Training Starting!")
- gan.train()
- print(" [*] Training finished!")
- else:
- if not args.recreate:
- print(" [*] Wiggle Started!")
- gan.wiggleEf()
- print(" [*] Wiggle finished!")
- else:
- print(" [*] Dataset recreation Started")
- gan.recreate()
- print(" [*] Dataset recreation finished")
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py
deleted file mode 100644
index f2678b321c295bcceaef945111ac3524be19d6e4..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .misc import add_prefix
-
-__all__ = ['add_prefix']
diff --git a/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py b/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py
deleted file mode 100644
index cdd69f2a24d47166425d785ef2767708b44b6593..0000000000000000000000000000000000000000
--- a/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from config import output_path_video, output_path_audio
-from logger import logger
-from resource_loader.video_loader_interface import VideoLoaderInterface
-
-
-class UploadedMediaLoader(VideoLoaderInterface):
- def __init__(self, uploaded_stream, original_name, media_type='video'):
- self.uploaded_stream = uploaded_stream
- self.original_name = original_name
- self.media_type = media_type # 'video' or 'audio'
- self.media_id = None
- self.filename = None
- self.output_path = None
- self.extract_filename()
- self.set_output_path()
-
- def extract_filename(self):
- self.filename = self.original_name.split(" - ", 1)[1]
- self.media_id = self.filename.rsplit(".", 1)[0]
-
- def set_output_path(self):
- if self.media_type == 'video':
- self.output_path = output_path_video
- elif self.media_type == 'audio':
- self.output_path = output_path_audio
- else:
- raise ValueError("Invalid media type")
-
- def download(self):
- with open(f"{self.output_path}/{self.filename}", "wb") as f:
- f.write(self.uploaded_stream.getvalue())
- logger.info(f"{self.media_type.capitalize()} processed: {self.original_name}")
diff --git a/spaces/STF-R/docker-test3/app/templates/prediction.html b/spaces/STF-R/docker-test3/app/templates/prediction.html
deleted file mode 100644
index 4366614474cbe8347297ec3d57a7b8b7bf72684d..0000000000000000000000000000000000000000
--- a/spaces/STF-R/docker-test3/app/templates/prediction.html
+++ /dev/null
@@ -1,48 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
- VIVADATA | {{iris_type}}
-
-
-
-
-
-
-
-
-
-
-
-
-
- VIVADATA - Flask Demo
-
-
-
- 🏵️🌺🌸 It's a {{iris_type|title}} 🏵️🌺🌸
-
-
-
-
-
-
-
- Back
-
-
-
-
-
-
-
-
diff --git a/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py b/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py
deleted file mode 100644
index 3706c57541c1b7d9004957422b52cd1e2191ae68..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-# flake8: noqa
-
-from ..utils import DummyObject, requires_backends
-
-
-class LMSDiscreteScheduler(metaclass=DummyObject):
- _backends = ["scipy"]
-
- def __init__(self, *args, **kwargs):
- requires_backends(self, ["scipy"])
diff --git a/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py b/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py
deleted file mode 100644
index 2e25dd918f2714ae9a2c21d14401663c371b5f38..0000000000000000000000000000000000000000
--- a/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py
+++ /dev/null
@@ -1,704 +0,0 @@
-from typing import Callable, Optional, Tuple
-
-import flax.linen as nn
-import jax
-import jax.numpy as jnp
-from flax.core.frozen_dict import FrozenDict, unfreeze
-from jax import lax
-from jax.random import PRNGKey
-from transformers import GPT2Config, FlaxViTModel, ViTConfig
-from transformers.modeling_flax_outputs import (
- FlaxCausalLMOutputWithCrossAttentions,
- FlaxSeq2SeqLMOutput,
- FlaxSeq2SeqModelOutput,
-)
-from transformers.models.bart.modeling_flax_bart import (
- shift_tokens_right,
-)
-from .modeling_flax_gpt2 import (
- FlaxGPT2Module,
- FlaxGPT2Model,
- FlaxPreTrainedModel
-)
-from transformers.models.vit.modeling_flax_vit import FlaxViTModule
-
-from .configuration_vit_gpt2 import ViTGPT2Config
-
-
-class FlaxViTGPT2Module(nn.Module):
- config: ViTGPT2Config
- dtype: jnp.dtype = jnp.float32 # the dtype of the computation
-
- def setup(self):
-
- self.encoder = FlaxViTModule(self.config.vit_config, dtype=self.dtype)
- self.decoder = FlaxGPT2Module(self.config.gpt2_config, dtype=self.dtype)
-
- def _get_encoder_module(self):
- return self.encoder
-
- def _get_decoder_module(self):
- return self.decoder
-
- def __call__(
- self,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- output_attentions: bool = False,
- output_hidden_states: bool = False,
- return_dict: bool = True,
- deterministic: bool = True,
- ):
- encoder_outputs = self.encoder(
- pixel_values=pixel_values,
- deterministic=deterministic,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- decoder_outputs = self.decoder(
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_hidden_states=encoder_outputs[0],
- encoder_attention_mask=encoder_attention_mask,
- deterministic=deterministic,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict
- )
-
- return FlaxSeq2SeqModelOutput(
- last_hidden_state=decoder_outputs.last_hidden_state,
- decoder_hidden_states=decoder_outputs.hidden_states,
- decoder_attentions=decoder_outputs.attentions,
- cross_attentions=decoder_outputs.cross_attentions,
- encoder_last_hidden_state=encoder_outputs.last_hidden_state,
- encoder_hidden_states=encoder_outputs.hidden_states,
- encoder_attentions=encoder_outputs.attentions,
- )
-
-
-class FlaxViTGPT2ForConditionalGenerationModule(nn.Module):
- config: ViTGPT2Config
- dtype: jnp.dtype = jnp.float32
- bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
-
- def setup(self):
- self.model = FlaxViTGPT2Module(config=self.config, dtype=self.dtype)
- self.lm_head = nn.Dense(
- self.model.decoder.embed_dim,
- use_bias=False,
- dtype=self.dtype,
- kernel_init=jax.nn.initializers.normal(
- self.config.gpt2_config.initializer_range, self.dtype
- ),
- )
- self.final_logits_bias = self.param(
- "final_logits_bias", self.bias_init, (1, self.model.decoder.embed_dim)
- )
-
- def _get_encoder_module(self):
- return self.model.encoder
-
- def _get_decoder_module(self):
- return self.model.decoder
-
- def __call__(
- self,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- output_attentions: bool = False,
- output_hidden_states: bool = False,
- return_dict: bool = True,
- deterministic: bool = True,
- ):
- outputs = self.model(
- pixel_values=pixel_values,
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_attention_mask=encoder_attention_mask,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=deterministic,
- )
-
- hidden_states = outputs[0]
- lm_logits = self.lm_head(hidden_states)
- lm_logits += self.final_logits_bias
-
- if not return_dict:
- output = (lm_logits,) + outputs[1:]
- return output
-
- return FlaxSeq2SeqLMOutput(
- logits=lm_logits,
- decoder_hidden_states=outputs.decoder_hidden_states,
- decoder_attentions=outputs.decoder_attentions,
- cross_attentions=outputs.cross_attentions,
- encoder_last_hidden_state=outputs.encoder_last_hidden_state,
- encoder_hidden_states=outputs.encoder_hidden_states,
- encoder_attentions=outputs.encoder_attentions,
- )
-
-class FlaxViTGPT2PreTrainedModel(FlaxPreTrainedModel):
- config_class = ViTGPT2Config
- base_model_prefix: str = "model"
- module_class: nn.Module = None
-
- def __init__(
- self,
- config: ViTGPT2Config,
- input_shape: Tuple = None,
- seed: int = 0,
- dtype: jnp.dtype = jnp.float32,
- **kwargs,
- ):
- if input_shape is None:
- input_shape = (
- (1, config.vit_config.image_size, config.vit_config.image_size, 3),
- (1, 1),
- )
-
- module = self.module_class(config=config, dtype=dtype, **kwargs)
- super().__init__(
- config, module, input_shape=input_shape, seed=seed, dtype=dtype
- )
-
- def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
- # init input tensors
- pixel_values = jax.random.normal(rng, input_shape[0])
- # # make sure initialization pass will work for FlaxBartForSequenceClassificationModule
- # input_ids = jax.ops.index_update(input_ids, (..., -1), self.config.eos_token_id)
-
- input_ids = jnp.zeros(input_shape[1], dtype="i4")
- attention_mask = jnp.ones_like(input_ids)
-
- batch_size, sequence_length = input_ids.shape
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- params_rng, dropout_rng = jax.random.split(rng)
- rngs = {"params": params_rng, "dropout": dropout_rng}
-
- return self.module.init(
- rngs,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- )["params"]
-
- def init_cache(self, batch_size, max_length, encoder_outputs):
-
- input_ids = jnp.ones((batch_size, max_length), dtype="i4")
- attention_mask = jnp.ones_like(input_ids)
- position_ids = jnp.broadcast_to(
- jnp.arange(jnp.atleast_2d(input_ids).shape[-1]),
- input_ids.shape,
- )
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- return decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
-
- init_variables = self.module.init(
- jax.random.PRNGKey(0),
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_hidden_states=encoder_outputs[0],
- init_cache=True,
- method=_decoder_forward, # we only need to call the decoder to init the cache
- )
- return unfreeze(init_variables["cache"])
-
- def encode(
- self,
- pixel_values: jnp.ndarray,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- def _encoder_forward(module, pixel_values, **kwargs):
- encode_module = module._get_encoder_module()
- return encode_module(pixel_values, **kwargs)
-
- return self.module.apply(
- {"params": params or self.params},
- pixel_values=jnp.array(pixel_values, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- method=_encoder_forward,
- )
-
- def decode(
- self,
- input_ids,
- encoder_outputs,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- past_key_values: dict = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
-
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- encoder_hidden_states = encoder_outputs[0]
- if encoder_attention_mask is None:
- batch_size, sequence_length = encoder_hidden_states.shape[:2]
- encoder_attention_mask = jnp.ones((batch_size, sequence_length))
-
- batch_size, sequence_length = input_ids.shape
- if attention_mask is None:
- attention_mask = jnp.ones((batch_size, sequence_length))
-
- if position_ids is None:
- if past_key_values is not None:
- raise ValueError(
- "Make sure to provide `position_ids` when passing `past_key_values`."
- )
-
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- inputs = {"params": params or self.params}
-
- # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
- # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
- # it can be changed by FlaxGPT2Attention module
- if past_key_values:
- inputs["cache"] = past_key_values
- mutable = ["cache"]
- else:
- mutable = False
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- return decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
-
- outputs = self.module.apply(
- inputs,
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- mutable=mutable,
- method=_decoder_forward,
- )
-
- # add updated cache to model output
- if past_key_values is not None and return_dict:
- outputs, past = outputs
- outputs["past_key_values"] = unfreeze(past["cache"])
- return outputs
- elif past_key_values is not None and not return_dict:
- outputs, past = outputs
- outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
-
- return outputs
-
- def __call__(
- self,
- pixel_values: jnp.ndarray,
- input_ids: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
-
- # # prepare encoder inputs
- # if encoder_attention_mask is None:
- # encoder_attention_mask = jnp.ones_like(input_ids)
-
- # if position_ids is None:
- # batch_size, sequence_length = input_ids.shape
- # position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
-
- # prepare decoder inputs
- # if decoder_input_ids is None:
- # decoder_input_ids = shift_tokens_right(
- # input_ids, self.config.pad_token_id, decoder_start_token_id=self.config.decoder_start_token_id
- # ) # TODO: Check how to use this
-
- if attention_mask is None:
- attention_mask = jnp.ones_like(input_ids)
- if position_ids is None:
- batch_size, sequence_length = input_ids.shape
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
-
- return self.module.apply(
- {"params": params or self.params},
- pixel_values=jnp.array(pixel_values, dtype=jnp.float32),
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- )
-
-
-class FlaxViTGPT2ForConditionalGeneration(FlaxViTGPT2PreTrainedModel):
- module_class = FlaxViTGPT2ForConditionalGenerationModule
- dtype: jnp.dtype = jnp.float32
-
- def decode(
- self,
- input_ids,
- encoder_outputs,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- past_key_values: dict = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- deterministic: bool = True,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- encoder_hidden_states = encoder_outputs[0]
- if encoder_attention_mask is None:
- batch_size, sequence_length = encoder_hidden_states.shape[:2]
- encoder_attention_mask = jnp.ones((batch_size, sequence_length))
-
- batch_size, sequence_length = input_ids.shape
- if attention_mask is None:
- attention_mask = jnp.ones((batch_size, sequence_length))
-
- if position_ids is None:
- if past_key_values is not None:
- raise ValueError(
- "Make sure to provide `position_ids` when passing `past_key_values`."
- )
-
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- inputs = {"params": params or self.params}
-
- # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
- # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
- # it can be changed by FlaxGPT2Attention module
- if past_key_values:
- inputs["cache"] = past_key_values
- mutable = ["cache"]
- else:
- mutable = False
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- outputs = decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
- hidden_states = outputs[0]
-
- if self.config.tie_word_embeddings:
- shared_embedding = module.model.variables["params"]["shared"][
- "embedding"
- ]
- lm_logits = module.lm_head.apply(
- {"params": {"kernel": shared_embedding.T}}, hidden_states
- )
- else:
- lm_logits = module.lm_head(hidden_states)
-
- lm_logits += module.final_logits_bias
- return lm_logits, outputs
-
- outputs = self.module.apply(
- inputs,
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=deterministic,
- rngs=rngs,
- mutable=mutable,
- method=_decoder_forward,
- )
-
- if past_key_values is None:
- lm_logits, outputs = outputs
- else:
- (lm_logits, outputs), past = outputs
-
- if return_dict:
- outputs = FlaxCausalLMOutputWithCrossAttentions(
- logits=lm_logits,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- cross_attentions=outputs.cross_attentions,
- )
- else:
- outputs = (lm_logits,) + outputs[1:]
-
- # add updated cache to model output
- if past_key_values is not None and return_dict:
- outputs["past_key_values"] = unfreeze(past["cache"])
- return outputs
- elif past_key_values is not None and not return_dict:
- outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
-
- return outputs
-
- def prepare_inputs_for_generation(
- self,
- input_ids,
- max_length,
- encoder_attention_mask: Optional[jnp.DeviceArray] = None,
- attention_mask: Optional[jnp.DeviceArray] = None,
- encoder_outputs=None,
- **kwargs,
- ):
- # initializing the cache
- batch_size, seq_length = input_ids.shape
-
- past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
- # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
- # But since the decoder uses a causal mask, those positions are masked anyways.
- # Thus we can create a single static attention_mask here, which is more efficient for compilation
- extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
- if attention_mask is not None:
- position_ids = attention_mask.cumsum(axis=-1) - 1
- extended_attention_mask = lax.dynamic_update_slice(
- extended_attention_mask, attention_mask, (0, 0)
- )
- else:
- position_ids = jnp.broadcast_to(
- jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)
- )
-
- return {
- "past_key_values": past_key_values,
- "encoder_outputs": encoder_outputs,
- "encoder_attention_mask": encoder_attention_mask,
- "attention_mask": extended_attention_mask,
- "position_ids": position_ids,
- }
-
- def update_inputs_for_generation(self, model_outputs, model_kwargs):
- model_kwargs["past_key_values"] = model_outputs.past_key_values
- model_kwargs["position_ids"] = (
- model_kwargs["position_ids"][:, -1:] + 1
- )
- return model_kwargs
-
- @classmethod
- def from_vit_gpt2_pretrained(
- cls,
- vit_model_name_or_path: str = None,
- gpt2_model_name_or_path: str = None,
- *model_args,
- **kwargs,
- ) -> FlaxViTGPT2PreTrainedModel:
-
- kwargs_gpt2 = {
- argument[len("gpt2_") :]: value
- for argument, value in kwargs.items()
- if argument.startswith("gpt2_")
- }
-
- kwargs_vit = {
- argument[len("vit_") :]: value
- for argument, value in kwargs.items()
- if argument.startswith("vit_")
- }
-
- # remove gpt2, vit kwargs from kwargs
- for key in kwargs_gpt2.keys():
- del kwargs["gpt2_" + key]
- for key in kwargs_vit.keys():
- del kwargs["vit_" + key]
-
- # Load and initialize the gpt2 and vit model
- gpt2_model = kwargs_gpt2.pop("model", None)
- if gpt2_model is None:
- assert (
- gpt2_model_name_or_path is not None
- ), "If `model` is not defined as an argument, a `gpt2_model_name_or_path` has to be defined"
-
- if "config" not in kwargs_gpt2:
- gpt2_config = GPT2Config.from_pretrained(gpt2_model_name_or_path)
- kwargs_gpt2["config"] = gpt2_config
-
- kwargs_gpt2["config"].add_cross_attention = True
- gpt2_model = FlaxGPT2Model.from_pretrained(
- gpt2_model_name_or_path, *model_args, **kwargs_gpt2
- )
-
- vit_model = kwargs_vit.pop("model", None)
- if vit_model is None:
- assert (
- vit_model_name_or_path is not None
- ), "If `model` is not defined as an argument, a `vit_model_name_or_path` has to be defined"
-
- if "config" not in kwargs_vit:
- vit_config = ViTConfig.from_pretrained(vit_model_name_or_path)
- kwargs_vit["config"] = vit_config
-
- vit_model = FlaxViTModel.from_pretrained(
- vit_model_name_or_path, *model_args, **kwargs_vit
- )
-
- # instantiate config with corresponding kwargs
- dtype = kwargs.pop("dtype", jnp.float32)
- config = ViTGPT2Config.from_vit_gpt2_configs(
- vit_model.config, gpt2_model.config, **kwargs
- )
-
- # init model
- model = cls(config, *model_args, dtype=dtype, **kwargs)
- model.params["model"]["encoder"] = vit_model.params
- model.params["model"]["decoder"] = gpt2_model.params
-
- return model
-
diff --git a/spaces/Seetha/IMA-pipeline-streamlit/app.py b/spaces/Seetha/IMA-pipeline-streamlit/app.py
deleted file mode 100644
index f0db4170922ebf1b9a612db7fac952e61207154a..0000000000000000000000000000000000000000
--- a/spaces/Seetha/IMA-pipeline-streamlit/app.py
+++ /dev/null
@@ -1,581 +0,0 @@
-# import all packages
-import requests
-import streamlit as st
-from sklearn.model_selection import StratifiedKFold
-from sklearn.model_selection import train_test_split
-from sklearn.model_selection import KFold
-# tokenizer
-from transformers import AutoTokenizer, DistilBertTokenizerFast
-# sequence tagging model + training-related
-from transformers import DistilBertForTokenClassification, Trainer, TrainingArguments
-import torch
-import sys
-import os
-from sklearn.metrics import classification_report
-from pandas import read_csv
-from sklearn.linear_model import LogisticRegression
-import sklearn.model_selection
-from sklearn.feature_extraction.text import TfidfTransformer
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.pipeline import Pipeline, FeatureUnion
-import math
-# from sklearn.metrics import accuracy_score
-# from sklearn.metrics import precision_recall_fscore_support
-import json
-import re
-import numpy as np
-import pandas as pd
-import nltk
-nltk.download("punkt")
-import string
-from sklearn.model_selection import train_test_split
-from transformers import AutoTokenizer, Trainer, TrainingArguments, AutoModelForSequenceClassification, AutoConfig
-from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
-import itertools
-from transformers import TextClassificationPipeline, TFAutoModelForSequenceClassification, AutoTokenizer
-from transformers import pipeline
-import pickle
-import csv
-import pdfplumber
-import pathlib
-import shutil
-import webbrowser
-from streamlit.components.v1 import html
-import streamlit.components.v1 as components
-from PyPDF2 import PdfReader
-from huggingface_hub import HfApi
-import io
-from datasets import load_dataset
-import time
-
-import huggingface_hub
-from huggingface_hub import Repository
-from datetime import datetime
-import pathlib as Path
-from requests import get
-import urllib.request
-# import gradio as gr
-# from gradio import inputs, outputs
-from datasets import load_dataset
-from huggingface_hub import HfApi, list_models
-import os
-from huggingface_hub import HfFileSystem
-from tensorflow.keras.models import Sequential, model_from_json
-#import tensorflow_datasets as tfds
-import tensorflow as tf
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-import spacy
-from tensorflow.keras.preprocessing.text import Tokenizer
-#from spacy import en_core_web_lg
-#import en_core_web_lg
-#nlp = en_core_web_lg.load()
-nlp = spacy.load('en_core_web_sm')
-
-#tfds.disable_progress_bar()
-MAX_SEQUENCE_LENGTH = 500
-
-# dataset = load_dataset('Seetha/Visualization', streaming=True)
-# df = pd.DataFrame.from_dict(dataset['train'])
-# DATASET_REPO_URL = "https://huggingface.co/datasets/Seetha/Visualization"
-# DATA_FILENAME = "level2.json"
-#DATA_FILE = os.path.join("data", DATA_FILENAME)
-DATASET_REPO_URL = "https://huggingface.co/datasets/Seetha/visual_files"
-DATA_FILENAME = "detailedResults.json"
-DATA_FILENAME1 = "level2.json"
-
-HF_TOKEN = os.environ.get("HF_TOKEN")
-#st.write("is none?", HF_TOKEN is None)
-
-def main():
-
- st.title("Text to Causal Knowledge Graph")
- st.sidebar.title("Please upload your text documents in one file here:")
- k=2
- seed = 1
- k1= 5
- text_list = []
- causal_sents = []
-
- uploaded_file = None
- try:
- uploaded_file = st.sidebar.file_uploader("Choose a file", type = "pdf")
- except:
- uploaded_file = PdfReader('sample_anno.pdf')
- st.error("Please upload your own PDF to be analyzed")
-
- if uploaded_file is not None:
- reader = PdfReader(uploaded_file)
- for page in reader.pages:
- text = page.extract_text()
- text_list.append(text)
- else:
- st.error("Please upload your own PDF to be analyzed")
- st.stop()
-
- text_list_final = [x.replace('\n', '') for x in text_list]
- text_list_final = re.sub('"', '', str(text_list_final))
-
- sentences = nltk.sent_tokenize(text_list_final)
-
- result =[]
- for i in sentences:
- result1 = i.lower()
- result2 = re.sub(r'[^\w\s]','',result1)
- result.append(result2)
-
- #st.write("--- %s seconds ---" % (time.time() - start_time))
- tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") #bert-base-uncased
-
- model_path = "checkpoint-2850"
-
- model = AutoModelForSequenceClassification.from_pretrained(model_path,id2label={0:'non-causal',1:'causal'})
-
- #st.write('sequence classification loaded')
- pipe1 = pipeline("text-classification", model=model,tokenizer=tokenizer)
- for sent in result:
- pred = pipe1(sent)
- for lab in pred:
- if lab['label'] == 'causal': #causal
- causal_sents.append(sent)
-
- # st.write('causal sentence classification finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
-
- model_name = "distilbert-base-cased"
- tokenizer = DistilBertTokenizerFast.from_pretrained(model_name,low_cpu_mem_usage=True)
-
- model_path1 = "DistilBertforTokenclassification"
-
- model = DistilBertForTokenClassification.from_pretrained(model_path1,low_cpu_mem_usage=True) #len(unique_tags),, num_labels= 7, , id2label={0:'CT',1:'E',2:'C',3:'O'}
- pipe = pipeline('ner', model=model, tokenizer=tokenizer,aggregation_strategy='simple') #grouped_entities=True
- st.write('DistilBERT loaded')
- sentence_pred = []
- class_list = []
- entity_list = []
- for k in causal_sents:
- pred= pipe(k)
- #st.write(pred)
- #st.write('preds')
- for i in pred:
- sentence_pred.append(k)
- class_list.append(i['word'])
- entity_list.append(i['entity_group'])
-
- # st.write('causality extraction finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
-
- # filename = 'Checkpoint-classification.sav'
- # loaded_model = pickle.load(open(filename, 'rb'))
- # loaded_vectorizer = pickle.load(open('vectorizefile_classification.pickle', 'rb'))
-
- # pipeline_test_output = loaded_vectorizer.transform(class_list)
- # predicted = loaded_model.predict(pipeline_test_output)
-
- tokenizer = Tokenizer(num_words=100000)
- tokenizer.fit_on_texts(class_list)
- word_index = tokenizer.word_index
- # text_embedding = np.zeros((len(word_index) + 1, 300))
- # for word, i in word_index.items():
- # text_embedding[i] = nlp(word).vector
- json_file = open('model.json', 'r')
- loaded_model_json = json_file.read()
- json_file.close()
- loaded_model = model_from_json(loaded_model_json)
- # load weights into new model
- loaded_model.load_weights("model.h5")
-
- loss = tf.keras.losses.CategoricalCrossentropy() #from_logits=True
- loaded_model.compile(loss=loss,optimizer=tf.keras.optimizers.Adam(1e-4))
-
- predictions = loaded_model.predict(pad_sequences(tokenizer.texts_to_sequences(class_list),maxlen=MAX_SEQUENCE_LENGTH))
- predicted = np.argmax(predictions,axis=1)
-
- st.write(predictions)
- st.write(predicted)
- # st.write('stakeholder taxonomy finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
- pred1 = predicted
- level0 = []
- count =0
- for i in predicted:
- if i == 3:
- level0.append('Non-Performance')
- count +=1
- else:
- level0.append('Performance')
- count +=1
-
- list_pred = {0: 'Customers',1:'Employees',2:'Investors',3:'Non-performance',4:'Society',5:'Unclassified'}
- pred_val = [list_pred[i] for i in pred1]
-
- #print('count',count)
- for ind,(sent,preds) in enumerate(zip(class_list,pred_val)):
- if 'customers' in sent or 'client' in sent or 'consumer' in sent or 'user' in sent:
- pred_val[ind] = 'Customers'
- elif 'investor' in sent or 'finance' in sent or 'shareholder' in sent or 'stockholder' in sent or 'owners' in sent:
- pred_val[ind] = 'Investors'
- elif 'employee' in sent or 'worker' in sent or 'staff' in sent:
- pred_val[ind] = 'Employees'
- elif 'society' in sent or 'societal' in sent or 'social responsib*' in sent or 'social performance' in sent or 'community' in sent:
- pred_val[ind] = 'Society'
-
- sent_id, unique = pd.factorize(sentence_pred)
-
- final_list = pd.DataFrame(
- {'Id': sent_id,
- 'Fullsentence': sentence_pred,
- 'Component': class_list,
- 'causeOrEffect': entity_list,
- 'Labellevel1': level0,
- 'Labellevel2': pred_val
- })
- s = final_list['Component'].shift(-1)
- m = s.str.startswith('##', na=False)
- final_list.loc[m, 'Component'] += (' ' + s[m])
-
-
- final_list1 = final_list[~final_list['Component'].astype(str).str.startswith('##')]
- li = []
- uni = final_list1['Id'].unique()
- for i in uni:
- df_new = final_list1[final_list1['Id'] == i]
- uni1 = df_new['Id'].unique()
-
- # if 'E' not in df_new.values:
- # li.append(uni1)
- # out = np.concatenate(li).ravel()
- # li_pan = pd.DataFrame(out,columns=['Id'])
- # df3 = pd.merge(final_list1, li_pan[['Id']], on='Id', how='left', indicator=True) \
- # .query("_merge == 'left_only'") \
- # .drop("_merge",axis=1)
-
- df3 = final_list1
- #df = df3.groupby(['Id','Fullsentence','causeOrEffect', 'Labellevel1', 'Labellevel2'])['Component'].apply(', '.join).reset_index()
- #st.write(df)
-
- #df = df3
- df3["causeOrEffect"].replace({"C": "cause", "E": "effect"}, inplace=True)
- df_final = df3[df3['causeOrEffect'] != 'CT']
- df3['New string'] = df_final['Component'].replace(r'[##]+', ' ', regex=True)
-
- df_final = df_final.drop("Component",axis=1)
- df_final.insert(2, "Component", df3['New string'], True)
-
- df_final1 = df_final[df_final['Component'].str.split().str.len().gt(1)]
- #st.write(df_final[df_final['Component'].str.len() != 1])
- #df_final1.to_csv('predictions.csv')
-
-# buffer = io.BytesIO()
-# with pd.ExcelWriter(buffer, engine="xlsxwriter") as writer:
-# df_final.to_excel(writer, sheet_name="Sheet1", index=False)
-# writer.close()
-
- count_NP_NP = 0
- count_NP_investor = 0
- count_NP_customer = 0
- count_NP_employees = 0
- count_NP_society = 0
-
- count_inv_np = 0
- count_inv_investor = 0
- count_inv_customer = 0
- count_inv_employee = 0
- count_inv_society = 0
-
- count_cus_np = 0
- count_cus_investor = 0
- count_cus_customer = 0
- count_cus_employee = 0
- count_cus_society = 0
-
- count_emp_np = 0
- count_emp_investor = 0
- count_emp_customer = 0
- count_emp_employee = 0
- count_emp_society = 0
-
- count_soc_np = 0
- count_soc_investor = 0
- count_soc_customer = 0
- count_soc_employee = 0
- count_soc_society = 0
- for i in range(0,df_final['Id'].max()):
- j = df_final.loc[df_final['Id'] == i]
- cause_tab = j.loc[j['causeOrEffect'] == 'cause']
- effect_tab = j.loc[j['causeOrEffect'] == 'effect']
- cause_coun_NP = (cause_tab.Labellevel2 == 'Non-performance').sum()
- effect_coun_NP = (effect_tab.Labellevel2 == 'Non-performance').sum()
-
- if (cause_coun_NP > 0) and (effect_coun_NP > 0):
- count_NP = cause_coun_NP if cause_coun_NP >= effect_coun_NP else effect_coun_NP
- else:
- count_NP = 0
- effect_NP_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_NP > 0) and (effect_NP_inv > 0):
- count_NP_inv = cause_coun_NP if cause_coun_NP >= effect_NP_inv else effect_NP_inv
- else:
- count_NP_inv = 0
- effect_NP_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_NP > 0) and (effect_NP_cus > 0):
- count_NP_cus = cause_coun_NP if cause_coun_NP >= effect_NP_cus else effect_NP_cus
- else:
- count_NP_cus = 0
- effect_NP_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_NP > 0) and (effect_NP_emp > 0):
- count_NP_emp = cause_coun_NP if cause_coun_NP >= effect_NP_emp else effect_NP_emp
- else:
- count_NP_emp = 0
- effect_NP_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_NP > 0) and (effect_NP_soc > 0):
- count_NP_soc = cause_coun_NP if cause_coun_NP >= effect_NP_soc else effect_NP_soc
- else:
- count_NP_soc = 0
-
- cause_coun_inv = (cause_tab.Labellevel2 == 'Investors').sum()
- effect_coun_inv = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_inv > 0) and (effect_coun_inv > 0):
- count_NP_inv = cause_coun_inv if cause_coun_inv >= effect_coun_inv else effect_coun_inv
- else:
- count_NP_inv = 0
-
- effect_inv_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_inv > 0) and (effect_inv_inv > 0):
- count_inv_inv = cause_coun_inv if cause_coun_inv >= effect_inv_inv else effect_inv_inv
- else:
- count_inv_inv = 0
- effect_inv_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_inv > 0) and (effect_inv_cus > 0):
- count_inv_cus = cause_coun_inv if cause_coun_inv >= effect_inv_cus else effect_inv_cus
- else:
- count_inv_cus = 0
- effect_inv_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_inv > 0) and (effect_inv_emp > 0):
- count_inv_emp = cause_coun_inv if cause_coun_inv >= effect_inv_emp else effect_inv_emp
- else:
- count_inv_emp = 0
-
- effect_inv_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_inv > 0) and (effect_inv_soc > 0):
- count_inv_soc = cause_coun_inv if cause_coun_inv >= effect_inv_soc else effect_inv_soc
- else:
- count_inv_soc = 0
-
- cause_coun_cus = (cause_tab.Labellevel2 == 'Customers').sum()
- effect_coun_cus = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_cus > 0) and (effect_coun_cus > 0):
- count_NP_cus = cause_coun_cus if cause_coun_cus >= effect_coun_cus else effect_coun_cus
- else:
- count_NP_cus = 0
-
- effect_cus_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_cus > 0) and (effect_cus_inv > 0):
- count_cus_inv = cause_coun_cus if cause_coun_cus >= effect_cus_inv else effect_cus_inv
- else:
- count_cus_inv = 0
-
- effect_cus_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_cus > 0) and (effect_cus_cus > 0):
- count_cus_cus = cause_coun_cus if cause_coun_cus >= effect_cus_cus else effect_cus_cus
- else:
- count_cus_cus = 0
-
- effect_cus_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_cus > 0) and (effect_cus_emp > 0):
- count_cus_emp = cause_coun_cus if cause_coun_cus >= effect_cus_emp else effect_cus_emp
- else:
- count_cus_emp = 0
-
- effect_cus_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_cus > 0) and (effect_cus_soc > 0):
- count_cus_soc = cause_coun_cus if cause_coun_cus >= effect_cus_soc else effect_cus_soc
- else:
- count_cus_soc = 0
-
- cause_coun_emp = (cause_tab.Labellevel2 == 'Employees').sum()
- effect_coun_emp = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_emp > 0) and (effect_coun_emp > 0):
- count_NP_emp = cause_coun_emp if cause_coun_emp >= effect_coun_emp else effect_coun_emp
- else:
- count_NP_emp = 0
-
- effect_emp_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_emp > 0) and (effect_emp_inv > 0):
- count_emp_inv = cause_coun_emp if cause_coun_emp >= effect_emp_inv else effect_emp_inv
- else:
- count_emp_inv = 0
-
- effect_emp_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_emp > 0) and (effect_emp_cus > 0):
- count_emp_cus = cause_coun_emp if cause_coun_emp >= effect_emp_cus else effect_emp_cus
- else:
- count_emp_cus = 0
-
- effect_emp_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_emp > 0) and (effect_emp_emp > 0):
- count_emp_emp = cause_coun_emp if cause_coun_emp >= effect_emp_emp else effect_emp_emp
- else:
- count_emp_emp = 0
-
- effect_emp_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_emp > 0) and (effect_emp_soc > 0):
- count_emp_soc = cause_coun_emp if cause_coun_emp >= effect_emp_soc else effect_emp_soc
- else:
- count_emp_soc = 0
-
- cause_coun_soc = (cause_tab.Labellevel2 == 'Society').sum()
- effect_coun_soc = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_soc > 0) and (effect_coun_soc > 0):
- count_NP_soc = cause_coun_soc if cause_coun_soc >= effect_coun_soc else effect_coun_soc
- else:
- count_NP_soc = 0
-
- effect_soc_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_soc > 0) and (effect_soc_inv > 0):
- count_soc_inv = cause_coun_soc if cause_coun_soc >= effect_soc_inv else effect_soc_inv
- else:
- count_soc_inv = 0
-
- effect_soc_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_soc > 0) and (effect_soc_cus > 0):
- count_soc_cus = cause_coun_soc if cause_coun_soc >= effect_soc_cus else effect_soc_cus
- else:
- count_soc_cus = 0
-
- effect_soc_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_soc > 0) and (effect_soc_emp > 0):
- count_soc_emp = cause_coun_soc if cause_coun_soc >= effect_soc_emp else effect_soc_emp
- else:
- count_soc_emp = 0
-
- effect_soc_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_soc > 0) and (effect_soc_soc > 0):
- count_soc_soc = cause_coun_soc if cause_coun_soc >= effect_soc_soc else effect_soc_soc
- else:
- count_soc_soc = 0
-
- count_NP_NP = count_NP_NP + count_NP
- count_NP_investor = count_NP_investor + count_NP_inv
- count_NP_customer = count_NP_customer + count_NP_cus
- count_NP_employees = count_NP_employees + count_NP_emp
- count_NP_society = count_NP_society + count_NP_soc
-
- count_inv_np = count_inv_np + count_NP_inv
- count_inv_investor = count_inv_investor + count_inv_inv
- count_inv_customer = count_inv_customer + count_inv_cus
- count_inv_employee = count_inv_employee + count_inv_emp
- count_inv_society = count_inv_society + count_inv_soc
-
- count_cus_np = count_cus_np + count_NP_cus
- count_cus_investor = count_cus_investor + count_cus_inv
- count_cus_customer = count_cus_customer + count_cus_cus
- count_cus_employee = count_cus_employee + count_cus_emp
- count_cus_society = count_cus_society + count_cus_soc
-
- count_emp_np = count_emp_np + count_NP_emp
- count_emp_investor = count_emp_investor + count_emp_inv
- count_emp_customer = count_emp_customer + count_emp_cus
- count_emp_employee = count_emp_employee + count_emp_emp
- count_emp_society = count_emp_society + count_emp_soc
-
- count_soc_np = count_soc_np + count_NP_soc
- count_soc_investor = count_soc_investor + count_soc_inv
- count_soc_customer = count_soc_customer + count_soc_cus
- count_soc_employee = count_soc_employee + count_soc_emp
- count_soc_society = count_soc_society + count_soc_soc
-
- df_tab = pd.DataFrame(columns = ['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'],index=['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'], dtype=object)
-
- df_tab.loc['Non-performance'] = [count_NP_NP, count_NP_investor, count_NP_customer, count_NP_employees, count_NP_society]
- df_tab.loc['Investors'] = [count_inv_np, count_inv_investor, count_inv_customer, count_inv_employee, count_inv_society]
- df_tab.loc['Customers'] = [count_cus_np, count_cus_investor, count_cus_customer, count_cus_employee, count_cus_society]
- df_tab.loc['Employees'] = [count_emp_np, count_emp_investor, count_emp_customer, count_emp_employee, count_emp_society]
- df_tab.loc['Society'] = [count_soc_np, count_soc_investor, count_soc_customer, count_soc_employee, count_soc_society]
-
-
-# df_tab = pd.DataFrame({
-# 'Non-performance': [count_NP_NP, count_NP_investor, count_NP_customer, count_NP_employees, count_NP_society],
-# 'Investors': [count_inv_np, count_inv_investor, count_inv_customer, count_inv_employee, count_inv_society],
-# 'Customers': [count_cus_np, count_cus_investor, count_cus_customer, count_cus_employee, count_cus_society],
-# 'Employees': [count_emp_np, count_emp_investor, count_emp_customer, count_emp_employee, count_emp_society],
-# 'Society': [count_soc_np, count_soc_investor, count_soc_customer, count_soc_employee, count_soc_society]},
-# index=['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'])
-
- #df_tab.to_csv('final_data.csv')
-
- buffer = io.BytesIO()
- with pd.ExcelWriter(buffer, engine="xlsxwriter") as writer:
- df_tab.to_excel(writer,sheet_name="count_result",index=False)
- df_final1.to_excel(writer,sheet_name="Detailed_results",index=False)
- writer.close()
- #df = pd.read_csv('final_data.csv', index_col=0)
-#474-515
- # Convert to JSON format
- json_data = []
- for row in df_tab.index:
- for col in df_tab.columns:
- json_data.append({
- 'source': row,
- 'target': col,
- 'value': int(df_tab.loc[row, col])
- })
-
- HfApi().delete_file(path_in_repo = DATA_FILENAME1 ,repo_id = 'Seetha/visual_files',token= HF_TOKEN,repo_type='dataset')
- #st.write('file-deleted')
- fs = HfFileSystem(token=HF_TOKEN)
- with fs.open('datasets/Seetha/visual_files/level2.json', 'w') as f:
- json.dump(json_data, f)
-
- df_final1.to_csv('predictions.csv')
- csv_file = "predictions.csv"
- json_file = "detailedResults.json"
-
- # Open the CSV file and read the data
- with open(csv_file, "r") as f:
- csv_data = csv.DictReader(f)
-
- # # Convert the CSV data to a list of dictionaries
- data_list = []
- for row in csv_data:
- data_list.append(dict(row))
-
- # # Convert the list of dictionaries to JSON
- json_data = json.dumps(data_list)
-
- HfApi().delete_file(path_in_repo = DATA_FILENAME ,repo_id = 'Seetha/visual_files',token= HF_TOKEN,repo_type='dataset')
- #st.write('file2-deleted')
- with fs.open('datasets/Seetha/visual_files/detailedResults.json','w') as fi:
- #data = json.load(fi)
- fi.write(json_data)
-
- def convert_df(df):
-
- #IMPORTANT: Cache the conversion to prevent computation on every rerun
-
- return df.to_csv().encode('utf-8')
-
-
-
- csv1 = convert_df(df_final1.astype(str))
- csv2 = convert_df(df_tab.astype(str))
-
- with st.container():
-
- st.download_button(label="Download the result table",data=buffer,file_name="t2cg_outputs.xlsx",mime="application/vnd.ms-excel")
- st.markdown('Click this link in a separate tab to view knowledge graph', unsafe_allow_html=True)
- # st.download_button(label="Download the detailed result table_csv",data=csv1,file_name='results.csv',mime='text/csv')
- # st.download_button(label="Download the result table_csv",data=csv2,file_name='final_data.csv',mime='text/csv')
-
-#with st.container():
- # Execute your app
- #st.title("Visualization example")
-# components.html(source_code)
- #html(my_html)
- #webbrowser.open('https://huggingface.co/spaces/Seetha/visual-knowledgegraph')
-# # embed streamlit docs in a streamlit app
-# #components.iframe("https://webpages.charlotte.edu/ltotapal/")
-
-
-
-if __name__ == '__main__':
- start_time = time.time()
- main()
diff --git a/spaces/Shiro26/MendoBERT_RE/app.py b/spaces/Shiro26/MendoBERT_RE/app.py
deleted file mode 100644
index 1bf256334d6604b5048b7f1054c9cb1915120f15..0000000000000000000000000000000000000000
--- a/spaces/Shiro26/MendoBERT_RE/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-
-model = pipeline("text-classification", model="/home/user/app/MendoBERT/", tokenizer="indolem/indobert-base-uncased")
-basemodel = pipeline("text-classification", model="/home/user/app/IndoLEM/", tokenizer="indolem/indobert-base-uncased")
-
-st.title(':blue[MendoBERT] - Relation Extraction :family:')
-
-if 'options' not in st.session_state:
- st.session_state['options'] = ""
-
-def button1_callback():
- st.session_state['options'] = "Kami menyimpulkan bahwa polimorfisme @GENE$ dan AGT tidak berkontribusi pada kerentanan genetik terhadap @DISEASE$ dan retinopati pada populasi Kaukasia Mediterania."
-def button2_callback():
- st.session_state['options'] = "Genotipe bayi PON1 RR dan @GENE$ CC dikaitkan dengan @DISEASE$ dalam populasi penelitian kami, yang menunjukkan kemungkinan peran variabilitas paraoxonase manusia dalam etiologi kelahiran prematur."
-
-
-placeholder = st.empty()
-
-st.info("Please replace the gene and disease that you want to get their relation predicted with @GENE\$ and \@DISEASE\$", icon="ℹ️")
-
-st.caption('_Examples_')
-st.button('Kami menyimpulkan bahwa polimorfisme \@GENE\$ dan AGT tidak berkontribusi pada kerentanan genetik terhadap \@DISEASE\$ dan retinopati pada populasi Kaukasia Mediterania.', use_container_width=True, on_click = button1_callback)
-st.button('Genotipe bayi PON1 RR dan \@GENE\$ CC dikaitkan dengan \@DISEASE\$ dalam populasi penelitian kami, yang menunjukkan kemungkinan peran variabilitas paraoxonase manusia dalam etiologi kelahiran prematur.', use_container_width=True, on_click = button2_callback)
-
-with placeholder:
- text = st.text_area('Enter some text: ', key = 'options')
-
-if text:
- st.subheader('MendoBERT')
- st.write(model(text))
- st.write("\n")
- st.subheader('IndoLEM')
- st.write(basemodel(text))
\ No newline at end of file
diff --git a/spaces/Shredder/CONBERT-2/README.md b/spaces/Shredder/CONBERT-2/README.md
deleted file mode 100644
index daf10e5da8dd31671c54c18d24b0e7d1d77b62f0..0000000000000000000000000000000000000000
--- a/spaces/Shredder/CONBERT-2/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: CONBERT 2
-emoji: 👁
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Stearns/Soar/soar_io.py b/spaces/Stearns/Soar/soar_io.py
deleted file mode 100644
index c05d97417b69a804b35207fb839957a91258a5d2..0000000000000000000000000000000000000000
--- a/spaces/Stearns/Soar/soar_io.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from pysoarlib import *
-
-## DEFINE MAIN CONNECTOR CLASS
-
-class GeneralSoarIOConnector(AgentConnector):
- def __init__(self, client):
- AgentConnector.__init__(self, client)
- client.add_print_event_handler(self.agent_print_collector)
- # client.execute_command("output callbacks on")
- # client.execute_command("output console on")
-
- self.agent_printout = ""
-
- def on_input_phase(self, input_link):
- pass
-
- def on_init_soar(self):
- self.reset()
-
- def on_output_event(self, command_name, root_id):
- pass
-
-
- def agent_print_collector(self, text):
- self.agent_printout += text+"\n"
-
- def get_agent_output(self):
- return self.agent_printout
-
- def reset(self):
- self.agent_printout = ""
-
-
-
-
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py
deleted file mode 100644
index 489b7c13d0ce82b769ba50412ecde0889d0b7851..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# encoding: utf-8
-"""
-Utilities for working with external processes.
-"""
-
-# Copyright (c) IPython Development Team.
-# Distributed under the terms of the Modified BSD License.
-
-
-import os
-import shutil
-import sys
-
-if sys.platform == 'win32':
- from ._process_win32 import system, getoutput, arg_split, check_pid
-elif sys.platform == 'cli':
- from ._process_cli import system, getoutput, arg_split, check_pid
-else:
- from ._process_posix import system, getoutput, arg_split, check_pid
-
-from ._process_common import getoutputerror, get_output_error_code, process_handler
-
-
-class FindCmdError(Exception):
- pass
-
-
-def find_cmd(cmd):
- """Find absolute path to executable cmd in a cross platform manner.
-
- This function tries to determine the full path to a command line program
- using `which` on Unix/Linux/OS X and `win32api` on Windows. Most of the
- time it will use the version that is first on the users `PATH`.
-
- Warning, don't use this to find IPython command line programs as there
- is a risk you will find the wrong one. Instead find those using the
- following code and looking for the application itself::
-
- import sys
- argv = [sys.executable, '-m', 'IPython']
-
- Parameters
- ----------
- cmd : str
- The command line program to look for.
- """
- path = shutil.which(cmd)
- if path is None:
- raise FindCmdError('command could not be found: %s' % cmd)
- return path
-
-
-def abbrev_cwd():
- """ Return abbreviated version of cwd, e.g. d:mydir """
- cwd = os.getcwd().replace('\\','/')
- drivepart = ''
- tail = cwd
- if sys.platform == 'win32':
- if len(cwd) < 4:
- return cwd
- drivepart,tail = os.path.splitdrive(cwd)
-
-
- parts = tail.split('/')
- if len(parts) > 2:
- tail = '/'.join(parts[-2:])
-
- return (drivepart + (
- cwd == '/' and '/' or tail))
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py
deleted file mode 100644
index 37d3db222ef2c7920628971a92e863d9915514c6..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import sys
-from typing import Any, Dict, List, Optional, Generic, TypeVar, cast
-from types import TracebackType
-
-if sys.version_info >= (3, 8):
- from importlib.metadata import entry_points
-else:
- from importlib_metadata import entry_points
-
-from toolz import curry
-
-
-PluginType = TypeVar("PluginType")
-
-
-class NoSuchEntryPoint(Exception):
- def __init__(self, group, name):
- self.group = group
- self.name = name
-
- def __str__(self):
- return f"No {self.name!r} entry point found in group {self.group!r}"
-
-
-class PluginEnabler:
- """Context manager for enabling plugins
-
- This object lets you use enable() as a context manager to
- temporarily enable a given plugin::
-
- with plugins.enable('name'):
- do_something() # 'name' plugin temporarily enabled
- # plugins back to original state
- """
-
- def __init__(self, registry: "PluginRegistry", name: str, **options):
- self.registry = registry # type: PluginRegistry
- self.name = name # type: str
- self.options = options # type: Dict[str, Any]
- self.original_state = registry._get_state() # type: Dict[str, Any]
- self.registry._enable(name, **options)
-
- def __enter__(self) -> "PluginEnabler":
- return self
-
- def __exit__(self, typ: type, value: Exception, traceback: TracebackType) -> None:
- self.registry._set_state(self.original_state)
-
- def __repr__(self) -> str:
- return "{}.enable({!r})".format(self.registry.__class__.__name__, self.name)
-
-
-class PluginRegistry(Generic[PluginType]):
- """A registry for plugins.
-
- This is a plugin registry that allows plugins to be loaded/registered
- in two ways:
-
- 1. Through an explicit call to ``.register(name, value)``.
- 2. By looking for other Python packages that are installed and provide
- a setuptools entry point group.
-
- When you create an instance of this class, provide the name of the
- entry point group to use::
-
- reg = PluginRegister('my_entrypoint_group')
-
- """
-
- # this is a mapping of name to error message to allow custom error messages
- # in case an entrypoint is not found
- entrypoint_err_messages = {} # type: Dict[str, str]
-
- # global settings is a key-value mapping of settings that are stored globally
- # in the registry rather than passed to the plugins
- _global_settings = {} # type: Dict[str, Any]
-
- def __init__(self, entry_point_group: str = "", plugin_type: type = object):
- """Create a PluginRegistry for a named entry point group.
-
- Parameters
- ==========
- entry_point_group: str
- The name of the entry point group.
- plugin_type: object
- A type that will optionally be used for runtime type checking of
- loaded plugins using isinstance.
- """
- self.entry_point_group = entry_point_group # type: str
- self.plugin_type = plugin_type # type: Optional[type]
- self._active = None # type: Optional[PluginType]
- self._active_name = "" # type: str
- self._plugins = {} # type: Dict[str, PluginType]
- self._options = {} # type: Dict[str, Any]
- self._global_settings = self.__class__._global_settings.copy() # type: dict
-
- def register(self, name: str, value: Optional[PluginType]) -> Optional[PluginType]:
- """Register a plugin by name and value.
-
- This method is used for explicit registration of a plugin and shouldn't be
- used to manage entry point managed plugins, which are auto-loaded.
-
- Parameters
- ==========
- name: str
- The name of the plugin.
- value: PluginType or None
- The actual plugin object to register or None to unregister that plugin.
-
- Returns
- =======
- plugin: PluginType or None
- The plugin that was registered or unregistered.
- """
- if value is None:
- return self._plugins.pop(name, None)
- else:
- assert isinstance(value, self.plugin_type) # type: ignore[arg-type] # Should ideally be fixed by better annotating plugin_type
- self._plugins[name] = value
- return value
-
- def names(self) -> List[str]:
- """List the names of the registered and entry points plugins."""
- exts = list(self._plugins.keys())
- e_points = importlib_metadata_get(self.entry_point_group)
- more_exts = [ep.name for ep in e_points]
- exts.extend(more_exts)
- return sorted(set(exts))
-
- def _get_state(self) -> Dict[str, Any]:
- """Return a dictionary representing the current state of the registry"""
- return {
- "_active": self._active,
- "_active_name": self._active_name,
- "_plugins": self._plugins.copy(),
- "_options": self._options.copy(),
- "_global_settings": self._global_settings.copy(),
- }
-
- def _set_state(self, state: Dict[str, Any]) -> None:
- """Reset the state of the registry"""
- assert set(state.keys()) == {
- "_active",
- "_active_name",
- "_plugins",
- "_options",
- "_global_settings",
- }
- for key, val in state.items():
- setattr(self, key, val)
-
- def _enable(self, name: str, **options) -> None:
- if name not in self._plugins:
- try:
- (ep,) = [
- ep
- for ep in importlib_metadata_get(self.entry_point_group)
- if ep.name == name
- ]
- except ValueError as err:
- if name in self.entrypoint_err_messages:
- raise ValueError(self.entrypoint_err_messages[name]) from err
- else:
- raise NoSuchEntryPoint(self.entry_point_group, name) from err
- value = cast(PluginType, ep.load())
- self.register(name, value)
- self._active_name = name
- self._active = self._plugins[name]
- for key in set(options.keys()) & set(self._global_settings.keys()):
- self._global_settings[key] = options.pop(key)
- self._options = options
-
- def enable(self, name: Optional[str] = None, **options) -> PluginEnabler:
- """Enable a plugin by name.
-
- This can be either called directly, or used as a context manager.
-
- Parameters
- ----------
- name : string (optional)
- The name of the plugin to enable. If not specified, then use the
- current active name.
- **options :
- Any additional parameters will be passed to the plugin as keyword
- arguments
-
- Returns
- -------
- PluginEnabler:
- An object that allows enable() to be used as a context manager
- """
- if name is None:
- name = self.active
- return PluginEnabler(self, name, **options)
-
- @property
- def active(self) -> str:
- """Return the name of the currently active plugin"""
- return self._active_name
-
- @property
- def options(self) -> Dict[str, Any]:
- """Return the current options dictionary"""
- return self._options
-
- def get(self) -> Optional[PluginType]:
- """Return the currently active plugin."""
- if self._options:
- return curry(self._active, **self._options)
- else:
- return self._active
-
- def __repr__(self) -> str:
- return "{}(active={!r}, registered={!r})" "".format(
- self.__class__.__name__, self._active_name, list(self.names())
- )
-
-
-def importlib_metadata_get(group):
- ep = entry_points()
- # 'select' was introduced in Python 3.10 and 'get' got deprecated
- # We don't check for Python version here as by checking with hasattr we
- # also get compatibility with the importlib_metadata package which had a different
- # deprecation cycle for 'get'
- if hasattr(ep, "select"):
- return ep.select(group=group)
- else:
- return ep.get(group, [])
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py
deleted file mode 100644
index bba2d3f7dfffa3bdbf921bdad4ca7143be97c2fd..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py
+++ /dev/null
@@ -1,143 +0,0 @@
-from __future__ import annotations
-
-import codecs
-from dataclasses import InitVar, dataclass, field
-from typing import Any, Callable, Mapping
-
-from ..abc import (
- AnyByteReceiveStream,
- AnyByteSendStream,
- AnyByteStream,
- ObjectReceiveStream,
- ObjectSendStream,
- ObjectStream,
-)
-
-
-@dataclass(eq=False)
-class TextReceiveStream(ObjectReceiveStream[str]):
- """
- Stream wrapper that decodes bytes to strings using the given encoding.
-
- Decoding is done using :class:`~codecs.IncrementalDecoder` which returns any completely
- received unicode characters as soon as they come in.
-
- :param transport_stream: any bytes-based receive stream
- :param encoding: character encoding to use for decoding bytes to strings (defaults to
- ``utf-8``)
- :param errors: handling scheme for decoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteReceiveStream
- encoding: InitVar[str] = "utf-8"
- errors: InitVar[str] = "strict"
- _decoder: codecs.IncrementalDecoder = field(init=False)
-
- def __post_init__(self, encoding: str, errors: str) -> None:
- decoder_class = codecs.getincrementaldecoder(encoding)
- self._decoder = decoder_class(errors=errors)
-
- async def receive(self) -> str:
- while True:
- chunk = await self.transport_stream.receive()
- decoded = self._decoder.decode(chunk)
- if decoded:
- return decoded
-
- async def aclose(self) -> None:
- await self.transport_stream.aclose()
- self._decoder.reset()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return self.transport_stream.extra_attributes
-
-
-@dataclass(eq=False)
-class TextSendStream(ObjectSendStream[str]):
- """
- Sends strings to the wrapped stream as bytes using the given encoding.
-
- :param AnyByteSendStream transport_stream: any bytes-based send stream
- :param str encoding: character encoding to use for encoding strings to bytes (defaults to
- ``utf-8``)
- :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteSendStream
- encoding: InitVar[str] = "utf-8"
- errors: str = "strict"
- _encoder: Callable[..., tuple[bytes, int]] = field(init=False)
-
- def __post_init__(self, encoding: str) -> None:
- self._encoder = codecs.getencoder(encoding)
-
- async def send(self, item: str) -> None:
- encoded = self._encoder(item, self.errors)[0]
- await self.transport_stream.send(encoded)
-
- async def aclose(self) -> None:
- await self.transport_stream.aclose()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return self.transport_stream.extra_attributes
-
-
-@dataclass(eq=False)
-class TextStream(ObjectStream[str]):
- """
- A bidirectional stream that decodes bytes to strings on receive and encodes strings to bytes on
- send.
-
- Extra attributes will be provided from both streams, with the receive stream providing the
- values in case of a conflict.
-
- :param AnyByteStream transport_stream: any bytes-based stream
- :param str encoding: character encoding to use for encoding/decoding strings to/from bytes
- (defaults to ``utf-8``)
- :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteStream
- encoding: InitVar[str] = "utf-8"
- errors: InitVar[str] = "strict"
- _receive_stream: TextReceiveStream = field(init=False)
- _send_stream: TextSendStream = field(init=False)
-
- def __post_init__(self, encoding: str, errors: str) -> None:
- self._receive_stream = TextReceiveStream(
- self.transport_stream, encoding=encoding, errors=errors
- )
- self._send_stream = TextSendStream(
- self.transport_stream, encoding=encoding, errors=errors
- )
-
- async def receive(self) -> str:
- return await self._receive_stream.receive()
-
- async def send(self, item: str) -> None:
- await self._send_stream.send(item)
-
- async def send_eof(self) -> None:
- await self.transport_stream.send_eof()
-
- async def aclose(self) -> None:
- await self._send_stream.aclose()
- await self._receive_stream.aclose()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return {
- **self._send_stream.extra_attributes,
- **self._receive_stream.extra_attributes,
- }
diff --git a/spaces/TH5314/newbing/src/lib/bots/bing/types.ts b/spaces/TH5314/newbing/src/lib/bots/bing/types.ts
deleted file mode 100644
index 02cd5e8b01e3529642d28dc1539bf958f4ac420b..0000000000000000000000000000000000000000
--- a/spaces/TH5314/newbing/src/lib/bots/bing/types.ts
+++ /dev/null
@@ -1,259 +0,0 @@
-export type Author = 'user' | 'system' | 'bot'
-
-export type BotId = 'bing'
-
-export enum BingConversationStyle {
- Creative = 'Creative',
- Balanced = 'Balanced',
- Precise = 'Precise'
-}
-
-export enum ErrorCode {
- CONVERSATION_LIMIT = 'CONVERSATION_LIMIT',
- BING_UNAUTHORIZED = 'BING_UNAUTHORIZED',
- BING_FORBIDDEN = 'BING_FORBIDDEN',
- BING_CAPTCHA = 'BING_CAPTCHA',
- THROTTLE_LIMIT = 'THROTTLE_LIMIT',
- NOTFOUND_ERROR = 'NOT_FOUND_ERROR',
- UNKOWN_ERROR = 'UNKOWN_ERROR',
- NETWORK_ERROR = 'NETWORK_ERROR',
-}
-
-export class ChatError extends Error {
- code: ErrorCode
- constructor(message: string, code: ErrorCode) {
- super(message)
- this.code = code
- }
-}
-
-export type ChatMessageModel = {
- id: string
- author: Author
- text: string
- error?: ChatError
- throttling?: Throttling
- sourceAttributions?: SourceAttribution[]
- suggestedResponses?: SuggestedResponse[]
-}
-
-export interface ConversationModel {
- messages: ChatMessageModel[]
-}
-
-export type Event =
- | {
- type: 'UPDATE_ANSWER'
- data: {
- text: string
- spokenText?: string
- sourceAttributions?: SourceAttribution[]
- suggestedResponses?: SuggestedResponse[]
- throttling?: Throttling
- }
- }
- | {
- type: 'DONE'
- }
- | {
- type: 'ERROR'
- error: ChatError
- }
-
-export interface SendMessageParams {
- prompt: string
- imageUrl?: string
- options: T
- onEvent: (event: Event) => void
- signal?: AbortSignal
-}
-
-export interface ConversationResponse {
- conversationId: string
- clientId: string
- conversationSignature: string
- result: {
- value: string
- message?: string
- }
-}
-
-export interface Telemetry {
- metrics?: null
- startTime: string
-}
-
-export interface ChatUpdateArgument {
- messages?: ChatResponseMessage[]
- throttling?: Throttling
- requestId: string
- result: null
-}
-
-export type ChatUpdateCompleteResponse = {
- type: 2
- invocationId: string
- item: ChatResponseItem
-} | {
- type: 1
- target: string
- arguments: ChatUpdateArgument[]
-} | {
- type: 3
- invocationId: string
-} | {
- type: 6 | 7
-}
-
-export interface ChatRequestResult {
- value: string
- serviceVersion: string
- error?: string
-}
-
-export interface ChatResponseItem {
- messages: ChatResponseMessage[]
- firstNewMessageIndex: number
- suggestedResponses: null
- conversationId: string
- requestId: string
- conversationExpiryTime: string
- telemetry: Telemetry
- result: ChatRequestResult
- throttling: Throttling
-}
-export enum InvocationEventType {
- Invocation = 1,
- StreamItem = 2,
- Completion = 3,
- StreamInvocation = 4,
- CancelInvocation = 5,
- Ping = 6,
- Close = 7,
-}
-
-// https://github.com/bytemate/bingchat-api/blob/main/src/lib.ts
-
-export interface ConversationInfo {
- conversationId: string
- clientId: string
- conversationSignature: string
- invocationId: number
- conversationStyle: BingConversationStyle
- prompt: string
- imageUrl?: string
-}
-
-export interface BingChatResponse {
- conversationSignature: string
- conversationId: string
- clientId: string
- invocationId: number
- conversationExpiryTime: Date
- response: string
- details: ChatResponseMessage
-}
-
-export interface Throttling {
- maxNumLongDocSummaryUserMessagesInConversation: number
- maxNumUserMessagesInConversation: number
- numLongDocSummaryUserMessagesInConversation: number
- numUserMessagesInConversation: number
-}
-
-export interface ChatResponseMessage {
- text: string
- spokenText?: string
- author: string
- createdAt: Date
- timestamp: Date
- messageId: string
- requestId: string
- offense: string
- adaptiveCards: AdaptiveCard[]
- sourceAttributions: SourceAttribution[]
- feedback: Feedback
- contentOrigin: string
- messageType?: string
- contentType?: string
- privacy: null
- suggestedResponses: SuggestedResponse[]
-}
-
-export interface AdaptiveCard {
- type: string
- version: string
- body: Body[]
-}
-
-export interface Body {
- type: string
- text: string
- wrap: boolean
- size?: string
-}
-
-export interface Feedback {
- tag: null
- updatedOn: null
- type: string
-}
-
-export interface SourceAttribution {
- providerDisplayName: string
- seeMoreUrl: string
- searchQuery: string
-}
-
-export interface SuggestedResponse {
- text: string
- author?: Author
- createdAt?: Date
- timestamp?: Date
- messageId?: string
- messageType?: string
- offense?: string
- feedback?: Feedback
- contentOrigin?: string
- privacy?: null
-}
-
-export interface KBlobRequest {
- knowledgeRequest: KnowledgeRequestContext
- imageBase64?: string
-}
-
-export interface KBlobResponse {
- blobId: string
- processedBlobId?: string
-}
-
-export interface KnowledgeRequestContext {
- imageInfo: ImageInfo;
- knowledgeRequest: KnowledgeRequest;
-}
-
-export interface ImageInfo {
- url?: string;
-}
-
-export interface KnowledgeRequest {
- invokedSkills: string[];
- subscriptionId: string;
- invokedSkillsRequestData: InvokedSkillsRequestData;
- convoData: ConvoData;
-}
-
-export interface ConvoData {
- convoid: string;
- convotone: BingConversationStyle;
-}
-
-export interface InvokedSkillsRequestData {
- enableFaceBlur: boolean;
-}
-
-export interface FileItem {
- url: string;
- status?: 'loading' | 'error' | 'loaded'
-}
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py
deleted file mode 100644
index 264d564dbda676b52f446c0d25433a15939a78a3..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py
+++ /dev/null
@@ -1,519 +0,0 @@
-"""
-This module uses ctypes to bind a whole bunch of functions and constants from
-SecureTransport. The goal here is to provide the low-level API to
-SecureTransport. These are essentially the C-level functions and constants, and
-they're pretty gross to work with.
-
-This code is a bastardised version of the code found in Will Bond's oscrypto
-library. An enormous debt is owed to him for blazing this trail for us. For
-that reason, this code should be considered to be covered both by urllib3's
-license and by oscrypto's:
-
- Copyright (c) 2015-2016 Will Bond
-
- Permission is hereby granted, free of charge, to any person obtaining a
- copy of this software and associated documentation files (the "Software"),
- to deal in the Software without restriction, including without limitation
- the rights to use, copy, modify, merge, publish, distribute, sublicense,
- and/or sell copies of the Software, and to permit persons to whom the
- Software is furnished to do so, subject to the following conditions:
-
- The above copyright notice and this permission notice shall be included in
- all copies or substantial portions of the Software.
-
- THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
- FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
- DEALINGS IN THE SOFTWARE.
-"""
-from __future__ import absolute_import
-
-import platform
-from ctypes import (
- CDLL,
- CFUNCTYPE,
- POINTER,
- c_bool,
- c_byte,
- c_char_p,
- c_int32,
- c_long,
- c_size_t,
- c_uint32,
- c_ulong,
- c_void_p,
-)
-from ctypes.util import find_library
-
-from ...packages.six import raise_from
-
-if platform.system() != "Darwin":
- raise ImportError("Only macOS is supported")
-
-version = platform.mac_ver()[0]
-version_info = tuple(map(int, version.split(".")))
-if version_info < (10, 8):
- raise OSError(
- "Only OS X 10.8 and newer are supported, not %s.%s"
- % (version_info[0], version_info[1])
- )
-
-
-def load_cdll(name, macos10_16_path):
- """Loads a CDLL by name, falling back to known path on 10.16+"""
- try:
- # Big Sur is technically 11 but we use 10.16 due to the Big Sur
- # beta being labeled as 10.16.
- if version_info >= (10, 16):
- path = macos10_16_path
- else:
- path = find_library(name)
- if not path:
- raise OSError # Caught and reraised as 'ImportError'
- return CDLL(path, use_errno=True)
- except OSError:
- raise_from(ImportError("The library %s failed to load" % name), None)
-
-
-Security = load_cdll(
- "Security", "/System/Library/Frameworks/Security.framework/Security"
-)
-CoreFoundation = load_cdll(
- "CoreFoundation",
- "/System/Library/Frameworks/CoreFoundation.framework/CoreFoundation",
-)
-
-
-Boolean = c_bool
-CFIndex = c_long
-CFStringEncoding = c_uint32
-CFData = c_void_p
-CFString = c_void_p
-CFArray = c_void_p
-CFMutableArray = c_void_p
-CFDictionary = c_void_p
-CFError = c_void_p
-CFType = c_void_p
-CFTypeID = c_ulong
-
-CFTypeRef = POINTER(CFType)
-CFAllocatorRef = c_void_p
-
-OSStatus = c_int32
-
-CFDataRef = POINTER(CFData)
-CFStringRef = POINTER(CFString)
-CFArrayRef = POINTER(CFArray)
-CFMutableArrayRef = POINTER(CFMutableArray)
-CFDictionaryRef = POINTER(CFDictionary)
-CFArrayCallBacks = c_void_p
-CFDictionaryKeyCallBacks = c_void_p
-CFDictionaryValueCallBacks = c_void_p
-
-SecCertificateRef = POINTER(c_void_p)
-SecExternalFormat = c_uint32
-SecExternalItemType = c_uint32
-SecIdentityRef = POINTER(c_void_p)
-SecItemImportExportFlags = c_uint32
-SecItemImportExportKeyParameters = c_void_p
-SecKeychainRef = POINTER(c_void_p)
-SSLProtocol = c_uint32
-SSLCipherSuite = c_uint32
-SSLContextRef = POINTER(c_void_p)
-SecTrustRef = POINTER(c_void_p)
-SSLConnectionRef = c_uint32
-SecTrustResultType = c_uint32
-SecTrustOptionFlags = c_uint32
-SSLProtocolSide = c_uint32
-SSLConnectionType = c_uint32
-SSLSessionOption = c_uint32
-
-
-try:
- Security.SecItemImport.argtypes = [
- CFDataRef,
- CFStringRef,
- POINTER(SecExternalFormat),
- POINTER(SecExternalItemType),
- SecItemImportExportFlags,
- POINTER(SecItemImportExportKeyParameters),
- SecKeychainRef,
- POINTER(CFArrayRef),
- ]
- Security.SecItemImport.restype = OSStatus
-
- Security.SecCertificateGetTypeID.argtypes = []
- Security.SecCertificateGetTypeID.restype = CFTypeID
-
- Security.SecIdentityGetTypeID.argtypes = []
- Security.SecIdentityGetTypeID.restype = CFTypeID
-
- Security.SecKeyGetTypeID.argtypes = []
- Security.SecKeyGetTypeID.restype = CFTypeID
-
- Security.SecCertificateCreateWithData.argtypes = [CFAllocatorRef, CFDataRef]
- Security.SecCertificateCreateWithData.restype = SecCertificateRef
-
- Security.SecCertificateCopyData.argtypes = [SecCertificateRef]
- Security.SecCertificateCopyData.restype = CFDataRef
-
- Security.SecCopyErrorMessageString.argtypes = [OSStatus, c_void_p]
- Security.SecCopyErrorMessageString.restype = CFStringRef
-
- Security.SecIdentityCreateWithCertificate.argtypes = [
- CFTypeRef,
- SecCertificateRef,
- POINTER(SecIdentityRef),
- ]
- Security.SecIdentityCreateWithCertificate.restype = OSStatus
-
- Security.SecKeychainCreate.argtypes = [
- c_char_p,
- c_uint32,
- c_void_p,
- Boolean,
- c_void_p,
- POINTER(SecKeychainRef),
- ]
- Security.SecKeychainCreate.restype = OSStatus
-
- Security.SecKeychainDelete.argtypes = [SecKeychainRef]
- Security.SecKeychainDelete.restype = OSStatus
-
- Security.SecPKCS12Import.argtypes = [
- CFDataRef,
- CFDictionaryRef,
- POINTER(CFArrayRef),
- ]
- Security.SecPKCS12Import.restype = OSStatus
-
- SSLReadFunc = CFUNCTYPE(OSStatus, SSLConnectionRef, c_void_p, POINTER(c_size_t))
- SSLWriteFunc = CFUNCTYPE(
- OSStatus, SSLConnectionRef, POINTER(c_byte), POINTER(c_size_t)
- )
-
- Security.SSLSetIOFuncs.argtypes = [SSLContextRef, SSLReadFunc, SSLWriteFunc]
- Security.SSLSetIOFuncs.restype = OSStatus
-
- Security.SSLSetPeerID.argtypes = [SSLContextRef, c_char_p, c_size_t]
- Security.SSLSetPeerID.restype = OSStatus
-
- Security.SSLSetCertificate.argtypes = [SSLContextRef, CFArrayRef]
- Security.SSLSetCertificate.restype = OSStatus
-
- Security.SSLSetCertificateAuthorities.argtypes = [SSLContextRef, CFTypeRef, Boolean]
- Security.SSLSetCertificateAuthorities.restype = OSStatus
-
- Security.SSLSetConnection.argtypes = [SSLContextRef, SSLConnectionRef]
- Security.SSLSetConnection.restype = OSStatus
-
- Security.SSLSetPeerDomainName.argtypes = [SSLContextRef, c_char_p, c_size_t]
- Security.SSLSetPeerDomainName.restype = OSStatus
-
- Security.SSLHandshake.argtypes = [SSLContextRef]
- Security.SSLHandshake.restype = OSStatus
-
- Security.SSLRead.argtypes = [SSLContextRef, c_char_p, c_size_t, POINTER(c_size_t)]
- Security.SSLRead.restype = OSStatus
-
- Security.SSLWrite.argtypes = [SSLContextRef, c_char_p, c_size_t, POINTER(c_size_t)]
- Security.SSLWrite.restype = OSStatus
-
- Security.SSLClose.argtypes = [SSLContextRef]
- Security.SSLClose.restype = OSStatus
-
- Security.SSLGetNumberSupportedCiphers.argtypes = [SSLContextRef, POINTER(c_size_t)]
- Security.SSLGetNumberSupportedCiphers.restype = OSStatus
-
- Security.SSLGetSupportedCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- POINTER(c_size_t),
- ]
- Security.SSLGetSupportedCiphers.restype = OSStatus
-
- Security.SSLSetEnabledCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- c_size_t,
- ]
- Security.SSLSetEnabledCiphers.restype = OSStatus
-
- Security.SSLGetNumberEnabledCiphers.argtype = [SSLContextRef, POINTER(c_size_t)]
- Security.SSLGetNumberEnabledCiphers.restype = OSStatus
-
- Security.SSLGetEnabledCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- POINTER(c_size_t),
- ]
- Security.SSLGetEnabledCiphers.restype = OSStatus
-
- Security.SSLGetNegotiatedCipher.argtypes = [SSLContextRef, POINTER(SSLCipherSuite)]
- Security.SSLGetNegotiatedCipher.restype = OSStatus
-
- Security.SSLGetNegotiatedProtocolVersion.argtypes = [
- SSLContextRef,
- POINTER(SSLProtocol),
- ]
- Security.SSLGetNegotiatedProtocolVersion.restype = OSStatus
-
- Security.SSLCopyPeerTrust.argtypes = [SSLContextRef, POINTER(SecTrustRef)]
- Security.SSLCopyPeerTrust.restype = OSStatus
-
- Security.SecTrustSetAnchorCertificates.argtypes = [SecTrustRef, CFArrayRef]
- Security.SecTrustSetAnchorCertificates.restype = OSStatus
-
- Security.SecTrustSetAnchorCertificatesOnly.argstypes = [SecTrustRef, Boolean]
- Security.SecTrustSetAnchorCertificatesOnly.restype = OSStatus
-
- Security.SecTrustEvaluate.argtypes = [SecTrustRef, POINTER(SecTrustResultType)]
- Security.SecTrustEvaluate.restype = OSStatus
-
- Security.SecTrustGetCertificateCount.argtypes = [SecTrustRef]
- Security.SecTrustGetCertificateCount.restype = CFIndex
-
- Security.SecTrustGetCertificateAtIndex.argtypes = [SecTrustRef, CFIndex]
- Security.SecTrustGetCertificateAtIndex.restype = SecCertificateRef
-
- Security.SSLCreateContext.argtypes = [
- CFAllocatorRef,
- SSLProtocolSide,
- SSLConnectionType,
- ]
- Security.SSLCreateContext.restype = SSLContextRef
-
- Security.SSLSetSessionOption.argtypes = [SSLContextRef, SSLSessionOption, Boolean]
- Security.SSLSetSessionOption.restype = OSStatus
-
- Security.SSLSetProtocolVersionMin.argtypes = [SSLContextRef, SSLProtocol]
- Security.SSLSetProtocolVersionMin.restype = OSStatus
-
- Security.SSLSetProtocolVersionMax.argtypes = [SSLContextRef, SSLProtocol]
- Security.SSLSetProtocolVersionMax.restype = OSStatus
-
- try:
- Security.SSLSetALPNProtocols.argtypes = [SSLContextRef, CFArrayRef]
- Security.SSLSetALPNProtocols.restype = OSStatus
- except AttributeError:
- # Supported only in 10.12+
- pass
-
- Security.SecCopyErrorMessageString.argtypes = [OSStatus, c_void_p]
- Security.SecCopyErrorMessageString.restype = CFStringRef
-
- Security.SSLReadFunc = SSLReadFunc
- Security.SSLWriteFunc = SSLWriteFunc
- Security.SSLContextRef = SSLContextRef
- Security.SSLProtocol = SSLProtocol
- Security.SSLCipherSuite = SSLCipherSuite
- Security.SecIdentityRef = SecIdentityRef
- Security.SecKeychainRef = SecKeychainRef
- Security.SecTrustRef = SecTrustRef
- Security.SecTrustResultType = SecTrustResultType
- Security.SecExternalFormat = SecExternalFormat
- Security.OSStatus = OSStatus
-
- Security.kSecImportExportPassphrase = CFStringRef.in_dll(
- Security, "kSecImportExportPassphrase"
- )
- Security.kSecImportItemIdentity = CFStringRef.in_dll(
- Security, "kSecImportItemIdentity"
- )
-
- # CoreFoundation time!
- CoreFoundation.CFRetain.argtypes = [CFTypeRef]
- CoreFoundation.CFRetain.restype = CFTypeRef
-
- CoreFoundation.CFRelease.argtypes = [CFTypeRef]
- CoreFoundation.CFRelease.restype = None
-
- CoreFoundation.CFGetTypeID.argtypes = [CFTypeRef]
- CoreFoundation.CFGetTypeID.restype = CFTypeID
-
- CoreFoundation.CFStringCreateWithCString.argtypes = [
- CFAllocatorRef,
- c_char_p,
- CFStringEncoding,
- ]
- CoreFoundation.CFStringCreateWithCString.restype = CFStringRef
-
- CoreFoundation.CFStringGetCStringPtr.argtypes = [CFStringRef, CFStringEncoding]
- CoreFoundation.CFStringGetCStringPtr.restype = c_char_p
-
- CoreFoundation.CFStringGetCString.argtypes = [
- CFStringRef,
- c_char_p,
- CFIndex,
- CFStringEncoding,
- ]
- CoreFoundation.CFStringGetCString.restype = c_bool
-
- CoreFoundation.CFDataCreate.argtypes = [CFAllocatorRef, c_char_p, CFIndex]
- CoreFoundation.CFDataCreate.restype = CFDataRef
-
- CoreFoundation.CFDataGetLength.argtypes = [CFDataRef]
- CoreFoundation.CFDataGetLength.restype = CFIndex
-
- CoreFoundation.CFDataGetBytePtr.argtypes = [CFDataRef]
- CoreFoundation.CFDataGetBytePtr.restype = c_void_p
-
- CoreFoundation.CFDictionaryCreate.argtypes = [
- CFAllocatorRef,
- POINTER(CFTypeRef),
- POINTER(CFTypeRef),
- CFIndex,
- CFDictionaryKeyCallBacks,
- CFDictionaryValueCallBacks,
- ]
- CoreFoundation.CFDictionaryCreate.restype = CFDictionaryRef
-
- CoreFoundation.CFDictionaryGetValue.argtypes = [CFDictionaryRef, CFTypeRef]
- CoreFoundation.CFDictionaryGetValue.restype = CFTypeRef
-
- CoreFoundation.CFArrayCreate.argtypes = [
- CFAllocatorRef,
- POINTER(CFTypeRef),
- CFIndex,
- CFArrayCallBacks,
- ]
- CoreFoundation.CFArrayCreate.restype = CFArrayRef
-
- CoreFoundation.CFArrayCreateMutable.argtypes = [
- CFAllocatorRef,
- CFIndex,
- CFArrayCallBacks,
- ]
- CoreFoundation.CFArrayCreateMutable.restype = CFMutableArrayRef
-
- CoreFoundation.CFArrayAppendValue.argtypes = [CFMutableArrayRef, c_void_p]
- CoreFoundation.CFArrayAppendValue.restype = None
-
- CoreFoundation.CFArrayGetCount.argtypes = [CFArrayRef]
- CoreFoundation.CFArrayGetCount.restype = CFIndex
-
- CoreFoundation.CFArrayGetValueAtIndex.argtypes = [CFArrayRef, CFIndex]
- CoreFoundation.CFArrayGetValueAtIndex.restype = c_void_p
-
- CoreFoundation.kCFAllocatorDefault = CFAllocatorRef.in_dll(
- CoreFoundation, "kCFAllocatorDefault"
- )
- CoreFoundation.kCFTypeArrayCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeArrayCallBacks"
- )
- CoreFoundation.kCFTypeDictionaryKeyCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeDictionaryKeyCallBacks"
- )
- CoreFoundation.kCFTypeDictionaryValueCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeDictionaryValueCallBacks"
- )
-
- CoreFoundation.CFTypeRef = CFTypeRef
- CoreFoundation.CFArrayRef = CFArrayRef
- CoreFoundation.CFStringRef = CFStringRef
- CoreFoundation.CFDictionaryRef = CFDictionaryRef
-
-except (AttributeError):
- raise ImportError("Error initializing ctypes")
-
-
-class CFConst(object):
- """
- A class object that acts as essentially a namespace for CoreFoundation
- constants.
- """
-
- kCFStringEncodingUTF8 = CFStringEncoding(0x08000100)
-
-
-class SecurityConst(object):
- """
- A class object that acts as essentially a namespace for Security constants.
- """
-
- kSSLSessionOptionBreakOnServerAuth = 0
-
- kSSLProtocol2 = 1
- kSSLProtocol3 = 2
- kTLSProtocol1 = 4
- kTLSProtocol11 = 7
- kTLSProtocol12 = 8
- # SecureTransport does not support TLS 1.3 even if there's a constant for it
- kTLSProtocol13 = 10
- kTLSProtocolMaxSupported = 999
-
- kSSLClientSide = 1
- kSSLStreamType = 0
-
- kSecFormatPEMSequence = 10
-
- kSecTrustResultInvalid = 0
- kSecTrustResultProceed = 1
- # This gap is present on purpose: this was kSecTrustResultConfirm, which
- # is deprecated.
- kSecTrustResultDeny = 3
- kSecTrustResultUnspecified = 4
- kSecTrustResultRecoverableTrustFailure = 5
- kSecTrustResultFatalTrustFailure = 6
- kSecTrustResultOtherError = 7
-
- errSSLProtocol = -9800
- errSSLWouldBlock = -9803
- errSSLClosedGraceful = -9805
- errSSLClosedNoNotify = -9816
- errSSLClosedAbort = -9806
-
- errSSLXCertChainInvalid = -9807
- errSSLCrypto = -9809
- errSSLInternal = -9810
- errSSLCertExpired = -9814
- errSSLCertNotYetValid = -9815
- errSSLUnknownRootCert = -9812
- errSSLNoRootCert = -9813
- errSSLHostNameMismatch = -9843
- errSSLPeerHandshakeFail = -9824
- errSSLPeerUserCancelled = -9839
- errSSLWeakPeerEphemeralDHKey = -9850
- errSSLServerAuthCompleted = -9841
- errSSLRecordOverflow = -9847
-
- errSecVerifyFailed = -67808
- errSecNoTrustSettings = -25263
- errSecItemNotFound = -25300
- errSecInvalidTrustSettings = -25262
-
- # Cipher suites. We only pick the ones our default cipher string allows.
- # Source: https://developer.apple.com/documentation/security/1550981-ssl_cipher_suite_values
- TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384 = 0xC02C
- TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384 = 0xC030
- TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256 = 0xC02B
- TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256 = 0xC02F
- TLS_ECDHE_ECDSA_WITH_CHACHA20_POLY1305_SHA256 = 0xCCA9
- TLS_ECDHE_RSA_WITH_CHACHA20_POLY1305_SHA256 = 0xCCA8
- TLS_DHE_RSA_WITH_AES_256_GCM_SHA384 = 0x009F
- TLS_DHE_RSA_WITH_AES_128_GCM_SHA256 = 0x009E
- TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA384 = 0xC024
- TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384 = 0xC028
- TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA = 0xC00A
- TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA = 0xC014
- TLS_DHE_RSA_WITH_AES_256_CBC_SHA256 = 0x006B
- TLS_DHE_RSA_WITH_AES_256_CBC_SHA = 0x0039
- TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256 = 0xC023
- TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256 = 0xC027
- TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA = 0xC009
- TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA = 0xC013
- TLS_DHE_RSA_WITH_AES_128_CBC_SHA256 = 0x0067
- TLS_DHE_RSA_WITH_AES_128_CBC_SHA = 0x0033
- TLS_RSA_WITH_AES_256_GCM_SHA384 = 0x009D
- TLS_RSA_WITH_AES_128_GCM_SHA256 = 0x009C
- TLS_RSA_WITH_AES_256_CBC_SHA256 = 0x003D
- TLS_RSA_WITH_AES_128_CBC_SHA256 = 0x003C
- TLS_RSA_WITH_AES_256_CBC_SHA = 0x0035
- TLS_RSA_WITH_AES_128_CBC_SHA = 0x002F
- TLS_AES_128_GCM_SHA256 = 0x1301
- TLS_AES_256_GCM_SHA384 = 0x1302
- TLS_AES_128_CCM_8_SHA256 = 0x1305
- TLS_AES_128_CCM_SHA256 = 0x1304
diff --git a/spaces/TeamTonic/MultiMed/app.py b/spaces/TeamTonic/MultiMed/app.py
deleted file mode 100644
index 29b1ad119a6d38fc2fca1fa616c0f221e9751208..0000000000000000000000000000000000000000
--- a/spaces/TeamTonic/MultiMed/app.py
+++ /dev/null
@@ -1,490 +0,0 @@
-# Welcome to Team Tonic's MultiMed
-
-from gradio_client import Client
-import os
-import numpy as np
-import base64
-import gradio as gr
-import requests
-import json
-import dotenv
-from scipy.io.wavfile import write
-import PIL
-from openai import OpenAI
-import time
-dotenv.load_dotenv()
-
-seamless_client = Client("facebook/seamless_m4t")
-HuggingFace_Token = os.getenv("HuggingFace_Token")
-
-def check_hallucination(assertion,citation):
- API_URL = "https://api-inference.huggingface.co/models/vectara/hallucination_evaluation_model"
- headers = {"Authorization": f"Bearer {HuggingFace_Token}"}
- payload = {"inputs" : f"{assertion} [SEP] {citation}"}
-
- response = requests.post(API_URL, headers=headers, json=payload,timeout=120)
- output = response.json()
- output = output[0][0]["score"]
-
- return f"**hullicination score:** {output}"
-
-
-
-def process_speech(input_language, audio_input):
- """
- processing sound using seamless_m4t
- """
- if audio_input is None :
- return "no audio or audio did not save yet \nplease try again ! "
- print(f"audio : {audio_input}")
- print(f"audio type : {type(audio_input)}")
- out = seamless_client.predict(
- "S2TT",
- "file",
- None,
- audio_input, #audio_name
- "",
- input_language,# source language
- input_language,# target language
- api_name="/run",
- )
- out = out[1] # get the text
- try :
- return f"{out}"
- except Exception as e :
- return f"{e}"
-
-
-
-
-def process_image(image) :
- img_name = f"{np.random.randint(0, 100)}.jpg"
- PIL.Image.fromarray(image.astype('uint8'), 'RGB').save(img_name)
- image = open(img_name, "rb").read()
- base64_image = base64_image = base64.b64encode(image).decode('utf-8')
- openai_api_key = os.getenv('OPENAI_API_KEY')
- # oai_org = os.getenv('OAI_ORG')
-
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {openai_api_key}"
- }
-
- payload = {
- "model": "gpt-4-vision-preview",
- "messages": [
- {
- "role": "user",
- "content": [
- {
- "type": "text",
- "text": "You are clinical consultant discussion training cases with students at TonicUniversity. Assess and describe the photo in minute detail. Explain why each area or item in the photograph would be inappropriate to describe if required. Pay attention to anatomy, symptoms and remedies. Propose a course of action based on your assessment. Exclude any other commentary:"
- },
- {
- "type": "image_url",
- "image_url": {
- "url": f"data:image/jpeg;base64,{base64_image}"
- }
- }
- ]
- }
- ],
- "max_tokens": 1200
- }
-
- response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
-
- try :
- out = response.json()
- out = out["choices"][0]["message"]["content"]
-
- return out
- except Exception as e :
- return f"{e}"
-
-
-def query_vectara(text):
- user_message = text
-
- # Read authentication parameters from the .env file
- CUSTOMER_ID = os.getenv('CUSTOMER_ID')
- CORPUS_ID = os.getenv('CORPUS_ID')
- API_KEY = os.getenv('API_KEY')
-
- # Define the headers
- api_key_header = {
- "customer-id": CUSTOMER_ID,
- "x-api-key": API_KEY
- }
-
- # Define the request body in the structure provided in the example
- request_body = {
- "query": [
- {
- "query": user_message,
- "queryContext": "",
- "start": 1,
- "numResults": 50,
- "contextConfig": {
- "charsBefore": 0,
- "charsAfter": 0,
- "sentencesBefore": 2,
- "sentencesAfter": 2,
- "startTag": "%START_SNIPPET%",
- "endTag": "%END_SNIPPET%",
- },
- "rerankingConfig": {
- "rerankerId": 272725718,
- "mmrConfig": {
- "diversityBias": 0.35
- }
- },
- "corpusKey": [
- {
- "customerId": CUSTOMER_ID,
- "corpusId": CORPUS_ID,
- "semantics": 0,
- "metadataFilter": "",
- "lexicalInterpolationConfig": {
- "lambda": 0
- },
- "dim": []
- }
- ],
- "summary": [
- {
- "maxSummarizedResults": 5,
- "responseLang": "auto",
- "summarizerPromptName": "vectara-summary-ext-v1.2.0"
- }
- ]
- }
- ]
- }
-
- # Make the API request using Gradio
- response = requests.post(
- "https://api.vectara.io/v1/query",
- json=request_body, # Use json to automatically serialize the request body
- verify=True,
- headers=api_key_header
- )
-
- if response.status_code == 200:
- query_data = response.json()
- if query_data:
- sources_info = []
-
- # Extract the summary.
- summary = query_data['responseSet'][0]['summary'][0]['text']
-
- # Iterate over all response sets
- for response_set in query_data.get('responseSet', []):
- # Extract sources
- # Limit to top 5 sources.
- for source in response_set.get('response', [])[:5]:
- source_metadata = source.get('metadata', [])
- source_info = {}
-
- for metadata in source_metadata:
- metadata_name = metadata.get('name', '')
- metadata_value = metadata.get('value', '')
-
- if metadata_name == 'title':
- source_info['title'] = metadata_value
- elif metadata_name == 'author':
- source_info['author'] = metadata_value
- elif metadata_name == 'pageNumber':
- source_info['page number'] = metadata_value
-
- if source_info:
- sources_info.append(source_info)
-
- result = {"summary": summary, "sources": sources_info}
- return f"{json.dumps(result, indent=2)}"
- else:
- return "No data found in the response."
- else:
- return f"Error: {response.status_code}"
-
-
-def convert_to_markdown(vectara_response_json):
- vectara_response = json.loads(vectara_response_json)
- if vectara_response:
- summary = vectara_response.get('summary', 'No summary available')
- sources_info = vectara_response.get('sources', [])
-
- # Format the summary as Markdown
- markdown_summary = f' {summary}\n\n'
-
- # Format the sources as a numbered list
- markdown_sources = ""
- for i, source_info in enumerate(sources_info):
- author = source_info.get('author', 'Unknown author')
- title = source_info.get('title', 'Unknown title')
- page_number = source_info.get('page number', 'Unknown page number')
- markdown_sources += f"{i+1}. {title} by {author}, Page {page_number}\n"
-
- return f"{markdown_summary}**Sources:**\n{markdown_sources}"
- else:
- return "No data found in the response."
-# Main function to handle the Gradio interface logic
-
-def process_summary_with_openai(summary):
- """
- This function takes a summary text as input and processes it with OpenAI's GPT model.
- """
- try:
- # Ensure that the OpenAI client is properly initialized
- client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
-
- # Create the prompt for OpenAI's completion
- prompt = "You are clinical consultant discussion training cases with students at TonicUniversity. Assess and describe the proper options in minute detail. Propose a course of action based on your assessment. You will recieve a summary assessment in a language, respond ONLY in the original language. Exclude any other commentary:"
-
- # Call the OpenAI API with the prompt and the summary
- completion = client.chat.completions.create(
- model="gpt-4-1106-preview", # Make sure to use the correct model name
- messages=[
- {"role": "system", "content": prompt},
- {"role": "user", "content": summary}
- ]
- )
-
- # Extract the content from the completion
- final_summary = completion.choices[0].message.content
- return final_summary
- except Exception as e:
- return str(e)
-
-
-def process_and_query(text=None):
- try:
- # augment the prompt before feeding it to vectara
- text = "the user asks the following to his health adviser " + text
- # If an image is provided, process it with OpenAI and use the response as the text query for Vectara
- # if image is not None:
- # text = process_image(image)
- # return "**Summary:** "+text
- # if audio is not None:
- # text = process_speech(audio)
- # # augment the prompt before feeding it to vectara
- # text = "the user asks the following to his health adviser " + text
-
-
-
- # Use the text to query Vectara
- vectara_response_json = query_vectara(text)
-
- # Convert the Vectara response to Markdown
- markdown_output = convert_to_markdown(vectara_response_json)
-
- # Process the summary with OpenAI
- final_response = process_summary_with_openai(markdown_output)
-
- # Return the processed summary along with the full output
- return f"**Summary**: {final_response}\n\n**Full output**:\n{markdown_output}"
- except Exception as e:
- return str(e)
-
- completion = client.chat.completions.create(
- model="gpt-3.5-turbo",
- messages=[
- {"role": "system", "content": prompt},
- {"role": "user", "content": markdown_output_final}
- ]
- )
- final_response= completion.choices[0].message.content
- return f"**Summary**: {final_response}\n\n**Full output**:\n{markdown_output}"
- except Exception as e:
- return str(e)
-
-
-# Define the Gradio interface
-# iface = gr.Interface(
-# fn=process_and_query,
-# inputs=[
-# gr.Textbox(label="Input Text"),
-# gr.Image(label="Upload Image"),
-# gr.Audio(label="talk in french",
-# sources=["microphone"]),
-# ],
-# outputs=[gr.Markdown(label="Output Text")],
-# title="👋🏻Welcome to ⚕🗣️😷MultiMed - Access Chat ⚕🗣️😷",
-# description='''
-# ### How To Use ⚕🗣️😷MultiMed⚕:
-# #### 🗣️📝Interact with ⚕🗣️😷MultiMed⚕ in any language using audio or text!
-# #### 🗣️📝 This is an educational and accessible conversational tool to improve wellness and sanitation in support of public health.
-# #### 📚🌟💼 The knowledge base is composed of publicly available medical and health sources in multiple languages. We also used [Kelvalya/MedAware](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset) that we processed and converted to HTML. The quality of the answers depends on the quality of the dataset, so if you want to see some data represented here, do [get in touch](https://discord.gg/GWpVpekp). You can also use 😷MultiMed⚕️ on your own data & in your own way by cloning this space. 🧬🔬🔍 Simply click here:
-# #### Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community on 👻Discord: [Discord](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)"
-# ''',
-# theme='ParityError/Anime',
-# examples=[
-# ["What is the proper treatment for buccal herpes?"],
-# ["Male, 40 presenting with swollen glands and a rash"],
-# ["How does cellular metabolism work TCA cycle"],
-# ["What special care must be provided to children with chicken pox?"],
-# ["When and how often should I wash my hands ?"],
-# ["بکل ہرپس کا صحیح علاج کیا ہے؟"],
-# ["구강 헤르페스의 적절한 치료법은 무엇입니까?"],
-# ["Je, ni matibabu gani sahihi kwa herpes ya buccal?"],
-# ],
-# )
-
-welcome_message = """
-# 👋🏻Welcome to ⚕🗣️😷MultiMed - Access Chat ⚕🗣️😷
-### How To Use ⚕🗣️😷MultiMed⚕:
-#### 🗣️📝Interact with ⚕🗣️😷MultiMed⚕ in any language using audio or text!
-#### 🗣️📝 This is an educational and accessible conversational tool to improve wellness and sanitation in support of public health.
-#### 📚🌟💼 The knowledge base is composed of publicly available medical and health sources in multiple languages. We also used [Kelvalya/MedAware](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset) that we processed and converted to HTML. The quality of the answers depends on the quality of the dataset, so if you want to see some data represented here, do [get in touch](https://discord.gg/GWpVpekp). You can also use 😷MultiMed⚕️ on your own data & in your own way by cloning this space. 🧬🔬🔍 Simply click here:
-#### Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community on 👻Discord: [Discord](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)"
-"""
-
-
-languages = [
- "Afrikaans",
- "Amharic",
- "Modern Standard Arabic",
- "Moroccan Arabic",
- "Egyptian Arabic",
- "Assamese",
- "Asturian",
- "North Azerbaijani",
- "Belarusian",
- "Bengali",
- "Bosnian",
- "Bulgarian",
- "Catalan",
- "Cebuano",
- "Czech",
- "Central Kurdish",
- "Mandarin Chinese",
- "Welsh",
- "Danish",
- "German",
- "Greek",
- "English",
- "Estonian",
- "Basque",
- "Finnish",
- "French",
- "West Central Oromo",
- "Irish",
- "Galician",
- "Gujarati",
- "Hebrew",
- "Hindi",
- "Croatian",
- "Hungarian",
- "Armenian",
- "Igbo",
- "Indonesian",
- "Icelandic",
- "Italian",
- "Javanese",
- "Japanese",
- "Kamba",
- "Kannada",
- "Georgian",
- "Kazakh",
- "Kabuverdianu",
- "Halh Mongolian",
- "Khmer",
- "Kyrgyz",
- "Korean",
- "Lao",
- "Lithuanian",
- "Luxembourgish",
- "Ganda",
- "Luo",
- "Standard Latvian",
- "Maithili",
- "Malayalam",
- "Marathi",
- "Macedonian",
- "Maltese",
- "Meitei",
- "Burmese",
- "Dutch",
- "Norwegian Nynorsk",
- "Norwegian Bokmål",
- "Nepali",
- "Nyanja",
- "Occitan",
- "Odia",
- "Punjabi",
- "Southern Pashto",
- "Western Persian",
- "Polish",
- "Portuguese",
- "Romanian",
- "Russian",
- "Slovak",
- "Slovenian",
- "Shona",
- "Sindhi",
- "Somali",
- "Spanish",
- "Serbian",
- "Swedish",
- "Swahili",
- "Tamil",
- "Telugu",
- "Tajik",
- "Tagalog",
- "Thai",
- "Turkish",
- "Ukrainian",
- "Urdu",
- "Northern Uzbek",
- "Vietnamese",
- "Xhosa",
- "Yoruba",
- "Cantonese",
- "Colloquial Malay",
- "Standard Malay",
- "Zulu"
-]
-
-
-with gr.Blocks(theme='ParityError/Anime') as iface :
- gr.Markdown(welcome_message)
- with gr.Tab("text summarization"):
- text_input = gr.Textbox(label="input text",lines=5)
- text_output = gr.Markdown(label="output text")
- text_button = gr.Button("process text")
- gr.Examples([
- ["What is the proper treatment for buccal herpes?"],
- ["Male, 40 presenting with swollen glands and a rash"],
- ["How does cellular metabolism work TCA cycle"],
- ["What special care must be provided to children with chicken pox?"],
- ["When and how often should I wash my hands?"],
- ["بکل ہرپس کا صحیح علاج کیا ہے؟"],
- ["구강 헤르페스의 적절한 치료법은 무엇입니까?"],
- ["Je, ni matibabu gani sahihi kwa herpes ya buccal?"],
- ],inputs=[text_input])
- with gr.Tab("image identification"):
- image_input = gr.Image(label="upload image")
- image_output = gr.Markdown(label="output text")
- image_button = gr.Button("process image")
- image_button.click(process_image, inputs=image_input, outputs=image_output)
- gr.Examples(["sick person.jpeg"],inputs=[image_input])
- with gr.Tab("speech to text"):
- input_language = gr.Dropdown(languages, label="select the language",value="English",interactive=True)
- audio_input = gr.Audio(label="speak",type="filepath",sources="microphone")
- audio_output = gr.Markdown(label="output text")
- audio_button = gr.Button("process audio")
- audio_button.click(process_speech, inputs=[input_language,audio_input], outputs=audio_output)
- gr.Examples([["English","sample_input.mp3"]],inputs=[input_language,audio_input])
- with gr.Tab("hallucination check"):
- assertion = gr.Textbox(label="assertion")
- citation = gr.Textbox(label="citation text")
- hullucination_output = gr.Markdown(label="output text")
- audio_button = gr.Button("check hallucination")
- gr.Examples([["i am drunk","sarah is pregnant"]],inputs=[assertion,citation])
- text_button.click(process_and_query, inputs=text_input, outputs=text_output)
- audio_button.click(check_hallucination,inputs=[assertion,citation],outputs=hullucination_output)
-
-
-
-
-iface.queue().launch(show_error=True,debug=True)
diff --git a/spaces/Thanarit/GPT-Detection-Demo/app.py b/spaces/Thanarit/GPT-Detection-Demo/app.py
deleted file mode 100644
index b0de7e28071553c6b86ec9a73b9881b4d540c0bc..0000000000000000000000000000000000000000
--- a/spaces/Thanarit/GPT-Detection-Demo/app.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-from ModelDriver import *
-import numpy as np
-
-# Add a title
-st.title('GPT Detection Demo')
-st.write("This is a demo for GPT detection. You can use this demo to test the model. The model is trained on two datasets: OpenGPT and CSAbstract. You can choose the model and dataset in the sidebar.")
-st.write("Reference on how we built Roberta Sentinel: https://arxiv.org/abs/2305.07969")
-
-# Add 4 options for 4 models
-ModelOption = st.sidebar.selectbox(
- 'Which Model do you want to use?',
- ('RobertaSentinel', 'RobertaClassifier'),
-)
-
-DatasetOption = st.sidebar.selectbox(
- 'Which Dataset the model was trained on?',
- ('OpenGPT', 'CSAbstract'),
-)
-
-
-text = st.text_area('Enter text here (max 500 words)', '')
-
-if st.button('Generate'):
- if ModelOption == 'RobertaSentinel':
- if DatasetOption == 'OpenGPT':
- result = RobertaSentinelOpenGPTInference(text)
- st.write("Model: RobertaSentinelOpenGPT")
- elif DatasetOption == 'CSAbstract':
- result = RobertaSentinelCSAbstractInference(text)
- st.write("Model: RobertaSentinelCSAbstract")
-
- elif ModelOption == 'RobertaClassifier':
- if DatasetOption == 'OpenGPT':
- result = RobertaClassifierOpenGPTInference(text)
- st.write("Model: RobertaClassifierOpenGPT")
- elif DatasetOption == 'CSAbstract':
- result = RobertaClassifierCSAbstractInference(text)
- st.write("Model: RobertaClassifierCSAbstract")
-
- Prediction = "Human Written" if not np.argmax(result) else "Machine Generated"
-
- st.write(f"Prediction: {Prediction} ")
- st.write(f"Probabilty:", max(result))
-
-
-
-
-
-
-
-
-
diff --git a/spaces/ThomasSimonini/Unity-MLAgents-Pyramids/index.html b/spaces/ThomasSimonini/Unity-MLAgents-Pyramids/index.html
deleted file mode 100644
index 37f4404c8056925b6523479300dec8d2406d5c96..0000000000000000000000000000000000000000
--- a/spaces/ThomasSimonini/Unity-MLAgents-Pyramids/index.html
+++ /dev/null
@@ -1,92 +0,0 @@
-
-
-
-
-
-
- Unity-MLAgents-Pyramids
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/Tirendaz/Text-Classification/app.py b/spaces/Tirendaz/Text-Classification/app.py
deleted file mode 100644
index 125b46d586913e6a24ccb8585102a076baf95d9d..0000000000000000000000000000000000000000
--- a/spaces/Tirendaz/Text-Classification/app.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-classifier = pipeline("sentiment-analysis", model="Tirendaz/my_distilbert_model")
-
-def text_classification(text):
- result= classifier(text)
- sentiment_label = result[0]['label']
- sentiment_score = result[0]['score']
- formatted_output = f"This sentiment is {sentiment_label} with the probability {sentiment_score*100:.2f}%"
- return formatted_output
-
-examples=["This is wonderful movie!", "The movie was really bad; I didn't like it."]
-
-io = gr.Interface(fn=text_classification,
- inputs= gr.Textbox(lines=2, label="Text", placeholder="Enter title here..."),
- outputs=gr.Textbox(lines=2, label="Text Classification Result"),
- title="Text Classification",
- description="Enter a text and see the text classification result!",
- examples=examples)
-
-io.launch()
-
diff --git a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Vercel.py b/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Vercel.py
deleted file mode 100644
index e5df9cf017e4c1a265f5c9d5e48eb5c10a56e60a..0000000000000000000000000000000000000000
--- a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Vercel.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import os
-import json
-import base64
-import execjs
-import queue
-import threading
-
-from curl_cffi import requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://play.vercel.ai'
-supports_stream = True
-needs_auth = False
-
-models = {
- 'claude-instant-v1': 'anthropic:claude-instant-v1',
- 'claude-v1': 'anthropic:claude-v1',
- 'alpaca-7b': 'replicate:replicate/alpaca-7b',
- 'stablelm-tuned-alpha-7b': 'replicate:stability-ai/stablelm-tuned-alpha-7b',
- 'bloom': 'huggingface:bigscience/bloom',
- 'bloomz': 'huggingface:bigscience/bloomz',
- 'flan-t5-xxl': 'huggingface:google/flan-t5-xxl',
- 'flan-ul2': 'huggingface:google/flan-ul2',
- 'gpt-neox-20b': 'huggingface:EleutherAI/gpt-neox-20b',
- 'oasst-sft-4-pythia-12b-epoch-3.5': 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5',
- 'santacoder': 'huggingface:bigcode/santacoder',
- 'command-medium-nightly': 'cohere:command-medium-nightly',
- 'command-xlarge-nightly': 'cohere:command-xlarge-nightly',
- 'code-cushman-001': 'openai:code-cushman-001',
- 'code-davinci-002': 'openai:code-davinci-002',
- 'gpt-3.5-turbo': 'openai:gpt-3.5-turbo',
- 'text-ada-001': 'openai:text-ada-001',
- 'text-babbage-001': 'openai:text-babbage-001',
- 'text-curie-001': 'openai:text-curie-001',
- 'text-davinci-002': 'openai:text-davinci-002',
- 'text-davinci-003': 'openai:text-davinci-003'
-}
-model = models.keys()
-
-vercel_models = {'anthropic:claude-instant-v1': {'id': 'anthropic:claude-instant-v1', 'provider': 'anthropic', 'providerHumanName': 'Anthropic', 'makerHumanName': 'Anthropic', 'minBillingTier': 'hobby', 'parameters': {'temperature': {'value': 1, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': ['\n\nHuman:'], 'range': []}}, 'name': 'claude-instant-v1'}, 'anthropic:claude-v1': {'id': 'anthropic:claude-v1', 'provider': 'anthropic', 'providerHumanName': 'Anthropic', 'makerHumanName': 'Anthropic', 'minBillingTier': 'hobby', 'parameters': {'temperature': {'value': 1, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': ['\n\nHuman:'], 'range': []}}, 'name': 'claude-v1'}, 'replicate:replicate/alpaca-7b': {'id': 'replicate:replicate/alpaca-7b', 'provider': 'replicate', 'providerHumanName': 'Replicate', 'makerHumanName': 'Stanford', 'parameters': {'temperature': {'value': 0.75, 'range': [0.01, 5]}, 'maximumLength': {'value': 200, 'range': [50, 512]}, 'topP': {'value': 0.95, 'range': [0.01, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'repetitionPenalty': {'value': 1.1765, 'range': [0.01, 5]}, 'stopSequences': {'value': [], 'range': []}}, 'version': '2014ee1247354f2e81c0b3650d71ca715bc1e610189855f134c30ecb841fae21', 'name': 'alpaca-7b'}, 'replicate:stability-ai/stablelm-tuned-alpha-7b': {'id': 'replicate:stability-ai/stablelm-tuned-alpha-7b', 'provider': 'replicate', 'makerHumanName': 'StabilityAI', 'providerHumanName': 'Replicate', 'parameters': {'temperature': {'value': 0.75, 'range': [0.01, 5]}, 'maximumLength': {'value': 200, 'range': [50, 512]}, 'topP': {'value': 0.95, 'range': [0.01, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'repetitionPenalty': {'value': 1.1765, 'range': [0.01, 5]}, 'stopSequences': {'value': [], 'range': []}}, 'version': '4a9a32b4fd86c2d047f1d271fa93972683ec6ef1cf82f402bd021f267330b50b', 'name': 'stablelm-tuned-alpha-7b'}, 'huggingface:bigscience/bloom': {'id': 'huggingface:bigscience/bloom', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigScience', 'instructions': "Do NOT talk to Bloom as an entity, it's not a chatbot but a webpage/blog/article completion model. For the best results: mimic a few words of a webpage similar to the content you want to generate. Start a sentence as if YOU were writing a blog, webpage, math post, coding article and Bloom will generate a coherent follow-up.", 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'bloom'}, 'huggingface:bigscience/bloomz': {'id': 'huggingface:bigscience/bloomz', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigScience', 'instructions': 'We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "Translate to English: Je t\'aime.", the model will most likely answer "I love you.".', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'bloomz'}, 'huggingface:google/flan-t5-xxl': {'id': 'huggingface:google/flan-t5-xxl', 'provider': 'huggingface', 'makerHumanName': 'Google', 'providerHumanName': 'HuggingFace', 'name': 'flan-t5-xxl', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}}, 'huggingface:google/flan-ul2': {'id': 'huggingface:google/flan-ul2', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'Google', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'flan-ul2'}, 'huggingface:EleutherAI/gpt-neox-20b': {'id': 'huggingface:EleutherAI/gpt-neox-20b', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'EleutherAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'gpt-neox-20b'}, 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5': {'id': 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'OpenAssistant', 'parameters': {'maximumLength': {'value': 200, 'range': [50, 1024]}, 'typicalP': {'value': 0.2, 'range': [0.1, 0.99]}, 'repetitionPenalty': {'value': 1, 'range': [0.1, 2]}}, 'name': 'oasst-sft-4-pythia-12b-epoch-3.5'}, 'huggingface:bigcode/santacoder': {
- 'id': 'huggingface:bigcode/santacoder', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigCode', 'instructions': 'The model was trained on GitHub code. As such it is not an instruction model and commands like "Write a function that computes the square root." do not work well. You should phrase commands like they occur in source code such as comments (e.g. # the following function computes the sqrt) or write a function signature and docstring and let the model complete the function body.', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'santacoder'}, 'cohere:command-medium-nightly': {'id': 'cohere:command-medium-nightly', 'provider': 'cohere', 'providerHumanName': 'Cohere', 'makerHumanName': 'Cohere', 'name': 'command-medium-nightly', 'parameters': {'temperature': {'value': 0.9, 'range': [0, 2]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0, 1]}, 'topK': {'value': 0, 'range': [0, 500]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'cohere:command-xlarge-nightly': {'id': 'cohere:command-xlarge-nightly', 'provider': 'cohere', 'providerHumanName': 'Cohere', 'makerHumanName': 'Cohere', 'name': 'command-xlarge-nightly', 'parameters': {'temperature': {'value': 0.9, 'range': [0, 2]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0, 1]}, 'topK': {'value': 0, 'range': [0, 500]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:gpt-4': {'id': 'openai:gpt-4', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'gpt-4', 'minBillingTier': 'pro', 'parameters': {'temperature': {'value': 0.7, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:code-cushman-001': {'id': 'openai:code-cushman-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'code-cushman-001'}, 'openai:code-davinci-002': {'id': 'openai:code-davinci-002', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'code-davinci-002'}, 'openai:gpt-3.5-turbo': {'id': 'openai:gpt-3.5-turbo', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.7, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'gpt-3.5-turbo'}, 'openai:text-ada-001': {'id': 'openai:text-ada-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-ada-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-babbage-001': {'id': 'openai:text-babbage-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-babbage-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-curie-001': {'id': 'openai:text-curie-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-curie-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-davinci-002': {'id': 'openai:text-davinci-002', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-davinci-002', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-davinci-003': {'id': 'openai:text-davinci-003', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-davinci-003', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}}
-
-
-# based on https://github.com/ading2210/vercel-llm-api // modified
-class Client:
- def __init__(self):
- self.session = requests.Session()
- self.headers = {
- 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110 Safari/537.36',
- 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8',
- 'Accept-Encoding': 'gzip, deflate, br',
- 'Accept-Language': 'en-US,en;q=0.5',
- 'Te': 'trailers',
- 'Upgrade-Insecure-Requests': '1'
- }
- self.session.headers.update(self.headers)
-
- def get_token(self):
- b64 = self.session.get('https://sdk.vercel.ai/openai.jpeg').text
- data = json.loads(base64.b64decode(b64))
-
- code = 'const globalThis = {data: `sentinel`}; function token() {return (%s)(%s)}' % (
- data['c'], data['a'])
-
- token_string = json.dumps(separators=(',', ':'),
- obj={'r': execjs.compile(code).call('token'), 't': data['t']})
-
- return base64.b64encode(token_string.encode()).decode()
-
- def get_default_params(self, model_id):
- return {key: param['value'] for key, param in vercel_models[model_id]['parameters'].items()}
-
- def generate(self, model_id: str, prompt: str, params: dict = {}):
- if not ':' in model_id:
- model_id = models[model_id]
-
- defaults = self.get_default_params(model_id)
-
- payload = defaults | params | {
- 'prompt': prompt,
- 'model': model_id,
- }
-
- headers = self.headers | {
- 'Accept-Encoding': 'gzip, deflate, br',
- 'Custom-Encoding': self.get_token(),
- 'Host': 'sdk.vercel.ai',
- 'Origin': 'https://sdk.vercel.ai',
- 'Referrer': 'https://sdk.vercel.ai',
- 'Sec-Fetch-Dest': 'empty',
- 'Sec-Fetch-Mode': 'cors',
- 'Sec-Fetch-Site': 'same-origin',
- }
-
- chunks_queue = queue.Queue()
- error = None
- response = None
-
- def callback(data):
- chunks_queue.put(data.decode())
-
- def request_thread():
- nonlocal response, error
- for _ in range(3):
- try:
- response = self.session.post('https://sdk.vercel.ai/api/generate',
- json=payload, headers=headers, content_callback=callback)
- response.raise_for_status()
-
- except Exception as e:
- if _ == 2:
- error = e
-
- else:
- continue
-
- thread = threading.Thread(target=request_thread, daemon=True)
- thread.start()
-
- text = ''
- index = 0
- while True:
- try:
- chunk = chunks_queue.get(block=True, timeout=0.1)
-
- except queue.Empty:
- if error:
- raise error
-
- elif response:
- break
-
- else:
- continue
-
- text += chunk
- lines = text.split('\n')
-
- if len(lines) - 1 > index:
- new = lines[index:-1]
- for word in new:
- yield json.loads(word)
- index = len(lines) - 1
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
- yield 'Vercel is currently not working.'
- return
-
- conversation = 'This is a conversation between a human and a language model, respond to the last message accordingly, referring to the past history of messages if needed.\n'
-
- for message in messages:
- conversation += '%s: %s\n' % (message['role'], message['content'])
-
- conversation += 'assistant: '
-
- completion = Client().generate(model, conversation)
-
- for token in completion:
- yield token
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/Vijish/Crop-CLIP/README.md b/spaces/Vijish/Crop-CLIP/README.md
deleted file mode 100644
index bdcae9f45efba75036d8cd67008634852f99dcfc..0000000000000000000000000000000000000000
--- a/spaces/Vijish/Crop-CLIP/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Crop CLIP
-emoji: 🦀
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Violette/Protogen_x3.4_Official_Release/README.md b/spaces/Violette/Protogen_x3.4_Official_Release/README.md
deleted file mode 100644
index f956b53e422114f032bad59a721dd2d563081a37..0000000000000000000000000000000000000000
--- a/spaces/Violette/Protogen_x3.4_Official_Release/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Protogen X3.4 Official Release
-emoji: 📈
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/tasks/__init__.py b/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/tasks/__init__.py
deleted file mode 100644
index ab1fb1c8289535cf9397bb9805c0cba3666ad26f..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/tasks/__init__.py
+++ /dev/null
@@ -1,26 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-from minigpt4.common.registry import registry
-from minigpt4.tasks.base_task import BaseTask
-from minigpt4.tasks.image_text_pretrain import ImageTextPretrainTask
-
-
-def setup_task(cfg):
- assert "task" in cfg.run_cfg, "Task name must be provided."
-
- task_name = cfg.run_cfg.task
- task = registry.get_task_class(task_name).setup_task(cfg=cfg)
- assert task is not None, "Task {} not properly registered.".format(task_name)
-
- return task
-
-
-__all__ = [
- "BaseTask",
- "ImageTextPretrainTask",
-]
diff --git a/spaces/Voicelab/vlT5-keywords-generation/README.md b/spaces/Voicelab/vlT5-keywords-generation/README.md
deleted file mode 100644
index bb0ec20c0d1e6457305ec098f8f8437c1d529252..0000000000000000000000000000000000000000
--- a/spaces/Voicelab/vlT5-keywords-generation/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: VlT5 Keywords Generation
-emoji: 🌍
-colorFrom: pink
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/utils.py b/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/utils.py
deleted file mode 100644
index ee4b01ddfbe8173965371b29f770f3e87615fe71..0000000000000000000000000000000000000000
--- a/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/utils.py
+++ /dev/null
@@ -1,225 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-import librosa
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict= {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})" .format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10,2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_audio_to_torch(full_path, target_sampling_rate):
- audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
- return torch.FloatTensor(audio.astype(np.float32))
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/XPMaster/KSA_Weather_Prediction/README.md b/spaces/XPMaster/KSA_Weather_Prediction/README.md
deleted file mode 100644
index eb0dad165265158f4093d00781e64bb2dc852b4f..0000000000000000000000000000000000000000
--- a/spaces/XPMaster/KSA_Weather_Prediction/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: KSA Weather Prediction
-emoji: ⚡
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/metrics.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/metrics.py
deleted file mode 100644
index 46fdddf3de2cf8d987ecb4c7d7cb3503afa995ad..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/metrics.py
+++ /dev/null
@@ -1,361 +0,0 @@
-"Implements various metrics to measure training accuracy"
-from .torch_core import *
-from .callback import *
-from .layers import *
-from .basic_train import LearnerCallback
-
-__all__ = ['error_rate', 'accuracy', 'accuracy_thresh', 'dice', 'exp_rmspe', 'fbeta','FBeta', 'mse', 'mean_squared_error',
- 'mae', 'mean_absolute_error', 'rmse', 'root_mean_squared_error', 'msle', 'mean_squared_logarithmic_error',
- 'explained_variance', 'r2_score', 'top_k_accuracy', 'KappaScore', 'ConfusionMatrix', 'MatthewsCorreff',
- 'Precision', 'Recall', 'R2Score', 'ExplainedVariance', 'ExpRMSPE', 'RMSE', 'Perplexity', 'AUROC', 'auc_roc_score',
- 'roc_curve', 'MultiLabelFbeta', 'foreground_acc']
-
-def fbeta(y_pred:Tensor, y_true:Tensor, thresh:float=0.2, beta:float=2, eps:float=1e-9, sigmoid:bool=True)->Rank0Tensor:
- "Computes the f_beta between `preds` and `targets`"
- beta2 = beta ** 2
- if sigmoid: y_pred = y_pred.sigmoid()
- y_pred = (y_pred>thresh).float()
- y_true = y_true.float()
- TP = (y_pred*y_true).sum(dim=1)
- prec = TP/(y_pred.sum(dim=1)+eps)
- rec = TP/(y_true.sum(dim=1)+eps)
- res = (prec*rec)/(prec*beta2+rec+eps)*(1+beta2)
- return res.mean()
-
-def accuracy(input:Tensor, targs:Tensor)->Rank0Tensor:
- "Computes accuracy with `targs` when `input` is bs * n_classes."
- n = targs.shape[0]
- input = input.argmax(dim=-1).view(n,-1)
- targs = targs.view(n,-1)
- return (input==targs).float().mean()
-
-def accuracy_thresh(y_pred:Tensor, y_true:Tensor, thresh:float=0.5, sigmoid:bool=True)->Rank0Tensor:
- "Computes accuracy when `y_pred` and `y_true` are the same size."
- if sigmoid: y_pred = y_pred.sigmoid()
- return ((y_pred>thresh)==y_true.byte()).float().mean()
-
-def top_k_accuracy(input:Tensor, targs:Tensor, k:int=5)->Rank0Tensor:
- "Computes the Top-k accuracy (target is in the top k predictions)."
- input = input.topk(k=k, dim=-1)[1]
- targs = targs.unsqueeze(dim=-1).expand_as(input)
- return (input == targs).max(dim=-1)[0].float().mean()
-
-def foreground_acc(input, target, void_code):
- "Computes non-background accuracy, e.g. camvid for multiclass segmentation"
- target = target.squeeze(1)
- mask = target != void_code
- return (input.argmax(dim=1)[mask]==target[mask]).float().mean()
-
-def error_rate(input:Tensor, targs:Tensor)->Rank0Tensor:
- "1 - `accuracy`"
- return 1 - accuracy(input, targs)
-
-def dice(input:Tensor, targs:Tensor, iou:bool=False, eps:float=1e-8)->Rank0Tensor:
- "Dice coefficient metric for binary target. If iou=True, returns iou metric, classic for segmentation problems."
- n = targs.shape[0]
- input = input.argmax(dim=1).view(n,-1)
- targs = targs.view(n,-1)
- intersect = (input * targs).sum().float()
- union = (input+targs).sum().float()
- if not iou: return (2. * intersect / union if union > 0 else union.new([1.]).squeeze())
- else: return (intersect / (union-intersect+eps) if union > 0 else union.new([1.]).squeeze())
-
-def psnr(input:Tensor, targs:Tensor)->Rank0Tensor:
- return 10 * (1. / mean_squared_error(input, targs)).log10()
-
-def exp_rmspe(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Exp RMSE between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- pred, targ = torch.exp(pred), torch.exp(targ)
- pct_var = (targ - pred)/targ
- return torch.sqrt((pct_var**2).mean())
-
-def mean_absolute_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean absolute error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return torch.abs(targ - pred).mean()
-
-def mean_squared_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean squared error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return F.mse_loss(pred, targ)
-
-def root_mean_squared_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Root mean squared error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return torch.sqrt(F.mse_loss(pred, targ))
-
-def mean_squared_logarithmic_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean squared logarithmic error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return F.mse_loss(torch.log(1 + pred), torch.log(1 + targ))
-
-def explained_variance(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Explained variance between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- var_pct = torch.var(targ - pred) / torch.var(targ)
- return 1 - var_pct
-
-def r2_score(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "R2 score (coefficient of determination) between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- u = torch.sum((targ - pred) ** 2)
- d = torch.sum((targ - targ.mean()) ** 2)
- return 1 - u / d
-
-class RegMetrics(Callback):
- "Stores predictions and targets to perform calculations on epoch end."
- def on_epoch_begin(self, **kwargs):
- self.targs, self.preds = Tensor([]), Tensor([])
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- assert last_output.numel() == last_target.numel(), "Expected same numbers of elements in pred & targ"
- self.preds = torch.cat((self.preds, last_output.cpu()))
- self.targs = torch.cat((self.targs, last_target.cpu()))
-
-class R2Score(RegMetrics):
- "Computes the R2 score (coefficient of determination)."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, r2_score(self.preds, self.targs))
-
-class ExplainedVariance(RegMetrics):
- "Computes the explained variance."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, explained_variance(self.preds, self.targs))
-
-class RMSE(RegMetrics):
- "Computes the root mean squared error."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, root_mean_squared_error(self.preds, self.targs))
-
-class ExpRMSPE(RegMetrics):
- "Computes the exponential of the root mean square error."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, exp_rmspe(self.preds, self.targs))
-
-# Aliases
-mse = mean_squared_error
-mae = mean_absolute_error
-msle = mean_squared_logarithmic_error
-rmse = root_mean_squared_error
-
-class ConfusionMatrix(Callback):
- "Computes the confusion matrix."
-
- def on_train_begin(self, **kwargs):
- self.n_classes = 0
-
- def on_epoch_begin(self, **kwargs):
- self.cm = None
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- preds = last_output.argmax(-1).view(-1).cpu()
- targs = last_target.cpu()
- if self.n_classes == 0:
- self.n_classes = last_output.shape[-1]
- self.x = torch.arange(0, self.n_classes)
- cm = ((preds==self.x[:, None]) & (targs==self.x[:, None, None])).sum(dim=2, dtype=torch.float32)
- if self.cm is None: self.cm = cm
- else: self.cm += cm
-
- def on_epoch_end(self, **kwargs):
- self.metric = self.cm
-
-@dataclass
-class CMScores(ConfusionMatrix):
- "Base class for metrics which rely on the calculation of the precision and/or recall score."
- average:Optional[str]="binary" # `binary`, `micro`, `macro`, `weigthed` or None
- pos_label:int=1 # 0 or 1
- eps:float=1e-9
-
- def _recall(self):
- rec = torch.diag(self.cm) / self.cm.sum(dim=1)
- if self.average is None: return rec
- else:
- if self.average == "micro": weights = self._weights(avg="weighted")
- else: weights = self._weights(avg=self.average)
- return (rec * weights).sum()
-
- def _precision(self):
- prec = torch.diag(self.cm) / self.cm.sum(dim=0)
- if self.average is None: return prec
- else:
- weights = self._weights(avg=self.average)
- return (prec * weights).sum()
-
- def _weights(self, avg:str):
- if self.n_classes != 2 and avg == "binary":
- avg = self.average = "macro"
- warn("average=`binary` was selected for a non binary case. Value for average has now been set to `macro` instead.")
- if avg == "binary":
- if self.pos_label not in (0, 1):
- self.pos_label = 1
- warn("Invalid value for pos_label. It has now been set to 1.")
- if self.pos_label == 1: return Tensor([0,1])
- else: return Tensor([1,0])
- elif avg == "micro": return self.cm.sum(dim=0) / self.cm.sum()
- elif avg == "macro": return torch.ones((self.n_classes,)) / self.n_classes
- elif avg == "weighted": return self.cm.sum(dim=1) / self.cm.sum()
-
-
-class Recall(CMScores):
- "Computes the Recall."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, self._recall())
-
-class Precision(CMScores):
- "Computes the Precision."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, self._precision())
-
-@dataclass
-class FBeta(CMScores):
- "Computes the F`beta` score."
- beta:float=2
-
- def on_train_begin(self, **kwargs):
- self.n_classes = 0
- self.beta2 = self.beta ** 2
- self.avg = self.average
- if self.average != "micro": self.average = None
-
- def on_epoch_end(self, last_metrics, **kwargs):
- prec = self._precision()
- rec = self._recall()
- metric = (1 + self.beta2) * prec * rec / (prec * self.beta2 + rec + self.eps)
- metric[metric != metric] = 0 # removing potential "nan"s
- if self.avg: metric = (self._weights(avg=self.avg) * metric).sum()
- return add_metrics(last_metrics, metric)
-
- def on_train_end(self, **kwargs): self.average = self.avg
-
-@dataclass
-class KappaScore(ConfusionMatrix):
- "Computes the rate of agreement (Cohens Kappa)."
- weights:Optional[str]=None # None, `linear`, or `quadratic`
-
- def on_epoch_end(self, last_metrics, **kwargs):
- sum0 = self.cm.sum(dim=0)
- sum1 = self.cm.sum(dim=1)
- expected = torch.einsum('i,j->ij', (sum0, sum1)) / sum0.sum()
- if self.weights is None:
- w = torch.ones((self.n_classes, self.n_classes))
- w[self.x, self.x] = 0
- elif self.weights == "linear" or self.weights == "quadratic":
- w = torch.zeros((self.n_classes, self.n_classes))
- w += torch.arange(self.n_classes, dtype=torch.float)
- w = torch.abs(w - torch.t(w)) if self.weights == "linear" else (w - torch.t(w)) ** 2
- else: raise ValueError('Unknown weights. Expected None, "linear", or "quadratic".')
- k = torch.sum(w * self.cm) / torch.sum(w * expected)
- return add_metrics(last_metrics, 1-k)
-
-@dataclass
-class MatthewsCorreff(ConfusionMatrix):
- "Computes the Matthews correlation coefficient."
- def on_epoch_end(self, last_metrics, **kwargs):
- t_sum = self.cm.sum(dim=1)
- p_sum = self.cm.sum(dim=0)
- n_correct = torch.trace(self.cm)
- n_samples = p_sum.sum()
- cov_ytyp = n_correct * n_samples - torch.dot(t_sum, p_sum)
- cov_ypyp = n_samples ** 2 - torch.dot(p_sum, p_sum)
- cov_ytyt = n_samples ** 2 - torch.dot(t_sum, t_sum)
- return add_metrics(last_metrics, cov_ytyp / torch.sqrt(cov_ytyt * cov_ypyp))
-
-class Perplexity(Callback):
- "Perplexity metric for language models."
- def on_epoch_begin(self, **kwargs): self.loss,self.len = 0.,0
-
- def on_batch_end(self, last_output, last_target, **kwargs):
- self.loss += last_target.size(1) * CrossEntropyFlat()(last_output, last_target)
- self.len += last_target.size(1)
-
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, torch.exp(self.loss / self.len))
-
-def auc_roc_score(input:Tensor, targ:Tensor):
- "Computes the area under the receiver operator characteristic (ROC) curve using the trapezoid method. Restricted binary classification tasks."
- fpr, tpr = roc_curve(input, targ)
- d = fpr[1:] - fpr[:-1]
- sl1, sl2 = [slice(None)], [slice(None)]
- sl1[-1], sl2[-1] = slice(1, None), slice(None, -1)
- return (d * (tpr[tuple(sl1)] + tpr[tuple(sl2)]) / 2.).sum(-1)
-
-def roc_curve(input:Tensor, targ:Tensor):
- "Computes the receiver operator characteristic (ROC) curve by determining the true positive ratio (TPR) and false positive ratio (FPR) for various classification thresholds. Restricted binary classification tasks."
- targ = (targ == 1)
- desc_score_indices = torch.flip(input.argsort(-1), [-1])
- input = input[desc_score_indices]
- targ = targ[desc_score_indices]
- d = input[1:] - input[:-1]
- distinct_value_indices = torch.nonzero(d).transpose(0,1)[0]
- threshold_idxs = torch.cat((distinct_value_indices, LongTensor([len(targ) - 1]).to(targ.device)))
- tps = torch.cumsum(targ * 1, dim=-1)[threshold_idxs]
- fps = (1 + threshold_idxs - tps)
- if tps[0] != 0 or fps[0] != 0:
- fps = torch.cat((LongTensor([0]), fps))
- tps = torch.cat((LongTensor([0]), tps))
- fpr, tpr = fps.float() / fps[-1], tps.float() / tps[-1]
- return fpr, tpr
-
-@dataclass
-class AUROC(Callback):
- "Computes the area under the curve (AUC) score based on the receiver operator characteristic (ROC) curve. Restricted to binary classification tasks."
- def on_epoch_begin(self, **kwargs):
- self.targs, self.preds = LongTensor([]), Tensor([])
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- last_output = F.softmax(last_output, dim=1)[:,-1]
- self.preds = torch.cat((self.preds, last_output.cpu()))
- self.targs = torch.cat((self.targs, last_target.cpu().long()))
-
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, auc_roc_score(self.preds, self.targs))
-
-class MultiLabelFbeta(LearnerCallback):
- "Computes the fbeta score for multilabel classification"
- # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html
- _order = -20
- def __init__(self, learn, beta=2, eps=1e-15, thresh=0.3, sigmoid=True, average="micro"):
- super().__init__(learn)
- self.eps, self.thresh, self.sigmoid, self.average, self.beta2 = \
- eps, thresh, sigmoid, average, beta**2
-
- def on_train_begin(self, **kwargs):
- self.c = self.learn.data.c
- if self.average != "none": self.learn.recorder.add_metric_names([f'{self.average}_fbeta'])
- else: self.learn.recorder.add_metric_names([f"fbeta_{c}" for c in self.learn.data.classes])
-
- def on_epoch_begin(self, **kwargs):
- dvc = self.learn.data.device
- self.tp = torch.zeros(self.c).to(dvc)
- self.total_pred = torch.zeros(self.c).to(dvc)
- self.total_targ = torch.zeros(self.c).to(dvc)
-
- def on_batch_end(self, last_output, last_target, **kwargs):
- pred, targ = (last_output.sigmoid() if self.sigmoid else last_output) > self.thresh, last_target.byte()
- m = pred*targ
- self.tp += m.sum(0).float()
- self.total_pred += pred.sum(0).float()
- self.total_targ += targ.sum(0).float()
-
- def fbeta_score(self, precision, recall):
- return (1 + self.beta2)*(precision*recall)/((self.beta2*precision + recall) + self.eps)
-
- def on_epoch_end(self, last_metrics, **kwargs):
- self.total_pred += self.eps
- self.total_targ += self.eps
- if self.average == "micro":
- precision, recall = self.tp.sum() / self.total_pred.sum(), self.tp.sum() / self.total_targ.sum()
- res = self.fbeta_score(precision, recall)
- elif self.average == "macro":
- res = self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ)).mean()
- elif self.average == "weighted":
- scores = self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ))
- res = (scores*self.total_targ).sum() / self.total_targ.sum()
- elif self.average == "none":
- res = listify(self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ)))
- else:
- raise Exception("Choose one of the average types: [micro, macro, weighted, none]")
-
- return add_metrics(last_metrics, res)
diff --git a/spaces/XzJosh/Taffy-Bert-VITS2/transforms.py b/spaces/XzJosh/Taffy-Bert-VITS2/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Taffy-Bert-VITS2/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
-
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {
- 'tails': tails,
- 'tail_bound': tail_bound
- }
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(
- inputs[..., None] >= bin_locations,
- dim=-1
- ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails='linear',
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == 'linear':
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError('{} tails are not implemented.'.format(tails))
-
- outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative
- )
-
- return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0., right=1., bottom=0., top=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError('Input to a transform is not within its domain')
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError('Minimal bin width too large for the number of bins')
- if min_bin_height * num_bins > 1.0:
- raise ValueError('Minimal bin height too large for the number of bins')
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (((inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta)
- + input_heights * (input_delta - input_derivatives)))
- b = (input_heights * input_derivatives
- - (inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta))
- c = - input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (input_delta * theta.pow(2)
- + input_derivatives * theta_one_minus_theta)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/XzJosh/Wenjing-Bert-VITS2/server.py b/spaces/XzJosh/Wenjing-Bert-VITS2/server.py
deleted file mode 100644
index c736ca4f95fec853950eef6654ef79856beffc0a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Wenjing-Bert-VITS2/server.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from flask import Flask, request, Response
-from io import BytesIO
-import torch
-from av import open as avopen
-
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-from scipy.io import wavfile
-
-# Flask Init
-app = Flask(__name__)
-app.config['JSON_AS_ASCII'] = False
-def get_text(text, language_str, hps):
- norm_text, phone, tone, word2ph = clean_text(text, language_str)
- print([f"{p}{t}" for p, t in zip(phone, tone)])
- phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
- if hps.data.add_blank:
- phone = commons.intersperse(phone, 0)
- tone = commons.intersperse(tone, 0)
- language = commons.intersperse(language, 0)
- for i in range(len(word2ph)):
- word2ph[i] = word2ph[i] * 2
- word2ph[0] += 1
- bert = get_bert(norm_text, word2ph, language_str)
-
- assert bert.shape[-1] == len(phone)
-
- phone = torch.LongTensor(phone)
- tone = torch.LongTensor(tone)
- language = torch.LongTensor(language)
-
- return bert, phone, tone, language
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w,length_scale,sid):
- bert, phones, tones, lang_ids = get_text(text,"ZH", hps,)
- with torch.no_grad():
- x_tst=phones.to(dev).unsqueeze(0)
- tones=tones.to(dev).unsqueeze(0)
- lang_ids=lang_ids.to(dev).unsqueeze(0)
- bert = bert.to(dev).unsqueeze(0)
- x_tst_lengths = torch.LongTensor([phones.size(0)]).to(dev)
- speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(dev)
- audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids,bert, sdp_ratio=sdp_ratio
- , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
- return audio
-
-def replace_punctuation(text, i=2):
- punctuation = "，。？！"
- for char in punctuation:
- text = text.replace(char, char * i)
- return text
-
-def wav2(i, o, format):
- inp = avopen(i, 'rb')
- out = avopen(o, 'wb', format=format)
- if format == "ogg": format = "libvorbis"
-
- ostream = out.add_stream(format)
-
- for frame in inp.decode(audio=0):
- for p in ostream.encode(frame): out.mux(p)
-
- for p in ostream.encode(None): out.mux(p)
-
- out.close()
- inp.close()
-
-# Load Generator
-hps = utils.get_hparams_from_file("./configs/config.json")
-
-dev='cuda'
-net_g = SynthesizerTrn(
- len(symbols),
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- n_speakers=hps.data.n_speakers,
- **hps.model).to(dev)
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("logs/G_649000.pth", net_g, None,skip_optimizer=True)
-
-@app.route("/",methods=['GET','POST'])
-def main():
- if request.method == 'GET':
- try:
- speaker = request.args.get('speaker')
- text = request.args.get('text').replace("/n","")
- sdp_ratio = float(request.args.get("sdp_ratio", 0.2))
- noise = float(request.args.get("noise", 0.5))
- noisew = float(request.args.get("noisew", 0.6))
- length = float(request.args.get("length", 1.2))
- if length >= 2:
- return "Too big length"
- if len(text) >=200:
- return "Too long text"
- fmt = request.args.get("format", "wav")
- if None in (speaker, text):
- return "Missing Parameter"
- if fmt not in ("mp3", "wav", "ogg"):
- return "Invalid Format"
- except:
- return "Invalid Parameter"
-
- with torch.no_grad():
- audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise, noise_scale_w=noisew, length_scale=length, sid=speaker)
-
- with BytesIO() as wav:
- wavfile.write(wav, hps.data.sampling_rate, audio)
- torch.cuda.empty_cache()
- if fmt == "wav":
- return Response(wav.getvalue(), mimetype="audio/wav")
- wav.seek(0, 0)
- with BytesIO() as ofp:
- wav2(wav, ofp, fmt)
- return Response(
- ofp.getvalue(),
- mimetype="audio/mpeg" if fmt == "mp3" else "audio/ogg"
- )
diff --git a/spaces/YlcldKlns/bing/src/components/settings.tsx b/spaces/YlcldKlns/bing/src/components/settings.tsx
deleted file mode 100644
index 80b8a2d3b252b875f5b6f7dfc2f6e3ad9cdfb22a..0000000000000000000000000000000000000000
--- a/spaces/YlcldKlns/bing/src/components/settings.tsx
+++ /dev/null
@@ -1,157 +0,0 @@
-import { useEffect, useState } from 'react'
-import { useAtom } from 'jotai'
-import { Switch } from '@headlessui/react'
-import { toast } from 'react-hot-toast'
-import { hashAtom, voiceAtom } from '@/state'
-import {
- Dialog,
- DialogContent,
- DialogDescription,
- DialogFooter,
- DialogHeader,
- DialogTitle
-} from '@/components/ui/dialog'
-import { Button } from './ui/button'
-import { Input } from './ui/input'
-import { ChunkKeys, parseCookies, extraCurlFromCookie, encodeHeadersToCookie, getCookie, setCookie } from '@/lib/utils'
-import { ExternalLink } from './external-link'
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-
-
-export function Settings() {
- const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
- const [loc, setLoc] = useAtom(hashAtom)
- const [curlValue, setCurlValue] = useState(extraCurlFromCookie(parseCookies(document.cookie, ChunkKeys)))
- const [imageOnly, setImageOnly] = useState(getCookie('IMAGE_ONLY') !== '0')
- const [enableTTS, setEnableTTS] = useAtom(voiceAtom)
-
- useEffect(() => {
- if (isCopied) {
- toast.success('复制成功')
- }
- }, [isCopied])
-
- if (loc === 'settings') {
- return (
-
- )
- } else if (loc === 'voice') {
- return (
-
- )
- }
- return null
-}
diff --git a/spaces/Yudha515/Rvc-Models/audiocraft/models/lm.py b/spaces/Yudha515/Rvc-Models/audiocraft/models/lm.py
deleted file mode 100644
index c8aad8f06797eef3293605056e1de14d07c56c2a..0000000000000000000000000000000000000000
--- a/spaces/Yudha515/Rvc-Models/audiocraft/models/lm.py
+++ /dev/null
@@ -1,527 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from dataclasses import dataclass
-from functools import partial
-import logging
-import math
-import typing as tp
-
-import torch
-from torch import nn
-
-from ..utils import utils
-from ..modules.streaming import StreamingModule, State
-from ..modules.transformer import StreamingTransformer, create_norm_fn
-from ..modules.conditioners import (
- ConditionFuser,
- ClassifierFreeGuidanceDropout,
- AttributeDropout,
- ConditioningProvider,
- ConditioningAttributes,
- ConditionType,
-)
-from ..modules.codebooks_patterns import CodebooksPatternProvider
-from ..modules.activations import get_activation_fn
-
-
-logger = logging.getLogger(__name__)
-ConditionTensors = tp.Dict[str, ConditionType]
-CFGConditions = tp.Union[ConditionTensors, tp.Tuple[ConditionTensors, ConditionTensors]]
-
-
-def get_init_fn(method: str, input_dim: int, init_depth: tp.Optional[int] = None):
- """LM layer initialization.
- Inspired from xlformers: https://github.com/fairinternal/xlformers
-
- Args:
- method (str): Method name for init function. Valid options are:
- 'gaussian', 'uniform'.
- input_dim (int): Input dimension of the initialized module.
- init_depth (Optional[int]): Optional init depth value used to rescale
- the standard deviation if defined.
- """
- # Compute std
- std = 1 / math.sqrt(input_dim)
- # Rescale with depth
- if init_depth is not None:
- std = std / math.sqrt(2 * init_depth)
-
- if method == 'gaussian':
- return partial(
- torch.nn.init.trunc_normal_, mean=0.0, std=std, a=-3 * std, b=3 * std
- )
- elif method == 'uniform':
- bound = math.sqrt(3) * std # ensure the standard deviation is `std`
- return partial(torch.nn.init.uniform_, a=-bound, b=bound)
- else:
- raise ValueError("Unsupported layer initialization method")
-
-
-def init_layer(m: nn.Module,
- method: str,
- init_depth: tp.Optional[int] = None,
- zero_bias_init: bool = False):
- """Wrapper around ``get_init_fn`` for proper initialization of LM modules.
-
- Args:
- m (nn.Module): Module to initialize.
- method (str): Method name for the init function.
- init_depth (Optional[int]): Optional init depth value used to rescale
- the standard deviation if defined.
- zero_bias_init (bool): Whether to initialize the bias to 0 or not.
- """
- if isinstance(m, nn.Linear):
- init_fn = get_init_fn(method, m.in_features, init_depth=init_depth)
- if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
- weight = m.weight.float()
- init_fn(weight)
- m.weight.data[:] = weight.half()
- else:
- init_fn(m.weight)
- if zero_bias_init and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.Embedding):
- init_fn = get_init_fn(method, m.embedding_dim, init_depth=None)
- if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
- weight = m.weight.float()
- init_fn(weight)
- m.weight.data[:] = weight.half()
- else:
- init_fn(m.weight)
-
-
-class ScaledEmbedding(nn.Embedding):
- """Boost learning rate for embeddings (with `scale`).
- """
- def __init__(self, *args, lr=None, **kwargs):
- super().__init__(*args, **kwargs)
- self.lr = lr
-
- def make_optim_group(self):
- group = {"params": list(self.parameters())}
- if self.lr is not None:
- group["lr"] = self.lr
- return group
-
-
-@dataclass
-class LMOutput:
- # The logits are already re-aligned with the input codes
- # hence no extra shift is required, e.g. when computing CE
- logits: torch.Tensor # [B, K, T, card]
- mask: torch.Tensor # [B, K, T]
-
-
-class LMModel(StreamingModule):
- """Transformer-based language model on multiple streams of codes.
-
- Args:
- pattern_provider (CodebooksPatternProvider): Pattern provider for codebook interleaving.
- condition_provider (MusicConditioningProvider): Conditioning provider from metadata.
- fuser (ConditionFuser): Fuser handling the fusing of conditions with language model input.
- n_q (int): Number of parallel streams to model.
- card (int): Cardinality, vocabulary size.
- dim (int): Dimension of the transformer encoder.
- num_heads (int): Number of heads for the transformer encoder.
- hidden_scale (int): Scale for hidden feed forward dimension of the transformer encoder.
- norm (str): Normalization method.
- norm_first (bool): Use pre-norm instead of post-norm.
- emb_lr (Optional[float]): Embedding-specific learning rate.
- bias_proj (bool): Use bias for output projections.
- weight_init (Optional[str]): Method for weight initialization.
- depthwise_init (Optional[str]): Method for depthwise weight initialization.
- zero_bias_init (bool): If true and bias in Linears, initialize bias to zeros.
- cfg_dropout (float): Classifier-free guidance dropout.
- cfg_coef (float): Classifier-free guidance coefficient.
- attribute_dropout (dict): Attribute dropout probabilities.
- two_step_cfg (bool): Whether to run classifier free-guidance with 2 distinct steps.
- **kwargs: Additional parameters for the transformer encoder.
- """
- def __init__(self, pattern_provider: CodebooksPatternProvider, condition_provider: ConditioningProvider,
- fuser: ConditionFuser, n_q: int = 8, card: int = 1024, dim: int = 128, num_heads: int = 8,
- hidden_scale: int = 4, norm: str = 'layer_norm', norm_first: bool = False,
- emb_lr: tp.Optional[float] = None, bias_proj: bool = True,
- weight_init: tp.Optional[str] = None, depthwise_init: tp.Optional[str] = None,
- zero_bias_init: bool = False, cfg_dropout: float = 0, cfg_coef: float = 1.0,
- attribute_dropout: tp.Dict[str, tp.Dict[str, float]] = {}, two_step_cfg: bool = False,
- **kwargs):
- super().__init__()
- self.cfg_coef = cfg_coef
- self.cfg_dropout = ClassifierFreeGuidanceDropout(p=cfg_dropout)
- self.att_dropout = AttributeDropout(p=attribute_dropout)
- self.condition_provider = condition_provider
- self.fuser = fuser
- self.card = card
- embed_dim = self.card + 1
- self.n_q = n_q
- self.dim = dim
- self.pattern_provider = pattern_provider
- self.two_step_cfg = two_step_cfg
- self.emb = nn.ModuleList([ScaledEmbedding(embed_dim, dim, lr=emb_lr) for _ in range(n_q)])
- if 'activation' in kwargs:
- kwargs['activation'] = get_activation_fn(kwargs['activation'])
- self.transformer = StreamingTransformer(
- d_model=dim, num_heads=num_heads, dim_feedforward=int(hidden_scale * dim),
- norm=norm, norm_first=norm_first, **kwargs)
- self.out_norm: tp.Optional[nn.Module] = None
- if norm_first:
- self.out_norm = create_norm_fn(norm, dim)
- self.linears = nn.ModuleList([nn.Linear(dim, self.card, bias=bias_proj) for _ in range(n_q)])
- self._init_weights(weight_init, depthwise_init, zero_bias_init)
- self._fsdp: tp.Optional[nn.Module]
- self.__dict__['_fsdp'] = None
-
- def _init_weights(self, weight_init: tp.Optional[str], depthwise_init: tp.Optional[str], zero_bias_init: bool):
- """Initialization of the transformer module weights.
-
- Args:
- weight_init (Optional[str]): Weight initialization strategy. See ``get_init_fn`` for valid options.
- depthwise_init (Optional[str]): Depwthwise initialization strategy. The following options are valid:
- 'current' where the depth corresponds to the current layer index or 'global' where the total number
- of layer is used as depth. If not set, no depthwise initialization strategy is used.
- zero_bias_init (bool): Whether to initalize bias to zero or not.
- """
- assert depthwise_init is None or depthwise_init in ['current', 'global']
- assert depthwise_init is None or weight_init is not None, \
- "If 'depthwise_init' is defined, a 'weight_init' method should be provided."
- assert not zero_bias_init or weight_init is not None, \
- "If 'zero_bias_init', a 'weight_init' method should be provided"
-
- if weight_init is None:
- return
-
- for emb_layer in self.emb:
- init_layer(emb_layer, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
- for layer_idx, tr_layer in enumerate(self.transformer.layers):
- depth = None
- if depthwise_init == 'current':
- depth = layer_idx + 1
- elif depthwise_init == 'global':
- depth = len(self.transformer.layers)
- init_fn = partial(init_layer, method=weight_init, init_depth=depth, zero_bias_init=zero_bias_init)
- tr_layer.apply(init_fn)
-
- for linear in self.linears:
- init_layer(linear, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
- @property
- def special_token_id(self) -> int:
- return self.card
-
- @property
- def num_codebooks(self) -> int:
- return self.n_q
-
- def forward(self, sequence: torch.Tensor,
- conditions: tp.List[ConditioningAttributes],
- condition_tensors: tp.Optional[ConditionTensors] = None) -> torch.Tensor:
- """Apply language model on sequence and conditions.
- Given a tensor of sequence of shape [B, K, S] with K the number of codebooks and
- S the sequence steps, return the logits with shape [B, card, K, S].
-
- Args:
- indices (torch.Tensor): indices of the codes to model.
- conditions (list[ConditioningAttributes]): conditionings to use when modeling
- the given codes. Note that when evaluating multiple time with the same conditioning
- you should pre-compute those and pass them as `condition_tensors`.
- condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning
- tensors, see `conditions`.
- Returns:
- torch.Tensor: Logits.
- """
- B, K, S = sequence.shape
- assert K == self.num_codebooks, 'Sequence shape must match the specified number of codebooks'
- input_ = sum([self.emb[k](sequence[:, k]) for k in range(K)])
- if condition_tensors is None:
- assert not self._is_streaming, "Conditions tensors should be precomputed when streaming."
- # apply dropout modules
- conditions = self.cfg_dropout(conditions)
- conditions = self.att_dropout(conditions)
- tokenized = self.condition_provider.tokenize(conditions)
- # encode conditions and fuse, both have a streaming cache to not recompute when generating.
- condition_tensors = self.condition_provider(tokenized)
- else:
- assert not conditions, "Shouldn't pass both conditions and condition_tensors."
-
- input_, cross_attention_input = self.fuser(input_, condition_tensors)
-
- out = self.transformer(input_, cross_attention_src=cross_attention_input)
- if self.out_norm:
- out = self.out_norm(out)
- logits = torch.stack([self.linears[k](out) for k in range(K)], dim=1) # [B, K, S, card]
-
- # remove the prefix from the model outputs
- if len(self.fuser.fuse2cond['prepend']) > 0:
- logits = logits[:, :, -S:]
-
- return logits # [B, K, S, card]
-
- def compute_predictions(
- self, codes: torch.Tensor,
- conditions: tp.List[ConditioningAttributes],
- condition_tensors: tp.Optional[ConditionTensors] = None) -> LMOutput:
- """Given an input tensor of codes [B, K, T] and list of conditions, runs the model
- forward using the specified codes interleaving pattern.
-
- Args:
- codes (torch.Tensor): Input codes of shape [B, K, T] with B the batch size,
- K the number of codebooks and T the number of timesteps.
- conditions (list[ConditioningAttributes]): conditionings to use when modeling
- the given codes. Note that when evaluating multiple time with the same conditioning
- you should pre-compute those and pass them as `condition_tensors`.
- condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning
- tensors, see `conditions`.
- Returns:
- LMOutput: Language model outputs
- logits (torch.Tensor) of shape [B, K, T, card] corresponding to the provided codes,
- i.e. the first item corresponds to logits to predict the first code, meaning that
- no additional shifting of codes and logits is required.
- mask (torch.Tensor) of shape [B, K, T], mask over valid and invalid positions.
- Given the specified interleaving strategies, parts of the logits and codes should
- not be considered as valid predictions because of invalid context.
- """
- B, K, T = codes.shape
- codes = codes.contiguous()
- # map codes [B, K, T] into pattern sequence [B, K, S] using special_token_id for masked tokens
- pattern = self.pattern_provider.get_pattern(T)
- sequence_codes, sequence_indexes, sequence_mask = pattern.build_pattern_sequence(
- codes, self.special_token_id, keep_only_valid_steps=True
- )
- # apply model on pattern sequence
- model = self if self._fsdp is None else self._fsdp
- logits = model(sequence_codes, conditions, condition_tensors) # [B, K, S, card]
- # map back the logits on pattern sequence to logits on original codes: [B, K, S, card] -> [B, K, T, card]
- # and provide the corresponding mask over invalid positions of tokens
- logits = logits.permute(0, 3, 1, 2) # [B, card, K, S]
- # note: we use nans as special token to make it obvious if we feed unexpected logits
- logits, logits_indexes, logits_mask = pattern.revert_pattern_logits(
- logits, float('nan'), keep_only_valid_steps=True
- )
- logits = logits.permute(0, 2, 3, 1) # [B, K, T, card]
- logits_mask = logits_mask[None, :, :].expand(B, -1, -1) # [K, T] -> [B, K, T]
- return LMOutput(logits, logits_mask)
-
- def _sample_next_token(self,
- sequence: torch.Tensor,
- cfg_conditions: CFGConditions,
- unconditional_state: State,
- use_sampling: bool = False,
- temp: float = 1.0,
- top_k: int = 0,
- top_p: float = 0.0,
- cfg_coef: tp.Optional[float] = None) -> torch.Tensor:
- """Sample next token from the model given a sequence and a set of conditions. The model supports
- multiple sampling strategies (greedy sampling, softmax, top-k, top-p...).
-
- Args:
- sequence (torch.Tensor): Current sequence of shape [B, K, S]
- with K corresponding to the number of codebooks and S the number of sequence steps.
- S = 1 in streaming mode, except for the first step that contains a bigger prompt.
- condition_tensors (Dict[str, ConditionType): Set of conditions. If CFG is used,
- should be twice the batch size, being the concatenation of the conditions + null conditions.
- use_sampling (bool): Whether to use a sampling strategy or not.
- temp (float): Sampling temperature.
- top_k (int): K for "top-k" sampling.
- top_p (float): P for "top-p" sampling.
- cfg_coef (float): classifier free guidance coefficient
- Returns:
- next_token (torch.Tensor): Next token tensor of shape [B, K, 1].
- """
- B = sequence.shape[0]
- cfg_coef = self.cfg_coef if cfg_coef is None else cfg_coef
- model = self if self._fsdp is None else self._fsdp
- if self.two_step_cfg and cfg_conditions != {}:
- assert isinstance(cfg_conditions, tuple)
- condition_tensors, null_condition_tensors = cfg_conditions
- cond_logits = model(sequence, conditions=[], condition_tensors=condition_tensors)
- state = self.get_streaming_state()
- self.set_streaming_state(unconditional_state)
- uncond_logits = model(sequence, conditions=[], condition_tensors=null_condition_tensors)
- unconditional_state.update(self.get_streaming_state())
- self.set_streaming_state(state)
- logits = uncond_logits + (cond_logits - uncond_logits) * self.cfg_coef
- else:
- assert isinstance(cfg_conditions, dict)
- condition_tensors = cfg_conditions
- if condition_tensors:
- # Preparing for CFG, predicting both conditional and unconditional logits.
- sequence = torch.cat([sequence, sequence], dim=0)
- all_logits = model(
- sequence,
- conditions=[], condition_tensors=condition_tensors)
- if condition_tensors:
- cond_logits, uncond_logits = all_logits.split(B, dim=0) # [B, K, T, card]
- logits = uncond_logits + (cond_logits - uncond_logits) * cfg_coef
- else:
- logits = all_logits
-
- logits = logits.permute(0, 1, 3, 2) # [B, K, card, T]
- logits = logits[..., -1] # [B x K x card]
-
- # Apply softmax for sampling if temp > 0. Else, do greedy sampling to avoid zero division error.
- if use_sampling and temp > 0.0:
- probs = torch.softmax(logits / temp, dim=-1)
- if top_p > 0.0:
- next_token = utils.sample_top_p(probs, p=top_p)
- elif top_k > 0:
- next_token = utils.sample_top_k(probs, k=top_k)
- else:
- next_token = utils.multinomial(probs, num_samples=1)
- else:
- next_token = torch.argmax(logits, dim=-1, keepdim=True)
-
- return next_token
-
- @torch.no_grad()
- def generate(self,
- prompt: tp.Optional[torch.Tensor] = None,
- conditions: tp.List[ConditioningAttributes] = [],
- num_samples: tp.Optional[int] = None,
- max_gen_len: int = 256,
- use_sampling: bool = True,
- temp: float = 1.0,
- top_k: int = 250,
- top_p: float = 0.0,
- cfg_coef: tp.Optional[float] = None,
- two_step_cfg: bool = False,
- remove_prompts: bool = False,
- check: bool = False,
- callback: tp.Optional[tp.Callable[[int, int], None]] = None) -> torch.Tensor:
- """Generate tokens sampling from the model given a prompt or unconditionally. Generation can
- be perform in a greedy fashion or using sampling with top K and top P strategies.
-
- Args:
- prompt (Optional[torch.Tensor]): Prompt tokens of shape [B, K, T].
- conditions_tensors (Dict[str, torch.Tensor]): Set of conditions or None.
- num_samples (int or None): Number of samples to generate when no prompt and no conditions are given.
- max_gen_len (int): Maximum generation length.
- use_sampling (bool): Whether to use a sampling strategy or not.
- temp (float): Sampling temperature.
- top_k (int): K for "top-k" sampling.
- top_p (float): P for "top-p" sampling.
- remove_prompts (bool): Whether to remove prompts from generation or not.
- Returns:
- torch.Tensor: Generated tokens.
- """
- assert not self.training, "generation shouldn't be used in training mode."
- first_param = next(iter(self.parameters()))
- device = first_param.device
-
- # Checking all input shapes are consistents.
- possible_num_samples = []
- if num_samples is not None:
- possible_num_samples.append(num_samples)
- elif prompt is not None:
- possible_num_samples.append(prompt.shape[0])
- elif conditions:
- possible_num_samples.append(len(conditions))
- else:
- possible_num_samples.append(1)
- assert [x == possible_num_samples[0] for x in possible_num_samples], "Inconsitent inputs shapes"
- num_samples = possible_num_samples[0]
-
- # below we create set of conditions: one conditional and one unconditional
- # to do that we merge the regular condition together with the null condition
- # we then do 1 forward pass instead of 2.
- # the reason for that is two-fold:
- # 1. it is about x2 faster than doing 2 forward passes
- # 2. avoid the streaming API treating the 2 passes as part of different time steps
- # We also support doing two different passes, in particular to ensure that
- # the padding structure is exactly the same between train anf test.
- # With a batch size of 1, this can be slower though.
- cfg_conditions: CFGConditions
- two_step_cfg = self.two_step_cfg if two_step_cfg is None else two_step_cfg
- if conditions:
- null_conditions = ClassifierFreeGuidanceDropout(p=1.0)(conditions)
- if two_step_cfg:
- cfg_conditions = (
- self.condition_provider(self.condition_provider.tokenize(conditions)),
- self.condition_provider(self.condition_provider.tokenize(null_conditions)),
- )
- else:
- conditions = conditions + null_conditions
- tokenized = self.condition_provider.tokenize(conditions)
- cfg_conditions = self.condition_provider(tokenized)
- else:
- cfg_conditions = {}
-
- if prompt is None:
- assert num_samples > 0
- prompt = torch.zeros((num_samples, self.num_codebooks, 0), dtype=torch.long, device=device)
-
- B, K, T = prompt.shape
- start_offset = T
- assert start_offset < max_gen_len
-
- pattern = self.pattern_provider.get_pattern(max_gen_len)
- # this token is used as default value for codes that are not generated yet
- unknown_token = -1
-
- # we generate codes up to the max_gen_len that will be mapped to the pattern sequence
- gen_codes = torch.full((B, K, max_gen_len), unknown_token, dtype=torch.long, device=device)
- # filling the gen_codes with the prompt if needed
- gen_codes[..., :start_offset] = prompt
- # create the gen_sequence with proper interleaving from the pattern: [B, K, S]
- gen_sequence, indexes, mask = pattern.build_pattern_sequence(gen_codes, self.special_token_id)
- # retrieve the start_offset in the sequence:
- # it is the first sequence step that contains the `start_offset` timestep
- start_offset_sequence = pattern.get_first_step_with_timesteps(start_offset)
- assert start_offset_sequence is not None
-
- with self.streaming():
- unconditional_state = self.get_streaming_state()
- prev_offset = 0
- gen_sequence_len = gen_sequence.shape[-1] # gen_sequence shape is [B, K, S]
- for offset in range(start_offset_sequence, gen_sequence_len):
- # get current sequence (note that the streaming API is providing the caching over previous offsets)
- curr_sequence = gen_sequence[..., prev_offset:offset]
- curr_mask = mask[None, ..., prev_offset:offset].expand(B, -1, -1)
- if check:
- # check coherence between mask and sequence
- assert (curr_sequence == torch.where(curr_mask, curr_sequence, self.special_token_id)).all()
- # should never happen as gen_sequence is filled progressively
- assert not (curr_sequence == unknown_token).any()
- # sample next token from the model, next token shape is [B, K, 1]
- next_token = self._sample_next_token(
- curr_sequence, cfg_conditions, unconditional_state, use_sampling, temp, top_k, top_p,
- cfg_coef=cfg_coef)
- # ensure the tokens that should be masked are properly set to special_token_id
- # as the model never output special_token_id
- valid_mask = mask[..., offset:offset+1].expand(B, -1, -1)
- next_token[~valid_mask] = self.special_token_id
- # ensure we don't overwrite prompt tokens, we only write over unknown tokens
- # (then mask tokens should be left as is as well, which is correct)
- gen_sequence[..., offset:offset+1] = torch.where(
- gen_sequence[..., offset:offset+1] == unknown_token,
- next_token, gen_sequence[..., offset:offset+1]
- )
- prev_offset = offset
- if callback is not None:
- callback(1 + offset - start_offset_sequence, gen_sequence_len - start_offset_sequence)
- unconditional_state.clear()
-
- # ensure sequence has been entirely filled
- assert not (gen_sequence == unknown_token).any()
- # ensure gen_sequence pattern and mask are matching
- # which means the gen_sequence is valid according to the pattern
- assert (
- gen_sequence == torch.where(mask[None, ...].expand(B, -1, -1), gen_sequence, self.special_token_id)
- ).all()
- # get back the codes, trimming the prompt if needed and cutting potentially incomplete timesteps
- out_codes, out_indexes, out_mask = pattern.revert_pattern_sequence(gen_sequence, special_token=unknown_token)
-
- # sanity checks over the returned codes and corresponding masks
- assert (out_codes[..., :max_gen_len] != unknown_token).all()
- assert (out_mask[..., :max_gen_len] == 1).all()
-
- out_start_offset = start_offset if remove_prompts else 0
- out_codes = out_codes[..., out_start_offset:max_gen_len]
-
- # ensure the returned codes are all valid
- assert (out_codes >= 0).all() and (out_codes <= self.card).all()
- return out_codes
diff --git a/spaces/YueMafighting/FollowYourPose/Dockerfile b/spaces/YueMafighting/FollowYourPose/Dockerfile
deleted file mode 100644
index 845d631472e9eca8e3bc2da734bb1b17eb72cc02..0000000000000000000000000000000000000000
--- a/spaces/YueMafighting/FollowYourPose/Dockerfile
+++ /dev/null
@@ -1,64 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update && \
- apt-get upgrade -y && \
- apt-get install -y --no-install-recommends \
- git \
- zip \
- unzip \
- git-lfs \
- wget \
- curl \
- # ffmpeg \
- ffmpeg \
- x264 \
- # python build dependencies \
- build-essential \
- libssl-dev \
- zlib1g-dev \
- libbz2-dev \
- libreadline-dev \
- libsqlite3-dev \
- libncursesw5-dev \
- xz-utils \
- tk-dev \
- libxml2-dev \
- libxmlsec1-dev \
- libffi-dev \
- liblzma-dev && \
- apt-get clean && \
- rm -rf /var/lib/apt/lists/*
-# RUN apt-get update && \
-# apt-get install zip
-# RUN wget https://github.com/ChenyangQiQi/FateZero/releases/download/v0.0.1/style.zip && unzip style.zip
-RUN useradd -m -u 1000 user
-RUN sudo apt-get install unzip
-USER user
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:${PATH}
-WORKDIR ${HOME}/app
-
-RUN curl https://pyenv.run | bash
-ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ENV PYTHON_VERSION=3.10.9
-RUN pyenv install ${PYTHON_VERSION} && \
- pyenv global ${PYTHON_VERSION} && \
- pyenv rehash && \
- pip install --no-cache-dir -U pip setuptools wheel
-
-RUN pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1
-COPY --chown=1000 requirements.txt /tmp/requirements.txt
-RUN pip install --no-cache-dir -U -r /tmp/requirements.txt
-
-COPY --chown=1000 . ${HOME}/app
-RUN ls -a
-# RUN cd ./FateZero/ckpt && bash download.sh
-RUN cd {WORKDIR}/data && bash download.sh
-ENV PYTHONPATH=${HOME}/app \
- PYTHONUNBUFFERED=1 \
- GRADIO_ALLOW_FLAGGING=never \
- GRADIO_NUM_PORTS=1 \
- GRADIO_SERVER_NAME=0.0.0.0 \
- GRADIO_THEME=huggingface \
- SYSTEM=spaces
-CMD ["python", "app.py"]
diff --git a/spaces/Yuliang/ECON/lib/common/render_utils.py b/spaces/Yuliang/ECON/lib/common/render_utils.py
deleted file mode 100644
index 389eab740fc341b9395e1e008b4fa91e9ef3cf83..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/common/render_utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: ps-license@tuebingen.mpg.de
-
-import math
-from typing import NewType
-
-import numpy as np
-import torch
-import trimesh
-from pytorch3d.renderer.mesh import rasterize_meshes
-from pytorch3d.structures import Meshes
-from torch import nn
-
-Tensor = NewType("Tensor", torch.Tensor)
-
-
-def solid_angles(points: Tensor, triangles: Tensor, thresh: float = 1e-8) -> Tensor:
- """Compute solid angle between the input points and triangles
- Follows the method described in:
- The Solid Angle of a Plane Triangle
- A. VAN OOSTEROM AND J. STRACKEE
- IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,
- VOL. BME-30, NO. 2, FEBRUARY 1983
- Parameters
- -----------
- points: BxQx3
- Tensor of input query points
- triangles: BxFx3x3
- Target triangles
- thresh: float
- float threshold
- Returns
- -------
- solid_angles: BxQxF
- A tensor containing the solid angle between all query points
- and input triangles
- """
- # Center the triangles on the query points. Size should be BxQxFx3x3
- centered_tris = triangles[:, None] - points[:, :, None, None]
-
- # BxQxFx3
- norms = torch.norm(centered_tris, dim=-1)
-
- # Should be BxQxFx3
- cross_prod = torch.cross(centered_tris[:, :, :, 1], centered_tris[:, :, :, 2], dim=-1)
- # Should be BxQxF
- numerator = (centered_tris[:, :, :, 0] * cross_prod).sum(dim=-1)
- del cross_prod
-
- dot01 = (centered_tris[:, :, :, 0] * centered_tris[:, :, :, 1]).sum(dim=-1)
- dot12 = (centered_tris[:, :, :, 1] * centered_tris[:, :, :, 2]).sum(dim=-1)
- dot02 = (centered_tris[:, :, :, 0] * centered_tris[:, :, :, 2]).sum(dim=-1)
- del centered_tris
-
- denominator = (
- norms.prod(dim=-1) + dot01 * norms[:, :, :, 2] + dot02 * norms[:, :, :, 1] +
- dot12 * norms[:, :, :, 0]
- )
- del dot01, dot12, dot02, norms
-
- # Should be BxQ
- solid_angle = torch.atan2(numerator, denominator)
- del numerator, denominator
-
- torch.cuda.empty_cache()
-
- return 2 * solid_angle
-
-
-def winding_numbers(points: Tensor, triangles: Tensor, thresh: float = 1e-8) -> Tensor:
- """Uses winding_numbers to compute inside/outside
- Robust inside-outside segmentation using generalized winding numbers
- Alec Jacobson,
- Ladislav Kavan,
- Olga Sorkine-Hornung
- Fast Winding Numbers for Soups and Clouds SIGGRAPH 2018
- Gavin Barill
- NEIL G. Dickson
- Ryan Schmidt
- David I.W. Levin
- and Alec Jacobson
- Parameters
- -----------
- points: BxQx3
- Tensor of input query points
- triangles: BxFx3x3
- Target triangles
- thresh: float
- float threshold
- Returns
- -------
- winding_numbers: BxQ
- A tensor containing the Generalized winding numbers
- """
- # The generalized winding number is the sum of solid angles of the point
- # with respect to all triangles.
- return (1 / (4 * math.pi) * solid_angles(points, triangles, thresh=thresh).sum(dim=-1))
-
-
-def batch_contains(verts, faces, points):
-
- B = verts.shape[0]
- N = points.shape[1]
-
- verts = verts.detach().cpu()
- faces = faces.detach().cpu()
- points = points.detach().cpu()
- contains = torch.zeros(B, N)
-
- for i in range(B):
- contains[i] = torch.as_tensor(trimesh.Trimesh(verts[i], faces[i]).contains(points[i]))
-
- return 2.0 * (contains - 0.5)
-
-
-def dict2obj(d):
- if not isinstance(d, dict):
- return d
-
- class C(object):
- pass
-
- o = C()
- for k in d:
- o.__dict__[k] = dict2obj(d[k])
- return o
-
-
-def face_vertices(vertices, faces):
- """
- :param vertices: [batch size, number of vertices, 3]
- :param faces: [batch size, number of faces, 3]
- :return: [batch size, number of faces, 3, 3]
- """
-
- bs, nv = vertices.shape[:2]
- bs, nf = faces.shape[:2]
- device = vertices.device
- faces = faces + (torch.arange(bs, dtype=torch.int32).to(device) * nv)[:, None, None]
- vertices = vertices.reshape((bs * nv, vertices.shape[-1]))
-
- return vertices[faces.long()]
-
-
-class Pytorch3dRasterizer(nn.Module):
- """Borrowed from https://github.com/facebookresearch/pytorch3d
- Notice:
- x,y,z are in image space, normalized
- can only render squared image now
- """
- def __init__(
- self, image_size=224, blur_radius=0.0, faces_per_pixel=1, device=torch.device("cuda:0")
- ):
- """
- use fixed raster_settings for rendering faces
- """
- super().__init__()
- raster_settings = {
- "image_size": image_size,
- "blur_radius": blur_radius,
- "faces_per_pixel": faces_per_pixel,
- "bin_size": -1,
- "max_faces_per_bin": None,
- "perspective_correct": False,
- "cull_backfaces": True,
- }
- raster_settings = dict2obj(raster_settings)
- self.raster_settings = raster_settings
- self.device = device
-
- def forward(self, vertices, faces, attributes=None):
- fixed_vertices = vertices.clone()
- fixed_vertices[..., :2] = -fixed_vertices[..., :2]
- meshes_screen = Meshes(verts=fixed_vertices.float(), faces=faces.long())
- raster_settings = self.raster_settings
- pix_to_face, zbuf, bary_coords, dists = rasterize_meshes(
- meshes_screen,
- image_size=raster_settings.image_size,
- blur_radius=raster_settings.blur_radius,
- faces_per_pixel=raster_settings.faces_per_pixel,
- bin_size=raster_settings.bin_size,
- max_faces_per_bin=raster_settings.max_faces_per_bin,
- perspective_correct=raster_settings.perspective_correct,
- )
- vismask = (pix_to_face > -1).float()
- D = attributes.shape[-1]
- attributes = attributes.clone()
- attributes = attributes.view(
- attributes.shape[0] * attributes.shape[1], 3, attributes.shape[-1]
- )
- N, H, W, K, _ = bary_coords.shape
- mask = pix_to_face == -1
- pix_to_face = pix_to_face.clone()
- pix_to_face[mask] = 0
- idx = pix_to_face.view(N * H * W * K, 1, 1).expand(N * H * W * K, 3, D)
- pixel_face_vals = attributes.gather(0, idx).view(N, H, W, K, 3, D)
- pixel_vals = (bary_coords[..., None] * pixel_face_vals).sum(dim=-2)
- pixel_vals[mask] = 0 # Replace masked values in output.
- pixel_vals = pixel_vals[:, :, :, 0].permute(0, 3, 1, 2)
- pixel_vals = torch.cat([pixel_vals, vismask[:, :, :, 0][:, None, :, :]], dim=1)
- return pixel_vals
-
- def get_texture(self, uvcoords, uvfaces, verts, faces, verts_color):
-
- batch_size = verts.shape[0]
- uv_verts_color = face_vertices(verts_color, faces.expand(batch_size, -1,
- -1)).to(self.device)
- uv_map = self.forward(
- uvcoords.expand(batch_size, -1, -1), uvfaces.expand(batch_size, -1, -1), uv_verts_color
- )[:, :3]
- uv_map_npy = np.flip(uv_map.squeeze(0).permute(1, 2, 0).cpu().numpy(), 0)
-
- return uv_map_npy
diff --git a/spaces/Yunshansongbai/SVC-Nahida/app.py b/spaces/Yunshansongbai/SVC-Nahida/app.py
deleted file mode 100644
index 7321a5fc776b31c97fe5729b6c4fb88a0e389ee1..0000000000000000000000000000000000000000
--- a/spaces/Yunshansongbai/SVC-Nahida/app.py
+++ /dev/null
@@ -1,194 +0,0 @@
-import io
-import os
-
-import gradio as gr
-import librosa
-import numpy as np
-import soundfile
-from inference.infer_tool import Svc
-import logging
-import os
-import paddle
-import requests
-import utils
-from spleeter import Separator
-import time
-from datetime import datetime, timedelta
-
-build_dir=os.getcwd()
-if build_dir == "/home/aistudio":
- build_dir += "/build"
-
-model_dir=build_dir+'/trained_models'
-
-model_list_path = model_dir + "/model_list.txt"
-
-# 筛选出文件夹
-models = []
-for filename in os.listdir(model_dir):
- # 判断文件名是否以 '.pdparams' 结尾，并且不包含后缀部分
- if filename.endswith('.pdparams') and os.path.splitext(filename)[0].isalpha():
- models.append(os.path.splitext(filename)[0])
-cache_model = {}
-
-def callback(text):
- if text == "reboot":
- os._exit(0)
- one_hour_later = datetime.now() + timedelta(hours=1)
- else:
- global start_time
- if time.time() - start_time >= 3600:
- os._exit(0)
- one_hour_later = datetime.now() + timedelta(hours=1)
- else:
- return text
-
-def separate_fn(song_input):
- try:
- if song_input is None:
- return "请上传歌曲",None,None,None,None
- params_2stems = {
- 'sample_rate': 44100,
- 'frame_length': 4096,
- 'frame_step': 1024,
- 'T': 512,
- 'F': 1024,
- 'num_instruments': ['vocals', 'instrumental'],
- 'output_dir': build_dir+'/output_2stems',
- 'checkpoint_path': build_dir+'/spleeter',
- 'use_elu': False}
- sampling_rate, song = song_input
- soundfile.write("temp.wav", song, sampling_rate, format="wav")
- # 初始化分离器
- sep = Separator(params_2stems)
- sep.separate('temp.wav')
- vocal_path = params_2stems["output_dir"]+"/temp-vocals.wav"
- instrumental_path = params_2stems["output_dir"]+"/temp-instrumental.wav"
- return "分离成功，请继续前往体验【转换】和【混音】",vocal_path,instrumental_path,vocal_path,instrumental_path
- except Exception as e:
- import traceback
- return traceback.format_exc() , None,None,None,None
-
-
-def convert_fn(model_name, input_audio,input_audio_micro, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale):
- try:
- if model_name in cache_model:
- model = cache_model[model_name]
- else:
- if paddle.device.is_compiled_with_cuda()==False and len(cache_model)!=0:
- return f"目前运行环境为CPU，受制于平台算力，每次启动本项目只允许加载1个模型，当前已加载{next(iter(cache_model))}",None,None
- config_path = f"{build_dir}/trained_models/config.json"
- model = Svc(f"{build_dir}/trained_models/{model_name}.pdparams", config_path,mode="test")
- cache_model[model_name] = model
- if input_audio is None and input_audio_micro is None:
- return "请上传音频", None,None
- if input_audio_micro is not None:
- input_audio = input_audio_micro
- sampling_rate, audio = input_audio
- duration = audio.shape[0] / sampling_rate
- audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio.transpose(1, 0))
- if sampling_rate != 16000:
- audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
- print(audio.shape)
- out_wav_path = "temp.wav"
- soundfile.write(out_wav_path, audio, 16000, format="wav")
- print(cluster_ratio, auto_f0, noise_scale)
- _audio = model.slice_inference(out_wav_path, model_name, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale)
- del model
- return "转换成功，请继续前往体验【混音】", (44100, _audio),(44100, _audio)
- except Exception as e:
- import traceback
- return traceback.format_exc() , None,None
-
-def compose_fn(input_vocal,input_instrumental,mixing_ratio=0.5):
- try:
- outlog = "混音成功"
- if input_vocal is None:
- return "请上传人声", None
- if input_instrumental is None:
- return "请上传伴奏", None
- vocal_sampling_rate, vocal = input_vocal
- vocal_duration = vocal.shape[0] / vocal_sampling_rate
- vocal = (vocal / np.iinfo(vocal.dtype).max).astype(np.float32)
- if len(vocal.shape) > 1:
- vocal = librosa.to_mono(vocal.transpose(1, 0))
- if vocal_sampling_rate != 44100:
- vocal = librosa.resample(vocal, orig_sr=vocal_sampling_rate, target_sr=44100)
-
- instrumental_sampling_rate, instrumental = input_instrumental
- instrumental_duration = instrumental.shape[0] / instrumental_sampling_rate
- instrumental = (instrumental / np.iinfo(instrumental.dtype).max).astype(np.float32)
- if len(instrumental.shape) > 1:
- instrumental = librosa.to_mono(instrumental.transpose(1, 0))
- if instrumental_sampling_rate != 44100:
- instrumental = librosa.resample(instrumental, orig_sr=instrumental_sampling_rate, target_sr=44100)
- if len(vocal)!=len(instrumental):
- min_length = min(len(vocal),len(instrumental))
- instrumental = instrumental[:min_length]
- vocal = vocal[:min_length]
- outlog = "人声伴奏长度不一致，已自动截断较长的音频"
-
- mixed_audio = (1 - mixing_ratio) * vocal + mixing_ratio * instrumental
- mixed_audio_data = mixed_audio.astype(np.float32)
- return outlog,(44100,mixed_audio_data)
- except Exception as e:
- import traceback
- return traceback.format_exc() , None
-
-
-app = gr.Blocks()
-
-with app:
- start_time = time.time()
- one_hour_later = datetime.now() + timedelta(hours=1)
- gr.Markdown('SVC歌声转换全流程体验（伴奏分离，转换，混音）
')
- with gr.Tabs() as tabs:
- with gr.TabItem("人声伴奏分离"):
- gr.Markdown('该项目人声分离的效果弱于UVR5，如自备分离好的伴奏和人声可跳过该步骤
')
- song_input = gr.Audio(label="上传歌曲（tips:上传后点击右上角✏可以进行歌曲剪辑）",interactive=True)
- gr.Examples(examples=[build_dir+"/examples/song/blue.wav",build_dir+"/examples/song/Counter_clockwise_Clock.wav",build_dir+"/examples/song/one_last_kiss.wav"],inputs=song_input,label="歌曲样例")
-
- btn_separate = gr.Button("人声伴奏分离", variant="primary")
- text_output1 = gr.Textbox(label="输出信息")
- vocal_output1 = gr.Audio(label="输出人声",interactive=False)
- instrumental_output1 = gr.Audio(label="输出伴奏",interactive=False)
- with gr.TabItem("转换"):
- model_name = gr.Dropdown(label="模型", choices=models, value="纳西妲")
- vocal_input1 = gr.Audio(label="上传人声",interactive=True)
- gr.Examples(examples=[build_dir+"/examples/vocals/blue_vocal.wav",build_dir+"/examples/vocals/Counter_clockwise_Clock_vocal.wav",build_dir+"/examples/vocals/one_last_kiss_vocal.wav"],inputs=vocal_input1,label="人声样例")
- btn_use_separate = gr.Button("使用【人声伴奏分离】分离的人声")
- micro_input = gr.Audio(label="麦克风输入（优先于上传的人声）",source="microphone",interactive=True)
- vc_transform = gr.Number(label="变调（半音数量,升八度12降八度-12）", value=0)
- cluster_ratio = gr.Number(label="聚类模型混合比例", value=0,visible=False)
- auto_f0 = gr.Checkbox(label="自动预测音高（转换歌声时不要打开，会严重跑调）", value=False)
- slice_db = gr.Number(label="静音分贝阈值（嘈杂的音频可以-30，干声保留呼吸可以-50）", value=-50)
- noise_scale = gr.Number(label="noise_scale", value=0.2)
- btn_convert = gr.Button("转换", variant="primary")
- text_output2 = gr.Textbox(label="输出信息")
- vc_output2 = gr.Audio(label="输出音频",interactive=False)
-
- with gr.TabItem("混音"):
- vocal_input2 = gr.Audio(label="上传人声",interactive=True)
- btn_use_convert = gr.Button("使用【转换】输出的人声")
- instrumental_input1 = gr.Audio(label="上传伴奏")
- gr.Examples(examples=[build_dir+"/examples/instrumental/blue_instrumental.wav",build_dir+"/examples/instrumental/Counter_clockwise_Clock_instrumental.wav",build_dir+"/examples/instrumental/one_last_kiss_instrumental.wav"],inputs=instrumental_input1,label="伴奏样例")
- btn_use_separate2 = gr.Button("使用【人声伴奏分离】分离的伴奏")
- mixing_ratio = gr.Slider(0, 1, value=0.75,step=0.01,label="混音比例（人声:伴奏）", info="人声:伴奏")
- btn_compose = gr.Button("混音", variant="primary")
- text_output3 = gr.Textbox(label="输出信息")
- song_output = gr.Audio(label="输出歌曲",interactive=False)
-
- with gr.TabItem("设置"):
- output = gr.Textbox(label="输出",placeholder=f"距离下一次允许重启时间为{one_hour_later}")
- btn_reboot = gr.Button("重启",variant="primary")
- btn_separate.click(separate_fn, song_input, [text_output1, vocal_output1,instrumental_output1,vocal_input1,instrumental_input1])
- btn_convert.click(convert_fn, [model_name, vocal_input1,micro_input,vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale], [text_output2, vc_output2,vocal_input2])
- btn_compose.click(compose_fn,[vocal_input2,instrumental_input1,mixing_ratio],[text_output3,song_output])
- btn_reboot.click(callback,output)
- btn_use_convert.click(lambda x:x,vc_output2,vocal_input2)
- btn_use_separate.click(lambda x:x,vocal_output1,vocal_input1)
- btn_use_separate2.click(lambda x:x,instrumental_output1,instrumental_input1)
-
-app.launch()
\ No newline at end of file
diff --git a/spaces/Ziqi/ReVersion/README.md b/spaces/Ziqi/ReVersion/README.md
deleted file mode 100644
index 1823df2c77ed395faf47aed9a4e04093a151848e..0000000000000000000000000000000000000000
--- a/spaces/Ziqi/ReVersion/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ReVersion
-emoji: 🐠
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/abcde1234www/tts/app.py b/spaces/abcde1234www/tts/app.py
deleted file mode 100644
index a97450c289843433835ebc4da6d5d885ea42167c..0000000000000000000000000000000000000000
--- a/spaces/abcde1234www/tts/app.py
+++ /dev/null
@@ -1,103 +0,0 @@
-"""
-Copyright 2022 Balacoon
-
-TTS interactive demo
-"""
-
-import logging
-from typing import cast
-
-import gradio as gr
-from balacoon_tts import TTS
-from huggingface_hub import hf_hub_download, list_repo_files
-
-# global tts module, initialized from a model selected
-tts = None
-
-
-def main():
- logging.basicConfig(level=logging.INFO)
-
- with gr.Blocks() as demo:
- gr.Markdown(
- """
- Text-to-Speech
-
- 1. Write an utterance to generate,
- 2. Select the model to synthesize with
- 3. Select speaker (only for multi-speaker models)
- 4. Hit "Generate" and listen to the result!
-
- When you select model for the first time,
- it will take a little time to download it.
- You can learn more about models available
- [here](https://huggingface.co/balacoon/tts),
- visit [Balacoon website](https://balacoon.com/) for more info.
- """
- )
- with gr.Row(variant="panel"):
- text = gr.Textbox(label="Text", placeholder="Type something here...")
-
- with gr.Row():
- with gr.Column(variant="panel"):
- repo_files = list_repo_files(repo_id="balacoon/tts")
- model_files = [x for x in repo_files if x.endswith(".addon")]
- model_name = gr.Dropdown(
- label="Model",
- choices=model_files,
- )
- with gr.Column(variant="panel"):
- speaker = gr.Dropdown(label="Speaker", choices=[])
-
- def set_model(model_name_str: str):
- """
- gets value from `model_name`, loads model,
- re-initializes tts object, gets list of
- speakers that model supports and set them to `speaker`
- """
- model_path = hf_hub_download(
- repo_id="balacoon/tts", filename=model_name_str
- )
- global tts
- tts = TTS(model_path)
- speakers = tts.get_speakers()
- if speakers:
- visible = True
- value = speakers[-1]
- else:
- visible = False
- value = ""
- return gr.Dropdown.update(
- choices=speakers, value=value, visible=visible
- )
-
- model_name.change(set_model, inputs=model_name, outputs=speaker)
-
- with gr.Row(variant="panel"):
- generate = gr.Button("Generate")
- with gr.Row(variant="panel"):
- audio = gr.Audio()
-
- def synthesize_audio(text_str: str, speaker_str: str = ""):
- """
- gets utterance to synthesize from `text` Textbox
- and speaker name from `speaker` dropdown list.
- speaker name might be empty for single-speaker models.
- Synthesizes the waveform and updates `audio` with it.
- """
- if not text_str:
- logging.info("text or speaker are not provided")
- return None
- global tts
- if len(text_str) > 1024:
- text_str = text_str[:1024]
- samples = cast(TTS, tts).synthesize(text_str, speaker_str)
- return gr.Audio.update(value=(24000, samples))
-
- generate.click(synthesize_audio, inputs=[text, speaker], outputs=audio)
-
- demo.launch()
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/tableau/setup.md b/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/tableau/setup.md
deleted file mode 100644
index 73dac977c474116928b47b28dbab6cc0705660c0..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/tableau/setup.md
+++ /dev/null
@@ -1,60 +0,0 @@
----
-title: Setup
----
-# Tableau Ingestion Guide: Setup & Prerequisites
-
-In order to configure ingestion from Tableau, you'll first have to enable Tableau Metadata API and you should have a user with Site Administrator Explorer permissions.
-
-## Tableau Prerequisites
-
-1. Grant `Site Administrator Explorer permissions` to a user
-
- A. Log in to Tableau Cloud https://sso.online.tableau.com/public/idp/SSO.
-
- B. Navigate to `Users`.
-
-
-
-
-
-
- C. **For New User**: Follow below steps to grant permission for new user.
-
- - Click `Add Users` -> `Add Users by Email`
-
-
-
-
-
- - Fill `Enter email addresses`, set `Site role` to `Site Administrator Explorer` and Click `Add Users`
-
-
-
-
-
-
- D. **For Existing User:** Follow below steps to grant permission for existing user.
-
- - Select a user and click `Actions` -> `Site Role`
-
-
-
-
-
- - Change user role to `Site Administrator Explorer`
-
-
-
-
-
-2. **Enable Tableau Metadata API:** This step is required only for Tableau Server. The Metadata API is installed with Tableau Server but disabled by default.
-
- - Open a command prompt as an admin on the initial node (*where TSM is installed*) in the cluster
- - Run the command: `tsm maintenance metadata-services enable`
-
-
-## Next Steps
-
-Once you've done all of the above in Tableau, it's time to [move on](configuration.md) to configuring the actual ingestion source within DataHub.
-
-*Need more help? Join the conversation in [Slack](http://slack.datahubproject.io)!*
\ No newline at end of file
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/multi_scale_deform_attn.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/multi_scale_deform_attn.py
deleted file mode 100644
index c52dda18b41705705b47dd0e995b124048c16fba..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/ops/multi_scale_deform_attn.py
+++ /dev/null
@@ -1,358 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-import warnings
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.autograd.function import Function, once_differentiable
-
-from annotator.uniformer.mmcv import deprecated_api_warning
-from annotator.uniformer.mmcv.cnn import constant_init, xavier_init
-from annotator.uniformer.mmcv.cnn.bricks.registry import ATTENTION
-from annotator.uniformer.mmcv.runner import BaseModule
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
- '_ext', ['ms_deform_attn_backward', 'ms_deform_attn_forward'])
-
-
-class MultiScaleDeformableAttnFunction(Function):
-
- @staticmethod
- def forward(ctx, value, value_spatial_shapes, value_level_start_index,
- sampling_locations, attention_weights, im2col_step):
- """GPU version of multi-scale deformable attention.
-
- Args:
- value (Tensor): The value has shape
- (bs, num_keys, mum_heads, embed_dims//num_heads)
- value_spatial_shapes (Tensor): Spatial shape of
- each feature map, has shape (num_levels, 2),
- last dimension 2 represent (h, w)
- sampling_locations (Tensor): The location of sampling points,
- has shape
- (bs ,num_queries, num_heads, num_levels, num_points, 2),
- the last dimension 2 represent (x, y).
- attention_weights (Tensor): The weight of sampling points used
- when calculate the attention, has shape
- (bs ,num_queries, num_heads, num_levels, num_points),
- im2col_step (Tensor): The step used in image to column.
-
- Returns:
- Tensor: has shape (bs, num_queries, embed_dims)
- """
-
- ctx.im2col_step = im2col_step
- output = ext_module.ms_deform_attn_forward(
- value,
- value_spatial_shapes,
- value_level_start_index,
- sampling_locations,
- attention_weights,
- im2col_step=ctx.im2col_step)
- ctx.save_for_backward(value, value_spatial_shapes,
- value_level_start_index, sampling_locations,
- attention_weights)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- """GPU version of backward function.
-
- Args:
- grad_output (Tensor): Gradient
- of output tensor of forward.
-
- Returns:
- Tuple[Tensor]: Gradient
- of input tensors in forward.
- """
- value, value_spatial_shapes, value_level_start_index,\
- sampling_locations, attention_weights = ctx.saved_tensors
- grad_value = torch.zeros_like(value)
- grad_sampling_loc = torch.zeros_like(sampling_locations)
- grad_attn_weight = torch.zeros_like(attention_weights)
-
- ext_module.ms_deform_attn_backward(
- value,
- value_spatial_shapes,
- value_level_start_index,
- sampling_locations,
- attention_weights,
- grad_output.contiguous(),
- grad_value,
- grad_sampling_loc,
- grad_attn_weight,
- im2col_step=ctx.im2col_step)
-
- return grad_value, None, None, \
- grad_sampling_loc, grad_attn_weight, None
-
-
-def multi_scale_deformable_attn_pytorch(value, value_spatial_shapes,
- sampling_locations, attention_weights):
- """CPU version of multi-scale deformable attention.
-
- Args:
- value (Tensor): The value has shape
- (bs, num_keys, mum_heads, embed_dims//num_heads)
- value_spatial_shapes (Tensor): Spatial shape of
- each feature map, has shape (num_levels, 2),
- last dimension 2 represent (h, w)
- sampling_locations (Tensor): The location of sampling points,
- has shape
- (bs ,num_queries, num_heads, num_levels, num_points, 2),
- the last dimension 2 represent (x, y).
- attention_weights (Tensor): The weight of sampling points used
- when calculate the attention, has shape
- (bs ,num_queries, num_heads, num_levels, num_points),
-
- Returns:
- Tensor: has shape (bs, num_queries, embed_dims)
- """
-
- bs, _, num_heads, embed_dims = value.shape
- _, num_queries, num_heads, num_levels, num_points, _ =\
- sampling_locations.shape
- value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes],
- dim=1)
- sampling_grids = 2 * sampling_locations - 1
- sampling_value_list = []
- for level, (H_, W_) in enumerate(value_spatial_shapes):
- # bs, H_*W_, num_heads, embed_dims ->
- # bs, H_*W_, num_heads*embed_dims ->
- # bs, num_heads*embed_dims, H_*W_ ->
- # bs*num_heads, embed_dims, H_, W_
- value_l_ = value_list[level].flatten(2).transpose(1, 2).reshape(
- bs * num_heads, embed_dims, H_, W_)
- # bs, num_queries, num_heads, num_points, 2 ->
- # bs, num_heads, num_queries, num_points, 2 ->
- # bs*num_heads, num_queries, num_points, 2
- sampling_grid_l_ = sampling_grids[:, :, :,
- level].transpose(1, 2).flatten(0, 1)
- # bs*num_heads, embed_dims, num_queries, num_points
- sampling_value_l_ = F.grid_sample(
- value_l_,
- sampling_grid_l_,
- mode='bilinear',
- padding_mode='zeros',
- align_corners=False)
- sampling_value_list.append(sampling_value_l_)
- # (bs, num_queries, num_heads, num_levels, num_points) ->
- # (bs, num_heads, num_queries, num_levels, num_points) ->
- # (bs, num_heads, 1, num_queries, num_levels*num_points)
- attention_weights = attention_weights.transpose(1, 2).reshape(
- bs * num_heads, 1, num_queries, num_levels * num_points)
- output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) *
- attention_weights).sum(-1).view(bs, num_heads * embed_dims,
- num_queries)
- return output.transpose(1, 2).contiguous()
-
-
-@ATTENTION.register_module()
-class MultiScaleDeformableAttention(BaseModule):
- """An attention module used in Deformable-Detr.
-
- `Deformable DETR: Deformable Transformers for End-to-End Object Detection.
- `_.
-
- Args:
- embed_dims (int): The embedding dimension of Attention.
- Default: 256.
- num_heads (int): Parallel attention heads. Default: 64.
- num_levels (int): The number of feature map used in
- Attention. Default: 4.
- num_points (int): The number of sampling points for
- each query in each head. Default: 4.
- im2col_step (int): The step used in image_to_column.
- Default: 64.
- dropout (float): A Dropout layer on `inp_identity`.
- Default: 0.1.
- batch_first (bool): Key, Query and Value are shape of
- (batch, n, embed_dim)
- or (n, batch, embed_dim). Default to False.
- norm_cfg (dict): Config dict for normalization layer.
- Default: None.
- init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
- Default: None.
- """
-
- def __init__(self,
- embed_dims=256,
- num_heads=8,
- num_levels=4,
- num_points=4,
- im2col_step=64,
- dropout=0.1,
- batch_first=False,
- norm_cfg=None,
- init_cfg=None):
- super().__init__(init_cfg)
- if embed_dims % num_heads != 0:
- raise ValueError(f'embed_dims must be divisible by num_heads, '
- f'but got {embed_dims} and {num_heads}')
- dim_per_head = embed_dims // num_heads
- self.norm_cfg = norm_cfg
- self.dropout = nn.Dropout(dropout)
- self.batch_first = batch_first
-
- # you'd better set dim_per_head to a power of 2
- # which is more efficient in the CUDA implementation
- def _is_power_of_2(n):
- if (not isinstance(n, int)) or (n < 0):
- raise ValueError(
- 'invalid input for _is_power_of_2: {} (type: {})'.format(
- n, type(n)))
- return (n & (n - 1) == 0) and n != 0
-
- if not _is_power_of_2(dim_per_head):
- warnings.warn(
- "You'd better set embed_dims in "
- 'MultiScaleDeformAttention to make '
- 'the dimension of each attention head a power of 2 '
- 'which is more efficient in our CUDA implementation.')
-
- self.im2col_step = im2col_step
- self.embed_dims = embed_dims
- self.num_levels = num_levels
- self.num_heads = num_heads
- self.num_points = num_points
- self.sampling_offsets = nn.Linear(
- embed_dims, num_heads * num_levels * num_points * 2)
- self.attention_weights = nn.Linear(embed_dims,
- num_heads * num_levels * num_points)
- self.value_proj = nn.Linear(embed_dims, embed_dims)
- self.output_proj = nn.Linear(embed_dims, embed_dims)
- self.init_weights()
-
- def init_weights(self):
- """Default initialization for Parameters of Module."""
- constant_init(self.sampling_offsets, 0.)
- thetas = torch.arange(
- self.num_heads,
- dtype=torch.float32) * (2.0 * math.pi / self.num_heads)
- grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
- grid_init = (grid_init /
- grid_init.abs().max(-1, keepdim=True)[0]).view(
- self.num_heads, 1, 1,
- 2).repeat(1, self.num_levels, self.num_points, 1)
- for i in range(self.num_points):
- grid_init[:, :, i, :] *= i + 1
-
- self.sampling_offsets.bias.data = grid_init.view(-1)
- constant_init(self.attention_weights, val=0., bias=0.)
- xavier_init(self.value_proj, distribution='uniform', bias=0.)
- xavier_init(self.output_proj, distribution='uniform', bias=0.)
- self._is_init = True
-
- @deprecated_api_warning({'residual': 'identity'},
- cls_name='MultiScaleDeformableAttention')
- def forward(self,
- query,
- key=None,
- value=None,
- identity=None,
- query_pos=None,
- key_padding_mask=None,
- reference_points=None,
- spatial_shapes=None,
- level_start_index=None,
- **kwargs):
- """Forward Function of MultiScaleDeformAttention.
-
- Args:
- query (Tensor): Query of Transformer with shape
- (num_query, bs, embed_dims).
- key (Tensor): The key tensor with shape
- `(num_key, bs, embed_dims)`.
- value (Tensor): The value tensor with shape
- `(num_key, bs, embed_dims)`.
- identity (Tensor): The tensor used for addition, with the
- same shape as `query`. Default None. If None,
- `query` will be used.
- query_pos (Tensor): The positional encoding for `query`.
- Default: None.
- key_pos (Tensor): The positional encoding for `key`. Default
- None.
- reference_points (Tensor): The normalized reference
- points with shape (bs, num_query, num_levels, 2),
- all elements is range in [0, 1], top-left (0,0),
- bottom-right (1, 1), including padding area.
- or (N, Length_{query}, num_levels, 4), add
- additional two dimensions is (w, h) to
- form reference boxes.
- key_padding_mask (Tensor): ByteTensor for `query`, with
- shape [bs, num_key].
- spatial_shapes (Tensor): Spatial shape of features in
- different levels. With shape (num_levels, 2),
- last dimension represents (h, w).
- level_start_index (Tensor): The start index of each level.
- A tensor has shape ``(num_levels, )`` and can be represented
- as [0, h_0*w_0, h_0*w_0+h_1*w_1, ...].
-
- Returns:
- Tensor: forwarded results with shape [num_query, bs, embed_dims].
- """
-
- if value is None:
- value = query
-
- if identity is None:
- identity = query
- if query_pos is not None:
- query = query + query_pos
- if not self.batch_first:
- # change to (bs, num_query ,embed_dims)
- query = query.permute(1, 0, 2)
- value = value.permute(1, 0, 2)
-
- bs, num_query, _ = query.shape
- bs, num_value, _ = value.shape
- assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value
-
- value = self.value_proj(value)
- if key_padding_mask is not None:
- value = value.masked_fill(key_padding_mask[..., None], 0.0)
- value = value.view(bs, num_value, self.num_heads, -1)
- sampling_offsets = self.sampling_offsets(query).view(
- bs, num_query, self.num_heads, self.num_levels, self.num_points, 2)
- attention_weights = self.attention_weights(query).view(
- bs, num_query, self.num_heads, self.num_levels * self.num_points)
- attention_weights = attention_weights.softmax(-1)
-
- attention_weights = attention_weights.view(bs, num_query,
- self.num_heads,
- self.num_levels,
- self.num_points)
- if reference_points.shape[-1] == 2:
- offset_normalizer = torch.stack(
- [spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
- sampling_locations = reference_points[:, :, None, :, None, :] \
- + sampling_offsets \
- / offset_normalizer[None, None, None, :, None, :]
- elif reference_points.shape[-1] == 4:
- sampling_locations = reference_points[:, :, None, :, None, :2] \
- + sampling_offsets / self.num_points \
- * reference_points[:, :, None, :, None, 2:] \
- * 0.5
- else:
- raise ValueError(
- f'Last dim of reference_points must be'
- f' 2 or 4, but get {reference_points.shape[-1]} instead.')
- if torch.cuda.is_available() and value.is_cuda:
- output = MultiScaleDeformableAttnFunction.apply(
- value, spatial_shapes, level_start_index, sampling_locations,
- attention_weights, self.im2col_step)
- else:
- output = multi_scale_deformable_attn_pytorch(
- value, spatial_shapes, sampling_locations, attention_weights)
-
- output = self.output_proj(output)
-
- if not self.batch_first:
- # (num_query, bs ,embed_dims)
- output = output.permute(1, 0, 2)
-
- return self.dropout(output) + identity
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/misc.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/misc.py
deleted file mode 100644
index 2c58d0d7fee9fe3d4519270ad8c1e998d0d8a18c..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/misc.py
+++ /dev/null
@@ -1,377 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import collections.abc
-import functools
-import itertools
-import subprocess
-import warnings
-from collections import abc
-from importlib import import_module
-from inspect import getfullargspec
-from itertools import repeat
-
-
-# From PyTorch internals
-def _ntuple(n):
-
- def parse(x):
- if isinstance(x, collections.abc.Iterable):
- return x
- return tuple(repeat(x, n))
-
- return parse
-
-
-to_1tuple = _ntuple(1)
-to_2tuple = _ntuple(2)
-to_3tuple = _ntuple(3)
-to_4tuple = _ntuple(4)
-to_ntuple = _ntuple
-
-
-def is_str(x):
- """Whether the input is an string instance.
-
- Note: This method is deprecated since python 2 is no longer supported.
- """
- return isinstance(x, str)
-
-
-def import_modules_from_strings(imports, allow_failed_imports=False):
- """Import modules from the given list of strings.
-
- Args:
- imports (list | str | None): The given module names to be imported.
- allow_failed_imports (bool): If True, the failed imports will return
- None. Otherwise, an ImportError is raise. Default: False.
-
- Returns:
- list[module] | module | None: The imported modules.
-
- Examples:
- >>> osp, sys = import_modules_from_strings(
- ... ['os.path', 'sys'])
- >>> import os.path as osp_
- >>> import sys as sys_
- >>> assert osp == osp_
- >>> assert sys == sys_
- """
- if not imports:
- return
- single_import = False
- if isinstance(imports, str):
- single_import = True
- imports = [imports]
- if not isinstance(imports, list):
- raise TypeError(
- f'custom_imports must be a list but got type {type(imports)}')
- imported = []
- for imp in imports:
- if not isinstance(imp, str):
- raise TypeError(
- f'{imp} is of type {type(imp)} and cannot be imported.')
- try:
- imported_tmp = import_module(imp)
- except ImportError:
- if allow_failed_imports:
- warnings.warn(f'{imp} failed to import and is ignored.',
- UserWarning)
- imported_tmp = None
- else:
- raise ImportError
- imported.append(imported_tmp)
- if single_import:
- imported = imported[0]
- return imported
-
-
-def iter_cast(inputs, dst_type, return_type=None):
- """Cast elements of an iterable object into some type.
-
- Args:
- inputs (Iterable): The input object.
- dst_type (type): Destination type.
- return_type (type, optional): If specified, the output object will be
- converted to this type, otherwise an iterator.
-
- Returns:
- iterator or specified type: The converted object.
- """
- if not isinstance(inputs, abc.Iterable):
- raise TypeError('inputs must be an iterable object')
- if not isinstance(dst_type, type):
- raise TypeError('"dst_type" must be a valid type')
-
- out_iterable = map(dst_type, inputs)
-
- if return_type is None:
- return out_iterable
- else:
- return return_type(out_iterable)
-
-
-def list_cast(inputs, dst_type):
- """Cast elements of an iterable object into a list of some type.
-
- A partial method of :func:`iter_cast`.
- """
- return iter_cast(inputs, dst_type, return_type=list)
-
-
-def tuple_cast(inputs, dst_type):
- """Cast elements of an iterable object into a tuple of some type.
-
- A partial method of :func:`iter_cast`.
- """
- return iter_cast(inputs, dst_type, return_type=tuple)
-
-
-def is_seq_of(seq, expected_type, seq_type=None):
- """Check whether it is a sequence of some type.
-
- Args:
- seq (Sequence): The sequence to be checked.
- expected_type (type): Expected type of sequence items.
- seq_type (type, optional): Expected sequence type.
-
- Returns:
- bool: Whether the sequence is valid.
- """
- if seq_type is None:
- exp_seq_type = abc.Sequence
- else:
- assert isinstance(seq_type, type)
- exp_seq_type = seq_type
- if not isinstance(seq, exp_seq_type):
- return False
- for item in seq:
- if not isinstance(item, expected_type):
- return False
- return True
-
-
-def is_list_of(seq, expected_type):
- """Check whether it is a list of some type.
-
- A partial method of :func:`is_seq_of`.
- """
- return is_seq_of(seq, expected_type, seq_type=list)
-
-
-def is_tuple_of(seq, expected_type):
- """Check whether it is a tuple of some type.
-
- A partial method of :func:`is_seq_of`.
- """
- return is_seq_of(seq, expected_type, seq_type=tuple)
-
-
-def slice_list(in_list, lens):
- """Slice a list into several sub lists by a list of given length.
-
- Args:
- in_list (list): The list to be sliced.
- lens(int or list): The expected length of each out list.
-
- Returns:
- list: A list of sliced list.
- """
- if isinstance(lens, int):
- assert len(in_list) % lens == 0
- lens = [lens] * int(len(in_list) / lens)
- if not isinstance(lens, list):
- raise TypeError('"indices" must be an integer or a list of integers')
- elif sum(lens) != len(in_list):
- raise ValueError('sum of lens and list length does not '
- f'match: {sum(lens)} != {len(in_list)}')
- out_list = []
- idx = 0
- for i in range(len(lens)):
- out_list.append(in_list[idx:idx + lens[i]])
- idx += lens[i]
- return out_list
-
-
-def concat_list(in_list):
- """Concatenate a list of list into a single list.
-
- Args:
- in_list (list): The list of list to be merged.
-
- Returns:
- list: The concatenated flat list.
- """
- return list(itertools.chain(*in_list))
-
-
-def check_prerequisites(
- prerequisites,
- checker,
- msg_tmpl='Prerequisites "{}" are required in method "{}" but not '
- 'found, please install them first.'): # yapf: disable
- """A decorator factory to check if prerequisites are satisfied.
-
- Args:
- prerequisites (str of list[str]): Prerequisites to be checked.
- checker (callable): The checker method that returns True if a
- prerequisite is meet, False otherwise.
- msg_tmpl (str): The message template with two variables.
-
- Returns:
- decorator: A specific decorator.
- """
-
- def wrap(func):
-
- @functools.wraps(func)
- def wrapped_func(*args, **kwargs):
- requirements = [prerequisites] if isinstance(
- prerequisites, str) else prerequisites
- missing = []
- for item in requirements:
- if not checker(item):
- missing.append(item)
- if missing:
- print(msg_tmpl.format(', '.join(missing), func.__name__))
- raise RuntimeError('Prerequisites not meet.')
- else:
- return func(*args, **kwargs)
-
- return wrapped_func
-
- return wrap
-
-
-def _check_py_package(package):
- try:
- import_module(package)
- except ImportError:
- return False
- else:
- return True
-
-
-def _check_executable(cmd):
- if subprocess.call(f'which {cmd}', shell=True) != 0:
- return False
- else:
- return True
-
-
-def requires_package(prerequisites):
- """A decorator to check if some python packages are installed.
-
- Example:
- >>> @requires_package('numpy')
- >>> func(arg1, args):
- >>> return numpy.zeros(1)
- array([0.])
- >>> @requires_package(['numpy', 'non_package'])
- >>> func(arg1, args):
- >>> return numpy.zeros(1)
- ImportError
- """
- return check_prerequisites(prerequisites, checker=_check_py_package)
-
-
-def requires_executable(prerequisites):
- """A decorator to check if some executable files are installed.
-
- Example:
- >>> @requires_executable('ffmpeg')
- >>> func(arg1, args):
- >>> print(1)
- 1
- """
- return check_prerequisites(prerequisites, checker=_check_executable)
-
-
-def deprecated_api_warning(name_dict, cls_name=None):
- """A decorator to check if some arguments are deprecate and try to replace
- deprecate src_arg_name to dst_arg_name.
-
- Args:
- name_dict(dict):
- key (str): Deprecate argument names.
- val (str): Expected argument names.
-
- Returns:
- func: New function.
- """
-
- def api_warning_wrapper(old_func):
-
- @functools.wraps(old_func)
- def new_func(*args, **kwargs):
- # get the arg spec of the decorated method
- args_info = getfullargspec(old_func)
- # get name of the function
- func_name = old_func.__name__
- if cls_name is not None:
- func_name = f'{cls_name}.{func_name}'
- if args:
- arg_names = args_info.args[:len(args)]
- for src_arg_name, dst_arg_name in name_dict.items():
- if src_arg_name in arg_names:
- warnings.warn(
- f'"{src_arg_name}" is deprecated in '
- f'`{func_name}`, please use "{dst_arg_name}" '
- 'instead')
- arg_names[arg_names.index(src_arg_name)] = dst_arg_name
- if kwargs:
- for src_arg_name, dst_arg_name in name_dict.items():
- if src_arg_name in kwargs:
-
- assert dst_arg_name not in kwargs, (
- f'The expected behavior is to replace '
- f'the deprecated key `{src_arg_name}` to '
- f'new key `{dst_arg_name}`, but got them '
- f'in the arguments at the same time, which '
- f'is confusing. `{src_arg_name} will be '
- f'deprecated in the future, please '
- f'use `{dst_arg_name}` instead.')
-
- warnings.warn(
- f'"{src_arg_name}" is deprecated in '
- f'`{func_name}`, please use "{dst_arg_name}" '
- 'instead')
- kwargs[dst_arg_name] = kwargs.pop(src_arg_name)
-
- # apply converted arguments to the decorated method
- output = old_func(*args, **kwargs)
- return output
-
- return new_func
-
- return api_warning_wrapper
-
-
-def is_method_overridden(method, base_class, derived_class):
- """Check if a method of base class is overridden in derived class.
-
- Args:
- method (str): the method name to check.
- base_class (type): the class of the base class.
- derived_class (type | Any): the class or instance of the derived class.
- """
- assert isinstance(base_class, type), \
- "base_class doesn't accept instance, Please pass class instead."
-
- if not isinstance(derived_class, type):
- derived_class = derived_class.__class__
-
- base_method = getattr(base_class, method)
- derived_method = getattr(derived_class, method)
- return derived_method != base_method
-
-
-def has_method(obj: object, method: str) -> bool:
- """Check whether the object has a method.
-
- Args:
- method (str): The method name to check.
- obj (object): The object to check.
-
- Returns:
- bool: True if the object has the method else False.
- """
- return hasattr(obj, method) and callable(getattr(obj, method))
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/momentum_updater.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/momentum_updater.py
deleted file mode 100644
index 60437756ceedf06055ec349df69a25465738d3f0..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/momentum_updater.py
+++ /dev/null
@@ -1,493 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import annotator.uniformer.mmcv as mmcv
-from .hook import HOOKS, Hook
-from .lr_updater import annealing_cos, annealing_linear, format_param
-
-
-class MomentumUpdaterHook(Hook):
-
- def __init__(self,
- by_epoch=True,
- warmup=None,
- warmup_iters=0,
- warmup_ratio=0.9):
- # validate the "warmup" argument
- if warmup is not None:
- if warmup not in ['constant', 'linear', 'exp']:
- raise ValueError(
- f'"{warmup}" is not a supported type for warming up, valid'
- ' types are "constant" and "linear"')
- if warmup is not None:
- assert warmup_iters > 0, \
- '"warmup_iters" must be a positive integer'
- assert 0 < warmup_ratio <= 1.0, \
- '"warmup_momentum" must be in range (0,1]'
-
- self.by_epoch = by_epoch
- self.warmup = warmup
- self.warmup_iters = warmup_iters
- self.warmup_ratio = warmup_ratio
-
- self.base_momentum = [] # initial momentum for all param groups
- self.regular_momentum = [
- ] # expected momentum if no warming up is performed
-
- def _set_momentum(self, runner, momentum_groups):
- if isinstance(runner.optimizer, dict):
- for k, optim in runner.optimizer.items():
- for param_group, mom in zip(optim.param_groups,
- momentum_groups[k]):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
- else:
- for param_group, mom in zip(runner.optimizer.param_groups,
- momentum_groups):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
-
- def get_momentum(self, runner, base_momentum):
- raise NotImplementedError
-
- def get_regular_momentum(self, runner):
- if isinstance(runner.optimizer, dict):
- momentum_groups = {}
- for k in runner.optimizer.keys():
- _momentum_group = [
- self.get_momentum(runner, _base_momentum)
- for _base_momentum in self.base_momentum[k]
- ]
- momentum_groups.update({k: _momentum_group})
- return momentum_groups
- else:
- return [
- self.get_momentum(runner, _base_momentum)
- for _base_momentum in self.base_momentum
- ]
-
- def get_warmup_momentum(self, cur_iters):
-
- def _get_warmup_momentum(cur_iters, regular_momentum):
- if self.warmup == 'constant':
- warmup_momentum = [
- _momentum / self.warmup_ratio
- for _momentum in self.regular_momentum
- ]
- elif self.warmup == 'linear':
- k = (1 - cur_iters / self.warmup_iters) * (1 -
- self.warmup_ratio)
- warmup_momentum = [
- _momentum / (1 - k) for _momentum in self.regular_mom
- ]
- elif self.warmup == 'exp':
- k = self.warmup_ratio**(1 - cur_iters / self.warmup_iters)
- warmup_momentum = [
- _momentum / k for _momentum in self.regular_mom
- ]
- return warmup_momentum
-
- if isinstance(self.regular_momentum, dict):
- momentum_groups = {}
- for key, regular_momentum in self.regular_momentum.items():
- momentum_groups[key] = _get_warmup_momentum(
- cur_iters, regular_momentum)
- return momentum_groups
- else:
- return _get_warmup_momentum(cur_iters, self.regular_momentum)
-
- def before_run(self, runner):
- # NOTE: when resuming from a checkpoint,
- # if 'initial_momentum' is not saved,
- # it will be set according to the optimizer params
- if isinstance(runner.optimizer, dict):
- self.base_momentum = {}
- for k, optim in runner.optimizer.items():
- for group in optim.param_groups:
- if 'momentum' in group.keys():
- group.setdefault('initial_momentum', group['momentum'])
- else:
- group.setdefault('initial_momentum', group['betas'][0])
- _base_momentum = [
- group['initial_momentum'] for group in optim.param_groups
- ]
- self.base_momentum.update({k: _base_momentum})
- else:
- for group in runner.optimizer.param_groups:
- if 'momentum' in group.keys():
- group.setdefault('initial_momentum', group['momentum'])
- else:
- group.setdefault('initial_momentum', group['betas'][0])
- self.base_momentum = [
- group['initial_momentum']
- for group in runner.optimizer.param_groups
- ]
-
- def before_train_epoch(self, runner):
- if not self.by_epoch:
- return
- self.regular_mom = self.get_regular_momentum(runner)
- self._set_momentum(runner, self.regular_mom)
-
- def before_train_iter(self, runner):
- cur_iter = runner.iter
- if not self.by_epoch:
- self.regular_mom = self.get_regular_momentum(runner)
- if self.warmup is None or cur_iter >= self.warmup_iters:
- self._set_momentum(runner, self.regular_mom)
- else:
- warmup_momentum = self.get_warmup_momentum(cur_iter)
- self._set_momentum(runner, warmup_momentum)
- elif self.by_epoch:
- if self.warmup is None or cur_iter > self.warmup_iters:
- return
- elif cur_iter == self.warmup_iters:
- self._set_momentum(runner, self.regular_mom)
- else:
- warmup_momentum = self.get_warmup_momentum(cur_iter)
- self._set_momentum(runner, warmup_momentum)
-
-
-@HOOKS.register_module()
-class StepMomentumUpdaterHook(MomentumUpdaterHook):
- """Step momentum scheduler with min value clipping.
-
- Args:
- step (int | list[int]): Step to decay the momentum. If an int value is
- given, regard it as the decay interval. If a list is given, decay
- momentum at these steps.
- gamma (float, optional): Decay momentum ratio. Default: 0.5.
- min_momentum (float, optional): Minimum momentum value to keep. If
- momentum after decay is lower than this value, it will be clipped
- accordingly. If None is given, we don't perform lr clipping.
- Default: None.
- """
-
- def __init__(self, step, gamma=0.5, min_momentum=None, **kwargs):
- if isinstance(step, list):
- assert mmcv.is_list_of(step, int)
- assert all([s > 0 for s in step])
- elif isinstance(step, int):
- assert step > 0
- else:
- raise TypeError('"step" must be a list or integer')
- self.step = step
- self.gamma = gamma
- self.min_momentum = min_momentum
- super(StepMomentumUpdaterHook, self).__init__(**kwargs)
-
- def get_momentum(self, runner, base_momentum):
- progress = runner.epoch if self.by_epoch else runner.iter
-
- # calculate exponential term
- if isinstance(self.step, int):
- exp = progress // self.step
- else:
- exp = len(self.step)
- for i, s in enumerate(self.step):
- if progress < s:
- exp = i
- break
-
- momentum = base_momentum * (self.gamma**exp)
- if self.min_momentum is not None:
- # clip to a minimum value
- momentum = max(momentum, self.min_momentum)
- return momentum
-
-
-@HOOKS.register_module()
-class CosineAnnealingMomentumUpdaterHook(MomentumUpdaterHook):
-
- def __init__(self, min_momentum=None, min_momentum_ratio=None, **kwargs):
- assert (min_momentum is None) ^ (min_momentum_ratio is None)
- self.min_momentum = min_momentum
- self.min_momentum_ratio = min_momentum_ratio
- super(CosineAnnealingMomentumUpdaterHook, self).__init__(**kwargs)
-
- def get_momentum(self, runner, base_momentum):
- if self.by_epoch:
- progress = runner.epoch
- max_progress = runner.max_epochs
- else:
- progress = runner.iter
- max_progress = runner.max_iters
- if self.min_momentum_ratio is not None:
- target_momentum = base_momentum * self.min_momentum_ratio
- else:
- target_momentum = self.min_momentum
- return annealing_cos(base_momentum, target_momentum,
- progress / max_progress)
-
-
-@HOOKS.register_module()
-class CyclicMomentumUpdaterHook(MomentumUpdaterHook):
- """Cyclic momentum Scheduler.
-
- Implement the cyclical momentum scheduler policy described in
- https://arxiv.org/pdf/1708.07120.pdf
-
- This momentum scheduler usually used together with the CyclicLRUpdater
- to improve the performance in the 3D detection area.
-
- Attributes:
- target_ratio (tuple[float]): Relative ratio of the lowest momentum and
- the highest momentum to the initial momentum.
- cyclic_times (int): Number of cycles during training
- step_ratio_up (float): The ratio of the increasing process of momentum
- in the total cycle.
- by_epoch (bool): Whether to update momentum by epoch.
- """
-
- def __init__(self,
- by_epoch=False,
- target_ratio=(0.85 / 0.95, 1),
- cyclic_times=1,
- step_ratio_up=0.4,
- **kwargs):
- if isinstance(target_ratio, float):
- target_ratio = (target_ratio, target_ratio / 1e5)
- elif isinstance(target_ratio, tuple):
- target_ratio = (target_ratio[0], target_ratio[0] / 1e5) \
- if len(target_ratio) == 1 else target_ratio
- else:
- raise ValueError('target_ratio should be either float '
- f'or tuple, got {type(target_ratio)}')
-
- assert len(target_ratio) == 2, \
- '"target_ratio" must be list or tuple of two floats'
- assert 0 <= step_ratio_up < 1.0, \
- '"step_ratio_up" must be in range [0,1)'
-
- self.target_ratio = target_ratio
- self.cyclic_times = cyclic_times
- self.step_ratio_up = step_ratio_up
- self.momentum_phases = [] # init momentum_phases
- # currently only support by_epoch=False
- assert not by_epoch, \
- 'currently only support "by_epoch" = False'
- super(CyclicMomentumUpdaterHook, self).__init__(by_epoch, **kwargs)
-
- def before_run(self, runner):
- super(CyclicMomentumUpdaterHook, self).before_run(runner)
- # initiate momentum_phases
- # total momentum_phases are separated as up and down
- max_iter_per_phase = runner.max_iters // self.cyclic_times
- iter_up_phase = int(self.step_ratio_up * max_iter_per_phase)
- self.momentum_phases.append(
- [0, iter_up_phase, max_iter_per_phase, 1, self.target_ratio[0]])
- self.momentum_phases.append([
- iter_up_phase, max_iter_per_phase, max_iter_per_phase,
- self.target_ratio[0], self.target_ratio[1]
- ])
-
- def get_momentum(self, runner, base_momentum):
- curr_iter = runner.iter
- for (start_iter, end_iter, max_iter_per_phase, start_ratio,
- end_ratio) in self.momentum_phases:
- curr_iter %= max_iter_per_phase
- if start_iter <= curr_iter < end_iter:
- progress = curr_iter - start_iter
- return annealing_cos(base_momentum * start_ratio,
- base_momentum * end_ratio,
- progress / (end_iter - start_iter))
-
-
-@HOOKS.register_module()
-class OneCycleMomentumUpdaterHook(MomentumUpdaterHook):
- """OneCycle momentum Scheduler.
-
- This momentum scheduler usually used together with the OneCycleLrUpdater
- to improve the performance.
-
- Args:
- base_momentum (float or list): Lower momentum boundaries in the cycle
- for each parameter group. Note that momentum is cycled inversely
- to learning rate; at the peak of a cycle, momentum is
- 'base_momentum' and learning rate is 'max_lr'.
- Default: 0.85
- max_momentum (float or list): Upper momentum boundaries in the cycle
- for each parameter group. Functionally,
- it defines the cycle amplitude (max_momentum - base_momentum).
- Note that momentum is cycled inversely
- to learning rate; at the start of a cycle, momentum is
- 'max_momentum' and learning rate is 'base_lr'
- Default: 0.95
- pct_start (float): The percentage of the cycle (in number of steps)
- spent increasing the learning rate.
- Default: 0.3
- anneal_strategy (str): {'cos', 'linear'}
- Specifies the annealing strategy: 'cos' for cosine annealing,
- 'linear' for linear annealing.
- Default: 'cos'
- three_phase (bool): If three_phase is True, use a third phase of the
- schedule to annihilate the learning rate according to
- final_div_factor instead of modifying the second phase (the first
- two phases will be symmetrical about the step indicated by
- pct_start).
- Default: False
- """
-
- def __init__(self,
- base_momentum=0.85,
- max_momentum=0.95,
- pct_start=0.3,
- anneal_strategy='cos',
- three_phase=False,
- **kwargs):
- # validate by_epoch, currently only support by_epoch=False
- if 'by_epoch' not in kwargs:
- kwargs['by_epoch'] = False
- else:
- assert not kwargs['by_epoch'], \
- 'currently only support "by_epoch" = False'
- if not isinstance(base_momentum, (float, list, dict)):
- raise ValueError('base_momentum must be the type among of float,'
- 'list or dict.')
- self._base_momentum = base_momentum
- if not isinstance(max_momentum, (float, list, dict)):
- raise ValueError('max_momentum must be the type among of float,'
- 'list or dict.')
- self._max_momentum = max_momentum
- # validate pct_start
- if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float):
- raise ValueError('Expected float between 0 and 1 pct_start, but '
- f'got {pct_start}')
- self.pct_start = pct_start
- # validate anneal_strategy
- if anneal_strategy not in ['cos', 'linear']:
- raise ValueError('anneal_strategy must by one of "cos" or '
- f'"linear", instead got {anneal_strategy}')
- elif anneal_strategy == 'cos':
- self.anneal_func = annealing_cos
- elif anneal_strategy == 'linear':
- self.anneal_func = annealing_linear
- self.three_phase = three_phase
- self.momentum_phases = [] # init momentum_phases
- super(OneCycleMomentumUpdaterHook, self).__init__(**kwargs)
-
- def before_run(self, runner):
- if isinstance(runner.optimizer, dict):
- for k, optim in runner.optimizer.items():
- if ('momentum' not in optim.defaults
- and 'betas' not in optim.defaults):
- raise ValueError('optimizer must support momentum with'
- 'option enabled')
- self.use_beta1 = 'betas' in optim.defaults
- _base_momentum = format_param(k, optim, self._base_momentum)
- _max_momentum = format_param(k, optim, self._max_momentum)
- for group, b_momentum, m_momentum in zip(
- optim.param_groups, _base_momentum, _max_momentum):
- if self.use_beta1:
- _, beta2 = group['betas']
- group['betas'] = (m_momentum, beta2)
- else:
- group['momentum'] = m_momentum
- group['base_momentum'] = b_momentum
- group['max_momentum'] = m_momentum
- else:
- optim = runner.optimizer
- if ('momentum' not in optim.defaults
- and 'betas' not in optim.defaults):
- raise ValueError('optimizer must support momentum with'
- 'option enabled')
- self.use_beta1 = 'betas' in optim.defaults
- k = type(optim).__name__
- _base_momentum = format_param(k, optim, self._base_momentum)
- _max_momentum = format_param(k, optim, self._max_momentum)
- for group, b_momentum, m_momentum in zip(optim.param_groups,
- _base_momentum,
- _max_momentum):
- if self.use_beta1:
- _, beta2 = group['betas']
- group['betas'] = (m_momentum, beta2)
- else:
- group['momentum'] = m_momentum
- group['base_momentum'] = b_momentum
- group['max_momentum'] = m_momentum
-
- if self.three_phase:
- self.momentum_phases.append({
- 'end_iter':
- float(self.pct_start * runner.max_iters) - 1,
- 'start_momentum':
- 'max_momentum',
- 'end_momentum':
- 'base_momentum'
- })
- self.momentum_phases.append({
- 'end_iter':
- float(2 * self.pct_start * runner.max_iters) - 2,
- 'start_momentum':
- 'base_momentum',
- 'end_momentum':
- 'max_momentum'
- })
- self.momentum_phases.append({
- 'end_iter': runner.max_iters - 1,
- 'start_momentum': 'max_momentum',
- 'end_momentum': 'max_momentum'
- })
- else:
- self.momentum_phases.append({
- 'end_iter':
- float(self.pct_start * runner.max_iters) - 1,
- 'start_momentum':
- 'max_momentum',
- 'end_momentum':
- 'base_momentum'
- })
- self.momentum_phases.append({
- 'end_iter': runner.max_iters - 1,
- 'start_momentum': 'base_momentum',
- 'end_momentum': 'max_momentum'
- })
-
- def _set_momentum(self, runner, momentum_groups):
- if isinstance(runner.optimizer, dict):
- for k, optim in runner.optimizer.items():
- for param_group, mom in zip(optim.param_groups,
- momentum_groups[k]):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
- else:
- for param_group, mom in zip(runner.optimizer.param_groups,
- momentum_groups):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
-
- def get_momentum(self, runner, param_group):
- curr_iter = runner.iter
- start_iter = 0
- for i, phase in enumerate(self.momentum_phases):
- end_iter = phase['end_iter']
- if curr_iter <= end_iter or i == len(self.momentum_phases) - 1:
- pct = (curr_iter - start_iter) / (end_iter - start_iter)
- momentum = self.anneal_func(
- param_group[phase['start_momentum']],
- param_group[phase['end_momentum']], pct)
- break
- start_iter = end_iter
- return momentum
-
- def get_regular_momentum(self, runner):
- if isinstance(runner.optimizer, dict):
- momentum_groups = {}
- for k, optim in runner.optimizer.items():
- _momentum_group = [
- self.get_momentum(runner, param_group)
- for param_group in optim.param_groups
- ]
- momentum_groups.update({k: _momentum_group})
- return momentum_groups
- else:
- momentum_groups = []
- for param_group in runner.optimizer.param_groups:
- momentum_groups.append(self.get_momentum(runner, param_group))
- return momentum_groups
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/core/evaluation/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/core/evaluation/__init__.py
deleted file mode 100644
index f7cc4b23413a0639e9de00eeb0bf600632d2c6cd..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmseg/core/evaluation/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from .class_names import get_classes, get_palette
-from .eval_hooks import DistEvalHook, EvalHook
-from .metrics import eval_metrics, mean_dice, mean_fscore, mean_iou
-
-__all__ = [
- 'EvalHook', 'DistEvalHook', 'mean_dice', 'mean_iou', 'mean_fscore',
- 'eval_metrics', 'get_classes', 'get_palette'
-]
diff --git a/spaces/abnerh/video-to-subs/README.md b/spaces/abnerh/video-to-subs/README.md
deleted file mode 100644
index ed32bb5f36f156684d9ebd5458b8a93e085d7e12..0000000000000000000000000000000000000000
--- a/spaces/abnerh/video-to-subs/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Video To Subs
-emoji: 🎙️
-colorFrom: white
-colorTo: blue
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/ai-guru/composer/static/_app/pages/index.svelte-9646dd5a.js b/spaces/ai-guru/composer/static/_app/pages/index.svelte-9646dd5a.js
deleted file mode 100644
index 510a133267536503ed780d792110a1ff8173e49a..0000000000000000000000000000000000000000
--- a/spaces/ai-guru/composer/static/_app/pages/index.svelte-9646dd5a.js
+++ /dev/null
@@ -1,5 +0,0 @@
-import{S as Z,i as x,s as ee,e as b,k as I,c as w,a as E,d as g,m as C,K as se,b as d,g as P,J as f,L as U,t as N,h as H,j as Ee,E as re,M as Re,N as R,O as fe,P as Ge,w as Y,x as J,y as K,Q as Ve,q as L,o as V,B as Q,R as ue,T as Fe,f as X,U as _e,V as ie,W as qe,n as $e,p as Te,X as We,v as Xe,u as Ye,Y as Je,l as Me}from"../chunks/index-7c452e28.js";import{w as le}from"../chunks/index-d282aaf8.js";const he=le("synth"),be=le("medium"),we=le("medium"),ve=le(!1),pe=le(""),ye=le(""),ke=le(""),ne={piano:"Piano",chamber:"Chamber Music",rock_and_metal:"Rock and Metal",synth:"Synthesizer",church:"Church",timpani_strings_harp:"Timpani, Contrabass, Harp",country:"Country",reggae:"Reggae-esque"},Ke={low:"Low",medium:"Medium",high:"High"},Qe={low:"Low",medium:"Medium",high:"High",very_high:"Very High"};function Ne(l,e,s){const t=l.slice();return t[4]=e[s],t[6]=s,t}function He(l){let e,s,t,o,n,a,i,c,u,p,r,_;return{c(){e=b("label"),s=b("div"),t=b("img"),a=I(),i=b("input"),u=I(),this.h()},l(m){e=w(m,"LABEL",{"data-selected":!0,class:!0});var T=E(e);s=w(T,"DIV",{class:!0});var v=E(s);t=w(v,"IMG",{src:!0,alt:!0,class:!0}),v.forEach(g),a=C(T),i=w(T,"INPUT",{type:!0,class:!0}),u=C(T),T.forEach(g),this.h()},h(){se(t.src,o=`static/${l[4]}.svg`)||d(t,"src",o),d(t,"alt",n=ne[l[4]]),d(t,"class","svelte-1r9pswz"),d(s,"class","svelte-1r9pswz"),d(i,"type","radio"),i.__value=c=l[4],i.value=i.__value,d(i,"class","svelte-1r9pswz"),l[3][0].push(i),d(e,"data-selected",p=l[0]===l[4]),d(e,"class","svelte-1r9pswz")},m(m,T){P(m,e,T),f(e,s),f(s,t),f(e,a),f(e,i),i.checked=i.__value===l[0],f(e,u),r||(_=U(i,"change",l[2]),r=!0)},p(m,T){T&1&&(i.checked=i.__value===m[0]),T&1&&p!==(p=m[0]===m[4])&&d(e,"data-selected",p)},d(m){m&&g(e),l[3][0].splice(l[3][0].indexOf(i),1),r=!1,_()}}}function Ze(l){let e,s,t=(ne[l[0]]||"Synthesizer")+"",o,n,a,i=l[1],c=[];for(let u=0;us(0,t=i));const o=Object.keys(ne),n=[[]];function a(){t=this.__value,he.set(t)}return[t,o,a,n]}class et extends Z{constructor(e){super(),x(this,e,xe,Ze,ee,{})}}function je(l,e,s){const t=l.slice();return t[5]=e[s],t}function Be(l){let e,s=l[1][l[5]]+"",t,o,n,a,i,c,u,p;return{c(){e=b("label"),t=N(s),o=I(),n=b("input"),i=I(),this.h()},l(r){e=w(r,"LABEL",{"data-selected":!0,class:!0});var _=E(e);t=H(_,s),o=C(_),n=w(_,"INPUT",{type:!0,class:!0}),i=C(_),_.forEach(g),this.h()},h(){d(n,"type","radio"),n.__value=a=l[5],n.value=n.__value,d(n,"class","svelte-1m848u0"),l[4][0].push(n),d(e,"data-selected",c=l[5]===l[0]),d(e,"class","svelte-1m848u0")},m(r,_){P(r,e,_),f(e,t),f(e,o),f(e,n),n.checked=n.__value===l[0],f(e,i),u||(p=U(n,"change",l[3]),u=!0)},p(r,_){_&2&&s!==(s=r[1][r[5]]+"")&&Ee(t,s),_&1&&(n.checked=n.__value===r[0]),_&1&&c!==(c=r[5]===r[0])&&d(e,"data-selected",c)},d(r){r&&g(e),l[4][0].splice(l[4][0].indexOf(n),1),u=!1,p()}}}function tt(l){let e,s=l[2],t=[];for(let o=0;o{"options"in c&&s(1,t=c.options),"selection"in c&&s(0,n=c.selection)},[n,t,o,i,a]}class Ue extends Z{constructor(e){super(),x(this,e,st,tt,ee,{options:1,selection:0})}}function nt(l){let e,s,t,o,n,a,i,c;function u(r){l[1](r)}let p={options:Ke};return l[0]!==void 0&&(p.selection=l[0]),a=new Ue({props:p}),fe.push(()=>Ge(a,"selection",u)),{c(){e=b("div"),s=b("fieldset"),t=b("legend"),o=N("Note density"),n=I(),Y(a.$$.fragment),this.h()},l(r){e=w(r,"DIV",{});var _=E(e);s=w(_,"FIELDSET",{class:!0});var m=E(s);t=w(m,"LEGEND",{class:!0});var T=E(t);o=H(T,"Note density"),T.forEach(g),n=C(m),J(a.$$.fragment,m),m.forEach(g),_.forEach(g),this.h()},h(){d(t,"class","svelte-1ikh8be"),d(s,"class","svelte-1ikh8be")},m(r,_){P(r,e,_),f(e,s),f(s,t),f(t,o),f(s,n),K(a,s,null),c=!0},p(r,[_]){const m={};!i&&_&1&&(i=!0,m.selection=r[0],Ve(()=>i=!1)),a.$set(m)},i(r){c||(L(a.$$.fragment,r),c=!0)},o(r){V(a.$$.fragment,r),c=!1},d(r){r&&g(e),Q(a)}}}function lt(l,e,s){let t;R(l,be,n=>s(0,t=n));function o(n){t=n,be.set(t)}return[t,o]}class at extends Z{constructor(e){super(),x(this,e,lt,nt,ee,{})}}function ot(l){let e,s,t,o,n,a,i,c;function u(r){l[1](r)}let p={options:Qe};return l[0]!==void 0&&(p.selection=l[0]),a=new Ue({props:p}),fe.push(()=>Ge(a,"selection",u)),{c(){e=b("div"),s=b("fieldset"),t=b("legend"),o=N("Temperature"),n=I(),Y(a.$$.fragment),this.h()},l(r){e=w(r,"DIV",{});var _=E(e);s=w(_,"FIELDSET",{class:!0});var m=E(s);t=w(m,"LEGEND",{class:!0});var T=E(t);o=H(T,"Temperature"),T.forEach(g),n=C(m),J(a.$$.fragment,m),m.forEach(g),_.forEach(g),this.h()},h(){d(t,"class","svelte-1ikh8be"),d(s,"class","svelte-1ikh8be")},m(r,_){P(r,e,_),f(e,s),f(s,t),f(t,o),f(s,n),K(a,s,null),c=!0},p(r,[_]){const m={};!i&&_&1&&(i=!0,m.selection=r[0],Ve(()=>i=!1)),a.$set(m)},i(r){c||(L(a.$$.fragment,r),c=!0)},o(r){V(a.$$.fragment,r),c=!1},d(r){r&&g(e),Q(a)}}}function rt(l,e,s){let t;R(l,we,n=>s(0,t=n));function o(n){t=n,we.set(t)}return[t,o]}class it extends Z{constructor(e){super(),x(this,e,rt,ot,ee,{})}}function ct(l){let e,s,t;return{c(){e=N("Compose "),s=b("img"),this.h()},l(o){e=H(o,"Compose "),s=w(o,"IMG",{src:!0,alt:!0,class:!0}),this.h()},h(){se(s.src,t="static/wand.svg")||d(s,"src",t),d(s,"alt","Magic wand"),d(s,"class","svelte-18w38ow")},m(o,n){P(o,e,n),P(o,s,n)},d(o){o&&g(e),o&&g(s)}}}function ut(l){let e;return{c(){e=N("Composing...")},l(s){e=H(s,"Composing...")},m(s,t){P(s,e,t)},d(s){s&&g(e)}}}function ft(l){let e,s,t;function o(i,c){return i[0]?ut:ct}let n=o(l),a=n(l);return{c(){e=b("button"),a.c(),this.h()},l(i){e=w(i,"BUTTON",{class:!0});var c=E(e);a.l(c),c.forEach(g),this.h()},h(){e.disabled=l[0],d(e,"class","svelte-18w38ow")},m(i,c){P(i,e,c),a.m(e,null),s||(t=U(e,"click",l[1]),s=!0)},p(i,[c]){n!==(n=o(i))&&(a.d(1),a=n(i),a&&(a.c(),a.m(e,null))),c&1&&(e.disabled=i[0])},i:re,o:re,d(i){i&&g(e),a.d(),s=!1,t()}}}function dt(l,e,s){let t,o,n,a,i,c,u;R(l,ve,v=>s(0,t=v)),R(l,ke,v=>s(2,o=v)),R(l,ye,v=>s(3,n=v)),R(l,pe,v=>s(4,a=v)),R(l,we,v=>s(5,i=v)),R(l,be,v=>s(6,c=v)),R(l,he,v=>s(7,u=v));const p=()=>{"mediaSession"in navigator&&(navigator.mediaSession.metadata=new MediaMetadata({title:`${ne[u]} Composition`,artist:"AI Guru Composer",album:"Hugging Face",artwork:[{src:"static/hugging-face-headphones.png",sizes:"512x512",type:"image/png"}]}))},r=async({music_style:v,density:y,temperature:D})=>{var A;const S=await fetch("task/create",{method:"POST",headers:{"Content-Type":"application/json"},body:JSON.stringify({music_style:v,density:y,temperature:D})});if(!S.ok||!((A=S.headers.get("content-type"))!=null&&A.includes("application/json")))throw new Error(`Unable to create composition: [${S.status}] ${await S.text()}`);return await S.json()},_=async v=>{var D;const y=await fetch(`task/poll?task_id=${v.task_id}`);if(!y.ok||!((D=y.headers.get("content-type"))!=null&&D.includes("application/json")))throw new Error(`Unable to create composition: [${y.status}] ${await y.text()}`);return await y.json()},m=async(v,y=1e3,D=100)=>(v=await _(v),v.status==="completed"||v.status==="failed"||D&&v.poll_count>D?v:(await new Promise(S=>setTimeout(S,y)),await m(v,y,D)));return[t,async()=>{ue(ve,t=!0,t);try{const v=await r({music_style:u,density:c,temperature:i}),y=await m(v),{audio:D,image:S,tokens:j}=y.output;ue(pe,a=D,a),ue(ye,n=S,n),ue(ke,o=j,o),p()}catch(v){console.error(v)}finally{ue(ve,t=!1,t)}}]}class _t extends Z{constructor(e){super(),x(this,e,dt,ft,ee,{})}}function ce(l,{delay:e=0,duration:s=400,easing:t=Fe}={}){const o=+getComputedStyle(l).opacity;return{delay:e,duration:s,easing:t,css:n=>`opacity: ${n*o}`}}function Le(l){let e,s,t,o;return{c(){e=b("img"),this.h()},l(n){e=w(n,"IMG",{class:!0,src:!0,alt:!0,draggable:!0}),this.h()},h(){d(e,"class","play-button svelte-1536b0c"),se(e.src,s="static/play.svg")||d(e,"src",s),d(e,"alt","Play button"),d(e,"draggable","false"),X(e,"width",l[7]>100?"20%":"7.5%",!1)},m(n,a){P(n,e,a),o=!0},p(n,a){a&128&&X(e,"width",n[7]>100?"20%":"7.5%",!1)},i(n){o||(_e(()=>{t||(t=ie(e,ce,{},!0)),t.run(1)}),o=!0)},o(n){t||(t=ie(e,ce,{},!1)),t.run(0),o=!1},d(n){n&&g(e),n&&t&&t.end()}}}function ht(l){let e,s,t,o,n,a,i,c=!1,u,p=!0,r,_,m=`translate(${Math.min(l[6]*(l[1]/(l[2]-.9)),l[6])}px, -2%)`,T,v,y,D,S,j,A,q,W;function z(){cancelAnimationFrame(u),a.paused||(u=Je(z),c=!0),l[15].call(a)}let $=l[3]&&Le(l);return{c(){e=b("section"),s=b("img"),o=I(),n=b("div"),a=b("audio"),r=I(),_=b("div"),T=I(),$&&$.c(),v=I(),y=b("a"),D=N("Download"),this.h()},l(h){e=w(h,"SECTION",{class:!0});var k=E(e);s=w(k,"IMG",{class:!0,src:!0,alt:!0}),o=C(k),n=w(k,"DIV",{class:!0,tabindex:!0});var G=E(n);a=w(G,"AUDIO",{src:!0,class:!0}),E(a).forEach(g),r=C(G),_=w(G,"DIV",{class:!0}),E(_).forEach(g),T=C(G),$&&$.l(G),G.forEach(g),v=C(k),y=w(k,"A",{href:!0,download:!0,class:!0});var F=E(y);D=H(F,"Download"),F.forEach(g),k.forEach(g),this.h()},h(){d(s,"class","notes svelte-1536b0c"),se(s.src,t=l[8])||d(s,"src",t),d(s,"alt",""),se(a.src,i=l[9])||d(a,"src",i),d(a,"class","svelte-1536b0c"),l[2]===void 0&&_e(()=>l[16].call(a)),d(_,"class","handle svelte-1536b0c"),X(_,"transform",m,!1),d(n,"class","player svelte-1536b0c"),d(n,"tabindex","0"),X(n,"width",l[6]+"px",!1),X(n,"height",l[7]+"px",!1),d(y,"href",l[9]),d(y,"download",S=`${ne[l[10]]} Composition - AI Guru ft. Hugging Face.wav`),d(y,"class","download svelte-1536b0c"),d(e,"class","svelte-1536b0c")},m(h,k){P(h,e,k),f(e,s),l[14](s),f(e,o),f(e,n),f(n,a),f(n,r),f(n,_),f(n,T),$&&$.m(n,null),l[18](n),f(e,v),f(e,y),f(y,D),l[20](e),A=!0,q||(W=[U(a,"timeupdate",z),U(a,"durationchange",l[16]),U(a,"play",l[17]),U(a,"pause",l[17]),U(n,"mousemove",l[11]),U(n,"touchmove",qe(l[12])),U(n,"keydown",l[13]),U(n,"click",l[19])],q=!0)},p(h,[k]){(!A||k&256&&!se(s.src,t=h[8]))&&d(s,"src",t),(!A||k&512&&!se(a.src,i=h[9]))&&d(a,"src",i),!c&&k&2&&!isNaN(h[1])&&(a.currentTime=h[1]),c=!1,k&8&&p!==(p=h[3])&&a[p?"pause":"play"](),k&70&&m!==(m=`translate(${Math.min(h[6]*(h[1]/(h[2]-.9)),h[6])}px, -2%)`)&&X(_,"transform",m,!1),h[3]?$?($.p(h,k),k&8&&L($,1)):($=Le(h),$.c(),L($,1),$.m(n,null)):$&&($e(),V($,1,1,()=>{$=null}),Te()),k&64&&X(n,"width",h[6]+"px",!1),k&128&&X(n,"height",h[7]+"px",!1),(!A||k&512)&&d(y,"href",h[9]),(!A||k&1024&&S!==(S=`${ne[h[10]]} Composition - AI Guru ft. Hugging Face.wav`))&&d(y,"download",S)},i(h){A||(L($),_e(()=>{j||(j=ie(e,ce,{},!0)),j.run(1)}),A=!0)},o(h){V($),j||(j=ie(e,ce,{},!1)),j.run(0),A=!1},d(h){h&&g(e),l[14](null),$&&$.d(),l[18](null),l[20](null),h&&j&&j.end(),q=!1,We(W)}}}function pt(l,e,s){let t,o,n;R(l,ye,h=>s(8,t=h)),R(l,pe,h=>s(9,o=h)),R(l,he,h=>s(10,n=h));let a,i,c,u=!0,p,r,_,m;const T=()=>{s(6,_=r&&r.clientWidth),s(7,m=r&&r.clientHeight)};Xe(()=>{T(),"mediaSession"in navigator&&(navigator.mediaSession.setActionHandler("play",()=>s(3,u=!1)),navigator.mediaSession.setActionHandler("pause",()=>s(3,u=!0)),navigator.mediaSession.setActionHandler("stop",()=>{s(3,u=!0),s(1,i=0)})),window.scrollTo({top:a.offsetTop,behavior:"smooth"})}),Ye(()=>{T()});const v=h=>{if(!c||!h.buttons)return;const{left:k,right:G}=p.getBoundingClientRect();s(1,i=c*(h.clientX-k)/(G-k))},y=h=>{if(!c)return;const{left:k,right:G}=p.getBoundingClientRect();s(1,i=c*(h.touches[0].clientX-k)/(G-k))},D=h=>{h.preventDefault(),h.code==="Space"&&s(3,u=!u),h.code==="ArrowLeft"&&s(1,i=i>=1?i-1:0),h.code==="ArrowRight"&&s(1,i=i<=c-1?i+1:c)};function S(h){fe[h?"unshift":"push"](()=>{r=h,s(5,r)})}function j(){i=this.currentTime,s(1,i)}function A(){c=this.duration,s(2,c)}function q(){u=this.paused,s(3,u)}function W(h){fe[h?"unshift":"push"](()=>{p=h,s(4,p)})}const z=()=>s(3,u=!u);function $(h){fe[h?"unshift":"push"](()=>{a=h,s(0,a)})}return[a,i,c,u,p,r,_,m,t,o,n,v,y,D,S,j,A,q,W,z,$]}class mt extends Z{constructor(e){super(),x(this,e,pt,ht,ee,{})}}function ze(l){let e,s,t,o,n,a,i,c;return{c(){e=b("section"),s=b("h2"),t=N("Tokenized notes"),o=I(),n=b("p"),a=N(l[0]),this.h()},l(u){e=w(u,"SECTION",{class:!0});var p=E(e);s=w(p,"H2",{});var r=E(s);t=H(r,"Tokenized notes"),r.forEach(g),o=C(p),n=w(p,"P",{class:!0});var _=E(n);a=H(_,l[0]),_.forEach(g),p.forEach(g),this.h()},h(){d(n,"class","svelte-4un5mw"),d(e,"class","svelte-4un5mw")},m(u,p){P(u,e,p),f(e,s),f(s,t),f(e,o),f(e,n),f(n,a),c=!0},p(u,p){(!c||p&1)&&Ee(a,u[0])},i(u){c||(_e(()=>{i||(i=ie(e,ce,{},!0)),i.run(1)}),c=!0)},o(u){i||(i=ie(e,ce,{},!1)),i.run(0),c=!1},d(u){u&&g(e),u&&i&&i.end()}}}function gt(l){let e,s,t=l[0]&&ze(l);return{c(){t&&t.c(),e=Me()},l(o){t&&t.l(o),e=Me()},m(o,n){t&&t.m(o,n),P(o,e,n),s=!0},p(o,[n]){o[0]?t?(t.p(o,n),n&1&&L(t,1)):(t=ze(o),t.c(),L(t,1),t.m(e.parentNode,e)):t&&($e(),V(t,1,1,()=>{t=null}),Te())},i(o){s||(L(t),s=!0)},o(o){V(t),s=!1},d(o){t&&t.d(o),o&&g(e)}}}function vt(l,e,s){let t;return R(l,ke,o=>s(0,t=o)),[t]}class bt extends Z{constructor(e){super(),x(this,e,vt,gt,ee,{})}}function Pe(l){let e,s,t,o;return e=new mt({}),t=new bt({}),{c(){Y(e.$$.fragment),s=I(),Y(t.$$.fragment)},l(n){J(e.$$.fragment,n),s=C(n),J(t.$$.fragment,n)},m(n,a){K(e,n,a),P(n,s,a),K(t,n,a),o=!0},i(n){o||(L(e.$$.fragment,n),L(t.$$.fragment,n),o=!0)},o(n){V(e.$$.fragment,n),V(t.$$.fragment,n),o=!1},d(n){Q(e,n),n&&g(s),Q(t,n)}}}function wt(l){let e,s,t,o,n,a,i,c,u,p,r,_,m,T,v,y,D,S,j,A,q,W,z,$,h,k,G,F,me,te,ge,de;$=new et({}),k=new at({}),F=new it({}),te=new _t({});let M=l[0]&&Pe();return{c(){e=b("main"),s=b("h1"),t=N("Composer"),o=I(),n=b("p"),a=N("Trained on fifteen thousand songs. One AI model. Infinite compositions."),i=I(),c=b("p"),u=N(`This space contains a deep neural network model that can compose music. You can use it to generate audio in
- different styles, 4 bars at a time.`),p=I(),r=b("p"),_=N("Developed by "),m=b("a"),T=N("Ron Au"),v=N(` and
- `),y=b("a"),D=N("Tristan Behrens"),S=N("."),j=I(),A=b("p"),q=N("Have fun! And always feel free to send us some feedback and share your compositions!"),W=I(),z=b("section"),Y($.$$.fragment),h=I(),Y(k.$$.fragment),G=I(),Y(F.$$.fragment),me=I(),Y(te.$$.fragment),ge=I(),M&&M.c(),this.h()},l(B){e=w(B,"MAIN",{class:!0});var O=E(e);s=w(O,"H1",{class:!0});var Ie=E(s);t=H(Ie,"Composer"),Ie.forEach(g),o=C(O),n=w(O,"P",{class:!0});var Ce=E(n);a=H(Ce,"Trained on fifteen thousand songs. One AI model. Infinite compositions."),Ce.forEach(g),i=C(O),c=w(O,"P",{class:!0});var De=E(c);u=H(De,`This space contains a deep neural network model that can compose music. You can use it to generate audio in
- different styles, 4 bars at a time.`),De.forEach(g),p=C(O),r=w(O,"P",{class:!0});var ae=E(r);_=H(ae,"Developed by "),m=w(ae,"A",{href:!0,rel:!0,target:!0});var Se=E(m);T=H(Se,"Ron Au"),Se.forEach(g),v=H(ae,` and
- `),y=w(ae,"A",{href:!0,rel:!0,target:!0});var Ae=E(y);D=H(Ae,"Tristan Behrens"),Ae.forEach(g),S=H(ae,"."),ae.forEach(g),j=C(O),A=w(O,"P",{class:!0});var Oe=E(A);q=H(Oe,"Have fun! And always feel free to send us some feedback and share your compositions!"),Oe.forEach(g),W=C(O),z=w(O,"SECTION",{id:!0,class:!0});var oe=E(z);J($.$$.fragment,oe),h=C(oe),J(k.$$.fragment,oe),G=C(oe),J(F.$$.fragment,oe),oe.forEach(g),me=C(O),J(te.$$.fragment,O),ge=C(O),M&&M.l(O),O.forEach(g),this.h()},h(){d(s,"class","svelte-1rfjlkw"),d(n,"class","heading svelte-1rfjlkw"),d(c,"class","svelte-1rfjlkw"),d(m,"href","https://twitter.com/ronvoluted"),d(m,"rel","noopener"),d(m,"target","_blank"),d(y,"href","https://twitter.com/DrTBehrens"),d(y,"rel","noopener"),d(y,"target","_blank"),d(r,"class","svelte-1rfjlkw"),d(A,"class","svelte-1rfjlkw"),d(z,"id","options"),d(z,"class","svelte-1rfjlkw"),d(e,"class","svelte-1rfjlkw")},m(B,O){P(B,e,O),f(e,s),f(s,t),f(e,o),f(e,n),f(n,a),f(e,i),f(e,c),f(c,u),f(e,p),f(e,r),f(r,_),f(r,m),f(m,T),f(r,v),f(r,y),f(y,D),f(r,S),f(e,j),f(e,A),f(A,q),f(e,W),f(e,z),K($,z,null),f(z,h),K(k,z,null),f(z,G),K(F,z,null),f(e,me),K(te,e,null),f(e,ge),M&&M.m(e,null),de=!0},p(B,[O]){B[0]?M?O&1&&L(M,1):(M=Pe(),M.c(),L(M,1),M.m(e,null)):M&&($e(),V(M,1,1,()=>{M=null}),Te())},i(B){de||(L($.$$.fragment,B),L(k.$$.fragment,B),L(F.$$.fragment,B),L(te.$$.fragment,B),L(M),de=!0)},o(B){V($.$$.fragment,B),V(k.$$.fragment,B),V(F.$$.fragment,B),V(te.$$.fragment,B),V(M),de=!1},d(B){B&&g(e),Q($),Q(k),Q(F),Q(te),M&&M.d()}}}function yt(l,e,s){let t;return R(l,pe,o=>s(0,t=o)),[t]}class $t extends Z{constructor(e){super(),x(this,e,yt,wt,ee,{})}}export{$t as default};
diff --git a/spaces/aimstack/bloom/Dockerfile b/spaces/aimstack/bloom/Dockerfile
deleted file mode 100644
index 1b5313643bce150166d4135b441cd315634a68b9..0000000000000000000000000000000000000000
--- a/spaces/aimstack/bloom/Dockerfile
+++ /dev/null
@@ -1,30 +0,0 @@
-FROM python:3.9
-
-
-RUN useradd -m -u 1000 aim_user
-
-# Switch to the "aim_user" user
-USER aim_user
-
-# Set home to the user's home directory
-ENV HOME=/home/aim_user \
- PATH=/home/aim_user/.local/bin:$PATH
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME
-
-# install the `aim` package on the latest version
-RUN pip install aim==4.0.0.dev6
-
-RUN aim telemetry off
-
-ENTRYPOINT ["/bin/sh", "-c"]
-
-COPY aim_repo.tar.gz .
-RUN tar xvzf aim_repo.tar.gz
-# have to run `aim init` in the directory that stores aim data for
-# otherwise `aim up` will prompt for confirmation to create the directory itself.
-# We run aim listening on 0.0.0.0 to expose all ports. Also, we run
-# using `--dev` to print verbose logs. Port 43800 is the default port of
-# `aim up` but explicit is better than implicit.
-CMD ["aim up --host 0.0.0.0 --port 7860 --workers 2"]
\ No newline at end of file
diff --git a/spaces/airus/img-to-music/constants.py b/spaces/airus/img-to-music/constants.py
deleted file mode 100644
index 86863d1b778d4c66f0d8e1e0b699f1bb937c1d50..0000000000000000000000000000000000000000
--- a/spaces/airus/img-to-music/constants.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import numpy as np
-import os
-
-MUBERT_LICENSE = os.environ.get('MUBERT_LICENSE')
-MUBERT_TOKEN = os.environ.get('MUBERT_TOKEN')
-
-MUBERT_MODE = "loop"
-MUBERT_TAGS_STRING = 'tribal,action,kids,neo-classic,run 130,pumped,jazz / funk,ethnic,dubtechno,reggae,acid jazz,liquidfunk,funk,witch house,tech house,underground,artists,mystical,disco,sensorium,r&b,agender,psychedelic trance / psytrance,peaceful,run 140,piano,run 160,setting,meditation,christmas,ambient,horror,cinematic,electro house,idm,bass,minimal,underscore,drums,glitchy,beautiful,technology,tribal house,country pop,jazz & funk,documentary,space,classical,valentines,chillstep,experimental,trap,new jack swing,drama,post-rock,tense,corporate,neutral,happy,analog,funky,spiritual,sberzvuk special,chill hop,dramatic,catchy,holidays,fitness 90,optimistic,orchestra,acid techno,energizing,romantic,minimal house,breaks,hyper pop,warm up,dreamy,dark,urban,microfunk,dub,nu disco,vogue,keys,hardcore,aggressive,indie,electro funk,beauty,relaxing,trance,pop,hiphop,soft,acoustic,chillrave / ethno-house,deep techno,angry,dance,fun,dubstep,tropical,latin pop,heroic,world music,inspirational,uplifting,atmosphere,art,epic,advertising,chillout,scary,spooky,slow ballad,saxophone,summer,erotic,jazzy,energy 100,kara mar,xmas,atmospheric,indie pop,hip-hop,yoga,reggaeton,lounge,travel,running,folk,chillrave & ethno-house,detective,darkambient,chill,fantasy,minimal techno,special,night,tropical house,downtempo,lullaby,meditative,upbeat,glitch hop,fitness,neurofunk,sexual,indie rock,future pop,jazz,cyberpunk,melancholic,happy hardcore,family / kids,synths,electric guitar,comedy,psychedelic trance & psytrance,edm,psychedelic rock,calm,zen,bells,podcast,melodic house,ethnic percussion,nature,heavy,bassline,indie dance,techno,drumnbass,synth pop,vaporwave,sad,8-bit,chillgressive,deep,orchestral,futuristic,hardtechno,nostalgic,big room,sci-fi,tutorial,joyful,pads,minimal 170,drill,ethnic 108,amusing,sleepy ambient,psychill,italo disco,lofi,house,acoustic guitar,bassline house,rock,k-pop,synthwave,deep house,electronica,gabber,nightlife,sport & fitness,road trip,celebration,electro,disco house,electronic'
-MUBERT_TAGS = np.array(MUBERT_TAGS_STRING.split(','))
\ No newline at end of file
diff --git a/spaces/akhaliq/Real-ESRGAN/realesrgan/data/realesrgan_paired_dataset.py b/spaces/akhaliq/Real-ESRGAN/realesrgan/data/realesrgan_paired_dataset.py
deleted file mode 100644
index 386c8d72496245dae8df033c2ebbd76b41ff45f1..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Real-ESRGAN/realesrgan/data/realesrgan_paired_dataset.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import os
-from basicsr.data.data_util import paired_paths_from_folder, paired_paths_from_lmdb
-from basicsr.data.transforms import augment, paired_random_crop
-from basicsr.utils import FileClient, imfrombytes, img2tensor
-from basicsr.utils.registry import DATASET_REGISTRY
-from torch.utils import data as data
-from torchvision.transforms.functional import normalize
-
-
-@DATASET_REGISTRY.register()
-class RealESRGANPairedDataset(data.Dataset):
- """Paired image dataset for image restoration.
-
- Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, noisy, etc) and GT image pairs.
-
- There are three modes:
- 1. 'lmdb': Use lmdb files.
- If opt['io_backend'] == lmdb.
- 2. 'meta_info': Use meta information file to generate paths.
- If opt['io_backend'] != lmdb and opt['meta_info'] is not None.
- 3. 'folder': Scan folders to generate paths.
- The rest.
-
- Args:
- opt (dict): Config for train datasets. It contains the following keys:
- dataroot_gt (str): Data root path for gt.
- dataroot_lq (str): Data root path for lq.
- meta_info (str): Path for meta information file.
- io_backend (dict): IO backend type and other kwarg.
- filename_tmpl (str): Template for each filename. Note that the template excludes the file extension.
- Default: '{}'.
- gt_size (int): Cropped patched size for gt patches.
- use_hflip (bool): Use horizontal flips.
- use_rot (bool): Use rotation (use vertical flip and transposing h
- and w for implementation).
-
- scale (bool): Scale, which will be added automatically.
- phase (str): 'train' or 'val'.
- """
-
- def __init__(self, opt):
- super(RealESRGANPairedDataset, self).__init__()
- self.opt = opt
- self.file_client = None
- self.io_backend_opt = opt['io_backend']
- # mean and std for normalizing the input images
- self.mean = opt['mean'] if 'mean' in opt else None
- self.std = opt['std'] if 'std' in opt else None
-
- self.gt_folder, self.lq_folder = opt['dataroot_gt'], opt['dataroot_lq']
- self.filename_tmpl = opt['filename_tmpl'] if 'filename_tmpl' in opt else '{}'
-
- # file client (lmdb io backend)
- if self.io_backend_opt['type'] == 'lmdb':
- self.io_backend_opt['db_paths'] = [self.lq_folder, self.gt_folder]
- self.io_backend_opt['client_keys'] = ['lq', 'gt']
- self.paths = paired_paths_from_lmdb([self.lq_folder, self.gt_folder], ['lq', 'gt'])
- elif 'meta_info' in self.opt and self.opt['meta_info'] is not None:
- # disk backend with meta_info
- # Each line in the meta_info describes the relative path to an image
- with open(self.opt['meta_info']) as fin:
- paths = [line.strip() for line in fin]
- self.paths = []
- for path in paths:
- gt_path, lq_path = path.split(', ')
- gt_path = os.path.join(self.gt_folder, gt_path)
- lq_path = os.path.join(self.lq_folder, lq_path)
- self.paths.append(dict([('gt_path', gt_path), ('lq_path', lq_path)]))
- else:
- # disk backend
- # it will scan the whole folder to get meta info
- # it will be time-consuming for folders with too many files. It is recommended using an extra meta txt file
- self.paths = paired_paths_from_folder([self.lq_folder, self.gt_folder], ['lq', 'gt'], self.filename_tmpl)
-
- def __getitem__(self, index):
- if self.file_client is None:
- self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt)
-
- scale = self.opt['scale']
-
- # Load gt and lq images. Dimension order: HWC; channel order: BGR;
- # image range: [0, 1], float32.
- gt_path = self.paths[index]['gt_path']
- img_bytes = self.file_client.get(gt_path, 'gt')
- img_gt = imfrombytes(img_bytes, float32=True)
- lq_path = self.paths[index]['lq_path']
- img_bytes = self.file_client.get(lq_path, 'lq')
- img_lq = imfrombytes(img_bytes, float32=True)
-
- # augmentation for training
- if self.opt['phase'] == 'train':
- gt_size = self.opt['gt_size']
- # random crop
- img_gt, img_lq = paired_random_crop(img_gt, img_lq, gt_size, scale, gt_path)
- # flip, rotation
- img_gt, img_lq = augment([img_gt, img_lq], self.opt['use_hflip'], self.opt['use_rot'])
-
- # BGR to RGB, HWC to CHW, numpy to tensor
- img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True)
- # normalize
- if self.mean is not None or self.std is not None:
- normalize(img_lq, self.mean, self.std, inplace=True)
- normalize(img_gt, self.mean, self.std, inplace=True)
-
- return {'lq': img_lq, 'gt': img_gt, 'lq_path': lq_path, 'gt_path': gt_path}
-
- def __len__(self):
- return len(self.paths)
diff --git a/spaces/akhaliq/SummerTime/evaluation/rougewe_metric.py b/spaces/akhaliq/SummerTime/evaluation/rougewe_metric.py
deleted file mode 100644
index b27aa0ce2266903a3aa898e6e1e4ea095ecbf1cf..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/SummerTime/evaluation/rougewe_metric.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from evaluation.summeval_metric import SummEvalMetric
-from typing import List, Dict
-
-import nltk
-
-
-class RougeWe(SummEvalMetric):
- metric_name = "rougeWE"
- range = (0, 1)
- higher_is_better = True
- requires_heavy_compute = True
-
- def __init__(self):
- from summ_eval.rouge_we_metric import RougeWeMetric
-
- nltk.download("stopwords")
- se_metric = RougeWeMetric()
- super(RougeWe, self).__init__(se_metric)
-
- def evaluate(
- self, inputs: List[str], targets: List[str], keys: List[str] = ["rouge_we_3_f"]
- ) -> Dict[str, float]:
- # TODO zhangir: update when dataset api is merged.
- return super(RougeWe, self).evaluate(inputs, targets, keys)
diff --git a/spaces/akhaliq/runwayml-stable-diffusion-v1-5/README.md b/spaces/akhaliq/runwayml-stable-diffusion-v1-5/README.md
deleted file mode 100644
index 26a6a20988103807b5f70ec8d8d68cb530640a10..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/runwayml-stable-diffusion-v1-5/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Runwayml Stable Diffusion V1 5
-emoji: 📉
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/alexiserodriguez/whisper-transcription-app/README.md b/spaces/alexiserodriguez/whisper-transcription-app/README.md
deleted file mode 100644
index e35a01a8bf10a8e6c8e19acf836343dc6a32e40e..0000000000000000000000000000000000000000
--- a/spaces/alexiserodriguez/whisper-transcription-app/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Whisper Transcription App
-emoji: 🐢
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/glibc.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/glibc.py
deleted file mode 100644
index 7bd3c20681d865cb4fa42617cf939b5512c7663f..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/glibc.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# The following comment should be removed at some point in the future.
-# mypy: strict-optional=False
-
-import os
-import sys
-from typing import Optional, Tuple
-
-
-def glibc_version_string() -> Optional[str]:
- "Returns glibc version string, or None if not using glibc."
- return glibc_version_string_confstr() or glibc_version_string_ctypes()
-
-
-def glibc_version_string_confstr() -> Optional[str]:
- "Primary implementation of glibc_version_string using os.confstr."
- # os.confstr is quite a bit faster than ctypes.DLL. It's also less likely
- # to be broken or missing. This strategy is used in the standard library
- # platform module:
- # https://github.com/python/cpython/blob/fcf1d003bf4f0100c9d0921ff3d70e1127ca1b71/Lib/platform.py#L175-L183
- if sys.platform == "win32":
- return None
- try:
- # os.confstr("CS_GNU_LIBC_VERSION") returns a string like "glibc 2.17":
- _, version = os.confstr("CS_GNU_LIBC_VERSION").split()
- except (AttributeError, OSError, ValueError):
- # os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)...
- return None
- return version
-
-
-def glibc_version_string_ctypes() -> Optional[str]:
- "Fallback implementation of glibc_version_string using ctypes."
-
- try:
- import ctypes
- except ImportError:
- return None
-
- # ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen
- # manpage says, "If filename is NULL, then the returned handle is for the
- # main program". This way we can let the linker do the work to figure out
- # which libc our process is actually using.
- process_namespace = ctypes.CDLL(None)
- try:
- gnu_get_libc_version = process_namespace.gnu_get_libc_version
- except AttributeError:
- # Symbol doesn't exist -> therefore, we are not linked to
- # glibc.
- return None
-
- # Call gnu_get_libc_version, which returns a string like "2.5"
- gnu_get_libc_version.restype = ctypes.c_char_p
- version_str = gnu_get_libc_version()
- # py2 / py3 compatibility:
- if not isinstance(version_str, str):
- version_str = version_str.decode("ascii")
-
- return version_str
-
-
-# platform.libc_ver regularly returns completely nonsensical glibc
-# versions. E.g. on my computer, platform says:
-#
-# ~$ python2.7 -c 'import platform; print(platform.libc_ver())'
-# ('glibc', '2.7')
-# ~$ python3.5 -c 'import platform; print(platform.libc_ver())'
-# ('glibc', '2.9')
-#
-# But the truth is:
-#
-# ~$ ldd --version
-# ldd (Debian GLIBC 2.22-11) 2.22
-#
-# This is unfortunate, because it means that the linehaul data on libc
-# versions that was generated by pip 8.1.2 and earlier is useless and
-# misleading. Solution: instead of using platform, use our code that actually
-# works.
-def libc_ver() -> Tuple[str, str]:
- """Try to determine the glibc version
-
- Returns a tuple of strings (lib, version) which default to empty strings
- in case the lookup fails.
- """
- glibc_version = glibc_version_string()
- if glibc_version is None:
- return ("", "")
- else:
- return ("glibc", glibc_version)
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/temp_dir.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/temp_dir.py
deleted file mode 100644
index 442679a758b7b4b965ce3e615a243a7a6d318b4e..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/utils/temp_dir.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import errno
-import itertools
-import logging
-import os.path
-import tempfile
-from contextlib import ExitStack, contextmanager
-from typing import Any, Dict, Iterator, Optional, TypeVar, Union
-
-from pip._internal.utils.misc import enum, rmtree
-
-logger = logging.getLogger(__name__)
-
-_T = TypeVar("_T", bound="TempDirectory")
-
-
-# Kinds of temporary directories. Only needed for ones that are
-# globally-managed.
-tempdir_kinds = enum(
- BUILD_ENV="build-env",
- EPHEM_WHEEL_CACHE="ephem-wheel-cache",
- REQ_BUILD="req-build",
-)
-
-
-_tempdir_manager: Optional[ExitStack] = None
-
-
-@contextmanager
-def global_tempdir_manager() -> Iterator[None]:
- global _tempdir_manager
- with ExitStack() as stack:
- old_tempdir_manager, _tempdir_manager = _tempdir_manager, stack
- try:
- yield
- finally:
- _tempdir_manager = old_tempdir_manager
-
-
-class TempDirectoryTypeRegistry:
- """Manages temp directory behavior"""
-
- def __init__(self) -> None:
- self._should_delete: Dict[str, bool] = {}
-
- def set_delete(self, kind: str, value: bool) -> None:
- """Indicate whether a TempDirectory of the given kind should be
- auto-deleted.
- """
- self._should_delete[kind] = value
-
- def get_delete(self, kind: str) -> bool:
- """Get configured auto-delete flag for a given TempDirectory type,
- default True.
- """
- return self._should_delete.get(kind, True)
-
-
-_tempdir_registry: Optional[TempDirectoryTypeRegistry] = None
-
-
-@contextmanager
-def tempdir_registry() -> Iterator[TempDirectoryTypeRegistry]:
- """Provides a scoped global tempdir registry that can be used to dictate
- whether directories should be deleted.
- """
- global _tempdir_registry
- old_tempdir_registry = _tempdir_registry
- _tempdir_registry = TempDirectoryTypeRegistry()
- try:
- yield _tempdir_registry
- finally:
- _tempdir_registry = old_tempdir_registry
-
-
-class _Default:
- pass
-
-
-_default = _Default()
-
-
-class TempDirectory:
- """Helper class that owns and cleans up a temporary directory.
-
- This class can be used as a context manager or as an OO representation of a
- temporary directory.
-
- Attributes:
- path
- Location to the created temporary directory
- delete
- Whether the directory should be deleted when exiting
- (when used as a contextmanager)
-
- Methods:
- cleanup()
- Deletes the temporary directory
-
- When used as a context manager, if the delete attribute is True, on
- exiting the context the temporary directory is deleted.
- """
-
- def __init__(
- self,
- path: Optional[str] = None,
- delete: Union[bool, None, _Default] = _default,
- kind: str = "temp",
- globally_managed: bool = False,
- ):
- super().__init__()
-
- if delete is _default:
- if path is not None:
- # If we were given an explicit directory, resolve delete option
- # now.
- delete = False
- else:
- # Otherwise, we wait until cleanup and see what
- # tempdir_registry says.
- delete = None
-
- # The only time we specify path is in for editables where it
- # is the value of the --src option.
- if path is None:
- path = self._create(kind)
-
- self._path = path
- self._deleted = False
- self.delete = delete
- self.kind = kind
-
- if globally_managed:
- assert _tempdir_manager is not None
- _tempdir_manager.enter_context(self)
-
- @property
- def path(self) -> str:
- assert not self._deleted, f"Attempted to access deleted path: {self._path}"
- return self._path
-
- def __repr__(self) -> str:
- return f"<{self.__class__.__name__} {self.path!r}>"
-
- def __enter__(self: _T) -> _T:
- return self
-
- def __exit__(self, exc: Any, value: Any, tb: Any) -> None:
- if self.delete is not None:
- delete = self.delete
- elif _tempdir_registry:
- delete = _tempdir_registry.get_delete(self.kind)
- else:
- delete = True
-
- if delete:
- self.cleanup()
-
- def _create(self, kind: str) -> str:
- """Create a temporary directory and store its path in self.path"""
- # We realpath here because some systems have their default tmpdir
- # symlinked to another directory. This tends to confuse build
- # scripts, so we canonicalize the path by traversing potential
- # symlinks here.
- path = os.path.realpath(tempfile.mkdtemp(prefix=f"pip-{kind}-"))
- logger.debug("Created temporary directory: %s", path)
- return path
-
- def cleanup(self) -> None:
- """Remove the temporary directory created and reset state"""
- self._deleted = True
- if not os.path.exists(self._path):
- return
- rmtree(self._path)
-
-
-class AdjacentTempDirectory(TempDirectory):
- """Helper class that creates a temporary directory adjacent to a real one.
-
- Attributes:
- original
- The original directory to create a temp directory for.
- path
- After calling create() or entering, contains the full
- path to the temporary directory.
- delete
- Whether the directory should be deleted when exiting
- (when used as a contextmanager)
-
- """
-
- # The characters that may be used to name the temp directory
- # We always prepend a ~ and then rotate through these until
- # a usable name is found.
- # pkg_resources raises a different error for .dist-info folder
- # with leading '-' and invalid metadata
- LEADING_CHARS = "-~.=%0123456789"
-
- def __init__(self, original: str, delete: Optional[bool] = None) -> None:
- self.original = original.rstrip("/\\")
- super().__init__(delete=delete)
-
- @classmethod
- def _generate_names(cls, name: str) -> Iterator[str]:
- """Generates a series of temporary names.
-
- The algorithm replaces the leading characters in the name
- with ones that are valid filesystem characters, but are not
- valid package names (for both Python and pip definitions of
- package).
- """
- for i in range(1, len(name)):
- for candidate in itertools.combinations_with_replacement(
- cls.LEADING_CHARS, i - 1
- ):
- new_name = "~" + "".join(candidate) + name[i:]
- if new_name != name:
- yield new_name
-
- # If we make it this far, we will have to make a longer name
- for i in range(len(cls.LEADING_CHARS)):
- for candidate in itertools.combinations_with_replacement(
- cls.LEADING_CHARS, i
- ):
- new_name = "~" + "".join(candidate) + name
- if new_name != name:
- yield new_name
-
- def _create(self, kind: str) -> str:
- root, name = os.path.split(self.original)
- for candidate in self._generate_names(name):
- path = os.path.join(root, candidate)
- try:
- os.mkdir(path)
- except OSError as ex:
- # Continue if the name exists already
- if ex.errno != errno.EEXIST:
- raise
- else:
- path = os.path.realpath(path)
- break
- else:
- # Final fallback on the default behavior.
- path = os.path.realpath(tempfile.mkdtemp(prefix=f"pip-{kind}-"))
-
- logger.debug("Created temporary directory: %s", path)
- return path
diff --git a/spaces/anaclaudia13ct/insect_detection/utils/segment/augmentations.py b/spaces/anaclaudia13ct/insect_detection/utils/segment/augmentations.py
deleted file mode 100644
index 169addedf0f58cf37e774e6a85eddff6eebc30be..0000000000000000000000000000000000000000
--- a/spaces/anaclaudia13ct/insect_detection/utils/segment/augmentations.py
+++ /dev/null
@@ -1,104 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Image augmentation functions
-"""
-
-import math
-import random
-
-import cv2
-import numpy as np
-
-from ..augmentations import box_candidates
-from ..general import resample_segments, segment2box
-
-
-def mixup(im, labels, segments, im2, labels2, segments2):
- # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
- r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
- im = (im * r + im2 * (1 - r)).astype(np.uint8)
- labels = np.concatenate((labels, labels2), 0)
- segments = np.concatenate((segments, segments2), 0)
- return im, labels, segments
-
-
-def random_perspective(im,
- targets=(),
- segments=(),
- degrees=10,
- translate=.1,
- scale=.1,
- shear=10,
- perspective=0.0,
- border=(0, 0)):
- # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
- # targets = [cls, xyxy]
-
- height = im.shape[0] + border[0] * 2 # shape(h,w,c)
- width = im.shape[1] + border[1] * 2
-
- # Center
- C = np.eye(3)
- C[0, 2] = -im.shape[1] / 2 # x translation (pixels)
- C[1, 2] = -im.shape[0] / 2 # y translation (pixels)
-
- # Perspective
- P = np.eye(3)
- P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
- P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
-
- # Rotation and Scale
- R = np.eye(3)
- a = random.uniform(-degrees, degrees)
- # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
- s = random.uniform(1 - scale, 1 + scale)
- # s = 2 ** random.uniform(-scale, scale)
- R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
-
- # Shear
- S = np.eye(3)
- S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
- S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
-
- # Translation
- T = np.eye(3)
- T[0, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * width) # x translation (pixels)
- T[1, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * height) # y translation (pixels)
-
- # Combined rotation matrix
- M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
- if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
- if perspective:
- im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
- else: # affine
- im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
-
- # Visualize
- # import matplotlib.pyplot as plt
- # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
- # ax[0].imshow(im[:, :, ::-1]) # base
- # ax[1].imshow(im2[:, :, ::-1]) # warped
-
- # Transform label coordinates
- n = len(targets)
- new_segments = []
- if n:
- new = np.zeros((n, 4))
- segments = resample_segments(segments) # upsample
- for i, segment in enumerate(segments):
- xy = np.ones((len(segment), 3))
- xy[:, :2] = segment
- xy = xy @ M.T # transform
- xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]) # perspective rescale or affine
-
- # clip
- new[i] = segment2box(xy, width, height)
- new_segments.append(xy)
-
- # filter candidates
- i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01)
- targets = targets[i]
- targets[:, 1:5] = new[i]
- new_segments = np.array(new_segments)[i]
-
- return im, targets, new_segments
diff --git a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/models/vitseg.py b/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/models/vitseg.py
deleted file mode 100644
index ed621431ddf930fcfa27b5929999776b96fede63..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/extensions/deforum/scripts/deforum_helpers/src/clipseg/models/vitseg.py
+++ /dev/null
@@ -1,286 +0,0 @@
-import math
-from posixpath import basename, dirname, join
-# import clip
-from clip.model import convert_weights
-import torch
-import json
-from torch import nn
-from torch.nn import functional as nnf
-from torch.nn.modules import activation
-from torch.nn.modules.activation import ReLU
-from torchvision import transforms
-
-normalize = transforms.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
-
-from torchvision.models import ResNet
-
-
-def process_prompts(conditional, prompt_list, conditional_map):
- # DEPRECATED
-
- # randomly sample a synonym
- words = [conditional_map[int(i)] for i in conditional]
- words = [syns[torch.multinomial(torch.ones(len(syns)), 1, replacement=True).item()] for syns in words]
- words = [w.replace('_', ' ') for w in words]
-
- if prompt_list is not None:
- prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
- prompts = [prompt_list[i] for i in prompt_indices]
- else:
- prompts = ['a photo of {}'] * (len(words))
-
- return [promt.format(w) for promt, w in zip(prompts, words)]
-
-
-class VITDenseBase(nn.Module):
-
- def rescaled_pos_emb(self, new_size):
- assert len(new_size) == 2
-
- a = self.model.positional_embedding[1:].T.view(1, 768, *self.token_shape)
- b = nnf.interpolate(a, new_size, mode='bicubic', align_corners=False).squeeze(0).view(768, new_size[0]*new_size[1]).T
- return torch.cat([self.model.positional_embedding[:1], b])
-
- def visual_forward(self, x_inp, extract_layers=(), skip=False, mask=None):
-
- with torch.no_grad():
-
- x_inp = nnf.interpolate(x_inp, (384, 384))
-
- x = self.model.patch_embed(x_inp)
- cls_token = self.model.cls_token.expand(x.shape[0], -1, -1) # stole cls_tokens impl from Phil Wang, thanks
- if self.model.dist_token is None:
- x = torch.cat((cls_token, x), dim=1)
- else:
- x = torch.cat((cls_token, self.model.dist_token.expand(x.shape[0], -1, -1), x), dim=1)
- x = self.model.pos_drop(x + self.model.pos_embed)
-
- activations = []
- for i, block in enumerate(self.model.blocks):
- x = block(x)
-
- if i in extract_layers:
- # permute to be compatible with CLIP
- activations += [x.permute(1,0,2)]
-
- x = self.model.norm(x)
- x = self.model.head(self.model.pre_logits(x[:, 0]))
-
- # again for CLIP compatibility
- # x = x.permute(1, 0, 2)
-
- return x, activations, None
-
- def sample_prompts(self, words, prompt_list=None):
-
- prompt_list = prompt_list if prompt_list is not None else self.prompt_list
-
- prompt_indices = torch.multinomial(torch.ones(len(prompt_list)), len(words), replacement=True)
- prompts = [prompt_list[i] for i in prompt_indices]
- return [promt.format(w) for promt, w in zip(prompts, words)]
-
- def get_cond_vec(self, conditional, batch_size):
- # compute conditional from a single string
- if conditional is not None and type(conditional) == str:
- cond = self.compute_conditional(conditional)
- cond = cond.repeat(batch_size, 1)
-
- # compute conditional from string list/tuple
- elif conditional is not None and type(conditional) in {list, tuple} and type(conditional[0]) == str:
- assert len(conditional) == batch_size
- cond = self.compute_conditional(conditional)
-
- # use conditional directly
- elif conditional is not None and type(conditional) == torch.Tensor and conditional.ndim == 2:
- cond = conditional
-
- # compute conditional from image
- elif conditional is not None and type(conditional) == torch.Tensor:
- with torch.no_grad():
- cond, _, _ = self.visual_forward(conditional)
- else:
- raise ValueError('invalid conditional')
- return cond
-
- def compute_conditional(self, conditional):
- import clip
-
- dev = next(self.parameters()).device
-
- if type(conditional) in {list, tuple}:
- text_tokens = clip.tokenize(conditional).to(dev)
- cond = self.clip_model.encode_text(text_tokens)
- else:
- if conditional in self.precomputed_prompts:
- cond = self.precomputed_prompts[conditional].float().to(dev)
- else:
- text_tokens = clip.tokenize([conditional]).to(dev)
- cond = self.clip_model.encode_text(text_tokens)[0]
-
- return cond
-
-
-class VITDensePredT(VITDenseBase):
-
- def __init__(self, extract_layers=(3, 6, 9), cond_layer=0, reduce_dim=128, n_heads=4, prompt='fixed',
- depth=3, extra_blocks=0, reduce_cond=None, fix_shift=False,
- learn_trans_conv_only=False, refine=None, limit_to_clip_only=False, upsample=False,
- add_calibration=False, process_cond=None, not_pretrained=False):
- super().__init__()
- # device = 'cpu'
-
- self.extract_layers = extract_layers
- self.cond_layer = cond_layer
- self.limit_to_clip_only = limit_to_clip_only
- self.process_cond = None
-
- if add_calibration:
- self.calibration_conds = 1
-
- self.upsample_proj = nn.Conv2d(reduce_dim, 1, kernel_size=1) if upsample else None
-
- self.add_activation1 = True
-
- import timm
- self.model = timm.create_model('vit_base_patch16_384', pretrained=True)
- self.model.head = nn.Linear(768, 512 if reduce_cond is None else reduce_cond)
-
- for p in self.model.parameters():
- p.requires_grad_(False)
-
- import clip
- self.clip_model, _ = clip.load('ViT-B/16', device='cpu', jit=False)
- # del self.clip_model.visual
-
-
- self.token_shape = (14, 14)
-
- # conditional
- if reduce_cond is not None:
- self.reduce_cond = nn.Linear(512, reduce_cond)
- for p in self.reduce_cond.parameters():
- p.requires_grad_(False)
- else:
- self.reduce_cond = None
-
- # self.film = AVAILABLE_BLOCKS['film'](512, 128)
- self.film_mul = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
- self.film_add = nn.Linear(512 if reduce_cond is None else reduce_cond, reduce_dim)
-
- # DEPRECATED
- # self.conditional_map = {c['id']: c['synonyms'] for c in json.load(open(cond_map))}
-
- assert len(self.extract_layers) == depth
-
- self.reduces = nn.ModuleList([nn.Linear(768, reduce_dim) for _ in range(depth)])
- self.blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(len(self.extract_layers))])
- self.extra_blocks = nn.ModuleList([nn.TransformerEncoderLayer(d_model=reduce_dim, nhead=n_heads) for _ in range(extra_blocks)])
-
- trans_conv_ks = (16, 16)
- self.trans_conv = nn.ConvTranspose2d(reduce_dim, 1, trans_conv_ks, stride=trans_conv_ks)
-
- # refinement and trans conv
-
- if learn_trans_conv_only:
- for p in self.parameters():
- p.requires_grad_(False)
-
- for p in self.trans_conv.parameters():
- p.requires_grad_(True)
-
- if prompt == 'fixed':
- self.prompt_list = ['a photo of a {}.']
