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- spaces/101-5/gpt4free/g4f/.v1/gpt4free/quora/mail.py +0 -80
- spaces/123Kumar/vits-uma-genshin-honkai123/README.md +0 -11
- spaces/1acneusushi/gradio-2dmoleculeeditor/data/ACDSee Ultimate v9.0.565 x64 [deepstatus] Serial Key keygen The Ultimate Guide to ACDSee Photo Editing Software.md +0 -172
- spaces/1acneusushi/gradio-2dmoleculeeditor/data/Command. .Conquer.Red.Alert2.Yuris.Revenge.REUP fitgirl repack Tips and tricks for mastering the game and defeating Yuri.md +0 -17
- spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Crack WinZip 24.0 What You Need to Know Before You Click.md +0 -26
- spaces/1gistliPinn/ChatGPT4/Examples/BIOMUTANT Password.md +0 -13
- spaces/1gistliPinn/ChatGPT4/Examples/Download Warcraft 3 Full _HOT_ Ko Can Cai Datl.md +0 -8
- spaces/1line/AutoGPT/tests/browse_tests.py +0 -26
- spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/3D Driving Class Game A Fun and Educational Driving Game for All Ages.md +0 -147
- spaces/1phancelerku/anime-remove-background/Enjoy Mortal Kombat 4 APK on Your Android Phone or Tablet.md +0 -102
- spaces/4Taps/SadTalker/README.md +0 -15
- spaces/801artistry/RVC801/demucs/audio.py +0 -172
- spaces/801artistry/RVC801/tools/torchgate/__init__.py +0 -12
- spaces/AIFILMS/StyleGANEX/datasets/inference_dataset.py +0 -22
- spaces/AIGC-Audio/Make_An_Audio/ldm/models/autoencoder.py +0 -474
- spaces/AIGC-Audio/Make_An_Audio/ldm/modules/encoders/CLAP/clap.py +0 -89
- spaces/AIWaves/Debate/src/agents/evolve.py +0 -17
- spaces/Abhilashvj/planogram-compliance/utils.py +0 -61
- spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/hteyun.py +0 -34
- spaces/Aer0xander/sd-to-diffusers/README.md +0 -14
- spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/holygrail/Factory.d.ts +0 -5
- spaces/AlgoveraAI/dcgan-crypto-punks/README.md +0 -38
- spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/configs/base.py +0 -56
- spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/eval_ijbc.py +0 -483
- spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_if.py +0 -1257
- spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py +0 -486
- spaces/Andy1621/uniformer_image_detection/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py +0 -92
- spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docker/Dockerfile +0 -75
- spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/openai/errors.py +0 -31
- spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/exp/upernet_global_small/test_config_g.py +0 -38
- spaces/Anonymous-sub/Rerender/ControlNet/gradio_scribble2image.py +0 -92
- spaces/Ariharasudhan/YoloV5/utils/plots.py +0 -575
- spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/namespaces.py +0 -107
- spaces/Audio-AGI/AudioSep/models/CLAP/training/distributed.py +0 -150
- spaces/Bart92/RVC_HF/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py +0 -90
- spaces/Benson/text-generation/Examples/Cmo Descargar Gratis Fuego Mx En El Ordenador Porttil Sin Bluestacks.md +0 -39
- spaces/Benson/text-generation/Examples/Descargar Bluecurve Home App.md +0 -76
- spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/formatters/_mapping.py +0 -23
- spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/pyparsing/common.py +0 -424
- spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/reduce_by_key.h +0 -23
- spaces/CVPR/WALT/mmdet/datasets/coco.py +0 -548
- spaces/CVPR/regionclip-demo/detectron2/engine/launch.py +0 -125
- spaces/CikeyQI/Yunzai/Yunzai/lib/tools/command.js +0 -118
- spaces/CikeyQI/meme-api/meme_generator/memes/douyin/__init__.py +0 -78
- spaces/Cletrason/Cletrason-toad-mario-movie/gradio_utils.py +0 -98
- spaces/CofAI/chat/g4f/Provider/Providers/DeepAi.py +0 -46
- spaces/CuriousDolphin/MobileSAM/README.md +0 -45
- spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/mtiLib/__init__.py +0 -1402
- spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio_client/serializing.py +0 -548
- spaces/DShrimp/PoseMaker/src/model.py +0 -219
spaces/101-5/gpt4free/g4f/.v1/gpt4free/quora/mail.py
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from json import loads
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from re import findall
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from time import sleep
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from fake_useragent import UserAgent
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from requests import Session
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class Emailnator:
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def __init__(self) -> None:
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self.client = Session()
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self.client.get("https://www.emailnator.com/", timeout=6)
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self.cookies = self.client.cookies.get_dict()
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self.client.headers = {
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"authority": "www.emailnator.com",
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"origin": "https://www.emailnator.com",
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"referer": "https://www.emailnator.com/",
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"user-agent": UserAgent().random,
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"x-xsrf-token": self.client.cookies.get("XSRF-TOKEN")[:-3] + "=",
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}
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self.email = None
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def get_mail(self):
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response = self.client.post(
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"https://www.emailnator.com/generate-email",
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json={
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"email": [
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"domain",
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"plusGmail",
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"dotGmail",
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]
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},
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)
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self.email = loads(response.text)["email"][0]
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return self.email
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def get_message(self):
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print("Waiting for message...")
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while True:
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sleep(2)
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mail_token = self.client.post("https://www.emailnator.com/message-list", json={"email": self.email})
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mail_token = loads(mail_token.text)["messageData"]
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if len(mail_token) == 2:
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print("Message received!")
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print(mail_token[1]["messageID"])
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break
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mail_context = self.client.post(
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"https://www.emailnator.com/message-list",
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json={
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"email": self.email,
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"messageID": mail_token[1]["messageID"],
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},
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)
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return mail_context.text
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def get_verification_code(self):
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message = self.get_message()
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code = findall(r';">(\d{6,7})</div>', message)[0]
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print(f"Verification code: {code}")
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return code
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def clear_inbox(self):
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print("Clearing inbox...")
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self.client.post(
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"https://www.emailnator.com/delete-all",
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json={"email": self.email},
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)
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print("Inbox cleared!")
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def __del__(self):
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if self.email:
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self.clear_inbox()
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spaces/123Kumar/vits-uma-genshin-honkai123/README.md
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---
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license: apache-2.0
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title: ' vits-uma-genshin-honkai'
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sdk: gradio
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sdk_version: 3.7
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emoji: 🐨
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colorTo: yellow
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pinned: false
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app_file: app.py
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duplicated_from: ikechan8370/vits-uma-genshin-honkai
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---
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/ACDSee Ultimate v9.0.565 x64 [deepstatus] Serial Key keygen The Ultimate Guide to ACDSee Photo Editing Software.md
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<h1>ACDSee Ultimate v9.0.565 x64 [deepstatus] Serial Key keygen</h1>
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<p>If you are looking for a powerful and versatile photo editing and management software, you might want to check out ACDSee Ultimate v9.0.565 x64 [deepstatus] Serial Key keygen. This is a torrent file that contains the full version of ACDSee Ultimate v9.0.565 x64, a serial key to activate it, and a keygen to generate more serial keys if needed.</p>
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<h2>ACDSee Ultimate v9.0.565 x64 [deepstatus] Serial Key keygen</h2><br /><p><b><b>Download File</b> — <a href="https://byltly.com/2uKwj0">https://byltly.com/2uKwj0</a></b></p><br /><br />
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<h2>Introduction</h2>
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<p>In this article, we will explain what ACDSee Ultimate is, what are its features, why you need a serial key and a keygen for it, how to download and install it, and how to use it for photo editing and management.</p>
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<h3>What is ACDSee Ultimate?</h3>
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<p>ACDSee Ultimate is a comprehensive photo editing and management software that offers everything you need to work with your digital images. It combines the features of ACDSee Pro, a professional photo editor and organizer, with ACDSee Photo Editor, a creative photo editor and enhancer.</p>
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<p>With ACDSee Ultimate, you can import, organize, view, edit, enhance, export, and share your photos in one application. You can also work with RAW files, layers, masks, filters, effects, brushes, and more.</p>
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<h3>What are the features of ACDSee Ultimate v9.0.565 x64?</h3>
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<p>ACDSee Ultimate v9.0.565 x64 is the latest version of ACDSee Ultimate that was released in 2015. It has many features that make it a powerful and versatile photo editing and management software.</p>
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<ul>
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<li>It supports 64-bit architecture for faster performance and larger file handling.</li>
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<p>You need to accept the license agreement, choose the destination folder, select the components to install, and click on "Install" to start the installation.</p>
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| 92 |
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<p>The installation may take a few minutes, depending on your computer's speed and performance.</p>
|
| 93 |
-
<h3>Step 4: Use the serial key and the keygen to activate the software</h3>
|
| 94 |
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<p>The fourth and final step is to use the serial key and the keygen to activate ACDSee Ultimate v9.0.565 x64 on your computer.</p>
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| 95 |
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<p>To use the serial key and the keygen, you need to open the folder named "Crack" in the folder where you extracted the files of ACDSee Ultimate v9.0.565 x64 [deepstatus] Serial Key keygen.</p>
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| 96 |
-
<p>In this folder, you will find two files: "keygen.exe" and "serial.txt".</p>
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| 97 |
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<p>You need to run the file named "keygen.exe" as administrator by right-clicking on it and choosing "Run as administrator".</p>
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| 98 |
-
<p>This will open a window that shows a random serial key for ACDSee Ultimate v9.0.565 x64.</p>
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| 99 |
-
<p>You need to copy this serial key and paste it in a text editor, such as Notepad or WordPad.</p>
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| 100 |
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<p>You can also use the serial key that is already provided in the file named "serial.txt" in the same folder.</p>
|
| 101 |
-
<p>Once you have copied or noted down a serial key for ACDSee Ultimate v9.0.565 x64, you need to launch the software by double-clicking on its icon on your desktop or in your start menu.</p>
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| 102 |
-
<p>This will open a window that asks you to activate ACDSee Ultimate v9.0.565 x64 online or offline.</p>
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| 103 |
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<p>You need to choose "Offline Activation" and click on "Next".</p>
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| 104 |
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<p>This will open another window that shows a request code for ACDSee Ultimate v9.0.565 x64.</p>
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| 105 |
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<p>You need to copy this request code and paste it in the keygen window where it says "Enter your request code here".</p>
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| 106 |
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<p>Then, you need to click on "Generate" in the keygen window.</p>
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| 107 |
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<p>This will generate an activation code for ACDSee Ultimate v9.0.565 x64 based on your request code.</p>
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| 108 |
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<p>You need to copy this activation code and paste it in the activation window where it says "Enter your activation code here".</p>
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| 109 |
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<p>Then, you need to click on "Activate" in the activation window.</p>
|
| 110 |
-
<p>This will activate ACDSee Ultimate v9.0.565 x64 on your computer and allow you to use it without any limitations.</p>
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| 111 |
-
<h2>How to use ACDSee Ultimate v9.0.565 x64 for photo editing and management</h2>
|
| 112 |
-
<p>Now that you have downloaded, installed, and activated ACDSee Ultimate v9.0.565 x64 on your computer, you can start using it for photo editing and management.</p>
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| 113 |
-
<p>In this section, we will show you how to import and organize your photos with ACDSee Ultimate v9.0.565 x64, how to edit your photos with ACDSee Ultimate v9.0.565 x64, and how to export and share your photos with ACDSee Ultimate v9.0.565 x64.</p>
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| 114 |
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<h3>How to import and organize your photos with ACDSee Ultimate v9.0.565 x64</h3>
|
| 115 |
-
<p>The first thing you need to do with ACDSee Ultimate v9.0.565 x64 is to import and organize your photos in its database.</p>
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<p>To import and organize your photos with ACDSee Ultimate v9.0.565 x64, you need to follow these steps:</p>
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| 117 |
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<ol>
|
| 118 |
-
<li>Open ACDSee Ultimate v9.0.565 x64 and switch to the Manage mode by clicking on its icon at the top left corner of the interface or pressing F1 on your keyboard.</li>
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| 119 |
-
<li>In the Manage mode, you can see a panel on the left side that shows various sources of your photos, such as folders, devices, cloud services, etc.</li>
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| 120 |
-
<li>To import photos from a folder on your computer or an external drive, you need to browse to that folder in the panel and select it.</li>
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| 121 |
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<li>To import photos from a device such as a camera or a smartphone, you need to connect that device to your computer via USB cable or Wi-Fi and select it in the panel.</li>
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<li>To import photos from a cloud service such as OneDrive or Dropbox, you need to sign in to that service with your account credentials and select it in the panel.</li>
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| 123 |
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the main area of the interface.</li>
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-
<li>To import photos from the grid to the database of ACDSee Ultimate v9.0.565 x64, you need to select the photos you want to import by clicking on them or using the Ctrl or Shift keys on your keyboard.</li>
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| 125 |
-
<li>Then, you need to click on the "Import" button at the top right corner of the interface or press Ctrl+I on your keyboard.</li>
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<li>This will open a window that lets you choose the destination folder, the file name format, the metadata options, and the import options for your photos.</li>
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| 127 |
-
<li>You can also create subfolders, rename files, add keywords, categories, ratings, and color labels to your photos in this window.</li>
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| 128 |
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<li>Once you have configured the import settings for your photos, you need to click on "Start Import" at the bottom right corner of the window.</li>
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| 129 |
-
<li>This will import your photos to the database of ACDSee Ultimate v9.0.565 x64 and show them in the destination folder in the panel.</li>
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| 130 |
-
</ol>
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-
<p>Once you have imported your photos to the database of ACDSee Ultimate v9.0.565 x64, you can organize them in various ways.</p>
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<p>Some of the ways to organize your photos with ACDSee Ultimate v9.0.565 x64 are:</p>
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<ul>
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-
<li>You can sort your photos by various criteria, such as name, date, size, rating, etc., by clicking on the "Sort By" button at the top right corner of the interface or pressing Ctrl+T on your keyboard.</li>
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| 135 |
-
<li>You can filter your photos by various criteria, such as keywords, categories, ratings, color labels, etc., by clicking on the "Filter By" button at the top right corner of the interface or pressing Ctrl+F on your keyboard.</li>
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| 136 |
-
<li>You can group your photos by various criteria, such as date, camera model, lens model, etc., by clicking on the "Group By" button at the top right corner of the interface or pressing Ctrl+G on your keyboard.</li>
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| 137 |
-
<li>You can create collections of your photos based on common themes or projects by clicking on the "Collections" tab in the panel and dragging and dropping your photos to a new or existing collection.</li>
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| 138 |
-
<li>You can create smart collections of your photos based on dynamic rules that automatically update as you add or modify your photos by clicking on the "Smart Collections" tab in the panel and creating a new smart collection with a name and a set of rules.</li>
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| 139 |
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</ul>
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| 140 |
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<h3>How to edit your photos with ACDSee Ultimate v9.0.565 x64</h3>
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| 141 |
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<p>The next thing you need to do with ACDSee Ultimate v9.0.565 x64 is to edit your photos to enhance their appearance and quality.</p>
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-
<p>To edit your photos with ACDSee Ultimate v9.0.565 x64, you need to follow these steps:</p>
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| 143 |
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<ol>
|
| 144 |
-
<li>Select a photo you want to edit in the Manage mode by clicking on it or using the arrow keys on your keyboard.</li>
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| 145 |
-
<li>Switch to the Develop mode by clicking on its icon at the top left corner of the interface or pressing F2 on your keyboard.</li>
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| 146 |
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<li>In the Develop mode, you can see a panel on the right side that shows various tools and settings for processing RAW files and making non-destructive adjustments to your photo.</li>
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the bottom right corner of the window.</li>
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<li>This will export your photos to the destination folder or share them via email or social media, depending on your choice.</li>
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</ol>
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<h2>Conclusion</h2>
|
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<p>In this article, we have explained what ACDSee Ultimate is, what are its features, why you need a serial key and a keygen for it, how to download and install it, and how to use it for photo editing and management.</p>
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<p>We have also provided a link to download ACDSee Ultimate v9.0.565 x64 [deepstatus] Serial Key keygen, which is a torrent file that contains the full version of ACDSee Ultimate v9.0.565 x64, a serial key to activate it, and a keygen to generate more serial keys if needed.</p>
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<p>By using ACDSee Ultimate v9.0.565 x64 [deepstatus] Serial Key keygen, you can enjoy a powerful and versatile photo editing and management software for free.</p>
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<p>However, we do not endorse or support piracy or illegal downloading of software. If you like ACDSee Ultimate v9.0.565 x64 and find it useful, you should buy it from its official website: https://www.acdsee.com/en/products/acdsee-ultimate</p>
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<p>This way, you can support the developers of ACDSee Ultimate v9.0.565 x64 and get access to updates, support, and more features.</p>
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<h2>FAQs</h2>
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<p>Here are some frequently asked questions about ACDSee Ultimate v9.0.565 x64 [deepstatus] Serial Key keygen:</p>
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<ol>
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<li>Q: Is ACDSee Ultimate v9.0.565 x64 compatible with Windows 10?</li>
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<li>A: Yes, ACDSee Ultimate v9.0.565 x64 is compatible with Windows 10 and other versions of Windows from Windows 7 to Windows 8.1.</li>
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<li>Q: Is ACDSee Ultimate v9.0.565 x64 safe to download and use?</li>
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<li>A: ACDSee Ultimate v9.0.565 x64 [deepstatus] Serial Key keygen is safe to download and use as long as you download it from [deepstatus], which is a trusted source of cracked software torrents.</li>
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<li>Q: Can I use ACDSee Ultimate v9.0.565 x64 on multiple computers?</li>
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<li>A: Yes, you can use ACDSee Ultimate v9.0.565 x64 on multiple computers as long as you have a valid serial key and a keygen for each computer.</li>
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<li>Q: Can I update ACDSee Ultimate v9.0.565 x64 to a newer version?</li>
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<li>A: No, you cannot update ACDSee Ultimate v9.0.565 x64 to a newer version as it will invalidate your serial key and activation code.</li>
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<li>Q: Can I get technical support for ACDSee Ultimate v9.0.565 x64?</li>
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<li>A: No, you cannot get technical support for ACDSee Ultimate v9.0.565 x64 as it is an illegal copy of the software.</li>
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</ol>
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</p> 0a6ba089eb<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Command. .Conquer.Red.Alert2.Yuris.Revenge.REUP fitgirl repack Tips and tricks for mastering the game and defeating Yuri.md
DELETED
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<br />
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<h1>Command and Conquer Red Alert 2 Yuri's Revenge: A Classic RTS Game</h1>
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<p>If you are a fan of real-time strategy (RTS) games, you have probably heard of <strong>Command and Conquer Red Alert 2 Yuri's Revenge</strong>. This game is an expansion pack to <strong>Command and Conquer Red Alert 2</strong>, one of the most popular and acclaimed RTS games of all time. In this article, we will tell you everything you need to know about this classic game, including what it is, why it is so good, how to play it on Windows 10, and more.</p>
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<h2>Introduction</h2>
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<p><strong>Command and Conquer Red Alert 2 Yuri's Revenge</strong> is an expansion pack to <strong>Command and Conquer Red Alert 2</strong>, a RTS game developed by Westwood Studios and published by Electronic Arts in October 2001 as part of the Command and Conquer series . The game is set in an alternate history where the Soviet Union has invaded the United States, but is defeated by the Allied forces. However, Yuri, the former Soviet advisor who betrayed his leader Romanov, has his own plans for world domination. He has built a secret army of mind-controlled soldiers and devices that can manipulate time and space. The Allies must stop him before he succeeds in his evil scheme.</p>
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<p><strong>Command and Conquer Red Alert 2 Yuri's Revenge</strong> is a classic RTS game because it offers a thrilling and engaging gameplay experience that combines strategy, action, humor, and creativity. The game has three playable factions, each with its own unique units, buildings, abilities, and campaign. The game also has a multiplayer mode that allows you to play online or offline with other players. The game has excellent graphics, sound, music, voice acting, and cutscenes that immerse you in the game world. The game also has a vibrant modding community that creates new content for the game.</p>
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<p>If you want to play <strong>Command and Conquer Red Alert 2 Yuri's Revenge</strong> on Windows 10, you will need to buy a digital copy of the game from the EA Origin Store or use your physical copy if you have one. You will also need to download and install the CnCNet patch that fixes many bugs and compatibility issues with Windows 10. After that, you can enjoy playing this classic game on your modern PC.</p>
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<h2>Gameplay</h2>
|
| 10 |
-
<h3>Factions</h3>
|
| 11 |
-
<p><strong>Command and Conquer Red Alert 2 Yuri's Revenge</strong> has three playable factions: Allies, Soviets, and Yuri. Each faction has its own strengths, weaknesses, strategies, and playstyles. Here is a brief overview of each faction:</p>
|
| 12 |
-
<ul>
|
| 13 |
-
<li><strong>Allies</strong>: The Allies are the defenders of freedom and democracy. They have advanced technology and versatile units that can adapt to different situations. They have access to units such as Prism Tanks, Mirage Tanks, Rocketeers, Chrono Legionnaires, Harriers, Dolphins, Aircraft Carriers, IFVs (which can change their weapons depending on what infantry they carry), Spies (which can disguise as enemy infantry), Engineers (which can capture enemy buildings), Tanya (a commando who can destroy buildings with C4 charges), Chrono Miner (a miner that can teleport to ore fields), Weather Control Device (a superweapon that can unleash a devastating lightning storm), Chronosphere (a superweapon that can teleport units anywhere on the map), Force Shield (a defensive structure that can create a temporary invulnerable barrier), Gap Generator (a defensive structure that can create a fog of war around itself), Robot Control Center (a building that allows you to control robotic units such as Terror Drones), Battle Fortress (a heavy transport vehicle that can carry five infantry units), Guardian GI (an anti-tank infantry unit that can deploy into a stationary turret), Sniper (an infantry unit that can kill enemy infantry with one shot), Navy SEAL (a commando who can swim underwater and plant C4 charges on ships), Night Hawk Transport (a stealth transport helicopter), Black Eagle (a fast jet fighter), Grand Cannon (a long-range defensive cannon).</li>
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| 14 |
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<li><strong>Soviets</strong>: The Soviets are the aggressors of communism and tyranny. They have powerful weapons and brute force units that can overwhelm their enemies. They have access to units such as Rhino Tanks, Apocalypse Tanks, Kirov Airships, Tesla Troopers, Flak Troopers, Desolators (which can deploy into a radiation emitter), Terrorists (which can explode themselves near enemy units or buildings), Crazy Ivans (which can attach bombs to friendly or enemy units or buildings), Boris (a commando who can call airstrikes on enemy buildings), Tesla Coil (a defensive structure that can zap enemy units with electricity), Iron Curtain (a superweapon that can make units invulnerable for a short time), Nuclear Missile Silo (a superweapon that can launch a nuclear missile at any location on the map), Sentry Gun (a defensive structure that can shoot enemy infantry), Flak Cannon (a defensive structure that can shoot enemy aircraft), Industrial Plant (a building that reduces the cost of vehicles by 25%), Siege Chopper (a helicopter that can transform into an artillery unit), Tesla Tank (a tank that fires electric bolts), V3 Launcher (a long-range missile launcher), Terror Drone (a robotic unit that can infiltrate enemy vehicles and destroy them from within), Psychic Sensor (a building that reveals nearby enemy units on the radar), Nuclear Reactor (a power plant that provides more power than other power plants but explodes violently when destroyed).</li>
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| 15 |
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<li><strong>Yuri</strong>: Yuri is the mastermind of mind control and psychic warfare. He has exotic units and buildings that can manipulate his enemies' minds or use unconventional tactics. He has access to units such as Lasher Tanks</p> 0a6ba089eb<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Crack WinZip 24.0 What You Need to Know Before You Click.md
DELETED
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<h1>How to Download Crack WinZip 24.0 for Free</h1>
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<p>WinZip 24.0 is a popular software that allows you to compress, encrypt, and backup your files. It can also help you save disk space, reduce email attachment size, and access your files from anywhere. However, WinZip 24.0 is not a free software and you need to buy a license to use it without any limitations.</p>
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<p>But what if you don't want to pay for a license? Is there a way to download crack WinZip 24.0 for free? The answer is yes, but it comes with some risks and drawbacks. In this article, we will explain what crack WinZip 24.0 is, how to download it, and why you should avoid it.</p>
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<h2>download crack winzip 24.0</h2><br /><p><b><b>Download</b> ✵✵✵ <a href="https://byltly.com/2uKA8m">https://byltly.com/2uKA8m</a></b></p><br /><br />
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<h2>What is Crack WinZip 24.0?</h2>
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<p>Crack WinZip 24.0 is a modified version of WinZip 24.0 that bypasses the activation process and allows you to use the software without a license. Crack WinZip 24.0 usually comes with a keygen, which is a program that generates serial numbers and activation codes for the software.</p>
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<p>Crack WinZip 24.0 may sound tempting, but it is illegal and unethical to use. By using crack WinZip 24.0, you are violating the terms and conditions of the software and infringing the intellectual property rights of the developers. You are also depriving them of their rightful income and discouraging them from creating more quality products.</p>
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<p>If you still want to download crack WinZip 24.0, despite the legal and ethical issues, you need to be careful and cautious. Crack WinZip 24.0 is not available on the official website of WinZip or any authorized reseller. You can only find it on some shady websites or torrent sites that offer pirated software.</p>
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<p>To download crack WinZip 24.0, you need to search for it on Google or any other search engine and look for the links that claim to provide it. You may find some websites that offer direct downloads or some torrent sites that offer magnet links . You need to click on the link and follow the instructions to download the file.</p>
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<p>However, before you download crack WinZip 24.0, you need to be aware of the risks and drawbacks of doing so.</p>
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<p>There are many reasons why you should avoid downloading crack WinZip 24.0 and use the official version instead. Here are some of them:</p>
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<li><b>Crack WinZip 24.0 may contain viruses or malware</b>. Since crack WinZip 24.0 is not verified by any trusted source, there is no guarantee that it is safe and clean. It may contain viruses or malware that can harm your computer or steal your personal information.</li>
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<li><b>Crack WinZip 24.0 may not work properly or at all</b>. Since crack WinZip 24.0 is not updated or supported by the developers, there is no guarantee that it will work properly or at all. It may have bugs, errors, or compatibility issues that can affect your user experience or damage your files.</li>
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<li><b>Crack WinZip 24.0 may expose you to legal troubles</b>. Since crack WinZip 24.0 is illegal and unethical to use, you may face legal troubles if you are caught using it. You may be sued by the developers or prosecuted by the authorities for violating their rights and laws.</li>
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<li><b>Crack WinZip 24.0 may deprive you of the benefits of the official version</b>. Since crack WinZip 24.0 is not registered or activated by the developers, you may miss out on the benefits of the official version such as updates, support, features, security, etc.</li>
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</ul>
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<h2>Conclusion</h2>
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<p>WinZip 24.0 is a powerful software that can help you compress, encrypt, and backup your files. However, it is not a free software and you need to buy a license to use it without any limitations.</p>
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<p>If you want to download crack</p> ddb901b051<br />
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spaces/1gistliPinn/ChatGPT4/Examples/BIOMUTANT Password.md
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<h2>BIOMUTANT Password</h2><br /><p><b><b>DOWNLOAD</b> ☑ <a href="https://imgfil.com/2uxXoB">https://imgfil.com/2uxXoB</a></b></p><br /><br />
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Just spotted this on the dashboard, Biomutant is part of a free game this week... with new passwords and a new username and password for this site only for ... BIOMUTANT.
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How can I find out who did it?
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Did they hack it?What do I need to do?
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I don't want them to be able to enter their passwords and download cheats.
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Does anyone know what i can do
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I don't know why this site uses the Biomutant username and password, but that shouldn't be a problem for you.
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If they are using it to hack, you will need to find a way to terminate them.
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It might be useful for you if you find a list of available IPs so you can track where they're coming from. 8a78ff9644<br />
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spaces/1gistliPinn/ChatGPT4/Examples/Download Warcraft 3 Full _HOT_ Ko Can Cai Datl.md
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<br />
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<p>once the operation is completed, you will be taken to the results page which shows you the properties you selected. the properties are listed alphabetically, which makes the process of selecting a property to buy easy. the prices listed also include a premium for a high end property. this is clearly displayed under the price column.</p>
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<p>however, there are three additional features included that make the download tool pretty useful. firstly, the search feature is very easy to use. here, you can search for properties by the county in which they are located. you can also narrow down the search results by the number of rooms and the number of off-street parking spots. finally, the browse feature displays properties by counties and property owners.</p>
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<h2>Download Warcraft 3 Full Ko Can Cai Datl</h2><br /><p><b><b>DOWNLOAD</b> ———>>> <a href="https://imgfil.com/2uxX6J">https://imgfil.com/2uxX6J</a></b></p><br /><br />
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<p>the final useful feature of the download tool is the ability to create property alerts. this feature enables you to keep up to date with all of the properties you are interested in and what price they have sold for over the past 24 hours.</p>
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<p>ebook is the much more popular word than digital book, of course, but for a different reason. in most cases, a book is something you read or buy. a digital book is essentially something that you download. while this much more common now, it takes a while for the distinction to disappear and it is pretty rare to find a book that isn't also available as a digital book. these days, a digital book is something that you can read on your e-reader or tablet, in which case it is called an e-book.<br /> a lot of people like to actually read the words on an actual paper book and the advent of e-books didn't kill this desire. it just means that you can read the book wherever you want to, from any device that is capable of connecting to the web.</p> 899543212b<br />
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spaces/1line/AutoGPT/tests/browse_tests.py
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import os
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import sys
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import unittest
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from bs4 import BeautifulSoup
|
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sys.path.append(os.path.abspath("../scripts"))
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| 8 |
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|
| 9 |
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from browse import extract_hyperlinks
|
| 10 |
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| 11 |
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| 12 |
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class TestBrowseLinks(unittest.TestCase):
|
| 13 |
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def test_extract_hyperlinks(self):
|
| 14 |
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body = """
|
| 15 |
-
<body>
|
| 16 |
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<a href="https://google.com">Google</a>
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<a href="foo.html">Foo</a>
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<div>Some other crap</div>
|
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-
</body>
|
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"""
|
| 21 |
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soup = BeautifulSoup(body, "html.parser")
|
| 22 |
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links = extract_hyperlinks(soup, "http://example.com")
|
| 23 |
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self.assertEqual(
|
| 24 |
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links,
|
| 25 |
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[("Google", "https://google.com"), ("Foo", "http://example.com/foo.html")],
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)
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/3D Driving Class Game A Fun and Educational Driving Game for All Ages.md
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<h1>Download 3D Driving Class Game: A Fun and Realistic Way to Learn How to Drive</h1>
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<p>Do you want to learn how to drive a car in a fun and realistic way? Do you want to practice your driving skills in various scenarios and situations? Do you want to compete with other drivers online and show off your driving abilities? If you answered yes to any of these questions, then you should download 3D Driving Class game, a simulation app that lets you experience driving in a virtual world.</p>
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<h2>What is 3D Driving Class Game?</h2>
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<p>3D Driving Class game is a simulation app that allows you to practice driving in a virtual world. You can choose from different cars and maps, follow the traffic rules, take driving tests, and earn points. You can also join the online multiplayer mode, chat with other drivers, challenge yourself with different driving modes, and compare your scores with other players on the leaderboards.</p>
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<h2>download 3d driving class game</h2><br /><p><b><b>DOWNLOAD</b> 🗸 <a href="https://urlin.us/2uSUWC">https://urlin.us/2uSUWC</a></b></p><br /><br />
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<h3>Features of 3D Driving Class Game</h3>
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<h4>- Realistic driving scenarios and traffic rules</h4>
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<p>One of the main features of 3D Driving Class game is that it offers realistic driving scenarios and traffic rules. You can experience driving in various environments, such as city, countryside, highway, mountain, desert, snow, night, rain, etc. You can also follow the traffic rules, such as speed limit, traffic lights, stop signs, lane change, parking, etc. You can learn how to drive safely and legally in different situations.</p>
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<h4>- Various cars and maps to choose from</h4>
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<p>Another feature of 3D Driving Class game is that it offers various cars and maps to choose from. You can select from different car models, such as sedan, hatchback, SUV, sports car, bus, truck, etc. You can also customize your car's color, engine, tires, etc. You can also select from different maps, such as Seoul, Busan, Jeju Island, etc. You can explore different places and enjoy the scenery.</p>
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<h4>- Online multiplayer mode and leaderboards</h4>
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<p>A third feature of 3D Driving Class game is that it offers online multiplayer mode and leaderboards. You can join the online multiplayer mode and chat with other drivers. You can also challenge yourself with different driving modes, such as free drive, time trial, racing, drifting, etc. You can also compare your scores and rankings with other players on the leaderboards. You can show off your driving skills and have fun with other drivers.</p>
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<h2>How to Download 3D Driving Class Game?</h2>
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<p>If you are interested in downloading 3D Driving Class game, you can do so for your Android, iOS, or PC devices. Here are the steps and requirements for each device:</p>
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<h3>Download 3D Driving Class Game for Android</h3>
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<h4>- Steps to download from Google Play Store</h4>
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<p>If you have an Android device, you can download 3D Driving Class game from the Google Play Store. Here are the steps to do so:</p>
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<ol>
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<li>Open the Google Play Store app on your Android device.</li>
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<li>Search for "3D Driving Class" in the search bar.</li>
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<li>Select the app from the list of results and tap on "Install".</li>
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<li>Wait for the app to download and install on your device.</li>
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<li>Launch the app and enjoy playing 3D Driving Class game.</li>
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</ol>
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<h4>- Requirements and permissions for Android devices</h4>
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<p>To download and play 3D Driving Class game on your Android device, you need to meet the following requirements and permissions:</p>
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<p>Download 3D Driving Class - Apps on Google Play<br />
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Download Fast&Grand 3D Driving Class Online for Free<br />
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Download 3D Driving Class Test Your Skills in Various Vehicles<br />
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Download 3D Driving Class Experience Realistic Driving Simulator <br />
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Download 3D Driving Class Practice Your Skills in Various Environments</p>
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<ul>
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<li>Your device must have Android 4.1 or higher version.</li>
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<li>Your device must have at least 100 MB of free storage space.</li>
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<li>Your device must have a stable internet connection.</li>
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<li>Your device must allow access to photos, media, files, camera, microphone, location, and other features.</li>
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</ul>
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<h3>Download 3D Driving Class Game for iOS</h3>
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<h4>- Steps to download from App Store</h4>
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<p>If you have an iOS device, you can download 3D Driving Class game from the App Store. Here are the steps to do so:</p>
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| 77 |
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<ol>
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| 78 |
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<li>Open the App Store app on your iOS device.</li>
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| 79 |
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<li>Search for "3D Driving Class" in the search bar.</li>
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<li>Select the app from the list of results and tap on "Get".</li>
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<li>Enter your Apple ID and password if prompted.</li>
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<li>Wait for the app to download and install on your device.</li>
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<li>Launch the app and enjoy playing 3D Driving Class game.</li>
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</ol>
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<h4>- Requirements and permissions for iOS devices</h4>
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<p>To download and play 3D Driving Class game on your iOS device, you need to meet the following requirements and permissions:</p>
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<ul>
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| 88 |
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<li>Your device must have iOS 9.0 or higher version.</li>
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<li>Your device must have at least 100 MB of free storage space.</li>
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<li>Your device must have a stable internet connection.</li>
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<li>Your device must allow access to photos, media, files, camera, microphone, location, and other features.</li>
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</ul> <h3>Download 3D Driving Class Game for PC</h3>
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<h4>- Steps to download from Web Browser</h4>
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<p>If you have a PC device, you can download 3D Driving Class game from the Web Browser. Here are the steps to do so:</p>
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| 95 |
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<ol>
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| 96 |
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<li>Open your Web Browser on your PC device.</li>
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| 97 |
-
<li>Go to the official website of 3D Driving Class game: <a href="">https://www.3ddrivingclass.com/</a></li>
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| 98 |
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<li>Click on the "Download" button on the homepage.</li>
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<li>Select the version of the game that suits your PC device (Windows or Mac).</li>
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<li>Save the file to your preferred location on your PC device.</li>
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<li>Run the file and follow the instructions to install the game on your PC device.</li>
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<li>Launch the game and enjoy playing 3D Driving Class game.</li>
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</ol>
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<h4>- Requirements and permissions for PC devices</h4>
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<p>To download and play 3D Driving Class game on your PC device, you need to meet the following requirements and permissions:</p>
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<ul>
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| 107 |
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<li>Your device must have Windows 7 or higher version or Mac OS X 10.9 or higher version.</li>
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<li>Your device must have at least 2 GB of RAM and 500 MB of free storage space.</li>
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<li>Your device must have a stable internet connection.</li>
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<li>Your device must allow access to photos, media, files, camera, microphone, location, and other features.</li>
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</ul>
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| 112 |
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<h2>Tips and Tricks for Playing 3D Driving Class Game</h2>
|
| 113 |
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<p>Now that you have downloaded 3D Driving Class game, you might be wondering how to play it and improve your driving skills. Here are some tips and tricks for playing 3D Driving Class game:</p>
|
| 114 |
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<h3>How to Pass the Driving Tests and Earn Points</h3>
|
| 115 |
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<h4>- Follow the traffic rules and signs</h4>
|
| 116 |
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<p>The first tip for playing 3D Driving Class game is to follow the traffic rules and signs. You can learn how to drive safely and legally by following the speed limit, traffic lights, stop signs, lane change, parking, etc. You can also take driving tests that will evaluate your driving knowledge and skills. You can earn points by passing the driving tests and completing the missions.</p>
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| 117 |
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<h4>- Use the indicators, mirrors, and brakes properly</h4>
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| 118 |
-
<p>The second tip for playing 3D Driving Class game is to use the indicators, mirrors, and brakes properly. You can learn how to drive smoothly and efficiently by using the indicators, mirrors, and brakes properly. You can use the indicators to signal your intentions to other drivers, such as turning left or right, changing lanes, etc. You can use the mirrors to check your surroundings and blind spots, such as rear view mirror, side mirror, etc. You can use the brakes to slow down or stop your car safely, such as normal brake, emergency brake, etc.</p>
|
| 119 |
-
<h4>- Avoid collisions and accidents</h4>
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<p>The third tip for playing 3D Driving Class game is to avoid collisions and accidents. You can learn how to drive carefully and responsibly by avoiding collisions and accidents. You can avoid collisions and accidents by keeping a safe distance from other cars, pedestrians, animals, objects, etc. You can also avoid collisions and accidents by driving at a reasonable speed, avoiding distractions, being alert, etc.</p> <h3>How to Customize Your Car and Map</h3>
|
| 121 |
-
<h4>- Choose from different car models and colors</h4>
|
| 122 |
-
<p>The fourth tip for playing 3D Driving Class game is to choose from different car models and colors. You can customize your car according to your preference and style by choosing from different car models and colors. You can choose from different car models, such as sedan, hatchback, SUV, sports car, bus, truck, etc. You can also choose from different car colors, such as red, blue, green, yellow, black, white, etc.</p>
|
| 123 |
-
<h4>- Select from various maps and environments</h4>
|
| 124 |
-
<p>The fifth tip for playing 3D Driving Class game is to select from various maps and environments. You can customize your map according to your interest and mood by selecting from various maps and environments. You can select from various maps, such as Seoul, Busan, Jeju Island, etc. You can also select from various environments, such as city, countryside, highway, mountain, desert, snow, night, rain, etc.</p>
|
| 125 |
-
<h4>- Adjust the camera angle and sound settings</h4>
|
| 126 |
-
<p>The sixth tip for playing 3D Driving Class game is to adjust the camera angle and sound settings. You can customize your view and sound according to your comfort and convenience by adjusting the camera angle and sound settings. You can adjust the camera angle to change the perspective of your driving, such as first-person view, third-person view, top-down view, etc. You can also adjust the sound settings to change the volume and quality of your driving, such as engine sound, horn sound, music sound, etc.</p>
|
| 127 |
-
<h3>How to Compete with Other Players Online</h3>
|
| 128 |
-
<h4>- Join the online multiplayer mode and chat with other drivers</h4>
|
| 129 |
-
<p>The seventh tip for playing 3D Driving Class game is to join the online multiplayer mode and chat with other drivers. You can have fun and socialize with other drivers by joining the online multiplayer mode and chat with other drivers. You can join the online multiplayer mode by tapping on the "Online" button on the main menu. You can chat with other drivers by tapping on the "Chat" button on the screen. You can also send emojis and stickers to express your emotions.</p>
|
| 130 |
-
<h4>- Challenge yourself with different driving modes and levels</h4>
|
| 131 |
-
<p>The eighth tip for playing 3D Driving Class game is to challenge yourself with different driving modes and levels. You can test your driving skills and improve your driving performance by challenging yourself with different driving modes and levels. You can challenge yourself with different driving modes, such as free drive, time trial, racing, drifting, etc. You can also challenge yourself with different driving levels, such as beginner, intermediate, advanced, expert, etc.</p>
|
| 132 |
-
<h4>- Compare your scores and rankings with other players on the leaderboards</h4>
|
| 133 |
-
<p>The ninth tip for playing 3D Driving Class game is to compare your scores and rankings with other players on the leaderboards. You can measure your progress and achievements by comparing your scores and rankings with other players on the leaderboards. You can compare your scores by tapping on the "Score" button on the screen. You can also compare your rankings by tapping on the "Ranking" button on the screen. You can see how you rank among other players in terms of points, tests passed, missions completed, etc.</p>
|
| 134 |
-
<h2>Conclusion</h2>
|
| 135 |
-
<p>In conclusion, 3D Driving Class game is a simulation app that lets you experience driving in a virtual world. You can learn how to drive safely and legally in different scenarios and situations. You can also customize your car and map according to your preference and style. You can also have fun and socialize with other drivers online by joining the online multiplayer mode and chat with other drivers. You can also test your driving skills and improve your driving performance by challenging yourself with different driving modes and levels. You can also measure your progress and achievements by comparing your scores and rankings with other players on the leaderboards.</p>
|
| 136 |
-
<p>If you are looking for a fun and realistic way to learn how to drive a car, you should download 3D Driving Class game today. It is a simulation app that will teach you how to drive in a virtual world.</p>
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| 137 |
-
<h2>FAQs</h2>
|
| 138 |
-
<p>Here are some frequently asked questions about 3D Driving Class game:</p>
|
| 139 |
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<ol>
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| 140 |
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<li>Q: Is 3D Driving Class game free to download and play?<br>A: Yes, 3D Driving Class game is free to download and play. However, it may contain ads and in-app purchases that require real money.</li>
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<li>Q: Is 3D Driving Class game suitable for children?<br>A: Yes, 3D Driving Class game is suitable for children who want to learn how to drive a car in a fun way. However, parental guidance is recommended for some of the content and features of the game.</li>
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<li>Q: How can I contact the developer of 3D Driving Class game?<br>A: You can contact the developer of 3D Driving Class game by sending an email to <a href="mailto:[email protected]">[email protected]</a> or by visiting their Facebook page: <a href="https://www.facebook.com/3ddrivingclass/">https://www.facebook.com/3ddrivingclass/</a></li>
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<li>Q: How can I update 3D Driving Class game?<br>A: You can update 3D Driving Class game by checking for updates on the Google Play Store, App Store, or Web Browser. You can also enable automatic updates on your device settings.</li>
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<li>Q: How can I uninstall 3D Driving Class game?<br>A: You can uninstall 3D Driving Class game by following the usual steps for uninstalling apps on your device. You can also delete the app data and cache on your device settings.</li>
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<h1>Mortal Kombat 4 APKCombo: How to Download and Play the Classic Fighting Game on Your Android Device</h1>
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<h2>Introduction</h2>
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<p>If you are a fan of fighting games, you probably have heard of Mortal Kombat, one of the most popular and influential franchises in the genre. Mortal Kombat is known for its brutal and bloody combat, its iconic characters, and its rich lore and story. One of the best entries in the series is Mortal Kombat 4, which was released in 1997 for arcades and later ported to various platforms, including PlayStation, Nintendo 64, PC, and Game Boy Color.</p>
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<p>Mortal Kombat 4 was the first game in the series to use 3D graphics, while still retaining the classic 2D gameplay. It also introduced a weapon system, allowing each character to use a special weapon during fights. The game features 15 playable characters, plus two secret ones, and a variety of stages and modes. The story revolves around the attack of the corrupted Elder God Shinnok, who seeks to conquer all the realms with the help of his loyal servant Quan Chi. The heroes of Earthrealm must stop him and his army of darkness before it is too late.</p>
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<p>But what if you want to play Mortal Kombat 4 on your Android device? Is it possible? The answer is yes, thanks to APKCombo, a website that allows you to download APK files for Android games and apps. In this article, we will show you how to download and play Mortal Kombat 4 APKCombo on your Android device, so you can enjoy this classic fighting game anytime and anywhere.</p>
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<h3>Step 1: Visit the APKCombo website</h3>
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<p>The first step is to visit the <a href="(^1^)">APKCombo website</a>, which is a reliable source for downloading APK files for Android games and apps. APK files are the installation files for Android applications, similar to EXE files for Windows. By downloading APK files from APKCombo, you can access games and apps that are not available on Google Play Store, or that are not compatible with your device.</p>
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<h3>Step 2: Search for Mortal Kombat 4</h3>
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<p>The next step is to search for Mortal Kombat 4 on the APKCombo website. You can use the search bar at the top of the page, or browse through the categories and filters. You will see a list of results related to Mortal Kombat 4, such as <a href="(^2^)">mortal kombat armagedon</a>, <a href="(^3^)">Guide Mortal Kombat 4</a>, and others. You want to select the one that says <strong>Mortal Kombat</strong>, which is the official name of Mortal Kombat 4 on Android.</p>
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<h3>Step 3: Choose the version and device compatibility</h3>
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<p>Once you select Mortal Kombat, you will see a page with more information about the game, such as its description, screenshots, ratings, reviews, and more. You will also see a section that says <strong>Download APK - Latest Version</strong>, which shows you the latest version of the game available for download. You can click on it to see more details about the version, such as its size, date, requirements, and compatibility.</p> <p>Before you download the APK file, you should check if it is compatible with your device. You can do this by looking at the <strong>Compatibility</strong> section, which shows you the minimum Android version and the supported architectures for the APK file. For example, if your device has Android 5.0 or higher and an ARMv7 processor, you can download the APK file that says <strong>Android 5.0+ (arm-v7a)</strong>. If your device has a different Android version or architecture, you should look for another APK file that matches your device.</p>
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<p>If you are not sure about your device's Android version or architecture, you can check them by going to your device's settings and looking for the <strong>About phone</strong> or <strong>About device</strong> option. There, you will see information such as your device's model, software version, kernel version, and more. You can also use apps like <a href="">CPU-Z</a> or <a href="">Droid Hardware Info</a> to get more details about your device's specifications.</p>
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<h3>Step 4: Download the APK file</h3>
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<p>Once you have chosen the APK file that is compatible with your device, you can download it by clicking on the <strong>Download APK</strong> button. You will see a pop-up window that asks you to confirm the download and shows you the file name and size. You can click on <strong>OK</strong> to start the download, or <strong>Cancel</strong> to abort it.</p>
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<p>The download will start in the background and you can see the progress in your notification bar. Depending on your internet speed and the file size, the download may take a few minutes or longer. You can also pause or resume the download by tapping on the notification.</p>
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<p>Now, you can install the APK file by tapping on it and following the instructions on the screen. You may see a screen that shows you the permissions that the app requires, such as access to your storage, camera, microphone, etc. Review them carefully and tap on <strong>Install</strong>. The installation will take a few seconds or longer, depending on your device's performance.</p>
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<h2>How to Play Mortal Kombat 4 APKCombo</h2>
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<h3>Step 1: Launch the game</h3>
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<p>After installing the APK file, you can launch the game by tapping on its icon on your home screen or app drawer. You will see a splash screen with the game's logo and then a loading screen with some tips and trivia. Wait for the game to load completely and then tap on the screen to continue.</p>
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<h3>Step 2: Choose your game mode and character</h3>
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<p>You will see a main menu with several options, such as <strong>New Game</strong>, <strong>Arcade Mode</strong>, <strong>Versus Mode</strong>, <strong>Tournament Mode</strong>, <strong>Practice Mode</strong>, <strong>Cheat Menu</strong>, and <strong>Options</strong>. You can choose any of these options depending on your preference and mood.</p>
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<p>If you want to play a single-player campaign with a storyline and cutscenes, choose <strong>New Game</strong>. If you want to play a classic arcade mode with a series of fights against different opponents, choose <strong>Arcade Mode</strong>. If you want to play against another player on the same device, choose <strong>Versus Mode</strong>. If you want to play in a tournament with up to 8 players on the same device, choose <strong>Tournament Mode</strong>. If you want to practice your moves and combos without any pressure, choose <strong>Practice Mode</strong <p>If you want to access some hidden features and cheats, such as changing the difficulty, unlocking characters, and enabling fatalities, choose <strong>Cheat Menu</strong>. If you want to adjust some settings, such as the sound, the controls, and the language, choose <strong>Options</strong>.</p>
|
| 78 |
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<p>After choosing your game mode, you will see a character selection screen with 15 playable characters, plus two secret ones. You can scroll through the characters by swiping left or right on the screen, or by using the arrows at the bottom. You can also see some information about each character, such as their name, origin, fighting style, and weapon. To select a character, tap on their portrait and confirm your choice by tapping on <strong>OK</strong>.</p>
|
| 79 |
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<h3>Step 3: Enjoy the gameplay and features</h3>
|
| 80 |
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<p>Once you have selected your character, you will see a stage selection screen with various locations to fight in. You can choose any stage by tapping on it, or let the game choose one randomly by tapping on <strong>Random</strong>. After choosing your stage, the game will start and you will see your character and your opponent facing each other.</p>
|
| 81 |
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<p>The gameplay of Mortal Kombat 4 APKCombo is similar to the original game, with some adaptations for the touch screen. You can move your character by using a virtual joystick on the left side of the screen, and perform attacks by using buttons on the right side of the screen. You can also use gestures to perform special moves and combos, such as swiping up, down, left, or right. You can see your health bar and your weapon bar at the top of the screen, and your opponent's health bar and weapon bar at the bottom of the screen.</p>
|
| 82 |
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<p>The goal of each fight is to deplete your opponent's health bar before they deplete yours. You can do this by using various attacks, such as punches, kicks, throws, weapons, and special moves. Each character has their own set of moves and abilities that you can discover by experimenting or by consulting the <strong>Move List</strong> option in the pause menu. You can also block your opponent's attacks by pressing the <strong>Block</strong> button on the right side of the screen.</p>
|
| 83 |
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<p>Each fight consists of two rounds, and you need to win two rounds to win the fight. If both you and your opponent have the same amount of health at the end of a round, it will result in a draw and a third round will be played. If you win a fight, you will see a victory pose and a message from your character. If you lose a fight, you will see a defeat pose and a message from your opponent.</p>
|
| 84 |
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<p>One of the most distinctive features of Mortal Kombat 4 APKCombo is the <strong>Fatality</strong>, which is a finishing move that you can perform on your opponent after winning the final round. A fatality is a brutal and gruesome attack that kills your opponent in a spectacular way, such as ripping their head off, impaling them with a weapon, or burning them alive. To perform a fatality, you need to follow a specific sequence of buttons or gestures that varies for each character and each stage. You can find out how to perform fatalities by using the <strong>Cheat Menu</strong> option in the main menu.</p>
|
| 85 |
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<h2>Conclusion</h2>
|
| 86 |
-
<p>Mortal Kombat 4 APKCombo is a great way to enjoy one of the best fighting games ever made on your Android device. It offers an authentic and faithful adaptation of the original game, with stunning 3D graphics, smooth gameplay, and tons of features and modes. You can download and play Mortal Kombat 4 APKCombo for free from APKCombo website, which is a reliable source for APK files for Android games and apps. All you need to do is follow the steps we have shown you in this article, and you will be ready to enter the realm of Mortal Kombat.</p>
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| 87 |
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<h2>FAQs</h2>
|
| 88 |
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<p>Here are some frequently asked questions about Mortal Kombat 4 APKCombo:</p>
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| 89 |
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<ul>
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| 90 |
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<li><strong>Q: Is Mortal Kombat 4 APKCombo safe to download and install?</strong></li>
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<li><strong>A: Yes,</strong> Mortal Kombat 4 APKCombo is safe to download and install from APKCombo website, which is a trusted source for APK files for Android games and apps. However, you should always be careful when downloading APK files from unknown sources, as they may contain malware or viruses that can harm your device. You should also scan any APK file with an antivirus app before installing it.</li>
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| 92 |
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<li><strong>Q: Is Mortal Kombat 4 APKCombo legal to download and play?</strong></li>
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<li><strong>A: Yes,</strong> Mortal Kombat 4 APKCombo is legal to download and play, as long as you own the original game or have the permission of the developer or publisher. APKCombo does not host or distribute any pirated or illegal content, and only provides links to APK files that are available on other websites. However, you should always respect the intellectual property rights of the creators and owners of the games and apps you download and play.</li>
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<li><strong>Q: How can I update Mortal Kombat 4 APKCombo to the latest version?</strong></li>
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<li><strong>A: To update Mortal Kombat 4 APKCombo to the latest version,</strong> you need to visit the APKCombo website again and look for the latest version of the game available for download. You can also use the <strong>Update</strong> option in the game's menu, which will redirect you to the APKCombo website. You can then download and install the new APK file over the old one, without losing your progress or data.</li>
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<li><strong>Q: How can I uninstall Mortal Kombat 4 APKCombo from my device?</strong></li>
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<li><strong>A: To uninstall Mortal Kombat 4 APKCombo from your device,</strong> you need to go to your device's settings and look for the <strong>Apps</strong> or <strong>Applications</strong> option. There, you will see a list of all the apps and games installed on your device. You can scroll through the list and find Mortal Kombat 4 APKCombo, and then tap on it. You will see a screen that shows you some information about the app, such as its size, permissions, storage, and more. You can also see an option that says <strong>Uninstall</strong>. Tap on it and confirm your choice by tapping on <strong>OK</strong>. The app will be removed from your device in a few seconds.</li>
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<li><strong>Q: Where can I find more information and tips about Mortal Kombat 4 APKCombo?</strong></li>
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<li><strong>A: If you want to find more information and tips about Mortal Kombat 4 APKCombo,</strong> you can visit the official website of the game, which is <a href="">https://www.mortalkombat.com/</a>. There, you can find news, updates, videos, screenshots, wallpapers, forums, and more. You can also visit some fan websites and blogs, such as <a href="">https://www.mortalkombatwarehouse.com/</a>, <a href="">https://www.mksecrets.net/</a>, and <a href="">https://www.mortalkombatonline.com/</a>. There, you can find guides, cheats, secrets, trivia, fan art, fan fiction, and more.</li>
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spaces/4Taps/SadTalker/README.md
DELETED
|
@@ -1,15 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: SadTalker
|
| 3 |
-
emoji: 😭
|
| 4 |
-
colorFrom: purple
|
| 5 |
-
colorTo: green
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.23.0
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
license: mit
|
| 11 |
-
duplicated_from: vinthony/SadTalker
|
| 12 |
-
---
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
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spaces/801artistry/RVC801/demucs/audio.py
DELETED
|
@@ -1,172 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Facebook, Inc. and its 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 |
-
import json
|
| 7 |
-
import subprocess as sp
|
| 8 |
-
from pathlib import Path
|
| 9 |
-
|
| 10 |
-
import julius
|
| 11 |
-
import numpy as np
|
| 12 |
-
import torch
|
| 13 |
-
|
| 14 |
-
from .utils import temp_filenames
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
def _read_info(path):
|
| 18 |
-
stdout_data = sp.check_output([
|
| 19 |
-
'ffprobe', "-loglevel", "panic",
|
| 20 |
-
str(path), '-print_format', 'json', '-show_format', '-show_streams'
|
| 21 |
-
])
|
| 22 |
-
return json.loads(stdout_data.decode('utf-8'))
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
class AudioFile:
|
| 26 |
-
"""
|
| 27 |
-
Allows to read audio from any format supported by ffmpeg, as well as resampling or
|
| 28 |
-
converting to mono on the fly. See :method:`read` for more details.
|
| 29 |
-
"""
|
| 30 |
-
def __init__(self, path: Path):
|
| 31 |
-
self.path = Path(path)
|
| 32 |
-
self._info = None
|
| 33 |
-
|
| 34 |
-
def __repr__(self):
|
| 35 |
-
features = [("path", self.path)]
|
| 36 |
-
features.append(("samplerate", self.samplerate()))
|
| 37 |
-
features.append(("channels", self.channels()))
|
| 38 |
-
features.append(("streams", len(self)))
|
| 39 |
-
features_str = ", ".join(f"{name}={value}" for name, value in features)
|
| 40 |
-
return f"AudioFile({features_str})"
|
| 41 |
-
|
| 42 |
-
@property
|
| 43 |
-
def info(self):
|
| 44 |
-
if self._info is None:
|
| 45 |
-
self._info = _read_info(self.path)
|
| 46 |
-
return self._info
|
| 47 |
-
|
| 48 |
-
@property
|
| 49 |
-
def duration(self):
|
| 50 |
-
return float(self.info['format']['duration'])
|
| 51 |
-
|
| 52 |
-
@property
|
| 53 |
-
def _audio_streams(self):
|
| 54 |
-
return [
|
| 55 |
-
index for index, stream in enumerate(self.info["streams"])
|
| 56 |
-
if stream["codec_type"] == "audio"
|
| 57 |
-
]
|
| 58 |
-
|
| 59 |
-
def __len__(self):
|
| 60 |
-
return len(self._audio_streams)
|
| 61 |
-
|
| 62 |
-
def channels(self, stream=0):
|
| 63 |
-
return int(self.info['streams'][self._audio_streams[stream]]['channels'])
|
| 64 |
-
|
| 65 |
-
def samplerate(self, stream=0):
|
| 66 |
-
return int(self.info['streams'][self._audio_streams[stream]]['sample_rate'])
|
| 67 |
-
|
| 68 |
-
def read(self,
|
| 69 |
-
seek_time=None,
|
| 70 |
-
duration=None,
|
| 71 |
-
streams=slice(None),
|
| 72 |
-
samplerate=None,
|
| 73 |
-
channels=None,
|
| 74 |
-
temp_folder=None):
|
| 75 |
-
"""
|
| 76 |
-
Slightly more efficient implementation than stempeg,
|
| 77 |
-
in particular, this will extract all stems at once
|
| 78 |
-
rather than having to loop over one file multiple times
|
| 79 |
-
for each stream.
|
| 80 |
-
|
| 81 |
-
Args:
|
| 82 |
-
seek_time (float): seek time in seconds or None if no seeking is needed.
|
| 83 |
-
duration (float): duration in seconds to extract or None to extract until the end.
|
| 84 |
-
streams (slice, int or list): streams to extract, can be a single int, a list or
|
| 85 |
-
a slice. If it is a slice or list, the output will be of size [S, C, T]
|
| 86 |
-
with S the number of streams, C the number of channels and T the number of samples.
|
| 87 |
-
If it is an int, the output will be [C, T].
|
| 88 |
-
samplerate (int): if provided, will resample on the fly. If None, no resampling will
|
| 89 |
-
be done. Original sampling rate can be obtained with :method:`samplerate`.
|
| 90 |
-
channels (int): if 1, will convert to mono. We do not rely on ffmpeg for that
|
| 91 |
-
as ffmpeg automatically scale by +3dB to conserve volume when playing on speakers.
|
| 92 |
-
See https://sound.stackexchange.com/a/42710.
|
| 93 |
-
Our definition of mono is simply the average of the two channels. Any other
|
| 94 |
-
value will be ignored.
|
| 95 |
-
temp_folder (str or Path or None): temporary folder to use for decoding.
|
| 96 |
-
|
| 97 |
-
|
| 98 |
-
"""
|
| 99 |
-
streams = np.array(range(len(self)))[streams]
|
| 100 |
-
single = not isinstance(streams, np.ndarray)
|
| 101 |
-
if single:
|
| 102 |
-
streams = [streams]
|
| 103 |
-
|
| 104 |
-
if duration is None:
|
| 105 |
-
target_size = None
|
| 106 |
-
query_duration = None
|
| 107 |
-
else:
|
| 108 |
-
target_size = int((samplerate or self.samplerate()) * duration)
|
| 109 |
-
query_duration = float((target_size + 1) / (samplerate or self.samplerate()))
|
| 110 |
-
|
| 111 |
-
with temp_filenames(len(streams)) as filenames:
|
| 112 |
-
command = ['ffmpeg', '-y']
|
| 113 |
-
command += ['-loglevel', 'panic']
|
| 114 |
-
if seek_time:
|
| 115 |
-
command += ['-ss', str(seek_time)]
|
| 116 |
-
command += ['-i', str(self.path)]
|
| 117 |
-
for stream, filename in zip(streams, filenames):
|
| 118 |
-
command += ['-map', f'0:{self._audio_streams[stream]}']
|
| 119 |
-
if query_duration is not None:
|
| 120 |
-
command += ['-t', str(query_duration)]
|
| 121 |
-
command += ['-threads', '1']
|
| 122 |
-
command += ['-f', 'f32le']
|
| 123 |
-
if samplerate is not None:
|
| 124 |
-
command += ['-ar', str(samplerate)]
|
| 125 |
-
command += [filename]
|
| 126 |
-
|
| 127 |
-
sp.run(command, check=True)
|
| 128 |
-
wavs = []
|
| 129 |
-
for filename in filenames:
|
| 130 |
-
wav = np.fromfile(filename, dtype=np.float32)
|
| 131 |
-
wav = torch.from_numpy(wav)
|
| 132 |
-
wav = wav.view(-1, self.channels()).t()
|
| 133 |
-
if channels is not None:
|
| 134 |
-
wav = convert_audio_channels(wav, channels)
|
| 135 |
-
if target_size is not None:
|
| 136 |
-
wav = wav[..., :target_size]
|
| 137 |
-
wavs.append(wav)
|
| 138 |
-
wav = torch.stack(wavs, dim=0)
|
| 139 |
-
if single:
|
| 140 |
-
wav = wav[0]
|
| 141 |
-
return wav
|
| 142 |
-
|
| 143 |
-
|
| 144 |
-
def convert_audio_channels(wav, channels=2):
|
| 145 |
-
"""Convert audio to the given number of channels."""
|
| 146 |
-
*shape, src_channels, length = wav.shape
|
| 147 |
-
if src_channels == channels:
|
| 148 |
-
pass
|
| 149 |
-
elif channels == 1:
|
| 150 |
-
# Case 1:
|
| 151 |
-
# The caller asked 1-channel audio, but the stream have multiple
|
| 152 |
-
# channels, downmix all channels.
|
| 153 |
-
wav = wav.mean(dim=-2, keepdim=True)
|
| 154 |
-
elif src_channels == 1:
|
| 155 |
-
# Case 2:
|
| 156 |
-
# The caller asked for multiple channels, but the input file have
|
| 157 |
-
# one single channel, replicate the audio over all channels.
|
| 158 |
-
wav = wav.expand(*shape, channels, length)
|
| 159 |
-
elif src_channels >= channels:
|
| 160 |
-
# Case 3:
|
| 161 |
-
# The caller asked for multiple channels, and the input file have
|
| 162 |
-
# more channels than requested. In that case return the first channels.
|
| 163 |
-
wav = wav[..., :channels, :]
|
| 164 |
-
else:
|
| 165 |
-
# Case 4: What is a reasonable choice here?
|
| 166 |
-
raise ValueError('The audio file has less channels than requested but is not mono.')
