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- spaces/17TheWord/RealESRGAN/setup.py +0 -107
- spaces/1gistliPinn/ChatGPT4/Examples/All Autodesk 2018 Products Crack Keygen (x86x64) !Latest Utorrent HOT.md +0 -6
- spaces/1gistliPinn/ChatGPT4/Examples/Download Driver Booster Pro Full Version.md +0 -21
- spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Anime X 1.1.9 APK How to Download and Use It on Your Android Device.md +0 -106
- spaces/1phancelerku/anime-remove-background/Caves (Roguelike) APK A Mod Menu for Every Play Style and Preference.md +0 -148
- spaces/1phancelerku/anime-remove-background/Experience the Legendary Stick War with MOD APK Features.md +0 -93
- spaces/801artistry/RVC801/lib/uvr5_pack/lib_v5/nets_537227KB.py +0 -123
- spaces/AI-Hobbyist/Hoyo-RVC/infer_pack/commons.py +0 -166
- spaces/AIConsultant/MusicGen/audiocraft/grids/compression/_explorers.py +0 -55
- spaces/ASJMO/freegpt/g4f/Provider/Providers/Phind.py +0 -36
- spaces/Adapter/CoAdapter/ldm/modules/extra_condition/openpose/util.py +0 -203
- spaces/Adapter/T2I-Adapter/docs/examples.md +0 -41
- spaces/Adr740/CV_XPLORER_POC/app.py +0 -38
- spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/kawaseblurpipeline-plugin.d.ts +0 -30
- spaces/AiiluoChen/webui/README.md +0 -20
- spaces/AlekseyCalvin/dreambooth-training3/convertosd.py +0 -302
- spaces/Alfasign/HuggingGPT-Lite/models_server.py +0 -779
- spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/models/facial_recognition/__init__.py +0 -0
- spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/TRANSLATING.md +0 -57
- spaces/Andy1621/uniformer_image_detection/mmdet/core/utils/misc.py +0 -61
- spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/accuracy.py +0 -78
- spaces/Andy1621/uniformer_image_detection/tools/deployment/pytorch2onnx.py +0 -244
- spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py +0 -4
- spaces/Andy1621/uniformer_image_segmentation/configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py +0 -2
- spaces/AnishKumbhar/ChatBot/text-generation-webui-main/cmd_wsl.bat +0 -11
- spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/silero_tts/tts_preprocessor.py +0 -200
- spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/memory.py +0 -25
- spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/appdirs.py +0 -608
- spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/py34compat.py +0 -13
- spaces/Bakar31/MLOps_Practice_Repo_1/README.md +0 -12
- spaces/Banbri/zcvzcv/src/lib/useImageDimension.ts +0 -20
- spaces/BartPoint/VoiceChange/infer_pack/commons.py +0 -166
- spaces/Benson/text-generation/Examples/Car Drift Game Download Apkpure.md +0 -58
- spaces/Benson/text-generation/Examples/Descargar Camin Simulador ltimo Para Ventanas 10.md +0 -97
- spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/pyparsing/core.py +0 -0
- spaces/Bilalst/Gradio_Youtube_Transcript_v2/app.py +0 -116
- spaces/Bonosa2/dall-e_image-generation/app.py +0 -43
- spaces/CVPR/LIVE/thrust/thrust/iterator/transform_output_iterator.h +0 -163
- spaces/CVPR/LIVE/thrust/thrust/iterator/zip_iterator.h +0 -245
- spaces/CVPR/WALT/mmdet/models/dense_heads/free_anchor_retina_head.py +0 -270
- spaces/CVPR/WALT/walt/datasets/walt_3d.py +0 -535
- spaces/Catmeow/AI_story_writing/README.md +0 -12
- spaces/ChandraMohanNayal/AutoGPT/autogpt/commands/file_operations.py +0 -267
- spaces/CofAI/chat.b4/client/css/message-input.css +0 -27
- spaces/CofAI/chat.b4/g4f/Provider/Providers/helpers/gpt4love.py +0 -48
- spaces/Cran-May/SEA-Streamlit/README.md +0 -14
- spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/charset_normalizer/version.py +0 -6
- spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/svgLib/path/__init__.py +0 -61
- spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/__main__.py +0 -100
- spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpx/_content.py +0 -238
spaces/17TheWord/RealESRGAN/setup.py
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#!/usr/bin/env python
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from setuptools import find_packages, setup
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import os
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import subprocess
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import time
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version_file = 'realesrgan/version.py'
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def readme():
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with open('README.md', encoding='utf-8') as f:
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content = f.read()
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return content
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def get_git_hash():
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def _minimal_ext_cmd(cmd):
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# construct minimal environment
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env = {}
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for k in ['SYSTEMROOT', 'PATH', 'HOME']:
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v = os.environ.get(k)
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if v is not None:
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env[k] = v
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# LANGUAGE is used on win32
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env['LANGUAGE'] = 'C'
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env['LANG'] = 'C'
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env['LC_ALL'] = 'C'
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out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
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return out
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try:
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out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
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sha = out.strip().decode('ascii')
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except OSError:
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sha = 'unknown'
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return sha
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def get_hash():
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if os.path.exists('.git'):
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sha = get_git_hash()[:7]
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else:
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sha = 'unknown'
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return sha
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def write_version_py():
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content = """# GENERATED VERSION FILE
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# TIME: {}
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__version__ = '{}'
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__gitsha__ = '{}'
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version_info = ({})
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"""
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sha = get_hash()
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with open('VERSION', 'r') as f:
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SHORT_VERSION = f.read().strip()
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VERSION_INFO = ', '.join([x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')])
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version_file_str = content.format(time.asctime(), SHORT_VERSION, sha, VERSION_INFO)
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with open(version_file, 'w') as f:
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f.write(version_file_str)
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def get_version():
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with open(version_file, 'r') as f:
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exec(compile(f.read(), version_file, 'exec'))
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return locals()['__version__']
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def get_requirements(filename='requirements.txt'):
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here = os.path.dirname(os.path.realpath(__file__))
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with open(os.path.join(here, filename), 'r') as f:
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requires = [line.replace('\n', '') for line in f.readlines()]
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return requires
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if __name__ == '__main__':
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write_version_py()
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setup(
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name='realesrgan',
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version=get_version(),
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description='Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration',
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long_description=readme(),
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long_description_content_type='text/markdown',
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author='Xintao Wang',
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author_email='[email protected]',
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keywords='computer vision, pytorch, image restoration, super-resolution, esrgan, real-esrgan',
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url='https://github.com/xinntao/Real-ESRGAN',
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include_package_data=True,
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packages=find_packages(exclude=('options', 'datasets', 'experiments', 'results', 'tb_logger', 'wandb')),
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classifiers=[
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'Development Status :: 4 - Beta',
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'License :: OSI Approved :: Apache Software License',
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'Operating System :: OS Independent',
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'Programming Language :: Python :: 3',
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'Programming Language :: Python :: 3.7',
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'Programming Language :: Python :: 3.8',
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],
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license='BSD-3-Clause License',
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setup_requires=['cython', 'numpy'],
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install_requires=get_requirements(),
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zip_safe=False)
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spaces/1gistliPinn/ChatGPT4/Examples/All Autodesk 2018 Products Crack Keygen (x86x64) !Latest Utorrent HOT.md
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spaces/1gistliPinn/ChatGPT4/Examples/Download Driver Booster Pro Full Version.md
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<br />
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<h1>How to Download and Install Driver Booster Pro Full Version for Windows</h1>
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<h2>Step 1: Download Driver Booster Pro</h2>
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<p>You can download Driver Booster Pro from the official website[^3^] or from other trusted sources such as FileCR[^1^] or 4DOWNLOAD[^2^]. The installation file is about 30 MB in size and supports Windows 11/10/8.1/8/7/Vista/XP. Here we will use the FileCR link as an example.</p>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Anime X 1.1.9 APK How to Download and Use It on Your Android Device.md
DELETED
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@@ -1,106 +0,0 @@
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<br />
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<h1>Anime X APK 1.1.9 Download: Watch Free Anime Online</h1>
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<tr><td>It offers a huge collection of anime videos from various sources and genres.</td><td>It may not have some of the latest or obscure titles that are available on other platforms.</td></tr>
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<tr><td>It has a high-quality video player that supports subtitles and other features.</td><td>It may encounter some buffering or loading issues depending on your internet connection or server availability.</td></tr>
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<tr><td>It has a simple and user-friendly interface that makes it easy to use.</td><td>It may contain some ads or pop-ups that can be annoying or intrusive.</td></tr>
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<tr><td>It is free and does not require any registration or subscription.</td><td>It may not be legal or safe in some countries or regions where anime piracy is prohibited or regulated.</td></tr>
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</table>
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<li><strong>AnimeFlix</strong>: This is another app that lets you watch and download anime videos from various sources, such as Gogoanime, 4Anime, AnimeDao, and more. It has a sleek and modern interface that makes it easy to browse and find what you want. It also has a dark mode option that reduces eye strain and saves battery life. You can download it from here .</li>
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<li><strong>AnimeGlare</strong>: This is an app that allows you to stream anime videos from multiple servers, such as Streamtape, Vidstreaming, Mp4upload, and more. It has a simple and minimalist interface that makes it fast and smooth to use. It also has a favorites list, a history list, and a random anime generator. You can download it from here .</li>
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<li><strong>AnimeZone</strong>: This is an app that enables you to watch and download anime videos from various websites, such as AnimeHeaven, AnimeFreak, AnimeUltima, and more. It has a colorful and attractive interface that makes it fun and enjoyable to use. It also has a chat room, a news section, and a request feature. You can download it from here .</li>
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</ul>
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<h2>Conclusion</h2>