- elif prompt == 'shuffle':
- self.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.']
- elif prompt == 'shuffle+':
- self.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.',
- 'a cropped photo of a {}.', 'a good photo of a {}.', 'a photo of one {}.',
- 'a bad photo of a {}.', 'a photo of the {}.']
- elif prompt == 'shuffle_clip':
- from models.clip_prompts import imagenet_templates
- self.prompt_list = imagenet_templates
-
- if process_cond is not None:
- if process_cond == 'clamp' or process_cond[0] == 'clamp':
-
- val = process_cond[1] if type(process_cond) in {list, tuple} else 0.2
-
- def clamp_vec(x):
- return torch.clamp(x, -val, val)
-
- self.process_cond = clamp_vec
-
- elif process_cond.endswith('.pth'):
-
- shift = torch.load(process_cond)
- def add_shift(x):
- return x + shift.to(x.device)
-
- self.process_cond = add_shift
-
- import pickle
- precomp = pickle.load(open('precomputed_prompt_vectors.pickle', 'rb'))
- self.precomputed_prompts = {k: torch.from_numpy(v) for k, v in precomp.items()}
-
-
- def forward(self, inp_image, conditional=None, return_features=False, mask=None):
-
- assert type(return_features) == bool
-
- # inp_image = inp_image.to(self.model.positional_embedding.device)
-
- if mask is not None:
- raise ValueError('mask not supported')
-
- # x_inp = normalize(inp_image)
- x_inp = inp_image
-
- bs, dev = inp_image.shape[0], x_inp.device
-
- inp_image_size = inp_image.shape[2:]
-
- cond = self.get_cond_vec(conditional, bs)
-
- visual_q, activations, _ = self.visual_forward(x_inp, extract_layers=[0] + list(self.extract_layers))
-
- activation1 = activations[0]
- activations = activations[1:]
-
- a = None
- for i, (activation, block, reduce) in enumerate(zip(activations[::-1], self.blocks, self.reduces)):
-
- if a is not None:
- a = reduce(activation) + a
- else:
- a = reduce(activation)
-
- if i == self.cond_layer:
- if self.reduce_cond is not None:
- cond = self.reduce_cond(cond)
-
- a = self.film_mul(cond) * a + self.film_add(cond)
-
- a = block(a)
-
- for block in self.extra_blocks:
- a = a + block(a)
-
- a = a[1:].permute(1, 2, 0) # rm cls token and -> BS, Feats, Tokens
-
- size = int(math.sqrt(a.shape[2]))
-
- a = a.view(bs, a.shape[1], size, size)
-
- if self.trans_conv is not None:
- a = self.trans_conv(a)
-
- if self.upsample_proj is not None:
- a = self.upsample_proj(a)
- a = nnf.interpolate(a, x_inp.shape[2:], mode='bilinear')
-
- a = nnf.interpolate(a, inp_image_size)
-
- if return_features:
- return a, visual_q, cond, [activation1] + activations
- else:
- return a,
diff --git a/spaces/aodianyun/stable-diffusion-webui/modules/script_callbacks.py b/spaces/aodianyun/stable-diffusion-webui/modules/script_callbacks.py
deleted file mode 100644
index c98c2395b6fe46ddec2a10cc6a54ee0f3ba248f5..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/modules/script_callbacks.py
+++ /dev/null
@@ -1,359 +0,0 @@
-import sys
-import traceback
-from collections import namedtuple
-import inspect
-from typing import Optional, Dict, Any
-
-from fastapi import FastAPI
-from gradio import Blocks
-
-
-def report_exception(c, job):
- print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
- print(traceback.format_exc(), file=sys.stderr)
-
-
-class ImageSaveParams:
- def __init__(self, image, p, filename, pnginfo):
- self.image = image
- """the PIL image itself"""
-
- self.p = p
- """p object with processing parameters; either StableDiffusionProcessing or an object with same fields"""
-
- self.filename = filename
- """name of file that the image would be saved to"""
-
- self.pnginfo = pnginfo
- """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""
-
-
-class CFGDenoiserParams:
- def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps):
- self.x = x
- """Latent image representation in the process of being denoised"""
-
- self.image_cond = image_cond
- """Conditioning image"""
-
- self.sigma = sigma
- """Current sigma noise step value"""
-
- self.sampling_step = sampling_step
- """Current Sampling step number"""
-
- self.total_sampling_steps = total_sampling_steps
- """Total number of sampling steps planned"""
-
-
-class CFGDenoisedParams:
- def __init__(self, x, sampling_step, total_sampling_steps):
- self.x = x
- """Latent image representation in the process of being denoised"""
-
- self.sampling_step = sampling_step
- """Current Sampling step number"""
-
- self.total_sampling_steps = total_sampling_steps
- """Total number of sampling steps planned"""
-
-
-class UiTrainTabParams:
- def __init__(self, txt2img_preview_params):
- self.txt2img_preview_params = txt2img_preview_params
-
-
-class ImageGridLoopParams:
- def __init__(self, imgs, cols, rows):
- self.imgs = imgs
- self.cols = cols
- self.rows = rows
-
-
-ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
-callback_map = dict(
- callbacks_app_started=[],
- callbacks_model_loaded=[],
- callbacks_ui_tabs=[],
- callbacks_ui_train_tabs=[],
- callbacks_ui_settings=[],
- callbacks_before_image_saved=[],
- callbacks_image_saved=[],
- callbacks_cfg_denoiser=[],
- callbacks_cfg_denoised=[],
- callbacks_before_component=[],
- callbacks_after_component=[],
- callbacks_image_grid=[],
- callbacks_infotext_pasted=[],
- callbacks_script_unloaded=[],
- callbacks_before_ui=[],
-)
-
-
-def clear_callbacks():
- for callback_list in callback_map.values():
- callback_list.clear()
-
-
-def app_started_callback(demo: Optional[Blocks], app: FastAPI):
- for c in callback_map['callbacks_app_started']:
- try:
- c.callback(demo, app)
- except Exception:
- report_exception(c, 'app_started_callback')
-
-
-def model_loaded_callback(sd_model):
- for c in callback_map['callbacks_model_loaded']:
- try:
- c.callback(sd_model)
- except Exception:
- report_exception(c, 'model_loaded_callback')
-
-
-def ui_tabs_callback():
- res = []
-
- for c in callback_map['callbacks_ui_tabs']:
- try:
- res += c.callback() or []
- except Exception:
- report_exception(c, 'ui_tabs_callback')
-
- return res
-
-
-def ui_train_tabs_callback(params: UiTrainTabParams):
- for c in callback_map['callbacks_ui_train_tabs']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'callbacks_ui_train_tabs')
-
-
-def ui_settings_callback():
- for c in callback_map['callbacks_ui_settings']:
- try:
- c.callback()
- except Exception:
- report_exception(c, 'ui_settings_callback')
-
-
-def before_image_saved_callback(params: ImageSaveParams):
- for c in callback_map['callbacks_before_image_saved']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'before_image_saved_callback')
-
-
-def image_saved_callback(params: ImageSaveParams):
- for c in callback_map['callbacks_image_saved']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'image_saved_callback')
-
-
-def cfg_denoiser_callback(params: CFGDenoiserParams):
- for c in callback_map['callbacks_cfg_denoiser']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'cfg_denoiser_callback')
-
-
-def cfg_denoised_callback(params: CFGDenoisedParams):
- for c in callback_map['callbacks_cfg_denoised']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'cfg_denoised_callback')
-
-
-def before_component_callback(component, **kwargs):
- for c in callback_map['callbacks_before_component']:
- try:
- c.callback(component, **kwargs)
- except Exception:
- report_exception(c, 'before_component_callback')
-
-
-def after_component_callback(component, **kwargs):
- for c in callback_map['callbacks_after_component']:
- try:
- c.callback(component, **kwargs)
- except Exception:
- report_exception(c, 'after_component_callback')
-
-
-def image_grid_callback(params: ImageGridLoopParams):
- for c in callback_map['callbacks_image_grid']:
- try:
- c.callback(params)
- except Exception:
- report_exception(c, 'image_grid')
-
-
-def infotext_pasted_callback(infotext: str, params: Dict[str, Any]):
- for c in callback_map['callbacks_infotext_pasted']:
- try:
- c.callback(infotext, params)
- except Exception:
- report_exception(c, 'infotext_pasted')
-
-
-def script_unloaded_callback():
- for c in reversed(callback_map['callbacks_script_unloaded']):
- try:
- c.callback()
- except Exception:
- report_exception(c, 'script_unloaded')
-
-
-def before_ui_callback():
- for c in reversed(callback_map['callbacks_before_ui']):
- try:
- c.callback()
- except Exception:
- report_exception(c, 'before_ui')
-
-
-def add_callback(callbacks, fun):
- stack = [x for x in inspect.stack() if x.filename != __file__]
- filename = stack[0].filename if len(stack) > 0 else 'unknown file'
-
- callbacks.append(ScriptCallback(filename, fun))
-
-
-def remove_current_script_callbacks():
- stack = [x for x in inspect.stack() if x.filename != __file__]
- filename = stack[0].filename if len(stack) > 0 else 'unknown file'
- if filename == 'unknown file':
- return
- for callback_list in callback_map.values():
- for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:
- callback_list.remove(callback_to_remove)
-
-
-def remove_callbacks_for_function(callback_func):
- for callback_list in callback_map.values():
- for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:
- callback_list.remove(callback_to_remove)
-
-
-def on_app_started(callback):
- """register a function to be called when the webui started, the gradio `Block` component and
- fastapi `FastAPI` object are passed as the arguments"""
- add_callback(callback_map['callbacks_app_started'], callback)
-
-
-def on_model_loaded(callback):
- """register a function to be called when the stable diffusion model is created; the model is
- passed as an argument; this function is also called when the script is reloaded. """
- add_callback(callback_map['callbacks_model_loaded'], callback)
-
-
-def on_ui_tabs(callback):
- """register a function to be called when the UI is creating new tabs.
- The function must either return a None, which means no new tabs to be added, or a list, where
- each element is a tuple:
- (gradio_component, title, elem_id)
-
- gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks)
- title is tab text displayed to user in the UI
- elem_id is HTML id for the tab
- """
- add_callback(callback_map['callbacks_ui_tabs'], callback)
-
-
-def on_ui_train_tabs(callback):
- """register a function to be called when the UI is creating new tabs for the train tab.
- Create your new tabs with gr.Tab.
- """
- add_callback(callback_map['callbacks_ui_train_tabs'], callback)
-
-
-def on_ui_settings(callback):
- """register a function to be called before UI settings are populated; add your settings
- by using shared.opts.add_option(shared.OptionInfo(...)) """
- add_callback(callback_map['callbacks_ui_settings'], callback)
-
-
-def on_before_image_saved(callback):
- """register a function to be called before an image is saved to a file.
- The callback is called with one argument:
- - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.
- """
- add_callback(callback_map['callbacks_before_image_saved'], callback)
-
-
-def on_image_saved(callback):
- """register a function to be called after an image is saved to a file.
- The callback is called with one argument:
- - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.
- """
- add_callback(callback_map['callbacks_image_saved'], callback)
-
-
-def on_cfg_denoiser(callback):
- """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
- The callback is called with one argument:
- - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.
- """
- add_callback(callback_map['callbacks_cfg_denoiser'], callback)
-
-
-def on_cfg_denoised(callback):
- """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
- The callback is called with one argument:
- - params: CFGDenoisedParams - parameters to be passed to the inner model and sampling state details.
- """
- add_callback(callback_map['callbacks_cfg_denoised'], callback)
-
-
-def on_before_component(callback):
- """register a function to be called before a component is created.
- The callback is called with arguments:
- - component - gradio component that is about to be created.
- - **kwargs - args to gradio.components.IOComponent.__init__ function
-
- Use elem_id/label fields of kwargs to figure out which component it is.
- This can be useful to inject your own components somewhere in the middle of vanilla UI.
- """
- add_callback(callback_map['callbacks_before_component'], callback)
-
-
-def on_after_component(callback):
- """register a function to be called after a component is created. See on_before_component for more."""
- add_callback(callback_map['callbacks_after_component'], callback)
-
-
-def on_image_grid(callback):
- """register a function to be called before making an image grid.
- The callback is called with one argument:
- - params: ImageGridLoopParams - parameters to be used for grid creation. Can be modified.
- """
- add_callback(callback_map['callbacks_image_grid'], callback)
-
-
-def on_infotext_pasted(callback):
- """register a function to be called before applying an infotext.
- The callback is called with two arguments:
- - infotext: str - raw infotext.
- - result: Dict[str, any] - parsed infotext parameters.
- """
- add_callback(callback_map['callbacks_infotext_pasted'], callback)
-
-
-def on_script_unloaded(callback):
- """register a function to be called before the script is unloaded. Any hooks/hijacks/monkeying about that
- the script did should be reverted here"""
-
- add_callback(callback_map['callbacks_script_unloaded'], callback)
-
-
-def on_before_ui(callback):
- """register a function to be called before the UI is created."""
-
- add_callback(callback_map['callbacks_before_ui'], callback)
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/fast_pitch_config.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/fast_pitch_config.py
deleted file mode 100644
index d086d26564450c60fa04a7f3a068506f4147d3be..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/tts/configs/fast_pitch_config.py
+++ /dev/null
@@ -1,183 +0,0 @@
-from dataclasses import dataclass, field
-from typing import List
-
-from TTS.tts.configs.shared_configs import BaseTTSConfig
-from TTS.tts.models.forward_tts import ForwardTTSArgs
-
-
-@dataclass
-class FastPitchConfig(BaseTTSConfig):
- """Configure `ForwardTTS` as FastPitch model.
-
- Example:
-
- >>> from TTS.tts.configs.fast_pitch_config import FastPitchConfig
- >>> config = FastPitchConfig()
-
- Args:
- model (str):
- Model name used for selecting the right model at initialization. Defaults to `fast_pitch`.
-
- base_model (str):
- Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
- the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
-
- model_args (Coqpit):
- Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
-
- data_dep_init_steps (int):
- Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
- Activation Normalization that pre-computes normalization stats at the beginning and use the same values
- for the rest. Defaults to 10.
-
- speakers_file (str):
- Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
- speaker names. Defaults to `None`.
-
- use_speaker_embedding (bool):
- enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
- in the multi-speaker mode. Defaults to False.
-
- use_d_vector_file (bool):
- enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
-
- d_vector_file (str):
- Path to the file including pre-computed speaker embeddings. Defaults to None.
-
- d_vector_dim (int):
- Dimension of the external speaker embeddings. Defaults to 0.
-
- optimizer (str):
- Name of the model optimizer. Defaults to `Adam`.
-
- optimizer_params (dict):
- Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
-
- lr_scheduler (str):
- Name of the learning rate scheduler. Defaults to `Noam`.
-
- lr_scheduler_params (dict):
- Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
-
- lr (float):
- Initial learning rate. Defaults to `1e-3`.
-
- grad_clip (float):
- Gradient norm clipping value. Defaults to `5.0`.
-
- spec_loss_type (str):
- Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- duration_loss_type (str):
- Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
-
- use_ssim_loss (bool):
- Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
-
- wd (float):
- Weight decay coefficient. Defaults to `1e-7`.
-
- ssim_loss_alpha (float):
- Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
-
- dur_loss_alpha (float):
- Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
-
- spec_loss_alpha (float):
- Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
-
- pitch_loss_alpha (float):
- Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0.
-
- binary_align_loss_alpha (float):
- Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
-
- binary_loss_warmup_epochs (float):
- Number of epochs to gradually increase the binary loss impact. Defaults to 150.
-
- min_seq_len (int):
- Minimum input sequence length to be used at training.
-
- max_seq_len (int):
- Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
-
- # dataset configs
- compute_f0(bool):
- Compute pitch. defaults to True
-
- f0_cache_path(str):
- pith cache path. defaults to None
- """
-
- model: str = "fast_pitch"
- base_model: str = "forward_tts"
-
- # model specific params
- model_args: ForwardTTSArgs = field(default_factory=ForwardTTSArgs)
-
- # multi-speaker settings
- num_speakers: int = 0
- speakers_file: str = None
- use_speaker_embedding: bool = False
- use_d_vector_file: bool = False
- d_vector_file: str = False
- d_vector_dim: int = 0
-
- # optimizer parameters
- optimizer: str = "Adam"
- optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
- lr_scheduler: str = "NoamLR"
- lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
- lr: float = 1e-4
- grad_clip: float = 5.0
-
- # loss params
- spec_loss_type: str = "mse"
- duration_loss_type: str = "mse"
- use_ssim_loss: bool = True
- ssim_loss_alpha: float = 1.0
- spec_loss_alpha: float = 1.0
- aligner_loss_alpha: float = 1.0
- pitch_loss_alpha: float = 0.1
- dur_loss_alpha: float = 0.1
- binary_align_loss_alpha: float = 0.1
- binary_loss_warmup_epochs: int = 150
-
- # overrides
- min_seq_len: int = 13
- max_seq_len: int = 200
- r: int = 1 # DO NOT CHANGE
-
- # dataset configs
- compute_f0: bool = True
- f0_cache_path: str = None
-
- # testing
- test_sentences: List[str] = field(
- default_factory=lambda: [
- "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- "Be a voice, not an echo.",
- "I'm sorry Dave. I'm afraid I can't do that.",
- "This cake is great. It's so delicious and moist.",
- "Prior to November 22, 1963.",
- ]
- )
-
- def __post_init__(self):
- # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
- if self.num_speakers > 0:
- self.model_args.num_speakers = self.num_speakers
-
- # speaker embedding settings
- if self.use_speaker_embedding:
- self.model_args.use_speaker_embedding = True
- if self.speakers_file:
- self.model_args.speakers_file = self.speakers_file
-
- # d-vector settings
- if self.use_d_vector_file:
- self.model_args.use_d_vector_file = True
- if self.d_vector_dim is not None and self.d_vector_dim > 0:
- self.model_args.d_vector_dim = self.d_vector_dim
- if self.d_vector_file:
- self.model_args.d_vector_file = self.d_vector_file
diff --git a/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/__init__.py b/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/__init__.py
deleted file mode 100644
index 4bae29fd5f85b41e4669302bd2603bc6924eddc7..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/Wav2Lip/face_detection/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# -*- coding: utf-8 -*-
-
-__author__ = """Adrian Bulat"""
-__email__ = 'adrian.bulat@nottingham.ac.uk'
-__version__ = '1.0.1'
-
-from .api import FaceAlignment, LandmarksType, NetworkSize
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/scatter_with_minimap.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/scatter_with_minimap.py
deleted file mode 100644
index 7de894c43ae56bf135411e9912ca3d396aa71dec..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/scatter_with_minimap.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""
-Scatter Plot with Minimap
--------------------------
-This example shows how to create a miniature version of a plot
-such that creating a selection in the miniature version
-adjusts the axis limits in another, more detailed view.
-"""
-# category: scatter plots
-
-import altair as alt
-from vega_datasets import data
-
-source = data.seattle_weather()
-
-zoom = alt.selection_interval(encodings=["x", "y"])
-
-minimap = (
- alt.Chart(source)
- .mark_point()
- .add_selection(zoom)
- .encode(
- x="date:T",
- y="temp_max:Q",
- color=alt.condition(zoom, "weather", alt.value("lightgray")),
- )
- .properties(
- width=200,
- height=200,
- title="Minimap -- click and drag to zoom in the detail view",
- )
-)
-
-detail = (
- alt.Chart(source)
- .mark_point()
- .encode(
- x=alt.X(
- "date:T", scale=alt.Scale(domain={"selection": zoom.name, "encoding": "x"})
- ),
- y=alt.Y(
- "temp_max:Q",
- scale=alt.Scale(domain={"selection": zoom.name, "encoding": "y"}),
- ),
- color="weather",
- )
- .properties(width=600, height=400, title="Seattle weather -- detail view")
-)
-
-detail | minimap
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/distributed/legacy_distributed_data_parallel.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/distributed/legacy_distributed_data_parallel.py
deleted file mode 100644
index cd434c7372ba30ea0e6f87e084230448f53480e9..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/distributed/legacy_distributed_data_parallel.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-A modified version of the legacy DistributedDataParallel module that uses c10d
-communication primitives. This version is simpler than the latest PyTorch
-version and is useful for debugging. Notably it does not overlap gradient
-communication with the backward pass, which makes it slower but more robust
-than the PyTorch version.
-
-This version also supports the *no_sync* context manager, which allows faster
-training with `--update-freq`.
-"""
-
-from collections import OrderedDict
-from contextlib import contextmanager
-
-import torch
-from torch import nn
-
-from fairseq.distributed import utils
-
-
-class LegacyDistributedDataParallel(nn.Module):
- """Implements distributed data parallelism at the module level.
-
- A simplified version of :class:`torch.nn.parallel.DistributedDataParallel`.
- This version uses a c10d process group for communication and does not
- broadcast buffers.
-
- Args:
- module (~torch.nn.Module): module to be parallelized
- process_group: the c10d process group to be used for distributed data
- parallel all-reduction.
- buffer_size (int, optional): number of elements to buffer before
- performing all-reduce (default: 256M).
- """
-
- def __init__(self, module, process_group, buffer_size=2**28):
- super().__init__()
-
- self.module = module
- self.process_group = process_group
- self.world_size = utils.get_world_size(self.process_group)
-
- # Never use a bigger buffer than the number of model params
- self.buffer_size = min(buffer_size, sum(p.numel() for p in module.parameters()))
- self.buffer = None
-
- # We can also forcibly accumulate grads locally and only do the
- # all-reduce at some later time
- self.accumulate_grads = False
-
- # make per-device lists of parameters
- paramlists = OrderedDict()
- for param in self.module.parameters():
- device = param.device
- if paramlists.get(device) is None:
- paramlists[device] = []
- paramlists[device] += [param]
- self.per_device_params = list(paramlists.values())
-
- @contextmanager
- def no_sync(self):
- """A context manager to disable gradient synchronization."""
- old_accumulate_grads = self.accumulate_grads
- self.accumulate_grads = True
- yield
- self.accumulate_grads = old_accumulate_grads
-
- def forward(self, *inputs, **kwargs):
- return self.module(*inputs, **kwargs)
-
- def all_reduce_grads(self):
- """
- This function must be called explicitly after backward to reduce
- gradients. There is no automatic hook like c10d.
- """
-
- def all_reduce_params(params):
- buffer = self.buffer
- nonzero_buffer = False
- if len(params) > 1:
- offset = 0
- for p in params:
- sz = p.numel()
- if p.grad is not None:
- buffer[offset : offset + sz].copy_(p.grad.data.view(-1))
- nonzero_buffer = True
- else:
- buffer[offset : offset + sz].zero_()
- offset += sz
- else:
- # we only have a single grad to all-reduce
- p = params[0]
- if p.grad is not None:
- buffer = p.grad.data
- nonzero_buffer = True
- elif p.numel() <= self.buffer.numel():
- buffer = buffer[: p.numel()]
- buffer.zero_()
- else:
- buffer = torch.zeros_like(p)
-
- if nonzero_buffer:
- buffer.div_(self.world_size)
-
- utils.all_reduce(buffer, self.process_group)
-
- # copy all-reduced grads back into their original place
- offset = 0
- for p in params:
- sz = p.numel()
- if p.grad is not None:
- p.grad.data.copy_(buffer[offset : offset + sz].view_as(p))
- else:
- p.grad = buffer[offset : offset + sz].view_as(p).clone()
- offset += sz
-
- def reduction_fn():
- # This function only needs to be called once
- if self.accumulate_grads:
- return
-
- if self.buffer is None:
- self.buffer = next(self.module.parameters()).new(self.buffer_size)
-
- for params in self.per_device_params:
- # All-reduce the gradients in buckets
- offset = 0
- buffered_params = []
- for param in params:
- if not param.requires_grad:
- continue
- if param.grad is None:
- param.grad = torch.zeros_like(param)
-
- if hasattr(param, "expert"):
- # Skip gradient sync for unshared parameters
- continue
-
- if param.grad.requires_grad:
- raise RuntimeError(
- "DistributedDataParallel only works "
- "with gradients that don't require "
- "grad"
- )
- sz = param.numel()
- if sz > self.buffer.numel():
- # all-reduce big params directly
- all_reduce_params([param])
- else:
- if offset + sz > self.buffer.numel():
- all_reduce_params(buffered_params)
- offset = 0
- buffered_params.clear()
- buffered_params.append(param)
- offset += sz
-
- if len(buffered_params) > 0:
- all_reduce_params(buffered_params)
-
- reduction_fn()
diff --git a/spaces/ashutosh1919/quantum-perceptron/quantum_perceptron/__init__.py b/spaces/ashutosh1919/quantum-perceptron/quantum_perceptron/__init__.py
deleted file mode 100644
index ed4e767fa8c5310969615aa941e5b41ce6ea5099..0000000000000000000000000000000000000000
--- a/spaces/ashutosh1919/quantum-perceptron/quantum_perceptron/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from quantum_perceptron.perceptron import Perceptron
diff --git a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Saim Mehmood.html b/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Saim Mehmood.html
deleted file mode 100644
index 74fbd5bb33874558aed6731c8c07572671215e80..0000000000000000000000000000000000000000
--- a/spaces/at2507/SM_NLP_RecoSys/Data/Mentor_interviews/Saim Mehmood.html
+++ /dev/null
@@ -1,134 +0,0 @@
-
-
-
- Saim Mehmood
-
-
-
-
-
- Saim Mehmood
-
-
- Interview
Saim was a menteeCareer- Moved to Canada back in Dec 2017
- grad school Jan 2018
- MSc in CS - research-oriented work
- graduated June 2020
- Sep 2019 got in touch with Omar
- Omar: "You already have a good project with your thesis", finish that up
- Covid hit, kept applying
- kept applying
- took me almost 2 years to find job
- worked as MLOps
- learned a lot throughout my journey, particularly about academia and industry
And how can you add value as a mentor?- selling yourself as a previous academic
- it's a long journey - expectation setting - you will hit a lot rejections!
- "I was getting rejection emails every day, and was happy to see them"
- market the project that you've done
- make sure they are technically sound
- Build a project if needed, post on medium
Mentorship exp- Been a TA during school
Ideal mentee- Recent CS grads would be easier, but open to other disciplines
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/drawingboard.min.css b/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/drawingboard.min.css
deleted file mode 100644
index 7d80478705de8fdceacbad40e5a85c19307be665..0000000000000000000000000000000000000000
--- a/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/drawingboard.min.css
+++ /dev/null
@@ -1,5 +0,0 @@
-/* drawingboard.js v0.4.6 - https://github.com/Leimi/drawingboard.js
-* Copyright (c) 2015 Emmanuel Pelletier
-* Licensed MIT */
-
-.drawing-board,.drawing-board *{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}.drawing-board-controls-hidden,.drawing-board-utils-hidden{display:none!important}.drawing-board{position:relative;display:block}.drawing-board-canvas-wrapper{position:relative;margin:0;border:1px solid #ddd}.drawing-board-canvas{position:absolute;top:0;left:0;width:auto;cursor:crosshair;z-index:20}.drawing-board-cursor{position:absolute;top:0;left:0;pointer-events:none;border-radius:50%;background:#ccc;background:rgba(0,0,0,.2);z-index:30}.drawing-board-control-colors-rainbows,.drawing-board-control-size .drawing-board-control-inner,.drawing-board-control-size-dropdown,.drawing-board-control>button{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;overflow:hidden;background-color:#eee;padding:2px 4px;border:1px solid #ccc;box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);-webkit-box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);height:28px}.drawing-board-control>button{cursor:pointer;min-width:28px;line-height:14px}.drawing-board-control>button:focus,.drawing-board-control>button:hover{background-color:#ddd}.drawing-board-control>button.active,.drawing-board-control>button:active{box-shadow:inset 0 1px 2px 0 rgba(0,0,0,.2);-webkit-box-shadow:inset 0 1px 2px 0 rgba(0,0,0,.2);background-color:#ddd}.drawing-board-control>button[disabled]{color:gray}.drawing-board-control>button[disabled].active,.drawing-board-control>button[disabled]:active,.drawing-board-control>button[disabled]:focus,.drawing-board-control>button[disabled]:hover{background-color:#eee;box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);-webkit-box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);cursor:default}.drawing-board-controls{margin:0 auto;text-align:center;font-size:0;display:table;border-spacing:9.33px 0;position:relative;min-height:28px}.drawing-board-controls[data-align=left]{margin:0;left:-9.33px}.drawing-board-controls[data-align=right]{margin:0 0 0 auto;right:-9.33px}.drawing-board-canvas-wrapper+.drawing-board-controls,.drawing-board-controls+.drawing-board-canvas-wrapper{margin-top:5px}.drawing-board-controls-hidden{height:0;min-height:0;padding:0;margin:0;border:0}.drawing-board-control{display:table-cell;border-collapse:separate;vertical-align:middle;font-size:16px;height:100%}.drawing-board-control-inner{position:relative;height:100%;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.drawing-board-control>button{margin:0;vertical-align:middle}.drawing-board-control-colors{font-size:0;line-height:0}.drawing-board-control-colors-current{border:1px solid #ccc;cursor:pointer;display:inline-block;width:26px;height:26px}.drawing-board-control-colors-rainbows{display:inline-block;position:absolute;left:0;top:33px;margin-left:0;z-index:100;width:250px;height:auto;padding:4px}.drawing-board-control-colors-rainbow{height:18px}.drawing-board-control-colors-picker:first-child{margin-right:5px}.drawing-board-control-colors-picker{display:inline-block;width:18px;height:18px;cursor:pointer}.drawing-board-control-colors-picker[data-color="rgba(255, 255, 255, 1)"]{width:16px;height:17px;border:1px solid #ccc;border-bottom:none}.drawing-board-control-colors-picker:hover{width:16px;height:16px;border:1px solid #555}.drawing-board-control-drawingmode>button{margin-right:2px}.drawing-board-control-drawingmode>button:last-child{margin-right:0}.drawing-board-control-drawingmode-pencil-button{overflow:hidden;background-image:url(data:image/png;base64,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);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-drawingmode-pencil-button:before{content:"";display:block;width:0;height:100%}.drawing-board-control-drawingmode-eraser-button{overflow:hidden;background-image:url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAkpJREFUeNp0kk+IElEcx39vFBc9+OfQRTAwzFt4CaYOKStj6MoeculStzoIQSB4kCVckmDx4iGCXWYJIqjoVOzO1l4qT1F7WfBWHvxzDPyTB3XUmXn93suRybUffHmP997n9/cRsFgwGARJkiAcDsPlwgEIeEZQAhCRAkgAlOD6SQP4rgMFDWVnYCAQgFgsBqFQCBwOByzZNQOotPHx1RNCCCipu6bfb+zSnslkeOQVILPrBkAirbws9btdTEWAzZPXpfepOzaeGMBXwe/3w3+MwTc3Dl+UeghTiskbBvR6Pbh18mZHB0jjmxvCKhIfR37s3r+Sevf8ca/T4TBF2HTSODuDxP7uNjrZFFbBk8lEzOVyspa4ykGYw2zfbTb/7ilvok1YhlVVFfP5vDydTkHXdXDdlhZOOnPY4/HA0YPtp3h6LFjh8XgsFgoFGTPgsKm1zDr8ajTQh8Fh5eGjZzjGI8yjKlgjF4tFGdd/YKYmRja24hw+zu3sYe2HiH3hYzQjl8tleTQanWtou93G6Qngdrth6+1+9h6hTULJZ/PeziJXKhV5OByeg1ut1gJOp9NZTdNOcQ419ot+ggp1qoLdBFmqVmNpm3A8Huewy+Wq1RH8QH9zmBlJJpMRdCIqiiIPBgN+2MCGsW/r8/kgGo1m0fmpzWarseayHlmNeL1eFiWC0cRqtSr3+/3FpSiKHMZtjU1glbFyfKgLTqfzEka9OJvNeDnzz1JnCaFmqOl8ZdJY1SiDOXCiXKg1NtG5DIt0y6ov3dE/AgwAENFWYYLj4mYAAAAASUVORK5CYII=);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-drawingmode-eraser-button:before{content:"";display:block;width:0;height:100%}.drawing-board-control-drawingmode-filler-button{overflow:hidden;background-image:url(data:image/png;base64,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);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-drawingmode-filler-button:before{content:"";display:block;width:0;height:100%}.drawing-board-control-navigation>button{font-family:Helvetica,Arial,sans-serif;font-size:14px;font-weight:700;margin-right:2px}.drawing-board-control-navigation>button:last-child{margin-right:0}.drawing-board-control-size[data-drawing-board-type=range] .drawing-board-control-inner{width:75px}.drawing-board-control-size[data-drawing-board-type=dropdown] .drawing-board-control-inner{overflow:visible}.drawing-board-control-size-range-input{position:relative;width:100%;z-index:100;margin:0;padding:0;border:0}.drawing-board-control-size-dropdown span,.drawing-board-control-size-dropdown-current span,.drawing-board-control-size-range-current{display:block;background:#333;opacity:.8}.drawing-board-control-size-range-current{display:inline-block;opacity:.15;position:absolute;pointer-events:none;left:50%;top:50%;z-index:50}.drawing-board-control-size-dropdown-current{display:block;height:100%;width:40px;overflow:hidden;position:relative}.drawing-board-control-size-dropdown-current span{position:absolute;left:50%;top:50%}.drawing-board-control-size-dropdown{position:absolute;left:-6px;top:33px;height:auto;list-style-type:none;margin:0;padding:0;z-index:100}.drawing-board-control-size-dropdown li{display:block;padding:4px;margin:3px 0;min-height:16px}.drawing-board-control-size-dropdown li:hover{background:#ccc}.drawing-board-control-size-dropdown span{margin:0 auto}.drawing-board-control-download-button{overflow:hidden;background-image:url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAoBJREFUeNqMkr9PU1EUx7/vR1tQ3yu10hAmTawtBSYSy2YccFOcnDQm/gewOLnj5GYMg4sdXFxkMca4OBAwdUBe/ZkIGFp+9tHXvh/3/fTcAm01aLzJybnn3nM+95tzrnDl6Tb+sibuTmWUWj3C6/Juk+LySUmyvt0FCKKA02ryOCy6LBiu15ngMbZ5DDCNBqIw6gKM+n4nECUJru3glKry8CpjQaHVYmC2rVH82DIMMMdGGARdwJ+SPNdFS9chx+MXDNMp/NzagWNatk/nQU/hiYAoih6FYTBCBs9zUXMCbAhx2OYOv351lPOJ3EwH4LteL6Dcp/Rfu3FrstDyIizt+agpaYxNDU0M9gl4v7Ck+TYrCYLQqZHUyTtdQBiutPSGUflczSXHs5lVKwZdSOBMvwztxVvN0RtzsiyXBFHsAvL5PBSnCpXV2getILFiE2SjspYbuZzPiDSZ2vOXmlvX5yQqTmMfg9ZXqtls1wnT09OHEyAq0aFLg/gSXsSWq9wWk+p9PrCoYTwcijdLOfE7UsEufN9HGIYnT4EnTGIXe1KqtNNIvuNnGamxfi7SgQD/nIJCTbzOPQ/SQh1pud7T4M6W/8qFIw/5WAr5m7Ozsw9UVc069Fls2yJzSC5/lnc9RhaHZVnfSqUnEgXP2oBqtYqBgYG2+mKxmOVADnAcB4yxHgD1RzehKKns/LyV4gUHBweQy+UyRkdH6UKJ6fQDFxcXoWkaXJeRuTgUGCdLQJ9bx72lGZimGWs2m+083oN+2iiFQiGxvLy8RrDzudyltgrG3N8U2G8CrPz4sGYYRqJSqWR4H/jNWbJhUjAWi8XG8R/L87yPpGCVttVfAgwAVpZR+8tZC08AAAAASUVORK5CYII=);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-download-button:before{content:"";display:block;width:0;height:100%}
\ No newline at end of file
diff --git a/spaces/awacke1/Bloom.Generative.Writer/generators/topic_to_abstract.py b/spaces/awacke1/Bloom.Generative.Writer/generators/topic_to_abstract.py
deleted file mode 100644
index a91131db41007e46ce1d9a9772bdd7e9645b9d2b..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Bloom.Generative.Writer/generators/topic_to_abstract.py
+++ /dev/null
@@ -1,6 +0,0 @@
-
-from .model import model
-
-
-def topic_to_abstract_generator(template):
- return model('topic', template)
diff --git a/spaces/awacke1/Streamlit-Data-Synthesis-Example/app.py b/spaces/awacke1/Streamlit-Data-Synthesis-Example/app.py
deleted file mode 100644
index 636e3a14af4ceae48968e372e88f840f3425c791..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Streamlit-Data-Synthesis-Example/app.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import streamlit as st
-import pandas as pd
-
-
-def generate_hospital_data():
- # Generate hospital data
- hospitals = {
- "city": ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"],
- "state": ["NY", "CA", "IL", "TX", "AZ"],
- "bed_count": [1200, 1500, 1100, 1300, 1400],
- }
- df = pd.DataFrame(hospitals)
- return df
-
-
-def generate_state_data():
- # Generate state data
- states = {
- "state": ["NY", "CA", "IL", "TX", "AZ"],
- "population": [20000000, 40000000, 13000000, 29000000, 7000000],
- "square_miles": [54556, 163696, 57914, 268596, 113990],
- }
- df = pd.DataFrame(states)
- return df
-
-
-def merge_datasets(hospitals_df, states_df):
- # Merge hospital and state data
- merged_df = pd.merge(hospitals_df, states_df, on="state")
- return merged_df
-
-
-def calculate_beds_per_capita(merged_df):
- # Calculate beds per capita
- merged_df["beds_per_capita"] = merged_df["bed_count"] / merged_df["population"]
- return merged_df
-
-
-def main():
- # Generate data
- hospitals_df = generate_hospital_data()
- states_df = generate_state_data()
-
- # Merge datasets
- merged_df = merge_datasets(hospitals_df, states_df)
-
- # Calculate beds per capita
- merged_df = calculate_beds_per_capita(merged_df)
-
- # Show merged and calculated data
- st.write(merged_df)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/class_names/README.md b/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/class_names/README.md
deleted file mode 100644
index 30d74d258442c7c65512eafab474568dd706c430..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/class_names/README.md
+++ /dev/null
@@ -1 +0,0 @@
-test
\ No newline at end of file
diff --git a/spaces/badayvedat/AudioSep/models/CLAP/open_clip/bert.py b/spaces/badayvedat/AudioSep/models/CLAP/open_clip/bert.py
deleted file mode 100644
index a83d96d2a77ed05198efc05837522bc88d2499cc..0000000000000000000000000000000000000000
--- a/spaces/badayvedat/AudioSep/models/CLAP/open_clip/bert.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from transformers import BertTokenizer, BertModel
-
-tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-model = BertModel.from_pretrained("bert-base-uncased")
-text = "Replace me by any text you'd like."
-
-
-def bert_embeddings(text):
- # text = "Replace me by any text you'd like."
- encoded_input = tokenizer(text, return_tensors="pt")
- output = model(**encoded_input)
- return output
-
-
-from transformers import RobertaTokenizer, RobertaModel
-
-tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
-model = RobertaModel.from_pretrained("roberta-base")
-text = "Replace me by any text you'd like."
-
-
-def Roberta_embeddings(text):
- # text = "Replace me by any text you'd like."
- encoded_input = tokenizer(text, return_tensors="pt")
- output = model(**encoded_input)
- return output
-
-
-from transformers import BartTokenizer, BartModel
-
-tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
-model = BartModel.from_pretrained("facebook/bart-base")
-text = "Replace me by any text you'd like."
-
-
-def bart_embeddings(text):
- # text = "Replace me by any text you'd like."
- encoded_input = tokenizer(text, return_tensors="pt")
- output = model(**encoded_input)
- return output
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DAmmoLoader.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DAmmoLoader.js
deleted file mode 100644
index 9239e109afef47ff228e1fa2005fd9a6643da7ae..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DAmmoLoader.js
+++ /dev/null
@@ -1,590 +0,0 @@
-/**
- * SEA3D+AMMO for Three.JS
- * @author Sunag / http://www.sunag.com.br/
- */
-
-'use strict';
-
-THREE.SEA3D.prototype.toAmmoVec3 = function ( v ) {
-
- return new Ammo.btVector3( v.x, v.y, v.z );
-
-};
-
-//
-// Sphere
-//
-
-THREE.SEA3D.prototype.readSphere = function ( sea ) {
-
- var shape = new Ammo.btSphereShape( sea.radius );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Box
-//
-
-THREE.SEA3D.prototype.readBox = function ( sea ) {
-
- var shape = new Ammo.btBoxShape( new Ammo.btVector3( sea.width * .5, sea.height * .5, sea.depth * .5 ) );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Cone
-//
-
-THREE.SEA3D.prototype.readCone = function ( sea ) {
-
- var shape = new Ammo.btConeShape( sea.radius, sea.height );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Cylinder
-//
-
-THREE.SEA3D.prototype.readCylinder = function ( sea ) {
-
- var shape = new Ammo.btCylinderShape( new Ammo.btVector3( sea.height, sea.radius, sea.radius ) );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Capsule
-//
-
-THREE.SEA3D.prototype.readCapsule = function ( sea ) {
-
- var shape = new Ammo.btCapsuleShape( sea.radius, sea.height );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Convex Geometry
-//
-
-THREE.SEA3D.prototype.readConvexGeometry = function ( sea ) {
-
- if ( this.config.convexHull ) {
-
- var shape = SEA3D.AMMO.createConvexHull( sea.geometry.tag, sea.subGeometryIndex );
-
- } else {
-
- var triMesh = SEA3D.AMMO.createTriangleMesh( sea.geometry.tag, sea.subGeometryIndex );
-
- var shape = new Ammo.btConvexTriangleMeshShape( triMesh, true );
-
- }
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Triangle Geometry
-//
-
-THREE.SEA3D.prototype.readTriangleGeometry = function ( sea ) {
-
- var triMesh = SEA3D.AMMO.createTriangleMesh( sea.geometry.tag, sea.subGeometryIndex );
-
- var shape = new Ammo.btBvhTriangleMeshShape( triMesh, true, true );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Compound
-//
-
-THREE.SEA3D.prototype.readCompound = function ( sea ) {
-
- var shape = new Ammo.btCompoundShape();
-
- for ( var i = 0; i < sea.compounds.length; i ++ ) {
-
- var compound = sea.compounds[ i ];
-
- THREE.SEA3D.MTXBUF.elements = compound.transform;
-
- var transform = SEA3D.AMMO.getTransformFromMatrix( THREE.SEA3D.MTXBUF );
-
- shape.addChildShape( transform, compound.shape.tag );
-
- }
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Rigid Body Base
-//
-
-THREE.SEA3D.prototype.readRigidBodyBase = function ( sea ) {
-
- var shape = sea.shape.tag,
- transform, target;
-
- if ( sea.target ) {
-
- target = sea.target.tag;
-
- target.physics = { enabled: true };
- target.updateMatrix();
-
- transform = SEA3D.AMMO.getTransformFromMatrix( sea.target.tag.matrix );
-
- } else {
-
- THREE.SEA3D.MTXBUF.fromArray( sea.transform );
-
- transform = SEA3D.AMMO.getTransformFromMatrix( THREE.SEA3D.MTXBUF );
-
- }
-
- var motionState = new Ammo.btDefaultMotionState( transform );
- var localInertia = new Ammo.btVector3( 0, 0, 0 );
-
- shape.calculateLocalInertia( sea.mass, localInertia );
-
- var info = new Ammo.btRigidBodyConstructionInfo( sea.mass, motionState, shape, localInertia );
- info.set_m_friction( sea.friction );
- info.set_m_restitution( sea.restitution );
- info.set_m_linearDamping( sea.linearDamping );
- info.set_m_angularDamping( sea.angularDamping );
-
- var rb = new Ammo.btRigidBody( info );
-
- if ( target ) {
-
- target.physics.rigidBody = rb;
-
- if ( sea.offset ) {
-
- var offset = new THREE.Matrix4();
- offset.fromArray( sea.offset );
-
- target.physics.offset = offset;
-
- }
-
- }
-
- Ammo.destroy( info );
-
- this.domain.rigidBodies = this.rigidBodies = this.rigidBodies || [];
- this.rigidBodies.push( this.objects[ "rb/" + sea.name ] = sea.tag = rb );
-
- return rb;
-
-};
-
-//
-// Rigid Body
-//
-
-THREE.SEA3D.prototype.readRigidBody = function ( sea ) {
-
- var rb = this.readRigidBodyBase( sea );
-
- SEA3D.AMMO.addRigidBody( rb, sea.target ? sea.target.tag : undefined, this.config.enabledPhysics );
-
-};
-
-//
-// Car Controller
-//
-
-THREE.SEA3D.prototype.readCarController = function ( sea ) {
-
- var body = this.readRigidBodyBase( sea );
-
- body.setActivationState( SEA3D.AMMO.DISABLE_DEACTIVATION );
-
- // Car
-
- var vehicleRayCaster = new Ammo.btDefaultVehicleRaycaster( SEA3D.AMMO.world );
-
- var tuning = new Ammo.btVehicleTuning();
-
- tuning.set_m_suspensionStiffness( sea.suspensionStiffness );
- tuning.set_m_suspensionDamping( sea.suspensionDamping );
- tuning.set_m_suspensionCompression( sea.suspensionCompression );
- tuning.set_m_maxSuspensionTravelCm( sea.maxSuspensionTravelCm );
- tuning.set_m_maxSuspensionForce( sea.maxSuspensionForce );
- tuning.set_m_frictionSlip( sea.frictionSlip );
-
- var vehicle = new Ammo.btRaycastVehicle( tuning, body, vehicleRayCaster ),
- wheels = [];
-
- vehicle.setCoordinateSystem( 0, 1, 2 );
-
- for ( var i = 0; i < sea.wheel.length; i ++ ) {
-
- var wheel = sea.wheel[ i ];
-
- var wheelInfo = vehicle.addWheel(
- this.toAmmoVec3( wheel.pos ),
- this.toAmmoVec3( wheel.dir ),
- this.toAmmoVec3( wheel.axle ),
- wheel.suspensionRestLength,
- wheel.radius,
- tuning,
- wheel.isFront
- );
-
- var target = wheels[ i ] = wheel.target ? wheel.target.tag : undefined;
-
- if ( target ) {
-
- target.physics = { enabled: true, rigidBody: wheelInfo };
-
- if ( wheel.offset ) {
-
- var offset = new THREE.Matrix4();
- offset.fromArray( wheel.offset );
-
- target.physics.offset = offset;
-
- }
-
- if ( target.parent ) {
-
- target.parent.remove( target );
-
- }
-
- if ( this.container ) {
-
- this.container.add( target );
-
- }
-
- }
-
- wheelInfo.set_m_suspensionStiffness( sea.suspensionStiffness );
- wheelInfo.set_m_wheelsDampingRelaxation( sea.dampingRelaxation );
- wheelInfo.set_m_wheelsDampingCompression( sea.dampingCompression );
- wheelInfo.set_m_frictionSlip( sea.frictionSlip );
-
- }
-
- SEA3D.AMMO.addVehicle( vehicle, wheels );
- SEA3D.AMMO.addRigidBody( body, sea.target ? sea.target.tag : undefined, this.config.enabledPhysics );
-
- this.domain.vehicles = this.vehicles = this.vehicles || [];
- this.vehicles.push( this.objects[ "vhc/" + sea.name ] = sea.tag = vehicle );
-
-};
-
-//
-// P2P Constraint
-//
-
-THREE.SEA3D.prototype.readP2PConstraint = function ( sea ) {
-
- var ctrt;
-
- if ( sea.targetB ) {
-
- ctrt = new Ammo.btPoint2PointConstraint(
- sea.targetA.tag,
- sea.targetB.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.pointB )
- );
-
- } else {
-
- ctrt = new Ammo.btPoint2PointConstraint(
- sea.targetA.tag,
- this.toAmmoVec3( sea.pointA )
- );
-
- }
-
- SEA3D.AMMO.addConstraint( ctrt );
-
- this.domain.constraints = this.constraints = this.constraints || [];
- this.constraints.push( this.objects[ "ctnt/" + sea.name ] = sea.tag = ctrt );
-
-};
-
-//
-// Hinge Constraint
-//
-
-THREE.SEA3D.prototype.readHingeConstraint = function ( sea ) {
-
- var ctrt;
-
- if ( sea.targetB ) {
-
- ctrt = new Ammo.btHingeConstraint(
- sea.targetA.tag,
- sea.targetB.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.pointB ),
- this.toAmmoVec3( sea.axisA ),
- this.toAmmoVec3( sea.axisB ),
- false
- );
-
- } else {
-
- ctrt = new Ammo.btHingeConstraint(
- sea.targetA.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.axisA ),
- false
- );
-
- }
-
- if ( sea.limit ) {
-
- ctrt.setLimit( sea.limit.low, sea.limit.high, sea.limit.softness, sea.limit.biasFactor, sea.limit.relaxationFactor );
-
- }
-
- if ( sea.angularMotor ) {
-
- ctrt.enableAngularMotor( true, sea.angularMotor.velocity, sea.angularMotor.impulse );
-
- }
-
- SEA3D.AMMO.addConstraint( ctrt );
-
- this.domain.constraints = this.constraints = this.constraints || [];
- this.constraints.push( this.objects[ "ctnt/" + sea.name ] = sea.tag = ctrt );
-
-};
-
-//
-// Cone Twist Constraint
-//
-
-THREE.SEA3D.prototype.readConeTwistConstraint = function ( sea ) {
-
- var ctrt;
-
- if ( sea.targetB ) {
-
- ctrt = new Ammo.btConeTwistConstraint(
- sea.targetA.tag,
- sea.targetB.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.pointB ),
- false
- );
-
- } else {
-
- ctrt = new Ammo.btConeTwistConstraint(
- sea.targetA.tag,
- this.toAmmoVec3( sea.pointA ),
- false
- );
-
- }
-
- SEA3D.AMMO.addConstraint( ctrt );
-
- this.domain.constraints = this.constraints = this.constraints || [];
- this.constraints.push( this.objects[ "ctnt/" + sea.name ] = sea.tag = ctrt );
-
-};
-
-//
-// Domain
-//
-
-THREE.SEA3D.Domain.prototype.enabledPhysics = function ( enabled ) {
-
- var i = this.rigidBodies ? this.rigidBodies.length : 0;
-
- while ( i -- ) {
-
- SEA3D.AMMO.setEnabledRigidBody( this.rigidBodies[ i ], enabled );
-
- }
-
-};
-
-THREE.SEA3D.Domain.prototype.applyContainerTransform = function () {
-
- this.container.updateMatrix();
-
- var matrix = this.container.matrix.clone();
-
- this.container.position.set( 0, 0, 0 );
- this.container.quaternion.set( 0, 0, 0, 1 );
- this.container.scale.set( 1, 1, 1 );
-
- this.applyTransform( matrix );
-
-};
-
-THREE.SEA3D.Domain.prototype.applyTransform = function ( matrix ) {
-
- var mtx = THREE.SEA3D.MTXBUF, vec = THREE.SEA3D.VECBUF;
-
- var i = this.rigidBodies ? this.rigidBodies.length : 0,
- childs = this.container ? this.container.children : [],
- targets = [];
-
- while ( i -- ) {
-
- var rb = this.rigidBodies[ i ],
- target = SEA3D.AMMO.getTargetByRigidBody( rb ),
- transform = rb.getWorldTransform(),
- transformMatrix = SEA3D.AMMO.getMatrixFromTransform( transform );
-
- transformMatrix.multiplyMatrices( transformMatrix, matrix );
-
- transform = SEA3D.AMMO.getTransformFromMatrix( transformMatrix );
-
- rb.setWorldTransform( transform );
-
- if ( target ) targets.push( target );
-
- }
-
- for ( i = 0; i < childs.length; i ++ ) {
-
- var obj3d = childs[ i ];
-
- if ( targets.indexOf( obj3d ) > - 1 ) continue;
-
- obj3d.updateMatrix();
-
- mtx.copy( obj3d.matrix );
-
- mtx.multiplyMatrices( matrix, mtx );
-
- obj3d.position.setFromMatrixPosition( mtx );
- obj3d.scale.setFromMatrixScale( mtx );
-
- // ignore rotation scale
-
- mtx.scale( vec.set( 1 / obj3d.scale.x, 1 / obj3d.scale.y, 1 / obj3d.scale.z ) );
- obj3d.quaternion.setFromRotationMatrix( mtx );
-
- }
-
-};
-
-//
-// Extension
-//
-
-THREE.SEA3D.Domain.prototype.getShape = THREE.SEA3D.prototype.getShape = function ( name ) {
-
- return this.objects[ "shpe/" + name ];
-
-};
-
-THREE.SEA3D.Domain.prototype.getRigidBody = THREE.SEA3D.prototype.getRigidBody = function ( name ) {
-
- return this.objects[ "rb/" + name ];
-
-};
-
-THREE.SEA3D.Domain.prototype.getConstraint = THREE.SEA3D.prototype.getConstraint = function ( name ) {
-
- return this.objects[ "ctnt/" + name ];
-
-};
-
-THREE.SEA3D.EXTENSIONS_LOADER.push( {
-
- parse: function () {
-
- delete this.shapes;
- delete this.rigidBodies;
- delete this.vehicles;
- delete this.constraints;
-
- },
-
- setTypeRead: function () {
-
- // CONFIG
-
- this.config.physics = this.config.physics !== undefined ? this.config.physics : true;
- this.config.convexHull = this.config.convexHull !== undefined ? this.config.convexHull : true;
- this.config.enabledPhysics = this.config.enabledPhysics !== undefined ? this.config.enabledPhysics : true;
-
- if ( this.config.physics ) {
-
- // SHAPES
-
- this.file.typeRead[ SEA3D.Sphere.prototype.type ] = this.readSphere;
- this.file.typeRead[ SEA3D.Box.prototype.type ] = this.readBox;
- this.file.typeRead[ SEA3D.Capsule.prototype.type ] = this.readCapsule;
- this.file.typeRead[ SEA3D.Cone.prototype.type ] = this.readCone;
- this.file.typeRead[ SEA3D.Cylinder.prototype.type ] = this.readCylinder;
- this.file.typeRead[ SEA3D.ConvexGeometry.prototype.type ] = this.readConvexGeometry;
- this.file.typeRead[ SEA3D.TriangleGeometry.prototype.type ] = this.readTriangleGeometry;
- this.file.typeRead[ SEA3D.Compound.prototype.type ] = this.readCompound;
-
- // CONSTRAINTS
-
- this.file.typeRead[ SEA3D.P2PConstraint.prototype.type ] = this.readP2PConstraint;
- this.file.typeRead[ SEA3D.HingeConstraint.prototype.type ] = this.readHingeConstraint;
- this.file.typeRead[ SEA3D.ConeTwistConstraint.prototype.type ] = this.readConeTwistConstraint;
-
- // PHYSICS
-
- this.file.typeRead[ SEA3D.RigidBody.prototype.type ] = this.readRigidBody;
- this.file.typeRead[ SEA3D.CarController.prototype.type ] = this.readCarController;
-
- }
-
- }
-} );
-
-THREE.SEA3D.EXTENSIONS_DOMAIN.push( {
-
- dispose: function () {
-
- var i;
-
- i = this.rigidBodies ? this.rigidBodies.length : 0;
- while ( i -- ) SEA3D.AMMO.removeRigidBody( this.rigidBodies[ i ], true );
-
- i = this.vehicles ? this.vehicles.length : 0;
- while ( i -- ) SEA3D.AMMO.removeVehicle( this.vehicles[ i ], true );
-
- i = this.constraints ? this.constraints.length : 0;
- while ( i -- ) SEA3D.AMMO.removeConstraint( this.constraints[ i ], true );
-
- i = this.shapes ? this.shapes.length : 0;
- while ( i -- ) Ammo.destroy( this.shapes[ i ] );
-
- }
-
-} );
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/ShadowMapViewer.js b/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/ShadowMapViewer.js
deleted file mode 100644
index 340efd3f339f776062b01678a0b75adaa67e5623..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/ShadowMapViewer.js
+++ /dev/null
@@ -1,208 +0,0 @@
-/**
- * @author arya-s / https://github.com/arya-s
- *
- * This is a helper for visualising a given light's shadow map.
- * It works for shadow casting lights: THREE.DirectionalLight and THREE.SpotLight.
- * It renders out the shadow map and displays it on a HUD.
- *
- * Example usage:
- * 1) Include \n\t\t" + head + "\n\t\n\t\n\t\t" + body + "\n\t\t\n
Note the model runs on CPU, so it may take a while to run the model.")
-
- with gr.Row():
- with gr.Column():
- input_image = gr.Image(source='upload', type="pil")
- grounding_caption = gr.Textbox(label="Detection Prompt")
- run_button = gr.Button(label="Run")
- with gr.Accordion("Advanced options", open=False):
- box_threshold = gr.Slider(
- label="Box Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
- )
- text_threshold = gr.Slider(
- label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
- )
-
- with gr.Column():
- gallery = gr.outputs.Image(
- type="pil",
- # label="grounding results"
- ).style(full_width=True, full_height=True)
- # gallery = gr.Gallery(label="Generated images", show_label=False).style(
- # grid=[1], height="auto", container=True, full_width=True, full_height=True)
-
- run_button.click(fn=run_grounding, inputs=[
- input_image, grounding_caption, box_threshold, text_threshold], outputs=[gallery])
- gr.Examples(
- [["watermelon.jpg", "watermelon", 0.25, 0.25]],
- inputs = [input_image, grounding_caption, box_threshold, text_threshold],
- outputs = [gallery],
- fn=run_grounding,
- cache_examples=True,
- label='Try this example input!'
- )
- block.launch(share=True, show_api=False, show_error=True)
-
diff --git a/spaces/Audio-AGI/WavJourney/README.md b/spaces/Audio-AGI/WavJourney/README.md
deleted file mode 100644
index 6514202f6a3a92fe09c96516f15d4e7848d85484..0000000000000000000000000000000000000000
--- a/spaces/Audio-AGI/WavJourney/README.md
+++ /dev/null
@@ -1,111 +0,0 @@
----
-title: WavJourney
-emoji: 🔥
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
-license: cc-by-nc-4.0
----
-# 🎵 WavJourney: Compositional Audio Creation with LLMs
-[](https://arxiv.org/abs/2307.14335) [](https://github.com/Audio-AGI/WavJourney/) [](https://audio-agi.github.io/WavJourney_demopage/) [](https://huggingface.co/spaces/Audio-AGI/WavJourney)
-
-
-This repository contains the official implementation of ["WavJourney: Compositional Audio Creation with Large Language Models"](https://audio-agi.github.io/WavJourney_demopage/WavJourney_arXiv.pdf).
-
-Starting with a text prompt, WavJourney can create audio content with engaging storylines encompassing personalized speakers, lifelike speech in context, emotionally resonant music compositions, and impactful sound effects that enhance the auditory experience. Check the audio examples in the [Project Page](https://audio-agi.github.io/WavJourney_demopage/)!
-
-
-
-
-
-
-## Preliminaries
-1. Install the environment:
-```bash
-bash ./scripts/EnvsSetup.sh
-```
-2. Activate the conda environment:
-```bash
-conda activate WavJourney
-```
-
-3. (Optional) You can modify the default configuration in `config.yaml`, check the details described in the configuration file.
-4. Pre-download the models (might take some time):
-```bash
-python scripts/download_models.py
-```
-
-5. Set the WAVJOURNEY_OPENAI_KEY in the environment variable for accessing [GPT-4 API](https://platform.openai.com/account/api-keys) [[Guidance](https://help.openai.com/en/articles/7102672-how-can-i-access-gpt-4)]
-```bash
-export WAVJOURNEY_OPENAI_KEY=your_openai_key_here
-```
-
-6. Set environment variables for using API services
-```bash
-# Set the port for the WAVJOURNEY service to 8021
-export WAVJOURNEY_SERVICE_PORT=8021
-
-# Set the URL for the WAVJOURNEY service to 127.0.0.1
-export WAVJOURNEY_SERVICE_URL=127.0.0.1
-
-# Limit the maximum script lines for WAVJOURNEY to 999
-export WAVJOURNEY_MAX_SCRIPT_LINES=999
-```
-
-
-7. Start Python API services (e.g., Text-to-Speech, Text-to-Audio)
-```bash
-bash scripts/start_services.sh
-```
-
-## Web APP
- ```bash
-bash scripts/start_ui.sh
- ```
-
-## Commandline Usage
- ```bash
- python wavjourney_cli.py -f --input-text "Generate a one-minute introduction to quantum mechanics"
- ```
-
-
-## Kill the services
-You can kill the running services via this command:
- ```bash
-python scripts/kill_services.py
- ```
-
-## (Advanced features) Speaker customization
-You can add voice presets to WavJourney to customize the voice actors. Simply provide the voice id, the description and a sample wav file, and WavJourney will pick the voice automatically based on the audio script. Predefined system voice presets are in `data/voice_presets`.
-
-You can manage voice presets via UI. Specifically, if you want to add voice to voice presets. Run the script via command line below:
-```bash
-python add_voice_preset.py --id "id" --desc "description" --wav-path path/to/wav --session-id ''
-```
-What makes for good voice prompt? See detailed instructions here.
-## Hardware requirement
-- The VRAM of the GPU in the default configuration should be greater than 16 GB.
-- Operation system: Linux.
-
-## Citation
-If you find this work useful, you can cite the paper below:
-
- @article{liu2023wavjourney,
- title = {WavJourney: Compositional Audio Creation with Large Language Models},
- author = {Liu, Xubo and Zhu, Zhongkai and Liu, Haohe and Yuan, Yi and Huang, Qiushi and Liang, Jinhua and Cao, Yin and Kong, Qiuqiang and Plumbley, Mark D and Wang, Wenwu},
- journal = {arXiv preprint arXiv:2307.14335},
- year = {2023}
- }
-
-[](https://www.buymeacoffee.com/liuxubo)
-
-## Appreciation
-- [Bark](https://github.com/suno-ai/bark) for a zero-shot text-to-speech synthesis model.
-- [AudioCraft](https://github.com/facebookresearch/audiocraft) for state-of-the-art audio generation models.
-
-## Disclaimer
-We are not responsible for audio generated using semantics created by this model. Just don't use it for illegal purposes.
-
diff --git a/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py b/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py
deleted file mode 100644
index be58431ed446bf6283c17274a41e37ec3eb24409..0000000000000000000000000000000000000000
--- a/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import os
-import asyncio
-import random
-from datetime import date, datetime, timezone, timedelta
-from ast import literal_eval
-
-import streamlit as st
-import openai
-
-from autoagents.utils.constants import MAIN_HEADER, MAIN_CAPTION, SAMPLE_QUESTIONS
-from autoagents.agents.search import ActionRunner
-
-from langchain.chat_models import ChatOpenAI
-
-
-async def run():
- output_acc = ""
- st.session_state["random"] = random.randint(0, 99)
- if "task" not in st.session_state:
- st.session_state.task = None
- if "model_name" not in st.session_state:
- st.session_state.model_name = "gpt-3.5-turbo"
-
- st.set_page_config(
- page_title="Search Agent",
- page_icon="🤖",
- layout="wide",
- initial_sidebar_state="expanded",
- )
-
- st.title(MAIN_HEADER)
- st.caption(MAIN_CAPTION)
-
- with st.form("my_form", clear_on_submit=False):
- st.markdown("", unsafe_allow_html=True)
- user_input = st.text_input(
- "You: ",
- key="input",
- placeholder="Ask me anything ...",
- label_visibility="hidden",
- )
-
- submitted = st.form_submit_button(
- "Search", help="Hit to submit the search query."
- )
-
- # Ask the user to enter their OpenAI API key
- if (api_key := st.sidebar.text_input("OpenAI api-key", type="password")):
- api_org = None
- else:
- api_key, api_org = os.getenv("OPENAI_API_KEY"), os.getenv("OPENAI_API_ORG")
- with st.sidebar:
- model_dict = {
- "gpt-3.5-turbo": "GPT-3.5-turbo",
- "gpt-4": "GPT-4 (Better but slower)",
- }
- st.radio(
- "OpenAI model",
- model_dict.keys(),
- key="model_name",
- format_func=lambda x: model_dict[x],
- )
-
- time_zone = str(datetime.now(timezone(timedelta(0))).astimezone().tzinfo)
- st.markdown(f"**The system time zone is {time_zone} and the date is {date.today()}**")
-
- st.markdown("**Example Queries:**")
- for q in SAMPLE_QUESTIONS:
- st.markdown(f"*{q}*")
-
- if not api_key:
- st.warning(
- "API key required to try this app. The API key is not stored in any form. [This](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key) might help."
- )
- elif api_org and st.session_state.model_name == "gpt-4":
- st.warning(
- "The free API key does not support GPT-4. Please switch to GPT-3.5-turbo or input your own API key."
- )
- else:
- outputq = asyncio.Queue()
- runner = ActionRunner(outputq,
- ChatOpenAI(openai_api_key=api_key,
- openai_organization=api_org,
- temperature=0,
- model_name=st.session_state.model_name),
- persist_logs=True) # log to HF-dataset
-
- async def cleanup(e):
- st.error(e)
- await st.session_state.task
- st.session_state.task = None
- st.stop()
-
- placeholder = st.empty()
-
- if user_input and submitted:
- if st.session_state.task is not None:
- with placeholder.container():
- st.session_state.task.cancel()
- st.warning("Previous search aborted", icon="⚠️")
-
- st.session_state.task = asyncio.create_task(
- runner.run(user_input, outputq)
- )
- iterations = 0
- with st.expander("Search Results", expanded=True):
- while True:
- with st.spinner("Wait for it..."):
- output = await outputq.get()
- placeholder.empty()
- if isinstance(output, Exception):
- if isinstance(output, openai.error.AuthenticationError):
- await cleanup(f"AuthenticationError: Invalid OpenAI API key.")
- elif isinstance(output, openai.error.InvalidRequestError) \
- and output._message == "The model: `gpt-4` does not exist":
- await cleanup(f"The free API key does not support GPT-4. Please switch to GPT-3.5-turbo or input your own API key.")
- elif isinstance(output, openai.error.OpenAIError):
- await cleanup(output)
- elif isinstance(output, RuntimeWarning):
- st.warning(output)
- continue
- else:
- await cleanup("Something went wrong. Please try searching again.")
- return
- try:
- output_fmt = literal_eval(output)
- st.json(output_fmt, expanded=False)
- st.write("---")
- iterations += 1
- except:
- output_acc += "\n" + output
- st.markdown(f" {output} ",
- unsafe_allow_html=True)
- if iterations >= runner.agent_executor.max_iterations:
- await cleanup(
- f"Maximum iterations ({iterations}) exceeded. You can try running the search again or try a variation of the query."
- )
- return
- if "Final Answer:" in output:
- break
- # Found the answer
- final_answer = await st.session_state.task
- final_answer = final_answer.replace("$", "\$")
- # st.success accepts md
- st.success(final_answer, icon="✅")
- st.balloons()
- st.session_state.task = None
- st.stop()
-
-if __name__ == "__main__":
- loop = asyncio.new_event_loop()
- loop.set_debug(enabled=False)
- loop.run_until_complete(run())
diff --git a/spaces/BAAI/vid2vid-zero/Dockerfile b/spaces/BAAI/vid2vid-zero/Dockerfile
deleted file mode 100644
index 9eaa78f8facc5066f018183f108824842f11b7e8..0000000000000000000000000000000000000000
--- a/spaces/BAAI/vid2vid-zero/Dockerfile
+++ /dev/null
@@ -1,57 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update && \
- apt-get upgrade -y && \
- apt-get install -y --no-install-recommends \
- git \
- git-lfs \
- wget \
- curl \
- # ffmpeg \
- ffmpeg \
- x264 \
- # python build dependencies \
- build-essential \
- libssl-dev \
- zlib1g-dev \
- libbz2-dev \
- libreadline-dev \
- libsqlite3-dev \
- libncursesw5-dev \
- xz-utils \
- tk-dev \
- libxml2-dev \
- libxmlsec1-dev \
- libffi-dev \
- liblzma-dev && \
- apt-get clean && \
- rm -rf /var/lib/apt/lists/*
-
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:${PATH}
-WORKDIR ${HOME}/app
-
-RUN curl https://pyenv.run | bash
-ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ENV PYTHON_VERSION=3.10.9
-RUN pyenv install ${PYTHON_VERSION} && \
- pyenv global ${PYTHON_VERSION} && \
- pyenv rehash && \
- pip install --no-cache-dir -U pip setuptools wheel
-
-RUN pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1
-COPY --chown=1000 requirements.txt /tmp/requirements.txt
-RUN pip install --no-cache-dir -U -r /tmp/requirements.txt
-
-COPY --chown=1000 . ${HOME}/app
-# RUN cd Tune-A-Video && patch -p1 < ../patch
-ENV PYTHONPATH=${HOME}/app \
- PYTHONUNBUFFERED=1 \
- GRADIO_ALLOW_FLAGGING=never \
- GRADIO_NUM_PORTS=1 \
- GRADIO_SERVER_NAME=0.0.0.0 \
- GRADIO_THEME=huggingface \
- SYSTEM=spaces
-CMD ["python", "app.py"]
diff --git a/spaces/Bala2-03-2003/MygenvioceAI/app.py b/spaces/Bala2-03-2003/MygenvioceAI/app.py
deleted file mode 100644
index ca8b6d40b4ab898c70da92f4a4298de2baf703dc..0000000000000000000000000000000000000000
--- a/spaces/Bala2-03-2003/MygenvioceAI/app.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import os
-import re
-import requests
-import json
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-PLAY_HT_API_KEY=os.getenv('PLAY_HT_API_KEY')
-PLAY_HT_USER_ID=os.getenv('PLAY_HT_USER_ID')
-
-PLAY_HT_VOICE_ID=os.getenv('PLAY_HT_VOICE_ID')
-play_ht_api_get_audio_url = "https://play.ht/api/v2/tts"
-
-
-template = """You are a helpful assistant to answer user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
- input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
- llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
- prompt=prompt,
- verbose=True,
- memory=memory,
-)
-
-headers = {
- "accept": "text/event-stream",
- "content-type": "application/json",
- "AUTHORIZATION": "Bearer "+ PLAY_HT_API_KEY,
- "X-USER-ID": PLAY_HT_USER_ID
-}
-
-
-def get_payload(text):
- return {
- "text": text,
- "voice": PLAY_HT_VOICE_ID,
- "quality": "medium",
- "output_format": "mp3",
- "speed": 1,
- "sample_rate": 24000,
- "seed": None,
- "temperature": None
- }
-
-def get_generated_audio(text):
- payload = get_payload(text)
- generated_response = {}
- try:
- response = requests.post(play_ht_api_get_audio_url, json=payload, headers=headers)
- response.raise_for_status()
- generated_response["type"]= 'SUCCESS'
- generated_response["response"] = response.text
- except requests.exceptions.RequestException as e:
- generated_response["type"]= 'ERROR'
- try:
- response_text = json.loads(response.text)
- if response_text['error_message']:
- generated_response["response"] = response_text['error_message']
- else:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["type"]= 'ERROR'
- generated_response["response"] = response.text
- return generated_response
-
-def extract_urls(text):
- # Define the regex pattern for URLs
- url_pattern = r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+[/\w\.-]*'
-
- # Find all occurrences of URLs in the text
- urls = re.findall(url_pattern, text)
-
- return urls
-
-def get_audio_reply_for_question(text):
- generated_audio_event = get_generated_audio(text)
- #From get_generated_audio, you will get events in a string format, from that we need to extract the url
- final_response = {
- "audio_url": '',
- "message": ''
- }
- if generated_audio_event["type"] == 'SUCCESS':
- audio_urls = extract_urls(generated_audio_event["response"])
- if len(audio_urls) == 0:
- final_response['message'] = "No audio file link found in generated event"
- else:
- final_response['audio_url'] = audio_urls[-1]
- else:
- final_response['message'] = generated_audio_event['response']
- return final_response
-
-def download_url(url):
- try:
- # Send a GET request to the URL to fetch the content
- final_response = {
- 'content':'',
- 'error':''
- }
- response = requests.get(url)
- # Check if the request was successful (status code 200)
- if response.status_code == 200:
- final_response['content'] = response.content
- else:
- final_response['error'] = f"Failed to download the URL. Status code: {response.status_code}"
- except Exception as e:
- final_response['error'] = f"Failed to download the URL. Error: {e}"
- return final_response
-
-def get_filename_from_url(url):
- # Use os.path.basename() to extract the file name from the URL
- file_name = os.path.basename(url)
- return file_name
-
-def get_text_response(user_message):
- response = llm_chain.predict(user_message = user_message)
- return response
-
-def get_text_response_and_audio_response(user_message):
- response = get_text_response(user_message) # Getting the reply from Open AI
- audio_reply_for_question_response = get_audio_reply_for_question(response)
- final_response = {
- 'output_file_path': '',
- 'message':''
- }
- audio_url = audio_reply_for_question_response['audio_url']
- if audio_url:
- output_file_path=get_filename_from_url(audio_url)
- download_url_response = download_url(audio_url)
- audio_content = download_url_response['content']
- if audio_content:
- with open(output_file_path, "wb") as audio_file:
- audio_file.write(audio_content)
- final_response['output_file_path'] = output_file_path
- else:
- final_response['message'] = download_url_response['error']
- else:
- final_response['message'] = audio_reply_for_question_response['message']
- return final_response
-
-def chat_bot_response(message, history):
- text_and_audio_response = get_text_response_and_audio_response(message)
- output_file_path = text_and_audio_response['output_file_path']
- if output_file_path:
- return (text_and_audio_response['output_file_path'],)
- else:
- return text_and_audio_response['message']
-
-demo = gr.ChatInterface(chat_bot_response,examples=["How are you doing?","What are your interests?","Which places do you like to visit?"])
-
-if __name__ == "__main__":
- demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/BartPoint/VoiceChange/app_multi.py b/spaces/BartPoint/VoiceChange/app_multi.py
deleted file mode 100644
index 67890355f12e2d2d5420ce8e31a722020f349b24..0000000000000000000000000000000000000000
--- a/spaces/BartPoint/VoiceChange/app_multi.py
+++ /dev/null
@@ -1,469 +0,0 @@
-from typing import Union
-
-from argparse import ArgumentParser
-
-import asyncio
-import json
-import hashlib
-from os import path, getenv
-
-import gradio as gr
-
-import torch
-
-import numpy as np
-import librosa
-
-import edge_tts
-
-import config
-import util
-from infer_pack.models import (
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono
-)
-from vc_infer_pipeline import VC
-
-# Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21 # noqa
-in_hf_space = getenv('SYSTEM') == 'spaces'
-
-# Argument parsing
-arg_parser = ArgumentParser()
-arg_parser.add_argument(
- '--hubert',
- default=getenv('RVC_HUBERT', 'hubert_base.pt'),
- help='path to hubert base model (default: hubert_base.pt)'
-)
-arg_parser.add_argument(
- '--config',
- default=getenv('RVC_MULTI_CFG', 'multi_config.json'),
- help='path to config file (default: multi_config.json)'
-)
-arg_parser.add_argument(
- '--api',
- action='store_true',
- help='enable api endpoint'
-)
-arg_parser.add_argument(
- '--cache-examples',
- action='store_true',
- help='enable example caching, please remember delete gradio_cached_examples folder when example config has been modified' # noqa
-)
-args = arg_parser.parse_args()
-
-app_css = '''
-#model_info img {
- max-width: 100px;
- max-height: 100px;
- float: right;
-}
-
-#model_info p {
- margin: unset;
-}
-'''
-
-app = gr.Blocks(
- theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate"),
- css=app_css,
- analytics_enabled=False
-)
-
-# Load hubert model
-hubert_model = util.load_hubert_model(config.device, args.hubert)
-hubert_model.eval()
-
-# Load models
-multi_cfg = json.load(open(args.config, 'r'))
-loaded_models = []
-
-for model_name in multi_cfg.get('models'):
- print(f'Loading model: {model_name}')
-
- # Load model info
- model_info = json.load(
- open(path.join('model', model_name, 'config.json'), 'r')
- )
-
- # Load RVC checkpoint
- cpt = torch.load(
- path.join('model', model_name, model_info['model']),
- map_location='cpu'
- )
- tgt_sr = cpt['config'][-1]
- cpt['config'][-3] = cpt['weight']['emb_g.weight'].shape[0] # n_spk
-
- if_f0 = cpt.get('f0', 1)
- net_g: Union[SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono]
- if if_f0 == 1:
- net_g = SynthesizerTrnMs768NSFsid(
- *cpt['config'],
- is_half=util.is_half(config.device)
- )
- else:
- net_g = SynthesizerTrnMs768NSFsid_nono(*cpt['config'])
-
- del net_g.enc_q
-
- # According to original code, this thing seems necessary.
- print(net_g.load_state_dict(cpt['weight'], strict=False))
-
- net_g.eval().to(config.device)
- net_g = net_g.half() if util.is_half(config.device) else net_g.float()
-
- vc = VC(tgt_sr, config)
-
- loaded_models.append(dict(
- name=model_name,
- metadata=model_info,
- vc=vc,
- net_g=net_g,
- if_f0=if_f0,
- target_sr=tgt_sr
- ))
-
-print(f'Models loaded: {len(loaded_models)}')
-
-# Edge TTS speakers
-tts_speakers_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) # noqa
-
-
-# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer-web.py#L118 # noqa
-def vc_func(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- if input_audio is None:
- return (None, 'Please provide input audio.')
-
- if model_index is None:
- return (None, 'Please select a model.')
-
- model = loaded_models[model_index]
-
- # Reference: so-vits
- (audio_samp, audio_npy) = input_audio
-
- # https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49
- # Can be change well, we will see
- if (audio_npy.shape[0] / audio_samp) > 320 and in_hf_space:
- return (None, 'Input audio is longer than 60 secs.')
-
- # Bloody hell: https://stackoverflow.com/questions/26921836/
- if audio_npy.dtype != np.float32: # :thonk:
- audio_npy = (
- audio_npy / np.iinfo(audio_npy.dtype).max
- ).astype(np.float32)
-
- if len(audio_npy.shape) > 1:
- audio_npy = librosa.to_mono(audio_npy.transpose(1, 0))
-
- if audio_samp != 16000:
- audio_npy = librosa.resample(
- audio_npy,
- orig_sr=audio_samp,
- target_sr=16000
- )
-
- pitch_int = int(pitch_adjust)
-
- resample = (
- 0 if resample_option == 'Disable resampling'
- else int(resample_option)
- )
-
- times = [0, 0, 0]
-
- checksum = hashlib.sha512()
- checksum.update(audio_npy.tobytes())
-
- output_audio = model['vc'].pipeline(
- hubert_model,
- model['net_g'],
- model['metadata'].get('speaker_id', 0),
- audio_npy,
- checksum.hexdigest(),
- times,
- pitch_int,
- f0_method,
- path.join('model', model['name'], model['metadata']['feat_index']),
- feat_ratio,
- model['if_f0'],
- filter_radius,
- model['target_sr'],
- resample,
- rms_mix_rate,
- 'v2'
- )
-
- out_sr = (
- resample if resample >= 16000 and model['target_sr'] != resample
- else model['target_sr']
- )
-
- print(f'npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s')
- return ((out_sr, output_audio), 'Success')
-
-
-async def edge_tts_vc_func(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- if input_text is None:
- return (None, 'Please provide TTS text.')
-
- if tts_speaker is None:
- return (None, 'Please select TTS speaker.')
-
- if model_index is None:
- return (None, 'Please select a model.')
-
- speaker = tts_speakers_list[tts_speaker]['ShortName']
- (tts_np, tts_sr) = await util.call_edge_tts(speaker, input_text)
- return vc_func(
- (tts_sr, tts_np),
- model_index,
- pitch_adjust,
- f0_method,
- feat_ratio,
- filter_radius,
- rms_mix_rate,
- resample_option
- )
-
-
-def update_model_info(model_index):
- if model_index is None:
- return str(
- '### Model info\n'
- 'Please select a model from dropdown above.'
- )
-
- model = loaded_models[model_index]
- model_icon = model['metadata'].get('icon', '')
-
- return str(
- '### Model info\n'
- ''
- '**{name}**\n\n'
- 'Author: {author}\n\n'
- 'Source: {source}\n\n'
- '{note}'
- ).format(
- name=model['metadata'].get('name'),
- author=model['metadata'].get('author', 'Anonymous'),
- source=model['metadata'].get('source', 'Unknown'),
- note=model['metadata'].get('note', ''),
- icon=(
- model_icon
- if model_icon.startswith(('http://', 'https://'))
- else '/file/model/%s/%s' % (model['name'], model_icon)
- )
- )
-
-
-def _example_vc(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- (audio, message) = vc_func(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
- )
- return (
- audio,
- message,
- update_model_info(model_index)
- )
-
-
-async def _example_edge_tts(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- (audio, message) = await edge_tts_vc_func(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method,
- feat_ratio, filter_radius, rms_mix_rate, resample_option
- )
- return (
- audio,
- message,
- update_model_info(model_index)
- )
-
-
-with app:
- gr.Markdown(
- '## A simplistic Web interface\n'
- 'RVC interface, project based on [RVC-WebUI](https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI)' # thx noqa
- 'A lot of inspiration from what\'s already out there, including [zomehwh/rvc-models](https://huggingface.co/spaces/zomehwh/rvc-models) & [DJQmUKV/rvc-inference](https://huggingface.co/spaces/DJQmUKV/rvc-inference).\n ' # thx noqa
- )
-
- with gr.Row():
- with gr.Column():
- with gr.Tab('Audio conversion'):
- input_audio = gr.Audio(label='Input audio')
-
- vc_convert_btn = gr.Button('Convert', variant='primary')
-
- with gr.Tab('TTS conversion'):
- tts_input = gr.TextArea(
- label='TTS input text'
- )
- tts_speaker = gr.Dropdown(
- [
- '%s (%s)' % (
- s['FriendlyName'],
- s['Gender']
- )
- for s in tts_speakers_list
- ],
- label='TTS speaker',
- type='index'
- )
-
- tts_convert_btn = gr.Button('Convert', variant='primary')
-
- pitch_adjust = gr.Slider(
- label='Pitch',
- minimum=-24,
- maximum=24,
- step=1,
- value=0
- )
- f0_method = gr.Radio(
- label='f0 methods',
- choices=['pm', 'harvest', 'crepe'],
- value='pm',
- interactive=True
- )
-
- with gr.Accordion('Advanced options', open=False):
- feat_ratio = gr.Slider(
- label='Feature ratio',
- minimum=0,
- maximum=1,
- step=0.1,
- value=0.6
- )
- filter_radius = gr.Slider(
- label='Filter radius',
- minimum=0,
- maximum=7,
- step=1,
- value=3
- )
- rms_mix_rate = gr.Slider(
- label='Volume envelope mix rate',
- minimum=0,
- maximum=1,
- step=0.1,
- value=1
- )
- resample_rate = gr.Dropdown(
- [
- 'Disable resampling',
- '16000',
- '22050',
- '44100',
- '48000'
- ],
- label='Resample rate',
- value='Disable resampling'
- )
-
- with gr.Column():
- # Model select
- model_index = gr.Dropdown(
- [
- '%s - %s' % (
- m['metadata'].get('source', 'Unknown'),
- m['metadata'].get('name')
- )
- for m in loaded_models
- ],
- label='Model',
- type='index'
- )
-
- # Model info
- with gr.Box():
- model_info = gr.Markdown(
- '### Model info\n'
- 'Please select a model from dropdown above.',
- elem_id='model_info'
- )
-
- output_audio = gr.Audio(label='Output audio')
- output_msg = gr.Textbox(label='Output message')
-
- multi_examples = multi_cfg.get('examples')
- if (
- multi_examples and
- multi_examples.get('vc') and multi_examples.get('tts_vc')
- ):
- with gr.Accordion('Sweet sweet examples', open=False):
- with gr.Row():
- # VC Example
- if multi_examples.get('vc'):
- gr.Examples(
- label='Audio conversion examples',
- examples=multi_examples.get('vc'),
- inputs=[
- input_audio, model_index, pitch_adjust, f0_method,
- feat_ratio
- ],
- outputs=[output_audio, output_msg, model_info],
- fn=_example_vc,
- cache_examples=args.cache_examples,
- run_on_click=args.cache_examples
- )
-
- # Edge TTS Example
- if multi_examples.get('tts_vc'):
- gr.Examples(
- label='TTS conversion examples',
- examples=multi_examples.get('tts_vc'),
- inputs=[
- tts_input, model_index, tts_speaker, pitch_adjust,
- f0_method, feat_ratio
- ],
- outputs=[output_audio, output_msg, model_info],
- fn=_example_edge_tts,
- cache_examples=args.cache_examples,
- run_on_click=args.cache_examples
- )
-
- vc_convert_btn.click(
- vc_func,
- [
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_rate
- ],
- [output_audio, output_msg],
- api_name='audio_conversion'
- )
-
- tts_convert_btn.click(
- edge_tts_vc_func,
- [
- tts_input, model_index, tts_speaker, pitch_adjust, f0_method,
- feat_ratio, filter_radius, rms_mix_rate, resample_rate
- ],
- [output_audio, output_msg],
- api_name='tts_conversion'
- )
-
- model_index.change(
- update_model_info,
- inputs=[model_index],
- outputs=[model_info],
- show_progress=False,
- queue=False
- )
-
-app.queue(
- concurrency_count=1,
- max_size=20,
- api_open=args.api
-).launch()
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md b/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md
deleted file mode 100644
index 231c36d39005d9275c61ccc88067d084ca775635..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md
+++ /dev/null
@@ -1,134 +0,0 @@
-
-Aparcamiento de coches multijugador Sudáfrica Edición APK Descargar: Una guía para los usuarios de Android
- Si usted está buscando un juego de estacionamiento de coches realista y divertido que ofrece más que solo estacionamiento, es posible que desee echa un vistazo a Parking Multijugador. Y si usted está en Sudáfrica o desea experimentar la cultura del automóvil de Sudáfrica, es posible que desee probar el Aparcamiento Multijugador South áfrica Edition APK. En este artículo, te diremos qué es el Car Parking Multiplayer, qué hace que South áfrica Edition sea diferente, cómo descargarlo e instalarlo en tu dispositivo Android, y algunos consejos y trucos para jugarlo.
- ¿Qué es el Aparcamiento Multijugador?
- Car Parking Multiplayer es un juego que puede engañarte con su nombre bastante engañoso. Pero, es mucho más que solo estar aparcando tu coche. Es una experiencia de mundo abierto donde se puede conducir gratis y sí, todavía trabajar en ese aparcamiento si lo desea. Incluso puedes saltar de tu coche y caminar. Hay diferentes áreas que se pueden explorar en el juego. Cada una es como su propio mundo abierto. Puedes optar por jugar en modo de un solo jugador o en modo online si quieres una escena más caótica (de forma divertida) .
-aparcamiento de coches multijugador sudáfrica edición apk descargar
DOWNLOAD === https://bltlly.com/2v6IE9
- Características de Aparcamiento Multijugador
- Aparcamiento de coches multijugador tiene las siguientes características :
-
-- Modo multijugador de mundo abierto
- - Caminar libremente
- - Mundo abierto libre con gasolineras reales y servicios de automóviles
- - Compite contra jugadores reales en las carreras multijugador
- - Intercambiar coches con jugadores reales
- - Miles de jugadores reales cada día
- - Lista de amigos
- - Chat de voz
- - Modo de policía
-
-
-- Personalización del coche
- - Suspensión ajustable, ángulo de rueda y más
- - Ajuste del motor: motor de intercambio, turbo, caja de cambios y escape
- - Visual auto tungs: Vynils dinámico, partes del cuerpo del coche
-
-
-- Mundo abierto de alta calidad
-
- - 100 coches con el interior real
- - 16 pieles de jugador
- - Edificios con interior
-
-
-- Juego interesante
- - 82 desafíos de estacionamiento y conducción en la vida real
- - Diferentes vehículos: Grúa, camioneta, camiones, coches deportivos y clásicos
-
-
-
- Opiniones de Aparcamiento multijugador
- Aparcamiento de coches multijugador ha recibido en su mayoría críticas positivas de los usuarios en Google Play Store y App Store. Tiene una calificación de 4.4 de 5 estrellas en Google Play Store y una calificación de 4.3 de 5 estrellas en App Store . Estos son algunos de los comentarios de los usuarios:
- "Increíble juego! No hay errores en este juego o retrasos ect. (base en el dispositivo que utiliza) Me encantan los gráficos y los coches. Los coches son tan realistas y los sonidos son increíbles. Me encanta cómo puedes personalizar tu coche y hacer que se vea genial. El modo multijugador es increíble. Puedes chatear con otros jugadores y competir con ellos. También puedes unirte a un clan o crear tu propio clan. Este juego es muy divertido y adictivo. Recomiendo este juego a todos los que aman los coches y los juegos de estacionamiento."
- "Este juego es muy bueno pero necesita algunas mejoras como agregar más coches, más mapas, más personalizaciones, más modos de juego, más desafíos, etc. Además, el juego se bloquea a veces y los controles no son muy suaves. Los gráficos son agradables, pero pueden ser mejores. El juego es divertido de jugar con amigos, pero se vuelve aburrido después de un tiempo. Espero que los desarrolladores actualicen el juego pronto y lo hagan más agradable."
-
- ¿Qué es el aparcamiento multijugador South áfrica Edition?
- Car Parking Multijugador South áfrica Edition es una versión modificada de Car Parking Multijugador que está especialmente diseñado para los usuarios sudafricanos o los fans de la cultura del coche de Sudáfrica. No es una versión oficial del juego, sino un archivo APK hecho por fans que se puede descargar e instalar en dispositivos Android .
- Diferencias entre Aparcamiento de coches multijugador y Aparcamiento de coches multijugador Sudáfrica Edición
- Car Parking Multijugador South áfrica Edition tiene algunas diferencias con el juego original Car Parking Multijugador, como :
-
-- Más coches que son populares en Sudáfrica, como BMW E30, VW Golf Mk1, Toyota Corolla, Nissan 1400, etc.
-- Más personalizaciones que reflejan la cultura del automóvil sudafricano, como llantas giratorias, escapes fuertes, pegatinas, banderas, etc.
-- Más mapas que se basan en ubicaciones reales en Sudáfrica, como Ciudad del Cabo, Johannesburgo, Durban, etc.
-- Más música inspirada en la escena musical sudafricana, como kwaito, gqom, amapiano, etc.
-- Más idiomas que se hablan en Sudáfrica, como afrikaans, zulú, xhosa, etc.
-
- Beneficios de Aparcamiento Multijugador South áfrica Edition
- Car Parking Multijugador South áfrica Edition tiene algunos beneficios para los usuarios que quieren disfrutar del juego con un toque sudafricano, como :
-
-- Más variedad y diversidad en términos de coches, personalizaciones, mapas, música e idiomas
-- Más diversión y emoción en términos de jugabilidad, gráficos, efectos de sonido e interacciones
-- Más conexión y comunidad con otros jugadores que comparten el mismo interés y pasión por la cultura del automóvil de Sudáfrica
-- Más soporte y actualizaciones de los desarrolladores que se dedican a mejorar el juego y agregar nuevas características
-
- Cómo descargar e instalar el estacionamiento de coches multijugador South áfrica Edition APK en dispositivos Android?
-
- Requisitos para descargar e instalar el estacionamiento de coches multijugador South áfrica Edition APK
- Para descargar e instalar Aparcamiento Multijugador South áfrica Edition APK en su dispositivo Android, es necesario tener :
-
-
-- Un dispositivo Android que se ejecuta en Android 4.1 o superior
-- Una conexión a Internet estable
-- Una aplicación de administrador de archivos que puede acceder al archivo APK
-- Una cantidad suficiente de espacio de almacenamiento en su dispositivo
-- Un permiso para instalar aplicaciones de fuentes desconocidas en la configuración del dispositivo
-
- Pasos para Descargar e Instalar Aparcamiento Multijugador South áfrica Edition APK
- Para descargar e instalar Aparcamiento Multijugador South áfrica Edition APK en su dispositivo Android, es necesario seguir estos pasos :
-
-- Ir a un sitio web de confianza que proporciona el enlace para descargar Car Parking Multi jugador South áfrica Edition APK. Por ejemplo, puede visitar [este sitio web] para obtener la última versión del archivo APK.
-- Haga clic en el botón de descarga y espere a que el archivo APK se descargue en su dispositivo.
-- Una vez que se complete la descarga, localizar el archivo APK en su dispositivo utilizando una aplicación de administrador de archivos. Puede encontrarlo en la carpeta Descargas o en cualquier otra carpeta donde lo haya guardado.
-- Toque en el archivo APK y seleccione Instalar. Es posible que vea un mensaje de advertencia que dice "Para su seguridad, el teléfono no está permitido instalar aplicaciones desconocidas de esta fuente". Si ves este mensaje, ve a la configuración del dispositivo y habilita la opción de instalar aplicaciones de fuentes desconocidas. Esta opción puede estar en Seguridad, Privacidad o Aplicaciones dependiendo del modelo de dispositivo y la versión de Android.
-
-- Espere a que termine el proceso de instalación. Puede tardar unos minutos dependiendo del rendimiento del dispositivo y la velocidad de Internet.
-- Una vez que se hace la instalación, se puede abrir el juego y disfrutar de aparcamiento multijugador South áfrica Edition en su dispositivo Android.
-
- Consejos y Trucos para Jugar Aparcamiento Multijugador South áfrica Edition
- Ahora que ha descargado e instalado Car Parking Multiplayer South áfrica Edition en su dispositivo Android, es posible que desee conocer algunos consejos y trucos para jugarlo. Estos son algunos de ellos:
- Cómo seleccionar un coche y un jugador
- Para seleccionar un coche y un jugador en Car Parking Multiplayer South áfrica Edition, debe hacer lo siguiente:
-
-- Toque en el icono de menú en la esquina superior izquierda de la pantalla.
-- Toque en Garaje para ver la lista de coches que usted posee o puede comprar.
-- Toque en el coche que desea utilizar y luego toque en Select.
-- Toque en Volver para volver al menú.
-- Toque en Perfil para ver la lista de jugadores entre los que puede elegir.
-- Toque en el reproductor que desea utilizar y luego toque en Select.
-- Toque en Volver para volver al menú.
-- Toque en Jugar para comenzar a jugar el juego con el coche y el jugador seleccionado.
-
- Cómo ajustar su coche y ajustar su relación de engranajes
- Para afinar su coche y ajustar su relación de engranajes en Car Parking Multiplayer South áfrica Edition, debe hacer lo siguiente:
-
-- Toque en el icono de menú en la esquina superior izquierda de la pantalla.
-- Toque en Garaje para ver la lista de coches que usted posee o puede comprar.
-- Toque en el coche que desea sintonizar y luego toque en Tune.
-- Verá cuatro pestañas: Motor, Suspensión, Ruedas y Cuerpo. Puede deslizar hacia la izquierda o hacia la derecha para cambiar entre ellas.
-
-- Para ajustar su relación de cambios, toque en Caja de cambios en la pestaña Motor. Verá un gráfico que muestra cómo cambia su velocidad con diferentes marchas. Puede arrastrar los puntos en el gráfico para cambiar la relación de transmisión para cada marcha. También puede pulsar en Auto o Manual para cambiar entre modos de transmisión automática o manual.
-- Una vez que haya terminado de ajustar su coche, toque en Guardar para aplicar los cambios.
-
- Cómo Deriva, Donut, y Burnout
- Para la deriva, donut, y burnout en el estacionamiento de coches multijugador South áfrica Edition, es necesario hacer lo siguiente:
-
-- Para la deriva, debe usar una combinación de dirección, acelerador, freno y freno de mano. Puede usar los controles de inclinación o táctiles para la dirección. Para iniciar la deriva, es necesario acelerar y girar bruscamente en una esquina. Luego, debe aplicar el freno de mano brevemente para hacer que sus ruedas traseras pierdan tracción. Después de eso, debe equilibrar el acelerador y la dirección para mantener el ángulo de deriva y la dirección. También puede utilizar el freno de mano o freno de mano de nuevo si necesita ajustar su deriva. Para terminar con la deriva, es necesario soltar el acelerador y la dirección y enderezar el coche.
-- Para la rosquilla, debe usar una combinación de dirección, acelerador y freno de mano. Puede usar controles de inclinación o táctiles para la dirección. Para comenzar a donar, debe acelerar y girar bruscamente en una dirección. Entonces, es necesario aplicar el freno de mano para hacer que su coche gire alrededor de su centro. Después de eso, es necesario mantener el acelerador y la dirección constante para mantener el círculo de la dona. También puede cambiar la dirección de su dona cambiando la dirección. Para terminar donuting, es necesario soltar el acelerador y el freno de mano y enderezar su coche.
-
-
- Conclusión
- Car Parking Multijugador South áfrica Edition es un juego que ofrece una experiencia de aparcamiento realista y divertido con un toque sudafricano. Tiene más coches, personalizaciones, mapas, música e idiomas que reflejan la cultura del automóvil sudafricano. También tiene un modo de mundo abierto, un modo de ajuste de coche, y un modo multijugador que hacen el juego más agradable y emocionante. Puede descargar e instalar Parking Multijugador South áfrica Edition APK en su dispositivo Android siguiendo los pasos en este artículo. También puedes usar los consejos y trucos de este artículo para mejorar tu juego y divertirte más.
- Preguntas frecuentes
- Aquí están algunas de las preguntas más frecuentes sobre Car Parking Multiplayer South áfrica Edition:
- ¿Es gratis Car Parking Multijugador South áfrica Edition?
- Sí, Parking Multijugador South áfrica Edition es gratis para descargar y jugar. Sin embargo, puede contener algunas compras en la aplicación que requieren dinero real.
- ¿Es seguro el aparcamiento multijugador South áfrica Edition?
- Sí, Parking Multijugador South áfrica Edition es seguro, siempre y cuando se descarga desde un sitio web de confianza. Sin embargo, siempre debe tener cuidado al descargar e instalar aplicaciones de fuentes desconocidas, ya que pueden contener virus o malware que pueden dañar su dispositivo.
- ¿Es compatible Car Parking Multiplayer South áfrica Edition con mi dispositivo?
- Car Parking Multijugador South áfrica Edition es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 4.1 o superior. Sin embargo, algunos dispositivos pueden tener diferentes especificaciones o problemas de rendimiento que pueden afectar la calidad o funcionalidad del juego.
- ¿Cómo puedo actualizar Car Parking Multijugador South áfrica Edition?
-
- ¿Cómo puedo contactar a los desarrolladores de Car Parking Multiplayer South áfrica Edition?
- Para contactar a los desarrolladores de Car Parking Multiplayer South áfrica Edition, puedes visitar su página de Facebook o su canal de YouTube y dejar un comentario o un mensaje.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md b/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md
deleted file mode 100644
index 2001fb4da4e341236f8b4ef59c584c7ee860ae80..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md
+++ /dev/null
@@ -1,76 +0,0 @@
-
-Nitro asfalto 9 leyendas Mod Apk: Una guía para los aficionados a las carreras
-Si eres un fan de los juegos de carreras, es posible que hayas oído hablar de Asphalt Nitro 9 Legends, uno de los juegos más populares y emocionantes del género. Este juego le permite tomar el volante de los coches reales de gama alta de renombrados fabricantes de automóviles legendarios, como Ferrari, Porsche, Lamborghini, y W Motors, entre muchas otras marcas internacionales. Puedes conducir, impulsar y realizar acrobacias en ubicaciones dinámicas de la vida real en modo individual o multijugador.
-Pero ¿y si quieres disfrutar del juego sin limitaciones ni restricciones? ¿Qué pasa si quieres tener dinero ilimitado y fichas, desbloquear todos los coches y pistas, personalizar sus vehículos y actualizarlos, y competir con otros jugadores en línea o fuera de línea? Bueno, hay una manera de hacerlo. Puede descargar e instalar Asphalt Nitro 9 Leyendas Mod Apk, una versión modificada del juego que le da acceso a todas estas características y más.
-asfalto nitro 9 leyendas mod apk
Download File ===== https://bltlly.com/2v6LmQ
-En este artículo, le guiará a través de las características, proceso de descarga, pasos de instalación, consejos y trucos, pros y contras, y la conclusión de Asphalt Nitro 9 Leyendas Mod Apk. Sigue leyendo para saber más.
- Características de Asphalt Nitro 9 Leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es una versión modificada del juego original que le da recursos ilimitados y características que mejoran su experiencia de juego. Estas son algunas de las características que puedes disfrutar con este mod apk:
-
-- Dinero ilimitado y fichas: Con este apk mod, nunca se quedará sin dinero o fichas para comprar nuevos coches, pistas, mejoras, o artículos. Puedes gastar todo lo que quieras sin preocuparte por tu presupuesto.
-
-- Personalizar sus vehículos y actualizarlos: Con este apk mod, puede personalizar sus vehículos de acuerdo a su preferencia. Puedes cambiar su color, pintura, calcomanías, ruedas, etc. También puedes mejorar su rendimiento mejorando su motor, transmisión, suspensión, frenos, nitro boost, etc.
-- Disfrutar de gráficos realistas y efectos de sonido: Con este apk mod, se puede disfrutar de los impresionantes gráficos y efectos de sonido del juego. El juego utiliza un motor de física realista que simula el movimiento y el comportamiento de los coches. El juego también cuenta con efectos de sonido de alta calidad que te hacen sentir como si estuvieras en una carrera real.
-- Compite con otros jugadores en línea o fuera de línea: Con este apk mod, puede competir con otros jugadores en línea o fuera de línea. Puedes unirte a carreras multijugador online y desafiar a tus amigos u otros jugadores de todo el mundo. También puedes jugar offline en modo carrera y completar varias misiones y desafíos.
-
- Cómo descargar e instalar Asphalt Nitro 9 leyendas Mod Apk
-Si desea descargar e instalar Asphalt Nitro 9 leyendas Mod Apk, es necesario seguir estos sencillos pasos:
-
-- Paso 1: Descargar el archivo apk mod de una fuente de confianza. Usted puede encontrar muchos sitios web que ofrecen el archivo apk mod para Asphalt Nitro 9 Leyendas, pero no todos ellos son seguros y fiables. Debes asegurarte de que el archivo que descargas esté libre de virus, malware o cualquier otro contenido dañino. Puede utilizar este enlace para descargar el archivo apk mod de forma segura.
-- Paso 2: Habilitar fuentes desconocidas en la configuración del dispositivo. Antes de poder instalar el archivo apk mod, debe habilitar fuentes desconocidas en la configuración de su dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a la configuración del dispositivo, luego a la seguridad, luego a fuentes desconocidas y conéctela.
-
-- Paso 4: Disfruta del juego con recursos y funciones ilimitadas. Ahora que ha instalado Asphalt Nitro 9 Leyendas Mod Apk, se puede disfrutar del juego con dinero ilimitado, fichas, coches, pistas, opciones de personalización, y más. También puedes competir con otros jugadores online o offline y divertirte.
-
- Consejos y trucos para jugar Asphalt Nitro 9 leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es un juego divertido y emocionante que le mantendrá entretenido durante horas. Sin embargo, si quieres mejorar tus habilidades y rendimiento en el juego, puedes seguir estos consejos y trucos:
-
-- Elija el coche adecuado para cada pista y modo. El juego ofrece una variedad de coches de diferentes marcas y categorías, pero no todos ellos son adecuados para cada pista o modo. Es necesario tener en cuenta la velocidad, aceleración, manejo, nitro, y otros factores de cada coche antes de elegir uno. Por ejemplo, si usted está jugando en una pista con curvas, es posible que desee elegir un coche con buen manejo y nitro. Si usted está jugando en una pista recta, es posible que desee elegir un coche con alta velocidad y aceleración.
-- Utilice nitro boost sabiamente y estratégicamente. Nitro boost es una de las características más importantes del juego, ya que puede darte una ventaja sobre tus oponentes. Sin embargo, necesitas usarlo sabiamente y estratégicamente. No debes usarlo todo de una vez ni desperdiciarlo en momentos innecesarios. Debes guardarlo para momentos cruciales, como adelantar a tus rivales, escapar de obstáculos o colisiones, o llegar a la meta. También debe usarlo en combinación con acrobacias y derivas para ganar más puntos y recompensas.
-
-- Evitar obstáculos y colisiones con otros coches. El juego cuenta con muchos obstáculos y desafíos que pueden obstaculizar su progreso o dañar su coche. Estos incluyen coches de tráfico, coches de policía, helicópteros, barricadas, picos, etc. Usted debe evitar estos obstáculos tanto como sea posible, ya que pueden ralentizar o dañar su coche. También debes evitar colisiones con otros coches, especialmente tus rivales, ya que pueden sacarte de la pista o hacerte perder tu posición. Usted debe tratar de esquivar o escapar de ellos, o utilizar su impulso nitro para alejarse de ellos.
-- Utilice diferentes ángulos de cámara y controles para adaptarse a sus preferencias. El juego ofrece diferentes ángulos de cámara y controles que puedes usar para jugar el juego. Puede elegir entre vista en primera persona, en tercera persona o de arriba hacia abajo, según su preferencia. También puede elegir entre los controles de inclinación, toque o toque, dependiendo de su comodidad. Puede cambiar estos ajustes en el menú de opciones del juego.
-
- Pros y contras de Asphalt Nitro 9 Leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es un gran juego que ofrece muchos beneficios y ventajas para los aficionados a las carreras. Sin embargo, también tiene algunos inconvenientes y desventajas que usted debe tener en cuenta. Estos son algunos de los pros y los contras de Asphalt Nitro 9 Leyendas Mod Apk:
-
-
-Pros
-Contras
-
-
-
-- Divertido, adictivo y desafiante juego
-- Dinero ilimitado y tokens
-- Desbloquear todos los coches y pistas
-- Personalizar sus vehículos y actualizarlos
-- Disfruta de gráficos realistas y efectos de sonido
-- Compite con otros jugadores online o offline
-
-- Puede causar retraso o estrellarse en algunos dispositivos
-- Puede no ser compatible con algunos dispositivos o versiones
-- No se puede actualizar regularmente o con frecuencia
-- No puede ser soportado por los desarrolladores o editores oficiales
-
-- Puede exponer su dispositivo a riesgos o amenazas de seguridad
-
-
- Conclusión
-En conclusión, Asfalto Nitro 9 Leyendas Mod Apk es un juego fantástico que le dará horas de diversión y emoción. Es una versión modificada del juego original que le da recursos ilimitados y características que mejoran su experiencia de juego. Puede descargarlo e instalarlo de forma fácil y segura siguiendo los pasos que proporcionamos en este artículo. También puedes seguir los consejos y trucos que compartimos para mejorar tus habilidades y rendimiento en el juego. Sin embargo, también debe ser consciente de los pros y los contras de Asphalt Nitro 9 Leyendas Mod Apk, y decidir si vale la pena jugar o no.
-Si usted es un fanático de las carreras, le recomendamos que pruebe Asphalt Nitro 9 Leyendas Mod Apk y ver por sí mismo lo increíble que es. No te arrepentirás.
- Preguntas frecuentes (preguntas frecuentes)
-Aquí están algunas de las preguntas y respuestas más comunes sobre Asphalt Nitro 9 Legends Mod Apk:
-
- Q: ¿Es libre de Asphalt Nitro 9 Leyendas Mod Apk?
-A: Sí, Asphalt Nitro 9 Leyendas Mod Apk es gratis para descargar y jugar. Usted no necesita pagar nada para disfrutar del juego.
- Q: ¿Es seguro Asphalt Nitro 9 Legends Mod Apk?
-A: Sí, Asfalto Nitro 9 Leyendas Mod Apk es seguro para descargar e instalar. Sin embargo, siempre debe descargarlo de una fuente de confianza y habilitar fuentes desconocidas en la configuración del dispositivo antes de instalarlo.
- Q: ¿Es legal Asphalt Nitro 9 Legends Mod Apk?
-A: No, Asphalt Nitro 9 Leyendas Mod Apk no es legal. Es una versión modificada del juego original que viola los términos y condiciones de los desarrolladores y editores oficiales. También puede infringir los derechos de propiedad intelectual del juego original.
- Q: Es Asphalt Nitro 9 Leyendas Mod Apk en línea o fuera de línea?
-
- Q: ¿Cómo puedo actualizar Asphalt Nitro 9 Legends Mod Apk?
-A: Para actualizar Asphalt Nitro 9 leyendas Mod Apk, es necesario descargar e instalar la última versión del archivo apk mod de una fuente de confianza. Es posible que tenga que desinstalar la versión anterior del archivo apk mod antes de instalar el nuevo.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md b/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md
deleted file mode 100644
index d872bc888664198a5fb77f20f85945d9c05ae2ef..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md
+++ /dev/null
@@ -1,76 +0,0 @@
-
-Camioneros de Europa 3 APK 36.6: Un juego de simulador de camiones realista y divertido
-Si eres un fan de los juegos de simuladores de camiones, es posible que hayas oído hablar de Truckers of Europe 3, un juego popular que te permite experimentar la vida de un conductor de camiones en Europa. En este artículo, te contaremos todo lo que necesitas saber sobre este juego, incluyendo qué es, cuáles son sus características, cómo descargarlo e instalarlo, cómo jugarlo y por qué deberías jugarlo. ¡Así que abróchate el cinturón y prepárate para un emocionante viaje!
-Introducción
-¿Qué es Camioneros de Europa 3?
-Truckers of Europe 3 es un juego de simulador de camioneros desarrollado por Jerryisgaming, un canal de YouTube que crea videos de juegos. El juego fue lanzado en 2020 y ha sido actualizado regularmente con nuevas características y mejoras. La última versión del juego es 0.36.6, que fue lanzado el 30 de septiembre de 2021.
-camioneros de europa 3 apk 36.6
Download Zip ❤❤❤ https://bltlly.com/2v6LwC
-¿Cuáles son las características de Camioneros de Europa 3?
-Truckers of Europe 3 tiene muchas características que lo convierten en uno de los mejores juegos de simuladores de camiones en el mercado. Algunas de estas características son:
-
-- Un gran mapa de Europa con más de 50 ciudades y países para explorar.
-- Una variedad de camiones y remolques para elegir, cada uno con diferentes especificaciones y rendimiento.
-- Un sistema de conducción realista con transmisión manual, volante, pedales, indicadores, espejos, luces, bocina, limpiaparabrisas, etc.
-- Un sistema de tráfico realista con coches, autobuses, camiones, motocicletas, policía, ambulancia, etc.
-- Un sistema meteorológico realista con ciclo de día y noche, lluvia, nieve, niebla, etc.
-- Un sistema de daños realista con desgaste de neumáticos, consumo de combustible, sobrecalentamiento del motor, etc.
-- Un modo de carrera con misiones, contratos, entrega de carga, ingresos, gastos, etc.
-- Un sistema de habilidades con niveles, puntos, ventajas, etc.
-- Un sistema de personalización con trabajos de pintura, accesorios, calcomanías, etc.
-
-
-Cómo descargar e instalar camioneros de Europa 3 APK 36.6?
-Requisitos
-Para descargar e instalar Camioneros de Europa 3 APK 36.6 en su dispositivo Android, es necesario cumplir con los siguientes requisitos:
-
-- Tu dispositivo debe tener la versión de Android 4.4 o superior.
-- Su dispositivo debe tener al menos 1 GB de RAM y 500 MB de espacio de almacenamiento libre.
-- Su dispositivo debe tener una conexión a Internet estable.
-- Su dispositivo debe permitir la instalación desde fuentes desconocidas. Puede habilitar esta opción yendo a Configuración > Seguridad > Fuentes desconocidas.
-
-Pasos
-Para descargar e instalar Camioneros de Europa 3 APK 36.6 en su dispositivo Android, debe seguir estos pasos:
-
-- Ir a este enlace y descargar el archivo APK.
-- Localice el archivo descargado en el administrador de archivos de su dispositivo y toque en él para iniciar el proceso de instalación.
-- Siga las instrucciones en la pantalla y conceda los permisos necesarios a la aplicación.
-- Espere a que la instalación se complete y luego inicie la aplicación desde el cajón de aplicaciones o la pantalla de inicio.
-- Disfruta jugando Camioneros de Europa 3 en tu dispositivo Android!
-
-Cómo jugar Camioneros de Europa 3?
-Elige tu camión y remolque
-Lo primero que tienes que hacer cuando empiezas a jugar Truckers of Europe 3 es elegir tu camión y remolque. Puede hacer esto yendo al menú del garaje y navegando a través de las opciones disponibles. También puede comprar nuevos camiones y remolques con el dinero que gana de sus misiones. Cada camión y remolque tiene diferentes atributos, como velocidad, potencia, capacidad de combustible, peso de carga, etc. Debe elegir el que se adapte a sus preferencias y necesidades.
-Conduce por toda Europa y entrega carga
-
-Mientras conduce, tendrá que seguir las reglas de tráfico y regulaciones, tales como límites de velocidad, semáforos, señales, etc. También tendrá que lidiar con condiciones de tráfico realistas, como automóviles, autobuses, camiones, motocicletas, policía, ambulancia, etc. También tendrá que enfrentarse a condiciones meteorológicas realistas, como lluvia, nieve, niebla, etc. También tendrá que controlar el estado de su camión, como el nivel de combustible, la temperatura del motor, la presión de los neumáticos, etc. También tendrá que prestar atención al estado de su conductor, como el nivel de fatiga, el nivel de hambre, etc. Tendrá que parar en gasolineras, áreas de descanso, restaurantes, etc. para llenar su tanque de combustible, descansar a su conductor, comer algo de comida, etc.
-Cuando llegue a su destino, tendrá que aparcar su camión y remolque en el lugar designado y descargar la carga. A continuación, recibirá su recompensa y comentarios sobre su rendimiento. También ganarás puntos de experiencia que te ayudarán a subir de nivel y desbloquear nuevas habilidades y beneficios.
-Personaliza tu camión y mejora tus habilidades
-Otro aspecto divertido de Truckers of Europe 3 es que puedes personalizar tu camión y mejorar tus habilidades. Puedes hacer esto yendo al menú del taller y gastando algo de dinero en varios artículos y servicios. Puede cambiar el color de su camión, agregar algunos accesorios, aplicar algunas calcomanías, etc. También puede reparar su camión si está dañado o afinarlo si no funciona bien.
-También puedes ir al menú de habilidades y gastar algunos puntos en varias habilidades y beneficios que mejorarán tus habilidades y beneficios como conductor de camión. Puede mejorar su eficiencia de combustible, manejo de carga, seguridad de conducción, habilidades de negociación, etc. También puede desbloquear nuevos tipos de carga, contratos, camiones, remolques, etc.
-
-¿Por qué deberías jugar Camioneros de Europa 3?
-Gráficos realistas y física
-
-Tráfico y clima diversos y dinámicos
-Otra razón por la que deberías jugar a Truckers of Europe 3 es porque tiene un tráfico diverso y dinámico y el clima hará que tu experiencia de conducción sea más desafiante y agradable. El juego tiene un gran mapa de Europa con más de 50 ciudades y países para explorar. Cada ciudad y país tiene sus propias características únicas, tales como monumentos, edificios, carreteras, señales, etc. El juego también tiene un sistema de tráfico diverso que incluye automóviles, autobuses, camiones, motocicletas, policía, ambulancia, etc. Cada vehículo tiene su propio comportamiento, velocidad, dirección, etc. El juego también tiene un sistema de tiempo dinámico que incluye ciclo de día y noche, lluvia, nieve, niebla, etc. Cada condición climática tiene su propio efecto en la visibilidad, tracción, manejo, etc. del camión y el remolque.
-Misiones y logros desafiantes y gratificantes
-Otra razón por la que deberías jugar a Truckers of Europe 3 es porque tiene misiones desafiantes y gratificantes y logros que te mantendrán motivado y entretenido. El juego tiene un modo de carrera que le permite comenzar como un conductor de camión novato y trabajar su camino hasta convertirse en un camionero profesional. Puede hacerlo completando varias misiones y contratos que implican la entrega de carga a diferentes destinos en toda Europa. También puede ganar dinero de sus entregas y gastarlo en la compra de nuevos camiones y remolques o la personalización de los existentes. También puede ganar puntos de experiencia de sus entregas y gastarlos en mejorar sus habilidades y beneficios. También puedes desbloquear nuevos tipos de carga, contratos, camiones, remolques, etc. a medida que avanzas en el juego.
-
-Conclusión
-Truckers of Europe 3 es un juego de simulador de camiones realista y divertido que te permite experimentar la vida de un conductor de camiones en Europa. Puede elegir su camión y remolque, conducir por toda Europa y entregar carga, personalizar su camión y mejorar sus habilidades, y ganar dinero y logros. El juego tiene gráficos y física realistas, tráfico y clima diversos y dinámicos, y misiones y logros desafiantes y gratificantes. Si usted está buscando un juego de simulador de camiones que le mantendrá enganchado durante horas, definitivamente debe probar Camioneros de Europa 3 APK 36.6.
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre los camioneros de Europa 3:
-
-- Q: ¿Los camioneros de Europa 3 son libres de jugar?
-- A: Sí, Truckers of Europe 3 es gratis. Puedes descargarlo e instalarlo desde el enlace proporcionado en este artículo. Sin embargo, el juego puede contener algunas compras en la aplicación que pueden mejorar su experiencia de juego.
-- Q: ¿Es seguro descargar e instalar Truckers of Europe 3?
-- A: Sí, Truckers of Europe 3 es seguro para descargar e instalar. El archivo APK proporcionado en este artículo es verificado y probado por nuestro equipo. Sin embargo, siempre debe descargar e instalar archivos APK de fuentes confiables solamente.
-- Q: ¿Es Truckers of Europe 3 compatible con mi dispositivo?
-- A: Camioneros de Europa 3 es compatible con la mayoría de los dispositivos Android que tienen la versión Android 4.4 o superior. Sin embargo, algunos dispositivos pueden no ser compatibles con el juego debido a limitaciones de hardware o software.
-- P: ¿Cómo puedo contactar al desarrollador de Truckers of Europe 3?
-- A: Puedes contactar al desarrollador de Truckers of Europe 3 visitando su canal de YouTube Jerryisgaming o su página de Facebook Jerryisgaming. También puedes dejar tus comentarios, sugerencias o consultas en la sección de comentarios de sus videos o publicaciones.
-- P: ¿Cómo puedo apoyar el desarrollo de Camioneros de Europa 3?
-
-
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md b/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md
deleted file mode 100644
index 516d398df61b62811ac5d43e8205d838c0714cbc..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md
+++ /dev/null
@@ -1,80 +0,0 @@
-
-Cómo descargar Genshin Impact Cloud y disfrutar del juego en cualquier lugar
-Genshin Impact es uno de los juegos más populares y exitosos de 2020, con millones de jugadores en todo el mundo. El juego es un RPG de acción de mundo abierto que te permite explorar un mundo vasto y hermoso llamado Teyvat, donde puedes conocer a varios personajes, luchar contra enemigos, reunir recursos y completar misiones. El juego está disponible en múltiples plataformas, como PC, PlayStation 4, PlayStation 5, iOS y Android. Sin embargo, si quieres jugar el juego en tu dispositivo móvil, puedes enfrentarte a algunos desafíos, como espacio de almacenamiento limitado, bajo rendimiento o dispositivos incompatibles. Ahí es donde entra Genshin Impact Cloud.
-¿Qué es la nube de impacto Genshin y por qué debe probarlo
-Genshin Impact Cloud es un servicio que te permite jugar el juego en tu dispositivo móvil sin descargar el juego entero
-Genshin Impact Cloud es una nueva característica que fue introducida por miHoYo, el desarrollador de Genshin Impact, en abril de 2021. Es un servicio de juegos en la nube que le permite transmitir el juego desde un servidor a su dispositivo móvil a través de Internet. Esto significa que no necesitas descargar o instalar el juego en tu dispositivo, lo que puede ahorrarte mucho espacio de almacenamiento. El tamaño del juego de Genshin Impact en dispositivos móviles es de alrededor de 5,2 GB, pero con Genshin Impact Cloud, solo necesitas descargar una aplicación de 56 MB de tamaño. Esto también puede ayudarle a evitar largos tiempos de carga o actualizaciones.
-descargar estrellas pelea hack ios
Download ===> https://bltlly.com/2v6KEj
-Genshin Impact Cloud tiene muchos beneficios, como ahorrar espacio de almacenamiento, mejorar el rendimiento y admitir cross-save
-Además de ahorrar espacio de almacenamiento, Genshin Impact Cloud también tiene otras ventajas que pueden mejorar su experiencia de juego. Por ejemplo:
-
-
-- Genshin Impact Cloud soporta cross-save y cross-play. Esto significa que puedes acceder a tu progreso y datos existentes desde otras plataformas iniciando sesión con tu cuenta miHoYo. También puedes jugar con tus amigos que usan diferentes dispositivos, como PC o PlayStation.
-- Genshin Impact Cloud es de uso gratuito. No es necesario pagar tarifas o suscripciones adicionales para utilizar el servicio. También puede hacer compras en la aplicación como de costumbre, y disfrutar de todas las actualizaciones y eventos del juego.
-
-Con Genshin Impact Cloud, puede disfrutar del juego en cualquier lugar y en cualquier momento, siempre y cuando tenga una conexión a Internet estable y un dispositivo compatible.
-Cómo descargar Genshin Impact Cloud en dispositivos Android
-Genshin Impact Cloud está disponible actualmente solo para usuarios de Android en Malasia y Singapur como prueba beta
-Genshin Impact Cloud todavía está en sus primeras etapas de desarrollo, y todavía no está disponible para todas las regiones y plataformas. Actualmente, el servicio solo está abierto para los usuarios de Android en Malasia y Singapur como una prueba beta. Esto significa que solo un número limitado de jugadores puede acceder al servicio, y puede haber algunos errores o errores durante el juego. miHoYo no ha anunciado cuándo se ampliará el servicio a otras regiones y plataformas, pero es probable que lo hagan en el futuro.
-Para descargar Genshin Impact Cloud, necesita tener una cuenta miHoYo y registrarse para la prueba beta en el sitio web oficial
-Si eres un usuario de Android en Malasia o Singapur, y quieres probar Genshin Impact Cloud, debes seguir estos pasos:
-
-- Crea una cuenta miHoYo si aún no tienes una. Puede hacer esto visitando https://account.mihoyo.com/#/register y llenando la información requerida.
-- Visite None:
- """
- Raise an option parsing error using parser.error().
-
- Args:
- parser: an OptionParser instance.
- option: an Option instance.
- msg: the error text.
- """
- msg = f"{option} error: {msg}"
- msg = textwrap.fill(" ".join(msg.split()))
- parser.error(msg)
-
-
-def make_option_group(group: Dict[str, Any], parser: ConfigOptionParser) -> OptionGroup:
- """
- Return an OptionGroup object
- group -- assumed to be dict with 'name' and 'options' keys
- parser -- an optparse Parser
- """
- option_group = OptionGroup(parser, group["name"])
- for option in group["options"]:
- option_group.add_option(option())
- return option_group
-
-
-def check_dist_restriction(options: Values, check_target: bool = False) -> None:
- """Function for determining if custom platform options are allowed.
-
- :param options: The OptionParser options.
- :param check_target: Whether or not to check if --target is being used.
- """
- dist_restriction_set = any(
- [
- options.python_version,
- options.platforms,
- options.abis,
- options.implementation,
- ]
- )
-
- binary_only = FormatControl(set(), {":all:"})
- sdist_dependencies_allowed = (
- options.format_control != binary_only and not options.ignore_dependencies
- )
-
- # Installations or downloads using dist restrictions must not combine
- # source distributions and dist-specific wheels, as they are not
- # guaranteed to be locally compatible.
- if dist_restriction_set and sdist_dependencies_allowed:
- raise CommandError(
- "When restricting platform and interpreter constraints using "
- "--python-version, --platform, --abi, or --implementation, "
- "either --no-deps must be set, or --only-binary=:all: must be "
- "set and --no-binary must not be set (or must be set to "
- ":none:)."
- )
-
- if check_target:
- if dist_restriction_set and not options.target_dir:
- raise CommandError(
- "Can not use any platform or abi specific options unless "
- "installing via '--target'"
- )
-
-
-def _path_option_check(option: Option, opt: str, value: str) -> str:
- return os.path.expanduser(value)
-
-
-def _package_name_option_check(option: Option, opt: str, value: str) -> str:
- return canonicalize_name(value)
-
-
-class PipOption(Option):
- TYPES = Option.TYPES + ("path", "package_name")
- TYPE_CHECKER = Option.TYPE_CHECKER.copy()
- TYPE_CHECKER["package_name"] = _package_name_option_check
- TYPE_CHECKER["path"] = _path_option_check
-
-
-###########
-# options #
-###########
-
-help_: Callable[..., Option] = partial(
- Option,
- "-h",
- "--help",
- dest="help",
- action="help",
- help="Show help.",
-)
-
-debug_mode: Callable[..., Option] = partial(
- Option,
- "--debug",
- dest="debug_mode",
- action="store_true",
- default=False,
- help=(
- "Let unhandled exceptions propagate outside the main subroutine, "
- "instead of logging them to stderr."
- ),
-)
-
-isolated_mode: Callable[..., Option] = partial(
- Option,
- "--isolated",
- dest="isolated_mode",
- action="store_true",
- default=False,
- help=(
- "Run pip in an isolated mode, ignoring environment variables and user "
- "configuration."
- ),
-)
-
-require_virtualenv: Callable[..., Option] = partial(
- Option,
- "--require-virtualenv",
- "--require-venv",
- dest="require_venv",
- action="store_true",
- default=False,
- help=(
- "Allow pip to only run in a virtual environment; "
- "exit with an error otherwise."
- ),
-)
-
-override_externally_managed: Callable[..., Option] = partial(
- Option,
- "--break-system-packages",
- dest="override_externally_managed",
- action="store_true",
- help="Allow pip to modify an EXTERNALLY-MANAGED Python installation",
-)
-
-python: Callable[..., Option] = partial(
- Option,
- "--python",
- dest="python",
- help="Run pip with the specified Python interpreter.",
-)
-
-verbose: Callable[..., Option] = partial(
- Option,
- "-v",
- "--verbose",
- dest="verbose",
- action="count",
- default=0,
- help="Give more output. Option is additive, and can be used up to 3 times.",
-)
-
-no_color: Callable[..., Option] = partial(
- Option,
- "--no-color",
- dest="no_color",
- action="store_true",
- default=False,
- help="Suppress colored output.",
-)
-
-version: Callable[..., Option] = partial(
- Option,
- "-V",
- "--version",
- dest="version",
- action="store_true",
- help="Show version and exit.",
-)
-
-quiet: Callable[..., Option] = partial(
- Option,
- "-q",
- "--quiet",
- dest="quiet",
- action="count",
- default=0,
- help=(
- "Give less output. Option is additive, and can be used up to 3"
- " times (corresponding to WARNING, ERROR, and CRITICAL logging"
- " levels)."
- ),
-)
-
-progress_bar: Callable[..., Option] = partial(
- Option,
- "--progress-bar",
- dest="progress_bar",
- type="choice",
- choices=["on", "off"],
- default="on",
- help="Specify whether the progress bar should be used [on, off] (default: on)",
-)
-
-log: Callable[..., Option] = partial(
- PipOption,
- "--log",
- "--log-file",
- "--local-log",
- dest="log",
- metavar="path",
- type="path",
- help="Path to a verbose appending log.",
-)
-
-no_input: Callable[..., Option] = partial(
- Option,
- # Don't ask for input
- "--no-input",
- dest="no_input",
- action="store_true",
- default=False,
- help="Disable prompting for input.",
-)
-
-keyring_provider: Callable[..., Option] = partial(
- Option,
- "--keyring-provider",
- dest="keyring_provider",
- choices=["auto", "disabled", "import", "subprocess"],
- default="auto",
- help=(
- "Enable the credential lookup via the keyring library if user input is allowed."
- " Specify which mechanism to use [disabled, import, subprocess]."
- " (default: disabled)"
- ),
-)
-
-proxy: Callable[..., Option] = partial(
- Option,
- "--proxy",
- dest="proxy",
- type="str",
- default="",
- help="Specify a proxy in the form scheme://[user:passwd@]proxy.server:port.",
-)
-
-retries: Callable[..., Option] = partial(
- Option,
- "--retries",
- dest="retries",
- type="int",
- default=5,
- help="Maximum number of retries each connection should attempt "
- "(default %default times).",
-)
-
-timeout: Callable[..., Option] = partial(
- Option,
- "--timeout",
- "--default-timeout",
- metavar="sec",
- dest="timeout",
- type="float",
- default=15,
- help="Set the socket timeout (default %default seconds).",
-)
-
-
-def exists_action() -> Option:
- return Option(
- # Option when path already exist
- "--exists-action",
- dest="exists_action",
- type="choice",
- choices=["s", "i", "w", "b", "a"],
- default=[],
- action="append",
- metavar="action",
- help="Default action when a path already exists: "
- "(s)witch, (i)gnore, (w)ipe, (b)ackup, (a)bort.",
- )
-
-
-cert: Callable[..., Option] = partial(
- PipOption,
- "--cert",
- dest="cert",
- type="path",
- metavar="path",
- help=(
- "Path to PEM-encoded CA certificate bundle. "
- "If provided, overrides the default. "
- "See 'SSL Certificate Verification' in pip documentation "
- "for more information."
- ),
-)
-
-client_cert: Callable[..., Option] = partial(
- PipOption,
- "--client-cert",
- dest="client_cert",
- type="path",
- default=None,
- metavar="path",
- help="Path to SSL client certificate, a single file containing the "
- "private key and the certificate in PEM format.",
-)
-
-index_url: Callable[..., Option] = partial(
- Option,
- "-i",
- "--index-url",
- "--pypi-url",
- dest="index_url",
- metavar="URL",
- default=PyPI.simple_url,
- help="Base URL of the Python Package Index (default %default). "
- "This should point to a repository compliant with PEP 503 "
- "(the simple repository API) or a local directory laid out "
- "in the same format.",
-)
-
-
-def extra_index_url() -> Option:
- return Option(
- "--extra-index-url",
- dest="extra_index_urls",
- metavar="URL",
- action="append",
- default=[],
- help="Extra URLs of package indexes to use in addition to "
- "--index-url. Should follow the same rules as "
- "--index-url.",
- )
-
-
-no_index: Callable[..., Option] = partial(
- Option,
- "--no-index",
- dest="no_index",
- action="store_true",
- default=False,
- help="Ignore package index (only looking at --find-links URLs instead).",
-)
-
-
-def find_links() -> Option:
- return Option(
- "-f",
- "--find-links",
- dest="find_links",
- action="append",
- default=[],
- metavar="url",
- help="If a URL or path to an html file, then parse for links to "
- "archives such as sdist (.tar.gz) or wheel (.whl) files. "
- "If a local path or file:// URL that's a directory, "
- "then look for archives in the directory listing. "
- "Links to VCS project URLs are not supported.",
- )
-
-
-def trusted_host() -> Option:
- return Option(
- "--trusted-host",
- dest="trusted_hosts",
- action="append",
- metavar="HOSTNAME",
- default=[],
- help="Mark this host or host:port pair as trusted, even though it "
- "does not have valid or any HTTPS.",
- )
-
-
-def constraints() -> Option:
- return Option(
- "-c",
- "--constraint",
- dest="constraints",
- action="append",
- default=[],
- metavar="file",
- help="Constrain versions using the given constraints file. "
- "This option can be used multiple times.",
- )
-
-
-def requirements() -> Option:
- return Option(
- "-r",
- "--requirement",
- dest="requirements",
- action="append",
- default=[],
- metavar="file",
- help="Install from the given requirements file. "
- "This option can be used multiple times.",
- )
-
-
-def editable() -> Option:
- return Option(
- "-e",
- "--editable",
- dest="editables",
- action="append",
- default=[],
- metavar="path/url",
- help=(
- "Install a project in editable mode (i.e. setuptools "
- '"develop mode") from a local project path or a VCS url.'
- ),
- )
-
-
-def _handle_src(option: Option, opt_str: str, value: str, parser: OptionParser) -> None:
- value = os.path.abspath(value)
- setattr(parser.values, option.dest, value)
-
-
-src: Callable[..., Option] = partial(
- PipOption,
- "--src",
- "--source",
- "--source-dir",
- "--source-directory",
- dest="src_dir",
- type="path",
- metavar="dir",
- default=get_src_prefix(),
- action="callback",
- callback=_handle_src,
- help="Directory to check out editable projects into. "
- 'The default in a virtualenv is "
/src". '
- 'The default for global installs is "/src".',
-)
-
-
-def _get_format_control(values: Values, option: Option) -> Any:
- """Get a format_control object."""
- return getattr(values, option.dest)
-
-
-def _handle_no_binary(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- existing = _get_format_control(parser.values, option)
- FormatControl.handle_mutual_excludes(
- value,
- existing.no_binary,
- existing.only_binary,
- )
-
-
-def _handle_only_binary(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- existing = _get_format_control(parser.values, option)
- FormatControl.handle_mutual_excludes(
- value,
- existing.only_binary,
- existing.no_binary,
- )
-
-
-def no_binary() -> Option:
- format_control = FormatControl(set(), set())
- return Option(
- "--no-binary",
- dest="format_control",
- action="callback",
- callback=_handle_no_binary,
- type="str",
- default=format_control,
- help="Do not use binary packages. Can be supplied multiple times, and "
- 'each time adds to the existing value. Accepts either ":all:" to '
- 'disable all binary packages, ":none:" to empty the set (notice '
- "the colons), or one or more package names with commas between "
- "them (no colons). Note that some packages are tricky to compile "
- "and may fail to install when this option is used on them.",
- )
-
-
-def only_binary() -> Option:
- format_control = FormatControl(set(), set())
- return Option(
- "--only-binary",
- dest="format_control",
- action="callback",
- callback=_handle_only_binary,
- type="str",
- default=format_control,
- help="Do not use source packages. Can be supplied multiple times, and "
- 'each time adds to the existing value. Accepts either ":all:" to '
- 'disable all source packages, ":none:" to empty the set, or one '
- "or more package names with commas between them. Packages "
- "without binary distributions will fail to install when this "
- "option is used on them.",
- )
-
-
-platforms: Callable[..., Option] = partial(
- Option,
- "--platform",
- dest="platforms",
- metavar="platform",
- action="append",
- default=None,
- help=(
- "Only use wheels compatible with . Defaults to the "
- "platform of the running system. Use this option multiple times to "
- "specify multiple platforms supported by the target interpreter."
- ),
-)
-
-
-# This was made a separate function for unit-testing purposes.
-def _convert_python_version(value: str) -> Tuple[Tuple[int, ...], Optional[str]]:
- """
- Convert a version string like "3", "37", or "3.7.3" into a tuple of ints.
-
- :return: A 2-tuple (version_info, error_msg), where `error_msg` is
- non-None if and only if there was a parsing error.
- """
- if not value:
- # The empty string is the same as not providing a value.
- return (None, None)
-
- parts = value.split(".")
- if len(parts) > 3:
- return ((), "at most three version parts are allowed")
-
- if len(parts) == 1:
- # Then we are in the case of "3" or "37".
- value = parts[0]
- if len(value) > 1:
- parts = [value[0], value[1:]]
-
- try:
- version_info = tuple(int(part) for part in parts)
- except ValueError:
- return ((), "each version part must be an integer")
-
- return (version_info, None)
-
-
-def _handle_python_version(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- """
- Handle a provided --python-version value.
- """
- version_info, error_msg = _convert_python_version(value)
- if error_msg is not None:
- msg = "invalid --python-version value: {!r}: {}".format(
- value,
- error_msg,
- )
- raise_option_error(parser, option=option, msg=msg)
-
- parser.values.python_version = version_info
-
-
-python_version: Callable[..., Option] = partial(
- Option,
- "--python-version",
- dest="python_version",
- metavar="python_version",
- action="callback",
- callback=_handle_python_version,
- type="str",
- default=None,
- help=dedent(
- """\
- The Python interpreter version to use for wheel and "Requires-Python"
- compatibility checks. Defaults to a version derived from the running
- interpreter. The version can be specified using up to three dot-separated
- integers (e.g. "3" for 3.0.0, "3.7" for 3.7.0, or "3.7.3"). A major-minor
- version can also be given as a string without dots (e.g. "37" for 3.7.0).
- """
- ),
-)
-
-
-implementation: Callable[..., Option] = partial(
- Option,
- "--implementation",
- dest="implementation",
- metavar="implementation",
- default=None,
- help=(
- "Only use wheels compatible with Python "
- "implementation , e.g. 'pp', 'jy', 'cp', "
- " or 'ip'. If not specified, then the current "
- "interpreter implementation is used. Use 'py' to force "
- "implementation-agnostic wheels."
- ),
-)
-
-
-abis: Callable[..., Option] = partial(
- Option,
- "--abi",
- dest="abis",
- metavar="abi",
- action="append",
- default=None,
- help=(
- "Only use wheels compatible with Python abi , e.g. 'pypy_41'. "
- "If not specified, then the current interpreter abi tag is used. "
- "Use this option multiple times to specify multiple abis supported "
- "by the target interpreter. Generally you will need to specify "
- "--implementation, --platform, and --python-version when using this "
- "option."
- ),
-)
-
-
-def add_target_python_options(cmd_opts: OptionGroup) -> None:
- cmd_opts.add_option(platforms())
- cmd_opts.add_option(python_version())
- cmd_opts.add_option(implementation())
- cmd_opts.add_option(abis())
-
-
-def make_target_python(options: Values) -> TargetPython:
- target_python = TargetPython(
- platforms=options.platforms,
- py_version_info=options.python_version,
- abis=options.abis,
- implementation=options.implementation,
- )
-
- return target_python
-
-
-def prefer_binary() -> Option:
- return Option(
- "--prefer-binary",
- dest="prefer_binary",
- action="store_true",
- default=False,
- help="Prefer older binary packages over newer source packages.",
- )
-
-
-cache_dir: Callable[..., Option] = partial(
- PipOption,
- "--cache-dir",
- dest="cache_dir",
- default=USER_CACHE_DIR,
- metavar="dir",
- type="path",
- help="Store the cache data in .",
-)
-
-
-def _handle_no_cache_dir(
- option: Option, opt: str, value: str, parser: OptionParser
-) -> None:
- """
- Process a value provided for the --no-cache-dir option.
-
- This is an optparse.Option callback for the --no-cache-dir option.
- """
- # The value argument will be None if --no-cache-dir is passed via the
- # command-line, since the option doesn't accept arguments. However,
- # the value can be non-None if the option is triggered e.g. by an
- # environment variable, like PIP_NO_CACHE_DIR=true.
- if value is not None:
- # Then parse the string value to get argument error-checking.
- try:
- strtobool(value)
- except ValueError as exc:
- raise_option_error(parser, option=option, msg=str(exc))
-
- # Originally, setting PIP_NO_CACHE_DIR to a value that strtobool()
- # converted to 0 (like "false" or "no") caused cache_dir to be disabled
- # rather than enabled (logic would say the latter). Thus, we disable
- # the cache directory not just on values that parse to True, but (for
- # backwards compatibility reasons) also on values that parse to False.
- # In other words, always set it to False if the option is provided in
- # some (valid) form.
- parser.values.cache_dir = False
-
-
-no_cache: Callable[..., Option] = partial(
- Option,
- "--no-cache-dir",
- dest="cache_dir",
- action="callback",
- callback=_handle_no_cache_dir,
- help="Disable the cache.",
-)
-
-no_deps: Callable[..., Option] = partial(
- Option,
- "--no-deps",
- "--no-dependencies",
- dest="ignore_dependencies",
- action="store_true",
- default=False,
- help="Don't install package dependencies.",
-)
-
-ignore_requires_python: Callable[..., Option] = partial(
- Option,
- "--ignore-requires-python",
- dest="ignore_requires_python",
- action="store_true",
- help="Ignore the Requires-Python information.",
-)
-
-no_build_isolation: Callable[..., Option] = partial(
- Option,
- "--no-build-isolation",
- dest="build_isolation",
- action="store_false",
- default=True,
- help="Disable isolation when building a modern source distribution. "
- "Build dependencies specified by PEP 518 must be already installed "
- "if this option is used.",
-)
-
-check_build_deps: Callable[..., Option] = partial(
- Option,
- "--check-build-dependencies",
- dest="check_build_deps",
- action="store_true",
- default=False,
- help="Check the build dependencies when PEP517 is used.",
-)
-
-
-def _handle_no_use_pep517(
- option: Option, opt: str, value: str, parser: OptionParser
-) -> None:
- """
- Process a value provided for the --no-use-pep517 option.
-
- This is an optparse.Option callback for the no_use_pep517 option.
- """
- # Since --no-use-pep517 doesn't accept arguments, the value argument
- # will be None if --no-use-pep517 is passed via the command-line.
- # However, the value can be non-None if the option is triggered e.g.
- # by an environment variable, for example "PIP_NO_USE_PEP517=true".
- if value is not None:
- msg = """A value was passed for --no-use-pep517,
- probably using either the PIP_NO_USE_PEP517 environment variable
- or the "no-use-pep517" config file option. Use an appropriate value
- of the PIP_USE_PEP517 environment variable or the "use-pep517"
- config file option instead.
- """
- raise_option_error(parser, option=option, msg=msg)
-
- # If user doesn't wish to use pep517, we check if setuptools and wheel are installed
- # and raise error if it is not.
- packages = ("setuptools", "wheel")
- if not all(importlib.util.find_spec(package) for package in packages):
- msg = (
- f"It is not possible to use --no-use-pep517 "
- f"without {' and '.join(packages)} installed."
- )
- raise_option_error(parser, option=option, msg=msg)
-
- # Otherwise, --no-use-pep517 was passed via the command-line.
- parser.values.use_pep517 = False
-
-
-use_pep517: Any = partial(
- Option,
- "--use-pep517",
- dest="use_pep517",
- action="store_true",
- default=None,
- help="Use PEP 517 for building source distributions "
- "(use --no-use-pep517 to force legacy behaviour).",
-)
-
-no_use_pep517: Any = partial(
- Option,
- "--no-use-pep517",
- dest="use_pep517",
- action="callback",
- callback=_handle_no_use_pep517,
- default=None,
- help=SUPPRESS_HELP,
-)
-
-
-def _handle_config_settings(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- key, sep, val = value.partition("=")
- if sep != "=":
- parser.error(f"Arguments to {opt_str} must be of the form KEY=VAL") # noqa
- dest = getattr(parser.values, option.dest)
- if dest is None:
- dest = {}
- setattr(parser.values, option.dest, dest)
- if key in dest:
- if isinstance(dest[key], list):
- dest[key].append(val)
- else:
- dest[key] = [dest[key], val]
- else:
- dest[key] = val
-
-
-config_settings: Callable[..., Option] = partial(
- Option,
- "-C",
- "--config-settings",
- dest="config_settings",
- type=str,
- action="callback",
- callback=_handle_config_settings,
- metavar="settings",
- help="Configuration settings to be passed to the PEP 517 build backend. "
- "Settings take the form KEY=VALUE. Use multiple --config-settings options "
- "to pass multiple keys to the backend.",
-)
-
-build_options: Callable[..., Option] = partial(
- Option,
- "--build-option",
- dest="build_options",
- metavar="options",
- action="append",
- help="Extra arguments to be supplied to 'setup.py bdist_wheel'.",
-)
-
-global_options: Callable[..., Option] = partial(
- Option,
- "--global-option",
- dest="global_options",
- action="append",
- metavar="options",
- help="Extra global options to be supplied to the setup.py "
- "call before the install or bdist_wheel command.",
-)
-
-no_clean: Callable[..., Option] = partial(
- Option,
- "--no-clean",
- action="store_true",
- default=False,
- help="Don't clean up build directories.",
-)
-
-pre: Callable[..., Option] = partial(
- Option,
- "--pre",
- action="store_true",
- default=False,
- help="Include pre-release and development versions. By default, "
- "pip only finds stable versions.",
-)
-
-disable_pip_version_check: Callable[..., Option] = partial(
- Option,
- "--disable-pip-version-check",
- dest="disable_pip_version_check",
- action="store_true",
- default=False,
- help="Don't periodically check PyPI to determine whether a new version "
- "of pip is available for download. Implied with --no-index.",
-)
-
-root_user_action: Callable[..., Option] = partial(
- Option,
- "--root-user-action",
- dest="root_user_action",
- default="warn",
- choices=["warn", "ignore"],
- help="Action if pip is run as a root user. By default, a warning message is shown.",
-)
-
-
-def _handle_merge_hash(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- """Given a value spelled "algo:digest", append the digest to a list
- pointed to in a dict by the algo name."""
- if not parser.values.hashes:
- parser.values.hashes = {}
- try:
- algo, digest = value.split(":", 1)
- except ValueError:
- parser.error(
- "Arguments to {} must be a hash name " # noqa
- "followed by a value, like --hash=sha256:"
- "abcde...".format(opt_str)
- )
- if algo not in STRONG_HASHES:
- parser.error(
- "Allowed hash algorithms for {} are {}.".format( # noqa
- opt_str, ", ".join(STRONG_HASHES)
- )
- )
- parser.values.hashes.setdefault(algo, []).append(digest)
-
-
-hash: Callable[..., Option] = partial(
- Option,
- "--hash",
- # Hash values eventually end up in InstallRequirement.hashes due to
- # __dict__ copying in process_line().
- dest="hashes",
- action="callback",
- callback=_handle_merge_hash,
- type="string",
- help="Verify that the package's archive matches this "
- "hash before installing. Example: --hash=sha256:abcdef...",
-)
-
-
-require_hashes: Callable[..., Option] = partial(
- Option,
- "--require-hashes",
- dest="require_hashes",
- action="store_true",
- default=False,
- help="Require a hash to check each requirement against, for "
- "repeatable installs. This option is implied when any package in a "
- "requirements file has a --hash option.",
-)
-
-
-list_path: Callable[..., Option] = partial(
- PipOption,
- "--path",
- dest="path",
- type="path",
- action="append",
- help="Restrict to the specified installation path for listing "
- "packages (can be used multiple times).",
-)
-
-
-def check_list_path_option(options: Values) -> None:
- if options.path and (options.user or options.local):
- raise CommandError("Cannot combine '--path' with '--user' or '--local'")
-
-
-list_exclude: Callable[..., Option] = partial(
- PipOption,
- "--exclude",
- dest="excludes",
- action="append",
- metavar="package",
- type="package_name",
- help="Exclude specified package from the output",
-)
-
-
-no_python_version_warning: Callable[..., Option] = partial(
- Option,
- "--no-python-version-warning",
- dest="no_python_version_warning",
- action="store_true",
- default=False,
- help="Silence deprecation warnings for upcoming unsupported Pythons.",
-)
-
-
-# Features that are now always on. A warning is printed if they are used.
-ALWAYS_ENABLED_FEATURES = [
- "no-binary-enable-wheel-cache", # always on since 23.1
-]
-
-use_new_feature: Callable[..., Option] = partial(
- Option,
- "--use-feature",
- dest="features_enabled",
- metavar="feature",
- action="append",
- default=[],
- choices=[
- "fast-deps",
- "truststore",
- ]
- + ALWAYS_ENABLED_FEATURES,
- help="Enable new functionality, that may be backward incompatible.",
-)
-
-use_deprecated_feature: Callable[..., Option] = partial(
- Option,
- "--use-deprecated",
- dest="deprecated_features_enabled",
- metavar="feature",
- action="append",
- default=[],
- choices=[
- "legacy-resolver",
- ],
- help=("Enable deprecated functionality, that will be removed in the future."),
-)
-
-
-##########
-# groups #
-##########
-
-general_group: Dict[str, Any] = {
- "name": "General Options",
- "options": [
- help_,
- debug_mode,
- isolated_mode,
- require_virtualenv,
- python,
- verbose,
- version,
- quiet,
- log,
- no_input,
- keyring_provider,
- proxy,
- retries,
- timeout,
- exists_action,
- trusted_host,
- cert,
- client_cert,
- cache_dir,
- no_cache,
- disable_pip_version_check,
- no_color,
- no_python_version_warning,
- use_new_feature,
- use_deprecated_feature,
- ],
-}
-
-index_group: Dict[str, Any] = {
- "name": "Package Index Options",
- "options": [
- index_url,
- extra_index_url,
- no_index,
- find_links,
- ],
-}
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py
deleted file mode 100644
index fd80d8c1129722b84771bd6a0f6ccfd57f5cf78e..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import re
-from ast import literal_eval
-from operator import attrgetter
-from typing import Callable, Iterable, List, Match, NamedTuple, Optional, Tuple, Union
-
-from ._emoji_replace import _emoji_replace
-from .emoji import EmojiVariant
-from .errors import MarkupError
-from .style import Style
-from .text import Span, Text
-
-RE_TAGS = re.compile(
- r"""((\\*)\[([a-z#/@][^[]*?)])""",
- re.VERBOSE,
-)
-
-RE_HANDLER = re.compile(r"^([\w.]*?)(\(.*?\))?$")
-
-
-class Tag(NamedTuple):
- """A tag in console markup."""
-
- name: str
- """The tag name. e.g. 'bold'."""
- parameters: Optional[str]
- """Any additional parameters after the name."""
-
- def __str__(self) -> str:
- return (
- self.name if self.parameters is None else f"{self.name} {self.parameters}"
- )
-
- @property
- def markup(self) -> str:
- """Get the string representation of this tag."""
- return (
- f"[{self.name}]"
- if self.parameters is None
- else f"[{self.name}={self.parameters}]"
- )
-
-
-_ReStringMatch = Match[str] # regex match object
-_ReSubCallable = Callable[[_ReStringMatch], str] # Callable invoked by re.sub
-_EscapeSubMethod = Callable[[_ReSubCallable, str], str] # Sub method of a compiled re
-
-
-def escape(
- markup: str,
- _escape: _EscapeSubMethod = re.compile(r"(\\*)(\[[a-z#/@][^[]*?])").sub,
-) -> str:
- """Escapes text so that it won't be interpreted as markup.
-
- Args:
- markup (str): Content to be inserted in to markup.
-
- Returns:
- str: Markup with square brackets escaped.
- """
-
- def escape_backslashes(match: Match[str]) -> str:
- """Called by re.sub replace matches."""
- backslashes, text = match.groups()
- return f"{backslashes}{backslashes}\\{text}"
-
- markup = _escape(escape_backslashes, markup)
- return markup
-
-
-def _parse(markup: str) -> Iterable[Tuple[int, Optional[str], Optional[Tag]]]:
- """Parse markup in to an iterable of tuples of (position, text, tag).
-
- Args:
- markup (str): A string containing console markup
-
- """
- position = 0
- _divmod = divmod
- _Tag = Tag
- for match in RE_TAGS.finditer(markup):
- full_text, escapes, tag_text = match.groups()
- start, end = match.span()
- if start > position:
- yield start, markup[position:start], None
- if escapes:
- backslashes, escaped = _divmod(len(escapes), 2)
- if backslashes:
- # Literal backslashes
- yield start, "\\" * backslashes, None
- start += backslashes * 2
- if escaped:
- # Escape of tag
- yield start, full_text[len(escapes) :], None
- position = end
- continue
- text, equals, parameters = tag_text.partition("=")
- yield start, None, _Tag(text, parameters if equals else None)
- position = end
- if position < len(markup):
- yield position, markup[position:], None
-
-
-def render(
- markup: str,
- style: Union[str, Style] = "",
- emoji: bool = True,
- emoji_variant: Optional[EmojiVariant] = None,
-) -> Text:
- """Render console markup in to a Text instance.
-
- Args:
- markup (str): A string containing console markup.
- emoji (bool, optional): Also render emoji code. Defaults to True.
-
- Raises:
- MarkupError: If there is a syntax error in the markup.
-
- Returns:
- Text: A test instance.
- """
- emoji_replace = _emoji_replace
- if "[" not in markup:
- return Text(
- emoji_replace(markup, default_variant=emoji_variant) if emoji else markup,
- style=style,
- )
- text = Text(style=style)
- append = text.append
- normalize = Style.normalize
-
- style_stack: List[Tuple[int, Tag]] = []
- pop = style_stack.pop
-
- spans: List[Span] = []
- append_span = spans.append
-
- _Span = Span
- _Tag = Tag
-
- def pop_style(style_name: str) -> Tuple[int, Tag]:
- """Pop tag matching given style name."""
- for index, (_, tag) in enumerate(reversed(style_stack), 1):
- if tag.name == style_name:
- return pop(-index)
- raise KeyError(style_name)
-
- for position, plain_text, tag in _parse(markup):
- if plain_text is not None:
- # Handle open brace escapes, where the brace is not part of a tag.
- plain_text = plain_text.replace("\\[", "[")
- append(emoji_replace(plain_text) if emoji else plain_text)
- elif tag is not None:
- if tag.name.startswith("/"): # Closing tag
- style_name = tag.name[1:].strip()
-
- if style_name: # explicit close
- style_name = normalize(style_name)
- try:
- start, open_tag = pop_style(style_name)
- except KeyError:
- raise MarkupError(
- f"closing tag '{tag.markup}' at position {position} doesn't match any open tag"
- ) from None
- else: # implicit close
- try:
- start, open_tag = pop()
- except IndexError:
- raise MarkupError(
- f"closing tag '[/]' at position {position} has nothing to close"
- ) from None
-
- if open_tag.name.startswith("@"):
- if open_tag.parameters:
- handler_name = ""
- parameters = open_tag.parameters.strip()
- handler_match = RE_HANDLER.match(parameters)
- if handler_match is not None:
- handler_name, match_parameters = handler_match.groups()
- parameters = (
- "()" if match_parameters is None else match_parameters
- )
-
- try:
- meta_params = literal_eval(parameters)
- except SyntaxError as error:
- raise MarkupError(
- f"error parsing {parameters!r} in {open_tag.parameters!r}; {error.msg}"
- )
- except Exception as error:
- raise MarkupError(
- f"error parsing {open_tag.parameters!r}; {error}"
- ) from None
-
- if handler_name:
- meta_params = (
- handler_name,
- meta_params
- if isinstance(meta_params, tuple)
- else (meta_params,),
- )
-
- else:
- meta_params = ()
-
- append_span(
- _Span(
- start, len(text), Style(meta={open_tag.name: meta_params})
- )
- )
- else:
- append_span(_Span(start, len(text), str(open_tag)))
-
- else: # Opening tag
- normalized_tag = _Tag(normalize(tag.name), tag.parameters)
- style_stack.append((len(text), normalized_tag))
-
- text_length = len(text)
- while style_stack:
- start, tag = style_stack.pop()
- style = str(tag)
- if style:
- append_span(_Span(start, text_length, style))
-
- text.spans = sorted(spans[::-1], key=attrgetter("start"))
- return text
-
-
-if __name__ == "__main__": # pragma: no cover
-
- MARKUP = [
- "[red]Hello World[/red]",
- "[magenta]Hello [b]World[/b]",
- "[bold]Bold[italic] bold and italic [/bold]italic[/italic]",
- "Click [link=https://www.willmcgugan.com]here[/link] to visit my Blog",
- ":warning-emoji: [bold red blink] DANGER![/]",
- ]
-
- from pip._vendor.rich import print
- from pip._vendor.rich.table import Table
-
- grid = Table("Markup", "Result", padding=(0, 1))
-
- for markup in MARKUP:
- grid.add_row(Text(markup), markup)
-
- print(grid)
diff --git a/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat b/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat
deleted file mode 100644
index 62a39a7584f4d7c5fbc31758e3e9e7eff700276d..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat
+++ /dev/null
@@ -1,22 +0,0 @@
-@echo off
-
-if defined _OLD_VIRTUAL_PROMPT (
- set "PROMPT=%_OLD_VIRTUAL_PROMPT%"
-)
-set _OLD_VIRTUAL_PROMPT=
-
-if defined _OLD_VIRTUAL_PYTHONHOME (
- set "PYTHONHOME=%_OLD_VIRTUAL_PYTHONHOME%"
- set _OLD_VIRTUAL_PYTHONHOME=
-)
-
-if defined _OLD_VIRTUAL_PATH (
- set "PATH=%_OLD_VIRTUAL_PATH%"
-)
-
-set _OLD_VIRTUAL_PATH=
-
-set VIRTUAL_ENV=
-set VIRTUAL_ENV_PROMPT=
-
-:END
diff --git a/spaces/CAMP-ViL/Xplainer/inference.py b/spaces/CAMP-ViL/Xplainer/inference.py
deleted file mode 100644
index 2ab6b61c2b1fa8039af58a5f5c4823b6c2f56cab..0000000000000000000000000000000000000000
--- a/spaces/CAMP-ViL/Xplainer/inference.py
+++ /dev/null
@@ -1,116 +0,0 @@
-import argparse
-import gc
-from pathlib import Path
-
-import torch
-from torch.utils.data import DataLoader
-from tqdm import tqdm
-
-from chestxray14 import ChestXray14Dataset
-from chexpert import CheXpertDataset
-from descriptors import disease_descriptors_chexpert, disease_descriptors_chestxray14
-from model import InferenceModel
-from utils import calculate_auroc
-
-torch.multiprocessing.set_sharing_strategy('file_system')
-
-
-def inference_chexpert():
- split = 'test'
- dataset = CheXpertDataset(f'data/chexpert/{split}_labels.csv') # also do test
- dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=lambda x: x, num_workers=0)
- inference_model = InferenceModel()
- all_descriptors = inference_model.get_all_descriptors(disease_descriptors_chexpert)
-
- all_labels = []
- all_probs_neg = []
-
- for batch in tqdm(dataloader):
- batch = batch[0]
- image_paths, labels, keys = batch
- image_paths = [Path(image_path) for image_path in image_paths]
- agg_probs = []
- agg_negative_probs = []
- for image_path in image_paths:
- probs, negative_probs = inference_model.get_descriptor_probs(image_path, descriptors=all_descriptors)
- agg_probs.append(probs)
- agg_negative_probs.append(negative_probs)
- probs = {} # Aggregated
- negative_probs = {} # Aggregated
- for key in agg_probs[0].keys():
- probs[key] = sum([p[key] for p in agg_probs]) / len(agg_probs) # Mean Aggregation
-
- for key in agg_negative_probs[0].keys():
- negative_probs[key] = sum([p[key] for p in agg_negative_probs]) / len(agg_negative_probs) # Mean Aggregation
-
- disease_probs, negative_disease_probs = inference_model.get_diseases_probs(disease_descriptors_chexpert, pos_probs=probs,
- negative_probs=negative_probs)
- predicted_diseases, prob_vector_neg_prompt = inference_model.get_predictions_bin_prompting(disease_descriptors_chexpert,
- disease_probs=disease_probs,
- negative_disease_probs=negative_disease_probs,
- keys=keys)
- all_labels.append(labels)
- all_probs_neg.append(prob_vector_neg_prompt)
-
- all_labels = torch.stack(all_labels)
- all_probs_neg = torch.stack(all_probs_neg)
-
- # evaluation
- existing_mask = sum(all_labels, 0) > 0
- all_labels_clean = all_labels[:, existing_mask]
- all_probs_neg_clean = all_probs_neg[:, existing_mask]
- all_keys_clean = [key for idx, key in enumerate(keys) if existing_mask[idx]]
-
- overall_auroc, per_disease_auroc = calculate_auroc(all_probs_neg_clean, all_labels_clean)
- print(f"AUROC: {overall_auroc:.5f}\n")
- for idx, key in enumerate(all_keys_clean):
- print(f'{key}: {per_disease_auroc[idx]:.5f}')
-
-
-def inference_chestxray14():
- dataset = ChestXray14Dataset(f'data/chestxray14/Data_Entry_2017_v2020_modified.csv')
- dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=lambda x: x, num_workers=1)
- inference_model = InferenceModel()
- all_descriptors = inference_model.get_all_descriptors(disease_descriptors_chestxray14)
-
- all_labels = []
- all_probs_neg = []
- for batch in tqdm(dataloader):
- batch = batch[0]
- image_path, labels, keys = batch
- image_path = Path(image_path)
- probs, negative_probs = inference_model.get_descriptor_probs(image_path, descriptors=all_descriptors)
- disease_probs, negative_disease_probs = inference_model.get_diseases_probs(disease_descriptors_chestxray14, pos_probs=probs,
- negative_probs=negative_probs)
- predicted_diseases, prob_vector_neg_prompt = inference_model.get_predictions_bin_prompting(disease_descriptors_chestxray14,
- disease_probs=disease_probs,
- negative_disease_probs=negative_disease_probs,
- keys=keys)
- all_labels.append(labels)
- all_probs_neg.append(prob_vector_neg_prompt)
- gc.collect()
-
- all_labels = torch.stack(all_labels)
- all_probs_neg = torch.stack(all_probs_neg)
-
- existing_mask = sum(all_labels, 0) > 0
- all_labels_clean = all_labels[:, existing_mask]
- all_probs_neg_clean = all_probs_neg[:, existing_mask]
- all_keys_clean = [key for idx, key in enumerate(keys) if existing_mask[idx]]
-
- overall_auroc, per_disease_auroc = calculate_auroc(all_probs_neg_clean[:, 1:], all_labels_clean[:, 1:])
- print(f"AUROC: {overall_auroc:.5f}\n")
- for idx, key in enumerate(all_keys_clean[1:]):
- print(f'{key}: {per_disease_auroc[idx]:.5f}')
-
-
-if __name__ == '__main__':
- # add argument parser
- parser = argparse.ArgumentParser()
- parser.add_argument('--dataset', type=str, default='chexpert', help='chexpert or chestxray14')
- args = parser.parse_args()
-
- if args.dataset == 'chexpert':
- inference_chexpert()
- elif args.dataset == 'chestxray14':
- inference_chestxray14()
diff --git a/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h b/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h
deleted file mode 100644
index e00d3ef054bd740b801e47cc1344e38621d8c055..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h
+++ /dev/null
@@ -1,253 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-
-namespace thrust
-{
-
-namespace detail
-{
-
-
-// adapted from http://www.boost.org/doc/libs/1_37_0/libs/iterator/doc/iterator_facade.html#iterator-category
-//
-// in our implementation, R need not be a reference type to result in a category
-// derived from forward_XXX_iterator_tag
-//
-// iterator-category(T,V,R) :=
-// if(T is convertible to input_host_iterator_tag
-// || T is convertible to output_host_iterator_tag
-// || T is convertible to input_device_iterator_tag
-// || T is convertible to output_device_iterator_tag
-// )
-// return T
-//
-// else if (T is not convertible to incrementable_traversal_tag)
-// the program is ill-formed
-//
-// else return a type X satisfying the following two constraints:
-//
-// 1. X is convertible to X1, and not to any more-derived
-// type, where X1 is defined by:
-//
-// if (T is convertible to forward_traversal_tag)
-// {
-// if (T is convertible to random_access_traversal_tag)
-// X1 = random_access_host_iterator_tag
-// else if (T is convertible to bidirectional_traversal_tag)
-// X1 = bidirectional_host_iterator_tag
-// else
-// X1 = forward_host_iterator_tag
-// }
-// else
-// {
-// if (T is convertible to single_pass_traversal_tag
-// && R is convertible to V)
-// X1 = input_host_iterator_tag
-// else
-// X1 = T
-// }
-//
-// 2. category-to-traversal(X) is convertible to the most
-// derived traversal tag type to which X is also convertible,
-// and not to any more-derived traversal tag type.
-
-
-template
- struct iterator_facade_default_category;
-
-
-// Thrust's implementation of iterator_facade_default_category is slightly
-// different from Boost's equivalent.
-// Thrust does not check is_convertible because Reference
-// may not be a complete type at this point, and implementations of is_convertible
-// typically require that both types be complete.
-// Instead, it simply assumes that if is_convertible,
-// then the category is input_iterator_tag
-
-
-// this is the function for standard system iterators
-template
- struct iterator_facade_default_category_std :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if< // XXX note we differ from Boost here
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_std
-
-
-// this is the function for host system iterators
-template
- struct iterator_facade_default_category_host :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if< // XXX note we differ from Boost here
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_host
-
-
-// this is the function for device system iterators
-template
- struct iterator_facade_default_category_device :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value, // XXX note we differ from Boost here
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_device
-
-
-// this is the function for any system iterators
-template
- struct iterator_facade_default_category_any
-{
- typedef thrust::detail::iterator_category_with_system_and_traversal<
- typename iterator_facade_default_category_std::type,
- thrust::any_system_tag,
- Traversal
- > type;
-}; // end iterator_facade_default_category_any
-
-
-template
- struct iterator_facade_default_category
- // check for any system
- : thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_any,
-
- // check for host system
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_host,
-
- // check for device system
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_device,
-
- // if we don't recognize the system, get a standard iterator category
- // and combine it with System & Traversal
- thrust::detail::identity_<
- thrust::detail::iterator_category_with_system_and_traversal<
- typename iterator_facade_default_category_std::type,
- System,
- Traversal
- >
- >
- >
- >
- >
-{};
-
-
-template
- struct iterator_facade_category_impl
-{
- typedef typename iterator_facade_default_category<
- System,Traversal,ValueParam,Reference
- >::type category;
-
- // we must be able to deduce both Traversal & System from category
- // otherwise, munge them all together
- typedef typename thrust::detail::eval_if<
- thrust::detail::and_<
- thrust::detail::is_same<
- Traversal,
- typename thrust::detail::iterator_category_to_traversal::type
- >,
- thrust::detail::is_same<
- System,
- typename thrust::detail::iterator_category_to_system::type
- >
- >::value,
- thrust::detail::identity_,
- thrust::detail::identity_ >
- >::type type;
-}; // end iterator_facade_category_impl
-
-
-template
- struct iterator_facade_category
-{
- typedef typename
- thrust::detail::eval_if<
- thrust::detail::is_iterator_category::value,
- thrust::detail::identity_, // categories are fine as-is
- iterator_facade_category_impl
- >::type type;
-}; // end iterator_facade_category
-
-
-} // end detail
-} // end thrust
-
diff --git a/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py b/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py
deleted file mode 100644
index a4f5ed44caefb1612df67785b1f4f0d9ec46ee93..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import torch.nn as nn
-from mmcv.cnn import ConvModule, xavier_init
-
-from ..builder import NECKS
-
-
-@NECKS.register_module()
-class ChannelMapper(nn.Module):
- r"""Channel Mapper to reduce/increase channels of backbone features.
-
- This is used to reduce/increase channels of backbone features.
-
- Args:
- in_channels (List[int]): Number of input channels per scale.
- out_channels (int): Number of output channels (used at each scale).
- kernel_size (int, optional): kernel_size for reducing channels (used
- at each scale). Default: 3.
- conv_cfg (dict, optional): Config dict for convolution layer.
- Default: None.
- norm_cfg (dict, optional): Config dict for normalization layer.
- Default: None.
- act_cfg (dict, optional): Config dict for activation layer in
- ConvModule. Default: dict(type='ReLU').
-
- Example:
- >>> import torch
- >>> in_channels = [2, 3, 5, 7]
- >>> scales = [340, 170, 84, 43]
- >>> inputs = [torch.rand(1, c, s, s)
- ... for c, s in zip(in_channels, scales)]
- >>> self = ChannelMapper(in_channels, 11, 3).eval()
- >>> outputs = self.forward(inputs)
- >>> for i in range(len(outputs)):
- ... print(f'outputs[{i}].shape = {outputs[i].shape}')
- outputs[0].shape = torch.Size([1, 11, 340, 340])
- outputs[1].shape = torch.Size([1, 11, 170, 170])
- outputs[2].shape = torch.Size([1, 11, 84, 84])
- outputs[3].shape = torch.Size([1, 11, 43, 43])
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size=3,
- conv_cfg=None,
- norm_cfg=None,
- act_cfg=dict(type='ReLU')):
- super(ChannelMapper, self).__init__()
- assert isinstance(in_channels, list)
-
- self.convs = nn.ModuleList()
- for in_channel in in_channels:
- self.convs.append(
- ConvModule(
- in_channel,
- out_channels,
- kernel_size,
- padding=(kernel_size - 1) // 2,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg))
-
- # default init_weights for conv(msra) and norm in ConvModule
- def init_weights(self):
- """Initialize the weights of ChannelMapper module."""
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- xavier_init(m, distribution='uniform')
-
- def forward(self, inputs):
- """Forward function."""
- assert len(inputs) == len(self.convs)
- outs = [self.convs[i](inputs[i]) for i in range(len(inputs))]
- return tuple(outs)
diff --git a/spaces/CVPR/regionclip-demo/datasets/README.md b/spaces/CVPR/regionclip-demo/datasets/README.md
deleted file mode 100644
index 0eb44cc3b23beeb1755ab8d12002d26f13434235..0000000000000000000000000000000000000000
--- a/spaces/CVPR/regionclip-demo/datasets/README.md
+++ /dev/null
@@ -1,140 +0,0 @@
-# Use Builtin Datasets
-
-A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog)
-for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc).
-This document explains how to setup the builtin datasets so they can be used by the above APIs.
-[Use Custom Datasets](https://detectron2.readthedocs.io/tutorials/datasets.html) gives a deeper dive on how to use `DatasetCatalog` and `MetadataCatalog`,
-and how to add new datasets to them.
-
-Detectron2 has builtin support for a few datasets.
-The datasets are assumed to exist in a directory specified by the environment variable
-`DETECTRON2_DATASETS`.
-Under this directory, detectron2 will look for datasets in the structure described below, if needed.
-```
-$DETECTRON2_DATASETS/
- coco/
- lvis/
- cityscapes/
- VOC20{07,12}/
-```
-
-You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`.
-If left unset, the default is `./datasets` relative to your current working directory.
-
-The [model zoo](https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md)
-contains configs and models that use these builtin datasets.
-
-## Expected dataset structure for [COCO instance/keypoint detection](https://cocodataset.org/#download):
-
-```
-coco/
- annotations/
- instances_{train,val}2017.json
- person_keypoints_{train,val}2017.json
- {train,val}2017/
- # image files that are mentioned in the corresponding json
-```
-
-You can use the 2014 version of the dataset as well.
-
-Some of the builtin tests (`dev/run_*_tests.sh`) uses a tiny version of the COCO dataset,
-which you can download with `./datasets/prepare_for_tests.sh`.
-
-## Expected dataset structure for PanopticFPN:
-
-Extract panoptic annotations from [COCO website](https://cocodataset.org/#download)
-into the following structure:
-```
-coco/
- annotations/
- panoptic_{train,val}2017.json
- panoptic_{train,val}2017/ # png annotations
- panoptic_stuff_{train,val}2017/ # generated by the script mentioned below
-```
-
-Install panopticapi by:
-```
-pip install git+https://github.com/cocodataset/panopticapi.git
-```
-Then, run `python datasets/prepare_panoptic_fpn.py`, to extract semantic annotations from panoptic annotations.
-
-## Expected dataset structure for [LVIS instance segmentation](https://www.lvisdataset.org/dataset):
-```
-coco/
- {train,val,test}2017/
-lvis/
- lvis_v0.5_{train,val}.json
- lvis_v0.5_image_info_test.json
- lvis_v1_{train,val}.json
- lvis_v1_image_info_test{,_challenge}.json
-```
-
-Install lvis-api by:
-```
-pip install git+https://github.com/lvis-dataset/lvis-api.git
-```
-
-To evaluate models trained on the COCO dataset using LVIS annotations,
-run `python datasets/prepare_cocofied_lvis.py` to prepare "cocofied" LVIS annotations.
-
-## Expected dataset structure for [cityscapes](https://www.cityscapes-dataset.com/downloads/):
-```
-cityscapes/
- gtFine/
- train/
- aachen/
- color.png, instanceIds.png, labelIds.png, polygons.json,
- labelTrainIds.png
- ...
- val/
- test/
- # below are generated Cityscapes panoptic annotation
- cityscapes_panoptic_train.json
- cityscapes_panoptic_train/
- cityscapes_panoptic_val.json
- cityscapes_panoptic_val/
- cityscapes_panoptic_test.json
- cityscapes_panoptic_test/
- leftImg8bit/
- train/
- val/
- test/
-```
-Install cityscapes scripts by:
-```
-pip install git+https://github.com/mcordts/cityscapesScripts.git
-```
-
-Note: to create labelTrainIds.png, first prepare the above structure, then run cityscapesescript with:
-```
-CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createTrainIdLabelImgs.py
-```
-These files are not needed for instance segmentation.
-
-Note: to generate Cityscapes panoptic dataset, run cityscapesescript with:
-```
-CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createPanopticImgs.py
-```
-These files are not needed for semantic and instance segmentation.
-
-## Expected dataset structure for [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/index.html):
-```
-VOC20{07,12}/
- Annotations/
- ImageSets/
- Main/
- trainval.txt
- test.txt
- # train.txt or val.txt, if you use these splits
- JPEGImages/
-```
-
-## Expected dataset structure for [ADE20k Scene Parsing](http://sceneparsing.csail.mit.edu/):
-```
-ADEChallengeData2016/
- annotations/
- annotations_detectron2/
- images/
- objectInfo150.txt
-```
-The directory `annotations_detectron2` is generated by running `python datasets/prepare_ade20k_sem_seg.py`.
diff --git a/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py b/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py
deleted file mode 100644
index ee1f6f1bcfa2fc3437fb6d5f579640482f1ad5f4..0000000000000000000000000000000000000000
--- a/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py
+++ /dev/null
@@ -1,397 +0,0 @@
-# -*- coding: utf-8 -*-
-import re
-from os.path import join, abspath, dirname
-from collections import defaultdict
-import epitran
-
-epi = epitran.Epitran("deu-Latn-nar")
-
-
-def mode_type(mode_in):
- """In the case of "sql", this will return an sqlite cursor."""
- if mode_in.lower() == "sql":
- import sqlite3
- conn = sqlite3.connect(join(abspath(dirname(__file__)), "./Resources/de.db"))
- return conn.cursor()
-
-
-#TESTS
-#NUMBERS ARE TOO HARD!
-
-
-
-def preprocess(words):
- """Returns a string of words stripped of punctuation"""
- punct_str = '!"#$%&\'()*+,-./:;<=>/?@[\\]^_`{|}~«» '
- return ' '.join([w.strip(punct_str).lower() for w in words.split()])
-
-
-def preserve_punc(words):
- """converts words to IPA and finds punctuation before and after the word."""
- words_preserved = []
- for w in words.split():
- punct_list = ["", preprocess(w), ""]
- before = re.search("^([^A-Za-z0-9]+)[A-Za-z]", w)
- after = re.search("[A-Za-z]([^A-Za-z0-9]+)$", w)
- if before:
- punct_list[0] = str(before.group(1))
- if after:
- punct_list[2] = str(after.group(1))
- words_preserved.append(punct_list)
- return words_preserved
-
-
-
-def apply_punct(triple, as_str=False):
- """places surrounding punctuation back on center on a list of preserve_punc triples"""
- if type(triple[0]) == list:
- for i, t in enumerate(triple):
- triple[i] = str(''.join(triple[i]))
- if as_str:
- return ' '.join(triple)
- return triple
- if as_str:
- return str(''.join(t for t in triple))
- return [''.join(t for t in triple)]
-
-
-def _punct_replace_word(original, transcription):
- """Get the IPA transcription of word with the original punctuation marks"""
- for i, trans_list in enumerate(transcription):
- for j, item in enumerate(trans_list):
- triple = [original[i][0]] + [item] + [original[i][2]]
- transcription[i][j] = apply_punct(triple, as_str=True)
- return transcription
-
-
-def fetch_words(words_in, db_type="sql"):
- """fetches a list of words from the database"""
- asset = mode_type(db_type)
- f_result = []
- if db_type.lower() == "sql":
- for word in words_in:
- asset.execute("SELECT Words, phonemes FROM De_words WHERE Words IN (?)", (word,))
- result = asset.fetchall()
- flag = True
- try:
- f_result.append(result.pop())
- flag = False
- except IndexError:
- pass
- if result == [] and flag is True:
- result = epi.transliterate(word)
- f_result.append((word, result))
- f_result = list(filter(None,f_result))
- f_set = set(f_result)
- d = defaultdict(list)
- for k, v in f_set:
- d[k].append(v)
- return list(d.items())
-
-def get_deu(tokens_in, db_type="sql"):
- """query the SQL database for the words and return the phonemes in the order of user_in"""
- result = fetch_words(tokens_in, db_type)
- ordered = []
- for word in tokens_in:
- this_word = [[i[1] for i in result if i[0] == word]][0]
- if this_word:
- ordered.append(this_word[0])
- else:
- ordered.append(["__IGNORE__" + word])
- return ordered
-
-
-def deu_to_ipa(deu_list, mark=True):
- """converts the deu word lists into IPA transcriptions"""
- symbols = {}
- ipa_list = [] # the final list of IPA tokens to be returned
- for word_list in deu_list:
- ipa_word_list = [] # the word list for each word
- for word in word_list:
- if re.sub("\d*", "", word.replace("__IGNORE__", "")) == "":
- pass # do not delete token if it's all numbers
- else:
- word = re.sub("[0-9]", "", word)
- ipa_form = ''
- if word.startswith("__IGNORE__"):
- ipa_form = word.replace("__IGNORE__", "")
- # mark words we couldn't transliterate with an asterisk:
-
- if mark:
- if not re.sub("\d*", "", ipa_form) == "":
- ipa_form += "*"
- else:
- for piece in word.split(" "):
- marked = False
- unmarked = piece
- if piece[0] in ["ˈ", "ˌ"] or piece[0] is None:
- marked = True
- mark = piece
- unmarked = piece[1:]
-
- if unmarked in symbols:
- if marked:
- ipa_form += mark + symbols[unmarked]
- else:
- ipa_form += symbols[unmarked]
-
- else:
- ipa_form += piece
- swap_list = [["ˈər", "əˈr"], ["ˈie", "iˈe"]]
- for sym in swap_list:
- if not ipa_form.startswith(sym[0]):
- ipa_form = ipa_form.replace(sym[0], sym[1])
- ipa_word_list.append(ipa_form)
- ipa_list.append(sorted(list(set(ipa_word_list))))
- return ipa_list
-
-
-def get_top(ipa_list):
- """Returns only the one result for a query. If multiple entries for words are found, only the first is used."""
- return ' '.join([word_list[-1] for word_list in ipa_list])
-
-
-def get_all(ipa_list):
- """utilizes an algorithm to discover and return all possible combinations of IPA transcriptions"""
- final_size = 1
- for word_list in ipa_list:
- final_size *= len(word_list)
- list_all = ["" for s in range(final_size)]
- for i in range(len(ipa_list)):
- if i == 0:
- swtich_rate = final_size / len(ipa_list[i])
- else:
- swtich_rate /= len(ipa_list[i])
- k = 0
- for j in range(final_size):
- if (j+1) % int(swtich_rate) == 0:
- k += 1
- if k == len(ipa_list[i]):
- k = 0
- list_all[j] = list_all[j] + ipa_list[i][k] + " "
- return sorted([sent[:-1] for sent in list_all])
-
-
-def ipa_list(words_in, keep_punct=True, db_type="sql"):
- """Returns a list of all the discovered IPA transcriptions for each word."""
- if type(words_in) == str:
- words = [preserve_punc(w.lower())[0] for w in words_in.split()]
- else:
- words = [preserve_punc(w.lower())[0] for w in words_in]
- deu = get_deu([w[1] for w in words], db_type=db_type)
- ipa = deu_to_ipa(deu)
- if keep_punct:
- ipa = _punct_replace_word(words, ipa)
- return ipa
-
-
-def isin_deu(word, db_type="sql"):
- """checks if a word is in the deu dictionary. Doesn't strip punctuation.
- If given more than one word, returns True only if all words are present."""
- if type(word) == str:
- word = [preprocess(w) for w in word.split()]
- results = fetch_words(word, db_type)
- as_set = list(set(t[0] for t in results))
- return len(as_set) == len(set(word))
-
-def replace_number(text):
- text = text.replace("1","eins ")
- text = text.replace("2","zwei ")
- text = text.replace("3","drei ")
- text = text.replace("4","vier ")
- text = text.replace("5","fünf ")
- text = text.replace("6","sechs ")
- text = text.replace("7","sieben ")
- text = text.replace("8","acht ")
- text = text.replace("9","neun ")
- text = text.replace("0","null ")
- return text
-
-
-
-def convert(text, retrieve_all=False, keep_punct=True, mode="sql"):
- """takes either a string or list of German words and converts them to IPA"""
- text = replace_number(text)
- ipa = ipa_list(
- words_in=text,
- keep_punct=keep_punct,
- db_type=mode)
- if retrieve_all:
- return get_all(ipa)
- return get_top(ipa)
-
-
-
-_decimal_number_re = re.compile(r'\d+\,\d+')
-_euros_pre = re.compile(r'€([0-9\,]*[0-9]+)')
-_euros_re = re.compile(r'([0-9\,]*[0-9]+)€')
-_ordinal_re = re.compile(r'(der |die |das )([0-9]+)\.')
-_clock_re=re.compile(r'\d{1,2}\:\d{2}')
-_number_re = re.compile(r'[0-9]+')
-
-def base(text):
- text = text.replace("1", "eins ")
- text = text.replace("2", "zwei ")
- text = text.replace("3", "drei ")
- text = text.replace("4", "vier ")
- text = text.replace("5", "fünf ")
- text = text.replace("6", "sechs ")
- text = text.replace("7", "sieben ")
- text = text.replace("8", "acht ")
- text = text.replace("9", "neun ")
- text = text.replace("0", "null ")
- return text
-
-def tens_to_word(num):
- tens = num[0]
- ones = num[1]
- ones_word = base(ones)
-
- if num =="10":
- return "zehn"
- elif num=="11":
- return "elf"
- elif num=="12":
- return "zwölf"
-
- if tens == "1":
- if ones == "6":
- ones_word = ones_word[:-1]
- elif ones == "7":
- ones_word = ones_word[:-2]
- return ones_word + "zehn"
- else:
- tens_word = base(tens)
- if ones == "1":
- ones_word = ones_word[:-1]
- if tens == "2":
- tens_word = "zwan"
- elif tens == "6":
- tens_word = tens_word[:-1]
- elif tens == "7":
- tens_word = tens_word[:-2]
- if tens == "3":
- tens_word += "ßig"
- else:
- tens_word += "zig"
- if ones == "0":
- return tens_word
- else:
- return ones_word + " und " + tens_word
-
-def huns_to_word(num):
- huns = num[0]
- tens = num[1]
-
- if huns == "1":
- huns_word= "hundert"
- else:
- huns_word = base(huns)+" hundert"
-
- remain = num_to_word(num[1:])
- if remain != "":
- remain = " " + remain
- return huns_word + remain
-
-def thos_to_word(num):
- thos = num[0]
- if thos == "1":
- thos_word= "tausend"
- else:
- thos_word = base(thos)+" tausend"
- remain=num_to_word(num[1:])
- if remain!="":
- remain=" "+remain
- return thos_word+remain
-
-def num_to_word(num):
- num=num.lstrip("0")
- if num=="":
- return("")
- digit=len(num)
- if digit==1:
- return base(num)
- elif digit==2:
- return tens_to_word(num)
- elif digit == 3:
- return huns_to_word(num)
- elif digit == 4:
- return thos_to_word(num)
- else:
- return base(num)
-
-def number_to_words(m):
- m=m.group(0).lstrip("0")
- if m=="":
- return"null"
- return num_to_word(m)
-
-def _expand_ordinal(m):
-
- pre=m.group(1)
- m = m.group(2).lstrip("0")
-
- if m=="":
- return"NULL"
- num=int(m)
- if num<=19 & num>=1:
- if num ==1:
- return "erste"
- elif num==3:
- return "dritte"
- elif num==7:
- return "siebte"
- elif num==8:
- return "achte"
- else:
- return pre + num_to_word(m) + "te"
- else:
- return pre + num_to_word(m) + "ste"
-
-def _expand_decimal(m):
- match=m.group(0)
- parts = match.split(',')
- if int(parts[0])==0:
- return '%s komma %s' % ("null", base(parts[1]))
- return '%s komma %s' % (num_to_word(parts[0]),base(parts[1]))
-
-def _expand_euros(m):
- match = m.group(1)
- parts = match.split(',')
- if len(parts) > 2:
- return match + ' euro' # Unexpected format
- euros = int(parts[0]) if parts[0] else 0
- cents = int(parts[1])*10 if len(parts) > 1 and parts[1] else 0
- if euros and cents:
- return '%s euro %s' % (euros, cents)
- elif euros:
- return '%s euro' % (euros)
- elif cents:
- return '%s cent' % (cents)
- else:
- return 'null euro'
-
-def _expand_clock(m):
- match = m.group(0)
- parts = match.split(':')
- if int(parts[0]) == 0:
- return '%s Uhr %s' % ("null",num_to_word(parts[1]))
- elif int(parts[0]) == 1:
- return '%s Uhr %s' % ("ein", num_to_word(parts[1]))
- return '%s Uhr %s' % (num_to_word(parts[0]),num_to_word(parts[1]))
-
-def normalize_numbers(text):
- text = re.sub(_euros_pre, _expand_euros, text)
- text = re.sub(_euros_re, _expand_euros, text)
- text = re.sub(_clock_re, _expand_clock, text)
- text = re.sub(_decimal_number_re, _expand_decimal, text)
- text = re.sub(_ordinal_re, _expand_ordinal, text)
- text = re.sub(_number_re, number_to_words, text)
- text=text.replace(" "," ")
- return text
-
-def collapse_whitespace(text):
- return re.sub(r'\s+', ' ', text)
-
-def mark_dark_l(text):
- return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
diff --git a/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py b/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py
deleted file mode 100644
index 1519b3095bc5250016066754ebe6c0328773f24a..0000000000000000000000000000000000000000
--- a/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from utils import randomize_seed_fn
-
-
-def create_demo(process, max_images=12, default_num_images=3):
- with gr.Blocks() as demo:
- with gr.Row():
- with gr.Column():
- image = gr.Image()
- prompt = gr.Textbox(label='Prompt')
- run_button = gr.Button('Run')
- with gr.Accordion('Advanced options', open=False):
- preprocessor_name = gr.Radio(label='Preprocessor',
- choices=['NormalBae', 'None'],
- type='value',
- value='NormalBae')
- num_samples = gr.Slider(label='Images',
- minimum=1,
- maximum=max_images,
- value=default_num_images,
- step=1)
- image_resolution = gr.Slider(label='Image resolution',
- minimum=256,
- maximum=512,
- value=512,
- step=256)
- preprocess_resolution = gr.Slider(
- label='Preprocess resolution',
- minimum=128,
- maximum=512,
- value=384,
- step=1)
- num_steps = gr.Slider(label='Number of steps',
- minimum=1,
- maximum=100,
- value=20,
- step=1)
- guidance_scale = gr.Slider(label='Guidance scale',
- minimum=0.1,
- maximum=30.0,
- value=9.0,
- step=0.1)
- seed = gr.Slider(label='Seed',
- minimum=0,
- maximum=1000000,
- step=1,
- value=0,
- randomize=True)
- randomize_seed = gr.Checkbox(label='Randomize seed',
- value=True)
- a_prompt = gr.Textbox(
- label='Additional prompt',
- value='best quality, extremely detailed')
- n_prompt = gr.Textbox(
- label='Negative prompt',
- value=
- 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
- )
- with gr.Column():
- result = gr.Gallery(label='Output', show_label=False).style(
- columns=2, object_fit='scale-down')
- inputs = [
- image,
- prompt,
- a_prompt,
- n_prompt,
- num_samples,
- image_resolution,
- preprocess_resolution,
- num_steps,
- guidance_scale,
- seed,
- preprocessor_name,
- ]
- prompt.submit(
- fn=randomize_seed_fn,
- inputs=[seed, randomize_seed],
- outputs=seed,
- ).then(
- fn=process,
- inputs=inputs,
- outputs=result,
- )
- run_button.click(
- fn=randomize_seed_fn,
- inputs=[seed, randomize_seed],
- outputs=seed,
- ).then(
- fn=process,
- inputs=inputs,
- outputs=result,
- api_name='normal',
- )
- return demo
-
-
-if __name__ == '__main__':
- from model import Model
- model = Model(task_name='NormalBae')
- demo = create_demo(model.process_normal)
- demo.queue().launch()
diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py
deleted file mode 100644
index 0a44b37fc5563deea8406c6ddf109860a56e9485..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from PIL.Image import Image as IMG
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.utils import save_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def knock(images: List[BuildImage], texts, args):
- img = images[0].convert("RGBA").square()
- # fmt: off
- locs = [(60, 308, 210, 195), (60, 308, 210, 198), (45, 330, 250, 172), (58, 320, 218, 180),
- (60, 310, 215, 193), (40, 320, 250, 285), (48, 308, 226, 192), (51, 301, 223, 200)]
- # fmt: on
- frames: List[IMG] = []
- for i in range(8):
- frame = BuildImage.open(img_dir / f"{i}.png")
- x, y, w, h = locs[i]
- frame.paste(img.resize((w, h)), (x, y), below=True)
- frames.append(frame.image)
- return save_gif(frames, 0.04)
-
-
-add_meme("knock", knock, min_images=1, max_images=1, keywords=["敲"])
diff --git a/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py b/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py
deleted file mode 100644
index 37f310fadd0b48b2f364877158fb2105d645fc03..0000000000000000000000000000000000000000
--- a/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import locale
-import json
-import os
-
-
-def load_language_list(language):
- with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f:
- language_list = json.load(f)
- return language_list
-
-
-class I18nAuto:
- def __init__(self, language=None):
- if language in ["Auto", None]:
- language = locale.getdefaultlocale()[
- 0
- ] # getlocale can't identify the system's language ((None, None))
- if not os.path.exists(f"./i18n/{language}.json"):
- language = "en_US"
- self.language = language
- # print("Use Language:", language)
- self.language_map = load_language_list(language)
-
- def __call__(self, key):
- return self.language_map.get(key, key)
-
- def print(self):
- print("Use Language:", self.language)
diff --git a/spaces/Codecooker/rvcapi/src/mdx.py b/spaces/Codecooker/rvcapi/src/mdx.py
deleted file mode 100644
index 575c456fbdf9e5b5955401f41a3a58ddc27267b3..0000000000000000000000000000000000000000
--- a/spaces/Codecooker/rvcapi/src/mdx.py
+++ /dev/null
@@ -1,287 +0,0 @@
-import gc
-import hashlib
-import os
-import queue
-import threading
-import warnings
-
-import librosa
-import numpy as np
-import onnxruntime as ort
-import soundfile as sf
-import torch
-from tqdm import tqdm
-
-warnings.filterwarnings("ignore")
-stem_naming = {'Vocals': 'Instrumental', 'Other': 'Instruments', 'Instrumental': 'Vocals', 'Drums': 'Drumless', 'Bass': 'Bassless'}
-
-
-class MDXModel:
- def __init__(self, device, dim_f, dim_t, n_fft, hop=1024, stem_name=None, compensation=1.000):
- self.dim_f = dim_f
- self.dim_t = dim_t
- self.dim_c = 4
- self.n_fft = n_fft
- self.hop = hop
- self.stem_name = stem_name
- self.compensation = compensation
-
- self.n_bins = self.n_fft // 2 + 1
- self.chunk_size = hop * (self.dim_t - 1)
- self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(device)
-
- out_c = self.dim_c
-
- self.freq_pad = torch.zeros([1, out_c, self.n_bins - self.dim_f, self.dim_t]).to(device)
-
- def stft(self, x):
- x = x.reshape([-1, self.chunk_size])
- x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True, return_complex=True)
- x = torch.view_as_real(x)
- x = x.permute([0, 3, 1, 2])
- x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 4, self.n_bins, self.dim_t])
- return x[:, :, :self.dim_f]
-
- def istft(self, x, freq_pad=None):
- freq_pad = self.freq_pad.repeat([x.shape[0], 1, 1, 1]) if freq_pad is None else freq_pad
- x = torch.cat([x, freq_pad], -2)
- # c = 4*2 if self.target_name=='*' else 2
- x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 2, self.n_bins, self.dim_t])
- x = x.permute([0, 2, 3, 1])
- x = x.contiguous()
- x = torch.view_as_complex(x)
- x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True)
- return x.reshape([-1, 2, self.chunk_size])
-
-
-class MDX:
- DEFAULT_SR = 44100
- # Unit: seconds
- DEFAULT_CHUNK_SIZE = 0 * DEFAULT_SR
- DEFAULT_MARGIN_SIZE = 1 * DEFAULT_SR
-
- DEFAULT_PROCESSOR = 0
-
- def __init__(self, model_path: str, params: MDXModel, processor=DEFAULT_PROCESSOR):
-
- # Set the device and the provider (CPU or CUDA)
- self.device = torch.device(f'cuda:{processor}') if processor >= 0 else torch.device('cpu')
- self.provider = ['CUDAExecutionProvider'] if processor >= 0 else ['CPUExecutionProvider']
-
- self.model = params
-
- # Load the ONNX model using ONNX Runtime
- self.ort = ort.InferenceSession(model_path, providers=self.provider)
- # Preload the model for faster performance
- self.ort.run(None, {'input': torch.rand(1, 4, params.dim_f, params.dim_t).numpy()})
- self.process = lambda spec: self.ort.run(None, {'input': spec.cpu().numpy()})[0]
-
- self.prog = None
-
- @staticmethod
- def get_hash(model_path):
- try:
- with open(model_path, 'rb') as f:
- f.seek(- 10000 * 1024, 2)
- model_hash = hashlib.md5(f.read()).hexdigest()
- except:
- model_hash = hashlib.md5(open(model_path, 'rb').read()).hexdigest()
-
- return model_hash
-
- @staticmethod
- def segment(wave, combine=True, chunk_size=DEFAULT_CHUNK_SIZE, margin_size=DEFAULT_MARGIN_SIZE):
- """
- Segment or join segmented wave array
-
- Args:
- wave: (np.array) Wave array to be segmented or joined
- combine: (bool) If True, combines segmented wave array. If False, segments wave array.
- chunk_size: (int) Size of each segment (in samples)
- margin_size: (int) Size of margin between segments (in samples)
-
- Returns:
- numpy array: Segmented or joined wave array
- """
-
- if combine:
- processed_wave = None # Initializing as None instead of [] for later numpy array concatenation
- for segment_count, segment in enumerate(wave):
- start = 0 if segment_count == 0 else margin_size
- end = None if segment_count == len(wave) - 1 else -margin_size
- if margin_size == 0:
- end = None
- if processed_wave is None: # Create array for first segment
- processed_wave = segment[:, start:end]
- else: # Concatenate to existing array for subsequent segments
- processed_wave = np.concatenate((processed_wave, segment[:, start:end]), axis=-1)
-
- else:
- processed_wave = []
- sample_count = wave.shape[-1]
-
- if chunk_size <= 0 or chunk_size > sample_count:
- chunk_size = sample_count
-
- if margin_size > chunk_size:
- margin_size = chunk_size
-
- for segment_count, skip in enumerate(range(0, sample_count, chunk_size)):
-
- margin = 0 if segment_count == 0 else margin_size
- end = min(skip + chunk_size + margin_size, sample_count)
- start = skip - margin
-
- cut = wave[:, start:end].copy()
- processed_wave.append(cut)
-
- if end == sample_count:
- break
-
- return processed_wave
-
- def pad_wave(self, wave):
- """
- Pad the wave array to match the required chunk size
-
- Args:
- wave: (np.array) Wave array to be padded
-
- Returns:
- tuple: (padded_wave, pad, trim)
- - padded_wave: Padded wave array
- - pad: Number of samples that were padded
- - trim: Number of samples that were trimmed
- """
- n_sample = wave.shape[1]
- trim = self.model.n_fft // 2
- gen_size = self.model.chunk_size - 2 * trim
- pad = gen_size - n_sample % gen_size
-
- # Padded wave
- wave_p = np.concatenate((np.zeros((2, trim)), wave, np.zeros((2, pad)), np.zeros((2, trim))), 1)
-
- mix_waves = []
- for i in range(0, n_sample + pad, gen_size):
- waves = np.array(wave_p[:, i:i + self.model.chunk_size])
- mix_waves.append(waves)
-
- mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(self.device)
-
- return mix_waves, pad, trim
-
- def _process_wave(self, mix_waves, trim, pad, q: queue.Queue, _id: int):
- """
- Process each wave segment in a multi-threaded environment
-
- Args:
- mix_waves: (torch.Tensor) Wave segments to be processed
- trim: (int) Number of samples trimmed during padding
- pad: (int) Number of samples padded during padding
- q: (queue.Queue) Queue to hold the processed wave segments
- _id: (int) Identifier of the processed wave segment
-
- Returns:
- numpy array: Processed wave segment
- """
- mix_waves = mix_waves.split(1)
- with torch.no_grad():
- pw = []
- for mix_wave in mix_waves:
- self.prog.update()
- spec = self.model.stft(mix_wave)
- processed_spec = torch.tensor(self.process(spec))
- processed_wav = self.model.istft(processed_spec.to(self.device))
- processed_wav = processed_wav[:, :, trim:-trim].transpose(0, 1).reshape(2, -1).cpu().numpy()
- pw.append(processed_wav)
- processed_signal = np.concatenate(pw, axis=-1)[:, :-pad]
- q.put({_id: processed_signal})
- return processed_signal
-
- def process_wave(self, wave: np.array, mt_threads=1):
- """
- Process the wave array in a multi-threaded environment
-
- Args:
- wave: (np.array) Wave array to be processed
- mt_threads: (int) Number of threads to be used for processing
-
- Returns:
- numpy array: Processed wave array
- """
- self.prog = tqdm(total=0)
- chunk = wave.shape[-1] // mt_threads
- waves = self.segment(wave, False, chunk)
-
- # Create a queue to hold the processed wave segments
- q = queue.Queue()
- threads = []
- for c, batch in enumerate(waves):
- mix_waves, pad, trim = self.pad_wave(batch)
- self.prog.total = len(mix_waves) * mt_threads
- thread = threading.Thread(target=self._process_wave, args=(mix_waves, trim, pad, q, c))
- thread.start()
- threads.append(thread)
- for thread in threads:
- thread.join()
- self.prog.close()
-
- processed_batches = []
- while not q.empty():
- processed_batches.append(q.get())
- processed_batches = [list(wave.values())[0] for wave in
- sorted(processed_batches, key=lambda d: list(d.keys())[0])]
- assert len(processed_batches) == len(waves), 'Incomplete processed batches, please reduce batch size!'
- return self.segment(processed_batches, True, chunk)
-
-
-def run_mdx(model_params, output_dir, model_path, filename, exclude_main=False, exclude_inversion=False, suffix=None, invert_suffix=None, denoise=False, keep_orig=True, m_threads=2):
- device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
-
- device_properties = torch.cuda.get_device_properties(device)
- vram_gb = device_properties.total_memory / 1024**3
- m_threads = 1 if vram_gb < 8 else 2
-
- model_hash = MDX.get_hash(model_path)
- mp = model_params.get(model_hash)
- model = MDXModel(
- device,
- dim_f=mp["mdx_dim_f_set"],
- dim_t=2 ** mp["mdx_dim_t_set"],
- n_fft=mp["mdx_n_fft_scale_set"],
- stem_name=mp["primary_stem"],
- compensation=mp["compensate"]
- )
-
- mdx_sess = MDX(model_path, model)
- wave, sr = librosa.load(filename, mono=False, sr=44100)
- # normalizing input wave gives better output
- peak = max(np.max(wave), abs(np.min(wave)))
- wave /= peak
- if denoise:
- wave_processed = -(mdx_sess.process_wave(-wave, m_threads)) + (mdx_sess.process_wave(wave, m_threads))
- wave_processed *= 0.5
- else:
- wave_processed = mdx_sess.process_wave(wave, m_threads)
- # return to previous peak
- wave_processed *= peak
- stem_name = model.stem_name if suffix is None else suffix
-
- main_filepath = None
- if not exclude_main:
- main_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav")
- sf.write(main_filepath, wave_processed.T, sr)
-
- invert_filepath = None
- if not exclude_inversion:
- diff_stem_name = stem_naming.get(stem_name) if invert_suffix is None else invert_suffix
- stem_name = f"{stem_name}_diff" if diff_stem_name is None else diff_stem_name
- invert_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav")
- sf.write(invert_filepath, (-wave_processed.T * model.compensation) + wave.T, sr)
-
- if not keep_orig:
- os.remove(filename)
-
- del mdx_sess, wave_processed, wave
- gc.collect()
- return main_filepath, invert_filepath
diff --git a/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py b/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py
deleted file mode 100644
index 76dce7e41c803a8055f3627cccb98deb51419b09..0000000000000000000000000000000000000000
--- a/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import argparse
-import glob
-import os
-
-
-def main(args):
- txt_file = open(args.meta_info, 'w')
- # sca images
- img_paths_gt = sorted(glob.glob(os.path.join(args.input[0], '*')))
- img_paths_lq = sorted(glob.glob(os.path.join(args.input[1], '*')))
-
- assert len(img_paths_gt) == len(img_paths_lq), ('GT folder and LQ folder should have the same length, but got '
- f'{len(img_paths_gt)} and {len(img_paths_lq)}.')
-
- for img_path_gt, img_path_lq in zip(img_paths_gt, img_paths_lq):
- # get the relative paths
- img_name_gt = os.path.relpath(img_path_gt, args.root[0])
- img_name_lq = os.path.relpath(img_path_lq, args.root[1])
- print(f'{img_name_gt}, {img_name_lq}')
- txt_file.write(f'{img_name_gt}, {img_name_lq}\n')
-
-
-if __name__ == '__main__':
- """This script is used to generate meta info (txt file) for paired images.
- """
- parser = argparse.ArgumentParser()
- parser.add_argument(
- '--input',
- nargs='+',
- default=['datasets/DF2K/DIV2K_train_HR_sub', 'datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub'],
- help='Input folder, should be [gt_folder, lq_folder]')
- parser.add_argument('--root', nargs='+', default=[None, None], help='Folder root, will use the ')
- parser.add_argument(
- '--meta_info',
- type=str,
- default='datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt',
- help='txt path for meta info')
- args = parser.parse_args()
-
- assert len(args.input) == 2, 'Input folder should have two elements: gt folder and lq folder'
- assert len(args.root) == 2, 'Root path should have two elements: root for gt folder and lq folder'
- os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)
- for i in range(2):
- if args.input[i].endswith('/'):
- args.input[i] = args.input[i][:-1]
- if args.root[i] is None:
- args.root[i] = os.path.dirname(args.input[i])
-
- main(args)
diff --git a/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py b/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py
deleted file mode 100644
index eb453a809d17e6dee04e158d1c68dc807478edef..0000000000000000000000000000000000000000
--- a/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Onodofthenorth/SD_PixelArt_SpriteSheet_Generator").launch()
\ No newline at end of file
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py
deleted file mode 100644
index bafc43a19d432290867a5c08b9820f2e4f79aea3..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py
+++ /dev/null
@@ -1,97 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# GD file handling
-#
-# History:
-# 1996-04-12 fl Created
-#
-# Copyright (c) 1997 by Secret Labs AB.
-# Copyright (c) 1996 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-"""
-.. note::
- This format cannot be automatically recognized, so the
- class is not registered for use with :py:func:`PIL.Image.open()`. To open a
- gd file, use the :py:func:`PIL.GdImageFile.open()` function instead.
-
-.. warning::
- THE GD FORMAT IS NOT DESIGNED FOR DATA INTERCHANGE. This
- implementation is provided for convenience and demonstrational
- purposes only.
-"""
-
-
-from . import ImageFile, ImagePalette, UnidentifiedImageError
-from ._binary import i16be as i16
-from ._binary import i32be as i32
-
-
-class GdImageFile(ImageFile.ImageFile):
- """
- Image plugin for the GD uncompressed format. Note that this format
- is not supported by the standard :py:func:`PIL.Image.open()` function. To use
- this plugin, you have to import the :py:mod:`PIL.GdImageFile` module and
- use the :py:func:`PIL.GdImageFile.open()` function.
- """
-
- format = "GD"
- format_description = "GD uncompressed images"
-
- def _open(self):
- # Header
- s = self.fp.read(1037)
-
- if i16(s) not in [65534, 65535]:
- msg = "Not a valid GD 2.x .gd file"
- raise SyntaxError(msg)
-
- self.mode = "L" # FIXME: "P"
- self._size = i16(s, 2), i16(s, 4)
-
- true_color = s[6]
- true_color_offset = 2 if true_color else 0
-
- # transparency index
- tindex = i32(s, 7 + true_color_offset)
- if tindex < 256:
- self.info["transparency"] = tindex
-
- self.palette = ImagePalette.raw(
- "XBGR", s[7 + true_color_offset + 4 : 7 + true_color_offset + 4 + 256 * 4]
- )
-
- self.tile = [
- (
- "raw",
- (0, 0) + self.size,
- 7 + true_color_offset + 4 + 256 * 4,
- ("L", 0, 1),
- )
- ]
-
-
-def open(fp, mode="r"):
- """
- Load texture from a GD image file.
-
- :param fp: GD file name, or an opened file handle.
- :param mode: Optional mode. In this version, if the mode argument
- is given, it must be "r".
- :returns: An image instance.
- :raises OSError: If the image could not be read.
- """
- if mode != "r":
- msg = "bad mode"
- raise ValueError(msg)
-
- try:
- return GdImageFile(fp)
- except SyntaxError as e:
- msg = "cannot identify this image file"
- raise UnidentifiedImageError(msg) from e
diff --git a/spaces/Dana19/ImageRecognition_FaceCount/README.md b/spaces/Dana19/ImageRecognition_FaceCount/README.md
deleted file mode 100644
index 8afdea6aa817a1ec3b056820a882b63deedc6772..0000000000000000000000000000000000000000
--- a/spaces/Dana19/ImageRecognition_FaceCount/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Headcount
-emoji: 💻
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.3
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/DemoLou/moe-tts/text/thai.py b/spaces/DemoLou/moe-tts/text/thai.py
deleted file mode 100644
index 998207c01a85c710a46db1ec8b62c39c2d94bc84..0000000000000000000000000000000000000000
--- a/spaces/DemoLou/moe-tts/text/thai.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import re
-from num_thai.thainumbers import NumThai
-
-
-num = NumThai()
-
-# List of (Latin alphabet, Thai) pairs:
-_latin_to_thai = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', 'เอ'),
- ('b','บี'),
- ('c','ซี'),
- ('d','ดี'),
- ('e','อี'),
- ('f','เอฟ'),
- ('g','จี'),
- ('h','เอช'),
- ('i','ไอ'),
- ('j','เจ'),
- ('k','เค'),
- ('l','แอล'),
- ('m','เอ็ม'),
- ('n','เอ็น'),
- ('o','โอ'),
- ('p','พี'),
- ('q','คิว'),
- ('r','แอร์'),
- ('s','เอส'),
- ('t','ที'),
- ('u','ยู'),
- ('v','วี'),
- ('w','ดับเบิลยู'),
- ('x','เอ็กซ์'),
- ('y','วาย'),
- ('z','ซี')
-]]
-
-
-def num_to_thai(text):
- return re.sub(r'(?:\d+(?:,?\d+)?)+(?:\.\d+(?:,?\d+)?)?', lambda x: ''.join(num.NumberToTextThai(float(x.group(0).replace(',', '')))), text)
-
-def latin_to_thai(text):
- for regex, replacement in _latin_to_thai:
- text = re.sub(regex, replacement, text)
- return text
diff --git a/spaces/Detomo/AI-Galary/README.md b/spaces/Detomo/AI-Galary/README.md
deleted file mode 100644
index 5772a1eb261aa38b6407c07b0633d4a12566eccf..0000000000000000000000000000000000000000
--- a/spaces/Detomo/AI-Galary/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AI Galary
-emoji: ⚡
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Detomo/generate_wifi_qrcode/app.py b/spaces/Detomo/generate_wifi_qrcode/app.py
deleted file mode 100644
index e6681ad55d07851290ef2f0f04f1e026a95b917a..0000000000000000000000000000000000000000
--- a/spaces/Detomo/generate_wifi_qrcode/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import wifi_qrcode_generator
-import gradio as gr
-
-examples = [['Home wifi', 'very complicated password', 'WPA']]
-
-def infer(wifi_name, wifi_pw, authentication_type):
- image = wifi_qrcode_generator.wifi_qrcode(
- wifi_name, False, authentication_type, wifi_pw
- )
- image.save('qrcode_wifi.png')
- return 'qrcode_wifi.png'
-
-description= "Generate code for accessing WIFI. Fill your wifi name, wifi password and select authentication type. Then click submit and enjoy 😎"
-
-iface = gr.Interface(
- fn=infer,
- title="Generate QR code for accessing WIFI 🥃",
- description=description,
- inputs=[
- gr.Textbox(
- label="Wifi name",
- lines=1,
- ),
- gr.Textbox(
- label="Wifi password",
- lines=1,
- ),
- gr.Dropdown(
- label='authentication_type',
- choices=["WPA", "WEP", "nopass"],
- value='WPA'
- ),
- ],
- outputs="image",
- examples=examples,
- article="Author: Vu Minh Chien", cache_examples=True).launch(enable_queue=True)
diff --git a/spaces/DiffusionArtco/scifi-art-creator/app.py b/spaces/DiffusionArtco/scifi-art-creator/app.py
deleted file mode 100644
index 236417b745e1681f1a9e1ae644d75254da2de370..0000000000000000000000000000000000000000
--- a/spaces/DiffusionArtco/scifi-art-creator/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import gradio as gr
-import requests
-from PIL import Image
-from io import BytesIO
-import base64
-
-api_url = "https://5cb20b40-572c-426f-9466-995256f9b6eb.id.repl.co/generate_image"
-
-def generate_image(model="Sci-Fi Diffusion", prompt="", seed=0, negative_prompt="", sampler="k_dpmpp_2s_a", steps=50):
- data = "?model=" + model + "&prompt=" + prompt + "&seed=" + str(seed) + "&negative_prompt=" + negative_prompt + "&sampler=" + sampler + "&steps=" + str(steps)
- response = requests.post(api_url + data, timeout=400)
- if response.status_code == 200:
- img_base64 = response.json()["url"]
- img_bytes = base64.b64decode(img_base64)
- img = Image.open(BytesIO(img_bytes))
- return img
- else:
- return None
-
-inputs = [
- gr.inputs.Dropdown([ 'Experience', 'FKing SciFi', 'Future Diffusion', 'JWST Deep Space Diffusion', 'Protogen Infinity', 'RCNZ Dumb Monkey', 'RealBiter', 'Robo-Diffusion', 'Sci-Fi Diffusion'], label="Model", default="Sci-Fi Diffusion"),
- gr.inputs.Textbox(label="Prompt"),
- gr.inputs.Number(label="Seed", default=0),
- gr.inputs.Textbox(label="Negative Prompt", default=""),
- gr.inputs.Dropdown(["k_lms", "k_heun", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "DDIM", "k_dpm_fast", "k_dpm_adaptive", "k_dpmpp_2m", "k_dpmpp_2s_a", "k_dpmpp_sde"], label="Sampler", default="k_dpmpp_2s_a"),
- gr.inputs.Number(label="Steps", default=50)
-]
-
-outputs = gr.outputs.Image(label="Generated Image", type="pil")
-
-interface = gr.Interface(generate_image, inputs, outputs, title="",
- description="
-#include
-#include
-#include "filtered_lrelu.h"
-
-//------------------------------------------------------------------------
-
-static std::tuple filtered_lrelu(
- torch::Tensor x, torch::Tensor fu, torch::Tensor fd, torch::Tensor b, torch::Tensor si,
- int up, int down, int px0, int px1, int py0, int py1, int sx, int sy, float gain, float slope, float clamp, bool flip_filters, bool writeSigns)
-{
- // Set CUDA device.
- TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
- const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-
- // Validate arguments.
- TORCH_CHECK(fu.device() == x.device() && fd.device() == x.device() && b.device() == x.device(), "all input tensors must reside on the same device");
- TORCH_CHECK(fu.dtype() == torch::kFloat && fd.dtype() == torch::kFloat, "fu and fd must be float32");
- TORCH_CHECK(b.dtype() == x.dtype(), "x and b must have the same dtype");
- TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat, "x and b must be float16 or float32");
- TORCH_CHECK(x.dim() == 4, "x must be rank 4");
- TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
- TORCH_CHECK(x.numel() > 0, "x is empty");
- TORCH_CHECK((fu.dim() == 1 || fu.dim() == 2) && (fd.dim() == 1 || fd.dim() == 2), "fu and fd must be rank 1 or 2");
- TORCH_CHECK(fu.size(0) <= INT_MAX && fu.size(-1) <= INT_MAX, "fu is too large");
- TORCH_CHECK(fd.size(0) <= INT_MAX && fd.size(-1) <= INT_MAX, "fd is too large");
- TORCH_CHECK(fu.numel() > 0, "fu is empty");
- TORCH_CHECK(fd.numel() > 0, "fd is empty");
- TORCH_CHECK(b.dim() == 1 && b.size(0) == x.size(1), "b must be a vector with the same number of channels as x");
- TORCH_CHECK(up >= 1 && down >= 1, "up and down must be at least 1");
-
- // Figure out how much shared memory is available on the device.
- int maxSharedBytes = 0;
- AT_CUDA_CHECK(cudaDeviceGetAttribute(&maxSharedBytes, cudaDevAttrMaxSharedMemoryPerBlockOptin, x.device().index()));
- int sharedKB = maxSharedBytes >> 10;
-
- // Populate enough launch parameters to check if a CUDA kernel exists.
- filtered_lrelu_kernel_params p;
- p.up = up;
- p.down = down;
- p.fuShape = make_int2((int)fu.size(-1), fu.dim() == 2 ? (int)fu.size(0) : 0); // shape [n, 0] indicates separable filter.
- p.fdShape = make_int2((int)fd.size(-1), fd.dim() == 2 ? (int)fd.size(0) : 0);
- filtered_lrelu_kernel_spec test_spec = choose_filtered_lrelu_kernel(p, sharedKB);
- if (!test_spec.exec)
- {
- // No kernel found - return empty tensors and indicate missing kernel with return code of -1.
- return std::make_tuple(torch::Tensor(), torch::Tensor(), -1);
- }
-
- // Input/output element size.
- int64_t sz = (x.dtype() == torch::kHalf) ? 2 : 4;
-
- // Input sizes.
- int64_t xw = (int)x.size(3);
- int64_t xh = (int)x.size(2);
- int64_t fut_w = (int)fu.size(-1) - 1;
- int64_t fut_h = (int)fu.size(0) - 1;
- int64_t fdt_w = (int)fd.size(-1) - 1;
- int64_t fdt_h = (int)fd.size(0) - 1;
-
- // Logical size of upsampled buffer.
- int64_t cw = xw * up + (px0 + px1) - fut_w;
- int64_t ch = xh * up + (py0 + py1) - fut_h;
- TORCH_CHECK(cw > fdt_w && ch > fdt_h, "upsampled buffer must be at least the size of downsampling filter");
- TORCH_CHECK(cw <= INT_MAX && ch <= INT_MAX, "upsampled buffer is too large");
-
- // Compute output size and allocate.
- int64_t yw = (cw - fdt_w + (down - 1)) / down;
- int64_t yh = (ch - fdt_h + (down - 1)) / down;
- TORCH_CHECK(yw > 0 && yh > 0, "output must be at least 1x1");
- TORCH_CHECK(yw <= INT_MAX && yh <= INT_MAX, "output is too large");
- torch::Tensor y = torch::empty({x.size(0), x.size(1), yh, yw}, x.options(), x.suggest_memory_format());
-
- // Allocate sign tensor.
- torch::Tensor so;
- torch::Tensor s = si;
- bool readSigns = !!s.numel();
- int64_t sw_active = 0; // Active width of sign tensor.
- if (writeSigns)
- {
- sw_active = yw * down - (down - 1) + fdt_w; // Active width in elements.
- int64_t sh = yh * down - (down - 1) + fdt_h; // Height = active height.
- int64_t sw = (sw_active + 15) & ~15; // Width = active width in elements, rounded up to multiple of 16.
- TORCH_CHECK(sh <= INT_MAX && (sw >> 2) <= INT_MAX, "signs is too large");
- s = so = torch::empty({x.size(0), x.size(1), sh, sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
- }
- else if (readSigns)
- sw_active = s.size(3) << 2;
-
- // Validate sign tensor if in use.
- if (readSigns || writeSigns)
- {
- TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
- TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
- TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
- TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
- TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
- TORCH_CHECK(s.size(2) <= INT_MAX && s.size(3) <= INT_MAX, "signs is too large");
- }
-
- // Populate rest of CUDA kernel parameters.
- p.x = x.data_ptr();
- p.y = y.data_ptr();
- p.b = b.data_ptr();
- p.s = (readSigns || writeSigns) ? s.data_ptr() : 0;
- p.fu = fu.data_ptr();
- p.fd = fd.data_ptr();
- p.pad0 = make_int2(px0, py0);
- p.gain = gain;
- p.slope = slope;
- p.clamp = clamp;
- p.flip = (flip_filters) ? 1 : 0;
- p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
- p.yShape = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
- p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3), (int)s.size(2)) : make_int2(0, 0); // Width is in bytes. Contiguous.
- p.sOfs = make_int2(sx, sy);
- p.swLimit = (sw_active + 3) >> 2; // Rounded up to bytes.
-
- // x, y, b strides are in bytes.
- p.xStride = make_longlong4(sz * x.stride(3), sz * x.stride(2), sz * x.stride(1), sz * x.stride(0));
- p.yStride = make_longlong4(sz * y.stride(3), sz * y.stride(2), sz * y.stride(1), sz * y.stride(0));
- p.bStride = sz * b.stride(0);
-
- // fu, fd strides are in elements.
- p.fuStride = make_longlong3(fu.stride(-1), fu.dim() == 2 ? fu.stride(0) : 0, 0);
- p.fdStride = make_longlong3(fd.stride(-1), fd.dim() == 2 ? fd.stride(0) : 0, 0);
-
- // Determine if indices don't fit in int32. Support negative strides although Torch currently never produces those.
- bool index64b = false;
- if (std::abs(p.bStride * x.size(1)) > INT_MAX) index64b = true;
- if (std::min(x.size(0) * p.xStride.w, 0ll) + std::min(x.size(1) * p.xStride.z, 0ll) + std::min(x.size(2) * p.xStride.y, 0ll) + std::min(x.size(3) * p.xStride.x, 0ll) < -INT_MAX) index64b = true;
- if (std::max(x.size(0) * p.xStride.w, 0ll) + std::max(x.size(1) * p.xStride.z, 0ll) + std::max(x.size(2) * p.xStride.y, 0ll) + std::max(x.size(3) * p.xStride.x, 0ll) > INT_MAX) index64b = true;
- if (std::min(y.size(0) * p.yStride.w, 0ll) + std::min(y.size(1) * p.yStride.z, 0ll) + std::min(y.size(2) * p.yStride.y, 0ll) + std::min(y.size(3) * p.yStride.x, 0ll) < -INT_MAX) index64b = true;
- if (std::max(y.size(0) * p.yStride.w, 0ll) + std::max(y.size(1) * p.yStride.z, 0ll) + std::max(y.size(2) * p.yStride.y, 0ll) + std::max(y.size(3) * p.yStride.x, 0ll) > INT_MAX) index64b = true;
- if (s.numel() > INT_MAX) index64b = true;
-
- // Choose CUDA kernel.
- filtered_lrelu_kernel_spec spec = { 0 };
- AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_cuda", [&]
- {
- if constexpr (sizeof(scalar_t) <= 4) // Exclude doubles. constexpr prevents template instantiation.
- {
- // Choose kernel based on index type, datatype and sign read/write modes.
- if (!index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if (!index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if (!index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- }
- });
- TORCH_CHECK(spec.exec, "internal error - CUDA kernel not found") // This should not happen because we tested earlier that kernel exists.
-
- // Launch CUDA kernel.
- void* args[] = {&p};
- int bx = spec.numWarps * 32;
- int gx = (p.yShape.x - 1) / spec.tileOut.x + 1;
- int gy = (p.yShape.y - 1) / spec.tileOut.y + 1;
- int gz = p.yShape.z * p.yShape.w;
-
- // Repeat multiple horizontal tiles in a CTA?
- if (spec.xrep)
- {
- p.tilesXrep = spec.xrep;
- p.tilesXdim = gx;
-
- gx = (gx + p.tilesXrep - 1) / p.tilesXrep;
- std::swap(gx, gy);
- }
- else
- {
- p.tilesXrep = 0;
- p.tilesXdim = 0;
- }
-
- // Launch filter setup kernel.
- AT_CUDA_CHECK(cudaLaunchKernel(spec.setup, 1, 1024, args, 0, at::cuda::getCurrentCUDAStream()));
-
- // Copy kernels to constant memory.
- if ( writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
- else if (!writeSigns && readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
- else if (!writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
-
- // Set cache and shared memory configurations for main kernel.
- AT_CUDA_CHECK(cudaFuncSetCacheConfig(spec.exec, cudaFuncCachePreferShared));
- if (spec.dynamicSharedKB) // Need dynamically allocated shared memory?
- AT_CUDA_CHECK(cudaFuncSetAttribute(spec.exec, cudaFuncAttributeMaxDynamicSharedMemorySize, spec.dynamicSharedKB << 10));
- AT_CUDA_CHECK(cudaFuncSetSharedMemConfig(spec.exec, cudaSharedMemBankSizeFourByte));
-
- // Launch main kernel.
- const int maxSubGz = 65535; // CUDA maximum for block z dimension.
- for (int zofs=0; zofs < gz; zofs += maxSubGz) // Do multiple launches if gz is too big.
- {
- p.blockZofs = zofs;
- int subGz = std::min(maxSubGz, gz - zofs);
- AT_CUDA_CHECK(cudaLaunchKernel(spec.exec, dim3(gx, gy, subGz), bx, args, spec.dynamicSharedKB << 10, at::cuda::getCurrentCUDAStream()));
- }
-
- // Done.
- return std::make_tuple(y, so, 0);
-}
-
-//------------------------------------------------------------------------
-
-static torch::Tensor filtered_lrelu_act(torch::Tensor x, torch::Tensor si, int sx, int sy, float gain, float slope, float clamp, bool writeSigns)
-{
- // Set CUDA device.
- TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
- const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-
- // Validate arguments.
- TORCH_CHECK(x.dim() == 4, "x must be rank 4");
- TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
- TORCH_CHECK(x.numel() > 0, "x is empty");
- TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat || x.dtype() == torch::kDouble, "x must be float16, float32 or float64");
-
- // Output signs if we don't have sign input.
- torch::Tensor so;
- torch::Tensor s = si;
- bool readSigns = !!s.numel();
- if (writeSigns)
- {
- int64_t sw = x.size(3);
- sw = (sw + 15) & ~15; // Round to a multiple of 16 for coalescing.
- s = so = torch::empty({x.size(0), x.size(1), x.size(2), sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
- }
-
- // Validate sign tensor if in use.
- if (readSigns || writeSigns)
- {
- TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
- TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
- TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
- TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
- TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
- TORCH_CHECK(s.size(2) <= INT_MAX && (s.size(3) << 2) <= INT_MAX, "signs tensor is too large");
- }
-
- // Initialize CUDA kernel parameters.
- filtered_lrelu_act_kernel_params p;
- p.x = x.data_ptr();
- p.s = (readSigns || writeSigns) ? s.data_ptr() : 0;
- p.gain = gain;
- p.slope = slope;
- p.clamp = clamp;
- p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
- p.xStride = make_longlong4(x.stride(3), x.stride(2), x.stride(1), x.stride(0));
- p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3) << 2, (int)s.size(2)) : make_int2(0, 0); // Width is in elements. Contiguous.
- p.sOfs = make_int2(sx, sy);
-
- // Choose CUDA kernel.
- void* func = 0;
- AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_act_cuda", [&]
- {
- if (writeSigns)
- func = choose_filtered_lrelu_act_kernel();
- else if (readSigns)
- func = choose_filtered_lrelu_act_kernel();
- else
- func = choose_filtered_lrelu_act_kernel();
- });
- TORCH_CHECK(func, "internal error - CUDA kernel not found");
-
- // Launch CUDA kernel.
- void* args[] = {&p};
- int bx = 128; // 4 warps per block.
-
- // Logical size of launch = writeSigns ? p.s : p.x
- uint32_t gx = writeSigns ? p.sShape.x : p.xShape.x;
- uint32_t gy = writeSigns ? p.sShape.y : p.xShape.y;
- uint32_t gz = p.xShape.z * p.xShape.w; // Same as in p.sShape if signs are in use.
- gx = (gx - 1) / bx + 1;
-
- // Make sure grid y and z dimensions are within CUDA launch limits. Kernel loops internally to do the rest.
- const uint32_t gmax = 65535;
- gy = std::min(gy, gmax);
- gz = std::min(gz, gmax);
-
- // Launch.
- AT_CUDA_CHECK(cudaLaunchKernel(func, dim3(gx, gy, gz), bx, args, 0, at::cuda::getCurrentCUDAStream()));
- return so;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
- m.def("filtered_lrelu", &filtered_lrelu); // The whole thing.
- m.def("filtered_lrelu_act_", &filtered_lrelu_act); // Activation and sign tensor handling only. Modifies data tensor in-place.
-}
-
-//------------------------------------------------------------------------
diff --git a/spaces/EronSamez/RVC_HFmeu/gui_v0.py b/spaces/EronSamez/RVC_HFmeu/gui_v0.py
deleted file mode 100644
index 88c3cf9eb1eaa0fa812b32ae4d3750b4ce0a8699..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/gui_v0.py
+++ /dev/null
@@ -1,786 +0,0 @@
-import os, sys, traceback, re
-
-import json
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from configs.config import Config
-
-Config = Config()
-import PySimpleGUI as sg
-import sounddevice as sd
-import noisereduce as nr
-import numpy as np
-from fairseq import checkpoint_utils
-import librosa, torch, pyworld, faiss, time, threading
-import torch.nn.functional as F
-import torchaudio.transforms as tat
-import scipy.signal as signal
-import torchcrepe
-
-# import matplotlib.pyplot as plt
-from lib.infer_pack.models import (
- SynthesizerTrnMs256NSFsid,
- SynthesizerTrnMs256NSFsid_nono,
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono,
-)
-from i18n import I18nAuto
-
-i18n = I18nAuto()
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-current_dir = os.getcwd()
-
-
-class RVC:
- def __init__(
- self, key, f0_method, hubert_path, pth_path, index_path, npy_path, index_rate
- ) -> None:
- """
- 初始化
- """
- try:
- self.f0_up_key = key
- self.time_step = 160 / 16000 * 1000
- self.f0_min = 50
- self.f0_max = 1100
- self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
- self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
- self.f0_method = f0_method
- self.sr = 16000
- self.window = 160
-
- # Get Torch Device
- if torch.cuda.is_available():
- self.torch_device = torch.device(
- f"cuda:{0 % torch.cuda.device_count()}"
- )
- elif torch.backends.mps.is_available():
- self.torch_device = torch.device("mps")
- else:
- self.torch_device = torch.device("cpu")
-
- if index_rate != 0:
- self.index = faiss.read_index(index_path)
- # self.big_npy = np.load(npy_path)
- self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
- print("index search enabled")
- self.index_rate = index_rate
- model_path = hubert_path
- print("load model(s) from {}".format(model_path))
- models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
- [model_path],
- suffix="",
- )
- self.model = models[0]
- self.model = self.model.to(device)
- if Config.is_half:
- self.model = self.model.half()
- else:
- self.model = self.model.float()
- self.model.eval()
- cpt = torch.load(pth_path, map_location="cpu")
- self.tgt_sr = cpt["config"][-1]
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- self.if_f0 = cpt.get("f0", 1)
- self.version = cpt.get("version", "v1")
- if self.version == "v1":
- if self.if_f0 == 1:
- self.net_g = SynthesizerTrnMs256NSFsid(
- *cpt["config"], is_half=Config.is_half
- )
- else:
- self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
- elif self.version == "v2":
- if self.if_f0 == 1:
- self.net_g = SynthesizerTrnMs768NSFsid(
- *cpt["config"], is_half=Config.is_half
- )
- else:
- self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
- del self.net_g.enc_q
- print(self.net_g.load_state_dict(cpt["weight"], strict=False))
- self.net_g.eval().to(device)
- if Config.is_half:
- self.net_g = self.net_g.half()
- else:
- self.net_g = self.net_g.float()
- except:
- print(traceback.format_exc())
-
- def get_regular_crepe_computation(self, x, f0_min, f0_max, model="full"):
- batch_size = 512
- # Compute pitch using first gpu
- audio = torch.tensor(np.copy(x))[None].float()
- f0, pd = torchcrepe.predict(
- audio,
- self.sr,
- self.window,
- f0_min,
- f0_max,
- model,
- batch_size=batch_size,
- device=self.torch_device,
- return_periodicity=True,
- )
- pd = torchcrepe.filter.median(pd, 3)
- f0 = torchcrepe.filter.mean(f0, 3)
- f0[pd < 0.1] = 0
- f0 = f0[0].cpu().numpy()
- return f0
-
- def get_harvest_computation(self, x, f0_min, f0_max):
- f0, t = pyworld.harvest(
- x.astype(np.double),
- fs=self.sr,
- f0_ceil=f0_max,
- f0_floor=f0_min,
- frame_period=10,
- )
- f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
- f0 = signal.medfilt(f0, 3)
- return f0
-
- def get_f0(self, x, f0_up_key, inp_f0=None):
- # Calculate Padding and f0 details here
- p_len = x.shape[0] // 512 # For Now This probs doesn't work
- x_pad = 1
- f0_min = 50
- f0_max = 1100
- f0_mel_min = 1127 * np.log(1 + f0_min / 700)
- f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
- f0 = 0
- # Here, check f0_methods and get their computations
- if self.f0_method == "harvest":
- f0 = self.get_harvest_computation(x, f0_min, f0_max)
- elif self.f0_method == "reg-crepe":
- f0 = self.get_regular_crepe_computation(x, f0_min, f0_max)
- elif self.f0_method == "reg-crepe-tiny":
- f0 = self.get_regular_crepe_computation(x, f0_min, f0_max, "tiny")
-
- # Calculate f0_course and f0_bak here
- f0 *= pow(2, f0_up_key / 12)
- # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- tf0 = self.sr // self.window # 每秒f0点数
- if inp_f0 is not None:
- delta_t = np.round(
- (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
- ).astype("int16")
- replace_f0 = np.interp(
- list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
- )
- shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
- f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
- # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- f0bak = f0.copy()
- f0_mel = 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
- f0_mel_max - f0_mel_min
- ) + 1
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > 255] = 255
- f0_coarse = np.rint(f0_mel).astype(np.int)
- return f0_coarse, f0bak # 1-0
-
- def infer(self, feats: torch.Tensor) -> np.ndarray:
- """
- 推理函数
- """
- audio = feats.clone().cpu().numpy()
- assert feats.dim() == 1, feats.dim()
- feats = feats.view(1, -1)
- padding_mask = torch.BoolTensor(feats.shape).fill_(False)
- if Config.is_half:
- feats = feats.half()
- else:
- feats = feats.float()
- inputs = {
- "source": feats.to(device),
- "padding_mask": padding_mask.to(device),
- "output_layer": 9 if self.version == "v1" else 12,
- }
- torch.cuda.synchronize()
- with torch.no_grad():
- logits = self.model.extract_features(**inputs)
- feats = (
- self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
- )
-
- ####索引优化
- try:
- if (
- hasattr(self, "index")
- and hasattr(self, "big_npy")
- and self.index_rate != 0
- ):
- npy = feats[0].cpu().numpy().astype("float32")
- score, ix = self.index.search(npy, k=8)
- weight = np.square(1 / score)
- weight /= weight.sum(axis=1, keepdims=True)
- npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
- if Config.is_half:
- npy = npy.astype("float16")
- feats = (
- torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
- + (1 - self.index_rate) * feats
- )
- else:
- print("index search FAIL or disabled")
- except:
- traceback.print_exc()
- print("index search FAIL")
- feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
- torch.cuda.synchronize()
- print(feats.shape)
- if self.if_f0 == 1:
- pitch, pitchf = self.get_f0(audio, self.f0_up_key)
- p_len = min(feats.shape[1], 13000, pitch.shape[0]) # 太大了爆显存
- else:
- pitch, pitchf = None, None
- p_len = min(feats.shape[1], 13000) # 太大了爆显存
- torch.cuda.synchronize()
- # print(feats.shape,pitch.shape)
- feats = feats[:, :p_len, :]
- if self.if_f0 == 1:
- pitch = pitch[:p_len]
- pitchf = pitchf[:p_len]
- pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
- pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
- p_len = torch.LongTensor([p_len]).to(device)
- ii = 0 # sid
- sid = torch.LongTensor([ii]).to(device)
- with torch.no_grad():
- if self.if_f0 == 1:
- infered_audio = (
- self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
- .data.cpu()
- .float()
- )
- else:
- infered_audio = (
- self.net_g.infer(feats, p_len, sid)[0][0, 0].data.cpu().float()
- )
- torch.cuda.synchronize()
- return infered_audio
-
-
-class GUIConfig:
- def __init__(self) -> None:
- self.hubert_path: str = ""
- self.pth_path: str = ""
- self.index_path: str = ""
- self.npy_path: str = ""
- self.f0_method: str = ""
- self.pitch: int = 12
- self.samplerate: int = 44100
- self.block_time: float = 1.0 # s
- self.buffer_num: int = 1
- self.threhold: int = -30
- self.crossfade_time: float = 0.08
- self.extra_time: float = 0.04
- self.I_noise_reduce = False
- self.O_noise_reduce = False
- self.index_rate = 0.3
-
-
-class GUI:
- def __init__(self) -> None:
- self.config = GUIConfig()
- self.flag_vc = False
-
- self.launcher()
-
- def load(self):
- (
- input_devices,
- output_devices,
- input_devices_indices,
- output_devices_indices,
- ) = self.get_devices()
- try:
- with open("values1.json", "r") as j:
- data = json.load(j)
- except:
- # Injecting f0_method into the json data
- with open("values1.json", "w") as j:
- data = {
- "pth_path": "",
- "index_path": "",
- "sg_input_device": input_devices[
- input_devices_indices.index(sd.default.device[0])
- ],
- "sg_output_device": output_devices[
- output_devices_indices.index(sd.default.device[1])
- ],
- "threhold": "-45",
- "pitch": "0",
- "index_rate": "0",
- "block_time": "1",
- "crossfade_length": "0.04",
- "extra_time": "1",
- }
- return data
-
- def launcher(self):
- data = self.load()
- sg.theme("DarkTeal12")
- input_devices, output_devices, _, _ = self.get_devices()
- layout = [
- [
- sg.Frame(
- title="Proudly forked by Mangio621",
- ),
- sg.Frame(
- title=i18n("Load model"),
- layout=[
- [
- sg.Input(
- default_text="hubert_base.pt",
- key="hubert_path",
- disabled=True,
- ),
- sg.FileBrowse(
- i18n("Hubert Model"),
- initial_folder=os.path.join(os.getcwd()),
- file_types=(("pt files", "*.pt"),),
- ),
- ],
- [
- sg.Input(
- default_text=data.get("pth_path", ""),
- key="pth_path",
- ),
- sg.FileBrowse(
- i18n("Select the .pth file"),
- initial_folder=os.path.join(os.getcwd(), "weights"),
- file_types=(("weight files", "*.pth"),),
- ),
- ],
- [
- sg.Input(
- default_text=data.get("index_path", ""),
- key="index_path",
- ),
- sg.FileBrowse(
- i18n("Select the .index file"),
- initial_folder=os.path.join(os.getcwd(), "logs"),
- file_types=(("index files", "*.index"),),
- ),
- ],
- [
- sg.Input(
- default_text="你不需要填写这个You don't need write this.",
- key="npy_path",
- disabled=True,
- ),
- sg.FileBrowse(
- i18n("Select the .npy file"),
- initial_folder=os.path.join(os.getcwd(), "logs"),
- file_types=(("feature files", "*.npy"),),
- ),
- ],
- ],
- ),
- ],
- [
- # Mangio f0 Selection frame Here
- sg.Frame(
- layout=[
- [
- sg.Radio(
- "Harvest", "f0_method", key="harvest", default=True
- ),
- sg.Radio("Crepe", "f0_method", key="reg-crepe"),
- sg.Radio("Crepe Tiny", "f0_method", key="reg-crepe-tiny"),
- ]
- ],
- title="Select an f0 Method",
- )
- ],
- [
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Input device")),
- sg.Combo(
- input_devices,
- key="sg_input_device",
- default_value=data.get("sg_input_device", ""),
- ),
- ],
- [
- sg.Text(i18n("Output device")),
- sg.Combo(
- output_devices,
- key="sg_output_device",
- default_value=data.get("sg_output_device", ""),
- ),
- ],
- ],
- title=i18n("Audio device (please use the same type of driver)"),
- )
- ],
- [
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Response threshold")),
- sg.Slider(
- range=(-60, 0),
- key="threhold",
- resolution=1,
- orientation="h",
- default_value=data.get("threhold", ""),
- ),
- ],
- [
- sg.Text(i18n("Pitch settings")),
- sg.Slider(
- range=(-24, 24),
- key="pitch",
- resolution=1,
- orientation="h",
- default_value=data.get("pitch", ""),
- ),
- ],
- [
- sg.Text(i18n("Index Rate")),
- sg.Slider(
- range=(0.0, 1.0),
- key="index_rate",
- resolution=0.01,
- orientation="h",
- default_value=data.get("index_rate", ""),
- ),
- ],
- ],
- title=i18n("General settings"),
- ),
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Sample length")),
- sg.Slider(
- range=(0.1, 3.0),
- key="block_time",
- resolution=0.1,
- orientation="h",
- default_value=data.get("block_time", ""),
- ),
- ],
- [
- sg.Text(i18n("Fade length")),
- sg.Slider(
- range=(0.01, 0.15),
- key="crossfade_length",
- resolution=0.01,
- orientation="h",
- default_value=data.get("crossfade_length", ""),
- ),
- ],
- [
- sg.Text(i18n("Extra推理时长")),
- sg.Slider(
- range=(0.05, 3.00),
- key="extra_time",
- resolution=0.01,
- orientation="h",
- default_value=data.get("extra_time", ""),
- ),
- ],
- [
- sg.Checkbox(i18n("Input noise reduction"), key="I_noise_reduce"),
- sg.Checkbox(i18n("Output noise reduction"), key="O_noise_reduce"),
- ],
- ],
- title=i18n("Performance settings"),
- ),
- ],
- [
- sg.Button(i18n("开始音频Convert"), key="start_vc"),
- sg.Button(i18n("停止音频Convert"), key="stop_vc"),
- sg.Text(i18n("Inference time (ms):")),
- sg.Text("0", key="infer_time"),
- ],
- ]
- self.window = sg.Window("RVC - GUI", layout=layout)
- self.event_handler()
-
- def event_handler(self):
- while True:
- event, values = self.window.read()
- if event == sg.WINDOW_CLOSED:
- self.flag_vc = False
- exit()
- if event == "start_vc" and self.flag_vc == False:
- if self.set_values(values) == True:
- print("using_cuda:" + str(torch.cuda.is_available()))
- self.start_vc()
- settings = {
- "pth_path": values["pth_path"],
- "index_path": values["index_path"],
- "f0_method": self.get_f0_method_from_radios(values),
- "sg_input_device": values["sg_input_device"],
- "sg_output_device": values["sg_output_device"],
- "threhold": values["threhold"],
- "pitch": values["pitch"],
- "index_rate": values["index_rate"],
- "block_time": values["block_time"],
- "crossfade_length": values["crossfade_length"],
- "extra_time": values["extra_time"],
- }
- with open("values1.json", "w") as j:
- json.dump(settings, j)
- if event == "stop_vc" and self.flag_vc == True:
- self.flag_vc = False
-
- # Function that returns the used f0 method in string format "harvest"
- def get_f0_method_from_radios(self, values):
- f0_array = [
- {"name": "harvest", "val": values["harvest"]},
- {"name": "reg-crepe", "val": values["reg-crepe"]},
- {"name": "reg-crepe-tiny", "val": values["reg-crepe-tiny"]},
- ]
- # Filter through to find a true value
- used_f0 = ""
- for f0 in f0_array:
- if f0["val"] == True:
- used_f0 = f0["name"]
- break
- if used_f0 == "":
- used_f0 = "harvest" # Default Harvest if used_f0 is empty somehow
- return used_f0
-
- def set_values(self, values):
- if len(values["pth_path"].strip()) == 0:
- sg.popup(i18n("Select the pth file"))
- return False
- if len(values["index_path"].strip()) == 0:
- sg.popup(i18n("Select the index file"))
- return False
- pattern = re.compile("[^\x00-\x7F]+")
- if pattern.findall(values["hubert_path"]):
- sg.popup(i18n("The hubert model path must not contain Chinese characters"))
- return False
- if pattern.findall(values["pth_path"]):
- sg.popup(i18n("The pth file path must not contain Chinese characters."))
- return False
- if pattern.findall(values["index_path"]):
- sg.popup(i18n("The index file path must not contain Chinese characters."))
- return False
- self.set_devices(values["sg_input_device"], values["sg_output_device"])
- self.config.hubert_path = os.path.join(current_dir, "hubert_base.pt")
- self.config.pth_path = values["pth_path"]
- self.config.index_path = values["index_path"]
- self.config.npy_path = values["npy_path"]
- self.config.f0_method = self.get_f0_method_from_radios(values)
- self.config.threhold = values["threhold"]
- self.config.pitch = values["pitch"]
- self.config.block_time = values["block_time"]
- self.config.crossfade_time = values["crossfade_length"]
- self.config.extra_time = values["extra_time"]
- self.config.I_noise_reduce = values["I_noise_reduce"]
- self.config.O_noise_reduce = values["O_noise_reduce"]
- self.config.index_rate = values["index_rate"]
- return True
-
- def start_vc(self):
- torch.cuda.empty_cache()
- self.flag_vc = True
- self.block_frame = int(self.config.block_time * self.config.samplerate)
- self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate)
- self.sola_search_frame = int(0.012 * self.config.samplerate)
- self.delay_frame = int(0.01 * self.config.samplerate) # 往前预留0.02s
- self.extra_frame = int(self.config.extra_time * self.config.samplerate)
- self.rvc = None
- self.rvc = RVC(
- self.config.pitch,
- self.config.f0_method,
- self.config.hubert_path,
- self.config.pth_path,
- self.config.index_path,
- self.config.npy_path,
- self.config.index_rate,
- )
- self.input_wav: np.ndarray = np.zeros(
- self.extra_frame
- + self.crossfade_frame
- + self.sola_search_frame
- + self.block_frame,
- dtype="float32",
- )
- self.output_wav: torch.Tensor = torch.zeros(
- self.block_frame, device=device, dtype=torch.float32
- )
- self.sola_buffer: torch.Tensor = torch.zeros(
- self.crossfade_frame, device=device, dtype=torch.float32
- )
- self.fade_in_window: torch.Tensor = torch.linspace(
- 0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32
- )
- self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
- self.resampler1 = tat.Resample(
- orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
- )
- self.resampler2 = tat.Resample(
- orig_freq=self.rvc.tgt_sr,
- new_freq=self.config.samplerate,
- dtype=torch.float32,
- )
- thread_vc = threading.Thread(target=self.soundinput)
- thread_vc.start()
-
- def soundinput(self):
- """
- 接受音频输入
- """
- with sd.Stream(
- channels=2,
- callback=self.audio_callback,
- blocksize=self.block_frame,
- samplerate=self.config.samplerate,
- dtype="float32",
- ):
- while self.flag_vc:
- time.sleep(self.config.block_time)
- print("Audio block passed.")
- print("ENDing VC")
-
- def audio_callback(
- self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
- ):
- """
- 音频处理
- """
- start_time = time.perf_counter()
- indata = librosa.to_mono(indata.T)
- if self.config.I_noise_reduce:
- indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate)
-
- """noise gate"""
- frame_length = 2048
- hop_length = 1024
- rms = librosa.feature.rms(
- y=indata, frame_length=frame_length, hop_length=hop_length
- )
- db_threhold = librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold
- # print(rms.shape,db.shape,db)
- for i in range(db_threhold.shape[0]):
- if db_threhold[i]:
- indata[i * hop_length : (i + 1) * hop_length] = 0
- self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata)
-
- # infer
- print("input_wav:" + str(self.input_wav.shape))
- # print('infered_wav:'+str(infer_wav.shape))
- infer_wav: torch.Tensor = self.resampler2(
- self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav)))
- )[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to(
- device
- )
- print("infer_wav:" + str(infer_wav.shape))
-
- # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
- cor_nom = F.conv1d(
- infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
- self.sola_buffer[None, None, :],
- )
- cor_den = torch.sqrt(
- F.conv1d(
- infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
- ** 2,
- torch.ones(1, 1, self.crossfade_frame, device=device),
- )
- + 1e-8
- )
- sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
- print("sola offset: " + str(int(sola_offset)))
-
- # crossfade
- self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
- self.output_wav[: self.crossfade_frame] *= self.fade_in_window
- self.output_wav[: self.crossfade_frame] += self.sola_buffer[:]
- if sola_offset < self.sola_search_frame:
- self.sola_buffer[:] = (
- infer_wav[
- -self.sola_search_frame
- - self.crossfade_frame
- + sola_offset : -self.sola_search_frame
- + sola_offset
- ]
- * self.fade_out_window
- )
- else:
- self.sola_buffer[:] = (
- infer_wav[-self.crossfade_frame :] * self.fade_out_window
- )
-
- if self.config.O_noise_reduce:
- outdata[:] = np.tile(
- nr.reduce_noise(
- y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
- ),
- (2, 1),
- ).T
- else:
- outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
- total_time = time.perf_counter() - start_time
- self.window["infer_time"].update(int(total_time * 1000))
- print("infer time:" + str(total_time))
- print("f0_method: " + str(self.config.f0_method))
-
- def get_devices(self, update: bool = True):
- """获取设备列表"""
- if update:
- sd._terminate()
- sd._initialize()
- devices = sd.query_devices()
- hostapis = sd.query_hostapis()
- for hostapi in hostapis:
- for device_idx in hostapi["devices"]:
- devices[device_idx]["hostapi_name"] = hostapi["name"]
- input_devices = [
- f"{d['name']} ({d['hostapi_name']})"
- for d in devices
- if d["max_input_channels"] > 0
- ]
- output_devices = [
- f"{d['name']} ({d['hostapi_name']})"
- for d in devices
- if d["max_output_channels"] > 0
- ]
- input_devices_indices = [
- d["index"] if "index" in d else d["name"]
- for d in devices
- if d["max_input_channels"] > 0
- ]
- output_devices_indices = [
- d["index"] if "index" in d else d["name"]
- for d in devices
- if d["max_output_channels"] > 0
- ]
- return (
- input_devices,
- output_devices,
- input_devices_indices,
- output_devices_indices,
- )
-
- def set_devices(self, input_device, output_device):
- """设置输出设备"""
- (
- input_devices,
- output_devices,
- input_device_indices,
- output_device_indices,
- ) = self.get_devices()
- sd.default.device[0] = input_device_indices[input_devices.index(input_device)]
- sd.default.device[1] = output_device_indices[
- output_devices.index(output_device)
- ]
- print("input device:" + str(sd.default.device[0]) + ":" + str(input_device))
- print("output device:" + str(sd.default.device[1]) + ":" + str(output_device))
-
-
-gui = GUI()
diff --git a/spaces/EuroPython2022/clickbaitonator/fudge/main.py b/spaces/EuroPython2022/clickbaitonator/fudge/main.py
deleted file mode 100644
index e8c2299b2449b6dd07d26c7ae678732b1dabca88..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/clickbaitonator/fudge/main.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import os
-import random
-import time
-import pickle
-import math
-from argparse import ArgumentParser
-
-from tqdm import tqdm
-import numpy as np
-import torch
-import torch.nn as nn
-
-from data import Dataset
-from model import Model
-from util import save_checkpoint, ProgressMeter, AverageMeter, num_params, pad_mask
-from constants import *
-
-
-def train(model, dataset, optimizer, criterion, epoch, args, data_start_index):
- model.train()
- if data_start_index == 0:
- dataset.shuffle('train', seed=epoch + args.seed)
- if args.epoch_max_len is not None:
- data_end_index = min(data_start_index + args.epoch_max_len, len(dataset.splits['train']))
- loader = dataset.loader('train', num_workers=args.num_workers, indices=list(range(data_start_index, data_end_index)))
- data_start_index = data_end_index if data_end_index < len(dataset.splits['train']) else 0
- else:
- loader = dataset.loader('train', num_workers=args.num_workers)
- loss_meter = AverageMeter('loss', ':6.4f')
- total_length = len(loader)
- progress = ProgressMeter(total_length, [loss_meter], prefix='Training: ')
- for batch_num, batch in enumerate(tqdm(loader, total=len(loader))):
- batch = [tensor.to(args.device) for tensor in batch]
- inputs, lengths, future_words, log_probs, labels, classification_targets, syllables_to_go, future_word_num_syllables, rhyme_group_index = batch
- if args.task not in ['formality', 'iambic']:
- if not args.debug and len(inputs) != args.batch_size: # it'll screw up the bias...?
- continue
- scores = model(inputs, lengths, future_words, log_probs, syllables_to_go, future_word_num_syllables, rhyme_group_index, run_classifier=True)
- if args.task == 'formality': # we're learning for all positions at once. scores are batch x seq
- expanded_labels = classification_targets.unsqueeze(1).expand(-1, scores.shape[1]) # batch x seq
- length_mask = pad_mask(lengths).permute(1, 0) # batch x seq
- loss = criterion(scores.flatten()[length_mask.flatten()==1], expanded_labels.flatten().float()[length_mask.flatten()==1])
- elif args.task in ['iambic', 'newline']:
- use_indices = classification_targets.flatten() != -1
- loss = criterion(scores.flatten()[use_indices], classification_targets.flatten().float()[use_indices])
- else: # topic, rhyme
- loss = criterion(scores.flatten(), labels.flatten().float())
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
- loss_meter.update(loss.detach(), len(labels))
- if batch_num % args.train_print_freq == 0:
- progress.display(batch_num)
- progress.display(total_length)
- return data_start_index
-
-
-def validate(model, dataset, criterion, epoch, args):
- model.eval()
- random.seed(0)
- loader = dataset.loader('val', num_workers=args.num_workers)
- loss_meter = AverageMeter('loss', ':6.4f')
- total_length = len(loader)
- progress = ProgressMeter(total_length, [loss_meter], prefix='Validation: ')
- with torch.no_grad():
- for batch_num, batch in enumerate(tqdm(loader, total=len(loader))):
- batch = [tensor.to(args.device) for tensor in batch]
- inputs, lengths, future_words, log_probs, labels, classification_targets, syllables_to_go, future_word_num_syllables, rhyme_group_index = batch
- if args.task not in ['formality', 'iambic']: # topic predictor
- if not args.debug and len(inputs) != args.batch_size:
- continue
- scores = model(inputs, lengths, future_words, log_probs, syllables_to_go, future_word_num_syllables, rhyme_group_index, run_classifier=True)
- if args.task == 'formality': # we're learning for all positions at once. scores are batch x seq
- expanded_labels = classification_targets.unsqueeze(1).expand(-1, scores.shape[1]) # batch x seq
- length_mask = pad_mask(lengths).permute(1, 0) # batch x seq
- loss = criterion(scores.flatten()[length_mask.flatten()==1], expanded_labels.flatten().float()[length_mask.flatten()==1])
- elif args.task in ['iambic', 'newline']:
- use_indices = classification_targets.flatten() != -1
- loss = criterion(scores.flatten()[use_indices], classification_targets.flatten().float()[use_indices])
- else: # topic, rhyme
- loss = criterion(scores.flatten(), labels.flatten().float())
- loss_meter.update(loss.detach(), len(labels))
- if batch_num % args.train_print_freq == 0:
- progress.display(batch_num)
- progress.display(total_length)
- return loss_meter.avg
-
-
-def main(args):
- dataset = Dataset(args)
- os.makedirs(args.save_dir, exist_ok=True)
- with open(os.path.join(args.save_dir, 'dataset_info'), 'wb') as wf:
- pickle.dump(dataset.dataset_info, wf)
- if args.task == 'rhyme':
- with open(os.path.join(args.save_dir, 'rhyme_info'), 'wb') as wf:
- pickle.dump(dataset.rhyme_info, wf)
- if args.ckpt:
- checkpoint = torch.load(args.ckpt, map_location=args.device)
- start_epoch = checkpoint['epoch'] + 1
- best_val_metric = checkpoint['best_metric']
- model_args = checkpoint['args']
- model = Model(model_args, dataset.gpt_pad_id, len(dataset.index2word), rhyme_group_size=len(dataset.index2rhyme_group) if args.task == 'rhyme' else None) # no need to get the glove embeddings when reloading since they're saved in model ckpt anyway
- model.load_state_dict(checkpoint['state_dict'])
- model = model.to(args.device)
- optimizer = torch.optim.Adam(model.parameters(), lr=model_args.lr)
- optimizer.load_state_dict(checkpoint['optimizer'])
- data_start_index = checkpoint['data_start_index']
- print("=> loaded checkpoint '{}' (epoch {})"
- .format(args.ckpt, checkpoint['epoch']))
- # NOTE: just import pdb after loading the model here if you want to play with it, it's easy
- # model.eval()
- # import pdb; pdb.set_trace()
- else:
- model = Model(args, dataset.gpt_pad_id, len(dataset.index2word), rhyme_group_size=len(dataset.index2rhyme_group) if args.task == 'rhyme' else None, glove_embeddings=dataset.glove_embeddings)
- model = model.to(args.device)
- optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
- best_val_metric = 1e8 # lower is better for BCE
- data_start_index = 0
- print('num params', num_params(model))
- criterion = nn.BCEWithLogitsLoss().to(args.device)
-
- if args.evaluate:
- epoch = 0
- validate(model, dataset, criterion, epoch, args)
- return
- for epoch in range(args.epochs):
- print("TRAINING: Epoch {} at {}".format(epoch, time.ctime()))
- data_start_index = train(model, dataset, optimizer, criterion, epoch, args, data_start_index)
- if epoch % args.validation_freq == 0:
- print("VALIDATION: Epoch {} at {}".format(epoch, time.ctime()))
- metric = validate(model, dataset, criterion, epoch, args)
-
- if not args.debug:
- if metric < best_val_metric:
- print('new best val metric', metric)
- best_val_metric = metric
- save_checkpoint({
- 'epoch': epoch,
- 'state_dict': model.state_dict(),
- 'best_metric': best_val_metric,
- 'optimizer': optimizer.state_dict(),
- 'data_start_index': data_start_index,
- 'args': args
- }, os.path.join(args.save_dir, 'model_best.pth.tar'))
- save_checkpoint({
- 'epoch': epoch,
- 'state_dict': model.state_dict(),
- 'best_metric': metric,
- 'optimizer': optimizer.state_dict(),
- 'data_start_index': data_start_index,
- 'args': args
- }, os.path.join(args.save_dir, 'model_epoch' + str(epoch) + '.pth.tar'))
-
-
-if __name__=='__main__':
- parser = ArgumentParser()
-
- # DATA
- parser.add_argument('--task', type=str, required=True, choices=['iambic', 'rhyme', 'newline', 'topic', 'formality', 'clickbait'])
- parser.add_argument('--data_dir', type=str, required=True)
- parser.add_argument('--glove_file', type=str, help='glove embedding init, for topic task')
-
- # SAVE/LOAD
- parser.add_argument('--save_dir', type=str, required=True, help='where to save ckpts')
- parser.add_argument('--ckpt', type=str, default=None, help='load ckpt from file if given')
- parser.add_argument('--dataset_info', type=str, help='saved dataset info')
- parser.add_argument('--rhyme_info', type=str, help='saved dataset rhyme info, for a ckpt with task==rhyme')
-
- # TRAINING
- parser.add_argument('--batch_size', type=int, default=128)
- parser.add_argument('--epochs', type=int, default=100)
- parser.add_argument('--epoch_max_len', type=int, default=None, help='max batches per epoch if set, for more frequent validation')
- parser.add_argument('--validation_freq', type=int, default=1, help='validate every X epochs')
- parser.add_argument('--lr', type=float, default=1e-3, help='Adam learning rate')
- parser.add_argument('--seed', type=int, default=1, help='random seed')
- parser.add_argument('--device', type=str, default='cuda', choices=['cpu', 'cuda'])
- parser.add_argument('--num_workers', type=int, default=20, help='num workers for data loader')
- parser.add_argument('--evaluate', action='store_true', default=False)
- parser.add_argument('--debug', action='store_true', default=False)
-
- # PRINTING
- parser.add_argument('--train_print_freq', type=int, default=100, help='how often to print metrics (every X batches)')
-
- args = parser.parse_args()
-
- random.seed(args.seed)
- np.random.seed(args.seed)
- torch.manual_seed(args.seed)
- if args.evaluate:
- assert args.ckpt is not None
-
- main(args)
\ No newline at end of file
diff --git a/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts b/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts
deleted file mode 100644
index 9ba0fe30a092c73c8321a6bb2502750471b133f2..0000000000000000000000000000000000000000
--- a/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts
+++ /dev/null
@@ -1,279 +0,0 @@
-import {
- ChatInterface,
- ChatModule,
- ChatRestModule,
- ChatWorkerClient,
-} from "@mlc-ai/web-llm";
-
-function getElementAndCheck(id: string): HTMLElement {
- const element = document.getElementById(id);
- if (element == null) {
- throw Error("Cannot find element " + id);
- }
- return element;
-}
-
-const appConfig = {
- model_list: [
- {
- model_url:
- "https://huggingface.co/Felladrin/mlc-chat-Mistral-7B-OpenOrca-q4f32_1/resolve/main/params/",
- local_id: "Mistral-7B-OpenOrca-q4f32_1",
- },
- ],
- model_lib_map: {
- "Mistral-7B-OpenOrca-q4f32_1":
- "https://huggingface.co/Felladrin/mlc-chat-Mistral-7B-OpenOrca-q4f32_1/resolve/main/Mistral-7B-OpenOrca-q4f32_1-webgpu.wasm",
- },
- use_web_worker: true,
-};
-
-class ChatUI {
- private uiChat: HTMLElement;
- private uiChatInput: HTMLInputElement;
- private uiChatInfoLabel: HTMLLabelElement;
- private chat: ChatInterface;
- private localChat: ChatInterface;
- private config = appConfig;
- private selectedModel: string;
- private chatLoaded = false;
- private requestInProgress = false;
- // We use a request chain to ensure that
- // all requests send to chat are sequentialized
- private chatRequestChain: Promise = Promise.resolve();
-
- constructor(chat: ChatInterface, localChat: ChatInterface) {
- // use web worker to run chat generation in background
- this.chat = chat;
- this.localChat = localChat;
- // get the elements
- this.uiChat = getElementAndCheck("chatui-chat");
- this.uiChatInput = getElementAndCheck("chatui-input") as HTMLInputElement;
- this.uiChatInfoLabel = getElementAndCheck(
- "chatui-info-label"
- ) as HTMLLabelElement;
- // register event handlers
- getElementAndCheck("chatui-reset-btn").onclick = () => {
- this.onReset();
- };
- getElementAndCheck("chatui-send-btn").onclick = () => {
- this.onGenerate();
- };
- // TODO: find other alternative triggers
- getElementAndCheck("chatui-input").onkeypress = (event) => {
- if (event.keyCode === 13) {
- this.onGenerate();
- }
- };
-
- const modelSelector = getElementAndCheck(
- "chatui-select"
- ) as HTMLSelectElement;
- for (let i = 0; i < this.config.model_list.length; ++i) {
- const item = this.config.model_list[i];
- const opt = document.createElement("option");
- opt.value = item.local_id;
- opt.innerHTML = item.local_id;
- opt.selected = i == 0;
- modelSelector.appendChild(opt);
- }
- // Append local server option to the model selector
- const localServerOpt = document.createElement("option");
- localServerOpt.value = "Local Server";
- localServerOpt.innerHTML = "Local Server";
- modelSelector.append(localServerOpt);
- this.selectedModel = modelSelector.value;
- modelSelector.onchange = () => {
- this.onSelectChange(modelSelector);
- };
- }
- /**
- * Push a task to the execution queue.
- *
- * @param task The task to be executed;
- */
- private pushTask(task: () => Promise) {
- const lastEvent = this.chatRequestChain;
- this.chatRequestChain = lastEvent.then(task);
- }
- // Event handlers
- // all event handler pushes the tasks to a queue
- // that get executed sequentially
- // the tasks previous tasks, which causes them to early stop
- // can be interrupted by chat.interruptGenerate
- private async onGenerate() {
- if (this.requestInProgress) {
- return;
- }
- this.pushTask(async () => {
- await this.asyncGenerate();
- });
- }
-
- private async onSelectChange(modelSelector: HTMLSelectElement) {
- if (this.requestInProgress) {
- // interrupt previous generation if any
- this.chat.interruptGenerate();
- }
- // try reset after previous requests finishes
- this.pushTask(async () => {
- await this.chat.resetChat();
- this.resetChatHistory();
- await this.unloadChat();
- this.selectedModel = modelSelector.value;
- await this.asyncInitChat();
- });
- }
-
- private async onReset() {
- if (this.requestInProgress) {
- // interrupt previous generation if any
- this.chat.interruptGenerate();
- }
- // try reset after previous requests finishes
- this.pushTask(async () => {
- await this.chat.resetChat();
- this.resetChatHistory();
- });
- }
-
- // Internal helper functions
- private appendMessage(kind, text) {
- if (kind == "init") {
- text = "[System Initalize] " + text;
- }
- if (this.uiChat === undefined) {
- throw Error("cannot find ui chat");
- }
- const msg = `
-
-
- ${text}
-
-
- `;
- this.uiChat.insertAdjacentHTML("beforeend", msg);
- this.uiChat.scrollTo(0, this.uiChat.scrollHeight);
- }
-
- private updateLastMessage(kind, text) {
- if (kind == "init") {
- text = "[System Initalize] " + text;
- }
- if (this.uiChat === undefined) {
- throw Error("cannot find ui chat");
- }
- const matches = this.uiChat.getElementsByClassName(`msg ${kind}-msg`);
- if (matches.length == 0) throw Error(`${kind} message do not exist`);
- const msg = matches[matches.length - 1];
- const msgText = msg.getElementsByClassName("msg-text");
- if (msgText.length != 1) throw Error("Expect msg-text");
- if (msgText[0].innerHTML == text) return;
- const list = text.split("\n").map((t) => {
- const item = document.createElement("div");
- item.textContent = t;
- return item;
- });
- msgText[0].innerHTML = "";
- list.forEach((item) => msgText[0].append(item));
- this.uiChat.scrollTo(0, this.uiChat.scrollHeight);
- }
-
- private resetChatHistory() {
- const clearTags = ["left", "right", "init", "error"];
- for (const tag of clearTags) {
- // need to unpack to list so the iterator don't get affected by mutation
- const matches = [...this.uiChat.getElementsByClassName(`msg ${tag}-msg`)];
- for (const item of matches) {
- this.uiChat.removeChild(item);
- }
- }
- if (this.uiChatInfoLabel !== undefined) {
- this.uiChatInfoLabel.innerHTML = "";
- }
- }
-
- private async asyncInitChat() {
- if (this.chatLoaded) return;
- this.requestInProgress = true;
- this.appendMessage("init", "");
- const initProgressCallback = (report) => {
- this.updateLastMessage("init", report.text);
- };
- this.chat.setInitProgressCallback(initProgressCallback);
-
- try {
- if (this.selectedModel != "Local Server") {
- await this.chat.reload(this.selectedModel, undefined, this.config);
- }
- } catch (err) {
- this.appendMessage("error", "Init error, " + err.toString());
- console.log(err.stack);
- this.unloadChat();
- this.requestInProgress = false;
- return;
- }
- this.requestInProgress = false;
- this.chatLoaded = true;
- }
-
- private async unloadChat() {
- await this.chat.unload();
- this.chatLoaded = false;
- }
-
- /**
- * Run generate
- */
- private async asyncGenerate() {
- await this.asyncInitChat();
- this.requestInProgress = true;
- const prompt = this.uiChatInput.value;
- if (prompt == "") {
- this.requestInProgress = false;
- return;
- }
-
- this.appendMessage("right", prompt);
- this.uiChatInput.value = "";
- this.uiChatInput.setAttribute("placeholder", "Generating...");
-
- this.appendMessage("left", "");
- const callbackUpdateResponse = (step, msg) => {
- if (msg.length === 0) return this.chat.interruptGenerate();
- this.updateLastMessage("left", msg);
- };
-
- try {
- if (this.selectedModel == "Local Server") {
- await this.localChat.generate(prompt, callbackUpdateResponse);
- this.uiChatInfoLabel.innerHTML =
- await this.localChat.runtimeStatsText();
- } else {
- await this.chat.generate(prompt, callbackUpdateResponse);
- this.uiChatInfoLabel.innerHTML = await this.chat.runtimeStatsText();
- }
- } catch (err) {
- this.appendMessage("error", "Generate error, " + err.toString());
- console.log(err.stack);
- await this.unloadChat();
- }
- this.uiChatInput.setAttribute("placeholder", "Enter your message...");
- this.requestInProgress = false;
- }
-}
-
-const useWebWorker = appConfig.use_web_worker;
-let chat: ChatInterface;
-let localChat: ChatInterface;
-
-if (useWebWorker) {
- chat = new ChatWorkerClient(
- new Worker(new URL("./worker.ts", import.meta.url), { type: "module" })
- );
- localChat = new ChatRestModule();
-} else {
- chat = new ChatModule();
- localChat = new ChatRestModule();
-}
-new ChatUI(chat, localChat);
diff --git a/spaces/Francesco/torch-cam-transformers/app.py b/spaces/Francesco/torch-cam-transformers/app.py
deleted file mode 100644
index 6c5bf7ea8344430235dfcc553c112be801348a50..0000000000000000000000000000000000000000
--- a/spaces/Francesco/torch-cam-transformers/app.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import streamlit as st
-
-import sys
-
-
-def main():
- # Wide mode
- st.set_page_config(layout="wide")
-
- # Designing the interface
- st.title(sys.version)
-
-
-main()
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py
deleted file mode 100644
index bf18572329019d7a8f1df01799eda207c16dd7ff..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py
+++ /dev/null
@@ -1,284 +0,0 @@
-import os
-import random
-import re
-import numpy as np
-import librosa
-import torch
-import random
-from utils import repeat_expand_2d
-from tqdm import tqdm
-from torch.utils.data import Dataset
-
-def traverse_dir(
- root_dir,
- extensions,
- amount=None,
- str_include=None,
- str_exclude=None,
- is_pure=False,
- is_sort=False,
- is_ext=True):
-
- file_list = []
- cnt = 0
- for root, _, files in os.walk(root_dir):
- for file in files:
- if any([file.endswith(f".{ext}") for ext in extensions]):
- # path
- mix_path = os.path.join(root, file)
- pure_path = mix_path[len(root_dir)+1:] if is_pure else mix_path
-
- # amount
- if (amount is not None) and (cnt == amount):
- if is_sort:
- file_list.sort()
- return file_list
-
- # check string
- if (str_include is not None) and (str_include not in pure_path):
- continue
- if (str_exclude is not None) and (str_exclude in pure_path):
- continue
-
- if not is_ext:
- ext = pure_path.split('.')[-1]
- pure_path = pure_path[:-(len(ext)+1)]
- file_list.append(pure_path)
- cnt += 1
- if is_sort:
- file_list.sort()
- return file_list
-
-
-def get_data_loaders(args, whole_audio=False):
- data_train = AudioDataset(
- filelists = args.data.training_files,
- waveform_sec=args.data.duration,
- hop_size=args.data.block_size,
- sample_rate=args.data.sampling_rate,
- load_all_data=args.train.cache_all_data,
- whole_audio=whole_audio,
- extensions=args.data.extensions,
- n_spk=args.model.n_spk,
- spk=args.spk,
- device=args.train.cache_device,
- fp16=args.train.cache_fp16,
- use_aug=True)
- loader_train = torch.utils.data.DataLoader(
- data_train ,
- batch_size=args.train.batch_size if not whole_audio else 1,
- shuffle=True,
- num_workers=args.train.num_workers if args.train.cache_device=='cpu' else 0,
- persistent_workers=(args.train.num_workers > 0) if args.train.cache_device=='cpu' else False,
- pin_memory=True if args.train.cache_device=='cpu' else False
- )
- data_valid = AudioDataset(
- filelists = args.data.validation_files,
- waveform_sec=args.data.duration,
- hop_size=args.data.block_size,
- sample_rate=args.data.sampling_rate,
- load_all_data=args.train.cache_all_data,
- whole_audio=True,
- spk=args.spk,
- extensions=args.data.extensions,
- n_spk=args.model.n_spk)
- loader_valid = torch.utils.data.DataLoader(
- data_valid,
- batch_size=1,
- shuffle=False,
- num_workers=0,
- pin_memory=True
- )
- return loader_train, loader_valid
-
-
-class AudioDataset(Dataset):
- def __init__(
- self,
- filelists,
- waveform_sec,
- hop_size,
- sample_rate,
- spk,
- load_all_data=True,
- whole_audio=False,
- extensions=['wav'],
- n_spk=1,
- device='cpu',
- fp16=False,
- use_aug=False,
- ):
- super().__init__()
-
- self.waveform_sec = waveform_sec
- self.sample_rate = sample_rate
- self.hop_size = hop_size
- self.filelists = filelists
- self.whole_audio = whole_audio
- self.use_aug = use_aug
- self.data_buffer={}
- self.pitch_aug_dict = {}
- # np.load(os.path.join(self.path_root, 'pitch_aug_dict.npy'), allow_pickle=True).item()
- if load_all_data:
- print('Load all the data filelists:', filelists)
- else:
- print('Load the f0, volume data filelists:', filelists)
- with open(filelists,"r") as f:
- self.paths = f.read().splitlines()
- for name_ext in tqdm(self.paths, total=len(self.paths)):
- name = os.path.splitext(name_ext)[0]
- path_audio = name_ext
- duration = librosa.get_duration(filename = path_audio, sr = self.sample_rate)
-
- path_f0 = name_ext + ".f0.npy"
- f0,_ = np.load(path_f0,allow_pickle=True)
- f0 = torch.from_numpy(np.array(f0,dtype=float)).float().unsqueeze(-1).to(device)
-
- path_volume = name_ext + ".vol.npy"
- volume = np.load(path_volume)
- volume = torch.from_numpy(volume).float().unsqueeze(-1).to(device)
-
- path_augvol = name_ext + ".aug_vol.npy"
- aug_vol = np.load(path_augvol)
- aug_vol = torch.from_numpy(aug_vol).float().unsqueeze(-1).to(device)
-
- if n_spk is not None and n_spk > 1:
- spk_name = name_ext.split("/")[-2]
- spk_id = spk[spk_name] if spk_name in spk else 0
- if spk_id < 0 or spk_id >= n_spk:
- raise ValueError(' [x] Muiti-speaker traing error : spk_id must be a positive integer from 0 to n_spk-1 ')
- else:
- spk_id = 0
- spk_id = torch.LongTensor(np.array([spk_id])).to(device)
-
- if load_all_data:
- '''
- audio, sr = librosa.load(path_audio, sr=self.sample_rate)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio)
- audio = torch.from_numpy(audio).to(device)
- '''
- path_mel = name_ext + ".mel.npy"
- mel = np.load(path_mel)
- mel = torch.from_numpy(mel).to(device)
-
- path_augmel = name_ext + ".aug_mel.npy"
- aug_mel,keyshift = np.load(path_augmel, allow_pickle=True)
- aug_mel = np.array(aug_mel,dtype=float)
- aug_mel = torch.from_numpy(aug_mel).to(device)
- self.pitch_aug_dict[name_ext] = keyshift
-
- path_units = name_ext + ".soft.pt"
- units = torch.load(path_units).to(device)
- units = units[0]
- units = repeat_expand_2d(units,f0.size(0)).transpose(0,1)
-
- if fp16:
- mel = mel.half()
- aug_mel = aug_mel.half()
- units = units.half()
-
- self.data_buffer[name_ext] = {
- 'duration': duration,
- 'mel': mel,
- 'aug_mel': aug_mel,
- 'units': units,
- 'f0': f0,
- 'volume': volume,
- 'aug_vol': aug_vol,
- 'spk_id': spk_id
- }
- else:
- path_augmel = name_ext + ".aug_mel.npy"
- aug_mel,keyshift = np.load(path_augmel, allow_pickle=True)
- self.pitch_aug_dict[name_ext] = keyshift
- self.data_buffer[name_ext] = {
- 'duration': duration,
- 'f0': f0,
- 'volume': volume,
- 'aug_vol': aug_vol,
- 'spk_id': spk_id
- }
-
-
- def __getitem__(self, file_idx):
- name_ext = self.paths[file_idx]
- data_buffer = self.data_buffer[name_ext]
- # check duration. if too short, then skip
- if data_buffer['duration'] < (self.waveform_sec + 0.1):
- return self.__getitem__( (file_idx + 1) % len(self.paths))
-
- # get item
- return self.get_data(name_ext, data_buffer)
-
- def get_data(self, name_ext, data_buffer):
- name = os.path.splitext(name_ext)[0]
- frame_resolution = self.hop_size / self.sample_rate
- duration = data_buffer['duration']
- waveform_sec = duration if self.whole_audio else self.waveform_sec
-
- # load audio
- idx_from = 0 if self.whole_audio else random.uniform(0, duration - waveform_sec - 0.1)
- start_frame = int(idx_from / frame_resolution)
- units_frame_len = int(waveform_sec / frame_resolution)
- aug_flag = random.choice([True, False]) and self.use_aug
- '''
- audio = data_buffer.get('audio')
- if audio is None:
- path_audio = os.path.join(self.path_root, 'audio', name) + '.wav'
- audio, sr = librosa.load(
- path_audio,
- sr = self.sample_rate,
- offset = start_frame * frame_resolution,
- duration = waveform_sec)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio)
- # clip audio into N seconds
- audio = audio[ : audio.shape[-1] // self.hop_size * self.hop_size]
- audio = torch.from_numpy(audio).float()
- else:
- audio = audio[start_frame * self.hop_size : (start_frame + units_frame_len) * self.hop_size]
- '''
- # load mel
- mel_key = 'aug_mel' if aug_flag else 'mel'
- mel = data_buffer.get(mel_key)
- if mel is None:
- mel = name_ext + ".mel.npy"
- mel = np.load(mel)
- mel = mel[start_frame : start_frame + units_frame_len]
- mel = torch.from_numpy(mel).float()
- else:
- mel = mel[start_frame : start_frame + units_frame_len]
-
- # load f0
- f0 = data_buffer.get('f0')
- aug_shift = 0
- if aug_flag:
- aug_shift = self.pitch_aug_dict[name_ext]
- f0_frames = 2 ** (aug_shift / 12) * f0[start_frame : start_frame + units_frame_len]
-
- # load units
- units = data_buffer.get('units')
- if units is None:
- path_units = name_ext + ".soft.pt"
- units = torch.load(path_units)
- units = units[0]
- units = repeat_expand_2d(units,f0.size(0)).transpose(0,1)
-
- units = units[start_frame : start_frame + units_frame_len]
-
- # load volume
- vol_key = 'aug_vol' if aug_flag else 'volume'
- volume = data_buffer.get(vol_key)
- volume_frames = volume[start_frame : start_frame + units_frame_len]
-
- # load spk_id
- spk_id = data_buffer.get('spk_id')
-
- # load shift
- aug_shift = torch.from_numpy(np.array([[aug_shift]])).float()
-
- return dict(mel=mel, f0=f0_frames, volume=volume_frames, units=units, spk_id=spk_id, aug_shift=aug_shift, name=name, name_ext=name_ext)
-
- def __len__(self):
- return len(self.paths)
\ No newline at end of file
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py
deleted file mode 100644
index 3bdb70ba9357e95ff05853dcc06437c3401ef3be..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import os
-from functools import lru_cache
-from typing import Union
-
-import ffmpeg
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-from .utils import exact_div
-
-from librosa.filters import mel as librosa_mel_fn
-
-# hard-coded audio hyperparameters
-SAMPLE_RATE = 16000
-N_FFT = 400
-N_MELS = 80
-HOP_LENGTH = 160
-CHUNK_LENGTH = 30
-N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk
-N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH) # 3000: number of frames in a mel spectrogram input
-
-
-def load_audio(file: str, sr: int = SAMPLE_RATE):
- """
- Open an audio file and read as mono waveform, resampling as necessary
-
- Parameters
- ----------
- file: str
- The audio file to open
-
- sr: int
- The sample rate to resample the audio if necessary
-
- Returns
- -------
- A NumPy array containing the audio waveform, in float32 dtype.
- """
- try:
- # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
- # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
- out, _ = (
- ffmpeg.input(file, threads=0)
- .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
- .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
- )
- except ffmpeg.Error as e:
- raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
-
- return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
-
-
-def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
- """
- Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
- """
- if torch.is_tensor(array):
- if array.shape[axis] > length:
- array = array.index_select(dim=axis, index=torch.arange(length, device=array.device))
-
- if array.shape[axis] < length:
- pad_widths = [(0, 0)] * array.ndim
- pad_widths[axis] = (0, length - array.shape[axis])
- array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
- else:
- if array.shape[axis] > length:
- array = array.take(indices=range(length), axis=axis)
-
- if array.shape[axis] < length:
- pad_widths = [(0, 0)] * array.ndim
- pad_widths[axis] = (0, length - array.shape[axis])
- array = np.pad(array, pad_widths)
-
- return array
-
-
-@lru_cache(maxsize=None)
-def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
- """
- load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
- Allows decoupling librosa dependency; saved using:
-
- np.savez_compressed(
- "mel_filters.npz",
- mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
- )
- """
- assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
- return torch.from_numpy(librosa_mel_fn(sr=SAMPLE_RATE,n_fft=N_FFT,n_mels=n_mels)).to(device)
-
-
-def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS):
- """
- Compute the log-Mel spectrogram of
-
- Parameters
- ----------
- audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
- The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
-
- n_mels: int
- The number of Mel-frequency filters, only 80 is supported
-
- Returns
- -------
- torch.Tensor, shape = (80, n_frames)
- A Tensor that contains the Mel spectrogram
- """
- if not torch.is_tensor(audio):
- if isinstance(audio, str):
- audio = load_audio(audio)
- audio = torch.from_numpy(audio)
-
- window = torch.hann_window(N_FFT).to(audio.device)
- stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
- magnitudes = stft[..., :-1].abs() ** 2
-
- filters = mel_filters(audio.device, n_mels)
- mel_spec = filters @ magnitudes
-
- log_spec = torch.clamp(mel_spec, min=1e-10).log10()
- log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
- log_spec = (log_spec + 4.0) / 4.0
- return log_spec
diff --git a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py b/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py
deleted file mode 100644
index 6817392ba8a9eb830351de89fb7afc5ad72f5e42..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py
+++ /dev/null
@@ -1,188 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
- 1. "english_cleaners" for English text
- 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
- the Unidecode library (https://pypi.python.org/pypi/Unidecode)
- 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
- the symbols in symbols.py to match your data).
-'''
-
-
-# Regular expression matching whitespace:
-
-
-import re
-import inflect
-from unidecode import unidecode
-import eng_to_ipa as ipa
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
-_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
-_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
-_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
-_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
-_number_re = re.compile(r'[0-9]+')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
- ('mrs', 'misess'),
- ('mr', 'mister'),
- ('dr', 'doctor'),
- ('st', 'saint'),
- ('co', 'company'),
- ('jr', 'junior'),
- ('maj', 'major'),
- ('gen', 'general'),
- ('drs', 'doctors'),
- ('rev', 'reverend'),
- ('lt', 'lieutenant'),
- ('hon', 'honorable'),
- ('sgt', 'sergeant'),
- ('capt', 'captain'),
- ('esq', 'esquire'),
- ('ltd', 'limited'),
- ('col', 'colonel'),
- ('ft', 'fort'),
-]]
-
-
-# List of (ipa, lazy ipa) pairs:
-_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('æ', 'e'),
- ('ɑ', 'a'),
- ('ɔ', 'o'),
- ('ð', 'z'),
- ('θ', 's'),
- ('ɛ', 'e'),
- ('ɪ', 'i'),
- ('ʊ', 'u'),
- ('ʒ', 'ʥ'),
- ('ʤ', 'ʥ'),
- ('ˈ', '↓'),
-]]
-
-# List of (ipa, lazy ipa2) pairs:
-_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('ð', 'z'),
- ('θ', 's'),
- ('ʒ', 'ʑ'),
- ('ʤ', 'dʑ'),
- ('ˈ', '↓'),
-]]
-
-# List of (ipa, ipa2) pairs
-_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('ʤ', 'dʒ'),
- ('ʧ', 'tʃ')
-]]
-
-
-def expand_abbreviations(text):
- for regex, replacement in _abbreviations:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def collapse_whitespace(text):
- return re.sub(r'\s+', ' ', text)
-
-
-def _remove_commas(m):
- return m.group(1).replace(',', '')
-
-
-def _expand_decimal_point(m):
- return m.group(1).replace('.', ' point ')
-
-
-def _expand_dollars(m):
- match = m.group(1)
- parts = match.split('.')
