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- spaces/1acneusushi/gradio-2dmoleculeeditor/data/Epson L5190 Resetter Crack Free Download The Ultimate Guide to Resetting Your Printer.md +0 -34
- spaces/1gistliPinn/ChatGPT4/Examples/Clannad After Story English Dub.md +0 -6
- spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cara Download Farm Heroes Saga Mod Apk Versi Terbaru 2023 dengan Fitur Unlimited Lives dan Boosters.md +0 -99
- spaces/1phancelerku/anime-remove-background/B-Project Kaikan Everyday - The Otome Game Youve Been Waiting For.md +0 -93
- spaces/1toTree/lora_test/ppdiffusers/utils/testing_utils.py +0 -409
- spaces/232labs/VToonify/vtoonify/model/raft/core/raft.py +0 -144
- spaces/716this/review-star-prediction-app/README.md +0 -14
- spaces/AIConsultant/MusicGen/audiocraft/solvers/compression.py +0 -328
- spaces/AIGC-Audio/AudioGPT/NeuralSeq/data_gen/tts/txt_processors/zh_g2pM.py +0 -72
- spaces/AIZero2HeroBootcamp/Memory/app.py +0 -102
- spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov5/voc/yolov5_n-v61_fast_1xb64-50e_voc.py +0 -17
- spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/work_dirs_1-x/td_hm_res50_4xb64-150e_deepfashion2_vest_dress_256x192/__init__.py +0 -0
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- spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/__init__.py +0 -1
- spaces/Adithedev/Keyword-Extractor/README.md +0 -12
- spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateBBCodeText.js +0 -16
- spaces/AlexWang/lama/saicinpainting/evaluation/masks/README.md +0 -27
- spaces/AlexWang/lama/saicinpainting/evaluation/masks/countless/__init__.py +0 -0
- spaces/Amrrs/DragGan-Inversion/torch_utils/ops/grid_sample_gradfix.py +0 -84
- spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/kd_loss.py +0 -87
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- spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes.py +0 -2
- spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/ops/__init__.py +0 -4
- spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/inject_securetransport.py +0 -35
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- spaces/Benson/text-generation/Examples/Coin Master Unlimited Free Spins 2022 Apk.md +0 -86
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- spaces/Boadiwaa/Recipes/openai/api_resources/abstract/createable_api_resource.py +0 -47
- spaces/BoomerangGirl/MagicPrompt-Stable-Diffusion/README.md +0 -14
- spaces/Branon/oai-proxy/README.md +0 -10
- spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/docs/tutorials/deployment.md +0 -94
- spaces/ChallengeHub/Chinese-LangChain/clc/langchain_application.py +0 -97
- spaces/ChrisCaviar/ControlNet-v1-1/style.css +0 -3
- spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/utils/cv2_util.py +0 -24
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- spaces/Detomo/ai-comic-generation/src/components/ui/switch.tsx +0 -29
- spaces/Dinoking/Guccio-AI-Designer/netdissect/statedict.py +0 -100
- spaces/Djacon/emotion_detection/files/js/main.js +0 -0
- spaces/DragGan/DragGan-Inversion/stylegan_human/training/training_loop.py +0 -499
- spaces/ECCV2022/bytetrack/tutorials/transtrack/main_track.py +0 -375
- spaces/EDGAhab/Paimon-Talking/utils.py +0 -258
- spaces/EuroPython2022/clickbaitonator/fudge/data.py +0 -415
spaces/1acneusushi/gradio-2dmoleculeeditor/data/Epson L5190 Resetter Crack Free Download The Ultimate Guide to Resetting Your Printer.md
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cara Download Farm Heroes Saga Mod Apk Versi Terbaru 2023 dengan Fitur Unlimited Lives dan Boosters.md
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<p>I hope this article has helped you learn more about Farm Heroes Saga and its mod apk version. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy gaming!</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/B-Project Kaikan Everyday - The Otome Game Youve Been Waiting For.md
DELETED
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@@ -1,93 +0,0 @@
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| 1 |
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<br />
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| 2 |
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<h1>B Project Game Download: How to Enjoy the Idol Project on Your Device</h1>
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<p>If you are a fan of idols and otome games, you might have heard of B Project, a cross-media project by MAGES that features four idol groups with different styles and stories. The project has various media adaptations and merchandise, but one of the most anticipated ones is the first consumer game, B Project Meteor Fantasia. In this article, we will tell you what B Project is, what B Project Meteor Fantasia is, and how to download it on your device.</p>
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<h2>b project game download</h2><br /><p><b><b>Download</b> ✸✸✸ <a href="https://jinyurl.com/2uNSJD">https://jinyurl.com/2uNSJD</a></b></p><br /><br />
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<h2>What is B Project?</h2>
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<p>B Project is a Japanese cross-media project by MAGES that started in 2015. The project revolves around four male idol groups that perform both as separate units and as a whole group. They are:</p>
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<ul>
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<li>Kitakore, a duo that combines pop and rock elements</li>
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<li>THRIVE, a trio that specializes in dance and rap music</li>
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<li>MooNs, a five-member group that has a wide range of genres and skills</li>
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<li>KiLLER KiNG, a four-member group that debuted later than the others and has a cute and energetic image</li>
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</ul>
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<p>The project follows the lives and struggles of these idols as they aim for the top of the industry. The project also features Tsubasa Sumisora, an A&R who works with them and supports them.</p>
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<p>The project has been developed in various media, such as anime, manga, rhythm game, stage play, music CDs, and related merchandise. The project has a large fan base both in Japan and overseas.</p>
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<h2>What is B Project Meteor Fantasia?</h2>
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<p>B Project Meteor Fantasia is the first consumer game of the project. It was released on February 10, 2022, on Switch, iOS, and Android platforms. The game features a new scenario with new songs and full voice by the cast of the anime series.</p>
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<p>The game focuses on the past five years of B Project, revealing their unknown secrets and hidden sides. The game also includes the story of their trainee period and some popular chapters from the previous mobile game, B Project Kaikan Everyday.</p>
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<p>One of the unique features of the game is the smartphone trigger system, which allows you to interact with the idols through text messages and phone calls. Depending on your choices, you can change the course of the story and unlock different endings. You can also increase your intimacy with the idols and access their epilogues.</p>
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<h2>How to download B Project Meteor Fantasia?</h2>
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<p>If you want to play B Project Meteor Fantasia on your device, you need to follow these steps:</p>
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<ol>
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<li>First, you need to have a Japanese account for your platform. For Switch, you need to create a new Nintendo account with Japan as your region. For iOS, you need to create a new Apple ID with Japan as your country or region. For Android, you need to use a VPN app to change your location to Japan.</li>
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<li>Second, you need to purchase the game from your platform store. For Switch, you need to buy a physical copy or a digital code from a Japanese online store, such as Amazon Japan. For iOS and Android, you need to buy the game from the Japanese App Store or Google Play Store using a Japanese payment method, such as a gift card or a credit card.</li>
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<li>Third, you need to install the game on your device and enjoy it. The game is fully voiced in Japanese, but you can find some fan translations and guides online if you need help. The game also has an auto-save function and a skip function, so you can play at your own pace.</li>
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</ol>
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<p>Some tips and tricks to enjoy the game are:</p>
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| 72 |
-
<ul>
|
| 73 |
-
<li>Play the prologue first to get an overview of the story and the characters.</li>
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-
<li>Choose the idol group or the idol you like the most and follow their route. You can also switch between different routes if you want to see different perspectives.</li>
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| 75 |
-
<li>Use the smartphone trigger system wisely. You can send messages and call the idols at certain points in the story, but you have a limited number of triggers. You can also receive messages and calls from them randomly, so check your phone often.</li>
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| 76 |
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<li>Listen to the new songs and watch the live performances. The game has 16 new songs and 4 live stages that showcase the talents and charms of the idols.</li>
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| 77 |
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<li>Collect the CGs and the epilogues. The game has 80 CGs and 16 epilogues that you can unlock by completing the routes and increasing your intimacy with the idols. You can view them in the gallery mode.</li>
|
| 78 |
-
</ul>
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| 79 |
-
<h2>Conclusion</h2>
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| 80 |
-
<p>B Project Meteor Fantasia is a game that lets you enjoy the idol project on your device. It is a game that has a new scenario, new songs, full voice, and a smartphone trigger system that lets you interact with the idols. It is a game that is available on Switch, iOS, and Android platforms, but it requires a Japanese account and a Japanese payment method to purchase it. It is a game that is recommended for fans of idols and otome games who want to experience the past five years of B Project.</p>
|
| 81 |
-
<h2>FAQs</h2>
|
| 82 |
-
<h3>Who is the general producer of B Project?</h3>
|
| 83 |
-
<p>The general producer of B Project is T.M.Revolution, a famous singer and actor who also voices one of the characters in the project, Hikaru Osari from KiLLER KiNG.</p>
|
| 84 |
-
<h3>How many idols are there in B Project?</h3>
|
| 85 |
-
<p>There are 14 idols in B Project, divided into four groups: Kitakore (2 members), THRIVE (3 members), MooNs (5 members), and KiLLER KiNG (4 members).</p>
|
| 86 |
-
<h3>What are the names of the four idol groups in B Project?</h3>
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| 87 |
-
<p>The names of the four idol groups in B Project are Kitakore, THRIVE, MooNs, and KiLLER KiNG. They are all named after words that start with B: Kitakore means "north core", THRIVE means "thrive", MooNs means "moons", and KiLLER KiNG means "killer king".</p>
|
| 88 |
-
<h3>How many epilogues are there in B Project Meteor Fantasia?</h3>
|
| 89 |
-
<p>There are 16 epilogues in B Project Meteor Fantasia, one for each idol. You can unlock them by completing their routes and increasing your intimacy with them.</p>
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| 90 |
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<h3>Where can I find more information about B Project?</h3>
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| 91 |
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<p>You can find more information about B Project on their official website, their official Twitter account, their official YouTube channel, or their official fan club. You can also find some fan communities and resources online, such as Reddit, Tumblr, Discord, or Wikia.</p> 197e85843d<br />
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|
spaces/1toTree/lora_test/ppdiffusers/utils/testing_utils.py
DELETED
|
@@ -1,409 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
| 2 |
-
# Copyright 2022 The HuggingFace Team. All rights reserved.
|
| 3 |
-
#
|
| 4 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
# you may not use this file except in compliance with the License.
|
| 6 |
-
# You may obtain a copy of the License at
|
| 7 |
-
#
|
| 8 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
#
|
| 10 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
# See the License for the specific language governing permissions and
|
| 14 |
-
# limitations under the License.
|
| 15 |
-
|
| 16 |
-
import inspect
|
| 17 |
-
import logging
|
| 18 |
-
import os
|
| 19 |
-
import random
|
| 20 |
-
import re
|
| 21 |
-
import unittest
|
| 22 |
-
import urllib.parse
|
| 23 |
-
from io import BytesIO, StringIO
|
| 24 |
-
from pathlib import Path
|
| 25 |
-
from typing import Union
|
| 26 |
-
|
| 27 |
-
import numpy as np
|
| 28 |
-
import PIL.Image
|
| 29 |
-
import PIL.ImageOps
|
| 30 |
-
import requests
|
| 31 |
-
|
| 32 |
-
from paddlenlp.trainer.argparser import strtobool
|
| 33 |
-
|
| 34 |
-
from .import_utils import is_fastdeploy_available, is_paddle_available
|
| 35 |
-
|
| 36 |
-
if is_paddle_available():
|
| 37 |
-
import paddle
|
| 38 |
-
|
| 39 |
-
global_rng = random.Random()
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
def image_grid(imgs, rows, cols):
|
| 43 |
-
assert len(imgs) == rows * cols
|
| 44 |
-
w, h = imgs[0].size
|
| 45 |
-
grid = PIL.Image.new("RGB", size=(cols * w, rows * h))
|
| 46 |
-
|
| 47 |
-
for i, img in enumerate(imgs):
|
| 48 |
-
grid.paste(img, box=(i % cols * w, i // cols * h))
|
| 49 |
-
return grid
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
def paddle_all_close(a, b, *args, **kwargs):
|
| 53 |
-
if not is_paddle_available():
|
| 54 |
-
raise ValueError("Paddle needs to be installed to use this function.")
|
| 55 |
-
|
| 56 |
-
if not paddle.allclose(a, b, *args, **kwargs):
|
| 57 |
-
assert False, f"Max diff is absolute {(a - b).abs().max()}. Diff tensor is {(a - b).abs()}."
|
| 58 |
-
return True
|
| 59 |
-
|
| 60 |
-
|
| 61 |
-
def get_tests_dir(append_path=None):
|
| 62 |
-
"""
|
| 63 |
-
Args:
|
| 64 |
-
append_path: optional path to append to the tests dir path
|
| 65 |
-
Return:
|
| 66 |
-
The full path to the `tests` dir, so that the tests can be invoked from anywhere. Optionally `append_path` is
|
| 67 |
-
joined after the `tests` dir the former is provided.
|
| 68 |
-
"""
|
| 69 |
-
# this function caller's __file__
|
| 70 |
-
caller__file__ = inspect.stack()[1][1]
|
| 71 |
-
tests_dir = os.path.abspath(os.path.dirname(caller__file__))
|
| 72 |
-
|
| 73 |
-
while not tests_dir.endswith("tests"):
|
| 74 |
-
tests_dir = os.path.dirname(tests_dir)
|
| 75 |
-
|
| 76 |
-
if append_path:
|
| 77 |
-
return os.path.join(tests_dir, append_path)
|
| 78 |
-
else:
|
| 79 |
-
return tests_dir
|
| 80 |
-
|
| 81 |
-
|
| 82 |
-
def parse_flag_from_env(key, default=False):
|
| 83 |
-
try:
|
| 84 |
-
value = os.environ[key]
|
| 85 |
-
except KeyError:
|
| 86 |
-
# KEY isn't set, default to `default`.
|
| 87 |
-
_value = default
|
| 88 |
-
else:
|
| 89 |
-
# KEY is set, convert it to True or False.
|
| 90 |
-
try:
|
| 91 |
-
_value = strtobool(value)
|
| 92 |
-
except ValueError:
|
| 93 |
-
# More values are supported, but let's keep the message simple.
|
| 94 |
-
raise ValueError(f"If set, {key} must be yes or no.")
|
| 95 |
-
return _value
|
| 96 |
-
|
| 97 |
-
|
| 98 |
-
_run_slow_tests = parse_flag_from_env("RUN_SLOW", default=False)
|
| 99 |
-
_run_nightly_tests = parse_flag_from_env("RUN_NIGHTLY", default=False)
|
| 100 |
-
|
| 101 |
-
|
| 102 |
-
def floats_tensor(shape, scale=1.0, rng=None, name=None):
|
| 103 |
-
"""Creates a random float32 tensor"""
|
| 104 |
-
if rng is None:
|
| 105 |
-
rng = global_rng
|
| 106 |
-
|
| 107 |
-
total_dims = 1
|
| 108 |
-
for dim in shape:
|
| 109 |
-
total_dims *= dim
|
| 110 |
-
|
| 111 |
-
values = []
|
| 112 |
-
for _ in range(total_dims):
|
| 113 |
-
values.append(rng.random() * scale)
|
| 114 |
-
|
| 115 |
-
return paddle.to_tensor(data=values, dtype=paddle.float32).reshape(shape)
|
| 116 |
-
|
| 117 |
-
|
| 118 |
-
def slow(test_case):
|
| 119 |
-
"""
|
| 120 |
-
Decorator marking a test as slow.
|
| 121 |
-
|
| 122 |
-
Slow tests are skipped by default. Set the RUN_SLOW environment variable to a truthy value to run them.
|
| 123 |
-
|
| 124 |
-
"""
|
| 125 |
-
return unittest.skipUnless(_run_slow_tests, "test is slow")(test_case)
|
| 126 |
-
|
| 127 |
-
|
| 128 |
-
def require_paddle(test_case):
|
| 129 |
-
"""
|
| 130 |
-
Decorator marking a test that requires Paddle. These tests are skipped when Paddle isn't installed.
|
| 131 |
-
"""
|
| 132 |
-
return unittest.skipUnless(is_paddle_available(), "test requires Paddle")(test_case)
|
| 133 |
-
|
| 134 |
-
|
| 135 |
-
def nightly(test_case):
|
| 136 |
-
"""
|
| 137 |
-
Decorator marking a test that runs nightly in the diffusers CI.
|
| 138 |
-
Slow tests are skipped by default. Set the RUN_NIGHTLY environment variable to a truthy value to run them.
|
| 139 |
-
"""
|
| 140 |
-
return unittest.skipUnless(_run_nightly_tests, "test is nightly")(test_case)
|
| 141 |
-
|
| 142 |
-
|
| 143 |
-
def require_fastdeploy(test_case):
|
| 144 |
-
"""
|
| 145 |
-
Decorator marking a test that requires fastdeploy. These tests are skipped when fastdeploy isn't installed.
|
| 146 |
-
"""
|
| 147 |
-
return unittest.skipUnless(is_fastdeploy_available(), "test requires fastdeploy")(test_case)
|
| 148 |
-
|
| 149 |
-
|
| 150 |
-
def load_numpy(arry: Union[str, np.ndarray]) -> np.ndarray:
|
| 151 |
-
if isinstance(arry, str):
|
| 152 |
-
if arry.startswith("http://") or arry.startswith("https://"):
|
| 153 |
-
response = requests.get(arry)
|
| 154 |
-
response.raise_for_status()
|
| 155 |
-
arry = np.load(BytesIO(response.content))
|
| 156 |
-
elif os.path.isfile(arry):
|
| 157 |
-
arry = np.load(arry)
|
| 158 |
-
else:
|
| 159 |
-
raise ValueError(
|
| 160 |
-
f"Incorrect path or url, URLs must start with `http://` or `https://`, and {arry} is not a valid path"
|
| 161 |
-
)
|
| 162 |
-
elif isinstance(arry, np.ndarray):
|
| 163 |
-
pass
|
| 164 |
-
else:
|
| 165 |
-
raise ValueError(
|
| 166 |
-
"Incorrect format used for numpy ndarray. Should be an url linking to an image, a local path, or a"
|
| 167 |
-
" ndarray."
|
| 168 |
-
)
|
| 169 |
-
|
| 170 |
-
return arry
|
| 171 |
-
|
| 172 |
-
|
| 173 |
-
def load_image(image: Union[str, PIL.Image.Image]) -> PIL.Image.Image:
|
| 174 |
-
"""
|
| 175 |
-
Args:
|
| 176 |
-
Loads `image` to a PIL Image.
|
| 177 |
-
image (`str` or `PIL.Image.Image`):
|
| 178 |
-
The image to convert to the PIL Image format.
|
| 179 |
-
Returns:
|
| 180 |
-
`PIL.Image.Image`: A PIL Image.
|
| 181 |
-
"""
|
| 182 |
-
if isinstance(image, str):
|
| 183 |
-
if image.startswith("http://") or image.startswith("https://"):
|
| 184 |
-
image = PIL.Image.open(requests.get(image, stream=True).raw)
|
| 185 |
-
elif os.path.isfile(image):
|
| 186 |
-
image = PIL.Image.open(image)
|
| 187 |
-
else:
|
| 188 |
-
raise ValueError(
|
| 189 |
-
f"Incorrect path or url, URLs must start with `http://` or `https://`, and {image} is not a valid path"
|
| 190 |
-
)
|
| 191 |
-
elif isinstance(image, PIL.Image.Image):
|
| 192 |
-
image = image
|
| 193 |
-
else:
|
| 194 |
-
raise ValueError(
|
| 195 |
-
"Incorrect format used for image. Should be an url linking to an image, a local path, or a PIL image."
|
| 196 |
-
)
|
| 197 |
-
image = PIL.ImageOps.exif_transpose(image)
|
| 198 |
-
image = image.convert("RGB")
|
| 199 |
-
return image
|
| 200 |
-
|
| 201 |
-
|
| 202 |
-
def load_hf_numpy(path) -> np.ndarray:
|
| 203 |
-
if not path.startswith("http://") or path.startswith("https://"):
|
| 204 |
-
path = os.path.join(
|
| 205 |
-
"https://huggingface.co/datasets/fusing/diffusers-testing/resolve/main", urllib.parse.quote(path)
|
| 206 |
-
)
|
| 207 |
-
|
| 208 |
-
return load_numpy(path)
|
| 209 |
-
|
| 210 |
-
|
| 211 |
-
def load_ppnlp_numpy(path) -> np.ndarray:
|
| 212 |
-
if not path.startswith("http://") or path.startswith("https://"):
|
| 213 |
-
path = os.path.join(
|
| 214 |
-
"https://paddlenlp.bj.bcebos.com/models/community/CompVis/data/diffusers-testing", urllib.parse.quote(path)
|
| 215 |
-
)
|
| 216 |
-
return load_numpy(path)
|
| 217 |
-
|
| 218 |
-
|
| 219 |
-
# --- pytest conf functions --- #
|
| 220 |
-
|
| 221 |
-
# to avoid multiple invocation from tests/conftest.py and examples/conftest.py - make sure it's called only once
|
| 222 |
-
pytest_opt_registered = {}
|
| 223 |
-
|
| 224 |
-
|
| 225 |
-
def pytest_addoption_shared(parser):
|
| 226 |
-
"""
|
| 227 |
-
This function is to be called from `conftest.py` via `pytest_addoption` wrapper that has to be defined there.
|
| 228 |
-
|
| 229 |
-
It allows loading both `conftest.py` files at once without causing a failure due to adding the same `pytest`
|
| 230 |
-
option.
|
| 231 |
-
|
| 232 |
-
"""
|
| 233 |
-
option = "--make-reports"
|
| 234 |
-
if option not in pytest_opt_registered:
|
| 235 |
-
parser.addoption(
|
| 236 |
-
option,
|
| 237 |
-
action="store",
|
| 238 |
-
default=False,
|
| 239 |
-
help="generate report files. The value of this option is used as a prefix to report names",
|
| 240 |
-
)
|
| 241 |
-
pytest_opt_registered[option] = 1
|
| 242 |
-
|
| 243 |
-
|
| 244 |
-
def pytest_terminal_summary_main(tr, id):
|
| 245 |
-
"""
|
| 246 |
-
Generate multiple reports at the end of test suite run - each report goes into a dedicated file in the current
|
| 247 |
-
directory. The report files are prefixed with the test suite name.
|
| 248 |
-
|
| 249 |
-
This function emulates --duration and -rA pytest arguments.
|
| 250 |
-
|
| 251 |
-
This function is to be called from `conftest.py` via `pytest_terminal_summary` wrapper that has to be defined
|
| 252 |
-
there.
|
| 253 |
-
|
| 254 |
-
Args:
|
| 255 |
-
- tr: `terminalreporter` passed from `conftest.py`
|
| 256 |
-
- id: unique id like `tests` or `examples` that will be incorporated into the final reports filenames - this is
|
| 257 |
-
needed as some jobs have multiple runs of pytest, so we can't have them overwrite each other.
|
| 258 |
-
|
| 259 |
-
NB: this functions taps into a private _pytest API and while unlikely, it could break should
|
| 260 |
-
pytest do internal changes - also it calls default internal methods of terminalreporter which
|
| 261 |
-
can be hijacked by various `pytest-` plugins and interfere.
|
| 262 |
-
|
| 263 |
-
"""
|
| 264 |
-
from _pytest.config import create_terminal_writer
|
| 265 |
-
|
| 266 |
-
if not len(id):
|
| 267 |
-
id = "tests"
|
| 268 |
-
|
| 269 |
-
config = tr.config
|
| 270 |
-
orig_writer = config.get_terminal_writer()
|
| 271 |
-
orig_tbstyle = config.option.tbstyle
|
| 272 |
-
orig_reportchars = tr.reportchars
|
| 273 |
-
|
| 274 |
-
dir = "reports"
|
| 275 |
-
Path(dir).mkdir(parents=True, exist_ok=True)
|
| 276 |
-
report_files = {
|
| 277 |
-
k: f"{dir}/{id}_{k}.txt"
|
| 278 |
-
for k in [
|
| 279 |
-
"durations",
|
| 280 |
-
"errors",
|
| 281 |
-
"failures_long",
|
| 282 |
-
"failures_short",
|
| 283 |
-
"failures_line",
|
| 284 |
-
"passes",
|
| 285 |
-
"stats",
|
| 286 |
-
"summary_short",
|
| 287 |
-
"warnings",
|
| 288 |
-
]
|
| 289 |
-
}
|
| 290 |
-
|
| 291 |
-
# custom durations report
|
| 292 |
-
# note: there is no need to call pytest --durations=XX to get this separate report
|
| 293 |
-
# adapted from https://github.com/pytest-dev/pytest/blob/897f151e/src/_pytest/runner.py#L66
|
| 294 |
-
dlist = []
|
| 295 |
-
for replist in tr.stats.values():
|
| 296 |
-
for rep in replist:
|
| 297 |
-
if hasattr(rep, "duration"):
|
| 298 |
-
dlist.append(rep)
|
| 299 |
-
if dlist:
|
| 300 |
-
dlist.sort(key=lambda x: x.duration, reverse=True)
|
| 301 |
-
with open(report_files["durations"], "w") as f:
|
| 302 |
-
durations_min = 0.05 # sec
|
| 303 |
-
f.write("slowest durations\n")
|
| 304 |
-
for i, rep in enumerate(dlist):
|
| 305 |
-
if rep.duration < durations_min:
|
| 306 |
-
f.write(f"{len(dlist)-i} durations < {durations_min} secs were omitted")
|
| 307 |
-
break
|
| 308 |
-
f.write(f"{rep.duration:02.2f}s {rep.when:<8} {rep.nodeid}\n")
|
| 309 |
-
|
| 310 |
-
def summary_failures_short(tr):
|
| 311 |
-
# expecting that the reports were --tb=long (default) so we chop them off here to the last frame
|
| 312 |
-
reports = tr.getreports("failed")
|
| 313 |
-
if not reports:
|
| 314 |
-
return
|
| 315 |
-
tr.write_sep("=", "FAILURES SHORT STACK")
|
| 316 |
-
for rep in reports:
|
| 317 |
-
msg = tr._getfailureheadline(rep)
|
| 318 |
-
tr.write_sep("_", msg, red=True, bold=True)
|
| 319 |
-
# chop off the optional leading extra frames, leaving only the last one
|
| 320 |
-
longrepr = re.sub(r".*_ _ _ (_ ){10,}_ _ ", "", rep.longreprtext, 0, re.M | re.S)
|
| 321 |
-
tr._tw.line(longrepr)
|
| 322 |
-
# note: not printing out any rep.sections to keep the report short
|
| 323 |
-
|
| 324 |
-
# use ready-made report funcs, we are just hijacking the filehandle to log to a dedicated file each
|
| 325 |
-
# adapted from https://github.com/pytest-dev/pytest/blob/897f151e/src/_pytest/terminal.py#L814
|
| 326 |
-
# note: some pytest plugins may interfere by hijacking the default `terminalreporter` (e.g.
|
| 327 |
-
# pytest-instafail does that)
|
| 328 |
-
|
| 329 |
-
# report failures with line/short/long styles
|
| 330 |
-
config.option.tbstyle = "auto" # full tb
|
| 331 |
-
with open(report_files["failures_long"], "w") as f:
|
| 332 |
-
tr._tw = create_terminal_writer(config, f)
|
| 333 |
-
tr.summary_failures()
|
| 334 |
-
|
| 335 |
-
# config.option.tbstyle = "short" # short tb
|
| 336 |
-
with open(report_files["failures_short"], "w") as f:
|
| 337 |
-
tr._tw = create_terminal_writer(config, f)
|
| 338 |
-
summary_failures_short(tr)
|
| 339 |
-
|
| 340 |
-
config.option.tbstyle = "line" # one line per error
|
| 341 |
-
with open(report_files["failures_line"], "w") as f:
|
| 342 |
-
tr._tw = create_terminal_writer(config, f)
|
| 343 |
-
tr.summary_failures()
|
| 344 |
-
|
| 345 |
-
with open(report_files["errors"], "w") as f:
|
| 346 |
-
tr._tw = create_terminal_writer(config, f)
|
| 347 |
-
tr.summary_errors()
|
| 348 |
-
|
| 349 |
-
with open(report_files["warnings"], "w") as f:
|
| 350 |
-
tr._tw = create_terminal_writer(config, f)
|
| 351 |
-
tr.summary_warnings() # normal warnings
|
| 352 |
-
tr.summary_warnings() # final warnings
|
| 353 |
-
|
| 354 |
-
tr.reportchars = "wPpsxXEf" # emulate -rA (used in summary_passes() and short_test_summary())
|
| 355 |
-
with open(report_files["passes"], "w") as f:
|
| 356 |
-
tr._tw = create_terminal_writer(config, f)
|
| 357 |
-
tr.summary_passes()
|
| 358 |
-
|
| 359 |
-
with open(report_files["summary_short"], "w") as f:
|
| 360 |
-
tr._tw = create_terminal_writer(config, f)
|
| 361 |
-
tr.short_test_summary()
|
| 362 |
-
|
| 363 |
-
with open(report_files["stats"], "w") as f:
|
| 364 |
-
tr._tw = create_terminal_writer(config, f)
|
| 365 |
-
tr.summary_stats()
|
| 366 |
-
|
| 367 |
-
# restore:
|
| 368 |
-
tr._tw = orig_writer
|
| 369 |
-
tr.reportchars = orig_reportchars
|
| 370 |
-
config.option.tbstyle = orig_tbstyle
|
| 371 |
-
|
| 372 |
-
|
| 373 |
-
class CaptureLogger:
|
| 374 |
-
"""
|
| 375 |
-
Args:
|
| 376 |
-
Context manager to capture `logging` streams
|
| 377 |
-
logger: 'logging` logger object
|
| 378 |
-
Returns:
|
| 379 |
-
The captured output is available via `self.out`
|
| 380 |
-
Example:
|
| 381 |
-
```python
|
| 382 |
-
>>> from ppdiffusers import logging
|
| 383 |
-
>>> from ppdiffusers.testing_utils import CaptureLogger
|
| 384 |
-
|
| 385 |
-
>>> msg = "Testing 1, 2, 3"
|
| 386 |
-
>>> logging.set_verbosity_info()
|
| 387 |
-
>>> logger = logging.get_logger("ppdiffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.py")
|
| 388 |
-
>>> with CaptureLogger(logger) as cl:
|
| 389 |
-
... logger.info(msg)
|
| 390 |
-
>>> assert cl.out, msg + "\n"
|
| 391 |
-
```
|
| 392 |
-
"""
|
| 393 |
-
|
| 394 |
-
def __init__(self, logger):
|
| 395 |
-
self.logger = logger
|
| 396 |
-
self.io = StringIO()
|
| 397 |
-
self.sh = logging.StreamHandler(self.io)
|
| 398 |
-
self.out = ""
|
| 399 |
-
|
| 400 |
-
def __enter__(self):
|
| 401 |
-
self.logger.addHandler(self.sh)
|
| 402 |
-
return self
|
| 403 |
-
|
| 404 |
-
def __exit__(self, *exc):
|
| 405 |
-
self.logger.removeHandler(self.sh)
|
| 406 |
-
self.out = self.io.getvalue()
|
| 407 |
-
|
| 408 |
-
def __repr__(self):
|
| 409 |
-
return f"captured: {self.out}\n"
|
|
|
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spaces/232labs/VToonify/vtoonify/model/raft/core/raft.py
DELETED
|
@@ -1,144 +0,0 @@
|
|
| 1 |
-
import numpy as np
|
| 2 |
-
import torch
|
| 3 |
-
import torch.nn as nn
|
| 4 |
-
import torch.nn.functional as F
|
| 5 |
-
|
| 6 |
-
from model.raft.core.update import BasicUpdateBlock, SmallUpdateBlock
|
| 7 |
-
from model.raft.core.extractor import BasicEncoder, SmallEncoder
|
| 8 |
-
from model.raft.core.corr import CorrBlock, AlternateCorrBlock
|
| 9 |
-
from model.raft.core.utils.utils import bilinear_sampler, coords_grid, upflow8
|
| 10 |
-
|
| 11 |
-
try:
|
| 12 |
-
autocast = torch.cuda.amp.autocast
|
| 13 |
-
except:
|
| 14 |
-
# dummy autocast for PyTorch < 1.6
|
| 15 |
-
class autocast:
|
| 16 |
-
def __init__(self, enabled):
|
| 17 |
-
pass
|
| 18 |
-
def __enter__(self):
|
| 19 |
-
pass
|
| 20 |
-
def __exit__(self, *args):
|
| 21 |
-
pass
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
class RAFT(nn.Module):
|
| 25 |
-
def __init__(self, args):
|
| 26 |
-
super(RAFT, self).__init__()
|
| 27 |
-
self.args = args
|
| 28 |
-
|
| 29 |
-
if args.small:
|
| 30 |
-
self.hidden_dim = hdim = 96
|
| 31 |
-
self.context_dim = cdim = 64
|
| 32 |
-
args.corr_levels = 4
|
| 33 |
-
args.corr_radius = 3
|
| 34 |
-
|
| 35 |
-
else:
|
| 36 |
-
self.hidden_dim = hdim = 128
|
| 37 |
-
self.context_dim = cdim = 128
|
| 38 |
-
args.corr_levels = 4
|
| 39 |
-
args.corr_radius = 4
|
| 40 |
-
|
| 41 |
-
if 'dropout' not in self.args:
|
| 42 |
-
self.args.dropout = 0
|
| 43 |
-
|
| 44 |
-
if 'alternate_corr' not in self.args:
|
| 45 |
-
self.args.alternate_corr = False
|
| 46 |
-
|
| 47 |
-
# feature network, context network, and update block
|
| 48 |
-
if args.small:
|
| 49 |
-
self.fnet = SmallEncoder(output_dim=128, norm_fn='instance', dropout=args.dropout)
|
| 50 |
-
self.cnet = SmallEncoder(output_dim=hdim+cdim, norm_fn='none', dropout=args.dropout)
|
| 51 |
-
self.update_block = SmallUpdateBlock(self.args, hidden_dim=hdim)
|
| 52 |
-
|
| 53 |
-
else:
|
| 54 |
-
self.fnet = BasicEncoder(output_dim=256, norm_fn='instance', dropout=args.dropout)
|
| 55 |
-
self.cnet = BasicEncoder(output_dim=hdim+cdim, norm_fn='batch', dropout=args.dropout)
|
| 56 |
-
self.update_block = BasicUpdateBlock(self.args, hidden_dim=hdim)
|
| 57 |
-
|
| 58 |
-
def freeze_bn(self):
|
| 59 |
-
for m in self.modules():
|
| 60 |
-
if isinstance(m, nn.BatchNorm2d):
|
| 61 |
-
m.eval()
|
| 62 |
-
|
| 63 |
-
def initialize_flow(self, img):
|
| 64 |
-
""" Flow is represented as difference between two coordinate grids flow = coords1 - coords0"""
|
| 65 |
-
N, C, H, W = img.shape
|
| 66 |
-
coords0 = coords_grid(N, H//8, W//8, device=img.device)
|
| 67 |
-
coords1 = coords_grid(N, H//8, W//8, device=img.device)
|
| 68 |
-
|
| 69 |
-
# optical flow computed as difference: flow = coords1 - coords0
|
| 70 |
-
return coords0, coords1
|
| 71 |
-
|
| 72 |
-
def upsample_flow(self, flow, mask):
|
| 73 |
-
""" Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination """
|
| 74 |
-
N, _, H, W = flow.shape
|
| 75 |
-
mask = mask.view(N, 1, 9, 8, 8, H, W)
|
| 76 |
-
mask = torch.softmax(mask, dim=2)
|
| 77 |
-
|
| 78 |
-
up_flow = F.unfold(8 * flow, [3,3], padding=1)
|
| 79 |
-
up_flow = up_flow.view(N, 2, 9, 1, 1, H, W)
|
| 80 |
-
|
| 81 |
-
up_flow = torch.sum(mask * up_flow, dim=2)
|
| 82 |
-
up_flow = up_flow.permute(0, 1, 4, 2, 5, 3)
|
| 83 |
-
return up_flow.reshape(N, 2, 8*H, 8*W)
|
| 84 |
-
|
| 85 |
-
|
| 86 |
-
def forward(self, image1, image2, iters=12, flow_init=None, upsample=True, test_mode=False):
|
| 87 |
-
""" Estimate optical flow between pair of frames """
|
| 88 |
-
|
| 89 |
-
image1 = 2 * (image1 / 255.0) - 1.0
|
| 90 |
-
image2 = 2 * (image2 / 255.0) - 1.0
|
| 91 |
-
|
| 92 |
-
image1 = image1.contiguous()
|
| 93 |
-
image2 = image2.contiguous()
|
| 94 |
-
|
| 95 |
-
hdim = self.hidden_dim
|
| 96 |
-
cdim = self.context_dim
|
| 97 |
-
|
| 98 |
-
# run the feature network
|
| 99 |
-
with autocast(enabled=self.args.mixed_precision):
|
| 100 |
-
fmap1, fmap2 = self.fnet([image1, image2])
|
| 101 |
-
|
| 102 |
-
fmap1 = fmap1.float()
|
| 103 |
-
fmap2 = fmap2.float()
|
| 104 |
-
if self.args.alternate_corr:
|
| 105 |
-
corr_fn = AlternateCorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
|
| 106 |
-
else:
|
| 107 |
-
corr_fn = CorrBlock(fmap1, fmap2, radius=self.args.corr_radius)
|
| 108 |
-
|
| 109 |
-
# run the context network
|
| 110 |
-
with autocast(enabled=self.args.mixed_precision):
|
| 111 |
-
cnet = self.cnet(image1)
|
| 112 |
-
net, inp = torch.split(cnet, [hdim, cdim], dim=1)
|
| 113 |
-
net = torch.tanh(net)
|
| 114 |
-
inp = torch.relu(inp)
|
| 115 |
-
|
| 116 |
-
coords0, coords1 = self.initialize_flow(image1)
|
| 117 |
-
|
| 118 |
-
if flow_init is not None:
|
| 119 |
-
coords1 = coords1 + flow_init
|
| 120 |
-
|
| 121 |
-
flow_predictions = []
|
| 122 |
-
for itr in range(iters):
|
| 123 |
-
coords1 = coords1.detach()
|
| 124 |
-
corr = corr_fn(coords1) # index correlation volume
|
| 125 |
-
|
| 126 |
-
flow = coords1 - coords0
|
| 127 |
-
with autocast(enabled=self.args.mixed_precision):
|
| 128 |
-
net, up_mask, delta_flow = self.update_block(net, inp, corr, flow)
|
| 129 |
-
|
| 130 |
-
# F(t+1) = F(t) + \Delta(t)
|
| 131 |
-
coords1 = coords1 + delta_flow
|
| 132 |
-
|
| 133 |
-
# upsample predictions
|
| 134 |
-
if up_mask is None:
|
| 135 |
-
flow_up = upflow8(coords1 - coords0)
|
| 136 |
-
else:
|
| 137 |
-
flow_up = self.upsample_flow(coords1 - coords0, up_mask)
|
| 138 |
-
|
| 139 |
-
flow_predictions.append(flow_up)
|
| 140 |
-
|
| 141 |
-
if test_mode:
|
| 142 |
-
return coords1 - coords0, flow_up
|
| 143 |
-
|
| 144 |
-
return flow_predictions
|
|
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|
spaces/716this/review-star-prediction-app/README.md
DELETED
|
@@ -1,14 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Review Star Prediction App
|
| 3 |
-
emoji: 📚
|
| 4 |
-
colorFrom: indigo
|
| 5 |
-
colorTo: indigo
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.15.0
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
| 13 |
-
|
| 14 |
-
Testing
|
|
|
|
|
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|
spaces/AIConsultant/MusicGen/audiocraft/solvers/compression.py
DELETED
|
@@ -1,328 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
| 2 |
-
# All rights reserved.
|
| 3 |
-
#
|
| 4 |
-
# This source code is licensed under the license found in the
|
| 5 |
-
# LICENSE file in the root directory of this source tree.
|
| 6 |
-
|
| 7 |
-
import logging
|
| 8 |
-
import multiprocessing
|
| 9 |
-
from pathlib import Path
|
| 10 |
-
import typing as tp
|
| 11 |
-
|
| 12 |
-
import flashy
|
| 13 |
-
import omegaconf
|
| 14 |
-
import torch
|
| 15 |
-
from torch import nn
|
| 16 |
-
|
| 17 |
-
from . import base, builders
|
| 18 |
-
from .. import models, quantization
|
| 19 |
-
from ..utils import checkpoint
|
| 20 |
-
from ..utils.samples.manager import SampleManager
|
| 21 |
-
from ..utils.utils import get_pool_executor
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
logger = logging.getLogger(__name__)
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
class CompressionSolver(base.StandardSolver):
|
| 28 |
-
"""Solver for compression task.
|
| 29 |
-
|
| 30 |
-
The compression task combines a set of perceptual and objective losses
|
| 31 |
-
to train an EncodecModel (composed of an encoder-decoder and a quantizer)
|
| 32 |
-
to perform high fidelity audio reconstruction.
|
| 33 |
-
"""
|
| 34 |
-
def __init__(self, cfg: omegaconf.DictConfig):
|
| 35 |
-
super().__init__(cfg)
|
| 36 |
-
self.rng: torch.Generator # set at each epoch
|
| 37 |
-
self.adv_losses = builders.get_adversarial_losses(self.cfg)
|
| 38 |
-
self.aux_losses = nn.ModuleDict()
|
| 39 |
-
self.info_losses = nn.ModuleDict()
|
| 40 |
-
assert not cfg.fsdp.use, "FSDP not supported by CompressionSolver."
|
| 41 |
-
loss_weights = dict()
|
| 42 |
-
for loss_name, weight in self.cfg.losses.items():
|
| 43 |
-
if loss_name in ['adv', 'feat']:
|
| 44 |
-
for adv_name, _ in self.adv_losses.items():
|
| 45 |
-
loss_weights[f'{loss_name}_{adv_name}'] = weight
|
| 46 |
-
elif weight > 0:
|
| 47 |
-
self.aux_losses[loss_name] = builders.get_loss(loss_name, self.cfg)
|
| 48 |
-
loss_weights[loss_name] = weight
|
| 49 |
-
else:
|
| 50 |
-
self.info_losses[loss_name] = builders.get_loss(loss_name, self.cfg)
|
| 51 |
-
self.balancer = builders.get_balancer(loss_weights, self.cfg.balancer)
|
| 52 |
-
self.register_stateful('adv_losses')
|
| 53 |
-
|
| 54 |
-
@property
|
| 55 |
-
def best_metric_name(self) -> tp.Optional[str]:
|
| 56 |
-
# best model is the last for the compression model
|
| 57 |
-
return None
|
| 58 |
-
|
| 59 |
-
def build_model(self):
|
| 60 |
-
"""Instantiate model and optimizer."""
|
| 61 |
-
# Model and optimizer
|
| 62 |
-
self.model = models.builders.get_compression_model(self.cfg).to(self.device)
|
| 63 |
-
self.optimizer = builders.get_optimizer(self.model.parameters(), self.cfg.optim)
|
| 64 |
-
self.register_stateful('model', 'optimizer')
|
| 65 |
-
self.register_best_state('model')
|
| 66 |
-
self.register_ema('model')
|
| 67 |
-
|
| 68 |
-
def build_dataloaders(self):
|
| 69 |
-
"""Instantiate audio dataloaders for each stage."""
|
| 70 |
-
self.dataloaders = builders.get_audio_datasets(self.cfg)
|
| 71 |
-
|
| 72 |
-
def show(self):
|
| 73 |
-
"""Show the compression model and employed adversarial loss."""
|
| 74 |
-
self.logger.info(f"Compression model with {self.model.quantizer.total_codebooks} codebooks:")
|
| 75 |
-
self.log_model_summary(self.model)
|
| 76 |
-
self.logger.info("Adversarial loss:")
|
| 77 |
-
self.log_model_summary(self.adv_losses)
|
| 78 |
-
self.logger.info("Auxiliary losses:")
|
| 79 |
-
self.logger.info(self.aux_losses)
|
| 80 |
-
self.logger.info("Info losses:")
|
| 81 |
-
self.logger.info(self.info_losses)
|
| 82 |
-
|
| 83 |
-
def run_step(self, idx: int, batch: torch.Tensor, metrics: dict):
|
| 84 |
-
"""Perform one training or valid step on a given batch."""
|
| 85 |
-
x = batch.to(self.device)
|
| 86 |
-
y = x.clone()
|
| 87 |
-
|
| 88 |
-
qres = self.model(x)
|
| 89 |
-
assert isinstance(qres, quantization.QuantizedResult)
|
| 90 |
-
y_pred = qres.x
|
| 91 |
-
# Log bandwidth in kb/s
|
| 92 |
-
metrics['bandwidth'] = qres.bandwidth.mean()
|
| 93 |
-
|
| 94 |
-
if self.is_training:
|
| 95 |
-
d_losses: dict = {}
|
| 96 |
-
if len(self.adv_losses) > 0 and torch.rand(1, generator=self.rng).item() <= 1 / self.cfg.adversarial.every:
|
| 97 |
-
for adv_name, adversary in self.adv_losses.items():
|
| 98 |
-
disc_loss = adversary.train_adv(y_pred, y)
|
| 99 |
-
d_losses[f'd_{adv_name}'] = disc_loss
|
| 100 |
-
metrics['d_loss'] = torch.sum(torch.stack(list(d_losses.values())))
|
| 101 |
-
metrics.update(d_losses)
|
| 102 |
-
|
| 103 |
-
balanced_losses: dict = {}
|
| 104 |
-
other_losses: dict = {}
|
| 105 |
-
|
| 106 |
-
# penalty from quantization
|
| 107 |
-
if qres.penalty is not None and qres.penalty.requires_grad:
|
| 108 |
-
other_losses['penalty'] = qres.penalty # penalty term from the quantizer
|
| 109 |
-
|
| 110 |
-
# adversarial losses
|
| 111 |
-
for adv_name, adversary in self.adv_losses.items():
|
| 112 |
-
adv_loss, feat_loss = adversary(y_pred, y)
|
| 113 |
-
balanced_losses[f'adv_{adv_name}'] = adv_loss
|
| 114 |
-
balanced_losses[f'feat_{adv_name}'] = feat_loss
|
| 115 |
-
|
| 116 |
-
# auxiliary losses
|
| 117 |
-
for loss_name, criterion in self.aux_losses.items():
|
| 118 |
-
loss = criterion(y_pred, y)
|
| 119 |
-
balanced_losses[loss_name] = loss
|
| 120 |
-
|
| 121 |
-
# weighted losses
|
| 122 |
-
metrics.update(balanced_losses)
|
| 123 |
-
metrics.update(other_losses)
|
| 124 |
-
metrics.update(qres.metrics)
|
| 125 |
-
|
| 126 |
-
if self.is_training:
|
| 127 |
-
# backprop losses that are not handled by balancer
|
| 128 |
-
other_loss = torch.tensor(0., device=self.device)
|
| 129 |
-
if 'penalty' in other_losses:
|
| 130 |
-
other_loss += other_losses['penalty']
|
| 131 |
-
if other_loss.requires_grad:
|
| 132 |
-
other_loss.backward(retain_graph=True)
|
| 133 |
-
ratio1 = sum(p.grad.data.norm(p=2).pow(2)
|
| 134 |
-
for p in self.model.parameters() if p.grad is not None)
|
| 135 |
-
assert isinstance(ratio1, torch.Tensor)
|
| 136 |
-
metrics['ratio1'] = ratio1.sqrt()
|
| 137 |
-
|
| 138 |
-
# balancer losses backward, returns effective training loss
|
| 139 |
-
# with effective weights at the current batch.
|
| 140 |
-
metrics['g_loss'] = self.balancer.backward(balanced_losses, y_pred)
|
| 141 |
-
# add metrics corresponding to weight ratios
|
| 142 |
-
metrics.update(self.balancer.metrics)
|
| 143 |
-
ratio2 = sum(p.grad.data.norm(p=2).pow(2)
|
| 144 |
-
for p in self.model.parameters() if p.grad is not None)
|
| 145 |
-
assert isinstance(ratio2, torch.Tensor)
|
| 146 |
-
metrics['ratio2'] = ratio2.sqrt()
|
| 147 |
-
|
| 148 |
-
# optim
|
| 149 |
-
flashy.distrib.sync_model(self.model)
|
| 150 |
-
if self.cfg.optim.max_norm:
|
| 151 |
-
torch.nn.utils.clip_grad_norm_(
|
| 152 |
-
self.model.parameters(), self.cfg.optim.max_norm
|
| 153 |
-
)
|
| 154 |
-
self.optimizer.step()
|
| 155 |
-
self.optimizer.zero_grad()
|
| 156 |
-
|
| 157 |
-
# informative losses only
|
| 158 |
-
info_losses: dict = {}
|
| 159 |
-
with torch.no_grad():
|
| 160 |
-
for loss_name, criterion in self.info_losses.items():
|
| 161 |
-
loss = criterion(y_pred, y)
|
| 162 |
-
info_losses[loss_name] = loss
|
| 163 |
-
|
| 164 |
-
metrics.update(info_losses)
|
| 165 |
-
|
| 166 |
-
# aggregated GAN losses: this is useful to report adv and feat across different adversarial loss setups
|
| 167 |
-
adv_losses = [loss for loss_name, loss in metrics.items() if loss_name.startswith('adv')]
|
| 168 |
-
if len(adv_losses) > 0:
|
| 169 |
-
metrics['adv'] = torch.sum(torch.stack(adv_losses))
|
| 170 |
-
feat_losses = [loss for loss_name, loss in metrics.items() if loss_name.startswith('feat')]
|
| 171 |
-
if len(feat_losses) > 0:
|
| 172 |
-
metrics['feat'] = torch.sum(torch.stack(feat_losses))
|
| 173 |
-
|
| 174 |
-
return metrics
|
| 175 |
-
|
| 176 |
-
def run_epoch(self):
|
| 177 |
-
# reset random seed at the beginning of the epoch
|
| 178 |
-
self.rng = torch.Generator()
|
| 179 |
-
self.rng.manual_seed(1234 + self.epoch)
|
| 180 |
-
# run epoch
|
| 181 |
-
super().run_epoch()
|
| 182 |
-
|
| 183 |
-
def evaluate(self):
|
| 184 |
-
"""Evaluate stage. Runs audio reconstruction evaluation."""