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<p>Anime X is one of the best apps for anime fans who want to watch free anime online on their Android devices. It has many features that make it stand out from other streaming platforms, such as a huge collection of anime videos, a high-quality video player, a download and share option, a simple and user-friendly interface, and a notification system. However, it also has some drawbacks, such as limited or outdated titles, buffering or loading issues, storage or data consumption, ads or pop-ups, and legal or safety concerns. Therefore, you should use it at your own risk and discretion. Alternatively, you can try some other apps that offer similar features, such as AnimeFlix, AnimeGlare, or AnimeZone.</p>
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<p>Here are some of the frequently asked questions about Anime X:</p>
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<ol>
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<li><strong>Is Anime X safe to use?</strong></li>
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<p>Anime X is not available on the Google Play Store or any other official app store. Therefore, you need to download it from third-party sources that may not be secure or reliable. Moreover, Anime X may contain some ads or pop-ups that can redirect you to malicious or inappropriate websites. Furthermore, Anime X may not be legal or safe in some countries or regions where anime piracy is prohibited or regulated. Therefore, you should use it at your own risk and discretion.</p>
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<li><strong>Does Anime X require any permissions?</strong></li>
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<p>Anime X requires some permissions to function properly on your device. These include access to your storage (to download videos), access to your network (to stream videos), access to your device settings (to change screen orientation), and access to your notifications (to alert you of new updates). You can grant or deny these permissions as per your preference.</p>
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<li><strong>How can I update Anime X?</strong></li>
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<p>Anime X does not have an auto-update feature. Therefore, you need to manually check for updates on the official website of Anime X or on the app itself. If there is a new version available, you can download and install it following the same steps as mentioned above.</p>
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<li><strong>How can I contact the developers of Anime X?</strong></li>
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<p>If you have any questions, suggestions, feedbacks, or complaints about Anime X, you can contact the developers via email at [email protected] or via their Facebook page . They will try to respond to you as soon as possible.</p>
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<li><strong>How can I support the developers of Anime X?</strong></li>
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<p>If you like Anime X and want to support the developers for their hard work and dedication, you can donate to them via PayPal or Patreon . You can also rate and review the app on their website or on their Facebook page . You can also share the app with your friends and family who love anime.</p>
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<br />
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<h1>What is Caves (Roguelike)?</h1>
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<p>Caves (Roguelike) is a pixel-art dungeon crawler game that challenges you to explore randomly generated underground levels, fight monsters, collect loot, and upgrade your character. The game is inspired by classic roguelike games such as Rogue, Nethack, and Dungeon Crawl Stone Soup, which means that every run is different and death is permanent. You can choose from various classes, skills, and items to customize your playstyle and strategy.</p>
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<p>The basic gameplay of Caves (Roguelike) is similar to other roguelike games. You start by choosing a class from the available options, such as warrior, mage, rogue, or priest. Each class has its own strengths, weaknesses, and skills. Then, you enter the dungeon and explore each floor by moving around with the arrow keys or tapping on the screen. You can interact with objects, such as chests, doors, switches, or stairs, by pressing the spacebar or tapping on them. You can also use items from your inventory by pressing the I key or tapping on the backpack icon. You can fight enemies by moving into them or using skills from your skill bar by pressing the number keys or tapping on the skill icons. You can also use potions or scrolls from your quick slots by pressing the Q or E keys or tapping on the potion or scroll icons. You can also access the game menu by pressing the ESC key or tapping on the menu icon. The game menu allows you to save, load, quit, or change the game settings. Your goal is to reach the deepest level of the dungeon and defeat the final boss. Along the way, you will find various items, such as weapons, armor, rings, amulets, or artifacts, that can improve your stats and abilities. You will also gain experience points and level up by killing enemies, which will allow you to increase your attributes and skills. However, you will also face many dangers, such as traps, curses, diseases, or hunger, that can hinder your progress and end your run. You have to be careful and smart to survive and succeed in Caves (Roguelike).</p>
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<p>Caves (Roguelike) is a challenging game that requires a lot of trial and error and learning from your mistakes. Here are some tips and tricks that can help you improve your performance and enjoyment of the game:</p>
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<ul>
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<p>A mod menu apk is a modified version of an original game application that allows you to access various cheats, hacks, or features that are not available in the official game. A mod menu apk usually has a menu interface that lets you toggle on or off different options and settings that can alter the gameplay in various ways. For example, you can enable unlimited money, health, ammo, or resources; unlock all levels, items, or characters; activate god mode, speed hack, or wall hack; or customize the graphics, sound, or controls of the game. A mod menu apk can enhance your gaming experience by making it more fun, easy, or interesting.</p>
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<li>Locate the downloaded mod menu apk file on your device and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to finish.</li>
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<p>Before you download and install Caves (Roguelike) mod menu apk, you should make sure that your device meets the minimum and recommended specifications for the game. Here are the requirements for Caves (Roguelike) mod menu apk:</p>
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<table>
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<td>Android 4.1 or higher</td>
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<td>100 MB of free storage space</td>
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<td>200 MB of free storage space or more</td>
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</tr>
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<td>A stable internet connection</td>
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</table>
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<p>There are many websites that offer Caves (Roguelike) mod menu apk for download, but not all of them are trustworthy or safe. Some of them may contain viruses, malware, or fake files that can harm your device or data. Therefore, you should be careful and selective when choosing a source for Caves (Roguelike) mod menu apk. Here are some of the reliable and safe websites where you can download Caves (Roguelike) mod menu apk:</p>
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<li>[Moddroid.com](^1^): This website provides a variety of modded games and apps for Android devices, including Caves (Roguelike) mod menu apk. The website is easy to use and has a fast download speed. The website also has a rating and review system that allows you to see the feedback and comments from other users.</li>
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<li>[Apkdone.com]: This website offers a large collection of premium and modded games and apps for Android devices, including Caves (Roguelike) mod menu apk. The website is user-friendly and has a simple design. The website also has a search and filter function that helps you find what you are looking for.</li>
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<li>[Apkhome.net]: This website features a wide range of free and modded games and apps for Android devices, including Caves (Roguelike) mod menu apk. The website is well-organized and has a clear layout. The website also has a blog section that provides news, updates, and tips about Android games and apps.</li>
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</ul>
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<h2>What features does Caves (Roguelike) mod menu apk have?</h2>
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<p>Caves (Roguelike) mod menu apk has many features that can enhance your gaming experience and make it more fun, easy, or interesting. Here are some of the features that Caves (Roguelike) mod menu apk provides:</p>
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<h3>Unlimited skills</h3>
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<p>This feature allows you to use any skill without cooldown or cost. This means that you can spam your skills as much as you want without worrying about running out of mana or waiting for them to recharge. This can give you an edge in combat and help you defeat enemies faster and easier.</p>
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<h4>How to activate unlimited skills</h4>
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| 126 |
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<p>To activate unlimited skills, you need to go to the mod menu by tapping on the icon on the top left corner of the screen. Then, you need to toggle on the option that says "Unlimited Skills". You will see a green check mark next to it when it is enabled. You can also toggle it off by tapping on it again.</p>
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<h3>God mode</h3>
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<p>This feature makes you invincible and immune to damage. This means that you can survive any attack or trap without losing any health or dying. This can make you unstoppable and fearless in exploring the dungeon and facing any enemy or boss.</p>
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<h4>How to activate god mode</h4>
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<p>To activate god mode, you need to go to the mod menu by tapping on the icon on the top left corner of the screen. Then, you need to toggle on the option that says "God Mode". You will see a green check mark next to it when it is enabled. You can also toggle it off by tapping on it again.</p>
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<h2>Conclusion</h2>
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| 132 |
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<p>Caves (Roguelike) is a pixel-art dungeon crawler game that offers a high level of replayability, difficulty, and variety. You can choose from various classes, skills, and items to customize your playstyle and strategy. You can also use a mod menu apk to access various cheats, hacks, or features that can alter the gameplay in various ways. However, you should also be aware of the risks and consequences of using a mod menu apk and use it responsibly and ethically. If you want to download and install Caves (Roguelike) mod menu apk, you can follow the steps and sources provided in this article. Have fun and enjoy the game!</p>
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<h3>FAQs</h3>
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<p>Here are some frequently asked questions and their answers about Caves (Roguelike) and its mod menu apk:</p>
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<ol>
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<li>Q: Is Caves (Roguelike) free to play?</li>
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<li>A: Yes, Caves (Roguelike) is free to play and download from the Google Play Store or other official sources. However, the game may contain ads or in-app purchases that require real money.</li>
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<li>Q: Is Caves (Roguelike) mod menu apk safe to use?</li>
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<li>A: It depends on the source and quality of the mod menu apk. Some mod menu apks may contain viruses, malware, or fake files that can harm your device or data. Therefore, you should only download and install mod menu apks from reliable and safe websites that have positive ratings and reviews from other users.</li>
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<li>Q: Is Caves (Roguelike) mod menu apk legal to use?</li>
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<li>A: It depends on the laws and regulations of your country or region. Some countries or regions may prohibit or restrict the use of mod menu apks or other forms of game modification or cheating. Therefore, you should check the terms of service and policies of the game developers or publishers and the laws and regulations of your country or region before using a mod menu apk.</li>
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<li>Q: How can I update Caves (Roguelike) mod menu apk?</li>
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<li>A: You can update Caves (Roguelike) mod menu apk by downloading and installing the latest version of the mod menu apk from the same source that you used before. However, you should also backup your game data before updating to avoid losing your progress or settings.</li>
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<li>Q: How can I uninstall Caves (Roguelike) mod menu apk?</li>
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<li>A: You can uninstall Caves (Roguelike) mod menu apk by deleting the mod menu apk file from your device or by using an uninstaller app that can remove all traces of the mod menu apk from your device. However, you should also backup your game data before uninstalling to avoid losing your progress or settings.</li>
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</ol></p> 401be4b1e0<br />