- if len(parts) > 2:
- return match + ' dollars' # Unexpected format
- dollars = int(parts[0]) if parts[0] else 0
- cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
- if dollars and cents:
- dollar_unit = 'dollar' if dollars == 1 else 'dollars'
- cent_unit = 'cent' if cents == 1 else 'cents'
- return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
- elif dollars:
- dollar_unit = 'dollar' if dollars == 1 else 'dollars'
- return '%s %s' % (dollars, dollar_unit)
- elif cents:
- cent_unit = 'cent' if cents == 1 else 'cents'
- return '%s %s' % (cents, cent_unit)
- else:
- return 'zero dollars'
-
-
-def _expand_ordinal(m):
- return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
- num = int(m.group(0))
- if num > 1000 and num < 3000:
- if num == 2000:
- return 'two thousand'
- elif num > 2000 and num < 2010:
- return 'two thousand ' + _inflect.number_to_words(num % 100)
- elif num % 100 == 0:
- return _inflect.number_to_words(num // 100) + ' hundred'
- else:
- return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
- else:
- return _inflect.number_to_words(num, andword='')
-
-
-def normalize_numbers(text):
- text = re.sub(_comma_number_re, _remove_commas, text)
- text = re.sub(_pounds_re, r'\1 pounds', text)
- text = re.sub(_dollars_re, _expand_dollars, text)
- text = re.sub(_decimal_number_re, _expand_decimal_point, text)
- text = re.sub(_ordinal_re, _expand_ordinal, text)
- text = re.sub(_number_re, _expand_number, text)
- return text
-
-
-def mark_dark_l(text):
- return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
-
-
-def english_to_ipa(text):
- text = unidecode(text).lower()
- text = expand_abbreviations(text)
- text = normalize_numbers(text)
- phonemes = ipa.convert(text)
- phonemes = collapse_whitespace(phonemes)
- return phonemes
-
-
-def english_to_lazy_ipa(text):
- text = english_to_ipa(text)
- for regex, replacement in _lazy_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def english_to_ipa2(text):
- text = english_to_ipa(text)
- text = mark_dark_l(text)
- for regex, replacement in _ipa_to_ipa2:
- text = re.sub(regex, replacement, text)
- return text.replace('...', '…')
-
-
-def english_to_lazy_ipa2(text):
- text = english_to_ipa(text)
- for regex, replacement in _lazy_ipa2:
- text = re.sub(regex, replacement, text)
- return text
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
deleted file mode 100644
index a02a814fe2f08b464454e8eb6e1c88004ab804f6..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
+++ /dev/null
@@ -1,27 +0,0 @@
-_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
-model = dict(
- pretrained='torchvision://resnet101',
- backbone=dict(depth=101),
- bbox_head=dict(
- with_deform=True,
- norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='Resize',
- img_scale=[(1333, 640), (1333, 800)],
- multiscale_mode='value',
- keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
deleted file mode 100644
index 4874121fd01e4024bfde445f451b7368c6834511..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './deeplabv3_r50-d8_480x480_40k_pascal_context_59.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
deleted file mode 100644
index 89d5e1ae0f3ef44626f3b5534c504cbce7389a32..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
- '../_base_/models/gcnet_r50-d8.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
- decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py
deleted file mode 100644
index 12f6d402a3c4a113d4c37be062790fa435b72104..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Evaluation with objective metrics for the pretrained AudioGen models.
-This grid takes signature from the training grid and runs evaluation-only stage.
-
-When running the grid for the first time, please use:
-REGEN=1 dora grid audiogen.audiogen_pretrained_16khz_eval
-and re-use the REGEN=1 option when the grid is changed to force regenerating it.
-
-Note that you need the proper metrics external libraries setup to use all
-the objective metrics activated in this grid. Refer to the README for more information.
-"""
-
-import os
-
-from ..musicgen._explorers import GenerationEvalExplorer
-from ...environment import AudioCraftEnvironment
-from ... import train
-
-
-def eval(launcher, batch_size: int = 32):
- opts = {
- 'dset': 'audio/audiocaps_16khz',
- 'solver/audiogen/evaluation': 'objective_eval',
- 'execute_only': 'evaluate',
- '+dataset.evaluate.batch_size': batch_size,
- '+metrics.fad.tf.batch_size': 32,
- }
- # binary for FAD computation: replace this path with your own path
- metrics_opts = {
- 'metrics.fad.tf.bin': '/data/home/jadecopet/local/usr/opt/google-research'
- }
- opt1 = {'generate.lm.use_sampling': True, 'generate.lm.top_k': 250, 'generate.lm.top_p': 0.}
- opt2 = {'transformer_lm.two_step_cfg': True}
-
- sub = launcher.bind(opts)
- sub.bind_(metrics_opts)
-
- # base objective metrics
- sub(opt1, opt2)
-
-
-@GenerationEvalExplorer
-def explorer(launcher):
- partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
- launcher.slurm_(gpus=4, partition=partitions)
-
- if 'REGEN' not in os.environ:
- folder = train.main.dora.dir / 'grids' / __name__.split('.', 2)[-1]
- with launcher.job_array():
- for sig in folder.iterdir():
- if not sig.is_symlink():
- continue
- xp = train.main.get_xp_from_sig(sig.name)
- launcher(xp.argv)
- return
-
- audiogen_base = launcher.bind(solver="audiogen/audiogen_base_16khz")
- audiogen_base.bind_({'autocast': False, 'fsdp.use': True})
-
- audiogen_base_medium = audiogen_base.bind({'continue_from': '//pretrained/facebook/audiogen-medium'})
- audiogen_base_medium.bind_({'model/lm/model_scale': 'medium'})
- eval(audiogen_base_medium, batch_size=128)
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py
deleted file mode 100644
index 5612ff5688d85fede0e605b244919e8081cb1da9..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Grid search file, simply list all the exp you want in `explorer`.
-Any new exp added there will be scheduled.
-You can cancel and experiment by commenting its line.
-
-This grid is a minimal example for debugging compression task
-and how to override parameters directly in a grid.
-Learn more about dora grids: https://github.com/facebookresearch/dora
-"""
-
-from ._explorers import CompressionExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@CompressionExplorer
-def explorer(launcher):
- partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
- launcher.slurm_(gpus=2, partition=partitions)
- launcher.bind_(solver='compression/debug')
-
- with launcher.job_array():
- # base debug task using config from solver=compression/debug
- launcher()
- # we can override parameters in the grid to launch additional xps
- launcher({'rvq.bins': 2048, 'rvq.n_q': 4})
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py
deleted file mode 100644
index 44f4b0a2c3c6c726857db8386491823dd85dde51..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import csv
-import json
-import logging
-from pathlib import Path
-import tempfile
-import typing as tp
-import subprocess
-import shutil
-
-import torch
-import torchaudio
-
-logger = logging.getLogger(__name__)
-
-
-class ViSQOL:
- """ViSQOL wrapper to run ViSQOL from Python using a pre-installed binary.
-
- To learn more about ViSQOL and how to build ViSQOL binary using bazel, please refer to the
- instructions available in the open source repository: https://github.com/google/visqol
-
- ViSQOL is capable of running in two modes:
-
- Audio Mode:
- When running in audio mode, input signals must have a 48kHz sample rate. Input should be resampled to 48kHz.
- Input signals can be multi-channel, but they will be down-mixed to mono for performing the comparison.
- Audio mode uses support vector regression, with the maximum range at ~4.75.
-
- Speech Mode:
- When running in speech mode, ViSQOL uses a wideband model. It therefore expects input sample rates of 16kHz.
- Input should be resampled to 16kHz.
- As part of the speech mode processing, a root mean square implementation for voice activity detection
- is performed on the reference signal to determine what parts of the signal have voice activity and
- should therefore be included in the comparison. The signal is normalized before performing the voice
- activity detection.
- Input signals can be multi-channel, but they will be down-mixed to mono for performing the comparison.
- Speech mode is scaled to have a maximum MOS of 5.0 to match previous version behavior.
-
- For more details, check the guidelines: https://github.com/google/visqol#general-guidelines-for-input
-
- Args:
- visqol_bin (str): Path to the ViSQOL binary.
- mode (str): ViSQOL computation mode, expecting "audio" or "speech".
- model (str): Name of the model to use for similarity to quality model.
- debug (bool): Whether to also get debug metrics from ViSQOL or not.
- """
- SAMPLE_RATES_MODES = {"audio": 48_000, "speech": 16_000}
- ALLOWED_SAMPLE_RATES = frozenset(SAMPLE_RATES_MODES.values())
-
- def __init__(self, bin: tp.Union[Path, str], mode: str = "audio",
- model: str = "libsvm_nu_svr_model.txt", debug: bool = False):
- assert bin is not None and Path(bin).exists(), f"Could not find ViSQOL binary in specified path: {bin}"
- self.visqol_bin = str(bin)
- self.visqol_mode = mode
- self.target_sr = self._get_target_sr(self.visqol_mode)
- self.model = model
- self.debug = debug
- assert Path(self.visqol_model).exists(), \
- f"Could not find the specified model in ViSQOL install: {self.visqol_model}"
-
- def _get_target_sr(self, mode: str) -> int:
- # returns target sampling rate for the corresponding ViSQOL mode.
- if mode not in ViSQOL.SAMPLE_RATES_MODES:
- raise ValueError(
- f"Unsupported mode! Allowed are: {', '.join(ViSQOL.SAMPLE_RATES_MODES.keys())}"
- )
- return ViSQOL.SAMPLE_RATES_MODES[mode]
-
- def _prepare_files(
- self, ref_sig: torch.Tensor, deg_sig: torch.Tensor, sr: int, target_sr: int, pad_with_silence: bool = False
- ):
- # prepare files for ViSQOL evaluation.
- assert target_sr in ViSQOL.ALLOWED_SAMPLE_RATES
- assert len(ref_sig) == len(deg_sig), (
- "Expects same number of ref and degraded inputs",
- f" but ref len {len(ref_sig)} != deg len {len(deg_sig)}"
- )
- # resample audio if needed
- if sr != target_sr:
- transform = torchaudio.transforms.Resample(sr, target_sr)
- pad = int(0.5 * target_sr)
- rs_ref = []
- rs_deg = []
- for i in range(len(ref_sig)):
- rs_ref_i = transform(ref_sig[i])
- rs_deg_i = transform(deg_sig[i])
- if pad_with_silence:
- rs_ref_i = torch.nn.functional.pad(rs_ref_i, (pad, pad), mode='constant', value=0)
- rs_deg_i = torch.nn.functional.pad(rs_deg_i, (pad, pad), mode='constant', value=0)
- rs_ref.append(rs_ref_i)
- rs_deg.append(rs_deg_i)
- ref_sig = torch.stack(rs_ref)
- deg_sig = torch.stack(rs_deg)
- # save audio chunks to tmp dir and create csv
- tmp_dir = Path(tempfile.mkdtemp())
- try:
- tmp_input_csv_path = tmp_dir / "input.csv"
- tmp_results_csv_path = tmp_dir / "results.csv"
- tmp_debug_json_path = tmp_dir / "debug.json"
- with open(tmp_input_csv_path, "w") as csv_file:
- csv_writer = csv.writer(csv_file)
- csv_writer.writerow(["reference", "degraded"])
- for i in range(len(ref_sig)):
- tmp_ref_filename = tmp_dir / f"ref_{i}.wav"
- tmp_deg_filename = tmp_dir / f"deg_{i}.wav"
- torchaudio.save(
- tmp_ref_filename,
- torch.clamp(ref_sig[i], min=-0.99, max=0.99),
- sample_rate=target_sr,
- bits_per_sample=16,
- encoding="PCM_S"
- )
- torchaudio.save(
- tmp_deg_filename,
- torch.clamp(deg_sig[i], min=-0.99, max=0.99),
- sample_rate=target_sr,
- bits_per_sample=16,
- encoding="PCM_S"
- )
- csv_writer.writerow([str(tmp_ref_filename), str(tmp_deg_filename)])
- return tmp_dir, tmp_input_csv_path, tmp_results_csv_path, tmp_debug_json_path
- except Exception as e:
- logger.error("Exception occurred when preparing files for ViSQOL: %s", e)
- return tmp_dir, None, None, None
-
- def _flush_files(self, tmp_dir: tp.Union[Path, str]):
- # flush tmp files used to compute ViSQOL.
- shutil.rmtree(str(tmp_dir))
-
- def _collect_moslqo_score(self, results_csv_path: tp.Union[Path, str]) -> float:
- # collect results for each evaluated pair and return averaged moslqo score.
- with open(results_csv_path, "r") as csv_file:
- reader = csv.DictReader(csv_file)
- moslqo_scores = [float(row["moslqo"]) for row in reader]
- if len(moslqo_scores) > 0:
- return sum(moslqo_scores) / len(moslqo_scores)
- else:
- return 0.0
-
- def _collect_debug_data(self, debug_json_path: tp.Union[Path, str]) -> dict:
- # collect debug data for the visqol inference.
- with open(debug_json_path, "r") as f:
- data = json.load(f)
- return data
-
- @property
- def visqol_model(self):
- return f'{self.visqol_bin}/model/{self.model}'
-
- def _run_visqol(
- self,
- input_csv_path: tp.Union[Path, str],
- results_csv_path: tp.Union[Path, str],
- debug_csv_path: tp.Optional[tp.Union[Path, str]],
- ):
- input_csv_path = str(input_csv_path)
- results_csv_path = str(results_csv_path)
- debug_csv_path = str(debug_csv_path)
- cmd = [
- f'{self.visqol_bin}/bazel-bin/visqol',
- '--batch_input_csv', f'{input_csv_path}',
- '--results_csv', f'{results_csv_path}'
- ]
- if debug_csv_path is not None:
- cmd += ['--output_debug', f'{debug_csv_path}']
- if self.visqol_mode == "speech":
- cmd += ['--use_speech_mode']
- cmd += ['--similarity_to_quality_model', f'{self.visqol_model}']
- result = subprocess.run(cmd, capture_output=True)
- if result.returncode:
- logger.error("Error with visqol: \n %s \n %s", result.stdout.decode(), result.stderr.decode())
- raise RuntimeError("Error while executing visqol")
- result.check_returncode()
-
- def __call__(
- self,
- ref_sig: torch.Tensor,
- deg_sig: torch.Tensor,
- sr: int,
- pad_with_silence: bool = False,
- ):
- """Calculate the ViSQOL metric for a pair of audio signals at a given sample rate.
- Args:
- ref_sig (torch.Tensor): Reference signals as [B, C, T].
- deg_sig (torch.Tensor): Degraded signals as [B, C, T].
- sr (int): Sample rate of the two audio signals.
- pad_with_silence (bool): Whether to pad the file with silences as recommended
- in visqol guidelines (see: https://github.com/google/visqol#general-guidelines-for-input).
- Returns:
- float: The ViSQOL score or mean score for the batch.
- """
- logger.debug(f"Calculating visqol with mode={self.visqol_mode} on {len(ref_sig)} samples")
- tmp_dir, input_csv, results_csv, debug_json = self._prepare_files(
- ref_sig, deg_sig, sr, self.target_sr, pad_with_silence
- )
- try:
- if input_csv and results_csv:
- self._run_visqol(
- input_csv,
- results_csv,
- debug_json if self.debug else None,
- )
- mosqol = self._collect_moslqo_score(results_csv)
- return mosqol
- else:
- raise RuntimeError("Something unexpected happened when running VISQOL!")
- except Exception as e:
- logger.error("Exception occurred when running ViSQOL: %s", e)
- finally:
- self._flush_files(tmp_dir)
diff --git a/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile b/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile
deleted file mode 100644
index 31b5f817b41d7d95055d9aa5d5e4f973abee0b45..0000000000000000000000000000000000000000
--- a/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile
+++ /dev/null
@@ -1,26 +0,0 @@
-IMG:=`cat Name`
-MOUNT:=
-PORT_MAP:=
-DOTENV:=.env
-CONTAINER_NAME:=sample-chatgpt-on-wechat
-
-echo:
- echo $(IMG)
-
-run_d:
- docker rm $(CONTAINER_NAME) || echo
- docker run -dt --name $(CONTAINER_NAME) $(PORT_MAP) \
- --env-file=$(DOTENV) \
- $(MOUNT) $(IMG)
-
-run_i:
- docker rm $(CONTAINER_NAME) || echo
- docker run -it --name $(CONTAINER_NAME) $(PORT_MAP) \
- --env-file=$(DOTENV) \
- $(MOUNT) $(IMG)
-
-stop:
- docker stop $(CONTAINER_NAME)
-
-rm: stop
- docker rm $(CONTAINER_NAME)
diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py
deleted file mode 100644
index 84e51fd71fca78131a3be3d0d35d808c07bb3a95..0000000000000000000000000000000000000000
--- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import torch.nn as nn
-import math
-
-
-def conv3x3(in_planes, out_planes, stride=1):
- """3x3 convolution with padding"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
- expansion = 1
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(BasicBlock, self).__init__()
- self.conv1 = conv3x3(inplanes, planes, stride)
- self.bn1 = nn.BatchNorm2d(planes)
- self.relu = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(planes, planes)
- self.bn2 = nn.BatchNorm2d(planes)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(Bottleneck, self).__init__()
- self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
- self.bn1 = nn.BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
- self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * 4)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
- out = self.relu(out)
-
- out = self.conv3(out)
- out = self.bn3(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class ResNet50(nn.Module):
- def __init__(self,mode='rgb'):
- self.inplanes = 64
- super(ResNet50, self).__init__()
- if(mode=='rgb'):
- self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- elif(mode=='rgbd'):
- self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- elif(mode=="share"):
- self.conv1=nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- self.conv1_d=nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- else:
- raise
- self.bn1 = nn.BatchNorm2d(64)
- self.relu = nn.ReLU(inplace=True)
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
- self.layer1 = self._make_layer(Bottleneck, 64, 3)
- self.layer2 = self._make_layer(Bottleneck, 128, 4, stride=2)
- self.layer3_1 = self._make_layer(Bottleneck, 256, 6, stride=2)
- self.layer4_1 = self._make_layer(Bottleneck, 512, 3, stride=2)
-
- self.inplanes = 512
-
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
- m.weight.data.normal_(0, math.sqrt(2. / n))
- elif isinstance(m, nn.BatchNorm2d):
- m.weight.data.fill_(1)
- m.bias.data.zero_()
-
- def _make_layer(self, block, planes, blocks, stride=1):
- downsample = None
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- nn.Conv2d(self.inplanes, planes * block.expansion,
- kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(planes * block.expansion),
- )
-
- layers = []
- layers.append(block(self.inplanes, planes, stride, downsample))
- self.inplanes = planes * block.expansion
- for i in range(1, blocks):
- layers.append(block(self.inplanes, planes))
-
- return nn.Sequential(*layers)
-
- def forward(self, x):
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
- x = self.maxpool(x)
-
- x = self.layer1(x)
- x = self.layer2(x)
- x1 = self.layer3_1(x)
- x1 = self.layer4_1(x1)
-
- return x1, x1
diff --git a/spaces/Hallucinate/demo/ldm/models/autoencoder.py b/spaces/Hallucinate/demo/ldm/models/autoencoder.py
deleted file mode 100644
index 6a9c4f45498561953b8085981609b2a3298a5473..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/ldm/models/autoencoder.py
+++ /dev/null
@@ -1,443 +0,0 @@
-import torch
-import pytorch_lightning as pl
-import torch.nn.functional as F
-from contextlib import contextmanager
-
-from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
-
-from ldm.modules.diffusionmodules.model import Encoder, Decoder
-from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
-
-from ldm.util import instantiate_from_config
-
-
-class VQModel(pl.LightningModule):
- def __init__(self,
- ddconfig,
- lossconfig,
- n_embed,
- embed_dim,
- ckpt_path=None,
- ignore_keys=[],
- image_key="image",
- colorize_nlabels=None,
- monitor=None,
- batch_resize_range=None,
- scheduler_config=None,
- lr_g_factor=1.0,
- remap=None,
- sane_index_shape=False, # tell vector quantizer to return indices as bhw
- use_ema=False
- ):
- super().__init__()
- self.embed_dim = embed_dim
- self.n_embed = n_embed
- self.image_key = image_key
- self.encoder = Encoder(**ddconfig)
- self.decoder = Decoder(**ddconfig)
- self.loss = instantiate_from_config(lossconfig)
- self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
- remap=remap,
- sane_index_shape=sane_index_shape)
- self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
- self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
- if colorize_nlabels is not None:
- assert type(colorize_nlabels)==int
- self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
- if monitor is not None:
- self.monitor = monitor
- self.batch_resize_range = batch_resize_range
- if self.batch_resize_range is not None:
- print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
-
- self.use_ema = use_ema
- if self.use_ema:
- self.model_ema = LitEma(self)
- print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
- if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
- self.scheduler_config = scheduler_config
- self.lr_g_factor = lr_g_factor
-
- @contextmanager
- def ema_scope(self, context=None):
- if self.use_ema:
- self.model_ema.store(self.parameters())
- self.model_ema.copy_to(self)
- if context is not None:
- print(f"{context}: Switched to EMA weights")
- try:
- yield None
- finally:
- if self.use_ema:
- self.model_ema.restore(self.parameters())
- if context is not None:
- print(f"{context}: Restored training weights")
-
- def init_from_ckpt(self, path, ignore_keys=list()):
- sd = torch.load(path, map_location="cpu")["state_dict"]
- keys = list(sd.keys())
- for k in keys:
- for ik in ignore_keys:
- if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
- del sd[k]
- missing, unexpected = self.load_state_dict(sd, strict=False)
- print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
- if len(missing) > 0:
- print(f"Missing Keys: {missing}")
- print(f"Unexpected Keys: {unexpected}")
-
- def on_train_batch_end(self, *args, **kwargs):
- if self.use_ema:
- self.model_ema(self)
-
- def encode(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- quant, emb_loss, info = self.quantize(h)
- return quant, emb_loss, info
-
- def encode_to_prequant(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- return h
-
- def decode(self, quant):
- quant = self.post_quant_conv(quant)
- dec = self.decoder(quant)
- return dec
-
- def decode_code(self, code_b):
- quant_b = self.quantize.embed_code(code_b)
- dec = self.decode(quant_b)
- return dec
-
- def forward(self, input, return_pred_indices=False):
- quant, diff, (_,_,ind) = self.encode(input)
- dec = self.decode(quant)
- if return_pred_indices:
- return dec, diff, ind
- return dec, diff
-
- def get_input(self, batch, k):
- x = batch[k]
- if len(x.shape) == 3:
- x = x[..., None]
- x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
- if self.batch_resize_range is not None:
- lower_size = self.batch_resize_range[0]
- upper_size = self.batch_resize_range[1]
- if self.global_step <= 4:
- # do the first few batches with max size to avoid later oom
- new_resize = upper_size
- else:
- new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
- if new_resize != x.shape[2]:
- x = F.interpolate(x, size=new_resize, mode="bicubic")
- x = x.detach()
- return x
-
- def training_step(self, batch, batch_idx, optimizer_idx):
- # https://github.com/pytorch/pytorch/issues/37142
- # try not to fool the heuristics
- x = self.get_input(batch, self.image_key)
- xrec, qloss, ind = self(x, return_pred_indices=True)
-
- if optimizer_idx == 0:
- # autoencode
- aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train",
- predicted_indices=ind)
-
- self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
- return aeloss
-
- if optimizer_idx == 1:
- # discriminator
- discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
- self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
- return discloss
-
- def validation_step(self, batch, batch_idx):
- log_dict = self._validation_step(batch, batch_idx)
- with self.ema_scope():
- log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
- return log_dict
-
- def _validation_step(self, batch, batch_idx, suffix=""):
- x = self.get_input(batch, self.image_key)
- xrec, qloss, ind = self(x, return_pred_indices=True)
- aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
- self.global_step,
- last_layer=self.get_last_layer(),
- split="val"+suffix,
- predicted_indices=ind
- )
-
- discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
- self.global_step,
- last_layer=self.get_last_layer(),
- split="val"+suffix,
- predicted_indices=ind
- )
- rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
- self.log(f"val{suffix}/rec_loss", rec_loss,
- prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
- self.log(f"val{suffix}/aeloss", aeloss,
- prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
- if version.parse(pl.__version__) >= version.parse('1.4.0'):
- del log_dict_ae[f"val{suffix}/rec_loss"]
- self.log_dict(log_dict_ae)
- self.log_dict(log_dict_disc)
- return self.log_dict
-
- def configure_optimizers(self):
- lr_d = self.learning_rate
- lr_g = self.lr_g_factor*self.learning_rate
- print("lr_d", lr_d)
- print("lr_g", lr_g)
- opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
- list(self.decoder.parameters())+
- list(self.quantize.parameters())+
- list(self.quant_conv.parameters())+
- list(self.post_quant_conv.parameters()),
- lr=lr_g, betas=(0.5, 0.9))
- opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
- lr=lr_d, betas=(0.5, 0.9))
-
- if self.scheduler_config is not None:
- scheduler = instantiate_from_config(self.scheduler_config)
-
- print("Setting up LambdaLR scheduler...")
- scheduler = [
- {
- 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
- 'interval': 'step',
- 'frequency': 1
- },
- {
- 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
- 'interval': 'step',
- 'frequency': 1
- },
- ]
- return [opt_ae, opt_disc], scheduler
- return [opt_ae, opt_disc], []
-
- def get_last_layer(self):
- return self.decoder.conv_out.weight
-
- def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
- log = dict()
- x = self.get_input(batch, self.image_key)
- x = x.to(self.device)
- if only_inputs:
- log["inputs"] = x
- return log
- xrec, _ = self(x)
- if x.shape[1] > 3:
- # colorize with random projection
- assert xrec.shape[1] > 3
- x = self.to_rgb(x)
- xrec = self.to_rgb(xrec)
- log["inputs"] = x
- log["reconstructions"] = xrec
- if plot_ema:
- with self.ema_scope():
- xrec_ema, _ = self(x)
- if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
- log["reconstructions_ema"] = xrec_ema
- return log
-
- def to_rgb(self, x):
- assert self.image_key == "segmentation"
- if not hasattr(self, "colorize"):
- self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
- x = F.conv2d(x, weight=self.colorize)
- x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
- return x
-
-
-class VQModelInterface(VQModel):
- def __init__(self, embed_dim, *args, **kwargs):
- super().__init__(embed_dim=embed_dim, *args, **kwargs)
- self.embed_dim = embed_dim
-
- def encode(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- return h
-
- def decode(self, h, force_not_quantize=False):
- # also go through quantization layer
- if not force_not_quantize:
- quant, emb_loss, info = self.quantize(h)
- else:
- quant = h
- quant = self.post_quant_conv(quant)
- dec = self.decoder(quant)
- return dec
-
-
-class AutoencoderKL(pl.LightningModule):
- def __init__(self,
- ddconfig,
- lossconfig,
- embed_dim,
- ckpt_path=None,
- ignore_keys=[],
- image_key="image",
- colorize_nlabels=None,
- monitor=None,
- ):
- super().__init__()
- self.image_key = image_key
- self.encoder = Encoder(**ddconfig)
- self.decoder = Decoder(**ddconfig)
- self.loss = instantiate_from_config(lossconfig)
- assert ddconfig["double_z"]
- self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
- self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
- self.embed_dim = embed_dim
- if colorize_nlabels is not None:
- assert type(colorize_nlabels)==int
- self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
- if monitor is not None:
- self.monitor = monitor
- if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-
- def init_from_ckpt(self, path, ignore_keys=list()):
- sd = torch.load(path, map_location="cpu")["state_dict"]
- keys = list(sd.keys())
- for k in keys:
- for ik in ignore_keys:
- if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
- del sd[k]
- self.load_state_dict(sd, strict=False)
- print(f"Restored from {path}")
-
- def encode(self, x):
- h = self.encoder(x)
- moments = self.quant_conv(h)
- posterior = DiagonalGaussianDistribution(moments)
- return posterior
-
- def decode(self, z):
- z = self.post_quant_conv(z)
- dec = self.decoder(z)
- return dec
-
- def forward(self, input, sample_posterior=True):
- posterior = self.encode(input)
- if sample_posterior:
- z = posterior.sample()
- else:
- z = posterior.mode()
- dec = self.decode(z)
- return dec, posterior
-
- def get_input(self, batch, k):
- x = batch[k]
- if len(x.shape) == 3:
- x = x[..., None]
- x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
- return x
-
- def training_step(self, batch, batch_idx, optimizer_idx):
- inputs = self.get_input(batch, self.image_key)
- reconstructions, posterior = self(inputs)
-
- if optimizer_idx == 0:
- # train encoder+decoder+logvar
- aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
- self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
- self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
- return aeloss
-
- if optimizer_idx == 1:
- # train the discriminator
- discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
-
- self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
- self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
- return discloss
-
- def validation_step(self, batch, batch_idx):
- inputs = self.get_input(batch, self.image_key)
- reconstructions, posterior = self(inputs)
- aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
- last_layer=self.get_last_layer(), split="val")
-
- discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
- last_layer=self.get_last_layer(), split="val")
-
- self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
- self.log_dict(log_dict_ae)
- self.log_dict(log_dict_disc)
- return self.log_dict
-
- def configure_optimizers(self):
- lr = self.learning_rate
- opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
- list(self.decoder.parameters())+
- list(self.quant_conv.parameters())+
- list(self.post_quant_conv.parameters()),
- lr=lr, betas=(0.5, 0.9))
- opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
- lr=lr, betas=(0.5, 0.9))
- return [opt_ae, opt_disc], []
-
- def get_last_layer(self):
- return self.decoder.conv_out.weight
-
- @torch.no_grad()
- def log_images(self, batch, only_inputs=False, **kwargs):
- log = dict()
- x = self.get_input(batch, self.image_key)
- x = x.to(self.device)
- if not only_inputs:
- xrec, posterior = self(x)
- if x.shape[1] > 3:
- # colorize with random projection
- assert xrec.shape[1] > 3
- x = self.to_rgb(x)
- xrec = self.to_rgb(xrec)
- log["samples"] = self.decode(torch.randn_like(posterior.sample()))
- log["reconstructions"] = xrec
- log["inputs"] = x
- return log
-
- def to_rgb(self, x):
- assert self.image_key == "segmentation"
- if not hasattr(self, "colorize"):
- self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
- x = F.conv2d(x, weight=self.colorize)
- x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
- return x
-
-
-class IdentityFirstStage(torch.nn.Module):
- def __init__(self, *args, vq_interface=False, **kwargs):
- self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff
- super().__init__()
-
- def encode(self, x, *args, **kwargs):
- return x
-
- def decode(self, x, *args, **kwargs):
- return x
-
- def quantize(self, x, *args, **kwargs):
- if self.vq_interface:
- return x, None, [None, None, None]
- return x
-
- def forward(self, x, *args, **kwargs):
- return x
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py b/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py
deleted file mode 100644
index 81676226e57ca519273b98328a1afe6961c37ce3..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py
+++ /dev/null
@@ -1,403 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA Authors. All rights reserved.
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-# http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Auto Config class."""
-import importlib
-import re
-import warnings
-from collections import OrderedDict
-from typing import List, Union
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.file_utils import CONFIG_NAME
-from transformers.utils import logging
-from .dynamic import get_class_from_dynamic_module
-
-
-logger = logging.get_logger(__name__)
-
-CONFIG_MAPPING_NAMES = OrderedDict(
- [
- # Add configs here
- ("roformer", "RoFormerConfig"),
- ("longformer", "LongformerConfig"),
- ]
-)
-
-CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
- [
- # Add archive maps here
- ("roformer", "ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
- ("longformer", "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
- ]
-)
-
-MODEL_NAMES_MAPPING = OrderedDict(
- [
- # Add full (and cased) model names here
- ("roformer", "Roformer"),
- ("longformer", "Longformer"),
- ]
-)
-
-SPECIAL_MODEL_TYPE_TO_MODULE_NAME = OrderedDict([("openai-gpt", "openai")])
-
-
-def model_type_to_module_name(key):
- """Converts a config key to the corresponding module."""
- # Special treatment
- if key in SPECIAL_MODEL_TYPE_TO_MODULE_NAME:
- return SPECIAL_MODEL_TYPE_TO_MODULE_NAME[key]
-
- return key.replace("-", "_")
-
-
-def config_class_to_model_type(config):
- """Converts a config class name to the corresponding model type"""
- for key, cls in CONFIG_MAPPING_NAMES.items():
- if cls == config:
- return key
- return None
-
-
-class _LazyConfigMapping(OrderedDict):
- """
- A dictionary that lazily load its values when they are requested.
- """
-
- def __init__(self, mapping):
- self._mapping = mapping
- self._extra_content = {}
- self._modules = {}
-
- def __getitem__(self, key):
- if key in self._extra_content:
- return self._extra_content[key]
- if key not in self._mapping:
- raise KeyError(key)
- value = self._mapping[key]
- module_name = model_type_to_module_name(key)
- if module_name not in self._modules:
- self._modules[module_name] = importlib.import_module(f".{module_name}", "fengshen.models")
-
- return getattr(self._modules[module_name], value)
-
- def keys(self):
- return list(self._mapping.keys()) + list(self._extra_content.keys())
-
- def values(self):
- return [self[k] for k in self._mapping.keys()] + list(self._extra_content.values())
-
- def items(self):
- return [(k, self[k]) for k in self._mapping.keys()] + list(self._extra_content.items())
-
- def __iter__(self):
- return iter(list(self._mapping.keys()) + list(self._extra_content.keys()))
-
- def __contains__(self, item):
- return item in self._mapping or item in self._extra_content
-
- def register(self, key, value):
- """
- Register a new configuration in this mapping.
- """
- if key in self._mapping.keys():
- raise ValueError(f"'{key}' is already used by a Transformers config, pick another name.")
- self._extra_content[key] = value
-
-
-CONFIG_MAPPING = _LazyConfigMapping(CONFIG_MAPPING_NAMES)
-
-
-class _LazyLoadAllMappings(OrderedDict):
- """
- A mapping that will load all pairs of key values at the first access (either by indexing, requestions keys, values,
- etc.)
-
- Args:
- mapping: The mapping to load.
- """
-
- def __init__(self, mapping):
- self._mapping = mapping
- self._initialized = False
- self._data = {}
-
- def _initialize(self):
- if self._initialized:
- return
- warnings.warn(
- "ALL_PRETRAINED_CONFIG_ARCHIVE_MAP is deprecated and will be removed in v5 of Transformers. "
- "It does not contain all available model checkpoints, far from it. Checkout hf.co/models for that.",
- FutureWarning,
- )
-
- for model_type, map_name in self._mapping.items():
- module_name = model_type_to_module_name(model_type)
- module = importlib.import_module(f".{module_name}", "transformers.models")
- mapping = getattr(module, map_name)
- self._data.update(mapping)
-
- self._initialized = True
-
- def __getitem__(self, key):
- self._initialize()
- return self._data[key]
-
- def keys(self):
- self._initialize()
- return self._data.keys()
-
- def values(self):
- self._initialize()
- return self._data.values()
-
- def items(self):
- self._initialize()
- return self._data.keys()
-
- def __iter__(self):
- self._initialize()
- return iter(self._data)
-
- def __contains__(self, item):
- self._initialize()
- return item in self._data
-
-
-ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = _LazyLoadAllMappings(CONFIG_ARCHIVE_MAP_MAPPING_NAMES)
-
-
-def _get_class_name(model_class: Union[str, List[str]]):
- if isinstance(model_class, (list, tuple)):
- return " or ".join([f"[`{c}`]" for c in model_class if c is not None])
- return f"[`{model_class}`]"
-
-
-def _list_model_options(indent, config_to_class=None, use_model_types=True):
- if config_to_class is None and not use_model_types:
- raise ValueError("Using `use_model_types=False` requires a `config_to_class` dictionary.")
- if use_model_types:
- if config_to_class is None:
- model_type_to_name = {model_type: f"[`{config}`]" for model_type, config in CONFIG_MAPPING_NAMES.items()}
- else:
- model_type_to_name = {
- model_type: _get_class_name(model_class)
- for model_type, model_class in config_to_class.items()
- if model_type in MODEL_NAMES_MAPPING
- }
- lines = [
- f"{indent}- **{model_type}** -- {model_type_to_name[model_type]} ({MODEL_NAMES_MAPPING[model_type]} model)"
- for model_type in sorted(model_type_to_name.keys())
- ]
- else:
- config_to_name = {
- CONFIG_MAPPING_NAMES[config]: _get_class_name(clas)
- for config, clas in config_to_class.items()
- if config in CONFIG_MAPPING_NAMES
- }
- config_to_model_name = {
- config: MODEL_NAMES_MAPPING[model_type] for model_type, config in CONFIG_MAPPING_NAMES.items()
- }
- lines = [
- f"{indent}- [`{config_name}`] configuration class: {config_to_name[config_name]} ({config_to_model_name[config_name]} model)"
- for config_name in sorted(config_to_name.keys())
- ]
- return "\n".join(lines)
-
-
-def replace_list_option_in_docstrings(config_to_class=None, use_model_types=True):
- def docstring_decorator(fn):
- docstrings = fn.__doc__
- lines = docstrings.split("\n")
- i = 0
- while i < len(lines) and re.search(r"^(\s*)List options\s*$", lines[i]) is None:
- i += 1
- if i < len(lines):
- indent = re.search(r"^(\s*)List options\s*$", lines[i]).groups()[0]
- if use_model_types:
- indent = f"{indent} "
- lines[i] = _list_model_options(indent, config_to_class=config_to_class, use_model_types=use_model_types)
- docstrings = "\n".join(lines)
- else:
- raise ValueError(
- f"The function {fn} should have an empty 'List options' in its docstring as placeholder, current docstring is:\n{docstrings}"
- )
- fn.__doc__ = docstrings
- return fn
-
- return docstring_decorator
-
-
-class AutoConfig:
- r"""
- This is a generic configuration class that will be instantiated as one of the configuration classes of the library
- when created with the [`~AutoConfig.from_pretrained`] class method.
-
- This class cannot be instantiated directly using `__init__()` (throws an error).
- """
-
- def __init__(self):
- raise EnvironmentError(
- "AutoConfig is designed to be instantiated "
- "using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method."
- )
-
- @classmethod
- def for_model(cls, model_type: str, *args, **kwargs):
- if model_type in CONFIG_MAPPING:
- config_class = CONFIG_MAPPING[model_type]
- return config_class(*args, **kwargs)
- raise ValueError(
- f"Unrecognized model identifier: {model_type}. Should contain one of {', '.join(CONFIG_MAPPING.keys())}"
- )
-
- @classmethod
- @replace_list_option_in_docstrings()
- def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
- r"""
- Instantiate one of the configuration classes of the library from a pretrained model configuration.
-
- The configuration class to instantiate is selected based on the `model_type` property of the config object that
- is loaded, or when it's missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:
-
- List options
-
- Args:
- pretrained_model_name_or_path (`str` or `os.PathLike`):
- Can be either:
-
- - A string, the *model id* of a pretrained model configuration hosted inside a model repo on
- huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or
- namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
- - A path to a *directory* containing a configuration file saved using the
- [`~PretrainedConfig.save_pretrained`] method, or the [`~PreTrainedModel.save_pretrained`] method,
- e.g., `./my_model_directory/`.
- - A path or url to a saved configuration JSON *file*, e.g.,
- `./my_model_directory/configuration.json`.
- cache_dir (`str` or `os.PathLike`, *optional*):
- Path to a directory in which a downloaded pretrained model configuration should be cached if the
- standard cache should not be used.
- force_download (`bool`, *optional*, defaults to `False`):
- Whether or not to force the (re-)download the model weights and configuration files and override the
- cached versions if they exist.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether or not to delete incompletely received files. Will attempt to resume the download if such a
- file exists.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
- 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- revision(`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
- git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
- identifier allowed by git.
- return_unused_kwargs (`bool`, *optional*, defaults to `False`):
- If `False`, then this function returns just the final configuration object.
-
- If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a
- dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
- part of `kwargs` which has not been used to update `config` and is otherwise ignored.
- trust_remote_code (`bool`, *optional*, defaults to `False`):
- Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
- should only be set to `True` for repositories you trust and in which you have read the code, as it will
- execute code present on the Hub on your local machine.
- kwargs(additional keyword arguments, *optional*):
- The values in kwargs of any keys which are configuration attributes will be used to override the loaded
- values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
- by the `return_unused_kwargs` keyword parameter.
-
- Examples:
-
- ```python
- >>> from transformers import AutoConfig
-
- >>> # Download configuration from huggingface.co and cache.
- >>> config = AutoConfig.from_pretrained("bert-base-uncased")
-
- >>> # Download configuration from huggingface.co (user-uploaded) and cache.
- >>> config = AutoConfig.from_pretrained("dbmdz/bert-base-german-cased")
-
- >>> # If configuration file is in a directory (e.g., was saved using *save_pretrained('./test/saved_model/')*).
- >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/")
-
- >>> # Load a specific configuration file.
- >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/my_configuration.json")
-
- >>> # Change some config attributes when loading a pretrained config.
- >>> config = AutoConfig.from_pretrained("bert-base-uncased", output_attentions=True, foo=False)
- >>> config.output_attentions
- True
-
- >>> config, unused_kwargs = AutoConfig.from_pretrained(
- ... "bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
- ... )
- >>> config.output_attentions
- True
-
- >>> config.unused_kwargs
- {'foo': False}
- ```"""
- kwargs["_from_auto"] = True
- kwargs["name_or_path"] = pretrained_model_name_or_path
- trust_remote_code = kwargs.pop("trust_remote_code", False)
- config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
- if "auto_map" in config_dict and "AutoConfig" in config_dict["auto_map"]:
- if not trust_remote_code:
- raise ValueError(
- f"Loading {pretrained_model_name_or_path} requires you to execute the configuration file in that repo "
- "on your local machine. Make sure you have read the code there to avoid malicious use, then set "
- "the option `trust_remote_code=True` to remove this error."
- )
- if kwargs.get("revision", None) is None:
- logger.warn(
- "Explicitly passing a `revision` is encouraged when loading a configuration with custom code to "
- "ensure no malicious code has been contributed in a newer revision."
- )
- class_ref = config_dict["auto_map"]["AutoConfig"]
- module_file, class_name = class_ref.split(".")
- config_class = get_class_from_dynamic_module(
- pretrained_model_name_or_path, module_file + ".py", class_name, **kwargs
- )
- return config_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
- elif "model_type" in config_dict:
- config_class = CONFIG_MAPPING[config_dict["model_type"]]
- return config_class.from_dict(config_dict, **kwargs)
- else:
- # Fallback: use pattern matching on the string.
- for pattern, config_class in CONFIG_MAPPING.items():
- if pattern in str(pretrained_model_name_or_path):
- return config_class.from_dict(config_dict, **kwargs)
-
- raise ValueError(
- f"Unrecognized model in {pretrained_model_name_or_path}. "
- f"Should have a `model_type` key in its {CONFIG_NAME}, or contain one of the following strings "
- f"in its name: {', '.join(CONFIG_MAPPING.keys())}"
- )
-
- @staticmethod
- def register(model_type, config):
- """
- Register a new configuration for this class.
-
- Args:
- model_type (`str`): The model type like "bert" or "gpt".
- config ([`PretrainedConfig`]): The config to register.
- """
- if issubclass(config, PretrainedConfig) and config.model_type != model_type:
- raise ValueError(
- "The config you are passing has a `model_type` attribute that is not consistent with the model type "
- f"you passed (config has {config.model_type} and you passed {model_type}. Fix one of those so they "
- "match!"
- )
- CONFIG_MAPPING.register(model_type, config)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py
deleted file mode 100644
index b6fb6e751cdedb2af4b1f6c0950557e187cd9519..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .scst_loss import ScstRewardCriterion
-from .label_smoothed_cross_entropy import AjustLabelSmoothedCrossEntropyCriterion
\ No newline at end of file
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py
deleted file mode 100644
index 56d63e3e1b5a036e0adf32480e2b66f371738013..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from dataclasses import dataclass, field
-
-import torch
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-from omegaconf import II
-
-
-@dataclass
-class LabelSmoothedCrossEntropyCriterionConfig(FairseqDataclass):
- label_smoothing: float = field(
- default=0.0,
- metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"},
- )
- report_accuracy: bool = field(
- default=False,
- metadata={"help": "report accuracy metric"},
- )
- ignore_prefix_size: int = field(
- default=0,
- metadata={"help": "Ignore first N tokens"},
- )
- sentence_avg: bool = II("optimization.sentence_avg")
-
-
-def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=None, reduce=True):
- if target.dim() == lprobs.dim() - 1:
- target = target.unsqueeze(-1)
- nll_loss = -lprobs.gather(dim=-1, index=target)
- smooth_loss = -lprobs.sum(dim=-1, keepdim=True)
- if ignore_index is not None:
- pad_mask = target.eq(ignore_index)
- nll_loss.masked_fill_(pad_mask, 0.0)
- smooth_loss.masked_fill_(pad_mask, 0.0)
- else:
- nll_loss = nll_loss.squeeze(-1)
- smooth_loss = smooth_loss.squeeze(-1)
- if reduce:
- nll_loss = nll_loss.sum()
- smooth_loss = smooth_loss.sum()
- eps_i = epsilon / (lprobs.size(-1) - 1)
- loss = (1.0 - epsilon - eps_i) * nll_loss + eps_i * smooth_loss
- return loss, nll_loss
-
-
-@register_criterion(
- "label_smoothed_cross_entropy", dataclass=LabelSmoothedCrossEntropyCriterionConfig
-)
-class LabelSmoothedCrossEntropyCriterion(FairseqCriterion):
- def __init__(
- self,
- task,
- sentence_avg,
- label_smoothing,
- ignore_prefix_size=0,
- report_accuracy=False,
- ):
- super().__init__(task)
- self.sentence_avg = sentence_avg
- self.eps = label_smoothing
- self.ignore_prefix_size = ignore_prefix_size
- self.report_accuracy = report_accuracy
-
- def forward(self, model, sample, reduce=True):
- """Compute the loss for the given sample.
-
- Returns a tuple with three elements:
- 1) the loss
- 2) the sample size, which is used as the denominator for the gradient
- 3) logging outputs to display while training
- """
- net_output = model(**sample["net_input"])
- loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce)
- sample_size = (
- sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
- )
- logging_output = {
- "loss": loss.data,
- "nll_loss": nll_loss.data,
- "ntokens": sample["ntokens"],
- "nsentences": sample["target"].size(0),
- "sample_size": sample_size,
- }
- if self.report_accuracy:
- n_correct, total = self.compute_accuracy(model, net_output, sample)
- logging_output["n_correct"] = utils.item(n_correct.data)
- logging_output["total"] = utils.item(total.data)
- return loss, sample_size, logging_output
-
- def get_lprobs_and_target(self, model, net_output, sample):
- lprobs = model.get_normalized_probs(net_output, log_probs=True)
- target = model.get_targets(sample, net_output)
- if self.ignore_prefix_size > 0:
- if getattr(lprobs, "batch_first", False):
- lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
- target = target[:, self.ignore_prefix_size :].contiguous()
- else:
- lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous()
- target = target[self.ignore_prefix_size :, :].contiguous()
- return lprobs.view(-1, lprobs.size(-1)), target.view(-1)
-
- def compute_loss(self, model, net_output, sample, reduce=True):
- lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
- loss, nll_loss = label_smoothed_nll_loss(
- lprobs,
- target,
- self.eps,
- ignore_index=self.padding_idx,
- reduce=reduce,
- )
- return loss, nll_loss
-
- def compute_accuracy(self, model, net_output, sample):
- lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
- mask = target.ne(self.padding_idx)
- n_correct = torch.sum(
- lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))
- )
- total = torch.sum(mask)
- return n_correct, total
-
- @classmethod
- def reduce_metrics(cls, logging_outputs) -> None:
- """Aggregate logging outputs from data parallel training."""
- loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
- nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs)
- ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
- sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
-
- metrics.log_scalar(
- "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
- )
- metrics.log_scalar(
- "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3
- )
- metrics.log_derived(
- "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
- )
-
- total = utils.item(sum(log.get("total", 0) for log in logging_outputs))
- if total > 0:
- metrics.log_scalar("total", total)
- n_correct = utils.item(
- sum(log.get("n_correct", 0) for log in logging_outputs)
- )
- metrics.log_scalar("n_correct", n_correct)
- metrics.log_derived(
- "accuracy",
- lambda meters: round(
- meters["n_correct"].sum * 100.0 / meters["total"].sum, 3
- )
- if meters["total"].sum > 0
- else float("nan"),
- )
-
- @staticmethod
- def logging_outputs_can_be_summed() -> bool:
- """
- Whether the logging outputs returned by `forward` can be summed
- across workers prior to calling `reduce_metrics`. Setting this
- to True will improves distributed training speed.
- """
- return True
diff --git a/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md b/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md
deleted file mode 100644
index d4654cdd019b1496d4c953944c9aced8a0dcc67a..0000000000000000000000000000000000000000
--- a/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Sail-rvc-Ai Hoshino From Oshi No Ko RVC V2 300 Epoch
-emoji: 📈
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Hina4867/bingo/src/components/ui/select.tsx b/spaces/Hina4867/bingo/src/components/ui/select.tsx
deleted file mode 100644
index 77f12c2996f541b97663de4c9e20ab34d4ec2fac..0000000000000000000000000000000000000000
--- a/spaces/Hina4867/bingo/src/components/ui/select.tsx
+++ /dev/null
@@ -1,123 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SelectPrimitive from '@radix-ui/react-select'
-
-import { cn } from '@/lib/utils'
-import {
- IconArrowDown,
- IconCheck,
- IconChevronUpDown
-} from '@/components/ui/icons'
-
-const Select = SelectPrimitive.Root
-
-const SelectGroup = SelectPrimitive.Group
-
-const SelectValue = SelectPrimitive.Value
-
-const SelectTrigger = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-
- {children}
-
-
-
-))
-SelectTrigger.displayName = SelectPrimitive.Trigger.displayName
-
-const SelectContent = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, position = 'popper', ...props }, ref) => (
-
-
-
- {children}
-
-
-
-))
-SelectContent.displayName = SelectPrimitive.Content.displayName
-
-const SelectLabel = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-
-
-
-
-
- {children}
-
-))
-SelectItem.displayName = SelectPrimitive.Item.displayName
-
-const SelectSeparator = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- {o.delete(i),o.size===0&&n&&(n(),n=null)}}return{set:a,update:l,subscribe:r}}var g;const E=((g=globalThis.__sveltekit_1d38e1f)==null?void 0:g.base)??"";var k;const S=((k=globalThis.__sveltekit_1d38e1f)==null?void 0:k.assets)??E,w="1698134506027",y="sveltekit:snapshot",I="sveltekit:scroll",x="sveltekit:index",_={tap:1,hover:2,viewport:3,eager:4,off:-1};function O(e){let t=e.baseURI;if(!t){const n=e.getElementsByTagName("base");t=n.length?n[0].href:e.URL}return t}function U(){return{x:pageXOffset,y:pageYOffset}}function f(e,t){return e.getAttribute(`data-sveltekit-${t}`)}const b={..._,"":_.hover};function v(e){let t=e.assignedSlot??e.parentNode;return(t==null?void 0:t.nodeType)===11&&(t=t.host),t}function L(e,t){for(;e&&e!==t;){if(e.nodeName.toUpperCase()==="A"&&e.hasAttribute("href"))return e;e=v(e)}}function N(e,t){let n;try{n=new URL(e instanceof SVGAElement?e.href.baseVal:e.href,document.baseURI)}catch{}const o=e instanceof SVGAElement?e.target.baseVal:e.target,a=!n||!!o||R(n,t)||(e.getAttribute("rel")||"").split(/\s+/).includes("external"),l=(n==null?void 0:n.origin)===location.origin&&e.hasAttribute("download");return{url:n,external:a,target:o,download:l}}function P(e){let t=null,n=null,o=null,a=null,l=null,r=null,s=e;for(;s&&s!==document.documentElement;)o===null&&(o=f(s,"preload-code")),a===null&&(a=f(s,"preload-data")),t===null&&(t=f(s,"keepfocus")),n===null&&(n=f(s,"noscroll")),l===null&&(l=f(s,"reload")),r===null&&(r=f(s,"replacestate")),s=v(s);return{preload_code:b[o??"off"],preload_data:b[a??"off"],keep_focus:t==="off"?!1:t===""?!0:null,noscroll:n==="off"?!1:n===""?!0:null,reload:l==="off"?!1:l===""?!0:null,replace_state:r==="off"?!1:r===""?!0:null}}function h(e){const t=p(e);let n=!0;function o(){n=!0,t.update(r=>r)}function a(r){n=!1,t.set(r)}function l(r){let s;return t.subscribe(u=>{(s===void 0||n&&u!==s)&&r(s=u)})}return{notify:o,set:a,subscribe:l}}function A(){const{set:e,subscribe:t}=p(!1);let n;async function o(){clearTimeout(n);try{const a=await fetch(`${S}/_app/version.json`,{headers:{pragma:"no-cache","cache-control":"no-cache"}});if(!a.ok)return!1;const r=(await a.json()).version!==w;return r&&(e(!0),clearTimeout(n)),r}catch{return!1}}return{subscribe:t,check:o}}function R(e,t){return e.origin!==location.origin||!e.pathname.startsWith(t)}function V(e){e.client}const Y={url:h({}),page:h({}),navigating:p(null),updated:A()};export{x as I,_ as P,I as S,y as a,N as b,P as c,U as d,E as e,L as f,O as g,V as h,R as i,Y as s};
diff --git a/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py b/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py
deleted file mode 100644
index f8e2eb0f15699f1b458a8445d0c1dd6229a21f77..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-import os, sys
-import subprocess
-import re
-from subprocess import check_call, check_output
-
-WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None)
-
-if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip():
- print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."')
- sys.exit(-1)
-
-
-BLEU_REGEX = re.compile("^BLEU\\S* = (\\S+) ")
-def run_eval_bleu(cmd):
- output = check_output(cmd, shell=True, stderr=subprocess.STDOUT).decode("utf-8").strip()
- print(output)
- bleu = -1.0
- for line in output.strip().split('\n'):
- m = BLEU_REGEX.search(line)
- if m is not None:
- bleu = m.groups()[0]
- bleu = float(bleu)
- break
- return bleu
-
-def check_data_test_bleu(raw_folder, data_lang_pairs):
- not_matchings = []
- for sacrebleu_set, src_tgts in data_lang_pairs:
- for src_tgt in src_tgts:
- print(f'checking test bleus for: {src_tgt} at {sacrebleu_set}')
- src, tgt = src_tgt.split('-')
- ssrc, stgt = src[:2], tgt[:2]
- if os.path.exists(f'{raw_folder}/test.{tgt}-{src}.{src}'):
- # reversed direction may have different test set
- test_src = f'{raw_folder}/test.{tgt}-{src}.{src}'
- else:
- test_src = f'{raw_folder}/test.{src}-{tgt}.{src}'
- cmd1 = f'cat {test_src} | sacrebleu -t "{sacrebleu_set}" -l {stgt}-{ssrc}; [ $? -eq 0 ] || echo ""'
- test_tgt = f'{raw_folder}/test.{src}-{tgt}.{tgt}'
- cmd2 = f'cat {test_tgt} | sacrebleu -t "{sacrebleu_set}" -l {ssrc}-{stgt}; [ $? -eq 0 ] || echo ""'
- bleu1 = run_eval_bleu(cmd1)
- if bleu1 != 100.0:
- not_matchings.append(f'{sacrebleu_set}:{src_tgt} source side not matching: {test_src}')
- bleu2 = run_eval_bleu(cmd2)
- if bleu2 != 100.0:
- not_matchings.append(f'{sacrebleu_set}:{src_tgt} target side not matching: {test_tgt}')
- return not_matchings
-
-if __name__ == "__main__":
- to_data_path = f'{WORKDIR_ROOT}/iwsltv2'
- not_matching = check_data_test_bleu(
- f'{to_data_path}/raw',
- [
- ('iwslt17', ['en_XX-ar_AR', 'en_XX-ko_KR', 'ar_AR-en_XX', 'ko_KR-en_XX']),
- ('iwslt17', ['en_XX-it_IT', 'en_XX-nl_XX', 'it_IT-en_XX', 'nl_XX-en_XX']),
- ('iwslt17/tst2015', ['en_XX-vi_VN', "vi_VN-en_XX"]),
- ]
- )
- if len(not_matching) > 0:
- print('the following datasets do not have matching test datasets:\n\t', '\n\t'.join(not_matching))
-
diff --git a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py b/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py
deleted file mode 100644
index 88597d62a505715091f9ba62d38bf0a85a31b95a..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import math
-import os
-os.environ["LRU_CACHE_CAPACITY"] = "3"
-import random
-import torch
-import torch.utils.data
-import numpy as np
-import librosa
-from librosa.util import normalize
-from librosa.filters import mel as librosa_mel_fn
-from scipy.io.wavfile import read
-import soundfile as sf
-
-def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
- sampling_rate = None
- try:
- data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
- except Exception as ex:
- print(f"'{full_path}' failed to load.\nException:")
- print(ex)
- if return_empty_on_exception:
- return [], sampling_rate or target_sr or 32000
- else:
- raise Exception(ex)
-
- if len(data.shape) > 1:
- data = data[:, 0]
- assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
-
- if np.issubdtype(data.dtype, np.integer): # if audio data is type int
- max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
- else: # if audio data is type fp32
- max_mag = max(np.amax(data), -np.amin(data))
- max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
-
- data = torch.FloatTensor(data.astype(np.float32))/max_mag
-
- if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
- return [], sampling_rate or target_sr or 32000
- if target_sr is not None and sampling_rate != target_sr:
- data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
- sampling_rate = target_sr
-
- return data, sampling_rate
-
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
- return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
-
-def dynamic_range_decompression(x, C=1):
- return np.exp(x) / C
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-def dynamic_range_decompression_torch(x, C=1):
- return torch.exp(x) / C
-
-class STFT():
- def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
- self.target_sr = sr
-
- self.n_mels = n_mels
- self.n_fft = n_fft
- self.win_size = win_size
- self.hop_length = hop_length
- self.fmin = fmin
- self.fmax = fmax
- self.clip_val = clip_val
- self.mel_basis = {}
- self.hann_window = {}
-
- def get_mel(self, y, center=False):
- sampling_rate = self.target_sr
- n_mels = self.n_mels
- n_fft = self.n_fft
- win_size = self.win_size
- hop_length = self.hop_length
- fmin = self.fmin
- fmax = self.fmax
- clip_val = self.clip_val
-
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- if fmax not in self.mel_basis:
- mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
- self.mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
- self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_length)/2), int((n_fft-hop_length)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)],
- center=center, pad_mode='reflect', normalized=False, onesided=True)
- # print(111,spec)
- spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
- # print(222,spec)
- spec = torch.matmul(self.mel_basis[str(fmax)+'_'+str(y.device)], spec)
- # print(333,spec)
- spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
- # print(444,spec)
- return spec
-
- def __call__(self, audiopath):
- audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
- spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
- return spect
-
-stft = STFT()
diff --git a/spaces/JUNGU/emotion-ko-state/README.md b/spaces/JUNGU/emotion-ko-state/README.md
deleted file mode 100644
index 48eb244083a0ac6a51b0298cd043231b60b3fde8..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/emotion-ko-state/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Emotion Ko State
-emoji: 🦀
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py b/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py
deleted file mode 100644
index 27fcd62482d0cf44e02fa1c339195be58cb745b0..0000000000000000000000000000000000000000
--- a/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import pinecone
-from colorama import Fore, Style
-
-from autogpt.llm_utils import create_embedding_with_ada
-from autogpt.logs import logger
-from autogpt.memory.base import MemoryProviderSingleton
-
-
-class PineconeMemory(MemoryProviderSingleton):
- def __init__(self, cfg):
- pinecone_api_key = cfg.pinecone_api_key
- pinecone_region = cfg.pinecone_region
- pinecone.init(api_key=pinecone_api_key, environment=pinecone_region)
- dimension = 1536
- metric = "cosine"
- pod_type = "p1"
- table_name = "auto-gpt"
- # this assumes we don't start with memory.
- # for now this works.
- # we'll need a more complicated and robust system if we want to start with
- # memory.
- self.vec_num = 0
-
- try:
- pinecone.whoami()
- except Exception as e:
- logger.typewriter_log(
- "FAILED TO CONNECT TO PINECONE",
- Fore.RED,
- Style.BRIGHT + str(e) + Style.RESET_ALL,
- )
- logger.double_check(
- "Please ensure you have setup and configured Pinecone properly for use."
- + f"You can check out {Fore.CYAN + Style.BRIGHT}"
- "https://github.com/Torantulino/Auto-GPT#-pinecone-api-key-setup"
- f"{Style.RESET_ALL} to ensure you've set up everything correctly."
- )
- exit(1)
-
- if table_name not in pinecone.list_indexes():
- pinecone.create_index(
- table_name, dimension=dimension, metric=metric, pod_type=pod_type
- )
- self.index = pinecone.Index(table_name)
-
- def add(self, data):
- vector = create_embedding_with_ada(data)
- # no metadata here. We may wish to change that long term.
- self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})])
- _text = f"Inserting data into memory at index: {self.vec_num}:\n data: {data}"
- self.vec_num += 1
- return _text
-
- def get(self, data):
- return self.get_relevant(data, 1)
-
- def clear(self):
- self.index.delete(deleteAll=True)
- return "Obliviated"
-
- def get_relevant(self, data, num_relevant=5):
- """
- Returns all the data in the memory that is relevant to the given data.
- :param data: The data to compare to.
- :param num_relevant: The number of relevant data to return. Defaults to 5
- """
- query_embedding = create_embedding_with_ada(data)
- results = self.index.query(
- query_embedding, top_k=num_relevant, include_metadata=True
- )
- sorted_results = sorted(results.matches, key=lambda x: x.score)
- return [str(item["metadata"]["raw_text"]) for item in sorted_results]
-
- def get_stats(self):
- return self.index.describe_index_stats()
diff --git a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py b/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py
deleted file mode 100644
index 1cc3b96da1275f1d01afb4c999f9c404d09b247a..0000000000000000000000000000000000000000
--- a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import cv2
-import numpy as np
-from PIL import Image
-import pickle
-import tensorflow as tf
-import os
-
-class rabbitsLogReg:
- def __init__(self,url) -> None:
- self.image = url
-
- def predict_image(self):
- # Load the model
- load_extractor = tf.keras.models.load_model("././Model/Rabbit/resnetLogreg/resnet_EXTRACTOR.h5")
-
- modelpath = "././Model/Rabbit/resnetLogreg/dataSaved.pkl"
-
- with open(modelpath, 'rb') as file:
- saved_data = pickle.load(file)
- animal_breed = saved_data['class_name']
- model = saved_data['logreg_model']
-
- im = Image.open(self.image)
- img = im.convert("RGB")
- img= np.asarray(img)
- image_resized= cv2.resize(img, (224,224))
- features = load_extractor.predict(np.expand_dims(image_resized, axis=0))
-
- reshaped_features = features.reshape(features.shape[0],-1)
- predicted_class = model.predict(reshaped_features)
- pred_prob = model.predict_proba(reshaped_features)[:2]
- prediction_probability = pred_prob[0][predicted_class[0]]
- predicted_class
-
- output_class= animal_breed[predicted_class[0]]
-
- return [output_class, prediction_probability]
diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md b/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md
deleted file mode 100644
index 80af4fbbfba5d15e1cb6d1f4b67808ca76fa37d7..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md
+++ /dev/null
@@ -1,140 +0,0 @@
-
-
-川虎 Chat 🐯 Chuanhu Chat
-
-
-
-
-
-
-
Lightweight and User-friendly Web-UI for LLMs including ChatGPT/ChatGLM/LLaMA
-
- Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search
- LaTeX rendering / Table rendering / Code highlighting
- Auto dark mode / Adaptive web interface / WeChat-like theme
- Multi-parameters tuning / Multi-API-Key support / Multi-user support
- Compatible with GPT-4 / Local deployment for LLMs
-
- Video Tutorial
- ·
- 2.0 Introduction
- ·
- 3.0 Introduction & Tutorial
- ||
- Online trial
- ·
- One-Click deployment
-
-
-
-
-
-
-
-## Supported LLM Models
-
-**LLM models via API**:
-
-- [ChatGPT](https://chat.openai.com) ([GPT-4](https://openai.com/product/gpt-4))
-- [Google PaLM](https://developers.generativeai.google/products/palm)
-- [Inspur Yuan 1.0](https://air.inspur.com/home)
-- [MiniMax](https://api.minimax.chat/)
-- [XMChat](https://github.com/MILVLG/xmchat)
-
-**LLM models via local deployment**:
-
-- [ChatGLM](https://github.com/THUDM/ChatGLM-6B) ([ChatGLM2](https://github.com/THUDM/ChatGLM2-6B))
-- [LLaMA](https://github.com/facebookresearch/llama)
-- [StableLM](https://github.com/Stability-AI/StableLM)
-- [MOSS](https://github.com/OpenLMLab/MOSS)
-
-## Usage Tips
-
-- To better control the ChatGPT, use System Prompt.
-- To use a Prompt Template, select the Prompt Template Collection file first, and then choose certain prompt from the drop-down menu.
-- To try again if the response is unsatisfactory, use `🔄 Regenerate` button.
-- To start a new line in the input box, press Shift + Enter keys.
-- To quickly switch between input history, press ↑ and ↓ key in the input box.
-- To deploy the program onto a server, set `"server_name": "0.0.0.0", "server_port" ,` in `config.json`.
-- To get a public shared link, set `"share": true,` in `config.json`. Please be noted that the program must be running in order to be accessed via a public link.
-- To use it in Hugging Face Spaces: It is recommended to **Duplicate Space** and run the program in your own Space for a faster and more secure experience.
-
-## Quickstart
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-Then make a copy of `config_example.json`, rename it to `config.json`, and then fill in your API-Key and other settings in the file.
-
-```shell
-python ChuanhuChatbot.py
-```
-
-A browser window will open and you will be able to chat with ChatGPT.
-
-> **Note**
->
-> Please check our [wiki page](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程) for detailed instructions.
-
-## Troubleshooting
-
-When you encounter problems, you should try manually pulling the latest changes of this project first. The steps are as follows:
-
-1. Download the latest code archive by clicking on `Download ZIP` on the webpage, or
- ```shell
- git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
- ```
-2. Try installing the dependencies again (as this project may have introduced new dependencies)
- ```
- pip install -r requirements.txt
- ```
-
-Generally, you can solve most problems by following these steps.
-
-If the problem still exists, please refer to this page: [Frequently Asked Questions (FAQ)](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-This page lists almost all the possible problems and solutions. Please read it carefully.
-
-## More Information
-
-More information could be found in our [wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki):
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-
-
-
-
-## Sponsor
-
-🐯 If you find this project helpful, feel free to buy me a coke or a cup of coffee~
-
-
-
-
diff --git a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py b/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py
deleted file mode 100644
index 59a73cc04025762d6490fcd2945a747d963def32..0000000000000000000000000000000000000000
--- a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from os import environ
-
-
-def assert_in(file, files_to_check):
- if file not in files_to_check:
- raise AssertionError("{} does not exist in the list".format(str(file)))
- return True
-
-
-def assert_in_env(check_list: list):
- for item in check_list:
- assert_in(item, environ.keys())
- return True
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py
deleted file mode 100644
index beb6e65c61f8a16b4376494123f31178cdb88bde..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py
+++ /dev/null
@@ -1,13 +0,0 @@
-CUSTOM_STREAMLIT_CSS = """
-div[data-testid="stBlock"] button {
- width: 100% !important;
- margin-bottom: 20px !important;
- border-color: #bfbfbf !important;
-}
-section[data-testid="stSidebar"] div {
- max-width: 10rem;
-}
-pre code {
- white-space: pre-wrap;
-}
-"""
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py
deleted file mode 100644
index 34bcffd6c0975377a54ae1ce89002be1dae8151d..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import matplotlib
-matplotlib.use('Agg')
-import time
-
-class Timer():
- ''' Timer for recording training time distribution. '''
- def __init__(self):
- self.prev_t = time.time()
- self.clear()
-
- def set(self):
- self.prev_t = time.time()
-
- def cnt(self, mode):
- self.time_table[mode] += time.time()-self.prev_t
- self.set()
- if mode == 'bw':
- self.click += 1
-
- def show(self):
- total_time = sum(self.time_table.values())
- self.time_table['avg'] = total_time/self.click
- self.time_table['rd'] = 100*self.time_table['rd']/total_time
- self.time_table['fw'] = 100*self.time_table['fw']/total_time
- self.time_table['bw'] = 100*self.time_table['bw']/total_time
- msg = '{avg:.3f} sec/step (rd {rd:.1f}% | fw {fw:.1f}% | bw {bw:.1f}%)'.format(
- **self.time_table)
- self.clear()
- return msg
-
- def clear(self):
- self.time_table = {'rd': 0, 'fw': 0, 'bw': 0}
- self.click = 0
-
-# Reference : https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/e2e_asr.py#L168
-
-def human_format(num):
- magnitude = 0
- while num >= 1000:
- magnitude += 1
- num /= 1000.0
- # add more suffixes if you need them
- return '{:3.1f}{}'.format(num, [' ', 'K', 'M', 'G', 'T', 'P'][magnitude])
-
-
-# provide easy access of attribute from dict, such abc.key
-class AttrDict(dict):
- def __init__(self, *args, **kwargs):
- super(AttrDict, self).__init__(*args, **kwargs)
- self.__dict__ = self
diff --git a/spaces/KevinQHLin/UniVTG/main/train_mr.py b/spaces/KevinQHLin/UniVTG/main/train_mr.py
deleted file mode 100644
index 1a10d029f81f86733d6dab71a3aee575917b092b..0000000000000000000000000000000000000000
--- a/spaces/KevinQHLin/UniVTG/main/train_mr.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import os
-import pdb
-import sys
-import time
-import json
-import pprint
-import random
-import numpy as np
-from tqdm import tqdm, trange
-from collections import defaultdict
-
-import torch
-import torch.nn as nn
-import torch.backends.cudnn as cudnn
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-
-sys.path.append('/data/home/qinghonglin/univtg')
-from main.config import BaseOptions, setup_model
-from main.dataset import \
- DatasetMR, start_end_collate_mr, prepare_batch_inputs_mr
-from main.inference_mr import eval_epoch, start_inference
-from utils.basic_utils import set_seed, AverageMeter, dict_to_markdown
-from utils.model_utils import count_parameters
-
-import logging
-logger = logging.getLogger(__name__)
-logging.basicConfig(format="%(asctime)s.%(msecs)03d:%(levelname)s:%(name)s - %(message)s",
- datefmt="%Y-%m-%d %H:%M:%S",
- level=logging.INFO)
-
-def train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer):
- logger.info(f"[Epoch {epoch_i+1}]")
- model.train()
- criterion.train()
-
- # init meters
- time_meters = defaultdict(AverageMeter)
- loss_meters = defaultdict(AverageMeter)
-
- num_training_examples = len(train_loader)
- timer_dataloading = time.time()
- for batch_idx, batch in tqdm(enumerate(train_loader),
- desc="Training Iteration",
- total=num_training_examples):
- time_meters["dataloading_time"].update(time.time() - timer_dataloading)
-
- timer_start = time.time()
- model_inputs, targets = prepare_batch_inputs_mr(batch[1], opt.device, non_blocking=opt.pin_memory)
- time_meters["prepare_inputs_time"].update(time.time() - timer_start)
-
- timer_start = time.time()
-
- # try:
- outputs = model(**model_inputs)
- loss_dict = criterion(outputs, targets)
- weight_dict = criterion.weight_dict
- losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
- time_meters["model_forward_time"].update(time.time() - timer_start)
-
- timer_start = time.time()
- optimizer.zero_grad()
- losses.backward()
-
- if opt.grad_clip > 0:
- nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
- optimizer.step()
- time_meters["model_backward_time"].update(time.time() - timer_start)
-
- loss_dict["loss_overall"] = float(losses) # for logging only
- for k, v in loss_dict.items():
- loss_meters[k].update(float(v) * weight_dict[k] if k in weight_dict else float(v))
-
- timer_dataloading = time.time()
-
- # print/add logs
- tb_writer.add_scalar("Train/lr", float(optimizer.param_groups[0]["lr"]), epoch_i+1)
- for k, v in loss_meters.items():
- tb_writer.add_scalar("Train/{}".format(k), v.avg, epoch_i+1)
-
- to_write = opt.train_log_txt_formatter.format(
- time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
- epoch=epoch_i+1,
- loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in loss_meters.items()]))
- with open(opt.train_log_filepath, "a") as f:
- f.write(to_write)
-
- logger.info("Epoch time stats:")
- for name, meter in time_meters.items():
- d = {k: f"{getattr(meter, k):.4f}" for k in ["max", "min", "avg"]}
- logger.info(f"{name} ==> {d}")
-
-
-def train(model, criterion, optimizer, lr_scheduler, train_dataset, val_dataset, opt):
- tb_writer = SummaryWriter(opt.tensorboard_log_dir)
- tb_writer.add_text("hyperparameters", dict_to_markdown(vars(opt), max_str_len=None))
- opt.train_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str}\n"
- opt.eval_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str} [Metrics] {eval_metrics_str}\n"
-
- train_loader = DataLoader(
- train_dataset,
- collate_fn=start_end_collate_mr,
- batch_size=opt.bsz,
- num_workers=opt.num_workers,
- shuffle=True,
- pin_memory=opt.pin_memory
- )
-
- prev_best_score = 0.
- es_cnt = 0
- if opt.start_epoch is None:
- start_epoch = -1 if opt.eval_init else 0
- else:
- start_epoch = opt.start_epoch
- save_submission_filename = "latest_{}_{}_preds.jsonl".format(opt.dset_name, opt.eval_split_name)
- for epoch_i in trange(start_epoch, opt.n_epoch, desc="Epoch"):
- if epoch_i > -1:
- train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer)
- lr_scheduler.step()
- eval_epoch_interval = opt.eval_epoch
- if opt.eval_path is not None and (epoch_i + 1) % eval_epoch_interval == 0:
- with torch.no_grad():
- metrics_no_nms, metrics_nms, eval_loss_meters, latest_file_paths = \
- eval_epoch(model, val_dataset, opt, save_submission_filename, epoch_i, criterion, tb_writer)
-
- # log
- to_write = opt.eval_log_txt_formatter.format(
- time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
- epoch=epoch_i,
- loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in eval_loss_meters.items()]),
- eval_metrics_str=json.dumps(metrics_no_nms))
-
- with open(opt.eval_log_filepath, "a") as f:
- f.write(to_write)
- logger.info("metrics_no_nms {}".format(pprint.pformat(metrics_no_nms["brief"], indent=4)))
- if metrics_nms is not None:
- logger.info("metrics_nms {}".format(pprint.pformat(metrics_nms["brief"], indent=4)))
-
- metrics = metrics_nms if metrics_nms is not None else metrics_no_nms
- for k, v in metrics["brief"].items():
- tb_writer.add_scalar(f"Eval/{k}", float(v), epoch_i+1)
-
- # stop_score = metrics["brief"]["MR-full-mAP"]
- # pdb.set_trace()
- stop_score = metrics["brief"][opt.main_metric]
- if stop_score > prev_best_score:
- es_cnt = 0
- prev_best_score = stop_score
-
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "lr_scheduler": lr_scheduler.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"))
-
- best_file_paths = [e.replace("latest", "best") for e in latest_file_paths]
- for src, tgt in zip(latest_file_paths, best_file_paths):
- os.renames(src, tgt)
- logger.info("The checkpoint file has been updated.")
- else:
- es_cnt += 1
- if opt.max_es_cnt != -1 and es_cnt > opt.max_es_cnt: # early stop
- with open(opt.train_log_filepath, "a") as f:
- f.write(f"Early Stop at epoch {epoch_i}")
- logger.info(f"\n>>>>> Early stop at epoch {epoch_i} {prev_best_score}\n")
- break
-
- # save ckpt
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "lr_scheduler": lr_scheduler.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_latest.ckpt"))
-
- if (epoch_i + 1) % opt.save_interval == 0 or (epoch_i + 1) % opt.lr_drop == 0: # additional copies
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", f"_e{epoch_i:04d}.ckpt"))
-
- if opt.debug:
- break
-
- tb_writer.close()
-
-
-def start_training():
- logger.info("Setup config, data and model...")
- opt = BaseOptions().parse()
- set_seed(opt.seed)
- if opt.debug: # keep the model run deterministically
- # 'cudnn.benchmark = True' enabled auto finding the best algorithm for a specific input/net config.
- # Enable this only when input size is fixed.
- cudnn.benchmark = False
- cudnn.deterministic = True
-
- dataset_config = dict(
- dset_name=opt.dset_name,
- data_path=opt.train_path,
- v_feat_dirs=opt.v_feat_dirs,
- q_feat_dir=opt.t_feat_dir,
- v_feat_dim=opt.v_feat_dim,
- q_feat_dim=opt.t_feat_dim,
- q_feat_type="last_hidden_state",
- max_q_l=opt.max_q_l,
- max_v_l=opt.max_v_l,
- ctx_mode=opt.ctx_mode,
- data_ratio=opt.data_ratio,
- normalize_v=not opt.no_norm_vfeat,
- normalize_t=not opt.no_norm_tfeat,
- clip_len=opt.clip_length,
- max_windows=opt.max_windows,
- span_loss_type=opt.span_loss_type,
- txt_drop_ratio=opt.txt_drop_ratio,
- use_cache=opt.use_cache,
- add_easy_negative=opt.add_easy_negative,
- easy_negative_only=opt.easy_negative_only
- )
-
- dataset_config["data_path"] = opt.train_path
- train_dataset = DatasetMR(**dataset_config)
-
- if opt.eval_path is not None:
- dataset_config["data_path"] = opt.eval_path
- dataset_config["txt_drop_ratio"] = 0
- dataset_config["q_feat_dir"] = opt.t_feat_dir.replace("txt_clip_asr", "txt_clip").replace("txt_clip_cap", "txt_clip") # for pretraining
- # dataset_config["load_labels"] = False # uncomment to calculate eval loss
- eval_dataset = DatasetMR(**dataset_config)
- else:
- eval_dataset = None
-
- if opt.lr_warmup > 0:
- # total_steps = opt.n_epoch * len(train_dataset) // opt.bsz
- total_steps = opt.n_epoch
- warmup_steps = opt.lr_warmup if opt.lr_warmup > 1 else int(opt.lr_warmup * total_steps)
- opt.lr_warmup = [warmup_steps, total_steps]
- model, criterion, optimizer, lr_scheduler = setup_model(opt)
- logger.info(f"Model {model}")
- count_parameters(model)
- logger.info("Start Training...")
- train(model, criterion, optimizer, lr_scheduler, train_dataset, eval_dataset, opt)
- return opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"), opt.eval_split_name, opt.eval_path, opt.debug
-
-
-if __name__ == '__main__':
- best_ckpt_path, eval_split_name, eval_path, debug = start_training()
- if not debug:
- input_args = ["--resume", best_ckpt_path,
- "--eval_split_name", eval_split_name,
- "--eval_path", eval_path]
-
- import sys
- sys.argv[1:] = input_args
- logger.info("\n\n\nFINISHED TRAINING!!!")
- logger.info("Evaluating model at {}".format(best_ckpt_path))
- logger.info("Input args {}".format(sys.argv[1:]))
- start_inference()
\ No newline at end of file
diff --git a/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md b/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md
deleted file mode 100644
index 1f55e11e80f6fc5ebbf42dade0266e3d4ee06ce4..0000000000000000000000000000000000000000
--- a/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
-# Transliteration Models for Indian languages
-These are models for transliteration involving Indian languages.
-The models are essentially Statistical Machine Translation systems trained using Moses over a
-character-level parallel corpora of transliterations. Hence, you will need Moses to use these transliteration models.
-The transliteration corpus has itself been mined in an unsupervised fashion from a translation corpus.
-
-Currently we have trained transliteration models for five language pairs: bn-hi, ta-hi, te-hi, en-hi and mr-hi
-
-Support for transliteration has been introduced in Moses from version 2.1
-So please ensure that you have minimum 2.1 version setup for Moses
-
-Commands to run the transliteration module using moses
-
-$moseshome/mosesdecoder/scripts/Transliteration/post-decoding-transliteration.pl \
---moses-src-dir $moseshome/mosesdecoder --external-bin-dir $moseshome/tools \
---transliteration-model-dir {path to transliteration model folder} --oov-file {path to file containing oov words, oovs are space separated with each line containing all oovs for the input line}\
- --input-file {input file to transliterated} --output-file {output file location} \
- --input-extension {input language code for eg. en} --output-extension {output language code for eg. hi} --language-model {path to language model} \
- --decoder $moseshome/mosesdecoder/bin/moses
-
-A sample execution of the model will be as follows:
-
-export moseshome={path to moses installation}
-$moseshome/mosesdecoder/scripts/Transliteration/post-decoding-transliteration.pl \
---moses-src-dir $moseshome/mosesdecoder --external-bin-dir $moseshome/tools \
---transliteration-model-dir /home/ratish/project/nlp_resources/indic_nlp_resources/transliterate/en-hi \
---oov-file /home/ratish/project/translit/input.oov \
- --input-file /home/ratish/project/translit/input.en \
- --output-file /home/ratish/project/translit/output.hi \
- --input-extension en --output-extension hi --language-model /home/ratish/project/translit/lm/nc.binlm.1 \
- --decoder $moseshome/mosesdecoder/bin/moses
-
-So far, we have seen the use of transliteration in a post-editing task for machine translation task.
-In case, the models are needed for purely transliteration purpose, the input file and OOV file are the same.
-Sample input file:
-New Delhi is capital of India
-India is worlds seventh largest nation in the World
-
-OOV file
-New Delhi is capital of India
-India is worlds seventh largest nation in the World
-
-On running the transliteration module, the output is:
-न्यू डेल्ही इस कैपिटल आफ इंडिया
-इंडिया इस वर्ल्ड सेवंथ लारगेस्ट नेशन इन थे वर्ल्ड
diff --git a/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py b/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py
deleted file mode 100644
index 980440eb3434e48b7cad90f0577c838a05d787b4..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Sequence
-
-from torch.utils.data import BatchSampler, Sampler
-
-from mmdet.registry import DATA_SAMPLERS
-
-
-# TODO: maybe replace with a data_loader wrapper
-@DATA_SAMPLERS.register_module()
-class AspectRatioBatchSampler(BatchSampler):
- """A sampler wrapper for grouping images with similar aspect ratio (< 1 or.
-
- >= 1) into a same batch.
-
- Args:
- sampler (Sampler): Base sampler.
- batch_size (int): Size of mini-batch.
- drop_last (bool): If ``True``, the sampler will drop the last batch if
- its size would be less than ``batch_size``.
- """
-
- def __init__(self,
- sampler: Sampler,
- batch_size: int,
- drop_last: bool = False) -> None:
- if not isinstance(sampler, Sampler):
- raise TypeError('sampler should be an instance of ``Sampler``, '
- f'but got {sampler}')
- if not isinstance(batch_size, int) or batch_size <= 0:
- raise ValueError('batch_size should be a positive integer value, '
- f'but got batch_size={batch_size}')
- self.sampler = sampler
- self.batch_size = batch_size
- self.drop_last = drop_last
- # two groups for w < h and w >= h
- self._aspect_ratio_buckets = [[] for _ in range(2)]
-
- def __iter__(self) -> Sequence[int]:
- for idx in self.sampler:
- data_info = self.sampler.dataset.get_data_info(idx)
- width, height = data_info['width'], data_info['height']
- bucket_id = 0 if width < height else 1
- bucket = self._aspect_ratio_buckets[bucket_id]
- bucket.append(idx)
- # yield a batch of indices in the same aspect ratio group
- if len(bucket) == self.batch_size:
- yield bucket[:]
- del bucket[:]
-
- # yield the rest data and reset the bucket
- left_data = self._aspect_ratio_buckets[0] + self._aspect_ratio_buckets[
- 1]
- self._aspect_ratio_buckets = [[] for _ in range(2)]
- while len(left_data) > 0:
- if len(left_data) <= self.batch_size:
- if not self.drop_last:
- yield left_data[:]
- left_data = []
- else:
- yield left_data[:self.batch_size]
- left_data = left_data[self.batch_size:]
-
- def __len__(self) -> int:
- if self.drop_last:
- return len(self.sampler) // self.batch_size
- else:
- return (len(self.sampler) + self.batch_size - 1) // self.batch_size
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py
deleted file mode 100644
index 430b06d085d94760d56a7ea083eaf23bd32b1f53..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py
+++ /dev/null
@@ -1,722 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, Scale
-from mmcv.ops import DeformConv2d
-from torch import Tensor
-
-from mmdet.registry import MODELS, TASK_UTILS
-from mmdet.structures.bbox import bbox_overlaps
-from mmdet.utils import (ConfigType, InstanceList, MultiConfig,
- OptInstanceList, RangeType, reduce_mean)
-from ..task_modules.prior_generators import MlvlPointGenerator
-from ..task_modules.samplers import PseudoSampler
-from ..utils import multi_apply
-from .atss_head import ATSSHead
-from .fcos_head import FCOSHead
-
-INF = 1e8
-
-
-@MODELS.register_module()
-class VFNetHead(ATSSHead, FCOSHead):
- """Head of `VarifocalNet (VFNet): An IoU-aware Dense Object
- Detector.`_.
-
- The VFNet predicts IoU-aware classification scores which mix the
- object presence confidence and object localization accuracy as the
- detection score. It is built on the FCOS architecture and uses ATSS
- for defining positive/negative training examples. The VFNet is trained
- with Varifocal Loss and empolys star-shaped deformable convolution to
- extract features for a bbox.
-
- Args:
- num_classes (int): Number of categories excluding the background
- category.
- in_channels (int): Number of channels in the input feature map.
- regress_ranges (Sequence[Tuple[int, int]]): Regress range of multiple
- level points.
- center_sampling (bool): If true, use center sampling. Defaults to False.
- center_sample_radius (float): Radius of center sampling. Defaults to 1.5.
- sync_num_pos (bool): If true, synchronize the number of positive
- examples across GPUs. Defaults to True
- gradient_mul (float): The multiplier to gradients from bbox refinement
- and recognition. Defaults to 0.1.
- bbox_norm_type (str): The bbox normalization type, 'reg_denom' or
- 'stride'. Defaults to reg_denom
- loss_cls_fl (:obj:`ConfigDict` or dict): Config of focal loss.
- use_vfl (bool): If true, use varifocal loss for training.
- Defaults to True.
- loss_cls (:obj:`ConfigDict` or dict): Config of varifocal loss.
- loss_bbox (:obj:`ConfigDict` or dict): Config of localization loss,
- GIoU Loss.
- loss_bbox (:obj:`ConfigDict` or dict): Config of localization
- refinement loss, GIoU Loss.
- norm_cfg (:obj:`ConfigDict` or dict): dictionary to construct and
- config norm layer. Defaults to norm_cfg=dict(type='GN',
- num_groups=32, requires_grad=True).
- use_atss (bool): If true, use ATSS to define positive/negative
- examples. Defaults to True.
- anchor_generator (:obj:`ConfigDict` or dict): Config of anchor
- generator for ATSS.
- init_cfg (:obj:`ConfigDict` or dict or list[dict] or
- list[:obj:`ConfigDict`]): Initialization config dict.
-
- Example:
- >>> self = VFNetHead(11, 7)
- >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
- >>> cls_score, bbox_pred, bbox_pred_refine= self.forward(feats)
- >>> assert len(cls_score) == len(self.scales)
- """ # noqa: E501
-
- def __init__(self,
- num_classes: int,
- in_channels: int,
- regress_ranges: RangeType = ((-1, 64), (64, 128), (128, 256),
- (256, 512), (512, INF)),
- center_sampling: bool = False,
- center_sample_radius: float = 1.5,
- sync_num_pos: bool = True,
- gradient_mul: float = 0.1,
- bbox_norm_type: str = 'reg_denom',
- loss_cls_fl: ConfigType = dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- use_vfl: bool = True,
- loss_cls: ConfigType = dict(
- type='VarifocalLoss',
- use_sigmoid=True,
- alpha=0.75,
- gamma=2.0,
- iou_weighted=True,
- loss_weight=1.0),
- loss_bbox: ConfigType = dict(
- type='GIoULoss', loss_weight=1.5),
- loss_bbox_refine: ConfigType = dict(
- type='GIoULoss', loss_weight=2.0),
- norm_cfg: ConfigType = dict(
- type='GN', num_groups=32, requires_grad=True),
- use_atss: bool = True,
- reg_decoded_bbox: bool = True,
- anchor_generator: ConfigType = dict(
- type='AnchorGenerator',
- ratios=[1.0],
- octave_base_scale=8,
- scales_per_octave=1,
- center_offset=0.0,
- strides=[8, 16, 32, 64, 128]),
- init_cfg: MultiConfig = dict(
- type='Normal',
- layer='Conv2d',
- std=0.01,
- override=dict(
- type='Normal',
- name='vfnet_cls',
- std=0.01,
- bias_prob=0.01)),
- **kwargs) -> None:
- # dcn base offsets, adapted from reppoints_head.py
- self.num_dconv_points = 9
- self.dcn_kernel = int(np.sqrt(self.num_dconv_points))
- self.dcn_pad = int((self.dcn_kernel - 1) / 2)
- dcn_base = np.arange(-self.dcn_pad,
- self.dcn_pad + 1).astype(np.float64)
- dcn_base_y = np.repeat(dcn_base, self.dcn_kernel)
- dcn_base_x = np.tile(dcn_base, self.dcn_kernel)
- dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape(
- (-1))
- self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1)
-
- super(FCOSHead, self).__init__(
- num_classes=num_classes,
- in_channels=in_channels,
- norm_cfg=norm_cfg,
- init_cfg=init_cfg,
- **kwargs)
- self.regress_ranges = regress_ranges
- self.reg_denoms = [
- regress_range[-1] for regress_range in regress_ranges
- ]
- self.reg_denoms[-1] = self.reg_denoms[-2] * 2
- self.center_sampling = center_sampling
- self.center_sample_radius = center_sample_radius
- self.sync_num_pos = sync_num_pos
- self.bbox_norm_type = bbox_norm_type
- self.gradient_mul = gradient_mul
- self.use_vfl = use_vfl
- if self.use_vfl:
- self.loss_cls = MODELS.build(loss_cls)
- else:
- self.loss_cls = MODELS.build(loss_cls_fl)
- self.loss_bbox = MODELS.build(loss_bbox)
- self.loss_bbox_refine = MODELS.build(loss_bbox_refine)
-
- # for getting ATSS targets
- self.use_atss = use_atss
- self.reg_decoded_bbox = reg_decoded_bbox
- self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
-
- self.anchor_center_offset = anchor_generator['center_offset']
-
- self.num_base_priors = self.prior_generator.num_base_priors[0]
-
- if self.train_cfg:
- self.assigner = TASK_UTILS.build(self.train_cfg['assigner'])
- if self.train_cfg.get('sampler', None) is not None:
- self.sampler = TASK_UTILS.build(
- self.train_cfg['sampler'], default_args=dict(context=self))
- else:
- self.sampler = PseudoSampler()
- # only be used in `get_atss_targets` when `use_atss` is True
- self.atss_prior_generator = TASK_UTILS.build(anchor_generator)
-
- self.fcos_prior_generator = MlvlPointGenerator(
- anchor_generator['strides'],
- self.anchor_center_offset if self.use_atss else 0.5)
-
- # In order to reuse the `get_bboxes` in `BaseDenseHead.
- # Only be used in testing phase.
- self.prior_generator = self.fcos_prior_generator
-
- def _init_layers(self) -> None:
- """Initialize layers of the head."""
- super(FCOSHead, self)._init_cls_convs()
- super(FCOSHead, self)._init_reg_convs()
- self.relu = nn.ReLU()
- self.vfnet_reg_conv = ConvModule(
- self.feat_channels,
- self.feat_channels,
- 3,
- stride=1,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- bias=self.conv_bias)
- self.vfnet_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
- self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
- self.vfnet_reg_refine_dconv = DeformConv2d(
- self.feat_channels,
- self.feat_channels,
- self.dcn_kernel,
- 1,
- padding=self.dcn_pad)
- self.vfnet_reg_refine = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
- self.scales_refine = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
- self.vfnet_cls_dconv = DeformConv2d(
- self.feat_channels,
- self.feat_channels,
- self.dcn_kernel,
- 1,
- padding=self.dcn_pad)
- self.vfnet_cls = nn.Conv2d(
- self.feat_channels, self.cls_out_channels, 3, padding=1)
-
- def forward(self, x: Tuple[Tensor]) -> Tuple[List[Tensor]]:
- """Forward features from the upstream network.
-
- Args:
- x (tuple[Tensor]): Features from the upstream network, each is
- a 4D-tensor.
-
- Returns:
- tuple:
-
- - cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level, each is a 4D-tensor, the channel number is
- num_points * num_classes.
- - bbox_preds (list[Tensor]): Box offsets for each
- scale level, each is a 4D-tensor, the channel number is
- num_points * 4.
- - bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level, each is a 4D-tensor, the channel
- number is num_points * 4.
- """
- return multi_apply(self.forward_single, x, self.scales,
- self.scales_refine, self.strides, self.reg_denoms)
-
- def forward_single(self, x: Tensor, scale: Scale, scale_refine: Scale,
- stride: int, reg_denom: int) -> tuple:
- """Forward features of a single scale level.
-
- Args:
- x (Tensor): FPN feature maps of the specified stride.
- scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
- the bbox prediction.
- scale_refine (:obj: `mmcv.cnn.Scale`): Learnable scale module to
- resize the refined bbox prediction.
- stride (int): The corresponding stride for feature maps,
- used to normalize the bbox prediction when
- bbox_norm_type = 'stride'.
- reg_denom (int): The corresponding regression range for feature
- maps, only used to normalize the bbox prediction when
- bbox_norm_type = 'reg_denom'.
-
- Returns:
- tuple: iou-aware cls scores for each box, bbox predictions and
- refined bbox predictions of input feature maps.
- """
- cls_feat = x
- reg_feat = x
-
- for cls_layer in self.cls_convs:
- cls_feat = cls_layer(cls_feat)
-
- for reg_layer in self.reg_convs:
- reg_feat = reg_layer(reg_feat)
-
- # predict the bbox_pred of different level
- reg_feat_init = self.vfnet_reg_conv(reg_feat)
- if self.bbox_norm_type == 'reg_denom':
- bbox_pred = scale(
- self.vfnet_reg(reg_feat_init)).float().exp() * reg_denom
- elif self.bbox_norm_type == 'stride':
- bbox_pred = scale(
- self.vfnet_reg(reg_feat_init)).float().exp() * stride
- else:
- raise NotImplementedError
-
- # compute star deformable convolution offsets
- # converting dcn_offset to reg_feat.dtype thus VFNet can be
- # trained with FP16
- dcn_offset = self.star_dcn_offset(bbox_pred, self.gradient_mul,
- stride).to(reg_feat.dtype)
-
- # refine the bbox_pred
- reg_feat = self.relu(self.vfnet_reg_refine_dconv(reg_feat, dcn_offset))
- bbox_pred_refine = scale_refine(
- self.vfnet_reg_refine(reg_feat)).float().exp()
- bbox_pred_refine = bbox_pred_refine * bbox_pred.detach()
-
- # predict the iou-aware cls score
- cls_feat = self.relu(self.vfnet_cls_dconv(cls_feat, dcn_offset))
- cls_score = self.vfnet_cls(cls_feat)
-
- if self.training:
- return cls_score, bbox_pred, bbox_pred_refine
- else:
- return cls_score, bbox_pred_refine
-
- def star_dcn_offset(self, bbox_pred: Tensor, gradient_mul: float,
- stride: int) -> Tensor:
- """Compute the star deformable conv offsets.
-
- Args:
- bbox_pred (Tensor): Predicted bbox distance offsets (l, r, t, b).
- gradient_mul (float): Gradient multiplier.
- stride (int): The corresponding stride for feature maps,
- used to project the bbox onto the feature map.
-
- Returns:
- Tensor: The offsets for deformable convolution.
- """
- dcn_base_offset = self.dcn_base_offset.type_as(bbox_pred)
- bbox_pred_grad_mul = (1 - gradient_mul) * bbox_pred.detach() + \
- gradient_mul * bbox_pred
- # map to the feature map scale
- bbox_pred_grad_mul = bbox_pred_grad_mul / stride
- N, C, H, W = bbox_pred.size()
-
- x1 = bbox_pred_grad_mul[:, 0, :, :]
- y1 = bbox_pred_grad_mul[:, 1, :, :]
- x2 = bbox_pred_grad_mul[:, 2, :, :]
- y2 = bbox_pred_grad_mul[:, 3, :, :]
- bbox_pred_grad_mul_offset = bbox_pred.new_zeros(
- N, 2 * self.num_dconv_points, H, W)
- bbox_pred_grad_mul_offset[:, 0, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 1, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 2, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 4, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 5, :, :] = x2 # x2
- bbox_pred_grad_mul_offset[:, 7, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 11, :, :] = x2 # x2
- bbox_pred_grad_mul_offset[:, 12, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 13, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 14, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 16, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 17, :, :] = x2 # x2
- dcn_offset = bbox_pred_grad_mul_offset - dcn_base_offset
-
- return dcn_offset
-
- def loss_by_feat(
- self,
- cls_scores: List[Tensor],
- bbox_preds: List[Tensor],
- bbox_preds_refine: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> dict:
- """Compute loss of the head.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level, each is a 4D-tensor, the channel number is
- num_points * num_classes.
- bbox_preds (list[Tensor]): Box offsets for each
- scale level, each is a 4D-tensor, the channel number is
- num_points * 4.
- bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level, each is a 4D-tensor, the channel
- number is num_points * 4.
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine)
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- all_level_points = self.fcos_prior_generator.grid_priors(
- featmap_sizes, bbox_preds[0].dtype, bbox_preds[0].device)
- labels, label_weights, bbox_targets, bbox_weights = self.get_targets(
- cls_scores,
- all_level_points,
- batch_gt_instances,
- batch_img_metas,
- batch_gt_instances_ignore=batch_gt_instances_ignore)
-
- num_imgs = cls_scores[0].size(0)
- # flatten cls_scores, bbox_preds and bbox_preds_refine
- flatten_cls_scores = [
- cls_score.permute(0, 2, 3,
- 1).reshape(-1,
- self.cls_out_channels).contiguous()
- for cls_score in cls_scores
- ]
- flatten_bbox_preds = [
- bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
- for bbox_pred in bbox_preds
- ]
- flatten_bbox_preds_refine = [
- bbox_pred_refine.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
- for bbox_pred_refine in bbox_preds_refine
- ]
- flatten_cls_scores = torch.cat(flatten_cls_scores)
- flatten_bbox_preds = torch.cat(flatten_bbox_preds)
- flatten_bbox_preds_refine = torch.cat(flatten_bbox_preds_refine)
- flatten_labels = torch.cat(labels)
- flatten_bbox_targets = torch.cat(bbox_targets)
- # repeat points to align with bbox_preds
- flatten_points = torch.cat(
- [points.repeat(num_imgs, 1) for points in all_level_points])
-
- # FG cat_id: [0, num_classes - 1], BG cat_id: num_classes
- bg_class_ind = self.num_classes
- pos_inds = torch.where(
- ((flatten_labels >= 0) & (flatten_labels < bg_class_ind)) > 0)[0]
- num_pos = len(pos_inds)
-
- pos_bbox_preds = flatten_bbox_preds[pos_inds]
- pos_bbox_preds_refine = flatten_bbox_preds_refine[pos_inds]
- pos_labels = flatten_labels[pos_inds]
-
- # sync num_pos across all gpus
- if self.sync_num_pos:
- num_pos_avg_per_gpu = reduce_mean(
- pos_inds.new_tensor(num_pos).float()).item()
- num_pos_avg_per_gpu = max(num_pos_avg_per_gpu, 1.0)
- else:
- num_pos_avg_per_gpu = num_pos
-
- pos_bbox_targets = flatten_bbox_targets[pos_inds]
- pos_points = flatten_points[pos_inds]
-
- pos_decoded_bbox_preds = self.bbox_coder.decode(
- pos_points, pos_bbox_preds)
- pos_decoded_target_preds = self.bbox_coder.decode(
- pos_points, pos_bbox_targets)
- iou_targets_ini = bbox_overlaps(
- pos_decoded_bbox_preds,
- pos_decoded_target_preds.detach(),
- is_aligned=True).clamp(min=1e-6)
- bbox_weights_ini = iou_targets_ini.clone().detach()
- bbox_avg_factor_ini = reduce_mean(
- bbox_weights_ini.sum()).clamp_(min=1).item()
-
- pos_decoded_bbox_preds_refine = \
- self.bbox_coder.decode(pos_points, pos_bbox_preds_refine)
- iou_targets_rf = bbox_overlaps(
- pos_decoded_bbox_preds_refine,
- pos_decoded_target_preds.detach(),
- is_aligned=True).clamp(min=1e-6)
- bbox_weights_rf = iou_targets_rf.clone().detach()
- bbox_avg_factor_rf = reduce_mean(
- bbox_weights_rf.sum()).clamp_(min=1).item()
-
- if num_pos > 0:
- loss_bbox = self.loss_bbox(
- pos_decoded_bbox_preds,
- pos_decoded_target_preds.detach(),
- weight=bbox_weights_ini,
- avg_factor=bbox_avg_factor_ini)
-
- loss_bbox_refine = self.loss_bbox_refine(
- pos_decoded_bbox_preds_refine,
- pos_decoded_target_preds.detach(),
- weight=bbox_weights_rf,
- avg_factor=bbox_avg_factor_rf)
-
- # build IoU-aware cls_score targets
- if self.use_vfl:
- pos_ious = iou_targets_rf.clone().detach()
- cls_iou_targets = torch.zeros_like(flatten_cls_scores)
- cls_iou_targets[pos_inds, pos_labels] = pos_ious
- else:
- loss_bbox = pos_bbox_preds.sum() * 0
- loss_bbox_refine = pos_bbox_preds_refine.sum() * 0
- if self.use_vfl:
- cls_iou_targets = torch.zeros_like(flatten_cls_scores)
-
- if self.use_vfl:
- loss_cls = self.loss_cls(
- flatten_cls_scores,
- cls_iou_targets,
- avg_factor=num_pos_avg_per_gpu)
- else:
- loss_cls = self.loss_cls(
- flatten_cls_scores,
- flatten_labels,
- weight=label_weights,
- avg_factor=num_pos_avg_per_gpu)
-
- return dict(
- loss_cls=loss_cls,
- loss_bbox=loss_bbox,
- loss_bbox_rf=loss_bbox_refine)
-
- def get_targets(
- self,
- cls_scores: List[Tensor],
- mlvl_points: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> tuple:
- """A wrapper for computing ATSS and FCOS targets for points in multiple
- images.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- tuple:
-
- - labels_list (list[Tensor]): Labels of each level.
- - label_weights (Tensor/None): Label weights of all levels.
- - bbox_targets_list (list[Tensor]): Regression targets of each
- level, (l, t, r, b).
- - bbox_weights (Tensor/None): Bbox weights of all levels.
- """
- if self.use_atss:
- return self.get_atss_targets(cls_scores, mlvl_points,
- batch_gt_instances, batch_img_metas,
- batch_gt_instances_ignore)
- else:
- self.norm_on_bbox = False
- return self.get_fcos_targets(mlvl_points, batch_gt_instances)
-
- def _get_targets_single(self, *args, **kwargs):
- """Avoid ambiguity in multiple inheritance."""
- if self.use_atss:
- return ATSSHead._get_targets_single(self, *args, **kwargs)
- else:
- return FCOSHead._get_targets_single(self, *args, **kwargs)
-
- def get_fcos_targets(self, points: List[Tensor],
- batch_gt_instances: InstanceList) -> tuple:
- """Compute FCOS regression and classification targets for points in
- multiple images.
-
- Args:
- points (list[Tensor]): Points of each fpn level, each has shape
- (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
-
- Returns:
- tuple:
-
- - labels (list[Tensor]): Labels of each level.
- - label_weights: None, to be compatible with ATSS targets.
- - bbox_targets (list[Tensor]): BBox targets of each level.
- - bbox_weights: None, to be compatible with ATSS targets.
- """
- labels, bbox_targets = FCOSHead.get_targets(self, points,
- batch_gt_instances)
- label_weights = None
- bbox_weights = None
- return labels, label_weights, bbox_targets, bbox_weights
-
- def get_anchors(self,
- featmap_sizes: List[Tuple],
- batch_img_metas: List[dict],
- device: str = 'cuda') -> tuple:
- """Get anchors according to feature map sizes.
-
- Args:
- featmap_sizes (list[tuple]): Multi-level feature map sizes.
- batch_img_metas (list[dict]): Image meta info.
- device (str): Device for returned tensors
-
- Returns:
- tuple:
-
- - anchor_list (list[Tensor]): Anchors of each image.
- - valid_flag_list (list[Tensor]): Valid flags of each image.
- """
- num_imgs = len(batch_img_metas)
-
- # since feature map sizes of all images are the same, we only compute
- # anchors for one time
- multi_level_anchors = self.atss_prior_generator.grid_priors(
- featmap_sizes, device=device)
- anchor_list = [multi_level_anchors for _ in range(num_imgs)]
-
- # for each image, we compute valid flags of multi level anchors
- valid_flag_list = []
- for img_id, img_meta in enumerate(batch_img_metas):
- multi_level_flags = self.atss_prior_generator.valid_flags(
- featmap_sizes, img_meta['pad_shape'], device=device)
- valid_flag_list.append(multi_level_flags)
-
- return anchor_list, valid_flag_list
-
- def get_atss_targets(
- self,
- cls_scores: List[Tensor],
- mlvl_points: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> tuple:
- """A wrapper for computing ATSS targets for points in multiple images.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- tuple:
-
- - labels_list (list[Tensor]): Labels of each level.
- - label_weights (Tensor): Label weights of all levels.
- - bbox_targets_list (list[Tensor]): Regression targets of each
- level, (l, t, r, b).
- - bbox_weights (Tensor): Bbox weights of all levels.
- """
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- assert len(
- featmap_sizes
- ) == self.atss_prior_generator.num_levels == \
- self.fcos_prior_generator.num_levels
-
- device = cls_scores[0].device
-
- anchor_list, valid_flag_list = self.get_anchors(
- featmap_sizes, batch_img_metas, device=device)
-
- cls_reg_targets = ATSSHead.get_targets(
- self,
- anchor_list,
- valid_flag_list,
- batch_gt_instances,
- batch_img_metas,
- batch_gt_instances_ignore,
- unmap_outputs=True)
-
- (anchor_list, labels_list, label_weights_list, bbox_targets_list,
- bbox_weights_list, avg_factor) = cls_reg_targets
-
- bbox_targets_list = [
- bbox_targets.reshape(-1, 4) for bbox_targets in bbox_targets_list
- ]
-
- num_imgs = len(batch_img_metas)
- # transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format
- bbox_targets_list = self.transform_bbox_targets(
- bbox_targets_list, mlvl_points, num_imgs)
-
- labels_list = [labels.reshape(-1) for labels in labels_list]
- label_weights_list = [
- label_weights.reshape(-1) for label_weights in label_weights_list
- ]
- bbox_weights_list = [
- bbox_weights.reshape(-1) for bbox_weights in bbox_weights_list
- ]
- label_weights = torch.cat(label_weights_list)
- bbox_weights = torch.cat(bbox_weights_list)
- return labels_list, label_weights, bbox_targets_list, bbox_weights
-
- def transform_bbox_targets(self, decoded_bboxes: List[Tensor],
- mlvl_points: List[Tensor],
- num_imgs: int) -> List[Tensor]:
- """Transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format.
-
- Args:
- decoded_bboxes (list[Tensor]): Regression targets of each level,
- in the form of (x1, y1, x2, y2).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- num_imgs (int): the number of images in a batch.
-
- Returns:
- bbox_targets (list[Tensor]): Regression targets of each level in
- the form of (l, t, r, b).
- """
- # TODO: Re-implemented in Class PointCoder
- assert len(decoded_bboxes) == len(mlvl_points)
- num_levels = len(decoded_bboxes)
- mlvl_points = [points.repeat(num_imgs, 1) for points in mlvl_points]
- bbox_targets = []
- for i in range(num_levels):
- bbox_target = self.bbox_coder.encode(mlvl_points[i],
- decoded_bboxes[i])
- bbox_targets.append(bbox_target)
-
- return bbox_targets
-
- def _load_from_state_dict(self, state_dict: dict, prefix: str,
- local_metadata: dict, strict: bool,
- missing_keys: Union[List[str], str],
- unexpected_keys: Union[List[str], str],
- error_msgs: Union[List[str], str]) -> None:
- """Override the method in the parent class to avoid changing para's
- name."""
- pass
diff --git a/spaces/Larvuz/instruct-pix2pix/edit_app.py b/spaces/Larvuz/instruct-pix2pix/edit_app.py
deleted file mode 100644
index 0359e815ad51b1a2291dd8943555568e452981ad..0000000000000000000000000000000000000000
--- a/spaces/Larvuz/instruct-pix2pix/edit_app.py
+++ /dev/null
@@ -1,192 +0,0 @@
-from __future__ import annotations
-
-import math
-import random
-
-import gradio as gr
-import torch
-from PIL import Image, ImageOps
-from diffusers import StableDiffusionInstructPix2PixPipeline
-
-
-help_text = """
-If you're not getting what you want, there may be a few reasons:
-1. Is the image not changing enough? Your Image CFG weight may be too high. This value dictates how similar the output should be to the input. It's possible your edit requires larger changes from the original image, and your Image CFG weight isn't allowing that. Alternatively, your Text CFG weight may be too low. This value dictates how much to listen to the text instruction. The default Image CFG of 1.5 and Text CFG of 7.5 are a good starting point, but aren't necessarily optimal for each edit. Try:
- * Decreasing the Image CFG weight, or
- * Increasing the Text CFG weight, or
-2. Conversely, is the image changing too much, such that the details in the original image aren't preserved? Try:
- * Increasing the Image CFG weight, or
- * Decreasing the Text CFG weight
-3. Try generating results with different random seeds by setting "Randomize Seed" and running generation multiple times. You can also try setting "Randomize CFG" to sample new Text CFG and Image CFG values each time.
-4. Rephrasing the instruction sometimes improves results (e.g., "turn him into a dog" vs. "make him a dog" vs. "as a dog").
-5. Increasing the number of steps sometimes improves results.
-6. Do faces look weird? The Stable Diffusion autoencoder has a hard time with faces that are small in the image. Try:
- * Cropping the image so the face takes up a larger portion of the frame.
-"""
-
-
-example_instructions = [
- "Make it a picasso painting",
- "as if it were by modigliani",
- "convert to a bronze statue",
- "Turn it into an anime.",
- "have it look like a graphic novel",
- "make him gain weight",
- "what would he look like bald?",
- "Have him smile",
- "Put him in a cocktail party.",
- "move him at the beach.",
- "add dramatic lighting",
- "Convert to black and white",
- "What if it were snowing?",
- "Give him a leather jacket",
- "Turn him into a cyborg!",
- "make him wear a beanie",
-]
-
-model_id = "timbrooks/instruct-pix2pix"
-
-def main():
- pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None).to("cuda")
- example_image = Image.open("imgs/example.jpg").convert("RGB")
-
- def load_example(
- steps: int,
- randomize_seed: bool,
- seed: int,
- randomize_cfg: bool,
- text_cfg_scale: float,
- image_cfg_scale: float,
- ):
- example_instruction = random.choice(example_instructions)
- return [example_image, example_instruction] + generate(
- example_image,
- example_instruction,
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- )
-
- def generate(
- input_image: Image.Image,
- instruction: str,
- steps: int,
- randomize_seed: bool,
- seed: int,
- randomize_cfg: bool,
- text_cfg_scale: float,
- image_cfg_scale: float,
- ):
- seed = random.randint(0, 100000) if randomize_seed else seed
- text_cfg_scale = round(random.uniform(6.0, 9.0), ndigits=2) if randomize_cfg else text_cfg_scale
- image_cfg_scale = round(random.uniform(1.2, 1.8), ndigits=2) if randomize_cfg else image_cfg_scale
-
- width, height = input_image.size
- factor = 512 / max(width, height)
- factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
- width = int((width * factor) // 64) * 64
- height = int((height * factor) // 64) * 64
- input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS)
-
- if instruction == "":
- return [input_image, seed]
-
- generator = torch.manual_seed(seed)
- edited_image = pipe(
- instruction, image=input_image,
- guidance_scale=text_cfg_scale, image_guidance_scale=image_cfg_scale,
- num_inference_steps=steps, generator=generator,
- ).images[0]
- return [seed, text_cfg_scale, image_cfg_scale, edited_image]
-
- def reset():
- return [0, "Randomize Seed", 1371, "Fix CFG", 7.5, 1.5, None]
-
- with gr.Blocks() as demo:
- gr.HTML("""
- InstructPix2Pix: Learning to Follow Image Editing Instructions
-
-For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
-
-
-
-
""")
- with gr.Row():
- with gr.Column(scale=1, min_width=100):
- generate_button = gr.Button("Generate")
- with gr.Column(scale=1, min_width=100):
- load_button = gr.Button("Load Example")
- with gr.Column(scale=1, min_width=100):
- reset_button = gr.Button("Reset")
- with gr.Column(scale=3):
- instruction = gr.Textbox(lines=1, label="Edit Instruction", interactive=True)
-
- with gr.Row():
- input_image = gr.Image(label="Input Image", type="pil", interactive=True)
- edited_image = gr.Image(label=f"Edited Image", type="pil", interactive=False)
- input_image.style(height=512, width=512)
- edited_image.style(height=512, width=512)
-
- with gr.Row():
- steps = gr.Number(value=50, precision=0, label="Steps", interactive=True)
- randomize_seed = gr.Radio(
- ["Fix Seed", "Randomize Seed"],
- value="Randomize Seed",
- type="index",
- show_label=False,
- interactive=True,
- )
- seed = gr.Number(value=1371, precision=0, label="Seed", interactive=True)
- randomize_cfg = gr.Radio(
- ["Fix CFG", "Randomize CFG"],
- value="Fix CFG",
- type="index",
- show_label=False,
- interactive=True,
- )
- text_cfg_scale = gr.Number(value=7.5, label=f"Text CFG", interactive=True)
- image_cfg_scale = gr.Number(value=1.5, label=f"Image CFG", interactive=True)
-
- gr.Markdown(help_text)
-
- load_button.click(
- fn=load_example,
- inputs=[
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- ],
- outputs=[input_image, instruction, seed, text_cfg_scale, image_cfg_scale, edited_image],
- )
- generate_button.click(
- fn=generate,
- inputs=[
- input_image,
- instruction,
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- ],
- outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image],
- )
- reset_button.click(
- fn=reset,
- inputs=[],
- outputs=[steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale, edited_image],
- )
-
- demo.queue(concurrency_count=1)
- demo.launch(share=False)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/Loren/Streamlit_OCR_comparator/README.md b/spaces/Loren/Streamlit_OCR_comparator/README.md
deleted file mode 100644
index 2894464cad0f3f4d163e1f8e72750b10259159da..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Streamlit OCR Comparator
-emoji: 📰🔍🔤
-colorFrom: indigo
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: Home.py
-tags: [streamlit, ocr]
-pinned: true
----
\ No newline at end of file
diff --git a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py b/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py
deleted file mode 100644
index 6d9b36c74b1808b56ded68cf080a689db7e0ee4e..0000000000000000000000000000000000000000
--- a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# -*- coding: utf-8 -*-
-# File : __init__.py
-# Author : Jiayuan Mao
-# Email : maojiayuan@gmail.com
-# Date : 27/01/2018
-#
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-from .batchnorm import set_sbn_eps_mode
-from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d
-from .batchnorm import patch_sync_batchnorm, convert_model
-from .replicate import DataParallelWithCallback, patch_replication_callback
diff --git a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py b/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py
deleted file mode 100644
index 9794e0fc3463a5e8fad05c037cce64683059a6d3..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.ERROR)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})".format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10, 2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r", encoding="utf-8") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
\ No newline at end of file
diff --git a/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py b/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py
deleted file mode 100644
index 3a5999ddc80faa0ce6a54389562c9f092b40bb03..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# encoding: utf-8
-"""
-This files contains pytest fixtures and other test related stuff.
-
-"""
-from __future__ import absolute_import, division, print_function
-
-import numpy as np
-import pytest
-
-# save numpy's current print options
-_NP_PRINT_OPTIONS = np.get_printoptions()
-
-
-@pytest.fixture(autouse=True)
-def setup_doctest(request):
- """Set up the environment for doctests (when run through pytest)."""
- np.set_printoptions(precision=5, edgeitems=2, suppress=True)
-
- def fin():
- """Restore the environment after doctests (when run through pytest)."""
- np.set_printoptions(**_NP_PRINT_OPTIONS)
-
- request.addfinalizer(fin)
diff --git a/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py b/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py
deleted file mode 100644
index 19e34a11def040b971b534b9ed3fda71d0a35c85..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py
+++ /dev/null
@@ -1,882 +0,0 @@
-# encoding: utf-8
-# pylint: disable=no-member
-# pylint: disable=invalid-name
-# pylint: disable=too-many-arguments
-# pylint: disable=wrong-import-position
-"""
-Evaluation package.
-
-"""
-
-from __future__ import absolute_import, division, print_function
-
-import numpy as np
-
-
-# evaluation helper functions
-def find_closest_matches(detections, annotations):
- """
- Find the closest annotation for each detection.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
-
- Returns
- -------
- indices : numpy array
- Indices of the closest matches.
-
- Notes
- -----
- The sequences must be ordered.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.int)
- # if only a single annotation is given
- if len(annotations) == 1:
- # return an array as long as the detections with indices 0
- return np.zeros(len(detections), dtype=np.int)
- # solution found at: http://stackoverflow.com/questions/8914491/
- indices = annotations.searchsorted(detections)
- indices = np.clip(indices, 1, len(annotations) - 1)
- left = annotations[indices - 1]
- right = annotations[indices]
- indices -= detections - left < right - detections
- # return the indices of the closest matches
- return indices
-
-
-def calc_errors(detections, annotations, matches=None):
- """
- Errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.float)
- # determine the closest annotations
- if matches is None:
- matches = find_closest_matches(detections, annotations)
- # calc error relative to those annotations
- errors = detections - annotations[matches]
- # return the errors
- return errors
-
-
-def calc_absolute_errors(detections, annotations, matches=None):
- """
- Absolute errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Absolute errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # return the errors
- return np.abs(calc_errors(detections, annotations, matches))
-
-
-def calc_relative_errors(detections, annotations, matches=None):
- """
- Relative errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Relative errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.float)
- # determine the closest annotations
- if matches is None:
- matches = find_closest_matches(detections, annotations)
- # calculate the absolute errors
- errors = calc_errors(detections, annotations, matches)
- # return the relative errors
- return np.abs(1 - (errors / annotations[matches]))
-
-
-# abstract evaluation base class
-class EvaluationMixin(object):
- """
- Evaluation mixin class.
-
- This class has a `name` attribute which is used for display purposes and
- defaults to 'None'.
-
- `METRIC_NAMES` is a list of tuples, containing the attribute's name and the
- corresponding label, e.g.:
-
- The attributes defined in `METRIC_NAMES` will be provided as an ordered
- dictionary as the `metrics` property unless the subclass overwrites the
- property.
-
- `FLOAT_FORMAT` is used to format floats.
-
- """
-
- # Example:
- # METRIC_NAMES = [
- # ('precision', 'Precision'),
- # ('recall', 'Recall'),
- # ('fmeasure', 'F-measure'),
- # ]
- name = None
- METRIC_NAMES = []
- FLOAT_FORMAT = '{:.3f}'
-
- @property
- def metrics(self):
- """Metrics as a dictionary."""
- from collections import OrderedDict
- metrics = OrderedDict()
- # metrics = {}
- for metric in [m[0] for m in self.METRIC_NAMES]:
- metrics[metric] = getattr(self, metric)
- return metrics
-
- def __len__(self):
- """Length of the evaluation object."""
- raise NotImplementedError('must be implemented by subclass.')
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- Notes
- -----
- This is a fallback method formatting the `metrics` dictionary in a
- human readable way. Classes inheriting from this mixin class should
- provide a method better suitable.
-
- """
- # pylint: disable=unused-argument
- import pprint
- return pprint.pformat(dict(self.metrics), indent=4)
-
-
-# evaluation classes
-class SimpleEvaluation(EvaluationMixin):
- """
- Simple Precision, Recall, F-measure and Accuracy evaluation based on the
- numbers of true/false positive/negative detections.
-
- Parameters
- ----------
- num_tp : int
- Number of true positive detections.
- num_fp : int
- Number of false positive detections.
- num_tn : int
- Number of true negative detections.
- num_fn : int
- Number of false negative detections.
- name : str
- Name to be displayed.
-
- Notes
- -----
- This class is only suitable for a 1-class evaluation problem.
-
- """
-
- METRIC_NAMES = [
- ('num_tp', 'No. of true positives'),
- ('num_fp', 'No. of false positives'),
- ('num_tn', 'No. of true negatives'),
- ('num_fn', 'No. of false negatives'),
- ('num_annotations', 'No. Annotations'),
- ('precision', 'Precision'),
- ('recall', 'Recall'),
- ('fmeasure', 'F-measure'),
- ('accuracy', 'Accuracy'),
- ]
-
- def __init__(self, num_tp=0, num_fp=0, num_tn=0, num_fn=0, name=None,
- **kwargs):
- # pylint: disable=unused-argument
- # hidden variables, to be able to overwrite them in subclasses
- self._num_tp = int(num_tp)
- self._num_fp = int(num_fp)
- self._num_tn = int(num_tn)
- self._num_fn = int(num_fn)
- # name of the evaluation
- self.name = name
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return self._num_tp
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return self._num_fp
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return self._num_tn
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return self._num_fn
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- return self.num_tp + self.num_fn
-
- def __len__(self):
- # the length equals the number of annotations
- return self.num_annotations
-
- @property
- def precision(self):
- """Precision."""
- # correct / retrieved
- retrieved = float(self.num_tp + self.num_fp)
- # if there are no positive predictions, none of them are wrong
- if retrieved == 0:
- return 1.
- return self.num_tp / retrieved
-
- @property
- def recall(self):
- """Recall."""
- # correct / relevant
- relevant = float(self.num_tp + self.num_fn)
- # if there are no positive annotations, we recalled all of them
- if relevant == 0:
- return 1.
- return self.num_tp / relevant
-
- @property
- def fmeasure(self):
- """F-measure."""
- # 2pr / (p+r)
- numerator = 2. * self.precision * self.recall
- if numerator == 0:
- return 0.
- return numerator / (self.precision + self.recall)
-
- @property
- def accuracy(self):
- """Accuracy."""
- # acc: (TP + TN) / (TP + FP + TN + FN)
- denominator = self.num_fp + self.num_fn + self.num_tp + self.num_tn
- if denominator == 0:
- return 1.
- numerator = float(self.num_tp + self.num_tn)
- if numerator == 0:
- return 0.
- return numerator / denominator
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
-
- """
- ret = ''
- if self.name is not None:
- ret += '%s\n ' % self.name
- ret += 'Annotations: %5d TP: %5d FP: %5d FN: %5d ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- return ret
-
- def __str__(self):
- return self.tostring()
-
-
-# evaluate Precision, Recall, F-measure and Accuracy with lists or numpy arrays
-class Evaluation(SimpleEvaluation):
- """
- Evaluation class for measuring Precision, Recall and F-measure based on
- numpy arrays or lists with true/false positive/negative detections.
-
- Parameters
- ----------
- tp : list or numpy array
- True positive detections.
- fp : list or numpy array
- False positive detections.
- tn : list or numpy array
- True negative detections.
- fn : list or numpy array
- False negative detections.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, tp=None, fp=None, tn=None, fn=None, **kwargs):
- # set default values
- if tp is None:
- tp = []
- if fp is None:
- fp = []
- if tn is None:
- tn = []
- if fn is None:
- fn = []
- # instantiate a SimpleEvaluation object
- super(Evaluation, self).__init__(**kwargs)
- # convert everything to numpy arrays and save them
- self.tp = np.asarray(list(tp), dtype=np.float)
- self.fp = np.asarray(list(fp), dtype=np.float)
- self.tn = np.asarray(list(tn), dtype=np.float)
- self.fn = np.asarray(list(fn), dtype=np.float)
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return len(self.tp)
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return len(self.fp)
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return len(self.tn)
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return len(self.fn)
-
-
-# class for evaluation of Precision, Recall, F-measure with 2D arrays
-class MultiClassEvaluation(Evaluation):
- """
- Evaluation class for measuring Precision, Recall and F-measure based on
- 2D numpy arrays with true/false positive/negative detections.
-
- Parameters
- ----------
- tp : list of tuples or numpy array, shape (num_tp, 2)
- True positive detections.
- fp : list of tuples or numpy array, shape (num_fp, 2)
- False positive detections.
- tn : list of tuples or numpy array, shape (num_tn, 2)
- True negative detections.
- fn : list of tuples or numpy array, shape (num_fn, 2)
- False negative detections.
- name : str
- Name to be displayed.
-
- Notes
- -----
- The second item of the tuples or the second column of the arrays denote
- the class the detection belongs to.
-
- """
- def __init__(self, tp=None, fp=None, tn=None, fn=None, **kwargs):
- # set default values
- if tp is None:
- tp = np.zeros((0, 2))
- if fp is None:
- fp = np.zeros((0, 2))
- if tn is None:
- tn = np.zeros((0, 2))
- if fn is None:
- fn = np.zeros((0, 2))
- super(MultiClassEvaluation, self).__init__(**kwargs)
- self.tp = np.asarray(tp, dtype=np.float)
- self.fp = np.asarray(fp, dtype=np.float)
- self.tn = np.asarray(tn, dtype=np.float)
- self.fn = np.asarray(fn, dtype=np.float)
-
- def tostring(self, verbose=False, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Parameters
- ----------
- verbose : bool
- Add evaluation for individual classes.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- ret = ''
-
- if verbose:
- # extract all classes
- classes = []
- if self.tp.any():
- classes = np.append(classes, np.unique(self.tp[:, 1]))
- if self.fp.any():
- classes = np.append(classes, np.unique(self.fp[:, 1]))
- if self.tn.any():
- classes = np.append(classes, np.unique(self.tn[:, 1]))
- if self.fn.any():
- classes = np.append(classes, np.unique(self.fn[:, 1]))
- for cls in sorted(np.unique(classes)):
- # extract the TP, FP, TN and FN of this class
- tp = self.tp[self.tp[:, 1] == cls]
- fp = self.fp[self.fp[:, 1] == cls]
- tn = self.tn[self.tn[:, 1] == cls]
- fn = self.fn[self.fn[:, 1] == cls]
- # evaluate them
- e = Evaluation(tp, fp, tn, fn, name='Class %s' % cls)
- # append to the output string
- ret += ' %s\n' % e.tostring(verbose=False)
- # normal formatting
- ret += 'Annotations: %5d TP: %5d FP: %4d FN: %4d ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- # return
- return ret
-
-
-# class for summing Evaluations
-class SumEvaluation(SimpleEvaluation):
- """
- Simple class for summing evaluations.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, eval_objects, name=None):
- # pylint: disable=super-init-not-called
- # Note: we want to inherit the evaluation functions/properties, no need
- # to call __super__, but we need to take care of 'name'
- if not isinstance(eval_objects, list):
- # wrap the given eval_object in a list
- eval_objects = [eval_objects]
- self.eval_objects = eval_objects
- self.name = name or 'sum for %d files' % len(self)
-
- def __len__(self):
- # just use the length of the evaluation objects
- return len(self.eval_objects)
-
- # redefine the counters (number of TP, FP, TN, FN & number of annotations)
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return sum(e.num_tp for e in self.eval_objects)
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return sum(e.num_fp for e in self.eval_objects)
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return sum(e.num_tn for e in self.eval_objects)
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return sum(e.num_fn for e in self.eval_objects)
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- return sum(e.num_annotations for e in self.eval_objects)
-
-
-# class for averaging Evaluations
-class MeanEvaluation(SumEvaluation):
- """
- Simple class for averaging evaluation.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, eval_objects, name=None, **kwargs):
- super(MeanEvaluation, self).__init__(eval_objects, **kwargs)
- # handle the 'name' here to be able to set a different default value
- self.name = name or 'mean for %d files' % len(self)
-
- # overwrite the properties to calculate the mean instead of the sum
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_tp for e in self.eval_objects])
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_fp for e in self.eval_objects])
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_tn for e in self.eval_objects])
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_fn for e in self.eval_objects])
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_annotations for e in self.eval_objects])
-
- @property
- def precision(self):
- """Precision."""
- return np.nanmean([e.precision for e in self.eval_objects])
-
- @property
- def recall(self):
- """Recall."""
- return np.nanmean([e.recall for e in self.eval_objects])
-
- @property
- def fmeasure(self):
- """F-measure."""
- return np.nanmean([e.fmeasure for e in self.eval_objects])
-
- @property
- def accuracy(self):
- """Accuracy."""
- return np.nanmean([e.accuracy for e in self.eval_objects])
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- ret = ''
- if self.name is not None:
- ret += '%s\n ' % self.name
- # TODO: unify this with SimpleEvaluation but
- # add option to provide field formatters (e.g. 3d or 5.2f)
- # format with floats instead of integers
- ret += 'Annotations: %5.2f TP: %5.2f FP: %5.2f FN: %5.2f ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- return ret
-
-
-def tostring(eval_objects, **kwargs):
- """
- Format the given evaluation objects as human readable strings.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- # pylint: disable=unused-argument
- return '\n'.join([e.tostring() for e in eval_objects])
-
-
-def tocsv(eval_objects, metric_names=None, float_format='{:.3f}', **kwargs):
- """
- Format the given evaluation objects as a CSV table.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- metric_names : list of tuples, optional
- List of tuples defining the name of the property corresponding to the
- metric, and the metric label e.g. ('fp', 'False Positives').
- float_format : str, optional
- How to format the metrics.
-
- Returns
- -------
- str
- CSV table representation of the evaluation objects.
-
- Notes
- -----
- If no `metric_names` are given, they will be extracted from the first
- evaluation object.
-
- """
- # pylint: disable=unused-argument
-
- if metric_names is None:
- # get the evaluation metrics from the first evaluation object
- metric_names = eval_objects[0].METRIC_NAMES
- metric_names, metric_labels = list(zip(*metric_names))
- # add header
- lines = ['Name,' + ','.join(metric_labels)]
- # TODO: use e.metrics dict?
- # add the evaluation objects
- for e in eval_objects:
- values = [float_format.format(getattr(e, mn)) for mn in metric_names]
- lines.append(e.name + ',' + ','.join(values))
- # return everything
- return '\n'.join(lines)
-
-
-def totex(eval_objects, metric_names=None, float_format='{:.3f}', **kwargs):
- """
- Format the given evaluation objects as a LaTeX table.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- metric_names : list of tuples, optional
- List of tuples defining the name of the property corresponding to the
- metric, and the metric label e.g. ('fp', 'False Positives').
- float_format : str, optional
- How to format the metrics.
-
- Returns
- -------
- str
- LaTeX table representation of the evaluation objects.
-
- Notes
- -----
- If no `metric_names` are given, they will be extracted from the first
- evaluation object.
-
- """
- # pylint: disable=unused-argument
-
- if metric_names is None:
- # get the evaluation metrics from the first evaluation object
- metric_names = eval_objects[0].METRIC_NAMES
- metric_names, metric_labels = list(zip(*metric_names))
- # add header
- lines = ['Name & ' + ' & '.join(metric_labels) + '\\\\']
- # TODO: use e.metrics dict
- # TODO: add a generic totable() function which accepts columns separator,
- # newline stuff (e.g. tex \\\\) and others
- # add the evaluation objects
- for e in eval_objects:
- values = [float_format.format(getattr(e, mn)) for mn in metric_names]
- lines.append(e.name + ' & ' + ' & '.join(values) + '\\\\')
- # return everything
- return '\n'.join(lines)
-
-
-def evaluation_io(parser, ann_suffix, det_suffix, ann_dir=None, det_dir=None):
- """
- Add evaluation input/output and formatting related arguments to an existing
- parser object.
-
- Parameters
- ----------
- parser : argparse parser instance
- Existing argparse parser object.
- ann_suffix : str
- Suffix of the annotation files.
- det_suffix : str
- Suffix of the detection files.
- ann_dir : str, optional
- Use only annotations from this folder (and sub-folders).
- det_dir : str, optional
- Use only detections from this folder (and sub-folders).
-
- Returns
- -------
- io_group : argparse argument group
- Evaluation input / output argument group.
- formatter_group : argparse argument group
- Evaluation formatter argument group.
-
- """
- import sys
- import argparse
- # general input output file handling
- parser.add_argument('files', nargs='*',
- help='files (or folders) to be evaluated')
- parser.add_argument('-o', dest='outfile', type=argparse.FileType('w'),
- default=sys.stdout,
- help='output file [default: STDOUT]')
- # file suffixes used for evaluation
- g = parser.add_argument_group('file/folder/suffix arguments')
- g.add_argument('-a', dest='ann_suffix', action='store', default=ann_suffix,
- help='suffix of the annotation files '
- '[default: %(default)s]')
- g.add_argument('--ann_dir', action='store', default=ann_dir,
- help='search only this directory (recursively) for '
- 'annotation files [default: %(default)s]')
- g.add_argument('-d', dest='det_suffix', action='store', default=det_suffix,
- help='suffix of the detection files [default: %(default)s]')
- g.add_argument('--det_dir', action='store', default=det_dir,
- help='search only this directory (recursively) for '
- 'detection files [default: %(default)s]')
- # option to ignore non-existing detections
- g.add_argument('-i', '--ignore_non_existing', action='store_true',
- help='ignore non-existing detections [default: raise a '
- 'warning and assume empty detections]')
- # verbose
- parser.add_argument('-v', '--verbose', action='count', default=0,
- help='increase verbosity level')
- # option to suppress warnings
- parser.add_argument('-q', '--quiet', action='store_true',
- help='suppress any warnings')
- # output format options
- parser.set_defaults(output_formatter=tostring)
- f = parser.add_argument_group('formatting arguments')
- formats = f.add_mutually_exclusive_group()
- formats.add_argument('--tex', dest='output_formatter',
- action='store_const', const=totex,
- help='format output to be used in .tex files')
- formats.add_argument('--csv', dest='output_formatter',
- action='store_const', const=tocsv,
- help='format output to be used in .csv files')
- # return the output formatting group so the caller can add more options
- return g, f
-
-
-# finally import the submodules
-from . import chords, beats, notes, onsets, tempo
-
-# import often used classes
-from .beats import BeatEvaluation, BeatMeanEvaluation
-from .chords import ChordEvaluation, ChordMeanEvaluation, ChordSumEvaluation
-from .key import KeyEvaluation, KeyMeanEvaluation
-from .notes import NoteEvaluation, NoteMeanEvaluation, NoteSumEvaluation
-from .onsets import OnsetEvaluation, OnsetMeanEvaluation, OnsetSumEvaluation
-from .tempo import TempoEvaluation, TempoMeanEvaluation
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py
deleted file mode 100644
index 6fc71979190323f44c09f8b7e1761cf49cd2d76b..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
- 'iou3d_boxes_iou_bev_forward', 'iou3d_nms_forward',
- 'iou3d_nms_normal_forward'
-])
-
-
-def boxes_iou_bev(boxes_a, boxes_b):
- """Calculate boxes IoU in the Bird's Eye View.
-
- Args:
- boxes_a (torch.Tensor): Input boxes a with shape (M, 5).
- boxes_b (torch.Tensor): Input boxes b with shape (N, 5).
-
- Returns:
- ans_iou (torch.Tensor): IoU result with shape (M, N).
- """
- ans_iou = boxes_a.new_zeros(
- torch.Size((boxes_a.shape[0], boxes_b.shape[0])))
-
- ext_module.iou3d_boxes_iou_bev_forward(boxes_a.contiguous(),
- boxes_b.contiguous(), ans_iou)
-
- return ans_iou
-
-
-def nms_bev(boxes, scores, thresh, pre_max_size=None, post_max_size=None):
- """NMS function GPU implementation (for BEV boxes). The overlap of two
- boxes for IoU calculation is defined as the exact overlapping area of the
- two boxes. In this function, one can also set ``pre_max_size`` and
- ``post_max_size``.
-
- Args:
- boxes (torch.Tensor): Input boxes with the shape of [N, 5]
- ([x1, y1, x2, y2, ry]).
- scores (torch.Tensor): Scores of boxes with the shape of [N].
- thresh (float): Overlap threshold of NMS.
- pre_max_size (int, optional): Max size of boxes before NMS.
- Default: None.
- post_max_size (int, optional): Max size of boxes after NMS.
- Default: None.
-
- Returns:
- torch.Tensor: Indexes after NMS.
- """
- assert boxes.size(1) == 5, 'Input boxes shape should be [N, 5]'
- order = scores.sort(0, descending=True)[1]
-
- if pre_max_size is not None:
- order = order[:pre_max_size]
- boxes = boxes[order].contiguous()
-
- keep = torch.zeros(boxes.size(0), dtype=torch.long)
- num_out = ext_module.iou3d_nms_forward(boxes, keep, thresh)
- keep = order[keep[:num_out].cuda(boxes.device)].contiguous()
- if post_max_size is not None:
- keep = keep[:post_max_size]
- return keep
-
-
-def nms_normal_bev(boxes, scores, thresh):
- """Normal NMS function GPU implementation (for BEV boxes). The overlap of
- two boxes for IoU calculation is defined as the exact overlapping area of
- the two boxes WITH their yaw angle set to 0.
-
- Args:
- boxes (torch.Tensor): Input boxes with shape (N, 5).
- scores (torch.Tensor): Scores of predicted boxes with shape (N).
- thresh (float): Overlap threshold of NMS.
-
- Returns:
- torch.Tensor: Remaining indices with scores in descending order.
- """
- assert boxes.shape[1] == 5, 'Input boxes shape should be [N, 5]'
- order = scores.sort(0, descending=True)[1]
-
- boxes = boxes[order].contiguous()
-
- keep = torch.zeros(boxes.size(0), dtype=torch.long)
- num_out = ext_module.iou3d_nms_normal_forward(boxes, keep, thresh)
- return order[keep[:num_out].cuda(boxes.device)].contiguous()
diff --git a/spaces/MetaWabbit/Auto-GPT/run.sh b/spaces/MetaWabbit/Auto-GPT/run.sh
deleted file mode 100644
index edcbc44155b9ca9df83e283fdf976472c13e6492..0000000000000000000000000000000000000000
--- a/spaces/MetaWabbit/Auto-GPT/run.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-python scripts/check_requirements.py requirements.txt
-if [ $? -eq 1 ]
-then
- echo Installing missing packages...
- pip install -r requirements.txt
-fi
-python -m autogpt $@
-read -p "Press any key to continue..."
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py b/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py
deleted file mode 100644
index 244425feef76bf89d4de939cb8a1914a6f0f47c6..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py
+++ /dev/null
@@ -1,387 +0,0 @@
-# Lint as: python3
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Unit tests for the classifier trainer models."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import copy
-import functools
-import json
-
-import os
-import sys
-
-from typing import Any, Callable, Iterable, Mapping, MutableMapping, Optional, Tuple
-
-from absl import flags
-from absl.testing import parameterized
-import tensorflow as tf
-
-from tensorflow.python.distribute import combinations
-from tensorflow.python.distribute import strategy_combinations
-from official.utils.flags import core as flags_core
-from official.vision.image_classification import classifier_trainer
-from official.vision.image_classification import dataset_factory
-from official.vision.image_classification import test_utils
-from official.vision.image_classification.configs import base_configs
-
-classifier_trainer.define_classifier_flags()
-
-
-def distribution_strategy_combinations() -> Iterable[Tuple[Any, ...]]:
- """Returns the combinations of end-to-end tests to run."""
- return combinations.combine(
- distribution=[
- strategy_combinations.default_strategy,
- strategy_combinations.tpu_strategy,
- strategy_combinations.one_device_strategy_gpu,
- strategy_combinations.mirrored_strategy_with_two_gpus,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset=[
- 'imagenet',
- ],
- )
-
-
-def get_params_override(params_override: Mapping[str, Any]) -> str:
- """Converts params_override dict to string command."""
- return '--params_override=' + json.dumps(params_override)
-
-
-def basic_params_override(dtype: str = 'float32') -> MutableMapping[str, Any]:
- """Returns a basic parameter configuration for testing."""
- return {
- 'train_dataset': {
- 'builder': 'synthetic',
- 'use_per_replica_batch_size': True,
- 'batch_size': 1,
- 'image_size': 224,
- 'dtype': dtype,
- },
- 'validation_dataset': {
- 'builder': 'synthetic',
- 'batch_size': 1,
- 'use_per_replica_batch_size': True,
- 'image_size': 224,
- 'dtype': dtype,
- },
- 'train': {
- 'steps': 1,
- 'epochs': 1,
- 'callbacks': {
- 'enable_checkpoint_and_export': True,
- 'enable_tensorboard': False,
- },
- },
- 'evaluation': {
- 'steps': 1,
- },
- }
-
-
-def get_trivial_model(num_classes: int) -> tf.keras.Model:
- """Creates and compiles trivial model for ImageNet dataset."""
- model = test_utils.trivial_model(num_classes=num_classes)
- lr = 0.01
- optimizer = tf.keras.optimizers.SGD(learning_rate=lr)
- loss_obj = tf.keras.losses.SparseCategoricalCrossentropy()
- model.compile(optimizer=optimizer,
- loss=loss_obj,
- run_eagerly=True)
- return model
-
-
-def get_trivial_data() -> tf.data.Dataset:
- """Gets trivial data in the ImageNet size."""
- def generate_data(_) -> tf.data.Dataset:
- image = tf.zeros(shape=(224, 224, 3), dtype=tf.float32)
- label = tf.zeros([1], dtype=tf.int32)
- return image, label
-
- dataset = tf.data.Dataset.range(1)
- dataset = dataset.repeat()
- dataset = dataset.map(generate_data,
- num_parallel_calls=tf.data.experimental.AUTOTUNE)
- dataset = dataset.prefetch(buffer_size=1).batch(1)
- return dataset
-
-
-def run_end_to_end(main: Callable[[Any], None],
- extra_flags: Optional[Iterable[str]] = None,
- model_dir: Optional[str] = None):
- """Runs the classifier trainer end-to-end."""
- extra_flags = [] if extra_flags is None else extra_flags
- args = [sys.argv[0], '--model_dir', model_dir] + extra_flags
- flags_core.parse_flags(argv=args)
- main(flags.FLAGS)
-
-
-class ClassifierTest(tf.test.TestCase, parameterized.TestCase):
- """Unit tests for Keras models."""
- _tempdir = None
-
- @classmethod
- def setUpClass(cls): # pylint: disable=invalid-name
- super(ClassifierTest, cls).setUpClass()
-
- def tearDown(self):
- super(ClassifierTest, self).tearDown()
- tf.io.gfile.rmtree(self.get_temp_dir())
-
- @combinations.generate(distribution_strategy_combinations())
- def test_end_to_end_train_and_eval(self, distribution, model, dataset):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override()),
- '--mode=train_and_eval',
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
-
- @combinations.generate(
- combinations.combine(
- distribution=[
- strategy_combinations.one_device_strategy_gpu,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset='imagenet',
- dtype='float16',
- ))
- def test_gpu_train(self, distribution, model, dataset, dtype):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override(dtype)),
- '--mode=train_and_eval',
- ]
-
- export_params = basic_params_override()
- export_path = os.path.join(model_dir, 'export')
- export_params['export'] = {}
- export_params['export']['destination'] = export_path
- export_flags = base_flags + [
- '--mode=export_only',
- get_params_override(export_params)
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
- run_end_to_end(main=run,
- extra_flags=export_flags,
- model_dir=model_dir)
- self.assertTrue(os.path.exists(export_path))
-
- @combinations.generate(
- combinations.combine(
- distribution=[
- strategy_combinations.tpu_strategy,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset='imagenet',
- dtype='bfloat16',
- ))
- def test_tpu_train(self, distribution, model, dataset, dtype):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override(dtype)),
- '--mode=train_and_eval',
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
-
- @combinations.generate(distribution_strategy_combinations())
- def test_end_to_end_invalid_mode(self, distribution, model, dataset):
- """Test the Keras EfficientNet model with `strategy`."""
- model_dir = self.get_temp_dir()
- extra_flags = [
- '--data_dir=not_used',
- '--mode=invalid_mode',
- '--model_type=' + model,
- '--dataset=' + dataset,
- get_params_override(basic_params_override()),
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- with self.assertRaises(ValueError):
- run_end_to_end(main=run, extra_flags=extra_flags, model_dir=model_dir)
-
-
-class UtilTests(parameterized.TestCase, tf.test.TestCase):
- """Tests for individual utility functions within classifier_trainer.py."""
-
- @parameterized.named_parameters(
- ('efficientnet-b0', 'efficientnet', 'efficientnet-b0', 224),
- ('efficientnet-b1', 'efficientnet', 'efficientnet-b1', 240),
- ('efficientnet-b2', 'efficientnet', 'efficientnet-b2', 260),
- ('efficientnet-b3', 'efficientnet', 'efficientnet-b3', 300),
- ('efficientnet-b4', 'efficientnet', 'efficientnet-b4', 380),
- ('efficientnet-b5', 'efficientnet', 'efficientnet-b5', 456),
- ('efficientnet-b6', 'efficientnet', 'efficientnet-b6', 528),
- ('efficientnet-b7', 'efficientnet', 'efficientnet-b7', 600),
- ('resnet', 'resnet', '', None),
- )
- def test_get_model_size(self, model, model_name, expected):
- config = base_configs.ExperimentConfig(
- model_name=model,
- model=base_configs.ModelConfig(
- model_params={
- 'model_name': model_name,
- },
- )
- )
- size = classifier_trainer.get_image_size_from_model(config)
- self.assertEqual(size, expected)
-
- @parameterized.named_parameters(
- ('dynamic', 'dynamic', None, 'dynamic'),
- ('scalar', 128., None, 128.),
- ('float32', None, 'float32', 1),
- ('float16', None, 'float16', 128),
- )
- def test_get_loss_scale(self, loss_scale, dtype, expected):
- config = base_configs.ExperimentConfig(
- runtime=base_configs.RuntimeConfig(
- loss_scale=loss_scale),
- train_dataset=dataset_factory.DatasetConfig(dtype=dtype))
- ls = classifier_trainer.get_loss_scale(config, fp16_default=128)
- self.assertEqual(ls, expected)
-
- @parameterized.named_parameters(
- ('float16', 'float16'),
- ('bfloat16', 'bfloat16')
- )
- def test_initialize(self, dtype):
- config = base_configs.ExperimentConfig(
- runtime=base_configs.RuntimeConfig(
- run_eagerly=False,
- enable_xla=False,
- per_gpu_thread_count=1,
- gpu_thread_mode='gpu_private',
- num_gpus=1,
- dataset_num_private_threads=1,
- ),
- train_dataset=dataset_factory.DatasetConfig(dtype=dtype),
- model=base_configs.ModelConfig(),
- )
-
- class EmptyClass:
- pass
- fake_ds_builder = EmptyClass()
- fake_ds_builder.dtype = dtype
- fake_ds_builder.config = EmptyClass()
- classifier_trainer.initialize(config, fake_ds_builder)
-
- def test_resume_from_checkpoint(self):
- """Tests functionality for resuming from checkpoint."""
- # Set the keras policy
- policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16')
- tf.keras.mixed_precision.experimental.set_policy(policy)
-
- # Get the model, datasets, and compile it.
- model = get_trivial_model(10)
-
- # Create the checkpoint
- model_dir = self.get_temp_dir()
- train_epochs = 1
- train_steps = 10
- ds = get_trivial_data()
- callbacks = [
- tf.keras.callbacks.ModelCheckpoint(
- os.path.join(model_dir, 'model.ckpt-{epoch:04d}'),
- save_weights_only=True)
- ]
- model.fit(
- ds,
- callbacks=callbacks,
- epochs=train_epochs,
- steps_per_epoch=train_steps)
-
- # Test load from checkpoint
- clean_model = get_trivial_model(10)
- weights_before_load = copy.deepcopy(clean_model.get_weights())
- initial_epoch = classifier_trainer.resume_from_checkpoint(
- model=clean_model,
- model_dir=model_dir,
- train_steps=train_steps)
- self.assertEqual(initial_epoch, 1)
- self.assertNotAllClose(weights_before_load, clean_model.get_weights())
-
- tf.io.gfile.rmtree(model_dir)
-
- def test_serialize_config(self):
- """Tests functionality for serializing data."""
- config = base_configs.ExperimentConfig()
- model_dir = self.get_temp_dir()
- classifier_trainer.serialize_config(params=config, model_dir=model_dir)
- saved_params_path = os.path.join(model_dir, 'params.yaml')
- self.assertTrue(os.path.exists(saved_params_path))
- tf.io.gfile.rmtree(model_dir)
-
-if __name__ == '__main__':
- tf.test.main()
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py b/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py
deleted file mode 100644
index 11fcb5305660ec71153ebfc12631f455a3464115..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py
+++ /dev/null
@@ -1,231 +0,0 @@
-# Lint as: python3
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Definitions for high level configuration groups.."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-
-from typing import Any, List, Mapping, Optional
-
-import dataclasses
-
-from official.modeling import hyperparams
-from official.modeling.hyperparams import config_definitions
-
-CallbacksConfig = config_definitions.CallbacksConfig
-TensorboardConfig = config_definitions.TensorboardConfig
-RuntimeConfig = config_definitions.RuntimeConfig
-
-
-@dataclasses.dataclass
-class ExportConfig(hyperparams.Config):
- """Configuration for exports.
-
- Attributes:
- checkpoint: the path to the checkpoint to export.
- destination: the path to where the checkpoint should be exported.
- """
- checkpoint: str = None
- destination: str = None
-
-
-@dataclasses.dataclass
-class MetricsConfig(hyperparams.Config):
- """Configuration for Metrics.
-
- Attributes:
- accuracy: Whether or not to track accuracy as a Callback. Defaults to None.
- top_5: Whether or not to track top_5_accuracy as a Callback. Defaults to
- None.
- """
- accuracy: bool = None
- top_5: bool = None
-
-
-@dataclasses.dataclass
-class TimeHistoryConfig(hyperparams.Config):
- """Configuration for the TimeHistory callback.
-
- Attributes:
- log_steps: Interval of steps between logging of batch level stats.
- """
- log_steps: int = None
-
-
-@dataclasses.dataclass
-class TrainConfig(hyperparams.Config):
- """Configuration for training.
-
- Attributes:
- resume_checkpoint: Whether or not to enable load checkpoint loading.
- Defaults to None.
- epochs: The number of training epochs to run. Defaults to None.
- steps: The number of steps to run per epoch. If None, then this will be
- inferred based on the number of images and batch size. Defaults to None.
- callbacks: An instance of CallbacksConfig.
- metrics: An instance of MetricsConfig.
- tensorboard: An instance of TensorboardConfig.
- set_epoch_loop: Whether or not to set `experimental_steps_per_execution` to
- equal the number of training steps in `model.compile`. This reduces the
- number of callbacks run per epoch which significantly improves end-to-end
- TPU training time.
- """
- resume_checkpoint: bool = None
- epochs: int = None
- steps: int = None
- callbacks: CallbacksConfig = CallbacksConfig()
- metrics: MetricsConfig = None
- tensorboard: TensorboardConfig = TensorboardConfig()
- time_history: TimeHistoryConfig = TimeHistoryConfig()
- set_epoch_loop: bool = False
-
-
-@dataclasses.dataclass
-class EvalConfig(hyperparams.Config):
- """Configuration for evaluation.
-
- Attributes:
- epochs_between_evals: The number of train epochs to run between evaluations.
- Defaults to None.
- steps: The number of eval steps to run during evaluation. If None, this will
- be inferred based on the number of images and batch size. Defaults to
- None.
- skip_eval: Whether or not to skip evaluation.
- """
- epochs_between_evals: int = None
- steps: int = None
- skip_eval: bool = False
-
-
-@dataclasses.dataclass
-class LossConfig(hyperparams.Config):
- """Configuration for Loss.
-
- Attributes:
- name: The name of the loss. Defaults to None.
- label_smoothing: Whether or not to apply label smoothing to the loss. This
- only applies to 'categorical_cross_entropy'.
- """
- name: str = None
- label_smoothing: float = None
-
-
-@dataclasses.dataclass
-class OptimizerConfig(hyperparams.Config):
- """Configuration for Optimizers.
-
- Attributes:
- name: The name of the optimizer. Defaults to None.
- decay: Decay or rho, discounting factor for gradient. Defaults to None.
- epsilon: Small value used to avoid 0 denominator. Defaults to None.
- momentum: Plain momentum constant. Defaults to None.
- nesterov: Whether or not to apply Nesterov momentum. Defaults to None.
- moving_average_decay: The amount of decay to apply. If 0 or None, then
- exponential moving average is not used. Defaults to None.
- lookahead: Whether or not to apply the lookahead optimizer. Defaults to
- None.
- beta_1: The exponential decay rate for the 1st moment estimates. Used in the
- Adam optimizers. Defaults to None.
- beta_2: The exponential decay rate for the 2nd moment estimates. Used in the
- Adam optimizers. Defaults to None.
- epsilon: Small value used to avoid 0 denominator. Defaults to 1e-7.
- """
- name: str = None
- decay: float = None
- epsilon: float = None
- momentum: float = None
- nesterov: bool = None
- moving_average_decay: Optional[float] = None
- lookahead: Optional[bool] = None
- beta_1: float = None
- beta_2: float = None
- epsilon: float = None
-
-
-@dataclasses.dataclass
-class LearningRateConfig(hyperparams.Config):
- """Configuration for learning rates.
-
- Attributes:
- name: The name of the learning rate. Defaults to None.
- initial_lr: The initial learning rate. Defaults to None.
- decay_epochs: The number of decay epochs. Defaults to None.
- decay_rate: The rate of decay. Defaults to None.
- warmup_epochs: The number of warmup epochs. Defaults to None.
- batch_lr_multiplier: The multiplier to apply to the base learning rate, if
- necessary. Defaults to None.
- examples_per_epoch: the number of examples in a single epoch. Defaults to
- None.
- boundaries: boundaries used in piecewise constant decay with warmup.
- multipliers: multipliers used in piecewise constant decay with warmup.
- scale_by_batch_size: Scale the learning rate by a fraction of the batch
- size. Set to 0 for no scaling (default).
- staircase: Apply exponential decay at discrete values instead of continuous.
- """
- name: str = None
- initial_lr: float = None
- decay_epochs: float = None
- decay_rate: float = None
- warmup_epochs: int = None
- examples_per_epoch: int = None
- boundaries: List[int] = None
- multipliers: List[float] = None
- scale_by_batch_size: float = 0.
- staircase: bool = None
-
-
-@dataclasses.dataclass
-class ModelConfig(hyperparams.Config):
- """Configuration for Models.
-
- Attributes:
- name: The name of the model. Defaults to None.
- model_params: The parameters used to create the model. Defaults to None.
- num_classes: The number of classes in the model. Defaults to None.
- loss: A `LossConfig` instance. Defaults to None.
- optimizer: An `OptimizerConfig` instance. Defaults to None.
- """
- name: str = None
- model_params: hyperparams.Config = None
- num_classes: int = None
- loss: LossConfig = None
- optimizer: OptimizerConfig = None
-
-
-@dataclasses.dataclass
-class ExperimentConfig(hyperparams.Config):
- """Base configuration for an image classification experiment.
-
- Attributes:
- model_dir: The directory to use when running an experiment.
- mode: e.g. 'train_and_eval', 'export'
- runtime: A `RuntimeConfig` instance.
- train: A `TrainConfig` instance.
- evaluation: An `EvalConfig` instance.
- model: A `ModelConfig` instance.
- export: An `ExportConfig` instance.
- """
- model_dir: str = None
- model_name: str = None
- mode: str = None
- runtime: RuntimeConfig = None
- train_dataset: Any = None
- validation_dataset: Any = None
- train: TrainConfig = None
- evaluation: EvalConfig = None
- model: ModelConfig = None
- export: ExportConfig = None
diff --git a/spaces/Navneet574/Heart_Disease_Prediciton/app.py b/spaces/Navneet574/Heart_Disease_Prediciton/app.py
deleted file mode 100644
index 30f66c36eeab6633487a32671d615f07ab544f93..0000000000000000000000000000000000000000
--- a/spaces/Navneet574/Heart_Disease_Prediciton/app.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import gradio as gr
-from joblib import load
-import numpy as np
-import pandas as pd
-
-AgeCategory = ['18-24', '25-29', '35-39', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80 or older']
-Binary_Input = ['Yes', 'No']
-Sex = ['Male', 'Female']
-Race = ['White', 'Black', 'Asian', 'American Indian/Alaskan Native', 'Hispanic', 'Other']
-Diabetic = ['Yes', 'No', 'No borderline diabetes', 'Yes (during pregnancy)']
-GeneralHealth = ['Excellent', 'Very good', 'Good', 'Fair', 'Poor']
-
-dic_ageCat = {'18-24':0, '25-29':1, '35-39':2, '35-39':3, '40-44':4, '45-49':5, '50-54':6, '55-59':7, '60-64':8, '65-69':9, '70-74':10, '75-79':11, '80 or older':12}
-dic_binary = {'Yes':1, 'No':0}
-dic_sex = {'Male':0, 'Female':1}
-dic_race = {'White':5, 'Black':2, 'Asian':0, 'American Indian/Alaskan Native':3, 'Hispanic':1, 'Other':4}
-dic_diabetic = {'Yes':2, 'No':0, 'No borderline diabetes':1, 'Yes (during pregnancy)':3}
-dic_generalHealth = {'Excellent':0, 'Very good':4, 'Good':2, 'Fair':1, 'Poor':3}
-
-
-def predict_price(BMI, Smoking, AlcoholDrinking, Stroke, PhysicalHealth, MentalHealth, DiffWalking, Sex, AgeCategory, Race, Diabetic, PhysicalActivity, GenHealth, SleepTime, Asthma, KidneyDisease, SkinCancer):
- model = load('Heart_Disease_Classification.jb')
-
- data = {
- 'BMI': [BMI],
- 'Smoking': dic_binary[Smoking],
- 'AlcoholDrinking': dic_binary[AlcoholDrinking],
- 'Stroke': dic_binary[Stroke],
- 'PhysicalHealth': [PhysicalHealth],
- 'MentalHealth': [MentalHealth],
- 'DiffWalking': dic_binary[DiffWalking],
- 'Sex': dic_sex[Sex],
- 'AgeCategory': dic_ageCat[AgeCategory],
- 'Race': dic_race[Race],
- 'Diabetic': dic_diabetic[Diabetic],
- 'PhysicalActivity': dic_binary[PhysicalActivity],
- 'GenHealth': dic_generalHealth[GenHealth],
- 'SleepTime': [SleepTime],
- 'Asthma': dic_binary[Asthma],
- 'KidneyDisease': dic_binary[KidneyDisease],
- 'SkinCancer': dic_binary[SkinCancer]
- }
-
- Xinp = pd.DataFrame(data)
- print(Xinp)
-
- stone = model.predict(Xinp)
-
- if stone == 1:
- return stone["YES"]
- else:
- return stone["NO"]
-
-ui = gr.Interface(
- fn=predict_price,inputs=[
- gr.inputs.Textbox(placeholder='BMI', default=0,
- numeric=True, label='Body Mass Index (BMI) (normal is between 18.5 and 24.9)'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Smoking', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Alcohol Drinking', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Stroke', default="No"),
- gr.inputs.Textbox(placeholder='Physical Health',
- default=0, numeric=True, label='Physical Health Score'),
- gr.inputs.Textbox(placeholder='MentalHealth', default=0,
- numeric=True, label='Mental Health Score'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Diff Walking', default="No"),
- gr.inputs.Dropdown(choices=Sex, label='Sex', default=""),
- gr.inputs.Dropdown(choices=AgeCategory, label='Age Category', default="18-24"),
- gr.inputs.Dropdown(choices=Race, label='Race', default="Asian"),
- gr.inputs.Dropdown(choices=Diabetic, label='Diabetic', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Physical Activity', default="Yes"),
- gr.inputs.Dropdown(choices=GeneralHealth, label='General Health', default="Excellent"),
- gr.inputs.Textbox(placeholder='Sleep Time',
- default='7', numeric=True, label='Sleep Time (normal is between 6-8 hrs)'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Asthma', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Kidney Disease', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Skin Cancer', default="No")
- ], outputs=[
- "text"
- ]
-)
-if __name__ == "__main__":
- ui.launch()
\ No newline at end of file
diff --git a/spaces/Nikhil0987/omm/convo.py b/spaces/Nikhil0987/omm/convo.py
deleted file mode 100644
index f5c87b19d82d3f1e1869101faf1205475b5ed3b9..0000000000000000000000000000000000000000
--- a/spaces/Nikhil0987/omm/convo.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from transformers import pipeline, Conversation
-import streamlit as st
-from streamlit_option_menu import option_menu
-
-chatbot = pipeline(task="conversational", model="microsoft/DialoGPT-medium")
-
-def Convo():
- if convo := st.chat_input("Enter your message"):
-
- conversation = Conversation(convo)
-
-
-
- # candidate_labels = ["HELP", "PROBLEM SOLVE", "GENERAL TALK"]
-
-
- ans = chatbot(conversation)
- # add_user_input = st.button("Add User Input")
-
- # conversation.add_user_input("{}".format(convo))
- # conversation = chatbot(conversation)
- with st.chat_message("assistant"):
- # ans
- st.write(ans.generated_responses[-1])
-
-
-# convo()
diff --git a/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py b/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py
deleted file mode 100644
index d9d372dcbb1bed2fffbfd8e81d6da749ceab730b..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py
+++ /dev/null
@@ -1,772 +0,0 @@
-"""
-This scripts plots examples of the images that get best and worse metrics
-"""
-print("Imports...", end="")
-import os
-import sys
-from argparse import ArgumentParser
-from pathlib import Path
-
-import matplotlib.patches as mpatches
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import seaborn as sns
-import yaml
-from imageio import imread
-from matplotlib.gridspec import GridSpec
-from skimage.color import rgba2rgb
-from sklearn.metrics.pairwise import euclidean_distances
-
-sys.path.append("../")
-
-from climategan.data import encode_mask_label
-from climategan.eval_metrics import edges_coherence_std_min
-from eval_masker import crop_and_resize
-
-# -----------------------
-# ----- Constants -----
-# -----------------------
-
-# Metrics
-metrics = ["error", "f05", "edge_coherence"]
-
-dict_metrics = {
- "names": {
- "tpr": "TPR, Recall, Sensitivity",
- "tnr": "TNR, Specificity, Selectivity",
- "fpr": "FPR",
- "fpt": "False positives relative to image size",
- "fnr": "FNR, Miss rate",
- "fnt": "False negatives relative to image size",
- "mpr": "May positive rate (MPR)",
- "mnr": "May negative rate (MNR)",
- "accuracy": "Accuracy (ignoring may)",
- "error": "Error",
- "f05": "F05 score",
- "precision": "Precision",
- "edge_coherence": "Edge coherence",
- "accuracy_must_may": "Accuracy (ignoring cannot)",
- },
- "key_metrics": ["error", "f05", "edge_coherence"],
-}
-
-
-# Colors
-colorblind_palette = sns.color_palette("colorblind")
-color_cannot = colorblind_palette[1]
-color_must = colorblind_palette[2]
-color_may = colorblind_palette[7]
-color_pred = colorblind_palette[4]
-
-icefire = sns.color_palette("icefire", as_cmap=False, n_colors=5)
-color_tp = icefire[0]
-color_tn = icefire[1]
-color_fp = icefire[4]
-color_fn = icefire[3]
-
-
-def parsed_args():
- """
- Parse and returns command-line args
-
- Returns:
- argparse.Namespace: the parsed arguments
- """
- parser = ArgumentParser()
- parser.add_argument(
- "--input_csv",
- default="ablations_metrics_20210311.csv",
- type=str,
- help="CSV containing the results of the ablation study",
- )
- parser.add_argument(
- "--output_dir",
- default=None,
- type=str,
- help="Output directory",
- )
- parser.add_argument(
- "--models_log_path",
- default=None,
- type=str,
- help="Path containing the log files of the models",
- )
- parser.add_argument(
- "--masker_test_set_dir",
- default=None,
- type=str,
- help="Directory containing the test images",
- )
- parser.add_argument(
- "--best_model",
- default="dada, msd_spade, pseudo",
- type=str,
- help="The string identifier of the best model",
- )
- parser.add_argument(
- "--dpi",
- default=200,
- type=int,
- help="DPI for the output images",
- )
- parser.add_argument(
- "--alpha",
- default=0.5,
- type=float,
- help="Transparency of labels shade",
- )
- parser.add_argument(
- "--percentile",
- default=0.05,
- type=float,
- help="Transparency of labels shade",
- )
- parser.add_argument(
- "--seed",
- default=None,
- type=int,
- help="Bootstrap random seed, for reproducibility",
- )
- parser.add_argument(
- "--no_images",
- action="store_true",
- default=False,
- help="Do not generate images",
- )
-
- return parser.parse_args()
-
-
-def map_color(arr, input_color, output_color, rtol=1e-09):
- """
- Maps one color to another
- """
- input_color_arr = np.tile(input_color, (arr.shape[:2] + (1,)))
- output = arr.copy()
- output[np.all(np.isclose(arr, input_color_arr, rtol=rtol), axis=2)] = output_color
- return output
-
-
-def plot_labels(ax, img, label, img_id, n_, add_title, do_legend):
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (255, 0, 0), color_cannot)
- label_colmap = map_color(label_colmap, (0, 0, 255), color_must)
- label_colmap = map_color(label_colmap, (0, 0, 0), color_may)
-
- ax.imshow(img)
- ax.imshow(label_colmap, alpha=0.5)
- ax.axis("off")
-
- if n_ in [1, 3, 5]:
- color_ = "green"
- else:
- color_ = "red"
-
- ax.text(
- -0.15,
- 0.5,
- img_id,
- color=color_,
- fontweight="roman",
- fontsize="x-large",
- horizontalalignment="left",
- verticalalignment="center",
- transform=ax.transAxes,
- )
-
- if add_title:
- ax.set_title("Labels", rotation=0, fontsize="x-large")
-
-
-def plot_pred(ax, img, pred, img_id, add_title, do_legend):
- pred = np.tile(np.expand_dims(pred, axis=2), reps=(1, 1, 3))
-
- pred_colmap = pred.astype(float)
- pred_colmap = map_color(pred_colmap, (1, 1, 1), color_pred)
- pred_colmap_ma = np.ma.masked_not_equal(pred_colmap, color_pred)
- pred_colmap_ma = pred_colmap_ma.mask * img + pred_colmap_ma
-
- ax.imshow(img)
- ax.imshow(pred_colmap_ma, alpha=0.5)
- ax.axis("off")
-
- if add_title:
- ax.set_title("Prediction", rotation=0, fontsize="x-large")
-
-
-def plot_correct_incorrect(
- ax, img_filename, img, metric, label, img_id, n_, add_title, do_legend
-):
- # FP
- fp_map = imread(
- model_path / "eval-metrics/fp" / "{}_fp.png".format(Path(img_filename).stem)
- )
- fp_map = np.tile(np.expand_dims(fp_map, axis=2), reps=(1, 1, 3))
-
- fp_map_colmap = fp_map.astype(float)
- fp_map_colmap = map_color(fp_map_colmap, (1, 1, 1), color_fp)
-
- # FN
- fn_map = imread(
- model_path / "eval-metrics/fn" / "{}_fn.png".format(Path(img_filename).stem)
- )
- fn_map = np.tile(np.expand_dims(fn_map, axis=2), reps=(1, 1, 3))
-
- fn_map_colmap = fn_map.astype(float)
- fn_map_colmap = map_color(fn_map_colmap, (1, 1, 1), color_fn)
-
- # TP
- tp_map = imread(
- model_path / "eval-metrics/tp" / "{}_tp.png".format(Path(img_filename).stem)
- )
- tp_map = np.tile(np.expand_dims(tp_map, axis=2), reps=(1, 1, 3))
-
- tp_map_colmap = tp_map.astype(float)
- tp_map_colmap = map_color(tp_map_colmap, (1, 1, 1), color_tp)
-
- # TN
- tn_map = imread(
- model_path / "eval-metrics/tn" / "{}_tn.png".format(Path(img_filename).stem)
- )
- tn_map = np.tile(np.expand_dims(tn_map, axis=2), reps=(1, 1, 3))
-
- tn_map_colmap = tn_map.astype(float)
- tn_map_colmap = map_color(tn_map_colmap, (1, 1, 1), color_tn)
-
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (0, 0, 0), color_may)
- label_colmap_ma = np.ma.masked_not_equal(label_colmap, color_may)
- label_colmap_ma = label_colmap_ma.mask * img + label_colmap_ma
-
- # Combine masks
- maps = fp_map_colmap + fn_map_colmap + tp_map_colmap + tn_map_colmap
- maps_ma = np.ma.masked_equal(maps, (0, 0, 0))
- maps_ma = maps_ma.mask * img + maps_ma
-
- ax.imshow(img)
- ax.imshow(label_colmap_ma, alpha=0.5)
- ax.imshow(maps_ma, alpha=0.5)
- ax.axis("off")
-
- if add_title:
- ax.set_title("Metric", rotation=0, fontsize="x-large")
-
-
-def plot_edge_coherence(ax, img, metric, label, pred, img_id, n_, add_title, do_legend):
- pred = np.tile(np.expand_dims(pred, axis=2), reps=(1, 1, 3))
-
- ec, pred_ec, label_ec = edges_coherence_std_min(
- np.squeeze(pred[:, :, 0]), np.squeeze(encode_mask_label(label, "flood"))
- )
-
- ##################
- # Edge distances #
- ##################
-
- # Location of edges
- pred_ec_coord = np.argwhere(pred_ec > 0)
- label_ec_coord = np.argwhere(label_ec > 0)
-
- # Normalized pairwise distances between pred and label
- dist_mat = np.divide(
- euclidean_distances(pred_ec_coord, label_ec_coord), pred_ec.shape[0]
- )
-
- # Standard deviation of the minimum distance from pred to label
- min_dist = np.min(dist_mat, axis=1) # noqa: F841
-
- #############
- # Make plot #
- #############
-
- pred_ec = np.tile(
- np.expand_dims(np.asarray(pred_ec > 0, dtype=float), axis=2), reps=(1, 1, 3)
- )
- pred_ec_colmap = map_color(pred_ec, (1, 1, 1), color_pred)
- pred_ec_colmap_ma = np.ma.masked_not_equal(pred_ec_colmap, color_pred) # noqa: F841
-
- label_ec = np.tile(
- np.expand_dims(np.asarray(label_ec > 0, dtype=float), axis=2), reps=(1, 1, 3)
- )
- label_ec_colmap = map_color(label_ec, (1, 1, 1), color_must)
- label_ec_colmap_ma = np.ma.masked_not_equal( # noqa: F841
- label_ec_colmap, color_must
- )
-
- # Combined pred and label edges
- combined_ec = pred_ec_colmap + label_ec_colmap
- combined_ec_ma = np.ma.masked_equal(combined_ec, (0, 0, 0))
- combined_ec_img = combined_ec_ma.mask * img + combined_ec
-
- # Pred
- pred_colmap = pred.astype(float)
- pred_colmap = map_color(pred_colmap, (1, 1, 1), color_pred)
- pred_colmap_ma = np.ma.masked_not_equal(pred_colmap, color_pred)
-
- # Must
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (0, 0, 255), color_must)
- label_colmap_ma = np.ma.masked_not_equal(label_colmap, color_must)
-
- # TP
- tp_map = imread(
- model_path / "eval-metrics/tp" / "{}_tp.png".format(Path(srs_sel.filename).stem)
- )
- tp_map = np.tile(np.expand_dims(tp_map, axis=2), reps=(1, 1, 3))
- tp_map_colmap = tp_map.astype(float)
- tp_map_colmap = map_color(tp_map_colmap, (1, 1, 1), color_tp)
- tp_map_colmap_ma = np.ma.masked_not_equal(tp_map_colmap, color_tp)
-
- # Combination
- comb_pred = (
- (pred_colmap_ma.mask ^ tp_map_colmap_ma.mask)
- & tp_map_colmap_ma.mask
- & combined_ec_ma.mask
- ) * pred_colmap
- comb_label = (
- (label_colmap_ma.mask ^ pred_colmap_ma.mask)
- & pred_colmap_ma.mask
- & combined_ec_ma.mask
- ) * label_colmap
- comb_tp = combined_ec_ma.mask * tp_map_colmap.copy()
- combined = comb_tp + comb_label + comb_pred
- combined_ma = np.ma.masked_equal(combined, (0, 0, 0))
- combined_ma = combined_ma.mask * combined_ec_img + combined_ma
-
- ax.imshow(combined_ec_img, alpha=1)
- ax.imshow(combined_ma, alpha=0.5)
- ax.axis("off")
-
- # Plot lines
- idx_sort_x = np.argsort(pred_ec_coord[:, 1])
- offset = 100
- for idx in range(offset, pred_ec_coord.shape[0], offset):
- y0, x0 = pred_ec_coord[idx_sort_x[idx], :]
- argmin = np.argmin(dist_mat[idx_sort_x[idx]])
- y1, x1 = label_ec_coord[argmin, :]
- ax.plot([x0, x1], [y0, y1], color="white", linewidth=0.5)
-
- if add_title:
- ax.set_title("Metric", rotation=0, fontsize="x-large")
-
-
-def plot_images_metric(
- axes, metric, img_filename, img_id, n_, srs_sel, add_title, do_legend
-):
-
- # Read images
- img_path = imgs_orig_path / img_filename
- label_path = labels_path / "{}_labeled.png".format(Path(img_filename).stem)
- img, label = crop_and_resize(img_path, label_path)
- img = rgba2rgb(img) if img.shape[-1] == 4 else img / 255.0
-
- pred = imread(
- model_path / "eval-metrics/pred" / "{}_pred.png".format(Path(img_filename).stem)
- )
-
- # Label
- plot_labels(axes[0], img, label, img_id, n_, add_title, do_legend)
-
- # Prediction
- plot_pred(axes[1], img, pred, img_id, add_title, do_legend)
-
- # Correct / incorrect
- if metric in ["error", "f05"]:
- plot_correct_incorrect(
- axes[2],
- img_filename,
- img,
- metric,
- label,
- img_id,
- n_,
- add_title,
- do_legend=False,
- )
- handles = []
- lw = 1.0
- handles.append(
- mpatches.Patch(facecolor=color_tp, label="TP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_tn, label="TN", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_fp, label="FP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_fn, label="FN", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(
- facecolor=color_may,
- label="May-be-flooded",
- linewidth=lw,
- alpha=0.66,
- )
- )
- labels = ["TP", "TN", "FP", "FN", "May-be-flooded"]
- if metric == "error":
- if n_ in [1, 3, 5]:
- title = "Low error rate"
- else:
- title = "High error rate"
- else:
- if n_ in [1, 3, 5]:
- title = "High F05 score"
- else:
- title = "Low F05 score"
- # Edge coherence
- elif metric == "edge_coherence":
- plot_edge_coherence(
- axes[2], img, metric, label, pred, img_id, n_, add_title, do_legend=False
- )
- handles = []
- lw = 1.0
- handles.append(
- mpatches.Patch(facecolor=color_tp, label="TP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_pred, label="pred", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(
- facecolor=color_must,
- label="Must-be-flooded",
- linewidth=lw,
- alpha=0.66,
- )
- )
- labels = ["TP", "Prediction", "Must-be-flooded"]
- if n_ in [1, 3, 5]:
- title = "High edge coherence"
- else:
- title = "Low edge coherence"
-
- else:
- raise ValueError
-
- labels_values_title = "Error: {:.4f} \nFO5: {:.4f} \nEdge coherence: {:.4f}".format(
- srs_sel.error, srs_sel.f05, srs_sel.edge_coherence
- )
-
- plot_legend(axes[3], img, handles, labels, labels_values_title, title)
-
-
-def plot_legend(ax, img, handles, labels, labels_values_title, title):
- img_ = np.zeros_like(img, dtype=np.uint8)
- img_.fill(255)
- ax.imshow(img_)
- ax.axis("off")
-
- leg1 = ax.legend(
- handles=handles,
- labels=labels,
- title=title,
- title_fontsize="medium",
- labelspacing=0.6,
- loc="upper left",
- fontsize="x-small",
- frameon=False,
- )
- leg1._legend_box.align = "left"
-
- leg2 = ax.legend(
- title=labels_values_title,
- title_fontsize="small",
- loc="lower left",
- frameon=False,
- )
- leg2._legend_box.align = "left"
-
- ax.add_artist(leg1)
-
-
-def scatterplot_metrics_pair(ax, df, x_metric, y_metric, dict_images):
-
- sns.scatterplot(data=df, x=x_metric, y=y_metric, ax=ax)
-
- # Set X-label
- ax.set_xlabel(dict_metrics["names"][x_metric], rotation=0, fontsize="medium")
-
- # Set Y-label
- ax.set_ylabel(dict_metrics["names"][y_metric], rotation=90, fontsize="medium")
-
- # Change spines
- sns.despine(ax=ax, left=True, bottom=True)
-
- annotate_scatterplot(ax, dict_images, x_metric, y_metric)
-
-
-def scatterplot_metrics(ax, df, df_all, dict_images, plot_all=False):
-
- # Other
- if plot_all:
- sns.scatterplot(
- data=df_all.loc[df_all.ground == True],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='+', alpha=0.25)
- sns.scatterplot(
- data=df_all.loc[df_all.instagan == True],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='x', alpha=0.25)
- sns.scatterplot(
- data=df_all.loc[(df_all.instagan == False) & (df_all.instagan == False) &
- (df_all.model_feats != args.best_model)],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='s', alpha=0.25)
-
- # Best model
- cmap_ = sns.cubehelix_palette(as_cmap=True)
- sns.scatterplot(
- data=df, x="error", y="f05", hue="edge_coherence", ax=ax, palette=cmap_
- )
-
- norm = plt.Normalize(df["edge_coherence"].min(), df["edge_coherence"].max())
- sm = plt.cm.ScalarMappable(cmap=cmap_, norm=norm)
- sm.set_array([])
-
- # Remove the legend and add a colorbar
- ax.get_legend().remove()
- ax_cbar = ax.figure.colorbar(sm)
- ax_cbar.set_label("Edge coherence", labelpad=8)
-
- # Set X-label
- ax.set_xlabel(dict_metrics["names"]["error"], rotation=0, fontsize="medium")
-
- # Set Y-label
- ax.set_ylabel(dict_metrics["names"]["f05"], rotation=90, fontsize="medium")
-
- annotate_scatterplot(ax, dict_images, "error", "f05")
-
- # Change spines
- sns.despine(ax=ax, left=True, bottom=True)
-
- # Set XY limits
- xlim = ax.get_xlim()
- ylim = ax.get_ylim()
- ax.set_xlim([0.0, xlim[1]])
- ax.set_ylim([ylim[0], 1.0])
-
-
-def annotate_scatterplot(ax, dict_images, x_metric, y_metric, offset=0.1):
- xlim = ax.get_xlim()
- ylim = ax.get_ylim()
- x_len = xlim[1] - xlim[0]
- y_len = ylim[1] - ylim[0]
- x_th = xlim[1] - x_len / 2.0
- y_th = ylim[1] - y_len / 2.0
- for text, d in dict_images.items():
- if text in ["B", "D", "F"]:
- x = d[x_metric]
- y = d[y_metric]
-
- x_text = x + x_len * offset if x < x_th else x - x_len * offset
- y_text = y + y_len * offset if y < y_th else y - y_len * offset
-
- ax.annotate(
- xy=(x, y),
- xycoords="data",
- xytext=(x_text, y_text),
- textcoords="data",
- text=text,
- arrowprops=dict(facecolor="black", shrink=0.05),
- fontsize="medium",
- color="black",
- )
- elif text == "A":
- x = (
- dict_images["A"][x_metric]
- + dict_images["C"][x_metric]
- + dict_images["E"][x_metric]
- ) / 3
- y = (
- dict_images["A"][y_metric]
- + dict_images["C"][y_metric]
- + dict_images["E"][y_metric]
- ) / 3
-
- x_text = x + x_len * 2 * offset if x < x_th else x - x_len * 2 * offset
- y_text = (
- y + y_len * 0.45 * offset if y < y_th else y - y_len * 0.45 * offset
- )
-
- ax.annotate(
- xy=(x, y),
- xycoords="data",
- xytext=(x_text, y_text),
- textcoords="data",
- text="A, C, E",
- arrowprops=dict(facecolor="black", shrink=0.05),
- fontsize="medium",
- color="black",
- )
-
-
-if __name__ == "__main__":
- # -----------------------------
- # ----- Parse arguments -----
- # -----------------------------
- args = parsed_args()
- print("Args:\n" + "\n".join([f" {k:20}: {v}" for k, v in vars(args).items()]))
-
- # Determine output dir
- if args.output_dir is None:
- output_dir = Path(os.environ["SLURM_TMPDIR"])
- else:
- output_dir = Path(args.output_dir)
- if not output_dir.exists():
- output_dir.mkdir(parents=True, exist_ok=False)
-
- # Store args
- output_yml = output_dir / "labels.yml"
- with open(output_yml, "w") as f:
- yaml.dump(vars(args), f)
-
- # Data dirs
- imgs_orig_path = Path(args.masker_test_set_dir) / "imgs"
- labels_path = Path(args.masker_test_set_dir) / "labels"
-
- # Read CSV
- df_all = pd.read_csv(args.input_csv, index_col="model_img_idx")
-
- # Select best model
- df = df_all.loc[df_all.model_feats == args.best_model]
- v_key, model_dir = df.model.unique()[0].split("/")
- model_path = Path(args.models_log_path) / "ablation-{}".format(v_key) / model_dir
-
- # Set up plot
- sns.reset_orig()
- sns.set(style="whitegrid")
- plt.rcParams.update({"font.family": "serif"})
- plt.rcParams.update(
- {
- "font.serif": [
- "Computer Modern Roman",
- "Times New Roman",
- "Utopia",
- "New Century Schoolbook",
- "Century Schoolbook L",
- "ITC Bookman",
- "Bookman",
- "Times",
- "Palatino",
- "Charter",
- "serif" "Bitstream Vera Serif",
- "DejaVu Serif",
- ]
- }
- )
-
- if args.seed:
- np.random.seed(args.seed)
- img_ids = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
- dict_images = {}
- idx = 0
-
- # Define grid of subplots
- grid_vmargin = 0.03 # Extent of the vertical margin between metric grids
- ax_hspace = 0.04 # Extent of the vertical space between axes of same grid
- ax_wspace = 0.05 # Extent of the horizontal space between axes of same grid
- n_grids = len(metrics)
- n_cols = 4
- n_rows = 2
- h_grid = (1.0 / n_grids) - ((n_grids - 1) * grid_vmargin) / n_grids
-
- fig1 = plt.figure(dpi=200, figsize=(11, 13))
-
- n_ = 0
- add_title = False
- for metric_id, metric in enumerate(metrics):
-
- # Create grid
- top_grid = 1.0 - metric_id * h_grid - metric_id * grid_vmargin
- bottom_grid = top_grid - h_grid
- gridspec = GridSpec(
- n_rows,
- n_cols,
- wspace=ax_wspace,
- hspace=ax_hspace,
- bottom=bottom_grid,
- top=top_grid,
- )
-
- # Select best
- if metric == "error":
- ascending = True
- else:
- ascending = False
- idx_rand = np.random.permutation(int(args.percentile * len(df)))[0]
- srs_sel = df.sort_values(by=metric, ascending=ascending).iloc[idx_rand]
- img_id = img_ids[idx]
- dict_images.update({img_id: srs_sel})
- # Read images
- img_filename = srs_sel.filename
-
- axes_row = [fig1.add_subplot(gridspec[0, c]) for c in range(n_cols)]
- if not args.no_images:
- n_ += 1
- if metric_id == 0:
- add_title = True
- plot_images_metric(
- axes_row,
- metric,
- img_filename,
- img_id,
- n_,
- srs_sel,
- add_title=add_title,
- do_legend=False,
- )
- add_title = False
-
- idx += 1
- print("1 more row done.")
- # Select worst
- if metric == "error":
- ascending = False
- else:
- ascending = True
- idx_rand = np.random.permutation(int(args.percentile * len(df)))[0]
- srs_sel = df.sort_values(by=metric, ascending=ascending).iloc[idx_rand]
- img_id = img_ids[idx]
- dict_images.update({img_id: srs_sel})
- # Read images
- img_filename = srs_sel.filename
-
- axes_row = [fig1.add_subplot(gridspec[1, c]) for c in range(n_cols)]
- if not args.no_images:
- n_ += 1
- plot_images_metric(
- axes_row,
- metric,
- img_filename,
- img_id,
- n_,
- srs_sel,
- add_title=add_title,
- do_legend=False,
- )
-
- idx += 1
- print("1 more row done.")
-
- output_fig = output_dir / "all_metrics.png"
-
- fig1.tight_layout() # (pad=1.5) #
- fig1.savefig(output_fig, dpi=fig1.dpi, bbox_inches="tight")
-
- # Scatter plot
- fig2 = plt.figure(dpi=200)
-
- scatterplot_metrics(fig2.gca(), df, df_all, dict_images)
-
- # fig2, axes = plt.subplots(nrows=1, ncols=3, dpi=200, figsize=(18, 5))
- #
- # scatterplot_metrics_pair(axes[0], df, "error", "f05", dict_images)
- # scatterplot_metrics_pair(axes[1], df, "error", "edge_coherence", dict_images)
- # scatterplot_metrics_pair(axes[2], df, "f05", "edge_coherence", dict_images)
-
- output_fig = output_dir / "scatterplots.png"
- fig2.savefig(output_fig, dpi=fig2.dpi, bbox_inches="tight")
diff --git a/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh b/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh
deleted file mode 100644
index d649238546b996ae1ad00f7753c04aeebc7aaa97..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-echo "Dowloading Script" && python download_labelbox.py
-
-echo "Merging Script" && python merge_labelbox_masks.py
-
-echo "Cleaning labeled"
-rm /Users/victor/Downloads/metrics-v2/labels/*
-cp /Users/victor/Downloads/labelbox_test_flood-v2/__labeled/* /Users/victor/Downloads/metrics-v2/labels
-
-echo "Create labeled images Script" && python create_labeled.py
\ No newline at end of file
diff --git a/spaces/Nunchakuka/FrenchAnonymizer/models.py b/spaces/Nunchakuka/FrenchAnonymizer/models.py
deleted file mode 100644
index 46b8aacb1bef18f6fad4c20c968b19125626799c..0000000000000000000000000000000000000000
--- a/spaces/Nunchakuka/FrenchAnonymizer/models.py
+++ /dev/null
@@ -1,351 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
-
-class Encoder(nn.Module):
- def __init__(self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
- def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
- resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(weight_norm(
- ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
- k, u, padding=(k-u)//2)))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel//(2**(i+1))
- for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i*self.num_kernels+j](x)
- else:
- xs += self.resblocks[i*self.num_kernels+j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- print('Removing weight norm...')
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
- ])
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ])
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2,3,5,7,11]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class SpeakerEncoder(torch.nn.Module):
- def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
- super(SpeakerEncoder, self).__init__()
- self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
- self.linear = nn.Linear(model_hidden_size, model_embedding_size)
- self.relu = nn.ReLU()
-
- def forward(self, mels):
- self.lstm.flatten_parameters()
- _, (hidden, _) = self.lstm(mels)
- embeds_raw = self.relu(self.linear(hidden[-1]))
- return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
-
- def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
- mel_slices = []
- for i in range(0, total_frames-partial_frames, partial_hop):
- mel_range = torch.arange(i, i+partial_frames)
- mel_slices.append(mel_range)
-
- return mel_slices
-
- def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
- mel_len = mel.size(1)
- last_mel = mel[:,-partial_frames:]
-
- if mel_len > partial_frames:
- mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
- mels = list(mel[:,s] for s in mel_slices)
- mels.append(last_mel)
- mels = torch.stack(tuple(mels), 0).squeeze(1)
-
- with torch.no_grad():
- partial_embeds = self(mels)
- embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
- #embed = embed / torch.linalg.norm(embed, 2)
- else:
- with torch.no_grad():
- embed = self(last_mel)
-
- return embed
-
-
-class SynthesizerTrn(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels,
- ssl_dim,
- use_spk,
- **kwargs):
-
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- self.ssl_dim = ssl_dim
- self.use_spk = use_spk
-
- self.enc_p = Encoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16)
- self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
- self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
- self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
- if not self.use_spk:
- self.enc_spk = SpeakerEncoder(model_hidden_size=gin_channels, model_embedding_size=gin_channels)
-
- def forward(self, c, spec, g=None, mel=None, c_lengths=None, spec_lengths=None):
- if c_lengths == None:
- c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
- if spec_lengths == None:
- spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
-
- if not self.use_spk:
- g = self.enc_spk(mel.transpose(1,2))
- g = g.unsqueeze(-1)
-
- _, m_p, logs_p, _ = self.enc_p(c, c_lengths)
- z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g)
- z_p = self.flow(z, spec_mask, g=g)
-
- z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size)
- o = self.dec(z_slice, g=g)
-
- return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, c, g=None, mel=None, c_lengths=None):
- if c_lengths == None:
- c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
- if not self.use_spk:
- g = self.enc_spk.embed_utterance(mel.transpose(1,2))
- g = g.unsqueeze(-1)
-
- z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths)
- z = self.flow(z_p, c_mask, g=g, reverse=True)
- o = self.dec(z * c_mask, g=g)
-
- return o
diff --git a/spaces/Nunchakuka/FrenchAnonymizer/utils.py b/spaces/Nunchakuka/FrenchAnonymizer/utils.py
deleted file mode 100644
index 1bd5b6185af6c9f1c270b8ba345bfc36d059e081..0000000000000000000000000000000000000000
--- a/spaces/Nunchakuka/FrenchAnonymizer/utils.py
+++ /dev/null
@@ -1,305 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-from torch.nn import functional as F
-from commons import sequence_mask
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def get_cmodel(rank):
- checkpoint = torch.load('wavlm/WavLM-Large.pt')
- cfg = WavLMConfig(checkpoint['cfg'])
- cmodel = WavLM(cfg).cuda(rank)
- cmodel.load_state_dict(checkpoint['model'])
- cmodel.eval()
- return cmodel
-
-
-def get_content(cmodel, y):
- with torch.no_grad():
- c = cmodel.extract_features(y.squeeze(1))[0]
- c = c.transpose(1, 2)
- return c
-
-
-def get_vocoder(rank):
- with open("hifigan/config.json", "r") as f:
- config = json.load(f)
- config = hifigan.AttrDict(config)
- vocoder = hifigan.Generator(config)
- ckpt = torch.load("hifigan/generator_v1")
- vocoder.load_state_dict(ckpt["generator"])
- vocoder.eval()
- vocoder.remove_weight_norm()
- vocoder.cuda(rank)
- return vocoder
-
-
-def transform(mel, height): # 68-92
- #r = np.random.random()
- #rate = r * 0.3 + 0.85 # 0.85-1.15
- #height = int(mel.size(-2) * rate)
- tgt = torchvision.transforms.functional.resize(mel, (height, mel.size(-1)))
- if height >= mel.size(-2):
- return tgt[:, :mel.size(-2), :]
- else:
- silence = tgt[:,-1:,:].repeat(1,mel.size(-2)-height,1)
- silence += torch.randn_like(silence) / 10
- return torch.cat((tgt, silence), 1)
-
-
-def stretch(mel, width): # 0.5-2
- return torchvision.transforms.functional.resize(mel, (mel.size(-2), width))
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict= {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})" .format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info("Saving model and optimizer state at iteration {} to {}".format(
- iteration, checkpoint_path))
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- torch.save({'model': state_dict,
- 'iteration': iteration,
- 'optimizer': optimizer.state_dict(),
- 'learning_rate': learning_rate}, checkpoint_path)
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
- for k, v in scalars.items():
- writer.add_scalar(k, v, global_step)
- for k, v in histograms.items():
- writer.add_histogram(k, v, global_step)
- for k, v in images.items():
- writer.add_image(k, v, global_step, dataformats='HWC')
- for k, v in audios.items():
- writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
- x = f_list[-1]
- print(x)
- return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10,2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py
deleted file mode 100644
index bb80d11a67cd75764a89f6f41915b0348ae96e92..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py
+++ /dev/null
@@ -1,428 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from multiprocessing import Pool
-
-import numpy as np
-from fairseq import options
-from fairseq.data import dictionary
-from fairseq.scoring import bleu
-
-from examples.noisychannel import (
- rerank_generate,
- rerank_options,
- rerank_score_bw,
- rerank_score_lm,
- rerank_utils,
-)
-
-
-def score_target_hypo(
- args, a, b, c, lenpen, target_outfile, hypo_outfile, write_hypos, normalize
-):
-
- print("lenpen", lenpen, "weight1", a, "weight2", b, "weight3", c)
- gen_output_lst, bitext1_lst, bitext2_lst, lm_res_lst = load_score_files(args)
- dict = dictionary.Dictionary()
- scorer = scorer = bleu.Scorer(
- bleu.BleuConfig(
- pad=dict.pad(),
- eos=dict.eos(),
- unk=dict.unk(),
- )
- )
-
- ordered_hypos = {}
- ordered_targets = {}
-
- for shard_id in range(len(bitext1_lst)):
- bitext1 = bitext1_lst[shard_id]
- bitext2 = bitext2_lst[shard_id]
- gen_output = gen_output_lst[shard_id]
- lm_res = lm_res_lst[shard_id]
-
- total = len(bitext1.rescore_source.keys())
- source_lst = []
- hypo_lst = []
- score_lst = []
- reference_lst = []
- j = 1
- best_score = -math.inf
-
- for i in range(total):
- # length is measured in terms of words, not bpe tokens, since models may not share the same bpe
- target_len = len(bitext1.rescore_hypo[i].split())
-
- if lm_res is not None:
- lm_score = lm_res.score[i]
- else:
- lm_score = 0
-
- if bitext2 is not None:
- bitext2_score = bitext2.rescore_score[i]
- bitext2_backwards = bitext2.backwards
- else:
- bitext2_score = None
- bitext2_backwards = None
-
- score = rerank_utils.get_score(
- a,
- b,
- c,
- target_len,
- bitext1.rescore_score[i],
- bitext2_score,
- lm_score=lm_score,
- lenpen=lenpen,
- src_len=bitext1.source_lengths[i],
- tgt_len=bitext1.target_lengths[i],
- bitext1_backwards=bitext1.backwards,
- bitext2_backwards=bitext2_backwards,
- normalize=normalize,
- )
-
- if score > best_score:
- best_score = score
- best_hypo = bitext1.rescore_hypo[i]
-
- if j == gen_output.num_hypos[i] or j == args.num_rescore:
- j = 1
- hypo_lst.append(best_hypo)
- score_lst.append(best_score)
- source_lst.append(bitext1.rescore_source[i])
- reference_lst.append(bitext1.rescore_target[i])
-
- best_score = -math.inf
- best_hypo = ""
- else:
- j += 1
-
- gen_keys = list(sorted(gen_output.no_bpe_target.keys()))
-
- for key in range(len(gen_keys)):
- if args.prefix_len is None:
- assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], (
- "pred and rescore hypo mismatch: i: "
- + str(key)
- + ", "
- + str(hypo_lst[key])
- + str(gen_keys[key])
- + str(gen_output.no_bpe_hypo[key])
- )
- sys_tok = dict.encode_line(hypo_lst[key])
- ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]])
- scorer.add(ref_tok, sys_tok)
-
- else:
- full_hypo = rerank_utils.get_full_from_prefix(
- hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]]
- )
- sys_tok = dict.encode_line(full_hypo)
- ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]])
- scorer.add(ref_tok, sys_tok)
-
- # if only one set of hyper parameters is provided, write the predictions to a file
- if write_hypos:
- # recover the orinal ids from n best list generation
- for key in range(len(gen_output.no_bpe_target)):
- if args.prefix_len is None:
- assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], (
- "pred and rescore hypo mismatch:"
- + "i:"
- + str(key)
- + str(hypo_lst[key])
- + str(gen_output.no_bpe_hypo[key])
- )
- ordered_hypos[gen_keys[key]] = hypo_lst[key]
- ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[
- gen_keys[key]
- ]
-
- else:
- full_hypo = rerank_utils.get_full_from_prefix(
- hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]]
- )
- ordered_hypos[gen_keys[key]] = full_hypo
- ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[
- gen_keys[key]
- ]
-
- # write the hypos in the original order from nbest list generation
- if args.num_shards == (len(bitext1_lst)):
- with open(target_outfile, "w") as t:
- with open(hypo_outfile, "w") as h:
- for key in range(len(ordered_hypos)):
- t.write(ordered_targets[key])
- h.write(ordered_hypos[key])
-
- res = scorer.result_string(4)
- if write_hypos:
- print(res)
- score = rerank_utils.parse_bleu_scoring(res)
- return score
-
-
-def match_target_hypo(args, target_outfile, hypo_outfile):
- """combine scores from the LM and bitext models, and write the top scoring hypothesis to a file"""
- if len(args.weight1) == 1:
- res = score_target_hypo(
- args,
- args.weight1[0],
- args.weight2[0],
- args.weight3[0],
- args.lenpen[0],
- target_outfile,
- hypo_outfile,
- True,
- args.normalize,
- )
- rerank_scores = [res]
- else:
- print("launching pool")
- with Pool(32) as p:
- rerank_scores = p.starmap(
- score_target_hypo,
- [
- (
- args,
- args.weight1[i],
- args.weight2[i],
- args.weight3[i],
- args.lenpen[i],
- target_outfile,
- hypo_outfile,
- False,
- args.normalize,
- )
- for i in range(len(args.weight1))
- ],
- )
-
- if len(rerank_scores) > 1:
- best_index = np.argmax(rerank_scores)
- best_score = rerank_scores[best_index]
- print("best score", best_score)
- print("best lenpen", args.lenpen[best_index])
- print("best weight1", args.weight1[best_index])
- print("best weight2", args.weight2[best_index])
- print("best weight3", args.weight3[best_index])
- return (
- args.lenpen[best_index],
- args.weight1[best_index],
- args.weight2[best_index],
- args.weight3[best_index],
- best_score,
- )
-
- else:
- return (
- args.lenpen[0],
- args.weight1[0],
- args.weight2[0],
- args.weight3[0],
- rerank_scores[0],
- )
-
-
-def load_score_files(args):
- if args.all_shards:
- shard_ids = list(range(args.num_shards))
- else:
- shard_ids = [args.shard_id]
-
- gen_output_lst = []
- bitext1_lst = []
- bitext2_lst = []
- lm_res1_lst = []
-
- for shard_id in shard_ids:
- using_nbest = args.nbest_list is not None
- (
- pre_gen,
- left_to_right_preprocessed_dir,
- right_to_left_preprocessed_dir,
- backwards_preprocessed_dir,
- lm_preprocessed_dir,
- ) = rerank_utils.get_directories(
- args.data_dir_name,
- args.num_rescore,
- args.gen_subset,
- args.gen_model_name,
- shard_id,
- args.num_shards,
- args.sampling,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- rerank1_is_gen = (
- args.gen_model == args.score_model1 and args.source_prefix_frac is None
- )
- rerank2_is_gen = (
- args.gen_model == args.score_model2 and args.source_prefix_frac is None
- )
-
- score1_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model1_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards1,
- )
- if args.score_model2 is not None:
- score2_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model2_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards2,
- )
- if args.language_model is not None:
- lm_score_file = rerank_utils.rescore_file_name(
- pre_gen, args.prefix_len, args.lm_name, lm_file=True
- )
-
- # get gen output
- predictions_bpe_file = pre_gen + "/generate_output_bpe.txt"
- if using_nbest:
- print("Using predefined n-best list from interactive.py")
- predictions_bpe_file = args.nbest_list
- gen_output = rerank_utils.BitextOutputFromGen(
- predictions_bpe_file,
- bpe_symbol=args.post_process,
- nbest=using_nbest,
- prefix_len=args.prefix_len,
- target_prefix_frac=args.target_prefix_frac,
- )
-
- if rerank1_is_gen:
- bitext1 = gen_output
- else:
- bitext1 = rerank_utils.BitextOutput(
- score1_file,
- args.backwards1,
- args.right_to_left1,
- args.post_process,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- if args.score_model2 is not None or args.nbest_list is not None:
- if rerank2_is_gen:
- bitext2 = gen_output
- else:
- bitext2 = rerank_utils.BitextOutput(
- score2_file,
- args.backwards2,
- args.right_to_left2,
- args.post_process,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- assert (
- bitext2.source_lengths == bitext1.source_lengths
- ), "source lengths for rescoring models do not match"
- assert (
- bitext2.target_lengths == bitext1.target_lengths
- ), "target lengths for rescoring models do not match"
- else:
- if args.diff_bpe:
- assert args.score_model2 is None
- bitext2 = gen_output
- else:
- bitext2 = None
-
- if args.language_model is not None:
- lm_res1 = rerank_utils.LMOutput(
- lm_score_file,
- args.lm_dict,
- args.prefix_len,
- args.post_process,
- args.target_prefix_frac,
- )
- else:
- lm_res1 = None
-
- gen_output_lst.append(gen_output)
- bitext1_lst.append(bitext1)
- bitext2_lst.append(bitext2)
- lm_res1_lst.append(lm_res1)
- return gen_output_lst, bitext1_lst, bitext2_lst, lm_res1_lst
-
-
-def rerank(args):
- if type(args.lenpen) is not list:
- args.lenpen = [args.lenpen]
- if type(args.weight1) is not list:
- args.weight1 = [args.weight1]
- if type(args.weight2) is not list:
- args.weight2 = [args.weight2]
- if type(args.weight3) is not list:
- args.weight3 = [args.weight3]
- if args.all_shards:
- shard_ids = list(range(args.num_shards))
- else:
- shard_ids = [args.shard_id]
-
- for shard_id in shard_ids:
- (
- pre_gen,
- left_to_right_preprocessed_dir,
- right_to_left_preprocessed_dir,
- backwards_preprocessed_dir,
- lm_preprocessed_dir,
- ) = rerank_utils.get_directories(
- args.data_dir_name,
- args.num_rescore,
- args.gen_subset,
- args.gen_model_name,
- shard_id,
- args.num_shards,
- args.sampling,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
- rerank_generate.gen_and_reprocess_nbest(args)
- rerank_score_bw.score_bw(args)
- rerank_score_lm.score_lm(args)
-
- if args.write_hypos is None:
- write_targets = pre_gen + "/matched_targets"
- write_hypos = pre_gen + "/matched_hypos"
- else:
- write_targets = args.write_hypos + "_targets" + args.gen_subset
- write_hypos = args.write_hypos + "_hypos" + args.gen_subset
-
- if args.all_shards:
- write_targets += "_all_shards"
- write_hypos += "_all_shards"
-
- (
- best_lenpen,
- best_weight1,
- best_weight2,
- best_weight3,
- best_score,
- ) = match_target_hypo(args, write_targets, write_hypos)
-
- return best_lenpen, best_weight1, best_weight2, best_weight3, best_score
-
-
-def cli_main():
- parser = rerank_options.get_reranking_parser()
- args = options.parse_args_and_arch(parser)
- rerank(args)
-
-
-if __name__ == "__main__":
- cli_main()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md
deleted file mode 100644
index 9d3d7b67dc08440b5f4d1c5a7ffcd4bd6e76c14f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md
+++ /dev/null
@@ -1,179 +0,0 @@
-# Examples of Training scripts for Non-autoregressive Machine Translation models
-
-### Non-autoregressive Transformer (NAT, Gu et al., 2017)
-Note that we need to have an additional module to perform "length prediction" (`--length-loss-factor`) before generating the whole sequence.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch nonautoregressive_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-### Fast Structured Decoding for Sequence Models (NAT-CRF, Sun et al., 2019)
-Note that we implemented a low-rank appromixated CRF model by setting `--crf-lowrank-approx=32` and `--crf-beam-approx=64` as discribed in the original paper. All other settings are the same as the vanilla NAT model.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch nacrf_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --word-ins-loss-factor 0.5 \
- --crf-lowrank-approx 32 \
- --crf-beam-approx 64 \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-### Non-autoregressive Transformer with Iterative Refinement (iNAT, Lee et al., 2018)
-Note that `--train-step` means how many iterations of refinement we used during training, and `--dae-ratio` controls the ratio of denoising auto-encoder training described in the original paper.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch iterative_nonautoregressive_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --train-step 4 \
- --dae-ratio 0.5 \
- --stochastic-approx \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-### Insertion Transformer (InsT, Stern et al., 2019)
-Note that we need to specify the "slot-loss" (uniform or balanced tree) described in the original paper. Here we use `--label-tau` to control the temperature.
-
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch insertion_transformer \
- --noise random_delete \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-### Mask Predict (CMLM, Ghazvininejad et al., 2019)
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch cmlm_transformer \
- --noise random_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-
-
-### Levenshtein Transformer (LevT, Gu et al., 2019)
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch levenshtein_transformer \
- --noise random_delete \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py
deleted file mode 100644
index 8b7eb2ec4fc5190c4dcdfe34b0259e6f448e18a9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""isort:skip_file"""
-
-from .dictionary import Dictionary, TruncatedDictionary
-
-from .fairseq_dataset import FairseqDataset, FairseqIterableDataset
-
-from .base_wrapper_dataset import BaseWrapperDataset
-
-from .add_target_dataset import AddTargetDataset
-from .append_token_dataset import AppendTokenDataset
-from .audio.raw_audio_dataset import BinarizedAudioDataset, FileAudioDataset
-from .audio.hubert_dataset import HubertDataset
-from .backtranslation_dataset import BacktranslationDataset
-from .bucket_pad_length_dataset import BucketPadLengthDataset
-from .colorize_dataset import ColorizeDataset
-from .concat_dataset import ConcatDataset
-from .concat_sentences_dataset import ConcatSentencesDataset
-from .denoising_dataset import DenoisingDataset
-from .id_dataset import IdDataset
-from .indexed_dataset import (
- IndexedCachedDataset,
- IndexedDataset,
- IndexedRawTextDataset,
- MMapIndexedDataset,
-)
-from .language_pair_dataset import LanguagePairDataset
-from .list_dataset import ListDataset
-from .lm_context_window_dataset import LMContextWindowDataset
-from .lru_cache_dataset import LRUCacheDataset
-from .mask_tokens_dataset import MaskTokensDataset
-from .monolingual_dataset import MonolingualDataset
-from .multi_corpus_sampled_dataset import MultiCorpusSampledDataset
-from .nested_dictionary_dataset import NestedDictionaryDataset
-from .noising import NoisingDataset
-from .numel_dataset import NumelDataset
-from .num_samples_dataset import NumSamplesDataset
-from .offset_tokens_dataset import OffsetTokensDataset
-from .pad_dataset import LeftPadDataset, PadDataset, RightPadDataset
-from .prepend_dataset import PrependDataset
-from .prepend_token_dataset import PrependTokenDataset
-from .raw_label_dataset import RawLabelDataset
-from .replace_dataset import ReplaceDataset
-from .resampling_dataset import ResamplingDataset
-from .roll_dataset import RollDataset
-from .round_robin_zip_datasets import RoundRobinZipDatasets
-from .sort_dataset import SortDataset
-from .strip_token_dataset import StripTokenDataset
-from .subsample_dataset import SubsampleDataset
-from .token_block_dataset import TokenBlockDataset
-from .transform_eos_dataset import TransformEosDataset
-from .transform_eos_lang_pair_dataset import TransformEosLangPairDataset
-from .shorten_dataset import TruncateDataset, RandomCropDataset
-from .multilingual.sampled_multi_dataset import SampledMultiDataset
-from .multilingual.sampled_multi_epoch_dataset import SampledMultiEpochDataset
-from .fasta_dataset import FastaDataset, EncodedFastaDataset
-
-from .iterators import (
- CountingIterator,
- EpochBatchIterator,
- GroupedIterator,
- ShardedIterator,
-)
-
-__all__ = [
- "AddTargetDataset",
- "AppendTokenDataset",
- "BacktranslationDataset",
- "BaseWrapperDataset",
- "BinarizedAudioDataset",
- "BucketPadLengthDataset",
- "ColorizeDataset",
- "ConcatDataset",
- "ConcatSentencesDataset",
- "CountingIterator",
- "DenoisingDataset",
- "Dictionary",
- "EncodedFastaDataset",
- "EpochBatchIterator",
- "FairseqDataset",
- "FairseqIterableDataset",
- "FastaDataset",
- "FileAudioDataset",
- "GroupedIterator",
- "HubertDataset",
- "IdDataset",
- "IndexedCachedDataset",
- "IndexedDataset",
- "IndexedRawTextDataset",
- "LanguagePairDataset",
- "LeftPadDataset",
- "ListDataset",
- "LMContextWindowDataset",
- "LRUCacheDataset",
- "MaskTokensDataset",
- "MMapIndexedDataset",
- "MonolingualDataset",
- "MultiCorpusSampledDataset",
- "NestedDictionaryDataset",
- "NoisingDataset",
- "NumelDataset",
- "NumSamplesDataset",
- "OffsetTokensDataset",
- "PadDataset",
- "PrependDataset",
- "PrependTokenDataset",
- "RandomCropDataset",
- "RawLabelDataset",
- "ResamplingDataset",
- "ReplaceDataset",
- "RightPadDataset",
- "RollDataset",
- "RoundRobinZipDatasets",
- "SampledMultiDataset",
- "SampledMultiEpochDataset",
- "ShardedIterator",
- "SortDataset",
- "StripTokenDataset",
- "SubsampleDataset",
- "TokenBlockDataset",
- "TransformEosDataset",
- "TransformEosLangPairDataset",
- "TruncateDataset",
- "TruncatedDictionary",
-]
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py
deleted file mode 100644
index 11c7dc1ea966a54f8915ef164377e40f90e851a1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-from fairseq.tasks import register_task
-from fairseq.tasks.speech_to_text import SpeechToTextTask
-from fairseq.tasks.translation import (
- TranslationTask, TranslationConfig
-)
-
-try:
- import examples.simultaneous_translation # noqa
- import_successful = True
-except BaseException:
- import_successful = False
-
-
-logger = logging.getLogger(__name__)
-
-
-def check_import(flag):
- if not flag:
- raise ImportError(
- "'examples.simultaneous_translation' is not correctly imported. "
- "Please considering `pip install -e $FAIRSEQ_DIR`."
- )
-
-
-@register_task("simul_speech_to_text")
-class SimulSpeechToTextTask(SpeechToTextTask):
- def __init__(self, args, tgt_dict):
- check_import(import_successful)
- super().__init__(args, tgt_dict)
-
-
-@register_task("simul_text_to_text", dataclass=TranslationConfig)
-class SimulTextToTextTask(TranslationTask):
- def __init__(self, cfg, src_dict, tgt_dict):
- check_import(import_successful)
- super().__init__(cfg, src_dict, tgt_dict)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md
deleted file mode 100644
index 57104230655c7c517d25904e634c53b6159ee60f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md
+++ /dev/null
@@ -1,42 +0,0 @@
-# Unit to Speech Model (unit2speech)
-
-Unit to speech model is modified Tacotron2 model that learns to synthesize speech from discrete speech units. All models are trained on quantized [LJSpeech](https://keithito.com/LJ-Speech-Dataset/).
-
-Upstream Units | Download Link
-|-|-
-Log Mel Filterbank + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km50/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km100/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km200/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km500/tts_checkpoint_best.pt)
-Modified CPC + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km50/tts_checkpoint_best.pt)
-Modified CPC + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km100/tts_checkpoint_best.pt)
-Modified CPC + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km200/tts_checkpoint_best.pt)
-Modified CPC + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km500/tts_checkpoint_best.pt)
-HuBERT Base + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km50/tts_checkpoint_best.pt)
-HuBERT Base + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km100/tts_checkpoint_best.pt)
-HuBERT Base + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km200/tts_checkpoint_best.pt)
-HuBERT Base + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km500/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km50/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km100/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km200/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km500/tts_checkpoint_best.pt)
-
-## Run inference using a unit2speech model
-* Install librosa, unidecode and inflect using `pip install librosa, unidecode, inflect`
-* Download [Waveglow checkpoint](https://dl.fbaipublicfiles.com/textless_nlp/gslm/waveglow_256channels_new.pt). This is the vocoder.
-
-Sample commnd to run inference using trained unit2speech models. Please note that the quantized audio to synthesized should be using the same units as the unit2speech model was trained with.
-```
-FAIRSEQ_ROOT=
-TTS_MODEL_PATH=
-QUANTIZED_UNIT_PATH=
-OUT_DIR=
-WAVEGLOW_PATH=
-
-PYTHONPATH=${FAIRSEQ_ROOT}:${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech python ${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py \
- --tts_model_path $TTS_MODEL_PATH \
- --quantized_unit_path $QUANTIZED_UNIT_PATH \
- --out_audio_dir $OUT_DIR \
- --waveglow_path $WAVEGLOW_PATH \
- --max_decoder_steps 2000
-```
\ No newline at end of file
diff --git a/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py b/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py
deleted file mode 100644
index 375e4d50872d5c68ee57ca17470a2ca425425eba..0000000000000000000000000000000000000000
--- a/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import re
-from unidecode import unidecode
-import pyopenjtalk
-
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
- r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
- r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ts', 'ʦ'),
- ('u', 'ɯ'),
- ('j', 'ʥ'),
- ('y', 'j'),
- ('ni', 'n^i'),
- ('nj', 'n^'),
- ('hi', 'çi'),
- ('hj', 'ç'),
- ('f', 'ɸ'),
- ('I', 'i*'),
- ('U', 'ɯ*'),
- ('r', 'ɾ')
-]]
-
-# List of (romaji, ipa2) pairs for marks:
-_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('u', 'ɯ'),
- ('ʧ', 'tʃ'),
- ('j', 'dʑ'),
- ('y', 'j'),
- ('ni', 'n^i'),
- ('nj', 'n^'),
- ('hi', 'çi'),
- ('hj', 'ç'),
- ('f', 'ɸ'),
- ('I', 'i*'),
- ('U', 'ɯ*'),
- ('r', 'ɾ')
-]]
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'Q([↑↓]*[kg])', r'k#\1'),
- (r'Q([↑↓]*[tdjʧ])', r't#\1'),
- (r'Q([↑↓]*[sʃ])', r's\1'),
- (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'N([↑↓]*[pbm])', r'm\1'),
- (r'N([↑↓]*[ʧʥj])', r'n^\1'),
- (r'N([↑↓]*[tdn])', r'n\1'),
- (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-def symbols_to_japanese(text):
- for regex, replacement in _symbols_to_japanese:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_romaji_with_accent(text):
- '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
- text = symbols_to_japanese(text)
- sentences = re.split(_japanese_marks, text)
- marks = re.findall(_japanese_marks, text)
- text = ''
- for i, sentence in enumerate(sentences):
- if re.match(_japanese_characters, sentence):
- if text != '':
- text += ' '
- labels = pyopenjtalk.extract_fullcontext(sentence)
- for n, label in enumerate(labels):
- phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
- if phoneme not in ['sil', 'pau']:
- text += phoneme.replace('ch', 'ʧ').replace('sh',
- 'ʃ').replace('cl', 'Q')
- else:
- continue
- # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
- a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
- a2 = int(re.search(r"\+(\d+)\+", label).group(1))
- a3 = int(re.search(r"\+(\d+)/", label).group(1))
- if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
- a2_next = -1
- else:
- a2_next = int(
- re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
- # Accent phrase boundary
- if a3 == 1 and a2_next == 1:
- text += ' '
- # Falling
- elif a1 == 0 and a2_next == a2 + 1:
- text += '↓'
- # Rising
- elif a2 == 1 and a2_next == 2:
- text += '↑'
- if i < len(marks):
- text += unidecode(marks[i]).replace(' ', '')
- return text
-
-
-def get_real_sokuon(text):
- for regex, replacement in _real_sokuon:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def get_real_hatsuon(text):
- for regex, replacement in _real_hatsuon:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa(text):
- text = japanese_to_romaji_with_accent(text).replace('...', '…')
- text = re.sub(
- r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
- text = get_real_sokuon(text)
- text = get_real_hatsuon(text)
- for regex, replacement in _romaji_to_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa2(text):
- text = japanese_to_romaji_with_accent(text).replace('...', '…')
- text = get_real_sokuon(text)
- text = get_real_hatsuon(text)
- for regex, replacement in _romaji_to_ipa2:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa3(text):
- text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
- 'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
- text = re.sub(
- r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
- text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
- return text
diff --git a/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py b/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py
deleted file mode 100644
index c215099fedacae35c6798fdd9b8420a447aa16bb..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-from audiocraft.quantization.vq import ResidualVectorQuantizer
-
-
-class TestResidualVectorQuantizer:
-
- def test_rvq(self):
- x = torch.randn(1, 16, 2048)
- vq = ResidualVectorQuantizer(n_q=8, dimension=16, bins=8)
- res = vq(x, 1.)
- assert res.x.shape == torch.Size([1, 16, 2048])
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py
deleted file mode 100644
index feb7a8222487756d38482da95183bbbcbbe96ed9..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py
+++ /dev/null
@@ -1,864 +0,0 @@
-
-import math
-import json
-import copy
-from typing import List, Dict
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.modeling.proposal_generator.build import PROPOSAL_GENERATOR_REGISTRY
-from detectron2.layers import ShapeSpec, cat
-from detectron2.structures import Instances, Boxes
-from detectron2.modeling import detector_postprocess
-from detectron2.utils.comm import get_world_size
-from detectron2.config import configurable
-
-from ..layers.heatmap_focal_loss import heatmap_focal_loss_jit
-from ..layers.heatmap_focal_loss import binary_heatmap_focal_loss
-from ..layers.iou_loss import IOULoss
-from ..layers.ml_nms import ml_nms
-from ..debug import debug_train, debug_test
-from .utils import reduce_sum, _transpose
-from .centernet_head import CenterNetHead
-
-__all__ = ["CenterNet"]
-
-INF = 100000000
-
-@PROPOSAL_GENERATOR_REGISTRY.register()
-class CenterNet(nn.Module):
- @configurable
- def __init__(self,
- # input_shape: Dict[str, ShapeSpec],
- in_channels=256,
- *,
- num_classes=80,
- in_features=("p3", "p4", "p5", "p6", "p7"),
- strides=(8, 16, 32, 64, 128),
- score_thresh=0.05,
- hm_min_overlap=0.8,
- loc_loss_type='giou',
- min_radius=4,
- hm_focal_alpha=0.25,
- hm_focal_beta=4,
- loss_gamma=2.0,
- reg_weight=2.0,
- not_norm_reg=True,
- with_agn_hm=False,
- only_proposal=False,
- as_proposal=False,
- not_nms=False,
- pos_weight=1.,
- neg_weight=1.,
- sigmoid_clamp=1e-4,
- ignore_high_fp=-1.,
- center_nms=False,
- sizes_of_interest=[[0,80],[64,160],[128,320],[256,640],[512,10000000]],
- more_pos=False,
- more_pos_thresh=0.2,
- more_pos_topk=9,
- pre_nms_topk_train=1000,
- pre_nms_topk_test=1000,
- post_nms_topk_train=100,
- post_nms_topk_test=100,
- nms_thresh_train=0.6,
- nms_thresh_test=0.6,
- no_reduce=False,
- debug=False,
- vis_thresh=0.5,
- pixel_mean=[103.530,116.280,123.675],
- pixel_std=[1.0,1.0,1.0],
- device='cuda',
- centernet_head=None,
- ):
- super().__init__()
- self.num_classes = num_classes
- self.in_features = in_features
- self.strides = strides
- self.score_thresh = score_thresh
- self.min_radius = min_radius
- self.hm_focal_alpha = hm_focal_alpha
- self.hm_focal_beta = hm_focal_beta
- self.loss_gamma = loss_gamma
- self.reg_weight = reg_weight
- self.not_norm_reg = not_norm_reg
- self.with_agn_hm = with_agn_hm
- self.only_proposal = only_proposal
- self.as_proposal = as_proposal
- self.not_nms = not_nms
- self.pos_weight = pos_weight
- self.neg_weight = neg_weight
- self.sigmoid_clamp = sigmoid_clamp
- self.ignore_high_fp = ignore_high_fp
- self.center_nms = center_nms
- self.sizes_of_interest = sizes_of_interest
- self.more_pos = more_pos
- self.more_pos_thresh = more_pos_thresh
- self.more_pos_topk = more_pos_topk
- self.pre_nms_topk_train = pre_nms_topk_train
- self.pre_nms_topk_test = pre_nms_topk_test
- self.post_nms_topk_train = post_nms_topk_train
- self.post_nms_topk_test = post_nms_topk_test
- self.nms_thresh_train = nms_thresh_train
- self.nms_thresh_test = nms_thresh_test
- self.no_reduce = no_reduce
- self.debug = debug
- self.vis_thresh = vis_thresh
- if self.center_nms:
- self.not_nms = True
- self.iou_loss = IOULoss(loc_loss_type)
- assert (not self.only_proposal) or self.with_agn_hm
- # delta for rendering heatmap
- self.delta = (1 - hm_min_overlap) / (1 + hm_min_overlap)
- if centernet_head is None:
- self.centernet_head = CenterNetHead(
- in_channels=in_channels,
- num_levels=len(in_features),
- with_agn_hm=with_agn_hm,
- only_proposal=only_proposal)
- else:
- self.centernet_head = centernet_head
- if self.debug:
- pixel_mean = torch.Tensor(pixel_mean).to(
- torch.device(device)).view(3, 1, 1)
- pixel_std = torch.Tensor(pixel_std).to(
- torch.device(device)).view(3, 1, 1)
- self.denormalizer = lambda x: x * pixel_std + pixel_mean
-
- @classmethod
- def from_config(cls, cfg, input_shape):
- ret = {
- # 'input_shape': input_shape,
- 'in_channels': input_shape[
- cfg.MODEL.CENTERNET.IN_FEATURES[0]].channels,
- 'num_classes': cfg.MODEL.CENTERNET.NUM_CLASSES,
- 'in_features': cfg.MODEL.CENTERNET.IN_FEATURES,
- 'strides': cfg.MODEL.CENTERNET.FPN_STRIDES,
- 'score_thresh': cfg.MODEL.CENTERNET.INFERENCE_TH,
- 'loc_loss_type': cfg.MODEL.CENTERNET.LOC_LOSS_TYPE,
- 'hm_min_overlap': cfg.MODEL.CENTERNET.HM_MIN_OVERLAP,
- 'min_radius': cfg.MODEL.CENTERNET.MIN_RADIUS,
- 'hm_focal_alpha': cfg.MODEL.CENTERNET.HM_FOCAL_ALPHA,
- 'hm_focal_beta': cfg.MODEL.CENTERNET.HM_FOCAL_BETA,
- 'loss_gamma': cfg.MODEL.CENTERNET.LOSS_GAMMA,
- 'reg_weight': cfg.MODEL.CENTERNET.REG_WEIGHT,
- 'not_norm_reg': cfg.MODEL.CENTERNET.NOT_NORM_REG,
- 'with_agn_hm': cfg.MODEL.CENTERNET.WITH_AGN_HM,
- 'only_proposal': cfg.MODEL.CENTERNET.ONLY_PROPOSAL,
- 'as_proposal': cfg.MODEL.CENTERNET.AS_PROPOSAL,
- 'not_nms': cfg.MODEL.CENTERNET.NOT_NMS,
- 'pos_weight': cfg.MODEL.CENTERNET.POS_WEIGHT,
- 'neg_weight': cfg.MODEL.CENTERNET.NEG_WEIGHT,
- 'sigmoid_clamp': cfg.MODEL.CENTERNET.SIGMOID_CLAMP,
- 'ignore_high_fp': cfg.MODEL.CENTERNET.IGNORE_HIGH_FP,
- 'center_nms': cfg.MODEL.CENTERNET.CENTER_NMS,
- 'sizes_of_interest': cfg.MODEL.CENTERNET.SOI,
- 'more_pos': cfg.MODEL.CENTERNET.MORE_POS,
- 'more_pos_thresh': cfg.MODEL.CENTERNET.MORE_POS_THRESH,
- 'more_pos_topk': cfg.MODEL.CENTERNET.MORE_POS_TOPK,
- 'pre_nms_topk_train': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TRAIN,
- 'pre_nms_topk_test': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TEST,
- 'post_nms_topk_train': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TRAIN,
- 'post_nms_topk_test': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TEST,
- 'nms_thresh_train': cfg.MODEL.CENTERNET.NMS_TH_TRAIN,
- 'nms_thresh_test': cfg.MODEL.CENTERNET.NMS_TH_TEST,
- 'no_reduce': cfg.MODEL.CENTERNET.NO_REDUCE,
- 'debug': cfg.DEBUG,
- 'vis_thresh': cfg.VIS_THRESH,
- 'pixel_mean': cfg.MODEL.PIXEL_MEAN,
- 'pixel_std': cfg.MODEL.PIXEL_STD,
- 'device': cfg.MODEL.DEVICE,
- 'centernet_head': CenterNetHead(
- cfg, [input_shape[f] for f in cfg.MODEL.CENTERNET.IN_FEATURES]),
- }
- return ret
-
-
- def forward(self, images, features_dict, gt_instances):
- features = [features_dict[f] for f in self.in_features]
- clss_per_level, reg_pred_per_level, agn_hm_pred_per_level = \
- self.centernet_head(features)
- grids = self.compute_grids(features)
- shapes_per_level = grids[0].new_tensor(
- [(x.shape[2], x.shape[3]) for x in reg_pred_per_level])
-
- if not self.training:
- return self.inference(
- images, clss_per_level, reg_pred_per_level,
- agn_hm_pred_per_level, grids)
- else:
- pos_inds, labels, reg_targets, flattened_hms = \
- self._get_ground_truth(
- grids, shapes_per_level, gt_instances)
- # logits_pred: M x F, reg_pred: M x 4, agn_hm_pred: M
- logits_pred, reg_pred, agn_hm_pred = self._flatten_outputs(
- clss_per_level, reg_pred_per_level, agn_hm_pred_per_level)
-
- if self.more_pos:
- # add more pixels as positive if \
- # 1. they are within the center3x3 region of an object
- # 2. their regression losses are small (= 0).squeeze(1)
- reg_pred = reg_pred[reg_inds]
- reg_targets_pos = reg_targets[reg_inds]
- reg_weight_map = flattened_hms.max(dim=1)[0]
- reg_weight_map = reg_weight_map[reg_inds]
- reg_weight_map = reg_weight_map * 0 + 1 \
- if self.not_norm_reg else reg_weight_map
- if self.no_reduce:
- reg_norm = max(reg_weight_map.sum(), 1)
- else:
- reg_norm = max(reduce_sum(reg_weight_map.sum()).item() / num_gpus, 1)
-
- reg_loss = self.reg_weight * self.iou_loss(
- reg_pred, reg_targets_pos, reg_weight_map,
- reduction='sum') / reg_norm
- losses['loss_centernet_loc'] = reg_loss
-
- if self.with_agn_hm:
- cat_agn_heatmap = flattened_hms.max(dim=1)[0] # M
- agn_pos_loss, agn_neg_loss = binary_heatmap_focal_loss(
- agn_hm_pred, cat_agn_heatmap, pos_inds,
- alpha=self.hm_focal_alpha,
- beta=self.hm_focal_beta,
- gamma=self.loss_gamma,
- sigmoid_clamp=self.sigmoid_clamp,
- ignore_high_fp=self.ignore_high_fp,
- )
- agn_pos_loss = self.pos_weight * agn_pos_loss / num_pos_avg
- agn_neg_loss = self.neg_weight * agn_neg_loss / num_pos_avg
- losses['loss_centernet_agn_pos'] = agn_pos_loss
- losses['loss_centernet_agn_neg'] = agn_neg_loss
-
- if self.debug:
- print('losses', losses)
- print('total_num_pos', total_num_pos)
- return losses
-
-
- def compute_grids(self, features):
- grids = []
- for level, feature in enumerate(features):
- h, w = feature.size()[-2:]
- shifts_x = torch.arange(
- 0, w * self.strides[level],
- step=self.strides[level],
- dtype=torch.float32, device=feature.device)
- shifts_y = torch.arange(
- 0, h * self.strides[level],
- step=self.strides[level],
- dtype=torch.float32, device=feature.device)
- shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
- shift_x = shift_x.reshape(-1)
- shift_y = shift_y.reshape(-1)
- grids_per_level = torch.stack((shift_x, shift_y), dim=1) + \
- self.strides[level] // 2
- grids.append(grids_per_level)
- return grids
-
-
- def _get_ground_truth(self, grids, shapes_per_level, gt_instances):
- '''
- Input:
- grids: list of tensors [(hl x wl, 2)]_l
- shapes_per_level: list of tuples L x 2:
- gt_instances: gt instances
- Retuen:
- pos_inds: N
- labels: N
- reg_targets: M x 4
- flattened_hms: M x C or M x 1
- N: number of objects in all images
- M: number of pixels from all FPN levels
- '''
-
- # get positive pixel index
- if not self.more_pos:
- pos_inds, labels = self._get_label_inds(
- gt_instances, shapes_per_level)
- else:
- pos_inds, labels = None, None
- heatmap_channels = self.num_classes
- L = len(grids)
- num_loc_list = [len(loc) for loc in grids]
- strides = torch.cat([
- shapes_per_level.new_ones(num_loc_list[l]) * self.strides[l] \
- for l in range(L)]).float() # M
- reg_size_ranges = torch.cat([
- shapes_per_level.new_tensor(self.sizes_of_interest[l]).float().view(
- 1, 2).expand(num_loc_list[l], 2) for l in range(L)]) # M x 2
- grids = torch.cat(grids, dim=0) # M x 2
- M = grids.shape[0]
-
- reg_targets = []
- flattened_hms = []
- for i in range(len(gt_instances)): # images
- boxes = gt_instances[i].gt_boxes.tensor # N x 4
- area = gt_instances[i].gt_boxes.area() # N
- gt_classes = gt_instances[i].gt_classes # N in [0, self.num_classes]
-
- N = boxes.shape[0]
- if N == 0:
- reg_targets.append(grids.new_zeros((M, 4)) - INF)
- flattened_hms.append(
- grids.new_zeros((
- M, 1 if self.only_proposal else heatmap_channels)))
- continue
-
- l = grids[:, 0].view(M, 1) - boxes[:, 0].view(1, N) # M x N
- t = grids[:, 1].view(M, 1) - boxes[:, 1].view(1, N) # M x N
- r = boxes[:, 2].view(1, N) - grids[:, 0].view(M, 1) # M x N
- b = boxes[:, 3].view(1, N) - grids[:, 1].view(M, 1) # M x N
- reg_target = torch.stack([l, t, r, b], dim=2) # M x N x 4
-
- centers = ((boxes[:, [0, 1]] + boxes[:, [2, 3]]) / 2) # N x 2
- centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2
- strides_expanded = strides.view(M, 1, 1).expand(M, N, 2)
- centers_discret = ((centers_expanded / strides_expanded).int() * \
- strides_expanded).float() + strides_expanded / 2 # M x N x 2
-
- is_peak = (((grids.view(M, 1, 2).expand(M, N, 2) - \
- centers_discret) ** 2).sum(dim=2) == 0) # M x N
- is_in_boxes = reg_target.min(dim=2)[0] > 0 # M x N
- is_center3x3 = self.get_center3x3(
- grids, centers, strides) & is_in_boxes # M x N
- is_cared_in_the_level = self.assign_reg_fpn(
- reg_target, reg_size_ranges) # M x N
- reg_mask = is_center3x3 & is_cared_in_the_level # M x N
-
- dist2 = ((grids.view(M, 1, 2).expand(M, N, 2) - \
- centers_expanded) ** 2).sum(dim=2) # M x N
- dist2[is_peak] = 0
- radius2 = self.delta ** 2 * 2 * area # N
- radius2 = torch.clamp(
- radius2, min=self.min_radius ** 2)
- weighted_dist2 = dist2 / radius2.view(1, N).expand(M, N) # M x N
- reg_target = self._get_reg_targets(
- reg_target, weighted_dist2.clone(), reg_mask, area) # M x 4
-
- if self.only_proposal:
- flattened_hm = self._create_agn_heatmaps_from_dist(
- weighted_dist2.clone()) # M x 1
- else:
- flattened_hm = self._create_heatmaps_from_dist(
- weighted_dist2.clone(), gt_classes,
- channels=heatmap_channels) # M x C
-
- reg_targets.append(reg_target)
- flattened_hms.append(flattened_hm)
-
- # transpose im first training_targets to level first ones
- reg_targets = _transpose(reg_targets, num_loc_list)
- flattened_hms = _transpose(flattened_hms, num_loc_list)
- for l in range(len(reg_targets)):
- reg_targets[l] = reg_targets[l] / float(self.strides[l])
- reg_targets = cat([x for x in reg_targets], dim=0) # MB x 4
- flattened_hms = cat([x for x in flattened_hms], dim=0) # MB x C
-
- return pos_inds, labels, reg_targets, flattened_hms
-
-
- def _get_label_inds(self, gt_instances, shapes_per_level):
- '''
- Inputs:
- gt_instances: [n_i], sum n_i = N
- shapes_per_level: L x 2 [(h_l, w_l)]_L
- Returns:
- pos_inds: N'
- labels: N'
- '''
- pos_inds = []
- labels = []
- L = len(self.strides)
- B = len(gt_instances)
- shapes_per_level = shapes_per_level.long()
- loc_per_level = (shapes_per_level[:, 0] * shapes_per_level[:, 1]).long() # L
- level_bases = []
- s = 0
- for l in range(L):
- level_bases.append(s)
- s = s + B * loc_per_level[l]
- level_bases = shapes_per_level.new_tensor(level_bases).long() # L
- strides_default = shapes_per_level.new_tensor(self.strides).float() # L
- for im_i in range(B):
- targets_per_im = gt_instances[im_i]
- bboxes = targets_per_im.gt_boxes.tensor # n x 4
- n = bboxes.shape[0]
- centers = ((bboxes[:, [0, 1]] + bboxes[:, [2, 3]]) / 2) # n x 2
- centers = centers.view(n, 1, 2).expand(n, L, 2)
- strides = strides_default.view(1, L, 1).expand(n, L, 2)
- centers_inds = (centers / strides).long() # n x L x 2
- Ws = shapes_per_level[:, 1].view(1, L).expand(n, L)
- pos_ind = level_bases.view(1, L).expand(n, L) + \
- im_i * loc_per_level.view(1, L).expand(n, L) + \
- centers_inds[:, :, 1] * Ws + \
- centers_inds[:, :, 0] # n x L
- is_cared_in_the_level = self.assign_fpn_level(bboxes)
- pos_ind = pos_ind[is_cared_in_the_level].view(-1)
- label = targets_per_im.gt_classes.view(
- n, 1).expand(n, L)[is_cared_in_the_level].view(-1)
-
- pos_inds.append(pos_ind) # n'
- labels.append(label) # n'
- pos_inds = torch.cat(pos_inds, dim=0).long()
- labels = torch.cat(labels, dim=0)
- return pos_inds, labels # N, N
-
-
- def assign_fpn_level(self, boxes):
- '''
- Inputs:
- boxes: n x 4
- size_ranges: L x 2
- Return:
- is_cared_in_the_level: n x L
- '''
- size_ranges = boxes.new_tensor(
- self.sizes_of_interest).view(len(self.sizes_of_interest), 2) # L x 2
- crit = ((boxes[:, 2:] - boxes[:, :2]) **2).sum(dim=1) ** 0.5 / 2 # n
- n, L = crit.shape[0], size_ranges.shape[0]
- crit = crit.view(n, 1).expand(n, L)
- size_ranges_expand = size_ranges.view(1, L, 2).expand(n, L, 2)
- is_cared_in_the_level = (crit >= size_ranges_expand[:, :, 0]) & \
- (crit <= size_ranges_expand[:, :, 1])
- return is_cared_in_the_level
-
-
- def assign_reg_fpn(self, reg_targets_per_im, size_ranges):
- '''
- TODO (Xingyi): merge it with assign_fpn_level
- Inputs:
- reg_targets_per_im: M x N x 4
- size_ranges: M x 2
- '''
- crit = ((reg_targets_per_im[:, :, :2] + \
- reg_targets_per_im[:, :, 2:])**2).sum(dim=2) ** 0.5 / 2 # M x N
- is_cared_in_the_level = (crit >= size_ranges[:, [0]]) & \
- (crit <= size_ranges[:, [1]])
- return is_cared_in_the_level
-
-
- def _get_reg_targets(self, reg_targets, dist, mask, area):
- '''
- reg_targets (M x N x 4): long tensor
- dist (M x N)
- is_*: M x N
- '''
- dist[mask == 0] = INF * 1.0
- min_dist, min_inds = dist.min(dim=1) # M
- reg_targets_per_im = reg_targets[
- range(len(reg_targets)), min_inds] # M x N x 4 --> M x 4
- reg_targets_per_im[min_dist == INF] = - INF
- return reg_targets_per_im
-
-
- def _create_heatmaps_from_dist(self, dist, labels, channels):
- '''
- dist: M x N
- labels: N
- return:
- heatmaps: M x C
- '''
- heatmaps = dist.new_zeros((dist.shape[0], channels))
- for c in range(channels):
- inds = (labels == c) # N
- if inds.int().sum() == 0:
- continue
- heatmaps[:, c] = torch.exp(-dist[:, inds].min(dim=1)[0])
- zeros = heatmaps[:, c] < 1e-4
- heatmaps[zeros, c] = 0
- return heatmaps
-
-
- def _create_agn_heatmaps_from_dist(self, dist):
- '''
- TODO (Xingyi): merge it with _create_heatmaps_from_dist
- dist: M x N
- return:
- heatmaps: M x 1
- '''
- heatmaps = dist.new_zeros((dist.shape[0], 1))
- heatmaps[:, 0] = torch.exp(-dist.min(dim=1)[0])
- zeros = heatmaps < 1e-4
- heatmaps[zeros] = 0
- return heatmaps
-
-
- def _flatten_outputs(self, clss, reg_pred, agn_hm_pred):
- # Reshape: (N, F, Hl, Wl) -> (N, Hl, Wl, F) -> (sum_l N*Hl*Wl, F)
- clss = cat([x.permute(0, 2, 3, 1).reshape(-1, x.shape[1]) \
- for x in clss], dim=0) if clss[0] is not None else None
- reg_pred = cat(
- [x.permute(0, 2, 3, 1).reshape(-1, 4) for x in reg_pred], dim=0)
- agn_hm_pred = cat([x.permute(0, 2, 3, 1).reshape(-1) \
- for x in agn_hm_pred], dim=0) if self.with_agn_hm else None
- return clss, reg_pred, agn_hm_pred
-
-
- def get_center3x3(self, locations, centers, strides):
- '''
- Inputs:
- locations: M x 2
- centers: N x 2
- strides: M
- '''
- M, N = locations.shape[0], centers.shape[0]
- locations_expanded = locations.view(M, 1, 2).expand(M, N, 2) # M x N x 2
- centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2
- strides_expanded = strides.view(M, 1, 1).expand(M, N, 2) # M x N
- centers_discret = ((centers_expanded / strides_expanded).int() * \
- strides_expanded).float() + strides_expanded / 2 # M x N x 2
- dist_x = (locations_expanded[:, :, 0] - centers_discret[:, :, 0]).abs()
- dist_y = (locations_expanded[:, :, 1] - centers_discret[:, :, 1]).abs()
- return (dist_x <= strides_expanded[:, :, 0]) & \
- (dist_y <= strides_expanded[:, :, 0])
-
-
- def inference(self, images, clss_per_level, reg_pred_per_level,
- agn_hm_pred_per_level, grids):
- logits_pred = [x.sigmoid() if x is not None else None \
- for x in clss_per_level]
- agn_hm_pred_per_level = [x.sigmoid() if x is not None else None \
- for x in agn_hm_pred_per_level]
-
- if self.only_proposal:
- proposals = self.predict_instances(
- grids, agn_hm_pred_per_level, reg_pred_per_level,
- images.image_sizes, [None for _ in agn_hm_pred_per_level])
- else:
- proposals = self.predict_instances(
- grids, logits_pred, reg_pred_per_level,
- images.image_sizes, agn_hm_pred_per_level)
- if self.as_proposal or self.only_proposal:
- for p in range(len(proposals)):
- proposals[p].proposal_boxes = proposals[p].get('pred_boxes')
- proposals[p].objectness_logits = proposals[p].get('scores')
- proposals[p].remove('pred_boxes')
-
- if self.debug:
- debug_test(
- [self.denormalizer(x) for x in images],
- logits_pred, reg_pred_per_level,
- agn_hm_pred_per_level, preds=proposals,
- vis_thresh=self.vis_thresh,
- debug_show_name=False)
- return proposals, {}
-
-
- def predict_instances(
- self, grids, logits_pred, reg_pred, image_sizes, agn_hm_pred,
- is_proposal=False):
- sampled_boxes = []
- for l in range(len(grids)):
- sampled_boxes.append(self.predict_single_level(
- grids[l], logits_pred[l], reg_pred[l] * self.strides[l],
- image_sizes, agn_hm_pred[l], l, is_proposal=is_proposal))
- boxlists = list(zip(*sampled_boxes))
- boxlists = [Instances.cat(boxlist) for boxlist in boxlists]
- boxlists = self.nms_and_topK(
- boxlists, nms=not self.not_nms)
- return boxlists
-
-
- def predict_single_level(
- self, grids, heatmap, reg_pred, image_sizes, agn_hm, level,
- is_proposal=False):
- N, C, H, W = heatmap.shape
- # put in the same format as grids
- if self.center_nms:
- heatmap_nms = nn.functional.max_pool2d(
- heatmap, (3, 3), stride=1, padding=1)
- heatmap = heatmap * (heatmap_nms == heatmap).float()
- heatmap = heatmap.permute(0, 2, 3, 1) # N x H x W x C
- heatmap = heatmap.reshape(N, -1, C) # N x HW x C
- box_regression = reg_pred.view(N, 4, H, W).permute(0, 2, 3, 1) # N x H x W x 4
- box_regression = box_regression.reshape(N, -1, 4)
-
- candidate_inds = heatmap > self.score_thresh # 0.05
- pre_nms_top_n = candidate_inds.view(N, -1).sum(1) # N
- pre_nms_topk = self.pre_nms_topk_train if self.training else self.pre_nms_topk_test
- pre_nms_top_n = pre_nms_top_n.clamp(max=pre_nms_topk) # N
-
- if agn_hm is not None:
- agn_hm = agn_hm.view(N, 1, H, W).permute(0, 2, 3, 1)
- agn_hm = agn_hm.reshape(N, -1)
- heatmap = heatmap * agn_hm[:, :, None]
-
- results = []
- for i in range(N):
- per_box_cls = heatmap[i] # HW x C
- per_candidate_inds = candidate_inds[i] # n
- per_box_cls = per_box_cls[per_candidate_inds] # n
-
- per_candidate_nonzeros = per_candidate_inds.nonzero() # n
- per_box_loc = per_candidate_nonzeros[:, 0] # n
- per_class = per_candidate_nonzeros[:, 1] # n
-
- per_box_regression = box_regression[i] # HW x 4
- per_box_regression = per_box_regression[per_box_loc] # n x 4
- per_grids = grids[per_box_loc] # n x 2
-
- per_pre_nms_top_n = pre_nms_top_n[i] # 1
-
- if per_candidate_inds.sum().item() > per_pre_nms_top_n.item():
- per_box_cls, top_k_indices = \
- per_box_cls.topk(per_pre_nms_top_n, sorted=False)
- per_class = per_class[top_k_indices]
- per_box_regression = per_box_regression[top_k_indices]
- per_grids = per_grids[top_k_indices]
-
- detections = torch.stack([
- per_grids[:, 0] - per_box_regression[:, 0],
- per_grids[:, 1] - per_box_regression[:, 1],
- per_grids[:, 0] + per_box_regression[:, 2],
- per_grids[:, 1] + per_box_regression[:, 3],
- ], dim=1) # n x 4
-
- # avoid invalid boxes in RoI heads
- detections[:, 2] = torch.max(detections[:, 2], detections[:, 0] + 0.01)
- detections[:, 3] = torch.max(detections[:, 3], detections[:, 1] + 0.01)
- boxlist = Instances(image_sizes[i])
- boxlist.scores = torch.sqrt(per_box_cls) \
- if self.with_agn_hm else per_box_cls # n
- # import pdb; pdb.set_trace()
- boxlist.pred_boxes = Boxes(detections)
- boxlist.pred_classes = per_class
- results.append(boxlist)
- return results
-
-
- def nms_and_topK(self, boxlists, nms=True):
- num_images = len(boxlists)
- results = []
- for i in range(num_images):
- nms_thresh = self.nms_thresh_train if self.training else \
- self.nms_thresh_test
- result = ml_nms(boxlists[i], nms_thresh) if nms else boxlists[i]
- if self.debug:
- print('#proposals before nms', len(boxlists[i]))
- print('#proposals after nms', len(result))
- num_dets = len(result)
- post_nms_topk = self.post_nms_topk_train if self.training else \
- self.post_nms_topk_test
- if num_dets > post_nms_topk:
- cls_scores = result.scores
- image_thresh, _ = torch.kthvalue(
- cls_scores.float().cpu(),
- num_dets - post_nms_topk + 1
- )
- keep = cls_scores >= image_thresh.item()
- keep = torch.nonzero(keep).squeeze(1)
- result = result[keep]
- if self.debug:
- print('#proposals after filter', len(result))
- results.append(result)
- return results
-
-
- def _add_more_pos(self, reg_pred, gt_instances, shapes_per_level):
- labels, level_masks, c33_inds, c33_masks, c33_regs = \
- self._get_c33_inds(gt_instances, shapes_per_level)
- N, L, K = labels.shape[0], len(self.strides), 9
- c33_inds[c33_masks == 0] = 0
- reg_pred_c33 = reg_pred[c33_inds].detach() # N x L x K
- invalid_reg = c33_masks == 0
- c33_regs_expand = c33_regs.view(N * L * K, 4).clamp(min=0)
- if N > 0:
- with torch.no_grad():
- c33_reg_loss = self.iou_loss(
- reg_pred_c33.view(N * L * K, 4),
- c33_regs_expand, None,
- reduction='none').view(N, L, K).detach() # N x L x K
- else:
- c33_reg_loss = reg_pred_c33.new_zeros((N, L, K)).detach()
- c33_reg_loss[invalid_reg] = INF # N x L x K
- c33_reg_loss.view(N * L, K)[level_masks.view(N * L), 4] = 0 # real center
- c33_reg_loss = c33_reg_loss.view(N, L * K)
- if N == 0:
- loss_thresh = c33_reg_loss.new_ones((N)).float()
- else:
- loss_thresh = torch.kthvalue(
- c33_reg_loss, self.more_pos_topk, dim=1)[0] # N
- loss_thresh[loss_thresh > self.more_pos_thresh] = self.more_pos_thresh # N
- new_pos = c33_reg_loss.view(N, L, K) < \
- loss_thresh.view(N, 1, 1).expand(N, L, K)
- pos_inds = c33_inds[new_pos].view(-1) # P
- labels = labels.view(N, 1, 1).expand(N, L, K)[new_pos].view(-1)
- return pos_inds, labels
-
-
- def _get_c33_inds(self, gt_instances, shapes_per_level):
- '''
- TODO (Xingyi): The current implementation is ugly. Refactor.
- Get the center (and the 3x3 region near center) locations of each objects
- Inputs:
- gt_instances: [n_i], sum n_i = N
- shapes_per_level: L x 2 [(h_l, w_l)]_L
- '''
- labels = []
- level_masks = []
- c33_inds = []
- c33_masks = []
- c33_regs = []
- L = len(self.strides)
- B = len(gt_instances)
- shapes_per_level = shapes_per_level.long()
- loc_per_level = (shapes_per_level[:, 0] * shapes_per_level[:, 1]).long() # L
- level_bases = []
- s = 0
- for l in range(L):
- level_bases.append(s)
- s = s + B * loc_per_level[l]
- level_bases = shapes_per_level.new_tensor(level_bases).long() # L
- strides_default = shapes_per_level.new_tensor(self.strides).float() # L
- K = 9
- dx = shapes_per_level.new_tensor([-1, 0, 1, -1, 0, 1, -1, 0, 1]).long()
- dy = shapes_per_level.new_tensor([-1, -1, -1, 0, 0, 0, 1, 1, 1]).long()
- for im_i in range(B):
- targets_per_im = gt_instances[im_i]
- bboxes = targets_per_im.gt_boxes.tensor # n x 4
- n = bboxes.shape[0]
- if n == 0:
- continue
- centers = ((bboxes[:, [0, 1]] + bboxes[:, [2, 3]]) / 2) # n x 2
- centers = centers.view(n, 1, 2).expand(n, L, 2)
-
- strides = strides_default.view(1, L, 1).expand(n, L, 2) #
- centers_inds = (centers / strides).long() # n x L x 2
- center_grids = centers_inds * strides + strides // 2# n x L x 2
- l = center_grids[:, :, 0] - bboxes[:, 0].view(n, 1).expand(n, L)
- t = center_grids[:, :, 1] - bboxes[:, 1].view(n, 1).expand(n, L)
- r = bboxes[:, 2].view(n, 1).expand(n, L) - center_grids[:, :, 0]
- b = bboxes[:, 3].view(n, 1).expand(n, L) - center_grids[:, :, 1] # n x L
- reg = torch.stack([l, t, r, b], dim=2) # n x L x 4
- reg = reg / strides_default.view(1, L, 1).expand(n, L, 4).float()
-
- Ws = shapes_per_level[:, 1].view(1, L).expand(n, L)
- Hs = shapes_per_level[:, 0].view(1, L).expand(n, L)
- expand_Ws = Ws.view(n, L, 1).expand(n, L, K)
- expand_Hs = Hs.view(n, L, 1).expand(n, L, K)
- label = targets_per_im.gt_classes.view(n).clone()
- mask = reg.min(dim=2)[0] >= 0 # n x L
- mask = mask & self.assign_fpn_level(bboxes)
- labels.append(label) # n
- level_masks.append(mask) # n x L
-
- Dy = dy.view(1, 1, K).expand(n, L, K)
- Dx = dx.view(1, 1, K).expand(n, L, K)
- c33_ind = level_bases.view(1, L, 1).expand(n, L, K) + \
- im_i * loc_per_level.view(1, L, 1).expand(n, L, K) + \
- (centers_inds[:, :, 1:2].expand(n, L, K) + Dy) * expand_Ws + \
- (centers_inds[:, :, 0:1].expand(n, L, K) + Dx) # n x L x K
-
- c33_mask = \
- ((centers_inds[:, :, 1:2].expand(n, L, K) + dy) < expand_Hs) & \
- ((centers_inds[:, :, 1:2].expand(n, L, K) + dy) >= 0) & \
- ((centers_inds[:, :, 0:1].expand(n, L, K) + dx) < expand_Ws) & \
- ((centers_inds[:, :, 0:1].expand(n, L, K) + dx) >= 0)
- # TODO (Xingyi): think about better way to implement this
- # Currently it hard codes the 3x3 region
- c33_reg = reg.view(n, L, 1, 4).expand(n, L, K, 4).clone()
- c33_reg[:, :, [0, 3, 6], 0] -= 1
- c33_reg[:, :, [0, 3, 6], 2] += 1
- c33_reg[:, :, [2, 5, 8], 0] += 1
- c33_reg[:, :, [2, 5, 8], 2] -= 1
- c33_reg[:, :, [0, 1, 2], 1] -= 1
- c33_reg[:, :, [0, 1, 2], 3] += 1
- c33_reg[:, :, [6, 7, 8], 1] += 1
- c33_reg[:, :, [6, 7, 8], 3] -= 1
- c33_mask = c33_mask & (c33_reg.min(dim=3)[0] >= 0) # n x L x K
- c33_inds.append(c33_ind)
- c33_masks.append(c33_mask)
- c33_regs.append(c33_reg)
-
- if len(level_masks) > 0:
- labels = torch.cat(labels, dim=0)
- level_masks = torch.cat(level_masks, dim=0)
- c33_inds = torch.cat(c33_inds, dim=0).long()
- c33_regs = torch.cat(c33_regs, dim=0)
- c33_masks = torch.cat(c33_masks, dim=0)
- else:
- labels = shapes_per_level.new_zeros((0)).long()
- level_masks = shapes_per_level.new_zeros((0, L)).bool()
- c33_inds = shapes_per_level.new_zeros((0, L, K)).long()
- c33_regs = shapes_per_level.new_zeros((0, L, K, 4)).float()
- c33_masks = shapes_per_level.new_zeros((0, L, K)).bool()
- return labels, level_masks, c33_inds, c33_masks, c33_regs # N x L, N x L x K
\ No newline at end of file
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh b/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh
deleted file mode 100644
index 6d2ba9a6265c0d5fa580035952a1f568dd8d9e44..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh
+++ /dev/null
@@ -1,37 +0,0 @@
-mkdir celeba-hq-dataset
-
-unzip data256x256.zip -d celeba-hq-dataset/
-
-# Reindex
-for i in `echo {00001..30000}`
-do
- mv 'celeba-hq-dataset/data256x256/'$i'.jpg' 'celeba-hq-dataset/data256x256/'$[10#$i - 1]'.jpg'
-done
-
-
-# Split: split train -> train & val
-cat fetch_data/train_shuffled.flist | shuf > celeba-hq-dataset/temp_train_shuffled.flist
-cat celeba-hq-dataset/temp_train_shuffled.flist | head -n 2000 > celeba-hq-dataset/val_shuffled.flist
-cat celeba-hq-dataset/temp_train_shuffled.flist | tail -n +2001 > celeba-hq-dataset/train_shuffled.flist
-cat fetch_data/val_shuffled.flist > celeba-hq-dataset/visual_test_shuffled.flist
-
-mkdir celeba-hq-dataset/train_256/
-mkdir celeba-hq-dataset/val_source_256/
-mkdir celeba-hq-dataset/visual_test_source_256/
-
-cat celeba-hq-dataset/train_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/train_256/
-cat celeba-hq-dataset/val_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/val_source_256/
-cat celeba-hq-dataset/visual_test_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/visual_test_source_256/
-
-
-# create location config celeba.yaml
-PWD=$(pwd)
-DATASET=${PWD}/celeba-hq-dataset
-CELEBA=${PWD}/configs/training/location/celeba.yaml
-
-touch $CELEBA
-echo "# @package _group_" >> $CELEBA
-echo "data_root_dir: ${DATASET}/" >> $CELEBA
-echo "out_root_dir: ${PWD}/experiments/" >> $CELEBA
-echo "tb_dir: ${PWD}/tb_logs/" >> $CELEBA
-echo "pretrained_models: ${PWD}/" >> $CELEBA
diff --git a/spaces/OpenMotionLab/MotionGPT/pyrender/tests/conftest.py b/spaces/OpenMotionLab/MotionGPT/pyrender/tests/conftest.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py b/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py
deleted file mode 100644
index 4e06ab8cbe6b43a355c0a9cfb3f2d688438d2c64..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py
+++ /dev/null
@@ -1,201 +0,0 @@
-# ------------------------------------------------------------------------------
-# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/cityscapes_evaluation.py
-# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
-# ------------------------------------------------------------------------------
-
-import glob
-import logging
-import numpy as np
-import os
-import tempfile
-from collections import OrderedDict
-import torch
-from PIL import Image
-
-from detectron2.data import MetadataCatalog
-from detectron2.utils import comm
-from detectron2.utils.file_io import PathManager
-
-from .evaluator import DatasetEvaluator
-
-
-class CityscapesEvaluator(DatasetEvaluator):
- """
- Base class for evaluation using cityscapes API.
- """
-
- def __init__(self, dataset_name):
- """
- Args:
- dataset_name (str): the name of the dataset.
- It must have the following metadata associated with it:
- "thing_classes", "gt_dir".
- """
- self._metadata = MetadataCatalog.get(dataset_name)
- self._cpu_device = torch.device("cpu")
- self._logger = logging.getLogger(__name__)
-
- def reset(self):
- self._working_dir = tempfile.TemporaryDirectory(prefix="cityscapes_eval_")
- self._temp_dir = self._working_dir.name
- # All workers will write to the same results directory
- # TODO this does not work in distributed training
- assert (
- comm.get_local_size() == comm.get_world_size()
- ), "CityscapesEvaluator currently do not work with multiple machines."
- self._temp_dir = comm.all_gather(self._temp_dir)[0]
- if self._temp_dir != self._working_dir.name:
- self._working_dir.cleanup()
- self._logger.info(
- "Writing cityscapes results to temporary directory {} ...".format(self._temp_dir)
- )
-
-
-class CityscapesInstanceEvaluator(CityscapesEvaluator):
- """
- Evaluate instance segmentation results on cityscapes dataset using cityscapes API.
-
- Note:
- * It does not work in multi-machine distributed training.
- * It contains a synchronization, therefore has to be used on all ranks.
- * Only the main process runs evaluation.
- """
-
- def process(self, inputs, outputs):
- from cityscapesscripts.helpers.labels import name2label
-
- for input, output in zip(inputs, outputs):
- file_name = input["file_name"]
- basename = os.path.splitext(os.path.basename(file_name))[0]
- pred_txt = os.path.join(self._temp_dir, basename + "_pred.txt")
-
- if "instances" in output:
- output = output["instances"].to(self._cpu_device)
- num_instances = len(output)
- with open(pred_txt, "w") as fout:
- for i in range(num_instances):
- pred_class = output.pred_classes[i]
- classes = self._metadata.stuff_classes[pred_class]
- class_id = name2label[classes].id
- score = output.scores[i]
- mask = output.pred_masks[i].numpy().astype("uint8")
- png_filename = os.path.join(
- self._temp_dir, basename + "_{}_{}.png".format(i, classes)
- )
-
- Image.fromarray(mask * 255).save(png_filename)
- fout.write(
- "{} {} {}\n".format(os.path.basename(png_filename), class_id, score)
- )
- else:
- # Cityscapes requires a prediction file for every ground truth image.
- with open(pred_txt, "w") as fout:
- pass
-
- def evaluate(self):
- """
- Returns:
- dict: has a key "segm", whose value is a dict of "AP" and "AP50".
- """
- comm.synchronize()
- if comm.get_rank() > 0:
- return
- import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval
-
- self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
-
- # set some global states in cityscapes evaluation API, before evaluating
- cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
- cityscapes_eval.args.predictionWalk = None
- cityscapes_eval.args.JSONOutput = False
- cityscapes_eval.args.colorized = False
- cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json")
-
- # These lines are adopted from
- # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
- gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
- groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_instanceIds.png"))
- assert len(
- groundTruthImgList
- ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
- cityscapes_eval.args.groundTruthSearch
- )
- predictionImgList = []
- for gt in groundTruthImgList:
- predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args))
- results = cityscapes_eval.evaluateImgLists(
- predictionImgList, groundTruthImgList, cityscapes_eval.args
- )["averages"]
-
- ret = OrderedDict()
- ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100}
- self._working_dir.cleanup()
- return ret
-
-
-class CityscapesSemSegEvaluator(CityscapesEvaluator):
- """
- Evaluate semantic segmentation results on cityscapes dataset using cityscapes API.
-
- Note:
- * It does not work in multi-machine distributed training.
- * It contains a synchronization, therefore has to be used on all ranks.
- * Only the main process runs evaluation.
- """
-
- def process(self, inputs, outputs):
- from cityscapesscripts.helpers.labels import trainId2label
-
- for input, output in zip(inputs, outputs):
- file_name = input["file_name"]
- basename = os.path.splitext(os.path.basename(file_name))[0]
- pred_filename = os.path.join(self._temp_dir, basename + "_pred.png")
-
- output = output["sem_seg"].argmax(dim=0).to(self._cpu_device).numpy()
- pred = 255 * np.ones(output.shape, dtype=np.uint8)
- for train_id, label in trainId2label.items():
- if label.ignoreInEval:
- continue
- pred[output == train_id] = label.id
- Image.fromarray(pred).save(pred_filename)
-
- def evaluate(self):
- comm.synchronize()
- if comm.get_rank() > 0:
- return
- # Load the Cityscapes eval script *after* setting the required env var,
- # since the script reads CITYSCAPES_DATASET into global variables at load time.
- import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval
-
- self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
-
- # set some global states in cityscapes evaluation API, before evaluating
- cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
- cityscapes_eval.args.predictionWalk = None
- cityscapes_eval.args.JSONOutput = False
- cityscapes_eval.args.colorized = False
-
- # These lines are adopted from
- # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa
- gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
- groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_labelIds.png"))
- assert len(
- groundTruthImgList
- ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
- cityscapes_eval.args.groundTruthSearch
- )
- predictionImgList = []
- for gt in groundTruthImgList:
- predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt))
- results = cityscapes_eval.evaluateImgLists(
- predictionImgList, groundTruthImgList, cityscapes_eval.args
- )
- ret = OrderedDict()
- ret["sem_seg"] = {
- "IoU": 100.0 * results["averageScoreClasses"],
- "iIoU": 100.0 * results["averageScoreInstClasses"],
- "IoU_sup": 100.0 * results["averageScoreCategories"],
- "iIoU_sup": 100.0 * results["averageScoreInstCategories"],
- }
- self._working_dir.cleanup()
- return ret
diff --git a/spaces/PeepDaSlan9/AutoGPT/benchmark/__init__.py b/spaces/PeepDaSlan9/AutoGPT/benchmark/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py b/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py
deleted file mode 100644
index ba0443e0c22481af2b451d8ccc5ab6e6f79cffb8..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py
+++ /dev/null
@@ -1,573 +0,0 @@
-import os
-import random
-from typing import Optional, Callable
-import json
-import glob
-import csv
-
-import numpy as np
-import torch
-import librosa
-import pyloudnorm as pyln
-from pedalboard import Pedalboard, Limiter, Gain, Compressor, Clipping
-
-from .dataset import (
- MusdbTrainDataset,
- MusdbValidDataset,
- apply_limitaug,
-)
-from utils import (
- load_wav_arbitrary_position_stereo,
- load_wav_specific_position_stereo,
- db2linear,
-)
-
-
-class DelimitTrainDataset(MusdbTrainDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- seq_duration: Optional[float] = 6.0,
- samples_per_track: int = 64,
- source_augmentations: Optional[Callable] = lambda audio: audio,
- sample_rate: int = 44100,
- seed: int = 42,
- limitaug_method: str = "limitaug",
- limitaug_mode: str = "normal_L",
- limitaug_custom_target_lufs: float = None,
- limitaug_custom_target_lufs_std: float = None,
- target_loudnorm_lufs: float = -14.0,
- target_limitaug_mode: str = None,
- target_limitaug_custom_target_lufs: float = None,
- target_limitaug_custom_target_lufs_std: float = None,
- custom_limiter_attack_range: list = [2.0, 2.0],
- custom_limiter_release_range: list = [200.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(
- target=target,
- root=root,
- seq_duration=seq_duration,
- samples_per_track=samples_per_track,
- source_augmentations=source_augmentations,
- sample_rate=sample_rate,
- seed=seed,
- limitaug_method=limitaug_method,
- limitaug_mode=limitaug_mode,
- limitaug_custom_target_lufs=limitaug_custom_target_lufs,
- limitaug_custom_target_lufs_std=limitaug_custom_target_lufs_std,
- target_loudnorm_lufs=target_loudnorm_lufs,
- custom_limiter_attack_range=custom_limiter_attack_range,
- custom_limiter_release_range=custom_limiter_release_range,
- *args,
- **kwargs,
- )
-
- self.target_limitaug_mode = target_limitaug_mode
-
- self.target_limitaug_custom_target_lufs = (target_limitaug_custom_target_lufs,)
- self.target_limitaug_custom_target_lufs_std = (
- target_limitaug_custom_target_lufs_std,
- )
- self.limitaug_mode_statistics["target_custom"] = [
- target_limitaug_custom_target_lufs,
- target_limitaug_custom_target_lufs_std,
- ]
-
- """
- Parameters
- ----------
- limitaug_method : str
- choose from ["linear_gain_increase", "limitaug", "limitaug_then_loudnorm", "only_loudnorm"]
- limitaug_mode : str
- choose from ["uniform", "normal", "normal_L", "normal_XL", "normal_short_term", "normal_L_short_term", "normal_XL_short_term", "custom"]
- limitaug_custom_target_lufs : float
- valid only when
- limitaug_mode == "custom"
- target_loudnorm_lufs : float
- valid only when
- limitaug_method == 'limitaug_then_loudnorm' or 'only_loudnorm'
- default is -14.
- To the best of my knowledge, Spotify and Youtube music is using -14 as a reference loudness normalization level.
- No special reason for the choice of -14 as target_loudnorm_lufs.
- target : str
- target name of the source to be separated, defaults to ``vocals``.
- root : str
- root path of MUSDB
- seq_duration : float
- training is performed in chunks of ``seq_duration`` (in seconds,
- defaults to ``None`` which loads the full audio track
- samples_per_track : int
- sets the number of samples, yielded from each track per epoch.
- Defaults to 64
- source_augmentations : list[callables]
- provide list of augmentation function that take a multi-channel
- audio file of shape (src, samples) as input and output. Defaults to
- no-augmentations (input = output)
- seed : int
- control randomness of dataset iterations
- args, kwargs : additional keyword arguments
- used to add further control for the musdb dataset
- initialization function.
- """
-
- # Get a limitaug result without target (individual stem source)
- def get_limitaug_mixture(self, mixture):
- if self.limitaug_method == "limitaug":
- self.board[1].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- )
-
- elif self.limitaug_method == "limitaug_then_loudnorm":
- self.board[1].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = (
- apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- target_loudnorm_lufs=self.target_loudnorm_lufs,
- ),
- )
-
- # Apply LimitAug using Custom Limiter
- elif self.limitaug_method == "custom_limiter_limitaug":
- # Change attack time of First compressor of the Limiter
- self.board[1].attack_ms = random.uniform(
- self.custom_limiter_attack_range[0], self.custom_limiter_attack_range[1]
- )
- # Change release time of First compressor of the Limiter
- self.board[1].release_ms = random.uniform(
- self.custom_limiter_release_range[0],
- self.custom_limiter_release_range[1],
- )
- # Change release time of Second compressor of the Limiter
- self.board[2].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- target_loudnorm_lufs=self.target_loudnorm_lufs,
- )
-
- # When we want to force NN to output an appropriately compressed target output
- if self.target_limitaug_mode:
- mixture_target_lufs = random.gauss(
- self.limitaug_mode_statistics[self.target_limitaug_mode][0],
- self.limitaug_mode_statistics[self.target_limitaug_mode][1],
- )
- mixture, target_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=mixture_target_lufs,
- loudness=mixture_lufs,
- )
-
- if np.isinf(mixture_lufs):
- mixture_loudnorm = mixture
- else:
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain, eps=0.0)
-
- return mixture_limited, mixture_loudnorm
-
- def __getitem__(self, index):
- audio_sources = []
-
- for k, source in enumerate(self.sources):
- # memorize index of target source
- if source == self.target: # if source is 'vocals'
- track_path = self.train_list[
- index // self.samples_per_track
- ] # we want to use # training samples per each track.
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration
- )
- else:
- track_path = random.choice(self.train_list)
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration
- )
- audio = self.source_augmentations(audio)
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- # apply linear mix over source index=0
- # and here, linear mixture is a target unlike in MusdbTrainDataset
- mixture = stems.sum(0)
- mixture_limited, mixture_loudnorm = self.get_limitaug_mixture(mixture)
- # We will give mixture_limited as an input and mixture_loudnorm as a target to the model.
-
- mixture_limited = np.clip(mixture_limited, -1.0, 1.0)
- mixture_limited = torch.as_tensor(mixture_limited, dtype=torch.float32)
- mixture_loudnorm = torch.as_tensor(mixture_loudnorm, dtype=torch.float32)
-
- return mixture_limited, mixture_loudnorm
-
-
-class OzoneTrainDataset(DelimitTrainDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- ozone_root: str = None,
- use_fixed: float = 0.1, # ratio of fixed samples
- seq_duration: Optional[float] = 6.0,
- samples_per_track: int = 64,
- source_augmentations: Optional[Callable] = lambda audio: audio,
- sample_rate: int = 44100,
- seed: int = 42,
- limitaug_method: str = "limitaug",
- limitaug_mode: str = "normal_L",
- limitaug_custom_target_lufs: float = None,
- limitaug_custom_target_lufs_std: float = None,
- target_loudnorm_lufs: float = -14.0,
- target_limitaug_mode: str = None,
- target_limitaug_custom_target_lufs: float = None,
- target_limitaug_custom_target_lufs_std: float = None,
- custom_limiter_attack_range: list = [2.0, 2.0],
- custom_limiter_release_range: list = [200.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(
- target,
- root,
- seq_duration,
- samples_per_track,
- source_augmentations,
- sample_rate,
- seed,
- limitaug_method,
- limitaug_mode,
- limitaug_custom_target_lufs,
- limitaug_custom_target_lufs_std,
- target_loudnorm_lufs,
- target_limitaug_mode,
- target_limitaug_custom_target_lufs,
- target_limitaug_custom_target_lufs_std,
- custom_limiter_attack_range,
- custom_limiter_release_range,
- *args,
- **kwargs,
- )
-
- self.ozone_root = ozone_root
- self.use_fixed = use_fixed
- self.list_train_fixed = glob.glob(f"{self.ozone_root}/ozone_train_fixed/*.wav")
- self.list_train_random = glob.glob(
- f"{self.ozone_root}/ozone_train_random/*.wav"
- )
- self.dict_train_random = {}
-
- # Load information of pre-generated random training examples
- list_csv_files = glob.glob(f"{self.ozone_root}/ozone_train_random_*.csv")
- list_csv_files.sort()
- for csv_file in list_csv_files:
- with open(csv_file, "r") as f:
- reader = csv.reader(f)
- next(reader)
- for row in reader:
- self.dict_train_random[row[0]] = {
- "max_threshold": float(row[1]),
- "max_character": float(row[2]),
- "vocals": {
- "name": row[3],
- "start_sec": float(row[4]),
- "gain": float(row[5]),
- "channelswap": bool(row[6]),
- },
- "bass": {
- "name": row[7],
- "start_sec": float(row[8]),
- "gain": float(row[9]),
- "channelswap": bool(row[10]),
- },
- "drums": {
- "name": row[11],
- "start_sec": float(row[12]),
- "gain": float(row[13]),
- "channelswap": bool(row[14]),
- },
- "other": {
- "name": row[15],
- "start_sec": float(row[16]),
- "gain": float(row[17]),
- "channelswap": bool(row[18]),
- },
- }
-
- def __getitem__(self, idx):
- use_fixed_prob = random.random()
-
- if use_fixed_prob <= self.use_fixed:
- # Fixed examples
- audio_path = random.choice(self.list_train_fixed)
- song_name = os.path.basename(audio_path).replace(".wav", "")
- mixture_limited, start_pos_sec = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration, return_pos=True
- )
-
- audio_sources = []
- track_path = f"{self.root}/train/{song_name}"
- for source in self.sources:
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_specific_position_stereo(
- audio_path,
- self.sample_rate,
- self.seq_duration,
- start_position=start_pos_sec,
- )
- audio_sources.append(audio)
-
- else:
- # Random examples
- # Load mixture_limited (pre-generated)
- audio_path = random.choice(self.list_train_random)
- seg_name = os.path.basename(audio_path).replace(".wav", "")
- mixture_limited, sr = librosa.load(
- audio_path, sr=self.sample_rate, mono=False
- )
-
- # Load mixture_unlimited (from the original musdb18, using metadata)
- audio_sources = []
- for source in self.sources:
- dict_seg_info = self.dict_train_random[seg_name]
- dict_seg_source_info = dict_seg_info[source]
- audio_path = (
- f"{self.root}/train/{dict_seg_source_info['name']}/{source}.wav"
- )
- audio = load_wav_specific_position_stereo(
- audio_path,
- self.sample_rate,
- self.seq_duration,
- start_position=dict_seg_source_info["start_sec"],
- )
-
- # apply augmentations
- audio = audio * dict_seg_source_info["gain"]
- if dict_seg_source_info["channelswap"]:
- audio = np.flip(audio, axis=0)
-
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
- mixture = stems.sum(axis=0)
- mixture_lufs = self.meter.integrated_loudness(mixture.T)
- if np.isinf(mixture_lufs):
- mixture_loudnorm = mixture
- else:
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain, eps=0.0)
-
- return mixture_limited, mixture_loudnorm
-
-
-class DelimitValidDataset(MusdbValidDataset):
- def __init__(
- self,
- target: str = "vocals",
- root: str = None,
- delimit_valid_root: str = None,
- valid_target_lufs: float = -8.05, # From the Table 1 of the "Towards robust music source separation on loud commercial music" paper, the average loudness of commerical music.
- target_loudnorm_lufs: float = -14.0,
- delimit_valid_L_root: str = None, # This will be used when using the target as compressed (normal_L) mixture.
- use_custom_limiter: bool = False,
- custom_limiter_attack_range: list = [0.1, 10.0],
- custom_limiter_release_range: list = [30.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(target=target, root=root, *args, **kwargs)
- self.delimit_valid_root = delimit_valid_root
- if self.delimit_valid_root:
- with open(f"{self.delimit_valid_root}/valid_loudness.json", "r") as f:
- self.dict_valid_loudness = json.load(f)
- self.delimit_valid_L_root = delimit_valid_L_root
- if self.delimit_valid_L_root:
- with open(f"{self.delimit_valid_L_root}/valid_loudness.json", "r") as f:
- self.dict_valid_L_loudness = json.load(f)
-
- self.valid_target_lufs = valid_target_lufs
- self.target_loudnorm_lufs = target_loudnorm_lufs
- self.meter = pyln.Meter(self.sample_rate)
- self.use_custom_limiter = use_custom_limiter
-
- if self.use_custom_limiter:
- print("using Custom limiter limitaug for validation!!")
- self.custom_limiter_attack_range = custom_limiter_attack_range
- self.custom_limiter_release_range = custom_limiter_release_range
- self.board = Pedalboard(
- [
- Gain(gain_db=0.0),
- Compressor(
- threshold_db=-10.0, ratio=4.0, attack_ms=2.0, release_ms=200.0
- ), # attack_ms and release_ms will be changed later.
- Compressor(
- threshold_db=0.0,
- ratio=1000.0,
- attack_ms=0.001,
- release_ms=100.0,
- ),
- Gain(gain_db=3.75),
- Clipping(threshold_db=0.0),
- ]
- ) # This implementation is the same as JUCE Limiter.
- # However, we want the first compressor to have a variable attack and release time.
- # Therefore, we use the Custom Limiter instead of the JUCE Limiter.
- else:
- self.board = Pedalboard(
- [Gain(gain_db=0.0), Limiter(threshold_db=0.0, release_ms=100.0)]
- ) # Currently, we are using a limiter with a release time of 100ms.
-
- def __getitem__(self, index):
- audio_sources = []
- target_ind = None
-
- for k, source in enumerate(self.sources):
- # memorize index of target source
- if source == self.target: # if source is 'vocals'
- target_ind = k
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- audio_path = f"{track_path}/{source}.wav"
- # audio = utils.load_wav_stereo(audio_path, self.sample_rate)
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
- else:
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- audio_path = f"{track_path}/{source}.wav"
- # audio = utils.load_wav_stereo(audio_path, self.sample_rate)
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
-
- audio = torch.as_tensor(audio, dtype=torch.float32)
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- # apply linear mix over source index=0
- # and here, linear mixture is a target unlike in MusdbTrainDataset
- mixture = stems.sum(0)
- if (
- self.delimit_valid_root
- ): # If there exists a pre-processed delimit valid dataset
- audio_path = f"{self.delimit_valid_root}/valid/{song_name}.wav"
- mixture_limited = librosa.load(audio_path, mono=False, sr=self.sample_rate)[
- 0
- ]
- mixture_lufs = self.dict_valid_loudness[song_name]
-
- else:
- if self.use_custom_limiter:
- custom_limiter_attack = random.uniform(
- self.custom_limiter_attack_range[0],
- self.custom_limiter_attack_range[1],
- )
- self.board[1].attack_ms = custom_limiter_attack
-
- custom_limiter_release = random.uniform(
- self.custom_limiter_release_range[0],
- self.custom_limiter_release_range[1],
- )
- self.board[1].release_ms = custom_limiter_release
-
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=self.valid_target_lufs,
- )
- else:
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=self.valid_target_lufs,
- # target_loudnorm_lufs=self.target_loudnorm_lufs,
- ) # mixture_limited is a limiter applied mixture
- # We will give mixture_limited as an input and mixture_loudnorm as a target to the model.
-
- if self.delimit_valid_L_root:
- audio_L_path = f"{self.delimit_valid_L_root}/valid/{song_name}.wav"
- mixture_loudnorm = librosa.load(
- audio_L_path, mono=False, sr=self.sample_rate
- )[0]
- mixture_lufs = self.dict_valid_L_loudness[song_name]
- mixture = mixture_loudnorm
-
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain)
-
- if self.use_custom_limiter:
- return (
- mixture_limited,
- mixture_loudnorm,
- song_name,
- mixture_lufs,
- custom_limiter_attack,
- custom_limiter_release,
- )
- else:
- return mixture_limited, mixture_loudnorm, song_name, mixture_lufs
-
-
-class OzoneValidDataset(MusdbValidDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- ozone_root: str = None,
- target_loudnorm_lufs: float = -14.0,
- *args,
- **kwargs,
- ) -> None:
- super().__init__(target=target, root=root, *args, **kwargs)
-
- self.ozone_root = ozone_root
- self.target_loudnorm_lufs = target_loudnorm_lufs
-
- with open(f"{self.ozone_root}/valid_loudness.json", "r") as f:
- self.dict_valid_loudness = json.load(f)
-
- def __getitem__(self, index):
- audio_sources = []
-
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- for k, source in enumerate(self.sources):
- audio_path = f"{track_path}/{source}.wav"
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- mixture = stems.sum(0)
-
- audio_path = f"{self.ozone_root}/ozone_train_fixed/{song_name}.wav"
- mixture_limited = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
-
- mixture_lufs = self.dict_valid_loudness[song_name]
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain)
-
- return mixture_limited, mixture_loudnorm, song_name, mixture_lufs
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py
deleted file mode 100644
index 75559579cf053abcc99538606cbb88c723faf783..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py
+++ /dev/null
@@ -1,282 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-
-import torch
-import torch.nn as nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair, _single
-
-from annotator.uniformer.mmcv.utils import deprecated_api_warning
-from ..cnn import CONV_LAYERS
-from ..utils import ext_loader, print_log
-
-ext_module = ext_loader.load_ext(
- '_ext',
- ['modulated_deform_conv_forward', 'modulated_deform_conv_backward'])
-
-
-class ModulatedDeformConv2dFunction(Function):
-
- @staticmethod
- def symbolic(g, input, offset, mask, weight, bias, stride, padding,
- dilation, groups, deform_groups):
- input_tensors = [input, offset, mask, weight]
- if bias is not None:
- input_tensors.append(bias)
- return g.op(
- 'mmcv::MMCVModulatedDeformConv2d',
- *input_tensors,
- stride_i=stride,
- padding_i=padding,
- dilation_i=dilation,
- groups_i=groups,
- deform_groups_i=deform_groups)
-
- @staticmethod
- def forward(ctx,
- input,
- offset,
- mask,
- weight,
- bias=None,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- deform_groups=1):
- if input is not None and input.dim() != 4:
- raise ValueError(
- f'Expected 4D tensor as input, got {input.dim()}D tensor \
- instead.')
- ctx.stride = _pair(stride)
- ctx.padding = _pair(padding)
- ctx.dilation = _pair(dilation)
- ctx.groups = groups
- ctx.deform_groups = deform_groups
- ctx.with_bias = bias is not None
- if not ctx.with_bias:
- bias = input.new_empty(0) # fake tensor
- # When pytorch version >= 1.6.0, amp is adopted for fp16 mode;
- # amp won't cast the type of model (float32), but "offset" is cast
- # to float16 by nn.Conv2d automatically, leading to the type
- # mismatch with input (when it is float32) or weight.
- # The flag for whether to use fp16 or amp is the type of "offset",
- # we cast weight and input to temporarily support fp16 and amp
- # whatever the pytorch version is.
- input = input.type_as(offset)
- weight = weight.type_as(input)
- ctx.save_for_backward(input, offset, mask, weight, bias)
- output = input.new_empty(
- ModulatedDeformConv2dFunction._output_size(ctx, input, weight))
- ctx._bufs = [input.new_empty(0), input.new_empty(0)]
- ext_module.modulated_deform_conv_forward(
- input,
- weight,
- bias,
- ctx._bufs[0],
- offset,
- mask,
- output,
- ctx._bufs[1],
- kernel_h=weight.size(2),
- kernel_w=weight.size(3),
- stride_h=ctx.stride[0],
- stride_w=ctx.stride[1],
- pad_h=ctx.padding[0],
- pad_w=ctx.padding[1],
- dilation_h=ctx.dilation[0],
- dilation_w=ctx.dilation[1],
- group=ctx.groups,
- deformable_group=ctx.deform_groups,
- with_bias=ctx.with_bias)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- input, offset, mask, weight, bias = ctx.saved_tensors
- grad_input = torch.zeros_like(input)
- grad_offset = torch.zeros_like(offset)
- grad_mask = torch.zeros_like(mask)
- grad_weight = torch.zeros_like(weight)
- grad_bias = torch.zeros_like(bias)
- grad_output = grad_output.contiguous()
- ext_module.modulated_deform_conv_backward(
- input,
- weight,
- bias,
- ctx._bufs[0],
- offset,
- mask,
- ctx._bufs[1],
- grad_input,
- grad_weight,
- grad_bias,
- grad_offset,
- grad_mask,
- grad_output,
- kernel_h=weight.size(2),
- kernel_w=weight.size(3),
- stride_h=ctx.stride[0],
- stride_w=ctx.stride[1],
- pad_h=ctx.padding[0],
- pad_w=ctx.padding[1],
- dilation_h=ctx.dilation[0],
- dilation_w=ctx.dilation[1],
- group=ctx.groups,
- deformable_group=ctx.deform_groups,
- with_bias=ctx.with_bias)
- if not ctx.with_bias:
- grad_bias = None
-
- return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias,
- None, None, None, None, None)
-
- @staticmethod
- def _output_size(ctx, input, weight):
- channels = weight.size(0)
- output_size = (input.size(0), channels)
- for d in range(input.dim() - 2):
- in_size = input.size(d + 2)
- pad = ctx.padding[d]
- kernel = ctx.dilation[d] * (weight.size(d + 2) - 1) + 1
- stride_ = ctx.stride[d]
- output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
- if not all(map(lambda s: s > 0, output_size)):
- raise ValueError(
- 'convolution input is too small (output would be ' +
- 'x'.join(map(str, output_size)) + ')')
- return output_size
-
-
-modulated_deform_conv2d = ModulatedDeformConv2dFunction.apply
-
-
-class ModulatedDeformConv2d(nn.Module):
-
- @deprecated_api_warning({'deformable_groups': 'deform_groups'},
- cls_name='ModulatedDeformConv2d')
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- deform_groups=1,
- bias=True):
- super(ModulatedDeformConv2d, self).__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.kernel_size = _pair(kernel_size)
- self.stride = _pair(stride)
- self.padding = _pair(padding)
- self.dilation = _pair(dilation)
- self.groups = groups
- self.deform_groups = deform_groups
- # enable compatibility with nn.Conv2d
- self.transposed = False
- self.output_padding = _single(0)
-
- self.weight = nn.Parameter(
- torch.Tensor(out_channels, in_channels // groups,
- *self.kernel_size))
- if bias:
- self.bias = nn.Parameter(torch.Tensor(out_channels))
- else:
- self.register_parameter('bias', None)
- self.init_weights()
-
- def init_weights(self):
- n = self.in_channels
- for k in self.kernel_size:
- n *= k
- stdv = 1. / math.sqrt(n)
- self.weight.data.uniform_(-stdv, stdv)
- if self.bias is not None:
- self.bias.data.zero_()
-
- def forward(self, x, offset, mask):
- return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,
- self.stride, self.padding,
- self.dilation, self.groups,
- self.deform_groups)
-
-
-@CONV_LAYERS.register_module('DCNv2')
-class ModulatedDeformConv2dPack(ModulatedDeformConv2d):
- """A ModulatedDeformable Conv Encapsulation that acts as normal Conv
- layers.
-
- Args:
- in_channels (int): Same as nn.Conv2d.
- out_channels (int): Same as nn.Conv2d.
- kernel_size (int or tuple[int]): Same as nn.Conv2d.
- stride (int): Same as nn.Conv2d, while tuple is not supported.
- padding (int): Same as nn.Conv2d, while tuple is not supported.