|
| 167 |
-
return wav
|
| 168 |
-
|
| 169 |
-
|
| 170 |
-
def convert_audio(wav, from_samplerate, to_samplerate, channels):
|
| 171 |
-
wav = convert_audio_channels(wav, channels)
|
| 172 |
-
return julius.resample_frac(wav, from_samplerate, to_samplerate)
|
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spaces/801artistry/RVC801/tools/torchgate/__init__.py
DELETED
|
@@ -1,12 +0,0 @@
|
|
| 1 |
-
"""
|
| 2 |
-
TorchGating is a PyTorch-based implementation of Spectral Gating
|
| 3 |
-
================================================
|
| 4 |
-
Author: Asaf Zorea
|
| 5 |
-
|
| 6 |
-
Contents
|
| 7 |
-
--------
|
| 8 |
-
torchgate imports all the functions from PyTorch, and in addition provides:
|
| 9 |
-
TorchGating --- A PyTorch module that applies a spectral gate to an input signal
|
| 10 |
-
|
| 11 |
-
"""
|
| 12 |
-
from .torchgate import TorchGate
|
|
|
|
|
|
|
|
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|
|
spaces/AIFILMS/StyleGANEX/datasets/inference_dataset.py
DELETED
|
@@ -1,22 +0,0 @@
|
|
| 1 |
-
from torch.utils.data import Dataset
|
| 2 |
-
from PIL import Image
|
| 3 |
-
from utils import data_utils
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
class InferenceDataset(Dataset):
|
| 7 |
-
|
| 8 |
-
def __init__(self, root, opts, transform=None):
|
| 9 |
-
self.paths = sorted(data_utils.make_dataset(root))
|
| 10 |
-
self.transform = transform
|
| 11 |
-
self.opts = opts
|
| 12 |
-
|
| 13 |
-
def __len__(self):
|
| 14 |
-
return len(self.paths)
|
| 15 |
-
|
| 16 |
-
def __getitem__(self, index):
|
| 17 |
-
from_path = self.paths[index]
|
| 18 |
-
from_im = Image.open(from_path)
|
| 19 |
-
from_im = from_im.convert('RGB') if self.opts.label_nc == 0 else from_im.convert('L')
|
| 20 |
-
if self.transform:
|
| 21 |
-
from_im = self.transform(from_im)
|
| 22 |
-
return from_im
|
|
|
|
|
|
|
|
|
|
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|
|
spaces/AIGC-Audio/Make_An_Audio/ldm/models/autoencoder.py
DELETED
|
@@ -1,474 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import torch
|
| 3 |
-
import pytorch_lightning as pl
|
| 4 |
-
import torch.nn.functional as F
|
| 5 |
-
from contextlib import contextmanager
|
| 6 |
-
from packaging import version
|
| 7 |
-
import numpy as np
|
| 8 |
-
from ldm.modules.diffusionmodules.model import Encoder, Decoder
|
| 9 |
-
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
|
| 10 |
-
from torch.optim.lr_scheduler import LambdaLR
|
| 11 |
-
from ldm.util import instantiate_from_config
|
| 12 |
-
# from icecream import ic
|
| 13 |
-
|
| 14 |
-
class VQModel(pl.LightningModule):
|
| 15 |
-
def __init__(self,
|
| 16 |
-
ddconfig,
|
| 17 |
-
lossconfig,
|
| 18 |
-
n_embed,
|
| 19 |
-
embed_dim,
|
| 20 |
-
ckpt_path=None,
|
| 21 |
-
ignore_keys=[],
|
| 22 |
-
image_key="image",
|
| 23 |
-
colorize_nlabels=None,
|
| 24 |
-
monitor=None,
|
| 25 |
-
batch_resize_range=None,
|
| 26 |
-
scheduler_config=None,
|
| 27 |
-
lr_g_factor=1.0,
|
| 28 |
-
remap=None,
|
| 29 |
-
sane_index_shape=False, # tell vector quantizer to return indices as bhw
|
| 30 |
-
use_ema=False
|
| 31 |
-
):
|
| 32 |
-
super().__init__()
|
| 33 |
-
self.embed_dim = embed_dim
|
| 34 |
-
self.n_embed = n_embed
|
| 35 |
-
self.image_key = image_key
|
| 36 |
-
self.encoder = Encoder(**ddconfig)
|
| 37 |
-
self.decoder = Decoder(**ddconfig)
|
| 38 |
-
self.loss = instantiate_from_config(lossconfig)
|
| 39 |
-
self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
|
| 40 |
-
remap=remap,
|
| 41 |
-
sane_index_shape=sane_index_shape)
|
| 42 |
-
self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
|
| 43 |
-
self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
|
| 44 |
-
if colorize_nlabels is not None:
|
| 45 |
-
assert type(colorize_nlabels)==int
|
| 46 |
-
self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
|
| 47 |
-
if monitor is not None:
|
| 48 |
-
self.monitor = monitor
|
| 49 |
-
self.batch_resize_range = batch_resize_range
|
| 50 |
-
if self.batch_resize_range is not None:
|
| 51 |
-
print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
|
| 52 |
-
|
| 53 |
-
self.use_ema = use_ema
|
| 54 |
-
if self.use_ema:
|
| 55 |
-
self.model_ema = LitEma(self)
|
| 56 |
-
print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
|
| 57 |
-
|
| 58 |
-
if ckpt_path is not None:
|
| 59 |
-
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
|
| 60 |
-
self.scheduler_config = scheduler_config
|
| 61 |
-
self.lr_g_factor = lr_g_factor
|
| 62 |
-
|
| 63 |
-
@contextmanager
|
| 64 |
-
def ema_scope(self, context=None):
|
| 65 |
-
if self.use_ema:
|
| 66 |
-
self.model_ema.store(self.parameters())
|
| 67 |
-
self.model_ema.copy_to(self)
|
| 68 |
-
if context is not None:
|
| 69 |
-
print(f"{context}: Switched to EMA weights")
|
| 70 |
-
try:
|
| 71 |
-
yield None
|
| 72 |
-
finally:
|
| 73 |
-
if self.use_ema:
|
| 74 |
-
self.model_ema.restore(self.parameters())
|
| 75 |
-
if context is not None:
|
| 76 |
-
print(f"{context}: Restored training weights")
|
| 77 |
-
|
| 78 |
-
def init_from_ckpt(self, path, ignore_keys=list()):
|
| 79 |
-
sd = torch.load(path, map_location="cpu")["state_dict"]
|
| 80 |
-
keys = list(sd.keys())
|
| 81 |
-
for k in keys:
|
| 82 |
-
for ik in ignore_keys:
|
| 83 |
-
if k.startswith(ik):
|
| 84 |
-
print("Deleting key {} from state_dict.".format(k))
|
| 85 |
-
del sd[k]
|
| 86 |
-
missing, unexpected = self.load_state_dict(sd, strict=False)
|
| 87 |
-
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
|
| 88 |
-
if len(missing) > 0:
|
| 89 |
-
print(f"Missing Keys: {missing}")
|
| 90 |
-
print(f"Unexpected Keys: {unexpected}")
|
| 91 |
-
|
| 92 |
-
def on_train_batch_end(self, *args, **kwargs):
|
| 93 |
-
if self.use_ema:
|
| 94 |
-
self.model_ema(self)
|
| 95 |
-
|
| 96 |
-
def encode(self, x):
|
| 97 |
-
h = self.encoder(x)
|
| 98 |
-
h = self.quant_conv(h)
|
| 99 |
-
quant, emb_loss, info = self.quantize(h)
|
| 100 |
-
return quant, emb_loss, info
|
| 101 |
-
|
| 102 |
-
def encode_to_prequant(self, x):
|
| 103 |
-
h = self.encoder(x)
|
| 104 |
-
h = self.quant_conv(h)
|
| 105 |
-
return h
|
| 106 |
-
|
| 107 |
-
def decode(self, quant):
|
| 108 |
-
quant = self.post_quant_conv(quant)
|
| 109 |
-
dec = self.decoder(quant)
|
| 110 |
-
return dec
|
| 111 |
-
|
| 112 |
-
def decode_code(self, code_b):
|
| 113 |
-
quant_b = self.quantize.embed_code(code_b)
|
| 114 |
-
dec = self.decode(quant_b)
|
| 115 |
-
return dec
|
| 116 |
-
|
| 117 |
-
def forward(self, input, return_pred_indices=False):
|
| 118 |
-
quant, diff, (_,_,ind) = self.encode(input)
|
| 119 |
-
dec = self.decode(quant)
|
| 120 |
-
if return_pred_indices:
|
| 121 |
-
return dec, diff, ind
|
| 122 |
-
return dec, diff
|
| 123 |
-
|
| 124 |
-
def get_input(self, batch, k):
|
| 125 |
-
x = batch[k]
|
| 126 |
-
if len(x.shape) == 3:
|
| 127 |
-
x = x[..., None]
|
| 128 |
-
x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
|
| 129 |
-
if self.batch_resize_range is not None:
|
| 130 |
-
lower_size = self.batch_resize_range[0]
|
| 131 |
-
upper_size = self.batch_resize_range[1]
|
| 132 |
-
if self.global_step <= 4:
|
| 133 |
-
# do the first few batches with max size to avoid later oom
|
| 134 |
-
new_resize = upper_size
|
| 135 |
-
else:
|
| 136 |
-
new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
|
| 137 |
-
if new_resize != x.shape[2]:
|
| 138 |
-
x = F.interpolate(x, size=new_resize, mode="bicubic")
|
| 139 |
-
x = x.detach()
|
| 140 |
-
return x
|
| 141 |
-
|
| 142 |
-
def training_step(self, batch, batch_idx, optimizer_idx):
|
| 143 |
-
# https://github.com/pytorch/pytorch/issues/37142
|
| 144 |
-
# try not to fool the heuristics
|
| 145 |
-
x = self.get_input(batch, self.image_key)
|
| 146 |
-
xrec, qloss, ind = self(x, return_pred_indices=True)
|
| 147 |
-
|
| 148 |
-
if optimizer_idx == 0:
|
| 149 |
-
# autoencode
|
| 150 |
-
aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
|
| 151 |
-
last_layer=self.get_last_layer(), split="train",
|
| 152 |
-
predicted_indices=ind)
|
| 153 |
-
|
| 154 |
-
self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
|
| 155 |
-
return aeloss
|
| 156 |
-
|
| 157 |
-
if optimizer_idx == 1:
|
| 158 |
-
# discriminator
|
| 159 |
-
discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
|
| 160 |
-
last_layer=self.get_last_layer(), split="train")
|
| 161 |
-
self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
|
| 162 |
-
return discloss
|
| 163 |
-
|
| 164 |
-
def validation_step(self, batch, batch_idx):
|
| 165 |
-
log_dict = self._validation_step(batch, batch_idx)
|
| 166 |
-
with self.ema_scope():
|
| 167 |
-
log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
|
| 168 |
-
return log_dict
|
| 169 |
-
|
| 170 |
-
def _validation_step(self, batch, batch_idx, suffix=""):
|
| 171 |
-
x = self.get_input(batch, self.image_key)
|
| 172 |
-
xrec, qloss, ind = self(x, return_pred_indices=True)
|
| 173 |
-
aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
|
| 174 |
-
self.global_step,
|
| 175 |
-
last_layer=self.get_last_layer(),
|
| 176 |
-
split="val"+suffix,
|
| 177 |
-
predicted_indices=ind
|
| 178 |
-
)
|
| 179 |
-
|
| 180 |
-
discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
|
| 181 |
-
self.global_step,
|
| 182 |
-
last_layer=self.get_last_layer(),
|
| 183 |
-
split="val"+suffix,
|
| 184 |
-
predicted_indices=ind
|
| 185 |
-
)
|
| 186 |
-
rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
|
| 187 |
-
self.log(f"val{suffix}/rec_loss", rec_loss,
|
| 188 |
-
prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
|
| 189 |
-
self.log(f"val{suffix}/aeloss", aeloss,
|
| 190 |
-
prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
|
| 191 |
-
if version.parse(pl.__version__) >= version.parse('1.4.0'):
|
| 192 |
-
del log_dict_ae[f"val{suffix}/rec_loss"]
|
| 193 |
-
self.log_dict(log_dict_ae)
|
| 194 |
-
self.log_dict(log_dict_disc)
|
| 195 |
-
return self.log_dict
|
| 196 |
-
|
| 197 |
-
def test_step(self, batch, batch_idx):
|
| 198 |
-
x = self.get_input(batch, self.image_key)
|
| 199 |
-
xrec, qloss, ind = self(x, return_pred_indices=True)
|
| 200 |
-
reconstructions = (xrec + 1)/2 # to mel scale
|
| 201 |
-
test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path)
|
| 202 |
-
savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class')
|
| 203 |
-
if not os.path.exists(savedir):
|
| 204 |
-
os.makedirs(savedir)
|
| 205 |
-
|
| 206 |
-
file_names = batch['f_name']
|
| 207 |
-
# print(f"reconstructions.shape:{reconstructions.shape}",file_names)
|
| 208 |
-
reconstructions = reconstructions.cpu().numpy().squeeze(1) # squuze channel dim
|
| 209 |
-
for b in range(reconstructions.shape[0]):
|
| 210 |
-
vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num
|
| 211 |
-
v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:]
|
| 212 |
-
save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}.npy')
|
| 213 |
-
np.save(save_img_path,reconstructions[b])
|
| 214 |
-
|
| 215 |
-
return None
|
| 216 |
-
|
| 217 |
-
def configure_optimizers(self):
|
| 218 |
-
lr_d = self.learning_rate
|
| 219 |
-
lr_g = self.lr_g_factor*self.learning_rate
|
| 220 |
-
print("lr_d", lr_d)
|
| 221 |
-
print("lr_g", lr_g)
|
| 222 |
-
opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
|
| 223 |
-
list(self.decoder.parameters())+
|
| 224 |
-
list(self.quantize.parameters())+
|
| 225 |
-
list(self.quant_conv.parameters())+
|
| 226 |
-
list(self.post_quant_conv.parameters()),
|
| 227 |
-
lr=lr_g, betas=(0.5, 0.9))
|
| 228 |
-
opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
|
| 229 |
-
lr=lr_d, betas=(0.5, 0.9))
|
| 230 |
-
|
| 231 |
-
if self.scheduler_config is not None:
|
| 232 |
-
scheduler = instantiate_from_config(self.scheduler_config)
|
| 233 |
-
|
| 234 |
-
print("Setting up LambdaLR scheduler...")
|
| 235 |
-
scheduler = [
|
| 236 |
-
{
|
| 237 |
-
'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
|
| 238 |
-
'interval': 'step',
|
| 239 |
-
'frequency': 1
|
| 240 |
-
},
|
| 241 |
-
{
|
| 242 |
-
'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
|
| 243 |
-
'interval': 'step',
|
| 244 |
-
'frequency': 1
|
| 245 |
-
},
|
| 246 |
-
]
|
| 247 |
-
return [opt_ae, opt_disc], scheduler
|
| 248 |
-
return [opt_ae, opt_disc], []
|
| 249 |
-
|
| 250 |
-
def get_last_layer(self):
|
| 251 |
-
return self.decoder.conv_out.weight
|
| 252 |
-
|
| 253 |
-
def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
|
| 254 |
-
log = dict()
|
| 255 |
-
x = self.get_input(batch, self.image_key)
|
| 256 |
-
x = x.to(self.device)
|
| 257 |
-
if only_inputs:
|
| 258 |
-
log["inputs"] = x
|
| 259 |
-
return log
|
| 260 |
-
xrec, _ = self(x)
|
| 261 |
-
if x.shape[1] > 3:
|
| 262 |
-
# colorize with random projection
|
| 263 |
-
assert xrec.shape[1] > 3
|
| 264 |
-
x = self.to_rgb(x)
|
| 265 |
-
xrec = self.to_rgb(xrec)
|
| 266 |
-
log["inputs"] = x
|
| 267 |
-
log["reconstructions"] = xrec
|
| 268 |
-
if plot_ema:
|
| 269 |
-
with self.ema_scope():
|
| 270 |
-
xrec_ema, _ = self(x)
|
| 271 |
-
if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
|
| 272 |
-
log["reconstructions_ema"] = xrec_ema
|
| 273 |
-
return log
|
| 274 |
-
|
| 275 |
-
def to_rgb(self, x):
|
| 276 |
-
assert self.image_key == "segmentation"
|
| 277 |
-
if not hasattr(self, "colorize"):
|
| 278 |
-
self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
|
| 279 |
-
x = F.conv2d(x, weight=self.colorize)
|
| 280 |
-
x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
|
| 281 |
-
return x
|
| 282 |
-
|
| 283 |
-
|
| 284 |
-
class VQModelInterface(VQModel):
|
| 285 |
-
def __init__(self, embed_dim, *args, **kwargs):
|
| 286 |
-
super().__init__(embed_dim=embed_dim, *args, **kwargs)
|
| 287 |
-
self.embed_dim = embed_dim
|
| 288 |
-
|
| 289 |
-
def encode(self, x):# VQModel的quantize写在encoder里,VQModelInterface则将其写在decoder里
|
| 290 |
-
h = self.encoder(x)
|
| 291 |
-
h = self.quant_conv(h)
|
| 292 |
-
return h
|
| 293 |
-
|
| 294 |
-
def decode(self, h, force_not_quantize=False):
|
| 295 |
-
# also go through quantization layer
|
| 296 |
-
if not force_not_quantize:
|
| 297 |
-
quant, emb_loss, info = self.quantize(h)
|
| 298 |
-
else:
|
| 299 |
-
quant = h
|
| 300 |
-
quant = self.post_quant_conv(quant)
|
| 301 |
-
dec = self.decoder(quant)
|
| 302 |
-
return dec
|
| 303 |
-
|
| 304 |
-
|
| 305 |
-
class AutoencoderKL(pl.LightningModule):
|
| 306 |
-
def __init__(self,
|
| 307 |
-
ddconfig,
|
| 308 |
-
lossconfig,
|
| 309 |
-
embed_dim,
|
| 310 |
-
ckpt_path=None,
|
| 311 |
-
ignore_keys=[],
|
| 312 |
-
image_key="image",
|
| 313 |
-
colorize_nlabels=None,
|
| 314 |
-
monitor=None,
|
| 315 |
-
):
|
| 316 |
-
super().__init__()
|
| 317 |
-
self.image_key = image_key
|
| 318 |
-
self.encoder = Encoder(**ddconfig)
|
| 319 |
-
self.decoder = Decoder(**ddconfig)
|
| 320 |
-
self.loss = instantiate_from_config(lossconfig)
|
| 321 |
-
assert ddconfig["double_z"]
|
| 322 |
-
self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
|
| 323 |
-
self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
|
| 324 |
-
self.embed_dim = embed_dim
|
| 325 |
-
if colorize_nlabels is not None:
|
| 326 |
-
assert type(colorize_nlabels)==int
|
| 327 |
-
self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
|
| 328 |
-
if monitor is not None:
|
| 329 |
-
self.monitor = monitor
|
| 330 |
-
if ckpt_path is not None:
|
| 331 |
-
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
|
| 332 |
-
# self.automatic_optimization = False # hjw for debug
|
| 333 |
-
|
| 334 |
-
def init_from_ckpt(self, path, ignore_keys=list()):
|
| 335 |
-
sd = torch.load(path, map_location="cpu")["state_dict"]
|
| 336 |
-
keys = list(sd.keys())
|
| 337 |
-
for k in keys:
|
| 338 |
-
for ik in ignore_keys:
|
| 339 |
-
if k.startswith(ik):
|
| 340 |
-
print("Deleting key {} from state_dict.".format(k))
|
| 341 |
-
del sd[k]
|
| 342 |
-
self.load_state_dict(sd, strict=False)
|
| 343 |
-
print(f"Restored from {path}")
|
| 344 |
-
|
| 345 |
-
def encode(self, x):
|
| 346 |
-
h = self.encoder(x)
|
| 347 |
-
moments = self.quant_conv(h)
|
| 348 |
-
posterior = DiagonalGaussianDistribution(moments)
|
| 349 |
-
return posterior
|
| 350 |
-
|
| 351 |
-
def decode(self, z):
|
| 352 |
-
z = self.post_quant_conv(z)
|
| 353 |
-
dec = self.decoder(z)
|
| 354 |
-
return dec
|
| 355 |
-
|
| 356 |
-
def forward(self, input, sample_posterior=True):
|
| 357 |
-
posterior = self.encode(input)
|
| 358 |
-
if sample_posterior:
|
| 359 |
-
z = posterior.sample()
|
| 360 |
-
else:
|
| 361 |
-
z = posterior.mode()
|
| 362 |
-
dec = self.decode(z)
|
| 363 |
-
return dec, posterior
|
| 364 |
-
|
| 365 |
-
def get_input(self, batch, k):
|
| 366 |
-
x = batch[k]
|
| 367 |
-
if len(x.shape) == 3:
|
| 368 |
-
x = x[..., None]
|
| 369 |
-
x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
|
| 370 |
-
return x
|
| 371 |
-
|
| 372 |
-
def training_step(self, batch, batch_idx, optimizer_idx):
|
| 373 |
-
inputs = self.get_input(batch, self.image_key)
|
| 374 |
-
reconstructions, posterior = self(inputs)
|
| 375 |
-
|
| 376 |
-
if optimizer_idx == 0:
|
| 377 |
-
# train encoder+decoder+logvar
|
| 378 |
-
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
|
| 379 |
-
last_layer=self.get_last_layer(), split="train")
|
| 380 |
-
self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
|
| 381 |
-
self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
|
| 382 |
-
return aeloss
|
| 383 |
-
|
| 384 |
-
if optimizer_idx == 1:
|
| 385 |
-
# train the discriminator
|
| 386 |
-
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
|
| 387 |
-
last_layer=self.get_last_layer(), split="train")
|
| 388 |
-
|
| 389 |
-
self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
|
| 390 |
-
self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
|
| 391 |
-
return discloss
|
| 392 |
-
|
| 393 |
-
def validation_step(self, batch, batch_idx):
|
| 394 |
-
# self.log_images(batch,only_inputs=False,save_dir='mel_result_ae13_26/fake_class')
|
| 395 |
-
return self.log_dict
|
| 396 |
-
|
| 397 |
-
def test_step(self, batch, batch_idx):
|
| 398 |
-
test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path)
|
| 399 |
-
savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class')
|
| 400 |
-
os.makedirs(savedir,exist_ok=True)
|
| 401 |
-
inputs = self.get_input(batch, self.image_key)# inputs shape:(b,c,mel_len,T) or (b,c,h,w)
|
| 402 |
-
# ic(inputs.shape)
|
| 403 |
-
# inputs = inputs[...,:624]
|
| 404 |
-
# ic(inputs.shape)
|
| 405 |
-
xrec, posterior = self(inputs)# reconstructions:(b,c,mel_len,T) or (b,c,h,w)
|
| 406 |
-
file_names = batch['f_name']
|
| 407 |
-
# print(f"reconstructions.shape:{reconstructions.shape}",file_names)
|
| 408 |
-
for b in range(len(file_names)):
|
| 409 |
-
rcon = (xrec[b].squeeze().detach().cpu().numpy() + 1) / 2 # to mel scale,squeeze channel dim
|
| 410 |
-
vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num
|
| 411 |
-
v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:]
|
| 412 |
-
save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}.npy')
|
| 413 |
-
np.save(save_img_path,rcon)
|
| 414 |
-
|
| 415 |
-
return None
|
| 416 |
-
|
| 417 |
-
def configure_optimizers(self):
|
| 418 |
-
lr = self.learning_rate
|
| 419 |
-
opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
|
| 420 |
-
list(self.decoder.parameters())+
|
| 421 |
-
list(self.quant_conv.parameters())+
|
| 422 |
-
list(self.post_quant_conv.parameters()),
|
| 423 |
-
lr=lr, betas=(0.5, 0.9))
|
| 424 |
-
opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
|
| 425 |
-
lr=lr, betas=(0.5, 0.9))
|
| 426 |
-
return [opt_ae, opt_disc], []
|
| 427 |
-
|
| 428 |
-
def get_last_layer(self):
|
| 429 |
-
return self.decoder.conv_out.weight
|
| 430 |
-
|
| 431 |
-
@torch.no_grad()
|
| 432 |
-
def log_images(self, batch, only_inputs=False,save_dir = 'mel_result_ae13_26_debug/fake_class', **kwargs): # 在main.py的on_validation_batch_end中调用
|
| 433 |
-
log = dict()
|
| 434 |
-
x = self.get_input(batch, self.image_key)
|
| 435 |
-
x = x.to(self.device)
|
| 436 |
-
if not only_inputs:
|
| 437 |
-
xrec, posterior = self(x)
|
| 438 |
-
if x.shape[1] > 3:
|
| 439 |
-
# colorize with random projection
|
| 440 |
-
assert xrec.shape[1] > 3
|
| 441 |
-
x = self.to_rgb(x)
|
| 442 |
-
xrec = self.to_rgb(xrec)
|
| 443 |
-
log["samples"] = self.decode(torch.randn_like(posterior.sample()))
|
| 444 |
-
log["reconstructions"] = xrec
|
| 445 |
-
log["inputs"] = x
|
| 446 |
-
return log
|
| 447 |
-
|
| 448 |
-
def to_rgb(self, x):
|
| 449 |
-
assert self.image_key == "segmentation"
|
| 450 |
-
if not hasattr(self, "colorize"):
|
| 451 |
-
self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
|
| 452 |
-
x = F.conv2d(x, weight=self.colorize)
|
| 453 |
-
x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
|
| 454 |
-
return x
|
| 455 |
-
|
| 456 |
-
|
| 457 |
-
class IdentityFirstStage(torch.nn.Module):
|
| 458 |
-
def __init__(self, *args, vq_interface=False, **kwargs):
|
| 459 |
-
self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff
|
| 460 |
-
super().__init__()
|
| 461 |
-
|
| 462 |
-
def encode(self, x, *args, **kwargs):
|
| 463 |
-
return x
|
| 464 |
-
|
| 465 |
-
def decode(self, x, *args, **kwargs):
|
| 466 |
-
return x
|
| 467 |
-
|
| 468 |
-
def quantize(self, x, *args, **kwargs):
|
| 469 |
-
if self.vq_interface:
|
| 470 |
-
return x, None, [None, None, None]
|
| 471 |
-
return x
|
| 472 |
-
|
| 473 |
-
def forward(self, x, *args, **kwargs):
|
| 474 |
-
return x
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|
spaces/AIGC-Audio/Make_An_Audio/ldm/modules/encoders/CLAP/clap.py
DELETED
|
@@ -1,89 +0,0 @@
|
|
| 1 |
-
import numpy as np
|
| 2 |
-
import torch
|
| 3 |
-
import torch.nn.functional as F
|
| 4 |
-
from torch import nn
|
| 5 |
-
from transformers import AutoModel
|
| 6 |
-
from .audio import get_audio_encoder
|
| 7 |
-
|
| 8 |
-
class Projection(nn.Module):
|
| 9 |
-
def __init__(self, d_in: int, d_out: int, p: float=0.5) -> None:
|
| 10 |
-
super().__init__()
|
| 11 |
-
self.linear1 = nn.Linear(d_in, d_out, bias=False)
|
| 12 |
-
self.linear2 = nn.Linear(d_out, d_out, bias=False)
|
| 13 |
-
self.layer_norm = nn.LayerNorm(d_out)
|
| 14 |
-
self.drop = nn.Dropout(p)
|
| 15 |
-
|
| 16 |
-
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
| 17 |
-
embed1 = self.linear1(x)
|
| 18 |
-
embed2 = self.drop(self.linear2(F.gelu(embed1)))
|
| 19 |
-
embeds = self.layer_norm(embed1 + embed2)
|
| 20 |
-
return embeds
|
| 21 |
-
|
| 22 |
-
class AudioEncoder(nn.Module):
|
| 23 |
-
def __init__(self, audioenc_name:str, d_in: int, d_out: int, sample_rate: int, window_size: int,
|
| 24 |
-
hop_size: int, mel_bins: int, fmin: int, fmax: int, classes_num: int) -> None:
|
| 25 |
-
super().__init__()
|
| 26 |
-
|
| 27 |
-
audio_encoder = get_audio_encoder(audioenc_name)
|
| 28 |
-
|
| 29 |
-
self.base = audio_encoder(
|
| 30 |
-
sample_rate, window_size,
|
| 31 |
-
hop_size, mel_bins, fmin, fmax,
|
| 32 |
-
classes_num, d_in)
|
| 33 |
-
|
| 34 |
-
self.projection = Projection(d_in, d_out)
|
| 35 |
-
|
| 36 |
-
def forward(self, x):
|
| 37 |
-
out_dict = self.base(x)
|
| 38 |
-
audio_features, audio_classification_output = out_dict['embedding'], out_dict['clipwise_output']
|
| 39 |
-
projected_vec = self.projection(audio_features)
|
| 40 |
-
return projected_vec, audio_classification_output
|
| 41 |
-
|
| 42 |
-
class TextEncoder(nn.Module):
|
| 43 |
-
def __init__(self, d_out: int, text_model: str, transformer_embed_dim: int) -> None:
|
| 44 |
-
super().__init__()
|
| 45 |
-
self.base = AutoModel.from_pretrained(text_model)
|
| 46 |
-
self.projection = Projection(transformer_embed_dim, d_out)
|
| 47 |
-
|
| 48 |
-
def forward(self, x):
|
| 49 |
-
out = self.base(**x)[0]
|
| 50 |
-
out = out[:, 0, :] # get CLS token output
|
| 51 |
-
projected_vec = self.projection(out)
|
| 52 |
-
return projected_vec
|
| 53 |
-
|
| 54 |
-
class CLAP(nn.Module):
|
| 55 |
-
def __init__(self,
|
| 56 |
-
# audio
|
| 57 |
-
audioenc_name: str,
|
| 58 |
-
sample_rate: int,
|
| 59 |
-
window_size: int,
|
| 60 |
-
hop_size: int,
|
| 61 |
-
mel_bins: int,
|
| 62 |
-
fmin: int,
|
| 63 |
-
fmax: int,
|
| 64 |
-
classes_num: int,
|
| 65 |
-
out_emb: int,
|
| 66 |
-
# text
|
| 67 |
-
text_model: str,
|
| 68 |
-
transformer_embed_dim: int,
|
| 69 |
-
# common
|
| 70 |
-
d_proj: int,
|
| 71 |
-
):
|
| 72 |
-
super().__init__()
|
| 73 |
-
|
| 74 |
-
|
| 75 |
-
self.audio_encoder = AudioEncoder(
|
| 76 |
-
audioenc_name, out_emb, d_proj,
|
| 77 |
-
sample_rate, window_size, hop_size, mel_bins, fmin, fmax, classes_num)
|
| 78 |
-
|
| 79 |
-
self.caption_encoder = TextEncoder(
|
| 80 |
-
d_proj, text_model, transformer_embed_dim
|
| 81 |
-
)
|
| 82 |
-
|
| 83 |
-
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
|
| 84 |
-
|
| 85 |
-
def forward(self, audio, text):
|
| 86 |
-
audio_embed, _ = self.audio_encoder(audio)
|
| 87 |
-
caption_embed = self.caption_encoder(text)
|
| 88 |
-
|
| 89 |
-
return caption_embed, audio_embed, self.logit_scale.exp()
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spaces/AIWaves/Debate/src/agents/evolve.py
DELETED
|
@@ -1,17 +0,0 @@
|
|
| 1 |
-
# coding=utf-8
|
| 2 |
-
# Copyright 2023 The AIWaves Inc. team.
|
| 3 |
-
|
| 4 |
-
#
|
| 5 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 6 |
-
# you may not use this file except in compliance with the License.
|
| 7 |
-
# You may obtain a copy of the License at
|
| 8 |
-
#
|
| 9 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 10 |
-
#
|
| 11 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 12 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 13 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 14 |
-
# See the License for the specific language governing permissions and
|
| 15 |
-
# limitations under the License.
|
| 16 |
-
|
| 17 |
-
"""self evolution of an LLM autonoumous agent"""
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spaces/Abhilashvj/planogram-compliance/utils.py
DELETED
|
@@ -1,61 +0,0 @@
|
|
| 1 |
-
import json
|
| 2 |
-
import os
|
| 3 |
-
|
| 4 |
-
# from sklearn.externals import joblib
|
| 5 |
-
import joblib
|
| 6 |
-
import numpy as np
|
| 7 |
-
import pandas as pd
|
| 8 |
-
|
| 9 |
-
# from .variables import old_ocr_req_cols
|
| 10 |
-
# from .skew_correction import PageSkewWraper
|
| 11 |
-
|
| 12 |
-
const_HW = 1.294117647
|
| 13 |
-
const_W = 600
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
def bucket_sort(df, colmn, ymax_col="ymax", ymin_col="ymin"):
|
| 17 |
-
df["line_number"] = 0
|
| 18 |
-
colmn.append("line_number")
|
| 19 |
-
array_value = df[colmn].values
|
| 20 |
-
start_index = Line_counter = counter = 0
|
| 21 |
-
ymax, ymin, line_no = (
|
| 22 |
-
colmn.index(ymax_col),
|
| 23 |
-
colmn.index(ymin_col),
|
| 24 |
-
colmn.index("line_number"),
|
| 25 |
-
)
|
| 26 |
-
while counter < len(array_value):
|
| 27 |
-
current_ymax = array_value[start_index][ymax]
|
| 28 |
-
for next_index in range(start_index, len(array_value)):
|
| 29 |
-
counter += 1
|
| 30 |
-
|
| 31 |
-
next_ymin = array_value[next_index][ymin]
|
| 32 |
-
next_ymax = array_value[next_index][ymax]
|
| 33 |
-
if current_ymax > next_ymin:
|
| 34 |
-
|
| 35 |
-
array_value[next_index][line_no] = Line_counter + 1
|
| 36 |
-
# if current_ymax < next_ymax:
|
| 37 |
-
|
| 38 |
-
# current_ymax = next_ymax
|
| 39 |
-
else:
|
| 40 |
-
counter -= 1
|
| 41 |
-
break
|
| 42 |
-
# print(counter, len(array_value), start_index)
|
| 43 |
-
start_index = counter
|
| 44 |
-
Line_counter += 1
|
| 45 |
-
return pd.DataFrame(array_value, columns=colmn)
|
| 46 |
-
|
| 47 |
-
|
| 48 |
-
def do_sorting(df):
|
| 49 |
-
df.sort_values(["ymin", "xmin"], ascending=True, inplace=True)
|
| 50 |
-
df["idx"] = df.index
|
| 51 |
-
if "line_number" in df.columns:
|
| 52 |
-
print("line number removed")
|
| 53 |
-
df.drop("line_number", axis=1, inplace=True)
|
| 54 |
-
req_colns = ["xmin", "ymin", "xmax", "ymax", "idx"]
|
| 55 |
-
temp_df = df.copy()
|
| 56 |
-
temp = bucket_sort(temp_df.copy(), req_colns)
|
| 57 |
-
df = df.merge(temp[["idx", "line_number"]], on="idx")
|
| 58 |
-
df.sort_values(["line_number", "xmin"], ascending=True, inplace=True)
|
| 59 |
-
df = df.reset_index(drop=True)
|
| 60 |
-
df = df.reset_index(drop=True)
|
| 61 |
-
return df
|
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|
spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/hteyun.py
DELETED
|
@@ -1,34 +0,0 @@
|
|
| 1 |
-
import requests
|
| 2 |
-
import os
|
| 3 |
-
import json
|
| 4 |
-
from ...typing import sha256, Dict, get_type_hints
|
| 5 |
-
|
| 6 |
-
url = 'https://hteyun.com'
|
| 7 |
-
model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0613']
|
| 8 |
-
supports_stream = True
|
| 9 |
-
needs_auth = False
|
| 10 |
-
|
| 11 |
-
def _create_completion(model: str, messages: list, stream: bool, temperature: float = 0.7, **kwargs):
|
| 12 |
-
headers = {
|
| 13 |
-
'Content-Type': 'application/json',
|
| 14 |
-
'Accept': 'application/json, text/plain, */*',
|
| 15 |
-
'Accept-Language': 'ru-RU,ru;q=0.9,en-US;q=0.8,en;q=0.7,ja;q=0.6,zh-TW;q=0.5,zh;q=0.4',
|
| 16 |
-
'Origin': 'https://hteyun.com',
|
| 17 |
-
'Referer': 'https://hteyun.com/chat/',
|
| 18 |
-
}
|
| 19 |
-
data = {
|
| 20 |
-
'messages': messages,
|
| 21 |
-
'model': model,
|
| 22 |
-
'systemMessage': 'You are ChatGPT, a large language model trained by OpenAI. Follow the user\'s instructions carefully. Respond using russian language.',
|
| 23 |
-
'temperature': 0.7,
|
| 24 |
-
'presence_penalty': 0,
|
| 25 |
-
}
|
| 26 |
-
response = requests.post(url + '/api/chat-stream', json=data, headers=headers, stream=True)
|
| 27 |
-
print(response.json())
|
| 28 |
-
|
| 29 |
-
# Извлечение текста из response
|
| 30 |
-
return response.json()['text']
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
|
| 34 |
-
'(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
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|
spaces/Aer0xander/sd-to-diffusers/README.md
DELETED
|
@@ -1,14 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: SD To Diffusers
|
| 3 |
-
emoji: 🎨➡️🧨
|
| 4 |
-
colorFrom: indigo
|
| 5 |
-
colorTo: blue
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.9.1
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
license: mit
|
| 11 |
-
duplicated_from: diffusers/sd-to-diffusers
|
| 12 |
-
---
|
| 13 |
-
|
| 14 |
-
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/holygrail/Factory.d.ts
DELETED
|
@@ -1,5 +0,0 @@
|
|
| 1 |
-
import HolyGrail from './HolyGrail';
|
| 2 |
-
|
| 3 |
-
export default function (
|
| 4 |
-
config?: HolyGrail.IConfig
|
| 5 |
-
): HolyGrail;
|
|
|
|
|
|
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|
|
spaces/AlgoveraAI/dcgan-crypto-punks/README.md
DELETED
|
@@ -1,38 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Dcgan Crypto Punks
|
| 3 |
-
emoji: 📚
|
| 4 |
-
colorFrom: purple
|
| 5 |
-
colorTo: blue
|
| 6 |
-
sdk: gradio
|
| 7 |
-
# sdk_version: 3.3
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
# Configuration
|
| 13 |
-
|
| 14 |
-
`title`: _string_
|
| 15 |
-
Display title for the Space
|
| 16 |
-
|
| 17 |
-
`emoji`: _string_
|
| 18 |
-
Space emoji (emoji-only character allowed)
|
| 19 |
-
|
| 20 |
-
`colorFrom`: _string_
|
| 21 |
-
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
|
| 22 |
-
|
| 23 |
-
`colorTo`: _string_
|
| 24 |
-
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
|
| 25 |
-
|
| 26 |
-
`sdk`: _string_
|
| 27 |
-
Can be either `gradio` or `streamlit`
|
| 28 |
-
|
| 29 |
-
`sdk_version` : _string_
|
| 30 |
-
Only applicable for `streamlit` SDK.
|
| 31 |
-
See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
|
| 32 |
-
|
| 33 |
-
`app_file`: _string_
|
| 34 |
-
Path to your main application file (which contains either `gradio` or `streamlit` Python code).
|
| 35 |
-
Path is relative to the root of the repository.
|
| 36 |
-
|
| 37 |
-
`pinned`: _boolean_
|
| 38 |
-
Whether the Space stays on top of your list.
|
|
|
|
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|
spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/configs/base.py
DELETED
|
@@ -1,56 +0,0 @@
|
|
| 1 |
-
from easydict import EasyDict as edict
|
| 2 |
-
|
| 3 |
-
# make training faster
|
| 4 |
-
# our RAM is 256G
|
| 5 |
-
# mount -t tmpfs -o size=140G tmpfs /train_tmp
|
| 6 |
-
|
| 7 |
-
config = edict()
|
| 8 |
-
config.loss = "arcface"
|
| 9 |
-
config.network = "r50"
|
| 10 |
-
config.resume = False
|
| 11 |
-
config.output = "ms1mv3_arcface_r50"
|
| 12 |
-
|
| 13 |
-
config.dataset = "ms1m-retinaface-t1"
|
| 14 |
-
config.embedding_size = 512
|
| 15 |
-
config.sample_rate = 1
|
| 16 |
-
config.fp16 = False
|
| 17 |
-
config.momentum = 0.9
|
| 18 |
-
config.weight_decay = 5e-4
|
| 19 |
-
config.batch_size = 128
|
| 20 |
-
config.lr = 0.1 # batch size is 512
|
| 21 |
-
|
| 22 |
-
if config.dataset == "emore":
|
| 23 |
-
config.rec = "/train_tmp/faces_emore"
|
| 24 |
-
config.num_classes = 85742
|
| 25 |
-
config.num_image = 5822653
|
| 26 |
-
config.num_epoch = 16
|
| 27 |
-
config.warmup_epoch = -1
|
| 28 |
-
config.decay_epoch = [8, 14, ]
|
| 29 |
-
config.val_targets = ["lfw", ]
|
| 30 |
-
|
| 31 |
-
elif config.dataset == "ms1m-retinaface-t1":
|
| 32 |
-
config.rec = "/train_tmp/ms1m-retinaface-t1"
|
| 33 |
-
config.num_classes = 93431
|
| 34 |
-
config.num_image = 5179510
|
| 35 |
-
config.num_epoch = 25
|
| 36 |
-
config.warmup_epoch = -1
|
| 37 |
-
config.decay_epoch = [11, 17, 22]
|
| 38 |
-
config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
|
| 39 |
-
|
| 40 |
-
elif config.dataset == "glint360k":
|
| 41 |
-
config.rec = "/train_tmp/glint360k"
|
| 42 |
-
config.num_classes = 360232
|
| 43 |
-
config.num_image = 17091657
|
| 44 |
-
config.num_epoch = 20
|
| 45 |
-
config.warmup_epoch = -1
|
| 46 |
-
config.decay_epoch = [8, 12, 15, 18]
|
| 47 |
-
config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
|
| 48 |
-
|
| 49 |
-
elif config.dataset == "webface":
|
| 50 |
-
config.rec = "/train_tmp/faces_webface_112x112"
|
| 51 |
-
config.num_classes = 10572
|
| 52 |
-
config.num_image = "forget"
|
| 53 |
-
config.num_epoch = 34
|
| 54 |
-
config.warmup_epoch = -1
|
| 55 |
-
config.decay_epoch = [20, 28, 32]
|
| 56 |
-
config.val_targets = ["lfw", "cfp_fp", "agedb_30"]
|
|
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spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/eval_ijbc.py
DELETED
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# coding: utf-8
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import os
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import pickle
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import matplotlib
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import pandas as pd
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import timeit
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import sklearn
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import argparse
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import cv2
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import numpy as np
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import torch
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from skimage import transform as trans
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from backbones import get_model
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from sklearn.metrics import roc_curve, auc
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from menpo.visualize.viewmatplotlib import sample_colours_from_colourmap
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from prettytable import PrettyTable
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from pathlib import Path
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import sys
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import warnings
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sys.path.insert(0, "../")
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warnings.filterwarnings("ignore")
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parser = argparse.ArgumentParser(description='do ijb test')
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# general
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parser.add_argument('--model-prefix', default='', help='path to load model.')
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parser.add_argument('--image-path', default='', type=str, help='')
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parser.add_argument('--result-dir', default='.', type=str, help='')
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parser.add_argument('--batch-size', default=128, type=int, help='')
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parser.add_argument('--network', default='iresnet50', type=str, help='')
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| 38 |
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parser.add_argument('--job', default='insightface', type=str, help='job name')
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parser.add_argument('--target', default='IJBC', type=str, help='target, set to IJBC or IJBB')
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args = parser.parse_args()
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target = args.target
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model_path = args.model_prefix
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image_path = args.image_path
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result_dir = args.result_dir
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| 46 |
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gpu_id = None
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| 47 |
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use_norm_score = True # if Ture, TestMode(N1)
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| 48 |
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use_detector_score = True # if Ture, TestMode(D1)
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| 49 |
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use_flip_test = True # if Ture, TestMode(F1)
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job = args.job
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batch_size = args.batch_size
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| 52 |
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| 53 |
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| 54 |
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class Embedding(object):
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def __init__(self, prefix, data_shape, batch_size=1):
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image_size = (112, 112)
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self.image_size = image_size
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weight = torch.load(prefix)
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resnet = get_model(args.network, dropout=0, fp16=False).cuda()
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resnet.load_state_dict(weight)
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model = torch.nn.DataParallel(resnet)
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self.model = model
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self.model.eval()
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src = np.array([
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[30.2946, 51.6963],
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[65.5318, 51.5014],
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[48.0252, 71.7366],
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[33.5493, 92.3655],
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[62.7299, 92.2041]], dtype=np.float32)
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src[:, 0] += 8.0
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self.src = src
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self.batch_size = batch_size
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self.data_shape = data_shape
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def get(self, rimg, landmark):
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assert landmark.shape[0] == 68 or landmark.shape[0] == 5
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assert landmark.shape[1] == 2
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if landmark.shape[0] == 68:
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landmark5 = np.zeros((5, 2), dtype=np.float32)
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landmark5[0] = (landmark[36] + landmark[39]) / 2
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landmark5[1] = (landmark[42] + landmark[45]) / 2
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landmark5[2] = landmark[30]
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landmark5[3] = landmark[48]
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landmark5[4] = landmark[54]
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else:
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landmark5 = landmark
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tform = trans.SimilarityTransform()
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tform.estimate(landmark5, self.src)
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M = tform.params[0:2, :]
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img = cv2.warpAffine(rimg,
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M, (self.image_size[1], self.image_size[0]),
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borderValue=0.0)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img_flip = np.fliplr(img)
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img = np.transpose(img, (2, 0, 1)) # 3*112*112, RGB
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img_flip = np.transpose(img_flip, (2, 0, 1))
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input_blob = np.zeros((2, 3, self.image_size[1], self.image_size[0]), dtype=np.uint8)
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input_blob[0] = img
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input_blob[1] = img_flip
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return input_blob
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@torch.no_grad()
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def forward_db(self, batch_data):
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imgs = torch.Tensor(batch_data).cuda()
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imgs.div_(255).sub_(0.5).div_(0.5)
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feat = self.model(imgs)
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feat = feat.reshape([self.batch_size, 2 * feat.shape[1]])
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return feat.cpu().numpy()
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# 将一个list尽量均分成n份，限制len(list)==n，份数大于原list内元素个数则分配空list[]
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def divideIntoNstrand(listTemp, n):
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twoList = [[] for i in range(n)]
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for i, e in enumerate(listTemp):
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twoList[i % n].append(e)
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return twoList
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def read_template_media_list(path):
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# ijb_meta = np.loadtxt(path, dtype=str)
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ijb_meta = pd.read_csv(path, sep=' ', header=None).values
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templates = ijb_meta[:, 1].astype(np.int)
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medias = ijb_meta[:, 2].astype(np.int)
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return templates, medias
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# In[ ]:
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def read_template_pair_list(path):
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# pairs = np.loadtxt(path, dtype=str)
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pairs = pd.read_csv(path, sep=' ', header=None).values
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# print(pairs.shape)
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# print(pairs[:, 0].astype(np.int))
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t1 = pairs[:, 0].astype(np.int)
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t2 = pairs[:, 1].astype(np.int)
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label = pairs[:, 2].astype(np.int)
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return t1, t2, label
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# In[ ]:
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def read_image_feature(path):
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with open(path, 'rb') as fid:
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img_feats = pickle.load(fid)
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return img_feats
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# In[ ]:
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def get_image_feature(img_path, files_list, model_path, epoch, gpu_id):
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batch_size = args.batch_size
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data_shape = (3, 112, 112)
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files = files_list
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print('files:', len(files))
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rare_size = len(files) % batch_size
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faceness_scores = []
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batch = 0
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img_feats = np.empty((len(files), 1024), dtype=np.float32)
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batch_data = np.empty((2 * batch_size, 3, 112, 112))
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embedding = Embedding(model_path, data_shape, batch_size)
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for img_index, each_line in enumerate(files[:len(files) - rare_size]):
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name_lmk_score = each_line.strip().split(' ')
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img_name = os.path.join(img_path, name_lmk_score[0])
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img = cv2.imread(img_name)
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lmk = np.array([float(x) for x in name_lmk_score[1:-1]],
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dtype=np.float32)
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lmk = lmk.reshape((5, 2))
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input_blob = embedding.get(img, lmk)
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batch_data[2 * (img_index - batch * batch_size)][:] = input_blob[0]
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batch_data[2 * (img_index - batch * batch_size) + 1][:] = input_blob[1]
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if (img_index + 1) % batch_size == 0:
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print('batch', batch)
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img_feats[batch * batch_size:batch * batch_size +
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batch_size][:] = embedding.forward_db(batch_data)
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batch += 1
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faceness_scores.append(name_lmk_score[-1])
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batch_data = np.empty((2 * rare_size, 3, 112, 112))
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embedding = Embedding(model_path, data_shape, rare_size)
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for img_index, each_line in enumerate(files[len(files) - rare_size:]):
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name_lmk_score = each_line.strip().split(' ')
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img_name = os.path.join(img_path, name_lmk_score[0])
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img = cv2.imread(img_name)
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lmk = np.array([float(x) for x in name_lmk_score[1:-1]],
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dtype=np.float32)
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lmk = lmk.reshape((5, 2))
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input_blob = embedding.get(img, lmk)
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batch_data[2 * img_index][:] = input_blob[0]
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batch_data[2 * img_index + 1][:] = input_blob[1]
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| 197 |
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if (img_index + 1) % rare_size == 0:
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| 198 |
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print('batch', batch)
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img_feats[len(files) -
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rare_size:][:] = embedding.forward_db(batch_data)
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batch += 1
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faceness_scores.append(name_lmk_score[-1])
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faceness_scores = np.array(faceness_scores).astype(np.float32)
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# img_feats = np.ones( (len(files), 1024), dtype=np.float32) * 0.01
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# faceness_scores = np.ones( (len(files), ), dtype=np.float32 )
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return img_feats, faceness_scores
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# In[ ]:
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def image2template_feature(img_feats=None, templates=None, medias=None):
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# ==========================================================
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# 1. face image feature l2 normalization. img_feats:[number_image x feats_dim]
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# 2. compute media feature.
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# 3. compute template feature.
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# ==========================================================
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unique_templates = np.unique(templates)
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template_feats = np.zeros((len(unique_templates), img_feats.shape[1]))
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for count_template, uqt in enumerate(unique_templates):
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(ind_t,) = np.where(templates == uqt)
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face_norm_feats = img_feats[ind_t]
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face_medias = medias[ind_t]
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unique_medias, unique_media_counts = np.unique(face_medias,
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return_counts=True)
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media_norm_feats = []
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for u, ct in zip(unique_medias, unique_media_counts):
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(ind_m,) = np.where(face_medias == u)
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if ct == 1:
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media_norm_feats += [face_norm_feats[ind_m]]
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else: # image features from the same video will be aggregated into one feature
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media_norm_feats += [
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np.mean(face_norm_feats[ind_m], axis=0, keepdims=True)
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]
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media_norm_feats = np.array(media_norm_feats)
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# media_norm_feats = media_norm_feats / np.sqrt(np.sum(media_norm_feats ** 2, -1, keepdims=True))
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| 239 |
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template_feats[count_template] = np.sum(media_norm_feats, axis=0)
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| 240 |
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if count_template % 2000 == 0:
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| 241 |
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print('Finish Calculating {} template features.'.format(
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| 242 |
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count_template))
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| 243 |
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# template_norm_feats = template_feats / np.sqrt(np.sum(template_feats ** 2, -1, keepdims=True))
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| 244 |
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template_norm_feats = sklearn.preprocessing.normalize(template_feats)
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| 245 |
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# print(template_norm_feats.shape)
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| 246 |
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return template_norm_feats, unique_templates
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| 247 |
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| 248 |
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| 249 |
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# In[ ]:
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| 250 |
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| 252 |
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def verification(template_norm_feats=None,
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unique_templates=None,
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p1=None,
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p2=None):
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| 256 |
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# ==========================================================
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| 257 |
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# Compute set-to-set Similarity Score.
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| 258 |
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# ==========================================================
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| 259 |
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template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)
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| 260 |
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for count_template, uqt in enumerate(unique_templates):
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| 261 |
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template2id[uqt] = count_template
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| 262 |
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| 263 |
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score = np.zeros((len(p1),)) # save cosine distance between pairs
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| 264 |
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| 265 |
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total_pairs = np.array(range(len(p1)))
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| 266 |
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batchsize = 100000 # small batchsize instead of all pairs in one batch due to the memory limiation
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| 267 |
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sublists = [
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| 268 |
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total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)
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]
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total_sublists = len(sublists)
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| 271 |
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for c, s in enumerate(sublists):
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| 272 |
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feat1 = template_norm_feats[template2id[p1[s]]]
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| 273 |
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feat2 = template_norm_feats[template2id[p2[s]]]
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| 274 |
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similarity_score = np.sum(feat1 * feat2, -1)
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| 275 |
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score[s] = similarity_score.flatten()
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| 276 |
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if c % 10 == 0:
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| 277 |
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print('Finish {}/{} pairs.'.format(c, total_sublists))
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| 278 |
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return score
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| 279 |
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| 280 |
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| 281 |
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# In[ ]:
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| 282 |
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def verification2(template_norm_feats=None,
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| 283 |
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unique_templates=None,
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| 284 |
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p1=None,
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| 285 |
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p2=None):
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| 286 |
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template2id = np.zeros((max(unique_templates) + 1, 1), dtype=int)
|
| 287 |
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for count_template, uqt in enumerate(unique_templates):
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| 288 |
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template2id[uqt] = count_template
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| 289 |
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score = np.zeros((len(p1),)) # save cosine distance between pairs
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| 290 |
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total_pairs = np.array(range(len(p1)))
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| 291 |
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batchsize = 100000 # small batchsize instead of all pairs in one batch due to the memory limiation
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| 292 |
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sublists = [
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| 293 |
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total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)
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| 294 |
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]
|
| 295 |
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total_sublists = len(sublists)
|
| 296 |
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for c, s in enumerate(sublists):
|
| 297 |
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feat1 = template_norm_feats[template2id[p1[s]]]
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| 298 |
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feat2 = template_norm_feats[template2id[p2[s]]]
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| 299 |
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similarity_score = np.sum(feat1 * feat2, -1)
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| 300 |
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score[s] = similarity_score.flatten()
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| 301 |
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if c % 10 == 0:
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| 302 |
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print('Finish {}/{} pairs.'.format(c, total_sublists))
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| 303 |
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return score
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| 304 |
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| 305 |
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| 306 |
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def read_score(path):
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| 307 |
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with open(path, 'rb') as fid:
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| 308 |
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img_feats = pickle.load(fid)
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| 309 |
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return img_feats
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| 310 |
-
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| 311 |
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|
| 312 |
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# # Step1: Load Meta Data
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| 313 |
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| 314 |
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# In[ ]:
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| 315 |
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| 316 |
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assert target == 'IJBC' or target == 'IJBB'
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| 317 |
-
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| 318 |
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# =============================================================
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| 319 |
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# load image and template relationships for template feature embedding
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| 320 |
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# tid --> template id, mid --> media id
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| 321 |
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# format:
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| 322 |
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# image_name tid mid
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| 323 |
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# =============================================================
|
| 324 |
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start = timeit.default_timer()
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| 325 |
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templates, medias = read_template_media_list(
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| 326 |
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os.path.join('%s/meta' % image_path,
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| 327 |
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'%s_face_tid_mid.txt' % target.lower()))
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| 328 |
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stop = timeit.default_timer()
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| 329 |
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print('Time: %.2f s. ' % (stop - start))
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| 330 |
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|
| 331 |
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# In[ ]:
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| 332 |
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| 333 |
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# =============================================================
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| 334 |
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# load template pairs for template-to-template verification
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| 335 |
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# tid : template id, label : 1/0
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| 336 |
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# format:
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| 337 |
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# tid_1 tid_2 label
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| 338 |
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# =============================================================
|
| 339 |
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start = timeit.default_timer()
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| 340 |
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p1, p2, label = read_template_pair_list(
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| 341 |
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os.path.join('%s/meta' % image_path,
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| 342 |
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'%s_template_pair_label.txt' % target.lower()))
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| 343 |
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stop = timeit.default_timer()
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| 344 |
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print('Time: %.2f s. ' % (stop - start))
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| 345 |
-
|
| 346 |
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# # Step 2: Get Image Features
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| 347 |
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|
| 348 |
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# In[ ]:
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| 349 |
-
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| 350 |
-
# =============================================================
|
| 351 |
-
# load image features
|
| 352 |
-
# format:
|
| 353 |
-
# img_feats: [image_num x feats_dim] (227630, 512)
|
| 354 |
-
# =============================================================
|
| 355 |
-
start = timeit.default_timer()
|
| 356 |
-
img_path = '%s/loose_crop' % image_path
|
| 357 |
-
img_list_path = '%s/meta/%s_name_5pts_score.txt' % (image_path, target.lower())
|
| 358 |
-
img_list = open(img_list_path)
|
| 359 |
-
files = img_list.readlines()
|
| 360 |
-
# files_list = divideIntoNstrand(files, rank_size)
|
| 361 |
-
files_list = files
|
| 362 |
-
|
| 363 |
-
# img_feats
|
| 364 |
-
# for i in range(rank_size):
|
| 365 |
-
img_feats, faceness_scores = get_image_feature(img_path, files_list,
|
| 366 |
-
model_path, 0, gpu_id)
|
| 367 |
-
stop = timeit.default_timer()
|
| 368 |
-
print('Time: %.2f s. ' % (stop - start))
|
| 369 |
-
print('Feature Shape: ({} , {}) .'.format(img_feats.shape[0],
|
| 370 |
-
img_feats.shape[1]))
|
| 371 |
-
|
| 372 |
-
# # Step3: Get Template Features
|
| 373 |
-
|
| 374 |
-
# In[ ]:
|
| 375 |
-
|
| 376 |
-
# =============================================================
|
| 377 |
-
# compute template features from image features.
|
| 378 |
-
# =============================================================
|
| 379 |
-
start = timeit.default_timer()
|
| 380 |
-
# ==========================================================
|
| 381 |
-
# Norm feature before aggregation into template feature?
|
| 382 |
-
# Feature norm from embedding network and faceness score are able to decrease weights for noise samples (not face).
|
| 383 |
-
# ==========================================================
|
| 384 |
-
# 1. FaceScore （Feature Norm）
|
| 385 |
-
# 2. FaceScore （Detector）
|
| 386 |
-
|
| 387 |
-
if use_flip_test:
|
| 388 |
-
# concat --- F1
|
| 389 |
-
# img_input_feats = img_feats
|
| 390 |
-
# add --- F2
|
| 391 |
-
img_input_feats = img_feats[:, 0:img_feats.shape[1] //
|
| 392 |
-
2] + img_feats[:, img_feats.shape[1] // 2:]
|
| 393 |
-
else:
|
| 394 |
-
img_input_feats = img_feats[:, 0:img_feats.shape[1] // 2]
|
| 395 |
-
|
| 396 |
-
if use_norm_score:
|
| 397 |
-
img_input_feats = img_input_feats
|
| 398 |
-
else:
|
| 399 |
-
# normalise features to remove norm information
|
| 400 |
-
img_input_feats = img_input_feats / np.sqrt(
|
| 401 |
-
np.sum(img_input_feats ** 2, -1, keepdims=True))
|
| 402 |
-
|
| 403 |
-
if use_detector_score:
|
| 404 |
-
print(img_input_feats.shape, faceness_scores.shape)
|
| 405 |
-
img_input_feats = img_input_feats * faceness_scores[:, np.newaxis]
|
| 406 |
-
else:
|
| 407 |
-
img_input_feats = img_input_feats
|
| 408 |
-
|
| 409 |
-
template_norm_feats, unique_templates = image2template_feature(
|
| 410 |
-
img_input_feats, templates, medias)
|
| 411 |
-
stop = timeit.default_timer()
|
| 412 |
-
print('Time: %.2f s. ' % (stop - start))
|
| 413 |
-
|
| 414 |
-
# # Step 4: Get Template Similarity Scores
|
| 415 |
-
|
| 416 |
-
# In[ ]:
|
| 417 |
-
|
| 418 |
-
# =============================================================
|
| 419 |
-
# compute verification scores between template pairs.
|
| 420 |
-
# =============================================================
|
| 421 |
-
start = timeit.default_timer()
|
| 422 |
-
score = verification(template_norm_feats, unique_templates, p1, p2)
|
| 423 |
-
stop = timeit.default_timer()
|
| 424 |
-
print('Time: %.2f s. ' % (stop - start))
|
| 425 |
-
|
| 426 |
-
# In[ ]:
|
| 427 |
-
save_path = os.path.join(result_dir, args.job)
|
| 428 |
-
# save_path = result_dir + '/%s_result' % target
|
| 429 |
-
|
| 430 |
-
if not os.path.exists(save_path):
|
| 431 |
-
os.makedirs(save_path)
|
| 432 |
-
|
| 433 |
-
score_save_file = os.path.join(save_path, "%s.npy" % target.lower())
|
| 434 |
-
np.save(score_save_file, score)
|
| 435 |
-
|
| 436 |
-
# # Step 5: Get ROC Curves and TPR@FPR Table
|
| 437 |
-
|
| 438 |
-
# In[ ]:
|
| 439 |
-
|
| 440 |
-
files = [score_save_file]
|
| 441 |
-
methods = []
|
| 442 |
-
scores = []
|
| 443 |
-
for file in files:
|
| 444 |
-
methods.append(Path(file).stem)
|
| 445 |
-
scores.append(np.load(file))
|
| 446 |
-
|
| 447 |
-
methods = np.array(methods)
|
| 448 |
-
scores = dict(zip(methods, scores))
|
| 449 |
-
colours = dict(
|
| 450 |
-
zip(methods, sample_colours_from_colourmap(methods.shape[0], 'Set2')))
|
| 451 |
-
x_labels = [10 ** -6, 10 ** -5, 10 ** -4, 10 ** -3, 10 ** -2, 10 ** -1]
|
| 452 |
-
tpr_fpr_table = PrettyTable(['Methods'] + [str(x) for x in x_labels])
|
| 453 |
-
fig = plt.figure()
|
| 454 |
-
for method in methods:
|
| 455 |
-
fpr, tpr, _ = roc_curve(label, scores[method])
|
| 456 |
-
roc_auc = auc(fpr, tpr)
|
| 457 |
-
fpr = np.flipud(fpr)
|
| 458 |
-
tpr = np.flipud(tpr) # select largest tpr at same fpr
|
| 459 |
-
plt.plot(fpr,
|
| 460 |
-
tpr,
|
| 461 |
-
color=colours[method],
|
| 462 |
-
lw=1,
|
| 463 |
-
label=('[%s (AUC = %0.4f %%)]' %
|
| 464 |
-
(method.split('-')[-1], roc_auc * 100)))
|
| 465 |
-
tpr_fpr_row = []
|
| 466 |
-
tpr_fpr_row.append("%s-%s" % (method, target))
|
| 467 |
-
for fpr_iter in np.arange(len(x_labels)):
|
| 468 |
-
_, min_index = min(
|
| 469 |
-
list(zip(abs(fpr - x_labels[fpr_iter]), range(len(fpr)))))
|
| 470 |
-
tpr_fpr_row.append('%.2f' % (tpr[min_index] * 100))
|
| 471 |
-
tpr_fpr_table.add_row(tpr_fpr_row)
|
| 472 |
-
plt.xlim([10 ** -6, 0.1])
|
| 473 |
-
plt.ylim([0.3, 1.0])
|
| 474 |
-
plt.grid(linestyle='--', linewidth=1)
|
| 475 |
-
plt.xticks(x_labels)
|
| 476 |
-
plt.yticks(np.linspace(0.3, 1.0, 8, endpoint=True))
|
| 477 |
-
plt.xscale('log')
|
| 478 |
-
plt.xlabel('False Positive Rate')
|
| 479 |
-
plt.ylabel('True Positive Rate')
|
| 480 |
-
plt.title('ROC on IJB')
|
| 481 |
-
plt.legend(loc="lower right")
|
| 482 |
-
fig.savefig(os.path.join(save_path, '%s.pdf' % target.lower()))
|
| 483 |
-
print(tpr_fpr_table)
|
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|
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_if.py
DELETED
|
@@ -1,1257 +0,0 @@
|
|
| 1 |
-
import argparse
|
| 2 |
-
import inspect
|
| 3 |
-
import os
|
| 4 |
-
|
| 5 |
-
import numpy as np
|
| 6 |
-
import torch
|
| 7 |
-
from torch.nn import functional as F
|
| 8 |
-
from transformers import CLIPConfig, CLIPImageProcessor, CLIPVisionModelWithProjection, T5EncoderModel, T5Tokenizer
|
| 9 |
-
|
| 10 |
-
from diffusers import DDPMScheduler, IFPipeline, IFSuperResolutionPipeline, UNet2DConditionModel
|
| 11 |
-
from diffusers.pipelines.deepfloyd_if.safety_checker import IFSafetyChecker
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
try:
|
| 15 |
-
from omegaconf import OmegaConf
|
| 16 |
-
except ImportError:
|
| 17 |
-
raise ImportError(
|
| 18 |
-
"OmegaConf is required to convert the IF checkpoints. Please install it with `pip install" " OmegaConf`."