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<h1>Stick War: Legacy MOD APK - The Ultimate Strategy Game for Android</h1>
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<p>If you are a fan of strategy games, you might have heard of Stick War: Legacy, one of the most popular and addictive web games ever. Now, you can enjoy this game on your Android device with Stick War: Legacy MOD APK, a modified version that gives you unlimited gems, unlocked skins and weapons, and no ads. In this article, we will tell you everything you need to know about Stick War: Legacy MOD APK, including its features, how to download and install it, and some tips and tricks for playing it.</p>
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<h2>Introduction</h2>
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<p>Stick War: Legacy is a strategy game that puts you in charge of an army of stickmen who are fighting against other stickmen nations. You can control each and every character in your army, from miners who gather resources, to swordsmen who slash enemies, to archers who shoot arrows from afar. You can also use spells and special abilities to turn the tide of the battle. Your goal is to conquer all the territories on the map and become the ultimate stickman leader.</p>
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<h3>What is Stick War: Legacy?</h3>
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<p>Stick War: Legacy is the official mobile version of the original web game, Stick War. It was developed by Max Games Studios and released in 2016. It has been downloaded over 100 million times on Google Play Store and has an average rating of 4.5 out of 5 stars. It features several game modes, such as Campaign, Endless Deads, Tournament, and Sandbox. It also has different difficulty levels, from Normal to Insane. You can play Stick War: Legacy for free, but you will have to watch ads and earn gems slowly.</p>
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<h3>What is Stick War: Legacy MOD APK?</h3>
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<p>Stick War: Legacy MOD APK is a modified version of the original game that gives you some advantages that make the game more fun and easy. With Stick War: Legacy MOD APK, you will get unlimited gems, which are the main currency in the game. You can use gems to buy skins and weapons for your units, upgrade your spells and abilities, and unlock new game modes. You will also get all the skins and weapons unlocked from the start, so you can customize your army as you wish. Moreover, you will not see any ads in the game, which can be annoying and distracting.</p>
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<p>Here are some of the main features of Stick War: Legacy MOD APK that make it worth downloading:</p>
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<p>Gems are essential in Stick War: Legacy, as they allow you to buy and upgrade various items in the game. However, earning gems in the original game can be slow and tedious, as you have to complete missions, watch ads, or spend real money. With Stick War: Legacy MOD APK, you will get unlimited gems from the start, so you can buy anything you want without worrying about running out.</p>
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<p>Skins and weapons are cosmetic items that change the appearance of your units. They do not affect their performance or stats, but they can make your army look more cool and unique. In the original game, you have to buy skins and weapons with gems or unlock them by playing certain game modes. With Stick War: Legacy MOD APK, you will get all the skins and weapons unlocked from the start, so you can choose your favorite ones without spending any gems.</p>
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<p> <p>Ads are one of the most annoying things in any game, especially when they interrupt your gameplay or force you to watch them to get rewards. In the original game, you have to watch ads to get extra gems, unlock game modes, or revive your units. With Stick War: Legacy MOD APK, you will not see any ads in the game, which will make your gaming experience more smooth and enjoyable.</p>
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<p>If you are interested in downloading and installing Stick War: Legacy MOD APK, you can follow these simple steps:</p>
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<p>Before you can install any APK file on your Android device, you have to enable the option of unknown sources, which allows you to install apps from sources other than Google Play Store. To do this, go to your device settings, then security, then unknown sources, and turn it on.</p>
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<p>Now, you can launch the game and enjoy all the features of Stick War: Legacy MOD APK. You will see that you have unlimited gems, unlocked skins and weapons, and no ads. You can start playing the game mode of your choice and conquer all the stickman nations.</p>
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<p>Stick War: Legacy MOD APK is a fun and easy game to play, but it can also be challenging and strategic at times. Here are some tips and tricks that can help you improve your skills and win more battles:</p>
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<h3>Choose Your Strategy Wisely</h3>
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<p>In Stick War: Legacy MOD APK, you can choose from different strategies to lead your army. You can either be aggressive and attack your enemies head-on, or defensive and build up your defenses and resources. You can also be balanced and mix both approaches. Each strategy has its pros and cons, so you have to consider the situation and the enemy before deciding. For example, if you are facing a strong enemy with powerful units, you might want to be defensive and wait for an opening. On the other hand, if you are facing a weak enemy with few units, you might want to be aggressive and finish them off quickly.</p>
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<h3>Upgrade Your Units and Spells</h3>
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<p>In Stick War: Legacy MOD APK, you can upgrade your units and spells with gems. Upgrading your units will increase their health, damage, speed, and range. Upgrading your spells will increase their power, duration, and cooldown. Upgrading is essential if you want to keep up with the increasing difficulty of the game. You should upgrade your units and spells regularly and evenly, so that they are all effective and useful in different situations.</p>
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<h3>Use Your Special Abilities</h3>
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<p>In Stick War: Legacy MOD APK, you can use special abilities that can give you an edge in battle. These abilities include summoning giants, controlling a single unit, casting spells, or using items. Each ability has a different effect and cost, so you have to use them wisely and sparingly. You should use your abilities when they are most needed or when they can make a big difference in the outcome of the battle. For example, you can use the giant ability to break through enemy defenses or crush their units. You can use the control ability to take over an enemy unit or a powerful unit of your own. You can use the spell ability to heal your units or damage your enemies. You can use the item ability to boost your units or hinder your enemies.</p>
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<h2>Conclusion</h2>
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<p>Stick War: Legacy MOD APK is a great game for anyone who loves strategy games and stickman games. It is fun, addictive, challenging, and rewarding. It has amazing graphics, sound effects, animations, and gameplay. It has various game modes, difficulty levels, skins, weapons, spells, abilities, and items. It has unlimited gems, unlocked skins and weapons, and no ads. It is easy to download and install, and easy to play. It is the ultimate strategy game for Android. If you are looking for a game that will keep you entertained for hours, you should definitely try Stick War: Legacy MOD APK. You will not regret it.</p>
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<p>Here are some of the frequently asked questions about Stick War: Legacy MOD APK:</p>
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<h3>Is Stick War: Legacy MOD APK safe to download and install?</h3>
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<p>Yes, Stick War: Legacy MOD APK is safe to download and install, as long as you use a reliable source and follow the instructions carefully. The APK file does not contain any viruses, malware, or spyware that can harm your device or compromise your privacy. However, you should always be careful when downloading and installing any APK file from unknown sources, as they might not be trustworthy or compatible with your device.</p>
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spaces/801artistry/RVC801/lib/uvr5_pack/lib_v5/nets_537227KB.py
DELETED
|
@@ -1,123 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
import numpy as np
|
| 3 |
-
from torch import nn
|
| 4 |
-
import torch.nn.functional as F
|
| 5 |
-
|
| 6 |
-
from . import layers_537238KB as layers
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
class BaseASPPNet(nn.Module):
|
| 10 |
-
def __init__(self, nin, ch, dilations=(4, 8, 16)):
|
| 11 |
-
super(BaseASPPNet, self).__init__()
|
| 12 |
-
self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
|
| 13 |
-
self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
|
| 14 |
-
self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
|
| 15 |
-
self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
|
| 16 |
-
|
| 17 |
-
self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
|
| 18 |
-
|
| 19 |
-
self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
|
| 20 |
-
self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
|
| 21 |
-
self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
|
| 22 |
-
self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
|
| 23 |
-
|
| 24 |
-
def __call__(self, x):
|
| 25 |
-
h, e1 = self.enc1(x)
|
| 26 |
-
h, e2 = self.enc2(h)
|
| 27 |
-
h, e3 = self.enc3(h)
|
| 28 |
-
h, e4 = self.enc4(h)
|
| 29 |
-
|
| 30 |
-
h = self.aspp(h)
|
| 31 |
-
|
| 32 |
-
h = self.dec4(h, e4)
|
| 33 |
-
h = self.dec3(h, e3)
|
| 34 |
-
h = self.dec2(h, e2)
|
| 35 |
-
h = self.dec1(h, e1)
|
| 36 |
-
|
| 37 |
-
return h
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
class CascadedASPPNet(nn.Module):
|
| 41 |
-
def __init__(self, n_fft):
|
| 42 |
-
super(CascadedASPPNet, self).__init__()
|
| 43 |
-
self.stg1_low_band_net = BaseASPPNet(2, 64)
|
| 44 |
-
self.stg1_high_band_net = BaseASPPNet(2, 64)
|
| 45 |
-
|
| 46 |
-
self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
|
| 47 |
-
self.stg2_full_band_net = BaseASPPNet(32, 64)
|
| 48 |
-
|
| 49 |
-
self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0)
|
| 50 |
-
self.stg3_full_band_net = BaseASPPNet(64, 128)
|
| 51 |
-
|
| 52 |
-
self.out = nn.Conv2d(128, 2, 1, bias=False)
|
| 53 |
-
self.aux1_out = nn.Conv2d(64, 2, 1, bias=False)
|
| 54 |
-
self.aux2_out = nn.Conv2d(64, 2, 1, bias=False)
|
| 55 |
-
|
| 56 |
-
self.max_bin = n_fft // 2
|
| 57 |
-
self.output_bin = n_fft // 2 + 1
|
| 58 |
-
|
| 59 |
-
self.offset = 128
|
| 60 |
-
|
| 61 |
-
def forward(self, x, aggressiveness=None):
|
| 62 |
-
mix = x.detach()
|
| 63 |
-
x = x.clone()
|
| 64 |
-
|
| 65 |
-
x = x[:, :, : self.max_bin]
|
| 66 |
-
|
| 67 |
-
bandw = x.size()[2] // 2
|
| 68 |
-
aux1 = torch.cat(
|
| 69 |
-
[
|
| 70 |
-
self.stg1_low_band_net(x[:, :, :bandw]),
|
| 71 |
-
self.stg1_high_band_net(x[:, :, bandw:]),
|
| 72 |
-
],
|
| 73 |
-
dim=2,
|
| 74 |
-
)
|
| 75 |
-
|
| 76 |
-
h = torch.cat([x, aux1], dim=1)
|
| 77 |
-
aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
|
| 78 |
-
|
| 79 |
-
h = torch.cat([x, aux1, aux2], dim=1)
|
| 80 |
-
h = self.stg3_full_band_net(self.stg3_bridge(h))
|
| 81 |
-
|
| 82 |
-
mask = torch.sigmoid(self.out(h))
|
| 83 |
-
mask = F.pad(
|
| 84 |
-
input=mask,
|
| 85 |
-
pad=(0, 0, 0, self.output_bin - mask.size()[2]),
|
| 86 |
-
mode="replicate",
|
| 87 |
-
)
|
| 88 |
-
|
| 89 |
-
if self.training:
|
| 90 |
-
aux1 = torch.sigmoid(self.aux1_out(aux1))
|
| 91 |
-
aux1 = F.pad(
|
| 92 |
-
input=aux1,
|
| 93 |
-
pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
|
| 94 |
-
mode="replicate",
|
| 95 |
-
)
|
| 96 |
-
aux2 = torch.sigmoid(self.aux2_out(aux2))
|
| 97 |
-
aux2 = F.pad(
|
| 98 |
-
input=aux2,
|
| 99 |
-
pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
|
| 100 |
-
mode="replicate",
|
| 101 |
-
)
|
| 102 |
-
return mask * mix, aux1 * mix, aux2 * mix
|
| 103 |
-
else:
|
| 104 |
-
if aggressiveness:
|
| 105 |
-
mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
|
| 106 |
-
mask[:, :, : aggressiveness["split_bin"]],
|
| 107 |
-
1 + aggressiveness["value"] / 3,
|
| 108 |
-
)
|
| 109 |
-
mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
|
| 110 |
-
mask[:, :, aggressiveness["split_bin"] :],
|
| 111 |
-
1 + aggressiveness["value"],
|
| 112 |
-
)
|
| 113 |
-
|
| 114 |
-
return mask * mix
|
| 115 |
-
|
| 116 |
-
def predict(self, x_mag, aggressiveness=None):
|
| 117 |
-
h = self.forward(x_mag, aggressiveness)
|
| 118 |
-
|
| 119 |
-
if self.offset > 0:
|
| 120 |
-
h = h[:, :, :, self.offset : -self.offset]
|
| 121 |
-
assert h.size()[3] > 0
|
| 122 |
-
|
| 123 |
-
return h
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spaces/AI-Hobbyist/Hoyo-RVC/infer_pack/commons.py
DELETED
|
@@ -1,166 +0,0 @@
|
|
| 1 |
-
import math
|
| 2 |
-
import numpy as np
|
| 3 |
-
import torch
|
| 4 |
-
from torch import nn
|
| 5 |
-
from torch.nn import functional as F
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
def init_weights(m, mean=0.0, std=0.01):
|
| 9 |
-
classname = m.__class__.__name__
|
| 10 |
-
if classname.find("Conv") != -1:
|
| 11 |
-
m.weight.data.normal_(mean, std)
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
def get_padding(kernel_size, dilation=1):
|
| 15 |
-
return int((kernel_size * dilation - dilation) / 2)
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
def convert_pad_shape(pad_shape):
|
| 19 |
-
l = pad_shape[::-1]
|
| 20 |
-
pad_shape = [item for sublist in l for item in sublist]
|
| 21 |
-
return pad_shape
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
def kl_divergence(m_p, logs_p, m_q, logs_q):
|
| 25 |
-
"""KL(P||Q)"""
|
| 26 |
-
kl = (logs_q - logs_p) - 0.5
|
| 27 |
-
kl += (
|
| 28 |
-
0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
|
| 29 |
-
)
|
| 30 |
-
return kl
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
def rand_gumbel(shape):
|
| 34 |
-
"""Sample from the Gumbel distribution, protect from overflows."""