- dilation (int): Same as nn.Conv2d, while tuple is not supported.
- groups (int): Same as nn.Conv2d.
- bias (bool or str): If specified as `auto`, it will be decided by the
- norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
- False.
- """
-
- _version = 2
-
- def __init__(self, *args, **kwargs):
- super(ModulatedDeformConv2dPack, self).__init__(*args, **kwargs)
- self.conv_offset = nn.Conv2d(
- self.in_channels,
- self.deform_groups * 3 * self.kernel_size[0] * self.kernel_size[1],
- kernel_size=self.kernel_size,
- stride=self.stride,
- padding=self.padding,
- dilation=self.dilation,
- bias=True)
- self.init_weights()
-
- def init_weights(self):
- super(ModulatedDeformConv2dPack, self).init_weights()
- if hasattr(self, 'conv_offset'):
- self.conv_offset.weight.data.zero_()
- self.conv_offset.bias.data.zero_()
-
- def forward(self, x):
- out = self.conv_offset(x)
- o1, o2, mask = torch.chunk(out, 3, dim=1)
- offset = torch.cat((o1, o2), dim=1)
- mask = torch.sigmoid(mask)
- return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,
- self.stride, self.padding,
- self.dilation, self.groups,
- self.deform_groups)
-
- def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
- missing_keys, unexpected_keys, error_msgs):
- version = local_metadata.get('version', None)
-
- if version is None or version < 2:
- # the key is different in early versions
- # In version < 2, ModulatedDeformConvPack
- # loads previous benchmark models.
- if (prefix + 'conv_offset.weight' not in state_dict
- and prefix[:-1] + '_offset.weight' in state_dict):
- state_dict[prefix + 'conv_offset.weight'] = state_dict.pop(
- prefix[:-1] + '_offset.weight')
- if (prefix + 'conv_offset.bias' not in state_dict
- and prefix[:-1] + '_offset.bias' in state_dict):
- state_dict[prefix +
- 'conv_offset.bias'] = state_dict.pop(prefix[:-1] +
- '_offset.bias')
-
- if version is not None and version > 1:
- print_log(
- f'ModulatedDeformConvPack {prefix.rstrip(".")} is upgraded to '
- 'version 2.',
- logger='root')
-
- super()._load_from_state_dict(state_dict, prefix, local_metadata,
- strict, missing_keys, unexpected_keys,
- error_msgs)
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py
deleted file mode 100644
index a802a74fd6c3610d9ae178e6201f47423eca7ad1..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py
+++ /dev/null
@@ -1,177 +0,0 @@
-from typing import List
-
-import torch
-from torch import nn as nn
-
-from annotator.uniformer.mmcv.runner import force_fp32
-from .furthest_point_sample import (furthest_point_sample,
- furthest_point_sample_with_dist)
-
-
-def calc_square_dist(point_feat_a, point_feat_b, norm=True):
- """Calculating square distance between a and b.
-
- Args:
- point_feat_a (Tensor): (B, N, C) Feature vector of each point.
- point_feat_b (Tensor): (B, M, C) Feature vector of each point.
- norm (Bool, optional): Whether to normalize the distance.
- Default: True.
-
- Returns:
- Tensor: (B, N, M) Distance between each pair points.
- """
- num_channel = point_feat_a.shape[-1]
- # [bs, n, 1]
- a_square = torch.sum(point_feat_a.unsqueeze(dim=2).pow(2), dim=-1)
- # [bs, 1, m]
- b_square = torch.sum(point_feat_b.unsqueeze(dim=1).pow(2), dim=-1)
-
- corr_matrix = torch.matmul(point_feat_a, point_feat_b.transpose(1, 2))
-
- dist = a_square + b_square - 2 * corr_matrix
- if norm:
- dist = torch.sqrt(dist) / num_channel
- return dist
-
-
-def get_sampler_cls(sampler_type):
- """Get the type and mode of points sampler.
-
- Args:
- sampler_type (str): The type of points sampler.
- The valid value are "D-FPS", "F-FPS", or "FS".
-
- Returns:
- class: Points sampler type.
- """
- sampler_mappings = {
- 'D-FPS': DFPSSampler,
- 'F-FPS': FFPSSampler,
- 'FS': FSSampler,
- }
- try:
- return sampler_mappings[sampler_type]
- except KeyError:
- raise KeyError(
- f'Supported `sampler_type` are {sampler_mappings.keys()}, but got \
- {sampler_type}')
-
-
-class PointsSampler(nn.Module):
- """Points sampling.
-
- Args:
- num_point (list[int]): Number of sample points.
- fps_mod_list (list[str], optional): Type of FPS method, valid mod
- ['F-FPS', 'D-FPS', 'FS'], Default: ['D-FPS'].
- F-FPS: using feature distances for FPS.
- D-FPS: using Euclidean distances of points for FPS.
- FS: using F-FPS and D-FPS simultaneously.
- fps_sample_range_list (list[int], optional):
- Range of points to apply FPS. Default: [-1].
- """
-
- def __init__(self,
- num_point: List[int],
- fps_mod_list: List[str] = ['D-FPS'],
- fps_sample_range_list: List[int] = [-1]):
- super().__init__()
- # FPS would be applied to different fps_mod in the list,
- # so the length of the num_point should be equal to
- # fps_mod_list and fps_sample_range_list.
- assert len(num_point) == len(fps_mod_list) == len(
- fps_sample_range_list)
- self.num_point = num_point
- self.fps_sample_range_list = fps_sample_range_list
- self.samplers = nn.ModuleList()
- for fps_mod in fps_mod_list:
- self.samplers.append(get_sampler_cls(fps_mod)())
- self.fp16_enabled = False
-
- @force_fp32()
- def forward(self, points_xyz, features):
- """
- Args:
- points_xyz (Tensor): (B, N, 3) xyz coordinates of the features.
- features (Tensor): (B, C, N) Descriptors of the features.
-
- Returns:
- Tensor: (B, npoint, sample_num) Indices of sampled points.
- """
- indices = []
- last_fps_end_index = 0
-
- for fps_sample_range, sampler, npoint in zip(
- self.fps_sample_range_list, self.samplers, self.num_point):
- assert fps_sample_range < points_xyz.shape[1]
-
- if fps_sample_range == -1:
- sample_points_xyz = points_xyz[:, last_fps_end_index:]
- if features is not None:
- sample_features = features[:, :, last_fps_end_index:]
- else:
- sample_features = None
- else:
- sample_points_xyz = \
- points_xyz[:, last_fps_end_index:fps_sample_range]
- if features is not None:
- sample_features = features[:, :, last_fps_end_index:
- fps_sample_range]
- else:
- sample_features = None
-
- fps_idx = sampler(sample_points_xyz.contiguous(), sample_features,
- npoint)
-
- indices.append(fps_idx + last_fps_end_index)
- last_fps_end_index += fps_sample_range
- indices = torch.cat(indices, dim=1)
-
- return indices
-
-
-class DFPSSampler(nn.Module):
- """Using Euclidean distances of points for FPS."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with D-FPS."""
- fps_idx = furthest_point_sample(points.contiguous(), npoint)
- return fps_idx
-
-
-class FFPSSampler(nn.Module):
- """Using feature distances for FPS."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with F-FPS."""
- assert features is not None, \
- 'feature input to FFPS_Sampler should not be None'
- features_for_fps = torch.cat([points, features.transpose(1, 2)], dim=2)
- features_dist = calc_square_dist(
- features_for_fps, features_for_fps, norm=False)
- fps_idx = furthest_point_sample_with_dist(features_dist, npoint)
- return fps_idx
-
-
-class FSSampler(nn.Module):
- """Using F-FPS and D-FPS simultaneously."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with FS_Sampling."""
- assert features is not None, \
- 'feature input to FS_Sampler should not be None'
- ffps_sampler = FFPSSampler()
- dfps_sampler = DFPSSampler()
- fps_idx_ffps = ffps_sampler(points, features, npoint)
- fps_idx_dfps = dfps_sampler(points, features, npoint)
- fps_idx = torch.cat([fps_idx_ffps, fps_idx_dfps], dim=1)
- return fps_idx
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py
deleted file mode 100644
index 332b242c03d1c5e80d4577df442a9a037b1816e1..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .base_pixel_sampler import BasePixelSampler
-from .ohem_pixel_sampler import OHEMPixelSampler
-
-__all__ = ['BasePixelSampler', 'OHEMPixelSampler']
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py
deleted file mode 100644
index c0e42756ee6fcd779387255391a30079a28f5e60..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import torch
-from torch import nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-from maskrcnn_benchmark import _C
-
-
-class _ROIPool(Function):
- @staticmethod
- def forward(ctx, input, roi, output_size, spatial_scale):
- ctx.output_size = _pair(output_size)
- ctx.spatial_scale = spatial_scale
- ctx.input_shape = input.size()
- output, argmax = _C.roi_pool_forward(
- input, roi, spatial_scale, output_size[0], output_size[1]
- )
- ctx.save_for_backward(input, roi, argmax)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- input, rois, argmax = ctx.saved_tensors
- output_size = ctx.output_size
- spatial_scale = ctx.spatial_scale
- bs, ch, h, w = ctx.input_shape
- grad_input = _C.roi_pool_backward(
- grad_output,
- input,
- rois,
- argmax,
- spatial_scale,
- output_size[0],
- output_size[1],
- bs,
- ch,
- h,
- w,
- )
- return grad_input, None, None, None
-
-
-roi_pool = _ROIPool.apply
-
-
-class ROIPool(nn.Module):
- def __init__(self, output_size, spatial_scale):
- super(ROIPool, self).__init__()
- self.output_size = output_size
- self.spatial_scale = spatial_scale
-
- def forward(self, input, rois):
- return roi_pool(input, rois, self.output_size, self.spatial_scale)
-
- def __repr__(self):
- tmpstr = self.__class__.__name__ + "("
- tmpstr += "output_size=" + str(self.output_size)
- tmpstr += ", spatial_scale=" + str(self.spatial_scale)
- tmpstr += ")"
- return tmpstr
diff --git a/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py b/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py
deleted file mode 100644
index e90ef2130a47ec52160709877972716352e04c9c..0000000000000000000000000000000000000000
--- a/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
- if bias is not None:
- rest_dim = [1] * (input.ndim - bias.ndim - 1)
- return (
- F.leaky_relu(
- input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope
- )
- * scale
- )
- else:
- return F.leaky_relu(input, negative_slope=0.2) * scale
-
-
-class EqualLinear(nn.Module):
- def __init__(
- self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1
- ):
- super().__init__()
- self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
- if bias:
- self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
- else:
- self.bias = None
- self.scale = (1 / math.sqrt(in_dim)) * lr_mul
- self.lr_mul = lr_mul
-
- def forward(self, input):
- out = F.linear(input, self.weight * self.scale)
- out = fused_leaky_relu(out, self.bias * self.lr_mul)
- return out
-
-
-class RandomLatentConverter(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.layers = nn.Sequential(*[EqualLinear(channels, channels, lr_mul=.1) for _ in range(5)],
- nn.Linear(channels, channels))
- self.channels = channels
-
- def forward(self, ref):
- r = torch.randn(ref.shape[0], self.channels, device=ref.device)
- y = self.layers(r)
- return y
-
-
-if __name__ == '__main__':
- model = RandomLatentConverter(512)
- model(torch.randn(5,512))
\ No newline at end of file
diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py
deleted file mode 100644
index 8d760f8a1a769bba657d4aea47857f48f7cd0d7a..0000000000000000000000000000000000000000
--- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py
+++ /dev/null
@@ -1,1832 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# Updated to account for UI changes from https://github.com/rkfg/audiocraft/blob/long/app.py
-# also released under the MIT license.
-
-import argparse
-from concurrent.futures import ProcessPoolExecutor
-import os
-from pathlib import Path
-import subprocess as sp
-from tempfile import NamedTemporaryFile
-import time
-import warnings
-import glob
-import re
-from PIL import Image
-from pydub import AudioSegment
-from datetime import datetime
-
-import json
-import shutil
-import taglib
-import torch
-import torchaudio
-import gradio as gr
-import numpy as np
-import typing as tp
-
-from audiocraft.data.audio_utils import convert_audio
-from audiocraft.data.audio import audio_write
-from audiocraft.models import AudioGen, MusicGen, MultiBandDiffusion
-from audiocraft.utils import ui
-import random, string
-
-version = "2.0.0a"
-
-theme = gr.themes.Base(
- primary_hue="lime",
- secondary_hue="lime",
- neutral_hue="neutral",
-).set(
- button_primary_background_fill_hover='*primary_500',
- button_primary_background_fill_hover_dark='*primary_500',
- button_secondary_background_fill_hover='*primary_500',
- button_secondary_background_fill_hover_dark='*primary_500'
-)
-
-MODEL = None # Last used model
-MODELS = None
-UNLOAD_MODEL = False
-MOVE_TO_CPU = False
-IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
-print(IS_BATCHED)
-MAX_BATCH_SIZE = 12
-BATCHED_DURATION = 15
-INTERRUPTING = False
-MBD = None
-# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
-_old_call = sp.call
-
-
-def generate_random_string(length):
- characters = string.ascii_letters + string.digits
- return ''.join(random.choice(characters) for _ in range(length))
-
-
-def resize_video(input_path, output_path, target_width, target_height):
- ffmpeg_cmd = [
- 'ffmpeg',
- '-y',
- '-i', input_path,
- '-vf', f'scale={target_width}:{target_height}',
- '-c:a', 'copy',
- output_path
- ]
- sp.run(ffmpeg_cmd)
-
-
-def _call_nostderr(*args, **kwargs):
- # Avoid ffmpeg vomiting on the logs.
- kwargs['stderr'] = sp.DEVNULL
- kwargs['stdout'] = sp.DEVNULL
- _old_call(*args, **kwargs)
-
-
-sp.call = _call_nostderr
-# Preallocating the pool of processes.
-pool = ProcessPoolExecutor(4)
-pool.__enter__()
-
-
-def interrupt():
- global INTERRUPTING
- INTERRUPTING = True
-
-
-class FileCleaner:
- def __init__(self, file_lifetime: float = 3600):
- self.file_lifetime = file_lifetime
- self.files = []
-
- def add(self, path: tp.Union[str, Path]):
- self._cleanup()
- self.files.append((time.time(), Path(path)))
-
- def _cleanup(self):
- now = time.time()
- for time_added, path in list(self.files):
- if now - time_added > self.file_lifetime:
- if path.exists():
- path.unlink()
- self.files.pop(0)
- else:
- break
-
-
-file_cleaner = FileCleaner()
-
-
-def make_waveform(*args, **kwargs):
- # Further remove some warnings.
- be = time.time()
- with warnings.catch_warnings():
- warnings.simplefilter('ignore')
- height = kwargs.pop('height')
- width = kwargs.pop('width')
- if height < 256:
- height = 256
- if width < 256:
- width = 256
- waveform_video = gr.make_waveform(*args, **kwargs)
- out = f"{generate_random_string(12)}.mp4"
- image = kwargs.get('bg_image', None)
- if image is None:
- resize_video(waveform_video, out, 900, 300)
- else:
- resize_video(waveform_video, out, width, height)
- print("Make a video took", time.time() - be)
- return out
-
-
-def load_model(version='GrandaddyShmax/musicgen-melody', custom_model=None, base_model='GrandaddyShmax/musicgen-medium', gen_type="music"):
- global MODEL, MODELS
- print("Loading model", version)
- if MODELS is None:
- if version == 'GrandaddyShmax/musicgen-custom':
- MODEL = MusicGen.get_pretrained(base_model)
- file_path = os.path.abspath("models/" + str(custom_model) + ".pt")
- MODEL.lm.load_state_dict(torch.load(file_path))
- else:
- if gen_type == "music":
- MODEL = MusicGen.get_pretrained(version)
- elif gen_type == "audio":
- MODEL = AudioGen.get_pretrained(version)
-
- return
-
- else:
- t1 = time.monotonic()
- if MODEL is not None:
- MODEL.to('cpu') # move to cache
- print("Previous model moved to CPU in %.2fs" % (time.monotonic() - t1))
- t1 = time.monotonic()
- if version != 'GrandaddyShmax/musicgen-custom' and MODELS.get(version) is None:
- print("Loading model %s from disk" % version)
- if gen_type == "music":
- result = MusicGen.get_pretrained(version)
- elif gen_type == "audio":
- result = AudioGen.get_pretrained(version)
- MODELS[version] = result
- print("Model loaded in %.2fs" % (time.monotonic() - t1))
- MODEL = result
- return
- result = MODELS[version].to('cuda')
- print("Cached model loaded in %.2fs" % (time.monotonic() - t1))
- MODEL = result
-
-def get_audio_info(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted. (Discord removes metadata from mp4 and wav files, so you can't use them)"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- global_prompt = str("\nGlobal Prompt: " + (data['global_prompt'] if data['global_prompt'] != "" else "none")) if 'global_prompt' in data else ""
- bpm = str("\nBPM: " + data['bpm']) if 'bpm' in data else ""
- key = str("\nKey: " + data['key']) if 'key' in data else ""
- scale = str("\nScale: " + data['scale']) if 'scale' in data else ""
- prompts = str("\nPrompts: " + (data['texts'] if data['texts'] != "['']" else "none")) if 'texts' in data else ""
- duration = str("\nDuration: " + data['duration']) if 'duration' in data else ""
- overlap = str("\nOverlap: " + data['overlap']) if 'overlap' in data else ""
- seed = str("\nSeed: " + data['seed']) if 'seed' in data else ""
- audio_mode = str("\nAudio Mode: " + data['audio_mode']) if 'audio_mode' in data else ""
- input_length = str("\nInput Length: " + data['input_length']) if 'input_length' in data else ""
- channel = str("\nChannel: " + data['channel']) if 'channel' in data else ""
- sr_select = str("\nSample Rate: " + data['sr_select']) if 'sr_select' in data else ""
- gen_type = str(data['generator'] + "gen-") if 'generator' in data else ""
- model = str("\nModel: " + gen_type + data['model']) if 'model' in data else ""
- custom_model = str("\nCustom Model: " + data['custom_model']) if 'custom_model' in data else ""
- base_model = str("\nBase Model: " + data['base_model']) if 'base_model' in data else ""
- decoder = str("\nDecoder: " + data['decoder']) if 'decoder' in data else ""
- topk = str("\nTopk: " + data['topk']) if 'topk' in data else ""
- topp = str("\nTopp: " + data['topp']) if 'topp' in data else ""
- temperature = str("\nTemperature: " + data['temperature']) if 'temperature' in data else ""
- cfg_coef = str("\nClassifier Free Guidance: " + data['cfg_coef']) if 'cfg_coef' in data else ""
- version = str("Version: " + data['version']) if 'version' in data else "Version: Unknown"
- info = str(version + global_prompt + bpm + key + scale + prompts + duration + overlap + seed + audio_mode + input_length + channel + sr_select + model + custom_model + base_model + decoder + topk + topp + temperature + cfg_coef)
- if info == "":
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted. (Discord removes metadata from mp4 and wav files, so you can't use them)"
- return info
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- #if 'global_prompt' not in data:
- #return "No tags found. Either the file is not generated by MusicGen+ V1.2.8a and higher or the tags are corrupted."
- global_prompt = str("\nGlobal Prompt: " + (data['global_prompt'] if data['global_prompt'] != "" else "none")) if 'global_prompt' in data else ""
- bpm = str("\nBPM: " + data['bpm']) if 'bpm' in data else ""
- key = str("\nKey: " + data['key']) if 'key' in data else ""
- scale = str("\nScale: " + data['scale']) if 'scale' in data else ""
- prompts = str("\nPrompts: " + (data['texts'] if data['texts'] != "['']" else "none")) if 'texts' in data else ""
- duration = str("\nDuration: " + data['duration']) if 'duration' in data else ""
- overlap = str("\nOverlap: " + data['overlap']) if 'overlap' in data else ""
- seed = str("\nSeed: " + data['seed']) if 'seed' in data else ""
- audio_mode = str("\nAudio Mode: " + data['audio_mode']) if 'audio_mode' in data else ""
- input_length = str("\nInput Length: " + data['input_length']) if 'input_length' in data else ""
- channel = str("\nChannel: " + data['channel']) if 'channel' in data else ""
- sr_select = str("\nSample Rate: " + data['sr_select']) if 'sr_select' in data else ""
- gen_type = str(data['generator'] + "gen-") if 'generator' in data else ""
- model = str("\nModel: " + gen_type + data['model']) if 'model' in data else ""
- custom_model = str("\nCustom Model: " + data['custom_model']) if 'custom_model' in data else ""
- base_model = str("\nBase Model: " + data['base_model']) if 'base_model' in data else ""
- decoder = str("\nDecoder: " + data['decoder']) if 'decoder' in data else ""
- topk = str("\nTopk: " + data['topk']) if 'topk' in data else ""
- topp = str("\nTopp: " + data['topp']) if 'topp' in data else ""
- temperature = str("\nTemperature: " + data['temperature']) if 'temperature' in data else ""
- cfg_coef = str("\nClassifier Free Guidance: " + data['cfg_coef']) if 'cfg_coef' in data else ""
- version = str("Version: " + data['version']) if 'version' in data else "Version: Unknown"
- info = str(version + global_prompt + bpm + key + scale + prompts + duration + overlap + seed + audio_mode + input_length + channel + sr_select + model + custom_model + base_model + decoder + topk + topp + temperature + cfg_coef)
- if info == "":
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted."
- return info
- else:
- return "Only .wav ,.mp4 and .json files are supported"
- else:
- return None
-
-
-def info_to_params(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- struc_prompt = (False if data['bpm'] == "none" else True) if 'bpm' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- bpm = (120 if data['bpm'] == "none" else int(data['bpm'])) if 'bpm' in data else 120
- key = ("C" if data['key'] == "none" else data['key']) if 'key' in data else "C"
- scale = ("Major" if data['scale'] == "none" else data['scale']) if 'scale' in data else "Major"
- model = data['model'] if 'model' in data else "large"
- custom_model = (data['custom_model'] if data['custom_model'] in get_available_models() else None) if 'custom_model' in data else None
- base_model = data['base_model'] if 'base_model' in data else "medium"
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- audio_mode = ("sample" if data['audio_mode'] == "none" else data['audio_mode']) if 'audio_mode' in data else "sample"
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, bpm, key, scale, model, custom_model, base_model, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], audio_mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- struc_prompt = (False if data['bpm'] == "none" else True) if 'bpm' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- bpm = (120 if data['bpm'] == "none" else int(data['bpm'])) if 'bpm' in data else 120
- key = ("C" if data['key'] == "none" else data['key']) if 'key' in data else "C"
- scale = ("Major" if data['scale'] == "none" else data['scale']) if 'scale' in data else "Major"
- model = data['model'] if 'model' in data else "large"
- custom_model = (data['custom_model'] if data['custom_model'] in get_available_models() else None) if 'custom_model' in data else None
- base_model = data['base_model'] if 'base_model' in data else "medium"
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- audio_mode = ("sample" if data['audio_mode'] == "none" else data['audio_mode']) if 'audio_mode' in data else "sample"
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, bpm, key, scale, model, custom_model, base_model, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], audio_mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- else:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-
-
-def info_to_params_a(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- struc_prompt = (False if data['global_prompt'] == "" else True) if 'global_prompt' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- struc_prompt = (False if data['global_prompt'] == "" else True) if 'global_prompt' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
-
- else:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- else:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-
-
-def make_pseudo_stereo (filename, sr_select, pan, delay):
- if pan:
- temp = AudioSegment.from_wav(filename)
- if sr_select != "32000":
- temp = temp.set_frame_rate(int(sr_select))
- left = temp.pan(-0.5) - 5
- right = temp.pan(0.6) - 5
- temp = left.overlay(right, position=5)
- temp.export(filename, format="wav")
- if delay:
- waveform, sample_rate = torchaudio.load(filename) # load mono WAV file
- delay_seconds = 0.01 # set delay 10ms
- delay_samples = int(delay_seconds * sample_rate) # Calculating delay value in number of samples
- stereo_waveform = torch.stack([waveform[0], torch.cat((torch.zeros(delay_samples), waveform[0][:-delay_samples]))]) # Generate a stereo file with original mono audio and delayed version
- torchaudio.save(filename, stereo_waveform, sample_rate)
- return
-
-
-def normalize_audio(audio_data):
- audio_data = audio_data.astype(np.float32)
- max_value = np.max(np.abs(audio_data))
- audio_data /= max_value
- return audio_data
-
-
-def load_diffusion():
- global MBD
- if MBD is None:
- print("loading MBD")
- MBD = MultiBandDiffusion.get_mbd_musicgen()
-
-
-def unload_diffusion():
- global MBD
- if MBD is not None:
- print("unloading MBD")
- MBD = None
-
-
-def _do_predictions(gen_type, texts, melodies, sample, trim_start, trim_end, duration, image, height, width, background, bar1, bar2, channel, sr_select, progress=False, **gen_kwargs):
- if gen_type == "music":
- maximum_size = 29.5
- elif gen_type == "audio":
- maximum_size = 9.5
- cut_size = 0
- input_length = 0
- sampleP = None
- if sample is not None:
- globalSR, sampleM = sample[0], sample[1]
- sampleM = normalize_audio(sampleM)
- sampleM = torch.from_numpy(sampleM).t()
- if sampleM.dim() == 1:
- sampleM = sampleM.unsqueeze(0)
- sample_length = sampleM.shape[sampleM.dim() - 1] / globalSR
- if trim_start >= sample_length:
- trim_start = sample_length - 0.5
- if trim_end >= sample_length:
- trim_end = sample_length - 0.5
- if trim_start + trim_end >= sample_length:
- tmp = sample_length - 0.5
- trim_start = tmp / 2
- trim_end = tmp / 2
- sampleM = sampleM[..., int(globalSR * trim_start):int(globalSR * (sample_length - trim_end))]
- sample_length = sample_length - (trim_start + trim_end)
- if sample_length > maximum_size:
- cut_size = sample_length - maximum_size
- sampleP = sampleM[..., :int(globalSR * cut_size)]
- sampleM = sampleM[..., int(globalSR * cut_size):]
- if sample_length >= duration:
- duration = sample_length + 0.5
- input_length = sample_length
- global MODEL
- MODEL.set_generation_params(duration=(duration - cut_size), **gen_kwargs)
- print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies], [None if sample is None else (sample[0], sample[1].shape)])
- be = time.time()
- processed_melodies = []
- if gen_type == "music":
- target_sr = 32000
- elif gen_type == "audio":
- target_sr = 16000
- target_ac = 1
-
- for melody in melodies:
- if melody is None:
- processed_melodies.append(None)
- else:
- sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
- if melody.dim() == 1:
- melody = melody[None]
- melody = melody[..., :int(sr * duration)]
- melody = convert_audio(melody, sr, target_sr, target_ac)
- processed_melodies.append(melody)
-
- if sample is not None:
- if sampleP is None:
- if gen_type == "music":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress
- )
- else:
- if sampleP.dim() > 1:
- sampleP = convert_audio(sampleP, globalSR, target_sr, target_ac)
- sampleP = sampleP.to(MODEL.device).float().unsqueeze(0)
- if gen_type == "music":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress
- )
- outputs = torch.cat([sampleP, outputs], 2)
-
- elif any(m is not None for m in processed_melodies):
- if gen_type == "music":
- outputs = MODEL.generate_with_chroma(
- descriptions=texts,
- melody_wavs=processed_melodies,
- melody_sample_rate=target_sr,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_with_chroma(
- descriptions=texts,
- melody_wavs=processed_melodies,
- melody_sample_rate=target_sr,
- progress=progress
- )
- else:
- if gen_type == "music":
- outputs = MODEL.generate(texts, progress=progress, return_tokens=USE_DIFFUSION)
- elif gen_type == "audio":
- outputs = MODEL.generate(texts, progress=progress)
-
- if USE_DIFFUSION:
- print("outputs: " + str(outputs))
- outputs_diffusion = MBD.tokens_to_wav(outputs[1])
- outputs = torch.cat([outputs[0], outputs_diffusion], dim=0)
- outputs = outputs.detach().cpu().float()
- backups = outputs
- if channel == "stereo":
- outputs = convert_audio(outputs, target_sr, int(sr_select), 2)
- elif channel == "mono" and sr_select != "32000":
- outputs = convert_audio(outputs, target_sr, int(sr_select), 1)
- out_files = []
- out_audios = []
- out_backup = []
- for output in outputs:
- with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
- audio_write(
- file.name, output, (MODEL.sample_rate if channel == "stereo effect" else int(sr_select)), strategy="loudness",
- loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
-
- if channel == "stereo effect":
- make_pseudo_stereo(file.name, sr_select, pan=True, delay=True);
-
- out_files.append(pool.submit(make_waveform, file.name, bg_image=image, bg_color=background, bars_color=(bar1, bar2), fg_alpha=1.0, bar_count=75, height=height, width=width))
- out_audios.append(file.name)
- file_cleaner.add(file.name)
- print(f'wav: {file.name}')
- for backup in backups:
- with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
- audio_write(
- file.name, backup, MODEL.sample_rate, strategy="loudness",
- loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
- out_backup.append(file.name)
- file_cleaner.add(file.name)
- res = [out_file.result() for out_file in out_files]
- res_audio = out_audios
- res_backup = out_backup
- for file in res:
- file_cleaner.add(file)
- print(f'video: {file}')
- print("batch finished", len(texts), time.time() - be)
- print("Tempfiles currently stored: ", len(file_cleaner.files))
- if MOVE_TO_CPU:
- MODEL.to('cpu')
- if UNLOAD_MODEL:
- MODEL = None
- torch.cuda.empty_cache()
- torch.cuda.ipc_collect()
- return res, res_audio, res_backup, input_length
-
-
-def predict_batched(texts, melodies):
- max_text_length = 512
- texts = [text[:max_text_length] for text in texts]
- load_model('melody')
- res = _do_predictions(texts, melodies, BATCHED_DURATION)
- return res
-
-
-def add_tags(filename, tags):
- json_string = None
-
- data = {
- "global_prompt": tags[0],
- "bpm": tags[1],
- "key": tags[2],
- "scale": tags[3],
- "texts": tags[4],
- "duration": tags[5],
- "overlap": tags[6],
- "seed": tags[7],
- "audio_mode": tags[8],
- "input_length": tags[9],
- "channel": tags[10],
- "sr_select": tags[11],
- "model": tags[12],
- "custom_model": tags[13],
- "base_model": tags[14],
- "decoder": tags[15],
- "topk": tags[16],
- "topp": tags[17],
- "temperature": tags[18],
- "cfg_coef": tags[19],
- "generator": tags[20],
- "version": version
- }
-
- json_string = json.dumps(data)
-
- if os.path.exists(filename):
- with taglib.File(filename, save_on_exit=True) as song:
- song.tags = {'COMMENT': json_string }
-
- json_file = open(tags[7] + '.json', 'w')
- json_file.write(json_string)
- json_file.close()
-
- return json_file.name;
-
-
-def save_outputs(mp4, wav_tmp, tags, gen_type):
- # mp4: .mp4 file name in root running folder of app.py
- # wav_tmp: temporary wav file located in %TEMP% folder
- # seed - used seed
- # exanple BgnJtr4Pn1AJ.mp4, C:\Users\Alex\AppData\Local\Temp\tmp4ermrebs.wav, 195123182343465
- # procedure read generated .mp4 and wav files, rename it by using seed as name,
- # and will store it to ./output/today_date/wav and ./output/today_date/mp4 folders.
- # if file with same seed number already exist its make postfix in name like seed(n)
- # where is n - consiqunce number 1-2-3-4 and so on
- # then we store generated mp4 and wav into destination folders.
-
- current_date = datetime.now().strftime("%Y%m%d")
- wav_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'wav')
- mp4_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'mp4')
- json_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'json')
- os.makedirs(wav_directory, exist_ok=True)
- os.makedirs(mp4_directory, exist_ok=True)
- os.makedirs(json_directory, exist_ok=True)
-
- filename = str(tags[7]) + '.wav'
- target = os.path.join(wav_directory, filename)
- counter = 1
- while os.path.exists(target):
- filename = str(tags[7]) + f'({counter})' + '.wav'
- target = os.path.join(wav_directory, filename)
- counter += 1
-
- shutil.copyfile(wav_tmp, target); # make copy of original file
- json_file = add_tags(target, tags);
-
- wav_target=target;
- target=target.replace('wav', 'mp4');
- mp4_target=target;
-
- mp4=r'./' +mp4;
- shutil.copyfile(mp4, target); # make copy of original file
- _ = add_tags(target, tags);
-
- target=target.replace('mp4', 'json'); # change the extension to json
- json_target=target; # store the json target
-
- with open(target, 'w') as f: # open a writable file object
- shutil.copyfile(json_file, target); # make copy of original file
-
- os.remove(json_file)
-
- return wav_target, mp4_target, json_target;
-
-
-def clear_cash():
- # delete all temporary files genegated my system
- current_date = datetime.now().date()
- current_directory = os.getcwd()
- files = glob.glob(os.path.join(current_directory, '*.mp4'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- temp_directory = os.environ.get('TEMP')
- files = glob.glob(os.path.join(temp_directory, 'tmp*.mp4'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- files = glob.glob(os.path.join(temp_directory, 'tmp*.wav'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- files = glob.glob(os.path.join(temp_directory, 'tmp*.png'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
- return
-
-
-def s2t(seconds, seconds2):
- # convert seconds to time format
- # seconds - time in seconds
- # return time in format 00:00
- m, s = divmod(seconds, 60)
- m2, s2 = divmod(seconds2, 60)
- if seconds != 0 and seconds < seconds2:
- s = s + 1
- return ("%02d:%02d - %02d:%02d" % (m, s, m2, s2))
-
-
-def calc_time(gen_type, s, duration, overlap, d0, d1, d2, d3, d4, d5, d6, d7, d8, d9):
- # calculate the time of generation
- # overlap - overlap in seconds
- # d0-d9 - drag
- # return time in seconds
- d_amount = [int(d0), int(d1), int(d2), int(d3), int(d4), int(d5), int(d6), int(d7), int(d8), int(d9)]
- calc = []
- tracks = []
- time = 0
- s = s - 1
- max_time = duration
- max_limit = 0
- if gen_type == "music":
- max_limit = 30
- elif gen_type == "audio":
- max_limit = 10
- track_add = max_limit - overlap
- tracks.append(max_limit + ((d_amount[0] - 1) * track_add))
- for i in range(1, 10):
- tracks.append(d_amount[i] * track_add)
-
- if tracks[0] >= max_time or s == 0:
- calc.append(s2t(time, max_time))
- time = max_time
- else:
- calc.append(s2t(time, tracks[0]))
- time = tracks[0]
-
- for i in range(1, 10):
- if time + tracks[i] >= max_time or i == s:
- calc.append(s2t(time, max_time))
- time = max_time
- else:
- calc.append(s2t(time, time + tracks[i]))
- time = time + tracks[i]
-
- return calc[0], calc[1], calc[2], calc[3], calc[4], calc[5], calc[6], calc[7], calc[8], calc[9]
-
-
-def predict_full(gen_type, model, decoder, custom_model, base_model, prompt_amount, struc_prompt, bpm, key, scale, global_prompt, p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, d0, d1, d2, d3, d4, d5, d6, d7, d8, d9, audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select, progress=gr.Progress()):
- global INTERRUPTING
- global USE_DIFFUSION
- INTERRUPTING = False
-
- if gen_type == "audio":
- custom_model = None
- base_model = "medium"
-
- if temperature < 0:
- raise gr.Error("Temperature must be >= 0.")
- if topk < 0:
- raise gr.Error("Topk must be non-negative.")
- if topp < 0:
- raise gr.Error("Topp must be non-negative.")
-
- if trim_start < 0:
- trim_start = 0
- if trim_end < 0:
- trim_end = 0
-
- topk = int(topk)
-
- if decoder == "MultiBand_Diffusion":
- USE_DIFFUSION = True
- load_diffusion()
- else:
- USE_DIFFUSION = False
- unload_diffusion()
-
- if gen_type == "music":
- model_shrt = model
- model = "GrandaddyShmax/musicgen-" + model
- elif gen_type == "audio":
- model_shrt = model
- model = "GrandaddyShmax/audiogen-" + model
- base_model_shrt = base_model
- base_model = "GrandaddyShmax/musicgen-" + base_model
-
- if MODEL is None or MODEL.name != (model):
- load_model(model, custom_model, base_model, gen_type)
- else:
- if MOVE_TO_CPU:
- MODEL.to('cuda')
-
- if seed < 0:
- seed = random.randint(0, 0xffff_ffff_ffff)
- torch.manual_seed(seed)
-
- def _progress(generated, to_generate):
- progress((min(generated, to_generate), to_generate))
- if INTERRUPTING:
- raise gr.Error("Interrupted.")
- MODEL.set_custom_progress_callback(_progress)
-
- audio_mode = "none"
- melody = None
- sample = None
- if audio:
- audio_mode = mode
- if mode == "sample":
- sample = audio
- elif mode == "melody":
- melody = audio
-
- base_model = "none" if model != "custom" else base_model
- custom_model = "none" if model != "custom" else custom_model
-
- text_cat = [p0, p1, p2, p3, p4, p5, p6, p7, p8, p9]
- drag_cat = [d0, d1, d2, d3, d4, d5, d6, d7, d8, d9]
- texts = []
- raw_texts = []
- ind = 0
- ind2 = 0
- while ind < prompt_amount:
- for ind2 in range(int(drag_cat[ind])):
- if not struc_prompt:
- texts.append(text_cat[ind])
- global_prompt = "none"
- bpm = "none"
- key = "none"
- scale = "none"
- raw_texts.append(text_cat[ind])
- else:
- if gen_type == "music":
- bpm_str = str(bpm) + " bpm"
- key_str = ", " + str(key) + " " + str(scale)
- global_str = (", " + str(global_prompt)) if str(global_prompt) != "" else ""
- elif gen_type == "audio":
- bpm_str = ""
- key_str = ""
- global_str = (str(global_prompt)) if str(global_prompt) != "" else ""
- texts_str = (", " + str(text_cat[ind])) if str(text_cat[ind]) != "" else ""
- texts.append(bpm_str + key_str + global_str + texts_str)
- raw_texts.append(text_cat[ind])
- ind2 = 0
- ind = ind + 1
-
- outs, outs_audio, outs_backup, input_length = _do_predictions(
- gen_type, [texts], [melody], sample, trim_start, trim_end, duration, image, height, width, background, bar1, bar2, channel, sr_select, progress=True,
- top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef, extend_stride=MODEL.max_duration-overlap)
- tags = [str(global_prompt), str(bpm), str(key), str(scale), str(raw_texts), str(duration), str(overlap), str(seed), str(audio_mode), str(input_length), str(channel), str(sr_select), str(model_shrt), str(custom_model), str(base_model_shrt), str(decoder), str(topk), str(topp), str(temperature), str(cfg_coef), str(gen_type)]
- wav_target, mp4_target, json_target = save_outputs(outs[0], outs_audio[0], tags, gen_type);
- # Removes the temporary files.
- for out in outs:
- os.remove(out)
- for out in outs_audio:
- os.remove(out)
-
- return mp4_target, wav_target, outs_backup[0], [mp4_target, wav_target, json_target], seed
-
-
-max_textboxes = 10
-
-
-def get_available_models():
- return sorted([re.sub('.pt$', '', item.name) for item in list(Path('models/').glob('*')) if item.name.endswith('.pt')])
-
-
-def toggle_audio_src(choice):
- if choice == "mic":
- return gr.update(source="microphone", value=None, label="Microphone")
- else:
- return gr.update(source="upload", value=None, label="File")
-
-
-def ui_full(launch_kwargs):
- with gr.Blocks(title='AudioCraft Plus', theme=theme) as interface:
- gr.Markdown(
- """
- # AudioCraft Plus - v2.0.0a
-
- ### An All-in-One AudioCraft WebUI
-
- Thanks to: facebookresearch, Camenduru, rkfg, oobabooga, AlexHK and GrandaddyShmax
- """
- )
- with gr.Tab("MusicGen"):
- gr.Markdown(
- """
- ### MusicGen
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Tab("Generation"):
- with gr.Accordion("Structure Prompts", open=False):
- with gr.Column():
- with gr.Row():
- struc_prompts = gr.Checkbox(label="Enable", value=False, interactive=True, container=False)
- bpm = gr.Number(label="BPM", value=120, interactive=True, scale=1, precision=0)
- key = gr.Dropdown(["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "Bb", "B"], label="Key", value="C", interactive=True)
- scale = gr.Dropdown(["Major", "Minor"], label="Scale", value="Major", interactive=True)
- with gr.Row():
- global_prompt = gr.Text(label="Global Prompt", interactive=True, scale=3)
- with gr.Row():
- s = gr.Slider(1, max_textboxes, value=1, step=1, label="Prompts:", interactive=True, scale=2)
- #s_mode = gr.Radio(["segmentation", "batch"], value="segmentation", interactive=True, scale=1, label="Generation Mode")
- with gr.Column():
- textboxes = []
- prompts = []
- repeats = []
- calcs = []
- with gr.Row():
- text0 = gr.Text(label="Input Text", interactive=True, scale=4)
- prompts.append(text0)
- drag0 = gr.Number(label="Repeat", value=1, interactive=True, scale=1)
- repeats.append(drag0)
- calc0 = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- calcs.append(calc0)
- for i in range(max_textboxes):
- with gr.Row(visible=False) as t:
- text = gr.Text(label="Input Text", interactive=True, scale=3)
- repeat = gr.Number(label="Repeat", minimum=1, value=1, interactive=True, scale=1)
- calc = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- textboxes.append(t)
- prompts.append(text)
- repeats.append(repeat)
- calcs.append(calc)
- to_calc = gr.Button("Calculate Timings", variant="secondary")
- with gr.Row():
- duration = gr.Slider(minimum=1, maximum=300, value=10, step=1, label="Duration", interactive=True)
- with gr.Row():
- overlap = gr.Slider(minimum=1, maximum=29, value=12, step=1, label="Overlap", interactive=True)
- with gr.Row():
- seed = gr.Number(label="Seed", value=-1, scale=4, precision=0, interactive=True)
- gr.Button('\U0001f3b2\ufe0f', scale=1).click(fn=lambda: -1, outputs=[seed], queue=False)
- reuse_seed = gr.Button('\u267b\ufe0f', scale=1)
-
- with gr.Tab("Audio"):
- with gr.Row():
- with gr.Column():
- input_type = gr.Radio(["file", "mic"], value="file", label="Input Type (optional)", interactive=True)
- mode = gr.Radio(["melody", "sample"], label="Input Audio Mode (optional)", value="sample", interactive=True)
- with gr.Row():
- trim_start = gr.Number(label="Trim Start", value=0, interactive=True)
- trim_end = gr.Number(label="Trim End", value=0, interactive=True)
- audio = gr.Audio(source="upload", type="numpy", label="Input Audio (optional)", interactive=True)
-
- with gr.Tab("Customization"):
- with gr.Row():
- with gr.Column():
- background = gr.ColorPicker(value="#0f0f0f", label="background color", interactive=True, scale=0)
- bar1 = gr.ColorPicker(value="#84cc16", label="bar color start", interactive=True, scale=0)
- bar2 = gr.ColorPicker(value="#10b981", label="bar color end", interactive=True, scale=0)
- with gr.Column():
- image = gr.Image(label="Background Image", type="filepath", interactive=True, scale=4)
- with gr.Row():
- height = gr.Number(label="Height", value=512, interactive=True)
- width = gr.Number(label="Width", value=768, interactive=True)
-
- with gr.Tab("Settings"):
- with gr.Row():
- channel = gr.Radio(["mono", "stereo", "stereo effect"], label="Output Audio Channels", value="stereo", interactive=True, scale=1)
- sr_select = gr.Dropdown(["11025", "16000", "22050", "24000", "32000", "44100", "48000"], label="Output Audio Sample Rate", value="48000", interactive=True)
- with gr.Row():
- model = gr.Radio(["melody", "small", "medium", "large", "custom"], label="Model", value="large", interactive=True, scale=1)
- with gr.Column():
- dropdown = gr.Dropdown(choices=get_available_models(), value=("No models found" if len(get_available_models()) < 1 else get_available_models()[0]), label='Custom Model (models folder)', elem_classes='slim-dropdown', interactive=True)
- ui.create_refresh_button(dropdown, lambda: None, lambda: {'choices': get_available_models()}, 'refresh-button')
- basemodel = gr.Radio(["small", "medium", "melody", "large"], label="Base Model", value="medium", interactive=True, scale=1)
- with gr.Row():
- decoder = gr.Radio(["Default", "MultiBand_Diffusion"], label="Decoder", value="Default", interactive=True)
- with gr.Row():
- topk = gr.Number(label="Top-k", value=250, interactive=True)
- topp = gr.Number(label="Top-p", value=0, interactive=True)
- temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
- cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
- with gr.Row():
- submit = gr.Button("Generate", variant="primary")
- # Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
- _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
- with gr.Column() as c:
- with gr.Tab("Output"):
- output = gr.Video(label="Generated Music", scale=0)
- with gr.Row():
- audio_only = gr.Audio(type="numpy", label="Audio Only", interactive=False)
- backup_only = gr.Audio(type="numpy", label="Backup Audio", interactive=False, visible=False)
- send_audio = gr.Button("Send to Input Audio")
- seed_used = gr.Number(label='Seed used', value=-1, interactive=False)
- download = gr.File(label="Generated Files", interactive=False)
- with gr.Tab("Wiki"):
- gr.Markdown(
- """
- - **[Generate (button)]:**
- Generates the music with the given settings and prompts.
-
- - **[Interrupt (button)]:**
- Stops the music generation as soon as it can, providing an incomplete output.
-
- ---
-
- ### Generation Tab:
-
- #### Structure Prompts:
-
- This feature helps reduce repetetive prompts by allowing you to set global prompts
- that will be used for all prompt segments.
-
- - **[Structure Prompts (checkbox)]:**
- Enable/Disable the structure prompts feature.
-
- - **[BPM (number)]:**
- Beats per minute of the generated music.
-
- - **[Key (dropdown)]:**
- The key of the generated music.
-
- - **[Scale (dropdown)]:**
- The scale of the generated music.
-
- - **[Global Prompt (text)]:**
- Here write the prompt that you wish to be used for all prompt segments.
-
- #### Multi-Prompt:
-
- This feature allows you to control the music, adding variation to different time segments.
- You have up to 10 prompt segments. the first prompt will always be 30s long
- the other prompts will be [30s - overlap].
- for example if the overlap is 10s, each prompt segment will be 20s.
-
- - **[Prompt Segments (number)]:**
- Amount of unique prompt to generate throughout the music generation.
-
- - **[Prompt/Input Text (prompt)]:**
- Here describe the music you wish the model to generate.
-
- - **[Repeat (number)]:**
- Write how many times this prompt will repeat (instead of wasting another prompt segment on the same prompt).
-
- - **[Time (text)]:**
- The time of the prompt segment.
-
- - **[Calculate Timings (button)]:**
- Calculates the timings of the prompt segments.
-
- - **[Duration (number)]:**
- How long you want the generated music to be (in seconds).
-
- - **[Overlap (number)]:**
- How much each new segment will reference the previous segment (in seconds).
- For example, if you choose 20s: Each new segment after the first one will reference the previous segment 20s
- and will generate only 10s of new music. The model can only process 30s of music.
-
- - **[Seed (number)]:**
- Your generated music id. If you wish to generate the exact same music,
- place the exact seed with the exact prompts
- (This way you can also extend specific song that was generated short).
-
- - **[Random Seed (button)]:**
- Gives "-1" as a seed, which counts as a random seed.
-
- - **[Copy Previous Seed (button)]:**
- Copies the seed from the output seed (if you don't feel like doing it manualy).
-
- ---
-
- ### Audio Tab:
-
- - **[Input Type (selection)]:**
- `File` mode allows you to upload an audio file to use as input
- `Mic` mode allows you to use your microphone as input
-
- - **[Input Audio Mode (selection)]:**
- `Melody` mode only works with the melody model: it conditions the music generation to reference the melody
- `Sample` mode works with any model: it gives a music sample to the model to generate its continuation.
-
- - **[Trim Start and Trim End (numbers)]:**
- `Trim Start` set how much you'd like to trim the input audio from the start
- `Trim End` same as the above but from the end
-
- - **[Input Audio (audio file)]:**
- Input here the audio you wish to use with "melody" or "sample" mode.
-
- ---
-
- ### Customization Tab:
-
- - **[Background Color (color)]:**
- Works only if you don't upload image. Color of the background of the waveform.
-
- - **[Bar Color Start (color)]:**
- First color of the waveform bars.
-
- - **[Bar Color End (color)]:**
- Second color of the waveform bars.
-
- - **[Background Image (image)]:**
- Background image that you wish to be attached to the generated video along with the waveform.
-
- - **[Height and Width (numbers)]:**
- Output video resolution, only works with image.
- (minimum height and width is 256).
-
- ---
-
- ### Settings Tab:
-
- - **[Output Audio Channels (selection)]:**
- With this you can select the amount of channels that you wish for your output audio.
- `mono` is a straightforward single channel audio
- `stereo` is a dual channel audio but it will sound more or less like mono
- `stereo effect` this one is also dual channel but uses tricks to simulate a stereo audio.
-
- - **[Output Audio Sample Rate (dropdown)]:**
- The output audio sample rate, the model default is 32000.
-
- - **[Model (selection)]:**
- Here you can choose which model you wish to use:
- `melody` model is based on the medium model with a unique feature that lets you use melody conditioning
- `small` model is trained on 300M parameters
- `medium` model is trained on 1.5B parameters
- `large` model is trained on 3.3B parameters
- `custom` model runs the custom model that you provided.
-
- - **[Custom Model (selection)]:**
- This dropdown will show you models that are placed in the `models` folder
- you must select `custom` in the model options in order to use it.
-
- - **[Refresh (button)]:**
- Refreshes the dropdown list for custom model.
-
- - **[Base Model (selection)]:**
- Choose here the model that your custom model is based on.
-
- - **[Decoder (selection)]:**
- Choose here the decoder that you wish to use:
- `Default` is the default decoder
- `MultiBand_Diffusion` is a decoder that uses diffusion to generate the audio.
-
- - **[Top-k (number)]:**
- is a parameter used in text generation models, including music generation models. It determines the number of most likely next tokens to consider at each step of the generation process. The model ranks all possible tokens based on their predicted probabilities, and then selects the top-k tokens from the ranked list. The model then samples from this reduced set of tokens to determine the next token in the generated sequence. A smaller value of k results in a more focused and deterministic output, while a larger value of k allows for more diversity in the generated music.
-
- - **[Top-p (number)]:**
- also known as nucleus sampling or probabilistic sampling, is another method used for token selection during text generation. Instead of specifying a fixed number like top-k, top-p considers the cumulative probability distribution of the ranked tokens. It selects the smallest possible set of tokens whose cumulative probability exceeds a certain threshold (usually denoted as p). The model then samples from this set to choose the next token. This approach ensures that the generated output maintains a balance between diversity and coherence, as it allows for a varying number of tokens to be considered based on their probabilities.
-
- - **[Temperature (number)]:**
- is a parameter that controls the randomness of the generated output. It is applied during the sampling process, where a higher temperature value results in more random and diverse outputs, while a lower temperature value leads to more deterministic and focused outputs. In the context of music generation, a higher temperature can introduce more variability and creativity into the generated music, but it may also lead to less coherent or structured compositions. On the other hand, a lower temperature can produce more repetitive and predictable music.
-
- - **[Classifier Free Guidance (number)]:**
- refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
- """
- )
- with gr.Tab("AudioGen"):
- gr.Markdown(
- """
- ### AudioGen
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Tab("Generation"):
- with gr.Accordion("Structure Prompts", open=False):
- with gr.Row():
- struc_prompts_a = gr.Checkbox(label="Enable", value=False, interactive=True, container=False)
- global_prompt_a = gr.Text(label="Global Prompt", interactive=True, scale=3)
- with gr.Row():
- s_a = gr.Slider(1, max_textboxes, value=1, step=1, label="Prompts:", interactive=True, scale=2)
- with gr.Column():
- textboxes_a = []
- prompts_a = []
- repeats_a = []
- calcs_a = []
- with gr.Row():
- text0_a = gr.Text(label="Input Text", interactive=True, scale=4)
- prompts_a.append(text0_a)
- drag0_a = gr.Number(label="Repeat", value=1, interactive=True, scale=1)
- repeats_a.append(drag0_a)
- calc0_a = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- calcs_a.append(calc0_a)
- for i in range(max_textboxes):
- with gr.Row(visible=False) as t_a:
- text_a = gr.Text(label="Input Text", interactive=True, scale=3)
- repeat_a = gr.Number(label="Repeat", minimum=1, value=1, interactive=True, scale=1)
- calc_a = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- textboxes_a.append(t_a)
- prompts_a.append(text_a)
- repeats_a.append(repeat_a)
- calcs_a.append(calc_a)
- to_calc_a = gr.Button("Calculate Timings", variant="secondary")
- with gr.Row():
- duration_a = gr.Slider(minimum=1, maximum=300, value=10, step=1, label="Duration", interactive=True)
- with gr.Row():
- overlap_a = gr.Slider(minimum=1, maximum=9, value=2, step=1, label="Overlap", interactive=True)
- with gr.Row():
- seed_a = gr.Number(label="Seed", value=-1, scale=4, precision=0, interactive=True)
- gr.Button('\U0001f3b2\ufe0f', scale=1).click(fn=lambda: -1, outputs=[seed_a], queue=False)
- reuse_seed_a = gr.Button('\u267b\ufe0f', scale=1)
-
- with gr.Tab("Audio"):
- with gr.Row():
- with gr.Column():
- input_type_a = gr.Radio(["file", "mic"], value="file", label="Input Type (optional)", interactive=True)
- mode_a = gr.Radio(["sample"], label="Input Audio Mode (optional)", value="sample", interactive=False, visible=False)
- with gr.Row():
- trim_start_a = gr.Number(label="Trim Start", value=0, interactive=True)
- trim_end_a = gr.Number(label="Trim End", value=0, interactive=True)
- audio_a = gr.Audio(source="upload", type="numpy", label="Input Audio (optional)", interactive=True)
-
- with gr.Tab("Customization"):
- with gr.Row():
- with gr.Column():
- background_a = gr.ColorPicker(value="#0f0f0f", label="background color", interactive=True, scale=0)
- bar1_a = gr.ColorPicker(value="#84cc16", label="bar color start", interactive=True, scale=0)
- bar2_a = gr.ColorPicker(value="#10b981", label="bar color end", interactive=True, scale=0)
- with gr.Column():
- image_a = gr.Image(label="Background Image", type="filepath", interactive=True, scale=4)
- with gr.Row():
- height_a = gr.Number(label="Height", value=512, interactive=True)
- width_a = gr.Number(label="Width", value=768, interactive=True)
-
- with gr.Tab("Settings"):
- with gr.Row():
- channel_a = gr.Radio(["mono", "stereo", "stereo effect"], label="Output Audio Channels", value="stereo", interactive=True, scale=1)
- sr_select_a = gr.Dropdown(["11025", "16000", "22050", "24000", "32000", "44100", "48000"], label="Output Audio Sample Rate", value="48000", interactive=True)
- with gr.Row():
- model_a = gr.Radio(["medium"], label="Model", value="medium", interactive=False, visible=False)
- decoder_a = gr.Radio(["Default"], label="Decoder", value="Default", interactive=False, visible=False)
- with gr.Row():
- topk_a = gr.Number(label="Top-k", value=250, interactive=True)
- topp_a = gr.Number(label="Top-p", value=0, interactive=True)
- temperature_a = gr.Number(label="Temperature", value=1.0, interactive=True)
- cfg_coef_a = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
- with gr.Row():
- submit_a = gr.Button("Generate", variant="primary")
- _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
- with gr.Column():
- with gr.Tab("Output"):
- output_a = gr.Video(label="Generated Audio", scale=0)
- with gr.Row():
- audio_only_a = gr.Audio(type="numpy", label="Audio Only", interactive=False)
- backup_only_a = gr.Audio(type="numpy", label="Backup Audio", interactive=False, visible=False)
- send_audio_a = gr.Button("Send to Input Audio")
- seed_used_a = gr.Number(label='Seed used', value=-1, interactive=False)
- download_a = gr.File(label="Generated Files", interactive=False)
- with gr.Tab("Wiki"):
- gr.Markdown(
- """
- - **[Generate (button)]:**
- Generates the audio with the given settings and prompts.
-
- - **[Interrupt (button)]:**
- Stops the audio generation as soon as it can, providing an incomplete output.
-
- ---
-
- ### Generation Tab:
-
- #### Structure Prompts:
-
- This feature helps reduce repetetive prompts by allowing you to set global prompts
- that will be used for all prompt segments.
-
- - **[Structure Prompts (checkbox)]:**
- Enable/Disable the structure prompts feature.
-
- - **[Global Prompt (text)]:**
- Here write the prompt that you wish to be used for all prompt segments.
-
- #### Multi-Prompt:
-
- This feature allows you to control the audio, adding variation to different time segments.
- You have up to 10 prompt segments. the first prompt will always be 10s long
- the other prompts will be [10s - overlap].
- for example if the overlap is 2s, each prompt segment will be 8s.
-
- - **[Prompt Segments (number)]:**
- Amount of unique prompt to generate throughout the audio generation.
-
- - **[Prompt/Input Text (prompt)]:**
- Here describe the audio you wish the model to generate.
-
- - **[Repeat (number)]:**
- Write how many times this prompt will repeat (instead of wasting another prompt segment on the same prompt).
-
- - **[Time (text)]:**
- The time of the prompt segment.
-
- - **[Calculate Timings (button)]:**
- Calculates the timings of the prompt segments.
-
- - **[Duration (number)]:**
- How long you want the generated audio to be (in seconds).
-
- - **[Overlap (number)]:**
- How much each new segment will reference the previous segment (in seconds).
- For example, if you choose 2s: Each new segment after the first one will reference the previous segment 2s
- and will generate only 8s of new audio. The model can only process 10s of music.
-
- - **[Seed (number)]:**
- Your generated audio id. If you wish to generate the exact same audio,
- place the exact seed with the exact prompts
- (This way you can also extend specific song that was generated short).
-
- - **[Random Seed (button)]:**
- Gives "-1" as a seed, which counts as a random seed.
-
- - **[Copy Previous Seed (button)]:**
- Copies the seed from the output seed (if you don't feel like doing it manualy).
-
- ---
-
- ### Audio Tab:
-
- - **[Input Type (selection)]:**
- `File` mode allows you to upload an audio file to use as input
- `Mic` mode allows you to use your microphone as input
-
- - **[Trim Start and Trim End (numbers)]:**
- `Trim Start` set how much you'd like to trim the input audio from the start
- `Trim End` same as the above but from the end
-
- - **[Input Audio (audio file)]:**
- Input here the audio you wish to use.
-
- ---
-
- ### Customization Tab:
-
- - **[Background Color (color)]:**
- Works only if you don't upload image. Color of the background of the waveform.
-
- - **[Bar Color Start (color)]:**
- First color of the waveform bars.
-
- - **[Bar Color End (color)]:**
- Second color of the waveform bars.
-
- - **[Background Image (image)]:**
- Background image that you wish to be attached to the generated video along with the waveform.
-
- - **[Height and Width (numbers)]:**
- Output video resolution, only works with image.
- (minimum height and width is 256).
-
- ---
-
- ### Settings Tab:
-
- - **[Output Audio Channels (selection)]:**
- With this you can select the amount of channels that you wish for your output audio.
- `mono` is a straightforward single channel audio
- `stereo` is a dual channel audio but it will sound more or less like mono
- `stereo effect` this one is also dual channel but uses tricks to simulate a stereo audio.
-
- - **[Output Audio Sample Rate (dropdown)]:**
- The output audio sample rate, the model default is 32000.
-
- - **[Top-k (number)]:**
- is a parameter used in text generation models, including music generation models. It determines the number of most likely next tokens to consider at each step of the generation process. The model ranks all possible tokens based on their predicted probabilities, and then selects the top-k tokens from the ranked list. The model then samples from this reduced set of tokens to determine the next token in the generated sequence. A smaller value of k results in a more focused and deterministic output, while a larger value of k allows for more diversity in the generated music.
-
- - **[Top-p (number)]:**
- also known as nucleus sampling or probabilistic sampling, is another method used for token selection during text generation. Instead of specifying a fixed number like top-k, top-p considers the cumulative probability distribution of the ranked tokens. It selects the smallest possible set of tokens whose cumulative probability exceeds a certain threshold (usually denoted as p). The model then samples from this set to choose the next token. This approach ensures that the generated output maintains a balance between diversity and coherence, as it allows for a varying number of tokens to be considered based on their probabilities.
-
- - **[Temperature (number)]:**
- is a parameter that controls the randomness of the generated output. It is applied during the sampling process, where a higher temperature value results in more random and diverse outputs, while a lower temperature value leads to more deterministic and focused outputs. In the context of music generation, a higher temperature can introduce more variability and creativity into the generated music, but it may also lead to less coherent or structured compositions. On the other hand, a lower temperature can produce more repetitive and predictable music.
-
- - **[Classifier Free Guidance (number)]:**
- refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
- """
- )
- with gr.Tab("Audio Info"):
- gr.Markdown(
- """
- ### Audio Info
- """
- )
- with gr.Row():
- with gr.Column():
- in_audio = gr.File(type="file", label="Input Any Audio", interactive=True)
- with gr.Row():
- send_gen = gr.Button("Send to MusicGen", variant="primary")
- send_gen_a = gr.Button("Send to AudioGen", variant="primary")
- with gr.Column():
- info = gr.Textbox(label="Audio Info", lines=10, interactive=False)
- with gr.Tab("Changelog"):
- gr.Markdown(
- """
- ## Changelog:
-
- ### v2.0.0a
-
- - Forgot to move all the update to app.py from temp2.py... oops
-
-
-
- ### v2.0.0
-
- - Changed name from MusicGen+ to AudioCraft Plus
-
- - Complete overhaul of the repo "backend" with the latest changes from the main facebookresearch repo
-
- - Added a new decoder: MultiBand_Diffusion
-
- - Added AudioGen: a new tab for generating audio
-
-
-
- ### v1.2.8c
-
- - Implemented Reverse compatibility for audio info tab with previous versions
-
-
-
- ### v1.2.8b
-
- - Fixed the error when loading default models
-
-
-
- ### v1.2.8a
-
- - Adapted Audio info tab to work with the new structure prompts feature
-
- - Now custom models actually work, make sure you select the correct base model
-
-
-
- ### v1.2.8
-
- - Now you will also recieve json file with metadata of generated audio
-
- - Added error messages in Audio Info tab
-
- - Added structure prompts: you can select bpm, key and global prompt for all prompts
-
- - Added time display next to each prompt, can be calculated with "Calculate Timings" button
-
-
-
- ### v1.2.7
-
- - When sending generated audio to Input Audio, it will send a backup audio with default settings
- (best for continuos generation)
-
- - Added Metadata to generated audio (Thanks to AlexHK ♥)
-
- - Added Audio Info tab that will display the metadata of the input audio
-
- - Added "send to Text2Audio" button in Audio Info tab
-
- - Generated audio is now stored in the "output" folder (Thanks to AlexHK ♥)
-
- - Added an output area with generated files and download buttons
-
- - Enhanced Stereo effect (Thanks to AlexHK ♥)
-
-
-
- ### v1.2.6
-
- - Added option to generate in stereo (instead of only mono)
-
- - Added dropdown for selecting output sample rate (model default is 32000)
-
-
-
- ### v1.2.5a
-
- - Added file cleaner (This comes from the main facebookresearch repo)
-
- - Reorganized a little, moved audio to a seperate tab
-
-
-
- ### v1.2.5
-
- - Gave a unique lime theme to the webui
-
- - Added additional output for audio only
-
- - Added button to send generated audio to Input Audio
-
- - Added option to trim Input Audio
-
-
-
- ### v1.2.4
-
- - Added mic input (This comes from the main facebookresearch repo)
-
-
-
- ### v1.2.3
-
- - Added option to change video size to fit the image you upload
-
-
-
- ### v1.2.2
-
- - Added Wiki, Changelog and About tabs
-
-
-
- ### v1.2.1
-
- - Added tabs and organized the entire interface
-
- - Added option to attach image to the output video
-
- - Added option to load fine-tuned models (Yet to be tested)
-
-
-
- ### v1.2.0
-
- - Added Multi-Prompt
-
-
-
- ### v1.1.3
-
- - Added customization options for generated waveform
-
-
-
- ### v1.1.2
-
- - Removed sample length limit: now you can input audio of any length as music sample
-
-
-
- ### v1.1.1
-
- - Improved music sample audio quality when using music continuation
-
-
-
- ### v1.1.0
-
- - Rebuilt the repo on top of the latest structure of the main MusicGen repo
-
- - Improved Music continuation feature
-
-
-
- ### v1.0.0 - Stable Version
-
- - Added Music continuation
- """
- )
- with gr.Tab("About"):
- gen_type = gr.Text(value="music", interactive=False, visible=False)
- gen_type_a = gr.Text(value="audio", interactive=False, visible=False)
- gr.Markdown(
- """
- This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
- presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
-
- ## MusicGen+ is an extended version of the original MusicGen by facebookresearch.
-
- ### Repo: https://github.com/GrandaddyShmax/audiocraft_plus/tree/plus
-
- ---
-
- ### This project was possible thanks to:
-
- #### GrandaddyShmax - https://github.com/GrandaddyShmax
-
- #### Camenduru - https://github.com/camenduru
-
- #### rkfg - https://github.com/rkfg
-
- #### oobabooga - https://github.com/oobabooga
-
- #### AlexHK - https://github.com/alanhk147
- """
- )
-
- send_gen.click(info_to_params, inputs=[in_audio], outputs=[decoder, struc_prompts, global_prompt, bpm, key, scale, model, dropdown, basemodel, s, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select], queue=False)
- reuse_seed.click(fn=lambda x: x, inputs=[seed_used], outputs=[seed], queue=False)
- send_audio.click(fn=lambda x: x, inputs=[backup_only], outputs=[audio], queue=False)
- submit.click(predict_full, inputs=[gen_type, model, decoder, dropdown, basemodel, s, struc_prompts, bpm, key, scale, global_prompt, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select], outputs=[output, audio_only, backup_only, download, seed_used])
- input_type.change(toggle_audio_src, input_type, [audio], queue=False, show_progress=False)
- to_calc.click(calc_time, inputs=[gen_type, s, duration, overlap, repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9]], outputs=[calcs[0], calcs[1], calcs[2], calcs[3], calcs[4], calcs[5], calcs[6], calcs[7], calcs[8], calcs[9]], queue=False)
-
- send_gen_a.click(info_to_params_a, inputs=[in_audio], outputs=[decoder_a, struc_prompts_a, global_prompt_a, s_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, channel_a, sr_select_a], queue=False)
- reuse_seed_a.click(fn=lambda x: x, inputs=[seed_used_a], outputs=[seed_a], queue=False)
- send_audio_a.click(fn=lambda x: x, inputs=[backup_only_a], outputs=[audio_a], queue=False)
- submit_a.click(predict_full, inputs=[gen_type_a, model_a, decoder_a, dropdown, basemodel, s_a, struc_prompts_a, bpm, key, scale, global_prompt_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], audio_a, mode_a, trim_start_a, trim_end_a, duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, image_a, height_a, width_a, background_a, bar1_a, bar2_a, channel_a, sr_select_a], outputs=[output_a, audio_only_a, backup_only_a, download_a, seed_used_a])
- input_type_a.change(toggle_audio_src, input_type_a, [audio_a], queue=False, show_progress=False)
- to_calc_a.click(calc_time, inputs=[gen_type_a, s_a, duration_a, overlap_a, repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9]], outputs=[calcs_a[0], calcs_a[1], calcs_a[2], calcs_a[3], calcs_a[4], calcs_a[5], calcs_a[6], calcs_a[7], calcs_a[8], calcs_a[9]], queue=False)
-
- in_audio.change(get_audio_info, in_audio, outputs=[info])
-
- def variable_outputs(k):
- k = int(k) - 1
- return [gr.Textbox.update(visible=True)]*k + [gr.Textbox.update(visible=False)]*(max_textboxes-k)
- def get_size(image):
- if image is not None:
- img = Image.open(image)
- img_height = img.height
- img_width = img.width
- if (img_height%2) != 0:
- img_height = img_height + 1
- if (img_width%2) != 0:
- img_width = img_width + 1
- return img_height, img_width
- else:
- return 512, 768
-
- image.change(get_size, image, outputs=[height, width])
- image_a.change(get_size, image_a, outputs=[height_a, width_a])
- s.change(variable_outputs, s, textboxes)
- s_a.change(variable_outputs, s_a, textboxes_a)
- interface.queue().launch(**launch_kwargs)
-
-
-def ui_batched(launch_kwargs):
- with gr.Blocks() as demo:
- gr.Markdown(
- """
- # MusicGen
-
- This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
- a simple and controllable model for music generation
- presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
-
-
-
- for longer sequences, more control and no queue.
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Row():
- text = gr.Text(label="Describe your music", lines=2, interactive=True)
- with gr.Column():
- radio = gr.Radio(["file", "mic"], value="file",
- label="Condition on a melody (optional) File or Mic")
- melody = gr.Audio(source="upload", type="numpy", label="File",
- interactive=True, elem_id="melody-input")
- with gr.Row():
- submit = gr.Button("Generate")
- with gr.Column():
- output = gr.Video(label="Generated Music")
- audio_output = gr.Audio(label="Generated Music (wav)", type='filepath')
- submit.click(predict_batched, inputs=[text, melody],
- outputs=[output, audio_output], batch=True, max_batch_size=MAX_BATCH_SIZE)
- radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
- gr.Examples(
- fn=predict_batched,
- examples=[
- [
- "An 80s driving pop song with heavy drums and synth pads in the background",
- "./assets/bach.mp3",
- ],
- [
- "A cheerful country song with acoustic guitars",
- "./assets/bolero_ravel.mp3",
- ],
- [
- "90s rock song with electric guitar and heavy drums",
- None,
- ],
- [
- "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
- "./assets/bach.mp3",
- ],
- [
- "lofi slow bpm electro chill with organic samples",
- None,
- ],
- ],
- inputs=[text, melody],
- outputs=[output]
- )
- gr.Markdown("""
- ### More details
-
- The model will generate 12 seconds of audio based on the description you provided.
- You can optionally provide a reference audio from which a broad melody will be extracted.
- The model will then try to follow both the description and melody provided.
- All samples are generated with the `melody` model.
-
- You can also use your own GPU or a Google Colab by following the instructions on our repo.
-
- See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
- for more details.
- """)
-
- demo.queue(max_size=8 * 4).launch(**launch_kwargs)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument(
- '--listen',
- type=str,
- default='0.0.0.0' if 'SPACE_ID' in os.environ else '127.0.0.1',
- help='IP to listen on for connections to Gradio',
- )
- parser.add_argument(
- '--username', type=str, default='', help='Username for authentication'
- )
- parser.add_argument(
- '--password', type=str, default='', help='Password for authentication'
- )
- parser.add_argument(
- '--server_port',
- type=int,
- default=0,
- help='Port to run the server listener on',
- )
- parser.add_argument(
- '--inbrowser', action='store_true', help='Open in browser'
- )
- parser.add_argument(
- '--share', action='store_true', help='Share the gradio UI'
- )
- parser.add_argument(
- '--unload_model', action='store_true', help='Unload the model after every generation to save GPU memory'
- )
-
- parser.add_argument(
- '--unload_to_cpu', action='store_true', help='Move the model to main RAM after every generation to save GPU memory but reload faster than after full unload (see above)'
- )
-
- parser.add_argument(
- '--cache', action='store_true', help='Cache models in RAM to quickly switch between them'
- )
-
- args = parser.parse_args()
- UNLOAD_MODEL = args.unload_model
- MOVE_TO_CPU = args.unload_to_cpu
- if args.cache:
- MODELS = {}
-
- launch_kwargs = {}
- launch_kwargs['server_name'] = args.listen
-
- if args.username and args.password:
- launch_kwargs['auth'] = (args.username, args.password)
- if args.server_port:
- launch_kwargs['server_port'] = args.server_port
- if args.inbrowser:
- launch_kwargs['inbrowser'] = args.inbrowser
- if args.share:
- launch_kwargs['share'] = args.share
-
- # Show the interface
- if IS_BATCHED:
- global USE_DIFFUSION
- USE_DIFFUSION = False
- ui_batched(launch_kwargs)
- else:
- ui_full(launch_kwargs)
\ No newline at end of file
diff --git a/spaces/PunPk/AI_FallingAsleepDriving/README.md b/spaces/PunPk/AI_FallingAsleepDriving/README.md
deleted file mode 100644
index 0855483dc5cdc696af1fe1d3502b584b42fde086..0000000000000000000000000000000000000000
--- a/spaces/PunPk/AI_FallingAsleepDriving/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: AI FallingAsleepDriving
-emoji: 📉
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RMXK/RVC_HFF/tensorlowest.py b/spaces/RMXK/RVC_HFF/tensorlowest.py
deleted file mode 100644
index eccd4dbf3494434e59f7defaae6ab91797263b90..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/tensorlowest.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from tensorboard.backend.event_processing import event_accumulator
-
-import os
-from shutil import copy2
-from re import search as RSearch
-import pandas as pd
-from ast import literal_eval as LEval
-
-weights_dir = 'weights/'
-
-def find_biggest_tensorboard(tensordir):
- try:
- files = [f for f in os.listdir(tensordir) if f.endswith('.0')]
- if not files:
- print("No files with the '.0' extension found!")
- return
-
- max_size = 0
- biggest_file = ""
-
- for file in files:
- file_path = os.path.join(tensordir, file)
- if os.path.isfile(file_path):
- file_size = os.path.getsize(file_path)
- if file_size > max_size:
- max_size = file_size
- biggest_file = file
-
- return biggest_file
-
- except FileNotFoundError:
- print("Couldn't find your model!")
- return
-
-def main(model_name, save_freq, lastmdls):
- global lowestval_weight_dir, scl
-
- tensordir = os.path.join('logs', model_name)
- lowestval_weight_dir = os.path.join(tensordir, "lowestvals")
-
- latest_file = find_biggest_tensorboard(tensordir)
-
- if latest_file is None:
- print("Couldn't find a valid tensorboard file!")
- return
-
- tfile = os.path.join(tensordir, latest_file)
-
- ea = event_accumulator.EventAccumulator(tfile,
- size_guidance={
- event_accumulator.COMPRESSED_HISTOGRAMS: 500,
- event_accumulator.IMAGES: 4,
- event_accumulator.AUDIO: 4,
- event_accumulator.SCALARS: 0,
- event_accumulator.HISTOGRAMS: 1,
- })
-
- ea.Reload()
- ea.Tags()
-
- scl = ea.Scalars('loss/g/total')
-
- listwstep = {}
-
- for val in scl:
- if (val.step // save_freq) * save_freq in [val.step for val in scl]:
- listwstep[float(val.value)] = (val.step // save_freq) * save_freq
-
- lowest_vals = sorted(listwstep.keys())[:lastmdls]
-
- sorted_dict = {value: step for value, step in listwstep.items() if value in lowest_vals}
-
- return sorted_dict
-
-def selectweights(model_name, file_dict, weights_dir, lowestval_weight_dir):
- os.makedirs(lowestval_weight_dir, exist_ok=True)
- logdir = []
- files = []
- lbldict = {
- 'Values': {},
- 'Names': {}
- }
- weights_dir_path = os.path.join(weights_dir, "")
- low_val_path = os.path.join(os.getcwd(), os.path.join(lowestval_weight_dir, ""))
-
- try:
- file_dict = LEval(file_dict)
- except Exception as e:
- print(f"Error! {e}")
- return f"Couldn't load tensorboard file! {e}"
-
- weights = [f for f in os.scandir(weights_dir)]
- for key, value in file_dict.items():
- pattern = fr"^{model_name}_.*_s{value}\.pth$"
- matching_weights = [f.name for f in weights if f.is_file() and RSearch(pattern, f.name)]
- for weight in matching_weights:
- source_path = weights_dir_path + weight
- destination_path = os.path.join(lowestval_weight_dir, weight)
-
- copy2(source_path, destination_path)
-
- logdir.append(f"File = {weight} Value: {key}, Step: {value}")
-
- lbldict['Names'][weight] = weight
- lbldict['Values'][weight] = key
-
- files.append(low_val_path + weight)
-
- print(f"File = {weight} Value: {key}, Step: {value}")
-
- yield ('\n'.join(logdir), files, pd.DataFrame(lbldict))
-
-
- return ''.join(logdir), files, pd.DataFrame(lbldict)
-
-
-if __name__ == "__main__":
- model = str(input("Enter the name of the model: "))
- sav_freq = int(input("Enter save frequency of the model: "))
- ds = main(model, sav_freq)
-
- if ds: selectweights(model, ds, weights_dir, lowestval_weight_dir)
-
\ No newline at end of file
diff --git a/spaces/Rekanice/hf_minimal_sushi/README.md b/spaces/Rekanice/hf_minimal_sushi/README.md
deleted file mode 100644
index 4b038ea069e1817e717615da6679be0a913f0f5d..0000000000000000000000000000000000000000
--- a/spaces/Rekanice/hf_minimal_sushi/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Hf Minimal Sushi
-emoji: 🌍
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RoCobo/WiggleGAN/main.py b/spaces/RoCobo/WiggleGAN/main.py
deleted file mode 100644
index 097c3b9aed9be52619095419859205b71110555c..0000000000000000000000000000000000000000
--- a/spaces/RoCobo/WiggleGAN/main.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import argparse
-import os
-import torch
-from WiggleGAN import WiggleGAN
-#from MyACGAN import MyACGAN
-#from MyGAN import MyGAN
-
-"""parsing and configuration"""
-
-
-def parse_args():
- desc = "Pytorch implementation of GAN collections"
- parser = argparse.ArgumentParser(description=desc)
-
- parser.add_argument('--gan_type', type=str, default='WiggleGAN',
- choices=['MyACGAN', 'MyGAN', 'WiggleGAN'],
- help='The type of GAN')
- parser.add_argument('--dataset', type=str, default='4cam',
- choices=['mnist', 'fashion-mnist', 'cifar10', 'cifar100', 'svhn', 'stl10', 'lsun-bed', '4cam'],
- help='The name of dataset')
- parser.add_argument('--split', type=str, default='', help='The split flag for svhn and stl10')
- parser.add_argument('--epoch', type=int, default=50, help='The number of epochs to run')
- parser.add_argument('--batch_size', type=int, default=16, help='The size of batch')
- parser.add_argument('--input_size', type=int, default=10, help='The size of input image')
- parser.add_argument('--save_dir', type=str, default='models',
- help='Directory name to save the model')
- parser.add_argument('--result_dir', type=str, default='results', help='Directory name to save the generated images')
- parser.add_argument('--log_dir', type=str, default='logs', help='Directory name to save training logs')
- parser.add_argument('--lrG', type=float, default=0.0002)
- parser.add_argument('--lrD', type=float, default=0.001)
- parser.add_argument('--beta1', type=float, default=0.5)
- parser.add_argument('--beta2', type=float, default=0.999)
- parser.add_argument('--gpu_mode', type=str2bool, default=True)
- parser.add_argument('--benchmark_mode', type=str2bool, default=True)
- parser.add_argument('--cameras', type=int, default=2)
- parser.add_argument('--imageDim', type=int, default=128)
- parser.add_argument('--epochV', type=int, default=0)
- parser.add_argument('--cIm', type=int, default=4)
- parser.add_argument('--seedLoad', type=str, default="-0000")
- parser.add_argument('--zGF', type=float, default=0.2)
- parser.add_argument('--zDF', type=float, default=0.2)
- parser.add_argument('--bF', type=float, default=0.2)
- parser.add_argument('--expandGen', type=int, default=3)
- parser.add_argument('--expandDis', type=int, default=3)
- parser.add_argument('--wiggleDepth', type=int, default=-1)
- parser.add_argument('--visdom', type=str2bool, default=True)
- parser.add_argument('--lambdaL1', type=int, default=100)
- parser.add_argument('--clipping', type=float, default=-1)
- parser.add_argument('--depth', type=str2bool, default=True)
- parser.add_argument('--recreate', type=str2bool, default=False)
- parser.add_argument('--name_wiggle', type=str, default='wiggle-result')
-
- return check_args(parser.parse_args())
-
-
-"""checking arguments"""
-
-def str2bool(v):
- if isinstance(v, bool):
- return v
- if v.lower() in ('yes', 'true', 't', 'y', '1'):
- return True
- elif v.lower() in ('no', 'false', 'f', 'n', '0'):
- return False
- else:
- raise argparse.ArgumentTypeError('Boolean value expected.')
-
-
-def check_args(args):
- # --save_dir
- if not os.path.exists(args.save_dir):
- os.makedirs(args.save_dir)
-
- # --result_dir
- if not os.path.exists(args.result_dir):
- os.makedirs(args.result_dir)
-
- # --result_dir
- if not os.path.exists(args.log_dir):
- os.makedirs(args.log_dir)
-
- # --epoch
- try:
- assert args.epoch >= 1
- except:
- print('number of epochs must be larger than or equal to one')
-
- # --batch_size
- try:
- assert args.batch_size >= 1
- except:
- print('batch size must be larger than or equal to one')
-
- return args
-
-
-"""main"""
-
-
-def main():
- # parse arguments
- args = parse_args()
- if args is None:
- exit()
-
- if args.benchmark_mode:
- torch.backends.cudnn.benchmark = True
-
- # declare instance for GAN
- if args.gan_type == 'WiggleGAN':
- gan = WiggleGAN(args)
- #elif args.gan_type == 'MyACGAN':
- # gan = MyACGAN(args)
- #elif args.gan_type == 'MyGAN':
- # gan = MyGAN(args)
- else:
- raise Exception("[!] There is no option for " + args.gan_type)
-
- # launch the graph in a session
- if (args.wiggleDepth < 0 and not args.recreate):
- print(" [*] Training Starting!")
- gan.train()
- print(" [*] Training finished!")
- else:
- if not args.recreate:
- print(" [*] Wiggle Started!")
- gan.wiggleEf()
- print(" [*] Wiggle finished!")
- else:
- print(" [*] Dataset recreation Started")
- gan.recreate()
- print(" [*] Dataset recreation finished")
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py
deleted file mode 100644
index f2678b321c295bcceaef945111ac3524be19d6e4..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .misc import add_prefix
-
-__all__ = ['add_prefix']
diff --git a/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py b/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py
deleted file mode 100644
index cdd69f2a24d47166425d785ef2767708b44b6593..0000000000000000000000000000000000000000
--- a/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from config import output_path_video, output_path_audio
-from logger import logger
-from resource_loader.video_loader_interface import VideoLoaderInterface
-
-
-class UploadedMediaLoader(VideoLoaderInterface):
- def __init__(self, uploaded_stream, original_name, media_type='video'):
- self.uploaded_stream = uploaded_stream
- self.original_name = original_name
- self.media_type = media_type # 'video' or 'audio'
- self.media_id = None
- self.filename = None
- self.output_path = None
- self.extract_filename()
- self.set_output_path()
-
- def extract_filename(self):
- self.filename = self.original_name.split(" - ", 1)[1]
- self.media_id = self.filename.rsplit(".", 1)[0]
-
- def set_output_path(self):
- if self.media_type == 'video':
- self.output_path = output_path_video
- elif self.media_type == 'audio':
- self.output_path = output_path_audio
- else:
- raise ValueError("Invalid media type")
-
- def download(self):
- with open(f"{self.output_path}/{self.filename}", "wb") as f:
- f.write(self.uploaded_stream.getvalue())
- logger.info(f"{self.media_type.capitalize()} processed: {self.original_name}")
diff --git a/spaces/STF-R/docker-test3/app/templates/prediction.html b/spaces/STF-R/docker-test3/app/templates/prediction.html
deleted file mode 100644
index 4366614474cbe8347297ec3d57a7b8b7bf72684d..0000000000000000000000000000000000000000
--- a/spaces/STF-R/docker-test3/app/templates/prediction.html
+++ /dev/null
@@ -1,48 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
- VIVADATA | {{iris_type}}
-
-
-
-
-
-
-
-
-
-
-
-
-
- VIVADATA - Flask Demo
-
-
-
- 🏵️🌺🌸 It's a {{iris_type|title}} 🏵️🌺🌸
-
-
-
-
-
-
-
- Back
-
-
-
-
-
-
-
-
diff --git a/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py b/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py
deleted file mode 100644
index 3706c57541c1b7d9004957422b52cd1e2191ae68..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-# flake8: noqa
-
-from ..utils import DummyObject, requires_backends
-
-
-class LMSDiscreteScheduler(metaclass=DummyObject):
- _backends = ["scipy"]
-
- def __init__(self, *args, **kwargs):
- requires_backends(self, ["scipy"])
diff --git a/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py b/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py
deleted file mode 100644
index 2e25dd918f2714ae9a2c21d14401663c371b5f38..0000000000000000000000000000000000000000
--- a/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py
+++ /dev/null
@@ -1,704 +0,0 @@
-from typing import Callable, Optional, Tuple
-
-import flax.linen as nn
-import jax
-import jax.numpy as jnp
-from flax.core.frozen_dict import FrozenDict, unfreeze
-from jax import lax
-from jax.random import PRNGKey
-from transformers import GPT2Config, FlaxViTModel, ViTConfig
-from transformers.modeling_flax_outputs import (
- FlaxCausalLMOutputWithCrossAttentions,
- FlaxSeq2SeqLMOutput,
- FlaxSeq2SeqModelOutput,
-)
-from transformers.models.bart.modeling_flax_bart import (
- shift_tokens_right,
-)
-from .modeling_flax_gpt2 import (
- FlaxGPT2Module,
- FlaxGPT2Model,
- FlaxPreTrainedModel
-)
-from transformers.models.vit.modeling_flax_vit import FlaxViTModule
-
-from .configuration_vit_gpt2 import ViTGPT2Config
-
-
-class FlaxViTGPT2Module(nn.Module):
- config: ViTGPT2Config
- dtype: jnp.dtype = jnp.float32 # the dtype of the computation
-
- def setup(self):
-
- self.encoder = FlaxViTModule(self.config.vit_config, dtype=self.dtype)
- self.decoder = FlaxGPT2Module(self.config.gpt2_config, dtype=self.dtype)
-
- def _get_encoder_module(self):
- return self.encoder
-
- def _get_decoder_module(self):
- return self.decoder
-
- def __call__(
- self,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- output_attentions: bool = False,
- output_hidden_states: bool = False,
- return_dict: bool = True,
- deterministic: bool = True,
- ):
- encoder_outputs = self.encoder(
- pixel_values=pixel_values,
- deterministic=deterministic,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- decoder_outputs = self.decoder(
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_hidden_states=encoder_outputs[0],
- encoder_attention_mask=encoder_attention_mask,
- deterministic=deterministic,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict
- )
-
- return FlaxSeq2SeqModelOutput(
- last_hidden_state=decoder_outputs.last_hidden_state,
- decoder_hidden_states=decoder_outputs.hidden_states,
- decoder_attentions=decoder_outputs.attentions,
- cross_attentions=decoder_outputs.cross_attentions,
- encoder_last_hidden_state=encoder_outputs.last_hidden_state,
- encoder_hidden_states=encoder_outputs.hidden_states,
- encoder_attentions=encoder_outputs.attentions,
- )
-
-
-class FlaxViTGPT2ForConditionalGenerationModule(nn.Module):
- config: ViTGPT2Config
- dtype: jnp.dtype = jnp.float32
- bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
-
- def setup(self):
- self.model = FlaxViTGPT2Module(config=self.config, dtype=self.dtype)
- self.lm_head = nn.Dense(
- self.model.decoder.embed_dim,
- use_bias=False,
- dtype=self.dtype,
- kernel_init=jax.nn.initializers.normal(
- self.config.gpt2_config.initializer_range, self.dtype
- ),
- )
- self.final_logits_bias = self.param(
- "final_logits_bias", self.bias_init, (1, self.model.decoder.embed_dim)
- )
-
- def _get_encoder_module(self):
- return self.model.encoder
-
- def _get_decoder_module(self):
- return self.model.decoder
-
- def __call__(
- self,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- output_attentions: bool = False,
- output_hidden_states: bool = False,
- return_dict: bool = True,
- deterministic: bool = True,
- ):
- outputs = self.model(
- pixel_values=pixel_values,
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_attention_mask=encoder_attention_mask,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=deterministic,
- )
-
- hidden_states = outputs[0]
- lm_logits = self.lm_head(hidden_states)
- lm_logits += self.final_logits_bias
-
- if not return_dict:
- output = (lm_logits,) + outputs[1:]
- return output
-
- return FlaxSeq2SeqLMOutput(
- logits=lm_logits,
- decoder_hidden_states=outputs.decoder_hidden_states,
- decoder_attentions=outputs.decoder_attentions,
- cross_attentions=outputs.cross_attentions,
- encoder_last_hidden_state=outputs.encoder_last_hidden_state,
- encoder_hidden_states=outputs.encoder_hidden_states,
- encoder_attentions=outputs.encoder_attentions,
- )
-
-class FlaxViTGPT2PreTrainedModel(FlaxPreTrainedModel):
- config_class = ViTGPT2Config
- base_model_prefix: str = "model"
- module_class: nn.Module = None
-
- def __init__(
- self,
- config: ViTGPT2Config,
- input_shape: Tuple = None,
- seed: int = 0,
- dtype: jnp.dtype = jnp.float32,
- **kwargs,
- ):
- if input_shape is None:
- input_shape = (
- (1, config.vit_config.image_size, config.vit_config.image_size, 3),
- (1, 1),
- )
-
- module = self.module_class(config=config, dtype=dtype, **kwargs)
- super().__init__(
- config, module, input_shape=input_shape, seed=seed, dtype=dtype
- )
-
- def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
- # init input tensors
- pixel_values = jax.random.normal(rng, input_shape[0])
- # # make sure initialization pass will work for FlaxBartForSequenceClassificationModule
- # input_ids = jax.ops.index_update(input_ids, (..., -1), self.config.eos_token_id)
-
- input_ids = jnp.zeros(input_shape[1], dtype="i4")
- attention_mask = jnp.ones_like(input_ids)
-
- batch_size, sequence_length = input_ids.shape
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- params_rng, dropout_rng = jax.random.split(rng)
- rngs = {"params": params_rng, "dropout": dropout_rng}
-
- return self.module.init(
- rngs,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- )["params"]
-
- def init_cache(self, batch_size, max_length, encoder_outputs):
-
- input_ids = jnp.ones((batch_size, max_length), dtype="i4")
- attention_mask = jnp.ones_like(input_ids)
- position_ids = jnp.broadcast_to(
- jnp.arange(jnp.atleast_2d(input_ids).shape[-1]),
- input_ids.shape,
- )
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- return decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
-
- init_variables = self.module.init(
- jax.random.PRNGKey(0),
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_hidden_states=encoder_outputs[0],
- init_cache=True,
- method=_decoder_forward, # we only need to call the decoder to init the cache
- )
- return unfreeze(init_variables["cache"])
-
- def encode(
- self,
- pixel_values: jnp.ndarray,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- def _encoder_forward(module, pixel_values, **kwargs):
- encode_module = module._get_encoder_module()
- return encode_module(pixel_values, **kwargs)
-
- return self.module.apply(
- {"params": params or self.params},
- pixel_values=jnp.array(pixel_values, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- method=_encoder_forward,
- )
-
- def decode(
- self,
- input_ids,
- encoder_outputs,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- past_key_values: dict = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
-
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- encoder_hidden_states = encoder_outputs[0]
- if encoder_attention_mask is None:
- batch_size, sequence_length = encoder_hidden_states.shape[:2]
- encoder_attention_mask = jnp.ones((batch_size, sequence_length))
-
- batch_size, sequence_length = input_ids.shape
- if attention_mask is None:
- attention_mask = jnp.ones((batch_size, sequence_length))
-
- if position_ids is None:
- if past_key_values is not None:
- raise ValueError(
- "Make sure to provide `position_ids` when passing `past_key_values`."
- )
-
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- inputs = {"params": params or self.params}
-
- # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
- # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
- # it can be changed by FlaxGPT2Attention module
- if past_key_values:
- inputs["cache"] = past_key_values
- mutable = ["cache"]
- else:
- mutable = False
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- return decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
-
- outputs = self.module.apply(
- inputs,
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- mutable=mutable,
- method=_decoder_forward,
- )
-
- # add updated cache to model output
- if past_key_values is not None and return_dict:
- outputs, past = outputs
- outputs["past_key_values"] = unfreeze(past["cache"])
- return outputs
- elif past_key_values is not None and not return_dict:
- outputs, past = outputs
- outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
-
- return outputs
-
- def __call__(
- self,
- pixel_values: jnp.ndarray,
- input_ids: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
-
- # # prepare encoder inputs
- # if encoder_attention_mask is None:
- # encoder_attention_mask = jnp.ones_like(input_ids)
-
- # if position_ids is None:
- # batch_size, sequence_length = input_ids.shape
- # position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
-
- # prepare decoder inputs
- # if decoder_input_ids is None:
- # decoder_input_ids = shift_tokens_right(
- # input_ids, self.config.pad_token_id, decoder_start_token_id=self.config.decoder_start_token_id
- # ) # TODO: Check how to use this
-
- if attention_mask is None:
- attention_mask = jnp.ones_like(input_ids)
- if position_ids is None:
- batch_size, sequence_length = input_ids.shape
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
-
- return self.module.apply(
- {"params": params or self.params},
- pixel_values=jnp.array(pixel_values, dtype=jnp.float32),
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- )
-
-
-class FlaxViTGPT2ForConditionalGeneration(FlaxViTGPT2PreTrainedModel):
- module_class = FlaxViTGPT2ForConditionalGenerationModule
- dtype: jnp.dtype = jnp.float32
-
- def decode(
- self,
- input_ids,
- encoder_outputs,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- past_key_values: dict = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- deterministic: bool = True,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- encoder_hidden_states = encoder_outputs[0]
- if encoder_attention_mask is None:
- batch_size, sequence_length = encoder_hidden_states.shape[:2]
- encoder_attention_mask = jnp.ones((batch_size, sequence_length))
-
- batch_size, sequence_length = input_ids.shape
- if attention_mask is None:
- attention_mask = jnp.ones((batch_size, sequence_length))
-
- if position_ids is None:
- if past_key_values is not None:
- raise ValueError(
- "Make sure to provide `position_ids` when passing `past_key_values`."
- )
-
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- inputs = {"params": params or self.params}
-
- # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
- # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
- # it can be changed by FlaxGPT2Attention module
- if past_key_values:
- inputs["cache"] = past_key_values
- mutable = ["cache"]
- else:
- mutable = False
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- outputs = decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
- hidden_states = outputs[0]
-
- if self.config.tie_word_embeddings:
- shared_embedding = module.model.variables["params"]["shared"][
- "embedding"
- ]
- lm_logits = module.lm_head.apply(
- {"params": {"kernel": shared_embedding.T}}, hidden_states
- )
- else:
- lm_logits = module.lm_head(hidden_states)
-
- lm_logits += module.final_logits_bias
- return lm_logits, outputs
-
- outputs = self.module.apply(
- inputs,
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=deterministic,
- rngs=rngs,
- mutable=mutable,
- method=_decoder_forward,
- )
-
- if past_key_values is None:
- lm_logits, outputs = outputs
- else:
- (lm_logits, outputs), past = outputs
-
- if return_dict:
- outputs = FlaxCausalLMOutputWithCrossAttentions(
- logits=lm_logits,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- cross_attentions=outputs.cross_attentions,
- )
- else:
- outputs = (lm_logits,) + outputs[1:]
-
- # add updated cache to model output
- if past_key_values is not None and return_dict:
- outputs["past_key_values"] = unfreeze(past["cache"])
- return outputs
- elif past_key_values is not None and not return_dict:
- outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
-
- return outputs
-
- def prepare_inputs_for_generation(
- self,
- input_ids,
- max_length,
- encoder_attention_mask: Optional[jnp.DeviceArray] = None,
- attention_mask: Optional[jnp.DeviceArray] = None,
- encoder_outputs=None,
- **kwargs,
- ):
- # initializing the cache
- batch_size, seq_length = input_ids.shape
-
- past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
- # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
- # But since the decoder uses a causal mask, those positions are masked anyways.
- # Thus we can create a single static attention_mask here, which is more efficient for compilation
- extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
- if attention_mask is not None:
- position_ids = attention_mask.cumsum(axis=-1) - 1
- extended_attention_mask = lax.dynamic_update_slice(
- extended_attention_mask, attention_mask, (0, 0)
- )
- else:
- position_ids = jnp.broadcast_to(
- jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)
- )
-
- return {
- "past_key_values": past_key_values,
- "encoder_outputs": encoder_outputs,
- "encoder_attention_mask": encoder_attention_mask,
- "attention_mask": extended_attention_mask,
- "position_ids": position_ids,
- }
-
- def update_inputs_for_generation(self, model_outputs, model_kwargs):
- model_kwargs["past_key_values"] = model_outputs.past_key_values
- model_kwargs["position_ids"] = (
- model_kwargs["position_ids"][:, -1:] + 1
- )
- return model_kwargs
-
- @classmethod
- def from_vit_gpt2_pretrained(
- cls,
- vit_model_name_or_path: str = None,
- gpt2_model_name_or_path: str = None,
- *model_args,
- **kwargs,
- ) -> FlaxViTGPT2PreTrainedModel:
-
- kwargs_gpt2 = {
- argument[len("gpt2_") :]: value
- for argument, value in kwargs.items()
- if argument.startswith("gpt2_")
- }
-
- kwargs_vit = {
- argument[len("vit_") :]: value
- for argument, value in kwargs.items()
- if argument.startswith("vit_")
- }
-
- # remove gpt2, vit kwargs from kwargs
- for key in kwargs_gpt2.keys():
- del kwargs["gpt2_" + key]
- for key in kwargs_vit.keys():
- del kwargs["vit_" + key]
-
- # Load and initialize the gpt2 and vit model
- gpt2_model = kwargs_gpt2.pop("model", None)
- if gpt2_model is None:
- assert (
- gpt2_model_name_or_path is not None
- ), "If `model` is not defined as an argument, a `gpt2_model_name_or_path` has to be defined"
-
- if "config" not in kwargs_gpt2:
- gpt2_config = GPT2Config.from_pretrained(gpt2_model_name_or_path)
- kwargs_gpt2["config"] = gpt2_config
-
- kwargs_gpt2["config"].add_cross_attention = True
- gpt2_model = FlaxGPT2Model.from_pretrained(
- gpt2_model_name_or_path, *model_args, **kwargs_gpt2
- )
-
- vit_model = kwargs_vit.pop("model", None)
- if vit_model is None:
- assert (
- vit_model_name_or_path is not None
- ), "If `model` is not defined as an argument, a `vit_model_name_or_path` has to be defined"
-
- if "config" not in kwargs_vit:
- vit_config = ViTConfig.from_pretrained(vit_model_name_or_path)
- kwargs_vit["config"] = vit_config
-
- vit_model = FlaxViTModel.from_pretrained(
- vit_model_name_or_path, *model_args, **kwargs_vit
- )
-
- # instantiate config with corresponding kwargs
- dtype = kwargs.pop("dtype", jnp.float32)
- config = ViTGPT2Config.from_vit_gpt2_configs(
- vit_model.config, gpt2_model.config, **kwargs
- )
-
- # init model
- model = cls(config, *model_args, dtype=dtype, **kwargs)
- model.params["model"]["encoder"] = vit_model.params
- model.params["model"]["decoder"] = gpt2_model.params
-
- return model
-
diff --git a/spaces/Seetha/IMA-pipeline-streamlit/app.py b/spaces/Seetha/IMA-pipeline-streamlit/app.py
deleted file mode 100644
index f0db4170922ebf1b9a612db7fac952e61207154a..0000000000000000000000000000000000000000
--- a/spaces/Seetha/IMA-pipeline-streamlit/app.py
+++ /dev/null
@@ -1,581 +0,0 @@
-# import all packages
-import requests
-import streamlit as st
-from sklearn.model_selection import StratifiedKFold
-from sklearn.model_selection import train_test_split
-from sklearn.model_selection import KFold
-# tokenizer
-from transformers import AutoTokenizer, DistilBertTokenizerFast
-# sequence tagging model + training-related
-from transformers import DistilBertForTokenClassification, Trainer, TrainingArguments
-import torch
-import sys
-import os
-from sklearn.metrics import classification_report
-from pandas import read_csv
-from sklearn.linear_model import LogisticRegression
-import sklearn.model_selection
-from sklearn.feature_extraction.text import TfidfTransformer
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.pipeline import Pipeline, FeatureUnion
-import math
-# from sklearn.metrics import accuracy_score
-# from sklearn.metrics import precision_recall_fscore_support
-import json
-import re
-import numpy as np
-import pandas as pd
-import nltk
-nltk.download("punkt")
-import string
-from sklearn.model_selection import train_test_split
-from transformers import AutoTokenizer, Trainer, TrainingArguments, AutoModelForSequenceClassification, AutoConfig
-from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
-import itertools
-from transformers import TextClassificationPipeline, TFAutoModelForSequenceClassification, AutoTokenizer
-from transformers import pipeline
-import pickle
-import csv
-import pdfplumber
-import pathlib
-import shutil
-import webbrowser
-from streamlit.components.v1 import html
-import streamlit.components.v1 as components
-from PyPDF2 import PdfReader
-from huggingface_hub import HfApi
-import io
-from datasets import load_dataset
-import time
-
-import huggingface_hub
-from huggingface_hub import Repository
-from datetime import datetime
-import pathlib as Path
-from requests import get
-import urllib.request
-# import gradio as gr
-# from gradio import inputs, outputs
-from datasets import load_dataset
-from huggingface_hub import HfApi, list_models
-import os
-from huggingface_hub import HfFileSystem
-from tensorflow.keras.models import Sequential, model_from_json
-#import tensorflow_datasets as tfds
-import tensorflow as tf
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-import spacy
-from tensorflow.keras.preprocessing.text import Tokenizer
-#from spacy import en_core_web_lg
-#import en_core_web_lg
-#nlp = en_core_web_lg.load()
-nlp = spacy.load('en_core_web_sm')
-
-#tfds.disable_progress_bar()
-MAX_SEQUENCE_LENGTH = 500
-
-# dataset = load_dataset('Seetha/Visualization', streaming=True)
-# df = pd.DataFrame.from_dict(dataset['train'])
-# DATASET_REPO_URL = "https://huggingface.co/datasets/Seetha/Visualization"
-# DATA_FILENAME = "level2.json"
-#DATA_FILE = os.path.join("data", DATA_FILENAME)
-DATASET_REPO_URL = "https://huggingface.co/datasets/Seetha/visual_files"
-DATA_FILENAME = "detailedResults.json"
-DATA_FILENAME1 = "level2.json"
-
-HF_TOKEN = os.environ.get("HF_TOKEN")
-#st.write("is none?", HF_TOKEN is None)
-
-def main():
-
- st.title("Text to Causal Knowledge Graph")
- st.sidebar.title("Please upload your text documents in one file here:")
- k=2
- seed = 1
- k1= 5
- text_list = []
- causal_sents = []
-
- uploaded_file = None
- try:
- uploaded_file = st.sidebar.file_uploader("Choose a file", type = "pdf")
- except:
- uploaded_file = PdfReader('sample_anno.pdf')
- st.error("Please upload your own PDF to be analyzed")
-
- if uploaded_file is not None:
- reader = PdfReader(uploaded_file)
- for page in reader.pages:
- text = page.extract_text()
- text_list.append(text)
- else:
- st.error("Please upload your own PDF to be analyzed")
- st.stop()
-
- text_list_final = [x.replace('\n', '') for x in text_list]
- text_list_final = re.sub('"', '', str(text_list_final))
-
- sentences = nltk.sent_tokenize(text_list_final)
-
- result =[]
- for i in sentences:
- result1 = i.lower()
- result2 = re.sub(r'[^\w\s]','',result1)
- result.append(result2)
-
- #st.write("--- %s seconds ---" % (time.time() - start_time))
- tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") #bert-base-uncased
-
- model_path = "checkpoint-2850"
-
- model = AutoModelForSequenceClassification.from_pretrained(model_path,id2label={0:'non-causal',1:'causal'})
-
- #st.write('sequence classification loaded')
- pipe1 = pipeline("text-classification", model=model,tokenizer=tokenizer)
- for sent in result:
- pred = pipe1(sent)
- for lab in pred:
- if lab['label'] == 'causal': #causal
- causal_sents.append(sent)
-
- # st.write('causal sentence classification finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
-
- model_name = "distilbert-base-cased"
- tokenizer = DistilBertTokenizerFast.from_pretrained(model_name,low_cpu_mem_usage=True)
-
- model_path1 = "DistilBertforTokenclassification"
-
- model = DistilBertForTokenClassification.from_pretrained(model_path1,low_cpu_mem_usage=True) #len(unique_tags),, num_labels= 7, , id2label={0:'CT',1:'E',2:'C',3:'O'}
- pipe = pipeline('ner', model=model, tokenizer=tokenizer,aggregation_strategy='simple') #grouped_entities=True
- st.write('DistilBERT loaded')
- sentence_pred = []
- class_list = []
- entity_list = []
- for k in causal_sents:
- pred= pipe(k)
- #st.write(pred)
- #st.write('preds')
- for i in pred:
- sentence_pred.append(k)
- class_list.append(i['word'])
- entity_list.append(i['entity_group'])
-
- # st.write('causality extraction finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
-
- # filename = 'Checkpoint-classification.sav'
- # loaded_model = pickle.load(open(filename, 'rb'))
- # loaded_vectorizer = pickle.load(open('vectorizefile_classification.pickle', 'rb'))
-
- # pipeline_test_output = loaded_vectorizer.transform(class_list)
- # predicted = loaded_model.predict(pipeline_test_output)
-
- tokenizer = Tokenizer(num_words=100000)
- tokenizer.fit_on_texts(class_list)
- word_index = tokenizer.word_index
- # text_embedding = np.zeros((len(word_index) + 1, 300))
- # for word, i in word_index.items():
- # text_embedding[i] = nlp(word).vector
- json_file = open('model.json', 'r')
- loaded_model_json = json_file.read()
- json_file.close()
- loaded_model = model_from_json(loaded_model_json)
- # load weights into new model
- loaded_model.load_weights("model.h5")
-
- loss = tf.keras.losses.CategoricalCrossentropy() #from_logits=True
- loaded_model.compile(loss=loss,optimizer=tf.keras.optimizers.Adam(1e-4))
-
- predictions = loaded_model.predict(pad_sequences(tokenizer.texts_to_sequences(class_list),maxlen=MAX_SEQUENCE_LENGTH))
- predicted = np.argmax(predictions,axis=1)
-
- st.write(predictions)
- st.write(predicted)
- # st.write('stakeholder taxonomy finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
- pred1 = predicted
- level0 = []
- count =0
- for i in predicted:
- if i == 3:
- level0.append('Non-Performance')
- count +=1
- else:
- level0.append('Performance')
- count +=1
-
- list_pred = {0: 'Customers',1:'Employees',2:'Investors',3:'Non-performance',4:'Society',5:'Unclassified'}
- pred_val = [list_pred[i] for i in pred1]
-
- #print('count',count)
- for ind,(sent,preds) in enumerate(zip(class_list,pred_val)):
- if 'customers' in sent or 'client' in sent or 'consumer' in sent or 'user' in sent:
- pred_val[ind] = 'Customers'
- elif 'investor' in sent or 'finance' in sent or 'shareholder' in sent or 'stockholder' in sent or 'owners' in sent:
- pred_val[ind] = 'Investors'
- elif 'employee' in sent or 'worker' in sent or 'staff' in sent:
- pred_val[ind] = 'Employees'
- elif 'society' in sent or 'societal' in sent or 'social responsib*' in sent or 'social performance' in sent or 'community' in sent:
- pred_val[ind] = 'Society'
-
- sent_id, unique = pd.factorize(sentence_pred)
-
- final_list = pd.DataFrame(
- {'Id': sent_id,
- 'Fullsentence': sentence_pred,
- 'Component': class_list,
- 'causeOrEffect': entity_list,
- 'Labellevel1': level0,
- 'Labellevel2': pred_val
- })
- s = final_list['Component'].shift(-1)
- m = s.str.startswith('##', na=False)
- final_list.loc[m, 'Component'] += (' ' + s[m])
-
-
- final_list1 = final_list[~final_list['Component'].astype(str).str.startswith('##')]
- li = []
- uni = final_list1['Id'].unique()
- for i in uni:
- df_new = final_list1[final_list1['Id'] == i]
- uni1 = df_new['Id'].unique()
-
- # if 'E' not in df_new.values:
- # li.append(uni1)
- # out = np.concatenate(li).ravel()
- # li_pan = pd.DataFrame(out,columns=['Id'])
- # df3 = pd.merge(final_list1, li_pan[['Id']], on='Id', how='left', indicator=True) \
- # .query("_merge == 'left_only'") \
- # .drop("_merge",axis=1)
-
- df3 = final_list1
- #df = df3.groupby(['Id','Fullsentence','causeOrEffect', 'Labellevel1', 'Labellevel2'])['Component'].apply(', '.join).reset_index()
- #st.write(df)
-
- #df = df3
- df3["causeOrEffect"].replace({"C": "cause", "E": "effect"}, inplace=True)
- df_final = df3[df3['causeOrEffect'] != 'CT']
- df3['New string'] = df_final['Component'].replace(r'[##]+', ' ', regex=True)
-
- df_final = df_final.drop("Component",axis=1)
- df_final.insert(2, "Component", df3['New string'], True)
-
- df_final1 = df_final[df_final['Component'].str.split().str.len().gt(1)]
- #st.write(df_final[df_final['Component'].str.len() != 1])
- #df_final1.to_csv('predictions.csv')
-
-# buffer = io.BytesIO()
-# with pd.ExcelWriter(buffer, engine="xlsxwriter") as writer:
-# df_final.to_excel(writer, sheet_name="Sheet1", index=False)
-# writer.close()
-
- count_NP_NP = 0
- count_NP_investor = 0
- count_NP_customer = 0
- count_NP_employees = 0
- count_NP_society = 0
-
- count_inv_np = 0
- count_inv_investor = 0
- count_inv_customer = 0
- count_inv_employee = 0
- count_inv_society = 0
-
- count_cus_np = 0
- count_cus_investor = 0
- count_cus_customer = 0
- count_cus_employee = 0
- count_cus_society = 0
-
- count_emp_np = 0
- count_emp_investor = 0
- count_emp_customer = 0
- count_emp_employee = 0
- count_emp_society = 0
-
- count_soc_np = 0
- count_soc_investor = 0
- count_soc_customer = 0
- count_soc_employee = 0
- count_soc_society = 0
- for i in range(0,df_final['Id'].max()):
- j = df_final.loc[df_final['Id'] == i]
- cause_tab = j.loc[j['causeOrEffect'] == 'cause']
- effect_tab = j.loc[j['causeOrEffect'] == 'effect']
- cause_coun_NP = (cause_tab.Labellevel2 == 'Non-performance').sum()
- effect_coun_NP = (effect_tab.Labellevel2 == 'Non-performance').sum()
-
- if (cause_coun_NP > 0) and (effect_coun_NP > 0):
- count_NP = cause_coun_NP if cause_coun_NP >= effect_coun_NP else effect_coun_NP
- else:
- count_NP = 0
- effect_NP_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_NP > 0) and (effect_NP_inv > 0):
- count_NP_inv = cause_coun_NP if cause_coun_NP >= effect_NP_inv else effect_NP_inv
- else:
- count_NP_inv = 0
- effect_NP_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_NP > 0) and (effect_NP_cus > 0):
- count_NP_cus = cause_coun_NP if cause_coun_NP >= effect_NP_cus else effect_NP_cus
- else:
- count_NP_cus = 0
- effect_NP_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_NP > 0) and (effect_NP_emp > 0):
- count_NP_emp = cause_coun_NP if cause_coun_NP >= effect_NP_emp else effect_NP_emp
- else:
- count_NP_emp = 0
- effect_NP_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_NP > 0) and (effect_NP_soc > 0):
- count_NP_soc = cause_coun_NP if cause_coun_NP >= effect_NP_soc else effect_NP_soc
- else:
- count_NP_soc = 0
-
- cause_coun_inv = (cause_tab.Labellevel2 == 'Investors').sum()
- effect_coun_inv = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_inv > 0) and (effect_coun_inv > 0):
- count_NP_inv = cause_coun_inv if cause_coun_inv >= effect_coun_inv else effect_coun_inv
- else:
- count_NP_inv = 0
-
- effect_inv_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_inv > 0) and (effect_inv_inv > 0):
- count_inv_inv = cause_coun_inv if cause_coun_inv >= effect_inv_inv else effect_inv_inv
- else:
- count_inv_inv = 0
- effect_inv_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_inv > 0) and (effect_inv_cus > 0):
- count_inv_cus = cause_coun_inv if cause_coun_inv >= effect_inv_cus else effect_inv_cus
- else:
- count_inv_cus = 0
- effect_inv_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_inv > 0) and (effect_inv_emp > 0):
- count_inv_emp = cause_coun_inv if cause_coun_inv >= effect_inv_emp else effect_inv_emp
- else:
- count_inv_emp = 0
-
- effect_inv_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_inv > 0) and (effect_inv_soc > 0):
- count_inv_soc = cause_coun_inv if cause_coun_inv >= effect_inv_soc else effect_inv_soc
- else:
- count_inv_soc = 0
-
- cause_coun_cus = (cause_tab.Labellevel2 == 'Customers').sum()
- effect_coun_cus = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_cus > 0) and (effect_coun_cus > 0):
- count_NP_cus = cause_coun_cus if cause_coun_cus >= effect_coun_cus else effect_coun_cus
- else:
- count_NP_cus = 0
-
- effect_cus_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_cus > 0) and (effect_cus_inv > 0):
- count_cus_inv = cause_coun_cus if cause_coun_cus >= effect_cus_inv else effect_cus_inv
- else:
- count_cus_inv = 0
-
- effect_cus_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_cus > 0) and (effect_cus_cus > 0):
- count_cus_cus = cause_coun_cus if cause_coun_cus >= effect_cus_cus else effect_cus_cus
- else:
- count_cus_cus = 0
-
- effect_cus_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_cus > 0) and (effect_cus_emp > 0):
- count_cus_emp = cause_coun_cus if cause_coun_cus >= effect_cus_emp else effect_cus_emp
- else:
- count_cus_emp = 0
-
- effect_cus_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_cus > 0) and (effect_cus_soc > 0):
- count_cus_soc = cause_coun_cus if cause_coun_cus >= effect_cus_soc else effect_cus_soc
- else:
- count_cus_soc = 0
-
- cause_coun_emp = (cause_tab.Labellevel2 == 'Employees').sum()
- effect_coun_emp = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_emp > 0) and (effect_coun_emp > 0):
- count_NP_emp = cause_coun_emp if cause_coun_emp >= effect_coun_emp else effect_coun_emp
- else:
- count_NP_emp = 0
-
- effect_emp_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_emp > 0) and (effect_emp_inv > 0):
- count_emp_inv = cause_coun_emp if cause_coun_emp >= effect_emp_inv else effect_emp_inv
- else:
- count_emp_inv = 0
-
- effect_emp_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_emp > 0) and (effect_emp_cus > 0):
- count_emp_cus = cause_coun_emp if cause_coun_emp >= effect_emp_cus else effect_emp_cus
- else:
- count_emp_cus = 0
-
- effect_emp_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_emp > 0) and (effect_emp_emp > 0):
- count_emp_emp = cause_coun_emp if cause_coun_emp >= effect_emp_emp else effect_emp_emp
- else:
- count_emp_emp = 0
-
- effect_emp_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_emp > 0) and (effect_emp_soc > 0):
- count_emp_soc = cause_coun_emp if cause_coun_emp >= effect_emp_soc else effect_emp_soc
- else:
- count_emp_soc = 0
-
- cause_coun_soc = (cause_tab.Labellevel2 == 'Society').sum()
- effect_coun_soc = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_soc > 0) and (effect_coun_soc > 0):
- count_NP_soc = cause_coun_soc if cause_coun_soc >= effect_coun_soc else effect_coun_soc
- else:
- count_NP_soc = 0
-
- effect_soc_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_soc > 0) and (effect_soc_inv > 0):
- count_soc_inv = cause_coun_soc if cause_coun_soc >= effect_soc_inv else effect_soc_inv
- else:
- count_soc_inv = 0
-
- effect_soc_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_soc > 0) and (effect_soc_cus > 0):
- count_soc_cus = cause_coun_soc if cause_coun_soc >= effect_soc_cus else effect_soc_cus
- else:
- count_soc_cus = 0
-
- effect_soc_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_soc > 0) and (effect_soc_emp > 0):
- count_soc_emp = cause_coun_soc if cause_coun_soc >= effect_soc_emp else effect_soc_emp
- else:
- count_soc_emp = 0
-
- effect_soc_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_soc > 0) and (effect_soc_soc > 0):
- count_soc_soc = cause_coun_soc if cause_coun_soc >= effect_soc_soc else effect_soc_soc
- else:
- count_soc_soc = 0
-
- count_NP_NP = count_NP_NP + count_NP
- count_NP_investor = count_NP_investor + count_NP_inv
- count_NP_customer = count_NP_customer + count_NP_cus
- count_NP_employees = count_NP_employees + count_NP_emp
- count_NP_society = count_NP_society + count_NP_soc
-
- count_inv_np = count_inv_np + count_NP_inv
- count_inv_investor = count_inv_investor + count_inv_inv
- count_inv_customer = count_inv_customer + count_inv_cus
- count_inv_employee = count_inv_employee + count_inv_emp
- count_inv_society = count_inv_society + count_inv_soc
-
- count_cus_np = count_cus_np + count_NP_cus
- count_cus_investor = count_cus_investor + count_cus_inv
- count_cus_customer = count_cus_customer + count_cus_cus
- count_cus_employee = count_cus_employee + count_cus_emp
- count_cus_society = count_cus_society + count_cus_soc
-
- count_emp_np = count_emp_np + count_NP_emp
- count_emp_investor = count_emp_investor + count_emp_inv
- count_emp_customer = count_emp_customer + count_emp_cus
- count_emp_employee = count_emp_employee + count_emp_emp
- count_emp_society = count_emp_society + count_emp_soc
-
- count_soc_np = count_soc_np + count_NP_soc
- count_soc_investor = count_soc_investor + count_soc_inv
- count_soc_customer = count_soc_customer + count_soc_cus
- count_soc_employee = count_soc_employee + count_soc_emp
- count_soc_society = count_soc_society + count_soc_soc
-
- df_tab = pd.DataFrame(columns = ['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'],index=['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'], dtype=object)
-
- df_tab.loc['Non-performance'] = [count_NP_NP, count_NP_investor, count_NP_customer, count_NP_employees, count_NP_society]
- df_tab.loc['Investors'] = [count_inv_np, count_inv_investor, count_inv_customer, count_inv_employee, count_inv_society]
- df_tab.loc['Customers'] = [count_cus_np, count_cus_investor, count_cus_customer, count_cus_employee, count_cus_society]
- df_tab.loc['Employees'] = [count_emp_np, count_emp_investor, count_emp_customer, count_emp_employee, count_emp_society]
- df_tab.loc['Society'] = [count_soc_np, count_soc_investor, count_soc_customer, count_soc_employee, count_soc_society]
-
-
-# df_tab = pd.DataFrame({
-# 'Non-performance': [count_NP_NP, count_NP_investor, count_NP_customer, count_NP_employees, count_NP_society],
-# 'Investors': [count_inv_np, count_inv_investor, count_inv_customer, count_inv_employee, count_inv_society],
-# 'Customers': [count_cus_np, count_cus_investor, count_cus_customer, count_cus_employee, count_cus_society],
-# 'Employees': [count_emp_np, count_emp_investor, count_emp_customer, count_emp_employee, count_emp_society],
-# 'Society': [count_soc_np, count_soc_investor, count_soc_customer, count_soc_employee, count_soc_society]},
-# index=['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'])
-
- #df_tab.to_csv('final_data.csv')
-
- buffer = io.BytesIO()
- with pd.ExcelWriter(buffer, engine="xlsxwriter") as writer:
- df_tab.to_excel(writer,sheet_name="count_result",index=False)
- df_final1.to_excel(writer,sheet_name="Detailed_results",index=False)
- writer.close()
- #df = pd.read_csv('final_data.csv', index_col=0)
-#474-515
- # Convert to JSON format
- json_data = []
- for row in df_tab.index:
- for col in df_tab.columns:
- json_data.append({
- 'source': row,
- 'target': col,
- 'value': int(df_tab.loc[row, col])
- })
-
- HfApi().delete_file(path_in_repo = DATA_FILENAME1 ,repo_id = 'Seetha/visual_files',token= HF_TOKEN,repo_type='dataset')
- #st.write('file-deleted')
- fs = HfFileSystem(token=HF_TOKEN)
- with fs.open('datasets/Seetha/visual_files/level2.json', 'w') as f:
- json.dump(json_data, f)
-
- df_final1.to_csv('predictions.csv')
- csv_file = "predictions.csv"
- json_file = "detailedResults.json"
-
- # Open the CSV file and read the data
- with open(csv_file, "r") as f:
- csv_data = csv.DictReader(f)
-
- # # Convert the CSV data to a list of dictionaries
- data_list = []
- for row in csv_data:
- data_list.append(dict(row))
-
- # # Convert the list of dictionaries to JSON
- json_data = json.dumps(data_list)
-
- HfApi().delete_file(path_in_repo = DATA_FILENAME ,repo_id = 'Seetha/visual_files',token= HF_TOKEN,repo_type='dataset')
- #st.write('file2-deleted')
- with fs.open('datasets/Seetha/visual_files/detailedResults.json','w') as fi:
- #data = json.load(fi)
- fi.write(json_data)
-
- def convert_df(df):
-
- #IMPORTANT: Cache the conversion to prevent computation on every rerun
-
- return df.to_csv().encode('utf-8')
-
-
-
- csv1 = convert_df(df_final1.astype(str))
- csv2 = convert_df(df_tab.astype(str))
-
- with st.container():
-
- st.download_button(label="Download the result table",data=buffer,file_name="t2cg_outputs.xlsx",mime="application/vnd.ms-excel")
- st.markdown('Click this link in a separate tab to view knowledge graph', unsafe_allow_html=True)
- # st.download_button(label="Download the detailed result table_csv",data=csv1,file_name='results.csv',mime='text/csv')
- # st.download_button(label="Download the result table_csv",data=csv2,file_name='final_data.csv',mime='text/csv')
-
-#with st.container():
- # Execute your app
- #st.title("Visualization example")
-# components.html(source_code)
- #html(my_html)
- #webbrowser.open('https://huggingface.co/spaces/Seetha/visual-knowledgegraph')
-# # embed streamlit docs in a streamlit app
-# #components.iframe("https://webpages.charlotte.edu/ltotapal/")
-
-
-
-if __name__ == '__main__':
- start_time = time.time()
- main()
diff --git a/spaces/Shiro26/MendoBERT_RE/app.py b/spaces/Shiro26/MendoBERT_RE/app.py
deleted file mode 100644
index 1bf256334d6604b5048b7f1054c9cb1915120f15..0000000000000000000000000000000000000000
--- a/spaces/Shiro26/MendoBERT_RE/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-
-model = pipeline("text-classification", model="/home/user/app/MendoBERT/", tokenizer="indolem/indobert-base-uncased")
-basemodel = pipeline("text-classification", model="/home/user/app/IndoLEM/", tokenizer="indolem/indobert-base-uncased")
-
-st.title(':blue[MendoBERT] - Relation Extraction :family:')
-
-if 'options' not in st.session_state:
- st.session_state['options'] = ""
-
-def button1_callback():
- st.session_state['options'] = "Kami menyimpulkan bahwa polimorfisme @GENE$ dan AGT tidak berkontribusi pada kerentanan genetik terhadap @DISEASE$ dan retinopati pada populasi Kaukasia Mediterania."
-def button2_callback():
- st.session_state['options'] = "Genotipe bayi PON1 RR dan @GENE$ CC dikaitkan dengan @DISEASE$ dalam populasi penelitian kami, yang menunjukkan kemungkinan peran variabilitas paraoxonase manusia dalam etiologi kelahiran prematur."
-
-
-placeholder = st.empty()
-
-st.info("Please replace the gene and disease that you want to get their relation predicted with @GENE\$ and \@DISEASE\$", icon="ℹ️")
-
-st.caption('_Examples_')
-st.button('Kami menyimpulkan bahwa polimorfisme \@GENE\$ dan AGT tidak berkontribusi pada kerentanan genetik terhadap \@DISEASE\$ dan retinopati pada populasi Kaukasia Mediterania.', use_container_width=True, on_click = button1_callback)
-st.button('Genotipe bayi PON1 RR dan \@GENE\$ CC dikaitkan dengan \@DISEASE\$ dalam populasi penelitian kami, yang menunjukkan kemungkinan peran variabilitas paraoxonase manusia dalam etiologi kelahiran prematur.', use_container_width=True, on_click = button2_callback)
-
-with placeholder:
- text = st.text_area('Enter some text: ', key = 'options')
-
-if text:
- st.subheader('MendoBERT')
- st.write(model(text))
- st.write("\n")
- st.subheader('IndoLEM')
- st.write(basemodel(text))
\ No newline at end of file
diff --git a/spaces/Shredder/CONBERT-2/README.md b/spaces/Shredder/CONBERT-2/README.md
deleted file mode 100644
index daf10e5da8dd31671c54c18d24b0e7d1d77b62f0..0000000000000000000000000000000000000000
--- a/spaces/Shredder/CONBERT-2/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: CONBERT 2
-emoji: 👁
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Stearns/Soar/soar_io.py b/spaces/Stearns/Soar/soar_io.py
deleted file mode 100644
index c05d97417b69a804b35207fb839957a91258a5d2..0000000000000000000000000000000000000000
--- a/spaces/Stearns/Soar/soar_io.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from pysoarlib import *
-
-## DEFINE MAIN CONNECTOR CLASS
-
-class GeneralSoarIOConnector(AgentConnector):
- def __init__(self, client):
- AgentConnector.__init__(self, client)
- client.add_print_event_handler(self.agent_print_collector)
- # client.execute_command("output callbacks on")
- # client.execute_command("output console on")
-
- self.agent_printout = ""
-
- def on_input_phase(self, input_link):
- pass
-
- def on_init_soar(self):
- self.reset()
-
- def on_output_event(self, command_name, root_id):
- pass
-
-
- def agent_print_collector(self, text):
- self.agent_printout += text+"\n"
-
- def get_agent_output(self):
- return self.agent_printout
-
- def reset(self):
- self.agent_printout = ""
-
-
-
-
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py
deleted file mode 100644
index 489b7c13d0ce82b769ba50412ecde0889d0b7851..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# encoding: utf-8
-"""
-Utilities for working with external processes.
-"""
-
-# Copyright (c) IPython Development Team.
-# Distributed under the terms of the Modified BSD License.
-
-
-import os
-import shutil
-import sys
-
-if sys.platform == 'win32':
- from ._process_win32 import system, getoutput, arg_split, check_pid
-elif sys.platform == 'cli':
- from ._process_cli import system, getoutput, arg_split, check_pid
-else:
- from ._process_posix import system, getoutput, arg_split, check_pid
-
-from ._process_common import getoutputerror, get_output_error_code, process_handler
-
-
-class FindCmdError(Exception):
- pass
-
-
-def find_cmd(cmd):
- """Find absolute path to executable cmd in a cross platform manner.
-
- This function tries to determine the full path to a command line program
- using `which` on Unix/Linux/OS X and `win32api` on Windows. Most of the
- time it will use the version that is first on the users `PATH`.
-
- Warning, don't use this to find IPython command line programs as there
- is a risk you will find the wrong one. Instead find those using the
- following code and looking for the application itself::
-
- import sys
- argv = [sys.executable, '-m', 'IPython']
-
- Parameters
- ----------
- cmd : str
- The command line program to look for.
- """
- path = shutil.which(cmd)
- if path is None:
- raise FindCmdError('command could not be found: %s' % cmd)
- return path
-
-
-def abbrev_cwd():
- """ Return abbreviated version of cwd, e.g. d:mydir """
- cwd = os.getcwd().replace('\\','/')
- drivepart = ''
- tail = cwd
- if sys.platform == 'win32':
- if len(cwd) < 4:
- return cwd
- drivepart,tail = os.path.splitdrive(cwd)
-
-
- parts = tail.split('/')
- if len(parts) > 2:
- tail = '/'.join(parts[-2:])
-
- return (drivepart + (
- cwd == '/' and '/' or tail))
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py
deleted file mode 100644
index 37d3db222ef2c7920628971a92e863d9915514c6..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import sys
-from typing import Any, Dict, List, Optional, Generic, TypeVar, cast
-from types import TracebackType
-
-if sys.version_info >= (3, 8):
- from importlib.metadata import entry_points
-else:
- from importlib_metadata import entry_points
-
-from toolz import curry
-
-
-PluginType = TypeVar("PluginType")
-
-
-class NoSuchEntryPoint(Exception):
- def __init__(self, group, name):
- self.group = group
- self.name = name
-
- def __str__(self):
- return f"No {self.name!r} entry point found in group {self.group!r}"
-
-
-class PluginEnabler:
- """Context manager for enabling plugins
-
- This object lets you use enable() as a context manager to
- temporarily enable a given plugin::
-
- with plugins.enable('name'):
- do_something() # 'name' plugin temporarily enabled
- # plugins back to original state
- """
-
- def __init__(self, registry: "PluginRegistry", name: str, **options):
- self.registry = registry # type: PluginRegistry
- self.name = name # type: str
- self.options = options # type: Dict[str, Any]
- self.original_state = registry._get_state() # type: Dict[str, Any]
- self.registry._enable(name, **options)
-
- def __enter__(self) -> "PluginEnabler":
- return self
-
- def __exit__(self, typ: type, value: Exception, traceback: TracebackType) -> None:
- self.registry._set_state(self.original_state)
-
- def __repr__(self) -> str:
- return "{}.enable({!r})".format(self.registry.__class__.__name__, self.name)
-
-
-class PluginRegistry(Generic[PluginType]):
- """A registry for plugins.
-
- This is a plugin registry that allows plugins to be loaded/registered
- in two ways:
-
- 1. Through an explicit call to ``.register(name, value)``.
- 2. By looking for other Python packages that are installed and provide
- a setuptools entry point group.
-
- When you create an instance of this class, provide the name of the
- entry point group to use::
-
- reg = PluginRegister('my_entrypoint_group')
-
- """
-
- # this is a mapping of name to error message to allow custom error messages
- # in case an entrypoint is not found
- entrypoint_err_messages = {} # type: Dict[str, str]
-
- # global settings is a key-value mapping of settings that are stored globally
- # in the registry rather than passed to the plugins
- _global_settings = {} # type: Dict[str, Any]
-
- def __init__(self, entry_point_group: str = "", plugin_type: type = object):
- """Create a PluginRegistry for a named entry point group.
-
- Parameters
- ==========
- entry_point_group: str
- The name of the entry point group.
- plugin_type: object
- A type that will optionally be used for runtime type checking of
- loaded plugins using isinstance.
- """
- self.entry_point_group = entry_point_group # type: str
- self.plugin_type = plugin_type # type: Optional[type]
- self._active = None # type: Optional[PluginType]
- self._active_name = "" # type: str
- self._plugins = {} # type: Dict[str, PluginType]
- self._options = {} # type: Dict[str, Any]
- self._global_settings = self.__class__._global_settings.copy() # type: dict
-
- def register(self, name: str, value: Optional[PluginType]) -> Optional[PluginType]:
- """Register a plugin by name and value.
-
- This method is used for explicit registration of a plugin and shouldn't be
- used to manage entry point managed plugins, which are auto-loaded.
-
- Parameters
- ==========
- name: str
- The name of the plugin.
- value: PluginType or None
- The actual plugin object to register or None to unregister that plugin.
-
- Returns
- =======
- plugin: PluginType or None
- The plugin that was registered or unregistered.
- """
- if value is None:
- return self._plugins.pop(name, None)
- else:
- assert isinstance(value, self.plugin_type) # type: ignore[arg-type] # Should ideally be fixed by better annotating plugin_type
- self._plugins[name] = value
- return value
-
- def names(self) -> List[str]:
- """List the names of the registered and entry points plugins."""
- exts = list(self._plugins.keys())
- e_points = importlib_metadata_get(self.entry_point_group)
- more_exts = [ep.name for ep in e_points]
- exts.extend(more_exts)
- return sorted(set(exts))
-
- def _get_state(self) -> Dict[str, Any]:
- """Return a dictionary representing the current state of the registry"""
- return {
- "_active": self._active,
- "_active_name": self._active_name,
- "_plugins": self._plugins.copy(),
- "_options": self._options.copy(),
- "_global_settings": self._global_settings.copy(),
- }
-
- def _set_state(self, state: Dict[str, Any]) -> None:
- """Reset the state of the registry"""
- assert set(state.keys()) == {
- "_active",
- "_active_name",
- "_plugins",
- "_options",
- "_global_settings",
- }
- for key, val in state.items():
- setattr(self, key, val)
-
- def _enable(self, name: str, **options) -> None:
- if name not in self._plugins:
- try:
- (ep,) = [
- ep
- for ep in importlib_metadata_get(self.entry_point_group)
- if ep.name == name
- ]
- except ValueError as err:
- if name in self.entrypoint_err_messages:
- raise ValueError(self.entrypoint_err_messages[name]) from err
- else:
- raise NoSuchEntryPoint(self.entry_point_group, name) from err
- value = cast(PluginType, ep.load())
- self.register(name, value)
- self._active_name = name
- self._active = self._plugins[name]
- for key in set(options.keys()) & set(self._global_settings.keys()):
- self._global_settings[key] = options.pop(key)
- self._options = options
-
- def enable(self, name: Optional[str] = None, **options) -> PluginEnabler:
- """Enable a plugin by name.
-
- This can be either called directly, or used as a context manager.
-
- Parameters
- ----------
- name : string (optional)
- The name of the plugin to enable. If not specified, then use the
- current active name.
- **options :
- Any additional parameters will be passed to the plugin as keyword
- arguments
-
- Returns
- -------
- PluginEnabler:
- An object that allows enable() to be used as a context manager
- """
- if name is None:
- name = self.active
- return PluginEnabler(self, name, **options)
-
- @property
- def active(self) -> str:
- """Return the name of the currently active plugin"""
- return self._active_name
-
- @property
- def options(self) -> Dict[str, Any]:
- """Return the current options dictionary"""
- return self._options
-
- def get(self) -> Optional[PluginType]:
- """Return the currently active plugin."""
- if self._options:
- return curry(self._active, **self._options)
- else:
- return self._active
-
- def __repr__(self) -> str:
- return "{}(active={!r}, registered={!r})" "".format(
- self.__class__.__name__, self._active_name, list(self.names())
- )
-
-
-def importlib_metadata_get(group):
- ep = entry_points()
- # 'select' was introduced in Python 3.10 and 'get' got deprecated
- # We don't check for Python version here as by checking with hasattr we
- # also get compatibility with the importlib_metadata package which had a different
- # deprecation cycle for 'get'
- if hasattr(ep, "select"):
- return ep.select(group=group)
- else:
- return ep.get(group, [])
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py
deleted file mode 100644
index bba2d3f7dfffa3bdbf921bdad4ca7143be97c2fd..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py
+++ /dev/null
@@ -1,143 +0,0 @@
-from __future__ import annotations
-
-import codecs
-from dataclasses import InitVar, dataclass, field
-from typing import Any, Callable, Mapping
-
-from ..abc import (
- AnyByteReceiveStream,
- AnyByteSendStream,
- AnyByteStream,
- ObjectReceiveStream,
- ObjectSendStream,
- ObjectStream,
-)
-
-
-@dataclass(eq=False)
-class TextReceiveStream(ObjectReceiveStream[str]):
- """
- Stream wrapper that decodes bytes to strings using the given encoding.
-
- Decoding is done using :class:`~codecs.IncrementalDecoder` which returns any completely
- received unicode characters as soon as they come in.
-
- :param transport_stream: any bytes-based receive stream
- :param encoding: character encoding to use for decoding bytes to strings (defaults to
- ``utf-8``)
- :param errors: handling scheme for decoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteReceiveStream
- encoding: InitVar[str] = "utf-8"
- errors: InitVar[str] = "strict"
- _decoder: codecs.IncrementalDecoder = field(init=False)
-
- def __post_init__(self, encoding: str, errors: str) -> None:
- decoder_class = codecs.getincrementaldecoder(encoding)
- self._decoder = decoder_class(errors=errors)
-
- async def receive(self) -> str:
- while True:
- chunk = await self.transport_stream.receive()
- decoded = self._decoder.decode(chunk)
- if decoded:
- return decoded
-
- async def aclose(self) -> None:
- await self.transport_stream.aclose()
- self._decoder.reset()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return self.transport_stream.extra_attributes
-
-
-@dataclass(eq=False)
-class TextSendStream(ObjectSendStream[str]):
- """
- Sends strings to the wrapped stream as bytes using the given encoding.
-
- :param AnyByteSendStream transport_stream: any bytes-based send stream
- :param str encoding: character encoding to use for encoding strings to bytes (defaults to
- ``utf-8``)
- :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteSendStream
- encoding: InitVar[str] = "utf-8"
- errors: str = "strict"
- _encoder: Callable[..., tuple[bytes, int]] = field(init=False)
-
- def __post_init__(self, encoding: str) -> None:
- self._encoder = codecs.getencoder(encoding)
-
- async def send(self, item: str) -> None:
- encoded = self._encoder(item, self.errors)[0]
- await self.transport_stream.send(encoded)
-
- async def aclose(self) -> None:
- await self.transport_stream.aclose()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return self.transport_stream.extra_attributes
-
-
-@dataclass(eq=False)
-class TextStream(ObjectStream[str]):
- """
- A bidirectional stream that decodes bytes to strings on receive and encodes strings to bytes on
- send.
-
- Extra attributes will be provided from both streams, with the receive stream providing the
- values in case of a conflict.
-
- :param AnyByteStream transport_stream: any bytes-based stream
- :param str encoding: character encoding to use for encoding/decoding strings to/from bytes
- (defaults to ``utf-8``)
- :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteStream
- encoding: InitVar[str] = "utf-8"
- errors: InitVar[str] = "strict"
- _receive_stream: TextReceiveStream = field(init=False)
- _send_stream: TextSendStream = field(init=False)
-
- def __post_init__(self, encoding: str, errors: str) -> None:
- self._receive_stream = TextReceiveStream(
- self.transport_stream, encoding=encoding, errors=errors
- )
- self._send_stream = TextSendStream(
- self.transport_stream, encoding=encoding, errors=errors
- )
-
- async def receive(self) -> str:
- return await self._receive_stream.receive()
-
- async def send(self, item: str) -> None:
- await self._send_stream.send(item)
-
- async def send_eof(self) -> None:
- await self.transport_stream.send_eof()
-
- async def aclose(self) -> None:
- await self._send_stream.aclose()
- await self._receive_stream.aclose()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return {
- **self._send_stream.extra_attributes,
- **self._receive_stream.extra_attributes,
- }
diff --git a/spaces/TH5314/newbing/src/lib/bots/bing/types.ts b/spaces/TH5314/newbing/src/lib/bots/bing/types.ts
deleted file mode 100644
index 02cd5e8b01e3529642d28dc1539bf958f4ac420b..0000000000000000000000000000000000000000
--- a/spaces/TH5314/newbing/src/lib/bots/bing/types.ts
+++ /dev/null
@@ -1,259 +0,0 @@
-export type Author = 'user' | 'system' | 'bot'
-
-export type BotId = 'bing'
-
-export enum BingConversationStyle {
- Creative = 'Creative',
- Balanced = 'Balanced',
- Precise = 'Precise'
-}
-
-export enum ErrorCode {
- CONVERSATION_LIMIT = 'CONVERSATION_LIMIT',
- BING_UNAUTHORIZED = 'BING_UNAUTHORIZED',
- BING_FORBIDDEN = 'BING_FORBIDDEN',
- BING_CAPTCHA = 'BING_CAPTCHA',
- THROTTLE_LIMIT = 'THROTTLE_LIMIT',
- NOTFOUND_ERROR = 'NOT_FOUND_ERROR',
- UNKOWN_ERROR = 'UNKOWN_ERROR',
- NETWORK_ERROR = 'NETWORK_ERROR',
-}
-
-export class ChatError extends Error {
- code: ErrorCode
- constructor(message: string, code: ErrorCode) {
- super(message)
- this.code = code
- }
-}
-
-export type ChatMessageModel = {
- id: string
- author: Author
- text: string
- error?: ChatError
- throttling?: Throttling
- sourceAttributions?: SourceAttribution[]
- suggestedResponses?: SuggestedResponse[]
-}
-
-export interface ConversationModel {
- messages: ChatMessageModel[]
-}
-
-export type Event =
- | {
- type: 'UPDATE_ANSWER'
- data: {
- text: string
- spokenText?: string
- sourceAttributions?: SourceAttribution[]
- suggestedResponses?: SuggestedResponse[]
- throttling?: Throttling
- }
- }
- | {
- type: 'DONE'
- }
- | {
- type: 'ERROR'
- error: ChatError
- }
-
-export interface SendMessageParams {
- prompt: string
- imageUrl?: string
- options: T
- onEvent: (event: Event) => void
- signal?: AbortSignal
-}
-
-export interface ConversationResponse {
- conversationId: string
- clientId: string
- conversationSignature: string
- result: {
- value: string
- message?: string
- }
-}
-
-export interface Telemetry {
- metrics?: null
- startTime: string
-}
-
-export interface ChatUpdateArgument {
- messages?: ChatResponseMessage[]
- throttling?: Throttling
- requestId: string
- result: null
-}
-
-export type ChatUpdateCompleteResponse = {
- type: 2
- invocationId: string
- item: ChatResponseItem
-} | {
- type: 1
- target: string
- arguments: ChatUpdateArgument[]
-} | {
- type: 3
- invocationId: string
-} | {
- type: 6 | 7
-}
-
-export interface ChatRequestResult {
- value: string
- serviceVersion: string
- error?: string
-}
-
-export interface ChatResponseItem {
- messages: ChatResponseMessage[]
- firstNewMessageIndex: number
- suggestedResponses: null
- conversationId: string
- requestId: string
- conversationExpiryTime: string
- telemetry: Telemetry
- result: ChatRequestResult
- throttling: Throttling
-}
-export enum InvocationEventType {
- Invocation = 1,
- StreamItem = 2,
- Completion = 3,
- StreamInvocation = 4,
- CancelInvocation = 5,
- Ping = 6,
- Close = 7,
-}
-
-// https://github.com/bytemate/bingchat-api/blob/main/src/lib.ts
-
-export interface ConversationInfo {
- conversationId: string
- clientId: string
- conversationSignature: string
- invocationId: number
- conversationStyle: BingConversationStyle
- prompt: string
- imageUrl?: string
-}
-
-export interface BingChatResponse {
- conversationSignature: string
- conversationId: string
- clientId: string
- invocationId: number
- conversationExpiryTime: Date
- response: string
- details: ChatResponseMessage
-}
-
-export interface Throttling {
- maxNumLongDocSummaryUserMessagesInConversation: number
- maxNumUserMessagesInConversation: number
- numLongDocSummaryUserMessagesInConversation: number
- numUserMessagesInConversation: number
-}
-
-export interface ChatResponseMessage {
- text: string
- spokenText?: string
- author: string
- createdAt: Date
- timestamp: Date
- messageId: string
- requestId: string
- offense: string
- adaptiveCards: AdaptiveCard[]
- sourceAttributions: SourceAttribution[]
- feedback: Feedback
- contentOrigin: string
- messageType?: string
- contentType?: string
- privacy: null
- suggestedResponses: SuggestedResponse[]
-}
-
-export interface AdaptiveCard {
- type: string
- version: string
- body: Body[]
-}
-
-export interface Body {
- type: string
- text: string
- wrap: boolean
- size?: string
-}
-
-export interface Feedback {
- tag: null
- updatedOn: null
- type: string
-}
-
-export interface SourceAttribution {
- providerDisplayName: string
- seeMoreUrl: string
- searchQuery: string
-}
-
-export interface SuggestedResponse {
- text: string
- author?: Author
- createdAt?: Date
- timestamp?: Date
- messageId?: string
- messageType?: string
- offense?: string
- feedback?: Feedback
- contentOrigin?: string
- privacy?: null
-}
-
-export interface KBlobRequest {
- knowledgeRequest: KnowledgeRequestContext
- imageBase64?: string
-}
-
-export interface KBlobResponse {
- blobId: string
- processedBlobId?: string
-}
-
-export interface KnowledgeRequestContext {
- imageInfo: ImageInfo;
- knowledgeRequest: KnowledgeRequest;
-}
-
-export interface ImageInfo {
- url?: string;
-}
-
-export interface KnowledgeRequest {
- invokedSkills: string[];
- subscriptionId: string;
- invokedSkillsRequestData: InvokedSkillsRequestData;
- convoData: ConvoData;
-}
-
-export interface ConvoData {
- convoid: string;
- convotone: BingConversationStyle;
-}
-
-export interface InvokedSkillsRequestData {
- enableFaceBlur: boolean;
-}
-
-export interface FileItem {
- url: string;
- status?: 'loading' | 'error' | 'loaded'
-}
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py
deleted file mode 100644
index 264d564dbda676b52f446c0d25433a15939a78a3..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py
+++ /dev/null
@@ -1,519 +0,0 @@
-"""
-This module uses ctypes to bind a whole bunch of functions and constants from
-SecureTransport. The goal here is to provide the low-level API to
-SecureTransport. These are essentially the C-level functions and constants, and
-they're pretty gross to work with.
-
-This code is a bastardised version of the code found in Will Bond's oscrypto
-library. An enormous debt is owed to him for blazing this trail for us. For
-that reason, this code should be considered to be covered both by urllib3's
-license and by oscrypto's:
-
- Copyright (c) 2015-2016 Will Bond
-
- Permission is hereby granted, free of charge, to any person obtaining a
- copy of this software and associated documentation files (the "Software"),
- to deal in the Software without restriction, including without limitation
- the rights to use, copy, modify, merge, publish, distribute, sublicense,
- and/or sell copies of the Software, and to permit persons to whom the
- Software is furnished to do so, subject to the following conditions:
-
- The above copyright notice and this permission notice shall be included in
- all copies or substantial portions of the Software.
-
- THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
- FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
- DEALINGS IN THE SOFTWARE.
-"""
-from __future__ import absolute_import
-
-import platform
-from ctypes import (
- CDLL,
- CFUNCTYPE,
- POINTER,
- c_bool,
- c_byte,
- c_char_p,
- c_int32,
- c_long,
- c_size_t,
- c_uint32,
- c_ulong,
- c_void_p,
-)
-from ctypes.util import find_library
-
-from ...packages.six import raise_from
-
-if platform.system() != "Darwin":
- raise ImportError("Only macOS is supported")
-
-version = platform.mac_ver()[0]
-version_info = tuple(map(int, version.split(".")))
-if version_info < (10, 8):
- raise OSError(
- "Only OS X 10.8 and newer are supported, not %s.%s"
- % (version_info[0], version_info[1])
- )
-
-
-def load_cdll(name, macos10_16_path):
- """Loads a CDLL by name, falling back to known path on 10.16+"""
- try:
- # Big Sur is technically 11 but we use 10.16 due to the Big Sur
- # beta being labeled as 10.16.
- if version_info >= (10, 16):
- path = macos10_16_path
- else:
- path = find_library(name)
- if not path:
- raise OSError # Caught and reraised as 'ImportError'
- return CDLL(path, use_errno=True)
- except OSError:
- raise_from(ImportError("The library %s failed to load" % name), None)
-
-
-Security = load_cdll(
- "Security", "/System/Library/Frameworks/Security.framework/Security"
-)
-CoreFoundation = load_cdll(
- "CoreFoundation",
- "/System/Library/Frameworks/CoreFoundation.framework/CoreFoundation",
-)
-
-
-Boolean = c_bool
-CFIndex = c_long
-CFStringEncoding = c_uint32
-CFData = c_void_p
-CFString = c_void_p
-CFArray = c_void_p
-CFMutableArray = c_void_p
-CFDictionary = c_void_p
-CFError = c_void_p
-CFType = c_void_p
-CFTypeID = c_ulong
-
-CFTypeRef = POINTER(CFType)
-CFAllocatorRef = c_void_p
-
-OSStatus = c_int32
-
-CFDataRef = POINTER(CFData)
-CFStringRef = POINTER(CFString)
-CFArrayRef = POINTER(CFArray)
-CFMutableArrayRef = POINTER(CFMutableArray)
-CFDictionaryRef = POINTER(CFDictionary)
-CFArrayCallBacks = c_void_p
-CFDictionaryKeyCallBacks = c_void_p
-CFDictionaryValueCallBacks = c_void_p
-
-SecCertificateRef = POINTER(c_void_p)
-SecExternalFormat = c_uint32
-SecExternalItemType = c_uint32
-SecIdentityRef = POINTER(c_void_p)
-SecItemImportExportFlags = c_uint32
-SecItemImportExportKeyParameters = c_void_p
-SecKeychainRef = POINTER(c_void_p)
-SSLProtocol = c_uint32
-SSLCipherSuite = c_uint32
-SSLContextRef = POINTER(c_void_p)
-SecTrustRef = POINTER(c_void_p)
-SSLConnectionRef = c_uint32
-SecTrustResultType = c_uint32
-SecTrustOptionFlags = c_uint32
-SSLProtocolSide = c_uint32
-SSLConnectionType = c_uint32
-SSLSessionOption = c_uint32
-
-
-try:
- Security.SecItemImport.argtypes = [
- CFDataRef,
- CFStringRef,
- POINTER(SecExternalFormat),
- POINTER(SecExternalItemType),
- SecItemImportExportFlags,
- POINTER(SecItemImportExportKeyParameters),
- SecKeychainRef,
- POINTER(CFArrayRef),
- ]
- Security.SecItemImport.restype = OSStatus
-
- Security.SecCertificateGetTypeID.argtypes = []
- Security.SecCertificateGetTypeID.restype = CFTypeID
-
- Security.SecIdentityGetTypeID.argtypes = []
- Security.SecIdentityGetTypeID.restype = CFTypeID
-
- Security.SecKeyGetTypeID.argtypes = []
- Security.SecKeyGetTypeID.restype = CFTypeID
-
- Security.SecCertificateCreateWithData.argtypes = [CFAllocatorRef, CFDataRef]
- Security.SecCertificateCreateWithData.restype = SecCertificateRef
-
- Security.SecCertificateCopyData.argtypes = [SecCertificateRef]
- Security.SecCertificateCopyData.restype = CFDataRef
-
- Security.SecCopyErrorMessageString.argtypes = [OSStatus, c_void_p]
- Security.SecCopyErrorMessageString.restype = CFStringRef
-
- Security.SecIdentityCreateWithCertificate.argtypes = [
- CFTypeRef,
- SecCertificateRef,
- POINTER(SecIdentityRef),
- ]
- Security.SecIdentityCreateWithCertificate.restype = OSStatus
-
- Security.SecKeychainCreate.argtypes = [
- c_char_p,
- c_uint32,
- c_void_p,
- Boolean,
- c_void_p,
- POINTER(SecKeychainRef),
- ]
- Security.SecKeychainCreate.restype = OSStatus
-
- Security.SecKeychainDelete.argtypes = [SecKeychainRef]
- Security.SecKeychainDelete.restype = OSStatus
-
- Security.SecPKCS12Import.argtypes = [
- CFDataRef,
- CFDictionaryRef,
- POINTER(CFArrayRef),
- ]
- Security.SecPKCS12Import.restype = OSStatus
-
- SSLReadFunc = CFUNCTYPE(OSStatus, SSLConnectionRef, c_void_p, POINTER(c_size_t))
- SSLWriteFunc = CFUNCTYPE(
- OSStatus, SSLConnectionRef, POINTER(c_byte), POINTER(c_size_t)
- )
-
- Security.SSLSetIOFuncs.argtypes = [SSLContextRef, SSLReadFunc, SSLWriteFunc]
- Security.SSLSetIOFuncs.restype = OSStatus
-
- Security.SSLSetPeerID.argtypes = [SSLContextRef, c_char_p, c_size_t]
- Security.SSLSetPeerID.restype = OSStatus
-
- Security.SSLSetCertificate.argtypes = [SSLContextRef, CFArrayRef]
- Security.SSLSetCertificate.restype = OSStatus
-
- Security.SSLSetCertificateAuthorities.argtypes = [SSLContextRef, CFTypeRef, Boolean]
- Security.SSLSetCertificateAuthorities.restype = OSStatus
-
- Security.SSLSetConnection.argtypes = [SSLContextRef, SSLConnectionRef]
- Security.SSLSetConnection.restype = OSStatus
-
- Security.SSLSetPeerDomainName.argtypes = [SSLContextRef, c_char_p, c_size_t]
- Security.SSLSetPeerDomainName.restype = OSStatus
-
- Security.SSLHandshake.argtypes = [SSLContextRef]
- Security.SSLHandshake.restype = OSStatus
-
- Security.SSLRead.argtypes = [SSLContextRef, c_char_p, c_size_t, POINTER(c_size_t)]
- Security.SSLRead.restype = OSStatus
-
- Security.SSLWrite.argtypes = [SSLContextRef, c_char_p, c_size_t, POINTER(c_size_t)]
- Security.SSLWrite.restype = OSStatus
-
- Security.SSLClose.argtypes = [SSLContextRef]
- Security.SSLClose.restype = OSStatus
-
- Security.SSLGetNumberSupportedCiphers.argtypes = [SSLContextRef, POINTER(c_size_t)]
- Security.SSLGetNumberSupportedCiphers.restype = OSStatus
-
- Security.SSLGetSupportedCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- POINTER(c_size_t),
- ]
- Security.SSLGetSupportedCiphers.restype = OSStatus
-
- Security.SSLSetEnabledCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- c_size_t,
- ]
- Security.SSLSetEnabledCiphers.restype = OSStatus
-
- Security.SSLGetNumberEnabledCiphers.argtype = [SSLContextRef, POINTER(c_size_t)]
- Security.SSLGetNumberEnabledCiphers.restype = OSStatus
-
- Security.SSLGetEnabledCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- POINTER(c_size_t),
- ]
- Security.SSLGetEnabledCiphers.restype = OSStatus
-
- Security.SSLGetNegotiatedCipher.argtypes = [SSLContextRef, POINTER(SSLCipherSuite)]
- Security.SSLGetNegotiatedCipher.restype = OSStatus
-
- Security.SSLGetNegotiatedProtocolVersion.argtypes = [
- SSLContextRef,
- POINTER(SSLProtocol),
- ]
- Security.SSLGetNegotiatedProtocolVersion.restype = OSStatus
-
- Security.SSLCopyPeerTrust.argtypes = [SSLContextRef, POINTER(SecTrustRef)]
- Security.SSLCopyPeerTrust.restype = OSStatus
-
- Security.SecTrustSetAnchorCertificates.argtypes = [SecTrustRef, CFArrayRef]
- Security.SecTrustSetAnchorCertificates.restype = OSStatus
-
- Security.SecTrustSetAnchorCertificatesOnly.argstypes = [SecTrustRef, Boolean]
- Security.SecTrustSetAnchorCertificatesOnly.restype = OSStatus
-
- Security.SecTrustEvaluate.argtypes = [SecTrustRef, POINTER(SecTrustResultType)]
- Security.SecTrustEvaluate.restype = OSStatus
-
- Security.SecTrustGetCertificateCount.argtypes = [SecTrustRef]
- Security.SecTrustGetCertificateCount.restype = CFIndex
-
- Security.SecTrustGetCertificateAtIndex.argtypes = [SecTrustRef, CFIndex]
- Security.SecTrustGetCertificateAtIndex.restype = SecCertificateRef
-
- Security.SSLCreateContext.argtypes = [
- CFAllocatorRef,
- SSLProtocolSide,
- SSLConnectionType,
- ]
- Security.SSLCreateContext.restype = SSLContextRef
-
- Security.SSLSetSessionOption.argtypes = [SSLContextRef, SSLSessionOption, Boolean]
- Security.SSLSetSessionOption.restype = OSStatus
-
- Security.SSLSetProtocolVersionMin.argtypes = [SSLContextRef, SSLProtocol]
- Security.SSLSetProtocolVersionMin.restype = OSStatus
-
- Security.SSLSetProtocolVersionMax.argtypes = [SSLContextRef, SSLProtocol]
- Security.SSLSetProtocolVersionMax.restype = OSStatus
-
- try:
- Security.SSLSetALPNProtocols.argtypes = [SSLContextRef, CFArrayRef]
- Security.SSLSetALPNProtocols.restype = OSStatus
- except AttributeError:
- # Supported only in 10.12+
- pass
-
- Security.SecCopyErrorMessageString.argtypes = [OSStatus, c_void_p]
- Security.SecCopyErrorMessageString.restype = CFStringRef
-
- Security.SSLReadFunc = SSLReadFunc
- Security.SSLWriteFunc = SSLWriteFunc
- Security.SSLContextRef = SSLContextRef
- Security.SSLProtocol = SSLProtocol
- Security.SSLCipherSuite = SSLCipherSuite
- Security.SecIdentityRef = SecIdentityRef
- Security.SecKeychainRef = SecKeychainRef
- Security.SecTrustRef = SecTrustRef
- Security.SecTrustResultType = SecTrustResultType
- Security.SecExternalFormat = SecExternalFormat
- Security.OSStatus = OSStatus
-
- Security.kSecImportExportPassphrase = CFStringRef.in_dll(
- Security, "kSecImportExportPassphrase"
- )
- Security.kSecImportItemIdentity = CFStringRef.in_dll(
- Security, "kSecImportItemIdentity"
- )
-
- # CoreFoundation time!
- CoreFoundation.CFRetain.argtypes = [CFTypeRef]
- CoreFoundation.CFRetain.restype = CFTypeRef
-
- CoreFoundation.CFRelease.argtypes = [CFTypeRef]
- CoreFoundation.CFRelease.restype = None
-
- CoreFoundation.CFGetTypeID.argtypes = [CFTypeRef]
- CoreFoundation.CFGetTypeID.restype = CFTypeID
-
- CoreFoundation.CFStringCreateWithCString.argtypes = [
- CFAllocatorRef,
- c_char_p,
- CFStringEncoding,
- ]
- CoreFoundation.CFStringCreateWithCString.restype = CFStringRef
-
- CoreFoundation.CFStringGetCStringPtr.argtypes = [CFStringRef, CFStringEncoding]
- CoreFoundation.CFStringGetCStringPtr.restype = c_char_p
-
- CoreFoundation.CFStringGetCString.argtypes = [
- CFStringRef,
- c_char_p,
- CFIndex,
- CFStringEncoding,
- ]
- CoreFoundation.CFStringGetCString.restype = c_bool
-
- CoreFoundation.CFDataCreate.argtypes = [CFAllocatorRef, c_char_p, CFIndex]
- CoreFoundation.CFDataCreate.restype = CFDataRef
-
- CoreFoundation.CFDataGetLength.argtypes = [CFDataRef]
- CoreFoundation.CFDataGetLength.restype = CFIndex
-
- CoreFoundation.CFDataGetBytePtr.argtypes = [CFDataRef]
- CoreFoundation.CFDataGetBytePtr.restype = c_void_p
-
- CoreFoundation.CFDictionaryCreate.argtypes = [
- CFAllocatorRef,
- POINTER(CFTypeRef),
- POINTER(CFTypeRef),
- CFIndex,
- CFDictionaryKeyCallBacks,
- CFDictionaryValueCallBacks,
- ]
- CoreFoundation.CFDictionaryCreate.restype = CFDictionaryRef
-
- CoreFoundation.CFDictionaryGetValue.argtypes = [CFDictionaryRef, CFTypeRef]
- CoreFoundation.CFDictionaryGetValue.restype = CFTypeRef
-
- CoreFoundation.CFArrayCreate.argtypes = [
- CFAllocatorRef,
- POINTER(CFTypeRef),
- CFIndex,
- CFArrayCallBacks,
- ]
- CoreFoundation.CFArrayCreate.restype = CFArrayRef
-
- CoreFoundation.CFArrayCreateMutable.argtypes = [
- CFAllocatorRef,
- CFIndex,
- CFArrayCallBacks,
- ]
- CoreFoundation.CFArrayCreateMutable.restype = CFMutableArrayRef
-
- CoreFoundation.CFArrayAppendValue.argtypes = [CFMutableArrayRef, c_void_p]
- CoreFoundation.CFArrayAppendValue.restype = None
-
- CoreFoundation.CFArrayGetCount.argtypes = [CFArrayRef]
- CoreFoundation.CFArrayGetCount.restype = CFIndex
-
- CoreFoundation.CFArrayGetValueAtIndex.argtypes = [CFArrayRef, CFIndex]
- CoreFoundation.CFArrayGetValueAtIndex.restype = c_void_p
-
- CoreFoundation.kCFAllocatorDefault = CFAllocatorRef.in_dll(
- CoreFoundation, "kCFAllocatorDefault"
- )
- CoreFoundation.kCFTypeArrayCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeArrayCallBacks"
- )
- CoreFoundation.kCFTypeDictionaryKeyCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeDictionaryKeyCallBacks"
- )
- CoreFoundation.kCFTypeDictionaryValueCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeDictionaryValueCallBacks"
- )
-
- CoreFoundation.CFTypeRef = CFTypeRef
- CoreFoundation.CFArrayRef = CFArrayRef
- CoreFoundation.CFStringRef = CFStringRef
- CoreFoundation.CFDictionaryRef = CFDictionaryRef
-
-except (AttributeError):
- raise ImportError("Error initializing ctypes")
-
-
-class CFConst(object):
- """
- A class object that acts as essentially a namespace for CoreFoundation
- constants.
- """
-
- kCFStringEncodingUTF8 = CFStringEncoding(0x08000100)
-
-
-class SecurityConst(object):
- """
- A class object that acts as essentially a namespace for Security constants.
- """
-
- kSSLSessionOptionBreakOnServerAuth = 0
-
- kSSLProtocol2 = 1
- kSSLProtocol3 = 2
- kTLSProtocol1 = 4
- kTLSProtocol11 = 7
- kTLSProtocol12 = 8
- # SecureTransport does not support TLS 1.3 even if there's a constant for it
- kTLSProtocol13 = 10
- kTLSProtocolMaxSupported = 999
-
- kSSLClientSide = 1
- kSSLStreamType = 0
-
- kSecFormatPEMSequence = 10
-
- kSecTrustResultInvalid = 0
- kSecTrustResultProceed = 1
- # This gap is present on purpose: this was kSecTrustResultConfirm, which
- # is deprecated.
- kSecTrustResultDeny = 3
- kSecTrustResultUnspecified = 4
- kSecTrustResultRecoverableTrustFailure = 5
- kSecTrustResultFatalTrustFailure = 6
- kSecTrustResultOtherError = 7
-
- errSSLProtocol = -9800
- errSSLWouldBlock = -9803
- errSSLClosedGraceful = -9805
- errSSLClosedNoNotify = -9816
- errSSLClosedAbort = -9806
-
- errSSLXCertChainInvalid = -9807
- errSSLCrypto = -9809
- errSSLInternal = -9810
- errSSLCertExpired = -9814
- errSSLCertNotYetValid = -9815
- errSSLUnknownRootCert = -9812
- errSSLNoRootCert = -9813
- errSSLHostNameMismatch = -9843
- errSSLPeerHandshakeFail = -9824
- errSSLPeerUserCancelled = -9839
- errSSLWeakPeerEphemeralDHKey = -9850
- errSSLServerAuthCompleted = -9841
- errSSLRecordOverflow = -9847
-
- errSecVerifyFailed = -67808
- errSecNoTrustSettings = -25263
- errSecItemNotFound = -25300
- errSecInvalidTrustSettings = -25262
-
- # Cipher suites. We only pick the ones our default cipher string allows.
- # Source: https://developer.apple.com/documentation/security/1550981-ssl_cipher_suite_values
- TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384 = 0xC02C
- TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384 = 0xC030
- TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256 = 0xC02B
- TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256 = 0xC02F
- TLS_ECDHE_ECDSA_WITH_CHACHA20_POLY1305_SHA256 = 0xCCA9
- TLS_ECDHE_RSA_WITH_CHACHA20_POLY1305_SHA256 = 0xCCA8
- TLS_DHE_RSA_WITH_AES_256_GCM_SHA384 = 0x009F
- TLS_DHE_RSA_WITH_AES_128_GCM_SHA256 = 0x009E
- TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA384 = 0xC024
- TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384 = 0xC028
- TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA = 0xC00A
- TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA = 0xC014
- TLS_DHE_RSA_WITH_AES_256_CBC_SHA256 = 0x006B
- TLS_DHE_RSA_WITH_AES_256_CBC_SHA = 0x0039
- TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256 = 0xC023
- TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256 = 0xC027
- TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA = 0xC009
- TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA = 0xC013
- TLS_DHE_RSA_WITH_AES_128_CBC_SHA256 = 0x0067
- TLS_DHE_RSA_WITH_AES_128_CBC_SHA = 0x0033
- TLS_RSA_WITH_AES_256_GCM_SHA384 = 0x009D
- TLS_RSA_WITH_AES_128_GCM_SHA256 = 0x009C
- TLS_RSA_WITH_AES_256_CBC_SHA256 = 0x003D
- TLS_RSA_WITH_AES_128_CBC_SHA256 = 0x003C
- TLS_RSA_WITH_AES_256_CBC_SHA = 0x0035
- TLS_RSA_WITH_AES_128_CBC_SHA = 0x002F
- TLS_AES_128_GCM_SHA256 = 0x1301
- TLS_AES_256_GCM_SHA384 = 0x1302
- TLS_AES_128_CCM_8_SHA256 = 0x1305
- TLS_AES_128_CCM_SHA256 = 0x1304
diff --git a/spaces/TeamTonic/MultiMed/app.py b/spaces/TeamTonic/MultiMed/app.py
deleted file mode 100644
index 29b1ad119a6d38fc2fca1fa616c0f221e9751208..0000000000000000000000000000000000000000
--- a/spaces/TeamTonic/MultiMed/app.py
+++ /dev/null
@@ -1,490 +0,0 @@
-# Welcome to Team Tonic's MultiMed
-
-from gradio_client import Client
-import os
-import numpy as np
-import base64
-import gradio as gr
-import requests
-import json
-import dotenv
-from scipy.io.wavfile import write
-import PIL
-from openai import OpenAI
-import time
-dotenv.load_dotenv()
-
-seamless_client = Client("facebook/seamless_m4t")
-HuggingFace_Token = os.getenv("HuggingFace_Token")
-
-def check_hallucination(assertion,citation):
- API_URL = "https://api-inference.huggingface.co/models/vectara/hallucination_evaluation_model"
- headers = {"Authorization": f"Bearer {HuggingFace_Token}"}
- payload = {"inputs" : f"{assertion} [SEP] {citation}"}
-
- response = requests.post(API_URL, headers=headers, json=payload,timeout=120)
- output = response.json()
- output = output[0][0]["score"]
-
- return f"**hullicination score:** {output}"
-
-
-
-def process_speech(input_language, audio_input):
- """
- processing sound using seamless_m4t
- """
- if audio_input is None :
- return "no audio or audio did not save yet \nplease try again ! "
- print(f"audio : {audio_input}")
- print(f"audio type : {type(audio_input)}")
- out = seamless_client.predict(
- "S2TT",
- "file",
- None,
- audio_input, #audio_name
- "",
- input_language,# source language
- input_language,# target language
- api_name="/run",
- )
- out = out[1] # get the text
- try :
- return f"{out}"
- except Exception as e :
- return f"{e}"
-
-
-
-
-def process_image(image) :
- img_name = f"{np.random.randint(0, 100)}.jpg"
- PIL.Image.fromarray(image.astype('uint8'), 'RGB').save(img_name)
- image = open(img_name, "rb").read()
- base64_image = base64_image = base64.b64encode(image).decode('utf-8')
- openai_api_key = os.getenv('OPENAI_API_KEY')
- # oai_org = os.getenv('OAI_ORG')
-
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {openai_api_key}"
- }
-
- payload = {
- "model": "gpt-4-vision-preview",
- "messages": [
- {
- "role": "user",
- "content": [
- {
- "type": "text",
- "text": "You are clinical consultant discussion training cases with students at TonicUniversity. Assess and describe the photo in minute detail. Explain why each area or item in the photograph would be inappropriate to describe if required. Pay attention to anatomy, symptoms and remedies. Propose a course of action based on your assessment. Exclude any other commentary:"
- },
- {
- "type": "image_url",
- "image_url": {
- "url": f"data:image/jpeg;base64,{base64_image}"
- }
- }
- ]
- }
- ],
- "max_tokens": 1200
- }
-
- response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
-
- try :
- out = response.json()
- out = out["choices"][0]["message"]["content"]
-
- return out
- except Exception as e :
- return f"{e}"
-
-
-def query_vectara(text):
- user_message = text
-
- # Read authentication parameters from the .env file
- CUSTOMER_ID = os.getenv('CUSTOMER_ID')
- CORPUS_ID = os.getenv('CORPUS_ID')
- API_KEY = os.getenv('API_KEY')
-
- # Define the headers
- api_key_header = {
- "customer-id": CUSTOMER_ID,
- "x-api-key": API_KEY
- }
-
- # Define the request body in the structure provided in the example
- request_body = {
- "query": [
- {
- "query": user_message,
- "queryContext": "",
- "start": 1,
- "numResults": 50,
- "contextConfig": {
- "charsBefore": 0,
- "charsAfter": 0,
- "sentencesBefore": 2,
- "sentencesAfter": 2,
- "startTag": "%START_SNIPPET%",
- "endTag": "%END_SNIPPET%",
- },
- "rerankingConfig": {
- "rerankerId": 272725718,
- "mmrConfig": {
- "diversityBias": 0.35
- }
- },
- "corpusKey": [
- {
- "customerId": CUSTOMER_ID,
- "corpusId": CORPUS_ID,
- "semantics": 0,
- "metadataFilter": "",
- "lexicalInterpolationConfig": {
- "lambda": 0
- },
- "dim": []
- }
- ],
- "summary": [
- {
- "maxSummarizedResults": 5,
- "responseLang": "auto",
- "summarizerPromptName": "vectara-summary-ext-v1.2.0"
- }
- ]
- }
- ]
- }
-
- # Make the API request using Gradio
- response = requests.post(
- "https://api.vectara.io/v1/query",
- json=request_body, # Use json to automatically serialize the request body
- verify=True,
- headers=api_key_header
- )
-
- if response.status_code == 200:
- query_data = response.json()
- if query_data:
- sources_info = []
-
- # Extract the summary.
- summary = query_data['responseSet'][0]['summary'][0]['text']
-
- # Iterate over all response sets
- for response_set in query_data.get('responseSet', []):
- # Extract sources
- # Limit to top 5 sources.
- for source in response_set.get('response', [])[:5]:
- source_metadata = source.get('metadata', [])
- source_info = {}
-
- for metadata in source_metadata:
- metadata_name = metadata.get('name', '')
- metadata_value = metadata.get('value', '')
-
- if metadata_name == 'title':
- source_info['title'] = metadata_value
- elif metadata_name == 'author':
- source_info['author'] = metadata_value
- elif metadata_name == 'pageNumber':
- source_info['page number'] = metadata_value
-
- if source_info:
- sources_info.append(source_info)
-
- result = {"summary": summary, "sources": sources_info}
- return f"{json.dumps(result, indent=2)}"
- else:
- return "No data found in the response."
- else:
- return f"Error: {response.status_code}"
-
-
-def convert_to_markdown(vectara_response_json):
- vectara_response = json.loads(vectara_response_json)
- if vectara_response:
- summary = vectara_response.get('summary', 'No summary available')
- sources_info = vectara_response.get('sources', [])
-
- # Format the summary as Markdown
- markdown_summary = f' {summary}\n\n'
-
- # Format the sources as a numbered list
- markdown_sources = ""
- for i, source_info in enumerate(sources_info):
- author = source_info.get('author', 'Unknown author')
- title = source_info.get('title', 'Unknown title')
- page_number = source_info.get('page number', 'Unknown page number')
- markdown_sources += f"{i+1}. {title} by {author}, Page {page_number}\n"
-
- return f"{markdown_summary}**Sources:**\n{markdown_sources}"
- else:
- return "No data found in the response."
-# Main function to handle the Gradio interface logic
-
-def process_summary_with_openai(summary):
- """
- This function takes a summary text as input and processes it with OpenAI's GPT model.
- """
- try:
- # Ensure that the OpenAI client is properly initialized
- client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
-
- # Create the prompt for OpenAI's completion
- prompt = "You are clinical consultant discussion training cases with students at TonicUniversity. Assess and describe the proper options in minute detail. Propose a course of action based on your assessment. You will recieve a summary assessment in a language, respond ONLY in the original language. Exclude any other commentary:"
-
- # Call the OpenAI API with the prompt and the summary
- completion = client.chat.completions.create(
- model="gpt-4-1106-preview", # Make sure to use the correct model name
- messages=[
- {"role": "system", "content": prompt},
- {"role": "user", "content": summary}
- ]
- )
-
- # Extract the content from the completion
- final_summary = completion.choices[0].message.content
- return final_summary
- except Exception as e:
- return str(e)
-
-
-def process_and_query(text=None):
- try:
- # augment the prompt before feeding it to vectara
- text = "the user asks the following to his health adviser " + text
- # If an image is provided, process it with OpenAI and use the response as the text query for Vectara
- # if image is not None:
- # text = process_image(image)
- # return "**Summary:** "+text
- # if audio is not None:
- # text = process_speech(audio)
- # # augment the prompt before feeding it to vectara
- # text = "the user asks the following to his health adviser " + text
-
-
-
- # Use the text to query Vectara
- vectara_response_json = query_vectara(text)
-
- # Convert the Vectara response to Markdown
- markdown_output = convert_to_markdown(vectara_response_json)
-
- # Process the summary with OpenAI
- final_response = process_summary_with_openai(markdown_output)
-
- # Return the processed summary along with the full output
- return f"**Summary**: {final_response}\n\n**Full output**:\n{markdown_output}"
- except Exception as e:
- return str(e)
-
- completion = client.chat.completions.create(
- model="gpt-3.5-turbo",
- messages=[
- {"role": "system", "content": prompt},
- {"role": "user", "content": markdown_output_final}
- ]
- )
- final_response= completion.choices[0].message.content
- return f"**Summary**: {final_response}\n\n**Full output**:\n{markdown_output}"
- except Exception as e:
- return str(e)
-
-
-# Define the Gradio interface
-# iface = gr.Interface(
-# fn=process_and_query,
-# inputs=[
-# gr.Textbox(label="Input Text"),
-# gr.Image(label="Upload Image"),
-# gr.Audio(label="talk in french",
-# sources=["microphone"]),
-# ],
-# outputs=[gr.Markdown(label="Output Text")],
-# title="👋🏻Welcome to ⚕🗣️😷MultiMed - Access Chat ⚕🗣️😷",
-# description='''
-# ### How To Use ⚕🗣️😷MultiMed⚕:
-# #### 🗣️📝Interact with ⚕🗣️😷MultiMed⚕ in any language using audio or text!
-# #### 🗣️📝 This is an educational and accessible conversational tool to improve wellness and sanitation in support of public health.
-# #### 📚🌟💼 The knowledge base is composed of publicly available medical and health sources in multiple languages. We also used [Kelvalya/MedAware](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset) that we processed and converted to HTML. The quality of the answers depends on the quality of the dataset, so if you want to see some data represented here, do [get in touch](https://discord.gg/GWpVpekp). You can also use 😷MultiMed⚕️ on your own data & in your own way by cloning this space. 🧬🔬🔍 Simply click here:
-# #### Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community on 👻Discord: [Discord](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)"
-# ''',
-# theme='ParityError/Anime',
-# examples=[
-# ["What is the proper treatment for buccal herpes?"],
-# ["Male, 40 presenting with swollen glands and a rash"],
-# ["How does cellular metabolism work TCA cycle"],
-# ["What special care must be provided to children with chicken pox?"],
-# ["When and how often should I wash my hands ?"],
-# ["بکل ہرپس کا صحیح علاج کیا ہے؟"],
-# ["구강 헤르페스의 적절한 치료법은 무엇입니까?"],
-# ["Je, ni matibabu gani sahihi kwa herpes ya buccal?"],
-# ],
-# )
-
-welcome_message = """
-# 👋🏻Welcome to ⚕🗣️😷MultiMed - Access Chat ⚕🗣️😷
-### How To Use ⚕🗣️😷MultiMed⚕:
-#### 🗣️📝Interact with ⚕🗣️😷MultiMed⚕ in any language using audio or text!
-#### 🗣️📝 This is an educational and accessible conversational tool to improve wellness and sanitation in support of public health.
-#### 📚🌟💼 The knowledge base is composed of publicly available medical and health sources in multiple languages. We also used [Kelvalya/MedAware](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset) that we processed and converted to HTML. The quality of the answers depends on the quality of the dataset, so if you want to see some data represented here, do [get in touch](https://discord.gg/GWpVpekp). You can also use 😷MultiMed⚕️ on your own data & in your own way by cloning this space. 🧬🔬🔍 Simply click here:
-#### Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community on 👻Discord: [Discord](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)"
-"""
-
-
-languages = [
- "Afrikaans",
- "Amharic",
- "Modern Standard Arabic",
- "Moroccan Arabic",
- "Egyptian Arabic",
- "Assamese",
- "Asturian",
- "North Azerbaijani",
- "Belarusian",
- "Bengali",
- "Bosnian",
- "Bulgarian",
- "Catalan",
- "Cebuano",
- "Czech",
- "Central Kurdish",
- "Mandarin Chinese",
- "Welsh",
- "Danish",
- "German",
- "Greek",
- "English",
- "Estonian",
- "Basque",
- "Finnish",
- "French",
- "West Central Oromo",
- "Irish",
- "Galician",
- "Gujarati",
- "Hebrew",
- "Hindi",
- "Croatian",
- "Hungarian",
- "Armenian",
- "Igbo",
- "Indonesian",
- "Icelandic",
- "Italian",
- "Javanese",
- "Japanese",
- "Kamba",
- "Kannada",
- "Georgian",
- "Kazakh",
- "Kabuverdianu",
- "Halh Mongolian",
- "Khmer",
- "Kyrgyz",
- "Korean",
- "Lao",
- "Lithuanian",
- "Luxembourgish",
- "Ganda",
- "Luo",
- "Standard Latvian",
- "Maithili",
- "Malayalam",
- "Marathi",
- "Macedonian",
- "Maltese",
- "Meitei",
- "Burmese",
- "Dutch",
- "Norwegian Nynorsk",
- "Norwegian Bokmål",
- "Nepali",
- "Nyanja",
- "Occitan",
- "Odia",
- "Punjabi",
- "Southern Pashto",
- "Western Persian",
- "Polish",
- "Portuguese",
- "Romanian",
- "Russian",
- "Slovak",
- "Slovenian",
- "Shona",
- "Sindhi",
- "Somali",
- "Spanish",
- "Serbian",
- "Swedish",
- "Swahili",
- "Tamil",
- "Telugu",
- "Tajik",
- "Tagalog",
- "Thai",
- "Turkish",
- "Ukrainian",
- "Urdu",
- "Northern Uzbek",
- "Vietnamese",
- "Xhosa",
- "Yoruba",
- "Cantonese",
- "Colloquial Malay",
- "Standard Malay",
- "Zulu"
-]
-
-
-with gr.Blocks(theme='ParityError/Anime') as iface :
- gr.Markdown(welcome_message)
- with gr.Tab("text summarization"):
- text_input = gr.Textbox(label="input text",lines=5)
- text_output = gr.Markdown(label="output text")
- text_button = gr.Button("process text")
- gr.Examples([
- ["What is the proper treatment for buccal herpes?"],
- ["Male, 40 presenting with swollen glands and a rash"],
- ["How does cellular metabolism work TCA cycle"],
- ["What special care must be provided to children with chicken pox?"],
- ["When and how often should I wash my hands?"],
- ["بکل ہرپس کا صحیح علاج کیا ہے؟"],
- ["구강 헤르페스의 적절한 치료법은 무엇입니까?"],
- ["Je, ni matibabu gani sahihi kwa herpes ya buccal?"],
- ],inputs=[text_input])
- with gr.Tab("image identification"):
- image_input = gr.Image(label="upload image")
- image_output = gr.Markdown(label="output text")
- image_button = gr.Button("process image")
- image_button.click(process_image, inputs=image_input, outputs=image_output)
- gr.Examples(["sick person.jpeg"],inputs=[image_input])
- with gr.Tab("speech to text"):
- input_language = gr.Dropdown(languages, label="select the language",value="English",interactive=True)
- audio_input = gr.Audio(label="speak",type="filepath",sources="microphone")
- audio_output = gr.Markdown(label="output text")
- audio_button = gr.Button("process audio")
- audio_button.click(process_speech, inputs=[input_language,audio_input], outputs=audio_output)
- gr.Examples([["English","sample_input.mp3"]],inputs=[input_language,audio_input])
- with gr.Tab("hallucination check"):
- assertion = gr.Textbox(label="assertion")
- citation = gr.Textbox(label="citation text")
- hullucination_output = gr.Markdown(label="output text")
- audio_button = gr.Button("check hallucination")
- gr.Examples([["i am drunk","sarah is pregnant"]],inputs=[assertion,citation])
- text_button.click(process_and_query, inputs=text_input, outputs=text_output)
- audio_button.click(check_hallucination,inputs=[assertion,citation],outputs=hullucination_output)
-
-
-
-
-iface.queue().launch(show_error=True,debug=True)
diff --git a/spaces/Thanarit/GPT-Detection-Demo/app.py b/spaces/Thanarit/GPT-Detection-Demo/app.py
deleted file mode 100644
index b0de7e28071553c6b86ec9a73b9881b4d540c0bc..0000000000000000000000000000000000000000
--- a/spaces/Thanarit/GPT-Detection-Demo/app.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-from ModelDriver import *
-import numpy as np
-
-# Add a title
-st.title('GPT Detection Demo')
-st.write("This is a demo for GPT detection. You can use this demo to test the model. The model is trained on two datasets: OpenGPT and CSAbstract. You can choose the model and dataset in the sidebar.")
-st.write("Reference on how we built Roberta Sentinel: https://arxiv.org/abs/2305.07969")
-
-# Add 4 options for 4 models
-ModelOption = st.sidebar.selectbox(
- 'Which Model do you want to use?',
- ('RobertaSentinel', 'RobertaClassifier'),
-)
-
-DatasetOption = st.sidebar.selectbox(
- 'Which Dataset the model was trained on?',
- ('OpenGPT', 'CSAbstract'),
-)
-
-
-text = st.text_area('Enter text here (max 500 words)', '')
-
-if st.button('Generate'):
- if ModelOption == 'RobertaSentinel':
- if DatasetOption == 'OpenGPT':
- result = RobertaSentinelOpenGPTInference(text)
- st.write("Model: RobertaSentinelOpenGPT")
- elif DatasetOption == 'CSAbstract':
- result = RobertaSentinelCSAbstractInference(text)
- st.write("Model: RobertaSentinelCSAbstract")
-
- elif ModelOption == 'RobertaClassifier':
- if DatasetOption == 'OpenGPT':
- result = RobertaClassifierOpenGPTInference(text)
- st.write("Model: RobertaClassifierOpenGPT")
- elif DatasetOption == 'CSAbstract':
- result = RobertaClassifierCSAbstractInference(text)
- st.write("Model: RobertaClassifierCSAbstract")
-
- Prediction = "Human Written" if not np.argmax(result) else "Machine Generated"
-
- st.write(f"Prediction: {Prediction} ")
- st.write(f"Probabilty:", max(result))
-
-
-
-
-
-
-
-
-
diff --git a/spaces/ThomasSimonini/Unity-MLAgents-Pyramids/index.html b/spaces/ThomasSimonini/Unity-MLAgents-Pyramids/index.html
deleted file mode 100644
index 37f4404c8056925b6523479300dec8d2406d5c96..0000000000000000000000000000000000000000
--- a/spaces/ThomasSimonini/Unity-MLAgents-Pyramids/index.html
+++ /dev/null
@@ -1,92 +0,0 @@
-
-
-
-
-
-
- ")
-
- with gr.Row():
- with gr.Column():
- input_image = gr.Image(source='upload', type="pil")
- grounding_caption = gr.Textbox(label="Detection Prompt")
- run_button = gr.Button(label="Run")
- with gr.Accordion("Advanced options", open=False):
- box_threshold = gr.Slider(
- label="Box Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
- )
- text_threshold = gr.Slider(
- label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
- )
-
- with gr.Column():
- gallery = gr.outputs.Image(
- type="pil",
- # label="grounding results"
- ).style(full_width=True, full_height=True)
- # gallery = gr.Gallery(label="Generated images", show_label=False).style(
- # grid=[1], height="auto", container=True, full_width=True, full_height=True)
-
- run_button.click(fn=run_grounding, inputs=[
- input_image, grounding_caption, box_threshold, text_threshold], outputs=[gallery])
- gr.Examples(
- [["watermelon.jpg", "watermelon", 0.25, 0.25]],
- inputs = [input_image, grounding_caption, box_threshold, text_threshold],
- outputs = [gallery],
- fn=run_grounding,
- cache_examples=True,
- label='Try this example input!'
- )
- block.launch(share=True, show_api=False, show_error=True)
-
diff --git a/spaces/Audio-AGI/WavJourney/README.md b/spaces/Audio-AGI/WavJourney/README.md
deleted file mode 100644
index 6514202f6a3a92fe09c96516f15d4e7848d85484..0000000000000000000000000000000000000000
--- a/spaces/Audio-AGI/WavJourney/README.md
+++ /dev/null
@@ -1,111 +0,0 @@
----
-title: WavJourney
-emoji: 🔥
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
-license: cc-by-nc-4.0
----
-# 🎵 WavJourney: Compositional Audio Creation with LLMs
-[](https://arxiv.org/abs/2307.14335) [](https://github.com/Audio-AGI/WavJourney/) [](https://audio-agi.github.io/WavJourney_demopage/) [](https://huggingface.co/spaces/Audio-AGI/WavJourney)
-
-
-This repository contains the official implementation of ["WavJourney: Compositional Audio Creation with Large Language Models"](https://audio-agi.github.io/WavJourney_demopage/WavJourney_arXiv.pdf).
-
-Starting with a text prompt, WavJourney can create audio content with engaging storylines encompassing personalized speakers, lifelike speech in context, emotionally resonant music compositions, and impactful sound effects that enhance the auditory experience. Check the audio examples in the [Project Page](https://audio-agi.github.io/WavJourney_demopage/)!
-
-
-
-
-
-
-## Preliminaries
-1. Install the environment:
-```bash
-bash ./scripts/EnvsSetup.sh
-```
-2. Activate the conda environment:
-```bash
-conda activate WavJourney
-```
-
-3. (Optional) You can modify the default configuration in `config.yaml`, check the details described in the configuration file.
-4. Pre-download the models (might take some time):
-```bash
-python scripts/download_models.py
-```
-
-5. Set the WAVJOURNEY_OPENAI_KEY in the environment variable for accessing [GPT-4 API](https://platform.openai.com/account/api-keys) [[Guidance](https://help.openai.com/en/articles/7102672-how-can-i-access-gpt-4)]
-```bash
-export WAVJOURNEY_OPENAI_KEY=your_openai_key_here
-```
-
-6. Set environment variables for using API services
-```bash
-# Set the port for the WAVJOURNEY service to 8021
-export WAVJOURNEY_SERVICE_PORT=8021
-
-# Set the URL for the WAVJOURNEY service to 127.0.0.1
-export WAVJOURNEY_SERVICE_URL=127.0.0.1
-
-# Limit the maximum script lines for WAVJOURNEY to 999
-export WAVJOURNEY_MAX_SCRIPT_LINES=999
-```
-
-
-7. Start Python API services (e.g., Text-to-Speech, Text-to-Audio)
-```bash
-bash scripts/start_services.sh
-```
-
-## Web APP
- ```bash
-bash scripts/start_ui.sh
- ```
-
-## Commandline Usage
- ```bash
- python wavjourney_cli.py -f --input-text "Generate a one-minute introduction to quantum mechanics"
- ```
-
-
-## Kill the services
-You can kill the running services via this command:
- ```bash
-python scripts/kill_services.py
- ```
-
-## (Advanced features) Speaker customization
-You can add voice presets to WavJourney to customize the voice actors. Simply provide the voice id, the description and a sample wav file, and WavJourney will pick the voice automatically based on the audio script. Predefined system voice presets are in `data/voice_presets`.
-
-You can manage voice presets via UI. Specifically, if you want to add voice to voice presets. Run the script via command line below:
-```bash
-python add_voice_preset.py --id "id" --desc "description" --wav-path path/to/wav --session-id ''
-```
-What makes for good voice prompt? See detailed instructions here.
-## Hardware requirement
-- The VRAM of the GPU in the default configuration should be greater than 16 GB.
-- Operation system: Linux.
-
-## Citation
-If you find this work useful, you can cite the paper below:
-
- @article{liu2023wavjourney,
- title = {WavJourney: Compositional Audio Creation with Large Language Models},
- author = {Liu, Xubo and Zhu, Zhongkai and Liu, Haohe and Yuan, Yi and Huang, Qiushi and Liang, Jinhua and Cao, Yin and Kong, Qiuqiang and Plumbley, Mark D and Wang, Wenwu},
- journal = {arXiv preprint arXiv:2307.14335},
- year = {2023}
- }
-
-[](https://www.buymeacoffee.com/liuxubo)
-
-## Appreciation
-- [Bark](https://github.com/suno-ai/bark) for a zero-shot text-to-speech synthesis model.
-- [AudioCraft](https://github.com/facebookresearch/audiocraft) for state-of-the-art audio generation models.
-
-## Disclaimer
-We are not responsible for audio generated using semantics created by this model. Just don't use it for illegal purposes.
-
diff --git a/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py b/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py
deleted file mode 100644
index be58431ed446bf6283c17274a41e37ec3eb24409..0000000000000000000000000000000000000000
--- a/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import os
-import asyncio
-import random
-from datetime import date, datetime, timezone, timedelta
-from ast import literal_eval
-
-import streamlit as st
-import openai
-
-from autoagents.utils.constants import MAIN_HEADER, MAIN_CAPTION, SAMPLE_QUESTIONS
-from autoagents.agents.search import ActionRunner
-
-from langchain.chat_models import ChatOpenAI
-
-
-async def run():
- output_acc = ""
- st.session_state["random"] = random.randint(0, 99)
- if "task" not in st.session_state:
- st.session_state.task = None
- if "model_name" not in st.session_state:
- st.session_state.model_name = "gpt-3.5-turbo"
-
- st.set_page_config(
- page_title="Search Agent",
- page_icon="🤖",
- layout="wide",
- initial_sidebar_state="expanded",
- )
-
- st.title(MAIN_HEADER)
- st.caption(MAIN_CAPTION)
-
- with st.form("my_form", clear_on_submit=False):
- st.markdown("", unsafe_allow_html=True)
- user_input = st.text_input(
- "You: ",
- key="input",
- placeholder="Ask me anything ...",
- label_visibility="hidden",
- )
-
- submitted = st.form_submit_button(
- "Search", help="Hit to submit the search query."
- )
-
- # Ask the user to enter their OpenAI API key
- if (api_key := st.sidebar.text_input("OpenAI api-key", type="password")):
- api_org = None
- else:
- api_key, api_org = os.getenv("OPENAI_API_KEY"), os.getenv("OPENAI_API_ORG")
- with st.sidebar:
- model_dict = {
- "gpt-3.5-turbo": "GPT-3.5-turbo",
- "gpt-4": "GPT-4 (Better but slower)",
- }
- st.radio(
- "OpenAI model",
- model_dict.keys(),
- key="model_name",
- format_func=lambda x: model_dict[x],
- )
-
- time_zone = str(datetime.now(timezone(timedelta(0))).astimezone().tzinfo)
- st.markdown(f"**The system time zone is {time_zone} and the date is {date.today()}**")
-
- st.markdown("**Example Queries:**")
- for q in SAMPLE_QUESTIONS:
- st.markdown(f"*{q}*")
-
- if not api_key:
- st.warning(
- "API key required to try this app. The API key is not stored in any form. [This](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key) might help."
- )
- elif api_org and st.session_state.model_name == "gpt-4":
- st.warning(
- "The free API key does not support GPT-4. Please switch to GPT-3.5-turbo or input your own API key."
- )
- else:
- outputq = asyncio.Queue()
- runner = ActionRunner(outputq,
- ChatOpenAI(openai_api_key=api_key,
- openai_organization=api_org,
- temperature=0,
- model_name=st.session_state.model_name),
- persist_logs=True) # log to HF-dataset
-
- async def cleanup(e):
- st.error(e)
- await st.session_state.task
- st.session_state.task = None
- st.stop()
-
- placeholder = st.empty()
-
- if user_input and submitted:
- if st.session_state.task is not None:
- with placeholder.container():
- st.session_state.task.cancel()
- st.warning("Previous search aborted", icon="⚠️")
-
- st.session_state.task = asyncio.create_task(
- runner.run(user_input, outputq)
- )
- iterations = 0
- with st.expander("Search Results", expanded=True):
- while True:
- with st.spinner("Wait for it..."):
- output = await outputq.get()
- placeholder.empty()
- if isinstance(output, Exception):
- if isinstance(output, openai.error.AuthenticationError):
- await cleanup(f"AuthenticationError: Invalid OpenAI API key.")
- elif isinstance(output, openai.error.InvalidRequestError) \
- and output._message == "The model: `gpt-4` does not exist":
- await cleanup(f"The free API key does not support GPT-4. Please switch to GPT-3.5-turbo or input your own API key.")
- elif isinstance(output, openai.error.OpenAIError):
- await cleanup(output)
- elif isinstance(output, RuntimeWarning):
- st.warning(output)
- continue
- else:
- await cleanup("Something went wrong. Please try searching again.")
- return
- try:
- output_fmt = literal_eval(output)
- st.json(output_fmt, expanded=False)
- st.write("---")
- iterations += 1
- except:
- output_acc += "\n" + output
- st.markdown(f" {output} ",
- unsafe_allow_html=True)
- if iterations >= runner.agent_executor.max_iterations:
- await cleanup(
- f"Maximum iterations ({iterations}) exceeded. You can try running the search again or try a variation of the query."
- )
- return
- if "Final Answer:" in output:
- break
- # Found the answer
- final_answer = await st.session_state.task
- final_answer = final_answer.replace("$", "\$")
- # st.success accepts md
- st.success(final_answer, icon="✅")
- st.balloons()
- st.session_state.task = None
- st.stop()
-
-if __name__ == "__main__":
- loop = asyncio.new_event_loop()
- loop.set_debug(enabled=False)
- loop.run_until_complete(run())
diff --git a/spaces/BAAI/vid2vid-zero/Dockerfile b/spaces/BAAI/vid2vid-zero/Dockerfile
deleted file mode 100644
index 9eaa78f8facc5066f018183f108824842f11b7e8..0000000000000000000000000000000000000000
--- a/spaces/BAAI/vid2vid-zero/Dockerfile
+++ /dev/null
@@ -1,57 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update && \
- apt-get upgrade -y && \
- apt-get install -y --no-install-recommends \
- git \
- git-lfs \
- wget \
- curl \
- # ffmpeg \
- ffmpeg \
- x264 \
- # python build dependencies \
- build-essential \
- libssl-dev \
- zlib1g-dev \
- libbz2-dev \
- libreadline-dev \
- libsqlite3-dev \
- libncursesw5-dev \
- xz-utils \
- tk-dev \
- libxml2-dev \
- libxmlsec1-dev \
- libffi-dev \
- liblzma-dev && \
- apt-get clean && \
- rm -rf /var/lib/apt/lists/*
-
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:${PATH}
-WORKDIR ${HOME}/app
-
-RUN curl https://pyenv.run | bash
-ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ENV PYTHON_VERSION=3.10.9
-RUN pyenv install ${PYTHON_VERSION} && \
- pyenv global ${PYTHON_VERSION} && \
- pyenv rehash && \
- pip install --no-cache-dir -U pip setuptools wheel
-
-RUN pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1
-COPY --chown=1000 requirements.txt /tmp/requirements.txt
-RUN pip install --no-cache-dir -U -r /tmp/requirements.txt
-
-COPY --chown=1000 . ${HOME}/app
-# RUN cd Tune-A-Video && patch -p1 < ../patch
-ENV PYTHONPATH=${HOME}/app \
- PYTHONUNBUFFERED=1 \
- GRADIO_ALLOW_FLAGGING=never \
- GRADIO_NUM_PORTS=1 \
- GRADIO_SERVER_NAME=0.0.0.0 \
- GRADIO_THEME=huggingface \
- SYSTEM=spaces
-CMD ["python", "app.py"]
diff --git a/spaces/Bala2-03-2003/MygenvioceAI/app.py b/spaces/Bala2-03-2003/MygenvioceAI/app.py
deleted file mode 100644
index ca8b6d40b4ab898c70da92f4a4298de2baf703dc..0000000000000000000000000000000000000000
--- a/spaces/Bala2-03-2003/MygenvioceAI/app.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import os
-import re
-import requests
-import json
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-PLAY_HT_API_KEY=os.getenv('PLAY_HT_API_KEY')
-PLAY_HT_USER_ID=os.getenv('PLAY_HT_USER_ID')
-
-PLAY_HT_VOICE_ID=os.getenv('PLAY_HT_VOICE_ID')
-play_ht_api_get_audio_url = "https://play.ht/api/v2/tts"
-
-
-template = """You are a helpful assistant to answer user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
- input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
- llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
- prompt=prompt,
- verbose=True,
- memory=memory,
-)
-
-headers = {
- "accept": "text/event-stream",
- "content-type": "application/json",
- "AUTHORIZATION": "Bearer "+ PLAY_HT_API_KEY,
- "X-USER-ID": PLAY_HT_USER_ID
-}
-
-
-def get_payload(text):
- return {
- "text": text,
- "voice": PLAY_HT_VOICE_ID,
- "quality": "medium",
- "output_format": "mp3",
- "speed": 1,
- "sample_rate": 24000,
- "seed": None,
- "temperature": None
- }
-
-def get_generated_audio(text):
- payload = get_payload(text)
- generated_response = {}
- try:
- response = requests.post(play_ht_api_get_audio_url, json=payload, headers=headers)
- response.raise_for_status()
- generated_response["type"]= 'SUCCESS'
- generated_response["response"] = response.text
- except requests.exceptions.RequestException as e:
- generated_response["type"]= 'ERROR'
- try:
- response_text = json.loads(response.text)
- if response_text['error_message']:
- generated_response["response"] = response_text['error_message']
- else:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["type"]= 'ERROR'
- generated_response["response"] = response.text
- return generated_response
-
-def extract_urls(text):
- # Define the regex pattern for URLs
- url_pattern = r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+[/\w\.-]*'
-
- # Find all occurrences of URLs in the text
- urls = re.findall(url_pattern, text)
-
- return urls
-
-def get_audio_reply_for_question(text):
- generated_audio_event = get_generated_audio(text)
- #From get_generated_audio, you will get events in a string format, from that we need to extract the url
- final_response = {
- "audio_url": '',
- "message": ''
- }
- if generated_audio_event["type"] == 'SUCCESS':
- audio_urls = extract_urls(generated_audio_event["response"])
- if len(audio_urls) == 0:
- final_response['message'] = "No audio file link found in generated event"
- else:
- final_response['audio_url'] = audio_urls[-1]
- else:
- final_response['message'] = generated_audio_event['response']
- return final_response
-
-def download_url(url):
- try:
- # Send a GET request to the URL to fetch the content
- final_response = {
- 'content':'',
- 'error':''
- }
- response = requests.get(url)
- # Check if the request was successful (status code 200)
- if response.status_code == 200:
- final_response['content'] = response.content
- else:
- final_response['error'] = f"Failed to download the URL. Status code: {response.status_code}"
- except Exception as e:
- final_response['error'] = f"Failed to download the URL. Error: {e}"
- return final_response
-
-def get_filename_from_url(url):
- # Use os.path.basename() to extract the file name from the URL
- file_name = os.path.basename(url)
- return file_name
-
-def get_text_response(user_message):
- response = llm_chain.predict(user_message = user_message)
- return response
-
-def get_text_response_and_audio_response(user_message):
- response = get_text_response(user_message) # Getting the reply from Open AI
- audio_reply_for_question_response = get_audio_reply_for_question(response)
- final_response = {
- 'output_file_path': '',
- 'message':''
- }
- audio_url = audio_reply_for_question_response['audio_url']
- if audio_url:
- output_file_path=get_filename_from_url(audio_url)
- download_url_response = download_url(audio_url)
- audio_content = download_url_response['content']
- if audio_content:
- with open(output_file_path, "wb") as audio_file:
- audio_file.write(audio_content)
- final_response['output_file_path'] = output_file_path
- else:
- final_response['message'] = download_url_response['error']
- else:
- final_response['message'] = audio_reply_for_question_response['message']
- return final_response
-
-def chat_bot_response(message, history):
- text_and_audio_response = get_text_response_and_audio_response(message)
- output_file_path = text_and_audio_response['output_file_path']
- if output_file_path:
- return (text_and_audio_response['output_file_path'],)
- else:
- return text_and_audio_response['message']
-
-demo = gr.ChatInterface(chat_bot_response,examples=["How are you doing?","What are your interests?","Which places do you like to visit?"])
-
-if __name__ == "__main__":
- demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/BartPoint/VoiceChange/app_multi.py b/spaces/BartPoint/VoiceChange/app_multi.py
deleted file mode 100644
index 67890355f12e2d2d5420ce8e31a722020f349b24..0000000000000000000000000000000000000000
--- a/spaces/BartPoint/VoiceChange/app_multi.py
+++ /dev/null
@@ -1,469 +0,0 @@
-from typing import Union
-
-from argparse import ArgumentParser
-
-import asyncio
-import json
-import hashlib
-from os import path, getenv
-
-import gradio as gr
-
-import torch
-
-import numpy as np
-import librosa
-
-import edge_tts
-
-import config
-import util
-from infer_pack.models import (
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono
-)
-from vc_infer_pipeline import VC
-
-# Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21 # noqa
-in_hf_space = getenv('SYSTEM') == 'spaces'
-
-# Argument parsing
-arg_parser = ArgumentParser()
-arg_parser.add_argument(
- '--hubert',
- default=getenv('RVC_HUBERT', 'hubert_base.pt'),
- help='path to hubert base model (default: hubert_base.pt)'
-)
-arg_parser.add_argument(
- '--config',
- default=getenv('RVC_MULTI_CFG', 'multi_config.json'),
- help='path to config file (default: multi_config.json)'
-)
-arg_parser.add_argument(
- '--api',
- action='store_true',
- help='enable api endpoint'
-)
-arg_parser.add_argument(
- '--cache-examples',
- action='store_true',
- help='enable example caching, please remember delete gradio_cached_examples folder when example config has been modified' # noqa
-)
-args = arg_parser.parse_args()
-
-app_css = '''
-#model_info img {
- max-width: 100px;
- max-height: 100px;
- float: right;
-}
-
-#model_info p {
- margin: unset;
-}
-'''
-
-app = gr.Blocks(
- theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate"),
- css=app_css,
- analytics_enabled=False
-)
-
-# Load hubert model
-hubert_model = util.load_hubert_model(config.device, args.hubert)
-hubert_model.eval()
-
-# Load models
-multi_cfg = json.load(open(args.config, 'r'))
-loaded_models = []
-
-for model_name in multi_cfg.get('models'):
- print(f'Loading model: {model_name}')
-
- # Load model info
- model_info = json.load(
- open(path.join('model', model_name, 'config.json'), 'r')
- )
-
- # Load RVC checkpoint
- cpt = torch.load(
- path.join('model', model_name, model_info['model']),
- map_location='cpu'
- )
- tgt_sr = cpt['config'][-1]
- cpt['config'][-3] = cpt['weight']['emb_g.weight'].shape[0] # n_spk
-
- if_f0 = cpt.get('f0', 1)
- net_g: Union[SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono]
- if if_f0 == 1:
- net_g = SynthesizerTrnMs768NSFsid(
- *cpt['config'],
- is_half=util.is_half(config.device)
- )
- else:
- net_g = SynthesizerTrnMs768NSFsid_nono(*cpt['config'])
-
- del net_g.enc_q
-
- # According to original code, this thing seems necessary.
- print(net_g.load_state_dict(cpt['weight'], strict=False))
-
- net_g.eval().to(config.device)
- net_g = net_g.half() if util.is_half(config.device) else net_g.float()
-
- vc = VC(tgt_sr, config)
-
- loaded_models.append(dict(
- name=model_name,
- metadata=model_info,
- vc=vc,
- net_g=net_g,
- if_f0=if_f0,
- target_sr=tgt_sr
- ))
-
-print(f'Models loaded: {len(loaded_models)}')
-
-# Edge TTS speakers
-tts_speakers_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) # noqa
-
-
-# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer-web.py#L118 # noqa
-def vc_func(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- if input_audio is None:
- return (None, 'Please provide input audio.')
-
- if model_index is None:
- return (None, 'Please select a model.')
-
- model = loaded_models[model_index]
-
- # Reference: so-vits
- (audio_samp, audio_npy) = input_audio
-
- # https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49
- # Can be change well, we will see
- if (audio_npy.shape[0] / audio_samp) > 320 and in_hf_space:
- return (None, 'Input audio is longer than 60 secs.')
-
- # Bloody hell: https://stackoverflow.com/questions/26921836/
- if audio_npy.dtype != np.float32: # :thonk:
- audio_npy = (
- audio_npy / np.iinfo(audio_npy.dtype).max
- ).astype(np.float32)
-
- if len(audio_npy.shape) > 1:
- audio_npy = librosa.to_mono(audio_npy.transpose(1, 0))
-
- if audio_samp != 16000:
- audio_npy = librosa.resample(
- audio_npy,
- orig_sr=audio_samp,
- target_sr=16000
- )
-
- pitch_int = int(pitch_adjust)
-
- resample = (
- 0 if resample_option == 'Disable resampling'
- else int(resample_option)
- )
-
- times = [0, 0, 0]
-
- checksum = hashlib.sha512()
- checksum.update(audio_npy.tobytes())
-
- output_audio = model['vc'].pipeline(
- hubert_model,
- model['net_g'],
- model['metadata'].get('speaker_id', 0),
- audio_npy,
- checksum.hexdigest(),
- times,
- pitch_int,
- f0_method,
- path.join('model', model['name'], model['metadata']['feat_index']),
- feat_ratio,
- model['if_f0'],
- filter_radius,
- model['target_sr'],
- resample,
- rms_mix_rate,
- 'v2'
- )
-
- out_sr = (
- resample if resample >= 16000 and model['target_sr'] != resample
- else model['target_sr']
- )
-
- print(f'npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s')
- return ((out_sr, output_audio), 'Success')
-
-
-async def edge_tts_vc_func(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- if input_text is None:
- return (None, 'Please provide TTS text.')
-
- if tts_speaker is None:
- return (None, 'Please select TTS speaker.')
-
- if model_index is None:
- return (None, 'Please select a model.')
-
- speaker = tts_speakers_list[tts_speaker]['ShortName']
- (tts_np, tts_sr) = await util.call_edge_tts(speaker, input_text)
- return vc_func(
- (tts_sr, tts_np),
- model_index,
- pitch_adjust,
- f0_method,
- feat_ratio,
- filter_radius,
- rms_mix_rate,
- resample_option
- )
-
-
-def update_model_info(model_index):
- if model_index is None:
- return str(
- '### Model info\n'
- 'Please select a model from dropdown above.'
- )
-
- model = loaded_models[model_index]
- model_icon = model['metadata'].get('icon', '')
-
- return str(
- '### Model info\n'
- ''
- '**{name}**\n\n'
- 'Author: {author}\n\n'
- 'Source: {source}\n\n'
- '{note}'
- ).format(
- name=model['metadata'].get('name'),
- author=model['metadata'].get('author', 'Anonymous'),
- source=model['metadata'].get('source', 'Unknown'),
- note=model['metadata'].get('note', ''),
- icon=(
- model_icon
- if model_icon.startswith(('http://', 'https://'))
- else '/file/model/%s/%s' % (model['name'], model_icon)
- )
- )
-
-
-def _example_vc(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- (audio, message) = vc_func(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
- )
- return (
- audio,
- message,
- update_model_info(model_index)
- )
-
-
-async def _example_edge_tts(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- (audio, message) = await edge_tts_vc_func(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method,
- feat_ratio, filter_radius, rms_mix_rate, resample_option
- )
- return (
- audio,
- message,
- update_model_info(model_index)
- )
-
-
-with app:
- gr.Markdown(
- '## A simplistic Web interface\n'
- 'RVC interface, project based on [RVC-WebUI](https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI)' # thx noqa
- 'A lot of inspiration from what\'s already out there, including [zomehwh/rvc-models](https://huggingface.co/spaces/zomehwh/rvc-models) & [DJQmUKV/rvc-inference](https://huggingface.co/spaces/DJQmUKV/rvc-inference).\n ' # thx noqa
- )
-
- with gr.Row():
- with gr.Column():
- with gr.Tab('Audio conversion'):
- input_audio = gr.Audio(label='Input audio')
-
- vc_convert_btn = gr.Button('Convert', variant='primary')
-
- with gr.Tab('TTS conversion'):
- tts_input = gr.TextArea(
- label='TTS input text'
- )
- tts_speaker = gr.Dropdown(
- [
- '%s (%s)' % (
- s['FriendlyName'],
- s['Gender']
- )
- for s in tts_speakers_list
- ],
- label='TTS speaker',
- type='index'
- )
-
- tts_convert_btn = gr.Button('Convert', variant='primary')
-
- pitch_adjust = gr.Slider(
- label='Pitch',
- minimum=-24,
- maximum=24,
- step=1,
- value=0
- )
- f0_method = gr.Radio(
- label='f0 methods',
- choices=['pm', 'harvest', 'crepe'],
- value='pm',
- interactive=True
- )
-
- with gr.Accordion('Advanced options', open=False):
- feat_ratio = gr.Slider(
- label='Feature ratio',
- minimum=0,
- maximum=1,
- step=0.1,
- value=0.6
- )
- filter_radius = gr.Slider(
- label='Filter radius',
- minimum=0,
- maximum=7,
- step=1,
- value=3
- )
- rms_mix_rate = gr.Slider(
- label='Volume envelope mix rate',
- minimum=0,
- maximum=1,
- step=0.1,
- value=1
- )
- resample_rate = gr.Dropdown(
- [
- 'Disable resampling',
- '16000',
- '22050',
- '44100',
- '48000'
- ],
- label='Resample rate',
- value='Disable resampling'
- )
-
- with gr.Column():
- # Model select
- model_index = gr.Dropdown(
- [
- '%s - %s' % (
- m['metadata'].get('source', 'Unknown'),
- m['metadata'].get('name')
- )
- for m in loaded_models
- ],
- label='Model',
- type='index'
- )
-
- # Model info
- with gr.Box():
- model_info = gr.Markdown(
- '### Model info\n'
- 'Please select a model from dropdown above.',
- elem_id='model_info'
- )
-
- output_audio = gr.Audio(label='Output audio')
- output_msg = gr.Textbox(label='Output message')
-
- multi_examples = multi_cfg.get('examples')
- if (
- multi_examples and
- multi_examples.get('vc') and multi_examples.get('tts_vc')
- ):
- with gr.Accordion('Sweet sweet examples', open=False):
- with gr.Row():
- # VC Example
- if multi_examples.get('vc'):
- gr.Examples(
- label='Audio conversion examples',
- examples=multi_examples.get('vc'),
- inputs=[
- input_audio, model_index, pitch_adjust, f0_method,
- feat_ratio
- ],
- outputs=[output_audio, output_msg, model_info],
- fn=_example_vc,
- cache_examples=args.cache_examples,
- run_on_click=args.cache_examples
- )
-
- # Edge TTS Example
- if multi_examples.get('tts_vc'):
- gr.Examples(
- label='TTS conversion examples',
- examples=multi_examples.get('tts_vc'),
- inputs=[
- tts_input, model_index, tts_speaker, pitch_adjust,
- f0_method, feat_ratio
- ],
- outputs=[output_audio, output_msg, model_info],
- fn=_example_edge_tts,
- cache_examples=args.cache_examples,
- run_on_click=args.cache_examples
- )
-
- vc_convert_btn.click(
- vc_func,
- [
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_rate
- ],
- [output_audio, output_msg],
- api_name='audio_conversion'
- )
-
- tts_convert_btn.click(
- edge_tts_vc_func,
- [
- tts_input, model_index, tts_speaker, pitch_adjust, f0_method,
- feat_ratio, filter_radius, rms_mix_rate, resample_rate
- ],
- [output_audio, output_msg],
- api_name='tts_conversion'
- )
-
- model_index.change(
- update_model_info,
- inputs=[model_index],
- outputs=[model_info],
- show_progress=False,
- queue=False
- )
-
-app.queue(
- concurrency_count=1,
- max_size=20,
- api_open=args.api
-).launch()
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md b/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md
deleted file mode 100644
index 231c36d39005d9275c61ccc88067d084ca775635..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md
+++ /dev/null
@@ -1,134 +0,0 @@
-
-Aparcamiento de coches multijugador Sudáfrica Edición APK Descargar: Una guía para los usuarios de Android
- Si usted está buscando un juego de estacionamiento de coches realista y divertido que ofrece más que solo estacionamiento, es posible que desee echa un vistazo a Parking Multijugador. Y si usted está en Sudáfrica o desea experimentar la cultura del automóvil de Sudáfrica, es posible que desee probar el Aparcamiento Multijugador South áfrica Edition APK. En este artículo, te diremos qué es el Car Parking Multiplayer, qué hace que South áfrica Edition sea diferente, cómo descargarlo e instalarlo en tu dispositivo Android, y algunos consejos y trucos para jugarlo.
- ¿Qué es el Aparcamiento Multijugador?
- Car Parking Multiplayer es un juego que puede engañarte con su nombre bastante engañoso. Pero, es mucho más que solo estar aparcando tu coche. Es una experiencia de mundo abierto donde se puede conducir gratis y sí, todavía trabajar en ese aparcamiento si lo desea. Incluso puedes saltar de tu coche y caminar. Hay diferentes áreas que se pueden explorar en el juego. Cada una es como su propio mundo abierto. Puedes optar por jugar en modo de un solo jugador o en modo online si quieres una escena más caótica (de forma divertida) .
-aparcamiento de coches multijugador sudáfrica edición apk descargar
DOWNLOAD === https://bltlly.com/2v6IE9
- Características de Aparcamiento Multijugador
- Aparcamiento de coches multijugador tiene las siguientes características :
-
-- Modo multijugador de mundo abierto
- - Caminar libremente
- - Mundo abierto libre con gasolineras reales y servicios de automóviles
- - Compite contra jugadores reales en las carreras multijugador
- - Intercambiar coches con jugadores reales
- - Miles de jugadores reales cada día
- - Lista de amigos
- - Chat de voz
- - Modo de policía
-
-
-- Personalización del coche
- - Suspensión ajustable, ángulo de rueda y más
- - Ajuste del motor: motor de intercambio, turbo, caja de cambios y escape
- - Visual auto tungs: Vynils dinámico, partes del cuerpo del coche
-
-
-- Mundo abierto de alta calidad
-
- - 100 coches con el interior real
- - 16 pieles de jugador
- - Edificios con interior
-
-
-- Juego interesante
- - 82 desafíos de estacionamiento y conducción en la vida real
- - Diferentes vehículos: Grúa, camioneta, camiones, coches deportivos y clásicos
-
-
-
- Opiniones de Aparcamiento multijugador
- Aparcamiento de coches multijugador ha recibido en su mayoría críticas positivas de los usuarios en Google Play Store y App Store. Tiene una calificación de 4.4 de 5 estrellas en Google Play Store y una calificación de 4.3 de 5 estrellas en App Store . Estos son algunos de los comentarios de los usuarios:
- "Increíble juego! No hay errores en este juego o retrasos ect. (base en el dispositivo que utiliza) Me encantan los gráficos y los coches. Los coches son tan realistas y los sonidos son increíbles. Me encanta cómo puedes personalizar tu coche y hacer que se vea genial. El modo multijugador es increíble. Puedes chatear con otros jugadores y competir con ellos. También puedes unirte a un clan o crear tu propio clan. Este juego es muy divertido y adictivo. Recomiendo este juego a todos los que aman los coches y los juegos de estacionamiento."
- "Este juego es muy bueno pero necesita algunas mejoras como agregar más coches, más mapas, más personalizaciones, más modos de juego, más desafíos, etc. Además, el juego se bloquea a veces y los controles no son muy suaves. Los gráficos son agradables, pero pueden ser mejores. El juego es divertido de jugar con amigos, pero se vuelve aburrido después de un tiempo. Espero que los desarrolladores actualicen el juego pronto y lo hagan más agradable."
-
- ¿Qué es el aparcamiento multijugador South áfrica Edition?
- Car Parking Multijugador South áfrica Edition es una versión modificada de Car Parking Multijugador que está especialmente diseñado para los usuarios sudafricanos o los fans de la cultura del coche de Sudáfrica. No es una versión oficial del juego, sino un archivo APK hecho por fans que se puede descargar e instalar en dispositivos Android .
- Diferencias entre Aparcamiento de coches multijugador y Aparcamiento de coches multijugador Sudáfrica Edición
- Car Parking Multijugador South áfrica Edition tiene algunas diferencias con el juego original Car Parking Multijugador, como :
-
-- Más coches que son populares en Sudáfrica, como BMW E30, VW Golf Mk1, Toyota Corolla, Nissan 1400, etc.
-- Más personalizaciones que reflejan la cultura del automóvil sudafricano, como llantas giratorias, escapes fuertes, pegatinas, banderas, etc.
-- Más mapas que se basan en ubicaciones reales en Sudáfrica, como Ciudad del Cabo, Johannesburgo, Durban, etc.
-- Más música inspirada en la escena musical sudafricana, como kwaito, gqom, amapiano, etc.
-- Más idiomas que se hablan en Sudáfrica, como afrikaans, zulú, xhosa, etc.
-
- Beneficios de Aparcamiento Multijugador South áfrica Edition
- Car Parking Multijugador South áfrica Edition tiene algunos beneficios para los usuarios que quieren disfrutar del juego con un toque sudafricano, como :
-
-- Más variedad y diversidad en términos de coches, personalizaciones, mapas, música e idiomas
-- Más diversión y emoción en términos de jugabilidad, gráficos, efectos de sonido e interacciones
-- Más conexión y comunidad con otros jugadores que comparten el mismo interés y pasión por la cultura del automóvil de Sudáfrica
-- Más soporte y actualizaciones de los desarrolladores que se dedican a mejorar el juego y agregar nuevas características
-
- Cómo descargar e instalar el estacionamiento de coches multijugador South áfrica Edition APK en dispositivos Android?
-
- Requisitos para descargar e instalar el estacionamiento de coches multijugador South áfrica Edition APK
- Para descargar e instalar Aparcamiento Multijugador South áfrica Edition APK en su dispositivo Android, es necesario tener :
-
-
-- Un dispositivo Android que se ejecuta en Android 4.1 o superior
-- Una conexión a Internet estable
-- Una aplicación de administrador de archivos que puede acceder al archivo APK
-- Una cantidad suficiente de espacio de almacenamiento en su dispositivo
-- Un permiso para instalar aplicaciones de fuentes desconocidas en la configuración del dispositivo
-
- Pasos para Descargar e Instalar Aparcamiento Multijugador South áfrica Edition APK
- Para descargar e instalar Aparcamiento Multijugador South áfrica Edition APK en su dispositivo Android, es necesario seguir estos pasos :
-
-- Ir a un sitio web de confianza que proporciona el enlace para descargar Car Parking Multi jugador South áfrica Edition APK. Por ejemplo, puede visitar [este sitio web] para obtener la última versión del archivo APK.
-- Haga clic en el botón de descarga y espere a que el archivo APK se descargue en su dispositivo.
-- Una vez que se complete la descarga, localizar el archivo APK en su dispositivo utilizando una aplicación de administrador de archivos. Puede encontrarlo en la carpeta Descargas o en cualquier otra carpeta donde lo haya guardado.
-- Toque en el archivo APK y seleccione Instalar. Es posible que vea un mensaje de advertencia que dice "Para su seguridad, el teléfono no está permitido instalar aplicaciones desconocidas de esta fuente". Si ves este mensaje, ve a la configuración del dispositivo y habilita la opción de instalar aplicaciones de fuentes desconocidas. Esta opción puede estar en Seguridad, Privacidad o Aplicaciones dependiendo del modelo de dispositivo y la versión de Android.
-
-- Espere a que termine el proceso de instalación. Puede tardar unos minutos dependiendo del rendimiento del dispositivo y la velocidad de Internet.
-- Una vez que se hace la instalación, se puede abrir el juego y disfrutar de aparcamiento multijugador South áfrica Edition en su dispositivo Android.
-
- Consejos y Trucos para Jugar Aparcamiento Multijugador South áfrica Edition
- Ahora que ha descargado e instalado Car Parking Multiplayer South áfrica Edition en su dispositivo Android, es posible que desee conocer algunos consejos y trucos para jugarlo. Estos son algunos de ellos:
- Cómo seleccionar un coche y un jugador
- Para seleccionar un coche y un jugador en Car Parking Multiplayer South áfrica Edition, debe hacer lo siguiente:
-
-- Toque en el icono de menú en la esquina superior izquierda de la pantalla.
-- Toque en Garaje para ver la lista de coches que usted posee o puede comprar.
-- Toque en el coche que desea utilizar y luego toque en Select.
-- Toque en Volver para volver al menú.
-- Toque en Perfil para ver la lista de jugadores entre los que puede elegir.
-- Toque en el reproductor que desea utilizar y luego toque en Select.
-- Toque en Volver para volver al menú.
-- Toque en Jugar para comenzar a jugar el juego con el coche y el jugador seleccionado.
-
- Cómo ajustar su coche y ajustar su relación de engranajes
- Para afinar su coche y ajustar su relación de engranajes en Car Parking Multiplayer South áfrica Edition, debe hacer lo siguiente:
-
-- Toque en el icono de menú en la esquina superior izquierda de la pantalla.
-- Toque en Garaje para ver la lista de coches que usted posee o puede comprar.
-- Toque en el coche que desea sintonizar y luego toque en Tune.
-- Verá cuatro pestañas: Motor, Suspensión, Ruedas y Cuerpo. Puede deslizar hacia la izquierda o hacia la derecha para cambiar entre ellas.
-
-- Para ajustar su relación de cambios, toque en Caja de cambios en la pestaña Motor. Verá un gráfico que muestra cómo cambia su velocidad con diferentes marchas. Puede arrastrar los puntos en el gráfico para cambiar la relación de transmisión para cada marcha. También puede pulsar en Auto o Manual para cambiar entre modos de transmisión automática o manual.
-- Una vez que haya terminado de ajustar su coche, toque en Guardar para aplicar los cambios.
-
- Cómo Deriva, Donut, y Burnout
- Para la deriva, donut, y burnout en el estacionamiento de coches multijugador South áfrica Edition, es necesario hacer lo siguiente:
-
-- Para la deriva, debe usar una combinación de dirección, acelerador, freno y freno de mano. Puede usar los controles de inclinación o táctiles para la dirección. Para iniciar la deriva, es necesario acelerar y girar bruscamente en una esquina. Luego, debe aplicar el freno de mano brevemente para hacer que sus ruedas traseras pierdan tracción. Después de eso, debe equilibrar el acelerador y la dirección para mantener el ángulo de deriva y la dirección. También puede utilizar el freno de mano o freno de mano de nuevo si necesita ajustar su deriva. Para terminar con la deriva, es necesario soltar el acelerador y la dirección y enderezar el coche.
-- Para la rosquilla, debe usar una combinación de dirección, acelerador y freno de mano. Puede usar controles de inclinación o táctiles para la dirección. Para comenzar a donar, debe acelerar y girar bruscamente en una dirección. Entonces, es necesario aplicar el freno de mano para hacer que su coche gire alrededor de su centro. Después de eso, es necesario mantener el acelerador y la dirección constante para mantener el círculo de la dona. También puede cambiar la dirección de su dona cambiando la dirección. Para terminar donuting, es necesario soltar el acelerador y el freno de mano y enderezar su coche.
-
-
- Conclusión
- Car Parking Multijugador South áfrica Edition es un juego que ofrece una experiencia de aparcamiento realista y divertido con un toque sudafricano. Tiene más coches, personalizaciones, mapas, música e idiomas que reflejan la cultura del automóvil sudafricano. También tiene un modo de mundo abierto, un modo de ajuste de coche, y un modo multijugador que hacen el juego más agradable y emocionante. Puede descargar e instalar Parking Multijugador South áfrica Edition APK en su dispositivo Android siguiendo los pasos en este artículo. También puedes usar los consejos y trucos de este artículo para mejorar tu juego y divertirte más.
- Preguntas frecuentes
- Aquí están algunas de las preguntas más frecuentes sobre Car Parking Multiplayer South áfrica Edition:
- ¿Es gratis Car Parking Multijugador South áfrica Edition?
- Sí, Parking Multijugador South áfrica Edition es gratis para descargar y jugar. Sin embargo, puede contener algunas compras en la aplicación que requieren dinero real.
- ¿Es seguro el aparcamiento multijugador South áfrica Edition?
- Sí, Parking Multijugador South áfrica Edition es seguro, siempre y cuando se descarga desde un sitio web de confianza. Sin embargo, siempre debe tener cuidado al descargar e instalar aplicaciones de fuentes desconocidas, ya que pueden contener virus o malware que pueden dañar su dispositivo.
- ¿Es compatible Car Parking Multiplayer South áfrica Edition con mi dispositivo?
- Car Parking Multijugador South áfrica Edition es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 4.1 o superior. Sin embargo, algunos dispositivos pueden tener diferentes especificaciones o problemas de rendimiento que pueden afectar la calidad o funcionalidad del juego.
- ¿Cómo puedo actualizar Car Parking Multijugador South áfrica Edition?
-
- ¿Cómo puedo contactar a los desarrolladores de Car Parking Multiplayer South áfrica Edition?
- Para contactar a los desarrolladores de Car Parking Multiplayer South áfrica Edition, puedes visitar su página de Facebook o su canal de YouTube y dejar un comentario o un mensaje.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md b/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md
deleted file mode 100644
index 2001fb4da4e341236f8b4ef59c584c7ee860ae80..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md
+++ /dev/null
@@ -1,76 +0,0 @@
-
-Nitro asfalto 9 leyendas Mod Apk: Una guía para los aficionados a las carreras
-Si eres un fan de los juegos de carreras, es posible que hayas oído hablar de Asphalt Nitro 9 Legends, uno de los juegos más populares y emocionantes del género. Este juego le permite tomar el volante de los coches reales de gama alta de renombrados fabricantes de automóviles legendarios, como Ferrari, Porsche, Lamborghini, y W Motors, entre muchas otras marcas internacionales. Puedes conducir, impulsar y realizar acrobacias en ubicaciones dinámicas de la vida real en modo individual o multijugador.
-Pero ¿y si quieres disfrutar del juego sin limitaciones ni restricciones? ¿Qué pasa si quieres tener dinero ilimitado y fichas, desbloquear todos los coches y pistas, personalizar sus vehículos y actualizarlos, y competir con otros jugadores en línea o fuera de línea? Bueno, hay una manera de hacerlo. Puede descargar e instalar Asphalt Nitro 9 Leyendas Mod Apk, una versión modificada del juego que le da acceso a todas estas características y más.
-asfalto nitro 9 leyendas mod apk
Download File ===== https://bltlly.com/2v6LmQ
-En este artículo, le guiará a través de las características, proceso de descarga, pasos de instalación, consejos y trucos, pros y contras, y la conclusión de Asphalt Nitro 9 Leyendas Mod Apk. Sigue leyendo para saber más.
- Características de Asphalt Nitro 9 Leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es una versión modificada del juego original que le da recursos ilimitados y características que mejoran su experiencia de juego. Estas son algunas de las características que puedes disfrutar con este mod apk:
-
-- Dinero ilimitado y fichas: Con este apk mod, nunca se quedará sin dinero o fichas para comprar nuevos coches, pistas, mejoras, o artículos. Puedes gastar todo lo que quieras sin preocuparte por tu presupuesto.
-
-- Personalizar sus vehículos y actualizarlos: Con este apk mod, puede personalizar sus vehículos de acuerdo a su preferencia. Puedes cambiar su color, pintura, calcomanías, ruedas, etc. También puedes mejorar su rendimiento mejorando su motor, transmisión, suspensión, frenos, nitro boost, etc.
-- Disfrutar de gráficos realistas y efectos de sonido: Con este apk mod, se puede disfrutar de los impresionantes gráficos y efectos de sonido del juego. El juego utiliza un motor de física realista que simula el movimiento y el comportamiento de los coches. El juego también cuenta con efectos de sonido de alta calidad que te hacen sentir como si estuvieras en una carrera real.
-- Compite con otros jugadores en línea o fuera de línea: Con este apk mod, puede competir con otros jugadores en línea o fuera de línea. Puedes unirte a carreras multijugador online y desafiar a tus amigos u otros jugadores de todo el mundo. También puedes jugar offline en modo carrera y completar varias misiones y desafíos.
-
- Cómo descargar e instalar Asphalt Nitro 9 leyendas Mod Apk
-Si desea descargar e instalar Asphalt Nitro 9 leyendas Mod Apk, es necesario seguir estos sencillos pasos:
-
-- Paso 1: Descargar el archivo apk mod de una fuente de confianza. Usted puede encontrar muchos sitios web que ofrecen el archivo apk mod para Asphalt Nitro 9 Leyendas, pero no todos ellos son seguros y fiables. Debes asegurarte de que el archivo que descargas esté libre de virus, malware o cualquier otro contenido dañino. Puede utilizar este enlace para descargar el archivo apk mod de forma segura.
-- Paso 2: Habilitar fuentes desconocidas en la configuración del dispositivo. Antes de poder instalar el archivo apk mod, debe habilitar fuentes desconocidas en la configuración de su dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a la configuración del dispositivo, luego a la seguridad, luego a fuentes desconocidas y conéctela.
-
-- Paso 4: Disfruta del juego con recursos y funciones ilimitadas. Ahora que ha instalado Asphalt Nitro 9 Leyendas Mod Apk, se puede disfrutar del juego con dinero ilimitado, fichas, coches, pistas, opciones de personalización, y más. También puedes competir con otros jugadores online o offline y divertirte.
-
- Consejos y trucos para jugar Asphalt Nitro 9 leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es un juego divertido y emocionante que le mantendrá entretenido durante horas. Sin embargo, si quieres mejorar tus habilidades y rendimiento en el juego, puedes seguir estos consejos y trucos:
-
-- Elija el coche adecuado para cada pista y modo. El juego ofrece una variedad de coches de diferentes marcas y categorías, pero no todos ellos son adecuados para cada pista o modo. Es necesario tener en cuenta la velocidad, aceleración, manejo, nitro, y otros factores de cada coche antes de elegir uno. Por ejemplo, si usted está jugando en una pista con curvas, es posible que desee elegir un coche con buen manejo y nitro. Si usted está jugando en una pista recta, es posible que desee elegir un coche con alta velocidad y aceleración.
-- Utilice nitro boost sabiamente y estratégicamente. Nitro boost es una de las características más importantes del juego, ya que puede darte una ventaja sobre tus oponentes. Sin embargo, necesitas usarlo sabiamente y estratégicamente. No debes usarlo todo de una vez ni desperdiciarlo en momentos innecesarios. Debes guardarlo para momentos cruciales, como adelantar a tus rivales, escapar de obstáculos o colisiones, o llegar a la meta. También debe usarlo en combinación con acrobacias y derivas para ganar más puntos y recompensas.
-
-- Evitar obstáculos y colisiones con otros coches. El juego cuenta con muchos obstáculos y desafíos que pueden obstaculizar su progreso o dañar su coche. Estos incluyen coches de tráfico, coches de policía, helicópteros, barricadas, picos, etc. Usted debe evitar estos obstáculos tanto como sea posible, ya que pueden ralentizar o dañar su coche. También debes evitar colisiones con otros coches, especialmente tus rivales, ya que pueden sacarte de la pista o hacerte perder tu posición. Usted debe tratar de esquivar o escapar de ellos, o utilizar su impulso nitro para alejarse de ellos.
-- Utilice diferentes ángulos de cámara y controles para adaptarse a sus preferencias. El juego ofrece diferentes ángulos de cámara y controles que puedes usar para jugar el juego. Puede elegir entre vista en primera persona, en tercera persona o de arriba hacia abajo, según su preferencia. También puede elegir entre los controles de inclinación, toque o toque, dependiendo de su comodidad. Puede cambiar estos ajustes en el menú de opciones del juego.
-
- Pros y contras de Asphalt Nitro 9 Leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es un gran juego que ofrece muchos beneficios y ventajas para los aficionados a las carreras. Sin embargo, también tiene algunos inconvenientes y desventajas que usted debe tener en cuenta. Estos son algunos de los pros y los contras de Asphalt Nitro 9 Leyendas Mod Apk:
-
-
-Pros
-Contras
-
-
-
-- Divertido, adictivo y desafiante juego
-- Dinero ilimitado y tokens
-- Desbloquear todos los coches y pistas
-- Personalizar sus vehículos y actualizarlos
-- Disfruta de gráficos realistas y efectos de sonido
-- Compite con otros jugadores online o offline
-
-- Puede causar retraso o estrellarse en algunos dispositivos
-- Puede no ser compatible con algunos dispositivos o versiones
-- No se puede actualizar regularmente o con frecuencia
-- No puede ser soportado por los desarrolladores o editores oficiales
-
-- Puede exponer su dispositivo a riesgos o amenazas de seguridad
-
-
- Conclusión
-En conclusión, Asfalto Nitro 9 Leyendas Mod Apk es un juego fantástico que le dará horas de diversión y emoción. Es una versión modificada del juego original que le da recursos ilimitados y características que mejoran su experiencia de juego. Puede descargarlo e instalarlo de forma fácil y segura siguiendo los pasos que proporcionamos en este artículo. También puedes seguir los consejos y trucos que compartimos para mejorar tus habilidades y rendimiento en el juego. Sin embargo, también debe ser consciente de los pros y los contras de Asphalt Nitro 9 Leyendas Mod Apk, y decidir si vale la pena jugar o no.
-Si usted es un fanático de las carreras, le recomendamos que pruebe Asphalt Nitro 9 Leyendas Mod Apk y ver por sí mismo lo increíble que es. No te arrepentirás.
- Preguntas frecuentes (preguntas frecuentes)
-Aquí están algunas de las preguntas y respuestas más comunes sobre Asphalt Nitro 9 Legends Mod Apk:
-
- Q: ¿Es libre de Asphalt Nitro 9 Leyendas Mod Apk?
-A: Sí, Asphalt Nitro 9 Leyendas Mod Apk es gratis para descargar y jugar. Usted no necesita pagar nada para disfrutar del juego.
- Q: ¿Es seguro Asphalt Nitro 9 Legends Mod Apk?
-A: Sí, Asfalto Nitro 9 Leyendas Mod Apk es seguro para descargar e instalar. Sin embargo, siempre debe descargarlo de una fuente de confianza y habilitar fuentes desconocidas en la configuración del dispositivo antes de instalarlo.
- Q: ¿Es legal Asphalt Nitro 9 Legends Mod Apk?
-A: No, Asphalt Nitro 9 Leyendas Mod Apk no es legal. Es una versión modificada del juego original que viola los términos y condiciones de los desarrolladores y editores oficiales. También puede infringir los derechos de propiedad intelectual del juego original.
- Q: Es Asphalt Nitro 9 Leyendas Mod Apk en línea o fuera de línea?
-
- Q: ¿Cómo puedo actualizar Asphalt Nitro 9 Legends Mod Apk?
-A: Para actualizar Asphalt Nitro 9 leyendas Mod Apk, es necesario descargar e instalar la última versión del archivo apk mod de una fuente de confianza. Es posible que tenga que desinstalar la versión anterior del archivo apk mod antes de instalar el nuevo.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md b/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md
deleted file mode 100644
index d872bc888664198a5fb77f20f85945d9c05ae2ef..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md
+++ /dev/null
@@ -1,76 +0,0 @@
-
-Camioneros de Europa 3 APK 36.6: Un juego de simulador de camiones realista y divertido
-Si eres un fan de los juegos de simuladores de camiones, es posible que hayas oído hablar de Truckers of Europe 3, un juego popular que te permite experimentar la vida de un conductor de camiones en Europa. En este artículo, te contaremos todo lo que necesitas saber sobre este juego, incluyendo qué es, cuáles son sus características, cómo descargarlo e instalarlo, cómo jugarlo y por qué deberías jugarlo. ¡Así que abróchate el cinturón y prepárate para un emocionante viaje!
-Introducción
-¿Qué es Camioneros de Europa 3?
-Truckers of Europe 3 es un juego de simulador de camioneros desarrollado por Jerryisgaming, un canal de YouTube que crea videos de juegos. El juego fue lanzado en 2020 y ha sido actualizado regularmente con nuevas características y mejoras. La última versión del juego es 0.36.6, que fue lanzado el 30 de septiembre de 2021.
-camioneros de europa 3 apk 36.6
Download Zip ❤❤❤ https://bltlly.com/2v6LwC
-¿Cuáles son las características de Camioneros de Europa 3?
-Truckers of Europe 3 tiene muchas características que lo convierten en uno de los mejores juegos de simuladores de camiones en el mercado. Algunas de estas características son:
-
-- Un gran mapa de Europa con más de 50 ciudades y países para explorar.
-- Una variedad de camiones y remolques para elegir, cada uno con diferentes especificaciones y rendimiento.
-- Un sistema de conducción realista con transmisión manual, volante, pedales, indicadores, espejos, luces, bocina, limpiaparabrisas, etc.
-- Un sistema de tráfico realista con coches, autobuses, camiones, motocicletas, policía, ambulancia, etc.
-- Un sistema meteorológico realista con ciclo de día y noche, lluvia, nieve, niebla, etc.
-- Un sistema de daños realista con desgaste de neumáticos, consumo de combustible, sobrecalentamiento del motor, etc.
-- Un modo de carrera con misiones, contratos, entrega de carga, ingresos, gastos, etc.
-- Un sistema de habilidades con niveles, puntos, ventajas, etc.
-- Un sistema de personalización con trabajos de pintura, accesorios, calcomanías, etc.
-
-
-Cómo descargar e instalar camioneros de Europa 3 APK 36.6?
-Requisitos
-Para descargar e instalar Camioneros de Europa 3 APK 36.6 en su dispositivo Android, es necesario cumplir con los siguientes requisitos:
-
-- Tu dispositivo debe tener la versión de Android 4.4 o superior.
-- Su dispositivo debe tener al menos 1 GB de RAM y 500 MB de espacio de almacenamiento libre.
-- Su dispositivo debe tener una conexión a Internet estable.
-- Su dispositivo debe permitir la instalación desde fuentes desconocidas. Puede habilitar esta opción yendo a Configuración > Seguridad > Fuentes desconocidas.
-
-Pasos
-Para descargar e instalar Camioneros de Europa 3 APK 36.6 en su dispositivo Android, debe seguir estos pasos:
-
-- Ir a este enlace y descargar el archivo APK.
-- Localice el archivo descargado en el administrador de archivos de su dispositivo y toque en él para iniciar el proceso de instalación.
-- Siga las instrucciones en la pantalla y conceda los permisos necesarios a la aplicación.
-- Espere a que la instalación se complete y luego inicie la aplicación desde el cajón de aplicaciones o la pantalla de inicio.
-- Disfruta jugando Camioneros de Europa 3 en tu dispositivo Android!
-
-Cómo jugar Camioneros de Europa 3?
-Elige tu camión y remolque
-Lo primero que tienes que hacer cuando empiezas a jugar Truckers of Europe 3 es elegir tu camión y remolque. Puede hacer esto yendo al menú del garaje y navegando a través de las opciones disponibles. También puede comprar nuevos camiones y remolques con el dinero que gana de sus misiones. Cada camión y remolque tiene diferentes atributos, como velocidad, potencia, capacidad de combustible, peso de carga, etc. Debe elegir el que se adapte a sus preferencias y necesidades.
-Conduce por toda Europa y entrega carga
-
-Mientras conduce, tendrá que seguir las reglas de tráfico y regulaciones, tales como límites de velocidad, semáforos, señales, etc. También tendrá que lidiar con condiciones de tráfico realistas, como automóviles, autobuses, camiones, motocicletas, policía, ambulancia, etc. También tendrá que enfrentarse a condiciones meteorológicas realistas, como lluvia, nieve, niebla, etc. También tendrá que controlar el estado de su camión, como el nivel de combustible, la temperatura del motor, la presión de los neumáticos, etc. También tendrá que prestar atención al estado de su conductor, como el nivel de fatiga, el nivel de hambre, etc. Tendrá que parar en gasolineras, áreas de descanso, restaurantes, etc. para llenar su tanque de combustible, descansar a su conductor, comer algo de comida, etc.
-Cuando llegue a su destino, tendrá que aparcar su camión y remolque en el lugar designado y descargar la carga. A continuación, recibirá su recompensa y comentarios sobre su rendimiento. También ganarás puntos de experiencia que te ayudarán a subir de nivel y desbloquear nuevas habilidades y beneficios.
-Personaliza tu camión y mejora tus habilidades
-Otro aspecto divertido de Truckers of Europe 3 es que puedes personalizar tu camión y mejorar tus habilidades. Puedes hacer esto yendo al menú del taller y gastando algo de dinero en varios artículos y servicios. Puede cambiar el color de su camión, agregar algunos accesorios, aplicar algunas calcomanías, etc. También puede reparar su camión si está dañado o afinarlo si no funciona bien.
-También puedes ir al menú de habilidades y gastar algunos puntos en varias habilidades y beneficios que mejorarán tus habilidades y beneficios como conductor de camión. Puede mejorar su eficiencia de combustible, manejo de carga, seguridad de conducción, habilidades de negociación, etc. También puede desbloquear nuevos tipos de carga, contratos, camiones, remolques, etc.
-
-¿Por qué deberías jugar Camioneros de Europa 3?
-Gráficos realistas y física
-
-Tráfico y clima diversos y dinámicos
-Otra razón por la que deberías jugar a Truckers of Europe 3 es porque tiene un tráfico diverso y dinámico y el clima hará que tu experiencia de conducción sea más desafiante y agradable. El juego tiene un gran mapa de Europa con más de 50 ciudades y países para explorar. Cada ciudad y país tiene sus propias características únicas, tales como monumentos, edificios, carreteras, señales, etc. El juego también tiene un sistema de tráfico diverso que incluye automóviles, autobuses, camiones, motocicletas, policía, ambulancia, etc. Cada vehículo tiene su propio comportamiento, velocidad, dirección, etc. El juego también tiene un sistema de tiempo dinámico que incluye ciclo de día y noche, lluvia, nieve, niebla, etc. Cada condición climática tiene su propio efecto en la visibilidad, tracción, manejo, etc. del camión y el remolque.
-Misiones y logros desafiantes y gratificantes
-Otra razón por la que deberías jugar a Truckers of Europe 3 es porque tiene misiones desafiantes y gratificantes y logros que te mantendrán motivado y entretenido. El juego tiene un modo de carrera que le permite comenzar como un conductor de camión novato y trabajar su camino hasta convertirse en un camionero profesional. Puede hacerlo completando varias misiones y contratos que implican la entrega de carga a diferentes destinos en toda Europa. También puede ganar dinero de sus entregas y gastarlo en la compra de nuevos camiones y remolques o la personalización de los existentes. También puede ganar puntos de experiencia de sus entregas y gastarlos en mejorar sus habilidades y beneficios. También puedes desbloquear nuevos tipos de carga, contratos, camiones, remolques, etc. a medida que avanzas en el juego.
-
-Conclusión
-Truckers of Europe 3 es un juego de simulador de camiones realista y divertido que te permite experimentar la vida de un conductor de camiones en Europa. Puede elegir su camión y remolque, conducir por toda Europa y entregar carga, personalizar su camión y mejorar sus habilidades, y ganar dinero y logros. El juego tiene gráficos y física realistas, tráfico y clima diversos y dinámicos, y misiones y logros desafiantes y gratificantes. Si usted está buscando un juego de simulador de camiones que le mantendrá enganchado durante horas, definitivamente debe probar Camioneros de Europa 3 APK 36.6.
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre los camioneros de Europa 3:
-
-- Q: ¿Los camioneros de Europa 3 son libres de jugar?
-- A: Sí, Truckers of Europe 3 es gratis. Puedes descargarlo e instalarlo desde el enlace proporcionado en este artículo. Sin embargo, el juego puede contener algunas compras en la aplicación que pueden mejorar su experiencia de juego.
-- Q: ¿Es seguro descargar e instalar Truckers of Europe 3?
-- A: Sí, Truckers of Europe 3 es seguro para descargar e instalar. El archivo APK proporcionado en este artículo es verificado y probado por nuestro equipo. Sin embargo, siempre debe descargar e instalar archivos APK de fuentes confiables solamente.
-- Q: ¿Es Truckers of Europe 3 compatible con mi dispositivo?
-- A: Camioneros de Europa 3 es compatible con la mayoría de los dispositivos Android que tienen la versión Android 4.4 o superior. Sin embargo, algunos dispositivos pueden no ser compatibles con el juego debido a limitaciones de hardware o software.
-- P: ¿Cómo puedo contactar al desarrollador de Truckers of Europe 3?
-- A: Puedes contactar al desarrollador de Truckers of Europe 3 visitando su canal de YouTube Jerryisgaming o su página de Facebook Jerryisgaming. También puedes dejar tus comentarios, sugerencias o consultas en la sección de comentarios de sus videos o publicaciones.
-- P: ¿Cómo puedo apoyar el desarrollo de Camioneros de Europa 3?
-
-
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md b/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md
deleted file mode 100644
index 516d398df61b62811ac5d43e8205d838c0714cbc..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md
+++ /dev/null
@@ -1,80 +0,0 @@
-
-Cómo descargar Genshin Impact Cloud y disfrutar del juego en cualquier lugar
-Genshin Impact es uno de los juegos más populares y exitosos de 2020, con millones de jugadores en todo el mundo. El juego es un RPG de acción de mundo abierto que te permite explorar un mundo vasto y hermoso llamado Teyvat, donde puedes conocer a varios personajes, luchar contra enemigos, reunir recursos y completar misiones. El juego está disponible en múltiples plataformas, como PC, PlayStation 4, PlayStation 5, iOS y Android. Sin embargo, si quieres jugar el juego en tu dispositivo móvil, puedes enfrentarte a algunos desafíos, como espacio de almacenamiento limitado, bajo rendimiento o dispositivos incompatibles. Ahí es donde entra Genshin Impact Cloud.
-¿Qué es la nube de impacto Genshin y por qué debe probarlo
-Genshin Impact Cloud es un servicio que te permite jugar el juego en tu dispositivo móvil sin descargar el juego entero
-Genshin Impact Cloud es una nueva característica que fue introducida por miHoYo, el desarrollador de Genshin Impact, en abril de 2021. Es un servicio de juegos en la nube que le permite transmitir el juego desde un servidor a su dispositivo móvil a través de Internet. Esto significa que no necesitas descargar o instalar el juego en tu dispositivo, lo que puede ahorrarte mucho espacio de almacenamiento. El tamaño del juego de Genshin Impact en dispositivos móviles es de alrededor de 5,2 GB, pero con Genshin Impact Cloud, solo necesitas descargar una aplicación de 56 MB de tamaño. Esto también puede ayudarle a evitar largos tiempos de carga o actualizaciones.
-descargar estrellas pelea hack ios
Download ===> https://bltlly.com/2v6KEj
-Genshin Impact Cloud tiene muchos beneficios, como ahorrar espacio de almacenamiento, mejorar el rendimiento y admitir cross-save
-Además de ahorrar espacio de almacenamiento, Genshin Impact Cloud también tiene otras ventajas que pueden mejorar su experiencia de juego. Por ejemplo:
-
-
-- Genshin Impact Cloud soporta cross-save y cross-play. Esto significa que puedes acceder a tu progreso y datos existentes desde otras plataformas iniciando sesión con tu cuenta miHoYo. También puedes jugar con tus amigos que usan diferentes dispositivos, como PC o PlayStation.
-- Genshin Impact Cloud es de uso gratuito. No es necesario pagar tarifas o suscripciones adicionales para utilizar el servicio. También puede hacer compras en la aplicación como de costumbre, y disfrutar de todas las actualizaciones y eventos del juego.
-
-Con Genshin Impact Cloud, puede disfrutar del juego en cualquier lugar y en cualquier momento, siempre y cuando tenga una conexión a Internet estable y un dispositivo compatible.
-Cómo descargar Genshin Impact Cloud en dispositivos Android
-Genshin Impact Cloud está disponible actualmente solo para usuarios de Android en Malasia y Singapur como prueba beta
-Genshin Impact Cloud todavía está en sus primeras etapas de desarrollo, y todavía no está disponible para todas las regiones y plataformas. Actualmente, el servicio solo está abierto para los usuarios de Android en Malasia y Singapur como una prueba beta. Esto significa que solo un número limitado de jugadores puede acceder al servicio, y puede haber algunos errores o errores durante el juego. miHoYo no ha anunciado cuándo se ampliará el servicio a otras regiones y plataformas, pero es probable que lo hagan en el futuro.
-Para descargar Genshin Impact Cloud, necesita tener una cuenta miHoYo y registrarse para la prueba beta en el sitio web oficial
-Si eres un usuario de Android en Malasia o Singapur, y quieres probar Genshin Impact Cloud, debes seguir estos pasos:
-
-- Crea una cuenta miHoYo si aún no tienes una. Puede hacer esto visitando https://account.mihoyo.com/#/register y llenando la información requerida.
-- Visite None:
- """
- Raise an option parsing error using parser.error().
-
- Args:
- parser: an OptionParser instance.
- option: an Option instance.
- msg: the error text.
- """
- msg = f"{option} error: {msg}"
- msg = textwrap.fill(" ".join(msg.split()))
- parser.error(msg)
-
-
-def make_option_group(group: Dict[str, Any], parser: ConfigOptionParser) -> OptionGroup:
- """
- Return an OptionGroup object
- group -- assumed to be dict with 'name' and 'options' keys
- parser -- an optparse Parser
- """
- option_group = OptionGroup(parser, group["name"])
- for option in group["options"]:
- option_group.add_option(option())
- return option_group
-
-
-def check_dist_restriction(options: Values, check_target: bool = False) -> None:
- """Function for determining if custom platform options are allowed.
-
- :param options: The OptionParser options.
- :param check_target: Whether or not to check if --target is being used.
- """
- dist_restriction_set = any(
- [
- options.python_version,
- options.platforms,
- options.abis,
- options.implementation,
- ]
- )
-
- binary_only = FormatControl(set(), {":all:"})
- sdist_dependencies_allowed = (
- options.format_control != binary_only and not options.ignore_dependencies
- )
-
- # Installations or downloads using dist restrictions must not combine
- # source distributions and dist-specific wheels, as they are not
- # guaranteed to be locally compatible.
- if dist_restriction_set and sdist_dependencies_allowed:
- raise CommandError(
- "When restricting platform and interpreter constraints using "
- "--python-version, --platform, --abi, or --implementation, "
- "either --no-deps must be set, or --only-binary=:all: must be "
- "set and --no-binary must not be set (or must be set to "
- ":none:)."
- )
-
- if check_target:
- if dist_restriction_set and not options.target_dir:
- raise CommandError(
- "Can not use any platform or abi specific options unless "
- "installing via '--target'"
- )
-
-
-def _path_option_check(option: Option, opt: str, value: str) -> str:
- return os.path.expanduser(value)
-
-
-def _package_name_option_check(option: Option, opt: str, value: str) -> str:
- return canonicalize_name(value)
-
-
-class PipOption(Option):
- TYPES = Option.TYPES + ("path", "package_name")
- TYPE_CHECKER = Option.TYPE_CHECKER.copy()
- TYPE_CHECKER["package_name"] = _package_name_option_check
- TYPE_CHECKER["path"] = _path_option_check
-
-
-###########
-# options #
-###########
-
-help_: Callable[..., Option] = partial(
- Option,
- "-h",
- "--help",
- dest="help",
- action="help",
- help="Show help.",
-)
-
-debug_mode: Callable[..., Option] = partial(
- Option,
- "--debug",
- dest="debug_mode",
- action="store_true",
- default=False,
- help=(
- "Let unhandled exceptions propagate outside the main subroutine, "
- "instead of logging them to stderr."
- ),
-)
-
-isolated_mode: Callable[..., Option] = partial(
- Option,
- "--isolated",
- dest="isolated_mode",
- action="store_true",
- default=False,
- help=(
- "Run pip in an isolated mode, ignoring environment variables and user "
- "configuration."
- ),
-)
-
-require_virtualenv: Callable[..., Option] = partial(
- Option,
- "--require-virtualenv",
- "--require-venv",
- dest="require_venv",
- action="store_true",
- default=False,
- help=(
- "Allow pip to only run in a virtual environment; "
- "exit with an error otherwise."
- ),
-)
-
-override_externally_managed: Callable[..., Option] = partial(
- Option,
- "--break-system-packages",
- dest="override_externally_managed",
- action="store_true",
- help="Allow pip to modify an EXTERNALLY-MANAGED Python installation",
-)
-
-python: Callable[..., Option] = partial(
- Option,
- "--python",
- dest="python",
- help="Run pip with the specified Python interpreter.",
-)
-
-verbose: Callable[..., Option] = partial(
- Option,
- "-v",
- "--verbose",
- dest="verbose",
- action="count",
- default=0,
- help="Give more output. Option is additive, and can be used up to 3 times.",
-)
-
-no_color: Callable[..., Option] = partial(
- Option,
- "--no-color",
- dest="no_color",
- action="store_true",
- default=False,
- help="Suppress colored output.",
-)
-
-version: Callable[..., Option] = partial(
- Option,
- "-V",
- "--version",
- dest="version",
- action="store_true",
- help="Show version and exit.",
-)
-
-quiet: Callable[..., Option] = partial(
- Option,
- "-q",
- "--quiet",
- dest="quiet",
- action="count",
- default=0,
- help=(
- "Give less output. Option is additive, and can be used up to 3"
- " times (corresponding to WARNING, ERROR, and CRITICAL logging"
- " levels)."
- ),
-)
-
-progress_bar: Callable[..., Option] = partial(
- Option,
- "--progress-bar",
- dest="progress_bar",
- type="choice",
- choices=["on", "off"],
- default="on",
- help="Specify whether the progress bar should be used [on, off] (default: on)",
-)
-
-log: Callable[..., Option] = partial(
- PipOption,
- "--log",
- "--log-file",
- "--local-log",
- dest="log",
- metavar="path",
- type="path",
- help="Path to a verbose appending log.",
-)
-
-no_input: Callable[..., Option] = partial(
- Option,
- # Don't ask for input
- "--no-input",
- dest="no_input",
- action="store_true",
- default=False,
- help="Disable prompting for input.",
-)
-
-keyring_provider: Callable[..., Option] = partial(
- Option,
- "--keyring-provider",
- dest="keyring_provider",
- choices=["auto", "disabled", "import", "subprocess"],
- default="auto",
- help=(
- "Enable the credential lookup via the keyring library if user input is allowed."
- " Specify which mechanism to use [disabled, import, subprocess]."
- " (default: disabled)"
- ),
-)
-
-proxy: Callable[..., Option] = partial(
- Option,
- "--proxy",
- dest="proxy",
- type="str",
- default="",
- help="Specify a proxy in the form scheme://[user:passwd@]proxy.server:port.",
-)
-
-retries: Callable[..., Option] = partial(
- Option,
- "--retries",
- dest="retries",
- type="int",
- default=5,
- help="Maximum number of retries each connection should attempt "
- "(default %default times).",
-)
-
-timeout: Callable[..., Option] = partial(
- Option,
- "--timeout",
- "--default-timeout",
- metavar="sec",
- dest="timeout",
- type="float",
- default=15,
- help="Set the socket timeout (default %default seconds).",
-)
-
-
-def exists_action() -> Option:
- return Option(
- # Option when path already exist
- "--exists-action",
- dest="exists_action",
- type="choice",
- choices=["s", "i", "w", "b", "a"],
- default=[],
- action="append",
- metavar="action",
- help="Default action when a path already exists: "
- "(s)witch, (i)gnore, (w)ipe, (b)ackup, (a)bort.",
- )
-
-
-cert: Callable[..., Option] = partial(
- PipOption,
- "--cert",
- dest="cert",
- type="path",
- metavar="path",
- help=(
- "Path to PEM-encoded CA certificate bundle. "
- "If provided, overrides the default. "
- "See 'SSL Certificate Verification' in pip documentation "
- "for more information."
- ),
-)
-
-client_cert: Callable[..., Option] = partial(
- PipOption,
- "--client-cert",
- dest="client_cert",
- type="path",
- default=None,
- metavar="path",
- help="Path to SSL client certificate, a single file containing the "
- "private key and the certificate in PEM format.",
-)
-
-index_url: Callable[..., Option] = partial(
- Option,
- "-i",
- "--index-url",
- "--pypi-url",
- dest="index_url",
- metavar="URL",
- default=PyPI.simple_url,
- help="Base URL of the Python Package Index (default %default). "
- "This should point to a repository compliant with PEP 503 "
- "(the simple repository API) or a local directory laid out "
- "in the same format.",
-)
-
-
-def extra_index_url() -> Option:
- return Option(
- "--extra-index-url",
- dest="extra_index_urls",
- metavar="URL",
- action="append",
- default=[],
- help="Extra URLs of package indexes to use in addition to "
- "--index-url. Should follow the same rules as "
- "--index-url.",
- )
-
-
-no_index: Callable[..., Option] = partial(
- Option,
- "--no-index",
- dest="no_index",
- action="store_true",
- default=False,
- help="Ignore package index (only looking at --find-links URLs instead).",
-)
-
-
-def find_links() -> Option:
- return Option(
- "-f",
- "--find-links",
- dest="find_links",
- action="append",
- default=[],
- metavar="url",
- help="If a URL or path to an html file, then parse for links to "
- "archives such as sdist (.tar.gz) or wheel (.whl) files. "
- "If a local path or file:// URL that's a directory, "
- "then look for archives in the directory listing. "
- "Links to VCS project URLs are not supported.",
- )
-
-
-def trusted_host() -> Option:
- return Option(
- "--trusted-host",
- dest="trusted_hosts",
- action="append",
- metavar="HOSTNAME",
- default=[],
- help="Mark this host or host:port pair as trusted, even though it "
- "does not have valid or any HTTPS.",
- )
-
-
-def constraints() -> Option:
- return Option(
- "-c",
- "--constraint",
- dest="constraints",
- action="append",
- default=[],
- metavar="file",
- help="Constrain versions using the given constraints file. "
- "This option can be used multiple times.",
- )
-
-
-def requirements() -> Option:
- return Option(
- "-r",
- "--requirement",
- dest="requirements",
- action="append",
- default=[],
- metavar="file",
- help="Install from the given requirements file. "
- "This option can be used multiple times.",
- )
-
-
-def editable() -> Option:
- return Option(
- "-e",
- "--editable",
- dest="editables",
- action="append",
- default=[],
- metavar="path/url",
- help=(
- "Install a project in editable mode (i.e. setuptools "
- '"develop mode") from a local project path or a VCS url.'
- ),
- )
-
-
-def _handle_src(option: Option, opt_str: str, value: str, parser: OptionParser) -> None:
- value = os.path.abspath(value)
- setattr(parser.values, option.dest, value)
-
-
-src: Callable[..., Option] = partial(
- PipOption,
- "--src",
- "--source",
- "--source-dir",
- "--source-directory",
- dest="src_dir",
- type="path",
- metavar="dir",
- default=get_src_prefix(),
- action="callback",
- callback=_handle_src,
- help="Directory to check out editable projects into. "
- 'The default in a virtualenv is "
/src". '
- 'The default for global installs is "/src".',
-)
-
-
-def _get_format_control(values: Values, option: Option) -> Any:
- """Get a format_control object."""
- return getattr(values, option.dest)
-
-
-def _handle_no_binary(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- existing = _get_format_control(parser.values, option)
- FormatControl.handle_mutual_excludes(
- value,
- existing.no_binary,
- existing.only_binary,
- )
-
-
-def _handle_only_binary(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- existing = _get_format_control(parser.values, option)
- FormatControl.handle_mutual_excludes(
- value,
- existing.only_binary,
- existing.no_binary,
- )
-
-
-def no_binary() -> Option:
- format_control = FormatControl(set(), set())
- return Option(
- "--no-binary",
- dest="format_control",
- action="callback",
- callback=_handle_no_binary,
- type="str",
- default=format_control,
- help="Do not use binary packages. Can be supplied multiple times, and "
- 'each time adds to the existing value. Accepts either ":all:" to '
- 'disable all binary packages, ":none:" to empty the set (notice '
- "the colons), or one or more package names with commas between "
- "them (no colons). Note that some packages are tricky to compile "
- "and may fail to install when this option is used on them.",
- )
-
-
-def only_binary() -> Option:
- format_control = FormatControl(set(), set())
- return Option(
- "--only-binary",
- dest="format_control",
- action="callback",
- callback=_handle_only_binary,
- type="str",
- default=format_control,
- help="Do not use source packages. Can be supplied multiple times, and "
- 'each time adds to the existing value. Accepts either ":all:" to '
- 'disable all source packages, ":none:" to empty the set, or one '
- "or more package names with commas between them. Packages "
- "without binary distributions will fail to install when this "
- "option is used on them.",
- )
-
-
-platforms: Callable[..., Option] = partial(
- Option,
- "--platform",
- dest="platforms",
- metavar="platform",
- action="append",
- default=None,
- help=(
- "Only use wheels compatible with . Defaults to the "
- "platform of the running system. Use this option multiple times to "
- "specify multiple platforms supported by the target interpreter."
- ),
-)
-
-
-# This was made a separate function for unit-testing purposes.
-def _convert_python_version(value: str) -> Tuple[Tuple[int, ...], Optional[str]]:
- """
- Convert a version string like "3", "37", or "3.7.3" into a tuple of ints.
-
- :return: A 2-tuple (version_info, error_msg), where `error_msg` is
- non-None if and only if there was a parsing error.
- """
- if not value:
- # The empty string is the same as not providing a value.
- return (None, None)
-
- parts = value.split(".")
- if len(parts) > 3:
- return ((), "at most three version parts are allowed")
-
- if len(parts) == 1:
- # Then we are in the case of "3" or "37".
- value = parts[0]
- if len(value) > 1:
- parts = [value[0], value[1:]]
-
- try:
- version_info = tuple(int(part) for part in parts)
- except ValueError:
- return ((), "each version part must be an integer")
-
- return (version_info, None)
-
-
-def _handle_python_version(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- """
- Handle a provided --python-version value.
- """
- version_info, error_msg = _convert_python_version(value)
- if error_msg is not None:
- msg = "invalid --python-version value: {!r}: {}".format(
- value,
- error_msg,
- )
- raise_option_error(parser, option=option, msg=msg)
-
- parser.values.python_version = version_info
-
-
-python_version: Callable[..., Option] = partial(
- Option,
- "--python-version",
- dest="python_version",
- metavar="python_version",
- action="callback",
- callback=_handle_python_version,
- type="str",
- default=None,
- help=dedent(
- """\
- The Python interpreter version to use for wheel and "Requires-Python"
- compatibility checks. Defaults to a version derived from the running
- interpreter. The version can be specified using up to three dot-separated
- integers (e.g. "3" for 3.0.0, "3.7" for 3.7.0, or "3.7.3"). A major-minor
- version can also be given as a string without dots (e.g. "37" for 3.7.0).
- """
- ),
-)
-
-
-implementation: Callable[..., Option] = partial(
- Option,
- "--implementation",
- dest="implementation",
- metavar="implementation",
- default=None,
- help=(
- "Only use wheels compatible with Python "
- "implementation , e.g. 'pp', 'jy', 'cp', "
- " or 'ip'. If not specified, then the current "
- "interpreter implementation is used. Use 'py' to force "
- "implementation-agnostic wheels."
- ),
-)
-
-
-abis: Callable[..., Option] = partial(
- Option,
- "--abi",
- dest="abis",
- metavar="abi",
- action="append",
- default=None,
- help=(
- "Only use wheels compatible with Python abi , e.g. 'pypy_41'. "
- "If not specified, then the current interpreter abi tag is used. "
- "Use this option multiple times to specify multiple abis supported "
- "by the target interpreter. Generally you will need to specify "
- "--implementation, --platform, and --python-version when using this "
- "option."
- ),
-)
-
-
-def add_target_python_options(cmd_opts: OptionGroup) -> None:
- cmd_opts.add_option(platforms())
- cmd_opts.add_option(python_version())
- cmd_opts.add_option(implementation())
- cmd_opts.add_option(abis())
-
-
-def make_target_python(options: Values) -> TargetPython:
- target_python = TargetPython(
- platforms=options.platforms,
- py_version_info=options.python_version,
- abis=options.abis,
- implementation=options.implementation,
- )
-
- return target_python
-
-
-def prefer_binary() -> Option:
- return Option(
- "--prefer-binary",
- dest="prefer_binary",
- action="store_true",
- default=False,
- help="Prefer older binary packages over newer source packages.",
- )
-
-
-cache_dir: Callable[..., Option] = partial(
- PipOption,
- "--cache-dir",
- dest="cache_dir",
- default=USER_CACHE_DIR,
- metavar="dir",
- type="path",
- help="Store the cache data in .",
-)
-
-
-def _handle_no_cache_dir(
- option: Option, opt: str, value: str, parser: OptionParser
-) -> None:
- """
- Process a value provided for the --no-cache-dir option.
-
- This is an optparse.Option callback for the --no-cache-dir option.
- """
- # The value argument will be None if --no-cache-dir is passed via the
- # command-line, since the option doesn't accept arguments. However,
- # the value can be non-None if the option is triggered e.g. by an
- # environment variable, like PIP_NO_CACHE_DIR=true.
- if value is not None:
- # Then parse the string value to get argument error-checking.
- try:
- strtobool(value)
- except ValueError as exc:
- raise_option_error(parser, option=option, msg=str(exc))
-
- # Originally, setting PIP_NO_CACHE_DIR to a value that strtobool()
- # converted to 0 (like "false" or "no") caused cache_dir to be disabled
- # rather than enabled (logic would say the latter). Thus, we disable
- # the cache directory not just on values that parse to True, but (for
- # backwards compatibility reasons) also on values that parse to False.
- # In other words, always set it to False if the option is provided in
- # some (valid) form.
- parser.values.cache_dir = False
-
-
-no_cache: Callable[..., Option] = partial(
- Option,
- "--no-cache-dir",
- dest="cache_dir",
- action="callback",
- callback=_handle_no_cache_dir,
- help="Disable the cache.",
-)
-
-no_deps: Callable[..., Option] = partial(
- Option,
- "--no-deps",
- "--no-dependencies",
- dest="ignore_dependencies",
- action="store_true",
- default=False,
- help="Don't install package dependencies.",
-)
-
-ignore_requires_python: Callable[..., Option] = partial(
- Option,
- "--ignore-requires-python",
- dest="ignore_requires_python",
- action="store_true",
- help="Ignore the Requires-Python information.",
-)
-
-no_build_isolation: Callable[..., Option] = partial(
- Option,
- "--no-build-isolation",
- dest="build_isolation",
- action="store_false",
- default=True,
- help="Disable isolation when building a modern source distribution. "
- "Build dependencies specified by PEP 518 must be already installed "
- "if this option is used.",
-)
-
-check_build_deps: Callable[..., Option] = partial(
- Option,
- "--check-build-dependencies",
- dest="check_build_deps",
- action="store_true",
- default=False,
- help="Check the build dependencies when PEP517 is used.",
-)
-
-
-def _handle_no_use_pep517(
- option: Option, opt: str, value: str, parser: OptionParser
-) -> None:
- """
- Process a value provided for the --no-use-pep517 option.
-
- This is an optparse.Option callback for the no_use_pep517 option.
- """
- # Since --no-use-pep517 doesn't accept arguments, the value argument
- # will be None if --no-use-pep517 is passed via the command-line.
- # However, the value can be non-None if the option is triggered e.g.
- # by an environment variable, for example "PIP_NO_USE_PEP517=true".
- if value is not None:
- msg = """A value was passed for --no-use-pep517,
- probably using either the PIP_NO_USE_PEP517 environment variable
- or the "no-use-pep517" config file option. Use an appropriate value
- of the PIP_USE_PEP517 environment variable or the "use-pep517"
- config file option instead.
- """
- raise_option_error(parser, option=option, msg=msg)
-
- # If user doesn't wish to use pep517, we check if setuptools and wheel are installed
- # and raise error if it is not.
- packages = ("setuptools", "wheel")
- if not all(importlib.util.find_spec(package) for package in packages):
- msg = (
- f"It is not possible to use --no-use-pep517 "
- f"without {' and '.join(packages)} installed."
- )
- raise_option_error(parser, option=option, msg=msg)
-
- # Otherwise, --no-use-pep517 was passed via the command-line.
- parser.values.use_pep517 = False
-
-
-use_pep517: Any = partial(
- Option,
- "--use-pep517",
- dest="use_pep517",
- action="store_true",
- default=None,
- help="Use PEP 517 for building source distributions "
- "(use --no-use-pep517 to force legacy behaviour).",
-)
-
-no_use_pep517: Any = partial(
- Option,
- "--no-use-pep517",
- dest="use_pep517",
- action="callback",
- callback=_handle_no_use_pep517,
- default=None,
- help=SUPPRESS_HELP,
-)
-
-
-def _handle_config_settings(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- key, sep, val = value.partition("=")
- if sep != "=":
- parser.error(f"Arguments to {opt_str} must be of the form KEY=VAL") # noqa
- dest = getattr(parser.values, option.dest)
- if dest is None:
- dest = {}
- setattr(parser.values, option.dest, dest)
- if key in dest:
- if isinstance(dest[key], list):
- dest[key].append(val)
- else:
- dest[key] = [dest[key], val]
- else:
- dest[key] = val
-
-
-config_settings: Callable[..., Option] = partial(
- Option,
- "-C",
- "--config-settings",
- dest="config_settings",
- type=str,
- action="callback",
- callback=_handle_config_settings,
- metavar="settings",
- help="Configuration settings to be passed to the PEP 517 build backend. "
- "Settings take the form KEY=VALUE. Use multiple --config-settings options "
- "to pass multiple keys to the backend.",
-)
-
-build_options: Callable[..., Option] = partial(
- Option,
- "--build-option",
- dest="build_options",
- metavar="options",
- action="append",
- help="Extra arguments to be supplied to 'setup.py bdist_wheel'.",
-)
-
-global_options: Callable[..., Option] = partial(
- Option,
- "--global-option",
- dest="global_options",
- action="append",
- metavar="options",
- help="Extra global options to be supplied to the setup.py "
- "call before the install or bdist_wheel command.",
-)
-
-no_clean: Callable[..., Option] = partial(
- Option,
- "--no-clean",
- action="store_true",
- default=False,
- help="Don't clean up build directories.",
-)
-
-pre: Callable[..., Option] = partial(
- Option,
- "--pre",
- action="store_true",
- default=False,
- help="Include pre-release and development versions. By default, "
- "pip only finds stable versions.",
-)
-
-disable_pip_version_check: Callable[..., Option] = partial(
- Option,
- "--disable-pip-version-check",
- dest="disable_pip_version_check",
- action="store_true",
- default=False,
- help="Don't periodically check PyPI to determine whether a new version "
- "of pip is available for download. Implied with --no-index.",
-)
-
-root_user_action: Callable[..., Option] = partial(
- Option,
- "--root-user-action",
- dest="root_user_action",
- default="warn",
- choices=["warn", "ignore"],
- help="Action if pip is run as a root user. By default, a warning message is shown.",
-)
-
-
-def _handle_merge_hash(
- option: Option, opt_str: str, value: str, parser: OptionParser
-) -> None:
- """Given a value spelled "algo:digest", append the digest to a list
- pointed to in a dict by the algo name."""
- if not parser.values.hashes:
- parser.values.hashes = {}
- try:
- algo, digest = value.split(":", 1)
- except ValueError:
- parser.error(
- "Arguments to {} must be a hash name " # noqa
- "followed by a value, like --hash=sha256:"
- "abcde...".format(opt_str)
- )
- if algo not in STRONG_HASHES:
- parser.error(
- "Allowed hash algorithms for {} are {}.".format( # noqa
- opt_str, ", ".join(STRONG_HASHES)
- )
- )
- parser.values.hashes.setdefault(algo, []).append(digest)
-
-
-hash: Callable[..., Option] = partial(
- Option,
- "--hash",
- # Hash values eventually end up in InstallRequirement.hashes due to
- # __dict__ copying in process_line().
- dest="hashes",
- action="callback",
- callback=_handle_merge_hash,
- type="string",
- help="Verify that the package's archive matches this "
- "hash before installing. Example: --hash=sha256:abcdef...",
-)
-
-
-require_hashes: Callable[..., Option] = partial(
- Option,
- "--require-hashes",
- dest="require_hashes",
- action="store_true",
- default=False,
- help="Require a hash to check each requirement against, for "
- "repeatable installs. This option is implied when any package in a "
- "requirements file has a --hash option.",
-)
-
-
-list_path: Callable[..., Option] = partial(
- PipOption,
- "--path",
- dest="path",
- type="path",
- action="append",
- help="Restrict to the specified installation path for listing "
- "packages (can be used multiple times).",
-)
-
-
-def check_list_path_option(options: Values) -> None:
- if options.path and (options.user or options.local):
- raise CommandError("Cannot combine '--path' with '--user' or '--local'")
-
-
-list_exclude: Callable[..., Option] = partial(
- PipOption,
- "--exclude",
- dest="excludes",
- action="append",
- metavar="package",
- type="package_name",
- help="Exclude specified package from the output",
-)
-
-
-no_python_version_warning: Callable[..., Option] = partial(
- Option,
- "--no-python-version-warning",
- dest="no_python_version_warning",
- action="store_true",
- default=False,
- help="Silence deprecation warnings for upcoming unsupported Pythons.",
-)
-
-
-# Features that are now always on. A warning is printed if they are used.
-ALWAYS_ENABLED_FEATURES = [
- "no-binary-enable-wheel-cache", # always on since 23.1
-]
-
-use_new_feature: Callable[..., Option] = partial(
- Option,
- "--use-feature",
- dest="features_enabled",
- metavar="feature",
- action="append",
- default=[],
- choices=[
- "fast-deps",
- "truststore",
- ]
- + ALWAYS_ENABLED_FEATURES,
- help="Enable new functionality, that may be backward incompatible.",
-)
-
-use_deprecated_feature: Callable[..., Option] = partial(
- Option,
- "--use-deprecated",
- dest="deprecated_features_enabled",
- metavar="feature",
- action="append",
- default=[],
- choices=[
- "legacy-resolver",
- ],
- help=("Enable deprecated functionality, that will be removed in the future."),
-)
-
-
-##########
-# groups #
-##########
-
-general_group: Dict[str, Any] = {
- "name": "General Options",
- "options": [
- help_,
- debug_mode,
- isolated_mode,
- require_virtualenv,
- python,
- verbose,
- version,
- quiet,
- log,
- no_input,
- keyring_provider,
- proxy,
- retries,
- timeout,
- exists_action,
- trusted_host,
- cert,
- client_cert,
- cache_dir,
- no_cache,
- disable_pip_version_check,
- no_color,
- no_python_version_warning,
- use_new_feature,
- use_deprecated_feature,
- ],
-}
-
-index_group: Dict[str, Any] = {
- "name": "Package Index Options",
- "options": [
- index_url,
- extra_index_url,
- no_index,
- find_links,
- ],
-}
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py
deleted file mode 100644
index fd80d8c1129722b84771bd6a0f6ccfd57f5cf78e..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import re
-from ast import literal_eval
-from operator import attrgetter
-from typing import Callable, Iterable, List, Match, NamedTuple, Optional, Tuple, Union
-
-from ._emoji_replace import _emoji_replace
-from .emoji import EmojiVariant
-from .errors import MarkupError
-from .style import Style
-from .text import Span, Text
-
-RE_TAGS = re.compile(
- r"""((\\*)\[([a-z#/@][^[]*?)])""",
- re.VERBOSE,
-)
-
-RE_HANDLER = re.compile(r"^([\w.]*?)(\(.*?\))?$")
-
-
-class Tag(NamedTuple):
- """A tag in console markup."""
-
- name: str
- """The tag name. e.g. 'bold'."""
- parameters: Optional[str]
- """Any additional parameters after the name."""
-
- def __str__(self) -> str:
- return (
- self.name if self.parameters is None else f"{self.name} {self.parameters}"
- )
-
- @property
- def markup(self) -> str:
- """Get the string representation of this tag."""
- return (
- f"[{self.name}]"
- if self.parameters is None
- else f"[{self.name}={self.parameters}]"
- )
-
-
-_ReStringMatch = Match[str] # regex match object
-_ReSubCallable = Callable[[_ReStringMatch], str] # Callable invoked by re.sub
-_EscapeSubMethod = Callable[[_ReSubCallable, str], str] # Sub method of a compiled re
-
-
-def escape(
- markup: str,
- _escape: _EscapeSubMethod = re.compile(r"(\\*)(\[[a-z#/@][^[]*?])").sub,
-) -> str:
- """Escapes text so that it won't be interpreted as markup.
-
- Args:
- markup (str): Content to be inserted in to markup.
-
- Returns:
- str: Markup with square brackets escaped.
- """
-
- def escape_backslashes(match: Match[str]) -> str:
- """Called by re.sub replace matches."""
- backslashes, text = match.groups()
- return f"{backslashes}{backslashes}\\{text}"
-
- markup = _escape(escape_backslashes, markup)
- return markup
-
-
-def _parse(markup: str) -> Iterable[Tuple[int, Optional[str], Optional[Tag]]]:
- """Parse markup in to an iterable of tuples of (position, text, tag).
-
- Args:
- markup (str): A string containing console markup
-
- """
- position = 0
- _divmod = divmod
- _Tag = Tag
- for match in RE_TAGS.finditer(markup):
- full_text, escapes, tag_text = match.groups()
- start, end = match.span()
- if start > position:
- yield start, markup[position:start], None
- if escapes:
- backslashes, escaped = _divmod(len(escapes), 2)
- if backslashes:
- # Literal backslashes
- yield start, "\\" * backslashes, None
- start += backslashes * 2
- if escaped:
- # Escape of tag
- yield start, full_text[len(escapes) :], None
- position = end
- continue
- text, equals, parameters = tag_text.partition("=")
- yield start, None, _Tag(text, parameters if equals else None)
- position = end
- if position < len(markup):
- yield position, markup[position:], None
-
-
-def render(
- markup: str,
- style: Union[str, Style] = "",
- emoji: bool = True,
- emoji_variant: Optional[EmojiVariant] = None,
-) -> Text:
- """Render console markup in to a Text instance.
-
- Args:
- markup (str): A string containing console markup.
- emoji (bool, optional): Also render emoji code. Defaults to True.
-
- Raises:
- MarkupError: If there is a syntax error in the markup.
-
- Returns:
- Text: A test instance.
- """
- emoji_replace = _emoji_replace
- if "[" not in markup:
- return Text(
- emoji_replace(markup, default_variant=emoji_variant) if emoji else markup,
- style=style,
- )
- text = Text(style=style)
- append = text.append
- normalize = Style.normalize
-
- style_stack: List[Tuple[int, Tag]] = []
- pop = style_stack.pop
-
- spans: List[Span] = []
- append_span = spans.append
-
- _Span = Span
- _Tag = Tag
-
- def pop_style(style_name: str) -> Tuple[int, Tag]:
- """Pop tag matching given style name."""
- for index, (_, tag) in enumerate(reversed(style_stack), 1):
- if tag.name == style_name:
- return pop(-index)
- raise KeyError(style_name)
-
- for position, plain_text, tag in _parse(markup):
- if plain_text is not None:
- # Handle open brace escapes, where the brace is not part of a tag.