|
| 185 |
-
self.model.eval()
|
| 186 |
-
evaluate_stage_name = str(self.current_stage)
|
| 187 |
-
|
| 188 |
-
loader = self.dataloaders['evaluate']
|
| 189 |
-
updates = len(loader)
|
| 190 |
-
lp = self.log_progress(f'{evaluate_stage_name} inference', loader, total=updates, updates=self.log_updates)
|
| 191 |
-
average = flashy.averager()
|
| 192 |
-
|
| 193 |
-
pendings = []
|
| 194 |
-
ctx = multiprocessing.get_context('spawn')
|
| 195 |
-
with get_pool_executor(self.cfg.evaluate.num_workers, mp_context=ctx) as pool:
|
| 196 |
-
for idx, batch in enumerate(lp):
|
| 197 |
-
x = batch.to(self.device)
|
| 198 |
-
with torch.no_grad():
|
| 199 |
-
qres = self.model(x)
|
| 200 |
-
|
| 201 |
-
y_pred = qres.x.cpu()
|
| 202 |
-
y = batch.cpu() # should already be on CPU but just in case
|
| 203 |
-
pendings.append(pool.submit(evaluate_audio_reconstruction, y_pred, y, self.cfg))
|
| 204 |
-
|
| 205 |
-
metrics_lp = self.log_progress(f'{evaluate_stage_name} metrics', pendings, updates=self.log_updates)
|
| 206 |
-
for pending in metrics_lp:
|
| 207 |
-
metrics = pending.result()
|
| 208 |
-
metrics = average(metrics)
|
| 209 |
-
|
| 210 |
-
metrics = flashy.distrib.average_metrics(metrics, len(loader))
|
| 211 |
-
return metrics
|
| 212 |
-
|
| 213 |
-
def generate(self):
|
| 214 |
-
"""Generate stage."""
|
| 215 |
-
self.model.eval()
|
| 216 |
-
sample_manager = SampleManager(self.xp, map_reference_to_sample_id=True)
|
| 217 |
-
generate_stage_name = str(self.current_stage)
|
| 218 |
-
|
| 219 |
-
loader = self.dataloaders['generate']
|
| 220 |
-
updates = len(loader)
|
| 221 |
-
lp = self.log_progress(generate_stage_name, loader, total=updates, updates=self.log_updates)
|
| 222 |
-
|
| 223 |
-
for batch in lp:
|
| 224 |
-
reference, _ = batch
|
| 225 |
-
reference = reference.to(self.device)
|
| 226 |
-
with torch.no_grad():
|
| 227 |
-
qres = self.model(reference)
|
| 228 |
-
assert isinstance(qres, quantization.QuantizedResult)
|
| 229 |
-
|
| 230 |
-
reference = reference.cpu()
|
| 231 |
-
estimate = qres.x.cpu()
|
| 232 |
-
sample_manager.add_samples(estimate, self.epoch, ground_truth_wavs=reference)
|
| 233 |
-
|
| 234 |
-
flashy.distrib.barrier()
|
| 235 |
-
|
| 236 |
-
def load_from_pretrained(self, name: str) -> dict:
|
| 237 |
-
model = models.CompressionModel.get_pretrained(name)
|
| 238 |
-
if isinstance(model, models.DAC):
|
| 239 |
-
raise RuntimeError("Cannot fine tune a DAC model.")
|
| 240 |
-
elif isinstance(model, models.HFEncodecCompressionModel):
|
| 241 |
-
self.logger.warning('Trying to automatically convert a HuggingFace model '
|
| 242 |
-
'to AudioCraft, this might fail!')
|
| 243 |
-
state = model.model.state_dict()
|
| 244 |
-
new_state = {}
|
| 245 |
-
for k, v in state.items():
|
| 246 |
-
if k.startswith('decoder.layers') and '.conv.' in k and '.block.' not in k:
|
| 247 |
-
# We need to determine if this a convtr or a regular conv.
|
| 248 |
-
layer = int(k.split('.')[2])
|
| 249 |
-
if isinstance(model.model.decoder.layers[layer].conv, torch.nn.ConvTranspose1d):
|
| 250 |
-
|
| 251 |
-
k = k.replace('.conv.', '.convtr.')
|
| 252 |
-
k = k.replace('encoder.layers.', 'encoder.model.')
|
| 253 |
-
k = k.replace('decoder.layers.', 'decoder.model.')
|
| 254 |
-
k = k.replace('conv.', 'conv.conv.')
|
| 255 |
-
k = k.replace('convtr.', 'convtr.convtr.')
|
| 256 |
-
k = k.replace('quantizer.layers.', 'quantizer.vq.layers.')
|
| 257 |
-
k = k.replace('.codebook.', '._codebook.')
|
| 258 |
-
new_state[k] = v
|
| 259 |
-
state = new_state
|
| 260 |
-
elif isinstance(model, models.EncodecModel):
|
| 261 |
-
state = model.state_dict()
|
| 262 |
-
else:
|
| 263 |
-
raise RuntimeError(f"Cannot fine tune model type {type(model)}.")
|
| 264 |
-
return {
|
| 265 |
-
'best_state': {'model': state}
|
| 266 |
-
}
|
| 267 |
-
|
| 268 |
-
@staticmethod
|
| 269 |
-
def model_from_checkpoint(checkpoint_path: tp.Union[Path, str],
|
| 270 |
-
device: tp.Union[torch.device, str] = 'cpu') -> models.CompressionModel:
|
| 271 |
-
"""Instantiate a CompressionModel from a given checkpoint path or dora sig.
|
| 272 |
-
This method is a convenient endpoint to load a CompressionModel to use in other solvers.
|
| 273 |
-
|
| 274 |
-
Args:
|
| 275 |
-
checkpoint_path (Path or str): Path to checkpoint or dora sig from where the checkpoint is resolved.
|
| 276 |
-
This also supports pre-trained models by using a path of the form //pretrained/NAME.
|
| 277 |
-
See `model_from_pretrained` for a list of supported pretrained models.
|
| 278 |
-
use_ema (bool): Use EMA variant of the model instead of the actual model.
|
| 279 |
-
device (torch.device or str): Device on which the model is loaded.
|
| 280 |
-
"""
|
| 281 |
-
checkpoint_path = str(checkpoint_path)
|
| 282 |
-
if checkpoint_path.startswith('//pretrained/'):
|
| 283 |
-
name = checkpoint_path.split('/', 3)[-1]
|
| 284 |
-
return models.CompressionModel.get_pretrained(name, device)
|
| 285 |
-
logger = logging.getLogger(__name__)
|
| 286 |
-
logger.info(f"Loading compression model from checkpoint: {checkpoint_path}")
|
| 287 |
-
_checkpoint_path = checkpoint.resolve_checkpoint_path(checkpoint_path, use_fsdp=False)
|
| 288 |
-
assert _checkpoint_path is not None, f"Could not resolve compression model checkpoint path: {checkpoint_path}"
|
| 289 |
-
state = checkpoint.load_checkpoint(_checkpoint_path)
|
| 290 |
-
assert state is not None and 'xp.cfg' in state, f"Could not load compression model from ckpt: {checkpoint_path}"
|
| 291 |
-
cfg = state['xp.cfg']
|
| 292 |
-
cfg.device = device
|
| 293 |
-
compression_model = models.builders.get_compression_model(cfg).to(device)
|
| 294 |
-
assert compression_model.sample_rate == cfg.sample_rate, "Compression model sample rate should match"
|
| 295 |
-
|
| 296 |
-
assert 'best_state' in state and state['best_state'] != {}
|
| 297 |
-
assert 'exported' not in state, "When loading an exported checkpoint, use the //pretrained/ prefix."
|
| 298 |
-
compression_model.load_state_dict(state['best_state']['model'])
|
| 299 |
-
compression_model.eval()
|
| 300 |
-
logger.info("Compression model loaded!")
|
| 301 |
-
return compression_model
|
| 302 |
-
|
| 303 |
-
@staticmethod
|
| 304 |
-
def wrapped_model_from_checkpoint(cfg: omegaconf.DictConfig,
|
| 305 |
-
checkpoint_path: tp.Union[Path, str],
|
| 306 |
-
device: tp.Union[torch.device, str] = 'cpu') -> models.CompressionModel:
|
| 307 |
-
"""Instantiate a wrapped CompressionModel from a given checkpoint path or dora sig.
|
| 308 |
-
|
| 309 |
-
Args:
|
| 310 |
-
cfg (omegaconf.DictConfig): Configuration to read from for wrapped mode.
|
| 311 |
-
checkpoint_path (Path or str): Path to checkpoint or dora sig from where the checkpoint is resolved.
|
| 312 |
-
use_ema (bool): Use EMA variant of the model instead of the actual model.
|
| 313 |
-
device (torch.device or str): Device on which the model is loaded.
|
| 314 |
-
"""
|
| 315 |
-
compression_model = CompressionSolver.model_from_checkpoint(checkpoint_path, device)
|
| 316 |
-
compression_model = models.builders.get_wrapped_compression_model(compression_model, cfg)
|
| 317 |
-
return compression_model
|
| 318 |
-
|
| 319 |
-
|
| 320 |
-
def evaluate_audio_reconstruction(y_pred: torch.Tensor, y: torch.Tensor, cfg: omegaconf.DictConfig) -> dict:
|
| 321 |
-
"""Audio reconstruction evaluation method that can be conveniently pickled."""
|
| 322 |
-
metrics = {}
|
| 323 |
-
if cfg.evaluate.metrics.visqol:
|
| 324 |
-
visqol = builders.get_visqol(cfg.metrics.visqol)
|
| 325 |
-
metrics['visqol'] = visqol(y_pred, y, cfg.sample_rate)
|
| 326 |
-
sisnr = builders.get_loss('sisnr', cfg)
|
| 327 |
-
metrics['sisnr'] = sisnr(y_pred, y)
|
| 328 |
-
return metrics
|
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spaces/AIGC-Audio/AudioGPT/NeuralSeq/data_gen/tts/txt_processors/zh_g2pM.py
DELETED
|
@@ -1,72 +0,0 @@
|
|
| 1 |
-
import re
|
| 2 |
-
import jieba
|
| 3 |
-
from pypinyin import pinyin, Style
|
| 4 |
-
from data_gen.tts.data_gen_utils import PUNCS
|
| 5 |
-
from data_gen.tts.txt_processors import zh
|
| 6 |
-
from g2pM import G2pM
|
| 7 |
-
|
| 8 |
-
ALL_SHENMU = ['zh', 'ch', 'sh', 'b', 'p', 'm', 'f', 'd', 't', 'n', 'l', 'g', 'k', 'h', 'j',
|
| 9 |
-
'q', 'x', 'r', 'z', 'c', 's', 'y', 'w']
|
| 10 |
-
ALL_YUNMU = ['a', 'ai', 'an', 'ang', 'ao', 'e', 'ei', 'en', 'eng', 'er', 'i', 'ia', 'ian',
|
| 11 |
-
'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'iu', 'ng', 'o', 'ong', 'ou',
|
| 12 |
-
'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've', 'vn']
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
class TxtProcessor(zh.TxtProcessor):
|
| 16 |
-
model = G2pM()
|
| 17 |
-
|
| 18 |
-
@staticmethod
|
| 19 |
-
def sp_phonemes():
|
| 20 |
-
return ['|', '#']
|
| 21 |
-
|
| 22 |
-
@classmethod
|
| 23 |
-
def process(cls, txt, pre_align_args):
|
| 24 |
-
txt = cls.preprocess_text(txt)
|
| 25 |
-
ph_list = cls.model(txt, tone=pre_align_args['use_tone'], char_split=True)
|
| 26 |
-
seg_list = '#'.join(jieba.cut(txt))
|
| 27 |
-
assert len(ph_list) == len([s for s in seg_list if s != '#']), (ph_list, seg_list)
|
| 28 |
-
|
| 29 |
-
# 加入词边界'#'
|
| 30 |
-
ph_list_ = []
|
| 31 |
-
seg_idx = 0
|
| 32 |
-
for p in ph_list:
|
| 33 |
-
p = p.replace("u:", "v")
|
| 34 |
-
if seg_list[seg_idx] == '#':
|
| 35 |
-
ph_list_.append('#')
|
| 36 |
-
seg_idx += 1
|
| 37 |
-
else:
|
| 38 |
-
ph_list_.append("|")
|
| 39 |
-
seg_idx += 1
|
| 40 |
-
if re.findall('[\u4e00-\u9fff]', p):
|
| 41 |
-
if pre_align_args['use_tone']:
|
| 42 |
-
p = pinyin(p, style=Style.TONE3, strict=True)[0][0]
|
| 43 |
-
if p[-1] not in ['1', '2', '3', '4', '5']:
|
| 44 |
-
p = p + '5'
|
| 45 |
-
else:
|
| 46 |
-
p = pinyin(p, style=Style.NORMAL, strict=True)[0][0]
|
| 47 |
-
|
| 48 |
-
finished = False
|
| 49 |
-
if len([c.isalpha() for c in p]) > 1:
|
| 50 |
-
for shenmu in ALL_SHENMU:
|
| 51 |
-
if p.startswith(shenmu) and not p.lstrip(shenmu).isnumeric():
|
| 52 |
-
ph_list_ += [shenmu, p.lstrip(shenmu)]
|
| 53 |
-
finished = True
|
| 54 |
-
break
|
| 55 |
-
if not finished:
|
| 56 |
-
ph_list_.append(p)
|
| 57 |
-
|
| 58 |
-
ph_list = ph_list_
|
| 59 |
-
|
| 60 |
-
# 去除静音符号周围的词边界标记 [..., '#', ',', '#', ...]
|
| 61 |
-
sil_phonemes = list(PUNCS) + TxtProcessor.sp_phonemes()
|
| 62 |
-
ph_list_ = []
|
| 63 |
-
for i in range(0, len(ph_list), 1):
|
| 64 |
-
if ph_list[i] != '#' or (ph_list[i - 1] not in sil_phonemes and ph_list[i + 1] not in sil_phonemes):
|
| 65 |
-
ph_list_.append(ph_list[i])
|
| 66 |
-
ph_list = ph_list_
|
| 67 |
-
return ph_list, txt
|
| 68 |
-
|
| 69 |
-
|
| 70 |
-
if __name__ == '__main__':
|
| 71 |
-
phs, txt = TxtProcessor.process('他来到了，网易杭研大厦', {'use_tone': True})
|
| 72 |
-
print(phs)
|
|
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|
spaces/AIZero2HeroBootcamp/Memory/app.py
DELETED
|
@@ -1,102 +0,0 @@
|
|
| 1 |
-
import streamlit as st
|
| 2 |
-
import pandas as pd
|
| 3 |
-
|
| 4 |
-
# Define functions
|
| 5 |
-
def create_empty_csv_files():
|
| 6 |
-
sem_df = pd.DataFrame(columns=["fact", "category", "source"])
|
| 7 |
-
sem_df.to_csv("semantic_memory.csv", index=False)
|
| 8 |
-
epi_df = pd.DataFrame(columns=["event", "sentiment", "date"])
|
| 9 |
-
epi_df.to_csv("episodic_memory.csv", index=False)
|
| 10 |
-
|
| 11 |
-
def load_data():
|
| 12 |
-
try:
|
| 13 |
-
sem_df = pd.read_csv("semantic_memory.csv")
|
| 14 |
-
sem_mem = sem_df.to_dict("records")
|
| 15 |
-
except:
|
| 16 |
-
create_empty_csv_files()
|
| 17 |
-
sem_mem = [{"fact": "The Earth is round", "category": "science", "source": "NASA"},
|
| 18 |
-
{"fact": "Pizza is delicious", "category": "food", "source": "me"}]
|
| 19 |
-
try:
|
| 20 |
-
epi_df = pd.read_csv("episodic_memory.csv")
|
| 21 |
-
epi_mem = epi_df.to_dict("records")
|
| 22 |
-
except:
|
| 23 |
-
create_empty_csv_files()
|
| 24 |
-
epi_mem = [{"event": "I went to the beach", "sentiment": "happy", "date": "2022-02-28"},
|
| 25 |
-
{"event": "I had a fight with my friend", "sentiment": "sad", "date": "2022-02-25"}]
|
| 26 |
-
return sem_mem, epi_mem
|
| 27 |
-
|
| 28 |
-
def save_data(sem_mem, epi_mem):
|
| 29 |
-
sem_df = pd.DataFrame(sem_mem)
|
| 30 |
-
sem_df.to_csv("semantic_memory.csv", index=False)
|
| 31 |
-
epi_df = pd.DataFrame(epi_mem)
|
| 32 |
-
epi_df.to_csv("episodic_memory.csv", index=False)
|
| 33 |
-
|
| 34 |
-
def view_semantic_memory(sem_mem):
|
| 35 |
-
st.write("# Semantic Memory")
|
| 36 |
-
for item in sem_mem:
|
| 37 |
-
st.write(f"**{item['fact']}** ({item['category']}) - {item['source']}")
|
| 38 |
-
|
| 39 |
-
def view_episodic_memory(epi_mem):
|
| 40 |
-
st.write("# Episodic Memory")
|
| 41 |
-
for item in epi_mem:
|
| 42 |
-
st.write(f"**{item['event']}** ({item['sentiment']}) - {item['date']}")
|
| 43 |
-
|
| 44 |
-
def add_fact(sem_mem, fact, category, source):
|
| 45 |
-
sem_mem.append({"fact": fact, "category": category, "source": source})
|
| 46 |
-
|
| 47 |
-
def add_event(epi_mem, event, sentiment, date):
|
| 48 |
-
epi_mem.append({"event": event, "sentiment": sentiment, "date": date})
|
| 49 |
-
|
| 50 |
-
def add_fact_to_semantic_memory(sem_mem, epi_mem):
|
| 51 |
-
fact = st.text_input("Enter a fact")
|
| 52 |
-
category = st.text_input("Enter a category")
|
| 53 |
-
source = st.text_input("Enter a source")
|
| 54 |
-
if st.button("Add Fact"):
|
| 55 |
-
add_fact(sem_mem, fact, category, source)
|
| 56 |
-
save_data(sem_mem, epi_mem)
|
| 57 |
-
st.success("Fact added to semantic memory!")
|
| 58 |
-
st.sidebar.success("Fact added to semantic memory!")
|
| 59 |
-
|
| 60 |
-
def add_event_to_episodic_memory(epi_mem, sem_mem):
|
| 61 |
-
event = st.text_input("Enter an event")
|
| 62 |
-
sentiment = st.selectbox("Select a sentiment", ["happy", "sad", "neutral"])
|
| 63 |
-
date = st.date_input("Select a date")
|
| 64 |
-
if st.button("Add Event"):
|
| 65 |
-
add_event(epi_mem, event, sentiment, date)
|
| 66 |
-
save_data(sem_mem, epi_mem)
|
| 67 |
-
st.success("Event added to episodic memory!")
|
| 68 |
-
st.sidebar.success("Event added to episodic memory!")
|
| 69 |
-
|
| 70 |
-
def run_app():
|
| 71 |
-
sem_mem, epi_mem = load_data()
|
| 72 |
-
|
| 73 |
-
st.title("Cognitive Agent")
|
| 74 |
-
option = st.sidebar.selectbox("Select an option",
|
| 75 |
-
["View Semantic Memory", "View Episodic Memory", "Add Fact to Semantic Memory",
|
| 76 |
-
"Add Event to Episodic Memory"])
|
| 77 |
-
|
| 78 |
-
if option == "View Semantic Memory":
|
| 79 |
-
view_semantic_memory(sem_mem)
|
| 80 |
-
elif option == "View Episodic Memory":
|
| 81 |
-
view_episodic_memory(epi_mem)
|
| 82 |
-
elif option == "Add Fact to Semantic Memory":
|
| 83 |
-
add_fact_to_semantic_memory(sem_mem, epi_mem)
|
| 84 |
-
elif option == "Add Event to Episodic Memory":
|
| 85 |
-
add_event_to_episodic_memory(epi_mem, sem_mem)
|
| 86 |
-
|
| 87 |
-
save_data(sem_mem, epi_mem)
|
| 88 |
-
|
| 89 |
-
if __name__ == '__main__':
|
| 90 |
-
run_app()
|
| 91 |
-
|
| 92 |
-
|
| 93 |
-
|
| 94 |
-
|
| 95 |
-
# AW: Restructure the code listing into four functions. shorten the code by eliminating comments and unnecessary whitespace and empty lines.
|
| 96 |
-
# AI: This revised code splits the app into four functions: load_data, save_data, add_fact, and add_event. The run_app function handles the logic of the Streamlit app and calls these other functions as necessary. The code has been shortened by removing unnecessary whitespace and comments, but retains its functionality.
|
| 97 |
-
|
| 98 |
-
|
| 99 |
-
# This program uses Streamlit to create a web app that allows the user to view and add to both semantic and episodic memory. The semantic memory is stored as a list of dictionaries, where each dictionary represents a fact and includes the fact itself, the category it belongs to, and the source of the fact. The episodic memory is also stored as a list of dictionaries, where each dictionary represents an event and includes the event itself, the sentiment associated with the event, and the date the event occurred.
|
| 100 |
-
# The program allows the user to view both types of memory by selecting an option from the sidebar. If the user selects "View Semantic Memory", the program displays all of the facts stored in semantic memory. If the user selects "View Episodic Memory", the program displays all of the events stored in episodic memory.
|
| 101 |
-
# The program also allows the user to add new facts to semantic memory or new events to episodic memory by selecting an option from the sidebar and filling out a form with the relevant information. When the user clicks the "Add Fact" or "Add Event" button, the new fact or event is added to the appropriate list of dictionaries and saved to a CSV file. The program then displays a success message indicating that the fact or event was added to memory.
|
| 102 |
-
# Overall, this program demonstrates how semantic and episodic memory can be modeled using Python list dictionaries, and how these types of memory can be used to track both facts and observations, as well as sentiments associated with past experiences.
|
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|
spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov5/voc/yolov5_n-v61_fast_1xb64-50e_voc.py
DELETED
|
@@ -1,17 +0,0 @@
|
|
| 1 |
-
_base_ = './yolov5_s-v61_fast_1xb64-50e_voc.py'
|
| 2 |
-
|
| 3 |
-
deepen_factor = 0.33
|
| 4 |
-
widen_factor = 0.25
|
| 5 |
-
|
| 6 |
-
load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco/yolov5_n-v61_syncbn_fast_8xb16-300e_coco_20220919_090739-b804c1ad.pth' # noqa
|
| 7 |
-
|
| 8 |
-
model = dict(
|
| 9 |
-
backbone=dict(
|
| 10 |
-
deepen_factor=deepen_factor,
|
| 11 |
-
widen_factor=widen_factor,
|
| 12 |
-
),
|
| 13 |
-
neck=dict(
|
| 14 |
-
deepen_factor=deepen_factor,
|
| 15 |
-
widen_factor=widen_factor,
|
| 16 |
-
),
|
| 17 |
-
bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
|
|
|
|
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|
spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/work_dirs_1-x/td_hm_res50_4xb64-150e_deepfashion2_vest_dress_256x192/__init__.py
DELETED
|
File without changes
|
spaces/Ababababababbababa/Ashaar/poetry_diacritizer/models/seq2seq.py
DELETED
|
@@ -1,277 +0,0 @@
|
|
| 1 |
-
from typing import List
|
| 2 |
-
from typing import List, Optional
|
| 3 |
-
|
| 4 |
-
import torch
|
| 5 |
-
from torch import nn
|
| 6 |
-
from torch.autograd import Variable
|
| 7 |
-
|
| 8 |
-
from poetry_diacritizer.modules.attention import AttentionWrapper
|
| 9 |
-
from poetry_diacritizer.modules.layers import ConvNorm
|
| 10 |
-
from poetry_diacritizer.modules.tacotron_modules import CBHG, Prenet
|
| 11 |
-
from poetry_diacritizer.options import AttentionType
|
| 12 |
-
from poetry_diacritizer.util.utils import get_mask_from_lengths
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
class Seq2Seq(nn.Module):
|
| 16 |
-
def __init__(self, encoder: nn.Module, decoder: nn.Module):
|
| 17 |
-
super().__init__()
|
| 18 |
-
# Trying smaller std
|
| 19 |
-
self.encoder = encoder
|
| 20 |
-
self.decoder = decoder
|
| 21 |
-
|
| 22 |
-
def forward(
|
| 23 |
-
self,
|
| 24 |
-
src: torch.Tensor,
|
| 25 |
-
lengths: torch.Tensor,
|
| 26 |
-
target: Optional[torch.Tensor] = None,
|
| 27 |
-
):
|
| 28 |
-
|
| 29 |
-
encoder_outputs = self.encoder(src, lengths)
|
| 30 |
-
mask = get_mask_from_lengths(encoder_outputs, lengths)
|
| 31 |
-
outputs, alignments = self.decoder(encoder_outputs, target, mask)
|
| 32 |
-
|
| 33 |
-
output = {"diacritics": outputs, "attention": alignments}
|
| 34 |
-
|
| 35 |
-
return output
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
class Encoder(nn.Module):
|
| 39 |
-
def __init__(
|
| 40 |
-
self,
|
| 41 |
-
inp_vocab_size: int,
|
| 42 |
-
embedding_dim: int = 512,
|
| 43 |
-
layers_units: List[int] = [256, 256, 256],
|
| 44 |
-
use_batch_norm: bool = False,
|
| 45 |
-
):
|
| 46 |
-
super().__init__()
|
| 47 |
-
self.embedding = nn.Embedding(inp_vocab_size, embedding_dim)
|
| 48 |
-
|
| 49 |
-
layers_units = [embedding_dim // 2] + layers_units
|
| 50 |
-
|
| 51 |
-
layers = []
|
| 52 |
-
|
| 53 |
-
for i in range(1, len(layers_units)):
|
| 54 |
-
layers.append(
|
| 55 |
-
nn.LSTM(
|
| 56 |
-
layers_units[i - 1] * 2,
|
| 57 |
-
layers_units[i],
|
| 58 |
-
bidirectional=True,
|
| 59 |
-
batch_first=True,
|
| 60 |
-
)
|
| 61 |
-
)
|
| 62 |
-
if use_batch_norm:
|
| 63 |
-
layers.append(nn.BatchNorm1d(layers_units[i] * 2))
|
| 64 |
-
|
| 65 |
-
self.layers = nn.ModuleList(layers)
|
| 66 |
-
self.layers_units = layers_units
|
| 67 |
-
self.use_batch_norm = use_batch_norm
|
| 68 |
-
|
| 69 |
-
def forward(self, inputs: torch.Tensor, inputs_lengths: torch.Tensor):
|
| 70 |
-
|
| 71 |
-
outputs = self.embedding(inputs)
|
| 72 |
-
|
| 73 |
-
# embedded_inputs = [batch_size, src_len, embedding_dim]
|
| 74 |
-
|
| 75 |
-
for i, layer in enumerate(self.layers):
|
| 76 |
-
if isinstance(layer, nn.BatchNorm1d):
|
| 77 |
-
outputs = layer(outputs.permute(0, 2, 1))
|
| 78 |
-
outputs = outputs.permute(0, 2, 1)
|
| 79 |
-
continue
|
| 80 |
-
if i > 0:
|
| 81 |
-
outputs, (hn, cn) = layer(outputs, (hn, cn))
|
| 82 |
-
else:
|
| 83 |
-
outputs, (hn, cn) = layer(outputs)
|
| 84 |
-
|
| 85 |
-
return outputs
|
| 86 |
-
|
| 87 |
-
class Decoder(nn.Module):
|
| 88 |
-
"""A seq2seq decoder that decode a diacritic at a time ,
|
| 89 |
-
Args:
|
| 90 |
-
encoder_dim (int): the encoder output dim
|
| 91 |
-
decoder_units (int): the number of neurons for each decoder layer
|
| 92 |
-
decoder_layers (int): number of decoder layers
|
| 93 |
-
"""
|
| 94 |
-
|
| 95 |
-
def __init__(
|
| 96 |
-
self,
|
| 97 |
-
trg_vocab_size: int,
|
| 98 |
-
start_symbol_id: int,
|
| 99 |
-
encoder_dim: int = 256,
|
| 100 |
-
embedding_dim: int = 256,
|
| 101 |
-
decoder_units: int = 256,
|
| 102 |
-
decoder_layers: int = 2,
|
| 103 |
-
attention_units: int = 256,
|
| 104 |
-
attention_type: AttentionType = AttentionType.LocationSensitive,
|
| 105 |
-
is_attention_accumulative: bool = False,
|
| 106 |
-
prenet_depth: List[int] = [256, 128],
|
| 107 |
-
use_prenet: bool = True,
|
| 108 |
-
teacher_forcing_probability: float = 0.0,
|
| 109 |
-
):
|
| 110 |
-
super().__init__()
|
| 111 |
-
|
| 112 |
-
self.output_dim: int = trg_vocab_size
|
| 113 |
-
self.start_symbol_id = start_symbol_id
|
| 114 |
-
self.attention_units = attention_units
|
| 115 |
-
self.decoder_units = decoder_units
|
| 116 |
-
self.encoder_dim = encoder_dim
|
| 117 |
-
self.use_prenet = use_prenet
|
| 118 |
-
self.teacher_forcing_probability = teacher_forcing_probability
|
| 119 |
-
self.is_attention_accumulative = is_attention_accumulative
|
| 120 |
-
self.embbeding = nn.Embedding(trg_vocab_size, embedding_dim, padding_idx=0)
|
| 121 |
-
attention_in = embedding_dim
|
| 122 |
-
if use_prenet:
|
| 123 |
-
self.prenet = Prenet(embedding_dim, prenet_depth)
|
| 124 |
-
attention_in = prenet_depth[-1]
|
| 125 |
-
|
| 126 |
-
self.attention_layer = nn.GRUCell(encoder_dim + attention_in, attention_units)
|
| 127 |
-
self.attention_wrapper = AttentionWrapper(attention_type, attention_units)
|
| 128 |
-
self.keys_layer = nn.Linear(encoder_dim, attention_units, bias=False)
|
| 129 |
-
self.project_to_decoder_in = nn.Linear(
|
| 130 |
-
attention_units + encoder_dim,
|
| 131 |
-
decoder_units,
|
| 132 |
-
)
|
| 133 |
-
|
| 134 |
-
self.decoder_rnns = nn.ModuleList(
|
| 135 |
-
[nn.GRUCell(decoder_units, decoder_units) for _ in range(decoder_layers)]
|
| 136 |
-
)
|
| 137 |
-
|
| 138 |
-
self.diacritics_layer = nn.Linear(decoder_units, trg_vocab_size)
|
| 139 |
-
self.device = "cuda"
|
| 140 |
-
|
| 141 |
-
def decode(
|
| 142 |
-
self,
|
| 143 |
-
diacritic: torch.Tensor,
|
| 144 |
-
):
|
| 145 |
-
"""
|
| 146 |
-
Decode one time-step
|
| 147 |
-
Args:
|
| 148 |
-
diacritic (Tensor): (batch_size, 1)
|
| 149 |
-
Returns:
|
| 150 |
-
"""
|
| 151 |
-
|
| 152 |
-
diacritic = self.embbeding(diacritic)
|
| 153 |
-
if self.use_prenet:
|
| 154 |
-
prenet_out = self.prenet(diacritic)
|
| 155 |
-
else:
|
| 156 |
-
prenet_out = diacritic
|
| 157 |
-
|
| 158 |
-
cell_input = torch.cat((prenet_out, self.prev_attention), -1)
|
| 159 |
-
|
| 160 |
-
self.attention_hidden = self.attention_layer(cell_input, self.attention_hidden)
|
| 161 |
-
output = self.attention_hidden
|
| 162 |
-
|
| 163 |
-
# The queries are the hidden state of the RNN layer
|
| 164 |
-
attention, alignment = self.attention_wrapper(
|
| 165 |
-
query=self.attention_hidden,
|
| 166 |
-
values=self.encoder_outputs,
|
| 167 |
-
keys=self.keys,
|
| 168 |
-
mask=self.mask,
|
| 169 |
-
prev_alignment=self.prev_alignment,
|
| 170 |
-
)
|
| 171 |
-
|
| 172 |
-
decoder_input = torch.cat((output, attention), -1)
|
| 173 |
-
|
| 174 |
-
decoder_input = self.project_to_decoder_in(decoder_input)
|
| 175 |
-
|
| 176 |
-
for idx in range(len(self.decoder_rnns)):
|
| 177 |
-
self.decoder_hiddens[idx] = self.decoder_rnns[idx](
|
| 178 |
-
decoder_input, self.decoder_hiddens[idx]
|
| 179 |
-
)
|
| 180 |
-
decoder_input = self.decoder_hiddens[idx] + decoder_input
|
| 181 |
-
|
| 182 |
-
output = decoder_input
|
| 183 |
-
|
| 184 |
-
output = self.diacritics_layer(output)
|
| 185 |
-
|
| 186 |
-
if self.is_attention_accumulative:
|
| 187 |
-
self.prev_alignment = self.prev_alignment + alignment
|
| 188 |
-
else:
|
| 189 |
-
self.prev_alignment = alignment
|
| 190 |
-
|
| 191 |
-
self.prev_attention = attention
|
| 192 |
-
|
| 193 |
-
return output, alignment
|
| 194 |
-
|
| 195 |
-
def inference(self):
|
| 196 |
-
"""Generate diacritics one at a time"""
|
| 197 |
-
batch_size = self.encoder_outputs.size(0)
|
| 198 |
-
trg_len = self.encoder_outputs.size(1)
|
| 199 |
-
diacritic = (
|
| 200 |
-
torch.full((batch_size,), self.start_symbol_id).to(self.device).long()
|
| 201 |
-
)
|
| 202 |
-
outputs, alignments = [], []
|
| 203 |
-
self.initialize()
|
| 204 |
-
|
| 205 |
-
for _ in range(trg_len):
|
| 206 |
-
output, alignment = self.decode(diacritic=diacritic)
|
| 207 |
-
|
| 208 |
-
outputs.append(output)
|
| 209 |
-
alignments.append(alignment)
|
| 210 |
-
diacritic = torch.max(output, 1).indices
|
| 211 |
-
|
| 212 |
-
alignments = torch.stack(alignments).transpose(0, 1)
|
| 213 |
-
outputs = torch.stack(outputs).transpose(0, 1).contiguous()
|
| 214 |
-
return outputs, alignments
|
| 215 |
-
|
| 216 |
-
def forward(
|
| 217 |
-
self,
|
| 218 |
-
encoder_outputs: torch.Tensor,
|
| 219 |
-
diacritics: Optional[torch.Tensor] = None,
|
| 220 |
-
input_mask: Optional[torch.Tensor] = None,
|
| 221 |
-
):
|
| 222 |
-
"""calculate forward propagation
|
| 223 |
-
Args:
|
| 224 |
-
encoder_outputs (Tensor): the output of the encoder
|
| 225 |
-
(batch_size, Tx, encoder_units * 2)
|
| 226 |
-
diacritics(Tensor): target sequence
|
| 227 |
-
input_mask (Tensor): the inputs mask (batch_size, Tx)
|
| 228 |
-
"""
|
| 229 |
-
self.mask = input_mask
|
| 230 |
-
self.encoder_outputs = encoder_outputs
|
| 231 |
-
self.keys = self.keys_layer(encoder_outputs)
|
| 232 |
-
|
| 233 |
-
if diacritics is None:
|
| 234 |
-
return self.inference()
|
| 235 |
-
|
| 236 |
-
batch_size = diacritics.size(0)
|
| 237 |
-
trg_len = diacritics.size(1)
|
| 238 |
-
|
| 239 |
-
# Init decoder states
|
| 240 |
-
outputs = []
|
| 241 |
-
alignments = []
|
| 242 |
-
|
| 243 |
-
self.initialize()
|
| 244 |
-
|
| 245 |
-
diacritic = (
|
| 246 |
-
torch.full((batch_size,), self.start_symbol_id).to(self.device).long()
|
| 247 |
-
)
|
| 248 |
-
|
| 249 |
-
for time in range(trg_len):
|
| 250 |
-
output, alignment = self.decode(diacritic=diacritic)
|
| 251 |
-
outputs += [output]
|
| 252 |
-
alignments += [alignment]
|
| 253 |
-
#if random.random() > self.teacher_forcing_probability:
|
| 254 |
-
diacritic = diacritics[:, time] # use training input
|
| 255 |
-
#else:
|
| 256 |
-
#diacritic = torch.max(output, 1).indices # use last output
|
| 257 |
-
|
| 258 |
-
alignments = torch.stack(alignments).transpose(0, 1)
|
| 259 |
-
outputs = torch.stack(outputs).transpose(0, 1).contiguous()
|
| 260 |
-
|
| 261 |
-
return outputs, alignments
|
| 262 |
-
|
| 263 |
-
def initialize(self):
|
| 264 |
-
"""Initialize the first step variables"""
|
| 265 |
-
batch_size = self.encoder_outputs.size(0)
|
| 266 |
-
src_len = self.encoder_outputs.size(1)
|
| 267 |
-
self.attention_hidden = Variable(
|
| 268 |
-
torch.zeros(batch_size, self.attention_units)
|
| 269 |
-
).to(self.device)
|
| 270 |
-
self.decoder_hiddens = [
|
| 271 |
-
Variable(torch.zeros(batch_size, self.decoder_units)).to(self.device)
|
| 272 |
-
for _ in range(len(self.decoder_rnns))
|
| 273 |
-
]
|
| 274 |
-
self.prev_attention = Variable(torch.zeros(batch_size, self.encoder_dim)).to(
|
| 275 |
-
self.device
|
| 276 |
-
)
|
| 277 |
-
self.prev_alignment = Variable(torch.zeros(batch_size, src_len)).to(self.device)
|
|
|
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|
spaces/Abhilashvj/planogram-compliance/utils/loggers/wandb/log_dataset.py
DELETED
|
@@ -1,43 +0,0 @@
|
|
| 1 |
-
import argparse
|
| 2 |
-
|
| 3 |
-
from wandb_utils import WandbLogger
|
| 4 |
-
|
| 5 |
-
from utils.general import LOGGER
|
| 6 |
-
|
| 7 |
-
WANDB_ARTIFACT_PREFIX = "wandb-artifact://"
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
def create_dataset_artifact(opt):
|
| 11 |
-
logger = WandbLogger(
|
| 12 |
-
opt, None, job_type="Dataset Creation"
|
| 13 |
-
) # TODO: return value unused
|
| 14 |
-
if not logger.wandb:
|
| 15 |
-
LOGGER.info(
|
| 16 |
-
"install wandb using `pip install wandb` to log the dataset"
|
| 17 |
-
)
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
if __name__ == "__main__":
|
| 21 |
-
parser = argparse.ArgumentParser()
|
| 22 |
-
parser.add_argument(
|
| 23 |
-
"--data", type=str, default="data/coco128.yaml", help="data.yaml path"
|
| 24 |
-
)
|
| 25 |
-
parser.add_argument(
|
| 26 |
-
"--single-cls",
|
| 27 |
-
action="store_true",
|
| 28 |
-
help="train as single-class dataset",
|
| 29 |
-
)
|
| 30 |
-
parser.add_argument(
|
| 31 |
-
"--project", type=str, default="YOLOv5", help="name of W&B Project"
|
| 32 |
-
)
|
| 33 |
-
parser.add_argument("--entity", default=None, help="W&B entity")
|
| 34 |
-
parser.add_argument(
|
| 35 |
-
"--name", type=str, default="log dataset", help="name of W&B run"
|
| 36 |
-
)
|
| 37 |
-
|
| 38 |
-
opt = parser.parse_args()
|
| 39 |
-
opt.resume = (
|
| 40 |
-
False # Explicitly disallow resume check for dataset upload job
|
| 41 |
-
)
|
| 42 |
-
|
| 43 |
-
create_dataset_artifact(opt)
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spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/__init__.py
DELETED
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@@ -1 +0,0 @@
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|
| 1 |
-
# -*- coding: utf-8 -*-
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|
spaces/Adithedev/Keyword-Extractor/README.md
DELETED
|
@@ -1,12 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Keyword Extractor
|
| 3 |
-
emoji: 🌍
|
| 4 |
-
colorFrom: green
|
| 5 |
-
colorTo: gray
|
| 6 |
-
sdk: streamlit
|
| 7 |
-
sdk_version: 1.25.0
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateBBCodeText.js
DELETED
|
@@ -1,16 +0,0 @@
|
|
| 1 |
-
import BBCodeText from '../../bbcodetext/BBCodeText.js';
|
| 2 |
-
import MergeStyle from './utils/MergeStyle.js';
|
| 3 |
-
import SetTextureProperties from './utils/SetTextureProperties.js';
|
| 4 |
-
|
| 5 |
-
var CreateBBCodeText = function (scene, data, view, styles, customBuilders) {
|
| 6 |
-
data = MergeStyle(data, styles);
|
| 7 |
-
|
| 8 |
-
var gameObject = new BBCodeText(scene, 0, 0, data.text, data);
|
| 9 |
-
|
| 10 |
-
SetTextureProperties(gameObject, data);
|
| 11 |
-
|
| 12 |
-
scene.add.existing(gameObject);
|
| 13 |
-
return gameObject;
|
| 14 |
-
}
|
| 15 |
-
|
| 16 |
-
export default CreateBBCodeText;
|
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|
spaces/AlexWang/lama/saicinpainting/evaluation/masks/README.md
DELETED
|
@@ -1,27 +0,0 @@
|
|
| 1 |
-
# Current algorithm
|
| 2 |
-
|
| 3 |
-
## Choice of mask objects
|
| 4 |
-
|
| 5 |
-
For identification of the objects which are suitable for mask obtaining, panoptic segmentation model
|
| 6 |
-
from [detectron2](https://github.com/facebookresearch/detectron2) trained on COCO. Categories of the detected instances
|
| 7 |
-
belong either to "stuff" or "things" types. We consider that instances of objects should have category belong
|
| 8 |
-
to "things". Besides, we set upper bound on area which is taken by the object — we consider that too big
|
| 9 |
-
area indicates either of the instance being a background or a main object which should not be removed.
|
| 10 |
-
|
| 11 |
-
## Choice of position for mask
|
| 12 |
-
|
| 13 |
-
We consider that input image has size 2^n x 2^m. We downsample it using
|
| 14 |
-
[COUNTLESS](https://github.com/william-silversmith/countless) algorithm so the width is equal to
|
| 15 |
-
64 = 2^8 = 2^{downsample_levels}.
|
| 16 |
-
|
| 17 |
-
### Augmentation
|
| 18 |
-
|
| 19 |
-
There are several parameters for augmentation:
|
| 20 |
-
- Scaling factor. We limit scaling to the case when a mask after scaling with pivot point in its center fits inside the
|
| 21 |
-
image completely.
|
| 22 |
-
-
|
| 23 |
-
|
| 24 |
-
### Shift
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
## Select
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|
spaces/AlexWang/lama/saicinpainting/evaluation/masks/countless/__init__.py
DELETED
|
File without changes
|
spaces/Amrrs/DragGan-Inversion/torch_utils/ops/grid_sample_gradfix.py
DELETED
|
@@ -1,84 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
| 2 |
-
#
|
| 3 |
-
# NVIDIA CORPORATION and its licensors retain all intellectual property
|
| 4 |
-
# and proprietary rights in and to this software, related documentation
|
| 5 |
-
# and any modifications thereto. Any use, reproduction, disclosure or
|
| 6 |
-
# distribution of this software and related documentation without an express
|
| 7 |
-
# license agreement from NVIDIA CORPORATION is strictly prohibited.
|
| 8 |
-
|
| 9 |
-
"""Custom replacement for `torch.nn.functional.grid_sample` that
|
| 10 |
-
supports arbitrarily high order gradients between the input and output.
|
| 11 |
-
Only works on 2D images and assumes
|
| 12 |
-
`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`."""
|
| 13 |
-
|
| 14 |
-
import torch
|
| 15 |
-
|
| 16 |
-
# pylint: disable=redefined-builtin
|
| 17 |
-
# pylint: disable=arguments-differ
|
| 18 |
-
# pylint: disable=protected-access
|
| 19 |
-
|
| 20 |
-
# ----------------------------------------------------------------------------
|
| 21 |
-
|
| 22 |
-
enabled = False # Enable the custom op by setting this to true.
|
| 23 |
-
|
| 24 |
-
# ----------------------------------------------------------------------------
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
def grid_sample(input, grid):
|
| 28 |
-
if _should_use_custom_op():
|
| 29 |
-
return _GridSample2dForward.apply(input, grid)
|
| 30 |
-
return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
|
| 31 |
-
|
| 32 |
-
# ----------------------------------------------------------------------------
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
def _should_use_custom_op():
|
| 36 |
-
return enabled
|
| 37 |
-
|
| 38 |
-
# ----------------------------------------------------------------------------
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
class _GridSample2dForward(torch.autograd.Function):
|
| 42 |
-
@staticmethod
|
| 43 |
-
def forward(ctx, input, grid):
|
| 44 |
-
assert input.ndim == 4
|
| 45 |
-
assert grid.ndim == 4
|
| 46 |
-
output = torch.nn.functional.grid_sample(
|
| 47 |
-
input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
|
| 48 |
-
ctx.save_for_backward(input, grid)
|
| 49 |
-
return output
|
| 50 |
-
|
| 51 |
-
@staticmethod
|
| 52 |
-
def backward(ctx, grad_output):
|
| 53 |
-
input, grid = ctx.saved_tensors
|
| 54 |
-
grad_input, grad_grid = _GridSample2dBackward.apply(
|
| 55 |
-
grad_output, input, grid)
|
| 56 |
-
return grad_input, grad_grid
|
| 57 |
-
|
| 58 |
-
# ----------------------------------------------------------------------------
|
| 59 |
-
|
| 60 |
-
|
| 61 |
-
class _GridSample2dBackward(torch.autograd.Function):
|
| 62 |
-
@staticmethod
|
| 63 |
-
def forward(ctx, grad_output, input, grid):
|
| 64 |
-
op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')
|
| 65 |
-
grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
|
| 66 |
-
ctx.save_for_backward(grid)
|
| 67 |
-
return grad_input, grad_grid
|
| 68 |
-
|
| 69 |
-
@staticmethod
|
| 70 |
-
def backward(ctx, grad2_grad_input, grad2_grad_grid):
|
| 71 |
-
_ = grad2_grad_grid # unused
|
| 72 |
-
grid, = ctx.saved_tensors
|
| 73 |
-
grad2_grad_output = None
|
| 74 |
-
grad2_input = None
|
| 75 |
-
grad2_grid = None
|
| 76 |
-
|
| 77 |
-
if ctx.needs_input_grad[0]:
|
| 78 |
-
grad2_grad_output = _GridSample2dForward.apply(
|
| 79 |
-
grad2_grad_input, grid)
|
| 80 |
-
|
| 81 |
-
assert not ctx.needs_input_grad[2]
|
| 82 |
-
return grad2_grad_output, grad2_input, grad2_grid
|
| 83 |
-
|
| 84 |
-
# ----------------------------------------------------------------------------
|
|
|
|
|
|
|
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|
spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/kd_loss.py
DELETED
|
@@ -1,87 +0,0 @@
|
|
| 1 |
-
import mmcv
|
| 2 |
-
import torch.nn as nn
|
| 3 |
-
import torch.nn.functional as F
|
| 4 |
-
|
| 5 |
-
from ..builder import LOSSES
|
| 6 |
-
from .utils import weighted_loss
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
@mmcv.jit(derivate=True, coderize=True)
|
| 10 |
-
@weighted_loss
|
| 11 |
-
def knowledge_distillation_kl_div_loss(pred,
|
| 12 |
-
soft_label,
|
| 13 |
-
T,
|
| 14 |
-
detach_target=True):
|
| 15 |
-
r"""Loss function for knowledge distilling using KL divergence.
|
| 16 |
-
|
| 17 |
-
Args:
|
| 18 |
-
pred (Tensor): Predicted logits with shape (N, n + 1).
|
| 19 |
-
soft_label (Tensor): Target logits with shape (N, N + 1).
|
| 20 |
-
T (int): Temperature for distillation.
|
| 21 |
-
detach_target (bool): Remove soft_label from automatic differentiation
|
| 22 |
-
|
| 23 |
-
Returns:
|
| 24 |
-
torch.Tensor: Loss tensor with shape (N,).
|
| 25 |
-
"""
|
| 26 |
-
assert pred.size() == soft_label.size()
|
| 27 |
-
target = F.softmax(soft_label / T, dim=1)
|
| 28 |
-
if detach_target:
|
| 29 |
-
target = target.detach()
|
| 30 |
-
|
| 31 |
-
kd_loss = F.kl_div(
|
| 32 |
-
F.log_softmax(pred / T, dim=1), target, reduction='none').mean(1) * (
|
| 33 |
-
T * T)
|
| 34 |
-
|
| 35 |
-
return kd_loss
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
@LOSSES.register_module()
|
| 39 |
-
class KnowledgeDistillationKLDivLoss(nn.Module):
|
| 40 |
-
"""Loss function for knowledge distilling using KL divergence.
|
| 41 |
-
|
| 42 |
-
Args:
|
| 43 |
-
reduction (str): Options are `'none'`, `'mean'` and `'sum'`.
|
| 44 |
-
loss_weight (float): Loss weight of current loss.