|
| 19 |
-
)
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
def parse_args():
|
| 23 |
-
parser = argparse.ArgumentParser()
|
| 24 |
-
|
| 25 |
-
parser.add_argument("--dump_path", required=False, default=None, type=str)
|
| 26 |
-
|
| 27 |
-
parser.add_argument("--dump_path_stage_2", required=False, default=None, type=str)
|
| 28 |
-
|
| 29 |
-
parser.add_argument("--dump_path_stage_3", required=False, default=None, type=str)
|
| 30 |
-
|
| 31 |
-
parser.add_argument("--unet_config", required=False, default=None, type=str, help="Path to unet config file")
|
| 32 |
-
|
| 33 |
-
parser.add_argument(
|
| 34 |
-
"--unet_checkpoint_path", required=False, default=None, type=str, help="Path to unet checkpoint file"
|
| 35 |
-
)
|
| 36 |
-
|
| 37 |
-
parser.add_argument(
|
| 38 |
-
"--unet_checkpoint_path_stage_2",
|
| 39 |
-
required=False,
|
| 40 |
-
default=None,
|
| 41 |
-
type=str,
|
| 42 |
-
help="Path to stage 2 unet checkpoint file",
|
| 43 |
-
)
|
| 44 |
-
|
| 45 |
-
parser.add_argument(
|
| 46 |
-
"--unet_checkpoint_path_stage_3",
|
| 47 |
-
required=False,
|
| 48 |
-
default=None,
|
| 49 |
-
type=str,
|
| 50 |
-
help="Path to stage 3 unet checkpoint file",
|
| 51 |
-
)
|
| 52 |
-
|
| 53 |
-
parser.add_argument("--p_head_path", type=str, required=True)
|
| 54 |
-
|
| 55 |
-
parser.add_argument("--w_head_path", type=str, required=True)
|
| 56 |
-
|
| 57 |
-
args = parser.parse_args()
|
| 58 |
-
|
| 59 |
-
return args
|
| 60 |
-
|
| 61 |
-
|
| 62 |
-
def main(args):
|
| 63 |
-
tokenizer = T5Tokenizer.from_pretrained("google/t5-v1_1-xxl")
|
| 64 |
-
text_encoder = T5EncoderModel.from_pretrained("google/t5-v1_1-xxl")
|
| 65 |
-
|
| 66 |
-
feature_extractor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14")
|
| 67 |
-
safety_checker = convert_safety_checker(p_head_path=args.p_head_path, w_head_path=args.w_head_path)
|
| 68 |
-
|
| 69 |
-
if args.unet_config is not None and args.unet_checkpoint_path is not None and args.dump_path is not None:
|
| 70 |
-
convert_stage_1_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args)
|
| 71 |
-
|
| 72 |
-
if args.unet_checkpoint_path_stage_2 is not None and args.dump_path_stage_2 is not None:
|
| 73 |
-
convert_super_res_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args, stage=2)
|
| 74 |
-
|
| 75 |
-
if args.unet_checkpoint_path_stage_3 is not None and args.dump_path_stage_3 is not None:
|
| 76 |
-
convert_super_res_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args, stage=3)
|
| 77 |
-
|
| 78 |
-
|
| 79 |
-
def convert_stage_1_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args):
|
| 80 |
-
unet = get_stage_1_unet(args.unet_config, args.unet_checkpoint_path)
|
| 81 |
-
|
| 82 |
-
scheduler = DDPMScheduler(
|
| 83 |
-
variance_type="learned_range",
|
| 84 |
-
beta_schedule="squaredcos_cap_v2",
|
| 85 |
-
prediction_type="epsilon",
|
| 86 |
-
thresholding=True,
|
| 87 |
-
dynamic_thresholding_ratio=0.95,
|
| 88 |
-
sample_max_value=1.5,
|
| 89 |
-
)
|
| 90 |
-
|
| 91 |
-
pipe = IFPipeline(
|
| 92 |
-
tokenizer=tokenizer,
|
| 93 |
-
text_encoder=text_encoder,
|
| 94 |
-
unet=unet,
|
| 95 |
-
scheduler=scheduler,
|
| 96 |
-
safety_checker=safety_checker,
|
| 97 |
-
feature_extractor=feature_extractor,
|
| 98 |
-
requires_safety_checker=True,
|
| 99 |
-
)
|
| 100 |
-
|
| 101 |
-
pipe.save_pretrained(args.dump_path)
|
| 102 |
-
|
| 103 |
-
|
| 104 |
-
def convert_super_res_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args, stage):
|
| 105 |
-
if stage == 2:
|
| 106 |
-
unet_checkpoint_path = args.unet_checkpoint_path_stage_2
|
| 107 |
-
sample_size = None
|
| 108 |
-
dump_path = args.dump_path_stage_2
|
| 109 |
-
elif stage == 3:
|
| 110 |
-
unet_checkpoint_path = args.unet_checkpoint_path_stage_3
|
| 111 |
-
sample_size = 1024
|
| 112 |
-
dump_path = args.dump_path_stage_3
|
| 113 |
-
else:
|
| 114 |
-
assert False
|
| 115 |
-
|
| 116 |
-
unet = get_super_res_unet(unet_checkpoint_path, verify_param_count=False, sample_size=sample_size)
|
| 117 |
-
|
| 118 |
-
image_noising_scheduler = DDPMScheduler(
|
| 119 |
-
beta_schedule="squaredcos_cap_v2",
|
| 120 |
-
)
|
| 121 |
-
|
| 122 |
-
scheduler = DDPMScheduler(
|
| 123 |
-
variance_type="learned_range",
|
| 124 |
-
beta_schedule="squaredcos_cap_v2",
|
| 125 |
-
prediction_type="epsilon",
|
| 126 |
-
thresholding=True,
|
| 127 |
-
dynamic_thresholding_ratio=0.95,
|
| 128 |
-
sample_max_value=1.0,
|
| 129 |
-
)
|
| 130 |
-
|
| 131 |
-
pipe = IFSuperResolutionPipeline(
|
| 132 |
-
tokenizer=tokenizer,
|
| 133 |
-
text_encoder=text_encoder,
|
| 134 |
-
unet=unet,
|
| 135 |
-
scheduler=scheduler,
|
| 136 |
-
image_noising_scheduler=image_noising_scheduler,
|
| 137 |
-
safety_checker=safety_checker,
|
| 138 |
-
feature_extractor=feature_extractor,
|
| 139 |
-
requires_safety_checker=True,
|
| 140 |
-
)
|
| 141 |
-
|
| 142 |
-
pipe.save_pretrained(dump_path)
|
| 143 |
-
|
| 144 |
-
|
| 145 |
-
def get_stage_1_unet(unet_config, unet_checkpoint_path):
|
| 146 |
-
original_unet_config = OmegaConf.load(unet_config)
|
| 147 |
-
original_unet_config = original_unet_config.params
|
| 148 |
-
|
| 149 |
-
unet_diffusers_config = create_unet_diffusers_config(original_unet_config)
|
| 150 |
-
|
| 151 |
-
unet = UNet2DConditionModel(**unet_diffusers_config)
|
| 152 |
-
|
| 153 |
-
device = "cuda" if torch.cuda.is_available() else "cpu"
|
| 154 |
-
unet_checkpoint = torch.load(unet_checkpoint_path, map_location=device)
|
| 155 |
-
|
| 156 |
-
converted_unet_checkpoint = convert_ldm_unet_checkpoint(
|
| 157 |
-
unet_checkpoint, unet_diffusers_config, path=unet_checkpoint_path
|
| 158 |
-
)
|
| 159 |
-
|
| 160 |
-
unet.load_state_dict(converted_unet_checkpoint)
|
| 161 |
-
|
| 162 |
-
return unet
|
| 163 |
-
|
| 164 |
-
|
| 165 |
-
def convert_safety_checker(p_head_path, w_head_path):
|
| 166 |
-
state_dict = {}
|
| 167 |
-
|
| 168 |
-
# p head
|
| 169 |
-
|
| 170 |
-
p_head = np.load(p_head_path)
|
| 171 |
-
|
| 172 |
-
p_head_weights = p_head["weights"]
|
| 173 |
-
p_head_weights = torch.from_numpy(p_head_weights)
|
| 174 |
-
p_head_weights = p_head_weights.unsqueeze(0)
|
| 175 |
-
|
| 176 |
-
p_head_biases = p_head["biases"]
|
| 177 |
-
p_head_biases = torch.from_numpy(p_head_biases)
|
| 178 |
-
p_head_biases = p_head_biases.unsqueeze(0)
|
| 179 |
-
|
| 180 |
-
state_dict["p_head.weight"] = p_head_weights
|
| 181 |
-
state_dict["p_head.bias"] = p_head_biases
|
| 182 |
-
|
| 183 |
-
# w head
|
| 184 |
-
|
| 185 |
-
w_head = np.load(w_head_path)
|
| 186 |
-
|
| 187 |
-
w_head_weights = w_head["weights"]
|
| 188 |
-
w_head_weights = torch.from_numpy(w_head_weights)
|
| 189 |
-
w_head_weights = w_head_weights.unsqueeze(0)
|
| 190 |
-
|
| 191 |
-
w_head_biases = w_head["biases"]
|
| 192 |
-
w_head_biases = torch.from_numpy(w_head_biases)
|
| 193 |
-
w_head_biases = w_head_biases.unsqueeze(0)
|
| 194 |
-
|
| 195 |
-
state_dict["w_head.weight"] = w_head_weights
|
| 196 |
-
state_dict["w_head.bias"] = w_head_biases
|
| 197 |
-
|
| 198 |
-
# vision model
|
| 199 |
-
|
| 200 |
-
vision_model = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
|
| 201 |
-
vision_model_state_dict = vision_model.state_dict()
|
| 202 |
-
|
| 203 |
-
for key, value in vision_model_state_dict.items():
|
| 204 |
-
key = f"vision_model.{key}"
|
| 205 |
-
state_dict[key] = value
|
| 206 |
-
|
| 207 |
-
# full model
|
| 208 |
-
|
| 209 |
-
config = CLIPConfig.from_pretrained("openai/clip-vit-large-patch14")
|
| 210 |
-
safety_checker = IFSafetyChecker(config)
|
| 211 |
-
|
| 212 |
-
safety_checker.load_state_dict(state_dict)
|
| 213 |
-
|
| 214 |
-
return safety_checker
|
| 215 |
-
|
| 216 |
-
|
| 217 |
-
def create_unet_diffusers_config(original_unet_config, class_embed_type=None):
|
| 218 |
-
attention_resolutions = parse_list(original_unet_config.attention_resolutions)
|
| 219 |
-
attention_resolutions = [original_unet_config.image_size // int(res) for res in attention_resolutions]
|
| 220 |
-
|
| 221 |
-
channel_mult = parse_list(original_unet_config.channel_mult)
|
| 222 |
-
block_out_channels = [original_unet_config.model_channels * mult for mult in channel_mult]
|
| 223 |
-
|
| 224 |
-
down_block_types = []
|
| 225 |
-
resolution = 1
|
| 226 |
-
|
| 227 |
-
for i in range(len(block_out_channels)):
|
| 228 |
-
if resolution in attention_resolutions:
|
| 229 |
-
block_type = "SimpleCrossAttnDownBlock2D"
|
| 230 |
-
elif original_unet_config.resblock_updown:
|
| 231 |
-
block_type = "ResnetDownsampleBlock2D"
|
| 232 |
-
else:
|
| 233 |
-
block_type = "DownBlock2D"
|
| 234 |
-
|
| 235 |
-
down_block_types.append(block_type)
|
| 236 |
-
|
| 237 |
-
if i != len(block_out_channels) - 1:
|
| 238 |
-
resolution *= 2
|
| 239 |
-
|
| 240 |
-
up_block_types = []
|
| 241 |
-
for i in range(len(block_out_channels)):
|
| 242 |
-
if resolution in attention_resolutions:
|
| 243 |
-
block_type = "SimpleCrossAttnUpBlock2D"
|
| 244 |
-
elif original_unet_config.resblock_updown:
|
| 245 |
-
block_type = "ResnetUpsampleBlock2D"
|
| 246 |
-
else:
|
| 247 |
-
block_type = "UpBlock2D"
|
| 248 |
-
up_block_types.append(block_type)
|
| 249 |
-
resolution //= 2
|
| 250 |
-
|
| 251 |
-
head_dim = original_unet_config.num_head_channels
|
| 252 |
-
|
| 253 |
-
use_linear_projection = (
|
| 254 |
-
original_unet_config.use_linear_in_transformer
|
| 255 |
-
if "use_linear_in_transformer" in original_unet_config
|
| 256 |
-
else False
|
| 257 |
-
)
|
| 258 |
-
if use_linear_projection:
|
| 259 |
-
# stable diffusion 2-base-512 and 2-768
|
| 260 |
-
if head_dim is None:
|
| 261 |
-
head_dim = [5, 10, 20, 20]
|
| 262 |
-
|
| 263 |
-
projection_class_embeddings_input_dim = None
|
| 264 |
-
|
| 265 |
-
if class_embed_type is None:
|
| 266 |
-
if "num_classes" in original_unet_config:
|
| 267 |
-
if original_unet_config.num_classes == "sequential":
|
| 268 |
-
class_embed_type = "projection"
|
| 269 |
-
assert "adm_in_channels" in original_unet_config
|
| 270 |
-
projection_class_embeddings_input_dim = original_unet_config.adm_in_channels
|
| 271 |
-
else:
|
| 272 |
-
raise NotImplementedError(
|
| 273 |
-
f"Unknown conditional unet num_classes config: {original_unet_config.num_classes}"
|
| 274 |
-
)
|
| 275 |
-
|
| 276 |
-
config = {
|
| 277 |
-
"sample_size": original_unet_config.image_size,
|
| 278 |
-
"in_channels": original_unet_config.in_channels,
|
| 279 |
-
"down_block_types": tuple(down_block_types),
|
| 280 |
-
"block_out_channels": tuple(block_out_channels),
|
| 281 |
-
"layers_per_block": original_unet_config.num_res_blocks,
|
| 282 |
-
"cross_attention_dim": original_unet_config.encoder_channels,
|
| 283 |
-
"attention_head_dim": head_dim,
|
| 284 |
-
"use_linear_projection": use_linear_projection,
|
| 285 |
-
"class_embed_type": class_embed_type,
|
| 286 |
-
"projection_class_embeddings_input_dim": projection_class_embeddings_input_dim,
|
| 287 |
-
"out_channels": original_unet_config.out_channels,
|
| 288 |
-
"up_block_types": tuple(up_block_types),
|
| 289 |
-
"upcast_attention": False, # TODO: guessing
|
| 290 |
-
"cross_attention_norm": "group_norm",
|
| 291 |
-
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
|
| 292 |
-
"addition_embed_type": "text",
|
| 293 |
-
"act_fn": "gelu",
|
| 294 |
-
}
|
| 295 |
-
|
| 296 |
-
if original_unet_config.use_scale_shift_norm:
|
| 297 |
-
config["resnet_time_scale_shift"] = "scale_shift"
|
| 298 |
-
|
| 299 |
-
if "encoder_dim" in original_unet_config:
|
| 300 |
-
config["encoder_hid_dim"] = original_unet_config.encoder_dim
|
| 301 |
-
|
| 302 |
-
return config
|
| 303 |
-
|
| 304 |
-
|
| 305 |
-
def convert_ldm_unet_checkpoint(unet_state_dict, config, path=None):
|
| 306 |
-
"""
|
| 307 |
-
Takes a state dict and a config, and returns a converted checkpoint.
|
| 308 |
-
"""
|
| 309 |
-
new_checkpoint = {}
|
| 310 |
-
|
| 311 |
-
new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
|
| 312 |
-
new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
|
| 313 |
-
new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
|
| 314 |
-
new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
|
| 315 |
-
|
| 316 |
-
if config["class_embed_type"] in [None, "identity"]:
|
| 317 |
-
# No parameters to port
|
| 318 |
-
...
|
| 319 |
-
elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
|
| 320 |
-
new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["label_emb.0.0.weight"]
|
| 321 |
-
new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["label_emb.0.0.bias"]
|
| 322 |
-
new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["label_emb.0.2.weight"]
|
| 323 |
-
new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["label_emb.0.2.bias"]
|
| 324 |
-
else:
|
| 325 |
-
raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}")
|
| 326 |
-
|
| 327 |
-
new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
|
| 328 |
-
new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
|
| 329 |
-
|
| 330 |
-
new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
|
| 331 |
-
new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
|
| 332 |
-
new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
|
| 333 |
-
new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
|
| 334 |
-
|
| 335 |
-
# Retrieves the keys for the input blocks only
|
| 336 |
-
num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
|
| 337 |
-
input_blocks = {
|
| 338 |
-
layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}." in key]
|
| 339 |
-
for layer_id in range(num_input_blocks)
|
| 340 |
-
}
|
| 341 |
-
|
| 342 |
-
# Retrieves the keys for the middle blocks only
|
| 343 |
-
num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
|
| 344 |
-
middle_blocks = {
|
| 345 |
-
layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
|
| 346 |
-
for layer_id in range(num_middle_blocks)
|
| 347 |
-
}
|
| 348 |
-
|
| 349 |
-
# Retrieves the keys for the output blocks only
|
| 350 |
-
num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
|
| 351 |
-
output_blocks = {
|
| 352 |
-
layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}." in key]
|
| 353 |
-
for layer_id in range(num_output_blocks)
|
| 354 |
-
}
|
| 355 |
-
|
| 356 |
-
for i in range(1, num_input_blocks):
|
| 357 |
-
block_id = (i - 1) // (config["layers_per_block"] + 1)
|
| 358 |
-
layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
|
| 359 |
-
|
| 360 |
-
resnets = [
|
| 361 |
-
key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
|
| 362 |
-
]
|
| 363 |
-
attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
|
| 364 |
-
|
| 365 |
-
if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
|
| 366 |
-
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
|
| 367 |
-
f"input_blocks.{i}.0.op.weight"
|
| 368 |
-
)
|
| 369 |
-
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
|
| 370 |
-
f"input_blocks.{i}.0.op.bias"
|
| 371 |
-
)
|
| 372 |
-
|
| 373 |
-
paths = renew_resnet_paths(resnets)
|
| 374 |
-
|
| 375 |
-
# TODO need better check than i in [4, 8, 12, 16]
|
| 376 |
-
block_type = config["down_block_types"][block_id]
|
| 377 |
-
if (block_type == "ResnetDownsampleBlock2D" or block_type == "SimpleCrossAttnDownBlock2D") and i in [
|
| 378 |
-
4,
|
| 379 |
-
8,
|
| 380 |
-
12,
|
| 381 |
-
16,
|
| 382 |
-
]:
|
| 383 |
-
meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.downsamplers.0"}
|
| 384 |
-
else:
|
| 385 |
-
meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
|
| 386 |
-
|
| 387 |
-
assign_to_checkpoint(
|
| 388 |
-
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
| 389 |
-
)
|
| 390 |
-
|
| 391 |
-
if len(attentions):
|
| 392 |
-
old_path = f"input_blocks.{i}.1"
|
| 393 |
-
new_path = f"down_blocks.{block_id}.attentions.{layer_in_block_id}"
|
| 394 |
-
|
| 395 |
-
assign_attention_to_checkpoint(
|
| 396 |
-
new_checkpoint=new_checkpoint,
|
| 397 |
-
unet_state_dict=unet_state_dict,
|
| 398 |
-
old_path=old_path,
|
| 399 |
-
new_path=new_path,
|
| 400 |
-
config=config,
|
| 401 |
-
)
|
| 402 |
-
|
| 403 |
-
paths = renew_attention_paths(attentions)
|
| 404 |
-
meta_path = {"old": old_path, "new": new_path}
|
| 405 |
-
assign_to_checkpoint(
|
| 406 |
-
paths,
|
| 407 |
-
new_checkpoint,
|
| 408 |
-
unet_state_dict,
|
| 409 |
-
additional_replacements=[meta_path],
|
| 410 |
-
config=config,
|
| 411 |
-
)
|
| 412 |
-
|
| 413 |
-
resnet_0 = middle_blocks[0]
|
| 414 |
-
attentions = middle_blocks[1]
|
| 415 |
-
resnet_1 = middle_blocks[2]
|
| 416 |
-
|
| 417 |
-
resnet_0_paths = renew_resnet_paths(resnet_0)
|
| 418 |
-
assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
|
| 419 |
-
|
| 420 |
-
resnet_1_paths = renew_resnet_paths(resnet_1)
|
| 421 |
-
assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
|
| 422 |
-
|
| 423 |
-
old_path = "middle_block.1"
|
| 424 |
-
new_path = "mid_block.attentions.0"
|
| 425 |
-
|
| 426 |
-
assign_attention_to_checkpoint(
|
| 427 |
-
new_checkpoint=new_checkpoint,
|
| 428 |
-
unet_state_dict=unet_state_dict,
|
| 429 |
-
old_path=old_path,
|
| 430 |
-
new_path=new_path,
|
| 431 |
-
config=config,
|
| 432 |
-
)
|
| 433 |
-
|
| 434 |
-
attentions_paths = renew_attention_paths(attentions)
|
| 435 |
-
meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
|
| 436 |
-
assign_to_checkpoint(
|
| 437 |
-
attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
| 438 |
-
)
|
| 439 |
-
|
| 440 |
-
for i in range(num_output_blocks):
|
| 441 |
-
block_id = i // (config["layers_per_block"] + 1)
|
| 442 |
-
layer_in_block_id = i % (config["layers_per_block"] + 1)
|
| 443 |
-
output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
|
| 444 |
-
output_block_list = {}
|
| 445 |
-
|
| 446 |
-
for layer in output_block_layers:
|
| 447 |
-
layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
|
| 448 |
-
if layer_id in output_block_list:
|
| 449 |
-
output_block_list[layer_id].append(layer_name)
|
| 450 |
-
else:
|
| 451 |
-
output_block_list[layer_id] = [layer_name]
|
| 452 |
-
|
| 453 |
-
# len(output_block_list) == 1 -> resnet
|
| 454 |
-
# len(output_block_list) == 2 -> resnet, attention
|
| 455 |
-
# len(output_block_list) == 3 -> resnet, attention, upscale resnet
|
| 456 |
-
|
| 457 |
-
if len(output_block_list) > 1:
|
| 458 |
-
resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
|
| 459 |
-
attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
|
| 460 |
-
|
| 461 |
-
paths = renew_resnet_paths(resnets)
|
| 462 |
-
|
| 463 |
-
meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
|
| 464 |
-
|
| 465 |
-
assign_to_checkpoint(
|
| 466 |
-
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
| 467 |
-
)
|
| 468 |
-
|
| 469 |
-
output_block_list = {k: sorted(v) for k, v in output_block_list.items()}
|
| 470 |
-
if ["conv.bias", "conv.weight"] in output_block_list.values():
|
| 471 |
-
index = list(output_block_list.values()).index(["conv.bias", "conv.weight"])
|
| 472 |
-
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
|
| 473 |
-
f"output_blocks.{i}.{index}.conv.weight"
|
| 474 |
-
]
|
| 475 |
-
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
|
| 476 |
-
f"output_blocks.{i}.{index}.conv.bias"
|
| 477 |
-
]
|
| 478 |
-
|
| 479 |
-
# Clear attentions as they have been attributed above.
|
| 480 |
-
if len(attentions) == 2:
|
| 481 |
-
attentions = []
|
| 482 |
-
|
| 483 |
-
if len(attentions):
|
| 484 |
-
old_path = f"output_blocks.{i}.1"
|
| 485 |
-
new_path = f"up_blocks.{block_id}.attentions.{layer_in_block_id}"
|
| 486 |
-
|
| 487 |
-
assign_attention_to_checkpoint(
|
| 488 |
-
new_checkpoint=new_checkpoint,
|
| 489 |
-
unet_state_dict=unet_state_dict,
|
| 490 |
-
old_path=old_path,
|
| 491 |
-
new_path=new_path,
|
| 492 |
-
config=config,
|
| 493 |
-
)
|
| 494 |
-
|
| 495 |
-
paths = renew_attention_paths(attentions)
|
| 496 |
-
meta_path = {
|
| 497 |
-
"old": old_path,
|
| 498 |
-
"new": new_path,
|
| 499 |
-
}
|
| 500 |
-
assign_to_checkpoint(
|
| 501 |
-
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
| 502 |
-
)
|
| 503 |
-
|
| 504 |
-
if len(output_block_list) == 3:
|
| 505 |
-
resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.2" in key]
|
| 506 |
-
paths = renew_resnet_paths(resnets)
|
| 507 |
-
meta_path = {"old": f"output_blocks.{i}.2", "new": f"up_blocks.{block_id}.upsamplers.0"}
|
| 508 |
-
assign_to_checkpoint(
|
| 509 |
-
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
| 510 |
-
)
|
| 511 |
-
else:
|
| 512 |
-
resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
|
| 513 |
-
for path in resnet_0_paths:
|
| 514 |
-
old_path = ".".join(["output_blocks", str(i), path["old"]])
|
| 515 |
-
new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
|
| 516 |
-
|
| 517 |
-
new_checkpoint[new_path] = unet_state_dict[old_path]
|
| 518 |
-
|
| 519 |
-
if "encoder_proj.weight" in unet_state_dict:
|
| 520 |
-
new_checkpoint["encoder_hid_proj.weight"] = unet_state_dict.pop("encoder_proj.weight")
|
| 521 |
-
new_checkpoint["encoder_hid_proj.bias"] = unet_state_dict.pop("encoder_proj.bias")
|
| 522 |
-
|
| 523 |
-
if "encoder_pooling.0.weight" in unet_state_dict:
|
| 524 |
-
new_checkpoint["add_embedding.norm1.weight"] = unet_state_dict.pop("encoder_pooling.0.weight")
|
| 525 |
-
new_checkpoint["add_embedding.norm1.bias"] = unet_state_dict.pop("encoder_pooling.0.bias")
|
| 526 |
-
|
| 527 |
-
new_checkpoint["add_embedding.pool.positional_embedding"] = unet_state_dict.pop(
|
| 528 |
-
"encoder_pooling.1.positional_embedding"
|
| 529 |
-
)
|
| 530 |
-
new_checkpoint["add_embedding.pool.k_proj.weight"] = unet_state_dict.pop("encoder_pooling.1.k_proj.weight")
|
| 531 |
-
new_checkpoint["add_embedding.pool.k_proj.bias"] = unet_state_dict.pop("encoder_pooling.1.k_proj.bias")
|
| 532 |
-
new_checkpoint["add_embedding.pool.q_proj.weight"] = unet_state_dict.pop("encoder_pooling.1.q_proj.weight")
|
| 533 |
-
new_checkpoint["add_embedding.pool.q_proj.bias"] = unet_state_dict.pop("encoder_pooling.1.q_proj.bias")
|
| 534 |
-
new_checkpoint["add_embedding.pool.v_proj.weight"] = unet_state_dict.pop("encoder_pooling.1.v_proj.weight")
|
| 535 |
-
new_checkpoint["add_embedding.pool.v_proj.bias"] = unet_state_dict.pop("encoder_pooling.1.v_proj.bias")
|
| 536 |
-
|
| 537 |
-
new_checkpoint["add_embedding.proj.weight"] = unet_state_dict.pop("encoder_pooling.2.weight")
|
| 538 |
-
new_checkpoint["add_embedding.proj.bias"] = unet_state_dict.pop("encoder_pooling.2.bias")
|
| 539 |
-
|
| 540 |
-
new_checkpoint["add_embedding.norm2.weight"] = unet_state_dict.pop("encoder_pooling.3.weight")
|
| 541 |
-
new_checkpoint["add_embedding.norm2.bias"] = unet_state_dict.pop("encoder_pooling.3.bias")
|
| 542 |
-
|
| 543 |
-
return new_checkpoint
|
| 544 |
-
|
| 545 |
-
|
| 546 |
-
def shave_segments(path, n_shave_prefix_segments=1):
|
| 547 |
-
"""
|
| 548 |
-
Removes segments. Positive values shave the first segments, negative shave the last segments.
|
| 549 |
-
"""
|
| 550 |
-
if n_shave_prefix_segments >= 0:
|
| 551 |
-
return ".".join(path.split(".")[n_shave_prefix_segments:])
|
| 552 |
-
else:
|
| 553 |
-
return ".".join(path.split(".")[:n_shave_prefix_segments])
|
| 554 |
-
|
| 555 |
-
|
| 556 |
-
def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
|
| 557 |
-
"""
|
| 558 |
-
Updates paths inside resnets to the new naming scheme (local renaming)
|
| 559 |
-
"""
|
| 560 |
-
mapping = []
|
| 561 |
-
for old_item in old_list:
|
| 562 |
-
new_item = old_item.replace("in_layers.0", "norm1")
|
| 563 |
-
new_item = new_item.replace("in_layers.2", "conv1")
|
| 564 |
-
|
| 565 |
-
new_item = new_item.replace("out_layers.0", "norm2")
|
| 566 |
-
new_item = new_item.replace("out_layers.3", "conv2")
|
| 567 |
-
|
| 568 |
-
new_item = new_item.replace("emb_layers.1", "time_emb_proj")
|
| 569 |
-
new_item = new_item.replace("skip_connection", "conv_shortcut")
|
| 570 |
-
|
| 571 |
-
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
|
| 572 |
-
|
| 573 |
-
mapping.append({"old": old_item, "new": new_item})
|
| 574 |
-
|
| 575 |
-
return mapping
|
| 576 |
-
|
| 577 |
-
|
| 578 |
-
def renew_attention_paths(old_list, n_shave_prefix_segments=0):
|
| 579 |
-
"""
|
| 580 |
-
Updates paths inside attentions to the new naming scheme (local renaming)
|
| 581 |
-
"""
|
| 582 |
-
mapping = []
|
| 583 |
-
for old_item in old_list:
|
| 584 |
-
new_item = old_item
|
| 585 |
-
|
| 586 |
-
if "qkv" in new_item:
|
| 587 |
-
continue
|
| 588 |
-
|
| 589 |
-
if "encoder_kv" in new_item:
|
| 590 |
-
continue
|
| 591 |
-
|
| 592 |
-
new_item = new_item.replace("norm.weight", "group_norm.weight")
|
| 593 |
-
new_item = new_item.replace("norm.bias", "group_norm.bias")
|
| 594 |
-
|
| 595 |
-
new_item = new_item.replace("proj_out.weight", "to_out.0.weight")
|
| 596 |
-
new_item = new_item.replace("proj_out.bias", "to_out.0.bias")
|
| 597 |
-
|
| 598 |
-
new_item = new_item.replace("norm_encoder.weight", "norm_cross.weight")
|
| 599 |
-
new_item = new_item.replace("norm_encoder.bias", "norm_cross.bias")
|
| 600 |
-
|
| 601 |
-
new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
|
| 602 |
-
|
| 603 |
-
mapping.append({"old": old_item, "new": new_item})
|
| 604 |
-
|
| 605 |
-
return mapping
|
| 606 |
-
|
| 607 |
-
|
| 608 |
-
def assign_attention_to_checkpoint(new_checkpoint, unet_state_dict, old_path, new_path, config):
|
| 609 |
-
qkv_weight = unet_state_dict.pop(f"{old_path}.qkv.weight")
|
| 610 |
-
qkv_weight = qkv_weight[:, :, 0]
|
| 611 |
-
|
| 612 |
-
qkv_bias = unet_state_dict.pop(f"{old_path}.qkv.bias")
|
| 613 |
-
|
| 614 |
-
is_cross_attn_only = "only_cross_attention" in config and config["only_cross_attention"]
|
| 615 |
-
|
| 616 |
-
split = 1 if is_cross_attn_only else 3
|
| 617 |
-
|
| 618 |
-
weights, bias = split_attentions(
|
| 619 |
-
weight=qkv_weight,
|
| 620 |
-
bias=qkv_bias,
|
| 621 |
-
split=split,
|
| 622 |
-
chunk_size=config["attention_head_dim"],
|
| 623 |
-
)
|
| 624 |
-
|
| 625 |
-
if is_cross_attn_only:
|
| 626 |
-
query_weight, q_bias = weights, bias
|
| 627 |
-
new_checkpoint[f"{new_path}.to_q.weight"] = query_weight[0]
|
| 628 |
-
new_checkpoint[f"{new_path}.to_q.bias"] = q_bias[0]
|
| 629 |
-
else:
|
| 630 |
-
[query_weight, key_weight, value_weight], [q_bias, k_bias, v_bias] = weights, bias
|
| 631 |
-
new_checkpoint[f"{new_path}.to_q.weight"] = query_weight
|
| 632 |
-
new_checkpoint[f"{new_path}.to_q.bias"] = q_bias
|
| 633 |
-
new_checkpoint[f"{new_path}.to_k.weight"] = key_weight
|
| 634 |
-
new_checkpoint[f"{new_path}.to_k.bias"] = k_bias
|
| 635 |
-
new_checkpoint[f"{new_path}.to_v.weight"] = value_weight
|
| 636 |
-
new_checkpoint[f"{new_path}.to_v.bias"] = v_bias
|
| 637 |
-
|
| 638 |
-
encoder_kv_weight = unet_state_dict.pop(f"{old_path}.encoder_kv.weight")
|
| 639 |
-
encoder_kv_weight = encoder_kv_weight[:, :, 0]
|
| 640 |
-
|
| 641 |
-
encoder_kv_bias = unet_state_dict.pop(f"{old_path}.encoder_kv.bias")
|
| 642 |
-
|
| 643 |
-
[encoder_k_weight, encoder_v_weight], [encoder_k_bias, encoder_v_bias] = split_attentions(
|
| 644 |
-
weight=encoder_kv_weight,
|
| 645 |
-
bias=encoder_kv_bias,
|
| 646 |
-
split=2,
|
| 647 |
-
chunk_size=config["attention_head_dim"],
|
| 648 |
-
)
|
| 649 |
-
|
| 650 |
-
new_checkpoint[f"{new_path}.add_k_proj.weight"] = encoder_k_weight
|
| 651 |
-
new_checkpoint[f"{new_path}.add_k_proj.bias"] = encoder_k_bias
|
| 652 |
-
new_checkpoint[f"{new_path}.add_v_proj.weight"] = encoder_v_weight
|
| 653 |
-
new_checkpoint[f"{new_path}.add_v_proj.bias"] = encoder_v_bias
|
| 654 |
-
|
| 655 |
-
|
| 656 |
-
def assign_to_checkpoint(paths, checkpoint, old_checkpoint, additional_replacements=None, config=None):
|
| 657 |
-
"""
|
| 658 |
-
This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits
|
| 659 |
-
attention layers, and takes into account additional replacements that may arise.
|
| 660 |
-
|
| 661 |
-
Assigns the weights to the new checkpoint.
|
| 662 |
-
"""
|
| 663 |
-
assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
|
| 664 |
-
|
| 665 |
-
for path in paths:
|
| 666 |
-
new_path = path["new"]
|
| 667 |
-
|
| 668 |
-
# Global renaming happens here
|
| 669 |
-
new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
|
| 670 |
-
new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
|
| 671 |
-
new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
|
| 672 |
-
|
| 673 |
-
if additional_replacements is not None:
|
| 674 |
-
for replacement in additional_replacements:
|
| 675 |
-
new_path = new_path.replace(replacement["old"], replacement["new"])
|
| 676 |
-
|
| 677 |
-
# proj_attn.weight has to be converted from conv 1D to linear
|
| 678 |
-
if "proj_attn.weight" in new_path or "to_out.0.weight" in new_path:
|
| 679 |
-
checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
|
| 680 |
-
else:
|
| 681 |
-
checkpoint[new_path] = old_checkpoint[path["old"]]
|
| 682 |
-
|
| 683 |
-
|
| 684 |
-
# TODO maybe document and/or can do more efficiently (build indices in for loop and extract once for each split?)
|
| 685 |
-
def split_attentions(*, weight, bias, split, chunk_size):
|
| 686 |
-
weights = [None] * split
|
| 687 |
-
biases = [None] * split
|
| 688 |
-
|
| 689 |
-
weights_biases_idx = 0
|
| 690 |
-
|
| 691 |
-
for starting_row_index in range(0, weight.shape[0], chunk_size):
|
| 692 |
-
row_indices = torch.arange(starting_row_index, starting_row_index + chunk_size)
|
| 693 |
-
|
| 694 |
-
weight_rows = weight[row_indices, :]
|
| 695 |
-
bias_rows = bias[row_indices]
|
| 696 |
-
|
| 697 |
-
if weights[weights_biases_idx] is None:
|
| 698 |
-
weights[weights_biases_idx] = weight_rows
|
| 699 |
-
biases[weights_biases_idx] = bias_rows
|
| 700 |
-
else:
|
| 701 |
-
assert weights[weights_biases_idx] is not None
|
| 702 |
-
weights[weights_biases_idx] = torch.concat([weights[weights_biases_idx], weight_rows])
|
| 703 |
-
biases[weights_biases_idx] = torch.concat([biases[weights_biases_idx], bias_rows])
|
| 704 |
-
|
| 705 |
-
weights_biases_idx = (weights_biases_idx + 1) % split
|
| 706 |
-
|
| 707 |
-
return weights, biases
|
| 708 |
-
|
| 709 |
-
|
| 710 |
-
def parse_list(value):
|
| 711 |
-
if isinstance(value, str):
|
| 712 |
-
value = value.split(",")
|
| 713 |
-
value = [int(v) for v in value]
|
| 714 |
-
elif isinstance(value, list):
|
| 715 |
-
pass
|
| 716 |
-
else:
|
| 717 |
-
raise ValueError(f"Can't parse list for type: {type(value)}")
|
| 718 |
-
|
| 719 |
-
return value
|
| 720 |
-
|
| 721 |
-
|
| 722 |
-
# below is copy and pasted from original convert_if_stage_2.py script
|
| 723 |
-
|
| 724 |
-
|
| 725 |
-
def get_super_res_unet(unet_checkpoint_path, verify_param_count=True, sample_size=None):
|
| 726 |
-
orig_path = unet_checkpoint_path
|
| 727 |
-
|
| 728 |
-
original_unet_config = OmegaConf.load(os.path.join(orig_path, "config.yml"))
|
| 729 |
-
original_unet_config = original_unet_config.params
|
| 730 |
-
|
| 731 |
-
unet_diffusers_config = superres_create_unet_diffusers_config(original_unet_config)
|
| 732 |
-
unet_diffusers_config["time_embedding_dim"] = original_unet_config.model_channels * int(
|
| 733 |
-
original_unet_config.channel_mult.split(",")[-1]
|
| 734 |
-
)
|
| 735 |
-
if original_unet_config.encoder_dim != original_unet_config.encoder_channels:
|
| 736 |
-
unet_diffusers_config["encoder_hid_dim"] = original_unet_config.encoder_dim
|
| 737 |
-
unet_diffusers_config["class_embed_type"] = "timestep"
|
| 738 |
-
unet_diffusers_config["addition_embed_type"] = "text"
|
| 739 |
-
|
| 740 |
-
unet_diffusers_config["time_embedding_act_fn"] = "gelu"
|
| 741 |
-
unet_diffusers_config["resnet_skip_time_act"] = True
|
| 742 |
-
unet_diffusers_config["resnet_out_scale_factor"] = 1 / 0.7071
|
| 743 |
-
unet_diffusers_config["mid_block_scale_factor"] = 1 / 0.7071
|
| 744 |
-
unet_diffusers_config["only_cross_attention"] = (
|
| 745 |
-
bool(original_unet_config.disable_self_attentions)
|
| 746 |
-
if (
|
| 747 |
-
"disable_self_attentions" in original_unet_config
|
| 748 |
-
and isinstance(original_unet_config.disable_self_attentions, int)
|
| 749 |
-
)
|
| 750 |
-
else True
|
| 751 |
-
)
|
| 752 |
-
|
| 753 |
-
if sample_size is None:
|
| 754 |
-
unet_diffusers_config["sample_size"] = original_unet_config.image_size
|
| 755 |
-
else:
|
| 756 |
-
# The second upscaler unet's sample size is incorrectly specified
|
| 757 |
-
# in the config and is instead hardcoded in source
|
| 758 |
-
unet_diffusers_config["sample_size"] = sample_size
|
| 759 |
-
|
| 760 |
-
unet_checkpoint = torch.load(os.path.join(unet_checkpoint_path, "pytorch_model.bin"), map_location="cpu")
|
| 761 |
-
|
| 762 |
-
if verify_param_count:
|
| 763 |
-
# check that architecture matches - is a bit slow
|
| 764 |
-
verify_param_count(orig_path, unet_diffusers_config)
|
| 765 |
-
|
| 766 |
-
converted_unet_checkpoint = superres_convert_ldm_unet_checkpoint(
|
| 767 |
-
unet_checkpoint, unet_diffusers_config, path=unet_checkpoint_path
|
| 768 |
-
)
|
| 769 |
-
converted_keys = converted_unet_checkpoint.keys()
|
| 770 |
-
|
| 771 |
-
model = UNet2DConditionModel(**unet_diffusers_config)
|
| 772 |
-
expected_weights = model.state_dict().keys()
|
| 773 |
-
|
| 774 |
-
diff_c_e = set(converted_keys) - set(expected_weights)
|
| 775 |
-
diff_e_c = set(expected_weights) - set(converted_keys)
|
| 776 |
-
|
| 777 |
-
assert len(diff_e_c) == 0, f"Expected, but not converted: {diff_e_c}"
|
| 778 |
-
assert len(diff_c_e) == 0, f"Converted, but not expected: {diff_c_e}"
|
| 779 |
-
|
| 780 |
-
model.load_state_dict(converted_unet_checkpoint)
|
| 781 |
-
|
| 782 |
-
return model
|
| 783 |
-
|
| 784 |
-
|
| 785 |
-
def superres_create_unet_diffusers_config(original_unet_config):
|
| 786 |
-
attention_resolutions = parse_list(original_unet_config.attention_resolutions)
|
| 787 |
-
attention_resolutions = [original_unet_config.image_size // int(res) for res in attention_resolutions]
|
| 788 |
-
|
| 789 |
-
channel_mult = parse_list(original_unet_config.channel_mult)
|
| 790 |
-
block_out_channels = [original_unet_config.model_channels * mult for mult in channel_mult]
|
| 791 |
-
|
| 792 |
-
down_block_types = []
|
| 793 |
-
resolution = 1
|
| 794 |
-
|
| 795 |
-
for i in range(len(block_out_channels)):
|
| 796 |
-
if resolution in attention_resolutions:
|
| 797 |
-
block_type = "SimpleCrossAttnDownBlock2D"
|
| 798 |
-
elif original_unet_config.resblock_updown:
|
| 799 |
-
block_type = "ResnetDownsampleBlock2D"
|
| 800 |
-
else:
|
| 801 |
-
block_type = "DownBlock2D"
|
| 802 |
-
|
| 803 |
-
down_block_types.append(block_type)
|
| 804 |
-
|
| 805 |
-
if i != len(block_out_channels) - 1:
|
| 806 |
-
resolution *= 2
|
| 807 |
-
|
| 808 |
-
up_block_types = []
|
| 809 |
-
for i in range(len(block_out_channels)):
|
| 810 |
-
if resolution in attention_resolutions:
|
| 811 |
-
block_type = "SimpleCrossAttnUpBlock2D"
|
| 812 |
-
elif original_unet_config.resblock_updown:
|
| 813 |
-
block_type = "ResnetUpsampleBlock2D"
|
| 814 |
-
else:
|
| 815 |
-
block_type = "UpBlock2D"
|
| 816 |
-
up_block_types.append(block_type)
|
| 817 |
-
resolution //= 2
|
| 818 |
-
|
| 819 |
-
head_dim = original_unet_config.num_head_channels
|
| 820 |
-
use_linear_projection = (
|
| 821 |
-
original_unet_config.use_linear_in_transformer
|
| 822 |
-
if "use_linear_in_transformer" in original_unet_config
|
| 823 |
-
else False
|
| 824 |
-
)
|
| 825 |
-
if use_linear_projection:
|
| 826 |
-
# stable diffusion 2-base-512 and 2-768
|
| 827 |
-
if head_dim is None:
|
| 828 |
-
head_dim = [5, 10, 20, 20]
|
| 829 |
-
|
| 830 |
-
class_embed_type = None
|
| 831 |
-
projection_class_embeddings_input_dim = None
|
| 832 |
-
|
| 833 |
-
if "num_classes" in original_unet_config:
|
| 834 |
-
if original_unet_config.num_classes == "sequential":
|
| 835 |
-
class_embed_type = "projection"
|
| 836 |
-
assert "adm_in_channels" in original_unet_config
|
| 837 |
-
projection_class_embeddings_input_dim = original_unet_config.adm_in_channels
|
| 838 |
-
else:
|
| 839 |
-
raise NotImplementedError(
|
| 840 |
-
f"Unknown conditional unet num_classes config: {original_unet_config.num_classes}"
|
| 841 |
-
)
|
| 842 |
-
|
| 843 |
-
config = {
|
| 844 |
-
"in_channels": original_unet_config.in_channels,
|
| 845 |
-
"down_block_types": tuple(down_block_types),
|
| 846 |
-
"block_out_channels": tuple(block_out_channels),
|
| 847 |
-
"layers_per_block": tuple(original_unet_config.num_res_blocks),
|
| 848 |
-
"cross_attention_dim": original_unet_config.encoder_channels,
|
| 849 |
-
"attention_head_dim": head_dim,
|
| 850 |
-
"use_linear_projection": use_linear_projection,
|
| 851 |
-
"class_embed_type": class_embed_type,
|
| 852 |
-
"projection_class_embeddings_input_dim": projection_class_embeddings_input_dim,
|
| 853 |
-
"out_channels": original_unet_config.out_channels,
|
| 854 |
-
"up_block_types": tuple(up_block_types),
|
| 855 |
-
"upcast_attention": False, # TODO: guessing
|
| 856 |
-
"cross_attention_norm": "group_norm",
|
| 857 |
-
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
|
| 858 |
-
"act_fn": "gelu",
|
| 859 |
-
}
|
| 860 |
-
|
| 861 |
-
if original_unet_config.use_scale_shift_norm:
|
| 862 |
-
config["resnet_time_scale_shift"] = "scale_shift"
|
| 863 |
-
|
| 864 |
-
return config
|
| 865 |
-
|
| 866 |
-
|
| 867 |
-
def superres_convert_ldm_unet_checkpoint(unet_state_dict, config, path=None, extract_ema=False):
|
| 868 |
-
"""
|
| 869 |
-
Takes a state dict and a config, and returns a converted checkpoint.
|
| 870 |
-
"""
|
| 871 |
-
new_checkpoint = {}
|
| 872 |
-
|
| 873 |
-
new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
|
| 874 |
-
new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
|
| 875 |
-
new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
|
| 876 |
-
new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
|
| 877 |
-
|
| 878 |
-
if config["class_embed_type"] is None:
|
| 879 |
-
# No parameters to port
|
| 880 |
-
...
|
| 881 |
-
elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
|
| 882 |
-
new_checkpoint["class_embedding.linear_1.weight"] = unet_state_dict["aug_proj.0.weight"]
|
| 883 |
-
new_checkpoint["class_embedding.linear_1.bias"] = unet_state_dict["aug_proj.0.bias"]
|
| 884 |
-
new_checkpoint["class_embedding.linear_2.weight"] = unet_state_dict["aug_proj.2.weight"]
|
| 885 |
-
new_checkpoint["class_embedding.linear_2.bias"] = unet_state_dict["aug_proj.2.bias"]
|
| 886 |
-
else:
|
| 887 |
-
raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}")
|
| 888 |
-
|
| 889 |
-
if "encoder_proj.weight" in unet_state_dict:
|
| 890 |
-
new_checkpoint["encoder_hid_proj.weight"] = unet_state_dict["encoder_proj.weight"]
|
| 891 |
-
new_checkpoint["encoder_hid_proj.bias"] = unet_state_dict["encoder_proj.bias"]
|
| 892 |
-
|
| 893 |
-
if "encoder_pooling.0.weight" in unet_state_dict:
|
| 894 |
-
mapping = {
|
| 895 |
-
"encoder_pooling.0": "add_embedding.norm1",
|
| 896 |
-
"encoder_pooling.1": "add_embedding.pool",
|
| 897 |
-
"encoder_pooling.2": "add_embedding.proj",
|
| 898 |
-
"encoder_pooling.3": "add_embedding.norm2",
|
| 899 |
-
}
|
| 900 |
-
for key in unet_state_dict.keys():
|
| 901 |
-
if key.startswith("encoder_pooling"):
|
| 902 |
-
prefix = key[: len("encoder_pooling.0")]
|
| 903 |
-
new_key = key.replace(prefix, mapping[prefix])
|
| 904 |
-
new_checkpoint[new_key] = unet_state_dict[key]
|
| 905 |
-
|
| 906 |
-
new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
|
| 907 |
-
new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
|
| 908 |
-
|
| 909 |
-
new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
|
| 910 |
-
new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
|
| 911 |
-
new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
|
| 912 |
-
new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
|
| 913 |
-
|
| 914 |
-
# Retrieves the keys for the input blocks only
|
| 915 |
-
num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
|
| 916 |
-
input_blocks = {
|
| 917 |
-
layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}." in key]
|
| 918 |
-
for layer_id in range(num_input_blocks)
|
| 919 |
-
}
|
| 920 |
-
|
| 921 |
-
# Retrieves the keys for the middle blocks only
|
| 922 |
-
num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
|
| 923 |
-
middle_blocks = {
|
| 924 |
-
layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
|
| 925 |
-
for layer_id in range(num_middle_blocks)
|
| 926 |
-
}
|
| 927 |
-
|
| 928 |
-
# Retrieves the keys for the output blocks only
|
| 929 |
-
num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
|
| 930 |
-
output_blocks = {
|
| 931 |
-
layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}." in key]
|
| 932 |
-
for layer_id in range(num_output_blocks)
|
| 933 |
-
}
|
| 934 |
-
if not isinstance(config["layers_per_block"], int):
|
| 935 |
-
layers_per_block_list = [e + 1 for e in config["layers_per_block"]]
|
| 936 |
-
layers_per_block_cumsum = list(np.cumsum(layers_per_block_list))
|
| 937 |
-
downsampler_ids = layers_per_block_cumsum
|
| 938 |
-
else:
|
| 939 |
-
# TODO need better check than i in [4, 8, 12, 16]
|
| 940 |
-
downsampler_ids = [4, 8, 12, 16]
|
| 941 |
-
|
| 942 |
-
for i in range(1, num_input_blocks):
|
| 943 |
-
if isinstance(config["layers_per_block"], int):
|
| 944 |
-
layers_per_block = config["layers_per_block"]
|
| 945 |
-
block_id = (i - 1) // (layers_per_block + 1)
|
| 946 |
-
layer_in_block_id = (i - 1) % (layers_per_block + 1)
|
| 947 |
-
else:
|
| 948 |
-
block_id = next(k for k, n in enumerate(layers_per_block_cumsum) if (i - 1) < n)
|
| 949 |
-
passed_blocks = layers_per_block_cumsum[block_id - 1] if block_id > 0 else 0
|
| 950 |
-
layer_in_block_id = (i - 1) - passed_blocks
|
| 951 |
-
|
| 952 |
-
resnets = [
|
| 953 |
-
key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
|
| 954 |
-
]
|
| 955 |
-
attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
|
| 956 |
-
|
| 957 |
-
if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
|
| 958 |
-
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
|
| 959 |
-
f"input_blocks.{i}.0.op.weight"
|
| 960 |
-
)
|
| 961 |
-
new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
|
| 962 |
-
f"input_blocks.{i}.0.op.bias"
|
| 963 |
-
)
|
| 964 |
-
|
| 965 |
-
paths = renew_resnet_paths(resnets)
|
| 966 |
-
|
| 967 |
-
block_type = config["down_block_types"][block_id]
|
| 968 |
-
if (
|
| 969 |
-
block_type == "ResnetDownsampleBlock2D" or block_type == "SimpleCrossAttnDownBlock2D"
|
| 970 |
-
) and i in downsampler_ids:
|
| 971 |
-
meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.downsamplers.0"}
|
| 972 |
-
else:
|
| 973 |
-
meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
|
| 974 |
-
|
| 975 |
-
assign_to_checkpoint(
|
| 976 |
-
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
| 977 |
-
)
|
| 978 |
-
|
| 979 |
-
if len(attentions):
|
| 980 |
-
old_path = f"input_blocks.{i}.1"
|
| 981 |
-
new_path = f"down_blocks.{block_id}.attentions.{layer_in_block_id}"
|
| 982 |
-
|
| 983 |
-
assign_attention_to_checkpoint(
|
| 984 |
-
new_checkpoint=new_checkpoint,
|
| 985 |
-
unet_state_dict=unet_state_dict,
|
| 986 |
-
old_path=old_path,
|
| 987 |
-
new_path=new_path,
|
| 988 |
-
config=config,
|
| 989 |
-
)
|
| 990 |
-
|
| 991 |
-
paths = renew_attention_paths(attentions)
|
| 992 |
-
meta_path = {"old": old_path, "new": new_path}
|
| 993 |
-
assign_to_checkpoint(
|
| 994 |
-
paths,
|
| 995 |
-
new_checkpoint,
|
| 996 |
-
unet_state_dict,
|
| 997 |
-
additional_replacements=[meta_path],
|
| 998 |
-
config=config,
|
| 999 |
-
)
|
| 1000 |
-
|
| 1001 |
-
resnet_0 = middle_blocks[0]
|
| 1002 |
-
attentions = middle_blocks[1]
|
| 1003 |
-
resnet_1 = middle_blocks[2]
|
| 1004 |
-
|
| 1005 |
-
resnet_0_paths = renew_resnet_paths(resnet_0)
|
| 1006 |
-
assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
|
| 1007 |
-
|
| 1008 |
-
resnet_1_paths = renew_resnet_paths(resnet_1)
|
| 1009 |
-
assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
|
| 1010 |
-
|
| 1011 |
-
old_path = "middle_block.1"
|
| 1012 |
-
new_path = "mid_block.attentions.0"
|
| 1013 |
-
|
| 1014 |
-
assign_attention_to_checkpoint(
|
| 1015 |
-
new_checkpoint=new_checkpoint,
|
| 1016 |
-
unet_state_dict=unet_state_dict,
|
| 1017 |
-
old_path=old_path,
|
| 1018 |
-
new_path=new_path,
|
| 1019 |
-
config=config,
|
| 1020 |
-
)
|
| 1021 |
-
|
| 1022 |
-
attentions_paths = renew_attention_paths(attentions)
|
| 1023 |
-
meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
|
| 1024 |
-
assign_to_checkpoint(
|
| 1025 |
-
attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
| 1026 |
-
)
|
| 1027 |
-
if not isinstance(config["layers_per_block"], int):
|
| 1028 |
-
layers_per_block_list = list(reversed([e + 1 for e in config["layers_per_block"]]))
|
| 1029 |
-
layers_per_block_cumsum = list(np.cumsum(layers_per_block_list))
|
| 1030 |
-
|
| 1031 |
-
for i in range(num_output_blocks):
|
| 1032 |
-
if isinstance(config["layers_per_block"], int):
|
| 1033 |
-
layers_per_block = config["layers_per_block"]
|
| 1034 |
-
block_id = i // (layers_per_block + 1)
|
| 1035 |
-
layer_in_block_id = i % (layers_per_block + 1)
|
| 1036 |
-
else:
|
| 1037 |
-
block_id = next(k for k, n in enumerate(layers_per_block_cumsum) if i < n)
|
| 1038 |
-
passed_blocks = layers_per_block_cumsum[block_id - 1] if block_id > 0 else 0
|
| 1039 |
-
layer_in_block_id = i - passed_blocks
|
| 1040 |
-
|
| 1041 |
-
output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
|
| 1042 |
-
output_block_list = {}
|
| 1043 |
-
|
| 1044 |
-
for layer in output_block_layers:
|
| 1045 |
-
layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
|
| 1046 |
-
if layer_id in output_block_list:
|
| 1047 |
-
output_block_list[layer_id].append(layer_name)
|
| 1048 |
-
else:
|
| 1049 |
-
output_block_list[layer_id] = [layer_name]
|
| 1050 |
-
|
| 1051 |
-
# len(output_block_list) == 1 -> resnet
|
| 1052 |
-
# len(output_block_list) == 2 -> resnet, attention or resnet, upscale resnet
|
| 1053 |
-
# len(output_block_list) == 3 -> resnet, attention, upscale resnet
|
| 1054 |
-
|
| 1055 |
-
if len(output_block_list) > 1:
|
| 1056 |
-
resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
|
| 1057 |
-
|
| 1058 |
-
has_attention = True
|
| 1059 |
-
if len(output_block_list) == 2 and any("in_layers" in k for k in output_block_list["1"]):
|
| 1060 |
-
has_attention = False
|
| 1061 |
-
|
| 1062 |
-
maybe_attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
|
| 1063 |
-
|
| 1064 |
-
paths = renew_resnet_paths(resnets)
|
| 1065 |
-
|
| 1066 |
-
meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
|
| 1067 |
-
|
| 1068 |
-
assign_to_checkpoint(
|
| 1069 |
-
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
| 1070 |
-
)
|
| 1071 |
-
|
| 1072 |
-
output_block_list = {k: sorted(v) for k, v in output_block_list.items()}
|
| 1073 |
-
if ["conv.bias", "conv.weight"] in output_block_list.values():
|
| 1074 |
-
index = list(output_block_list.values()).index(["conv.bias", "conv.weight"])
|
| 1075 |
-
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
|
| 1076 |
-
f"output_blocks.{i}.{index}.conv.weight"
|
| 1077 |
-
]
|
| 1078 |
-
new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
|
| 1079 |
-
f"output_blocks.{i}.{index}.conv.bias"
|
| 1080 |
-
]
|
| 1081 |
-
|
| 1082 |
-
# this layer was no attention
|
| 1083 |
-
has_attention = False
|
| 1084 |
-
maybe_attentions = []
|
| 1085 |
-
|
| 1086 |
-
if has_attention:
|
| 1087 |
-
old_path = f"output_blocks.{i}.1"
|
| 1088 |
-
new_path = f"up_blocks.{block_id}.attentions.{layer_in_block_id}"
|
| 1089 |
-
|
| 1090 |
-
assign_attention_to_checkpoint(
|
| 1091 |
-
new_checkpoint=new_checkpoint,
|
| 1092 |
-
unet_state_dict=unet_state_dict,
|
| 1093 |
-
old_path=old_path,
|
| 1094 |
-
new_path=new_path,
|
| 1095 |
-
config=config,
|
| 1096 |
-
)
|
| 1097 |
-
|
| 1098 |
-
paths = renew_attention_paths(maybe_attentions)
|
| 1099 |
-
meta_path = {
|
| 1100 |
-
"old": old_path,
|
| 1101 |
-
"new": new_path,
|
| 1102 |
-
}
|
| 1103 |
-
assign_to_checkpoint(
|
| 1104 |
-
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
| 1105 |
-
)
|
| 1106 |
-
|
| 1107 |
-
if len(output_block_list) == 3 or (not has_attention and len(maybe_attentions) > 0):
|
| 1108 |
-
layer_id = len(output_block_list) - 1
|
| 1109 |
-
resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.{layer_id}" in key]
|
| 1110 |
-
paths = renew_resnet_paths(resnets)
|
| 1111 |
-
meta_path = {"old": f"output_blocks.{i}.{layer_id}", "new": f"up_blocks.{block_id}.upsamplers.0"}
|
| 1112 |
-
assign_to_checkpoint(
|
| 1113 |
-
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
|
| 1114 |
-
)
|
| 1115 |
-
else:
|
| 1116 |
-
resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
|
| 1117 |
-
for path in resnet_0_paths:
|
| 1118 |
-
old_path = ".".join(["output_blocks", str(i), path["old"]])
|
| 1119 |
-
new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
|
| 1120 |
-
|
| 1121 |
-
new_checkpoint[new_path] = unet_state_dict[old_path]
|
| 1122 |
-
|
| 1123 |
-
return new_checkpoint
|
| 1124 |
-
|
| 1125 |
-
|
| 1126 |
-
def verify_param_count(orig_path, unet_diffusers_config):
|
| 1127 |
-
if "-II-" in orig_path:
|
| 1128 |
-
from deepfloyd_if.modules import IFStageII
|
| 1129 |
-
|
| 1130 |
-
if_II = IFStageII(device="cpu", dir_or_name=orig_path)
|
| 1131 |
-
elif "-III-" in orig_path:
|
| 1132 |
-
from deepfloyd_if.modules import IFStageIII
|
| 1133 |
-
|
| 1134 |
-
if_II = IFStageIII(device="cpu", dir_or_name=orig_path)
|
| 1135 |
-
else:
|
| 1136 |
-
assert f"Weird name. Should have -II- or -III- in path: {orig_path}"
|
| 1137 |
-
|
| 1138 |
-
unet = UNet2DConditionModel(**unet_diffusers_config)
|
| 1139 |
-
|
| 1140 |
-
# in params
|
| 1141 |
-
assert_param_count(unet.time_embedding, if_II.model.time_embed)
|
| 1142 |
-
assert_param_count(unet.conv_in, if_II.model.input_blocks[:1])
|
| 1143 |
-
|
| 1144 |
-
# downblocks
|
| 1145 |
-
assert_param_count(unet.down_blocks[0], if_II.model.input_blocks[1:4])
|
| 1146 |
-
assert_param_count(unet.down_blocks[1], if_II.model.input_blocks[4:7])
|
| 1147 |
-
assert_param_count(unet.down_blocks[2], if_II.model.input_blocks[7:11])
|
| 1148 |
-
|
| 1149 |
-
if "-II-" in orig_path:
|
| 1150 |
-
assert_param_count(unet.down_blocks[3], if_II.model.input_blocks[11:17])
|
| 1151 |
-
assert_param_count(unet.down_blocks[4], if_II.model.input_blocks[17:])
|
| 1152 |
-
if "-III-" in orig_path:
|
| 1153 |
-
assert_param_count(unet.down_blocks[3], if_II.model.input_blocks[11:15])
|
| 1154 |
-
assert_param_count(unet.down_blocks[4], if_II.model.input_blocks[15:20])
|
| 1155 |
-
assert_param_count(unet.down_blocks[5], if_II.model.input_blocks[20:])
|
| 1156 |
-
|
| 1157 |
-
# mid block
|
| 1158 |
-
assert_param_count(unet.mid_block, if_II.model.middle_block)
|
| 1159 |
-
|
| 1160 |
-
# up block
|
| 1161 |
-
if "-II-" in orig_path:
|
| 1162 |
-
assert_param_count(unet.up_blocks[0], if_II.model.output_blocks[:6])
|
| 1163 |
-
assert_param_count(unet.up_blocks[1], if_II.model.output_blocks[6:12])
|
| 1164 |
-
assert_param_count(unet.up_blocks[2], if_II.model.output_blocks[12:16])
|
| 1165 |
-
assert_param_count(unet.up_blocks[3], if_II.model.output_blocks[16:19])
|
| 1166 |
-
assert_param_count(unet.up_blocks[4], if_II.model.output_blocks[19:])
|
| 1167 |
-
if "-III-" in orig_path:
|
| 1168 |
-
assert_param_count(unet.up_blocks[0], if_II.model.output_blocks[:5])
|
| 1169 |
-
assert_param_count(unet.up_blocks[1], if_II.model.output_blocks[5:10])
|
| 1170 |
-
assert_param_count(unet.up_blocks[2], if_II.model.output_blocks[10:14])
|
| 1171 |
-
assert_param_count(unet.up_blocks[3], if_II.model.output_blocks[14:18])
|
| 1172 |
-
assert_param_count(unet.up_blocks[4], if_II.model.output_blocks[18:21])
|
| 1173 |
-
assert_param_count(unet.up_blocks[5], if_II.model.output_blocks[21:24])
|
| 1174 |
-
|
| 1175 |
-
# out params
|
| 1176 |
-
assert_param_count(unet.conv_norm_out, if_II.model.out[0])
|
| 1177 |
-
assert_param_count(unet.conv_out, if_II.model.out[2])
|
| 1178 |
-
|
| 1179 |
-
# make sure all model architecture has same param count
|
| 1180 |
-
assert_param_count(unet, if_II.model)
|
| 1181 |
-
|
| 1182 |
-
|
| 1183 |
-
def assert_param_count(model_1, model_2):
|
| 1184 |
-
count_1 = sum(p.numel() for p in model_1.parameters())
|
| 1185 |
-
count_2 = sum(p.numel() for p in model_2.parameters())
|
| 1186 |
-
assert count_1 == count_2, f"{model_1.__class__}: {count_1} != {model_2.__class__}: {count_2}"
|
| 1187 |
-
|
| 1188 |
-
|
| 1189 |
-
def superres_check_against_original(dump_path, unet_checkpoint_path):
|
| 1190 |
-
model_path = dump_path
|
| 1191 |
-
model = UNet2DConditionModel.from_pretrained(model_path)
|
| 1192 |
-
model.to("cuda")
|
| 1193 |
-
orig_path = unet_checkpoint_path
|
| 1194 |
-
|
| 1195 |
-
if "-II-" in orig_path:
|
| 1196 |
-
from deepfloyd_if.modules import IFStageII
|
| 1197 |
-
|
| 1198 |
-
if_II_model = IFStageII(device="cuda", dir_or_name=orig_path, model_kwargs={"precision": "fp32"}).model
|
| 1199 |
-
elif "-III-" in orig_path:
|
| 1200 |
-
from deepfloyd_if.modules import IFStageIII
|
| 1201 |
-
|
| 1202 |
-
if_II_model = IFStageIII(device="cuda", dir_or_name=orig_path, model_kwargs={"precision": "fp32"}).model
|
| 1203 |
-
|
| 1204 |
-
batch_size = 1
|
| 1205 |
-
channels = model.in_channels // 2
|
| 1206 |
-
height = model.sample_size
|
| 1207 |
-
width = model.sample_size
|
| 1208 |
-
height = 1024
|
| 1209 |
-
width = 1024
|
| 1210 |
-
|
| 1211 |
-
torch.manual_seed(0)
|
| 1212 |
-
|
| 1213 |
-
latents = torch.randn((batch_size, channels, height, width), device=model.device)
|
| 1214 |
-
image_small = torch.randn((batch_size, channels, height // 4, width // 4), device=model.device)
|
| 1215 |
-
|
| 1216 |
-
interpolate_antialias = {}
|
| 1217 |
-
if "antialias" in inspect.signature(F.interpolate).parameters:
|
| 1218 |
-
interpolate_antialias["antialias"] = True
|
| 1219 |
-
image_upscaled = F.interpolate(
|
| 1220 |
-
image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias
|
| 1221 |
-
)
|
| 1222 |
-
|
| 1223 |
-
latent_model_input = torch.cat([latents, image_upscaled], dim=1).to(model.dtype)
|
| 1224 |
-
t = torch.tensor([5], device=model.device).to(model.dtype)
|
| 1225 |
-
|
| 1226 |
-
seq_len = 64
|
| 1227 |
-
encoder_hidden_states = torch.randn((batch_size, seq_len, model.config.encoder_hid_dim), device=model.device).to(
|
| 1228 |
-
model.dtype
|
| 1229 |
-
)
|
| 1230 |
-
|
| 1231 |
-
fake_class_labels = torch.tensor([t], device=model.device).to(model.dtype)
|
| 1232 |
-
|
| 1233 |
-
with torch.no_grad():
|
| 1234 |
-
out = if_II_model(latent_model_input, t, aug_steps=fake_class_labels, text_emb=encoder_hidden_states)
|
| 1235 |
-
|
| 1236 |
-
if_II_model.to("cpu")
|
| 1237 |
-
del if_II_model
|
| 1238 |
-
import gc
|
| 1239 |
-
|
| 1240 |
-
torch.cuda.empty_cache()
|
| 1241 |
-
gc.collect()
|
| 1242 |
-
print(50 * "=")
|
| 1243 |
-
|
| 1244 |
-
with torch.no_grad():
|
| 1245 |
-
noise_pred = model(
|
| 1246 |
-
sample=latent_model_input,
|
| 1247 |
-
encoder_hidden_states=encoder_hidden_states,
|
| 1248 |
-
class_labels=fake_class_labels,
|
| 1249 |
-
timestep=t,
|
| 1250 |
-
).sample
|
| 1251 |
-
|
| 1252 |
-
print("Out shape", noise_pred.shape)
|
| 1253 |
-
print("Diff", (out - noise_pred).abs().sum())
|
| 1254 |
-
|
| 1255 |
-
|
| 1256 |
-
if __name__ == "__main__":
|
| 1257 |
-
main(parse_args())
|
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spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py
DELETED
|
@@ -1,486 +0,0 @@
|
|
| 1 |
-
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
| 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 inspect
|
| 16 |
-
from typing import Callable, List, Optional, Union
|
| 17 |
-
|
| 18 |
-
import numpy as np
|
| 19 |
-
import torch
|
| 20 |
-
from transformers import CLIPImageProcessor, CLIPTokenizer
|
| 21 |
-
|
| 22 |
-
from ...configuration_utils import FrozenDict
|
| 23 |
-
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
|
| 24 |
-
from ...utils import deprecate, logging
|
| 25 |
-
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
|
| 26 |
-
from ..pipeline_utils import DiffusionPipeline
|
| 27 |
-
from . import StableDiffusionPipelineOutput
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
logger = logging.get_logger(__name__)
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
class OnnxStableDiffusionPipeline(DiffusionPipeline):
|
| 34 |
-
vae_encoder: OnnxRuntimeModel
|
| 35 |
-
vae_decoder: OnnxRuntimeModel
|
| 36 |
-
text_encoder: OnnxRuntimeModel
|
| 37 |
-
tokenizer: CLIPTokenizer
|
| 38 |
-
unet: OnnxRuntimeModel
|
| 39 |
-
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
|
| 40 |
-
safety_checker: OnnxRuntimeModel
|
| 41 |
-
feature_extractor: CLIPImageProcessor
|
| 42 |
-
|
| 43 |
-
_optional_components = ["safety_checker", "feature_extractor"]
|
| 44 |
-
_is_onnx = True
|
| 45 |
-
|
| 46 |
-
def __init__(
|
| 47 |
-
self,
|
| 48 |
-
vae_encoder: OnnxRuntimeModel,
|
| 49 |
-
vae_decoder: OnnxRuntimeModel,
|
| 50 |
-
text_encoder: OnnxRuntimeModel,
|
| 51 |
-
tokenizer: CLIPTokenizer,
|
| 52 |
-
unet: OnnxRuntimeModel,
|
| 53 |
-
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
|
| 54 |
-
safety_checker: OnnxRuntimeModel,
|
| 55 |
-
feature_extractor: CLIPImageProcessor,
|
| 56 |
-
requires_safety_checker: bool = True,
|
| 57 |
-
):
|
| 58 |
-
super().__init__()
|
| 59 |
-
|
| 60 |
-
if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
|
| 61 |
-
deprecation_message = (
|
| 62 |
-
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
|
| 63 |
-
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
|
| 64 |
-
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
|
| 65 |
-
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
|
| 66 |
-
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
|
| 67 |
-
" file"
|
| 68 |
-
)
|
| 69 |
-
deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
|
| 70 |
-
new_config = dict(scheduler.config)
|
| 71 |
-
new_config["steps_offset"] = 1
|
| 72 |
-
scheduler._internal_dict = FrozenDict(new_config)
|
| 73 |
-
|
| 74 |
-
if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
|
| 75 |
-
deprecation_message = (
|
| 76 |
-
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
|
| 77 |
-
" `clip_sample` should be set to False in the configuration file. Please make sure to update the"
|
| 78 |
-
" config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
|
| 79 |
-
" future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
|
| 80 |
-
" nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
|
| 81 |
-
)
|
| 82 |
-
deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
|
| 83 |
-
new_config = dict(scheduler.config)
|
| 84 |
-
new_config["clip_sample"] = False
|
| 85 |
-
scheduler._internal_dict = FrozenDict(new_config)
|
| 86 |
-
|
| 87 |
-
if safety_checker is None and requires_safety_checker:
|
| 88 |
-
logger.warning(
|
| 89 |
-
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
|
| 90 |
-
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
|
| 91 |
-
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
|
| 92 |
-
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
|
| 93 |
-
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
|
| 94 |
-
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
|
| 95 |
-
)
|
| 96 |
-
|
| 97 |
-
if safety_checker is not None and feature_extractor is None:
|
| 98 |
-
raise ValueError(
|
| 99 |
-
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
|
| 100 |
-
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
|
| 101 |
-
)
|
| 102 |
-
|
| 103 |
-
self.register_modules(
|
| 104 |
-
vae_encoder=vae_encoder,
|
| 105 |
-
vae_decoder=vae_decoder,
|
| 106 |
-
text_encoder=text_encoder,
|
| 107 |
-
tokenizer=tokenizer,
|
| 108 |
-
unet=unet,
|
| 109 |
-
scheduler=scheduler,
|
| 110 |
-
safety_checker=safety_checker,
|
| 111 |
-
feature_extractor=feature_extractor,
|
| 112 |
-
)
|
| 113 |
-
self.register_to_config(requires_safety_checker=requires_safety_checker)
|
| 114 |
-
|
| 115 |
-
def _encode_prompt(
|
| 116 |
-
self,
|
| 117 |
-
prompt: Union[str, List[str]],
|
| 118 |
-
num_images_per_prompt: Optional[int],
|
| 119 |
-
do_classifier_free_guidance: bool,
|
| 120 |
-
negative_prompt: Optional[str],
|
| 121 |
-
prompt_embeds: Optional[np.ndarray] = None,
|
| 122 |
-
negative_prompt_embeds: Optional[np.ndarray] = None,
|
| 123 |
-
):
|
| 124 |
-
r"""
|
| 125 |
-
Encodes the prompt into text encoder hidden states.