- plain_text = plain_text.replace("\\[", "[")
- append(emoji_replace(plain_text) if emoji else plain_text)
- elif tag is not None:
- if tag.name.startswith("/"): # Closing tag
- style_name = tag.name[1:].strip()
-
- if style_name: # explicit close
- style_name = normalize(style_name)
- try:
- start, open_tag = pop_style(style_name)
- except KeyError:
- raise MarkupError(
- f"closing tag '{tag.markup}' at position {position} doesn't match any open tag"
- ) from None
- else: # implicit close
- try:
- start, open_tag = pop()
- except IndexError:
- raise MarkupError(
- f"closing tag '[/]' at position {position} has nothing to close"
- ) from None
-
- if open_tag.name.startswith("@"):
- if open_tag.parameters:
- handler_name = ""
- parameters = open_tag.parameters.strip()
- handler_match = RE_HANDLER.match(parameters)
- if handler_match is not None:
- handler_name, match_parameters = handler_match.groups()
- parameters = (
- "()" if match_parameters is None else match_parameters
- )
-
- try:
- meta_params = literal_eval(parameters)
- except SyntaxError as error:
- raise MarkupError(
- f"error parsing {parameters!r} in {open_tag.parameters!r}; {error.msg}"
- )
- except Exception as error:
- raise MarkupError(
- f"error parsing {open_tag.parameters!r}; {error}"
- ) from None
-
- if handler_name:
- meta_params = (
- handler_name,
- meta_params
- if isinstance(meta_params, tuple)
- else (meta_params,),
- )
-
- else:
- meta_params = ()
-
- append_span(
- _Span(
- start, len(text), Style(meta={open_tag.name: meta_params})
- )
- )
- else:
- append_span(_Span(start, len(text), str(open_tag)))
-
- else: # Opening tag
- normalized_tag = _Tag(normalize(tag.name), tag.parameters)
- style_stack.append((len(text), normalized_tag))
-
- text_length = len(text)
- while style_stack:
- start, tag = style_stack.pop()
- style = str(tag)
- if style:
- append_span(_Span(start, text_length, style))
-
- text.spans = sorted(spans[::-1], key=attrgetter("start"))
- return text
-
-
-if __name__ == "__main__": # pragma: no cover
-
- MARKUP = [
- "[red]Hello World[/red]",
- "[magenta]Hello [b]World[/b]",
- "[bold]Bold[italic] bold and italic [/bold]italic[/italic]",
- "Click [link=https://www.willmcgugan.com]here[/link] to visit my Blog",
- ":warning-emoji: [bold red blink] DANGER![/]",
- ]
-
- from pip._vendor.rich import print
- from pip._vendor.rich.table import Table
-
- grid = Table("Markup", "Result", padding=(0, 1))
-
- for markup in MARKUP:
- grid.add_row(Text(markup), markup)
-
- print(grid)
diff --git a/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat b/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat
deleted file mode 100644
index 62a39a7584f4d7c5fbc31758e3e9e7eff700276d..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat
+++ /dev/null
@@ -1,22 +0,0 @@
-@echo off
-
-if defined _OLD_VIRTUAL_PROMPT (
- set "PROMPT=%_OLD_VIRTUAL_PROMPT%"
-)
-set _OLD_VIRTUAL_PROMPT=
-
-if defined _OLD_VIRTUAL_PYTHONHOME (
- set "PYTHONHOME=%_OLD_VIRTUAL_PYTHONHOME%"
- set _OLD_VIRTUAL_PYTHONHOME=
-)
-
-if defined _OLD_VIRTUAL_PATH (
- set "PATH=%_OLD_VIRTUAL_PATH%"
-)
-
-set _OLD_VIRTUAL_PATH=
-
-set VIRTUAL_ENV=
-set VIRTUAL_ENV_PROMPT=
-
-:END
diff --git a/spaces/CAMP-ViL/Xplainer/inference.py b/spaces/CAMP-ViL/Xplainer/inference.py
deleted file mode 100644
index 2ab6b61c2b1fa8039af58a5f5c4823b6c2f56cab..0000000000000000000000000000000000000000
--- a/spaces/CAMP-ViL/Xplainer/inference.py
+++ /dev/null
@@ -1,116 +0,0 @@
-import argparse
-import gc
-from pathlib import Path
-
-import torch
-from torch.utils.data import DataLoader
-from tqdm import tqdm
-
-from chestxray14 import ChestXray14Dataset
-from chexpert import CheXpertDataset
-from descriptors import disease_descriptors_chexpert, disease_descriptors_chestxray14
-from model import InferenceModel
-from utils import calculate_auroc
-
-torch.multiprocessing.set_sharing_strategy('file_system')
-
-
-def inference_chexpert():
- split = 'test'
- dataset = CheXpertDataset(f'data/chexpert/{split}_labels.csv') # also do test
- dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=lambda x: x, num_workers=0)
- inference_model = InferenceModel()
- all_descriptors = inference_model.get_all_descriptors(disease_descriptors_chexpert)
-
- all_labels = []
- all_probs_neg = []
-
- for batch in tqdm(dataloader):
- batch = batch[0]
- image_paths, labels, keys = batch
- image_paths = [Path(image_path) for image_path in image_paths]
- agg_probs = []
- agg_negative_probs = []
- for image_path in image_paths:
- probs, negative_probs = inference_model.get_descriptor_probs(image_path, descriptors=all_descriptors)
- agg_probs.append(probs)
- agg_negative_probs.append(negative_probs)
- probs = {} # Aggregated
- negative_probs = {} # Aggregated
- for key in agg_probs[0].keys():
- probs[key] = sum([p[key] for p in agg_probs]) / len(agg_probs) # Mean Aggregation
-
- for key in agg_negative_probs[0].keys():
- negative_probs[key] = sum([p[key] for p in agg_negative_probs]) / len(agg_negative_probs) # Mean Aggregation
-
- disease_probs, negative_disease_probs = inference_model.get_diseases_probs(disease_descriptors_chexpert, pos_probs=probs,
- negative_probs=negative_probs)
- predicted_diseases, prob_vector_neg_prompt = inference_model.get_predictions_bin_prompting(disease_descriptors_chexpert,
- disease_probs=disease_probs,
- negative_disease_probs=negative_disease_probs,
- keys=keys)
- all_labels.append(labels)
- all_probs_neg.append(prob_vector_neg_prompt)
-
- all_labels = torch.stack(all_labels)
- all_probs_neg = torch.stack(all_probs_neg)
-
- # evaluation
- existing_mask = sum(all_labels, 0) > 0
- all_labels_clean = all_labels[:, existing_mask]
- all_probs_neg_clean = all_probs_neg[:, existing_mask]
- all_keys_clean = [key for idx, key in enumerate(keys) if existing_mask[idx]]
-
- overall_auroc, per_disease_auroc = calculate_auroc(all_probs_neg_clean, all_labels_clean)
- print(f"AUROC: {overall_auroc:.5f}\n")
- for idx, key in enumerate(all_keys_clean):
- print(f'{key}: {per_disease_auroc[idx]:.5f}')
-
-
-def inference_chestxray14():
- dataset = ChestXray14Dataset(f'data/chestxray14/Data_Entry_2017_v2020_modified.csv')
- dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=lambda x: x, num_workers=1)
- inference_model = InferenceModel()
- all_descriptors = inference_model.get_all_descriptors(disease_descriptors_chestxray14)
-
- all_labels = []
- all_probs_neg = []
- for batch in tqdm(dataloader):
- batch = batch[0]
- image_path, labels, keys = batch
- image_path = Path(image_path)
- probs, negative_probs = inference_model.get_descriptor_probs(image_path, descriptors=all_descriptors)
- disease_probs, negative_disease_probs = inference_model.get_diseases_probs(disease_descriptors_chestxray14, pos_probs=probs,
- negative_probs=negative_probs)
- predicted_diseases, prob_vector_neg_prompt = inference_model.get_predictions_bin_prompting(disease_descriptors_chestxray14,
- disease_probs=disease_probs,
- negative_disease_probs=negative_disease_probs,
- keys=keys)
- all_labels.append(labels)
- all_probs_neg.append(prob_vector_neg_prompt)
- gc.collect()
-
- all_labels = torch.stack(all_labels)
- all_probs_neg = torch.stack(all_probs_neg)
-
- existing_mask = sum(all_labels, 0) > 0
- all_labels_clean = all_labels[:, existing_mask]
- all_probs_neg_clean = all_probs_neg[:, existing_mask]
- all_keys_clean = [key for idx, key in enumerate(keys) if existing_mask[idx]]
-
- overall_auroc, per_disease_auroc = calculate_auroc(all_probs_neg_clean[:, 1:], all_labels_clean[:, 1:])
- print(f"AUROC: {overall_auroc:.5f}\n")
- for idx, key in enumerate(all_keys_clean[1:]):
- print(f'{key}: {per_disease_auroc[idx]:.5f}')
-
-
-if __name__ == '__main__':
- # add argument parser
- parser = argparse.ArgumentParser()
- parser.add_argument('--dataset', type=str, default='chexpert', help='chexpert or chestxray14')
- args = parser.parse_args()
-
- if args.dataset == 'chexpert':
- inference_chexpert()
- elif args.dataset == 'chestxray14':
- inference_chestxray14()
diff --git a/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h b/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h
deleted file mode 100644
index e00d3ef054bd740b801e47cc1344e38621d8c055..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h
+++ /dev/null
@@ -1,253 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-
-namespace thrust
-{
-
-namespace detail
-{
-
-
-// adapted from http://www.boost.org/doc/libs/1_37_0/libs/iterator/doc/iterator_facade.html#iterator-category
-//
-// in our implementation, R need not be a reference type to result in a category
-// derived from forward_XXX_iterator_tag
-//
-// iterator-category(T,V,R) :=
-// if(T is convertible to input_host_iterator_tag
-// || T is convertible to output_host_iterator_tag
-// || T is convertible to input_device_iterator_tag
-// || T is convertible to output_device_iterator_tag
-// )
-// return T
-//
-// else if (T is not convertible to incrementable_traversal_tag)
-// the program is ill-formed
-//
-// else return a type X satisfying the following two constraints:
-//
-// 1. X is convertible to X1, and not to any more-derived
-// type, where X1 is defined by:
-//
-// if (T is convertible to forward_traversal_tag)
-// {
-// if (T is convertible to random_access_traversal_tag)
-// X1 = random_access_host_iterator_tag
-// else if (T is convertible to bidirectional_traversal_tag)
-// X1 = bidirectional_host_iterator_tag
-// else
-// X1 = forward_host_iterator_tag
-// }
-// else
-// {
-// if (T is convertible to single_pass_traversal_tag
-// && R is convertible to V)
-// X1 = input_host_iterator_tag
-// else
-// X1 = T
-// }
-//
-// 2. category-to-traversal(X) is convertible to the most
-// derived traversal tag type to which X is also convertible,
-// and not to any more-derived traversal tag type.
-
-
-template
- struct iterator_facade_default_category;
-
-
-// Thrust's implementation of iterator_facade_default_category is slightly
-// different from Boost's equivalent.
-// Thrust does not check is_convertible because Reference
-// may not be a complete type at this point, and implementations of is_convertible
-// typically require that both types be complete.
-// Instead, it simply assumes that if is_convertible,
-// then the category is input_iterator_tag
-
-
-// this is the function for standard system iterators
-template
- struct iterator_facade_default_category_std :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if< // XXX note we differ from Boost here
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_std
-
-
-// this is the function for host system iterators
-template
- struct iterator_facade_default_category_host :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if< // XXX note we differ from Boost here
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_host
-
-
-// this is the function for device system iterators
-template
- struct iterator_facade_default_category_device :
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >,
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value, // XXX note we differ from Boost here
- thrust::detail::identity_,
- thrust::detail::identity_
- >
- >
-{
-}; // end iterator_facade_default_category_device
-
-
-// this is the function for any system iterators
-template
- struct iterator_facade_default_category_any
-{
- typedef thrust::detail::iterator_category_with_system_and_traversal<
- typename iterator_facade_default_category_std::type,
- thrust::any_system_tag,
- Traversal
- > type;
-}; // end iterator_facade_default_category_any
-
-
-template
- struct iterator_facade_default_category
- // check for any system
- : thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_any,
-
- // check for host system
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_host,
-
- // check for device system
- thrust::detail::eval_if<
- thrust::detail::is_convertible::value,
- iterator_facade_default_category_device,
-
- // if we don't recognize the system, get a standard iterator category
- // and combine it with System & Traversal
- thrust::detail::identity_<
- thrust::detail::iterator_category_with_system_and_traversal<
- typename iterator_facade_default_category_std::type,
- System,
- Traversal
- >
- >
- >
- >
- >
-{};
-
-
-template
- struct iterator_facade_category_impl
-{
- typedef typename iterator_facade_default_category<
- System,Traversal,ValueParam,Reference
- >::type category;
-
- // we must be able to deduce both Traversal & System from category
- // otherwise, munge them all together
- typedef typename thrust::detail::eval_if<
- thrust::detail::and_<
- thrust::detail::is_same<
- Traversal,
- typename thrust::detail::iterator_category_to_traversal::type
- >,
- thrust::detail::is_same<
- System,
- typename thrust::detail::iterator_category_to_system::type
- >
- >::value,
- thrust::detail::identity_,
- thrust::detail::identity_ >
- >::type type;
-}; // end iterator_facade_category_impl
-
-
-template
- struct iterator_facade_category
-{
- typedef typename
- thrust::detail::eval_if<
- thrust::detail::is_iterator_category::value,
- thrust::detail::identity_, // categories are fine as-is
- iterator_facade_category_impl
- >::type type;
-}; // end iterator_facade_category
-
-
-} // end detail
-} // end thrust
-
diff --git a/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py b/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py
deleted file mode 100644
index a4f5ed44caefb1612df67785b1f4f0d9ec46ee93..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/necks/channel_mapper.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import torch.nn as nn
-from mmcv.cnn import ConvModule, xavier_init
-
-from ..builder import NECKS
-
-
-@NECKS.register_module()
-class ChannelMapper(nn.Module):
- r"""Channel Mapper to reduce/increase channels of backbone features.
-
- This is used to reduce/increase channels of backbone features.
-
- Args:
- in_channels (List[int]): Number of input channels per scale.
- out_channels (int): Number of output channels (used at each scale).
- kernel_size (int, optional): kernel_size for reducing channels (used
- at each scale). Default: 3.
- conv_cfg (dict, optional): Config dict for convolution layer.
- Default: None.
- norm_cfg (dict, optional): Config dict for normalization layer.
- Default: None.
- act_cfg (dict, optional): Config dict for activation layer in
- ConvModule. Default: dict(type='ReLU').
-
- Example:
- >>> import torch
- >>> in_channels = [2, 3, 5, 7]
- >>> scales = [340, 170, 84, 43]
- >>> inputs = [torch.rand(1, c, s, s)
- ... for c, s in zip(in_channels, scales)]
- >>> self = ChannelMapper(in_channels, 11, 3).eval()
- >>> outputs = self.forward(inputs)
- >>> for i in range(len(outputs)):
- ... print(f'outputs[{i}].shape = {outputs[i].shape}')
- outputs[0].shape = torch.Size([1, 11, 340, 340])
- outputs[1].shape = torch.Size([1, 11, 170, 170])
- outputs[2].shape = torch.Size([1, 11, 84, 84])
- outputs[3].shape = torch.Size([1, 11, 43, 43])
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size=3,
- conv_cfg=None,
- norm_cfg=None,
- act_cfg=dict(type='ReLU')):
- super(ChannelMapper, self).__init__()
- assert isinstance(in_channels, list)
-
- self.convs = nn.ModuleList()
- for in_channel in in_channels:
- self.convs.append(
- ConvModule(
- in_channel,
- out_channels,
- kernel_size,
- padding=(kernel_size - 1) // 2,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg))
-
- # default init_weights for conv(msra) and norm in ConvModule
- def init_weights(self):
- """Initialize the weights of ChannelMapper module."""
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- xavier_init(m, distribution='uniform')
-
- def forward(self, inputs):
- """Forward function."""
- assert len(inputs) == len(self.convs)
- outs = [self.convs[i](inputs[i]) for i in range(len(inputs))]
- return tuple(outs)
diff --git a/spaces/CVPR/regionclip-demo/datasets/README.md b/spaces/CVPR/regionclip-demo/datasets/README.md
deleted file mode 100644
index 0eb44cc3b23beeb1755ab8d12002d26f13434235..0000000000000000000000000000000000000000
--- a/spaces/CVPR/regionclip-demo/datasets/README.md
+++ /dev/null
@@ -1,140 +0,0 @@
-# Use Builtin Datasets
-
-A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog)
-for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc).
-This document explains how to setup the builtin datasets so they can be used by the above APIs.
-[Use Custom Datasets](https://detectron2.readthedocs.io/tutorials/datasets.html) gives a deeper dive on how to use `DatasetCatalog` and `MetadataCatalog`,
-and how to add new datasets to them.
-
-Detectron2 has builtin support for a few datasets.
-The datasets are assumed to exist in a directory specified by the environment variable
-`DETECTRON2_DATASETS`.
-Under this directory, detectron2 will look for datasets in the structure described below, if needed.
-```
-$DETECTRON2_DATASETS/
- coco/
- lvis/
- cityscapes/
- VOC20{07,12}/
-```
-
-You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`.
-If left unset, the default is `./datasets` relative to your current working directory.
-
-The [model zoo](https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md)
-contains configs and models that use these builtin datasets.
-
-## Expected dataset structure for [COCO instance/keypoint detection](https://cocodataset.org/#download):
-
-```
-coco/
- annotations/
- instances_{train,val}2017.json
- person_keypoints_{train,val}2017.json
- {train,val}2017/
- # image files that are mentioned in the corresponding json
-```
-
-You can use the 2014 version of the dataset as well.
-
-Some of the builtin tests (`dev/run_*_tests.sh`) uses a tiny version of the COCO dataset,
-which you can download with `./datasets/prepare_for_tests.sh`.
-
-## Expected dataset structure for PanopticFPN:
-
-Extract panoptic annotations from [COCO website](https://cocodataset.org/#download)
-into the following structure:
-```
-coco/
- annotations/
- panoptic_{train,val}2017.json
- panoptic_{train,val}2017/ # png annotations
- panoptic_stuff_{train,val}2017/ # generated by the script mentioned below
-```
-
-Install panopticapi by:
-```
-pip install git+https://github.com/cocodataset/panopticapi.git
-```
-Then, run `python datasets/prepare_panoptic_fpn.py`, to extract semantic annotations from panoptic annotations.
-
-## Expected dataset structure for [LVIS instance segmentation](https://www.lvisdataset.org/dataset):
-```
-coco/
- {train,val,test}2017/
-lvis/
- lvis_v0.5_{train,val}.json
- lvis_v0.5_image_info_test.json
- lvis_v1_{train,val}.json
- lvis_v1_image_info_test{,_challenge}.json
-```
-
-Install lvis-api by:
-```
-pip install git+https://github.com/lvis-dataset/lvis-api.git
-```
-
-To evaluate models trained on the COCO dataset using LVIS annotations,
-run `python datasets/prepare_cocofied_lvis.py` to prepare "cocofied" LVIS annotations.
-
-## Expected dataset structure for [cityscapes](https://www.cityscapes-dataset.com/downloads/):
-```
-cityscapes/
- gtFine/
- train/
- aachen/
- color.png, instanceIds.png, labelIds.png, polygons.json,
- labelTrainIds.png
- ...
- val/
- test/
- # below are generated Cityscapes panoptic annotation
- cityscapes_panoptic_train.json
- cityscapes_panoptic_train/
- cityscapes_panoptic_val.json
- cityscapes_panoptic_val/
- cityscapes_panoptic_test.json
- cityscapes_panoptic_test/
- leftImg8bit/
- train/
- val/
- test/
-```
-Install cityscapes scripts by:
-```
-pip install git+https://github.com/mcordts/cityscapesScripts.git
-```
-
-Note: to create labelTrainIds.png, first prepare the above structure, then run cityscapesescript with:
-```
-CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createTrainIdLabelImgs.py
-```
-These files are not needed for instance segmentation.
-
-Note: to generate Cityscapes panoptic dataset, run cityscapesescript with:
-```
-CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createPanopticImgs.py
-```
-These files are not needed for semantic and instance segmentation.
-
-## Expected dataset structure for [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/index.html):
-```
-VOC20{07,12}/
- Annotations/
- ImageSets/
- Main/
- trainval.txt
- test.txt
- # train.txt or val.txt, if you use these splits
- JPEGImages/
-```
-
-## Expected dataset structure for [ADE20k Scene Parsing](http://sceneparsing.csail.mit.edu/):
-```
-ADEChallengeData2016/
- annotations/
- annotations_detectron2/
- images/
- objectInfo150.txt
-```
-The directory `annotations_detectron2` is generated by running `python datasets/prepare_ade20k_sem_seg.py`.
diff --git a/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py b/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py
deleted file mode 100644
index ee1f6f1bcfa2fc3437fb6d5f579640482f1ad5f4..0000000000000000000000000000000000000000
--- a/spaces/CarlDennis/HYTTS/text/ger_to_ipa.py
+++ /dev/null
@@ -1,397 +0,0 @@
-# -*- coding: utf-8 -*-
-import re
-from os.path import join, abspath, dirname
-from collections import defaultdict
-import epitran
-
-epi = epitran.Epitran("deu-Latn-nar")
-
-
-def mode_type(mode_in):
- """In the case of "sql", this will return an sqlite cursor."""
- if mode_in.lower() == "sql":
- import sqlite3
- conn = sqlite3.connect(join(abspath(dirname(__file__)), "./Resources/de.db"))
- return conn.cursor()
-
-
-#TESTS
-#NUMBERS ARE TOO HARD!
-
-
-
-def preprocess(words):
- """Returns a string of words stripped of punctuation"""
- punct_str = '!"#$%&\'()*+,-./:;<=>/?@[\\]^_`{|}~«» '
- return ' '.join([w.strip(punct_str).lower() for w in words.split()])
-
-
-def preserve_punc(words):
- """converts words to IPA and finds punctuation before and after the word."""
- words_preserved = []
- for w in words.split():
- punct_list = ["", preprocess(w), ""]
- before = re.search("^([^A-Za-z0-9]+)[A-Za-z]", w)
- after = re.search("[A-Za-z]([^A-Za-z0-9]+)$", w)
- if before:
- punct_list[0] = str(before.group(1))
- if after:
- punct_list[2] = str(after.group(1))
- words_preserved.append(punct_list)
- return words_preserved
-
-
-
-def apply_punct(triple, as_str=False):
- """places surrounding punctuation back on center on a list of preserve_punc triples"""
- if type(triple[0]) == list:
- for i, t in enumerate(triple):
- triple[i] = str(''.join(triple[i]))
- if as_str:
- return ' '.join(triple)
- return triple
- if as_str:
- return str(''.join(t for t in triple))
- return [''.join(t for t in triple)]
-
-
-def _punct_replace_word(original, transcription):
- """Get the IPA transcription of word with the original punctuation marks"""
- for i, trans_list in enumerate(transcription):
- for j, item in enumerate(trans_list):
- triple = [original[i][0]] + [item] + [original[i][2]]
- transcription[i][j] = apply_punct(triple, as_str=True)
- return transcription
-
-
-def fetch_words(words_in, db_type="sql"):
- """fetches a list of words from the database"""
- asset = mode_type(db_type)
- f_result = []
- if db_type.lower() == "sql":
- for word in words_in:
- asset.execute("SELECT Words, phonemes FROM De_words WHERE Words IN (?)", (word,))
- result = asset.fetchall()
- flag = True
- try:
- f_result.append(result.pop())
- flag = False
- except IndexError:
- pass
- if result == [] and flag is True:
- result = epi.transliterate(word)
- f_result.append((word, result))
- f_result = list(filter(None,f_result))
- f_set = set(f_result)
- d = defaultdict(list)
- for k, v in f_set:
- d[k].append(v)
- return list(d.items())
-
-def get_deu(tokens_in, db_type="sql"):
- """query the SQL database for the words and return the phonemes in the order of user_in"""
- result = fetch_words(tokens_in, db_type)
- ordered = []
- for word in tokens_in:
- this_word = [[i[1] for i in result if i[0] == word]][0]
- if this_word:
- ordered.append(this_word[0])
- else:
- ordered.append(["__IGNORE__" + word])
- return ordered
-
-
-def deu_to_ipa(deu_list, mark=True):
- """converts the deu word lists into IPA transcriptions"""
- symbols = {}
- ipa_list = [] # the final list of IPA tokens to be returned
- for word_list in deu_list:
- ipa_word_list = [] # the word list for each word
- for word in word_list:
- if re.sub("\d*", "", word.replace("__IGNORE__", "")) == "":
- pass # do not delete token if it's all numbers
- else:
- word = re.sub("[0-9]", "", word)
- ipa_form = ''
- if word.startswith("__IGNORE__"):
- ipa_form = word.replace("__IGNORE__", "")
- # mark words we couldn't transliterate with an asterisk:
-
- if mark:
- if not re.sub("\d*", "", ipa_form) == "":
- ipa_form += "*"
- else:
- for piece in word.split(" "):
- marked = False
- unmarked = piece
- if piece[0] in ["ˈ", "ˌ"] or piece[0] is None:
- marked = True
- mark = piece
- unmarked = piece[1:]
-
- if unmarked in symbols:
- if marked:
- ipa_form += mark + symbols[unmarked]
- else:
- ipa_form += symbols[unmarked]
-
- else:
- ipa_form += piece
- swap_list = [["ˈər", "əˈr"], ["ˈie", "iˈe"]]
- for sym in swap_list:
- if not ipa_form.startswith(sym[0]):
- ipa_form = ipa_form.replace(sym[0], sym[1])
- ipa_word_list.append(ipa_form)
- ipa_list.append(sorted(list(set(ipa_word_list))))
- return ipa_list
-
-
-def get_top(ipa_list):
- """Returns only the one result for a query. If multiple entries for words are found, only the first is used."""
- return ' '.join([word_list[-1] for word_list in ipa_list])
-
-
-def get_all(ipa_list):
- """utilizes an algorithm to discover and return all possible combinations of IPA transcriptions"""
- final_size = 1
- for word_list in ipa_list:
- final_size *= len(word_list)
- list_all = ["" for s in range(final_size)]
- for i in range(len(ipa_list)):
- if i == 0:
- swtich_rate = final_size / len(ipa_list[i])
- else:
- swtich_rate /= len(ipa_list[i])
- k = 0
- for j in range(final_size):
- if (j+1) % int(swtich_rate) == 0:
- k += 1
- if k == len(ipa_list[i]):
- k = 0
- list_all[j] = list_all[j] + ipa_list[i][k] + " "
- return sorted([sent[:-1] for sent in list_all])
-
-
-def ipa_list(words_in, keep_punct=True, db_type="sql"):
- """Returns a list of all the discovered IPA transcriptions for each word."""
- if type(words_in) == str:
- words = [preserve_punc(w.lower())[0] for w in words_in.split()]
- else:
- words = [preserve_punc(w.lower())[0] for w in words_in]
- deu = get_deu([w[1] for w in words], db_type=db_type)
- ipa = deu_to_ipa(deu)
- if keep_punct:
- ipa = _punct_replace_word(words, ipa)
- return ipa
-
-
-def isin_deu(word, db_type="sql"):
- """checks if a word is in the deu dictionary. Doesn't strip punctuation.
- If given more than one word, returns True only if all words are present."""
- if type(word) == str:
- word = [preprocess(w) for w in word.split()]
- results = fetch_words(word, db_type)
- as_set = list(set(t[0] for t in results))
- return len(as_set) == len(set(word))
-
-def replace_number(text):
- text = text.replace("1","eins ")
- text = text.replace("2","zwei ")
- text = text.replace("3","drei ")
- text = text.replace("4","vier ")
- text = text.replace("5","fünf ")
- text = text.replace("6","sechs ")
- text = text.replace("7","sieben ")
- text = text.replace("8","acht ")
- text = text.replace("9","neun ")
- text = text.replace("0","null ")
- return text
-
-
-
-def convert(text, retrieve_all=False, keep_punct=True, mode="sql"):
- """takes either a string or list of German words and converts them to IPA"""
- text = replace_number(text)
- ipa = ipa_list(
- words_in=text,
- keep_punct=keep_punct,
- db_type=mode)
- if retrieve_all:
- return get_all(ipa)
- return get_top(ipa)
-
-
-
-_decimal_number_re = re.compile(r'\d+\,\d+')
-_euros_pre = re.compile(r'€([0-9\,]*[0-9]+)')
-_euros_re = re.compile(r'([0-9\,]*[0-9]+)€')
-_ordinal_re = re.compile(r'(der |die |das )([0-9]+)\.')
-_clock_re=re.compile(r'\d{1,2}\:\d{2}')
-_number_re = re.compile(r'[0-9]+')
-
-def base(text):
- text = text.replace("1", "eins ")
- text = text.replace("2", "zwei ")
- text = text.replace("3", "drei ")
- text = text.replace("4", "vier ")
- text = text.replace("5", "fünf ")
- text = text.replace("6", "sechs ")
- text = text.replace("7", "sieben ")
- text = text.replace("8", "acht ")
- text = text.replace("9", "neun ")
- text = text.replace("0", "null ")
- return text
-
-def tens_to_word(num):
- tens = num[0]
- ones = num[1]
- ones_word = base(ones)
-
- if num =="10":
- return "zehn"
- elif num=="11":
- return "elf"
- elif num=="12":
- return "zwölf"
-
- if tens == "1":
- if ones == "6":
- ones_word = ones_word[:-1]
- elif ones == "7":
- ones_word = ones_word[:-2]
- return ones_word + "zehn"
- else:
- tens_word = base(tens)
- if ones == "1":
- ones_word = ones_word[:-1]
- if tens == "2":
- tens_word = "zwan"
- elif tens == "6":
- tens_word = tens_word[:-1]
- elif tens == "7":
- tens_word = tens_word[:-2]
- if tens == "3":
- tens_word += "ßig"
- else:
- tens_word += "zig"
- if ones == "0":
- return tens_word
- else:
- return ones_word + " und " + tens_word
-
-def huns_to_word(num):
- huns = num[0]
- tens = num[1]
-
- if huns == "1":
- huns_word= "hundert"
- else:
- huns_word = base(huns)+" hundert"
-
- remain = num_to_word(num[1:])
- if remain != "":
- remain = " " + remain
- return huns_word + remain
-
-def thos_to_word(num):
- thos = num[0]
- if thos == "1":
- thos_word= "tausend"
- else:
- thos_word = base(thos)+" tausend"
- remain=num_to_word(num[1:])
- if remain!="":
- remain=" "+remain
- return thos_word+remain
-
-def num_to_word(num):
- num=num.lstrip("0")
- if num=="":
- return("")
- digit=len(num)
- if digit==1:
- return base(num)
- elif digit==2:
- return tens_to_word(num)
- elif digit == 3:
- return huns_to_word(num)
- elif digit == 4:
- return thos_to_word(num)
- else:
- return base(num)
-
-def number_to_words(m):
- m=m.group(0).lstrip("0")
- if m=="":
- return"null"
- return num_to_word(m)
-
-def _expand_ordinal(m):
-
- pre=m.group(1)
- m = m.group(2).lstrip("0")
-
- if m=="":
- return"NULL"
- num=int(m)
- if num<=19 & num>=1:
- if num ==1:
- return "erste"
- elif num==3:
- return "dritte"
- elif num==7:
- return "siebte"
- elif num==8:
- return "achte"
- else:
- return pre + num_to_word(m) + "te"
- else:
- return pre + num_to_word(m) + "ste"
-
-def _expand_decimal(m):
- match=m.group(0)
- parts = match.split(',')
- if int(parts[0])==0:
- return '%s komma %s' % ("null", base(parts[1]))
- return '%s komma %s' % (num_to_word(parts[0]),base(parts[1]))
-
-def _expand_euros(m):
- match = m.group(1)
- parts = match.split(',')
- if len(parts) > 2:
- return match + ' euro' # Unexpected format
- euros = int(parts[0]) if parts[0] else 0
- cents = int(parts[1])*10 if len(parts) > 1 and parts[1] else 0
- if euros and cents:
- return '%s euro %s' % (euros, cents)
- elif euros:
- return '%s euro' % (euros)
- elif cents:
- return '%s cent' % (cents)
- else:
- return 'null euro'
-
-def _expand_clock(m):
- match = m.group(0)
- parts = match.split(':')
- if int(parts[0]) == 0:
- return '%s Uhr %s' % ("null",num_to_word(parts[1]))
- elif int(parts[0]) == 1:
- return '%s Uhr %s' % ("ein", num_to_word(parts[1]))
- return '%s Uhr %s' % (num_to_word(parts[0]),num_to_word(parts[1]))
-
-def normalize_numbers(text):
- text = re.sub(_euros_pre, _expand_euros, text)
- text = re.sub(_euros_re, _expand_euros, text)
- text = re.sub(_clock_re, _expand_clock, text)
- text = re.sub(_decimal_number_re, _expand_decimal, text)
- text = re.sub(_ordinal_re, _expand_ordinal, text)
- text = re.sub(_number_re, number_to_words, text)
- text=text.replace(" "," ")
- return text
-
-def collapse_whitespace(text):
- return re.sub(r'\s+', ' ', text)
-
-def mark_dark_l(text):
- return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
diff --git a/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py b/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py
deleted file mode 100644
index 1519b3095bc5250016066754ebe6c0328773f24a..0000000000000000000000000000000000000000
--- a/spaces/ChrisCaviar/ControlNet-v1-1/app_normal.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from utils import randomize_seed_fn
-
-
-def create_demo(process, max_images=12, default_num_images=3):
- with gr.Blocks() as demo:
- with gr.Row():
- with gr.Column():
- image = gr.Image()
- prompt = gr.Textbox(label='Prompt')
- run_button = gr.Button('Run')
- with gr.Accordion('Advanced options', open=False):
- preprocessor_name = gr.Radio(label='Preprocessor',
- choices=['NormalBae', 'None'],
- type='value',
- value='NormalBae')
- num_samples = gr.Slider(label='Images',
- minimum=1,
- maximum=max_images,
- value=default_num_images,
- step=1)
- image_resolution = gr.Slider(label='Image resolution',
- minimum=256,
- maximum=512,
- value=512,
- step=256)
- preprocess_resolution = gr.Slider(
- label='Preprocess resolution',
- minimum=128,
- maximum=512,
- value=384,
- step=1)
- num_steps = gr.Slider(label='Number of steps',
- minimum=1,
- maximum=100,
- value=20,
- step=1)
- guidance_scale = gr.Slider(label='Guidance scale',
- minimum=0.1,
- maximum=30.0,
- value=9.0,
- step=0.1)
- seed = gr.Slider(label='Seed',
- minimum=0,
- maximum=1000000,
- step=1,
- value=0,
- randomize=True)
- randomize_seed = gr.Checkbox(label='Randomize seed',
- value=True)
- a_prompt = gr.Textbox(
- label='Additional prompt',
- value='best quality, extremely detailed')
- n_prompt = gr.Textbox(
- label='Negative prompt',
- value=
- 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
- )
- with gr.Column():
- result = gr.Gallery(label='Output', show_label=False).style(
- columns=2, object_fit='scale-down')
- inputs = [
- image,
- prompt,
- a_prompt,
- n_prompt,
- num_samples,
- image_resolution,
- preprocess_resolution,
- num_steps,
- guidance_scale,
- seed,
- preprocessor_name,
- ]
- prompt.submit(
- fn=randomize_seed_fn,
- inputs=[seed, randomize_seed],
- outputs=seed,
- ).then(
- fn=process,
- inputs=inputs,
- outputs=result,
- )
- run_button.click(
- fn=randomize_seed_fn,
- inputs=[seed, randomize_seed],
- outputs=seed,
- ).then(
- fn=process,
- inputs=inputs,
- outputs=result,
- api_name='normal',
- )
- return demo
-
-
-if __name__ == '__main__':
- from model import Model
- model = Model(task_name='NormalBae')
- demo = create_demo(model.process_normal)
- demo.queue().launch()
diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py
deleted file mode 100644
index 0a44b37fc5563deea8406c6ddf109860a56e9485..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/meme_generator/memes/knock/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from PIL.Image import Image as IMG
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.utils import save_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def knock(images: List[BuildImage], texts, args):
- img = images[0].convert("RGBA").square()
- # fmt: off
- locs = [(60, 308, 210, 195), (60, 308, 210, 198), (45, 330, 250, 172), (58, 320, 218, 180),
- (60, 310, 215, 193), (40, 320, 250, 285), (48, 308, 226, 192), (51, 301, 223, 200)]
- # fmt: on
- frames: List[IMG] = []
- for i in range(8):
- frame = BuildImage.open(img_dir / f"{i}.png")
- x, y, w, h = locs[i]
- frame.paste(img.resize((w, h)), (x, y), below=True)
- frames.append(frame.image)
- return save_gif(frames, 0.04)
-
-
-add_meme("knock", knock, min_images=1, max_images=1, keywords=["敲"])
diff --git a/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py b/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py
deleted file mode 100644
index 37f310fadd0b48b2f364877158fb2105d645fc03..0000000000000000000000000000000000000000
--- a/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import locale
-import json
-import os
-
-
-def load_language_list(language):
- with open(f"./i18n/{language}.json", "r", encoding="utf-8") as f:
- language_list = json.load(f)
- return language_list
-
-
-class I18nAuto:
- def __init__(self, language=None):
- if language in ["Auto", None]:
- language = locale.getdefaultlocale()[
- 0
- ] # getlocale can't identify the system's language ((None, None))
- if not os.path.exists(f"./i18n/{language}.json"):
- language = "en_US"
- self.language = language
- # print("Use Language:", language)
- self.language_map = load_language_list(language)
-
- def __call__(self, key):
- return self.language_map.get(key, key)
-
- def print(self):
- print("Use Language:", self.language)
diff --git a/spaces/Codecooker/rvcapi/src/mdx.py b/spaces/Codecooker/rvcapi/src/mdx.py
deleted file mode 100644
index 575c456fbdf9e5b5955401f41a3a58ddc27267b3..0000000000000000000000000000000000000000
--- a/spaces/Codecooker/rvcapi/src/mdx.py
+++ /dev/null
@@ -1,287 +0,0 @@
-import gc
-import hashlib
-import os
-import queue
-import threading
-import warnings
-
-import librosa
-import numpy as np
-import onnxruntime as ort
-import soundfile as sf
-import torch
-from tqdm import tqdm
-
-warnings.filterwarnings("ignore")
-stem_naming = {'Vocals': 'Instrumental', 'Other': 'Instruments', 'Instrumental': 'Vocals', 'Drums': 'Drumless', 'Bass': 'Bassless'}
-
-
-class MDXModel:
- def __init__(self, device, dim_f, dim_t, n_fft, hop=1024, stem_name=None, compensation=1.000):
- self.dim_f = dim_f
- self.dim_t = dim_t
- self.dim_c = 4
- self.n_fft = n_fft
- self.hop = hop
- self.stem_name = stem_name
- self.compensation = compensation
-
- self.n_bins = self.n_fft // 2 + 1
- self.chunk_size = hop * (self.dim_t - 1)
- self.window = torch.hann_window(window_length=self.n_fft, periodic=True).to(device)
-
- out_c = self.dim_c
-
- self.freq_pad = torch.zeros([1, out_c, self.n_bins - self.dim_f, self.dim_t]).to(device)
-
- def stft(self, x):
- x = x.reshape([-1, self.chunk_size])
- x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True, return_complex=True)
- x = torch.view_as_real(x)
- x = x.permute([0, 3, 1, 2])
- x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 4, self.n_bins, self.dim_t])
- return x[:, :, :self.dim_f]
-
- def istft(self, x, freq_pad=None):
- freq_pad = self.freq_pad.repeat([x.shape[0], 1, 1, 1]) if freq_pad is None else freq_pad
- x = torch.cat([x, freq_pad], -2)
- # c = 4*2 if self.target_name=='*' else 2
- x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape([-1, 2, self.n_bins, self.dim_t])
- x = x.permute([0, 2, 3, 1])
- x = x.contiguous()
- x = torch.view_as_complex(x)
- x = torch.istft(x, n_fft=self.n_fft, hop_length=self.hop, window=self.window, center=True)
- return x.reshape([-1, 2, self.chunk_size])
-
-
-class MDX:
- DEFAULT_SR = 44100
- # Unit: seconds
- DEFAULT_CHUNK_SIZE = 0 * DEFAULT_SR
- DEFAULT_MARGIN_SIZE = 1 * DEFAULT_SR
-
- DEFAULT_PROCESSOR = 0
-
- def __init__(self, model_path: str, params: MDXModel, processor=DEFAULT_PROCESSOR):
-
- # Set the device and the provider (CPU or CUDA)
- self.device = torch.device(f'cuda:{processor}') if processor >= 0 else torch.device('cpu')
- self.provider = ['CUDAExecutionProvider'] if processor >= 0 else ['CPUExecutionProvider']
-
- self.model = params
-
- # Load the ONNX model using ONNX Runtime
- self.ort = ort.InferenceSession(model_path, providers=self.provider)
- # Preload the model for faster performance
- self.ort.run(None, {'input': torch.rand(1, 4, params.dim_f, params.dim_t).numpy()})
- self.process = lambda spec: self.ort.run(None, {'input': spec.cpu().numpy()})[0]
-
- self.prog = None
-
- @staticmethod
- def get_hash(model_path):
- try:
- with open(model_path, 'rb') as f:
- f.seek(- 10000 * 1024, 2)
- model_hash = hashlib.md5(f.read()).hexdigest()
- except:
- model_hash = hashlib.md5(open(model_path, 'rb').read()).hexdigest()
-
- return model_hash
-
- @staticmethod
- def segment(wave, combine=True, chunk_size=DEFAULT_CHUNK_SIZE, margin_size=DEFAULT_MARGIN_SIZE):
- """
- Segment or join segmented wave array
-
- Args:
- wave: (np.array) Wave array to be segmented or joined
- combine: (bool) If True, combines segmented wave array. If False, segments wave array.
- chunk_size: (int) Size of each segment (in samples)
- margin_size: (int) Size of margin between segments (in samples)
-
- Returns:
- numpy array: Segmented or joined wave array
- """
-
- if combine:
- processed_wave = None # Initializing as None instead of [] for later numpy array concatenation
- for segment_count, segment in enumerate(wave):
- start = 0 if segment_count == 0 else margin_size
- end = None if segment_count == len(wave) - 1 else -margin_size
- if margin_size == 0:
- end = None
- if processed_wave is None: # Create array for first segment
- processed_wave = segment[:, start:end]
- else: # Concatenate to existing array for subsequent segments
- processed_wave = np.concatenate((processed_wave, segment[:, start:end]), axis=-1)
-
- else:
- processed_wave = []
- sample_count = wave.shape[-1]
-
- if chunk_size <= 0 or chunk_size > sample_count:
- chunk_size = sample_count
-
- if margin_size > chunk_size:
- margin_size = chunk_size
-
- for segment_count, skip in enumerate(range(0, sample_count, chunk_size)):
-
- margin = 0 if segment_count == 0 else margin_size
- end = min(skip + chunk_size + margin_size, sample_count)
- start = skip - margin
-
- cut = wave[:, start:end].copy()
- processed_wave.append(cut)
-
- if end == sample_count:
- break
-
- return processed_wave
-
- def pad_wave(self, wave):
- """
- Pad the wave array to match the required chunk size
-
- Args:
- wave: (np.array) Wave array to be padded
-
- Returns:
- tuple: (padded_wave, pad, trim)
- - padded_wave: Padded wave array
- - pad: Number of samples that were padded
- - trim: Number of samples that were trimmed
- """
- n_sample = wave.shape[1]
- trim = self.model.n_fft // 2
- gen_size = self.model.chunk_size - 2 * trim
- pad = gen_size - n_sample % gen_size
-
- # Padded wave
- wave_p = np.concatenate((np.zeros((2, trim)), wave, np.zeros((2, pad)), np.zeros((2, trim))), 1)
-
- mix_waves = []
- for i in range(0, n_sample + pad, gen_size):
- waves = np.array(wave_p[:, i:i + self.model.chunk_size])
- mix_waves.append(waves)
-
- mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(self.device)
-
- return mix_waves, pad, trim
-
- def _process_wave(self, mix_waves, trim, pad, q: queue.Queue, _id: int):
- """
- Process each wave segment in a multi-threaded environment
-
- Args:
- mix_waves: (torch.Tensor) Wave segments to be processed
- trim: (int) Number of samples trimmed during padding
- pad: (int) Number of samples padded during padding
- q: (queue.Queue) Queue to hold the processed wave segments
- _id: (int) Identifier of the processed wave segment
-
- Returns:
- numpy array: Processed wave segment
- """
- mix_waves = mix_waves.split(1)
- with torch.no_grad():
- pw = []
- for mix_wave in mix_waves:
- self.prog.update()
- spec = self.model.stft(mix_wave)
- processed_spec = torch.tensor(self.process(spec))
- processed_wav = self.model.istft(processed_spec.to(self.device))
- processed_wav = processed_wav[:, :, trim:-trim].transpose(0, 1).reshape(2, -1).cpu().numpy()
- pw.append(processed_wav)
- processed_signal = np.concatenate(pw, axis=-1)[:, :-pad]
- q.put({_id: processed_signal})
- return processed_signal
-
- def process_wave(self, wave: np.array, mt_threads=1):
- """
- Process the wave array in a multi-threaded environment
-
- Args:
- wave: (np.array) Wave array to be processed
- mt_threads: (int) Number of threads to be used for processing
-
- Returns:
- numpy array: Processed wave array
- """
- self.prog = tqdm(total=0)
- chunk = wave.shape[-1] // mt_threads
- waves = self.segment(wave, False, chunk)
-
- # Create a queue to hold the processed wave segments
- q = queue.Queue()
- threads = []
- for c, batch in enumerate(waves):
- mix_waves, pad, trim = self.pad_wave(batch)
- self.prog.total = len(mix_waves) * mt_threads
- thread = threading.Thread(target=self._process_wave, args=(mix_waves, trim, pad, q, c))
- thread.start()
- threads.append(thread)
- for thread in threads:
- thread.join()
- self.prog.close()
-
- processed_batches = []
- while not q.empty():
- processed_batches.append(q.get())
- processed_batches = [list(wave.values())[0] for wave in
- sorted(processed_batches, key=lambda d: list(d.keys())[0])]
- assert len(processed_batches) == len(waves), 'Incomplete processed batches, please reduce batch size!'
- return self.segment(processed_batches, True, chunk)
-
-
-def run_mdx(model_params, output_dir, model_path, filename, exclude_main=False, exclude_inversion=False, suffix=None, invert_suffix=None, denoise=False, keep_orig=True, m_threads=2):
- device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
-
- device_properties = torch.cuda.get_device_properties(device)
- vram_gb = device_properties.total_memory / 1024**3
- m_threads = 1 if vram_gb < 8 else 2
-
- model_hash = MDX.get_hash(model_path)
- mp = model_params.get(model_hash)
- model = MDXModel(
- device,
- dim_f=mp["mdx_dim_f_set"],
- dim_t=2 ** mp["mdx_dim_t_set"],
- n_fft=mp["mdx_n_fft_scale_set"],
- stem_name=mp["primary_stem"],
- compensation=mp["compensate"]
- )
-
- mdx_sess = MDX(model_path, model)
- wave, sr = librosa.load(filename, mono=False, sr=44100)
- # normalizing input wave gives better output
- peak = max(np.max(wave), abs(np.min(wave)))
- wave /= peak
- if denoise:
- wave_processed = -(mdx_sess.process_wave(-wave, m_threads)) + (mdx_sess.process_wave(wave, m_threads))
- wave_processed *= 0.5
- else:
- wave_processed = mdx_sess.process_wave(wave, m_threads)
- # return to previous peak
- wave_processed *= peak
- stem_name = model.stem_name if suffix is None else suffix
-
- main_filepath = None
- if not exclude_main:
- main_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav")
- sf.write(main_filepath, wave_processed.T, sr)
-
- invert_filepath = None
- if not exclude_inversion:
- diff_stem_name = stem_naming.get(stem_name) if invert_suffix is None else invert_suffix
- stem_name = f"{stem_name}_diff" if diff_stem_name is None else diff_stem_name
- invert_filepath = os.path.join(output_dir, f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav")
- sf.write(invert_filepath, (-wave_processed.T * model.compensation) + wave.T, sr)
-
- if not keep_orig:
- os.remove(filename)
-
- del mdx_sess, wave_processed, wave
- gc.collect()
- return main_filepath, invert_filepath
diff --git a/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py b/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py
deleted file mode 100644
index 76dce7e41c803a8055f3627cccb98deb51419b09..0000000000000000000000000000000000000000
--- a/spaces/Cvandi/remake/scripts/generate_meta_info_pairdata.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import argparse
-import glob
-import os
-
-
-def main(args):
- txt_file = open(args.meta_info, 'w')
- # sca images
- img_paths_gt = sorted(glob.glob(os.path.join(args.input[0], '*')))
- img_paths_lq = sorted(glob.glob(os.path.join(args.input[1], '*')))
-
- assert len(img_paths_gt) == len(img_paths_lq), ('GT folder and LQ folder should have the same length, but got '
- f'{len(img_paths_gt)} and {len(img_paths_lq)}.')
-
- for img_path_gt, img_path_lq in zip(img_paths_gt, img_paths_lq):
- # get the relative paths
- img_name_gt = os.path.relpath(img_path_gt, args.root[0])
- img_name_lq = os.path.relpath(img_path_lq, args.root[1])
- print(f'{img_name_gt}, {img_name_lq}')
- txt_file.write(f'{img_name_gt}, {img_name_lq}\n')
-
-
-if __name__ == '__main__':
- """This script is used to generate meta info (txt file) for paired images.
- """
- parser = argparse.ArgumentParser()
- parser.add_argument(
- '--input',
- nargs='+',
- default=['datasets/DF2K/DIV2K_train_HR_sub', 'datasets/DF2K/DIV2K_train_LR_bicubic_X4_sub'],
- help='Input folder, should be [gt_folder, lq_folder]')
- parser.add_argument('--root', nargs='+', default=[None, None], help='Folder root, will use the ')
- parser.add_argument(
- '--meta_info',
- type=str,
- default='datasets/DF2K/meta_info/meta_info_DIV2K_sub_pair.txt',
- help='txt path for meta info')
- args = parser.parse_args()
-
- assert len(args.input) == 2, 'Input folder should have two elements: gt folder and lq folder'
- assert len(args.root) == 2, 'Root path should have two elements: root for gt folder and lq folder'
- os.makedirs(os.path.dirname(args.meta_info), exist_ok=True)
- for i in range(2):
- if args.input[i].endswith('/'):
- args.input[i] = args.input[i][:-1]
- if args.root[i] is None:
- args.root[i] = os.path.dirname(args.input[i])
-
- main(args)
diff --git a/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py b/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py
deleted file mode 100644
index eb453a809d17e6dee04e158d1c68dc807478edef..0000000000000000000000000000000000000000
--- a/spaces/DEVINKofficial/Onodofthenorth-SD_PixelArt_SpriteSheet_Generator/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Onodofthenorth/SD_PixelArt_SpriteSheet_Generator").launch()
\ No newline at end of file
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py
deleted file mode 100644
index bafc43a19d432290867a5c08b9820f2e4f79aea3..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/GdImageFile.py
+++ /dev/null
@@ -1,97 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# GD file handling
-#
-# History:
-# 1996-04-12 fl Created
-#
-# Copyright (c) 1997 by Secret Labs AB.
-# Copyright (c) 1996 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-"""
-.. note::
- This format cannot be automatically recognized, so the
- class is not registered for use with :py:func:`PIL.Image.open()`. To open a
- gd file, use the :py:func:`PIL.GdImageFile.open()` function instead.
-
-.. warning::
- THE GD FORMAT IS NOT DESIGNED FOR DATA INTERCHANGE. This
- implementation is provided for convenience and demonstrational
- purposes only.
-"""
-
-
-from . import ImageFile, ImagePalette, UnidentifiedImageError
-from ._binary import i16be as i16
-from ._binary import i32be as i32
-
-
-class GdImageFile(ImageFile.ImageFile):
- """
- Image plugin for the GD uncompressed format. Note that this format
- is not supported by the standard :py:func:`PIL.Image.open()` function. To use
- this plugin, you have to import the :py:mod:`PIL.GdImageFile` module and
- use the :py:func:`PIL.GdImageFile.open()` function.
- """
-
- format = "GD"
- format_description = "GD uncompressed images"
-
- def _open(self):
- # Header
- s = self.fp.read(1037)
-
- if i16(s) not in [65534, 65535]:
- msg = "Not a valid GD 2.x .gd file"
- raise SyntaxError(msg)
-
- self.mode = "L" # FIXME: "P"
- self._size = i16(s, 2), i16(s, 4)
-
- true_color = s[6]
- true_color_offset = 2 if true_color else 0
-
- # transparency index
- tindex = i32(s, 7 + true_color_offset)
- if tindex < 256:
- self.info["transparency"] = tindex
-
- self.palette = ImagePalette.raw(
- "XBGR", s[7 + true_color_offset + 4 : 7 + true_color_offset + 4 + 256 * 4]
- )
-
- self.tile = [
- (
- "raw",
- (0, 0) + self.size,
- 7 + true_color_offset + 4 + 256 * 4,
- ("L", 0, 1),
- )
- ]
-
-
-def open(fp, mode="r"):
- """
- Load texture from a GD image file.
-
- :param fp: GD file name, or an opened file handle.
- :param mode: Optional mode. In this version, if the mode argument
- is given, it must be "r".
- :returns: An image instance.
- :raises OSError: If the image could not be read.
- """
- if mode != "r":
- msg = "bad mode"
- raise ValueError(msg)
-
- try:
- return GdImageFile(fp)
- except SyntaxError as e:
- msg = "cannot identify this image file"
- raise UnidentifiedImageError(msg) from e
diff --git a/spaces/Dana19/ImageRecognition_FaceCount/README.md b/spaces/Dana19/ImageRecognition_FaceCount/README.md
deleted file mode 100644
index 8afdea6aa817a1ec3b056820a882b63deedc6772..0000000000000000000000000000000000000000
--- a/spaces/Dana19/ImageRecognition_FaceCount/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Headcount
-emoji: 💻
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.3
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/DemoLou/moe-tts/text/thai.py b/spaces/DemoLou/moe-tts/text/thai.py
deleted file mode 100644
index 998207c01a85c710a46db1ec8b62c39c2d94bc84..0000000000000000000000000000000000000000
--- a/spaces/DemoLou/moe-tts/text/thai.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import re
-from num_thai.thainumbers import NumThai
-
-
-num = NumThai()
-
-# List of (Latin alphabet, Thai) pairs:
-_latin_to_thai = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
- ('a', 'เอ'),
- ('b','บี'),
- ('c','ซี'),
- ('d','ดี'),
- ('e','อี'),
- ('f','เอฟ'),
- ('g','จี'),
- ('h','เอช'),
- ('i','ไอ'),
- ('j','เจ'),
- ('k','เค'),
- ('l','แอล'),
- ('m','เอ็ม'),
- ('n','เอ็น'),
- ('o','โอ'),
- ('p','พี'),
- ('q','คิว'),
- ('r','แอร์'),
- ('s','เอส'),
- ('t','ที'),
- ('u','ยู'),
- ('v','วี'),
- ('w','ดับเบิลยู'),
- ('x','เอ็กซ์'),
- ('y','วาย'),
- ('z','ซี')
-]]
-
-
-def num_to_thai(text):
- return re.sub(r'(?:\d+(?:,?\d+)?)+(?:\.\d+(?:,?\d+)?)?', lambda x: ''.join(num.NumberToTextThai(float(x.group(0).replace(',', '')))), text)
-
-def latin_to_thai(text):
- for regex, replacement in _latin_to_thai:
- text = re.sub(regex, replacement, text)
- return text
diff --git a/spaces/Detomo/AI-Galary/README.md b/spaces/Detomo/AI-Galary/README.md
deleted file mode 100644
index 5772a1eb261aa38b6407c07b0633d4a12566eccf..0000000000000000000000000000000000000000
--- a/spaces/Detomo/AI-Galary/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AI Galary
-emoji: ⚡
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Detomo/generate_wifi_qrcode/app.py b/spaces/Detomo/generate_wifi_qrcode/app.py
deleted file mode 100644
index e6681ad55d07851290ef2f0f04f1e026a95b917a..0000000000000000000000000000000000000000
--- a/spaces/Detomo/generate_wifi_qrcode/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import wifi_qrcode_generator
-import gradio as gr
-
-examples = [['Home wifi', 'very complicated password', 'WPA']]
-
-def infer(wifi_name, wifi_pw, authentication_type):
- image = wifi_qrcode_generator.wifi_qrcode(
- wifi_name, False, authentication_type, wifi_pw
- )
- image.save('qrcode_wifi.png')
- return 'qrcode_wifi.png'
-
-description= "Generate code for accessing WIFI. Fill your wifi name, wifi password and select authentication type. Then click submit and enjoy 😎"
-
-iface = gr.Interface(
- fn=infer,
- title="Generate QR code for accessing WIFI 🥃",
- description=description,
- inputs=[
- gr.Textbox(
- label="Wifi name",
- lines=1,
- ),
- gr.Textbox(
- label="Wifi password",
- lines=1,
- ),
- gr.Dropdown(
- label='authentication_type',
- choices=["WPA", "WEP", "nopass"],
- value='WPA'
- ),
- ],
- outputs="image",
- examples=examples,
- article="Author: Vu Minh Chien", cache_examples=True).launch(enable_queue=True)
diff --git a/spaces/DiffusionArtco/scifi-art-creator/app.py b/spaces/DiffusionArtco/scifi-art-creator/app.py
deleted file mode 100644
index 236417b745e1681f1a9e1ae644d75254da2de370..0000000000000000000000000000000000000000
--- a/spaces/DiffusionArtco/scifi-art-creator/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import gradio as gr
-import requests
-from PIL import Image
-from io import BytesIO
-import base64
-
-api_url = "https://5cb20b40-572c-426f-9466-995256f9b6eb.id.repl.co/generate_image"
-
-def generate_image(model="Sci-Fi Diffusion", prompt="", seed=0, negative_prompt="", sampler="k_dpmpp_2s_a", steps=50):
- data = "?model=" + model + "&prompt=" + prompt + "&seed=" + str(seed) + "&negative_prompt=" + negative_prompt + "&sampler=" + sampler + "&steps=" + str(steps)
- response = requests.post(api_url + data, timeout=400)
- if response.status_code == 200:
- img_base64 = response.json()["url"]
- img_bytes = base64.b64decode(img_base64)
- img = Image.open(BytesIO(img_bytes))
- return img
- else:
- return None
-
-inputs = [
- gr.inputs.Dropdown([ 'Experience', 'FKing SciFi', 'Future Diffusion', 'JWST Deep Space Diffusion', 'Protogen Infinity', 'RCNZ Dumb Monkey', 'RealBiter', 'Robo-Diffusion', 'Sci-Fi Diffusion'], label="Model", default="Sci-Fi Diffusion"),
- gr.inputs.Textbox(label="Prompt"),
- gr.inputs.Number(label="Seed", default=0),
- gr.inputs.Textbox(label="Negative Prompt", default=""),
- gr.inputs.Dropdown(["k_lms", "k_heun", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "DDIM", "k_dpm_fast", "k_dpm_adaptive", "k_dpmpp_2m", "k_dpmpp_2s_a", "k_dpmpp_sde"], label="Sampler", default="k_dpmpp_2s_a"),
- gr.inputs.Number(label="Steps", default=50)
-]
-
-outputs = gr.outputs.Image(label="Generated Image", type="pil")
-
-interface = gr.Interface(generate_image, inputs, outputs, title="",
- description="
-#include
-#include
-#include "filtered_lrelu.h"
-
-//------------------------------------------------------------------------
-
-static std::tuple filtered_lrelu(
- torch::Tensor x, torch::Tensor fu, torch::Tensor fd, torch::Tensor b, torch::Tensor si,
- int up, int down, int px0, int px1, int py0, int py1, int sx, int sy, float gain, float slope, float clamp, bool flip_filters, bool writeSigns)
-{
- // Set CUDA device.
- TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
- const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-
- // Validate arguments.
- TORCH_CHECK(fu.device() == x.device() && fd.device() == x.device() && b.device() == x.device(), "all input tensors must reside on the same device");
- TORCH_CHECK(fu.dtype() == torch::kFloat && fd.dtype() == torch::kFloat, "fu and fd must be float32");
- TORCH_CHECK(b.dtype() == x.dtype(), "x and b must have the same dtype");
- TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat, "x and b must be float16 or float32");
- TORCH_CHECK(x.dim() == 4, "x must be rank 4");
- TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
- TORCH_CHECK(x.numel() > 0, "x is empty");
- TORCH_CHECK((fu.dim() == 1 || fu.dim() == 2) && (fd.dim() == 1 || fd.dim() == 2), "fu and fd must be rank 1 or 2");
- TORCH_CHECK(fu.size(0) <= INT_MAX && fu.size(-1) <= INT_MAX, "fu is too large");
- TORCH_CHECK(fd.size(0) <= INT_MAX && fd.size(-1) <= INT_MAX, "fd is too large");
- TORCH_CHECK(fu.numel() > 0, "fu is empty");
- TORCH_CHECK(fd.numel() > 0, "fd is empty");
- TORCH_CHECK(b.dim() == 1 && b.size(0) == x.size(1), "b must be a vector with the same number of channels as x");
- TORCH_CHECK(up >= 1 && down >= 1, "up and down must be at least 1");
-
- // Figure out how much shared memory is available on the device.
- int maxSharedBytes = 0;
- AT_CUDA_CHECK(cudaDeviceGetAttribute(&maxSharedBytes, cudaDevAttrMaxSharedMemoryPerBlockOptin, x.device().index()));
- int sharedKB = maxSharedBytes >> 10;
-
- // Populate enough launch parameters to check if a CUDA kernel exists.
- filtered_lrelu_kernel_params p;
- p.up = up;
- p.down = down;
- p.fuShape = make_int2((int)fu.size(-1), fu.dim() == 2 ? (int)fu.size(0) : 0); // shape [n, 0] indicates separable filter.
- p.fdShape = make_int2((int)fd.size(-1), fd.dim() == 2 ? (int)fd.size(0) : 0);
- filtered_lrelu_kernel_spec test_spec = choose_filtered_lrelu_kernel(p, sharedKB);
- if (!test_spec.exec)
- {
- // No kernel found - return empty tensors and indicate missing kernel with return code of -1.
- return std::make_tuple(torch::Tensor(), torch::Tensor(), -1);
- }
-
- // Input/output element size.
- int64_t sz = (x.dtype() == torch::kHalf) ? 2 : 4;
-
- // Input sizes.
- int64_t xw = (int)x.size(3);
- int64_t xh = (int)x.size(2);
- int64_t fut_w = (int)fu.size(-1) - 1;
- int64_t fut_h = (int)fu.size(0) - 1;
- int64_t fdt_w = (int)fd.size(-1) - 1;
- int64_t fdt_h = (int)fd.size(0) - 1;
-
- // Logical size of upsampled buffer.
- int64_t cw = xw * up + (px0 + px1) - fut_w;
- int64_t ch = xh * up + (py0 + py1) - fut_h;
- TORCH_CHECK(cw > fdt_w && ch > fdt_h, "upsampled buffer must be at least the size of downsampling filter");
- TORCH_CHECK(cw <= INT_MAX && ch <= INT_MAX, "upsampled buffer is too large");
-
- // Compute output size and allocate.
- int64_t yw = (cw - fdt_w + (down - 1)) / down;
- int64_t yh = (ch - fdt_h + (down - 1)) / down;
- TORCH_CHECK(yw > 0 && yh > 0, "output must be at least 1x1");
- TORCH_CHECK(yw <= INT_MAX && yh <= INT_MAX, "output is too large");
- torch::Tensor y = torch::empty({x.size(0), x.size(1), yh, yw}, x.options(), x.suggest_memory_format());
-
- // Allocate sign tensor.
- torch::Tensor so;
- torch::Tensor s = si;
- bool readSigns = !!s.numel();
- int64_t sw_active = 0; // Active width of sign tensor.
- if (writeSigns)
- {
- sw_active = yw * down - (down - 1) + fdt_w; // Active width in elements.
- int64_t sh = yh * down - (down - 1) + fdt_h; // Height = active height.
- int64_t sw = (sw_active + 15) & ~15; // Width = active width in elements, rounded up to multiple of 16.
- TORCH_CHECK(sh <= INT_MAX && (sw >> 2) <= INT_MAX, "signs is too large");
- s = so = torch::empty({x.size(0), x.size(1), sh, sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
- }
- else if (readSigns)
- sw_active = s.size(3) << 2;
-
- // Validate sign tensor if in use.
- if (readSigns || writeSigns)
- {
- TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
- TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
- TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
- TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
- TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
- TORCH_CHECK(s.size(2) <= INT_MAX && s.size(3) <= INT_MAX, "signs is too large");
- }
-
- // Populate rest of CUDA kernel parameters.
- p.x = x.data_ptr();
- p.y = y.data_ptr();
- p.b = b.data_ptr();
- p.s = (readSigns || writeSigns) ? s.data_ptr() : 0;
- p.fu = fu.data_ptr();
- p.fd = fd.data_ptr();
- p.pad0 = make_int2(px0, py0);
- p.gain = gain;
- p.slope = slope;
- p.clamp = clamp;
- p.flip = (flip_filters) ? 1 : 0;
- p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
- p.yShape = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
- p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3), (int)s.size(2)) : make_int2(0, 0); // Width is in bytes. Contiguous.
- p.sOfs = make_int2(sx, sy);
- p.swLimit = (sw_active + 3) >> 2; // Rounded up to bytes.
-
- // x, y, b strides are in bytes.
- p.xStride = make_longlong4(sz * x.stride(3), sz * x.stride(2), sz * x.stride(1), sz * x.stride(0));
- p.yStride = make_longlong4(sz * y.stride(3), sz * y.stride(2), sz * y.stride(1), sz * y.stride(0));
- p.bStride = sz * b.stride(0);
-
- // fu, fd strides are in elements.
- p.fuStride = make_longlong3(fu.stride(-1), fu.dim() == 2 ? fu.stride(0) : 0, 0);
- p.fdStride = make_longlong3(fd.stride(-1), fd.dim() == 2 ? fd.stride(0) : 0, 0);
-
- // Determine if indices don't fit in int32. Support negative strides although Torch currently never produces those.
- bool index64b = false;
- if (std::abs(p.bStride * x.size(1)) > INT_MAX) index64b = true;
- if (std::min(x.size(0) * p.xStride.w, 0ll) + std::min(x.size(1) * p.xStride.z, 0ll) + std::min(x.size(2) * p.xStride.y, 0ll) + std::min(x.size(3) * p.xStride.x, 0ll) < -INT_MAX) index64b = true;
- if (std::max(x.size(0) * p.xStride.w, 0ll) + std::max(x.size(1) * p.xStride.z, 0ll) + std::max(x.size(2) * p.xStride.y, 0ll) + std::max(x.size(3) * p.xStride.x, 0ll) > INT_MAX) index64b = true;
- if (std::min(y.size(0) * p.yStride.w, 0ll) + std::min(y.size(1) * p.yStride.z, 0ll) + std::min(y.size(2) * p.yStride.y, 0ll) + std::min(y.size(3) * p.yStride.x, 0ll) < -INT_MAX) index64b = true;
- if (std::max(y.size(0) * p.yStride.w, 0ll) + std::max(y.size(1) * p.yStride.z, 0ll) + std::max(y.size(2) * p.yStride.y, 0ll) + std::max(y.size(3) * p.yStride.x, 0ll) > INT_MAX) index64b = true;
- if (s.numel() > INT_MAX) index64b = true;
-
- // Choose CUDA kernel.
- filtered_lrelu_kernel_spec spec = { 0 };
- AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_cuda", [&]
- {
- if constexpr (sizeof(scalar_t) <= 4) // Exclude doubles. constexpr prevents template instantiation.
- {
- // Choose kernel based on index type, datatype and sign read/write modes.
- if (!index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if (!index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if (!index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && !writeSigns && readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- else if ( index64b && !writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel(p, sharedKB);
- }
- });
- TORCH_CHECK(spec.exec, "internal error - CUDA kernel not found") // This should not happen because we tested earlier that kernel exists.
-
- // Launch CUDA kernel.
- void* args[] = {&p};
- int bx = spec.numWarps * 32;
- int gx = (p.yShape.x - 1) / spec.tileOut.x + 1;
- int gy = (p.yShape.y - 1) / spec.tileOut.y + 1;
- int gz = p.yShape.z * p.yShape.w;
-
- // Repeat multiple horizontal tiles in a CTA?
- if (spec.xrep)
- {
- p.tilesXrep = spec.xrep;
- p.tilesXdim = gx;
-
- gx = (gx + p.tilesXrep - 1) / p.tilesXrep;
- std::swap(gx, gy);
- }
- else
- {
- p.tilesXrep = 0;
- p.tilesXdim = 0;
- }
-
- // Launch filter setup kernel.
- AT_CUDA_CHECK(cudaLaunchKernel(spec.setup, 1, 1024, args, 0, at::cuda::getCurrentCUDAStream()));
-
- // Copy kernels to constant memory.
- if ( writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
- else if (!writeSigns && readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
- else if (!writeSigns && !readSigns) AT_CUDA_CHECK((copy_filters(at::cuda::getCurrentCUDAStream())));
-
- // Set cache and shared memory configurations for main kernel.
- AT_CUDA_CHECK(cudaFuncSetCacheConfig(spec.exec, cudaFuncCachePreferShared));
- if (spec.dynamicSharedKB) // Need dynamically allocated shared memory?
- AT_CUDA_CHECK(cudaFuncSetAttribute(spec.exec, cudaFuncAttributeMaxDynamicSharedMemorySize, spec.dynamicSharedKB << 10));
- AT_CUDA_CHECK(cudaFuncSetSharedMemConfig(spec.exec, cudaSharedMemBankSizeFourByte));
-
- // Launch main kernel.
- const int maxSubGz = 65535; // CUDA maximum for block z dimension.
- for (int zofs=0; zofs < gz; zofs += maxSubGz) // Do multiple launches if gz is too big.
- {
- p.blockZofs = zofs;
- int subGz = std::min(maxSubGz, gz - zofs);
- AT_CUDA_CHECK(cudaLaunchKernel(spec.exec, dim3(gx, gy, subGz), bx, args, spec.dynamicSharedKB << 10, at::cuda::getCurrentCUDAStream()));
- }
-
- // Done.
- return std::make_tuple(y, so, 0);
-}
-
-//------------------------------------------------------------------------
-
-static torch::Tensor filtered_lrelu_act(torch::Tensor x, torch::Tensor si, int sx, int sy, float gain, float slope, float clamp, bool writeSigns)
-{
- // Set CUDA device.
- TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
- const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-
- // Validate arguments.
- TORCH_CHECK(x.dim() == 4, "x must be rank 4");
- TORCH_CHECK(x.size(0) * x.size(1) <= INT_MAX && x.size(2) <= INT_MAX && x.size(3) <= INT_MAX, "x is too large");
- TORCH_CHECK(x.numel() > 0, "x is empty");
- TORCH_CHECK(x.dtype() == torch::kHalf || x.dtype() == torch::kFloat || x.dtype() == torch::kDouble, "x must be float16, float32 or float64");
-
- // Output signs if we don't have sign input.
- torch::Tensor so;
- torch::Tensor s = si;
- bool readSigns = !!s.numel();
- if (writeSigns)
- {
- int64_t sw = x.size(3);
- sw = (sw + 15) & ~15; // Round to a multiple of 16 for coalescing.
- s = so = torch::empty({x.size(0), x.size(1), x.size(2), sw >> 2}, x.options().dtype(torch::kUInt8), at::MemoryFormat::Contiguous);
- }
-
- // Validate sign tensor if in use.
- if (readSigns || writeSigns)
- {
- TORCH_CHECK(s.is_contiguous(), "signs must be contiguous");
- TORCH_CHECK(s.dtype() == torch::kUInt8, "signs must be uint8");
- TORCH_CHECK(s.device() == x.device(), "signs must reside on the same device as x");
- TORCH_CHECK(s.dim() == 4, "signs must be rank 4");
- TORCH_CHECK(s.size(0) == x.size(0) && s.size(1) == x.size(1), "signs must have same batch & channels as x");
- TORCH_CHECK(s.size(2) <= INT_MAX && (s.size(3) << 2) <= INT_MAX, "signs tensor is too large");
- }
-
- // Initialize CUDA kernel parameters.
- filtered_lrelu_act_kernel_params p;
- p.x = x.data_ptr();
- p.s = (readSigns || writeSigns) ? s.data_ptr() : 0;
- p.gain = gain;
- p.slope = slope;
- p.clamp = clamp;
- p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
- p.xStride = make_longlong4(x.stride(3), x.stride(2), x.stride(1), x.stride(0));
- p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3) << 2, (int)s.size(2)) : make_int2(0, 0); // Width is in elements. Contiguous.
- p.sOfs = make_int2(sx, sy);
-
- // Choose CUDA kernel.
- void* func = 0;
- AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_act_cuda", [&]
- {
- if (writeSigns)
- func = choose_filtered_lrelu_act_kernel();
- else if (readSigns)
- func = choose_filtered_lrelu_act_kernel();
- else
- func = choose_filtered_lrelu_act_kernel();
- });
- TORCH_CHECK(func, "internal error - CUDA kernel not found");
-
- // Launch CUDA kernel.
- void* args[] = {&p};
- int bx = 128; // 4 warps per block.
-
- // Logical size of launch = writeSigns ? p.s : p.x
- uint32_t gx = writeSigns ? p.sShape.x : p.xShape.x;
- uint32_t gy = writeSigns ? p.sShape.y : p.xShape.y;
- uint32_t gz = p.xShape.z * p.xShape.w; // Same as in p.sShape if signs are in use.
- gx = (gx - 1) / bx + 1;
-
- // Make sure grid y and z dimensions are within CUDA launch limits. Kernel loops internally to do the rest.
- const uint32_t gmax = 65535;
- gy = std::min(gy, gmax);
- gz = std::min(gz, gmax);
-
- // Launch.
- AT_CUDA_CHECK(cudaLaunchKernel(func, dim3(gx, gy, gz), bx, args, 0, at::cuda::getCurrentCUDAStream()));
- return so;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
- m.def("filtered_lrelu", &filtered_lrelu); // The whole thing.
- m.def("filtered_lrelu_act_", &filtered_lrelu_act); // Activation and sign tensor handling only. Modifies data tensor in-place.
-}
-
-//------------------------------------------------------------------------
diff --git a/spaces/EronSamez/RVC_HFmeu/gui_v0.py b/spaces/EronSamez/RVC_HFmeu/gui_v0.py
deleted file mode 100644
index 88c3cf9eb1eaa0fa812b32ae4d3750b4ce0a8699..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/gui_v0.py
+++ /dev/null
@@ -1,786 +0,0 @@
-import os, sys, traceback, re
-
-import json
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-from configs.config import Config
-
-Config = Config()
-import PySimpleGUI as sg
-import sounddevice as sd
-import noisereduce as nr
-import numpy as np
-from fairseq import checkpoint_utils
-import librosa, torch, pyworld, faiss, time, threading
-import torch.nn.functional as F
-import torchaudio.transforms as tat
-import scipy.signal as signal
-import torchcrepe
-
-# import matplotlib.pyplot as plt
-from lib.infer_pack.models import (
- SynthesizerTrnMs256NSFsid,
- SynthesizerTrnMs256NSFsid_nono,
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono,
-)
-from i18n import I18nAuto
-
-i18n = I18nAuto()
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-current_dir = os.getcwd()
-
-
-class RVC:
- def __init__(
- self, key, f0_method, hubert_path, pth_path, index_path, npy_path, index_rate
- ) -> None:
- """
- 初始化
- """
- try:
- self.f0_up_key = key
- self.time_step = 160 / 16000 * 1000
- self.f0_min = 50
- self.f0_max = 1100
- self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
- self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
- self.f0_method = f0_method
- self.sr = 16000
- self.window = 160
-
- # Get Torch Device
- if torch.cuda.is_available():
- self.torch_device = torch.device(
- f"cuda:{0 % torch.cuda.device_count()}"
- )
- elif torch.backends.mps.is_available():
- self.torch_device = torch.device("mps")
- else:
- self.torch_device = torch.device("cpu")
-
- if index_rate != 0:
- self.index = faiss.read_index(index_path)
- # self.big_npy = np.load(npy_path)
- self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
- print("index search enabled")
- self.index_rate = index_rate
- model_path = hubert_path
- print("load model(s) from {}".format(model_path))
- models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
- [model_path],
- suffix="",
- )
- self.model = models[0]
- self.model = self.model.to(device)
- if Config.is_half:
- self.model = self.model.half()
- else:
- self.model = self.model.float()
- self.model.eval()
- cpt = torch.load(pth_path, map_location="cpu")
- self.tgt_sr = cpt["config"][-1]
- cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
- self.if_f0 = cpt.get("f0", 1)
- self.version = cpt.get("version", "v1")
- if self.version == "v1":
- if self.if_f0 == 1:
- self.net_g = SynthesizerTrnMs256NSFsid(
- *cpt["config"], is_half=Config.is_half
- )
- else:
- self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
- elif self.version == "v2":
- if self.if_f0 == 1:
- self.net_g = SynthesizerTrnMs768NSFsid(
- *cpt["config"], is_half=Config.is_half
- )
- else:
- self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
- del self.net_g.enc_q
- print(self.net_g.load_state_dict(cpt["weight"], strict=False))
- self.net_g.eval().to(device)
- if Config.is_half:
- self.net_g = self.net_g.half()
- else:
- self.net_g = self.net_g.float()
- except:
- print(traceback.format_exc())
-
- def get_regular_crepe_computation(self, x, f0_min, f0_max, model="full"):
- batch_size = 512
- # Compute pitch using first gpu
- audio = torch.tensor(np.copy(x))[None].float()
- f0, pd = torchcrepe.predict(
- audio,
- self.sr,
- self.window,
- f0_min,
- f0_max,
- model,
- batch_size=batch_size,
- device=self.torch_device,
- return_periodicity=True,
- )
- pd = torchcrepe.filter.median(pd, 3)
- f0 = torchcrepe.filter.mean(f0, 3)
- f0[pd < 0.1] = 0
- f0 = f0[0].cpu().numpy()
- return f0
-
- def get_harvest_computation(self, x, f0_min, f0_max):
- f0, t = pyworld.harvest(
- x.astype(np.double),
- fs=self.sr,
- f0_ceil=f0_max,
- f0_floor=f0_min,
- frame_period=10,
- )
- f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
- f0 = signal.medfilt(f0, 3)
- return f0
-
- def get_f0(self, x, f0_up_key, inp_f0=None):
- # Calculate Padding and f0 details here
- p_len = x.shape[0] // 512 # For Now This probs doesn't work
- x_pad = 1
- f0_min = 50
- f0_max = 1100
- f0_mel_min = 1127 * np.log(1 + f0_min / 700)
- f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
- f0 = 0
- # Here, check f0_methods and get their computations
- if self.f0_method == "harvest":
- f0 = self.get_harvest_computation(x, f0_min, f0_max)
- elif self.f0_method == "reg-crepe":
- f0 = self.get_regular_crepe_computation(x, f0_min, f0_max)
- elif self.f0_method == "reg-crepe-tiny":
- f0 = self.get_regular_crepe_computation(x, f0_min, f0_max, "tiny")
-
- # Calculate f0_course and f0_bak here
- f0 *= pow(2, f0_up_key / 12)
- # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- tf0 = self.sr // self.window # 每秒f0点数
- if inp_f0 is not None:
- delta_t = np.round(
- (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
- ).astype("int16")
- replace_f0 = np.interp(
- list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
- )
- shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
- f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
- # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- f0bak = f0.copy()
- f0_mel = 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
- f0_mel_max - f0_mel_min
- ) + 1
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > 255] = 255
- f0_coarse = np.rint(f0_mel).astype(np.int)
- return f0_coarse, f0bak # 1-0
-
- def infer(self, feats: torch.Tensor) -> np.ndarray:
- """
- 推理函数
- """
- audio = feats.clone().cpu().numpy()
- assert feats.dim() == 1, feats.dim()
- feats = feats.view(1, -1)
- padding_mask = torch.BoolTensor(feats.shape).fill_(False)
- if Config.is_half:
- feats = feats.half()
- else:
- feats = feats.float()
- inputs = {
- "source": feats.to(device),
- "padding_mask": padding_mask.to(device),
- "output_layer": 9 if self.version == "v1" else 12,
- }
- torch.cuda.synchronize()
- with torch.no_grad():
- logits = self.model.extract_features(**inputs)
- feats = (
- self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
- )
-
- ####索引优化
- try:
- if (
- hasattr(self, "index")
- and hasattr(self, "big_npy")
- and self.index_rate != 0
- ):
- npy = feats[0].cpu().numpy().astype("float32")
- score, ix = self.index.search(npy, k=8)
- weight = np.square(1 / score)
- weight /= weight.sum(axis=1, keepdims=True)
- npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
- if Config.is_half:
- npy = npy.astype("float16")
- feats = (
- torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate
- + (1 - self.index_rate) * feats
- )
- else:
- print("index search FAIL or disabled")
- except:
- traceback.print_exc()
- print("index search FAIL")
- feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
- torch.cuda.synchronize()
- print(feats.shape)
- if self.if_f0 == 1:
- pitch, pitchf = self.get_f0(audio, self.f0_up_key)
- p_len = min(feats.shape[1], 13000, pitch.shape[0]) # 太大了爆显存
- else:
- pitch, pitchf = None, None
- p_len = min(feats.shape[1], 13000) # 太大了爆显存
- torch.cuda.synchronize()
- # print(feats.shape,pitch.shape)
- feats = feats[:, :p_len, :]
- if self.if_f0 == 1:
- pitch = pitch[:p_len]
- pitchf = pitchf[:p_len]
- pitch = torch.LongTensor(pitch).unsqueeze(0).to(device)
- pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device)
- p_len = torch.LongTensor([p_len]).to(device)
- ii = 0 # sid
- sid = torch.LongTensor([ii]).to(device)
- with torch.no_grad():
- if self.if_f0 == 1:
- infered_audio = (
- self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
- .data.cpu()
- .float()
- )
- else:
- infered_audio = (
- self.net_g.infer(feats, p_len, sid)[0][0, 0].data.cpu().float()
- )
- torch.cuda.synchronize()
- return infered_audio
-
-
-class GUIConfig:
- def __init__(self) -> None:
- self.hubert_path: str = ""
- self.pth_path: str = ""
- self.index_path: str = ""
- self.npy_path: str = ""
- self.f0_method: str = ""
- self.pitch: int = 12
- self.samplerate: int = 44100
- self.block_time: float = 1.0 # s
- self.buffer_num: int = 1
- self.threhold: int = -30
- self.crossfade_time: float = 0.08
- self.extra_time: float = 0.04
- self.I_noise_reduce = False
- self.O_noise_reduce = False
- self.index_rate = 0.3
-
-
-class GUI:
- def __init__(self) -> None:
- self.config = GUIConfig()
- self.flag_vc = False
-
- self.launcher()
-
- def load(self):
- (
- input_devices,
- output_devices,
- input_devices_indices,
- output_devices_indices,
- ) = self.get_devices()
- try:
- with open("values1.json", "r") as j:
- data = json.load(j)
- except:
- # Injecting f0_method into the json data
- with open("values1.json", "w") as j:
- data = {
- "pth_path": "",
- "index_path": "",
- "sg_input_device": input_devices[
- input_devices_indices.index(sd.default.device[0])
- ],
- "sg_output_device": output_devices[
- output_devices_indices.index(sd.default.device[1])
- ],
- "threhold": "-45",
- "pitch": "0",
- "index_rate": "0",
- "block_time": "1",
- "crossfade_length": "0.04",
- "extra_time": "1",
- }
- return data
-
- def launcher(self):
- data = self.load()
- sg.theme("DarkTeal12")
- input_devices, output_devices, _, _ = self.get_devices()
- layout = [
- [
- sg.Frame(
- title="Proudly forked by Mangio621",
- ),
- sg.Frame(
- title=i18n("Load model"),
- layout=[
- [
- sg.Input(
- default_text="hubert_base.pt",
- key="hubert_path",
- disabled=True,
- ),
- sg.FileBrowse(
- i18n("Hubert Model"),
- initial_folder=os.path.join(os.getcwd()),
- file_types=(("pt files", "*.pt"),),
- ),
- ],
- [
- sg.Input(
- default_text=data.get("pth_path", ""),
- key="pth_path",
- ),
- sg.FileBrowse(
- i18n("Select the .pth file"),
- initial_folder=os.path.join(os.getcwd(), "weights"),
- file_types=(("weight files", "*.pth"),),
- ),
- ],
- [
- sg.Input(
- default_text=data.get("index_path", ""),
- key="index_path",
- ),
- sg.FileBrowse(
- i18n("Select the .index file"),
- initial_folder=os.path.join(os.getcwd(), "logs"),
- file_types=(("index files", "*.index"),),
- ),
- ],
- [
- sg.Input(
- default_text="你不需要填写这个You don't need write this.",
- key="npy_path",
- disabled=True,
- ),
- sg.FileBrowse(
- i18n("Select the .npy file"),
- initial_folder=os.path.join(os.getcwd(), "logs"),
- file_types=(("feature files", "*.npy"),),
- ),
- ],
- ],
- ),
- ],
- [
- # Mangio f0 Selection frame Here
- sg.Frame(
- layout=[
- [
- sg.Radio(
- "Harvest", "f0_method", key="harvest", default=True
- ),
- sg.Radio("Crepe", "f0_method", key="reg-crepe"),
- sg.Radio("Crepe Tiny", "f0_method", key="reg-crepe-tiny"),
- ]
- ],
- title="Select an f0 Method",
- )
- ],
- [
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Input device")),
- sg.Combo(
- input_devices,
- key="sg_input_device",
- default_value=data.get("sg_input_device", ""),
- ),
- ],
- [
- sg.Text(i18n("Output device")),
- sg.Combo(
- output_devices,
- key="sg_output_device",
- default_value=data.get("sg_output_device", ""),
- ),
- ],
- ],
- title=i18n("Audio device (please use the same type of driver)"),
- )
- ],
- [
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Response threshold")),
- sg.Slider(
- range=(-60, 0),
- key="threhold",
- resolution=1,
- orientation="h",
- default_value=data.get("threhold", ""),
- ),
- ],
- [
- sg.Text(i18n("Pitch settings")),
- sg.Slider(
- range=(-24, 24),
- key="pitch",
- resolution=1,
- orientation="h",
- default_value=data.get("pitch", ""),
- ),
- ],
- [
- sg.Text(i18n("Index Rate")),
- sg.Slider(
- range=(0.0, 1.0),
- key="index_rate",
- resolution=0.01,
- orientation="h",
- default_value=data.get("index_rate", ""),
- ),
- ],
- ],
- title=i18n("General settings"),
- ),
- sg.Frame(
- layout=[
- [
- sg.Text(i18n("Sample length")),
- sg.Slider(
- range=(0.1, 3.0),
- key="block_time",
- resolution=0.1,
- orientation="h",
- default_value=data.get("block_time", ""),
- ),
- ],
- [
- sg.Text(i18n("Fade length")),
- sg.Slider(
- range=(0.01, 0.15),
- key="crossfade_length",
- resolution=0.01,
- orientation="h",
- default_value=data.get("crossfade_length", ""),
- ),
- ],
- [
- sg.Text(i18n("Extra推理时长")),
- sg.Slider(
- range=(0.05, 3.00),
- key="extra_time",
- resolution=0.01,
- orientation="h",
- default_value=data.get("extra_time", ""),
- ),
- ],
- [
- sg.Checkbox(i18n("Input noise reduction"), key="I_noise_reduce"),
- sg.Checkbox(i18n("Output noise reduction"), key="O_noise_reduce"),
- ],
- ],
- title=i18n("Performance settings"),
- ),
- ],
- [
- sg.Button(i18n("开始音频Convert"), key="start_vc"),
- sg.Button(i18n("停止音频Convert"), key="stop_vc"),
- sg.Text(i18n("Inference time (ms):")),
- sg.Text("0", key="infer_time"),
- ],
- ]
- self.window = sg.Window("RVC - GUI", layout=layout)
- self.event_handler()
-
- def event_handler(self):
- while True:
- event, values = self.window.read()
- if event == sg.WINDOW_CLOSED:
- self.flag_vc = False
- exit()
- if event == "start_vc" and self.flag_vc == False:
- if self.set_values(values) == True:
- print("using_cuda:" + str(torch.cuda.is_available()))
- self.start_vc()
- settings = {
- "pth_path": values["pth_path"],
- "index_path": values["index_path"],
- "f0_method": self.get_f0_method_from_radios(values),
- "sg_input_device": values["sg_input_device"],
- "sg_output_device": values["sg_output_device"],
- "threhold": values["threhold"],
- "pitch": values["pitch"],
- "index_rate": values["index_rate"],
- "block_time": values["block_time"],
- "crossfade_length": values["crossfade_length"],
- "extra_time": values["extra_time"],
- }
- with open("values1.json", "w") as j:
- json.dump(settings, j)
- if event == "stop_vc" and self.flag_vc == True:
- self.flag_vc = False
-
- # Function that returns the used f0 method in string format "harvest"
- def get_f0_method_from_radios(self, values):
- f0_array = [
- {"name": "harvest", "val": values["harvest"]},
- {"name": "reg-crepe", "val": values["reg-crepe"]},
- {"name": "reg-crepe-tiny", "val": values["reg-crepe-tiny"]},
- ]
- # Filter through to find a true value
- used_f0 = ""
- for f0 in f0_array:
- if f0["val"] == True:
- used_f0 = f0["name"]
- break
- if used_f0 == "":
- used_f0 = "harvest" # Default Harvest if used_f0 is empty somehow
- return used_f0
-
- def set_values(self, values):
- if len(values["pth_path"].strip()) == 0:
- sg.popup(i18n("Select the pth file"))
- return False
- if len(values["index_path"].strip()) == 0:
- sg.popup(i18n("Select the index file"))
- return False
- pattern = re.compile("[^\x00-\x7F]+")
- if pattern.findall(values["hubert_path"]):
- sg.popup(i18n("The hubert model path must not contain Chinese characters"))
- return False
- if pattern.findall(values["pth_path"]):
- sg.popup(i18n("The pth file path must not contain Chinese characters."))
- return False
- if pattern.findall(values["index_path"]):
- sg.popup(i18n("The index file path must not contain Chinese characters."))
- return False
- self.set_devices(values["sg_input_device"], values["sg_output_device"])
- self.config.hubert_path = os.path.join(current_dir, "hubert_base.pt")
- self.config.pth_path = values["pth_path"]
- self.config.index_path = values["index_path"]
- self.config.npy_path = values["npy_path"]
- self.config.f0_method = self.get_f0_method_from_radios(values)
- self.config.threhold = values["threhold"]
- self.config.pitch = values["pitch"]
- self.config.block_time = values["block_time"]
- self.config.crossfade_time = values["crossfade_length"]
- self.config.extra_time = values["extra_time"]
- self.config.I_noise_reduce = values["I_noise_reduce"]
- self.config.O_noise_reduce = values["O_noise_reduce"]
- self.config.index_rate = values["index_rate"]
- return True
-
- def start_vc(self):
- torch.cuda.empty_cache()
- self.flag_vc = True
- self.block_frame = int(self.config.block_time * self.config.samplerate)
- self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate)
- self.sola_search_frame = int(0.012 * self.config.samplerate)
- self.delay_frame = int(0.01 * self.config.samplerate) # 往前预留0.02s
- self.extra_frame = int(self.config.extra_time * self.config.samplerate)
- self.rvc = None
- self.rvc = RVC(
- self.config.pitch,
- self.config.f0_method,
- self.config.hubert_path,
- self.config.pth_path,
- self.config.index_path,
- self.config.npy_path,
- self.config.index_rate,
- )
- self.input_wav: np.ndarray = np.zeros(
- self.extra_frame
- + self.crossfade_frame
- + self.sola_search_frame
- + self.block_frame,
- dtype="float32",
- )
- self.output_wav: torch.Tensor = torch.zeros(
- self.block_frame, device=device, dtype=torch.float32
- )
- self.sola_buffer: torch.Tensor = torch.zeros(
- self.crossfade_frame, device=device, dtype=torch.float32
- )
- self.fade_in_window: torch.Tensor = torch.linspace(
- 0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32
- )
- self.fade_out_window: torch.Tensor = 1 - self.fade_in_window
- self.resampler1 = tat.Resample(
- orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32
- )
- self.resampler2 = tat.Resample(
- orig_freq=self.rvc.tgt_sr,
- new_freq=self.config.samplerate,
- dtype=torch.float32,
- )
- thread_vc = threading.Thread(target=self.soundinput)
- thread_vc.start()
-
- def soundinput(self):
- """
- 接受音频输入
- """
- with sd.Stream(
- channels=2,
- callback=self.audio_callback,
- blocksize=self.block_frame,
- samplerate=self.config.samplerate,
- dtype="float32",
- ):
- while self.flag_vc:
- time.sleep(self.config.block_time)
- print("Audio block passed.")
- print("ENDing VC")
-
- def audio_callback(
- self, indata: np.ndarray, outdata: np.ndarray, frames, times, status
- ):
- """
- 音频处理
- """
- start_time = time.perf_counter()
- indata = librosa.to_mono(indata.T)
- if self.config.I_noise_reduce:
- indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate)
-
- """noise gate"""
- frame_length = 2048
- hop_length = 1024
- rms = librosa.feature.rms(
- y=indata, frame_length=frame_length, hop_length=hop_length
- )
- db_threhold = librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold
- # print(rms.shape,db.shape,db)
- for i in range(db_threhold.shape[0]):
- if db_threhold[i]:
- indata[i * hop_length : (i + 1) * hop_length] = 0
- self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata)
-
- # infer
- print("input_wav:" + str(self.input_wav.shape))
- # print('infered_wav:'+str(infer_wav.shape))
- infer_wav: torch.Tensor = self.resampler2(
- self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav)))
- )[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to(
- device
- )
- print("infer_wav:" + str(infer_wav.shape))
-
- # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC
- cor_nom = F.conv1d(
- infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame],
- self.sola_buffer[None, None, :],
- )
- cor_den = torch.sqrt(
- F.conv1d(
- infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame]
- ** 2,
- torch.ones(1, 1, self.crossfade_frame, device=device),
- )
- + 1e-8
- )
- sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
- print("sola offset: " + str(int(sola_offset)))
-
- # crossfade
- self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
- self.output_wav[: self.crossfade_frame] *= self.fade_in_window
- self.output_wav[: self.crossfade_frame] += self.sola_buffer[:]
- if sola_offset < self.sola_search_frame:
- self.sola_buffer[:] = (
- infer_wav[
- -self.sola_search_frame
- - self.crossfade_frame
- + sola_offset : -self.sola_search_frame
- + sola_offset
- ]
- * self.fade_out_window
- )
- else:
- self.sola_buffer[:] = (
- infer_wav[-self.crossfade_frame :] * self.fade_out_window
- )
-
- if self.config.O_noise_reduce:
- outdata[:] = np.tile(
- nr.reduce_noise(
- y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate
- ),
- (2, 1),
- ).T
- else:
- outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
- total_time = time.perf_counter() - start_time
- self.window["infer_time"].update(int(total_time * 1000))
- print("infer time:" + str(total_time))
- print("f0_method: " + str(self.config.f0_method))
-
- def get_devices(self, update: bool = True):
- """获取设备列表"""
- if update:
- sd._terminate()
- sd._initialize()
- devices = sd.query_devices()
- hostapis = sd.query_hostapis()
- for hostapi in hostapis:
- for device_idx in hostapi["devices"]:
- devices[device_idx]["hostapi_name"] = hostapi["name"]
- input_devices = [
- f"{d['name']} ({d['hostapi_name']})"
- for d in devices
- if d["max_input_channels"] > 0
- ]
- output_devices = [
- f"{d['name']} ({d['hostapi_name']})"
- for d in devices
- if d["max_output_channels"] > 0
- ]
- input_devices_indices = [
- d["index"] if "index" in d else d["name"]
- for d in devices
- if d["max_input_channels"] > 0
- ]
- output_devices_indices = [
- d["index"] if "index" in d else d["name"]
- for d in devices
- if d["max_output_channels"] > 0
- ]
- return (
- input_devices,
- output_devices,
- input_devices_indices,
- output_devices_indices,
- )
-
- def set_devices(self, input_device, output_device):
- """设置输出设备"""
- (
- input_devices,
- output_devices,
- input_device_indices,
- output_device_indices,
- ) = self.get_devices()
- sd.default.device[0] = input_device_indices[input_devices.index(input_device)]
- sd.default.device[1] = output_device_indices[
- output_devices.index(output_device)
- ]
- print("input device:" + str(sd.default.device[0]) + ":" + str(input_device))
- print("output device:" + str(sd.default.device[1]) + ":" + str(output_device))
-
-
-gui = GUI()
diff --git a/spaces/EuroPython2022/clickbaitonator/fudge/main.py b/spaces/EuroPython2022/clickbaitonator/fudge/main.py
deleted file mode 100644
index e8c2299b2449b6dd07d26c7ae678732b1dabca88..0000000000000000000000000000000000000000
--- a/spaces/EuroPython2022/clickbaitonator/fudge/main.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import os
-import random
-import time
-import pickle
-import math
-from argparse import ArgumentParser
-
-from tqdm import tqdm
-import numpy as np
-import torch
-import torch.nn as nn
-
-from data import Dataset
-from model import Model
-from util import save_checkpoint, ProgressMeter, AverageMeter, num_params, pad_mask
-from constants import *
-
-
-def train(model, dataset, optimizer, criterion, epoch, args, data_start_index):
- model.train()
- if data_start_index == 0:
- dataset.shuffle('train', seed=epoch + args.seed)
- if args.epoch_max_len is not None:
- data_end_index = min(data_start_index + args.epoch_max_len, len(dataset.splits['train']))
- loader = dataset.loader('train', num_workers=args.num_workers, indices=list(range(data_start_index, data_end_index)))
- data_start_index = data_end_index if data_end_index < len(dataset.splits['train']) else 0
- else:
- loader = dataset.loader('train', num_workers=args.num_workers)
- loss_meter = AverageMeter('loss', ':6.4f')
- total_length = len(loader)
- progress = ProgressMeter(total_length, [loss_meter], prefix='Training: ')
- for batch_num, batch in enumerate(tqdm(loader, total=len(loader))):
- batch = [tensor.to(args.device) for tensor in batch]
- inputs, lengths, future_words, log_probs, labels, classification_targets, syllables_to_go, future_word_num_syllables, rhyme_group_index = batch
- if args.task not in ['formality', 'iambic']:
- if not args.debug and len(inputs) != args.batch_size: # it'll screw up the bias...?
- continue
- scores = model(inputs, lengths, future_words, log_probs, syllables_to_go, future_word_num_syllables, rhyme_group_index, run_classifier=True)
- if args.task == 'formality': # we're learning for all positions at once. scores are batch x seq
- expanded_labels = classification_targets.unsqueeze(1).expand(-1, scores.shape[1]) # batch x seq
- length_mask = pad_mask(lengths).permute(1, 0) # batch x seq
- loss = criterion(scores.flatten()[length_mask.flatten()==1], expanded_labels.flatten().float()[length_mask.flatten()==1])
- elif args.task in ['iambic', 'newline']:
- use_indices = classification_targets.flatten() != -1
- loss = criterion(scores.flatten()[use_indices], classification_targets.flatten().float()[use_indices])
- else: # topic, rhyme
- loss = criterion(scores.flatten(), labels.flatten().float())
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
- loss_meter.update(loss.detach(), len(labels))
- if batch_num % args.train_print_freq == 0:
- progress.display(batch_num)
- progress.display(total_length)
- return data_start_index
-
-
-def validate(model, dataset, criterion, epoch, args):
- model.eval()
- random.seed(0)
- loader = dataset.loader('val', num_workers=args.num_workers)
- loss_meter = AverageMeter('loss', ':6.4f')
- total_length = len(loader)
- progress = ProgressMeter(total_length, [loss_meter], prefix='Validation: ')
- with torch.no_grad():
- for batch_num, batch in enumerate(tqdm(loader, total=len(loader))):
- batch = [tensor.to(args.device) for tensor in batch]
- inputs, lengths, future_words, log_probs, labels, classification_targets, syllables_to_go, future_word_num_syllables, rhyme_group_index = batch
- if args.task not in ['formality', 'iambic']: # topic predictor
- if not args.debug and len(inputs) != args.batch_size:
- continue
- scores = model(inputs, lengths, future_words, log_probs, syllables_to_go, future_word_num_syllables, rhyme_group_index, run_classifier=True)
- if args.task == 'formality': # we're learning for all positions at once. scores are batch x seq
- expanded_labels = classification_targets.unsqueeze(1).expand(-1, scores.shape[1]) # batch x seq
- length_mask = pad_mask(lengths).permute(1, 0) # batch x seq
- loss = criterion(scores.flatten()[length_mask.flatten()==1], expanded_labels.flatten().float()[length_mask.flatten()==1])
- elif args.task in ['iambic', 'newline']:
- use_indices = classification_targets.flatten() != -1
- loss = criterion(scores.flatten()[use_indices], classification_targets.flatten().float()[use_indices])
- else: # topic, rhyme
- loss = criterion(scores.flatten(), labels.flatten().float())
- loss_meter.update(loss.detach(), len(labels))
- if batch_num % args.train_print_freq == 0:
- progress.display(batch_num)
- progress.display(total_length)
- return loss_meter.avg
-
-
-def main(args):
- dataset = Dataset(args)
- os.makedirs(args.save_dir, exist_ok=True)
- with open(os.path.join(args.save_dir, 'dataset_info'), 'wb') as wf:
- pickle.dump(dataset.dataset_info, wf)
- if args.task == 'rhyme':
- with open(os.path.join(args.save_dir, 'rhyme_info'), 'wb') as wf:
- pickle.dump(dataset.rhyme_info, wf)
- if args.ckpt:
- checkpoint = torch.load(args.ckpt, map_location=args.device)
- start_epoch = checkpoint['epoch'] + 1
- best_val_metric = checkpoint['best_metric']
- model_args = checkpoint['args']
- model = Model(model_args, dataset.gpt_pad_id, len(dataset.index2word), rhyme_group_size=len(dataset.index2rhyme_group) if args.task == 'rhyme' else None) # no need to get the glove embeddings when reloading since they're saved in model ckpt anyway
- model.load_state_dict(checkpoint['state_dict'])
- model = model.to(args.device)
- optimizer = torch.optim.Adam(model.parameters(), lr=model_args.lr)
- optimizer.load_state_dict(checkpoint['optimizer'])
- data_start_index = checkpoint['data_start_index']
- print("=> loaded checkpoint '{}' (epoch {})"
- .format(args.ckpt, checkpoint['epoch']))
- # NOTE: just import pdb after loading the model here if you want to play with it, it's easy
- # model.eval()
- # import pdb; pdb.set_trace()
- else:
- model = Model(args, dataset.gpt_pad_id, len(dataset.index2word), rhyme_group_size=len(dataset.index2rhyme_group) if args.task == 'rhyme' else None, glove_embeddings=dataset.glove_embeddings)
- model = model.to(args.device)
- optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
- best_val_metric = 1e8 # lower is better for BCE
- data_start_index = 0
- print('num params', num_params(model))
- criterion = nn.BCEWithLogitsLoss().to(args.device)
-
- if args.evaluate:
- epoch = 0
- validate(model, dataset, criterion, epoch, args)
- return
- for epoch in range(args.epochs):
- print("TRAINING: Epoch {} at {}".format(epoch, time.ctime()))
- data_start_index = train(model, dataset, optimizer, criterion, epoch, args, data_start_index)
- if epoch % args.validation_freq == 0:
- print("VALIDATION: Epoch {} at {}".format(epoch, time.ctime()))
- metric = validate(model, dataset, criterion, epoch, args)
-
- if not args.debug:
- if metric < best_val_metric:
- print('new best val metric', metric)
- best_val_metric = metric
- save_checkpoint({
- 'epoch': epoch,
- 'state_dict': model.state_dict(),
- 'best_metric': best_val_metric,
- 'optimizer': optimizer.state_dict(),
- 'data_start_index': data_start_index,
- 'args': args
- }, os.path.join(args.save_dir, 'model_best.pth.tar'))
- save_checkpoint({
- 'epoch': epoch,
- 'state_dict': model.state_dict(),
- 'best_metric': metric,
- 'optimizer': optimizer.state_dict(),
- 'data_start_index': data_start_index,
- 'args': args
- }, os.path.join(args.save_dir, 'model_epoch' + str(epoch) + '.pth.tar'))
-
-
-if __name__=='__main__':
- parser = ArgumentParser()
-
- # DATA
- parser.add_argument('--task', type=str, required=True, choices=['iambic', 'rhyme', 'newline', 'topic', 'formality', 'clickbait'])
- parser.add_argument('--data_dir', type=str, required=True)
- parser.add_argument('--glove_file', type=str, help='glove embedding init, for topic task')
-
- # SAVE/LOAD
- parser.add_argument('--save_dir', type=str, required=True, help='where to save ckpts')
- parser.add_argument('--ckpt', type=str, default=None, help='load ckpt from file if given')
- parser.add_argument('--dataset_info', type=str, help='saved dataset info')
- parser.add_argument('--rhyme_info', type=str, help='saved dataset rhyme info, for a ckpt with task==rhyme')
-
- # TRAINING
- parser.add_argument('--batch_size', type=int, default=128)
- parser.add_argument('--epochs', type=int, default=100)
- parser.add_argument('--epoch_max_len', type=int, default=None, help='max batches per epoch if set, for more frequent validation')
- parser.add_argument('--validation_freq', type=int, default=1, help='validate every X epochs')
- parser.add_argument('--lr', type=float, default=1e-3, help='Adam learning rate')
- parser.add_argument('--seed', type=int, default=1, help='random seed')
- parser.add_argument('--device', type=str, default='cuda', choices=['cpu', 'cuda'])
- parser.add_argument('--num_workers', type=int, default=20, help='num workers for data loader')
- parser.add_argument('--evaluate', action='store_true', default=False)
- parser.add_argument('--debug', action='store_true', default=False)
-
- # PRINTING
- parser.add_argument('--train_print_freq', type=int, default=100, help='how often to print metrics (every X batches)')
-
- args = parser.parse_args()
-
- random.seed(args.seed)
- np.random.seed(args.seed)
- torch.manual_seed(args.seed)
- if args.evaluate:
- assert args.ckpt is not None
-
- main(args)
\ No newline at end of file
diff --git a/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts b/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts
deleted file mode 100644
index 9ba0fe30a092c73c8321a6bb2502750471b133f2..0000000000000000000000000000000000000000
--- a/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca/src/index.ts
+++ /dev/null
@@ -1,279 +0,0 @@
-import {
- ChatInterface,
- ChatModule,
- ChatRestModule,
- ChatWorkerClient,
-} from "@mlc-ai/web-llm";
-
-function getElementAndCheck(id: string): HTMLElement {
- const element = document.getElementById(id);
- if (element == null) {
- throw Error("Cannot find element " + id);
- }
- return element;
-}
-
-const appConfig = {
- model_list: [
- {
- model_url:
- "https://huggingface.co/Felladrin/mlc-chat-Mistral-7B-OpenOrca-q4f32_1/resolve/main/params/",
- local_id: "Mistral-7B-OpenOrca-q4f32_1",
- },
- ],
- model_lib_map: {
- "Mistral-7B-OpenOrca-q4f32_1":
- "https://huggingface.co/Felladrin/mlc-chat-Mistral-7B-OpenOrca-q4f32_1/resolve/main/Mistral-7B-OpenOrca-q4f32_1-webgpu.wasm",
- },
- use_web_worker: true,
-};
-
-class ChatUI {
- private uiChat: HTMLElement;
- private uiChatInput: HTMLInputElement;
- private uiChatInfoLabel: HTMLLabelElement;
- private chat: ChatInterface;
- private localChat: ChatInterface;
- private config = appConfig;
- private selectedModel: string;
- private chatLoaded = false;
- private requestInProgress = false;
- // We use a request chain to ensure that
- // all requests send to chat are sequentialized
- private chatRequestChain: Promise = Promise.resolve();
-
- constructor(chat: ChatInterface, localChat: ChatInterface) {
- // use web worker to run chat generation in background
- this.chat = chat;
- this.localChat = localChat;
- // get the elements
- this.uiChat = getElementAndCheck("chatui-chat");
- this.uiChatInput = getElementAndCheck("chatui-input") as HTMLInputElement;
- this.uiChatInfoLabel = getElementAndCheck(
- "chatui-info-label"
- ) as HTMLLabelElement;
- // register event handlers
- getElementAndCheck("chatui-reset-btn").onclick = () => {
- this.onReset();
- };
- getElementAndCheck("chatui-send-btn").onclick = () => {
- this.onGenerate();
- };
- // TODO: find other alternative triggers
- getElementAndCheck("chatui-input").onkeypress = (event) => {
- if (event.keyCode === 13) {
- this.onGenerate();
- }
- };
-
- const modelSelector = getElementAndCheck(
- "chatui-select"
- ) as HTMLSelectElement;
- for (let i = 0; i < this.config.model_list.length; ++i) {
- const item = this.config.model_list[i];
- const opt = document.createElement("option");
- opt.value = item.local_id;
- opt.innerHTML = item.local_id;
- opt.selected = i == 0;
- modelSelector.appendChild(opt);
- }
- // Append local server option to the model selector
- const localServerOpt = document.createElement("option");
- localServerOpt.value = "Local Server";
- localServerOpt.innerHTML = "Local Server";
- modelSelector.append(localServerOpt);
- this.selectedModel = modelSelector.value;
- modelSelector.onchange = () => {
- this.onSelectChange(modelSelector);
- };
- }
- /**
- * Push a task to the execution queue.
- *
- * @param task The task to be executed;
- */
- private pushTask(task: () => Promise) {
- const lastEvent = this.chatRequestChain;
- this.chatRequestChain = lastEvent.then(task);
- }
- // Event handlers
- // all event handler pushes the tasks to a queue
- // that get executed sequentially
- // the tasks previous tasks, which causes them to early stop
- // can be interrupted by chat.interruptGenerate
- private async onGenerate() {
- if (this.requestInProgress) {
- return;
- }
- this.pushTask(async () => {
- await this.asyncGenerate();
- });
- }
-
- private async onSelectChange(modelSelector: HTMLSelectElement) {
- if (this.requestInProgress) {
- // interrupt previous generation if any
- this.chat.interruptGenerate();
- }
- // try reset after previous requests finishes
- this.pushTask(async () => {
- await this.chat.resetChat();
- this.resetChatHistory();
- await this.unloadChat();
- this.selectedModel = modelSelector.value;
- await this.asyncInitChat();
- });
- }
-
- private async onReset() {
- if (this.requestInProgress) {
- // interrupt previous generation if any
- this.chat.interruptGenerate();
- }
- // try reset after previous requests finishes
- this.pushTask(async () => {
- await this.chat.resetChat();
- this.resetChatHistory();
- });
- }
-
- // Internal helper functions
- private appendMessage(kind, text) {
- if (kind == "init") {
- text = "[System Initalize] " + text;
- }
- if (this.uiChat === undefined) {
- throw Error("cannot find ui chat");
- }
- const msg = `
-
-
- ${text}
-
-
- `;
- this.uiChat.insertAdjacentHTML("beforeend", msg);
- this.uiChat.scrollTo(0, this.uiChat.scrollHeight);
- }
-
- private updateLastMessage(kind, text) {
- if (kind == "init") {
- text = "[System Initalize] " + text;
- }
- if (this.uiChat === undefined) {
- throw Error("cannot find ui chat");
- }
- const matches = this.uiChat.getElementsByClassName(`msg ${kind}-msg`);
- if (matches.length == 0) throw Error(`${kind} message do not exist`);
- const msg = matches[matches.length - 1];
- const msgText = msg.getElementsByClassName("msg-text");
- if (msgText.length != 1) throw Error("Expect msg-text");
- if (msgText[0].innerHTML == text) return;
- const list = text.split("\n").map((t) => {
- const item = document.createElement("div");
- item.textContent = t;
- return item;
- });
- msgText[0].innerHTML = "";
- list.forEach((item) => msgText[0].append(item));
- this.uiChat.scrollTo(0, this.uiChat.scrollHeight);
- }
-
- private resetChatHistory() {
- const clearTags = ["left", "right", "init", "error"];
- for (const tag of clearTags) {
- // need to unpack to list so the iterator don't get affected by mutation
- const matches = [...this.uiChat.getElementsByClassName(`msg ${tag}-msg`)];
- for (const item of matches) {
- this.uiChat.removeChild(item);
- }
- }
- if (this.uiChatInfoLabel !== undefined) {
- this.uiChatInfoLabel.innerHTML = "";
- }
- }
-
- private async asyncInitChat() {
- if (this.chatLoaded) return;
- this.requestInProgress = true;
- this.appendMessage("init", "");
- const initProgressCallback = (report) => {
- this.updateLastMessage("init", report.text);
- };
- this.chat.setInitProgressCallback(initProgressCallback);
-
- try {
- if (this.selectedModel != "Local Server") {
- await this.chat.reload(this.selectedModel, undefined, this.config);
- }
- } catch (err) {
- this.appendMessage("error", "Init error, " + err.toString());
- console.log(err.stack);
- this.unloadChat();
- this.requestInProgress = false;
- return;
- }
- this.requestInProgress = false;
- this.chatLoaded = true;
- }
-
- private async unloadChat() {
- await this.chat.unload();
- this.chatLoaded = false;
- }
-
- /**
- * Run generate
- */
- private async asyncGenerate() {
- await this.asyncInitChat();
- this.requestInProgress = true;
- const prompt = this.uiChatInput.value;
- if (prompt == "") {
- this.requestInProgress = false;
- return;
- }
-
- this.appendMessage("right", prompt);
- this.uiChatInput.value = "";
- this.uiChatInput.setAttribute("placeholder", "Generating...");
-
- this.appendMessage("left", "");
- const callbackUpdateResponse = (step, msg) => {
- if (msg.length === 0) return this.chat.interruptGenerate();
- this.updateLastMessage("left", msg);
- };
-
- try {
- if (this.selectedModel == "Local Server") {
- await this.localChat.generate(prompt, callbackUpdateResponse);
- this.uiChatInfoLabel.innerHTML =
- await this.localChat.runtimeStatsText();
- } else {
- await this.chat.generate(prompt, callbackUpdateResponse);
- this.uiChatInfoLabel.innerHTML = await this.chat.runtimeStatsText();
- }
- } catch (err) {
- this.appendMessage("error", "Generate error, " + err.toString());
- console.log(err.stack);
- await this.unloadChat();
- }
- this.uiChatInput.setAttribute("placeholder", "Enter your message...");
- this.requestInProgress = false;
- }
-}
-
-const useWebWorker = appConfig.use_web_worker;
-let chat: ChatInterface;
-let localChat: ChatInterface;
-
-if (useWebWorker) {
- chat = new ChatWorkerClient(
- new Worker(new URL("./worker.ts", import.meta.url), { type: "module" })
- );
- localChat = new ChatRestModule();
-} else {
- chat = new ChatModule();
- localChat = new ChatRestModule();
-}
-new ChatUI(chat, localChat);
diff --git a/spaces/Francesco/torch-cam-transformers/app.py b/spaces/Francesco/torch-cam-transformers/app.py
deleted file mode 100644
index 6c5bf7ea8344430235dfcc553c112be801348a50..0000000000000000000000000000000000000000
--- a/spaces/Francesco/torch-cam-transformers/app.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import streamlit as st
-
-import sys
-
-
-def main():
- # Wide mode
- st.set_page_config(layout="wide")
-
- # Designing the interface
- st.title(sys.version)
-
-
-main()
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py
deleted file mode 100644
index bf18572329019d7a8f1df01799eda207c16dd7ff..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py
+++ /dev/null
@@ -1,284 +0,0 @@
-import os
-import random
-import re
-import numpy as np
-import librosa
-import torch
-import random
-from utils import repeat_expand_2d
-from tqdm import tqdm
-from torch.utils.data import Dataset
-
-def traverse_dir(
- root_dir,
- extensions,
- amount=None,
- str_include=None,
- str_exclude=None,
- is_pure=False,
- is_sort=False,
- is_ext=True):
-
- file_list = []
- cnt = 0
- for root, _, files in os.walk(root_dir):
- for file in files:
- if any([file.endswith(f".{ext}") for ext in extensions]):
- # path
- mix_path = os.path.join(root, file)
- pure_path = mix_path[len(root_dir)+1:] if is_pure else mix_path
-
- # amount
- if (amount is not None) and (cnt == amount):
- if is_sort:
- file_list.sort()
- return file_list
-
- # check string
- if (str_include is not None) and (str_include not in pure_path):
- continue
- if (str_exclude is not None) and (str_exclude in pure_path):
- continue
-
- if not is_ext:
- ext = pure_path.split('.')[-1]
- pure_path = pure_path[:-(len(ext)+1)]
- file_list.append(pure_path)
- cnt += 1
- if is_sort:
- file_list.sort()
- return file_list
-
-
-def get_data_loaders(args, whole_audio=False):
- data_train = AudioDataset(
- filelists = args.data.training_files,
- waveform_sec=args.data.duration,
- hop_size=args.data.block_size,
- sample_rate=args.data.sampling_rate,
- load_all_data=args.train.cache_all_data,
- whole_audio=whole_audio,
- extensions=args.data.extensions,
- n_spk=args.model.n_spk,
- spk=args.spk,
- device=args.train.cache_device,
- fp16=args.train.cache_fp16,
- use_aug=True)
- loader_train = torch.utils.data.DataLoader(
- data_train ,
- batch_size=args.train.batch_size if not whole_audio else 1,
- shuffle=True,
- num_workers=args.train.num_workers if args.train.cache_device=='cpu' else 0,
- persistent_workers=(args.train.num_workers > 0) if args.train.cache_device=='cpu' else False,
- pin_memory=True if args.train.cache_device=='cpu' else False
- )
- data_valid = AudioDataset(
- filelists = args.data.validation_files,
- waveform_sec=args.data.duration,
- hop_size=args.data.block_size,
- sample_rate=args.data.sampling_rate,
- load_all_data=args.train.cache_all_data,
- whole_audio=True,
- spk=args.spk,
- extensions=args.data.extensions,
- n_spk=args.model.n_spk)
- loader_valid = torch.utils.data.DataLoader(
- data_valid,
- batch_size=1,
- shuffle=False,
- num_workers=0,
- pin_memory=True
- )
- return loader_train, loader_valid
-
-
-class AudioDataset(Dataset):
- def __init__(
- self,
- filelists,
- waveform_sec,
- hop_size,
- sample_rate,
- spk,
- load_all_data=True,
- whole_audio=False,
- extensions=['wav'],
- n_spk=1,
- device='cpu',
- fp16=False,
- use_aug=False,
- ):
- super().__init__()
-
- self.waveform_sec = waveform_sec
- self.sample_rate = sample_rate
- self.hop_size = hop_size
- self.filelists = filelists
- self.whole_audio = whole_audio
- self.use_aug = use_aug
- self.data_buffer={}
- self.pitch_aug_dict = {}
- # np.load(os.path.join(self.path_root, 'pitch_aug_dict.npy'), allow_pickle=True).item()
- if load_all_data:
- print('Load all the data filelists:', filelists)
- else:
- print('Load the f0, volume data filelists:', filelists)
- with open(filelists,"r") as f:
- self.paths = f.read().splitlines()
- for name_ext in tqdm(self.paths, total=len(self.paths)):
- name = os.path.splitext(name_ext)[0]
- path_audio = name_ext
- duration = librosa.get_duration(filename = path_audio, sr = self.sample_rate)
-
- path_f0 = name_ext + ".f0.npy"
- f0,_ = np.load(path_f0,allow_pickle=True)
- f0 = torch.from_numpy(np.array(f0,dtype=float)).float().unsqueeze(-1).to(device)
-
- path_volume = name_ext + ".vol.npy"
- volume = np.load(path_volume)
- volume = torch.from_numpy(volume).float().unsqueeze(-1).to(device)
-
- path_augvol = name_ext + ".aug_vol.npy"
- aug_vol = np.load(path_augvol)
- aug_vol = torch.from_numpy(aug_vol).float().unsqueeze(-1).to(device)
-
- if n_spk is not None and n_spk > 1:
- spk_name = name_ext.split("/")[-2]
- spk_id = spk[spk_name] if spk_name in spk else 0
- if spk_id < 0 or spk_id >= n_spk:
- raise ValueError(' [x] Muiti-speaker traing error : spk_id must be a positive integer from 0 to n_spk-1 ')
- else:
- spk_id = 0
- spk_id = torch.LongTensor(np.array([spk_id])).to(device)
-
- if load_all_data:
- '''
- audio, sr = librosa.load(path_audio, sr=self.sample_rate)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio)
- audio = torch.from_numpy(audio).to(device)
- '''
- path_mel = name_ext + ".mel.npy"
- mel = np.load(path_mel)
- mel = torch.from_numpy(mel).to(device)
-
- path_augmel = name_ext + ".aug_mel.npy"
- aug_mel,keyshift = np.load(path_augmel, allow_pickle=True)
- aug_mel = np.array(aug_mel,dtype=float)
- aug_mel = torch.from_numpy(aug_mel).to(device)
- self.pitch_aug_dict[name_ext] = keyshift
-
- path_units = name_ext + ".soft.pt"
- units = torch.load(path_units).to(device)
- units = units[0]
- units = repeat_expand_2d(units,f0.size(0)).transpose(0,1)
-
- if fp16:
- mel = mel.half()
- aug_mel = aug_mel.half()
- units = units.half()
-
- self.data_buffer[name_ext] = {
- 'duration': duration,
- 'mel': mel,
- 'aug_mel': aug_mel,
- 'units': units,
- 'f0': f0,
- 'volume': volume,
- 'aug_vol': aug_vol,
- 'spk_id': spk_id
- }
- else:
- path_augmel = name_ext + ".aug_mel.npy"
- aug_mel,keyshift = np.load(path_augmel, allow_pickle=True)
- self.pitch_aug_dict[name_ext] = keyshift
- self.data_buffer[name_ext] = {
- 'duration': duration,
- 'f0': f0,
- 'volume': volume,
- 'aug_vol': aug_vol,
- 'spk_id': spk_id
- }
-
-
- def __getitem__(self, file_idx):
- name_ext = self.paths[file_idx]
- data_buffer = self.data_buffer[name_ext]
- # check duration. if too short, then skip
- if data_buffer['duration'] < (self.waveform_sec + 0.1):
- return self.__getitem__( (file_idx + 1) % len(self.paths))
-
- # get item
- return self.get_data(name_ext, data_buffer)
-
- def get_data(self, name_ext, data_buffer):
- name = os.path.splitext(name_ext)[0]
- frame_resolution = self.hop_size / self.sample_rate
- duration = data_buffer['duration']
- waveform_sec = duration if self.whole_audio else self.waveform_sec
-
- # load audio
- idx_from = 0 if self.whole_audio else random.uniform(0, duration - waveform_sec - 0.1)
- start_frame = int(idx_from / frame_resolution)
- units_frame_len = int(waveform_sec / frame_resolution)
- aug_flag = random.choice([True, False]) and self.use_aug
- '''
- audio = data_buffer.get('audio')
- if audio is None:
- path_audio = os.path.join(self.path_root, 'audio', name) + '.wav'
- audio, sr = librosa.load(
- path_audio,
- sr = self.sample_rate,
- offset = start_frame * frame_resolution,
- duration = waveform_sec)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio)
- # clip audio into N seconds
- audio = audio[ : audio.shape[-1] // self.hop_size * self.hop_size]
- audio = torch.from_numpy(audio).float()
- else:
- audio = audio[start_frame * self.hop_size : (start_frame + units_frame_len) * self.hop_size]
- '''
- # load mel
- mel_key = 'aug_mel' if aug_flag else 'mel'
- mel = data_buffer.get(mel_key)
- if mel is None:
- mel = name_ext + ".mel.npy"
- mel = np.load(mel)
- mel = mel[start_frame : start_frame + units_frame_len]
- mel = torch.from_numpy(mel).float()
- else:
- mel = mel[start_frame : start_frame + units_frame_len]
-
- # load f0
- f0 = data_buffer.get('f0')
- aug_shift = 0
- if aug_flag:
- aug_shift = self.pitch_aug_dict[name_ext]
- f0_frames = 2 ** (aug_shift / 12) * f0[start_frame : start_frame + units_frame_len]
-
- # load units
- units = data_buffer.get('units')
- if units is None:
- path_units = name_ext + ".soft.pt"
- units = torch.load(path_units)
- units = units[0]
- units = repeat_expand_2d(units,f0.size(0)).transpose(0,1)
-
- units = units[start_frame : start_frame + units_frame_len]
-
- # load volume
- vol_key = 'aug_vol' if aug_flag else 'volume'
- volume = data_buffer.get(vol_key)
- volume_frames = volume[start_frame : start_frame + units_frame_len]
-
- # load spk_id
- spk_id = data_buffer.get('spk_id')
-
- # load shift
- aug_shift = torch.from_numpy(np.array([[aug_shift]])).float()
-
- return dict(mel=mel, f0=f0_frames, volume=volume_frames, units=units, spk_id=spk_id, aug_shift=aug_shift, name=name, name_ext=name_ext)
-
- def __len__(self):
- return len(self.paths)
\ No newline at end of file
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py
deleted file mode 100644
index 3bdb70ba9357e95ff05853dcc06437c3401ef3be..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import os
-from functools import lru_cache
-from typing import Union
-
-import ffmpeg
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-from .utils import exact_div
-
-from librosa.filters import mel as librosa_mel_fn
-
-# hard-coded audio hyperparameters
-SAMPLE_RATE = 16000
-N_FFT = 400
-N_MELS = 80
-HOP_LENGTH = 160
-CHUNK_LENGTH = 30
-N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk
-N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH) # 3000: number of frames in a mel spectrogram input
-
-
-def load_audio(file: str, sr: int = SAMPLE_RATE):
- """
- Open an audio file and read as mono waveform, resampling as necessary
-
- Parameters
- ----------
- file: str
- The audio file to open
-
- sr: int
- The sample rate to resample the audio if necessary
-
- Returns
- -------
- A NumPy array containing the audio waveform, in float32 dtype.
- """
- try:
- # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
- # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
- out, _ = (
- ffmpeg.input(file, threads=0)
- .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
- .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
- )
- except ffmpeg.Error as e:
- raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
-
- return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
-
-
-def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
- """
- Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
- """
- if torch.is_tensor(array):
- if array.shape[axis] > length:
- array = array.index_select(dim=axis, index=torch.arange(length, device=array.device))
-
- if array.shape[axis] < length:
- pad_widths = [(0, 0)] * array.ndim
- pad_widths[axis] = (0, length - array.shape[axis])
- array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
- else:
- if array.shape[axis] > length:
- array = array.take(indices=range(length), axis=axis)
-
- if array.shape[axis] < length:
- pad_widths = [(0, 0)] * array.ndim
- pad_widths[axis] = (0, length - array.shape[axis])
- array = np.pad(array, pad_widths)
-
- return array
-
-
-@lru_cache(maxsize=None)
-def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
- """
- load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
- Allows decoupling librosa dependency; saved using:
-
- np.savez_compressed(
- "mel_filters.npz",
- mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
- )
- """
- assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
- return torch.from_numpy(librosa_mel_fn(sr=SAMPLE_RATE,n_fft=N_FFT,n_mels=n_mels)).to(device)
-
-
-def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS):
- """
- Compute the log-Mel spectrogram of
-
- Parameters
- ----------
- audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
- The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
-
- n_mels: int
- The number of Mel-frequency filters, only 80 is supported
-
- Returns
- -------
- torch.Tensor, shape = (80, n_frames)
- A Tensor that contains the Mel spectrogram
- """
- if not torch.is_tensor(audio):
- if isinstance(audio, str):
- audio = load_audio(audio)
- audio = torch.from_numpy(audio)
-
- window = torch.hann_window(N_FFT).to(audio.device)
- stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
- magnitudes = stft[..., :-1].abs() ** 2
-
- filters = mel_filters(audio.device, n_mels)
- mel_spec = filters @ magnitudes
-
- log_spec = torch.clamp(mel_spec, min=1e-10).log10()
- log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
- log_spec = (log_spec + 4.0) / 4.0
- return log_spec
diff --git a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py b/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py
deleted file mode 100644
index 6817392ba8a9eb830351de89fb7afc5ad72f5e42..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py
+++ /dev/null
@@ -1,188 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
- 1. "english_cleaners" for English text
- 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
- the Unidecode library (https://pypi.python.org/pypi/Unidecode)
- 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
- the symbols in symbols.py to match your data).
-'''
-
-
-# Regular expression matching whitespace:
-
-
-import re
-import inflect
-from unidecode import unidecode
-import eng_to_ipa as ipa
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
-_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
-_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
-_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
-_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
-_number_re = re.compile(r'[0-9]+')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
- ('mrs', 'misess'),
- ('mr', 'mister'),
- ('dr', 'doctor'),
- ('st', 'saint'),
- ('co', 'company'),
- ('jr', 'junior'),
- ('maj', 'major'),
- ('gen', 'general'),
- ('drs', 'doctors'),
- ('rev', 'reverend'),
- ('lt', 'lieutenant'),
- ('hon', 'honorable'),
- ('sgt', 'sergeant'),
- ('capt', 'captain'),
- ('esq', 'esquire'),
- ('ltd', 'limited'),
- ('col', 'colonel'),
- ('ft', 'fort'),
-]]
-
-
-# List of (ipa, lazy ipa) pairs:
-_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('æ', 'e'),
- ('ɑ', 'a'),
- ('ɔ', 'o'),
- ('ð', 'z'),
- ('θ', 's'),
- ('ɛ', 'e'),
- ('ɪ', 'i'),
- ('ʊ', 'u'),
- ('ʒ', 'ʥ'),
- ('ʤ', 'ʥ'),
- ('ˈ', '↓'),
-]]
-
-# List of (ipa, lazy ipa2) pairs:
-_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('ð', 'z'),
- ('θ', 's'),
- ('ʒ', 'ʑ'),
- ('ʤ', 'dʑ'),
- ('ˈ', '↓'),
-]]
-
-# List of (ipa, ipa2) pairs
-_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('r', 'ɹ'),
- ('ʤ', 'dʒ'),
- ('ʧ', 'tʃ')
-]]
-
-
-def expand_abbreviations(text):
- for regex, replacement in _abbreviations:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def collapse_whitespace(text):
- return re.sub(r'\s+', ' ', text)
-
-
-def _remove_commas(m):
- return m.group(1).replace(',', '')
-
-
-def _expand_decimal_point(m):
- return m.group(1).replace('.', ' point ')
-
-
-def _expand_dollars(m):
- match = m.group(1)
- parts = match.split('.')
- if len(parts) > 2:
- return match + ' dollars' # Unexpected format
- dollars = int(parts[0]) if parts[0] else 0
- cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
- if dollars and cents:
- dollar_unit = 'dollar' if dollars == 1 else 'dollars'
- cent_unit = 'cent' if cents == 1 else 'cents'
- return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
- elif dollars:
- dollar_unit = 'dollar' if dollars == 1 else 'dollars'
- return '%s %s' % (dollars, dollar_unit)
- elif cents:
- cent_unit = 'cent' if cents == 1 else 'cents'
- return '%s %s' % (cents, cent_unit)
- else:
- return 'zero dollars'
-
-
-def _expand_ordinal(m):
- return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
- num = int(m.group(0))
- if num > 1000 and num < 3000:
- if num == 2000:
- return 'two thousand'
- elif num > 2000 and num < 2010:
- return 'two thousand ' + _inflect.number_to_words(num % 100)
- elif num % 100 == 0:
- return _inflect.number_to_words(num // 100) + ' hundred'
- else:
- return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
- else:
- return _inflect.number_to_words(num, andword='')
-
-
-def normalize_numbers(text):
- text = re.sub(_comma_number_re, _remove_commas, text)
- text = re.sub(_pounds_re, r'\1 pounds', text)
- text = re.sub(_dollars_re, _expand_dollars, text)
- text = re.sub(_decimal_number_re, _expand_decimal_point, text)
- text = re.sub(_ordinal_re, _expand_ordinal, text)
- text = re.sub(_number_re, _expand_number, text)
- return text
-
-
-def mark_dark_l(text):
- return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
-
-
-def english_to_ipa(text):
- text = unidecode(text).lower()
- text = expand_abbreviations(text)
- text = normalize_numbers(text)
- phonemes = ipa.convert(text)
- phonemes = collapse_whitespace(phonemes)
- return phonemes
-
-
-def english_to_lazy_ipa(text):
- text = english_to_ipa(text)
- for regex, replacement in _lazy_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def english_to_ipa2(text):
- text = english_to_ipa(text)
- text = mark_dark_l(text)
- for regex, replacement in _ipa_to_ipa2:
- text = re.sub(regex, replacement, text)
- return text.replace('...', '…')
-
-
-def english_to_lazy_ipa2(text):
- text = english_to_ipa(text)
- for regex, replacement in _lazy_ipa2:
- text = re.sub(regex, replacement, text)
- return text
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
deleted file mode 100644
index a02a814fe2f08b464454e8eb6e1c88004ab804f6..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
+++ /dev/null
@@ -1,27 +0,0 @@
-_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
-model = dict(
- pretrained='torchvision://resnet101',
- backbone=dict(depth=101),
- bbox_head=dict(
- with_deform=True,
- norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='Resize',
- img_scale=[(1333, 640), (1333, 800)],
- multiscale_mode='value',
- keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
deleted file mode 100644
index 4874121fd01e4024bfde445f451b7368c6834511..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './deeplabv3_r50-d8_480x480_40k_pascal_context_59.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
deleted file mode 100644
index 89d5e1ae0f3ef44626f3b5534c504cbce7389a32..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
- '../_base_/models/gcnet_r50-d8.py', '../_base_/datasets/ade20k.py',
- '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
- decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py
deleted file mode 100644
index 12f6d402a3c4a113d4c37be062790fa435b72104..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Evaluation with objective metrics for the pretrained AudioGen models.
-This grid takes signature from the training grid and runs evaluation-only stage.
-
-When running the grid for the first time, please use:
-REGEN=1 dora grid audiogen.audiogen_pretrained_16khz_eval
-and re-use the REGEN=1 option when the grid is changed to force regenerating it.
-
-Note that you need the proper metrics external libraries setup to use all
-the objective metrics activated in this grid. Refer to the README for more information.
-"""
-
-import os
-
-from ..musicgen._explorers import GenerationEvalExplorer
-from ...environment import AudioCraftEnvironment
-from ... import train
-
-
-def eval(launcher, batch_size: int = 32):
- opts = {
- 'dset': 'audio/audiocaps_16khz',
- 'solver/audiogen/evaluation': 'objective_eval',
- 'execute_only': 'evaluate',
- '+dataset.evaluate.batch_size': batch_size,
- '+metrics.fad.tf.batch_size': 32,
- }
- # binary for FAD computation: replace this path with your own path
- metrics_opts = {
- 'metrics.fad.tf.bin': '/data/home/jadecopet/local/usr/opt/google-research'
- }
- opt1 = {'generate.lm.use_sampling': True, 'generate.lm.top_k': 250, 'generate.lm.top_p': 0.}
- opt2 = {'transformer_lm.two_step_cfg': True}
-
- sub = launcher.bind(opts)
- sub.bind_(metrics_opts)
-
- # base objective metrics
- sub(opt1, opt2)
-
-
-@GenerationEvalExplorer
-def explorer(launcher):
- partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
- launcher.slurm_(gpus=4, partition=partitions)
-
- if 'REGEN' not in os.environ:
- folder = train.main.dora.dir / 'grids' / __name__.split('.', 2)[-1]
- with launcher.job_array():
- for sig in folder.iterdir():
- if not sig.is_symlink():
- continue
- xp = train.main.get_xp_from_sig(sig.name)
- launcher(xp.argv)
- return
-
- audiogen_base = launcher.bind(solver="audiogen/audiogen_base_16khz")
- audiogen_base.bind_({'autocast': False, 'fsdp.use': True})
-
- audiogen_base_medium = audiogen_base.bind({'continue_from': '//pretrained/facebook/audiogen-medium'})
- audiogen_base_medium.bind_({'model/lm/model_scale': 'medium'})
- eval(audiogen_base_medium, batch_size=128)
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py
deleted file mode 100644
index 5612ff5688d85fede0e605b244919e8081cb1da9..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Grid search file, simply list all the exp you want in `explorer`.
-Any new exp added there will be scheduled.
-You can cancel and experiment by commenting its line.
-
-This grid is a minimal example for debugging compression task
-and how to override parameters directly in a grid.
-Learn more about dora grids: https://github.com/facebookresearch/dora
-"""
-
-from ._explorers import CompressionExplorer
-from ...environment import AudioCraftEnvironment
-
-
-@CompressionExplorer
-def explorer(launcher):
- partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global'])
- launcher.slurm_(gpus=2, partition=partitions)
- launcher.bind_(solver='compression/debug')
-
- with launcher.job_array():
- # base debug task using config from solver=compression/debug
- launcher()
- # we can override parameters in the grid to launch additional xps
- launcher({'rvq.bins': 2048, 'rvq.n_q': 4})
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py
deleted file mode 100644
index 44f4b0a2c3c6c726857db8386491823dd85dde51..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py
+++ /dev/null
@@ -1,216 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import csv
-import json
-import logging
-from pathlib import Path
-import tempfile
-import typing as tp
-import subprocess
-import shutil
-
-import torch
-import torchaudio
-
-logger = logging.getLogger(__name__)
-
-
-class ViSQOL:
- """ViSQOL wrapper to run ViSQOL from Python using a pre-installed binary.
-
- To learn more about ViSQOL and how to build ViSQOL binary using bazel, please refer to the
- instructions available in the open source repository: https://github.com/google/visqol
-
- ViSQOL is capable of running in two modes:
-
- Audio Mode:
- When running in audio mode, input signals must have a 48kHz sample rate. Input should be resampled to 48kHz.
- Input signals can be multi-channel, but they will be down-mixed to mono for performing the comparison.
- Audio mode uses support vector regression, with the maximum range at ~4.75.
-
- Speech Mode:
- When running in speech mode, ViSQOL uses a wideband model. It therefore expects input sample rates of 16kHz.
- Input should be resampled to 16kHz.
- As part of the speech mode processing, a root mean square implementation for voice activity detection
- is performed on the reference signal to determine what parts of the signal have voice activity and
- should therefore be included in the comparison. The signal is normalized before performing the voice
- activity detection.
- Input signals can be multi-channel, but they will be down-mixed to mono for performing the comparison.
- Speech mode is scaled to have a maximum MOS of 5.0 to match previous version behavior.
-
- For more details, check the guidelines: https://github.com/google/visqol#general-guidelines-for-input
-
- Args:
- visqol_bin (str): Path to the ViSQOL binary.
- mode (str): ViSQOL computation mode, expecting "audio" or "speech".
- model (str): Name of the model to use for similarity to quality model.
- debug (bool): Whether to also get debug metrics from ViSQOL or not.
- """
- SAMPLE_RATES_MODES = {"audio": 48_000, "speech": 16_000}
- ALLOWED_SAMPLE_RATES = frozenset(SAMPLE_RATES_MODES.values())
-
- def __init__(self, bin: tp.Union[Path, str], mode: str = "audio",
- model: str = "libsvm_nu_svr_model.txt", debug: bool = False):
- assert bin is not None and Path(bin).exists(), f"Could not find ViSQOL binary in specified path: {bin}"
- self.visqol_bin = str(bin)
- self.visqol_mode = mode
- self.target_sr = self._get_target_sr(self.visqol_mode)
- self.model = model
- self.debug = debug
- assert Path(self.visqol_model).exists(), \
- f"Could not find the specified model in ViSQOL install: {self.visqol_model}"
-
- def _get_target_sr(self, mode: str) -> int:
- # returns target sampling rate for the corresponding ViSQOL mode.
- if mode not in ViSQOL.SAMPLE_RATES_MODES:
- raise ValueError(
- f"Unsupported mode! Allowed are: {', '.join(ViSQOL.SAMPLE_RATES_MODES.keys())}"
- )
- return ViSQOL.SAMPLE_RATES_MODES[mode]
-
- def _prepare_files(
- self, ref_sig: torch.Tensor, deg_sig: torch.Tensor, sr: int, target_sr: int, pad_with_silence: bool = False
- ):
- # prepare files for ViSQOL evaluation.
- assert target_sr in ViSQOL.ALLOWED_SAMPLE_RATES
- assert len(ref_sig) == len(deg_sig), (
- "Expects same number of ref and degraded inputs",
- f" but ref len {len(ref_sig)} != deg len {len(deg_sig)}"
- )
- # resample audio if needed
- if sr != target_sr:
- transform = torchaudio.transforms.Resample(sr, target_sr)
- pad = int(0.5 * target_sr)
- rs_ref = []
- rs_deg = []
- for i in range(len(ref_sig)):
- rs_ref_i = transform(ref_sig[i])
- rs_deg_i = transform(deg_sig[i])
- if pad_with_silence:
- rs_ref_i = torch.nn.functional.pad(rs_ref_i, (pad, pad), mode='constant', value=0)
- rs_deg_i = torch.nn.functional.pad(rs_deg_i, (pad, pad), mode='constant', value=0)
- rs_ref.append(rs_ref_i)
- rs_deg.append(rs_deg_i)
- ref_sig = torch.stack(rs_ref)
- deg_sig = torch.stack(rs_deg)
- # save audio chunks to tmp dir and create csv
- tmp_dir = Path(tempfile.mkdtemp())
- try:
- tmp_input_csv_path = tmp_dir / "input.csv"
- tmp_results_csv_path = tmp_dir / "results.csv"
- tmp_debug_json_path = tmp_dir / "debug.json"
- with open(tmp_input_csv_path, "w") as csv_file:
- csv_writer = csv.writer(csv_file)
- csv_writer.writerow(["reference", "degraded"])
- for i in range(len(ref_sig)):
- tmp_ref_filename = tmp_dir / f"ref_{i}.wav"
- tmp_deg_filename = tmp_dir / f"deg_{i}.wav"
- torchaudio.save(
- tmp_ref_filename,
- torch.clamp(ref_sig[i], min=-0.99, max=0.99),
- sample_rate=target_sr,
- bits_per_sample=16,
- encoding="PCM_S"
- )
- torchaudio.save(
- tmp_deg_filename,
- torch.clamp(deg_sig[i], min=-0.99, max=0.99),
- sample_rate=target_sr,
- bits_per_sample=16,
- encoding="PCM_S"
- )
- csv_writer.writerow([str(tmp_ref_filename), str(tmp_deg_filename)])
- return tmp_dir, tmp_input_csv_path, tmp_results_csv_path, tmp_debug_json_path
- except Exception as e:
- logger.error("Exception occurred when preparing files for ViSQOL: %s", e)
- return tmp_dir, None, None, None
-
- def _flush_files(self, tmp_dir: tp.Union[Path, str]):
- # flush tmp files used to compute ViSQOL.
- shutil.rmtree(str(tmp_dir))
-
- def _collect_moslqo_score(self, results_csv_path: tp.Union[Path, str]) -> float:
- # collect results for each evaluated pair and return averaged moslqo score.
- with open(results_csv_path, "r") as csv_file:
- reader = csv.DictReader(csv_file)
- moslqo_scores = [float(row["moslqo"]) for row in reader]
- if len(moslqo_scores) > 0:
- return sum(moslqo_scores) / len(moslqo_scores)
- else:
- return 0.0
-
- def _collect_debug_data(self, debug_json_path: tp.Union[Path, str]) -> dict:
- # collect debug data for the visqol inference.
- with open(debug_json_path, "r") as f:
- data = json.load(f)
- return data
-
- @property
- def visqol_model(self):
- return f'{self.visqol_bin}/model/{self.model}'
-
- def _run_visqol(
- self,
- input_csv_path: tp.Union[Path, str],
- results_csv_path: tp.Union[Path, str],
- debug_csv_path: tp.Optional[tp.Union[Path, str]],
- ):
- input_csv_path = str(input_csv_path)
- results_csv_path = str(results_csv_path)
- debug_csv_path = str(debug_csv_path)
- cmd = [
- f'{self.visqol_bin}/bazel-bin/visqol',
- '--batch_input_csv', f'{input_csv_path}',
- '--results_csv', f'{results_csv_path}'
- ]
- if debug_csv_path is not None:
- cmd += ['--output_debug', f'{debug_csv_path}']
- if self.visqol_mode == "speech":
- cmd += ['--use_speech_mode']
- cmd += ['--similarity_to_quality_model', f'{self.visqol_model}']
- result = subprocess.run(cmd, capture_output=True)
- if result.returncode:
- logger.error("Error with visqol: \n %s \n %s", result.stdout.decode(), result.stderr.decode())
- raise RuntimeError("Error while executing visqol")
- result.check_returncode()
-
- def __call__(
- self,
- ref_sig: torch.Tensor,
- deg_sig: torch.Tensor,
- sr: int,
- pad_with_silence: bool = False,
- ):
- """Calculate the ViSQOL metric for a pair of audio signals at a given sample rate.
- Args:
- ref_sig (torch.Tensor): Reference signals as [B, C, T].
- deg_sig (torch.Tensor): Degraded signals as [B, C, T].
- sr (int): Sample rate of the two audio signals.
- pad_with_silence (bool): Whether to pad the file with silences as recommended
- in visqol guidelines (see: https://github.com/google/visqol#general-guidelines-for-input).
- Returns:
- float: The ViSQOL score or mean score for the batch.
- """
- logger.debug(f"Calculating visqol with mode={self.visqol_mode} on {len(ref_sig)} samples")
- tmp_dir, input_csv, results_csv, debug_json = self._prepare_files(
- ref_sig, deg_sig, sr, self.target_sr, pad_with_silence
- )
- try:
- if input_csv and results_csv:
- self._run_visqol(
- input_csv,
- results_csv,
- debug_json if self.debug else None,
- )
- mosqol = self._collect_moslqo_score(results_csv)
- return mosqol
- else:
- raise RuntimeError("Something unexpected happened when running VISQOL!")
- except Exception as e:
- logger.error("Exception occurred when running ViSQOL: %s", e)
- finally:
- self._flush_files(tmp_dir)
diff --git a/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile b/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile
deleted file mode 100644
index 31b5f817b41d7d95055d9aa5d5e4f973abee0b45..0000000000000000000000000000000000000000
--- a/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile
+++ /dev/null
@@ -1,26 +0,0 @@
-IMG:=`cat Name`
-MOUNT:=
-PORT_MAP:=
-DOTENV:=.env
-CONTAINER_NAME:=sample-chatgpt-on-wechat
-
-echo:
- echo $(IMG)
-
-run_d:
- docker rm $(CONTAINER_NAME) || echo
- docker run -dt --name $(CONTAINER_NAME) $(PORT_MAP) \
- --env-file=$(DOTENV) \
- $(MOUNT) $(IMG)
-
-run_i:
- docker rm $(CONTAINER_NAME) || echo
- docker run -it --name $(CONTAINER_NAME) $(PORT_MAP) \
- --env-file=$(DOTENV) \
- $(MOUNT) $(IMG)
-
-stop:
- docker stop $(CONTAINER_NAME)
-
-rm: stop
- docker rm $(CONTAINER_NAME)
diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py
deleted file mode 100644
index 84e51fd71fca78131a3be3d0d35d808c07bb3a95..0000000000000000000000000000000000000000
--- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py
+++ /dev/null
@@ -1,145 +0,0 @@
-import torch.nn as nn
-import math
-
-
-def conv3x3(in_planes, out_planes, stride=1):
- """3x3 convolution with padding"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
- expansion = 1
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(BasicBlock, self).__init__()
- self.conv1 = conv3x3(inplanes, planes, stride)
- self.bn1 = nn.BatchNorm2d(planes)
- self.relu = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(planes, planes)
- self.bn2 = nn.BatchNorm2d(planes)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(Bottleneck, self).__init__()
- self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
- self.bn1 = nn.BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
- self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * 4)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
- out = self.relu(out)
-
- out = self.conv3(out)
- out = self.bn3(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class ResNet50(nn.Module):
- def __init__(self,mode='rgb'):
- self.inplanes = 64
- super(ResNet50, self).__init__()
- if(mode=='rgb'):
- self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- elif(mode=='rgbd'):
- self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- elif(mode=="share"):
- self.conv1=nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- self.conv1_d=nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
- bias=False)
- else:
- raise
- self.bn1 = nn.BatchNorm2d(64)
- self.relu = nn.ReLU(inplace=True)
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
- self.layer1 = self._make_layer(Bottleneck, 64, 3)
- self.layer2 = self._make_layer(Bottleneck, 128, 4, stride=2)
- self.layer3_1 = self._make_layer(Bottleneck, 256, 6, stride=2)
- self.layer4_1 = self._make_layer(Bottleneck, 512, 3, stride=2)
-
- self.inplanes = 512
-
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
- m.weight.data.normal_(0, math.sqrt(2. / n))
- elif isinstance(m, nn.BatchNorm2d):
- m.weight.data.fill_(1)
- m.bias.data.zero_()
-
- def _make_layer(self, block, planes, blocks, stride=1):
- downsample = None
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- nn.Conv2d(self.inplanes, planes * block.expansion,
- kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(planes * block.expansion),
- )
-
- layers = []
- layers.append(block(self.inplanes, planes, stride, downsample))
- self.inplanes = planes * block.expansion
- for i in range(1, blocks):
- layers.append(block(self.inplanes, planes))
-
- return nn.Sequential(*layers)
-
- def forward(self, x):
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
- x = self.maxpool(x)
-
- x = self.layer1(x)
- x = self.layer2(x)
- x1 = self.layer3_1(x)
- x1 = self.layer4_1(x1)
-
- return x1, x1
diff --git a/spaces/Hallucinate/demo/ldm/models/autoencoder.py b/spaces/Hallucinate/demo/ldm/models/autoencoder.py
deleted file mode 100644
index 6a9c4f45498561953b8085981609b2a3298a5473..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/ldm/models/autoencoder.py
+++ /dev/null
@@ -1,443 +0,0 @@
-import torch
-import pytorch_lightning as pl
-import torch.nn.functional as F
-from contextlib import contextmanager
-
-from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
-
-from ldm.modules.diffusionmodules.model import Encoder, Decoder
-from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
-
-from ldm.util import instantiate_from_config
-
-
-class VQModel(pl.LightningModule):
- def __init__(self,
- ddconfig,
- lossconfig,
- n_embed,
- embed_dim,
- ckpt_path=None,
- ignore_keys=[],
- image_key="image",
- colorize_nlabels=None,
- monitor=None,
- batch_resize_range=None,
- scheduler_config=None,
- lr_g_factor=1.0,
- remap=None,
- sane_index_shape=False, # tell vector quantizer to return indices as bhw
- use_ema=False
- ):
- super().__init__()
- self.embed_dim = embed_dim
- self.n_embed = n_embed
- self.image_key = image_key
- self.encoder = Encoder(**ddconfig)
- self.decoder = Decoder(**ddconfig)
- self.loss = instantiate_from_config(lossconfig)
- self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
- remap=remap,
- sane_index_shape=sane_index_shape)
- self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
- self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
- if colorize_nlabels is not None:
- assert type(colorize_nlabels)==int
- self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
- if monitor is not None:
- self.monitor = monitor
- self.batch_resize_range = batch_resize_range
- if self.batch_resize_range is not None:
- print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
-
- self.use_ema = use_ema
- if self.use_ema:
- self.model_ema = LitEma(self)
- print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
-
- if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
- self.scheduler_config = scheduler_config
- self.lr_g_factor = lr_g_factor
-
- @contextmanager
- def ema_scope(self, context=None):
- if self.use_ema:
- self.model_ema.store(self.parameters())
- self.model_ema.copy_to(self)
- if context is not None:
- print(f"{context}: Switched to EMA weights")
- try:
- yield None
- finally:
- if self.use_ema:
- self.model_ema.restore(self.parameters())
- if context is not None:
- print(f"{context}: Restored training weights")
-
- def init_from_ckpt(self, path, ignore_keys=list()):
- sd = torch.load(path, map_location="cpu")["state_dict"]
- keys = list(sd.keys())
- for k in keys:
- for ik in ignore_keys:
- if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
- del sd[k]
- missing, unexpected = self.load_state_dict(sd, strict=False)
- print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
- if len(missing) > 0:
- print(f"Missing Keys: {missing}")
- print(f"Unexpected Keys: {unexpected}")
-
- def on_train_batch_end(self, *args, **kwargs):
- if self.use_ema:
- self.model_ema(self)
-
- def encode(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- quant, emb_loss, info = self.quantize(h)
- return quant, emb_loss, info
-
- def encode_to_prequant(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- return h
-
- def decode(self, quant):
- quant = self.post_quant_conv(quant)
- dec = self.decoder(quant)
- return dec
-
- def decode_code(self, code_b):
- quant_b = self.quantize.embed_code(code_b)
- dec = self.decode(quant_b)
- return dec
-
- def forward(self, input, return_pred_indices=False):
- quant, diff, (_,_,ind) = self.encode(input)
- dec = self.decode(quant)
- if return_pred_indices:
- return dec, diff, ind
- return dec, diff
-
- def get_input(self, batch, k):
- x = batch[k]
- if len(x.shape) == 3:
- x = x[..., None]
- x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
- if self.batch_resize_range is not None:
- lower_size = self.batch_resize_range[0]
- upper_size = self.batch_resize_range[1]
- if self.global_step <= 4:
- # do the first few batches with max size to avoid later oom
- new_resize = upper_size
- else:
- new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
- if new_resize != x.shape[2]:
- x = F.interpolate(x, size=new_resize, mode="bicubic")
- x = x.detach()
- return x
-
- def training_step(self, batch, batch_idx, optimizer_idx):
- # https://github.com/pytorch/pytorch/issues/37142
- # try not to fool the heuristics
- x = self.get_input(batch, self.image_key)
- xrec, qloss, ind = self(x, return_pred_indices=True)
-
- if optimizer_idx == 0:
- # autoencode
- aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train",
- predicted_indices=ind)
-
- self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
- return aeloss
-
- if optimizer_idx == 1:
- # discriminator
- discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
- self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
- return discloss
-
- def validation_step(self, batch, batch_idx):
- log_dict = self._validation_step(batch, batch_idx)
- with self.ema_scope():
- log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
- return log_dict
-
- def _validation_step(self, batch, batch_idx, suffix=""):
- x = self.get_input(batch, self.image_key)
- xrec, qloss, ind = self(x, return_pred_indices=True)
- aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
- self.global_step,
- last_layer=self.get_last_layer(),
- split="val"+suffix,
- predicted_indices=ind
- )
-
- discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
- self.global_step,
- last_layer=self.get_last_layer(),
- split="val"+suffix,
- predicted_indices=ind
- )
- rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
- self.log(f"val{suffix}/rec_loss", rec_loss,
- prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
- self.log(f"val{suffix}/aeloss", aeloss,
- prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
- if version.parse(pl.__version__) >= version.parse('1.4.0'):
- del log_dict_ae[f"val{suffix}/rec_loss"]
- self.log_dict(log_dict_ae)
- self.log_dict(log_dict_disc)
- return self.log_dict
-
- def configure_optimizers(self):
- lr_d = self.learning_rate
- lr_g = self.lr_g_factor*self.learning_rate
- print("lr_d", lr_d)
- print("lr_g", lr_g)
- opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
- list(self.decoder.parameters())+
- list(self.quantize.parameters())+
- list(self.quant_conv.parameters())+
- list(self.post_quant_conv.parameters()),
- lr=lr_g, betas=(0.5, 0.9))
- opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
- lr=lr_d, betas=(0.5, 0.9))
-
- if self.scheduler_config is not None:
- scheduler = instantiate_from_config(self.scheduler_config)
-
- print("Setting up LambdaLR scheduler...")
- scheduler = [
- {
- 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
- 'interval': 'step',
- 'frequency': 1
- },
- {
- 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
- 'interval': 'step',
- 'frequency': 1
- },
- ]
- return [opt_ae, opt_disc], scheduler
- return [opt_ae, opt_disc], []
-
- def get_last_layer(self):
- return self.decoder.conv_out.weight
-
- def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
- log = dict()
- x = self.get_input(batch, self.image_key)
- x = x.to(self.device)
- if only_inputs:
- log["inputs"] = x
- return log
- xrec, _ = self(x)
- if x.shape[1] > 3:
- # colorize with random projection
- assert xrec.shape[1] > 3
- x = self.to_rgb(x)
- xrec = self.to_rgb(xrec)
- log["inputs"] = x
- log["reconstructions"] = xrec
- if plot_ema:
- with self.ema_scope():
- xrec_ema, _ = self(x)
- if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
- log["reconstructions_ema"] = xrec_ema
- return log
-
- def to_rgb(self, x):
- assert self.image_key == "segmentation"
- if not hasattr(self, "colorize"):
- self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
- x = F.conv2d(x, weight=self.colorize)
- x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
- return x
-
-
-class VQModelInterface(VQModel):
- def __init__(self, embed_dim, *args, **kwargs):
- super().__init__(embed_dim=embed_dim, *args, **kwargs)
- self.embed_dim = embed_dim
-
- def encode(self, x):
- h = self.encoder(x)
- h = self.quant_conv(h)
- return h
-
- def decode(self, h, force_not_quantize=False):
- # also go through quantization layer
- if not force_not_quantize:
- quant, emb_loss, info = self.quantize(h)
- else:
- quant = h
- quant = self.post_quant_conv(quant)
- dec = self.decoder(quant)
- return dec
-
-
-class AutoencoderKL(pl.LightningModule):
- def __init__(self,
- ddconfig,
- lossconfig,
- embed_dim,
- ckpt_path=None,
- ignore_keys=[],
- image_key="image",
- colorize_nlabels=None,
- monitor=None,
- ):
- super().__init__()
- self.image_key = image_key
- self.encoder = Encoder(**ddconfig)
- self.decoder = Decoder(**ddconfig)
- self.loss = instantiate_from_config(lossconfig)
- assert ddconfig["double_z"]
- self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
- self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
- self.embed_dim = embed_dim
- if colorize_nlabels is not None:
- assert type(colorize_nlabels)==int
- self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
- if monitor is not None:
- self.monitor = monitor
- if ckpt_path is not None:
- self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
-
- def init_from_ckpt(self, path, ignore_keys=list()):
- sd = torch.load(path, map_location="cpu")["state_dict"]
- keys = list(sd.keys())
- for k in keys:
- for ik in ignore_keys:
- if k.startswith(ik):
- print("Deleting key {} from state_dict.".format(k))
- del sd[k]
- self.load_state_dict(sd, strict=False)
- print(f"Restored from {path}")
-
- def encode(self, x):
- h = self.encoder(x)
- moments = self.quant_conv(h)
- posterior = DiagonalGaussianDistribution(moments)
- return posterior
-
- def decode(self, z):
- z = self.post_quant_conv(z)
- dec = self.decoder(z)
- return dec
-
- def forward(self, input, sample_posterior=True):
- posterior = self.encode(input)
- if sample_posterior:
- z = posterior.sample()
- else:
- z = posterior.mode()
- dec = self.decode(z)
- return dec, posterior
-
- def get_input(self, batch, k):
- x = batch[k]
- if len(x.shape) == 3:
- x = x[..., None]
- x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
- return x
-
- def training_step(self, batch, batch_idx, optimizer_idx):
- inputs = self.get_input(batch, self.image_key)
- reconstructions, posterior = self(inputs)
-
- if optimizer_idx == 0:
- # train encoder+decoder+logvar
- aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
- self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
- self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
- return aeloss
-
- if optimizer_idx == 1:
- # train the discriminator
- discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
- last_layer=self.get_last_layer(), split="train")
-
- self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
- self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
- return discloss
-
- def validation_step(self, batch, batch_idx):
- inputs = self.get_input(batch, self.image_key)
- reconstructions, posterior = self(inputs)
- aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
- last_layer=self.get_last_layer(), split="val")
-
- discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
- last_layer=self.get_last_layer(), split="val")
-
- self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
- self.log_dict(log_dict_ae)
- self.log_dict(log_dict_disc)
- return self.log_dict
-
- def configure_optimizers(self):
- lr = self.learning_rate
- opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
- list(self.decoder.parameters())+
- list(self.quant_conv.parameters())+
- list(self.post_quant_conv.parameters()),
- lr=lr, betas=(0.5, 0.9))
- opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
- lr=lr, betas=(0.5, 0.9))
- return [opt_ae, opt_disc], []
-
- def get_last_layer(self):
- return self.decoder.conv_out.weight
-
- @torch.no_grad()
- def log_images(self, batch, only_inputs=False, **kwargs):
- log = dict()
- x = self.get_input(batch, self.image_key)
- x = x.to(self.device)
- if not only_inputs:
- xrec, posterior = self(x)
- if x.shape[1] > 3:
- # colorize with random projection
- assert xrec.shape[1] > 3
- x = self.to_rgb(x)
- xrec = self.to_rgb(xrec)
- log["samples"] = self.decode(torch.randn_like(posterior.sample()))
- log["reconstructions"] = xrec
- log["inputs"] = x
- return log
-
- def to_rgb(self, x):
- assert self.image_key == "segmentation"
- if not hasattr(self, "colorize"):
- self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
- x = F.conv2d(x, weight=self.colorize)
- x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
- return x
-
-
-class IdentityFirstStage(torch.nn.Module):
- def __init__(self, *args, vq_interface=False, **kwargs):
- self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff
- super().__init__()
-
- def encode(self, x, *args, **kwargs):
- return x
-
- def decode(self, x, *args, **kwargs):
- return x
-
- def quantize(self, x, *args, **kwargs):
- if self.vq_interface:
- return x, None, [None, None, None]
- return x
-
- def forward(self, x, *args, **kwargs):
- return x
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py b/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py
deleted file mode 100644
index 81676226e57ca519273b98328a1afe6961c37ce3..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py
+++ /dev/null
@@ -1,403 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA Authors. All rights reserved.
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-# http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Auto Config class."""
-import importlib
-import re
-import warnings
-from collections import OrderedDict
-from typing import List, Union
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.file_utils import CONFIG_NAME
-from transformers.utils import logging
-from .dynamic import get_class_from_dynamic_module
-
-
-logger = logging.get_logger(__name__)
-
-CONFIG_MAPPING_NAMES = OrderedDict(
- [
- # Add configs here
- ("roformer", "RoFormerConfig"),
- ("longformer", "LongformerConfig"),
- ]
-)
-
-CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
- [
- # Add archive maps here
- ("roformer", "ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
- ("longformer", "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
- ]
-)
-
-MODEL_NAMES_MAPPING = OrderedDict(
- [
- # Add full (and cased) model names here
- ("roformer", "Roformer"),
- ("longformer", "Longformer"),
- ]
-)
-
-SPECIAL_MODEL_TYPE_TO_MODULE_NAME = OrderedDict([("openai-gpt", "openai")])
-
-
-def model_type_to_module_name(key):
- """Converts a config key to the corresponding module."""
- # Special treatment
- if key in SPECIAL_MODEL_TYPE_TO_MODULE_NAME:
- return SPECIAL_MODEL_TYPE_TO_MODULE_NAME[key]
-
- return key.replace("-", "_")
-
-
-def config_class_to_model_type(config):
- """Converts a config class name to the corresponding model type"""
- for key, cls in CONFIG_MAPPING_NAMES.items():
- if cls == config:
- return key
- return None
-
-
-class _LazyConfigMapping(OrderedDict):
- """
- A dictionary that lazily load its values when they are requested.
- """
-
- def __init__(self, mapping):
- self._mapping = mapping
- self._extra_content = {}
- self._modules = {}
-
- def __getitem__(self, key):
- if key in self._extra_content:
- return self._extra_content[key]
- if key not in self._mapping:
- raise KeyError(key)
- value = self._mapping[key]
- module_name = model_type_to_module_name(key)
- if module_name not in self._modules:
- self._modules[module_name] = importlib.import_module(f".{module_name}", "fengshen.models")
-
- return getattr(self._modules[module_name], value)
-
- def keys(self):
- return list(self._mapping.keys()) + list(self._extra_content.keys())
-
- def values(self):
- return [self[k] for k in self._mapping.keys()] + list(self._extra_content.values())
-
- def items(self):
- return [(k, self[k]) for k in self._mapping.keys()] + list(self._extra_content.items())
-
- def __iter__(self):
- return iter(list(self._mapping.keys()) + list(self._extra_content.keys()))
-
- def __contains__(self, item):
- return item in self._mapping or item in self._extra_content
-
- def register(self, key, value):
- """
- Register a new configuration in this mapping.
- """
- if key in self._mapping.keys():
- raise ValueError(f"'{key}' is already used by a Transformers config, pick another name.")
- self._extra_content[key] = value
-
-
-CONFIG_MAPPING = _LazyConfigMapping(CONFIG_MAPPING_NAMES)
-
-
-class _LazyLoadAllMappings(OrderedDict):
- """
- A mapping that will load all pairs of key values at the first access (either by indexing, requestions keys, values,
- etc.)
-
- Args:
- mapping: The mapping to load.
- """
-
- def __init__(self, mapping):
- self._mapping = mapping
- self._initialized = False
- self._data = {}
-
- def _initialize(self):
- if self._initialized:
- return
- warnings.warn(
- "ALL_PRETRAINED_CONFIG_ARCHIVE_MAP is deprecated and will be removed in v5 of Transformers. "
- "It does not contain all available model checkpoints, far from it. Checkout hf.co/models for that.",
- FutureWarning,
- )
-
- for model_type, map_name in self._mapping.items():
- module_name = model_type_to_module_name(model_type)
- module = importlib.import_module(f".{module_name}", "transformers.models")
- mapping = getattr(module, map_name)
- self._data.update(mapping)
-
- self._initialized = True
-
- def __getitem__(self, key):
- self._initialize()
- return self._data[key]
-
- def keys(self):
- self._initialize()
- return self._data.keys()
-
- def values(self):
- self._initialize()
- return self._data.values()
-
- def items(self):
- self._initialize()
- return self._data.keys()
-
- def __iter__(self):
- self._initialize()
- return iter(self._data)
-
- def __contains__(self, item):
- self._initialize()
- return item in self._data
-
-
-ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = _LazyLoadAllMappings(CONFIG_ARCHIVE_MAP_MAPPING_NAMES)
-
-
-def _get_class_name(model_class: Union[str, List[str]]):
- if isinstance(model_class, (list, tuple)):
- return " or ".join([f"[`{c}`]" for c in model_class if c is not None])
- return f"[`{model_class}`]"
-
-
-def _list_model_options(indent, config_to_class=None, use_model_types=True):
- if config_to_class is None and not use_model_types:
- raise ValueError("Using `use_model_types=False` requires a `config_to_class` dictionary.")
- if use_model_types:
- if config_to_class is None:
- model_type_to_name = {model_type: f"[`{config}`]" for model_type, config in CONFIG_MAPPING_NAMES.items()}
- else:
- model_type_to_name = {
- model_type: _get_class_name(model_class)
- for model_type, model_class in config_to_class.items()
- if model_type in MODEL_NAMES_MAPPING
- }
- lines = [
- f"{indent}- **{model_type}** -- {model_type_to_name[model_type]} ({MODEL_NAMES_MAPPING[model_type]} model)"
- for model_type in sorted(model_type_to_name.keys())
- ]
- else:
- config_to_name = {
- CONFIG_MAPPING_NAMES[config]: _get_class_name(clas)
- for config, clas in config_to_class.items()
- if config in CONFIG_MAPPING_NAMES
- }
- config_to_model_name = {
- config: MODEL_NAMES_MAPPING[model_type] for model_type, config in CONFIG_MAPPING_NAMES.items()
- }
- lines = [
- f"{indent}- [`{config_name}`] configuration class: {config_to_name[config_name]} ({config_to_model_name[config_name]} model)"
- for config_name in sorted(config_to_name.keys())
- ]
- return "\n".join(lines)
-
-
-def replace_list_option_in_docstrings(config_to_class=None, use_model_types=True):
- def docstring_decorator(fn):
- docstrings = fn.__doc__
- lines = docstrings.split("\n")
- i = 0
- while i < len(lines) and re.search(r"^(\s*)List options\s*$", lines[i]) is None:
- i += 1
- if i < len(lines):
- indent = re.search(r"^(\s*)List options\s*$", lines[i]).groups()[0]
- if use_model_types:
- indent = f"{indent} "
- lines[i] = _list_model_options(indent, config_to_class=config_to_class, use_model_types=use_model_types)
- docstrings = "\n".join(lines)
- else:
- raise ValueError(
- f"The function {fn} should have an empty 'List options' in its docstring as placeholder, current docstring is:\n{docstrings}"
- )
- fn.__doc__ = docstrings
- return fn
-
- return docstring_decorator
-
-
-class AutoConfig:
- r"""
- This is a generic configuration class that will be instantiated as one of the configuration classes of the library
- when created with the [`~AutoConfig.from_pretrained`] class method.
-
- This class cannot be instantiated directly using `__init__()` (throws an error).
- """
-
- def __init__(self):
- raise EnvironmentError(
- "AutoConfig is designed to be instantiated "
- "using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method."
- )
-
- @classmethod
- def for_model(cls, model_type: str, *args, **kwargs):
- if model_type in CONFIG_MAPPING:
- config_class = CONFIG_MAPPING[model_type]
- return config_class(*args, **kwargs)
- raise ValueError(
- f"Unrecognized model identifier: {model_type}. Should contain one of {', '.join(CONFIG_MAPPING.keys())}"
- )
-
- @classmethod
- @replace_list_option_in_docstrings()
- def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
- r"""
- Instantiate one of the configuration classes of the library from a pretrained model configuration.
-
- The configuration class to instantiate is selected based on the `model_type` property of the config object that
- is loaded, or when it's missing, by falling back to using pattern matching on `pretrained_model_name_or_path`:
-
- List options
-
- Args:
- pretrained_model_name_or_path (`str` or `os.PathLike`):
- Can be either:
-
- - A string, the *model id* of a pretrained model configuration hosted inside a model repo on
- huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or
- namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`.
- - A path to a *directory* containing a configuration file saved using the
- [`~PretrainedConfig.save_pretrained`] method, or the [`~PreTrainedModel.save_pretrained`] method,
- e.g., `./my_model_directory/`.
- - A path or url to a saved configuration JSON *file*, e.g.,
- `./my_model_directory/configuration.json`.
- cache_dir (`str` or `os.PathLike`, *optional*):
- Path to a directory in which a downloaded pretrained model configuration should be cached if the
- standard cache should not be used.
- force_download (`bool`, *optional*, defaults to `False`):
- Whether or not to force the (re-)download the model weights and configuration files and override the
- cached versions if they exist.
- resume_download (`bool`, *optional*, defaults to `False`):
- Whether or not to delete incompletely received files. Will attempt to resume the download if such a
- file exists.
- proxies (`Dict[str, str]`, *optional*):
- A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
- 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
- revision(`str`, *optional*, defaults to `"main"`):
- The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
- git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
- identifier allowed by git.
- return_unused_kwargs (`bool`, *optional*, defaults to `False`):
- If `False`, then this function returns just the final configuration object.
-
- If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a
- dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
- part of `kwargs` which has not been used to update `config` and is otherwise ignored.
- trust_remote_code (`bool`, *optional*, defaults to `False`):
- Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
- should only be set to `True` for repositories you trust and in which you have read the code, as it will
- execute code present on the Hub on your local machine.
- kwargs(additional keyword arguments, *optional*):
- The values in kwargs of any keys which are configuration attributes will be used to override the loaded
- values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
- by the `return_unused_kwargs` keyword parameter.
-
- Examples:
-
- ```python
- >>> from transformers import AutoConfig
-
- >>> # Download configuration from huggingface.co and cache.
- >>> config = AutoConfig.from_pretrained("bert-base-uncased")
-
- >>> # Download configuration from huggingface.co (user-uploaded) and cache.
- >>> config = AutoConfig.from_pretrained("dbmdz/bert-base-german-cased")
-
- >>> # If configuration file is in a directory (e.g., was saved using *save_pretrained('./test/saved_model/')*).
- >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/")
-
- >>> # Load a specific configuration file.
- >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/my_configuration.json")
-
- >>> # Change some config attributes when loading a pretrained config.
- >>> config = AutoConfig.from_pretrained("bert-base-uncased", output_attentions=True, foo=False)
- >>> config.output_attentions
- True
-
- >>> config, unused_kwargs = AutoConfig.from_pretrained(
- ... "bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
- ... )
- >>> config.output_attentions
- True
-
- >>> config.unused_kwargs
- {'foo': False}
- ```"""
- kwargs["_from_auto"] = True
- kwargs["name_or_path"] = pretrained_model_name_or_path
- trust_remote_code = kwargs.pop("trust_remote_code", False)
- config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
- if "auto_map" in config_dict and "AutoConfig" in config_dict["auto_map"]:
- if not trust_remote_code:
- raise ValueError(
- f"Loading {pretrained_model_name_or_path} requires you to execute the configuration file in that repo "
- "on your local machine. Make sure you have read the code there to avoid malicious use, then set "
- "the option `trust_remote_code=True` to remove this error."
- )
- if kwargs.get("revision", None) is None:
- logger.warn(
- "Explicitly passing a `revision` is encouraged when loading a configuration with custom code to "
- "ensure no malicious code has been contributed in a newer revision."
- )
- class_ref = config_dict["auto_map"]["AutoConfig"]
- module_file, class_name = class_ref.split(".")
- config_class = get_class_from_dynamic_module(
- pretrained_model_name_or_path, module_file + ".py", class_name, **kwargs
- )
- return config_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
- elif "model_type" in config_dict:
- config_class = CONFIG_MAPPING[config_dict["model_type"]]
- return config_class.from_dict(config_dict, **kwargs)
- else:
- # Fallback: use pattern matching on the string.
- for pattern, config_class in CONFIG_MAPPING.items():
- if pattern in str(pretrained_model_name_or_path):
- return config_class.from_dict(config_dict, **kwargs)
-
- raise ValueError(
- f"Unrecognized model in {pretrained_model_name_or_path}. "
- f"Should have a `model_type` key in its {CONFIG_NAME}, or contain one of the following strings "
- f"in its name: {', '.join(CONFIG_MAPPING.keys())}"
- )
-
- @staticmethod
- def register(model_type, config):
- """
- Register a new configuration for this class.
-
- Args:
- model_type (`str`): The model type like "bert" or "gpt".
- config ([`PretrainedConfig`]): The config to register.
- """
- if issubclass(config, PretrainedConfig) and config.model_type != model_type:
- raise ValueError(
- "The config you are passing has a `model_type` attribute that is not consistent with the model type "
- f"you passed (config has {config.model_type} and you passed {model_type}. Fix one of those so they "
- "match!"
- )
- CONFIG_MAPPING.register(model_type, config)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py
deleted file mode 100644
index b6fb6e751cdedb2af4b1f6c0950557e187cd9519..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from .scst_loss import ScstRewardCriterion
-from .label_smoothed_cross_entropy import AjustLabelSmoothedCrossEntropyCriterion
\ No newline at end of file
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py
deleted file mode 100644
index 56d63e3e1b5a036e0adf32480e2b66f371738013..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from dataclasses import dataclass, field
-
-import torch
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-from omegaconf import II
-
-
-@dataclass
-class LabelSmoothedCrossEntropyCriterionConfig(FairseqDataclass):
- label_smoothing: float = field(
- default=0.0,
- metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"},
- )
- report_accuracy: bool = field(
- default=False,
- metadata={"help": "report accuracy metric"},
- )
- ignore_prefix_size: int = field(
- default=0,
- metadata={"help": "Ignore first N tokens"},
- )
- sentence_avg: bool = II("optimization.sentence_avg")
-
-
-def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=None, reduce=True):
- if target.dim() == lprobs.dim() - 1:
- target = target.unsqueeze(-1)
- nll_loss = -lprobs.gather(dim=-1, index=target)
- smooth_loss = -lprobs.sum(dim=-1, keepdim=True)
- if ignore_index is not None:
- pad_mask = target.eq(ignore_index)
- nll_loss.masked_fill_(pad_mask, 0.0)
- smooth_loss.masked_fill_(pad_mask, 0.0)
- else:
- nll_loss = nll_loss.squeeze(-1)
- smooth_loss = smooth_loss.squeeze(-1)
- if reduce:
- nll_loss = nll_loss.sum()
- smooth_loss = smooth_loss.sum()
- eps_i = epsilon / (lprobs.size(-1) - 1)
- loss = (1.0 - epsilon - eps_i) * nll_loss + eps_i * smooth_loss
- return loss, nll_loss
-
-
-@register_criterion(
- "label_smoothed_cross_entropy", dataclass=LabelSmoothedCrossEntropyCriterionConfig
-)
-class LabelSmoothedCrossEntropyCriterion(FairseqCriterion):
- def __init__(
- self,
- task,
- sentence_avg,
- label_smoothing,
- ignore_prefix_size=0,
- report_accuracy=False,
- ):
- super().__init__(task)
- self.sentence_avg = sentence_avg
- self.eps = label_smoothing
- self.ignore_prefix_size = ignore_prefix_size
- self.report_accuracy = report_accuracy
-
- def forward(self, model, sample, reduce=True):
- """Compute the loss for the given sample.
-
- Returns a tuple with three elements:
- 1) the loss
- 2) the sample size, which is used as the denominator for the gradient
- 3) logging outputs to display while training
- """
- net_output = model(**sample["net_input"])
- loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce)
- sample_size = (
- sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
- )
- logging_output = {
- "loss": loss.data,
- "nll_loss": nll_loss.data,
- "ntokens": sample["ntokens"],
- "nsentences": sample["target"].size(0),
- "sample_size": sample_size,
- }
- if self.report_accuracy:
- n_correct, total = self.compute_accuracy(model, net_output, sample)
- logging_output["n_correct"] = utils.item(n_correct.data)
- logging_output["total"] = utils.item(total.data)
- return loss, sample_size, logging_output
-
- def get_lprobs_and_target(self, model, net_output, sample):
- lprobs = model.get_normalized_probs(net_output, log_probs=True)
- target = model.get_targets(sample, net_output)
- if self.ignore_prefix_size > 0:
- if getattr(lprobs, "batch_first", False):
- lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous()
- target = target[:, self.ignore_prefix_size :].contiguous()
- else:
- lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous()
- target = target[self.ignore_prefix_size :, :].contiguous()
- return lprobs.view(-1, lprobs.size(-1)), target.view(-1)
-
- def compute_loss(self, model, net_output, sample, reduce=True):
- lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
- loss, nll_loss = label_smoothed_nll_loss(
- lprobs,
- target,
- self.eps,
- ignore_index=self.padding_idx,
- reduce=reduce,
- )
- return loss, nll_loss
-
- def compute_accuracy(self, model, net_output, sample):
- lprobs, target = self.get_lprobs_and_target(model, net_output, sample)
- mask = target.ne(self.padding_idx)
- n_correct = torch.sum(
- lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask))
- )
- total = torch.sum(mask)
- return n_correct, total
-
- @classmethod
- def reduce_metrics(cls, logging_outputs) -> None:
- """Aggregate logging outputs from data parallel training."""
- loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
- nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs)
- ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
- sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
-
- metrics.log_scalar(
- "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
- )
- metrics.log_scalar(
- "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3
- )
- metrics.log_derived(
- "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
- )
-
- total = utils.item(sum(log.get("total", 0) for log in logging_outputs))
- if total > 0:
- metrics.log_scalar("total", total)
- n_correct = utils.item(
- sum(log.get("n_correct", 0) for log in logging_outputs)
- )
- metrics.log_scalar("n_correct", n_correct)
- metrics.log_derived(
- "accuracy",
- lambda meters: round(
- meters["n_correct"].sum * 100.0 / meters["total"].sum, 3
- )
- if meters["total"].sum > 0
- else float("nan"),
- )
-
- @staticmethod
- def logging_outputs_can_be_summed() -> bool:
- """
- Whether the logging outputs returned by `forward` can be summed
- across workers prior to calling `reduce_metrics`. Setting this
- to True will improves distributed training speed.
- """
- return True
diff --git a/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md b/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md
deleted file mode 100644
index d4654cdd019b1496d4c953944c9aced8a0dcc67a..0000000000000000000000000000000000000000
--- a/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Sail-rvc-Ai Hoshino From Oshi No Ko RVC V2 300 Epoch
-emoji: 📈
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Hina4867/bingo/src/components/ui/select.tsx b/spaces/Hina4867/bingo/src/components/ui/select.tsx
deleted file mode 100644
index 77f12c2996f541b97663de4c9e20ab34d4ec2fac..0000000000000000000000000000000000000000
--- a/spaces/Hina4867/bingo/src/components/ui/select.tsx
+++ /dev/null
@@ -1,123 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SelectPrimitive from '@radix-ui/react-select'
-
-import { cn } from '@/lib/utils'
-import {
- IconArrowDown,
- IconCheck,
- IconChevronUpDown
-} from '@/components/ui/icons'
-
-const Select = SelectPrimitive.Root
-
-const SelectGroup = SelectPrimitive.Group
-
-const SelectValue = SelectPrimitive.Value
-
-const SelectTrigger = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-
- {children}
-
-
-
-))
-SelectTrigger.displayName = SelectPrimitive.Trigger.displayName
-
-const SelectContent = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, position = 'popper', ...props }, ref) => (
-
-
-
- {children}
-
-
-
-))
-SelectContent.displayName = SelectPrimitive.Content.displayName
-
-const SelectLabel = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-
-
-
-
-
- {children}
-
-))
-SelectItem.displayName = SelectPrimitive.Item.displayName
-
-const SelectSeparator = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- {o.delete(i),o.size===0&&n&&(n(),n=null)}}return{set:a,update:l,subscribe:r}}var g;const E=((g=globalThis.__sveltekit_1d38e1f)==null?void 0:g.base)??"";var k;const S=((k=globalThis.__sveltekit_1d38e1f)==null?void 0:k.assets)??E,w="1698134506027",y="sveltekit:snapshot",I="sveltekit:scroll",x="sveltekit:index",_={tap:1,hover:2,viewport:3,eager:4,off:-1};function O(e){let t=e.baseURI;if(!t){const n=e.getElementsByTagName("base");t=n.length?n[0].href:e.URL}return t}function U(){return{x:pageXOffset,y:pageYOffset}}function f(e,t){return e.getAttribute(`data-sveltekit-${t}`)}const b={..._,"":_.hover};function v(e){let t=e.assignedSlot??e.parentNode;return(t==null?void 0:t.nodeType)===11&&(t=t.host),t}function L(e,t){for(;e&&e!==t;){if(e.nodeName.toUpperCase()==="A"&&e.hasAttribute("href"))return e;e=v(e)}}function N(e,t){let n;try{n=new URL(e instanceof SVGAElement?e.href.baseVal:e.href,document.baseURI)}catch{}const o=e instanceof SVGAElement?e.target.baseVal:e.target,a=!n||!!o||R(n,t)||(e.getAttribute("rel")||"").split(/\s+/).includes("external"),l=(n==null?void 0:n.origin)===location.origin&&e.hasAttribute("download");return{url:n,external:a,target:o,download:l}}function P(e){let t=null,n=null,o=null,a=null,l=null,r=null,s=e;for(;s&&s!==document.documentElement;)o===null&&(o=f(s,"preload-code")),a===null&&(a=f(s,"preload-data")),t===null&&(t=f(s,"keepfocus")),n===null&&(n=f(s,"noscroll")),l===null&&(l=f(s,"reload")),r===null&&(r=f(s,"replacestate")),s=v(s);return{preload_code:b[o??"off"],preload_data:b[a??"off"],keep_focus:t==="off"?!1:t===""?!0:null,noscroll:n==="off"?!1:n===""?!0:null,reload:l==="off"?!1:l===""?!0:null,replace_state:r==="off"?!1:r===""?!0:null}}function h(e){const t=p(e);let n=!0;function o(){n=!0,t.update(r=>r)}function a(r){n=!1,t.set(r)}function l(r){let s;return t.subscribe(u=>{(s===void 0||n&&u!==s)&&r(s=u)})}return{notify:o,set:a,subscribe:l}}function A(){const{set:e,subscribe:t}=p(!1);let n;async function o(){clearTimeout(n);try{const a=await fetch(`${S}/_app/version.json`,{headers:{pragma:"no-cache","cache-control":"no-cache"}});if(!a.ok)return!1;const r=(await a.json()).version!==w;return r&&(e(!0),clearTimeout(n)),r}catch{return!1}}return{subscribe:t,check:o}}function R(e,t){return e.origin!==location.origin||!e.pathname.startsWith(t)}function V(e){e.client}const Y={url:h({}),page:h({}),navigating:p(null),updated:A()};export{x as I,_ as P,I as S,y as a,N as b,P as c,U as d,E as e,L as f,O as g,V as h,R as i,Y as s};
diff --git a/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py b/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py
deleted file mode 100644
index f8e2eb0f15699f1b458a8445d0c1dd6229a21f77..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/multilingual/data_scripts/check_iswlt_test_data.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-import os, sys
-import subprocess
-import re
-from subprocess import check_call, check_output
-
-WORKDIR_ROOT = os.environ.get('WORKDIR_ROOT', None)
-
-if WORKDIR_ROOT is None or not WORKDIR_ROOT.strip():
- print('please specify your working directory root in OS environment variable WORKDIR_ROOT. Exitting..."')
- sys.exit(-1)
-
-
-BLEU_REGEX = re.compile("^BLEU\\S* = (\\S+) ")
-def run_eval_bleu(cmd):
- output = check_output(cmd, shell=True, stderr=subprocess.STDOUT).decode("utf-8").strip()
- print(output)
- bleu = -1.0
- for line in output.strip().split('\n'):
- m = BLEU_REGEX.search(line)
- if m is not None:
- bleu = m.groups()[0]
- bleu = float(bleu)
- break
- return bleu
-
-def check_data_test_bleu(raw_folder, data_lang_pairs):
- not_matchings = []
- for sacrebleu_set, src_tgts in data_lang_pairs:
- for src_tgt in src_tgts:
- print(f'checking test bleus for: {src_tgt} at {sacrebleu_set}')
- src, tgt = src_tgt.split('-')
- ssrc, stgt = src[:2], tgt[:2]
- if os.path.exists(f'{raw_folder}/test.{tgt}-{src}.{src}'):
- # reversed direction may have different test set
- test_src = f'{raw_folder}/test.{tgt}-{src}.{src}'
- else:
- test_src = f'{raw_folder}/test.{src}-{tgt}.{src}'
- cmd1 = f'cat {test_src} | sacrebleu -t "{sacrebleu_set}" -l {stgt}-{ssrc}; [ $? -eq 0 ] || echo ""'
- test_tgt = f'{raw_folder}/test.{src}-{tgt}.{tgt}'
- cmd2 = f'cat {test_tgt} | sacrebleu -t "{sacrebleu_set}" -l {ssrc}-{stgt}; [ $? -eq 0 ] || echo ""'
- bleu1 = run_eval_bleu(cmd1)
- if bleu1 != 100.0:
- not_matchings.append(f'{sacrebleu_set}:{src_tgt} source side not matching: {test_src}')
- bleu2 = run_eval_bleu(cmd2)
- if bleu2 != 100.0:
- not_matchings.append(f'{sacrebleu_set}:{src_tgt} target side not matching: {test_tgt}')
- return not_matchings
-
-if __name__ == "__main__":
- to_data_path = f'{WORKDIR_ROOT}/iwsltv2'
- not_matching = check_data_test_bleu(
- f'{to_data_path}/raw',
- [
- ('iwslt17', ['en_XX-ar_AR', 'en_XX-ko_KR', 'ar_AR-en_XX', 'ko_KR-en_XX']),
- ('iwslt17', ['en_XX-it_IT', 'en_XX-nl_XX', 'it_IT-en_XX', 'nl_XX-en_XX']),
- ('iwslt17/tst2015', ['en_XX-vi_VN', "vi_VN-en_XX"]),
- ]
- )
- if len(not_matching) > 0:
- print('the following datasets do not have matching test datasets:\n\t', '\n\t'.join(not_matching))
-
diff --git a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py b/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py
deleted file mode 100644
index 88597d62a505715091f9ba62d38bf0a85a31b95a..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/so-vits-svc-4.0-ikaros2/vdecoder/hifigan/nvSTFT.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import math
-import os
-os.environ["LRU_CACHE_CAPACITY"] = "3"
-import random
-import torch
-import torch.utils.data
-import numpy as np
-import librosa
-from librosa.util import normalize
-from librosa.filters import mel as librosa_mel_fn
-from scipy.io.wavfile import read
-import soundfile as sf
-
-def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
- sampling_rate = None
- try:
- data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
- except Exception as ex:
- print(f"'{full_path}' failed to load.\nException:")
- print(ex)
- if return_empty_on_exception:
- return [], sampling_rate or target_sr or 32000
- else:
- raise Exception(ex)
-
- if len(data.shape) > 1:
- data = data[:, 0]
- assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
-
- if np.issubdtype(data.dtype, np.integer): # if audio data is type int
- max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
- else: # if audio data is type fp32
- max_mag = max(np.amax(data), -np.amin(data))
- max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
-
- data = torch.FloatTensor(data.astype(np.float32))/max_mag
-
- if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
- return [], sampling_rate or target_sr or 32000
- if target_sr is not None and sampling_rate != target_sr:
- data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
- sampling_rate = target_sr
-
- return data, sampling_rate
-
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
- return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
-
-def dynamic_range_decompression(x, C=1):
- return np.exp(x) / C
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-def dynamic_range_decompression_torch(x, C=1):
- return torch.exp(x) / C
-
-class STFT():
- def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
- self.target_sr = sr
-
- self.n_mels = n_mels
- self.n_fft = n_fft
- self.win_size = win_size
- self.hop_length = hop_length
- self.fmin = fmin
- self.fmax = fmax
- self.clip_val = clip_val
- self.mel_basis = {}
- self.hann_window = {}
-
- def get_mel(self, y, center=False):
- sampling_rate = self.target_sr
- n_mels = self.n_mels
- n_fft = self.n_fft
- win_size = self.win_size
- hop_length = self.hop_length
- fmin = self.fmin
- fmax = self.fmax
- clip_val = self.clip_val
-
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- if fmax not in self.mel_basis:
- mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
- self.mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
- self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_length)/2), int((n_fft-hop_length)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)],
- center=center, pad_mode='reflect', normalized=False, onesided=True)
- # print(111,spec)
- spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
- # print(222,spec)
- spec = torch.matmul(self.mel_basis[str(fmax)+'_'+str(y.device)], spec)
- # print(333,spec)
- spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
- # print(444,spec)
- return spec
-
- def __call__(self, audiopath):
- audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
- spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
- return spect
-
-stft = STFT()
diff --git a/spaces/JUNGU/emotion-ko-state/README.md b/spaces/JUNGU/emotion-ko-state/README.md
deleted file mode 100644
index 48eb244083a0ac6a51b0298cd043231b60b3fde8..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/emotion-ko-state/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Emotion Ko State
-emoji: 🦀
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py b/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py
deleted file mode 100644
index 27fcd62482d0cf44e02fa1c339195be58cb745b0..0000000000000000000000000000000000000000
--- a/spaces/Jamkonams/AutoGPT/autogpt/memory/pinecone.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import pinecone
-from colorama import Fore, Style
-
-from autogpt.llm_utils import create_embedding_with_ada
-from autogpt.logs import logger
-from autogpt.memory.base import MemoryProviderSingleton
-
-
-class PineconeMemory(MemoryProviderSingleton):
- def __init__(self, cfg):
- pinecone_api_key = cfg.pinecone_api_key
- pinecone_region = cfg.pinecone_region
- pinecone.init(api_key=pinecone_api_key, environment=pinecone_region)
- dimension = 1536
- metric = "cosine"
- pod_type = "p1"
- table_name = "auto-gpt"
- # this assumes we don't start with memory.
- # for now this works.
- # we'll need a more complicated and robust system if we want to start with
- # memory.
- self.vec_num = 0
-
- try:
- pinecone.whoami()
- except Exception as e:
- logger.typewriter_log(
- "FAILED TO CONNECT TO PINECONE",
- Fore.RED,
- Style.BRIGHT + str(e) + Style.RESET_ALL,
- )
- logger.double_check(
- "Please ensure you have setup and configured Pinecone properly for use."
- + f"You can check out {Fore.CYAN + Style.BRIGHT}"
- "https://github.com/Torantulino/Auto-GPT#-pinecone-api-key-setup"
- f"{Style.RESET_ALL} to ensure you've set up everything correctly."
- )
- exit(1)
-
- if table_name not in pinecone.list_indexes():
- pinecone.create_index(
- table_name, dimension=dimension, metric=metric, pod_type=pod_type
- )
- self.index = pinecone.Index(table_name)
-
- def add(self, data):
- vector = create_embedding_with_ada(data)
- # no metadata here. We may wish to change that long term.
- self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})])
- _text = f"Inserting data into memory at index: {self.vec_num}:\n data: {data}"
- self.vec_num += 1
- return _text
-
- def get(self, data):
- return self.get_relevant(data, 1)
-
- def clear(self):
- self.index.delete(deleteAll=True)
- return "Obliviated"
-
- def get_relevant(self, data, num_relevant=5):
- """
- Returns all the data in the memory that is relevant to the given data.
- :param data: The data to compare to.
- :param num_relevant: The number of relevant data to return. Defaults to 5
- """
- query_embedding = create_embedding_with_ada(data)
- results = self.index.query(
- query_embedding, top_k=num_relevant, include_metadata=True
- )
- sorted_results = sorted(results.matches, key=lambda x: x.score)
- return [str(item["metadata"]["raw_text"]) for item in sorted_results]
-
- def get_stats(self):
- return self.index.describe_index_stats()
diff --git a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py b/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py
deleted file mode 100644
index 1cc3b96da1275f1d01afb4c999f9c404d09b247a..0000000000000000000000000000000000000000
--- a/spaces/JohnCalimoso/animalbreedidentificationversion1.5/Control/Rabbit/con_rabbit_logreg.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import cv2
-import numpy as np
-from PIL import Image
-import pickle
-import tensorflow as tf
-import os
-
-class rabbitsLogReg:
- def __init__(self,url) -> None:
- self.image = url
-
- def predict_image(self):
- # Load the model
- load_extractor = tf.keras.models.load_model("././Model/Rabbit/resnetLogreg/resnet_EXTRACTOR.h5")
-
- modelpath = "././Model/Rabbit/resnetLogreg/dataSaved.pkl"
-
- with open(modelpath, 'rb') as file:
- saved_data = pickle.load(file)
- animal_breed = saved_data['class_name']
- model = saved_data['logreg_model']
-
- im = Image.open(self.image)
- img = im.convert("RGB")
- img= np.asarray(img)
- image_resized= cv2.resize(img, (224,224))
- features = load_extractor.predict(np.expand_dims(image_resized, axis=0))
-
- reshaped_features = features.reshape(features.shape[0],-1)
- predicted_class = model.predict(reshaped_features)
- pred_prob = model.predict_proba(reshaped_features)[:2]
- prediction_probability = pred_prob[0][predicted_class[0]]
- predicted_class
-
- output_class= animal_breed[predicted_class[0]]
-
- return [output_class, prediction_probability]
diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md b/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md
deleted file mode 100644
index 80af4fbbfba5d15e1cb6d1f4b67808ca76fa37d7..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/ChuanhuChatGPT/readme/README_en.md
+++ /dev/null
@@ -1,140 +0,0 @@
-
-
-川虎 Chat 🐯 Chuanhu Chat
-
-
-
-
-
-
-
Lightweight and User-friendly Web-UI for LLMs including ChatGPT/ChatGLM/LLaMA
-
- Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search
- LaTeX rendering / Table rendering / Code highlighting
- Auto dark mode / Adaptive web interface / WeChat-like theme
- Multi-parameters tuning / Multi-API-Key support / Multi-user support
- Compatible with GPT-4 / Local deployment for LLMs
-
- Video Tutorial
- ·
- 2.0 Introduction
- ·
- 3.0 Introduction & Tutorial
- ||
- Online trial
- ·
- One-Click deployment
-
-
-
-
-
-
-
-## Supported LLM Models
-
-**LLM models via API**:
-
-- [ChatGPT](https://chat.openai.com) ([GPT-4](https://openai.com/product/gpt-4))
-- [Google PaLM](https://developers.generativeai.google/products/palm)
-- [Inspur Yuan 1.0](https://air.inspur.com/home)
-- [MiniMax](https://api.minimax.chat/)
-- [XMChat](https://github.com/MILVLG/xmchat)
-
-**LLM models via local deployment**:
-
-- [ChatGLM](https://github.com/THUDM/ChatGLM-6B) ([ChatGLM2](https://github.com/THUDM/ChatGLM2-6B))
-- [LLaMA](https://github.com/facebookresearch/llama)
-- [StableLM](https://github.com/Stability-AI/StableLM)
-- [MOSS](https://github.com/OpenLMLab/MOSS)
-
-## Usage Tips
-
-- To better control the ChatGPT, use System Prompt.
-- To use a Prompt Template, select the Prompt Template Collection file first, and then choose certain prompt from the drop-down menu.
-- To try again if the response is unsatisfactory, use `🔄 Regenerate` button.
-- To start a new line in the input box, press Shift + Enter keys.
-- To quickly switch between input history, press ↑ and ↓ key in the input box.
-- To deploy the program onto a server, set `"server_name": "0.0.0.0", "server_port" ,` in `config.json`.
-- To get a public shared link, set `"share": true,` in `config.json`. Please be noted that the program must be running in order to be accessed via a public link.
-- To use it in Hugging Face Spaces: It is recommended to **Duplicate Space** and run the program in your own Space for a faster and more secure experience.
-
-## Quickstart
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-Then make a copy of `config_example.json`, rename it to `config.json`, and then fill in your API-Key and other settings in the file.
-
-```shell
-python ChuanhuChatbot.py
-```
-
-A browser window will open and you will be able to chat with ChatGPT.
-
-> **Note**
->
-> Please check our [wiki page](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程) for detailed instructions.
-
-## Troubleshooting
-
-When you encounter problems, you should try manually pulling the latest changes of this project first. The steps are as follows:
-
-1. Download the latest code archive by clicking on `Download ZIP` on the webpage, or
- ```shell
- git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
- ```
-2. Try installing the dependencies again (as this project may have introduced new dependencies)
- ```
- pip install -r requirements.txt
- ```
-
-Generally, you can solve most problems by following these steps.
-
-If the problem still exists, please refer to this page: [Frequently Asked Questions (FAQ)](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-This page lists almost all the possible problems and solutions. Please read it carefully.
-
-## More Information
-
-More information could be found in our [wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki):
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-
-
-
-
-## Sponsor
-
-🐯 If you find this project helpful, feel free to buy me a coke or a cup of coffee~
-
-
-
-
diff --git a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py b/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py
deleted file mode 100644
index 59a73cc04025762d6490fcd2945a747d963def32..0000000000000000000000000000000000000000
--- a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from os import environ
-
-
-def assert_in(file, files_to_check):
- if file not in files_to_check:
- raise AssertionError("{} does not exist in the list".format(str(file)))
- return True
-
-
-def assert_in_env(check_list: list):
- for item in check_list:
- assert_in(item, environ.keys())
- return True
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py
deleted file mode 100644
index beb6e65c61f8a16b4376494123f31178cdb88bde..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py
+++ /dev/null
@@ -1,13 +0,0 @@
-CUSTOM_STREAMLIT_CSS = """
-div[data-testid="stBlock"] button {
- width: 100% !important;
- margin-bottom: 20px !important;
- border-color: #bfbfbf !important;
-}
-section[data-testid="stSidebar"] div {
- max-width: 10rem;
-}
-pre code {
- white-space: pre-wrap;
-}
-"""
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py
deleted file mode 100644
index 34bcffd6c0975377a54ae1ce89002be1dae8151d..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import matplotlib
-matplotlib.use('Agg')
-import time
-
-class Timer():
- ''' Timer for recording training time distribution. '''
- def __init__(self):
- self.prev_t = time.time()
- self.clear()
-
- def set(self):
- self.prev_t = time.time()
-
- def cnt(self, mode):
- self.time_table[mode] += time.time()-self.prev_t
- self.set()
- if mode == 'bw':
- self.click += 1
-
- def show(self):
- total_time = sum(self.time_table.values())
- self.time_table['avg'] = total_time/self.click
- self.time_table['rd'] = 100*self.time_table['rd']/total_time
- self.time_table['fw'] = 100*self.time_table['fw']/total_time
- self.time_table['bw'] = 100*self.time_table['bw']/total_time
- msg = '{avg:.3f} sec/step (rd {rd:.1f}% | fw {fw:.1f}% | bw {bw:.1f}%)'.format(
- **self.time_table)
- self.clear()
- return msg
-
- def clear(self):
- self.time_table = {'rd': 0, 'fw': 0, 'bw': 0}
- self.click = 0
-
-# Reference : https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/e2e_asr.py#L168
-
-def human_format(num):
- magnitude = 0
- while num >= 1000:
- magnitude += 1
- num /= 1000.0
- # add more suffixes if you need them
- return '{:3.1f}{}'.format(num, [' ', 'K', 'M', 'G', 'T', 'P'][magnitude])
-
-
-# provide easy access of attribute from dict, such abc.key
-class AttrDict(dict):
- def __init__(self, *args, **kwargs):
- super(AttrDict, self).__init__(*args, **kwargs)
- self.__dict__ = self
diff --git a/spaces/KevinQHLin/UniVTG/main/train_mr.py b/spaces/KevinQHLin/UniVTG/main/train_mr.py
deleted file mode 100644
index 1a10d029f81f86733d6dab71a3aee575917b092b..0000000000000000000000000000000000000000
--- a/spaces/KevinQHLin/UniVTG/main/train_mr.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import os
-import pdb
-import sys
-import time
-import json
-import pprint
-import random
-import numpy as np
-from tqdm import tqdm, trange
-from collections import defaultdict
-
-import torch
-import torch.nn as nn
-import torch.backends.cudnn as cudnn
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-
-sys.path.append('/data/home/qinghonglin/univtg')
-from main.config import BaseOptions, setup_model
-from main.dataset import \
- DatasetMR, start_end_collate_mr, prepare_batch_inputs_mr
-from main.inference_mr import eval_epoch, start_inference
-from utils.basic_utils import set_seed, AverageMeter, dict_to_markdown
-from utils.model_utils import count_parameters
-
-import logging
-logger = logging.getLogger(__name__)
-logging.basicConfig(format="%(asctime)s.%(msecs)03d:%(levelname)s:%(name)s - %(message)s",
- datefmt="%Y-%m-%d %H:%M:%S",
- level=logging.INFO)
-
-def train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer):
- logger.info(f"[Epoch {epoch_i+1}]")
- model.train()
- criterion.train()
-
- # init meters
- time_meters = defaultdict(AverageMeter)
- loss_meters = defaultdict(AverageMeter)
-
- num_training_examples = len(train_loader)
- timer_dataloading = time.time()
- for batch_idx, batch in tqdm(enumerate(train_loader),
- desc="Training Iteration",
- total=num_training_examples):
- time_meters["dataloading_time"].update(time.time() - timer_dataloading)
-
- timer_start = time.time()
- model_inputs, targets = prepare_batch_inputs_mr(batch[1], opt.device, non_blocking=opt.pin_memory)
- time_meters["prepare_inputs_time"].update(time.time() - timer_start)
-
- timer_start = time.time()
-
- # try:
- outputs = model(**model_inputs)
- loss_dict = criterion(outputs, targets)
- weight_dict = criterion.weight_dict
- losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
- time_meters["model_forward_time"].update(time.time() - timer_start)
-
- timer_start = time.time()
- optimizer.zero_grad()
- losses.backward()
-
- if opt.grad_clip > 0:
- nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
- optimizer.step()
- time_meters["model_backward_time"].update(time.time() - timer_start)
-
- loss_dict["loss_overall"] = float(losses) # for logging only
- for k, v in loss_dict.items():
- loss_meters[k].update(float(v) * weight_dict[k] if k in weight_dict else float(v))
-
- timer_dataloading = time.time()
-
- # print/add logs
- tb_writer.add_scalar("Train/lr", float(optimizer.param_groups[0]["lr"]), epoch_i+1)
- for k, v in loss_meters.items():
- tb_writer.add_scalar("Train/{}".format(k), v.avg, epoch_i+1)
-
- to_write = opt.train_log_txt_formatter.format(
- time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
- epoch=epoch_i+1,
- loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in loss_meters.items()]))
- with open(opt.train_log_filepath, "a") as f:
- f.write(to_write)
-
- logger.info("Epoch time stats:")
- for name, meter in time_meters.items():
- d = {k: f"{getattr(meter, k):.4f}" for k in ["max", "min", "avg"]}
- logger.info(f"{name} ==> {d}")
-
-
-def train(model, criterion, optimizer, lr_scheduler, train_dataset, val_dataset, opt):
- tb_writer = SummaryWriter(opt.tensorboard_log_dir)
- tb_writer.add_text("hyperparameters", dict_to_markdown(vars(opt), max_str_len=None))
- opt.train_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str}\n"
- opt.eval_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str} [Metrics] {eval_metrics_str}\n"
-
- train_loader = DataLoader(
- train_dataset,
- collate_fn=start_end_collate_mr,
- batch_size=opt.bsz,
- num_workers=opt.num_workers,
- shuffle=True,
- pin_memory=opt.pin_memory
- )
-
- prev_best_score = 0.
- es_cnt = 0
- if opt.start_epoch is None:
- start_epoch = -1 if opt.eval_init else 0
- else:
- start_epoch = opt.start_epoch
- save_submission_filename = "latest_{}_{}_preds.jsonl".format(opt.dset_name, opt.eval_split_name)
- for epoch_i in trange(start_epoch, opt.n_epoch, desc="Epoch"):
- if epoch_i > -1:
- train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer)
- lr_scheduler.step()
- eval_epoch_interval = opt.eval_epoch
- if opt.eval_path is not None and (epoch_i + 1) % eval_epoch_interval == 0:
- with torch.no_grad():
- metrics_no_nms, metrics_nms, eval_loss_meters, latest_file_paths = \
- eval_epoch(model, val_dataset, opt, save_submission_filename, epoch_i, criterion, tb_writer)
-
- # log
- to_write = opt.eval_log_txt_formatter.format(
- time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
- epoch=epoch_i,
- loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in eval_loss_meters.items()]),
- eval_metrics_str=json.dumps(metrics_no_nms))
-
- with open(opt.eval_log_filepath, "a") as f:
- f.write(to_write)
- logger.info("metrics_no_nms {}".format(pprint.pformat(metrics_no_nms["brief"], indent=4)))
- if metrics_nms is not None:
- logger.info("metrics_nms {}".format(pprint.pformat(metrics_nms["brief"], indent=4)))
-
- metrics = metrics_nms if metrics_nms is not None else metrics_no_nms
- for k, v in metrics["brief"].items():
- tb_writer.add_scalar(f"Eval/{k}", float(v), epoch_i+1)
-
- # stop_score = metrics["brief"]["MR-full-mAP"]
- # pdb.set_trace()
- stop_score = metrics["brief"][opt.main_metric]
- if stop_score > prev_best_score:
- es_cnt = 0
- prev_best_score = stop_score
-
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "lr_scheduler": lr_scheduler.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"))
-
- best_file_paths = [e.replace("latest", "best") for e in latest_file_paths]
- for src, tgt in zip(latest_file_paths, best_file_paths):
- os.renames(src, tgt)
- logger.info("The checkpoint file has been updated.")
- else:
- es_cnt += 1
- if opt.max_es_cnt != -1 and es_cnt > opt.max_es_cnt: # early stop
- with open(opt.train_log_filepath, "a") as f:
- f.write(f"Early Stop at epoch {epoch_i}")
- logger.info(f"\n>>>>> Early stop at epoch {epoch_i} {prev_best_score}\n")
- break
-
- # save ckpt
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "lr_scheduler": lr_scheduler.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_latest.ckpt"))
-
- if (epoch_i + 1) % opt.save_interval == 0 or (epoch_i + 1) % opt.lr_drop == 0: # additional copies
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", f"_e{epoch_i:04d}.ckpt"))
-
- if opt.debug:
- break
-
- tb_writer.close()
-
-
-def start_training():
- logger.info("Setup config, data and model...")
- opt = BaseOptions().parse()
- set_seed(opt.seed)
- if opt.debug: # keep the model run deterministically
- # 'cudnn.benchmark = True' enabled auto finding the best algorithm for a specific input/net config.
- # Enable this only when input size is fixed.
- cudnn.benchmark = False
- cudnn.deterministic = True
-
- dataset_config = dict(
- dset_name=opt.dset_name,
- data_path=opt.train_path,
- v_feat_dirs=opt.v_feat_dirs,
- q_feat_dir=opt.t_feat_dir,
- v_feat_dim=opt.v_feat_dim,
- q_feat_dim=opt.t_feat_dim,
- q_feat_type="last_hidden_state",
- max_q_l=opt.max_q_l,
- max_v_l=opt.max_v_l,
- ctx_mode=opt.ctx_mode,
- data_ratio=opt.data_ratio,
- normalize_v=not opt.no_norm_vfeat,
- normalize_t=not opt.no_norm_tfeat,
- clip_len=opt.clip_length,
- max_windows=opt.max_windows,
- span_loss_type=opt.span_loss_type,
- txt_drop_ratio=opt.txt_drop_ratio,
- use_cache=opt.use_cache,
- add_easy_negative=opt.add_easy_negative,
- easy_negative_only=opt.easy_negative_only
- )
-
- dataset_config["data_path"] = opt.train_path
- train_dataset = DatasetMR(**dataset_config)
-
- if opt.eval_path is not None:
- dataset_config["data_path"] = opt.eval_path
- dataset_config["txt_drop_ratio"] = 0
- dataset_config["q_feat_dir"] = opt.t_feat_dir.replace("txt_clip_asr", "txt_clip").replace("txt_clip_cap", "txt_clip") # for pretraining
- # dataset_config["load_labels"] = False # uncomment to calculate eval loss
- eval_dataset = DatasetMR(**dataset_config)
- else:
- eval_dataset = None
-
- if opt.lr_warmup > 0:
- # total_steps = opt.n_epoch * len(train_dataset) // opt.bsz
- total_steps = opt.n_epoch
- warmup_steps = opt.lr_warmup if opt.lr_warmup > 1 else int(opt.lr_warmup * total_steps)
- opt.lr_warmup = [warmup_steps, total_steps]
- model, criterion, optimizer, lr_scheduler = setup_model(opt)
- logger.info(f"Model {model}")
- count_parameters(model)
- logger.info("Start Training...")
- train(model, criterion, optimizer, lr_scheduler, train_dataset, eval_dataset, opt)
- return opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"), opt.eval_split_name, opt.eval_path, opt.debug
-
-
-if __name__ == '__main__':
- best_ckpt_path, eval_split_name, eval_path, debug = start_training()
- if not debug:
- input_args = ["--resume", best_ckpt_path,
- "--eval_split_name", eval_split_name,
- "--eval_path", eval_path]
-
- import sys
- sys.argv[1:] = input_args
- logger.info("\n\n\nFINISHED TRAINING!!!")
- logger.info("Evaluating model at {}".format(best_ckpt_path))
- logger.info("Input args {}".format(sys.argv[1:]))
- start_inference()
\ No newline at end of file
diff --git a/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md b/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md
deleted file mode 100644
index 1f55e11e80f6fc5ebbf42dade0266e3d4ee06ce4..0000000000000000000000000000000000000000
--- a/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
-# Transliteration Models for Indian languages
-These are models for transliteration involving Indian languages.
-The models are essentially Statistical Machine Translation systems trained using Moses over a
-character-level parallel corpora of transliterations. Hence, you will need Moses to use these transliteration models.
-The transliteration corpus has itself been mined in an unsupervised fashion from a translation corpus.
-
-Currently we have trained transliteration models for five language pairs: bn-hi, ta-hi, te-hi, en-hi and mr-hi
-
-Support for transliteration has been introduced in Moses from version 2.1
-So please ensure that you have minimum 2.1 version setup for Moses
-
-Commands to run the transliteration module using moses
-
-$moseshome/mosesdecoder/scripts/Transliteration/post-decoding-transliteration.pl \
---moses-src-dir $moseshome/mosesdecoder --external-bin-dir $moseshome/tools \
---transliteration-model-dir {path to transliteration model folder} --oov-file {path to file containing oov words, oovs are space separated with each line containing all oovs for the input line}\
- --input-file {input file to transliterated} --output-file {output file location} \
- --input-extension {input language code for eg. en} --output-extension {output language code for eg. hi} --language-model {path to language model} \
- --decoder $moseshome/mosesdecoder/bin/moses
-
-A sample execution of the model will be as follows:
-
-export moseshome={path to moses installation}
-$moseshome/mosesdecoder/scripts/Transliteration/post-decoding-transliteration.pl \
---moses-src-dir $moseshome/mosesdecoder --external-bin-dir $moseshome/tools \
---transliteration-model-dir /home/ratish/project/nlp_resources/indic_nlp_resources/transliterate/en-hi \
---oov-file /home/ratish/project/translit/input.oov \
- --input-file /home/ratish/project/translit/input.en \
- --output-file /home/ratish/project/translit/output.hi \
- --input-extension en --output-extension hi --language-model /home/ratish/project/translit/lm/nc.binlm.1 \
- --decoder $moseshome/mosesdecoder/bin/moses
-
-So far, we have seen the use of transliteration in a post-editing task for machine translation task.
-In case, the models are needed for purely transliteration purpose, the input file and OOV file are the same.
-Sample input file:
-New Delhi is capital of India
-India is worlds seventh largest nation in the World
-
-OOV file
-New Delhi is capital of India
-India is worlds seventh largest nation in the World
-
-On running the transliteration module, the output is:
-न्यू डेल्ही इस कैपिटल आफ इंडिया
-इंडिया इस वर्ल्ड सेवंथ लारगेस्ट नेशन इन थे वर्ल्ड
diff --git a/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py b/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py
deleted file mode 100644
index 980440eb3434e48b7cad90f0577c838a05d787b4..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Sequence
-
-from torch.utils.data import BatchSampler, Sampler
-
-from mmdet.registry import DATA_SAMPLERS
-
-
-# TODO: maybe replace with a data_loader wrapper
-@DATA_SAMPLERS.register_module()
-class AspectRatioBatchSampler(BatchSampler):
- """A sampler wrapper for grouping images with similar aspect ratio (< 1 or.
-
- >= 1) into a same batch.
-
- Args:
- sampler (Sampler): Base sampler.
- batch_size (int): Size of mini-batch.
- drop_last (bool): If ``True``, the sampler will drop the last batch if
- its size would be less than ``batch_size``.
- """
-
- def __init__(self,
- sampler: Sampler,
- batch_size: int,
- drop_last: bool = False) -> None:
- if not isinstance(sampler, Sampler):
- raise TypeError('sampler should be an instance of ``Sampler``, '
- f'but got {sampler}')
- if not isinstance(batch_size, int) or batch_size <= 0:
- raise ValueError('batch_size should be a positive integer value, '
- f'but got batch_size={batch_size}')
- self.sampler = sampler
- self.batch_size = batch_size
- self.drop_last = drop_last
- # two groups for w < h and w >= h
- self._aspect_ratio_buckets = [[] for _ in range(2)]
-
- def __iter__(self) -> Sequence[int]:
- for idx in self.sampler:
- data_info = self.sampler.dataset.get_data_info(idx)
- width, height = data_info['width'], data_info['height']
- bucket_id = 0 if width < height else 1
- bucket = self._aspect_ratio_buckets[bucket_id]
- bucket.append(idx)
- # yield a batch of indices in the same aspect ratio group
- if len(bucket) == self.batch_size:
- yield bucket[:]
- del bucket[:]
-
- # yield the rest data and reset the bucket
- left_data = self._aspect_ratio_buckets[0] + self._aspect_ratio_buckets[
- 1]
- self._aspect_ratio_buckets = [[] for _ in range(2)]
- while len(left_data) > 0:
- if len(left_data) <= self.batch_size:
- if not self.drop_last:
- yield left_data[:]
- left_data = []
- else:
- yield left_data[:self.batch_size]
- left_data = left_data[self.batch_size:]
-
- def __len__(self) -> int:
- if self.drop_last:
- return len(self.sampler) // self.batch_size
- else:
- return (len(self.sampler) + self.batch_size - 1) // self.batch_size
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py
deleted file mode 100644
index 430b06d085d94760d56a7ea083eaf23bd32b1f53..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py
+++ /dev/null
@@ -1,722 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, Scale
-from mmcv.ops import DeformConv2d
-from torch import Tensor
-
-from mmdet.registry import MODELS, TASK_UTILS
-from mmdet.structures.bbox import bbox_overlaps
-from mmdet.utils import (ConfigType, InstanceList, MultiConfig,
- OptInstanceList, RangeType, reduce_mean)
-from ..task_modules.prior_generators import MlvlPointGenerator
-from ..task_modules.samplers import PseudoSampler
-from ..utils import multi_apply
-from .atss_head import ATSSHead
-from .fcos_head import FCOSHead
-
-INF = 1e8
-
-
-@MODELS.register_module()
-class VFNetHead(ATSSHead, FCOSHead):
- """Head of `VarifocalNet (VFNet): An IoU-aware Dense Object
- Detector.`_.
-
- The VFNet predicts IoU-aware classification scores which mix the
- object presence confidence and object localization accuracy as the
- detection score. It is built on the FCOS architecture and uses ATSS
- for defining positive/negative training examples. The VFNet is trained
- with Varifocal Loss and empolys star-shaped deformable convolution to
- extract features for a bbox.
-
- Args:
- num_classes (int): Number of categories excluding the background
- category.
- in_channels (int): Number of channels in the input feature map.
- regress_ranges (Sequence[Tuple[int, int]]): Regress range of multiple
- level points.
- center_sampling (bool): If true, use center sampling. Defaults to False.
- center_sample_radius (float): Radius of center sampling. Defaults to 1.5.
- sync_num_pos (bool): If true, synchronize the number of positive
- examples across GPUs. Defaults to True
- gradient_mul (float): The multiplier to gradients from bbox refinement
- and recognition. Defaults to 0.1.
- bbox_norm_type (str): The bbox normalization type, 'reg_denom' or
- 'stride'. Defaults to reg_denom
- loss_cls_fl (:obj:`ConfigDict` or dict): Config of focal loss.
- use_vfl (bool): If true, use varifocal loss for training.
- Defaults to True.
- loss_cls (:obj:`ConfigDict` or dict): Config of varifocal loss.
- loss_bbox (:obj:`ConfigDict` or dict): Config of localization loss,
- GIoU Loss.
- loss_bbox (:obj:`ConfigDict` or dict): Config of localization
- refinement loss, GIoU Loss.
- norm_cfg (:obj:`ConfigDict` or dict): dictionary to construct and
- config norm layer. Defaults to norm_cfg=dict(type='GN',
- num_groups=32, requires_grad=True).
- use_atss (bool): If true, use ATSS to define positive/negative
- examples. Defaults to True.
- anchor_generator (:obj:`ConfigDict` or dict): Config of anchor
- generator for ATSS.
- init_cfg (:obj:`ConfigDict` or dict or list[dict] or
- list[:obj:`ConfigDict`]): Initialization config dict.
-
- Example:
- >>> self = VFNetHead(11, 7)
- >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
- >>> cls_score, bbox_pred, bbox_pred_refine= self.forward(feats)
- >>> assert len(cls_score) == len(self.scales)
- """ # noqa: E501
-
- def __init__(self,
- num_classes: int,
- in_channels: int,
- regress_ranges: RangeType = ((-1, 64), (64, 128), (128, 256),
- (256, 512), (512, INF)),
- center_sampling: bool = False,
- center_sample_radius: float = 1.5,
- sync_num_pos: bool = True,
- gradient_mul: float = 0.1,
- bbox_norm_type: str = 'reg_denom',
- loss_cls_fl: ConfigType = dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- use_vfl: bool = True,
- loss_cls: ConfigType = dict(
- type='VarifocalLoss',
- use_sigmoid=True,
- alpha=0.75,
- gamma=2.0,
- iou_weighted=True,
- loss_weight=1.0),
- loss_bbox: ConfigType = dict(
- type='GIoULoss', loss_weight=1.5),
- loss_bbox_refine: ConfigType = dict(
- type='GIoULoss', loss_weight=2.0),
- norm_cfg: ConfigType = dict(
- type='GN', num_groups=32, requires_grad=True),
- use_atss: bool = True,
- reg_decoded_bbox: bool = True,
- anchor_generator: ConfigType = dict(
- type='AnchorGenerator',
- ratios=[1.0],
- octave_base_scale=8,
- scales_per_octave=1,
- center_offset=0.0,
- strides=[8, 16, 32, 64, 128]),
- init_cfg: MultiConfig = dict(
- type='Normal',
- layer='Conv2d',
- std=0.01,
- override=dict(
- type='Normal',
- name='vfnet_cls',
- std=0.01,
- bias_prob=0.01)),
- **kwargs) -> None:
- # dcn base offsets, adapted from reppoints_head.py
- self.num_dconv_points = 9
- self.dcn_kernel = int(np.sqrt(self.num_dconv_points))
- self.dcn_pad = int((self.dcn_kernel - 1) / 2)
- dcn_base = np.arange(-self.dcn_pad,
- self.dcn_pad + 1).astype(np.float64)
- dcn_base_y = np.repeat(dcn_base, self.dcn_kernel)
- dcn_base_x = np.tile(dcn_base, self.dcn_kernel)
- dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape(
- (-1))
- self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1)
-
- super(FCOSHead, self).__init__(
- num_classes=num_classes,
- in_channels=in_channels,
- norm_cfg=norm_cfg,
- init_cfg=init_cfg,
- **kwargs)
- self.regress_ranges = regress_ranges
- self.reg_denoms = [
- regress_range[-1] for regress_range in regress_ranges
- ]
- self.reg_denoms[-1] = self.reg_denoms[-2] * 2
- self.center_sampling = center_sampling
- self.center_sample_radius = center_sample_radius
- self.sync_num_pos = sync_num_pos
- self.bbox_norm_type = bbox_norm_type
- self.gradient_mul = gradient_mul
- self.use_vfl = use_vfl
- if self.use_vfl:
- self.loss_cls = MODELS.build(loss_cls)
- else:
- self.loss_cls = MODELS.build(loss_cls_fl)
- self.loss_bbox = MODELS.build(loss_bbox)
- self.loss_bbox_refine = MODELS.build(loss_bbox_refine)
-
- # for getting ATSS targets
- self.use_atss = use_atss
- self.reg_decoded_bbox = reg_decoded_bbox
- self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
-
- self.anchor_center_offset = anchor_generator['center_offset']
-
- self.num_base_priors = self.prior_generator.num_base_priors[0]
-
- if self.train_cfg:
- self.assigner = TASK_UTILS.build(self.train_cfg['assigner'])
- if self.train_cfg.get('sampler', None) is not None:
- self.sampler = TASK_UTILS.build(
- self.train_cfg['sampler'], default_args=dict(context=self))
- else:
- self.sampler = PseudoSampler()
- # only be used in `get_atss_targets` when `use_atss` is True
- self.atss_prior_generator = TASK_UTILS.build(anchor_generator)
-
- self.fcos_prior_generator = MlvlPointGenerator(
- anchor_generator['strides'],
- self.anchor_center_offset if self.use_atss else 0.5)
-
- # In order to reuse the `get_bboxes` in `BaseDenseHead.
- # Only be used in testing phase.
- self.prior_generator = self.fcos_prior_generator
-
- def _init_layers(self) -> None:
- """Initialize layers of the head."""
- super(FCOSHead, self)._init_cls_convs()
- super(FCOSHead, self)._init_reg_convs()
- self.relu = nn.ReLU()
- self.vfnet_reg_conv = ConvModule(
- self.feat_channels,
- self.feat_channels,
- 3,
- stride=1,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- bias=self.conv_bias)
- self.vfnet_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
- self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
- self.vfnet_reg_refine_dconv = DeformConv2d(
- self.feat_channels,
- self.feat_channels,
- self.dcn_kernel,
- 1,
- padding=self.dcn_pad)
- self.vfnet_reg_refine = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
- self.scales_refine = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
- self.vfnet_cls_dconv = DeformConv2d(
- self.feat_channels,
- self.feat_channels,
- self.dcn_kernel,
- 1,
- padding=self.dcn_pad)
- self.vfnet_cls = nn.Conv2d(
- self.feat_channels, self.cls_out_channels, 3, padding=1)
-
- def forward(self, x: Tuple[Tensor]) -> Tuple[List[Tensor]]:
- """Forward features from the upstream network.
-
- Args:
- x (tuple[Tensor]): Features from the upstream network, each is
- a 4D-tensor.
-
- Returns:
- tuple:
-
- - cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level, each is a 4D-tensor, the channel number is
- num_points * num_classes.
- - bbox_preds (list[Tensor]): Box offsets for each
- scale level, each is a 4D-tensor, the channel number is
- num_points * 4.
- - bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level, each is a 4D-tensor, the channel
- number is num_points * 4.
- """
- return multi_apply(self.forward_single, x, self.scales,
- self.scales_refine, self.strides, self.reg_denoms)
-
- def forward_single(self, x: Tensor, scale: Scale, scale_refine: Scale,
- stride: int, reg_denom: int) -> tuple:
- """Forward features of a single scale level.
-
- Args:
- x (Tensor): FPN feature maps of the specified stride.
- scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
- the bbox prediction.
- scale_refine (:obj: `mmcv.cnn.Scale`): Learnable scale module to
- resize the refined bbox prediction.
- stride (int): The corresponding stride for feature maps,
- used to normalize the bbox prediction when
- bbox_norm_type = 'stride'.
- reg_denom (int): The corresponding regression range for feature
- maps, only used to normalize the bbox prediction when
- bbox_norm_type = 'reg_denom'.
-
- Returns:
- tuple: iou-aware cls scores for each box, bbox predictions and
- refined bbox predictions of input feature maps.
- """
- cls_feat = x
- reg_feat = x
-
- for cls_layer in self.cls_convs:
- cls_feat = cls_layer(cls_feat)
-
- for reg_layer in self.reg_convs:
- reg_feat = reg_layer(reg_feat)
-
- # predict the bbox_pred of different level
- reg_feat_init = self.vfnet_reg_conv(reg_feat)
- if self.bbox_norm_type == 'reg_denom':
- bbox_pred = scale(
- self.vfnet_reg(reg_feat_init)).float().exp() * reg_denom
- elif self.bbox_norm_type == 'stride':
- bbox_pred = scale(
- self.vfnet_reg(reg_feat_init)).float().exp() * stride
- else:
- raise NotImplementedError
-
- # compute star deformable convolution offsets
- # converting dcn_offset to reg_feat.dtype thus VFNet can be
- # trained with FP16
- dcn_offset = self.star_dcn_offset(bbox_pred, self.gradient_mul,
- stride).to(reg_feat.dtype)
-
- # refine the bbox_pred
- reg_feat = self.relu(self.vfnet_reg_refine_dconv(reg_feat, dcn_offset))
- bbox_pred_refine = scale_refine(
- self.vfnet_reg_refine(reg_feat)).float().exp()
- bbox_pred_refine = bbox_pred_refine * bbox_pred.detach()
-
- # predict the iou-aware cls score
- cls_feat = self.relu(self.vfnet_cls_dconv(cls_feat, dcn_offset))
- cls_score = self.vfnet_cls(cls_feat)
-
- if self.training:
- return cls_score, bbox_pred, bbox_pred_refine
- else:
- return cls_score, bbox_pred_refine
-
- def star_dcn_offset(self, bbox_pred: Tensor, gradient_mul: float,
- stride: int) -> Tensor:
- """Compute the star deformable conv offsets.
-
- Args:
- bbox_pred (Tensor): Predicted bbox distance offsets (l, r, t, b).
- gradient_mul (float): Gradient multiplier.
- stride (int): The corresponding stride for feature maps,
- used to project the bbox onto the feature map.
-
- Returns:
- Tensor: The offsets for deformable convolution.
- """
- dcn_base_offset = self.dcn_base_offset.type_as(bbox_pred)
- bbox_pred_grad_mul = (1 - gradient_mul) * bbox_pred.detach() + \
- gradient_mul * bbox_pred
- # map to the feature map scale
- bbox_pred_grad_mul = bbox_pred_grad_mul / stride
- N, C, H, W = bbox_pred.size()
-
- x1 = bbox_pred_grad_mul[:, 0, :, :]
- y1 = bbox_pred_grad_mul[:, 1, :, :]
- x2 = bbox_pred_grad_mul[:, 2, :, :]
- y2 = bbox_pred_grad_mul[:, 3, :, :]
- bbox_pred_grad_mul_offset = bbox_pred.new_zeros(
- N, 2 * self.num_dconv_points, H, W)
- bbox_pred_grad_mul_offset[:, 0, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 1, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 2, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 4, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 5, :, :] = x2 # x2
- bbox_pred_grad_mul_offset[:, 7, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 11, :, :] = x2 # x2
- bbox_pred_grad_mul_offset[:, 12, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 13, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 14, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 16, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 17, :, :] = x2 # x2
- dcn_offset = bbox_pred_grad_mul_offset - dcn_base_offset
-
- return dcn_offset
-
- def loss_by_feat(
- self,
- cls_scores: List[Tensor],
- bbox_preds: List[Tensor],
- bbox_preds_refine: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> dict:
- """Compute loss of the head.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level, each is a 4D-tensor, the channel number is
- num_points * num_classes.
- bbox_preds (list[Tensor]): Box offsets for each
- scale level, each is a 4D-tensor, the channel number is
- num_points * 4.
- bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level, each is a 4D-tensor, the channel
- number is num_points * 4.
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine)
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- all_level_points = self.fcos_prior_generator.grid_priors(
- featmap_sizes, bbox_preds[0].dtype, bbox_preds[0].device)
- labels, label_weights, bbox_targets, bbox_weights = self.get_targets(
- cls_scores,
- all_level_points,
- batch_gt_instances,
- batch_img_metas,
- batch_gt_instances_ignore=batch_gt_instances_ignore)
-
- num_imgs = cls_scores[0].size(0)
- # flatten cls_scores, bbox_preds and bbox_preds_refine
- flatten_cls_scores = [
- cls_score.permute(0, 2, 3,
- 1).reshape(-1,
- self.cls_out_channels).contiguous()
- for cls_score in cls_scores
- ]
- flatten_bbox_preds = [
- bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
- for bbox_pred in bbox_preds
- ]
- flatten_bbox_preds_refine = [
- bbox_pred_refine.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
- for bbox_pred_refine in bbox_preds_refine
- ]
- flatten_cls_scores = torch.cat(flatten_cls_scores)
- flatten_bbox_preds = torch.cat(flatten_bbox_preds)
- flatten_bbox_preds_refine = torch.cat(flatten_bbox_preds_refine)
- flatten_labels = torch.cat(labels)
- flatten_bbox_targets = torch.cat(bbox_targets)
- # repeat points to align with bbox_preds
- flatten_points = torch.cat(
- [points.repeat(num_imgs, 1) for points in all_level_points])
-
- # FG cat_id: [0, num_classes - 1], BG cat_id: num_classes
- bg_class_ind = self.num_classes
- pos_inds = torch.where(
- ((flatten_labels >= 0) & (flatten_labels < bg_class_ind)) > 0)[0]
- num_pos = len(pos_inds)
-
- pos_bbox_preds = flatten_bbox_preds[pos_inds]
- pos_bbox_preds_refine = flatten_bbox_preds_refine[pos_inds]
- pos_labels = flatten_labels[pos_inds]
-
- # sync num_pos across all gpus
- if self.sync_num_pos:
- num_pos_avg_per_gpu = reduce_mean(
- pos_inds.new_tensor(num_pos).float()).item()
- num_pos_avg_per_gpu = max(num_pos_avg_per_gpu, 1.0)
- else:
- num_pos_avg_per_gpu = num_pos
-
- pos_bbox_targets = flatten_bbox_targets[pos_inds]
- pos_points = flatten_points[pos_inds]
-
- pos_decoded_bbox_preds = self.bbox_coder.decode(
- pos_points, pos_bbox_preds)
- pos_decoded_target_preds = self.bbox_coder.decode(
- pos_points, pos_bbox_targets)
- iou_targets_ini = bbox_overlaps(
- pos_decoded_bbox_preds,
- pos_decoded_target_preds.detach(),
- is_aligned=True).clamp(min=1e-6)
- bbox_weights_ini = iou_targets_ini.clone().detach()
- bbox_avg_factor_ini = reduce_mean(
- bbox_weights_ini.sum()).clamp_(min=1).item()
-
- pos_decoded_bbox_preds_refine = \
- self.bbox_coder.decode(pos_points, pos_bbox_preds_refine)
- iou_targets_rf = bbox_overlaps(
- pos_decoded_bbox_preds_refine,
- pos_decoded_target_preds.detach(),
- is_aligned=True).clamp(min=1e-6)
- bbox_weights_rf = iou_targets_rf.clone().detach()
- bbox_avg_factor_rf = reduce_mean(
- bbox_weights_rf.sum()).clamp_(min=1).item()
-
- if num_pos > 0:
- loss_bbox = self.loss_bbox(
- pos_decoded_bbox_preds,
- pos_decoded_target_preds.detach(),
- weight=bbox_weights_ini,
- avg_factor=bbox_avg_factor_ini)
-
- loss_bbox_refine = self.loss_bbox_refine(
- pos_decoded_bbox_preds_refine,
- pos_decoded_target_preds.detach(),
- weight=bbox_weights_rf,
- avg_factor=bbox_avg_factor_rf)
-
- # build IoU-aware cls_score targets
- if self.use_vfl:
- pos_ious = iou_targets_rf.clone().detach()
- cls_iou_targets = torch.zeros_like(flatten_cls_scores)
- cls_iou_targets[pos_inds, pos_labels] = pos_ious
- else:
- loss_bbox = pos_bbox_preds.sum() * 0
- loss_bbox_refine = pos_bbox_preds_refine.sum() * 0
- if self.use_vfl:
- cls_iou_targets = torch.zeros_like(flatten_cls_scores)
-
- if self.use_vfl:
- loss_cls = self.loss_cls(
- flatten_cls_scores,
- cls_iou_targets,
- avg_factor=num_pos_avg_per_gpu)
- else:
- loss_cls = self.loss_cls(
- flatten_cls_scores,
- flatten_labels,
- weight=label_weights,
- avg_factor=num_pos_avg_per_gpu)
-
- return dict(
- loss_cls=loss_cls,
- loss_bbox=loss_bbox,
- loss_bbox_rf=loss_bbox_refine)
-
- def get_targets(
- self,
- cls_scores: List[Tensor],
- mlvl_points: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> tuple:
- """A wrapper for computing ATSS and FCOS targets for points in multiple
- images.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- tuple:
-
- - labels_list (list[Tensor]): Labels of each level.
- - label_weights (Tensor/None): Label weights of all levels.
- - bbox_targets_list (list[Tensor]): Regression targets of each
- level, (l, t, r, b).
- - bbox_weights (Tensor/None): Bbox weights of all levels.
- """
- if self.use_atss:
- return self.get_atss_targets(cls_scores, mlvl_points,
- batch_gt_instances, batch_img_metas,
- batch_gt_instances_ignore)
- else:
- self.norm_on_bbox = False
- return self.get_fcos_targets(mlvl_points, batch_gt_instances)
-
- def _get_targets_single(self, *args, **kwargs):
- """Avoid ambiguity in multiple inheritance."""
- if self.use_atss:
- return ATSSHead._get_targets_single(self, *args, **kwargs)
- else:
- return FCOSHead._get_targets_single(self, *args, **kwargs)
-
- def get_fcos_targets(self, points: List[Tensor],
- batch_gt_instances: InstanceList) -> tuple:
- """Compute FCOS regression and classification targets for points in
- multiple images.
-
- Args:
- points (list[Tensor]): Points of each fpn level, each has shape
- (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
-
- Returns:
- tuple:
-
- - labels (list[Tensor]): Labels of each level.
- - label_weights: None, to be compatible with ATSS targets.
- - bbox_targets (list[Tensor]): BBox targets of each level.
- - bbox_weights: None, to be compatible with ATSS targets.
- """
- labels, bbox_targets = FCOSHead.get_targets(self, points,
- batch_gt_instances)
- label_weights = None
- bbox_weights = None
- return labels, label_weights, bbox_targets, bbox_weights
-
- def get_anchors(self,
- featmap_sizes: List[Tuple],
- batch_img_metas: List[dict],
- device: str = 'cuda') -> tuple:
- """Get anchors according to feature map sizes.
-
- Args:
- featmap_sizes (list[tuple]): Multi-level feature map sizes.
- batch_img_metas (list[dict]): Image meta info.
- device (str): Device for returned tensors
-
- Returns:
- tuple:
-
- - anchor_list (list[Tensor]): Anchors of each image.
- - valid_flag_list (list[Tensor]): Valid flags of each image.
- """
- num_imgs = len(batch_img_metas)
-
- # since feature map sizes of all images are the same, we only compute
- # anchors for one time
- multi_level_anchors = self.atss_prior_generator.grid_priors(
- featmap_sizes, device=device)
- anchor_list = [multi_level_anchors for _ in range(num_imgs)]
-
- # for each image, we compute valid flags of multi level anchors
- valid_flag_list = []
- for img_id, img_meta in enumerate(batch_img_metas):
- multi_level_flags = self.atss_prior_generator.valid_flags(
- featmap_sizes, img_meta['pad_shape'], device=device)
- valid_flag_list.append(multi_level_flags)
-
- return anchor_list, valid_flag_list
-
- def get_atss_targets(
- self,
- cls_scores: List[Tensor],
- mlvl_points: List[Tensor],
- batch_gt_instances: InstanceList,
- batch_img_metas: List[dict],
- batch_gt_instances_ignore: OptInstanceList = None) -> tuple:
- """A wrapper for computing ATSS targets for points in multiple images.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes`` and ``labels``
- attributes.
- batch_img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
- Batch of gt_instances_ignore. It includes ``bboxes`` attribute
- data that is ignored during training and testing.
- Defaults to None.
-
- Returns:
- tuple:
-
- - labels_list (list[Tensor]): Labels of each level.
- - label_weights (Tensor): Label weights of all levels.
- - bbox_targets_list (list[Tensor]): Regression targets of each
- level, (l, t, r, b).
- - bbox_weights (Tensor): Bbox weights of all levels.
- """
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- assert len(
- featmap_sizes
- ) == self.atss_prior_generator.num_levels == \
- self.fcos_prior_generator.num_levels
-
- device = cls_scores[0].device
-
- anchor_list, valid_flag_list = self.get_anchors(
- featmap_sizes, batch_img_metas, device=device)
-
- cls_reg_targets = ATSSHead.get_targets(
- self,
- anchor_list,
- valid_flag_list,
- batch_gt_instances,
- batch_img_metas,
- batch_gt_instances_ignore,
- unmap_outputs=True)
-
- (anchor_list, labels_list, label_weights_list, bbox_targets_list,
- bbox_weights_list, avg_factor) = cls_reg_targets
-
- bbox_targets_list = [
- bbox_targets.reshape(-1, 4) for bbox_targets in bbox_targets_list
- ]
-
- num_imgs = len(batch_img_metas)
- # transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format
- bbox_targets_list = self.transform_bbox_targets(
- bbox_targets_list, mlvl_points, num_imgs)
-
- labels_list = [labels.reshape(-1) for labels in labels_list]
- label_weights_list = [
- label_weights.reshape(-1) for label_weights in label_weights_list
- ]
- bbox_weights_list = [
- bbox_weights.reshape(-1) for bbox_weights in bbox_weights_list
- ]
- label_weights = torch.cat(label_weights_list)
- bbox_weights = torch.cat(bbox_weights_list)
- return labels_list, label_weights, bbox_targets_list, bbox_weights
-
- def transform_bbox_targets(self, decoded_bboxes: List[Tensor],
- mlvl_points: List[Tensor],
- num_imgs: int) -> List[Tensor]:
- """Transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format.
-
- Args:
- decoded_bboxes (list[Tensor]): Regression targets of each level,
- in the form of (x1, y1, x2, y2).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- num_imgs (int): the number of images in a batch.
-
- Returns:
- bbox_targets (list[Tensor]): Regression targets of each level in
- the form of (l, t, r, b).
- """
- # TODO: Re-implemented in Class PointCoder
- assert len(decoded_bboxes) == len(mlvl_points)
- num_levels = len(decoded_bboxes)
- mlvl_points = [points.repeat(num_imgs, 1) for points in mlvl_points]
- bbox_targets = []
- for i in range(num_levels):
- bbox_target = self.bbox_coder.encode(mlvl_points[i],
- decoded_bboxes[i])
- bbox_targets.append(bbox_target)
-
- return bbox_targets
-
- def _load_from_state_dict(self, state_dict: dict, prefix: str,
- local_metadata: dict, strict: bool,
- missing_keys: Union[List[str], str],
- unexpected_keys: Union[List[str], str],
- error_msgs: Union[List[str], str]) -> None:
- """Override the method in the parent class to avoid changing para's
- name."""
- pass
diff --git a/spaces/Larvuz/instruct-pix2pix/edit_app.py b/spaces/Larvuz/instruct-pix2pix/edit_app.py
deleted file mode 100644
index 0359e815ad51b1a2291dd8943555568e452981ad..0000000000000000000000000000000000000000
--- a/spaces/Larvuz/instruct-pix2pix/edit_app.py
+++ /dev/null
@@ -1,192 +0,0 @@
-from __future__ import annotations
-
-import math
-import random
-
-import gradio as gr
-import torch
-from PIL import Image, ImageOps
-from diffusers import StableDiffusionInstructPix2PixPipeline
-
-
-help_text = """
-If you're not getting what you want, there may be a few reasons:
-1. Is the image not changing enough? Your Image CFG weight may be too high. This value dictates how similar the output should be to the input. It's possible your edit requires larger changes from the original image, and your Image CFG weight isn't allowing that. Alternatively, your Text CFG weight may be too low. This value dictates how much to listen to the text instruction. The default Image CFG of 1.5 and Text CFG of 7.5 are a good starting point, but aren't necessarily optimal for each edit. Try:
- * Decreasing the Image CFG weight, or
- * Increasing the Text CFG weight, or
-2. Conversely, is the image changing too much, such that the details in the original image aren't preserved? Try:
- * Increasing the Image CFG weight, or
- * Decreasing the Text CFG weight
-3. Try generating results with different random seeds by setting "Randomize Seed" and running generation multiple times. You can also try setting "Randomize CFG" to sample new Text CFG and Image CFG values each time.
-4. Rephrasing the instruction sometimes improves results (e.g., "turn him into a dog" vs. "make him a dog" vs. "as a dog").
-5. Increasing the number of steps sometimes improves results.
-6. Do faces look weird? The Stable Diffusion autoencoder has a hard time with faces that are small in the image. Try:
- * Cropping the image so the face takes up a larger portion of the frame.
-"""
-
-
-example_instructions = [
- "Make it a picasso painting",
- "as if it were by modigliani",
- "convert to a bronze statue",
- "Turn it into an anime.",
- "have it look like a graphic novel",
- "make him gain weight",
- "what would he look like bald?",
- "Have him smile",
- "Put him in a cocktail party.",
- "move him at the beach.",
- "add dramatic lighting",
- "Convert to black and white",
- "What if it were snowing?",
- "Give him a leather jacket",
- "Turn him into a cyborg!",
- "make him wear a beanie",
-]
-
-model_id = "timbrooks/instruct-pix2pix"
-
-def main():
- pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None).to("cuda")
- example_image = Image.open("imgs/example.jpg").convert("RGB")
-
- def load_example(
- steps: int,
- randomize_seed: bool,
- seed: int,
- randomize_cfg: bool,
- text_cfg_scale: float,
- image_cfg_scale: float,
- ):
- example_instruction = random.choice(example_instructions)
- return [example_image, example_instruction] + generate(
- example_image,
- example_instruction,
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- )
-
- def generate(
- input_image: Image.Image,
- instruction: str,
- steps: int,
- randomize_seed: bool,
- seed: int,
- randomize_cfg: bool,
- text_cfg_scale: float,
- image_cfg_scale: float,
- ):
- seed = random.randint(0, 100000) if randomize_seed else seed
- text_cfg_scale = round(random.uniform(6.0, 9.0), ndigits=2) if randomize_cfg else text_cfg_scale
- image_cfg_scale = round(random.uniform(1.2, 1.8), ndigits=2) if randomize_cfg else image_cfg_scale
-
- width, height = input_image.size
- factor = 512 / max(width, height)
- factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height)
- width = int((width * factor) // 64) * 64
- height = int((height * factor) // 64) * 64
- input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS)
-
- if instruction == "":
- return [input_image, seed]
-
- generator = torch.manual_seed(seed)
- edited_image = pipe(
- instruction, image=input_image,
- guidance_scale=text_cfg_scale, image_guidance_scale=image_cfg_scale,
- num_inference_steps=steps, generator=generator,
- ).images[0]
- return [seed, text_cfg_scale, image_cfg_scale, edited_image]
-
- def reset():
- return [0, "Randomize Seed", 1371, "Fix CFG", 7.5, 1.5, None]
-
- with gr.Blocks() as demo:
- gr.HTML("""
- InstructPix2Pix: Learning to Follow Image Editing Instructions
-
-For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.
-
-
-
-
""")
- with gr.Row():
- with gr.Column(scale=1, min_width=100):
- generate_button = gr.Button("Generate")
- with gr.Column(scale=1, min_width=100):
- load_button = gr.Button("Load Example")
- with gr.Column(scale=1, min_width=100):
- reset_button = gr.Button("Reset")
- with gr.Column(scale=3):
- instruction = gr.Textbox(lines=1, label="Edit Instruction", interactive=True)
-
- with gr.Row():
- input_image = gr.Image(label="Input Image", type="pil", interactive=True)
- edited_image = gr.Image(label=f"Edited Image", type="pil", interactive=False)
- input_image.style(height=512, width=512)
- edited_image.style(height=512, width=512)
-
- with gr.Row():
- steps = gr.Number(value=50, precision=0, label="Steps", interactive=True)
- randomize_seed = gr.Radio(
- ["Fix Seed", "Randomize Seed"],
- value="Randomize Seed",
- type="index",
- show_label=False,
- interactive=True,
- )
- seed = gr.Number(value=1371, precision=0, label="Seed", interactive=True)
- randomize_cfg = gr.Radio(
- ["Fix CFG", "Randomize CFG"],
- value="Fix CFG",
- type="index",
- show_label=False,
- interactive=True,
- )
- text_cfg_scale = gr.Number(value=7.5, label=f"Text CFG", interactive=True)
- image_cfg_scale = gr.Number(value=1.5, label=f"Image CFG", interactive=True)
-
- gr.Markdown(help_text)
-
- load_button.click(
- fn=load_example,
- inputs=[
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- ],
- outputs=[input_image, instruction, seed, text_cfg_scale, image_cfg_scale, edited_image],
- )
- generate_button.click(
- fn=generate,
- inputs=[
- input_image,
- instruction,
- steps,
- randomize_seed,
- seed,
- randomize_cfg,
- text_cfg_scale,
- image_cfg_scale,
- ],
- outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image],
- )
- reset_button.click(
- fn=reset,
- inputs=[],
- outputs=[steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale, edited_image],
- )
-
- demo.queue(concurrency_count=1)
- demo.launch(share=False)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/Loren/Streamlit_OCR_comparator/README.md b/spaces/Loren/Streamlit_OCR_comparator/README.md
deleted file mode 100644
index 2894464cad0f3f4d163e1f8e72750b10259159da..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Streamlit OCR Comparator
-emoji: 📰🔍🔤
-colorFrom: indigo
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: Home.py
-tags: [streamlit, ocr]
-pinned: true
----
\ No newline at end of file
diff --git a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py b/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py
deleted file mode 100644
index 6d9b36c74b1808b56ded68cf080a689db7e0ee4e..0000000000000000000000000000000000000000
--- a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# -*- coding: utf-8 -*-
-# File : __init__.py
-# Author : Jiayuan Mao
-# Email : maojiayuan@gmail.com
-# Date : 27/01/2018
-#
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-from .batchnorm import set_sbn_eps_mode
-from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d
-from .batchnorm import patch_sync_batchnorm, convert_model
-from .replicate import DataParallelWithCallback, patch_replication_callback
diff --git a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py b/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py
deleted file mode 100644
index 9794e0fc3463a5e8fad05c037cce64683059a6d3..0000000000000000000000000000000000000000
--- a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.ERROR)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict = {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})".format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10, 2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r", encoding="utf-8") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
\ No newline at end of file
diff --git a/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py b/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py
deleted file mode 100644
index 3a5999ddc80faa0ce6a54389562c9f092b40bb03..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# encoding: utf-8
-"""
-This files contains pytest fixtures and other test related stuff.
-
-"""
-from __future__ import absolute_import, division, print_function
-
-import numpy as np
-import pytest
-
-# save numpy's current print options
-_NP_PRINT_OPTIONS = np.get_printoptions()
-
-
-@pytest.fixture(autouse=True)
-def setup_doctest(request):
- """Set up the environment for doctests (when run through pytest)."""
- np.set_printoptions(precision=5, edgeitems=2, suppress=True)
-
- def fin():
- """Restore the environment after doctests (when run through pytest)."""
- np.set_printoptions(**_NP_PRINT_OPTIONS)
-
- request.addfinalizer(fin)
diff --git a/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py b/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py
deleted file mode 100644
index 19e34a11def040b971b534b9ed3fda71d0a35c85..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py
+++ /dev/null
@@ -1,882 +0,0 @@
-# encoding: utf-8
-# pylint: disable=no-member
-# pylint: disable=invalid-name
-# pylint: disable=too-many-arguments
-# pylint: disable=wrong-import-position
-"""
-Evaluation package.
-
-"""
-
-from __future__ import absolute_import, division, print_function
-
-import numpy as np
-
-
-# evaluation helper functions
-def find_closest_matches(detections, annotations):
- """
- Find the closest annotation for each detection.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
-
- Returns
- -------
- indices : numpy array
- Indices of the closest matches.
-
- Notes
- -----
- The sequences must be ordered.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.int)
- # if only a single annotation is given
- if len(annotations) == 1:
- # return an array as long as the detections with indices 0
- return np.zeros(len(detections), dtype=np.int)
- # solution found at: http://stackoverflow.com/questions/8914491/
- indices = annotations.searchsorted(detections)
- indices = np.clip(indices, 1, len(annotations) - 1)
- left = annotations[indices - 1]
- right = annotations[indices]
- indices -= detections - left < right - detections
- # return the indices of the closest matches
- return indices
-
-
-def calc_errors(detections, annotations, matches=None):
- """
- Errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.float)
- # determine the closest annotations
- if matches is None:
- matches = find_closest_matches(detections, annotations)
- # calc error relative to those annotations
- errors = detections - annotations[matches]
- # return the errors
- return errors
-
-
-def calc_absolute_errors(detections, annotations, matches=None):
- """
- Absolute errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Absolute errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # return the errors
- return np.abs(calc_errors(detections, annotations, matches))
-
-
-def calc_relative_errors(detections, annotations, matches=None):
- """
- Relative errors of the detections to the closest annotations.
-
- Parameters
- ----------
- detections : list or numpy array
- Detected events.
- annotations : list or numpy array
- Annotated events.
- matches : list or numpy array
- Indices of the closest events.
-
- Returns
- -------
- errors : numpy array
- Relative errors.
-
- Notes
- -----
- The sequences must be ordered. To speed up the calculation, a list of
- pre-computed indices of the closest matches can be used.
-
- """
- # make sure the arrays have the correct types
- detections = np.asarray(detections, dtype=np.float)
- annotations = np.asarray(annotations, dtype=np.float)
- if matches is not None:
- matches = np.asarray(matches, dtype=np.int)
- # TODO: right now, it only works with 1D arrays
- if detections.ndim > 1 or annotations.ndim > 1:
- raise NotImplementedError('please implement multi-dim support')
- # if no detections or annotations are given
- if len(detections) == 0 or len(annotations) == 0:
- # return a empty array
- return np.zeros(0, dtype=np.float)
- # determine the closest annotations
- if matches is None:
- matches = find_closest_matches(detections, annotations)
- # calculate the absolute errors
- errors = calc_errors(detections, annotations, matches)
- # return the relative errors
- return np.abs(1 - (errors / annotations[matches]))
-
-
-# abstract evaluation base class
-class EvaluationMixin(object):
- """
- Evaluation mixin class.
-
- This class has a `name` attribute which is used for display purposes and
- defaults to 'None'.
-
- `METRIC_NAMES` is a list of tuples, containing the attribute's name and the
- corresponding label, e.g.:
-
- The attributes defined in `METRIC_NAMES` will be provided as an ordered
- dictionary as the `metrics` property unless the subclass overwrites the
- property.
-
- `FLOAT_FORMAT` is used to format floats.
-
- """
-
- # Example:
- # METRIC_NAMES = [
- # ('precision', 'Precision'),
- # ('recall', 'Recall'),
- # ('fmeasure', 'F-measure'),
- # ]
- name = None
- METRIC_NAMES = []
- FLOAT_FORMAT = '{:.3f}'
-
- @property
- def metrics(self):
- """Metrics as a dictionary."""
- from collections import OrderedDict
- metrics = OrderedDict()
- # metrics = {}
- for metric in [m[0] for m in self.METRIC_NAMES]:
- metrics[metric] = getattr(self, metric)
- return metrics
-
- def __len__(self):
- """Length of the evaluation object."""
- raise NotImplementedError('must be implemented by subclass.')
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- Notes
- -----
- This is a fallback method formatting the `metrics` dictionary in a
- human readable way. Classes inheriting from this mixin class should
- provide a method better suitable.
-
- """
- # pylint: disable=unused-argument
- import pprint
- return pprint.pformat(dict(self.metrics), indent=4)
-
-
-# evaluation classes
-class SimpleEvaluation(EvaluationMixin):
- """
- Simple Precision, Recall, F-measure and Accuracy evaluation based on the
- numbers of true/false positive/negative detections.
-
- Parameters
- ----------
- num_tp : int
- Number of true positive detections.
- num_fp : int
- Number of false positive detections.
- num_tn : int
- Number of true negative detections.
- num_fn : int
- Number of false negative detections.
- name : str
- Name to be displayed.
-
- Notes
- -----
- This class is only suitable for a 1-class evaluation problem.
-
- """
-
- METRIC_NAMES = [
- ('num_tp', 'No. of true positives'),
- ('num_fp', 'No. of false positives'),
- ('num_tn', 'No. of true negatives'),
- ('num_fn', 'No. of false negatives'),
- ('num_annotations', 'No. Annotations'),
- ('precision', 'Precision'),
- ('recall', 'Recall'),
- ('fmeasure', 'F-measure'),
- ('accuracy', 'Accuracy'),
- ]
-
- def __init__(self, num_tp=0, num_fp=0, num_tn=0, num_fn=0, name=None,
- **kwargs):
- # pylint: disable=unused-argument
- # hidden variables, to be able to overwrite them in subclasses
- self._num_tp = int(num_tp)
- self._num_fp = int(num_fp)
- self._num_tn = int(num_tn)
- self._num_fn = int(num_fn)
- # name of the evaluation
- self.name = name
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return self._num_tp
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return self._num_fp
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return self._num_tn
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return self._num_fn
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- return self.num_tp + self.num_fn
-
- def __len__(self):
- # the length equals the number of annotations
- return self.num_annotations
-
- @property
- def precision(self):
- """Precision."""
- # correct / retrieved
- retrieved = float(self.num_tp + self.num_fp)
- # if there are no positive predictions, none of them are wrong
- if retrieved == 0:
- return 1.
- return self.num_tp / retrieved
-
- @property
- def recall(self):
- """Recall."""
- # correct / relevant
- relevant = float(self.num_tp + self.num_fn)
- # if there are no positive annotations, we recalled all of them
- if relevant == 0:
- return 1.
- return self.num_tp / relevant
-
- @property
- def fmeasure(self):
- """F-measure."""
- # 2pr / (p+r)
- numerator = 2. * self.precision * self.recall
- if numerator == 0:
- return 0.
- return numerator / (self.precision + self.recall)
-
- @property
- def accuracy(self):
- """Accuracy."""
- # acc: (TP + TN) / (TP + FP + TN + FN)
- denominator = self.num_fp + self.num_fn + self.num_tp + self.num_tn
- if denominator == 0:
- return 1.
- numerator = float(self.num_tp + self.num_tn)
- if numerator == 0:
- return 0.
- return numerator / denominator
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
-
- """
- ret = ''
- if self.name is not None:
- ret += '%s\n ' % self.name
- ret += 'Annotations: %5d TP: %5d FP: %5d FN: %5d ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- return ret
-
- def __str__(self):
- return self.tostring()
-
-
-# evaluate Precision, Recall, F-measure and Accuracy with lists or numpy arrays
-class Evaluation(SimpleEvaluation):
- """
- Evaluation class for measuring Precision, Recall and F-measure based on
- numpy arrays or lists with true/false positive/negative detections.
-
- Parameters
- ----------
- tp : list or numpy array
- True positive detections.
- fp : list or numpy array
- False positive detections.
- tn : list or numpy array
- True negative detections.
- fn : list or numpy array
- False negative detections.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, tp=None, fp=None, tn=None, fn=None, **kwargs):
- # set default values
- if tp is None:
- tp = []
- if fp is None:
- fp = []
- if tn is None:
- tn = []
- if fn is None:
- fn = []
- # instantiate a SimpleEvaluation object
- super(Evaluation, self).__init__(**kwargs)
- # convert everything to numpy arrays and save them
- self.tp = np.asarray(list(tp), dtype=np.float)
- self.fp = np.asarray(list(fp), dtype=np.float)
- self.tn = np.asarray(list(tn), dtype=np.float)
- self.fn = np.asarray(list(fn), dtype=np.float)
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return len(self.tp)
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return len(self.fp)
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return len(self.tn)
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return len(self.fn)
-
-
-# class for evaluation of Precision, Recall, F-measure with 2D arrays
-class MultiClassEvaluation(Evaluation):
- """
- Evaluation class for measuring Precision, Recall and F-measure based on
- 2D numpy arrays with true/false positive/negative detections.
-
- Parameters
- ----------
- tp : list of tuples or numpy array, shape (num_tp, 2)
- True positive detections.
- fp : list of tuples or numpy array, shape (num_fp, 2)
- False positive detections.
- tn : list of tuples or numpy array, shape (num_tn, 2)
- True negative detections.
- fn : list of tuples or numpy array, shape (num_fn, 2)
- False negative detections.
- name : str
- Name to be displayed.
-
- Notes
- -----
- The second item of the tuples or the second column of the arrays denote
- the class the detection belongs to.
-
- """
- def __init__(self, tp=None, fp=None, tn=None, fn=None, **kwargs):
- # set default values
- if tp is None:
- tp = np.zeros((0, 2))
- if fp is None:
- fp = np.zeros((0, 2))
- if tn is None:
- tn = np.zeros((0, 2))
- if fn is None:
- fn = np.zeros((0, 2))
- super(MultiClassEvaluation, self).__init__(**kwargs)
- self.tp = np.asarray(tp, dtype=np.float)
- self.fp = np.asarray(fp, dtype=np.float)
- self.tn = np.asarray(tn, dtype=np.float)
- self.fn = np.asarray(fn, dtype=np.float)
-
- def tostring(self, verbose=False, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Parameters
- ----------
- verbose : bool
- Add evaluation for individual classes.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- ret = ''
-
- if verbose:
- # extract all classes
- classes = []
- if self.tp.any():
- classes = np.append(classes, np.unique(self.tp[:, 1]))
- if self.fp.any():
- classes = np.append(classes, np.unique(self.fp[:, 1]))
- if self.tn.any():
- classes = np.append(classes, np.unique(self.tn[:, 1]))
- if self.fn.any():
- classes = np.append(classes, np.unique(self.fn[:, 1]))
- for cls in sorted(np.unique(classes)):
- # extract the TP, FP, TN and FN of this class
- tp = self.tp[self.tp[:, 1] == cls]
- fp = self.fp[self.fp[:, 1] == cls]
- tn = self.tn[self.tn[:, 1] == cls]
- fn = self.fn[self.fn[:, 1] == cls]
- # evaluate them
- e = Evaluation(tp, fp, tn, fn, name='Class %s' % cls)
- # append to the output string
- ret += ' %s\n' % e.tostring(verbose=False)
- # normal formatting
- ret += 'Annotations: %5d TP: %5d FP: %4d FN: %4d ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- # return
- return ret
-
-
-# class for summing Evaluations
-class SumEvaluation(SimpleEvaluation):
- """
- Simple class for summing evaluations.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, eval_objects, name=None):
- # pylint: disable=super-init-not-called
- # Note: we want to inherit the evaluation functions/properties, no need
- # to call __super__, but we need to take care of 'name'
- if not isinstance(eval_objects, list):
- # wrap the given eval_object in a list
- eval_objects = [eval_objects]
- self.eval_objects = eval_objects
- self.name = name or 'sum for %d files' % len(self)
-
- def __len__(self):
- # just use the length of the evaluation objects
- return len(self.eval_objects)
-
- # redefine the counters (number of TP, FP, TN, FN & number of annotations)
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- return sum(e.num_tp for e in self.eval_objects)
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- return sum(e.num_fp for e in self.eval_objects)
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- return sum(e.num_tn for e in self.eval_objects)
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- return sum(e.num_fn for e in self.eval_objects)
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- return sum(e.num_annotations for e in self.eval_objects)
-
-
-# class for averaging Evaluations
-class MeanEvaluation(SumEvaluation):
- """
- Simple class for averaging evaluation.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- name : str
- Name to be displayed.
-
- """
-
- def __init__(self, eval_objects, name=None, **kwargs):
- super(MeanEvaluation, self).__init__(eval_objects, **kwargs)
- # handle the 'name' here to be able to set a different default value
- self.name = name or 'mean for %d files' % len(self)
-
- # overwrite the properties to calculate the mean instead of the sum
-
- @property
- def num_tp(self):
- """Number of true positive detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_tp for e in self.eval_objects])
-
- @property
- def num_fp(self):
- """Number of false positive detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_fp for e in self.eval_objects])
-
- @property
- def num_tn(self):
- """Number of true negative detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_tn for e in self.eval_objects])
-
- @property
- def num_fn(self):
- """Number of false negative detections."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_fn for e in self.eval_objects])
-
- @property
- def num_annotations(self):
- """Number of annotations."""
- if not self.eval_objects:
- return 0.
- return np.nanmean([e.num_annotations for e in self.eval_objects])
-
- @property
- def precision(self):
- """Precision."""
- return np.nanmean([e.precision for e in self.eval_objects])
-
- @property
- def recall(self):
- """Recall."""
- return np.nanmean([e.recall for e in self.eval_objects])
-
- @property
- def fmeasure(self):
- """F-measure."""
- return np.nanmean([e.fmeasure for e in self.eval_objects])
-
- @property
- def accuracy(self):
- """Accuracy."""
- return np.nanmean([e.accuracy for e in self.eval_objects])
-
- def tostring(self, **kwargs):
- """
- Format the evaluation metrics as a human readable string.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- ret = ''
- if self.name is not None:
- ret += '%s\n ' % self.name
- # TODO: unify this with SimpleEvaluation but
- # add option to provide field formatters (e.g. 3d or 5.2f)
- # format with floats instead of integers
- ret += 'Annotations: %5.2f TP: %5.2f FP: %5.2f FN: %5.2f ' \
- 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \
- (self.num_annotations, self.num_tp, self.num_fp, self.num_fn,
- self.precision, self.recall, self.fmeasure, self.accuracy)
- return ret
-
-
-def tostring(eval_objects, **kwargs):
- """
- Format the given evaluation objects as human readable strings.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
-
- Returns
- -------
- str
- Evaluation metrics formatted as a human readable string.
-
- """
- # pylint: disable=unused-argument
- return '\n'.join([e.tostring() for e in eval_objects])
-
-
-def tocsv(eval_objects, metric_names=None, float_format='{:.3f}', **kwargs):
- """
- Format the given evaluation objects as a CSV table.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- metric_names : list of tuples, optional
- List of tuples defining the name of the property corresponding to the
- metric, and the metric label e.g. ('fp', 'False Positives').
- float_format : str, optional
- How to format the metrics.
-
- Returns
- -------
- str
- CSV table representation of the evaluation objects.
-
- Notes
- -----
- If no `metric_names` are given, they will be extracted from the first
- evaluation object.
-
- """
- # pylint: disable=unused-argument
-
- if metric_names is None:
- # get the evaluation metrics from the first evaluation object
- metric_names = eval_objects[0].METRIC_NAMES
- metric_names, metric_labels = list(zip(*metric_names))
- # add header
- lines = ['Name,' + ','.join(metric_labels)]
- # TODO: use e.metrics dict?
- # add the evaluation objects
- for e in eval_objects:
- values = [float_format.format(getattr(e, mn)) for mn in metric_names]
- lines.append(e.name + ',' + ','.join(values))
- # return everything
- return '\n'.join(lines)
-
-
-def totex(eval_objects, metric_names=None, float_format='{:.3f}', **kwargs):
- """
- Format the given evaluation objects as a LaTeX table.
-
- Parameters
- ----------
- eval_objects : list
- Evaluation objects.
- metric_names : list of tuples, optional
- List of tuples defining the name of the property corresponding to the
- metric, and the metric label e.g. ('fp', 'False Positives').
- float_format : str, optional
- How to format the metrics.
-
- Returns
- -------
- str
- LaTeX table representation of the evaluation objects.
-
- Notes
- -----
- If no `metric_names` are given, they will be extracted from the first
- evaluation object.
-
- """
- # pylint: disable=unused-argument
-
- if metric_names is None:
- # get the evaluation metrics from the first evaluation object
- metric_names = eval_objects[0].METRIC_NAMES
- metric_names, metric_labels = list(zip(*metric_names))
- # add header
- lines = ['Name & ' + ' & '.join(metric_labels) + '\\\\']
- # TODO: use e.metrics dict
- # TODO: add a generic totable() function which accepts columns separator,
- # newline stuff (e.g. tex \\\\) and others
- # add the evaluation objects
- for e in eval_objects:
- values = [float_format.format(getattr(e, mn)) for mn in metric_names]
- lines.append(e.name + ' & ' + ' & '.join(values) + '\\\\')
- # return everything
- return '\n'.join(lines)
-
-
-def evaluation_io(parser, ann_suffix, det_suffix, ann_dir=None, det_dir=None):
- """
- Add evaluation input/output and formatting related arguments to an existing
- parser object.
-
- Parameters
- ----------
- parser : argparse parser instance
- Existing argparse parser object.
- ann_suffix : str
- Suffix of the annotation files.
- det_suffix : str
- Suffix of the detection files.
- ann_dir : str, optional
- Use only annotations from this folder (and sub-folders).
- det_dir : str, optional
- Use only detections from this folder (and sub-folders).
-
- Returns
- -------
- io_group : argparse argument group
- Evaluation input / output argument group.
- formatter_group : argparse argument group
- Evaluation formatter argument group.
-
- """
- import sys
- import argparse
- # general input output file handling
- parser.add_argument('files', nargs='*',
- help='files (or folders) to be evaluated')
- parser.add_argument('-o', dest='outfile', type=argparse.FileType('w'),
- default=sys.stdout,
- help='output file [default: STDOUT]')
- # file suffixes used for evaluation
- g = parser.add_argument_group('file/folder/suffix arguments')
- g.add_argument('-a', dest='ann_suffix', action='store', default=ann_suffix,
- help='suffix of the annotation files '
- '[default: %(default)s]')
- g.add_argument('--ann_dir', action='store', default=ann_dir,
- help='search only this directory (recursively) for '
- 'annotation files [default: %(default)s]')
- g.add_argument('-d', dest='det_suffix', action='store', default=det_suffix,
- help='suffix of the detection files [default: %(default)s]')
- g.add_argument('--det_dir', action='store', default=det_dir,
- help='search only this directory (recursively) for '
- 'detection files [default: %(default)s]')
- # option to ignore non-existing detections
- g.add_argument('-i', '--ignore_non_existing', action='store_true',
- help='ignore non-existing detections [default: raise a '
- 'warning and assume empty detections]')
- # verbose
- parser.add_argument('-v', '--verbose', action='count', default=0,
- help='increase verbosity level')
- # option to suppress warnings
- parser.add_argument('-q', '--quiet', action='store_true',
- help='suppress any warnings')
- # output format options
- parser.set_defaults(output_formatter=tostring)
- f = parser.add_argument_group('formatting arguments')
- formats = f.add_mutually_exclusive_group()
- formats.add_argument('--tex', dest='output_formatter',
- action='store_const', const=totex,
- help='format output to be used in .tex files')
- formats.add_argument('--csv', dest='output_formatter',
- action='store_const', const=tocsv,
- help='format output to be used in .csv files')
- # return the output formatting group so the caller can add more options
- return g, f
-
-
-# finally import the submodules
-from . import chords, beats, notes, onsets, tempo
-
-# import often used classes
-from .beats import BeatEvaluation, BeatMeanEvaluation
-from .chords import ChordEvaluation, ChordMeanEvaluation, ChordSumEvaluation
-from .key import KeyEvaluation, KeyMeanEvaluation
-from .notes import NoteEvaluation, NoteMeanEvaluation, NoteSumEvaluation
-from .onsets import OnsetEvaluation, OnsetMeanEvaluation, OnsetSumEvaluation
-from .tempo import TempoEvaluation, TempoMeanEvaluation
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py
deleted file mode 100644
index 6fc71979190323f44c09f8b7e1761cf49cd2d76b..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
- 'iou3d_boxes_iou_bev_forward', 'iou3d_nms_forward',
- 'iou3d_nms_normal_forward'
-])
-
-
-def boxes_iou_bev(boxes_a, boxes_b):
- """Calculate boxes IoU in the Bird's Eye View.
-
- Args:
- boxes_a (torch.Tensor): Input boxes a with shape (M, 5).
- boxes_b (torch.Tensor): Input boxes b with shape (N, 5).
-
- Returns:
- ans_iou (torch.Tensor): IoU result with shape (M, N).
- """
- ans_iou = boxes_a.new_zeros(
- torch.Size((boxes_a.shape[0], boxes_b.shape[0])))
-
- ext_module.iou3d_boxes_iou_bev_forward(boxes_a.contiguous(),
- boxes_b.contiguous(), ans_iou)
-
- return ans_iou
-
-
-def nms_bev(boxes, scores, thresh, pre_max_size=None, post_max_size=None):
- """NMS function GPU implementation (for BEV boxes). The overlap of two
- boxes for IoU calculation is defined as the exact overlapping area of the
- two boxes. In this function, one can also set ``pre_max_size`` and
- ``post_max_size``.
-
- Args:
- boxes (torch.Tensor): Input boxes with the shape of [N, 5]
- ([x1, y1, x2, y2, ry]).
- scores (torch.Tensor): Scores of boxes with the shape of [N].
- thresh (float): Overlap threshold of NMS.
- pre_max_size (int, optional): Max size of boxes before NMS.
- Default: None.
- post_max_size (int, optional): Max size of boxes after NMS.
- Default: None.
-
- Returns:
- torch.Tensor: Indexes after NMS.
- """
- assert boxes.size(1) == 5, 'Input boxes shape should be [N, 5]'
- order = scores.sort(0, descending=True)[1]
-
- if pre_max_size is not None:
- order = order[:pre_max_size]
- boxes = boxes[order].contiguous()
-
- keep = torch.zeros(boxes.size(0), dtype=torch.long)
- num_out = ext_module.iou3d_nms_forward(boxes, keep, thresh)
- keep = order[keep[:num_out].cuda(boxes.device)].contiguous()
- if post_max_size is not None:
- keep = keep[:post_max_size]
- return keep
-
-
-def nms_normal_bev(boxes, scores, thresh):
- """Normal NMS function GPU implementation (for BEV boxes). The overlap of
- two boxes for IoU calculation is defined as the exact overlapping area of
- the two boxes WITH their yaw angle set to 0.
-
- Args:
- boxes (torch.Tensor): Input boxes with shape (N, 5).
- scores (torch.Tensor): Scores of predicted boxes with shape (N).
- thresh (float): Overlap threshold of NMS.
-
- Returns:
- torch.Tensor: Remaining indices with scores in descending order.
- """
- assert boxes.shape[1] == 5, 'Input boxes shape should be [N, 5]'
- order = scores.sort(0, descending=True)[1]
-
- boxes = boxes[order].contiguous()
-
- keep = torch.zeros(boxes.size(0), dtype=torch.long)
- num_out = ext_module.iou3d_nms_normal_forward(boxes, keep, thresh)
- return order[keep[:num_out].cuda(boxes.device)].contiguous()
diff --git a/spaces/MetaWabbit/Auto-GPT/run.sh b/spaces/MetaWabbit/Auto-GPT/run.sh
deleted file mode 100644
index edcbc44155b9ca9df83e283fdf976472c13e6492..0000000000000000000000000000000000000000
--- a/spaces/MetaWabbit/Auto-GPT/run.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-python scripts/check_requirements.py requirements.txt
-if [ $? -eq 1 ]
-then
- echo Installing missing packages...
- pip install -r requirements.txt
-fi
-python -m autogpt $@
-read -p "Press any key to continue..."
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py b/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py
deleted file mode 100644
index 244425feef76bf89d4de939cb8a1914a6f0f47c6..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py
+++ /dev/null
@@ -1,387 +0,0 @@
-# Lint as: python3
-# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Unit tests for the classifier trainer models."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import copy
-import functools
-import json
-
-import os
-import sys
-
-from typing import Any, Callable, Iterable, Mapping, MutableMapping, Optional, Tuple
-
-from absl import flags
-from absl.testing import parameterized
-import tensorflow as tf
-
-from tensorflow.python.distribute import combinations
-from tensorflow.python.distribute import strategy_combinations
-from official.utils.flags import core as flags_core
-from official.vision.image_classification import classifier_trainer
-from official.vision.image_classification import dataset_factory
-from official.vision.image_classification import test_utils
-from official.vision.image_classification.configs import base_configs
-
-classifier_trainer.define_classifier_flags()
-
-
-def distribution_strategy_combinations() -> Iterable[Tuple[Any, ...]]:
- """Returns the combinations of end-to-end tests to run."""
- return combinations.combine(
- distribution=[
- strategy_combinations.default_strategy,
- strategy_combinations.tpu_strategy,
- strategy_combinations.one_device_strategy_gpu,
- strategy_combinations.mirrored_strategy_with_two_gpus,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset=[
- 'imagenet',
- ],
- )
-
-
-def get_params_override(params_override: Mapping[str, Any]) -> str:
- """Converts params_override dict to string command."""
- return '--params_override=' + json.dumps(params_override)
-
-
-def basic_params_override(dtype: str = 'float32') -> MutableMapping[str, Any]:
- """Returns a basic parameter configuration for testing."""
- return {
- 'train_dataset': {
- 'builder': 'synthetic',
- 'use_per_replica_batch_size': True,
- 'batch_size': 1,
- 'image_size': 224,
- 'dtype': dtype,
- },
- 'validation_dataset': {
- 'builder': 'synthetic',
- 'batch_size': 1,
- 'use_per_replica_batch_size': True,
- 'image_size': 224,
- 'dtype': dtype,
- },
- 'train': {
- 'steps': 1,
- 'epochs': 1,
- 'callbacks': {
- 'enable_checkpoint_and_export': True,
- 'enable_tensorboard': False,
- },
- },
- 'evaluation': {
- 'steps': 1,
- },
- }
-
-
-def get_trivial_model(num_classes: int) -> tf.keras.Model:
- """Creates and compiles trivial model for ImageNet dataset."""
- model = test_utils.trivial_model(num_classes=num_classes)
- lr = 0.01
- optimizer = tf.keras.optimizers.SGD(learning_rate=lr)
- loss_obj = tf.keras.losses.SparseCategoricalCrossentropy()
- model.compile(optimizer=optimizer,
- loss=loss_obj,
- run_eagerly=True)
- return model
-
-
-def get_trivial_data() -> tf.data.Dataset:
- """Gets trivial data in the ImageNet size."""
- def generate_data(_) -> tf.data.Dataset:
- image = tf.zeros(shape=(224, 224, 3), dtype=tf.float32)
- label = tf.zeros([1], dtype=tf.int32)
- return image, label
-
- dataset = tf.data.Dataset.range(1)
- dataset = dataset.repeat()
- dataset = dataset.map(generate_data,
- num_parallel_calls=tf.data.experimental.AUTOTUNE)
- dataset = dataset.prefetch(buffer_size=1).batch(1)
- return dataset
-
-
-def run_end_to_end(main: Callable[[Any], None],
- extra_flags: Optional[Iterable[str]] = None,
- model_dir: Optional[str] = None):
- """Runs the classifier trainer end-to-end."""
- extra_flags = [] if extra_flags is None else extra_flags
- args = [sys.argv[0], '--model_dir', model_dir] + extra_flags
- flags_core.parse_flags(argv=args)
- main(flags.FLAGS)
-
-
-class ClassifierTest(tf.test.TestCase, parameterized.TestCase):
- """Unit tests for Keras models."""
- _tempdir = None
-
- @classmethod
- def setUpClass(cls): # pylint: disable=invalid-name
- super(ClassifierTest, cls).setUpClass()
-
- def tearDown(self):
- super(ClassifierTest, self).tearDown()
- tf.io.gfile.rmtree(self.get_temp_dir())
-
- @combinations.generate(distribution_strategy_combinations())
- def test_end_to_end_train_and_eval(self, distribution, model, dataset):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override()),
- '--mode=train_and_eval',
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
-
- @combinations.generate(
- combinations.combine(
- distribution=[
- strategy_combinations.one_device_strategy_gpu,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset='imagenet',
- dtype='float16',
- ))
- def test_gpu_train(self, distribution, model, dataset, dtype):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override(dtype)),
- '--mode=train_and_eval',
- ]
-
- export_params = basic_params_override()
- export_path = os.path.join(model_dir, 'export')
- export_params['export'] = {}
- export_params['export']['destination'] = export_path
- export_flags = base_flags + [
- '--mode=export_only',
- get_params_override(export_params)
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
- run_end_to_end(main=run,
- extra_flags=export_flags,
- model_dir=model_dir)
- self.assertTrue(os.path.exists(export_path))
-
- @combinations.generate(
- combinations.combine(
- distribution=[
- strategy_combinations.tpu_strategy,
- ],
- model=[
- 'efficientnet',
- 'resnet',
- ],
- mode='eager',
- dataset='imagenet',
- dtype='bfloat16',
- ))
- def test_tpu_train(self, distribution, model, dataset, dtype):
- """Test train_and_eval and export for Keras classifier models."""
- # Some parameters are not defined as flags (e.g. cannot run
- # classifier_train.py --batch_size=...) by design, so use
- # "--params_override=..." instead
- model_dir = self.get_temp_dir()
- base_flags = [
- '--data_dir=not_used',
- '--model_type=' + model,
- '--dataset=' + dataset,
- ]
- train_and_eval_flags = base_flags + [
- get_params_override(basic_params_override(dtype)),
- '--mode=train_and_eval',
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- run_end_to_end(main=run,
- extra_flags=train_and_eval_flags,
- model_dir=model_dir)
-
- @combinations.generate(distribution_strategy_combinations())
- def test_end_to_end_invalid_mode(self, distribution, model, dataset):
- """Test the Keras EfficientNet model with `strategy`."""
- model_dir = self.get_temp_dir()
- extra_flags = [
- '--data_dir=not_used',
- '--mode=invalid_mode',
- '--model_type=' + model,
- '--dataset=' + dataset,
- get_params_override(basic_params_override()),
- ]
-
- run = functools.partial(classifier_trainer.run,
- strategy_override=distribution)
- with self.assertRaises(ValueError):
- run_end_to_end(main=run, extra_flags=extra_flags, model_dir=model_dir)
-
-
-class UtilTests(parameterized.TestCase, tf.test.TestCase):
- """Tests for individual utility functions within classifier_trainer.py."""
-
- @parameterized.named_parameters(
- ('efficientnet-b0', 'efficientnet', 'efficientnet-b0', 224),
- ('efficientnet-b1', 'efficientnet', 'efficientnet-b1', 240),
- ('efficientnet-b2', 'efficientnet', 'efficientnet-b2', 260),
- ('efficientnet-b3', 'efficientnet', 'efficientnet-b3', 300),
- ('efficientnet-b4', 'efficientnet', 'efficientnet-b4', 380),
- ('efficientnet-b5', 'efficientnet', 'efficientnet-b5', 456),
- ('efficientnet-b6', 'efficientnet', 'efficientnet-b6', 528),
- ('efficientnet-b7', 'efficientnet', 'efficientnet-b7', 600),
- ('resnet', 'resnet', '', None),
- )
- def test_get_model_size(self, model, model_name, expected):
- config = base_configs.ExperimentConfig(
- model_name=model,
- model=base_configs.ModelConfig(
- model_params={
- 'model_name': model_name,
- },
- )
- )
- size = classifier_trainer.get_image_size_from_model(config)
- self.assertEqual(size, expected)
-
- @parameterized.named_parameters(
- ('dynamic', 'dynamic', None, 'dynamic'),
- ('scalar', 128., None, 128.),
- ('float32', None, 'float32', 1),
- ('float16', None, 'float16', 128),
- )
- def test_get_loss_scale(self, loss_scale, dtype, expected):
- config = base_configs.ExperimentConfig(
- runtime=base_configs.RuntimeConfig(
- loss_scale=loss_scale),
- train_dataset=dataset_factory.DatasetConfig(dtype=dtype))
- ls = classifier_trainer.get_loss_scale(config, fp16_default=128)
- self.assertEqual(ls, expected)
-
- @parameterized.named_parameters(
- ('float16', 'float16'),
- ('bfloat16', 'bfloat16')
- )
- def test_initialize(self, dtype):
- config = base_configs.ExperimentConfig(
- runtime=base_configs.RuntimeConfig(
- run_eagerly=False,
- enable_xla=False,
- per_gpu_thread_count=1,
- gpu_thread_mode='gpu_private',
- num_gpus=1,
- dataset_num_private_threads=1,
- ),
- train_dataset=dataset_factory.DatasetConfig(dtype=dtype),
- model=base_configs.ModelConfig(),
- )
-
- class EmptyClass:
- pass
- fake_ds_builder = EmptyClass()
- fake_ds_builder.dtype = dtype
- fake_ds_builder.config = EmptyClass()
- classifier_trainer.initialize(config, fake_ds_builder)
-
- def test_resume_from_checkpoint(self):
- """Tests functionality for resuming from checkpoint."""
- # Set the keras policy
- policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16')
- tf.keras.mixed_precision.experimental.set_policy(policy)
-
- # Get the model, datasets, and compile it.
- model = get_trivial_model(10)
-
- # Create the checkpoint
- model_dir = self.get_temp_dir()
- train_epochs = 1
- train_steps = 10
- ds = get_trivial_data()
- callbacks = [
- tf.keras.callbacks.ModelCheckpoint(
- os.path.join(model_dir, 'model.ckpt-{epoch:04d}'),
- save_weights_only=True)
- ]
- model.fit(
- ds,
- callbacks=callbacks,
- epochs=train_epochs,
- steps_per_epoch=train_steps)
-
- # Test load from checkpoint
- clean_model = get_trivial_model(10)
- weights_before_load = copy.deepcopy(clean_model.get_weights())
- initial_epoch = classifier_trainer.resume_from_checkpoint(
- model=clean_model,
- model_dir=model_dir,
- train_steps=train_steps)
- self.assertEqual(initial_epoch, 1)
- self.assertNotAllClose(weights_before_load, clean_model.get_weights())
-
- tf.io.gfile.rmtree(model_dir)
-
- def test_serialize_config(self):
- """Tests functionality for serializing data."""
- config = base_configs.ExperimentConfig()
- model_dir = self.get_temp_dir()
- classifier_trainer.serialize_config(params=config, model_dir=model_dir)
- saved_params_path = os.path.join(model_dir, 'params.yaml')
- self.assertTrue(os.path.exists(saved_params_path))
- tf.io.gfile.rmtree(model_dir)
-
-if __name__ == '__main__':
- tf.test.main()
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py b/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py
deleted file mode 100644
index 11fcb5305660ec71153ebfc12631f455a3464115..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py
+++ /dev/null
@@ -1,231 +0,0 @@
-# Lint as: python3
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Definitions for high level configuration groups.."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-
-from typing import Any, List, Mapping, Optional
-
-import dataclasses
-
-from official.modeling import hyperparams
-from official.modeling.hyperparams import config_definitions
-
-CallbacksConfig = config_definitions.CallbacksConfig
-TensorboardConfig = config_definitions.TensorboardConfig
-RuntimeConfig = config_definitions.RuntimeConfig
-
-
-@dataclasses.dataclass
-class ExportConfig(hyperparams.Config):
- """Configuration for exports.
-
- Attributes:
- checkpoint: the path to the checkpoint to export.
- destination: the path to where the checkpoint should be exported.
- """
- checkpoint: str = None
- destination: str = None
-
-
-@dataclasses.dataclass
-class MetricsConfig(hyperparams.Config):
- """Configuration for Metrics.
-
- Attributes:
- accuracy: Whether or not to track accuracy as a Callback. Defaults to None.
- top_5: Whether or not to track top_5_accuracy as a Callback. Defaults to
- None.
- """
- accuracy: bool = None
- top_5: bool = None
-
-
-@dataclasses.dataclass
-class TimeHistoryConfig(hyperparams.Config):
- """Configuration for the TimeHistory callback.
-
- Attributes:
- log_steps: Interval of steps between logging of batch level stats.
- """
- log_steps: int = None
-
-
-@dataclasses.dataclass
-class TrainConfig(hyperparams.Config):
- """Configuration for training.
-
- Attributes:
- resume_checkpoint: Whether or not to enable load checkpoint loading.
- Defaults to None.
- epochs: The number of training epochs to run. Defaults to None.
- steps: The number of steps to run per epoch. If None, then this will be
- inferred based on the number of images and batch size. Defaults to None.
- callbacks: An instance of CallbacksConfig.
- metrics: An instance of MetricsConfig.
- tensorboard: An instance of TensorboardConfig.