|
| 45 |
-
T (int): Temperature for distillation.
|
| 46 |
-
"""
|
| 47 |
-
|
| 48 |
-
def __init__(self, reduction='mean', loss_weight=1.0, T=10):
|
| 49 |
-
super(KnowledgeDistillationKLDivLoss, self).__init__()
|
| 50 |
-
assert T >= 1
|
| 51 |
-
self.reduction = reduction
|
| 52 |
-
self.loss_weight = loss_weight
|
| 53 |
-
self.T = T
|
| 54 |
-
|
| 55 |
-
def forward(self,
|
| 56 |
-
pred,
|
| 57 |
-
soft_label,
|
| 58 |
-
weight=None,
|
| 59 |
-
avg_factor=None,
|
| 60 |
-
reduction_override=None):
|
| 61 |
-
"""Forward function.
|
| 62 |
-
|
| 63 |
-
Args:
|
| 64 |
-
pred (Tensor): Predicted logits with shape (N, n + 1).
|
| 65 |
-
soft_label (Tensor): Target logits with shape (N, N + 1).
|
| 66 |
-
weight (torch.Tensor, optional): The weight of loss for each
|
| 67 |
-
prediction. Defaults to None.
|
| 68 |
-
avg_factor (int, optional): Average factor that is used to average
|
| 69 |
-
the loss. Defaults to None.
|
| 70 |
-
reduction_override (str, optional): The reduction method used to
|
| 71 |
-
override the original reduction method of the loss.
|
| 72 |
-
Defaults to None.
|
| 73 |
-
"""
|
| 74 |
-
assert reduction_override in (None, 'none', 'mean', 'sum')
|
| 75 |
-
|
| 76 |
-
reduction = (
|
| 77 |
-
reduction_override if reduction_override else self.reduction)
|
| 78 |
-
|
| 79 |
-
loss_kd = self.loss_weight * knowledge_distillation_kl_div_loss(
|
| 80 |
-
pred,
|
| 81 |
-
soft_label,
|
| 82 |
-
weight,
|
| 83 |
-
reduction=reduction,
|
| 84 |
-
avg_factor=avg_factor,
|
| 85 |
-
T=self.T)
|
| 86 |
-
|
| 87 |
-
return loss_kd
|
|
|
|
|
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|
spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py
DELETED
|
@@ -1,2 +0,0 @@
|
|
| 1 |
-
_base_ = './deeplabv3_r50-d8_512x1024_80k_cityscapes.py'
|
| 2 |
-
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
|
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|
|
|
|
|
|
spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r101-d8_769x769_40k_cityscapes.py
DELETED
|
@@ -1,2 +0,0 @@
|
|
| 1 |
-
_base_ = './nonlocal_r50-d8_769x769_40k_cityscapes.py'
|
| 2 |
-
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
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spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/ops/__init__.py
DELETED
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@@ -1,4 +0,0 @@
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| 1 |
-
from .encoding import Encoding
|
| 2 |
-
from .wrappers import Upsample, resize
|
| 3 |
-
|
| 4 |
-
__all__ = ['Upsample', 'resize', 'Encoding']
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spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/inject_securetransport.py
DELETED
|
@@ -1,35 +0,0 @@
|
|
| 1 |
-
"""A helper module that injects SecureTransport, on import.
|
| 2 |
-
|
| 3 |
-
The import should be done as early as possible, to ensure all requests and
|
| 4 |
-
sessions (or whatever) are created after injecting SecureTransport.
|
| 5 |
-
|
| 6 |
-
Note that we only do the injection on macOS, when the linked OpenSSL is too
|
| 7 |
-
old to handle TLSv1.2.
|
| 8 |
-
"""
|
| 9 |
-
|
| 10 |
-
import sys
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
def inject_securetransport() -> None:
|
| 14 |
-
# Only relevant on macOS
|
| 15 |
-
if sys.platform != "darwin":
|
| 16 |
-
return
|
| 17 |
-
|
| 18 |
-
try:
|
| 19 |
-
import ssl
|
| 20 |
-
except ImportError:
|
| 21 |
-
return
|
| 22 |
-
|
| 23 |
-
# Checks for OpenSSL 1.0.1
|
| 24 |
-
if ssl.OPENSSL_VERSION_NUMBER >= 0x1000100F:
|
| 25 |
-
return
|
| 26 |
-
|
| 27 |
-
try:
|
| 28 |
-
from pip._vendor.urllib3.contrib import securetransport
|
| 29 |
-
except (ImportError, OSError):
|
| 30 |
-
return
|
| 31 |
-
|
| 32 |
-
securetransport.inject_into_urllib3()
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
inject_securetransport()
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spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/distlib/__init__.py
DELETED
|
@@ -1,23 +0,0 @@
|
|
| 1 |
-
# -*- coding: utf-8 -*-
|
| 2 |
-
#
|
| 3 |
-
# Copyright (C) 2012-2022 Vinay Sajip.
|
| 4 |
-
# Licensed to the Python Software Foundation under a contributor agreement.
|
| 5 |
-
# See LICENSE.txt and CONTRIBUTORS.txt.
|
| 6 |
-
#
|
| 7 |
-
import logging
|
| 8 |
-
|
| 9 |
-
__version__ = '0.3.6'
|
| 10 |
-
|
| 11 |
-
class DistlibException(Exception):
|
| 12 |
-
pass
|
| 13 |
-
|
| 14 |
-
try:
|
| 15 |
-
from logging import NullHandler
|
| 16 |
-
except ImportError: # pragma: no cover
|
| 17 |
-
class NullHandler(logging.Handler):
|
| 18 |
-
def handle(self, record): pass
|
| 19 |
-
def emit(self, record): pass
|
| 20 |
-
def createLock(self): self.lock = None
|
| 21 |
-
|
| 22 |
-
logger = logging.getLogger(__name__)
|
| 23 |
-
logger.addHandler(NullHandler())
|
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spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/importlib_resources/simple.py
DELETED
|
@@ -1,116 +0,0 @@
|
|
| 1 |
-
"""
|
| 2 |
-
Interface adapters for low-level readers.
|
| 3 |
-
"""
|
| 4 |
-
|
| 5 |
-
import abc
|
| 6 |
-
import io
|
| 7 |
-
import itertools
|
| 8 |
-
from typing import BinaryIO, List
|
| 9 |
-
|
| 10 |
-
from .abc import Traversable, TraversableResources
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
class SimpleReader(abc.ABC):
|
| 14 |
-
"""
|
| 15 |
-
The minimum, low-level interface required from a resource
|
| 16 |
-
provider.
|
| 17 |
-
"""
|
| 18 |
-
|
| 19 |
-
@abc.abstractproperty
|
| 20 |
-
def package(self):
|
| 21 |
-
# type: () -> str
|
| 22 |
-
"""
|
| 23 |
-
The name of the package for which this reader loads resources.
|
| 24 |
-
"""
|
| 25 |
-
|
| 26 |
-
@abc.abstractmethod
|
| 27 |
-
def children(self):
|
| 28 |
-
# type: () -> List['SimpleReader']
|
| 29 |
-
"""
|
| 30 |
-
Obtain an iterable of SimpleReader for available
|
| 31 |
-
child containers (e.g. directories).
|
| 32 |
-
"""
|
| 33 |
-
|
| 34 |
-
@abc.abstractmethod
|
| 35 |
-
def resources(self):
|
| 36 |
-
# type: () -> List[str]
|
| 37 |
-
"""
|
| 38 |
-
Obtain available named resources for this virtual package.
|
| 39 |
-
"""
|
| 40 |
-
|
| 41 |
-
@abc.abstractmethod
|
| 42 |
-
def open_binary(self, resource):
|
| 43 |
-
# type: (str) -> BinaryIO
|
| 44 |
-
"""
|
| 45 |
-
Obtain a File-like for a named resource.
|
| 46 |
-
"""
|
| 47 |
-
|
| 48 |
-
@property
|
| 49 |
-
def name(self):
|
| 50 |
-
return self.package.split('.')[-1]
|
| 51 |
-
|
| 52 |
-
|
| 53 |
-
class ResourceHandle(Traversable):
|
| 54 |
-
"""
|
| 55 |
-
Handle to a named resource in a ResourceReader.
|
| 56 |
-
"""
|
| 57 |
-
|
| 58 |
-
def __init__(self, parent, name):
|
| 59 |
-
# type: (ResourceContainer, str) -> None
|
| 60 |
-
self.parent = parent
|
| 61 |
-
self.name = name # type: ignore
|
| 62 |
-
|
| 63 |
-
def is_file(self):
|
| 64 |
-
return True
|
| 65 |
-
|
| 66 |
-
def is_dir(self):
|
| 67 |
-
return False
|
| 68 |
-
|
| 69 |
-
def open(self, mode='r', *args, **kwargs):
|
| 70 |
-
stream = self.parent.reader.open_binary(self.name)
|
| 71 |
-
if 'b' not in mode:
|
| 72 |
-
stream = io.TextIOWrapper(*args, **kwargs)
|
| 73 |
-
return stream
|
| 74 |
-
|
| 75 |
-
def joinpath(self, name):
|
| 76 |
-
raise RuntimeError("Cannot traverse into a resource")
|
| 77 |
-
|
| 78 |
-
|
| 79 |
-
class ResourceContainer(Traversable):
|
| 80 |
-
"""
|
| 81 |
-
Traversable container for a package's resources via its reader.
|
| 82 |
-
"""
|
| 83 |
-
|
| 84 |
-
def __init__(self, reader):
|
| 85 |
-
# type: (SimpleReader) -> None
|
| 86 |
-
self.reader = reader
|
| 87 |
-
|
| 88 |
-
def is_dir(self):
|
| 89 |
-
return True
|
| 90 |
-
|
| 91 |
-
def is_file(self):
|
| 92 |
-
return False
|
| 93 |
-
|
| 94 |
-
def iterdir(self):
|
| 95 |
-
files = (ResourceHandle(self, name) for name in self.reader.resources)
|
| 96 |
-
dirs = map(ResourceContainer, self.reader.children())
|
| 97 |
-
return itertools.chain(files, dirs)
|
| 98 |
-
|
| 99 |
-
def open(self, *args, **kwargs):
|
| 100 |
-
raise IsADirectoryError()
|
| 101 |
-
|
| 102 |
-
def joinpath(self, name):
|
| 103 |
-
return next(
|
| 104 |
-
traversable for traversable in self.iterdir() if traversable.name == name
|
| 105 |
-
)
|
| 106 |
-
|
| 107 |
-
|
| 108 |
-
class TraversableReader(TraversableResources, SimpleReader):
|
| 109 |
-
"""
|
| 110 |
-
A TraversableResources based on SimpleReader. Resource providers
|
| 111 |
-
may derive from this class to provide the TraversableResources
|
| 112 |
-
interface by supplying the SimpleReader interface.
|
| 113 |
-
"""
|
| 114 |
-
|
| 115 |
-
def files(self):
|
| 116 |
-
return ResourceContainer(self)
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|
spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/config/__init__.py
DELETED
|
@@ -1,24 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates.
|
| 2 |
-
from .compat import downgrade_config, upgrade_config
|
| 3 |
-
from .config import CfgNode, get_cfg, global_cfg, set_global_cfg, configurable
|
| 4 |
-
from .instantiate import instantiate
|
| 5 |
-
from .lazy import LazyCall, LazyConfig
|
| 6 |
-
|
| 7 |
-
__all__ = [
|
| 8 |
-
"CfgNode",
|
| 9 |
-
"get_cfg",
|
| 10 |
-
"global_cfg",
|
| 11 |
-
"set_global_cfg",
|
| 12 |
-
"downgrade_config",
|
| 13 |
-
"upgrade_config",
|
| 14 |
-
"configurable",
|
| 15 |
-
"instantiate",
|
| 16 |
-
"LazyCall",
|
| 17 |
-
"LazyConfig",
|
| 18 |
-
]
|
| 19 |
-
|
| 20 |
-
|
| 21 |
-
from detectron2.utils.env import fixup_module_metadata
|
| 22 |
-
|
| 23 |
-
fixup_module_metadata(__name__, globals(), __all__)
|
| 24 |
-
del fixup_module_metadata
|
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|
spaces/Bajr/softly/Dockerfile
DELETED
|
@@ -1,15 +0,0 @@
|
|
| 1 |
-
FROM node:20-bullseye-slim
|
| 2 |
-
RUN apt-get update && \
|
| 3 |
-
apt-get install -y git
|
| 4 |
-
EXPOSE 7860
|
| 5 |
-
WORKDIR /home/node
|
| 6 |
-
RUN mkdir app && chown node:node app
|
| 7 |
-
USER node
|
| 8 |
-
WORKDIR /home/node/app
|
| 9 |
-
RUN mkdir source_code
|
| 10 |
-
COPY Dockerfile greeting.md* .env* ./
|
| 11 |
-
COPY run.sh ./
|
| 12 |
-
USER root
|
| 13 |
-
RUN chmod +x ./run.sh
|
| 14 |
-
USER node
|
| 15 |
-
CMD ["/bin/bash", "./run.sh"]
|
|
|
|
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|
|
spaces/Benson/text-generation/Examples/Coin Master Unlimited Free Spins 2022 Apk.md
DELETED
|
@@ -1,86 +0,0 @@
|
|
| 1 |
-
<br />
|
| 2 |
-
<h1>Coin Master unlimited free spins 2022 apk: how to get more spins and coins</h1>
|
| 3 |
-
<p>Coin Master is one of the most popular and addictive mobile games, combining strategy, chance and fun. In this game, your goal is to build your own Viking village, attack and loot other players' villages, and protect your territory from invaders. To do all this, you need to spin a slot machine that will give you different rewards such as coins, shields, hammers, or free spins. </p>
|
| 4 |
-
<h2>coin master unlimited free spins 2022 apk</h2><br /><p><b><b>Download</b> ✒ ✒ ✒ <a href="https://bltlly.com/2v6LV2">https://bltlly.com/2v6LV2</a></b></p><br /><br />
|
| 5 |
-
<p>But the free spins are not infinite, and they sell out quickly. That’s why many players look for ways to get more spins and coins without having to pay or wait. One of the most tempting options is to use the Coin Master Unlimited Free Spins 2022 apk, a modified app that allows you to get infinite spins in the game. But what exactly is this apk, how does it work, and what advantages and disadvantages does it have? In this article we tell you everything. </p>
|
| 6 |
-
<h2>What is Coin Master and why is it so popular? </h2>
|
| 7 |
-
<p>Before going into details about the Coin Master 2022 unlimited free spins apk, let’s briefly explain what Coin Master is and why it has become one of the most downloaded and played games in the world. </p>
|
| 8 |
-
<h3>A strategy and chance game that engages you</h3>
|
| 9 |
-
<p>Coin Master is a free game for Android and iOS that was launched in 2010 by the Israeli company Moon Active. Since then, it has accumulated over 100 million downloads and generated over $500 million in revenue. The game is based on a simple but very addictive concept: spin a slot machine to get different rewards to help you build your Viking village. </p>
|
| 10 |
-
|
| 11 |
-
<h3>How to play Coin Master and build your viking village</h3>
|
| 12 |
-
<p>The game is very easy to play, but also requires some strategy and planning. At first, you get 75 free spins to start spinning the slot machine. Every time you spin the machine, you spend one spin and get one of the following rewards:</p>
|
| 13 |
-
<p></p>
|
| 14 |
-
<ul>
|
| 15 |
-
<li>Coins: they are used to buy and improve the buildings of your village. The more advanced the level of your village, the more coins you will need to build and upgrade. </li>
|
| 16 |
-
<li>Hammers: allow you to attack other players' villages and steal some of their coins. You can choose which player to attack or let the game do it for you. Each attack gives you the option to hit one of the target player’s four buildings. If you manage to destroy all four buildings, you get an extra bonus. </li>
|
| 17 |
-
<li>Shields: protect you from attacks by other players. Each shield blocks an attack, but you can only have a maximum of three shields at a time. If you already have three shields and get another one, it becomes coins. </li>
|
| 18 |
-
<li>Pigs: they give you the opportunity to loot the village of the player who occupies the first place in the ranking of the game. You can choose from four places to dig and find a hidden treasure. The treasure can contain coins, free spins, or a surprise bag. </li>
|
| 19 |
-
<li>Free spins: they give you more spins to keep playing. You can get between two and ten free spins every time they appear on the machine. </li>
|
| 20 |
-
</ul>
|
| 21 |
-
<p>When you run out of free spins, you can wait for them to recharge every hour, buy more with real money, or get more with other methods that we will see later. </p>
|
| 22 |
-
<h2>How to get unlimited free spins in Coin Master? </h2>
|
| 23 |
-
|
| 24 |
-
<h3>Legal and safe methods to get more spins</h3>
|
| 25 |
-
<p>These are the methods that the game itself offers you to get more free spins without violating its rules or endangering your account or your device. They are as follows:</p>
|
| 26 |
-
<h4>Wait for hourly re-runs</h4>
|
| 27 |
-
<p>This is the most basic and simple method, but also the slowest. The game gives you five free spins per hour, up to a maximum of 50 free spins per day. If you don’t want to spend money or risk using other methods, you can simply wait for hourly spins to reload and play sparingly. </p>
|
| 28 |
-
<h4>Invite your Facebook friends to play</h4>
|
| 29 |
-
<p>This is one of the most effective and easy methods to get more free spins. The game rewards you with 40 free spins for each friend you invite to play Coin Master via Facebook. Just send them an invitation from the game and wait for them to accept it and install the game on their devices. Also, by connecting your Facebook account with the game, you can see your friends' villages and compete with them. </p>
|
| 30 |
-
<h4>Follow the official social networks of Coin Master</h4>
|
| 31 |
-
<p>Another way to get more free spins is to follow the official social networks of Coin Master, such as Facebook, Twitter, Instagram, or YouTube. In these networks, the game usually publishes links and codes that give you free spins or coins if you claim them on time. You can also participate in sweepstakes, contests, and surveys that the game periodically performs to reward its followers with free spins. </p>
|
| 32 |
-
<h4>View ads in exchange for extra spins</h4>
|
| 33 |
-
|
| 34 |
-
<h4>Participate in special events and missions</h4>
|
| 35 |
-
<p>Coin Master usually organizes special events and missions that give you the opportunity to get more free spins and other rewards. These events and missions can vary in duration, difficulty, and theme, but they always offer you an incentive to play more and get more profits. For example, there may be seasonal events, such as Christmas or Halloween, or themed events, such as the Lion King or the Vikings. To participate in these events and missions, simply enter the game and follow the instructions given. </p>
|
| 36 |
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<h4>Complete letter collections</h4>
|
| 37 |
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<p>Another way to get more free spins is to complete the card collections that the game offers you. Cards are collectibles that you can get by turning the machine or opening chests. There are different types of cards, each with its own value and rarity. By completing a card collection, you get a reward that can include free spins, coins, or pets. Pets are creatures that accompany you in the game and give you different advantages, such as multiplying your coins or your attacks. </p>
|
| 38 |
-
<h3>Illegal and risky methods to get infinite spins</h3>
|
| 39 |
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<p>These are the methods that some players use to get infinite spins in Coin Master, but they are not legal or safe. These methods violate the rules of the game and can endanger your account or device. They are as follows:</p>
|
| 40 |
-
<h4>Using online print generators</h4>
|
| 41 |
-
<p>Some websites or apps promise you unlimited free spins for Coin Master if you enter your username or email. These generators often ask you to complete a survey or verify that you are human to give you the free spins. However, these generators do not really work and only seek to get your personal data or waste your time. </p>
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| 42 |
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| 43 |
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<p>Another option that some players choose is to download modified or hacked Coin Master apps that supposedly give you infinite spins in the game. These apps are altered versions of the original game that are downloaded from unofficial sources. However, these apps may contain viruses or malware that damage your device or steal your information. They can also be detected by the game and cause your account to be banned. </p>
|
| 44 |
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<h4>Using game tricks or glitches</h4>
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| 45 |
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<p>Finally, some players try to take advantage of some tricks or glitches of the game to get more free spins. These tricks or glitches are errors or failures of the game that can be exploited to obtain advantages. For example, some players change the date and time of their device to trick the game and get more free spins. However, these tricks or glitches do not always work and can cause problems in the operation of the game or in synchronization with the server. </p>
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| 46 |
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<h2>What are the advantages of using the Coin Master apk unlimited 2022 free spins? </h2>
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| 47 |
-
<p>Now that we know what methods exist to get more free spins in Coin Master, let’s focus on the Coin Master apk unlimited free spins 2022, a modified app that allows you to get infinite spins in the game. What are the advantages of using this apk? Here are some of the possible advantages:</p>
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| 48 |
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<h3>Enjoy the game without limits or restrictions</h3>
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| 49 |
-
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| 50 |
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<h3>Save money and time in the game</h3>
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| 51 |
-
<p>Another advantage of using the Coin Master Unlimited 2022 free spins apk is that you can save money and time in the game. With infinite spins, you won’t have to spend real money buying more spins or coins, which can save you a lot in the long run. Plus, with infinite spins, you won’t have to waste time watching ads, completing surveys, or following social media for more spins. You can spend all your time playing and having fun. </p>
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| 52 |
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<h3>Increase your chances of winning and advancing</h3>
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| 53 |
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<p>Finally, another advantage of using the Coin Master apk unlimited free spins 2022 is that you can increase your chances of winning and advancing in the game. By having infinite spins, you can get more coins, shields, hammers, pigs, and cards to help you build your village, attack and loot other players' villages, and protect your territory from invaders. You can also participate in more events and special missions that give you more rewards and benefits. Thus, you can progress faster and easier in the game and reach the highest levels. </p>
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| 54 |
-
<h2>What are the disadvantages or risks of using the Coin Master apk unlimited 2022 free spins? </h2>
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| 55 |
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<p>But not all are advantages when using the Coin Master apk unlimited free spins 2022. This app also has its disadvantages and risks, which you should consider before deciding whether to use it or not. Here are some of the possible disadvantages and risks:</p>
|
| 56 |
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<h3>Violating game terms and conditions of use</h3>
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| 57 |
-
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| 58 |
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<h3>Expose your account and your personal data to possible bans or thefts</h3>
|
| 59 |
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<p>Another disadvantage or risk of using the Coin Master Unlimited 2022 Free Spins apk is that you are exposing your account and your personal data to possible bans or thefts. By using this app, you are giving access to an unofficial source to your account and your personal information, such as your name, email address, or credit card. This can pose a risk to your security and privacy, as you may be the victim of a ban by the game or a theft by third parties. </p>
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| 60 |
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<h3>Damaging your device with viruses or malware</h3>
|
| 61 |
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<p>Finally, another disadvantage or risk of using the Coin Master apk unlimited free spins 2022 is that you are damaging your device with viruses or malware. By downloading and installing this application from an unofficial source, you are exposing your device to possible viruses or malware that may infect or damage it. This can affect the performance or operation of your device, and even lead to loss or deletion of your personal data or files. </p>
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| 62 |
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<h2>Conclusion: Is it worth using the Coin Master apk unlimited 2022 free spins? </h2>
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| 63 |
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<p>After analyzing the advantages and disadvantages of using the Coin Master apk unlimited free spins 2022, we can conclude that this is a very tempting but also very risky option. While it is true that this application allows you to enjoy the game without limits or restrictions, it is also true that it exposes you to possible legal, security, and fun problems. </p>
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| 64 |
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<p>Therefore, our recommendation is that you do not use the Coin Master apk unlimited 2022 free spins, and that you opt for legal and safe methods to get more free spins in the game. In this way, you will be able to play Coin Master responsibly, safely, and fun, without compromising your account, your device, or your gaming experience. </p>
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<p>Below, we present some of the most frequently asked questions players get about the Coin Master apk unlimited free spins 2022, and their respective answers. </p>
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| 67 |
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<h3>Where can I download the Coin Master apk unlimited free spins 2022? </h3>
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| 68 |
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<p>We cannot give you an exact answer to this question, as the Coin Master Unlimited Free Spins 2022 apk is not an official or licensed app by the game. Therefore, you won’t find it in the official app stores, such as Google Play or App Store. You will need to search for it on unofficial websites or forums, but be aware that these sources may not be reliable or secure. </p>
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| 69 |
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<h3>How to install unlimited Coin Master Free Spins 2022 apk? </h3>
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| 70 |
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<p>To install the Coin Master apk unlimited free spins 2022, you will need to follow a few steps similar to those followed to install any other application from an external source. Here are the general steps:</p>
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| 71 |
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<ol>
|
| 72 |
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<li>Download the apk file from the source you have chosen. </li>
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| 73 |
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<li>Go to your device settings and activate the option to allow the installation of applications from unknown sources. </li>
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| 74 |
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<li>Find the apk file in your device’s download folder and open it. </li>
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| 75 |
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<li>Follow the instructions on the screen to complete the installation. </li>
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| 76 |
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<li>Open the app and enjoy unlimited free spins in Coin Master.</li>
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| 77 |
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</ol>
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| 78 |
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<p>Remember that these steps may vary according to the type and model of your device, and according to the version of the apk you have downloaded. </p>
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| 79 |
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<h3>Is it safe to use Coin Master apk unlimited 2022 free spins? </h3>
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| 81 |
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<h3>Is it legal to use Coin Master apk unlimited 2022 free spins? </h3>
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<p>No, it is not legal to use the Coin Master apk unlimited 2022 free spins. By using this application, you are violating the terms and conditions of use of the game, which expressly prohibit the use of any software or tool that alters or modifies the normal operation of the game or that confers an unfair advantage on other players. This can lead to serious misconduct with legal consequences. </p>
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<h3>Is it fun to use the Coin Master apk unlimited free spins 2022? </h3>
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<p>We cannot answer this question for you, as it depends on your personal tastes and preferences. But we can tell you that using the Coin Master apk unlimited free spins 2022 can make you lose the fun and challenge of the game. By having infinite spins, the game loses its essence and its grace, as there is no challenge or excitement when spinning the machine. Also, by using the apk of Coin Master unlimited free spins 2022, you are missing out on the interaction and competition with other players, which are part of the appeal of the game. That’s why we suggest you play Coin Master naturally and honestly, and enjoy the game as designed. </p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Descarga De Archivos Zip Facebook Lite.md
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<h1>Descarga de archivos Zip de Facebook Lite: Una guía para usuarios de Android</h1>
|
| 3 |
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<p>Facebook es una de las plataformas de redes sociales más populares del mundo, con miles de millones de usuarios. Sin embargo, no todo el mundo tiene acceso a una conexión a Internet rápida y estable, o un teléfono inteligente potente y espacioso. Es por eso que Facebook creó Facebook Lite, una versión más ligera y rápida de la aplicación que funciona en todas las condiciones de red y dispositivos Android. En este artículo, te mostraremos cómo descargar el archivo zip de Facebook Lite para Android y cómo instalarlo en tu teléfono. </p>
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| 4 |
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<h2>descarga de archivos zip facebook lite</h2><br /><p><b><b>Download File</b> ⇒ <a href="https://bltlly.com/2v6Kse">https://bltlly.com/2v6Kse</a></b></p><br /><br />
|
| 5 |
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<h2>¿Qué es Facebook Lite y por qué usarlo? </h2>
|
| 6 |
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<p>Facebook Lite es una aplicación de teléfono inteligente desarrollada por Facebook para teléfonos Android de baja potencia y áreas con conexiones a Internet lentas o inestables. Es una versión reducida de la aplicación estándar de Facebook, que utiliza menos datos, espacio de almacenamiento y batería. También se carga rápidamente y funciona en todas las redes, incluyendo 2G. </p>
|
| 7 |
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<h3>Características y beneficios de Facebook Lite</h3>
|
| 8 |
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<p>Con Facebook Lite, todavía tienes acceso a todas las características básicas de Facebook, como:</p>
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| 9 |
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<ul>
|
| 10 |
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<li>Publicar en su línea de tiempo, fotos de gusto, comentarios sobre los mensajes, y la edición de su perfil y grupos</li>
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| 11 |
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<li>Encontrar amigos y familiares, y mantenerse en contacto con ellos a través de mensajes y llamadas</li>
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| 12 |
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<li>Compartir fotos, vídeos, memes y otro contenido</li>
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| 13 |
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<li>Recibir notificaciones cuando alguien le gusta o comentarios en sus mensajes</li>
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| 14 |
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<li>Encontrar eventos locales, RSVPing, y hacer planes con amigos</li>
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| 15 |
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<li>Siguiendo a sus celebridades favoritas, marcas, sitios web, artistas o equipos deportivos</li>
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| 16 |
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<li>Comprar y vender localmente en Facebook Marketplace</li>
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| 17 |
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</ul>
|
| 18 |
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<p>Algunas ventajas de usar Facebook Lite son:</p>
|
| 19 |
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<ul>
|
| 20 |
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<li>Se instala rápido - la aplicación es más pequeña que 10 MB, por lo que es rápida de descargar y utiliza menos espacio de almacenamiento</li>
|
| 21 |
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<li>Funciona en los teléfonos Android antiguos - se puede utilizar en los teléfonos Android más antiguos no soportados por la aplicación regular de Facebook</li>
|
| 22 |
-
|
| 23 |
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<li>Carga rápidamente - está optimizado para velocidad y rendimiento, incluso en conexiones lentas</li>
|
| 24 |
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<li>Funciona en todas las redes - está diseñado para redes 2G y áreas con conexiones a Internet pobres o inestables</li>
|
| 25 |
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</ul>
|
| 26 |
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<h3>Compatibilidad y requisitos de Facebook Lite</h3>
|
| 27 |
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<p>Facebook Lite es compatible con dispositivos Android con Android 2.3 o superior. También es compatible con la mayoría de los idiomas hablados en todo el mundo. Necesita una conexión a Internet activa para usar la aplicación, ya sea a través de Wi-Fi o datos móviles. También necesita una cuenta de Facebook para iniciar sesión en la aplicación. </p>
|
| 28 |
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<p></p>
|
| 29 |
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<h2>Cómo descargar el archivo zip de Facebook Lite para Android</h2>
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| 30 |
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<p>Hay diferentes maneras de descargar el archivo zip de Facebook Lite para Android. Aquí están algunos de ellos:</p>
|
| 31 |
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<h3>Descargar desde el sitio web oficial</h3>
|
| 32 |
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<p>Puede descargar el archivo zip de Facebook Lite desde el sitio web oficial de Facebook. Estos son los pasos:</p>
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| 33 |
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<ol>
|
| 34 |
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<li>Ir a [1](https://www.facebook.com/lite) en su navegador</li>
|
| 35 |
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<li>Toque en el botón "Descargar" </li>
|
| 36 |
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<li>Aparecerá una ventana emergente pidiéndole que guarde el archivo. Toque en "OK"</li>
|
| 37 |
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<li>El archivo zip se descargará en la carpeta de descarga predeterminada del dispositivo</li>
|
| 38 |
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</ol>
|
| 39 |
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<h3>Descargar de Google Play Store</h3>
|
| 40 |
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<p>También puede descargar el archivo zip de Facebook Lite desde Google Play Store. Estos son los pasos:</p>
|
| 41 |
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<ol>
|
| 42 |
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<li>Abre Google Play Store en tu dispositivo</li>
|
| 43 |
-
<li>Buscar "Facebook Lite" en la barra de búsqueda</li>
|
| 44 |
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<li> <li>Pulse el botón "Instalar" </li>
|
| 45 |
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<li>Espere a que la aplicación se descargue e instale en su dispositivo</li>
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| 46 |
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<li>Abra la aplicación e inicie sesión con su cuenta de Facebook</li>
|
| 47 |
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</ol>
|
| 48 |
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<h3>Descarga de fuentes de terceros</h3>
|
| 49 |
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<p>También puede descargar el archivo zip de Facebook Lite de fuentes de terceros, como APKPure, APKMirror o Uptodown. Sin embargo, debe tener cuidado al descargar de estas fuentes, ya que pueden contener malware o virus. También debe comprobar las revisiones y calificaciones de la aplicación antes de descargar. Estos son los pasos:</p>
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| 50 |
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<ol>
|
| 51 |
-
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| 52 |
-
<li>Toque en el botón "Descargar" </li>
|
| 53 |
-
<li>Aparecerá una ventana emergente pidiéndole que guarde el archivo. Toque en "OK"</li>
|
| 54 |
-
<li>El archivo zip se descargará en la carpeta de descarga predeterminada del dispositivo</li>
|
| 55 |
-
</ol>
|
| 56 |
-
<h2>Cómo instalar el archivo zip de Facebook Lite en Android</h2>
|
| 57 |
-
<p>Después de descargar el archivo zip de Facebook Lite, debe instalarlo en su dispositivo. Estos son los pasos:</p>
|
| 58 |
-
<h3>Habilitar la opción de fuentes desconocidas</h3>
|
| 59 |
-
<p>Antes de instalar el archivo zip de Facebook Lite, debe habilitar la opción de fuentes desconocidas en su dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Estos son los pasos:</p>
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| 60 |
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<ol>
|
| 61 |
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<li>Ir a la configuración del dispositivo</li>
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| 62 |
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<li>Toque en "Seguridad" o "Privacidad"</li>
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| 63 |
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<li>Buscar y habilitar la opción "Fuentes desconocidas" </li>
|
| 64 |
-
<li>Aparecerá un mensaje de advertencia. Toque en "OK"</li>
|
| 65 |
-
</ol>
|
| 66 |
-
<h3>Extraer el archivo zip usando una aplicación de administrador de archivos</h3>
|
| 67 |
-
<p>Para instalar el archivo zip de Facebook Lite, primero debe extraerlo usando una aplicación de administrador de archivos. Puede usar cualquier aplicación de administrador de archivos que admita archivos zip, como ES File Explorer, ZArchiver o RAR. Estos son los pasos:</p>
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| 68 |
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<ol>
|
| 69 |
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<li>Abra la aplicación de administrador de archivos en su dispositivo</li>
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| 70 |
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<li>Vaya a la carpeta donde descargó el archivo zip de Facebook Lite</li>
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| 71 |
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<li>Mantenga pulsado el archivo zip hasta que aparezca un menú</li>
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| 72 |
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<li>Seleccione "Extraer" o "Descomprimir"</li>
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| 73 |
-
<li>Elija una carpeta de destino donde desea extraer los archivos</li>
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| 74 |
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<li>Espere a que el proceso de extracción termine</li>
|
| 75 |
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</ol>
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| 76 |
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<h3>Instalar el archivo apk y poner en marcha la aplicación</h3>
|
| 77 |
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<p>Después de extraer el archivo zip de Facebook Lite, encontrará un archivo apk en la carpeta de destino. Este es el archivo de instalación de Facebook Lite. Estos son los pasos:</p>
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| 78 |
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<ol>
|
| 79 |
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<li>Toque en el archivo apk para abrirlo</li>
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| 80 |
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<li>Aparecerá una ventana emergente pidiéndole que instale la aplicación. Toque en "Instalar"</li>
|
| 81 |
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<li>Espere a que termine el proceso de instalación</li>
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| 82 |
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<li>Aparecerá un mensaje diciendo que la aplicación está instalada. Toque en "Abrir"</li>
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| 84 |
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</ol>
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| 85 |
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<h2>Conclusión y preguntas frecuentes</h2>
|
| 86 |
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<p>En este artículo, le hemos mostrado cómo descargar el archivo zip de Facebook Lite para Android y cómo instalarlo en su dispositivo. También hemos explicado qué es Facebook Lite y por qué debería usarlo. Esperamos que esta guía haya sido útil e informativa para usted. </p>
|
| 87 |
-
<h3>Resumen de los puntos principales</h3>
|
| 88 |
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<ul>
|
| 89 |
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<li>Facebook Lite es una versión más ligera y rápida de Facebook que funciona en todas las condiciones de red y dispositivos Android. </li>
|
| 90 |
-
<li>Puede descargar el archivo zip de Facebook Lite de diferentes fuentes, como el sitio web oficial, Google Play Store o sitios web de terceros. </li>
|
| 91 |
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<li> Es necesario habilitar la opción de fuentes desconocidas, extraer el archivo zip, e instalar el archivo apk para utilizar Facebook Lite en su dispositivo. </li>
|
| 92 |
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</ul>
|
| 93 |
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<h3>Preguntas frecuentes</h3>
|
| 94 |
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<borde de la tabla="1">
|
| 95 |
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<tr><td><b>Question</b></td><td><b>Answer</b></td></tr>
|
| 96 |
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<tr><td>¿Cuál es la diferencia entre Facebook y Facebook Lite? </td><td>Facebook Lite es una versión simplificada de Facebook que utiliza menos datos, espacio de almacenamiento y batería. También funciona en teléfonos Android más antiguos y conexiones a Internet más lentas. </td></tr>
|
| 97 |
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<tr><td>¿Es seguro usar Facebook Lite? </td><td>Facebook Lite es seguro de usar si lo descarga de fuentes confiables, como el sitio web oficial o Google Play Store. Sin embargo, debe tener cuidado al descargar de sitios web de terceros, ya que pueden contener malware o virus. </td></tr> <tr><td>¿Cómo puedo actualizar Facebook Lite? </td><td>Puedes actualizar Facebook Lite yendo a Google Play Store y buscando actualizaciones. También puede descargar la última versión del archivo zip de Facebook Lite desde el sitio web oficial u otras fuentes e instalarlo en su dispositivo. </td></tr>
|
| 98 |
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|
| 99 |
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<tr><td>¿Puedo usar Facebook Lite y Facebook al mismo tiempo? </td><td>Sí, puedes usar Facebook Lite y Facebook al mismo tiempo en tu dispositivo. Sin embargo, tendrá que iniciar sesión con diferentes cuentas o usar diferentes modos, como modo de incógnito o invitado. </td></tr>
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</tabla></p> 64aa2da5cf<br />
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spaces/Boadiwaa/Recipes/openai/api_resources/abstract/createable_api_resource.py
DELETED
|
@@ -1,47 +0,0 @@
|
|
| 1 |
-
from openai import api_requestor, util, error
|
| 2 |
-
from openai.api_resources.abstract.api_resource import APIResource
|
| 3 |
-
from openai.util import ApiType
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
class CreateableAPIResource(APIResource):
|
| 7 |
-
plain_old_data = False
|
| 8 |
-
|
| 9 |
-
@classmethod
|
| 10 |
-
def create(
|
| 11 |
-
cls,
|
| 12 |
-
api_key=None,
|
| 13 |
-
api_base=None,
|
| 14 |
-
api_type=None,
|
| 15 |
-
request_id=None,
|
| 16 |
-
api_version=None,
|
| 17 |
-
organization=None,
|
| 18 |
-
**params,
|
| 19 |
-
):
|
| 20 |
-
requestor = api_requestor.APIRequestor(
|
| 21 |
-
api_key,
|
| 22 |
-
api_base=api_base,
|
| 23 |
-
api_type=api_type,
|
| 24 |
-
api_version=api_version,
|
| 25 |
-
organization=organization,
|
| 26 |
-
)
|
| 27 |
-
typed_api_type, api_version = cls._get_api_type_and_version(api_type, api_version)
|
| 28 |
-
|
| 29 |
-
if typed_api_type == ApiType.AZURE:
|
| 30 |
-
base = cls.class_url()
|
| 31 |
-
url = "/%s%s?api-version=%s" % (cls.azure_api_prefix, base, api_version)
|
| 32 |
-
elif typed_api_type == ApiType.OPEN_AI:
|
| 33 |
-
url = cls.class_url()
|
| 34 |
-
else:
|
| 35 |
-
raise error.InvalidAPIType('Unsupported API type %s' % api_type)
|
| 36 |
-
|
| 37 |
-
response, _, api_key = requestor.request(
|
| 38 |
-
"post", url, params, request_id=request_id
|
| 39 |
-
)
|
| 40 |
-
|
| 41 |
-
return util.convert_to_openai_object(
|
| 42 |
-
response,
|
| 43 |
-
api_key,
|
| 44 |
-
api_version,
|
| 45 |
-
organization,
|
| 46 |
-
plain_old_data=cls.plain_old_data,
|
| 47 |
-
)
|
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|
spaces/BoomerangGirl/MagicPrompt-Stable-Diffusion/README.md
DELETED
|
@@ -1,14 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: MagicPrompt Stable Diffusion
|
| 3 |
-
emoji: 😻
|
| 4 |
-
colorFrom: red
|
| 5 |
-
colorTo: indigo
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.3.1
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
license: mit
|
| 11 |
-
duplicated_from: Gustavosta/MagicPrompt-Stable-Diffusion
|
| 12 |
-
---
|
| 13 |
-
|
| 14 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
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|
spaces/Branon/oai-proxy/README.md
DELETED
|
@@ -1,10 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Branon
|
| 3 |
-
emoji: 🤓
|
| 4 |
-
colorFrom: red
|
| 5 |
-
colorTo: purple
|
| 6 |
-
sdk: docker
|
| 7 |
-
pinned: false
|
| 8 |
-
---
|
| 9 |
-
|
| 10 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
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|
spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/docs/tutorials/deployment.md
DELETED
|
@@ -1,94 +0,0 @@
|
|
| 1 |
-
# Deployment
|
| 2 |
-
|
| 3 |
-
## Caffe2 Deployment
|
| 4 |
-
We currently support converting a detectron2 model to Caffe2 format through ONNX.
|
| 5 |
-
The converted Caffe2 model is able to run without detectron2 dependency in either Python or C++.
|
| 6 |
-
It has a runtime optimized for CPU & mobile inference, but not for GPU inference.
|
| 7 |
-
|
| 8 |
-
Caffe2 conversion requires PyTorch ≥ 1.4 and ONNX ≥ 1.6.
|
| 9 |
-
|
| 10 |
-
### Coverage
|
| 11 |
-
|
| 12 |
-
It supports 3 most common meta architectures: `GeneralizedRCNN`, `RetinaNet`, `PanopticFPN`,
|
| 13 |
-
and most official models under these 3 meta architectures.
|
| 14 |
-
|
| 15 |
-
Users' custom extensions under these architectures (added through registration) are supported
|
| 16 |
-
as long as they do not contain control flow or operators not available in Caffe2 (e.g. deformable convolution).
|
| 17 |
-
For example, custom backbones and heads are often supported out of the box.
|
| 18 |
-
|
| 19 |
-
### Usage
|
| 20 |
-
|
| 21 |
-
The conversion APIs are documented at [the API documentation](../modules/export.html).
|
| 22 |
-
We provide a tool, `caffe2_converter.py` as an example that uses
|
| 23 |
-
these APIs to convert a standard model.
|
| 24 |
-
|
| 25 |
-
To convert an official Mask R-CNN trained on COCO, first
|
| 26 |
-
[prepare the COCO dataset](../../datasets/), then pick the model from [Model Zoo](../../MODEL_ZOO.md), and run:
|
| 27 |
-
```
|
| 28 |
-
cd tools/deploy/ && ./caffe2_converter.py --config-file ../../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
|
| 29 |
-
--output ./caffe2_model --run-eval \
|
| 30 |
-
MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl \
|
| 31 |
-
MODEL.DEVICE cpu
|
| 32 |
-
```
|
| 33 |
-
|
| 34 |
-
Note that:
|
| 35 |
-
1. The conversion needs valid sample inputs & weights to trace the model. That's why the script requires the dataset.
|
| 36 |
-
You can modify the script to obtain sample inputs in other ways.
|
| 37 |
-
2. GPU conversion is supported only with Pytorch's master. So we use `MODEL.DEVICE cpu`.
|
| 38 |
-
3. With the `--run-eval` flag, it will evaluate the converted models to verify its accuracy.
|
| 39 |
-
The accuracy is typically slightly different (within 0.1 AP) from PyTorch due to
|
| 40 |
-
numerical precisions between different implementations.
|
| 41 |
-
It's recommended to always verify the accuracy in case your custom model is not supported by the
|
| 42 |
-
conversion.
|
| 43 |
-
|
| 44 |
-
The converted model is available at the specified `caffe2_model/` directory. Two files `model.pb`
|
| 45 |
-
and `model_init.pb` that contain network structure and network parameters are necessary for deployment.
|
| 46 |
-
These files can then be loaded in C++ or Python using Caffe2's APIs.
|
| 47 |
-
|
| 48 |
-
The script generates `model.svg` file which contains a visualization of the network.
|
| 49 |
-
You can also load `model.pb` to tools such as [netron](https://github.com/lutzroeder/netron) to visualize it.
|
| 50 |
-
|
| 51 |
-
### Use the model in C++/Python
|
| 52 |
-
|
| 53 |
-
The model can be loaded in C++. An example [caffe2_mask_rcnn.cpp](../../tools/deploy/) is given,
|
| 54 |
-
which performs CPU inference using `COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x`.
|
| 55 |
-
|
| 56 |
-
The C++ code needs to be built with:
|
| 57 |
-
* `libtorch.so`, `libc10.so`
|
| 58 |
-
* gflags, glog, opencv
|
| 59 |
-
* protobuf headers that match the version of your caffe2
|
| 60 |
-
* MKL headers if caffe2 is built with MKL
|
| 61 |
-
* `-D_GLIBCXX_USE_CXX11_ABI=` equals `torch._C._GLIBCXX_USE_CXX11_ABI`
|
| 62 |
-
|
| 63 |
-
As an example, the following works inside official detectron2 docker:
|
| 64 |
-
```
|
| 65 |
-
apt install libgflags-dev libgoogle-glog-dev
|
| 66 |
-
pip install mkl-include
|
| 67 |
-
wget https://github.com/protocolbuffers/protobuf/releases/download/v3.6.1/protobuf-cpp-3.6.1.tar.gz
|
| 68 |
-
tar xf protobuf-cpp-3.6.1.tar.gz
|
| 69 |
-
export TORCH_ROOT=/home/appuser/.local/lib/python3.6/site-packages/torch/
|
| 70 |
-
g++ -O2 caffe2_mask_rcnn.cpp `pkg-config --libs --cflags opencv` -Iprotobuf-3.6.1/src/ \
|
| 71 |
-
-lgflags -lglog -I$TORCH_ROOT/include -L$TORCH_ROOT/lib -lc10 -ltorch \
|
| 72 |
-
-I/home/appuser/.local/include -D_GLIBCXX_USE_CXX11_ABI=0 -o caffe2_mask_rcnn
|
| 73 |
-
|
| 74 |
-
export LD_LIBRARY_PATH=$TORCH_ROOT/lib
|
| 75 |
-
./caffe2_mask_rcnn --predict_net=./model.pb --init_net=./model_init.pb --input=input.jpg
|
| 76 |
-
```
|
| 77 |
-
|
| 78 |
-
Note that:
|
| 79 |
-
|
| 80 |
-
* All converted models (the .pb files) take two input tensors:
|
| 81 |
-
"data" is an NCHW image, and "im_info" is an Nx3 tensor consisting of (height, width, 1.0) for
|
| 82 |
-
each image (the shape of "data" might be larger than that in "im_info" due to padding).
|
| 83 |
-
|
| 84 |
-
* The converted models do not contain post-processing operations that
|
| 85 |
-
transform raw layer outputs into formatted predictions.
|
| 86 |
-
The example only produces raw outputs (28x28 masks) from the final
|
| 87 |
-
layers that are not post-processed, because in actual deployment, an application often needs
|
| 88 |
-
its custom lightweight post-processing (e.g. full-image masks for every detected object is often not necessary).
|
| 89 |
-
|
| 90 |
-
We also provide a python wrapper around the converted model, in the
|
| 91 |
-
[Caffe2Model.__call__](../modules/export.html#detectron2.export.Caffe2Model.__call__) method.
|
| 92 |
-
This method has an interface that's identical to the [pytorch versions of models](models.html),
|
| 93 |
-
and it internally applies pre/post-processing code to match the formats.
|
| 94 |
-
They can serve as a reference for pre/post-processing in actual deployment.
|
|
|
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|
spaces/ChallengeHub/Chinese-LangChain/clc/langchain_application.py
DELETED
|
@@ -1,97 +0,0 @@
|
|
| 1 |
-
#!/usr/bin/env python
|
| 2 |
-
# -*- coding:utf-8 _*-
|
| 3 |
-
"""
|
| 4 |
-
@author:quincy qiang
|
| 5 |
-
@license: Apache Licence
|
| 6 |
-
@file: model.py
|
| 7 |
-
@time: 2023/04/17
|
| 8 |
-
@contact: [email protected]
|
| 9 |
-
@software: PyCharm
|
| 10 |
-
@description: coding..