|
| 35 |
-
uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
|
| 36 |
-
return -torch.log(-torch.log(uniform_samples))
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
def rand_gumbel_like(x):
|
| 40 |
-
g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
|
| 41 |
-
return g
|
| 42 |
-
|
| 43 |
-
|
| 44 |
-
def slice_segments(x, ids_str, segment_size=4):
|
| 45 |
-
ret = torch.zeros_like(x[:, :, :segment_size])
|
| 46 |
-
for i in range(x.size(0)):
|
| 47 |
-
idx_str = ids_str[i]
|
| 48 |
-
idx_end = idx_str + segment_size
|
| 49 |
-
ret[i] = x[i, :, idx_str:idx_end]
|
| 50 |
-
return ret
|
| 51 |
-
|
| 52 |
-
|
| 53 |
-
def slice_segments2(x, ids_str, segment_size=4):
|
| 54 |
-
ret = torch.zeros_like(x[:, :segment_size])
|
| 55 |
-
for i in range(x.size(0)):
|
| 56 |
-
idx_str = ids_str[i]
|
| 57 |
-
idx_end = idx_str + segment_size
|
| 58 |
-
ret[i] = x[i, idx_str:idx_end]
|
| 59 |
-
return ret
|
| 60 |
-
|
| 61 |
-
|
| 62 |
-
def rand_slice_segments(x, x_lengths=None, segment_size=4):
|
| 63 |
-
b, d, t = x.size()
|
| 64 |
-
if x_lengths is None:
|
| 65 |
-
x_lengths = t
|
| 66 |
-
ids_str_max = x_lengths - segment_size + 1
|
| 67 |
-
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
| 68 |
-
ret = slice_segments(x, ids_str, segment_size)
|
| 69 |
-
return ret, ids_str
|
| 70 |
-
|
| 71 |
-
|
| 72 |
-
def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
|
| 73 |
-
position = torch.arange(length, dtype=torch.float)
|
| 74 |
-
num_timescales = channels // 2
|
| 75 |
-
log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
|
| 76 |
-
num_timescales - 1
|
| 77 |
-
)
|
| 78 |
-
inv_timescales = min_timescale * torch.exp(
|
| 79 |
-
torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
|
| 80 |
-
)
|
| 81 |
-
scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
|
| 82 |
-
signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
|
| 83 |
-
signal = F.pad(signal, [0, 0, 0, channels % 2])
|
| 84 |
-
signal = signal.view(1, channels, length)
|
| 85 |
-
return signal
|
| 86 |
-
|
| 87 |
-
|
| 88 |
-
def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
|
| 89 |
-
b, channels, length = x.size()
|
| 90 |
-
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
| 91 |
-
return x + signal.to(dtype=x.dtype, device=x.device)
|
| 92 |
-
|
| 93 |
-
|
| 94 |
-
def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
|
| 95 |
-
b, channels, length = x.size()
|
| 96 |
-
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
| 97 |
-
return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
|
| 98 |
-
|
| 99 |
-
|
| 100 |
-
def subsequent_mask(length):
|
| 101 |
-
mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
|
| 102 |
-
return mask
|
| 103 |
-
|
| 104 |
-
|
| 105 |
-
@torch.jit.script
|
| 106 |
-
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
| 107 |
-
n_channels_int = n_channels[0]
|
| 108 |
-
in_act = input_a + input_b
|
| 109 |
-
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
| 110 |
-
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
| 111 |
-
acts = t_act * s_act
|
| 112 |
-
return acts
|
| 113 |
-
|
| 114 |
-
|
| 115 |
-
def convert_pad_shape(pad_shape):
|
| 116 |
-
l = pad_shape[::-1]
|
| 117 |
-
pad_shape = [item for sublist in l for item in sublist]
|
| 118 |
-
return pad_shape
|
| 119 |
-
|
| 120 |
-
|
| 121 |
-
def shift_1d(x):
|
| 122 |
-
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
|
| 123 |
-
return x
|
| 124 |
-
|
| 125 |
-
|
| 126 |
-
def sequence_mask(length, max_length=None):
|
| 127 |
-
if max_length is None:
|
| 128 |
-
max_length = length.max()
|
| 129 |
-
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
| 130 |
-
return x.unsqueeze(0) < length.unsqueeze(1)
|
| 131 |
-
|
| 132 |
-
|
| 133 |
-
def generate_path(duration, mask):
|
| 134 |
-
"""
|
| 135 |
-
duration: [b, 1, t_x]
|
| 136 |
-
mask: [b, 1, t_y, t_x]
|
| 137 |
-
"""
|
| 138 |
-
device = duration.device
|
| 139 |
-
|
| 140 |
-
b, _, t_y, t_x = mask.shape
|
| 141 |
-
cum_duration = torch.cumsum(duration, -1)
|
| 142 |
-
|
| 143 |
-
cum_duration_flat = cum_duration.view(b * t_x)
|
| 144 |
-
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
|
| 145 |
-
path = path.view(b, t_x, t_y)
|
| 146 |
-
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
|
| 147 |
-
path = path.unsqueeze(1).transpose(2, 3) * mask
|
| 148 |
-
return path
|
| 149 |
-
|
| 150 |
-
|
| 151 |
-
def clip_grad_value_(parameters, clip_value, norm_type=2):
|
| 152 |
-
if isinstance(parameters, torch.Tensor):
|
| 153 |
-
parameters = [parameters]
|
| 154 |
-
parameters = list(filter(lambda p: p.grad is not None, parameters))
|
| 155 |
-
norm_type = float(norm_type)
|
| 156 |
-
if clip_value is not None:
|
| 157 |
-
clip_value = float(clip_value)
|
| 158 |
-
|
| 159 |
-
total_norm = 0
|
| 160 |
-
for p in parameters:
|
| 161 |
-
param_norm = p.grad.data.norm(norm_type)
|
| 162 |
-
total_norm += param_norm.item() ** norm_type
|
| 163 |
-
if clip_value is not None:
|
| 164 |
-
p.grad.data.clamp_(min=-clip_value, max=clip_value)
|
| 165 |
-
total_norm = total_norm ** (1.0 / norm_type)
|
| 166 |
-
return total_norm
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spaces/AIConsultant/MusicGen/audiocraft/grids/compression/_explorers.py
DELETED
|
@@ -1,55 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
| 2 |
-
# All rights reserved.
|
| 3 |
-
#
|
| 4 |
-
# This source code is licensed under the license found in the
|
| 5 |
-
# LICENSE file in the root directory of this source tree.
|
| 6 |
-
|
| 7 |
-
import treetable as tt
|
| 8 |
-
|
| 9 |
-
from .._base_explorers import BaseExplorer
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
class CompressionExplorer(BaseExplorer):
|
| 13 |
-
eval_metrics = ["sisnr", "visqol"]
|
| 14 |
-
|
| 15 |
-
def stages(self):
|
| 16 |
-
return ["train", "valid", "evaluate"]
|
| 17 |
-
|
| 18 |
-
def get_grid_meta(self):
|
| 19 |
-
"""Returns the list of Meta information to display for each XP/job.
|
| 20 |
-
"""
|
| 21 |
-
return [
|
| 22 |
-
tt.leaf("index", align=">"),
|
| 23 |
-
tt.leaf("name", wrap=140),
|
| 24 |
-
tt.leaf("state"),
|
| 25 |
-
tt.leaf("sig", align=">"),
|
| 26 |
-
]
|
| 27 |
-
|
| 28 |
-
def get_grid_metrics(self):
|
| 29 |
-
"""Return the metrics that should be displayed in the tracking table.
|
| 30 |
-
"""
|
| 31 |
-
return [
|
| 32 |
-
tt.group(
|
| 33 |
-
"train",
|
| 34 |
-
[
|
| 35 |
-
tt.leaf("epoch"),
|
| 36 |
-
tt.leaf("bandwidth", ".2f"),
|
| 37 |
-
tt.leaf("adv", ".4f"),
|
| 38 |
-
tt.leaf("d_loss", ".4f"),
|
| 39 |
-
],
|
| 40 |
-
align=">",
|
| 41 |
-
),
|
| 42 |
-
tt.group(
|
| 43 |
-
"valid",
|
| 44 |
-
[
|
| 45 |
-
tt.leaf("bandwidth", ".2f"),
|
| 46 |
-
tt.leaf("adv", ".4f"),
|
| 47 |
-
tt.leaf("msspec", ".4f"),
|
| 48 |
-
tt.leaf("sisnr", ".2f"),
|
| 49 |
-
],
|
| 50 |
-
align=">",
|
| 51 |
-
),
|
| 52 |
-
tt.group(
|
| 53 |
-
"evaluate", [tt.leaf(name, ".3f") for name in self.eval_metrics], align=">"
|
| 54 |
-
),
|
| 55 |
-
]
|
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|
spaces/ASJMO/freegpt/g4f/Provider/Providers/Phind.py
DELETED
|
@@ -1,36 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import json
|
| 3 |
-
import time
|
| 4 |
-
import subprocess
|
| 5 |
-
|
| 6 |
-
from ...typing import sha256, Dict, get_type_hints
|
| 7 |
-
|
| 8 |
-
url = 'https://phind.com'
|
| 9 |
-
model = ['gpt-4']
|
| 10 |
-
supports_stream = True
|
| 11 |
-
|
| 12 |
-
def _create_completion(model: str, messages: list, stream: bool, **kwargs):
|
| 13 |
-
|
| 14 |
-
path = os.path.dirname(os.path.realpath(__file__))
|
| 15 |
-
config = json.dumps({
|
| 16 |
-
'model': model,
|
| 17 |
-
'messages': messages}, separators=(',', ':'))
|
| 18 |
-
|
| 19 |
-
cmd = ['python', f'{path}/helpers/phind.py', config]
|
| 20 |
-
|
| 21 |
-
p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
|
| 22 |
-
|
| 23 |
-
for line in iter(p.stdout.readline, b''):
|
| 24 |
-
if b'<title>Just a moment...</title>' in line:
|
| 25 |
-
os.system('clear' if os.name == 'posix' else 'cls')
|
| 26 |
-
yield 'Clouflare error, please try again...'