- set_epoch_loop: Whether or not to set `experimental_steps_per_execution` to
- equal the number of training steps in `model.compile`. This reduces the
- number of callbacks run per epoch which significantly improves end-to-end
- TPU training time.
- """
- resume_checkpoint: bool = None
- epochs: int = None
- steps: int = None
- callbacks: CallbacksConfig = CallbacksConfig()
- metrics: MetricsConfig = None
- tensorboard: TensorboardConfig = TensorboardConfig()
- time_history: TimeHistoryConfig = TimeHistoryConfig()
- set_epoch_loop: bool = False
-
-
-@dataclasses.dataclass
-class EvalConfig(hyperparams.Config):
- """Configuration for evaluation.
-
- Attributes:
- epochs_between_evals: The number of train epochs to run between evaluations.
- Defaults to None.
- steps: The number of eval steps to run during evaluation. If None, this will
- be inferred based on the number of images and batch size. Defaults to
- None.
- skip_eval: Whether or not to skip evaluation.
- """
- epochs_between_evals: int = None
- steps: int = None
- skip_eval: bool = False
-
-
-@dataclasses.dataclass
-class LossConfig(hyperparams.Config):
- """Configuration for Loss.
-
- Attributes:
- name: The name of the loss. Defaults to None.
- label_smoothing: Whether or not to apply label smoothing to the loss. This
- only applies to 'categorical_cross_entropy'.
- """
- name: str = None
- label_smoothing: float = None
-
-
-@dataclasses.dataclass
-class OptimizerConfig(hyperparams.Config):
- """Configuration for Optimizers.
-
- Attributes:
- name: The name of the optimizer. Defaults to None.
- decay: Decay or rho, discounting factor for gradient. Defaults to None.
- epsilon: Small value used to avoid 0 denominator. Defaults to None.
- momentum: Plain momentum constant. Defaults to None.
- nesterov: Whether or not to apply Nesterov momentum. Defaults to None.
- moving_average_decay: The amount of decay to apply. If 0 or None, then
- exponential moving average is not used. Defaults to None.
- lookahead: Whether or not to apply the lookahead optimizer. Defaults to
- None.
- beta_1: The exponential decay rate for the 1st moment estimates. Used in the
- Adam optimizers. Defaults to None.
- beta_2: The exponential decay rate for the 2nd moment estimates. Used in the
- Adam optimizers. Defaults to None.
- epsilon: Small value used to avoid 0 denominator. Defaults to 1e-7.
- """
- name: str = None
- decay: float = None
- epsilon: float = None
- momentum: float = None
- nesterov: bool = None
- moving_average_decay: Optional[float] = None
- lookahead: Optional[bool] = None
- beta_1: float = None
- beta_2: float = None
- epsilon: float = None
-
-
-@dataclasses.dataclass
-class LearningRateConfig(hyperparams.Config):
- """Configuration for learning rates.
-
- Attributes:
- name: The name of the learning rate. Defaults to None.
- initial_lr: The initial learning rate. Defaults to None.
- decay_epochs: The number of decay epochs. Defaults to None.
- decay_rate: The rate of decay. Defaults to None.
- warmup_epochs: The number of warmup epochs. Defaults to None.
- batch_lr_multiplier: The multiplier to apply to the base learning rate, if
- necessary. Defaults to None.
- examples_per_epoch: the number of examples in a single epoch. Defaults to
- None.
- boundaries: boundaries used in piecewise constant decay with warmup.
- multipliers: multipliers used in piecewise constant decay with warmup.
- scale_by_batch_size: Scale the learning rate by a fraction of the batch
- size. Set to 0 for no scaling (default).
- staircase: Apply exponential decay at discrete values instead of continuous.
- """
- name: str = None
- initial_lr: float = None
- decay_epochs: float = None
- decay_rate: float = None
- warmup_epochs: int = None
- examples_per_epoch: int = None
- boundaries: List[int] = None
- multipliers: List[float] = None
- scale_by_batch_size: float = 0.
- staircase: bool = None
-
-
-@dataclasses.dataclass
-class ModelConfig(hyperparams.Config):
- """Configuration for Models.
-
- Attributes:
- name: The name of the model. Defaults to None.
- model_params: The parameters used to create the model. Defaults to None.
- num_classes: The number of classes in the model. Defaults to None.
- loss: A `LossConfig` instance. Defaults to None.
- optimizer: An `OptimizerConfig` instance. Defaults to None.
- """
- name: str = None
- model_params: hyperparams.Config = None
- num_classes: int = None
- loss: LossConfig = None
- optimizer: OptimizerConfig = None
-
-
-@dataclasses.dataclass
-class ExperimentConfig(hyperparams.Config):
- """Base configuration for an image classification experiment.
-
- Attributes:
- model_dir: The directory to use when running an experiment.
- mode: e.g. 'train_and_eval', 'export'
- runtime: A `RuntimeConfig` instance.
- train: A `TrainConfig` instance.
- evaluation: An `EvalConfig` instance.
- model: A `ModelConfig` instance.
- export: An `ExportConfig` instance.
- """
- model_dir: str = None
- model_name: str = None
- mode: str = None
- runtime: RuntimeConfig = None
- train_dataset: Any = None
- validation_dataset: Any = None
- train: TrainConfig = None
- evaluation: EvalConfig = None
- model: ModelConfig = None
- export: ExportConfig = None
diff --git a/spaces/Navneet574/Heart_Disease_Prediciton/app.py b/spaces/Navneet574/Heart_Disease_Prediciton/app.py
deleted file mode 100644
index 30f66c36eeab6633487a32671d615f07ab544f93..0000000000000000000000000000000000000000
--- a/spaces/Navneet574/Heart_Disease_Prediciton/app.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import gradio as gr
-from joblib import load
-import numpy as np
-import pandas as pd
-
-AgeCategory = ['18-24', '25-29', '35-39', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80 or older']
-Binary_Input = ['Yes', 'No']
-Sex = ['Male', 'Female']
-Race = ['White', 'Black', 'Asian', 'American Indian/Alaskan Native', 'Hispanic', 'Other']
-Diabetic = ['Yes', 'No', 'No borderline diabetes', 'Yes (during pregnancy)']
-GeneralHealth = ['Excellent', 'Very good', 'Good', 'Fair', 'Poor']
-
-dic_ageCat = {'18-24':0, '25-29':1, '35-39':2, '35-39':3, '40-44':4, '45-49':5, '50-54':6, '55-59':7, '60-64':8, '65-69':9, '70-74':10, '75-79':11, '80 or older':12}
-dic_binary = {'Yes':1, 'No':0}
-dic_sex = {'Male':0, 'Female':1}
-dic_race = {'White':5, 'Black':2, 'Asian':0, 'American Indian/Alaskan Native':3, 'Hispanic':1, 'Other':4}
-dic_diabetic = {'Yes':2, 'No':0, 'No borderline diabetes':1, 'Yes (during pregnancy)':3}
-dic_generalHealth = {'Excellent':0, 'Very good':4, 'Good':2, 'Fair':1, 'Poor':3}
-
-
-def predict_price(BMI, Smoking, AlcoholDrinking, Stroke, PhysicalHealth, MentalHealth, DiffWalking, Sex, AgeCategory, Race, Diabetic, PhysicalActivity, GenHealth, SleepTime, Asthma, KidneyDisease, SkinCancer):
- model = load('Heart_Disease_Classification.jb')
-
- data = {
- 'BMI': [BMI],
- 'Smoking': dic_binary[Smoking],
- 'AlcoholDrinking': dic_binary[AlcoholDrinking],
- 'Stroke': dic_binary[Stroke],
- 'PhysicalHealth': [PhysicalHealth],
- 'MentalHealth': [MentalHealth],
- 'DiffWalking': dic_binary[DiffWalking],
- 'Sex': dic_sex[Sex],
- 'AgeCategory': dic_ageCat[AgeCategory],
- 'Race': dic_race[Race],
- 'Diabetic': dic_diabetic[Diabetic],
- 'PhysicalActivity': dic_binary[PhysicalActivity],
- 'GenHealth': dic_generalHealth[GenHealth],
- 'SleepTime': [SleepTime],
- 'Asthma': dic_binary[Asthma],
- 'KidneyDisease': dic_binary[KidneyDisease],
- 'SkinCancer': dic_binary[SkinCancer]
- }
-
- Xinp = pd.DataFrame(data)
- print(Xinp)
-
- stone = model.predict(Xinp)
-
- if stone == 1:
- return stone["YES"]
- else:
- return stone["NO"]
-
-ui = gr.Interface(
- fn=predict_price,inputs=[
- gr.inputs.Textbox(placeholder='BMI', default=0,
- numeric=True, label='Body Mass Index (BMI) (normal is between 18.5 and 24.9)'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Smoking', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Alcohol Drinking', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Stroke', default="No"),
- gr.inputs.Textbox(placeholder='Physical Health',
- default=0, numeric=True, label='Physical Health Score'),
- gr.inputs.Textbox(placeholder='MentalHealth', default=0,
- numeric=True, label='Mental Health Score'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Diff Walking', default="No"),
- gr.inputs.Dropdown(choices=Sex, label='Sex', default=""),
- gr.inputs.Dropdown(choices=AgeCategory, label='Age Category', default="18-24"),
- gr.inputs.Dropdown(choices=Race, label='Race', default="Asian"),
- gr.inputs.Dropdown(choices=Diabetic, label='Diabetic', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Physical Activity', default="Yes"),
- gr.inputs.Dropdown(choices=GeneralHealth, label='General Health', default="Excellent"),
- gr.inputs.Textbox(placeholder='Sleep Time',
- default='7', numeric=True, label='Sleep Time (normal is between 6-8 hrs)'),
- gr.inputs.Dropdown(choices=Binary_Input, label='Asthma', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Kidney Disease', default="No"),
- gr.inputs.Dropdown(choices=Binary_Input, label='Skin Cancer', default="No")
- ], outputs=[
- "text"
- ]
-)
-if __name__ == "__main__":
- ui.launch()
\ No newline at end of file
diff --git a/spaces/Nikhil0987/omm/convo.py b/spaces/Nikhil0987/omm/convo.py
deleted file mode 100644
index f5c87b19d82d3f1e1869101faf1205475b5ed3b9..0000000000000000000000000000000000000000
--- a/spaces/Nikhil0987/omm/convo.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from transformers import pipeline, Conversation
-import streamlit as st
-from streamlit_option_menu import option_menu
-
-chatbot = pipeline(task="conversational", model="microsoft/DialoGPT-medium")
-
-def Convo():
- if convo := st.chat_input("Enter your message"):
-
- conversation = Conversation(convo)
-
-
-
- # candidate_labels = ["HELP", "PROBLEM SOLVE", "GENERAL TALK"]
-
-
- ans = chatbot(conversation)
- # add_user_input = st.button("Add User Input")
-
- # conversation.add_user_input("{}".format(convo))
- # conversation = chatbot(conversation)
- with st.chat_message("assistant"):
- # ans
- st.write(ans.generated_responses[-1])
-
-
-# convo()
diff --git a/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py b/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py
deleted file mode 100644
index d9d372dcbb1bed2fffbfd8e81d6da749ceab730b..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py
+++ /dev/null
@@ -1,772 +0,0 @@
-"""
-This scripts plots examples of the images that get best and worse metrics
-"""
-print("Imports...", end="")
-import os
-import sys
-from argparse import ArgumentParser
-from pathlib import Path
-
-import matplotlib.patches as mpatches
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import seaborn as sns
-import yaml
-from imageio import imread
-from matplotlib.gridspec import GridSpec
-from skimage.color import rgba2rgb
-from sklearn.metrics.pairwise import euclidean_distances
-
-sys.path.append("../")
-
-from climategan.data import encode_mask_label
-from climategan.eval_metrics import edges_coherence_std_min
-from eval_masker import crop_and_resize
-
-# -----------------------
-# ----- Constants -----
-# -----------------------
-
-# Metrics
-metrics = ["error", "f05", "edge_coherence"]
-
-dict_metrics = {
- "names": {
- "tpr": "TPR, Recall, Sensitivity",
- "tnr": "TNR, Specificity, Selectivity",
- "fpr": "FPR",
- "fpt": "False positives relative to image size",
- "fnr": "FNR, Miss rate",
- "fnt": "False negatives relative to image size",
- "mpr": "May positive rate (MPR)",
- "mnr": "May negative rate (MNR)",
- "accuracy": "Accuracy (ignoring may)",
- "error": "Error",
- "f05": "F05 score",
- "precision": "Precision",
- "edge_coherence": "Edge coherence",
- "accuracy_must_may": "Accuracy (ignoring cannot)",
- },
- "key_metrics": ["error", "f05", "edge_coherence"],
-}
-
-
-# Colors
-colorblind_palette = sns.color_palette("colorblind")
-color_cannot = colorblind_palette[1]
-color_must = colorblind_palette[2]
-color_may = colorblind_palette[7]
-color_pred = colorblind_palette[4]
-
-icefire = sns.color_palette("icefire", as_cmap=False, n_colors=5)
-color_tp = icefire[0]
-color_tn = icefire[1]
-color_fp = icefire[4]
-color_fn = icefire[3]
-
-
-def parsed_args():
- """
- Parse and returns command-line args
-
- Returns:
- argparse.Namespace: the parsed arguments
- """
- parser = ArgumentParser()
- parser.add_argument(
- "--input_csv",
- default="ablations_metrics_20210311.csv",
- type=str,
- help="CSV containing the results of the ablation study",
- )
- parser.add_argument(
- "--output_dir",
- default=None,
- type=str,
- help="Output directory",
- )
- parser.add_argument(
- "--models_log_path",
- default=None,
- type=str,
- help="Path containing the log files of the models",
- )
- parser.add_argument(
- "--masker_test_set_dir",
- default=None,
- type=str,
- help="Directory containing the test images",
- )
- parser.add_argument(
- "--best_model",
- default="dada, msd_spade, pseudo",
- type=str,
- help="The string identifier of the best model",
- )
- parser.add_argument(
- "--dpi",
- default=200,
- type=int,
- help="DPI for the output images",
- )
- parser.add_argument(
- "--alpha",
- default=0.5,
- type=float,
- help="Transparency of labels shade",
- )
- parser.add_argument(
- "--percentile",
- default=0.05,
- type=float,
- help="Transparency of labels shade",
- )
- parser.add_argument(
- "--seed",
- default=None,
- type=int,
- help="Bootstrap random seed, for reproducibility",
- )
- parser.add_argument(
- "--no_images",
- action="store_true",
- default=False,
- help="Do not generate images",
- )
-
- return parser.parse_args()
-
-
-def map_color(arr, input_color, output_color, rtol=1e-09):
- """
- Maps one color to another
- """
- input_color_arr = np.tile(input_color, (arr.shape[:2] + (1,)))
- output = arr.copy()
- output[np.all(np.isclose(arr, input_color_arr, rtol=rtol), axis=2)] = output_color
- return output
-
-
-def plot_labels(ax, img, label, img_id, n_, add_title, do_legend):
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (255, 0, 0), color_cannot)
- label_colmap = map_color(label_colmap, (0, 0, 255), color_must)
- label_colmap = map_color(label_colmap, (0, 0, 0), color_may)
-
- ax.imshow(img)
- ax.imshow(label_colmap, alpha=0.5)
- ax.axis("off")
-
- if n_ in [1, 3, 5]:
- color_ = "green"
- else:
- color_ = "red"
-
- ax.text(
- -0.15,
- 0.5,
- img_id,
- color=color_,
- fontweight="roman",
- fontsize="x-large",
- horizontalalignment="left",
- verticalalignment="center",
- transform=ax.transAxes,
- )
-
- if add_title:
- ax.set_title("Labels", rotation=0, fontsize="x-large")
-
-
-def plot_pred(ax, img, pred, img_id, add_title, do_legend):
- pred = np.tile(np.expand_dims(pred, axis=2), reps=(1, 1, 3))
-
- pred_colmap = pred.astype(float)
- pred_colmap = map_color(pred_colmap, (1, 1, 1), color_pred)
- pred_colmap_ma = np.ma.masked_not_equal(pred_colmap, color_pred)
- pred_colmap_ma = pred_colmap_ma.mask * img + pred_colmap_ma
-
- ax.imshow(img)
- ax.imshow(pred_colmap_ma, alpha=0.5)
- ax.axis("off")
-
- if add_title:
- ax.set_title("Prediction", rotation=0, fontsize="x-large")
-
-
-def plot_correct_incorrect(
- ax, img_filename, img, metric, label, img_id, n_, add_title, do_legend
-):
- # FP
- fp_map = imread(
- model_path / "eval-metrics/fp" / "{}_fp.png".format(Path(img_filename).stem)
- )
- fp_map = np.tile(np.expand_dims(fp_map, axis=2), reps=(1, 1, 3))
-
- fp_map_colmap = fp_map.astype(float)
- fp_map_colmap = map_color(fp_map_colmap, (1, 1, 1), color_fp)
-
- # FN
- fn_map = imread(
- model_path / "eval-metrics/fn" / "{}_fn.png".format(Path(img_filename).stem)
- )
- fn_map = np.tile(np.expand_dims(fn_map, axis=2), reps=(1, 1, 3))
-
- fn_map_colmap = fn_map.astype(float)
- fn_map_colmap = map_color(fn_map_colmap, (1, 1, 1), color_fn)
-
- # TP
- tp_map = imread(
- model_path / "eval-metrics/tp" / "{}_tp.png".format(Path(img_filename).stem)
- )
- tp_map = np.tile(np.expand_dims(tp_map, axis=2), reps=(1, 1, 3))
-
- tp_map_colmap = tp_map.astype(float)
- tp_map_colmap = map_color(tp_map_colmap, (1, 1, 1), color_tp)
-
- # TN
- tn_map = imread(
- model_path / "eval-metrics/tn" / "{}_tn.png".format(Path(img_filename).stem)
- )
- tn_map = np.tile(np.expand_dims(tn_map, axis=2), reps=(1, 1, 3))
-
- tn_map_colmap = tn_map.astype(float)
- tn_map_colmap = map_color(tn_map_colmap, (1, 1, 1), color_tn)
-
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (0, 0, 0), color_may)
- label_colmap_ma = np.ma.masked_not_equal(label_colmap, color_may)
- label_colmap_ma = label_colmap_ma.mask * img + label_colmap_ma
-
- # Combine masks
- maps = fp_map_colmap + fn_map_colmap + tp_map_colmap + tn_map_colmap
- maps_ma = np.ma.masked_equal(maps, (0, 0, 0))
- maps_ma = maps_ma.mask * img + maps_ma
-
- ax.imshow(img)
- ax.imshow(label_colmap_ma, alpha=0.5)
- ax.imshow(maps_ma, alpha=0.5)
- ax.axis("off")
-
- if add_title:
- ax.set_title("Metric", rotation=0, fontsize="x-large")
-
-
-def plot_edge_coherence(ax, img, metric, label, pred, img_id, n_, add_title, do_legend):
- pred = np.tile(np.expand_dims(pred, axis=2), reps=(1, 1, 3))
-
- ec, pred_ec, label_ec = edges_coherence_std_min(
- np.squeeze(pred[:, :, 0]), np.squeeze(encode_mask_label(label, "flood"))
- )
-
- ##################
- # Edge distances #
- ##################
-
- # Location of edges
- pred_ec_coord = np.argwhere(pred_ec > 0)
- label_ec_coord = np.argwhere(label_ec > 0)
-
- # Normalized pairwise distances between pred and label
- dist_mat = np.divide(
- euclidean_distances(pred_ec_coord, label_ec_coord), pred_ec.shape[0]
- )
-
- # Standard deviation of the minimum distance from pred to label
- min_dist = np.min(dist_mat, axis=1) # noqa: F841
-
- #############
- # Make plot #
- #############
-
- pred_ec = np.tile(
- np.expand_dims(np.asarray(pred_ec > 0, dtype=float), axis=2), reps=(1, 1, 3)
- )
- pred_ec_colmap = map_color(pred_ec, (1, 1, 1), color_pred)
- pred_ec_colmap_ma = np.ma.masked_not_equal(pred_ec_colmap, color_pred) # noqa: F841
-
- label_ec = np.tile(
- np.expand_dims(np.asarray(label_ec > 0, dtype=float), axis=2), reps=(1, 1, 3)
- )
- label_ec_colmap = map_color(label_ec, (1, 1, 1), color_must)
- label_ec_colmap_ma = np.ma.masked_not_equal( # noqa: F841
- label_ec_colmap, color_must
- )
-
- # Combined pred and label edges
- combined_ec = pred_ec_colmap + label_ec_colmap
- combined_ec_ma = np.ma.masked_equal(combined_ec, (0, 0, 0))
- combined_ec_img = combined_ec_ma.mask * img + combined_ec
-
- # Pred
- pred_colmap = pred.astype(float)
- pred_colmap = map_color(pred_colmap, (1, 1, 1), color_pred)
- pred_colmap_ma = np.ma.masked_not_equal(pred_colmap, color_pred)
-
- # Must
- label_colmap = label.astype(float)
- label_colmap = map_color(label_colmap, (0, 0, 255), color_must)
- label_colmap_ma = np.ma.masked_not_equal(label_colmap, color_must)
-
- # TP
- tp_map = imread(
- model_path / "eval-metrics/tp" / "{}_tp.png".format(Path(srs_sel.filename).stem)
- )
- tp_map = np.tile(np.expand_dims(tp_map, axis=2), reps=(1, 1, 3))
- tp_map_colmap = tp_map.astype(float)
- tp_map_colmap = map_color(tp_map_colmap, (1, 1, 1), color_tp)
- tp_map_colmap_ma = np.ma.masked_not_equal(tp_map_colmap, color_tp)
-
- # Combination
- comb_pred = (
- (pred_colmap_ma.mask ^ tp_map_colmap_ma.mask)
- & tp_map_colmap_ma.mask
- & combined_ec_ma.mask
- ) * pred_colmap
- comb_label = (
- (label_colmap_ma.mask ^ pred_colmap_ma.mask)
- & pred_colmap_ma.mask
- & combined_ec_ma.mask
- ) * label_colmap
- comb_tp = combined_ec_ma.mask * tp_map_colmap.copy()
- combined = comb_tp + comb_label + comb_pred
- combined_ma = np.ma.masked_equal(combined, (0, 0, 0))
- combined_ma = combined_ma.mask * combined_ec_img + combined_ma
-
- ax.imshow(combined_ec_img, alpha=1)
- ax.imshow(combined_ma, alpha=0.5)
- ax.axis("off")
-
- # Plot lines
- idx_sort_x = np.argsort(pred_ec_coord[:, 1])
- offset = 100
- for idx in range(offset, pred_ec_coord.shape[0], offset):
- y0, x0 = pred_ec_coord[idx_sort_x[idx], :]
- argmin = np.argmin(dist_mat[idx_sort_x[idx]])
- y1, x1 = label_ec_coord[argmin, :]
- ax.plot([x0, x1], [y0, y1], color="white", linewidth=0.5)
-
- if add_title:
- ax.set_title("Metric", rotation=0, fontsize="x-large")
-
-
-def plot_images_metric(
- axes, metric, img_filename, img_id, n_, srs_sel, add_title, do_legend
-):
-
- # Read images
- img_path = imgs_orig_path / img_filename
- label_path = labels_path / "{}_labeled.png".format(Path(img_filename).stem)
- img, label = crop_and_resize(img_path, label_path)
- img = rgba2rgb(img) if img.shape[-1] == 4 else img / 255.0
-
- pred = imread(
- model_path / "eval-metrics/pred" / "{}_pred.png".format(Path(img_filename).stem)
- )
-
- # Label
- plot_labels(axes[0], img, label, img_id, n_, add_title, do_legend)
-
- # Prediction
- plot_pred(axes[1], img, pred, img_id, add_title, do_legend)
-
- # Correct / incorrect
- if metric in ["error", "f05"]:
- plot_correct_incorrect(
- axes[2],
- img_filename,
- img,
- metric,
- label,
- img_id,
- n_,
- add_title,
- do_legend=False,
- )
- handles = []
- lw = 1.0
- handles.append(
- mpatches.Patch(facecolor=color_tp, label="TP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_tn, label="TN", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_fp, label="FP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_fn, label="FN", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(
- facecolor=color_may,
- label="May-be-flooded",
- linewidth=lw,
- alpha=0.66,
- )
- )
- labels = ["TP", "TN", "FP", "FN", "May-be-flooded"]
- if metric == "error":
- if n_ in [1, 3, 5]:
- title = "Low error rate"
- else:
- title = "High error rate"
- else:
- if n_ in [1, 3, 5]:
- title = "High F05 score"
- else:
- title = "Low F05 score"
- # Edge coherence
- elif metric == "edge_coherence":
- plot_edge_coherence(
- axes[2], img, metric, label, pred, img_id, n_, add_title, do_legend=False
- )
- handles = []
- lw = 1.0
- handles.append(
- mpatches.Patch(facecolor=color_tp, label="TP", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(facecolor=color_pred, label="pred", linewidth=lw, alpha=0.66)
- )
- handles.append(
- mpatches.Patch(
- facecolor=color_must,
- label="Must-be-flooded",
- linewidth=lw,
- alpha=0.66,
- )
- )
- labels = ["TP", "Prediction", "Must-be-flooded"]
- if n_ in [1, 3, 5]:
- title = "High edge coherence"
- else:
- title = "Low edge coherence"
-
- else:
- raise ValueError
-
- labels_values_title = "Error: {:.4f} \nFO5: {:.4f} \nEdge coherence: {:.4f}".format(
- srs_sel.error, srs_sel.f05, srs_sel.edge_coherence
- )
-
- plot_legend(axes[3], img, handles, labels, labels_values_title, title)
-
-
-def plot_legend(ax, img, handles, labels, labels_values_title, title):
- img_ = np.zeros_like(img, dtype=np.uint8)
- img_.fill(255)
- ax.imshow(img_)
- ax.axis("off")
-
- leg1 = ax.legend(
- handles=handles,
- labels=labels,
- title=title,
- title_fontsize="medium",
- labelspacing=0.6,
- loc="upper left",
- fontsize="x-small",
- frameon=False,
- )
- leg1._legend_box.align = "left"
-
- leg2 = ax.legend(
- title=labels_values_title,
- title_fontsize="small",
- loc="lower left",
- frameon=False,
- )
- leg2._legend_box.align = "left"
-
- ax.add_artist(leg1)
-
-
-def scatterplot_metrics_pair(ax, df, x_metric, y_metric, dict_images):
-
- sns.scatterplot(data=df, x=x_metric, y=y_metric, ax=ax)
-
- # Set X-label
- ax.set_xlabel(dict_metrics["names"][x_metric], rotation=0, fontsize="medium")
-
- # Set Y-label
- ax.set_ylabel(dict_metrics["names"][y_metric], rotation=90, fontsize="medium")
-
- # Change spines
- sns.despine(ax=ax, left=True, bottom=True)
-
- annotate_scatterplot(ax, dict_images, x_metric, y_metric)
-
-
-def scatterplot_metrics(ax, df, df_all, dict_images, plot_all=False):
-
- # Other
- if plot_all:
- sns.scatterplot(
- data=df_all.loc[df_all.ground == True],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='+', alpha=0.25)
- sns.scatterplot(
- data=df_all.loc[df_all.instagan == True],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='x', alpha=0.25)
- sns.scatterplot(
- data=df_all.loc[(df_all.instagan == False) & (df_all.instagan == False) &
- (df_all.model_feats != args.best_model)],
- x="error", y="f05", hue="edge_coherence", ax=ax,
- marker='s', alpha=0.25)
-
- # Best model
- cmap_ = sns.cubehelix_palette(as_cmap=True)
- sns.scatterplot(
- data=df, x="error", y="f05", hue="edge_coherence", ax=ax, palette=cmap_
- )
-
- norm = plt.Normalize(df["edge_coherence"].min(), df["edge_coherence"].max())
- sm = plt.cm.ScalarMappable(cmap=cmap_, norm=norm)
- sm.set_array([])
-
- # Remove the legend and add a colorbar
- ax.get_legend().remove()
- ax_cbar = ax.figure.colorbar(sm)
- ax_cbar.set_label("Edge coherence", labelpad=8)
-
- # Set X-label
- ax.set_xlabel(dict_metrics["names"]["error"], rotation=0, fontsize="medium")
-
- # Set Y-label
- ax.set_ylabel(dict_metrics["names"]["f05"], rotation=90, fontsize="medium")
-
- annotate_scatterplot(ax, dict_images, "error", "f05")
-
- # Change spines
- sns.despine(ax=ax, left=True, bottom=True)
-
- # Set XY limits
- xlim = ax.get_xlim()
- ylim = ax.get_ylim()
- ax.set_xlim([0.0, xlim[1]])
- ax.set_ylim([ylim[0], 1.0])
-
-
-def annotate_scatterplot(ax, dict_images, x_metric, y_metric, offset=0.1):
- xlim = ax.get_xlim()
- ylim = ax.get_ylim()
- x_len = xlim[1] - xlim[0]
- y_len = ylim[1] - ylim[0]
- x_th = xlim[1] - x_len / 2.0
- y_th = ylim[1] - y_len / 2.0
- for text, d in dict_images.items():
- if text in ["B", "D", "F"]:
- x = d[x_metric]
- y = d[y_metric]
-
- x_text = x + x_len * offset if x < x_th else x - x_len * offset
- y_text = y + y_len * offset if y < y_th else y - y_len * offset
-
- ax.annotate(
- xy=(x, y),
- xycoords="data",
- xytext=(x_text, y_text),
- textcoords="data",
- text=text,
- arrowprops=dict(facecolor="black", shrink=0.05),
- fontsize="medium",
- color="black",
- )
- elif text == "A":
- x = (
- dict_images["A"][x_metric]
- + dict_images["C"][x_metric]
- + dict_images["E"][x_metric]
- ) / 3
- y = (
- dict_images["A"][y_metric]
- + dict_images["C"][y_metric]
- + dict_images["E"][y_metric]
- ) / 3
-
- x_text = x + x_len * 2 * offset if x < x_th else x - x_len * 2 * offset
- y_text = (
- y + y_len * 0.45 * offset if y < y_th else y - y_len * 0.45 * offset
- )
-
- ax.annotate(
- xy=(x, y),
- xycoords="data",
- xytext=(x_text, y_text),
- textcoords="data",
- text="A, C, E",
- arrowprops=dict(facecolor="black", shrink=0.05),
- fontsize="medium",
- color="black",
- )
-
-
-if __name__ == "__main__":
- # -----------------------------
- # ----- Parse arguments -----
- # -----------------------------
- args = parsed_args()
- print("Args:\n" + "\n".join([f" {k:20}: {v}" for k, v in vars(args).items()]))
-
- # Determine output dir
- if args.output_dir is None:
- output_dir = Path(os.environ["SLURM_TMPDIR"])
- else:
- output_dir = Path(args.output_dir)
- if not output_dir.exists():
- output_dir.mkdir(parents=True, exist_ok=False)
-
- # Store args
- output_yml = output_dir / "labels.yml"
- with open(output_yml, "w") as f:
- yaml.dump(vars(args), f)
-
- # Data dirs
- imgs_orig_path = Path(args.masker_test_set_dir) / "imgs"
- labels_path = Path(args.masker_test_set_dir) / "labels"
-
- # Read CSV
- df_all = pd.read_csv(args.input_csv, index_col="model_img_idx")
-
- # Select best model
- df = df_all.loc[df_all.model_feats == args.best_model]
- v_key, model_dir = df.model.unique()[0].split("/")
- model_path = Path(args.models_log_path) / "ablation-{}".format(v_key) / model_dir
-
- # Set up plot
- sns.reset_orig()
- sns.set(style="whitegrid")
- plt.rcParams.update({"font.family": "serif"})
- plt.rcParams.update(
- {
- "font.serif": [
- "Computer Modern Roman",
- "Times New Roman",
- "Utopia",
- "New Century Schoolbook",
- "Century Schoolbook L",
- "ITC Bookman",
- "Bookman",
- "Times",
- "Palatino",
- "Charter",
- "serif" "Bitstream Vera Serif",
- "DejaVu Serif",
- ]
- }
- )
-
- if args.seed:
- np.random.seed(args.seed)
- img_ids = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
- dict_images = {}
- idx = 0
-
- # Define grid of subplots
- grid_vmargin = 0.03 # Extent of the vertical margin between metric grids
- ax_hspace = 0.04 # Extent of the vertical space between axes of same grid
- ax_wspace = 0.05 # Extent of the horizontal space between axes of same grid
- n_grids = len(metrics)
- n_cols = 4
- n_rows = 2
- h_grid = (1.0 / n_grids) - ((n_grids - 1) * grid_vmargin) / n_grids
-
- fig1 = plt.figure(dpi=200, figsize=(11, 13))
-
- n_ = 0
- add_title = False
- for metric_id, metric in enumerate(metrics):
-
- # Create grid
- top_grid = 1.0 - metric_id * h_grid - metric_id * grid_vmargin
- bottom_grid = top_grid - h_grid
- gridspec = GridSpec(
- n_rows,
- n_cols,
- wspace=ax_wspace,
- hspace=ax_hspace,
- bottom=bottom_grid,
- top=top_grid,
- )
-
- # Select best
- if metric == "error":
- ascending = True
- else:
- ascending = False
- idx_rand = np.random.permutation(int(args.percentile * len(df)))[0]
- srs_sel = df.sort_values(by=metric, ascending=ascending).iloc[idx_rand]
- img_id = img_ids[idx]
- dict_images.update({img_id: srs_sel})
- # Read images
- img_filename = srs_sel.filename
-
- axes_row = [fig1.add_subplot(gridspec[0, c]) for c in range(n_cols)]
- if not args.no_images:
- n_ += 1
- if metric_id == 0:
- add_title = True
- plot_images_metric(
- axes_row,
- metric,
- img_filename,
- img_id,
- n_,
- srs_sel,
- add_title=add_title,
- do_legend=False,
- )
- add_title = False
-
- idx += 1
- print("1 more row done.")
- # Select worst
- if metric == "error":
- ascending = False
- else:
- ascending = True
- idx_rand = np.random.permutation(int(args.percentile * len(df)))[0]
- srs_sel = df.sort_values(by=metric, ascending=ascending).iloc[idx_rand]
- img_id = img_ids[idx]
- dict_images.update({img_id: srs_sel})
- # Read images
- img_filename = srs_sel.filename
-
- axes_row = [fig1.add_subplot(gridspec[1, c]) for c in range(n_cols)]
- if not args.no_images:
- n_ += 1
- plot_images_metric(
- axes_row,
- metric,
- img_filename,
- img_id,
- n_,
- srs_sel,
- add_title=add_title,
- do_legend=False,
- )
-
- idx += 1
- print("1 more row done.")
-
- output_fig = output_dir / "all_metrics.png"
-
- fig1.tight_layout() # (pad=1.5) #
- fig1.savefig(output_fig, dpi=fig1.dpi, bbox_inches="tight")
-
- # Scatter plot
- fig2 = plt.figure(dpi=200)
-
- scatterplot_metrics(fig2.gca(), df, df_all, dict_images)
-
- # fig2, axes = plt.subplots(nrows=1, ncols=3, dpi=200, figsize=(18, 5))
- #
- # scatterplot_metrics_pair(axes[0], df, "error", "f05", dict_images)
- # scatterplot_metrics_pair(axes[1], df, "error", "edge_coherence", dict_images)
- # scatterplot_metrics_pair(axes[2], df, "f05", "edge_coherence", dict_images)
-
- output_fig = output_dir / "scatterplots.png"
- fig2.savefig(output_fig, dpi=fig2.dpi, bbox_inches="tight")
diff --git a/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh b/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh
deleted file mode 100644
index d649238546b996ae1ad00f7753c04aeebc7aaa97..0000000000000000000000000000000000000000
--- a/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-echo "Dowloading Script" && python download_labelbox.py
-
-echo "Merging Script" && python merge_labelbox_masks.py
-
-echo "Cleaning labeled"
-rm /Users/victor/Downloads/metrics-v2/labels/*
-cp /Users/victor/Downloads/labelbox_test_flood-v2/__labeled/* /Users/victor/Downloads/metrics-v2/labels
-
-echo "Create labeled images Script" && python create_labeled.py
\ No newline at end of file
diff --git a/spaces/Nunchakuka/FrenchAnonymizer/models.py b/spaces/Nunchakuka/FrenchAnonymizer/models.py
deleted file mode 100644
index 46b8aacb1bef18f6fad4c20c968b19125626799c..0000000000000000000000000000000000000000
--- a/spaces/Nunchakuka/FrenchAnonymizer/models.py
+++ /dev/null
@@ -1,351 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
-
-class Encoder(nn.Module):
- def __init__(self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
- def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
- resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(weight_norm(
- ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
- k, u, padding=(k-u)//2)))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel//(2**(i+1))
- for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i*self.num_kernels+j](x)
- else:
- xs += self.resblocks[i*self.num_kernels+j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- print('Removing weight norm...')
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
- ])
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ])
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2,3,5,7,11]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class SpeakerEncoder(torch.nn.Module):
- def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
- super(SpeakerEncoder, self).__init__()
- self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
- self.linear = nn.Linear(model_hidden_size, model_embedding_size)
- self.relu = nn.ReLU()
-
- def forward(self, mels):
- self.lstm.flatten_parameters()
- _, (hidden, _) = self.lstm(mels)
- embeds_raw = self.relu(self.linear(hidden[-1]))
- return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
-
- def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
- mel_slices = []
- for i in range(0, total_frames-partial_frames, partial_hop):
- mel_range = torch.arange(i, i+partial_frames)
- mel_slices.append(mel_range)
-
- return mel_slices
-
- def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
- mel_len = mel.size(1)
- last_mel = mel[:,-partial_frames:]
-
- if mel_len > partial_frames:
- mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
- mels = list(mel[:,s] for s in mel_slices)
- mels.append(last_mel)
- mels = torch.stack(tuple(mels), 0).squeeze(1)
-
- with torch.no_grad():
- partial_embeds = self(mels)
- embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
- #embed = embed / torch.linalg.norm(embed, 2)
- else:
- with torch.no_grad():
- embed = self(last_mel)
-
- return embed
-
-
-class SynthesizerTrn(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(self,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- gin_channels,
- ssl_dim,
- use_spk,
- **kwargs):
-
- super().__init__()
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.gin_channels = gin_channels
- self.ssl_dim = ssl_dim
- self.use_spk = use_spk
-
- self.enc_p = Encoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16)
- self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
- self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
- self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
- if not self.use_spk:
- self.enc_spk = SpeakerEncoder(model_hidden_size=gin_channels, model_embedding_size=gin_channels)
-
- def forward(self, c, spec, g=None, mel=None, c_lengths=None, spec_lengths=None):
- if c_lengths == None:
- c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
- if spec_lengths == None:
- spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device)
-
- if not self.use_spk:
- g = self.enc_spk(mel.transpose(1,2))
- g = g.unsqueeze(-1)
-
- _, m_p, logs_p, _ = self.enc_p(c, c_lengths)
- z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g)
- z_p = self.flow(z, spec_mask, g=g)
-
- z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size)
- o = self.dec(z_slice, g=g)
-
- return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, c, g=None, mel=None, c_lengths=None):
- if c_lengths == None:
- c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
- if not self.use_spk:
- g = self.enc_spk.embed_utterance(mel.transpose(1,2))
- g = g.unsqueeze(-1)
-
- z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths)
- z = self.flow(z_p, c_mask, g=g, reverse=True)
- o = self.dec(z * c_mask, g=g)
-
- return o
diff --git a/spaces/Nunchakuka/FrenchAnonymizer/utils.py b/spaces/Nunchakuka/FrenchAnonymizer/utils.py
deleted file mode 100644
index 1bd5b6185af6c9f1c270b8ba345bfc36d059e081..0000000000000000000000000000000000000000
--- a/spaces/Nunchakuka/FrenchAnonymizer/utils.py
+++ /dev/null
@@ -1,305 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-from torch.nn import functional as F
-from commons import sequence_mask
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def get_cmodel(rank):
- checkpoint = torch.load('wavlm/WavLM-Large.pt')
- cfg = WavLMConfig(checkpoint['cfg'])
- cmodel = WavLM(cfg).cuda(rank)
- cmodel.load_state_dict(checkpoint['model'])
- cmodel.eval()
- return cmodel
-
-
-def get_content(cmodel, y):
- with torch.no_grad():
- c = cmodel.extract_features(y.squeeze(1))[0]
- c = c.transpose(1, 2)
- return c
-
-
-def get_vocoder(rank):
- with open("hifigan/config.json", "r") as f:
- config = json.load(f)
- config = hifigan.AttrDict(config)
- vocoder = hifigan.Generator(config)
- ckpt = torch.load("hifigan/generator_v1")
- vocoder.load_state_dict(ckpt["generator"])
- vocoder.eval()
- vocoder.remove_weight_norm()
- vocoder.cuda(rank)
- return vocoder
-
-
-def transform(mel, height): # 68-92
- #r = np.random.random()
- #rate = r * 0.3 + 0.85 # 0.85-1.15
- #height = int(mel.size(-2) * rate)
- tgt = torchvision.transforms.functional.resize(mel, (height, mel.size(-1)))
- if height >= mel.size(-2):
- return tgt[:, :mel.size(-2), :]
- else:
- silence = tgt[:,-1:,:].repeat(1,mel.size(-2)-height,1)
- silence += torch.randn_like(silence) / 10
- return torch.cat((tgt, silence), 1)
-
-
-def stretch(mel, width): # 0.5-2
- return torchvision.transforms.functional.resize(mel, (mel.size(-2), width))
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict= {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})" .format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
- logger.info("Saving model and optimizer state at iteration {} to {}".format(
- iteration, checkpoint_path))
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- torch.save({'model': state_dict,
- 'iteration': iteration,
- 'optimizer': optimizer.state_dict(),
- 'learning_rate': learning_rate}, checkpoint_path)
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
- for k, v in scalars.items():
- writer.add_scalar(k, v, global_step)
- for k, v in histograms.items():
- writer.add_histogram(k, v, global_step)
- for k, v in images.items():
- writer.add_image(k, v, global_step, dataformats='HWC')
- for k, v in audios.items():
- writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
- f_list = glob.glob(os.path.join(dir_path, regex))
- f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
- x = f_list[-1]
- print(x)
- return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10,2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_wav_to_torch(full_path):
- sampling_rate, data = read(full_path)
- return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py
deleted file mode 100644
index bb80d11a67cd75764a89f6f41915b0348ae96e92..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py
+++ /dev/null
@@ -1,428 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from multiprocessing import Pool
-
-import numpy as np
-from fairseq import options
-from fairseq.data import dictionary
-from fairseq.scoring import bleu
-
-from examples.noisychannel import (
- rerank_generate,
- rerank_options,
- rerank_score_bw,
- rerank_score_lm,
- rerank_utils,
-)
-
-
-def score_target_hypo(
- args, a, b, c, lenpen, target_outfile, hypo_outfile, write_hypos, normalize
-):
-
- print("lenpen", lenpen, "weight1", a, "weight2", b, "weight3", c)
- gen_output_lst, bitext1_lst, bitext2_lst, lm_res_lst = load_score_files(args)
- dict = dictionary.Dictionary()
- scorer = scorer = bleu.Scorer(
- bleu.BleuConfig(
- pad=dict.pad(),
- eos=dict.eos(),
- unk=dict.unk(),
- )
- )
-
- ordered_hypos = {}
- ordered_targets = {}
-
- for shard_id in range(len(bitext1_lst)):
- bitext1 = bitext1_lst[shard_id]
- bitext2 = bitext2_lst[shard_id]
- gen_output = gen_output_lst[shard_id]
- lm_res = lm_res_lst[shard_id]
-
- total = len(bitext1.rescore_source.keys())
- source_lst = []
- hypo_lst = []
- score_lst = []
- reference_lst = []
- j = 1
- best_score = -math.inf
-
- for i in range(total):
- # length is measured in terms of words, not bpe tokens, since models may not share the same bpe
- target_len = len(bitext1.rescore_hypo[i].split())
-
- if lm_res is not None:
- lm_score = lm_res.score[i]
- else:
- lm_score = 0
-
- if bitext2 is not None:
- bitext2_score = bitext2.rescore_score[i]
- bitext2_backwards = bitext2.backwards
- else:
- bitext2_score = None
- bitext2_backwards = None
-
- score = rerank_utils.get_score(
- a,
- b,
- c,
- target_len,
- bitext1.rescore_score[i],
- bitext2_score,
- lm_score=lm_score,
- lenpen=lenpen,
- src_len=bitext1.source_lengths[i],
- tgt_len=bitext1.target_lengths[i],
- bitext1_backwards=bitext1.backwards,
- bitext2_backwards=bitext2_backwards,
- normalize=normalize,
- )
-
- if score > best_score:
- best_score = score
- best_hypo = bitext1.rescore_hypo[i]
-
- if j == gen_output.num_hypos[i] or j == args.num_rescore:
- j = 1
- hypo_lst.append(best_hypo)
- score_lst.append(best_score)
- source_lst.append(bitext1.rescore_source[i])
- reference_lst.append(bitext1.rescore_target[i])
-
- best_score = -math.inf
- best_hypo = ""
- else:
- j += 1
-
- gen_keys = list(sorted(gen_output.no_bpe_target.keys()))
-
- for key in range(len(gen_keys)):
- if args.prefix_len is None:
- assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], (
- "pred and rescore hypo mismatch: i: "
- + str(key)
- + ", "
- + str(hypo_lst[key])
- + str(gen_keys[key])
- + str(gen_output.no_bpe_hypo[key])
- )
- sys_tok = dict.encode_line(hypo_lst[key])
- ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]])
- scorer.add(ref_tok, sys_tok)
-
- else:
- full_hypo = rerank_utils.get_full_from_prefix(
- hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]]
- )
- sys_tok = dict.encode_line(full_hypo)
- ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]])
- scorer.add(ref_tok, sys_tok)
-
- # if only one set of hyper parameters is provided, write the predictions to a file
- if write_hypos:
- # recover the orinal ids from n best list generation
- for key in range(len(gen_output.no_bpe_target)):
- if args.prefix_len is None:
- assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], (
- "pred and rescore hypo mismatch:"
- + "i:"
- + str(key)
- + str(hypo_lst[key])
- + str(gen_output.no_bpe_hypo[key])
- )
- ordered_hypos[gen_keys[key]] = hypo_lst[key]
- ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[
- gen_keys[key]
- ]
-
- else:
- full_hypo = rerank_utils.get_full_from_prefix(
- hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]]
- )
- ordered_hypos[gen_keys[key]] = full_hypo
- ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[
- gen_keys[key]
- ]
-
- # write the hypos in the original order from nbest list generation
- if args.num_shards == (len(bitext1_lst)):
- with open(target_outfile, "w") as t:
- with open(hypo_outfile, "w") as h:
- for key in range(len(ordered_hypos)):
- t.write(ordered_targets[key])
- h.write(ordered_hypos[key])
-
- res = scorer.result_string(4)
- if write_hypos:
- print(res)
- score = rerank_utils.parse_bleu_scoring(res)
- return score
-
-
-def match_target_hypo(args, target_outfile, hypo_outfile):
- """combine scores from the LM and bitext models, and write the top scoring hypothesis to a file"""
- if len(args.weight1) == 1:
- res = score_target_hypo(
- args,
- args.weight1[0],
- args.weight2[0],
- args.weight3[0],
- args.lenpen[0],
- target_outfile,
- hypo_outfile,
- True,
- args.normalize,
- )
- rerank_scores = [res]
- else:
- print("launching pool")
- with Pool(32) as p:
- rerank_scores = p.starmap(
- score_target_hypo,
- [
- (
- args,
- args.weight1[i],
- args.weight2[i],
- args.weight3[i],
- args.lenpen[i],
- target_outfile,
- hypo_outfile,
- False,
- args.normalize,
- )
- for i in range(len(args.weight1))
- ],
- )
-
- if len(rerank_scores) > 1:
- best_index = np.argmax(rerank_scores)
- best_score = rerank_scores[best_index]
- print("best score", best_score)
- print("best lenpen", args.lenpen[best_index])
- print("best weight1", args.weight1[best_index])
- print("best weight2", args.weight2[best_index])
- print("best weight3", args.weight3[best_index])
- return (
- args.lenpen[best_index],
- args.weight1[best_index],
- args.weight2[best_index],
- args.weight3[best_index],
- best_score,
- )
-
- else:
- return (
- args.lenpen[0],
- args.weight1[0],
- args.weight2[0],
- args.weight3[0],
- rerank_scores[0],
- )
-
-
-def load_score_files(args):
- if args.all_shards:
- shard_ids = list(range(args.num_shards))
- else:
- shard_ids = [args.shard_id]
-
- gen_output_lst = []
- bitext1_lst = []
- bitext2_lst = []
- lm_res1_lst = []
-
- for shard_id in shard_ids:
- using_nbest = args.nbest_list is not None
- (
- pre_gen,
- left_to_right_preprocessed_dir,
- right_to_left_preprocessed_dir,
- backwards_preprocessed_dir,
- lm_preprocessed_dir,
- ) = rerank_utils.get_directories(
- args.data_dir_name,
- args.num_rescore,
- args.gen_subset,
- args.gen_model_name,
- shard_id,
- args.num_shards,
- args.sampling,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- rerank1_is_gen = (
- args.gen_model == args.score_model1 and args.source_prefix_frac is None
- )
- rerank2_is_gen = (
- args.gen_model == args.score_model2 and args.source_prefix_frac is None
- )
-
- score1_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model1_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards1,
- )
- if args.score_model2 is not None:
- score2_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model2_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards2,
- )
- if args.language_model is not None:
- lm_score_file = rerank_utils.rescore_file_name(
- pre_gen, args.prefix_len, args.lm_name, lm_file=True
- )
-
- # get gen output
- predictions_bpe_file = pre_gen + "/generate_output_bpe.txt"
- if using_nbest:
- print("Using predefined n-best list from interactive.py")
- predictions_bpe_file = args.nbest_list
- gen_output = rerank_utils.BitextOutputFromGen(
- predictions_bpe_file,
- bpe_symbol=args.post_process,
- nbest=using_nbest,
- prefix_len=args.prefix_len,
- target_prefix_frac=args.target_prefix_frac,
- )
-
- if rerank1_is_gen:
- bitext1 = gen_output
- else:
- bitext1 = rerank_utils.BitextOutput(
- score1_file,
- args.backwards1,
- args.right_to_left1,
- args.post_process,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- if args.score_model2 is not None or args.nbest_list is not None:
- if rerank2_is_gen:
- bitext2 = gen_output
- else:
- bitext2 = rerank_utils.BitextOutput(
- score2_file,
- args.backwards2,
- args.right_to_left2,
- args.post_process,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
-
- assert (
- bitext2.source_lengths == bitext1.source_lengths
- ), "source lengths for rescoring models do not match"
- assert (
- bitext2.target_lengths == bitext1.target_lengths
- ), "target lengths for rescoring models do not match"
- else:
- if args.diff_bpe:
- assert args.score_model2 is None
- bitext2 = gen_output
- else:
- bitext2 = None
-
- if args.language_model is not None:
- lm_res1 = rerank_utils.LMOutput(
- lm_score_file,
- args.lm_dict,
- args.prefix_len,
- args.post_process,
- args.target_prefix_frac,
- )
- else:
- lm_res1 = None
-
- gen_output_lst.append(gen_output)
- bitext1_lst.append(bitext1)
- bitext2_lst.append(bitext2)
- lm_res1_lst.append(lm_res1)
- return gen_output_lst, bitext1_lst, bitext2_lst, lm_res1_lst
-
-
-def rerank(args):
- if type(args.lenpen) is not list:
- args.lenpen = [args.lenpen]
- if type(args.weight1) is not list:
- args.weight1 = [args.weight1]
- if type(args.weight2) is not list:
- args.weight2 = [args.weight2]
- if type(args.weight3) is not list:
- args.weight3 = [args.weight3]
- if args.all_shards:
- shard_ids = list(range(args.num_shards))
- else:
- shard_ids = [args.shard_id]
-
- for shard_id in shard_ids:
- (
- pre_gen,
- left_to_right_preprocessed_dir,
- right_to_left_preprocessed_dir,
- backwards_preprocessed_dir,
- lm_preprocessed_dir,
- ) = rerank_utils.get_directories(
- args.data_dir_name,
- args.num_rescore,
- args.gen_subset,
- args.gen_model_name,
- shard_id,
- args.num_shards,
- args.sampling,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
- rerank_generate.gen_and_reprocess_nbest(args)
- rerank_score_bw.score_bw(args)
- rerank_score_lm.score_lm(args)
-
- if args.write_hypos is None:
- write_targets = pre_gen + "/matched_targets"
- write_hypos = pre_gen + "/matched_hypos"
- else:
- write_targets = args.write_hypos + "_targets" + args.gen_subset
- write_hypos = args.write_hypos + "_hypos" + args.gen_subset
-
- if args.all_shards:
- write_targets += "_all_shards"
- write_hypos += "_all_shards"
-
- (
- best_lenpen,
- best_weight1,
- best_weight2,
- best_weight3,
- best_score,
- ) = match_target_hypo(args, write_targets, write_hypos)
-
- return best_lenpen, best_weight1, best_weight2, best_weight3, best_score
-
-
-def cli_main():
- parser = rerank_options.get_reranking_parser()
- args = options.parse_args_and_arch(parser)
- rerank(args)
-
-
-if __name__ == "__main__":
- cli_main()
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md
deleted file mode 100644
index 9d3d7b67dc08440b5f4d1c5a7ffcd4bd6e76c14f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md
+++ /dev/null
@@ -1,179 +0,0 @@
-# Examples of Training scripts for Non-autoregressive Machine Translation models
-
-### Non-autoregressive Transformer (NAT, Gu et al., 2017)
-Note that we need to have an additional module to perform "length prediction" (`--length-loss-factor`) before generating the whole sequence.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch nonautoregressive_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-### Fast Structured Decoding for Sequence Models (NAT-CRF, Sun et al., 2019)
-Note that we implemented a low-rank appromixated CRF model by setting `--crf-lowrank-approx=32` and `--crf-beam-approx=64` as discribed in the original paper. All other settings are the same as the vanilla NAT model.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch nacrf_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --word-ins-loss-factor 0.5 \
- --crf-lowrank-approx 32 \
- --crf-beam-approx 64 \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-### Non-autoregressive Transformer with Iterative Refinement (iNAT, Lee et al., 2018)
-Note that `--train-step` means how many iterations of refinement we used during training, and `--dae-ratio` controls the ratio of denoising auto-encoder training described in the original paper.
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch iterative_nonautoregressive_transformer \
- --noise full_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --pred-length-offset \
- --length-loss-factor 0.1 \
- --train-step 4 \
- --dae-ratio 0.5 \
- --stochastic-approx \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-### Insertion Transformer (InsT, Stern et al., 2019)
-Note that we need to specify the "slot-loss" (uniform or balanced tree) described in the original paper. Here we use `--label-tau` to control the temperature.
-
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch insertion_transformer \
- --noise random_delete \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-### Mask Predict (CMLM, Ghazvininejad et al., 2019)
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch cmlm_transformer \
- --noise random_mask \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
-
-
-
-
-### Levenshtein Transformer (LevT, Gu et al., 2019)
-```bash
-fairseq-train \
- data-bin/wmt14_en_de_distill \
- --save-dir checkpoints \
- --ddp-backend=legacy_ddp \
- --task translation_lev \
- --criterion nat_loss \
- --arch levenshtein_transformer \
- --noise random_delete \
- --share-all-embeddings \
- --optimizer adam --adam-betas '(0.9,0.98)' \
- --lr 0.0005 --lr-scheduler inverse_sqrt \
- --stop-min-lr '1e-09' --warmup-updates 10000 \
- --warmup-init-lr '1e-07' --label-smoothing 0.1 \
- --dropout 0.3 --weight-decay 0.01 \
- --decoder-learned-pos \
- --encoder-learned-pos \
- --apply-bert-init \
- --log-format 'simple' --log-interval 100 \
- --fixed-validation-seed 7 \
- --max-tokens 8000 \
- --save-interval-updates 10000 \
- --max-update 300000
-```
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py
deleted file mode 100644
index 8b7eb2ec4fc5190c4dcdfe34b0259e6f448e18a9..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""isort:skip_file"""
-
-from .dictionary import Dictionary, TruncatedDictionary
-
-from .fairseq_dataset import FairseqDataset, FairseqIterableDataset
-
-from .base_wrapper_dataset import BaseWrapperDataset
-
-from .add_target_dataset import AddTargetDataset
-from .append_token_dataset import AppendTokenDataset
-from .audio.raw_audio_dataset import BinarizedAudioDataset, FileAudioDataset
-from .audio.hubert_dataset import HubertDataset
-from .backtranslation_dataset import BacktranslationDataset
-from .bucket_pad_length_dataset import BucketPadLengthDataset
-from .colorize_dataset import ColorizeDataset
-from .concat_dataset import ConcatDataset
-from .concat_sentences_dataset import ConcatSentencesDataset
-from .denoising_dataset import DenoisingDataset
-from .id_dataset import IdDataset
-from .indexed_dataset import (
- IndexedCachedDataset,
- IndexedDataset,
- IndexedRawTextDataset,
- MMapIndexedDataset,
-)
-from .language_pair_dataset import LanguagePairDataset
-from .list_dataset import ListDataset
-from .lm_context_window_dataset import LMContextWindowDataset
-from .lru_cache_dataset import LRUCacheDataset
-from .mask_tokens_dataset import MaskTokensDataset
-from .monolingual_dataset import MonolingualDataset
-from .multi_corpus_sampled_dataset import MultiCorpusSampledDataset
-from .nested_dictionary_dataset import NestedDictionaryDataset
-from .noising import NoisingDataset
-from .numel_dataset import NumelDataset
-from .num_samples_dataset import NumSamplesDataset
-from .offset_tokens_dataset import OffsetTokensDataset
-from .pad_dataset import LeftPadDataset, PadDataset, RightPadDataset
-from .prepend_dataset import PrependDataset
-from .prepend_token_dataset import PrependTokenDataset
-from .raw_label_dataset import RawLabelDataset
-from .replace_dataset import ReplaceDataset
-from .resampling_dataset import ResamplingDataset
-from .roll_dataset import RollDataset
-from .round_robin_zip_datasets import RoundRobinZipDatasets
-from .sort_dataset import SortDataset
-from .strip_token_dataset import StripTokenDataset
-from .subsample_dataset import SubsampleDataset
-from .token_block_dataset import TokenBlockDataset
-from .transform_eos_dataset import TransformEosDataset
-from .transform_eos_lang_pair_dataset import TransformEosLangPairDataset
-from .shorten_dataset import TruncateDataset, RandomCropDataset
-from .multilingual.sampled_multi_dataset import SampledMultiDataset
-from .multilingual.sampled_multi_epoch_dataset import SampledMultiEpochDataset
-from .fasta_dataset import FastaDataset, EncodedFastaDataset
-
-from .iterators import (
- CountingIterator,
- EpochBatchIterator,
- GroupedIterator,
- ShardedIterator,
-)
-
-__all__ = [
- "AddTargetDataset",
- "AppendTokenDataset",
- "BacktranslationDataset",
- "BaseWrapperDataset",
- "BinarizedAudioDataset",
- "BucketPadLengthDataset",
- "ColorizeDataset",
- "ConcatDataset",
- "ConcatSentencesDataset",
- "CountingIterator",
- "DenoisingDataset",
- "Dictionary",
- "EncodedFastaDataset",
- "EpochBatchIterator",
- "FairseqDataset",
- "FairseqIterableDataset",
- "FastaDataset",
- "FileAudioDataset",
- "GroupedIterator",
- "HubertDataset",
- "IdDataset",
- "IndexedCachedDataset",
- "IndexedDataset",
- "IndexedRawTextDataset",
- "LanguagePairDataset",
- "LeftPadDataset",
- "ListDataset",
- "LMContextWindowDataset",
- "LRUCacheDataset",
- "MaskTokensDataset",
- "MMapIndexedDataset",
- "MonolingualDataset",
- "MultiCorpusSampledDataset",
- "NestedDictionaryDataset",
- "NoisingDataset",
- "NumelDataset",
- "NumSamplesDataset",
- "OffsetTokensDataset",
- "PadDataset",
- "PrependDataset",
- "PrependTokenDataset",
- "RandomCropDataset",
- "RawLabelDataset",
- "ResamplingDataset",
- "ReplaceDataset",
- "RightPadDataset",
- "RollDataset",
- "RoundRobinZipDatasets",
- "SampledMultiDataset",
- "SampledMultiEpochDataset",
- "ShardedIterator",
- "SortDataset",
- "StripTokenDataset",
- "SubsampleDataset",
- "TokenBlockDataset",
- "TransformEosDataset",
- "TransformEosLangPairDataset",
- "TruncateDataset",
- "TruncatedDictionary",
-]
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py
deleted file mode 100644
index 11c7dc1ea966a54f8915ef164377e40f90e851a1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-from fairseq.tasks import register_task
-from fairseq.tasks.speech_to_text import SpeechToTextTask
-from fairseq.tasks.translation import (
- TranslationTask, TranslationConfig
-)
-
-try:
- import examples.simultaneous_translation # noqa
- import_successful = True
-except BaseException:
- import_successful = False
-
-
-logger = logging.getLogger(__name__)
-
-
-def check_import(flag):
- if not flag:
- raise ImportError(
- "'examples.simultaneous_translation' is not correctly imported. "
- "Please considering `pip install -e $FAIRSEQ_DIR`."
- )
-
-
-@register_task("simul_speech_to_text")
-class SimulSpeechToTextTask(SpeechToTextTask):
- def __init__(self, args, tgt_dict):
- check_import(import_successful)
- super().__init__(args, tgt_dict)
-
-
-@register_task("simul_text_to_text", dataclass=TranslationConfig)
-class SimulTextToTextTask(TranslationTask):
- def __init__(self, cfg, src_dict, tgt_dict):
- check_import(import_successful)
- super().__init__(cfg, src_dict, tgt_dict)
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md
deleted file mode 100644
index 57104230655c7c517d25904e634c53b6159ee60f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md
+++ /dev/null
@@ -1,42 +0,0 @@
-# Unit to Speech Model (unit2speech)
-
-Unit to speech model is modified Tacotron2 model that learns to synthesize speech from discrete speech units. All models are trained on quantized [LJSpeech](https://keithito.com/LJ-Speech-Dataset/).
-
-Upstream Units | Download Link
-|-|-
-Log Mel Filterbank + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km50/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km100/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km200/tts_checkpoint_best.pt)
-Log Mel Filterbank + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km500/tts_checkpoint_best.pt)
-Modified CPC + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km50/tts_checkpoint_best.pt)
-Modified CPC + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km100/tts_checkpoint_best.pt)
-Modified CPC + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km200/tts_checkpoint_best.pt)
-Modified CPC + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km500/tts_checkpoint_best.pt)
-HuBERT Base + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km50/tts_checkpoint_best.pt)
-HuBERT Base + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km100/tts_checkpoint_best.pt)
-HuBERT Base + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km200/tts_checkpoint_best.pt)
-HuBERT Base + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km500/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km50/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km100/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km200/tts_checkpoint_best.pt)
-wav2vec 2.0 Large + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km500/tts_checkpoint_best.pt)
-
-## Run inference using a unit2speech model
-* Install librosa, unidecode and inflect using `pip install librosa, unidecode, inflect`
-* Download [Waveglow checkpoint](https://dl.fbaipublicfiles.com/textless_nlp/gslm/waveglow_256channels_new.pt). This is the vocoder.
-
-Sample commnd to run inference using trained unit2speech models. Please note that the quantized audio to synthesized should be using the same units as the unit2speech model was trained with.
-```
-FAIRSEQ_ROOT=
-TTS_MODEL_PATH=
-QUANTIZED_UNIT_PATH=
-OUT_DIR=
-WAVEGLOW_PATH=
-
-PYTHONPATH=${FAIRSEQ_ROOT}:${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech python ${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py \
- --tts_model_path $TTS_MODEL_PATH \
- --quantized_unit_path $QUANTIZED_UNIT_PATH \
- --out_audio_dir $OUT_DIR \
- --waveglow_path $WAVEGLOW_PATH \
- --max_decoder_steps 2000
-```
\ No newline at end of file
diff --git a/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py b/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py
deleted file mode 100644
index 375e4d50872d5c68ee57ca17470a2ca425425eba..0000000000000000000000000000000000000000
--- a/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import re
-from unidecode import unidecode
-import pyopenjtalk
-
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
- r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
- r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ts', 'ʦ'),
- ('u', 'ɯ'),
- ('j', 'ʥ'),
- ('y', 'j'),
- ('ni', 'n^i'),
- ('nj', 'n^'),
- ('hi', 'çi'),
- ('hj', 'ç'),
- ('f', 'ɸ'),
- ('I', 'i*'),
- ('U', 'ɯ*'),
- ('r', 'ɾ')
-]]
-
-# List of (romaji, ipa2) pairs for marks:
-_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('u', 'ɯ'),
- ('ʧ', 'tʃ'),
- ('j', 'dʑ'),
- ('y', 'j'),
- ('ni', 'n^i'),
- ('nj', 'n^'),
- ('hi', 'çi'),
- ('hj', 'ç'),
- ('f', 'ɸ'),
- ('I', 'i*'),
- ('U', 'ɯ*'),
- ('r', 'ɾ')
-]]
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'Q([↑↓]*[kg])', r'k#\1'),
- (r'Q([↑↓]*[tdjʧ])', r't#\1'),
- (r'Q([↑↓]*[sʃ])', r's\1'),
- (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'N([↑↓]*[pbm])', r'm\1'),
- (r'N([↑↓]*[ʧʥj])', r'n^\1'),
- (r'N([↑↓]*[tdn])', r'n\1'),
- (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-def symbols_to_japanese(text):
- for regex, replacement in _symbols_to_japanese:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_romaji_with_accent(text):
- '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
- text = symbols_to_japanese(text)
- sentences = re.split(_japanese_marks, text)
- marks = re.findall(_japanese_marks, text)
- text = ''
- for i, sentence in enumerate(sentences):
- if re.match(_japanese_characters, sentence):
- if text != '':
- text += ' '
- labels = pyopenjtalk.extract_fullcontext(sentence)
- for n, label in enumerate(labels):
- phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
- if phoneme not in ['sil', 'pau']:
- text += phoneme.replace('ch', 'ʧ').replace('sh',
- 'ʃ').replace('cl', 'Q')
- else:
- continue
- # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
- a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
- a2 = int(re.search(r"\+(\d+)\+", label).group(1))
- a3 = int(re.search(r"\+(\d+)/", label).group(1))
- if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
- a2_next = -1
- else:
- a2_next = int(
- re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
- # Accent phrase boundary
- if a3 == 1 and a2_next == 1:
- text += ' '
- # Falling
- elif a1 == 0 and a2_next == a2 + 1:
- text += '↓'
- # Rising
- elif a2 == 1 and a2_next == 2:
- text += '↑'
- if i < len(marks):
- text += unidecode(marks[i]).replace(' ', '')
- return text
-
-
-def get_real_sokuon(text):
- for regex, replacement in _real_sokuon:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def get_real_hatsuon(text):
- for regex, replacement in _real_hatsuon:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa(text):
- text = japanese_to_romaji_with_accent(text).replace('...', '…')
- text = re.sub(
- r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
- text = get_real_sokuon(text)
- text = get_real_hatsuon(text)
- for regex, replacement in _romaji_to_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa2(text):
- text = japanese_to_romaji_with_accent(text).replace('...', '…')
- text = get_real_sokuon(text)
- text = get_real_hatsuon(text)
- for regex, replacement in _romaji_to_ipa2:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa3(text):
- text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
- 'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
- text = re.sub(
- r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
- text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
- return text
diff --git a/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py b/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py
deleted file mode 100644
index c215099fedacae35c6798fdd9b8420a447aa16bb..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-from audiocraft.quantization.vq import ResidualVectorQuantizer
-
-
-class TestResidualVectorQuantizer:
-
- def test_rvq(self):
- x = torch.randn(1, 16, 2048)
- vq = ResidualVectorQuantizer(n_q=8, dimension=16, bins=8)
- res = vq(x, 1.)
- assert res.x.shape == torch.Size([1, 16, 2048])
diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py
deleted file mode 100644
index feb7a8222487756d38482da95183bbbcbbe96ed9..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py
+++ /dev/null
@@ -1,864 +0,0 @@
-
-import math
-import json
-import copy
-from typing import List, Dict
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.modeling.proposal_generator.build import PROPOSAL_GENERATOR_REGISTRY
-from detectron2.layers import ShapeSpec, cat
-from detectron2.structures import Instances, Boxes
-from detectron2.modeling import detector_postprocess
-from detectron2.utils.comm import get_world_size
-from detectron2.config import configurable
-
-from ..layers.heatmap_focal_loss import heatmap_focal_loss_jit
-from ..layers.heatmap_focal_loss import binary_heatmap_focal_loss
-from ..layers.iou_loss import IOULoss
-from ..layers.ml_nms import ml_nms
-from ..debug import debug_train, debug_test
-from .utils import reduce_sum, _transpose
-from .centernet_head import CenterNetHead
-
-__all__ = ["CenterNet"]
-
-INF = 100000000
-
-@PROPOSAL_GENERATOR_REGISTRY.register()
-class CenterNet(nn.Module):
- @configurable
- def __init__(self,
- # input_shape: Dict[str, ShapeSpec],
- in_channels=256,
- *,
- num_classes=80,
- in_features=("p3", "p4", "p5", "p6", "p7"),
- strides=(8, 16, 32, 64, 128),
- score_thresh=0.05,
- hm_min_overlap=0.8,
- loc_loss_type='giou',
- min_radius=4,
- hm_focal_alpha=0.25,
- hm_focal_beta=4,
- loss_gamma=2.0,
- reg_weight=2.0,
- not_norm_reg=True,
- with_agn_hm=False,
- only_proposal=False,
- as_proposal=False,
- not_nms=False,
- pos_weight=1.,
- neg_weight=1.,
- sigmoid_clamp=1e-4,
- ignore_high_fp=-1.,
- center_nms=False,
- sizes_of_interest=[[0,80],[64,160],[128,320],[256,640],[512,10000000]],
- more_pos=False,
- more_pos_thresh=0.2,
- more_pos_topk=9,
- pre_nms_topk_train=1000,
- pre_nms_topk_test=1000,
- post_nms_topk_train=100,
- post_nms_topk_test=100,
- nms_thresh_train=0.6,
- nms_thresh_test=0.6,
- no_reduce=False,
- debug=False,
- vis_thresh=0.5,
- pixel_mean=[103.530,116.280,123.675],
- pixel_std=[1.0,1.0,1.0],
- device='cuda',
- centernet_head=None,
- ):
- super().__init__()
- self.num_classes = num_classes
- self.in_features = in_features
- self.strides = strides
- self.score_thresh = score_thresh
- self.min_radius = min_radius
- self.hm_focal_alpha = hm_focal_alpha
- self.hm_focal_beta = hm_focal_beta
- self.loss_gamma = loss_gamma
- self.reg_weight = reg_weight
- self.not_norm_reg = not_norm_reg
- self.with_agn_hm = with_agn_hm
- self.only_proposal = only_proposal
- self.as_proposal = as_proposal
- self.not_nms = not_nms
- self.pos_weight = pos_weight
- self.neg_weight = neg_weight
- self.sigmoid_clamp = sigmoid_clamp
- self.ignore_high_fp = ignore_high_fp
- self.center_nms = center_nms
- self.sizes_of_interest = sizes_of_interest
- self.more_pos = more_pos
- self.more_pos_thresh = more_pos_thresh
- self.more_pos_topk = more_pos_topk
- self.pre_nms_topk_train = pre_nms_topk_train
- self.pre_nms_topk_test = pre_nms_topk_test
- self.post_nms_topk_train = post_nms_topk_train
- self.post_nms_topk_test = post_nms_topk_test
- self.nms_thresh_train = nms_thresh_train
- self.nms_thresh_test = nms_thresh_test
- self.no_reduce = no_reduce
- self.debug = debug
- self.vis_thresh = vis_thresh
- if self.center_nms:
- self.not_nms = True
- self.iou_loss = IOULoss(loc_loss_type)
- assert (not self.only_proposal) or self.with_agn_hm
- # delta for rendering heatmap
- self.delta = (1 - hm_min_overlap) / (1 + hm_min_overlap)
- if centernet_head is None:
- self.centernet_head = CenterNetHead(
- in_channels=in_channels,
- num_levels=len(in_features),
- with_agn_hm=with_agn_hm,
- only_proposal=only_proposal)
- else:
- self.centernet_head = centernet_head
- if self.debug:
- pixel_mean = torch.Tensor(pixel_mean).to(
- torch.device(device)).view(3, 1, 1)
- pixel_std = torch.Tensor(pixel_std).to(
- torch.device(device)).view(3, 1, 1)
- self.denormalizer = lambda x: x * pixel_std + pixel_mean
-
- @classmethod
- def from_config(cls, cfg, input_shape):
- ret = {
- # 'input_shape': input_shape,
- 'in_channels': input_shape[
- cfg.MODEL.CENTERNET.IN_FEATURES[0]].channels,
- 'num_classes': cfg.MODEL.CENTERNET.NUM_CLASSES,
- 'in_features': cfg.MODEL.CENTERNET.IN_FEATURES,
- 'strides': cfg.MODEL.CENTERNET.FPN_STRIDES,
- 'score_thresh': cfg.MODEL.CENTERNET.INFERENCE_TH,
- 'loc_loss_type': cfg.MODEL.CENTERNET.LOC_LOSS_TYPE,
- 'hm_min_overlap': cfg.MODEL.CENTERNET.HM_MIN_OVERLAP,
- 'min_radius': cfg.MODEL.CENTERNET.MIN_RADIUS,
- 'hm_focal_alpha': cfg.MODEL.CENTERNET.HM_FOCAL_ALPHA,
- 'hm_focal_beta': cfg.MODEL.CENTERNET.HM_FOCAL_BETA,
- 'loss_gamma': cfg.MODEL.CENTERNET.LOSS_GAMMA,
- 'reg_weight': cfg.MODEL.CENTERNET.REG_WEIGHT,
- 'not_norm_reg': cfg.MODEL.CENTERNET.NOT_NORM_REG,
- 'with_agn_hm': cfg.MODEL.CENTERNET.WITH_AGN_HM,
- 'only_proposal': cfg.MODEL.CENTERNET.ONLY_PROPOSAL,
- 'as_proposal': cfg.MODEL.CENTERNET.AS_PROPOSAL,
- 'not_nms': cfg.MODEL.CENTERNET.NOT_NMS,
- 'pos_weight': cfg.MODEL.CENTERNET.POS_WEIGHT,
- 'neg_weight': cfg.MODEL.CENTERNET.NEG_WEIGHT,
- 'sigmoid_clamp': cfg.MODEL.CENTERNET.SIGMOID_CLAMP,
- 'ignore_high_fp': cfg.MODEL.CENTERNET.IGNORE_HIGH_FP,
- 'center_nms': cfg.MODEL.CENTERNET.CENTER_NMS,
- 'sizes_of_interest': cfg.MODEL.CENTERNET.SOI,
- 'more_pos': cfg.MODEL.CENTERNET.MORE_POS,
- 'more_pos_thresh': cfg.MODEL.CENTERNET.MORE_POS_THRESH,
- 'more_pos_topk': cfg.MODEL.CENTERNET.MORE_POS_TOPK,
- 'pre_nms_topk_train': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TRAIN,
- 'pre_nms_topk_test': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TEST,
- 'post_nms_topk_train': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TRAIN,
- 'post_nms_topk_test': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TEST,
- 'nms_thresh_train': cfg.MODEL.CENTERNET.NMS_TH_TRAIN,
- 'nms_thresh_test': cfg.MODEL.CENTERNET.NMS_TH_TEST,
- 'no_reduce': cfg.MODEL.CENTERNET.NO_REDUCE,
- 'debug': cfg.DEBUG,
- 'vis_thresh': cfg.VIS_THRESH,
- 'pixel_mean': cfg.MODEL.PIXEL_MEAN,
- 'pixel_std': cfg.MODEL.PIXEL_STD,
- 'device': cfg.MODEL.DEVICE,
- 'centernet_head': CenterNetHead(
- cfg, [input_shape[f] for f in cfg.MODEL.CENTERNET.IN_FEATURES]),
- }
- return ret
-
-
- def forward(self, images, features_dict, gt_instances):
- features = [features_dict[f] for f in self.in_features]
- clss_per_level, reg_pred_per_level, agn_hm_pred_per_level = \
- self.centernet_head(features)
- grids = self.compute_grids(features)
- shapes_per_level = grids[0].new_tensor(
- [(x.shape[2], x.shape[3]) for x in reg_pred_per_level])
-
- if not self.training:
- return self.inference(
- images, clss_per_level, reg_pred_per_level,
- agn_hm_pred_per_level, grids)
- else:
- pos_inds, labels, reg_targets, flattened_hms = \
- self._get_ground_truth(
- grids, shapes_per_level, gt_instances)
- # logits_pred: M x F, reg_pred: M x 4, agn_hm_pred: M
- logits_pred, reg_pred, agn_hm_pred = self._flatten_outputs(
- clss_per_level, reg_pred_per_level, agn_hm_pred_per_level)
-
- if self.more_pos:
- # add more pixels as positive if \
- # 1. they are within the center3x3 region of an object
- # 2. their regression losses are small (= 0).squeeze(1)
- reg_pred = reg_pred[reg_inds]
- reg_targets_pos = reg_targets[reg_inds]
- reg_weight_map = flattened_hms.max(dim=1)[0]
- reg_weight_map = reg_weight_map[reg_inds]
- reg_weight_map = reg_weight_map * 0 + 1 \
- if self.not_norm_reg else reg_weight_map
- if self.no_reduce:
- reg_norm = max(reg_weight_map.sum(), 1)
- else:
- reg_norm = max(reduce_sum(reg_weight_map.sum()).item() / num_gpus, 1)
-
- reg_loss = self.reg_weight * self.iou_loss(
- reg_pred, reg_targets_pos, reg_weight_map,
- reduction='sum') / reg_norm
- losses['loss_centernet_loc'] = reg_loss
-
- if self.with_agn_hm:
- cat_agn_heatmap = flattened_hms.max(dim=1)[0] # M
- agn_pos_loss, agn_neg_loss = binary_heatmap_focal_loss(
- agn_hm_pred, cat_agn_heatmap, pos_inds,
- alpha=self.hm_focal_alpha,
- beta=self.hm_focal_beta,
- gamma=self.loss_gamma,
- sigmoid_clamp=self.sigmoid_clamp,
- ignore_high_fp=self.ignore_high_fp,
- )
- agn_pos_loss = self.pos_weight * agn_pos_loss / num_pos_avg
- agn_neg_loss = self.neg_weight * agn_neg_loss / num_pos_avg
- losses['loss_centernet_agn_pos'] = agn_pos_loss
- losses['loss_centernet_agn_neg'] = agn_neg_loss
-
- if self.debug:
- print('losses', losses)
- print('total_num_pos', total_num_pos)
- return losses
-
-
- def compute_grids(self, features):
- grids = []
- for level, feature in enumerate(features):
- h, w = feature.size()[-2:]
- shifts_x = torch.arange(
- 0, w * self.strides[level],
- step=self.strides[level],
- dtype=torch.float32, device=feature.device)
- shifts_y = torch.arange(
- 0, h * self.strides[level],
- step=self.strides[level],
- dtype=torch.float32, device=feature.device)
- shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
- shift_x = shift_x.reshape(-1)
- shift_y = shift_y.reshape(-1)
- grids_per_level = torch.stack((shift_x, shift_y), dim=1) + \
- self.strides[level] // 2
- grids.append(grids_per_level)
- return grids
-
-
- def _get_ground_truth(self, grids, shapes_per_level, gt_instances):
- '''
- Input:
- grids: list of tensors [(hl x wl, 2)]_l
- shapes_per_level: list of tuples L x 2:
- gt_instances: gt instances
- Retuen:
- pos_inds: N
- labels: N
- reg_targets: M x 4
- flattened_hms: M x C or M x 1
- N: number of objects in all images
- M: number of pixels from all FPN levels
- '''
-
- # get positive pixel index
- if not self.more_pos:
- pos_inds, labels = self._get_label_inds(
- gt_instances, shapes_per_level)
- else:
- pos_inds, labels = None, None
- heatmap_channels = self.num_classes
- L = len(grids)
- num_loc_list = [len(loc) for loc in grids]
- strides = torch.cat([
- shapes_per_level.new_ones(num_loc_list[l]) * self.strides[l] \
- for l in range(L)]).float() # M
- reg_size_ranges = torch.cat([
- shapes_per_level.new_tensor(self.sizes_of_interest[l]).float().view(
- 1, 2).expand(num_loc_list[l], 2) for l in range(L)]) # M x 2
- grids = torch.cat(grids, dim=0) # M x 2
- M = grids.shape[0]
-
- reg_targets = []
- flattened_hms = []
- for i in range(len(gt_instances)): # images
- boxes = gt_instances[i].gt_boxes.tensor # N x 4
- area = gt_instances[i].gt_boxes.area() # N
- gt_classes = gt_instances[i].gt_classes # N in [0, self.num_classes]
-
- N = boxes.shape[0]
- if N == 0:
- reg_targets.append(grids.new_zeros((M, 4)) - INF)
- flattened_hms.append(
- grids.new_zeros((
- M, 1 if self.only_proposal else heatmap_channels)))
- continue
-
- l = grids[:, 0].view(M, 1) - boxes[:, 0].view(1, N) # M x N
- t = grids[:, 1].view(M, 1) - boxes[:, 1].view(1, N) # M x N
- r = boxes[:, 2].view(1, N) - grids[:, 0].view(M, 1) # M x N
- b = boxes[:, 3].view(1, N) - grids[:, 1].view(M, 1) # M x N
- reg_target = torch.stack([l, t, r, b], dim=2) # M x N x 4
-
- centers = ((boxes[:, [0, 1]] + boxes[:, [2, 3]]) / 2) # N x 2
- centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2
- strides_expanded = strides.view(M, 1, 1).expand(M, N, 2)
- centers_discret = ((centers_expanded / strides_expanded).int() * \
- strides_expanded).float() + strides_expanded / 2 # M x N x 2
-
- is_peak = (((grids.view(M, 1, 2).expand(M, N, 2) - \
- centers_discret) ** 2).sum(dim=2) == 0) # M x N
- is_in_boxes = reg_target.min(dim=2)[0] > 0 # M x N
- is_center3x3 = self.get_center3x3(
- grids, centers, strides) & is_in_boxes # M x N
- is_cared_in_the_level = self.assign_reg_fpn(
- reg_target, reg_size_ranges) # M x N
- reg_mask = is_center3x3 & is_cared_in_the_level # M x N
-
- dist2 = ((grids.view(M, 1, 2).expand(M, N, 2) - \
- centers_expanded) ** 2).sum(dim=2) # M x N
- dist2[is_peak] = 0
- radius2 = self.delta ** 2 * 2 * area # N
- radius2 = torch.clamp(
- radius2, min=self.min_radius ** 2)
- weighted_dist2 = dist2 / radius2.view(1, N).expand(M, N) # M x N
- reg_target = self._get_reg_targets(
- reg_target, weighted_dist2.clone(), reg_mask, area) # M x 4
-
- if self.only_proposal:
- flattened_hm = self._create_agn_heatmaps_from_dist(
- weighted_dist2.clone()) # M x 1
- else:
- flattened_hm = self._create_heatmaps_from_dist(
- weighted_dist2.clone(), gt_classes,
- channels=heatmap_channels) # M x C
-
- reg_targets.append(reg_target)
- flattened_hms.append(flattened_hm)
-
- # transpose im first training_targets to level first ones
- reg_targets = _transpose(reg_targets, num_loc_list)
- flattened_hms = _transpose(flattened_hms, num_loc_list)
- for l in range(len(reg_targets)):
- reg_targets[l] = reg_targets[l] / float(self.strides[l])
- reg_targets = cat([x for x in reg_targets], dim=0) # MB x 4
- flattened_hms = cat([x for x in flattened_hms], dim=0) # MB x C
-
- return pos_inds, labels, reg_targets, flattened_hms
-
-
- def _get_label_inds(self, gt_instances, shapes_per_level):
- '''
- Inputs:
- gt_instances: [n_i], sum n_i = N
- shapes_per_level: L x 2 [(h_l, w_l)]_L
- Returns:
- pos_inds: N'
- labels: N'
- '''
- pos_inds = []
- labels = []
- L = len(self.strides)
- B = len(gt_instances)
- shapes_per_level = shapes_per_level.long()
- loc_per_level = (shapes_per_level[:, 0] * shapes_per_level[:, 1]).long() # L
- level_bases = []
- s = 0
- for l in range(L):
- level_bases.append(s)
- s = s + B * loc_per_level[l]
- level_bases = shapes_per_level.new_tensor(level_bases).long() # L
- strides_default = shapes_per_level.new_tensor(self.strides).float() # L
- for im_i in range(B):
- targets_per_im = gt_instances[im_i]
- bboxes = targets_per_im.gt_boxes.tensor # n x 4
- n = bboxes.shape[0]
- centers = ((bboxes[:, [0, 1]] + bboxes[:, [2, 3]]) / 2) # n x 2
- centers = centers.view(n, 1, 2).expand(n, L, 2)
- strides = strides_default.view(1, L, 1).expand(n, L, 2)
- centers_inds = (centers / strides).long() # n x L x 2
- Ws = shapes_per_level[:, 1].view(1, L).expand(n, L)
- pos_ind = level_bases.view(1, L).expand(n, L) + \
- im_i * loc_per_level.view(1, L).expand(n, L) + \
- centers_inds[:, :, 1] * Ws + \
- centers_inds[:, :, 0] # n x L
- is_cared_in_the_level = self.assign_fpn_level(bboxes)
- pos_ind = pos_ind[is_cared_in_the_level].view(-1)
- label = targets_per_im.gt_classes.view(
- n, 1).expand(n, L)[is_cared_in_the_level].view(-1)
-
- pos_inds.append(pos_ind) # n'
- labels.append(label) # n'
- pos_inds = torch.cat(pos_inds, dim=0).long()
- labels = torch.cat(labels, dim=0)
- return pos_inds, labels # N, N
-
-
- def assign_fpn_level(self, boxes):
- '''
- Inputs:
- boxes: n x 4
- size_ranges: L x 2
- Return:
- is_cared_in_the_level: n x L
- '''
- size_ranges = boxes.new_tensor(
- self.sizes_of_interest).view(len(self.sizes_of_interest), 2) # L x 2
- crit = ((boxes[:, 2:] - boxes[:, :2]) **2).sum(dim=1) ** 0.5 / 2 # n
- n, L = crit.shape[0], size_ranges.shape[0]
- crit = crit.view(n, 1).expand(n, L)
- size_ranges_expand = size_ranges.view(1, L, 2).expand(n, L, 2)
- is_cared_in_the_level = (crit >= size_ranges_expand[:, :, 0]) & \
- (crit <= size_ranges_expand[:, :, 1])
- return is_cared_in_the_level
-
-
- def assign_reg_fpn(self, reg_targets_per_im, size_ranges):
- '''
- TODO (Xingyi): merge it with assign_fpn_level
- Inputs:
- reg_targets_per_im: M x N x 4
- size_ranges: M x 2
- '''
- crit = ((reg_targets_per_im[:, :, :2] + \
- reg_targets_per_im[:, :, 2:])**2).sum(dim=2) ** 0.5 / 2 # M x N
- is_cared_in_the_level = (crit >= size_ranges[:, [0]]) & \
- (crit <= size_ranges[:, [1]])
- return is_cared_in_the_level
-
-
- def _get_reg_targets(self, reg_targets, dist, mask, area):
- '''
- reg_targets (M x N x 4): long tensor
- dist (M x N)
- is_*: M x N
- '''
- dist[mask == 0] = INF * 1.0
- min_dist, min_inds = dist.min(dim=1) # M
- reg_targets_per_im = reg_targets[
- range(len(reg_targets)), min_inds] # M x N x 4 --> M x 4
- reg_targets_per_im[min_dist == INF] = - INF
- return reg_targets_per_im
-
-
- def _create_heatmaps_from_dist(self, dist, labels, channels):
- '''
- dist: M x N
- labels: N
- return:
- heatmaps: M x C
- '''
- heatmaps = dist.new_zeros((dist.shape[0], channels))
- for c in range(channels):
- inds = (labels == c) # N
- if inds.int().sum() == 0:
- continue
- heatmaps[:, c] = torch.exp(-dist[:, inds].min(dim=1)[0])
- zeros = heatmaps[:, c] < 1e-4
- heatmaps[zeros, c] = 0
- return heatmaps
-
-
- def _create_agn_heatmaps_from_dist(self, dist):
- '''
- TODO (Xingyi): merge it with _create_heatmaps_from_dist
- dist: M x N
- return:
- heatmaps: M x 1
- '''
- heatmaps = dist.new_zeros((dist.shape[0], 1))
- heatmaps[:, 0] = torch.exp(-dist.min(dim=1)[0])
- zeros = heatmaps < 1e-4
- heatmaps[zeros] = 0
- return heatmaps
-
-
- def _flatten_outputs(self, clss, reg_pred, agn_hm_pred):
- # Reshape: (N, F, Hl, Wl) -> (N, Hl, Wl, F) -> (sum_l N*Hl*Wl, F)
- clss = cat([x.permute(0, 2, 3, 1).reshape(-1, x.shape[1]) \
- for x in clss], dim=0) if clss[0] is not None else None
- reg_pred = cat(
- [x.permute(0, 2, 3, 1).reshape(-1, 4) for x in reg_pred], dim=0)
- agn_hm_pred = cat([x.permute(0, 2, 3, 1).reshape(-1) \
- for x in agn_hm_pred], dim=0) if self.with_agn_hm else None
- return clss, reg_pred, agn_hm_pred
-
-
- def get_center3x3(self, locations, centers, strides):
- '''
- Inputs:
- locations: M x 2
- centers: N x 2
- strides: M
- '''
- M, N = locations.shape[0], centers.shape[0]
- locations_expanded = locations.view(M, 1, 2).expand(M, N, 2) # M x N x 2
- centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2
- strides_expanded = strides.view(M, 1, 1).expand(M, N, 2) # M x N
- centers_discret = ((centers_expanded / strides_expanded).int() * \
- strides_expanded).float() + strides_expanded / 2 # M x N x 2
- dist_x = (locations_expanded[:, :, 0] - centers_discret[:, :, 0]).abs()
- dist_y = (locations_expanded[:, :, 1] - centers_discret[:, :, 1]).abs()
- return (dist_x <= strides_expanded[:, :, 0]) & \
- (dist_y <= strides_expanded[:, :, 0])
-
-
- def inference(self, images, clss_per_level, reg_pred_per_level,
- agn_hm_pred_per_level, grids):
- logits_pred = [x.sigmoid() if x is not None else None \
- for x in clss_per_level]
- agn_hm_pred_per_level = [x.sigmoid() if x is not None else None \
- for x in agn_hm_pred_per_level]
-
- if self.only_proposal:
- proposals = self.predict_instances(
- grids, agn_hm_pred_per_level, reg_pred_per_level,
- images.image_sizes, [None for _ in agn_hm_pred_per_level])
- else:
- proposals = self.predict_instances(
- grids, logits_pred, reg_pred_per_level,
- images.image_sizes, agn_hm_pred_per_level)
- if self.as_proposal or self.only_proposal:
- for p in range(len(proposals)):
- proposals[p].proposal_boxes = proposals[p].get('pred_boxes')
- proposals[p].objectness_logits = proposals[p].get('scores')
- proposals[p].remove('pred_boxes')
-
- if self.debug:
- debug_test(
- [self.denormalizer(x) for x in images],
- logits_pred, reg_pred_per_level,
- agn_hm_pred_per_level, preds=proposals,
- vis_thresh=self.vis_thresh,
- debug_show_name=False)
- return proposals, {}
-
-
- def predict_instances(
- self, grids, logits_pred, reg_pred, image_sizes, agn_hm_pred,
- is_proposal=False):
- sampled_boxes = []
- for l in range(len(grids)):
- sampled_boxes.append(self.predict_single_level(
- grids[l], logits_pred[l], reg_pred[l] * self.strides[l],
- image_sizes, agn_hm_pred[l], l, is_proposal=is_proposal))
- boxlists = list(zip(*sampled_boxes))
- boxlists = [Instances.cat(boxlist) for boxlist in boxlists]
- boxlists = self.nms_and_topK(
- boxlists, nms=not self.not_nms)
- return boxlists
-
-
- def predict_single_level(
- self, grids, heatmap, reg_pred, image_sizes, agn_hm, level,
- is_proposal=False):
- N, C, H, W = heatmap.shape
- # put in the same format as grids
- if self.center_nms:
- heatmap_nms = nn.functional.max_pool2d(
- heatmap, (3, 3), stride=1, padding=1)
- heatmap = heatmap * (heatmap_nms == heatmap).float()
- heatmap = heatmap.permute(0, 2, 3, 1) # N x H x W x C
- heatmap = heatmap.reshape(N, -1, C) # N x HW x C
- box_regression = reg_pred.view(N, 4, H, W).permute(0, 2, 3, 1) # N x H x W x 4
- box_regression = box_regression.reshape(N, -1, 4)
-
- candidate_inds = heatmap > self.score_thresh # 0.05
- pre_nms_top_n = candidate_inds.view(N, -1).sum(1) # N
- pre_nms_topk = self.pre_nms_topk_train if self.training else self.pre_nms_topk_test
- pre_nms_top_n = pre_nms_top_n.clamp(max=pre_nms_topk) # N
-
- if agn_hm is not None:
- agn_hm = agn_hm.view(N, 1, H, W).permute(0, 2, 3, 1)
- agn_hm = agn_hm.reshape(N, -1)
- heatmap = heatmap * agn_hm[:, :, None]
-
- results = []
- for i in range(N):
- per_box_cls = heatmap[i] # HW x C
- per_candidate_inds = candidate_inds[i] # n
- per_box_cls = per_box_cls[per_candidate_inds] # n
-
- per_candidate_nonzeros = per_candidate_inds.nonzero() # n
- per_box_loc = per_candidate_nonzeros[:, 0] # n
- per_class = per_candidate_nonzeros[:, 1] # n
-
- per_box_regression = box_regression[i] # HW x 4
- per_box_regression = per_box_regression[per_box_loc] # n x 4
- per_grids = grids[per_box_loc] # n x 2
-
- per_pre_nms_top_n = pre_nms_top_n[i] # 1
-
- if per_candidate_inds.sum().item() > per_pre_nms_top_n.item():
- per_box_cls, top_k_indices = \
- per_box_cls.topk(per_pre_nms_top_n, sorted=False)
- per_class = per_class[top_k_indices]
- per_box_regression = per_box_regression[top_k_indices]
- per_grids = per_grids[top_k_indices]
-
- detections = torch.stack([
- per_grids[:, 0] - per_box_regression[:, 0],
- per_grids[:, 1] - per_box_regression[:, 1],
- per_grids[:, 0] + per_box_regression[:, 2],
- per_grids[:, 1] + per_box_regression[:, 3],
- ], dim=1) # n x 4
-
- # avoid invalid boxes in RoI heads
- detections[:, 2] = torch.max(detections[:, 2], detections[:, 0] + 0.01)
- detections[:, 3] = torch.max(detections[:, 3], detections[:, 1] + 0.01)
- boxlist = Instances(image_sizes[i])
- boxlist.scores = torch.sqrt(per_box_cls) \
- if self.with_agn_hm else per_box_cls # n
- # import pdb; pdb.set_trace()
- boxlist.pred_boxes = Boxes(detections)
- boxlist.pred_classes = per_class
- results.append(boxlist)
- return results
-
-
- def nms_and_topK(self, boxlists, nms=True):
- num_images = len(boxlists)
- results = []
- for i in range(num_images):
- nms_thresh = self.nms_thresh_train if self.training else \
- self.nms_thresh_test
- result = ml_nms(boxlists[i], nms_thresh) if nms else boxlists[i]
- if self.debug:
- print('#proposals before nms', len(boxlists[i]))
- print('#proposals after nms', len(result))
- num_dets = len(result)
- post_nms_topk = self.post_nms_topk_train if self.training else \
- self.post_nms_topk_test
- if num_dets > post_nms_topk:
- cls_scores = result.scores
- image_thresh, _ = torch.kthvalue(
- cls_scores.float().cpu(),
- num_dets - post_nms_topk + 1
- )
- keep = cls_scores >= image_thresh.item()
- keep = torch.nonzero(keep).squeeze(1)
- result = result[keep]
- if self.debug:
- print('#proposals after filter', len(result))
- results.append(result)
- return results
-
-
- def _add_more_pos(self, reg_pred, gt_instances, shapes_per_level):
- labels, level_masks, c33_inds, c33_masks, c33_regs = \
- self._get_c33_inds(gt_instances, shapes_per_level)
- N, L, K = labels.shape[0], len(self.strides), 9
- c33_inds[c33_masks == 0] = 0
- reg_pred_c33 = reg_pred[c33_inds].detach() # N x L x K
- invalid_reg = c33_masks == 0
- c33_regs_expand = c33_regs.view(N * L * K, 4).clamp(min=0)
- if N > 0:
- with torch.no_grad():
- c33_reg_loss = self.iou_loss(
- reg_pred_c33.view(N * L * K, 4),
- c33_regs_expand, None,
- reduction='none').view(N, L, K).detach() # N x L x K
- else:
- c33_reg_loss = reg_pred_c33.new_zeros((N, L, K)).detach()
- c33_reg_loss[invalid_reg] = INF # N x L x K
- c33_reg_loss.view(N * L, K)[level_masks.view(N * L), 4] = 0 # real center
- c33_reg_loss = c33_reg_loss.view(N, L * K)
- if N == 0:
- loss_thresh = c33_reg_loss.new_ones((N)).float()
- else:
- loss_thresh = torch.kthvalue(
- c33_reg_loss, self.more_pos_topk, dim=1)[0] # N
- loss_thresh[loss_thresh > self.more_pos_thresh] = self.more_pos_thresh # N
- new_pos = c33_reg_loss.view(N, L, K) < \
- loss_thresh.view(N, 1, 1).expand(N, L, K)
- pos_inds = c33_inds[new_pos].view(-1) # P
- labels = labels.view(N, 1, 1).expand(N, L, K)[new_pos].view(-1)
- return pos_inds, labels
-
-
- def _get_c33_inds(self, gt_instances, shapes_per_level):
- '''
- TODO (Xingyi): The current implementation is ugly. Refactor.
- Get the center (and the 3x3 region near center) locations of each objects
- Inputs:
- gt_instances: [n_i], sum n_i = N
- shapes_per_level: L x 2 [(h_l, w_l)]_L
- '''
- labels = []
- level_masks = []
- c33_inds = []
- c33_masks = []
- c33_regs = []
- L = len(self.strides)
- B = len(gt_instances)
- shapes_per_level = shapes_per_level.long()
- loc_per_level = (shapes_per_level[:, 0] * shapes_per_level[:, 1]).long() # L
- level_bases = []
- s = 0
- for l in range(L):
- level_bases.append(s)
- s = s + B * loc_per_level[l]
- level_bases = shapes_per_level.new_tensor(level_bases).long() # L
- strides_default = shapes_per_level.new_tensor(self.strides).float() # L
- K = 9
- dx = shapes_per_level.new_tensor([-1, 0, 1, -1, 0, 1, -1, 0, 1]).long()
- dy = shapes_per_level.new_tensor([-1, -1, -1, 0, 0, 0, 1, 1, 1]).long()
- for im_i in range(B):
- targets_per_im = gt_instances[im_i]
- bboxes = targets_per_im.gt_boxes.tensor # n x 4
- n = bboxes.shape[0]
- if n == 0:
- continue
- centers = ((bboxes[:, [0, 1]] + bboxes[:, [2, 3]]) / 2) # n x 2
- centers = centers.view(n, 1, 2).expand(n, L, 2)
-
- strides = strides_default.view(1, L, 1).expand(n, L, 2) #
- centers_inds = (centers / strides).long() # n x L x 2
- center_grids = centers_inds * strides + strides // 2# n x L x 2
- l = center_grids[:, :, 0] - bboxes[:, 0].view(n, 1).expand(n, L)
- t = center_grids[:, :, 1] - bboxes[:, 1].view(n, 1).expand(n, L)
- r = bboxes[:, 2].view(n, 1).expand(n, L) - center_grids[:, :, 0]
- b = bboxes[:, 3].view(n, 1).expand(n, L) - center_grids[:, :, 1] # n x L
- reg = torch.stack([l, t, r, b], dim=2) # n x L x 4
- reg = reg / strides_default.view(1, L, 1).expand(n, L, 4).float()
-
- Ws = shapes_per_level[:, 1].view(1, L).expand(n, L)
- Hs = shapes_per_level[:, 0].view(1, L).expand(n, L)
- expand_Ws = Ws.view(n, L, 1).expand(n, L, K)
- expand_Hs = Hs.view(n, L, 1).expand(n, L, K)
- label = targets_per_im.gt_classes.view(n).clone()
- mask = reg.min(dim=2)[0] >= 0 # n x L
- mask = mask & self.assign_fpn_level(bboxes)
- labels.append(label) # n
- level_masks.append(mask) # n x L
-
- Dy = dy.view(1, 1, K).expand(n, L, K)
- Dx = dx.view(1, 1, K).expand(n, L, K)
- c33_ind = level_bases.view(1, L, 1).expand(n, L, K) + \
- im_i * loc_per_level.view(1, L, 1).expand(n, L, K) + \
- (centers_inds[:, :, 1:2].expand(n, L, K) + Dy) * expand_Ws + \
- (centers_inds[:, :, 0:1].expand(n, L, K) + Dx) # n x L x K
-
- c33_mask = \
- ((centers_inds[:, :, 1:2].expand(n, L, K) + dy) < expand_Hs) & \
- ((centers_inds[:, :, 1:2].expand(n, L, K) + dy) >= 0) & \
- ((centers_inds[:, :, 0:1].expand(n, L, K) + dx) < expand_Ws) & \
- ((centers_inds[:, :, 0:1].expand(n, L, K) + dx) >= 0)
- # TODO (Xingyi): think about better way to implement this
- # Currently it hard codes the 3x3 region
- c33_reg = reg.view(n, L, 1, 4).expand(n, L, K, 4).clone()
- c33_reg[:, :, [0, 3, 6], 0] -= 1
- c33_reg[:, :, [0, 3, 6], 2] += 1
- c33_reg[:, :, [2, 5, 8], 0] += 1
- c33_reg[:, :, [2, 5, 8], 2] -= 1
- c33_reg[:, :, [0, 1, 2], 1] -= 1
- c33_reg[:, :, [0, 1, 2], 3] += 1
- c33_reg[:, :, [6, 7, 8], 1] += 1
- c33_reg[:, :, [6, 7, 8], 3] -= 1
- c33_mask = c33_mask & (c33_reg.min(dim=3)[0] >= 0) # n x L x K
- c33_inds.append(c33_ind)
- c33_masks.append(c33_mask)
- c33_regs.append(c33_reg)
-
- if len(level_masks) > 0:
- labels = torch.cat(labels, dim=0)
- level_masks = torch.cat(level_masks, dim=0)
- c33_inds = torch.cat(c33_inds, dim=0).long()
- c33_regs = torch.cat(c33_regs, dim=0)
- c33_masks = torch.cat(c33_masks, dim=0)
- else:
- labels = shapes_per_level.new_zeros((0)).long()
- level_masks = shapes_per_level.new_zeros((0, L)).bool()
- c33_inds = shapes_per_level.new_zeros((0, L, K)).long()
- c33_regs = shapes_per_level.new_zeros((0, L, K, 4)).float()
- c33_masks = shapes_per_level.new_zeros((0, L, K)).bool()
- return labels, level_masks, c33_inds, c33_masks, c33_regs # N x L, N x L x K
\ No newline at end of file
diff --git a/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh b/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh
deleted file mode 100644
index 6d2ba9a6265c0d5fa580035952a1f568dd8d9e44..0000000000000000000000000000000000000000
--- a/spaces/OpenGVLab/InternGPT/third-party/lama/fetch_data/celebahq_dataset_prepare.sh
+++ /dev/null
@@ -1,37 +0,0 @@
-mkdir celeba-hq-dataset
-
-unzip data256x256.zip -d celeba-hq-dataset/
-
-# Reindex
-for i in `echo {00001..30000}`
-do
- mv 'celeba-hq-dataset/data256x256/'$i'.jpg' 'celeba-hq-dataset/data256x256/'$[10#$i - 1]'.jpg'
-done
-
-
-# Split: split train -> train & val
-cat fetch_data/train_shuffled.flist | shuf > celeba-hq-dataset/temp_train_shuffled.flist
-cat celeba-hq-dataset/temp_train_shuffled.flist | head -n 2000 > celeba-hq-dataset/val_shuffled.flist
-cat celeba-hq-dataset/temp_train_shuffled.flist | tail -n +2001 > celeba-hq-dataset/train_shuffled.flist
-cat fetch_data/val_shuffled.flist > celeba-hq-dataset/visual_test_shuffled.flist
-
-mkdir celeba-hq-dataset/train_256/
-mkdir celeba-hq-dataset/val_source_256/
-mkdir celeba-hq-dataset/visual_test_source_256/
-
-cat celeba-hq-dataset/train_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/train_256/
-cat celeba-hq-dataset/val_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/val_source_256/
-cat celeba-hq-dataset/visual_test_shuffled.flist | xargs -I {} mv celeba-hq-dataset/data256x256/{} celeba-hq-dataset/visual_test_source_256/
-
-
-# create location config celeba.yaml
-PWD=$(pwd)
-DATASET=${PWD}/celeba-hq-dataset
-CELEBA=${PWD}/configs/training/location/celeba.yaml
-
-touch $CELEBA
-echo "# @package _group_" >> $CELEBA
-echo "data_root_dir: ${DATASET}/" >> $CELEBA
-echo "out_root_dir: ${PWD}/experiments/" >> $CELEBA
-echo "tb_dir: ${PWD}/tb_logs/" >> $CELEBA
-echo "pretrained_models: ${PWD}/" >> $CELEBA
diff --git a/spaces/OpenMotionLab/MotionGPT/pyrender/tests/conftest.py b/spaces/OpenMotionLab/MotionGPT/pyrender/tests/conftest.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py b/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py
deleted file mode 100644
index 4e06ab8cbe6b43a355c0a9cfb3f2d688438d2c64..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py
+++ /dev/null
@@ -1,201 +0,0 @@
-# ------------------------------------------------------------------------------
-# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/cityscapes_evaluation.py
-# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
-# ------------------------------------------------------------------------------
-
-import glob
-import logging
-import numpy as np
-import os
-import tempfile
-from collections import OrderedDict
-import torch
-from PIL import Image
-
-from detectron2.data import MetadataCatalog
-from detectron2.utils import comm
-from detectron2.utils.file_io import PathManager
-
-from .evaluator import DatasetEvaluator
-
-
-class CityscapesEvaluator(DatasetEvaluator):
- """
- Base class for evaluation using cityscapes API.
- """
-
- def __init__(self, dataset_name):
- """
- Args:
- dataset_name (str): the name of the dataset.
- It must have the following metadata associated with it:
- "thing_classes", "gt_dir".
- """
- self._metadata = MetadataCatalog.get(dataset_name)
- self._cpu_device = torch.device("cpu")
- self._logger = logging.getLogger(__name__)
-
- def reset(self):
- self._working_dir = tempfile.TemporaryDirectory(prefix="cityscapes_eval_")
- self._temp_dir = self._working_dir.name
- # All workers will write to the same results directory
- # TODO this does not work in distributed training
- assert (
- comm.get_local_size() == comm.get_world_size()
- ), "CityscapesEvaluator currently do not work with multiple machines."
- self._temp_dir = comm.all_gather(self._temp_dir)[0]
- if self._temp_dir != self._working_dir.name:
- self._working_dir.cleanup()
- self._logger.info(
- "Writing cityscapes results to temporary directory {} ...".format(self._temp_dir)
- )
-
-
-class CityscapesInstanceEvaluator(CityscapesEvaluator):
- """
- Evaluate instance segmentation results on cityscapes dataset using cityscapes API.
-
- Note:
- * It does not work in multi-machine distributed training.
- * It contains a synchronization, therefore has to be used on all ranks.
- * Only the main process runs evaluation.
- """
-
- def process(self, inputs, outputs):
- from cityscapesscripts.helpers.labels import name2label
-
- for input, output in zip(inputs, outputs):
- file_name = input["file_name"]
- basename = os.path.splitext(os.path.basename(file_name))[0]
- pred_txt = os.path.join(self._temp_dir, basename + "_pred.txt")
-
- if "instances" in output:
- output = output["instances"].to(self._cpu_device)
- num_instances = len(output)
- with open(pred_txt, "w") as fout:
- for i in range(num_instances):
- pred_class = output.pred_classes[i]
- classes = self._metadata.stuff_classes[pred_class]
- class_id = name2label[classes].id
- score = output.scores[i]
- mask = output.pred_masks[i].numpy().astype("uint8")
- png_filename = os.path.join(
- self._temp_dir, basename + "_{}_{}.png".format(i, classes)
- )
-
- Image.fromarray(mask * 255).save(png_filename)
- fout.write(
- "{} {} {}\n".format(os.path.basename(png_filename), class_id, score)
- )
- else:
- # Cityscapes requires a prediction file for every ground truth image.
- with open(pred_txt, "w") as fout:
- pass
-
- def evaluate(self):
- """
- Returns:
- dict: has a key "segm", whose value is a dict of "AP" and "AP50".
- """
- comm.synchronize()
- if comm.get_rank() > 0:
- return
- import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval
-
- self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
-
- # set some global states in cityscapes evaluation API, before evaluating
- cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
- cityscapes_eval.args.predictionWalk = None
- cityscapes_eval.args.JSONOutput = False
- cityscapes_eval.args.colorized = False
- cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json")
-
- # These lines are adopted from
- # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
- gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
- groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_instanceIds.png"))
- assert len(
- groundTruthImgList
- ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
- cityscapes_eval.args.groundTruthSearch
- )
- predictionImgList = []
- for gt in groundTruthImgList:
- predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args))
- results = cityscapes_eval.evaluateImgLists(
- predictionImgList, groundTruthImgList, cityscapes_eval.args
- )["averages"]
-
- ret = OrderedDict()
- ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100}
- self._working_dir.cleanup()
- return ret
-
-
-class CityscapesSemSegEvaluator(CityscapesEvaluator):
- """
- Evaluate semantic segmentation results on cityscapes dataset using cityscapes API.
-
- Note:
- * It does not work in multi-machine distributed training.
- * It contains a synchronization, therefore has to be used on all ranks.
- * Only the main process runs evaluation.
- """
-
- def process(self, inputs, outputs):
- from cityscapesscripts.helpers.labels import trainId2label
-
- for input, output in zip(inputs, outputs):
- file_name = input["file_name"]
- basename = os.path.splitext(os.path.basename(file_name))[0]
- pred_filename = os.path.join(self._temp_dir, basename + "_pred.png")
-
- output = output["sem_seg"].argmax(dim=0).to(self._cpu_device).numpy()
- pred = 255 * np.ones(output.shape, dtype=np.uint8)
- for train_id, label in trainId2label.items():
- if label.ignoreInEval:
- continue
- pred[output == train_id] = label.id
- Image.fromarray(pred).save(pred_filename)
-
- def evaluate(self):
- comm.synchronize()
- if comm.get_rank() > 0:
- return
- # Load the Cityscapes eval script *after* setting the required env var,
- # since the script reads CITYSCAPES_DATASET into global variables at load time.
- import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval
-
- self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
-
- # set some global states in cityscapes evaluation API, before evaluating
- cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
- cityscapes_eval.args.predictionWalk = None
- cityscapes_eval.args.JSONOutput = False
- cityscapes_eval.args.colorized = False
-
- # These lines are adopted from
- # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa
- gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
- groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_labelIds.png"))
- assert len(
- groundTruthImgList
- ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
- cityscapes_eval.args.groundTruthSearch
- )
- predictionImgList = []
- for gt in groundTruthImgList:
- predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt))
- results = cityscapes_eval.evaluateImgLists(
- predictionImgList, groundTruthImgList, cityscapes_eval.args
- )
- ret = OrderedDict()
- ret["sem_seg"] = {
- "IoU": 100.0 * results["averageScoreClasses"],
- "iIoU": 100.0 * results["averageScoreInstClasses"],
- "IoU_sup": 100.0 * results["averageScoreCategories"],
- "iIoU_sup": 100.0 * results["averageScoreInstCategories"],
- }
- self._working_dir.cleanup()
- return ret
diff --git a/spaces/PeepDaSlan9/AutoGPT/benchmark/__init__.py b/spaces/PeepDaSlan9/AutoGPT/benchmark/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py b/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py
deleted file mode 100644
index ba0443e0c22481af2b451d8ccc5ab6e6f79cffb8..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/De-limiter/dataloader/delimit_dataset.py
+++ /dev/null
@@ -1,573 +0,0 @@
-import os
-import random
-from typing import Optional, Callable
-import json
-import glob
-import csv
-
-import numpy as np
-import torch
-import librosa
-import pyloudnorm as pyln
-from pedalboard import Pedalboard, Limiter, Gain, Compressor, Clipping
-
-from .dataset import (
- MusdbTrainDataset,
- MusdbValidDataset,
- apply_limitaug,
-)
-from utils import (
- load_wav_arbitrary_position_stereo,
- load_wav_specific_position_stereo,
- db2linear,
-)
-
-
-class DelimitTrainDataset(MusdbTrainDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- seq_duration: Optional[float] = 6.0,
- samples_per_track: int = 64,
- source_augmentations: Optional[Callable] = lambda audio: audio,
- sample_rate: int = 44100,
- seed: int = 42,
- limitaug_method: str = "limitaug",
- limitaug_mode: str = "normal_L",
- limitaug_custom_target_lufs: float = None,
- limitaug_custom_target_lufs_std: float = None,
- target_loudnorm_lufs: float = -14.0,
- target_limitaug_mode: str = None,
- target_limitaug_custom_target_lufs: float = None,
- target_limitaug_custom_target_lufs_std: float = None,
- custom_limiter_attack_range: list = [2.0, 2.0],
- custom_limiter_release_range: list = [200.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(
- target=target,
- root=root,
- seq_duration=seq_duration,
- samples_per_track=samples_per_track,
- source_augmentations=source_augmentations,
- sample_rate=sample_rate,
- seed=seed,
- limitaug_method=limitaug_method,
- limitaug_mode=limitaug_mode,
- limitaug_custom_target_lufs=limitaug_custom_target_lufs,
- limitaug_custom_target_lufs_std=limitaug_custom_target_lufs_std,
- target_loudnorm_lufs=target_loudnorm_lufs,
- custom_limiter_attack_range=custom_limiter_attack_range,
- custom_limiter_release_range=custom_limiter_release_range,
- *args,
- **kwargs,
- )
-
- self.target_limitaug_mode = target_limitaug_mode
-
- self.target_limitaug_custom_target_lufs = (target_limitaug_custom_target_lufs,)
- self.target_limitaug_custom_target_lufs_std = (
- target_limitaug_custom_target_lufs_std,
- )
- self.limitaug_mode_statistics["target_custom"] = [
- target_limitaug_custom_target_lufs,
- target_limitaug_custom_target_lufs_std,
- ]
-
- """
- Parameters
- ----------
- limitaug_method : str
- choose from ["linear_gain_increase", "limitaug", "limitaug_then_loudnorm", "only_loudnorm"]
- limitaug_mode : str
- choose from ["uniform", "normal", "normal_L", "normal_XL", "normal_short_term", "normal_L_short_term", "normal_XL_short_term", "custom"]
- limitaug_custom_target_lufs : float
- valid only when
- limitaug_mode == "custom"
- target_loudnorm_lufs : float
- valid only when
- limitaug_method == 'limitaug_then_loudnorm' or 'only_loudnorm'
- default is -14.
- To the best of my knowledge, Spotify and Youtube music is using -14 as a reference loudness normalization level.
- No special reason for the choice of -14 as target_loudnorm_lufs.
- target : str
- target name of the source to be separated, defaults to ``vocals``.
- root : str
- root path of MUSDB
- seq_duration : float
- training is performed in chunks of ``seq_duration`` (in seconds,
- defaults to ``None`` which loads the full audio track
- samples_per_track : int
- sets the number of samples, yielded from each track per epoch.
- Defaults to 64
- source_augmentations : list[callables]
- provide list of augmentation function that take a multi-channel
- audio file of shape (src, samples) as input and output. Defaults to
- no-augmentations (input = output)
- seed : int
- control randomness of dataset iterations
- args, kwargs : additional keyword arguments
- used to add further control for the musdb dataset
- initialization function.
- """
-
- # Get a limitaug result without target (individual stem source)
- def get_limitaug_mixture(self, mixture):
- if self.limitaug_method == "limitaug":
- self.board[1].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- )
-
- elif self.limitaug_method == "limitaug_then_loudnorm":
- self.board[1].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = (
- apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- target_loudnorm_lufs=self.target_loudnorm_lufs,
- ),
- )
-
- # Apply LimitAug using Custom Limiter
- elif self.limitaug_method == "custom_limiter_limitaug":
- # Change attack time of First compressor of the Limiter
- self.board[1].attack_ms = random.uniform(
- self.custom_limiter_attack_range[0], self.custom_limiter_attack_range[1]
- )
- # Change release time of First compressor of the Limiter
- self.board[1].release_ms = random.uniform(
- self.custom_limiter_release_range[0],
- self.custom_limiter_release_range[1],
- )
- # Change release time of Second compressor of the Limiter
- self.board[2].release_ms = random.uniform(30.0, 200.0)
- target_lufs = self.sample_target_lufs()
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=target_lufs,
- target_loudnorm_lufs=self.target_loudnorm_lufs,
- )
-
- # When we want to force NN to output an appropriately compressed target output
- if self.target_limitaug_mode:
- mixture_target_lufs = random.gauss(
- self.limitaug_mode_statistics[self.target_limitaug_mode][0],
- self.limitaug_mode_statistics[self.target_limitaug_mode][1],
- )
- mixture, target_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=mixture_target_lufs,
- loudness=mixture_lufs,
- )
-
- if np.isinf(mixture_lufs):
- mixture_loudnorm = mixture
- else:
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain, eps=0.0)
-
- return mixture_limited, mixture_loudnorm
-
- def __getitem__(self, index):
- audio_sources = []
-
- for k, source in enumerate(self.sources):
- # memorize index of target source
- if source == self.target: # if source is 'vocals'
- track_path = self.train_list[
- index // self.samples_per_track
- ] # we want to use # training samples per each track.
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration
- )
- else:
- track_path = random.choice(self.train_list)
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration
- )
- audio = self.source_augmentations(audio)
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- # apply linear mix over source index=0
- # and here, linear mixture is a target unlike in MusdbTrainDataset
- mixture = stems.sum(0)
- mixture_limited, mixture_loudnorm = self.get_limitaug_mixture(mixture)
- # We will give mixture_limited as an input and mixture_loudnorm as a target to the model.
-
- mixture_limited = np.clip(mixture_limited, -1.0, 1.0)
- mixture_limited = torch.as_tensor(mixture_limited, dtype=torch.float32)
- mixture_loudnorm = torch.as_tensor(mixture_loudnorm, dtype=torch.float32)
-
- return mixture_limited, mixture_loudnorm
-
-
-class OzoneTrainDataset(DelimitTrainDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- ozone_root: str = None,
- use_fixed: float = 0.1, # ratio of fixed samples
- seq_duration: Optional[float] = 6.0,
- samples_per_track: int = 64,
- source_augmentations: Optional[Callable] = lambda audio: audio,
- sample_rate: int = 44100,
- seed: int = 42,
- limitaug_method: str = "limitaug",
- limitaug_mode: str = "normal_L",
- limitaug_custom_target_lufs: float = None,
- limitaug_custom_target_lufs_std: float = None,
- target_loudnorm_lufs: float = -14.0,
- target_limitaug_mode: str = None,
- target_limitaug_custom_target_lufs: float = None,
- target_limitaug_custom_target_lufs_std: float = None,
- custom_limiter_attack_range: list = [2.0, 2.0],
- custom_limiter_release_range: list = [200.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(
- target,
- root,
- seq_duration,
- samples_per_track,
- source_augmentations,
- sample_rate,
- seed,
- limitaug_method,
- limitaug_mode,
- limitaug_custom_target_lufs,
- limitaug_custom_target_lufs_std,
- target_loudnorm_lufs,
- target_limitaug_mode,
- target_limitaug_custom_target_lufs,
- target_limitaug_custom_target_lufs_std,
- custom_limiter_attack_range,
- custom_limiter_release_range,
- *args,
- **kwargs,
- )
-
- self.ozone_root = ozone_root
- self.use_fixed = use_fixed
- self.list_train_fixed = glob.glob(f"{self.ozone_root}/ozone_train_fixed/*.wav")
- self.list_train_random = glob.glob(
- f"{self.ozone_root}/ozone_train_random/*.wav"
- )
- self.dict_train_random = {}
-
- # Load information of pre-generated random training examples
- list_csv_files = glob.glob(f"{self.ozone_root}/ozone_train_random_*.csv")
- list_csv_files.sort()
- for csv_file in list_csv_files:
- with open(csv_file, "r") as f:
- reader = csv.reader(f)
- next(reader)
- for row in reader:
- self.dict_train_random[row[0]] = {
- "max_threshold": float(row[1]),
- "max_character": float(row[2]),
- "vocals": {
- "name": row[3],
- "start_sec": float(row[4]),
- "gain": float(row[5]),
- "channelswap": bool(row[6]),
- },
- "bass": {
- "name": row[7],
- "start_sec": float(row[8]),
- "gain": float(row[9]),
- "channelswap": bool(row[10]),
- },
- "drums": {
- "name": row[11],
- "start_sec": float(row[12]),
- "gain": float(row[13]),
- "channelswap": bool(row[14]),
- },
- "other": {
- "name": row[15],
- "start_sec": float(row[16]),
- "gain": float(row[17]),
- "channelswap": bool(row[18]),
- },
- }
-
- def __getitem__(self, idx):
- use_fixed_prob = random.random()
-
- if use_fixed_prob <= self.use_fixed:
- # Fixed examples
- audio_path = random.choice(self.list_train_fixed)
- song_name = os.path.basename(audio_path).replace(".wav", "")
- mixture_limited, start_pos_sec = load_wav_arbitrary_position_stereo(
- audio_path, self.sample_rate, self.seq_duration, return_pos=True
- )
-
- audio_sources = []
- track_path = f"{self.root}/train/{song_name}"
- for source in self.sources:
- audio_path = f"{track_path}/{source}.wav"
- audio = load_wav_specific_position_stereo(
- audio_path,
- self.sample_rate,
- self.seq_duration,
- start_position=start_pos_sec,
- )
- audio_sources.append(audio)
-
- else:
- # Random examples
- # Load mixture_limited (pre-generated)
- audio_path = random.choice(self.list_train_random)
- seg_name = os.path.basename(audio_path).replace(".wav", "")
- mixture_limited, sr = librosa.load(
- audio_path, sr=self.sample_rate, mono=False
- )
-
- # Load mixture_unlimited (from the original musdb18, using metadata)
- audio_sources = []
- for source in self.sources:
- dict_seg_info = self.dict_train_random[seg_name]
- dict_seg_source_info = dict_seg_info[source]
- audio_path = (
- f"{self.root}/train/{dict_seg_source_info['name']}/{source}.wav"
- )
- audio = load_wav_specific_position_stereo(
- audio_path,
- self.sample_rate,
- self.seq_duration,
- start_position=dict_seg_source_info["start_sec"],
- )
-
- # apply augmentations
- audio = audio * dict_seg_source_info["gain"]
- if dict_seg_source_info["channelswap"]:
- audio = np.flip(audio, axis=0)
-
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
- mixture = stems.sum(axis=0)
- mixture_lufs = self.meter.integrated_loudness(mixture.T)
- if np.isinf(mixture_lufs):
- mixture_loudnorm = mixture
- else:
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain, eps=0.0)
-
- return mixture_limited, mixture_loudnorm
-
-
-class DelimitValidDataset(MusdbValidDataset):
- def __init__(
- self,
- target: str = "vocals",
- root: str = None,
- delimit_valid_root: str = None,
- valid_target_lufs: float = -8.05, # From the Table 1 of the "Towards robust music source separation on loud commercial music" paper, the average loudness of commerical music.
- target_loudnorm_lufs: float = -14.0,
- delimit_valid_L_root: str = None, # This will be used when using the target as compressed (normal_L) mixture.
- use_custom_limiter: bool = False,
- custom_limiter_attack_range: list = [0.1, 10.0],
- custom_limiter_release_range: list = [30.0, 200.0],
- *args,
- **kwargs,
- ) -> None:
- super().__init__(target=target, root=root, *args, **kwargs)
- self.delimit_valid_root = delimit_valid_root
- if self.delimit_valid_root:
- with open(f"{self.delimit_valid_root}/valid_loudness.json", "r") as f:
- self.dict_valid_loudness = json.load(f)
- self.delimit_valid_L_root = delimit_valid_L_root
- if self.delimit_valid_L_root:
- with open(f"{self.delimit_valid_L_root}/valid_loudness.json", "r") as f:
- self.dict_valid_L_loudness = json.load(f)
-
- self.valid_target_lufs = valid_target_lufs
- self.target_loudnorm_lufs = target_loudnorm_lufs
- self.meter = pyln.Meter(self.sample_rate)
- self.use_custom_limiter = use_custom_limiter
-
- if self.use_custom_limiter:
- print("using Custom limiter limitaug for validation!!")
- self.custom_limiter_attack_range = custom_limiter_attack_range
- self.custom_limiter_release_range = custom_limiter_release_range
- self.board = Pedalboard(
- [
- Gain(gain_db=0.0),
- Compressor(
- threshold_db=-10.0, ratio=4.0, attack_ms=2.0, release_ms=200.0
- ), # attack_ms and release_ms will be changed later.
- Compressor(
- threshold_db=0.0,
- ratio=1000.0,
- attack_ms=0.001,
- release_ms=100.0,
- ),
- Gain(gain_db=3.75),
- Clipping(threshold_db=0.0),
- ]
- ) # This implementation is the same as JUCE Limiter.
- # However, we want the first compressor to have a variable attack and release time.
- # Therefore, we use the Custom Limiter instead of the JUCE Limiter.
- else:
- self.board = Pedalboard(
- [Gain(gain_db=0.0), Limiter(threshold_db=0.0, release_ms=100.0)]
- ) # Currently, we are using a limiter with a release time of 100ms.
-
- def __getitem__(self, index):
- audio_sources = []
- target_ind = None
-
- for k, source in enumerate(self.sources):
- # memorize index of target source
- if source == self.target: # if source is 'vocals'
- target_ind = k
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- audio_path = f"{track_path}/{source}.wav"
- # audio = utils.load_wav_stereo(audio_path, self.sample_rate)
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
- else:
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- audio_path = f"{track_path}/{source}.wav"
- # audio = utils.load_wav_stereo(audio_path, self.sample_rate)
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
-
- audio = torch.as_tensor(audio, dtype=torch.float32)
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- # apply linear mix over source index=0
- # and here, linear mixture is a target unlike in MusdbTrainDataset
- mixture = stems.sum(0)
- if (
- self.delimit_valid_root
- ): # If there exists a pre-processed delimit valid dataset
- audio_path = f"{self.delimit_valid_root}/valid/{song_name}.wav"
- mixture_limited = librosa.load(audio_path, mono=False, sr=self.sample_rate)[
- 0
- ]
- mixture_lufs = self.dict_valid_loudness[song_name]
-
- else:
- if self.use_custom_limiter:
- custom_limiter_attack = random.uniform(
- self.custom_limiter_attack_range[0],
- self.custom_limiter_attack_range[1],
- )
- self.board[1].attack_ms = custom_limiter_attack
-
- custom_limiter_release = random.uniform(
- self.custom_limiter_release_range[0],
- self.custom_limiter_release_range[1],
- )
- self.board[1].release_ms = custom_limiter_release
-
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=self.valid_target_lufs,
- )
- else:
- mixture_limited, mixture_lufs = apply_limitaug(
- mixture,
- self.board,
- self.meter,
- self.sample_rate,
- target_lufs=self.valid_target_lufs,
- # target_loudnorm_lufs=self.target_loudnorm_lufs,
- ) # mixture_limited is a limiter applied mixture
- # We will give mixture_limited as an input and mixture_loudnorm as a target to the model.
-
- if self.delimit_valid_L_root:
- audio_L_path = f"{self.delimit_valid_L_root}/valid/{song_name}.wav"
- mixture_loudnorm = librosa.load(
- audio_L_path, mono=False, sr=self.sample_rate
- )[0]
- mixture_lufs = self.dict_valid_L_loudness[song_name]
- mixture = mixture_loudnorm
-
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain)
-
- if self.use_custom_limiter:
- return (
- mixture_limited,
- mixture_loudnorm,
- song_name,
- mixture_lufs,
- custom_limiter_attack,
- custom_limiter_release,
- )
- else:
- return mixture_limited, mixture_loudnorm, song_name, mixture_lufs
-
-
-class OzoneValidDataset(MusdbValidDataset):
- def __init__(
- self,
- target: str = "all",
- root: str = None,
- ozone_root: str = None,
- target_loudnorm_lufs: float = -14.0,
- *args,
- **kwargs,
- ) -> None:
- super().__init__(target=target, root=root, *args, **kwargs)
-
- self.ozone_root = ozone_root
- self.target_loudnorm_lufs = target_loudnorm_lufs
-
- with open(f"{self.ozone_root}/valid_loudness.json", "r") as f:
- self.dict_valid_loudness = json.load(f)
-
- def __getitem__(self, index):
- audio_sources = []
-
- track_path = self.valid_list[index]
- song_name = os.path.basename(track_path)
- for k, source in enumerate(self.sources):
- audio_path = f"{track_path}/{source}.wav"
- audio = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
- audio_sources.append(audio)
-
- stems = np.stack(audio_sources, axis=0)
-
- mixture = stems.sum(0)
-
- audio_path = f"{self.ozone_root}/ozone_train_fixed/{song_name}.wav"
- mixture_limited = librosa.load(audio_path, mono=False, sr=self.sample_rate)[0]
-
- mixture_lufs = self.dict_valid_loudness[song_name]
- augmented_gain = self.target_loudnorm_lufs - mixture_lufs
- mixture_loudnorm = mixture * db2linear(augmented_gain)
-
- return mixture_limited, mixture_loudnorm, song_name, mixture_lufs
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py
deleted file mode 100644
index 75559579cf053abcc99538606cbb88c723faf783..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/modulated_deform_conv.py
+++ /dev/null
@@ -1,282 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-
-import torch
-import torch.nn as nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair, _single
-
-from annotator.uniformer.mmcv.utils import deprecated_api_warning
-from ..cnn import CONV_LAYERS
-from ..utils import ext_loader, print_log
-
-ext_module = ext_loader.load_ext(
- '_ext',
- ['modulated_deform_conv_forward', 'modulated_deform_conv_backward'])
-
-
-class ModulatedDeformConv2dFunction(Function):
-
- @staticmethod
- def symbolic(g, input, offset, mask, weight, bias, stride, padding,
- dilation, groups, deform_groups):
- input_tensors = [input, offset, mask, weight]
- if bias is not None:
- input_tensors.append(bias)
- return g.op(
- 'mmcv::MMCVModulatedDeformConv2d',
- *input_tensors,
- stride_i=stride,
- padding_i=padding,
- dilation_i=dilation,
- groups_i=groups,
- deform_groups_i=deform_groups)
-
- @staticmethod
- def forward(ctx,
- input,
- offset,
- mask,
- weight,
- bias=None,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- deform_groups=1):
- if input is not None and input.dim() != 4:
- raise ValueError(
- f'Expected 4D tensor as input, got {input.dim()}D tensor \
- instead.')
- ctx.stride = _pair(stride)
- ctx.padding = _pair(padding)
- ctx.dilation = _pair(dilation)
- ctx.groups = groups
- ctx.deform_groups = deform_groups
- ctx.with_bias = bias is not None
- if not ctx.with_bias:
- bias = input.new_empty(0) # fake tensor
- # When pytorch version >= 1.6.0, amp is adopted for fp16 mode;
- # amp won't cast the type of model (float32), but "offset" is cast
- # to float16 by nn.Conv2d automatically, leading to the type
- # mismatch with input (when it is float32) or weight.
- # The flag for whether to use fp16 or amp is the type of "offset",
- # we cast weight and input to temporarily support fp16 and amp
- # whatever the pytorch version is.
- input = input.type_as(offset)
- weight = weight.type_as(input)
- ctx.save_for_backward(input, offset, mask, weight, bias)
- output = input.new_empty(
- ModulatedDeformConv2dFunction._output_size(ctx, input, weight))
- ctx._bufs = [input.new_empty(0), input.new_empty(0)]
- ext_module.modulated_deform_conv_forward(
- input,
- weight,
- bias,
- ctx._bufs[0],
- offset,
- mask,
- output,
- ctx._bufs[1],
- kernel_h=weight.size(2),
- kernel_w=weight.size(3),
- stride_h=ctx.stride[0],
- stride_w=ctx.stride[1],
- pad_h=ctx.padding[0],
- pad_w=ctx.padding[1],
- dilation_h=ctx.dilation[0],
- dilation_w=ctx.dilation[1],
- group=ctx.groups,
- deformable_group=ctx.deform_groups,
- with_bias=ctx.with_bias)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- input, offset, mask, weight, bias = ctx.saved_tensors
- grad_input = torch.zeros_like(input)
- grad_offset = torch.zeros_like(offset)
- grad_mask = torch.zeros_like(mask)
- grad_weight = torch.zeros_like(weight)
- grad_bias = torch.zeros_like(bias)
- grad_output = grad_output.contiguous()
- ext_module.modulated_deform_conv_backward(
- input,
- weight,
- bias,
- ctx._bufs[0],
- offset,
- mask,
- ctx._bufs[1],
- grad_input,
- grad_weight,
- grad_bias,
- grad_offset,
- grad_mask,
- grad_output,
- kernel_h=weight.size(2),
- kernel_w=weight.size(3),
- stride_h=ctx.stride[0],
- stride_w=ctx.stride[1],
- pad_h=ctx.padding[0],
- pad_w=ctx.padding[1],
- dilation_h=ctx.dilation[0],
- dilation_w=ctx.dilation[1],
- group=ctx.groups,
- deformable_group=ctx.deform_groups,
- with_bias=ctx.with_bias)
- if not ctx.with_bias:
- grad_bias = None
-
- return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias,
- None, None, None, None, None)
-
- @staticmethod
- def _output_size(ctx, input, weight):
- channels = weight.size(0)
- output_size = (input.size(0), channels)
- for d in range(input.dim() - 2):
- in_size = input.size(d + 2)
- pad = ctx.padding[d]
- kernel = ctx.dilation[d] * (weight.size(d + 2) - 1) + 1
- stride_ = ctx.stride[d]
- output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
- if not all(map(lambda s: s > 0, output_size)):
- raise ValueError(
- 'convolution input is too small (output would be ' +
- 'x'.join(map(str, output_size)) + ')')
- return output_size
-
-
-modulated_deform_conv2d = ModulatedDeformConv2dFunction.apply
-
-
-class ModulatedDeformConv2d(nn.Module):
-
- @deprecated_api_warning({'deformable_groups': 'deform_groups'},
- cls_name='ModulatedDeformConv2d')
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- deform_groups=1,
- bias=True):
- super(ModulatedDeformConv2d, self).__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.kernel_size = _pair(kernel_size)
- self.stride = _pair(stride)
- self.padding = _pair(padding)
- self.dilation = _pair(dilation)
- self.groups = groups
- self.deform_groups = deform_groups
- # enable compatibility with nn.Conv2d
- self.transposed = False
- self.output_padding = _single(0)
-
- self.weight = nn.Parameter(
- torch.Tensor(out_channels, in_channels // groups,
- *self.kernel_size))
- if bias:
- self.bias = nn.Parameter(torch.Tensor(out_channels))
- else:
- self.register_parameter('bias', None)
- self.init_weights()
-
- def init_weights(self):
- n = self.in_channels
- for k in self.kernel_size:
- n *= k
- stdv = 1. / math.sqrt(n)
- self.weight.data.uniform_(-stdv, stdv)
- if self.bias is not None:
- self.bias.data.zero_()
-
- def forward(self, x, offset, mask):
- return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,
- self.stride, self.padding,
- self.dilation, self.groups,
- self.deform_groups)
-
-
-@CONV_LAYERS.register_module('DCNv2')
-class ModulatedDeformConv2dPack(ModulatedDeformConv2d):
- """A ModulatedDeformable Conv Encapsulation that acts as normal Conv
- layers.
-
- Args:
- in_channels (int): Same as nn.Conv2d.
- out_channels (int): Same as nn.Conv2d.
- kernel_size (int or tuple[int]): Same as nn.Conv2d.
- stride (int): Same as nn.Conv2d, while tuple is not supported.
- padding (int): Same as nn.Conv2d, while tuple is not supported.
- dilation (int): Same as nn.Conv2d, while tuple is not supported.
- groups (int): Same as nn.Conv2d.
- bias (bool or str): If specified as `auto`, it will be decided by the
- norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
- False.
- """
-
- _version = 2
-
- def __init__(self, *args, **kwargs):
- super(ModulatedDeformConv2dPack, self).__init__(*args, **kwargs)
- self.conv_offset = nn.Conv2d(
- self.in_channels,
- self.deform_groups * 3 * self.kernel_size[0] * self.kernel_size[1],
- kernel_size=self.kernel_size,
- stride=self.stride,
- padding=self.padding,
- dilation=self.dilation,
- bias=True)
- self.init_weights()
-
- def init_weights(self):
- super(ModulatedDeformConv2dPack, self).init_weights()
- if hasattr(self, 'conv_offset'):
- self.conv_offset.weight.data.zero_()
- self.conv_offset.bias.data.zero_()
-
- def forward(self, x):
- out = self.conv_offset(x)
- o1, o2, mask = torch.chunk(out, 3, dim=1)
- offset = torch.cat((o1, o2), dim=1)
- mask = torch.sigmoid(mask)
- return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,
- self.stride, self.padding,
- self.dilation, self.groups,
- self.deform_groups)
-
- def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
- missing_keys, unexpected_keys, error_msgs):
- version = local_metadata.get('version', None)
-
- if version is None or version < 2:
- # the key is different in early versions
- # In version < 2, ModulatedDeformConvPack
- # loads previous benchmark models.
- if (prefix + 'conv_offset.weight' not in state_dict
- and prefix[:-1] + '_offset.weight' in state_dict):
- state_dict[prefix + 'conv_offset.weight'] = state_dict.pop(
- prefix[:-1] + '_offset.weight')
- if (prefix + 'conv_offset.bias' not in state_dict
- and prefix[:-1] + '_offset.bias' in state_dict):
- state_dict[prefix +
- 'conv_offset.bias'] = state_dict.pop(prefix[:-1] +
- '_offset.bias')
-
- if version is not None and version > 1:
- print_log(
- f'ModulatedDeformConvPack {prefix.rstrip(".")} is upgraded to '
- 'version 2.',
- logger='root')
-
- super()._load_from_state_dict(state_dict, prefix, local_metadata,
- strict, missing_keys, unexpected_keys,
- error_msgs)
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py
deleted file mode 100644
index a802a74fd6c3610d9ae178e6201f47423eca7ad1..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/points_sampler.py
+++ /dev/null
@@ -1,177 +0,0 @@
-from typing import List
-
-import torch
-from torch import nn as nn
-
-from annotator.uniformer.mmcv.runner import force_fp32
-from .furthest_point_sample import (furthest_point_sample,
- furthest_point_sample_with_dist)
-
-
-def calc_square_dist(point_feat_a, point_feat_b, norm=True):
- """Calculating square distance between a and b.
-
- Args:
- point_feat_a (Tensor): (B, N, C) Feature vector of each point.
- point_feat_b (Tensor): (B, M, C) Feature vector of each point.
- norm (Bool, optional): Whether to normalize the distance.
- Default: True.
-
- Returns:
- Tensor: (B, N, M) Distance between each pair points.
- """
- num_channel = point_feat_a.shape[-1]
- # [bs, n, 1]
- a_square = torch.sum(point_feat_a.unsqueeze(dim=2).pow(2), dim=-1)
- # [bs, 1, m]
- b_square = torch.sum(point_feat_b.unsqueeze(dim=1).pow(2), dim=-1)
-
- corr_matrix = torch.matmul(point_feat_a, point_feat_b.transpose(1, 2))
-
- dist = a_square + b_square - 2 * corr_matrix
- if norm:
- dist = torch.sqrt(dist) / num_channel
- return dist
-
-
-def get_sampler_cls(sampler_type):
- """Get the type and mode of points sampler.
-
- Args:
- sampler_type (str): The type of points sampler.
- The valid value are "D-FPS", "F-FPS", or "FS".
-
- Returns:
- class: Points sampler type.
- """
- sampler_mappings = {
- 'D-FPS': DFPSSampler,
- 'F-FPS': FFPSSampler,
- 'FS': FSSampler,
- }
- try:
- return sampler_mappings[sampler_type]
- except KeyError:
- raise KeyError(
- f'Supported `sampler_type` are {sampler_mappings.keys()}, but got \
- {sampler_type}')
-
-
-class PointsSampler(nn.Module):
- """Points sampling.
-
- Args:
- num_point (list[int]): Number of sample points.
- fps_mod_list (list[str], optional): Type of FPS method, valid mod
- ['F-FPS', 'D-FPS', 'FS'], Default: ['D-FPS'].
- F-FPS: using feature distances for FPS.
- D-FPS: using Euclidean distances of points for FPS.
- FS: using F-FPS and D-FPS simultaneously.
- fps_sample_range_list (list[int], optional):
- Range of points to apply FPS. Default: [-1].
- """
-
- def __init__(self,
- num_point: List[int],
- fps_mod_list: List[str] = ['D-FPS'],
- fps_sample_range_list: List[int] = [-1]):
- super().__init__()
- # FPS would be applied to different fps_mod in the list,
- # so the length of the num_point should be equal to
- # fps_mod_list and fps_sample_range_list.
- assert len(num_point) == len(fps_mod_list) == len(
- fps_sample_range_list)
- self.num_point = num_point
- self.fps_sample_range_list = fps_sample_range_list
- self.samplers = nn.ModuleList()
- for fps_mod in fps_mod_list:
- self.samplers.append(get_sampler_cls(fps_mod)())
- self.fp16_enabled = False
-
- @force_fp32()
- def forward(self, points_xyz, features):
- """
- Args:
- points_xyz (Tensor): (B, N, 3) xyz coordinates of the features.
- features (Tensor): (B, C, N) Descriptors of the features.
-
- Returns:
- Tensor: (B, npoint, sample_num) Indices of sampled points.
- """
- indices = []
- last_fps_end_index = 0
-
- for fps_sample_range, sampler, npoint in zip(
- self.fps_sample_range_list, self.samplers, self.num_point):
- assert fps_sample_range < points_xyz.shape[1]
-
- if fps_sample_range == -1:
- sample_points_xyz = points_xyz[:, last_fps_end_index:]
- if features is not None:
- sample_features = features[:, :, last_fps_end_index:]
- else:
- sample_features = None
- else:
- sample_points_xyz = \
- points_xyz[:, last_fps_end_index:fps_sample_range]
- if features is not None:
- sample_features = features[:, :, last_fps_end_index:
- fps_sample_range]
- else:
- sample_features = None
-
- fps_idx = sampler(sample_points_xyz.contiguous(), sample_features,
- npoint)
-
- indices.append(fps_idx + last_fps_end_index)
- last_fps_end_index += fps_sample_range
- indices = torch.cat(indices, dim=1)
-
- return indices
-
-
-class DFPSSampler(nn.Module):
- """Using Euclidean distances of points for FPS."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with D-FPS."""
- fps_idx = furthest_point_sample(points.contiguous(), npoint)
- return fps_idx
-
-
-class FFPSSampler(nn.Module):
- """Using feature distances for FPS."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with F-FPS."""
- assert features is not None, \
- 'feature input to FFPS_Sampler should not be None'
- features_for_fps = torch.cat([points, features.transpose(1, 2)], dim=2)
- features_dist = calc_square_dist(
- features_for_fps, features_for_fps, norm=False)
- fps_idx = furthest_point_sample_with_dist(features_dist, npoint)
- return fps_idx
-
-
-class FSSampler(nn.Module):
- """Using F-FPS and D-FPS simultaneously."""
-
- def __init__(self):
- super().__init__()
-
- def forward(self, points, features, npoint):
- """Sampling points with FS_Sampling."""
- assert features is not None, \
- 'feature input to FS_Sampler should not be None'
- ffps_sampler = FFPSSampler()
- dfps_sampler = DFPSSampler()
- fps_idx_ffps = ffps_sampler(points, features, npoint)
- fps_idx_dfps = dfps_sampler(points, features, npoint)
- fps_idx = torch.cat([fps_idx_ffps, fps_idx_dfps], dim=1)
- return fps_idx
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py
deleted file mode 100644
index 332b242c03d1c5e80d4577df442a9a037b1816e1..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/core/seg/sampler/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .base_pixel_sampler import BasePixelSampler
-from .ohem_pixel_sampler import OHEMPixelSampler
-
-__all__ = ['BasePixelSampler', 'OHEMPixelSampler']
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py
deleted file mode 100644
index c0e42756ee6fcd779387255391a30079a28f5e60..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/layers/roi_pool.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import torch
-from torch import nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
-
-from maskrcnn_benchmark import _C
-
-
-class _ROIPool(Function):
- @staticmethod
- def forward(ctx, input, roi, output_size, spatial_scale):
- ctx.output_size = _pair(output_size)
- ctx.spatial_scale = spatial_scale
- ctx.input_shape = input.size()
- output, argmax = _C.roi_pool_forward(
- input, roi, spatial_scale, output_size[0], output_size[1]
- )
- ctx.save_for_backward(input, roi, argmax)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- input, rois, argmax = ctx.saved_tensors
- output_size = ctx.output_size
- spatial_scale = ctx.spatial_scale
- bs, ch, h, w = ctx.input_shape
- grad_input = _C.roi_pool_backward(
- grad_output,
- input,
- rois,
- argmax,
- spatial_scale,
- output_size[0],
- output_size[1],
- bs,
- ch,
- h,
- w,
- )
- return grad_input, None, None, None
-
-
-roi_pool = _ROIPool.apply
-
-
-class ROIPool(nn.Module):
- def __init__(self, output_size, spatial_scale):
- super(ROIPool, self).__init__()
- self.output_size = output_size
- self.spatial_scale = spatial_scale
-
- def forward(self, input, rois):
- return roi_pool(input, rois, self.output_size, self.spatial_scale)
-
- def __repr__(self):
- tmpstr = self.__class__.__name__ + "("
- tmpstr += "output_size=" + str(self.output_size)
- tmpstr += ", spatial_scale=" + str(self.spatial_scale)
- tmpstr += ")"
- return tmpstr
diff --git a/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py b/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py
deleted file mode 100644
index e90ef2130a47ec52160709877972716352e04c9c..0000000000000000000000000000000000000000
--- a/spaces/Pranjal12345/Text_to_Speech/tortoise/models/random_latent_generator.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
- if bias is not None:
- rest_dim = [1] * (input.ndim - bias.ndim - 1)
- return (
- F.leaky_relu(
- input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope
- )
- * scale
- )
- else:
- return F.leaky_relu(input, negative_slope=0.2) * scale
-
-
-class EqualLinear(nn.Module):
- def __init__(
- self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1
- ):
- super().__init__()
- self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
- if bias:
- self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
- else:
- self.bias = None
- self.scale = (1 / math.sqrt(in_dim)) * lr_mul
- self.lr_mul = lr_mul
-
- def forward(self, input):
- out = F.linear(input, self.weight * self.scale)
- out = fused_leaky_relu(out, self.bias * self.lr_mul)
- return out
-
-
-class RandomLatentConverter(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.layers = nn.Sequential(*[EqualLinear(channels, channels, lr_mul=.1) for _ in range(5)],
- nn.Linear(channels, channels))
- self.channels = channels
-
- def forward(self, ref):
- r = torch.randn(ref.shape[0], self.channels, device=ref.device)
- y = self.layers(r)
- return y
-
-
-if __name__ == '__main__':
- model = RandomLatentConverter(512)
- model(torch.randn(5,512))
\ No newline at end of file
diff --git a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py b/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py
deleted file mode 100644
index 8d760f8a1a769bba657d4aea47857f48f7cd0d7a..0000000000000000000000000000000000000000
--- a/spaces/Prof-Reza/Audiocraft_Music-Audio_Generation/app.py
+++ /dev/null
@@ -1,1832 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# Updated to account for UI changes from https://github.com/rkfg/audiocraft/blob/long/app.py
-# also released under the MIT license.
-
-import argparse
-from concurrent.futures import ProcessPoolExecutor
-import os
-from pathlib import Path
-import subprocess as sp
-from tempfile import NamedTemporaryFile
-import time
-import warnings
-import glob
-import re
-from PIL import Image
-from pydub import AudioSegment
-from datetime import datetime
-
-import json
-import shutil
-import taglib
-import torch
-import torchaudio
-import gradio as gr
-import numpy as np
-import typing as tp
-
-from audiocraft.data.audio_utils import convert_audio
-from audiocraft.data.audio import audio_write
-from audiocraft.models import AudioGen, MusicGen, MultiBandDiffusion
-from audiocraft.utils import ui
-import random, string
-
-version = "2.0.0a"
-
-theme = gr.themes.Base(
- primary_hue="lime",
- secondary_hue="lime",
- neutral_hue="neutral",
-).set(
- button_primary_background_fill_hover='*primary_500',
- button_primary_background_fill_hover_dark='*primary_500',
- button_secondary_background_fill_hover='*primary_500',
- button_secondary_background_fill_hover_dark='*primary_500'
-)
-
-MODEL = None # Last used model
-MODELS = None
-UNLOAD_MODEL = False
-MOVE_TO_CPU = False
-IS_BATCHED = "facebook/MusicGen" in os.environ.get('SPACE_ID', '')
-print(IS_BATCHED)
-MAX_BATCH_SIZE = 12
-BATCHED_DURATION = 15
-INTERRUPTING = False
-MBD = None
-# We have to wrap subprocess call to clean a bit the log when using gr.make_waveform
-_old_call = sp.call
-
-
-def generate_random_string(length):
- characters = string.ascii_letters + string.digits
- return ''.join(random.choice(characters) for _ in range(length))
-
-
-def resize_video(input_path, output_path, target_width, target_height):
- ffmpeg_cmd = [
- 'ffmpeg',
- '-y',
- '-i', input_path,
- '-vf', f'scale={target_width}:{target_height}',
- '-c:a', 'copy',
- output_path
- ]
- sp.run(ffmpeg_cmd)
-
-
-def _call_nostderr(*args, **kwargs):
- # Avoid ffmpeg vomiting on the logs.
- kwargs['stderr'] = sp.DEVNULL
- kwargs['stdout'] = sp.DEVNULL
- _old_call(*args, **kwargs)
-
-
-sp.call = _call_nostderr
-# Preallocating the pool of processes.
-pool = ProcessPoolExecutor(4)
-pool.__enter__()
-
-
-def interrupt():
- global INTERRUPTING
- INTERRUPTING = True
-
-
-class FileCleaner:
- def __init__(self, file_lifetime: float = 3600):
- self.file_lifetime = file_lifetime
- self.files = []
-
- def add(self, path: tp.Union[str, Path]):
- self._cleanup()
- self.files.append((time.time(), Path(path)))
-
- def _cleanup(self):
- now = time.time()
- for time_added, path in list(self.files):
- if now - time_added > self.file_lifetime:
- if path.exists():
- path.unlink()
- self.files.pop(0)
- else:
- break
-
-
-file_cleaner = FileCleaner()
-
-
-def make_waveform(*args, **kwargs):
- # Further remove some warnings.
- be = time.time()
- with warnings.catch_warnings():
- warnings.simplefilter('ignore')
- height = kwargs.pop('height')
- width = kwargs.pop('width')
- if height < 256:
- height = 256
- if width < 256:
- width = 256
- waveform_video = gr.make_waveform(*args, **kwargs)
- out = f"{generate_random_string(12)}.mp4"
- image = kwargs.get('bg_image', None)
- if image is None:
- resize_video(waveform_video, out, 900, 300)
- else:
- resize_video(waveform_video, out, width, height)
- print("Make a video took", time.time() - be)
- return out
-
-
-def load_model(version='GrandaddyShmax/musicgen-melody', custom_model=None, base_model='GrandaddyShmax/musicgen-medium', gen_type="music"):
- global MODEL, MODELS
- print("Loading model", version)
- if MODELS is None:
- if version == 'GrandaddyShmax/musicgen-custom':
- MODEL = MusicGen.get_pretrained(base_model)
- file_path = os.path.abspath("models/" + str(custom_model) + ".pt")
- MODEL.lm.load_state_dict(torch.load(file_path))
- else:
- if gen_type == "music":
- MODEL = MusicGen.get_pretrained(version)
- elif gen_type == "audio":
- MODEL = AudioGen.get_pretrained(version)
-
- return
-
- else:
- t1 = time.monotonic()
- if MODEL is not None:
- MODEL.to('cpu') # move to cache
- print("Previous model moved to CPU in %.2fs" % (time.monotonic() - t1))
- t1 = time.monotonic()
- if version != 'GrandaddyShmax/musicgen-custom' and MODELS.get(version) is None:
- print("Loading model %s from disk" % version)
- if gen_type == "music":
- result = MusicGen.get_pretrained(version)
- elif gen_type == "audio":
- result = AudioGen.get_pretrained(version)
- MODELS[version] = result
- print("Model loaded in %.2fs" % (time.monotonic() - t1))
- MODEL = result
- return
- result = MODELS[version].to('cuda')
- print("Cached model loaded in %.2fs" % (time.monotonic() - t1))
- MODEL = result
-
-def get_audio_info(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted. (Discord removes metadata from mp4 and wav files, so you can't use them)"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- global_prompt = str("\nGlobal Prompt: " + (data['global_prompt'] if data['global_prompt'] != "" else "none")) if 'global_prompt' in data else ""
- bpm = str("\nBPM: " + data['bpm']) if 'bpm' in data else ""
- key = str("\nKey: " + data['key']) if 'key' in data else ""
- scale = str("\nScale: " + data['scale']) if 'scale' in data else ""
- prompts = str("\nPrompts: " + (data['texts'] if data['texts'] != "['']" else "none")) if 'texts' in data else ""
- duration = str("\nDuration: " + data['duration']) if 'duration' in data else ""
- overlap = str("\nOverlap: " + data['overlap']) if 'overlap' in data else ""
- seed = str("\nSeed: " + data['seed']) if 'seed' in data else ""
- audio_mode = str("\nAudio Mode: " + data['audio_mode']) if 'audio_mode' in data else ""
- input_length = str("\nInput Length: " + data['input_length']) if 'input_length' in data else ""
- channel = str("\nChannel: " + data['channel']) if 'channel' in data else ""
- sr_select = str("\nSample Rate: " + data['sr_select']) if 'sr_select' in data else ""
- gen_type = str(data['generator'] + "gen-") if 'generator' in data else ""
- model = str("\nModel: " + gen_type + data['model']) if 'model' in data else ""
- custom_model = str("\nCustom Model: " + data['custom_model']) if 'custom_model' in data else ""
- base_model = str("\nBase Model: " + data['base_model']) if 'base_model' in data else ""
- decoder = str("\nDecoder: " + data['decoder']) if 'decoder' in data else ""
- topk = str("\nTopk: " + data['topk']) if 'topk' in data else ""
- topp = str("\nTopp: " + data['topp']) if 'topp' in data else ""
- temperature = str("\nTemperature: " + data['temperature']) if 'temperature' in data else ""
- cfg_coef = str("\nClassifier Free Guidance: " + data['cfg_coef']) if 'cfg_coef' in data else ""
- version = str("Version: " + data['version']) if 'version' in data else "Version: Unknown"
- info = str(version + global_prompt + bpm + key + scale + prompts + duration + overlap + seed + audio_mode + input_length + channel + sr_select + model + custom_model + base_model + decoder + topk + topp + temperature + cfg_coef)
- if info == "":
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted. (Discord removes metadata from mp4 and wav files, so you can't use them)"
- return info
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- #if 'global_prompt' not in data:
- #return "No tags found. Either the file is not generated by MusicGen+ V1.2.8a and higher or the tags are corrupted."
- global_prompt = str("\nGlobal Prompt: " + (data['global_prompt'] if data['global_prompt'] != "" else "none")) if 'global_prompt' in data else ""
- bpm = str("\nBPM: " + data['bpm']) if 'bpm' in data else ""
- key = str("\nKey: " + data['key']) if 'key' in data else ""
- scale = str("\nScale: " + data['scale']) if 'scale' in data else ""
- prompts = str("\nPrompts: " + (data['texts'] if data['texts'] != "['']" else "none")) if 'texts' in data else ""
- duration = str("\nDuration: " + data['duration']) if 'duration' in data else ""
- overlap = str("\nOverlap: " + data['overlap']) if 'overlap' in data else ""
- seed = str("\nSeed: " + data['seed']) if 'seed' in data else ""
- audio_mode = str("\nAudio Mode: " + data['audio_mode']) if 'audio_mode' in data else ""
- input_length = str("\nInput Length: " + data['input_length']) if 'input_length' in data else ""
- channel = str("\nChannel: " + data['channel']) if 'channel' in data else ""
- sr_select = str("\nSample Rate: " + data['sr_select']) if 'sr_select' in data else ""
- gen_type = str(data['generator'] + "gen-") if 'generator' in data else ""
- model = str("\nModel: " + gen_type + data['model']) if 'model' in data else ""
- custom_model = str("\nCustom Model: " + data['custom_model']) if 'custom_model' in data else ""
- base_model = str("\nBase Model: " + data['base_model']) if 'base_model' in data else ""
- decoder = str("\nDecoder: " + data['decoder']) if 'decoder' in data else ""
- topk = str("\nTopk: " + data['topk']) if 'topk' in data else ""
- topp = str("\nTopp: " + data['topp']) if 'topp' in data else ""
- temperature = str("\nTemperature: " + data['temperature']) if 'temperature' in data else ""
- cfg_coef = str("\nClassifier Free Guidance: " + data['cfg_coef']) if 'cfg_coef' in data else ""
- version = str("Version: " + data['version']) if 'version' in data else "Version: Unknown"
- info = str(version + global_prompt + bpm + key + scale + prompts + duration + overlap + seed + audio_mode + input_length + channel + sr_select + model + custom_model + base_model + decoder + topk + topp + temperature + cfg_coef)
- if info == "":
- return "No tags found. Either the file is not generated by MusicGen+ V1.2.7 and higher or the tags are corrupted."
- return info
- else:
- return "Only .wav ,.mp4 and .json files are supported"
- else:
- return None
-
-
-def info_to_params(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- struc_prompt = (False if data['bpm'] == "none" else True) if 'bpm' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- bpm = (120 if data['bpm'] == "none" else int(data['bpm'])) if 'bpm' in data else 120
- key = ("C" if data['key'] == "none" else data['key']) if 'key' in data else "C"
- scale = ("Major" if data['scale'] == "none" else data['scale']) if 'scale' in data else "Major"
- model = data['model'] if 'model' in data else "large"
- custom_model = (data['custom_model'] if data['custom_model'] in get_available_models() else None) if 'custom_model' in data else None
- base_model = data['base_model'] if 'base_model' in data else "medium"
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- audio_mode = ("sample" if data['audio_mode'] == "none" else data['audio_mode']) if 'audio_mode' in data else "sample"
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, bpm, key, scale, model, custom_model, base_model, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], audio_mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- struc_prompt = (False if data['bpm'] == "none" else True) if 'bpm' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- bpm = (120 if data['bpm'] == "none" else int(data['bpm'])) if 'bpm' in data else 120
- key = ("C" if data['key'] == "none" else data['key']) if 'key' in data else "C"
- scale = ("Major" if data['scale'] == "none" else data['scale']) if 'scale' in data else "Major"
- model = data['model'] if 'model' in data else "large"
- custom_model = (data['custom_model'] if data['custom_model'] in get_available_models() else None) if 'custom_model' in data else None
- base_model = data['base_model'] if 'base_model' in data else "medium"
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- audio_mode = ("sample" if data['audio_mode'] == "none" else data['audio_mode']) if 'audio_mode' in data else "sample"
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, bpm, key, scale, model, custom_model, base_model, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], audio_mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- else:
- return "Default", False, "", 120, "C", "Major", "large", None, "medium", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "sample", 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-
-
-def info_to_params_a(audio_path):
- if audio_path is not None:
- if audio_path.name.endswith(".wav") or audio_path.name.endswith(".mp4") or audio_path.name.endswith(".json"):
- if not audio_path.name.endswith(".json"):
- with taglib.File(audio_path.name, save_on_exit=False) as song:
- if 'COMMENT' not in song.tags:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- json_string = song.tags['COMMENT'][0]
- data = json.loads(json_string)
- struc_prompt = (False if data['global_prompt'] == "" else True) if 'global_prompt' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
- else:
- with open(audio_path.name) as json_file:
- data = json.load(json_file)
- struc_prompt = (False if data['global_prompt'] == "" else True) if 'global_prompt' in data else False
- global_prompt = data['global_prompt'] if 'global_prompt' in data else ""
- decoder = data['decoder'] if 'decoder' in data else "Default"
- if 'texts' not in data:
- unique_prompts = 1
- text = ["", "", "", "", "", "", "", "", "", ""]
- repeat = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
- else:
- s = data['texts']
- s = re.findall(r"'(.*?)'", s)
- text = []
- repeat = []
- i = 0
- for elem in s:
- if elem.strip():
- if i == 0 or elem != s[i-1]:
- text.append(elem)
- repeat.append(1)
- else:
- repeat[-1] += 1
- i += 1
- text.extend([""] * (10 - len(text)))
- repeat.extend([1] * (10 - len(repeat)))
- unique_prompts = len([t for t in text if t])
- duration = int(data['duration']) if 'duration' in data else 10
- topk = float(data['topk']) if 'topk' in data else 250
- topp = float(data['topp']) if 'topp' in data else 0
- temperature = float(data['temperature']) if 'temperature' in data else 1.0
- cfg_coef = float(data['cfg_coef']) if 'cfg_coef' in data else 5.0
- seed = int(data['seed']) if 'seed' in data else -1
- overlap = int(data['overlap']) if 'overlap' in data else 12
- channel = data['channel'] if 'channel' in data else "stereo"
- sr_select = data['sr_select'] if 'sr_select' in data else "48000"
- return decoder, struc_prompt, global_prompt, unique_prompts, text[0], text[1], text[2], text[3], text[4], text[5], text[6], text[7], text[8], text[9], repeat[0], repeat[1], repeat[2], repeat[3], repeat[4], repeat[5], repeat[6], repeat[7], repeat[8], repeat[9], duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select
-
- else:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
- else:
- return "Default", False, "", 1, "", "", "", "", "", "", "", "", "", "", 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 250, 0, 1.0, 5.0, -1, 12, "stereo", "48000"
-
-
-def make_pseudo_stereo (filename, sr_select, pan, delay):
- if pan:
- temp = AudioSegment.from_wav(filename)
- if sr_select != "32000":
- temp = temp.set_frame_rate(int(sr_select))
- left = temp.pan(-0.5) - 5
- right = temp.pan(0.6) - 5
- temp = left.overlay(right, position=5)
- temp.export(filename, format="wav")
- if delay:
- waveform, sample_rate = torchaudio.load(filename) # load mono WAV file
- delay_seconds = 0.01 # set delay 10ms
- delay_samples = int(delay_seconds * sample_rate) # Calculating delay value in number of samples
- stereo_waveform = torch.stack([waveform[0], torch.cat((torch.zeros(delay_samples), waveform[0][:-delay_samples]))]) # Generate a stereo file with original mono audio and delayed version
- torchaudio.save(filename, stereo_waveform, sample_rate)
- return
-
-
-def normalize_audio(audio_data):
- audio_data = audio_data.astype(np.float32)
- max_value = np.max(np.abs(audio_data))
- audio_data /= max_value
- return audio_data
-
-
-def load_diffusion():
- global MBD
- if MBD is None:
- print("loading MBD")
- MBD = MultiBandDiffusion.get_mbd_musicgen()
-
-
-def unload_diffusion():
- global MBD
- if MBD is not None:
- print("unloading MBD")
- MBD = None
-
-
-def _do_predictions(gen_type, texts, melodies, sample, trim_start, trim_end, duration, image, height, width, background, bar1, bar2, channel, sr_select, progress=False, **gen_kwargs):
- if gen_type == "music":
- maximum_size = 29.5
- elif gen_type == "audio":
- maximum_size = 9.5
- cut_size = 0
- input_length = 0
- sampleP = None
- if sample is not None:
- globalSR, sampleM = sample[0], sample[1]
- sampleM = normalize_audio(sampleM)
- sampleM = torch.from_numpy(sampleM).t()
- if sampleM.dim() == 1:
- sampleM = sampleM.unsqueeze(0)
- sample_length = sampleM.shape[sampleM.dim() - 1] / globalSR
- if trim_start >= sample_length:
- trim_start = sample_length - 0.5
- if trim_end >= sample_length:
- trim_end = sample_length - 0.5
- if trim_start + trim_end >= sample_length:
- tmp = sample_length - 0.5
- trim_start = tmp / 2
- trim_end = tmp / 2
- sampleM = sampleM[..., int(globalSR * trim_start):int(globalSR * (sample_length - trim_end))]
- sample_length = sample_length - (trim_start + trim_end)
- if sample_length > maximum_size:
- cut_size = sample_length - maximum_size
- sampleP = sampleM[..., :int(globalSR * cut_size)]
- sampleM = sampleM[..., int(globalSR * cut_size):]
- if sample_length >= duration:
- duration = sample_length + 0.5
- input_length = sample_length
- global MODEL
- MODEL.set_generation_params(duration=(duration - cut_size), **gen_kwargs)
- print("new batch", len(texts), texts, [None if m is None else (m[0], m[1].shape) for m in melodies], [None if sample is None else (sample[0], sample[1].shape)])
- be = time.time()
- processed_melodies = []
- if gen_type == "music":
- target_sr = 32000
- elif gen_type == "audio":
- target_sr = 16000
- target_ac = 1
-
- for melody in melodies:
- if melody is None:
- processed_melodies.append(None)
- else:
- sr, melody = melody[0], torch.from_numpy(melody[1]).to(MODEL.device).float().t()
- if melody.dim() == 1:
- melody = melody[None]
- melody = melody[..., :int(sr * duration)]
- melody = convert_audio(melody, sr, target_sr, target_ac)
- processed_melodies.append(melody)
-
- if sample is not None:
- if sampleP is None:
- if gen_type == "music":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress
- )
- else:
- if sampleP.dim() > 1:
- sampleP = convert_audio(sampleP, globalSR, target_sr, target_ac)
- sampleP = sampleP.to(MODEL.device).float().unsqueeze(0)
- if gen_type == "music":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_continuation(
- prompt=sampleM,
- prompt_sample_rate=globalSR,
- descriptions=texts,
- progress=progress
- )
- outputs = torch.cat([sampleP, outputs], 2)
-
- elif any(m is not None for m in processed_melodies):
- if gen_type == "music":
- outputs = MODEL.generate_with_chroma(
- descriptions=texts,
- melody_wavs=processed_melodies,
- melody_sample_rate=target_sr,
- progress=progress,
- return_tokens=USE_DIFFUSION
- )
- elif gen_type == "audio":
- outputs = MODEL.generate_with_chroma(
- descriptions=texts,
- melody_wavs=processed_melodies,
- melody_sample_rate=target_sr,
- progress=progress
- )
- else:
- if gen_type == "music":
- outputs = MODEL.generate(texts, progress=progress, return_tokens=USE_DIFFUSION)
- elif gen_type == "audio":
- outputs = MODEL.generate(texts, progress=progress)
-
- if USE_DIFFUSION:
- print("outputs: " + str(outputs))
- outputs_diffusion = MBD.tokens_to_wav(outputs[1])
- outputs = torch.cat([outputs[0], outputs_diffusion], dim=0)
- outputs = outputs.detach().cpu().float()
- backups = outputs
- if channel == "stereo":
- outputs = convert_audio(outputs, target_sr, int(sr_select), 2)
- elif channel == "mono" and sr_select != "32000":
- outputs = convert_audio(outputs, target_sr, int(sr_select), 1)
- out_files = []
- out_audios = []
- out_backup = []
- for output in outputs:
- with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
- audio_write(
- file.name, output, (MODEL.sample_rate if channel == "stereo effect" else int(sr_select)), strategy="loudness",
- loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
-
- if channel == "stereo effect":
- make_pseudo_stereo(file.name, sr_select, pan=True, delay=True);
-
- out_files.append(pool.submit(make_waveform, file.name, bg_image=image, bg_color=background, bars_color=(bar1, bar2), fg_alpha=1.0, bar_count=75, height=height, width=width))
- out_audios.append(file.name)
- file_cleaner.add(file.name)
- print(f'wav: {file.name}')
- for backup in backups:
- with NamedTemporaryFile("wb", suffix=".wav", delete=False) as file:
- audio_write(
- file.name, backup, MODEL.sample_rate, strategy="loudness",
- loudness_headroom_db=16, loudness_compressor=True, add_suffix=False)
- out_backup.append(file.name)
- file_cleaner.add(file.name)
- res = [out_file.result() for out_file in out_files]
- res_audio = out_audios
- res_backup = out_backup
- for file in res:
- file_cleaner.add(file)
- print(f'video: {file}')
- print("batch finished", len(texts), time.time() - be)
- print("Tempfiles currently stored: ", len(file_cleaner.files))
- if MOVE_TO_CPU:
- MODEL.to('cpu')
- if UNLOAD_MODEL:
- MODEL = None
- torch.cuda.empty_cache()
- torch.cuda.ipc_collect()
- return res, res_audio, res_backup, input_length
-
-
-def predict_batched(texts, melodies):
- max_text_length = 512
- texts = [text[:max_text_length] for text in texts]
- load_model('melody')
- res = _do_predictions(texts, melodies, BATCHED_DURATION)
- return res
-
-
-def add_tags(filename, tags):
- json_string = None
-
- data = {
- "global_prompt": tags[0],
- "bpm": tags[1],
- "key": tags[2],
- "scale": tags[3],
- "texts": tags[4],
- "duration": tags[5],
- "overlap": tags[6],
- "seed": tags[7],
- "audio_mode": tags[8],
- "input_length": tags[9],
- "channel": tags[10],
- "sr_select": tags[11],
- "model": tags[12],
- "custom_model": tags[13],
- "base_model": tags[14],
- "decoder": tags[15],
- "topk": tags[16],
- "topp": tags[17],
- "temperature": tags[18],
- "cfg_coef": tags[19],
- "generator": tags[20],
- "version": version
- }
-
- json_string = json.dumps(data)
-
- if os.path.exists(filename):
- with taglib.File(filename, save_on_exit=True) as song:
- song.tags = {'COMMENT': json_string }
-
- json_file = open(tags[7] + '.json', 'w')
- json_file.write(json_string)
- json_file.close()
-
- return json_file.name;
-
-
-def save_outputs(mp4, wav_tmp, tags, gen_type):
- # mp4: .mp4 file name in root running folder of app.py
- # wav_tmp: temporary wav file located in %TEMP% folder
- # seed - used seed
- # exanple BgnJtr4Pn1AJ.mp4, C:\Users\Alex\AppData\Local\Temp\tmp4ermrebs.wav, 195123182343465
- # procedure read generated .mp4 and wav files, rename it by using seed as name,
- # and will store it to ./output/today_date/wav and ./output/today_date/mp4 folders.
- # if file with same seed number already exist its make postfix in name like seed(n)
- # where is n - consiqunce number 1-2-3-4 and so on
- # then we store generated mp4 and wav into destination folders.
-
- current_date = datetime.now().strftime("%Y%m%d")
- wav_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'wav')
- mp4_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'mp4')
- json_directory = os.path.join(os.getcwd(), 'output', current_date, gen_type,'json')
- os.makedirs(wav_directory, exist_ok=True)
- os.makedirs(mp4_directory, exist_ok=True)
- os.makedirs(json_directory, exist_ok=True)
-
- filename = str(tags[7]) + '.wav'
- target = os.path.join(wav_directory, filename)
- counter = 1
- while os.path.exists(target):
- filename = str(tags[7]) + f'({counter})' + '.wav'
- target = os.path.join(wav_directory, filename)
- counter += 1
-
- shutil.copyfile(wav_tmp, target); # make copy of original file
- json_file = add_tags(target, tags);
-
- wav_target=target;
- target=target.replace('wav', 'mp4');
- mp4_target=target;
-
- mp4=r'./' +mp4;
- shutil.copyfile(mp4, target); # make copy of original file
- _ = add_tags(target, tags);
-
- target=target.replace('mp4', 'json'); # change the extension to json
- json_target=target; # store the json target
-
- with open(target, 'w') as f: # open a writable file object
- shutil.copyfile(json_file, target); # make copy of original file
-
- os.remove(json_file)
-
- return wav_target, mp4_target, json_target;
-
-
-def clear_cash():
- # delete all temporary files genegated my system
- current_date = datetime.now().date()
- current_directory = os.getcwd()
- files = glob.glob(os.path.join(current_directory, '*.mp4'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- temp_directory = os.environ.get('TEMP')
- files = glob.glob(os.path.join(temp_directory, 'tmp*.mp4'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- files = glob.glob(os.path.join(temp_directory, 'tmp*.wav'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
-
- files = glob.glob(os.path.join(temp_directory, 'tmp*.png'))
- for file in files:
- creation_date = datetime.fromtimestamp(os.path.getctime(file)).date()
- if creation_date == current_date:
- os.remove(file)
- return
-
-
-def s2t(seconds, seconds2):
- # convert seconds to time format
- # seconds - time in seconds
- # return time in format 00:00
- m, s = divmod(seconds, 60)
- m2, s2 = divmod(seconds2, 60)
- if seconds != 0 and seconds < seconds2:
- s = s + 1
- return ("%02d:%02d - %02d:%02d" % (m, s, m2, s2))
-
-
-def calc_time(gen_type, s, duration, overlap, d0, d1, d2, d3, d4, d5, d6, d7, d8, d9):
- # calculate the time of generation
- # overlap - overlap in seconds
- # d0-d9 - drag
- # return time in seconds
- d_amount = [int(d0), int(d1), int(d2), int(d3), int(d4), int(d5), int(d6), int(d7), int(d8), int(d9)]
- calc = []
- tracks = []
- time = 0
- s = s - 1
- max_time = duration
- max_limit = 0
- if gen_type == "music":
- max_limit = 30
- elif gen_type == "audio":
- max_limit = 10
- track_add = max_limit - overlap
- tracks.append(max_limit + ((d_amount[0] - 1) * track_add))
- for i in range(1, 10):
- tracks.append(d_amount[i] * track_add)
-
- if tracks[0] >= max_time or s == 0:
- calc.append(s2t(time, max_time))
- time = max_time
- else:
- calc.append(s2t(time, tracks[0]))
- time = tracks[0]
-
- for i in range(1, 10):
- if time + tracks[i] >= max_time or i == s:
- calc.append(s2t(time, max_time))
- time = max_time
- else:
- calc.append(s2t(time, time + tracks[i]))
- time = time + tracks[i]
-
- return calc[0], calc[1], calc[2], calc[3], calc[4], calc[5], calc[6], calc[7], calc[8], calc[9]
-
-
-def predict_full(gen_type, model, decoder, custom_model, base_model, prompt_amount, struc_prompt, bpm, key, scale, global_prompt, p0, p1, p2, p3, p4, p5, p6, p7, p8, p9, d0, d1, d2, d3, d4, d5, d6, d7, d8, d9, audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select, progress=gr.Progress()):
- global INTERRUPTING
- global USE_DIFFUSION
- INTERRUPTING = False
-
- if gen_type == "audio":
- custom_model = None
- base_model = "medium"
-
- if temperature < 0:
- raise gr.Error("Temperature must be >= 0.")
- if topk < 0:
- raise gr.Error("Topk must be non-negative.")
- if topp < 0:
- raise gr.Error("Topp must be non-negative.")
-
- if trim_start < 0:
- trim_start = 0
- if trim_end < 0:
- trim_end = 0
-
- topk = int(topk)
-
- if decoder == "MultiBand_Diffusion":
- USE_DIFFUSION = True
- load_diffusion()
- else:
- USE_DIFFUSION = False
- unload_diffusion()
-
- if gen_type == "music":
- model_shrt = model
- model = "GrandaddyShmax/musicgen-" + model
- elif gen_type == "audio":
- model_shrt = model
- model = "GrandaddyShmax/audiogen-" + model
- base_model_shrt = base_model
- base_model = "GrandaddyShmax/musicgen-" + base_model
-
- if MODEL is None or MODEL.name != (model):
- load_model(model, custom_model, base_model, gen_type)
- else:
- if MOVE_TO_CPU:
- MODEL.to('cuda')
-
- if seed < 0:
- seed = random.randint(0, 0xffff_ffff_ffff)
- torch.manual_seed(seed)
-
- def _progress(generated, to_generate):
- progress((min(generated, to_generate), to_generate))
- if INTERRUPTING:
- raise gr.Error("Interrupted.")
- MODEL.set_custom_progress_callback(_progress)
-
- audio_mode = "none"
- melody = None
- sample = None
- if audio:
- audio_mode = mode
- if mode == "sample":
- sample = audio
- elif mode == "melody":
- melody = audio
-
- base_model = "none" if model != "custom" else base_model
- custom_model = "none" if model != "custom" else custom_model
-
- text_cat = [p0, p1, p2, p3, p4, p5, p6, p7, p8, p9]
- drag_cat = [d0, d1, d2, d3, d4, d5, d6, d7, d8, d9]
- texts = []
- raw_texts = []
- ind = 0
- ind2 = 0
- while ind < prompt_amount:
- for ind2 in range(int(drag_cat[ind])):
- if not struc_prompt:
- texts.append(text_cat[ind])
- global_prompt = "none"
- bpm = "none"
- key = "none"
- scale = "none"
- raw_texts.append(text_cat[ind])
- else:
- if gen_type == "music":
- bpm_str = str(bpm) + " bpm"
- key_str = ", " + str(key) + " " + str(scale)
- global_str = (", " + str(global_prompt)) if str(global_prompt) != "" else ""
- elif gen_type == "audio":
- bpm_str = ""
- key_str = ""
- global_str = (str(global_prompt)) if str(global_prompt) != "" else ""
- texts_str = (", " + str(text_cat[ind])) if str(text_cat[ind]) != "" else ""
- texts.append(bpm_str + key_str + global_str + texts_str)
- raw_texts.append(text_cat[ind])
- ind2 = 0
- ind = ind + 1
-
- outs, outs_audio, outs_backup, input_length = _do_predictions(
- gen_type, [texts], [melody], sample, trim_start, trim_end, duration, image, height, width, background, bar1, bar2, channel, sr_select, progress=True,
- top_k=topk, top_p=topp, temperature=temperature, cfg_coef=cfg_coef, extend_stride=MODEL.max_duration-overlap)
- tags = [str(global_prompt), str(bpm), str(key), str(scale), str(raw_texts), str(duration), str(overlap), str(seed), str(audio_mode), str(input_length), str(channel), str(sr_select), str(model_shrt), str(custom_model), str(base_model_shrt), str(decoder), str(topk), str(topp), str(temperature), str(cfg_coef), str(gen_type)]
- wav_target, mp4_target, json_target = save_outputs(outs[0], outs_audio[0], tags, gen_type);
- # Removes the temporary files.
- for out in outs:
- os.remove(out)
- for out in outs_audio:
- os.remove(out)
-
- return mp4_target, wav_target, outs_backup[0], [mp4_target, wav_target, json_target], seed
-
-
-max_textboxes = 10
-
-
-def get_available_models():
- return sorted([re.sub('.pt$', '', item.name) for item in list(Path('models/').glob('*')) if item.name.endswith('.pt')])
-
-
-def toggle_audio_src(choice):
- if choice == "mic":
- return gr.update(source="microphone", value=None, label="Microphone")
- else:
- return gr.update(source="upload", value=None, label="File")
-
-
-def ui_full(launch_kwargs):
- with gr.Blocks(title='AudioCraft Plus', theme=theme) as interface:
- gr.Markdown(
- """
- # AudioCraft Plus - v2.0.0a
-
- ### An All-in-One AudioCraft WebUI
-
- Thanks to: facebookresearch, Camenduru, rkfg, oobabooga, AlexHK and GrandaddyShmax
- """
- )
- with gr.Tab("MusicGen"):
- gr.Markdown(
- """
- ### MusicGen
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Tab("Generation"):
- with gr.Accordion("Structure Prompts", open=False):
- with gr.Column():
- with gr.Row():
- struc_prompts = gr.Checkbox(label="Enable", value=False, interactive=True, container=False)
- bpm = gr.Number(label="BPM", value=120, interactive=True, scale=1, precision=0)
- key = gr.Dropdown(["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "Bb", "B"], label="Key", value="C", interactive=True)
- scale = gr.Dropdown(["Major", "Minor"], label="Scale", value="Major", interactive=True)
- with gr.Row():
- global_prompt = gr.Text(label="Global Prompt", interactive=True, scale=3)
- with gr.Row():
- s = gr.Slider(1, max_textboxes, value=1, step=1, label="Prompts:", interactive=True, scale=2)
- #s_mode = gr.Radio(["segmentation", "batch"], value="segmentation", interactive=True, scale=1, label="Generation Mode")
- with gr.Column():
- textboxes = []
- prompts = []
- repeats = []
- calcs = []
- with gr.Row():
- text0 = gr.Text(label="Input Text", interactive=True, scale=4)
- prompts.append(text0)
- drag0 = gr.Number(label="Repeat", value=1, interactive=True, scale=1)
- repeats.append(drag0)
- calc0 = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- calcs.append(calc0)
- for i in range(max_textboxes):
- with gr.Row(visible=False) as t:
- text = gr.Text(label="Input Text", interactive=True, scale=3)
- repeat = gr.Number(label="Repeat", minimum=1, value=1, interactive=True, scale=1)
- calc = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- textboxes.append(t)
- prompts.append(text)
- repeats.append(repeat)
- calcs.append(calc)
- to_calc = gr.Button("Calculate Timings", variant="secondary")
- with gr.Row():
- duration = gr.Slider(minimum=1, maximum=300, value=10, step=1, label="Duration", interactive=True)
- with gr.Row():
- overlap = gr.Slider(minimum=1, maximum=29, value=12, step=1, label="Overlap", interactive=True)
- with gr.Row():
- seed = gr.Number(label="Seed", value=-1, scale=4, precision=0, interactive=True)
- gr.Button('\U0001f3b2\ufe0f', scale=1).click(fn=lambda: -1, outputs=[seed], queue=False)
- reuse_seed = gr.Button('\u267b\ufe0f', scale=1)
-
- with gr.Tab("Audio"):
- with gr.Row():
- with gr.Column():
- input_type = gr.Radio(["file", "mic"], value="file", label="Input Type (optional)", interactive=True)
- mode = gr.Radio(["melody", "sample"], label="Input Audio Mode (optional)", value="sample", interactive=True)
- with gr.Row():
- trim_start = gr.Number(label="Trim Start", value=0, interactive=True)
- trim_end = gr.Number(label="Trim End", value=0, interactive=True)
- audio = gr.Audio(source="upload", type="numpy", label="Input Audio (optional)", interactive=True)
-
- with gr.Tab("Customization"):
- with gr.Row():
- with gr.Column():
- background = gr.ColorPicker(value="#0f0f0f", label="background color", interactive=True, scale=0)
- bar1 = gr.ColorPicker(value="#84cc16", label="bar color start", interactive=True, scale=0)
- bar2 = gr.ColorPicker(value="#10b981", label="bar color end", interactive=True, scale=0)
- with gr.Column():
- image = gr.Image(label="Background Image", type="filepath", interactive=True, scale=4)
- with gr.Row():
- height = gr.Number(label="Height", value=512, interactive=True)
- width = gr.Number(label="Width", value=768, interactive=True)
-
- with gr.Tab("Settings"):
- with gr.Row():
- channel = gr.Radio(["mono", "stereo", "stereo effect"], label="Output Audio Channels", value="stereo", interactive=True, scale=1)
- sr_select = gr.Dropdown(["11025", "16000", "22050", "24000", "32000", "44100", "48000"], label="Output Audio Sample Rate", value="48000", interactive=True)
- with gr.Row():
- model = gr.Radio(["melody", "small", "medium", "large", "custom"], label="Model", value="large", interactive=True, scale=1)
- with gr.Column():
- dropdown = gr.Dropdown(choices=get_available_models(), value=("No models found" if len(get_available_models()) < 1 else get_available_models()[0]), label='Custom Model (models folder)', elem_classes='slim-dropdown', interactive=True)
- ui.create_refresh_button(dropdown, lambda: None, lambda: {'choices': get_available_models()}, 'refresh-button')
- basemodel = gr.Radio(["small", "medium", "melody", "large"], label="Base Model", value="medium", interactive=True, scale=1)
- with gr.Row():
- decoder = gr.Radio(["Default", "MultiBand_Diffusion"], label="Decoder", value="Default", interactive=True)
- with gr.Row():
- topk = gr.Number(label="Top-k", value=250, interactive=True)
- topp = gr.Number(label="Top-p", value=0, interactive=True)
- temperature = gr.Number(label="Temperature", value=1.0, interactive=True)
- cfg_coef = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
- with gr.Row():
- submit = gr.Button("Generate", variant="primary")
- # Adapted from https://github.com/rkfg/audiocraft/blob/long/app.py, MIT license.
- _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
- with gr.Column() as c:
- with gr.Tab("Output"):
- output = gr.Video(label="Generated Music", scale=0)
- with gr.Row():
- audio_only = gr.Audio(type="numpy", label="Audio Only", interactive=False)
- backup_only = gr.Audio(type="numpy", label="Backup Audio", interactive=False, visible=False)
- send_audio = gr.Button("Send to Input Audio")
- seed_used = gr.Number(label='Seed used', value=-1, interactive=False)
- download = gr.File(label="Generated Files", interactive=False)
- with gr.Tab("Wiki"):
- gr.Markdown(
- """
- - **[Generate (button)]:**
- Generates the music with the given settings and prompts.
-
- - **[Interrupt (button)]:**
- Stops the music generation as soon as it can, providing an incomplete output.
-
- ---
-
- ### Generation Tab:
-
- #### Structure Prompts:
-
- This feature helps reduce repetetive prompts by allowing you to set global prompts
- that will be used for all prompt segments.
-
- - **[Structure Prompts (checkbox)]:**
- Enable/Disable the structure prompts feature.
-
- - **[BPM (number)]:**
- Beats per minute of the generated music.
-
- - **[Key (dropdown)]:**
- The key of the generated music.
-
- - **[Scale (dropdown)]:**
- The scale of the generated music.
-
- - **[Global Prompt (text)]:**
- Here write the prompt that you wish to be used for all prompt segments.
-
- #### Multi-Prompt:
-
- This feature allows you to control the music, adding variation to different time segments.
- You have up to 10 prompt segments. the first prompt will always be 30s long
- the other prompts will be [30s - overlap].
- for example if the overlap is 10s, each prompt segment will be 20s.
-
- - **[Prompt Segments (number)]:**
- Amount of unique prompt to generate throughout the music generation.
-
- - **[Prompt/Input Text (prompt)]:**
- Here describe the music you wish the model to generate.
-
- - **[Repeat (number)]:**
- Write how many times this prompt will repeat (instead of wasting another prompt segment on the same prompt).
-
- - **[Time (text)]:**
- The time of the prompt segment.
-
- - **[Calculate Timings (button)]:**
- Calculates the timings of the prompt segments.
-
- - **[Duration (number)]:**
- How long you want the generated music to be (in seconds).
-
- - **[Overlap (number)]:**
- How much each new segment will reference the previous segment (in seconds).
- For example, if you choose 20s: Each new segment after the first one will reference the previous segment 20s
- and will generate only 10s of new music. The model can only process 30s of music.
-
- - **[Seed (number)]:**
- Your generated music id. If you wish to generate the exact same music,
- place the exact seed with the exact prompts
- (This way you can also extend specific song that was generated short).
-
- - **[Random Seed (button)]:**
- Gives "-1" as a seed, which counts as a random seed.
-
- - **[Copy Previous Seed (button)]:**
- Copies the seed from the output seed (if you don't feel like doing it manualy).
-
- ---
-
- ### Audio Tab:
-
- - **[Input Type (selection)]:**
- `File` mode allows you to upload an audio file to use as input
- `Mic` mode allows you to use your microphone as input
-
- - **[Input Audio Mode (selection)]:**
- `Melody` mode only works with the melody model: it conditions the music generation to reference the melody
- `Sample` mode works with any model: it gives a music sample to the model to generate its continuation.
-
- - **[Trim Start and Trim End (numbers)]:**
- `Trim Start` set how much you'd like to trim the input audio from the start
- `Trim End` same as the above but from the end
-
- - **[Input Audio (audio file)]:**
- Input here the audio you wish to use with "melody" or "sample" mode.
-
- ---
-
- ### Customization Tab:
-
- - **[Background Color (color)]:**
- Works only if you don't upload image. Color of the background of the waveform.
-
- - **[Bar Color Start (color)]:**
- First color of the waveform bars.
-
- - **[Bar Color End (color)]:**
- Second color of the waveform bars.
-
- - **[Background Image (image)]:**
- Background image that you wish to be attached to the generated video along with the waveform.
-
- - **[Height and Width (numbers)]:**
- Output video resolution, only works with image.
- (minimum height and width is 256).
-
- ---
-
- ### Settings Tab:
-
- - **[Output Audio Channels (selection)]:**
- With this you can select the amount of channels that you wish for your output audio.
- `mono` is a straightforward single channel audio
- `stereo` is a dual channel audio but it will sound more or less like mono
- `stereo effect` this one is also dual channel but uses tricks to simulate a stereo audio.
-
- - **[Output Audio Sample Rate (dropdown)]:**
- The output audio sample rate, the model default is 32000.
-
- - **[Model (selection)]:**
- Here you can choose which model you wish to use:
- `melody` model is based on the medium model with a unique feature that lets you use melody conditioning
- `small` model is trained on 300M parameters
- `medium` model is trained on 1.5B parameters
- `large` model is trained on 3.3B parameters
- `custom` model runs the custom model that you provided.
-
- - **[Custom Model (selection)]:**
- This dropdown will show you models that are placed in the `models` folder
- you must select `custom` in the model options in order to use it.
-
- - **[Refresh (button)]:**
- Refreshes the dropdown list for custom model.
-
- - **[Base Model (selection)]:**
- Choose here the model that your custom model is based on.
-
- - **[Decoder (selection)]:**
- Choose here the decoder that you wish to use:
- `Default` is the default decoder
- `MultiBand_Diffusion` is a decoder that uses diffusion to generate the audio.
-
- - **[Top-k (number)]:**
- is a parameter used in text generation models, including music generation models. It determines the number of most likely next tokens to consider at each step of the generation process. The model ranks all possible tokens based on their predicted probabilities, and then selects the top-k tokens from the ranked list. The model then samples from this reduced set of tokens to determine the next token in the generated sequence. A smaller value of k results in a more focused and deterministic output, while a larger value of k allows for more diversity in the generated music.
-
- - **[Top-p (number)]:**
- also known as nucleus sampling or probabilistic sampling, is another method used for token selection during text generation. Instead of specifying a fixed number like top-k, top-p considers the cumulative probability distribution of the ranked tokens. It selects the smallest possible set of tokens whose cumulative probability exceeds a certain threshold (usually denoted as p). The model then samples from this set to choose the next token. This approach ensures that the generated output maintains a balance between diversity and coherence, as it allows for a varying number of tokens to be considered based on their probabilities.
-
- - **[Temperature (number)]:**
- is a parameter that controls the randomness of the generated output. It is applied during the sampling process, where a higher temperature value results in more random and diverse outputs, while a lower temperature value leads to more deterministic and focused outputs. In the context of music generation, a higher temperature can introduce more variability and creativity into the generated music, but it may also lead to less coherent or structured compositions. On the other hand, a lower temperature can produce more repetitive and predictable music.
-
- - **[Classifier Free Guidance (number)]:**
- refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
- """
- )
- with gr.Tab("AudioGen"):
- gr.Markdown(
- """
- ### AudioGen
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Tab("Generation"):
- with gr.Accordion("Structure Prompts", open=False):
- with gr.Row():
- struc_prompts_a = gr.Checkbox(label="Enable", value=False, interactive=True, container=False)
- global_prompt_a = gr.Text(label="Global Prompt", interactive=True, scale=3)
- with gr.Row():
- s_a = gr.Slider(1, max_textboxes, value=1, step=1, label="Prompts:", interactive=True, scale=2)
- with gr.Column():
- textboxes_a = []
- prompts_a = []
- repeats_a = []
- calcs_a = []
- with gr.Row():
- text0_a = gr.Text(label="Input Text", interactive=True, scale=4)
- prompts_a.append(text0_a)
- drag0_a = gr.Number(label="Repeat", value=1, interactive=True, scale=1)
- repeats_a.append(drag0_a)
- calc0_a = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- calcs_a.append(calc0_a)
- for i in range(max_textboxes):
- with gr.Row(visible=False) as t_a:
- text_a = gr.Text(label="Input Text", interactive=True, scale=3)
- repeat_a = gr.Number(label="Repeat", minimum=1, value=1, interactive=True, scale=1)
- calc_a = gr.Text(interactive=False, value="00:00 - 00:00", scale=1, label="Time")
- textboxes_a.append(t_a)
- prompts_a.append(text_a)
- repeats_a.append(repeat_a)
- calcs_a.append(calc_a)
- to_calc_a = gr.Button("Calculate Timings", variant="secondary")
- with gr.Row():
- duration_a = gr.Slider(minimum=1, maximum=300, value=10, step=1, label="Duration", interactive=True)
- with gr.Row():
- overlap_a = gr.Slider(minimum=1, maximum=9, value=2, step=1, label="Overlap", interactive=True)
- with gr.Row():
- seed_a = gr.Number(label="Seed", value=-1, scale=4, precision=0, interactive=True)
- gr.Button('\U0001f3b2\ufe0f', scale=1).click(fn=lambda: -1, outputs=[seed_a], queue=False)
- reuse_seed_a = gr.Button('\u267b\ufe0f', scale=1)
-
- with gr.Tab("Audio"):
- with gr.Row():
- with gr.Column():
- input_type_a = gr.Radio(["file", "mic"], value="file", label="Input Type (optional)", interactive=True)
- mode_a = gr.Radio(["sample"], label="Input Audio Mode (optional)", value="sample", interactive=False, visible=False)
- with gr.Row():
- trim_start_a = gr.Number(label="Trim Start", value=0, interactive=True)
- trim_end_a = gr.Number(label="Trim End", value=0, interactive=True)
- audio_a = gr.Audio(source="upload", type="numpy", label="Input Audio (optional)", interactive=True)
-
- with gr.Tab("Customization"):
- with gr.Row():
- with gr.Column():
- background_a = gr.ColorPicker(value="#0f0f0f", label="background color", interactive=True, scale=0)
- bar1_a = gr.ColorPicker(value="#84cc16", label="bar color start", interactive=True, scale=0)
- bar2_a = gr.ColorPicker(value="#10b981", label="bar color end", interactive=True, scale=0)
- with gr.Column():
- image_a = gr.Image(label="Background Image", type="filepath", interactive=True, scale=4)
- with gr.Row():
- height_a = gr.Number(label="Height", value=512, interactive=True)
- width_a = gr.Number(label="Width", value=768, interactive=True)
-
- with gr.Tab("Settings"):
- with gr.Row():
- channel_a = gr.Radio(["mono", "stereo", "stereo effect"], label="Output Audio Channels", value="stereo", interactive=True, scale=1)
- sr_select_a = gr.Dropdown(["11025", "16000", "22050", "24000", "32000", "44100", "48000"], label="Output Audio Sample Rate", value="48000", interactive=True)
- with gr.Row():
- model_a = gr.Radio(["medium"], label="Model", value="medium", interactive=False, visible=False)
- decoder_a = gr.Radio(["Default"], label="Decoder", value="Default", interactive=False, visible=False)
- with gr.Row():
- topk_a = gr.Number(label="Top-k", value=250, interactive=True)
- topp_a = gr.Number(label="Top-p", value=0, interactive=True)
- temperature_a = gr.Number(label="Temperature", value=1.0, interactive=True)
- cfg_coef_a = gr.Number(label="Classifier Free Guidance", value=3.0, interactive=True)
- with gr.Row():
- submit_a = gr.Button("Generate", variant="primary")
- _ = gr.Button("Interrupt").click(fn=interrupt, queue=False)
- with gr.Column():
- with gr.Tab("Output"):
- output_a = gr.Video(label="Generated Audio", scale=0)
- with gr.Row():
- audio_only_a = gr.Audio(type="numpy", label="Audio Only", interactive=False)
- backup_only_a = gr.Audio(type="numpy", label="Backup Audio", interactive=False, visible=False)
- send_audio_a = gr.Button("Send to Input Audio")
- seed_used_a = gr.Number(label='Seed used', value=-1, interactive=False)
- download_a = gr.File(label="Generated Files", interactive=False)
- with gr.Tab("Wiki"):
- gr.Markdown(
- """
- - **[Generate (button)]:**
- Generates the audio with the given settings and prompts.
-
- - **[Interrupt (button)]:**
- Stops the audio generation as soon as it can, providing an incomplete output.
-
- ---
-
- ### Generation Tab:
-
- #### Structure Prompts:
-
- This feature helps reduce repetetive prompts by allowing you to set global prompts
- that will be used for all prompt segments.
-
- - **[Structure Prompts (checkbox)]:**
- Enable/Disable the structure prompts feature.
-
- - **[Global Prompt (text)]:**
- Here write the prompt that you wish to be used for all prompt segments.
-
- #### Multi-Prompt:
-
- This feature allows you to control the audio, adding variation to different time segments.
- You have up to 10 prompt segments. the first prompt will always be 10s long
- the other prompts will be [10s - overlap].
- for example if the overlap is 2s, each prompt segment will be 8s.
-
- - **[Prompt Segments (number)]:**
- Amount of unique prompt to generate throughout the audio generation.
-
- - **[Prompt/Input Text (prompt)]:**
- Here describe the audio you wish the model to generate.
-
- - **[Repeat (number)]:**
- Write how many times this prompt will repeat (instead of wasting another prompt segment on the same prompt).
-
- - **[Time (text)]:**
- The time of the prompt segment.
-
- - **[Calculate Timings (button)]:**
- Calculates the timings of the prompt segments.
-
- - **[Duration (number)]:**
- How long you want the generated audio to be (in seconds).
-
- - **[Overlap (number)]:**
- How much each new segment will reference the previous segment (in seconds).
- For example, if you choose 2s: Each new segment after the first one will reference the previous segment 2s
- and will generate only 8s of new audio. The model can only process 10s of music.
-
- - **[Seed (number)]:**
- Your generated audio id. If you wish to generate the exact same audio,
- place the exact seed with the exact prompts
- (This way you can also extend specific song that was generated short).
-
- - **[Random Seed (button)]:**
- Gives "-1" as a seed, which counts as a random seed.
-
- - **[Copy Previous Seed (button)]:**
- Copies the seed from the output seed (if you don't feel like doing it manualy).
-
- ---
-
- ### Audio Tab:
-
- - **[Input Type (selection)]:**
- `File` mode allows you to upload an audio file to use as input
- `Mic` mode allows you to use your microphone as input
-
- - **[Trim Start and Trim End (numbers)]:**
- `Trim Start` set how much you'd like to trim the input audio from the start
- `Trim End` same as the above but from the end
-
- - **[Input Audio (audio file)]:**
- Input here the audio you wish to use.
-
- ---
-
- ### Customization Tab:
-
- - **[Background Color (color)]:**
- Works only if you don't upload image. Color of the background of the waveform.
-
- - **[Bar Color Start (color)]:**
- First color of the waveform bars.
-
- - **[Bar Color End (color)]:**
- Second color of the waveform bars.
-
- - **[Background Image (image)]:**
- Background image that you wish to be attached to the generated video along with the waveform.
-
- - **[Height and Width (numbers)]:**
- Output video resolution, only works with image.
- (minimum height and width is 256).
-
- ---
-
- ### Settings Tab:
-
- - **[Output Audio Channels (selection)]:**
- With this you can select the amount of channels that you wish for your output audio.
- `mono` is a straightforward single channel audio
- `stereo` is a dual channel audio but it will sound more or less like mono
- `stereo effect` this one is also dual channel but uses tricks to simulate a stereo audio.
-
- - **[Output Audio Sample Rate (dropdown)]:**
- The output audio sample rate, the model default is 32000.
-
- - **[Top-k (number)]:**
- is a parameter used in text generation models, including music generation models. It determines the number of most likely next tokens to consider at each step of the generation process. The model ranks all possible tokens based on their predicted probabilities, and then selects the top-k tokens from the ranked list. The model then samples from this reduced set of tokens to determine the next token in the generated sequence. A smaller value of k results in a more focused and deterministic output, while a larger value of k allows for more diversity in the generated music.
-
- - **[Top-p (number)]:**
- also known as nucleus sampling or probabilistic sampling, is another method used for token selection during text generation. Instead of specifying a fixed number like top-k, top-p considers the cumulative probability distribution of the ranked tokens. It selects the smallest possible set of tokens whose cumulative probability exceeds a certain threshold (usually denoted as p). The model then samples from this set to choose the next token. This approach ensures that the generated output maintains a balance between diversity and coherence, as it allows for a varying number of tokens to be considered based on their probabilities.
-
- - **[Temperature (number)]:**
- is a parameter that controls the randomness of the generated output. It is applied during the sampling process, where a higher temperature value results in more random and diverse outputs, while a lower temperature value leads to more deterministic and focused outputs. In the context of music generation, a higher temperature can introduce more variability and creativity into the generated music, but it may also lead to less coherent or structured compositions. On the other hand, a lower temperature can produce more repetitive and predictable music.
-
- - **[Classifier Free Guidance (number)]:**
- refers to a technique used in some music generation models where a separate classifier network is trained to provide guidance or control over the generated music. This classifier is trained on labeled data to recognize specific musical characteristics or styles. During the generation process, the output of the generator model is evaluated by the classifier, and the generator is encouraged to produce music that aligns with the desired characteristics or style. This approach allows for more fine-grained control over the generated music, enabling users to specify certain attributes they want the model to capture.
- """
- )
- with gr.Tab("Audio Info"):
- gr.Markdown(
- """
- ### Audio Info
- """
- )
- with gr.Row():
- with gr.Column():
- in_audio = gr.File(type="file", label="Input Any Audio", interactive=True)
- with gr.Row():
- send_gen = gr.Button("Send to MusicGen", variant="primary")
- send_gen_a = gr.Button("Send to AudioGen", variant="primary")
- with gr.Column():
- info = gr.Textbox(label="Audio Info", lines=10, interactive=False)
- with gr.Tab("Changelog"):
- gr.Markdown(
- """
- ## Changelog:
-
- ### v2.0.0a
-
- - Forgot to move all the update to app.py from temp2.py... oops
-
-
-
- ### v2.0.0
-
- - Changed name from MusicGen+ to AudioCraft Plus
-
- - Complete overhaul of the repo "backend" with the latest changes from the main facebookresearch repo
-
- - Added a new decoder: MultiBand_Diffusion
-
- - Added AudioGen: a new tab for generating audio
-
-
-
- ### v1.2.8c
-
- - Implemented Reverse compatibility for audio info tab with previous versions
-
-
-
- ### v1.2.8b
-
- - Fixed the error when loading default models
-
-
-
- ### v1.2.8a
-
- - Adapted Audio info tab to work with the new structure prompts feature
-
- - Now custom models actually work, make sure you select the correct base model
-
-
-
- ### v1.2.8
-
- - Now you will also recieve json file with metadata of generated audio
-
- - Added error messages in Audio Info tab
-
- - Added structure prompts: you can select bpm, key and global prompt for all prompts
-
- - Added time display next to each prompt, can be calculated with "Calculate Timings" button
-
-
-
- ### v1.2.7
-
- - When sending generated audio to Input Audio, it will send a backup audio with default settings
- (best for continuos generation)
-
- - Added Metadata to generated audio (Thanks to AlexHK ♥)
-
- - Added Audio Info tab that will display the metadata of the input audio
-
- - Added "send to Text2Audio" button in Audio Info tab
-
- - Generated audio is now stored in the "output" folder (Thanks to AlexHK ♥)
-
- - Added an output area with generated files and download buttons
-
- - Enhanced Stereo effect (Thanks to AlexHK ♥)
-
-
-
- ### v1.2.6
-
- - Added option to generate in stereo (instead of only mono)
-
- - Added dropdown for selecting output sample rate (model default is 32000)
-
-
-
- ### v1.2.5a
-
- - Added file cleaner (This comes from the main facebookresearch repo)
-
- - Reorganized a little, moved audio to a seperate tab
-
-
-
- ### v1.2.5
-
- - Gave a unique lime theme to the webui
-
- - Added additional output for audio only
-
- - Added button to send generated audio to Input Audio
-
- - Added option to trim Input Audio
-
-
-
- ### v1.2.4
-
- - Added mic input (This comes from the main facebookresearch repo)
-
-
-
- ### v1.2.3
-
- - Added option to change video size to fit the image you upload
-
-
-
- ### v1.2.2
-
- - Added Wiki, Changelog and About tabs
-
-
-
- ### v1.2.1
-
- - Added tabs and organized the entire interface
-
- - Added option to attach image to the output video
-
- - Added option to load fine-tuned models (Yet to be tested)
-
-
-
- ### v1.2.0
-
- - Added Multi-Prompt
-
-
-
- ### v1.1.3
-
- - Added customization options for generated waveform
-
-
-
- ### v1.1.2
-
- - Removed sample length limit: now you can input audio of any length as music sample
-
-
-
- ### v1.1.1
-
- - Improved music sample audio quality when using music continuation
-
-
-
- ### v1.1.0
-
- - Rebuilt the repo on top of the latest structure of the main MusicGen repo
-
- - Improved Music continuation feature
-
-
-
- ### v1.0.0 - Stable Version
-
- - Added Music continuation
- """
- )
- with gr.Tab("About"):
- gen_type = gr.Text(value="music", interactive=False, visible=False)
- gen_type_a = gr.Text(value="audio", interactive=False, visible=False)
- gr.Markdown(
- """
- This is your private demo for [MusicGen](https://github.com/facebookresearch/audiocraft), a simple and controllable model for music generation
- presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284)
-
- ## MusicGen+ is an extended version of the original MusicGen by facebookresearch.
-
- ### Repo: https://github.com/GrandaddyShmax/audiocraft_plus/tree/plus
-
- ---
-
- ### This project was possible thanks to:
-
- #### GrandaddyShmax - https://github.com/GrandaddyShmax
-
- #### Camenduru - https://github.com/camenduru
-
- #### rkfg - https://github.com/rkfg
-
- #### oobabooga - https://github.com/oobabooga
-
- #### AlexHK - https://github.com/alanhk147
- """
- )
-
- send_gen.click(info_to_params, inputs=[in_audio], outputs=[decoder, struc_prompts, global_prompt, bpm, key, scale, model, dropdown, basemodel, s, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], mode, duration, topk, topp, temperature, cfg_coef, seed, overlap, channel, sr_select], queue=False)
- reuse_seed.click(fn=lambda x: x, inputs=[seed_used], outputs=[seed], queue=False)
- send_audio.click(fn=lambda x: x, inputs=[backup_only], outputs=[audio], queue=False)
- submit.click(predict_full, inputs=[gen_type, model, decoder, dropdown, basemodel, s, struc_prompts, bpm, key, scale, global_prompt, prompts[0], prompts[1], prompts[2], prompts[3], prompts[4], prompts[5], prompts[6], prompts[7], prompts[8], prompts[9], repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9], audio, mode, trim_start, trim_end, duration, topk, topp, temperature, cfg_coef, seed, overlap, image, height, width, background, bar1, bar2, channel, sr_select], outputs=[output, audio_only, backup_only, download, seed_used])
- input_type.change(toggle_audio_src, input_type, [audio], queue=False, show_progress=False)
- to_calc.click(calc_time, inputs=[gen_type, s, duration, overlap, repeats[0], repeats[1], repeats[2], repeats[3], repeats[4], repeats[5], repeats[6], repeats[7], repeats[8], repeats[9]], outputs=[calcs[0], calcs[1], calcs[2], calcs[3], calcs[4], calcs[5], calcs[6], calcs[7], calcs[8], calcs[9]], queue=False)
-
- send_gen_a.click(info_to_params_a, inputs=[in_audio], outputs=[decoder_a, struc_prompts_a, global_prompt_a, s_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, channel_a, sr_select_a], queue=False)
- reuse_seed_a.click(fn=lambda x: x, inputs=[seed_used_a], outputs=[seed_a], queue=False)
- send_audio_a.click(fn=lambda x: x, inputs=[backup_only_a], outputs=[audio_a], queue=False)
- submit_a.click(predict_full, inputs=[gen_type_a, model_a, decoder_a, dropdown, basemodel, s_a, struc_prompts_a, bpm, key, scale, global_prompt_a, prompts_a[0], prompts_a[1], prompts_a[2], prompts_a[3], prompts_a[4], prompts_a[5], prompts_a[6], prompts_a[7], prompts_a[8], prompts_a[9], repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9], audio_a, mode_a, trim_start_a, trim_end_a, duration_a, topk_a, topp_a, temperature_a, cfg_coef_a, seed_a, overlap_a, image_a, height_a, width_a, background_a, bar1_a, bar2_a, channel_a, sr_select_a], outputs=[output_a, audio_only_a, backup_only_a, download_a, seed_used_a])
- input_type_a.change(toggle_audio_src, input_type_a, [audio_a], queue=False, show_progress=False)
- to_calc_a.click(calc_time, inputs=[gen_type_a, s_a, duration_a, overlap_a, repeats_a[0], repeats_a[1], repeats_a[2], repeats_a[3], repeats_a[4], repeats_a[5], repeats_a[6], repeats_a[7], repeats_a[8], repeats_a[9]], outputs=[calcs_a[0], calcs_a[1], calcs_a[2], calcs_a[3], calcs_a[4], calcs_a[5], calcs_a[6], calcs_a[7], calcs_a[8], calcs_a[9]], queue=False)
-
- in_audio.change(get_audio_info, in_audio, outputs=[info])
-
- def variable_outputs(k):
- k = int(k) - 1
- return [gr.Textbox.update(visible=True)]*k + [gr.Textbox.update(visible=False)]*(max_textboxes-k)
- def get_size(image):
- if image is not None:
- img = Image.open(image)
- img_height = img.height
- img_width = img.width
- if (img_height%2) != 0:
- img_height = img_height + 1
- if (img_width%2) != 0:
- img_width = img_width + 1
- return img_height, img_width
- else:
- return 512, 768
-
- image.change(get_size, image, outputs=[height, width])
- image_a.change(get_size, image_a, outputs=[height_a, width_a])
- s.change(variable_outputs, s, textboxes)
- s_a.change(variable_outputs, s_a, textboxes_a)
- interface.queue().launch(**launch_kwargs)
-
-
-def ui_batched(launch_kwargs):
- with gr.Blocks() as demo:
- gr.Markdown(
- """
- # MusicGen
-
- This is the demo for [MusicGen](https://github.com/facebookresearch/audiocraft),
- a simple and controllable model for music generation
- presented at: ["Simple and Controllable Music Generation"](https://huggingface.co/papers/2306.05284).
-
-
-
- for longer sequences, more control and no queue.
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Row():
- text = gr.Text(label="Describe your music", lines=2, interactive=True)
- with gr.Column():
- radio = gr.Radio(["file", "mic"], value="file",
- label="Condition on a melody (optional) File or Mic")
- melody = gr.Audio(source="upload", type="numpy", label="File",
- interactive=True, elem_id="melody-input")
- with gr.Row():
- submit = gr.Button("Generate")
- with gr.Column():
- output = gr.Video(label="Generated Music")
- audio_output = gr.Audio(label="Generated Music (wav)", type='filepath')
- submit.click(predict_batched, inputs=[text, melody],
- outputs=[output, audio_output], batch=True, max_batch_size=MAX_BATCH_SIZE)
- radio.change(toggle_audio_src, radio, [melody], queue=False, show_progress=False)
- gr.Examples(
- fn=predict_batched,
- examples=[
- [
- "An 80s driving pop song with heavy drums and synth pads in the background",
- "./assets/bach.mp3",
- ],
- [
- "A cheerful country song with acoustic guitars",
- "./assets/bolero_ravel.mp3",
- ],
- [
- "90s rock song with electric guitar and heavy drums",
- None,
- ],
- [
- "a light and cheerly EDM track, with syncopated drums, aery pads, and strong emotions bpm: 130",
- "./assets/bach.mp3",
- ],
- [
- "lofi slow bpm electro chill with organic samples",
- None,
- ],
- ],
- inputs=[text, melody],
- outputs=[output]
- )
- gr.Markdown("""
- ### More details
-
- The model will generate 12 seconds of audio based on the description you provided.
- You can optionally provide a reference audio from which a broad melody will be extracted.
- The model will then try to follow both the description and melody provided.
- All samples are generated with the `melody` model.
-
- You can also use your own GPU or a Google Colab by following the instructions on our repo.
-
- See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
- for more details.
- """)
-
- demo.queue(max_size=8 * 4).launch(**launch_kwargs)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument(
- '--listen',
- type=str,
- default='0.0.0.0' if 'SPACE_ID' in os.environ else '127.0.0.1',
- help='IP to listen on for connections to Gradio',
- )
- parser.add_argument(
- '--username', type=str, default='', help='Username for authentication'
- )
- parser.add_argument(
- '--password', type=str, default='', help='Password for authentication'
- )
- parser.add_argument(
- '--server_port',
- type=int,
- default=0,
- help='Port to run the server listener on',
- )
- parser.add_argument(
- '--inbrowser', action='store_true', help='Open in browser'
- )
- parser.add_argument(
- '--share', action='store_true', help='Share the gradio UI'
- )
- parser.add_argument(
- '--unload_model', action='store_true', help='Unload the model after every generation to save GPU memory'
- )
-
- parser.add_argument(
- '--unload_to_cpu', action='store_true', help='Move the model to main RAM after every generation to save GPU memory but reload faster than after full unload (see above)'
- )
-
- parser.add_argument(
- '--cache', action='store_true', help='Cache models in RAM to quickly switch between them'
- )
-
- args = parser.parse_args()
- UNLOAD_MODEL = args.unload_model
- MOVE_TO_CPU = args.unload_to_cpu
- if args.cache:
- MODELS = {}
-
- launch_kwargs = {}
- launch_kwargs['server_name'] = args.listen
-
- if args.username and args.password:
- launch_kwargs['auth'] = (args.username, args.password)
- if args.server_port:
- launch_kwargs['server_port'] = args.server_port
- if args.inbrowser:
- launch_kwargs['inbrowser'] = args.inbrowser
- if args.share:
- launch_kwargs['share'] = args.share
-
- # Show the interface
- if IS_BATCHED:
- global USE_DIFFUSION
- USE_DIFFUSION = False
- ui_batched(launch_kwargs)
- else:
- ui_full(launch_kwargs)
\ No newline at end of file
diff --git a/spaces/PunPk/AI_FallingAsleepDriving/README.md b/spaces/PunPk/AI_FallingAsleepDriving/README.md
deleted file mode 100644
index 0855483dc5cdc696af1fe1d3502b584b42fde086..0000000000000000000000000000000000000000
--- a/spaces/PunPk/AI_FallingAsleepDriving/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: AI FallingAsleepDriving
-emoji: 📉
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RMXK/RVC_HFF/tensorlowest.py b/spaces/RMXK/RVC_HFF/tensorlowest.py
deleted file mode 100644
index eccd4dbf3494434e59f7defaae6ab91797263b90..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/tensorlowest.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from tensorboard.backend.event_processing import event_accumulator
-
-import os
-from shutil import copy2
-from re import search as RSearch
-import pandas as pd
-from ast import literal_eval as LEval
-
-weights_dir = 'weights/'
-
-def find_biggest_tensorboard(tensordir):
- try:
- files = [f for f in os.listdir(tensordir) if f.endswith('.0')]
- if not files:
- print("No files with the '.0' extension found!")
- return
-
- max_size = 0
- biggest_file = ""
-
- for file in files:
- file_path = os.path.join(tensordir, file)
- if os.path.isfile(file_path):
- file_size = os.path.getsize(file_path)
- if file_size > max_size:
- max_size = file_size
- biggest_file = file
-
- return biggest_file
-
- except FileNotFoundError:
- print("Couldn't find your model!")
- return
-
-def main(model_name, save_freq, lastmdls):
- global lowestval_weight_dir, scl
-
- tensordir = os.path.join('logs', model_name)
- lowestval_weight_dir = os.path.join(tensordir, "lowestvals")
-
- latest_file = find_biggest_tensorboard(tensordir)
-
- if latest_file is None:
- print("Couldn't find a valid tensorboard file!")
- return
-
- tfile = os.path.join(tensordir, latest_file)
-
- ea = event_accumulator.EventAccumulator(tfile,
- size_guidance={
- event_accumulator.COMPRESSED_HISTOGRAMS: 500,
- event_accumulator.IMAGES: 4,
- event_accumulator.AUDIO: 4,
- event_accumulator.SCALARS: 0,
- event_accumulator.HISTOGRAMS: 1,
- })
-
- ea.Reload()
- ea.Tags()
-
- scl = ea.Scalars('loss/g/total')
-
- listwstep = {}
-
- for val in scl:
- if (val.step // save_freq) * save_freq in [val.step for val in scl]:
- listwstep[float(val.value)] = (val.step // save_freq) * save_freq
-
- lowest_vals = sorted(listwstep.keys())[:lastmdls]
-
- sorted_dict = {value: step for value, step in listwstep.items() if value in lowest_vals}
-
- return sorted_dict
-
-def selectweights(model_name, file_dict, weights_dir, lowestval_weight_dir):
- os.makedirs(lowestval_weight_dir, exist_ok=True)
- logdir = []
- files = []
- lbldict = {
- 'Values': {},
- 'Names': {}
- }
- weights_dir_path = os.path.join(weights_dir, "")
- low_val_path = os.path.join(os.getcwd(), os.path.join(lowestval_weight_dir, ""))
-
- try:
- file_dict = LEval(file_dict)
- except Exception as e:
- print(f"Error! {e}")
- return f"Couldn't load tensorboard file! {e}"
-
- weights = [f for f in os.scandir(weights_dir)]
- for key, value in file_dict.items():
- pattern = fr"^{model_name}_.*_s{value}\.pth$"
- matching_weights = [f.name for f in weights if f.is_file() and RSearch(pattern, f.name)]
- for weight in matching_weights:
- source_path = weights_dir_path + weight
- destination_path = os.path.join(lowestval_weight_dir, weight)
-
- copy2(source_path, destination_path)
-
- logdir.append(f"File = {weight} Value: {key}, Step: {value}")
-
- lbldict['Names'][weight] = weight
- lbldict['Values'][weight] = key
-
- files.append(low_val_path + weight)
-
- print(f"File = {weight} Value: {key}, Step: {value}")
-
- yield ('\n'.join(logdir), files, pd.DataFrame(lbldict))
-
-
- return ''.join(logdir), files, pd.DataFrame(lbldict)
-
-
-if __name__ == "__main__":
- model = str(input("Enter the name of the model: "))
- sav_freq = int(input("Enter save frequency of the model: "))
- ds = main(model, sav_freq)
-
- if ds: selectweights(model, ds, weights_dir, lowestval_weight_dir)
-
\ No newline at end of file
diff --git a/spaces/Rekanice/hf_minimal_sushi/README.md b/spaces/Rekanice/hf_minimal_sushi/README.md
deleted file mode 100644
index 4b038ea069e1817e717615da6679be0a913f0f5d..0000000000000000000000000000000000000000
--- a/spaces/Rekanice/hf_minimal_sushi/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Hf Minimal Sushi
-emoji: 🌍
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.1.4
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/RoCobo/WiggleGAN/main.py b/spaces/RoCobo/WiggleGAN/main.py
deleted file mode 100644
index 097c3b9aed9be52619095419859205b71110555c..0000000000000000000000000000000000000000
--- a/spaces/RoCobo/WiggleGAN/main.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import argparse
-import os
-import torch
-from WiggleGAN import WiggleGAN
-#from MyACGAN import MyACGAN
-#from MyGAN import MyGAN
-
-"""parsing and configuration"""
-
-
-def parse_args():
- desc = "Pytorch implementation of GAN collections"
- parser = argparse.ArgumentParser(description=desc)
-
- parser.add_argument('--gan_type', type=str, default='WiggleGAN',
- choices=['MyACGAN', 'MyGAN', 'WiggleGAN'],
- help='The type of GAN')
- parser.add_argument('--dataset', type=str, default='4cam',
- choices=['mnist', 'fashion-mnist', 'cifar10', 'cifar100', 'svhn', 'stl10', 'lsun-bed', '4cam'],
- help='The name of dataset')
- parser.add_argument('--split', type=str, default='', help='The split flag for svhn and stl10')
- parser.add_argument('--epoch', type=int, default=50, help='The number of epochs to run')
- parser.add_argument('--batch_size', type=int, default=16, help='The size of batch')
- parser.add_argument('--input_size', type=int, default=10, help='The size of input image')
- parser.add_argument('--save_dir', type=str, default='models',
- help='Directory name to save the model')
- parser.add_argument('--result_dir', type=str, default='results', help='Directory name to save the generated images')
- parser.add_argument('--log_dir', type=str, default='logs', help='Directory name to save training logs')
- parser.add_argument('--lrG', type=float, default=0.0002)
- parser.add_argument('--lrD', type=float, default=0.001)
- parser.add_argument('--beta1', type=float, default=0.5)
- parser.add_argument('--beta2', type=float, default=0.999)
- parser.add_argument('--gpu_mode', type=str2bool, default=True)
- parser.add_argument('--benchmark_mode', type=str2bool, default=True)
- parser.add_argument('--cameras', type=int, default=2)
- parser.add_argument('--imageDim', type=int, default=128)
- parser.add_argument('--epochV', type=int, default=0)
- parser.add_argument('--cIm', type=int, default=4)
- parser.add_argument('--seedLoad', type=str, default="-0000")
- parser.add_argument('--zGF', type=float, default=0.2)
- parser.add_argument('--zDF', type=float, default=0.2)
- parser.add_argument('--bF', type=float, default=0.2)
- parser.add_argument('--expandGen', type=int, default=3)
- parser.add_argument('--expandDis', type=int, default=3)
- parser.add_argument('--wiggleDepth', type=int, default=-1)
- parser.add_argument('--visdom', type=str2bool, default=True)
- parser.add_argument('--lambdaL1', type=int, default=100)
- parser.add_argument('--clipping', type=float, default=-1)
- parser.add_argument('--depth', type=str2bool, default=True)
- parser.add_argument('--recreate', type=str2bool, default=False)
- parser.add_argument('--name_wiggle', type=str, default='wiggle-result')
-
- return check_args(parser.parse_args())
-
-
-"""checking arguments"""
-
-def str2bool(v):
- if isinstance(v, bool):
- return v
- if v.lower() in ('yes', 'true', 't', 'y', '1'):
- return True
- elif v.lower() in ('no', 'false', 'f', 'n', '0'):
- return False
- else:
- raise argparse.ArgumentTypeError('Boolean value expected.')
-
-
-def check_args(args):
- # --save_dir
- if not os.path.exists(args.save_dir):
- os.makedirs(args.save_dir)
-
- # --result_dir
- if not os.path.exists(args.result_dir):
- os.makedirs(args.result_dir)
-
- # --result_dir
- if not os.path.exists(args.log_dir):
- os.makedirs(args.log_dir)
-
- # --epoch
- try:
- assert args.epoch >= 1
- except:
- print('number of epochs must be larger than or equal to one')
-
- # --batch_size
- try:
- assert args.batch_size >= 1
- except:
- print('batch size must be larger than or equal to one')
-
- return args
-
-
-"""main"""
-
-
-def main():
- # parse arguments
- args = parse_args()
- if args is None:
- exit()
-
- if args.benchmark_mode:
- torch.backends.cudnn.benchmark = True
-
- # declare instance for GAN
- if args.gan_type == 'WiggleGAN':
- gan = WiggleGAN(args)
- #elif args.gan_type == 'MyACGAN':
- # gan = MyACGAN(args)
- #elif args.gan_type == 'MyGAN':
- # gan = MyGAN(args)
- else:
- raise Exception("[!] There is no option for " + args.gan_type)
-
- # launch the graph in a session
- if (args.wiggleDepth < 0 and not args.recreate):
- print(" [*] Training Starting!")
- gan.train()
- print(" [*] Training finished!")
- else:
- if not args.recreate:
- print(" [*] Wiggle Started!")
- gan.wiggleEf()
- print(" [*] Wiggle finished!")
- else:
- print(" [*] Dataset recreation Started")
- gan.recreate()
- print(" [*] Dataset recreation finished")
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py
deleted file mode 100644
index f2678b321c295bcceaef945111ac3524be19d6e4..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/utils/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .misc import add_prefix
-
-__all__ = ['add_prefix']
diff --git a/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py b/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py
deleted file mode 100644
index cdd69f2a24d47166425d785ef2767708b44b6593..0000000000000000000000000000000000000000
--- a/spaces/SAAZIZI/SummarizeAV/resource_loader/uploaded_media_loader.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from config import output_path_video, output_path_audio
-from logger import logger
-from resource_loader.video_loader_interface import VideoLoaderInterface
-
-
-class UploadedMediaLoader(VideoLoaderInterface):
- def __init__(self, uploaded_stream, original_name, media_type='video'):
- self.uploaded_stream = uploaded_stream
- self.original_name = original_name
- self.media_type = media_type # 'video' or 'audio'
- self.media_id = None
- self.filename = None
- self.output_path = None
- self.extract_filename()
- self.set_output_path()
-
- def extract_filename(self):
- self.filename = self.original_name.split(" - ", 1)[1]
- self.media_id = self.filename.rsplit(".", 1)[0]
-
- def set_output_path(self):
- if self.media_type == 'video':
- self.output_path = output_path_video
- elif self.media_type == 'audio':
- self.output_path = output_path_audio
- else:
- raise ValueError("Invalid media type")
-
- def download(self):
- with open(f"{self.output_path}/{self.filename}", "wb") as f:
- f.write(self.uploaded_stream.getvalue())
- logger.info(f"{self.media_type.capitalize()} processed: {self.original_name}")
diff --git a/spaces/STF-R/docker-test3/app/templates/prediction.html b/spaces/STF-R/docker-test3/app/templates/prediction.html
deleted file mode 100644
index 4366614474cbe8347297ec3d57a7b8b7bf72684d..0000000000000000000000000000000000000000
--- a/spaces/STF-R/docker-test3/app/templates/prediction.html
+++ /dev/null
@@ -1,48 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
- VIVADATA | {{iris_type}}
-
-
-
-
-
-
-
-
-
-
-
-
-
- VIVADATA - Flask Demo
-
-
-
- 🏵️🌺🌸 It's a {{iris_type|title}} 🏵️🌺🌸
-
-
-
-
-
-
-
- Back
-
-
-
-
-
-
-
-
diff --git a/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py b/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py
deleted file mode 100644
index 3706c57541c1b7d9004957422b52cd1e2191ae68..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# This file is autogenerated by the command `make fix-copies`, do not edit.
-# flake8: noqa
-
-from ..utils import DummyObject, requires_backends
-
-
-class LMSDiscreteScheduler(metaclass=DummyObject):
- _backends = ["scipy"]
-
- def __init__(self, *args, **kwargs):
- requires_backends(self, ["scipy"])
diff --git a/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py b/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py
deleted file mode 100644
index 2e25dd918f2714ae9a2c21d14401663c371b5f38..0000000000000000000000000000000000000000
--- a/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py
+++ /dev/null
@@ -1,704 +0,0 @@
-from typing import Callable, Optional, Tuple
-
-import flax.linen as nn
-import jax
-import jax.numpy as jnp
-from flax.core.frozen_dict import FrozenDict, unfreeze
-from jax import lax
-from jax.random import PRNGKey
-from transformers import GPT2Config, FlaxViTModel, ViTConfig
-from transformers.modeling_flax_outputs import (
- FlaxCausalLMOutputWithCrossAttentions,
- FlaxSeq2SeqLMOutput,
- FlaxSeq2SeqModelOutput,
-)
-from transformers.models.bart.modeling_flax_bart import (
- shift_tokens_right,
-)
-from .modeling_flax_gpt2 import (
- FlaxGPT2Module,
- FlaxGPT2Model,
- FlaxPreTrainedModel
-)
-from transformers.models.vit.modeling_flax_vit import FlaxViTModule
-
-from .configuration_vit_gpt2 import ViTGPT2Config
-
-
-class FlaxViTGPT2Module(nn.Module):
- config: ViTGPT2Config
- dtype: jnp.dtype = jnp.float32 # the dtype of the computation
-
- def setup(self):
-
- self.encoder = FlaxViTModule(self.config.vit_config, dtype=self.dtype)
- self.decoder = FlaxGPT2Module(self.config.gpt2_config, dtype=self.dtype)
-
- def _get_encoder_module(self):
- return self.encoder
-
- def _get_decoder_module(self):
- return self.decoder
-
- def __call__(
- self,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- output_attentions: bool = False,
- output_hidden_states: bool = False,
- return_dict: bool = True,
- deterministic: bool = True,
- ):
- encoder_outputs = self.encoder(
- pixel_values=pixel_values,
- deterministic=deterministic,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- )
-
- decoder_outputs = self.decoder(
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_hidden_states=encoder_outputs[0],
- encoder_attention_mask=encoder_attention_mask,
- deterministic=deterministic,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict
- )
-
- return FlaxSeq2SeqModelOutput(
- last_hidden_state=decoder_outputs.last_hidden_state,
- decoder_hidden_states=decoder_outputs.hidden_states,
- decoder_attentions=decoder_outputs.attentions,
- cross_attentions=decoder_outputs.cross_attentions,
- encoder_last_hidden_state=encoder_outputs.last_hidden_state,
- encoder_hidden_states=encoder_outputs.hidden_states,
- encoder_attentions=encoder_outputs.attentions,
- )
-
-
-class FlaxViTGPT2ForConditionalGenerationModule(nn.Module):
- config: ViTGPT2Config
- dtype: jnp.dtype = jnp.float32
- bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros
-
- def setup(self):
- self.model = FlaxViTGPT2Module(config=self.config, dtype=self.dtype)
- self.lm_head = nn.Dense(
- self.model.decoder.embed_dim,
- use_bias=False,
- dtype=self.dtype,
- kernel_init=jax.nn.initializers.normal(
- self.config.gpt2_config.initializer_range, self.dtype
- ),
- )
- self.final_logits_bias = self.param(
- "final_logits_bias", self.bias_init, (1, self.model.decoder.embed_dim)
- )
-
- def _get_encoder_module(self):
- return self.model.encoder
-
- def _get_decoder_module(self):
- return self.model.decoder
-
- def __call__(
- self,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- output_attentions: bool = False,
- output_hidden_states: bool = False,
- return_dict: bool = True,
- deterministic: bool = True,
- ):
- outputs = self.model(
- pixel_values=pixel_values,
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_attention_mask=encoder_attention_mask,
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=deterministic,
- )
-
- hidden_states = outputs[0]
- lm_logits = self.lm_head(hidden_states)
- lm_logits += self.final_logits_bias
-
- if not return_dict:
- output = (lm_logits,) + outputs[1:]
- return output
-
- return FlaxSeq2SeqLMOutput(
- logits=lm_logits,
- decoder_hidden_states=outputs.decoder_hidden_states,
- decoder_attentions=outputs.decoder_attentions,
- cross_attentions=outputs.cross_attentions,
- encoder_last_hidden_state=outputs.encoder_last_hidden_state,
- encoder_hidden_states=outputs.encoder_hidden_states,
- encoder_attentions=outputs.encoder_attentions,
- )
-
-class FlaxViTGPT2PreTrainedModel(FlaxPreTrainedModel):
- config_class = ViTGPT2Config
- base_model_prefix: str = "model"
- module_class: nn.Module = None
-
- def __init__(
- self,
- config: ViTGPT2Config,
- input_shape: Tuple = None,
- seed: int = 0,
- dtype: jnp.dtype = jnp.float32,
- **kwargs,
- ):
- if input_shape is None:
- input_shape = (
- (1, config.vit_config.image_size, config.vit_config.image_size, 3),
- (1, 1),
- )
-
- module = self.module_class(config=config, dtype=dtype, **kwargs)
- super().__init__(
- config, module, input_shape=input_shape, seed=seed, dtype=dtype
- )
-
- def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict:
- # init input tensors
- pixel_values = jax.random.normal(rng, input_shape[0])
- # # make sure initialization pass will work for FlaxBartForSequenceClassificationModule
- # input_ids = jax.ops.index_update(input_ids, (..., -1), self.config.eos_token_id)
-
- input_ids = jnp.zeros(input_shape[1], dtype="i4")
- attention_mask = jnp.ones_like(input_ids)
-
- batch_size, sequence_length = input_ids.shape
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- params_rng, dropout_rng = jax.random.split(rng)
- rngs = {"params": params_rng, "dropout": dropout_rng}
-
- return self.module.init(
- rngs,
- pixel_values,
- input_ids,
- attention_mask,
- position_ids,
- )["params"]
-
- def init_cache(self, batch_size, max_length, encoder_outputs):
-
- input_ids = jnp.ones((batch_size, max_length), dtype="i4")
- attention_mask = jnp.ones_like(input_ids)
- position_ids = jnp.broadcast_to(
- jnp.arange(jnp.atleast_2d(input_ids).shape[-1]),
- input_ids.shape,
- )
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- return decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
-
- init_variables = self.module.init(
- jax.random.PRNGKey(0),
- input_ids=input_ids,
- attention_mask=attention_mask,
- position_ids=position_ids,
- encoder_hidden_states=encoder_outputs[0],
- init_cache=True,
- method=_decoder_forward, # we only need to call the decoder to init the cache
- )
- return unfreeze(init_variables["cache"])
-
- def encode(
- self,
- pixel_values: jnp.ndarray,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- def _encoder_forward(module, pixel_values, **kwargs):
- encode_module = module._get_encoder_module()
- return encode_module(pixel_values, **kwargs)
-
- return self.module.apply(
- {"params": params or self.params},
- pixel_values=jnp.array(pixel_values, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- method=_encoder_forward,
- )
-
- def decode(
- self,
- input_ids,
- encoder_outputs,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- past_key_values: dict = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
-
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- encoder_hidden_states = encoder_outputs[0]
- if encoder_attention_mask is None:
- batch_size, sequence_length = encoder_hidden_states.shape[:2]
- encoder_attention_mask = jnp.ones((batch_size, sequence_length))
-
- batch_size, sequence_length = input_ids.shape
- if attention_mask is None:
- attention_mask = jnp.ones((batch_size, sequence_length))
-
- if position_ids is None:
- if past_key_values is not None:
- raise ValueError(
- "Make sure to provide `position_ids` when passing `past_key_values`."
- )
-
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- inputs = {"params": params or self.params}
-
- # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
- # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
- # it can be changed by FlaxGPT2Attention module
- if past_key_values:
- inputs["cache"] = past_key_values
- mutable = ["cache"]
- else:
- mutable = False
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- return decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
-
- outputs = self.module.apply(
- inputs,
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- mutable=mutable,
- method=_decoder_forward,
- )
-
- # add updated cache to model output
- if past_key_values is not None and return_dict:
- outputs, past = outputs
- outputs["past_key_values"] = unfreeze(past["cache"])
- return outputs
- elif past_key_values is not None and not return_dict:
- outputs, past = outputs
- outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
-
- return outputs
-
- def __call__(
- self,
- pixel_values: jnp.ndarray,
- input_ids: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- train: bool = False,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))
-
- # # prepare encoder inputs
- # if encoder_attention_mask is None:
- # encoder_attention_mask = jnp.ones_like(input_ids)
-
- # if position_ids is None:
- # batch_size, sequence_length = input_ids.shape
- # position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
-
- # prepare decoder inputs
- # if decoder_input_ids is None:
- # decoder_input_ids = shift_tokens_right(
- # input_ids, self.config.pad_token_id, decoder_start_token_id=self.config.decoder_start_token_id
- # ) # TODO: Check how to use this
-
- if attention_mask is None:
- attention_mask = jnp.ones_like(input_ids)
- if position_ids is None:
- batch_size, sequence_length = input_ids.shape
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
-
- return self.module.apply(
- {"params": params or self.params},
- pixel_values=jnp.array(pixel_values, dtype=jnp.float32),
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=not train,
- rngs=rngs,
- )
-
-
-class FlaxViTGPT2ForConditionalGeneration(FlaxViTGPT2PreTrainedModel):
- module_class = FlaxViTGPT2ForConditionalGenerationModule
- dtype: jnp.dtype = jnp.float32
-
- def decode(
- self,
- input_ids,
- encoder_outputs,
- encoder_attention_mask: Optional[jnp.ndarray] = None,
- attention_mask: Optional[jnp.ndarray] = None,
- position_ids: Optional[jnp.ndarray] = None,
- past_key_values: dict = None,
- output_attentions: Optional[bool] = None,
- output_hidden_states: Optional[bool] = None,
- return_dict: Optional[bool] = None,
- deterministic: bool = True,
- params: dict = None,
- dropout_rng: PRNGKey = None,
- ):
- output_attentions = (
- output_attentions
- if output_attentions is not None
- else self.config.output_attentions
- )
- output_hidden_states = (
- output_hidden_states
- if output_hidden_states is not None
- else self.config.output_hidden_states
- )
- return_dict = (
- return_dict if return_dict is not None else self.config.return_dict
- )
-
- encoder_hidden_states = encoder_outputs[0]
- if encoder_attention_mask is None:
- batch_size, sequence_length = encoder_hidden_states.shape[:2]
- encoder_attention_mask = jnp.ones((batch_size, sequence_length))
-
- batch_size, sequence_length = input_ids.shape
- if attention_mask is None:
- attention_mask = jnp.ones((batch_size, sequence_length))
-
- if position_ids is None:
- if past_key_values is not None:
- raise ValueError(
- "Make sure to provide `position_ids` when passing `past_key_values`."
- )
-
- position_ids = jnp.broadcast_to(
- jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
- )
-
- # Handle any PRNG if needed
- rngs = {}
- if dropout_rng is not None:
- rngs["dropout"] = dropout_rng
-
- inputs = {"params": params or self.params}
-
- # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
- # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
- # it can be changed by FlaxGPT2Attention module
- if past_key_values:
- inputs["cache"] = past_key_values
- mutable = ["cache"]
- else:
- mutable = False
-
- def _decoder_forward(
- module,
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- ):
- decoder_module = module._get_decoder_module()
- outputs = decoder_module(
- input_ids,
- attention_mask,
- position_ids,
- **kwargs,
- )
- hidden_states = outputs[0]
-
- if self.config.tie_word_embeddings:
- shared_embedding = module.model.variables["params"]["shared"][
- "embedding"
- ]
- lm_logits = module.lm_head.apply(
- {"params": {"kernel": shared_embedding.T}}, hidden_states
- )
- else:
- lm_logits = module.lm_head(hidden_states)
-
- lm_logits += module.final_logits_bias
- return lm_logits, outputs
-
- outputs = self.module.apply(
- inputs,
- input_ids=jnp.array(input_ids, dtype="i4"),
- attention_mask=jnp.array(attention_mask, dtype="i4"),
- position_ids=jnp.array(position_ids, dtype="i4"),
- encoder_hidden_states=encoder_hidden_states,
- encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
- output_attentions=output_attentions,
- output_hidden_states=output_hidden_states,
- return_dict=return_dict,
- deterministic=deterministic,
- rngs=rngs,
- mutable=mutable,
- method=_decoder_forward,
- )
-
- if past_key_values is None:
- lm_logits, outputs = outputs
- else:
- (lm_logits, outputs), past = outputs
-
- if return_dict:
- outputs = FlaxCausalLMOutputWithCrossAttentions(
- logits=lm_logits,
- hidden_states=outputs.hidden_states,
- attentions=outputs.attentions,
- cross_attentions=outputs.cross_attentions,
- )
- else:
- outputs = (lm_logits,) + outputs[1:]
-
- # add updated cache to model output
- if past_key_values is not None and return_dict:
- outputs["past_key_values"] = unfreeze(past["cache"])
- return outputs
- elif past_key_values is not None and not return_dict:
- outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
-
- return outputs
-
- def prepare_inputs_for_generation(
- self,
- input_ids,
- max_length,
- encoder_attention_mask: Optional[jnp.DeviceArray] = None,
- attention_mask: Optional[jnp.DeviceArray] = None,
- encoder_outputs=None,
- **kwargs,
- ):
- # initializing the cache
- batch_size, seq_length = input_ids.shape
-
- past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
- # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
- # But since the decoder uses a causal mask, those positions are masked anyways.
- # Thus we can create a single static attention_mask here, which is more efficient for compilation
- extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
- if attention_mask is not None:
- position_ids = attention_mask.cumsum(axis=-1) - 1
- extended_attention_mask = lax.dynamic_update_slice(
- extended_attention_mask, attention_mask, (0, 0)
- )
- else:
- position_ids = jnp.broadcast_to(
- jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)
- )
-
- return {
- "past_key_values": past_key_values,
- "encoder_outputs": encoder_outputs,
- "encoder_attention_mask": encoder_attention_mask,
- "attention_mask": extended_attention_mask,
- "position_ids": position_ids,
- }
-
- def update_inputs_for_generation(self, model_outputs, model_kwargs):
- model_kwargs["past_key_values"] = model_outputs.past_key_values
- model_kwargs["position_ids"] = (
- model_kwargs["position_ids"][:, -1:] + 1
- )
- return model_kwargs
-
- @classmethod
- def from_vit_gpt2_pretrained(
- cls,
- vit_model_name_or_path: str = None,
- gpt2_model_name_or_path: str = None,
- *model_args,
- **kwargs,
- ) -> FlaxViTGPT2PreTrainedModel:
-
- kwargs_gpt2 = {
- argument[len("gpt2_") :]: value
- for argument, value in kwargs.items()
- if argument.startswith("gpt2_")
- }
-
- kwargs_vit = {
- argument[len("vit_") :]: value
- for argument, value in kwargs.items()
- if argument.startswith("vit_")
- }
-
- # remove gpt2, vit kwargs from kwargs
- for key in kwargs_gpt2.keys():
- del kwargs["gpt2_" + key]
- for key in kwargs_vit.keys():
- del kwargs["vit_" + key]
-
- # Load and initialize the gpt2 and vit model
- gpt2_model = kwargs_gpt2.pop("model", None)
- if gpt2_model is None:
- assert (
- gpt2_model_name_or_path is not None
- ), "If `model` is not defined as an argument, a `gpt2_model_name_or_path` has to be defined"
-
- if "config" not in kwargs_gpt2:
- gpt2_config = GPT2Config.from_pretrained(gpt2_model_name_or_path)
- kwargs_gpt2["config"] = gpt2_config
-
- kwargs_gpt2["config"].add_cross_attention = True
- gpt2_model = FlaxGPT2Model.from_pretrained(
- gpt2_model_name_or_path, *model_args, **kwargs_gpt2
- )
-
- vit_model = kwargs_vit.pop("model", None)
- if vit_model is None:
- assert (
- vit_model_name_or_path is not None
- ), "If `model` is not defined as an argument, a `vit_model_name_or_path` has to be defined"
-
- if "config" not in kwargs_vit:
- vit_config = ViTConfig.from_pretrained(vit_model_name_or_path)
- kwargs_vit["config"] = vit_config
-
- vit_model = FlaxViTModel.from_pretrained(
- vit_model_name_or_path, *model_args, **kwargs_vit
- )
-
- # instantiate config with corresponding kwargs
- dtype = kwargs.pop("dtype", jnp.float32)
- config = ViTGPT2Config.from_vit_gpt2_configs(
- vit_model.config, gpt2_model.config, **kwargs
- )
-
- # init model
- model = cls(config, *model_args, dtype=dtype, **kwargs)
- model.params["model"]["encoder"] = vit_model.params
- model.params["model"]["decoder"] = gpt2_model.params
-
- return model
-
diff --git a/spaces/Seetha/IMA-pipeline-streamlit/app.py b/spaces/Seetha/IMA-pipeline-streamlit/app.py
deleted file mode 100644
index f0db4170922ebf1b9a612db7fac952e61207154a..0000000000000000000000000000000000000000
--- a/spaces/Seetha/IMA-pipeline-streamlit/app.py
+++ /dev/null
@@ -1,581 +0,0 @@
-# import all packages
-import requests
-import streamlit as st
-from sklearn.model_selection import StratifiedKFold
-from sklearn.model_selection import train_test_split
-from sklearn.model_selection import KFold
-# tokenizer
-from transformers import AutoTokenizer, DistilBertTokenizerFast
-# sequence tagging model + training-related
-from transformers import DistilBertForTokenClassification, Trainer, TrainingArguments
-import torch
-import sys
-import os
-from sklearn.metrics import classification_report
-from pandas import read_csv
-from sklearn.linear_model import LogisticRegression
-import sklearn.model_selection
-from sklearn.feature_extraction.text import TfidfTransformer
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.pipeline import Pipeline, FeatureUnion
-import math
-# from sklearn.metrics import accuracy_score
-# from sklearn.metrics import precision_recall_fscore_support
-import json
-import re
-import numpy as np
-import pandas as pd
-import nltk
-nltk.download("punkt")
-import string
-from sklearn.model_selection import train_test_split
-from transformers import AutoTokenizer, Trainer, TrainingArguments, AutoModelForSequenceClassification, AutoConfig
-from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
-import itertools
-from transformers import TextClassificationPipeline, TFAutoModelForSequenceClassification, AutoTokenizer
-from transformers import pipeline
-import pickle
-import csv
-import pdfplumber
-import pathlib
-import shutil
-import webbrowser
-from streamlit.components.v1 import html
-import streamlit.components.v1 as components
-from PyPDF2 import PdfReader
-from huggingface_hub import HfApi
-import io
-from datasets import load_dataset
-import time
-
-import huggingface_hub
-from huggingface_hub import Repository
-from datetime import datetime
-import pathlib as Path
-from requests import get
-import urllib.request
-# import gradio as gr
-# from gradio import inputs, outputs
-from datasets import load_dataset
-from huggingface_hub import HfApi, list_models
-import os
-from huggingface_hub import HfFileSystem
-from tensorflow.keras.models import Sequential, model_from_json
-#import tensorflow_datasets as tfds
-import tensorflow as tf
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-import spacy
-from tensorflow.keras.preprocessing.text import Tokenizer
-#from spacy import en_core_web_lg
-#import en_core_web_lg
-#nlp = en_core_web_lg.load()
-nlp = spacy.load('en_core_web_sm')
-
-#tfds.disable_progress_bar()
-MAX_SEQUENCE_LENGTH = 500
-
-# dataset = load_dataset('Seetha/Visualization', streaming=True)
-# df = pd.DataFrame.from_dict(dataset['train'])
-# DATASET_REPO_URL = "https://huggingface.co/datasets/Seetha/Visualization"
-# DATA_FILENAME = "level2.json"
-#DATA_FILE = os.path.join("data", DATA_FILENAME)
-DATASET_REPO_URL = "https://huggingface.co/datasets/Seetha/visual_files"
-DATA_FILENAME = "detailedResults.json"
-DATA_FILENAME1 = "level2.json"
-
-HF_TOKEN = os.environ.get("HF_TOKEN")
-#st.write("is none?", HF_TOKEN is None)
-
-def main():
-
- st.title("Text to Causal Knowledge Graph")
- st.sidebar.title("Please upload your text documents in one file here:")
- k=2
- seed = 1
- k1= 5
- text_list = []
- causal_sents = []
-
- uploaded_file = None
- try:
- uploaded_file = st.sidebar.file_uploader("Choose a file", type = "pdf")
- except:
- uploaded_file = PdfReader('sample_anno.pdf')
- st.error("Please upload your own PDF to be analyzed")
-
- if uploaded_file is not None:
- reader = PdfReader(uploaded_file)
- for page in reader.pages:
- text = page.extract_text()
- text_list.append(text)
- else:
- st.error("Please upload your own PDF to be analyzed")
- st.stop()
-
- text_list_final = [x.replace('\n', '') for x in text_list]
- text_list_final = re.sub('"', '', str(text_list_final))
-
- sentences = nltk.sent_tokenize(text_list_final)
-
- result =[]
- for i in sentences:
- result1 = i.lower()
- result2 = re.sub(r'[^\w\s]','',result1)
- result.append(result2)
-
- #st.write("--- %s seconds ---" % (time.time() - start_time))
- tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") #bert-base-uncased
-
- model_path = "checkpoint-2850"
-
- model = AutoModelForSequenceClassification.from_pretrained(model_path,id2label={0:'non-causal',1:'causal'})
-
- #st.write('sequence classification loaded')
- pipe1 = pipeline("text-classification", model=model,tokenizer=tokenizer)
- for sent in result:
- pred = pipe1(sent)
- for lab in pred:
- if lab['label'] == 'causal': #causal
- causal_sents.append(sent)
-
- # st.write('causal sentence classification finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
-
- model_name = "distilbert-base-cased"
- tokenizer = DistilBertTokenizerFast.from_pretrained(model_name,low_cpu_mem_usage=True)
-
- model_path1 = "DistilBertforTokenclassification"
-
- model = DistilBertForTokenClassification.from_pretrained(model_path1,low_cpu_mem_usage=True) #len(unique_tags),, num_labels= 7, , id2label={0:'CT',1:'E',2:'C',3:'O'}
- pipe = pipeline('ner', model=model, tokenizer=tokenizer,aggregation_strategy='simple') #grouped_entities=True
- st.write('DistilBERT loaded')
- sentence_pred = []
- class_list = []
- entity_list = []
- for k in causal_sents:
- pred= pipe(k)
- #st.write(pred)
- #st.write('preds')
- for i in pred:
- sentence_pred.append(k)
- class_list.append(i['word'])
- entity_list.append(i['entity_group'])
-
- # st.write('causality extraction finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
-
- # filename = 'Checkpoint-classification.sav'
- # loaded_model = pickle.load(open(filename, 'rb'))
- # loaded_vectorizer = pickle.load(open('vectorizefile_classification.pickle', 'rb'))
-
- # pipeline_test_output = loaded_vectorizer.transform(class_list)
- # predicted = loaded_model.predict(pipeline_test_output)
-
- tokenizer = Tokenizer(num_words=100000)
- tokenizer.fit_on_texts(class_list)
- word_index = tokenizer.word_index
- # text_embedding = np.zeros((len(word_index) + 1, 300))
- # for word, i in word_index.items():
- # text_embedding[i] = nlp(word).vector
- json_file = open('model.json', 'r')
- loaded_model_json = json_file.read()
- json_file.close()
- loaded_model = model_from_json(loaded_model_json)
- # load weights into new model
- loaded_model.load_weights("model.h5")
-
- loss = tf.keras.losses.CategoricalCrossentropy() #from_logits=True
- loaded_model.compile(loss=loss,optimizer=tf.keras.optimizers.Adam(1e-4))
-
- predictions = loaded_model.predict(pad_sequences(tokenizer.texts_to_sequences(class_list),maxlen=MAX_SEQUENCE_LENGTH))
- predicted = np.argmax(predictions,axis=1)
-
- st.write(predictions)
- st.write(predicted)
- # st.write('stakeholder taxonomy finished')
- # st.write("--- %s seconds ---" % (time.time() - start_time))
- pred1 = predicted
- level0 = []
- count =0
- for i in predicted:
- if i == 3:
- level0.append('Non-Performance')
- count +=1
- else:
- level0.append('Performance')
- count +=1
-
- list_pred = {0: 'Customers',1:'Employees',2:'Investors',3:'Non-performance',4:'Society',5:'Unclassified'}
- pred_val = [list_pred[i] for i in pred1]
-
- #print('count',count)
- for ind,(sent,preds) in enumerate(zip(class_list,pred_val)):
- if 'customers' in sent or 'client' in sent or 'consumer' in sent or 'user' in sent:
- pred_val[ind] = 'Customers'
- elif 'investor' in sent or 'finance' in sent or 'shareholder' in sent or 'stockholder' in sent or 'owners' in sent:
- pred_val[ind] = 'Investors'
- elif 'employee' in sent or 'worker' in sent or 'staff' in sent:
- pred_val[ind] = 'Employees'
- elif 'society' in sent or 'societal' in sent or 'social responsib*' in sent or 'social performance' in sent or 'community' in sent:
- pred_val[ind] = 'Society'
-
- sent_id, unique = pd.factorize(sentence_pred)
-
- final_list = pd.DataFrame(
- {'Id': sent_id,
- 'Fullsentence': sentence_pred,
- 'Component': class_list,
- 'causeOrEffect': entity_list,
- 'Labellevel1': level0,
- 'Labellevel2': pred_val
- })
- s = final_list['Component'].shift(-1)
- m = s.str.startswith('##', na=False)
- final_list.loc[m, 'Component'] += (' ' + s[m])
-
-
- final_list1 = final_list[~final_list['Component'].astype(str).str.startswith('##')]
- li = []
- uni = final_list1['Id'].unique()
- for i in uni:
- df_new = final_list1[final_list1['Id'] == i]
- uni1 = df_new['Id'].unique()
-
- # if 'E' not in df_new.values:
- # li.append(uni1)
- # out = np.concatenate(li).ravel()
- # li_pan = pd.DataFrame(out,columns=['Id'])
- # df3 = pd.merge(final_list1, li_pan[['Id']], on='Id', how='left', indicator=True) \
- # .query("_merge == 'left_only'") \
- # .drop("_merge",axis=1)
-
- df3 = final_list1
- #df = df3.groupby(['Id','Fullsentence','causeOrEffect', 'Labellevel1', 'Labellevel2'])['Component'].apply(', '.join).reset_index()
- #st.write(df)
-
- #df = df3
- df3["causeOrEffect"].replace({"C": "cause", "E": "effect"}, inplace=True)
- df_final = df3[df3['causeOrEffect'] != 'CT']
- df3['New string'] = df_final['Component'].replace(r'[##]+', ' ', regex=True)
-
- df_final = df_final.drop("Component",axis=1)
- df_final.insert(2, "Component", df3['New string'], True)
-
- df_final1 = df_final[df_final['Component'].str.split().str.len().gt(1)]
- #st.write(df_final[df_final['Component'].str.len() != 1])
- #df_final1.to_csv('predictions.csv')
-
-# buffer = io.BytesIO()
-# with pd.ExcelWriter(buffer, engine="xlsxwriter") as writer:
-# df_final.to_excel(writer, sheet_name="Sheet1", index=False)
-# writer.close()
-
- count_NP_NP = 0
- count_NP_investor = 0
- count_NP_customer = 0
- count_NP_employees = 0
- count_NP_society = 0
-
- count_inv_np = 0
- count_inv_investor = 0
- count_inv_customer = 0
- count_inv_employee = 0
- count_inv_society = 0
-
- count_cus_np = 0
- count_cus_investor = 0
- count_cus_customer = 0
- count_cus_employee = 0
- count_cus_society = 0
-
- count_emp_np = 0
- count_emp_investor = 0
- count_emp_customer = 0
- count_emp_employee = 0
- count_emp_society = 0
-
- count_soc_np = 0
- count_soc_investor = 0
- count_soc_customer = 0
- count_soc_employee = 0
- count_soc_society = 0
- for i in range(0,df_final['Id'].max()):
- j = df_final.loc[df_final['Id'] == i]
- cause_tab = j.loc[j['causeOrEffect'] == 'cause']
- effect_tab = j.loc[j['causeOrEffect'] == 'effect']
- cause_coun_NP = (cause_tab.Labellevel2 == 'Non-performance').sum()
- effect_coun_NP = (effect_tab.Labellevel2 == 'Non-performance').sum()
-
- if (cause_coun_NP > 0) and (effect_coun_NP > 0):
- count_NP = cause_coun_NP if cause_coun_NP >= effect_coun_NP else effect_coun_NP
- else:
- count_NP = 0
- effect_NP_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_NP > 0) and (effect_NP_inv > 0):
- count_NP_inv = cause_coun_NP if cause_coun_NP >= effect_NP_inv else effect_NP_inv
- else:
- count_NP_inv = 0
- effect_NP_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_NP > 0) and (effect_NP_cus > 0):
- count_NP_cus = cause_coun_NP if cause_coun_NP >= effect_NP_cus else effect_NP_cus
- else:
- count_NP_cus = 0
- effect_NP_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_NP > 0) and (effect_NP_emp > 0):
- count_NP_emp = cause_coun_NP if cause_coun_NP >= effect_NP_emp else effect_NP_emp
- else:
- count_NP_emp = 0
- effect_NP_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_NP > 0) and (effect_NP_soc > 0):
- count_NP_soc = cause_coun_NP if cause_coun_NP >= effect_NP_soc else effect_NP_soc
- else:
- count_NP_soc = 0
-
- cause_coun_inv = (cause_tab.Labellevel2 == 'Investors').sum()
- effect_coun_inv = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_inv > 0) and (effect_coun_inv > 0):
- count_NP_inv = cause_coun_inv if cause_coun_inv >= effect_coun_inv else effect_coun_inv
- else:
- count_NP_inv = 0
-
- effect_inv_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_inv > 0) and (effect_inv_inv > 0):
- count_inv_inv = cause_coun_inv if cause_coun_inv >= effect_inv_inv else effect_inv_inv
- else:
- count_inv_inv = 0
- effect_inv_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_inv > 0) and (effect_inv_cus > 0):
- count_inv_cus = cause_coun_inv if cause_coun_inv >= effect_inv_cus else effect_inv_cus
- else:
- count_inv_cus = 0
- effect_inv_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_inv > 0) and (effect_inv_emp > 0):
- count_inv_emp = cause_coun_inv if cause_coun_inv >= effect_inv_emp else effect_inv_emp
- else:
- count_inv_emp = 0
-
- effect_inv_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_inv > 0) and (effect_inv_soc > 0):
- count_inv_soc = cause_coun_inv if cause_coun_inv >= effect_inv_soc else effect_inv_soc
- else:
- count_inv_soc = 0
-
- cause_coun_cus = (cause_tab.Labellevel2 == 'Customers').sum()
- effect_coun_cus = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_cus > 0) and (effect_coun_cus > 0):
- count_NP_cus = cause_coun_cus if cause_coun_cus >= effect_coun_cus else effect_coun_cus
- else:
- count_NP_cus = 0
-
- effect_cus_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_cus > 0) and (effect_cus_inv > 0):
- count_cus_inv = cause_coun_cus if cause_coun_cus >= effect_cus_inv else effect_cus_inv
- else:
- count_cus_inv = 0
-
- effect_cus_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_cus > 0) and (effect_cus_cus > 0):
- count_cus_cus = cause_coun_cus if cause_coun_cus >= effect_cus_cus else effect_cus_cus
- else:
- count_cus_cus = 0
-
- effect_cus_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_cus > 0) and (effect_cus_emp > 0):
- count_cus_emp = cause_coun_cus if cause_coun_cus >= effect_cus_emp else effect_cus_emp
- else:
- count_cus_emp = 0
-
- effect_cus_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_cus > 0) and (effect_cus_soc > 0):
- count_cus_soc = cause_coun_cus if cause_coun_cus >= effect_cus_soc else effect_cus_soc
- else:
- count_cus_soc = 0
-
- cause_coun_emp = (cause_tab.Labellevel2 == 'Employees').sum()
- effect_coun_emp = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_emp > 0) and (effect_coun_emp > 0):
- count_NP_emp = cause_coun_emp if cause_coun_emp >= effect_coun_emp else effect_coun_emp
- else:
- count_NP_emp = 0
-
- effect_emp_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_emp > 0) and (effect_emp_inv > 0):
- count_emp_inv = cause_coun_emp if cause_coun_emp >= effect_emp_inv else effect_emp_inv
- else:
- count_emp_inv = 0
-
- effect_emp_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_emp > 0) and (effect_emp_cus > 0):
- count_emp_cus = cause_coun_emp if cause_coun_emp >= effect_emp_cus else effect_emp_cus
- else:
- count_emp_cus = 0
-
- effect_emp_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_emp > 0) and (effect_emp_emp > 0):
- count_emp_emp = cause_coun_emp if cause_coun_emp >= effect_emp_emp else effect_emp_emp
- else:
- count_emp_emp = 0
-
- effect_emp_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_emp > 0) and (effect_emp_soc > 0):
- count_emp_soc = cause_coun_emp if cause_coun_emp >= effect_emp_soc else effect_emp_soc
- else:
- count_emp_soc = 0
-
- cause_coun_soc = (cause_tab.Labellevel2 == 'Society').sum()
- effect_coun_soc = (effect_tab.Labellevel2 == 'Non-performance').sum()
- if (cause_coun_soc > 0) and (effect_coun_soc > 0):
- count_NP_soc = cause_coun_soc if cause_coun_soc >= effect_coun_soc else effect_coun_soc
- else:
- count_NP_soc = 0
-
- effect_soc_inv = (effect_tab.Labellevel2 == 'Investors').sum()
- if (cause_coun_soc > 0) and (effect_soc_inv > 0):
- count_soc_inv = cause_coun_soc if cause_coun_soc >= effect_soc_inv else effect_soc_inv
- else:
- count_soc_inv = 0
-
- effect_soc_cus = (effect_tab.Labellevel2 == 'Customers').sum()
- if (cause_coun_soc > 0) and (effect_soc_cus > 0):
- count_soc_cus = cause_coun_soc if cause_coun_soc >= effect_soc_cus else effect_soc_cus
- else:
- count_soc_cus = 0
-
- effect_soc_emp = (effect_tab.Labellevel2 == 'Employees').sum()
- if (cause_coun_soc > 0) and (effect_soc_emp > 0):
- count_soc_emp = cause_coun_soc if cause_coun_soc >= effect_soc_emp else effect_soc_emp
- else:
- count_soc_emp = 0
-
- effect_soc_soc = (effect_tab.Labellevel2 == 'Society').sum()
- if (cause_coun_soc > 0) and (effect_soc_soc > 0):
- count_soc_soc = cause_coun_soc if cause_coun_soc >= effect_soc_soc else effect_soc_soc
- else:
- count_soc_soc = 0
-
- count_NP_NP = count_NP_NP + count_NP
- count_NP_investor = count_NP_investor + count_NP_inv
- count_NP_customer = count_NP_customer + count_NP_cus
- count_NP_employees = count_NP_employees + count_NP_emp
- count_NP_society = count_NP_society + count_NP_soc
-
- count_inv_np = count_inv_np + count_NP_inv
- count_inv_investor = count_inv_investor + count_inv_inv
- count_inv_customer = count_inv_customer + count_inv_cus
- count_inv_employee = count_inv_employee + count_inv_emp
- count_inv_society = count_inv_society + count_inv_soc
-
- count_cus_np = count_cus_np + count_NP_cus
- count_cus_investor = count_cus_investor + count_cus_inv
- count_cus_customer = count_cus_customer + count_cus_cus
- count_cus_employee = count_cus_employee + count_cus_emp
- count_cus_society = count_cus_society + count_cus_soc
-
- count_emp_np = count_emp_np + count_NP_emp
- count_emp_investor = count_emp_investor + count_emp_inv
- count_emp_customer = count_emp_customer + count_emp_cus
- count_emp_employee = count_emp_employee + count_emp_emp
- count_emp_society = count_emp_society + count_emp_soc
-
- count_soc_np = count_soc_np + count_NP_soc
- count_soc_investor = count_soc_investor + count_soc_inv
- count_soc_customer = count_soc_customer + count_soc_cus
- count_soc_employee = count_soc_employee + count_soc_emp
- count_soc_society = count_soc_society + count_soc_soc
-
- df_tab = pd.DataFrame(columns = ['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'],index=['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'], dtype=object)
-
- df_tab.loc['Non-performance'] = [count_NP_NP, count_NP_investor, count_NP_customer, count_NP_employees, count_NP_society]
- df_tab.loc['Investors'] = [count_inv_np, count_inv_investor, count_inv_customer, count_inv_employee, count_inv_society]
- df_tab.loc['Customers'] = [count_cus_np, count_cus_investor, count_cus_customer, count_cus_employee, count_cus_society]
- df_tab.loc['Employees'] = [count_emp_np, count_emp_investor, count_emp_customer, count_emp_employee, count_emp_society]
- df_tab.loc['Society'] = [count_soc_np, count_soc_investor, count_soc_customer, count_soc_employee, count_soc_society]
-
-
-# df_tab = pd.DataFrame({
-# 'Non-performance': [count_NP_NP, count_NP_investor, count_NP_customer, count_NP_employees, count_NP_society],
-# 'Investors': [count_inv_np, count_inv_investor, count_inv_customer, count_inv_employee, count_inv_society],
-# 'Customers': [count_cus_np, count_cus_investor, count_cus_customer, count_cus_employee, count_cus_society],
-# 'Employees': [count_emp_np, count_emp_investor, count_emp_customer, count_emp_employee, count_emp_society],
-# 'Society': [count_soc_np, count_soc_investor, count_soc_customer, count_soc_employee, count_soc_society]},
-# index=['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'])
-
- #df_tab.to_csv('final_data.csv')
-
- buffer = io.BytesIO()
- with pd.ExcelWriter(buffer, engine="xlsxwriter") as writer:
- df_tab.to_excel(writer,sheet_name="count_result",index=False)
- df_final1.to_excel(writer,sheet_name="Detailed_results",index=False)
- writer.close()
- #df = pd.read_csv('final_data.csv', index_col=0)
-#474-515
- # Convert to JSON format
- json_data = []
- for row in df_tab.index:
- for col in df_tab.columns:
- json_data.append({
- 'source': row,
- 'target': col,
- 'value': int(df_tab.loc[row, col])
- })
-
- HfApi().delete_file(path_in_repo = DATA_FILENAME1 ,repo_id = 'Seetha/visual_files',token= HF_TOKEN,repo_type='dataset')
- #st.write('file-deleted')
- fs = HfFileSystem(token=HF_TOKEN)
- with fs.open('datasets/Seetha/visual_files/level2.json', 'w') as f:
- json.dump(json_data, f)
-
- df_final1.to_csv('predictions.csv')
- csv_file = "predictions.csv"
- json_file = "detailedResults.json"
-
- # Open the CSV file and read the data
- with open(csv_file, "r") as f:
- csv_data = csv.DictReader(f)
-
- # # Convert the CSV data to a list of dictionaries
- data_list = []
- for row in csv_data:
- data_list.append(dict(row))
-
- # # Convert the list of dictionaries to JSON
- json_data = json.dumps(data_list)
-
- HfApi().delete_file(path_in_repo = DATA_FILENAME ,repo_id = 'Seetha/visual_files',token= HF_TOKEN,repo_type='dataset')
- #st.write('file2-deleted')
- with fs.open('datasets/Seetha/visual_files/detailedResults.json','w') as fi:
- #data = json.load(fi)
- fi.write(json_data)
-
- def convert_df(df):
-
- #IMPORTANT: Cache the conversion to prevent computation on every rerun
-
- return df.to_csv().encode('utf-8')
-
-
-
- csv1 = convert_df(df_final1.astype(str))
- csv2 = convert_df(df_tab.astype(str))
-
- with st.container():
-
- st.download_button(label="Download the result table",data=buffer,file_name="t2cg_outputs.xlsx",mime="application/vnd.ms-excel")
- st.markdown('Click this link in a separate tab to view knowledge graph', unsafe_allow_html=True)
- # st.download_button(label="Download the detailed result table_csv",data=csv1,file_name='results.csv',mime='text/csv')
- # st.download_button(label="Download the result table_csv",data=csv2,file_name='final_data.csv',mime='text/csv')
-
-#with st.container():
- # Execute your app
- #st.title("Visualization example")
-# components.html(source_code)
- #html(my_html)
- #webbrowser.open('https://huggingface.co/spaces/Seetha/visual-knowledgegraph')
-# # embed streamlit docs in a streamlit app
-# #components.iframe("https://webpages.charlotte.edu/ltotapal/")
-
-
-
-if __name__ == '__main__':
- start_time = time.time()
- main()
diff --git a/spaces/Shiro26/MendoBERT_RE/app.py b/spaces/Shiro26/MendoBERT_RE/app.py
deleted file mode 100644
index 1bf256334d6604b5048b7f1054c9cb1915120f15..0000000000000000000000000000000000000000
--- a/spaces/Shiro26/MendoBERT_RE/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-
-model = pipeline("text-classification", model="/home/user/app/MendoBERT/", tokenizer="indolem/indobert-base-uncased")
-basemodel = pipeline("text-classification", model="/home/user/app/IndoLEM/", tokenizer="indolem/indobert-base-uncased")
-
-st.title(':blue[MendoBERT] - Relation Extraction :family:')
-
-if 'options' not in st.session_state:
- st.session_state['options'] = ""
-
-def button1_callback():
- st.session_state['options'] = "Kami menyimpulkan bahwa polimorfisme @GENE$ dan AGT tidak berkontribusi pada kerentanan genetik terhadap @DISEASE$ dan retinopati pada populasi Kaukasia Mediterania."
-def button2_callback():
- st.session_state['options'] = "Genotipe bayi PON1 RR dan @GENE$ CC dikaitkan dengan @DISEASE$ dalam populasi penelitian kami, yang menunjukkan kemungkinan peran variabilitas paraoxonase manusia dalam etiologi kelahiran prematur."
-
-
-placeholder = st.empty()
-
-st.info("Please replace the gene and disease that you want to get their relation predicted with @GENE\$ and \@DISEASE\$", icon="ℹ️")
-
-st.caption('_Examples_')
-st.button('Kami menyimpulkan bahwa polimorfisme \@GENE\$ dan AGT tidak berkontribusi pada kerentanan genetik terhadap \@DISEASE\$ dan retinopati pada populasi Kaukasia Mediterania.', use_container_width=True, on_click = button1_callback)
-st.button('Genotipe bayi PON1 RR dan \@GENE\$ CC dikaitkan dengan \@DISEASE\$ dalam populasi penelitian kami, yang menunjukkan kemungkinan peran variabilitas paraoxonase manusia dalam etiologi kelahiran prematur.', use_container_width=True, on_click = button2_callback)
-
-with placeholder:
- text = st.text_area('Enter some text: ', key = 'options')
-
-if text:
- st.subheader('MendoBERT')
- st.write(model(text))
- st.write("\n")
- st.subheader('IndoLEM')
- st.write(basemodel(text))
\ No newline at end of file
diff --git a/spaces/Shredder/CONBERT-2/README.md b/spaces/Shredder/CONBERT-2/README.md
deleted file mode 100644
index daf10e5da8dd31671c54c18d24b0e7d1d77b62f0..0000000000000000000000000000000000000000
--- a/spaces/Shredder/CONBERT-2/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: CONBERT 2
-emoji: 👁
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Stearns/Soar/soar_io.py b/spaces/Stearns/Soar/soar_io.py
deleted file mode 100644
index c05d97417b69a804b35207fb839957a91258a5d2..0000000000000000000000000000000000000000
--- a/spaces/Stearns/Soar/soar_io.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from pysoarlib import *
-
-## DEFINE MAIN CONNECTOR CLASS
-
-class GeneralSoarIOConnector(AgentConnector):
- def __init__(self, client):
- AgentConnector.__init__(self, client)
- client.add_print_event_handler(self.agent_print_collector)
- # client.execute_command("output callbacks on")
- # client.execute_command("output console on")
-
- self.agent_printout = ""
-
- def on_input_phase(self, input_link):
- pass
-
- def on_init_soar(self):
- self.reset()
-
- def on_output_event(self, command_name, root_id):
- pass
-
-
- def agent_print_collector(self, text):
- self.agent_printout += text+"\n"
-
- def get_agent_output(self):
- return self.agent_printout
-
- def reset(self):
- self.agent_printout = ""
-
-
-
-
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py
deleted file mode 100644
index 489b7c13d0ce82b769ba50412ecde0889d0b7851..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# encoding: utf-8
-"""
-Utilities for working with external processes.
-"""
-
-# Copyright (c) IPython Development Team.
-# Distributed under the terms of the Modified BSD License.
-
-
-import os
-import shutil
-import sys
-
-if sys.platform == 'win32':
- from ._process_win32 import system, getoutput, arg_split, check_pid
-elif sys.platform == 'cli':
- from ._process_cli import system, getoutput, arg_split, check_pid
-else:
- from ._process_posix import system, getoutput, arg_split, check_pid
-
-from ._process_common import getoutputerror, get_output_error_code, process_handler
-
-
-class FindCmdError(Exception):
- pass
-
-
-def find_cmd(cmd):
- """Find absolute path to executable cmd in a cross platform manner.
-
- This function tries to determine the full path to a command line program
- using `which` on Unix/Linux/OS X and `win32api` on Windows. Most of the
- time it will use the version that is first on the users `PATH`.
-
- Warning, don't use this to find IPython command line programs as there
- is a risk you will find the wrong one. Instead find those using the
- following code and looking for the application itself::
-
- import sys
- argv = [sys.executable, '-m', 'IPython']
-
- Parameters
- ----------
- cmd : str
- The command line program to look for.
- """
- path = shutil.which(cmd)
- if path is None:
- raise FindCmdError('command could not be found: %s' % cmd)
- return path
-
-
-def abbrev_cwd():
- """ Return abbreviated version of cwd, e.g. d:mydir """
- cwd = os.getcwd().replace('\\','/')
- drivepart = ''
- tail = cwd
- if sys.platform == 'win32':
- if len(cwd) < 4:
- return cwd
- drivepart,tail = os.path.splitdrive(cwd)
-
-
- parts = tail.split('/')
- if len(parts) > 2:
- tail = '/'.join(parts[-2:])
-
- return (drivepart + (
- cwd == '/' and '/' or tail))
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py
deleted file mode 100644
index 37d3db222ef2c7920628971a92e863d9915514c6..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py
+++ /dev/null
@@ -1,228 +0,0 @@
-import sys
-from typing import Any, Dict, List, Optional, Generic, TypeVar, cast
-from types import TracebackType
-
-if sys.version_info >= (3, 8):
- from importlib.metadata import entry_points
-else:
- from importlib_metadata import entry_points
-
-from toolz import curry
-
-
-PluginType = TypeVar("PluginType")
-
-
-class NoSuchEntryPoint(Exception):
- def __init__(self, group, name):
- self.group = group
- self.name = name
-
- def __str__(self):
- return f"No {self.name!r} entry point found in group {self.group!r}"
-
-
-class PluginEnabler:
- """Context manager for enabling plugins
-
- This object lets you use enable() as a context manager to
- temporarily enable a given plugin::
-
- with plugins.enable('name'):
- do_something() # 'name' plugin temporarily enabled
- # plugins back to original state
- """
-
- def __init__(self, registry: "PluginRegistry", name: str, **options):
- self.registry = registry # type: PluginRegistry
- self.name = name # type: str
- self.options = options # type: Dict[str, Any]
- self.original_state = registry._get_state() # type: Dict[str, Any]
- self.registry._enable(name, **options)
-
- def __enter__(self) -> "PluginEnabler":
- return self
-
- def __exit__(self, typ: type, value: Exception, traceback: TracebackType) -> None:
- self.registry._set_state(self.original_state)
-
- def __repr__(self) -> str:
- return "{}.enable({!r})".format(self.registry.__class__.__name__, self.name)
-
-
-class PluginRegistry(Generic[PluginType]):
- """A registry for plugins.
-
- This is a plugin registry that allows plugins to be loaded/registered
- in two ways:
-
- 1. Through an explicit call to ``.register(name, value)``.
- 2. By looking for other Python packages that are installed and provide
- a setuptools entry point group.
-
- When you create an instance of this class, provide the name of the
- entry point group to use::
-
- reg = PluginRegister('my_entrypoint_group')
-
- """
-
- # this is a mapping of name to error message to allow custom error messages
- # in case an entrypoint is not found
- entrypoint_err_messages = {} # type: Dict[str, str]
-
- # global settings is a key-value mapping of settings that are stored globally
- # in the registry rather than passed to the plugins
- _global_settings = {} # type: Dict[str, Any]
-
- def __init__(self, entry_point_group: str = "", plugin_type: type = object):
- """Create a PluginRegistry for a named entry point group.
-
- Parameters
- ==========
- entry_point_group: str
- The name of the entry point group.
- plugin_type: object
- A type that will optionally be used for runtime type checking of
- loaded plugins using isinstance.
- """
- self.entry_point_group = entry_point_group # type: str
- self.plugin_type = plugin_type # type: Optional[type]
- self._active = None # type: Optional[PluginType]
- self._active_name = "" # type: str
- self._plugins = {} # type: Dict[str, PluginType]
- self._options = {} # type: Dict[str, Any]
- self._global_settings = self.__class__._global_settings.copy() # type: dict
-
- def register(self, name: str, value: Optional[PluginType]) -> Optional[PluginType]:
- """Register a plugin by name and value.
-
- This method is used for explicit registration of a plugin and shouldn't be
- used to manage entry point managed plugins, which are auto-loaded.
-
- Parameters
- ==========
- name: str
- The name of the plugin.
- value: PluginType or None
- The actual plugin object to register or None to unregister that plugin.
-
- Returns
- =======
- plugin: PluginType or None
- The plugin that was registered or unregistered.
- """
- if value is None:
- return self._plugins.pop(name, None)
- else:
- assert isinstance(value, self.plugin_type) # type: ignore[arg-type] # Should ideally be fixed by better annotating plugin_type
- self._plugins[name] = value
- return value
-
- def names(self) -> List[str]:
- """List the names of the registered and entry points plugins."""
- exts = list(self._plugins.keys())
- e_points = importlib_metadata_get(self.entry_point_group)
- more_exts = [ep.name for ep in e_points]
- exts.extend(more_exts)
- return sorted(set(exts))
-
- def _get_state(self) -> Dict[str, Any]:
- """Return a dictionary representing the current state of the registry"""
- return {
- "_active": self._active,
- "_active_name": self._active_name,
- "_plugins": self._plugins.copy(),
- "_options": self._options.copy(),
- "_global_settings": self._global_settings.copy(),
- }
-
- def _set_state(self, state: Dict[str, Any]) -> None:
- """Reset the state of the registry"""
- assert set(state.keys()) == {
- "_active",
- "_active_name",
- "_plugins",
- "_options",
- "_global_settings",
- }
- for key, val in state.items():
- setattr(self, key, val)
-
- def _enable(self, name: str, **options) -> None:
- if name not in self._plugins:
- try:
- (ep,) = [
- ep
- for ep in importlib_metadata_get(self.entry_point_group)
- if ep.name == name
- ]
- except ValueError as err:
- if name in self.entrypoint_err_messages:
- raise ValueError(self.entrypoint_err_messages[name]) from err
- else:
- raise NoSuchEntryPoint(self.entry_point_group, name) from err
- value = cast(PluginType, ep.load())
- self.register(name, value)
- self._active_name = name
- self._active = self._plugins[name]
- for key in set(options.keys()) & set(self._global_settings.keys()):
- self._global_settings[key] = options.pop(key)
- self._options = options
-
- def enable(self, name: Optional[str] = None, **options) -> PluginEnabler:
- """Enable a plugin by name.
-
- This can be either called directly, or used as a context manager.
-
- Parameters
- ----------
- name : string (optional)
- The name of the plugin to enable. If not specified, then use the
- current active name.
- **options :
- Any additional parameters will be passed to the plugin as keyword
- arguments
-
- Returns
- -------
- PluginEnabler:
- An object that allows enable() to be used as a context manager
- """
- if name is None:
- name = self.active
- return PluginEnabler(self, name, **options)
-
- @property
- def active(self) -> str:
- """Return the name of the currently active plugin"""
- return self._active_name
-
- @property
- def options(self) -> Dict[str, Any]:
- """Return the current options dictionary"""
- return self._options
-
- def get(self) -> Optional[PluginType]:
- """Return the currently active plugin."""
- if self._options:
- return curry(self._active, **self._options)
- else:
- return self._active
-
- def __repr__(self) -> str:
- return "{}(active={!r}, registered={!r})" "".format(
- self.__class__.__name__, self._active_name, list(self.names())
- )
-
-
-def importlib_metadata_get(group):
- ep = entry_points()
- # 'select' was introduced in Python 3.10 and 'get' got deprecated
- # We don't check for Python version here as by checking with hasattr we
- # also get compatibility with the importlib_metadata package which had a different
- # deprecation cycle for 'get'
- if hasattr(ep, "select"):
- return ep.select(group=group)
- else:
- return ep.get(group, [])
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py
deleted file mode 100644
index bba2d3f7dfffa3bdbf921bdad4ca7143be97c2fd..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py
+++ /dev/null
@@ -1,143 +0,0 @@
-from __future__ import annotations
-
-import codecs
-from dataclasses import InitVar, dataclass, field
-from typing import Any, Callable, Mapping
-
-from ..abc import (
- AnyByteReceiveStream,
- AnyByteSendStream,
- AnyByteStream,
- ObjectReceiveStream,
- ObjectSendStream,
- ObjectStream,
-)
-
-
-@dataclass(eq=False)
-class TextReceiveStream(ObjectReceiveStream[str]):
- """
- Stream wrapper that decodes bytes to strings using the given encoding.
-
- Decoding is done using :class:`~codecs.IncrementalDecoder` which returns any completely
- received unicode characters as soon as they come in.
-
- :param transport_stream: any bytes-based receive stream
- :param encoding: character encoding to use for decoding bytes to strings (defaults to
- ``utf-8``)
- :param errors: handling scheme for decoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteReceiveStream
- encoding: InitVar[str] = "utf-8"
- errors: InitVar[str] = "strict"
- _decoder: codecs.IncrementalDecoder = field(init=False)
-
- def __post_init__(self, encoding: str, errors: str) -> None:
- decoder_class = codecs.getincrementaldecoder(encoding)
- self._decoder = decoder_class(errors=errors)
-
- async def receive(self) -> str:
- while True:
- chunk = await self.transport_stream.receive()
- decoded = self._decoder.decode(chunk)
- if decoded:
- return decoded
-
- async def aclose(self) -> None:
- await self.transport_stream.aclose()
- self._decoder.reset()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return self.transport_stream.extra_attributes
-
-
-@dataclass(eq=False)
-class TextSendStream(ObjectSendStream[str]):
- """
- Sends strings to the wrapped stream as bytes using the given encoding.
-
- :param AnyByteSendStream transport_stream: any bytes-based send stream
- :param str encoding: character encoding to use for encoding strings to bytes (defaults to
- ``utf-8``)
- :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteSendStream
- encoding: InitVar[str] = "utf-8"
- errors: str = "strict"
- _encoder: Callable[..., tuple[bytes, int]] = field(init=False)
-
- def __post_init__(self, encoding: str) -> None:
- self._encoder = codecs.getencoder(encoding)
-
- async def send(self, item: str) -> None:
- encoded = self._encoder(item, self.errors)[0]
- await self.transport_stream.send(encoded)
-
- async def aclose(self) -> None:
- await self.transport_stream.aclose()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return self.transport_stream.extra_attributes
-
-
-@dataclass(eq=False)
-class TextStream(ObjectStream[str]):
- """
- A bidirectional stream that decodes bytes to strings on receive and encodes strings to bytes on
- send.