|
| 11 |
-
"""
|
| 12 |
-
from langchain.chains import RetrievalQA
|
| 13 |
-
from langchain.prompts.prompt import PromptTemplate
|
| 14 |
-
|
| 15 |
-
from clc.config import LangChainCFG
|
| 16 |
-
from clc.gpt_service import ChatGLMService
|
| 17 |
-
from clc.source_service import SourceService
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
class LangChainApplication(object):
|
| 21 |
-
def __init__(self, config):
|
| 22 |
-
self.config = config
|
| 23 |
-
self.llm_service = ChatGLMService()
|
| 24 |
-
self.llm_service.load_model(model_name_or_path=self.config.llm_model_name)
|
| 25 |
-
self.source_service = SourceService(config)
|
| 26 |
-
|
| 27 |
-
# if self.config.kg_vector_stores is None:
|
| 28 |
-
# print("init a source vector store")
|
| 29 |
-
# self.source_service.init_source_vector()
|
| 30 |
-
# else:
|
| 31 |
-
# print("load zh_wikipedia source vector store ")
|
| 32 |
-
# try:
|
| 33 |
-
# self.source_service.load_vector_store(self.config.kg_vector_stores['初始化知识库'])
|
| 34 |
-
# except Exception as e:
|
| 35 |
-
# self.source_service.init_source_vector()
|
| 36 |
-
|
| 37 |
-
def get_knowledge_based_answer(self, query,
|
| 38 |
-
history_len=5,
|
| 39 |
-
temperature=0.1,
|
| 40 |
-
top_p=0.9,
|
| 41 |
-
top_k=4,
|
| 42 |
-
web_content='',
|
| 43 |
-
chat_history=[]):
|
| 44 |
-
if web_content:
|
| 45 |
-
prompt_template = f"""基于以下已知信息，简洁和专业的来回答用户的问题。
|
| 46 |
-
如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。
|
| 47 |
-
已知网络检索内容：{web_content}""" + """
|
| 48 |
-
已知内容:
|
| 49 |
-
{context}
|
| 50 |
-
问题:
|
| 51 |
-
{question}"""
|
| 52 |
-
else:
|
| 53 |
-
prompt_template = """基于以下已知信息，简洁和专业的来回答用户的问题。
|
| 54 |
-
如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。
|
| 55 |
-
已知内容:
|
| 56 |
-
{context}
|
| 57 |
-
问题:
|
| 58 |
-
{question}"""
|
| 59 |
-
prompt = PromptTemplate(template=prompt_template,
|
| 60 |
-
input_variables=["context", "question"])
|
| 61 |
-
self.llm_service.history = chat_history[-history_len:] if history_len > 0 else []
|
| 62 |
-
|
| 63 |
-
self.llm_service.temperature = temperature
|
| 64 |
-
self.llm_service.top_p = top_p
|
| 65 |
-
|
| 66 |
-
knowledge_chain = RetrievalQA.from_llm(
|
| 67 |
-
llm=self.llm_service,
|
| 68 |
-
retriever=self.source_service.vector_store.as_retriever(
|
| 69 |
-
search_kwargs={"k": top_k}),
|
| 70 |
-
prompt=prompt)
|
| 71 |
-
knowledge_chain.combine_documents_chain.document_prompt = PromptTemplate(
|
| 72 |
-
input_variables=["page_content"], template="{page_content}")
|
| 73 |
-
|
| 74 |
-
knowledge_chain.return_source_documents = True
|
| 75 |
-
|
| 76 |
-
result = knowledge_chain({"query": query})
|
| 77 |
-
return result
|
| 78 |
-
|
| 79 |
-
def get_llm_answer(self, query='', web_content=''):
|
| 80 |
-
if web_content:
|
| 81 |
-
prompt = f'基于网络检索内容：{web_content}，回答以下问题{query}'
|
| 82 |
-
else:
|
| 83 |
-
prompt = query
|
| 84 |
-
result = self.llm_service._call(prompt)
|
| 85 |
-
return result
|
| 86 |
-
|
| 87 |
-
|
| 88 |
-
if __name__ == '__main__':
|
| 89 |
-
config = LangChainCFG()
|
| 90 |
-
application = LangChainApplication(config)
|
| 91 |
-
# result = application.get_knowledge_based_answer('马保国是谁')
|
| 92 |
-
# print(result)
|
| 93 |
-
# application.source_service.add_document('/home/searchgpt/yq/Knowledge-ChatGLM/docs/added/马保国.txt')
|
| 94 |
-
# result = application.get_knowledge_based_answer('马保国是谁')
|
| 95 |
-
# print(result)
|
| 96 |
-
result = application.get_llm_answer('马保国是谁')
|
| 97 |
-
print(result)
|
|
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spaces/ChrisCaviar/ControlNet-v1-1/style.css
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
h1 {
|
| 2 |
-
text-align: center;
|
| 3 |
-
}
|
|
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|
|
|
|
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|
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|
spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/utils/cv2_util.py
DELETED
|
@@ -1,24 +0,0 @@
|
|
| 1 |
-
"""
|
| 2 |
-
Module for cv2 utility functions and maintaining version compatibility
|
| 3 |
-
between 3.x and 4.x
|
| 4 |
-
"""
|
| 5 |
-
import cv2
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
def findContours(*args, **kwargs):
|
| 9 |
-
"""
|
| 10 |
-
Wraps cv2.findContours to maintain compatiblity between versions
|
| 11 |
-
3 and 4
|
| 12 |
-
|
| 13 |
-
Returns:
|
| 14 |
-
contours, hierarchy
|
| 15 |
-
"""
|
| 16 |
-
if cv2.__version__.startswith('4'):
|
| 17 |
-
contours, hierarchy = cv2.findContours(*args, **kwargs)
|
| 18 |
-
elif cv2.__version__.startswith('3'):
|
| 19 |
-
_, contours, hierarchy = cv2.findContours(*args, **kwargs)
|
| 20 |
-
else:
|
| 21 |
-
raise AssertionError(
|
| 22 |
-
'cv2 must be either version 3 or 4 to call this method')
|
| 23 |
-
|
| 24 |
-
return contours, hierarchy
|
|
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|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiohttp/web_urldispatcher.py
DELETED
|
@@ -1,1220 +0,0 @@
|
|
| 1 |
-
import abc
|
| 2 |
-
import asyncio
|
| 3 |
-
import base64
|
| 4 |
-
import hashlib
|
| 5 |
-
import inspect
|
| 6 |
-
import keyword
|
| 7 |
-
import os
|
| 8 |
-
import re
|
| 9 |
-
import warnings
|
| 10 |
-
from contextlib import contextmanager
|
| 11 |
-
from functools import wraps
|
| 12 |
-
from pathlib import Path
|
| 13 |
-
from types import MappingProxyType
|
| 14 |
-
from typing import (
|
| 15 |
-
TYPE_CHECKING,
|
| 16 |
-
Any,
|
| 17 |
-
Awaitable,
|
| 18 |
-
Callable,
|
| 19 |
-
Container,
|
| 20 |
-
Dict,
|
| 21 |
-
Generator,
|
| 22 |
-
Iterable,
|
| 23 |
-
Iterator,
|
| 24 |
-
List,
|
| 25 |
-
Mapping,
|
| 26 |
-
Optional,
|
| 27 |
-
Pattern,
|
| 28 |
-
Set,
|
| 29 |
-
Sized,
|
| 30 |
-
Tuple,
|
| 31 |
-
Type,
|
| 32 |
-
Union,
|
| 33 |
-
cast,
|
| 34 |
-
)
|
| 35 |
-
|
| 36 |
-
from yarl import URL, __version__ as yarl_version # type: ignore[attr-defined]
|
| 37 |
-
|
| 38 |
-
from . import hdrs
|
| 39 |
-
from .abc import AbstractMatchInfo, AbstractRouter, AbstractView
|
| 40 |
-
from .helpers import DEBUG
|
| 41 |
-
from .http import HttpVersion11
|
| 42 |
-
from .typedefs import Final, Handler, PathLike, TypedDict
|
| 43 |
-
from .web_exceptions import (
|
| 44 |
-
HTTPException,
|
| 45 |
-
HTTPExpectationFailed,
|
| 46 |
-
HTTPForbidden,
|
| 47 |
-
HTTPMethodNotAllowed,
|
| 48 |
-
HTTPNotFound,
|
| 49 |
-
)
|
| 50 |
-
from .web_fileresponse import FileResponse
|
| 51 |
-
from .web_request import Request
|
| 52 |
-
from .web_response import Response, StreamResponse
|
| 53 |
-
from .web_routedef import AbstractRouteDef
|
| 54 |
-
|
| 55 |
-
__all__ = (
|
| 56 |
-
"UrlDispatcher",
|
| 57 |
-
"UrlMappingMatchInfo",
|
| 58 |
-
"AbstractResource",
|
| 59 |
-
"Resource",
|
| 60 |
-
"PlainResource",
|
| 61 |
-
"DynamicResource",
|
| 62 |
-
"AbstractRoute",
|
| 63 |
-
"ResourceRoute",
|
| 64 |
-
"StaticResource",
|
| 65 |
-
"View",
|
| 66 |
-
)
|
| 67 |
-
|
| 68 |
-
|
| 69 |
-
if TYPE_CHECKING: # pragma: no cover
|
| 70 |
-
from .web_app import Application
|
| 71 |
-
|
| 72 |
-
BaseDict = Dict[str, str]
|
| 73 |
-
else:
|
| 74 |
-
BaseDict = dict
|
| 75 |
-
|
| 76 |
-
YARL_VERSION: Final[Tuple[int, ...]] = tuple(map(int, yarl_version.split(".")[:2]))
|
| 77 |
-
|
| 78 |
-
HTTP_METHOD_RE: Final[Pattern[str]] = re.compile(
|
| 79 |
-
r"^[0-9A-Za-z!#\$%&'\*\+\-\.\^_`\|~]+$"
|
| 80 |
-
)
|
| 81 |
-
ROUTE_RE: Final[Pattern[str]] = re.compile(
|
| 82 |
-
r"(\{[_a-zA-Z][^{}]*(?:\{[^{}]*\}[^{}]*)*\})"
|
| 83 |
-
)
|
| 84 |
-
PATH_SEP: Final[str] = re.escape("/")
|
| 85 |
-
|
| 86 |
-
|
| 87 |
-
_ExpectHandler = Callable[[Request], Awaitable[None]]
|
| 88 |
-
_Resolve = Tuple[Optional["UrlMappingMatchInfo"], Set[str]]
|
| 89 |
-
|
| 90 |
-
|
| 91 |
-
class _InfoDict(TypedDict, total=False):
|
| 92 |
-
path: str
|
| 93 |
-
|
| 94 |
-
formatter: str
|
| 95 |
-
pattern: Pattern[str]
|
| 96 |
-
|
| 97 |
-
directory: Path
|
| 98 |
-
prefix: str
|
| 99 |
-
routes: Mapping[str, "AbstractRoute"]
|
| 100 |
-
|
| 101 |
-
app: "Application"
|
| 102 |
-
|
| 103 |
-
domain: str
|
| 104 |
-
|
| 105 |
-
rule: "AbstractRuleMatching"
|
| 106 |
-
|
| 107 |
-
http_exception: HTTPException
|
| 108 |
-
|
| 109 |
-
|
| 110 |
-
class AbstractResource(Sized, Iterable["AbstractRoute"]):
|
| 111 |
-
def __init__(self, *, name: Optional[str] = None) -> None:
|
| 112 |
-
self._name = name
|
| 113 |
-
|
| 114 |
-
@property
|
| 115 |
-
def name(self) -> Optional[str]:
|
| 116 |
-
return self._name
|
| 117 |
-
|
| 118 |
-
@property
|
| 119 |
-
@abc.abstractmethod
|
| 120 |
-
def canonical(self) -> str:
|
| 121 |
-
"""Exposes the resource's canonical path.
|
| 122 |
-
|
| 123 |
-
For example '/foo/bar/{name}'
|
| 124 |
-
|
| 125 |
-
"""
|
| 126 |
-
|
| 127 |
-
@abc.abstractmethod # pragma: no branch
|
| 128 |
-
def url_for(self, **kwargs: str) -> URL:
|
| 129 |
-
"""Construct url for resource with additional params."""
|
| 130 |
-
|
| 131 |
-
@abc.abstractmethod # pragma: no branch
|
| 132 |
-
async def resolve(self, request: Request) -> _Resolve:
|
| 133 |
-
"""Resolve resource.
|
| 134 |
-
|
| 135 |
-
Return (UrlMappingMatchInfo, allowed_methods) pair.
|
| 136 |
-
"""
|
| 137 |
-
|
| 138 |
-
@abc.abstractmethod
|
| 139 |
-
def add_prefix(self, prefix: str) -> None:
|
| 140 |
-
"""Add a prefix to processed URLs.
|
| 141 |
-
|
| 142 |
-
Required for subapplications support.
|
| 143 |
-
"""
|
| 144 |
-
|
| 145 |
-
@abc.abstractmethod
|
| 146 |
-
def get_info(self) -> _InfoDict:
|
| 147 |
-
"""Return a dict with additional info useful for introspection"""
|
| 148 |
-
|
| 149 |
-
def freeze(self) -> None:
|
| 150 |
-
pass
|
| 151 |
-
|
| 152 |
-
@abc.abstractmethod
|
| 153 |
-
def raw_match(self, path: str) -> bool:
|
| 154 |
-
"""Perform a raw match against path"""
|
| 155 |
-
|
| 156 |
-
|
| 157 |
-
class AbstractRoute(abc.ABC):
|
| 158 |
-
def __init__(
|
| 159 |
-
self,
|
| 160 |
-
method: str,
|
| 161 |
-
handler: Union[Handler, Type[AbstractView]],
|
| 162 |
-
*,
|
| 163 |
-
expect_handler: Optional[_ExpectHandler] = None,
|
| 164 |
-
resource: Optional[AbstractResource] = None,
|
| 165 |
-
) -> None:
|
| 166 |
-
|
| 167 |
-
if expect_handler is None:
|
| 168 |
-
expect_handler = _default_expect_handler
|
| 169 |
-
|
| 170 |
-
assert asyncio.iscoroutinefunction(
|
| 171 |
-
expect_handler
|
| 172 |
-
), f"Coroutine is expected, got {expect_handler!r}"
|
| 173 |
-
|
| 174 |
-
method = method.upper()
|
| 175 |
-
if not HTTP_METHOD_RE.match(method):
|
| 176 |
-
raise ValueError(f"{method} is not allowed HTTP method")
|
| 177 |
-
|
| 178 |
-
assert callable(handler), handler
|
| 179 |
-
if asyncio.iscoroutinefunction(handler):
|
| 180 |
-
pass
|
| 181 |
-
elif inspect.isgeneratorfunction(handler):
|
| 182 |
-
warnings.warn(
|
| 183 |
-
"Bare generators are deprecated, " "use @coroutine wrapper",
|
| 184 |
-
DeprecationWarning,
|
| 185 |
-
)
|
| 186 |
-
elif isinstance(handler, type) and issubclass(handler, AbstractView):
|
| 187 |
-
pass
|
| 188 |
-
else:
|
| 189 |
-
warnings.warn(
|
| 190 |
-
"Bare functions are deprecated, " "use async ones", DeprecationWarning
|
| 191 |
-
)
|
| 192 |
-
|
| 193 |
-
@wraps(handler)
|
| 194 |
-
async def handler_wrapper(request: Request) -> StreamResponse:
|
| 195 |
-
result = old_handler(request)
|
| 196 |
-
if asyncio.iscoroutine(result):
|
| 197 |
-
return await result
|
| 198 |
-
return result # type: ignore[return-value]
|
| 199 |
-
|
| 200 |
-
old_handler = handler
|
| 201 |
-
handler = handler_wrapper
|
| 202 |
-
|
| 203 |
-
self._method = method
|
| 204 |
-
self._handler = handler
|
| 205 |
-
self._expect_handler = expect_handler
|
| 206 |
-
self._resource = resource
|
| 207 |
-
|
| 208 |
-
@property
|
| 209 |
-
def method(self) -> str:
|
| 210 |
-
return self._method
|
| 211 |
-
|
| 212 |
-
@property
|
| 213 |
-
def handler(self) -> Handler:
|
| 214 |
-
return self._handler
|
| 215 |
-
|
| 216 |
-
@property
|
| 217 |
-
@abc.abstractmethod
|
| 218 |
-
def name(self) -> Optional[str]:
|
| 219 |
-
"""Optional route's name, always equals to resource's name."""
|
| 220 |
-
|
| 221 |
-
@property
|
| 222 |
-
def resource(self) -> Optional[AbstractResource]:
|
| 223 |
-
return self._resource
|
| 224 |
-
|
| 225 |
-
@abc.abstractmethod
|
| 226 |
-
def get_info(self) -> _InfoDict:
|
| 227 |
-
"""Return a dict with additional info useful for introspection"""
|
| 228 |
-
|
| 229 |
-
@abc.abstractmethod # pragma: no branch
|
| 230 |
-
def url_for(self, *args: str, **kwargs: str) -> URL:
|
| 231 |
-
"""Construct url for route with additional params."""
|
| 232 |
-
|
| 233 |
-
async def handle_expect_header(self, request: Request) -> None:
|
| 234 |
-
await self._expect_handler(request)
|
| 235 |
-
|
| 236 |
-
|
| 237 |
-
class UrlMappingMatchInfo(BaseDict, AbstractMatchInfo):
|
| 238 |
-
def __init__(self, match_dict: Dict[str, str], route: AbstractRoute):
|
| 239 |
-
super().__init__(match_dict)
|
| 240 |
-
self._route = route
|
| 241 |
-
self._apps: List[Application] = []
|
| 242 |
-
self._current_app: Optional[Application] = None
|
| 243 |
-
self._frozen = False
|
| 244 |
-
|
| 245 |
-
@property
|
| 246 |
-
def handler(self) -> Handler:
|
| 247 |
-
return self._route.handler
|
| 248 |
-
|
| 249 |
-
@property
|
| 250 |
-
def route(self) -> AbstractRoute:
|
| 251 |
-
return self._route
|
| 252 |
-
|
| 253 |
-
@property
|
| 254 |
-
def expect_handler(self) -> _ExpectHandler:
|
| 255 |
-
return self._route.handle_expect_header
|
| 256 |
-
|
| 257 |
-
@property
|
| 258 |
-
def http_exception(self) -> Optional[HTTPException]:
|
| 259 |
-
return None
|
| 260 |
-
|
| 261 |
-
def get_info(self) -> _InfoDict: # type: ignore[override]
|
| 262 |
-
return self._route.get_info()
|
| 263 |
-
|
| 264 |
-
@property
|
| 265 |
-
def apps(self) -> Tuple["Application", ...]:
|
| 266 |
-
return tuple(self._apps)
|
| 267 |
-
|
| 268 |
-
def add_app(self, app: "Application") -> None:
|
| 269 |
-
if self._frozen:
|
| 270 |
-
raise RuntimeError("Cannot change apps stack after .freeze() call")
|
| 271 |
-
if self._current_app is None:
|
| 272 |
-
self._current_app = app
|
| 273 |
-
self._apps.insert(0, app)
|
| 274 |
-
|
| 275 |
-
@property
|
| 276 |
-
def current_app(self) -> "Application":
|
| 277 |
-
app = self._current_app
|
| 278 |
-
assert app is not None
|
| 279 |
-
return app
|
| 280 |
-
|
| 281 |
-
@contextmanager
|
| 282 |
-
def set_current_app(self, app: "Application") -> Generator[None, None, None]:
|
| 283 |
-
if DEBUG: # pragma: no cover
|
| 284 |
-
if app not in self._apps:
|
| 285 |
-
raise RuntimeError(
|
| 286 |
-
"Expected one of the following apps {!r}, got {!r}".format(
|
| 287 |
-
self._apps, app
|
| 288 |
-
)
|
| 289 |
-
)
|
| 290 |
-
prev = self._current_app
|
| 291 |
-
self._current_app = app
|
| 292 |
-
try:
|
| 293 |
-
yield
|
| 294 |
-
finally:
|
| 295 |
-
self._current_app = prev
|
| 296 |
-
|
| 297 |
-
def freeze(self) -> None:
|
| 298 |
-
self._frozen = True
|
| 299 |
-
|
| 300 |
-
def __repr__(self) -> str:
|
| 301 |
-
return f"<MatchInfo {super().__repr__()}: {self._route}>"
|
| 302 |
-
|
| 303 |
-
|
| 304 |
-
class MatchInfoError(UrlMappingMatchInfo):
|
| 305 |
-
def __init__(self, http_exception: HTTPException) -> None:
|
| 306 |
-
self._exception = http_exception
|
| 307 |
-
super().__init__({}, SystemRoute(self._exception))
|
| 308 |
-
|
| 309 |
-
@property
|
| 310 |
-
def http_exception(self) -> HTTPException:
|
| 311 |
-
return self._exception
|
| 312 |
-
|
| 313 |
-
def __repr__(self) -> str:
|
| 314 |
-
return "<MatchInfoError {}: {}>".format(
|
| 315 |
-
self._exception.status, self._exception.reason
|
| 316 |
-
)
|
| 317 |
-
|
| 318 |
-
|
| 319 |
-
async def _default_expect_handler(request: Request) -> None:
|
| 320 |
-
"""Default handler for Expect header.
|
| 321 |
-
|
| 322 |
-
Just send "100 Continue" to client.
|
| 323 |
-
raise HTTPExpectationFailed if value of header is not "100-continue"
|
| 324 |
-
"""
|
| 325 |
-
expect = request.headers.get(hdrs.EXPECT, "")
|
| 326 |
-
if request.version == HttpVersion11:
|
| 327 |
-
if expect.lower() == "100-continue":
|
| 328 |
-
await request.writer.write(b"HTTP/1.1 100 Continue\r\n\r\n")
|
| 329 |
-
else:
|
| 330 |
-
raise HTTPExpectationFailed(text="Unknown Expect: %s" % expect)
|
| 331 |
-
|
| 332 |
-
|
| 333 |
-
class Resource(AbstractResource):
|
| 334 |
-
def __init__(self, *, name: Optional[str] = None) -> None:
|
| 335 |
-
super().__init__(name=name)
|
| 336 |
-
self._routes: List[ResourceRoute] = []
|
| 337 |
-
|
| 338 |
-
def add_route(
|
| 339 |
-
self,
|
| 340 |
-
method: str,
|
| 341 |
-
handler: Union[Type[AbstractView], Handler],
|
| 342 |
-
*,
|
| 343 |
-
expect_handler: Optional[_ExpectHandler] = None,
|
| 344 |
-
) -> "ResourceRoute":
|
| 345 |
-
|
| 346 |
-
for route_obj in self._routes:
|
| 347 |
-
if route_obj.method == method or route_obj.method == hdrs.METH_ANY:
|
| 348 |
-
raise RuntimeError(
|
| 349 |
-
"Added route will never be executed, "
|
| 350 |
-
"method {route.method} is already "
|
| 351 |
-
"registered".format(route=route_obj)
|
| 352 |
-
)
|
| 353 |
-
|
| 354 |
-
route_obj = ResourceRoute(method, handler, self, expect_handler=expect_handler)
|
| 355 |
-
self.register_route(route_obj)
|
| 356 |
-
return route_obj
|
| 357 |
-
|
| 358 |
-
def register_route(self, route: "ResourceRoute") -> None:
|
| 359 |
-
assert isinstance(
|
| 360 |
-
route, ResourceRoute
|
| 361 |
-
), f"Instance of Route class is required, got {route!r}"
|
| 362 |
-
self._routes.append(route)
|
| 363 |
-
|
| 364 |
-
async def resolve(self, request: Request) -> _Resolve:
|
| 365 |
-
allowed_methods: Set[str] = set()
|
| 366 |
-
|
| 367 |
-
match_dict = self._match(request.rel_url.raw_path)
|
| 368 |
-
if match_dict is None:
|
| 369 |
-
return None, allowed_methods
|
| 370 |
-
|
| 371 |
-
for route_obj in self._routes:
|
| 372 |
-
route_method = route_obj.method
|
| 373 |
-
allowed_methods.add(route_method)
|
| 374 |
-
|
| 375 |
-
if route_method == request.method or route_method == hdrs.METH_ANY:
|
| 376 |
-
return (UrlMappingMatchInfo(match_dict, route_obj), allowed_methods)
|
| 377 |
-
else:
|
| 378 |
-
return None, allowed_methods
|
| 379 |
-
|
| 380 |
-
@abc.abstractmethod
|
| 381 |
-
def _match(self, path: str) -> Optional[Dict[str, str]]:
|
| 382 |
-
pass # pragma: no cover
|
| 383 |
-
|
| 384 |
-
def __len__(self) -> int:
|
| 385 |
-
return len(self._routes)
|
| 386 |
-
|
| 387 |
-
def __iter__(self) -> Iterator[AbstractRoute]:
|
| 388 |
-
return iter(self._routes)
|
| 389 |
-
|
| 390 |
-
# TODO: implement all abstract methods
|
| 391 |
-
|
| 392 |
-
|
| 393 |
-
class PlainResource(Resource):
|
| 394 |
-
def __init__(self, path: str, *, name: Optional[str] = None) -> None:
|
| 395 |
-
super().__init__(name=name)
|
| 396 |
-
assert not path or path.startswith("/")
|
| 397 |
-
self._path = path
|
| 398 |
-
|
| 399 |
-
@property
|
| 400 |
-
def canonical(self) -> str:
|
| 401 |
-
return self._path
|
| 402 |
-
|
| 403 |
-
def freeze(self) -> None:
|
| 404 |
-
if not self._path:
|
| 405 |
-
self._path = "/"
|
| 406 |
-
|
| 407 |
-
def add_prefix(self, prefix: str) -> None:
|
| 408 |
-
assert prefix.startswith("/")
|
| 409 |
-
assert not prefix.endswith("/")
|
| 410 |
-
assert len(prefix) > 1
|
| 411 |
-
self._path = prefix + self._path
|
| 412 |
-
|
| 413 |
-
def _match(self, path: str) -> Optional[Dict[str, str]]:
|
| 414 |
-
# string comparison is about 10 times faster than regexp matching
|
| 415 |
-
if self._path == path:
|
| 416 |
-
return {}
|
| 417 |
-
else:
|
| 418 |
-
return None
|
| 419 |
-
|
| 420 |
-
def raw_match(self, path: str) -> bool:
|
| 421 |
-
return self._path == path
|
| 422 |
-
|
| 423 |
-
def get_info(self) -> _InfoDict:
|
| 424 |
-
return {"path": self._path}
|
| 425 |
-
|
| 426 |
-
def url_for(self) -> URL: # type: ignore[override]
|
| 427 |
-
return URL.build(path=self._path, encoded=True)
|
| 428 |
-
|
| 429 |
-
def __repr__(self) -> str:
|
| 430 |
-
name = "'" + self.name + "' " if self.name is not None else ""
|
| 431 |
-
return f"<PlainResource {name} {self._path}>"
|
| 432 |
-
|
| 433 |
-
|
| 434 |
-
class DynamicResource(Resource):
|
| 435 |
-
|
| 436 |
-
DYN = re.compile(r"\{(?P<var>[_a-zA-Z][_a-zA-Z0-9]*)\}")
|
| 437 |
-
DYN_WITH_RE = re.compile(r"\{(?P<var>[_a-zA-Z][_a-zA-Z0-9]*):(?P<re>.+)\}")
|
| 438 |
-
GOOD = r"[^{}/]+"
|
| 439 |
-
|
| 440 |
-
def __init__(self, path: str, *, name: Optional[str] = None) -> None:
|
| 441 |
-
super().__init__(name=name)
|
| 442 |
-
pattern = ""
|
| 443 |
-
formatter = ""
|
| 444 |
-
for part in ROUTE_RE.split(path):
|
| 445 |
-
match = self.DYN.fullmatch(part)
|
| 446 |
-
if match:
|
| 447 |
-
pattern += "(?P<{}>{})".format(match.group("var"), self.GOOD)
|
| 448 |
-
formatter += "{" + match.group("var") + "}"
|
| 449 |
-
continue
|
| 450 |
-
|
| 451 |
-
match = self.DYN_WITH_RE.fullmatch(part)
|
| 452 |
-
if match:
|
| 453 |
-
pattern += "(?P<{var}>{re})".format(**match.groupdict())
|
| 454 |
-
formatter += "{" + match.group("var") + "}"
|
| 455 |
-
continue
|
| 456 |
-
|
| 457 |
-
if "{" in part or "}" in part:
|
| 458 |
-
raise ValueError(f"Invalid path '{path}'['{part}']")
|
| 459 |
-
|
| 460 |
-
part = _requote_path(part)
|
| 461 |
-
formatter += part
|
| 462 |
-
pattern += re.escape(part)
|
| 463 |
-
|
| 464 |
-
try:
|
| 465 |
-
compiled = re.compile(pattern)
|
| 466 |
-
except re.error as exc:
|
| 467 |
-
raise ValueError(f"Bad pattern '{pattern}': {exc}") from None
|
| 468 |
-
assert compiled.pattern.startswith(PATH_SEP)
|
| 469 |
-
assert formatter.startswith("/")
|
| 470 |
-
self._pattern = compiled
|
| 471 |
-
self._formatter = formatter
|
| 472 |
-
|
| 473 |
-
@property
|
| 474 |
-
def canonical(self) -> str:
|
| 475 |
-
return self._formatter
|
| 476 |
-
|
| 477 |
-
def add_prefix(self, prefix: str) -> None:
|
| 478 |
-
assert prefix.startswith("/")
|
| 479 |
-
assert not prefix.endswith("/")
|
| 480 |
-
assert len(prefix) > 1
|
| 481 |
-
self._pattern = re.compile(re.escape(prefix) + self._pattern.pattern)
|
| 482 |
-
self._formatter = prefix + self._formatter
|
| 483 |
-
|
| 484 |
-
def _match(self, path: str) -> Optional[Dict[str, str]]:
|
| 485 |
-
match = self._pattern.fullmatch(path)
|
| 486 |
-
if match is None:
|
| 487 |
-
return None
|
| 488 |
-
else:
|
| 489 |
-
return {
|
| 490 |
-
key: _unquote_path(value) for key, value in match.groupdict().items()
|
| 491 |
-
}
|
| 492 |
-
|
| 493 |
-
def raw_match(self, path: str) -> bool:
|
| 494 |
-
return self._formatter == path
|
| 495 |
-
|
| 496 |
-
def get_info(self) -> _InfoDict:
|
| 497 |
-
return {"formatter": self._formatter, "pattern": self._pattern}
|
| 498 |
-
|
| 499 |
-
def url_for(self, **parts: str) -> URL:
|
| 500 |
-
url = self._formatter.format_map({k: _quote_path(v) for k, v in parts.items()})
|
| 501 |
-
return URL.build(path=url, encoded=True)
|
| 502 |
-
|
| 503 |
-
def __repr__(self) -> str:
|
| 504 |
-
name = "'" + self.name + "' " if self.name is not None else ""
|
| 505 |
-
return "<DynamicResource {name} {formatter}>".format(
|
| 506 |
-
name=name, formatter=self._formatter
|
| 507 |
-
)
|
| 508 |
-
|
| 509 |
-
|
| 510 |
-
class PrefixResource(AbstractResource):
|
| 511 |
-
def __init__(self, prefix: str, *, name: Optional[str] = None) -> None:
|
| 512 |
-
assert not prefix or prefix.startswith("/"), prefix
|
| 513 |
-
assert prefix in ("", "/") or not prefix.endswith("/"), prefix
|
| 514 |
-
super().__init__(name=name)
|
| 515 |
-
self._prefix = _requote_path(prefix)
|
| 516 |
-
self._prefix2 = self._prefix + "/"
|
| 517 |
-
|
| 518 |
-
@property
|
| 519 |
-
def canonical(self) -> str:
|
| 520 |
-
return self._prefix
|
| 521 |
-
|
| 522 |
-
def add_prefix(self, prefix: str) -> None:
|
| 523 |
-
assert prefix.startswith("/")
|
| 524 |
-
assert not prefix.endswith("/")
|
| 525 |
-
assert len(prefix) > 1
|
| 526 |
-
self._prefix = prefix + self._prefix
|
| 527 |
-
self._prefix2 = self._prefix + "/"
|
| 528 |
-
|
| 529 |
-
def raw_match(self, prefix: str) -> bool:
|
| 530 |
-
return False
|
| 531 |
-
|
| 532 |
-
# TODO: impl missing abstract methods
|
| 533 |
-
|
| 534 |
-
|
| 535 |
-
class StaticResource(PrefixResource):
|
| 536 |
-
VERSION_KEY = "v"
|
| 537 |
-
|
| 538 |
-
def __init__(
|
| 539 |
-
self,
|
| 540 |
-
prefix: str,
|
| 541 |
-
directory: PathLike,
|
| 542 |
-
*,
|
| 543 |
-
name: Optional[str] = None,
|
| 544 |
-
expect_handler: Optional[_ExpectHandler] = None,
|
| 545 |
-
chunk_size: int = 256 * 1024,
|
| 546 |
-
show_index: bool = False,
|
| 547 |
-
follow_symlinks: bool = False,
|
| 548 |
-
append_version: bool = False,
|
| 549 |
-
) -> None:
|
| 550 |
-
super().__init__(prefix, name=name)
|
| 551 |
-
try:
|
| 552 |
-
directory = Path(directory)
|
| 553 |
-
if str(directory).startswith("~"):
|
| 554 |
-
directory = Path(os.path.expanduser(str(directory)))
|
| 555 |
-
directory = directory.resolve()
|
| 556 |
-
if not directory.is_dir():
|
| 557 |
-
raise ValueError("Not a directory")
|
| 558 |
-
except (FileNotFoundError, ValueError) as error:
|
| 559 |
-
raise ValueError(f"No directory exists at '{directory}'") from error
|
| 560 |
-
self._directory = directory
|
| 561 |
-
self._show_index = show_index
|
| 562 |
-
self._chunk_size = chunk_size
|
| 563 |
-
self._follow_symlinks = follow_symlinks
|
| 564 |
-
self._expect_handler = expect_handler
|
| 565 |
-
self._append_version = append_version
|
| 566 |
-
|
| 567 |
-
self._routes = {
|
| 568 |
-
"GET": ResourceRoute(
|
| 569 |
-
"GET", self._handle, self, expect_handler=expect_handler
|
| 570 |
-
),
|
| 571 |
-
"HEAD": ResourceRoute(
|
| 572 |
-
"HEAD", self._handle, self, expect_handler=expect_handler
|
| 573 |
-
),
|
| 574 |
-
}
|
| 575 |
-
|
| 576 |
-
def url_for( # type: ignore[override]
|
| 577 |
-
self,
|
| 578 |
-
*,
|
| 579 |
-
filename: Union[str, Path],
|
| 580 |
-
append_version: Optional[bool] = None,
|
| 581 |
-
) -> URL:
|
| 582 |
-
if append_version is None:
|
| 583 |
-
append_version = self._append_version
|
| 584 |
-
if isinstance(filename, Path):
|
| 585 |
-
filename = str(filename)
|
| 586 |
-
filename = filename.lstrip("/")
|
| 587 |
-
|
| 588 |
-
url = URL.build(path=self._prefix, encoded=True)
|
| 589 |
-
# filename is not encoded
|
| 590 |
-
if YARL_VERSION < (1, 6):
|
| 591 |
-
url = url / filename.replace("%", "%25")
|
| 592 |
-
else:
|
| 593 |
-
url = url / filename
|
| 594 |
-
|
| 595 |
-
if append_version:
|
| 596 |
-
try:
|
| 597 |
-
filepath = self._directory.joinpath(filename).resolve()
|
| 598 |
-
if not self._follow_symlinks:
|
| 599 |
-
filepath.relative_to(self._directory)
|
| 600 |
-
except (ValueError, FileNotFoundError):
|
| 601 |
-
# ValueError for case when path point to symlink
|
| 602 |
-
# with follow_symlinks is False
|
| 603 |
-
return url # relatively safe
|
| 604 |
-
if filepath.is_file():
|
| 605 |
-
# TODO cache file content
|
| 606 |
-
# with file watcher for cache invalidation
|
| 607 |
-
with filepath.open("rb") as f:
|
| 608 |
-
file_bytes = f.read()
|
| 609 |
-
h = self._get_file_hash(file_bytes)
|
| 610 |
-
url = url.with_query({self.VERSION_KEY: h})
|
| 611 |
-
return url
|
| 612 |
-
return url
|
| 613 |
-
|
| 614 |
-
@staticmethod
|
| 615 |
-
def _get_file_hash(byte_array: bytes) -> str:
|
| 616 |
-
m = hashlib.sha256() # todo sha256 can be configurable param
|
| 617 |
-
m.update(byte_array)
|
| 618 |
-
b64 = base64.urlsafe_b64encode(m.digest())
|
| 619 |
-
return b64.decode("ascii")
|
| 620 |
-
|
| 621 |
-
def get_info(self) -> _InfoDict:
|
| 622 |
-
return {
|
| 623 |
-
"directory": self._directory,
|
| 624 |
-
"prefix": self._prefix,
|
| 625 |
-
"routes": self._routes,
|
| 626 |
-
}
|
| 627 |
-
|
| 628 |
-
def set_options_route(self, handler: Handler) -> None:
|
| 629 |
-
if "OPTIONS" in self._routes:
|
| 630 |
-
raise RuntimeError("OPTIONS route was set already")
|
| 631 |
-
self._routes["OPTIONS"] = ResourceRoute(
|
| 632 |
-
"OPTIONS", handler, self, expect_handler=self._expect_handler
|
| 633 |
-
)
|
| 634 |
-
|
| 635 |
-
async def resolve(self, request: Request) -> _Resolve:
|
| 636 |
-
path = request.rel_url.raw_path
|
| 637 |
-
method = request.method
|
| 638 |
-
allowed_methods = set(self._routes)
|
| 639 |
-
if not path.startswith(self._prefix2) and path != self._prefix:
|
| 640 |
-
return None, set()
|
| 641 |
-
|
| 642 |
-
if method not in allowed_methods:
|
| 643 |
-
return None, allowed_methods
|
| 644 |
-
|
| 645 |
-
match_dict = {"filename": _unquote_path(path[len(self._prefix) + 1 :])}
|
| 646 |
-
return (UrlMappingMatchInfo(match_dict, self._routes[method]), allowed_methods)
|
| 647 |
-
|
| 648 |
-
def __len__(self) -> int:
|
| 649 |
-
return len(self._routes)
|
| 650 |
-
|
| 651 |
-
def __iter__(self) -> Iterator[AbstractRoute]:
|
| 652 |
-
return iter(self._routes.values())
|
| 653 |
-
|
| 654 |
-
async def _handle(self, request: Request) -> StreamResponse:
|
| 655 |
-
rel_url = request.match_info["filename"]
|
| 656 |
-
try:
|
| 657 |
-
filename = Path(rel_url)
|
| 658 |
-
if filename.anchor:
|
| 659 |
-
# rel_url is an absolute name like
|
| 660 |
-
# /static/\\machine_name\c$ or /static/D:\path
|
| 661 |
-
# where the static dir is totally different
|
| 662 |
-
raise HTTPForbidden()
|
| 663 |
-
filepath = self._directory.joinpath(filename).resolve()
|
| 664 |
-
if not self._follow_symlinks:
|
| 665 |
-
filepath.relative_to(self._directory)
|
| 666 |
-
except (ValueError, FileNotFoundError) as error:
|
| 667 |
-
# relatively safe
|
| 668 |
-
raise HTTPNotFound() from error
|
| 669 |
-
except HTTPForbidden:
|
| 670 |
-
raise
|
| 671 |
-
except Exception as error:
|
| 672 |
-
# perm error or other kind!
|
| 673 |
-
request.app.logger.exception(error)
|
| 674 |
-
raise HTTPNotFound() from error
|
| 675 |
-
|
| 676 |
-
# on opening a dir, load its contents if allowed
|
| 677 |
-
if filepath.is_dir():
|
| 678 |
-
if self._show_index:
|
| 679 |
-
try:
|
| 680 |
-
return Response(
|
| 681 |
-
text=self._directory_as_html(filepath), content_type="text/html"
|
| 682 |
-
)
|
| 683 |
-
except PermissionError:
|
| 684 |
-
raise HTTPForbidden()
|
| 685 |
-
else:
|
| 686 |
-
raise HTTPForbidden()
|
| 687 |
-
elif filepath.is_file():
|
| 688 |
-
return FileResponse(filepath, chunk_size=self._chunk_size)
|
| 689 |
-
else:
|
| 690 |
-
raise HTTPNotFound
|
| 691 |
-
|
| 692 |
-
def _directory_as_html(self, filepath: Path) -> str:
|
| 693 |
-
# returns directory's index as html
|
| 694 |
-
|
| 695 |
-
# sanity check
|
| 696 |
-
assert filepath.is_dir()
|
| 697 |
-
|
| 698 |
-
relative_path_to_dir = filepath.relative_to(self._directory).as_posix()
|
| 699 |
-
index_of = f"Index of /{relative_path_to_dir}"
|
| 700 |
-
h1 = f"<h1>{index_of}</h1>"
|
| 701 |
-
|
| 702 |
-
index_list = []
|
| 703 |
-
dir_index = filepath.iterdir()
|
| 704 |
-
for _file in sorted(dir_index):
|
| 705 |
-
# show file url as relative to static path
|
| 706 |
-
rel_path = _file.relative_to(self._directory).as_posix()
|
| 707 |
-
file_url = self._prefix + "/" + rel_path
|
| 708 |
-
|
| 709 |
-
# if file is a directory, add '/' to the end of the name
|
| 710 |
-
if _file.is_dir():
|
| 711 |
-
file_name = f"{_file.name}/"
|
| 712 |
-
else:
|
| 713 |
-
file_name = _file.name
|
| 714 |
-
|
| 715 |
-
index_list.append(
|
| 716 |
-
'<li><a href="{url}">{name}</a></li>'.format(
|
| 717 |
-
url=file_url, name=file_name
|
| 718 |
-
)
|
| 719 |
-
)
|
| 720 |
-
ul = "<ul>\n{}\n</ul>".format("\n".join(index_list))
|
| 721 |
-
body = f"<body>\n{h1}\n{ul}\n</body>"
|
| 722 |
-
|
| 723 |
-
head_str = f"<head>\n<title>{index_of}</title>\n</head>"
|
| 724 |
-
html = f"<html>\n{head_str}\n{body}\n</html>"
|
| 725 |
-
|
| 726 |
-
return html
|
| 727 |
-
|
| 728 |
-
def __repr__(self) -> str:
|
| 729 |
-
name = "'" + self.name + "'" if self.name is not None else ""
|
| 730 |
-
return "<StaticResource {name} {path} -> {directory!r}>".format(
|
| 731 |
-
name=name, path=self._prefix, directory=self._directory
|
| 732 |
-
)
|
| 733 |
-
|
| 734 |
-
|
| 735 |
-
class PrefixedSubAppResource(PrefixResource):
|
| 736 |
-
def __init__(self, prefix: str, app: "Application") -> None:
|
| 737 |
-
super().__init__(prefix)
|
| 738 |
-
self._app = app
|
| 739 |
-
for resource in app.router.resources():
|
| 740 |
-
resource.add_prefix(prefix)
|
| 741 |
-
|
| 742 |
-
def add_prefix(self, prefix: str) -> None:
|
| 743 |
-
super().add_prefix(prefix)
|
| 744 |
-
for resource in self._app.router.resources():
|
| 745 |
-
resource.add_prefix(prefix)
|
| 746 |
-
|
| 747 |
-
def url_for(self, *args: str, **kwargs: str) -> URL:
|
| 748 |
-
raise RuntimeError(".url_for() is not supported " "by sub-application root")
|
| 749 |
-
|
| 750 |
-
def get_info(self) -> _InfoDict:
|
| 751 |
-
return {"app": self._app, "prefix": self._prefix}
|
| 752 |
-
|
| 753 |
-
async def resolve(self, request: Request) -> _Resolve:
|
| 754 |
-
if (
|
| 755 |
-
not request.url.raw_path.startswith(self._prefix2)
|
| 756 |
-
and request.url.raw_path != self._prefix
|
| 757 |
-
):
|
| 758 |
-
return None, set()
|
| 759 |
-
match_info = await self._app.router.resolve(request)
|
| 760 |
-
match_info.add_app(self._app)
|
| 761 |
-
if isinstance(match_info.http_exception, HTTPMethodNotAllowed):
|
| 762 |
-
methods = match_info.http_exception.allowed_methods
|
| 763 |
-
else:
|
| 764 |
-
methods = set()
|
| 765 |
-
return match_info, methods
|
| 766 |
-
|
| 767 |
-
def __len__(self) -> int:
|
| 768 |
-
return len(self._app.router.routes())
|
| 769 |
-
|
| 770 |
-
def __iter__(self) -> Iterator[AbstractRoute]:
|
| 771 |
-
return iter(self._app.router.routes())
|
| 772 |
-
|
| 773 |
-
def __repr__(self) -> str:
|
| 774 |
-
return "<PrefixedSubAppResource {prefix} -> {app!r}>".format(
|
| 775 |
-
prefix=self._prefix, app=self._app
|
| 776 |
-
)
|
| 777 |
-
|
| 778 |
-
|
| 779 |
-
class AbstractRuleMatching(abc.ABC):
|
| 780 |
-
@abc.abstractmethod # pragma: no branch
|
| 781 |
-
async def match(self, request: Request) -> bool:
|
| 782 |
-
"""Return bool if the request satisfies the criteria"""
|
| 783 |
-
|
| 784 |
-
@abc.abstractmethod # pragma: no branch
|
| 785 |
-
def get_info(self) -> _InfoDict:
|
| 786 |
-
"""Return a dict with additional info useful for introspection"""
|
| 787 |
-
|
| 788 |
-
@property
|
| 789 |
-
@abc.abstractmethod # pragma: no branch
|
| 790 |
-
def canonical(self) -> str:
|
| 791 |
-
"""Return a str"""
|
| 792 |
-
|
| 793 |
-
|
| 794 |
-
class Domain(AbstractRuleMatching):
|
| 795 |
-
re_part = re.compile(r"(?!-)[a-z\d-]{1,63}(?<!-)")
|
| 796 |
-
|
| 797 |
-
def __init__(self, domain: str) -> None:
|
| 798 |
-
super().__init__()
|
| 799 |
-
self._domain = self.validation(domain)
|
| 800 |
-
|
| 801 |
-
@property
|
| 802 |
-
def canonical(self) -> str:
|
| 803 |
-
return self._domain
|
| 804 |
-
|
| 805 |
-
def validation(self, domain: str) -> str:
|
| 806 |
-
if not isinstance(domain, str):
|
| 807 |
-
raise TypeError("Domain must be str")
|
| 808 |
-
domain = domain.rstrip(".").lower()
|
| 809 |
-
if not domain:
|
| 810 |
-
raise ValueError("Domain cannot be empty")
|
| 811 |
-
elif "://" in domain:
|
| 812 |
-
raise ValueError("Scheme not supported")
|
| 813 |
-
url = URL("http://" + domain)
|
| 814 |
-
assert url.raw_host is not None
|
| 815 |
-
if not all(self.re_part.fullmatch(x) for x in url.raw_host.split(".")):
|
| 816 |
-
raise ValueError("Domain not valid")
|
| 817 |
-
if url.port == 80:
|
| 818 |
-
return url.raw_host
|
| 819 |
-
return f"{url.raw_host}:{url.port}"
|
| 820 |
-
|
| 821 |
-
async def match(self, request: Request) -> bool:
|
| 822 |
-
host = request.headers.get(hdrs.HOST)
|
| 823 |
-
if not host:
|
| 824 |
-
return False
|
| 825 |
-
return self.match_domain(host)
|
| 826 |
-
|
| 827 |
-
def match_domain(self, host: str) -> bool:
|
| 828 |
-
return host.lower() == self._domain
|
| 829 |
-
|
| 830 |
-
def get_info(self) -> _InfoDict:
|
| 831 |
-
return {"domain": self._domain}
|
| 832 |
-
|
| 833 |
-
|
| 834 |
-
class MaskDomain(Domain):
|
| 835 |
-
re_part = re.compile(r"(?!-)[a-z\d\*-]{1,63}(?<!-)")
|
| 836 |
-
|
| 837 |
-
def __init__(self, domain: str) -> None:
|
| 838 |
-
super().__init__(domain)
|
| 839 |
-
mask = self._domain.replace(".", r"\.").replace("*", ".*")
|
| 840 |
-
self._mask = re.compile(mask)
|
| 841 |
-
|
| 842 |
-
@property
|
| 843 |
-
def canonical(self) -> str:
|
| 844 |
-
return self._mask.pattern
|
| 845 |
-
|
| 846 |
-
def match_domain(self, host: str) -> bool:
|
| 847 |
-
return self._mask.fullmatch(host) is not None
|
| 848 |
-
|
| 849 |
-
|
| 850 |
-
class MatchedSubAppResource(PrefixedSubAppResource):
|
| 851 |
-
def __init__(self, rule: AbstractRuleMatching, app: "Application") -> None:
|
| 852 |
-
AbstractResource.__init__(self)
|
| 853 |
-
self._prefix = ""
|
| 854 |
-
self._app = app
|
| 855 |
-
self._rule = rule
|
| 856 |
-
|
| 857 |
-
@property
|
| 858 |
-
def canonical(self) -> str:
|
| 859 |
-
return self._rule.canonical
|
| 860 |
-
|
| 861 |
-
def get_info(self) -> _InfoDict:
|
| 862 |
-
return {"app": self._app, "rule": self._rule}
|
| 863 |
-
|
| 864 |
-
async def resolve(self, request: Request) -> _Resolve:
|
| 865 |
-
if not await self._rule.match(request):
|
| 866 |
-
return None, set()
|
| 867 |
-
match_info = await self._app.router.resolve(request)
|
| 868 |
-
match_info.add_app(self._app)
|
| 869 |
-
if isinstance(match_info.http_exception, HTTPMethodNotAllowed):
|
| 870 |
-
methods = match_info.http_exception.allowed_methods
|
| 871 |
-
else:
|
| 872 |
-
methods = set()
|
| 873 |
-
return match_info, methods
|
| 874 |
-
|
| 875 |
-
def __repr__(self) -> str:
|
| 876 |
-
return "<MatchedSubAppResource -> {app!r}>" "".format(app=self._app)
|
| 877 |
-
|
| 878 |
-
|
| 879 |
-
class ResourceRoute(AbstractRoute):
|
| 880 |
-
"""A route with resource"""
|
| 881 |
-
|
| 882 |
-
def __init__(
|
| 883 |
-
self,
|
| 884 |
-
method: str,
|
| 885 |
-
handler: Union[Handler, Type[AbstractView]],
|
| 886 |
-
resource: AbstractResource,
|
| 887 |
-
*,
|
| 888 |
-
expect_handler: Optional[_ExpectHandler] = None,
|
| 889 |
-
) -> None:
|
| 890 |
-
super().__init__(
|
| 891 |
-
method, handler, expect_handler=expect_handler, resource=resource
|
| 892 |
-
)
|
| 893 |
-
|
| 894 |
-
def __repr__(self) -> str:
|
| 895 |
-
return "<ResourceRoute [{method}] {resource} -> {handler!r}".format(
|
| 896 |
-
method=self.method, resource=self._resource, handler=self.handler
|
| 897 |
-
)
|
| 898 |
-
|
| 899 |
-
@property
|
| 900 |
-
def name(self) -> Optional[str]:
|
| 901 |
-
if self._resource is None:
|
| 902 |
-
return None
|
| 903 |
-
return self._resource.name
|
| 904 |
-
|
| 905 |
-
def url_for(self, *args: str, **kwargs: str) -> URL:
|
| 906 |
-
"""Construct url for route with additional params."""