|
| 27 |
-
os._exit(0)
|
| 28 |
-
|
| 29 |
-
else:
|
| 30 |
-
if b'ping - 2023-' in line:
|
| 31 |
-
continue
|
| 32 |
-
|
| 33 |
-
yield line.decode('cp1251') #[:-1]
|
| 34 |
-
|
| 35 |
-
params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
|
| 36 |
-
'(%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/Adapter/CoAdapter/ldm/modules/extra_condition/openpose/util.py
DELETED
|
@@ -1,203 +0,0 @@
|
|
| 1 |
-
import math
|
| 2 |
-
|
| 3 |
-
import cv2
|
| 4 |
-
import matplotlib
|
| 5 |
-
import numpy as np
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
def padRightDownCorner(img, stride, padValue):
|
| 9 |
-
h = img.shape[0]
|
| 10 |
-
w = img.shape[1]
|
| 11 |
-
|
| 12 |
-
pad = 4 * [None]
|
| 13 |
-
pad[0] = 0 # up
|
| 14 |
-
pad[1] = 0 # left
|
| 15 |
-
pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down
|
| 16 |
-
pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right
|
| 17 |
-
|
| 18 |
-
img_padded = img
|
| 19 |
-
pad_up = np.tile(img_padded[0:1, :, :] * 0 + padValue, (pad[0], 1, 1))
|
| 20 |
-
img_padded = np.concatenate((pad_up, img_padded), axis=0)
|
| 21 |
-
pad_left = np.tile(img_padded[:, 0:1, :] * 0 + padValue, (1, pad[1], 1))
|
| 22 |
-
img_padded = np.concatenate((pad_left, img_padded), axis=1)
|
| 23 |
-
pad_down = np.tile(img_padded[-2:-1, :, :] * 0 + padValue, (pad[2], 1, 1))
|
| 24 |
-
img_padded = np.concatenate((img_padded, pad_down), axis=0)
|
| 25 |
-
pad_right = np.tile(img_padded[:, -2:-1, :] * 0 + padValue, (1, pad[3], 1))
|
| 26 |
-
img_padded = np.concatenate((img_padded, pad_right), axis=1)
|
| 27 |
-
|
| 28 |
-
return img_padded, pad
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
# transfer caffe model to pytorch which will match the layer name
|
| 32 |
-
def transfer(model, model_weights):
|
| 33 |
-
transfered_model_weights = {}
|
| 34 |
-
for weights_name in model.state_dict().keys():
|
| 35 |
-
transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])]
|
| 36 |
-
return transfered_model_weights
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
# draw the body keypoint and lims
|
| 40 |
-
def draw_bodypose(canvas, candidate, subset):
|
| 41 |
-
stickwidth = 4
|
| 42 |
-
limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \
|
| 43 |
-
[10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \
|
| 44 |
-
[1, 16], [16, 18], [3, 17], [6, 18]]
|
| 45 |
-
|
| 46 |
-
colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
|
| 47 |
-
[0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
|
| 48 |
-
[170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
|
| 49 |
-
for i in range(18):
|
| 50 |
-
for n in range(len(subset)):
|
| 51 |
-
index = int(subset[n][i])
|
| 52 |
-
if index == -1:
|
| 53 |
-
continue
|
| 54 |
-
x, y = candidate[index][0:2]
|
| 55 |
-
cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1)
|
| 56 |
-
for i in range(17):
|
| 57 |
-
for n in range(len(subset)):
|
| 58 |
-
index = subset[n][np.array(limbSeq[i]) - 1]
|
| 59 |
-
if -1 in index:
|
| 60 |
-
continue
|
| 61 |
-
cur_canvas = canvas.copy()
|
| 62 |
-
Y = candidate[index.astype(int), 0]
|
| 63 |
-
X = candidate[index.astype(int), 1]
|
| 64 |
-
mX = np.mean(X)
|
| 65 |
-
mY = np.mean(Y)
|
| 66 |
-
length = ((X[0] - X[1])**2 + (Y[0] - Y[1])**2)**0.5
|
| 67 |
-
angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
|
| 68 |
-
polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
|
| 69 |
-
cv2.fillConvexPoly(cur_canvas, polygon, colors[i])
|
| 70 |
-
canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
|
| 71 |
-
# plt.imsave("preview.jpg", canvas[:, :, [2, 1, 0]])
|
| 72 |
-
# plt.imshow(canvas[:, :, [2, 1, 0]])
|
| 73 |
-
return canvas
|
| 74 |
-
|
| 75 |
-
|
| 76 |
-
# image drawed by opencv is not good.
|
| 77 |
-
def draw_handpose(canvas, all_hand_peaks, show_number=False):
|
| 78 |
-
edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \
|
| 79 |
-
[10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]]
|
| 80 |
-
|
| 81 |
-
for peaks in all_hand_peaks:
|
| 82 |
-
for ie, e in enumerate(edges):
|
| 83 |
-
if np.sum(np.all(peaks[e], axis=1) == 0) == 0:
|
| 84 |
-
x1, y1 = peaks[e[0]]
|
| 85 |
-
x2, y2 = peaks[e[1]]
|
| 86 |
-
cv2.line(
|
| 87 |
-
canvas, (x1, y1), (x2, y2),
|
| 88 |
-
matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255,
|
| 89 |
-
thickness=2)
|
| 90 |
-
|
| 91 |
-
for i, keyponit in enumerate(peaks):
|
| 92 |
-
x, y = keyponit
|
| 93 |
-
cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
|
| 94 |
-
if show_number:
|
| 95 |
-
cv2.putText(canvas, str(i), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 0), lineType=cv2.LINE_AA)
|
| 96 |
-
return canvas
|
| 97 |
-
|
| 98 |
-
|
| 99 |
-
# detect hand according to body pose keypoints
|
| 100 |
-
# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp
|
| 101 |
-
def handDetect(candidate, subset, oriImg):
|
| 102 |
-
# right hand: wrist 4, elbow 3, shoulder 2
|
| 103 |
-
# left hand: wrist 7, elbow 6, shoulder 5
|
| 104 |
-
ratioWristElbow = 0.33
|
| 105 |
-
detect_result = []
|
| 106 |
-
image_height, image_width = oriImg.shape[0:2]
|
| 107 |
-
for person in subset.astype(int):
|
| 108 |
-
# if any of three not detected
|
| 109 |
-
has_left = np.sum(person[[5, 6, 7]] == -1) == 0
|
| 110 |
-
has_right = np.sum(person[[2, 3, 4]] == -1) == 0
|
| 111 |
-
if not (has_left or has_right):
|
| 112 |
-
continue
|
| 113 |
-
hands = []
|
| 114 |
-
#left hand
|
| 115 |
-
if has_left:
|
| 116 |
-
left_shoulder_index, left_elbow_index, left_wrist_index = person[[5, 6, 7]]
|
| 117 |
-
x1, y1 = candidate[left_shoulder_index][:2]
|
| 118 |
-
x2, y2 = candidate[left_elbow_index][:2]
|
| 119 |
-
x3, y3 = candidate[left_wrist_index][:2]
|
| 120 |
-
hands.append([x1, y1, x2, y2, x3, y3, True])
|
| 121 |
-
# right hand
|
| 122 |
-
if has_right:
|
| 123 |
-
right_shoulder_index, right_elbow_index, right_wrist_index = person[[2, 3, 4]]
|
| 124 |
-
x1, y1 = candidate[right_shoulder_index][:2]
|
| 125 |
-
x2, y2 = candidate[right_elbow_index][:2]
|
| 126 |
-
x3, y3 = candidate[right_wrist_index][:2]
|
| 127 |
-
hands.append([x1, y1, x2, y2, x3, y3, False])
|
| 128 |
-
|
| 129 |
-
for x1, y1, x2, y2, x3, y3, is_left in hands:
|
| 130 |
-
# pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox
|
| 131 |
-
# handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]);
|
| 132 |
-
# handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]);
|
| 133 |
-
# const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow);
|
| 134 |
-
# const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder);
|
| 135 |
-
# handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder);
|
| 136 |
-
x = x3 + ratioWristElbow * (x3 - x2)
|
| 137 |
-
y = y3 + ratioWristElbow * (y3 - y2)
|
| 138 |
-
distanceWristElbow = math.sqrt((x3 - x2)**2 + (y3 - y2)**2)
|
| 139 |
-
distanceElbowShoulder = math.sqrt((x2 - x1)**2 + (y2 - y1)**2)
|
| 140 |
-
width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder)
|
| 141 |
-
# x-y refers to the center --> offset to topLeft point
|
| 142 |
-
# handRectangle.x -= handRectangle.width / 2.f;
|
| 143 |
-
# handRectangle.y -= handRectangle.height / 2.f;
|
| 144 |
-
x -= width / 2
|
| 145 |
-
y -= width / 2 # width = height
|
| 146 |
-
# overflow the image
|
| 147 |
-
if x < 0: x = 0
|
| 148 |
-
if y < 0: y = 0
|
| 149 |
-
width1 = width
|
| 150 |
-
width2 = width
|
| 151 |
-
if x + width > image_width: width1 = image_width - x
|
| 152 |
-
if y + width > image_height: width2 = image_height - y
|
| 153 |
-
width = min(width1, width2)
|
| 154 |
-
# the max hand box value is 20 pixels
|
| 155 |
-
if width >= 20:
|
| 156 |
-
detect_result.append([int(x), int(y), int(width), is_left])
|
| 157 |
-
'''
|
| 158 |
-
return value: [[x, y, w, True if left hand else False]].