-
- Extra attributes will be provided from both streams, with the receive stream providing the
- values in case of a conflict.
-
- :param AnyByteStream transport_stream: any bytes-based stream
- :param str encoding: character encoding to use for encoding/decoding strings to/from bytes
- (defaults to ``utf-8``)
- :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the
- `codecs module documentation`_ for a comprehensive list of options)
-
- .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects
- """
-
- transport_stream: AnyByteStream
- encoding: InitVar[str] = "utf-8"
- errors: InitVar[str] = "strict"
- _receive_stream: TextReceiveStream = field(init=False)
- _send_stream: TextSendStream = field(init=False)
-
- def __post_init__(self, encoding: str, errors: str) -> None:
- self._receive_stream = TextReceiveStream(
- self.transport_stream, encoding=encoding, errors=errors
- )
- self._send_stream = TextSendStream(
- self.transport_stream, encoding=encoding, errors=errors
- )
-
- async def receive(self) -> str:
- return await self._receive_stream.receive()
-
- async def send(self, item: str) -> None:
- await self._send_stream.send(item)
-
- async def send_eof(self) -> None:
- await self.transport_stream.send_eof()
-
- async def aclose(self) -> None:
- await self._send_stream.aclose()
- await self._receive_stream.aclose()
-
- @property
- def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]:
- return {
- **self._send_stream.extra_attributes,
- **self._receive_stream.extra_attributes,
- }
diff --git a/spaces/TH5314/newbing/src/lib/bots/bing/types.ts b/spaces/TH5314/newbing/src/lib/bots/bing/types.ts
deleted file mode 100644
index 02cd5e8b01e3529642d28dc1539bf958f4ac420b..0000000000000000000000000000000000000000
--- a/spaces/TH5314/newbing/src/lib/bots/bing/types.ts
+++ /dev/null
@@ -1,259 +0,0 @@
-export type Author = 'user' | 'system' | 'bot'
-
-export type BotId = 'bing'
-
-export enum BingConversationStyle {
- Creative = 'Creative',
- Balanced = 'Balanced',
- Precise = 'Precise'
-}
-
-export enum ErrorCode {
- CONVERSATION_LIMIT = 'CONVERSATION_LIMIT',
- BING_UNAUTHORIZED = 'BING_UNAUTHORIZED',
- BING_FORBIDDEN = 'BING_FORBIDDEN',
- BING_CAPTCHA = 'BING_CAPTCHA',
- THROTTLE_LIMIT = 'THROTTLE_LIMIT',
- NOTFOUND_ERROR = 'NOT_FOUND_ERROR',
- UNKOWN_ERROR = 'UNKOWN_ERROR',
- NETWORK_ERROR = 'NETWORK_ERROR',
-}
-
-export class ChatError extends Error {
- code: ErrorCode
- constructor(message: string, code: ErrorCode) {
- super(message)
- this.code = code
- }
-}
-
-export type ChatMessageModel = {
- id: string
- author: Author
- text: string
- error?: ChatError
- throttling?: Throttling
- sourceAttributions?: SourceAttribution[]
- suggestedResponses?: SuggestedResponse[]
-}
-
-export interface ConversationModel {
- messages: ChatMessageModel[]
-}
-
-export type Event =
- | {
- type: 'UPDATE_ANSWER'
- data: {
- text: string
- spokenText?: string
- sourceAttributions?: SourceAttribution[]
- suggestedResponses?: SuggestedResponse[]
- throttling?: Throttling
- }
- }
- | {
- type: 'DONE'
- }
- | {
- type: 'ERROR'
- error: ChatError
- }
-
-export interface SendMessageParams {
- prompt: string
- imageUrl?: string
- options: T
- onEvent: (event: Event) => void
- signal?: AbortSignal
-}
-
-export interface ConversationResponse {
- conversationId: string
- clientId: string
- conversationSignature: string
- result: {
- value: string
- message?: string
- }
-}
-
-export interface Telemetry {
- metrics?: null
- startTime: string
-}
-
-export interface ChatUpdateArgument {
- messages?: ChatResponseMessage[]
- throttling?: Throttling
- requestId: string
- result: null
-}
-
-export type ChatUpdateCompleteResponse = {
- type: 2
- invocationId: string
- item: ChatResponseItem
-} | {
- type: 1
- target: string
- arguments: ChatUpdateArgument[]
-} | {
- type: 3
- invocationId: string
-} | {
- type: 6 | 7
-}
-
-export interface ChatRequestResult {
- value: string
- serviceVersion: string
- error?: string
-}
-
-export interface ChatResponseItem {
- messages: ChatResponseMessage[]
- firstNewMessageIndex: number
- suggestedResponses: null
- conversationId: string
- requestId: string
- conversationExpiryTime: string
- telemetry: Telemetry
- result: ChatRequestResult
- throttling: Throttling
-}
-export enum InvocationEventType {
- Invocation = 1,
- StreamItem = 2,
- Completion = 3,
- StreamInvocation = 4,
- CancelInvocation = 5,
- Ping = 6,
- Close = 7,
-}
-
-// https://github.com/bytemate/bingchat-api/blob/main/src/lib.ts
-
-export interface ConversationInfo {
- conversationId: string
- clientId: string
- conversationSignature: string
- invocationId: number
- conversationStyle: BingConversationStyle
- prompt: string
- imageUrl?: string
-}
-
-export interface BingChatResponse {
- conversationSignature: string
- conversationId: string
- clientId: string
- invocationId: number
- conversationExpiryTime: Date
- response: string
- details: ChatResponseMessage
-}
-
-export interface Throttling {
- maxNumLongDocSummaryUserMessagesInConversation: number
- maxNumUserMessagesInConversation: number
- numLongDocSummaryUserMessagesInConversation: number
- numUserMessagesInConversation: number
-}
-
-export interface ChatResponseMessage {
- text: string
- spokenText?: string
- author: string
- createdAt: Date
- timestamp: Date
- messageId: string
- requestId: string
- offense: string
- adaptiveCards: AdaptiveCard[]
- sourceAttributions: SourceAttribution[]
- feedback: Feedback
- contentOrigin: string
- messageType?: string
- contentType?: string
- privacy: null
- suggestedResponses: SuggestedResponse[]
-}
-
-export interface AdaptiveCard {
- type: string
- version: string
- body: Body[]
-}
-
-export interface Body {
- type: string
- text: string
- wrap: boolean
- size?: string
-}
-
-export interface Feedback {
- tag: null
- updatedOn: null
- type: string
-}
-
-export interface SourceAttribution {
- providerDisplayName: string
- seeMoreUrl: string
- searchQuery: string
-}
-
-export interface SuggestedResponse {
- text: string
- author?: Author
- createdAt?: Date
- timestamp?: Date
- messageId?: string
- messageType?: string
- offense?: string
- feedback?: Feedback
- contentOrigin?: string
- privacy?: null
-}
-
-export interface KBlobRequest {
- knowledgeRequest: KnowledgeRequestContext
- imageBase64?: string
-}
-
-export interface KBlobResponse {
- blobId: string
- processedBlobId?: string
-}
-
-export interface KnowledgeRequestContext {
- imageInfo: ImageInfo;
- knowledgeRequest: KnowledgeRequest;
-}
-
-export interface ImageInfo {
- url?: string;
-}
-
-export interface KnowledgeRequest {
- invokedSkills: string[];
- subscriptionId: string;
- invokedSkillsRequestData: InvokedSkillsRequestData;
- convoData: ConvoData;
-}
-
-export interface ConvoData {
- convoid: string;
- convotone: BingConversationStyle;
-}
-
-export interface InvokedSkillsRequestData {
- enableFaceBlur: boolean;
-}
-
-export interface FileItem {
- url: string;
- status?: 'loading' | 'error' | 'loaded'
-}
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py
deleted file mode 100644
index 264d564dbda676b52f446c0d25433a15939a78a3..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py
+++ /dev/null
@@ -1,519 +0,0 @@
-"""
-This module uses ctypes to bind a whole bunch of functions and constants from
-SecureTransport. The goal here is to provide the low-level API to
-SecureTransport. These are essentially the C-level functions and constants, and
-they're pretty gross to work with.
-
-This code is a bastardised version of the code found in Will Bond's oscrypto
-library. An enormous debt is owed to him for blazing this trail for us. For
-that reason, this code should be considered to be covered both by urllib3's
-license and by oscrypto's:
-
- Copyright (c) 2015-2016 Will Bond
-
- Permission is hereby granted, free of charge, to any person obtaining a
- copy of this software and associated documentation files (the "Software"),
- to deal in the Software without restriction, including without limitation
- the rights to use, copy, modify, merge, publish, distribute, sublicense,
- and/or sell copies of the Software, and to permit persons to whom the
- Software is furnished to do so, subject to the following conditions:
-
- The above copyright notice and this permission notice shall be included in
- all copies or substantial portions of the Software.
-
- THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
- FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
- DEALINGS IN THE SOFTWARE.
-"""
-from __future__ import absolute_import
-
-import platform
-from ctypes import (
- CDLL,
- CFUNCTYPE,
- POINTER,
- c_bool,
- c_byte,
- c_char_p,
- c_int32,
- c_long,
- c_size_t,
- c_uint32,
- c_ulong,
- c_void_p,
-)
-from ctypes.util import find_library
-
-from ...packages.six import raise_from
-
-if platform.system() != "Darwin":
- raise ImportError("Only macOS is supported")
-
-version = platform.mac_ver()[0]
-version_info = tuple(map(int, version.split(".")))
-if version_info < (10, 8):
- raise OSError(
- "Only OS X 10.8 and newer are supported, not %s.%s"
- % (version_info[0], version_info[1])
- )
-
-
-def load_cdll(name, macos10_16_path):
- """Loads a CDLL by name, falling back to known path on 10.16+"""
- try:
- # Big Sur is technically 11 but we use 10.16 due to the Big Sur
- # beta being labeled as 10.16.
- if version_info >= (10, 16):
- path = macos10_16_path
- else:
- path = find_library(name)
- if not path:
- raise OSError # Caught and reraised as 'ImportError'
- return CDLL(path, use_errno=True)
- except OSError:
- raise_from(ImportError("The library %s failed to load" % name), None)
-
-
-Security = load_cdll(
- "Security", "/System/Library/Frameworks/Security.framework/Security"
-)
-CoreFoundation = load_cdll(
- "CoreFoundation",
- "/System/Library/Frameworks/CoreFoundation.framework/CoreFoundation",
-)
-
-
-Boolean = c_bool
-CFIndex = c_long
-CFStringEncoding = c_uint32
-CFData = c_void_p
-CFString = c_void_p
-CFArray = c_void_p
-CFMutableArray = c_void_p
-CFDictionary = c_void_p
-CFError = c_void_p
-CFType = c_void_p
-CFTypeID = c_ulong
-
-CFTypeRef = POINTER(CFType)
-CFAllocatorRef = c_void_p
-
-OSStatus = c_int32
-
-CFDataRef = POINTER(CFData)
-CFStringRef = POINTER(CFString)
-CFArrayRef = POINTER(CFArray)
-CFMutableArrayRef = POINTER(CFMutableArray)
-CFDictionaryRef = POINTER(CFDictionary)
-CFArrayCallBacks = c_void_p
-CFDictionaryKeyCallBacks = c_void_p
-CFDictionaryValueCallBacks = c_void_p
-
-SecCertificateRef = POINTER(c_void_p)
-SecExternalFormat = c_uint32
-SecExternalItemType = c_uint32
-SecIdentityRef = POINTER(c_void_p)
-SecItemImportExportFlags = c_uint32
-SecItemImportExportKeyParameters = c_void_p
-SecKeychainRef = POINTER(c_void_p)
-SSLProtocol = c_uint32
-SSLCipherSuite = c_uint32
-SSLContextRef = POINTER(c_void_p)
-SecTrustRef = POINTER(c_void_p)
-SSLConnectionRef = c_uint32
-SecTrustResultType = c_uint32
-SecTrustOptionFlags = c_uint32
-SSLProtocolSide = c_uint32
-SSLConnectionType = c_uint32
-SSLSessionOption = c_uint32
-
-
-try:
- Security.SecItemImport.argtypes = [
- CFDataRef,
- CFStringRef,
- POINTER(SecExternalFormat),
- POINTER(SecExternalItemType),
- SecItemImportExportFlags,
- POINTER(SecItemImportExportKeyParameters),
- SecKeychainRef,
- POINTER(CFArrayRef),
- ]
- Security.SecItemImport.restype = OSStatus
-
- Security.SecCertificateGetTypeID.argtypes = []
- Security.SecCertificateGetTypeID.restype = CFTypeID
-
- Security.SecIdentityGetTypeID.argtypes = []
- Security.SecIdentityGetTypeID.restype = CFTypeID
-
- Security.SecKeyGetTypeID.argtypes = []
- Security.SecKeyGetTypeID.restype = CFTypeID
-
- Security.SecCertificateCreateWithData.argtypes = [CFAllocatorRef, CFDataRef]
- Security.SecCertificateCreateWithData.restype = SecCertificateRef
-
- Security.SecCertificateCopyData.argtypes = [SecCertificateRef]
- Security.SecCertificateCopyData.restype = CFDataRef
-
- Security.SecCopyErrorMessageString.argtypes = [OSStatus, c_void_p]
- Security.SecCopyErrorMessageString.restype = CFStringRef
-
- Security.SecIdentityCreateWithCertificate.argtypes = [
- CFTypeRef,
- SecCertificateRef,
- POINTER(SecIdentityRef),
- ]
- Security.SecIdentityCreateWithCertificate.restype = OSStatus
-
- Security.SecKeychainCreate.argtypes = [
- c_char_p,
- c_uint32,
- c_void_p,
- Boolean,
- c_void_p,
- POINTER(SecKeychainRef),
- ]
- Security.SecKeychainCreate.restype = OSStatus
-
- Security.SecKeychainDelete.argtypes = [SecKeychainRef]
- Security.SecKeychainDelete.restype = OSStatus
-
- Security.SecPKCS12Import.argtypes = [
- CFDataRef,
- CFDictionaryRef,
- POINTER(CFArrayRef),
- ]
- Security.SecPKCS12Import.restype = OSStatus
-
- SSLReadFunc = CFUNCTYPE(OSStatus, SSLConnectionRef, c_void_p, POINTER(c_size_t))
- SSLWriteFunc = CFUNCTYPE(
- OSStatus, SSLConnectionRef, POINTER(c_byte), POINTER(c_size_t)
- )
-
- Security.SSLSetIOFuncs.argtypes = [SSLContextRef, SSLReadFunc, SSLWriteFunc]
- Security.SSLSetIOFuncs.restype = OSStatus
-
- Security.SSLSetPeerID.argtypes = [SSLContextRef, c_char_p, c_size_t]
- Security.SSLSetPeerID.restype = OSStatus
-
- Security.SSLSetCertificate.argtypes = [SSLContextRef, CFArrayRef]
- Security.SSLSetCertificate.restype = OSStatus
-
- Security.SSLSetCertificateAuthorities.argtypes = [SSLContextRef, CFTypeRef, Boolean]
- Security.SSLSetCertificateAuthorities.restype = OSStatus
-
- Security.SSLSetConnection.argtypes = [SSLContextRef, SSLConnectionRef]
- Security.SSLSetConnection.restype = OSStatus
-
- Security.SSLSetPeerDomainName.argtypes = [SSLContextRef, c_char_p, c_size_t]
- Security.SSLSetPeerDomainName.restype = OSStatus
-
- Security.SSLHandshake.argtypes = [SSLContextRef]
- Security.SSLHandshake.restype = OSStatus
-
- Security.SSLRead.argtypes = [SSLContextRef, c_char_p, c_size_t, POINTER(c_size_t)]
- Security.SSLRead.restype = OSStatus
-
- Security.SSLWrite.argtypes = [SSLContextRef, c_char_p, c_size_t, POINTER(c_size_t)]
- Security.SSLWrite.restype = OSStatus
-
- Security.SSLClose.argtypes = [SSLContextRef]
- Security.SSLClose.restype = OSStatus
-
- Security.SSLGetNumberSupportedCiphers.argtypes = [SSLContextRef, POINTER(c_size_t)]
- Security.SSLGetNumberSupportedCiphers.restype = OSStatus
-
- Security.SSLGetSupportedCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- POINTER(c_size_t),
- ]
- Security.SSLGetSupportedCiphers.restype = OSStatus
-
- Security.SSLSetEnabledCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- c_size_t,
- ]
- Security.SSLSetEnabledCiphers.restype = OSStatus
-
- Security.SSLGetNumberEnabledCiphers.argtype = [SSLContextRef, POINTER(c_size_t)]
- Security.SSLGetNumberEnabledCiphers.restype = OSStatus
-
- Security.SSLGetEnabledCiphers.argtypes = [
- SSLContextRef,
- POINTER(SSLCipherSuite),
- POINTER(c_size_t),
- ]
- Security.SSLGetEnabledCiphers.restype = OSStatus
-
- Security.SSLGetNegotiatedCipher.argtypes = [SSLContextRef, POINTER(SSLCipherSuite)]
- Security.SSLGetNegotiatedCipher.restype = OSStatus
-
- Security.SSLGetNegotiatedProtocolVersion.argtypes = [
- SSLContextRef,
- POINTER(SSLProtocol),
- ]
- Security.SSLGetNegotiatedProtocolVersion.restype = OSStatus
-
- Security.SSLCopyPeerTrust.argtypes = [SSLContextRef, POINTER(SecTrustRef)]
- Security.SSLCopyPeerTrust.restype = OSStatus
-
- Security.SecTrustSetAnchorCertificates.argtypes = [SecTrustRef, CFArrayRef]
- Security.SecTrustSetAnchorCertificates.restype = OSStatus
-
- Security.SecTrustSetAnchorCertificatesOnly.argstypes = [SecTrustRef, Boolean]
- Security.SecTrustSetAnchorCertificatesOnly.restype = OSStatus
-
- Security.SecTrustEvaluate.argtypes = [SecTrustRef, POINTER(SecTrustResultType)]
- Security.SecTrustEvaluate.restype = OSStatus
-
- Security.SecTrustGetCertificateCount.argtypes = [SecTrustRef]
- Security.SecTrustGetCertificateCount.restype = CFIndex
-
- Security.SecTrustGetCertificateAtIndex.argtypes = [SecTrustRef, CFIndex]
- Security.SecTrustGetCertificateAtIndex.restype = SecCertificateRef
-
- Security.SSLCreateContext.argtypes = [
- CFAllocatorRef,
- SSLProtocolSide,
- SSLConnectionType,
- ]
- Security.SSLCreateContext.restype = SSLContextRef
-
- Security.SSLSetSessionOption.argtypes = [SSLContextRef, SSLSessionOption, Boolean]
- Security.SSLSetSessionOption.restype = OSStatus
-
- Security.SSLSetProtocolVersionMin.argtypes = [SSLContextRef, SSLProtocol]
- Security.SSLSetProtocolVersionMin.restype = OSStatus
-
- Security.SSLSetProtocolVersionMax.argtypes = [SSLContextRef, SSLProtocol]
- Security.SSLSetProtocolVersionMax.restype = OSStatus
-
- try:
- Security.SSLSetALPNProtocols.argtypes = [SSLContextRef, CFArrayRef]
- Security.SSLSetALPNProtocols.restype = OSStatus
- except AttributeError:
- # Supported only in 10.12+
- pass
-
- Security.SecCopyErrorMessageString.argtypes = [OSStatus, c_void_p]
- Security.SecCopyErrorMessageString.restype = CFStringRef
-
- Security.SSLReadFunc = SSLReadFunc
- Security.SSLWriteFunc = SSLWriteFunc
- Security.SSLContextRef = SSLContextRef
- Security.SSLProtocol = SSLProtocol
- Security.SSLCipherSuite = SSLCipherSuite
- Security.SecIdentityRef = SecIdentityRef
- Security.SecKeychainRef = SecKeychainRef
- Security.SecTrustRef = SecTrustRef
- Security.SecTrustResultType = SecTrustResultType
- Security.SecExternalFormat = SecExternalFormat
- Security.OSStatus = OSStatus
-
- Security.kSecImportExportPassphrase = CFStringRef.in_dll(
- Security, "kSecImportExportPassphrase"
- )
- Security.kSecImportItemIdentity = CFStringRef.in_dll(
- Security, "kSecImportItemIdentity"
- )
-
- # CoreFoundation time!
- CoreFoundation.CFRetain.argtypes = [CFTypeRef]
- CoreFoundation.CFRetain.restype = CFTypeRef
-
- CoreFoundation.CFRelease.argtypes = [CFTypeRef]
- CoreFoundation.CFRelease.restype = None
-
- CoreFoundation.CFGetTypeID.argtypes = [CFTypeRef]
- CoreFoundation.CFGetTypeID.restype = CFTypeID
-
- CoreFoundation.CFStringCreateWithCString.argtypes = [
- CFAllocatorRef,
- c_char_p,
- CFStringEncoding,
- ]
- CoreFoundation.CFStringCreateWithCString.restype = CFStringRef
-
- CoreFoundation.CFStringGetCStringPtr.argtypes = [CFStringRef, CFStringEncoding]
- CoreFoundation.CFStringGetCStringPtr.restype = c_char_p
-
- CoreFoundation.CFStringGetCString.argtypes = [
- CFStringRef,
- c_char_p,
- CFIndex,
- CFStringEncoding,
- ]
- CoreFoundation.CFStringGetCString.restype = c_bool
-
- CoreFoundation.CFDataCreate.argtypes = [CFAllocatorRef, c_char_p, CFIndex]
- CoreFoundation.CFDataCreate.restype = CFDataRef
-
- CoreFoundation.CFDataGetLength.argtypes = [CFDataRef]
- CoreFoundation.CFDataGetLength.restype = CFIndex
-
- CoreFoundation.CFDataGetBytePtr.argtypes = [CFDataRef]
- CoreFoundation.CFDataGetBytePtr.restype = c_void_p
-
- CoreFoundation.CFDictionaryCreate.argtypes = [
- CFAllocatorRef,
- POINTER(CFTypeRef),
- POINTER(CFTypeRef),
- CFIndex,
- CFDictionaryKeyCallBacks,
- CFDictionaryValueCallBacks,
- ]
- CoreFoundation.CFDictionaryCreate.restype = CFDictionaryRef
-
- CoreFoundation.CFDictionaryGetValue.argtypes = [CFDictionaryRef, CFTypeRef]
- CoreFoundation.CFDictionaryGetValue.restype = CFTypeRef
-
- CoreFoundation.CFArrayCreate.argtypes = [
- CFAllocatorRef,
- POINTER(CFTypeRef),
- CFIndex,
- CFArrayCallBacks,
- ]
- CoreFoundation.CFArrayCreate.restype = CFArrayRef
-
- CoreFoundation.CFArrayCreateMutable.argtypes = [
- CFAllocatorRef,
- CFIndex,
- CFArrayCallBacks,
- ]
- CoreFoundation.CFArrayCreateMutable.restype = CFMutableArrayRef
-
- CoreFoundation.CFArrayAppendValue.argtypes = [CFMutableArrayRef, c_void_p]
- CoreFoundation.CFArrayAppendValue.restype = None
-
- CoreFoundation.CFArrayGetCount.argtypes = [CFArrayRef]
- CoreFoundation.CFArrayGetCount.restype = CFIndex
-
- CoreFoundation.CFArrayGetValueAtIndex.argtypes = [CFArrayRef, CFIndex]
- CoreFoundation.CFArrayGetValueAtIndex.restype = c_void_p
-
- CoreFoundation.kCFAllocatorDefault = CFAllocatorRef.in_dll(
- CoreFoundation, "kCFAllocatorDefault"
- )
- CoreFoundation.kCFTypeArrayCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeArrayCallBacks"
- )
- CoreFoundation.kCFTypeDictionaryKeyCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeDictionaryKeyCallBacks"
- )
- CoreFoundation.kCFTypeDictionaryValueCallBacks = c_void_p.in_dll(
- CoreFoundation, "kCFTypeDictionaryValueCallBacks"
- )
-
- CoreFoundation.CFTypeRef = CFTypeRef
- CoreFoundation.CFArrayRef = CFArrayRef
- CoreFoundation.CFStringRef = CFStringRef
- CoreFoundation.CFDictionaryRef = CFDictionaryRef
-
-except (AttributeError):
- raise ImportError("Error initializing ctypes")
-
-
-class CFConst(object):
- """
- A class object that acts as essentially a namespace for CoreFoundation
- constants.
- """
-
- kCFStringEncodingUTF8 = CFStringEncoding(0x08000100)
-
-
-class SecurityConst(object):
- """
- A class object that acts as essentially a namespace for Security constants.
- """
-
- kSSLSessionOptionBreakOnServerAuth = 0
-
- kSSLProtocol2 = 1
- kSSLProtocol3 = 2
- kTLSProtocol1 = 4
- kTLSProtocol11 = 7
- kTLSProtocol12 = 8
- # SecureTransport does not support TLS 1.3 even if there's a constant for it
- kTLSProtocol13 = 10
- kTLSProtocolMaxSupported = 999
-
- kSSLClientSide = 1
- kSSLStreamType = 0
-
- kSecFormatPEMSequence = 10
-
- kSecTrustResultInvalid = 0
- kSecTrustResultProceed = 1
- # This gap is present on purpose: this was kSecTrustResultConfirm, which
- # is deprecated.
- kSecTrustResultDeny = 3
- kSecTrustResultUnspecified = 4
- kSecTrustResultRecoverableTrustFailure = 5
- kSecTrustResultFatalTrustFailure = 6
- kSecTrustResultOtherError = 7
-
- errSSLProtocol = -9800
- errSSLWouldBlock = -9803
- errSSLClosedGraceful = -9805
- errSSLClosedNoNotify = -9816
- errSSLClosedAbort = -9806
-
- errSSLXCertChainInvalid = -9807
- errSSLCrypto = -9809
- errSSLInternal = -9810
- errSSLCertExpired = -9814
- errSSLCertNotYetValid = -9815
- errSSLUnknownRootCert = -9812
- errSSLNoRootCert = -9813
- errSSLHostNameMismatch = -9843
- errSSLPeerHandshakeFail = -9824
- errSSLPeerUserCancelled = -9839
- errSSLWeakPeerEphemeralDHKey = -9850
- errSSLServerAuthCompleted = -9841
- errSSLRecordOverflow = -9847
-
- errSecVerifyFailed = -67808
- errSecNoTrustSettings = -25263
- errSecItemNotFound = -25300
- errSecInvalidTrustSettings = -25262
-
- # Cipher suites. We only pick the ones our default cipher string allows.
- # Source: https://developer.apple.com/documentation/security/1550981-ssl_cipher_suite_values
- TLS_ECDHE_ECDSA_WITH_AES_256_GCM_SHA384 = 0xC02C
- TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384 = 0xC030
- TLS_ECDHE_ECDSA_WITH_AES_128_GCM_SHA256 = 0xC02B
- TLS_ECDHE_RSA_WITH_AES_128_GCM_SHA256 = 0xC02F
- TLS_ECDHE_ECDSA_WITH_CHACHA20_POLY1305_SHA256 = 0xCCA9
- TLS_ECDHE_RSA_WITH_CHACHA20_POLY1305_SHA256 = 0xCCA8
- TLS_DHE_RSA_WITH_AES_256_GCM_SHA384 = 0x009F
- TLS_DHE_RSA_WITH_AES_128_GCM_SHA256 = 0x009E
- TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA384 = 0xC024
- TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384 = 0xC028
- TLS_ECDHE_ECDSA_WITH_AES_256_CBC_SHA = 0xC00A
- TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA = 0xC014
- TLS_DHE_RSA_WITH_AES_256_CBC_SHA256 = 0x006B
- TLS_DHE_RSA_WITH_AES_256_CBC_SHA = 0x0039
- TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256 = 0xC023
- TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256 = 0xC027
- TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA = 0xC009
- TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA = 0xC013
- TLS_DHE_RSA_WITH_AES_128_CBC_SHA256 = 0x0067
- TLS_DHE_RSA_WITH_AES_128_CBC_SHA = 0x0033
- TLS_RSA_WITH_AES_256_GCM_SHA384 = 0x009D
- TLS_RSA_WITH_AES_128_GCM_SHA256 = 0x009C
- TLS_RSA_WITH_AES_256_CBC_SHA256 = 0x003D
- TLS_RSA_WITH_AES_128_CBC_SHA256 = 0x003C
- TLS_RSA_WITH_AES_256_CBC_SHA = 0x0035
- TLS_RSA_WITH_AES_128_CBC_SHA = 0x002F
- TLS_AES_128_GCM_SHA256 = 0x1301
- TLS_AES_256_GCM_SHA384 = 0x1302
- TLS_AES_128_CCM_8_SHA256 = 0x1305
- TLS_AES_128_CCM_SHA256 = 0x1304
diff --git a/spaces/TeamTonic/MultiMed/app.py b/spaces/TeamTonic/MultiMed/app.py
deleted file mode 100644
index 29b1ad119a6d38fc2fca1fa616c0f221e9751208..0000000000000000000000000000000000000000
--- a/spaces/TeamTonic/MultiMed/app.py
+++ /dev/null
@@ -1,490 +0,0 @@
-# Welcome to Team Tonic's MultiMed
-
-from gradio_client import Client
-import os
-import numpy as np
-import base64
-import gradio as gr
-import requests
-import json
-import dotenv
-from scipy.io.wavfile import write
-import PIL
-from openai import OpenAI
-import time
-dotenv.load_dotenv()
-
-seamless_client = Client("facebook/seamless_m4t")
-HuggingFace_Token = os.getenv("HuggingFace_Token")
-
-def check_hallucination(assertion,citation):
- API_URL = "https://api-inference.huggingface.co/models/vectara/hallucination_evaluation_model"
- headers = {"Authorization": f"Bearer {HuggingFace_Token}"}
- payload = {"inputs" : f"{assertion} [SEP] {citation}"}
-
- response = requests.post(API_URL, headers=headers, json=payload,timeout=120)
- output = response.json()
- output = output[0][0]["score"]
-
- return f"**hullicination score:** {output}"
-
-
-
-def process_speech(input_language, audio_input):
- """
- processing sound using seamless_m4t
- """
- if audio_input is None :
- return "no audio or audio did not save yet \nplease try again ! "
- print(f"audio : {audio_input}")
- print(f"audio type : {type(audio_input)}")
- out = seamless_client.predict(
- "S2TT",
- "file",
- None,
- audio_input, #audio_name
- "",
- input_language,# source language
- input_language,# target language
- api_name="/run",
- )
- out = out[1] # get the text
- try :
- return f"{out}"
- except Exception as e :
- return f"{e}"
-
-
-
-
-def process_image(image) :
- img_name = f"{np.random.randint(0, 100)}.jpg"
- PIL.Image.fromarray(image.astype('uint8'), 'RGB').save(img_name)
- image = open(img_name, "rb").read()
- base64_image = base64_image = base64.b64encode(image).decode('utf-8')
- openai_api_key = os.getenv('OPENAI_API_KEY')
- # oai_org = os.getenv('OAI_ORG')
-
- headers = {
- "Content-Type": "application/json",
- "Authorization": f"Bearer {openai_api_key}"
- }
-
- payload = {
- "model": "gpt-4-vision-preview",
- "messages": [
- {
- "role": "user",
- "content": [
- {
- "type": "text",
- "text": "You are clinical consultant discussion training cases with students at TonicUniversity. Assess and describe the photo in minute detail. Explain why each area or item in the photograph would be inappropriate to describe if required. Pay attention to anatomy, symptoms and remedies. Propose a course of action based on your assessment. Exclude any other commentary:"
- },
- {
- "type": "image_url",
- "image_url": {
- "url": f"data:image/jpeg;base64,{base64_image}"
- }
- }
- ]
- }
- ],
- "max_tokens": 1200
- }
-
- response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
-
- try :
- out = response.json()
- out = out["choices"][0]["message"]["content"]
-
- return out
- except Exception as e :
- return f"{e}"
-
-
-def query_vectara(text):
- user_message = text
-
- # Read authentication parameters from the .env file
- CUSTOMER_ID = os.getenv('CUSTOMER_ID')
- CORPUS_ID = os.getenv('CORPUS_ID')
- API_KEY = os.getenv('API_KEY')
-
- # Define the headers
- api_key_header = {
- "customer-id": CUSTOMER_ID,
- "x-api-key": API_KEY
- }
-
- # Define the request body in the structure provided in the example
- request_body = {
- "query": [
- {
- "query": user_message,
- "queryContext": "",
- "start": 1,
- "numResults": 50,
- "contextConfig": {
- "charsBefore": 0,
- "charsAfter": 0,
- "sentencesBefore": 2,
- "sentencesAfter": 2,
- "startTag": "%START_SNIPPET%",
- "endTag": "%END_SNIPPET%",
- },
- "rerankingConfig": {
- "rerankerId": 272725718,
- "mmrConfig": {
- "diversityBias": 0.35
- }
- },
- "corpusKey": [
- {
- "customerId": CUSTOMER_ID,
- "corpusId": CORPUS_ID,
- "semantics": 0,
- "metadataFilter": "",
- "lexicalInterpolationConfig": {
- "lambda": 0
- },
- "dim": []
- }
- ],
- "summary": [
- {
- "maxSummarizedResults": 5,
- "responseLang": "auto",
- "summarizerPromptName": "vectara-summary-ext-v1.2.0"
- }
- ]
- }
- ]
- }
-
- # Make the API request using Gradio
- response = requests.post(
- "https://api.vectara.io/v1/query",
- json=request_body, # Use json to automatically serialize the request body
- verify=True,
- headers=api_key_header
- )
-
- if response.status_code == 200:
- query_data = response.json()
- if query_data:
- sources_info = []
-
- # Extract the summary.
- summary = query_data['responseSet'][0]['summary'][0]['text']
-
- # Iterate over all response sets
- for response_set in query_data.get('responseSet', []):
- # Extract sources
- # Limit to top 5 sources.
- for source in response_set.get('response', [])[:5]:
- source_metadata = source.get('metadata', [])
- source_info = {}
-
- for metadata in source_metadata:
- metadata_name = metadata.get('name', '')
- metadata_value = metadata.get('value', '')
-
- if metadata_name == 'title':
- source_info['title'] = metadata_value
- elif metadata_name == 'author':
- source_info['author'] = metadata_value
- elif metadata_name == 'pageNumber':
- source_info['page number'] = metadata_value
-
- if source_info:
- sources_info.append(source_info)
-
- result = {"summary": summary, "sources": sources_info}
- return f"{json.dumps(result, indent=2)}"
- else:
- return "No data found in the response."
- else:
- return f"Error: {response.status_code}"
-
-
-def convert_to_markdown(vectara_response_json):
- vectara_response = json.loads(vectara_response_json)
- if vectara_response:
- summary = vectara_response.get('summary', 'No summary available')
- sources_info = vectara_response.get('sources', [])
-
- # Format the summary as Markdown
- markdown_summary = f' {summary}\n\n'
-
- # Format the sources as a numbered list
- markdown_sources = ""
- for i, source_info in enumerate(sources_info):
- author = source_info.get('author', 'Unknown author')
- title = source_info.get('title', 'Unknown title')
- page_number = source_info.get('page number', 'Unknown page number')
- markdown_sources += f"{i+1}. {title} by {author}, Page {page_number}\n"
-
- return f"{markdown_summary}**Sources:**\n{markdown_sources}"
- else:
- return "No data found in the response."
-# Main function to handle the Gradio interface logic
-
-def process_summary_with_openai(summary):
- """
- This function takes a summary text as input and processes it with OpenAI's GPT model.
- """
- try:
- # Ensure that the OpenAI client is properly initialized
- client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
-
- # Create the prompt for OpenAI's completion
- prompt = "You are clinical consultant discussion training cases with students at TonicUniversity. Assess and describe the proper options in minute detail. Propose a course of action based on your assessment. You will recieve a summary assessment in a language, respond ONLY in the original language. Exclude any other commentary:"
-
- # Call the OpenAI API with the prompt and the summary
- completion = client.chat.completions.create(
- model="gpt-4-1106-preview", # Make sure to use the correct model name
- messages=[
- {"role": "system", "content": prompt},
- {"role": "user", "content": summary}
- ]
- )
-
- # Extract the content from the completion
- final_summary = completion.choices[0].message.content
- return final_summary
- except Exception as e:
- return str(e)
-
-
-def process_and_query(text=None):
- try:
- # augment the prompt before feeding it to vectara
- text = "the user asks the following to his health adviser " + text
- # If an image is provided, process it with OpenAI and use the response as the text query for Vectara
- # if image is not None:
- # text = process_image(image)
- # return "**Summary:** "+text
- # if audio is not None:
- # text = process_speech(audio)
- # # augment the prompt before feeding it to vectara
- # text = "the user asks the following to his health adviser " + text
-
-
-
- # Use the text to query Vectara
- vectara_response_json = query_vectara(text)
-
- # Convert the Vectara response to Markdown
- markdown_output = convert_to_markdown(vectara_response_json)
-
- # Process the summary with OpenAI
- final_response = process_summary_with_openai(markdown_output)
-
- # Return the processed summary along with the full output
- return f"**Summary**: {final_response}\n\n**Full output**:\n{markdown_output}"
- except Exception as e:
- return str(e)
-
- completion = client.chat.completions.create(
- model="gpt-3.5-turbo",
- messages=[
- {"role": "system", "content": prompt},
- {"role": "user", "content": markdown_output_final}
- ]
- )
- final_response= completion.choices[0].message.content
- return f"**Summary**: {final_response}\n\n**Full output**:\n{markdown_output}"
- except Exception as e:
- return str(e)
-
-
-# Define the Gradio interface
-# iface = gr.Interface(
-# fn=process_and_query,
-# inputs=[
-# gr.Textbox(label="Input Text"),
-# gr.Image(label="Upload Image"),
-# gr.Audio(label="talk in french",
-# sources=["microphone"]),
-# ],
-# outputs=[gr.Markdown(label="Output Text")],
-# title="👋🏻Welcome to ⚕🗣️😷MultiMed - Access Chat ⚕🗣️😷",
-# description='''
-# ### How To Use ⚕🗣️😷MultiMed⚕:
-# #### 🗣️📝Interact with ⚕🗣️😷MultiMed⚕ in any language using audio or text!
-# #### 🗣️📝 This is an educational and accessible conversational tool to improve wellness and sanitation in support of public health.
-# #### 📚🌟💼 The knowledge base is composed of publicly available medical and health sources in multiple languages. We also used [Kelvalya/MedAware](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset) that we processed and converted to HTML. The quality of the answers depends on the quality of the dataset, so if you want to see some data represented here, do [get in touch](https://discord.gg/GWpVpekp). You can also use 😷MultiMed⚕️ on your own data & in your own way by cloning this space. 🧬🔬🔍 Simply click here:
-# #### Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community on 👻Discord: [Discord](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)"
-# ''',
-# theme='ParityError/Anime',
-# examples=[
-# ["What is the proper treatment for buccal herpes?"],
-# ["Male, 40 presenting with swollen glands and a rash"],
-# ["How does cellular metabolism work TCA cycle"],
-# ["What special care must be provided to children with chicken pox?"],
-# ["When and how often should I wash my hands ?"],
-# ["بکل ہرپس کا صحیح علاج کیا ہے؟"],
-# ["구강 헤르페스의 적절한 치료법은 무엇입니까?"],
-# ["Je, ni matibabu gani sahihi kwa herpes ya buccal?"],
-# ],
-# )
-
-welcome_message = """
-# 👋🏻Welcome to ⚕🗣️😷MultiMed - Access Chat ⚕🗣️😷
-### How To Use ⚕🗣️😷MultiMed⚕:
-#### 🗣️📝Interact with ⚕🗣️😷MultiMed⚕ in any language using audio or text!
-#### 🗣️📝 This is an educational and accessible conversational tool to improve wellness and sanitation in support of public health.
-#### 📚🌟💼 The knowledge base is composed of publicly available medical and health sources in multiple languages. We also used [Kelvalya/MedAware](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset) that we processed and converted to HTML. The quality of the answers depends on the quality of the dataset, so if you want to see some data represented here, do [get in touch](https://discord.gg/GWpVpekp). You can also use 😷MultiMed⚕️ on your own data & in your own way by cloning this space. 🧬🔬🔍 Simply click here: - - -## Preliminaries -1. Install the environment: -```bash -bash ./scripts/EnvsSetup.sh -``` -2. Activate the conda environment: -```bash -conda activate WavJourney -``` - -3. (Optional) You can modify the default configuration in `config.yaml`, check the details described in the configuration file. -4. Pre-download the models (might take some time): -```bash -python scripts/download_models.py -``` - -5. Set the WAVJOURNEY_OPENAI_KEY in the environment variable for accessing [GPT-4 API](https://platform.openai.com/account/api-keys) [[Guidance](https://help.openai.com/en/articles/7102672-how-can-i-access-gpt-4)] -```bash -export WAVJOURNEY_OPENAI_KEY=your_openai_key_here -``` - -6. Set environment variables for using API services -```bash -# Set the port for the WAVJOURNEY service to 8021 -export WAVJOURNEY_SERVICE_PORT=8021 - -# Set the URL for the WAVJOURNEY service to 127.0.0.1 -export WAVJOURNEY_SERVICE_URL=127.0.0.1 - -# Limit the maximum script lines for WAVJOURNEY to 999 -export WAVJOURNEY_MAX_SCRIPT_LINES=999 -``` - - -7. Start Python API services (e.g., Text-to-Speech, Text-to-Audio) -```bash -bash scripts/start_services.sh -``` - -## Web APP - ```bash -bash scripts/start_ui.sh - ``` - -## Commandline Usage - ```bash - python wavjourney_cli.py -f --input-text "Generate a one-minute introduction to quantum mechanics" - ``` - - -## Kill the services -You can kill the running services via this command: - ```bash -python scripts/kill_services.py - ``` - -## (Advanced features) Speaker customization -You can add voice presets to WavJourney to customize the voice actors. Simply provide the voice id, the description and a sample wav file, and WavJourney will pick the voice automatically based on the audio script. Predefined system voice presets are in `data/voice_presets`. - -You can manage voice presets via UI. Specifically, if you want to add voice to voice presets. Run the script via command line below: -```bash -python add_voice_preset.py --id "id" --desc "description" --wav-path path/to/wav --session-id '' -``` -What makes for good voice prompt? See detailed instructions here. -## Hardware requirement -- The VRAM of the GPU in the default configuration should be greater than 16 GB. -- Operation system: Linux. - -## Citation -If you find this work useful, you can cite the paper below: - - @article{liu2023wavjourney, - title = {WavJourney: Compositional Audio Creation with Large Language Models}, - author = {Liu, Xubo and Zhu, Zhongkai and Liu, Haohe and Yuan, Yi and Huang, Qiushi and Liang, Jinhua and Cao, Yin and Kong, Qiuqiang and Plumbley, Mark D and Wang, Wenwu}, - journal = {arXiv preprint arXiv:2307.14335}, - year = {2023} - } - -[](https://www.buymeacoffee.com/liuxubo) - -## Appreciation -- [Bark](https://github.com/suno-ai/bark) for a zero-shot text-to-speech synthesis model. -- [AudioCraft](https://github.com/facebookresearch/audiocraft) for state-of-the-art audio generation models. - -## Disclaimer -We are not responsible for audio generated using semantics created by this model. Just don't use it for illegal purposes. - diff --git a/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py b/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py deleted file mode 100644 index be58431ed446bf6283c17274a41e37ec3eb24409..0000000000000000000000000000000000000000 --- a/spaces/AutoLLM/AutoAgents/autoagents/spaces/app.py +++ /dev/null @@ -1,153 +0,0 @@ -import os -import asyncio -import random -from datetime import date, datetime, timezone, timedelta -from ast import literal_eval - -import streamlit as st -import openai - -from autoagents.utils.constants import MAIN_HEADER, MAIN_CAPTION, SAMPLE_QUESTIONS -from autoagents.agents.search import ActionRunner - -from langchain.chat_models import ChatOpenAI - - -async def run(): - output_acc = "" - st.session_state["random"] = random.randint(0, 99) - if "task" not in st.session_state: - st.session_state.task = None - if "model_name" not in st.session_state: - st.session_state.model_name = "gpt-3.5-turbo" - - st.set_page_config( - page_title="Search Agent", - page_icon="🤖", - layout="wide", - initial_sidebar_state="expanded", - ) - - st.title(MAIN_HEADER) - st.caption(MAIN_CAPTION) - - with st.form("my_form", clear_on_submit=False): - st.markdown("", unsafe_allow_html=True) - user_input = st.text_input( - "You: ", - key="input", - placeholder="Ask me anything ...", - label_visibility="hidden", - ) - - submitted = st.form_submit_button( - "Search", help="Hit to submit the search query." - ) - - # Ask the user to enter their OpenAI API key - if (api_key := st.sidebar.text_input("OpenAI api-key", type="password")): - api_org = None - else: - api_key, api_org = os.getenv("OPENAI_API_KEY"), os.getenv("OPENAI_API_ORG") - with st.sidebar: - model_dict = { - "gpt-3.5-turbo": "GPT-3.5-turbo", - "gpt-4": "GPT-4 (Better but slower)", - } - st.radio( - "OpenAI model", - model_dict.keys(), - key="model_name", - format_func=lambda x: model_dict[x], - ) - - time_zone = str(datetime.now(timezone(timedelta(0))).astimezone().tzinfo) - st.markdown(f"**The system time zone is {time_zone} and the date is {date.today()}**") - - st.markdown("**Example Queries:**") - for q in SAMPLE_QUESTIONS: - st.markdown(f"*{q}*") - - if not api_key: - st.warning( - "API key required to try this app. The API key is not stored in any form. [This](https://help.openai.com/en/articles/4936850-where-do-i-find-my-secret-api-key) might help." - ) - elif api_org and st.session_state.model_name == "gpt-4": - st.warning( - "The free API key does not support GPT-4. Please switch to GPT-3.5-turbo or input your own API key." - ) - else: - outputq = asyncio.Queue() - runner = ActionRunner(outputq, - ChatOpenAI(openai_api_key=api_key, - openai_organization=api_org, - temperature=0, - model_name=st.session_state.model_name), - persist_logs=True) # log to HF-dataset - - async def cleanup(e): - st.error(e) - await st.session_state.task - st.session_state.task = None - st.stop() - - placeholder = st.empty() - - if user_input and submitted: - if st.session_state.task is not None: - with placeholder.container(): - st.session_state.task.cancel() - st.warning("Previous search aborted", icon="⚠️") - - st.session_state.task = asyncio.create_task( - runner.run(user_input, outputq) - ) - iterations = 0 - with st.expander("Search Results", expanded=True): - while True: - with st.spinner("Wait for it..."): - output = await outputq.get() - placeholder.empty() - if isinstance(output, Exception): - if isinstance(output, openai.error.AuthenticationError): - await cleanup(f"AuthenticationError: Invalid OpenAI API key.") - elif isinstance(output, openai.error.InvalidRequestError) \ - and output._message == "The model: `gpt-4` does not exist": - await cleanup(f"The free API key does not support GPT-4. Please switch to GPT-3.5-turbo or input your own API key.") - elif isinstance(output, openai.error.OpenAIError): - await cleanup(output) - elif isinstance(output, RuntimeWarning): - st.warning(output) - continue - else: - await cleanup("Something went wrong. Please try searching again.") - return - try: - output_fmt = literal_eval(output) - st.json(output_fmt, expanded=False) - st.write("---") - iterations += 1 - except: - output_acc += "\n" + output - st.markdown(f"
{output}
",
- unsafe_allow_html=True)
- if iterations >= runner.agent_executor.max_iterations:
- await cleanup(
- f"Maximum iterations ({iterations}) exceeded. You can try running the search again or try a variation of the query."
- )
- return
- if "Final Answer:" in output:
- break
- # Found the answer
- final_answer = await st.session_state.task
- final_answer = final_answer.replace("$", "\$")
- # st.success accepts md
- st.success(final_answer, icon="✅")
- st.balloons()
- st.session_state.task = None
- st.stop()
-
-if __name__ == "__main__":
- loop = asyncio.new_event_loop()
- loop.set_debug(enabled=False)
- loop.run_until_complete(run())
diff --git a/spaces/BAAI/vid2vid-zero/Dockerfile b/spaces/BAAI/vid2vid-zero/Dockerfile
deleted file mode 100644
index 9eaa78f8facc5066f018183f108824842f11b7e8..0000000000000000000000000000000000000000
--- a/spaces/BAAI/vid2vid-zero/Dockerfile
+++ /dev/null
@@ -1,57 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update && \
- apt-get upgrade -y && \
- apt-get install -y --no-install-recommends \
- git \
- git-lfs \
- wget \
- curl \
- # ffmpeg \
- ffmpeg \
- x264 \
- # python build dependencies \
- build-essential \
- libssl-dev \
- zlib1g-dev \
- libbz2-dev \
- libreadline-dev \
- libsqlite3-dev \
- libncursesw5-dev \
- xz-utils \
- tk-dev \
- libxml2-dev \
- libxmlsec1-dev \
- libffi-dev \
- liblzma-dev && \
- apt-get clean && \
- rm -rf /var/lib/apt/lists/*
-
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:${PATH}
-WORKDIR ${HOME}/app
-
-RUN curl https://pyenv.run | bash
-ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ENV PYTHON_VERSION=3.10.9
-RUN pyenv install ${PYTHON_VERSION} && \
- pyenv global ${PYTHON_VERSION} && \
- pyenv rehash && \
- pip install --no-cache-dir -U pip setuptools wheel
-
-RUN pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1
-COPY --chown=1000 requirements.txt /tmp/requirements.txt
-RUN pip install --no-cache-dir -U -r /tmp/requirements.txt
-
-COPY --chown=1000 . ${HOME}/app
-# RUN cd Tune-A-Video && patch -p1 < ../patch
-ENV PYTHONPATH=${HOME}/app \
- PYTHONUNBUFFERED=1 \
- GRADIO_ALLOW_FLAGGING=never \
- GRADIO_NUM_PORTS=1 \
- GRADIO_SERVER_NAME=0.0.0.0 \
- GRADIO_THEME=huggingface \
- SYSTEM=spaces
-CMD ["python", "app.py"]
diff --git a/spaces/Bala2-03-2003/MygenvioceAI/app.py b/spaces/Bala2-03-2003/MygenvioceAI/app.py
deleted file mode 100644
index ca8b6d40b4ab898c70da92f4a4298de2baf703dc..0000000000000000000000000000000000000000
--- a/spaces/Bala2-03-2003/MygenvioceAI/app.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import os
-import re
-import requests
-import json
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-PLAY_HT_API_KEY=os.getenv('PLAY_HT_API_KEY')
-PLAY_HT_USER_ID=os.getenv('PLAY_HT_USER_ID')
-
-PLAY_HT_VOICE_ID=os.getenv('PLAY_HT_VOICE_ID')
-play_ht_api_get_audio_url = "https://play.ht/api/v2/tts"
-
-
-template = """You are a helpful assistant to answer user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
- input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
- llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
- prompt=prompt,
- verbose=True,
- memory=memory,
-)
-
-headers = {
- "accept": "text/event-stream",
- "content-type": "application/json",
- "AUTHORIZATION": "Bearer "+ PLAY_HT_API_KEY,
- "X-USER-ID": PLAY_HT_USER_ID
-}
-
-
-def get_payload(text):
- return {
- "text": text,
- "voice": PLAY_HT_VOICE_ID,
- "quality": "medium",
- "output_format": "mp3",
- "speed": 1,
- "sample_rate": 24000,
- "seed": None,
- "temperature": None
- }
-
-def get_generated_audio(text):
- payload = get_payload(text)
- generated_response = {}
- try:
- response = requests.post(play_ht_api_get_audio_url, json=payload, headers=headers)
- response.raise_for_status()
- generated_response["type"]= 'SUCCESS'
- generated_response["response"] = response.text
- except requests.exceptions.RequestException as e:
- generated_response["type"]= 'ERROR'
- try:
- response_text = json.loads(response.text)
- if response_text['error_message']:
- generated_response["response"] = response_text['error_message']
- else:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["type"]= 'ERROR'
- generated_response["response"] = response.text
- return generated_response
-
-def extract_urls(text):
- # Define the regex pattern for URLs
- url_pattern = r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+[/\w\.-]*'
-
- # Find all occurrences of URLs in the text
- urls = re.findall(url_pattern, text)
-
- return urls
-
-def get_audio_reply_for_question(text):
- generated_audio_event = get_generated_audio(text)
- #From get_generated_audio, you will get events in a string format, from that we need to extract the url
- final_response = {
- "audio_url": '',
- "message": ''
- }
- if generated_audio_event["type"] == 'SUCCESS':
- audio_urls = extract_urls(generated_audio_event["response"])
- if len(audio_urls) == 0:
- final_response['message'] = "No audio file link found in generated event"
- else:
- final_response['audio_url'] = audio_urls[-1]
- else:
- final_response['message'] = generated_audio_event['response']
- return final_response
-
-def download_url(url):
- try:
- # Send a GET request to the URL to fetch the content
- final_response = {
- 'content':'',
- 'error':''
- }
- response = requests.get(url)
- # Check if the request was successful (status code 200)
- if response.status_code == 200:
- final_response['content'] = response.content
- else:
- final_response['error'] = f"Failed to download the URL. Status code: {response.status_code}"
- except Exception as e:
- final_response['error'] = f"Failed to download the URL. Error: {e}"
- return final_response
-
-def get_filename_from_url(url):
- # Use os.path.basename() to extract the file name from the URL
- file_name = os.path.basename(url)
- return file_name
-
-def get_text_response(user_message):
- response = llm_chain.predict(user_message = user_message)
- return response
-
-def get_text_response_and_audio_response(user_message):
- response = get_text_response(user_message) # Getting the reply from Open AI
- audio_reply_for_question_response = get_audio_reply_for_question(response)
- final_response = {
- 'output_file_path': '',
- 'message':''
- }
- audio_url = audio_reply_for_question_response['audio_url']
- if audio_url:
- output_file_path=get_filename_from_url(audio_url)
- download_url_response = download_url(audio_url)
- audio_content = download_url_response['content']
- if audio_content:
- with open(output_file_path, "wb") as audio_file:
- audio_file.write(audio_content)
- final_response['output_file_path'] = output_file_path
- else:
- final_response['message'] = download_url_response['error']
- else:
- final_response['message'] = audio_reply_for_question_response['message']
- return final_response
-
-def chat_bot_response(message, history):
- text_and_audio_response = get_text_response_and_audio_response(message)
- output_file_path = text_and_audio_response['output_file_path']
- if output_file_path:
- return (text_and_audio_response['output_file_path'],)
- else:
- return text_and_audio_response['message']
-
-demo = gr.ChatInterface(chat_bot_response,examples=["How are you doing?","What are your interests?","Which places do you like to visit?"])
-
-if __name__ == "__main__":
- demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/BartPoint/VoiceChange/app_multi.py b/spaces/BartPoint/VoiceChange/app_multi.py
deleted file mode 100644
index 67890355f12e2d2d5420ce8e31a722020f349b24..0000000000000000000000000000000000000000
--- a/spaces/BartPoint/VoiceChange/app_multi.py
+++ /dev/null
@@ -1,469 +0,0 @@
-from typing import Union
-
-from argparse import ArgumentParser
-
-import asyncio
-import json
-import hashlib
-from os import path, getenv
-
-import gradio as gr
-
-import torch
-
-import numpy as np
-import librosa
-
-import edge_tts
-
-import config
-import util
-from infer_pack.models import (
- SynthesizerTrnMs768NSFsid,
- SynthesizerTrnMs768NSFsid_nono
-)
-from vc_infer_pipeline import VC
-
-# Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21 # noqa
-in_hf_space = getenv('SYSTEM') == 'spaces'
-
-# Argument parsing
-arg_parser = ArgumentParser()
-arg_parser.add_argument(
- '--hubert',
- default=getenv('RVC_HUBERT', 'hubert_base.pt'),
- help='path to hubert base model (default: hubert_base.pt)'
-)
-arg_parser.add_argument(
- '--config',
- default=getenv('RVC_MULTI_CFG', 'multi_config.json'),
- help='path to config file (default: multi_config.json)'
-)
-arg_parser.add_argument(
- '--api',
- action='store_true',
- help='enable api endpoint'
-)
-arg_parser.add_argument(
- '--cache-examples',
- action='store_true',
- help='enable example caching, please remember delete gradio_cached_examples folder when example config has been modified' # noqa
-)
-args = arg_parser.parse_args()
-
-app_css = '''
-#model_info img {
- max-width: 100px;
- max-height: 100px;
- float: right;
-}
-
-#model_info p {
- margin: unset;
-}
-'''
-
-app = gr.Blocks(
- theme=gr.themes.Soft(primary_hue="orange", secondary_hue="slate"),
- css=app_css,
- analytics_enabled=False
-)
-
-# Load hubert model
-hubert_model = util.load_hubert_model(config.device, args.hubert)
-hubert_model.eval()
-
-# Load models
-multi_cfg = json.load(open(args.config, 'r'))
-loaded_models = []
-
-for model_name in multi_cfg.get('models'):
- print(f'Loading model: {model_name}')
-
- # Load model info
- model_info = json.load(
- open(path.join('model', model_name, 'config.json'), 'r')
- )
-
- # Load RVC checkpoint
- cpt = torch.load(
- path.join('model', model_name, model_info['model']),
- map_location='cpu'
- )
- tgt_sr = cpt['config'][-1]
- cpt['config'][-3] = cpt['weight']['emb_g.weight'].shape[0] # n_spk
-
- if_f0 = cpt.get('f0', 1)
- net_g: Union[SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono]
- if if_f0 == 1:
- net_g = SynthesizerTrnMs768NSFsid(
- *cpt['config'],
- is_half=util.is_half(config.device)
- )
- else:
- net_g = SynthesizerTrnMs768NSFsid_nono(*cpt['config'])
-
- del net_g.enc_q
-
- # According to original code, this thing seems necessary.
- print(net_g.load_state_dict(cpt['weight'], strict=False))
-
- net_g.eval().to(config.device)
- net_g = net_g.half() if util.is_half(config.device) else net_g.float()
-
- vc = VC(tgt_sr, config)
-
- loaded_models.append(dict(
- name=model_name,
- metadata=model_info,
- vc=vc,
- net_g=net_g,
- if_f0=if_f0,
- target_sr=tgt_sr
- ))
-
-print(f'Models loaded: {len(loaded_models)}')
-
-# Edge TTS speakers
-tts_speakers_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices()) # noqa
-
-
-# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer-web.py#L118 # noqa
-def vc_func(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- if input_audio is None:
- return (None, 'Please provide input audio.')
-
- if model_index is None:
- return (None, 'Please select a model.')
-
- model = loaded_models[model_index]
-
- # Reference: so-vits
- (audio_samp, audio_npy) = input_audio
-
- # https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49
- # Can be change well, we will see
- if (audio_npy.shape[0] / audio_samp) > 320 and in_hf_space:
- return (None, 'Input audio is longer than 60 secs.')
-
- # Bloody hell: https://stackoverflow.com/questions/26921836/
- if audio_npy.dtype != np.float32: # :thonk:
- audio_npy = (
- audio_npy / np.iinfo(audio_npy.dtype).max
- ).astype(np.float32)
-
- if len(audio_npy.shape) > 1:
- audio_npy = librosa.to_mono(audio_npy.transpose(1, 0))
-
- if audio_samp != 16000:
- audio_npy = librosa.resample(
- audio_npy,
- orig_sr=audio_samp,
- target_sr=16000
- )
-
- pitch_int = int(pitch_adjust)
-
- resample = (
- 0 if resample_option == 'Disable resampling'
- else int(resample_option)
- )
-
- times = [0, 0, 0]
-
- checksum = hashlib.sha512()
- checksum.update(audio_npy.tobytes())
-
- output_audio = model['vc'].pipeline(
- hubert_model,
- model['net_g'],
- model['metadata'].get('speaker_id', 0),
- audio_npy,
- checksum.hexdigest(),
- times,
- pitch_int,
- f0_method,
- path.join('model', model['name'], model['metadata']['feat_index']),
- feat_ratio,
- model['if_f0'],
- filter_radius,
- model['target_sr'],
- resample,
- rms_mix_rate,
- 'v2'
- )
-
- out_sr = (
- resample if resample >= 16000 and model['target_sr'] != resample
- else model['target_sr']
- )
-
- print(f'npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s')
- return ((out_sr, output_audio), 'Success')
-
-
-async def edge_tts_vc_func(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- if input_text is None:
- return (None, 'Please provide TTS text.')
-
- if tts_speaker is None:
- return (None, 'Please select TTS speaker.')
-
- if model_index is None:
- return (None, 'Please select a model.')
-
- speaker = tts_speakers_list[tts_speaker]['ShortName']
- (tts_np, tts_sr) = await util.call_edge_tts(speaker, input_text)
- return vc_func(
- (tts_sr, tts_np),
- model_index,
- pitch_adjust,
- f0_method,
- feat_ratio,
- filter_radius,
- rms_mix_rate,
- resample_option
- )
-
-
-def update_model_info(model_index):
- if model_index is None:
- return str(
- '### Model info\n'
- 'Please select a model from dropdown above.'
- )
-
- model = loaded_models[model_index]
- model_icon = model['metadata'].get('icon', '')
-
- return str(
- '### Model info\n'
- ''
- '**{name}**\n\n'
- 'Author: {author}\n\n'
- 'Source: {source}\n\n'
- '{note}'
- ).format(
- name=model['metadata'].get('name'),
- author=model['metadata'].get('author', 'Anonymous'),
- source=model['metadata'].get('source', 'Unknown'),
- note=model['metadata'].get('note', ''),
- icon=(
- model_icon
- if model_icon.startswith(('http://', 'https://'))
- else '/file/model/%s/%s' % (model['name'], model_icon)
- )
- )
-
-
-def _example_vc(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- (audio, message) = vc_func(
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
- )
- return (
- audio,
- message,
- update_model_info(model_index)
- )
-
-
-async def _example_edge_tts(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_option
-):
- (audio, message) = await edge_tts_vc_func(
- input_text, model_index, tts_speaker, pitch_adjust, f0_method,
- feat_ratio, filter_radius, rms_mix_rate, resample_option
- )
- return (
- audio,
- message,
- update_model_info(model_index)
- )
-
-
-with app:
- gr.Markdown(
- '## A simplistic Web interface\n'
- 'RVC interface, project based on [RVC-WebUI](https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI)' # thx noqa
- 'A lot of inspiration from what\'s already out there, including [zomehwh/rvc-models](https://huggingface.co/spaces/zomehwh/rvc-models) & [DJQmUKV/rvc-inference](https://huggingface.co/spaces/DJQmUKV/rvc-inference).\n ' # thx noqa
- )
-
- with gr.Row():
- with gr.Column():
- with gr.Tab('Audio conversion'):
- input_audio = gr.Audio(label='Input audio')
-
- vc_convert_btn = gr.Button('Convert', variant='primary')
-
- with gr.Tab('TTS conversion'):
- tts_input = gr.TextArea(
- label='TTS input text'
- )
- tts_speaker = gr.Dropdown(
- [
- '%s (%s)' % (
- s['FriendlyName'],
- s['Gender']
- )
- for s in tts_speakers_list
- ],
- label='TTS speaker',
- type='index'
- )
-
- tts_convert_btn = gr.Button('Convert', variant='primary')
-
- pitch_adjust = gr.Slider(
- label='Pitch',
- minimum=-24,
- maximum=24,
- step=1,
- value=0
- )
- f0_method = gr.Radio(
- label='f0 methods',
- choices=['pm', 'harvest', 'crepe'],
- value='pm',
- interactive=True
- )
-
- with gr.Accordion('Advanced options', open=False):
- feat_ratio = gr.Slider(
- label='Feature ratio',
- minimum=0,
- maximum=1,
- step=0.1,
- value=0.6
- )
- filter_radius = gr.Slider(
- label='Filter radius',
- minimum=0,
- maximum=7,
- step=1,
- value=3
- )
- rms_mix_rate = gr.Slider(
- label='Volume envelope mix rate',
- minimum=0,
- maximum=1,
- step=0.1,
- value=1
- )
- resample_rate = gr.Dropdown(
- [
- 'Disable resampling',
- '16000',
- '22050',
- '44100',
- '48000'
- ],
- label='Resample rate',
- value='Disable resampling'
- )
-
- with gr.Column():
- # Model select
- model_index = gr.Dropdown(
- [
- '%s - %s' % (
- m['metadata'].get('source', 'Unknown'),
- m['metadata'].get('name')
- )
- for m in loaded_models
- ],
- label='Model',
- type='index'
- )
-
- # Model info
- with gr.Box():
- model_info = gr.Markdown(
- '### Model info\n'
- 'Please select a model from dropdown above.',
- elem_id='model_info'
- )
-
- output_audio = gr.Audio(label='Output audio')
- output_msg = gr.Textbox(label='Output message')
-
- multi_examples = multi_cfg.get('examples')
- if (
- multi_examples and
- multi_examples.get('vc') and multi_examples.get('tts_vc')
- ):
- with gr.Accordion('Sweet sweet examples', open=False):
- with gr.Row():
- # VC Example
- if multi_examples.get('vc'):
- gr.Examples(
- label='Audio conversion examples',
- examples=multi_examples.get('vc'),
- inputs=[
- input_audio, model_index, pitch_adjust, f0_method,
- feat_ratio
- ],
- outputs=[output_audio, output_msg, model_info],
- fn=_example_vc,
- cache_examples=args.cache_examples,
- run_on_click=args.cache_examples
- )
-
- # Edge TTS Example
- if multi_examples.get('tts_vc'):
- gr.Examples(
- label='TTS conversion examples',
- examples=multi_examples.get('tts_vc'),
- inputs=[
- tts_input, model_index, tts_speaker, pitch_adjust,
- f0_method, feat_ratio
- ],
- outputs=[output_audio, output_msg, model_info],
- fn=_example_edge_tts,
- cache_examples=args.cache_examples,
- run_on_click=args.cache_examples
- )
-
- vc_convert_btn.click(
- vc_func,
- [
- input_audio, model_index, pitch_adjust, f0_method, feat_ratio,
- filter_radius, rms_mix_rate, resample_rate
- ],
- [output_audio, output_msg],
- api_name='audio_conversion'
- )
-
- tts_convert_btn.click(
- edge_tts_vc_func,
- [
- tts_input, model_index, tts_speaker, pitch_adjust, f0_method,
- feat_ratio, filter_radius, rms_mix_rate, resample_rate
- ],
- [output_audio, output_msg],
- api_name='tts_conversion'
- )
-
- model_index.change(
- update_model_info,
- inputs=[model_index],
- outputs=[model_info],
- show_progress=False,
- queue=False
- )
-
-app.queue(
- concurrency_count=1,
- max_size=20,
- api_open=args.api
-).launch()
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md b/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md
deleted file mode 100644
index 231c36d39005d9275c61ccc88067d084ca775635..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Sudfrica Edicin Apk Descargar.md
+++ /dev/null
@@ -1,134 +0,0 @@
-
-Aparcamiento de coches multijugador Sudáfrica Edición APK Descargar: Una guía para los usuarios de Android
-Si usted está buscando un juego de estacionamiento de coches realista y divertido que ofrece más que solo estacionamiento, es posible que desee echa un vistazo a Parking Multijugador. Y si usted está en Sudáfrica o desea experimentar la cultura del automóvil de Sudáfrica, es posible que desee probar el Aparcamiento Multijugador South áfrica Edition APK. En este artículo, te diremos qué es el Car Parking Multiplayer, qué hace que South áfrica Edition sea diferente, cómo descargarlo e instalarlo en tu dispositivo Android, y algunos consejos y trucos para jugarlo.
-¿Qué es el Aparcamiento Multijugador?
-Car Parking Multiplayer es un juego que puede engañarte con su nombre bastante engañoso. Pero, es mucho más que solo estar aparcando tu coche. Es una experiencia de mundo abierto donde se puede conducir gratis y sí, todavía trabajar en ese aparcamiento si lo desea. Incluso puedes saltar de tu coche y caminar. Hay diferentes áreas que se pueden explorar en el juego. Cada una es como su propio mundo abierto. Puedes optar por jugar en modo de un solo jugador o en modo online si quieres una escena más caótica (de forma divertida) .
-aparcamiento de coches multijugador sudáfrica edición apk descargar
DOWNLOAD === https://bltlly.com/2v6IE9
-
Características de Aparcamiento Multijugador
-Aparcamiento de coches multijugador tiene las siguientes características :
--
-
- Modo multijugador de mundo abierto
-
-
- Caminar libremente -
- Mundo abierto libre con gasolineras reales y servicios de automóviles -
- Compite contra jugadores reales en las carreras multijugador -
- Intercambiar coches con jugadores reales -
- Miles de jugadores reales cada día -
- Lista de amigos -
- Chat de voz -
- Modo de policía -
- - Personalización del coche
-
-
- Suspensión ajustable, ángulo de rueda y más -
- Ajuste del motor: motor de intercambio, turbo, caja de cambios y escape -
- Visual auto tungs: Vynils dinámico, partes del cuerpo del coche -
- - Mundo abierto de alta calidad
-
-
-
- 100 coches con el interior real -
- 16 pieles de jugador -
- Edificios con interior -
- - Juego interesante
-
-
- 82 desafíos de estacionamiento y conducción en la vida real -
- Diferentes vehículos: Grúa, camioneta, camiones, coches deportivos y clásicos -
-
Opiniones de Aparcamiento multijugador
-Aparcamiento de coches multijugador ha recibido en su mayoría críticas positivas de los usuarios en Google Play Store y App Store. Tiene una calificación de 4.4 de 5 estrellas en Google Play Store y una calificación de 4.3 de 5 estrellas en App Store . Estos son algunos de los comentarios de los usuarios:
--"Increíble juego! No hay errores en este juego o retrasos ect. (base en el dispositivo que utiliza) Me encantan los gráficos y los coches. Los coches son tan realistas y los sonidos son increíbles. Me encanta cómo puedes personalizar tu coche y hacer que se vea genial. El modo multijugador es increíble. Puedes chatear con otros jugadores y competir con ellos. También puedes unirte a un clan o crear tu propio clan. Este juego es muy divertido y adictivo. Recomiendo este juego a todos los que aman los coches y los juegos de estacionamiento."
- -"Este juego es muy bueno pero necesita algunas mejoras como agregar más coches, más mapas, más personalizaciones, más modos de juego, más desafíos, etc. Además, el juego se bloquea a veces y los controles no son muy suaves. Los gráficos son agradables, pero pueden ser mejores. El juego es divertido de jugar con amigos, pero se vuelve aburrido después de un tiempo. Espero que los desarrolladores actualicen el juego pronto y lo hagan más agradable."
¿Qué es el aparcamiento multijugador South áfrica Edition?
-Car Parking Multijugador South áfrica Edition es una versión modificada de Car Parking Multijugador que está especialmente diseñado para los usuarios sudafricanos o los fans de la cultura del coche de Sudáfrica. No es una versión oficial del juego, sino un archivo APK hecho por fans que se puede descargar e instalar en dispositivos Android .
-Diferencias entre Aparcamiento de coches multijugador y Aparcamiento de coches multijugador Sudáfrica Edición
-Car Parking Multijugador South áfrica Edition tiene algunas diferencias con el juego original Car Parking Multijugador, como :
--
-
- Más coches que son populares en Sudáfrica, como BMW E30, VW Golf Mk1, Toyota Corolla, Nissan 1400, etc. -
- Más personalizaciones que reflejan la cultura del automóvil sudafricano, como llantas giratorias, escapes fuertes, pegatinas, banderas, etc. -
- Más mapas que se basan en ubicaciones reales en Sudáfrica, como Ciudad del Cabo, Johannesburgo, Durban, etc. -
- Más música inspirada en la escena musical sudafricana, como kwaito, gqom, amapiano, etc. -
- Más idiomas que se hablan en Sudáfrica, como afrikaans, zulú, xhosa, etc. -
Beneficios de Aparcamiento Multijugador South áfrica Edition
-Car Parking Multijugador South áfrica Edition tiene algunos beneficios para los usuarios que quieren disfrutar del juego con un toque sudafricano, como :
--
-
- Más variedad y diversidad en términos de coches, personalizaciones, mapas, música e idiomas -
- Más diversión y emoción en términos de jugabilidad, gráficos, efectos de sonido e interacciones -
- Más conexión y comunidad con otros jugadores que comparten el mismo interés y pasión por la cultura del automóvil de Sudáfrica -
- Más soporte y actualizaciones de los desarrolladores que se dedican a mejorar el juego y agregar nuevas características -
Cómo descargar e instalar el estacionamiento de coches multijugador South áfrica Edition APK en dispositivos Android?
- -Requisitos para descargar e instalar el estacionamiento de coches multijugador South áfrica Edition APK
-Para descargar e instalar Aparcamiento Multijugador South áfrica Edition APK en su dispositivo Android, es necesario tener :
- --
-
- Un dispositivo Android que se ejecuta en Android 4.1 o superior -
- Una conexión a Internet estable -
- Una aplicación de administrador de archivos que puede acceder al archivo APK -
- Una cantidad suficiente de espacio de almacenamiento en su dispositivo -
- Un permiso para instalar aplicaciones de fuentes desconocidas en la configuración del dispositivo -
Pasos para Descargar e Instalar Aparcamiento Multijugador South áfrica Edition APK
-Para descargar e instalar Aparcamiento Multijugador South áfrica Edition APK en su dispositivo Android, es necesario seguir estos pasos :
--
-
- Ir a un sitio web de confianza que proporciona el enlace para descargar Car Parking Multi jugador South áfrica Edition APK. Por ejemplo, puede visitar [este sitio web] para obtener la última versión del archivo APK. -
- Haga clic en el botón de descarga y espere a que el archivo APK se descargue en su dispositivo. -
- Una vez que se complete la descarga, localizar el archivo APK en su dispositivo utilizando una aplicación de administrador de archivos. Puede encontrarlo en la carpeta Descargas o en cualquier otra carpeta donde lo haya guardado. -
- Toque en el archivo APK y seleccione Instalar. Es posible que vea un mensaje de advertencia que dice "Para su seguridad, el teléfono no está permitido instalar aplicaciones desconocidas de esta fuente". Si ves este mensaje, ve a la configuración del dispositivo y habilita la opción de instalar aplicaciones de fuentes desconocidas. Esta opción puede estar en Seguridad, Privacidad o Aplicaciones dependiendo del modelo de dispositivo y la versión de Android. - -
- Espere a que termine el proceso de instalación. Puede tardar unos minutos dependiendo del rendimiento del dispositivo y la velocidad de Internet. -
- Una vez que se hace la instalación, se puede abrir el juego y disfrutar de aparcamiento multijugador South áfrica Edition en su dispositivo Android. -
Consejos y Trucos para Jugar Aparcamiento Multijugador South áfrica Edition
-Ahora que ha descargado e instalado Car Parking Multiplayer South áfrica Edition en su dispositivo Android, es posible que desee conocer algunos consejos y trucos para jugarlo. Estos son algunos de ellos:
-Cómo seleccionar un coche y un jugador
-Para seleccionar un coche y un jugador en Car Parking Multiplayer South áfrica Edition, debe hacer lo siguiente:
--
-
- Toque en el icono de menú en la esquina superior izquierda de la pantalla. -
- Toque en Garaje para ver la lista de coches que usted posee o puede comprar. -
- Toque en el coche que desea utilizar y luego toque en Select. -
- Toque en Volver para volver al menú. -
- Toque en Perfil para ver la lista de jugadores entre los que puede elegir. -
- Toque en el reproductor que desea utilizar y luego toque en Select. -
- Toque en Volver para volver al menú. -
- Toque en Jugar para comenzar a jugar el juego con el coche y el jugador seleccionado. -
Cómo ajustar su coche y ajustar su relación de engranajes
-Para afinar su coche y ajustar su relación de engranajes en Car Parking Multiplayer South áfrica Edition, debe hacer lo siguiente:
--
-
- Toque en el icono de menú en la esquina superior izquierda de la pantalla. -
- Toque en Garaje para ver la lista de coches que usted posee o puede comprar. -
- Toque en el coche que desea sintonizar y luego toque en Tune. -
- Verá cuatro pestañas: Motor, Suspensión, Ruedas y Cuerpo. Puede deslizar hacia la izquierda o hacia la derecha para cambiar entre ellas. - -
- Para ajustar su relación de cambios, toque en Caja de cambios en la pestaña Motor. Verá un gráfico que muestra cómo cambia su velocidad con diferentes marchas. Puede arrastrar los puntos en el gráfico para cambiar la relación de transmisión para cada marcha. También puede pulsar en Auto o Manual para cambiar entre modos de transmisión automática o manual. -
- Una vez que haya terminado de ajustar su coche, toque en Guardar para aplicar los cambios. -
Cómo Deriva, Donut, y Burnout
-Para la deriva, donut, y burnout en el estacionamiento de coches multijugador South áfrica Edition, es necesario hacer lo siguiente:
--
-
- Para la deriva, debe usar una combinación de dirección, acelerador, freno y freno de mano. Puede usar los controles de inclinación o táctiles para la dirección. Para iniciar la deriva, es necesario acelerar y girar bruscamente en una esquina. Luego, debe aplicar el freno de mano brevemente para hacer que sus ruedas traseras pierdan tracción. Después de eso, debe equilibrar el acelerador y la dirección para mantener el ángulo de deriva y la dirección. También puede utilizar el freno de mano o freno de mano de nuevo si necesita ajustar su deriva. Para terminar con la deriva, es necesario soltar el acelerador y la dirección y enderezar el coche. -
- Para la rosquilla, debe usar una combinación de dirección, acelerador y freno de mano. Puede usar controles de inclinación o táctiles para la dirección. Para comenzar a donar, debe acelerar y girar bruscamente en una dirección. Entonces, es necesario aplicar el freno de mano para hacer que su coche gire alrededor de su centro. Después de eso, es necesario mantener el acelerador y la dirección constante para mantener el círculo de la dona. También puede cambiar la dirección de su dona cambiando la dirección. Para terminar donuting, es necesario soltar el acelerador y el freno de mano y enderezar su coche. - -
Conclusión
-Car Parking Multijugador South áfrica Edition es un juego que ofrece una experiencia de aparcamiento realista y divertido con un toque sudafricano. Tiene más coches, personalizaciones, mapas, música e idiomas que reflejan la cultura del automóvil sudafricano. También tiene un modo de mundo abierto, un modo de ajuste de coche, y un modo multijugador que hacen el juego más agradable y emocionante. Puede descargar e instalar Parking Multijugador South áfrica Edition APK en su dispositivo Android siguiendo los pasos en este artículo. También puedes usar los consejos y trucos de este artículo para mejorar tu juego y divertirte más.
-Preguntas frecuentes
-Aquí están algunas de las preguntas más frecuentes sobre Car Parking Multiplayer South áfrica Edition:
-¿Es gratis Car Parking Multijugador South áfrica Edition?
-Sí, Parking Multijugador South áfrica Edition es gratis para descargar y jugar. Sin embargo, puede contener algunas compras en la aplicación que requieren dinero real.
-¿Es seguro el aparcamiento multijugador South áfrica Edition?
-Sí, Parking Multijugador South áfrica Edition es seguro, siempre y cuando se descarga desde un sitio web de confianza. Sin embargo, siempre debe tener cuidado al descargar e instalar aplicaciones de fuentes desconocidas, ya que pueden contener virus o malware que pueden dañar su dispositivo.
-¿Es compatible Car Parking Multiplayer South áfrica Edition con mi dispositivo?
-Car Parking Multijugador South áfrica Edition es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 4.1 o superior. Sin embargo, algunos dispositivos pueden tener diferentes especificaciones o problemas de rendimiento que pueden afectar la calidad o funcionalidad del juego.
-¿Cómo puedo actualizar Car Parking Multijugador South áfrica Edition?
- -¿Cómo puedo contactar a los desarrolladores de Car Parking Multiplayer South áfrica Edition?
-Para contactar a los desarrolladores de Car Parking Multiplayer South áfrica Edition, puedes visitar su página de Facebook o su canal de YouTube y dejar un comentario o un mensaje.
64aa2da5cf-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md b/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md deleted file mode 100644 index 2001fb4da4e341236f8b4ef59c584c7ee860ae80..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Asfalto Nitro 9 Leyendas Mod Apk.md +++ /dev/null @@ -1,76 +0,0 @@ - -
Nitro asfalto 9 leyendas Mod Apk: Una guía para los aficionados a las carreras
-Si eres un fan de los juegos de carreras, es posible que hayas oído hablar de Asphalt Nitro 9 Legends, uno de los juegos más populares y emocionantes del género. Este juego le permite tomar el volante de los coches reales de gama alta de renombrados fabricantes de automóviles legendarios, como Ferrari, Porsche, Lamborghini, y W Motors, entre muchas otras marcas internacionales. Puedes conducir, impulsar y realizar acrobacias en ubicaciones dinámicas de la vida real en modo individual o multijugador.
-Pero ¿y si quieres disfrutar del juego sin limitaciones ni restricciones? ¿Qué pasa si quieres tener dinero ilimitado y fichas, desbloquear todos los coches y pistas, personalizar sus vehículos y actualizarlos, y competir con otros jugadores en línea o fuera de línea? Bueno, hay una manera de hacerlo. Puede descargar e instalar Asphalt Nitro 9 Leyendas Mod Apk, una versión modificada del juego que le da acceso a todas estas características y más.
-asfalto nitro 9 leyendas mod apk
Download File ===== https://bltlly.com/2v6LmQ
-
En este artículo, le guiará a través de las características, proceso de descarga, pasos de instalación, consejos y trucos, pros y contras, y la conclusión de Asphalt Nitro 9 Leyendas Mod Apk. Sigue leyendo para saber más.
-Características de Asphalt Nitro 9 Leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es una versión modificada del juego original que le da recursos ilimitados y características que mejoran su experiencia de juego. Estas son algunas de las características que puedes disfrutar con este mod apk:
--
-
- Dinero ilimitado y fichas: Con este apk mod, nunca se quedará sin dinero o fichas para comprar nuevos coches, pistas, mejoras, o artículos. Puedes gastar todo lo que quieras sin preocuparte por tu presupuesto. - -
- Personalizar sus vehículos y actualizarlos: Con este apk mod, puede personalizar sus vehículos de acuerdo a su preferencia. Puedes cambiar su color, pintura, calcomanías, ruedas, etc. También puedes mejorar su rendimiento mejorando su motor, transmisión, suspensión, frenos, nitro boost, etc. -
- Disfrutar de gráficos realistas y efectos de sonido: Con este apk mod, se puede disfrutar de los impresionantes gráficos y efectos de sonido del juego. El juego utiliza un motor de física realista que simula el movimiento y el comportamiento de los coches. El juego también cuenta con efectos de sonido de alta calidad que te hacen sentir como si estuvieras en una carrera real. -
- Compite con otros jugadores en línea o fuera de línea: Con este apk mod, puede competir con otros jugadores en línea o fuera de línea. Puedes unirte a carreras multijugador online y desafiar a tus amigos u otros jugadores de todo el mundo. También puedes jugar offline en modo carrera y completar varias misiones y desafíos. -
Cómo descargar e instalar Asphalt Nitro 9 leyendas Mod Apk
-Si desea descargar e instalar Asphalt Nitro 9 leyendas Mod Apk, es necesario seguir estos sencillos pasos:
--
-
- Paso 1: Descargar el archivo apk mod de una fuente de confianza. Usted puede encontrar muchos sitios web que ofrecen el archivo apk mod para Asphalt Nitro 9 Leyendas, pero no todos ellos son seguros y fiables. Debes asegurarte de que el archivo que descargas esté libre de virus, malware o cualquier otro contenido dañino. Puede utilizar este enlace para descargar el archivo apk mod de forma segura. -
- Paso 2: Habilitar fuentes desconocidas en la configuración del dispositivo. Antes de poder instalar el archivo apk mod, debe habilitar fuentes desconocidas en la configuración de su dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a la configuración del dispositivo, luego a la seguridad, luego a fuentes desconocidas y conéctela. - -
- Paso 4: Disfruta del juego con recursos y funciones ilimitadas. Ahora que ha instalado Asphalt Nitro 9 Leyendas Mod Apk, se puede disfrutar del juego con dinero ilimitado, fichas, coches, pistas, opciones de personalización, y más. También puedes competir con otros jugadores online o offline y divertirte. -
Consejos y trucos para jugar Asphalt Nitro 9 leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es un juego divertido y emocionante que le mantendrá entretenido durante horas. Sin embargo, si quieres mejorar tus habilidades y rendimiento en el juego, puedes seguir estos consejos y trucos:
--
-
- Elija el coche adecuado para cada pista y modo. El juego ofrece una variedad de coches de diferentes marcas y categorías, pero no todos ellos son adecuados para cada pista o modo. Es necesario tener en cuenta la velocidad, aceleración, manejo, nitro, y otros factores de cada coche antes de elegir uno. Por ejemplo, si usted está jugando en una pista con curvas, es posible que desee elegir un coche con buen manejo y nitro. Si usted está jugando en una pista recta, es posible que desee elegir un coche con alta velocidad y aceleración. -
- Utilice nitro boost sabiamente y estratégicamente. Nitro boost es una de las características más importantes del juego, ya que puede darte una ventaja sobre tus oponentes. Sin embargo, necesitas usarlo sabiamente y estratégicamente. No debes usarlo todo de una vez ni desperdiciarlo en momentos innecesarios. Debes guardarlo para momentos cruciales, como adelantar a tus rivales, escapar de obstáculos o colisiones, o llegar a la meta. También debe usarlo en combinación con acrobacias y derivas para ganar más puntos y recompensas. - -
- Evitar obstáculos y colisiones con otros coches. El juego cuenta con muchos obstáculos y desafíos que pueden obstaculizar su progreso o dañar su coche. Estos incluyen coches de tráfico, coches de policía, helicópteros, barricadas, picos, etc. Usted debe evitar estos obstáculos tanto como sea posible, ya que pueden ralentizar o dañar su coche. También debes evitar colisiones con otros coches, especialmente tus rivales, ya que pueden sacarte de la pista o hacerte perder tu posición. Usted debe tratar de esquivar o escapar de ellos, o utilizar su impulso nitro para alejarse de ellos. -
- Utilice diferentes ángulos de cámara y controles para adaptarse a sus preferencias. El juego ofrece diferentes ángulos de cámara y controles que puedes usar para jugar el juego. Puede elegir entre vista en primera persona, en tercera persona o de arriba hacia abajo, según su preferencia. También puede elegir entre los controles de inclinación, toque o toque, dependiendo de su comodidad. Puede cambiar estos ajustes en el menú de opciones del juego. -
Pros y contras de Asphalt Nitro 9 Leyendas Mod Apk
-Asfalto Nitro 9 Leyendas Mod Apk es un gran juego que ofrece muchos beneficios y ventajas para los aficionados a las carreras. Sin embargo, también tiene algunos inconvenientes y desventajas que usted debe tener en cuenta. Estos son algunos de los pros y los contras de Asphalt Nitro 9 Leyendas Mod Apk:
--
-
- Divertido, adictivo y desafiante juego -
- Dinero ilimitado y tokens -
- Desbloquear todos los coches y pistas -
- Personalizar sus vehículos y actualizarlos -
- Disfruta de gráficos realistas y efectos de sonido -
- Compite con otros jugadores online o offline -
-
-
- Puede causar retraso o estrellarse en algunos dispositivos -
- Puede no ser compatible con algunos dispositivos o versiones -
- No se puede actualizar regularmente o con frecuencia -
- No puede ser soportado por los desarrolladores o editores oficiales - -
- Puede exponer su dispositivo a riesgos o amenazas de seguridad -
Conclusión
-En conclusión, Asfalto Nitro 9 Leyendas Mod Apk es un juego fantástico que le dará horas de diversión y emoción. Es una versión modificada del juego original que le da recursos ilimitados y características que mejoran su experiencia de juego. Puede descargarlo e instalarlo de forma fácil y segura siguiendo los pasos que proporcionamos en este artículo. También puedes seguir los consejos y trucos que compartimos para mejorar tus habilidades y rendimiento en el juego. Sin embargo, también debe ser consciente de los pros y los contras de Asphalt Nitro 9 Leyendas Mod Apk, y decidir si vale la pena jugar o no.
-Si usted es un fanático de las carreras, le recomendamos que pruebe Asphalt Nitro 9 Leyendas Mod Apk y ver por sí mismo lo increíble que es. No te arrepentirás.
-Preguntas frecuentes (preguntas frecuentes)
-Aquí están algunas de las preguntas y respuestas más comunes sobre Asphalt Nitro 9 Legends Mod Apk:
- -Q: ¿Es libre de Asphalt Nitro 9 Leyendas Mod Apk?
-A: Sí, Asphalt Nitro 9 Leyendas Mod Apk es gratis para descargar y jugar. Usted no necesita pagar nada para disfrutar del juego.
-Q: ¿Es seguro Asphalt Nitro 9 Legends Mod Apk?
-A: Sí, Asfalto Nitro 9 Leyendas Mod Apk es seguro para descargar e instalar. Sin embargo, siempre debe descargarlo de una fuente de confianza y habilitar fuentes desconocidas en la configuración del dispositivo antes de instalarlo.
-Q: ¿Es legal Asphalt Nitro 9 Legends Mod Apk?
-A: No, Asphalt Nitro 9 Leyendas Mod Apk no es legal. Es una versión modificada del juego original que viola los términos y condiciones de los desarrolladores y editores oficiales. También puede infringir los derechos de propiedad intelectual del juego original.
-Q: Es Asphalt Nitro 9 Leyendas Mod Apk en línea o fuera de línea?
- -Q: ¿Cómo puedo actualizar Asphalt Nitro 9 Legends Mod Apk?
-A: Para actualizar Asphalt Nitro 9 leyendas Mod Apk, es necesario descargar e instalar la última versión del archivo apk mod de una fuente de confianza. Es posible que tenga que desinstalar la versión anterior del archivo apk mod antes de instalar el nuevo.
64aa2da5cf-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md b/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md deleted file mode 100644 index d872bc888664198a5fb77f20f85945d9c05ae2ef..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Camioneros De Europa 3 Apk 36.6.md +++ /dev/null @@ -1,76 +0,0 @@ - -
Camioneros de Europa 3 APK 36.6: Un juego de simulador de camiones realista y divertido
-Si eres un fan de los juegos de simuladores de camiones, es posible que hayas oído hablar de Truckers of Europe 3, un juego popular que te permite experimentar la vida de un conductor de camiones en Europa. En este artículo, te contaremos todo lo que necesitas saber sobre este juego, incluyendo qué es, cuáles son sus características, cómo descargarlo e instalarlo, cómo jugarlo y por qué deberías jugarlo. ¡Así que abróchate el cinturón y prepárate para un emocionante viaje!
-Introducción
-¿Qué es Camioneros de Europa 3?
-Truckers of Europe 3 es un juego de simulador de camioneros desarrollado por Jerryisgaming, un canal de YouTube que crea videos de juegos. El juego fue lanzado en 2020 y ha sido actualizado regularmente con nuevas características y mejoras. La última versión del juego es 0.36.6, que fue lanzado el 30 de septiembre de 2021.
-camioneros de europa 3 apk 36.6
Download Zip ❤❤❤ https://bltlly.com/2v6LwC
-
¿Cuáles son las características de Camioneros de Europa 3?
-Truckers of Europe 3 tiene muchas características que lo convierten en uno de los mejores juegos de simuladores de camiones en el mercado. Algunas de estas características son:
--
-
- Un gran mapa de Europa con más de 50 ciudades y países para explorar. -
- Una variedad de camiones y remolques para elegir, cada uno con diferentes especificaciones y rendimiento. -
- Un sistema de conducción realista con transmisión manual, volante, pedales, indicadores, espejos, luces, bocina, limpiaparabrisas, etc. -
- Un sistema de tráfico realista con coches, autobuses, camiones, motocicletas, policía, ambulancia, etc. -
- Un sistema meteorológico realista con ciclo de día y noche, lluvia, nieve, niebla, etc. -
- Un sistema de daños realista con desgaste de neumáticos, consumo de combustible, sobrecalentamiento del motor, etc. -
- Un modo de carrera con misiones, contratos, entrega de carga, ingresos, gastos, etc. -
- Un sistema de habilidades con niveles, puntos, ventajas, etc. -
- Un sistema de personalización con trabajos de pintura, accesorios, calcomanías, etc. - -
Cómo descargar e instalar camioneros de Europa 3 APK 36.6?
-Requisitos
-Para descargar e instalar Camioneros de Europa 3 APK 36.6 en su dispositivo Android, es necesario cumplir con los siguientes requisitos:
--
-
- Tu dispositivo debe tener la versión de Android 4.4 o superior. -
- Su dispositivo debe tener al menos 1 GB de RAM y 500 MB de espacio de almacenamiento libre. -
- Su dispositivo debe tener una conexión a Internet estable. -
- Su dispositivo debe permitir la instalación desde fuentes desconocidas. Puede habilitar esta opción yendo a Configuración > Seguridad > Fuentes desconocidas. -
Pasos
-Para descargar e instalar Camioneros de Europa 3 APK 36.6 en su dispositivo Android, debe seguir estos pasos:
--
-
- Ir a este enlace y descargar el archivo APK. -
- Localice el archivo descargado en el administrador de archivos de su dispositivo y toque en él para iniciar el proceso de instalación. -
- Siga las instrucciones en la pantalla y conceda los permisos necesarios a la aplicación. -
- Espere a que la instalación se complete y luego inicie la aplicación desde el cajón de aplicaciones o la pantalla de inicio. -
- Disfruta jugando Camioneros de Europa 3 en tu dispositivo Android! -
Cómo jugar Camioneros de Europa 3?
-Elige tu camión y remolque
-Lo primero que tienes que hacer cuando empiezas a jugar Truckers of Europe 3 es elegir tu camión y remolque. Puede hacer esto yendo al menú del garaje y navegando a través de las opciones disponibles. También puede comprar nuevos camiones y remolques con el dinero que gana de sus misiones. Cada camión y remolque tiene diferentes atributos, como velocidad, potencia, capacidad de combustible, peso de carga, etc. Debe elegir el que se adapte a sus preferencias y necesidades.
-Conduce por toda Europa y entrega carga
- -Mientras conduce, tendrá que seguir las reglas de tráfico y regulaciones, tales como límites de velocidad, semáforos, señales, etc. También tendrá que lidiar con condiciones de tráfico realistas, como automóviles, autobuses, camiones, motocicletas, policía, ambulancia, etc. También tendrá que enfrentarse a condiciones meteorológicas realistas, como lluvia, nieve, niebla, etc. También tendrá que controlar el estado de su camión, como el nivel de combustible, la temperatura del motor, la presión de los neumáticos, etc. También tendrá que prestar atención al estado de su conductor, como el nivel de fatiga, el nivel de hambre, etc. Tendrá que parar en gasolineras, áreas de descanso, restaurantes, etc. para llenar su tanque de combustible, descansar a su conductor, comer algo de comida, etc.
-Cuando llegue a su destino, tendrá que aparcar su camión y remolque en el lugar designado y descargar la carga. A continuación, recibirá su recompensa y comentarios sobre su rendimiento. También ganarás puntos de experiencia que te ayudarán a subir de nivel y desbloquear nuevas habilidades y beneficios.
-Personaliza tu camión y mejora tus habilidades
-Otro aspecto divertido de Truckers of Europe 3 es que puedes personalizar tu camión y mejorar tus habilidades. Puedes hacer esto yendo al menú del taller y gastando algo de dinero en varios artículos y servicios. Puede cambiar el color de su camión, agregar algunos accesorios, aplicar algunas calcomanías, etc. También puede reparar su camión si está dañado o afinarlo si no funciona bien.
-También puedes ir al menú de habilidades y gastar algunos puntos en varias habilidades y beneficios que mejorarán tus habilidades y beneficios como conductor de camión. Puede mejorar su eficiencia de combustible, manejo de carga, seguridad de conducción, habilidades de negociación, etc. También puede desbloquear nuevos tipos de carga, contratos, camiones, remolques, etc.
- -¿Por qué deberías jugar Camioneros de Europa 3?
-Gráficos realistas y física
- -Tráfico y clima diversos y dinámicos
-Otra razón por la que deberías jugar a Truckers of Europe 3 es porque tiene un tráfico diverso y dinámico y el clima hará que tu experiencia de conducción sea más desafiante y agradable. El juego tiene un gran mapa de Europa con más de 50 ciudades y países para explorar. Cada ciudad y país tiene sus propias características únicas, tales como monumentos, edificios, carreteras, señales, etc. El juego también tiene un sistema de tráfico diverso que incluye automóviles, autobuses, camiones, motocicletas, policía, ambulancia, etc. Cada vehículo tiene su propio comportamiento, velocidad, dirección, etc. El juego también tiene un sistema de tiempo dinámico que incluye ciclo de día y noche, lluvia, nieve, niebla, etc. Cada condición climática tiene su propio efecto en la visibilidad, tracción, manejo, etc. del camión y el remolque.
-Misiones y logros desafiantes y gratificantes
-Otra razón por la que deberías jugar a Truckers of Europe 3 es porque tiene misiones desafiantes y gratificantes y logros que te mantendrán motivado y entretenido. El juego tiene un modo de carrera que le permite comenzar como un conductor de camión novato y trabajar su camino hasta convertirse en un camionero profesional. Puede hacerlo completando varias misiones y contratos que implican la entrega de carga a diferentes destinos en toda Europa. También puede ganar dinero de sus entregas y gastarlo en la compra de nuevos camiones y remolques o la personalización de los existentes. También puede ganar puntos de experiencia de sus entregas y gastarlos en mejorar sus habilidades y beneficios. También puedes desbloquear nuevos tipos de carga, contratos, camiones, remolques, etc. a medida que avanzas en el juego.
- -Conclusión
-Truckers of Europe 3 es un juego de simulador de camiones realista y divertido que te permite experimentar la vida de un conductor de camiones en Europa. Puede elegir su camión y remolque, conducir por toda Europa y entregar carga, personalizar su camión y mejorar sus habilidades, y ganar dinero y logros. El juego tiene gráficos y física realistas, tráfico y clima diversos y dinámicos, y misiones y logros desafiantes y gratificantes. Si usted está buscando un juego de simulador de camiones que le mantendrá enganchado durante horas, definitivamente debe probar Camioneros de Europa 3 APK 36.6.
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre los camioneros de Europa 3:
--
-
- Q: ¿Los camioneros de Europa 3 son libres de jugar? -
- A: Sí, Truckers of Europe 3 es gratis. Puedes descargarlo e instalarlo desde el enlace proporcionado en este artículo. Sin embargo, el juego puede contener algunas compras en la aplicación que pueden mejorar su experiencia de juego. -
- Q: ¿Es seguro descargar e instalar Truckers of Europe 3? -
- A: Sí, Truckers of Europe 3 es seguro para descargar e instalar. El archivo APK proporcionado en este artículo es verificado y probado por nuestro equipo. Sin embargo, siempre debe descargar e instalar archivos APK de fuentes confiables solamente. -
- Q: ¿Es Truckers of Europe 3 compatible con mi dispositivo? -
- A: Camioneros de Europa 3 es compatible con la mayoría de los dispositivos Android que tienen la versión Android 4.4 o superior. Sin embargo, algunos dispositivos pueden no ser compatibles con el juego debido a limitaciones de hardware o software. -
- P: ¿Cómo puedo contactar al desarrollador de Truckers of Europe 3? -
- A: Puedes contactar al desarrollador de Truckers of Europe 3 visitando su canal de YouTube Jerryisgaming o su página de Facebook Jerryisgaming. También puedes dejar tus comentarios, sugerencias o consultas en la sección de comentarios de sus videos o publicaciones. -
- P: ¿Cómo puedo apoyar el desarrollo de Camioneros de Europa 3? - -
-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md b/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md deleted file mode 100644 index 516d398df61b62811ac5d43e8205d838c0714cbc..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Estrellas Pelea Hack Ios.md +++ /dev/null @@ -1,80 +0,0 @@ - -
Cómo descargar Genshin Impact Cloud y disfrutar del juego en cualquier lugar
-Genshin Impact es uno de los juegos más populares y exitosos de 2020, con millones de jugadores en todo el mundo. El juego es un RPG de acción de mundo abierto que te permite explorar un mundo vasto y hermoso llamado Teyvat, donde puedes conocer a varios personajes, luchar contra enemigos, reunir recursos y completar misiones. El juego está disponible en múltiples plataformas, como PC, PlayStation 4, PlayStation 5, iOS y Android. Sin embargo, si quieres jugar el juego en tu dispositivo móvil, puedes enfrentarte a algunos desafíos, como espacio de almacenamiento limitado, bajo rendimiento o dispositivos incompatibles. Ahí es donde entra Genshin Impact Cloud.
-¿Qué es la nube de impacto Genshin y por qué debe probarlo
-Genshin Impact Cloud es un servicio que te permite jugar el juego en tu dispositivo móvil sin descargar el juego entero
-Genshin Impact Cloud es una nueva característica que fue introducida por miHoYo, el desarrollador de Genshin Impact, en abril de 2021. Es un servicio de juegos en la nube que le permite transmitir el juego desde un servidor a su dispositivo móvil a través de Internet. Esto significa que no necesitas descargar o instalar el juego en tu dispositivo, lo que puede ahorrarte mucho espacio de almacenamiento. El tamaño del juego de Genshin Impact en dispositivos móviles es de alrededor de 5,2 GB, pero con Genshin Impact Cloud, solo necesitas descargar una aplicación de 56 MB de tamaño. Esto también puede ayudarle a evitar largos tiempos de carga o actualizaciones.
-descargar estrellas pelea hack ios
Download ===> https://bltlly.com/2v6KEj
-
Genshin Impact Cloud tiene muchos beneficios, como ahorrar espacio de almacenamiento, mejorar el rendimiento y admitir cross-save
-Además de ahorrar espacio de almacenamiento, Genshin Impact Cloud también tiene otras ventajas que pueden mejorar su experiencia de juego. Por ejemplo:
--
-
-
- Genshin Impact Cloud soporta cross-save y cross-play. Esto significa que puedes acceder a tu progreso y datos existentes desde otras plataformas iniciando sesión con tu cuenta miHoYo. También puedes jugar con tus amigos que usan diferentes dispositivos, como PC o PlayStation. -
- Genshin Impact Cloud es de uso gratuito. No es necesario pagar tarifas o suscripciones adicionales para utilizar el servicio. También puede hacer compras en la aplicación como de costumbre, y disfrutar de todas las actualizaciones y eventos del juego. -
Con Genshin Impact Cloud, puede disfrutar del juego en cualquier lugar y en cualquier momento, siempre y cuando tenga una conexión a Internet estable y un dispositivo compatible.
-Cómo descargar Genshin Impact Cloud en dispositivos Android
-Genshin Impact Cloud está disponible actualmente solo para usuarios de Android en Malasia y Singapur como prueba beta
-Genshin Impact Cloud todavía está en sus primeras etapas de desarrollo, y todavía no está disponible para todas las regiones y plataformas. Actualmente, el servicio solo está abierto para los usuarios de Android en Malasia y Singapur como una prueba beta. Esto significa que solo un número limitado de jugadores puede acceder al servicio, y puede haber algunos errores o errores durante el juego. miHoYo no ha anunciado cuándo se ampliará el servicio a otras regiones y plataformas, pero es probable que lo hagan en el futuro.
-Para descargar Genshin Impact Cloud, necesita tener una cuenta miHoYo y registrarse para la prueba beta en el sitio web oficial
-Si eres un usuario de Android en Malasia o Singapur, y quieres probar Genshin Impact Cloud, debes seguir estos pasos:
--
-
- Crea una cuenta miHoYo si aún no tienes una. Puede hacer esto visitando https://account.mihoyo.com/#/register y llenando la información requerida. -
- Visite None:
- """
- Raise an option parsing error using parser.error().
-
- Args:
- parser: an OptionParser instance.
- option: an Option instance.
- msg: the error text.
- """
- msg = f"{option} error: {msg}"
- msg = textwrap.fill(" ".join(msg.split()))
- parser.error(msg)
-
-
-def make_option_group(group: Dict[str, Any], parser: ConfigOptionParser) -> OptionGroup:
- """
- Return an OptionGroup object
- group -- assumed to be dict with 'name' and 'options' keys
- parser -- an optparse Parser
- """
- option_group = OptionGroup(parser, group["name"])
- for option in group["options"]:
- option_group.add_option(option())
- return option_group
-
-
-def check_dist_restriction(options: Values, check_target: bool = False) -> None:
- """Function for determining if custom platform options are allowed.
-
- :param options: The OptionParser options.
- :param check_target: Whether or not to check if --target is being used.
- """
- dist_restriction_set = any(
- [
- options.python_version,
- options.platforms,
- options.abis,
- options.implementation,
- ]
- )
-
- binary_only = FormatControl(set(), {":all:"})
- sdist_dependencies_allowed = (
- options.format_control != binary_only and not options.ignore_dependencies
- )
-
- # Installations or downloads using dist restrictions must not combine
- # source distributions and dist-specific wheels, as they are not
- # guaranteed to be locally compatible.
- if dist_restriction_set and sdist_dependencies_allowed:
- raise CommandError(
- "When restricting platform and interpreter constraints using "
- "--python-version, --platform, --abi, or --implementation, "
- "either --no-deps must be set, or --only-binary=:all: must be "
- "set and --no-binary must not be set (or must be set to "
- ":none:)."
- )
-
- if check_target:
- if dist_restriction_set and not options.target_dir:
- raise CommandError(
- "Can not use any platform or abi specific options unless "
- "installing via '--target'"
- )
-
-
-def _path_option_check(option: Option, opt: str, value: str) -> str:
- return os.path.expanduser(value)
-
-
-def _package_name_option_check(option: Option, opt: str, value: str) -> str:
- return canonicalize_name(value)
-
-
-class PipOption(Option):
- TYPES = Option.TYPES + ("path", "package_name")
- TYPE_CHECKER = Option.TYPE_CHECKER.copy()
- TYPE_CHECKER["package_name"] = _package_name_option_check
- TYPE_CHECKER["path"] = _path_option_check
-
-
-###########
-# options #
-###########
-
-help_: Callable[..., Option] = partial(
- Option,
- "-h",
- "--help",
- dest="help",
- action="help",
- help="Show help.",
-)
-
-debug_mode: Callable[..., Option] = partial(
- Option,
- "--debug",
- dest="debug_mode",
- action="store_true",
- default=False,
- help=(
- "Let unhandled exceptions propagate outside the main subroutine, "
- "instead of logging them to stderr."
- ),
-)
-
-isolated_mode: Callable[..., Option] = partial(
- Option,
- "--isolated",
- dest="isolated_mode",
- action="store_true",
- default=False,
- help=(
- "Run pip in an isolated mode, ignoring environment variables and user "
- "configuration."
- ),
-)
-
-require_virtualenv: Callable[..., Option] = partial(
- Option,
- "--require-virtualenv",
- "--require-venv",
- dest="require_venv",
- action="store_true",
- default=False,
- help=(
- "Allow pip to only run in a virtual environment; "
- "exit with an error otherwise."
- ),
-)
-
-override_externally_managed: Callable[..., Option] = partial(
- Option,
- "--break-system-packages",
- dest="override_externally_managed",
- action="store_true",
- help="Allow pip to modify an EXTERNALLY-MANAGED Python installation",
-)
-
-python: Callable[..., Option] = partial(
- Option,
- "--python",
- dest="python",
- help="Run pip with the specified Python interpreter.",
-)
-
-verbose: Callable[..., Option] = partial(
- Option,
- "-v",
- "--verbose",
- dest="verbose",
- action="count",
- default=0,
- help="Give more output. Option is additive, and can be used up to 3 times.",
-)
-
-no_color: Callable[..., Option] = partial(
- Option,
- "--no-color",
- dest="no_color",
- action="store_true",
- default=False,
- help="Suppress colored output.",
-)
-
-version: Callable[..., Option] = partial(
- Option,
- "-V",
- "--version",
- dest="version",
- action="store_true",
- help="Show version and exit.",
-)
-
-quiet: Callable[..., Option] = partial(
- Option,
- "-q",
- "--quiet",
- dest="quiet",
- action="count",
- default=0,
- help=(
- "Give less output. Option is additive, and can be used up to 3"
- " times (corresponding to WARNING, ERROR, and CRITICAL logging"
- " levels)."
- ),
-)
-
-progress_bar: Callable[..., Option] = partial(
- Option,
- "--progress-bar",
- dest="progress_bar",
- type="choice",
- choices=["on", "off"],
- default="on",
- help="Specify whether the progress bar should be used [on, off] (default: on)",
-)
-
-log: Callable[..., Option] = partial(
- PipOption,
- "--log",
- "--log-file",
- "--local-log",
- dest="log",
- metavar="path",
- type="path",
- help="Path to a verbose appending log.",
-)
-
-no_input: Callable[..., Option] = partial(
- Option,
- # Don't ask for input
- "--no-input",
- dest="no_input",
- action="store_true",
- default=False,
- help="Disable prompting for input.",
-)
-
-keyring_provider: Callable[..., Option] = partial(
- Option,
- "--keyring-provider",
- dest="keyring_provider",
- choices=["auto", "disabled", "import", "subprocess"],
- default="auto",
- help=(
- "Enable the credential lookup via the keyring library if user input is allowed."
- " Specify which mechanism to use [disabled, import, subprocess]."
- " (default: disabled)"
- ),
-)
-
-proxy: Callable[..., Option] = partial(
- Option,
- "--proxy",
- dest="proxy",
- type="str",
- default="",
- help="Specify a proxy in the form scheme://[user:passwd@]proxy.server:port.",
-)
-
-retries: Callable[..., Option] = partial(
- Option,
- "--retries",
- dest="retries",
- type="int",
- default=5,
- help="Maximum number of retries each connection should attempt "
- "(default %default times).",
-)
-
-timeout: Callable[..., Option] = partial(
- Option,
- "--timeout",
- "--default-timeout",
- metavar="sec",
- dest="timeout",
- type="float",
- default=15,
- help="Set the socket timeout (default %default seconds).",
-)
-
-
-def exists_action() -> Option:
- return Option(
- # Option when path already exist
- "--exists-action",
- dest="exists_action",
- type="choice",
- choices=["s", "i", "w", "b", "a"],
- default=[],
- action="append",
- metavar="action",
- help="Default action when a path already exists: "
- "(s)witch, (i)gnore, (w)ipe, (b)ackup, (a)bort.",
- )
-
-
-cert: Callable[..., Option] = partial(
- PipOption,
- "--cert",
- dest="cert",
- type="path",
- metavar="path",
- help=(
- "Path to PEM-encoded CA certificate bundle. "
- "If provided, overrides the default. "
- "See 'SSL Certificate Verification' in pip documentation "
- "for more information."
- ),
-)
-
-client_cert: Callable[..., Option] = partial(
- PipOption,
- "--client-cert",
- dest="client_cert",
- type="path",
- default=None,
- metavar="path",
- help="Path to SSL client certificate, a single file containing the "
- "private key and the certificate in PEM format.",
-)
-
-index_url: Callable[..., Option] = partial(
- Option,
- "-i",
- "--index-url",
- "--pypi-url",
- dest="index_url",
- metavar="URL",
- default=PyPI.simple_url,
- help="Base URL of the Python Package Index (default %default). "
- "This should point to a repository compliant with PEP 503 "
- "(the simple repository API) or a local directory laid out "
- "in the same format.",
-)
-
-
-def extra_index_url() -> Option:
- return Option(
- "--extra-index-url",
- dest="extra_index_urls",
- metavar="URL",
- action="append",
- default=[],
- help="Extra URLs of package indexes to use in addition to "
- "--index-url. Should follow the same rules as "
- "--index-url.",
- )
-
-
-no_index: Callable[..., Option] = partial(
- Option,
- "--no-index",
- dest="no_index",
- action="store_true",
- default=False,
- help="Ignore package index (only looking at --find-links URLs instead).",
-)
-
-
-def find_links() -> Option:
- return Option(
- "-f",
- "--find-links",
- dest="find_links",
- action="append",
- default=[],
- metavar="url",
- help="If a URL or path to an html file, then parse for links to "
- "archives such as sdist (.tar.gz) or wheel (.whl) files. "
- "If a local path or file:// URL that's a directory, "
- "then look for archives in the directory listing. "
- "Links to VCS project URLs are not supported.",
- )
-
-
-def trusted_host() -> Option:
- return Option(
- "--trusted-host",
- dest="trusted_hosts",
- action="append",
- metavar="HOSTNAME",
- default=[],
- help="Mark this host or host:port pair as trusted, even though it "
- "does not have valid or any HTTPS.",
- )
-
-
-def constraints() -> Option:
- return Option(
- "-c",
- "--constraint",
- dest="constraints",
- action="append",
- default=[],
- metavar="file",
- help="Constrain versions using the given constraints file. "
- "This option can be used multiple times.",
- )
-
-
-def requirements() -> Option:
- return Option(
- "-r",
- "--requirement",
- dest="requirements",
- action="append",
- default=[],
- metavar="file",
- help="Install from the given requirements file. "
- "This option can be used multiple times.",
- )
-
-
-def editable() -> Option:
- return Option(
- "-e",
- "--editable",
- dest="editables",
- action="append",
- default=[],
- metavar="path/url",
- help=(
- "Install a project in editable mode (i.e. setuptools "
- '"develop mode") from a local project path or a VCS url.'
- ),
- )
-
-
-def _handle_src(option: Option, opt_str: str, value: str, parser: OptionParser) -> None:
- value = os.path.abspath(value)
- setattr(parser.values, option.dest, value)
-
-
-src: Callable[..., Option] = partial(
- PipOption,
- "--src",
- "--source",
- "--source-dir",
- "--source-directory",
- dest="src_dir",
- type="path",
- metavar="dir",
- default=get_src_prefix(),
- action="callback",
- callback=_handle_src,
- help="Directory to check out editable projects into. "
- 'The default in a virtualenv is "
/src". ' - 'The default for global installs is " /src".', -) - - -def _get_format_control(values: Values, option: Option) -> Any: - """Get a format_control object.""" - return getattr(values, option.dest) - - -def _handle_no_binary( - option: Option, opt_str: str, value: str, parser: OptionParser -) -> None: - existing = _get_format_control(parser.values, option) - FormatControl.handle_mutual_excludes( - value, - existing.no_binary, - existing.only_binary, - ) - - -def _handle_only_binary( - option: Option, opt_str: str, value: str, parser: OptionParser -) -> None: - existing = _get_format_control(parser.values, option) - FormatControl.handle_mutual_excludes( - value, - existing.only_binary, - existing.no_binary, - ) - - -def no_binary() -> Option: - format_control = FormatControl(set(), set()) - return Option( - "--no-binary", - dest="format_control", - action="callback", - callback=_handle_no_binary, - type="str", - default=format_control, - help="Do not use binary packages. Can be supplied multiple times, and " - 'each time adds to the existing value. Accepts either ":all:" to ' - 'disable all binary packages, ":none:" to empty the set (notice ' - "the colons), or one or more package names with commas between " - "them (no colons). Note that some packages are tricky to compile " - "and may fail to install when this option is used on them.", - ) - - -def only_binary() -> Option: - format_control = FormatControl(set(), set()) - return Option( - "--only-binary", - dest="format_control", - action="callback", - callback=_handle_only_binary, - type="str", - default=format_control, - help="Do not use source packages. Can be supplied multiple times, and " - 'each time adds to the existing value. Accepts either ":all:" to ' - 'disable all source packages, ":none:" to empty the set, or one ' - "or more package names with commas between them. Packages " - "without binary distributions will fail to install when this " - "option is used on them.", - ) - - -platforms: Callable[..., Option] = partial( - Option, - "--platform", - dest="platforms", - metavar="platform", - action="append", - default=None, - help=( - "Only use wheels compatible with . Defaults to the " - "platform of the running system. Use this option multiple times to " - "specify multiple platforms supported by the target interpreter." - ), -) - - -# This was made a separate function for unit-testing purposes. -def _convert_python_version(value: str) -> Tuple[Tuple[int, ...], Optional[str]]: - """ - Convert a version string like "3", "37", or "3.7.3" into a tuple of ints. - - :return: A 2-tuple (version_info, error_msg), where `error_msg` is - non-None if and only if there was a parsing error. - """ - if not value: - # The empty string is the same as not providing a value. - return (None, None) - - parts = value.split(".") - if len(parts) > 3: - return ((), "at most three version parts are allowed") - - if len(parts) == 1: - # Then we are in the case of "3" or "37". - value = parts[0] - if len(value) > 1: - parts = [value[0], value[1:]] - - try: - version_info = tuple(int(part) for part in parts) - except ValueError: - return ((), "each version part must be an integer") - - return (version_info, None) - - -def _handle_python_version( - option: Option, opt_str: str, value: str, parser: OptionParser -) -> None: - """ - Handle a provided --python-version value. - """ - version_info, error_msg = _convert_python_version(value) - if error_msg is not None: - msg = "invalid --python-version value: {!r}: {}".format( - value, - error_msg, - ) - raise_option_error(parser, option=option, msg=msg) - - parser.values.python_version = version_info - - -python_version: Callable[..., Option] = partial( - Option, - "--python-version", - dest="python_version", - metavar="python_version", - action="callback", - callback=_handle_python_version, - type="str", - default=None, - help=dedent( - """\ - The Python interpreter version to use for wheel and "Requires-Python" - compatibility checks. Defaults to a version derived from the running - interpreter. The version can be specified using up to three dot-separated - integers (e.g. "3" for 3.0.0, "3.7" for 3.7.0, or "3.7.3"). A major-minor - version can also be given as a string without dots (e.g. "37" for 3.7.0). - """ - ), -) - - -implementation: Callable[..., Option] = partial( - Option, - "--implementation", - dest="implementation", - metavar="implementation", - default=None, - help=( - "Only use wheels compatible with Python " - "implementation , e.g. 'pp', 'jy', 'cp', " - " or 'ip'. If not specified, then the current " - "interpreter implementation is used. Use 'py' to force " - "implementation-agnostic wheels." - ), -) - - -abis: Callable[..., Option] = partial( - Option, - "--abi", - dest="abis", - metavar="abi", - action="append", - default=None, - help=( - "Only use wheels compatible with Python abi , e.g. 'pypy_41'. " - "If not specified, then the current interpreter abi tag is used. " - "Use this option multiple times to specify multiple abis supported " - "by the target interpreter. Generally you will need to specify " - "--implementation, --platform, and --python-version when using this " - "option." - ), -) - - -def add_target_python_options(cmd_opts: OptionGroup) -> None: - cmd_opts.add_option(platforms()) - cmd_opts.add_option(python_version()) - cmd_opts.add_option(implementation()) - cmd_opts.add_option(abis()) - - -def make_target_python(options: Values) -> TargetPython: - target_python = TargetPython( - platforms=options.platforms, - py_version_info=options.python_version, - abis=options.abis, - implementation=options.implementation, - ) - - return target_python - - -def prefer_binary() -> Option: - return Option( - "--prefer-binary", - dest="prefer_binary", - action="store_true", - default=False, - help="Prefer older binary packages over newer source packages.", - ) - - -cache_dir: Callable[..., Option] = partial( - PipOption, - "--cache-dir", - dest="cache_dir", - default=USER_CACHE_DIR, - metavar="dir", - type="path", - help="Store the cache data in .", -) - - -def _handle_no_cache_dir( - option: Option, opt: str, value: str, parser: OptionParser -) -> None: - """ - Process a value provided for the --no-cache-dir option. - - This is an optparse.Option callback for the --no-cache-dir option. - """ - # The value argument will be None if --no-cache-dir is passed via the - # command-line, since the option doesn't accept arguments. However, - # the value can be non-None if the option is triggered e.g. by an - # environment variable, like PIP_NO_CACHE_DIR=true. - if value is not None: - # Then parse the string value to get argument error-checking. - try: - strtobool(value) - except ValueError as exc: - raise_option_error(parser, option=option, msg=str(exc)) - - # Originally, setting PIP_NO_CACHE_DIR to a value that strtobool() - # converted to 0 (like "false" or "no") caused cache_dir to be disabled - # rather than enabled (logic would say the latter). Thus, we disable - # the cache directory not just on values that parse to True, but (for - # backwards compatibility reasons) also on values that parse to False. - # In other words, always set it to False if the option is provided in - # some (valid) form. - parser.values.cache_dir = False - - -no_cache: Callable[..., Option] = partial( - Option, - "--no-cache-dir", - dest="cache_dir", - action="callback", - callback=_handle_no_cache_dir, - help="Disable the cache.", -) - -no_deps: Callable[..., Option] = partial( - Option, - "--no-deps", - "--no-dependencies", - dest="ignore_dependencies", - action="store_true", - default=False, - help="Don't install package dependencies.", -) - -ignore_requires_python: Callable[..., Option] = partial( - Option, - "--ignore-requires-python", - dest="ignore_requires_python", - action="store_true", - help="Ignore the Requires-Python information.", -) - -no_build_isolation: Callable[..., Option] = partial( - Option, - "--no-build-isolation", - dest="build_isolation", - action="store_false", - default=True, - help="Disable isolation when building a modern source distribution. " - "Build dependencies specified by PEP 518 must be already installed " - "if this option is used.", -) - -check_build_deps: Callable[..., Option] = partial( - Option, - "--check-build-dependencies", - dest="check_build_deps", - action="store_true", - default=False, - help="Check the build dependencies when PEP517 is used.", -) - - -def _handle_no_use_pep517( - option: Option, opt: str, value: str, parser: OptionParser -) -> None: - """ - Process a value provided for the --no-use-pep517 option. - - This is an optparse.Option callback for the no_use_pep517 option. - """ - # Since --no-use-pep517 doesn't accept arguments, the value argument - # will be None if --no-use-pep517 is passed via the command-line. - # However, the value can be non-None if the option is triggered e.g. - # by an environment variable, for example "PIP_NO_USE_PEP517=true". - if value is not None: - msg = """A value was passed for --no-use-pep517, - probably using either the PIP_NO_USE_PEP517 environment variable - or the "no-use-pep517" config file option. Use an appropriate value - of the PIP_USE_PEP517 environment variable or the "use-pep517" - config file option instead. - """ - raise_option_error(parser, option=option, msg=msg) - - # If user doesn't wish to use pep517, we check if setuptools and wheel are installed - # and raise error if it is not. - packages = ("setuptools", "wheel") - if not all(importlib.util.find_spec(package) for package in packages): - msg = ( - f"It is not possible to use --no-use-pep517 " - f"without {' and '.join(packages)} installed." - ) - raise_option_error(parser, option=option, msg=msg) - - # Otherwise, --no-use-pep517 was passed via the command-line. - parser.values.use_pep517 = False - - -use_pep517: Any = partial( - Option, - "--use-pep517", - dest="use_pep517", - action="store_true", - default=None, - help="Use PEP 517 for building source distributions " - "(use --no-use-pep517 to force legacy behaviour).", -) - -no_use_pep517: Any = partial( - Option, - "--no-use-pep517", - dest="use_pep517", - action="callback", - callback=_handle_no_use_pep517, - default=None, - help=SUPPRESS_HELP, -) - - -def _handle_config_settings( - option: Option, opt_str: str, value: str, parser: OptionParser -) -> None: - key, sep, val = value.partition("=") - if sep != "=": - parser.error(f"Arguments to {opt_str} must be of the form KEY=VAL") # noqa - dest = getattr(parser.values, option.dest) - if dest is None: - dest = {} - setattr(parser.values, option.dest, dest) - if key in dest: - if isinstance(dest[key], list): - dest[key].append(val) - else: - dest[key] = [dest[key], val] - else: - dest[key] = val - - -config_settings: Callable[..., Option] = partial( - Option, - "-C", - "--config-settings", - dest="config_settings", - type=str, - action="callback", - callback=_handle_config_settings, - metavar="settings", - help="Configuration settings to be passed to the PEP 517 build backend. " - "Settings take the form KEY=VALUE. Use multiple --config-settings options " - "to pass multiple keys to the backend.", -) - -build_options: Callable[..., Option] = partial( - Option, - "--build-option", - dest="build_options", - metavar="options", - action="append", - help="Extra arguments to be supplied to 'setup.py bdist_wheel'.", -) - -global_options: Callable[..., Option] = partial( - Option, - "--global-option", - dest="global_options", - action="append", - metavar="options", - help="Extra global options to be supplied to the setup.py " - "call before the install or bdist_wheel command.", -) - -no_clean: Callable[..., Option] = partial( - Option, - "--no-clean", - action="store_true", - default=False, - help="Don't clean up build directories.", -) - -pre: Callable[..., Option] = partial( - Option, - "--pre", - action="store_true", - default=False, - help="Include pre-release and development versions. By default, " - "pip only finds stable versions.", -) - -disable_pip_version_check: Callable[..., Option] = partial( - Option, - "--disable-pip-version-check", - dest="disable_pip_version_check", - action="store_true", - default=False, - help="Don't periodically check PyPI to determine whether a new version " - "of pip is available for download. Implied with --no-index.", -) - -root_user_action: Callable[..., Option] = partial( - Option, - "--root-user-action", - dest="root_user_action", - default="warn", - choices=["warn", "ignore"], - help="Action if pip is run as a root user. By default, a warning message is shown.", -) - - -def _handle_merge_hash( - option: Option, opt_str: str, value: str, parser: OptionParser -) -> None: - """Given a value spelled "algo:digest", append the digest to a list - pointed to in a dict by the algo name.""" - if not parser.values.hashes: - parser.values.hashes = {} - try: - algo, digest = value.split(":", 1) - except ValueError: - parser.error( - "Arguments to {} must be a hash name " # noqa - "followed by a value, like --hash=sha256:" - "abcde...".format(opt_str) - ) - if algo not in STRONG_HASHES: - parser.error( - "Allowed hash algorithms for {} are {}.".format( # noqa - opt_str, ", ".join(STRONG_HASHES) - ) - ) - parser.values.hashes.setdefault(algo, []).append(digest) - - -hash: Callable[..., Option] = partial( - Option, - "--hash", - # Hash values eventually end up in InstallRequirement.hashes due to - # __dict__ copying in process_line(). - dest="hashes", - action="callback", - callback=_handle_merge_hash, - type="string", - help="Verify that the package's archive matches this " - "hash before installing. Example: --hash=sha256:abcdef...", -) - - -require_hashes: Callable[..., Option] = partial( - Option, - "--require-hashes", - dest="require_hashes", - action="store_true", - default=False, - help="Require a hash to check each requirement against, for " - "repeatable installs. This option is implied when any package in a " - "requirements file has a --hash option.", -) - - -list_path: Callable[..., Option] = partial( - PipOption, - "--path", - dest="path", - type="path", - action="append", - help="Restrict to the specified installation path for listing " - "packages (can be used multiple times).", -) - - -def check_list_path_option(options: Values) -> None: - if options.path and (options.user or options.local): - raise CommandError("Cannot combine '--path' with '--user' or '--local'") - - -list_exclude: Callable[..., Option] = partial( - PipOption, - "--exclude", - dest="excludes", - action="append", - metavar="package", - type="package_name", - help="Exclude specified package from the output", -) - - -no_python_version_warning: Callable[..., Option] = partial( - Option, - "--no-python-version-warning", - dest="no_python_version_warning", - action="store_true", - default=False, - help="Silence deprecation warnings for upcoming unsupported Pythons.", -) - - -# Features that are now always on. A warning is printed if they are used. -ALWAYS_ENABLED_FEATURES = [ - "no-binary-enable-wheel-cache", # always on since 23.1 -] - -use_new_feature: Callable[..., Option] = partial( - Option, - "--use-feature", - dest="features_enabled", - metavar="feature", - action="append", - default=[], - choices=[ - "fast-deps", - "truststore", - ] - + ALWAYS_ENABLED_FEATURES, - help="Enable new functionality, that may be backward incompatible.", -) - -use_deprecated_feature: Callable[..., Option] = partial( - Option, - "--use-deprecated", - dest="deprecated_features_enabled", - metavar="feature", - action="append", - default=[], - choices=[ - "legacy-resolver", - ], - help=("Enable deprecated functionality, that will be removed in the future."), -) - - -########## -# groups # -########## - -general_group: Dict[str, Any] = { - "name": "General Options", - "options": [ - help_, - debug_mode, - isolated_mode, - require_virtualenv, - python, - verbose, - version, - quiet, - log, - no_input, - keyring_provider, - proxy, - retries, - timeout, - exists_action, - trusted_host, - cert, - client_cert, - cache_dir, - no_cache, - disable_pip_version_check, - no_color, - no_python_version_warning, - use_new_feature, - use_deprecated_feature, - ], -} - -index_group: Dict[str, Any] = { - "name": "Package Index Options", - "options": [ - index_url, - extra_index_url, - no_index, - find_links, - ], -} diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py deleted file mode 100644 index fd80d8c1129722b84771bd6a0f6ccfd57f5cf78e..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/markup.py +++ /dev/null @@ -1,246 +0,0 @@ -import re -from ast import literal_eval -from operator import attrgetter -from typing import Callable, Iterable, List, Match, NamedTuple, Optional, Tuple, Union - -from ._emoji_replace import _emoji_replace -from .emoji import EmojiVariant -from .errors import MarkupError -from .style import Style -from .text import Span, Text - -RE_TAGS = re.compile( - r"""((\\*)\[([a-z#/@][^[]*?)])""", - re.VERBOSE, -) - -RE_HANDLER = re.compile(r"^([\w.]*?)(\(.*?\))?$") - - -class Tag(NamedTuple): - """A tag in console markup.""" - - name: str - """The tag name. e.g. 'bold'.""" - parameters: Optional[str] - """Any additional parameters after the name.""" - - def __str__(self) -> str: - return ( - self.name if self.parameters is None else f"{self.name} {self.parameters}" - ) - - @property - def markup(self) -> str: - """Get the string representation of this tag.""" - return ( - f"[{self.name}]" - if self.parameters is None - else f"[{self.name}={self.parameters}]" - ) - - -_ReStringMatch = Match[str] # regex match object -_ReSubCallable = Callable[[_ReStringMatch], str] # Callable invoked by re.sub -_EscapeSubMethod = Callable[[_ReSubCallable, str], str] # Sub method of a compiled re - - -def escape( - markup: str, - _escape: _EscapeSubMethod = re.compile(r"(\\*)(\[[a-z#/@][^[]*?])").sub, -) -> str: - """Escapes text so that it won't be interpreted as markup. - - Args: - markup (str): Content to be inserted in to markup. - - Returns: - str: Markup with square brackets escaped. - """ - - def escape_backslashes(match: Match[str]) -> str: - """Called by re.sub replace matches.""" - backslashes, text = match.groups() - return f"{backslashes}{backslashes}\\{text}" - - markup = _escape(escape_backslashes, markup) - return markup - - -def _parse(markup: str) -> Iterable[Tuple[int, Optional[str], Optional[Tag]]]: - """Parse markup in to an iterable of tuples of (position, text, tag). - - Args: - markup (str): A string containing console markup - - """ - position = 0 - _divmod = divmod - _Tag = Tag - for match in RE_TAGS.finditer(markup): - full_text, escapes, tag_text = match.groups() - start, end = match.span() - if start > position: - yield start, markup[position:start], None - if escapes: - backslashes, escaped = _divmod(len(escapes), 2) - if backslashes: - # Literal backslashes - yield start, "\\" * backslashes, None - start += backslashes * 2 - if escaped: - # Escape of tag - yield start, full_text[len(escapes) :], None - position = end - continue - text, equals, parameters = tag_text.partition("=") - yield start, None, _Tag(text, parameters if equals else None) - position = end - if position < len(markup): - yield position, markup[position:], None - - -def render( - markup: str, - style: Union[str, Style] = "", - emoji: bool = True, - emoji_variant: Optional[EmojiVariant] = None, -) -> Text: - """Render console markup in to a Text instance. - - Args: - markup (str): A string containing console markup. - emoji (bool, optional): Also render emoji code. Defaults to True. - - Raises: - MarkupError: If there is a syntax error in the markup. - - Returns: - Text: A test instance. - """ - emoji_replace = _emoji_replace - if "[" not in markup: - return Text( - emoji_replace(markup, default_variant=emoji_variant) if emoji else markup, - style=style, - ) - text = Text(style=style) - append = text.append - normalize = Style.normalize - - style_stack: List[Tuple[int, Tag]] = [] - pop = style_stack.pop - - spans: List[Span] = [] - append_span = spans.append - - _Span = Span - _Tag = Tag - - def pop_style(style_name: str) -> Tuple[int, Tag]: - """Pop tag matching given style name.""" - for index, (_, tag) in enumerate(reversed(style_stack), 1): - if tag.name == style_name: - return pop(-index) - raise KeyError(style_name) - - for position, plain_text, tag in _parse(markup): - if plain_text is not None: - # Handle open brace escapes, where the brace is not part of a tag. - plain_text = plain_text.replace("\\[", "[") - append(emoji_replace(plain_text) if emoji else plain_text) - elif tag is not None: - if tag.name.startswith("/"): # Closing tag - style_name = tag.name[1:].strip() - - if style_name: # explicit close - style_name = normalize(style_name) - try: - start, open_tag = pop_style(style_name) - except KeyError: - raise MarkupError( - f"closing tag '{tag.markup}' at position {position} doesn't match any open tag" - ) from None - else: # implicit close - try: - start, open_tag = pop() - except IndexError: - raise MarkupError( - f"closing tag '[/]' at position {position} has nothing to close" - ) from None - - if open_tag.name.startswith("@"): - if open_tag.parameters: - handler_name = "" - parameters = open_tag.parameters.strip() - handler_match = RE_HANDLER.match(parameters) - if handler_match is not None: - handler_name, match_parameters = handler_match.groups() - parameters = ( - "()" if match_parameters is None else match_parameters - ) - - try: - meta_params = literal_eval(parameters) - except SyntaxError as error: - raise MarkupError( - f"error parsing {parameters!r} in {open_tag.parameters!r}; {error.msg}" - ) - except Exception as error: - raise MarkupError( - f"error parsing {open_tag.parameters!r}; {error}" - ) from None - - if handler_name: - meta_params = ( - handler_name, - meta_params - if isinstance(meta_params, tuple) - else (meta_params,), - ) - - else: - meta_params = () - - append_span( - _Span( - start, len(text), Style(meta={open_tag.name: meta_params}) - ) - ) - else: - append_span(_Span(start, len(text), str(open_tag))) - - else: # Opening tag - normalized_tag = _Tag(normalize(tag.name), tag.parameters) - style_stack.append((len(text), normalized_tag)) - - text_length = len(text) - while style_stack: - start, tag = style_stack.pop() - style = str(tag) - if style: - append_span(_Span(start, text_length, style)) - - text.spans = sorted(spans[::-1], key=attrgetter("start")) - return text - - -if __name__ == "__main__": # pragma: no cover - - MARKUP = [ - "[red]Hello World[/red]", - "[magenta]Hello [b]World[/b]", - "[bold]Bold[italic] bold and italic [/bold]italic[/italic]", - "Click [link=https://www.willmcgugan.com]here[/link] to visit my Blog", - ":warning-emoji: [bold red blink] DANGER![/]", - ] - - from pip._vendor.rich import print - from pip._vendor.rich.table import Table - - grid = Table("Markup", "Result", padding=(0, 1)) - - for markup in MARKUP: - grid.add_row(Text(markup), markup) - - print(grid) diff --git a/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat b/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat deleted file mode 100644 index 62a39a7584f4d7c5fbc31758e3e9e7eff700276d..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Scripts/deactivate.bat +++ /dev/null @@ -1,22 +0,0 @@ -@echo off - -if defined _OLD_VIRTUAL_PROMPT ( - set "PROMPT=%_OLD_VIRTUAL_PROMPT%" -) -set _OLD_VIRTUAL_PROMPT= - -if defined _OLD_VIRTUAL_PYTHONHOME ( - set "PYTHONHOME=%_OLD_VIRTUAL_PYTHONHOME%" - set _OLD_VIRTUAL_PYTHONHOME= -) - -if defined _OLD_VIRTUAL_PATH ( - set "PATH=%_OLD_VIRTUAL_PATH%" -) - -set _OLD_VIRTUAL_PATH= - -set VIRTUAL_ENV= -set VIRTUAL_ENV_PROMPT= - -:END diff --git a/spaces/CAMP-ViL/Xplainer/inference.py b/spaces/CAMP-ViL/Xplainer/inference.py deleted file mode 100644 index 2ab6b61c2b1fa8039af58a5f5c4823b6c2f56cab..0000000000000000000000000000000000000000 --- a/spaces/CAMP-ViL/Xplainer/inference.py +++ /dev/null @@ -1,116 +0,0 @@ -import argparse -import gc -from pathlib import Path - -import torch -from torch.utils.data import DataLoader -from tqdm import tqdm - -from chestxray14 import ChestXray14Dataset -from chexpert import CheXpertDataset -from descriptors import disease_descriptors_chexpert, disease_descriptors_chestxray14 -from model import InferenceModel -from utils import calculate_auroc - -torch.multiprocessing.set_sharing_strategy('file_system') - - -def inference_chexpert(): - split = 'test' - dataset = CheXpertDataset(f'data/chexpert/{split}_labels.csv') # also do test - dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=lambda x: x, num_workers=0) - inference_model = InferenceModel() - all_descriptors = inference_model.get_all_descriptors(disease_descriptors_chexpert) - - all_labels = [] - all_probs_neg = [] - - for batch in tqdm(dataloader): - batch = batch[0] - image_paths, labels, keys = batch - image_paths = [Path(image_path) for image_path in image_paths] - agg_probs = [] - agg_negative_probs = [] - for image_path in image_paths: - probs, negative_probs = inference_model.get_descriptor_probs(image_path, descriptors=all_descriptors) - agg_probs.append(probs) - agg_negative_probs.append(negative_probs) - probs = {} # Aggregated - negative_probs = {} # Aggregated - for key in agg_probs[0].keys(): - probs[key] = sum([p[key] for p in agg_probs]) / len(agg_probs) # Mean Aggregation - - for key in agg_negative_probs[0].keys(): - negative_probs[key] = sum([p[key] for p in agg_negative_probs]) / len(agg_negative_probs) # Mean Aggregation - - disease_probs, negative_disease_probs = inference_model.get_diseases_probs(disease_descriptors_chexpert, pos_probs=probs, - negative_probs=negative_probs) - predicted_diseases, prob_vector_neg_prompt = inference_model.get_predictions_bin_prompting(disease_descriptors_chexpert, - disease_probs=disease_probs, - negative_disease_probs=negative_disease_probs, - keys=keys) - all_labels.append(labels) - all_probs_neg.append(prob_vector_neg_prompt) - - all_labels = torch.stack(all_labels) - all_probs_neg = torch.stack(all_probs_neg) - - # evaluation - existing_mask = sum(all_labels, 0) > 0 - all_labels_clean = all_labels[:, existing_mask] - all_probs_neg_clean = all_probs_neg[:, existing_mask] - all_keys_clean = [key for idx, key in enumerate(keys) if existing_mask[idx]] - - overall_auroc, per_disease_auroc = calculate_auroc(all_probs_neg_clean, all_labels_clean) - print(f"AUROC: {overall_auroc:.5f}\n") - for idx, key in enumerate(all_keys_clean): - print(f'{key}: {per_disease_auroc[idx]:.5f}') - - -def inference_chestxray14(): - dataset = ChestXray14Dataset(f'data/chestxray14/Data_Entry_2017_v2020_modified.csv') - dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=lambda x: x, num_workers=1) - inference_model = InferenceModel() - all_descriptors = inference_model.get_all_descriptors(disease_descriptors_chestxray14) - - all_labels = [] - all_probs_neg = [] - for batch in tqdm(dataloader): - batch = batch[0] - image_path, labels, keys = batch - image_path = Path(image_path) - probs, negative_probs = inference_model.get_descriptor_probs(image_path, descriptors=all_descriptors) - disease_probs, negative_disease_probs = inference_model.get_diseases_probs(disease_descriptors_chestxray14, pos_probs=probs, - negative_probs=negative_probs) - predicted_diseases, prob_vector_neg_prompt = inference_model.get_predictions_bin_prompting(disease_descriptors_chestxray14, - disease_probs=disease_probs, - negative_disease_probs=negative_disease_probs, - keys=keys) - all_labels.append(labels) - all_probs_neg.append(prob_vector_neg_prompt) - gc.collect() - - all_labels = torch.stack(all_labels) - all_probs_neg = torch.stack(all_probs_neg) - - existing_mask = sum(all_labels, 0) > 0 - all_labels_clean = all_labels[:, existing_mask] - all_probs_neg_clean = all_probs_neg[:, existing_mask] - all_keys_clean = [key for idx, key in enumerate(keys) if existing_mask[idx]] - - overall_auroc, per_disease_auroc = calculate_auroc(all_probs_neg_clean[:, 1:], all_labels_clean[:, 1:]) - print(f"AUROC: {overall_auroc:.5f}\n") - for idx, key in enumerate(all_keys_clean[1:]): - print(f'{key}: {per_disease_auroc[idx]:.5f}') - - -if __name__ == '__main__': - # add argument parser - parser = argparse.ArgumentParser() - parser.add_argument('--dataset', type=str, default='chexpert', help='chexpert or chestxray14') - args = parser.parse_args() - - if args.dataset == 'chexpert': - inference_chexpert() - elif args.dataset == 'chestxray14': - inference_chestxray14() diff --git a/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h b/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h deleted file mode 100644 index e00d3ef054bd740b801e47cc1344e38621d8c055..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/iterator/detail/iterator_facade_category.h +++ /dev/null @@ -1,253 +0,0 @@ -/* - * Copyright 2008-2013 NVIDIA Corporation - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -namespace thrust -{ - -namespace detail -{ - - -// adapted from http://www.boost.org/doc/libs/1_37_0/libs/iterator/doc/iterator_facade.html#iterator-category -// -// in our implementation, R need not be a reference type to result in a category -// derived from forward_XXX_iterator_tag -// -// iterator-category(T,V,R) := -// if(T is convertible to input_host_iterator_tag -// || T is convertible to output_host_iterator_tag -// || T is convertible to input_device_iterator_tag -// || T is convertible to output_device_iterator_tag -// ) -// return T -// -// else if (T is not convertible to incrementable_traversal_tag) -// the program is ill-formed -// -// else return a type X satisfying the following two constraints: -// -// 1. X is convertible to X1, and not to any more-derived -// type, where X1 is defined by: -// -// if (T is convertible to forward_traversal_tag) -// { -// if (T is convertible to random_access_traversal_tag) -// X1 = random_access_host_iterator_tag -// else if (T is convertible to bidirectional_traversal_tag) -// X1 = bidirectional_host_iterator_tag -// else -// X1 = forward_host_iterator_tag -// } -// else -// { -// if (T is convertible to single_pass_traversal_tag -// && R is convertible to V) -// X1 = input_host_iterator_tag -// else -// X1 = T -// } -// -// 2. category-to-traversal(X) is convertible to the most -// derived traversal tag type to which X is also convertible, -// and not to any more-derived traversal tag type. - - -template - struct iterator_facade_default_category; - - -// Thrust's implementation of iterator_facade_default_category is slightly -// different from Boost's equivalent. -// Thrust does not check is_convertible - struct iterator_facade_default_category_std : - thrust::detail::eval_if< - thrust::detail::is_convertible - > - > -{ -}; // end iterator_facade_default_category_std - - -// this is the function for host system iterators -template - struct iterator_facade_default_category_host : - thrust::detail::eval_if< - thrust::detail::is_convertible - > - > -{ -}; // end iterator_facade_default_category_host - - -// this is the function for device system iterators -template - struct iterator_facade_default_category_device : - thrust::detail::eval_if< - thrust::detail::is_convertible - > - > -{ -}; // end iterator_facade_default_category_device - - -// this is the function for any system iterators -template - struct iterator_facade_default_category_any -{ - typedef thrust::detail::iterator_category_with_system_and_traversal< - typename iterator_facade_default_category_std - struct iterator_facade_default_category - // check for any system - : thrust::detail::eval_if< - thrust::detail::is_convertible - struct iterator_facade_category_impl -{ - typedef typename iterator_facade_default_category< - System,Traversal,ValueParam,Reference - >::type category; - - // we must be able to deduce both Traversal & System from category - // otherwise, munge them all together - typedef typename thrust::detail::eval_if< - thrust::detail::and_< - thrust::detail::is_same< - Traversal, - typename thrust::detail::iterator_category_to_traversal ::type - >, - thrust::detail::is_same< - System, - typename thrust::detail::iterator_category_to_system ::type - > - >::value, - thrust::detail::identity_ , - thrust::detail::identity_ - struct iterator_facade_category -{ - typedef typename - thrust::detail::eval_if< - thrust::detail::is_iterator_category ::value, - thrust::detail::identity_ , // categories are fine as-is - iterator_facade_category_impl ", - examples=[]) - -interface.launch() - diff --git a/spaces/DragGan/DragGan-Inversion/stylegan_human/utils/ImagesDataset.py b/spaces/DragGan/DragGan-Inversion/stylegan_human/utils/ImagesDataset.py deleted file mode 100644 index ffe6106ff3d52b1c6c13ecab48618f2786bbfee5..0000000000000000000000000000000000000000 --- a/spaces/DragGan/DragGan-Inversion/stylegan_human/utils/ImagesDataset.py +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) SenseTime Research. All rights reserved. - - -import os -from torch.utils.data import Dataset -from PIL import Image - -from utils.data_utils import make_dataset - - -class ImagesDataset(Dataset): - - def __init__(self, source_root, source_transform=None): - self.source_paths = sorted(make_dataset(source_root)) - self.source_transform = source_transform - - def __len__(self): - return len(self.source_paths) - - def __getitem__(self, index): - fname, from_path = self.source_paths[index] - from_im = Image.open(from_path).convert('RGB') - - if self.source_transform: - from_im = self.source_transform(from_im) - - return fname, from_im diff --git a/spaces/DragGan/DragGan-Inversion/torch_utils/ops/filtered_lrelu.cpp b/spaces/DragGan/DragGan-Inversion/torch_utils/ops/filtered_lrelu.cpp deleted file mode 100644 index ff4149b8b46b54d2f400ae10e44d19f20503ba1f..0000000000000000000000000000000000000000 --- a/spaces/DragGan/DragGan-Inversion/torch_utils/ops/filtered_lrelu.cpp +++ /dev/null @@ -1,300 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include -#include -#include -#include "filtered_lrelu.h" - -//------------------------------------------------------------------------ - -static std::tuple () : 0; - p.fu = fu.data_ptr (); - p.fd = fd.data_ptr (); - p.pad0 = make_int2(px0, py0); - p.gain = gain; - p.slope = slope; - p.clamp = clamp; - p.flip = (flip_filters) ? 1 : 0; - p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0)); - p.yShape = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0)); - p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3), (int)s.size(2)) : make_int2(0, 0); // Width is in bytes. Contiguous. - p.sOfs = make_int2(sx, sy); - p.swLimit = (sw_active + 3) >> 2; // Rounded up to bytes. - - // x, y, b strides are in bytes. - p.xStride = make_longlong4(sz * x.stride(3), sz * x.stride(2), sz * x.stride(1), sz * x.stride(0)); - p.yStride = make_longlong4(sz * y.stride(3), sz * y.stride(2), sz * y.stride(1), sz * y.stride(0)); - p.bStride = sz * b.stride(0); - - // fu, fd strides are in elements. - p.fuStride = make_longlong3(fu.stride(-1), fu.dim() == 2 ? fu.stride(0) : 0, 0); - p.fdStride = make_longlong3(fd.stride(-1), fd.dim() == 2 ? fd.stride(0) : 0, 0); - - // Determine if indices don't fit in int32. Support negative strides although Torch currently never produces those. - bool index64b = false; - if (std::abs(p.bStride * x.size(1)) > INT_MAX) index64b = true; - if (std::min(x.size(0) * p.xStride.w, 0ll) + std::min(x.size(1) * p.xStride.z, 0ll) + std::min(x.size(2) * p.xStride.y, 0ll) + std::min(x.size(3) * p.xStride.x, 0ll) < -INT_MAX) index64b = true; - if (std::max(x.size(0) * p.xStride.w, 0ll) + std::max(x.size(1) * p.xStride.z, 0ll) + std::max(x.size(2) * p.xStride.y, 0ll) + std::max(x.size(3) * p.xStride.x, 0ll) > INT_MAX) index64b = true; - if (std::min(y.size(0) * p.yStride.w, 0ll) + std::min(y.size(1) * p.yStride.z, 0ll) + std::min(y.size(2) * p.yStride.y, 0ll) + std::min(y.size(3) * p.yStride.x, 0ll) < -INT_MAX) index64b = true; - if (std::max(y.size(0) * p.yStride.w, 0ll) + std::max(y.size(1) * p.yStride.z, 0ll) + std::max(y.size(2) * p.yStride.y, 0ll) + std::max(y.size(3) * p.yStride.x, 0ll) > INT_MAX) index64b = true; - if (s.numel() > INT_MAX) index64b = true; - - // Choose CUDA kernel. - filtered_lrelu_kernel_spec spec = { 0 }; - AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_cuda", [&] - { - if constexpr (sizeof(scalar_t) <= 4) // Exclude doubles. constexpr prevents template instantiation. - { - // Choose kernel based on index type, datatype and sign read/write modes. - if (!index64b && writeSigns && !readSigns) spec = choose_filtered_lrelu_kernel () : 0; - p.gain = gain; - p.slope = slope; - p.clamp = clamp; - p.xShape = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0)); - p.xStride = make_longlong4(x.stride(3), x.stride(2), x.stride(1), x.stride(0)); - p.sShape = (readSigns || writeSigns) ? make_int2((int)s.size(3) << 2, (int)s.size(2)) : make_int2(0, 0); // Width is in elements. Contiguous. - p.sOfs = make_int2(sx, sy); - - // Choose CUDA kernel. - void* func = 0; - AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "filtered_lrelu_act_cuda", [&] - { - if (writeSigns) - func = choose_filtered_lrelu_act_kernel = Promise.resolve(); - - constructor(chat: ChatInterface, localChat: ChatInterface) { - // use web worker to run chat generation in background - this.chat = chat; - this.localChat = localChat; - // get the elements - this.uiChat = getElementAndCheck("chatui-chat"); - this.uiChatInput = getElementAndCheck("chatui-input") as HTMLInputElement; - this.uiChatInfoLabel = getElementAndCheck( - "chatui-info-label" - ) as HTMLLabelElement; - // register event handlers - getElementAndCheck("chatui-reset-btn").onclick = () => { - this.onReset(); - }; - getElementAndCheck("chatui-send-btn").onclick = () => { - this.onGenerate(); - }; - // TODO: find other alternative triggers - getElementAndCheck("chatui-input").onkeypress = (event) => { - if (event.keyCode === 13) { - this.onGenerate(); - } - }; - - const modelSelector = getElementAndCheck( - "chatui-select" - ) as HTMLSelectElement; - for (let i = 0; i < this.config.model_list.length; ++i) { - const item = this.config.model_list[i]; - const opt = document.createElement("option"); - opt.value = item.local_id; - opt.innerHTML = item.local_id; - opt.selected = i == 0; - modelSelector.appendChild(opt); - } - // Append local server option to the model selector - const localServerOpt = document.createElement("option"); - localServerOpt.value = "Local Server"; - localServerOpt.innerHTML = "Local Server"; - modelSelector.append(localServerOpt); - this.selectedModel = modelSelector.value; - modelSelector.onchange = () => { - this.onSelectChange(modelSelector); - }; - } - /** - * Push a task to the execution queue. - * - * @param task The task to be executed; - */ - private pushTask(task: () => Promise ) { - const lastEvent = this.chatRequestChain; - this.chatRequestChain = lastEvent.then(task); - } - // Event handlers - // all event handler pushes the tasks to a queue - // that get executed sequentially - // the tasks previous tasks, which causes them to early stop - // can be interrupted by chat.interruptGenerate - private async onGenerate() { - if (this.requestInProgress) { - return; - } - this.pushTask(async () => { - await this.asyncGenerate(); - }); - } - - private async onSelectChange(modelSelector: HTMLSelectElement) { - if (this.requestInProgress) { - // interrupt previous generation if any - this.chat.interruptGenerate(); - } - // try reset after previous requests finishes - this.pushTask(async () => { - await this.chat.resetChat(); - this.resetChatHistory(); - await this.unloadChat(); - this.selectedModel = modelSelector.value; - await this.asyncInitChat(); - }); - } - - private async onReset() { - if (this.requestInProgress) { - // interrupt previous generation if any - this.chat.interruptGenerate(); - } - // try reset after previous requests finishes - this.pushTask(async () => { - await this.chat.resetChat(); - this.resetChatHistory(); - }); - } - - // Internal helper functions - private appendMessage(kind, text) { - if (kind == "init") { - text = "[System Initalize] " + text; - } - if (this.uiChat === undefined) { - throw Error("cannot find ui chat"); - } - const msg = ` - -- `; - this.uiChat.insertAdjacentHTML("beforeend", msg); - this.uiChat.scrollTo(0, this.uiChat.scrollHeight); - } - - private updateLastMessage(kind, text) { - if (kind == "init") { - text = "[System Initalize] " + text; - } - if (this.uiChat === undefined) { - throw Error("cannot find ui chat"); - } - const matches = this.uiChat.getElementsByClassName(`msg ${kind}-msg`); - if (matches.length == 0) throw Error(`${kind} message do not exist`); - const msg = matches[matches.length - 1]; - const msgText = msg.getElementsByClassName("msg-text"); - if (msgText.length != 1) throw Error("Expect msg-text"); - if (msgText[0].innerHTML == text) return; - const list = text.split("\n").map((t) => { - const item = document.createElement("div"); - item.textContent = t; - return item; - }); - msgText[0].innerHTML = ""; - list.forEach((item) => msgText[0].append(item)); - this.uiChat.scrollTo(0, this.uiChat.scrollHeight); - } - - private resetChatHistory() { - const clearTags = ["left", "right", "init", "error"]; - for (const tag of clearTags) { - // need to unpack to list so the iterator don't get affected by mutation - const matches = [...this.uiChat.getElementsByClassName(`msg ${tag}-msg`)]; - for (const item of matches) { - this.uiChat.removeChild(item); - } - } - if (this.uiChatInfoLabel !== undefined) { - this.uiChatInfoLabel.innerHTML = ""; - } - } - - private async asyncInitChat() { - if (this.chatLoaded) return; - this.requestInProgress = true; - this.appendMessage("init", ""); - const initProgressCallback = (report) => { - this.updateLastMessage("init", report.text); - }; - this.chat.setInitProgressCallback(initProgressCallback); - - try { - if (this.selectedModel != "Local Server") { - await this.chat.reload(this.selectedModel, undefined, this.config); - } - } catch (err) { - this.appendMessage("error", "Init error, " + err.toString()); - console.log(err.stack); - this.unloadChat(); - this.requestInProgress = false; - return; - } - this.requestInProgress = false; - this.chatLoaded = true; - } - - private async unloadChat() { - await this.chat.unload(); - this.chatLoaded = false; - } - - /** - * Run generate - */ - private async asyncGenerate() { - await this.asyncInitChat(); - this.requestInProgress = true; - const prompt = this.uiChatInput.value; - if (prompt == "") { - this.requestInProgress = false; - return; - } - - this.appendMessage("right", prompt); - this.uiChatInput.value = ""; - this.uiChatInput.setAttribute("placeholder", "Generating..."); - - this.appendMessage("left", ""); - const callbackUpdateResponse = (step, msg) => { - if (msg.length === 0) return this.chat.interruptGenerate(); - this.updateLastMessage("left", msg); - }; - - try { - if (this.selectedModel == "Local Server") { - await this.localChat.generate(prompt, callbackUpdateResponse); - this.uiChatInfoLabel.innerHTML = - await this.localChat.runtimeStatsText(); - } else { - await this.chat.generate(prompt, callbackUpdateResponse); - this.uiChatInfoLabel.innerHTML = await this.chat.runtimeStatsText(); - } - } catch (err) { - this.appendMessage("error", "Generate error, " + err.toString()); - console.log(err.stack); - await this.unloadChat(); - } - this.uiChatInput.setAttribute("placeholder", "Enter your message..."); - this.requestInProgress = false; - } -} - -const useWebWorker = appConfig.use_web_worker; -let chat: ChatInterface; -let localChat: ChatInterface; - -if (useWebWorker) { - chat = new ChatWorkerClient( - new Worker(new URL("./worker.ts", import.meta.url), { type: "module" }) - ); - localChat = new ChatRestModule(); -} else { - chat = new ChatModule(); - localChat = new ChatRestModule(); -} -new ChatUI(chat, localChat); diff --git a/spaces/Francesco/torch-cam-transformers/app.py b/spaces/Francesco/torch-cam-transformers/app.py deleted file mode 100644 index 6c5bf7ea8344430235dfcc553c112be801348a50..0000000000000000000000000000000000000000 --- a/spaces/Francesco/torch-cam-transformers/app.py +++ /dev/null @@ -1,14 +0,0 @@ -import streamlit as st - -import sys - - -def main(): - # Wide mode - st.set_page_config(layout="wide") - - # Designing the interface - st.title(sys.version) - - -main() diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py deleted file mode 100644 index bf18572329019d7a8f1df01799eda207c16dd7ff..0000000000000000000000000000000000000000 --- a/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/data_loaders.py +++ /dev/null @@ -1,284 +0,0 @@ -import os -import random -import re -import numpy as np -import librosa -import torch -import random -from utils import repeat_expand_2d -from tqdm import tqdm -from torch.utils.data import Dataset - -def traverse_dir( - root_dir, - extensions, - amount=None, - str_include=None, - str_exclude=None, - is_pure=False, - is_sort=False, - is_ext=True): - - file_list = [] - cnt = 0 - for root, _, files in os.walk(root_dir): - for file in files: - if any([file.endswith(f".{ext}") for ext in extensions]): - # path - mix_path = os.path.join(root, file) - pure_path = mix_path[len(root_dir)+1:] if is_pure else mix_path - - # amount - if (amount is not None) and (cnt == amount): - if is_sort: - file_list.sort() - return file_list - - # check string - if (str_include is not None) and (str_include not in pure_path): - continue - if (str_exclude is not None) and (str_exclude in pure_path): - continue - - if not is_ext: - ext = pure_path.split('.')[-1] - pure_path = pure_path[:-(len(ext)+1)] - file_list.append(pure_path) - cnt += 1 - if is_sort: - file_list.sort() - return file_list - - -def get_data_loaders(args, whole_audio=False): - data_train = AudioDataset( - filelists = args.data.training_files, - waveform_sec=args.data.duration, - hop_size=args.data.block_size, - sample_rate=args.data.sampling_rate, - load_all_data=args.train.cache_all_data, - whole_audio=whole_audio, - extensions=args.data.extensions, - n_spk=args.model.n_spk, - spk=args.spk, - device=args.train.cache_device, - fp16=args.train.cache_fp16, - use_aug=True) - loader_train = torch.utils.data.DataLoader( - data_train , - batch_size=args.train.batch_size if not whole_audio else 1, - shuffle=True, - num_workers=args.train.num_workers if args.train.cache_device=='cpu' else 0, - persistent_workers=(args.train.num_workers > 0) if args.train.cache_device=='cpu' else False, - pin_memory=True if args.train.cache_device=='cpu' else False - ) - data_valid = AudioDataset( - filelists = args.data.validation_files, - waveform_sec=args.data.duration, - hop_size=args.data.block_size, - sample_rate=args.data.sampling_rate, - load_all_data=args.train.cache_all_data, - whole_audio=True, - spk=args.spk, - extensions=args.data.extensions, - n_spk=args.model.n_spk) - loader_valid = torch.utils.data.DataLoader( - data_valid, - batch_size=1, - shuffle=False, - num_workers=0, - pin_memory=True - ) - return loader_train, loader_valid - - -class AudioDataset(Dataset): - def __init__( - self, - filelists, - waveform_sec, - hop_size, - sample_rate, - spk, - load_all_data=True, - whole_audio=False, - extensions=['wav'], - n_spk=1, - device='cpu', - fp16=False, - use_aug=False, - ): - super().__init__() - - self.waveform_sec = waveform_sec - self.sample_rate = sample_rate - self.hop_size = hop_size - self.filelists = filelists - self.whole_audio = whole_audio - self.use_aug = use_aug - self.data_buffer={} - self.pitch_aug_dict = {} - # np.load(os.path.join(self.path_root, 'pitch_aug_dict.npy'), allow_pickle=True).item() - if load_all_data: - print('Load all the data filelists:', filelists) - else: - print('Load the f0, volume data filelists:', filelists) - with open(filelists,"r") as f: - self.paths = f.read().splitlines() - for name_ext in tqdm(self.paths, total=len(self.paths)): - name = os.path.splitext(name_ext)[0] - path_audio = name_ext - duration = librosa.get_duration(filename = path_audio, sr = self.sample_rate) - - path_f0 = name_ext + ".f0.npy" - f0,_ = np.load(path_f0,allow_pickle=True) - f0 = torch.from_numpy(np.array(f0,dtype=float)).float().unsqueeze(-1).to(device) - - path_volume = name_ext + ".vol.npy" - volume = np.load(path_volume) - volume = torch.from_numpy(volume).float().unsqueeze(-1).to(device) - - path_augvol = name_ext + ".aug_vol.npy" - aug_vol = np.load(path_augvol) - aug_vol = torch.from_numpy(aug_vol).float().unsqueeze(-1).to(device) - - if n_spk is not None and n_spk > 1: - spk_name = name_ext.split("/")[-2] - spk_id = spk[spk_name] if spk_name in spk else 0 - if spk_id < 0 or spk_id >= n_spk: - raise ValueError(' [x] Muiti-speaker traing error : spk_id must be a positive integer from 0 to n_spk-1 ') - else: - spk_id = 0 - spk_id = torch.LongTensor(np.array([spk_id])).to(device) - - if load_all_data: - ''' - audio, sr = librosa.load(path_audio, sr=self.sample_rate) - if len(audio.shape) > 1: - audio = librosa.to_mono(audio) - audio = torch.from_numpy(audio).to(device) - ''' - path_mel = name_ext + ".mel.npy" - mel = np.load(path_mel) - mel = torch.from_numpy(mel).to(device) - - path_augmel = name_ext + ".aug_mel.npy" - aug_mel,keyshift = np.load(path_augmel, allow_pickle=True) - aug_mel = np.array(aug_mel,dtype=float) - aug_mel = torch.from_numpy(aug_mel).to(device) - self.pitch_aug_dict[name_ext] = keyshift - - path_units = name_ext + ".soft.pt" - units = torch.load(path_units).to(device) - units = units[0] - units = repeat_expand_2d(units,f0.size(0)).transpose(0,1) - - if fp16: - mel = mel.half() - aug_mel = aug_mel.half() - units = units.half() - - self.data_buffer[name_ext] = { - 'duration': duration, - 'mel': mel, - 'aug_mel': aug_mel, - 'units': units, - 'f0': f0, - 'volume': volume, - 'aug_vol': aug_vol, - 'spk_id': spk_id - } - else: - path_augmel = name_ext + ".aug_mel.npy" - aug_mel,keyshift = np.load(path_augmel, allow_pickle=True) - self.pitch_aug_dict[name_ext] = keyshift - self.data_buffer[name_ext] = { - 'duration': duration, - 'f0': f0, - 'volume': volume, - 'aug_vol': aug_vol, - 'spk_id': spk_id - } - - - def __getitem__(self, file_idx): - name_ext = self.paths[file_idx] - data_buffer = self.data_buffer[name_ext] - # check duration. if too short, then skip - if data_buffer['duration'] < (self.waveform_sec + 0.1): - return self.__getitem__( (file_idx + 1) % len(self.paths)) - - # get item - return self.get_data(name_ext, data_buffer) - - def get_data(self, name_ext, data_buffer): - name = os.path.splitext(name_ext)[0] - frame_resolution = self.hop_size / self.sample_rate - duration = data_buffer['duration'] - waveform_sec = duration if self.whole_audio else self.waveform_sec - - # load audio - idx_from = 0 if self.whole_audio else random.uniform(0, duration - waveform_sec - 0.1) - start_frame = int(idx_from / frame_resolution) - units_frame_len = int(waveform_sec / frame_resolution) - aug_flag = random.choice([True, False]) and self.use_aug - ''' - audio = data_buffer.get('audio') - if audio is None: - path_audio = os.path.join(self.path_root, 'audio', name) + '.wav' - audio, sr = librosa.load( - path_audio, - sr = self.sample_rate, - offset = start_frame * frame_resolution, - duration = waveform_sec) - if len(audio.shape) > 1: - audio = librosa.to_mono(audio) - # clip audio into N seconds - audio = audio[ : audio.shape[-1] // self.hop_size * self.hop_size] - audio = torch.from_numpy(audio).float() - else: - audio = audio[start_frame * self.hop_size : (start_frame + units_frame_len) * self.hop_size] - ''' - # load mel - mel_key = 'aug_mel' if aug_flag else 'mel' - mel = data_buffer.get(mel_key) - if mel is None: - mel = name_ext + ".mel.npy" - mel = np.load(mel) - mel = mel[start_frame : start_frame + units_frame_len] - mel = torch.from_numpy(mel).float() - else: - mel = mel[start_frame : start_frame + units_frame_len] - - # load f0 - f0 = data_buffer.get('f0') - aug_shift = 0 - if aug_flag: - aug_shift = self.pitch_aug_dict[name_ext] - f0_frames = 2 ** (aug_shift / 12) * f0[start_frame : start_frame + units_frame_len] - - # load units - units = data_buffer.get('units') - if units is None: - path_units = name_ext + ".soft.pt" - units = torch.load(path_units) - units = units[0] - units = repeat_expand_2d(units,f0.size(0)).transpose(0,1) - - units = units[start_frame : start_frame + units_frame_len] - - # load volume - vol_key = 'aug_vol' if aug_flag else 'volume' - volume = data_buffer.get(vol_key) - volume_frames = volume[start_frame : start_frame + units_frame_len] - - # load spk_id - spk_id = data_buffer.get('spk_id') - - # load shift - aug_shift = torch.from_numpy(np.array([[aug_shift]])).float() - - return dict(mel=mel, f0=f0_frames, volume=volume_frames, units=units, spk_id=spk_id, aug_shift=aug_shift, name=name, name_ext=name_ext) - - def __len__(self): - return len(self.paths) \ No newline at end of file diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py deleted file mode 100644 index 3bdb70ba9357e95ff05853dcc06437c3401ef3be..0000000000000000000000000000000000000000 --- a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/audio.py +++ /dev/null @@ -1,125 +0,0 @@ -import os -from functools import lru_cache -from typing import Union - -import ffmpeg -import numpy as np -import torch -import torch.nn.functional as F - -from .utils import exact_div - -from librosa.filters import mel as librosa_mel_fn - -# hard-coded audio hyperparameters -SAMPLE_RATE = 16000 -N_FFT = 400 -N_MELS = 80 -HOP_LENGTH = 160 -CHUNK_LENGTH = 30 -N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE # 480000: number of samples in a chunk -N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH) # 3000: number of frames in a mel spectrogram input - - -def load_audio(file: str, sr: int = SAMPLE_RATE): - """ - Open an audio file and read as mono waveform, resampling as necessary - - Parameters - ---------- - file: str - The audio file to open - - sr: int - The sample rate to resample the audio if necessary - - Returns - ------- - A NumPy array containing the audio waveform, in float32 dtype. - """ - try: - # This launches a subprocess to decode audio while down-mixing and resampling as necessary. - # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed. - out, _ = ( - ffmpeg.input(file, threads=0) - .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr) - .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True) - ) - except ffmpeg.Error as e: - raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e - - return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0 - - -def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1): - """ - Pad or trim the audio array to N_SAMPLES, as expected by the encoder. - """ - if torch.is_tensor(array): - if array.shape[axis] > length: - array = array.index_select(dim=axis, index=torch.arange(length, device=array.device)) - - if array.shape[axis] < length: - pad_widths = [(0, 0)] * array.ndim - pad_widths[axis] = (0, length - array.shape[axis]) - array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes]) - else: - if array.shape[axis] > length: - array = array.take(indices=range(length), axis=axis) - - if array.shape[axis] < length: - pad_widths = [(0, 0)] * array.ndim - pad_widths[axis] = (0, length - array.shape[axis]) - array = np.pad(array, pad_widths) - - return array - - -@lru_cache(maxsize=None) -def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor: - """ - load the mel filterbank matrix for projecting STFT into a Mel spectrogram. - Allows decoupling librosa dependency; saved using: - - np.savez_compressed( - "mel_filters.npz", - mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80), - ) - """ - assert n_mels == 80, f"Unsupported n_mels: {n_mels}" - return torch.from_numpy(librosa_mel_fn(sr=SAMPLE_RATE,n_fft=N_FFT,n_mels=n_mels)).to(device) - - -def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS): - """ - Compute the log-Mel spectrogram of - - Parameters - ---------- - audio: Union[str, np.ndarray, torch.Tensor], shape = (*) - The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz - - n_mels: int - The number of Mel-frequency filters, only 80 is supported - - Returns - ------- - torch.Tensor, shape = (80, n_frames) - A Tensor that contains the Mel spectrogram - """ - if not torch.is_tensor(audio): - if isinstance(audio, str): - audio = load_audio(audio) - audio = torch.from_numpy(audio) - - window = torch.hann_window(N_FFT).to(audio.device) - stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True) - magnitudes = stft[..., :-1].abs() ** 2 - - filters = mel_filters(audio.device, n_mels) - mel_spec = filters @ magnitudes - - log_spec = torch.clamp(mel_spec, min=1e-10).log10() - log_spec = torch.maximum(log_spec, log_spec.max() - 8.0) - log_spec = (log_spec + 4.0) / 4.0 - return log_spec diff --git a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py b/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py deleted file mode 100644 index 6817392ba8a9eb830351de89fb7afc5ad72f5e42..0000000000000000000000000000000000000000 --- a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/english.py +++ /dev/null @@ -1,188 +0,0 @@ -""" from https://github.com/keithito/tacotron """ - -''' -Cleaners are transformations that run over the input text at both training and eval time. - -Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners" -hyperparameter. Some cleaners are English-specific. You'll typically want to use: - 1. "english_cleaners" for English text - 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using - the Unidecode library (https://pypi.python.org/pypi/Unidecode) - 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update - the symbols in symbols.py to match your data). -''' - - -# Regular expression matching whitespace: - - -import re -import inflect -from unidecode import unidecode -import eng_to_ipa as ipa -_inflect = inflect.engine() -_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])') -_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)') -_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)') -_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)') -_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)') -_number_re = re.compile(r'[0-9]+') - -# List of (regular expression, replacement) pairs for abbreviations: -_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [ - ('mrs', 'misess'), - ('mr', 'mister'), - ('dr', 'doctor'), - ('st', 'saint'), - ('co', 'company'), - ('jr', 'junior'), - ('maj', 'major'), - ('gen', 'general'), - ('drs', 'doctors'), - ('rev', 'reverend'), - ('lt', 'lieutenant'), - ('hon', 'honorable'), - ('sgt', 'sergeant'), - ('capt', 'captain'), - ('esq', 'esquire'), - ('ltd', 'limited'), - ('col', 'colonel'), - ('ft', 'fort'), -]] - - -# List of (ipa, lazy ipa) pairs: -_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('r', 'ɹ'), - ('æ', 'e'), - ('ɑ', 'a'), - ('ɔ', 'o'), - ('ð', 'z'), - ('θ', 's'), - ('ɛ', 'e'), - ('ɪ', 'i'), - ('ʊ', 'u'), - ('ʒ', 'ʥ'), - ('ʤ', 'ʥ'), - ('ˈ', '↓'), -]] - -# List of (ipa, lazy ipa2) pairs: -_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('r', 'ɹ'), - ('ð', 'z'), - ('θ', 's'), - ('ʒ', 'ʑ'), - ('ʤ', 'dʑ'), - ('ˈ', '↓'), -]] - -# List of (ipa, ipa2) pairs -_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('r', 'ɹ'), - ('ʤ', 'dʒ'), - ('ʧ', 'tʃ') -]] - - -def expand_abbreviations(text): - for regex, replacement in _abbreviations: - text = re.sub(regex, replacement, text) - return text - - -def collapse_whitespace(text): - return re.sub(r'\s+', ' ', text) - - -def _remove_commas(m): - return m.group(1).replace(',', '') - - -def _expand_decimal_point(m): - return m.group(1).replace('.', ' point ') - - -def _expand_dollars(m): - match = m.group(1) - parts = match.split('.') - if len(parts) > 2: - return match + ' dollars' # Unexpected format - dollars = int(parts[0]) if parts[0] else 0 - cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 - if dollars and cents: - dollar_unit = 'dollar' if dollars == 1 else 'dollars' - cent_unit = 'cent' if cents == 1 else 'cents' - return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit) - elif dollars: - dollar_unit = 'dollar' if dollars == 1 else 'dollars' - return '%s %s' % (dollars, dollar_unit) - elif cents: - cent_unit = 'cent' if cents == 1 else 'cents' - return '%s %s' % (cents, cent_unit) - else: - return 'zero dollars' - - -def _expand_ordinal(m): - return _inflect.number_to_words(m.group(0)) - - -def _expand_number(m): - num = int(m.group(0)) - if num > 1000 and num < 3000: - if num == 2000: - return 'two thousand' - elif num > 2000 and num < 2010: - return 'two thousand ' + _inflect.number_to_words(num % 100) - elif num % 100 == 0: - return _inflect.number_to_words(num // 100) + ' hundred' - else: - return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ') - else: - return _inflect.number_to_words(num, andword='') - - -def normalize_numbers(text): - text = re.sub(_comma_number_re, _remove_commas, text) - text = re.sub(_pounds_re, r'\1 pounds', text) - text = re.sub(_dollars_re, _expand_dollars, text) - text = re.sub(_decimal_number_re, _expand_decimal_point, text) - text = re.sub(_ordinal_re, _expand_ordinal, text) - text = re.sub(_number_re, _expand_number, text) - return text - - -def mark_dark_l(text): - return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text) - - -def english_to_ipa(text): - text = unidecode(text).lower() - text = expand_abbreviations(text) - text = normalize_numbers(text) - phonemes = ipa.convert(text) - phonemes = collapse_whitespace(phonemes) - return phonemes - - -def english_to_lazy_ipa(text): - text = english_to_ipa(text) - for regex, replacement in _lazy_ipa: - text = re.sub(regex, replacement, text) - return text - - -def english_to_ipa2(text): - text = english_to_ipa(text) - text = mark_dark_l(text) - for regex, replacement in _ipa_to_ipa2: - text = re.sub(regex, replacement, text) - return text.replace('...', '…') - - -def english_to_lazy_ipa2(text): - text = english_to_ipa(text) - for regex, replacement in _lazy_ipa2: - text = re.sub(regex, replacement, text) - return text diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py deleted file mode 100644 index a02a814fe2f08b464454e8eb6e1c88004ab804f6..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py +++ /dev/null @@ -1,27 +0,0 @@ -_base_ = './fovea_r50_fpn_4x4_1x_coco.py' -model = dict( - pretrained='torchvision://resnet101', - backbone=dict(depth=101), - bbox_head=dict( - with_deform=True, - norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) -img_norm_cfg = dict( - mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations', with_bbox=True), - dict( - type='Resize', - img_scale=[(1333, 640), (1333, 800)], - multiscale_mode='value', - keep_ratio=True), - dict(type='RandomFlip', flip_ratio=0.5), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='DefaultFormatBundle'), - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), -] -data = dict(train=dict(pipeline=train_pipeline)) -# learning policy -lr_config = dict(step=[16, 22]) -runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py deleted file mode 100644 index 4874121fd01e4024bfde445f451b7368c6834511..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_480x480_40k_pascal_context_59.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './deeplabv3_r50-d8_480x480_40k_pascal_context_59.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py deleted file mode 100644 index 89d5e1ae0f3ef44626f3b5534c504cbce7389a32..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/gcnet/gcnet_r50-d8_512x512_80k_ade20k.py +++ /dev/null @@ -1,6 +0,0 @@ -_base_ = [ - '../_base_/models/gcnet_r50-d8.py', '../_base_/datasets/ade20k.py', - '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py' -] -model = dict( - decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150)) diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py deleted file mode 100644 index 12f6d402a3c4a113d4c37be062790fa435b72104..0000000000000000000000000000000000000000 --- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/audiogen/audiogen_pretrained_16khz_eval.py +++ /dev/null @@ -1,68 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -""" -Evaluation with objective metrics for the pretrained AudioGen models. -This grid takes signature from the training grid and runs evaluation-only stage. - -When running the grid for the first time, please use: -REGEN=1 dora grid audiogen.audiogen_pretrained_16khz_eval -and re-use the REGEN=1 option when the grid is changed to force regenerating it. - -Note that you need the proper metrics external libraries setup to use all -the objective metrics activated in this grid. Refer to the README for more information. -""" - -import os - -from ..musicgen._explorers import GenerationEvalExplorer -from ...environment import AudioCraftEnvironment -from ... import train - - -def eval(launcher, batch_size: int = 32): - opts = { - 'dset': 'audio/audiocaps_16khz', - 'solver/audiogen/evaluation': 'objective_eval', - 'execute_only': 'evaluate', - '+dataset.evaluate.batch_size': batch_size, - '+metrics.fad.tf.batch_size': 32, - } - # binary for FAD computation: replace this path with your own path - metrics_opts = { - 'metrics.fad.tf.bin': '/data/home/jadecopet/local/usr/opt/google-research' - } - opt1 = {'generate.lm.use_sampling': True, 'generate.lm.top_k': 250, 'generate.lm.top_p': 0.} - opt2 = {'transformer_lm.two_step_cfg': True} - - sub = launcher.bind(opts) - sub.bind_(metrics_opts) - - # base objective metrics - sub(opt1, opt2) - - -@GenerationEvalExplorer -def explorer(launcher): - partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global']) - launcher.slurm_(gpus=4, partition=partitions) - - if 'REGEN' not in os.environ: - folder = train.main.dora.dir / 'grids' / __name__.split('.', 2)[-1] - with launcher.job_array(): - for sig in folder.iterdir(): - if not sig.is_symlink(): - continue - xp = train.main.get_xp_from_sig(sig.name) - launcher(xp.argv) - return - - audiogen_base = launcher.bind(solver="audiogen/audiogen_base_16khz") - audiogen_base.bind_({'autocast': False, 'fsdp.use': True}) - - audiogen_base_medium = audiogen_base.bind({'continue_from': '//pretrained/facebook/audiogen-medium'}) - audiogen_base_medium.bind_({'model/lm/model_scale': 'medium'}) - eval(audiogen_base_medium, batch_size=128) diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py deleted file mode 100644 index 5612ff5688d85fede0e605b244919e8081cb1da9..0000000000000000000000000000000000000000 --- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/grids/compression/debug.py +++ /dev/null @@ -1,31 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -""" -Grid search file, simply list all the exp you want in `explorer`. -Any new exp added there will be scheduled. -You can cancel and experiment by commenting its line. - -This grid is a minimal example for debugging compression task -and how to override parameters directly in a grid. -Learn more about dora grids: https://github.com/facebookresearch/dora -""" - -from ._explorers import CompressionExplorer -from ...environment import AudioCraftEnvironment - - -@CompressionExplorer -def explorer(launcher): - partitions = AudioCraftEnvironment.get_slurm_partitions(['team', 'global']) - launcher.slurm_(gpus=2, partition=partitions) - launcher.bind_(solver='compression/debug') - - with launcher.job_array(): - # base debug task using config from solver=compression/debug - launcher() - # we can override parameters in the grid to launch additional xps - launcher({'rvq.bins': 2048, 'rvq.n_q': 4}) diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py deleted file mode 100644 index 44f4b0a2c3c6c726857db8386491823dd85dde51..0000000000000000000000000000000000000000 --- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/metrics/visqol.py +++ /dev/null @@ -1,216 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import csv -import json -import logging -from pathlib import Path -import tempfile -import typing as tp -import subprocess -import shutil - -import torch -import torchaudio - -logger = logging.getLogger(__name__) - - -class ViSQOL: - """ViSQOL wrapper to run ViSQOL from Python using a pre-installed binary. - - To learn more about ViSQOL and how to build ViSQOL binary using bazel, please refer to the - instructions available in the open source repository: https://github.com/google/visqol - - ViSQOL is capable of running in two modes: - - Audio Mode: - When running in audio mode, input signals must have a 48kHz sample rate. Input should be resampled to 48kHz. - Input signals can be multi-channel, but they will be down-mixed to mono for performing the comparison. - Audio mode uses support vector regression, with the maximum range at ~4.75. - - Speech Mode: - When running in speech mode, ViSQOL uses a wideband model. It therefore expects input sample rates of 16kHz. - Input should be resampled to 16kHz. - As part of the speech mode processing, a root mean square implementation for voice activity detection - is performed on the reference signal to determine what parts of the signal have voice activity and - should therefore be included in the comparison. The signal is normalized before performing the voice - activity detection. - Input signals can be multi-channel, but they will be down-mixed to mono for performing the comparison. - Speech mode is scaled to have a maximum MOS of 5.0 to match previous version behavior. - - For more details, check the guidelines: https://github.com/google/visqol#general-guidelines-for-input - - Args: - visqol_bin (str): Path to the ViSQOL binary. - mode (str): ViSQOL computation mode, expecting "audio" or "speech". - model (str): Name of the model to use for similarity to quality model. - debug (bool): Whether to also get debug metrics from ViSQOL or not. - """ - SAMPLE_RATES_MODES = {"audio": 48_000, "speech": 16_000} - ALLOWED_SAMPLE_RATES = frozenset(SAMPLE_RATES_MODES.values()) - - def __init__(self, bin: tp.Union[Path, str], mode: str = "audio", - model: str = "libsvm_nu_svr_model.txt", debug: bool = False): - assert bin is not None and Path(bin).exists(), f"Could not find ViSQOL binary in specified path: {bin}" - self.visqol_bin = str(bin) - self.visqol_mode = mode - self.target_sr = self._get_target_sr(self.visqol_mode) - self.model = model - self.debug = debug - assert Path(self.visqol_model).exists(), \ - f"Could not find the specified model in ViSQOL install: {self.visqol_model}" - - def _get_target_sr(self, mode: str) -> int: - # returns target sampling rate for the corresponding ViSQOL mode. - if mode not in ViSQOL.SAMPLE_RATES_MODES: - raise ValueError( - f"Unsupported mode! Allowed are: {', '.join(ViSQOL.SAMPLE_RATES_MODES.keys())}" - ) - return ViSQOL.SAMPLE_RATES_MODES[mode] - - def _prepare_files( - self, ref_sig: torch.Tensor, deg_sig: torch.Tensor, sr: int, target_sr: int, pad_with_silence: bool = False - ): - # prepare files for ViSQOL evaluation. - assert target_sr in ViSQOL.ALLOWED_SAMPLE_RATES - assert len(ref_sig) == len(deg_sig), ( - "Expects same number of ref and degraded inputs", - f" but ref len {len(ref_sig)} != deg len {len(deg_sig)}" - ) - # resample audio if needed - if sr != target_sr: - transform = torchaudio.transforms.Resample(sr, target_sr) - pad = int(0.5 * target_sr) - rs_ref = [] - rs_deg = [] - for i in range(len(ref_sig)): - rs_ref_i = transform(ref_sig[i]) - rs_deg_i = transform(deg_sig[i]) - if pad_with_silence: - rs_ref_i = torch.nn.functional.pad(rs_ref_i, (pad, pad), mode='constant', value=0) - rs_deg_i = torch.nn.functional.pad(rs_deg_i, (pad, pad), mode='constant', value=0) - rs_ref.append(rs_ref_i) - rs_deg.append(rs_deg_i) - ref_sig = torch.stack(rs_ref) - deg_sig = torch.stack(rs_deg) - # save audio chunks to tmp dir and create csv - tmp_dir = Path(tempfile.mkdtemp()) - try: - tmp_input_csv_path = tmp_dir / "input.csv" - tmp_results_csv_path = tmp_dir / "results.csv" - tmp_debug_json_path = tmp_dir / "debug.json" - with open(tmp_input_csv_path, "w") as csv_file: - csv_writer = csv.writer(csv_file) - csv_writer.writerow(["reference", "degraded"]) - for i in range(len(ref_sig)): - tmp_ref_filename = tmp_dir / f"ref_{i}.wav" - tmp_deg_filename = tmp_dir / f"deg_{i}.wav" - torchaudio.save( - tmp_ref_filename, - torch.clamp(ref_sig[i], min=-0.99, max=0.99), - sample_rate=target_sr, - bits_per_sample=16, - encoding="PCM_S" - ) - torchaudio.save( - tmp_deg_filename, - torch.clamp(deg_sig[i], min=-0.99, max=0.99), - sample_rate=target_sr, - bits_per_sample=16, - encoding="PCM_S" - ) - csv_writer.writerow([str(tmp_ref_filename), str(tmp_deg_filename)]) - return tmp_dir, tmp_input_csv_path, tmp_results_csv_path, tmp_debug_json_path - except Exception as e: - logger.error("Exception occurred when preparing files for ViSQOL: %s", e) - return tmp_dir, None, None, None - - def _flush_files(self, tmp_dir: tp.Union[Path, str]): - # flush tmp files used to compute ViSQOL. - shutil.rmtree(str(tmp_dir)) - - def _collect_moslqo_score(self, results_csv_path: tp.Union[Path, str]) -> float: - # collect results for each evaluated pair and return averaged moslqo score. - with open(results_csv_path, "r") as csv_file: - reader = csv.DictReader(csv_file) - moslqo_scores = [float(row["moslqo"]) for row in reader] - if len(moslqo_scores) > 0: - return sum(moslqo_scores) / len(moslqo_scores) - else: - return 0.0 - - def _collect_debug_data(self, debug_json_path: tp.Union[Path, str]) -> dict: - # collect debug data for the visqol inference. - with open(debug_json_path, "r") as f: - data = json.load(f) - return data - - @property - def visqol_model(self): - return f'{self.visqol_bin}/model/{self.model}' - - def _run_visqol( - self, - input_csv_path: tp.Union[Path, str], - results_csv_path: tp.Union[Path, str], - debug_csv_path: tp.Optional[tp.Union[Path, str]], - ): - input_csv_path = str(input_csv_path) - results_csv_path = str(results_csv_path) - debug_csv_path = str(debug_csv_path) - cmd = [ - f'{self.visqol_bin}/bazel-bin/visqol', - '--batch_input_csv', f'{input_csv_path}', - '--results_csv', f'{results_csv_path}' - ] - if debug_csv_path is not None: - cmd += ['--output_debug', f'{debug_csv_path}'] - if self.visqol_mode == "speech": - cmd += ['--use_speech_mode'] - cmd += ['--similarity_to_quality_model', f'{self.visqol_model}'] - result = subprocess.run(cmd, capture_output=True) - if result.returncode: - logger.error("Error with visqol: \n %s \n %s", result.stdout.decode(), result.stderr.decode()) - raise RuntimeError("Error while executing visqol") - result.check_returncode() - - def __call__( - self, - ref_sig: torch.Tensor, - deg_sig: torch.Tensor, - sr: int, - pad_with_silence: bool = False, - ): - """Calculate the ViSQOL metric for a pair of audio signals at a given sample rate. - Args: - ref_sig (torch.Tensor): Reference signals as [B, C, T]. - deg_sig (torch.Tensor): Degraded signals as [B, C, T]. - sr (int): Sample rate of the two audio signals. - pad_with_silence (bool): Whether to pad the file with silences as recommended - in visqol guidelines (see: https://github.com/google/visqol#general-guidelines-for-input). - Returns: - float: The ViSQOL score or mean score for the batch. - """ - logger.debug(f"Calculating visqol with mode={self.visqol_mode} on {len(ref_sig)} samples") - tmp_dir, input_csv, results_csv, debug_json = self._prepare_files( - ref_sig, deg_sig, sr, self.target_sr, pad_with_silence - ) - try: - if input_csv and results_csv: - self._run_visqol( - input_csv, - results_csv, - debug_json if self.debug else None, - ) - mosqol = self._collect_moslqo_score(results_csv) - return mosqol - else: - raise RuntimeError("Something unexpected happened when running VISQOL!") - except Exception as e: - logger.error("Exception occurred when running ViSQOL: %s", e) - finally: - self._flush_files(tmp_dir) diff --git a/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile b/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile deleted file mode 100644 index 31b5f817b41d7d95055d9aa5d5e4f973abee0b45..0000000000000000000000000000000000000000 --- a/spaces/GuXiaoBei/wechat-chatbot/docker/sample-chatgpt-on-wechat/Makefile +++ /dev/null @@ -1,26 +0,0 @@ -IMG:=`cat Name` -MOUNT:= -PORT_MAP:= -DOTENV:=.env -CONTAINER_NAME:=sample-chatgpt-on-wechat - -echo: - echo $(IMG) - -run_d: - docker rm $(CONTAINER_NAME) || echo - docker run -dt --name $(CONTAINER_NAME) $(PORT_MAP) \ - --env-file=$(DOTENV) \ - $(MOUNT) $(IMG) - -run_i: - docker rm $(CONTAINER_NAME) || echo - docker run -it --name $(CONTAINER_NAME) $(PORT_MAP) \ - --env-file=$(DOTENV) \ - $(MOUNT) $(IMG) - -stop: - docker stop $(CONTAINER_NAME) - -rm: stop - docker rm $(CONTAINER_NAME) diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py deleted file mode 100644 index 84e51fd71fca78131a3be3d0d35d808c07bb3a95..0000000000000000000000000000000000000000 --- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/BBSNet/ResNet.py +++ /dev/null @@ -1,145 +0,0 @@ -import torch.nn as nn -import math - - -def conv3x3(in_planes, out_planes, stride=1): - """3x3 convolution with padding""" - return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, - padding=1, bias=False) - - -class BasicBlock(nn.Module): - expansion = 1 - - def __init__(self, inplanes, planes, stride=1, downsample=None): - super(BasicBlock, self).__init__() - self.conv1 = conv3x3(inplanes, planes, stride) - self.bn1 = nn.BatchNorm2d(planes) - self.relu = nn.ReLU(inplace=True) - self.conv2 = conv3x3(planes, planes) - self.bn2 = nn.BatchNorm2d(planes) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - - -class Bottleneck(nn.Module): - expansion = 4 - - def __init__(self, inplanes, planes, stride=1, downsample=None): - super(Bottleneck, self).__init__() - self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) - self.bn1 = nn.BatchNorm2d(planes) - self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, - padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(planes) - self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) - self.bn3 = nn.BatchNorm2d(planes * 4) - self.relu = nn.ReLU(inplace=True) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - out = self.relu(out) - - out = self.conv3(out) - out = self.bn3(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - - -class ResNet50(nn.Module): - def __init__(self,mode='rgb'): - self.inplanes = 64 - super(ResNet50, self).__init__() - if(mode=='rgb'): - self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, - bias=False) - elif(mode=='rgbd'): - self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, - bias=False) - elif(mode=="share"): - self.conv1=nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, - bias=False) - self.conv1_d=nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, - bias=False) - else: - raise - self.bn1 = nn.BatchNorm2d(64) - self.relu = nn.ReLU(inplace=True) - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - self.layer1 = self._make_layer(Bottleneck, 64, 3) - self.layer2 = self._make_layer(Bottleneck, 128, 4, stride=2) - self.layer3_1 = self._make_layer(Bottleneck, 256, 6, stride=2) - self.layer4_1 = self._make_layer(Bottleneck, 512, 3, stride=2) - - self.inplanes = 512 - - for m in self.modules(): - if isinstance(m, nn.Conv2d): - n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels - m.weight.data.normal_(0, math.sqrt(2. / n)) - elif isinstance(m, nn.BatchNorm2d): - m.weight.data.fill_(1) - m.bias.data.zero_() - - def _make_layer(self, block, planes, blocks, stride=1): - downsample = None - if stride != 1 or self.inplanes != planes * block.expansion: - downsample = nn.Sequential( - nn.Conv2d(self.inplanes, planes * block.expansion, - kernel_size=1, stride=stride, bias=False), - nn.BatchNorm2d(planes * block.expansion), - ) - - layers = [] - layers.append(block(self.inplanes, planes, stride, downsample)) - self.inplanes = planes * block.expansion - for i in range(1, blocks): - layers.append(block(self.inplanes, planes)) - - return nn.Sequential(*layers) - - def forward(self, x): - x = self.conv1(x) - x = self.bn1(x) - x = self.relu(x) - x = self.maxpool(x) - - x = self.layer1(x) - x = self.layer2(x) - x1 = self.layer3_1(x) - x1 = self.layer4_1(x1) - - return x1, x1 diff --git a/spaces/Hallucinate/demo/ldm/models/autoencoder.py b/spaces/Hallucinate/demo/ldm/models/autoencoder.py deleted file mode 100644 index 6a9c4f45498561953b8085981609b2a3298a5473..0000000000000000000000000000000000000000 --- a/spaces/Hallucinate/demo/ldm/models/autoencoder.py +++ /dev/null @@ -1,443 +0,0 @@ -import torch -import pytorch_lightning as pl -import torch.nn.functional as F -from contextlib import contextmanager - -from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer - -from ldm.modules.diffusionmodules.model import Encoder, Decoder -from ldm.modules.distributions.distributions import DiagonalGaussianDistribution - -from ldm.util import instantiate_from_config - - -class VQModel(pl.LightningModule): - def __init__(self, - ddconfig, - lossconfig, - n_embed, - embed_dim, - ckpt_path=None, - ignore_keys=[], - image_key="image", - colorize_nlabels=None, - monitor=None, - batch_resize_range=None, - scheduler_config=None, - lr_g_factor=1.0, - remap=None, - sane_index_shape=False, # tell vector quantizer to return indices as bhw - use_ema=False - ): - super().__init__() - self.embed_dim = embed_dim - self.n_embed = n_embed - self.image_key = image_key - self.encoder = Encoder(**ddconfig) - self.decoder = Decoder(**ddconfig) - self.loss = instantiate_from_config(lossconfig) - self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25, - remap=remap, - sane_index_shape=sane_index_shape) - self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1) - self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) - if colorize_nlabels is not None: - assert type(colorize_nlabels)==int - self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) - if monitor is not None: - self.monitor = monitor - self.batch_resize_range = batch_resize_range - if self.batch_resize_range is not None: - print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.") - - self.use_ema = use_ema - if self.use_ema: - self.model_ema = LitEma(self) - print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") - - if ckpt_path is not None: - self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) - self.scheduler_config = scheduler_config - self.lr_g_factor = lr_g_factor - - @contextmanager - def ema_scope(self, context=None): - if self.use_ema: - self.model_ema.store(self.parameters()) - self.model_ema.copy_to(self) - if context is not None: - print(f"{context}: Switched to EMA weights") - try: - yield None - finally: - if self.use_ema: - self.model_ema.restore(self.parameters()) - if context is not None: - print(f"{context}: Restored training weights") - - def init_from_ckpt(self, path, ignore_keys=list()): - sd = torch.load(path, map_location="cpu")["state_dict"] - keys = list(sd.keys()) - for k in keys: - for ik in ignore_keys: - if k.startswith(ik): - print("Deleting key {} from state_dict.".format(k)) - del sd[k] - missing, unexpected = self.load_state_dict(sd, strict=False) - print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") - if len(missing) > 0: - print(f"Missing Keys: {missing}") - print(f"Unexpected Keys: {unexpected}") - - def on_train_batch_end(self, *args, **kwargs): - if self.use_ema: - self.model_ema(self) - - def encode(self, x): - h = self.encoder(x) - h = self.quant_conv(h) - quant, emb_loss, info = self.quantize(h) - return quant, emb_loss, info - - def encode_to_prequant(self, x): - h = self.encoder(x) - h = self.quant_conv(h) - return h - - def decode(self, quant): - quant = self.post_quant_conv(quant) - dec = self.decoder(quant) - return dec - - def decode_code(self, code_b): - quant_b = self.quantize.embed_code(code_b) - dec = self.decode(quant_b) - return dec - - def forward(self, input, return_pred_indices=False): - quant, diff, (_,_,ind) = self.encode(input) - dec = self.decode(quant) - if return_pred_indices: - return dec, diff, ind - return dec, diff - - def get_input(self, batch, k): - x = batch[k] - if len(x.shape) == 3: - x = x[..., None] - x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() - if self.batch_resize_range is not None: - lower_size = self.batch_resize_range[0] - upper_size = self.batch_resize_range[1] - if self.global_step <= 4: - # do the first few batches with max size to avoid later oom - new_resize = upper_size - else: - new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16)) - if new_resize != x.shape[2]: - x = F.interpolate(x, size=new_resize, mode="bicubic") - x = x.detach() - return x - - def training_step(self, batch, batch_idx, optimizer_idx): - # https://github.com/pytorch/pytorch/issues/37142 - # try not to fool the heuristics - x = self.get_input(batch, self.image_key) - xrec, qloss, ind = self(x, return_pred_indices=True) - - if optimizer_idx == 0: - # autoencode - aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, - last_layer=self.get_last_layer(), split="train", - predicted_indices=ind) - - self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True) - return aeloss - - if optimizer_idx == 1: - # discriminator - discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step, - last_layer=self.get_last_layer(), split="train") - self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True) - return discloss - - def validation_step(self, batch, batch_idx): - log_dict = self._validation_step(batch, batch_idx) - with self.ema_scope(): - log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema") - return log_dict - - def _validation_step(self, batch, batch_idx, suffix=""): - x = self.get_input(batch, self.image_key) - xrec, qloss, ind = self(x, return_pred_indices=True) - aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0, - self.global_step, - last_layer=self.get_last_layer(), - split="val"+suffix, - predicted_indices=ind - ) - - discloss, log_dict_disc = self.loss(qloss, x, xrec, 1, - self.global_step, - last_layer=self.get_last_layer(), - split="val"+suffix, - predicted_indices=ind - ) - rec_loss = log_dict_ae[f"val{suffix}/rec_loss"] - self.log(f"val{suffix}/rec_loss", rec_loss, - prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) - self.log(f"val{suffix}/aeloss", aeloss, - prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True) - if version.parse(pl.__version__) >= version.parse('1.4.0'): - del log_dict_ae[f"val{suffix}/rec_loss"] - self.log_dict(log_dict_ae) - self.log_dict(log_dict_disc) - return self.log_dict - - def configure_optimizers(self): - lr_d = self.learning_rate - lr_g = self.lr_g_factor*self.learning_rate - print("lr_d", lr_d) - print("lr_g", lr_g) - opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ - list(self.decoder.parameters())+ - list(self.quantize.parameters())+ - list(self.quant_conv.parameters())+ - list(self.post_quant_conv.parameters()), - lr=lr_g, betas=(0.5, 0.9)) - opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), - lr=lr_d, betas=(0.5, 0.9)) - - if self.scheduler_config is not None: - scheduler = instantiate_from_config(self.scheduler_config) - - print("Setting up LambdaLR scheduler...") - scheduler = [ - { - 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule), - 'interval': 'step', - 'frequency': 1 - }, - { - 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule), - 'interval': 'step', - 'frequency': 1 - }, - ] - return [opt_ae, opt_disc], scheduler - return [opt_ae, opt_disc], [] - - def get_last_layer(self): - return self.decoder.conv_out.weight - - def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs): - log = dict() - x = self.get_input(batch, self.image_key) - x = x.to(self.device) - if only_inputs: - log["inputs"] = x - return log - xrec, _ = self(x) - if x.shape[1] > 3: - # colorize with random projection - assert xrec.shape[1] > 3 - x = self.to_rgb(x) - xrec = self.to_rgb(xrec) - log["inputs"] = x - log["reconstructions"] = xrec - if plot_ema: - with self.ema_scope(): - xrec_ema, _ = self(x) - if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema) - log["reconstructions_ema"] = xrec_ema - return log - - def to_rgb(self, x): - assert self.image_key == "segmentation" - if not hasattr(self, "colorize"): - self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) - x = F.conv2d(x, weight=self.colorize) - x = 2.*(x-x.min())/(x.max()-x.min()) - 1. - return x - - -class VQModelInterface(VQModel): - def __init__(self, embed_dim, *args, **kwargs): - super().__init__(embed_dim=embed_dim, *args, **kwargs) - self.embed_dim = embed_dim - - def encode(self, x): - h = self.encoder(x) - h = self.quant_conv(h) - return h - - def decode(self, h, force_not_quantize=False): - # also go through quantization layer - if not force_not_quantize: - quant, emb_loss, info = self.quantize(h) - else: - quant = h - quant = self.post_quant_conv(quant) - dec = self.decoder(quant) - return dec - - -class AutoencoderKL(pl.LightningModule): - def __init__(self, - ddconfig, - lossconfig, - embed_dim, - ckpt_path=None, - ignore_keys=[], - image_key="image", - colorize_nlabels=None, - monitor=None, - ): - super().__init__() - self.image_key = image_key - self.encoder = Encoder(**ddconfig) - self.decoder = Decoder(**ddconfig) - self.loss = instantiate_from_config(lossconfig) - assert ddconfig["double_z"] - self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) - self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) - self.embed_dim = embed_dim - if colorize_nlabels is not None: - assert type(colorize_nlabels)==int - self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) - if monitor is not None: - self.monitor = monitor - if ckpt_path is not None: - self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) - - def init_from_ckpt(self, path, ignore_keys=list()): - sd = torch.load(path, map_location="cpu")["state_dict"] - keys = list(sd.keys()) - for k in keys: - for ik in ignore_keys: - if k.startswith(ik): - print("Deleting key {} from state_dict.".format(k)) - del sd[k] - self.load_state_dict(sd, strict=False) - print(f"Restored from {path}") - - def encode(self, x): - h = self.encoder(x) - moments = self.quant_conv(h) - posterior = DiagonalGaussianDistribution(moments) - return posterior - - def decode(self, z): - z = self.post_quant_conv(z) - dec = self.decoder(z) - return dec - - def forward(self, input, sample_posterior=True): - posterior = self.encode(input) - if sample_posterior: - z = posterior.sample() - else: - z = posterior.mode() - dec = self.decode(z) - return dec, posterior - - def get_input(self, batch, k): - x = batch[k] - if len(x.shape) == 3: - x = x[..., None] - x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() - return x - - def training_step(self, batch, batch_idx, optimizer_idx): - inputs = self.get_input(batch, self.image_key) - reconstructions, posterior = self(inputs) - - if optimizer_idx == 0: - # train encoder+decoder+logvar - aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, - last_layer=self.get_last_layer(), split="train") - self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) - self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False) - return aeloss - - if optimizer_idx == 1: - # train the discriminator - discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step, - last_layer=self.get_last_layer(), split="train") - - self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True) - self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False) - return discloss - - def validation_step(self, batch, batch_idx): - inputs = self.get_input(batch, self.image_key) - reconstructions, posterior = self(inputs) - aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step, - last_layer=self.get_last_layer(), split="val") - - discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step, - last_layer=self.get_last_layer(), split="val") - - self.log("val/rec_loss", log_dict_ae["val/rec_loss"]) - self.log_dict(log_dict_ae) - self.log_dict(log_dict_disc) - return self.log_dict - - def configure_optimizers(self): - lr = self.learning_rate - opt_ae = torch.optim.Adam(list(self.encoder.parameters())+ - list(self.decoder.parameters())+ - list(self.quant_conv.parameters())+ - list(self.post_quant_conv.parameters()), - lr=lr, betas=(0.5, 0.9)) - opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(), - lr=lr, betas=(0.5, 0.9)) - return [opt_ae, opt_disc], [] - - def get_last_layer(self): - return self.decoder.conv_out.weight - - @torch.no_grad() - def log_images(self, batch, only_inputs=False, **kwargs): - log = dict() - x = self.get_input(batch, self.image_key) - x = x.to(self.device) - if not only_inputs: - xrec, posterior = self(x) - if x.shape[1] > 3: - # colorize with random projection - assert xrec.shape[1] > 3 - x = self.to_rgb(x) - xrec = self.to_rgb(xrec) - log["samples"] = self.decode(torch.randn_like(posterior.sample())) - log["reconstructions"] = xrec - log["inputs"] = x - return log - - def to_rgb(self, x): - assert self.image_key == "segmentation" - if not hasattr(self, "colorize"): - self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) - x = F.conv2d(x, weight=self.colorize) - x = 2.*(x-x.min())/(x.max()-x.min()) - 1. - return x - - -class IdentityFirstStage(torch.nn.Module): - def __init__(self, *args, vq_interface=False, **kwargs): - self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff - super().__init__() - - def encode(self, x, *args, **kwargs): - return x - - def decode(self, x, *args, **kwargs): - return x - - def quantize(self, x, *args, **kwargs): - if self.vq_interface: - return x, None, [None, None, None] - return x - - def forward(self, x, *args, **kwargs): - return x diff --git a/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py b/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py deleted file mode 100644 index 81676226e57ca519273b98328a1afe6961c37ce3..0000000000000000000000000000000000000000 --- a/spaces/HaloMaster/chinesesummary/fengshen/models/auto/configuration_auto.py +++ /dev/null @@ -1,403 +0,0 @@ -# coding=utf-8 -# Copyright 2021 The IDEA Authors. All rights reserved. - -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at - -# http://www.apache.org/licenses/LICENSE-2.0 - -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" Auto Config class.""" -import importlib -import re -import warnings -from collections import OrderedDict -from typing import List, Union - -from transformers.configuration_utils import PretrainedConfig -from transformers.file_utils import CONFIG_NAME -from transformers.utils import logging -from .dynamic import get_class_from_dynamic_module - - -logger = logging.get_logger(__name__) - -CONFIG_MAPPING_NAMES = OrderedDict( - [ - # Add configs here - ("roformer", "RoFormerConfig"), - ("longformer", "LongformerConfig"), - ] -) - -CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict( - [ - # Add archive maps here - ("roformer", "ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"), - ("longformer", "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"), - ] -) - -MODEL_NAMES_MAPPING = OrderedDict( - [ - # Add full (and cased) model names here - ("roformer", "Roformer"), - ("longformer", "Longformer"), - ] -) - -SPECIAL_MODEL_TYPE_TO_MODULE_NAME = OrderedDict([("openai-gpt", "openai")]) - - -def model_type_to_module_name(key): - """Converts a config key to the corresponding module.""" - # Special treatment - if key in SPECIAL_MODEL_TYPE_TO_MODULE_NAME: - return SPECIAL_MODEL_TYPE_TO_MODULE_NAME[key] - - return key.replace("-", "_") - - -def config_class_to_model_type(config): - """Converts a config class name to the corresponding model type""" - for key, cls in CONFIG_MAPPING_NAMES.items(): - if cls == config: - return key - return None - - -class _LazyConfigMapping(OrderedDict): - """ - A dictionary that lazily load its values when they are requested. - """ - - def __init__(self, mapping): - self._mapping = mapping - self._extra_content = {} - self._modules = {} - - def __getitem__(self, key): - if key in self._extra_content: - return self._extra_content[key] - if key not in self._mapping: - raise KeyError(key) - value = self._mapping[key] - module_name = model_type_to_module_name(key) - if module_name not in self._modules: - self._modules[module_name] = importlib.import_module(f".{module_name}", "fengshen.models") - - return getattr(self._modules[module_name], value) - - def keys(self): - return list(self._mapping.keys()) + list(self._extra_content.keys()) - - def values(self): - return [self[k] for k in self._mapping.keys()] + list(self._extra_content.values()) - - def items(self): - return [(k, self[k]) for k in self._mapping.keys()] + list(self._extra_content.items()) - - def __iter__(self): - return iter(list(self._mapping.keys()) + list(self._extra_content.keys())) - - def __contains__(self, item): - return item in self._mapping or item in self._extra_content - - def register(self, key, value): - """ - Register a new configuration in this mapping. - """ - if key in self._mapping.keys(): - raise ValueError(f"'{key}' is already used by a Transformers config, pick another name.") - self._extra_content[key] = value - - -CONFIG_MAPPING = _LazyConfigMapping(CONFIG_MAPPING_NAMES) - - -class _LazyLoadAllMappings(OrderedDict): - """ - A mapping that will load all pairs of key values at the first access (either by indexing, requestions keys, values, - etc.) - - Args: - mapping: The mapping to load. - """ - - def __init__(self, mapping): - self._mapping = mapping - self._initialized = False - self._data = {} - - def _initialize(self): - if self._initialized: - return - warnings.warn( - "ALL_PRETRAINED_CONFIG_ARCHIVE_MAP is deprecated and will be removed in v5 of Transformers. " - "It does not contain all available model checkpoints, far from it. Checkout hf.co/models for that.", - FutureWarning, - ) - - for model_type, map_name in self._mapping.items(): - module_name = model_type_to_module_name(model_type) - module = importlib.import_module(f".{module_name}", "transformers.models") - mapping = getattr(module, map_name) - self._data.update(mapping) - - self._initialized = True - - def __getitem__(self, key): - self._initialize() - return self._data[key] - - def keys(self): - self._initialize() - return self._data.keys() - - def values(self): - self._initialize() - return self._data.values() - - def items(self): - self._initialize() - return self._data.keys() - - def __iter__(self): - self._initialize() - return iter(self._data) - - def __contains__(self, item): - self._initialize() - return item in self._data - - -ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = _LazyLoadAllMappings(CONFIG_ARCHIVE_MAP_MAPPING_NAMES) - - -def _get_class_name(model_class: Union[str, List[str]]): - if isinstance(model_class, (list, tuple)): - return " or ".join([f"[`{c}`]" for c in model_class if c is not None]) - return f"[`{model_class}`]" - - -def _list_model_options(indent, config_to_class=None, use_model_types=True): - if config_to_class is None and not use_model_types: - raise ValueError("Using `use_model_types=False` requires a `config_to_class` dictionary.") - if use_model_types: - if config_to_class is None: - model_type_to_name = {model_type: f"[`{config}`]" for model_type, config in CONFIG_MAPPING_NAMES.items()} - else: - model_type_to_name = { - model_type: _get_class_name(model_class) - for model_type, model_class in config_to_class.items() - if model_type in MODEL_NAMES_MAPPING - } - lines = [ - f"{indent}- **{model_type}** -- {model_type_to_name[model_type]} ({MODEL_NAMES_MAPPING[model_type]} model)" - for model_type in sorted(model_type_to_name.keys()) - ] - else: - config_to_name = { - CONFIG_MAPPING_NAMES[config]: _get_class_name(clas) - for config, clas in config_to_class.items() - if config in CONFIG_MAPPING_NAMES - } - config_to_model_name = { - config: MODEL_NAMES_MAPPING[model_type] for model_type, config in CONFIG_MAPPING_NAMES.items() - } - lines = [ - f"{indent}- [`{config_name}`] configuration class: {config_to_name[config_name]} ({config_to_model_name[config_name]} model)" - for config_name in sorted(config_to_name.keys()) - ] - return "\n".join(lines) - - -def replace_list_option_in_docstrings(config_to_class=None, use_model_types=True): - def docstring_decorator(fn): - docstrings = fn.__doc__ - lines = docstrings.split("\n") - i = 0 - while i < len(lines) and re.search(r"^(\s*)List options\s*$", lines[i]) is None: - i += 1 - if i < len(lines): - indent = re.search(r"^(\s*)List options\s*$", lines[i]).groups()[0] - if use_model_types: - indent = f"{indent} " - lines[i] = _list_model_options(indent, config_to_class=config_to_class, use_model_types=use_model_types) - docstrings = "\n".join(lines) - else: - raise ValueError( - f"The function {fn} should have an empty 'List options' in its docstring as placeholder, current docstring is:\n{docstrings}" - ) - fn.__doc__ = docstrings - return fn - - return docstring_decorator - - -class AutoConfig: - r""" - This is a generic configuration class that will be instantiated as one of the configuration classes of the library - when created with the [`~AutoConfig.from_pretrained`] class method. - - This class cannot be instantiated directly using `__init__()` (throws an error). - """ - - def __init__(self): - raise EnvironmentError( - "AutoConfig is designed to be instantiated " - "using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method." - ) - - @classmethod - def for_model(cls, model_type: str, *args, **kwargs): - if model_type in CONFIG_MAPPING: - config_class = CONFIG_MAPPING[model_type] - return config_class(*args, **kwargs) - raise ValueError( - f"Unrecognized model identifier: {model_type}. Should contain one of {', '.join(CONFIG_MAPPING.keys())}" - ) - - @classmethod - @replace_list_option_in_docstrings() - def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): - r""" - Instantiate one of the configuration classes of the library from a pretrained model configuration. - - The configuration class to instantiate is selected based on the `model_type` property of the config object that - is loaded, or when it's missing, by falling back to using pattern matching on `pretrained_model_name_or_path`: - - List options - - Args: - pretrained_model_name_or_path (`str` or `os.PathLike`): - Can be either: - - - A string, the *model id* of a pretrained model configuration hosted inside a model repo on - huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or - namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - - A path to a *directory* containing a configuration file saved using the - [`~PretrainedConfig.save_pretrained`] method, or the [`~PreTrainedModel.save_pretrained`] method, - e.g., `./my_model_directory/`. - - A path or url to a saved configuration JSON *file*, e.g., - `./my_model_directory/configuration.json`. - cache_dir (`str` or `os.PathLike`, *optional*): - Path to a directory in which a downloaded pretrained model configuration should be cached if the - standard cache should not be used. - force_download (`bool`, *optional*, defaults to `False`): - Whether or not to force the (re-)download the model weights and configuration files and override the - cached versions if they exist. - resume_download (`bool`, *optional*, defaults to `False`): - Whether or not to delete incompletely received files. Will attempt to resume the download if such a - file exists. - proxies (`Dict[str, str]`, *optional*): - A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', - 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. - revision(`str`, *optional*, defaults to `"main"`): - The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a - git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any - identifier allowed by git. - return_unused_kwargs (`bool`, *optional*, defaults to `False`): - If `False`, then this function returns just the final configuration object. - - If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a - dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the - part of `kwargs` which has not been used to update `config` and is otherwise ignored. - trust_remote_code (`bool`, *optional*, defaults to `False`): - Whether or not to allow for custom models defined on the Hub in their own modeling files. This option - should only be set to `True` for repositories you trust and in which you have read the code, as it will - execute code present on the Hub on your local machine. - kwargs(additional keyword arguments, *optional*): - The values in kwargs of any keys which are configuration attributes will be used to override the loaded - values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled - by the `return_unused_kwargs` keyword parameter. - - Examples: - - ```python - >>> from transformers import AutoConfig - - >>> # Download configuration from huggingface.co and cache. - >>> config = AutoConfig.from_pretrained("bert-base-uncased") - - >>> # Download configuration from huggingface.co (user-uploaded) and cache. - >>> config = AutoConfig.from_pretrained("dbmdz/bert-base-german-cased") - - >>> # If configuration file is in a directory (e.g., was saved using *save_pretrained('./test/saved_model/')*). - >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/") - - >>> # Load a specific configuration file. - >>> config = AutoConfig.from_pretrained("./test/bert_saved_model/my_configuration.json") - - >>> # Change some config attributes when loading a pretrained config. - >>> config = AutoConfig.from_pretrained("bert-base-uncased", output_attentions=True, foo=False) - >>> config.output_attentions - True - - >>> config, unused_kwargs = AutoConfig.from_pretrained( - ... "bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True - ... ) - >>> config.output_attentions - True - - >>> config.unused_kwargs - {'foo': False} - ```""" - kwargs["_from_auto"] = True - kwargs["name_or_path"] = pretrained_model_name_or_path - trust_remote_code = kwargs.pop("trust_remote_code", False) - config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs) - if "auto_map" in config_dict and "AutoConfig" in config_dict["auto_map"]: - if not trust_remote_code: - raise ValueError( - f"Loading {pretrained_model_name_or_path} requires you to execute the configuration file in that repo " - "on your local machine. Make sure you have read the code there to avoid malicious use, then set " - "the option `trust_remote_code=True` to remove this error." - ) - if kwargs.get("revision", None) is None: - logger.warn( - "Explicitly passing a `revision` is encouraged when loading a configuration with custom code to " - "ensure no malicious code has been contributed in a newer revision." - ) - class_ref = config_dict["auto_map"]["AutoConfig"] - module_file, class_name = class_ref.split(".") - config_class = get_class_from_dynamic_module( - pretrained_model_name_or_path, module_file + ".py", class_name, **kwargs - ) - return config_class.from_pretrained(pretrained_model_name_or_path, **kwargs) - elif "model_type" in config_dict: - config_class = CONFIG_MAPPING[config_dict["model_type"]] - return config_class.from_dict(config_dict, **kwargs) - else: - # Fallback: use pattern matching on the string. - for pattern, config_class in CONFIG_MAPPING.items(): - if pattern in str(pretrained_model_name_or_path): - return config_class.from_dict(config_dict, **kwargs) - - raise ValueError( - f"Unrecognized model in {pretrained_model_name_or_path}. " - f"Should have a `model_type` key in its {CONFIG_NAME}, or contain one of the following strings " - f"in its name: {', '.join(CONFIG_MAPPING.keys())}" - ) - - @staticmethod - def register(model_type, config): - """ - Register a new configuration for this class. - - Args: - model_type (`str`): The model type like "bert" or "gpt". - config ([`PretrainedConfig`]): The config to register. - """ - if issubclass(config, PretrainedConfig) and config.model_type != model_type: - raise ValueError( - "The config you are passing has a `model_type` attribute that is not consistent with the model type " - f"you passed (config has {config.model_type} and you passed {model_type}. Fix one of those so they " - "match!" - ) - CONFIG_MAPPING.register(model_type, config) diff --git a/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py deleted file mode 100644 index b6fb6e751cdedb2af4b1f6c0950557e187cd9519..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/criterions/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .scst_loss import ScstRewardCriterion -from .label_smoothed_cross_entropy import AjustLabelSmoothedCrossEntropyCriterion \ No newline at end of file diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py deleted file mode 100644 index 56d63e3e1b5a036e0adf32480e2b66f371738013..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/criterions/label_smoothed_cross_entropy.py +++ /dev/null @@ -1,170 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from dataclasses import dataclass, field - -import torch -from fairseq import metrics, utils -from fairseq.criterions import FairseqCriterion, register_criterion -from fairseq.dataclass import FairseqDataclass -from omegaconf import II - - -@dataclass -class LabelSmoothedCrossEntropyCriterionConfig(FairseqDataclass): - label_smoothing: float = field( - default=0.0, - metadata={"help": "epsilon for label smoothing, 0 means no label smoothing"}, - ) - report_accuracy: bool = field( - default=False, - metadata={"help": "report accuracy metric"}, - ) - ignore_prefix_size: int = field( - default=0, - metadata={"help": "Ignore first N tokens"}, - ) - sentence_avg: bool = II("optimization.sentence_avg") - - -def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=None, reduce=True): - if target.dim() == lprobs.dim() - 1: - target = target.unsqueeze(-1) - nll_loss = -lprobs.gather(dim=-1, index=target) - smooth_loss = -lprobs.sum(dim=-1, keepdim=True) - if ignore_index is not None: - pad_mask = target.eq(ignore_index) - nll_loss.masked_fill_(pad_mask, 0.0) - smooth_loss.masked_fill_(pad_mask, 0.0) - else: - nll_loss = nll_loss.squeeze(-1) - smooth_loss = smooth_loss.squeeze(-1) - if reduce: - nll_loss = nll_loss.sum() - smooth_loss = smooth_loss.sum() - eps_i = epsilon / (lprobs.size(-1) - 1) - loss = (1.0 - epsilon - eps_i) * nll_loss + eps_i * smooth_loss - return loss, nll_loss - - -@register_criterion( - "label_smoothed_cross_entropy", dataclass=LabelSmoothedCrossEntropyCriterionConfig -) -class LabelSmoothedCrossEntropyCriterion(FairseqCriterion): - def __init__( - self, - task, - sentence_avg, - label_smoothing, - ignore_prefix_size=0, - report_accuracy=False, - ): - super().__init__(task) - self.sentence_avg = sentence_avg - self.eps = label_smoothing - self.ignore_prefix_size = ignore_prefix_size - self.report_accuracy = report_accuracy - - def forward(self, model, sample, reduce=True): - """Compute the loss for the given sample. - - Returns a tuple with three elements: - 1) the loss - 2) the sample size, which is used as the denominator for the gradient - 3) logging outputs to display while training - """ - net_output = model(**sample["net_input"]) - loss, nll_loss = self.compute_loss(model, net_output, sample, reduce=reduce) - sample_size = ( - sample["target"].size(0) if self.sentence_avg else sample["ntokens"] - ) - logging_output = { - "loss": loss.data, - "nll_loss": nll_loss.data, - "ntokens": sample["ntokens"], - "nsentences": sample["target"].size(0), - "sample_size": sample_size, - } - if self.report_accuracy: - n_correct, total = self.compute_accuracy(model, net_output, sample) - logging_output["n_correct"] = utils.item(n_correct.data) - logging_output["total"] = utils.item(total.data) - return loss, sample_size, logging_output - - def get_lprobs_and_target(self, model, net_output, sample): - lprobs = model.get_normalized_probs(net_output, log_probs=True) - target = model.get_targets(sample, net_output) - if self.ignore_prefix_size > 0: - if getattr(lprobs, "batch_first", False): - lprobs = lprobs[:, self.ignore_prefix_size :, :].contiguous() - target = target[:, self.ignore_prefix_size :].contiguous() - else: - lprobs = lprobs[self.ignore_prefix_size :, :, :].contiguous() - target = target[self.ignore_prefix_size :, :].contiguous() - return lprobs.view(-1, lprobs.size(-1)), target.view(-1) - - def compute_loss(self, model, net_output, sample, reduce=True): - lprobs, target = self.get_lprobs_and_target(model, net_output, sample) - loss, nll_loss = label_smoothed_nll_loss( - lprobs, - target, - self.eps, - ignore_index=self.padding_idx, - reduce=reduce, - ) - return loss, nll_loss - - def compute_accuracy(self, model, net_output, sample): - lprobs, target = self.get_lprobs_and_target(model, net_output, sample) - mask = target.ne(self.padding_idx) - n_correct = torch.sum( - lprobs.argmax(1).masked_select(mask).eq(target.masked_select(mask)) - ) - total = torch.sum(mask) - return n_correct, total - - @classmethod - def reduce_metrics(cls, logging_outputs) -> None: - """Aggregate logging outputs from data parallel training.""" - loss_sum = sum(log.get("loss", 0) for log in logging_outputs) - nll_loss_sum = sum(log.get("nll_loss", 0) for log in logging_outputs) - ntokens = sum(log.get("ntokens", 0) for log in logging_outputs) - sample_size = sum(log.get("sample_size", 0) for log in logging_outputs) - - metrics.log_scalar( - "loss", loss_sum / sample_size / math.log(2), sample_size, round=3 - ) - metrics.log_scalar( - "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3 - ) - metrics.log_derived( - "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg) - ) - - total = utils.item(sum(log.get("total", 0) for log in logging_outputs)) - if total > 0: - metrics.log_scalar("total", total) - n_correct = utils.item( - sum(log.get("n_correct", 0) for log in logging_outputs) - ) - metrics.log_scalar("n_correct", n_correct) - metrics.log_derived( - "accuracy", - lambda meters: round( - meters["n_correct"].sum * 100.0 / meters["total"].sum, 3 - ) - if meters["total"].sum > 0 - else float("nan"), - ) - - @staticmethod - def logging_outputs_can_be_summed() -> bool: - """ - Whether the logging outputs returned by `forward` can be summed - across workers prior to calling `reduce_metrics`. Setting this - to True will improves distributed training speed. - """ - return True diff --git a/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md b/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md deleted file mode 100644 index d4654cdd019b1496d4c953944c9aced8a0dcc67a..0000000000000000000000000000000000000000 --- a/spaces/HelloMimosa/sail-rvc-Ai_Hoshino__From_Oshi_no_Ko___RVC_v2__300_Epoch/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Sail-rvc-Ai Hoshino From Oshi No Ko RVC V2 300 Epoch -emoji: 📈 -colorFrom: yellow -colorTo: red -sdk: gradio -sdk_version: 3.39.0 -app_file: app.py -pinned: false -license: openrail ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Hina4867/bingo/src/components/ui/select.tsx b/spaces/Hina4867/bingo/src/components/ui/select.tsx deleted file mode 100644 index 77f12c2996f541b97663de4c9e20ab34d4ec2fac..0000000000000000000000000000000000000000 --- a/spaces/Hina4867/bingo/src/components/ui/select.tsx +++ /dev/null @@ -1,123 +0,0 @@ -'use client' - -import * as React from 'react' -import * as SelectPrimitive from '@radix-ui/react-select' - -import { cn } from '@/lib/utils' -import { - IconArrowDown, - IconCheck, - IconChevronUpDown -} from '@/components/ui/icons' - -const Select = SelectPrimitive.Root - -const SelectGroup = SelectPrimitive.Group - -const SelectValue = SelectPrimitive.Value - -const SelectTrigger = React.forwardRef< - React.ElementRef--${text}-, - React.ComponentPropsWithoutRef ->(({ className, children, ...props }, ref) => ( - - {children} - -)) -SelectTrigger.displayName = SelectPrimitive.Trigger.displayName - -const SelectContent = React.forwardRef< - React.ElementRef- -- , - React.ComponentPropsWithoutRef ->(({ className, children, position = 'popper', ...props }, ref) => ( - - -)) -SelectContent.displayName = SelectPrimitive.Content.displayName - -const SelectLabel = React.forwardRef< - React.ElementRef- -- {children} - -, - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - -)) -SelectLabel.displayName = SelectPrimitive.Label.displayName - -const SelectItem = React.forwardRef< - React.ElementRef , - React.ComponentPropsWithoutRef ->(({ className, children, ...props }, ref) => ( - - - -)) -SelectItem.displayName = SelectPrimitive.Item.displayName - -const SelectSeparator = React.forwardRef< - React.ElementRef- - -- {children} -, - React.ComponentPropsWithoutRef ->(({ className, ...props }, ref) => ( - -)) -SelectSeparator.displayName = SelectPrimitive.Separator.displayName - -export { - Select, - SelectGroup, - SelectValue, - SelectTrigger, - SelectContent, - SelectLabel, - SelectItem, - SelectSeparator -} diff --git a/spaces/HugoDzz/super-godot-galaxy/build/_app/immutable/chunks/singletons.1f11d8d9.js b/spaces/HugoDzz/super-godot-galaxy/build/_app/immutable/chunks/singletons.1f11d8d9.js deleted file mode 100644 index 1d6d785958b2ff6017a2a3b5f00230cf6a9bb08e..0000000000000000000000000000000000000000 --- a/spaces/HugoDzz/super-godot-galaxy/build/_app/immutable/chunks/singletons.1f11d8d9.js +++ /dev/null @@ -1 +0,0 @@ -import{H as d,s as m}from"./index.9af7eb9c.js";const c=[];function p(e,t=d){let n;const o=new Set;function a(s){if(m(e,s)&&(e=s,n)){const u=!c.length;for(const i of o)i[1](),c.push(i,e);if(u){for(let i=0;i 川虎 Chat 🐯 Chuanhu Chat
-- -- -## Supported LLM Models - -**LLM models via API**: - -- [ChatGPT](https://chat.openai.com) ([GPT-4](https://openai.com/product/gpt-4)) -- [Google PaLM](https://developers.generativeai.google/products/palm) -- [Inspur Yuan 1.0](https://air.inspur.com/home) -- [MiniMax](https://api.minimax.chat/) -- [XMChat](https://github.com/MILVLG/xmchat) - -**LLM models via local deployment**: - -- [ChatGLM](https://github.com/THUDM/ChatGLM-6B) ([ChatGLM2](https://github.com/THUDM/ChatGLM2-6B)) -- [LLaMA](https://github.com/facebookresearch/llama) -- [StableLM](https://github.com/Stability-AI/StableLM) -- [MOSS](https://github.com/OpenLMLab/MOSS) - -## Usage Tips - -- To better control the ChatGPT, use System Prompt. -- To use a Prompt Template, select the Prompt Template Collection file first, and then choose certain prompt from the drop-down menu. -- To try again if the response is unsatisfactory, use `🔄 Regenerate` button. -- To start a new line in the input box, press Shift + Enter keys. -- To quickly switch between input history, press ↑ and ↓ key in the input box. -- To deploy the program onto a server, set `"server_name": "0.0.0.0", "server_port"-
Lightweight and User-friendly Web-UI for LLMs including ChatGPT/ChatGLM/LLaMA
-- Streaming / Unlimited conversations / Save history / Preset prompts / Chat with files / Web search
- Video Tutorial - · - 2.0 Introduction - · - 3.0 Introduction & Tutorial - || - Online trial - · - One-Click deployment - -
- LaTeX rendering / Table rendering / Code highlighting
- Auto dark mode / Adaptive web interface / WeChat-like theme
- Multi-parameters tuning / Multi-API-Key support / Multi-user support
- Compatible with GPT-4 / Local deployment for LLMs --
- -
- ,` in `config.json`. -- To get a public shared link, set `"share": true,` in `config.json`. Please be noted that the program must be running in order to be accessed via a public link. -- To use it in Hugging Face Spaces: It is recommended to **Duplicate Space** and run the program in your own Space for a faster and more secure experience. - -## Quickstart - -```shell -git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git -cd ChuanhuChatGPT -pip install -r requirements.txt -``` - -Then make a copy of `config_example.json`, rename it to `config.json`, and then fill in your API-Key and other settings in the file. - -```shell -python ChuanhuChatbot.py -``` - -A browser window will open and you will be able to chat with ChatGPT. - -> **Note** -> -> Please check our [wiki page](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程) for detailed instructions. - -## Troubleshooting - -When you encounter problems, you should try manually pulling the latest changes of this project first. The steps are as follows: - -1. Download the latest code archive by clicking on `Download ZIP` on the webpage, or - ```shell - git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f - ``` -2. Try installing the dependencies again (as this project may have introduced new dependencies) - ``` - pip install -r requirements.txt - ``` - -Generally, you can solve most problems by following these steps. - -If the problem still exists, please refer to this page: [Frequently Asked Questions (FAQ)](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题) - -This page lists almost all the possible problems and solutions. Please read it carefully. - -## More Information - -More information could be found in our [wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki): - -- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization) -- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南) -- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目) -- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志) -- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可) - -## Starchart - -[](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date) - -## Contributors - - -
diff --git a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py b/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py
deleted file mode 100644
index 59a73cc04025762d6490fcd2945a747d963def32..0000000000000000000000000000000000000000
--- a/spaces/KdaiP/yolov8-deepsort-tracking/deep_sort/utils/asserts.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from os import environ
-
-
-def assert_in(file, files_to_check):
- if file not in files_to_check:
- raise AssertionError("{} does not exist in the list".format(str(file)))
- return True
-
-
-def assert_in_env(check_list: list):
- for item in check_list:
- assert_in(item, environ.keys())
- return True
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py
deleted file mode 100644
index beb6e65c61f8a16b4376494123f31178cdb88bde..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/mkgui/base/ui/streamlit_utils.py
+++ /dev/null
@@ -1,13 +0,0 @@
-CUSTOM_STREAMLIT_CSS = """
-div[data-testid="stBlock"] button {
- width: 100% !important;
- margin-bottom: 20px !important;
- border-color: #bfbfbf !important;
-}
-section[data-testid="stSidebar"] div {
- max-width: 10rem;
-}
-pre code {
- white-space: pre-wrap;
-}
-"""
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py
deleted file mode 100644
index 34bcffd6c0975377a54ae1ce89002be1dae8151d..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/utils/util.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import matplotlib
-matplotlib.use('Agg')
-import time
-
-class Timer():
- ''' Timer for recording training time distribution. '''
- def __init__(self):
- self.prev_t = time.time()
- self.clear()
-
- def set(self):
- self.prev_t = time.time()
-
- def cnt(self, mode):
- self.time_table[mode] += time.time()-self.prev_t
- self.set()
- if mode == 'bw':
- self.click += 1
-
- def show(self):
- total_time = sum(self.time_table.values())
- self.time_table['avg'] = total_time/self.click
- self.time_table['rd'] = 100*self.time_table['rd']/total_time
- self.time_table['fw'] = 100*self.time_table['fw']/total_time
- self.time_table['bw'] = 100*self.time_table['bw']/total_time
- msg = '{avg:.3f} sec/step (rd {rd:.1f}% | fw {fw:.1f}% | bw {bw:.1f}%)'.format(
- **self.time_table)
- self.clear()
- return msg
-
- def clear(self):
- self.time_table = {'rd': 0, 'fw': 0, 'bw': 0}
- self.click = 0
-
-# Reference : https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/e2e_asr.py#L168
-
-def human_format(num):
- magnitude = 0
- while num >= 1000:
- magnitude += 1
- num /= 1000.0
- # add more suffixes if you need them
- return '{:3.1f}{}'.format(num, [' ', 'K', 'M', 'G', 'T', 'P'][magnitude])
-
-
-# provide easy access of attribute from dict, such abc.key
-class AttrDict(dict):
- def __init__(self, *args, **kwargs):
- super(AttrDict, self).__init__(*args, **kwargs)
- self.__dict__ = self
diff --git a/spaces/KevinQHLin/UniVTG/main/train_mr.py b/spaces/KevinQHLin/UniVTG/main/train_mr.py
deleted file mode 100644
index 1a10d029f81f86733d6dab71a3aee575917b092b..0000000000000000000000000000000000000000
--- a/spaces/KevinQHLin/UniVTG/main/train_mr.py
+++ /dev/null
@@ -1,266 +0,0 @@
-import os
-import pdb
-import sys
-import time
-import json
-import pprint
-import random
-import numpy as np
-from tqdm import tqdm, trange
-from collections import defaultdict
-
-import torch
-import torch.nn as nn
-import torch.backends.cudnn as cudnn
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-
-sys.path.append('/data/home/qinghonglin/univtg')
-from main.config import BaseOptions, setup_model
-from main.dataset import \
- DatasetMR, start_end_collate_mr, prepare_batch_inputs_mr
-from main.inference_mr import eval_epoch, start_inference
-from utils.basic_utils import set_seed, AverageMeter, dict_to_markdown
-from utils.model_utils import count_parameters
-
-import logging
-logger = logging.getLogger(__name__)
-logging.basicConfig(format="%(asctime)s.%(msecs)03d:%(levelname)s:%(name)s - %(message)s",
- datefmt="%Y-%m-%d %H:%M:%S",
- level=logging.INFO)
-
-def train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer):
- logger.info(f"[Epoch {epoch_i+1}]")
- model.train()
- criterion.train()
-
- # init meters
- time_meters = defaultdict(AverageMeter)
- loss_meters = defaultdict(AverageMeter)
-
- num_training_examples = len(train_loader)
- timer_dataloading = time.time()
- for batch_idx, batch in tqdm(enumerate(train_loader),
- desc="Training Iteration",
- total=num_training_examples):
- time_meters["dataloading_time"].update(time.time() - timer_dataloading)
-
- timer_start = time.time()
- model_inputs, targets = prepare_batch_inputs_mr(batch[1], opt.device, non_blocking=opt.pin_memory)
- time_meters["prepare_inputs_time"].update(time.time() - timer_start)
-
- timer_start = time.time()
-
- # try:
- outputs = model(**model_inputs)
- loss_dict = criterion(outputs, targets)
- weight_dict = criterion.weight_dict
- losses = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict)
- time_meters["model_forward_time"].update(time.time() - timer_start)
-
- timer_start = time.time()
- optimizer.zero_grad()
- losses.backward()
-
- if opt.grad_clip > 0:
- nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip)
- optimizer.step()
- time_meters["model_backward_time"].update(time.time() - timer_start)
-
- loss_dict["loss_overall"] = float(losses) # for logging only
- for k, v in loss_dict.items():
- loss_meters[k].update(float(v) * weight_dict[k] if k in weight_dict else float(v))
-
- timer_dataloading = time.time()
-
- # print/add logs
- tb_writer.add_scalar("Train/lr", float(optimizer.param_groups[0]["lr"]), epoch_i+1)
- for k, v in loss_meters.items():
- tb_writer.add_scalar("Train/{}".format(k), v.avg, epoch_i+1)
-
- to_write = opt.train_log_txt_formatter.format(
- time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
- epoch=epoch_i+1,
- loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in loss_meters.items()]))
- with open(opt.train_log_filepath, "a") as f:
- f.write(to_write)
-
- logger.info("Epoch time stats:")
- for name, meter in time_meters.items():
- d = {k: f"{getattr(meter, k):.4f}" for k in ["max", "min", "avg"]}
- logger.info(f"{name} ==> {d}")
-
-
-def train(model, criterion, optimizer, lr_scheduler, train_dataset, val_dataset, opt):
- tb_writer = SummaryWriter(opt.tensorboard_log_dir)
- tb_writer.add_text("hyperparameters", dict_to_markdown(vars(opt), max_str_len=None))
- opt.train_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str}\n"
- opt.eval_log_txt_formatter = "{time_str} [Epoch] {epoch:03d} [Loss] {loss_str} [Metrics] {eval_metrics_str}\n"
-
- train_loader = DataLoader(
- train_dataset,
- collate_fn=start_end_collate_mr,
- batch_size=opt.bsz,
- num_workers=opt.num_workers,
- shuffle=True,
- pin_memory=opt.pin_memory
- )
-
- prev_best_score = 0.
- es_cnt = 0
- if opt.start_epoch is None:
- start_epoch = -1 if opt.eval_init else 0
- else:
- start_epoch = opt.start_epoch
- save_submission_filename = "latest_{}_{}_preds.jsonl".format(opt.dset_name, opt.eval_split_name)
- for epoch_i in trange(start_epoch, opt.n_epoch, desc="Epoch"):
- if epoch_i > -1:
- train_epoch(model, criterion, train_loader, optimizer, opt, epoch_i, tb_writer)
- lr_scheduler.step()
- eval_epoch_interval = opt.eval_epoch
- if opt.eval_path is not None and (epoch_i + 1) % eval_epoch_interval == 0:
- with torch.no_grad():
- metrics_no_nms, metrics_nms, eval_loss_meters, latest_file_paths = \
- eval_epoch(model, val_dataset, opt, save_submission_filename, epoch_i, criterion, tb_writer)
-
- # log
- to_write = opt.eval_log_txt_formatter.format(
- time_str=time.strftime("%Y_%m_%d_%H_%M_%S"),
- epoch=epoch_i,
- loss_str=" ".join(["{} {:.4f}".format(k, v.avg) for k, v in eval_loss_meters.items()]),
- eval_metrics_str=json.dumps(metrics_no_nms))
-
- with open(opt.eval_log_filepath, "a") as f:
- f.write(to_write)
- logger.info("metrics_no_nms {}".format(pprint.pformat(metrics_no_nms["brief"], indent=4)))
- if metrics_nms is not None:
- logger.info("metrics_nms {}".format(pprint.pformat(metrics_nms["brief"], indent=4)))
-
- metrics = metrics_nms if metrics_nms is not None else metrics_no_nms
- for k, v in metrics["brief"].items():
- tb_writer.add_scalar(f"Eval/{k}", float(v), epoch_i+1)
-
- # stop_score = metrics["brief"]["MR-full-mAP"]
- # pdb.set_trace()
- stop_score = metrics["brief"][opt.main_metric]
- if stop_score > prev_best_score:
- es_cnt = 0
- prev_best_score = stop_score
-
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "lr_scheduler": lr_scheduler.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"))
-
- best_file_paths = [e.replace("latest", "best") for e in latest_file_paths]
- for src, tgt in zip(latest_file_paths, best_file_paths):
- os.renames(src, tgt)
- logger.info("The checkpoint file has been updated.")
- else:
- es_cnt += 1
- if opt.max_es_cnt != -1 and es_cnt > opt.max_es_cnt: # early stop
- with open(opt.train_log_filepath, "a") as f:
- f.write(f"Early Stop at epoch {epoch_i}")
- logger.info(f"\n>>>>> Early stop at epoch {epoch_i} {prev_best_score}\n")
- break
-
- # save ckpt
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "lr_scheduler": lr_scheduler.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", "_latest.ckpt"))
-
- if (epoch_i + 1) % opt.save_interval == 0 or (epoch_i + 1) % opt.lr_drop == 0: # additional copies
- checkpoint = {
- "model": model.state_dict(),
- "optimizer": optimizer.state_dict(),
- "epoch": epoch_i,
- "opt": opt
- }
- torch.save(checkpoint, opt.ckpt_filepath.replace(".ckpt", f"_e{epoch_i:04d}.ckpt"))
-
- if opt.debug:
- break
-
- tb_writer.close()
-
-
-def start_training():
- logger.info("Setup config, data and model...")
- opt = BaseOptions().parse()
- set_seed(opt.seed)
- if opt.debug: # keep the model run deterministically
- # 'cudnn.benchmark = True' enabled auto finding the best algorithm for a specific input/net config.
- # Enable this only when input size is fixed.
- cudnn.benchmark = False
- cudnn.deterministic = True
-
- dataset_config = dict(
- dset_name=opt.dset_name,
- data_path=opt.train_path,
- v_feat_dirs=opt.v_feat_dirs,
- q_feat_dir=opt.t_feat_dir,
- v_feat_dim=opt.v_feat_dim,
- q_feat_dim=opt.t_feat_dim,
- q_feat_type="last_hidden_state",
- max_q_l=opt.max_q_l,
- max_v_l=opt.max_v_l,
- ctx_mode=opt.ctx_mode,
- data_ratio=opt.data_ratio,
- normalize_v=not opt.no_norm_vfeat,
- normalize_t=not opt.no_norm_tfeat,
- clip_len=opt.clip_length,
- max_windows=opt.max_windows,
- span_loss_type=opt.span_loss_type,
- txt_drop_ratio=opt.txt_drop_ratio,
- use_cache=opt.use_cache,
- add_easy_negative=opt.add_easy_negative,
- easy_negative_only=opt.easy_negative_only
- )
-
- dataset_config["data_path"] = opt.train_path
- train_dataset = DatasetMR(**dataset_config)
-
- if opt.eval_path is not None:
- dataset_config["data_path"] = opt.eval_path
- dataset_config["txt_drop_ratio"] = 0
- dataset_config["q_feat_dir"] = opt.t_feat_dir.replace("txt_clip_asr", "txt_clip").replace("txt_clip_cap", "txt_clip") # for pretraining
- # dataset_config["load_labels"] = False # uncomment to calculate eval loss
- eval_dataset = DatasetMR(**dataset_config)
- else:
- eval_dataset = None
-
- if opt.lr_warmup > 0:
- # total_steps = opt.n_epoch * len(train_dataset) // opt.bsz
- total_steps = opt.n_epoch
- warmup_steps = opt.lr_warmup if opt.lr_warmup > 1 else int(opt.lr_warmup * total_steps)
- opt.lr_warmup = [warmup_steps, total_steps]
- model, criterion, optimizer, lr_scheduler = setup_model(opt)
- logger.info(f"Model {model}")
- count_parameters(model)
- logger.info("Start Training...")
- train(model, criterion, optimizer, lr_scheduler, train_dataset, eval_dataset, opt)
- return opt.ckpt_filepath.replace(".ckpt", "_best.ckpt"), opt.eval_split_name, opt.eval_path, opt.debug
-
-
-if __name__ == '__main__':
- best_ckpt_path, eval_split_name, eval_path, debug = start_training()
- if not debug:
- input_args = ["--resume", best_ckpt_path,
- "--eval_split_name", eval_split_name,
- "--eval_path", eval_path]
-
- import sys
- sys.argv[1:] = input_args
- logger.info("\n\n\nFINISHED TRAINING!!!")
- logger.info("Evaluating model at {}".format(best_ckpt_path))
- logger.info("Input args {}".format(sys.argv[1:]))
- start_inference()
\ No newline at end of file
diff --git a/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md b/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md
deleted file mode 100644
index 1f55e11e80f6fc5ebbf42dade0266e3d4ee06ce4..0000000000000000000000000000000000000000
--- a/spaces/Kimata/Sanskrit-TTS/indic_nlp_resources/transliterate/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
-# Transliteration Models for Indian languages
-These are models for transliteration involving Indian languages.
-The models are essentially Statistical Machine Translation systems trained using Moses over a
-character-level parallel corpora of transliterations. Hence, you will need Moses to use these transliteration models.
-The transliteration corpus has itself been mined in an unsupervised fashion from a translation corpus.
-
-Currently we have trained transliteration models for five language pairs: bn-hi, ta-hi, te-hi, en-hi and mr-hi
-
-Support for transliteration has been introduced in Moses from version 2.1
-So please ensure that you have minimum 2.1 version setup for Moses
-
-Commands to run the transliteration module using moses
-
-$moseshome/mosesdecoder/scripts/Transliteration/post-decoding-transliteration.pl \
---moses-src-dir $moseshome/mosesdecoder --external-bin-dir $moseshome/tools \
---transliteration-model-dir {path to transliteration model folder} --oov-file {path to file containing oov words, oovs are space separated with each line containing all oovs for the input line}\
- --input-file {input file to transliterated} --output-file {output file location} \
- --input-extension {input language code for eg. en} --output-extension {output language code for eg. hi} --language-model {path to language model} \
- --decoder $moseshome/mosesdecoder/bin/moses
-
-A sample execution of the model will be as follows:
-
-export moseshome={path to moses installation}
-$moseshome/mosesdecoder/scripts/Transliteration/post-decoding-transliteration.pl \
---moses-src-dir $moseshome/mosesdecoder --external-bin-dir $moseshome/tools \
---transliteration-model-dir /home/ratish/project/nlp_resources/indic_nlp_resources/transliterate/en-hi \
---oov-file /home/ratish/project/translit/input.oov \
- --input-file /home/ratish/project/translit/input.en \
- --output-file /home/ratish/project/translit/output.hi \
- --input-extension en --output-extension hi --language-model /home/ratish/project/translit/lm/nc.binlm.1 \
- --decoder $moseshome/mosesdecoder/bin/moses
-
-So far, we have seen the use of transliteration in a post-editing task for machine translation task.
-In case, the models are needed for purely transliteration purpose, the input file and OOV file are the same.
-Sample input file:
-New Delhi is capital of India
-India is worlds seventh largest nation in the World
-
-OOV file
-New Delhi is capital of India
-India is worlds seventh largest nation in the World
-
-On running the transliteration module, the output is:
-न्यू डेल्ही इस कैपिटल आफ इंडिया
-इंडिया इस वर्ल्ड सेवंथ लारगेस्ट नेशन इन थे वर्ल्ड
diff --git a/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py b/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py
deleted file mode 100644
index 980440eb3434e48b7cad90f0577c838a05d787b4..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/datasets/samplers/batch_sampler.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import Sequence
-
-from torch.utils.data import BatchSampler, Sampler
-
-from mmdet.registry import DATA_SAMPLERS
-
-
-# TODO: maybe replace with a data_loader wrapper
-@DATA_SAMPLERS.register_module()
-class AspectRatioBatchSampler(BatchSampler):
- """A sampler wrapper for grouping images with similar aspect ratio (< 1 or.
-
- >= 1) into a same batch.
-
- Args:
- sampler (Sampler): Base sampler.
- batch_size (int): Size of mini-batch.
- drop_last (bool): If ``True``, the sampler will drop the last batch if
- its size would be less than ``batch_size``.