|
| 126 |
-
|
| 127 |
-
Args:
|
| 128 |
-
prompt (`str` or `List[str]`):
|
| 129 |
-
prompt to be encoded
|
| 130 |
-
num_images_per_prompt (`int`):
|
| 131 |
-
number of images that should be generated per prompt
|
| 132 |
-
do_classifier_free_guidance (`bool`):
|
| 133 |
-
whether to use classifier free guidance or not
|
| 134 |
-
negative_prompt (`str` or `List[str]`):
|
| 135 |
-
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
|
| 136 |
-
if `guidance_scale` is less than `1`).
|
| 137 |
-
prompt_embeds (`np.ndarray`, *optional*):
|
| 138 |
-
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
| 139 |
-
provided, text embeddings will be generated from `prompt` input argument.
|
| 140 |
-
negative_prompt_embeds (`np.ndarray`, *optional*):
|
| 141 |
-
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
| 142 |
-
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
| 143 |
-
argument.
|
| 144 |
-
"""
|
| 145 |
-
if prompt is not None and isinstance(prompt, str):
|
| 146 |
-
batch_size = 1
|
| 147 |
-
elif prompt is not None and isinstance(prompt, list):
|
| 148 |
-
batch_size = len(prompt)
|
| 149 |
-
else:
|
| 150 |
-
batch_size = prompt_embeds.shape[0]
|
| 151 |
-
|
| 152 |
-
if prompt_embeds is None:
|
| 153 |
-
# get prompt text embeddings
|
| 154 |
-
text_inputs = self.tokenizer(
|
| 155 |
-
prompt,
|
| 156 |
-
padding="max_length",
|
| 157 |
-
max_length=self.tokenizer.model_max_length,
|
| 158 |
-
truncation=True,
|
| 159 |
-
return_tensors="np",
|
| 160 |
-
)
|
| 161 |
-
text_input_ids = text_inputs.input_ids
|
| 162 |
-
untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="np").input_ids
|
| 163 |
-
|
| 164 |
-
if not np.array_equal(text_input_ids, untruncated_ids):
|
| 165 |
-
removed_text = self.tokenizer.batch_decode(
|
| 166 |
-
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
|
| 167 |
-
)
|
| 168 |
-
logger.warning(
|
| 169 |
-
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
| 170 |
-
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
|
| 171 |
-
)
|
| 172 |
-
|
| 173 |
-
prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
|
| 174 |
-
|
| 175 |
-
prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)
|
| 176 |
-
|
| 177 |
-
# get unconditional embeddings for classifier free guidance
|
| 178 |
-
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
| 179 |
-
uncond_tokens: List[str]
|
| 180 |
-
if negative_prompt is None:
|
| 181 |
-
uncond_tokens = [""] * batch_size
|
| 182 |
-
elif type(prompt) is not type(negative_prompt):
|
| 183 |
-
raise TypeError(
|
| 184 |
-
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
| 185 |
-
f" {type(prompt)}."
|
| 186 |
-
)
|
| 187 |
-
elif isinstance(negative_prompt, str):
|
| 188 |
-
uncond_tokens = [negative_prompt] * batch_size
|
| 189 |
-
elif batch_size != len(negative_prompt):
|
| 190 |
-
raise ValueError(
|
| 191 |
-
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
| 192 |
-
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
| 193 |
-
" the batch size of `prompt`."
|
| 194 |
-
)
|
| 195 |
-
else:
|
| 196 |
-
uncond_tokens = negative_prompt
|
| 197 |
-
|
| 198 |
-
max_length = prompt_embeds.shape[1]
|
| 199 |
-
uncond_input = self.tokenizer(
|
| 200 |
-
uncond_tokens,
|
| 201 |
-
padding="max_length",
|
| 202 |
-
max_length=max_length,
|
| 203 |
-
truncation=True,
|
| 204 |
-
return_tensors="np",
|
| 205 |
-
)
|
| 206 |
-
negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]
|
| 207 |
-
|
| 208 |
-
if do_classifier_free_guidance:
|
| 209 |
-
negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)
|
| 210 |
-
|
| 211 |
-
# For classifier free guidance, we need to do two forward passes.
|
| 212 |
-
# Here we concatenate the unconditional and text embeddings into a single batch
|
| 213 |
-
# to avoid doing two forward passes
|
| 214 |
-
prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds])
|
| 215 |
-
|
| 216 |
-
return prompt_embeds
|
| 217 |
-
|
| 218 |
-
def check_inputs(
|
| 219 |
-
self,
|
| 220 |
-
prompt: Union[str, List[str]],
|
| 221 |
-
height: Optional[int],
|
| 222 |
-
width: Optional[int],
|
| 223 |
-
callback_steps: int,
|
| 224 |
-
negative_prompt: Optional[str] = None,
|
| 225 |
-
prompt_embeds: Optional[np.ndarray] = None,
|
| 226 |
-
negative_prompt_embeds: Optional[np.ndarray] = None,
|
| 227 |
-
):
|
| 228 |
-
if height % 8 != 0 or width % 8 != 0:
|
| 229 |
-
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
| 230 |
-
|
| 231 |
-
if (callback_steps is None) or (
|
| 232 |
-
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
|
| 233 |
-
):
|
| 234 |
-
raise ValueError(
|
| 235 |
-
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
|
| 236 |
-
f" {type(callback_steps)}."
|
| 237 |
-
)
|
| 238 |
-
|
| 239 |
-
if prompt is not None and prompt_embeds is not None:
|
| 240 |
-
raise ValueError(
|
| 241 |
-
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
| 242 |
-
" only forward one of the two."
|
| 243 |
-
)
|
| 244 |
-
elif prompt is None and prompt_embeds is None:
|
| 245 |
-
raise ValueError(
|
| 246 |
-
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
|
| 247 |
-
)
|
| 248 |
-
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
|
| 249 |
-
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
| 250 |
-
|
| 251 |
-
if negative_prompt is not None and negative_prompt_embeds is not None:
|
| 252 |
-
raise ValueError(
|
| 253 |
-
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
|
| 254 |
-
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
| 255 |
-
)
|
| 256 |
-
|
| 257 |
-
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
| 258 |
-
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
| 259 |
-
raise ValueError(
|
| 260 |
-
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
| 261 |
-
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
| 262 |
-
f" {negative_prompt_embeds.shape}."
|
| 263 |
-
)
|
| 264 |
-
|
| 265 |
-
def __call__(
|
| 266 |
-
self,
|
| 267 |
-
prompt: Union[str, List[str]] = None,
|
| 268 |
-
height: Optional[int] = 512,
|
| 269 |
-
width: Optional[int] = 512,
|
| 270 |
-
num_inference_steps: Optional[int] = 50,
|
| 271 |
-
guidance_scale: Optional[float] = 7.5,
|
| 272 |
-
negative_prompt: Optional[Union[str, List[str]]] = None,
|
| 273 |
-
num_images_per_prompt: Optional[int] = 1,
|
| 274 |
-
eta: Optional[float] = 0.0,
|
| 275 |
-
generator: Optional[np.random.RandomState] = None,
|
| 276 |
-
latents: Optional[np.ndarray] = None,
|
| 277 |
-
prompt_embeds: Optional[np.ndarray] = None,
|
| 278 |
-
negative_prompt_embeds: Optional[np.ndarray] = None,
|
| 279 |
-
output_type: Optional[str] = "pil",
|
| 280 |
-
return_dict: bool = True,
|
| 281 |
-
callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
|
| 282 |
-
callback_steps: int = 1,
|
| 283 |
-
):
|
| 284 |
-
r"""
|
| 285 |
-
Function invoked when calling the pipeline for generation.
|
| 286 |
-
|
| 287 |
-
Args:
|
| 288 |
-
prompt (`str` or `List[str]`, *optional*):
|
| 289 |
-
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
|
| 290 |
-
instead.
|
| 291 |
-
image (`PIL.Image.Image` or List[`PIL.Image.Image`] or `torch.FloatTensor`):
|
| 292 |
-
`Image`, or tensor representing an image batch which will be upscaled. *
|
| 293 |
-
num_inference_steps (`int`, *optional*, defaults to 50):
|
| 294 |
-
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
| 295 |
-
expense of slower inference.
|
| 296 |
-
guidance_scale (`float`, *optional*, defaults to 7.5):
|
| 297 |
-
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
| 298 |
-
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
| 299 |
-
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
| 300 |
-
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
| 301 |
-
usually at the expense of lower image quality.
|
| 302 |
-
negative_prompt (`str` or `List[str]`, *optional*):
|
| 303 |
-
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
| 304 |
-
`negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale`
|
| 305 |
-
is less than `1`).
|
| 306 |
-
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
| 307 |
-
The number of images to generate per prompt.
|
| 308 |
-
eta (`float`, *optional*, defaults to 0.0):
|
| 309 |
-
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
| 310 |
-
[`schedulers.DDIMScheduler`], will be ignored for others.
|
| 311 |
-
generator (`np.random.RandomState`, *optional*):
|
| 312 |
-
One or a list of [numpy generator(s)](TODO) to make generation deterministic.
|
| 313 |
-
latents (`np.ndarray`, *optional*):
|
| 314 |
-
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
| 315 |
-
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
| 316 |
-
tensor will ge generated by sampling using the supplied random `generator`.
|
| 317 |
-
prompt_embeds (`np.ndarray`, *optional*):
|
| 318 |
-
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
| 319 |
-
provided, text embeddings will be generated from `prompt` input argument.
|
| 320 |
-
negative_prompt_embeds (`np.ndarray`, *optional*):
|
| 321 |
-
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
| 322 |
-
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
| 323 |
-
argument.
|
| 324 |
-
output_type (`str`, *optional*, defaults to `"pil"`):
|
| 325 |
-
The output format of the generate image. Choose between
|
| 326 |
-
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
| 327 |
-
return_dict (`bool`, *optional*, defaults to `True`):
|
| 328 |
-
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
| 329 |
-
plain tuple.
|
| 330 |
-
callback (`Callable`, *optional*):
|
| 331 |
-
A function that will be called every `callback_steps` steps during inference. The function will be
|
| 332 |
-
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
| 333 |
-
callback_steps (`int`, *optional*, defaults to 1):
|
| 334 |
-
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
| 335 |
-
called at every step.
|
| 336 |
-
|
| 337 |
-
Returns:
|
| 338 |
-
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
| 339 |
-
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
| 340 |
-
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
| 341 |
-
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
| 342 |
-
(nsfw) content, according to the `safety_checker`.
|
| 343 |
-
"""
|
| 344 |
-
|
| 345 |
-
# check inputs. Raise error if not correct
|
| 346 |
-
self.check_inputs(
|
| 347 |
-
prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds
|
| 348 |
-
)
|
| 349 |
-
|
| 350 |
-
# define call parameters
|
| 351 |
-
if prompt is not None and isinstance(prompt, str):
|
| 352 |
-
batch_size = 1
|
| 353 |
-
elif prompt is not None and isinstance(prompt, list):
|
| 354 |
-
batch_size = len(prompt)
|
| 355 |
-
else:
|
| 356 |
-
batch_size = prompt_embeds.shape[0]
|
| 357 |
-
|
| 358 |
-
if generator is None:
|
| 359 |
-
generator = np.random
|
| 360 |
-
|
| 361 |
-
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
| 362 |
-
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
| 363 |
-
# corresponds to doing no classifier free guidance.
|
| 364 |
-
do_classifier_free_guidance = guidance_scale > 1.0
|
| 365 |
-
|
| 366 |
-
prompt_embeds = self._encode_prompt(
|
| 367 |
-
prompt,
|
| 368 |
-
num_images_per_prompt,
|
| 369 |
-
do_classifier_free_guidance,
|
| 370 |
-
negative_prompt,
|
| 371 |
-
prompt_embeds=prompt_embeds,
|
| 372 |
-
negative_prompt_embeds=negative_prompt_embeds,
|
| 373 |
-
)
|
| 374 |
-
|
| 375 |
-
# get the initial random noise unless the user supplied it
|
| 376 |
-
latents_dtype = prompt_embeds.dtype
|
| 377 |
-
latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8)
|
| 378 |
-
if latents is None:
|
| 379 |
-
latents = generator.randn(*latents_shape).astype(latents_dtype)
|
| 380 |
-
elif latents.shape != latents_shape:
|
| 381 |
-
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
|
| 382 |
-
|
| 383 |
-
# set timesteps
|
| 384 |
-
self.scheduler.set_timesteps(num_inference_steps)
|
| 385 |
-
|
| 386 |
-
latents = latents * np.float64(self.scheduler.init_noise_sigma)
|
| 387 |
-
|
| 388 |
-
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
| 389 |
-
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
| 390 |
-
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
| 391 |
-
# and should be between [0, 1]
|
| 392 |
-
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
| 393 |
-
extra_step_kwargs = {}
|
| 394 |
-
if accepts_eta:
|
| 395 |
-
extra_step_kwargs["eta"] = eta
|
| 396 |
-
|
| 397 |
-
timestep_dtype = next(
|
| 398 |
-
(input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
|
| 399 |
-
)
|
| 400 |
-
timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
|
| 401 |
-
|
| 402 |
-
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
|
| 403 |
-
# expand the latents if we are doing classifier free guidance
|
| 404 |
-
latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
|
| 405 |
-
latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
|
| 406 |
-
latent_model_input = latent_model_input.cpu().numpy()
|
| 407 |
-
|
| 408 |
-
# predict the noise residual
|
| 409 |
-
timestep = np.array([t], dtype=timestep_dtype)
|
| 410 |
-
noise_pred = self.unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)
|
| 411 |
-
noise_pred = noise_pred[0]
|
| 412 |
-
|
| 413 |
-
# perform guidance
|
| 414 |
-
if do_classifier_free_guidance:
|
| 415 |
-
noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
|
| 416 |
-
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
| 417 |
-
|
| 418 |
-
# compute the previous noisy sample x_t -> x_t-1
|
| 419 |
-
scheduler_output = self.scheduler.step(
|
| 420 |
-
torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
|
| 421 |
-
)
|
| 422 |
-
latents = scheduler_output.prev_sample.numpy()
|
| 423 |
-
|
| 424 |
-
# call the callback, if provided
|
| 425 |
-
if callback is not None and i % callback_steps == 0:
|
| 426 |
-
callback(i, t, latents)
|
| 427 |
-
|
| 428 |
-
latents = 1 / 0.18215 * latents
|
| 429 |
-
# image = self.vae_decoder(latent_sample=latents)[0]
|
| 430 |
-
# it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
|
| 431 |
-
image = np.concatenate(
|
| 432 |
-
[self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
|
| 433 |
-
)
|
| 434 |
-
|
| 435 |
-
image = np.clip(image / 2 + 0.5, 0, 1)
|
| 436 |
-
image = image.transpose((0, 2, 3, 1))
|
| 437 |
-
|
| 438 |
-
if self.safety_checker is not None:
|
| 439 |
-
safety_checker_input = self.feature_extractor(
|
| 440 |
-
self.numpy_to_pil(image), return_tensors="np"
|
| 441 |
-
).pixel_values.astype(image.dtype)
|
| 442 |
-
|
| 443 |
-
images, has_nsfw_concept = [], []
|
| 444 |
-
for i in range(image.shape[0]):
|
| 445 |
-
image_i, has_nsfw_concept_i = self.safety_checker(
|
| 446 |
-
clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
|
| 447 |
-
)
|
| 448 |
-
images.append(image_i)
|
| 449 |
-
has_nsfw_concept.append(has_nsfw_concept_i[0])
|
| 450 |
-
image = np.concatenate(images)
|
| 451 |
-
else:
|
| 452 |
-
has_nsfw_concept = None
|
| 453 |
-
|
| 454 |
-
if output_type == "pil":
|
| 455 |
-
image = self.numpy_to_pil(image)
|
| 456 |
-
|
| 457 |
-
if not return_dict:
|
| 458 |
-
return (image, has_nsfw_concept)
|
| 459 |
-
|
| 460 |
-
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|
| 461 |
-
|
| 462 |
-
|
| 463 |
-
class StableDiffusionOnnxPipeline(OnnxStableDiffusionPipeline):
|
| 464 |
-
def __init__(
|
| 465 |
-
self,
|
| 466 |
-
vae_encoder: OnnxRuntimeModel,
|
| 467 |
-
vae_decoder: OnnxRuntimeModel,
|
| 468 |
-
text_encoder: OnnxRuntimeModel,
|
| 469 |
-
tokenizer: CLIPTokenizer,
|
| 470 |
-
unet: OnnxRuntimeModel,
|
| 471 |
-
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
|
| 472 |
-
safety_checker: OnnxRuntimeModel,
|
| 473 |
-
feature_extractor: CLIPImageProcessor,
|
| 474 |
-
):
|
| 475 |
-
deprecation_message = "Please use `OnnxStableDiffusionPipeline` instead of `StableDiffusionOnnxPipeline`."
|
| 476 |
-
deprecate("StableDiffusionOnnxPipeline", "1.0.0", deprecation_message)
|
| 477 |
-
super().__init__(
|
| 478 |
-
vae_encoder=vae_encoder,
|
| 479 |
-
vae_decoder=vae_decoder,
|
| 480 |
-
text_encoder=text_encoder,
|
| 481 |
-
tokenizer=tokenizer,
|
| 482 |
-
unet=unet,
|
| 483 |
-
scheduler=scheduler,
|
| 484 |
-
safety_checker=safety_checker,
|
| 485 |
-
feature_extractor=feature_extractor,
|
| 486 |
-
)
|
|
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spaces/Andy1621/uniformer_image_detection/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py
DELETED
|
@@ -1,92 +0,0 @@
|
|
| 1 |
-
_base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py'
|
| 2 |
-
rpn_weight = 0.7
|
| 3 |
-
model = dict(
|
| 4 |
-
rpn_head=dict(
|
| 5 |
-
_delete_=True,
|
| 6 |
-
type='CascadeRPNHead',
|
| 7 |
-
num_stages=2,
|
| 8 |
-
stages=[
|
| 9 |
-
dict(
|
| 10 |
-
type='StageCascadeRPNHead',
|
| 11 |
-
in_channels=256,
|
| 12 |
-
feat_channels=256,
|
| 13 |
-
anchor_generator=dict(
|
| 14 |
-
type='AnchorGenerator',
|
| 15 |
-
scales=[8],
|
| 16 |
-
ratios=[1.0],
|
| 17 |
-
strides=[4, 8, 16, 32, 64]),
|
| 18 |
-
adapt_cfg=dict(type='dilation', dilation=3),
|
| 19 |
-
bridged_feature=True,
|
| 20 |
-
sampling=False,
|
| 21 |
-
with_cls=False,
|
| 22 |
-
reg_decoded_bbox=True,
|
| 23 |
-
bbox_coder=dict(
|
| 24 |
-
type='DeltaXYWHBBoxCoder',
|
| 25 |
-
target_means=(.0, .0, .0, .0),
|
| 26 |
-
target_stds=(0.1, 0.1, 0.5, 0.5)),
|
| 27 |
-
loss_bbox=dict(
|
| 28 |
-
type='IoULoss', linear=True,
|
| 29 |
-
loss_weight=10.0 * rpn_weight)),
|
| 30 |
-
dict(
|
| 31 |
-
type='StageCascadeRPNHead',
|
| 32 |
-
in_channels=256,
|
| 33 |
-
feat_channels=256,
|
| 34 |
-
adapt_cfg=dict(type='offset'),
|
| 35 |
-
bridged_feature=False,
|
| 36 |
-
sampling=True,
|
| 37 |
-
with_cls=True,
|
| 38 |
-
reg_decoded_bbox=True,
|
| 39 |
-
bbox_coder=dict(
|
| 40 |
-
type='DeltaXYWHBBoxCoder',
|
| 41 |
-
target_means=(.0, .0, .0, .0),
|
| 42 |
-
target_stds=(0.05, 0.05, 0.1, 0.1)),
|
| 43 |
-
loss_cls=dict(
|
| 44 |
-
type='CrossEntropyLoss',
|
| 45 |
-
use_sigmoid=True,
|
| 46 |
-
loss_weight=1.0 * rpn_weight),
|
| 47 |
-
loss_bbox=dict(
|
| 48 |
-
type='IoULoss', linear=True,
|
| 49 |
-
loss_weight=10.0 * rpn_weight))
|
| 50 |
-
]),
|
| 51 |
-
roi_head=dict(
|
| 52 |
-
bbox_head=dict(
|
| 53 |
-
bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]),
|
| 54 |
-
loss_cls=dict(
|
| 55 |
-
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5),
|
| 56 |
-
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
|
| 57 |
-
# model training and testing settings
|
| 58 |
-
train_cfg=dict(
|
| 59 |
-
rpn=[
|
| 60 |
-
dict(
|
| 61 |
-
assigner=dict(
|
| 62 |
-
type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5),
|
| 63 |
-
allowed_border=-1,
|
| 64 |
-
pos_weight=-1,
|
| 65 |
-
debug=False),
|
| 66 |
-
dict(
|
| 67 |
-
assigner=dict(
|
| 68 |
-
type='MaxIoUAssigner',
|
| 69 |
-
pos_iou_thr=0.7,
|
| 70 |
-
neg_iou_thr=0.7,
|
| 71 |
-
min_pos_iou=0.3,
|
| 72 |
-
ignore_iof_thr=-1),
|
| 73 |
-
sampler=dict(
|
| 74 |
-
type='RandomSampler',
|
| 75 |
-
num=256,
|
| 76 |
-
pos_fraction=0.5,
|
| 77 |
-
neg_pos_ub=-1,
|
| 78 |
-
add_gt_as_proposals=False),
|
| 79 |
-
allowed_border=-1,
|
| 80 |
-
pos_weight=-1,
|
| 81 |
-
debug=False)
|
| 82 |
-
],
|
| 83 |
-
rpn_proposal=dict(max_per_img=300, nms=dict(iou_threshold=0.8)),
|
| 84 |
-
rcnn=dict(
|
| 85 |
-
assigner=dict(
|
| 86 |
-
pos_iou_thr=0.65, neg_iou_thr=0.65, min_pos_iou=0.65),
|
| 87 |
-
sampler=dict(type='RandomSampler', num=256))),
|
| 88 |
-
test_cfg=dict(
|
| 89 |
-
rpn=dict(max_per_img=300, nms=dict(iou_threshold=0.8)),
|
| 90 |
-
rcnn=dict(score_thr=1e-3)))
|
| 91 |
-
optimizer_config = dict(
|
| 92 |
-
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
|
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|
spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docker/Dockerfile
DELETED
|
@@ -1,75 +0,0 @@
|
|
| 1 |
-
FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 as builder
|
| 2 |
-
|
| 3 |
-
RUN --mount=type=cache,target=/var/cache/apt,sharing=locked,rw apt-get update && \
|
| 4 |
-
apt-get install --no-install-recommends -y git vim build-essential python3-dev python3-venv && \
|
| 5 |
-
rm -rf /var/lib/apt/lists/*
|
| 6 |
-
|
| 7 |
-
RUN git clone --depth=1 https://github.com/oobabooga/GPTQ-for-LLaMa /build
|
| 8 |
-
|
| 9 |
-
WORKDIR /build
|
| 10 |
-
|
| 11 |
-
RUN --mount=type=cache,target=/root/.cache/pip,rw \
|
| 12 |
-
python3 -m venv /build/venv && \
|
| 13 |
-
. /build/venv/bin/activate && \
|
| 14 |
-
pip3 install --upgrade pip setuptools wheel && \
|
| 15 |
-
pip3 install torch torchvision torchaudio && \
|
| 16 |
-
pip3 install -r requirements.txt
|
| 17 |
-
|
| 18 |
-
# https://developer.nvidia.com/cuda-gpus
|
| 19 |
-
# for a rtx 2060: ARG TORCH_CUDA_ARCH_LIST="7.5"
|
| 20 |
-
ARG TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST:-3.5;5.0;6.0;6.1;7.0;7.5;8.0;8.6+PTX}"
|
| 21 |
-
RUN . /build/venv/bin/activate && \
|
| 22 |
-
python3 setup_cuda.py bdist_wheel -d .
|
| 23 |
-
|
| 24 |
-
FROM nvidia/cuda:11.8.0-runtime-ubuntu22.04
|
| 25 |
-
|
| 26 |
-
LABEL maintainer="Your Name <[email protected]>"
|
| 27 |
-
LABEL description="Docker image for GPTQ-for-LLaMa and Text Generation WebUI"
|
| 28 |
-
|
| 29 |
-
RUN --mount=type=cache,target=/var/cache/apt,sharing=locked,rw apt-get update && \
|
| 30 |
-
apt-get install --no-install-recommends -y python3-dev libportaudio2 libasound-dev git python3 python3-pip make g++ ffmpeg && \
|
| 31 |
-
rm -rf /var/lib/apt/lists/*
|
| 32 |
-
|
| 33 |
-
RUN --mount=type=cache,target=/root/.cache/pip,rw pip3 install virtualenv
|
| 34 |
-
RUN mkdir /app
|
| 35 |
-
|
| 36 |
-
WORKDIR /app
|
| 37 |
-
|
| 38 |
-
ARG WEBUI_VERSION
|
| 39 |
-
RUN test -n "${WEBUI_VERSION}" && git reset --hard ${WEBUI_VERSION} || echo "Using provided webui source"
|
| 40 |
-
|
| 41 |
-
# Create virtualenv
|
| 42 |
-
RUN virtualenv /app/venv
|
| 43 |
-
RUN --mount=type=cache,target=/root/.cache/pip,rw \
|
| 44 |
-
. /app/venv/bin/activate && \
|
| 45 |
-
pip3 install --upgrade pip setuptools wheel && \
|
| 46 |
-
pip3 install torch torchvision torchaudio sentence_transformers xformers
|
| 47 |
-
|
| 48 |
-
# Copy and install GPTQ-for-LLaMa
|
| 49 |
-
COPY --from=builder /build /app/repositories/GPTQ-for-LLaMa
|
| 50 |
-
RUN --mount=type=cache,target=/root/.cache/pip,rw \
|
| 51 |
-
. /app/venv/bin/activate && \
|
| 52 |
-
pip3 install /app/repositories/GPTQ-for-LLaMa/*.whl
|
| 53 |
-
|
| 54 |
-
# Install main requirements
|
| 55 |
-
COPY requirements.txt /app/requirements.txt
|
| 56 |
-
RUN --mount=type=cache,target=/root/.cache/pip,rw \
|
| 57 |
-
. /app/venv/bin/activate && \
|
| 58 |
-
pip3 install -r requirements.txt
|
| 59 |
-
|
| 60 |
-
COPY . /app/
|
| 61 |
-
|
| 62 |
-
RUN cp /app/venv/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda118.so /app/venv/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so
|
| 63 |
-
|
| 64 |
-
# Install extension requirements
|
| 65 |
-
RUN --mount=type=cache,target=/root/.cache/pip,rw \
|
| 66 |
-
. /app/venv/bin/activate && \
|
| 67 |
-
for ext in /app/extensions/*/requirements.txt; do \
|
| 68 |
-
cd "$(dirname "$ext")"; \
|
| 69 |
-
pip3 install -r requirements.txt; \
|
| 70 |
-
done
|
| 71 |
-
|
| 72 |
-
ENV CLI_ARGS=""
|
| 73 |
-
|
| 74 |
-
EXPOSE ${CONTAINER_PORT:-7860} ${CONTAINER_API_PORT:-5000} ${CONTAINER_API_STREAM_PORT:-5005}
|
| 75 |
-
CMD . /app/venv/bin/activate && python3 server.py ${CLI_ARGS}
|
|
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|
spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/openai/errors.py
DELETED
|
@@ -1,31 +0,0 @@
|
|
| 1 |
-
class OpenAIError(Exception):
|
| 2 |
-
def __init__(self, message=None, code=500, internal_message=''):
|
| 3 |
-
self.message = message
|
| 4 |
-
self.code = code
|
| 5 |
-
self.internal_message = internal_message
|
| 6 |
-
|
| 7 |
-
def __repr__(self):
|
| 8 |
-
return "%s(message=%r, code=%d)" % (
|
| 9 |
-
self.__class__.__name__,
|
| 10 |
-
self.message,
|
| 11 |
-
self.code,
|
| 12 |
-
)
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
class InvalidRequestError(OpenAIError):
|
| 16 |
-
def __init__(self, message, param, code=400, internal_message=''):
|
| 17 |
-
super().__init__(message, code, internal_message)
|
| 18 |
-
self.param = param
|
| 19 |
-
|
| 20 |
-
def __repr__(self):
|
| 21 |
-
return "%s(message=%r, code=%d, param=%s)" % (
|
| 22 |
-
self.__class__.__name__,
|
| 23 |
-
self.message,
|
| 24 |
-
self.code,
|
| 25 |
-
self.param,
|
| 26 |
-
)
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
class ServiceUnavailableError(OpenAIError):
|
| 30 |
-
def __init__(self, message="Service unavailable, please try again later.", code=503, internal_message=''):
|
| 31 |
-
super().__init__(message, code, internal_message)
|
|
|
|
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|
spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/exp/upernet_global_small/test_config_g.py
DELETED
|
@@ -1,38 +0,0 @@
|
|
| 1 |
-
_base_ = [
|
| 2 |
-
'../../configs/_base_/models/upernet_uniformer.py',
|
| 3 |
-
'../../configs/_base_/datasets/ade20k.py',
|
| 4 |
-
'../../configs/_base_/default_runtime.py',
|
| 5 |
-
'../../configs/_base_/schedules/schedule_160k.py'
|
| 6 |
-
]
|
| 7 |
-
model = dict(
|
| 8 |
-
backbone=dict(
|
| 9 |
-
type='UniFormer',
|
| 10 |
-
embed_dim=[64, 128, 320, 512],
|
| 11 |
-
layers=[3, 4, 8, 3],
|
| 12 |
-
head_dim=64,
|
| 13 |
-
drop_path_rate=0.25,
|
| 14 |
-
windows=False,
|
| 15 |
-
hybrid=False,
|
| 16 |
-
),
|
| 17 |
-
decode_head=dict(
|
| 18 |
-
in_channels=[64, 128, 320, 512],
|
| 19 |
-
num_classes=150
|
| 20 |
-
),
|
| 21 |
-
auxiliary_head=dict(
|
| 22 |
-
in_channels=320,
|
| 23 |
-
num_classes=150
|
| 24 |
-
))
|
| 25 |
-
|
| 26 |
-
# AdamW optimizer, no weight decay for position embedding & layer norm in backbone
|
| 27 |
-
optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01,
|
| 28 |
-
paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
|
| 29 |
-
'relative_position_bias_table': dict(decay_mult=0.),
|
| 30 |
-
'norm': dict(decay_mult=0.)}))
|
| 31 |
-
|
| 32 |
-
lr_config = dict(_delete_=True, policy='poly',
|
| 33 |
-
warmup='linear',
|
| 34 |
-
warmup_iters=1500,
|
| 35 |
-
warmup_ratio=1e-6,
|
| 36 |
-
power=1.0, min_lr=0.0, by_epoch=False)
|
| 37 |
-
|
| 38 |
-
data=dict(samples_per_gpu=2)
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spaces/Anonymous-sub/Rerender/ControlNet/gradio_scribble2image.py
DELETED
|
@@ -1,92 +0,0 @@
|
|
| 1 |
-
from share import *
|
| 2 |
-
import config
|
| 3 |
-
|
| 4 |
-
import cv2
|
| 5 |
-
import einops
|
| 6 |
-
import gradio as gr
|
| 7 |
-
import numpy as np
|
| 8 |
-
import torch
|
| 9 |
-
import random
|
| 10 |
-
|
| 11 |
-
from pytorch_lightning import seed_everything
|
| 12 |
-
from annotator.util import resize_image, HWC3
|
| 13 |
-
from cldm.model import create_model, load_state_dict
|
| 14 |
-
from cldm.ddim_hacked import DDIMSampler
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
model = create_model('./models/cldm_v15.yaml').cpu()
|
| 18 |
-
model.load_state_dict(load_state_dict('./models/control_sd15_scribble.pth', location='cuda'))
|
| 19 |
-
model = model.cuda()
|
| 20 |
-
ddim_sampler = DDIMSampler(model)
|
| 21 |
-
|
| 22 |
-
|
| 23 |
-
def process(input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta):
|
| 24 |
-
with torch.no_grad():
|
| 25 |
-
img = resize_image(HWC3(input_image), image_resolution)
|
| 26 |
-
H, W, C = img.shape
|
| 27 |
-
|
| 28 |
-
detected_map = np.zeros_like(img, dtype=np.uint8)
|
| 29 |
-
detected_map[np.min(img, axis=2) < 127] = 255
|
| 30 |
-
|
| 31 |
-
control = torch.from_numpy(detected_map.copy()).float().cuda() / 255.0
|
| 32 |
-
control = torch.stack([control for _ in range(num_samples)], dim=0)
|
| 33 |
-
control = einops.rearrange(control, 'b h w c -> b c h w').clone()
|
| 34 |
-
|
| 35 |
-
if seed == -1:
|
| 36 |
-
seed = random.randint(0, 65535)
|
| 37 |
-
seed_everything(seed)
|
| 38 |
-
|
| 39 |
-
if config.save_memory:
|
| 40 |
-
model.low_vram_shift(is_diffusing=False)
|
| 41 |
-
|
| 42 |
-
cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
|
| 43 |
-
un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
|
| 44 |
-
shape = (4, H // 8, W // 8)
|
| 45 |
-
|
| 46 |
-
if config.save_memory:
|
| 47 |
-
model.low_vram_shift(is_diffusing=True)
|
| 48 |
-
|
| 49 |
-
model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13) # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
|
| 50 |
-
samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
|
| 51 |
-
shape, cond, verbose=False, eta=eta,
|
| 52 |
-
unconditional_guidance_scale=scale,
|
| 53 |
-
unconditional_conditioning=un_cond)
|
| 54 |
-
|
| 55 |
-
if config.save_memory:
|
| 56 |
-
model.low_vram_shift(is_diffusing=False)
|
| 57 |
-
|
| 58 |
-
x_samples = model.decode_first_stage(samples)
|
| 59 |
-
x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
|
| 60 |
-
|
| 61 |
-
results = [x_samples[i] for i in range(num_samples)]
|
| 62 |
-
return [255 - detected_map] + results
|
| 63 |
-
|
| 64 |
-
|
| 65 |
-
block = gr.Blocks().queue()
|
| 66 |
-
with block:
|
| 67 |
-
with gr.Row():
|
| 68 |
-
gr.Markdown("## Control Stable Diffusion with Scribble Maps")
|
| 69 |
-
with gr.Row():
|
| 70 |
-
with gr.Column():
|
| 71 |
-
input_image = gr.Image(source='upload', type="numpy")
|
| 72 |
-
prompt = gr.Textbox(label="Prompt")
|
| 73 |
-
run_button = gr.Button(label="Run")
|
| 74 |
-
with gr.Accordion("Advanced options", open=False):
|
| 75 |
-
num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
|
| 76 |
-
image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
|
| 77 |
-
strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
|
| 78 |
-
guess_mode = gr.Checkbox(label='Guess Mode', value=False)
|
| 79 |
-
ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
|
| 80 |
-
scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
|
| 81 |
-
seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
|
| 82 |
-
eta = gr.Number(label="eta (DDIM)", value=0.0)
|
| 83 |
-
a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed')
|
| 84 |
-
n_prompt = gr.Textbox(label="Negative Prompt",
|
| 85 |
-
value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality')
|
| 86 |
-
with gr.Column():
|
| 87 |
-
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
|
| 88 |
-
ips = [input_image, prompt, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta]
|
| 89 |
-
run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
|
| 90 |
-
|
| 91 |
-
|
| 92 |
-
block.launch(server_name='0.0.0.0')
|
|
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|
spaces/Ariharasudhan/YoloV5/utils/plots.py
DELETED
|
@@ -1,575 +0,0 @@
|
|
| 1 |
-
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
| 2 |
-
"""
|
| 3 |
-
Plotting utils
|
| 4 |
-
"""
|
| 5 |
-
|
| 6 |
-
import contextlib
|
| 7 |
-
import math
|
| 8 |
-
import os
|
| 9 |
-
from copy import copy
|
| 10 |
-
from pathlib import Path
|
| 11 |
-
from urllib.error import URLError
|
| 12 |
-
|
| 13 |
-
import cv2
|
| 14 |
-
import matplotlib
|
| 15 |
-
import matplotlib.pyplot as plt
|
| 16 |
-
import numpy as np
|
| 17 |
-
import pandas as pd
|
| 18 |
-
import seaborn as sn
|
| 19 |
-
import torch
|
| 20 |
-
from PIL import Image, ImageDraw, ImageFont
|
| 21 |
-
|
| 22 |
-
from utils import TryExcept, threaded
|
| 23 |
-
from utils.general import (CONFIG_DIR, FONT, LOGGER, check_font, check_requirements, clip_boxes, increment_path,
|
| 24 |
-
is_ascii, xywh2xyxy, xyxy2xywh)
|
| 25 |
-
from utils.metrics import fitness
|
| 26 |
-
from utils.segment.general import scale_image
|
| 27 |
-
|
| 28 |
-
# Settings
|
| 29 |
-
RANK = int(os.getenv('RANK', -1))
|
| 30 |
-
matplotlib.rc('font', **{'size': 11})
|
| 31 |
-
matplotlib.use('Agg') # for writing to files only
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
class Colors:
|
| 35 |
-
# Ultralytics color palette https://ultralytics.com/
|
| 36 |
-
def __init__(self):
|
| 37 |
-
# hex = matplotlib.colors.TABLEAU_COLORS.values()
|
| 38 |
-
hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
|
| 39 |
-
'2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
|
| 40 |
-
self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
|
| 41 |
-
self.n = len(self.palette)
|
| 42 |
-
|
| 43 |
-
def __call__(self, i, bgr=False):
|
| 44 |
-
c = self.palette[int(i) % self.n]
|
| 45 |
-
return (c[2], c[1], c[0]) if bgr else c
|
| 46 |
-
|
| 47 |
-
@staticmethod
|
| 48 |
-
def hex2rgb(h): # rgb order (PIL)
|
| 49 |
-
return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
colors = Colors() # create instance for 'from utils.plots import colors'
|
| 53 |
-
|
| 54 |
-
|
| 55 |
-
def check_pil_font(font=FONT, size=10):
|
| 56 |
-
# Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
|
| 57 |
-
font = Path(font)
|
| 58 |
-
font = font if font.exists() else (CONFIG_DIR / font.name)
|
| 59 |
-
try:
|
| 60 |
-
return ImageFont.truetype(str(font) if font.exists() else font.name, size)
|
| 61 |
-
except Exception: # download if missing
|
| 62 |
-
try:
|
| 63 |
-
check_font(font)
|
| 64 |
-
return ImageFont.truetype(str(font), size)
|
| 65 |
-
except TypeError:
|
| 66 |
-
check_requirements('Pillow>=8.4.0') # known issue https://github.com/ultralytics/yolov5/issues/5374
|
| 67 |
-
except URLError: # not online
|
| 68 |
-
return ImageFont.load_default()
|
| 69 |
-
|
| 70 |
-
|
| 71 |
-
class Annotator:
|
| 72 |
-
# YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
|
| 73 |
-
def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
|
| 74 |
-
assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
|
| 75 |
-
non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic
|
| 76 |
-
self.pil = pil or non_ascii
|
| 77 |
-
if self.pil: # use PIL
|
| 78 |
-
self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
|
| 79 |
-
self.draw = ImageDraw.Draw(self.im)
|
| 80 |
-
self.font = check_pil_font(font='Arial.Unicode.ttf' if non_ascii else font,
|
| 81 |
-
size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
|
| 82 |
-
else: # use cv2
|
| 83 |
-
self.im = im
|
| 84 |
-
self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
|
| 85 |
-
|
| 86 |
-
def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
|
| 87 |
-
# Add one xyxy box to image with label
|
| 88 |
-
if self.pil or not is_ascii(label):
|
| 89 |
-
self.draw.rectangle(box, width=self.lw, outline=color) # box
|
| 90 |
-
if label:
|
| 91 |
-
w, h = self.font.getsize(label) # text width, height
|
| 92 |
-
outside = box[1] - h >= 0 # label fits outside box
|
| 93 |
-
self.draw.rectangle(
|
| 94 |
-
(box[0], box[1] - h if outside else box[1], box[0] + w + 1,
|
| 95 |
-
box[1] + 1 if outside else box[1] + h + 1),
|
| 96 |
-
fill=color,
|
| 97 |
-
)
|
| 98 |
-
# self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0
|
| 99 |
-
self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
|
| 100 |
-
else: # cv2
|
| 101 |
-
p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
|
| 102 |
-
cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
|
| 103 |
-
if label:
|
| 104 |
-
tf = max(self.lw - 1, 1) # font thickness
|
| 105 |
-
w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
|
| 106 |
-
outside = p1[1] - h >= 3
|
| 107 |
-
p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
|
| 108 |
-
cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled
|
| 109 |
-
cv2.putText(self.im,
|
| 110 |
-
label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
|
| 111 |
-
0,
|
| 112 |
-
self.lw / 3,
|
| 113 |
-
txt_color,
|
| 114 |
-
thickness=tf,
|
| 115 |
-
lineType=cv2.LINE_AA)
|
| 116 |
-
|
| 117 |
-
def masks(self, masks, colors, im_gpu=None, alpha=0.5):
|
| 118 |
-
"""Plot masks at once.
|
| 119 |
-
Args:
|
| 120 |
-
masks (tensor): predicted masks on cuda, shape: [n, h, w]
|
| 121 |
-
colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n]
|
| 122 |
-
im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1]
|
| 123 |
-
alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque
|
| 124 |
-
"""
|
| 125 |
-
if self.pil:
|
| 126 |
-
# convert to numpy first
|
| 127 |
-
self.im = np.asarray(self.im).copy()
|
| 128 |
-
if im_gpu is None:
|
| 129 |
-
# Add multiple masks of shape(h,w,n) with colors list([r,g,b], [r,g,b], ...)
|
| 130 |
-
if len(masks) == 0:
|
| 131 |
-
return
|
| 132 |
-
if isinstance(masks, torch.Tensor):
|
| 133 |
-
masks = torch.as_tensor(masks, dtype=torch.uint8)
|
| 134 |
-
masks = masks.permute(1, 2, 0).contiguous()
|
| 135 |
-
masks = masks.cpu().numpy()
|
| 136 |
-
# masks = np.ascontiguousarray(masks.transpose(1, 2, 0))
|
| 137 |
-
masks = scale_image(masks.shape[:2], masks, self.im.shape)
|
| 138 |
-
masks = np.asarray(masks, dtype=np.float32)
|
| 139 |
-
colors = np.asarray(colors, dtype=np.float32) # shape(n,3)
|
| 140 |
-
s = masks.sum(2, keepdims=True).clip(0, 1) # add all masks together
|
| 141 |
-
masks = (masks @ colors).clip(0, 255) # (h,w,n) @ (n,3) = (h,w,3)
|
| 142 |
-
self.im[:] = masks * alpha + self.im * (1 - s * alpha)
|
| 143 |
-
else:
|
| 144 |
-
if len(masks) == 0:
|
| 145 |
-
self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
|
| 146 |
-
colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0
|
| 147 |
-
colors = colors[:, None, None] # shape(n,1,1,3)
|
| 148 |
-
masks = masks.unsqueeze(3) # shape(n,h,w,1)
|
| 149 |
-
masks_color = masks * (colors * alpha) # shape(n,h,w,3)
|
| 150 |
-
|
| 151 |
-
inv_alph_masks = (1 - masks * alpha).cumprod(0) # shape(n,h,w,1)
|
| 152 |
-
mcs = (masks_color * inv_alph_masks).sum(0) * 2 # mask color summand shape(n,h,w,3)
|
| 153 |
-
|
| 154 |
-
im_gpu = im_gpu.flip(dims=[0]) # flip channel
|
| 155 |
-
im_gpu = im_gpu.permute(1, 2, 0).contiguous() # shape(h,w,3)
|
| 156 |
-
im_gpu = im_gpu * inv_alph_masks[-1] + mcs
|
| 157 |
-
im_mask = (im_gpu * 255).byte().cpu().numpy()
|
| 158 |
-
self.im[:] = scale_image(im_gpu.shape, im_mask, self.im.shape)
|
| 159 |
-
if self.pil:
|
| 160 |
-
# convert im back to PIL and update draw
|
| 161 |
-
self.fromarray(self.im)
|
| 162 |
-
|
| 163 |
-
def rectangle(self, xy, fill=None, outline=None, width=1):
|
| 164 |
-
# Add rectangle to image (PIL-only)
|
| 165 |
-
self.draw.rectangle(xy, fill, outline, width)
|
| 166 |
-
|
| 167 |
-
def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'):
|
| 168 |
-
# Add text to image (PIL-only)
|
| 169 |
-
if anchor == 'bottom': # start y from font bottom
|
| 170 |
-
w, h = self.font.getsize(text) # text width, height
|
| 171 |
-
xy[1] += 1 - h
|
| 172 |
-
self.draw.text(xy, text, fill=txt_color, font=self.font)
|
| 173 |
-
|
| 174 |
-
def fromarray(self, im):
|
| 175 |
-
# Update self.im from a numpy array
|
| 176 |
-
self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
|
| 177 |
-
self.draw = ImageDraw.Draw(self.im)
|
| 178 |
-
|
| 179 |
-
def result(self):
|
| 180 |
-
# Return annotated image as array
|
| 181 |
-
return np.asarray(self.im)
|
| 182 |
-
|
| 183 |
-
|
| 184 |
-
def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
|
| 185 |
-
"""
|
| 186 |
-
x: Features to be visualized
|
| 187 |
-
module_type: Module type
|
| 188 |
-
stage: Module stage within model
|
| 189 |
-
n: Maximum number of feature maps to plot
|
| 190 |
-
save_dir: Directory to save results
|
| 191 |
-
"""
|
| 192 |
-
if 'Detect' not in module_type:
|
| 193 |
-
batch, channels, height, width = x.shape # batch, channels, height, width
|
| 194 |
-
if height > 1 and width > 1:
|
| 195 |
-
f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename
|
| 196 |
-
|
| 197 |
-
blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels
|
| 198 |
-
n = min(n, channels) # number of plots
|
| 199 |
-
fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols
|
| 200 |
-
ax = ax.ravel()
|
| 201 |
-
plt.subplots_adjust(wspace=0.05, hspace=0.05)
|
| 202 |
-
for i in range(n):
|
| 203 |
-
ax[i].imshow(blocks[i].squeeze()) # cmap='gray'
|
| 204 |
-
ax[i].axis('off')
|
| 205 |
-
|
| 206 |
-
LOGGER.info(f'Saving {f}... ({n}/{channels})')
|
| 207 |
-
plt.savefig(f, dpi=300, bbox_inches='tight')
|
| 208 |
-
plt.close()
|
| 209 |
-
np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy()) # npy save
|
| 210 |
-
|
| 211 |
-
|
| 212 |
-
def hist2d(x, y, n=100):
|
| 213 |
-
# 2d histogram used in labels.png and evolve.png
|
| 214 |
-
xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
|
| 215 |
-
hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
|
| 216 |
-
xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
|
| 217 |
-
yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)
|
| 218 |
-
return np.log(hist[xidx, yidx])
|
| 219 |
-
|
| 220 |
-
|
| 221 |
-
def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
|
| 222 |
-
from scipy.signal import butter, filtfilt
|
| 223 |
-
|
| 224 |
-
# https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy
|
| 225 |
-
def butter_lowpass(cutoff, fs, order):
|
| 226 |
-
nyq = 0.5 * fs
|
| 227 |
-
normal_cutoff = cutoff / nyq
|
| 228 |
-
return butter(order, normal_cutoff, btype='low', analog=False)
|
| 229 |
-
|
| 230 |
-
b, a = butter_lowpass(cutoff, fs, order=order)
|
| 231 |
-
return filtfilt(b, a, data) # forward-backward filter
|
| 232 |
-
|
| 233 |
-
|
| 234 |
-
def output_to_target(output, max_det=300):
|
| 235 |
-
# Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting
|
| 236 |
-
targets = []
|
| 237 |
-
for i, o in enumerate(output):
|
| 238 |
-
box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)
|
| 239 |
-
j = torch.full((conf.shape[0], 1), i)
|
| 240 |
-
targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1))
|
| 241 |
-
return torch.cat(targets, 0).numpy()
|
| 242 |
-
|
| 243 |
-
|
| 244 |
-
@threaded
|
| 245 |
-
def plot_images(images, targets, paths=None, fname='images.jpg', names=None):
|
| 246 |
-
# Plot image grid with labels
|
| 247 |
-
if isinstance(images, torch.Tensor):
|
| 248 |
-
images = images.cpu().float().numpy()
|
| 249 |
-
if isinstance(targets, torch.Tensor):
|
| 250 |
-
targets = targets.cpu().numpy()
|
| 251 |
-
|
| 252 |
-
max_size = 1920 # max image size
|
| 253 |
-
max_subplots = 16 # max image subplots, i.e. 4x4
|
| 254 |
-
bs, _, h, w = images.shape # batch size, _, height, width
|
| 255 |
-
bs = min(bs, max_subplots) # limit plot images
|
| 256 |
-
ns = np.ceil(bs ** 0.5) # number of subplots (square)
|
| 257 |
-
if np.max(images[0]) <= 1:
|
| 258 |
-
images *= 255 # de-normalise (optional)
|
| 259 |
-
|
| 260 |
-
# Build Image
|
| 261 |
-
mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init
|
| 262 |
-
for i, im in enumerate(images):
|
| 263 |
-
if i == max_subplots: # if last batch has fewer images than we expect
|
| 264 |
-
break
|
| 265 |
-
x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
|
| 266 |
-
im = im.transpose(1, 2, 0)
|
| 267 |
-
mosaic[y:y + h, x:x + w, :] = im
|
| 268 |
-
|
| 269 |
-
# Resize (optional)
|
| 270 |
-
scale = max_size / ns / max(h, w)
|
| 271 |
-
if scale < 1:
|
| 272 |
-
h = math.ceil(scale * h)
|
| 273 |
-
w = math.ceil(scale * w)
|
| 274 |
-
mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
|
| 275 |
-
|
| 276 |
-
# Annotate
|
| 277 |
-
fs = int((h + w) * ns * 0.01) # font size
|
| 278 |
-
annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)
|
| 279 |
-
for i in range(i + 1):
|
| 280 |
-
x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
|
| 281 |
-
annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
|
| 282 |
-
if paths:
|
| 283 |
-
annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
|
| 284 |
-
if len(targets) > 0:
|
| 285 |
-
ti = targets[targets[:, 0] == i] # image targets
|
| 286 |
-
boxes = xywh2xyxy(ti[:, 2:6]).T
|
| 287 |
-
classes = ti[:, 1].astype('int')
|
| 288 |
-
labels = ti.shape[1] == 6 # labels if no conf column
|
| 289 |
-
conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred)
|
| 290 |
-
|
| 291 |
-
if boxes.shape[1]:
|
| 292 |
-
if boxes.max() <= 1.01: # if normalized with tolerance 0.01
|
| 293 |
-
boxes[[0, 2]] *= w # scale to pixels
|
| 294 |
-
boxes[[1, 3]] *= h
|
| 295 |
-
elif scale < 1: # absolute coords need scale if image scales
|
| 296 |
-
boxes *= scale
|
| 297 |
-
boxes[[0, 2]] += x
|
| 298 |
-
boxes[[1, 3]] += y
|
| 299 |
-
for j, box in enumerate(boxes.T.tolist()):
|
| 300 |
-
cls = classes[j]
|
| 301 |
-
color = colors(cls)
|
| 302 |
-
cls = names[cls] if names else cls
|
| 303 |
-
if labels or conf[j] > 0.25: # 0.25 conf thresh
|
| 304 |
-
label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'
|
| 305 |
-
annotator.box_label(box, label, color=color)
|
| 306 |
-
annotator.im.save(fname) # save
|
| 307 |
-
|
| 308 |
-
|
| 309 |
-
def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
|
| 310 |
-
# Plot LR simulating training for full epochs
|
| 311 |
-
optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals
|
| 312 |
-
y = []
|
| 313 |
-
for _ in range(epochs):
|
| 314 |
-
scheduler.step()
|
| 315 |
-
y.append(optimizer.param_groups[0]['lr'])
|
| 316 |
-
plt.plot(y, '.-', label='LR')
|
| 317 |
-
plt.xlabel('epoch')
|
| 318 |
-
plt.ylabel('LR')
|
| 319 |
-
plt.grid()
|
| 320 |
-
plt.xlim(0, epochs)
|
| 321 |
-
plt.ylim(0)
|
| 322 |
-
plt.savefig(Path(save_dir) / 'LR.png', dpi=200)
|
| 323 |
-
plt.close()
|
| 324 |
-
|
| 325 |
-
|
| 326 |
-
def plot_val_txt(): # from utils.plots import *; plot_val()
|
| 327 |
-
# Plot val.txt histograms
|
| 328 |
-
x = np.loadtxt('val.txt', dtype=np.float32)
|
| 329 |
-
box = xyxy2xywh(x[:, :4])
|
| 330 |
-
cx, cy = box[:, 0], box[:, 1]
|
| 331 |
-
|
| 332 |
-
fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)
|
| 333 |
-
ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
|
| 334 |
-
ax.set_aspect('equal')
|
| 335 |
-
plt.savefig('hist2d.png', dpi=300)
|
| 336 |
-
|
| 337 |
-
fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)
|
| 338 |
-
ax[0].hist(cx, bins=600)
|
| 339 |
-
ax[1].hist(cy, bins=600)
|
| 340 |
-
plt.savefig('hist1d.png', dpi=200)
|
| 341 |
-
|
| 342 |
-
|
| 343 |
-
def plot_targets_txt(): # from utils.plots import *; plot_targets_txt()
|
| 344 |
-
# Plot targets.txt histograms
|
| 345 |
-
x = np.loadtxt('targets.txt', dtype=np.float32).T
|
| 346 |
-
s = ['x targets', 'y targets', 'width targets', 'height targets']
|
| 347 |
-
fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
|
| 348 |
-
ax = ax.ravel()
|
| 349 |
-
for i in range(4):
|
| 350 |
-
ax[i].hist(x[i], bins=100, label=f'{x[i].mean():.3g} +/- {x[i].std():.3g}')
|
| 351 |
-
ax[i].legend()
|
| 352 |
-
ax[i].set_title(s[i])
|
| 353 |
-
plt.savefig('targets.jpg', dpi=200)
|
| 354 |
-
|
| 355 |
-
|
| 356 |
-
def plot_val_study(file='', dir='', x=None): # from utils.plots import *; plot_val_study()
|
| 357 |
-
# Plot file=study.txt generated by val.py (or plot all study*.txt in dir)
|
| 358 |
-
save_dir = Path(file).parent if file else Path(dir)
|
| 359 |
-
plot2 = False # plot additional results
|
| 360 |
-
if plot2:
|
| 361 |
-
ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()
|
| 362 |
-
|
| 363 |
-
fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
|
| 364 |
-
# for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:
|
| 365 |
-
for f in sorted(save_dir.glob('study*.txt')):
|
| 366 |
-
y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
|
| 367 |
-
x = np.arange(y.shape[1]) if x is None else np.array(x)
|
| 368 |
-
if plot2:
|
| 369 |
-
s = ['P', 'R', '[email protected]', '[email protected]:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)']
|
| 370 |
-
for i in range(7):
|
| 371 |
-
ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
|
| 372 |
-
ax[i].set_title(s[i])
|
| 373 |
-
|
| 374 |
-
j = y[3].argmax() + 1
|
| 375 |
-
ax2.plot(y[5, 1:j],
|
| 376 |
-
y[3, 1:j] * 1E2,
|
| 377 |
-
'.-',
|
| 378 |
-
linewidth=2,
|
| 379 |
-
markersize=8,
|
| 380 |
-
label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
|
| 381 |
-
|
| 382 |
-
ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
|
| 383 |
-
'k.-',
|
| 384 |
-
linewidth=2,
|
| 385 |
-
markersize=8,
|
| 386 |
-
alpha=.25,
|
| 387 |
-
label='EfficientDet')
|
| 388 |
-
|
| 389 |
-
ax2.grid(alpha=0.2)
|
| 390 |
-
ax2.set_yticks(np.arange(20, 60, 5))
|
| 391 |
-
ax2.set_xlim(0, 57)
|
| 392 |
-
ax2.set_ylim(25, 55)
|
| 393 |
-
ax2.set_xlabel('GPU Speed (ms/img)')
|
| 394 |
-
ax2.set_ylabel('COCO AP val')
|
| 395 |
-
ax2.legend(loc='lower right')
|
| 396 |
-
f = save_dir / 'study.png'
|
| 397 |
-
print(f'Saving {f}...')