|
| 907 |
-
assert self._resource is not None
|
| 908 |
-
return self._resource.url_for(*args, **kwargs)
|
| 909 |
-
|
| 910 |
-
def get_info(self) -> _InfoDict:
|
| 911 |
-
assert self._resource is not None
|
| 912 |
-
return self._resource.get_info()
|
| 913 |
-
|
| 914 |
-
|
| 915 |
-
class SystemRoute(AbstractRoute):
|
| 916 |
-
def __init__(self, http_exception: HTTPException) -> None:
|
| 917 |
-
super().__init__(hdrs.METH_ANY, self._handle)
|
| 918 |
-
self._http_exception = http_exception
|
| 919 |
-
|
| 920 |
-
def url_for(self, *args: str, **kwargs: str) -> URL:
|
| 921 |
-
raise RuntimeError(".url_for() is not allowed for SystemRoute")
|
| 922 |
-
|
| 923 |
-
@property
|
| 924 |
-
def name(self) -> Optional[str]:
|
| 925 |
-
return None
|
| 926 |
-
|
| 927 |
-
def get_info(self) -> _InfoDict:
|
| 928 |
-
return {"http_exception": self._http_exception}
|
| 929 |
-
|
| 930 |
-
async def _handle(self, request: Request) -> StreamResponse:
|
| 931 |
-
raise self._http_exception
|
| 932 |
-
|
| 933 |
-
@property
|
| 934 |
-
def status(self) -> int:
|
| 935 |
-
return self._http_exception.status
|
| 936 |
-
|
| 937 |
-
@property
|
| 938 |
-
def reason(self) -> str:
|
| 939 |
-
return self._http_exception.reason
|
| 940 |
-
|
| 941 |
-
def __repr__(self) -> str:
|
| 942 |
-
return "<SystemRoute {self.status}: {self.reason}>".format(self=self)
|
| 943 |
-
|
| 944 |
-
|
| 945 |
-
class View(AbstractView):
|
| 946 |
-
async def _iter(self) -> StreamResponse:
|
| 947 |
-
if self.request.method not in hdrs.METH_ALL:
|
| 948 |
-
self._raise_allowed_methods()
|
| 949 |
-
method: Callable[[], Awaitable[StreamResponse]] = getattr(
|
| 950 |
-
self, self.request.method.lower(), None
|
| 951 |
-
)
|
| 952 |
-
if method is None:
|
| 953 |
-
self._raise_allowed_methods()
|
| 954 |
-
resp = await method()
|
| 955 |
-
return resp
|
| 956 |
-
|
| 957 |
-
def __await__(self) -> Generator[Any, None, StreamResponse]:
|
| 958 |
-
return self._iter().__await__()
|
| 959 |
-
|
| 960 |
-
def _raise_allowed_methods(self) -> None:
|
| 961 |
-
allowed_methods = {m for m in hdrs.METH_ALL if hasattr(self, m.lower())}
|
| 962 |
-
raise HTTPMethodNotAllowed(self.request.method, allowed_methods)
|
| 963 |
-
|
| 964 |
-
|
| 965 |
-
class ResourcesView(Sized, Iterable[AbstractResource], Container[AbstractResource]):
|
| 966 |
-
def __init__(self, resources: List[AbstractResource]) -> None:
|
| 967 |
-
self._resources = resources
|
| 968 |
-
|
| 969 |
-
def __len__(self) -> int:
|
| 970 |
-
return len(self._resources)
|
| 971 |
-
|
| 972 |
-
def __iter__(self) -> Iterator[AbstractResource]:
|
| 973 |
-
yield from self._resources
|
| 974 |
-
|
| 975 |
-
def __contains__(self, resource: object) -> bool:
|
| 976 |
-
return resource in self._resources
|
| 977 |
-
|
| 978 |
-
|
| 979 |
-
class RoutesView(Sized, Iterable[AbstractRoute], Container[AbstractRoute]):
|
| 980 |
-
def __init__(self, resources: List[AbstractResource]):
|
| 981 |
-
self._routes: List[AbstractRoute] = []
|
| 982 |
-
for resource in resources:
|
| 983 |
-
for route in resource:
|
| 984 |
-
self._routes.append(route)
|
| 985 |
-
|
| 986 |
-
def __len__(self) -> int:
|
| 987 |
-
return len(self._routes)
|
| 988 |
-
|
| 989 |
-
def __iter__(self) -> Iterator[AbstractRoute]:
|
| 990 |
-
yield from self._routes
|
| 991 |
-
|
| 992 |
-
def __contains__(self, route: object) -> bool:
|
| 993 |
-
return route in self._routes
|
| 994 |
-
|
| 995 |
-
|
| 996 |
-
class UrlDispatcher(AbstractRouter, Mapping[str, AbstractResource]):
|
| 997 |
-
|
| 998 |
-
NAME_SPLIT_RE = re.compile(r"[.:-]")
|
| 999 |
-
|
| 1000 |
-
def __init__(self) -> None:
|
| 1001 |
-
super().__init__()
|
| 1002 |
-
self._resources: List[AbstractResource] = []
|
| 1003 |
-
self._named_resources: Dict[str, AbstractResource] = {}
|
| 1004 |
-
|
| 1005 |
-
async def resolve(self, request: Request) -> UrlMappingMatchInfo:
|
| 1006 |
-
method = request.method
|
| 1007 |
-
allowed_methods: Set[str] = set()
|
| 1008 |
-
|
| 1009 |
-
for resource in self._resources:
|
| 1010 |
-
match_dict, allowed = await resource.resolve(request)
|
| 1011 |
-
if match_dict is not None:
|
| 1012 |
-
return match_dict
|
| 1013 |
-
else:
|
| 1014 |
-
allowed_methods |= allowed
|
| 1015 |
-
|
| 1016 |
-
if allowed_methods:
|
| 1017 |
-
return MatchInfoError(HTTPMethodNotAllowed(method, allowed_methods))
|
| 1018 |
-
else:
|
| 1019 |
-
return MatchInfoError(HTTPNotFound())
|
| 1020 |
-
|
| 1021 |
-
def __iter__(self) -> Iterator[str]:
|
| 1022 |
-
return iter(self._named_resources)
|
| 1023 |
-
|
| 1024 |
-
def __len__(self) -> int:
|
| 1025 |
-
return len(self._named_resources)
|
| 1026 |
-
|
| 1027 |
-
def __contains__(self, resource: object) -> bool:
|
| 1028 |
-
return resource in self._named_resources
|
| 1029 |
-
|
| 1030 |
-
def __getitem__(self, name: str) -> AbstractResource:
|
| 1031 |
-
return self._named_resources[name]
|
| 1032 |
-
|
| 1033 |
-
def resources(self) -> ResourcesView:
|
| 1034 |
-
return ResourcesView(self._resources)
|
| 1035 |
-
|
| 1036 |
-
def routes(self) -> RoutesView:
|
| 1037 |
-
return RoutesView(self._resources)
|
| 1038 |
-
|
| 1039 |
-
def named_resources(self) -> Mapping[str, AbstractResource]:
|
| 1040 |
-
return MappingProxyType(self._named_resources)
|
| 1041 |
-
|
| 1042 |
-
def register_resource(self, resource: AbstractResource) -> None:
|
| 1043 |
-
assert isinstance(
|
| 1044 |
-
resource, AbstractResource
|
| 1045 |
-
), f"Instance of AbstractResource class is required, got {resource!r}"
|
| 1046 |
-
if self.frozen:
|
| 1047 |
-
raise RuntimeError("Cannot register a resource into frozen router.")
|
| 1048 |
-
|
| 1049 |
-
name = resource.name
|
| 1050 |
-
|
| 1051 |
-
if name is not None:
|
| 1052 |
-
parts = self.NAME_SPLIT_RE.split(name)
|
| 1053 |
-
for part in parts:
|
| 1054 |
-
if keyword.iskeyword(part):
|
| 1055 |
-
raise ValueError(
|
| 1056 |
-
f"Incorrect route name {name!r}, "
|
| 1057 |
-
"python keywords cannot be used "
|
| 1058 |
-
"for route name"
|
| 1059 |
-
)
|
| 1060 |
-
if not part.isidentifier():
|
| 1061 |
-
raise ValueError(
|
| 1062 |
-
"Incorrect route name {!r}, "
|
| 1063 |
-
"the name should be a sequence of "
|
| 1064 |
-
"python identifiers separated "
|
| 1065 |
-
"by dash, dot or column".format(name)
|
| 1066 |
-
)
|
| 1067 |
-
if name in self._named_resources:
|
| 1068 |
-
raise ValueError(
|
| 1069 |
-
"Duplicate {!r}, "
|
| 1070 |
-
"already handled by {!r}".format(name, self._named_resources[name])
|
| 1071 |
-
)
|
| 1072 |
-
self._named_resources[name] = resource
|
| 1073 |
-
self._resources.append(resource)
|
| 1074 |
-
|
| 1075 |
-
def add_resource(self, path: str, *, name: Optional[str] = None) -> Resource:
|
| 1076 |
-
if path and not path.startswith("/"):
|
| 1077 |
-
raise ValueError("path should be started with / or be empty")
|
| 1078 |
-
# Reuse last added resource if path and name are the same
|
| 1079 |
-
if self._resources:
|
| 1080 |
-
resource = self._resources[-1]
|
| 1081 |
-
if resource.name == name and resource.raw_match(path):
|
| 1082 |
-
return cast(Resource, resource)
|
| 1083 |
-
if not ("{" in path or "}" in path or ROUTE_RE.search(path)):
|
| 1084 |
-
resource = PlainResource(_requote_path(path), name=name)
|
| 1085 |
-
self.register_resource(resource)
|
| 1086 |
-
return resource
|
| 1087 |
-
resource = DynamicResource(path, name=name)
|
| 1088 |
-
self.register_resource(resource)
|
| 1089 |
-
return resource
|
| 1090 |
-
|
| 1091 |
-
def add_route(
|
| 1092 |
-
self,
|
| 1093 |
-
method: str,
|
| 1094 |
-
path: str,
|
| 1095 |
-
handler: Union[Handler, Type[AbstractView]],
|
| 1096 |
-
*,
|
| 1097 |
-
name: Optional[str] = None,
|
| 1098 |
-
expect_handler: Optional[_ExpectHandler] = None,
|
| 1099 |
-
) -> AbstractRoute:
|
| 1100 |
-
resource = self.add_resource(path, name=name)
|
| 1101 |
-
return resource.add_route(method, handler, expect_handler=expect_handler)
|
| 1102 |
-
|
| 1103 |
-
def add_static(
|
| 1104 |
-
self,
|
| 1105 |
-
prefix: str,
|
| 1106 |
-
path: PathLike,
|
| 1107 |
-
*,
|
| 1108 |
-
name: Optional[str] = None,
|
| 1109 |
-
expect_handler: Optional[_ExpectHandler] = None,
|
| 1110 |
-
chunk_size: int = 256 * 1024,
|
| 1111 |
-
show_index: bool = False,
|
| 1112 |
-
follow_symlinks: bool = False,
|
| 1113 |
-
append_version: bool = False,
|
| 1114 |
-
) -> AbstractResource:
|
| 1115 |
-
"""Add static files view.
|
| 1116 |
-
|
| 1117 |
-
prefix - url prefix
|
| 1118 |
-
path - folder with files
|
| 1119 |
-
|
| 1120 |
-
"""
|
| 1121 |
-
assert prefix.startswith("/")
|
| 1122 |
-
if prefix.endswith("/"):
|
| 1123 |
-
prefix = prefix[:-1]
|
| 1124 |
-
resource = StaticResource(
|
| 1125 |
-
prefix,
|
| 1126 |
-
path,
|
| 1127 |
-
name=name,
|
| 1128 |
-
expect_handler=expect_handler,
|
| 1129 |
-
chunk_size=chunk_size,
|
| 1130 |
-
show_index=show_index,
|
| 1131 |
-
follow_symlinks=follow_symlinks,
|
| 1132 |
-
append_version=append_version,
|
| 1133 |
-
)
|
| 1134 |
-
self.register_resource(resource)
|
| 1135 |
-
return resource
|
| 1136 |
-
|
| 1137 |
-
def add_head(self, path: str, handler: Handler, **kwargs: Any) -> AbstractRoute:
|
| 1138 |
-
"""Shortcut for add_route with method HEAD."""
|
| 1139 |
-
return self.add_route(hdrs.METH_HEAD, path, handler, **kwargs)
|
| 1140 |
-
|
| 1141 |
-
def add_options(self, path: str, handler: Handler, **kwargs: Any) -> AbstractRoute:
|
| 1142 |
-
"""Shortcut for add_route with method OPTIONS."""
|
| 1143 |
-
return self.add_route(hdrs.METH_OPTIONS, path, handler, **kwargs)
|
| 1144 |
-
|
| 1145 |
-
def add_get(
|
| 1146 |
-
self,
|
| 1147 |
-
path: str,
|
| 1148 |
-
handler: Handler,
|
| 1149 |
-
*,
|
| 1150 |
-
name: Optional[str] = None,
|
| 1151 |
-
allow_head: bool = True,
|
| 1152 |
-
**kwargs: Any,
|
| 1153 |
-
) -> AbstractRoute:
|
| 1154 |
-
"""Shortcut for add_route with method GET.
|
| 1155 |
-
|
| 1156 |
-
If allow_head is true, another
|
| 1157 |
-
route is added allowing head requests to the same endpoint.
|
| 1158 |
-
"""
|
| 1159 |
-
resource = self.add_resource(path, name=name)
|
| 1160 |
-
if allow_head:
|
| 1161 |
-
resource.add_route(hdrs.METH_HEAD, handler, **kwargs)
|
| 1162 |
-
return resource.add_route(hdrs.METH_GET, handler, **kwargs)
|
| 1163 |
-
|
| 1164 |
-
def add_post(self, path: str, handler: Handler, **kwargs: Any) -> AbstractRoute:
|
| 1165 |
-
"""Shortcut for add_route with method POST."""
|
| 1166 |
-
return self.add_route(hdrs.METH_POST, path, handler, **kwargs)
|
| 1167 |
-
|
| 1168 |
-
def add_put(self, path: str, handler: Handler, **kwargs: Any) -> AbstractRoute:
|
| 1169 |
-
"""Shortcut for add_route with method PUT."""
|
| 1170 |
-
return self.add_route(hdrs.METH_PUT, path, handler, **kwargs)
|
| 1171 |
-
|
| 1172 |
-
def add_patch(self, path: str, handler: Handler, **kwargs: Any) -> AbstractRoute:
|
| 1173 |
-
"""Shortcut for add_route with method PATCH."""
|
| 1174 |
-
return self.add_route(hdrs.METH_PATCH, path, handler, **kwargs)
|
| 1175 |
-
|
| 1176 |
-
def add_delete(self, path: str, handler: Handler, **kwargs: Any) -> AbstractRoute:
|
| 1177 |
-
"""Shortcut for add_route with method DELETE."""
|
| 1178 |
-
return self.add_route(hdrs.METH_DELETE, path, handler, **kwargs)
|
| 1179 |
-
|
| 1180 |
-
def add_view(
|
| 1181 |
-
self, path: str, handler: Type[AbstractView], **kwargs: Any
|
| 1182 |
-
) -> AbstractRoute:
|
| 1183 |
-
"""Shortcut for add_route with ANY methods for a class-based view."""
|
| 1184 |
-
return self.add_route(hdrs.METH_ANY, path, handler, **kwargs)
|
| 1185 |
-
|
| 1186 |
-
def freeze(self) -> None:
|
| 1187 |
-
super().freeze()
|
| 1188 |
-
for resource in self._resources:
|
| 1189 |
-
resource.freeze()
|
| 1190 |
-
|
| 1191 |
-
def add_routes(self, routes: Iterable[AbstractRouteDef]) -> List[AbstractRoute]:
|
| 1192 |
-
"""Append routes to route table.
|
| 1193 |
-
|
| 1194 |
-
Parameter should be a sequence of RouteDef objects.
|
| 1195 |
-
|
| 1196 |
-
Returns a list of registered AbstractRoute instances.
|
| 1197 |
-
"""
|
| 1198 |
-
registered_routes = []
|
| 1199 |
-
for route_def in routes:
|
| 1200 |
-
registered_routes.extend(route_def.register(self))
|
| 1201 |
-
return registered_routes
|
| 1202 |
-
|
| 1203 |
-
|
| 1204 |
-
def _quote_path(value: str) -> str:
|
| 1205 |
-
if YARL_VERSION < (1, 6):
|
| 1206 |
-
value = value.replace("%", "%25")
|
| 1207 |
-
return URL.build(path=value, encoded=False).raw_path
|
| 1208 |
-
|
| 1209 |
-
|
| 1210 |
-
def _unquote_path(value: str) -> str:
|
| 1211 |
-
return URL.build(path=value, encoded=True).path
|
| 1212 |
-
|
| 1213 |
-
|
| 1214 |
-
def _requote_path(value: str) -> str:
|
| 1215 |
-
# Quote non-ascii characters and other characters which must be quoted,
|
| 1216 |
-
# but preserve existing %-sequences.
|
| 1217 |
-
result = _quote_path(value)
|
| 1218 |
-
if "%" in value:
|
| 1219 |
-
result = result.replace("%25", "%")
|
| 1220 |
-
return result
|
|
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|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/params.py
DELETED
|
@@ -1,760 +0,0 @@
|
|
| 1 |
-
import warnings
|
| 2 |
-
from enum import Enum
|
| 3 |
-
from typing import Any, Callable, Dict, List, Optional, Sequence, Union
|
| 4 |
-
|
| 5 |
-
from pydantic.fields import FieldInfo
|
| 6 |
-
from typing_extensions import Annotated, deprecated
|
| 7 |
-
|
| 8 |
-
from ._compat import PYDANTIC_V2, Undefined
|
| 9 |
-
|
| 10 |
-
_Unset: Any = Undefined
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
class ParamTypes(Enum):
|
| 14 |
-
query = "query"
|
| 15 |
-
header = "header"
|
| 16 |
-
path = "path"
|
| 17 |
-
cookie = "cookie"
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
class Param(FieldInfo):
|
| 21 |
-
in_: ParamTypes
|
| 22 |
-
|
| 23 |
-
def __init__(
|
| 24 |
-
self,
|
| 25 |
-
default: Any = Undefined,
|
| 26 |
-
*,
|
| 27 |
-
default_factory: Union[Callable[[], Any], None] = _Unset,
|
| 28 |
-
annotation: Optional[Any] = None,
|
| 29 |
-
alias: Optional[str] = None,
|
| 30 |
-
alias_priority: Union[int, None] = _Unset,
|
| 31 |
-
# TODO: update when deprecating Pydantic v1, import these types
|
| 32 |
-
# validation_alias: str | AliasPath | AliasChoices | None
|
| 33 |
-
validation_alias: Union[str, None] = None,
|
| 34 |
-
serialization_alias: Union[str, None] = None,
|
| 35 |
-
title: Optional[str] = None,
|
| 36 |
-
description: Optional[str] = None,
|
| 37 |
-
gt: Optional[float] = None,
|
| 38 |
-
ge: Optional[float] = None,
|
| 39 |
-
lt: Optional[float] = None,
|
| 40 |
-
le: Optional[float] = None,
|
| 41 |
-
min_length: Optional[int] = None,
|
| 42 |
-
max_length: Optional[int] = None,
|
| 43 |
-
pattern: Optional[str] = None,
|
| 44 |
-
regex: Annotated[
|
| 45 |
-
Optional[str],
|
| 46 |
-
deprecated(
|
| 47 |
-
"Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
|
| 48 |
-
),
|
| 49 |
-
] = None,
|
| 50 |
-
discriminator: Union[str, None] = None,
|
| 51 |
-
strict: Union[bool, None] = _Unset,
|
| 52 |
-
multiple_of: Union[float, None] = _Unset,
|
| 53 |
-
allow_inf_nan: Union[bool, None] = _Unset,
|
| 54 |
-
max_digits: Union[int, None] = _Unset,
|
| 55 |
-
decimal_places: Union[int, None] = _Unset,
|
| 56 |
-
examples: Optional[List[Any]] = None,
|
| 57 |
-
example: Annotated[
|
| 58 |
-
Optional[Any],
|
| 59 |
-
deprecated(
|
| 60 |
-
"Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
|
| 61 |
-
"although still supported. Use examples instead."
|
| 62 |
-
),
|
| 63 |
-
] = _Unset,
|
| 64 |
-
deprecated: Optional[bool] = None,
|
| 65 |
-
include_in_schema: bool = True,
|
| 66 |
-
json_schema_extra: Union[Dict[str, Any], None] = None,
|
| 67 |
-
**extra: Any,
|
| 68 |
-
):
|
| 69 |
-
self.deprecated = deprecated
|
| 70 |
-
if example is not _Unset:
|
| 71 |
-
warnings.warn(
|
| 72 |
-
"`example` has been depreacated, please use `examples` instead",
|
| 73 |
-
category=DeprecationWarning,
|
| 74 |
-
stacklevel=4,
|
| 75 |
-
)
|
| 76 |
-
self.example = example
|
| 77 |
-
self.include_in_schema = include_in_schema
|
| 78 |
-
kwargs = dict(
|
| 79 |
-
default=default,
|
| 80 |
-
default_factory=default_factory,
|
| 81 |
-
alias=alias,
|
| 82 |
-
title=title,
|
| 83 |
-
description=description,
|
| 84 |
-
gt=gt,
|
| 85 |
-
ge=ge,
|
| 86 |
-
lt=lt,
|
| 87 |
-
le=le,
|
| 88 |
-
min_length=min_length,
|
| 89 |
-
max_length=max_length,
|
| 90 |
-
discriminator=discriminator,
|
| 91 |
-
multiple_of=multiple_of,
|
| 92 |
-
allow_nan=allow_inf_nan,
|
| 93 |
-
max_digits=max_digits,
|
| 94 |
-
decimal_places=decimal_places,
|
| 95 |
-
**extra,
|
| 96 |
-
)
|
| 97 |
-
if examples is not None:
|
| 98 |
-
kwargs["examples"] = examples
|
| 99 |
-
if regex is not None:
|
| 100 |
-
warnings.warn(
|
| 101 |
-
"`regex` has been depreacated, please use `pattern` instead",
|
| 102 |
-
category=DeprecationWarning,
|
| 103 |
-
stacklevel=4,
|
| 104 |
-
)
|
| 105 |
-
current_json_schema_extra = json_schema_extra or extra
|
| 106 |
-
if PYDANTIC_V2:
|
| 107 |
-
kwargs.update(
|
| 108 |
-
{
|
| 109 |
-
"annotation": annotation,
|
| 110 |
-
"alias_priority": alias_priority,
|
| 111 |
-
"validation_alias": validation_alias,
|
| 112 |
-
"serialization_alias": serialization_alias,
|
| 113 |
-
"strict": strict,
|
| 114 |
-
"json_schema_extra": current_json_schema_extra,
|
| 115 |
-
}
|
| 116 |
-
)
|
| 117 |
-
kwargs["pattern"] = pattern or regex
|
| 118 |
-
else:
|
| 119 |
-
kwargs["regex"] = pattern or regex
|
| 120 |
-
kwargs.update(**current_json_schema_extra)
|
| 121 |
-
use_kwargs = {k: v for k, v in kwargs.items() if v is not _Unset}
|
| 122 |
-
|
| 123 |
-
super().__init__(**use_kwargs)
|
| 124 |
-
|
| 125 |
-
def __repr__(self) -> str:
|
| 126 |
-
return f"{self.__class__.__name__}({self.default})"
|
| 127 |
-
|
| 128 |
-
|
| 129 |
-
class Path(Param):
|
| 130 |
-
in_ = ParamTypes.path
|
| 131 |
-
|
| 132 |
-
def __init__(
|
| 133 |
-
self,
|
| 134 |
-
default: Any = ...,
|
| 135 |
-
*,
|
| 136 |
-
default_factory: Union[Callable[[], Any], None] = _Unset,
|
| 137 |
-
annotation: Optional[Any] = None,
|
| 138 |
-
alias: Optional[str] = None,
|
| 139 |
-
alias_priority: Union[int, None] = _Unset,
|
| 140 |
-
# TODO: update when deprecating Pydantic v1, import these types
|
| 141 |
-
# validation_alias: str | AliasPath | AliasChoices | None
|
| 142 |
-
validation_alias: Union[str, None] = None,
|
| 143 |
-
serialization_alias: Union[str, None] = None,
|
| 144 |
-
title: Optional[str] = None,
|
| 145 |
-
description: Optional[str] = None,
|
| 146 |
-
gt: Optional[float] = None,
|
| 147 |
-
ge: Optional[float] = None,
|
| 148 |
-
lt: Optional[float] = None,
|
| 149 |
-
le: Optional[float] = None,
|
| 150 |
-
min_length: Optional[int] = None,
|
| 151 |
-
max_length: Optional[int] = None,
|
| 152 |
-
pattern: Optional[str] = None,
|
| 153 |
-
regex: Annotated[
|
| 154 |
-
Optional[str],
|
| 155 |
-
deprecated(
|
| 156 |
-
"Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
|
| 157 |
-
),
|
| 158 |
-
] = None,
|
| 159 |
-
discriminator: Union[str, None] = None,
|
| 160 |
-
strict: Union[bool, None] = _Unset,
|
| 161 |
-
multiple_of: Union[float, None] = _Unset,
|
| 162 |
-
allow_inf_nan: Union[bool, None] = _Unset,
|
| 163 |
-
max_digits: Union[int, None] = _Unset,
|
| 164 |
-
decimal_places: Union[int, None] = _Unset,
|
| 165 |
-
examples: Optional[List[Any]] = None,
|
| 166 |
-
example: Annotated[
|
| 167 |
-
Optional[Any],
|
| 168 |
-
deprecated(
|
| 169 |
-
"Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
|
| 170 |
-
"although still supported. Use examples instead."
|
| 171 |
-
),
|
| 172 |
-
] = _Unset,
|
| 173 |
-
deprecated: Optional[bool] = None,
|
| 174 |
-
include_in_schema: bool = True,
|
| 175 |
-
json_schema_extra: Union[Dict[str, Any], None] = None,
|
| 176 |
-
**extra: Any,
|
| 177 |
-
):
|
| 178 |
-
assert default is ..., "Path parameters cannot have a default value"
|
| 179 |
-
self.in_ = self.in_
|
| 180 |
-
super().__init__(
|
| 181 |
-
default=default,
|
| 182 |
-
default_factory=default_factory,
|
| 183 |
-
annotation=annotation,
|
| 184 |
-
alias=alias,
|
| 185 |
-
alias_priority=alias_priority,
|
| 186 |
-
validation_alias=validation_alias,
|
| 187 |
-
serialization_alias=serialization_alias,
|
| 188 |
-
title=title,
|
| 189 |
-
description=description,
|
| 190 |
-
gt=gt,
|
| 191 |
-
ge=ge,
|
| 192 |
-
lt=lt,
|
| 193 |
-
le=le,
|
| 194 |
-
min_length=min_length,
|
| 195 |
-
max_length=max_length,
|
| 196 |
-
pattern=pattern,
|
| 197 |
-
regex=regex,
|
| 198 |
-
discriminator=discriminator,
|
| 199 |
-
strict=strict,
|
| 200 |
-
multiple_of=multiple_of,
|
| 201 |
-
allow_inf_nan=allow_inf_nan,
|
| 202 |
-
max_digits=max_digits,
|
| 203 |
-
decimal_places=decimal_places,
|
| 204 |
-
deprecated=deprecated,
|
| 205 |
-
example=example,
|
| 206 |
-
examples=examples,
|
| 207 |
-
include_in_schema=include_in_schema,
|
| 208 |
-
json_schema_extra=json_schema_extra,
|
| 209 |
-
**extra,
|
| 210 |
-
)
|
| 211 |
-
|
| 212 |
-
|
| 213 |
-
class Query(Param):
|
| 214 |
-
in_ = ParamTypes.query
|
| 215 |
-
|
| 216 |
-
def __init__(
|
| 217 |
-
self,
|
| 218 |
-
default: Any = Undefined,
|
| 219 |
-
*,
|
| 220 |
-
default_factory: Union[Callable[[], Any], None] = _Unset,
|
| 221 |
-
annotation: Optional[Any] = None,
|
| 222 |
-
alias: Optional[str] = None,
|
| 223 |
-
alias_priority: Union[int, None] = _Unset,
|
| 224 |
-
# TODO: update when deprecating Pydantic v1, import these types
|
| 225 |
-
# validation_alias: str | AliasPath | AliasChoices | None
|
| 226 |
-
validation_alias: Union[str, None] = None,
|
| 227 |
-
serialization_alias: Union[str, None] = None,
|
| 228 |
-
title: Optional[str] = None,
|
| 229 |
-
description: Optional[str] = None,
|
| 230 |
-
gt: Optional[float] = None,
|
| 231 |
-
ge: Optional[float] = None,
|
| 232 |
-
lt: Optional[float] = None,
|
| 233 |
-
le: Optional[float] = None,
|
| 234 |
-
min_length: Optional[int] = None,
|
| 235 |
-
max_length: Optional[int] = None,
|
| 236 |
-
pattern: Optional[str] = None,
|
| 237 |
-
regex: Annotated[
|
| 238 |
-
Optional[str],
|
| 239 |
-
deprecated(
|
| 240 |
-
"Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
|
| 241 |
-
),
|
| 242 |
-
] = None,
|
| 243 |
-
discriminator: Union[str, None] = None,
|
| 244 |
-
strict: Union[bool, None] = _Unset,
|
| 245 |
-
multiple_of: Union[float, None] = _Unset,
|
| 246 |
-
allow_inf_nan: Union[bool, None] = _Unset,
|
| 247 |
-
max_digits: Union[int, None] = _Unset,
|
| 248 |
-
decimal_places: Union[int, None] = _Unset,
|
| 249 |
-
examples: Optional[List[Any]] = None,
|
| 250 |
-
example: Annotated[
|
| 251 |
-
Optional[Any],
|
| 252 |
-
deprecated(
|
| 253 |
-
"Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
|
| 254 |
-
"although still supported. Use examples instead."
|
| 255 |
-
),
|
| 256 |
-
] = _Unset,
|
| 257 |
-
deprecated: Optional[bool] = None,
|
| 258 |
-
include_in_schema: bool = True,
|
| 259 |
-
json_schema_extra: Union[Dict[str, Any], None] = None,
|
| 260 |
-
**extra: Any,
|
| 261 |
-
):
|
| 262 |
-
super().__init__(
|
| 263 |
-
default=default,
|
| 264 |
-
default_factory=default_factory,
|
| 265 |
-
annotation=annotation,
|
| 266 |
-
alias=alias,
|
| 267 |
-
alias_priority=alias_priority,
|
| 268 |
-
validation_alias=validation_alias,
|
| 269 |
-
serialization_alias=serialization_alias,
|
| 270 |
-
title=title,
|
| 271 |
-
description=description,
|
| 272 |
-
gt=gt,
|
| 273 |
-
ge=ge,
|
| 274 |
-
lt=lt,
|
| 275 |
-
le=le,
|
| 276 |
-
min_length=min_length,
|
| 277 |
-
max_length=max_length,
|
| 278 |
-
pattern=pattern,
|
| 279 |
-
regex=regex,
|
| 280 |
-
discriminator=discriminator,
|
| 281 |
-
strict=strict,
|
| 282 |
-
multiple_of=multiple_of,
|
| 283 |
-
allow_inf_nan=allow_inf_nan,
|
| 284 |
-
max_digits=max_digits,
|
| 285 |
-
decimal_places=decimal_places,
|
| 286 |
-
deprecated=deprecated,
|
| 287 |
-
example=example,
|
| 288 |
-
examples=examples,
|
| 289 |
-
include_in_schema=include_in_schema,
|
| 290 |
-
json_schema_extra=json_schema_extra,
|
| 291 |
-
**extra,
|
| 292 |
-
)
|
| 293 |
-
|
| 294 |
-
|
| 295 |
-
class Header(Param):
|
| 296 |
-
in_ = ParamTypes.header
|
| 297 |
-
|
| 298 |
-
def __init__(
|
| 299 |
-
self,
|
| 300 |
-
default: Any = Undefined,
|
| 301 |
-
*,
|
| 302 |
-
default_factory: Union[Callable[[], Any], None] = _Unset,
|
| 303 |
-
annotation: Optional[Any] = None,
|
| 304 |
-
alias: Optional[str] = None,
|
| 305 |
-
alias_priority: Union[int, None] = _Unset,
|
| 306 |
-
# TODO: update when deprecating Pydantic v1, import these types
|
| 307 |
-
# validation_alias: str | AliasPath | AliasChoices | None
|
| 308 |
-
validation_alias: Union[str, None] = None,
|
| 309 |
-
serialization_alias: Union[str, None] = None,
|
| 310 |
-
convert_underscores: bool = True,
|
| 311 |
-
title: Optional[str] = None,
|
| 312 |
-
description: Optional[str] = None,
|
| 313 |
-
gt: Optional[float] = None,
|
| 314 |
-
ge: Optional[float] = None,
|
| 315 |
-
lt: Optional[float] = None,
|
| 316 |
-
le: Optional[float] = None,
|
| 317 |
-
min_length: Optional[int] = None,
|
| 318 |
-
max_length: Optional[int] = None,
|
| 319 |
-
pattern: Optional[str] = None,
|
| 320 |
-
regex: Annotated[
|
| 321 |
-
Optional[str],
|
| 322 |
-
deprecated(
|
| 323 |
-
"Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
|
| 324 |
-
),
|
| 325 |
-
] = None,
|
| 326 |
-
discriminator: Union[str, None] = None,
|
| 327 |
-
strict: Union[bool, None] = _Unset,
|
| 328 |
-
multiple_of: Union[float, None] = _Unset,
|
| 329 |
-
allow_inf_nan: Union[bool, None] = _Unset,
|
| 330 |
-
max_digits: Union[int, None] = _Unset,
|
| 331 |
-
decimal_places: Union[int, None] = _Unset,
|
| 332 |
-
examples: Optional[List[Any]] = None,
|
| 333 |
-
example: Annotated[
|
| 334 |
-
Optional[Any],
|
| 335 |
-
deprecated(
|
| 336 |
-
"Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
|
| 337 |
-
"although still supported. Use examples instead."
|
| 338 |
-
),
|
| 339 |
-
] = _Unset,
|
| 340 |
-
deprecated: Optional[bool] = None,
|
| 341 |
-
include_in_schema: bool = True,
|
| 342 |
-
json_schema_extra: Union[Dict[str, Any], None] = None,
|
| 343 |
-
**extra: Any,
|
| 344 |
-
):
|
| 345 |
-
self.convert_underscores = convert_underscores
|
| 346 |
-
super().__init__(
|
| 347 |
-
default=default,
|
| 348 |
-
default_factory=default_factory,
|
| 349 |
-
annotation=annotation,
|
| 350 |
-
alias=alias,
|
| 351 |
-
alias_priority=alias_priority,
|
| 352 |
-
validation_alias=validation_alias,
|
| 353 |
-
serialization_alias=serialization_alias,
|
| 354 |
-
title=title,
|
| 355 |
-
description=description,
|
| 356 |
-
gt=gt,
|
| 357 |
-
ge=ge,
|
| 358 |
-
lt=lt,
|
| 359 |
-
le=le,
|
| 360 |
-
min_length=min_length,
|
| 361 |
-
max_length=max_length,
|
| 362 |
-
pattern=pattern,
|
| 363 |
-
regex=regex,
|
| 364 |
-
discriminator=discriminator,
|
| 365 |
-
strict=strict,
|
| 366 |
-
multiple_of=multiple_of,
|
| 367 |
-
allow_inf_nan=allow_inf_nan,
|
| 368 |
-
max_digits=max_digits,
|
| 369 |
-
decimal_places=decimal_places,
|
| 370 |
-
deprecated=deprecated,
|
| 371 |
-
example=example,
|
| 372 |
-
examples=examples,
|
| 373 |
-
include_in_schema=include_in_schema,
|
| 374 |
-
json_schema_extra=json_schema_extra,
|
| 375 |
-
**extra,
|
| 376 |
-
)
|
| 377 |
-
|
| 378 |
-
|
| 379 |
-
class Cookie(Param):
|
| 380 |
-
in_ = ParamTypes.cookie
|
| 381 |
-
|
| 382 |
-
def __init__(
|
| 383 |
-
self,
|
| 384 |
-
default: Any = Undefined,
|
| 385 |
-
*,
|
| 386 |
-
default_factory: Union[Callable[[], Any], None] = _Unset,
|
| 387 |
-
annotation: Optional[Any] = None,
|
| 388 |
-
alias: Optional[str] = None,
|
| 389 |
-
alias_priority: Union[int, None] = _Unset,
|
| 390 |
-
# TODO: update when deprecating Pydantic v1, import these types
|
| 391 |
-
# validation_alias: str | AliasPath | AliasChoices | None
|
| 392 |
-
validation_alias: Union[str, None] = None,
|
| 393 |
-
serialization_alias: Union[str, None] = None,
|
| 394 |
-
title: Optional[str] = None,
|
| 395 |
-
description: Optional[str] = None,
|
| 396 |
-
gt: Optional[float] = None,
|
| 397 |
-
ge: Optional[float] = None,
|
| 398 |
-
lt: Optional[float] = None,
|
| 399 |
-
le: Optional[float] = None,
|
| 400 |
-
min_length: Optional[int] = None,
|
| 401 |
-
max_length: Optional[int] = None,
|
| 402 |
-
pattern: Optional[str] = None,
|
| 403 |
-
regex: Annotated[
|
| 404 |
-
Optional[str],
|
| 405 |
-
deprecated(
|
| 406 |
-
"Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
|
| 407 |
-
),
|
| 408 |
-
] = None,
|
| 409 |
-
discriminator: Union[str, None] = None,
|
| 410 |
-
strict: Union[bool, None] = _Unset,
|
| 411 |
-
multiple_of: Union[float, None] = _Unset,
|
| 412 |
-
allow_inf_nan: Union[bool, None] = _Unset,
|
| 413 |
-
max_digits: Union[int, None] = _Unset,
|
| 414 |
-
decimal_places: Union[int, None] = _Unset,
|
| 415 |
-
examples: Optional[List[Any]] = None,
|
| 416 |
-
example: Annotated[
|
| 417 |
-
Optional[Any],
|
| 418 |
-
deprecated(
|
| 419 |
-
"Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
|
| 420 |
-
"although still supported. Use examples instead."
|
| 421 |
-
),
|
| 422 |
-
] = _Unset,
|
| 423 |
-
deprecated: Optional[bool] = None,
|
| 424 |
-
include_in_schema: bool = True,
|
| 425 |
-
json_schema_extra: Union[Dict[str, Any], None] = None,
|
| 426 |
-
**extra: Any,
|
| 427 |
-
):
|
| 428 |
-
super().__init__(
|
| 429 |
-
default=default,
|
| 430 |
-
default_factory=default_factory,
|
| 431 |
-
annotation=annotation,
|
| 432 |
-
alias=alias,
|
| 433 |
-
alias_priority=alias_priority,
|
| 434 |
-
validation_alias=validation_alias,
|
| 435 |
-
serialization_alias=serialization_alias,
|
| 436 |
-
title=title,
|
| 437 |
-
description=description,
|
| 438 |
-
gt=gt,
|
| 439 |
-
ge=ge,
|
| 440 |
-
lt=lt,
|
| 441 |
-
le=le,
|
| 442 |
-
min_length=min_length,
|
| 443 |
-
max_length=max_length,
|
| 444 |
-
pattern=pattern,
|
| 445 |
-
regex=regex,
|
| 446 |
-
discriminator=discriminator,
|
| 447 |
-
strict=strict,
|
| 448 |
-
multiple_of=multiple_of,
|
| 449 |
-
allow_inf_nan=allow_inf_nan,
|
| 450 |
-
max_digits=max_digits,
|
| 451 |
-
decimal_places=decimal_places,
|
| 452 |
-
deprecated=deprecated,
|
| 453 |
-
example=example,
|
| 454 |
-
examples=examples,
|
| 455 |
-
include_in_schema=include_in_schema,
|
| 456 |
-
json_schema_extra=json_schema_extra,
|
| 457 |
-
**extra,
|
| 458 |
-
)
|
| 459 |
-
|
| 460 |
-
|
| 461 |
-
class Body(FieldInfo):
|
| 462 |
-
def __init__(
|
| 463 |
-
self,
|
| 464 |
-
default: Any = Undefined,
|
| 465 |
-
*,
|
| 466 |
-
default_factory: Union[Callable[[], Any], None] = _Unset,
|
| 467 |
-
annotation: Optional[Any] = None,
|
| 468 |
-
embed: bool = False,
|
| 469 |
-
media_type: str = "application/json",
|
| 470 |
-
alias: Optional[str] = None,
|
| 471 |
-
alias_priority: Union[int, None] = _Unset,
|
| 472 |
-
# TODO: update when deprecating Pydantic v1, import these types
|
| 473 |
-
# validation_alias: str | AliasPath | AliasChoices | None
|
| 474 |
-
validation_alias: Union[str, None] = None,
|
| 475 |
-
serialization_alias: Union[str, None] = None,
|
| 476 |
-
title: Optional[str] = None,
|
| 477 |
-
description: Optional[str] = None,
|
| 478 |
-
gt: Optional[float] = None,
|
| 479 |
-
ge: Optional[float] = None,
|
| 480 |
-
lt: Optional[float] = None,
|
| 481 |
-
le: Optional[float] = None,
|
| 482 |
-
min_length: Optional[int] = None,
|
| 483 |
-
max_length: Optional[int] = None,
|
| 484 |
-
pattern: Optional[str] = None,
|
| 485 |
-
regex: Annotated[
|
| 486 |
-
Optional[str],
|
| 487 |
-
deprecated(
|
| 488 |
-
"Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
|
| 489 |
-
),
|
| 490 |
-
] = None,
|
| 491 |
-
discriminator: Union[str, None] = None,
|
| 492 |
-
strict: Union[bool, None] = _Unset,
|
| 493 |
-
multiple_of: Union[float, None] = _Unset,
|
| 494 |
-
allow_inf_nan: Union[bool, None] = _Unset,
|
| 495 |
-
max_digits: Union[int, None] = _Unset,
|
| 496 |
-
decimal_places: Union[int, None] = _Unset,
|
| 497 |
-
examples: Optional[List[Any]] = None,
|
| 498 |
-
example: Annotated[
|
| 499 |
-
Optional[Any],
|
| 500 |
-
deprecated(
|
| 501 |
-
"Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
|
| 502 |
-
"although still supported. Use examples instead."
|
| 503 |
-
),
|
| 504 |
-
] = _Unset,
|
| 505 |
-
deprecated: Optional[bool] = None,
|
| 506 |
-
include_in_schema: bool = True,
|
| 507 |
-
json_schema_extra: Union[Dict[str, Any], None] = None,
|
| 508 |
-
**extra: Any,
|
| 509 |
-
):
|
| 510 |
-
self.embed = embed
|
| 511 |
-
self.media_type = media_type
|
| 512 |
-
self.deprecated = deprecated
|
| 513 |
-
if example is not _Unset:
|
| 514 |
-
warnings.warn(
|
| 515 |
-
"`example` has been depreacated, please use `examples` instead",
|
| 516 |
-
category=DeprecationWarning,
|
| 517 |
-
stacklevel=4,
|
| 518 |
-
)
|
| 519 |
-
self.example = example
|
| 520 |
-
self.include_in_schema = include_in_schema
|
| 521 |
-
kwargs = dict(
|
| 522 |
-
default=default,
|
| 523 |
-
default_factory=default_factory,
|
| 524 |
-
alias=alias,
|
| 525 |
-
title=title,
|
| 526 |
-
description=description,
|
| 527 |
-
gt=gt,
|
| 528 |
-
ge=ge,
|
| 529 |
-
lt=lt,
|
| 530 |
-
le=le,
|
| 531 |
-
min_length=min_length,
|
| 532 |
-
max_length=max_length,
|
| 533 |
-
discriminator=discriminator,
|
| 534 |
-
multiple_of=multiple_of,
|
| 535 |
-
allow_nan=allow_inf_nan,
|
| 536 |
-
max_digits=max_digits,
|
| 537 |
-
decimal_places=decimal_places,
|
| 538 |
-
**extra,
|
| 539 |
-
)
|
| 540 |
-
if examples is not None:
|
| 541 |
-
kwargs["examples"] = examples
|
| 542 |
-
if regex is not None:
|
| 543 |
-
warnings.warn(
|
| 544 |
-
"`regex` has been depreacated, please use `pattern` instead",
|
| 545 |
-
category=DeprecationWarning,
|
| 546 |
-
stacklevel=4,
|
| 547 |
-
)
|
| 548 |
-
current_json_schema_extra = json_schema_extra or extra
|
| 549 |
-
if PYDANTIC_V2:
|
| 550 |
-
kwargs.update(
|
| 551 |
-
{
|
| 552 |
-
"annotation": annotation,
|
| 553 |
-
"alias_priority": alias_priority,
|
| 554 |
-
"validation_alias": validation_alias,
|
| 555 |
-
"serialization_alias": serialization_alias,
|
| 556 |
-
"strict": strict,
|
| 557 |
-
"json_schema_extra": current_json_schema_extra,
|
| 558 |
-
}
|
| 559 |
-
)
|
| 560 |
-
kwargs["pattern"] = pattern or regex
|
| 561 |
-
else:
|
| 562 |
-
kwargs["regex"] = pattern or regex
|
| 563 |
-
kwargs.update(**current_json_schema_extra)
|
| 564 |
-
|
| 565 |
-
use_kwargs = {k: v for k, v in kwargs.items() if v is not _Unset}
|
| 566 |
-
|
| 567 |
-
super().__init__(**use_kwargs)
|
| 568 |
-
|
| 569 |
-
def __repr__(self) -> str:
|
| 570 |
-
return f"{self.__class__.__name__}({self.default})"
|
| 571 |
-
|
| 572 |
-
|
| 573 |
-
class Form(Body):
|
| 574 |
-
def __init__(
|
| 575 |
-
self,
|
| 576 |
-
default: Any = Undefined,
|
| 577 |
-
*,
|
| 578 |
-
default_factory: Union[Callable[[], Any], None] = _Unset,
|
| 579 |
-
annotation: Optional[Any] = None,
|
| 580 |
-
media_type: str = "application/x-www-form-urlencoded",
|
| 581 |
-
alias: Optional[str] = None,
|
| 582 |
-
alias_priority: Union[int, None] = _Unset,
|
| 583 |
-
# TODO: update when deprecating Pydantic v1, import these types
|
| 584 |
-
# validation_alias: str | AliasPath | AliasChoices | None
|
| 585 |
-
validation_alias: Union[str, None] = None,
|
| 586 |
-
serialization_alias: Union[str, None] = None,
|
| 587 |
-
title: Optional[str] = None,
|
| 588 |
-
description: Optional[str] = None,
|
| 589 |
-
gt: Optional[float] = None,
|
| 590 |
-
ge: Optional[float] = None,
|
| 591 |
-
lt: Optional[float] = None,
|
| 592 |
-
le: Optional[float] = None,
|
| 593 |
-
min_length: Optional[int] = None,
|
| 594 |
-
max_length: Optional[int] = None,
|
| 595 |
-
pattern: Optional[str] = None,
|
| 596 |
-
regex: Annotated[
|
| 597 |
-
Optional[str],
|
| 598 |
-
deprecated(
|
| 599 |
-
"Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
|
| 600 |
-
),
|
| 601 |
-
] = None,
|
| 602 |
-
discriminator: Union[str, None] = None,
|
| 603 |
-
strict: Union[bool, None] = _Unset,
|
| 604 |
-
multiple_of: Union[float, None] = _Unset,
|
| 605 |
-
allow_inf_nan: Union[bool, None] = _Unset,
|
| 606 |
-
max_digits: Union[int, None] = _Unset,
|
| 607 |
-
decimal_places: Union[int, None] = _Unset,
|
| 608 |
-
examples: Optional[List[Any]] = None,
|
| 609 |
-
example: Annotated[
|
| 610 |
-
Optional[Any],
|
| 611 |
-
deprecated(
|
| 612 |
-
"Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
|
| 613 |
-
"although still supported. Use examples instead."