|
| 159 |
-
width=height since the network require squared input.
|
| 160 |
-
x, y is the coordinate of top left
|
| 161 |
-
'''
|
| 162 |
-
return detect_result
|
| 163 |
-
|
| 164 |
-
|
| 165 |
-
# get max index of 2d array
|
| 166 |
-
def npmax(array):
|
| 167 |
-
arrayindex = array.argmax(1)
|
| 168 |
-
arrayvalue = array.max(1)
|
| 169 |
-
i = arrayvalue.argmax()
|
| 170 |
-
j = arrayindex[i]
|
| 171 |
-
return i, j
|
| 172 |
-
|
| 173 |
-
|
| 174 |
-
def HWC3(x):
|
| 175 |
-
assert x.dtype == np.uint8
|
| 176 |
-
if x.ndim == 2:
|
| 177 |
-
x = x[:, :, None]
|
| 178 |
-
assert x.ndim == 3
|
| 179 |
-
H, W, C = x.shape
|
| 180 |
-
assert C == 1 or C == 3 or C == 4
|
| 181 |
-
if C == 3:
|
| 182 |
-
return x
|
| 183 |
-
if C == 1:
|
| 184 |
-
return np.concatenate([x, x, x], axis=2)
|
| 185 |
-
if C == 4:
|
| 186 |
-
color = x[:, :, 0:3].astype(np.float32)
|
| 187 |
-
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
|
| 188 |
-
y = color * alpha + 255.0 * (1.0 - alpha)
|
| 189 |
-
y = y.clip(0, 255).astype(np.uint8)
|
| 190 |
-
return y
|
| 191 |
-
|
| 192 |
-
|
| 193 |
-
def resize_image(input_image, resolution):
|
| 194 |
-
H, W, C = input_image.shape
|
| 195 |
-
H = float(H)
|
| 196 |
-
W = float(W)
|
| 197 |
-
k = float(resolution) / min(H, W)
|
| 198 |
-
H *= k
|
| 199 |
-
W *= k
|
| 200 |
-
H = int(np.round(H / 64.0)) * 64
|
| 201 |
-
W = int(np.round(W / 64.0)) * 64
|
| 202 |
-
img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
|
| 203 |
-
return img
|
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|
spaces/Adapter/T2I-Adapter/docs/examples.md
DELETED
|
@@ -1,41 +0,0 @@
|
|
| 1 |
-
# Demos
|
| 2 |
-
|
| 3 |
-
## Style Adapter
|
| 4 |
-
|
| 5 |
-
<p align="center">
|
| 6 |
-
<img src="https://user-images.githubusercontent.com/17445847/222734169-d47789e8-e83c-48c2-80ef-a896c2bafbb0.png" height=450>
|
| 7 |
-
</p>
|
| 8 |
-
|
| 9 |
-
## Color Adapter (Spatial Palette)
|
| 10 |
-
|
| 11 |
-
<p align="center">
|
| 12 |
-
<img src="https://user-images.githubusercontent.com/17445847/222915829-ccfb0366-13a8-484a-9561-627fabd87d29.png" height=450>
|
| 13 |
-
</p>
|
| 14 |
-
|
| 15 |
-
## Openpose Adapter
|
| 16 |
-
|
| 17 |
-
<p align="center">
|
| 18 |
-
<img src="https://user-images.githubusercontent.com/17445847/222733916-dc26a66e-d786-4407-8889-b81804862b1a.png" height=450>
|
| 19 |
-
</p>
|
| 20 |
-
|
| 21 |
-
## Canny Adapter (Edge)
|
| 22 |
-
|
| 23 |
-
<p align="center">
|
| 24 |
-
<img src="https://user-images.githubusercontent.com/17445847/222915813-c8f264bd-1be6-4496-97ff-aec4f6b53788.png" height=450>
|
| 25 |
-
</p>
|
| 26 |
-
|
| 27 |
-
## Multi-adapters
|
| 28 |
-
<p align="center">
|
| 29 |
-
<img src="https://user-images.githubusercontent.com/17445847/220939329-379f88b7-444f-4a3a-9de0-8f90605d1d34.png" height=450>
|
| 30 |
-
</p>
|
| 31 |
-
|
| 32 |
-
<div align="center">
|
| 33 |
-
|
| 34 |
-
*T2I adapters naturally support using multiple adapters together.*
|
| 35 |
-
|
| 36 |
-
</div><br />
|
| 37 |
-
The testing script usage for this example is similar to the command line given below, except that we replaced the pretrained SD model with Anything 4.5 and Kenshi
|
| 38 |
-
|
| 39 |
-
>python test_composable_adapters.py --prompt "1gril, computer desk, best quality, extremely detailed" --neg_prompt "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality" --depth_cond_path examples/depth/desk_depth.png --depth_cond_weight 1.0 --depth_ckpt models/t2iadapter_depth_sd14v1.pth --depth_type_in depth --pose_cond_path examples/keypose/person_keypose.png --pose_cond_weight 1.5 --ckpt models/anything-v4.0-pruned.ckpt --n_sample 4 --max_resolution 524288
|
| 40 |
-
|
| 41 |
-
[Image source](https://twitter.com/toyxyz3/status/1628375164781211648)
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|
spaces/Adr740/CV_XPLORER_POC/app.py
DELETED
|
@@ -1,38 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
from functools import partial
|
| 3 |
-
import os
|
| 4 |
-
from get_cv import get_cv
|
| 5 |
-
|
| 6 |
-
title = "CV Indexing par Intelligence Artificielle"
|
| 7 |
-
desc = "Ceci est un outil qui vous aide à trouver rapidement des CV pertinents en fonction des descriptions de poste. Il suffit de taper simplement ce que vous recherchez dans la zone ci-dessous.\n\n Avec l'aide de l'IA, cet outil est conçu pour simplifier votre recherche de CV en suggérant des résultats qui correspondent le mieux à vos besoins. Vous n'avez qu'à saisir les termes pertinents qui décrivent le poste que vous recherchez et l'outil vous présentera une liste de CV adaptés à vos critères. Cela vous permettra de gagner du temps et de trouver plus facilement les candidats idéaux pour votre entreprise.\n\n"
|
| 8 |
-
# warning = "Warning!"
|
| 9 |
-
disclaimer = "#### Attention! La méthode utilisée est stochastique et par conséquent les résultats peuvent parfois ne pas respecter parfaitement la requête. SI CELA ARRIVE : essayez d'adapter votre demande en reformulant ou en fournissant plus d'informations, cela fonctionne mieux avec des textes plus longs (fiche de poste par exemple)"
|
| 10 |
-
def iter_grid(n_rows, n_cols):
|
| 11 |
-
for _ in range(n_rows):
|
| 12 |
-
with gr.Row():
|
| 13 |
-
for _ in range(n_cols):
|
| 14 |
-
with gr.Column():
|
| 15 |
-
yield
|
| 16 |
-
with gr.Blocks(title=title) as demo:
|
| 17 |
-
gr.Markdown(f"## {title}")
|
| 18 |
-
gr.Markdown(desc)
|
| 19 |
-
gr.Markdown(disclaimer)
|
| 20 |
-
with gr.Row():
|
| 21 |
-
with gr.Column(scale=4):
|
| 22 |
-
text_area = gr.Textbox(placeholder="Écrivez ici", lines=3, label="Décrivez le type de candidat que vous chechez ou copiez collez une fiche de poste")
|
| 23 |
-
with gr.Column(scale=1):
|
| 24 |
-
number_to_display = gr.Number(value=10,label = "Nombre de candidats à afficher")
|
| 25 |
-
submit_button = gr.Button(value="Rechercher des candidats")
|
| 26 |
-
pass
|
| 27 |
-
|
| 28 |
-
fn = partial(get_cv)
|
| 29 |
-
|
| 30 |
-
with gr.Accordion("Tous les résultats:"):
|
| 31 |
-
ll = gr.Markdown("Vide")
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
submit_button.click(fn=fn, inputs=[text_area,number_to_display], outputs=[ll])
|
| 35 |
-
|
| 36 |
-
login = os.environ.get("login")
|
| 37 |
-
pwd = os.environ.get("pwd")
|
| 38 |
-
demo.launch(enable_queue=True,max_threads=40)
|
|
|
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|
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/kawaseblurpipeline-plugin.d.ts
DELETED
|
@@ -1,30 +0,0 @@
|
|
| 1 |
-
// import * as Phaser from 'phaser';
|
| 2 |
-
import KawaseBlurFilterPostFxPipeline from './kawaseblurpipeline';
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
export default KawaseBlurFilterPipelinePlugin;
|
| 6 |
-
|
| 7 |
-
declare namespace KawaseBlurFilterPipelinePlugin {
|
| 8 |
-
|
| 9 |
-
interface IConfig extends KawaseBlurFilterPostFxPipeline.IConfig {
|
| 10 |
-
name?: string,
|
| 11 |
-
}
|
| 12 |
-
|
| 13 |
-
}
|
| 14 |
-
|
| 15 |
-
declare class KawaseBlurFilterPipelinePlugin extends Phaser.Plugins.BasePlugin {
|
| 16 |
-
add(
|
| 17 |
-
gameObject: Phaser.GameObjects.GameObject | Phaser.Cameras.Scene2D.Camera,
|
| 18 |
-
config?: KawaseBlurFilterPipelinePlugin.IConfig
|
| 19 |
-
): KawaseBlurFilterPostFxPipeline;
|
| 20 |
-
|
| 21 |
-
remove(
|
| 22 |
-
gameObject: Phaser.GameObjects.GameObject,
|
| 23 |
-
name?: string
|
| 24 |
-
): this;
|
| 25 |
-
|
| 26 |
-
get(
|
| 27 |
-
gameObject: Phaser.GameObjects.GameObject,
|
| 28 |
-
name?: string
|
| 29 |
-
): KawaseBlurFilterPostFxPipeline | KawaseBlurFilterPostFxPipeline[];
|
| 30 |
-
}
|
|
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|
|
spaces/AiiluoChen/webui/README.md
DELETED
|
@@ -1,20 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Stable Diffusion Web UI
|
| 3 |
-
emoji: 🚧
|
| 4 |
-
colorFrom: yellow
|
| 5 |
-
colorTo: yellow
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.9
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
duplicated_from: camenduru/webui
|
| 11 |
-
---
|
| 12 |
-
|
| 13 |
-
## Stable Diffusion Web UI
|
| 14 |
-
[https://github.com/AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
|
| 15 |
-
|
| 16 |
-
## Documentation
|
| 17 |
-
[https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki)