|
| 398 |
-
plt.savefig(f, dpi=300)
|
| 399 |
-
|
| 400 |
-
|
| 401 |
-
@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395
|
| 402 |
-
def plot_labels(labels, names=(), save_dir=Path('')):
|
| 403 |
-
# plot dataset labels
|
| 404 |
-
LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
|
| 405 |
-
c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes
|
| 406 |
-
nc = int(c.max() + 1) # number of classes
|
| 407 |
-
x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
|
| 408 |
-
|
| 409 |
-
# seaborn correlogram
|
| 410 |
-
sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
|
| 411 |
-
plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
|
| 412 |
-
plt.close()
|
| 413 |
-
|
| 414 |
-
# matplotlib labels
|
| 415 |
-
matplotlib.use('svg') # faster
|
| 416 |
-
ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
|
| 417 |
-
y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
|
| 418 |
-
with contextlib.suppress(Exception): # color histogram bars by class
|
| 419 |
-
[y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195
|
| 420 |
-
ax[0].set_ylabel('instances')
|
| 421 |
-
if 0 < len(names) < 30:
|
| 422 |
-
ax[0].set_xticks(range(len(names)))
|
| 423 |
-
ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10)
|
| 424 |
-
else:
|
| 425 |
-
ax[0].set_xlabel('classes')
|
| 426 |
-
sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
|
| 427 |
-
sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
|
| 428 |
-
|
| 429 |
-
# rectangles
|
| 430 |
-
labels[:, 1:3] = 0.5 # center
|
| 431 |
-
labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000
|
| 432 |
-
img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)
|
| 433 |
-
for cls, *box in labels[:1000]:
|
| 434 |
-
ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot
|
| 435 |
-
ax[1].imshow(img)
|
| 436 |
-
ax[1].axis('off')
|
| 437 |
-
|
| 438 |
-
for a in [0, 1, 2, 3]:
|
| 439 |
-
for s in ['top', 'right', 'left', 'bottom']:
|
| 440 |
-
ax[a].spines[s].set_visible(False)
|
| 441 |
-
|
| 442 |
-
plt.savefig(save_dir / 'labels.jpg', dpi=200)
|
| 443 |
-
matplotlib.use('Agg')
|
| 444 |
-
plt.close()
|
| 445 |
-
|
| 446 |
-
|
| 447 |
-
def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path('images.jpg')):
|
| 448 |
-
# Show classification image grid with labels (optional) and predictions (optional)
|
| 449 |
-
from utils.augmentations import denormalize
|
| 450 |
-
|
| 451 |
-
names = names or [f'class{i}' for i in range(1000)]
|
| 452 |
-
blocks = torch.chunk(denormalize(im.clone()).cpu().float(), len(im),
|
| 453 |
-
dim=0) # select batch index 0, block by channels
|
| 454 |
-
n = min(len(blocks), nmax) # number of plots
|
| 455 |
-
m = min(8, round(n ** 0.5)) # 8 x 8 default
|
| 456 |
-
fig, ax = plt.subplots(math.ceil(n / m), m) # 8 rows x n/8 cols
|
| 457 |
-
ax = ax.ravel() if m > 1 else [ax]
|
| 458 |
-
# plt.subplots_adjust(wspace=0.05, hspace=0.05)
|
| 459 |
-
for i in range(n):
|
| 460 |
-
ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0))
|
| 461 |
-
ax[i].axis('off')
|
| 462 |
-
if labels is not None:
|
| 463 |
-
s = names[labels[i]] + (f'—{names[pred[i]]}' if pred is not None else '')
|
| 464 |
-
ax[i].set_title(s, fontsize=8, verticalalignment='top')
|
| 465 |
-
plt.savefig(f, dpi=300, bbox_inches='tight')
|
| 466 |
-
plt.close()
|
| 467 |
-
if verbose:
|
| 468 |
-
LOGGER.info(f"Saving {f}")
|
| 469 |
-
if labels is not None:
|
| 470 |
-
LOGGER.info('True: ' + ' '.join(f'{names[i]:3s}' for i in labels[:nmax]))
|
| 471 |
-
if pred is not None:
|
| 472 |
-
LOGGER.info('Predicted:' + ' '.join(f'{names[i]:3s}' for i in pred[:nmax]))
|
| 473 |
-
return f
|
| 474 |
-
|
| 475 |
-
|
| 476 |
-
def plot_evolve(evolve_csv='path/to/evolve.csv'): # from utils.plots import *; plot_evolve()
|
| 477 |
-
# Plot evolve.csv hyp evolution results
|
| 478 |
-
evolve_csv = Path(evolve_csv)
|
| 479 |
-
data = pd.read_csv(evolve_csv)
|
| 480 |
-
keys = [x.strip() for x in data.columns]
|
| 481 |
-
x = data.values
|
| 482 |
-
f = fitness(x)
|
| 483 |
-
j = np.argmax(f) # max fitness index
|
| 484 |
-
plt.figure(figsize=(10, 12), tight_layout=True)
|
| 485 |
-
matplotlib.rc('font', **{'size': 8})
|
| 486 |
-
print(f'Best results from row {j} of {evolve_csv}:')
|
| 487 |
-
for i, k in enumerate(keys[7:]):
|
| 488 |
-
v = x[:, 7 + i]
|
| 489 |
-
mu = v[j] # best single result
|
| 490 |
-
plt.subplot(6, 5, i + 1)
|
| 491 |
-
plt.scatter(v, f, c=hist2d(v, f, 20), cmap='viridis', alpha=.8, edgecolors='none')
|
| 492 |
-
plt.plot(mu, f.max(), 'k+', markersize=15)
|
| 493 |
-
plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9}) # limit to 40 characters
|
| 494 |
-
if i % 5 != 0:
|
| 495 |
-
plt.yticks([])
|
| 496 |
-
print(f'{k:>15}: {mu:.3g}')
|
| 497 |
-
f = evolve_csv.with_suffix('.png') # filename
|
| 498 |
-
plt.savefig(f, dpi=200)
|
| 499 |
-
plt.close()
|
| 500 |
-
print(f'Saved {f}')
|
| 501 |
-
|
| 502 |
-
|
| 503 |
-
def plot_results(file='path/to/results.csv', dir=''):
|
| 504 |
-
# Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
|
| 505 |
-
save_dir = Path(file).parent if file else Path(dir)
|
| 506 |
-
fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
|
| 507 |
-
ax = ax.ravel()
|
| 508 |
-
files = list(save_dir.glob('results*.csv'))
|
| 509 |
-
assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
|
| 510 |
-
for f in files:
|
| 511 |
-
try:
|
| 512 |
-
data = pd.read_csv(f)
|
| 513 |
-
s = [x.strip() for x in data.columns]
|
| 514 |
-
x = data.values[:, 0]
|
| 515 |
-
for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
|
| 516 |
-
y = data.values[:, j].astype('float')
|
| 517 |
-
# y[y == 0] = np.nan # don't show zero values
|
| 518 |
-
ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
|
| 519 |
-
ax[i].set_title(s[j], fontsize=12)
|
| 520 |
-
# if j in [8, 9, 10]: # share train and val loss y axes
|
| 521 |
-
# ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
|
| 522 |
-
except Exception as e:
|
| 523 |
-
LOGGER.info(f'Warning: Plotting error for {f}: {e}')
|
| 524 |
-
ax[1].legend()
|
| 525 |
-
fig.savefig(save_dir / 'results.png', dpi=200)
|
| 526 |
-
plt.close()
|
| 527 |
-
|
| 528 |
-
|
| 529 |
-
def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
|
| 530 |
-
# Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection()
|
| 531 |
-
ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()
|
| 532 |
-
s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS']
|
| 533 |
-
files = list(Path(save_dir).glob('frames*.txt'))
|
| 534 |
-
for fi, f in enumerate(files):
|
| 535 |
-
try:
|
| 536 |
-
results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows
|
| 537 |
-
n = results.shape[1] # number of rows
|
| 538 |
-
x = np.arange(start, min(stop, n) if stop else n)
|
| 539 |
-
results = results[:, x]
|
| 540 |
-
t = (results[0] - results[0].min()) # set t0=0s
|
| 541 |
-
results[0] = x
|
| 542 |
-
for i, a in enumerate(ax):
|
| 543 |
-
if i < len(results):
|
| 544 |
-
label = labels[fi] if len(labels) else f.stem.replace('frames_', '')
|
| 545 |
-
a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5)
|
| 546 |
-
a.set_title(s[i])
|
| 547 |
-
a.set_xlabel('time (s)')
|
| 548 |
-
# if fi == len(files) - 1:
|
| 549 |
-
# a.set_ylim(bottom=0)
|
| 550 |
-
for side in ['top', 'right']:
|
| 551 |
-
a.spines[side].set_visible(False)
|
| 552 |
-
else:
|
| 553 |
-
a.remove()
|
| 554 |
-
except Exception as e:
|
| 555 |
-
print(f'Warning: Plotting error for {f}; {e}')
|
| 556 |
-
ax[1].legend()
|
| 557 |
-
plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)
|
| 558 |
-
|
| 559 |
-
|
| 560 |
-
def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True):
|
| 561 |
-
# Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
|
| 562 |
-
xyxy = torch.tensor(xyxy).view(-1, 4)
|
| 563 |
-
b = xyxy2xywh(xyxy) # boxes
|
| 564 |
-
if square:
|
| 565 |
-
b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square
|
| 566 |
-
b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad
|
| 567 |
-
xyxy = xywh2xyxy(b).long()
|
| 568 |
-
clip_boxes(xyxy, im.shape)
|
| 569 |
-
crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
|
| 570 |
-
if save:
|
| 571 |
-
file.parent.mkdir(parents=True, exist_ok=True) # make directory
|
| 572 |
-
f = str(increment_path(file).with_suffix('.jpg'))
|
| 573 |
-
# cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue
|
| 574 |
-
Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB
|
| 575 |
-
return crop
|
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|
spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/namespaces.py
DELETED
|
@@ -1,107 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
from distutils import log
|
| 3 |
-
import itertools
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
flatten = itertools.chain.from_iterable
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
class Installer:
|
| 10 |
-
|
| 11 |
-
nspkg_ext = '-nspkg.pth'
|
| 12 |
-
|
| 13 |
-
def install_namespaces(self):
|
| 14 |
-
nsp = self._get_all_ns_packages()
|
| 15 |
-
if not nsp:
|
| 16 |
-
return
|
| 17 |
-
filename, ext = os.path.splitext(self._get_target())
|
| 18 |
-
filename += self.nspkg_ext
|
| 19 |
-
self.outputs.append(filename)
|
| 20 |
-
log.info("Installing %s", filename)
|
| 21 |
-
lines = map(self._gen_nspkg_line, nsp)
|
| 22 |
-
|
| 23 |
-
if self.dry_run:
|
| 24 |
-
# always generate the lines, even in dry run
|
| 25 |
-
list(lines)
|
| 26 |
-
return
|
| 27 |
-
|
| 28 |
-
with open(filename, 'wt') as f:
|
| 29 |
-
f.writelines(lines)
|
| 30 |
-
|
| 31 |
-
def uninstall_namespaces(self):
|
| 32 |
-
filename, ext = os.path.splitext(self._get_target())
|
| 33 |
-
filename += self.nspkg_ext
|
| 34 |
-
if not os.path.exists(filename):
|
| 35 |
-
return
|
| 36 |
-
log.info("Removing %s", filename)
|
| 37 |
-
os.remove(filename)
|
| 38 |
-
|
| 39 |
-
def _get_target(self):
|
| 40 |
-
return self.target
|
| 41 |
-
|
| 42 |
-
_nspkg_tmpl = (
|
| 43 |
-
"import sys, types, os",
|
| 44 |
-
"has_mfs = sys.version_info > (3, 5)",
|
| 45 |
-
"p = os.path.join(%(root)s, *%(pth)r)",
|
| 46 |
-
"importlib = has_mfs and __import__('importlib.util')",
|
| 47 |
-
"has_mfs and __import__('importlib.machinery')",
|
| 48 |
-
(
|
| 49 |
-
"m = has_mfs and "
|
| 50 |
-
"sys.modules.setdefault(%(pkg)r, "
|
| 51 |
-
"importlib.util.module_from_spec("
|
| 52 |
-
"importlib.machinery.PathFinder.find_spec(%(pkg)r, "
|
| 53 |
-
"[os.path.dirname(p)])))"
|
| 54 |
-
),
|
| 55 |
-
(
|
| 56 |
-
"m = m or "
|
| 57 |
-
"sys.modules.setdefault(%(pkg)r, types.ModuleType(%(pkg)r))"
|
| 58 |
-
),
|
| 59 |
-
"mp = (m or []) and m.__dict__.setdefault('__path__',[])",
|
| 60 |
-
"(p not in mp) and mp.append(p)",
|
| 61 |
-
)
|
| 62 |
-
"lines for the namespace installer"
|
| 63 |
-
|
| 64 |
-
_nspkg_tmpl_multi = (
|
| 65 |
-
'm and setattr(sys.modules[%(parent)r], %(child)r, m)',
|
| 66 |
-
)
|
| 67 |
-
"additional line(s) when a parent package is indicated"
|
| 68 |
-
|
| 69 |
-
def _get_root(self):
|
| 70 |
-
return "sys._getframe(1).f_locals['sitedir']"
|
| 71 |
-
|
| 72 |
-
def _gen_nspkg_line(self, pkg):
|
| 73 |
-
pth = tuple(pkg.split('.'))
|
| 74 |
-
root = self._get_root()
|
| 75 |
-
tmpl_lines = self._nspkg_tmpl
|
| 76 |
-
parent, sep, child = pkg.rpartition('.')
|
| 77 |
-
if parent:
|
| 78 |
-
tmpl_lines += self._nspkg_tmpl_multi
|
| 79 |
-
return ';'.join(tmpl_lines) % locals() + '\n'
|
| 80 |
-
|
| 81 |
-
def _get_all_ns_packages(self):
|
| 82 |
-
"""Return sorted list of all package namespaces"""
|
| 83 |
-
pkgs = self.distribution.namespace_packages or []
|
| 84 |
-
return sorted(flatten(map(self._pkg_names, pkgs)))
|
| 85 |
-
|
| 86 |
-
@staticmethod
|
| 87 |
-
def _pkg_names(pkg):
|
| 88 |
-
"""
|
| 89 |
-
Given a namespace package, yield the components of that
|
| 90 |
-
package.
|
| 91 |
-
|
| 92 |
-
>>> names = Installer._pkg_names('a.b.c')
|
| 93 |
-
>>> set(names) == set(['a', 'a.b', 'a.b.c'])
|
| 94 |
-
True
|
| 95 |
-
"""
|
| 96 |
-
parts = pkg.split('.')
|
| 97 |
-
while parts:
|
| 98 |
-
yield '.'.join(parts)
|
| 99 |
-
parts.pop()
|
| 100 |
-
|
| 101 |
-
|
| 102 |
-
class DevelopInstaller(Installer):
|
| 103 |
-
def _get_root(self):
|
| 104 |
-
return repr(str(self.egg_path))
|
| 105 |
-
|
| 106 |
-
def _get_target(self):
|
| 107 |
-
return self.egg_link
|
|
|
|
|
|
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|
|
spaces/Audio-AGI/AudioSep/models/CLAP/training/distributed.py
DELETED
|
@@ -1,150 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
|
| 3 |
-
import torch
|
| 4 |
-
import socket
|
| 5 |
-
|
| 6 |
-
try:
|
| 7 |
-
import horovod.torch as hvd
|
| 8 |
-
except ImportError:
|
| 9 |
-
hvd = None
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
def is_global_master(args):
|
| 13 |
-
return args.rank == 0
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
def is_local_master(args):
|
| 17 |
-
return args.local_rank == 0
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
def is_master(args, local=False):
|
| 21 |
-
return is_local_master(args) if local else is_global_master(args)
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
def is_using_horovod():
|
| 25 |
-
# NOTE w/ horovod run, OMPI vars should be set, but w/ SLURM PMI vars will be set
|
| 26 |
-
# Differentiating between horovod and DDP use via SLURM may not be possible, so horovod arg still required...
|
| 27 |
-
ompi_vars = ["OMPI_COMM_WORLD_RANK", "OMPI_COMM_WORLD_SIZE"]
|
| 28 |
-
pmi_vars = ["PMI_RANK", "PMI_SIZE"]
|
| 29 |
-
if all([var in os.environ for var in ompi_vars]) or all(
|
| 30 |
-
[var in os.environ for var in pmi_vars]
|
| 31 |
-
):
|
| 32 |
-
return True
|
| 33 |
-
else:
|
| 34 |
-
return False
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
def is_using_distributed():
|
| 38 |
-
if "WORLD_SIZE" in os.environ:
|
| 39 |
-
return int(os.environ["WORLD_SIZE"]) > 1
|
| 40 |
-
if "SLURM_NTASKS" in os.environ:
|
| 41 |
-
return int(os.environ["SLURM_NTASKS"]) > 1
|
| 42 |
-
return False
|
| 43 |
-
|
| 44 |
-
|
| 45 |
-
def world_info_from_env():
|
| 46 |
-
local_rank = 0
|
| 47 |
-
for v in (
|
| 48 |
-
"SLURM_LOCALID",
|
| 49 |
-
"MPI_LOCALRANKID",
|
| 50 |
-
"OMPI_COMM_WORLD_LOCAL_RANK",
|
| 51 |
-
"LOCAL_RANK",
|
| 52 |
-
):
|
| 53 |
-
if v in os.environ:
|
| 54 |
-
local_rank = int(os.environ[v])
|
| 55 |
-
break
|
| 56 |
-
global_rank = 0
|
| 57 |
-
for v in ("SLURM_PROCID", "PMI_RANK", "OMPI_COMM_WORLD_RANK", "RANK"):
|
| 58 |
-
if v in os.environ:
|
| 59 |
-
global_rank = int(os.environ[v])
|
| 60 |
-
break
|
| 61 |
-
world_size = 1
|
| 62 |
-
for v in ("SLURM_NTASKS", "PMI_SIZE", "OMPI_COMM_WORLD_SIZE", "WORLD_SIZE"):
|
| 63 |
-
if v in os.environ:
|
| 64 |
-
world_size = int(os.environ[v])
|
| 65 |
-
break
|
| 66 |
-
|
| 67 |
-
return local_rank, global_rank, world_size
|
| 68 |
-
|
| 69 |
-
|
| 70 |
-
def init_distributed_device(args):
|
| 71 |
-
# Distributed training = training on more than one GPU.
|
| 72 |
-
# Works in both single and multi-node scenarios.
|
| 73 |
-
args.distributed = False
|
| 74 |
-
args.world_size = 1
|
| 75 |
-
args.rank = 0 # global rank
|
| 76 |
-
args.local_rank = 0
|
| 77 |
-
if args.horovod:
|
| 78 |
-
assert hvd is not None, "Horovod is not installed"
|
| 79 |
-
hvd.init()
|
| 80 |
-
world_size = int(os.environ["OMPI_COMM_WORLD_SIZE"])
|
| 81 |
-
world_rank = int(os.environ["OMPI_COMM_WORLD_RANK"])
|
| 82 |
-
local_rank = int(os.environ["OMPI_COMM_WORLD_LOCAL_RANK"])
|
| 83 |
-
args.local_rank = local_rank
|
| 84 |
-
args.rank = world_rank
|
| 85 |
-
args.world_size = world_size
|
| 86 |
-
# args.local_rank = int(hvd.local_rank())
|
| 87 |
-
# args.rank = hvd.rank()
|
| 88 |
-
# args.world_size = hvd.size()
|
| 89 |
-
args.distributed = True
|
| 90 |
-
os.environ["LOCAL_RANK"] = str(args.local_rank)
|
| 91 |
-
os.environ["RANK"] = str(args.rank)
|
| 92 |
-
os.environ["WORLD_SIZE"] = str(args.world_size)
|
| 93 |
-
print(
|
| 94 |
-
f"Distributed training: local_rank={args.local_rank}, "
|
| 95 |
-
f"rank={args.rank}, world_size={args.world_size}, "
|
| 96 |
-
f"hostname={socket.gethostname()}, pid={os.getpid()}"
|
| 97 |
-
)
|
| 98 |
-
elif is_using_distributed():
|
| 99 |
-
if "SLURM_PROCID" in os.environ:
|
| 100 |
-
# DDP via SLURM
|
| 101 |
-
args.local_rank, args.rank, args.world_size = world_info_from_env()
|
| 102 |
-
# SLURM var -> torch.distributed vars in case needed
|
| 103 |
-
os.environ["LOCAL_RANK"] = str(args.local_rank)
|
| 104 |
-
os.environ["RANK"] = str(args.rank)
|
| 105 |
-
os.environ["WORLD_SIZE"] = str(args.world_size)
|
| 106 |
-
torch.distributed.init_process_group(
|
| 107 |
-
backend=args.dist_backend,
|
| 108 |
-
init_method=args.dist_url,
|
| 109 |
-
world_size=args.world_size,
|
| 110 |
-
rank=args.rank,
|
| 111 |
-
)
|
| 112 |
-
elif "OMPI_COMM_WORLD_SIZE" in os.environ: # using Summit cluster
|
| 113 |
-
world_size = int(os.environ["OMPI_COMM_WORLD_SIZE"])
|
| 114 |
-
world_rank = int(os.environ["OMPI_COMM_WORLD_RANK"])
|
| 115 |
-
local_rank = int(os.environ["OMPI_COMM_WORLD_LOCAL_RANK"])
|
| 116 |
-
args.local_rank = local_rank
|
| 117 |
-
args.rank = world_rank
|
| 118 |
-
args.world_size = world_size
|
| 119 |
-
torch.distributed.init_process_group(
|
| 120 |
-
backend=args.dist_backend,
|
| 121 |
-
init_method=args.dist_url,
|
| 122 |
-
world_size=args.world_size,
|
| 123 |
-
rank=args.rank,
|
| 124 |
-
)
|
| 125 |
-
else:
|
| 126 |
-
# DDP via torchrun, torch.distributed.launch
|
| 127 |
-
args.local_rank, _, _ = world_info_from_env()
|
| 128 |
-
torch.distributed.init_process_group(
|
| 129 |
-
backend=args.dist_backend, init_method=args.dist_url
|
| 130 |
-
)
|
| 131 |
-
args.world_size = torch.distributed.get_world_size()
|
| 132 |
-
args.rank = torch.distributed.get_rank()
|
| 133 |
-
args.distributed = True
|
| 134 |
-
print(
|
| 135 |
-
f"Distributed training: local_rank={args.local_rank}, "
|
| 136 |
-
f"rank={args.rank}, world_size={args.world_size}, "
|
| 137 |
-
f"hostname={socket.gethostname()}, pid={os.getpid()}"
|
| 138 |
-
)
|
| 139 |
-
|
| 140 |
-
if torch.cuda.is_available():
|
| 141 |
-
if args.distributed and not args.no_set_device_rank:
|
| 142 |
-
device = "cuda:%d" % args.local_rank
|
| 143 |
-
else:
|
| 144 |
-
device = "cuda:0"
|
| 145 |
-
torch.cuda.set_device(device)
|
| 146 |
-
else:
|
| 147 |
-
device = "cpu"
|
| 148 |
-
args.device = device
|
| 149 |
-
device = torch.device(device)
|
| 150 |
-
return device
|
|
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|
spaces/Bart92/RVC_HF/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
DELETED
|
@@ -1,90 +0,0 @@
|
|
| 1 |
-
from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
|
| 2 |
-
import pyworld
|
| 3 |
-
import numpy as np
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
class DioF0Predictor(F0Predictor):
|
| 7 |
-
def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
|
| 8 |
-
self.hop_length = hop_length
|
| 9 |
-
self.f0_min = f0_min
|
| 10 |
-
self.f0_max = f0_max
|
| 11 |
-
self.sampling_rate = sampling_rate
|
| 12 |
-
|
| 13 |
-
def interpolate_f0(self, f0):
|
| 14 |
-
"""
|
| 15 |
-
对F0进行插值处理
|
| 16 |
-
"""
|
| 17 |
-
|
| 18 |
-
data = np.reshape(f0, (f0.size, 1))
|
| 19 |
-
|
| 20 |
-
vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
|
| 21 |
-
vuv_vector[data > 0.0] = 1.0
|
| 22 |
-
vuv_vector[data <= 0.0] = 0.0
|
| 23 |
-
|
| 24 |
-
ip_data = data
|
| 25 |
-
|
| 26 |
-
frame_number = data.size
|
| 27 |
-
last_value = 0.0
|
| 28 |
-
for i in range(frame_number):
|
| 29 |
-
if data[i] <= 0.0:
|
| 30 |
-
j = i + 1
|
| 31 |
-
for j in range(i + 1, frame_number):
|
| 32 |
-
if data[j] > 0.0:
|
| 33 |
-
break
|
| 34 |
-
if j < frame_number - 1:
|
| 35 |
-
if last_value > 0.0:
|
| 36 |
-
step = (data[j] - data[i - 1]) / float(j - i)
|
| 37 |
-
for k in range(i, j):
|
| 38 |
-
ip_data[k] = data[i - 1] + step * (k - i + 1)
|
| 39 |
-
else:
|
| 40 |
-
for k in range(i, j):
|
| 41 |
-
ip_data[k] = data[j]
|
| 42 |
-
else:
|
| 43 |
-
for k in range(i, frame_number):
|
| 44 |
-
ip_data[k] = last_value
|
| 45 |
-
else:
|
| 46 |
-
ip_data[i] = data[i] # 这里可能存在一个没有必要的拷贝
|
| 47 |
-
last_value = data[i]
|
| 48 |
-
|
| 49 |
-
return ip_data[:, 0], vuv_vector[:, 0]
|
| 50 |
-
|
| 51 |
-
def resize_f0(self, x, target_len):
|
| 52 |
-
source = np.array(x)
|
| 53 |
-
source[source < 0.001] = np.nan
|
| 54 |
-
target = np.interp(
|
| 55 |
-
np.arange(0, len(source) * target_len, len(source)) / target_len,
|
| 56 |
-
np.arange(0, len(source)),
|
| 57 |
-
source,
|
| 58 |
-
)
|
| 59 |
-
res = np.nan_to_num(target)
|
| 60 |
-
return res
|
| 61 |
-
|
| 62 |
-
def compute_f0(self, wav, p_len=None):
|
| 63 |
-
if p_len is None:
|
| 64 |
-
p_len = wav.shape[0] // self.hop_length
|
| 65 |
-
f0, t = pyworld.dio(
|
| 66 |
-
wav.astype(np.double),
|
| 67 |
-
fs=self.sampling_rate,
|
| 68 |
-
f0_floor=self.f0_min,
|
| 69 |
-
f0_ceil=self.f0_max,
|
| 70 |
-
frame_period=1000 * self.hop_length / self.sampling_rate,
|
| 71 |
-
)
|
| 72 |
-
f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
|
| 73 |
-
for index, pitch in enumerate(f0):
|
| 74 |
-
f0[index] = round(pitch, 1)
|
| 75 |
-
return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
|
| 76 |
-
|
| 77 |
-
def compute_f0_uv(self, wav, p_len=None):
|
| 78 |
-
if p_len is None:
|
| 79 |
-
p_len = wav.shape[0] // self.hop_length
|
| 80 |
-
f0, t = pyworld.dio(
|
| 81 |
-
wav.astype(np.double),
|
| 82 |
-
fs=self.sampling_rate,
|
| 83 |
-
f0_floor=self.f0_min,
|
| 84 |
-
f0_ceil=self.f0_max,
|
| 85 |
-
frame_period=1000 * self.hop_length / self.sampling_rate,
|
| 86 |
-
)
|
| 87 |
-
f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
|
| 88 |
-
for index, pitch in enumerate(f0):
|
| 89 |
-
f0[index] = round(pitch, 1)
|
| 90 |
-
return self.interpolate_f0(self.resize_f0(f0, p_len))
|
|
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spaces/Benson/text-generation/Examples/Cmo Descargar Gratis Fuego Mx En El Ordenador Porttil Sin Bluestacks.md
DELETED
|
@@ -1,39 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
<h1>Cómo descargar gratis fuego Max en el ordenador portátil sin Bluestacks</h1>
|
| 3 |
-
<p>Free Fire Max es un popular juego battle royale que ofrece una experiencia de juego premium con gráficos ultra HD y efectos impresionantes. El juego está diseñado para dispositivos móviles, pero algunos jugadores pueden querer disfrutarlo en una pantalla más grande con mejores controles. Sin embargo, no todo el mundo tiene un ordenador portátil de gran alcance que puede ejecutar emuladores de Android como Bluestacks sin problemas. Si usted es uno de ellos, no se preocupe, hay otras maneras de jugar fuego libre máx en el ordenador portátil sin bluestacks. En este artículo, le mostraremos dos métodos que puede tratar de descargar y jugar gratis fuego máx en el ordenador portátil sin bluestacks. </p>
|
| 4 |
-
<h2>Método 1: Usando el emulador de Gameloop</h2>
|
| 5 |
-
<p>Gameloop es un emulador de Android desarrollado por Tencent, la misma empresa que creó el fuego libre máx. Está optimizado para juegos y admite varios títulos, incluido el fuego libre máx. Estos son los pasos para usar Gameloop para jugar el fuego libre máximo en la computadora portátil sin bluestacks:</p>
|
| 6 |
-
<h2>cómo descargar gratis fuego máx en el ordenador portátil sin bluestacks</h2><br /><p><b><b>Download Zip</b> ↔ <a href="https://bltlly.com/2v6JdO">https://bltlly.com/2v6JdO</a></b></p><br /><br />
|
| 7 |
-
<ol>
|
| 8 |
-
<li><b>Descargar Gameloop</b> desde el <a href="( 1 )">sitio web oficial</a>. </li>
|
| 9 |
-
<li><b>Instalar y ejecutar Gameloop</b> en su ordenador portátil. Es posible que tenga que permitir algunos permisos y aceptar algunos términos y condiciones. </li>
|
| 10 |
-
<li><b>Búsqueda de fuego libre máx</b> en la pestaña del juego y haga clic en el botón de descarga. </li>
|
| 11 |
-
<li><b>Espera la descarga y la instalación</b> para completar y lanzar el juego desde la pantalla de inicio. </li>
|
| 12 |
-
</ol>
|
| 13 |
-
<p>Ahora puedes jugar a fuego libre máx en tu laptop usando el emulador de Gameloop. Puede personalizar la configuración, los controles y los gráficos según sus preferencias. También puede utilizar el teclado y el ratón para jugar el juego más cómodamente. </p>
|
| 14 |
-
<h2>Método 2: Usando SO Prime</h2>
|
| 15 |
-
|
| 16 |
-
<ol>
|
| 17 |
-
<li><b>Descargar Prime OS</b> desde su <a href="( 2 )">sitio web oficial</a>. Elija la versión que se adapte a las especificaciones de su computadora portátil. </li>
|
| 18 |
-
<li><b>Instale Prime OS</b> en el disco duro del sistema. Puede usar una unidad flash USB o un DVD para crear un medio de arranque. Siga las instrucciones del sitio web para completar el proceso de instalación. </li>
|
| 19 |
-
<li><b>Reinicie el sistema</b> y seleccione Sistema operativo primario en el menú de arranque. El primer arranque puede tardar algún tiempo en configurar los ajustes. </li>
|
| 20 |
-
<li><b>Inicie sesión en la cuenta de Google Play</b> e instale fire max gratis desde la aplicación Playstore. También puede descargarlo de otras fuentes si lo prefiere. </li>
|
| 21 |
-
</ol>
|
| 22 |
-
<p>Ahora puede jugar fuego libre máx en su computadora portátil usando Prime OS. Puede cambiar entre Windows y Android en cualquier momento reiniciando su sistema. También puede disfrutar de otras características de Prime OS, como AWD Launcher, acceso root y soporte multiventana. </p>
|
| 23 |
-
<h2>Conclusión</h2>
|
| 24 |
-
<p>En este artículo, le hemos mostrado cómo descargar gratis fuego máximo en el ordenador portátil sin bluestacks utilizando dos métodos: emulador de Gameloop y Prime OS. Ambos métodos tienen sus propias ventajas y desventajas, por lo que puede elegir el que se adapte a sus necesidades y preferencias. Jugar fuego libre máx en el ordenador portátil sin bluestacks puede darle una mejor experiencia de juego con gráficos más altos, un rendimiento más rápido y controles más fáciles. Sin embargo, también debe ser consciente de los riesgos y desafíos potenciales, como problemas de compatibilidad, amenazas de seguridad y requisitos del sistema. Esperamos que este artículo le ha ayudado a aprender cómo descargar gratis fuego máx en el ordenador portátil sin bluestacks. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. </p>
|
| 25 |
-
<h2>Preguntas frecuentes</h2>
|
| 26 |
-
<p>Aquí hay algunas preguntas frecuentes relacionadas con el tema de cómo descargar gratis fuego máx en el ordenador portátil sin bluestacks:</p>
|
| 27 |
-
<h3> ¿Es compatible con Windows 10? </h3>
|
| 28 |
-
|
| 29 |
-
<h3>¿Es el fuego libre máx mejor que el fuego libre? </h3>
|
| 30 |
-
<p>Free fire max es una versión mejorada de free fire que ofrece gráficos, efectos y características mejorados. También tiene un mapa más grande, más armas y más modos de juego. Sin embargo, el fuego libre máximo también requiere más espacio de almacenamiento, RAM y CPU que el fuego libre. Por lo tanto, puede no funcionar sin problemas en dispositivos de gama baja. </p>
|
| 31 |
-
<h3>¿Puedo jugar a fuego libre máx con jugadores de fuego libre? </h3>
|
| 32 |
-
<p>Sí, puedes jugar a fuego libre máximo con jugadores de fuego libre, ya que ambos juegos comparten el mismo servidor y sistema de cuenta. También puede transferir su progreso y los datos de fuego libre a fuego libre máx sin ningún tipo de molestia. </p>
|
| 33 |
-
<h3> ¿Cuáles son los requisitos mínimos del sistema para jugar fuego libre máx en la computadora portátil? </h3>
|
| 34 |
-
<p>Los requisitos mínimos del sistema para jugar fuego libre máx en la computadora portátil varían dependiendo del método que utilice. Para el emulador de Gameloop, necesita al menos 4 GB de RAM, 4 GB de espacio en disco y un procesador de doble núcleo. Para Prime OS, necesita al menos 2 GB de RAM, 16 GB de espacio en disco y un procesador de 64 bits. </p>
|
| 35 |
-
<h3> ¿Es seguro jugar fuego libre máximo en el ordenador portátil sin bluestacks? </h3>
|
| 36 |
-
<p>Jugando fuego libre máx en el ordenador portátil sin bluestacks es generalmente seguro, siempre y cuando se utiliza un método confiable y confiable. Sin embargo, también debe tener cuidado con las fuentes que descarga, los permisos que otorga y el software antivirus que usa. También debes evitar usar hacks o trucos que puedan comprometer tu cuenta o dispositivo. </p>
|
| 37 |
-
<p></p> 64aa2da5cf<br />
|
| 38 |
-
<br />
|
| 39 |
-
<br />
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spaces/Benson/text-generation/Examples/Descargar Bluecurve Home App.md
DELETED
|
@@ -1,76 +0,0 @@
|
|
| 1 |
-
<br />
|
| 2 |
-
<h1>Cómo descargar y usar la aplicación de inicio BlueCurve</h1>
|
| 3 |
-
<p>¿Quieres tener una mejor experiencia WiFi en casa? ¿Quieres gestionar tu red doméstica y dispositivos conectados con facilidad? ¿Quieres disfrutar de los beneficios de la integración del hogar inteligente? Si respondiste sí a cualquiera de estas preguntas, entonces necesitas la aplicación BlueCurve Home. </p>
|
| 4 |
-
<h2>descargar bluecurve home app</h2><br /><p><b><b>Download File</b> ► <a href="https://bltlly.com/2v6Ldi">https://bltlly.com/2v6Ldi</a></b></p><br /><br />
|
| 5 |
-
<p>BlueCurve Home es un panel digital que le permite configurar, monitorear y controlar su red WiFi doméstica desde cualquier lugar con su dispositivo conectado. Está disponible de forma gratuita para los clientes de Internet de Shaw que tengan una pasarela Fibre+. En este artículo, le mostraremos cómo descargar y usar la aplicación, así como sus características, beneficios y consejos para solucionar problemas. </p>
|
| 6 |
-
<h2>Cómo descargar la aplicación para dispositivos Android e iOS</h2>
|
| 7 |
-
<p>Descargar la aplicación BlueCurve Home es fácil. Solo tienes que seguir estos pasos:</p>
|
| 8 |
-
<ol>
|
| 9 |
-
<li>Ir al <a href="( 1 )">Google Play Store</a> o el <a href="( 3 )">Apple App Store</a> en su dispositivo. </li>
|
| 10 |
-
<li>Buscar "Shaw BlueCurve Inicio" y toque en el icono de la aplicación. </li>
|
| 11 |
-
<li>Toque en Instalar o Obtener para descargar la aplicación de forma gratuita. </li>
|
| 12 |
-
<li>Espere a que la aplicación termine de descargar e instalar en su dispositivo. </li>
|
| 13 |
-
</ol>
|
| 14 |
-
<h2>Cómo iniciar sesión con su ID y contraseña de Shaw</h2>
|
| 15 |
-
<p>Para usar la aplicación BlueCurve Home, debe iniciar sesión con su nombre de usuario y contraseña de Shaw ID. Si aún no tiene un ID de Shaw, puede crear uno en <a href="https://register.shaw.ca/">https://register.shaw.ca/</a>. Una vez que tenga su ID de Shaw, siga estos pasos:</p>
|
| 16 |
-
<ol>
|
| 17 |
-
<li>Abra la aplicación BlueCurve Home en su dispositivo. </li>
|
| 18 |
-
<li>Introduzca su nombre de usuario y contraseña de Shaw en los campos proporcionados. </li>
|
| 19 |
-
<li>Toque en Iniciar sesión para acceder a la aplicación. </li>
|
| 20 |
-
</ol>
|
| 21 |
-
|
| 22 |
-
<h2>Características de BlueCurve Inicio</h2>
|
| 23 |
-
<p>La aplicación BlueCurve Home tiene varias características que le ayudan a personalizar, controlar y proteger su red doméstica. Estas son algunas de ellas:</p>
|
| 24 |
-
<p></p>
|
| 25 |
-
<h3>Configuración y personalización de WiFi</h3>
|
| 26 |
-
<p>Con esta función, puede:</p>
|
| 27 |
-
<ul>
|
| 28 |
-
<li> Ver dispositivos conectados a su red doméstica y darles apodos para una fácil referencia. </li>
|
| 29 |
-
<li>Crear perfiles para que pueda asignar dispositivos a los miembros de su familia. </li>
|
| 30 |
-
<li> Vea y cambie fácilmente su nombre y contraseña de red WiFi. </li>
|
| 31 |
-
<li>Comparte tu WiFi con invitados o familiares con un código QR o un enlace. </li>
|
| 32 |
-
<li> Ver y gestionar todas las notificaciones de red anteriores desde el centro de notificaciones. </li>
|
| 33 |
-
</ul>
|
| 34 |
-
<h3>Controles parentales</h3>
|
| 35 |
-
<p>Con esta función, puede:</p>
|
| 36 |
-
<ul>
|
| 37 |
-
<li>Configurar controles parentales para bloquear contenido inapropiado y sitios web para sus hijos. </li>
|
| 38 |
-
<li>Pausa el acceso a Internet para dispositivos o perfiles específicos con un toque de un botón. </li>
|
| 39 |
-
<li>Monitorear y administrar el tiempo activo y el uso de datos de los miembros de su familia. </li>
|
| 40 |
-
<li>Proteja su red de malware, phishing y otras amenazas en línea con Shaw Secure de McAfee.</li>
|
| 41 |
-
</ul>
|
| 42 |
-
<h3>Solución de problemas</h3>
|
| 43 |
-
<p>Con esta función, puede:</p>
|
| 44 |
-
<ul>
|
| 45 |
-
<li>Ejecute una prueba de velocidad para comprobar el rendimiento de Internet y compararlo con su plan. </li>
|
| 46 |
-
<li>Reinicie su puerta de enlace de forma remota si experimenta algún problema de conectividad. </li>
|
| 47 |
-
<li>Encuentra consejos y soluciones para problemas WiFi comunes en la sección de ayuda de la aplicación. </li>
|
| 48 |
-
<li>Póngase en contacto con el soporte de Shaw directamente desde la aplicación si necesita más ayuda. </li>
|
| 49 |
-
</ul>
|
| 50 |
-
<h2>Beneficios de BlueCurve Inicio</h2>
|
| 51 |
-
<p>La aplicación BlueCurve Home no solo hace que su red WiFi sea más fácil de administrar, sino que también mejora su experiencia WiFi de muchas maneras. Estos son algunos de los beneficios de usar la aplicación:</p>
|
| 52 |
-
<h3>Experiencia WiFi mejorada</h3>
|
| 53 |
-
|
| 54 |
-
<h3>Integración en el hogar inteligente</h3>
|
| 55 |
-
<p>La aplicación BlueCurve Home también funciona con otros dispositivos domésticos inteligentes que mejoran su comodidad, comodidad y entretenimiento. Por ejemplo, puede usar la aplicación para conectarse y administrar BlueCurve Pods, que son dispositivos pequeños que extienden su cobertura WiFi a áreas de difícil acceso de su hogar. También puede utilizar la aplicación para integrar BlueCurve TV, que es el mejor servicio de televisión de Shaw que le permite ver contenido en vivo y bajo demanda en cualquier pantalla. Además, puede usar la aplicación para controlar otros dispositivos domésticos inteligentes, como luces, termostatos, cámaras y más, con comandos de voz o gestos. </p>
|
| 56 |
-
<h3>Atención al cliente</h3>
|
| 57 |
-
<p>Si alguna vez necesita ayuda con BlueCurve Home o cualquier otro servicio de Shaw, puede contar con su equipo de atención al cliente para ayudarle. Puede contactarlos por teléfono, chat, correo electrónico o redes sociales. También puede utilizar la aplicación para programar una devolución de llamada o una cita de servicio a su conveniencia. El equipo de atención al cliente de Shaw está disponible 24/7 para responder a sus preguntas y resolver sus problemas. </p>
|
| 58 |
-
<h2>Conclusión</h2>
|
| 59 |
-
<p>BlueCurve Home es una aplicación imprescindible para los clientes de Internet de Shaw que quieren tener más control y flexibilidad sobre su red doméstica y dispositivos conectados. Le permite configurar, monitorear y personalizar su red WiFi desde cualquier lugar con su dispositivo. También ofrece funciones como controles parentales, solución de problemas e integración del hogar inteligente que mejoran su experiencia WiFi y hacen su vida más fácil. Para descargar la aplicación gratis, visita Google Play Store o Apple App Store hoy. </p>
|
| 60 |
-
<h2>Preguntas frecuentes</h2>
|
| 61 |
-
<p>Aquí hay algunas preguntas y respuestas frecuentes sobre BlueCurve Home:</p>
|
| 62 |
-
<h4>Q: ¿Necesito pagar extra para usar BlueCurve Home? </h4>
|
| 63 |
-
<p>A: No, BlueCurve Home está incluido con su servicio de Internet Shaw sin costo adicional. Sin embargo, necesita tener un módem Fibre+ Gateway para usar la aplicación. </p>
|
| 64 |
-
<h4>Q: ¿Cuántos dispositivos puedo conectar a mi red BlueCurve Home? </h4>
|
| 65 |
-
|
| 66 |
-
<h4>Q: ¿Cómo puedo actualizar la aplicación BlueCurve Home? </h4>
|
| 67 |
-
<p>A: La aplicación BlueCurve Home se actualizará automáticamente cuando haya una nueva versión disponible. También puede comprobar las actualizaciones manualmente yendo a la tienda de aplicaciones de su dispositivo y tocando en Actualizar si hay una. </p>
|
| 68 |
-
<h4>Q: ¿Cómo puedo eliminar la aplicación BlueCurve Home? </h4>
|
| 69 |
-
<p>A: Si desea eliminar la aplicación BlueCurve Home de su dispositivo, puede seguir estos pasos:</p>
|
| 70 |
-
<ol>
|
| 71 |
-
<li>En los dispositivos Android, toque y mantenga pulsado el icono de la aplicación hasta que aparezca un menú. Luego toque en Desinstalar y confirmar. </li>
|
| 72 |
-
<li>En los dispositivos iOS, toque y mantenga pulsado el icono de la aplicación hasta que comience a temblar. Luego toque en el icono de X en la esquina superior izquierda del icono de la aplicación y confirme. </li>
|
| 73 |
-
</ol> <h4>Q: ¿Cómo accedo a la aplicación BlueCurve Home en mi computadora? </h4>
|
| 74 |
-
<p>A: Puede acceder a la aplicación BlueCurve Home en su computadora yendo a <a href="https://home.shaw.ca/">https://home.shaw.ca/</a> e iniciando sesión con su ID de Shaw y contraseña. Puede utilizar cualquier navegador web que soporte HTML5, como Chrome, Firefox, Safari o Edge.</p> 64aa2da5cf<br />
|
| 75 |
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<br />
|
| 76 |
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spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pygments/formatters/_mapping.py
DELETED
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@@ -1,23 +0,0 @@
|
|
| 1 |
-
# Automatically generated by scripts/gen_mapfiles.py.
|
| 2 |
-
# DO NOT EDIT BY HAND; run `make mapfiles` instead.
|
| 3 |
-
|
| 4 |
-
FORMATTERS = {
|
| 5 |
-
'BBCodeFormatter': ('pygments.formatters.bbcode', 'BBCode', ('bbcode', 'bb'), (), 'Format tokens with BBcodes. These formatting codes are used by many bulletin boards, so you can highlight your sourcecode with pygments before posting it there.'),
|
| 6 |
-
'BmpImageFormatter': ('pygments.formatters.img', 'img_bmp', ('bmp', 'bitmap'), ('*.bmp',), 'Create a bitmap image from source code. This uses the Python Imaging Library to generate a pixmap from the source code.'),
|
| 7 |
-
'GifImageFormatter': ('pygments.formatters.img', 'img_gif', ('gif',), ('*.gif',), 'Create a GIF image from source code. This uses the Python Imaging Library to generate a pixmap from the source code.'),
|
| 8 |
-
'GroffFormatter': ('pygments.formatters.groff', 'groff', ('groff', 'troff', 'roff'), (), 'Format tokens with groff escapes to change their color and font style.'),
|
| 9 |
-
'HtmlFormatter': ('pygments.formatters.html', 'HTML', ('html',), ('*.html', '*.htm'), "Format tokens as HTML 4 ``<span>`` tags within a ``<pre>`` tag, wrapped in a ``<div>`` tag. The ``<div>``'s CSS class can be set by the `cssclass` option."),
|
| 10 |
-
'IRCFormatter': ('pygments.formatters.irc', 'IRC', ('irc', 'IRC'), (), 'Format tokens with IRC color sequences'),
|
| 11 |
-
'ImageFormatter': ('pygments.formatters.img', 'img', ('img', 'IMG', 'png'), ('*.png',), 'Create a PNG image from source code. This uses the Python Imaging Library to generate a pixmap from the source code.'),
|
| 12 |
-
'JpgImageFormatter': ('pygments.formatters.img', 'img_jpg', ('jpg', 'jpeg'), ('*.jpg',), 'Create a JPEG image from source code. This uses the Python Imaging Library to generate a pixmap from the source code.'),
|
| 13 |
-
'LatexFormatter': ('pygments.formatters.latex', 'LaTeX', ('latex', 'tex'), ('*.tex',), 'Format tokens as LaTeX code. This needs the `fancyvrb` and `color` standard packages.'),
|
| 14 |
-
'NullFormatter': ('pygments.formatters.other', 'Text only', ('text', 'null'), ('*.txt',), 'Output the text unchanged without any formatting.'),
|
| 15 |
-
'PangoMarkupFormatter': ('pygments.formatters.pangomarkup', 'Pango Markup', ('pango', 'pangomarkup'), (), 'Format tokens as Pango Markup code. It can then be rendered to an SVG.'),
|
| 16 |
-
'RawTokenFormatter': ('pygments.formatters.other', 'Raw tokens', ('raw', 'tokens'), ('*.raw',), 'Format tokens as a raw representation for storing token streams.'),
|
| 17 |
-
'RtfFormatter': ('pygments.formatters.rtf', 'RTF', ('rtf',), ('*.rtf',), 'Format tokens as RTF markup. This formatter automatically outputs full RTF documents with color information and other useful stuff. Perfect for Copy and Paste into Microsoft(R) Word(R) documents.'),
|
| 18 |
-
'SvgFormatter': ('pygments.formatters.svg', 'SVG', ('svg',), ('*.svg',), 'Format tokens as an SVG graphics file. This formatter is still experimental. Each line of code is a ``<text>`` element with explicit ``x`` and ``y`` coordinates containing ``<tspan>`` elements with the individual token styles.'),
|
| 19 |
-
'Terminal256Formatter': ('pygments.formatters.terminal256', 'Terminal256', ('terminal256', 'console256', '256'), (), 'Format tokens with ANSI color sequences, for output in a 256-color terminal or console. Like in `TerminalFormatter` color sequences are terminated at newlines, so that paging the output works correctly.'),
|
| 20 |
-
'TerminalFormatter': ('pygments.formatters.terminal', 'Terminal', ('terminal', 'console'), (), 'Format tokens with ANSI color sequences, for output in a text console. Color sequences are terminated at newlines, so that paging the output works correctly.'),
|
| 21 |
-
'TerminalTrueColorFormatter': ('pygments.formatters.terminal256', 'TerminalTrueColor', ('terminal16m', 'console16m', '16m'), (), 'Format tokens with ANSI color sequences, for output in a true-color terminal or console. Like in `TerminalFormatter` color sequences are terminated at newlines, so that paging the output works correctly.'),
|
| 22 |
-
'TestcaseFormatter': ('pygments.formatters.other', 'Testcase', ('testcase',), (), 'Format tokens as appropriate for a new testcase.'),
|
| 23 |
-
}
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|
spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/pyparsing/common.py
DELETED
|
@@ -1,424 +0,0 @@
|
|
| 1 |
-
# common.py
|
| 2 |
-
from .core import *
|
| 3 |
-
from .helpers import delimited_list, any_open_tag, any_close_tag
|
| 4 |
-
from datetime import datetime
|
| 5 |
-
|
| 6 |
-
|
| 7 |
-
# some other useful expressions - using lower-case class name since we are really using this as a namespace
|
| 8 |
-
class pyparsing_common:
|
| 9 |
-
"""Here are some common low-level expressions that may be useful in
|
| 10 |
-
jump-starting parser development:
|
| 11 |
-
|
| 12 |
-
- numeric forms (:class:`integers<integer>`, :class:`reals<real>`,
|
| 13 |
-
:class:`scientific notation<sci_real>`)
|
| 14 |
-
- common :class:`programming identifiers<identifier>`
|
| 15 |
-
- network addresses (:class:`MAC<mac_address>`,
|
| 16 |
-
:class:`IPv4<ipv4_address>`, :class:`IPv6<ipv6_address>`)
|
| 17 |
-
- ISO8601 :class:`dates<iso8601_date>` and
|
| 18 |
-
:class:`datetime<iso8601_datetime>`
|
| 19 |
-
- :class:`UUID<uuid>`
|
| 20 |
-
- :class:`comma-separated list<comma_separated_list>`
|
| 21 |
-
- :class:`url`
|
| 22 |
-
|
| 23 |
-
Parse actions:
|
| 24 |
-
|
| 25 |
-
- :class:`convertToInteger`
|
| 26 |
-
- :class:`convertToFloat`
|
| 27 |
-
- :class:`convertToDate`
|
| 28 |
-
- :class:`convertToDatetime`
|
| 29 |
-
- :class:`stripHTMLTags`
|
| 30 |
-
- :class:`upcaseTokens`
|
| 31 |
-
- :class:`downcaseTokens`
|
| 32 |
-
|
| 33 |
-
Example::
|
| 34 |
-
|
| 35 |
-
pyparsing_common.number.runTests('''
|
| 36 |
-
# any int or real number, returned as the appropriate type
|
| 37 |
-
100
|
| 38 |
-
-100
|
| 39 |
-
+100
|
| 40 |
-
3.14159
|
| 41 |
-
6.02e23
|
| 42 |
-
1e-12
|
| 43 |
-
''')
|
| 44 |
-
|
| 45 |
-
pyparsing_common.fnumber.runTests('''
|
| 46 |
-
# any int or real number, returned as float
|
| 47 |
-
100
|
| 48 |
-
-100
|
| 49 |
-
+100
|
| 50 |
-
3.14159
|
| 51 |
-
6.02e23
|
| 52 |
-
1e-12
|
| 53 |
-
''')
|
| 54 |
-
|
| 55 |
-
pyparsing_common.hex_integer.runTests('''
|
| 56 |
-
# hex numbers
|
| 57 |
-
100
|
| 58 |
-
FF
|
| 59 |
-
''')
|
| 60 |
-
|
| 61 |
-
pyparsing_common.fraction.runTests('''
|
| 62 |
-
# fractions
|
| 63 |
-
1/2
|
| 64 |
-
-3/4
|
| 65 |
-
''')
|
| 66 |
-
|
| 67 |
-
pyparsing_common.mixed_integer.runTests('''
|
| 68 |
-
# mixed fractions
|
| 69 |
-
1
|
| 70 |
-
1/2
|
| 71 |
-
-3/4
|
| 72 |
-
1-3/4
|
| 73 |
-
''')
|
| 74 |
-
|
| 75 |
-
import uuid
|
| 76 |
-
pyparsing_common.uuid.setParseAction(tokenMap(uuid.UUID))
|
| 77 |
-
pyparsing_common.uuid.runTests('''
|
| 78 |
-
# uuid
|
| 79 |
-
12345678-1234-5678-1234-567812345678
|
| 80 |
-
''')
|
| 81 |
-
|
| 82 |
-
prints::
|
| 83 |
-
|
| 84 |
-
# any int or real number, returned as the appropriate type
|
| 85 |
-
100
|
| 86 |
-
[100]
|
| 87 |
-
|
| 88 |
-
-100
|
| 89 |
-
[-100]
|
| 90 |
-
|
| 91 |
-
+100
|
| 92 |
-
[100]
|
| 93 |
-
|
| 94 |
-
3.14159
|
| 95 |
-
[3.14159]
|
| 96 |
-
|
| 97 |
-
6.02e23
|
| 98 |
-
[6.02e+23]
|
| 99 |
-
|
| 100 |
-
1e-12
|
| 101 |
-
[1e-12]
|
| 102 |
-
|
| 103 |
-
# any int or real number, returned as float
|
| 104 |
-
100
|
| 105 |
-
[100.0]
|
| 106 |
-
|
| 107 |
-
-100
|
| 108 |
-
[-100.0]
|
| 109 |
-
|
| 110 |
-
+100
|
| 111 |
-
[100.0]
|
| 112 |
-
|
| 113 |
-
3.14159
|
| 114 |
-
[3.14159]
|
| 115 |
-
|
| 116 |
-
6.02e23
|
| 117 |
-
[6.02e+23]
|
| 118 |
-
|
| 119 |
-
1e-12
|
| 120 |
-
[1e-12]
|
| 121 |
-
|
| 122 |
-
# hex numbers
|
| 123 |
-
100
|
| 124 |
-
[256]
|
| 125 |
-
|
| 126 |
-
FF
|
| 127 |
-
[255]
|
| 128 |
-
|
| 129 |
-
# fractions
|
| 130 |
-
1/2
|
| 131 |
-
[0.5]
|
| 132 |
-
|
| 133 |
-
-3/4
|
| 134 |
-
[-0.75]
|
| 135 |
-
|
| 136 |
-
# mixed fractions
|
| 137 |
-
1
|
| 138 |
-
[1]
|
| 139 |
-
|
| 140 |
-
1/2
|
| 141 |
-
[0.5]
|
| 142 |
-
|
| 143 |
-
-3/4
|
| 144 |
-
[-0.75]
|
| 145 |
-
|
| 146 |
-
1-3/4
|
| 147 |
-
[1.75]
|
| 148 |
-
|
| 149 |
-
# uuid
|
| 150 |
-
12345678-1234-5678-1234-567812345678
|
| 151 |
-
[UUID('12345678-1234-5678-1234-567812345678')]
|
| 152 |
-
"""
|
| 153 |
-
|
| 154 |
-
convert_to_integer = token_map(int)
|
| 155 |
-
"""
|
| 156 |
-
Parse action for converting parsed integers to Python int
|
| 157 |
-
"""
|
| 158 |
-
|
| 159 |
-
convert_to_float = token_map(float)
|
| 160 |
-
"""
|
| 161 |
-
Parse action for converting parsed numbers to Python float
|
| 162 |
-
"""
|
| 163 |
-
|
| 164 |
-
integer = Word(nums).set_name("integer").set_parse_action(convert_to_integer)
|
| 165 |
-
"""expression that parses an unsigned integer, returns an int"""
|
| 166 |
-
|
| 167 |
-
hex_integer = (
|
| 168 |
-
Word(hexnums).set_name("hex integer").set_parse_action(token_map(int, 16))
|
| 169 |
-
)
|
| 170 |
-
"""expression that parses a hexadecimal integer, returns an int"""
|
| 171 |
-
|
| 172 |
-
signed_integer = (
|
| 173 |
-
Regex(r"[+-]?\d+")
|
| 174 |
-
.set_name("signed integer")
|
| 175 |
-
.set_parse_action(convert_to_integer)
|
| 176 |
-
)
|
| 177 |
-
"""expression that parses an integer with optional leading sign, returns an int"""
|
| 178 |
-
|
| 179 |
-
fraction = (
|
| 180 |
-
signed_integer().set_parse_action(convert_to_float)
|
| 181 |
-
+ "/"
|
| 182 |
-
+ signed_integer().set_parse_action(convert_to_float)
|
| 183 |
-
).set_name("fraction")
|
| 184 |
-
"""fractional expression of an integer divided by an integer, returns a float"""
|
| 185 |
-
fraction.add_parse_action(lambda tt: tt[0] / tt[-1])
|
| 186 |
-
|
| 187 |
-
mixed_integer = (
|
| 188 |
-
fraction | signed_integer + Opt(Opt("-").suppress() + fraction)
|
| 189 |
-
).set_name("fraction or mixed integer-fraction")
|
| 190 |
-
"""mixed integer of the form 'integer - fraction', with optional leading integer, returns float"""
|
| 191 |
-
mixed_integer.add_parse_action(sum)
|
| 192 |
-
|
| 193 |
-
real = (
|
| 194 |
-
Regex(r"[+-]?(?:\d+\.\d*|\.\d+)")
|
| 195 |
-
.set_name("real number")
|
| 196 |
-
.set_parse_action(convert_to_float)
|
| 197 |
-
)
|
| 198 |
-
"""expression that parses a floating point number and returns a float"""
|
| 199 |
-
|
| 200 |
-
sci_real = (
|
| 201 |
-
Regex(r"[+-]?(?:\d+(?:[eE][+-]?\d+)|(?:\d+\.\d*|\.\d+)(?:[eE][+-]?\d+)?)")