|
| 614 |
-
),
|
| 615 |
-
] = _Unset,
|
| 616 |
-
deprecated: Optional[bool] = None,
|
| 617 |
-
include_in_schema: bool = True,
|
| 618 |
-
json_schema_extra: Union[Dict[str, Any], None] = None,
|
| 619 |
-
**extra: Any,
|
| 620 |
-
):
|
| 621 |
-
super().__init__(
|
| 622 |
-
default=default,
|
| 623 |
-
default_factory=default_factory,
|
| 624 |
-
annotation=annotation,
|
| 625 |
-
embed=True,
|
| 626 |
-
media_type=media_type,
|
| 627 |
-
alias=alias,
|
| 628 |
-
alias_priority=alias_priority,
|
| 629 |
-
validation_alias=validation_alias,
|
| 630 |
-
serialization_alias=serialization_alias,
|
| 631 |
-
title=title,
|
| 632 |
-
description=description,
|
| 633 |
-
gt=gt,
|
| 634 |
-
ge=ge,
|
| 635 |
-
lt=lt,
|
| 636 |
-
le=le,
|
| 637 |
-
min_length=min_length,
|
| 638 |
-
max_length=max_length,
|
| 639 |
-
pattern=pattern,
|
| 640 |
-
regex=regex,
|
| 641 |
-
discriminator=discriminator,
|
| 642 |
-
strict=strict,
|
| 643 |
-
multiple_of=multiple_of,
|
| 644 |
-
allow_inf_nan=allow_inf_nan,
|
| 645 |
-
max_digits=max_digits,
|
| 646 |
-
decimal_places=decimal_places,
|
| 647 |
-
deprecated=deprecated,
|
| 648 |
-
example=example,
|
| 649 |
-
examples=examples,
|
| 650 |
-
include_in_schema=include_in_schema,
|
| 651 |
-
json_schema_extra=json_schema_extra,
|
| 652 |
-
**extra,
|
| 653 |
-
)
|
| 654 |
-
|
| 655 |
-
|
| 656 |
-
class File(Form):
|
| 657 |
-
def __init__(
|
| 658 |
-
self,
|
| 659 |
-
default: Any = Undefined,
|
| 660 |
-
*,
|
| 661 |
-
default_factory: Union[Callable[[], Any], None] = _Unset,
|
| 662 |
-
annotation: Optional[Any] = None,
|
| 663 |
-
media_type: str = "multipart/form-data",
|
| 664 |
-
alias: Optional[str] = None,
|
| 665 |
-
alias_priority: Union[int, None] = _Unset,
|
| 666 |
-
# TODO: update when deprecating Pydantic v1, import these types
|
| 667 |
-
# validation_alias: str | AliasPath | AliasChoices | None
|
| 668 |
-
validation_alias: Union[str, None] = None,
|
| 669 |
-
serialization_alias: Union[str, None] = None,
|
| 670 |
-
title: Optional[str] = None,
|
| 671 |
-
description: Optional[str] = None,
|
| 672 |
-
gt: Optional[float] = None,
|
| 673 |
-
ge: Optional[float] = None,
|
| 674 |
-
lt: Optional[float] = None,
|
| 675 |
-
le: Optional[float] = None,
|
| 676 |
-
min_length: Optional[int] = None,
|
| 677 |
-
max_length: Optional[int] = None,
|
| 678 |
-
pattern: Optional[str] = None,
|
| 679 |
-
regex: Annotated[
|
| 680 |
-
Optional[str],
|
| 681 |
-
deprecated(
|
| 682 |
-
"Deprecated in FastAPI 0.100.0 and Pydantic v2, use `pattern` instead."
|
| 683 |
-
),
|
| 684 |
-
] = None,
|
| 685 |
-
discriminator: Union[str, None] = None,
|
| 686 |
-
strict: Union[bool, None] = _Unset,
|
| 687 |
-
multiple_of: Union[float, None] = _Unset,
|
| 688 |
-
allow_inf_nan: Union[bool, None] = _Unset,
|
| 689 |
-
max_digits: Union[int, None] = _Unset,
|
| 690 |
-
decimal_places: Union[int, None] = _Unset,
|
| 691 |
-
examples: Optional[List[Any]] = None,
|
| 692 |
-
example: Annotated[
|
| 693 |
-
Optional[Any],
|
| 694 |
-
deprecated(
|
| 695 |
-
"Deprecated in OpenAPI 3.1.0 that now uses JSON Schema 2020-12, "
|
| 696 |
-
"although still supported. Use examples instead."
|
| 697 |
-
),
|
| 698 |
-
] = _Unset,
|
| 699 |
-
deprecated: Optional[bool] = None,
|
| 700 |
-
include_in_schema: bool = True,
|
| 701 |
-
json_schema_extra: Union[Dict[str, Any], None] = None,
|
| 702 |
-
**extra: Any,
|
| 703 |
-
):
|
| 704 |
-
super().__init__(
|
| 705 |
-
default=default,
|
| 706 |
-
default_factory=default_factory,
|
| 707 |
-
annotation=annotation,
|
| 708 |
-
media_type=media_type,
|
| 709 |
-
alias=alias,
|
| 710 |
-
alias_priority=alias_priority,
|
| 711 |
-
validation_alias=validation_alias,
|
| 712 |
-
serialization_alias=serialization_alias,
|
| 713 |
-
title=title,
|
| 714 |
-
description=description,
|
| 715 |
-
gt=gt,
|
| 716 |
-
ge=ge,
|
| 717 |
-
lt=lt,
|
| 718 |
-
le=le,
|
| 719 |
-
min_length=min_length,
|
| 720 |
-
max_length=max_length,
|
| 721 |
-
pattern=pattern,
|
| 722 |
-
regex=regex,
|
| 723 |
-
discriminator=discriminator,
|
| 724 |
-
strict=strict,
|
| 725 |
-
multiple_of=multiple_of,
|
| 726 |
-
allow_inf_nan=allow_inf_nan,
|
| 727 |
-
max_digits=max_digits,
|
| 728 |
-
decimal_places=decimal_places,
|
| 729 |
-
deprecated=deprecated,
|
| 730 |
-
example=example,
|
| 731 |
-
examples=examples,
|
| 732 |
-
include_in_schema=include_in_schema,
|
| 733 |
-
json_schema_extra=json_schema_extra,
|
| 734 |
-
**extra,
|
| 735 |
-
)
|
| 736 |
-
|
| 737 |
-
|
| 738 |
-
class Depends:
|
| 739 |
-
def __init__(
|
| 740 |
-
self, dependency: Optional[Callable[..., Any]] = None, *, use_cache: bool = True
|
| 741 |
-
):
|
| 742 |
-
self.dependency = dependency
|
| 743 |
-
self.use_cache = use_cache
|
| 744 |
-
|
| 745 |
-
def __repr__(self) -> str:
|
| 746 |
-
attr = getattr(self.dependency, "__name__", type(self.dependency).__name__)
|
| 747 |
-
cache = "" if self.use_cache else ", use_cache=False"
|
| 748 |
-
return f"{self.__class__.__name__}({attr}{cache})"
|
| 749 |
-
|
| 750 |
-
|
| 751 |
-
class Security(Depends):
|
| 752 |
-
def __init__(
|
| 753 |
-
self,
|
| 754 |
-
dependency: Optional[Callable[..., Any]] = None,
|
| 755 |
-
*,
|
| 756 |
-
scopes: Optional[Sequence[str]] = None,
|
| 757 |
-
use_cache: bool = True,
|
| 758 |
-
):
|
| 759 |
-
super().__init__(dependency=dependency, use_cache=use_cache)
|
| 760 |
-
self.scopes = scopes or []
|
|
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|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I_V_.py
DELETED
|
@@ -1,20 +0,0 @@
|
|
| 1 |
-
from fontTools.misc.textTools import strjoin, tobytes, tostr
|
| 2 |
-
from . import asciiTable
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
class table_T_S_I_V_(asciiTable.asciiTable):
|
| 6 |
-
def toXML(self, writer, ttFont):
|
| 7 |
-
data = tostr(self.data)
|
| 8 |
-
# removing null bytes. XXX needed??
|
| 9 |
-
data = data.split("\0")
|
| 10 |
-
data = strjoin(data)
|
| 11 |
-
writer.begintag("source")
|
| 12 |
-
writer.newline()
|
| 13 |
-
writer.write_noindent(data.replace("\r", "\n"))
|
| 14 |
-
writer.newline()
|
| 15 |
-
writer.endtag("source")
|
| 16 |
-
writer.newline()
|
| 17 |
-
|
| 18 |
-
def fromXML(self, name, attrs, content, ttFont):
|
| 19 |
-
lines = strjoin(content).split("\n")
|
| 20 |
-
self.data = tobytes("\r".join(lines[1:-1]))
|
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|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fsspec/implementations/ftp.py
DELETED
|
@@ -1,380 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import sys
|
| 3 |
-
import uuid
|
| 4 |
-
import warnings
|
| 5 |
-
from ftplib import FTP, Error, error_perm
|
| 6 |
-
from typing import Any
|
| 7 |
-
|
| 8 |
-
from ..spec import AbstractBufferedFile, AbstractFileSystem
|
| 9 |
-
from ..utils import infer_storage_options, isfilelike
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
class FTPFileSystem(AbstractFileSystem):
|
| 13 |
-
"""A filesystem over classic FTP"""
|
| 14 |
-
|
| 15 |
-
root_marker = "/"
|
| 16 |
-
cachable = False
|
| 17 |
-
protocol = "ftp"
|
| 18 |
-
|
| 19 |
-
def __init__(
|
| 20 |
-
self,
|
| 21 |
-
host,
|
| 22 |
-
port=21,
|
| 23 |
-
username=None,
|
| 24 |
-
password=None,
|
| 25 |
-
acct=None,
|
| 26 |
-
block_size=None,
|
| 27 |
-
tempdir=None,
|
| 28 |
-
timeout=30,
|
| 29 |
-
encoding="utf-8",
|
| 30 |
-
**kwargs,
|
| 31 |
-
):
|
| 32 |
-
"""
|
| 33 |
-
You can use _get_kwargs_from_urls to get some kwargs from
|
| 34 |
-
a reasonable FTP url.
|
| 35 |
-
|
| 36 |
-
Authentication will be anonymous if username/password are not
|
| 37 |
-
given.
|
| 38 |
-
|
| 39 |
-
Parameters
|
| 40 |
-
----------
|
| 41 |
-
host: str
|
| 42 |
-
The remote server name/ip to connect to
|
| 43 |
-
port: int
|
| 44 |
-
Port to connect with
|
| 45 |
-
username: str or None
|
| 46 |
-
If authenticating, the user's identifier
|
| 47 |
-
password: str of None
|
| 48 |
-
User's password on the server, if using
|
| 49 |
-
acct: str or None
|
| 50 |
-
Some servers also need an "account" string for auth
|
| 51 |
-
block_size: int or None
|
| 52 |
-
If given, the read-ahead or write buffer size.
|
| 53 |
-
tempdir: str
|
| 54 |
-
Directory on remote to put temporary files when in a transaction
|
| 55 |
-
timeout: int
|
| 56 |
-
Timeout of the ftp connection in seconds
|
| 57 |
-
encoding: str
|
| 58 |
-
Encoding to use for directories and filenames in FTP connection
|
| 59 |
-
"""
|
| 60 |
-
super(FTPFileSystem, self).__init__(**kwargs)
|
| 61 |
-
self.host = host
|
| 62 |
-
self.port = port
|
| 63 |
-
self.tempdir = tempdir or "/tmp"
|
| 64 |
-
self.cred = username, password, acct
|
| 65 |
-
self.timeout = timeout
|
| 66 |
-
self.encoding = encoding
|
| 67 |
-
if block_size is not None:
|
| 68 |
-
self.blocksize = block_size
|
| 69 |
-
else:
|
| 70 |
-
self.blocksize = 2**16
|
| 71 |
-
self._connect()
|
| 72 |
-
|
| 73 |
-
def _connect(self):
|
| 74 |
-
if sys.version_info >= (3, 9):
|
| 75 |
-
self.ftp = FTP(timeout=self.timeout, encoding=self.encoding)
|
| 76 |
-
elif self.encoding:
|
| 77 |
-
warnings.warn("`encoding` not supported for python<3.9, ignoring")
|
| 78 |
-
self.ftp = FTP(timeout=self.timeout)
|
| 79 |
-
else:
|
| 80 |
-
self.ftp = FTP(timeout=self.timeout)
|
| 81 |
-
self.ftp.connect(self.host, self.port)
|
| 82 |
-
self.ftp.login(*self.cred)
|
| 83 |
-
|
| 84 |
-
@classmethod
|
| 85 |
-
def _strip_protocol(cls, path):
|
| 86 |
-
return "/" + infer_storage_options(path)["path"].lstrip("/").rstrip("/")
|
| 87 |
-
|
| 88 |
-
@staticmethod
|
| 89 |
-
def _get_kwargs_from_urls(urlpath):
|
| 90 |
-
out = infer_storage_options(urlpath)
|
| 91 |
-
out.pop("path", None)
|
| 92 |
-
out.pop("protocol", None)
|
| 93 |
-
return out
|
| 94 |
-
|
| 95 |
-
def ls(self, path, detail=True, **kwargs):
|
| 96 |
-
path = self._strip_protocol(path)
|
| 97 |
-
out = []
|
| 98 |
-
if path not in self.dircache:
|
| 99 |
-
try:
|
| 100 |
-
try:
|
| 101 |
-
out = [
|
| 102 |
-
(fn, details)
|
| 103 |
-
for (fn, details) in self.ftp.mlsd(path)
|
| 104 |
-
if fn not in [".", ".."]
|
| 105 |
-
and details["type"] not in ["pdir", "cdir"]
|
| 106 |
-
]
|
| 107 |
-
except error_perm:
|
| 108 |
-
out = _mlsd2(self.ftp, path) # Not platform independent
|
| 109 |
-
for fn, details in out:
|
| 110 |
-
if path == "/":
|
| 111 |
-
path = "" # just for forming the names, below
|
| 112 |
-
details["name"] = "/".join([path, fn.lstrip("/")])
|
| 113 |
-
if details["type"] == "file":
|
| 114 |
-
details["size"] = int(details["size"])
|
| 115 |
-
else:
|
| 116 |
-
details["size"] = 0
|
| 117 |
-
if details["type"] == "dir":
|
| 118 |
-
details["type"] = "directory"
|
| 119 |
-
self.dircache[path] = out
|
| 120 |
-
except Error:
|
| 121 |
-
try:
|
| 122 |
-
info = self.info(path)
|
| 123 |
-
if info["type"] == "file":
|
| 124 |
-
out = [(path, info)]
|
| 125 |
-
except (Error, IndexError):
|
| 126 |
-
raise FileNotFoundError(path)
|
| 127 |
-
files = self.dircache.get(path, out)
|
| 128 |
-
if not detail:
|
| 129 |
-
return sorted([fn for fn, details in files])
|
| 130 |
-
return [details for fn, details in files]
|
| 131 |
-
|
| 132 |
-
def info(self, path, **kwargs):
|
| 133 |
-
# implement with direct method
|
| 134 |
-
path = self._strip_protocol(path)
|
| 135 |
-
if path == "/":
|
| 136 |
-
# special case, since this dir has no real entry
|
| 137 |
-
return {"name": "/", "size": 0, "type": "directory"}
|
| 138 |
-
files = self.ls(self._parent(path).lstrip("/"), True)
|
| 139 |
-
try:
|
| 140 |
-
out = [f for f in files if f["name"] == path][0]
|
| 141 |
-
except IndexError:
|
| 142 |
-
raise FileNotFoundError(path)
|
| 143 |
-
return out
|
| 144 |
-
|
| 145 |
-
def get_file(self, rpath, lpath, **kwargs):
|
| 146 |
-
if self.isdir(rpath):
|
| 147 |
-
if not os.path.exists(lpath):
|
| 148 |
-
os.mkdir(lpath)
|
| 149 |
-
return
|
| 150 |
-
if isfilelike(lpath):
|
| 151 |
-
outfile = lpath
|
| 152 |
-
else:
|
| 153 |
-
outfile = open(lpath, "wb")
|
| 154 |
-
|
| 155 |
-
def cb(x):
|
| 156 |
-
outfile.write(x)
|
| 157 |
-
|
| 158 |
-
self.ftp.retrbinary(
|
| 159 |
-
"RETR %s" % rpath,
|
| 160 |
-
blocksize=self.blocksize,
|
| 161 |
-
callback=cb,
|
| 162 |
-
)
|
| 163 |
-
if not isfilelike(lpath):
|
| 164 |
-
outfile.close()
|
| 165 |
-
|
| 166 |
-
def cat_file(self, path, start=None, end=None, **kwargs):
|
| 167 |
-
if end is not None:
|
| 168 |
-
return super().cat_file(path, start, end, **kwargs)
|
| 169 |
-
out = []
|
| 170 |
-
|
| 171 |
-
def cb(x):
|
| 172 |
-
out.append(x)
|
| 173 |
-
|
| 174 |
-
self.ftp.retrbinary(
|
| 175 |
-
"RETR %s" % path,
|
| 176 |
-
blocksize=self.blocksize,
|
| 177 |
-
rest=start,
|
| 178 |
-
callback=cb,
|
| 179 |
-
)
|
| 180 |
-
return b"".join(out)
|
| 181 |
-
|
| 182 |
-
def _open(
|
| 183 |
-
self,
|
| 184 |
-
path,
|
| 185 |
-
mode="rb",
|
| 186 |
-
block_size=None,
|
| 187 |
-
cache_options=None,
|
| 188 |
-
autocommit=True,
|
| 189 |
-
**kwargs,
|
| 190 |
-
):
|
| 191 |
-
path = self._strip_protocol(path)
|
| 192 |
-
block_size = block_size or self.blocksize
|
| 193 |
-
return FTPFile(
|
| 194 |
-
self,
|
| 195 |
-
path,
|
| 196 |
-
mode=mode,
|
| 197 |
-
block_size=block_size,
|
| 198 |
-
tempdir=self.tempdir,
|
| 199 |
-
autocommit=autocommit,
|
| 200 |
-
cache_options=cache_options,
|
| 201 |
-
)
|
| 202 |
-
|
| 203 |
-
def _rm(self, path):
|
| 204 |
-
path = self._strip_protocol(path)
|
| 205 |
-
self.ftp.delete(path)
|
| 206 |
-
self.invalidate_cache(self._parent(path))
|
| 207 |
-
|
| 208 |
-
def rm(self, path, recursive=False, maxdepth=None):
|
| 209 |
-
paths = self.expand_path(path, recursive=recursive, maxdepth=maxdepth)
|
| 210 |
-
for p in reversed(paths):
|
| 211 |
-
if self.isfile(p):
|
| 212 |
-
self.rm_file(p)
|
| 213 |
-
else:
|
| 214 |
-
self.rmdir(p)
|
| 215 |
-
|
| 216 |
-
def mkdir(self, path: str, create_parents: bool = True, **kwargs: Any) -> None:
|
| 217 |
-
path = self._strip_protocol(path)
|
| 218 |
-
parent = self._parent(path)
|
| 219 |
-
if parent != self.root_marker and not self.exists(parent) and create_parents:
|
| 220 |
-
self.mkdir(parent, create_parents=create_parents)
|
| 221 |
-
|
| 222 |
-
self.ftp.mkd(path)
|
| 223 |
-
self.invalidate_cache(self._parent(path))
|
| 224 |
-
|
| 225 |
-
def makedirs(self, path: str, exist_ok: bool = False) -> None:
|
| 226 |
-
path = self._strip_protocol(path)
|
| 227 |
-
if self.exists(path):
|
| 228 |
-
# NB: "/" does not "exist" as it has no directory entry
|
| 229 |
-
if not exist_ok:
|
| 230 |
-
raise FileExistsError(f"{path} exists without `exist_ok`")
|
| 231 |
-
# exists_ok=True -> no-op
|
| 232 |
-
else:
|
| 233 |
-
self.mkdir(path, create_parents=True)
|
| 234 |
-
|
| 235 |
-
def rmdir(self, path):
|
| 236 |
-
path = self._strip_protocol(path)
|
| 237 |
-
self.ftp.rmd(path)
|
| 238 |
-
self.invalidate_cache(self._parent(path))
|
| 239 |
-
|
| 240 |
-
def mv(self, path1, path2, **kwargs):
|
| 241 |
-
path1 = self._strip_protocol(path1)
|
| 242 |
-
path2 = self._strip_protocol(path2)
|
| 243 |
-
self.ftp.rename(path1, path2)
|
| 244 |
-
self.invalidate_cache(self._parent(path1))
|
| 245 |
-
self.invalidate_cache(self._parent(path2))
|
| 246 |
-
|
| 247 |
-
def __del__(self):
|
| 248 |
-
self.ftp.close()
|
| 249 |
-
|
| 250 |
-
def invalidate_cache(self, path=None):
|
| 251 |
-
if path is None:
|
| 252 |
-
self.dircache.clear()
|
| 253 |
-
else:
|
| 254 |
-
self.dircache.pop(path, None)
|
| 255 |
-
super(FTPFileSystem, self).invalidate_cache(path)
|
| 256 |
-
|
| 257 |
-
|
| 258 |
-
class TransferDone(Exception):
|
| 259 |
-
"""Internal exception to break out of transfer"""
|
| 260 |
-
|
| 261 |
-
pass
|
| 262 |
-
|
| 263 |
-
|
| 264 |
-
class FTPFile(AbstractBufferedFile):
|
| 265 |
-
"""Interact with a remote FTP file with read/write buffering"""
|
| 266 |
-
|
| 267 |
-
def __init__(
|
| 268 |
-
self,
|
| 269 |
-
fs,
|
| 270 |
-
path,
|
| 271 |
-
mode="rb",
|
| 272 |
-
block_size="default",
|
| 273 |
-
autocommit=True,
|
| 274 |
-
cache_type="readahead",
|
| 275 |
-
cache_options=None,
|
| 276 |
-
**kwargs,
|
| 277 |
-
):
|
| 278 |
-
super().__init__(
|
| 279 |
-
fs,
|
| 280 |
-
path,
|
| 281 |
-
mode=mode,
|
| 282 |
-
block_size=block_size,
|
| 283 |
-
autocommit=autocommit,
|
| 284 |
-
cache_type=cache_type,
|
| 285 |
-
cache_options=cache_options,
|
| 286 |
-
**kwargs,
|
| 287 |
-
)
|
| 288 |
-
if not autocommit:
|
| 289 |
-
self.target = self.path
|
| 290 |
-
self.path = "/".join([kwargs["tempdir"], str(uuid.uuid4())])
|
| 291 |
-
|
| 292 |
-
def commit(self):
|
| 293 |
-
self.fs.mv(self.path, self.target)
|
| 294 |
-
|
| 295 |
-
def discard(self):
|
| 296 |
-
self.fs.rm(self.path)
|
| 297 |
-
|
| 298 |
-
def _fetch_range(self, start, end):
|
| 299 |
-
"""Get bytes between given byte limits
|
| 300 |
-
|
| 301 |
-
Implemented by raising an exception in the fetch callback when the
|
| 302 |
-
number of bytes received reaches the requested amount.
|
| 303 |
-
|
| 304 |
-
Will fail if the server does not respect the REST command on
|
| 305 |
-
retrieve requests.
|
| 306 |
-
"""
|
| 307 |
-
out = []
|
| 308 |
-
total = [0]
|
| 309 |
-
|
| 310 |
-
def callback(x):
|
| 311 |
-
total[0] += len(x)
|
| 312 |
-
if total[0] > end - start:
|
| 313 |
-
out.append(x[: (end - start) - total[0]])
|
| 314 |
-
if end < self.size:
|
| 315 |
-
raise TransferDone
|
| 316 |
-
else:
|
| 317 |
-
out.append(x)
|
| 318 |
-
|
| 319 |
-
if total[0] == end - start and end < self.size:
|
| 320 |
-
raise TransferDone
|
| 321 |
-
|
| 322 |
-
try:
|
| 323 |
-
self.fs.ftp.retrbinary(
|
| 324 |
-
"RETR %s" % self.path,
|
| 325 |
-
blocksize=self.blocksize,
|
| 326 |
-
rest=start,
|
| 327 |
-
callback=callback,
|
| 328 |
-
)
|
| 329 |
-
except TransferDone:
|
| 330 |
-
try:
|
| 331 |
-
# stop transfer, we got enough bytes for this block
|
| 332 |
-
self.fs.ftp.abort()
|
| 333 |
-
self.fs.ftp.getmultiline()
|
| 334 |
-
except Error:
|
| 335 |
-
self.fs._connect()
|
| 336 |
-
|
| 337 |
-
return b"".join(out)
|
| 338 |
-
|
| 339 |
-
def _upload_chunk(self, final=False):
|
| 340 |
-
self.buffer.seek(0)
|
| 341 |
-
self.fs.ftp.storbinary(
|
| 342 |
-
"STOR " + self.path, self.buffer, blocksize=self.blocksize, rest=self.offset
|
| 343 |
-
)
|
| 344 |
-
return True
|
| 345 |
-
|
| 346 |
-
|
| 347 |
-
def _mlsd2(ftp, path="."):
|
| 348 |
-
"""
|
| 349 |
-
Fall back to using `dir` instead of `mlsd` if not supported.
|
| 350 |
-
|
| 351 |
-
This parses a Linux style `ls -l` response to `dir`, but the response may
|
| 352 |
-
be platform dependent.
|
| 353 |
-
|
| 354 |
-
Parameters
|
| 355 |
-
----------
|
| 356 |
-
ftp: ftplib.FTP
|
| 357 |
-
path: str
|
| 358 |
-
Expects to be given path, but defaults to ".".
|
| 359 |
-
"""
|
| 360 |
-
lines = []
|
| 361 |
-
minfo = []
|
| 362 |
-
ftp.dir(path, lines.append)
|
| 363 |
-
for line in lines:
|
| 364 |
-
line = line.split()
|
| 365 |
-
this = (
|
| 366 |
-
line[-1],
|
| 367 |
-
{
|
| 368 |
-
"modify": " ".join(line[5:8]),
|
| 369 |
-
"unix.owner": line[2],
|
| 370 |
-
"unix.group": line[3],
|
| 371 |
-
"unix.mode": line[0],
|
| 372 |
-
"size": line[4],
|
| 373 |
-
},
|
| 374 |
-
)
|
| 375 |
-
if "d" == this[1]["unix.mode"][0]:
|
| 376 |
-
this[1]["type"] = "dir"
|
| 377 |
-
else:
|
| 378 |
-
this[1]["type"] = "file"
|
| 379 |
-
minfo.append(this)
|
| 380 |
-
return minfo
|
|
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|
spaces/DeepDrivePL/PaddleSeg-Matting/matting/model/resnet_vd.py
DELETED
|
@@ -1,368 +0,0 @@
|
|
| 1 |
-
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
|
| 2 |
-
#
|
| 3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
-
# you may not use this file except in compliance with the License.
|
| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
|
| 14 |
-
|
| 15 |
-
import paddle
|
| 16 |
-
import paddle.nn as nn
|
| 17 |
-
import paddle.nn.functional as F
|
| 18 |
-
|
| 19 |
-
from paddleseg.cvlibs import manager
|
| 20 |
-
from paddleseg.models import layers
|
| 21 |
-
from paddleseg.utils import utils
|
| 22 |
-
|
| 23 |
-
__all__ = [
|
| 24 |
-
"ResNet18_vd", "ResNet34_vd", "ResNet50_vd", "ResNet101_vd", "ResNet152_vd"
|
| 25 |
-
]
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
class ConvBNLayer(nn.Layer):
|
| 29 |
-
def __init__(
|
| 30 |
-
self,
|
| 31 |
-
in_channels,
|
| 32 |
-
out_channels,
|
| 33 |
-
kernel_size,
|
| 34 |
-
stride=1,
|
| 35 |
-
dilation=1,
|
| 36 |
-
groups=1,
|
| 37 |
-
is_vd_mode=False,
|
| 38 |
-
act=None,
|
| 39 |
-
):
|
| 40 |
-
super(ConvBNLayer, self).__init__()
|
| 41 |
-
|
| 42 |
-
self.is_vd_mode = is_vd_mode
|
| 43 |
-
self._pool2d_avg = nn.AvgPool2D(
|
| 44 |
-
kernel_size=2, stride=2, padding=0, ceil_mode=True)
|
| 45 |
-
self._conv = nn.Conv2D(
|
| 46 |
-
in_channels=in_channels,
|
| 47 |
-
out_channels=out_channels,
|
| 48 |
-
kernel_size=kernel_size,
|
| 49 |
-
stride=stride,
|
| 50 |
-
padding=(kernel_size - 1) // 2 if dilation == 1 else 0,
|
| 51 |
-
dilation=dilation,
|
| 52 |
-
groups=groups,
|
| 53 |
-
bias_attr=False)
|
| 54 |
-
|
| 55 |
-
self._batch_norm = layers.SyncBatchNorm(out_channels)
|
| 56 |
-
self._act_op = layers.Activation(act=act)
|
| 57 |
-
|
| 58 |
-
def forward(self, inputs):
|
| 59 |
-
if self.is_vd_mode:
|
| 60 |
-
inputs = self._pool2d_avg(inputs)
|
| 61 |
-
y = self._conv(inputs)
|
| 62 |
-
y = self._batch_norm(y)
|
| 63 |
-
y = self._act_op(y)
|
| 64 |
-
|
| 65 |
-
return y
|
| 66 |
-
|
| 67 |
-
|
| 68 |
-
class BottleneckBlock(nn.Layer):
|
| 69 |
-
def __init__(self,
|
| 70 |
-
in_channels,
|
| 71 |
-
out_channels,
|
| 72 |
-
stride,
|
| 73 |
-
shortcut=True,
|
| 74 |
-
if_first=False,
|
| 75 |
-
dilation=1):
|
| 76 |
-
super(BottleneckBlock, self).__init__()
|
| 77 |
-
|
| 78 |
-
self.conv0 = ConvBNLayer(
|
| 79 |
-
in_channels=in_channels,
|
| 80 |
-
out_channels=out_channels,
|
| 81 |
-
kernel_size=1,
|
| 82 |
-
act='relu')
|
| 83 |
-
|
| 84 |
-
self.dilation = dilation
|
| 85 |
-
|
| 86 |
-
self.conv1 = ConvBNLayer(
|
| 87 |
-
in_channels=out_channels,
|
| 88 |
-
out_channels=out_channels,
|
| 89 |
-
kernel_size=3,
|
| 90 |
-
stride=stride,
|
| 91 |
-
act='relu',
|
| 92 |
-
dilation=dilation)
|
| 93 |
-
self.conv2 = ConvBNLayer(
|
| 94 |
-
in_channels=out_channels,
|
| 95 |
-
out_channels=out_channels * 4,
|
| 96 |
-
kernel_size=1,
|
| 97 |
-
act=None)
|
| 98 |
-
|
| 99 |
-
if not shortcut:
|
| 100 |
-
self.short = ConvBNLayer(
|
| 101 |
-
in_channels=in_channels,
|
| 102 |
-
out_channels=out_channels * 4,
|
| 103 |
-
kernel_size=1,
|
| 104 |
-
stride=1,
|
| 105 |
-
is_vd_mode=False if if_first or stride == 1 else True)
|
| 106 |
-
|
| 107 |
-
self.shortcut = shortcut
|
| 108 |
-
|
| 109 |
-
def forward(self, inputs):
|
| 110 |
-
y = self.conv0(inputs)
|
| 111 |
-
|
| 112 |
-
####################################################################
|
| 113 |
-
# If given dilation rate > 1, using corresponding padding.
|
| 114 |
-
# The performance drops down without the follow padding.
|
| 115 |
-
if self.dilation > 1:
|
| 116 |
-
padding = self.dilation
|
| 117 |
-
y = F.pad(y, [padding, padding, padding, padding])
|
| 118 |
-
#####################################################################
|
| 119 |
-
|
| 120 |
-
conv1 = self.conv1(y)
|
| 121 |
-
conv2 = self.conv2(conv1)
|
| 122 |
-
|
| 123 |
-
if self.shortcut:
|
| 124 |
-
short = inputs
|
| 125 |
-
else:
|
| 126 |
-
short = self.short(inputs)
|
| 127 |
-
|
| 128 |
-
y = paddle.add(x=short, y=conv2)
|
| 129 |
-
y = F.relu(y)
|
| 130 |
-
return y
|
| 131 |
-
|
| 132 |
-
|
| 133 |
-
class BasicBlock(nn.Layer):
|
| 134 |
-
def __init__(self,
|
| 135 |
-
in_channels,
|
| 136 |
-
out_channels,
|
| 137 |
-
stride,
|
| 138 |
-
shortcut=True,
|
| 139 |
-
if_first=False):
|
| 140 |
-
super(BasicBlock, self).__init__()
|
| 141 |
-
self.stride = stride
|
| 142 |
-
self.conv0 = ConvBNLayer(
|
| 143 |
-
in_channels=in_channels,
|
| 144 |
-
out_channels=out_channels,
|
| 145 |
-
kernel_size=3,
|
| 146 |
-
stride=stride,
|
| 147 |
-
act='relu')
|
| 148 |
-
self.conv1 = ConvBNLayer(
|
| 149 |
-
in_channels=out_channels,
|
| 150 |
-
out_channels=out_channels,
|
| 151 |
-
kernel_size=3,
|
| 152 |
-
act=None)
|
| 153 |
-
|
| 154 |
-
if not shortcut:
|
| 155 |
-
self.short = ConvBNLayer(
|
| 156 |
-
in_channels=in_channels,
|
| 157 |
-
out_channels=out_channels,
|
| 158 |
-
kernel_size=1,
|
| 159 |
-
stride=1,
|
| 160 |
-
is_vd_mode=False if if_first else True)
|
| 161 |
-
|
| 162 |
-
self.shortcut = shortcut
|
| 163 |
-
|
| 164 |
-
def forward(self, inputs):
|
| 165 |
-
y = self.conv0(inputs)
|
| 166 |
-
conv1 = self.conv1(y)
|
| 167 |
-
|
| 168 |
-
if self.shortcut:
|
| 169 |
-
short = inputs
|
| 170 |
-
else:
|
| 171 |
-
short = self.short(inputs)
|
| 172 |
-
y = paddle.add(x=short, y=conv1)
|
| 173 |
-
y = F.relu(y)
|
| 174 |
-
|
| 175 |
-
return y
|
| 176 |
-
|
| 177 |
-
|
| 178 |
-
class ResNet_vd(nn.Layer):
|
| 179 |
-
"""
|
| 180 |
-
The ResNet_vd implementation based on PaddlePaddle.
|
| 181 |
-
|
| 182 |
-
The original article refers to Jingdong
|
| 183 |
-
Tong He, et, al. "Bag of Tricks for Image Classification with Convolutional Neural Networks"
|
| 184 |
-
(https://arxiv.org/pdf/1812.01187.pdf).
|
| 185 |
-
|
| 186 |
-
Args:
|
| 187 |
-
layers (int, optional): The layers of ResNet_vd. The supported layers are (18, 34, 50, 101, 152, 200). Default: 50.
|
| 188 |
-
output_stride (int, optional): The stride of output features compared to input images. It is 8 or 16. Default: 8.
|
| 189 |
-
multi_grid (tuple|list, optional): The grid of stage4. Defult: (1, 1, 1).
|
| 190 |
-
pretrained (str, optional): The path of pretrained model.
|
| 191 |
-
|
| 192 |
-
"""
|
| 193 |
-
|
| 194 |
-
def __init__(self,
|
| 195 |
-
input_channels=3,
|
| 196 |
-
layers=50,
|
| 197 |
-
output_stride=32,
|
| 198 |
-
multi_grid=(1, 1, 1),
|
| 199 |
-
pretrained=None):
|
| 200 |
-
super(ResNet_vd, self).__init__()
|
| 201 |
-
|
| 202 |
-
self.conv1_logit = None # for gscnn shape stream
|
| 203 |
-
self.layers = layers
|
| 204 |
-
supported_layers = [18, 34, 50, 101, 152, 200]
|
| 205 |
-
assert layers in supported_layers, \
|
| 206 |
-
"supported layers are {} but input layer is {}".format(
|
| 207 |
-
supported_layers, layers)
|
| 208 |
-
|
| 209 |
-
if layers == 18:
|
| 210 |
-
depth = [2, 2, 2, 2]
|
| 211 |
-
elif layers == 34 or layers == 50:
|
| 212 |
-
depth = [3, 4, 6, 3]
|
| 213 |
-
elif layers == 101:
|
| 214 |
-
depth = [3, 4, 23, 3]
|
| 215 |
-
elif layers == 152:
|
| 216 |
-
depth = [3, 8, 36, 3]
|
| 217 |
-
elif layers == 200:
|
| 218 |
-
depth = [3, 12, 48, 3]
|
| 219 |
-
num_channels = [64, 256, 512, 1024
|
| 220 |
-
] if layers >= 50 else [64, 64, 128, 256]
|
| 221 |
-
num_filters = [64, 128, 256, 512]
|
| 222 |
-
|
| 223 |
-
# for channels of four returned stages
|
| 224 |
-
self.feat_channels = [c * 4 for c in num_filters
|
| 225 |
-
] if layers >= 50 else num_filters
|
| 226 |
-
self.feat_channels = [64] + self.feat_channels
|
| 227 |
-
|
| 228 |
-
dilation_dict = None
|
| 229 |
-
if output_stride == 8:
|
| 230 |
-
dilation_dict = {2: 2, 3: 4}
|
| 231 |
-
elif output_stride == 16:
|
| 232 |
-
dilation_dict = {3: 2}
|
| 233 |
-
|
| 234 |
-
self.conv1_1 = ConvBNLayer(
|
| 235 |
-
in_channels=input_channels,
|
| 236 |
-
out_channels=32,
|
| 237 |
-
kernel_size=3,
|
| 238 |
-
stride=2,
|
| 239 |
-
act='relu')
|
| 240 |
-
self.conv1_2 = ConvBNLayer(
|
| 241 |
-
in_channels=32,
|
| 242 |
-
out_channels=32,
|
| 243 |
-
kernel_size=3,
|
| 244 |
-
stride=1,
|
| 245 |
-
act='relu')
|
| 246 |
-
self.conv1_3 = ConvBNLayer(
|
| 247 |
-
in_channels=32,
|
| 248 |
-
out_channels=64,
|
| 249 |
-
kernel_size=3,
|
| 250 |
-
stride=1,
|
| 251 |
-
act='relu')
|
| 252 |
-
self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
|
| 253 |
-
|
| 254 |
-
# self.block_list = []
|
| 255 |
-
self.stage_list = []
|
| 256 |
-
if layers >= 50:
|
| 257 |
-
for block in range(len(depth)):
|
| 258 |
-
shortcut = False
|
| 259 |
-
block_list = []
|
| 260 |
-
for i in range(depth[block]):
|
| 261 |
-
if layers in [101, 152] and block == 2:
|
| 262 |
-
if i == 0:
|
| 263 |
-
conv_name = "res" + str(block + 2) + "a"
|
| 264 |
-
else:
|
| 265 |
-
conv_name = "res" + str(block + 2) + "b" + str(i)
|
| 266 |
-
else:
|
| 267 |
-
conv_name = "res" + str(block + 2) + chr(97 + i)
|
| 268 |
-
|
| 269 |
-
###############################################################################
|
| 270 |
-
# Add dilation rate for some segmentation tasks, if dilation_dict is not None.
|
| 271 |
-
dilation_rate = dilation_dict[
|
| 272 |
-
block] if dilation_dict and block in dilation_dict else 1
|
| 273 |
-
|
| 274 |
-
# Actually block here is 'stage', and i is 'block' in 'stage'
|
| 275 |
-
# At the stage 4, expand the the dilation_rate if given multi_grid
|
| 276 |
-
if block == 3:
|
| 277 |
-
dilation_rate = dilation_rate * multi_grid[i]
|
| 278 |
-
###############################################################################
|
| 279 |
-
|
| 280 |
-
bottleneck_block = self.add_sublayer(
|
| 281 |
-
'bb_%d_%d' % (block, i),
|
| 282 |
-
BottleneckBlock(
|
| 283 |
-
in_channels=num_channels[block]
|
| 284 |
-
if i == 0 else num_filters[block] * 4,
|
| 285 |
-
out_channels=num_filters[block],
|
| 286 |
-
stride=2 if i == 0 and block != 0
|
| 287 |
-
and dilation_rate == 1 else 1,
|
| 288 |
-
shortcut=shortcut,
|
| 289 |
-
if_first=block == i == 0,
|
| 290 |
-
dilation=dilation_rate))
|
| 291 |
-
|
| 292 |
-
block_list.append(bottleneck_block)
|
| 293 |
-
shortcut = True
|
| 294 |
-
self.stage_list.append(block_list)
|
| 295 |
-
else:
|
| 296 |
-
for block in range(len(depth)):
|
| 297 |
-
shortcut = False
|
| 298 |
-
block_list = []
|
| 299 |
-
for i in range(depth[block]):
|
| 300 |
-
conv_name = "res" + str(block + 2) + chr(97 + i)
|
| 301 |
-
basic_block = self.add_sublayer(
|
| 302 |
-
'bb_%d_%d' % (block, i),
|
| 303 |
-
BasicBlock(
|
| 304 |
-
in_channels=num_channels[block]
|
| 305 |
-
if i == 0 else num_filters[block],
|
| 306 |
-
out_channels=num_filters[block],
|
| 307 |
-
stride=2 if i == 0 and block != 0 else 1,
|
| 308 |
-
shortcut=shortcut,
|
| 309 |
-
if_first=block == i == 0))
|
| 310 |
-
block_list.append(basic_block)
|
| 311 |
-
shortcut = True
|
| 312 |
-
self.stage_list.append(block_list)
|
| 313 |
-
|
| 314 |
-
self.pretrained = pretrained
|
| 315 |
-
self.init_weight()
|
| 316 |
-
|
| 317 |
-
def forward(self, inputs):
|
| 318 |
-
feat_list = []
|
| 319 |
-
y = self.conv1_1(inputs)
|
| 320 |
-
y = self.conv1_2(y)
|
| 321 |
-
y = self.conv1_3(y)
|
| 322 |
-
feat_list.append(y)
|
| 323 |
-
|
| 324 |
-
y = self.pool2d_max(y)
|
| 325 |
-
|
| 326 |
-
# A feature list saves the output feature map of each stage.
|
| 327 |
-
for stage in self.stage_list:
|
| 328 |
-
for block in stage:
|
| 329 |
-
y = block(y)
|
| 330 |
-
feat_list.append(y)
|
| 331 |
-
|
| 332 |
-
return feat_list
|
| 333 |
-
|
| 334 |
-
def init_weight(self):
|
| 335 |
-
utils.load_pretrained_model(self, self.pretrained)
|
| 336 |
-
|
| 337 |
-
|
| 338 |
-
@manager.BACKBONES.add_component
|
| 339 |
-
def ResNet18_vd(**args):
|
| 340 |
-
model = ResNet_vd(layers=18, **args)
|
| 341 |
-
return model
|
| 342 |
-
|
| 343 |
-
|
| 344 |
-
def ResNet34_vd(**args):
|
| 345 |
-
model = ResNet_vd(layers=34, **args)
|
| 346 |
-
return model
|
| 347 |
-
|
| 348 |
-
|
| 349 |
-
@manager.BACKBONES.add_component
|
| 350 |
-
def ResNet50_vd(**args):
|
| 351 |
-
model = ResNet_vd(layers=50, **args)
|
| 352 |
-
return model
|
| 353 |
-
|
| 354 |
-
|
| 355 |
-
@manager.BACKBONES.add_component
|
| 356 |
-
def ResNet101_vd(**args):
|
| 357 |
-
model = ResNet_vd(layers=101, **args)
|
| 358 |
-
return model
|
| 359 |
-
|
| 360 |
-
|
| 361 |
-
def ResNet152_vd(**args):
|
| 362 |
-
model = ResNet_vd(layers=152, **args)
|
| 363 |
-
return model
|
| 364 |
-
|
| 365 |
-
|
| 366 |
-
def ResNet200_vd(**args):
|
| 367 |
-
model = ResNet_vd(layers=200, **args)
|
| 368 |
-
return model
|
|
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spaces/Detomo/ai-comic-generation/src/components/ui/switch.tsx
DELETED
|
@@ -1,29 +0,0 @@
|
|
| 1 |
-
"use client"
|
| 2 |
-
|
| 3 |
-
import * as React from "react"
|
| 4 |
-
import * as SwitchPrimitives from "@radix-ui/react-switch"
|
| 5 |
-
|
| 6 |
-
import { cn } from "@/lib/utils"
|
| 7 |
-
|
| 8 |
-
const Switch = React.forwardRef<
|
| 9 |
-
React.ElementRef<typeof SwitchPrimitives.Root>,
|
| 10 |
-
React.ComponentPropsWithoutRef<typeof SwitchPrimitives.Root>
|
| 11 |
-
>(({ className, ...props }, ref) => (
|
| 12 |
-
<SwitchPrimitives.Root
|
| 13 |
-
className={cn(
|
| 14 |
-
"peer inline-flex h-[24px] w-[44px] shrink-0 cursor-pointer items-center rounded-full border-2 border-transparent transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-stone-400 focus-visible:ring-offset-2 focus-visible:ring-offset-white disabled:cursor-not-allowed disabled:opacity-50 data-[state=checked]:bg-stone-900 data-[state=unchecked]:bg-stone-200 dark:focus-visible:ring-stone-800 dark:focus-visible:ring-offset-stone-950 dark:data-[state=checked]:bg-stone-50 dark:data-[state=unchecked]:bg-stone-800",
|
| 15 |
-
className
|
| 16 |
-
)}
|
| 17 |
-
{...props}
|
| 18 |
-
ref={ref}
|
| 19 |
-
>
|
| 20 |
-
<SwitchPrimitives.Thumb
|
| 21 |
-
className={cn(
|
| 22 |
-
"pointer-events-none block h-5 w-5 rounded-full bg-white shadow-lg ring-0 transition-transform data-[state=checked]:translate-x-5 data-[state=unchecked]:translate-x-0 dark:bg-stone-950"
|
| 23 |
-
)}
|
| 24 |
-
/>
|
| 25 |
-
</SwitchPrimitives.Root>
|
| 26 |
-
))
|
| 27 |
-
Switch.displayName = SwitchPrimitives.Root.displayName
|
| 28 |
-
|
| 29 |
-
export { Switch }
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|
spaces/Dinoking/Guccio-AI-Designer/netdissect/statedict.py
DELETED
|
@@ -1,100 +0,0 @@
|
|
| 1 |
-
'''
|
| 2 |
-
Utilities for dealing with simple state dicts as npz files instead of pth files.
|
| 3 |
-
'''
|
| 4 |
-
|
| 5 |
-
import torch
|
| 6 |
-
from collections.abc import MutableMapping, Mapping
|
| 7 |
-
|
| 8 |
-
def load_from_numpy_dict(model, numpy_dict, prefix='', examples=None):
|
| 9 |
-
'''
|
| 10 |
-
Loads a model from numpy_dict using load_state_dict.
|
| 11 |
-
Converts numpy types to torch types using the current state_dict
|
| 12 |
-
of the model to determine types and devices for the tensors.
|
| 13 |
-
Supports loading a subdict by prepending the given prefix to all keys.
|
| 14 |
-
'''
|
| 15 |
-
if prefix:
|
| 16 |
-
if not prefix.endswith('.'):
|
| 17 |
-
prefix = prefix + '.'
|
| 18 |
-
numpy_dict = PrefixSubDict(numpy_dict, prefix)
|
| 19 |
-
if examples is None:
|
| 20 |
-
exampels = model.state_dict()
|
| 21 |
-
torch_state_dict = TorchTypeMatchingDict(numpy_dict, examples)
|
| 22 |
-
model.load_state_dict(torch_state_dict)
|
| 23 |
-
|
| 24 |
-
def save_to_numpy_dict(model, numpy_dict, prefix=''):
|
| 25 |
-
'''
|
| 26 |
-
Saves a model by copying tensors to numpy_dict.
|
| 27 |
-
Converts torch types to numpy types using `t.detach().cpu().numpy()`.
|
| 28 |
-
Supports saving a subdict by prepending the given prefix to all keys.
|
| 29 |
-
'''
|
| 30 |
-
if prefix:
|
| 31 |
-
if not prefix.endswith('.'):
|
| 32 |
-
prefix = prefix + '.'
|
| 33 |
-
for k, v in model.numpy_dict().items():
|
| 34 |
-
if isinstance(v, torch.Tensor):
|
| 35 |
-
v = v.detach().cpu().numpy()
|
| 36 |
-
numpy_dict[prefix + k] = v
|
| 37 |
-
|
| 38 |
-
class TorchTypeMatchingDict(Mapping):
|
| 39 |
-
'''
|
| 40 |
-
Provides a view of a dict of numpy values as torch tensors, where the
|
| 41 |
-
types are converted to match the types and devices in the given
|
| 42 |
-
dict of examples.
|
| 43 |
-
'''
|
| 44 |
-
def __init__(self, data, examples):
|
| 45 |
-
self.data = data
|
| 46 |
-
self.examples = examples
|
| 47 |
-
self.cached_data = {}
|
| 48 |
-
def __getitem__(self, key):
|
| 49 |
-
if key in self.cached_data:
|
| 50 |
-
return self.cached_data[key]
|
| 51 |
-
val = self.data[key]
|
| 52 |
-
if key not in self.examples:
|
| 53 |
-
return val
|
| 54 |
-
example = self.examples.get(key, None)
|
| 55 |
-
example_type = type(example)
|
| 56 |
-
if example is not None and type(val) != example_type:
|
| 57 |
-
if isinstance(example, torch.Tensor):
|
| 58 |
-
val = torch.from_numpy(val)
|
| 59 |
-
else:
|
| 60 |
-
val = example_type(val)
|
| 61 |
-
if isinstance(example, torch.Tensor):
|
| 62 |
-
val = val.to(dtype=example.dtype, device=example.device)
|
| 63 |
-
self.cached_data[key] = val
|
| 64 |
-
return val
|
| 65 |
-
def __iter__(self):
|
| 66 |
-
return self.data.keys()
|
| 67 |
-
def __len__(self):
|
| 68 |
-
return len(self.data)
|
| 69 |
-
|
| 70 |
-
class PrefixSubDict(MutableMapping):
|
| 71 |
-
'''
|
| 72 |
-
Provides a view of the subset of a dict where string keys begin with
|
| 73 |
-
the given prefix. The prefix is stripped from all keys of the view.
|
| 74 |
-
'''
|
| 75 |
-
def __init__(self, data, prefix=''):
|
| 76 |
-
self.data = data
|
| 77 |
-
self.prefix = prefix
|
| 78 |
-
self._cached_keys = None
|
| 79 |
-
def __getitem__(self, key):
|
| 80 |
-
return self.data[self.prefix + key]
|
| 81 |
-
def __setitem__(self, key, value):
|
| 82 |
-
pkey = self.prefix + key
|
| 83 |
-
if self._cached_keys is not None and pkey not in self.data:
|
| 84 |
-
self._cached_keys = None
|
| 85 |
-
self.data[pkey] = value
|
| 86 |
-
def __delitem__(self, key):
|
| 87 |
-
pkey = self.prefix + key
|
| 88 |
-
if self._cached_keys is not None and pkey in self.data:
|
| 89 |
-
self._cached_keys = None
|
| 90 |
-
del self.data[pkey]
|
| 91 |
-
def __cached_keys(self):
|
| 92 |
-
if self._cached_keys is None:
|
| 93 |
-
plen = len(self.prefix)
|
| 94 |
-
self._cached_keys = list(k[plen:] for k in self.data
|
| 95 |
-
if k.startswith(self.prefix))
|
| 96 |
-
return self._cached_keys
|
| 97 |
-
def __iter__(self):
|
| 98 |
-
return iter(self.__cached_keys())
|
| 99 |
-
def __len__(self):
|
| 100 |
-
return len(self.__cached_keys())
|
|
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|
spaces/Djacon/emotion_detection/files/js/main.js
DELETED
|
The diff for this file is too large to render.