|
| 18 |
-
|
| 19 |
-
## Models License
|
| 20 |
-
https://huggingface.co/spaces/CompVis/stable-diffusion-license
|
|
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|
|
spaces/AlekseyCalvin/dreambooth-training3/convertosd.py
DELETED
|
@@ -1,302 +0,0 @@
|
|
| 1 |
-
# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
|
| 2 |
-
# *Only* converts the UNet, VAE, and Text Encoder.
|
| 3 |
-
# Does not convert optimizer state or any other thing.
|
| 4 |
-
|
| 5 |
-
import argparse
|
| 6 |
-
import os.path as osp
|
| 7 |
-
import re
|
| 8 |
-
|
| 9 |
-
import torch
|
| 10 |
-
import gc
|
| 11 |
-
|
| 12 |
-
# =================#
|
| 13 |
-
# UNet Conversion #
|
| 14 |
-
# =================#
|
| 15 |
-
|
| 16 |
-
unet_conversion_map = [
|
| 17 |
-
# (stable-diffusion, HF Diffusers)
|
| 18 |
-
("time_embed.0.weight", "time_embedding.linear_1.weight"),
|
| 19 |
-
("time_embed.0.bias", "time_embedding.linear_1.bias"),
|
| 20 |
-
("time_embed.2.weight", "time_embedding.linear_2.weight"),
|
| 21 |
-
("time_embed.2.bias", "time_embedding.linear_2.bias"),
|
| 22 |
-
("input_blocks.0.0.weight", "conv_in.weight"),
|
| 23 |
-
("input_blocks.0.0.bias", "conv_in.bias"),
|
| 24 |
-
("out.0.weight", "conv_norm_out.weight"),
|
| 25 |
-
("out.0.bias", "conv_norm_out.bias"),
|
| 26 |
-
("out.2.weight", "conv_out.weight"),
|
| 27 |
-
("out.2.bias", "conv_out.bias"),
|
| 28 |
-
]
|
| 29 |
-
|
| 30 |
-
unet_conversion_map_resnet = [
|
| 31 |
-
# (stable-diffusion, HF Diffusers)
|
| 32 |
-
("in_layers.0", "norm1"),
|
| 33 |
-
("in_layers.2", "conv1"),
|
| 34 |
-
("out_layers.0", "norm2"),
|
| 35 |
-
("out_layers.3", "conv2"),
|
| 36 |
-
("emb_layers.1", "time_emb_proj"),
|
| 37 |
-
("skip_connection", "conv_shortcut"),
|
| 38 |
-
]
|
| 39 |
-
|
| 40 |
-
unet_conversion_map_layer = []
|
| 41 |
-
# hardcoded number of downblocks and resnets/attentions...
|
| 42 |
-
# would need smarter logic for other networks.
|
| 43 |
-
for i in range(4):
|
| 44 |
-
# loop over downblocks/upblocks
|
| 45 |
-
|
| 46 |
-
for j in range(2):
|
| 47 |
-
# loop over resnets/attentions for downblocks
|
| 48 |
-
hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
|
| 49 |
-
sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
|
| 50 |
-
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
|
| 51 |
-
|
| 52 |
-
if i < 3:
|
| 53 |
-
# no attention layers in down_blocks.3
|
| 54 |
-
hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
|
| 55 |
-
sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
|
| 56 |
-
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
|
| 57 |
-
|
| 58 |
-
for j in range(3):
|
| 59 |
-
# loop over resnets/attentions for upblocks
|
| 60 |
-
hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
|
| 61 |
-
sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
|
| 62 |
-
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
|
| 63 |
-
|
| 64 |
-
if i > 0:
|
| 65 |
-
# no attention layers in up_blocks.0
|
| 66 |
-
hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
|
| 67 |
-
sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
|
| 68 |
-
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
|
| 69 |
-
|
| 70 |
-
if i < 3:
|
| 71 |
-
# no downsample in down_blocks.3
|
| 72 |
-
hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
|
| 73 |
-
sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
|
| 74 |
-
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
|
| 75 |
-
|
| 76 |
-
# no upsample in up_blocks.3
|
| 77 |
-
hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
|
| 78 |
-
sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
|
| 79 |
-
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
|
| 80 |
-
|
| 81 |
-
hf_mid_atn_prefix = "mid_block.attentions.0."
|
| 82 |
-
sd_mid_atn_prefix = "middle_block.1."
|
| 83 |
-
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
|
| 84 |
-
|
| 85 |
-
for j in range(2):
|
| 86 |
-
hf_mid_res_prefix = f"mid_block.resnets.{j}."
|
| 87 |
-
sd_mid_res_prefix = f"middle_block.{2*j}."
|
| 88 |
-
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
|
| 89 |
-
|
| 90 |
-
|
| 91 |
-
def convert_unet_state_dict(unet_state_dict):
|
| 92 |
-
# buyer beware: this is a *brittle* function,
|
| 93 |
-
# and correct output requires that all of these pieces interact in
|
| 94 |
-
# the exact order in which I have arranged them.
|
| 95 |
-
mapping = {k: k for k in unet_state_dict.keys()}
|
| 96 |
-
for sd_name, hf_name in unet_conversion_map:
|
| 97 |
-
mapping[hf_name] = sd_name
|
| 98 |
-
for k, v in mapping.items():
|
| 99 |
-
if "resnets" in k:
|
| 100 |
-
for sd_part, hf_part in unet_conversion_map_resnet:
|
| 101 |
-
v = v.replace(hf_part, sd_part)
|
| 102 |
-
mapping[k] = v
|
| 103 |
-
for k, v in mapping.items():
|
| 104 |
-
for sd_part, hf_part in unet_conversion_map_layer:
|
| 105 |
-
v = v.replace(hf_part, sd_part)
|
| 106 |
-
mapping[k] = v
|
| 107 |
-
new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
|
| 108 |
-
return new_state_dict
|
| 109 |
-
|
| 110 |
-
|
| 111 |
-
# ================#
|
| 112 |
-
# VAE Conversion #
|
| 113 |
-
# ================#
|
| 114 |
-
|
| 115 |
-
vae_conversion_map = [
|
| 116 |
-
# (stable-diffusion, HF Diffusers)
|
| 117 |
-
("nin_shortcut", "conv_shortcut"),
|
| 118 |
-
("norm_out", "conv_norm_out"),
|
| 119 |
-
("mid.attn_1.", "mid_block.attentions.0."),
|
| 120 |
-
]
|
| 121 |
-
|
| 122 |
-
for i in range(4):
|
| 123 |
-
# down_blocks have two resnets
|
| 124 |
-
for j in range(2):
|
| 125 |
-
hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
|
| 126 |
-
sd_down_prefix = f"encoder.down.{i}.block.{j}."
|
| 127 |
-
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
|
| 128 |
-
|
| 129 |
-
if i < 3:
|
| 130 |
-
hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
|
| 131 |
-
sd_downsample_prefix = f"down.{i}.downsample."
|
| 132 |
-
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
|
| 133 |
-
|
| 134 |
-
hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
|
| 135 |
-
sd_upsample_prefix = f"up.{3-i}.upsample."
|
| 136 |
-
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
|
| 137 |
-
|
| 138 |
-
# up_blocks have three resnets
|
| 139 |
-
# also, up blocks in hf are numbered in reverse from sd
|
| 140 |
-
for j in range(3):
|
| 141 |
-
hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
|
| 142 |
-
sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
|
| 143 |
-
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
|
| 144 |
-
|
| 145 |
-
# this part accounts for mid blocks in both the encoder and the decoder
|
| 146 |
-
for i in range(2):
|
| 147 |
-
hf_mid_res_prefix = f"mid_block.resnets.{i}."
|
| 148 |
-
sd_mid_res_prefix = f"mid.block_{i+1}."
|
| 149 |
-
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
|
| 150 |
-
|
| 151 |
-
|
| 152 |
-
vae_conversion_map_attn = [
|
| 153 |
-
# (stable-diffusion, HF Diffusers)
|
| 154 |
-
("norm.", "group_norm."),
|
| 155 |
-
("q.", "query."),
|
| 156 |
-
("k.", "key."),
|
| 157 |
-
("v.", "value."),
|
| 158 |
-
("proj_out.", "proj_attn."),
|
| 159 |
-
]
|
| 160 |
-
|
| 161 |
-
|
| 162 |
-
def reshape_weight_for_sd(w):
|
| 163 |
-
# convert HF linear weights to SD conv2d weights
|
| 164 |
-
return w.reshape(*w.shape, 1, 1)
|
| 165 |
-
|
| 166 |
-
|
| 167 |
-
def convert_vae_state_dict(vae_state_dict):
|
| 168 |
-
mapping = {k: k for k in vae_state_dict.keys()}
|
| 169 |
-
for k, v in mapping.items():
|
| 170 |
-
for sd_part, hf_part in vae_conversion_map:
|
| 171 |
-
v = v.replace(hf_part, sd_part)
|
| 172 |
-
mapping[k] = v
|
| 173 |
-
for k, v in mapping.items():
|
| 174 |
-
if "attentions" in k:
|
| 175 |
-
for sd_part, hf_part in vae_conversion_map_attn:
|
| 176 |
-
v = v.replace(hf_part, sd_part)
|
| 177 |
-
mapping[k] = v
|
| 178 |
-
new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
|
| 179 |
-
weights_to_convert = ["q", "k", "v", "proj_out"]
|
| 180 |
-
print("Converting to CKPT ...")