|
| 202 |
-
.set_name("real number with scientific notation")
|
| 203 |
-
.set_parse_action(convert_to_float)
|
| 204 |
-
)
|
| 205 |
-
"""expression that parses a floating point number with optional
|
| 206 |
-
scientific notation and returns a float"""
|
| 207 |
-
|
| 208 |
-
# streamlining this expression makes the docs nicer-looking
|
| 209 |
-
number = (sci_real | real | signed_integer).setName("number").streamline()
|
| 210 |
-
"""any numeric expression, returns the corresponding Python type"""
|
| 211 |
-
|
| 212 |
-
fnumber = (
|
| 213 |
-
Regex(r"[+-]?\d+\.?\d*([eE][+-]?\d+)?")
|
| 214 |
-
.set_name("fnumber")
|
| 215 |
-
.set_parse_action(convert_to_float)
|
| 216 |
-
)
|
| 217 |
-
"""any int or real number, returned as float"""
|
| 218 |
-
|
| 219 |
-
identifier = Word(identchars, identbodychars).set_name("identifier")
|
| 220 |
-
"""typical code identifier (leading alpha or '_', followed by 0 or more alphas, nums, or '_')"""
|
| 221 |
-
|
| 222 |
-
ipv4_address = Regex(
|
| 223 |
-
r"(25[0-5]|2[0-4][0-9]|1?[0-9]{1,2})(\.(25[0-5]|2[0-4][0-9]|1?[0-9]{1,2})){3}"
|
| 224 |
-
).set_name("IPv4 address")
|
| 225 |
-
"IPv4 address (``0.0.0.0 - 255.255.255.255``)"
|
| 226 |
-
|
| 227 |
-
_ipv6_part = Regex(r"[0-9a-fA-F]{1,4}").set_name("hex_integer")
|
| 228 |
-
_full_ipv6_address = (_ipv6_part + (":" + _ipv6_part) * 7).set_name(
|
| 229 |
-
"full IPv6 address"
|
| 230 |
-
)
|
| 231 |
-
_short_ipv6_address = (
|
| 232 |
-
Opt(_ipv6_part + (":" + _ipv6_part) * (0, 6))
|
| 233 |
-
+ "::"
|
| 234 |
-
+ Opt(_ipv6_part + (":" + _ipv6_part) * (0, 6))
|
| 235 |
-
).set_name("short IPv6 address")
|
| 236 |
-
_short_ipv6_address.add_condition(
|
| 237 |
-
lambda t: sum(1 for tt in t if pyparsing_common._ipv6_part.matches(tt)) < 8
|
| 238 |
-
)
|
| 239 |
-
_mixed_ipv6_address = ("::ffff:" + ipv4_address).set_name("mixed IPv6 address")
|
| 240 |
-
ipv6_address = Combine(
|
| 241 |
-
(_full_ipv6_address | _mixed_ipv6_address | _short_ipv6_address).set_name(
|
| 242 |
-
"IPv6 address"
|
| 243 |
-
)
|
| 244 |
-
).set_name("IPv6 address")
|
| 245 |
-
"IPv6 address (long, short, or mixed form)"
|
| 246 |
-
|
| 247 |
-
mac_address = Regex(
|
| 248 |
-
r"[0-9a-fA-F]{2}([:.-])[0-9a-fA-F]{2}(?:\1[0-9a-fA-F]{2}){4}"
|
| 249 |
-
).set_name("MAC address")
|
| 250 |
-
"MAC address xx:xx:xx:xx:xx (may also have '-' or '.' delimiters)"
|
| 251 |
-
|
| 252 |
-
@staticmethod
|
| 253 |
-
def convert_to_date(fmt: str = "%Y-%m-%d"):
|
| 254 |
-
"""
|
| 255 |
-
Helper to create a parse action for converting parsed date string to Python datetime.date
|
| 256 |
-
|
| 257 |
-
Params -
|
| 258 |
-
- fmt - format to be passed to datetime.strptime (default= ``"%Y-%m-%d"``)
|
| 259 |
-
|
| 260 |
-
Example::
|
| 261 |
-
|
| 262 |
-
date_expr = pyparsing_common.iso8601_date.copy()
|
| 263 |
-
date_expr.setParseAction(pyparsing_common.convertToDate())
|
| 264 |
-
print(date_expr.parseString("1999-12-31"))
|
| 265 |
-
|
| 266 |
-
prints::
|
| 267 |
-
|
| 268 |
-
[datetime.date(1999, 12, 31)]
|
| 269 |
-
"""
|
| 270 |
-
|
| 271 |
-
def cvt_fn(ss, ll, tt):
|
| 272 |
-
try:
|
| 273 |
-
return datetime.strptime(tt[0], fmt).date()
|
| 274 |
-
except ValueError as ve:
|
| 275 |
-
raise ParseException(ss, ll, str(ve))
|
| 276 |
-
|
| 277 |
-
return cvt_fn
|
| 278 |
-
|
| 279 |
-
@staticmethod
|
| 280 |
-
def convert_to_datetime(fmt: str = "%Y-%m-%dT%H:%M:%S.%f"):
|
| 281 |
-
"""Helper to create a parse action for converting parsed
|
| 282 |
-
datetime string to Python datetime.datetime
|
| 283 |
-
|
| 284 |
-
Params -
|
| 285 |
-
- fmt - format to be passed to datetime.strptime (default= ``"%Y-%m-%dT%H:%M:%S.%f"``)
|
| 286 |
-
|
| 287 |
-
Example::
|
| 288 |
-
|
| 289 |
-
dt_expr = pyparsing_common.iso8601_datetime.copy()
|
| 290 |
-
dt_expr.setParseAction(pyparsing_common.convertToDatetime())
|
| 291 |
-
print(dt_expr.parseString("1999-12-31T23:59:59.999"))
|
| 292 |
-
|
| 293 |
-
prints::
|
| 294 |
-
|
| 295 |
-
[datetime.datetime(1999, 12, 31, 23, 59, 59, 999000)]
|
| 296 |
-
"""
|
| 297 |
-
|
| 298 |
-
def cvt_fn(s, l, t):
|
| 299 |
-
try:
|
| 300 |
-
return datetime.strptime(t[0], fmt)
|
| 301 |
-
except ValueError as ve:
|
| 302 |
-
raise ParseException(s, l, str(ve))
|
| 303 |
-
|
| 304 |
-
return cvt_fn
|
| 305 |
-
|
| 306 |
-
iso8601_date = Regex(
|
| 307 |
-
r"(?P<year>\d{4})(?:-(?P<month>\d\d)(?:-(?P<day>\d\d))?)?"
|
| 308 |
-
).set_name("ISO8601 date")
|
| 309 |
-
"ISO8601 date (``yyyy-mm-dd``)"
|
| 310 |
-
|
| 311 |
-
iso8601_datetime = Regex(
|
| 312 |
-
r"(?P<year>\d{4})-(?P<month>\d\d)-(?P<day>\d\d)[T ](?P<hour>\d\d):(?P<minute>\d\d)(:(?P<second>\d\d(\.\d*)?)?)?(?P<tz>Z|[+-]\d\d:?\d\d)?"
|
| 313 |
-
).set_name("ISO8601 datetime")
|
| 314 |
-
"ISO8601 datetime (``yyyy-mm-ddThh:mm:ss.s(Z|+-00:00)``) - trailing seconds, milliseconds, and timezone optional; accepts separating ``'T'`` or ``' '``"
|
| 315 |
-
|
| 316 |
-
uuid = Regex(r"[0-9a-fA-F]{8}(-[0-9a-fA-F]{4}){3}-[0-9a-fA-F]{12}").set_name("UUID")
|
| 317 |
-
"UUID (``xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx``)"
|
| 318 |
-
|
| 319 |
-
_html_stripper = any_open_tag.suppress() | any_close_tag.suppress()
|
| 320 |
-
|
| 321 |
-
@staticmethod
|
| 322 |
-
def strip_html_tags(s: str, l: int, tokens: ParseResults):
|
| 323 |
-
"""Parse action to remove HTML tags from web page HTML source
|
| 324 |
-
|
| 325 |
-
Example::
|
| 326 |
-
|
| 327 |
-
# strip HTML links from normal text
|
| 328 |
-
text = '<td>More info at the <a href="https://github.com/pyparsing/pyparsing/wiki">pyparsing</a> wiki page</td>'
|
| 329 |
-
td, td_end = makeHTMLTags("TD")
|
| 330 |
-
table_text = td + SkipTo(td_end).setParseAction(pyparsing_common.stripHTMLTags)("body") + td_end
|
| 331 |
-
print(table_text.parseString(text).body)
|
| 332 |
-
|
| 333 |
-
Prints::
|
| 334 |
-
|
| 335 |
-
More info at the pyparsing wiki page
|
| 336 |
-
"""
|
| 337 |
-
return pyparsing_common._html_stripper.transform_string(tokens[0])
|
| 338 |
-
|
| 339 |
-
_commasepitem = (
|
| 340 |
-
Combine(
|
| 341 |
-
OneOrMore(
|
| 342 |
-
~Literal(",")
|
| 343 |
-
+ ~LineEnd()
|
| 344 |
-
+ Word(printables, exclude_chars=",")
|
| 345 |
-
+ Opt(White(" \t") + ~FollowedBy(LineEnd() | ","))
|
| 346 |
-
)
|
| 347 |
-
)
|
| 348 |
-
.streamline()
|
| 349 |
-
.set_name("commaItem")
|
| 350 |
-
)
|
| 351 |
-
comma_separated_list = delimited_list(
|
| 352 |
-
Opt(quoted_string.copy() | _commasepitem, default="")
|
| 353 |
-
).set_name("comma separated list")
|
| 354 |
-
"""Predefined expression of 1 or more printable words or quoted strings, separated by commas."""
|
| 355 |
-
|
| 356 |
-
upcase_tokens = staticmethod(token_map(lambda t: t.upper()))
|
| 357 |
-
"""Parse action to convert tokens to upper case."""
|
| 358 |
-
|
| 359 |
-
downcase_tokens = staticmethod(token_map(lambda t: t.lower()))
|
| 360 |
-
"""Parse action to convert tokens to lower case."""
|
| 361 |
-
|
| 362 |
-
# fmt: off
|
| 363 |
-
url = Regex(
|
| 364 |
-
# https://mathiasbynens.be/demo/url-regex
|
| 365 |
-
# https://gist.github.com/dperini/729294
|
| 366 |
-
r"^" +
|
| 367 |
-
# protocol identifier (optional)
|
| 368 |
-
# short syntax // still required
|
| 369 |
-
r"(?:(?:(?P<scheme>https?|ftp):)?\/\/)" +
|
| 370 |
-
# user:pass BasicAuth (optional)
|
| 371 |
-
r"(?:(?P<auth>\S+(?::\S*)?)@)?" +
|
| 372 |
-
r"(?P<host>" +
|
| 373 |
-
# IP address exclusion
|
| 374 |
-
# private & local networks
|
| 375 |
-
r"(?!(?:10|127)(?:\.\d{1,3}){3})" +
|
| 376 |
-
r"(?!(?:169\.254|192\.168)(?:\.\d{1,3}){2})" +
|
| 377 |
-
r"(?!172\.(?:1[6-9]|2\d|3[0-1])(?:\.\d{1,3}){2})" +
|
| 378 |
-
# IP address dotted notation octets
|
| 379 |
-
# excludes loopback network 0.0.0.0
|
| 380 |
-
# excludes reserved space >= 224.0.0.0
|
| 381 |
-
# excludes network & broadcast addresses
|
| 382 |
-
# (first & last IP address of each class)
|
| 383 |
-
r"(?:[1-9]\d?|1\d\d|2[01]\d|22[0-3])" +
|
| 384 |
-
r"(?:\.(?:1?\d{1,2}|2[0-4]\d|25[0-5])){2}" +
|
| 385 |
-
r"(?:\.(?:[1-9]\d?|1\d\d|2[0-4]\d|25[0-4]))" +
|
| 386 |
-
r"|" +
|
| 387 |
-
# host & domain names, may end with dot
|
| 388 |
-
# can be replaced by a shortest alternative
|
| 389 |
-
# (?![-_])(?:[-\w\u00a1-\uffff]{0,63}[^-_]\.)+
|
| 390 |
-
r"(?:" +
|
| 391 |
-
r"(?:" +
|
| 392 |
-
r"[a-z0-9\u00a1-\uffff]" +
|
| 393 |
-
r"[a-z0-9\u00a1-\uffff_-]{0,62}" +
|
| 394 |
-
r")?" +
|
| 395 |
-
r"[a-z0-9\u00a1-\uffff]\." +
|
| 396 |
-
r")+" +
|
| 397 |
-
# TLD identifier name, may end with dot
|
| 398 |
-
r"(?:[a-z\u00a1-\uffff]{2,}\.?)" +
|
| 399 |
-
r")" +
|
| 400 |
-
# port number (optional)
|
| 401 |
-
r"(:(?P<port>\d{2,5}))?" +
|
| 402 |
-
# resource path (optional)
|
| 403 |
-
r"(?P<path>\/[^?# ]*)?" +
|
| 404 |
-
# query string (optional)
|
| 405 |
-
r"(\?(?P<query>[^#]*))?" +
|
| 406 |
-
# fragment (optional)
|
| 407 |
-
r"(#(?P<fragment>\S*))?" +
|
| 408 |
-
r"$"
|
| 409 |
-
).set_name("url")
|
| 410 |
-
# fmt: on
|
| 411 |
-
|
| 412 |
-
# pre-PEP8 compatibility names
|
| 413 |
-
convertToInteger = convert_to_integer
|
| 414 |
-
convertToFloat = convert_to_float
|
| 415 |
-
convertToDate = convert_to_date
|
| 416 |
-
convertToDatetime = convert_to_datetime
|
| 417 |
-
stripHTMLTags = strip_html_tags
|
| 418 |
-
upcaseTokens = upcase_tokens
|
| 419 |
-
downcaseTokens = downcase_tokens
|
| 420 |
-
|
| 421 |
-
|
| 422 |
-
_builtin_exprs = [
|
| 423 |
-
v for v in vars(pyparsing_common).values() if isinstance(v, ParserElement)
|
| 424 |
-
]
|
|
|
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|
spaces/CVPR/LIVE/thrust/thrust/system/cpp/detail/reduce_by_key.h
DELETED
|
@@ -1,23 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2008-2013 NVIDIA Corporation
|
| 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 |
-
|
| 17 |
-
#pragma once
|
| 18 |
-
|
| 19 |
-
#include <thrust/detail/config.h>
|
| 20 |
-
|
| 21 |
-
// this system inherits reduce_by_key
|
| 22 |
-
#include <thrust/system/detail/sequential/reduce_by_key.h>
|
| 23 |
-
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|
spaces/CVPR/WALT/mmdet/datasets/coco.py
DELETED
|
@@ -1,548 +0,0 @@
|
|
| 1 |
-
import itertools
|
| 2 |
-
import logging
|
| 3 |
-
import os.path as osp
|
| 4 |
-
import tempfile
|
| 5 |
-
from collections import OrderedDict
|
| 6 |
-
|
| 7 |
-
import mmcv
|
| 8 |
-
import numpy as np
|
| 9 |
-
import pycocotools
|
| 10 |
-
from mmcv.utils import print_log
|
| 11 |
-
from pycocotools.coco import COCO
|
| 12 |
-
from pycocotools.cocoeval import COCOeval
|
| 13 |
-
from terminaltables import AsciiTable
|
| 14 |
-
|
| 15 |
-
from mmdet.core import eval_recalls
|
| 16 |
-
from .builder import DATASETS
|
| 17 |
-
from .custom import CustomDataset
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
@DATASETS.register_module()
|
| 21 |
-
class CocoDataset(CustomDataset):
|
| 22 |
-
|
| 23 |
-
CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
|
| 24 |
-
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
|
| 25 |
-
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
|
| 26 |
-
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
|
| 27 |
-
'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
|
| 28 |
-
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
|
| 29 |
-
'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
|
| 30 |
-
'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
|
| 31 |
-
'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
|
| 32 |
-
'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
|
| 33 |
-
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
|
| 34 |
-
'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
|
| 35 |
-
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
|
| 36 |
-
'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush')
|
| 37 |
-
|
| 38 |
-
def load_annotations(self, ann_file):
|
| 39 |
-
"""Load annotation from COCO style annotation file.
|
| 40 |
-
|
| 41 |
-
Args:
|
| 42 |
-
ann_file (str): Path of annotation file.
|
| 43 |
-
|
| 44 |
-
Returns:
|
| 45 |
-
list[dict]: Annotation info from COCO api.
|
| 46 |
-
"""
|
| 47 |
-
if not getattr(pycocotools, '__version__', '0') >= '12.0.2':
|
| 48 |
-
raise AssertionError(
|
| 49 |
-
'Incompatible version of pycocotools is installed. '
|
| 50 |
-
'Run pip uninstall pycocotools first. Then run pip '
|
| 51 |
-
'install mmpycocotools to install open-mmlab forked '
|
| 52 |
-
'pycocotools.')
|
| 53 |
-
|
| 54 |
-
self.coco = COCO(ann_file)
|
| 55 |
-
self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)
|
| 56 |
-
self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
|
| 57 |
-
self.img_ids = self.coco.get_img_ids()
|
| 58 |
-
data_infos = []
|
| 59 |
-
total_ann_ids = []
|
| 60 |
-
for i in self.img_ids:
|
| 61 |
-
info = self.coco.load_imgs([i])[0]
|
| 62 |
-
info['filename'] = info['file_name']
|
| 63 |
-
data_infos.append(info)
|
| 64 |
-
ann_ids = self.coco.get_ann_ids(img_ids=[i])
|
| 65 |
-
total_ann_ids.extend(ann_ids)
|
| 66 |
-
assert len(set(total_ann_ids)) == len(
|
| 67 |
-
total_ann_ids), f"Annotation ids in '{ann_file}' are not unique!"
|
| 68 |
-
return data_infos
|
| 69 |
-
|
| 70 |
-
def get_ann_info(self, idx):
|
| 71 |
-
"""Get COCO annotation by index.
|
| 72 |
-
|
| 73 |
-
Args:
|
| 74 |
-
idx (int): Index of data.
|
| 75 |
-
|
| 76 |
-
Returns:
|
| 77 |
-
dict: Annotation info of specified index.
|
| 78 |
-
"""
|
| 79 |
-
|
| 80 |
-
img_id = self.data_infos[idx]['id']
|
| 81 |
-
ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
|
| 82 |
-
ann_info = self.coco.load_anns(ann_ids)
|
| 83 |
-
return self._parse_ann_info(self.data_infos[idx], ann_info)
|
| 84 |
-
|
| 85 |
-
def get_cat_ids(self, idx):
|
| 86 |
-
"""Get COCO category ids by index.
|
| 87 |
-
|
| 88 |
-
Args:
|
| 89 |
-
idx (int): Index of data.
|
| 90 |
-
|
| 91 |
-
Returns:
|
| 92 |
-
list[int]: All categories in the image of specified index.
|
| 93 |
-
"""
|
| 94 |
-
|
| 95 |
-
img_id = self.data_infos[idx]['id']
|
| 96 |
-
ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
|
| 97 |
-
ann_info = self.coco.load_anns(ann_ids)
|
| 98 |
-
return [ann['category_id'] for ann in ann_info]
|
| 99 |
-
|
| 100 |
-
def _filter_imgs(self, min_size=32):
|
| 101 |
-
"""Filter images too small or without ground truths."""
|
| 102 |
-
valid_inds = []
|
| 103 |
-
# obtain images that contain annotation
|
| 104 |
-
ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values())
|
| 105 |
-
# obtain images that contain annotations of the required categories
|
| 106 |
-
ids_in_cat = set()
|
| 107 |
-
for i, class_id in enumerate(self.cat_ids):
|
| 108 |
-
ids_in_cat |= set(self.coco.cat_img_map[class_id])
|
| 109 |
-
# merge the image id sets of the two conditions and use the merged set
|
| 110 |
-
# to filter out images if self.filter_empty_gt=True
|
| 111 |
-
ids_in_cat &= ids_with_ann
|
| 112 |
-
|
| 113 |
-
valid_img_ids = []
|
| 114 |
-
for i, img_info in enumerate(self.data_infos):
|
| 115 |
-
img_id = self.img_ids[i]
|
| 116 |
-
if self.filter_empty_gt and img_id not in ids_in_cat:
|
| 117 |
-
continue
|
| 118 |
-
if min(img_info['width'], img_info['height']) >= min_size:
|
| 119 |
-
valid_inds.append(i)
|
| 120 |
-
valid_img_ids.append(img_id)
|
| 121 |
-
self.img_ids = valid_img_ids
|
| 122 |
-
return valid_inds
|
| 123 |
-
|
| 124 |
-
def _parse_ann_info(self, img_info, ann_info):
|
| 125 |
-
"""Parse bbox and mask annotation.
|
| 126 |
-
|
| 127 |
-
Args:
|
| 128 |
-
ann_info (list[dict]): Annotation info of an image.
|
| 129 |
-
with_mask (bool): Whether to parse mask annotations.
|
| 130 |
-
|
| 131 |
-
Returns:
|
| 132 |
-
dict: A dict containing the following keys: bboxes, bboxes_ignore,\
|
| 133 |
-
labels, masks, seg_map. "masks" are raw annotations and not \
|
| 134 |
-
decoded into binary masks.
|
| 135 |
-
"""
|
| 136 |
-
gt_bboxes = []
|
| 137 |
-
gt_labels = []
|
| 138 |
-
gt_bboxes_ignore = []
|
| 139 |
-
gt_masks_ann = []
|
| 140 |
-
for i, ann in enumerate(ann_info):
|
| 141 |
-
if ann.get('ignore', False):
|
| 142 |
-
continue
|
| 143 |
-
x1, y1, w, h = ann['bbox']
|
| 144 |
-
inter_w = max(0, min(x1 + w, img_info['width']) - max(x1, 0))
|
| 145 |
-
inter_h = max(0, min(y1 + h, img_info['height']) - max(y1, 0))
|
| 146 |
-
if inter_w * inter_h == 0:
|
| 147 |
-
continue
|
| 148 |
-
if ann['area'] <= 0 or w < 1 or h < 1:
|
| 149 |
-
continue
|
| 150 |
-
if ann['category_id'] not in self.cat_ids:
|
| 151 |
-
continue
|
| 152 |
-
bbox = [x1, y1, x1 + w, y1 + h]
|
| 153 |
-
if ann.get('iscrowd', False):
|
| 154 |
-
gt_bboxes_ignore.append(bbox)
|
| 155 |
-
else:
|
| 156 |
-
gt_bboxes.append(bbox)
|
| 157 |
-
gt_labels.append(self.cat2label[ann['category_id']])
|
| 158 |
-
gt_masks_ann.append(ann.get('segmentation', None))
|
| 159 |
-
|
| 160 |
-
if gt_bboxes:
|
| 161 |
-
gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
|
| 162 |
-
gt_labels = np.array(gt_labels, dtype=np.int64)
|
| 163 |
-
else:
|
| 164 |
-
gt_bboxes = np.zeros((0, 4), dtype=np.float32)
|
| 165 |
-
gt_labels = np.array([], dtype=np.int64)
|
| 166 |
-
|
| 167 |
-
if gt_bboxes_ignore:
|
| 168 |
-
gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)
|
| 169 |
-
else:
|
| 170 |
-
gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)
|
| 171 |
-
|
| 172 |
-
seg_map = img_info['filename'].replace('jpg', 'png')
|
| 173 |
-
|
| 174 |
-
ann = dict(
|
| 175 |
-
bboxes=gt_bboxes,
|
| 176 |
-
labels=gt_labels,
|
| 177 |
-
bboxes_ignore=gt_bboxes_ignore,
|
| 178 |
-
masks=gt_masks_ann,
|
| 179 |
-
seg_map=seg_map)
|
| 180 |
-
|
| 181 |
-
return ann
|
| 182 |
-
|
| 183 |
-
def xyxy2xywh(self, bbox):
|
| 184 |
-
"""Convert ``xyxy`` style bounding boxes to ``xywh`` style for COCO
|
| 185 |
-
evaluation.
|
| 186 |
-
|
| 187 |
-
Args:
|
| 188 |
-
bbox (numpy.ndarray): The bounding boxes, shape (4, ), in
|
| 189 |
-
``xyxy`` order.
|
| 190 |
-
|
| 191 |
-
Returns:
|
| 192 |
-
list[float]: The converted bounding boxes, in ``xywh`` order.
|
| 193 |
-
"""
|
| 194 |
-
|
| 195 |
-
_bbox = bbox.tolist()
|
| 196 |
-
return [
|
| 197 |
-
_bbox[0],
|
| 198 |
-
_bbox[1],
|
| 199 |
-
_bbox[2] - _bbox[0],
|
| 200 |
-
_bbox[3] - _bbox[1],
|
| 201 |
-
]
|
| 202 |
-
|
| 203 |
-
def _proposal2json(self, results):
|
| 204 |
-
"""Convert proposal results to COCO json style."""
|
| 205 |
-
json_results = []
|
| 206 |
-
for idx in range(len(self)):
|
| 207 |
-
img_id = self.img_ids[idx]
|
| 208 |
-
bboxes = results[idx]
|
| 209 |
-
for i in range(bboxes.shape[0]):
|
| 210 |
-
data = dict()
|
| 211 |
-
data['image_id'] = img_id
|
| 212 |
-
data['bbox'] = self.xyxy2xywh(bboxes[i])
|
| 213 |
-
data['score'] = float(bboxes[i][4])
|
| 214 |
-
data['category_id'] = 1
|
| 215 |
-
json_results.append(data)
|
| 216 |
-
return json_results
|
| 217 |
-
|
| 218 |
-
def _det2json(self, results):
|
| 219 |
-
"""Convert detection results to COCO json style."""
|
| 220 |
-
json_results = []
|
| 221 |
-
for idx in range(len(self)):
|
| 222 |
-
img_id = self.img_ids[idx]
|
| 223 |
-
result = results[idx]
|
| 224 |
-
for label in range(len(result)):
|
| 225 |
-
bboxes = result[label]
|
| 226 |
-
for i in range(bboxes.shape[0]):
|
| 227 |
-
data = dict()
|
| 228 |
-
data['image_id'] = img_id
|
| 229 |
-
data['bbox'] = self.xyxy2xywh(bboxes[i])
|
| 230 |
-
data['score'] = float(bboxes[i][4])
|
| 231 |
-
data['category_id'] = self.cat_ids[label]
|
| 232 |
-
json_results.append(data)
|
| 233 |
-
return json_results
|
| 234 |
-
|
| 235 |
-
def _segm2json(self, results):
|
| 236 |
-
"""Convert instance segmentation results to COCO json style."""
|
| 237 |
-
bbox_json_results = []
|
| 238 |
-
segm_json_results = []
|
| 239 |
-
for idx in range(len(self)):
|
| 240 |
-
img_id = self.img_ids[idx]
|
| 241 |
-
det, seg = results[idx]
|
| 242 |
-
for label in range(len(det)):
|
| 243 |
-
# bbox results
|
| 244 |
-
bboxes = det[label]
|
| 245 |
-
for i in range(bboxes.shape[0]):
|
| 246 |
-
data = dict()
|
| 247 |
-
data['image_id'] = img_id
|
| 248 |
-
data['bbox'] = self.xyxy2xywh(bboxes[i])
|
| 249 |
-
data['score'] = float(bboxes[i][4])
|
| 250 |
-
data['category_id'] = self.cat_ids[label]
|
| 251 |
-
bbox_json_results.append(data)
|
| 252 |
-
|
| 253 |
-
# segm results
|
| 254 |
-
# some detectors use different scores for bbox and mask
|
| 255 |
-
if isinstance(seg, tuple):
|
| 256 |
-
segms = seg[0][label]
|
| 257 |
-
mask_score = seg[1][label]
|
| 258 |
-
else:
|
| 259 |
-
segms = seg[label]
|
| 260 |
-
mask_score = [bbox[4] for bbox in bboxes]
|
| 261 |
-
for i in range(bboxes.shape[0]):
|
| 262 |
-
data = dict()
|
| 263 |
-
data['image_id'] = img_id
|
| 264 |
-
data['bbox'] = self.xyxy2xywh(bboxes[i])
|
| 265 |
-
data['score'] = float(mask_score[i])
|
| 266 |
-
data['category_id'] = self.cat_ids[label]
|
| 267 |
-
if isinstance(segms[i]['counts'], bytes):
|
| 268 |
-
segms[i]['counts'] = segms[i]['counts'].decode()
|
| 269 |
-
data['segmentation'] = segms[i]
|
| 270 |
-
segm_json_results.append(data)
|
| 271 |
-
return bbox_json_results, segm_json_results
|
| 272 |
-
|
| 273 |
-
def results2json(self, results, outfile_prefix):
|
| 274 |
-
"""Dump the detection results to a COCO style json file.
|
| 275 |
-
|
| 276 |
-
There are 3 types of results: proposals, bbox predictions, mask
|
| 277 |
-
predictions, and they have different data types. This method will
|
| 278 |
-
automatically recognize the type, and dump them to json files.
|
| 279 |
-
|
| 280 |
-
Args:
|
| 281 |
-
results (list[list | tuple | ndarray]): Testing results of the
|
| 282 |
-
dataset.
|
| 283 |
-
outfile_prefix (str): The filename prefix of the json files. If the
|
| 284 |
-
prefix is "somepath/xxx", the json files will be named
|
| 285 |
-
"somepath/xxx.bbox.json", "somepath/xxx.segm.json",
|
| 286 |
-
"somepath/xxx.proposal.json".
|
| 287 |
-
|
| 288 |
-
Returns:
|
| 289 |
-
dict[str: str]: Possible keys are "bbox", "segm", "proposal", and \
|
| 290 |
-
values are corresponding filenames.
|
| 291 |
-
"""
|
| 292 |
-
result_files = dict()
|
| 293 |
-
if isinstance(results[0], list):
|
| 294 |
-
json_results = self._det2json(results)
|
| 295 |
-
result_files['bbox'] = f'{outfile_prefix}.bbox.json'
|
| 296 |
-
result_files['proposal'] = f'{outfile_prefix}.bbox.json'
|
| 297 |
-
mmcv.dump(json_results, result_files['bbox'])
|
| 298 |
-
elif isinstance(results[0], tuple):
|
| 299 |
-
json_results = self._segm2json(results)
|
| 300 |
-
result_files['bbox'] = f'{outfile_prefix}.bbox.json'
|
| 301 |
-
result_files['proposal'] = f'{outfile_prefix}.bbox.json'
|
| 302 |
-
result_files['segm'] = f'{outfile_prefix}.segm.json'
|
| 303 |
-
mmcv.dump(json_results[0], result_files['bbox'])
|
| 304 |
-
mmcv.dump(json_results[1], result_files['segm'])
|
| 305 |
-
elif isinstance(results[0], np.ndarray):
|
| 306 |
-
json_results = self._proposal2json(results)
|
| 307 |
-
result_files['proposal'] = f'{outfile_prefix}.proposal.json'
|
| 308 |
-
mmcv.dump(json_results, result_files['proposal'])
|
| 309 |
-
else:
|
| 310 |
-
raise TypeError('invalid type of results')
|
| 311 |
-
return result_files
|
| 312 |
-
|
| 313 |
-
def fast_eval_recall(self, results, proposal_nums, iou_thrs, logger=None):
|
| 314 |
-
gt_bboxes = []
|
| 315 |
-
for i in range(len(self.img_ids)):
|
| 316 |
-
ann_ids = self.coco.get_ann_ids(img_ids=self.img_ids[i])
|
| 317 |
-
ann_info = self.coco.load_anns(ann_ids)
|
| 318 |
-
if len(ann_info) == 0:
|
| 319 |
-
gt_bboxes.append(np.zeros((0, 4)))
|
| 320 |
-
continue
|
| 321 |
-
bboxes = []
|
| 322 |
-
for ann in ann_info:
|
| 323 |
-
if ann.get('ignore', False) or ann['iscrowd']:
|
| 324 |
-
continue
|
| 325 |
-
x1, y1, w, h = ann['bbox']
|
| 326 |
-
bboxes.append([x1, y1, x1 + w, y1 + h])
|
| 327 |
-
bboxes = np.array(bboxes, dtype=np.float32)
|
| 328 |
-
if bboxes.shape[0] == 0:
|
| 329 |
-
bboxes = np.zeros((0, 4))
|
| 330 |
-
gt_bboxes.append(bboxes)
|
| 331 |
-
|
| 332 |
-
recalls = eval_recalls(
|
| 333 |
-
gt_bboxes, results, proposal_nums, iou_thrs, logger=logger)
|
| 334 |
-
ar = recalls.mean(axis=1)
|
| 335 |
-
return ar
|
| 336 |
-
|
| 337 |
-
def format_results(self, results, jsonfile_prefix=None, **kwargs):
|
| 338 |
-
"""Format the results to json (standard format for COCO evaluation).
|
| 339 |
-
|
| 340 |
-
Args:
|
| 341 |
-
results (list[tuple | numpy.ndarray]): Testing results of the
|
| 342 |
-
dataset.
|
| 343 |
-
jsonfile_prefix (str | None): The prefix of json files. It includes
|
| 344 |
-
the file path and the prefix of filename, e.g., "a/b/prefix".
|
| 345 |
-
If not specified, a temp file will be created. Default: None.
|
| 346 |
-
|
| 347 |
-
Returns:
|
| 348 |
-
tuple: (result_files, tmp_dir), result_files is a dict containing \
|
| 349 |
-
the json filepaths, tmp_dir is the temporal directory created \
|
| 350 |
-
for saving json files when jsonfile_prefix is not specified.
|
| 351 |
-
"""
|
| 352 |
-
assert isinstance(results, list), 'results must be a list'
|
| 353 |
-
assert len(results) == len(self), (
|
| 354 |
-
'The length of results is not equal to the dataset len: {} != {}'.
|
| 355 |
-
format(len(results), len(self)))
|
| 356 |
-
|
| 357 |
-
if jsonfile_prefix is None:
|
| 358 |
-
tmp_dir = tempfile.TemporaryDirectory()
|
| 359 |
-
jsonfile_prefix = osp.join(tmp_dir.name, 'results')
|
| 360 |
-
else:
|
| 361 |
-
tmp_dir = None
|
| 362 |
-
result_files = self.results2json(results, jsonfile_prefix)
|
| 363 |
-
return result_files, tmp_dir
|
| 364 |
-
|
| 365 |
-
def evaluate(self,
|
| 366 |
-
results,
|
| 367 |
-
metric='bbox',
|
| 368 |
-
logger=None,
|
| 369 |
-
jsonfile_prefix=None,
|
| 370 |
-
classwise=False,
|
| 371 |
-
proposal_nums=(100, 300, 1000),
|
| 372 |
-
iou_thrs=None,
|
| 373 |
-
metric_items=None):
|
| 374 |
-
"""Evaluation in COCO protocol.
|
| 375 |
-
|
| 376 |
-
Args:
|
| 377 |
-
results (list[list | tuple]): Testing results of the dataset.
|
| 378 |
-
metric (str | list[str]): Metrics to be evaluated. Options are
|
| 379 |
-
'bbox', 'segm', 'proposal', 'proposal_fast'.
|
| 380 |
-
logger (logging.Logger | str | None): Logger used for printing
|
| 381 |
-
related information during evaluation. Default: None.
|
| 382 |
-
jsonfile_prefix (str | None): The prefix of json files. It includes
|
| 383 |
-
the file path and the prefix of filename, e.g., "a/b/prefix".
|
| 384 |
-
If not specified, a temp file will be created. Default: None.
|
| 385 |
-
classwise (bool): Whether to evaluating the AP for each class.
|
| 386 |
-
proposal_nums (Sequence[int]): Proposal number used for evaluating
|
| 387 |
-
recalls, such as recall@100, recall@1000.
|
| 388 |
-
Default: (100, 300, 1000).
|
| 389 |
-
iou_thrs (Sequence[float], optional): IoU threshold used for
|
| 390 |
-
evaluating recalls/mAPs. If set to a list, the average of all
|
| 391 |
-
IoUs will also be computed. If not specified, [0.50, 0.55,
|
| 392 |
-
0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95] will be used.
|
| 393 |
-
Default: None.
|
| 394 |
-
metric_items (list[str] | str, optional): Metric items that will
|
| 395 |
-
be returned. If not specified, ``['AR@100', 'AR@300',
|
| 396 |
-
'AR@1000', 'AR_s@1000', 'AR_m@1000', 'AR_l@1000' ]`` will be
|
| 397 |
-
used when ``metric=='proposal'``, ``['mAP', 'mAP_50', 'mAP_75',
|
| 398 |
-
'mAP_s', 'mAP_m', 'mAP_l']`` will be used when
|
| 399 |
-
``metric=='bbox' or metric=='segm'``.
|
| 400 |
-
|
| 401 |
-
Returns:
|
| 402 |
-
dict[str, float]: COCO style evaluation metric.
|
| 403 |
-
"""
|
| 404 |
-
|
| 405 |
-
metrics = metric if isinstance(metric, list) else [metric]
|
| 406 |
-
allowed_metrics = ['bbox', 'segm', 'proposal', 'proposal_fast']
|
| 407 |
-
for metric in metrics:
|
| 408 |
-
if metric not in allowed_metrics:
|
| 409 |
-
raise KeyError(f'metric {metric} is not supported')
|
| 410 |
-
if iou_thrs is None:
|
| 411 |
-
iou_thrs = np.linspace(
|
| 412 |
-
.5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True)
|
| 413 |
-
if metric_items is not None:
|
| 414 |
-
if not isinstance(metric_items, list):
|
| 415 |
-
metric_items = [metric_items]
|
| 416 |
-
|
| 417 |
-
#result_files, tmp_dir = self.format_results(results, jsonfile_prefix)
|
| 418 |
-
|
| 419 |
-
eval_results = OrderedDict()
|
| 420 |
-
cocoGt = self.coco
|
| 421 |
-
print(cocoGt['images'])
|
| 422 |
-
asas
|
| 423 |
-
for metric in metrics:
|
| 424 |
-
msg = f'Evaluating {metric}...'
|
| 425 |
-
if logger is None:
|
| 426 |
-
msg = '\n' + msg
|
| 427 |
-
print_log(msg, logger=logger)
|
| 428 |
-
|
| 429 |
-
if metric == 'proposal_fast':
|
| 430 |
-
ar = self.fast_eval_recall(
|
| 431 |
-
results, proposal_nums, iou_thrs, logger='silent')
|
| 432 |
-
log_msg = []
|
| 433 |
-
for i, num in enumerate(proposal_nums):
|
| 434 |
-
eval_results[f'AR@{num}'] = ar[i]
|
| 435 |
-
log_msg.append(f'\nAR@{num}\t{ar[i]:.4f}')
|
| 436 |
-
log_msg = ''.join(log_msg)
|
| 437 |
-
print_log(log_msg, logger=logger)
|
| 438 |
-
continue
|
| 439 |
-
|
| 440 |
-
if metric not in result_files:
|
| 441 |
-
raise KeyError(f'{metric} is not in results')
|
| 442 |
-
try:
|
| 443 |
-
cocoDt = cocoGt.loadRes(result_files[metric])
|
| 444 |
-
except IndexError:
|
| 445 |
-
print_log(
|
| 446 |
-
'The testing results of the whole dataset is empty.',
|
| 447 |
-
logger=logger,
|
| 448 |
-
level=logging.ERROR)
|
| 449 |
-
break
|
| 450 |
-
|
| 451 |
-
iou_type = 'bbox' if metric == 'proposal' else metric
|
| 452 |
-
cocoEval = COCOeval(cocoGt, cocoDt, iou_type)
|
| 453 |
-
cocoEval.params.catIds = self.cat_ids
|
| 454 |
-
cocoEval.params.imgIds = self.img_ids
|
| 455 |
-
cocoEval.params.maxDets = list(proposal_nums)
|
| 456 |
-
cocoEval.params.iouThrs = iou_thrs
|
| 457 |
-
# mapping of cocoEval.stats
|
| 458 |
-
coco_metric_names = {
|
| 459 |
-
'mAP': 0,
|
| 460 |
-
'mAP_50': 1,
|
| 461 |
-
'mAP_75': 2,
|
| 462 |
-
'mAP_s': 3,
|
| 463 |
-
'mAP_m': 4,
|
| 464 |
-
'mAP_l': 5,
|
| 465 |
-
'AR@100': 6,
|
| 466 |
-
'AR@300': 7,
|
| 467 |
-
'AR@1000': 8,
|
| 468 |
-
'AR_s@1000': 9,
|
| 469 |
-
'AR_m@1000': 10,
|
| 470 |
-
'AR_l@1000': 11
|
| 471 |
-
}
|
| 472 |
-
if metric_items is not None:
|
| 473 |
-
for metric_item in metric_items:
|
| 474 |
-
if metric_item not in coco_metric_names:
|
| 475 |
-
raise KeyError(
|
| 476 |
-
f'metric item {metric_item} is not supported')
|
| 477 |
-
|
| 478 |
-
if metric == 'proposal':
|
| 479 |
-
cocoEval.params.useCats = 0
|
| 480 |
-
cocoEval.evaluate()
|
| 481 |
-
cocoEval.accumulate()
|
| 482 |
-
cocoEval.summarize()
|
| 483 |
-
if metric_items is None:
|
| 484 |
-
metric_items = [
|
| 485 |
-
'AR@100', 'AR@300', 'AR@1000', 'AR_s@1000',
|
| 486 |
-
'AR_m@1000', 'AR_l@1000'
|
| 487 |
-
]
|
| 488 |
-
|
| 489 |
-
for item in metric_items:
|
| 490 |
-
val = float(
|
| 491 |
-
f'{cocoEval.stats[coco_metric_names[item]]:.3f}')
|
| 492 |
-
eval_results[item] = val
|
| 493 |
-
else:
|
| 494 |
-
cocoEval.evaluate()
|
| 495 |
-
cocoEval.accumulate()
|
| 496 |
-
cocoEval.summarize()
|
| 497 |
-
if classwise: # Compute per-category AP
|
| 498 |
-
# Compute per-category AP
|
| 499 |
-
# from https://github.com/facebookresearch/detectron2/
|
| 500 |
-
precisions = cocoEval.eval['precision']
|
| 501 |
-
# precision: (iou, recall, cls, area range, max dets)
|
| 502 |
-
assert len(self.cat_ids) == precisions.shape[2]
|
| 503 |
-
|
| 504 |
-
results_per_category = []
|
| 505 |
-
for idx, catId in enumerate(self.cat_ids):
|
| 506 |
-
# area range index 0: all area ranges
|
| 507 |
-
# max dets index -1: typically 100 per image
|
| 508 |
-
nm = self.coco.loadCats(catId)[0]
|
| 509 |
-
precision = precisions[:, :, idx, 0, -1]
|
| 510 |
-
precision = precision[precision > -1]
|
| 511 |
-
if precision.size:
|
| 512 |
-
ap = np.mean(precision)
|
| 513 |
-
else:
|
| 514 |
-
ap = float('nan')
|
| 515 |
-
results_per_category.append(
|
| 516 |
-
(f'{nm["name"]}', f'{float(ap):0.3f}'))
|
| 517 |
-
|
| 518 |
-
num_columns = min(6, len(results_per_category) * 2)
|
| 519 |
-
results_flatten = list(
|
| 520 |
-
itertools.chain(*results_per_category))
|
| 521 |
-
headers = ['category', 'AP'] * (num_columns // 2)
|
| 522 |
-
results_2d = itertools.zip_longest(*[
|
| 523 |
-
results_flatten[i::num_columns]
|
| 524 |
-
for i in range(num_columns)
|
| 525 |
-
])
|
| 526 |
-
table_data = [headers]
|
| 527 |
-
table_data += [result for result in results_2d]
|
| 528 |
-
table = AsciiTable(table_data)
|
| 529 |
-
print_log('\n' + table.table, logger=logger)
|
| 530 |
-
|
| 531 |
-
if metric_items is None:
|
| 532 |
-
metric_items = [
|
| 533 |
-
'mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l'
|
| 534 |
-
]
|
| 535 |
-
|
| 536 |
-
for metric_item in metric_items:
|
| 537 |
-
key = f'{metric}_{metric_item}'
|
| 538 |
-
val = float(
|
| 539 |
-
f'{cocoEval.stats[coco_metric_names[metric_item]]:.3f}'
|
| 540 |
-
)
|
| 541 |
-
eval_results[key] = val
|
| 542 |
-
ap = cocoEval.stats[:6]
|
| 543 |
-
eval_results[f'{metric}_mAP_copypaste'] = (
|
| 544 |
-
f'{ap[0]:.3f} {ap[1]:.3f} {ap[2]:.3f} {ap[3]:.3f} '
|
| 545 |
-
f'{ap[4]:.3f} {ap[5]:.3f}')
|
| 546 |
-
if tmp_dir is not None:
|
| 547 |
-
tmp_dir.cleanup()
|
| 548 |
-
return eval_results
|
|
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|
spaces/CVPR/regionclip-demo/detectron2/engine/launch.py
DELETED
|
@@ -1,125 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates.
|
| 2 |
-
import logging
|
| 3 |
-
from datetime import timedelta
|
| 4 |
-
import torch
|
| 5 |
-
import torch.distributed as dist
|
| 6 |
-
import torch.multiprocessing as mp
|
| 7 |
-
|
| 8 |
-
from detectron2.utils import comm
|
| 9 |
-
|
| 10 |
-
__all__ = ["DEFAULT_TIMEOUT", "launch"]
|
| 11 |
-
|
| 12 |
-
DEFAULT_TIMEOUT = timedelta(minutes=30)
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
def _find_free_port():
|
| 16 |
-
import socket
|
| 17 |
-
|
| 18 |
-
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
|
| 19 |
-
# Binding to port 0 will cause the OS to find an available port for us
|
| 20 |
-
sock.bind(("", 0))
|
| 21 |
-
port = sock.getsockname()[1]
|
| 22 |
-
sock.close()
|
| 23 |
-
# NOTE: there is still a chance the port could be taken by other processes.
|
| 24 |
-
return port
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
def launch(
|
| 28 |
-
main_func,
|
| 29 |
-
num_gpus_per_machine,
|
| 30 |
-
num_machines=1,
|
| 31 |
-
machine_rank=0,
|
| 32 |
-
dist_url=None,
|
| 33 |
-
args=(),
|
| 34 |
-
timeout=DEFAULT_TIMEOUT,
|
| 35 |
-
):
|
| 36 |
-
"""
|
| 37 |
-
Launch multi-gpu or distributed training.
|
| 38 |
-
This function must be called on all machines involved in the training.
|
| 39 |
-
It will spawn child processes (defined by ``num_gpus_per_machine``) on each machine.
|
| 40 |
-
|
| 41 |
-
Args:
|
| 42 |
-
main_func: a function that will be called by `main_func(*args)`
|
| 43 |
-
num_gpus_per_machine (int): number of GPUs per machine
|
| 44 |
-
num_machines (int): the total number of machines
|
| 45 |
-
machine_rank (int): the rank of this machine
|
| 46 |
-
dist_url (str): url to connect to for distributed jobs, including protocol
|
| 47 |
-
e.g. "tcp://127.0.0.1:8686".
|
| 48 |
-
Can be set to "auto" to automatically select a free port on localhost
|
| 49 |
-
timeout (timedelta): timeout of the distributed workers
|
| 50 |
-
args (tuple): arguments passed to main_func
|
| 51 |
-
"""
|
| 52 |
-
world_size = num_machines * num_gpus_per_machine
|
| 53 |
-
if world_size > 1:
|
| 54 |
-
# https://github.com/pytorch/pytorch/pull/14391
|
| 55 |
-
# TODO prctl in spawned processes
|
| 56 |
-
|
| 57 |
-
if dist_url == "auto":
|
| 58 |
-
assert num_machines == 1, "dist_url=auto not supported in multi-machine jobs."
|
| 59 |
-
port = _find_free_port()
|
| 60 |
-
dist_url = f"tcp://127.0.0.1:{port}"
|
| 61 |
-
if num_machines > 1 and dist_url.startswith("file://"):
|
| 62 |
-
logger = logging.getLogger(__name__)
|
| 63 |
-
logger.warning(
|
| 64 |
-
"file:// is not a reliable init_method in multi-machine jobs. Prefer tcp://"
|
| 65 |
-
)
|
| 66 |
-
|
| 67 |
-
mp.spawn(
|
| 68 |
-
_distributed_worker,
|
| 69 |
-
nprocs=num_gpus_per_machine,
|
| 70 |
-
args=(
|
| 71 |
-
main_func,
|
| 72 |
-
world_size,
|
| 73 |
-
num_gpus_per_machine,
|
| 74 |
-
machine_rank,
|
| 75 |
-
dist_url,
|
| 76 |
-
args,
|
| 77 |
-
timeout,
|
| 78 |
-
),
|
| 79 |
-
daemon=False,
|
| 80 |
-
)
|
| 81 |
-
else:
|
| 82 |
-
main_func(*args)
|
| 83 |
-
|
| 84 |
-
|
| 85 |
-
def _distributed_worker(
|
| 86 |
-
local_rank,
|
| 87 |
-
main_func,
|
| 88 |
-
world_size,
|
| 89 |
-
num_gpus_per_machine,
|
| 90 |
-
machine_rank,
|
| 91 |
-
dist_url,
|
| 92 |
-
args,
|
| 93 |
-
timeout=DEFAULT_TIMEOUT,
|
| 94 |
-
):
|
| 95 |
-
assert torch.cuda.is_available(), "cuda is not available. Please check your installation."
|
| 96 |
-
global_rank = machine_rank * num_gpus_per_machine + local_rank
|
| 97 |
-
try:
|
| 98 |
-
dist.init_process_group(
|
| 99 |
-
backend="NCCL",
|
| 100 |
-
init_method=dist_url,
|
| 101 |
-
world_size=world_size,
|
| 102 |
-
rank=global_rank,
|
| 103 |
-
timeout=timeout,
|
| 104 |
-
)
|
| 105 |
-
except Exception as e:
|
| 106 |
-
logger = logging.getLogger(__name__)
|
| 107 |
-
logger.error("Process group URL: {}".format(dist_url))
|
| 108 |
-
raise e
|
| 109 |
-
# synchronize is needed here to prevent a possible timeout after calling init_process_group
|
| 110 |
-
# See: https://github.com/facebookresearch/maskrcnn-benchmark/issues/172
|
| 111 |
-
comm.synchronize()
|
| 112 |
-
|
| 113 |
-
assert num_gpus_per_machine <= torch.cuda.device_count()
|
| 114 |
-
torch.cuda.set_device(local_rank)
|
| 115 |
-
|
| 116 |
-
# Setup the local process group (which contains ranks within the same machine)
|
| 117 |
-
assert comm._LOCAL_PROCESS_GROUP is None
|
| 118 |
-
num_machines = world_size // num_gpus_per_machine
|
| 119 |
-
for i in range(num_machines):
|
| 120 |
-
ranks_on_i = list(range(i * num_gpus_per_machine, (i + 1) * num_gpus_per_machine))
|
| 121 |
-
pg = dist.new_group(ranks_on_i)
|
| 122 |
-
if i == machine_rank:
|
| 123 |
-
comm._LOCAL_PROCESS_GROUP = pg
|
| 124 |
-
|
| 125 |
-
main_func(*args)
|
|
|
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|
|
spaces/CikeyQI/Yunzai/Yunzai/lib/tools/command.js
DELETED
|
@@ -1,118 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
import '../config/init.js'
|
| 3 |
-
import log4js from 'log4js'
|
| 4 |
-
import PluginsLoader from '../plugins/loader.js'
|
| 5 |
-
import cfg from '../config/config.js'
|
| 6 |
-
|
| 7 |
-
class Command {
|
| 8 |
-
constructor () {
|
| 9 |
-
this.command = ''
|
| 10 |
-
// this.setLog()
|
| 11 |
-
/** 全局Bot */
|
| 12 |
-
global.Bot = {}
|
| 13 |
-
}
|
| 14 |
-
|
| 15 |
-
/**
|
| 16 |
-
* @param type 命令配置类型，默认default
|
| 17 |
-
*/
|
| 18 |
-
async run (type = 'default') {
|
| 19 |
-
/** 加载icqq事件监听 */
|
| 20 |
-
await PluginsLoader.load()
|
| 21 |
-
/** 获取命令行参数 */
|
| 22 |
-
this.getCommand()
|
| 23 |
-
/** 伪造消息 */
|
| 24 |
-
let e = this.fakeE(type)
|
| 25 |
-
|
| 26 |
-
/** 插件处理消息 */
|
| 27 |
-
await PluginsLoader.deal(e)
|
| 28 |
-
}
|
| 29 |
-
|
| 30 |
-
/** 设置命令 */
|
| 31 |
-
getCommand () {
|
| 32 |
-
if (process.argv[2]) {
|
| 33 |
-
this.command = '#' + process.argv[2].replace(/#|＃|井/g, '#').trim()
|
| 34 |
-
}
|
| 35 |
-
}
|
| 36 |
-
|
| 37 |
-
fakeE (id = 'default') {
|
| 38 |
-
/** 获取配置 */
|
| 39 |
-
let data = cfg.getYaml('test', id)
|
| 40 |
-
let text = this.command || data.text || ''
|
| 41 |
-
logger.info(`测试命令 [${text}]`)
|
| 42 |
-
let e = {
|
| 43 |
-
test: true,
|
| 44 |
-
self_id: 10000,
|
| 45 |
-
time: new Date().getTime(),
|
| 46 |
-
post_type: data.post_type || 'message',
|
| 47 |
-
message_type: data.message_type || 'group',
|
| 48 |
-
sub_type: data.sub_type || 'normal',
|
| 49 |
-
group_id: data.group_id || 826198224,
|
| 50 |
-
group_name: data.group_name || '测试群',
|
| 51 |
-
user_id: data.user_id,
|
| 52 |
-
anonymous: null,
|
| 53 |
-
message: [{ type: 'text', text }],
|
| 54 |
-
raw_message: text,
|
| 55 |
-
font: '微软雅黑',
|
| 56 |
-
sender: {
|
| 57 |
-
user_id: data.user_id,
|
| 58 |
-
nickname: '测试',
|
| 59 |
-
card: data.card,
|
| 60 |
-
sex: 'male',
|
| 61 |
-
age: 0,
|
| 62 |
-
area: 'unknown',
|
| 63 |
-
level: 2,
|
| 64 |
-
role: 'owner',
|
| 65 |
-
title: ''
|
| 66 |
-
},
|
| 67 |
-
group: {
|
| 68 |
-
mute_left: 0,
|
| 69 |
-
sendMsg: (msg) => {
|
| 70 |
-
logger.info(`回复内容 ${msg}`)
|
| 71 |
-
}
|
| 72 |
-
},
|
| 73 |
-
friend: {
|
| 74 |
-
getFileUrl: (fid) => {
|
| 75 |
-
return data.message[0].url
|
| 76 |
-
}
|
| 77 |
-
},
|
| 78 |
-
message_id: 'JzHU0DACliIAAAD3RzTh1WBOIC48',
|
| 79 |
-
reply: async (msg) => {
|
| 80 |
-
logger.info(`回复内容 ${msg}`)
|
| 81 |
-
},
|
| 82 |
-
toString: () => {
|
| 83 |
-
return text
|
| 84 |
-
}
|
| 85 |
-
}
|
| 86 |
-
|
| 87 |
-
if (data.message) {
|
| 88 |
-
e.message = data.message
|
| 89 |
-
}
|
| 90 |
-
|
| 91 |
-
return e
|
| 92 |
-
}
|
| 93 |
-
|
| 94 |
-
/** 日志 */
|
| 95 |
-
setLog () {
|
| 96 |
-
log4js.configure({
|
| 97 |
-
appenders: {
|
| 98 |
-
// 设置控制台输出 （默认日志级别是关闭的（即不会输出日志））
|
| 99 |
-
out: {
|
| 100 |
-
type: 'console',
|
| 101 |
-
layout: {
|
| 102 |
-
type: 'pattern',
|
| 103 |
-
pattern: '[%d{hh:mm:ss.SSS}][%[%5.5p%]] - %m'
|
| 104 |
-
}
|
| 105 |
-
}
|
| 106 |
-
},
|
| 107 |
-
// 不同等级的日志追加到不同的输出位置：appenders: ['out', 'allLog'] categories 作为getLogger方法的键名对应
|
| 108 |
-
categories: {
|
| 109 |
-
// appenders:采用的appender,取上面appenders项,level:设置级别
|
| 110 |
-
default: { appenders: ['out'], level: 'debug' }
|
| 111 |
-
}
|
| 112 |
-
})
|
| 113 |
-
global.logger = log4js.getLogger('[test]')
|
| 114 |
-
logger.level = 'debug'
|
| 115 |
-
}
|
| 116 |
-
}
|
| 117 |
-
|
| 118 |
-
export default new Command()
|
|
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spaces/CikeyQI/meme-api/meme_generator/memes/douyin/__init__.py
DELETED
|
@@ -1,78 +0,0 @@
|
|
| 1 |
-
import math
|
| 2 |
-
import random
|
| 3 |
-
from typing import List
|
| 4 |
-
|
| 5 |
-
from PIL.Image import Image as IMG
|
| 6 |
-
from pil_utils import BuildImage, Text2Image
|
| 7 |
-
|
| 8 |
-
from meme_generator import add_meme
|
| 9 |
-
from meme_generator.utils import save_gif
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
def douyin(images, texts: List[str], args):
|
| 13 |
-
text = texts[0]
|
| 14 |
-
text = " ".join(text.splitlines())
|
| 15 |
-
fontsize = 200
|
| 16 |
-
offset = round(fontsize * 0.05)
|
| 17 |
-
px = 70
|
| 18 |
-
py = 30
|
| 19 |
-
bg_color = "#1C0B1B"
|
| 20 |
-
frame = Text2Image.from_text(
|
| 21 |
-
text, fontsize, fill="#FF0050", stroke_fill="#FF0050", stroke_width=5
|
| 22 |
-
).to_image(bg_color=bg_color, padding=(px + offset * 2, py + offset * 2, px, py))
|
| 23 |
-
Text2Image.from_text(
|
| 24 |
-
text, fontsize, fill="#00F5EB", stroke_fill="#00F5EB", stroke_width=5
|
| 25 |
-
).draw_on_image(frame, (px, py))
|
| 26 |
-
Text2Image.from_text(
|
| 27 |
-
text, fontsize, fill="white", stroke_fill="white", stroke_width=5
|
| 28 |
-
).draw_on_image(frame, (px + offset, py + offset))
|
| 29 |
-
frame = BuildImage(frame)
|
| 30 |
-
|
| 31 |
-
width = frame.width - px
|
| 32 |
-
height = frame.height - py
|
| 33 |
-
frame_num = 10
|
| 34 |
-
devide_num = 6
|
| 35 |
-
seed = 20 * 0.05
|
| 36 |
-
frames: List[IMG] = []
|
| 37 |
-
for _ in range(frame_num):
|
| 38 |
-
new_frame = frame.copy()
|
| 39 |
-
h_seeds = [
|
| 40 |
-
math.fabs(math.sin(random.random() * devide_num)) for _ in range(devide_num)
|
| 41 |
-
]
|
| 42 |
-
h_seed_sum = sum(h_seeds)
|
| 43 |
-
h_seeds = [s / h_seed_sum for s in h_seeds]
|
| 44 |
-
direction = 1
|
| 45 |
-
last_yn = 0
|
| 46 |
-
last_h = 0
|
| 47 |
-
for i in range(devide_num):
|
| 48 |
-
yn = last_yn + last_h
|
| 49 |
-
h = max(round(height * h_seeds[i]), 2)
|
| 50 |
-
last_yn = yn
|
| 51 |
-
last_h = h
|
| 52 |
-
direction = -direction
|
| 53 |
-
piece = new_frame.copy().crop((px, yn, px + width, yn + h))
|
| 54 |
-
new_frame.paste(piece, (px + round(i * direction * seed), yn))
|
| 55 |
-
# 透视变换
|
| 56 |
-
move_x = 64
|
| 57 |
-
points = (
|
| 58 |
-
(move_x, 0),
|
| 59 |
-
(new_frame.width + move_x, 0),
|
| 60 |
-
(new_frame.width, new_frame.height),
|
| 61 |
-
(0, new_frame.height),
|
| 62 |
-
)
|
| 63 |
-
new_frame = new_frame.perspective(points)
|
| 64 |
-
bg = BuildImage.new("RGBA", new_frame.size, bg_color)
|
| 65 |
-
bg.paste(new_frame, alpha=True)
|
| 66 |
-
frames.append(bg.image)
|
| 67 |
-
|
| 68 |
-
return save_gif(frames, 0.2)
|
| 69 |
-
|
| 70 |
-
|
| 71 |
-
add_meme(
|
| 72 |
-
"douyin",
|
| 73 |
-
douyin,
|
| 74 |
-
min_texts=1,
|
| 75 |
-
max_texts=1,
|
| 76 |
-
default_texts=["douyin"],
|
| 77 |
-
keywords=["douyin"],
|
| 78 |
-
)
|
|
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|
spaces/Cletrason/Cletrason-toad-mario-movie/gradio_utils.py
DELETED
|
@@ -1,98 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
|
| 3 |
-
# App Canny utils
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
def edge_path_to_video_path(edge_path):
|
| 7 |
-
video_path = edge_path
|
| 8 |
-
|
| 9 |
-
vid_name = edge_path.split("/")[-1]
|
| 10 |
-
if vid_name == "butterfly.mp4":
|
| 11 |
-
video_path = "__assets__/canny_videos_mp4_2fps/butterfly.mp4"
|
| 12 |
-
elif vid_name == "deer.mp4":
|
| 13 |
-
video_path = "__assets__/canny_videos_mp4_2fps/deer.mp4"
|
| 14 |
-
elif vid_name == "fox.mp4":
|
| 15 |
-
video_path = "__assets__/canny_videos_mp4_2fps/fox.mp4"
|
| 16 |
-
elif vid_name == "girl_dancing.mp4":
|
| 17 |
-
video_path = "__assets__/canny_videos_mp4_2fps/girl_dancing.mp4"
|
| 18 |
-
elif vid_name == "girl_turning.mp4":
|
| 19 |
-
video_path = "__assets__/canny_videos_mp4_2fps/girl_turning.mp4"
|
| 20 |
-
elif vid_name == "halloween.mp4":
|
| 21 |
-
video_path = "__assets__/canny_videos_mp4_2fps/halloween.mp4"
|
| 22 |
-
elif vid_name == "santa.mp4":
|
| 23 |
-
video_path = "__assets__/canny_videos_mp4_2fps/santa.mp4"
|
| 24 |
-
|
| 25 |
-
assert os.path.isfile(video_path)
|
| 26 |
-
return video_path
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
# App Pose utils
|
| 30 |
-
def motion_to_video_path(motion):
|
| 31 |
-
videos = [
|
| 32 |
-
"__assets__/poses_skeleton_gifs/dance1_corr.mp4",
|
| 33 |
-
"__assets__/poses_skeleton_gifs/dance2_corr.mp4",
|
| 34 |
-
"__assets__/poses_skeleton_gifs/dance3_corr.mp4",
|
| 35 |
-
"__assets__/poses_skeleton_gifs/dance4_corr.mp4",
|
| 36 |
-
"__assets__/poses_skeleton_gifs/dance5_corr.mp4"
|
| 37 |
-
]
|
| 38 |
-
if len(motion.split(" ")) > 1 and motion.split(" ")[1].isnumeric():
|
| 39 |
-
id = int(motion.split(" ")[1]) - 1
|
| 40 |
-
return videos[id]
|
| 41 |
-
else:
|
| 42 |
-
return motion
|
| 43 |
-
|
| 44 |
-
|
| 45 |
-
# App Canny Dreambooth utils
|
| 46 |
-
def get_video_from_canny_selection(canny_selection):
|
| 47 |
-
if canny_selection == "woman1":
|
| 48 |
-
input_video_path = "__assets__/db_files_2fps/woman1.mp4"
|
| 49 |
-
|
| 50 |
-
elif canny_selection == "woman2":
|
| 51 |
-
input_video_path = "__assets__/db_files_2fps/woman2.mp4"
|
| 52 |
-
|
| 53 |
-
elif canny_selection == "man1":
|
| 54 |
-
input_video_path = "__assets__/db_files_2fps/man1.mp4"
|
| 55 |
-
|
| 56 |
-
elif canny_selection == "woman3":
|
| 57 |
-
input_video_path = "__assets__/db_files_2fps/woman3.mp4"
|
| 58 |
-
else:
|
| 59 |
-
input_video_path = canny_selection
|
| 60 |
-
|
| 61 |
-
assert os.path.isfile(input_video_path)
|
| 62 |
-
return input_video_path
|
| 63 |
-
|
| 64 |
-
|
| 65 |
-
def get_model_from_db_selection(db_selection):
|
| 66 |
-
if db_selection == "Anime DB":
|
| 67 |
-
input_video_path = 'PAIR/text2video-zero-controlnet-canny-anime'
|
| 68 |
-
elif db_selection == "Avatar DB":
|
| 69 |
-
input_video_path = 'PAIR/text2video-zero-controlnet-canny-avatar'
|
| 70 |
-
elif db_selection == "GTA-5 DB":
|
| 71 |
-
input_video_path = 'PAIR/text2video-zero-controlnet-canny-gta5'
|
| 72 |
-
elif db_selection == "Arcane DB":
|
| 73 |
-
input_video_path = 'PAIR/text2video-zero-controlnet-canny-arcane'
|
| 74 |
-
else:
|
| 75 |
-
input_video_path = db_selection
|
| 76 |
-
|
| 77 |
-
return input_video_path
|
| 78 |
-
|
| 79 |
-
|
| 80 |
-
def get_db_name_from_id(id):
|
| 81 |
-
db_names = ["Anime DB", "Arcane DB", "GTA-5 DB", "Avatar DB"]
|
| 82 |
-
return db_names[id]
|
| 83 |
-
|
| 84 |
-
|
| 85 |
-
def get_canny_name_from_id(id):
|
| 86 |
-
canny_names = ["woman1", "woman2", "man1", "woman3"]
|
| 87 |
-
return canny_names[id]
|
| 88 |
-
|
| 89 |
-
|
| 90 |
-
def logo_name_to_path(name):
|
| 91 |
-
logo_paths = {
|
| 92 |
-
'Picsart AI Research': '__assets__/pair_watermark.png',
|
| 93 |
-
'Text2Video-Zero': '__assets__/t2v-z_watermark.png',
|
| 94 |
-
'None': None
|
| 95 |
-
}
|
| 96 |
-
if name in logo_paths:
|
| 97 |
-
return logo_paths[name]
|
| 98 |
-
return name
|
|
|
|
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|
spaces/CofAI/chat/g4f/Provider/Providers/DeepAi.py
DELETED
|
@@ -1,46 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import json
|
| 3 |
-
import random
|
| 4 |
-
import hashlib
|
| 5 |
-
import requests
|
| 6 |
-
|
| 7 |
-
from ...typing import sha256, Dict, get_type_hints
|
| 8 |
-
|
| 9 |
-
url = 'https://deepai.org'
|
| 10 |
-
model = ['gpt-3.5-turbo']
|
| 11 |
-
supports_stream = True
|
| 12 |
-
needs_auth = False
|
| 13 |
-
|
| 14 |
-
def _create_completion(model: str, messages: list, stream: bool, **kwargs):
|
| 15 |
-
def md5(text: str) -> str:
|
| 16 |
-
return hashlib.md5(text.encode()).hexdigest()[::-1]
|
| 17 |
-
|
| 18 |
-
|
| 19 |
-
def get_api_key(user_agent: str) -> str:
|
| 20 |
-
part1 = str(random.randint(0, 10**11))
|
| 21 |
-
part2 = md5(user_agent + md5(user_agent + md5(user_agent + part1 + "x")))
|
| 22 |
-
|
| 23 |
-
return f"tryit-{part1}-{part2}"
|
| 24 |
-
|
| 25 |
-
user_agent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36'
|
| 26 |
-
|
| 27 |
-
headers = {
|
| 28 |
-
"api-key": get_api_key(user_agent),
|
| 29 |
-
"user-agent": user_agent
|
| 30 |
-
}
|
| 31 |
-
|
| 32 |
-
files = {
|
| 33 |
-
"chat_style": (None, "chat"),
|
| 34 |
-
"chatHistory": (None, json.dumps(messages))
|
| 35 |
-
}
|
| 36 |
-
|
| 37 |
-
r = requests.post("https://api.deepai.org/chat_response", headers=headers, files=files, stream=True)
|
| 38 |
-
|
| 39 |
-
for chunk in r.iter_content(chunk_size=None):
|
| 40 |
-
r.raise_for_status()
|
| 41 |
-
yield chunk.decode()
|
| 42 |
-
|
| 43 |
-
|
| 44 |
-
params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
|
| 45 |
-
'(%s)' % ', '.join(
|
| 46 |
-
[f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
|
|
|
|
|
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|
spaces/CuriousDolphin/MobileSAM/README.md
DELETED
|
@@ -1,45 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: MobileSAM
|
| 3 |
-
emoji: 🐠
|
| 4 |
-
colorFrom: indigo
|
| 5 |
-
colorTo: yellow
|
| 6 |
-
sdk: gradio
|
| 7 |
-
python_version: 3.8.10
|
| 8 |
-
sdk_version: 3.35.2
|
| 9 |
-
app_file: app.py
|
| 10 |
-
pinned: false
|
| 11 |
-
license: apache-2.0
|
| 12 |
-
duplicated_from: dhkim2810/MobileSAM
|
| 13 |
-
---
|
| 14 |
-
|
| 15 |
-
# Faster Segment Anything(MobileSAM)
|
| 16 |
-
|
| 17 |
-
Official PyTorch Implementation of the <a href="https://github.com/ChaoningZhang/MobileSAM">.