See raw diff
|
|
|
spaces/DragGan/DragGan-Inversion/stylegan_human/training/training_loop.py
DELETED
|
@@ -1,499 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
| 2 |
-
#
|
| 3 |
-
# NVIDIA CORPORATION and its licensors retain all intellectual property
|
| 4 |
-
# and proprietary rights in and to this software, related documentation
|
| 5 |
-
# and any modifications thereto. Any use, reproduction, disclosure or
|
| 6 |
-
# distribution of this software and related documentation without an express
|
| 7 |
-
# license agreement from NVIDIA CORPORATION is strictly prohibited.
|
| 8 |
-
|
| 9 |
-
"""Main training loop."""
|
| 10 |
-
|
| 11 |
-
import os
|
| 12 |
-
import time
|
| 13 |
-
import copy
|
| 14 |
-
import json
|
| 15 |
-
import pickle
|
| 16 |
-
import psutil
|
| 17 |
-
import PIL.Image
|
| 18 |
-
import numpy as np
|
| 19 |
-
import torch
|
| 20 |
-
import dnnlib
|
| 21 |
-
from torch_utils import misc
|
| 22 |
-
from torch_utils import training_stats
|
| 23 |
-
from torch_utils.ops import conv2d_gradfix
|
| 24 |
-
from torch_utils.ops import grid_sample_gradfix
|
| 25 |
-
|
| 26 |
-
import legacy
|
| 27 |
-
from metrics import metric_main
|
| 28 |
-
|
| 29 |
-
# ----------------------------------------------------------------------------
|
| 30 |
-
|
| 31 |
-
|
| 32 |
-
def setup_snapshot_image_grid(training_set, random_seed=0):
|
| 33 |
-
rnd = np.random.RandomState(random_seed)
|
| 34 |
-
gw = np.clip(7680 // training_set.image_shape[2], 7, 32)
|
| 35 |
-
gh = np.clip(4320 // training_set.image_shape[1], 4, 32)
|
| 36 |
-
|
| 37 |
-
# No labels => show random subset of training samples.
|
| 38 |
-
if not training_set.has_labels:
|
| 39 |
-
all_indices = list(range(len(training_set)))
|
| 40 |
-
rnd.shuffle(all_indices)
|
| 41 |
-
grid_indices = [all_indices[i %
|
| 42 |
-
len(all_indices)] for i in range(gw * gh)]
|
| 43 |
-
|
| 44 |
-
else:
|
| 45 |
-
# Group training samples by label.
|
| 46 |
-
label_groups = dict() # label => [idx, ...]
|
| 47 |
-
for idx in range(len(training_set)):
|
| 48 |
-
label = tuple(training_set.get_details(idx).raw_label.flat[::-1])
|
| 49 |
-
if label not in label_groups:
|
| 50 |
-
label_groups[label] = []
|
| 51 |
-
label_groups[label].append(idx)
|
| 52 |
-
|
| 53 |
-
# Reorder.
|
| 54 |
-
label_order = sorted(label_groups.keys())
|
| 55 |
-
for label in label_order:
|
| 56 |
-
rnd.shuffle(label_groups[label])
|
| 57 |
-
|
| 58 |
-
# Organize into grid.
|
| 59 |
-
grid_indices = []
|
| 60 |
-
for y in range(gh):
|
| 61 |
-
label = label_order[y % len(label_order)]
|
| 62 |
-
indices = label_groups[label]
|
| 63 |
-
grid_indices += [indices[x % len(indices)] for x in range(gw)]
|
| 64 |
-
label_groups[label] = [
|
| 65 |
-
indices[(i + gw) % len(indices)] for i in range(len(indices))]
|
| 66 |
-
|
| 67 |
-
# Load data.
|
| 68 |
-
images, labels = zip(*[training_set[i] for i in grid_indices])
|
| 69 |
-
return (gw, gh), np.stack(images), np.stack(labels)
|
| 70 |
-
|
| 71 |
-
# ----------------------------------------------------------------------------
|
| 72 |
-
|
| 73 |
-
|
| 74 |
-
def save_image_grid(img, fname, drange, grid_size):
|
| 75 |
-
lo, hi = drange
|
| 76 |
-
img = np.asarray(img, dtype=np.float32)
|
| 77 |
-
img = (img - lo) * (255 / (hi - lo))
|
| 78 |
-
img = np.rint(img).clip(0, 255).astype(np.uint8)
|
| 79 |
-
|
| 80 |
-
gw, gh = grid_size
|
| 81 |
-
_N, C, H, W = img.shape
|
| 82 |
-
img = img.reshape([gh, gw, C, H, W])
|
| 83 |
-
img = img.transpose(0, 3, 1, 4, 2)
|
| 84 |
-
img = img.reshape([gh * H, gw * W, C])
|
| 85 |
-
|
| 86 |
-
assert C in [1, 3]
|
| 87 |
-
if C == 1:
|
| 88 |
-
PIL.Image.fromarray(img[:, :, 0], 'L').save(fname)
|
| 89 |
-
if C == 3:
|
| 90 |
-
PIL.Image.fromarray(img, 'RGB').save(fname)
|
| 91 |
-
|
| 92 |
-
# ----------------------------------------------------------------------------
|
| 93 |
-
|
| 94 |
-
|
| 95 |
-
def training_loop(
|
| 96 |
-
run_dir='.', # Output directory.
|
| 97 |
-
training_set_kwargs={}, # Options for training set.
|
| 98 |
-
data_loader_kwargs={}, # Options for torch.utils.data.DataLoader.
|
| 99 |
-
G_kwargs={}, # Options for generator network.
|
| 100 |
-
D_kwargs={}, # Options for discriminator network.
|
| 101 |
-
G_opt_kwargs={}, # Options for generator optimizer.
|
| 102 |
-
D_opt_kwargs={}, # Options for discriminator optimizer.
|
| 103 |
-
# Options for augmentation pipeline. None = disable.
|
| 104 |
-
augment_kwargs=None,
|
| 105 |
-
loss_kwargs={}, # Options for loss function.
|
| 106 |
-
metrics=[], # Metrics to evaluate during training.
|
| 107 |
-
random_seed=0, # Global random seed.
|
| 108 |
-
num_gpus=1, # Number of GPUs participating in the training.
|
| 109 |
-
rank=0, # Rank of the current process in [0, num_gpus[.
|
| 110 |
-
# Total batch size for one training iteration. Can be larger than batch_gpu * num_gpus.
|
| 111 |
-
batch_size=4,
|
| 112 |
-
batch_gpu=4, # Number of samples processed at a time by one GPU.
|
| 113 |
-
# Half-life of the exponential moving average (EMA) of generator weights.
|
| 114 |
-
ema_kimg=10,
|
| 115 |
-
ema_rampup=0.05, # EMA ramp-up coefficient. None = no rampup.
|
| 116 |
-
# How often to perform regularization for G? None = disable lazy regularization.
|
| 117 |
-
G_reg_interval=None,
|
| 118 |
-
# How often to perform regularization for D? None = disable lazy regularization.
|
| 119 |
-
D_reg_interval=16,
|
| 120 |
-
augment_p=0, # Initial value of augmentation probability.
|
| 121 |
-
ada_target=None, # ADA target value. None = fixed p.
|
| 122 |
-
ada_interval=4, # How often to perform ADA adjustment?
|
| 123 |
-
# ADA adjustment speed, measured in how many kimg it takes for p to increase/decrease by one unit.
|
| 124 |
-
ada_kimg=500,
|
| 125 |
-
# Total length of the training, measured in thousands of real images.
|
| 126 |
-
total_kimg=25000,
|
| 127 |
-
kimg_per_tick=4, # Progress snapshot interval.
|
| 128 |
-
# How often to save image snapshots? None = disable.
|
| 129 |
-
image_snapshot_ticks=50,
|
| 130 |
-
# How often to save network snapshots? None = disable.
|
| 131 |
-
network_snapshot_ticks=50,
|
| 132 |
-
resume_pkl=None, # Network pickle to resume training from.
|
| 133 |
-
resume_kimg=0, # First kimg to report when resuming training.
|
| 134 |
-
cudnn_benchmark=True, # Enable torch.backends.cudnn.benchmark?
|
| 135 |
-
# Callback function for determining whether to abort training. Must return consistent results across ranks.
|
| 136 |
-
abort_fn=None,
|
| 137 |
-
# Callback function for updating training progress. Called for all ranks.
|
| 138 |
-
progress_fn=None,
|
| 139 |
-
):
|
| 140 |
-
# Initialize.
|
| 141 |
-
start_time = time.time()
|
| 142 |
-
device = torch.device('cuda', rank)
|
| 143 |
-
np.random.seed(random_seed * num_gpus + rank)
|
| 144 |
-
torch.manual_seed(random_seed * num_gpus + rank)
|
| 145 |
-
# Improves training speed.
|
| 146 |
-
torch.backends.cudnn.benchmark = cudnn_benchmark
|
| 147 |
-
# Improves numerical accuracy.
|
| 148 |
-
torch.backends.cuda.matmul.allow_tf32 = False
|
| 149 |
-
# Improves numerical accuracy.
|
| 150 |
-
torch.backends.cudnn.allow_tf32 = False
|
| 151 |
-
# Improves training speed.
|
| 152 |
-
conv2d_gradfix.enabled = True
|
| 153 |
-
# Avoids errors with the augmentation pipe.
|
| 154 |
-
grid_sample_gradfix.enabled = True
|
| 155 |
-
|
| 156 |
-
# Load training set.
|
| 157 |
-
if rank == 0:
|
| 158 |
-
print('Loading training set...')
|
| 159 |
-
training_set = dnnlib.util.construct_class_by_name(
|
| 160 |
-
**training_set_kwargs) # subclass of training.dataset.Dataset
|
| 161 |
-
training_set_sampler = misc.InfiniteSampler(
|
| 162 |
-
dataset=training_set, rank=rank, num_replicas=num_gpus, seed=random_seed)
|
| 163 |
-
training_set_iterator = iter(torch.utils.data.DataLoader(
|
| 164 |
-
dataset=training_set, sampler=training_set_sampler, batch_size=batch_size//num_gpus, **data_loader_kwargs))
|
| 165 |
-
if rank == 0:
|
| 166 |
-
print()
|
| 167 |
-
print('Num images: ', len(training_set))
|
| 168 |
-
print('Image shape:', training_set.image_shape)
|
| 169 |
-
print('Label shape:', training_set.label_shape)
|
| 170 |
-
print()
|
| 171 |
-
|
| 172 |
-
# Construct networks.
|
| 173 |
-
if rank == 0:
|
| 174 |
-
print('Constructing networks...')
|
| 175 |
-
common_kwargs = dict(c_dim=training_set.label_dim,
|
| 176 |
-
img_resolution=training_set.resolution, img_channels=training_set.num_channels)
|
| 177 |
-
G = dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs).train(
|
| 178 |
-
).requires_grad_(False).to(device) # subclass of torch.nn.Module
|
| 179 |
-
D = dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs).train(
|
| 180 |
-
).requires_grad_(False).to(device) # subclass of torch.nn.Module
|
| 181 |
-
G_ema = copy.deepcopy(G).eval()
|
| 182 |
-
|
| 183 |
-
# Resume from existing pickle.
|
| 184 |
-
if (resume_pkl is not None) and (rank == 0):
|
| 185 |
-
print(f'Resuming from "{resume_pkl}"')
|
| 186 |
-
with dnnlib.util.open_url(resume_pkl) as f:
|
| 187 |
-
resume_data = legacy.load_network_pkl(f)
|
| 188 |
-
for name, module in [('G', G), ('D', D), ('G_ema', G_ema)]:
|
| 189 |
-
misc.copy_params_and_buffers(
|
| 190 |
-
resume_data[name], module, require_all=False)
|
| 191 |
-
|
| 192 |
-
# Print network summary tables.
|
| 193 |
-
if rank == 0:
|
| 194 |
-
z = torch.empty([batch_gpu, G.z_dim], device=device)
|
| 195 |
-
c = torch.empty([batch_gpu, G.c_dim], device=device)
|
| 196 |
-
img = misc.print_module_summary(G, [z, c])
|
| 197 |
-
misc.print_module_summary(D, [img, c])
|
| 198 |
-
|
| 199 |
-
# Setup augmentation.
|
| 200 |
-
if rank == 0:
|
| 201 |
-
print('Setting up augmentation...')
|
| 202 |
-
augment_pipe = None
|
| 203 |
-
ada_stats = None
|
| 204 |
-
if (augment_kwargs is not None) and (augment_p > 0 or ada_target is not None):
|
| 205 |
-
augment_pipe = dnnlib.util.construct_class_by_name(
|
| 206 |
-
**augment_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module
|
| 207 |
-
augment_pipe.p.copy_(torch.as_tensor(augment_p))
|
| 208 |
-
if ada_target is not None:
|
| 209 |
-
ada_stats = training_stats.Collector(regex='Loss/signs/real')
|
| 210 |
-
|
| 211 |
-
# Distribute across GPUs.
|
| 212 |
-
if rank == 0:
|
| 213 |
-
print(f'Distributing across {num_gpus} GPUs...')
|
| 214 |
-
for module in [G, D, G_ema, augment_pipe]:
|
| 215 |
-
if module is not None and num_gpus > 1:
|
| 216 |
-
for param in misc.params_and_buffers(module):
|
| 217 |
-
torch.distributed.broadcast(param, src=0)
|
| 218 |
-
|
| 219 |
-
# Setup training phases.
|
| 220 |
-
if rank == 0:
|
| 221 |
-
print('Setting up training phases...')
|
| 222 |
-
loss = dnnlib.util.construct_class_by_name(
|
| 223 |
-
device=device, G=G, D=D, augment_pipe=augment_pipe, **loss_kwargs) # subclass of training.loss.Loss
|
| 224 |
-
phases = []
|
| 225 |
-
for name, module, opt_kwargs, reg_interval in [('G', G, G_opt_kwargs, G_reg_interval), ('D', D, D_opt_kwargs, D_reg_interval)]:
|
| 226 |
-
if reg_interval is None:
|
| 227 |
-
opt = dnnlib.util.construct_class_by_name(
|
| 228 |
-
params=module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer
|
| 229 |
-
phases += [dnnlib.EasyDict(name=name+'both',
|
| 230 |
-
module=module, opt=opt, interval=1)]
|
| 231 |
-
else: # Lazy regularization.
|
| 232 |
-
mb_ratio = reg_interval / (reg_interval + 1)
|
| 233 |
-
opt_kwargs = dnnlib.EasyDict(opt_kwargs)
|
| 234 |
-
opt_kwargs.lr = opt_kwargs.lr * mb_ratio
|
| 235 |
-
opt_kwargs.betas = [beta ** mb_ratio for beta in opt_kwargs.betas]
|
| 236 |
-
opt = dnnlib.util.construct_class_by_name(
|
| 237 |
-
module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer
|
| 238 |
-
phases += [dnnlib.EasyDict(name=name+'main',
|
| 239 |
-
module=module, opt=opt, interval=1)]
|
| 240 |
-
phases += [dnnlib.EasyDict(name=name+'reg',
|
| 241 |
-
module=module, opt=opt, interval=reg_interval)]
|
| 242 |
-
for phase in phases:
|
| 243 |
-
phase.start_event = None
|
| 244 |
-
phase.end_event = None
|
| 245 |
-
if rank == 0:
|
| 246 |
-
phase.start_event = torch.cuda.Event(enable_timing=True)
|
| 247 |
-
phase.end_event = torch.cuda.Event(enable_timing=True)
|
| 248 |
-
|
| 249 |
-
# Export sample images.
|
| 250 |
-
grid_size = None
|
| 251 |
-
grid_z = None
|
| 252 |
-
grid_c = None
|
| 253 |
-
if rank == 0:
|
| 254 |
-
print('Exporting sample images...')
|
| 255 |
-
grid_size, images, labels = setup_snapshot_image_grid(
|
| 256 |
-
training_set=training_set)
|
| 257 |
-
save_image_grid(images, os.path.join(run_dir, 'reals.png'),
|
| 258 |
-
drange=[0, 255], grid_size=grid_size)
|
| 259 |
-
grid_z = torch.randn([labels.shape[0], G.z_dim],
|
| 260 |
-
device=device).split(batch_gpu)
|
| 261 |
-
grid_c = torch.from_numpy(labels).to(device).split(batch_gpu)
|
| 262 |
-
images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu()
|
| 263 |
-
for z, c in zip(grid_z, grid_c)]).numpy()
|
| 264 |
-
save_image_grid(images, os.path.join(
|
| 265 |
-
run_dir, 'fakes_init.png'), drange=[-1, 1], grid_size=grid_size)
|
| 266 |
-
|
| 267 |
-
# Initialize logs.
|
| 268 |
-
if rank == 0:
|
| 269 |
-
print('Initializing logs...')
|
| 270 |
-
stats_collector = training_stats.Collector(regex='.*')
|
| 271 |
-
stats_metrics = dict()
|
| 272 |
-
stats_jsonl = None
|
| 273 |
-
stats_tfevents = None
|
| 274 |
-
if rank == 0:
|
| 275 |
-
stats_jsonl = open(os.path.join(run_dir, 'stats.jsonl'), 'wt')
|
| 276 |
-
try:
|
| 277 |
-
import torch.utils.tensorboard as tensorboard
|
| 278 |
-
stats_tfevents = tensorboard.SummaryWriter(run_dir)
|
| 279 |
-
except ImportError as err:
|
| 280 |
-
print('Skipping tfevents export:', err)
|
| 281 |
-
|
| 282 |
-
# Train.
|
| 283 |
-
if rank == 0:
|
| 284 |
-
print(f'Training for {total_kimg} kimg...')
|
| 285 |
-
print()
|
| 286 |
-
cur_nimg = resume_kimg * 1000
|
| 287 |
-
cur_tick = 0
|
| 288 |
-
tick_start_nimg = cur_nimg
|
| 289 |
-
tick_start_time = time.time()
|
| 290 |
-
maintenance_time = tick_start_time - start_time
|
| 291 |
-
batch_idx = 0
|
| 292 |
-
if progress_fn is not None:
|
| 293 |
-
progress_fn(0, total_kimg)
|
| 294 |
-
while True:
|
| 295 |
-
|
| 296 |
-
# Fetch training data.
|
| 297 |
-
with torch.autograd.profiler.record_function('data_fetch'):
|
| 298 |
-
phase_real_img, phase_real_c = next(training_set_iterator)
|
| 299 |
-
phase_real_img = (phase_real_img.to(device).to(
|
| 300 |
-
torch.float32) / 127.5 - 1).split(batch_gpu)
|
| 301 |
-
phase_real_c = phase_real_c.to(device).split(batch_gpu)
|
| 302 |
-
all_gen_z = torch.randn(
|
| 303 |
-
[len(phases) * batch_size, G.z_dim], device=device)
|
| 304 |
-
all_gen_z = [phase_gen_z.split(
|
| 305 |
-
batch_gpu) for phase_gen_z in all_gen_z.split(batch_size)]
|
| 306 |
-
all_gen_c = [training_set.get_label(np.random.randint(
|
| 307 |
-
len(training_set))) for _ in range(len(phases) * batch_size)]
|
| 308 |
-
all_gen_c = torch.from_numpy(
|
| 309 |
-
np.stack(all_gen_c)).pin_memory().to(device)
|
| 310 |
-
all_gen_c = [phase_gen_c.split(
|
| 311 |
-
batch_gpu) for phase_gen_c in all_gen_c.split(batch_size)]
|
| 312 |
-
|
| 313 |
-
# Execute training phases.
|
| 314 |
-
for phase, phase_gen_z, phase_gen_c in zip(phases, all_gen_z, all_gen_c):
|
| 315 |
-
if batch_idx % phase.interval != 0:
|
| 316 |
-
continue
|
| 317 |
-
if phase.start_event is not None:
|
| 318 |
-
phase.start_event.record(torch.cuda.current_stream(device))
|
| 319 |
-
|
| 320 |
-
# Accumulate gradients.
|
| 321 |
-
phase.opt.zero_grad(set_to_none=True)
|
| 322 |
-
phase.module.requires_grad_(True)
|
| 323 |
-
for real_img, real_c, gen_z, gen_c in zip(phase_real_img, phase_real_c, phase_gen_z, phase_gen_c):
|
| 324 |
-
loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c,
|
| 325 |
-
gen_z=gen_z, gen_c=gen_c, gain=phase.interval, cur_nimg=cur_nimg)
|
| 326 |
-
phase.module.requires_grad_(False)
|
| 327 |
-
|
| 328 |
-
# Update weights.
|
| 329 |
-
with torch.autograd.profiler.record_function(phase.name + '_opt'):
|
| 330 |
-
params = [param for param in phase.module.parameters()
|
| 331 |
-
if param.grad is not None]
|
| 332 |
-
if len(params) > 0:
|
| 333 |
-
flat = torch.cat([param.grad.flatten()
|
| 334 |
-
for param in params])
|
| 335 |
-
if num_gpus > 1:
|
| 336 |
-
torch.distributed.all_reduce(flat)
|
| 337 |
-
flat /= num_gpus
|
| 338 |
-
misc.nan_to_num(flat, nan=0, posinf=1e5,
|
| 339 |
-
neginf=-1e5, out=flat)
|
| 340 |
-
grads = flat.split([param.numel() for param in params])
|
| 341 |
-
for param, grad in zip(params, grads):
|
| 342 |
-
param.grad = grad.reshape(param.shape)
|
| 343 |
-
phase.opt.step()
|
| 344 |
-
|
| 345 |
-
# Phase done.
|
| 346 |
-
if phase.end_event is not None:
|
| 347 |
-
phase.end_event.record(torch.cuda.current_stream(device))
|
| 348 |
-
|
| 349 |
-
# Update G_ema.
|
| 350 |
-
with torch.autograd.profiler.record_function('Gema'):
|
| 351 |
-
ema_nimg = ema_kimg * 1000
|
| 352 |
-
if ema_rampup is not None:
|
| 353 |
-
ema_nimg = min(ema_nimg, cur_nimg * ema_rampup)
|
| 354 |
-
ema_beta = 0.5 ** (batch_size / max(ema_nimg, 1e-8))
|
| 355 |
-
for p_ema, p in zip(G_ema.parameters(), G.parameters()):
|
| 356 |
-
p_ema.copy_(p.lerp(p_ema, ema_beta))
|
| 357 |
-
for b_ema, b in zip(G_ema.buffers(), G.buffers()):
|
| 358 |
-
b_ema.copy_(b)
|
| 359 |
-
|
| 360 |
-
# Update state.
|
| 361 |
-
cur_nimg += batch_size
|
| 362 |
-
batch_idx += 1
|
| 363 |
-
|
| 364 |
-
# Execute ADA heuristic.
|
| 365 |
-
if (ada_stats is not None) and (batch_idx % ada_interval == 0):
|
| 366 |
-
ada_stats.update()
|
| 367 |
-
adjust = np.sign(ada_stats['Loss/signs/real'] - ada_target) * \
|
| 368 |
-
(batch_size * ada_interval) / (ada_kimg * 1000)
|
| 369 |
-
augment_pipe.p.copy_(
|
| 370 |
-
(augment_pipe.p + adjust).max(misc.constant(0, device=device)))
|
| 371 |
-
|
| 372 |
-
# Perform maintenance tasks once per tick.
|
| 373 |
-
done = (cur_nimg >= total_kimg * 1000)
|
| 374 |
-
if (not done) and (cur_tick != 0) and (cur_nimg < tick_start_nimg + kimg_per_tick * 1000):
|
| 375 |
-
continue
|
| 376 |
-
|
| 377 |
-
# Print status line, accumulating the same information in training_stats.
|
| 378 |
-
tick_end_time = time.time()
|
| 379 |
-
fields = []
|
| 380 |
-
fields += [
|
| 381 |
-
f"tick {training_stats.report0('Progress/tick', cur_tick):<5d}"]
|
| 382 |
-
fields += [
|
| 383 |
-
f"kimg {training_stats.report0('Progress/kimg', cur_nimg / 1e3):<8.1f}"]
|
| 384 |
-
fields += [
|
| 385 |
-
f"time {dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}"]
|
| 386 |
-
fields += [
|
| 387 |
-
f"sec/tick {training_stats.report0('Timing/sec_per_tick', tick_end_time - tick_start_time):<7.1f}"]
|
| 388 |
-
fields += [
|
| 389 |
-
f"sec/kimg {training_stats.report0('Timing/sec_per_kimg', (tick_end_time - tick_start_time) / (cur_nimg - tick_start_nimg) * 1e3):<7.2f}"]
|
| 390 |
-
fields += [
|
| 391 |
-
f"maintenance {training_stats.report0('Timing/maintenance_sec', maintenance_time):<6.1f}"]
|
| 392 |
-
fields += [
|
| 393 |
-
f"cpumem {training_stats.report0('Resources/cpu_mem_gb', psutil.Process(os.getpid()).memory_info().rss / 2**30):<6.2f}"]
|
| 394 |
-
fields += [
|
| 395 |
-
f"gpumem {training_stats.report0('Resources/peak_gpu_mem_gb', torch.cuda.max_memory_allocated(device) / 2**30):<6.2f}"]
|
| 396 |
-
fields += [
|
| 397 |
-
f"reserved {training_stats.report0('Resources/peak_gpu_mem_reserved_gb', torch.cuda.max_memory_reserved(device) / 2**30):<6.2f}"]
|
| 398 |
-
torch.cuda.reset_peak_memory_stats()
|
| 399 |
-
fields += [
|
| 400 |
-
f"augment {training_stats.report0('Progress/augment', float(augment_pipe.p.cpu()) if augment_pipe is not None else 0):.3f}"]
|
| 401 |
-
training_stats.report0('Timing/total_hours',
|
| 402 |
-
(tick_end_time - start_time) / (60 * 60))
|
| 403 |
-
training_stats.report0('Timing/total_days',
|
| 404 |
-
(tick_end_time - start_time) / (24 * 60 * 60))
|
| 405 |
-
if rank == 0:
|
| 406 |
-
print(' '.join(fields))
|
| 407 |
-
|
| 408 |
-
# Check for abort.
|
| 409 |
-
if (not done) and (abort_fn is not None) and abort_fn():
|
| 410 |
-
done = True
|
| 411 |
-
if rank == 0:
|
| 412 |
-
print()
|
| 413 |
-
print('Aborting...')
|
| 414 |
-
|
| 415 |
-
# Save image snapshot.
|
| 416 |
-
if (rank == 0) and (image_snapshot_ticks is not None) and (done or cur_tick % image_snapshot_ticks == 0):
|
| 417 |
-
images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu()
|
| 418 |
-
for z, c in zip(grid_z, grid_c)]).numpy()
|
| 419 |
-
save_image_grid(images, os.path.join(
|
| 420 |
-
run_dir, f'fakes{cur_nimg//1000:06d}.png'), drange=[-1, 1], grid_size=grid_size)
|
| 421 |
-
|
| 422 |
-
# Save network snapshot.
|
| 423 |
-
snapshot_pkl = None
|
| 424 |
-
snapshot_data = None
|
| 425 |
-
if (network_snapshot_ticks is not None) and (done or cur_tick % network_snapshot_ticks == 0):
|
| 426 |
-
snapshot_data = dict(G=G, D=D, G_ema=G_ema, augment_pipe=augment_pipe,
|
| 427 |
-
training_set_kwargs=dict(training_set_kwargs))
|
| 428 |
-
for key, value in snapshot_data.items():
|
| 429 |
-
if isinstance(value, torch.nn.Module):
|
| 430 |
-
value = copy.deepcopy(value).eval().requires_grad_(False)
|
| 431 |
-
if num_gpus > 1:
|
| 432 |
-
misc.check_ddp_consistency(
|
| 433 |
-
value, ignore_regex=r'.*\.[^.]+_(avg|ema)')
|
| 434 |
-
for param in misc.params_and_buffers(value):
|
| 435 |
-
torch.distributed.broadcast(param, src=0)
|
| 436 |
-
snapshot_data[key] = value.cpu()
|
| 437 |
-
del value # conserve memory
|
| 438 |
-
snapshot_pkl = os.path.join(
|
| 439 |
-
run_dir, f'network-snapshot-{cur_nimg//1000:06d}.pkl')
|
| 440 |
-
if rank == 0:
|
| 441 |
-
with open(snapshot_pkl, 'wb') as f:
|
| 442 |
-
pickle.dump(snapshot_data, f)
|
| 443 |
-
|
| 444 |
-
# Evaluate metrics.
|
| 445 |
-
if (snapshot_data is not None) and (len(metrics) > 0):
|
| 446 |
-
if rank == 0:
|
| 447 |
-
print('Evaluating metrics...')
|
| 448 |
-
for metric in metrics:
|
| 449 |
-
result_dict = metric_main.calc_metric(metric=metric, G=snapshot_data['G_ema'],
|
| 450 |
-
dataset_kwargs=training_set_kwargs, num_gpus=num_gpus, rank=rank, device=device)
|
| 451 |
-
if rank == 0:
|
| 452 |
-
metric_main.report_metric(
|
| 453 |
-
result_dict, run_dir=run_dir, snapshot_pkl=snapshot_pkl)
|
| 454 |
-
stats_metrics.update(result_dict.results)
|
| 455 |
-
del snapshot_data # conserve memory
|
| 456 |
-
|
| 457 |
-
# Collect statistics.
|
| 458 |
-
for phase in phases:
|
| 459 |
-
value = []
|
| 460 |
-
if (phase.start_event is not None) and (phase.end_event is not None):
|
| 461 |
-
phase.end_event.synchronize()
|
| 462 |
-
value = phase.start_event.elapsed_time(phase.end_event)
|
| 463 |
-
training_stats.report0('Timing/' + phase.name, value)
|
| 464 |
-
stats_collector.update()
|
| 465 |
-
stats_dict = stats_collector.as_dict()
|
| 466 |
-
|
| 467 |
-
# Update logs.
|
| 468 |
-
timestamp = time.time()
|
| 469 |
-
if stats_jsonl is not None:
|
| 470 |
-
fields = dict(stats_dict, timestamp=timestamp)
|
| 471 |
-
stats_jsonl.write(json.dumps(fields) + '\n')
|
| 472 |
-
stats_jsonl.flush()
|
| 473 |
-
if stats_tfevents is not None:
|
| 474 |
-
global_step = int(cur_nimg / 1e3)
|
| 475 |
-
walltime = timestamp - start_time
|
| 476 |
-
for name, value in stats_dict.items():
|
| 477 |
-
stats_tfevents.add_scalar(
|
| 478 |
-
name, value.mean, global_step=global_step, walltime=walltime)
|
| 479 |
-
for name, value in stats_metrics.items():
|
| 480 |
-
stats_tfevents.add_scalar(
|
| 481 |
-
f'Metrics/{name}', value, global_step=global_step, walltime=walltime)
|
| 482 |
-
stats_tfevents.flush()
|
| 483 |
-
if progress_fn is not None:
|
| 484 |
-
progress_fn(cur_nimg // 1000, total_kimg)
|
| 485 |
-
|
| 486 |
-
# Update state.
|
| 487 |
-
cur_tick += 1
|
| 488 |
-
tick_start_nimg = cur_nimg
|
| 489 |
-
tick_start_time = time.time()
|
| 490 |
-
maintenance_time = tick_start_time - tick_end_time
|
| 491 |
-
if done:
|
| 492 |
-
break
|
| 493 |
-
|
| 494 |
-
# Done.
|
| 495 |
-
if rank == 0:
|
| 496 |
-
print()
|
| 497 |
-
print('Exiting...')
|
| 498 |
-
|
| 499 |
-
# ----------------------------------------------------------------------------
|
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|
|
spaces/ECCV2022/bytetrack/tutorials/transtrack/main_track.py
DELETED
|
@@ -1,375 +0,0 @@
|
|
| 1 |
-
# Modified by Peize Sun, Rufeng Zhang
|
| 2 |
-
# ------------------------------------------------------------------------
|
| 3 |
-
# Deformable DETR
|
| 4 |
-
# Copyright (c) 2020 SenseTime. All Rights Reserved.
|
| 5 |
-
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
|
| 6 |
-
# ------------------------------------------------------------------------
|
| 7 |
-
# Modified from DETR (https://github.com/facebookresearch/detr)
|
| 8 |
-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
|
| 9 |
-
# ------------------------------------------------------------------------
|
| 10 |
-
import argparse
|
| 11 |
-
import datetime
|
| 12 |
-
import json
|
| 13 |
-
import random
|
| 14 |
-
import time
|
| 15 |
-
from pathlib import Path
|
| 16 |
-
|
| 17 |
-
import numpy as np
|
| 18 |
-
import torch
|
| 19 |
-
from torch.utils.data import DataLoader
|
| 20 |
-
import datasets
|
| 21 |
-
import util.misc as utils
|
| 22 |
-
import datasets.samplers as samplers
|
| 23 |
-
from datasets import build_dataset, get_coco_api_from_dataset
|
| 24 |
-
from engine_track import evaluate, train_one_epoch, evaluate_track
|
| 25 |
-
from models import build_tracktrain_model, build_tracktest_model, build_model
|
| 26 |
-
from models import Tracker
|
| 27 |
-
from models import save_track
|
| 28 |
-
from mot_online.byte_tracker import BYTETracker
|
| 29 |
-
|
| 30 |
-
from collections import defaultdict
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
def get_args_parser():
|
| 34 |
-
parser = argparse.ArgumentParser('Deformable DETR Detector', add_help=False)
|
| 35 |
-
parser.add_argument('--lr', default=2e-4, type=float)
|
| 36 |
-
parser.add_argument('--lr_backbone_names', default=["backbone.0"], type=str, nargs='+')
|
| 37 |
-
parser.add_argument('--lr_backbone', default=2e-5, type=float)
|
| 38 |
-
parser.add_argument('--lr_linear_proj_names', default=['reference_points', 'sampling_offsets'], type=str, nargs='+')
|
| 39 |
-
parser.add_argument('--lr_linear_proj_mult', default=0.1, type=float)
|
| 40 |
-
parser.add_argument('--batch_size', default=1, type=int)
|
| 41 |
-
parser.add_argument('--weight_decay', default=1e-4, type=float)
|
| 42 |
-
parser.add_argument('--epochs', default=50, type=int)
|
| 43 |
-
parser.add_argument('--lr_drop', default=40, type=int)
|
| 44 |
-
parser.add_argument('--lr_drop_epochs', default=None, type=int, nargs='+')
|
| 45 |
-
parser.add_argument('--clip_max_norm', default=0.1, type=float,
|
| 46 |
-
help='gradient clipping max norm')
|
| 47 |
-
|
| 48 |
-
parser.add_argument('--sgd', action='store_true')
|
| 49 |
-
|
| 50 |
-
# Variants of Deformable DETR
|
| 51 |
-
parser.add_argument('--with_box_refine', default=True, action='store_true')
|
| 52 |
-
parser.add_argument('--two_stage', default=False, action='store_true')
|
| 53 |
-
|
| 54 |
-
# Model parameters
|
| 55 |
-
parser.add_argument('--frozen_weights', type=str, default=None,
|
| 56 |
-
help="Path to the pretrained model. If set, only the mask head will be trained")
|
| 57 |
-
|
| 58 |
-
# * Backbone
|
| 59 |
-
parser.add_argument('--backbone', default='resnet50', type=str,
|
| 60 |
-
help="Name of the convolutional backbone to use")
|
| 61 |
-
parser.add_argument('--dilation', action='store_true',
|
| 62 |
-
help="If true, we replace stride with dilation in the last convolutional block (DC5)")
|
| 63 |
-
parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'),
|
| 64 |
-
help="Type of positional embedding to use on top of the image features")
|
| 65 |
-
parser.add_argument('--position_embedding_scale', default=2 * np.pi, type=float,
|
| 66 |
-
help="position / size * scale")
|
| 67 |
-
parser.add_argument('--num_feature_levels', default=4, type=int, help='number of feature levels')
|
| 68 |
-
|
| 69 |
-
# * Transformer
|
| 70 |
-
parser.add_argument('--enc_layers', default=6, type=int,
|
| 71 |
-
help="Number of encoding layers in the transformer")
|
| 72 |
-
parser.add_argument('--dec_layers', default=6, type=int,
|
| 73 |
-
help="Number of decoding layers in the transformer")
|
| 74 |
-
parser.add_argument('--dim_feedforward', default=1024, type=int,
|
| 75 |
-
help="Intermediate size of the feedforward layers in the transformer blocks")
|
| 76 |
-
parser.add_argument('--hidden_dim', default=256, type=int,
|
| 77 |
-
help="Size of the embeddings (dimension of the transformer)")
|
| 78 |
-
parser.add_argument('--dropout', default=0.1, type=float,
|
| 79 |
-
help="Dropout applied in the transformer")
|
| 80 |
-
parser.add_argument('--nheads', default=8, type=int,
|
| 81 |
-
help="Number of attention heads inside the transformer's attentions")
|
| 82 |
-
parser.add_argument('--num_queries', default=500, type=int,
|
| 83 |
-
help="Number of query slots")
|
| 84 |
-
parser.add_argument('--dec_n_points', default=4, type=int)
|
| 85 |
-
parser.add_argument('--enc_n_points', default=4, type=int)
|
| 86 |
-
|
| 87 |
-
# * Segmentation
|
| 88 |
-
parser.add_argument('--masks', action='store_true',
|
| 89 |
-
help="Train segmentation head if the flag is provided")
|
| 90 |
-
|
| 91 |
-
# Loss
|
| 92 |
-
parser.add_argument('--no_aux_loss', dest='aux_loss', action='store_false',
|
| 93 |
-
help="Disables auxiliary decoding losses (loss at each layer)")
|
| 94 |
-
|
| 95 |
-
# * Matcher
|
| 96 |
-
parser.add_argument('--set_cost_class', default=2, type=float,
|
| 97 |
-
help="Class coefficient in the matching cost")
|
| 98 |
-
parser.add_argument('--set_cost_bbox', default=5, type=float,
|
| 99 |
-
help="L1 box coefficient in the matching cost")
|
| 100 |
-
parser.add_argument('--set_cost_giou', default=2, type=float,
|
| 101 |
-
help="giou box coefficient in the matching cost")
|
| 102 |
-
|
| 103 |
-
# * Loss coefficients
|
| 104 |
-
parser.add_argument('--mask_loss_coef', default=1, type=float)
|
| 105 |
-
parser.add_argument('--dice_loss_coef', default=1, type=float)
|
| 106 |
-
parser.add_argument('--cls_loss_coef', default=2, type=float)
|
| 107 |
-
parser.add_argument('--bbox_loss_coef', default=5, type=float)
|
| 108 |
-
parser.add_argument('--giou_loss_coef', default=2, type=float)
|
| 109 |
-
parser.add_argument('--focal_alpha', default=0.25, type=float)
|
| 110 |
-
parser.add_argument('--id_loss_coef', default=1, type=float)
|
| 111 |
-
|
| 112 |
-
# dataset parameters
|
| 113 |
-
parser.add_argument('--dataset_file', default='coco')
|
| 114 |
-
parser.add_argument('--coco_path', default='./data/coco', type=str)
|
| 115 |
-
parser.add_argument('--coco_panoptic_path', type=str)
|
| 116 |
-
parser.add_argument('--remove_difficult', action='store_true')
|
| 117 |
-
|
| 118 |
-
parser.add_argument('--output_dir', default='',
|
| 119 |
-
help='path where to save, empty for no saving')
|
| 120 |
-
parser.add_argument('--device', default='cuda',
|
| 121 |
-
help='device to use for training / testing')
|
| 122 |
-
parser.add_argument('--seed', default=42, type=int)
|
| 123 |
-
parser.add_argument('--resume', default='', help='resume from checkpoint')
|
| 124 |
-
parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
|
| 125 |
-
help='start epoch')
|
| 126 |
-
parser.add_argument('--eval', action='store_true')
|
| 127 |
-
parser.add_argument('--num_workers', default=2, type=int)
|
| 128 |
-
parser.add_argument('--cache_mode', default=False, action='store_true', help='whether to cache images on memory')
|
| 129 |
-
|
| 130 |
-
# PyTorch checkpointing for saving memory (torch.utils.checkpoint.checkpoint)
|
| 131 |
-
parser.add_argument('--checkpoint_enc_ffn', default=False, action='store_true')
|
| 132 |
-
parser.add_argument('--checkpoint_dec_ffn', default=False, action='store_true')
|
| 133 |
-
|
| 134 |
-
# appended for track.
|
| 135 |
-
parser.add_argument('--track_train_split', default='train', type=str)
|
| 136 |
-
parser.add_argument('--track_eval_split', default='val', type=str)
|
| 137 |
-
parser.add_argument('--track_thresh', default=0.4, type=float)
|
| 138 |
-
parser.add_argument('--reid_shared', default=False, type=bool)
|
| 139 |
-
parser.add_argument('--reid_dim', default=128, type=int)
|
| 140 |
-
parser.add_argument('--num_ids', default=360, type=int)
|
| 141 |
-
|
| 142 |
-
|
| 143 |
-
# detector for track.
|
| 144 |
-
parser.add_argument('--det_val', default=False, action='store_true')
|
| 145 |
-
|
| 146 |
-
|
| 147 |
-
return parser
|
| 148 |
-
|
| 149 |
-
|
| 150 |
-
def main(args):
|
| 151 |
-
utils.init_distributed_mode(args)
|
| 152 |
-
print("git:\n {}\n".format(utils.get_sha()))
|
| 153 |
-
|
| 154 |
-
if args.frozen_weights is not None:
|
| 155 |
-
assert args.masks, "Frozen training is meant for segmentation only"
|
| 156 |
-
print(args)
|
| 157 |
-
|
| 158 |
-
device = torch.device(args.device)
|
| 159 |
-
|
| 160 |
-
# fix the seed for reproducibility
|
| 161 |
-
seed = args.seed + utils.get_rank()
|
| 162 |
-
torch.manual_seed(seed)
|
| 163 |
-
np.random.seed(seed)
|
| 164 |
-
random.seed(seed)
|
| 165 |
-
|
| 166 |
-
if args.det_val:
|
| 167 |
-
assert args.eval, 'only support eval mode of detector for track'
|
| 168 |
-
model, criterion, postprocessors = build_model(args)
|
| 169 |
-
elif args.eval:
|
| 170 |
-
model, criterion, postprocessors = build_tracktest_model(args)
|
| 171 |
-
else:
|
| 172 |
-
model, criterion, postprocessors = build_tracktrain_model(args)
|
| 173 |
-
|
| 174 |
-
model.to(device)
|
| 175 |
-
|
| 176 |
-
model_without_ddp = model
|
| 177 |
-
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
|
| 178 |
-
print('number of params:', n_parameters)
|
| 179 |
-
|
| 180 |
-
dataset_train = build_dataset(image_set=args.track_train_split, args=args)
|
| 181 |
-
dataset_val = build_dataset(image_set=args.track_eval_split, args=args)
|
| 182 |
-
|
| 183 |
-
if args.distributed:
|
| 184 |
-
if args.cache_mode:
|
| 185 |
-
sampler_train = samplers.NodeDistributedSampler(dataset_train)
|
| 186 |
-
sampler_val = samplers.NodeDistributedSampler(dataset_val, shuffle=False)
|
| 187 |
-
else:
|
| 188 |
-
sampler_train = samplers.DistributedSampler(dataset_train)
|
| 189 |
-
sampler_val = samplers.DistributedSampler(dataset_val, shuffle=False)
|
| 190 |
-
else:
|
| 191 |
-
sampler_train = torch.utils.data.RandomSampler(dataset_train)
|
| 192 |
-
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
|
| 193 |
-
|
| 194 |
-
batch_sampler_train = torch.utils.data.BatchSampler(
|
| 195 |
-
sampler_train, args.batch_size, drop_last=True)
|
| 196 |
-
|
| 197 |
-
data_loader_train = DataLoader(dataset_train, batch_sampler=batch_sampler_train,
|
| 198 |
-
collate_fn=utils.collate_fn, num_workers=args.num_workers,
|
| 199 |
-
pin_memory=True)
|
| 200 |
-
data_loader_val = DataLoader(dataset_val, args.batch_size, sampler=sampler_val,
|
| 201 |
-
drop_last=False, collate_fn=utils.collate_fn, num_workers=args.num_workers,
|
| 202 |
-
pin_memory=True)
|
| 203 |
-
|
| 204 |
-
# lr_backbone_names = ["backbone.0", "backbone.neck", "input_proj", "transformer.encoder"]
|
| 205 |
-
def match_name_keywords(n, name_keywords):
|
| 206 |
-
out = False
|
| 207 |
-
for b in name_keywords:
|
| 208 |
-
if b in n:
|
| 209 |
-
out = True
|
| 210 |
-
break
|
| 211 |
-
return out
|
| 212 |
-
|
| 213 |
-
for n, p in model_without_ddp.named_parameters():
|
| 214 |
-
print(n)
|
| 215 |
-
|
| 216 |
-
param_dicts = [
|
| 217 |
-
{
|
| 218 |
-
"params":
|
| 219 |
-
[p for n, p in model_without_ddp.named_parameters()
|
| 220 |
-
if not match_name_keywords(n, args.lr_backbone_names) and not match_name_keywords(n, args.lr_linear_proj_names) and p.requires_grad],
|
| 221 |
-
"lr": args.lr,
|
| 222 |
-
},
|
| 223 |
-
{
|
| 224 |
-
"params": [p for n, p in model_without_ddp.named_parameters() if match_name_keywords(n, args.lr_backbone_names) and p.requires_grad],
|
| 225 |
-
"lr": args.lr_backbone,
|
| 226 |
-
},
|
| 227 |
-
{
|
| 228 |
-
"params": [p for n, p in model_without_ddp.named_parameters() if match_name_keywords(n, args.lr_linear_proj_names) and p.requires_grad],
|
| 229 |
-
"lr": args.lr * args.lr_linear_proj_mult,
|
| 230 |
-
}
|
| 231 |
-
]
|
| 232 |
-
if args.sgd:
|
| 233 |
-
optimizer = torch.optim.SGD(param_dicts, lr=args.lr, momentum=0.9,
|
| 234 |
-
weight_decay=args.weight_decay)
|
| 235 |
-
else:
|
| 236 |
-
optimizer = torch.optim.AdamW(param_dicts, lr=args.lr,
|
| 237 |
-
weight_decay=args.weight_decay)
|
| 238 |
-
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, args.lr_drop)
|
| 239 |
-
|
| 240 |
-
if args.distributed:
|
| 241 |
-
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
|
| 242 |
-
model_without_ddp = model.module
|
| 243 |
-
|
| 244 |
-
if args.dataset_file == "coco_panoptic":
|
| 245 |
-
# We also evaluate AP during panoptic training, on original coco DS
|
| 246 |
-
coco_val = datasets.coco.build("val", args)
|
| 247 |
-
base_ds = get_coco_api_from_dataset(coco_val)
|
| 248 |
-
else:
|
| 249 |
-
base_ds = get_coco_api_from_dataset(dataset_val)
|
| 250 |
-
|
| 251 |
-
if args.frozen_weights is not None:
|
| 252 |
-
checkpoint = torch.load(args.frozen_weights, map_location='cpu')
|
| 253 |
-
model_without_ddp.detr.load_state_dict(checkpoint['model'])
|
| 254 |
-
|
| 255 |
-
output_dir = Path(args.output_dir)
|
| 256 |
-
if args.resume:
|
| 257 |
-
if args.resume.startswith('https'):
|
| 258 |
-
checkpoint = torch.hub.load_state_dict_from_url(
|
| 259 |
-
args.resume, map_location='cpu', check_hash=True)
|
| 260 |
-
else:
|
| 261 |
-
checkpoint = torch.load(args.resume, map_location='cpu')
|
| 262 |
-
missing_keys, unexpected_keys = model_without_ddp.load_state_dict(checkpoint['model'], strict=False)
|
| 263 |
-
unexpected_keys = [k for k in unexpected_keys if not (k.endswith('total_params') or k.endswith('total_ops'))]
|
| 264 |
-
if len(missing_keys) > 0:
|
| 265 |
-
print('Missing Keys: {}'.format(missing_keys))
|
| 266 |
-
if len(unexpected_keys) > 0:
|
| 267 |
-
print('Unexpected Keys: {}'.format(unexpected_keys))
|
| 268 |
-
if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
|
| 269 |
-
import copy
|
| 270 |
-
p_groups = copy.deepcopy(optimizer.param_groups)
|
| 271 |
-
optimizer.load_state_dict(checkpoint['optimizer'])
|
| 272 |
-
for pg, pg_old in zip(optimizer.param_groups, p_groups):
|
| 273 |
-
pg['lr'] = pg_old['lr']
|
| 274 |
-
pg['initial_lr'] = pg_old['initial_lr']
|
| 275 |
-
print(optimizer.param_groups)
|
| 276 |
-
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
|
| 277 |
-
# todo: this is a hack for doing experiment that resume from checkpoint and also modify lr scheduler (e.g., decrease lr in advance).