- """
-
- def __init__(self,
- sampler: Sampler,
- batch_size: int,
- drop_last: bool = False) -> None:
- if not isinstance(sampler, Sampler):
- raise TypeError('sampler should be an instance of ``Sampler``, '
- f'but got {sampler}')
- if not isinstance(batch_size, int) or batch_size <= 0:
- raise ValueError('batch_size should be a positive integer value, '
- f'but got batch_size={batch_size}')
- self.sampler = sampler
- self.batch_size = batch_size
- self.drop_last = drop_last
- # two groups for w < h and w >= h
- self._aspect_ratio_buckets = [[] for _ in range(2)]
-
- def __iter__(self) -> Sequence[int]:
- for idx in self.sampler:
- data_info = self.sampler.dataset.get_data_info(idx)
- width, height = data_info['width'], data_info['height']
- bucket_id = 0 if width < height else 1
- bucket = self._aspect_ratio_buckets[bucket_id]
- bucket.append(idx)
- # yield a batch of indices in the same aspect ratio group
- if len(bucket) == self.batch_size:
- yield bucket[:]
- del bucket[:]
-
- # yield the rest data and reset the bucket
- left_data = self._aspect_ratio_buckets[0] + self._aspect_ratio_buckets[
- 1]
- self._aspect_ratio_buckets = [[] for _ in range(2)]
- while len(left_data) > 0:
- if len(left_data) <= self.batch_size:
- if not self.drop_last:
- yield left_data[:]
- left_data = []
- else:
- yield left_data[:self.batch_size]
- left_data = left_data[self.batch_size:]
-
- def __len__(self) -> int:
- if self.drop_last:
- return len(self.sampler) // self.batch_size
- else:
- return (len(self.sampler) + self.batch_size - 1) // self.batch_size
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py
deleted file mode 100644
index 430b06d085d94760d56a7ea083eaf23bd32b1f53..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/vfnet_head.py
+++ /dev/null
@@ -1,722 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List, Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, Scale
-from mmcv.ops import DeformConv2d
-from torch import Tensor
-
-from mmdet.registry import MODELS, TASK_UTILS
-from mmdet.structures.bbox import bbox_overlaps
-from mmdet.utils import (ConfigType, InstanceList, MultiConfig,
- OptInstanceList, RangeType, reduce_mean)
-from ..task_modules.prior_generators import MlvlPointGenerator
-from ..task_modules.samplers import PseudoSampler
-from ..utils import multi_apply
-from .atss_head import ATSSHead
-from .fcos_head import FCOSHead
-
-INF = 1e8
-
-
-@MODELS.register_module()
-class VFNetHead(ATSSHead, FCOSHead):
- """Head of `VarifocalNet (VFNet): An IoU-aware Dense Object
- Detector.- InstructPix2Pix: Learning to Follow Image Editing Instructions -
-For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. -
""") - with gr.Row(): - with gr.Column(scale=1, min_width=100): - generate_button = gr.Button("Generate") - with gr.Column(scale=1, min_width=100): - load_button = gr.Button("Load Example") - with gr.Column(scale=1, min_width=100): - reset_button = gr.Button("Reset") - with gr.Column(scale=3): - instruction = gr.Textbox(lines=1, label="Edit Instruction", interactive=True) - - with gr.Row(): - input_image = gr.Image(label="Input Image", type="pil", interactive=True) - edited_image = gr.Image(label=f"Edited Image", type="pil", interactive=False) - input_image.style(height=512, width=512) - edited_image.style(height=512, width=512) - - with gr.Row(): - steps = gr.Number(value=50, precision=0, label="Steps", interactive=True) - randomize_seed = gr.Radio( - ["Fix Seed", "Randomize Seed"], - value="Randomize Seed", - type="index", - show_label=False, - interactive=True, - ) - seed = gr.Number(value=1371, precision=0, label="Seed", interactive=True) - randomize_cfg = gr.Radio( - ["Fix CFG", "Randomize CFG"], - value="Fix CFG", - type="index", - show_label=False, - interactive=True, - ) - text_cfg_scale = gr.Number(value=7.5, label=f"Text CFG", interactive=True) - image_cfg_scale = gr.Number(value=1.5, label=f"Image CFG", interactive=True) - - gr.Markdown(help_text) - - load_button.click( - fn=load_example, - inputs=[ - steps, - randomize_seed, - seed, - randomize_cfg, - text_cfg_scale, - image_cfg_scale, - ], - outputs=[input_image, instruction, seed, text_cfg_scale, image_cfg_scale, edited_image], - ) - generate_button.click( - fn=generate, - inputs=[ - input_image, - instruction, - steps, - randomize_seed, - seed, - randomize_cfg, - text_cfg_scale, - image_cfg_scale, - ], - outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image], - ) - reset_button.click( - fn=reset, - inputs=[], - outputs=[steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale, edited_image], - ) - - demo.queue(concurrency_count=1) - demo.launch(share=False) - - -if __name__ == "__main__": - main() diff --git a/spaces/Loren/Streamlit_OCR_comparator/README.md b/spaces/Loren/Streamlit_OCR_comparator/README.md deleted file mode 100644 index 2894464cad0f3f4d163e1f8e72750b10259159da..0000000000000000000000000000000000000000 --- a/spaces/Loren/Streamlit_OCR_comparator/README.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: Streamlit OCR Comparator -emoji: 📰🔍🔤 -colorFrom: indigo -colorTo: gray -sdk: streamlit -sdk_version: 1.10.0 -app_file: Home.py -tags: [streamlit, ocr] -pinned: true ---- \ No newline at end of file diff --git a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py b/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py deleted file mode 100644 index 6d9b36c74b1808b56ded68cf080a689db7e0ee4e..0000000000000000000000000000000000000000 --- a/spaces/MCkernick/Image_Restoration_Colorization/Global/detection_models/Synchronized-BatchNorm-PyTorch/sync_batchnorm/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# -*- coding: utf-8 -*- -# File : __init__.py -# Author : Jiayuan Mao -# Email : maojiayuan@gmail.com -# Date : 27/01/2018 -# -# This file is part of Synchronized-BatchNorm-PyTorch. -# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch -# Distributed under MIT License. - -from .batchnorm import set_sbn_eps_mode -from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d -from .batchnorm import patch_sync_batchnorm, convert_model -from .replicate import DataParallelWithCallback, patch_replication_callback diff --git a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py b/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py deleted file mode 100644 index 9794e0fc3463a5e8fad05c037cce64683059a6d3..0000000000000000000000000000000000000000 --- a/spaces/Mahiruoshi/Lovelive_Nijigasaki_VITS/utils.py +++ /dev/null @@ -1,226 +0,0 @@ -import os -import glob -import sys -import argparse -import logging -import json -import subprocess -import numpy as np -from scipy.io.wavfile import read -import torch - -MATPLOTLIB_FLAG = False - -logging.basicConfig(stream=sys.stdout, level=logging.ERROR) -logger = logging - - -def load_checkpoint(checkpoint_path, model, optimizer=None): - assert os.path.isfile(checkpoint_path) - checkpoint_dict = torch.load(checkpoint_path, map_location='cpu') - iteration = checkpoint_dict['iteration'] - learning_rate = checkpoint_dict['learning_rate'] - if optimizer is not None: - optimizer.load_state_dict(checkpoint_dict['optimizer']) - saved_state_dict = checkpoint_dict['model'] - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - new_state_dict = {} - for k, v in state_dict.items(): - try: - new_state_dict[k] = saved_state_dict[k] - except: - logger.info("%s is not in the checkpoint" % k) - new_state_dict[k] = v - if hasattr(model, 'module'): - model.module.load_state_dict(new_state_dict) - else: - model.load_state_dict(new_state_dict) - logger.info("Loaded checkpoint '{}' (iteration {})".format( - checkpoint_path, iteration)) - return model, optimizer, learning_rate, iteration - - -def plot_spectrogram_to_numpy(spectrogram): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(10, 2)) - im = ax.imshow(spectrogram, aspect="auto", origin="lower", - interpolation='none') - plt.colorbar(im, ax=ax) - plt.xlabel("Frames") - plt.ylabel("Channels") - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def plot_alignment_to_numpy(alignment, info=None): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(6, 4)) - im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower', - interpolation='none') - fig.colorbar(im, ax=ax) - xlabel = 'Decoder timestep' - if info is not None: - xlabel += '\n\n' + info - plt.xlabel(xlabel) - plt.ylabel('Encoder timestep') - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def load_wav_to_torch(full_path): - sampling_rate, data = read(full_path) - return torch.FloatTensor(data.astype(np.float32)), sampling_rate - - -def load_filepaths_and_text(filename, split="|"): - with open(filename, encoding='utf-8') as f: - filepaths_and_text = [line.strip().split(split) for line in f] - return filepaths_and_text - - -def get_hparams(init=True): - parser = argparse.ArgumentParser() - parser.add_argument('-c', '--config', type=str, default="./configs/base.json", - help='JSON file for configuration') - parser.add_argument('-m', '--model', type=str, required=True, - help='Model name') - - args = parser.parse_args() - model_dir = os.path.join("./logs", args.model) - - if not os.path.exists(model_dir): - os.makedirs(model_dir) - - config_path = args.config - config_save_path = os.path.join(model_dir, "config.json") - if init: - with open(config_path, "r") as f: - data = f.read() - with open(config_save_path, "w") as f: - f.write(data) - else: - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_dir(model_dir): - config_save_path = os.path.join(model_dir, "config.json") - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_file(config_path): - with open(config_path, "r", encoding="utf-8") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - return hparams - - -def check_git_hash(model_dir): - source_dir = os.path.dirname(os.path.realpath(__file__)) - if not os.path.exists(os.path.join(source_dir, ".git")): - logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format( - source_dir - )) - return - - cur_hash = subprocess.getoutput("git rev-parse HEAD") - - path = os.path.join(model_dir, "githash") - if os.path.exists(path): - saved_hash = open(path).read() - if saved_hash != cur_hash: - logger.warn("git hash values are different. {}(saved) != {}(current)".format( - saved_hash[:8], cur_hash[:8])) - else: - open(path, "w").write(cur_hash) - - -def get_logger(model_dir, filename="train.log"): - global logger - logger = logging.getLogger(os.path.basename(model_dir)) - logger.setLevel(logging.DEBUG) - - formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s") - if not os.path.exists(model_dir): - os.makedirs(model_dir) - h = logging.FileHandler(os.path.join(model_dir, filename)) - h.setLevel(logging.DEBUG) - h.setFormatter(formatter) - logger.addHandler(h) - return logger - - -class HParams(): - def __init__(self, **kwargs): - for k, v in kwargs.items(): - if type(v) == dict: - v = HParams(**v) - self[k] = v - - def keys(self): - return self.__dict__.keys() - - def items(self): - return self.__dict__.items() - - def values(self): - return self.__dict__.values() - - def __len__(self): - return len(self.__dict__) - - def __getitem__(self, key): - return getattr(self, key) - - def __setitem__(self, key, value): - return setattr(self, key, value) - - def __contains__(self, key): - return key in self.__dict__ - - def __repr__(self): - return self.__dict__.__repr__() \ No newline at end of file diff --git a/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py b/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py deleted file mode 100644 index 3a5999ddc80faa0ce6a54389562c9f092b40bb03..0000000000000000000000000000000000000000 --- a/spaces/Marshalls/testmtd/feature_extraction/madmom/conftest.py +++ /dev/null @@ -1,24 +0,0 @@ -# encoding: utf-8 -""" -This files contains pytest fixtures and other test related stuff. - -""" -from __future__ import absolute_import, division, print_function - -import numpy as np -import pytest - -# save numpy's current print options -_NP_PRINT_OPTIONS = np.get_printoptions() - - -@pytest.fixture(autouse=True) -def setup_doctest(request): - """Set up the environment for doctests (when run through pytest).""" - np.set_printoptions(precision=5, edgeitems=2, suppress=True) - - def fin(): - """Restore the environment after doctests (when run through pytest).""" - np.set_printoptions(**_NP_PRINT_OPTIONS) - - request.addfinalizer(fin) diff --git a/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py b/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py deleted file mode 100644 index 19e34a11def040b971b534b9ed3fda71d0a35c85..0000000000000000000000000000000000000000 --- a/spaces/Marshalls/testmtd/feature_extraction/madmom/evaluation/__init__.py +++ /dev/null @@ -1,882 +0,0 @@ -# encoding: utf-8 -# pylint: disable=no-member -# pylint: disable=invalid-name -# pylint: disable=too-many-arguments -# pylint: disable=wrong-import-position -""" -Evaluation package. - -""" - -from __future__ import absolute_import, division, print_function - -import numpy as np - - -# evaluation helper functions -def find_closest_matches(detections, annotations): - """ - Find the closest annotation for each detection. - - Parameters - ---------- - detections : list or numpy array - Detected events. - annotations : list or numpy array - Annotated events. - - Returns - ------- - indices : numpy array - Indices of the closest matches. - - Notes - ----- - The sequences must be ordered. - - """ - # make sure the arrays have the correct types - detections = np.asarray(detections, dtype=np.float) - annotations = np.asarray(annotations, dtype=np.float) - # TODO: right now, it only works with 1D arrays - if detections.ndim > 1 or annotations.ndim > 1: - raise NotImplementedError('please implement multi-dim support') - # if no detections or annotations are given - if len(detections) == 0 or len(annotations) == 0: - # return a empty array - return np.zeros(0, dtype=np.int) - # if only a single annotation is given - if len(annotations) == 1: - # return an array as long as the detections with indices 0 - return np.zeros(len(detections), dtype=np.int) - # solution found at: http://stackoverflow.com/questions/8914491/ - indices = annotations.searchsorted(detections) - indices = np.clip(indices, 1, len(annotations) - 1) - left = annotations[indices - 1] - right = annotations[indices] - indices -= detections - left < right - detections - # return the indices of the closest matches - return indices - - -def calc_errors(detections, annotations, matches=None): - """ - Errors of the detections to the closest annotations. - - Parameters - ---------- - detections : list or numpy array - Detected events. - annotations : list or numpy array - Annotated events. - matches : list or numpy array - Indices of the closest events. - - Returns - ------- - errors : numpy array - Errors. - - Notes - ----- - The sequences must be ordered. To speed up the calculation, a list of - pre-computed indices of the closest matches can be used. - - """ - # make sure the arrays have the correct types - detections = np.asarray(detections, dtype=np.float) - annotations = np.asarray(annotations, dtype=np.float) - if matches is not None: - matches = np.asarray(matches, dtype=np.int) - # TODO: right now, it only works with 1D arrays - if detections.ndim > 1 or annotations.ndim > 1: - raise NotImplementedError('please implement multi-dim support') - # if no detections or annotations are given - if len(detections) == 0 or len(annotations) == 0: - # return a empty array - return np.zeros(0, dtype=np.float) - # determine the closest annotations - if matches is None: - matches = find_closest_matches(detections, annotations) - # calc error relative to those annotations - errors = detections - annotations[matches] - # return the errors - return errors - - -def calc_absolute_errors(detections, annotations, matches=None): - """ - Absolute errors of the detections to the closest annotations. - - Parameters - ---------- - detections : list or numpy array - Detected events. - annotations : list or numpy array - Annotated events. - matches : list or numpy array - Indices of the closest events. - - Returns - ------- - errors : numpy array - Absolute errors. - - Notes - ----- - The sequences must be ordered. To speed up the calculation, a list of - pre-computed indices of the closest matches can be used. - - """ - # make sure the arrays have the correct types - detections = np.asarray(detections, dtype=np.float) - annotations = np.asarray(annotations, dtype=np.float) - if matches is not None: - matches = np.asarray(matches, dtype=np.int) - # TODO: right now, it only works with 1D arrays - if detections.ndim > 1 or annotations.ndim > 1: - raise NotImplementedError('please implement multi-dim support') - # return the errors - return np.abs(calc_errors(detections, annotations, matches)) - - -def calc_relative_errors(detections, annotations, matches=None): - """ - Relative errors of the detections to the closest annotations. - - Parameters - ---------- - detections : list or numpy array - Detected events. - annotations : list or numpy array - Annotated events. - matches : list or numpy array - Indices of the closest events. - - Returns - ------- - errors : numpy array - Relative errors. - - Notes - ----- - The sequences must be ordered. To speed up the calculation, a list of - pre-computed indices of the closest matches can be used. - - """ - # make sure the arrays have the correct types - detections = np.asarray(detections, dtype=np.float) - annotations = np.asarray(annotations, dtype=np.float) - if matches is not None: - matches = np.asarray(matches, dtype=np.int) - # TODO: right now, it only works with 1D arrays - if detections.ndim > 1 or annotations.ndim > 1: - raise NotImplementedError('please implement multi-dim support') - # if no detections or annotations are given - if len(detections) == 0 or len(annotations) == 0: - # return a empty array - return np.zeros(0, dtype=np.float) - # determine the closest annotations - if matches is None: - matches = find_closest_matches(detections, annotations) - # calculate the absolute errors - errors = calc_errors(detections, annotations, matches) - # return the relative errors - return np.abs(1 - (errors / annotations[matches])) - - -# abstract evaluation base class -class EvaluationMixin(object): - """ - Evaluation mixin class. - - This class has a `name` attribute which is used for display purposes and - defaults to 'None'. - - `METRIC_NAMES` is a list of tuples, containing the attribute's name and the - corresponding label, e.g.: - - The attributes defined in `METRIC_NAMES` will be provided as an ordered - dictionary as the `metrics` property unless the subclass overwrites the - property. - - `FLOAT_FORMAT` is used to format floats. - - """ - - # Example: - # METRIC_NAMES = [ - # ('precision', 'Precision'), - # ('recall', 'Recall'), - # ('fmeasure', 'F-measure'), - # ] - name = None - METRIC_NAMES = [] - FLOAT_FORMAT = '{:.3f}' - - @property - def metrics(self): - """Metrics as a dictionary.""" - from collections import OrderedDict - metrics = OrderedDict() - # metrics = {} - for metric in [m[0] for m in self.METRIC_NAMES]: - metrics[metric] = getattr(self, metric) - return metrics - - def __len__(self): - """Length of the evaluation object.""" - raise NotImplementedError('must be implemented by subclass.') - - def tostring(self, **kwargs): - """ - Format the evaluation metrics as a human readable string. - - Returns - ------- - str - Evaluation metrics formatted as a human readable string. - - Notes - ----- - This is a fallback method formatting the `metrics` dictionary in a - human readable way. Classes inheriting from this mixin class should - provide a method better suitable. - - """ - # pylint: disable=unused-argument - import pprint - return pprint.pformat(dict(self.metrics), indent=4) - - -# evaluation classes -class SimpleEvaluation(EvaluationMixin): - """ - Simple Precision, Recall, F-measure and Accuracy evaluation based on the - numbers of true/false positive/negative detections. - - Parameters - ---------- - num_tp : int - Number of true positive detections. - num_fp : int - Number of false positive detections. - num_tn : int - Number of true negative detections. - num_fn : int - Number of false negative detections. - name : str - Name to be displayed. - - Notes - ----- - This class is only suitable for a 1-class evaluation problem. - - """ - - METRIC_NAMES = [ - ('num_tp', 'No. of true positives'), - ('num_fp', 'No. of false positives'), - ('num_tn', 'No. of true negatives'), - ('num_fn', 'No. of false negatives'), - ('num_annotations', 'No. Annotations'), - ('precision', 'Precision'), - ('recall', 'Recall'), - ('fmeasure', 'F-measure'), - ('accuracy', 'Accuracy'), - ] - - def __init__(self, num_tp=0, num_fp=0, num_tn=0, num_fn=0, name=None, - **kwargs): - # pylint: disable=unused-argument - # hidden variables, to be able to overwrite them in subclasses - self._num_tp = int(num_tp) - self._num_fp = int(num_fp) - self._num_tn = int(num_tn) - self._num_fn = int(num_fn) - # name of the evaluation - self.name = name - - @property - def num_tp(self): - """Number of true positive detections.""" - return self._num_tp - - @property - def num_fp(self): - """Number of false positive detections.""" - return self._num_fp - - @property - def num_tn(self): - """Number of true negative detections.""" - return self._num_tn - - @property - def num_fn(self): - """Number of false negative detections.""" - return self._num_fn - - @property - def num_annotations(self): - """Number of annotations.""" - return self.num_tp + self.num_fn - - def __len__(self): - # the length equals the number of annotations - return self.num_annotations - - @property - def precision(self): - """Precision.""" - # correct / retrieved - retrieved = float(self.num_tp + self.num_fp) - # if there are no positive predictions, none of them are wrong - if retrieved == 0: - return 1. - return self.num_tp / retrieved - - @property - def recall(self): - """Recall.""" - # correct / relevant - relevant = float(self.num_tp + self.num_fn) - # if there are no positive annotations, we recalled all of them - if relevant == 0: - return 1. - return self.num_tp / relevant - - @property - def fmeasure(self): - """F-measure.""" - # 2pr / (p+r) - numerator = 2. * self.precision * self.recall - if numerator == 0: - return 0. - return numerator / (self.precision + self.recall) - - @property - def accuracy(self): - """Accuracy.""" - # acc: (TP + TN) / (TP + FP + TN + FN) - denominator = self.num_fp + self.num_fn + self.num_tp + self.num_tn - if denominator == 0: - return 1. - numerator = float(self.num_tp + self.num_tn) - if numerator == 0: - return 0. - return numerator / denominator - - def tostring(self, **kwargs): - """ - Format the evaluation metrics as a human readable string. - - Returns - ------- - str - Evaluation metrics formatted as a human readable string. - - - """ - ret = '' - if self.name is not None: - ret += '%s\n ' % self.name - ret += 'Annotations: %5d TP: %5d FP: %5d FN: %5d ' \ - 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \ - (self.num_annotations, self.num_tp, self.num_fp, self.num_fn, - self.precision, self.recall, self.fmeasure, self.accuracy) - return ret - - def __str__(self): - return self.tostring() - - -# evaluate Precision, Recall, F-measure and Accuracy with lists or numpy arrays -class Evaluation(SimpleEvaluation): - """ - Evaluation class for measuring Precision, Recall and F-measure based on - numpy arrays or lists with true/false positive/negative detections. - - Parameters - ---------- - tp : list or numpy array - True positive detections. - fp : list or numpy array - False positive detections. - tn : list or numpy array - True negative detections. - fn : list or numpy array - False negative detections. - name : str - Name to be displayed. - - """ - - def __init__(self, tp=None, fp=None, tn=None, fn=None, **kwargs): - # set default values - if tp is None: - tp = [] - if fp is None: - fp = [] - if tn is None: - tn = [] - if fn is None: - fn = [] - # instantiate a SimpleEvaluation object - super(Evaluation, self).__init__(**kwargs) - # convert everything to numpy arrays and save them - self.tp = np.asarray(list(tp), dtype=np.float) - self.fp = np.asarray(list(fp), dtype=np.float) - self.tn = np.asarray(list(tn), dtype=np.float) - self.fn = np.asarray(list(fn), dtype=np.float) - - @property - def num_tp(self): - """Number of true positive detections.""" - return len(self.tp) - - @property - def num_fp(self): - """Number of false positive detections.""" - return len(self.fp) - - @property - def num_tn(self): - """Number of true negative detections.""" - return len(self.tn) - - @property - def num_fn(self): - """Number of false negative detections.""" - return len(self.fn) - - -# class for evaluation of Precision, Recall, F-measure with 2D arrays -class MultiClassEvaluation(Evaluation): - """ - Evaluation class for measuring Precision, Recall and F-measure based on - 2D numpy arrays with true/false positive/negative detections. - - Parameters - ---------- - tp : list of tuples or numpy array, shape (num_tp, 2) - True positive detections. - fp : list of tuples or numpy array, shape (num_fp, 2) - False positive detections. - tn : list of tuples or numpy array, shape (num_tn, 2) - True negative detections. - fn : list of tuples or numpy array, shape (num_fn, 2) - False negative detections. - name : str - Name to be displayed. - - Notes - ----- - The second item of the tuples or the second column of the arrays denote - the class the detection belongs to. - - """ - def __init__(self, tp=None, fp=None, tn=None, fn=None, **kwargs): - # set default values - if tp is None: - tp = np.zeros((0, 2)) - if fp is None: - fp = np.zeros((0, 2)) - if tn is None: - tn = np.zeros((0, 2)) - if fn is None: - fn = np.zeros((0, 2)) - super(MultiClassEvaluation, self).__init__(**kwargs) - self.tp = np.asarray(tp, dtype=np.float) - self.fp = np.asarray(fp, dtype=np.float) - self.tn = np.asarray(tn, dtype=np.float) - self.fn = np.asarray(fn, dtype=np.float) - - def tostring(self, verbose=False, **kwargs): - """ - Format the evaluation metrics as a human readable string. - - Parameters - ---------- - verbose : bool - Add evaluation for individual classes. - - Returns - ------- - str - Evaluation metrics formatted as a human readable string. - - """ - ret = '' - - if verbose: - # extract all classes - classes = [] - if self.tp.any(): - classes = np.append(classes, np.unique(self.tp[:, 1])) - if self.fp.any(): - classes = np.append(classes, np.unique(self.fp[:, 1])) - if self.tn.any(): - classes = np.append(classes, np.unique(self.tn[:, 1])) - if self.fn.any(): - classes = np.append(classes, np.unique(self.fn[:, 1])) - for cls in sorted(np.unique(classes)): - # extract the TP, FP, TN and FN of this class - tp = self.tp[self.tp[:, 1] == cls] - fp = self.fp[self.fp[:, 1] == cls] - tn = self.tn[self.tn[:, 1] == cls] - fn = self.fn[self.fn[:, 1] == cls] - # evaluate them - e = Evaluation(tp, fp, tn, fn, name='Class %s' % cls) - # append to the output string - ret += ' %s\n' % e.tostring(verbose=False) - # normal formatting - ret += 'Annotations: %5d TP: %5d FP: %4d FN: %4d ' \ - 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \ - (self.num_annotations, self.num_tp, self.num_fp, self.num_fn, - self.precision, self.recall, self.fmeasure, self.accuracy) - # return - return ret - - -# class for summing Evaluations -class SumEvaluation(SimpleEvaluation): - """ - Simple class for summing evaluations. - - Parameters - ---------- - eval_objects : list - Evaluation objects. - name : str - Name to be displayed. - - """ - - def __init__(self, eval_objects, name=None): - # pylint: disable=super-init-not-called - # Note: we want to inherit the evaluation functions/properties, no need - # to call __super__, but we need to take care of 'name' - if not isinstance(eval_objects, list): - # wrap the given eval_object in a list - eval_objects = [eval_objects] - self.eval_objects = eval_objects - self.name = name or 'sum for %d files' % len(self) - - def __len__(self): - # just use the length of the evaluation objects - return len(self.eval_objects) - - # redefine the counters (number of TP, FP, TN, FN & number of annotations) - - @property - def num_tp(self): - """Number of true positive detections.""" - return sum(e.num_tp for e in self.eval_objects) - - @property - def num_fp(self): - """Number of false positive detections.""" - return sum(e.num_fp for e in self.eval_objects) - - @property - def num_tn(self): - """Number of true negative detections.""" - return sum(e.num_tn for e in self.eval_objects) - - @property - def num_fn(self): - """Number of false negative detections.""" - return sum(e.num_fn for e in self.eval_objects) - - @property - def num_annotations(self): - """Number of annotations.""" - return sum(e.num_annotations for e in self.eval_objects) - - -# class for averaging Evaluations -class MeanEvaluation(SumEvaluation): - """ - Simple class for averaging evaluation. - - Parameters - ---------- - eval_objects : list - Evaluation objects. - name : str - Name to be displayed. - - """ - - def __init__(self, eval_objects, name=None, **kwargs): - super(MeanEvaluation, self).__init__(eval_objects, **kwargs) - # handle the 'name' here to be able to set a different default value - self.name = name or 'mean for %d files' % len(self) - - # overwrite the properties to calculate the mean instead of the sum - - @property - def num_tp(self): - """Number of true positive detections.""" - if not self.eval_objects: - return 0. - return np.nanmean([e.num_tp for e in self.eval_objects]) - - @property - def num_fp(self): - """Number of false positive detections.""" - if not self.eval_objects: - return 0. - return np.nanmean([e.num_fp for e in self.eval_objects]) - - @property - def num_tn(self): - """Number of true negative detections.""" - if not self.eval_objects: - return 0. - return np.nanmean([e.num_tn for e in self.eval_objects]) - - @property - def num_fn(self): - """Number of false negative detections.""" - if not self.eval_objects: - return 0. - return np.nanmean([e.num_fn for e in self.eval_objects]) - - @property - def num_annotations(self): - """Number of annotations.""" - if not self.eval_objects: - return 0. - return np.nanmean([e.num_annotations for e in self.eval_objects]) - - @property - def precision(self): - """Precision.""" - return np.nanmean([e.precision for e in self.eval_objects]) - - @property - def recall(self): - """Recall.""" - return np.nanmean([e.recall for e in self.eval_objects]) - - @property - def fmeasure(self): - """F-measure.""" - return np.nanmean([e.fmeasure for e in self.eval_objects]) - - @property - def accuracy(self): - """Accuracy.""" - return np.nanmean([e.accuracy for e in self.eval_objects]) - - def tostring(self, **kwargs): - """ - Format the evaluation metrics as a human readable string. - - Returns - ------- - str - Evaluation metrics formatted as a human readable string. - - """ - ret = '' - if self.name is not None: - ret += '%s\n ' % self.name - # TODO: unify this with SimpleEvaluation but - # add option to provide field formatters (e.g. 3d or 5.2f) - # format with floats instead of integers - ret += 'Annotations: %5.2f TP: %5.2f FP: %5.2f FN: %5.2f ' \ - 'Precision: %.3f Recall: %.3f F-measure: %.3f Acc: %.3f' % \ - (self.num_annotations, self.num_tp, self.num_fp, self.num_fn, - self.precision, self.recall, self.fmeasure, self.accuracy) - return ret - - -def tostring(eval_objects, **kwargs): - """ - Format the given evaluation objects as human readable strings. - - Parameters - ---------- - eval_objects : list - Evaluation objects. - - Returns - ------- - str - Evaluation metrics formatted as a human readable string. - - """ - # pylint: disable=unused-argument - return '\n'.join([e.tostring() for e in eval_objects]) - - -def tocsv(eval_objects, metric_names=None, float_format='{:.3f}', **kwargs): - """ - Format the given evaluation objects as a CSV table. - - Parameters - ---------- - eval_objects : list - Evaluation objects. - metric_names : list of tuples, optional - List of tuples defining the name of the property corresponding to the - metric, and the metric label e.g. ('fp', 'False Positives'). - float_format : str, optional - How to format the metrics. - - Returns - ------- - str - CSV table representation of the evaluation objects. - - Notes - ----- - If no `metric_names` are given, they will be extracted from the first - evaluation object. - - """ - # pylint: disable=unused-argument - - if metric_names is None: - # get the evaluation metrics from the first evaluation object - metric_names = eval_objects[0].METRIC_NAMES - metric_names, metric_labels = list(zip(*metric_names)) - # add header - lines = ['Name,' + ','.join(metric_labels)] - # TODO: use e.metrics dict? - # add the evaluation objects - for e in eval_objects: - values = [float_format.format(getattr(e, mn)) for mn in metric_names] - lines.append(e.name + ',' + ','.join(values)) - # return everything - return '\n'.join(lines) - - -def totex(eval_objects, metric_names=None, float_format='{:.3f}', **kwargs): - """ - Format the given evaluation objects as a LaTeX table. - - Parameters - ---------- - eval_objects : list - Evaluation objects. - metric_names : list of tuples, optional - List of tuples defining the name of the property corresponding to the - metric, and the metric label e.g. ('fp', 'False Positives'). - float_format : str, optional - How to format the metrics. - - Returns - ------- - str - LaTeX table representation of the evaluation objects. - - Notes - ----- - If no `metric_names` are given, they will be extracted from the first - evaluation object. - - """ - # pylint: disable=unused-argument - - if metric_names is None: - # get the evaluation metrics from the first evaluation object - metric_names = eval_objects[0].METRIC_NAMES - metric_names, metric_labels = list(zip(*metric_names)) - # add header - lines = ['Name & ' + ' & '.join(metric_labels) + '\\\\'] - # TODO: use e.metrics dict - # TODO: add a generic totable() function which accepts columns separator, - # newline stuff (e.g. tex \\\\) and others - # add the evaluation objects - for e in eval_objects: - values = [float_format.format(getattr(e, mn)) for mn in metric_names] - lines.append(e.name + ' & ' + ' & '.join(values) + '\\\\') - # return everything - return '\n'.join(lines) - - -def evaluation_io(parser, ann_suffix, det_suffix, ann_dir=None, det_dir=None): - """ - Add evaluation input/output and formatting related arguments to an existing - parser object. - - Parameters - ---------- - parser : argparse parser instance - Existing argparse parser object. - ann_suffix : str - Suffix of the annotation files. - det_suffix : str - Suffix of the detection files. - ann_dir : str, optional - Use only annotations from this folder (and sub-folders). - det_dir : str, optional - Use only detections from this folder (and sub-folders). - - Returns - ------- - io_group : argparse argument group - Evaluation input / output argument group. - formatter_group : argparse argument group - Evaluation formatter argument group. - - """ - import sys - import argparse - # general input output file handling - parser.add_argument('files', nargs='*', - help='files (or folders) to be evaluated') - parser.add_argument('-o', dest='outfile', type=argparse.FileType('w'), - default=sys.stdout, - help='output file [default: STDOUT]') - # file suffixes used for evaluation - g = parser.add_argument_group('file/folder/suffix arguments') - g.add_argument('-a', dest='ann_suffix', action='store', default=ann_suffix, - help='suffix of the annotation files ' - '[default: %(default)s]') - g.add_argument('--ann_dir', action='store', default=ann_dir, - help='search only this directory (recursively) for ' - 'annotation files [default: %(default)s]') - g.add_argument('-d', dest='det_suffix', action='store', default=det_suffix, - help='suffix of the detection files [default: %(default)s]') - g.add_argument('--det_dir', action='store', default=det_dir, - help='search only this directory (recursively) for ' - 'detection files [default: %(default)s]') - # option to ignore non-existing detections - g.add_argument('-i', '--ignore_non_existing', action='store_true', - help='ignore non-existing detections [default: raise a ' - 'warning and assume empty detections]') - # verbose - parser.add_argument('-v', '--verbose', action='count', default=0, - help='increase verbosity level') - # option to suppress warnings - parser.add_argument('-q', '--quiet', action='store_true', - help='suppress any warnings') - # output format options - parser.set_defaults(output_formatter=tostring) - f = parser.add_argument_group('formatting arguments') - formats = f.add_mutually_exclusive_group() - formats.add_argument('--tex', dest='output_formatter', - action='store_const', const=totex, - help='format output to be used in .tex files') - formats.add_argument('--csv', dest='output_formatter', - action='store_const', const=tocsv, - help='format output to be used in .csv files') - # return the output formatting group so the caller can add more options - return g, f - - -# finally import the submodules -from . import chords, beats, notes, onsets, tempo - -# import often used classes -from .beats import BeatEvaluation, BeatMeanEvaluation -from .chords import ChordEvaluation, ChordMeanEvaluation, ChordSumEvaluation -from .key import KeyEvaluation, KeyMeanEvaluation -from .notes import NoteEvaluation, NoteMeanEvaluation, NoteSumEvaluation -from .onsets import OnsetEvaluation, OnsetMeanEvaluation, OnsetSumEvaluation -from .tempo import TempoEvaluation, TempoMeanEvaluation diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py deleted file mode 100644 index 6fc71979190323f44c09f8b7e1761cf49cd2d76b..0000000000000000000000000000000000000000 --- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/ops/iou3d.py +++ /dev/null @@ -1,85 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import torch - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', [ - 'iou3d_boxes_iou_bev_forward', 'iou3d_nms_forward', - 'iou3d_nms_normal_forward' -]) - - -def boxes_iou_bev(boxes_a, boxes_b): - """Calculate boxes IoU in the Bird's Eye View. - - Args: - boxes_a (torch.Tensor): Input boxes a with shape (M, 5). - boxes_b (torch.Tensor): Input boxes b with shape (N, 5). - - Returns: - ans_iou (torch.Tensor): IoU result with shape (M, N). - """ - ans_iou = boxes_a.new_zeros( - torch.Size((boxes_a.shape[0], boxes_b.shape[0]))) - - ext_module.iou3d_boxes_iou_bev_forward(boxes_a.contiguous(), - boxes_b.contiguous(), ans_iou) - - return ans_iou - - -def nms_bev(boxes, scores, thresh, pre_max_size=None, post_max_size=None): - """NMS function GPU implementation (for BEV boxes). The overlap of two - boxes for IoU calculation is defined as the exact overlapping area of the - two boxes. In this function, one can also set ``pre_max_size`` and - ``post_max_size``. - - Args: - boxes (torch.Tensor): Input boxes with the shape of [N, 5] - ([x1, y1, x2, y2, ry]). - scores (torch.Tensor): Scores of boxes with the shape of [N]. - thresh (float): Overlap threshold of NMS. - pre_max_size (int, optional): Max size of boxes before NMS. - Default: None. - post_max_size (int, optional): Max size of boxes after NMS. - Default: None. - - Returns: - torch.Tensor: Indexes after NMS. - """ - assert boxes.size(1) == 5, 'Input boxes shape should be [N, 5]' - order = scores.sort(0, descending=True)[1] - - if pre_max_size is not None: - order = order[:pre_max_size] - boxes = boxes[order].contiguous() - - keep = torch.zeros(boxes.size(0), dtype=torch.long) - num_out = ext_module.iou3d_nms_forward(boxes, keep, thresh) - keep = order[keep[:num_out].cuda(boxes.device)].contiguous() - if post_max_size is not None: - keep = keep[:post_max_size] - return keep - - -def nms_normal_bev(boxes, scores, thresh): - """Normal NMS function GPU implementation (for BEV boxes). The overlap of - two boxes for IoU calculation is defined as the exact overlapping area of - the two boxes WITH their yaw angle set to 0. - - Args: - boxes (torch.Tensor): Input boxes with shape (N, 5). - scores (torch.Tensor): Scores of predicted boxes with shape (N). - thresh (float): Overlap threshold of NMS. - - Returns: - torch.Tensor: Remaining indices with scores in descending order. - """ - assert boxes.shape[1] == 5, 'Input boxes shape should be [N, 5]' - order = scores.sort(0, descending=True)[1] - - boxes = boxes[order].contiguous() - - keep = torch.zeros(boxes.size(0), dtype=torch.long) - num_out = ext_module.iou3d_nms_normal_forward(boxes, keep, thresh) - return order[keep[:num_out].cuda(boxes.device)].contiguous() diff --git a/spaces/MetaWabbit/Auto-GPT/run.sh b/spaces/MetaWabbit/Auto-GPT/run.sh deleted file mode 100644 index edcbc44155b9ca9df83e283fdf976472c13e6492..0000000000000000000000000000000000000000 --- a/spaces/MetaWabbit/Auto-GPT/run.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash -python scripts/check_requirements.py requirements.txt -if [ $? -eq 1 ] -then - echo Installing missing packages... - pip install -r requirements.txt -fi -python -m autogpt $@ -read -p "Press any key to continue..." diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py b/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py deleted file mode 100644 index 244425feef76bf89d4de939cb8a1914a6f0f47c6..0000000000000000000000000000000000000000 --- a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/classifier_trainer_test.py +++ /dev/null @@ -1,387 +0,0 @@ -# Lint as: python3 -# Copyright 2017 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Unit tests for the classifier trainer models.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import copy -import functools -import json - -import os -import sys - -from typing import Any, Callable, Iterable, Mapping, MutableMapping, Optional, Tuple - -from absl import flags -from absl.testing import parameterized -import tensorflow as tf - -from tensorflow.python.distribute import combinations -from tensorflow.python.distribute import strategy_combinations -from official.utils.flags import core as flags_core -from official.vision.image_classification import classifier_trainer -from official.vision.image_classification import dataset_factory -from official.vision.image_classification import test_utils -from official.vision.image_classification.configs import base_configs - -classifier_trainer.define_classifier_flags() - - -def distribution_strategy_combinations() -> Iterable[Tuple[Any, ...]]: - """Returns the combinations of end-to-end tests to run.""" - return combinations.combine( - distribution=[ - strategy_combinations.default_strategy, - strategy_combinations.tpu_strategy, - strategy_combinations.one_device_strategy_gpu, - strategy_combinations.mirrored_strategy_with_two_gpus, - ], - model=[ - 'efficientnet', - 'resnet', - ], - mode='eager', - dataset=[ - 'imagenet', - ], - ) - - -def get_params_override(params_override: Mapping[str, Any]) -> str: - """Converts params_override dict to string command.""" - return '--params_override=' + json.dumps(params_override) - - -def basic_params_override(dtype: str = 'float32') -> MutableMapping[str, Any]: - """Returns a basic parameter configuration for testing.""" - return { - 'train_dataset': { - 'builder': 'synthetic', - 'use_per_replica_batch_size': True, - 'batch_size': 1, - 'image_size': 224, - 'dtype': dtype, - }, - 'validation_dataset': { - 'builder': 'synthetic', - 'batch_size': 1, - 'use_per_replica_batch_size': True, - 'image_size': 224, - 'dtype': dtype, - }, - 'train': { - 'steps': 1, - 'epochs': 1, - 'callbacks': { - 'enable_checkpoint_and_export': True, - 'enable_tensorboard': False, - }, - }, - 'evaluation': { - 'steps': 1, - }, - } - - -def get_trivial_model(num_classes: int) -> tf.keras.Model: - """Creates and compiles trivial model for ImageNet dataset.""" - model = test_utils.trivial_model(num_classes=num_classes) - lr = 0.01 - optimizer = tf.keras.optimizers.SGD(learning_rate=lr) - loss_obj = tf.keras.losses.SparseCategoricalCrossentropy() - model.compile(optimizer=optimizer, - loss=loss_obj, - run_eagerly=True) - return model - - -def get_trivial_data() -> tf.data.Dataset: - """Gets trivial data in the ImageNet size.""" - def generate_data(_) -> tf.data.Dataset: - image = tf.zeros(shape=(224, 224, 3), dtype=tf.float32) - label = tf.zeros([1], dtype=tf.int32) - return image, label - - dataset = tf.data.Dataset.range(1) - dataset = dataset.repeat() - dataset = dataset.map(generate_data, - num_parallel_calls=tf.data.experimental.AUTOTUNE) - dataset = dataset.prefetch(buffer_size=1).batch(1) - return dataset - - -def run_end_to_end(main: Callable[[Any], None], - extra_flags: Optional[Iterable[str]] = None, - model_dir: Optional[str] = None): - """Runs the classifier trainer end-to-end.""" - extra_flags = [] if extra_flags is None else extra_flags - args = [sys.argv[0], '--model_dir', model_dir] + extra_flags - flags_core.parse_flags(argv=args) - main(flags.FLAGS) - - -class ClassifierTest(tf.test.TestCase, parameterized.TestCase): - """Unit tests for Keras models.""" - _tempdir = None - - @classmethod - def setUpClass(cls): # pylint: disable=invalid-name - super(ClassifierTest, cls).setUpClass() - - def tearDown(self): - super(ClassifierTest, self).tearDown() - tf.io.gfile.rmtree(self.get_temp_dir()) - - @combinations.generate(distribution_strategy_combinations()) - def test_end_to_end_train_and_eval(self, distribution, model, dataset): - """Test train_and_eval and export for Keras classifier models.""" - # Some parameters are not defined as flags (e.g. cannot run - # classifier_train.py --batch_size=...) by design, so use - # "--params_override=..." instead - model_dir = self.get_temp_dir() - base_flags = [ - '--data_dir=not_used', - '--model_type=' + model, - '--dataset=' + dataset, - ] - train_and_eval_flags = base_flags + [ - get_params_override(basic_params_override()), - '--mode=train_and_eval', - ] - - run = functools.partial(classifier_trainer.run, - strategy_override=distribution) - run_end_to_end(main=run, - extra_flags=train_and_eval_flags, - model_dir=model_dir) - - @combinations.generate( - combinations.combine( - distribution=[ - strategy_combinations.one_device_strategy_gpu, - ], - model=[ - 'efficientnet', - 'resnet', - ], - mode='eager', - dataset='imagenet', - dtype='float16', - )) - def test_gpu_train(self, distribution, model, dataset, dtype): - """Test train_and_eval and export for Keras classifier models.""" - # Some parameters are not defined as flags (e.g. cannot run - # classifier_train.py --batch_size=...) by design, so use - # "--params_override=..." instead - model_dir = self.get_temp_dir() - base_flags = [ - '--data_dir=not_used', - '--model_type=' + model, - '--dataset=' + dataset, - ] - train_and_eval_flags = base_flags + [ - get_params_override(basic_params_override(dtype)), - '--mode=train_and_eval', - ] - - export_params = basic_params_override() - export_path = os.path.join(model_dir, 'export') - export_params['export'] = {} - export_params['export']['destination'] = export_path - export_flags = base_flags + [ - '--mode=export_only', - get_params_override(export_params) - ] - - run = functools.partial(classifier_trainer.run, - strategy_override=distribution) - run_end_to_end(main=run, - extra_flags=train_and_eval_flags, - model_dir=model_dir) - run_end_to_end(main=run, - extra_flags=export_flags, - model_dir=model_dir) - self.assertTrue(os.path.exists(export_path)) - - @combinations.generate( - combinations.combine( - distribution=[ - strategy_combinations.tpu_strategy, - ], - model=[ - 'efficientnet', - 'resnet', - ], - mode='eager', - dataset='imagenet', - dtype='bfloat16', - )) - def test_tpu_train(self, distribution, model, dataset, dtype): - """Test train_and_eval and export for Keras classifier models.""" - # Some parameters are not defined as flags (e.g. cannot run - # classifier_train.py --batch_size=...) by design, so use - # "--params_override=..." instead - model_dir = self.get_temp_dir() - base_flags = [ - '--data_dir=not_used', - '--model_type=' + model, - '--dataset=' + dataset, - ] - train_and_eval_flags = base_flags + [ - get_params_override(basic_params_override(dtype)), - '--mode=train_and_eval', - ] - - run = functools.partial(classifier_trainer.run, - strategy_override=distribution) - run_end_to_end(main=run, - extra_flags=train_and_eval_flags, - model_dir=model_dir) - - @combinations.generate(distribution_strategy_combinations()) - def test_end_to_end_invalid_mode(self, distribution, model, dataset): - """Test the Keras EfficientNet model with `strategy`.""" - model_dir = self.get_temp_dir() - extra_flags = [ - '--data_dir=not_used', - '--mode=invalid_mode', - '--model_type=' + model, - '--dataset=' + dataset, - get_params_override(basic_params_override()), - ] - - run = functools.partial(classifier_trainer.run, - strategy_override=distribution) - with self.assertRaises(ValueError): - run_end_to_end(main=run, extra_flags=extra_flags, model_dir=model_dir) - - -class UtilTests(parameterized.TestCase, tf.test.TestCase): - """Tests for individual utility functions within classifier_trainer.py.""" - - @parameterized.named_parameters( - ('efficientnet-b0', 'efficientnet', 'efficientnet-b0', 224), - ('efficientnet-b1', 'efficientnet', 'efficientnet-b1', 240), - ('efficientnet-b2', 'efficientnet', 'efficientnet-b2', 260), - ('efficientnet-b3', 'efficientnet', 'efficientnet-b3', 300), - ('efficientnet-b4', 'efficientnet', 'efficientnet-b4', 380), - ('efficientnet-b5', 'efficientnet', 'efficientnet-b5', 456), - ('efficientnet-b6', 'efficientnet', 'efficientnet-b6', 528), - ('efficientnet-b7', 'efficientnet', 'efficientnet-b7', 600), - ('resnet', 'resnet', '', None), - ) - def test_get_model_size(self, model, model_name, expected): - config = base_configs.ExperimentConfig( - model_name=model, - model=base_configs.ModelConfig( - model_params={ - 'model_name': model_name, - }, - ) - ) - size = classifier_trainer.get_image_size_from_model(config) - self.assertEqual(size, expected) - - @parameterized.named_parameters( - ('dynamic', 'dynamic', None, 'dynamic'), - ('scalar', 128., None, 128.), - ('float32', None, 'float32', 1), - ('float16', None, 'float16', 128), - ) - def test_get_loss_scale(self, loss_scale, dtype, expected): - config = base_configs.ExperimentConfig( - runtime=base_configs.RuntimeConfig( - loss_scale=loss_scale), - train_dataset=dataset_factory.DatasetConfig(dtype=dtype)) - ls = classifier_trainer.get_loss_scale(config, fp16_default=128) - self.assertEqual(ls, expected) - - @parameterized.named_parameters( - ('float16', 'float16'), - ('bfloat16', 'bfloat16') - ) - def test_initialize(self, dtype): - config = base_configs.ExperimentConfig( - runtime=base_configs.RuntimeConfig( - run_eagerly=False, - enable_xla=False, - per_gpu_thread_count=1, - gpu_thread_mode='gpu_private', - num_gpus=1, - dataset_num_private_threads=1, - ), - train_dataset=dataset_factory.DatasetConfig(dtype=dtype), - model=base_configs.ModelConfig(), - ) - - class EmptyClass: - pass - fake_ds_builder = EmptyClass() - fake_ds_builder.dtype = dtype - fake_ds_builder.config = EmptyClass() - classifier_trainer.initialize(config, fake_ds_builder) - - def test_resume_from_checkpoint(self): - """Tests functionality for resuming from checkpoint.""" - # Set the keras policy - policy = tf.keras.mixed_precision.experimental.Policy('mixed_bfloat16') - tf.keras.mixed_precision.experimental.set_policy(policy) - - # Get the model, datasets, and compile it. - model = get_trivial_model(10) - - # Create the checkpoint - model_dir = self.get_temp_dir() - train_epochs = 1 - train_steps = 10 - ds = get_trivial_data() - callbacks = [ - tf.keras.callbacks.ModelCheckpoint( - os.path.join(model_dir, 'model.ckpt-{epoch:04d}'), - save_weights_only=True) - ] - model.fit( - ds, - callbacks=callbacks, - epochs=train_epochs, - steps_per_epoch=train_steps) - - # Test load from checkpoint - clean_model = get_trivial_model(10) - weights_before_load = copy.deepcopy(clean_model.get_weights()) - initial_epoch = classifier_trainer.resume_from_checkpoint( - model=clean_model, - model_dir=model_dir, - train_steps=train_steps) - self.assertEqual(initial_epoch, 1) - self.assertNotAllClose(weights_before_load, clean_model.get_weights()) - - tf.io.gfile.rmtree(model_dir) - - def test_serialize_config(self): - """Tests functionality for serializing data.""" - config = base_configs.ExperimentConfig() - model_dir = self.get_temp_dir() - classifier_trainer.serialize_config(params=config, model_dir=model_dir) - saved_params_path = os.path.join(model_dir, 'params.yaml') - self.assertTrue(os.path.exists(saved_params_path)) - tf.io.gfile.rmtree(model_dir) - -if __name__ == '__main__': - tf.test.main() diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py b/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py deleted file mode 100644 index 11fcb5305660ec71153ebfc12631f455a3464115..0000000000000000000000000000000000000000 --- a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/base_configs.py +++ /dev/null @@ -1,231 +0,0 @@ -# Lint as: python3 -# Copyright 2019 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== -"""Definitions for high level configuration groups..""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - - -from typing import Any, List, Mapping, Optional - -import dataclasses - -from official.modeling import hyperparams -from official.modeling.hyperparams import config_definitions - -CallbacksConfig = config_definitions.CallbacksConfig -TensorboardConfig = config_definitions.TensorboardConfig -RuntimeConfig = config_definitions.RuntimeConfig - - -@dataclasses.dataclass -class ExportConfig(hyperparams.Config): - """Configuration for exports. - - Attributes: - checkpoint: the path to the checkpoint to export. - destination: the path to where the checkpoint should be exported. - """ - checkpoint: str = None - destination: str = None - - -@dataclasses.dataclass -class MetricsConfig(hyperparams.Config): - """Configuration for Metrics. - - Attributes: - accuracy: Whether or not to track accuracy as a Callback. Defaults to None. - top_5: Whether or not to track top_5_accuracy as a Callback. Defaults to - None. - """ - accuracy: bool = None - top_5: bool = None - - -@dataclasses.dataclass -class TimeHistoryConfig(hyperparams.Config): - """Configuration for the TimeHistory callback. - - Attributes: - log_steps: Interval of steps between logging of batch level stats. - """ - log_steps: int = None - - -@dataclasses.dataclass -class TrainConfig(hyperparams.Config): - """Configuration for training. - - Attributes: - resume_checkpoint: Whether or not to enable load checkpoint loading. - Defaults to None. - epochs: The number of training epochs to run. Defaults to None. - steps: The number of steps to run per epoch. If None, then this will be - inferred based on the number of images and batch size. Defaults to None. - callbacks: An instance of CallbacksConfig. - metrics: An instance of MetricsConfig. - tensorboard: An instance of TensorboardConfig. - set_epoch_loop: Whether or not to set `experimental_steps_per_execution` to - equal the number of training steps in `model.compile`. This reduces the - number of callbacks run per epoch which significantly improves end-to-end - TPU training time. - """ - resume_checkpoint: bool = None - epochs: int = None - steps: int = None - callbacks: CallbacksConfig = CallbacksConfig() - metrics: MetricsConfig = None - tensorboard: TensorboardConfig = TensorboardConfig() - time_history: TimeHistoryConfig = TimeHistoryConfig() - set_epoch_loop: bool = False - - -@dataclasses.dataclass -class EvalConfig(hyperparams.Config): - """Configuration for evaluation. - - Attributes: - epochs_between_evals: The number of train epochs to run between evaluations. - Defaults to None. - steps: The number of eval steps to run during evaluation. If None, this will - be inferred based on the number of images and batch size. Defaults to - None. - skip_eval: Whether or not to skip evaluation. - """ - epochs_between_evals: int = None - steps: int = None - skip_eval: bool = False - - -@dataclasses.dataclass -class LossConfig(hyperparams.Config): - """Configuration for Loss. - - Attributes: - name: The name of the loss. Defaults to None. - label_smoothing: Whether or not to apply label smoothing to the loss. This - only applies to 'categorical_cross_entropy'. - """ - name: str = None - label_smoothing: float = None - - -@dataclasses.dataclass -class OptimizerConfig(hyperparams.Config): - """Configuration for Optimizers. - - Attributes: - name: The name of the optimizer. Defaults to None. - decay: Decay or rho, discounting factor for gradient. Defaults to None. - epsilon: Small value used to avoid 0 denominator. Defaults to None. - momentum: Plain momentum constant. Defaults to None. - nesterov: Whether or not to apply Nesterov momentum. Defaults to None. - moving_average_decay: The amount of decay to apply. If 0 or None, then - exponential moving average is not used. Defaults to None. - lookahead: Whether or not to apply the lookahead optimizer. Defaults to - None. - beta_1: The exponential decay rate for the 1st moment estimates. Used in the - Adam optimizers. Defaults to None. - beta_2: The exponential decay rate for the 2nd moment estimates. Used in the - Adam optimizers. Defaults to None. - epsilon: Small value used to avoid 0 denominator. Defaults to 1e-7. - """ - name: str = None - decay: float = None - epsilon: float = None - momentum: float = None - nesterov: bool = None - moving_average_decay: Optional[float] = None - lookahead: Optional[bool] = None - beta_1: float = None - beta_2: float = None - epsilon: float = None - - -@dataclasses.dataclass -class LearningRateConfig(hyperparams.Config): - """Configuration for learning rates. - - Attributes: - name: The name of the learning rate. Defaults to None. - initial_lr: The initial learning rate. Defaults to None. - decay_epochs: The number of decay epochs. Defaults to None. - decay_rate: The rate of decay. Defaults to None. - warmup_epochs: The number of warmup epochs. Defaults to None. - batch_lr_multiplier: The multiplier to apply to the base learning rate, if - necessary. Defaults to None. - examples_per_epoch: the number of examples in a single epoch. Defaults to - None. - boundaries: boundaries used in piecewise constant decay with warmup. - multipliers: multipliers used in piecewise constant decay with warmup. - scale_by_batch_size: Scale the learning rate by a fraction of the batch - size. Set to 0 for no scaling (default). - staircase: Apply exponential decay at discrete values instead of continuous. - """ - name: str = None - initial_lr: float = None - decay_epochs: float = None - decay_rate: float = None - warmup_epochs: int = None - examples_per_epoch: int = None - boundaries: List[int] = None - multipliers: List[float] = None - scale_by_batch_size: float = 0. - staircase: bool = None - - -@dataclasses.dataclass -class ModelConfig(hyperparams.Config): - """Configuration for Models. - - Attributes: - name: The name of the model. Defaults to None. - model_params: The parameters used to create the model. Defaults to None. - num_classes: The number of classes in the model. Defaults to None. - loss: A `LossConfig` instance. Defaults to None. - optimizer: An `OptimizerConfig` instance. Defaults to None. - """ - name: str = None - model_params: hyperparams.Config = None - num_classes: int = None - loss: LossConfig = None - optimizer: OptimizerConfig = None - - -@dataclasses.dataclass -class ExperimentConfig(hyperparams.Config): - """Base configuration for an image classification experiment. - - Attributes: - model_dir: The directory to use when running an experiment. - mode: e.g. 'train_and_eval', 'export' - runtime: A `RuntimeConfig` instance. - train: A `TrainConfig` instance. - evaluation: An `EvalConfig` instance. - model: A `ModelConfig` instance. - export: An `ExportConfig` instance. - """ - model_dir: str = None - model_name: str = None - mode: str = None - runtime: RuntimeConfig = None - train_dataset: Any = None - validation_dataset: Any = None - train: TrainConfig = None - evaluation: EvalConfig = None - model: ModelConfig = None - export: ExportConfig = None diff --git a/spaces/Navneet574/Heart_Disease_Prediciton/app.py b/spaces/Navneet574/Heart_Disease_Prediciton/app.py deleted file mode 100644 index 30f66c36eeab6633487a32671d615f07ab544f93..0000000000000000000000000000000000000000 --- a/spaces/Navneet574/Heart_Disease_Prediciton/app.py +++ /dev/null @@ -1,82 +0,0 @@ -import gradio as gr -from joblib import load -import numpy as np -import pandas as pd - -AgeCategory = ['18-24', '25-29', '35-39', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80 or older'] -Binary_Input = ['Yes', 'No'] -Sex = ['Male', 'Female'] -Race = ['White', 'Black', 'Asian', 'American Indian/Alaskan Native', 'Hispanic', 'Other'] -Diabetic = ['Yes', 'No', 'No borderline diabetes', 'Yes (during pregnancy)'] -GeneralHealth = ['Excellent', 'Very good', 'Good', 'Fair', 'Poor'] - -dic_ageCat = {'18-24':0, '25-29':1, '35-39':2, '35-39':3, '40-44':4, '45-49':5, '50-54':6, '55-59':7, '60-64':8, '65-69':9, '70-74':10, '75-79':11, '80 or older':12} -dic_binary = {'Yes':1, 'No':0} -dic_sex = {'Male':0, 'Female':1} -dic_race = {'White':5, 'Black':2, 'Asian':0, 'American Indian/Alaskan Native':3, 'Hispanic':1, 'Other':4} -dic_diabetic = {'Yes':2, 'No':0, 'No borderline diabetes':1, 'Yes (during pregnancy)':3} -dic_generalHealth = {'Excellent':0, 'Very good':4, 'Good':2, 'Fair':1, 'Poor':3} - - -def predict_price(BMI, Smoking, AlcoholDrinking, Stroke, PhysicalHealth, MentalHealth, DiffWalking, Sex, AgeCategory, Race, Diabetic, PhysicalActivity, GenHealth, SleepTime, Asthma, KidneyDisease, SkinCancer): - model = load('Heart_Disease_Classification.jb') - - data = { - 'BMI': [BMI], - 'Smoking': dic_binary[Smoking], - 'AlcoholDrinking': dic_binary[AlcoholDrinking], - 'Stroke': dic_binary[Stroke], - 'PhysicalHealth': [PhysicalHealth], - 'MentalHealth': [MentalHealth], - 'DiffWalking': dic_binary[DiffWalking], - 'Sex': dic_sex[Sex], - 'AgeCategory': dic_ageCat[AgeCategory], - 'Race': dic_race[Race], - 'Diabetic': dic_diabetic[Diabetic], - 'PhysicalActivity': dic_binary[PhysicalActivity], - 'GenHealth': dic_generalHealth[GenHealth], - 'SleepTime': [SleepTime], - 'Asthma': dic_binary[Asthma], - 'KidneyDisease': dic_binary[KidneyDisease], - 'SkinCancer': dic_binary[SkinCancer] - } - - Xinp = pd.DataFrame(data) - print(Xinp) - - stone = model.predict(Xinp) - - if stone == 1: - return stone["YES"] - else: - return stone["NO"] - -ui = gr.Interface( - fn=predict_price,inputs=[ - gr.inputs.Textbox(placeholder='BMI', default=0, - numeric=True, label='Body Mass Index (BMI) (normal is between 18.5 and 24.9)'), - gr.inputs.Dropdown(choices=Binary_Input, label='Smoking', default="No"), - gr.inputs.Dropdown(choices=Binary_Input, label='Alcohol Drinking', default="No"), - gr.inputs.Dropdown(choices=Binary_Input, label='Stroke', default="No"), - gr.inputs.Textbox(placeholder='Physical Health', - default=0, numeric=True, label='Physical Health Score'), - gr.inputs.Textbox(placeholder='MentalHealth', default=0, - numeric=True, label='Mental Health Score'), - gr.inputs.Dropdown(choices=Binary_Input, label='Diff Walking', default="No"), - gr.inputs.Dropdown(choices=Sex, label='Sex', default=""), - gr.inputs.Dropdown(choices=AgeCategory, label='Age Category', default="18-24"), - gr.inputs.Dropdown(choices=Race, label='Race', default="Asian"), - gr.inputs.Dropdown(choices=Diabetic, label='Diabetic', default="No"), - gr.inputs.Dropdown(choices=Binary_Input, label='Physical Activity', default="Yes"), - gr.inputs.Dropdown(choices=GeneralHealth, label='General Health', default="Excellent"), - gr.inputs.Textbox(placeholder='Sleep Time', - default='7', numeric=True, label='Sleep Time (normal is between 6-8 hrs)'), - gr.inputs.Dropdown(choices=Binary_Input, label='Asthma', default="No"), - gr.inputs.Dropdown(choices=Binary_Input, label='Kidney Disease', default="No"), - gr.inputs.Dropdown(choices=Binary_Input, label='Skin Cancer', default="No") - ], outputs=[ - "text" - ] -) -if __name__ == "__main__": - ui.launch() \ No newline at end of file diff --git a/spaces/Nikhil0987/omm/convo.py b/spaces/Nikhil0987/omm/convo.py deleted file mode 100644 index f5c87b19d82d3f1e1869101faf1205475b5ed3b9..0000000000000000000000000000000000000000 --- a/spaces/Nikhil0987/omm/convo.py +++ /dev/null @@ -1,27 +0,0 @@ -from transformers import pipeline, Conversation -import streamlit as st -from streamlit_option_menu import option_menu - -chatbot = pipeline(task="conversational", model="microsoft/DialoGPT-medium") - -def Convo(): - if convo := st.chat_input("Enter your message"): - - conversation = Conversation(convo) - - - - # candidate_labels = ["HELP", "PROBLEM SOLVE", "GENERAL TALK"] - - - ans = chatbot(conversation) - # add_user_input = st.button("Add User Input") - - # conversation.add_user_input("{}".format(convo)) - # conversation = chatbot(conversation) - with st.chat_message("assistant"): - # ans - st.write(ans.generated_responses[-1]) - - -# convo() diff --git a/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py b/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py deleted file mode 100644 index d9d372dcbb1bed2fffbfd8e81d6da749ceab730b..0000000000000000000000000000000000000000 --- a/spaces/NimaBoscarino/climategan/figures/metrics_onefig.py +++ /dev/null @@ -1,772 +0,0 @@ -""" -This scripts plots examples of the images that get best and worse metrics -""" -print("Imports...", end="") -import os -import sys -from argparse import ArgumentParser -from pathlib import Path - -import matplotlib.patches as mpatches -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import seaborn as sns -import yaml -from imageio import imread -from matplotlib.gridspec import GridSpec -from skimage.color import rgba2rgb -from sklearn.metrics.pairwise import euclidean_distances - -sys.path.append("../") - -from climategan.data import encode_mask_label -from climategan.eval_metrics import edges_coherence_std_min -from eval_masker import crop_and_resize - -# ----------------------- -# ----- Constants ----- -# ----------------------- - -# Metrics -metrics = ["error", "f05", "edge_coherence"] - -dict_metrics = { - "names": { - "tpr": "TPR, Recall, Sensitivity", - "tnr": "TNR, Specificity, Selectivity", - "fpr": "FPR", - "fpt": "False positives relative to image size", - "fnr": "FNR, Miss rate", - "fnt": "False negatives relative to image size", - "mpr": "May positive rate (MPR)", - "mnr": "May negative rate (MNR)", - "accuracy": "Accuracy (ignoring may)", - "error": "Error", - "f05": "F05 score", - "precision": "Precision", - "edge_coherence": "Edge coherence", - "accuracy_must_may": "Accuracy (ignoring cannot)", - }, - "key_metrics": ["error", "f05", "edge_coherence"], -} - - -# Colors -colorblind_palette = sns.color_palette("colorblind") -color_cannot = colorblind_palette[1] -color_must = colorblind_palette[2] -color_may = colorblind_palette[7] -color_pred = colorblind_palette[4] - -icefire = sns.color_palette("icefire", as_cmap=False, n_colors=5) -color_tp = icefire[0] -color_tn = icefire[1] -color_fp = icefire[4] -color_fn = icefire[3] - - -def parsed_args(): - """ - Parse and returns command-line args - - Returns: - argparse.Namespace: the parsed arguments - """ - parser = ArgumentParser() - parser.add_argument( - "--input_csv", - default="ablations_metrics_20210311.csv", - type=str, - help="CSV containing the results of the ablation study", - ) - parser.add_argument( - "--output_dir", - default=None, - type=str, - help="Output directory", - ) - parser.add_argument( - "--models_log_path", - default=None, - type=str, - help="Path containing the log files of the models", - ) - parser.add_argument( - "--masker_test_set_dir", - default=None, - type=str, - help="Directory containing the test images", - ) - parser.add_argument( - "--best_model", - default="dada, msd_spade, pseudo", - type=str, - help="The string identifier of the best model", - ) - parser.add_argument( - "--dpi", - default=200, - type=int, - help="DPI for the output images", - ) - parser.add_argument( - "--alpha", - default=0.5, - type=float, - help="Transparency of labels shade", - ) - parser.add_argument( - "--percentile", - default=0.05, - type=float, - help="Transparency of labels shade", - ) - parser.add_argument( - "--seed", - default=None, - type=int, - help="Bootstrap random seed, for reproducibility", - ) - parser.add_argument( - "--no_images", - action="store_true", - default=False, - help="Do not generate images", - ) - - return parser.parse_args() - - -def map_color(arr, input_color, output_color, rtol=1e-09): - """ - Maps one color to another - """ - input_color_arr = np.tile(input_color, (arr.shape[:2] + (1,))) - output = arr.copy() - output[np.all(np.isclose(arr, input_color_arr, rtol=rtol), axis=2)] = output_color - return output - - -def plot_labels(ax, img, label, img_id, n_, add_title, do_legend): - label_colmap = label.astype(float) - label_colmap = map_color(label_colmap, (255, 0, 0), color_cannot) - label_colmap = map_color(label_colmap, (0, 0, 255), color_must) - label_colmap = map_color(label_colmap, (0, 0, 0), color_may) - - ax.imshow(img) - ax.imshow(label_colmap, alpha=0.5) - ax.axis("off") - - if n_ in [1, 3, 5]: - color_ = "green" - else: - color_ = "red" - - ax.text( - -0.15, - 0.5, - img_id, - color=color_, - fontweight="roman", - fontsize="x-large", - horizontalalignment="left", - verticalalignment="center", - transform=ax.transAxes, - ) - - if add_title: - ax.set_title("Labels", rotation=0, fontsize="x-large") - - -def plot_pred(ax, img, pred, img_id, add_title, do_legend): - pred = np.tile(np.expand_dims(pred, axis=2), reps=(1, 1, 3)) - - pred_colmap = pred.astype(float) - pred_colmap = map_color(pred_colmap, (1, 1, 1), color_pred) - pred_colmap_ma = np.ma.masked_not_equal(pred_colmap, color_pred) - pred_colmap_ma = pred_colmap_ma.mask * img + pred_colmap_ma - - ax.imshow(img) - ax.imshow(pred_colmap_ma, alpha=0.5) - ax.axis("off") - - if add_title: - ax.set_title("Prediction", rotation=0, fontsize="x-large") - - -def plot_correct_incorrect( - ax, img_filename, img, metric, label, img_id, n_, add_title, do_legend -): - # FP - fp_map = imread( - model_path / "eval-metrics/fp" / "{}_fp.png".format(Path(img_filename).stem) - ) - fp_map = np.tile(np.expand_dims(fp_map, axis=2), reps=(1, 1, 3)) - - fp_map_colmap = fp_map.astype(float) - fp_map_colmap = map_color(fp_map_colmap, (1, 1, 1), color_fp) - - # FN - fn_map = imread( - model_path / "eval-metrics/fn" / "{}_fn.png".format(Path(img_filename).stem) - ) - fn_map = np.tile(np.expand_dims(fn_map, axis=2), reps=(1, 1, 3)) - - fn_map_colmap = fn_map.astype(float) - fn_map_colmap = map_color(fn_map_colmap, (1, 1, 1), color_fn) - - # TP - tp_map = imread( - model_path / "eval-metrics/tp" / "{}_tp.png".format(Path(img_filename).stem) - ) - tp_map = np.tile(np.expand_dims(tp_map, axis=2), reps=(1, 1, 3)) - - tp_map_colmap = tp_map.astype(float) - tp_map_colmap = map_color(tp_map_colmap, (1, 1, 1), color_tp) - - # TN - tn_map = imread( - model_path / "eval-metrics/tn" / "{}_tn.png".format(Path(img_filename).stem) - ) - tn_map = np.tile(np.expand_dims(tn_map, axis=2), reps=(1, 1, 3)) - - tn_map_colmap = tn_map.astype(float) - tn_map_colmap = map_color(tn_map_colmap, (1, 1, 1), color_tn) - - label_colmap = label.astype(float) - label_colmap = map_color(label_colmap, (0, 0, 0), color_may) - label_colmap_ma = np.ma.masked_not_equal(label_colmap, color_may) - label_colmap_ma = label_colmap_ma.mask * img + label_colmap_ma - - # Combine masks - maps = fp_map_colmap + fn_map_colmap + tp_map_colmap + tn_map_colmap - maps_ma = np.ma.masked_equal(maps, (0, 0, 0)) - maps_ma = maps_ma.mask * img + maps_ma - - ax.imshow(img) - ax.imshow(label_colmap_ma, alpha=0.5) - ax.imshow(maps_ma, alpha=0.5) - ax.axis("off") - - if add_title: - ax.set_title("Metric", rotation=0, fontsize="x-large") - - -def plot_edge_coherence(ax, img, metric, label, pred, img_id, n_, add_title, do_legend): - pred = np.tile(np.expand_dims(pred, axis=2), reps=(1, 1, 3)) - - ec, pred_ec, label_ec = edges_coherence_std_min( - np.squeeze(pred[:, :, 0]), np.squeeze(encode_mask_label(label, "flood")) - ) - - ################## - # Edge distances # - ################## - - # Location of edges - pred_ec_coord = np.argwhere(pred_ec > 0) - label_ec_coord = np.argwhere(label_ec > 0) - - # Normalized pairwise distances between pred and label - dist_mat = np.divide( - euclidean_distances(pred_ec_coord, label_ec_coord), pred_ec.shape[0] - ) - - # Standard deviation of the minimum distance from pred to label - min_dist = np.min(dist_mat, axis=1) # noqa: F841 - - ############# - # Make plot # - ############# - - pred_ec = np.tile( - np.expand_dims(np.asarray(pred_ec > 0, dtype=float), axis=2), reps=(1, 1, 3) - ) - pred_ec_colmap = map_color(pred_ec, (1, 1, 1), color_pred) - pred_ec_colmap_ma = np.ma.masked_not_equal(pred_ec_colmap, color_pred) # noqa: F841 - - label_ec = np.tile( - np.expand_dims(np.asarray(label_ec > 0, dtype=float), axis=2), reps=(1, 1, 3) - ) - label_ec_colmap = map_color(label_ec, (1, 1, 1), color_must) - label_ec_colmap_ma = np.ma.masked_not_equal( # noqa: F841 - label_ec_colmap, color_must - ) - - # Combined pred and label edges - combined_ec = pred_ec_colmap + label_ec_colmap - combined_ec_ma = np.ma.masked_equal(combined_ec, (0, 0, 0)) - combined_ec_img = combined_ec_ma.mask * img + combined_ec - - # Pred - pred_colmap = pred.astype(float) - pred_colmap = map_color(pred_colmap, (1, 1, 1), color_pred) - pred_colmap_ma = np.ma.masked_not_equal(pred_colmap, color_pred) - - # Must - label_colmap = label.astype(float) - label_colmap = map_color(label_colmap, (0, 0, 255), color_must) - label_colmap_ma = np.ma.masked_not_equal(label_colmap, color_must) - - # TP - tp_map = imread( - model_path / "eval-metrics/tp" / "{}_tp.png".format(Path(srs_sel.filename).stem) - ) - tp_map = np.tile(np.expand_dims(tp_map, axis=2), reps=(1, 1, 3)) - tp_map_colmap = tp_map.astype(float) - tp_map_colmap = map_color(tp_map_colmap, (1, 1, 1), color_tp) - tp_map_colmap_ma = np.ma.masked_not_equal(tp_map_colmap, color_tp) - - # Combination - comb_pred = ( - (pred_colmap_ma.mask ^ tp_map_colmap_ma.mask) - & tp_map_colmap_ma.mask - & combined_ec_ma.mask - ) * pred_colmap - comb_label = ( - (label_colmap_ma.mask ^ pred_colmap_ma.mask) - & pred_colmap_ma.mask - & combined_ec_ma.mask - ) * label_colmap - comb_tp = combined_ec_ma.mask * tp_map_colmap.copy() - combined = comb_tp + comb_label + comb_pred - combined_ma = np.ma.masked_equal(combined, (0, 0, 0)) - combined_ma = combined_ma.mask * combined_ec_img + combined_ma - - ax.imshow(combined_ec_img, alpha=1) - ax.imshow(combined_ma, alpha=0.5) - ax.axis("off") - - # Plot lines - idx_sort_x = np.argsort(pred_ec_coord[:, 1]) - offset = 100 - for idx in range(offset, pred_ec_coord.shape[0], offset): - y0, x0 = pred_ec_coord[idx_sort_x[idx], :] - argmin = np.argmin(dist_mat[idx_sort_x[idx]]) - y1, x1 = label_ec_coord[argmin, :] - ax.plot([x0, x1], [y0, y1], color="white", linewidth=0.5) - - if add_title: - ax.set_title("Metric", rotation=0, fontsize="x-large") - - -def plot_images_metric( - axes, metric, img_filename, img_id, n_, srs_sel, add_title, do_legend -): - - # Read images - img_path = imgs_orig_path / img_filename - label_path = labels_path / "{}_labeled.png".format(Path(img_filename).stem) - img, label = crop_and_resize(img_path, label_path) - img = rgba2rgb(img) if img.shape[-1] == 4 else img / 255.0 - - pred = imread( - model_path / "eval-metrics/pred" / "{}_pred.png".format(Path(img_filename).stem) - ) - - # Label - plot_labels(axes[0], img, label, img_id, n_, add_title, do_legend) - - # Prediction - plot_pred(axes[1], img, pred, img_id, add_title, do_legend) - - # Correct / incorrect - if metric in ["error", "f05"]: - plot_correct_incorrect( - axes[2], - img_filename, - img, - metric, - label, - img_id, - n_, - add_title, - do_legend=False, - ) - handles = [] - lw = 1.0 - handles.append( - mpatches.Patch(facecolor=color_tp, label="TP", linewidth=lw, alpha=0.66) - ) - handles.append( - mpatches.Patch(facecolor=color_tn, label="TN", linewidth=lw, alpha=0.66) - ) - handles.append( - mpatches.Patch(facecolor=color_fp, label="FP", linewidth=lw, alpha=0.66) - ) - handles.append( - mpatches.Patch(facecolor=color_fn, label="FN", linewidth=lw, alpha=0.66) - ) - handles.append( - mpatches.Patch( - facecolor=color_may, - label="May-be-flooded", - linewidth=lw, - alpha=0.66, - ) - ) - labels = ["TP", "TN", "FP", "FN", "May-be-flooded"] - if metric == "error": - if n_ in [1, 3, 5]: - title = "Low error rate" - else: - title = "High error rate" - else: - if n_ in [1, 3, 5]: - title = "High F05 score" - else: - title = "Low F05 score" - # Edge coherence - elif metric == "edge_coherence": - plot_edge_coherence( - axes[2], img, metric, label, pred, img_id, n_, add_title, do_legend=False - ) - handles = [] - lw = 1.0 - handles.append( - mpatches.Patch(facecolor=color_tp, label="TP", linewidth=lw, alpha=0.66) - ) - handles.append( - mpatches.Patch(facecolor=color_pred, label="pred", linewidth=lw, alpha=0.66) - ) - handles.append( - mpatches.Patch( - facecolor=color_must, - label="Must-be-flooded", - linewidth=lw, - alpha=0.66, - ) - ) - labels = ["TP", "Prediction", "Must-be-flooded"] - if n_ in [1, 3, 5]: - title = "High edge coherence" - else: - title = "Low edge coherence" - - else: - raise ValueError - - labels_values_title = "Error: {:.4f} \nFO5: {:.4f} \nEdge coherence: {:.4f}".format( - srs_sel.error, srs_sel.f05, srs_sel.edge_coherence - ) - - plot_legend(axes[3], img, handles, labels, labels_values_title, title) - - -def plot_legend(ax, img, handles, labels, labels_values_title, title): - img_ = np.zeros_like(img, dtype=np.uint8) - img_.fill(255) - ax.imshow(img_) - ax.axis("off") - - leg1 = ax.legend( - handles=handles, - labels=labels, - title=title, - title_fontsize="medium", - labelspacing=0.6, - loc="upper left", - fontsize="x-small", - frameon=False, - ) - leg1._legend_box.align = "left" - - leg2 = ax.legend( - title=labels_values_title, - title_fontsize="small", - loc="lower left", - frameon=False, - ) - leg2._legend_box.align = "left" - - ax.add_artist(leg1) - - -def scatterplot_metrics_pair(ax, df, x_metric, y_metric, dict_images): - - sns.scatterplot(data=df, x=x_metric, y=y_metric, ax=ax) - - # Set X-label - ax.set_xlabel(dict_metrics["names"][x_metric], rotation=0, fontsize="medium") - - # Set Y-label - ax.set_ylabel(dict_metrics["names"][y_metric], rotation=90, fontsize="medium") - - # Change spines - sns.despine(ax=ax, left=True, bottom=True) - - annotate_scatterplot(ax, dict_images, x_metric, y_metric) - - -def scatterplot_metrics(ax, df, df_all, dict_images, plot_all=False): - - # Other - if plot_all: - sns.scatterplot( - data=df_all.loc[df_all.ground == True], - x="error", y="f05", hue="edge_coherence", ax=ax, - marker='+', alpha=0.25) - sns.scatterplot( - data=df_all.loc[df_all.instagan == True], - x="error", y="f05", hue="edge_coherence", ax=ax, - marker='x', alpha=0.25) - sns.scatterplot( - data=df_all.loc[(df_all.instagan == False) & (df_all.instagan == False) & - (df_all.model_feats != args.best_model)], - x="error", y="f05", hue="edge_coherence", ax=ax, - marker='s', alpha=0.25) - - # Best model - cmap_ = sns.cubehelix_palette(as_cmap=True) - sns.scatterplot( - data=df, x="error", y="f05", hue="edge_coherence", ax=ax, palette=cmap_ - ) - - norm = plt.Normalize(df["edge_coherence"].min(), df["edge_coherence"].max()) - sm = plt.cm.ScalarMappable(cmap=cmap_, norm=norm) - sm.set_array([]) - - # Remove the legend and add a colorbar - ax.get_legend().remove() - ax_cbar = ax.figure.colorbar(sm) - ax_cbar.set_label("Edge coherence", labelpad=8) - - # Set X-label - ax.set_xlabel(dict_metrics["names"]["error"], rotation=0, fontsize="medium") - - # Set Y-label - ax.set_ylabel(dict_metrics["names"]["f05"], rotation=90, fontsize="medium") - - annotate_scatterplot(ax, dict_images, "error", "f05") - - # Change spines - sns.despine(ax=ax, left=True, bottom=True) - - # Set XY limits - xlim = ax.get_xlim() - ylim = ax.get_ylim() - ax.set_xlim([0.0, xlim[1]]) - ax.set_ylim([ylim[0], 1.0]) - - -def annotate_scatterplot(ax, dict_images, x_metric, y_metric, offset=0.1): - xlim = ax.get_xlim() - ylim = ax.get_ylim() - x_len = xlim[1] - xlim[0] - y_len = ylim[1] - ylim[0] - x_th = xlim[1] - x_len / 2.0 - y_th = ylim[1] - y_len / 2.0 - for text, d in dict_images.items(): - if text in ["B", "D", "F"]: - x = d[x_metric] - y = d[y_metric] - - x_text = x + x_len * offset if x < x_th else x - x_len * offset - y_text = y + y_len * offset if y < y_th else y - y_len * offset - - ax.annotate( - xy=(x, y), - xycoords="data", - xytext=(x_text, y_text), - textcoords="data", - text=text, - arrowprops=dict(facecolor="black", shrink=0.05), - fontsize="medium", - color="black", - ) - elif text == "A": - x = ( - dict_images["A"][x_metric] - + dict_images["C"][x_metric] - + dict_images["E"][x_metric] - ) / 3 - y = ( - dict_images["A"][y_metric] - + dict_images["C"][y_metric] - + dict_images["E"][y_metric] - ) / 3 - - x_text = x + x_len * 2 * offset if x < x_th else x - x_len * 2 * offset - y_text = ( - y + y_len * 0.45 * offset if y < y_th else y - y_len * 0.45 * offset - ) - - ax.annotate( - xy=(x, y), - xycoords="data", - xytext=(x_text, y_text), - textcoords="data", - text="A, C, E", - arrowprops=dict(facecolor="black", shrink=0.05), - fontsize="medium", - color="black", - ) - - -if __name__ == "__main__": - # ----------------------------- - # ----- Parse arguments ----- - # ----------------------------- - args = parsed_args() - print("Args:\n" + "\n".join([f" {k:20}: {v}" for k, v in vars(args).items()])) - - # Determine output dir - if args.output_dir is None: - output_dir = Path(os.environ["SLURM_TMPDIR"]) - else: - output_dir = Path(args.output_dir) - if not output_dir.exists(): - output_dir.mkdir(parents=True, exist_ok=False) - - # Store args - output_yml = output_dir / "labels.yml" - with open(output_yml, "w") as f: - yaml.dump(vars(args), f) - - # Data dirs - imgs_orig_path = Path(args.masker_test_set_dir) / "imgs" - labels_path = Path(args.masker_test_set_dir) / "labels" - - # Read CSV - df_all = pd.read_csv(args.input_csv, index_col="model_img_idx") - - # Select best model - df = df_all.loc[df_all.model_feats == args.best_model] - v_key, model_dir = df.model.unique()[0].split("/") - model_path = Path(args.models_log_path) / "ablation-{}".format(v_key) / model_dir - - # Set up plot - sns.reset_orig() - sns.set(style="whitegrid") - plt.rcParams.update({"font.family": "serif"}) - plt.rcParams.update( - { - "font.serif": [ - "Computer Modern Roman", - "Times New Roman", - "Utopia", - "New Century Schoolbook", - "Century Schoolbook L", - "ITC Bookman", - "Bookman", - "Times", - "Palatino", - "Charter", - "serif" "Bitstream Vera Serif", - "DejaVu Serif", - ] - } - ) - - if args.seed: - np.random.seed(args.seed) - img_ids = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" - dict_images = {} - idx = 0 - - # Define grid of subplots - grid_vmargin = 0.03 # Extent of the vertical margin between metric grids - ax_hspace = 0.04 # Extent of the vertical space between axes of same grid - ax_wspace = 0.05 # Extent of the horizontal space between axes of same grid - n_grids = len(metrics) - n_cols = 4 - n_rows = 2 - h_grid = (1.0 / n_grids) - ((n_grids - 1) * grid_vmargin) / n_grids - - fig1 = plt.figure(dpi=200, figsize=(11, 13)) - - n_ = 0 - add_title = False - for metric_id, metric in enumerate(metrics): - - # Create grid - top_grid = 1.0 - metric_id * h_grid - metric_id * grid_vmargin - bottom_grid = top_grid - h_grid - gridspec = GridSpec( - n_rows, - n_cols, - wspace=ax_wspace, - hspace=ax_hspace, - bottom=bottom_grid, - top=top_grid, - ) - - # Select best - if metric == "error": - ascending = True - else: - ascending = False - idx_rand = np.random.permutation(int(args.percentile * len(df)))[0] - srs_sel = df.sort_values(by=metric, ascending=ascending).iloc[idx_rand] - img_id = img_ids[idx] - dict_images.update({img_id: srs_sel}) - # Read images - img_filename = srs_sel.filename - - axes_row = [fig1.add_subplot(gridspec[0, c]) for c in range(n_cols)] - if not args.no_images: - n_ += 1 - if metric_id == 0: - add_title = True - plot_images_metric( - axes_row, - metric, - img_filename, - img_id, - n_, - srs_sel, - add_title=add_title, - do_legend=False, - ) - add_title = False - - idx += 1 - print("1 more row done.") - # Select worst - if metric == "error": - ascending = False - else: - ascending = True - idx_rand = np.random.permutation(int(args.percentile * len(df)))[0] - srs_sel = df.sort_values(by=metric, ascending=ascending).iloc[idx_rand] - img_id = img_ids[idx] - dict_images.update({img_id: srs_sel}) - # Read images - img_filename = srs_sel.filename - - axes_row = [fig1.add_subplot(gridspec[1, c]) for c in range(n_cols)] - if not args.no_images: - n_ += 1 - plot_images_metric( - axes_row, - metric, - img_filename, - img_id, - n_, - srs_sel, - add_title=add_title, - do_legend=False, - ) - - idx += 1 - print("1 more row done.") - - output_fig = output_dir / "all_metrics.png" - - fig1.tight_layout() # (pad=1.5) # - fig1.savefig(output_fig, dpi=fig1.dpi, bbox_inches="tight") - - # Scatter plot - fig2 = plt.figure(dpi=200) - - scatterplot_metrics(fig2.gca(), df, df_all, dict_images) - - # fig2, axes = plt.subplots(nrows=1, ncols=3, dpi=200, figsize=(18, 5)) - # - # scatterplot_metrics_pair(axes[0], df, "error", "f05", dict_images) - # scatterplot_metrics_pair(axes[1], df, "error", "edge_coherence", dict_images) - # scatterplot_metrics_pair(axes[2], df, "f05", "edge_coherence", dict_images) - - output_fig = output_dir / "scatterplots.png" - fig2.savefig(output_fig, dpi=fig2.dpi, bbox_inches="tight") diff --git a/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh b/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh deleted file mode 100644 index d649238546b996ae1ad00f7753c04aeebc7aaa97..0000000000000000000000000000000000000000 --- a/spaces/NimaBoscarino/climategan/utils_scripts/make-labelbox.sh +++ /dev/null @@ -1,9 +0,0 @@ -echo "Dowloading Script" && python download_labelbox.py - -echo "Merging Script" && python merge_labelbox_masks.py - -echo "Cleaning labeled" -rm /Users/victor/Downloads/metrics-v2/labels/* -cp /Users/victor/Downloads/labelbox_test_flood-v2/__labeled/* /Users/victor/Downloads/metrics-v2/labels - -echo "Create labeled images Script" && python create_labeled.py \ No newline at end of file diff --git a/spaces/Nunchakuka/FrenchAnonymizer/models.py b/spaces/Nunchakuka/FrenchAnonymizer/models.py deleted file mode 100644 index 46b8aacb1bef18f6fad4c20c968b19125626799c..0000000000000000000000000000000000000000 --- a/spaces/Nunchakuka/FrenchAnonymizer/models.py +++ /dev/null @@ -1,351 +0,0 @@ -import copy -import math -import torch -from torch import nn -from torch.nn import functional as F - -import commons -import modules - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from commons import init_weights, get_padding - - -class ResidualCouplingBlock(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0): - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.flows = nn.ModuleList() - for i in range(n_flows): - self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True)) - self.flows.append(modules.Flip()) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - - -class Encoder(nn.Module): - def __init__(self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, g=None): - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - x = self.pre(x) * x_mask - x = self.enc(x, x_mask, g=g) - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - return z, m, logs, x_mask - - -class Generator(torch.nn.Module): - def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0): - super(Generator, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3) - resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append(weight_norm( - ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)), - k, u, padding=(k-u)//2))) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel//(2**(i+1)) - for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - def forward(self, x, g=None): - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i*self.num_kernels+j](x) - else: - xs += self.resblocks[i*self.num_kernels+j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print('Removing weight norm...') - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), - ]) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv1d(1, 16, 15, 1, padding=7)), - norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), - norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ]) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2,3,5,7,11] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class SpeakerEncoder(torch.nn.Module): - def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256): - super(SpeakerEncoder, self).__init__() - self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True) - self.linear = nn.Linear(model_hidden_size, model_embedding_size) - self.relu = nn.ReLU() - - def forward(self, mels): - self.lstm.flatten_parameters() - _, (hidden, _) = self.lstm(mels) - embeds_raw = self.relu(self.linear(hidden[-1])) - return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True) - - def compute_partial_slices(self, total_frames, partial_frames, partial_hop): - mel_slices = [] - for i in range(0, total_frames-partial_frames, partial_hop): - mel_range = torch.arange(i, i+partial_frames) - mel_slices.append(mel_range) - - return mel_slices - - def embed_utterance(self, mel, partial_frames=128, partial_hop=64): - mel_len = mel.size(1) - last_mel = mel[:,-partial_frames:] - - if mel_len > partial_frames: - mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop) - mels = list(mel[:,s] for s in mel_slices) - mels.append(last_mel) - mels = torch.stack(tuple(mels), 0).squeeze(1) - - with torch.no_grad(): - partial_embeds = self(mels) - embed = torch.mean(partial_embeds, axis=0).unsqueeze(0) - #embed = embed / torch.linalg.norm(embed, 2) - else: - with torch.no_grad(): - embed = self(last_mel) - - return embed - - -class SynthesizerTrn(nn.Module): - """ - Synthesizer for Training - """ - - def __init__(self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels, - ssl_dim, - use_spk, - **kwargs): - - super().__init__() - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.gin_channels = gin_channels - self.ssl_dim = ssl_dim - self.use_spk = use_spk - - self.enc_p = Encoder(ssl_dim, inter_channels, hidden_channels, 5, 1, 16) - self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) - self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) - self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) - - if not self.use_spk: - self.enc_spk = SpeakerEncoder(model_hidden_size=gin_channels, model_embedding_size=gin_channels) - - def forward(self, c, spec, g=None, mel=None, c_lengths=None, spec_lengths=None): - if c_lengths == None: - c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device) - if spec_lengths == None: - spec_lengths = (torch.ones(spec.size(0)) * spec.size(-1)).to(spec.device) - - if not self.use_spk: - g = self.enc_spk(mel.transpose(1,2)) - g = g.unsqueeze(-1) - - _, m_p, logs_p, _ = self.enc_p(c, c_lengths) - z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g) - z_p = self.flow(z, spec_mask, g=g) - - z_slice, ids_slice = commons.rand_slice_segments(z, spec_lengths, self.segment_size) - o = self.dec(z_slice, g=g) - - return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, c, g=None, mel=None, c_lengths=None): - if c_lengths == None: - c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device) - if not self.use_spk: - g = self.enc_spk.embed_utterance(mel.transpose(1,2)) - g = g.unsqueeze(-1) - - z_p, m_p, logs_p, c_mask = self.enc_p(c, c_lengths) - z = self.flow(z_p, c_mask, g=g, reverse=True) - o = self.dec(z * c_mask, g=g) - - return o diff --git a/spaces/Nunchakuka/FrenchAnonymizer/utils.py b/spaces/Nunchakuka/FrenchAnonymizer/utils.py deleted file mode 100644 index 1bd5b6185af6c9f1c270b8ba345bfc36d059e081..0000000000000000000000000000000000000000 --- a/spaces/Nunchakuka/FrenchAnonymizer/utils.py +++ /dev/null @@ -1,305 +0,0 @@ -import os -import sys -import argparse -import logging -import json -import subprocess -import numpy as np -from scipy.io.wavfile import read -import torch -from torch.nn import functional as F -from commons import sequence_mask - -MATPLOTLIB_FLAG = False - -logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) -logger = logging - - -def get_cmodel(rank): - checkpoint = torch.load('wavlm/WavLM-Large.pt') - cfg = WavLMConfig(checkpoint['cfg']) - cmodel = WavLM(cfg).cuda(rank) - cmodel.load_state_dict(checkpoint['model']) - cmodel.eval() - return cmodel - - -def get_content(cmodel, y): - with torch.no_grad(): - c = cmodel.extract_features(y.squeeze(1))[0] - c = c.transpose(1, 2) - return c - - -def get_vocoder(rank): - with open("hifigan/config.json", "r") as f: - config = json.load(f) - config = hifigan.AttrDict(config) - vocoder = hifigan.Generator(config) - ckpt = torch.load("hifigan/generator_v1") - vocoder.load_state_dict(ckpt["generator"]) - vocoder.eval() - vocoder.remove_weight_norm() - vocoder.cuda(rank) - return vocoder - - -def transform(mel, height): # 68-92 - #r = np.random.random() - #rate = r * 0.3 + 0.85 # 0.85-1.15 - #height = int(mel.size(-2) * rate) - tgt = torchvision.transforms.functional.resize(mel, (height, mel.size(-1))) - if height >= mel.size(-2): - return tgt[:, :mel.size(-2), :] - else: - silence = tgt[:,-1:,:].repeat(1,mel.size(-2)-height,1) - silence += torch.randn_like(silence) / 10 - return torch.cat((tgt, silence), 1) - - -def stretch(mel, width): # 0.5-2 - return torchvision.transforms.functional.resize(mel, (mel.size(-2), width)) - - -def load_checkpoint(checkpoint_path, model, optimizer=None): - assert os.path.isfile(checkpoint_path) - checkpoint_dict = torch.load(checkpoint_path, map_location='cpu') - iteration = checkpoint_dict['iteration'] - learning_rate = checkpoint_dict['learning_rate'] - if optimizer is not None: - optimizer.load_state_dict(checkpoint_dict['optimizer']) - saved_state_dict = checkpoint_dict['model'] - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - new_state_dict= {} - for k, v in state_dict.items(): - try: - new_state_dict[k] = saved_state_dict[k] - except: - logger.info("%s is not in the checkpoint" % k) - new_state_dict[k] = v - if hasattr(model, 'module'): - model.module.load_state_dict(new_state_dict) - else: - model.load_state_dict(new_state_dict) - logger.info("Loaded checkpoint '{}' (iteration {})" .format( - checkpoint_path, iteration)) - return model, optimizer, learning_rate, iteration - - -def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path): - logger.info("Saving model and optimizer state at iteration {} to {}".format( - iteration, checkpoint_path)) - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - torch.save({'model': state_dict, - 'iteration': iteration, - 'optimizer': optimizer.state_dict(), - 'learning_rate': learning_rate}, checkpoint_path) - - -def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050): - for k, v in scalars.items(): - writer.add_scalar(k, v, global_step) - for k, v in histograms.items(): - writer.add_histogram(k, v, global_step) - for k, v in images.items(): - writer.add_image(k, v, global_step, dataformats='HWC') - for k, v in audios.items(): - writer.add_audio(k, v, global_step, audio_sampling_rate) - - -def latest_checkpoint_path(dir_path, regex="G_*.pth"): - f_list = glob.glob(os.path.join(dir_path, regex)) - f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f)))) - x = f_list[-1] - print(x) - return x - - -def plot_spectrogram_to_numpy(spectrogram): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(10,2)) - im = ax.imshow(spectrogram, aspect="auto", origin="lower", - interpolation='none') - plt.colorbar(im, ax=ax) - plt.xlabel("Frames") - plt.ylabel("Channels") - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def plot_alignment_to_numpy(alignment, info=None): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(6, 4)) - im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower', - interpolation='none') - fig.colorbar(im, ax=ax) - xlabel = 'Decoder timestep' - if info is not None: - xlabel += '\n\n' + info - plt.xlabel(xlabel) - plt.ylabel('Encoder timestep') - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def load_wav_to_torch(full_path): - sampling_rate, data = read(full_path) - return torch.FloatTensor(data.astype(np.float32)), sampling_rate - - -def load_filepaths_and_text(filename, split="|"): - with open(filename, encoding='utf-8') as f: - filepaths_and_text = [line.strip().split(split) for line in f] - return filepaths_and_text - - -def get_hparams(init=True): - parser = argparse.ArgumentParser() - parser.add_argument('-c', '--config', type=str, default="./configs/base.json", - help='JSON file for configuration') - parser.add_argument('-m', '--model', type=str, required=True, - help='Model name') - - args = parser.parse_args() - model_dir = os.path.join("./logs", args.model) - - if not os.path.exists(model_dir): - os.makedirs(model_dir) - - config_path = args.config - config_save_path = os.path.join(model_dir, "config.json") - if init: - with open(config_path, "r") as f: - data = f.read() - with open(config_save_path, "w") as f: - f.write(data) - else: - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_dir(model_dir): - config_save_path = os.path.join(model_dir, "config.json") - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams =HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_file(config_path): - with open(config_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams =HParams(**config) - return hparams - - -def check_git_hash(model_dir): - source_dir = os.path.dirname(os.path.realpath(__file__)) - if not os.path.exists(os.path.join(source_dir, ".git")): - logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format( - source_dir - )) - return - - cur_hash = subprocess.getoutput("git rev-parse HEAD") - - path = os.path.join(model_dir, "githash") - if os.path.exists(path): - saved_hash = open(path).read() - if saved_hash != cur_hash: - logger.warn("git hash values are different. {}(saved) != {}(current)".format( - saved_hash[:8], cur_hash[:8])) - else: - open(path, "w").write(cur_hash) - - -def get_logger(model_dir, filename="train.log"): - global logger - logger = logging.getLogger(os.path.basename(model_dir)) - logger.setLevel(logging.DEBUG) - - formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s") - if not os.path.exists(model_dir): - os.makedirs(model_dir) - h = logging.FileHandler(os.path.join(model_dir, filename)) - h.setLevel(logging.DEBUG) - h.setFormatter(formatter) - logger.addHandler(h) - return logger - - -class HParams(): - def __init__(self, **kwargs): - for k, v in kwargs.items(): - if type(v) == dict: - v = HParams(**v) - self[k] = v - - def keys(self): - return self.__dict__.keys() - - def items(self): - return self.__dict__.items() - - def values(self): - return self.__dict__.values() - - def __len__(self): - return len(self.__dict__) - - def __getitem__(self, key): - return getattr(self, key) - - def __setitem__(self, key, value): - return setattr(self, key, value) - - def __contains__(self, key): - return key in self.__dict__ - - def __repr__(self): - return self.__dict__.__repr__() diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py deleted file mode 100644 index bb80d11a67cd75764a89f6f41915b0348ae96e92..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/noisychannel/rerank.py +++ /dev/null @@ -1,428 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from multiprocessing import Pool - -import numpy as np -from fairseq import options -from fairseq.data import dictionary -from fairseq.scoring import bleu - -from examples.noisychannel import ( - rerank_generate, - rerank_options, - rerank_score_bw, - rerank_score_lm, - rerank_utils, -) - - -def score_target_hypo( - args, a, b, c, lenpen, target_outfile, hypo_outfile, write_hypos, normalize -): - - print("lenpen", lenpen, "weight1", a, "weight2", b, "weight3", c) - gen_output_lst, bitext1_lst, bitext2_lst, lm_res_lst = load_score_files(args) - dict = dictionary.Dictionary() - scorer = scorer = bleu.Scorer( - bleu.BleuConfig( - pad=dict.pad(), - eos=dict.eos(), - unk=dict.unk(), - ) - ) - - ordered_hypos = {} - ordered_targets = {} - - for shard_id in range(len(bitext1_lst)): - bitext1 = bitext1_lst[shard_id] - bitext2 = bitext2_lst[shard_id] - gen_output = gen_output_lst[shard_id] - lm_res = lm_res_lst[shard_id] - - total = len(bitext1.rescore_source.keys()) - source_lst = [] - hypo_lst = [] - score_lst = [] - reference_lst = [] - j = 1 - best_score = -math.inf - - for i in range(total): - # length is measured in terms of words, not bpe tokens, since models may not share the same bpe - target_len = len(bitext1.rescore_hypo[i].split()) - - if lm_res is not None: - lm_score = lm_res.score[i] - else: - lm_score = 0 - - if bitext2 is not None: - bitext2_score = bitext2.rescore_score[i] - bitext2_backwards = bitext2.backwards - else: - bitext2_score = None - bitext2_backwards = None - - score = rerank_utils.get_score( - a, - b, - c, - target_len, - bitext1.rescore_score[i], - bitext2_score, - lm_score=lm_score, - lenpen=lenpen, - src_len=bitext1.source_lengths[i], - tgt_len=bitext1.target_lengths[i], - bitext1_backwards=bitext1.backwards, - bitext2_backwards=bitext2_backwards, - normalize=normalize, - ) - - if score > best_score: - best_score = score - best_hypo = bitext1.rescore_hypo[i] - - if j == gen_output.num_hypos[i] or j == args.num_rescore: - j = 1 - hypo_lst.append(best_hypo) - score_lst.append(best_score) - source_lst.append(bitext1.rescore_source[i]) - reference_lst.append(bitext1.rescore_target[i]) - - best_score = -math.inf - best_hypo = "" - else: - j += 1 - - gen_keys = list(sorted(gen_output.no_bpe_target.keys())) - - for key in range(len(gen_keys)): - if args.prefix_len is None: - assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], ( - "pred and rescore hypo mismatch: i: " - + str(key) - + ", " - + str(hypo_lst[key]) - + str(gen_keys[key]) - + str(gen_output.no_bpe_hypo[key]) - ) - sys_tok = dict.encode_line(hypo_lst[key]) - ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]]) - scorer.add(ref_tok, sys_tok) - - else: - full_hypo = rerank_utils.get_full_from_prefix( - hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]] - ) - sys_tok = dict.encode_line(full_hypo) - ref_tok = dict.encode_line(gen_output.no_bpe_target[gen_keys[key]]) - scorer.add(ref_tok, sys_tok) - - # if only one set of hyper parameters is provided, write the predictions to a file - if write_hypos: - # recover the orinal ids from n best list generation - for key in range(len(gen_output.no_bpe_target)): - if args.prefix_len is None: - assert hypo_lst[key] in gen_output.no_bpe_hypo[gen_keys[key]], ( - "pred and rescore hypo mismatch:" - + "i:" - + str(key) - + str(hypo_lst[key]) - + str(gen_output.no_bpe_hypo[key]) - ) - ordered_hypos[gen_keys[key]] = hypo_lst[key] - ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[ - gen_keys[key] - ] - - else: - full_hypo = rerank_utils.get_full_from_prefix( - hypo_lst[key], gen_output.no_bpe_hypo[gen_keys[key]] - ) - ordered_hypos[gen_keys[key]] = full_hypo - ordered_targets[gen_keys[key]] = gen_output.no_bpe_target[ - gen_keys[key] - ] - - # write the hypos in the original order from nbest list generation - if args.num_shards == (len(bitext1_lst)): - with open(target_outfile, "w") as t: - with open(hypo_outfile, "w") as h: - for key in range(len(ordered_hypos)): - t.write(ordered_targets[key]) - h.write(ordered_hypos[key]) - - res = scorer.result_string(4) - if write_hypos: - print(res) - score = rerank_utils.parse_bleu_scoring(res) - return score - - -def match_target_hypo(args, target_outfile, hypo_outfile): - """combine scores from the LM and bitext models, and write the top scoring hypothesis to a file""" - if len(args.weight1) == 1: - res = score_target_hypo( - args, - args.weight1[0], - args.weight2[0], - args.weight3[0], - args.lenpen[0], - target_outfile, - hypo_outfile, - True, - args.normalize, - ) - rerank_scores = [res] - else: - print("launching pool") - with Pool(32) as p: - rerank_scores = p.starmap( - score_target_hypo, - [ - ( - args, - args.weight1[i], - args.weight2[i], - args.weight3[i], - args.lenpen[i], - target_outfile, - hypo_outfile, - False, - args.normalize, - ) - for i in range(len(args.weight1)) - ], - ) - - if len(rerank_scores) > 1: - best_index = np.argmax(rerank_scores) - best_score = rerank_scores[best_index] - print("best score", best_score) - print("best lenpen", args.lenpen[best_index]) - print("best weight1", args.weight1[best_index]) - print("best weight2", args.weight2[best_index]) - print("best weight3", args.weight3[best_index]) - return ( - args.lenpen[best_index], - args.weight1[best_index], - args.weight2[best_index], - args.weight3[best_index], - best_score, - ) - - else: - return ( - args.lenpen[0], - args.weight1[0], - args.weight2[0], - args.weight3[0], - rerank_scores[0], - ) - - -def load_score_files(args): - if args.all_shards: - shard_ids = list(range(args.num_shards)) - else: - shard_ids = [args.shard_id] - - gen_output_lst = [] - bitext1_lst = [] - bitext2_lst = [] - lm_res1_lst = [] - - for shard_id in shard_ids: - using_nbest = args.nbest_list is not None - ( - pre_gen, - left_to_right_preprocessed_dir, - right_to_left_preprocessed_dir, - backwards_preprocessed_dir, - lm_preprocessed_dir, - ) = rerank_utils.get_directories( - args.data_dir_name, - args.num_rescore, - args.gen_subset, - args.gen_model_name, - shard_id, - args.num_shards, - args.sampling, - args.prefix_len, - args.target_prefix_frac, - args.source_prefix_frac, - ) - - rerank1_is_gen = ( - args.gen_model == args.score_model1 and args.source_prefix_frac is None - ) - rerank2_is_gen = ( - args.gen_model == args.score_model2 and args.source_prefix_frac is None - ) - - score1_file = rerank_utils.rescore_file_name( - pre_gen, - args.prefix_len, - args.model1_name, - target_prefix_frac=args.target_prefix_frac, - source_prefix_frac=args.source_prefix_frac, - backwards=args.backwards1, - ) - if args.score_model2 is not None: - score2_file = rerank_utils.rescore_file_name( - pre_gen, - args.prefix_len, - args.model2_name, - target_prefix_frac=args.target_prefix_frac, - source_prefix_frac=args.source_prefix_frac, - backwards=args.backwards2, - ) - if args.language_model is not None: - lm_score_file = rerank_utils.rescore_file_name( - pre_gen, args.prefix_len, args.lm_name, lm_file=True - ) - - # get gen output - predictions_bpe_file = pre_gen + "/generate_output_bpe.txt" - if using_nbest: - print("Using predefined n-best list from interactive.py") - predictions_bpe_file = args.nbest_list - gen_output = rerank_utils.BitextOutputFromGen( - predictions_bpe_file, - bpe_symbol=args.post_process, - nbest=using_nbest, - prefix_len=args.prefix_len, - target_prefix_frac=args.target_prefix_frac, - ) - - if rerank1_is_gen: - bitext1 = gen_output - else: - bitext1 = rerank_utils.BitextOutput( - score1_file, - args.backwards1, - args.right_to_left1, - args.post_process, - args.prefix_len, - args.target_prefix_frac, - args.source_prefix_frac, - ) - - if args.score_model2 is not None or args.nbest_list is not None: - if rerank2_is_gen: - bitext2 = gen_output - else: - bitext2 = rerank_utils.BitextOutput( - score2_file, - args.backwards2, - args.right_to_left2, - args.post_process, - args.prefix_len, - args.target_prefix_frac, - args.source_prefix_frac, - ) - - assert ( - bitext2.source_lengths == bitext1.source_lengths - ), "source lengths for rescoring models do not match" - assert ( - bitext2.target_lengths == bitext1.target_lengths - ), "target lengths for rescoring models do not match" - else: - if args.diff_bpe: - assert args.score_model2 is None - bitext2 = gen_output - else: - bitext2 = None - - if args.language_model is not None: - lm_res1 = rerank_utils.LMOutput( - lm_score_file, - args.lm_dict, - args.prefix_len, - args.post_process, - args.target_prefix_frac, - ) - else: - lm_res1 = None - - gen_output_lst.append(gen_output) - bitext1_lst.append(bitext1) - bitext2_lst.append(bitext2) - lm_res1_lst.append(lm_res1) - return gen_output_lst, bitext1_lst, bitext2_lst, lm_res1_lst - - -def rerank(args): - if type(args.lenpen) is not list: - args.lenpen = [args.lenpen] - if type(args.weight1) is not list: - args.weight1 = [args.weight1] - if type(args.weight2) is not list: - args.weight2 = [args.weight2] - if type(args.weight3) is not list: - args.weight3 = [args.weight3] - if args.all_shards: - shard_ids = list(range(args.num_shards)) - else: - shard_ids = [args.shard_id] - - for shard_id in shard_ids: - ( - pre_gen, - left_to_right_preprocessed_dir, - right_to_left_preprocessed_dir, - backwards_preprocessed_dir, - lm_preprocessed_dir, - ) = rerank_utils.get_directories( - args.data_dir_name, - args.num_rescore, - args.gen_subset, - args.gen_model_name, - shard_id, - args.num_shards, - args.sampling, - args.prefix_len, - args.target_prefix_frac, - args.source_prefix_frac, - ) - rerank_generate.gen_and_reprocess_nbest(args) - rerank_score_bw.score_bw(args) - rerank_score_lm.score_lm(args) - - if args.write_hypos is None: - write_targets = pre_gen + "/matched_targets" - write_hypos = pre_gen + "/matched_hypos" - else: - write_targets = args.write_hypos + "_targets" + args.gen_subset - write_hypos = args.write_hypos + "_hypos" + args.gen_subset - - if args.all_shards: - write_targets += "_all_shards" - write_hypos += "_all_shards" - - ( - best_lenpen, - best_weight1, - best_weight2, - best_weight3, - best_score, - ) = match_target_hypo(args, write_targets, write_hypos) - - return best_lenpen, best_weight1, best_weight2, best_weight3, best_score - - -def cli_main(): - parser = rerank_options.get_reranking_parser() - args = options.parse_args_and_arch(parser) - rerank(args) - - -if __name__ == "__main__": - cli_main() diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md deleted file mode 100644 index 9d3d7b67dc08440b5f4d1c5a7ffcd4bd6e76c14f..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/nonautoregressive_translation/scripts.md +++ /dev/null @@ -1,179 +0,0 @@ -# Examples of Training scripts for Non-autoregressive Machine Translation models - -### Non-autoregressive Transformer (NAT, Gu et al., 2017) -Note that we need to have an additional module to perform "length prediction" (`--length-loss-factor`) before generating the whole sequence. -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch nonautoregressive_transformer \ - --noise full_mask \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --pred-length-offset \ - --length-loss-factor 0.1 \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` - -### Fast Structured Decoding for Sequence Models (NAT-CRF, Sun et al., 2019) -Note that we implemented a low-rank appromixated CRF model by setting `--crf-lowrank-approx=32` and `--crf-beam-approx=64` as discribed in the original paper. All other settings are the same as the vanilla NAT model. -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch nacrf_transformer \ - --noise full_mask \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --pred-length-offset \ - --length-loss-factor 0.1 \ - --word-ins-loss-factor 0.5 \ - --crf-lowrank-approx 32 \ - --crf-beam-approx 64 \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` - - -### Non-autoregressive Transformer with Iterative Refinement (iNAT, Lee et al., 2018) -Note that `--train-step` means how many iterations of refinement we used during training, and `--dae-ratio` controls the ratio of denoising auto-encoder training described in the original paper. -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch iterative_nonautoregressive_transformer \ - --noise full_mask \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --pred-length-offset \ - --length-loss-factor 0.1 \ - --train-step 4 \ - --dae-ratio 0.5 \ - --stochastic-approx \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` - -### Insertion Transformer (InsT, Stern et al., 2019) -Note that we need to specify the "slot-loss" (uniform or balanced tree) described in the original paper. Here we use `--label-tau` to control the temperature. - -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch insertion_transformer \ - --noise random_delete \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` - - -### Mask Predict (CMLM, Ghazvininejad et al., 2019) -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch cmlm_transformer \ - --noise random_mask \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` - - - - -### Levenshtein Transformer (LevT, Gu et al., 2019) -```bash -fairseq-train \ - data-bin/wmt14_en_de_distill \ - --save-dir checkpoints \ - --ddp-backend=legacy_ddp \ - --task translation_lev \ - --criterion nat_loss \ - --arch levenshtein_transformer \ - --noise random_delete \ - --share-all-embeddings \ - --optimizer adam --adam-betas '(0.9,0.98)' \ - --lr 0.0005 --lr-scheduler inverse_sqrt \ - --stop-min-lr '1e-09' --warmup-updates 10000 \ - --warmup-init-lr '1e-07' --label-smoothing 0.1 \ - --dropout 0.3 --weight-decay 0.01 \ - --decoder-learned-pos \ - --encoder-learned-pos \ - --apply-bert-init \ - --log-format 'simple' --log-interval 100 \ - --fixed-validation-seed 7 \ - --max-tokens 8000 \ - --save-interval-updates 10000 \ - --max-update 300000 -``` diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py deleted file mode 100644 index 8b7eb2ec4fc5190c4dcdfe34b0259e6f448e18a9..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/__init__.py +++ /dev/null @@ -1,128 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -"""isort:skip_file""" - -from .dictionary import Dictionary, TruncatedDictionary - -from .fairseq_dataset import FairseqDataset, FairseqIterableDataset - -from .base_wrapper_dataset import BaseWrapperDataset - -from .add_target_dataset import AddTargetDataset -from .append_token_dataset import AppendTokenDataset -from .audio.raw_audio_dataset import BinarizedAudioDataset, FileAudioDataset -from .audio.hubert_dataset import HubertDataset -from .backtranslation_dataset import BacktranslationDataset -from .bucket_pad_length_dataset import BucketPadLengthDataset -from .colorize_dataset import ColorizeDataset -from .concat_dataset import ConcatDataset -from .concat_sentences_dataset import ConcatSentencesDataset -from .denoising_dataset import DenoisingDataset -from .id_dataset import IdDataset -from .indexed_dataset import ( - IndexedCachedDataset, - IndexedDataset, - IndexedRawTextDataset, - MMapIndexedDataset, -) -from .language_pair_dataset import LanguagePairDataset -from .list_dataset import ListDataset -from .lm_context_window_dataset import LMContextWindowDataset -from .lru_cache_dataset import LRUCacheDataset -from .mask_tokens_dataset import MaskTokensDataset -from .monolingual_dataset import MonolingualDataset -from .multi_corpus_sampled_dataset import MultiCorpusSampledDataset -from .nested_dictionary_dataset import NestedDictionaryDataset -from .noising import NoisingDataset -from .numel_dataset import NumelDataset -from .num_samples_dataset import NumSamplesDataset -from .offset_tokens_dataset import OffsetTokensDataset -from .pad_dataset import LeftPadDataset, PadDataset, RightPadDataset -from .prepend_dataset import PrependDataset -from .prepend_token_dataset import PrependTokenDataset -from .raw_label_dataset import RawLabelDataset -from .replace_dataset import ReplaceDataset -from .resampling_dataset import ResamplingDataset -from .roll_dataset import RollDataset -from .round_robin_zip_datasets import RoundRobinZipDatasets -from .sort_dataset import SortDataset -from .strip_token_dataset import StripTokenDataset -from .subsample_dataset import SubsampleDataset -from .token_block_dataset import TokenBlockDataset -from .transform_eos_dataset import TransformEosDataset -from .transform_eos_lang_pair_dataset import TransformEosLangPairDataset -from .shorten_dataset import TruncateDataset, RandomCropDataset -from .multilingual.sampled_multi_dataset import SampledMultiDataset -from .multilingual.sampled_multi_epoch_dataset import SampledMultiEpochDataset -from .fasta_dataset import FastaDataset, EncodedFastaDataset - -from .iterators import ( - CountingIterator, - EpochBatchIterator, - GroupedIterator, - ShardedIterator, -) - -__all__ = [ - "AddTargetDataset", - "AppendTokenDataset", - "BacktranslationDataset", - "BaseWrapperDataset", - "BinarizedAudioDataset", - "BucketPadLengthDataset", - "ColorizeDataset", - "ConcatDataset", - "ConcatSentencesDataset", - "CountingIterator", - "DenoisingDataset", - "Dictionary", - "EncodedFastaDataset", - "EpochBatchIterator", - "FairseqDataset", - "FairseqIterableDataset", - "FastaDataset", - "FileAudioDataset", - "GroupedIterator", - "HubertDataset", - "IdDataset", - "IndexedCachedDataset", - "IndexedDataset", - "IndexedRawTextDataset", - "LanguagePairDataset", - "LeftPadDataset", - "ListDataset", - "LMContextWindowDataset", - "LRUCacheDataset", - "MaskTokensDataset", - "MMapIndexedDataset", - "MonolingualDataset", - "MultiCorpusSampledDataset", - "NestedDictionaryDataset", - "NoisingDataset", - "NumelDataset", - "NumSamplesDataset", - "OffsetTokensDataset", - "PadDataset", - "PrependDataset", - "PrependTokenDataset", - "RandomCropDataset", - "RawLabelDataset", - "ResamplingDataset", - "ReplaceDataset", - "RightPadDataset", - "RollDataset", - "RoundRobinZipDatasets", - "SampledMultiDataset", - "SampledMultiEpochDataset", - "ShardedIterator", - "SortDataset", - "StripTokenDataset", - "SubsampleDataset", - "TokenBlockDataset", - "TransformEosDataset", - "TransformEosLangPairDataset", - "TruncateDataset", - "TruncatedDictionary", -] diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py deleted file mode 100644 index 11c7dc1ea966a54f8915ef164377e40f90e851a1..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/simultaneous_translation.py +++ /dev/null @@ -1,42 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -from fairseq.tasks import register_task -from fairseq.tasks.speech_to_text import SpeechToTextTask -from fairseq.tasks.translation import ( - TranslationTask, TranslationConfig -) - -try: - import examples.simultaneous_translation # noqa - import_successful = True -except BaseException: - import_successful = False - - -logger = logging.getLogger(__name__) - - -def check_import(flag): - if not flag: - raise ImportError( - "'examples.simultaneous_translation' is not correctly imported. " - "Please considering `pip install -e $FAIRSEQ_DIR`." - ) - - -@register_task("simul_speech_to_text") -class SimulSpeechToTextTask(SpeechToTextTask): - def __init__(self, args, tgt_dict): - check_import(import_successful) - super().__init__(args, tgt_dict) - - -@register_task("simul_text_to_text", dataclass=TranslationConfig) -class SimulTextToTextTask(TranslationTask): - def __init__(self, cfg, src_dict, tgt_dict): - check_import(import_successful) - super().__init__(cfg, src_dict, tgt_dict) diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md deleted file mode 100644 index 57104230655c7c517d25904e634c53b6159ee60f..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/textless_nlp/gslm/unit2speech/README.md +++ /dev/null @@ -1,42 +0,0 @@ -# Unit to Speech Model (unit2speech) - -Unit to speech model is modified Tacotron2 model that learns to synthesize speech from discrete speech units. All models are trained on quantized [LJSpeech](https://keithito.com/LJ-Speech-Dataset/). - -Upstream Units | Download Link -|-|- -Log Mel Filterbank + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km50/tts_checkpoint_best.pt) -Log Mel Filterbank + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km100/tts_checkpoint_best.pt) -Log Mel Filterbank + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km200/tts_checkpoint_best.pt) -Log Mel Filterbank + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/logmel/tts_km500/tts_checkpoint_best.pt) -Modified CPC + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km50/tts_checkpoint_best.pt) -Modified CPC + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km100/tts_checkpoint_best.pt) -Modified CPC + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km200/tts_checkpoint_best.pt) -Modified CPC + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/cpc/tts_km500/tts_checkpoint_best.pt) -HuBERT Base + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km50/tts_checkpoint_best.pt) -HuBERT Base + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km100/tts_checkpoint_best.pt) -HuBERT Base + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km200/tts_checkpoint_best.pt) -HuBERT Base + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/hubert/tts_km500/tts_checkpoint_best.pt) -wav2vec 2.0 Large + KM50 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km50/tts_checkpoint_best.pt) -wav2vec 2.0 Large + KM100 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km100/tts_checkpoint_best.pt) -wav2vec 2.0 Large + KM200 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km200/tts_checkpoint_best.pt) -wav2vec 2.0 Large + KM500 | [download](https://dl.fbaipublicfiles.com/textless_nlp/gslm/w2v2/tts_km500/tts_checkpoint_best.pt) - -## Run inference using a unit2speech model -* Install librosa, unidecode and inflect using `pip install librosa, unidecode, inflect` -* Download [Waveglow checkpoint](https://dl.fbaipublicfiles.com/textless_nlp/gslm/waveglow_256channels_new.pt). This is the vocoder. - -Sample commnd to run inference using trained unit2speech models. Please note that the quantized audio to synthesized should be using the same units as the unit2speech model was trained with. -``` -FAIRSEQ_ROOT=
- --TTS_MODEL_PATH= -QUANTIZED_UNIT_PATH= -OUT_DIR= -WAVEGLOW_PATH= - -PYTHONPATH=${FAIRSEQ_ROOT}:${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech python ${FAIRSEQ_ROOT}/examples/textless_nlp/gslm/unit2speech/synthesize_audio_from_units.py \ - --tts_model_path $TTS_MODEL_PATH \ - --quantized_unit_path $QUANTIZED_UNIT_PATH \ - --out_audio_dir $OUT_DIR \ - --waveglow_path $WAVEGLOW_PATH \ - --max_decoder_steps 2000 -``` \ No newline at end of file diff --git a/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py b/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py deleted file mode 100644 index 375e4d50872d5c68ee57ca17470a2ca425425eba..0000000000000000000000000000000000000000 --- a/spaces/ORI-Muchim/BlueArchiveTTS/text/japanese.py +++ /dev/null @@ -1,153 +0,0 @@ -import re -from unidecode import unidecode -import pyopenjtalk - - -# Regular expression matching Japanese without punctuation marks: -_japanese_characters = re.compile( - r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]') - -# Regular expression matching non-Japanese characters or punctuation marks: -_japanese_marks = re.compile( - r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]') - -# List of (symbol, Japanese) pairs for marks: -_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('％', 'パーセント') -]] - -# List of (romaji, ipa) pairs for marks: -_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('ts', 'ʦ'), - ('u', 'ɯ'), - ('j', 'ʥ'), - ('y', 'j'), - ('ni', 'n^i'), - ('nj', 'n^'), - ('hi', 'çi'), - ('hj', 'ç'), - ('f', 'ɸ'), - ('I', 'i*'), - ('U', 'ɯ*'), - ('r', 'ɾ') -]] - -# List of (romaji, ipa2) pairs for marks: -_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('u', 'ɯ'), - ('ʧ', 'tʃ'), - ('j', 'dʑ'), - ('y', 'j'), - ('ni', 'n^i'), - ('nj', 'n^'), - ('hi', 'çi'), - ('hj', 'ç'), - ('f', 'ɸ'), - ('I', 'i*'), - ('U', 'ɯ*'), - ('r', 'ɾ') -]] - -# List of (consonant, sokuon) pairs: -_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [ - (r'Q([↑↓]*[kg])', r'k#\1'), - (r'Q([↑↓]*[tdjʧ])', r't#\1'), - (r'Q([↑↓]*[sʃ])', r's\1'), - (r'Q([↑↓]*[pb])', r'p#\1') -]] - -# List of (consonant, hatsuon) pairs: -_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [ - (r'N([↑↓]*[pbm])', r'm\1'), - (r'N([↑↓]*[ʧʥj])', r'n^\1'), - (r'N([↑↓]*[tdn])', r'n\1'), - (r'N([↑↓]*[kg])', r'ŋ\1') -]] - - -def symbols_to_japanese(text): - for regex, replacement in _symbols_to_japanese: - text = re.sub(regex, replacement, text) - return text - - -def japanese_to_romaji_with_accent(text): - '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html''' - text = symbols_to_japanese(text) - sentences = re.split(_japanese_marks, text) - marks = re.findall(_japanese_marks, text) - text = '' - for i, sentence in enumerate(sentences): - if re.match(_japanese_characters, sentence): - if text != '': - text += ' ' - labels = pyopenjtalk.extract_fullcontext(sentence) - for n, label in enumerate(labels): - phoneme = re.search(r'\-([^\+]*)\+', label).group(1) - if phoneme not in ['sil', 'pau']: - text += phoneme.replace('ch', 'ʧ').replace('sh', - 'ʃ').replace('cl', 'Q') - else: - continue - # n_moras = int(re.search(r'/F:(\d+)_', label).group(1)) - a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1)) - a2 = int(re.search(r"\+(\d+)\+", label).group(1)) - a3 = int(re.search(r"\+(\d+)/", label).group(1)) - if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']: - a2_next = -1 - else: - a2_next = int( - re.search(r"\+(\d+)\+", labels[n + 1]).group(1)) - # Accent phrase boundary - if a3 == 1 and a2_next == 1: - text += ' ' - # Falling - elif a1 == 0 and a2_next == a2 + 1: - text += '↓' - # Rising - elif a2 == 1 and a2_next == 2: - text += '↑' - if i < len(marks): - text += unidecode(marks[i]).replace(' ', '') - return text - - -def get_real_sokuon(text): - for regex, replacement in _real_sokuon: - text = re.sub(regex, replacement, text) - return text - - -def get_real_hatsuon(text): - for regex, replacement in _real_hatsuon: - text = re.sub(regex, replacement, text) - return text - - -def japanese_to_ipa(text): - text = japanese_to_romaji_with_accent(text).replace('...', '…') - text = re.sub( - r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text) - text = get_real_sokuon(text) - text = get_real_hatsuon(text) - for regex, replacement in _romaji_to_ipa: - text = re.sub(regex, replacement, text) - return text - - -def japanese_to_ipa2(text): - text = japanese_to_romaji_with_accent(text).replace('...', '…') - text = get_real_sokuon(text) - text = get_real_hatsuon(text) - for regex, replacement in _romaji_to_ipa2: - text = re.sub(regex, replacement, text) - return text - - -def japanese_to_ipa3(text): - text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace( - 'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a') - text = re.sub( - r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text) - text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text) - return text diff --git a/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py b/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py deleted file mode 100644 index c215099fedacae35c6798fdd9b8420a447aa16bb..0000000000000000000000000000000000000000 --- a/spaces/Omnibus/MusicGen/tests/quantization/test_vq.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import torch - -from audiocraft.quantization.vq import ResidualVectorQuantizer - - -class TestResidualVectorQuantizer: - - def test_rvq(self): - x = torch.randn(1, 16, 2048) - vq = ResidualVectorQuantizer(n_q=8, dimension=16, bins=8) - res = vq(x, 1.) - assert res.x.shape == torch.Size([1, 16, 2048]) diff --git a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py b/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py deleted file mode 100644 index feb7a8222487756d38482da95183bbbcbbe96ed9..0000000000000000000000000000000000000000 --- a/spaces/OpenGVLab/InternGPT/iGPT/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/modeling/dense_heads/centernet.py +++ /dev/null @@ -1,864 +0,0 @@ - -import math -import json -import copy -from typing import List, Dict -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F - -from detectron2.modeling.proposal_generator.build import PROPOSAL_GENERATOR_REGISTRY -from detectron2.layers import ShapeSpec, cat -from detectron2.structures import Instances, Boxes -from detectron2.modeling import detector_postprocess -from detectron2.utils.comm import get_world_size -from detectron2.config import configurable - -from ..layers.heatmap_focal_loss import heatmap_focal_loss_jit -from ..layers.heatmap_focal_loss import binary_heatmap_focal_loss -from ..layers.iou_loss import IOULoss -from ..layers.ml_nms import ml_nms -from ..debug import debug_train, debug_test -from .utils import reduce_sum, _transpose -from .centernet_head import CenterNetHead - -__all__ = ["CenterNet"] - -INF = 100000000 - -@PROPOSAL_GENERATOR_REGISTRY.register() -class CenterNet(nn.Module): - @configurable - def __init__(self, - # input_shape: Dict[str, ShapeSpec], - in_channels=256, - *, - num_classes=80, - in_features=("p3", "p4", "p5", "p6", "p7"), - strides=(8, 16, 32, 64, 128), - score_thresh=0.05, - hm_min_overlap=0.8, - loc_loss_type='giou', - min_radius=4, - hm_focal_alpha=0.25, - hm_focal_beta=4, - loss_gamma=2.0, - reg_weight=2.0, - not_norm_reg=True, - with_agn_hm=False, - only_proposal=False, - as_proposal=False, - not_nms=False, - pos_weight=1., - neg_weight=1., - sigmoid_clamp=1e-4, - ignore_high_fp=-1., - center_nms=False, - sizes_of_interest=[[0,80],[64,160],[128,320],[256,640],[512,10000000]], - more_pos=False, - more_pos_thresh=0.2, - more_pos_topk=9, - pre_nms_topk_train=1000, - pre_nms_topk_test=1000, - post_nms_topk_train=100, - post_nms_topk_test=100, - nms_thresh_train=0.6, - nms_thresh_test=0.6, - no_reduce=False, - debug=False, - vis_thresh=0.5, - pixel_mean=[103.530,116.280,123.675], - pixel_std=[1.0,1.0,1.0], - device='cuda', - centernet_head=None, - ): - super().__init__() - self.num_classes = num_classes - self.in_features = in_features - self.strides = strides - self.score_thresh = score_thresh - self.min_radius = min_radius - self.hm_focal_alpha = hm_focal_alpha - self.hm_focal_beta = hm_focal_beta - self.loss_gamma = loss_gamma - self.reg_weight = reg_weight - self.not_norm_reg = not_norm_reg - self.with_agn_hm = with_agn_hm - self.only_proposal = only_proposal - self.as_proposal = as_proposal - self.not_nms = not_nms - self.pos_weight = pos_weight - self.neg_weight = neg_weight - self.sigmoid_clamp = sigmoid_clamp - self.ignore_high_fp = ignore_high_fp - self.center_nms = center_nms - self.sizes_of_interest = sizes_of_interest - self.more_pos = more_pos - self.more_pos_thresh = more_pos_thresh - self.more_pos_topk = more_pos_topk - self.pre_nms_topk_train = pre_nms_topk_train - self.pre_nms_topk_test = pre_nms_topk_test - self.post_nms_topk_train = post_nms_topk_train - self.post_nms_topk_test = post_nms_topk_test - self.nms_thresh_train = nms_thresh_train - self.nms_thresh_test = nms_thresh_test - self.no_reduce = no_reduce - self.debug = debug - self.vis_thresh = vis_thresh - if self.center_nms: - self.not_nms = True - self.iou_loss = IOULoss(loc_loss_type) - assert (not self.only_proposal) or self.with_agn_hm - # delta for rendering heatmap - self.delta = (1 - hm_min_overlap) / (1 + hm_min_overlap) - if centernet_head is None: - self.centernet_head = CenterNetHead( - in_channels=in_channels, - num_levels=len(in_features), - with_agn_hm=with_agn_hm, - only_proposal=only_proposal) - else: - self.centernet_head = centernet_head - if self.debug: - pixel_mean = torch.Tensor(pixel_mean).to( - torch.device(device)).view(3, 1, 1) - pixel_std = torch.Tensor(pixel_std).to( - torch.device(device)).view(3, 1, 1) - self.denormalizer = lambda x: x * pixel_std + pixel_mean - - @classmethod - def from_config(cls, cfg, input_shape): - ret = { - # 'input_shape': input_shape, - 'in_channels': input_shape[ - cfg.MODEL.CENTERNET.IN_FEATURES[0]].channels, - 'num_classes': cfg.MODEL.CENTERNET.NUM_CLASSES, - 'in_features': cfg.MODEL.CENTERNET.IN_FEATURES, - 'strides': cfg.MODEL.CENTERNET.FPN_STRIDES, - 'score_thresh': cfg.MODEL.CENTERNET.INFERENCE_TH, - 'loc_loss_type': cfg.MODEL.CENTERNET.LOC_LOSS_TYPE, - 'hm_min_overlap': cfg.MODEL.CENTERNET.HM_MIN_OVERLAP, - 'min_radius': cfg.MODEL.CENTERNET.MIN_RADIUS, - 'hm_focal_alpha': cfg.MODEL.CENTERNET.HM_FOCAL_ALPHA, - 'hm_focal_beta': cfg.MODEL.CENTERNET.HM_FOCAL_BETA, - 'loss_gamma': cfg.MODEL.CENTERNET.LOSS_GAMMA, - 'reg_weight': cfg.MODEL.CENTERNET.REG_WEIGHT, - 'not_norm_reg': cfg.MODEL.CENTERNET.NOT_NORM_REG, - 'with_agn_hm': cfg.MODEL.CENTERNET.WITH_AGN_HM, - 'only_proposal': cfg.MODEL.CENTERNET.ONLY_PROPOSAL, - 'as_proposal': cfg.MODEL.CENTERNET.AS_PROPOSAL, - 'not_nms': cfg.MODEL.CENTERNET.NOT_NMS, - 'pos_weight': cfg.MODEL.CENTERNET.POS_WEIGHT, - 'neg_weight': cfg.MODEL.CENTERNET.NEG_WEIGHT, - 'sigmoid_clamp': cfg.MODEL.CENTERNET.SIGMOID_CLAMP, - 'ignore_high_fp': cfg.MODEL.CENTERNET.IGNORE_HIGH_FP, - 'center_nms': cfg.MODEL.CENTERNET.CENTER_NMS, - 'sizes_of_interest': cfg.MODEL.CENTERNET.SOI, - 'more_pos': cfg.MODEL.CENTERNET.MORE_POS, - 'more_pos_thresh': cfg.MODEL.CENTERNET.MORE_POS_THRESH, - 'more_pos_topk': cfg.MODEL.CENTERNET.MORE_POS_TOPK, - 'pre_nms_topk_train': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TRAIN, - 'pre_nms_topk_test': cfg.MODEL.CENTERNET.PRE_NMS_TOPK_TEST, - 'post_nms_topk_train': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TRAIN, - 'post_nms_topk_test': cfg.MODEL.CENTERNET.POST_NMS_TOPK_TEST, - 'nms_thresh_train': cfg.MODEL.CENTERNET.NMS_TH_TRAIN, - 'nms_thresh_test': cfg.MODEL.CENTERNET.NMS_TH_TEST, - 'no_reduce': cfg.MODEL.CENTERNET.NO_REDUCE, - 'debug': cfg.DEBUG, - 'vis_thresh': cfg.VIS_THRESH, - 'pixel_mean': cfg.MODEL.PIXEL_MEAN, - 'pixel_std': cfg.MODEL.PIXEL_STD, - 'device': cfg.MODEL.DEVICE, - 'centernet_head': CenterNetHead( - cfg, [input_shape[f] for f in cfg.MODEL.CENTERNET.IN_FEATURES]), - } - return ret - - - def forward(self, images, features_dict, gt_instances): - features = [features_dict[f] for f in self.in_features] - clss_per_level, reg_pred_per_level, agn_hm_pred_per_level = \ - self.centernet_head(features) - grids = self.compute_grids(features) - shapes_per_level = grids[0].new_tensor( - [(x.shape[2], x.shape[3]) for x in reg_pred_per_level]) - - if not self.training: - return self.inference( - images, clss_per_level, reg_pred_per_level, - agn_hm_pred_per_level, grids) - else: - pos_inds, labels, reg_targets, flattened_hms = \ - self._get_ground_truth( - grids, shapes_per_level, gt_instances) - # logits_pred: M x F, reg_pred: M x 4, agn_hm_pred: M - logits_pred, reg_pred, agn_hm_pred = self._flatten_outputs( - clss_per_level, reg_pred_per_level, agn_hm_pred_per_level) - - if self.more_pos: - # add more pixels as positive if \ - # 1. they are within the center3x3 region of an object - # 2. their regression losses are small (
- -VIVADATA | {{iris_type}} - - - - - - - - - - - --- - diff --git a/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py b/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py deleted file mode 100644 index 3706c57541c1b7d9004957422b52cd1e2191ae68..0000000000000000000000000000000000000000 --- a/spaces/Salesforce/EDICT/my_diffusers/utils/dummy_scipy_objects.py +++ /dev/null @@ -1,11 +0,0 @@ -# This file is autogenerated by the command `make fix-copies`, do not edit. -# flake8: noqa - -from ..utils import DummyObject, requires_backends - - -class LMSDiscreteScheduler(metaclass=DummyObject): - _backends = ["scipy"] - - def __init__(self, *args, **kwargs): - requires_backends(self, ["scipy"]) diff --git a/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py b/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py deleted file mode 100644 index 2e25dd918f2714ae9a2c21d14401663c371b5f38..0000000000000000000000000000000000000000 --- a/spaces/ScottRobertsXR/image-captioning-01/vit_gpt2/modeling_flax_vit_gpt2.py +++ /dev/null @@ -1,704 +0,0 @@ -from typing import Callable, Optional, Tuple - -import flax.linen as nn -import jax -import jax.numpy as jnp -from flax.core.frozen_dict import FrozenDict, unfreeze -from jax import lax -from jax.random import PRNGKey -from transformers import GPT2Config, FlaxViTModel, ViTConfig -from transformers.modeling_flax_outputs import ( - FlaxCausalLMOutputWithCrossAttentions, - FlaxSeq2SeqLMOutput, - FlaxSeq2SeqModelOutput, -) -from transformers.models.bart.modeling_flax_bart import ( - shift_tokens_right, -) -from .modeling_flax_gpt2 import ( - FlaxGPT2Module, - FlaxGPT2Model, - FlaxPreTrainedModel -) -from transformers.models.vit.modeling_flax_vit import FlaxViTModule - -from .configuration_vit_gpt2 import ViTGPT2Config - - -class FlaxViTGPT2Module(nn.Module): - config: ViTGPT2Config - dtype: jnp.dtype = jnp.float32 # the dtype of the computation - - def setup(self): - - self.encoder = FlaxViTModule(self.config.vit_config, dtype=self.dtype) - self.decoder = FlaxGPT2Module(self.config.gpt2_config, dtype=self.dtype) - - def _get_encoder_module(self): - return self.encoder - - def _get_decoder_module(self): - return self.decoder - - def __call__( - self, - pixel_values, - input_ids, - attention_mask, - position_ids, - encoder_attention_mask: Optional[jnp.ndarray] = None, - output_attentions: bool = False, - output_hidden_states: bool = False, - return_dict: bool = True, - deterministic: bool = True, - ): - encoder_outputs = self.encoder( - pixel_values=pixel_values, - deterministic=deterministic, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - decoder_outputs = self.decoder( - input_ids=input_ids, - attention_mask=attention_mask, - position_ids=position_ids, - encoder_hidden_states=encoder_outputs[0], - encoder_attention_mask=encoder_attention_mask, - deterministic=deterministic, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict - ) - - return FlaxSeq2SeqModelOutput( - last_hidden_state=decoder_outputs.last_hidden_state, - decoder_hidden_states=decoder_outputs.hidden_states, - decoder_attentions=decoder_outputs.attentions, - cross_attentions=decoder_outputs.cross_attentions, - encoder_last_hidden_state=encoder_outputs.last_hidden_state, - encoder_hidden_states=encoder_outputs.hidden_states, - encoder_attentions=encoder_outputs.attentions, - ) - - -class FlaxViTGPT2ForConditionalGenerationModule(nn.Module): - config: ViTGPT2Config - dtype: jnp.dtype = jnp.float32 - bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros - - def setup(self): - self.model = FlaxViTGPT2Module(config=self.config, dtype=self.dtype) - self.lm_head = nn.Dense( - self.model.decoder.embed_dim, - use_bias=False, - dtype=self.dtype, - kernel_init=jax.nn.initializers.normal( - self.config.gpt2_config.initializer_range, self.dtype - ), - ) - self.final_logits_bias = self.param( - "final_logits_bias", self.bias_init, (1, self.model.decoder.embed_dim) - ) - - def _get_encoder_module(self): - return self.model.encoder - - def _get_decoder_module(self): - return self.model.decoder - - def __call__( - self, - pixel_values, - input_ids, - attention_mask, - position_ids, - encoder_attention_mask: Optional[jnp.ndarray] = None, - output_attentions: bool = False, - output_hidden_states: bool = False, - return_dict: bool = True, - deterministic: bool = True, - ): - outputs = self.model( - pixel_values=pixel_values, - input_ids=input_ids, - attention_mask=attention_mask, - position_ids=position_ids, - encoder_attention_mask=encoder_attention_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - deterministic=deterministic, - ) - - hidden_states = outputs[0] - lm_logits = self.lm_head(hidden_states) - lm_logits += self.final_logits_bias - - if not return_dict: - output = (lm_logits,) + outputs[1:] - return output - - return FlaxSeq2SeqLMOutput( - logits=lm_logits, - decoder_hidden_states=outputs.decoder_hidden_states, - decoder_attentions=outputs.decoder_attentions, - cross_attentions=outputs.cross_attentions, - encoder_last_hidden_state=outputs.encoder_last_hidden_state, - encoder_hidden_states=outputs.encoder_hidden_states, - encoder_attentions=outputs.encoder_attentions, - ) - -class FlaxViTGPT2PreTrainedModel(FlaxPreTrainedModel): - config_class = ViTGPT2Config - base_model_prefix: str = "model" - module_class: nn.Module = None - - def __init__( - self, - config: ViTGPT2Config, - input_shape: Tuple = None, - seed: int = 0, - dtype: jnp.dtype = jnp.float32, - **kwargs, - ): - if input_shape is None: - input_shape = ( - (1, config.vit_config.image_size, config.vit_config.image_size, 3), - (1, 1), - ) - - module = self.module_class(config=config, dtype=dtype, **kwargs) - super().__init__( - config, module, input_shape=input_shape, seed=seed, dtype=dtype - ) - - def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple) -> FrozenDict: - # init input tensors - pixel_values = jax.random.normal(rng, input_shape[0]) - # # make sure initialization pass will work for FlaxBartForSequenceClassificationModule - # input_ids = jax.ops.index_update(input_ids, (..., -1), self.config.eos_token_id) - - input_ids = jnp.zeros(input_shape[1], dtype="i4") - attention_mask = jnp.ones_like(input_ids) - - batch_size, sequence_length = input_ids.shape - position_ids = jnp.broadcast_to( - jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) - ) - - params_rng, dropout_rng = jax.random.split(rng) - rngs = {"params": params_rng, "dropout": dropout_rng} - - return self.module.init( - rngs, - pixel_values, - input_ids, - attention_mask, - position_ids, - )["params"] - - def init_cache(self, batch_size, max_length, encoder_outputs): - - input_ids = jnp.ones((batch_size, max_length), dtype="i4") - attention_mask = jnp.ones_like(input_ids) - position_ids = jnp.broadcast_to( - jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), - input_ids.shape, - ) - - def _decoder_forward( - module, - input_ids, - attention_mask, - position_ids, - **kwargs, - ): - decoder_module = module._get_decoder_module() - return decoder_module( - input_ids, - attention_mask, - position_ids, - **kwargs, - ) - - init_variables = self.module.init( - jax.random.PRNGKey(0), - input_ids=input_ids, - attention_mask=attention_mask, - position_ids=position_ids, - encoder_hidden_states=encoder_outputs[0], - init_cache=True, - method=_decoder_forward, # we only need to call the decoder to init the cache - ) - return unfreeze(init_variables["cache"]) - - def encode( - self, - pixel_values: jnp.ndarray, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - train: bool = False, - params: dict = None, - dropout_rng: PRNGKey = None, - ): - output_attentions = ( - output_attentions - if output_attentions is not None - else self.config.output_attentions - ) - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None - else self.config.output_hidden_states - ) - return_dict = ( - return_dict if return_dict is not None else self.config.return_dict - ) - - # Handle any PRNG if needed - rngs = {} - if dropout_rng is not None: - rngs["dropout"] = dropout_rng - - def _encoder_forward(module, pixel_values, **kwargs): - encode_module = module._get_encoder_module() - return encode_module(pixel_values, **kwargs) - - return self.module.apply( - {"params": params or self.params}, - pixel_values=jnp.array(pixel_values, dtype="i4"), - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - deterministic=not train, - rngs=rngs, - method=_encoder_forward, - ) - - def decode( - self, - input_ids, - encoder_outputs, - encoder_attention_mask: Optional[jnp.ndarray] = None, - attention_mask: Optional[jnp.ndarray] = None, - position_ids: Optional[jnp.ndarray] = None, - past_key_values: dict = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - train: bool = False, - params: dict = None, - dropout_rng: PRNGKey = None, - ): - - output_attentions = ( - output_attentions - if output_attentions is not None - else self.config.output_attentions - ) - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None - else self.config.output_hidden_states - ) - return_dict = ( - return_dict if return_dict is not None else self.config.return_dict - ) - - encoder_hidden_states = encoder_outputs[0] - if encoder_attention_mask is None: - batch_size, sequence_length = encoder_hidden_states.shape[:2] - encoder_attention_mask = jnp.ones((batch_size, sequence_length)) - - batch_size, sequence_length = input_ids.shape - if attention_mask is None: - attention_mask = jnp.ones((batch_size, sequence_length)) - - if position_ids is None: - if past_key_values is not None: - raise ValueError( - "Make sure to provide `position_ids` when passing `past_key_values`." - ) - - position_ids = jnp.broadcast_to( - jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) - ) - - # Handle any PRNG if needed - rngs = {} - if dropout_rng is not None: - rngs["dropout"] = dropout_rng - - inputs = {"params": params or self.params} - - # if past_key_values are passed then cache is already initialized a private flag init_cache has to be - # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that - # it can be changed by FlaxGPT2Attention module - if past_key_values: - inputs["cache"] = past_key_values - mutable = ["cache"] - else: - mutable = False - - def _decoder_forward( - module, - input_ids, - attention_mask, - position_ids, - **kwargs, - ): - decoder_module = module._get_decoder_module() - return decoder_module( - input_ids, - attention_mask, - position_ids, - **kwargs, - ) - - outputs = self.module.apply( - inputs, - input_ids=jnp.array(input_ids, dtype="i4"), - attention_mask=jnp.array(attention_mask, dtype="i4"), - position_ids=jnp.array(position_ids, dtype="i4"), - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - deterministic=not train, - rngs=rngs, - mutable=mutable, - method=_decoder_forward, - ) - - # add updated cache to model output - if past_key_values is not None and return_dict: - outputs, past = outputs - outputs["past_key_values"] = unfreeze(past["cache"]) - return outputs - elif past_key_values is not None and not return_dict: - outputs, past = outputs - outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] - - return outputs - - def __call__( - self, - pixel_values: jnp.ndarray, - input_ids: Optional[jnp.ndarray] = None, - attention_mask: Optional[jnp.ndarray] = None, - position_ids: Optional[jnp.ndarray] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - train: bool = False, - params: dict = None, - dropout_rng: PRNGKey = None, - ): - output_attentions = ( - output_attentions - if output_attentions is not None - else self.config.output_attentions - ) - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None - else self.config.output_hidden_states - ) - return_dict = ( - return_dict if return_dict is not None else self.config.return_dict - ) - - pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) - - # # prepare encoder inputs - # if encoder_attention_mask is None: - # encoder_attention_mask = jnp.ones_like(input_ids) - - # if position_ids is None: - # batch_size, sequence_length = input_ids.shape - # position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) - - # prepare decoder inputs - # if decoder_input_ids is None: - # decoder_input_ids = shift_tokens_right( - # input_ids, self.config.pad_token_id, decoder_start_token_id=self.config.decoder_start_token_id - # ) # TODO: Check how to use this - - if attention_mask is None: - attention_mask = jnp.ones_like(input_ids) - if position_ids is None: - batch_size, sequence_length = input_ids.shape - position_ids = jnp.broadcast_to( - jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) - ) - - # Handle any PRNG if needed - rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} - - return self.module.apply( - {"params": params or self.params}, - pixel_values=jnp.array(pixel_values, dtype=jnp.float32), - input_ids=jnp.array(input_ids, dtype="i4"), - attention_mask=jnp.array(attention_mask, dtype="i4"), - position_ids=jnp.array(position_ids, dtype="i4"), - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - deterministic=not train, - rngs=rngs, - ) - - -class FlaxViTGPT2ForConditionalGeneration(FlaxViTGPT2PreTrainedModel): - module_class = FlaxViTGPT2ForConditionalGenerationModule - dtype: jnp.dtype = jnp.float32 - - def decode( - self, - input_ids, - encoder_outputs, - encoder_attention_mask: Optional[jnp.ndarray] = None, - attention_mask: Optional[jnp.ndarray] = None, - position_ids: Optional[jnp.ndarray] = None, - past_key_values: dict = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - deterministic: bool = True, - params: dict = None, - dropout_rng: PRNGKey = None, - ): - output_attentions = ( - output_attentions - if output_attentions is not None - else self.config.output_attentions - ) - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None - else self.config.output_hidden_states - ) - return_dict = ( - return_dict if return_dict is not None else self.config.return_dict - ) - - encoder_hidden_states = encoder_outputs[0] - if encoder_attention_mask is None: - batch_size, sequence_length = encoder_hidden_states.shape[:2] - encoder_attention_mask = jnp.ones((batch_size, sequence_length)) - - batch_size, sequence_length = input_ids.shape - if attention_mask is None: - attention_mask = jnp.ones((batch_size, sequence_length)) - - if position_ids is None: - if past_key_values is not None: - raise ValueError( - "Make sure to provide `position_ids` when passing `past_key_values`." - ) - - position_ids = jnp.broadcast_to( - jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) - ) - - # Handle any PRNG if needed - rngs = {} - if dropout_rng is not None: - rngs["dropout"] = dropout_rng - - inputs = {"params": params or self.params} - - # if past_key_values are passed then cache is already initialized a private flag init_cache has to be - # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that - # it can be changed by FlaxGPT2Attention module - if past_key_values: - inputs["cache"] = past_key_values - mutable = ["cache"] - else: - mutable = False - - def _decoder_forward( - module, - input_ids, - attention_mask, - position_ids, - **kwargs, - ): - decoder_module = module._get_decoder_module() - outputs = decoder_module( - input_ids, - attention_mask, - position_ids, - **kwargs, - ) - hidden_states = outputs[0] - - if self.config.tie_word_embeddings: - shared_embedding = module.model.variables["params"]["shared"][ - "embedding" - ] - lm_logits = module.lm_head.apply( - {"params": {"kernel": shared_embedding.T}}, hidden_states - ) - else: - lm_logits = module.lm_head(hidden_states) - - lm_logits += module.final_logits_bias - return lm_logits, outputs - - outputs = self.module.apply( - inputs, - input_ids=jnp.array(input_ids, dtype="i4"), - attention_mask=jnp.array(attention_mask, dtype="i4"), - position_ids=jnp.array(position_ids, dtype="i4"), - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - deterministic=deterministic, - rngs=rngs, - mutable=mutable, - method=_decoder_forward, - ) - - if past_key_values is None: - lm_logits, outputs = outputs - else: - (lm_logits, outputs), past = outputs - - if return_dict: - outputs = FlaxCausalLMOutputWithCrossAttentions( - logits=lm_logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - cross_attentions=outputs.cross_attentions, - ) - else: - outputs = (lm_logits,) + outputs[1:] - - # add updated cache to model output - if past_key_values is not None and return_dict: - outputs["past_key_values"] = unfreeze(past["cache"]) - return outputs - elif past_key_values is not None and not return_dict: - outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] - - return outputs - - def prepare_inputs_for_generation( - self, - input_ids, - max_length, - encoder_attention_mask: Optional[jnp.DeviceArray] = None, - attention_mask: Optional[jnp.DeviceArray] = None, - encoder_outputs=None, - **kwargs, - ): - # initializing the cache - batch_size, seq_length = input_ids.shape - - past_key_values = self.init_cache(batch_size, max_length, encoder_outputs) - # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. - # But since the decoder uses a causal mask, those positions are masked anyways. - # Thus we can create a single static attention_mask here, which is more efficient for compilation - extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") - if attention_mask is not None: - position_ids = attention_mask.cumsum(axis=-1) - 1 - extended_attention_mask = lax.dynamic_update_slice( - extended_attention_mask, attention_mask, (0, 0) - ) - else: - position_ids = jnp.broadcast_to( - jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length) - ) - - return { - "past_key_values": past_key_values, - "encoder_outputs": encoder_outputs, - "encoder_attention_mask": encoder_attention_mask, - "attention_mask": extended_attention_mask, - "position_ids": position_ids, - } - - def update_inputs_for_generation(self, model_outputs, model_kwargs): - model_kwargs["past_key_values"] = model_outputs.past_key_values - model_kwargs["position_ids"] = ( - model_kwargs["position_ids"][:, -1:] + 1 - ) - return model_kwargs - - @classmethod - def from_vit_gpt2_pretrained( - cls, - vit_model_name_or_path: str = None, - gpt2_model_name_or_path: str = None, - *model_args, - **kwargs, - ) -> FlaxViTGPT2PreTrainedModel: - - kwargs_gpt2 = { - argument[len("gpt2_") :]: value - for argument, value in kwargs.items() - if argument.startswith("gpt2_") - } - - kwargs_vit = { - argument[len("vit_") :]: value - for argument, value in kwargs.items() - if argument.startswith("vit_") - } - - # remove gpt2, vit kwargs from kwargs - for key in kwargs_gpt2.keys(): - del kwargs["gpt2_" + key] - for key in kwargs_vit.keys(): - del kwargs["vit_" + key] - - # Load and initialize the gpt2 and vit model - gpt2_model = kwargs_gpt2.pop("model", None) - if gpt2_model is None: - assert ( - gpt2_model_name_or_path is not None - ), "If `model` is not defined as an argument, a `gpt2_model_name_or_path` has to be defined" - - if "config" not in kwargs_gpt2: - gpt2_config = GPT2Config.from_pretrained(gpt2_model_name_or_path) - kwargs_gpt2["config"] = gpt2_config - - kwargs_gpt2["config"].add_cross_attention = True - gpt2_model = FlaxGPT2Model.from_pretrained( - gpt2_model_name_or_path, *model_args, **kwargs_gpt2 - ) - - vit_model = kwargs_vit.pop("model", None) - if vit_model is None: - assert ( - vit_model_name_or_path is not None - ), "If `model` is not defined as an argument, a `vit_model_name_or_path` has to be defined" - - if "config" not in kwargs_vit: - vit_config = ViTConfig.from_pretrained(vit_model_name_or_path) - kwargs_vit["config"] = vit_config - - vit_model = FlaxViTModel.from_pretrained( - vit_model_name_or_path, *model_args, **kwargs_vit - ) - - # instantiate config with corresponding kwargs - dtype = kwargs.pop("dtype", jnp.float32) - config = ViTGPT2Config.from_vit_gpt2_configs( - vit_model.config, gpt2_model.config, **kwargs - ) - - # init model - model = cls(config, *model_args, dtype=dtype, **kwargs) - model.params["model"]["encoder"] = vit_model.params - model.params["model"]["decoder"] = gpt2_model.params - - return model - diff --git a/spaces/Seetha/IMA-pipeline-streamlit/app.py b/spaces/Seetha/IMA-pipeline-streamlit/app.py deleted file mode 100644 index f0db4170922ebf1b9a612db7fac952e61207154a..0000000000000000000000000000000000000000 --- a/spaces/Seetha/IMA-pipeline-streamlit/app.py +++ /dev/null @@ -1,581 +0,0 @@ -# import all packages -import requests -import streamlit as st -from sklearn.model_selection import StratifiedKFold -from sklearn.model_selection import train_test_split -from sklearn.model_selection import KFold -# tokenizer -from transformers import AutoTokenizer, DistilBertTokenizerFast -# sequence tagging model + training-related -from transformers import DistilBertForTokenClassification, Trainer, TrainingArguments -import torch -import sys -import os -from sklearn.metrics import classification_report -from pandas import read_csv -from sklearn.linear_model import LogisticRegression -import sklearn.model_selection -from sklearn.feature_extraction.text import TfidfTransformer -from sklearn.feature_extraction.text import CountVectorizer -from sklearn.pipeline import Pipeline, FeatureUnion -import math -# from sklearn.metrics import accuracy_score -# from sklearn.metrics import precision_recall_fscore_support -import json -import re -import numpy as np -import pandas as pd -import nltk -nltk.download("punkt") -import string -from sklearn.model_selection import train_test_split -from transformers import AutoTokenizer, Trainer, TrainingArguments, AutoModelForSequenceClassification, AutoConfig -from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler -import itertools -from transformers import TextClassificationPipeline, TFAutoModelForSequenceClassification, AutoTokenizer -from transformers import pipeline -import pickle -import csv -import pdfplumber -import pathlib -import shutil -import webbrowser -from streamlit.components.v1 import html -import streamlit.components.v1 as components -from PyPDF2 import PdfReader -from huggingface_hub import HfApi -import io -from datasets import load_dataset -import time - -import huggingface_hub -from huggingface_hub import Repository -from datetime import datetime -import pathlib as Path -from requests import get -import urllib.request -# import gradio as gr -# from gradio import inputs, outputs -from datasets import load_dataset -from huggingface_hub import HfApi, list_models -import os -from huggingface_hub import HfFileSystem -from tensorflow.keras.models import Sequential, model_from_json -#import tensorflow_datasets as tfds -import tensorflow as tf -from tensorflow.keras.preprocessing.sequence import pad_sequences -import spacy -from tensorflow.keras.preprocessing.text import Tokenizer -#from spacy import en_core_web_lg -#import en_core_web_lg -#nlp = en_core_web_lg.load() -nlp = spacy.load('en_core_web_sm') - -#tfds.disable_progress_bar() -MAX_SEQUENCE_LENGTH = 500 - -# dataset = load_dataset('Seetha/Visualization', streaming=True) -# df = pd.DataFrame.from_dict(dataset['train']) -# DATASET_REPO_URL = "https://huggingface.co/datasets/Seetha/Visualization" -# DATA_FILENAME = "level2.json" -#DATA_FILE = os.path.join("data", DATA_FILENAME) -DATASET_REPO_URL = "https://huggingface.co/datasets/Seetha/visual_files" -DATA_FILENAME = "detailedResults.json" -DATA_FILENAME1 = "level2.json" - -HF_TOKEN = os.environ.get("HF_TOKEN") -#st.write("is none?", HF_TOKEN is None) - -def main(): - - st.title("Text to Causal Knowledge Graph") - st.sidebar.title("Please upload your text documents in one file here:") - k=2 - seed = 1 - k1= 5 - text_list = [] - causal_sents = [] - - uploaded_file = None - try: - uploaded_file = st.sidebar.file_uploader("Choose a file", type = "pdf") - except: - uploaded_file = PdfReader('sample_anno.pdf') - st.error("Please upload your own PDF to be analyzed") - - if uploaded_file is not None: - reader = PdfReader(uploaded_file) - for page in reader.pages: - text = page.extract_text() - text_list.append(text) - else: - st.error("Please upload your own PDF to be analyzed") - st.stop() - - text_list_final = [x.replace('\n', '') for x in text_list] - text_list_final = re.sub('"', '', str(text_list_final)) - - sentences = nltk.sent_tokenize(text_list_final) - - result =[] - for i in sentences: - result1 = i.lower() - result2 = re.sub(r'[^\w\s]','',result1) - result.append(result2) - - #st.write("--- %s seconds ---" % (time.time() - start_time)) - tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") #bert-base-uncased - - model_path = "checkpoint-2850" - - model = AutoModelForSequenceClassification.from_pretrained(model_path,id2label={0:'non-causal',1:'causal'}) - - #st.write('sequence classification loaded') - pipe1 = pipeline("text-classification", model=model,tokenizer=tokenizer) - for sent in result: - pred = pipe1(sent) - for lab in pred: - if lab['label'] == 'causal': #causal - causal_sents.append(sent) - - # st.write('causal sentence classification finished') - # st.write("--- %s seconds ---" % (time.time() - start_time)) - - model_name = "distilbert-base-cased" - tokenizer = DistilBertTokenizerFast.from_pretrained(model_name,low_cpu_mem_usage=True) - - model_path1 = "DistilBertforTokenclassification" - - model = DistilBertForTokenClassification.from_pretrained(model_path1,low_cpu_mem_usage=True) #len(unique_tags),, num_labels= 7, , id2label={0:'CT',1:'E',2:'C',3:'O'} - pipe = pipeline('ner', model=model, tokenizer=tokenizer,aggregation_strategy='simple') #grouped_entities=True - st.write('DistilBERT loaded') - sentence_pred = [] - class_list = [] - entity_list = [] - for k in causal_sents: - pred= pipe(k) - #st.write(pred) - #st.write('preds') - for i in pred: - sentence_pred.append(k) - class_list.append(i['word']) - entity_list.append(i['entity_group']) - - # st.write('causality extraction finished') - # st.write("--- %s seconds ---" % (time.time() - start_time)) - - # filename = 'Checkpoint-classification.sav' - # loaded_model = pickle.load(open(filename, 'rb')) - # loaded_vectorizer = pickle.load(open('vectorizefile_classification.pickle', 'rb')) - - # pipeline_test_output = loaded_vectorizer.transform(class_list) - # predicted = loaded_model.predict(pipeline_test_output) - - tokenizer = Tokenizer(num_words=100000) - tokenizer.fit_on_texts(class_list) - word_index = tokenizer.word_index - # text_embedding = np.zeros((len(word_index) + 1, 300)) - # for word, i in word_index.items(): - # text_embedding[i] = nlp(word).vector - json_file = open('model.json', 'r') - loaded_model_json = json_file.read() - json_file.close() - loaded_model = model_from_json(loaded_model_json) - # load weights into new model - loaded_model.load_weights("model.h5") - - loss = tf.keras.losses.CategoricalCrossentropy() #from_logits=True - loaded_model.compile(loss=loss,optimizer=tf.keras.optimizers.Adam(1e-4)) - - predictions = loaded_model.predict(pad_sequences(tokenizer.texts_to_sequences(class_list),maxlen=MAX_SEQUENCE_LENGTH)) - predicted = np.argmax(predictions,axis=1) - - st.write(predictions) - st.write(predicted) - # st.write('stakeholder taxonomy finished') - # st.write("--- %s seconds ---" % (time.time() - start_time)) - pred1 = predicted - level0 = [] - count =0 - for i in predicted: - if i == 3: - level0.append('Non-Performance') - count +=1 - else: - level0.append('Performance') - count +=1 - - list_pred = {0: 'Customers',1:'Employees',2:'Investors',3:'Non-performance',4:'Society',5:'Unclassified'} - pred_val = [list_pred[i] for i in pred1] - - #print('count',count) - for ind,(sent,preds) in enumerate(zip(class_list,pred_val)): - if 'customers' in sent or 'client' in sent or 'consumer' in sent or 'user' in sent: - pred_val[ind] = 'Customers' - elif 'investor' in sent or 'finance' in sent or 'shareholder' in sent or 'stockholder' in sent or 'owners' in sent: - pred_val[ind] = 'Investors' - elif 'employee' in sent or 'worker' in sent or 'staff' in sent: - pred_val[ind] = 'Employees' - elif 'society' in sent or 'societal' in sent or 'social responsib*' in sent or 'social performance' in sent or 'community' in sent: - pred_val[ind] = 'Society' - - sent_id, unique = pd.factorize(sentence_pred) - - final_list = pd.DataFrame( - {'Id': sent_id, - 'Fullsentence': sentence_pred, - 'Component': class_list, - 'causeOrEffect': entity_list, - 'Labellevel1': level0, - 'Labellevel2': pred_val - }) - s = final_list['Component'].shift(-1) - m = s.str.startswith('##', na=False) - final_list.loc[m, 'Component'] += (' ' + s[m]) - - - final_list1 = final_list[~final_list['Component'].astype(str).str.startswith('##')] - li = [] - uni = final_list1['Id'].unique() - for i in uni: - df_new = final_list1[final_list1['Id'] == i] - uni1 = df_new['Id'].unique() - - # if 'E' not in df_new.values: - # li.append(uni1) - # out = np.concatenate(li).ravel() - # li_pan = pd.DataFrame(out,columns=['Id']) - # df3 = pd.merge(final_list1, li_pan[['Id']], on='Id', how='left', indicator=True) \ - # .query("_merge == 'left_only'") \ - # .drop("_merge",axis=1) - - df3 = final_list1 - #df = df3.groupby(['Id','Fullsentence','causeOrEffect', 'Labellevel1', 'Labellevel2'])['Component'].apply(', '.join).reset_index() - #st.write(df) - - #df = df3 - df3["causeOrEffect"].replace({"C": "cause", "E": "effect"}, inplace=True) - df_final = df3[df3['causeOrEffect'] != 'CT'] - df3['New string'] = df_final['Component'].replace(r'[##]+', ' ', regex=True) - - df_final = df_final.drop("Component",axis=1) - df_final.insert(2, "Component", df3['New string'], True) - - df_final1 = df_final[df_final['Component'].str.split().str.len().gt(1)] - #st.write(df_final[df_final['Component'].str.len() != 1]) - #df_final1.to_csv('predictions.csv') - -# buffer = io.BytesIO() -# with pd.ExcelWriter(buffer, engine="xlsxwriter") as writer: -# df_final.to_excel(writer, sheet_name="Sheet1", index=False) -# writer.close() - - count_NP_NP = 0 - count_NP_investor = 0 - count_NP_customer = 0 - count_NP_employees = 0 - count_NP_society = 0 - - count_inv_np = 0 - count_inv_investor = 0 - count_inv_customer = 0 - count_inv_employee = 0 - count_inv_society = 0 - - count_cus_np = 0 - count_cus_investor = 0 - count_cus_customer = 0 - count_cus_employee = 0 - count_cus_society = 0 - - count_emp_np = 0 - count_emp_investor = 0 - count_emp_customer = 0 - count_emp_employee = 0 - count_emp_society = 0 - - count_soc_np = 0 - count_soc_investor = 0 - count_soc_customer = 0 - count_soc_employee = 0 - count_soc_society = 0 - for i in range(0,df_final['Id'].max()): - j = df_final.loc[df_final['Id'] == i] - cause_tab = j.loc[j['causeOrEffect'] == 'cause'] - effect_tab = j.loc[j['causeOrEffect'] == 'effect'] - cause_coun_NP = (cause_tab.Labellevel2 == 'Non-performance').sum() - effect_coun_NP = (effect_tab.Labellevel2 == 'Non-performance').sum() - - if (cause_coun_NP > 0) and (effect_coun_NP > 0): - count_NP = cause_coun_NP if cause_coun_NP >= effect_coun_NP else effect_coun_NP - else: - count_NP = 0 - effect_NP_inv = (effect_tab.Labellevel2 == 'Investors').sum() - if (cause_coun_NP > 0) and (effect_NP_inv > 0): - count_NP_inv = cause_coun_NP if cause_coun_NP >= effect_NP_inv else effect_NP_inv - else: - count_NP_inv = 0 - effect_NP_cus = (effect_tab.Labellevel2 == 'Customers').sum() - if (cause_coun_NP > 0) and (effect_NP_cus > 0): - count_NP_cus = cause_coun_NP if cause_coun_NP >= effect_NP_cus else effect_NP_cus - else: - count_NP_cus = 0 - effect_NP_emp = (effect_tab.Labellevel2 == 'Employees').sum() - if (cause_coun_NP > 0) and (effect_NP_emp > 0): - count_NP_emp = cause_coun_NP if cause_coun_NP >= effect_NP_emp else effect_NP_emp - else: - count_NP_emp = 0 - effect_NP_soc = (effect_tab.Labellevel2 == 'Society').sum() - if (cause_coun_NP > 0) and (effect_NP_soc > 0): - count_NP_soc = cause_coun_NP if cause_coun_NP >= effect_NP_soc else effect_NP_soc - else: - count_NP_soc = 0 - - cause_coun_inv = (cause_tab.Labellevel2 == 'Investors').sum() - effect_coun_inv = (effect_tab.Labellevel2 == 'Non-performance').sum() - if (cause_coun_inv > 0) and (effect_coun_inv > 0): - count_NP_inv = cause_coun_inv if cause_coun_inv >= effect_coun_inv else effect_coun_inv - else: - count_NP_inv = 0 - - effect_inv_inv = (effect_tab.Labellevel2 == 'Investors').sum() - if (cause_coun_inv > 0) and (effect_inv_inv > 0): - count_inv_inv = cause_coun_inv if cause_coun_inv >= effect_inv_inv else effect_inv_inv - else: - count_inv_inv = 0 - effect_inv_cus = (effect_tab.Labellevel2 == 'Customers').sum() - if (cause_coun_inv > 0) and (effect_inv_cus > 0): - count_inv_cus = cause_coun_inv if cause_coun_inv >= effect_inv_cus else effect_inv_cus - else: - count_inv_cus = 0 - effect_inv_emp = (effect_tab.Labellevel2 == 'Employees').sum() - if (cause_coun_inv > 0) and (effect_inv_emp > 0): - count_inv_emp = cause_coun_inv if cause_coun_inv >= effect_inv_emp else effect_inv_emp - else: - count_inv_emp = 0 - - effect_inv_soc = (effect_tab.Labellevel2 == 'Society').sum() - if (cause_coun_inv > 0) and (effect_inv_soc > 0): - count_inv_soc = cause_coun_inv if cause_coun_inv >= effect_inv_soc else effect_inv_soc - else: - count_inv_soc = 0 - - cause_coun_cus = (cause_tab.Labellevel2 == 'Customers').sum() - effect_coun_cus = (effect_tab.Labellevel2 == 'Non-performance').sum() - if (cause_coun_cus > 0) and (effect_coun_cus > 0): - count_NP_cus = cause_coun_cus if cause_coun_cus >= effect_coun_cus else effect_coun_cus - else: - count_NP_cus = 0 - - effect_cus_inv = (effect_tab.Labellevel2 == 'Investors').sum() - if (cause_coun_cus > 0) and (effect_cus_inv > 0): - count_cus_inv = cause_coun_cus if cause_coun_cus >= effect_cus_inv else effect_cus_inv - else: - count_cus_inv = 0 - - effect_cus_cus = (effect_tab.Labellevel2 == 'Customers').sum() - if (cause_coun_cus > 0) and (effect_cus_cus > 0): - count_cus_cus = cause_coun_cus if cause_coun_cus >= effect_cus_cus else effect_cus_cus - else: - count_cus_cus = 0 - - effect_cus_emp = (effect_tab.Labellevel2 == 'Employees').sum() - if (cause_coun_cus > 0) and (effect_cus_emp > 0): - count_cus_emp = cause_coun_cus if cause_coun_cus >= effect_cus_emp else effect_cus_emp - else: - count_cus_emp = 0 - - effect_cus_soc = (effect_tab.Labellevel2 == 'Society').sum() - if (cause_coun_cus > 0) and (effect_cus_soc > 0): - count_cus_soc = cause_coun_cus if cause_coun_cus >= effect_cus_soc else effect_cus_soc - else: - count_cus_soc = 0 - - cause_coun_emp = (cause_tab.Labellevel2 == 'Employees').sum() - effect_coun_emp = (effect_tab.Labellevel2 == 'Non-performance').sum() - if (cause_coun_emp > 0) and (effect_coun_emp > 0): - count_NP_emp = cause_coun_emp if cause_coun_emp >= effect_coun_emp else effect_coun_emp - else: - count_NP_emp = 0 - - effect_emp_inv = (effect_tab.Labellevel2 == 'Investors').sum() - if (cause_coun_emp > 0) and (effect_emp_inv > 0): - count_emp_inv = cause_coun_emp if cause_coun_emp >= effect_emp_inv else effect_emp_inv - else: - count_emp_inv = 0 - - effect_emp_cus = (effect_tab.Labellevel2 == 'Customers').sum() - if (cause_coun_emp > 0) and (effect_emp_cus > 0): - count_emp_cus = cause_coun_emp if cause_coun_emp >= effect_emp_cus else effect_emp_cus - else: - count_emp_cus = 0 - - effect_emp_emp = (effect_tab.Labellevel2 == 'Employees').sum() - if (cause_coun_emp > 0) and (effect_emp_emp > 0): - count_emp_emp = cause_coun_emp if cause_coun_emp >= effect_emp_emp else effect_emp_emp - else: - count_emp_emp = 0 - - effect_emp_soc = (effect_tab.Labellevel2 == 'Society').sum() - if (cause_coun_emp > 0) and (effect_emp_soc > 0): - count_emp_soc = cause_coun_emp if cause_coun_emp >= effect_emp_soc else effect_emp_soc - else: - count_emp_soc = 0 - - cause_coun_soc = (cause_tab.Labellevel2 == 'Society').sum() - effect_coun_soc = (effect_tab.Labellevel2 == 'Non-performance').sum() - if (cause_coun_soc > 0) and (effect_coun_soc > 0): - count_NP_soc = cause_coun_soc if cause_coun_soc >= effect_coun_soc else effect_coun_soc - else: - count_NP_soc = 0 - - effect_soc_inv = (effect_tab.Labellevel2 == 'Investors').sum() - if (cause_coun_soc > 0) and (effect_soc_inv > 0): - count_soc_inv = cause_coun_soc if cause_coun_soc >= effect_soc_inv else effect_soc_inv - else: - count_soc_inv = 0 - - effect_soc_cus = (effect_tab.Labellevel2 == 'Customers').sum() - if (cause_coun_soc > 0) and (effect_soc_cus > 0): - count_soc_cus = cause_coun_soc if cause_coun_soc >= effect_soc_cus else effect_soc_cus - else: - count_soc_cus = 0 - - effect_soc_emp = (effect_tab.Labellevel2 == 'Employees').sum() - if (cause_coun_soc > 0) and (effect_soc_emp > 0): - count_soc_emp = cause_coun_soc if cause_coun_soc >= effect_soc_emp else effect_soc_emp - else: - count_soc_emp = 0 - - effect_soc_soc = (effect_tab.Labellevel2 == 'Society').sum() - if (cause_coun_soc > 0) and (effect_soc_soc > 0): - count_soc_soc = cause_coun_soc if cause_coun_soc >= effect_soc_soc else effect_soc_soc - else: - count_soc_soc = 0 - - count_NP_NP = count_NP_NP + count_NP - count_NP_investor = count_NP_investor + count_NP_inv - count_NP_customer = count_NP_customer + count_NP_cus - count_NP_employees = count_NP_employees + count_NP_emp - count_NP_society = count_NP_society + count_NP_soc - - count_inv_np = count_inv_np + count_NP_inv - count_inv_investor = count_inv_investor + count_inv_inv - count_inv_customer = count_inv_customer + count_inv_cus - count_inv_employee = count_inv_employee + count_inv_emp - count_inv_society = count_inv_society + count_inv_soc - - count_cus_np = count_cus_np + count_NP_cus - count_cus_investor = count_cus_investor + count_cus_inv - count_cus_customer = count_cus_customer + count_cus_cus - count_cus_employee = count_cus_employee + count_cus_emp - count_cus_society = count_cus_society + count_cus_soc - - count_emp_np = count_emp_np + count_NP_emp - count_emp_investor = count_emp_investor + count_emp_inv - count_emp_customer = count_emp_customer + count_emp_cus - count_emp_employee = count_emp_employee + count_emp_emp - count_emp_society = count_emp_society + count_emp_soc - - count_soc_np = count_soc_np + count_NP_soc - count_soc_investor = count_soc_investor + count_soc_inv - count_soc_customer = count_soc_customer + count_soc_cus - count_soc_employee = count_soc_employee + count_soc_emp - count_soc_society = count_soc_society + count_soc_soc - - df_tab = pd.DataFrame(columns = ['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'],index=['Non-performance', 'Investors', 'Customers', 'Employees', 'Society'], dtype=object) - - df_tab.loc['Non-performance'] = [count_NP_NP, count_NP_investor, count_NP_customer, count_NP_employees, count_NP_society] - df_tab.loc['Investors'] = [count_inv_np, count_inv_investor, count_inv_customer, count_inv_employee, count_inv_society] - df_tab.loc['Customers'] = [count_cus_np, count_cus_investor, count_cus_customer, count_cus_employee, count_cus_society] - df_tab.loc['Employees'] = [count_emp_np, count_emp_investor, count_emp_customer, count_emp_employee, count_emp_society] - df_tab.loc['Society'] = [count_soc_np, count_soc_investor, count_soc_customer, count_soc_employee, count_soc_society] - - -# df_tab = pd.DataFrame({ -# 'Non-performance': [count_NP_NP, count_NP_investor, count_NP_customer, count_NP_employees, count_NP_society], -# 'Investors': [count_inv_np, count_inv_investor, count_inv_customer, count_inv_employee, count_inv_society], -# 'Customers': [count_cus_np, count_cus_investor, count_cus_customer, count_cus_employee, count_cus_society], -# 'Employees': [count_emp_np, count_emp_investor, count_emp_customer, count_emp_employee, count_emp_society], -# 'Society': [count_soc_np, count_soc_investor, count_soc_customer, count_soc_employee, count_soc_society]}, -# index=['Non-performance', 'Investors', 'Customers', 'Employees', 'Society']) - - #df_tab.to_csv('final_data.csv') - - buffer = io.BytesIO() - with pd.ExcelWriter(buffer, engine="xlsxwriter") as writer: - df_tab.to_excel(writer,sheet_name="count_result",index=False) - df_final1.to_excel(writer,sheet_name="Detailed_results",index=False) - writer.close() - #df = pd.read_csv('final_data.csv', index_col=0) -#474-515 - # Convert to JSON format - json_data = [] - for row in df_tab.index: - for col in df_tab.columns: - json_data.append({ - 'source': row, - 'target': col, - 'value': int(df_tab.loc[row, col]) - }) - - HfApi().delete_file(path_in_repo = DATA_FILENAME1 ,repo_id = 'Seetha/visual_files',token= HF_TOKEN,repo_type='dataset') - #st.write('file-deleted') - fs = HfFileSystem(token=HF_TOKEN) - with fs.open('datasets/Seetha/visual_files/level2.json', 'w') as f: - json.dump(json_data, f) - - df_final1.to_csv('predictions.csv') - csv_file = "predictions.csv" - json_file = "detailedResults.json" - - # Open the CSV file and read the data - with open(csv_file, "r") as f: - csv_data = csv.DictReader(f) - - # # Convert the CSV data to a list of dictionaries - data_list = [] - for row in csv_data: - data_list.append(dict(row)) - - # # Convert the list of dictionaries to JSON - json_data = json.dumps(data_list) - - HfApi().delete_file(path_in_repo = DATA_FILENAME ,repo_id = 'Seetha/visual_files',token= HF_TOKEN,repo_type='dataset') - #st.write('file2-deleted') - with fs.open('datasets/Seetha/visual_files/detailedResults.json','w') as fi: - #data = json.load(fi) - fi.write(json_data) - - def convert_df(df): - - #IMPORTANT: Cache the conversion to prevent computation on every rerun - - return df.to_csv().encode('utf-8') - - - - csv1 = convert_df(df_final1.astype(str)) - csv2 = convert_df(df_tab.astype(str)) - - with st.container(): - - st.download_button(label="Download the result table",data=buffer,file_name="t2cg_outputs.xlsx",mime="application/vnd.ms-excel") - st.markdown('Click this link in a separate tab to view knowledge graph', unsafe_allow_html=True) - # st.download_button(label="Download the detailed result table_csv",data=csv1,file_name='results.csv',mime='text/csv') - # st.download_button(label="Download the result table_csv",data=csv2,file_name='final_data.csv',mime='text/csv') - -#with st.container(): - # Execute your app - #st.title("Visualization example") -# components.html(source_code) - #html(my_html) - #webbrowser.open('https://huggingface.co/spaces/Seetha/visual-knowledgegraph') -# # embed streamlit docs in a streamlit app -# #components.iframe("https://webpages.charlotte.edu/ltotapal/") - - - -if __name__ == '__main__': - start_time = time.time() - main() diff --git a/spaces/Shiro26/MendoBERT_RE/app.py b/spaces/Shiro26/MendoBERT_RE/app.py deleted file mode 100644 index 1bf256334d6604b5048b7f1054c9cb1915120f15..0000000000000000000000000000000000000000 --- a/spaces/Shiro26/MendoBERT_RE/app.py +++ /dev/null @@ -1,34 +0,0 @@ -import streamlit as st -from transformers import pipeline - -model = pipeline("text-classification", model="/home/user/app/MendoBERT/", tokenizer="indolem/indobert-base-uncased") -basemodel = pipeline("text-classification", model="/home/user/app/IndoLEM/", tokenizer="indolem/indobert-base-uncased") - -st.title(':blue[MendoBERT] - Relation Extraction :family:') - -if 'options' not in st.session_state: - st.session_state['options'] = "" - -def button1_callback(): - st.session_state['options'] = "Kami menyimpulkan bahwa polimorfisme @GENE$ dan AGT tidak berkontribusi pada kerentanan genetik terhadap @DISEASE$ dan retinopati pada populasi Kaukasia Mediterania." -def button2_callback(): - st.session_state['options'] = "Genotipe bayi PON1 RR dan @GENE$ CC dikaitkan dengan @DISEASE$ dalam populasi penelitian kami, yang menunjukkan kemungkinan peran variabilitas paraoxonase manusia dalam etiologi kelahiran prematur." - - -placeholder = st.empty() - -st.info("Please replace the gene and disease that you want to get their relation predicted with @GENE\$ and \@DISEASE\$", icon="ℹ️") - -st.caption('_Examples_') -st.button('Kami menyimpulkan bahwa polimorfisme \@GENE\$ dan AGT tidak berkontribusi pada kerentanan genetik terhadap \@DISEASE\$ dan retinopati pada populasi Kaukasia Mediterania.', use_container_width=True, on_click = button1_callback) -st.button('Genotipe bayi PON1 RR dan \@GENE\$ CC dikaitkan dengan \@DISEASE\$ dalam populasi penelitian kami, yang menunjukkan kemungkinan peran variabilitas paraoxonase manusia dalam etiologi kelahiran prematur.', use_container_width=True, on_click = button2_callback) - -with placeholder: - text = st.text_area('Enter some text: ', key = 'options') - -if text: - st.subheader('MendoBERT') - st.write(model(text)) - st.write("\n") - st.subheader('IndoLEM') - st.write(basemodel(text)) \ No newline at end of file diff --git a/spaces/Shredder/CONBERT-2/README.md b/spaces/Shredder/CONBERT-2/README.md deleted file mode 100644 index daf10e5da8dd31671c54c18d24b0e7d1d77b62f0..0000000000000000000000000000000000000000 --- a/spaces/Shredder/CONBERT-2/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: CONBERT 2 -emoji: 👁 -colorFrom: purple -colorTo: blue -sdk: gradio -sdk_version: 3.2 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Stearns/Soar/soar_io.py b/spaces/Stearns/Soar/soar_io.py deleted file mode 100644 index c05d97417b69a804b35207fb839957a91258a5d2..0000000000000000000000000000000000000000 --- a/spaces/Stearns/Soar/soar_io.py +++ /dev/null @@ -1,35 +0,0 @@ -from pysoarlib import * - -## DEFINE MAIN CONNECTOR CLASS - -class GeneralSoarIOConnector(AgentConnector): - def __init__(self, client): - AgentConnector.__init__(self, client) - client.add_print_event_handler(self.agent_print_collector) - # client.execute_command("output callbacks on") - # client.execute_command("output console on") - - self.agent_printout = "" - - def on_input_phase(self, input_link): - pass - - def on_init_soar(self): - self.reset() - - def on_output_event(self, command_name, root_id): - pass - - - def agent_print_collector(self, text): - self.agent_printout += text+"\n" - - def get_agent_output(self): - return self.agent_printout - - def reset(self): - self.agent_printout = "" - - - - diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py deleted file mode 100644 index 489b7c13d0ce82b769ba50412ecde0889d0b7851..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/utils/process.py +++ /dev/null @@ -1,69 +0,0 @@ -# encoding: utf-8 -""" -Utilities for working with external processes. -""" - -# Copyright (c) IPython Development Team. -# Distributed under the terms of the Modified BSD License. - - -import os -import shutil -import sys - -if sys.platform == 'win32': - from ._process_win32 import system, getoutput, arg_split, check_pid -elif sys.platform == 'cli': - from ._process_cli import system, getoutput, arg_split, check_pid -else: - from ._process_posix import system, getoutput, arg_split, check_pid - -from ._process_common import getoutputerror, get_output_error_code, process_handler - - -class FindCmdError(Exception): - pass - - -def find_cmd(cmd): - """Find absolute path to executable cmd in a cross platform manner. - - This function tries to determine the full path to a command line program - using `which` on Unix/Linux/OS X and `win32api` on Windows. Most of the - time it will use the version that is first on the users `PATH`. - - Warning, don't use this to find IPython command line programs as there - is a risk you will find the wrong one. Instead find those using the - following code and looking for the application itself:: - - import sys - argv = [sys.executable, '-m', 'IPython'] - - Parameters - ---------- - cmd : str - The command line program to look for. - """ - path = shutil.which(cmd) - if path is None: - raise FindCmdError('command could not be found: %s' % cmd) - return path - - -def abbrev_cwd(): - """ Return abbreviated version of cwd, e.g. d:mydir """ - cwd = os.getcwd().replace('\\','/') - drivepart = '' - tail = cwd - if sys.platform == 'win32': - if len(cwd) < 4: - return cwd - drivepart,tail = os.path.splitdrive(cwd) - - - parts = tail.split('/') - if len(parts) > 2: - tail = '/'.join(parts[-2:]) - - return (drivepart + ( - cwd == '/' and '/' or tail)) diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py deleted file mode 100644 index 37d3db222ef2c7920628971a92e863d9915514c6..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/altair/utils/plugin_registry.py +++ /dev/null @@ -1,228 +0,0 @@ -import sys -from typing import Any, Dict, List, Optional, Generic, TypeVar, cast -from types import TracebackType - -if sys.version_info >= (3, 8): - from importlib.metadata import entry_points -else: - from importlib_metadata import entry_points - -from toolz import curry - - -PluginType = TypeVar("PluginType") - - -class NoSuchEntryPoint(Exception): - def __init__(self, group, name): - self.group = group - self.name = name - - def __str__(self): - return f"No {self.name!r} entry point found in group {self.group!r}" - - -class PluginEnabler: - """Context manager for enabling plugins - - This object lets you use enable() as a context manager to - temporarily enable a given plugin:: - - with plugins.enable('name'): - do_something() # 'name' plugin temporarily enabled - # plugins back to original state - """ - - def __init__(self, registry: "PluginRegistry", name: str, **options): - self.registry = registry # type: PluginRegistry - self.name = name # type: str - self.options = options # type: Dict[str, Any] - self.original_state = registry._get_state() # type: Dict[str, Any] - self.registry._enable(name, **options) - - def __enter__(self) -> "PluginEnabler": - return self - - def __exit__(self, typ: type, value: Exception, traceback: TracebackType) -> None: - self.registry._set_state(self.original_state) - - def __repr__(self) -> str: - return "{}.enable({!r})".format(self.registry.__class__.__name__, self.name) - - -class PluginRegistry(Generic[PluginType]): - """A registry for plugins. - - This is a plugin registry that allows plugins to be loaded/registered - in two ways: - - 1. Through an explicit call to ``.register(name, value)``. - 2. By looking for other Python packages that are installed and provide - a setuptools entry point group. - - When you create an instance of this class, provide the name of the - entry point group to use:: - - reg = PluginRegister('my_entrypoint_group') - - """ - - # this is a mapping of name to error message to allow custom error messages - # in case an entrypoint is not found - entrypoint_err_messages = {} # type: Dict[str, str] - - # global settings is a key-value mapping of settings that are stored globally - # in the registry rather than passed to the plugins - _global_settings = {} # type: Dict[str, Any] - - def __init__(self, entry_point_group: str = "", plugin_type: type = object): - """Create a PluginRegistry for a named entry point group. - - Parameters - ========== - entry_point_group: str - The name of the entry point group. - plugin_type: object - A type that will optionally be used for runtime type checking of - loaded plugins using isinstance. - """ - self.entry_point_group = entry_point_group # type: str - self.plugin_type = plugin_type # type: Optional[type] - self._active = None # type: Optional[PluginType] - self._active_name = "" # type: str - self._plugins = {} # type: Dict[str, PluginType] - self._options = {} # type: Dict[str, Any] - self._global_settings = self.__class__._global_settings.copy() # type: dict - - def register(self, name: str, value: Optional[PluginType]) -> Optional[PluginType]: - """Register a plugin by name and value. - - This method is used for explicit registration of a plugin and shouldn't be - used to manage entry point managed plugins, which are auto-loaded. - - Parameters - ========== - name: str - The name of the plugin. - value: PluginType or None - The actual plugin object to register or None to unregister that plugin. - - Returns - ======= - plugin: PluginType or None - The plugin that was registered or unregistered. - """ - if value is None: - return self._plugins.pop(name, None) - else: - assert isinstance(value, self.plugin_type) # type: ignore[arg-type] # Should ideally be fixed by better annotating plugin_type - self._plugins[name] = value - return value - - def names(self) -> List[str]: - """List the names of the registered and entry points plugins.""" - exts = list(self._plugins.keys()) - e_points = importlib_metadata_get(self.entry_point_group) - more_exts = [ep.name for ep in e_points] - exts.extend(more_exts) - return sorted(set(exts)) - - def _get_state(self) -> Dict[str, Any]: - """Return a dictionary representing the current state of the registry""" - return { - "_active": self._active, - "_active_name": self._active_name, - "_plugins": self._plugins.copy(), - "_options": self._options.copy(), - "_global_settings": self._global_settings.copy(), - } - - def _set_state(self, state: Dict[str, Any]) -> None: - """Reset the state of the registry""" - assert set(state.keys()) == { - "_active", - "_active_name", - "_plugins", - "_options", - "_global_settings", - } - for key, val in state.items(): - setattr(self, key, val) - - def _enable(self, name: str, **options) -> None: - if name not in self._plugins: - try: - (ep,) = [ - ep - for ep in importlib_metadata_get(self.entry_point_group) - if ep.name == name - ] - except ValueError as err: - if name in self.entrypoint_err_messages: - raise ValueError(self.entrypoint_err_messages[name]) from err - else: - raise NoSuchEntryPoint(self.entry_point_group, name) from err - value = cast(PluginType, ep.load()) - self.register(name, value) - self._active_name = name - self._active = self._plugins[name] - for key in set(options.keys()) & set(self._global_settings.keys()): - self._global_settings[key] = options.pop(key) - self._options = options - - def enable(self, name: Optional[str] = None, **options) -> PluginEnabler: - """Enable a plugin by name. - - This can be either called directly, or used as a context manager. - - Parameters - ---------- - name : string (optional) - The name of the plugin to enable. If not specified, then use the - current active name. - **options : - Any additional parameters will be passed to the plugin as keyword - arguments - - Returns - ------- - PluginEnabler: - An object that allows enable() to be used as a context manager - """ - if name is None: - name = self.active - return PluginEnabler(self, name, **options) - - @property - def active(self) -> str: - """Return the name of the currently active plugin""" - return self._active_name - - @property - def options(self) -> Dict[str, Any]: - """Return the current options dictionary""" - return self._options - - def get(self) -> Optional[PluginType]: - """Return the currently active plugin.""" - if self._options: - return curry(self._active, **self._options) - else: - return self._active - - def __repr__(self) -> str: - return "{}(active={!r}, registered={!r})" "".format( - self.__class__.__name__, self._active_name, list(self.names()) - ) - - -def importlib_metadata_get(group): - ep = entry_points() - # 'select' was introduced in Python 3.10 and 'get' got deprecated - # We don't check for Python version here as by checking with hasattr we - # also get compatibility with the importlib_metadata package which had a different - # deprecation cycle for 'get' - if hasattr(ep, "select"): - return ep.select(group=group) - else: - return ep.get(group, []) diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py deleted file mode 100644 index bba2d3f7dfffa3bdbf921bdad4ca7143be97c2fd..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/streams/text.py +++ /dev/null @@ -1,143 +0,0 @@ -from __future__ import annotations - -import codecs -from dataclasses import InitVar, dataclass, field -from typing import Any, Callable, Mapping - -from ..abc import ( - AnyByteReceiveStream, - AnyByteSendStream, - AnyByteStream, - ObjectReceiveStream, - ObjectSendStream, - ObjectStream, -) - - -@dataclass(eq=False) -class TextReceiveStream(ObjectReceiveStream[str]): - """ - Stream wrapper that decodes bytes to strings using the given encoding. - - Decoding is done using :class:`~codecs.IncrementalDecoder` which returns any completely - received unicode characters as soon as they come in. - - :param transport_stream: any bytes-based receive stream - :param encoding: character encoding to use for decoding bytes to strings (defaults to - ``utf-8``) - :param errors: handling scheme for decoding errors (defaults to ``strict``; see the - `codecs module documentation`_ for a comprehensive list of options) - - .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects - """ - - transport_stream: AnyByteReceiveStream - encoding: InitVar[str] = "utf-8" - errors: InitVar[str] = "strict" - _decoder: codecs.IncrementalDecoder = field(init=False) - - def __post_init__(self, encoding: str, errors: str) -> None: - decoder_class = codecs.getincrementaldecoder(encoding) - self._decoder = decoder_class(errors=errors) - - async def receive(self) -> str: - while True: - chunk = await self.transport_stream.receive() - decoded = self._decoder.decode(chunk) - if decoded: - return decoded - - async def aclose(self) -> None: - await self.transport_stream.aclose() - self._decoder.reset() - - @property - def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]: - return self.transport_stream.extra_attributes - - -@dataclass(eq=False) -class TextSendStream(ObjectSendStream[str]): - """ - Sends strings to the wrapped stream as bytes using the given encoding. - - :param AnyByteSendStream transport_stream: any bytes-based send stream - :param str encoding: character encoding to use for encoding strings to bytes (defaults to - ``utf-8``) - :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the - `codecs module documentation`_ for a comprehensive list of options) - - .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects - """ - - transport_stream: AnyByteSendStream - encoding: InitVar[str] = "utf-8" - errors: str = "strict" - _encoder: Callable[..., tuple[bytes, int]] = field(init=False) - - def __post_init__(self, encoding: str) -> None: - self._encoder = codecs.getencoder(encoding) - - async def send(self, item: str) -> None: - encoded = self._encoder(item, self.errors)[0] - await self.transport_stream.send(encoded) - - async def aclose(self) -> None: - await self.transport_stream.aclose() - - @property - def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]: - return self.transport_stream.extra_attributes - - -@dataclass(eq=False) -class TextStream(ObjectStream[str]): - """ - A bidirectional stream that decodes bytes to strings on receive and encodes strings to bytes on - send. - - Extra attributes will be provided from both streams, with the receive stream providing the - values in case of a conflict. - - :param AnyByteStream transport_stream: any bytes-based stream - :param str encoding: character encoding to use for encoding/decoding strings to/from bytes - (defaults to ``utf-8``) - :param str errors: handling scheme for encoding errors (defaults to ``strict``; see the - `codecs module documentation`_ for a comprehensive list of options) - - .. _codecs module documentation: https://docs.python.org/3/library/codecs.html#codec-objects - """ - - transport_stream: AnyByteStream - encoding: InitVar[str] = "utf-8" - errors: InitVar[str] = "strict" - _receive_stream: TextReceiveStream = field(init=False) - _send_stream: TextSendStream = field(init=False) - - def __post_init__(self, encoding: str, errors: str) -> None: - self._receive_stream = TextReceiveStream( - self.transport_stream, encoding=encoding, errors=errors - ) - self._send_stream = TextSendStream( - self.transport_stream, encoding=encoding, errors=errors - ) - - async def receive(self) -> str: - return await self._receive_stream.receive() - - async def send(self, item: str) -> None: - await self._send_stream.send(item) - - async def send_eof(self) -> None: - await self.transport_stream.send_eof() - - async def aclose(self) -> None: - await self._send_stream.aclose() - await self._receive_stream.aclose() - - @property - def extra_attributes(self) -> Mapping[Any, Callable[[], Any]]: - return { - **self._send_stream.extra_attributes, - **self._receive_stream.extra_attributes, - } diff --git a/spaces/TH5314/newbing/src/lib/bots/bing/types.ts b/spaces/TH5314/newbing/src/lib/bots/bing/types.ts deleted file mode 100644 index 02cd5e8b01e3529642d28dc1539bf958f4ac420b..0000000000000000000000000000000000000000 --- a/spaces/TH5314/newbing/src/lib/bots/bing/types.ts +++ /dev/null @@ -1,259 +0,0 @@ -export type Author = 'user' | 'system' | 'bot' - -export type BotId = 'bing' - -export enum BingConversationStyle { - Creative = 'Creative', - Balanced = 'Balanced', - Precise = 'Precise' -} - -export enum ErrorCode { - CONVERSATION_LIMIT = 'CONVERSATION_LIMIT', - BING_UNAUTHORIZED = 'BING_UNAUTHORIZED', - BING_FORBIDDEN = 'BING_FORBIDDEN', - BING_CAPTCHA = 'BING_CAPTCHA', - THROTTLE_LIMIT = 'THROTTLE_LIMIT', - NOTFOUND_ERROR = 'NOT_FOUND_ERROR', - UNKOWN_ERROR = 'UNKOWN_ERROR', - NETWORK_ERROR = 'NETWORK_ERROR', -} - -export class ChatError extends Error { - code: ErrorCode - constructor(message: string, code: ErrorCode) { - super(message) - this.code = code - } -} - -export type ChatMessageModel = { - id: string - author: Author - text: string - error?: ChatError - throttling?: Throttling - sourceAttributions?: SourceAttribution[] - suggestedResponses?: SuggestedResponse[] -} - -export interface ConversationModel { - messages: ChatMessageModel[] -} - -export type Event = - | { - type: 'UPDATE_ANSWER' - data: { - text: string - spokenText?: string - sourceAttributions?: SourceAttribution[] - suggestedResponses?: SuggestedResponse[] - throttling?: Throttling - } - } - | { - type: 'DONE' - } - | { - type: 'ERROR' - error: ChatError - } - -export interface SendMessageParams-- -VIVADATA - Flask Demo
---🏵️🌺🌸 It's a {{iris_type|title}} 🏵️🌺🌸
--- -
-
-- Back -- -
- - -{ - prompt: string - imageUrl?: string - options: T - onEvent: (event: Event) => void - signal?: AbortSignal -} - -export interface ConversationResponse { - conversationId: string - clientId: string - conversationSignature: string - result: { - value: string - message?: string - } -} - -export interface Telemetry { - metrics?: null - startTime: string -} - -export interface ChatUpdateArgument { - messages?: ChatResponseMessage[] - throttling?: Throttling - requestId: string - result: null -} - -export type ChatUpdateCompleteResponse = { - type: 2 - invocationId: string - item: ChatResponseItem -} | { - type: 1 - target: string - arguments: ChatUpdateArgument[] -} | { - type: 3 - invocationId: string -} | { - type: 6 | 7 -} - -export interface ChatRequestResult { - value: string - serviceVersion: string - error?: string -} - -export interface ChatResponseItem { - messages: ChatResponseMessage[] - firstNewMessageIndex: number - suggestedResponses: null - conversationId: string - requestId: string - conversationExpiryTime: string - telemetry: Telemetry - result: ChatRequestResult - throttling: Throttling -} -export enum InvocationEventType { - Invocation = 1, - StreamItem = 2, - Completion = 3, - StreamInvocation = 4, - CancelInvocation = 5, - Ping = 6, - Close = 7, -} - -// https://github.com/bytemate/bingchat-api/blob/main/src/lib.ts - -export interface ConversationInfo { - conversationId: string - clientId: string - conversationSignature: string - invocationId: number - conversationStyle: BingConversationStyle - prompt: string - imageUrl?: string -} - -export interface BingChatResponse { - conversationSignature: string - conversationId: string - clientId: string - invocationId: number - conversationExpiryTime: Date - response: string - details: ChatResponseMessage -} - -export interface Throttling { - maxNumLongDocSummaryUserMessagesInConversation: number - maxNumUserMessagesInConversation: number - numLongDocSummaryUserMessagesInConversation: number - numUserMessagesInConversation: number -} - -export interface ChatResponseMessage { - text: string - spokenText?: string - author: string - createdAt: Date - timestamp: Date - messageId: string - requestId: string - offense: string - adaptiveCards: AdaptiveCard[] - sourceAttributions: SourceAttribution[] - feedback: Feedback - contentOrigin: string - messageType?: string - contentType?: string - privacy: null - suggestedResponses: SuggestedResponse[] -} - -export interface AdaptiveCard { - type: string - version: string - body: Body[] -} - -export interface Body { - type: string - text: string - wrap: boolean - size?: string -} - -export interface Feedback { - tag: null - updatedOn: null - type: string -} - -export interface SourceAttribution { - providerDisplayName: string - seeMoreUrl: string - searchQuery: string -} - -export interface SuggestedResponse { - text: string - author?: Author - createdAt?: Date - timestamp?: Date - messageId?: string - messageType?: string - offense?: string - feedback?: Feedback - contentOrigin?: string - privacy?: null -} - -export interface KBlobRequest { - knowledgeRequest: KnowledgeRequestContext - imageBase64?: string -} - -export interface KBlobResponse { - blobId: string - processedBlobId?: string -} - -export interface KnowledgeRequestContext { - imageInfo: ImageInfo; - knowledgeRequest: KnowledgeRequest; -} - -export interface ImageInfo { - url?: string; -} - -export interface KnowledgeRequest { - invokedSkills: string[]; - subscriptionId: string; - invokedSkillsRequestData: InvokedSkillsRequestData; - convoData: ConvoData; -} - -export interface ConvoData { - convoid: string; - convotone: BingConversationStyle; -} - -export interface InvokedSkillsRequestData { - enableFaceBlur: boolean; -} - -export interface FileItem { - url: string; - status?: 'loading' | 'error' | 'loaded' -} diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py deleted file mode 100644 index 264d564dbda676b52f446c0d25433a15939a78a3..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/contrib/_securetransport/bindings.py +++ /dev/null @@ -1,519 +0,0 @@ -""" -This module uses ctypes to bind a whole bunch of functions and constants from -SecureTransport. The goal here is to provide the low-level API to -SecureTransport. These are essentially the C-level functions and constants, and -they're pretty gross to work with. - -This code is a bastardised version of the code found in Will Bond's oscrypto -library. An enormous debt is owed to him for blazing this trail for us. For -that reason, this code should be considered to be covered both by urllib3's -license and by oscrypto's: - - Copyright (c) 2015-2016 Will Bond
- 
- 
-
-
-
-
-
-
-
-
diff --git a/spaces/Tirendaz/Text-Classification/app.py b/spaces/Tirendaz/Text-Classification/app.py
deleted file mode 100644
index 125b46d586913e6a24ccb8585102a076baf95d9d..0000000000000000000000000000000000000000
--- a/spaces/Tirendaz/Text-Classification/app.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-classifier = pipeline("sentiment-analysis", model="Tirendaz/my_distilbert_model")
-
-def text_classification(text):
- result= classifier(text)
- sentiment_label = result[0]['label']
- sentiment_score = result[0]['score']
- formatted_output = f"This sentiment is {sentiment_label} with the probability {sentiment_score*100:.2f}%"
- return formatted_output
-
-examples=["This is wonderful movie!", "The movie was really bad; I didn't like it."]
-
-io = gr.Interface(fn=text_classification,
- inputs= gr.Textbox(lines=2, label="Text", placeholder="Enter title here..."),
- outputs=gr.Textbox(lines=2, label="Text Classification Result"),
- title="Text Classification",
- description="Enter a text and see the text classification result!",
- examples=examples)
-
-io.launch()
-
diff --git a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Vercel.py b/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Vercel.py
deleted file mode 100644
index e5df9cf017e4c1a265f5c9d5e48eb5c10a56e60a..0000000000000000000000000000000000000000
--- a/spaces/VickyKira/NASAGPT/g4f/Provider/Providers/Vercel.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import os
-import json
-import base64
-import execjs
-import queue
-import threading
-
-from curl_cffi import requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://play.vercel.ai'
-supports_stream = True
-needs_auth = False
-
-models = {
- 'claude-instant-v1': 'anthropic:claude-instant-v1',
- 'claude-v1': 'anthropic:claude-v1',
- 'alpaca-7b': 'replicate:replicate/alpaca-7b',
- 'stablelm-tuned-alpha-7b': 'replicate:stability-ai/stablelm-tuned-alpha-7b',
- 'bloom': 'huggingface:bigscience/bloom',
- 'bloomz': 'huggingface:bigscience/bloomz',
- 'flan-t5-xxl': 'huggingface:google/flan-t5-xxl',
- 'flan-ul2': 'huggingface:google/flan-ul2',
- 'gpt-neox-20b': 'huggingface:EleutherAI/gpt-neox-20b',
- 'oasst-sft-4-pythia-12b-epoch-3.5': 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5',
- 'santacoder': 'huggingface:bigcode/santacoder',
- 'command-medium-nightly': 'cohere:command-medium-nightly',
- 'command-xlarge-nightly': 'cohere:command-xlarge-nightly',
- 'code-cushman-001': 'openai:code-cushman-001',
- 'code-davinci-002': 'openai:code-davinci-002',
- 'gpt-3.5-turbo': 'openai:gpt-3.5-turbo',
- 'text-ada-001': 'openai:text-ada-001',
- 'text-babbage-001': 'openai:text-babbage-001',
- 'text-curie-001': 'openai:text-curie-001',
- 'text-davinci-002': 'openai:text-davinci-002',
- 'text-davinci-003': 'openai:text-davinci-003'
-}
-model = models.keys()
-
-vercel_models = {'anthropic:claude-instant-v1': {'id': 'anthropic:claude-instant-v1', 'provider': 'anthropic', 'providerHumanName': 'Anthropic', 'makerHumanName': 'Anthropic', 'minBillingTier': 'hobby', 'parameters': {'temperature': {'value': 1, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': ['\n\nHuman:'], 'range': []}}, 'name': 'claude-instant-v1'}, 'anthropic:claude-v1': {'id': 'anthropic:claude-v1', 'provider': 'anthropic', 'providerHumanName': 'Anthropic', 'makerHumanName': 'Anthropic', 'minBillingTier': 'hobby', 'parameters': {'temperature': {'value': 1, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': ['\n\nHuman:'], 'range': []}}, 'name': 'claude-v1'}, 'replicate:replicate/alpaca-7b': {'id': 'replicate:replicate/alpaca-7b', 'provider': 'replicate', 'providerHumanName': 'Replicate', 'makerHumanName': 'Stanford', 'parameters': {'temperature': {'value': 0.75, 'range': [0.01, 5]}, 'maximumLength': {'value': 200, 'range': [50, 512]}, 'topP': {'value': 0.95, 'range': [0.01, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'repetitionPenalty': {'value': 1.1765, 'range': [0.01, 5]}, 'stopSequences': {'value': [], 'range': []}}, 'version': '2014ee1247354f2e81c0b3650d71ca715bc1e610189855f134c30ecb841fae21', 'name': 'alpaca-7b'}, 'replicate:stability-ai/stablelm-tuned-alpha-7b': {'id': 'replicate:stability-ai/stablelm-tuned-alpha-7b', 'provider': 'replicate', 'makerHumanName': 'StabilityAI', 'providerHumanName': 'Replicate', 'parameters': {'temperature': {'value': 0.75, 'range': [0.01, 5]}, 'maximumLength': {'value': 200, 'range': [50, 512]}, 'topP': {'value': 0.95, 'range': [0.01, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'repetitionPenalty': {'value': 1.1765, 'range': [0.01, 5]}, 'stopSequences': {'value': [], 'range': []}}, 'version': '4a9a32b4fd86c2d047f1d271fa93972683ec6ef1cf82f402bd021f267330b50b', 'name': 'stablelm-tuned-alpha-7b'}, 'huggingface:bigscience/bloom': {'id': 'huggingface:bigscience/bloom', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigScience', 'instructions': "Do NOT talk to Bloom as an entity, it's not a chatbot but a webpage/blog/article completion model. For the best results: mimic a few words of a webpage similar to the content you want to generate. Start a sentence as if YOU were writing a blog, webpage, math post, coding article and Bloom will generate a coherent follow-up.", 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'bloom'}, 'huggingface:bigscience/bloomz': {'id': 'huggingface:bigscience/bloomz', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigScience', 'instructions': 'We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "Translate to English: Je t\'aime.", the model will most likely answer "I love you.".', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'bloomz'}, 'huggingface:google/flan-t5-xxl': {'id': 'huggingface:google/flan-t5-xxl', 'provider': 'huggingface', 'makerHumanName': 'Google', 'providerHumanName': 'HuggingFace', 'name': 'flan-t5-xxl', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}}, 'huggingface:google/flan-ul2': {'id': 'huggingface:google/flan-ul2', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'Google', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'flan-ul2'}, 'huggingface:EleutherAI/gpt-neox-20b': {'id': 'huggingface:EleutherAI/gpt-neox-20b', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'EleutherAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'gpt-neox-20b'}, 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5': {'id': 'huggingface:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'OpenAssistant', 'parameters': {'maximumLength': {'value': 200, 'range': [50, 1024]}, 'typicalP': {'value': 0.2, 'range': [0.1, 0.99]}, 'repetitionPenalty': {'value': 1, 'range': [0.1, 2]}}, 'name': 'oasst-sft-4-pythia-12b-epoch-3.5'}, 'huggingface:bigcode/santacoder': {
- 'id': 'huggingface:bigcode/santacoder', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigCode', 'instructions': 'The model was trained on GitHub code. As such it is not an instruction model and commands like "Write a function that computes the square root." do not work well. You should phrase commands like they occur in source code such as comments (e.g. # the following function computes the sqrt) or write a function signature and docstring and let the model complete the function body.', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'santacoder'}, 'cohere:command-medium-nightly': {'id': 'cohere:command-medium-nightly', 'provider': 'cohere', 'providerHumanName': 'Cohere', 'makerHumanName': 'Cohere', 'name': 'command-medium-nightly', 'parameters': {'temperature': {'value': 0.9, 'range': [0, 2]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0, 1]}, 'topK': {'value': 0, 'range': [0, 500]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'cohere:command-xlarge-nightly': {'id': 'cohere:command-xlarge-nightly', 'provider': 'cohere', 'providerHumanName': 'Cohere', 'makerHumanName': 'Cohere', 'name': 'command-xlarge-nightly', 'parameters': {'temperature': {'value': 0.9, 'range': [0, 2]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0, 1]}, 'topK': {'value': 0, 'range': [0, 500]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:gpt-4': {'id': 'openai:gpt-4', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'gpt-4', 'minBillingTier': 'pro', 'parameters': {'temperature': {'value': 0.7, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:code-cushman-001': {'id': 'openai:code-cushman-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'code-cushman-001'}, 'openai:code-davinci-002': {'id': 'openai:code-davinci-002', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'code-davinci-002'}, 'openai:gpt-3.5-turbo': {'id': 'openai:gpt-3.5-turbo', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.7, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'gpt-3.5-turbo'}, 'openai:text-ada-001': {'id': 'openai:text-ada-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-ada-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-babbage-001': {'id': 'openai:text-babbage-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-babbage-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-curie-001': {'id': 'openai:text-curie-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-curie-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-davinci-002': {'id': 'openai:text-davinci-002', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-davinci-002', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-davinci-003': {'id': 'openai:text-davinci-003', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-davinci-003', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}}
-
-
-# based on https://github.com/ading2210/vercel-llm-api // modified
-class Client:
- def __init__(self):
- self.session = requests.Session()
- self.headers = {
- 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110 Safari/537.36',
- 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8',
- 'Accept-Encoding': 'gzip, deflate, br',
- 'Accept-Language': 'en-US,en;q=0.5',
- 'Te': 'trailers',
- 'Upgrade-Insecure-Requests': '1'
- }
- self.session.headers.update(self.headers)
-
- def get_token(self):
- b64 = self.session.get('https://sdk.vercel.ai/openai.jpeg').text
- data = json.loads(base64.b64decode(b64))
-
- code = 'const globalThis = {data: `sentinel`}; function token() {return (%s)(%s)}' % (
- data['c'], data['a'])
-
- token_string = json.dumps(separators=(',', ':'),
- obj={'r': execjs.compile(code).call('token'), 't': data['t']})
-
- return base64.b64encode(token_string.encode()).decode()
-
- def get_default_params(self, model_id):
- return {key: param['value'] for key, param in vercel_models[model_id]['parameters'].items()}
-
- def generate(self, model_id: str, prompt: str, params: dict = {}):
- if not ':' in model_id:
- model_id = models[model_id]
-
- defaults = self.get_default_params(model_id)
-
- payload = defaults | params | {
- 'prompt': prompt,
- 'model': model_id,
- }
-
- headers = self.headers | {
- 'Accept-Encoding': 'gzip, deflate, br',
- 'Custom-Encoding': self.get_token(),
- 'Host': 'sdk.vercel.ai',
- 'Origin': 'https://sdk.vercel.ai',
- 'Referrer': 'https://sdk.vercel.ai',
- 'Sec-Fetch-Dest': 'empty',
- 'Sec-Fetch-Mode': 'cors',
- 'Sec-Fetch-Site': 'same-origin',
- }
-
- chunks_queue = queue.Queue()
- error = None
- response = None
-
- def callback(data):
- chunks_queue.put(data.decode())
-
- def request_thread():
- nonlocal response, error
- for _ in range(3):
- try:
- response = self.session.post('https://sdk.vercel.ai/api/generate',
- json=payload, headers=headers, content_callback=callback)
- response.raise_for_status()
-
- except Exception as e:
- if _ == 2:
- error = e
-
- else:
- continue
-
- thread = threading.Thread(target=request_thread, daemon=True)
- thread.start()
-
- text = ''
- index = 0
- while True:
- try:
- chunk = chunks_queue.get(block=True, timeout=0.1)
-
- except queue.Empty:
- if error:
- raise error
-
- elif response:
- break
-
- else:
- continue
-
- text += chunk
- lines = text.split('\n')
-
- if len(lines) - 1 > index:
- new = lines[index:-1]
- for word in new:
- yield json.loads(word)
- index = len(lines) - 1
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
- yield 'Vercel is currently not working.'
- return
-
- conversation = 'This is a conversation between a human and a language model, respond to the last message accordingly, referring to the past history of messages if needed.\n'
-
- for message in messages:
- conversation += '%s: %s\n' % (message['role'], message['content'])
-
- conversation += 'assistant: '
-
- completion = Client().generate(model, conversation)
-
- for token in completion:
- yield token
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file
diff --git a/spaces/Vijish/Crop-CLIP/README.md b/spaces/Vijish/Crop-CLIP/README.md
deleted file mode 100644
index bdcae9f45efba75036d8cd67008634852f99dcfc..0000000000000000000000000000000000000000
--- a/spaces/Vijish/Crop-CLIP/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Crop CLIP
-emoji: 🦀
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Violette/Protogen_x3.4_Official_Release/README.md b/spaces/Violette/Protogen_x3.4_Official_Release/README.md
deleted file mode 100644
index f956b53e422114f032bad59a721dd2d563081a37..0000000000000000000000000000000000000000
--- a/spaces/Violette/Protogen_x3.4_Official_Release/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Protogen X3.4 Official Release
-emoji: 📈
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/tasks/__init__.py b/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/tasks/__init__.py
deleted file mode 100644
index ab1fb1c8289535cf9397bb9805c0cba3666ad26f..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/tasks/__init__.py
+++ /dev/null
@@ -1,26 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-from minigpt4.common.registry import registry
-from minigpt4.tasks.base_task import BaseTask
-from minigpt4.tasks.image_text_pretrain import ImageTextPretrainTask
-
-
-def setup_task(cfg):
- assert "task" in cfg.run_cfg, "Task name must be provided."
-
- task_name = cfg.run_cfg.task
- task = registry.get_task_class(task_name).setup_task(cfg=cfg)
- assert task is not None, "Task {} not properly registered.".format(task_name)
-
- return task
-
-
-__all__ = [
- "BaseTask",
- "ImageTextPretrainTask",
-]
diff --git a/spaces/Voicelab/vlT5-keywords-generation/README.md b/spaces/Voicelab/vlT5-keywords-generation/README.md
deleted file mode 100644
index bb0ec20c0d1e6457305ec098f8f8437c1d529252..0000000000000000000000000000000000000000
--- a/spaces/Voicelab/vlT5-keywords-generation/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: VlT5 Keywords Generation
-emoji: 🌍
-colorFrom: pink
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/utils.py b/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/utils.py
deleted file mode 100644
index ee4b01ddfbe8173965371b29f770f3e87615fe71..0000000000000000000000000000000000000000
--- a/spaces/WorldlineChanger/sayashi-vits-uma-genshin-honkai/utils.py
+++ /dev/null
@@ -1,225 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-import librosa
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
- assert os.path.isfile(checkpoint_path)
- checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
- iteration = checkpoint_dict['iteration']
- learning_rate = checkpoint_dict['learning_rate']
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- saved_state_dict = checkpoint_dict['model']
- if hasattr(model, 'module'):
- state_dict = model.module.state_dict()
- else:
- state_dict = model.state_dict()
- new_state_dict= {}
- for k, v in state_dict.items():
- try:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- logger.info("Loaded checkpoint '{}' (iteration {})" .format(
- checkpoint_path, iteration))
- return model, optimizer, learning_rate, iteration
-
-
-def plot_spectrogram_to_numpy(spectrogram):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(10,2))
- im = ax.imshow(spectrogram, aspect="auto", origin="lower",
- interpolation='none')
- plt.colorbar(im, ax=ax)
- plt.xlabel("Frames")
- plt.ylabel("Channels")
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
- global MATPLOTLIB_FLAG
- if not MATPLOTLIB_FLAG:
- import matplotlib
- matplotlib.use("Agg")
- MATPLOTLIB_FLAG = True
- mpl_logger = logging.getLogger('matplotlib')
- mpl_logger.setLevel(logging.WARNING)
- import matplotlib.pylab as plt
- import numpy as np
-
- fig, ax = plt.subplots(figsize=(6, 4))
- im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
- interpolation='none')
- fig.colorbar(im, ax=ax)
- xlabel = 'Decoder timestep'
- if info is not None:
- xlabel += '\n\n' + info
- plt.xlabel(xlabel)
- plt.ylabel('Encoder timestep')
- plt.tight_layout()
-
- fig.canvas.draw()
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- plt.close()
- return data
-
-
-def load_audio_to_torch(full_path, target_sampling_rate):
- audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
- return torch.FloatTensor(audio.astype(np.float32))
-
-
-def load_filepaths_and_text(filename, split="|"):
- with open(filename, encoding='utf-8') as f:
- filepaths_and_text = [line.strip().split(split) for line in f]
- return filepaths_and_text
-
-
-def get_hparams(init=True):
- parser = argparse.ArgumentParser()
- parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
- help='JSON file for configuration')
- parser.add_argument('-m', '--model', type=str, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = os.path.join("./logs", args.model)
-
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
-
- config_path = args.config
- config_save_path = os.path.join(model_dir, "config.json")
- if init:
- with open(config_path, "r") as f:
- data = f.read()
- with open(config_save_path, "w") as f:
- f.write(data)
- else:
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_dir(model_dir):
- config_save_path = os.path.join(model_dir, "config.json")
- with open(config_save_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- hparams.model_dir = model_dir
- return hparams
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams =HParams(**config)
- return hparams
-
-
-def check_git_hash(model_dir):
- source_dir = os.path.dirname(os.path.realpath(__file__))
- if not os.path.exists(os.path.join(source_dir, ".git")):
- logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
- source_dir
- ))
- return
-
- cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
- path = os.path.join(model_dir, "githash")
- if os.path.exists(path):
- saved_hash = open(path).read()
- if saved_hash != cur_hash:
- logger.warn("git hash values are different. {}(saved) != {}(current)".format(
- saved_hash[:8], cur_hash[:8]))
- else:
- open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
- global logger
- logger = logging.getLogger(os.path.basename(model_dir))
- logger.setLevel(logging.DEBUG)
-
- formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
- if not os.path.exists(model_dir):
- os.makedirs(model_dir)
- h = logging.FileHandler(os.path.join(model_dir, filename))
- h.setLevel(logging.DEBUG)
- h.setFormatter(formatter)
- logger.addHandler(h)
- return logger
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
diff --git a/spaces/XPMaster/KSA_Weather_Prediction/README.md b/spaces/XPMaster/KSA_Weather_Prediction/README.md
deleted file mode 100644
index eb0dad165265158f4093d00781e64bb2dc852b4f..0000000000000000000000000000000000000000
--- a/spaces/XPMaster/KSA_Weather_Prediction/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: KSA Weather Prediction
-emoji: ⚡
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/metrics.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/metrics.py
deleted file mode 100644
index 46fdddf3de2cf8d987ecb4c7d7cb3503afa995ad..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/metrics.py
+++ /dev/null
@@ -1,361 +0,0 @@
-"Implements various metrics to measure training accuracy"
-from .torch_core import *
-from .callback import *
-from .layers import *
-from .basic_train import LearnerCallback
-
-__all__ = ['error_rate', 'accuracy', 'accuracy_thresh', 'dice', 'exp_rmspe', 'fbeta','FBeta', 'mse', 'mean_squared_error',
- 'mae', 'mean_absolute_error', 'rmse', 'root_mean_squared_error', 'msle', 'mean_squared_logarithmic_error',
- 'explained_variance', 'r2_score', 'top_k_accuracy', 'KappaScore', 'ConfusionMatrix', 'MatthewsCorreff',
- 'Precision', 'Recall', 'R2Score', 'ExplainedVariance', 'ExpRMSPE', 'RMSE', 'Perplexity', 'AUROC', 'auc_roc_score',
- 'roc_curve', 'MultiLabelFbeta', 'foreground_acc']
-
-def fbeta(y_pred:Tensor, y_true:Tensor, thresh:float=0.2, beta:float=2, eps:float=1e-9, sigmoid:bool=True)->Rank0Tensor:
- "Computes the f_beta between `preds` and `targets`"
- beta2 = beta ** 2
- if sigmoid: y_pred = y_pred.sigmoid()
- y_pred = (y_pred>thresh).float()
- y_true = y_true.float()
- TP = (y_pred*y_true).sum(dim=1)
- prec = TP/(y_pred.sum(dim=1)+eps)
- rec = TP/(y_true.sum(dim=1)+eps)
- res = (prec*rec)/(prec*beta2+rec+eps)*(1+beta2)
- return res.mean()
-
-def accuracy(input:Tensor, targs:Tensor)->Rank0Tensor:
- "Computes accuracy with `targs` when `input` is bs * n_classes."
- n = targs.shape[0]
- input = input.argmax(dim=-1).view(n,-1)
- targs = targs.view(n,-1)
- return (input==targs).float().mean()
-
-def accuracy_thresh(y_pred:Tensor, y_true:Tensor, thresh:float=0.5, sigmoid:bool=True)->Rank0Tensor:
- "Computes accuracy when `y_pred` and `y_true` are the same size."
- if sigmoid: y_pred = y_pred.sigmoid()
- return ((y_pred>thresh)==y_true.byte()).float().mean()
-
-def top_k_accuracy(input:Tensor, targs:Tensor, k:int=5)->Rank0Tensor:
- "Computes the Top-k accuracy (target is in the top k predictions)."
- input = input.topk(k=k, dim=-1)[1]
- targs = targs.unsqueeze(dim=-1).expand_as(input)
- return (input == targs).max(dim=-1)[0].float().mean()
-
-def foreground_acc(input, target, void_code):
- "Computes non-background accuracy, e.g. camvid for multiclass segmentation"
- target = target.squeeze(1)
- mask = target != void_code
- return (input.argmax(dim=1)[mask]==target[mask]).float().mean()
-
-def error_rate(input:Tensor, targs:Tensor)->Rank0Tensor:
- "1 - `accuracy`"
- return 1 - accuracy(input, targs)
-
-def dice(input:Tensor, targs:Tensor, iou:bool=False, eps:float=1e-8)->Rank0Tensor:
- "Dice coefficient metric for binary target. If iou=True, returns iou metric, classic for segmentation problems."
- n = targs.shape[0]
- input = input.argmax(dim=1).view(n,-1)
- targs = targs.view(n,-1)
- intersect = (input * targs).sum().float()
- union = (input+targs).sum().float()
- if not iou: return (2. * intersect / union if union > 0 else union.new([1.]).squeeze())
- else: return (intersect / (union-intersect+eps) if union > 0 else union.new([1.]).squeeze())
-
-def psnr(input:Tensor, targs:Tensor)->Rank0Tensor:
- return 10 * (1. / mean_squared_error(input, targs)).log10()
-
-def exp_rmspe(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Exp RMSE between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- pred, targ = torch.exp(pred), torch.exp(targ)
- pct_var = (targ - pred)/targ
- return torch.sqrt((pct_var**2).mean())
-
-def mean_absolute_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean absolute error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return torch.abs(targ - pred).mean()
-
-def mean_squared_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean squared error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return F.mse_loss(pred, targ)
-
-def root_mean_squared_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Root mean squared error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return torch.sqrt(F.mse_loss(pred, targ))
-
-def mean_squared_logarithmic_error(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Mean squared logarithmic error between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- return F.mse_loss(torch.log(1 + pred), torch.log(1 + targ))
-
-def explained_variance(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "Explained variance between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- var_pct = torch.var(targ - pred) / torch.var(targ)
- return 1 - var_pct
-
-def r2_score(pred:Tensor, targ:Tensor)->Rank0Tensor:
- "R2 score (coefficient of determination) between `pred` and `targ`."
- pred,targ = flatten_check(pred,targ)
- u = torch.sum((targ - pred) ** 2)
- d = torch.sum((targ - targ.mean()) ** 2)
- return 1 - u / d
-
-class RegMetrics(Callback):
- "Stores predictions and targets to perform calculations on epoch end."
- def on_epoch_begin(self, **kwargs):
- self.targs, self.preds = Tensor([]), Tensor([])
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- assert last_output.numel() == last_target.numel(), "Expected same numbers of elements in pred & targ"
- self.preds = torch.cat((self.preds, last_output.cpu()))
- self.targs = torch.cat((self.targs, last_target.cpu()))
-
-class R2Score(RegMetrics):
- "Computes the R2 score (coefficient of determination)."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, r2_score(self.preds, self.targs))
-
-class ExplainedVariance(RegMetrics):
- "Computes the explained variance."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, explained_variance(self.preds, self.targs))
-
-class RMSE(RegMetrics):
- "Computes the root mean squared error."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, root_mean_squared_error(self.preds, self.targs))
-
-class ExpRMSPE(RegMetrics):
- "Computes the exponential of the root mean square error."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, exp_rmspe(self.preds, self.targs))
-
-# Aliases
-mse = mean_squared_error
-mae = mean_absolute_error
-msle = mean_squared_logarithmic_error
-rmse = root_mean_squared_error
-
-class ConfusionMatrix(Callback):
- "Computes the confusion matrix."
-
- def on_train_begin(self, **kwargs):
- self.n_classes = 0
-
- def on_epoch_begin(self, **kwargs):
- self.cm = None
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- preds = last_output.argmax(-1).view(-1).cpu()
- targs = last_target.cpu()
- if self.n_classes == 0:
- self.n_classes = last_output.shape[-1]
- self.x = torch.arange(0, self.n_classes)
- cm = ((preds==self.x[:, None]) & (targs==self.x[:, None, None])).sum(dim=2, dtype=torch.float32)
- if self.cm is None: self.cm = cm
- else: self.cm += cm
-
- def on_epoch_end(self, **kwargs):
- self.metric = self.cm
-
-@dataclass
-class CMScores(ConfusionMatrix):
- "Base class for metrics which rely on the calculation of the precision and/or recall score."
- average:Optional[str]="binary" # `binary`, `micro`, `macro`, `weigthed` or None
- pos_label:int=1 # 0 or 1
- eps:float=1e-9
-
- def _recall(self):
- rec = torch.diag(self.cm) / self.cm.sum(dim=1)
- if self.average is None: return rec
- else:
- if self.average == "micro": weights = self._weights(avg="weighted")
- else: weights = self._weights(avg=self.average)
- return (rec * weights).sum()
-
- def _precision(self):
- prec = torch.diag(self.cm) / self.cm.sum(dim=0)
- if self.average is None: return prec
- else:
- weights = self._weights(avg=self.average)
- return (prec * weights).sum()
-
- def _weights(self, avg:str):
- if self.n_classes != 2 and avg == "binary":
- avg = self.average = "macro"
- warn("average=`binary` was selected for a non binary case. Value for average has now been set to `macro` instead.")
- if avg == "binary":
- if self.pos_label not in (0, 1):
- self.pos_label = 1
- warn("Invalid value for pos_label. It has now been set to 1.")
- if self.pos_label == 1: return Tensor([0,1])
- else: return Tensor([1,0])
- elif avg == "micro": return self.cm.sum(dim=0) / self.cm.sum()
- elif avg == "macro": return torch.ones((self.n_classes,)) / self.n_classes
- elif avg == "weighted": return self.cm.sum(dim=1) / self.cm.sum()
-
-
-class Recall(CMScores):
- "Computes the Recall."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, self._recall())
-
-class Precision(CMScores):
- "Computes the Precision."
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, self._precision())
-
-@dataclass
-class FBeta(CMScores):
- "Computes the F`beta` score."
- beta:float=2
-
- def on_train_begin(self, **kwargs):
- self.n_classes = 0
- self.beta2 = self.beta ** 2
- self.avg = self.average
- if self.average != "micro": self.average = None
-
- def on_epoch_end(self, last_metrics, **kwargs):
- prec = self._precision()
- rec = self._recall()
- metric = (1 + self.beta2) * prec * rec / (prec * self.beta2 + rec + self.eps)
- metric[metric != metric] = 0 # removing potential "nan"s
- if self.avg: metric = (self._weights(avg=self.avg) * metric).sum()
- return add_metrics(last_metrics, metric)
-
- def on_train_end(self, **kwargs): self.average = self.avg
-
-@dataclass
-class KappaScore(ConfusionMatrix):
- "Computes the rate of agreement (Cohens Kappa)."
- weights:Optional[str]=None # None, `linear`, or `quadratic`
-
- def on_epoch_end(self, last_metrics, **kwargs):
- sum0 = self.cm.sum(dim=0)
- sum1 = self.cm.sum(dim=1)
- expected = torch.einsum('i,j->ij', (sum0, sum1)) / sum0.sum()
- if self.weights is None:
- w = torch.ones((self.n_classes, self.n_classes))
- w[self.x, self.x] = 0
- elif self.weights == "linear" or self.weights == "quadratic":
- w = torch.zeros((self.n_classes, self.n_classes))
- w += torch.arange(self.n_classes, dtype=torch.float)
- w = torch.abs(w - torch.t(w)) if self.weights == "linear" else (w - torch.t(w)) ** 2
- else: raise ValueError('Unknown weights. Expected None, "linear", or "quadratic".')
- k = torch.sum(w * self.cm) / torch.sum(w * expected)
- return add_metrics(last_metrics, 1-k)
-
-@dataclass
-class MatthewsCorreff(ConfusionMatrix):
- "Computes the Matthews correlation coefficient."
- def on_epoch_end(self, last_metrics, **kwargs):
- t_sum = self.cm.sum(dim=1)
- p_sum = self.cm.sum(dim=0)
- n_correct = torch.trace(self.cm)
- n_samples = p_sum.sum()
- cov_ytyp = n_correct * n_samples - torch.dot(t_sum, p_sum)
- cov_ypyp = n_samples ** 2 - torch.dot(p_sum, p_sum)
- cov_ytyt = n_samples ** 2 - torch.dot(t_sum, t_sum)
- return add_metrics(last_metrics, cov_ytyp / torch.sqrt(cov_ytyt * cov_ypyp))
-
-class Perplexity(Callback):
- "Perplexity metric for language models."
- def on_epoch_begin(self, **kwargs): self.loss,self.len = 0.,0
-
- def on_batch_end(self, last_output, last_target, **kwargs):
- self.loss += last_target.size(1) * CrossEntropyFlat()(last_output, last_target)
- self.len += last_target.size(1)
-
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, torch.exp(self.loss / self.len))
-
-def auc_roc_score(input:Tensor, targ:Tensor):
- "Computes the area under the receiver operator characteristic (ROC) curve using the trapezoid method. Restricted binary classification tasks."
- fpr, tpr = roc_curve(input, targ)
- d = fpr[1:] - fpr[:-1]
- sl1, sl2 = [slice(None)], [slice(None)]
- sl1[-1], sl2[-1] = slice(1, None), slice(None, -1)
- return (d * (tpr[tuple(sl1)] + tpr[tuple(sl2)]) / 2.).sum(-1)
-
-def roc_curve(input:Tensor, targ:Tensor):
- "Computes the receiver operator characteristic (ROC) curve by determining the true positive ratio (TPR) and false positive ratio (FPR) for various classification thresholds. Restricted binary classification tasks."
- targ = (targ == 1)
- desc_score_indices = torch.flip(input.argsort(-1), [-1])
- input = input[desc_score_indices]
- targ = targ[desc_score_indices]
- d = input[1:] - input[:-1]
- distinct_value_indices = torch.nonzero(d).transpose(0,1)[0]
- threshold_idxs = torch.cat((distinct_value_indices, LongTensor([len(targ) - 1]).to(targ.device)))
- tps = torch.cumsum(targ * 1, dim=-1)[threshold_idxs]
- fps = (1 + threshold_idxs - tps)
- if tps[0] != 0 or fps[0] != 0:
- fps = torch.cat((LongTensor([0]), fps))
- tps = torch.cat((LongTensor([0]), tps))
- fpr, tpr = fps.float() / fps[-1], tps.float() / tps[-1]
- return fpr, tpr
-
-@dataclass
-class AUROC(Callback):
- "Computes the area under the curve (AUC) score based on the receiver operator characteristic (ROC) curve. Restricted to binary classification tasks."
- def on_epoch_begin(self, **kwargs):
- self.targs, self.preds = LongTensor([]), Tensor([])
-
- def on_batch_end(self, last_output:Tensor, last_target:Tensor, **kwargs):
- last_output = F.softmax(last_output, dim=1)[:,-1]
- self.preds = torch.cat((self.preds, last_output.cpu()))
- self.targs = torch.cat((self.targs, last_target.cpu().long()))
-
- def on_epoch_end(self, last_metrics, **kwargs):
- return add_metrics(last_metrics, auc_roc_score(self.preds, self.targs))
-
-class MultiLabelFbeta(LearnerCallback):
- "Computes the fbeta score for multilabel classification"
- # https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html
- _order = -20
- def __init__(self, learn, beta=2, eps=1e-15, thresh=0.3, sigmoid=True, average="micro"):
- super().__init__(learn)
- self.eps, self.thresh, self.sigmoid, self.average, self.beta2 = \
- eps, thresh, sigmoid, average, beta**2
-
- def on_train_begin(self, **kwargs):
- self.c = self.learn.data.c
- if self.average != "none": self.learn.recorder.add_metric_names([f'{self.average}_fbeta'])
- else: self.learn.recorder.add_metric_names([f"fbeta_{c}" for c in self.learn.data.classes])
-
- def on_epoch_begin(self, **kwargs):
- dvc = self.learn.data.device
- self.tp = torch.zeros(self.c).to(dvc)
- self.total_pred = torch.zeros(self.c).to(dvc)
- self.total_targ = torch.zeros(self.c).to(dvc)
-
- def on_batch_end(self, last_output, last_target, **kwargs):
- pred, targ = (last_output.sigmoid() if self.sigmoid else last_output) > self.thresh, last_target.byte()
- m = pred*targ
- self.tp += m.sum(0).float()
- self.total_pred += pred.sum(0).float()
- self.total_targ += targ.sum(0).float()
-
- def fbeta_score(self, precision, recall):
- return (1 + self.beta2)*(precision*recall)/((self.beta2*precision + recall) + self.eps)
-
- def on_epoch_end(self, last_metrics, **kwargs):
- self.total_pred += self.eps
- self.total_targ += self.eps
- if self.average == "micro":
- precision, recall = self.tp.sum() / self.total_pred.sum(), self.tp.sum() / self.total_targ.sum()
- res = self.fbeta_score(precision, recall)
- elif self.average == "macro":
- res = self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ)).mean()
- elif self.average == "weighted":
- scores = self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ))
- res = (scores*self.total_targ).sum() / self.total_targ.sum()
- elif self.average == "none":
- res = listify(self.fbeta_score((self.tp / self.total_pred), (self.tp / self.total_targ)))
- else:
- raise Exception("Choose one of the average types: [micro, macro, weighted, none]")
-
- return add_metrics(last_metrics, res)
diff --git a/spaces/XzJosh/Taffy-Bert-VITS2/transforms.py b/spaces/XzJosh/Taffy-Bert-VITS2/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Taffy-Bert-VITS2/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
-
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {
- 'tails': tails,
- 'tail_bound': tail_bound
- }
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(
- inputs[..., None] >= bin_locations,
- dim=-1
- ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails='linear',
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == 'linear':
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError('{} tails are not implemented.'.format(tails))
-
- outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative
- )
-
- return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0., right=1., bottom=0., top=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError('Input to a transform is not within its domain')
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError('Minimal bin width too large for the number of bins')
- if min_bin_height * num_bins > 1.0:
- raise ValueError('Minimal bin height too large for the number of bins')
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (((inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta)
- + input_heights * (input_delta - input_derivatives)))
- b = (input_heights * input_derivatives
- - (inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta))
- c = - input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (input_delta * theta.pow(2)
- + input_derivatives * theta_one_minus_theta)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/XzJosh/Wenjing-Bert-VITS2/server.py b/spaces/XzJosh/Wenjing-Bert-VITS2/server.py
deleted file mode 100644
index c736ca4f95fec853950eef6654ef79856beffc0a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Wenjing-Bert-VITS2/server.py
+++ /dev/null
@@ -1,123 +0,0 @@
-from flask import Flask, request, Response
-from io import BytesIO
-import torch
-from av import open as avopen
-
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-from scipy.io import wavfile
-
-# Flask Init
-app = Flask(__name__)
-app.config['JSON_AS_ASCII'] = False
-def get_text(text, language_str, hps):
- norm_text, phone, tone, word2ph = clean_text(text, language_str)
- print([f"{p}{t}" for p, t in zip(phone, tone)])
- phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
- if hps.data.add_blank:
- phone = commons.intersperse(phone, 0)
- tone = commons.intersperse(tone, 0)
- language = commons.intersperse(language, 0)
- for i in range(len(word2ph)):
- word2ph[i] = word2ph[i] * 2
- word2ph[0] += 1
- bert = get_bert(norm_text, word2ph, language_str)
-
- assert bert.shape[-1] == len(phone)
-
- phone = torch.LongTensor(phone)
- tone = torch.LongTensor(tone)
- language = torch.LongTensor(language)
-
- return bert, phone, tone, language
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w,length_scale,sid):
- bert, phones, tones, lang_ids = get_text(text,"ZH", hps,)
- with torch.no_grad():
- x_tst=phones.to(dev).unsqueeze(0)
- tones=tones.to(dev).unsqueeze(0)
- lang_ids=lang_ids.to(dev).unsqueeze(0)
- bert = bert.to(dev).unsqueeze(0)
- x_tst_lengths = torch.LongTensor([phones.size(0)]).to(dev)
- speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(dev)
- audio = net_g.infer(x_tst, x_tst_lengths, speakers, tones, lang_ids,bert, sdp_ratio=sdp_ratio
- , noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale)[0][0,0].data.cpu().float().numpy()
- return audio
-
-def replace_punctuation(text, i=2):
- punctuation = "，。？！"
- for char in punctuation:
- text = text.replace(char, char * i)
- return text
-
-def wav2(i, o, format):
- inp = avopen(i, 'rb')
- out = avopen(o, 'wb', format=format)
- if format == "ogg": format = "libvorbis"
-
- ostream = out.add_stream(format)
-
- for frame in inp.decode(audio=0):
- for p in ostream.encode(frame): out.mux(p)
-
- for p in ostream.encode(None): out.mux(p)
-
- out.close()
- inp.close()
-
-# Load Generator
-hps = utils.get_hparams_from_file("./configs/config.json")
-
-dev='cuda'
-net_g = SynthesizerTrn(
- len(symbols),
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- n_speakers=hps.data.n_speakers,
- **hps.model).to(dev)
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("logs/G_649000.pth", net_g, None,skip_optimizer=True)
-
-@app.route("/",methods=['GET','POST'])
-def main():
- if request.method == 'GET':
- try:
- speaker = request.args.get('speaker')
- text = request.args.get('text').replace("/n","")
- sdp_ratio = float(request.args.get("sdp_ratio", 0.2))
- noise = float(request.args.get("noise", 0.5))
- noisew = float(request.args.get("noisew", 0.6))
- length = float(request.args.get("length", 1.2))
- if length >= 2:
- return "Too big length"
- if len(text) >=200:
- return "Too long text"
- fmt = request.args.get("format", "wav")
- if None in (speaker, text):
- return "Missing Parameter"
- if fmt not in ("mp3", "wav", "ogg"):
- return "Invalid Format"
- except:
- return "Invalid Parameter"
-
- with torch.no_grad():
- audio = infer(text, sdp_ratio=sdp_ratio, noise_scale=noise, noise_scale_w=noisew, length_scale=length, sid=speaker)
-
- with BytesIO() as wav:
- wavfile.write(wav, hps.data.sampling_rate, audio)
- torch.cuda.empty_cache()
- if fmt == "wav":
- return Response(wav.getvalue(), mimetype="audio/wav")
- wav.seek(0, 0)
- with BytesIO() as ofp:
- wav2(wav, ofp, fmt)
- return Response(
- ofp.getvalue(),
- mimetype="audio/mpeg" if fmt == "mp3" else "audio/ogg"
- )
diff --git a/spaces/YlcldKlns/bing/src/components/settings.tsx b/spaces/YlcldKlns/bing/src/components/settings.tsx
deleted file mode 100644
index 80b8a2d3b252b875f5b6f7dfc2f6e3ad9cdfb22a..0000000000000000000000000000000000000000
--- a/spaces/YlcldKlns/bing/src/components/settings.tsx
+++ /dev/null
@@ -1,157 +0,0 @@
-import { useEffect, useState } from 'react'
-import { useAtom } from 'jotai'
-import { Switch } from '@headlessui/react'
-import { toast } from 'react-hot-toast'
-import { hashAtom, voiceAtom } from '@/state'
-import {
- Dialog,
- DialogContent,
- DialogDescription,
- DialogFooter,
- DialogHeader,
- DialogTitle
-} from '@/components/ui/dialog'
-import { Button } from './ui/button'
-import { Input } from './ui/input'
-import { ChunkKeys, parseCookies, extraCurlFromCookie, encodeHeadersToCookie, getCookie, setCookie } from '@/lib/utils'
-import { ExternalLink } from './external-link'
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-
-
-export function Settings() {
- const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
- const [loc, setLoc] = useAtom(hashAtom)
- const [curlValue, setCurlValue] = useState(extraCurlFromCookie(parseCookies(document.cookie, ChunkKeys)))
- const [imageOnly, setImageOnly] = useState(getCookie('IMAGE_ONLY') !== '0')
- const [enableTTS, setEnableTTS] = useAtom(voiceAtom)
-
- useEffect(() => {
- if (isCopied) {
- toast.success('复制成功')
- }
- }, [isCopied])
-
- if (loc === 'settings') {
- return (
-
- )
- } else if (loc === 'voice') {
- return (
-
- )
- }
- return null
-}
diff --git a/spaces/Yudha515/Rvc-Models/audiocraft/models/lm.py b/spaces/Yudha515/Rvc-Models/audiocraft/models/lm.py
deleted file mode 100644
index c8aad8f06797eef3293605056e1de14d07c56c2a..0000000000000000000000000000000000000000
--- a/spaces/Yudha515/Rvc-Models/audiocraft/models/lm.py
+++ /dev/null
@@ -1,527 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from dataclasses import dataclass
-from functools import partial
-import logging
-import math
-import typing as tp
-
-import torch
-from torch import nn
-
-from ..utils import utils
-from ..modules.streaming import StreamingModule, State
-from ..modules.transformer import StreamingTransformer, create_norm_fn
-from ..modules.conditioners import (
- ConditionFuser,
- ClassifierFreeGuidanceDropout,
- AttributeDropout,
- ConditioningProvider,
- ConditioningAttributes,
- ConditionType,
-)
-from ..modules.codebooks_patterns import CodebooksPatternProvider
-from ..modules.activations import get_activation_fn
-
-
-logger = logging.getLogger(__name__)
-ConditionTensors = tp.Dict[str, ConditionType]
-CFGConditions = tp.Union[ConditionTensors, tp.Tuple[ConditionTensors, ConditionTensors]]
-
-
-def get_init_fn(method: str, input_dim: int, init_depth: tp.Optional[int] = None):
- """LM layer initialization.
- Inspired from xlformers: https://github.com/fairinternal/xlformers
-
- Args:
- method (str): Method name for init function. Valid options are:
- 'gaussian', 'uniform'.
- input_dim (int): Input dimension of the initialized module.
- init_depth (Optional[int]): Optional init depth value used to rescale
- the standard deviation if defined.
- """
- # Compute std
- std = 1 / math.sqrt(input_dim)
- # Rescale with depth
- if init_depth is not None:
- std = std / math.sqrt(2 * init_depth)
-
- if method == 'gaussian':
- return partial(
- torch.nn.init.trunc_normal_, mean=0.0, std=std, a=-3 * std, b=3 * std
- )
- elif method == 'uniform':
- bound = math.sqrt(3) * std # ensure the standard deviation is `std`
- return partial(torch.nn.init.uniform_, a=-bound, b=bound)
- else:
- raise ValueError("Unsupported layer initialization method")
-
-
-def init_layer(m: nn.Module,
- method: str,
- init_depth: tp.Optional[int] = None,
- zero_bias_init: bool = False):
- """Wrapper around ``get_init_fn`` for proper initialization of LM modules.
-
- Args:
- m (nn.Module): Module to initialize.
- method (str): Method name for the init function.
- init_depth (Optional[int]): Optional init depth value used to rescale
- the standard deviation if defined.
- zero_bias_init (bool): Whether to initialize the bias to 0 or not.
- """
- if isinstance(m, nn.Linear):
- init_fn = get_init_fn(method, m.in_features, init_depth=init_depth)
- if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
- weight = m.weight.float()
- init_fn(weight)
- m.weight.data[:] = weight.half()
- else:
- init_fn(m.weight)
- if zero_bias_init and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.Embedding):
- init_fn = get_init_fn(method, m.embedding_dim, init_depth=None)
- if m.weight.device.type == 'cpu' and m.weight.dtype == torch.float16:
- weight = m.weight.float()
- init_fn(weight)
- m.weight.data[:] = weight.half()
- else:
- init_fn(m.weight)
-
-
-class ScaledEmbedding(nn.Embedding):
- """Boost learning rate for embeddings (with `scale`).
- """
- def __init__(self, *args, lr=None, **kwargs):
- super().__init__(*args, **kwargs)
- self.lr = lr
-
- def make_optim_group(self):
- group = {"params": list(self.parameters())}
- if self.lr is not None:
- group["lr"] = self.lr
- return group
-
-
-@dataclass
-class LMOutput:
- # The logits are already re-aligned with the input codes
- # hence no extra shift is required, e.g. when computing CE
- logits: torch.Tensor # [B, K, T, card]
- mask: torch.Tensor # [B, K, T]
-
-
-class LMModel(StreamingModule):
- """Transformer-based language model on multiple streams of codes.
-
- Args:
- pattern_provider (CodebooksPatternProvider): Pattern provider for codebook interleaving.
- condition_provider (MusicConditioningProvider): Conditioning provider from metadata.
- fuser (ConditionFuser): Fuser handling the fusing of conditions with language model input.
- n_q (int): Number of parallel streams to model.
- card (int): Cardinality, vocabulary size.
- dim (int): Dimension of the transformer encoder.
- num_heads (int): Number of heads for the transformer encoder.
- hidden_scale (int): Scale for hidden feed forward dimension of the transformer encoder.
- norm (str): Normalization method.
- norm_first (bool): Use pre-norm instead of post-norm.
- emb_lr (Optional[float]): Embedding-specific learning rate.
- bias_proj (bool): Use bias for output projections.
- weight_init (Optional[str]): Method for weight initialization.
- depthwise_init (Optional[str]): Method for depthwise weight initialization.
- zero_bias_init (bool): If true and bias in Linears, initialize bias to zeros.
- cfg_dropout (float): Classifier-free guidance dropout.
- cfg_coef (float): Classifier-free guidance coefficient.
- attribute_dropout (dict): Attribute dropout probabilities.
- two_step_cfg (bool): Whether to run classifier free-guidance with 2 distinct steps.
- **kwargs: Additional parameters for the transformer encoder.
- """
- def __init__(self, pattern_provider: CodebooksPatternProvider, condition_provider: ConditioningProvider,
- fuser: ConditionFuser, n_q: int = 8, card: int = 1024, dim: int = 128, num_heads: int = 8,
- hidden_scale: int = 4, norm: str = 'layer_norm', norm_first: bool = False,
- emb_lr: tp.Optional[float] = None, bias_proj: bool = True,
- weight_init: tp.Optional[str] = None, depthwise_init: tp.Optional[str] = None,
- zero_bias_init: bool = False, cfg_dropout: float = 0, cfg_coef: float = 1.0,
- attribute_dropout: tp.Dict[str, tp.Dict[str, float]] = {}, two_step_cfg: bool = False,
- **kwargs):
- super().__init__()
- self.cfg_coef = cfg_coef
- self.cfg_dropout = ClassifierFreeGuidanceDropout(p=cfg_dropout)
- self.att_dropout = AttributeDropout(p=attribute_dropout)
- self.condition_provider = condition_provider
- self.fuser = fuser
- self.card = card
- embed_dim = self.card + 1
- self.n_q = n_q
- self.dim = dim
- self.pattern_provider = pattern_provider
- self.two_step_cfg = two_step_cfg
- self.emb = nn.ModuleList([ScaledEmbedding(embed_dim, dim, lr=emb_lr) for _ in range(n_q)])
- if 'activation' in kwargs:
- kwargs['activation'] = get_activation_fn(kwargs['activation'])
- self.transformer = StreamingTransformer(
- d_model=dim, num_heads=num_heads, dim_feedforward=int(hidden_scale * dim),
- norm=norm, norm_first=norm_first, **kwargs)
- self.out_norm: tp.Optional[nn.Module] = None
- if norm_first:
- self.out_norm = create_norm_fn(norm, dim)
- self.linears = nn.ModuleList([nn.Linear(dim, self.card, bias=bias_proj) for _ in range(n_q)])
- self._init_weights(weight_init, depthwise_init, zero_bias_init)
- self._fsdp: tp.Optional[nn.Module]
- self.__dict__['_fsdp'] = None
-
- def _init_weights(self, weight_init: tp.Optional[str], depthwise_init: tp.Optional[str], zero_bias_init: bool):
- """Initialization of the transformer module weights.
-
- Args:
- weight_init (Optional[str]): Weight initialization strategy. See ``get_init_fn`` for valid options.
- depthwise_init (Optional[str]): Depwthwise initialization strategy. The following options are valid:
- 'current' where the depth corresponds to the current layer index or 'global' where the total number
- of layer is used as depth. If not set, no depthwise initialization strategy is used.
- zero_bias_init (bool): Whether to initalize bias to zero or not.
- """
- assert depthwise_init is None or depthwise_init in ['current', 'global']
- assert depthwise_init is None or weight_init is not None, \
- "If 'depthwise_init' is defined, a 'weight_init' method should be provided."
- assert not zero_bias_init or weight_init is not None, \
- "If 'zero_bias_init', a 'weight_init' method should be provided"
-
- if weight_init is None:
- return
-
- for emb_layer in self.emb:
- init_layer(emb_layer, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
- for layer_idx, tr_layer in enumerate(self.transformer.layers):
- depth = None
- if depthwise_init == 'current':
- depth = layer_idx + 1
- elif depthwise_init == 'global':
- depth = len(self.transformer.layers)
- init_fn = partial(init_layer, method=weight_init, init_depth=depth, zero_bias_init=zero_bias_init)
- tr_layer.apply(init_fn)
-
- for linear in self.linears:
- init_layer(linear, method=weight_init, init_depth=None, zero_bias_init=zero_bias_init)
-
- @property
- def special_token_id(self) -> int:
- return self.card
-
- @property
- def num_codebooks(self) -> int:
- return self.n_q
-
- def forward(self, sequence: torch.Tensor,
- conditions: tp.List[ConditioningAttributes],
- condition_tensors: tp.Optional[ConditionTensors] = None) -> torch.Tensor:
- """Apply language model on sequence and conditions.
- Given a tensor of sequence of shape [B, K, S] with K the number of codebooks and
- S the sequence steps, return the logits with shape [B, card, K, S].
-
- Args:
- indices (torch.Tensor): indices of the codes to model.
- conditions (list[ConditioningAttributes]): conditionings to use when modeling
- the given codes. Note that when evaluating multiple time with the same conditioning
- you should pre-compute those and pass them as `condition_tensors`.
- condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning
- tensors, see `conditions`.
- Returns:
- torch.Tensor: Logits.
- """
- B, K, S = sequence.shape
- assert K == self.num_codebooks, 'Sequence shape must match the specified number of codebooks'
- input_ = sum([self.emb[k](sequence[:, k]) for k in range(K)])
- if condition_tensors is None:
- assert not self._is_streaming, "Conditions tensors should be precomputed when streaming."
- # apply dropout modules
- conditions = self.cfg_dropout(conditions)
- conditions = self.att_dropout(conditions)
- tokenized = self.condition_provider.tokenize(conditions)
- # encode conditions and fuse, both have a streaming cache to not recompute when generating.
- condition_tensors = self.condition_provider(tokenized)
- else:
- assert not conditions, "Shouldn't pass both conditions and condition_tensors."
-
- input_, cross_attention_input = self.fuser(input_, condition_tensors)
-
- out = self.transformer(input_, cross_attention_src=cross_attention_input)
- if self.out_norm:
- out = self.out_norm(out)
- logits = torch.stack([self.linears[k](out) for k in range(K)], dim=1) # [B, K, S, card]
-
- # remove the prefix from the model outputs
- if len(self.fuser.fuse2cond['prepend']) > 0:
- logits = logits[:, :, -S:]
-
- return logits # [B, K, S, card]
-
- def compute_predictions(
- self, codes: torch.Tensor,
- conditions: tp.List[ConditioningAttributes],
- condition_tensors: tp.Optional[ConditionTensors] = None) -> LMOutput:
- """Given an input tensor of codes [B, K, T] and list of conditions, runs the model
- forward using the specified codes interleaving pattern.
-
- Args:
- codes (torch.Tensor): Input codes of shape [B, K, T] with B the batch size,
- K the number of codebooks and T the number of timesteps.
- conditions (list[ConditioningAttributes]): conditionings to use when modeling
- the given codes. Note that when evaluating multiple time with the same conditioning
- you should pre-compute those and pass them as `condition_tensors`.
- condition_tensors (dict[str, ConditionType] or None): pre-computed conditioning
- tensors, see `conditions`.
- Returns:
- LMOutput: Language model outputs
- logits (torch.Tensor) of shape [B, K, T, card] corresponding to the provided codes,
- i.e. the first item corresponds to logits to predict the first code, meaning that
- no additional shifting of codes and logits is required.
- mask (torch.Tensor) of shape [B, K, T], mask over valid and invalid positions.
- Given the specified interleaving strategies, parts of the logits and codes should
- not be considered as valid predictions because of invalid context.
- """
- B, K, T = codes.shape
- codes = codes.contiguous()
- # map codes [B, K, T] into pattern sequence [B, K, S] using special_token_id for masked tokens
- pattern = self.pattern_provider.get_pattern(T)
- sequence_codes, sequence_indexes, sequence_mask = pattern.build_pattern_sequence(
- codes, self.special_token_id, keep_only_valid_steps=True
- )
- # apply model on pattern sequence
- model = self if self._fsdp is None else self._fsdp
- logits = model(sequence_codes, conditions, condition_tensors) # [B, K, S, card]
- # map back the logits on pattern sequence to logits on original codes: [B, K, S, card] -> [B, K, T, card]
- # and provide the corresponding mask over invalid positions of tokens
- logits = logits.permute(0, 3, 1, 2) # [B, card, K, S]
- # note: we use nans as special token to make it obvious if we feed unexpected logits
- logits, logits_indexes, logits_mask = pattern.revert_pattern_logits(
- logits, float('nan'), keep_only_valid_steps=True
- )
- logits = logits.permute(0, 2, 3, 1) # [B, K, T, card]
- logits_mask = logits_mask[None, :, :].expand(B, -1, -1) # [K, T] -> [B, K, T]
- return LMOutput(logits, logits_mask)
-
- def _sample_next_token(self,
- sequence: torch.Tensor,
- cfg_conditions: CFGConditions,
- unconditional_state: State,
- use_sampling: bool = False,
- temp: float = 1.0,
- top_k: int = 0,
- top_p: float = 0.0,
- cfg_coef: tp.Optional[float] = None) -> torch.Tensor:
- """Sample next token from the model given a sequence and a set of conditions. The model supports
- multiple sampling strategies (greedy sampling, softmax, top-k, top-p...).
-
- Args:
- sequence (torch.Tensor): Current sequence of shape [B, K, S]
- with K corresponding to the number of codebooks and S the number of sequence steps.
- S = 1 in streaming mode, except for the first step that contains a bigger prompt.
- condition_tensors (Dict[str, ConditionType): Set of conditions. If CFG is used,
- should be twice the batch size, being the concatenation of the conditions + null conditions.
- use_sampling (bool): Whether to use a sampling strategy or not.
- temp (float): Sampling temperature.
- top_k (int): K for "top-k" sampling.
- top_p (float): P for "top-p" sampling.
- cfg_coef (float): classifier free guidance coefficient
- Returns:
- next_token (torch.Tensor): Next token tensor of shape [B, K, 1].
- """
- B = sequence.shape[0]
- cfg_coef = self.cfg_coef if cfg_coef is None else cfg_coef
- model = self if self._fsdp is None else self._fsdp
- if self.two_step_cfg and cfg_conditions != {}:
- assert isinstance(cfg_conditions, tuple)
- condition_tensors, null_condition_tensors = cfg_conditions
- cond_logits = model(sequence, conditions=[], condition_tensors=condition_tensors)
- state = self.get_streaming_state()
- self.set_streaming_state(unconditional_state)
- uncond_logits = model(sequence, conditions=[], condition_tensors=null_condition_tensors)
- unconditional_state.update(self.get_streaming_state())
- self.set_streaming_state(state)
- logits = uncond_logits + (cond_logits - uncond_logits) * self.cfg_coef
- else:
- assert isinstance(cfg_conditions, dict)
- condition_tensors = cfg_conditions
- if condition_tensors:
- # Preparing for CFG, predicting both conditional and unconditional logits.
- sequence = torch.cat([sequence, sequence], dim=0)
- all_logits = model(
- sequence,
- conditions=[], condition_tensors=condition_tensors)
- if condition_tensors:
- cond_logits, uncond_logits = all_logits.split(B, dim=0) # [B, K, T, card]
- logits = uncond_logits + (cond_logits - uncond_logits) * cfg_coef
- else:
- logits = all_logits
-
- logits = logits.permute(0, 1, 3, 2) # [B, K, card, T]
- logits = logits[..., -1] # [B x K x card]
-
- # Apply softmax for sampling if temp > 0. Else, do greedy sampling to avoid zero division error.
- if use_sampling and temp > 0.0:
- probs = torch.softmax(logits / temp, dim=-1)
- if top_p > 0.0:
- next_token = utils.sample_top_p(probs, p=top_p)
- elif top_k > 0:
- next_token = utils.sample_top_k(probs, k=top_k)
- else:
- next_token = utils.multinomial(probs, num_samples=1)
- else:
- next_token = torch.argmax(logits, dim=-1, keepdim=True)
-
- return next_token
-
- @torch.no_grad()
- def generate(self,
- prompt: tp.Optional[torch.Tensor] = None,
- conditions: tp.List[ConditioningAttributes] = [],
- num_samples: tp.Optional[int] = None,
- max_gen_len: int = 256,
- use_sampling: bool = True,
- temp: float = 1.0,
- top_k: int = 250,
- top_p: float = 0.0,
- cfg_coef: tp.Optional[float] = None,
- two_step_cfg: bool = False,
- remove_prompts: bool = False,
- check: bool = False,
- callback: tp.Optional[tp.Callable[[int, int], None]] = None) -> torch.Tensor:
- """Generate tokens sampling from the model given a prompt or unconditionally. Generation can
- be perform in a greedy fashion or using sampling with top K and top P strategies.
-
- Args:
- prompt (Optional[torch.Tensor]): Prompt tokens of shape [B, K, T].
- conditions_tensors (Dict[str, torch.Tensor]): Set of conditions or None.
- num_samples (int or None): Number of samples to generate when no prompt and no conditions are given.
- max_gen_len (int): Maximum generation length.
- use_sampling (bool): Whether to use a sampling strategy or not.
- temp (float): Sampling temperature.
- top_k (int): K for "top-k" sampling.
- top_p (float): P for "top-p" sampling.
- remove_prompts (bool): Whether to remove prompts from generation or not.
- Returns:
- torch.Tensor: Generated tokens.
- """
- assert not self.training, "generation shouldn't be used in training mode."
- first_param = next(iter(self.parameters()))
- device = first_param.device
-
- # Checking all input shapes are consistents.
- possible_num_samples = []
- if num_samples is not None:
- possible_num_samples.append(num_samples)
- elif prompt is not None:
- possible_num_samples.append(prompt.shape[0])
- elif conditions:
- possible_num_samples.append(len(conditions))
- else:
- possible_num_samples.append(1)
- assert [x == possible_num_samples[0] for x in possible_num_samples], "Inconsitent inputs shapes"
- num_samples = possible_num_samples[0]
-
- # below we create set of conditions: one conditional and one unconditional
- # to do that we merge the regular condition together with the null condition
- # we then do 1 forward pass instead of 2.
- # the reason for that is two-fold:
- # 1. it is about x2 faster than doing 2 forward passes
- # 2. avoid the streaming API treating the 2 passes as part of different time steps
- # We also support doing two different passes, in particular to ensure that
- # the padding structure is exactly the same between train anf test.
- # With a batch size of 1, this can be slower though.
- cfg_conditions: CFGConditions
- two_step_cfg = self.two_step_cfg if two_step_cfg is None else two_step_cfg
- if conditions:
- null_conditions = ClassifierFreeGuidanceDropout(p=1.0)(conditions)
- if two_step_cfg:
- cfg_conditions = (
- self.condition_provider(self.condition_provider.tokenize(conditions)),
- self.condition_provider(self.condition_provider.tokenize(null_conditions)),
- )
- else:
- conditions = conditions + null_conditions
- tokenized = self.condition_provider.tokenize(conditions)
- cfg_conditions = self.condition_provider(tokenized)
- else:
- cfg_conditions = {}
-
- if prompt is None:
- assert num_samples > 0
- prompt = torch.zeros((num_samples, self.num_codebooks, 0), dtype=torch.long, device=device)
-
- B, K, T = prompt.shape
- start_offset = T
- assert start_offset < max_gen_len
-
- pattern = self.pattern_provider.get_pattern(max_gen_len)
- # this token is used as default value for codes that are not generated yet
- unknown_token = -1
-
- # we generate codes up to the max_gen_len that will be mapped to the pattern sequence
- gen_codes = torch.full((B, K, max_gen_len), unknown_token, dtype=torch.long, device=device)
- # filling the gen_codes with the prompt if needed
- gen_codes[..., :start_offset] = prompt
- # create the gen_sequence with proper interleaving from the pattern: [B, K, S]
- gen_sequence, indexes, mask = pattern.build_pattern_sequence(gen_codes, self.special_token_id)
- # retrieve the start_offset in the sequence:
- # it is the first sequence step that contains the `start_offset` timestep
- start_offset_sequence = pattern.get_first_step_with_timesteps(start_offset)
- assert start_offset_sequence is not None
-
- with self.streaming():
- unconditional_state = self.get_streaming_state()
- prev_offset = 0
- gen_sequence_len = gen_sequence.shape[-1] # gen_sequence shape is [B, K, S]
- for offset in range(start_offset_sequence, gen_sequence_len):
- # get current sequence (note that the streaming API is providing the caching over previous offsets)
- curr_sequence = gen_sequence[..., prev_offset:offset]
- curr_mask = mask[None, ..., prev_offset:offset].expand(B, -1, -1)
- if check:
- # check coherence between mask and sequence
- assert (curr_sequence == torch.where(curr_mask, curr_sequence, self.special_token_id)).all()
- # should never happen as gen_sequence is filled progressively
- assert not (curr_sequence == unknown_token).any()
- # sample next token from the model, next token shape is [B, K, 1]
- next_token = self._sample_next_token(
- curr_sequence, cfg_conditions, unconditional_state, use_sampling, temp, top_k, top_p,
- cfg_coef=cfg_coef)
- # ensure the tokens that should be masked are properly set to special_token_id
- # as the model never output special_token_id
- valid_mask = mask[..., offset:offset+1].expand(B, -1, -1)
- next_token[~valid_mask] = self.special_token_id
- # ensure we don't overwrite prompt tokens, we only write over unknown tokens
- # (then mask tokens should be left as is as well, which is correct)
- gen_sequence[..., offset:offset+1] = torch.where(
- gen_sequence[..., offset:offset+1] == unknown_token,
- next_token, gen_sequence[..., offset:offset+1]
- )
- prev_offset = offset
- if callback is not None:
- callback(1 + offset - start_offset_sequence, gen_sequence_len - start_offset_sequence)
- unconditional_state.clear()
-
- # ensure sequence has been entirely filled
- assert not (gen_sequence == unknown_token).any()
- # ensure gen_sequence pattern and mask are matching
- # which means the gen_sequence is valid according to the pattern
- assert (
- gen_sequence == torch.where(mask[None, ...].expand(B, -1, -1), gen_sequence, self.special_token_id)
- ).all()
- # get back the codes, trimming the prompt if needed and cutting potentially incomplete timesteps
- out_codes, out_indexes, out_mask = pattern.revert_pattern_sequence(gen_sequence, special_token=unknown_token)
-
- # sanity checks over the returned codes and corresponding masks
- assert (out_codes[..., :max_gen_len] != unknown_token).all()
- assert (out_mask[..., :max_gen_len] == 1).all()
-
- out_start_offset = start_offset if remove_prompts else 0
- out_codes = out_codes[..., out_start_offset:max_gen_len]
-
- # ensure the returned codes are all valid
- assert (out_codes >= 0).all() and (out_codes <= self.card).all()
- return out_codes
diff --git a/spaces/YueMafighting/FollowYourPose/Dockerfile b/spaces/YueMafighting/FollowYourPose/Dockerfile
deleted file mode 100644
index 845d631472e9eca8e3bc2da734bb1b17eb72cc02..0000000000000000000000000000000000000000
--- a/spaces/YueMafighting/FollowYourPose/Dockerfile
+++ /dev/null
@@ -1,64 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update && \
- apt-get upgrade -y && \
- apt-get install -y --no-install-recommends \
- git \
- zip \
- unzip \
- git-lfs \
- wget \
- curl \
- # ffmpeg \
- ffmpeg \
- x264 \
- # python build dependencies \
- build-essential \
- libssl-dev \
- zlib1g-dev \
- libbz2-dev \
- libreadline-dev \
- libsqlite3-dev \
- libncursesw5-dev \
- xz-utils \
- tk-dev \
- libxml2-dev \
- libxmlsec1-dev \
- libffi-dev \
- liblzma-dev && \
- apt-get clean && \
- rm -rf /var/lib/apt/lists/*
-# RUN apt-get update && \
-# apt-get install zip
-# RUN wget https://github.com/ChenyangQiQi/FateZero/releases/download/v0.0.1/style.zip && unzip style.zip
-RUN useradd -m -u 1000 user
-RUN sudo apt-get install unzip
-USER user
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:${PATH}
-WORKDIR ${HOME}/app
-
-RUN curl https://pyenv.run | bash
-ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ENV PYTHON_VERSION=3.10.9
-RUN pyenv install ${PYTHON_VERSION} && \
- pyenv global ${PYTHON_VERSION} && \
- pyenv rehash && \
- pip install --no-cache-dir -U pip setuptools wheel
-
-RUN pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1
-COPY --chown=1000 requirements.txt /tmp/requirements.txt
-RUN pip install --no-cache-dir -U -r /tmp/requirements.txt
-
-COPY --chown=1000 . ${HOME}/app
-RUN ls -a
-# RUN cd ./FateZero/ckpt && bash download.sh
-RUN cd {WORKDIR}/data && bash download.sh
-ENV PYTHONPATH=${HOME}/app \
- PYTHONUNBUFFERED=1 \
- GRADIO_ALLOW_FLAGGING=never \
- GRADIO_NUM_PORTS=1 \
- GRADIO_SERVER_NAME=0.0.0.0 \
- GRADIO_THEME=huggingface \
- SYSTEM=spaces
-CMD ["python", "app.py"]
diff --git a/spaces/Yuliang/ECON/lib/common/render_utils.py b/spaces/Yuliang/ECON/lib/common/render_utils.py
deleted file mode 100644
index 389eab740fc341b9395e1e008b4fa91e9ef3cf83..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/common/render_utils.py
+++ /dev/null
@@ -1,226 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
-# holder of all proprietary rights on this computer program.
-# You can only use this computer program if you have closed
-# a license agreement with MPG or you get the right to use the computer
-# program from someone who is authorized to grant you that right.
-# Any use of the computer program without a valid license is prohibited and
-# liable to prosecution.
-#
-# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
-# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
-# for Intelligent Systems. All rights reserved.
-#
-# Contact: ps-license@tuebingen.mpg.de
-
-import math
-from typing import NewType
-
-import numpy as np
-import torch
-import trimesh
-from pytorch3d.renderer.mesh import rasterize_meshes
-from pytorch3d.structures import Meshes
-from torch import nn
-
-Tensor = NewType("Tensor", torch.Tensor)
-
-
-def solid_angles(points: Tensor, triangles: Tensor, thresh: float = 1e-8) -> Tensor:
- """Compute solid angle between the input points and triangles
- Follows the method described in:
- The Solid Angle of a Plane Triangle
- A. VAN OOSTEROM AND J. STRACKEE
- IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,
- VOL. BME-30, NO. 2, FEBRUARY 1983
- Parameters
- -----------
- points: BxQx3
- Tensor of input query points
- triangles: BxFx3x3
- Target triangles
- thresh: float
- float threshold
- Returns
- -------
- solid_angles: BxQxF
- A tensor containing the solid angle between all query points
- and input triangles
- """
- # Center the triangles on the query points. Size should be BxQxFx3x3
- centered_tris = triangles[:, None] - points[:, :, None, None]
-
- # BxQxFx3
- norms = torch.norm(centered_tris, dim=-1)
-
- # Should be BxQxFx3
- cross_prod = torch.cross(centered_tris[:, :, :, 1], centered_tris[:, :, :, 2], dim=-1)
- # Should be BxQxF
- numerator = (centered_tris[:, :, :, 0] * cross_prod).sum(dim=-1)
- del cross_prod
-
- dot01 = (centered_tris[:, :, :, 0] * centered_tris[:, :, :, 1]).sum(dim=-1)
- dot12 = (centered_tris[:, :, :, 1] * centered_tris[:, :, :, 2]).sum(dim=-1)
- dot02 = (centered_tris[:, :, :, 0] * centered_tris[:, :, :, 2]).sum(dim=-1)
- del centered_tris
-
- denominator = (
- norms.prod(dim=-1) + dot01 * norms[:, :, :, 2] + dot02 * norms[:, :, :, 1] +
- dot12 * norms[:, :, :, 0]
- )
- del dot01, dot12, dot02, norms
-
- # Should be BxQ
- solid_angle = torch.atan2(numerator, denominator)
- del numerator, denominator
-
- torch.cuda.empty_cache()
-
- return 2 * solid_angle
-
-
-def winding_numbers(points: Tensor, triangles: Tensor, thresh: float = 1e-8) -> Tensor:
- """Uses winding_numbers to compute inside/outside
- Robust inside-outside segmentation using generalized winding numbers
- Alec Jacobson,
- Ladislav Kavan,
- Olga Sorkine-Hornung
- Fast Winding Numbers for Soups and Clouds SIGGRAPH 2018
- Gavin Barill
- NEIL G. Dickson
- Ryan Schmidt
- David I.W. Levin
- and Alec Jacobson
- Parameters
- -----------
- points: BxQx3
- Tensor of input query points
- triangles: BxFx3x3
- Target triangles
- thresh: float
- float threshold
- Returns
- -------
- winding_numbers: BxQ
- A tensor containing the Generalized winding numbers
- """
- # The generalized winding number is the sum of solid angles of the point
- # with respect to all triangles.
- return (1 / (4 * math.pi) * solid_angles(points, triangles, thresh=thresh).sum(dim=-1))
-
-
-def batch_contains(verts, faces, points):
-
- B = verts.shape[0]
- N = points.shape[1]
-
- verts = verts.detach().cpu()
- faces = faces.detach().cpu()
- points = points.detach().cpu()
- contains = torch.zeros(B, N)
-
- for i in range(B):
- contains[i] = torch.as_tensor(trimesh.Trimesh(verts[i], faces[i]).contains(points[i]))
-
- return 2.0 * (contains - 0.5)
-
-
-def dict2obj(d):
- if not isinstance(d, dict):
- return d
-
- class C(object):
- pass
-
- o = C()
- for k in d:
- o.__dict__[k] = dict2obj(d[k])
- return o
-
-
-def face_vertices(vertices, faces):
- """
- :param vertices: [batch size, number of vertices, 3]
- :param faces: [batch size, number of faces, 3]
- :return: [batch size, number of faces, 3, 3]
- """
-
- bs, nv = vertices.shape[:2]
- bs, nf = faces.shape[:2]
- device = vertices.device
- faces = faces + (torch.arange(bs, dtype=torch.int32).to(device) * nv)[:, None, None]
- vertices = vertices.reshape((bs * nv, vertices.shape[-1]))
-
- return vertices[faces.long()]
-
-
-class Pytorch3dRasterizer(nn.Module):
- """Borrowed from https://github.com/facebookresearch/pytorch3d
- Notice:
- x,y,z are in image space, normalized
- can only render squared image now
- """
- def __init__(
- self, image_size=224, blur_radius=0.0, faces_per_pixel=1, device=torch.device("cuda:0")
- ):
- """
- use fixed raster_settings for rendering faces
- """
- super().__init__()
- raster_settings = {
- "image_size": image_size,
- "blur_radius": blur_radius,
- "faces_per_pixel": faces_per_pixel,
- "bin_size": -1,
- "max_faces_per_bin": None,
- "perspective_correct": False,
- "cull_backfaces": True,
- }
- raster_settings = dict2obj(raster_settings)
- self.raster_settings = raster_settings
- self.device = device
-
- def forward(self, vertices, faces, attributes=None):
- fixed_vertices = vertices.clone()
- fixed_vertices[..., :2] = -fixed_vertices[..., :2]
- meshes_screen = Meshes(verts=fixed_vertices.float(), faces=faces.long())
- raster_settings = self.raster_settings
- pix_to_face, zbuf, bary_coords, dists = rasterize_meshes(
- meshes_screen,
- image_size=raster_settings.image_size,
- blur_radius=raster_settings.blur_radius,
- faces_per_pixel=raster_settings.faces_per_pixel,
- bin_size=raster_settings.bin_size,
- max_faces_per_bin=raster_settings.max_faces_per_bin,
- perspective_correct=raster_settings.perspective_correct,
- )
- vismask = (pix_to_face > -1).float()
- D = attributes.shape[-1]
- attributes = attributes.clone()
- attributes = attributes.view(
- attributes.shape[0] * attributes.shape[1], 3, attributes.shape[-1]
- )
- N, H, W, K, _ = bary_coords.shape
- mask = pix_to_face == -1
- pix_to_face = pix_to_face.clone()
- pix_to_face[mask] = 0
- idx = pix_to_face.view(N * H * W * K, 1, 1).expand(N * H * W * K, 3, D)
- pixel_face_vals = attributes.gather(0, idx).view(N, H, W, K, 3, D)
- pixel_vals = (bary_coords[..., None] * pixel_face_vals).sum(dim=-2)
- pixel_vals[mask] = 0 # Replace masked values in output.
- pixel_vals = pixel_vals[:, :, :, 0].permute(0, 3, 1, 2)
- pixel_vals = torch.cat([pixel_vals, vismask[:, :, :, 0][:, None, :, :]], dim=1)
- return pixel_vals
-
- def get_texture(self, uvcoords, uvfaces, verts, faces, verts_color):
-
- batch_size = verts.shape[0]
- uv_verts_color = face_vertices(verts_color, faces.expand(batch_size, -1,
- -1)).to(self.device)
- uv_map = self.forward(
- uvcoords.expand(batch_size, -1, -1), uvfaces.expand(batch_size, -1, -1), uv_verts_color
- )[:, :3]
- uv_map_npy = np.flip(uv_map.squeeze(0).permute(1, 2, 0).cpu().numpy(), 0)
-
- return uv_map_npy
diff --git a/spaces/Yunshansongbai/SVC-Nahida/app.py b/spaces/Yunshansongbai/SVC-Nahida/app.py
deleted file mode 100644
index 7321a5fc776b31c97fe5729b6c4fb88a0e389ee1..0000000000000000000000000000000000000000
--- a/spaces/Yunshansongbai/SVC-Nahida/app.py
+++ /dev/null
@@ -1,194 +0,0 @@
-import io
-import os
-
-import gradio as gr
-import librosa
-import numpy as np
-import soundfile
-from inference.infer_tool import Svc
-import logging
-import os
-import paddle
-import requests
-import utils
-from spleeter import Separator
-import time
-from datetime import datetime, timedelta
-
-build_dir=os.getcwd()
-if build_dir == "/home/aistudio":
- build_dir += "/build"
-
-model_dir=build_dir+'/trained_models'
-
-model_list_path = model_dir + "/model_list.txt"
-
-# 筛选出文件夹
-models = []
-for filename in os.listdir(model_dir):
- # 判断文件名是否以 '.pdparams' 结尾，并且不包含后缀部分
- if filename.endswith('.pdparams') and os.path.splitext(filename)[0].isalpha():
- models.append(os.path.splitext(filename)[0])
-cache_model = {}
-
-def callback(text):
- if text == "reboot":
- os._exit(0)
- one_hour_later = datetime.now() + timedelta(hours=1)
- else:
- global start_time
- if time.time() - start_time >= 3600:
- os._exit(0)
- one_hour_later = datetime.now() + timedelta(hours=1)
- else:
- return text
-
-def separate_fn(song_input):
- try:
- if song_input is None:
- return "请上传歌曲",None,None,None,None
- params_2stems = {
- 'sample_rate': 44100,
- 'frame_length': 4096,
- 'frame_step': 1024,
- 'T': 512,
- 'F': 1024,
- 'num_instruments': ['vocals', 'instrumental'],
- 'output_dir': build_dir+'/output_2stems',
- 'checkpoint_path': build_dir+'/spleeter',
- 'use_elu': False}
- sampling_rate, song = song_input
- soundfile.write("temp.wav", song, sampling_rate, format="wav")
- # 初始化分离器
- sep = Separator(params_2stems)
- sep.separate('temp.wav')
- vocal_path = params_2stems["output_dir"]+"/temp-vocals.wav"
- instrumental_path = params_2stems["output_dir"]+"/temp-instrumental.wav"
- return "分离成功，请继续前往体验【转换】和【混音】",vocal_path,instrumental_path,vocal_path,instrumental_path
- except Exception as e:
- import traceback
- return traceback.format_exc() , None,None,None,None
-
-
-def convert_fn(model_name, input_audio,input_audio_micro, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale):
- try:
- if model_name in cache_model:
- model = cache_model[model_name]
- else:
- if paddle.device.is_compiled_with_cuda()==False and len(cache_model)!=0:
- return f"目前运行环境为CPU，受制于平台算力，每次启动本项目只允许加载1个模型，当前已加载{next(iter(cache_model))}",None,None
- config_path = f"{build_dir}/trained_models/config.json"
- model = Svc(f"{build_dir}/trained_models/{model_name}.pdparams", config_path,mode="test")
- cache_model[model_name] = model
- if input_audio is None and input_audio_micro is None:
- return "请上传音频", None,None
- if input_audio_micro is not None:
- input_audio = input_audio_micro
- sampling_rate, audio = input_audio
- duration = audio.shape[0] / sampling_rate
- audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio.transpose(1, 0))
- if sampling_rate != 16000:
- audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
- print(audio.shape)
- out_wav_path = "temp.wav"
- soundfile.write(out_wav_path, audio, 16000, format="wav")
- print(cluster_ratio, auto_f0, noise_scale)
- _audio = model.slice_inference(out_wav_path, model_name, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale)
- del model
- return "转换成功，请继续前往体验【混音】", (44100, _audio),(44100, _audio)
- except Exception as e:
- import traceback
- return traceback.format_exc() , None,None
-
-def compose_fn(input_vocal,input_instrumental,mixing_ratio=0.5):
- try:
- outlog = "混音成功"
- if input_vocal is None:
- return "请上传人声", None
- if input_instrumental is None:
- return "请上传伴奏", None
- vocal_sampling_rate, vocal = input_vocal
- vocal_duration = vocal.shape[0] / vocal_sampling_rate
- vocal = (vocal / np.iinfo(vocal.dtype).max).astype(np.float32)
- if len(vocal.shape) > 1:
- vocal = librosa.to_mono(vocal.transpose(1, 0))
- if vocal_sampling_rate != 44100:
- vocal = librosa.resample(vocal, orig_sr=vocal_sampling_rate, target_sr=44100)
-
- instrumental_sampling_rate, instrumental = input_instrumental
- instrumental_duration = instrumental.shape[0] / instrumental_sampling_rate
- instrumental = (instrumental / np.iinfo(instrumental.dtype).max).astype(np.float32)
- if len(instrumental.shape) > 1:
- instrumental = librosa.to_mono(instrumental.transpose(1, 0))
- if instrumental_sampling_rate != 44100:
- instrumental = librosa.resample(instrumental, orig_sr=instrumental_sampling_rate, target_sr=44100)
- if len(vocal)!=len(instrumental):
- min_length = min(len(vocal),len(instrumental))
- instrumental = instrumental[:min_length]
- vocal = vocal[:min_length]
- outlog = "人声伴奏长度不一致，已自动截断较长的音频"
-
- mixed_audio = (1 - mixing_ratio) * vocal + mixing_ratio * instrumental
- mixed_audio_data = mixed_audio.astype(np.float32)
- return outlog,(44100,mixed_audio_data)
- except Exception as e:
- import traceback
- return traceback.format_exc() , None
-
-
-app = gr.Blocks()
-
-with app:
- start_time = time.time()
- one_hour_later = datetime.now() + timedelta(hours=1)
- gr.Markdown('
SVC歌声转换全流程体验（伴奏分离，转换，混音）
') - with gr.Tabs() as tabs: - with gr.TabItem("人声伴奏分离"): - gr.Markdown('该项目人声分离的效果弱于UVR5，如自备分离好的伴奏和人声可跳过该步骤
') - song_input = gr.Audio(label="上传歌曲（tips:上传后点击右上角✏可以进行歌曲剪辑）",interactive=True) - gr.Examples(examples=[build_dir+"/examples/song/blue.wav",build_dir+"/examples/song/Counter_clockwise_Clock.wav",build_dir+"/examples/song/one_last_kiss.wav"],inputs=song_input,label="歌曲样例") - - btn_separate = gr.Button("人声伴奏分离", variant="primary") - text_output1 = gr.Textbox(label="输出信息") - vocal_output1 = gr.Audio(label="输出人声",interactive=False) - instrumental_output1 = gr.Audio(label="输出伴奏",interactive=False) - with gr.TabItem("转换"): - model_name = gr.Dropdown(label="模型", choices=models, value="纳西妲") - vocal_input1 = gr.Audio(label="上传人声",interactive=True) - gr.Examples(examples=[build_dir+"/examples/vocals/blue_vocal.wav",build_dir+"/examples/vocals/Counter_clockwise_Clock_vocal.wav",build_dir+"/examples/vocals/one_last_kiss_vocal.wav"],inputs=vocal_input1,label="人声样例") - btn_use_separate = gr.Button("使用【人声伴奏分离】分离的人声") - micro_input = gr.Audio(label="麦克风输入（优先于上传的人声）",source="microphone",interactive=True) - vc_transform = gr.Number(label="变调（半音数量,升八度12降八度-12）", value=0) - cluster_ratio = gr.Number(label="聚类模型混合比例", value=0,visible=False) - auto_f0 = gr.Checkbox(label="自动预测音高（转换歌声时不要打开，会严重跑调）", value=False) - slice_db = gr.Number(label="静音分贝阈值（嘈杂的音频可以-30，干声保留呼吸可以-50）", value=-50) - noise_scale = gr.Number(label="noise_scale", value=0.2) - btn_convert = gr.Button("转换", variant="primary") - text_output2 = gr.Textbox(label="输出信息") - vc_output2 = gr.Audio(label="输出音频",interactive=False) - - with gr.TabItem("混音"): - vocal_input2 = gr.Audio(label="上传人声",interactive=True) - btn_use_convert = gr.Button("使用【转换】输出的人声") - instrumental_input1 = gr.Audio(label="上传伴奏") - gr.Examples(examples=[build_dir+"/examples/instrumental/blue_instrumental.wav",build_dir+"/examples/instrumental/Counter_clockwise_Clock_instrumental.wav",build_dir+"/examples/instrumental/one_last_kiss_instrumental.wav"],inputs=instrumental_input1,label="伴奏样例") - btn_use_separate2 = gr.Button("使用【人声伴奏分离】分离的伴奏") - mixing_ratio = gr.Slider(0, 1, value=0.75,step=0.01,label="混音比例（人声:伴奏）", info="人声:伴奏") - btn_compose = gr.Button("混音", variant="primary") - text_output3 = gr.Textbox(label="输出信息") - song_output = gr.Audio(label="输出歌曲",interactive=False) - - with gr.TabItem("设置"): - output = gr.Textbox(label="输出",placeholder=f"距离下一次允许重启时间为{one_hour_later}") - btn_reboot = gr.Button("重启",variant="primary") - btn_separate.click(separate_fn, song_input, [text_output1, vocal_output1,instrumental_output1,vocal_input1,instrumental_input1]) - btn_convert.click(convert_fn, [model_name, vocal_input1,micro_input,vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale], [text_output2, vc_output2,vocal_input2]) - btn_compose.click(compose_fn,[vocal_input2,instrumental_input1,mixing_ratio],[text_output3,song_output]) - btn_reboot.click(callback,output) - btn_use_convert.click(lambda x:x,vc_output2,vocal_input2) - btn_use_separate.click(lambda x:x,vocal_output1,vocal_input1) - btn_use_separate2.click(lambda x:x,instrumental_output1,instrumental_input1) - -app.launch() \ No newline at end of file diff --git a/spaces/Ziqi/ReVersion/README.md b/spaces/Ziqi/ReVersion/README.md deleted file mode 100644 index 1823df2c77ed395faf47aed9a4e04093a151848e..0000000000000000000000000000000000000000 --- a/spaces/Ziqi/ReVersion/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: ReVersion -emoji: 🐠 -colorFrom: pink -colorTo: purple -sdk: gradio -sdk_version: 3.23.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/abcde1234www/tts/app.py b/spaces/abcde1234www/tts/app.py deleted file mode 100644 index a97450c289843433835ebc4da6d5d885ea42167c..0000000000000000000000000000000000000000 --- a/spaces/abcde1234www/tts/app.py +++ /dev/null @@ -1,103 +0,0 @@ -""" -Copyright 2022 Balacoon - -TTS interactive demo -""" - -import logging -from typing import cast - -import gradio as gr -from balacoon_tts import TTS -from huggingface_hub import hf_hub_download, list_repo_files - -# global tts module, initialized from a model selected -tts = None - - -def main(): - logging.basicConfig(level=logging.INFO) - - with gr.Blocks() as demo: - gr.Markdown( - """ -Text-to-Speech
- - 1. Write an utterance to generate, - 2. Select the model to synthesize with - 3. Select speaker (only for multi-speaker models) - 4. Hit "Generate" and listen to the result! - - When you select model for the first time, - it will take a little time to download it. - You can learn more about models available - [here](https://huggingface.co/balacoon/tts), - visit [Balacoon website](https://balacoon.com/) for more info. - """ - ) - with gr.Row(variant="panel"): - text = gr.Textbox(label="Text", placeholder="Type something here...") - - with gr.Row(): - with gr.Column(variant="panel"): - repo_files = list_repo_files(repo_id="balacoon/tts") - model_files = [x for x in repo_files if x.endswith(".addon")] - model_name = gr.Dropdown( - label="Model", - choices=model_files, - ) - with gr.Column(variant="panel"): - speaker = gr.Dropdown(label="Speaker", choices=[]) - - def set_model(model_name_str: str): - """ - gets value from `model_name`, loads model, - re-initializes tts object, gets list of - speakers that model supports and set them to `speaker` - """ - model_path = hf_hub_download( - repo_id="balacoon/tts", filename=model_name_str - ) - global tts - tts = TTS(model_path) - speakers = tts.get_speakers() - if speakers: - visible = True - value = speakers[-1] - else: - visible = False - value = "" - return gr.Dropdown.update( - choices=speakers, value=value, visible=visible - ) - - model_name.change(set_model, inputs=model_name, outputs=speaker) - - with gr.Row(variant="panel"): - generate = gr.Button("Generate") - with gr.Row(variant="panel"): - audio = gr.Audio() - - def synthesize_audio(text_str: str, speaker_str: str = ""): - """ - gets utterance to synthesize from `text` Textbox - and speaker name from `speaker` dropdown list. - speaker name might be empty for single-speaker models. - Synthesizes the waveform and updates `audio` with it. - """ - if not text_str: - logging.info("text or speaker are not provided") - return None - global tts - if len(text_str) > 1024: - text_str = text_str[:1024] - samples = cast(TTS, tts).synthesize(text_str, speaker_str) - return gr.Audio.update(value=(24000, samples)) - - generate.click(synthesize_audio, inputs=[text, speaker], outputs=audio) - - demo.launch() - - -if __name__ == "__main__": - main() diff --git a/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/tableau/setup.md b/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/tableau/setup.md deleted file mode 100644 index 73dac977c474116928b47b28dbab6cc0705660c0..0000000000000000000000000000000000000000 --- a/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/tableau/setup.md +++ /dev/null @@ -1,60 +0,0 @@ ---- -title: Setup ---- -# Tableau Ingestion Guide: Setup & Prerequisites - -In order to configure ingestion from Tableau, you'll first have to enable Tableau Metadata API and you should have a user with Site Administrator Explorer permissions. - -## Tableau Prerequisites - -1. Grant `Site Administrator Explorer permissions` to a user - - A. Log in to Tableau Cloud https://sso.online.tableau.com/public/idp/SSO. - - B. Navigate to `Users`. - -
-
-
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/drawingboard.min.css b/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/drawingboard.min.css
deleted file mode 100644
index 7d80478705de8fdceacbad40e5a85c19307be665..0000000000000000000000000000000000000000
--- a/spaces/awaawawawa/iurf7irfuyytruyyugb/ui/media/drawingboard.min.css
+++ /dev/null
@@ -1,5 +0,0 @@
-/* drawingboard.js v0.4.6 - https://github.com/Leimi/drawingboard.js
-* Copyright (c) 2015 Emmanuel Pelletier
-* Licensed MIT */
-
-.drawing-board,.drawing-board *{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box}.drawing-board-controls-hidden,.drawing-board-utils-hidden{display:none!important}.drawing-board{position:relative;display:block}.drawing-board-canvas-wrapper{position:relative;margin:0;border:1px solid #ddd}.drawing-board-canvas{position:absolute;top:0;left:0;width:auto;cursor:crosshair;z-index:20}.drawing-board-cursor{position:absolute;top:0;left:0;pointer-events:none;border-radius:50%;background:#ccc;background:rgba(0,0,0,.2);z-index:30}.drawing-board-control-colors-rainbows,.drawing-board-control-size .drawing-board-control-inner,.drawing-board-control-size-dropdown,.drawing-board-control>button{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;overflow:hidden;background-color:#eee;padding:2px 4px;border:1px solid #ccc;box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);-webkit-box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);height:28px}.drawing-board-control>button{cursor:pointer;min-width:28px;line-height:14px}.drawing-board-control>button:focus,.drawing-board-control>button:hover{background-color:#ddd}.drawing-board-control>button.active,.drawing-board-control>button:active{box-shadow:inset 0 1px 2px 0 rgba(0,0,0,.2);-webkit-box-shadow:inset 0 1px 2px 0 rgba(0,0,0,.2);background-color:#ddd}.drawing-board-control>button[disabled]{color:gray}.drawing-board-control>button[disabled].active,.drawing-board-control>button[disabled]:active,.drawing-board-control>button[disabled]:focus,.drawing-board-control>button[disabled]:hover{background-color:#eee;box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);-webkit-box-shadow:0 1px 3px -2px #121212,inset 0 2px 5px 0 rgba(255,255,255,.3);cursor:default}.drawing-board-controls{margin:0 auto;text-align:center;font-size:0;display:table;border-spacing:9.33px 0;position:relative;min-height:28px}.drawing-board-controls[data-align=left]{margin:0;left:-9.33px}.drawing-board-controls[data-align=right]{margin:0 0 0 auto;right:-9.33px}.drawing-board-canvas-wrapper+.drawing-board-controls,.drawing-board-controls+.drawing-board-canvas-wrapper{margin-top:5px}.drawing-board-controls-hidden{height:0;min-height:0;padding:0;margin:0;border:0}.drawing-board-control{display:table-cell;border-collapse:separate;vertical-align:middle;font-size:16px;height:100%}.drawing-board-control-inner{position:relative;height:100%;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}.drawing-board-control>button{margin:0;vertical-align:middle}.drawing-board-control-colors{font-size:0;line-height:0}.drawing-board-control-colors-current{border:1px solid #ccc;cursor:pointer;display:inline-block;width:26px;height:26px}.drawing-board-control-colors-rainbows{display:inline-block;position:absolute;left:0;top:33px;margin-left:0;z-index:100;width:250px;height:auto;padding:4px}.drawing-board-control-colors-rainbow{height:18px}.drawing-board-control-colors-picker:first-child{margin-right:5px}.drawing-board-control-colors-picker{display:inline-block;width:18px;height:18px;cursor:pointer}.drawing-board-control-colors-picker[data-color="rgba(255, 255, 255, 1)"]{width:16px;height:17px;border:1px solid #ccc;border-bottom:none}.drawing-board-control-colors-picker:hover{width:16px;height:16px;border:1px solid #555}.drawing-board-control-drawingmode>button{margin-right:2px}.drawing-board-control-drawingmode>button:last-child{margin-right:0}.drawing-board-control-drawingmode-pencil-button{overflow:hidden;background-image:url(data:image/png;base64,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);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-drawingmode-pencil-button:before{content:"";display:block;width:0;height:100%}.drawing-board-control-drawingmode-eraser-button{overflow:hidden;background-image:url(data:image/png;base64,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);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-drawingmode-eraser-button:before{content:"";display:block;width:0;height:100%}.drawing-board-control-drawingmode-filler-button{overflow:hidden;background-image:url(data:image/png;base64,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);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-drawingmode-filler-button:before{content:"";display:block;width:0;height:100%}.drawing-board-control-navigation>button{font-family:Helvetica,Arial,sans-serif;font-size:14px;font-weight:700;margin-right:2px}.drawing-board-control-navigation>button:last-child{margin-right:0}.drawing-board-control-size[data-drawing-board-type=range] .drawing-board-control-inner{width:75px}.drawing-board-control-size[data-drawing-board-type=dropdown] .drawing-board-control-inner{overflow:visible}.drawing-board-control-size-range-input{position:relative;width:100%;z-index:100;margin:0;padding:0;border:0}.drawing-board-control-size-dropdown span,.drawing-board-control-size-dropdown-current span,.drawing-board-control-size-range-current{display:block;background:#333;opacity:.8}.drawing-board-control-size-range-current{display:inline-block;opacity:.15;position:absolute;pointer-events:none;left:50%;top:50%;z-index:50}.drawing-board-control-size-dropdown-current{display:block;height:100%;width:40px;overflow:hidden;position:relative}.drawing-board-control-size-dropdown-current span{position:absolute;left:50%;top:50%}.drawing-board-control-size-dropdown{position:absolute;left:-6px;top:33px;height:auto;list-style-type:none;margin:0;padding:0;z-index:100}.drawing-board-control-size-dropdown li{display:block;padding:4px;margin:3px 0;min-height:16px}.drawing-board-control-size-dropdown li:hover{background:#ccc}.drawing-board-control-size-dropdown span{margin:0 auto}.drawing-board-control-download-button{overflow:hidden;background-image:url(data:image/png;base64,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);background-position:50% 50%;background-repeat:no-repeat}.drawing-board-control-download-button:before{content:"";display:block;width:0;height:100%}
\ No newline at end of file
diff --git a/spaces/awacke1/Bloom.Generative.Writer/generators/topic_to_abstract.py b/spaces/awacke1/Bloom.Generative.Writer/generators/topic_to_abstract.py
deleted file mode 100644
index a91131db41007e46ce1d9a9772bdd7e9645b9d2b..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Bloom.Generative.Writer/generators/topic_to_abstract.py
+++ /dev/null
@@ -1,6 +0,0 @@
-
-from .model import model
-
-
-def topic_to_abstract_generator(template):
- return model('topic', template)
diff --git a/spaces/awacke1/Streamlit-Data-Synthesis-Example/app.py b/spaces/awacke1/Streamlit-Data-Synthesis-Example/app.py
deleted file mode 100644
index 636e3a14af4ceae48968e372e88f840f3425c791..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Streamlit-Data-Synthesis-Example/app.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import streamlit as st
-import pandas as pd
-
-
-def generate_hospital_data():
- # Generate hospital data
- hospitals = {
- "city": ["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"],
- "state": ["NY", "CA", "IL", "TX", "AZ"],
- "bed_count": [1200, 1500, 1100, 1300, 1400],
- }
- df = pd.DataFrame(hospitals)
- return df
-
-
-def generate_state_data():
- # Generate state data
- states = {
- "state": ["NY", "CA", "IL", "TX", "AZ"],
- "population": [20000000, 40000000, 13000000, 29000000, 7000000],
- "square_miles": [54556, 163696, 57914, 268596, 113990],
- }
- df = pd.DataFrame(states)
- return df
-
-
-def merge_datasets(hospitals_df, states_df):
- # Merge hospital and state data
- merged_df = pd.merge(hospitals_df, states_df, on="state")
- return merged_df
-
-
-def calculate_beds_per_capita(merged_df):
- # Calculate beds per capita
- merged_df["beds_per_capita"] = merged_df["bed_count"] / merged_df["population"]
- return merged_df
-
-
-def main():
- # Generate data
- hospitals_df = generate_hospital_data()
- states_df = generate_state_data()
-
- # Merge datasets
- merged_df = merge_datasets(hospitals_df, states_df)
-
- # Calculate beds per capita
- merged_df = calculate_beds_per_capita(merged_df)
-
- # Show merged and calculated data
- st.write(merged_df)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/class_names/README.md b/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/class_names/README.md
deleted file mode 100644
index 30d74d258442c7c65512eafab474568dd706c430..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Webcam-Object-Recognition-Yolo-n-Coco/class_names/README.md
+++ /dev/null
@@ -1 +0,0 @@
-test
\ No newline at end of file
diff --git a/spaces/badayvedat/AudioSep/models/CLAP/open_clip/bert.py b/spaces/badayvedat/AudioSep/models/CLAP/open_clip/bert.py
deleted file mode 100644
index a83d96d2a77ed05198efc05837522bc88d2499cc..0000000000000000000000000000000000000000
--- a/spaces/badayvedat/AudioSep/models/CLAP/open_clip/bert.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from transformers import BertTokenizer, BertModel
-
-tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-model = BertModel.from_pretrained("bert-base-uncased")
-text = "Replace me by any text you'd like."
-
-
-def bert_embeddings(text):
- # text = "Replace me by any text you'd like."
- encoded_input = tokenizer(text, return_tensors="pt")
- output = model(**encoded_input)
- return output
-
-
-from transformers import RobertaTokenizer, RobertaModel
-
-tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
-model = RobertaModel.from_pretrained("roberta-base")
-text = "Replace me by any text you'd like."
-
-
-def Roberta_embeddings(text):
- # text = "Replace me by any text you'd like."
- encoded_input = tokenizer(text, return_tensors="pt")
- output = model(**encoded_input)
- return output
-
-
-from transformers import BartTokenizer, BartModel
-
-tokenizer = BartTokenizer.from_pretrained("facebook/bart-base")
-model = BartModel.from_pretrained("facebook/bart-base")
-text = "Replace me by any text you'd like."
-
-
-def bart_embeddings(text):
- # text = "Replace me by any text you'd like."
- encoded_input = tokenizer(text, return_tensors="pt")
- output = model(**encoded_input)
- return output
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DAmmoLoader.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DAmmoLoader.js
deleted file mode 100644
index 9239e109afef47ff228e1fa2005fd9a6643da7ae..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/sea3d/physics/SEA3DAmmoLoader.js
+++ /dev/null
@@ -1,590 +0,0 @@
-/**
- * SEA3D+AMMO for Three.JS
- * @author Sunag / http://www.sunag.com.br/
- */
-
-'use strict';
-
-THREE.SEA3D.prototype.toAmmoVec3 = function ( v ) {
-
- return new Ammo.btVector3( v.x, v.y, v.z );
-
-};
-
-//
-// Sphere
-//
-
-THREE.SEA3D.prototype.readSphere = function ( sea ) {
-
- var shape = new Ammo.btSphereShape( sea.radius );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Box
-//
-
-THREE.SEA3D.prototype.readBox = function ( sea ) {
-
- var shape = new Ammo.btBoxShape( new Ammo.btVector3( sea.width * .5, sea.height * .5, sea.depth * .5 ) );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Cone
-//
-
-THREE.SEA3D.prototype.readCone = function ( sea ) {
-
- var shape = new Ammo.btConeShape( sea.radius, sea.height );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Cylinder
-//
-
-THREE.SEA3D.prototype.readCylinder = function ( sea ) {
-
- var shape = new Ammo.btCylinderShape( new Ammo.btVector3( sea.height, sea.radius, sea.radius ) );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Capsule
-//
-
-THREE.SEA3D.prototype.readCapsule = function ( sea ) {
-
- var shape = new Ammo.btCapsuleShape( sea.radius, sea.height );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Convex Geometry
-//
-
-THREE.SEA3D.prototype.readConvexGeometry = function ( sea ) {
-
- if ( this.config.convexHull ) {
-
- var shape = SEA3D.AMMO.createConvexHull( sea.geometry.tag, sea.subGeometryIndex );
-
- } else {
-
- var triMesh = SEA3D.AMMO.createTriangleMesh( sea.geometry.tag, sea.subGeometryIndex );
-
- var shape = new Ammo.btConvexTriangleMeshShape( triMesh, true );
-
- }
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Triangle Geometry
-//
-
-THREE.SEA3D.prototype.readTriangleGeometry = function ( sea ) {
-
- var triMesh = SEA3D.AMMO.createTriangleMesh( sea.geometry.tag, sea.subGeometryIndex );
-
- var shape = new Ammo.btBvhTriangleMeshShape( triMesh, true, true );
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Compound
-//
-
-THREE.SEA3D.prototype.readCompound = function ( sea ) {
-
- var shape = new Ammo.btCompoundShape();
-
- for ( var i = 0; i < sea.compounds.length; i ++ ) {
-
- var compound = sea.compounds[ i ];
-
- THREE.SEA3D.MTXBUF.elements = compound.transform;
-
- var transform = SEA3D.AMMO.getTransformFromMatrix( THREE.SEA3D.MTXBUF );
-
- shape.addChildShape( transform, compound.shape.tag );
-
- }
-
- this.domain.shapes = this.shapes = this.shapes || [];
- this.shapes.push( this.objects[ "shpe/" + sea.name ] = sea.tag = shape );
-
-};
-
-//
-// Rigid Body Base
-//
-
-THREE.SEA3D.prototype.readRigidBodyBase = function ( sea ) {
-
- var shape = sea.shape.tag,
- transform, target;
-
- if ( sea.target ) {
-
- target = sea.target.tag;
-
- target.physics = { enabled: true };
- target.updateMatrix();
-
- transform = SEA3D.AMMO.getTransformFromMatrix( sea.target.tag.matrix );
-
- } else {
-
- THREE.SEA3D.MTXBUF.fromArray( sea.transform );
-
- transform = SEA3D.AMMO.getTransformFromMatrix( THREE.SEA3D.MTXBUF );
-
- }
-
- var motionState = new Ammo.btDefaultMotionState( transform );
- var localInertia = new Ammo.btVector3( 0, 0, 0 );
-
- shape.calculateLocalInertia( sea.mass, localInertia );
-
- var info = new Ammo.btRigidBodyConstructionInfo( sea.mass, motionState, shape, localInertia );
- info.set_m_friction( sea.friction );
- info.set_m_restitution( sea.restitution );
- info.set_m_linearDamping( sea.linearDamping );
- info.set_m_angularDamping( sea.angularDamping );
-
- var rb = new Ammo.btRigidBody( info );
-
- if ( target ) {
-
- target.physics.rigidBody = rb;
-
- if ( sea.offset ) {
-
- var offset = new THREE.Matrix4();
- offset.fromArray( sea.offset );
-
- target.physics.offset = offset;
-
- }
-
- }
-
- Ammo.destroy( info );
-
- this.domain.rigidBodies = this.rigidBodies = this.rigidBodies || [];
- this.rigidBodies.push( this.objects[ "rb/" + sea.name ] = sea.tag = rb );
-
- return rb;
-
-};
-
-//
-// Rigid Body
-//
-
-THREE.SEA3D.prototype.readRigidBody = function ( sea ) {
-
- var rb = this.readRigidBodyBase( sea );
-
- SEA3D.AMMO.addRigidBody( rb, sea.target ? sea.target.tag : undefined, this.config.enabledPhysics );
-
-};
-
-//
-// Car Controller
-//
-
-THREE.SEA3D.prototype.readCarController = function ( sea ) {
-
- var body = this.readRigidBodyBase( sea );
-
- body.setActivationState( SEA3D.AMMO.DISABLE_DEACTIVATION );
-
- // Car
-
- var vehicleRayCaster = new Ammo.btDefaultVehicleRaycaster( SEA3D.AMMO.world );
-
- var tuning = new Ammo.btVehicleTuning();
-
- tuning.set_m_suspensionStiffness( sea.suspensionStiffness );
- tuning.set_m_suspensionDamping( sea.suspensionDamping );
- tuning.set_m_suspensionCompression( sea.suspensionCompression );
- tuning.set_m_maxSuspensionTravelCm( sea.maxSuspensionTravelCm );
- tuning.set_m_maxSuspensionForce( sea.maxSuspensionForce );
- tuning.set_m_frictionSlip( sea.frictionSlip );
-
- var vehicle = new Ammo.btRaycastVehicle( tuning, body, vehicleRayCaster ),
- wheels = [];
-
- vehicle.setCoordinateSystem( 0, 1, 2 );
-
- for ( var i = 0; i < sea.wheel.length; i ++ ) {
-
- var wheel = sea.wheel[ i ];
-
- var wheelInfo = vehicle.addWheel(
- this.toAmmoVec3( wheel.pos ),
- this.toAmmoVec3( wheel.dir ),
- this.toAmmoVec3( wheel.axle ),
- wheel.suspensionRestLength,
- wheel.radius,
- tuning,
- wheel.isFront
- );
-
- var target = wheels[ i ] = wheel.target ? wheel.target.tag : undefined;
-
- if ( target ) {
-
- target.physics = { enabled: true, rigidBody: wheelInfo };
-
- if ( wheel.offset ) {
-
- var offset = new THREE.Matrix4();
- offset.fromArray( wheel.offset );
-
- target.physics.offset = offset;
-
- }
-
- if ( target.parent ) {
-
- target.parent.remove( target );
-
- }
-
- if ( this.container ) {
-
- this.container.add( target );
-
- }
-
- }
-
- wheelInfo.set_m_suspensionStiffness( sea.suspensionStiffness );
- wheelInfo.set_m_wheelsDampingRelaxation( sea.dampingRelaxation );
- wheelInfo.set_m_wheelsDampingCompression( sea.dampingCompression );
- wheelInfo.set_m_frictionSlip( sea.frictionSlip );
-
- }
-
- SEA3D.AMMO.addVehicle( vehicle, wheels );
- SEA3D.AMMO.addRigidBody( body, sea.target ? sea.target.tag : undefined, this.config.enabledPhysics );
-
- this.domain.vehicles = this.vehicles = this.vehicles || [];
- this.vehicles.push( this.objects[ "vhc/" + sea.name ] = sea.tag = vehicle );
-
-};
-
-//
-// P2P Constraint
-//
-
-THREE.SEA3D.prototype.readP2PConstraint = function ( sea ) {
-
- var ctrt;
-
- if ( sea.targetB ) {
-
- ctrt = new Ammo.btPoint2PointConstraint(
- sea.targetA.tag,
- sea.targetB.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.pointB )
- );
-
- } else {
-
- ctrt = new Ammo.btPoint2PointConstraint(
- sea.targetA.tag,
- this.toAmmoVec3( sea.pointA )
- );
-
- }
-
- SEA3D.AMMO.addConstraint( ctrt );
-
- this.domain.constraints = this.constraints = this.constraints || [];
- this.constraints.push( this.objects[ "ctnt/" + sea.name ] = sea.tag = ctrt );
-
-};
-
-//
-// Hinge Constraint
-//
-
-THREE.SEA3D.prototype.readHingeConstraint = function ( sea ) {
-
- var ctrt;
-
- if ( sea.targetB ) {
-
- ctrt = new Ammo.btHingeConstraint(
- sea.targetA.tag,
- sea.targetB.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.pointB ),
- this.toAmmoVec3( sea.axisA ),
- this.toAmmoVec3( sea.axisB ),
- false
- );
-
- } else {
-
- ctrt = new Ammo.btHingeConstraint(
- sea.targetA.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.axisA ),
- false
- );
-
- }
-
- if ( sea.limit ) {
-
- ctrt.setLimit( sea.limit.low, sea.limit.high, sea.limit.softness, sea.limit.biasFactor, sea.limit.relaxationFactor );
-
- }
-
- if ( sea.angularMotor ) {
-
- ctrt.enableAngularMotor( true, sea.angularMotor.velocity, sea.angularMotor.impulse );
-
- }
-
- SEA3D.AMMO.addConstraint( ctrt );
-
- this.domain.constraints = this.constraints = this.constraints || [];
- this.constraints.push( this.objects[ "ctnt/" + sea.name ] = sea.tag = ctrt );
-
-};
-
-//
-// Cone Twist Constraint
-//
-
-THREE.SEA3D.prototype.readConeTwistConstraint = function ( sea ) {
-
- var ctrt;
-
- if ( sea.targetB ) {
-
- ctrt = new Ammo.btConeTwistConstraint(
- sea.targetA.tag,
- sea.targetB.tag,
- this.toAmmoVec3( sea.pointA ),
- this.toAmmoVec3( sea.pointB ),
- false
- );
-
- } else {
-
- ctrt = new Ammo.btConeTwistConstraint(
- sea.targetA.tag,
- this.toAmmoVec3( sea.pointA ),
- false
- );
-
- }
-
- SEA3D.AMMO.addConstraint( ctrt );
-
- this.domain.constraints = this.constraints = this.constraints || [];
- this.constraints.push( this.objects[ "ctnt/" + sea.name ] = sea.tag = ctrt );
-
-};
-
-//
-// Domain
-//
-
-THREE.SEA3D.Domain.prototype.enabledPhysics = function ( enabled ) {
-
- var i = this.rigidBodies ? this.rigidBodies.length : 0;
-
- while ( i -- ) {
-
- SEA3D.AMMO.setEnabledRigidBody( this.rigidBodies[ i ], enabled );
-
- }
-
-};
-
-THREE.SEA3D.Domain.prototype.applyContainerTransform = function () {
-
- this.container.updateMatrix();
-
- var matrix = this.container.matrix.clone();
-
- this.container.position.set( 0, 0, 0 );
- this.container.quaternion.set( 0, 0, 0, 1 );
- this.container.scale.set( 1, 1, 1 );
-
- this.applyTransform( matrix );
-
-};
-
-THREE.SEA3D.Domain.prototype.applyTransform = function ( matrix ) {
-
- var mtx = THREE.SEA3D.MTXBUF, vec = THREE.SEA3D.VECBUF;
-
- var i = this.rigidBodies ? this.rigidBodies.length : 0,
- childs = this.container ? this.container.children : [],
- targets = [];
-
- while ( i -- ) {
-
- var rb = this.rigidBodies[ i ],
- target = SEA3D.AMMO.getTargetByRigidBody( rb ),
- transform = rb.getWorldTransform(),
- transformMatrix = SEA3D.AMMO.getMatrixFromTransform( transform );
-
- transformMatrix.multiplyMatrices( transformMatrix, matrix );
-
- transform = SEA3D.AMMO.getTransformFromMatrix( transformMatrix );
-
- rb.setWorldTransform( transform );
-
- if ( target ) targets.push( target );
-
- }
-
- for ( i = 0; i < childs.length; i ++ ) {
-
- var obj3d = childs[ i ];
-
- if ( targets.indexOf( obj3d ) > - 1 ) continue;
-
- obj3d.updateMatrix();
-
- mtx.copy( obj3d.matrix );
-
- mtx.multiplyMatrices( matrix, mtx );
-
- obj3d.position.setFromMatrixPosition( mtx );
- obj3d.scale.setFromMatrixScale( mtx );
-
- // ignore rotation scale
-
- mtx.scale( vec.set( 1 / obj3d.scale.x, 1 / obj3d.scale.y, 1 / obj3d.scale.z ) );
- obj3d.quaternion.setFromRotationMatrix( mtx );
-
- }
-
-};
-
-//
-// Extension
-//
-
-THREE.SEA3D.Domain.prototype.getShape = THREE.SEA3D.prototype.getShape = function ( name ) {
-
- return this.objects[ "shpe/" + name ];
-
-};
-
-THREE.SEA3D.Domain.prototype.getRigidBody = THREE.SEA3D.prototype.getRigidBody = function ( name ) {
-
- return this.objects[ "rb/" + name ];
-
-};
-
-THREE.SEA3D.Domain.prototype.getConstraint = THREE.SEA3D.prototype.getConstraint = function ( name ) {
-
- return this.objects[ "ctnt/" + name ];
-
-};
-
-THREE.SEA3D.EXTENSIONS_LOADER.push( {
-
- parse: function () {
-
- delete this.shapes;
- delete this.rigidBodies;
- delete this.vehicles;
- delete this.constraints;
-
- },
-
- setTypeRead: function () {
-
- // CONFIG
-
- this.config.physics = this.config.physics !== undefined ? this.config.physics : true;
- this.config.convexHull = this.config.convexHull !== undefined ? this.config.convexHull : true;
- this.config.enabledPhysics = this.config.enabledPhysics !== undefined ? this.config.enabledPhysics : true;
-
- if ( this.config.physics ) {
-
- // SHAPES
-
- this.file.typeRead[ SEA3D.Sphere.prototype.type ] = this.readSphere;
- this.file.typeRead[ SEA3D.Box.prototype.type ] = this.readBox;
- this.file.typeRead[ SEA3D.Capsule.prototype.type ] = this.readCapsule;
- this.file.typeRead[ SEA3D.Cone.prototype.type ] = this.readCone;
- this.file.typeRead[ SEA3D.Cylinder.prototype.type ] = this.readCylinder;
- this.file.typeRead[ SEA3D.ConvexGeometry.prototype.type ] = this.readConvexGeometry;
- this.file.typeRead[ SEA3D.TriangleGeometry.prototype.type ] = this.readTriangleGeometry;
- this.file.typeRead[ SEA3D.Compound.prototype.type ] = this.readCompound;
-
- // CONSTRAINTS
-
- this.file.typeRead[ SEA3D.P2PConstraint.prototype.type ] = this.readP2PConstraint;
- this.file.typeRead[ SEA3D.HingeConstraint.prototype.type ] = this.readHingeConstraint;
- this.file.typeRead[ SEA3D.ConeTwistConstraint.prototype.type ] = this.readConeTwistConstraint;
-
- // PHYSICS
-
- this.file.typeRead[ SEA3D.RigidBody.prototype.type ] = this.readRigidBody;
- this.file.typeRead[ SEA3D.CarController.prototype.type ] = this.readCarController;
-
- }
-
- }
-} );
-
-THREE.SEA3D.EXTENSIONS_DOMAIN.push( {
-
- dispose: function () {
-
- var i;
-
- i = this.rigidBodies ? this.rigidBodies.length : 0;
- while ( i -- ) SEA3D.AMMO.removeRigidBody( this.rigidBodies[ i ], true );
-
- i = this.vehicles ? this.vehicles.length : 0;
- while ( i -- ) SEA3D.AMMO.removeVehicle( this.vehicles[ i ], true );
-
- i = this.constraints ? this.constraints.length : 0;
- while ( i -- ) SEA3D.AMMO.removeConstraint( this.constraints[ i ], true );
-
- i = this.shapes ? this.shapes.length : 0;
- while ( i -- ) Ammo.destroy( this.shapes[ i ] );
-
- }
-
-} );
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/ShadowMapViewer.js b/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/ShadowMapViewer.js
deleted file mode 100644
index 340efd3f339f776062b01678a0b75adaa67e5623..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/jsm/utils/ShadowMapViewer.js
+++ /dev/null
@@ -1,208 +0,0 @@
-/**
- * @author arya-s / https://github.com/arya-s
- *
- * This is a helper for visualising a given light's shadow map.
- * It works for shadow casting lights: THREE.DirectionalLight and THREE.SpotLight.
- * It renders out the shadow map and displays it on a HUD.
- *
- * Example usage:
- * 1) Include \n\t\t" + head + "\n\t\n\t\n\t\t" + body + "\n\t\t\nSaim Mehmood
- -
-
Saim was a mentee
-
-
-
-
- Interview
Saim was a mentee
Career
- Moved to Canada back in Dec 2017
- grad school Jan 2018
- MSc in CS - research-oriented work
- graduated June 2020
- Sep 2019 got in touch with Omar
- Omar: "You already have a good project with your thesis", finish that up
- Covid hit, kept applying
- kept applying
- took me almost 2 years to find job
- worked as MLOps
- learned a lot throughout my journey, particularly about academia and industry
And how can you add value as a mentor?
- selling yourself as a previous academic
- it's a long journey - expectation setting - you will hit a lot rejections!
- "I was getting rejection emails every day, and was happy to see them"
- market the project that you've done
- make sure they are technically sound
- Build a project if needed, post on medium
Mentorship exp
- Been a TA during school
Ideal mentee
- Recent CS grads would be easier, but open to other disciplines
-
Questions about SM?