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
**MobileSAM** performs on par with the original SAM (at least visually) and keeps exactly the same pipeline as the original SAM except for a change on the image encoder.
|
| 21 |
-
Specifically, we replace the original heavyweight ViT-H encoder (632M) with a much smaller Tiny-ViT (5M). On a single GPU, MobileSAM runs around 12ms per image: 8ms on the image encoder and 4ms on the mask decoder.
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
## License
|
| 25 |
-
|
| 26 |
-
The model is licensed under the [Apache 2.0 license](LICENSE).
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
## Acknowledgement
|
| 30 |
-
|
| 31 |
-
- [Segment Anything](https://segment-anything.com/) provides the SA-1B dataset and the base codes.
|
| 32 |
-
- [TinyViT](https://github.com/microsoft/Cream/tree/main/TinyViT) provides codes and pre-trained models.
|
| 33 |
-
|
| 34 |
-
## Citing MobileSAM
|
| 35 |
-
|
| 36 |
-
If you find this project useful for your research, please consider citing the following BibTeX entry.
|
| 37 |
-
|
| 38 |
-
```bibtex
|
| 39 |
-
@article{mobile_sam,
|
| 40 |
-
title={Faster Segment Anything: Towards Lightweight SAM for Mobile Applications},
|
| 41 |
-
author={Zhang, Chaoning and Han, Dongshen and Qiao, Yu and Kim, Jung Uk and Bae, Sung Ho and Lee, Seungkyu and Hong, Choong Seon},
|
| 42 |
-
journal={arXiv preprint arXiv:2306.14289},
|
| 43 |
-
year={2023}
|
| 44 |
-
}
|
| 45 |
-
```
|
|
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|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/mtiLib/__init__.py
DELETED
|
@@ -1,1402 +0,0 @@
|
|
| 1 |
-
#!/usr/bin/python
|
| 2 |
-
|
| 3 |
-
# FontDame-to-FontTools for OpenType Layout tables
|
| 4 |
-
#
|
| 5 |
-
# Source language spec is available at:
|
| 6 |
-
# http://monotype.github.io/OpenType_Table_Source/otl_source.html
|
| 7 |
-
# https://github.com/Monotype/OpenType_Table_Source/
|
| 8 |
-
|
| 9 |
-
from fontTools import ttLib
|
| 10 |
-
from fontTools.ttLib.tables._c_m_a_p import cmap_classes
|
| 11 |
-
from fontTools.ttLib.tables import otTables as ot
|
| 12 |
-
from fontTools.ttLib.tables.otBase import ValueRecord, valueRecordFormatDict
|
| 13 |
-
from fontTools.otlLib import builder as otl
|
| 14 |
-
from contextlib import contextmanager
|
| 15 |
-
from fontTools.ttLib import newTable
|
| 16 |
-
from fontTools.feaLib.lookupDebugInfo import LOOKUP_DEBUG_ENV_VAR, LOOKUP_DEBUG_INFO_KEY
|
| 17 |
-
from operator import setitem
|
| 18 |
-
import os
|
| 19 |
-
import logging
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
class MtiLibError(Exception):
|
| 23 |
-
pass
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
class ReferenceNotFoundError(MtiLibError):
|
| 27 |
-
pass
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
class FeatureNotFoundError(ReferenceNotFoundError):
|
| 31 |
-
pass
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
class LookupNotFoundError(ReferenceNotFoundError):
|
| 35 |
-
pass
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
log = logging.getLogger("fontTools.mtiLib")
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
def makeGlyph(s):
|
| 42 |
-
if s[:2] in ["U ", "u "]:
|
| 43 |
-
return ttLib.TTFont._makeGlyphName(int(s[2:], 16))
|
| 44 |
-
elif s[:2] == "# ":
|
| 45 |
-
return "glyph%.5d" % int(s[2:])
|
| 46 |
-
assert s.find(" ") < 0, "Space found in glyph name: %s" % s
|
| 47 |
-
assert s, "Glyph name is empty"
|
| 48 |
-
return s
|
| 49 |
-
|
| 50 |
-
|
| 51 |
-
def makeGlyphs(l):
|
| 52 |
-
return [makeGlyph(g) for g in l]
|
| 53 |
-
|
| 54 |
-
|
| 55 |
-
def mapLookup(sym, mapping):
|
| 56 |
-
# Lookups are addressed by name. So resolved them using a map if available.
|
| 57 |
-
# Fallback to parsing as lookup index if a map isn't provided.
|
| 58 |
-
if mapping is not None:
|
| 59 |
-
try:
|
| 60 |
-
idx = mapping[sym]
|
| 61 |
-
except KeyError:
|
| 62 |
-
raise LookupNotFoundError(sym)
|
| 63 |
-
else:
|
| 64 |
-
idx = int(sym)
|
| 65 |
-
return idx
|
| 66 |
-
|
| 67 |
-
|
| 68 |
-
def mapFeature(sym, mapping):
|
| 69 |
-
# Features are referenced by index according the spec. So, if symbol is an
|
| 70 |
-
# integer, use it directly. Otherwise look up in the map if provided.
|
| 71 |
-
try:
|
| 72 |
-
idx = int(sym)
|
| 73 |
-
except ValueError:
|
| 74 |
-
try:
|
| 75 |
-
idx = mapping[sym]
|
| 76 |
-
except KeyError:
|
| 77 |
-
raise FeatureNotFoundError(sym)
|
| 78 |
-
return idx
|
| 79 |
-
|
| 80 |
-
|
| 81 |
-
def setReference(mapper, mapping, sym, setter, collection, key):
|
| 82 |
-
try:
|
| 83 |
-
mapped = mapper(sym, mapping)
|
| 84 |
-
except ReferenceNotFoundError as e:
|
| 85 |
-
try:
|
| 86 |
-
if mapping is not None:
|
| 87 |
-
mapping.addDeferredMapping(
|
| 88 |
-
lambda ref: setter(collection, key, ref), sym, e
|
| 89 |
-
)
|
| 90 |
-
return
|
| 91 |
-
except AttributeError:
|
| 92 |
-
pass
|
| 93 |
-
raise
|
| 94 |
-
setter(collection, key, mapped)
|
| 95 |
-
|
| 96 |
-
|
| 97 |
-
class DeferredMapping(dict):
|
| 98 |
-
def __init__(self):
|
| 99 |
-
self._deferredMappings = []
|
| 100 |
-
|
| 101 |
-
def addDeferredMapping(self, setter, sym, e):
|
| 102 |
-
log.debug("Adding deferred mapping for symbol '%s' %s", sym, type(e).__name__)
|
| 103 |
-
self._deferredMappings.append((setter, sym, e))
|
| 104 |
-
|
| 105 |
-
def applyDeferredMappings(self):
|
| 106 |
-
for setter, sym, e in self._deferredMappings:
|
| 107 |
-
log.debug(
|
| 108 |
-
"Applying deferred mapping for symbol '%s' %s", sym, type(e).__name__
|
| 109 |
-
)
|
| 110 |
-
try:
|
| 111 |
-
mapped = self[sym]
|
| 112 |
-
except KeyError:
|
| 113 |
-
raise e
|
| 114 |
-
setter(mapped)
|
| 115 |
-
log.debug("Set to %s", mapped)
|
| 116 |
-
self._deferredMappings = []
|
| 117 |
-
|
| 118 |
-
|
| 119 |
-
def parseScriptList(lines, featureMap=None):
|
| 120 |
-
self = ot.ScriptList()
|
| 121 |
-
records = []
|
| 122 |
-
with lines.between("script table"):
|
| 123 |
-
for line in lines:
|
| 124 |
-
while len(line) < 4:
|
| 125 |
-
line.append("")
|
| 126 |
-
scriptTag, langSysTag, defaultFeature, features = line
|
| 127 |
-
log.debug("Adding script %s language-system %s", scriptTag, langSysTag)
|
| 128 |
-
|
| 129 |
-
langSys = ot.LangSys()
|
| 130 |
-
langSys.LookupOrder = None
|
| 131 |
-
if defaultFeature:
|
| 132 |
-
setReference(
|
| 133 |
-
mapFeature,
|
| 134 |
-
featureMap,
|
| 135 |
-
defaultFeature,
|
| 136 |
-
setattr,
|
| 137 |
-
langSys,
|
| 138 |
-
"ReqFeatureIndex",
|
| 139 |
-
)
|
| 140 |
-
else:
|
| 141 |
-
langSys.ReqFeatureIndex = 0xFFFF
|
| 142 |
-
syms = stripSplitComma(features)
|
| 143 |
-
langSys.FeatureIndex = theList = [3] * len(syms)
|
| 144 |
-
for i, sym in enumerate(syms):
|
| 145 |
-
setReference(mapFeature, featureMap, sym, setitem, theList, i)
|
| 146 |
-
langSys.FeatureCount = len(langSys.FeatureIndex)
|
| 147 |
-
|
| 148 |
-
script = [s for s in records if s.ScriptTag == scriptTag]
|
| 149 |
-
if script:
|
| 150 |
-
script = script[0].Script
|
| 151 |
-
else:
|
| 152 |
-
scriptRec = ot.ScriptRecord()
|
| 153 |
-
scriptRec.ScriptTag = scriptTag + " " * (4 - len(scriptTag))
|
| 154 |
-
scriptRec.Script = ot.Script()
|
| 155 |
-
records.append(scriptRec)
|
| 156 |
-
script = scriptRec.Script
|
| 157 |
-
script.DefaultLangSys = None
|
| 158 |
-
script.LangSysRecord = []
|
| 159 |
-
script.LangSysCount = 0
|
| 160 |
-
|
| 161 |
-
if langSysTag == "default":
|
| 162 |
-
script.DefaultLangSys = langSys
|
| 163 |
-
else:
|
| 164 |
-
langSysRec = ot.LangSysRecord()
|
| 165 |
-
langSysRec.LangSysTag = langSysTag + " " * (4 - len(langSysTag))
|
| 166 |
-
langSysRec.LangSys = langSys
|
| 167 |
-
script.LangSysRecord.append(langSysRec)
|
| 168 |
-
script.LangSysCount = len(script.LangSysRecord)
|
| 169 |
-
|
| 170 |
-
for script in records:
|
| 171 |
-
script.Script.LangSysRecord = sorted(
|
| 172 |
-
script.Script.LangSysRecord, key=lambda rec: rec.LangSysTag
|
| 173 |
-
)
|
| 174 |
-
self.ScriptRecord = sorted(records, key=lambda rec: rec.ScriptTag)
|
| 175 |
-
self.ScriptCount = len(self.ScriptRecord)
|
| 176 |
-
return self
|
| 177 |
-
|
| 178 |
-
|
| 179 |
-
def parseFeatureList(lines, lookupMap=None, featureMap=None):
|
| 180 |
-
self = ot.FeatureList()
|
| 181 |
-
self.FeatureRecord = []
|
| 182 |
-
with lines.between("feature table"):
|
| 183 |
-
for line in lines:
|
| 184 |
-
name, featureTag, lookups = line
|
| 185 |
-
if featureMap is not None:
|
| 186 |
-
assert name not in featureMap, "Duplicate feature name: %s" % name
|
| 187 |
-
featureMap[name] = len(self.FeatureRecord)
|
| 188 |
-
# If feature name is integer, make sure it matches its index.
|
| 189 |
-
try:
|
| 190 |
-
assert int(name) == len(self.FeatureRecord), "%d %d" % (
|
| 191 |
-
name,
|
| 192 |
-
len(self.FeatureRecord),
|
| 193 |
-
)
|
| 194 |
-
except ValueError:
|
| 195 |
-
pass
|
| 196 |
-
featureRec = ot.FeatureRecord()
|
| 197 |
-
featureRec.FeatureTag = featureTag
|
| 198 |
-
featureRec.Feature = ot.Feature()
|
| 199 |
-
self.FeatureRecord.append(featureRec)
|
| 200 |
-
feature = featureRec.Feature
|
| 201 |
-
feature.FeatureParams = None
|
| 202 |
-
syms = stripSplitComma(lookups)
|
| 203 |
-
feature.LookupListIndex = theList = [None] * len(syms)
|
| 204 |
-
for i, sym in enumerate(syms):
|
| 205 |
-
setReference(mapLookup, lookupMap, sym, setitem, theList, i)
|
| 206 |
-
feature.LookupCount = len(feature.LookupListIndex)
|
| 207 |
-
|
| 208 |
-
self.FeatureCount = len(self.FeatureRecord)
|
| 209 |
-
return self
|
| 210 |
-
|
| 211 |
-
|
| 212 |
-
def parseLookupFlags(lines):
|
| 213 |
-
flags = 0
|
| 214 |
-
filterset = None
|
| 215 |
-
allFlags = [
|
| 216 |
-
"righttoleft",
|
| 217 |
-
"ignorebaseglyphs",
|
| 218 |
-
"ignoreligatures",
|
| 219 |
-
"ignoremarks",
|
| 220 |
-
"markattachmenttype",
|
| 221 |
-
"markfiltertype",
|
| 222 |
-
]
|
| 223 |
-
while lines.peeks()[0].lower() in allFlags:
|
| 224 |
-
line = next(lines)
|
| 225 |
-
flag = {
|
| 226 |
-
"righttoleft": 0x0001,
|
| 227 |
-
"ignorebaseglyphs": 0x0002,
|
| 228 |
-
"ignoreligatures": 0x0004,
|
| 229 |
-
"ignoremarks": 0x0008,
|
| 230 |
-
}.get(line[0].lower())
|
| 231 |
-
if flag:
|
| 232 |
-
assert line[1].lower() in ["yes", "no"], line[1]
|
| 233 |
-
if line[1].lower() == "yes":
|
| 234 |
-
flags |= flag
|
| 235 |
-
continue
|
| 236 |
-
if line[0].lower() == "markattachmenttype":
|
| 237 |
-
flags |= int(line[1]) << 8
|
| 238 |
-
continue
|
| 239 |
-
if line[0].lower() == "markfiltertype":
|
| 240 |
-
flags |= 0x10
|
| 241 |
-
filterset = int(line[1])
|
| 242 |
-
return flags, filterset
|
| 243 |
-
|
| 244 |
-
|
| 245 |
-
def parseSingleSubst(lines, font, _lookupMap=None):
|
| 246 |
-
mapping = {}
|
| 247 |
-
for line in lines:
|
| 248 |
-
assert len(line) == 2, line
|
| 249 |
-
line = makeGlyphs(line)
|
| 250 |
-
mapping[line[0]] = line[1]
|
| 251 |
-
return otl.buildSingleSubstSubtable(mapping)
|
| 252 |
-
|
| 253 |
-
|
| 254 |
-
def parseMultiple(lines, font, _lookupMap=None):
|
| 255 |
-
mapping = {}
|
| 256 |
-
for line in lines:
|
| 257 |
-
line = makeGlyphs(line)
|
| 258 |
-
mapping[line[0]] = line[1:]
|
| 259 |
-
return otl.buildMultipleSubstSubtable(mapping)
|
| 260 |
-
|
| 261 |
-
|
| 262 |
-
def parseAlternate(lines, font, _lookupMap=None):
|
| 263 |
-
mapping = {}
|
| 264 |
-
for line in lines:
|
| 265 |
-
line = makeGlyphs(line)
|
| 266 |
-
mapping[line[0]] = line[1:]
|
| 267 |
-
return otl.buildAlternateSubstSubtable(mapping)
|
| 268 |
-
|
| 269 |
-
|
| 270 |
-
def parseLigature(lines, font, _lookupMap=None):
|
| 271 |
-
mapping = {}
|
| 272 |
-
for line in lines:
|
| 273 |
-
assert len(line) >= 2, line
|
| 274 |
-
line = makeGlyphs(line)
|
| 275 |
-
mapping[tuple(line[1:])] = line[0]
|
| 276 |
-
return otl.buildLigatureSubstSubtable(mapping)
|
| 277 |
-
|
| 278 |
-
|
| 279 |
-
def parseSinglePos(lines, font, _lookupMap=None):
|
| 280 |
-
values = {}
|
| 281 |
-
for line in lines:
|
| 282 |
-
assert len(line) == 3, line
|
| 283 |
-
w = line[0].title().replace(" ", "")
|
| 284 |
-
assert w in valueRecordFormatDict
|
| 285 |
-
g = makeGlyph(line[1])
|
| 286 |
-
v = int(line[2])
|
| 287 |
-
if g not in values:
|
| 288 |
-
values[g] = ValueRecord()
|
| 289 |
-
assert not hasattr(values[g], w), (g, w)
|
| 290 |
-
setattr(values[g], w, v)
|
| 291 |
-
return otl.buildSinglePosSubtable(values, font.getReverseGlyphMap())
|
| 292 |
-
|
| 293 |
-
|
| 294 |
-
def parsePair(lines, font, _lookupMap=None):
|
| 295 |
-
self = ot.PairPos()
|
| 296 |
-
self.ValueFormat1 = self.ValueFormat2 = 0
|
| 297 |
-
typ = lines.peeks()[0].split()[0].lower()
|
| 298 |
-
if typ in ("left", "right"):
|
| 299 |
-
self.Format = 1
|
| 300 |
-
values = {}
|
| 301 |
-
for line in lines:
|
| 302 |
-
assert len(line) == 4, line
|
| 303 |
-
side = line[0].split()[0].lower()
|
| 304 |
-
assert side in ("left", "right"), side
|
| 305 |
-
what = line[0][len(side) :].title().replace(" ", "")
|
| 306 |
-
mask = valueRecordFormatDict[what][0]
|
| 307 |
-
glyph1, glyph2 = makeGlyphs(line[1:3])
|
| 308 |
-
value = int(line[3])
|
| 309 |
-
if not glyph1 in values:
|
| 310 |
-
values[glyph1] = {}
|
| 311 |
-
if not glyph2 in values[glyph1]:
|
| 312 |
-
values[glyph1][glyph2] = (ValueRecord(), ValueRecord())
|
| 313 |
-
rec2 = values[glyph1][glyph2]
|
| 314 |
-
if side == "left":
|
| 315 |
-
self.ValueFormat1 |= mask
|
| 316 |
-
vr = rec2[0]
|
| 317 |
-
else:
|
| 318 |
-
self.ValueFormat2 |= mask
|
| 319 |
-
vr = rec2[1]
|
| 320 |
-
assert not hasattr(vr, what), (vr, what)
|
| 321 |
-
setattr(vr, what, value)
|
| 322 |
-
self.Coverage = makeCoverage(set(values.keys()), font)
|
| 323 |
-
self.PairSet = []
|
| 324 |
-
for glyph1 in self.Coverage.glyphs:
|
| 325 |
-
values1 = values[glyph1]
|
| 326 |
-
pairset = ot.PairSet()
|
| 327 |
-
records = pairset.PairValueRecord = []
|
| 328 |
-
for glyph2 in sorted(values1.keys(), key=font.getGlyphID):
|
| 329 |
-
values2 = values1[glyph2]
|
| 330 |
-
pair = ot.PairValueRecord()
|
| 331 |
-
pair.SecondGlyph = glyph2
|
| 332 |
-
pair.Value1 = values2[0]
|
| 333 |
-
pair.Value2 = values2[1] if self.ValueFormat2 else None
|
| 334 |
-
records.append(pair)
|
| 335 |
-
pairset.PairValueCount = len(pairset.PairValueRecord)
|
| 336 |
-
self.PairSet.append(pairset)
|
| 337 |
-
self.PairSetCount = len(self.PairSet)
|
| 338 |
-
elif typ.endswith("class"):
|
| 339 |
-
self.Format = 2
|
| 340 |
-
classDefs = [None, None]
|
| 341 |
-
while lines.peeks()[0].endswith("class definition begin"):
|
| 342 |
-
typ = lines.peek()[0][: -len("class definition begin")].lower()
|
| 343 |
-
idx, klass = {
|
| 344 |
-
"first": (0, ot.ClassDef1),
|
| 345 |
-
"second": (1, ot.ClassDef2),
|
| 346 |
-
}[typ]
|
| 347 |
-
assert classDefs[idx] is None
|
| 348 |
-
classDefs[idx] = parseClassDef(lines, font, klass=klass)
|
| 349 |
-
self.ClassDef1, self.ClassDef2 = classDefs
|
| 350 |
-
self.Class1Count, self.Class2Count = (
|
| 351 |
-
1 + max(c.classDefs.values()) for c in classDefs
|
| 352 |
-
)
|
| 353 |
-
self.Class1Record = [ot.Class1Record() for i in range(self.Class1Count)]
|
| 354 |
-
for rec1 in self.Class1Record:
|
| 355 |
-
rec1.Class2Record = [ot.Class2Record() for j in range(self.Class2Count)]
|
| 356 |
-
for rec2 in rec1.Class2Record:
|
| 357 |
-
rec2.Value1 = ValueRecord()
|
| 358 |
-
rec2.Value2 = ValueRecord()
|
| 359 |
-
for line in lines:
|
| 360 |
-
assert len(line) == 4, line
|
| 361 |
-
side = line[0].split()[0].lower()
|
| 362 |
-
assert side in ("left", "right"), side
|
| 363 |
-
what = line[0][len(side) :].title().replace(" ", "")
|
| 364 |
-
mask = valueRecordFormatDict[what][0]
|
| 365 |
-
class1, class2, value = (int(x) for x in line[1:4])
|
| 366 |
-
rec2 = self.Class1Record[class1].Class2Record[class2]
|
| 367 |
-
if side == "left":
|
| 368 |
-
self.ValueFormat1 |= mask
|
| 369 |
-
vr = rec2.Value1
|
| 370 |
-
else:
|
| 371 |
-
self.ValueFormat2 |= mask
|
| 372 |
-
vr = rec2.Value2
|
| 373 |
-
assert not hasattr(vr, what), (vr, what)
|
| 374 |
-
setattr(vr, what, value)
|
| 375 |
-
for rec1 in self.Class1Record:
|
| 376 |
-
for rec2 in rec1.Class2Record:
|
| 377 |
-
rec2.Value1 = ValueRecord(self.ValueFormat1, rec2.Value1)
|
| 378 |
-
rec2.Value2 = (
|
| 379 |
-
ValueRecord(self.ValueFormat2, rec2.Value2)
|
| 380 |
-
if self.ValueFormat2
|
| 381 |
-
else None
|
| 382 |
-
)
|
| 383 |
-
|
| 384 |
-
self.Coverage = makeCoverage(set(self.ClassDef1.classDefs.keys()), font)
|
| 385 |
-
else:
|
| 386 |
-
assert 0, typ
|
| 387 |
-
return self
|
| 388 |
-
|
| 389 |
-
|
| 390 |
-
def parseKernset(lines, font, _lookupMap=None):
|
| 391 |
-
typ = lines.peeks()[0].split()[0].lower()
|
| 392 |
-
if typ in ("left", "right"):
|
| 393 |
-
with lines.until(
|
| 394 |
-
("firstclass definition begin", "secondclass definition begin")
|
| 395 |
-
):
|
| 396 |
-
return parsePair(lines, font)
|
| 397 |
-
return parsePair(lines, font)
|
| 398 |
-
|
| 399 |
-
|
| 400 |
-
def makeAnchor(data, klass=ot.Anchor):
|
| 401 |
-
assert len(data) <= 2
|
| 402 |
-
anchor = klass()
|
| 403 |
-
anchor.Format = 1
|
| 404 |
-
anchor.XCoordinate, anchor.YCoordinate = intSplitComma(data[0])
|
| 405 |
-
if len(data) > 1 and data[1] != "":
|
| 406 |
-
anchor.Format = 2
|
| 407 |
-
anchor.AnchorPoint = int(data[1])
|
| 408 |
-
return anchor
|
| 409 |
-
|
| 410 |
-
|
| 411 |
-
def parseCursive(lines, font, _lookupMap=None):
|
| 412 |
-
records = {}
|
| 413 |
-
for line in lines:
|
| 414 |
-
assert len(line) in [3, 4], line
|
| 415 |
-
idx, klass = {
|
| 416 |
-
"entry": (0, ot.EntryAnchor),
|
| 417 |
-
"exit": (1, ot.ExitAnchor),
|
| 418 |
-
}[line[0]]
|
| 419 |
-
glyph = makeGlyph(line[1])
|
| 420 |
-
if glyph not in records:
|
| 421 |
-
records[glyph] = [None, None]
|
| 422 |
-
assert records[glyph][idx] is None, (glyph, idx)
|
| 423 |
-
records[glyph][idx] = makeAnchor(line[2:], klass)
|
| 424 |
-
return otl.buildCursivePosSubtable(records, font.getReverseGlyphMap())
|
| 425 |
-
|
| 426 |
-
|
| 427 |
-
def makeMarkRecords(data, coverage, c):
|
| 428 |
-
records = []
|
| 429 |
-
for glyph in coverage.glyphs:
|
| 430 |
-
klass, anchor = data[glyph]
|
| 431 |
-
record = c.MarkRecordClass()
|
| 432 |
-
record.Class = klass
|
| 433 |
-
setattr(record, c.MarkAnchor, anchor)
|
| 434 |
-
records.append(record)
|
| 435 |
-
return records
|
| 436 |
-
|
| 437 |
-
|
| 438 |
-
def makeBaseRecords(data, coverage, c, classCount):
|
| 439 |
-
records = []
|
| 440 |
-
idx = {}
|
| 441 |
-
for glyph in coverage.glyphs:
|
| 442 |
-
idx[glyph] = len(records)
|
| 443 |
-
record = c.BaseRecordClass()
|
| 444 |
-
anchors = [None] * classCount
|
| 445 |
-
setattr(record, c.BaseAnchor, anchors)
|
| 446 |
-
records.append(record)
|
| 447 |
-
for (glyph, klass), anchor in data.items():
|
| 448 |
-
record = records[idx[glyph]]
|
| 449 |
-
anchors = getattr(record, c.BaseAnchor)
|
| 450 |
-
assert anchors[klass] is None, (glyph, klass)
|
| 451 |
-
anchors[klass] = anchor
|
| 452 |
-
return records
|
| 453 |
-
|
| 454 |
-
|
| 455 |
-
def makeLigatureRecords(data, coverage, c, classCount):
|
| 456 |
-
records = [None] * len(coverage.glyphs)
|
| 457 |
-
idx = {g: i for i, g in enumerate(coverage.glyphs)}
|
| 458 |
-
|
| 459 |
-
for (glyph, klass, compIdx, compCount), anchor in data.items():
|
| 460 |
-
record = records[idx[glyph]]
|
| 461 |
-
if record is None:
|
| 462 |
-
record = records[idx[glyph]] = ot.LigatureAttach()
|
| 463 |
-
record.ComponentCount = compCount
|
| 464 |
-
record.ComponentRecord = [ot.ComponentRecord() for i in range(compCount)]
|
| 465 |
-
for compRec in record.ComponentRecord:
|
| 466 |
-
compRec.LigatureAnchor = [None] * classCount
|
| 467 |
-
assert record.ComponentCount == compCount, (
|
| 468 |
-
glyph,
|
| 469 |
-
record.ComponentCount,
|
| 470 |
-
compCount,
|
| 471 |
-
)
|
| 472 |
-
|
| 473 |
-
anchors = record.ComponentRecord[compIdx - 1].LigatureAnchor
|
| 474 |
-
assert anchors[klass] is None, (glyph, compIdx, klass)
|
| 475 |
-
anchors[klass] = anchor
|
| 476 |
-
return records
|
| 477 |
-
|
| 478 |
-
|
| 479 |
-
def parseMarkToSomething(lines, font, c):
|
| 480 |
-
self = c.Type()
|
| 481 |
-
self.Format = 1
|
| 482 |
-
markData = {}
|
| 483 |
-
baseData = {}
|
| 484 |
-
Data = {
|
| 485 |
-
"mark": (markData, c.MarkAnchorClass),
|
| 486 |
-
"base": (baseData, c.BaseAnchorClass),
|
| 487 |
-
"ligature": (baseData, c.BaseAnchorClass),
|
| 488 |
-
}
|
| 489 |
-
maxKlass = 0
|
| 490 |
-
for line in lines:
|
| 491 |
-
typ = line[0]
|
| 492 |
-
assert typ in ("mark", "base", "ligature")
|
| 493 |
-
glyph = makeGlyph(line[1])
|
| 494 |
-
data, anchorClass = Data[typ]
|
| 495 |
-
extraItems = 2 if typ == "ligature" else 0
|
| 496 |
-
extras = tuple(int(i) for i in line[2 : 2 + extraItems])
|
| 497 |
-
klass = int(line[2 + extraItems])
|
| 498 |
-
anchor = makeAnchor(line[3 + extraItems :], anchorClass)
|
| 499 |
-
if typ == "mark":
|
| 500 |
-
key, value = glyph, (klass, anchor)
|
| 501 |
-
else:
|
| 502 |
-
key, value = ((glyph, klass) + extras), anchor
|
| 503 |
-
assert key not in data, key
|
| 504 |
-
data[key] = value
|
| 505 |
-
maxKlass = max(maxKlass, klass)
|
| 506 |
-
|
| 507 |
-
# Mark
|
| 508 |
-
markCoverage = makeCoverage(set(markData.keys()), font, c.MarkCoverageClass)
|
| 509 |
-
markArray = c.MarkArrayClass()
|
| 510 |
-
markRecords = makeMarkRecords(markData, markCoverage, c)
|
| 511 |
-
setattr(markArray, c.MarkRecord, markRecords)
|
| 512 |
-
setattr(markArray, c.MarkCount, len(markRecords))
|
| 513 |
-
setattr(self, c.MarkCoverage, markCoverage)
|
| 514 |
-
setattr(self, c.MarkArray, markArray)
|
| 515 |
-
self.ClassCount = maxKlass + 1
|
| 516 |
-
|
| 517 |
-
# Base
|
| 518 |
-
self.classCount = 0 if not baseData else 1 + max(k[1] for k, v in baseData.items())
|
| 519 |
-
baseCoverage = makeCoverage(
|
| 520 |
-
set([k[0] for k in baseData.keys()]), font, c.BaseCoverageClass
|
| 521 |
-
)
|
| 522 |
-
baseArray = c.BaseArrayClass()
|
| 523 |
-
if c.Base == "Ligature":
|
| 524 |
-
baseRecords = makeLigatureRecords(baseData, baseCoverage, c, self.classCount)
|
| 525 |
-
else:
|
| 526 |
-
baseRecords = makeBaseRecords(baseData, baseCoverage, c, self.classCount)
|
| 527 |
-
setattr(baseArray, c.BaseRecord, baseRecords)
|
| 528 |
-
setattr(baseArray, c.BaseCount, len(baseRecords))
|
| 529 |
-
setattr(self, c.BaseCoverage, baseCoverage)
|
| 530 |
-
setattr(self, c.BaseArray, baseArray)
|
| 531 |
-
|
| 532 |
-
return self
|
| 533 |
-
|
| 534 |
-
|
| 535 |
-
class MarkHelper(object):
|
| 536 |
-
def __init__(self):
|
| 537 |
-
for Which in ("Mark", "Base"):
|
| 538 |
-
for What in ("Coverage", "Array", "Count", "Record", "Anchor"):
|
| 539 |
-
key = Which + What
|
| 540 |
-
if Which == "Mark" and What in ("Count", "Record", "Anchor"):
|
| 541 |
-
value = key
|
| 542 |
-
else:
|
| 543 |
-
value = getattr(self, Which) + What
|
| 544 |
-
if value == "LigatureRecord":
|
| 545 |
-
value = "LigatureAttach"
|
| 546 |
-
setattr(self, key, value)
|
| 547 |
-
if What != "Count":
|
| 548 |
-
klass = getattr(ot, value)
|
| 549 |
-
setattr(self, key + "Class", klass)
|
| 550 |
-
|
| 551 |
-
|
| 552 |
-
class MarkToBaseHelper(MarkHelper):
|
| 553 |
-
Mark = "Mark"
|
| 554 |
-
Base = "Base"
|
| 555 |
-
Type = ot.MarkBasePos
|
| 556 |
-
|
| 557 |
-
|
| 558 |
-
class MarkToMarkHelper(MarkHelper):
|
| 559 |
-
Mark = "Mark1"
|
| 560 |
-
Base = "Mark2"
|
| 561 |
-
Type = ot.MarkMarkPos
|
| 562 |
-
|
| 563 |
-
|
| 564 |
-
class MarkToLigatureHelper(MarkHelper):
|
| 565 |
-
Mark = "Mark"
|
| 566 |
-
Base = "Ligature"
|
| 567 |
-
Type = ot.MarkLigPos
|
| 568 |
-
|
| 569 |
-
|
| 570 |
-
def parseMarkToBase(lines, font, _lookupMap=None):
|
| 571 |
-
return parseMarkToSomething(lines, font, MarkToBaseHelper())
|
| 572 |
-
|
| 573 |
-
|
| 574 |
-
def parseMarkToMark(lines, font, _lookupMap=None):
|
| 575 |
-
return parseMarkToSomething(lines, font, MarkToMarkHelper())
|
| 576 |
-
|
| 577 |
-
|
| 578 |
-
def parseMarkToLigature(lines, font, _lookupMap=None):
|
| 579 |
-
return parseMarkToSomething(lines, font, MarkToLigatureHelper())
|
| 580 |
-
|
| 581 |
-
|
| 582 |
-
def stripSplitComma(line):
|
| 583 |
-
return [s.strip() for s in line.split(",")] if line else []
|
| 584 |
-
|
| 585 |
-
|
| 586 |
-
def intSplitComma(line):
|
| 587 |
-
return [int(i) for i in line.split(",")] if line else []
|
| 588 |
-
|
| 589 |
-
|
| 590 |
-
# Copied from fontTools.subset
|
| 591 |
-
class ContextHelper(object):
|
| 592 |
-
def __init__(self, klassName, Format):
|
| 593 |
-
if klassName.endswith("Subst"):
|
| 594 |
-
Typ = "Sub"
|
| 595 |
-
Type = "Subst"
|
| 596 |
-
else:
|
| 597 |
-
Typ = "Pos"
|
| 598 |
-
Type = "Pos"
|
| 599 |
-
if klassName.startswith("Chain"):
|
| 600 |
-
Chain = "Chain"
|
| 601 |
-
InputIdx = 1
|
| 602 |
-
DataLen = 3
|
| 603 |
-
else:
|
| 604 |
-
Chain = ""
|
| 605 |
-
InputIdx = 0
|
| 606 |
-
DataLen = 1
|
| 607 |
-
ChainTyp = Chain + Typ
|
| 608 |
-
|
| 609 |
-
self.Typ = Typ
|
| 610 |
-
self.Type = Type
|
| 611 |
-
self.Chain = Chain
|
| 612 |
-
self.ChainTyp = ChainTyp
|
| 613 |
-
self.InputIdx = InputIdx
|
| 614 |
-
self.DataLen = DataLen
|
| 615 |
-
|
| 616 |
-
self.LookupRecord = Type + "LookupRecord"
|
| 617 |
-
|
| 618 |
-
if Format == 1:
|
| 619 |
-
Coverage = lambda r: r.Coverage
|
| 620 |
-
ChainCoverage = lambda r: r.Coverage
|
| 621 |
-
ContextData = lambda r: (None,)
|
| 622 |
-
ChainContextData = lambda r: (None, None, None)
|
| 623 |
-
SetContextData = None
|
| 624 |
-
SetChainContextData = None
|
| 625 |
-
RuleData = lambda r: (r.Input,)
|
| 626 |
-
ChainRuleData = lambda r: (r.Backtrack, r.Input, r.LookAhead)
|
| 627 |
-
|
| 628 |
-
def SetRuleData(r, d):
|
| 629 |
-
(r.Input,) = d
|
| 630 |
-
(r.GlyphCount,) = (len(x) + 1 for x in d)
|
| 631 |
-
|
| 632 |
-
def ChainSetRuleData(r, d):
|
| 633 |
-
(r.Backtrack, r.Input, r.LookAhead) = d
|
| 634 |
-
(
|
| 635 |
-
r.BacktrackGlyphCount,
|
| 636 |
-
r.InputGlyphCount,
|
| 637 |
-
r.LookAheadGlyphCount,
|
| 638 |
-
) = (len(d[0]), len(d[1]) + 1, len(d[2]))
|
| 639 |
-
|
| 640 |
-
elif Format == 2:
|
| 641 |
-
Coverage = lambda r: r.Coverage
|
| 642 |
-
ChainCoverage = lambda r: r.Coverage
|
| 643 |
-
ContextData = lambda r: (r.ClassDef,)
|
| 644 |
-
ChainContextData = lambda r: (
|
| 645 |
-
r.BacktrackClassDef,
|
| 646 |
-
r.InputClassDef,
|
| 647 |
-
r.LookAheadClassDef,
|
| 648 |
-
)
|
| 649 |
-
|
| 650 |
-
def SetContextData(r, d):
|
| 651 |
-
(r.ClassDef,) = d
|
| 652 |
-
|
| 653 |
-
def SetChainContextData(r, d):
|
| 654 |
-
(r.BacktrackClassDef, r.InputClassDef, r.LookAheadClassDef) = d
|
| 655 |
-
|
| 656 |
-
RuleData = lambda r: (r.Class,)
|
| 657 |
-
ChainRuleData = lambda r: (r.Backtrack, r.Input, r.LookAhead)
|
| 658 |
-
|
| 659 |
-
def SetRuleData(r, d):
|
| 660 |
-
(r.Class,) = d
|
| 661 |
-
(r.GlyphCount,) = (len(x) + 1 for x in d)
|
| 662 |
-
|
| 663 |
-
def ChainSetRuleData(r, d):
|
| 664 |
-
(r.Backtrack, r.Input, r.LookAhead) = d
|
| 665 |
-
(
|
| 666 |
-
r.BacktrackGlyphCount,
|
| 667 |
-
r.InputGlyphCount,
|
| 668 |
-
r.LookAheadGlyphCount,
|
| 669 |
-
) = (len(d[0]), len(d[1]) + 1, len(d[2]))
|
| 670 |
-
|
| 671 |
-
elif Format == 3:
|
| 672 |
-
Coverage = lambda r: r.Coverage[0]
|
| 673 |
-
ChainCoverage = lambda r: r.InputCoverage[0]
|
| 674 |
-
ContextData = None
|
| 675 |
-
ChainContextData = None
|
| 676 |
-
SetContextData = None
|
| 677 |
-
SetChainContextData = None
|
| 678 |
-
RuleData = lambda r: r.Coverage
|
| 679 |
-
ChainRuleData = lambda r: (
|
| 680 |
-
r.BacktrackCoverage + r.InputCoverage + r.LookAheadCoverage
|
| 681 |
-
)
|
| 682 |
-
|
| 683 |
-
def SetRuleData(r, d):
|
| 684 |
-
(r.Coverage,) = d
|
| 685 |
-
(r.GlyphCount,) = (len(x) for x in d)
|
| 686 |
-
|
| 687 |
-
def ChainSetRuleData(r, d):
|
| 688 |
-
(r.BacktrackCoverage, r.InputCoverage, r.LookAheadCoverage) = d
|
| 689 |
-
(
|
| 690 |
-
r.BacktrackGlyphCount,
|
| 691 |
-
r.InputGlyphCount,
|
| 692 |
-
r.LookAheadGlyphCount,
|
| 693 |
-
) = (len(x) for x in d)
|
| 694 |
-
|
| 695 |
-
else:
|
| 696 |
-
assert 0, "unknown format: %s" % Format
|
| 697 |
-
|
| 698 |
-
if Chain:
|
| 699 |
-
self.Coverage = ChainCoverage
|
| 700 |
-
self.ContextData = ChainContextData
|
| 701 |
-
self.SetContextData = SetChainContextData
|
| 702 |
-
self.RuleData = ChainRuleData
|
| 703 |
-
self.SetRuleData = ChainSetRuleData
|
| 704 |
-
else:
|
| 705 |
-
self.Coverage = Coverage
|
| 706 |
-
self.ContextData = ContextData
|
| 707 |
-
self.SetContextData = SetContextData
|
| 708 |
-
self.RuleData = RuleData
|
| 709 |
-
self.SetRuleData = SetRuleData
|
| 710 |
-
|
| 711 |
-
if Format == 1:
|
| 712 |
-
self.Rule = ChainTyp + "Rule"
|
| 713 |
-
self.RuleCount = ChainTyp + "RuleCount"
|
| 714 |
-
self.RuleSet = ChainTyp + "RuleSet"
|
| 715 |
-
self.RuleSetCount = ChainTyp + "RuleSetCount"
|
| 716 |
-
self.Intersect = lambda glyphs, c, r: [r] if r in glyphs else []
|
| 717 |
-
elif Format == 2:
|
| 718 |
-
self.Rule = ChainTyp + "ClassRule"
|
| 719 |
-
self.RuleCount = ChainTyp + "ClassRuleCount"
|
| 720 |
-
self.RuleSet = ChainTyp + "ClassSet"
|
| 721 |
-
self.RuleSetCount = ChainTyp + "ClassSetCount"
|
| 722 |
-
self.Intersect = lambda glyphs, c, r: (
|
| 723 |
-
c.intersect_class(glyphs, r)
|
| 724 |
-
if c
|
| 725 |
-
else (set(glyphs) if r == 0 else set())
|
| 726 |
-
)
|
| 727 |
-
|
| 728 |
-
self.ClassDef = "InputClassDef" if Chain else "ClassDef"
|
| 729 |
-
self.ClassDefIndex = 1 if Chain else 0
|
| 730 |
-
self.Input = "Input" if Chain else "Class"
|
| 731 |
-
|
| 732 |
-
|
| 733 |
-
def parseLookupRecords(items, klassName, lookupMap=None):
|
| 734 |
-
klass = getattr(ot, klassName)
|
| 735 |
-
lst = []
|
| 736 |
-
for item in items:
|
| 737 |
-
rec = klass()
|
| 738 |
-
item = stripSplitComma(item)
|
| 739 |
-
assert len(item) == 2, item
|
| 740 |
-
idx = int(item[0])
|
| 741 |
-
assert idx > 0, idx
|
| 742 |
-
rec.SequenceIndex = idx - 1
|
| 743 |
-
setReference(mapLookup, lookupMap, item[1], setattr, rec, "LookupListIndex")
|
| 744 |
-
lst.append(rec)
|
| 745 |
-
return lst
|
| 746 |
-
|
| 747 |
-
|
| 748 |
-
def makeClassDef(classDefs, font, klass=ot.Coverage):
|
| 749 |
-
if not classDefs:
|
| 750 |
-
return None
|
| 751 |
-
self = klass()
|
| 752 |
-
self.classDefs = dict(classDefs)
|
| 753 |
-
return self
|
| 754 |
-
|
| 755 |
-
|
| 756 |
-
def parseClassDef(lines, font, klass=ot.ClassDef):
|
| 757 |
-
classDefs = {}
|
| 758 |
-
with lines.between("class definition"):
|
| 759 |
-
for line in lines:
|
| 760 |
-
glyph = makeGlyph(line[0])
|
| 761 |
-
assert glyph not in classDefs, glyph
|
| 762 |
-
classDefs[glyph] = int(line[1])
|
| 763 |
-
return makeClassDef(classDefs, font, klass)
|
| 764 |
-
|
| 765 |
-
|
| 766 |
-
def makeCoverage(glyphs, font, klass=ot.Coverage):
|
| 767 |
-
if not glyphs:
|
| 768 |
-
return None
|
| 769 |
-
if isinstance(glyphs, set):
|
| 770 |
-
glyphs = sorted(glyphs)
|
| 771 |
-
coverage = klass()
|
| 772 |
-
coverage.glyphs = sorted(set(glyphs), key=font.getGlyphID)
|
| 773 |
-
return coverage
|
| 774 |
-
|
| 775 |
-
|
| 776 |
-
def parseCoverage(lines, font, klass=ot.Coverage):
|
| 777 |
-
glyphs = []
|
| 778 |
-
with lines.between("coverage definition"):
|
| 779 |
-
for line in lines:
|
| 780 |
-
glyphs.append(makeGlyph(line[0]))
|
| 781 |
-
return makeCoverage(glyphs, font, klass)
|
| 782 |
-
|
| 783 |
-
|
| 784 |
-
def bucketizeRules(self, c, rules, bucketKeys):
|
| 785 |
-
buckets = {}
|
| 786 |
-
for seq, recs in rules:
|
| 787 |
-
buckets.setdefault(seq[c.InputIdx][0], []).append(
|
| 788 |
-
(tuple(s[1 if i == c.InputIdx else 0 :] for i, s in enumerate(seq)), recs)
|
| 789 |
-
)
|
| 790 |
-
|
| 791 |
-
rulesets = []
|
| 792 |
-
for firstGlyph in bucketKeys:
|
| 793 |
-
if firstGlyph not in buckets:
|
| 794 |
-
rulesets.append(None)
|
| 795 |
-
continue
|
| 796 |
-
thisRules = []
|
| 797 |
-
for seq, recs in buckets[firstGlyph]:
|
| 798 |
-
rule = getattr(ot, c.Rule)()
|
| 799 |
-
c.SetRuleData(rule, seq)
|
| 800 |
-
setattr(rule, c.Type + "Count", len(recs))
|
| 801 |
-
setattr(rule, c.LookupRecord, recs)
|
| 802 |
-
thisRules.append(rule)
|
| 803 |
-
|
| 804 |
-
ruleset = getattr(ot, c.RuleSet)()
|
| 805 |
-
setattr(ruleset, c.Rule, thisRules)
|
| 806 |
-
setattr(ruleset, c.RuleCount, len(thisRules))
|
| 807 |
-
rulesets.append(ruleset)
|
| 808 |
-
|
| 809 |
-
setattr(self, c.RuleSet, rulesets)
|
| 810 |
-
setattr(self, c.RuleSetCount, len(rulesets))
|
| 811 |
-
|
| 812 |
-
|
| 813 |
-
def parseContext(lines, font, Type, lookupMap=None):
|
| 814 |
-
self = getattr(ot, Type)()
|
| 815 |
-
typ = lines.peeks()[0].split()[0].lower()
|
| 816 |
-
if typ == "glyph":
|
| 817 |
-
self.Format = 1
|
| 818 |
-
log.debug("Parsing %s format %s", Type, self.Format)
|
| 819 |
-
c = ContextHelper(Type, self.Format)
|
| 820 |
-
rules = []
|
| 821 |
-
for line in lines:
|
| 822 |
-
assert line[0].lower() == "glyph", line[0]
|
| 823 |
-
while len(line) < 1 + c.DataLen:
|
| 824 |
-
line.append("")
|
| 825 |
-
seq = tuple(makeGlyphs(stripSplitComma(i)) for i in line[1 : 1 + c.DataLen])
|
| 826 |
-
recs = parseLookupRecords(line[1 + c.DataLen :], c.LookupRecord, lookupMap)
|
| 827 |
-
rules.append((seq, recs))
|
| 828 |
-
|
| 829 |
-
firstGlyphs = set(seq[c.InputIdx][0] for seq, recs in rules)
|
| 830 |
-
self.Coverage = makeCoverage(firstGlyphs, font)
|
| 831 |
-
bucketizeRules(self, c, rules, self.Coverage.glyphs)
|
| 832 |
-
elif typ.endswith("class"):
|
| 833 |
-
self.Format = 2
|
| 834 |
-
log.debug("Parsing %s format %s", Type, self.Format)
|
| 835 |
-
c = ContextHelper(Type, self.Format)
|
| 836 |
-
classDefs = [None] * c.DataLen
|
| 837 |
-
while lines.peeks()[0].endswith("class definition begin"):
|
| 838 |
-
typ = lines.peek()[0][: -len("class definition begin")].lower()
|
| 839 |
-
idx, klass = {
|
| 840 |
-
1: {
|
| 841 |
-
"": (0, ot.ClassDef),
|
| 842 |
-
},
|
| 843 |
-
3: {
|
| 844 |
-
"backtrack": (0, ot.BacktrackClassDef),
|
| 845 |
-
"": (1, ot.InputClassDef),
|
| 846 |
-
"lookahead": (2, ot.LookAheadClassDef),
|
| 847 |
-
},
|
| 848 |
-
}[c.DataLen][typ]
|
| 849 |
-
assert classDefs[idx] is None, idx
|
| 850 |
-
classDefs[idx] = parseClassDef(lines, font, klass=klass)
|
| 851 |
-
c.SetContextData(self, classDefs)
|
| 852 |
-
rules = []
|
| 853 |
-
for line in lines:
|
| 854 |
-
assert line[0].lower().startswith("class"), line[0]
|
| 855 |
-
while len(line) < 1 + c.DataLen:
|
| 856 |
-
line.append("")
|
| 857 |
-
seq = tuple(intSplitComma(i) for i in line[1 : 1 + c.DataLen])
|
| 858 |
-
recs = parseLookupRecords(line[1 + c.DataLen :], c.LookupRecord, lookupMap)
|
| 859 |
-
rules.append((seq, recs))
|
| 860 |
-
firstClasses = set(seq[c.InputIdx][0] for seq, recs in rules)
|
| 861 |
-
firstGlyphs = set(
|
| 862 |
-
g for g, c in classDefs[c.InputIdx].classDefs.items() if c in firstClasses
|
| 863 |
-
)
|
| 864 |
-
self.Coverage = makeCoverage(firstGlyphs, font)
|
| 865 |
-
bucketizeRules(self, c, rules, range(max(firstClasses) + 1))
|
| 866 |
-
elif typ.endswith("coverage"):
|
| 867 |
-
self.Format = 3
|
| 868 |
-
log.debug("Parsing %s format %s", Type, self.Format)
|
| 869 |
-
c = ContextHelper(Type, self.Format)
|
| 870 |
-
coverages = tuple([] for i in range(c.DataLen))
|
| 871 |
-
while lines.peeks()[0].endswith("coverage definition begin"):
|
| 872 |
-
typ = lines.peek()[0][: -len("coverage definition begin")].lower()
|
| 873 |
-
idx, klass = {
|
| 874 |
-
1: {
|
| 875 |
-
"": (0, ot.Coverage),
|
| 876 |
-
},
|
| 877 |
-
3: {
|
| 878 |
-
"backtrack": (0, ot.BacktrackCoverage),
|
| 879 |
-
"input": (1, ot.InputCoverage),
|
| 880 |
-
"lookahead": (2, ot.LookAheadCoverage),
|
| 881 |
-
},
|
| 882 |
-
}[c.DataLen][typ]
|
| 883 |
-
coverages[idx].append(parseCoverage(lines, font, klass=klass))
|
| 884 |
-
c.SetRuleData(self, coverages)
|
| 885 |
-
lines = list(lines)
|
| 886 |
-
assert len(lines) == 1
|
| 887 |
-
line = lines[0]
|
| 888 |
-
assert line[0].lower() == "coverage", line[0]
|
| 889 |
-
recs = parseLookupRecords(line[1:], c.LookupRecord, lookupMap)
|
| 890 |
-
setattr(self, c.Type + "Count", len(recs))
|
| 891 |
-
setattr(self, c.LookupRecord, recs)
|
| 892 |
-
else:
|
| 893 |
-
assert 0, typ
|
| 894 |
-
return self
|
| 895 |
-
|
| 896 |
-
|
| 897 |
-
def parseContextSubst(lines, font, lookupMap=None):
|
| 898 |
-
return parseContext(lines, font, "ContextSubst", lookupMap=lookupMap)
|
| 899 |
-
|
| 900 |
-
|
| 901 |
-
def parseContextPos(lines, font, lookupMap=None):
|
| 902 |
-
return parseContext(lines, font, "ContextPos", lookupMap=lookupMap)
|
| 903 |
-
|
| 904 |
-
|
| 905 |
-
def parseChainedSubst(lines, font, lookupMap=None):
|
| 906 |
-
return parseContext(lines, font, "ChainContextSubst", lookupMap=lookupMap)
|
| 907 |
-
|
| 908 |
-
|
| 909 |
-
def parseChainedPos(lines, font, lookupMap=None):
|
| 910 |
-
return parseContext(lines, font, "ChainContextPos", lookupMap=lookupMap)
|
| 911 |
-
|
| 912 |
-
|
| 913 |
-
def parseReverseChainedSubst(lines, font, _lookupMap=None):
|
| 914 |
-
self = ot.ReverseChainSingleSubst()
|
| 915 |
-
self.Format = 1
|
| 916 |
-
coverages = ([], [])
|
| 917 |
-
while lines.peeks()[0].endswith("coverage definition begin"):
|
| 918 |
-
typ = lines.peek()[0][: -len("coverage definition begin")].lower()
|
| 919 |
-
idx, klass = {
|
| 920 |
-
"backtrack": (0, ot.BacktrackCoverage),
|
| 921 |
-
"lookahead": (1, ot.LookAheadCoverage),
|
| 922 |
-
}[typ]
|
| 923 |
-
coverages[idx].append(parseCoverage(lines, font, klass=klass))
|
| 924 |
-
self.BacktrackCoverage = coverages[0]
|
| 925 |
-
self.BacktrackGlyphCount = len(self.BacktrackCoverage)
|
| 926 |
-
self.LookAheadCoverage = coverages[1]
|
| 927 |
-
self.LookAheadGlyphCount = len(self.LookAheadCoverage)
|
| 928 |
-
mapping = {}
|
| 929 |
-
for line in lines:
|
| 930 |
-
assert len(line) == 2, line
|
| 931 |
-
line = makeGlyphs(line)
|
| 932 |
-
mapping[line[0]] = line[1]
|
| 933 |
-
self.Coverage = makeCoverage(set(mapping.keys()), font)
|
| 934 |
-
self.Substitute = [mapping[k] for k in self.Coverage.glyphs]
|
| 935 |
-
self.GlyphCount = len(self.Substitute)
|
| 936 |
-
return self
|
| 937 |
-
|
| 938 |
-
|
| 939 |
-
def parseLookup(lines, tableTag, font, lookupMap=None):
|
| 940 |
-
line = lines.expect("lookup")
|
| 941 |
-
_, name, typ = line
|
| 942 |
-
log.debug("Parsing lookup type %s %s", typ, name)
|
| 943 |
-
lookup = ot.Lookup()
|
| 944 |
-
lookup.LookupFlag, filterset = parseLookupFlags(lines)
|
| 945 |
-
if filterset is not None:
|
| 946 |
-
lookup.MarkFilteringSet = filterset
|
| 947 |
-
lookup.LookupType, parseLookupSubTable = {
|
| 948 |
-
"GSUB": {
|
| 949 |
-
"single": (1, parseSingleSubst),
|
| 950 |
-
"multiple": (2, parseMultiple),
|
| 951 |
-
"alternate": (3, parseAlternate),
|
| 952 |
-
"ligature": (4, parseLigature),
|
| 953 |
-
"context": (5, parseContextSubst),
|
| 954 |
-
"chained": (6, parseChainedSubst),
|
| 955 |
-
"reversechained": (8, parseReverseChainedSubst),
|
| 956 |
-
},
|
| 957 |
-
"GPOS": {
|
| 958 |
-
"single": (1, parseSinglePos),
|
| 959 |
-
"pair": (2, parsePair),
|
| 960 |
-
"kernset": (2, parseKernset),
|
| 961 |
-
"cursive": (3, parseCursive),
|
| 962 |
-
"mark to base": (4, parseMarkToBase),
|
| 963 |
-
"mark to ligature": (5, parseMarkToLigature),
|
| 964 |
-
"mark to mark": (6, parseMarkToMark),
|
| 965 |
-
"context": (7, parseContextPos),
|
| 966 |
-
"chained": (8, parseChainedPos),
|
| 967 |
-
},
|
| 968 |
-
}[tableTag][typ]
|
| 969 |
-
|
| 970 |
-
with lines.until("lookup end"):
|
| 971 |
-
subtables = []
|
| 972 |
-
|
| 973 |
-
while lines.peek():
|
| 974 |
-
with lines.until(("% subtable", "subtable end")):
|
| 975 |
-
while lines.peek():
|
| 976 |
-
subtable = parseLookupSubTable(lines, font, lookupMap)
|
| 977 |
-
assert lookup.LookupType == subtable.LookupType
|
| 978 |
-
subtables.append(subtable)
|
| 979 |
-
if lines.peeks()[0] in ("% subtable", "subtable end"):
|
| 980 |
-
next(lines)
|
| 981 |
-
lines.expect("lookup end")
|
| 982 |
-
|
| 983 |
-
lookup.SubTable = subtables
|
| 984 |
-
lookup.SubTableCount = len(lookup.SubTable)
|
| 985 |
-
if lookup.SubTableCount == 0:
|
| 986 |
-
# Remove this return when following is fixed:
|
| 987 |
-
# https://github.com/fonttools/fonttools/issues/789
|
| 988 |
-
return None
|
| 989 |
-
return lookup
|
| 990 |
-
|
| 991 |
-
|
| 992 |
-
def parseGSUBGPOS(lines, font, tableTag):
|
| 993 |
-
container = ttLib.getTableClass(tableTag)()
|
| 994 |
-
lookupMap = DeferredMapping()
|
| 995 |
-
featureMap = DeferredMapping()
|
| 996 |
-
assert tableTag in ("GSUB", "GPOS")
|
| 997 |
-
log.debug("Parsing %s", tableTag)
|
| 998 |
-
self = getattr(ot, tableTag)()
|
| 999 |
-
self.Version = 0x00010000
|
| 1000 |
-
fields = {
|
| 1001 |
-
"script table begin": (
|
| 1002 |
-
"ScriptList",
|
| 1003 |
-
lambda lines: parseScriptList(lines, featureMap),
|
| 1004 |
-
),
|
| 1005 |
-
"feature table begin": (
|
| 1006 |
-
"FeatureList",
|
| 1007 |
-
lambda lines: parseFeatureList(lines, lookupMap, featureMap),
|
| 1008 |
-
),
|
| 1009 |
-
"lookup": ("LookupList", None),
|
| 1010 |
-
}
|
| 1011 |
-
for attr, parser in fields.values():
|
| 1012 |
-
setattr(self, attr, None)
|
| 1013 |
-
while lines.peek() is not None:
|
| 1014 |
-
typ = lines.peek()[0].lower()
|
| 1015 |
-
if typ not in fields:
|
| 1016 |
-
log.debug("Skipping %s", lines.peek())
|
| 1017 |
-
next(lines)
|
| 1018 |
-
continue
|
| 1019 |
-
attr, parser = fields[typ]
|
| 1020 |
-
if typ == "lookup":
|
| 1021 |
-
if self.LookupList is None:
|
| 1022 |
-
self.LookupList = ot.LookupList()
|
| 1023 |
-
self.LookupList.Lookup = []
|
| 1024 |
-
_, name, _ = lines.peek()
|
| 1025 |
-
lookup = parseLookup(lines, tableTag, font, lookupMap)
|
| 1026 |
-
if lookupMap is not None:
|
| 1027 |
-
assert name not in lookupMap, "Duplicate lookup name: %s" % name
|
| 1028 |
-
lookupMap[name] = len(self.LookupList.Lookup)
|
| 1029 |
-
else:
|
| 1030 |
-
assert int(name) == len(self.LookupList.Lookup), "%d %d" % (
|
| 1031 |
-
name,
|
| 1032 |
-
len(self.Lookup),
|
| 1033 |
-
)
|
| 1034 |
-
self.LookupList.Lookup.append(lookup)
|
| 1035 |
-
else:
|
| 1036 |
-
assert getattr(self, attr) is None, attr
|
| 1037 |
-
setattr(self, attr, parser(lines))
|
| 1038 |
-
if self.LookupList:
|
| 1039 |
-
self.LookupList.LookupCount = len(self.LookupList.Lookup)
|
| 1040 |
-
if lookupMap is not None:
|
| 1041 |
-
lookupMap.applyDeferredMappings()
|
| 1042 |
-
if os.environ.get(LOOKUP_DEBUG_ENV_VAR):
|
| 1043 |
-
if "Debg" not in font:
|
| 1044 |
-
font["Debg"] = newTable("Debg")
|
| 1045 |
-
font["Debg"].data = {}
|
| 1046 |
-
debug = (
|
| 1047 |
-
font["Debg"]
|
| 1048 |
-
.data.setdefault(LOOKUP_DEBUG_INFO_KEY, {})
|
| 1049 |
-
.setdefault(tableTag, {})
|
| 1050 |
-
)
|
| 1051 |
-
for name, lookup in lookupMap.items():
|
| 1052 |
-
debug[str(lookup)] = ["", name, ""]
|
| 1053 |
-
|
| 1054 |
-
featureMap.applyDeferredMappings()
|
| 1055 |
-
container.table = self
|
| 1056 |
-
return container
|
| 1057 |
-
|
| 1058 |
-
|
| 1059 |
-
def parseGSUB(lines, font):
|
| 1060 |
-
return parseGSUBGPOS(lines, font, "GSUB")
|
| 1061 |
-
|
| 1062 |
-
|
| 1063 |
-
def parseGPOS(lines, font):
|
| 1064 |
-
return parseGSUBGPOS(lines, font, "GPOS")
|
| 1065 |
-
|
| 1066 |
-
|
| 1067 |
-
def parseAttachList(lines, font):
|
| 1068 |
-
points = {}
|
| 1069 |
-
with lines.between("attachment list"):
|
| 1070 |
-
for line in lines:
|
| 1071 |
-
glyph = makeGlyph(line[0])
|
| 1072 |
-
assert glyph not in points, glyph
|
| 1073 |
-
points[glyph] = [int(i) for i in line[1:]]
|
| 1074 |
-
return otl.buildAttachList(points, font.getReverseGlyphMap())
|
| 1075 |
-
|
| 1076 |
-
|
| 1077 |
-
def parseCaretList(lines, font):
|
| 1078 |
-
carets = {}
|
| 1079 |
-
with lines.between("carets"):
|
| 1080 |
-
for line in lines:
|
| 1081 |
-
glyph = makeGlyph(line[0])
|
| 1082 |
-
assert glyph not in carets, glyph
|
| 1083 |
-
num = int(line[1])
|
| 1084 |
-
thisCarets = [int(i) for i in line[2:]]
|
| 1085 |
-
assert num == len(thisCarets), line
|
| 1086 |
-
carets[glyph] = thisCarets
|
| 1087 |
-
return otl.buildLigCaretList(carets, {}, font.getReverseGlyphMap())
|
| 1088 |
-
|
| 1089 |
-
|
| 1090 |
-
def makeMarkFilteringSets(sets, font):
|
| 1091 |
-
self = ot.MarkGlyphSetsDef()
|
| 1092 |
-
self.MarkSetTableFormat = 1
|
| 1093 |
-
self.MarkSetCount = 1 + max(sets.keys())
|
| 1094 |
-
self.Coverage = [None] * self.MarkSetCount
|
| 1095 |
-
for k, v in sorted(sets.items()):
|
| 1096 |
-
self.Coverage[k] = makeCoverage(set(v), font)
|
| 1097 |
-
return self
|
| 1098 |
-
|
| 1099 |
-
|
| 1100 |
-
def parseMarkFilteringSets(lines, font):
|
| 1101 |
-
sets = {}
|
| 1102 |
-
with lines.between("set definition"):
|
| 1103 |
-
for line in lines:
|
| 1104 |
-
assert len(line) == 2, line
|
| 1105 |
-
glyph = makeGlyph(line[0])
|
| 1106 |
-
# TODO accept set names
|
| 1107 |
-
st = int(line[1])
|
| 1108 |
-
if st not in sets:
|
| 1109 |
-
sets[st] = []
|
| 1110 |
-
sets[st].append(glyph)
|
| 1111 |
-
return makeMarkFilteringSets(sets, font)
|
| 1112 |
-
|
| 1113 |
-
|
| 1114 |
-
def parseGDEF(lines, font):
|
| 1115 |
-
container = ttLib.getTableClass("GDEF")()
|
| 1116 |
-
log.debug("Parsing GDEF")
|
| 1117 |
-
self = ot.GDEF()
|
| 1118 |
-
fields = {
|
| 1119 |
-
"class definition begin": (
|
| 1120 |
-
"GlyphClassDef",
|
| 1121 |
-
lambda lines, font: parseClassDef(lines, font, klass=ot.GlyphClassDef),
|
| 1122 |
-
),
|
| 1123 |
-
"attachment list begin": ("AttachList", parseAttachList),
|
| 1124 |
-
"carets begin": ("LigCaretList", parseCaretList),
|
| 1125 |
-
"mark attachment class definition begin": (
|
| 1126 |
-
"MarkAttachClassDef",
|
| 1127 |
-
lambda lines, font: parseClassDef(lines, font, klass=ot.MarkAttachClassDef),
|
| 1128 |
-
),
|
| 1129 |
-
"markfilter set definition begin": ("MarkGlyphSetsDef", parseMarkFilteringSets),
|
| 1130 |
-
}
|
| 1131 |
-
for attr, parser in fields.values():
|
| 1132 |
-
setattr(self, attr, None)
|
| 1133 |
-
while lines.peek() is not None:
|
| 1134 |
-
typ = lines.peek()[0].lower()
|
| 1135 |
-
if typ not in fields:
|
| 1136 |
-
log.debug("Skipping %s", typ)
|
| 1137 |
-
next(lines)
|
| 1138 |
-
continue
|
| 1139 |
-
attr, parser = fields[typ]
|
| 1140 |
-
assert getattr(self, attr) is None, attr
|
| 1141 |
-
setattr(self, attr, parser(lines, font))
|
| 1142 |
-
self.Version = 0x00010000 if self.MarkGlyphSetsDef is None else 0x00010002
|
| 1143 |
-
container.table = self
|
| 1144 |
-
return container
|
| 1145 |
-
|
| 1146 |
-
|
| 1147 |
-
def parseCmap(lines, font):
|
| 1148 |
-
container = ttLib.getTableClass("cmap")()
|
| 1149 |
-
log.debug("Parsing cmap")
|
| 1150 |
-
tables = []
|
| 1151 |
-
while lines.peek() is not None:
|
| 1152 |
-
lines.expect("cmap subtable %d" % len(tables))
|
| 1153 |
-
platId, encId, fmt, lang = [
|
| 1154 |
-
parseCmapId(lines, field)
|
| 1155 |
-
for field in ("platformID", "encodingID", "format", "language")
|
| 1156 |
-
]
|
| 1157 |
-
table = cmap_classes[fmt](fmt)
|
| 1158 |
-
table.platformID = platId
|
| 1159 |
-
table.platEncID = encId
|
| 1160 |
-
table.language = lang
|
| 1161 |
-
table.cmap = {}
|
| 1162 |
-
line = next(lines)
|
| 1163 |
-
while line[0] != "end subtable":
|
| 1164 |
-
table.cmap[int(line[0], 16)] = line[1]
|
| 1165 |
-
line = next(lines)
|
| 1166 |
-
tables.append(table)
|
| 1167 |
-
container.tableVersion = 0
|
| 1168 |
-
container.tables = tables
|
| 1169 |
-
return container
|
| 1170 |
-
|
| 1171 |
-
|
| 1172 |
-
def parseCmapId(lines, field):
|
| 1173 |
-
line = next(lines)
|
| 1174 |
-
assert field == line[0]
|
| 1175 |
-
return int(line[1])
|
| 1176 |
-
|
| 1177 |
-
|
| 1178 |
-
def parseTable(lines, font, tableTag=None):
|
| 1179 |
-
log.debug("Parsing table")
|
| 1180 |
-
line = lines.peeks()
|
| 1181 |
-
tag = None
|
| 1182 |
-
if line[0].split()[0] == "FontDame":
|
| 1183 |
-
tag = line[0].split()[1]
|
| 1184 |
-
elif " ".join(line[0].split()[:3]) == "Font Chef Table":
|
| 1185 |
-
tag = line[0].split()[3]
|
| 1186 |
-
if tag is not None:
|
| 1187 |
-
next(lines)
|
| 1188 |
-
tag = tag.ljust(4)
|
| 1189 |
-
if tableTag is None:
|
| 1190 |
-
tableTag = tag
|
| 1191 |
-
else:
|
| 1192 |
-
assert tableTag == tag, (tableTag, tag)
|
| 1193 |
-
|
| 1194 |
-
assert (
|
| 1195 |
-
tableTag is not None
|
| 1196 |
-
), "Don't know what table to parse and data doesn't specify"
|
| 1197 |
-
|
| 1198 |
-
return {
|
| 1199 |
-
"GSUB": parseGSUB,
|
| 1200 |
-
"GPOS": parseGPOS,
|
| 1201 |
-
"GDEF": parseGDEF,
|
| 1202 |
-
"cmap": parseCmap,
|
| 1203 |
-
}[tableTag](lines, font)
|
| 1204 |
-
|
| 1205 |
-
|
| 1206 |
-
class Tokenizer(object):
|
| 1207 |
-
def __init__(self, f):
|
| 1208 |
-
# TODO BytesIO / StringIO as needed? also, figure out whether we work on bytes or unicode
|
| 1209 |
-
lines = iter(f)
|
| 1210 |
-
try:
|
| 1211 |
-
self.filename = f.name
|
| 1212 |
-
except:
|
| 1213 |
-
self.filename = None
|
| 1214 |
-
self.lines = iter(lines)
|
| 1215 |
-
self.line = ""
|
| 1216 |
-
self.lineno = 0
|
| 1217 |
-
self.stoppers = []
|
| 1218 |
-
self.buffer = None
|
| 1219 |
-
|
| 1220 |
-
def __iter__(self):
|
| 1221 |
-
return self
|
| 1222 |
-
|
| 1223 |
-
def _next_line(self):
|
| 1224 |
-
self.lineno += 1
|
| 1225 |
-
line = self.line = next(self.lines)
|
| 1226 |
-
line = [s.strip() for s in line.split("\t")]
|
| 1227 |
-
if len(line) == 1 and not line[0]:
|
| 1228 |
-
del line[0]
|
| 1229 |
-
if line and not line[-1]:
|
| 1230 |
-
log.warning("trailing tab found on line %d: %s" % (self.lineno, self.line))
|
| 1231 |
-
while line and not line[-1]:
|
| 1232 |
-
del line[-1]
|
| 1233 |
-
return line
|
| 1234 |
-
|
| 1235 |
-
def _next_nonempty(self):
|
| 1236 |
-
while True:
|
| 1237 |
-
line = self._next_line()
|
| 1238 |
-
# Skip comments and empty lines
|
| 1239 |
-
if line and line[0] and (line[0][0] != "%" or line[0] == "% subtable"):
|
| 1240 |
-
return line
|
| 1241 |
-
|
| 1242 |
-
def _next_buffered(self):
|
| 1243 |
-
if self.buffer:
|
| 1244 |
-
ret = self.buffer
|
| 1245 |
-
self.buffer = None
|
| 1246 |
-
return ret
|
| 1247 |
-
else:
|
| 1248 |
-
return self._next_nonempty()
|
| 1249 |
-
|
| 1250 |
-
def __next__(self):
|
| 1251 |
-
line = self._next_buffered()
|
| 1252 |
-
if line[0].lower() in self.stoppers:
|
| 1253 |
-
self.buffer = line
|
| 1254 |
-
raise StopIteration
|
| 1255 |
-
return line
|
| 1256 |
-
|
| 1257 |
-
def next(self):
|
| 1258 |
-
return self.__next__()
|
| 1259 |
-
|
| 1260 |
-
def peek(self):
|
| 1261 |
-
if not self.buffer:
|
| 1262 |
-
try:
|
| 1263 |
-
self.buffer = self._next_nonempty()
|
| 1264 |
-
except StopIteration:
|
| 1265 |
-
return None
|
| 1266 |
-
if self.buffer[0].lower() in self.stoppers:
|
| 1267 |
-
return None
|
| 1268 |
-
return self.buffer
|
| 1269 |
-
|
| 1270 |
-
def peeks(self):
|
| 1271 |
-
ret = self.peek()
|
| 1272 |
-
return ret if ret is not None else ("",)
|
| 1273 |
-
|
| 1274 |
-
@contextmanager
|
| 1275 |
-
def between(self, tag):
|
| 1276 |
-
start = tag + " begin"
|
| 1277 |
-
end = tag + " end"
|
| 1278 |
-
self.expectendswith(start)
|
| 1279 |
-
self.stoppers.append(end)
|
| 1280 |
-
yield
|
| 1281 |
-
del self.stoppers[-1]
|
| 1282 |
-
self.expect(tag + " end")
|
| 1283 |
-
|
| 1284 |
-
@contextmanager
|
| 1285 |
-
def until(self, tags):
|
| 1286 |
-
if type(tags) is not tuple:
|
| 1287 |
-
tags = (tags,)
|
| 1288 |
-
self.stoppers.extend(tags)
|
| 1289 |
-
yield
|
| 1290 |
-
del self.stoppers[-len(tags) :]
|
| 1291 |
-
|
| 1292 |
-
def expect(self, s):
|
| 1293 |
-
line = next(self)
|
| 1294 |
-
tag = line[0].lower()
|
| 1295 |
-
assert tag == s, "Expected '%s', got '%s'" % (s, tag)
|
| 1296 |
-
return line
|
| 1297 |
-
|
| 1298 |
-
def expectendswith(self, s):
|
| 1299 |
-
line = next(self)
|
| 1300 |
-
tag = line[0].lower()
|
| 1301 |
-
assert tag.endswith(s), "Expected '*%s', got '%s'" % (s, tag)
|
| 1302 |
-
return line
|
| 1303 |
-
|
| 1304 |
-
|
| 1305 |
-
def build(f, font, tableTag=None):
|
| 1306 |
-
"""Convert a Monotype font layout file to an OpenType layout object
|
| 1307 |
-
|
| 1308 |
-
A font object must be passed, but this may be a "dummy" font; it is only
|
| 1309 |
-
used for sorting glyph sets when making coverage tables and to hold the
|
| 1310 |
-
OpenType layout table while it is being built.