|
| 278 |
-
args.override_resumed_lr_drop = True
|
| 279 |
-
if args.override_resumed_lr_drop:
|
| 280 |
-
print('Warning: (hack) args.override_resumed_lr_drop is set to True, so args.lr_drop would override lr_drop in resumed lr_scheduler.')
|
| 281 |
-
lr_scheduler.step_size = args.lr_drop
|
| 282 |
-
lr_scheduler.base_lrs = list(map(lambda group: group['initial_lr'], optimizer.param_groups))
|
| 283 |
-
lr_scheduler.step(lr_scheduler.last_epoch)
|
| 284 |
-
args.start_epoch = checkpoint['epoch'] + 1
|
| 285 |
-
# check the resumed model
|
| 286 |
-
# if not args.eval:
|
| 287 |
-
# test_stats, coco_evaluator, _ = evaluate(
|
| 288 |
-
# model, criterion, postprocessors, data_loader_val, base_ds, device, args.output_dir
|
| 289 |
-
# )
|
| 290 |
-
|
| 291 |
-
if args.eval:
|
| 292 |
-
assert args.batch_size == 1, print("Now only support 1.")
|
| 293 |
-
# tracker = MOTXTracker(score_thresh=args.track_thresh)
|
| 294 |
-
# test_stats, coco_evaluator, res_tracks = evaluate(model, criterion, postprocessors, data_loader_val,
|
| 295 |
-
# base_ds, device, args.output_dir, tracker=tracker,
|
| 296 |
-
# phase='eval', det_val=args.det_val)
|
| 297 |
-
tracker = BYTETracker(args)
|
| 298 |
-
test_stats, coco_evaluator, res_tracks = evaluate_track(args, model, criterion, postprocessors, data_loader_val,
|
| 299 |
-
base_ds, device, args.output_dir, tracker=tracker,
|
| 300 |
-
phase='eval', det_val=args.det_val)
|
| 301 |
-
if args.output_dir:
|
| 302 |
-
utils.save_on_master(coco_evaluator.coco_eval["bbox"].eval, output_dir / "eval.pth")
|
| 303 |
-
if res_tracks is not None:
|
| 304 |
-
print("Creating video index for {}.".format(args.dataset_file))
|
| 305 |
-
video_to_images = defaultdict(list)
|
| 306 |
-
video_names = defaultdict()
|
| 307 |
-
for _, info in dataset_val.coco.imgs.items():
|
| 308 |
-
video_to_images[info["video_id"]].append({"image_id": info["id"],
|
| 309 |
-
"frame_id": info["frame_id"]})
|
| 310 |
-
video_name = info["file_name"].split("/")[0]
|
| 311 |
-
if video_name not in video_names:
|
| 312 |
-
video_names[info["video_id"]] = video_name
|
| 313 |
-
assert len(video_to_images) == len(video_names)
|
| 314 |
-
# save mot results.
|
| 315 |
-
save_track(res_tracks, args.output_dir, video_to_images, video_names, args.track_eval_split)
|
| 316 |
-
|
| 317 |
-
return
|
| 318 |
-
|
| 319 |
-
print("Start training")
|
| 320 |
-
start_time = time.time()
|
| 321 |
-
for epoch in range(args.start_epoch, args.epochs):
|
| 322 |
-
if args.distributed:
|
| 323 |
-
sampler_train.set_epoch(epoch)
|
| 324 |
-
train_stats = train_one_epoch(
|
| 325 |
-
model, criterion, data_loader_train, optimizer, device, epoch, args.clip_max_norm)
|
| 326 |
-
lr_scheduler.step()
|
| 327 |
-
if args.output_dir:
|
| 328 |
-
checkpoint_paths = [output_dir / 'checkpoint.pth']
|
| 329 |
-
# extra checkpoint before LR drop and every 5 epochs
|
| 330 |
-
if (epoch + 1) % args.lr_drop == 0 or (epoch + 1) % 5 == 0:
|
| 331 |
-
checkpoint_paths.append(output_dir / f'checkpoint{epoch:04}.pth')
|
| 332 |
-
for checkpoint_path in checkpoint_paths:
|
| 333 |
-
utils.save_on_master({
|
| 334 |
-
'model': model_without_ddp.state_dict(),
|
| 335 |
-
'optimizer': optimizer.state_dict(),
|
| 336 |
-
'lr_scheduler': lr_scheduler.state_dict(),
|
| 337 |
-
'epoch': epoch,
|
| 338 |
-
'args': args,
|
| 339 |
-
}, checkpoint_path)
|
| 340 |
-
if epoch % 10 == 0 or epoch > args.epochs - 5:
|
| 341 |
-
test_stats, coco_evaluator, _ = evaluate(
|
| 342 |
-
model, criterion, postprocessors, data_loader_val, base_ds, device, args.output_dir,
|
| 343 |
-
)
|
| 344 |
-
|
| 345 |
-
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
|
| 346 |
-
**{f'test_{k}': v for k, v in test_stats.items()},
|
| 347 |
-
'epoch': epoch,
|
| 348 |
-
'n_parameters': n_parameters}
|
| 349 |
-
|
| 350 |
-
if args.output_dir and utils.is_main_process():
|
| 351 |
-
with (output_dir / "log.txt").open("a") as f:
|
| 352 |
-
f.write(json.dumps(log_stats) + "\n")
|
| 353 |
-
|
| 354 |
-
# for evaluation logs
|
| 355 |
-
if coco_evaluator is not None:
|
| 356 |
-
(output_dir / 'eval').mkdir(exist_ok=True)
|
| 357 |
-
if "bbox" in coco_evaluator.coco_eval:
|
| 358 |
-
filenames = ['latest.pth']
|
| 359 |
-
if epoch % 50 == 0:
|
| 360 |
-
filenames.append(f'{epoch:03}.pth')
|
| 361 |
-
for name in filenames:
|
| 362 |
-
torch.save(coco_evaluator.coco_eval["bbox"].eval,
|
| 363 |
-
output_dir / "eval" / name)
|
| 364 |
-
|
| 365 |
-
total_time = time.time() - start_time
|
| 366 |
-
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
|
| 367 |
-
print('Training time {}'.format(total_time_str))
|
| 368 |
-
|
| 369 |
-
|
| 370 |
-
if __name__ == '__main__':
|
| 371 |
-
parser = argparse.ArgumentParser('Deformable DETR training and evaluation script', parents=[get_args_parser()])
|
| 372 |
-
args = parser.parse_args()
|
| 373 |
-
if args.output_dir:
|
| 374 |
-
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
|
| 375 |
-
main(args)
|
|
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|
spaces/EDGAhab/Paimon-Talking/utils.py
DELETED
|
@@ -1,258 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import glob
|
| 3 |
-
import sys
|
| 4 |
-
import argparse
|
| 5 |
-
import logging
|
| 6 |
-
import json
|
| 7 |
-
import subprocess
|
| 8 |
-
import numpy as np
|
| 9 |
-
from scipy.io.wavfile import read
|
| 10 |
-
import torch
|
| 11 |
-
|
| 12 |
-
MATPLOTLIB_FLAG = False
|
| 13 |
-
|
| 14 |
-
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
|
| 15 |
-
logger = logging
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
def load_checkpoint(checkpoint_path, model, optimizer=None):
|
| 19 |
-
assert os.path.isfile(checkpoint_path)
|
| 20 |
-
checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
|
| 21 |
-
iteration = checkpoint_dict['iteration']
|
| 22 |
-
learning_rate = checkpoint_dict['learning_rate']
|
| 23 |
-
if optimizer is not None:
|
| 24 |
-
optimizer.load_state_dict(checkpoint_dict['optimizer'])
|
| 25 |
-
saved_state_dict = checkpoint_dict['model']
|
| 26 |
-
if hasattr(model, 'module'):
|
| 27 |
-
state_dict = model.module.state_dict()
|
| 28 |
-
else:
|
| 29 |
-
state_dict = model.state_dict()
|
| 30 |
-
new_state_dict= {}
|
| 31 |
-
for k, v in state_dict.items():
|
| 32 |
-
try:
|
| 33 |
-
new_state_dict[k] = saved_state_dict[k]
|
| 34 |
-
except:
|
| 35 |
-
logger.info("%s is not in the checkpoint" % k)
|
| 36 |
-
new_state_dict[k] = v
|
| 37 |
-
if hasattr(model, 'module'):
|
| 38 |
-
model.module.load_state_dict(new_state_dict)
|
| 39 |
-
else:
|
| 40 |
-
model.load_state_dict(new_state_dict)
|
| 41 |
-
logger.info("Loaded checkpoint '{}' (iteration {})" .format(
|
| 42 |
-
checkpoint_path, iteration))
|
| 43 |
-
return model, optimizer, learning_rate, iteration
|
| 44 |
-
|
| 45 |
-
|
| 46 |
-
def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
|
| 47 |
-
logger.info("Saving model and optimizer state at iteration {} to {}".format(
|
| 48 |
-
iteration, checkpoint_path))
|
| 49 |
-
if hasattr(model, 'module'):
|
| 50 |
-
state_dict = model.module.state_dict()
|
| 51 |
-
else:
|
| 52 |
-
state_dict = model.state_dict()
|
| 53 |
-
torch.save({'model': state_dict,
|
| 54 |
-
'iteration': iteration,
|
| 55 |
-
'optimizer': optimizer.state_dict(),
|
| 56 |
-
'learning_rate': learning_rate}, checkpoint_path)
|
| 57 |
-
|
| 58 |
-
|
| 59 |
-
def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
|
| 60 |
-
for k, v in scalars.items():
|
| 61 |
-
writer.add_scalar(k, v, global_step)
|
| 62 |
-
for k, v in histograms.items():
|
| 63 |
-
writer.add_histogram(k, v, global_step)
|
| 64 |
-
for k, v in images.items():
|
| 65 |
-
writer.add_image(k, v, global_step, dataformats='HWC')
|
| 66 |
-
for k, v in audios.items():
|
| 67 |
-
writer.add_audio(k, v, global_step, audio_sampling_rate)
|
| 68 |
-
|
| 69 |
-
|
| 70 |
-
def latest_checkpoint_path(dir_path, regex="G_*.pth"):
|
| 71 |
-
f_list = glob.glob(os.path.join(dir_path, regex))
|
| 72 |
-
f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
|
| 73 |
-
x = f_list[-1]
|
| 74 |
-
print(x)
|
| 75 |
-
return x
|
| 76 |
-
|
| 77 |
-
|
| 78 |
-
def plot_spectrogram_to_numpy(spectrogram):
|
| 79 |
-
global MATPLOTLIB_FLAG
|
| 80 |
-
if not MATPLOTLIB_FLAG:
|
| 81 |
-
import matplotlib
|
| 82 |
-
matplotlib.use("Agg")
|
| 83 |
-
MATPLOTLIB_FLAG = True
|
| 84 |
-
mpl_logger = logging.getLogger('matplotlib')
|
| 85 |
-
mpl_logger.setLevel(logging.WARNING)
|
| 86 |
-
import matplotlib.pylab as plt
|
| 87 |
-
import numpy as np
|
| 88 |
-
|
| 89 |
-
fig, ax = plt.subplots(figsize=(10,2))
|
| 90 |
-
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
|
| 91 |
-
interpolation='none')
|
| 92 |
-
plt.colorbar(im, ax=ax)
|
| 93 |
-
plt.xlabel("Frames")
|
| 94 |
-
plt.ylabel("Channels")
|
| 95 |
-
plt.tight_layout()
|
| 96 |
-
|
| 97 |
-
fig.canvas.draw()
|
| 98 |
-
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
| 99 |
-
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
| 100 |
-
plt.close()
|
| 101 |
-
return data
|
| 102 |
-
|
| 103 |
-
|
| 104 |
-
def plot_alignment_to_numpy(alignment, info=None):
|
| 105 |
-
global MATPLOTLIB_FLAG
|
| 106 |
-
if not MATPLOTLIB_FLAG:
|
| 107 |
-
import matplotlib
|
| 108 |
-
matplotlib.use("Agg")
|
| 109 |
-
MATPLOTLIB_FLAG = True
|
| 110 |
-
mpl_logger = logging.getLogger('matplotlib')
|
| 111 |
-
mpl_logger.setLevel(logging.WARNING)
|
| 112 |
-
import matplotlib.pylab as plt
|
| 113 |
-
import numpy as np
|
| 114 |
-
|
| 115 |
-
fig, ax = plt.subplots(figsize=(6, 4))
|
| 116 |
-
im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
|
| 117 |
-
interpolation='none')
|
| 118 |
-
fig.colorbar(im, ax=ax)
|
| 119 |
-
xlabel = 'Decoder timestep'
|
| 120 |
-
if info is not None:
|
| 121 |
-
xlabel += '\n\n' + info
|
| 122 |
-
plt.xlabel(xlabel)
|
| 123 |
-
plt.ylabel('Encoder timestep')
|
| 124 |
-
plt.tight_layout()
|
| 125 |
-
|
| 126 |
-
fig.canvas.draw()
|
| 127 |
-
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
| 128 |
-
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
| 129 |
-
plt.close()
|
| 130 |
-
return data
|
| 131 |
-
|
| 132 |
-
|
| 133 |
-
def load_wav_to_torch(full_path):
|
| 134 |
-
sampling_rate, data = read(full_path)
|
| 135 |
-
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
|
| 136 |
-
|
| 137 |
-
|
| 138 |
-
def load_filepaths_and_text(filename, split="|"):
|
| 139 |
-
with open(filename, encoding='utf-8') as f:
|
| 140 |
-
filepaths_and_text = [line.strip().split(split) for line in f]
|
| 141 |
-
return filepaths_and_text
|
| 142 |
-
|
| 143 |
-
|
| 144 |
-
def get_hparams(init=True):
|
| 145 |
-
parser = argparse.ArgumentParser()
|
| 146 |
-
parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
|
| 147 |
-
help='JSON file for configuration')
|
| 148 |
-
parser.add_argument('-m', '--model', type=str, required=True,
|
| 149 |
-
help='Model name')
|
| 150 |
-
|
| 151 |
-
args = parser.parse_args()
|
| 152 |
-
model_dir = os.path.join("./logs", args.model)
|
| 153 |
-
|
| 154 |
-
if not os.path.exists(model_dir):
|
| 155 |
-
os.makedirs(model_dir)
|
| 156 |
-
|
| 157 |
-
config_path = args.config
|
| 158 |
-
config_save_path = os.path.join(model_dir, "config.json")
|
| 159 |
-
if init:
|
| 160 |
-
with open(config_path, "r") as f:
|
| 161 |
-
data = f.read()
|
| 162 |
-
with open(config_save_path, "w") as f:
|
| 163 |
-
f.write(data)
|
| 164 |
-
else:
|
| 165 |
-
with open(config_save_path, "r") as f:
|
| 166 |
-
data = f.read()
|
| 167 |
-
config = json.loads(data)
|
| 168 |
-
|
| 169 |
-
hparams = HParams(**config)
|
| 170 |
-
hparams.model_dir = model_dir
|
| 171 |
-
return hparams
|
| 172 |
-
|
| 173 |
-
|
| 174 |
-
def get_hparams_from_dir(model_dir):
|
| 175 |
-
config_save_path = os.path.join(model_dir, "config.json")
|
| 176 |
-
with open(config_save_path, "r") as f:
|
| 177 |
-
data = f.read()
|
| 178 |
-
config = json.loads(data)
|
| 179 |
-
|
| 180 |
-
hparams =HParams(**config)
|
| 181 |
-
hparams.model_dir = model_dir
|
| 182 |
-
return hparams
|
| 183 |
-
|
| 184 |
-
|
| 185 |
-
def get_hparams_from_file(config_path):
|
| 186 |
-
with open(config_path, "r") as f:
|
| 187 |
-
data = f.read()
|
| 188 |
-
config = json.loads(data)
|
| 189 |
-
|
| 190 |
-
hparams =HParams(**config)
|
| 191 |
-
return hparams
|
| 192 |
-
|
| 193 |
-
|
| 194 |
-
def check_git_hash(model_dir):
|
| 195 |
-
source_dir = os.path.dirname(os.path.realpath(__file__))
|
| 196 |
-
if not os.path.exists(os.path.join(source_dir, ".git")):
|
| 197 |
-
logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
|
| 198 |
-
source_dir
|
| 199 |
-
))
|
| 200 |
-
return
|
| 201 |
-
|
| 202 |
-
cur_hash = subprocess.getoutput("git rev-parse HEAD")
|
| 203 |
-
|
| 204 |
-
path = os.path.join(model_dir, "githash")
|
| 205 |
-
if os.path.exists(path):
|
| 206 |
-
saved_hash = open(path).read()
|
| 207 |
-
if saved_hash != cur_hash:
|
| 208 |
-
logger.warn("git hash values are different. {}(saved) != {}(current)".format(
|
| 209 |
-
saved_hash[:8], cur_hash[:8]))
|
| 210 |
-
else:
|
| 211 |
-
open(path, "w").write(cur_hash)
|
| 212 |
-
|
| 213 |
-
|
| 214 |
-
def get_logger(model_dir, filename="train.log"):
|
| 215 |
-
global logger
|
| 216 |
-
logger = logging.getLogger(os.path.basename(model_dir))
|
| 217 |
-
logger.setLevel(logging.DEBUG)
|
| 218 |
-
|
| 219 |
-
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
|
| 220 |
-
if not os.path.exists(model_dir):
|
| 221 |
-
os.makedirs(model_dir)
|
| 222 |
-
h = logging.FileHandler(os.path.join(model_dir, filename))
|
| 223 |
-
h.setLevel(logging.DEBUG)
|
| 224 |
-
h.setFormatter(formatter)
|
| 225 |
-
logger.addHandler(h)
|
| 226 |
-
return logger
|
| 227 |
-
|
| 228 |
-
|
| 229 |
-
class HParams():
|
| 230 |
-
def __init__(self, **kwargs):
|
| 231 |
-
for k, v in kwargs.items():
|
| 232 |
-
if type(v) == dict:
|
| 233 |
-
v = HParams(**v)
|
| 234 |
-
self[k] = v
|
| 235 |
-
|
| 236 |
-
def keys(self):
|
| 237 |
-
return self.__dict__.keys()
|
| 238 |
-
|
| 239 |
-
def items(self):
|
| 240 |
-
return self.__dict__.items()
|
| 241 |
-
|
| 242 |
-
def values(self):
|
| 243 |
-
return self.__dict__.values()
|
| 244 |
-
|
| 245 |
-
def __len__(self):
|
| 246 |
-
return len(self.__dict__)
|
| 247 |
-
|
| 248 |
-
def __getitem__(self, key):
|
| 249 |
-
return getattr(self, key)
|
| 250 |
-
|
| 251 |
-
def __setitem__(self, key, value):
|
| 252 |
-
return setattr(self, key, value)
|
| 253 |
-
|
| 254 |
-
def __contains__(self, key):
|
| 255 |
-
return key in self.__dict__
|
| 256 |
-
|
| 257 |
-
def __repr__(self):
|
| 258 |
-
return self.__dict__.__repr__()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
spaces/EuroPython2022/clickbaitonator/fudge/data.py
DELETED
|
@@ -1,415 +0,0 @@
|
|
| 1 |
-
import random
|
| 2 |
-
import math
|
| 3 |
-
import os
|
| 4 |
-
import pickle
|
| 5 |
-
from collections import defaultdict, namedtuple
|
| 6 |
-
import string
|
| 7 |
-
|
| 8 |
-
os.environ['TOKENIZERS_PARALLELISM'] = 'false' # turn off since we're using multiple threads for loading anyway
|
| 9 |
-
|
| 10 |
-
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline, set_seed, GPT2Tokenizer, GPT2Model
|
| 11 |
-
import numpy as np
|
| 12 |
-
from tqdm import tqdm
|
| 13 |
-
import torch
|
| 14 |
-
|
| 15 |
-
from fudge.util import suppress_stdout
|
| 16 |
-
from fudge.poetry_util import is_iambic, count_syllables, get_rhymes, get_rhyme_group
|
| 17 |
-
from fudge.constants import *
|
| 18 |
-
|
| 19 |
-
DatasetInfo = namedtuple('DatasetInfo',
|
| 20 |
-
['index2word', 'word2index', 'total_words', 'vocab', 'glove_embeddings'])
|
| 21 |
-
RhymeInfo = namedtuple('RhymeInfo',
|
| 22 |
-
['word2rhyme_group', 'rhyme_group_counts', 'rhyme_groups', 'index2rhyme_group', 'rhyme_group2index', 'total_rhyme_groups'])
|
| 23 |
-
|
| 24 |
-
def collate(batch):
|
| 25 |
-
pad_id = batch[0][4]
|
| 26 |
-
inputs = [b[0] for b in batch]
|
| 27 |
-
lengths = torch.LongTensor([b[1] for b in batch])
|
| 28 |
-
max_length = lengths.max()
|
| 29 |
-
for i in range(len(inputs)):
|
| 30 |
-
if len(inputs[i]) < max_length:
|
| 31 |
-
inputs[i] = torch.cat([inputs[i], torch.zeros(max_length - len(inputs[i])).long()], dim=0) # actually 0 is fine as pad since it's masked out
|
| 32 |
-
inputs = torch.stack(inputs, dim=0)
|
| 33 |
-
future_words = torch.LongTensor([b[2] for b in batch]).unsqueeze(0).expand(len(batch), -1).clone() # batch x N=batch
|
| 34 |
-
labels = torch.zeros_like(future_words).long()
|
| 35 |
-
labels = labels.scatter(1, torch.arange(len(batch)).unsqueeze(1), torch.ones(len(batch)).long().unsqueeze(1)).clone()
|
| 36 |
-
log_probs = torch.Tensor([b[3] for b in batch])
|
| 37 |
-
classification_labels = [b[5] for b in batch] # batch
|
| 38 |
-
if type(classification_labels[0]) == list:
|
| 39 |
-
for i in range(len(classification_labels)):
|
| 40 |
-
assert len(classification_labels[i]) == lengths[i]
|
| 41 |
-
if len(classification_labels[i]) < max_length:
|
| 42 |
-
classification_labels[i] = torch.cat([torch.LongTensor(classification_labels[i]), -1 + torch.zeros(max_length - len(classification_labels[i])).long()], dim=0)
|
| 43 |
-
else:
|
| 44 |
-
classification_labels[i] = torch.LongTensor(classification_labels[i])
|
| 45 |
-
classification_labels = torch.stack(classification_labels, dim=0) # batch x seq
|
| 46 |
-
else:
|
| 47 |
-
assert type(classification_labels[0]) == int
|
| 48 |
-
classification_labels = torch.LongTensor(classification_labels) # they're just int labels
|
| 49 |
-
syllables_to_go = torch.LongTensor([b[6] for b in batch])
|
| 50 |
-
future_word_num_syllables = torch.LongTensor([b[7] for b in batch])
|
| 51 |
-
rhyme_group_index = torch.LongTensor([b[8] for b in batch])
|
| 52 |
-
return (inputs, lengths, future_words, log_probs, labels, classification_labels, syllables_to_go, future_word_num_syllables, rhyme_group_index)
|
| 53 |
-
|
| 54 |
-
|
| 55 |
-
def load_rhyme_info(index2word, vocab):
|
| 56 |
-
word2rhyme_group = defaultdict(lambda: UNKNOWN_RHYME_GROUP)
|
| 57 |
-
rhyme_group_counts = defaultdict(lambda: 0)
|
| 58 |
-
rhyme_groups = set()
|
| 59 |
-
for word in index2word:
|
| 60 |
-
try:
|
| 61 |
-
rhyme_group = get_rhyme_group(word)
|
| 62 |
-
word2rhyme_group[word] = rhyme_group
|
| 63 |
-
rhyme_group_counts[rhyme_group] += (vocab[word] if word in vocab else 1) # for rare words not in vocab, just use 1
|
| 64 |
-
rhyme_groups.add(rhyme_group)
|
| 65 |
-
except:
|
| 66 |
-
rhyme_group_counts[UNKNOWN_RHYME_GROUP] += (vocab[word] if word in vocab else 1)
|
| 67 |
-
index2rhyme_group = [UNKNOWN_RHYME_GROUP] + sorted(list(rhyme_groups))
|
| 68 |
-
rhyme_group2index = {s: i for i, s in enumerate(index2rhyme_group)}
|
| 69 |
-
total_rhyme_groups = sum(rhyme_group_counts.values())
|
| 70 |
-
|
| 71 |
-
return RhymeInfo(word2rhyme_group=dict(word2rhyme_group),
|
| 72 |
-
rhyme_group_counts=dict(rhyme_group_counts),
|
| 73 |
-
rhyme_groups=rhyme_groups,
|
| 74 |
-
index2rhyme_group=index2rhyme_group,
|
| 75 |
-
rhyme_group2index=rhyme_group2index,
|
| 76 |
-
total_rhyme_groups=total_rhyme_groups)
|
| 77 |
-
|
| 78 |
-
|
| 79 |
-
class Dataset:
|
| 80 |
-
def __init__(self, args):
|
| 81 |
-
print('loading data')
|
| 82 |
-
random.seed(args.seed)
|
| 83 |
-
self.batch_size = args.batch_size
|
| 84 |
-
self.data_dir = args.data_dir
|
| 85 |
-
self.topic = args.task == 'topic'
|
| 86 |
-
self.formality = args.task == 'formality'
|
| 87 |
-
self.iambic = args.task == 'iambic'
|
| 88 |
-
self.rhyme = args.task == 'rhyme'
|
| 89 |
-
self.newline = args.task == 'newline'
|
| 90 |
-
|
| 91 |
-
self.tokenizer = AutoTokenizer.from_pretrained(FORMALITY_MODEL_STRING if self.formality else TOPIC_MODEL_STRING)
|
| 92 |
-
self.tokenizer.add_special_tokens({'pad_token': PAD_TOKEN})
|
| 93 |
-
self.gpt_pad_id = self.tokenizer.encode(PAD_TOKEN)[0] # actually just the vocab size
|
| 94 |
-
sentences = []
|
| 95 |
-
self.vocab = defaultdict(lambda: 0)
|
| 96 |
-
if self.formality:
|
| 97 |
-
self.vocab['placeholder'] = 1 # anything so we don't crash
|
| 98 |
-
train, val, test = [], [], []
|
| 99 |
-
for category, label in [('formal', 1), ('informal', 0)]:
|
| 100 |
-
with open(os.path.join(args.data_dir, 'train', category), 'r') as rf:
|
| 101 |
-
for i, line in enumerate(rf):
|
| 102 |
-
if len(line) > FORMALITY_MAX_LEN:
|
| 103 |
-
line = ' '.join(line.strip()[:FORMALITY_MAX_LEN].split()[:-1]) # cutoff words until below max len; chosen so only ~20 examples affected in dataset
|
| 104 |
-
if i < FORMALITY_VAL_SIZE // 2:
|
| 105 |
-
val.append((line.strip(), label))
|
| 106 |
-
else:
|
| 107 |
-
train.append((line.strip(), label))
|
| 108 |
-
with open(os.path.join(args.data_dir, 'test', category), 'r') as rf:
|
| 109 |
-
for line in rf:
|
| 110 |
-
if len(line) > FORMALITY_MAX_LEN:
|
| 111 |
-
line = ' '.join(line.strip()[:FORMALITY_MAX_LEN].split()[:-1]) # cutoff words until below max len
|
| 112 |
-
test.append((line.strip(), label))
|
| 113 |
-
self.splits = {}
|
| 114 |
-
self.splits['train'], self.splits['val'], self.splits['test'] = train, val, test
|
| 115 |
-
else: # topic / poetry
|
| 116 |
-
for root, _, filenames in os.walk(args.data_dir):
|
| 117 |
-
for fname in filenames:
|
| 118 |
-
with open(os.path.join(root, fname), 'r') as rf:
|
| 119 |
-
for line in rf:
|
| 120 |
-
sentences.append(line.strip())
|
| 121 |
-
for word in line.strip().split(' '):
|
| 122 |
-
self.vocab[word] += 1
|
| 123 |
-
random.shuffle(sentences)
|
| 124 |
-
self.splits = {}
|
| 125 |
-
if args.debug:
|
| 126 |
-
self.splits['val'] = sentences
|
| 127 |
-
self.splits['test'] = sentences
|
| 128 |
-
self.splits['train'] = sentences
|
| 129 |
-
else:
|
| 130 |
-
self.splits['val'] = sentences[:TOPIC_VAL_SIZE]
|
| 131 |
-
self.splits['test'] = sentences[TOPIC_VAL_SIZE:2*TOPIC_VAL_SIZE]
|
| 132 |
-
self.splits['train'] = sentences[2*TOPIC_VAL_SIZE:]
|
| 133 |
-
|
| 134 |
-
if args.dataset_info is not None:
|
| 135 |
-
print('loading dataset info from file')
|
| 136 |
-
with open(args.dataset_info, 'rb') as rf:
|
| 137 |
-
dataset_info = pickle.load(rf)
|
| 138 |
-
self.vocab, self.total_words, self.index2word, self.word2index, self.glove_embeddings = \
|
| 139 |
-
dataset_info.vocab, dataset_info.total_words, dataset_info.index2word, dataset_info.word2index, dataset_info.glove_embeddings
|
| 140 |
-
self.dataset_info = dataset_info
|
| 141 |
-
else:
|
| 142 |
-
print('generating dataset info from scratch')
|
| 143 |
-
words_values = list(self.vocab.items())
|
| 144 |
-
words_values = sorted(words_values, key=lambda x: x[1], reverse=True)
|
| 145 |
-
if args.glove_file is None:
|
| 146 |
-
print('no glove embeddings given')
|
| 147 |
-
for word, _ in words_values[VOCAB_SIZE:]: # only use somewhat common tokens
|
| 148 |
-
del self.vocab[word]
|
| 149 |
-
glove_embeddings = None
|
| 150 |
-
else:
|
| 151 |
-
print('loading glove embeddings')
|
| 152 |
-
glove_embeddings = {}
|
| 153 |
-
with open(args.glove_file, 'r') as rf:
|
| 154 |
-
for i, line in enumerate(rf):
|
| 155 |
-
if i % GLOVE_PRINT_PROGRESS_FREQ == 0:
|
| 156 |
-
print(i)
|
| 157 |
-
line = line.strip().split()
|
| 158 |
-
if len(line) != GLOVE_DIM + 1:
|
| 159 |
-
continue # skip multi-word embeddings which are rare anyway
|
| 160 |
-
glove_embeddings[line[0]] = [float(x) for x in line[1:]]
|
| 161 |
-
for word, _ in words_values:
|
| 162 |
-
if word not in glove_embeddings:
|
| 163 |
-
del self.vocab[word]
|
| 164 |
-
self.total_words = sum(self.vocab.values())
|
| 165 |
-
self.index2word = [PAD_TOKEN] + sorted(list(self.vocab.keys()))
|
| 166 |
-
self.word2index = {s: i for i, s in enumerate(self.index2word)}
|
| 167 |
-
self.vocab = dict(self.vocab) # so we can pickle later
|
| 168 |
-
if glove_embeddings is None:
|
| 169 |
-
self.glove_embeddings = None
|
| 170 |
-
else:
|
| 171 |
-
self.glove_embeddings = torch.stack([torch.zeros(GLOVE_DIM)] + [torch.Tensor(glove_embeddings[word]) for word in self.index2word[1:]], dim=0)
|
| 172 |
-
|
| 173 |
-
self.dataset_info = DatasetInfo(index2word=self.index2word,
|
| 174 |
-
word2index=self.word2index,
|
| 175 |
-
total_words=self.total_words,
|
| 176 |
-
vocab=self.vocab,
|
| 177 |
-
glove_embeddings=self.glove_embeddings)
|
| 178 |
-
|
| 179 |
-
if self.rhyme:
|
| 180 |
-
if args.rhyme_info is not None:
|
| 181 |
-
print('loading rhyme info from file')
|
| 182 |
-
with open(args.rhyme_info, 'rb') as rf:
|
| 183 |
-
self.rhyme_info = pickle.load(rf)
|
| 184 |
-
else:
|
| 185 |
-
self.rhyme_info = load_rhyme_info(self.index2word, self.vocab)
|
| 186 |
-
self.word2rhyme_group, self.rhyme_group_counts, self.rhyme_groups, self.index2rhyme_group, self.rhyme_group2index, self.total_rhyme_groups = \
|
| 187 |
-
defaultdict(lambda: UNKNOWN_RHYME_GROUP, self.rhyme_info.word2rhyme_group), self.rhyme_info.rhyme_group_counts, self.rhyme_info.rhyme_groups, self.rhyme_info.index2rhyme_group, self.rhyme_info.rhyme_group2index, self.rhyme_info.total_rhyme_groups
|
| 188 |
-
|
| 189 |
-
print('done loading data')
|
| 190 |
-
print('split sizes:')
|
| 191 |
-
for key in ['train', 'val', 'test']:
|
| 192 |
-
print(key, len(self.splits[key]))
|
| 193 |
-
if not self.formality:
|
| 194 |
-
print('total words', self.total_words)
|
| 195 |
-
print('vocab size', len(self.index2word))
|
| 196 |
-
|
| 197 |
-
|
| 198 |
-
def shuffle(self, split, seed=None):
|
| 199 |
-
assert split in ['train', 'val', 'test']
|
| 200 |
-
if seed is not None:
|
| 201 |
-
random.seed(seed)
|
| 202 |
-
random.shuffle(self.splits[split])
|
| 203 |
-
|
| 204 |
-
|
| 205 |
-
def loader(self, split, num_workers=20, indices=None):
|
| 206 |
-
assert split in ['train', 'val', 'test']
|
| 207 |
-
data = self.splits[split] if indices is None else [self.splits[split][i] for i in indices]
|
| 208 |
-
return torch.utils.data.DataLoader(SplitLoader(data, self), batch_size=self.batch_size, pin_memory=True, collate_fn=collate, num_workers=num_workers)
|
| 209 |
-
|
| 210 |
-
|
| 211 |
-
class SplitLoader(torch.utils.data.IterableDataset):
|
| 212 |
-
def __init__(self, data, parent):
|
| 213 |
-
super(SplitLoader).__init__()
|
| 214 |
-
self.data = data
|
| 215 |
-
self.pos = 0
|
| 216 |
-
self.parent = parent
|
| 217 |
-
|
| 218 |
-
|
| 219 |
-
def __len__(self):
|
| 220 |
-
return len(self.data)
|
| 221 |
-
|
| 222 |
-
|
| 223 |
-
def __iter__(self):
|
| 224 |
-
return self
|
| 225 |
-
|
| 226 |
-
|
| 227 |
-
def __next__(self):
|
| 228 |
-
increment = 1
|
| 229 |
-
worker_info = torch.utils.data.get_worker_info()
|
| 230 |
-
if worker_info is not None: # # in a worker process
|
| 231 |
-
increment = worker_info.num_workers
|
| 232 |
-
worker_id = worker_info.id
|
| 233 |
-
if self.pos == 0:
|
| 234 |
-
self.pos = worker_id
|
| 235 |
-
valid = False
|
| 236 |
-
while not valid:
|
| 237 |
-
if self.pos >= len(self):
|
| 238 |
-
raise StopIteration
|
| 239 |
-
if self.parent.topic:
|
| 240 |
-
failed = False
|
| 241 |
-
future_word_num_syllables, rhyme_group_index, syllables_to_go = -1, -1, -1
|
| 242 |
-
raw_sentence, classification_label = self.data[self.pos], -1
|
| 243 |
-
original_sentence = raw_sentence.split()
|
| 244 |
-
sentence = self.parent.tokenizer.encode(raw_sentence, return_tensors='pt')[0]
|
| 245 |
-
length = len(sentence)
|
| 246 |
-
min_sentence_length = MIN_SENTENCE_LENGTH
|
| 247 |
-
if len(sentence) > min_sentence_length: # set to 3. well, everything in data is > 3 for the bag of words task
|
| 248 |
-
pos_to_split = random.randint(1, length - 1) # for lm, learn all positions at once
|
| 249 |
-
inp = sentence[:pos_to_split]
|
| 250 |
-
length = len(inp)
|
| 251 |
-
num_words_in_input = len(self.parent.tokenizer.decode(inp).split())
|
| 252 |
-
if not failed and num_words_in_input < len(original_sentence):
|
| 253 |
-
future_word_position_max = len(original_sentence) - 1
|
| 254 |
-
future_word_position = random.randint(num_words_in_input-1, future_word_position_max) # allow the last possibly partial word though
|
| 255 |
-
future_word = original_sentence[future_word_position]
|
| 256 |
-
unstripped_future_word = future_word
|
| 257 |
-
future_word = future_word.strip().strip(string.punctuation) # NOTE: we didn't strip punctuation for the topic bag of words paper experiments for our method. it doesn't make much difference, though.
|
| 258 |
-
if not failed and future_word in self.parent.word2index.keys():
|
| 259 |
-
word_log_prob = math.log(self.parent.vocab[future_word] / self.parent.total_words) # roughly baseline prob of word under noise model
|
| 260 |
-
future_word = self.parent.word2index[future_word]
|
| 261 |
-
pad_id = self.parent.gpt_pad_id
|
| 262 |
-
example = (inp, length, future_word, word_log_prob, pad_id, classification_label, syllables_to_go, future_word_num_syllables, rhyme_group_index)
|
| 263 |
-
valid = not failed
|
| 264 |
-
elif self.parent.formality:
|
| 265 |
-
future_word_num_syllables, rhyme_group_index, syllables_to_go = -1, -1, -1
|
| 266 |
-
raw_sentence, classification_label = self.data[self.pos]
|
| 267 |
-
original_sentence = raw_sentence.split()
|
| 268 |
-
sentence = self.parent.tokenizer.encode(raw_sentence, return_tensors='pt')[0]
|
| 269 |
-
length = len(sentence)
|
| 270 |
-
min_sentence_length = MIN_SENTENCE_LENGTH
|
| 271 |
-
if len(sentence) > min_sentence_length: # set to 3. well, everything in data is > 3 for the bag of words task
|
| 272 |
-
pos_to_split = length # no need to split; we're going to train on all possible prefixes simultaneously for efficiency
|
| 273 |
-
inp = sentence[:pos_to_split]
|
| 274 |
-
length = len(inp)
|
| 275 |
-
num_words_in_input = len(self.parent.tokenizer.decode(inp).split())
|
| 276 |
-
# only look up to 10 words ahead if we're doing count syllables, since we'll filter out anything more than 10 syllables ahead anyway
|
| 277 |
-
future_word_position_max = len(original_sentence) - 1
|
| 278 |
-
future_word_position = 0
|
| 279 |
-
future_word = 'placeholder'
|
| 280 |
-
unstripped_future_word = future_word
|
| 281 |
-
future_word = future_word.strip().strip(string.punctuation) # NOTE: we didn't strip punctuation for the topic bag of words paper experiments for our method. it doesn't make much difference, though.
|
| 282 |
-
word_log_prob, future_word = 0, 0
|
| 283 |
-
pad_id = self.parent.gpt_pad_id
|
| 284 |
-
example = (inp, length, future_word, word_log_prob, pad_id, classification_label, syllables_to_go, future_word_num_syllables, rhyme_group_index)
|
| 285 |
-
valid = True
|
| 286 |
-
elif self.parent.iambic:
|
| 287 |
-
failed = False
|
| 288 |
-
future_word_num_syllables, rhyme_group_index, syllables_to_go = -1, -1, -1
|
| 289 |
-
raw_sentence, classification_label = self.data[self.pos], -1
|
| 290 |
-
original_sentence = raw_sentence.split()
|
| 291 |
-
sentence = self.parent.tokenizer.encode(raw_sentence, return_tensors='pt')[0]
|
| 292 |
-
length = len(sentence)
|
| 293 |
-
min_sentence_length = MIN_SENTENCE_LENGTH
|
| 294 |
-
if len(sentence) > min_sentence_length: # set to 3. well, everything in data is > 3 for the bag of words task
|
| 295 |
-
pos_to_split = random.randint(0, length - 1)
|
| 296 |
-
# try to get a subseq of exactly 10 syllables
|
| 297 |
-
inp = sentence[pos_to_split:]
|
| 298 |
-
num_syllables = 0
|
| 299 |
-
checked = False
|
| 300 |
-
for i in range(1, len(inp)):
|
| 301 |
-
decoded = self.parent.tokenizer.decode(inp[:i])
|
| 302 |
-
num_syllables = count_syllables(decoded)
|
| 303 |
-
if num_syllables > POETRY_LINE_SYLLABLES:
|
| 304 |
-
inp = inp[:i-1] # might get a few data points where the split is in the middle of a word, but it should be ok for learning.
|
| 305 |
-
last_line_length = i-1
|
| 306 |
-
decoded = self.parent.tokenizer.decode(inp)
|
| 307 |
-
num_syllables = count_syllables(decoded)
|
| 308 |
-
checked = True
|
| 309 |
-
break
|
| 310 |
-
if not checked or num_syllables != POETRY_LINE_SYLLABLES:
|
| 311 |
-
failed = True
|
| 312 |
-
length = len(inp)
|
| 313 |
-
num_words_in_input = len(self.parent.tokenizer.decode(inp).split())
|
| 314 |
-
classification_label = [is_iambic(self.parent.tokenizer.decode(inp)) for _ in range(length)] # predict for whole seq including future
|
| 315 |
-
# only look up to 10 words ahead if we're doing count syllables, since we'll filter out anything more than 10 syllables ahead anyway
|
| 316 |
-
future_word_position_max = len(original_sentence) - 1
|
| 317 |
-
future_word_position = 0
|
| 318 |
-
future_word = 'placeholder'
|
| 319 |
-
unstripped_future_word = future_word
|
| 320 |
-
future_word = future_word.strip().strip(string.punctuation) # NOTE: we didn't strip punctuation for the topic bag of words paper experiments for our method. it doesn't make much difference, though.
|
| 321 |
-
if not failed:
|
| 322 |
-
word_log_prob, future_word = 0, 0
|
| 323 |
-
pad_id = self.parent.gpt_pad_id
|
| 324 |
-
example = (inp, length, future_word, word_log_prob, pad_id, classification_label, syllables_to_go, future_word_num_syllables, rhyme_group_index)
|
| 325 |
-
valid = not failed
|
| 326 |
-
elif self.parent.rhyme:
|
| 327 |
-
failed = False
|
| 328 |
-
future_word_num_syllables, rhyme_group_index = -1, -1
|
| 329 |
-
raw_sentence, classification_label = self.data[self.pos], -1
|
| 330 |
-
original_sentence = raw_sentence.split()
|
| 331 |
-
sentence = self.parent.tokenizer.encode(raw_sentence, return_tensors='pt')[0]
|
| 332 |
-
length = len(sentence)
|
| 333 |
-
min_sentence_length = MIN_SENTENCE_LENGTH
|
| 334 |
-
if len(sentence) > min_sentence_length: # set to 3. well, everything in data is > 3 for the bag of words task
|
| 335 |
-
pos_to_split = random.randint(1, length - 1) # for lm, learn all positions at once
|
| 336 |
-
inp = sentence[:pos_to_split]
|
| 337 |
-
length = len(inp)
|
| 338 |
-
num_words_in_input = len(self.parent.tokenizer.decode(inp).split())
|
| 339 |
-
if not failed and num_words_in_input < len(original_sentence):
|
| 340 |
-
# only look up to 10 words ahead if we're doing count syllables, since we'll filter out anything more than 10 syllables ahead anyway
|
| 341 |
-
future_word_position_max = min(len(original_sentence) - 1, num_words_in_input + MAX_COUNT_SYLLABLE_DIST)
|
| 342 |
-
future_word_position = random.randint(num_words_in_input-1, future_word_position_max) # allow the last possibly partial word though
|
| 343 |
-
future_word = original_sentence[future_word_position]
|
| 344 |
-
unstripped_future_word = future_word
|
| 345 |
-
future_word = future_word.strip().strip(string.punctuation) # NOTE: we didn't strip punctuation for the topic bag of words paper experiments for our method. it doesn't make much difference, though.
|
| 346 |
-
|
| 347 |
-
words_in_between = original_sentence[num_words_in_input-1:future_word_position+1]
|
| 348 |
-
syllables_to_go = count_syllables(' '.join(words_in_between))
|
| 349 |
-
if syllables_to_go > MAX_COUNT_SYLLABLE_DIST:
|
| 350 |
-
failed = True
|
| 351 |
-
future_word_num_syllables = count_syllables(future_word)
|
| 352 |
-
rhyme_group = self.parent.word2rhyme_group[future_word]
|
| 353 |
-
rhyme_group_index = self.parent.rhyme_group2index[rhyme_group]
|
| 354 |
-
# truncate context a bit since we're just doing couplets. random length from 1 to max desired length for this purpose.
|
| 355 |
-
desired_length = random.randint(1, MAX_COUNT_SYLLABLE_INPUT_LENGTH)
|
| 356 |
-
inp = inp[-desired_length:]
|
| 357 |
-
length = len(inp)
|
| 358 |
-
|
| 359 |
-
if not failed and future_word in self.parent.word2index.keys():
|
| 360 |
-
word_log_prob = math.log(self.parent.rhyme_group_counts[rhyme_group] / self.parent.total_rhyme_groups)
|
| 361 |
-
future_word = rhyme_group_index # future conditioning is just the rhyme group in this case
|
| 362 |
-
pad_id = self.parent.gpt_pad_id
|
| 363 |
-
example = (inp, length, future_word, word_log_prob, pad_id, classification_label, syllables_to_go, future_word_num_syllables, rhyme_group_index)
|
| 364 |
-
valid = not failed
|
| 365 |
-
elif self.parent.newline:
|
| 366 |
-
failed = False
|
| 367 |
-
future_word_num_syllables, rhyme_group_index = -1, -1
|
| 368 |
-
raw_sentence, classification_label = self.data[self.pos], -1
|
| 369 |
-
original_sentence = raw_sentence.split()
|
| 370 |
-
sentence = self.parent.tokenizer.encode(raw_sentence, return_tensors='pt')[0]
|
| 371 |
-
length = len(sentence)
|
| 372 |
-
min_sentence_length = MIN_SENTENCE_LENGTH
|
| 373 |
-
if len(sentence) > min_sentence_length: # set to 3. well, everything in data is > 3 for the bag of words task
|
| 374 |
-
pos_to_split = random.randint(1, length - 1) # for lm, learn all positions at once
|
| 375 |
-
inp = sentence[:pos_to_split]
|
| 376 |
-
while pos_to_split < len(sentence):
|
| 377 |
-
if len(self.parent.tokenizer.decode(inp).split()) == len(self.parent.tokenizer.decode(sentence[:pos_to_split + 1]).split()):
|
| 378 |
-
pos_to_split += 1
|
| 379 |
-
inp = sentence[:pos_to_split]
|
| 380 |
-
else:
|
| 381 |
-
break
|
| 382 |
-
length = len(inp)
|
| 383 |
-
num_words_in_input = len(self.parent.tokenizer.decode(inp).split())
|
| 384 |
-
if not failed and num_words_in_input < len(original_sentence):
|
| 385 |
-
# only look up to 10 words ahead if we're doing count syllables, since we'll filter out anything more than 10 syllables ahead anyway
|
| 386 |
-
future_word_position_max = len(original_sentence) - 1
|
| 387 |
-
future_word_position = random.randint(num_words_in_input-1, future_word_position_max) # allow the last possibly partial word though
|
| 388 |
-
future_word = original_sentence[future_word_position]
|
| 389 |
-
unstripped_future_word = future_word
|
| 390 |
-
future_word = future_word.strip().strip(string.punctuation) # NOTE: we didn't strip punctuation for the topic bag of words paper experiments for our method. it doesn't make much difference, though.
|
| 391 |
-
|
| 392 |
-
# future_word = original_sentence[-1] # useful for debugging
|
| 393 |
-
words_in_between = original_sentence[num_words_in_input-1:future_word_position+1]
|
| 394 |
-
syllables_to_go = count_syllables(' '.join(words_in_between))
|
| 395 |
-
if syllables_to_go > MAX_COUNT_SYLLABLE_DIST:
|
| 396 |
-
failed = True
|
| 397 |
-
# truncate context a bit since we're just doing couplets. random length from 1 to max desired length for this purpose.
|
| 398 |
-
desired_length = random.randint(1, MAX_COUNT_SYLLABLE_INPUT_LENGTH)
|
| 399 |
-
# desired_length = 10 # useful for debugging
|
| 400 |
-
inp = inp[-desired_length:]
|
| 401 |
-
length = len(inp)
|
| 402 |
-
true_label = 1 if unstripped_future_word.strip()[-1] in PHRASE_ENDS else 0 # common ways to end a phrase
|
| 403 |
-
classification_label = [-1 for _ in range(length)]
|
| 404 |
-
classification_label[-1] = true_label # only learn at the last position
|
| 405 |
-
if not failed and future_word in self.parent.word2index.keys():
|
| 406 |
-
word_log_prob = math.log(self.parent.vocab[future_word] / self.parent.total_words) # roughly baseline prob of word under noise model
|
| 407 |
-
future_word = self.parent.word2index[future_word]
|
| 408 |
-
pad_id = self.parent.gpt_pad_id
|
| 409 |
-
example = (inp, length, future_word, word_log_prob, pad_id, classification_label, syllables_to_go, future_word_num_syllables, rhyme_group_index)
|
| 410 |
-
valid = not failed
|
| 411 |
-
else:
|
| 412 |
-
raise NotImplementedError
|
| 413 |
-
|
| 414 |
-
self.pos += increment
|
| 415 |
-
return example
|
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