|
| 1311 |
-
|
| 1312 |
-
Args:
|
| 1313 |
-
f: A file object.
|
| 1314 |
-
font (TTFont): A font object.
|
| 1315 |
-
tableTag (string): If provided, asserts that the file contains data for the
|
| 1316 |
-
given OpenType table.
|
| 1317 |
-
|
| 1318 |
-
Returns:
|
| 1319 |
-
An object representing the table. (e.g. ``table_G_S_U_B_``)
|
| 1320 |
-
"""
|
| 1321 |
-
lines = Tokenizer(f)
|
| 1322 |
-
return parseTable(lines, font, tableTag=tableTag)
|
| 1323 |
-
|
| 1324 |
-
|
| 1325 |
-
def main(args=None, font=None):
|
| 1326 |
-
"""Convert a FontDame OTL file to TTX XML
|
| 1327 |
-
|
| 1328 |
-
Writes XML output to stdout.
|
| 1329 |
-
|
| 1330 |
-
Args:
|
| 1331 |
-
args: Command line arguments (``--font``, ``--table``, input files).
|
| 1332 |
-
"""
|
| 1333 |
-
import sys
|
| 1334 |
-
from fontTools import configLogger
|
| 1335 |
-
from fontTools.misc.testTools import MockFont
|
| 1336 |
-
|
| 1337 |
-
if args is None:
|
| 1338 |
-
args = sys.argv[1:]
|
| 1339 |
-
|
| 1340 |
-
# configure the library logger (for >= WARNING)
|
| 1341 |
-
configLogger()
|
| 1342 |
-
# comment this out to enable debug messages from mtiLib's logger
|
| 1343 |
-
# log.setLevel(logging.DEBUG)
|
| 1344 |
-
|
| 1345 |
-
import argparse
|
| 1346 |
-
|
| 1347 |
-
parser = argparse.ArgumentParser(
|
| 1348 |
-
"fonttools mtiLib",
|
| 1349 |
-
description=main.__doc__,
|
| 1350 |
-
)
|
| 1351 |
-
|
| 1352 |
-
parser.add_argument(
|
| 1353 |
-
"--font",
|
| 1354 |
-
"-f",
|
| 1355 |
-
metavar="FILE",
|
| 1356 |
-
dest="font",
|
| 1357 |
-
help="Input TTF files (used for glyph classes and sorting coverage tables)",
|
| 1358 |
-
)
|
| 1359 |
-
parser.add_argument(
|
| 1360 |
-
"--table",
|
| 1361 |
-
"-t",
|
| 1362 |
-
metavar="TABLE",
|
| 1363 |
-
dest="tableTag",
|
| 1364 |
-
help="Table to fill (sniffed from input file if not provided)",
|
| 1365 |
-
)
|
| 1366 |
-
parser.add_argument(
|
| 1367 |
-
"inputs", metavar="FILE", type=str, nargs="+", help="Input FontDame .txt files"
|
| 1368 |
-
)
|
| 1369 |
-
|
| 1370 |
-
args = parser.parse_args(args)
|
| 1371 |
-
|
| 1372 |
-
if font is None:
|
| 1373 |
-
if args.font:
|
| 1374 |
-
font = ttLib.TTFont(args.font)
|
| 1375 |
-
else:
|
| 1376 |
-
font = MockFont()
|
| 1377 |
-
|
| 1378 |
-
for f in args.inputs:
|
| 1379 |
-
log.debug("Processing %s", f)
|
| 1380 |
-
with open(f, "rt", encoding="utf-8") as f:
|
| 1381 |
-
table = build(f, font, tableTag=args.tableTag)
|
| 1382 |
-
blob = table.compile(font) # Make sure it compiles
|
| 1383 |
-
decompiled = table.__class__()
|
| 1384 |
-
decompiled.decompile(blob, font) # Make sure it decompiles!
|
| 1385 |
-
|
| 1386 |
-
# continue
|
| 1387 |
-
from fontTools.misc import xmlWriter
|
| 1388 |
-
|
| 1389 |
-
tag = table.tableTag
|
| 1390 |
-
writer = xmlWriter.XMLWriter(sys.stdout)
|
| 1391 |
-
writer.begintag(tag)
|
| 1392 |
-
writer.newline()
|
| 1393 |
-
# table.toXML(writer, font)
|
| 1394 |
-
decompiled.toXML(writer, font)
|
| 1395 |
-
writer.endtag(tag)
|
| 1396 |
-
writer.newline()
|
| 1397 |
-
|
| 1398 |
-
|
| 1399 |
-
if __name__ == "__main__":
|
| 1400 |
-
import sys
|
| 1401 |
-
|
| 1402 |
-
sys.exit(main())
|
|
|
|
|
|
|
|
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|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio_client/serializing.py
DELETED
|
@@ -1,548 +0,0 @@
|
|
| 1 |
-
from __future__ import annotations
|
| 2 |
-
|
| 3 |
-
import json
|
| 4 |
-
import os
|
| 5 |
-
import uuid
|
| 6 |
-
from pathlib import Path
|
| 7 |
-
from typing import Any
|
| 8 |
-
|
| 9 |
-
from gradio_client import media_data, utils
|
| 10 |
-
from gradio_client.data_classes import FileData
|
| 11 |
-
|
| 12 |
-
with open(Path(__file__).parent / "types.json") as f:
|
| 13 |
-
serializer_types = json.load(f)
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
class Serializable:
|
| 17 |
-
def serialized_info(self):
|
| 18 |
-
"""
|
| 19 |
-
The typing information for this component as a dictionary whose values are a list of 2 strings: [Python type, language-agnostic description].
|
| 20 |
-
Keys of the dictionary are: raw_input, raw_output, serialized_input, serialized_output
|
| 21 |
-
"""
|
| 22 |
-
return self.api_info()
|
| 23 |
-
|
| 24 |
-
def api_info(self) -> dict[str, list[str]]:
|
| 25 |
-
"""
|
| 26 |
-
The typing information for this component as a dictionary whose values are a list of 2 strings: [Python type, language-agnostic description].
|
| 27 |
-
Keys of the dictionary are: raw_input, raw_output, serialized_input, serialized_output
|
| 28 |
-
"""
|
| 29 |
-
raise NotImplementedError()
|
| 30 |
-
|
| 31 |
-
def example_inputs(self) -> dict[str, Any]:
|
| 32 |
-
"""
|
| 33 |
-
The example inputs for this component as a dictionary whose values are example inputs compatible with this component.
|
| 34 |
-
Keys of the dictionary are: raw, serialized
|
| 35 |
-
"""
|
| 36 |
-
raise NotImplementedError()
|
| 37 |
-
|
| 38 |
-
# For backwards compatibility
|
| 39 |
-
def input_api_info(self) -> tuple[str, str]:
|
| 40 |
-
api_info = self.api_info()
|
| 41 |
-
return (api_info["serialized_input"][0], api_info["serialized_input"][1])
|
| 42 |
-
|
| 43 |
-
# For backwards compatibility
|
| 44 |
-
def output_api_info(self) -> tuple[str, str]:
|
| 45 |
-
api_info = self.api_info()
|
| 46 |
-
return (api_info["serialized_output"][0], api_info["serialized_output"][1])
|
| 47 |
-
|
| 48 |
-
def serialize(self, x: Any, load_dir: str | Path = ""):
|
| 49 |
-
"""
|
| 50 |
-
Convert data from human-readable format to serialized format for a browser.
|
| 51 |
-
"""
|
| 52 |
-
return x
|
| 53 |
-
|
| 54 |
-
def deserialize(
|
| 55 |
-
self,
|
| 56 |
-
x: Any,
|
| 57 |
-
save_dir: str | Path | None = None,
|
| 58 |
-
root_url: str | None = None,
|
| 59 |
-
hf_token: str | None = None,
|
| 60 |
-
):
|
| 61 |
-
"""
|
| 62 |
-
Convert data from serialized format for a browser to human-readable format.
|
| 63 |
-
"""
|
| 64 |
-
return x
|
| 65 |
-
|
| 66 |
-
|
| 67 |
-
class SimpleSerializable(Serializable):
|
| 68 |
-
"""General class that does not perform any serialization or deserialization."""
|
| 69 |
-
|
| 70 |
-
def api_info(self) -> dict[str, bool | dict]:
|
| 71 |
-
return {
|
| 72 |
-
"info": serializer_types["SimpleSerializable"],
|
| 73 |
-
"serialized_info": False,
|
| 74 |
-
}
|
| 75 |
-
|
| 76 |
-
def example_inputs(self) -> dict[str, Any]:
|
| 77 |
-
return {
|
| 78 |
-
"raw": None,
|
| 79 |
-
"serialized": None,
|
| 80 |
-
}
|
| 81 |
-
|
| 82 |
-
|
| 83 |
-
class StringSerializable(Serializable):
|
| 84 |
-
"""Expects a string as input/output but performs no serialization."""
|
| 85 |
-
|
| 86 |
-
def api_info(self) -> dict[str, bool | dict]:
|
| 87 |
-
return {
|
| 88 |
-
"info": serializer_types["StringSerializable"],
|
| 89 |
-
"serialized_info": False,
|
| 90 |
-
}
|
| 91 |
-
|
| 92 |
-
def example_inputs(self) -> dict[str, Any]:
|
| 93 |
-
return {
|
| 94 |
-
"raw": "Howdy!",
|
| 95 |
-
"serialized": "Howdy!",
|
| 96 |
-
}
|
| 97 |
-
|
| 98 |
-
|
| 99 |
-
class ListStringSerializable(Serializable):
|
| 100 |
-
"""Expects a list of strings as input/output but performs no serialization."""
|
| 101 |
-
|
| 102 |
-
def api_info(self) -> dict[str, bool | dict]:
|
| 103 |
-
return {
|
| 104 |
-
"info": serializer_types["ListStringSerializable"],
|
| 105 |
-
"serialized_info": False,
|
| 106 |
-
}
|
| 107 |
-
|
| 108 |
-
def example_inputs(self) -> dict[str, Any]:
|
| 109 |
-
return {
|
| 110 |
-
"raw": ["Howdy!", "Merhaba"],
|
| 111 |
-
"serialized": ["Howdy!", "Merhaba"],
|
| 112 |
-
}
|
| 113 |
-
|
| 114 |
-
|
| 115 |
-
class BooleanSerializable(Serializable):
|
| 116 |
-
"""Expects a boolean as input/output but performs no serialization."""
|
| 117 |
-
|
| 118 |
-
def api_info(self) -> dict[str, bool | dict]:
|
| 119 |
-
return {
|
| 120 |
-
"info": serializer_types["BooleanSerializable"],
|
| 121 |
-
"serialized_info": False,
|
| 122 |
-
}
|
| 123 |
-
|
| 124 |
-
def example_inputs(self) -> dict[str, Any]:
|
| 125 |
-
return {
|
| 126 |
-
"raw": True,
|
| 127 |
-
"serialized": True,
|
| 128 |
-
}
|
| 129 |
-
|
| 130 |
-
|
| 131 |
-
class NumberSerializable(Serializable):
|
| 132 |
-
"""Expects a number (int/float) as input/output but performs no serialization."""
|
| 133 |
-
|
| 134 |
-
def api_info(self) -> dict[str, bool | dict]:
|
| 135 |
-
return {
|
| 136 |
-
"info": serializer_types["NumberSerializable"],
|
| 137 |
-
"serialized_info": False,
|
| 138 |
-
}
|
| 139 |
-
|
| 140 |
-
def example_inputs(self) -> dict[str, Any]:
|
| 141 |
-
return {
|
| 142 |
-
"raw": 5,
|
| 143 |
-
"serialized": 5,
|
| 144 |
-
}
|
| 145 |
-
|
| 146 |
-
|
| 147 |
-
class ImgSerializable(Serializable):
|
| 148 |
-
"""Expects a base64 string as input/output which is serialized to a filepath."""
|
| 149 |
-
|
| 150 |
-
def serialized_info(self):
|
| 151 |
-
return {"type": "string", "description": "filepath or URL to image"}
|
| 152 |
-
|
| 153 |
-
def api_info(self) -> dict[str, bool | dict]:
|
| 154 |
-
return {"info": serializer_types["ImgSerializable"], "serialized_info": True}
|
| 155 |
-
|
| 156 |
-
def example_inputs(self) -> dict[str, Any]:
|
| 157 |
-
return {
|
| 158 |
-
"raw": media_data.BASE64_IMAGE,
|
| 159 |
-
"serialized": "https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png",
|
| 160 |
-
}
|
| 161 |
-
|
| 162 |
-
def serialize(
|
| 163 |
-
self,
|
| 164 |
-
x: str | None,
|
| 165 |
-
load_dir: str | Path = "",
|
| 166 |
-
) -> str | None:
|
| 167 |
-
"""
|
| 168 |
-
Convert from human-friendly version of a file (string filepath) to a serialized
|
| 169 |
-
representation (base64).
|
| 170 |
-
Parameters:
|
| 171 |
-
x: String path to file to serialize
|
| 172 |
-
load_dir: Path to directory containing x
|
| 173 |
-
"""
|
| 174 |
-
if not x:
|
| 175 |
-
return None
|
| 176 |
-
if utils.is_http_url_like(x):
|
| 177 |
-
return utils.encode_url_to_base64(x)
|
| 178 |
-
return utils.encode_file_to_base64(Path(load_dir) / x)
|
| 179 |
-
|
| 180 |
-
def deserialize(
|
| 181 |
-
self,
|
| 182 |
-
x: str | None,
|
| 183 |
-
save_dir: str | Path | None = None,
|
| 184 |
-
root_url: str | None = None,
|
| 185 |
-
hf_token: str | None = None,
|
| 186 |
-
) -> str | None:
|
| 187 |
-
"""
|
| 188 |
-
Convert from serialized representation of a file (base64) to a human-friendly
|
| 189 |
-
version (string filepath). Optionally, save the file to the directory specified by save_dir
|
| 190 |
-
Parameters:
|
| 191 |
-
x: Base64 representation of image to deserialize into a string filepath
|
| 192 |
-
save_dir: Path to directory to save the deserialized image to
|
| 193 |
-
root_url: Ignored
|
| 194 |
-
hf_token: Ignored
|
| 195 |
-
"""
|
| 196 |
-
if x is None or x == "":
|
| 197 |
-
return None
|
| 198 |
-
file = utils.decode_base64_to_file(x, dir=save_dir)
|
| 199 |
-
return file.name
|
| 200 |
-
|
| 201 |
-
|
| 202 |
-
class FileSerializable(Serializable):
|
| 203 |
-
"""Expects a dict with base64 representation of object as input/output which is serialized to a filepath."""
|
| 204 |
-
|
| 205 |
-
def serialized_info(self):
|
| 206 |
-
return self._single_file_serialized_info()
|
| 207 |
-
|
| 208 |
-
def _single_file_api_info(self):
|
| 209 |
-
return {
|
| 210 |
-
"info": serializer_types["SingleFileSerializable"],
|
| 211 |
-
"serialized_info": True,
|
| 212 |
-
}
|
| 213 |
-
|
| 214 |
-
def _single_file_serialized_info(self):
|
| 215 |
-
return {"type": "string", "description": "filepath or URL to file"}
|
| 216 |
-
|
| 217 |
-
def _multiple_file_serialized_info(self):
|
| 218 |
-
return {
|
| 219 |
-
"type": "array",
|
| 220 |
-
"description": "List of filepath(s) or URL(s) to files",
|
| 221 |
-
"items": {"type": "string", "description": "filepath or URL to file"},
|
| 222 |
-
}
|
| 223 |
-
|
| 224 |
-
def _multiple_file_api_info(self):
|
| 225 |
-
return {
|
| 226 |
-
"info": serializer_types["MultipleFileSerializable"],
|
| 227 |
-
"serialized_info": True,
|
| 228 |
-
}
|
| 229 |
-
|
| 230 |
-
def api_info(self) -> dict[str, dict | bool]:
|
| 231 |
-
return self._single_file_api_info()
|
| 232 |
-
|
| 233 |
-
def example_inputs(self) -> dict[str, Any]:
|
| 234 |
-
return self._single_file_example_inputs()
|
| 235 |
-
|
| 236 |
-
def _single_file_example_inputs(self) -> dict[str, Any]:
|
| 237 |
-
return {
|
| 238 |
-
"raw": {"is_file": False, "data": media_data.BASE64_FILE},
|
| 239 |
-
"serialized": "https://github.com/gradio-app/gradio/raw/main/test/test_files/sample_file.pdf",
|
| 240 |
-
}
|
| 241 |
-
|
| 242 |
-
def _multiple_file_example_inputs(self) -> dict[str, Any]:
|
| 243 |
-
return {
|
| 244 |
-
"raw": [{"is_file": False, "data": media_data.BASE64_FILE}],
|
| 245 |
-
"serialized": [
|
| 246 |
-
"https://github.com/gradio-app/gradio/raw/main/test/test_files/sample_file.pdf"
|
| 247 |
-
],
|
| 248 |
-
}
|
| 249 |
-
|
| 250 |
-
def _serialize_single(
|
| 251 |
-
self, x: str | FileData | None, load_dir: str | Path = ""
|
| 252 |
-
) -> FileData | None:
|
| 253 |
-
if x is None or isinstance(x, dict):
|
| 254 |
-
return x
|
| 255 |
-
if utils.is_http_url_like(x):
|
| 256 |
-
filename = x
|
| 257 |
-
size = None
|
| 258 |
-
else:
|
| 259 |
-
filename = str(Path(load_dir) / x)
|
| 260 |
-
size = Path(filename).stat().st_size
|
| 261 |
-
return {
|
| 262 |
-
"name": filename,
|
| 263 |
-
"data": utils.encode_url_or_file_to_base64(filename),
|
| 264 |
-
"orig_name": Path(filename).name,
|
| 265 |
-
"is_file": False,
|
| 266 |
-
"size": size,
|
| 267 |
-
}
|
| 268 |
-
|
| 269 |
-
def _deserialize_single(
|
| 270 |
-
self,
|
| 271 |
-
x: str | FileData | None,
|
| 272 |
-
save_dir: str | None = None,
|
| 273 |
-
root_url: str | None = None,
|
| 274 |
-
hf_token: str | None = None,
|
| 275 |
-
) -> str | None:
|
| 276 |
-
if x is None:
|
| 277 |
-
return None
|
| 278 |
-
if isinstance(x, str):
|
| 279 |
-
file_name = utils.decode_base64_to_file(x, dir=save_dir).name
|
| 280 |
-
elif isinstance(x, dict):
|
| 281 |
-
if x.get("is_file"):
|
| 282 |
-
filepath = x.get("name")
|
| 283 |
-
assert filepath is not None, f"The 'name' field is missing in {x}"
|
| 284 |
-
if root_url is not None:
|
| 285 |
-
file_name = utils.download_tmp_copy_of_file(
|
| 286 |
-
root_url + "file=" + filepath,
|
| 287 |
-
hf_token=hf_token,
|
| 288 |
-
dir=save_dir,
|
| 289 |
-
)
|
| 290 |
-
else:
|
| 291 |
-
file_name = utils.create_tmp_copy_of_file(filepath, dir=save_dir)
|
| 292 |
-
else:
|
| 293 |
-
data = x.get("data")
|
| 294 |
-
assert data is not None, f"The 'data' field is missing in {x}"
|
| 295 |
-
file_name = utils.decode_base64_to_file(data, dir=save_dir).name
|
| 296 |
-
else:
|
| 297 |
-
raise ValueError(
|
| 298 |
-
f"A FileSerializable component can only deserialize a string or a dict, not a {type(x)}: {x}"
|
| 299 |
-
)
|
| 300 |
-
return file_name
|
| 301 |
-
|
| 302 |
-
def serialize(
|
| 303 |
-
self,
|
| 304 |
-
x: str | FileData | None | list[str | FileData | None],
|
| 305 |
-
load_dir: str | Path = "",
|
| 306 |
-
) -> FileData | None | list[FileData | None]:
|
| 307 |
-
"""
|
| 308 |
-
Convert from human-friendly version of a file (string filepath) to a
|
| 309 |
-
serialized representation (base64)
|
| 310 |
-
Parameters:
|
| 311 |
-
x: String path to file to serialize
|
| 312 |
-
load_dir: Path to directory containing x
|
| 313 |
-
"""
|
| 314 |
-
if x is None or x == "":
|
| 315 |
-
return None
|
| 316 |
-
if isinstance(x, list):
|
| 317 |
-
return [self._serialize_single(f, load_dir=load_dir) for f in x]
|
| 318 |
-
else:
|
| 319 |
-
return self._serialize_single(x, load_dir=load_dir)
|
| 320 |
-
|
| 321 |
-
def deserialize(
|
| 322 |
-
self,
|
| 323 |
-
x: str | FileData | None | list[str | FileData | None],
|
| 324 |
-
save_dir: Path | str | None = None,
|
| 325 |
-
root_url: str | None = None,
|
| 326 |
-
hf_token: str | None = None,
|
| 327 |
-
) -> str | None | list[str | None]:
|
| 328 |
-
"""
|
| 329 |
-
Convert from serialized representation of a file (base64) to a human-friendly
|
| 330 |
-
version (string filepath). Optionally, save the file to the directory specified by `save_dir`
|
| 331 |
-
Parameters:
|
| 332 |
-
x: Base64 representation of file to deserialize into a string filepath
|
| 333 |
-
save_dir: Path to directory to save the deserialized file to
|
| 334 |
-
root_url: If this component is loaded from an external Space, this is the URL of the Space.
|
| 335 |
-
hf_token: If this component is loaded from an external private Space, this is the access token for the Space
|
| 336 |
-
"""
|
| 337 |
-
if x is None:
|
| 338 |
-
return None
|
| 339 |
-
if isinstance(save_dir, Path):
|
| 340 |
-
save_dir = str(save_dir)
|
| 341 |
-
if isinstance(x, list):
|
| 342 |
-
return [
|
| 343 |
-
self._deserialize_single(
|
| 344 |
-
f, save_dir=save_dir, root_url=root_url, hf_token=hf_token
|
| 345 |
-
)
|
| 346 |
-
for f in x
|
| 347 |
-
]
|
| 348 |
-
else:
|
| 349 |
-
return self._deserialize_single(
|
| 350 |
-
x, save_dir=save_dir, root_url=root_url, hf_token=hf_token
|
| 351 |
-
)
|
| 352 |
-
|
| 353 |
-
|
| 354 |
-
class VideoSerializable(FileSerializable):
|
| 355 |
-
def serialized_info(self):
|
| 356 |
-
return {"type": "string", "description": "filepath or URL to video file"}
|
| 357 |
-
|
| 358 |
-
def api_info(self) -> dict[str, dict | bool]:
|
| 359 |
-
return {"info": serializer_types["FileSerializable"], "serialized_info": True}
|
| 360 |
-
|
| 361 |
-
def example_inputs(self) -> dict[str, Any]:
|
| 362 |
-
return {
|
| 363 |
-
"raw": {"is_file": False, "data": media_data.BASE64_VIDEO},
|
| 364 |
-
"serialized": "https://github.com/gradio-app/gradio/raw/main/test/test_files/video_sample.mp4",
|
| 365 |
-
}
|
| 366 |
-
|
| 367 |
-
def serialize(
|
| 368 |
-
self, x: str | None, load_dir: str | Path = ""
|
| 369 |
-
) -> tuple[FileData | None, None]:
|
| 370 |
-
return (super().serialize(x, load_dir), None) # type: ignore
|
| 371 |
-
|
| 372 |
-
def deserialize(
|
| 373 |
-
self,
|
| 374 |
-
x: tuple[FileData | None, FileData | None] | None,
|
| 375 |
-
save_dir: Path | str | None = None,
|
| 376 |
-
root_url: str | None = None,
|
| 377 |
-
hf_token: str | None = None,
|
| 378 |
-
) -> str | tuple[str | None, str | None] | None:
|
| 379 |
-
"""
|
| 380 |
-
Convert from serialized representation of a file (base64) to a human-friendly
|
| 381 |
-
version (string filepath). Optionally, save the file to the directory specified by `save_dir`
|
| 382 |
-
"""
|
| 383 |
-
if isinstance(x, (tuple, list)):
|
| 384 |
-
assert len(x) == 2, f"Expected tuple of length 2. Received: {x}"
|
| 385 |
-
x_as_list = [x[0], x[1]]
|
| 386 |
-
else:
|
| 387 |
-
raise ValueError(f"Expected tuple of length 2. Received: {x}")
|
| 388 |
-
deserialized_file = super().deserialize(x_as_list, save_dir, root_url, hf_token) # type: ignore
|
| 389 |
-
if isinstance(deserialized_file, list):
|
| 390 |
-
return deserialized_file[0] # ignore subtitles
|
| 391 |
-
|
| 392 |
-
|
| 393 |
-
class JSONSerializable(Serializable):
|
| 394 |
-
def serialized_info(self):
|
| 395 |
-
return {"type": "string", "description": "filepath to JSON file"}
|
| 396 |
-
|
| 397 |
-
def api_info(self) -> dict[str, dict | bool]:
|
| 398 |
-
return {"info": serializer_types["JSONSerializable"], "serialized_info": True}
|
| 399 |
-
|
| 400 |
-
def example_inputs(self) -> dict[str, Any]:
|
| 401 |
-
return {
|
| 402 |
-
"raw": {"a": 1, "b": 2},
|
| 403 |
-
"serialized": None,
|
| 404 |
-
}
|
| 405 |
-
|
| 406 |
-
def serialize(
|
| 407 |
-
self,
|
| 408 |
-
x: str | None,
|
| 409 |
-
load_dir: str | Path = "",
|
| 410 |
-
) -> dict | list | None:
|
| 411 |
-
"""
|
| 412 |
-
Convert from a a human-friendly version (string path to json file) to a
|
| 413 |
-
serialized representation (json string)
|
| 414 |
-
Parameters:
|
| 415 |
-
x: String path to json file to read to get json string
|
| 416 |
-
load_dir: Path to directory containing x
|
| 417 |
-
"""
|
| 418 |
-
if x is None or x == "":
|
| 419 |
-
return None
|
| 420 |
-
return utils.file_to_json(Path(load_dir) / x)
|
| 421 |
-
|
| 422 |
-
def deserialize(
|
| 423 |
-
self,
|
| 424 |
-
x: str | dict | list,
|
| 425 |
-
save_dir: str | Path | None = None,
|
| 426 |
-
root_url: str | None = None,
|
| 427 |
-
hf_token: str | None = None,
|
| 428 |
-
) -> str | None:
|
| 429 |
-
"""
|
| 430 |
-
Convert from serialized representation (json string) to a human-friendly
|
| 431 |
-
version (string path to json file). Optionally, save the file to the directory specified by `save_dir`
|
| 432 |
-
Parameters:
|
| 433 |
-
x: Json string
|
| 434 |
-
save_dir: Path to save the deserialized json file to
|
| 435 |
-
root_url: Ignored
|
| 436 |
-
hf_token: Ignored
|
| 437 |
-
"""
|
| 438 |
-
if x is None:
|
| 439 |
-
return None
|
| 440 |
-
return utils.dict_or_str_to_json_file(x, dir=save_dir).name
|
| 441 |
-
|
| 442 |
-
|
| 443 |
-
class GallerySerializable(Serializable):
|
| 444 |
-
def serialized_info(self):
|
| 445 |
-
return {
|
| 446 |
-
"type": "string",
|
| 447 |
-
"description": "path to directory with images and a file associating images with captions called captions.json",
|
| 448 |
-
}
|
| 449 |
-
|
| 450 |
-
def api_info(self) -> dict[str, dict | bool]:
|
| 451 |
-
return {
|
| 452 |
-
"info": serializer_types["GallerySerializable"],
|
| 453 |
-
"serialized_info": True,
|
| 454 |
-
}
|
| 455 |
-
|
| 456 |
-
def example_inputs(self) -> dict[str, Any]:
|
| 457 |
-
return {
|
| 458 |
-
"raw": [media_data.BASE64_IMAGE] * 2,
|
| 459 |
-
"serialized": [
|
| 460 |
-
"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png",
|
| 461 |
-
]
|
| 462 |
-
* 2,
|
| 463 |
-
}
|
| 464 |
-
|
| 465 |
-
def serialize(
|
| 466 |
-
self, x: str | None, load_dir: str | Path = ""
|
| 467 |
-
) -> list[list[str | None]] | None:
|
| 468 |
-
if x is None or x == "":
|
| 469 |
-
return None
|
| 470 |
-
files = []
|
| 471 |
-
captions_file = Path(x) / "captions.json"
|
| 472 |
-
with captions_file.open("r") as captions_json:
|
| 473 |
-
captions = json.load(captions_json)
|
| 474 |
-
for file_name, caption in captions.items():
|
| 475 |
-
img = FileSerializable().serialize(file_name)
|
| 476 |
-
files.append([img, caption])
|
| 477 |
-
return files
|
| 478 |
-
|
| 479 |
-
def deserialize(
|
| 480 |
-
self,
|
| 481 |
-
x: list[list[str | None]] | None,
|
| 482 |
-
save_dir: str = "",
|
| 483 |
-
root_url: str | None = None,
|
| 484 |
-
hf_token: str | None = None,
|
| 485 |
-
) -> None | str:
|
| 486 |
-
if x is None:
|
| 487 |
-
return None
|
| 488 |
-
gallery_path = Path(save_dir) / str(uuid.uuid4())
|
| 489 |
-
gallery_path.mkdir(exist_ok=True, parents=True)
|
| 490 |
-
captions = {}
|
| 491 |
-
for img_data in x:
|
| 492 |
-
if isinstance(img_data, (list, tuple)):
|
| 493 |
-
img_data, caption = img_data
|
| 494 |
-
else:
|
| 495 |
-
caption = None
|
| 496 |
-
name = FileSerializable().deserialize(
|
| 497 |
-
img_data, gallery_path, root_url=root_url, hf_token=hf_token
|
| 498 |
-
)
|
| 499 |
-
captions[name] = caption
|
| 500 |
-
captions_file = gallery_path / "captions.json"
|
| 501 |
-
with captions_file.open("w") as captions_json:
|
| 502 |
-
json.dump(captions, captions_json)
|
| 503 |
-
return os.path.abspath(gallery_path)
|
| 504 |
-
|
| 505 |
-
|
| 506 |
-
SERIALIZER_MAPPING = {}
|
| 507 |
-
for cls in Serializable.__subclasses__():
|
| 508 |
-
SERIALIZER_MAPPING[cls.__name__] = cls
|
| 509 |
-
for subcls in cls.__subclasses__():
|
| 510 |
-
SERIALIZER_MAPPING[subcls.__name__] = subcls
|
| 511 |
-
|
| 512 |
-
SERIALIZER_MAPPING["Serializable"] = SimpleSerializable
|
| 513 |
-
SERIALIZER_MAPPING["File"] = FileSerializable
|
| 514 |
-
SERIALIZER_MAPPING["UploadButton"] = FileSerializable
|
| 515 |
-
|
| 516 |
-
COMPONENT_MAPPING: dict[str, type] = {
|
| 517 |
-
"textbox": StringSerializable,
|
| 518 |
-
"number": NumberSerializable,
|
| 519 |
-
"slider": NumberSerializable,
|
| 520 |
-
"checkbox": BooleanSerializable,
|
| 521 |
-
"checkboxgroup": ListStringSerializable,
|
| 522 |
-
"radio": StringSerializable,
|
| 523 |
-
"dropdown": SimpleSerializable,
|
| 524 |
-
"image": ImgSerializable,
|
| 525 |
-
"video": FileSerializable,
|
| 526 |
-
"audio": FileSerializable,
|
| 527 |
-
"file": FileSerializable,
|
| 528 |
-
"dataframe": JSONSerializable,
|
| 529 |
-
"timeseries": JSONSerializable,
|
| 530 |
-
"state": SimpleSerializable,
|
| 531 |
-
"button": StringSerializable,
|
| 532 |
-
"uploadbutton": FileSerializable,
|
| 533 |
-
"colorpicker": StringSerializable,
|
| 534 |
-
"label": JSONSerializable,
|
| 535 |
-
"highlightedtext": JSONSerializable,
|
| 536 |
-
"json": JSONSerializable,
|
| 537 |
-
"html": StringSerializable,
|
| 538 |
-
"gallery": GallerySerializable,
|
| 539 |
-
"chatbot": JSONSerializable,
|
| 540 |
-
"model3d": FileSerializable,
|
| 541 |
-
"plot": JSONSerializable,
|
| 542 |
-
"barplot": JSONSerializable,
|
| 543 |
-
"lineplot": JSONSerializable,
|
| 544 |
-
"scatterplot": JSONSerializable,
|
| 545 |
-
"markdown": StringSerializable,
|
| 546 |
-
"code": StringSerializable,
|
| 547 |
-
"annotatedimage": JSONSerializable,
|
| 548 |
-
}
|
|
|
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|
spaces/DShrimp/PoseMaker/src/model.py
DELETED
|
@@ -1,219 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
from collections import OrderedDict
|
| 3 |
-
|
| 4 |
-
import torch
|
| 5 |
-
import torch.nn as nn
|
| 6 |
-
|
| 7 |
-
def make_layers(block, no_relu_layers):
|
| 8 |
-
layers = []
|
| 9 |
-
for layer_name, v in block.items():
|
| 10 |
-
if 'pool' in layer_name:
|
| 11 |
-
layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1],
|
| 12 |
-
padding=v[2])
|
| 13 |
-
layers.append((layer_name, layer))
|
| 14 |
-
else:
|
| 15 |
-
conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],
|
| 16 |
-
kernel_size=v[2], stride=v[3],
|
| 17 |
-
padding=v[4])
|
| 18 |
-
layers.append((layer_name, conv2d))
|
| 19 |
-
if layer_name not in no_relu_layers:
|
| 20 |
-
layers.append(('relu_'+layer_name, nn.ReLU(inplace=True)))
|
| 21 |
-
|
| 22 |
-
return nn.Sequential(OrderedDict(layers))
|
| 23 |
-
|
| 24 |
-
class bodypose_model(nn.Module):
|
| 25 |
-
def __init__(self):
|
| 26 |
-
super(bodypose_model, self).__init__()
|
| 27 |
-
|
| 28 |
-
# these layers have no relu layer
|
| 29 |
-
no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\
|
| 30 |
-
'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\
|
| 31 |
-
'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\
|
| 32 |
-
'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1']
|
| 33 |
-
blocks = {}
|
| 34 |
-
block0 = OrderedDict([
|
| 35 |
-
('conv1_1', [3, 64, 3, 1, 1]),
|
| 36 |
-
('conv1_2', [64, 64, 3, 1, 1]),
|
| 37 |
-
('pool1_stage1', [2, 2, 0]),
|
| 38 |
-
('conv2_1', [64, 128, 3, 1, 1]),
|
| 39 |
-
('conv2_2', [128, 128, 3, 1, 1]),
|
| 40 |
-
('pool2_stage1', [2, 2, 0]),
|
| 41 |
-
('conv3_1', [128, 256, 3, 1, 1]),
|
| 42 |
-
('conv3_2', [256, 256, 3, 1, 1]),
|
| 43 |
-
('conv3_3', [256, 256, 3, 1, 1]),
|
| 44 |
-
('conv3_4', [256, 256, 3, 1, 1]),
|
| 45 |
-
('pool3_stage1', [2, 2, 0]),
|
| 46 |
-
('conv4_1', [256, 512, 3, 1, 1]),
|
| 47 |
-
('conv4_2', [512, 512, 3, 1, 1]),
|
| 48 |
-
('conv4_3_CPM', [512, 256, 3, 1, 1]),
|
| 49 |
-
('conv4_4_CPM', [256, 128, 3, 1, 1])
|
| 50 |
-
])
|
| 51 |
-
|
| 52 |
-
|
| 53 |
-
# Stage 1
|
| 54 |
-
block1_1 = OrderedDict([
|
| 55 |
-
('conv5_1_CPM_L1', [128, 128, 3, 1, 1]),
|
| 56 |
-
('conv5_2_CPM_L1', [128, 128, 3, 1, 1]),
|
| 57 |
-
('conv5_3_CPM_L1', [128, 128, 3, 1, 1]),
|
| 58 |
-
('conv5_4_CPM_L1', [128, 512, 1, 1, 0]),
|
| 59 |
-
('conv5_5_CPM_L1', [512, 38, 1, 1, 0])
|
| 60 |
-
])
|
| 61 |
-
|
| 62 |
-
block1_2 = OrderedDict([
|
| 63 |
-
('conv5_1_CPM_L2', [128, 128, 3, 1, 1]),
|
| 64 |
-
('conv5_2_CPM_L2', [128, 128, 3, 1, 1]),
|
| 65 |
-
('conv5_3_CPM_L2', [128, 128, 3, 1, 1]),
|
| 66 |
-
('conv5_4_CPM_L2', [128, 512, 1, 1, 0]),
|
| 67 |
-
('conv5_5_CPM_L2', [512, 19, 1, 1, 0])
|
| 68 |
-
])
|
| 69 |
-
blocks['block1_1'] = block1_1
|
| 70 |
-
blocks['block1_2'] = block1_2
|
| 71 |
-
|
| 72 |
-
self.model0 = make_layers(block0, no_relu_layers)
|
| 73 |
-
|
| 74 |
-
# Stages 2 - 6
|
| 75 |
-
for i in range(2, 7):
|
| 76 |
-
blocks['block%d_1' % i] = OrderedDict([
|
| 77 |
-
('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]),
|
| 78 |
-
('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]),
|
| 79 |
-
('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]),
|
| 80 |
-
('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]),
|
| 81 |
-
('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]),
|
| 82 |
-
('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]),
|
| 83 |
-
('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0])
|
| 84 |
-
])
|
| 85 |
-
|
| 86 |
-
blocks['block%d_2' % i] = OrderedDict([
|
| 87 |
-
('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]),
|
| 88 |
-
('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]),
|
| 89 |
-
('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]),
|
| 90 |
-
('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]),
|
| 91 |
-
('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]),
|
| 92 |
-
('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]),
|
| 93 |
-
('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0])
|
| 94 |
-
])
|
| 95 |
-
|
| 96 |
-
for k in blocks.keys():
|
| 97 |
-
blocks[k] = make_layers(blocks[k], no_relu_layers)
|
| 98 |
-
|
| 99 |
-
self.model1_1 = blocks['block1_1']
|
| 100 |
-
self.model2_1 = blocks['block2_1']
|
| 101 |
-
self.model3_1 = blocks['block3_1']
|
| 102 |
-
self.model4_1 = blocks['block4_1']
|
| 103 |
-
self.model5_1 = blocks['block5_1']
|
| 104 |
-
self.model6_1 = blocks['block6_1']
|
| 105 |
-
|
| 106 |
-
self.model1_2 = blocks['block1_2']
|
| 107 |
-
self.model2_2 = blocks['block2_2']
|
| 108 |
-
self.model3_2 = blocks['block3_2']
|
| 109 |
-
self.model4_2 = blocks['block4_2']
|
| 110 |
-
self.model5_2 = blocks['block5_2']
|
| 111 |
-
self.model6_2 = blocks['block6_2']
|
| 112 |
-
|
| 113 |
-
|
| 114 |
-
def forward(self, x):
|
| 115 |
-
|
| 116 |
-
out1 = self.model0(x)
|
| 117 |
-
|
| 118 |
-
out1_1 = self.model1_1(out1)
|
| 119 |
-
out1_2 = self.model1_2(out1)
|
| 120 |
-
out2 = torch.cat([out1_1, out1_2, out1], 1)
|
| 121 |
-
|
| 122 |
-
out2_1 = self.model2_1(out2)
|
| 123 |
-
out2_2 = self.model2_2(out2)
|
| 124 |
-
out3 = torch.cat([out2_1, out2_2, out1], 1)
|
| 125 |
-
|
| 126 |
-
out3_1 = self.model3_1(out3)
|
| 127 |
-
out3_2 = self.model3_2(out3)
|
| 128 |
-
out4 = torch.cat([out3_1, out3_2, out1], 1)
|
| 129 |
-
|
| 130 |
-
out4_1 = self.model4_1(out4)
|
| 131 |
-
out4_2 = self.model4_2(out4)
|
| 132 |
-
out5 = torch.cat([out4_1, out4_2, out1], 1)
|
| 133 |
-
|
| 134 |
-
out5_1 = self.model5_1(out5)
|
| 135 |
-
out5_2 = self.model5_2(out5)
|
| 136 |
-
out6 = torch.cat([out5_1, out5_2, out1], 1)
|
| 137 |
-
|
| 138 |
-
out6_1 = self.model6_1(out6)
|
| 139 |
-
out6_2 = self.model6_2(out6)
|
| 140 |
-
|
| 141 |
-
return out6_1, out6_2
|
| 142 |
-
|
| 143 |
-
class handpose_model(nn.Module):
|
| 144 |
-
def __init__(self):
|
| 145 |
-
super(handpose_model, self).__init__()
|
| 146 |
-
|
| 147 |
-
# these layers have no relu layer
|
| 148 |
-
no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\
|
| 149 |
-
'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6']
|
| 150 |
-
# stage 1
|
| 151 |
-
block1_0 = OrderedDict([
|
| 152 |
-
('conv1_1', [3, 64, 3, 1, 1]),
|
| 153 |
-
('conv1_2', [64, 64, 3, 1, 1]),
|
| 154 |
-
('pool1_stage1', [2, 2, 0]),
|
| 155 |
-
('conv2_1', [64, 128, 3, 1, 1]),
|
| 156 |
-
('conv2_2', [128, 128, 3, 1, 1]),
|
| 157 |
-
('pool2_stage1', [2, 2, 0]),
|
| 158 |
-
('conv3_1', [128, 256, 3, 1, 1]),
|
| 159 |
-
('conv3_2', [256, 256, 3, 1, 1]),
|
| 160 |
-
('conv3_3', [256, 256, 3, 1, 1]),
|
| 161 |
-
('conv3_4', [256, 256, 3, 1, 1]),
|
| 162 |
-
('pool3_stage1', [2, 2, 0]),
|
| 163 |
-
('conv4_1', [256, 512, 3, 1, 1]),
|
| 164 |
-
('conv4_2', [512, 512, 3, 1, 1]),
|
| 165 |
-
('conv4_3', [512, 512, 3, 1, 1]),
|
| 166 |
-
('conv4_4', [512, 512, 3, 1, 1]),
|
| 167 |
-
('conv5_1', [512, 512, 3, 1, 1]),
|
| 168 |
-
('conv5_2', [512, 512, 3, 1, 1]),
|
| 169 |
-
('conv5_3_CPM', [512, 128, 3, 1, 1])
|
| 170 |
-
])
|
| 171 |
-
|
| 172 |
-
block1_1 = OrderedDict([
|
| 173 |
-
('conv6_1_CPM', [128, 512, 1, 1, 0]),
|
| 174 |
-
('conv6_2_CPM', [512, 22, 1, 1, 0])
|
| 175 |
-
])
|
| 176 |
-
|
| 177 |
-
blocks = {}
|
| 178 |
-
blocks['block1_0'] = block1_0
|
| 179 |
-
blocks['block1_1'] = block1_1
|
| 180 |
-
|
| 181 |
-
# stage 2-6
|
| 182 |
-
for i in range(2, 7):
|
| 183 |
-
blocks['block%d' % i] = OrderedDict([
|
| 184 |
-
('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]),
|
| 185 |
-
('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]),
|
| 186 |
-
('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]),
|
| 187 |
-
('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]),
|
| 188 |
-
('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]),
|
| 189 |
-
('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]),
|
| 190 |
-
('Mconv7_stage%d' % i, [128, 22, 1, 1, 0])
|
| 191 |
-
])
|
| 192 |
-
|
| 193 |
-
for k in blocks.keys():
|
| 194 |
-
blocks[k] = make_layers(blocks[k], no_relu_layers)
|
| 195 |
-
|
| 196 |
-
self.model1_0 = blocks['block1_0']
|
| 197 |
-
self.model1_1 = blocks['block1_1']
|
| 198 |
-
self.model2 = blocks['block2']
|
| 199 |
-
self.model3 = blocks['block3']
|
| 200 |
-
self.model4 = blocks['block4']
|
| 201 |
-
self.model5 = blocks['block5']
|
| 202 |
-
self.model6 = blocks['block6']
|
| 203 |
-
|
| 204 |
-
def forward(self, x):
|
| 205 |
-
out1_0 = self.model1_0(x)
|
| 206 |
-
out1_1 = self.model1_1(out1_0)
|
| 207 |
-
concat_stage2 = torch.cat([out1_1, out1_0], 1)
|
| 208 |
-
out_stage2 = self.model2(concat_stage2)
|
| 209 |
-
concat_stage3 = torch.cat([out_stage2, out1_0], 1)
|
| 210 |
-
out_stage3 = self.model3(concat_stage3)
|
| 211 |
-
concat_stage4 = torch.cat([out_stage3, out1_0], 1)
|
| 212 |
-
out_stage4 = self.model4(concat_stage4)
|
| 213 |
-
concat_stage5 = torch.cat([out_stage4, out1_0], 1)
|
| 214 |
-
out_stage5 = self.model5(concat_stage5)
|
| 215 |
-
concat_stage6 = torch.cat([out_stage5, out1_0], 1)
|
| 216 |
-
out_stage6 = self.model6(concat_stage6)
|
| 217 |
-
return out_stage6
|
| 218 |
-
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| 219 |
-
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