|
| 181 |
-
for k, v in new_state_dict.items():
|
| 182 |
-
for weight_name in weights_to_convert:
|
| 183 |
-
if f"mid.attn_1.{weight_name}.weight" in k:
|
| 184 |
-
print(f"Reshaping {k} for SD format")
|
| 185 |
-
new_state_dict[k] = reshape_weight_for_sd(v)
|
| 186 |
-
return new_state_dict
|
| 187 |
-
|
| 188 |
-
|
| 189 |
-
# =========================#
|
| 190 |
-
# Text Encoder Conversion #
|
| 191 |
-
# =========================#
|
| 192 |
-
|
| 193 |
-
|
| 194 |
-
textenc_conversion_lst = [
|
| 195 |
-
# (stable-diffusion, HF Diffusers)
|
| 196 |
-
("resblocks.", "text_model.encoder.layers."),
|
| 197 |
-
("ln_1", "layer_norm1"),
|
| 198 |
-
("ln_2", "layer_norm2"),
|
| 199 |
-
(".c_fc.", ".fc1."),
|
| 200 |
-
(".c_proj.", ".fc2."),
|
| 201 |
-
(".attn", ".self_attn"),
|
| 202 |
-
("ln_final.", "transformer.text_model.final_layer_norm."),
|
| 203 |
-
("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
|
| 204 |
-
("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
|
| 205 |
-
]
|
| 206 |
-
protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
|
| 207 |
-
textenc_pattern = re.compile("|".join(protected.keys()))
|
| 208 |
-
|
| 209 |
-
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
|
| 210 |
-
code2idx = {"q": 0, "k": 1, "v": 2}
|
| 211 |
-
|
| 212 |
-
|
| 213 |
-
def convert_text_enc_state_dict_v20(text_enc_dict):
|
| 214 |
-
new_state_dict = {}
|
| 215 |
-
capture_qkv_weight = {}
|
| 216 |
-
capture_qkv_bias = {}
|
| 217 |
-
for k, v in text_enc_dict.items():
|
| 218 |
-
if (
|
| 219 |
-
k.endswith(".self_attn.q_proj.weight")
|
| 220 |
-
or k.endswith(".self_attn.k_proj.weight")
|
| 221 |
-
or k.endswith(".self_attn.v_proj.weight")
|
| 222 |
-
):
|
| 223 |
-
k_pre = k[: -len(".q_proj.weight")]
|
| 224 |
-
k_code = k[-len("q_proj.weight")]
|
| 225 |
-
if k_pre not in capture_qkv_weight:
|
| 226 |
-
capture_qkv_weight[k_pre] = [None, None, None]
|
| 227 |
-
capture_qkv_weight[k_pre][code2idx[k_code]] = v
|
| 228 |
-
continue
|
| 229 |
-
|
| 230 |
-
if (
|
| 231 |
-
k.endswith(".self_attn.q_proj.bias")
|
| 232 |
-
or k.endswith(".self_attn.k_proj.bias")
|
| 233 |
-
or k.endswith(".self_attn.v_proj.bias")
|
| 234 |
-
):
|
| 235 |
-
k_pre = k[: -len(".q_proj.bias")]
|
| 236 |
-
k_code = k[-len("q_proj.bias")]
|
| 237 |
-
if k_pre not in capture_qkv_bias:
|
| 238 |
-
capture_qkv_bias[k_pre] = [None, None, None]
|
| 239 |
-
capture_qkv_bias[k_pre][code2idx[k_code]] = v
|
| 240 |
-
continue
|
| 241 |
-
|
| 242 |
-
relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k)
|
| 243 |
-
new_state_dict[relabelled_key] = v
|
| 244 |
-
|
| 245 |
-
for k_pre, tensors in capture_qkv_weight.items():
|
| 246 |
-
if None in tensors:
|
| 247 |
-
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
|
| 248 |
-
relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
|
| 249 |
-
new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors)
|
| 250 |
-
|
| 251 |
-
for k_pre, tensors in capture_qkv_bias.items():
|
| 252 |
-
if None in tensors:
|
| 253 |
-
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
|
| 254 |
-
relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
|
| 255 |
-
new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors)
|
| 256 |
-
|
| 257 |
-
return new_state_dict
|
| 258 |
-
|
| 259 |
-
|
| 260 |
-
def convert_text_enc_state_dict(text_enc_dict):
|
| 261 |
-
return text_enc_dict
|
| 262 |
-
|
| 263 |
-
|
| 264 |
-
def convert(model_path, checkpoint_path):
|
| 265 |
-
unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
|
| 266 |
-
vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
|
| 267 |
-
text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
|
| 268 |
-
|
| 269 |
-
# Convert the UNet model
|
| 270 |
-
unet_state_dict = torch.load(unet_path, map_location="cpu")
|
| 271 |
-
unet_state_dict = convert_unet_state_dict(unet_state_dict)
|
| 272 |
-
unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
|
| 273 |
-
|
| 274 |
-
# Convert the VAE model
|
| 275 |
-
vae_state_dict = torch.load(vae_path, map_location="cpu")
|
| 276 |
-
vae_state_dict = convert_vae_state_dict(vae_state_dict)
|
| 277 |
-
vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
|
| 278 |
-
|
| 279 |
-
# Convert the text encoder model
|
| 280 |
-
text_enc_dict = torch.load(text_enc_path, map_location="cpu")
|
| 281 |
-
|
| 282 |
-
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
|
| 283 |
-
is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict
|
| 284 |
-
|
| 285 |
-
if is_v20_model:
|
| 286 |
-
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
|
| 287 |
-
text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()}
|
| 288 |
-
text_enc_dict = convert_text_enc_state_dict_v20(text_enc_dict)
|
| 289 |
-
text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
|
| 290 |
-
else:
|
| 291 |
-
text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
|
| 292 |
-
text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
|
| 293 |
-
|
| 294 |
-
# Put together new checkpoint
|
| 295 |
-
state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
|
| 296 |
-
state_dict = {k: v.half() for k, v in state_dict.items()}
|
| 297 |
-
state_dict = {"state_dict": state_dict}
|
| 298 |
-
torch.save(state_dict, checkpoint_path)
|
| 299 |
-
del state_dict, text_enc_dict, vae_state_dict, unet_state_dict
|
| 300 |
-
torch.cuda.empty_cache()
|
| 301 |
-
gc.collect()
|
| 302 |
-
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|
spaces/Alfasign/HuggingGPT-Lite/models_server.py
DELETED
|
@@ -1,779 +0,0 @@
|
|
| 1 |
-
import argparse
|
| 2 |
-
import logging
|
| 3 |
-
import random
|
| 4 |
-
import uuid
|
| 5 |
-
import numpy as np
|
| 6 |
-
from transformers import pipeline
|
| 7 |
-
from diffusers import (
|
| 8 |
-
DiffusionPipeline,
|
| 9 |
-
StableDiffusionControlNetPipeline,
|
| 10 |
-
ControlNetModel,
|
| 11 |
-
UniPCMultistepScheduler,
|
| 12 |
-
)
|
| 13 |
-
from diffusers.utils import load_image
|
| 14 |
-
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
|
| 15 |
-
from diffusers.utils import export_to_video
|
| 16 |
-
from transformers import BlipProcessor, BlipForConditionalGeneration
|
| 17 |
-
from transformers import (
|
| 18 |
-
TrOCRProcessor,
|
| 19 |
-
VisionEncoderDecoderModel,
|
| 20 |
-
ViTImageProcessor,
|
| 21 |
-
AutoTokenizer,
|
| 22 |
-
)
|
| 23 |
-
from datasets import load_dataset
|
| 24 |
-
from PIL import Image
|
| 25 |
-
import io
|
| 26 |
-
from torchvision import transforms
|
| 27 |
-
import torch
|
| 28 |
-
import torchaudio
|
| 29 |
-
from speechbrain.pretrained import WaveformEnhancement
|
| 30 |
-
import joblib
|
| 31 |
-
from huggingface_hub import hf_hub_url, cached_download
|
| 32 |
-
from transformers import AutoImageProcessor, TimesformerForVideoClassification
|
| 33 |
-
from transformers import (
|
| 34 |
-
MaskFormerFeatureExtractor,
|
| 35 |
-
MaskFormerForInstanceSegmentation,
|
| 36 |
-
AutoFeatureExtractor,
|
| 37 |
-
)
|
| 38 |
-
from controlnet_aux import (
|
| 39 |
-
OpenposeDetector,
|
| 40 |
-
MLSDdetector,
|
| 41 |
-
HEDdetector,
|
| 42 |
-
CannyDetector,
|
| 43 |
-
MidasDetector,
|
| 44 |
-
)
|
| 45 |
-
from controlnet_aux.open_pose.body import Body
|
| 46 |
-
from controlnet_aux.mlsd.models.mbv2_mlsd_large import MobileV2_MLSD_Large
|
| 47 |
-
from controlnet_aux.hed import Network
|
| 48 |
-
from transformers import DPTForDepthEstimation, DPTFeatureExtractor
|
| 49 |
-
import warnings
|
| 50 |
-
import time
|
| 51 |
-
from espnet2.bin.tts_inference import Text2Speech
|
| 52 |
-
import soundfile as sf
|
| 53 |
-
from asteroid.models import BaseModel
|
| 54 |
-
import traceback
|
| 55 |
-
import os
|
| 56 |
-
import yaml
|
| 57 |
-
|
| 58 |
-
warnings.filterwarnings("ignore")
|
| 59 |
-
|
| 60 |
-
parser = argparse.ArgumentParser()
|
| 61 |
-
parser.add_argument("--config", type=str, default="config.yaml")
|
| 62 |
-
args = parser.parse_args()
|
| 63 |
-
|
| 64 |
-
if __name__ != "__main__":
|
| 65 |
-
args.config = "config.gradio.yaml"
|
| 66 |
-
|
| 67 |
-
logger = logging.getLogger(__name__)
|
| 68 |
-
logger.setLevel(logging.INFO)
|
| 69 |
-
handler = logging.StreamHandler()
|
| 70 |
-
handler.setLevel(logging.INFO)
|
| 71 |
-
formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
|
| 72 |
-
handler.setFormatter(formatter)
|
| 73 |
-
logger.addHandler(handler)
|
| 74 |
-
|
| 75 |
-
config = yaml.load(open(args.config, "r"), Loader=yaml.FullLoader)
|
| 76 |
-
|
| 77 |
-
local_deployment = config["local_deployment"]
|
| 78 |
-
if config["inference_mode"] == "huggingface":
|
| 79 |
-
local_deployment = "none"
|
| 80 |
-
|
| 81 |
-
PROXY = None
|
| 82 |
-
if config["proxy"]:
|
| 83 |
-
PROXY = {
|
| 84 |
-
"https": config["proxy"],
|
| 85 |
-
}
|
| 86 |
-
|
| 87 |
-
start = time.time()
|
| 88 |
-
|
| 89 |
-
# local_models = "models/"
|
| 90 |
-
local_models = ""
|
| 91 |
-
|
| 92 |
-
|
| 93 |
-
def load_pipes(local_deployment):
|
| 94 |
-
other_pipes = {}
|
| 95 |
-
standard_pipes = {}
|
| 96 |
-
controlnet_sd_pipes = {}
|
| 97 |
-
if local_deployment in ["full"]:
|
| 98 |
-
other_pipes = {
|
| 99 |
-
# "Salesforce/blip-image-captioning-large": {
|
| 100 |
-
# "model": BlipForConditionalGeneration.from_pretrained(f"Salesforce/blip-image-captioning-large"),
|
| 101 |
-
# "processor": BlipProcessor.from_pretrained(f"Salesforce/blip-image-captioning-large"),
|
| 102 |
-
# "device": "cpu"
|
| 103 |
-
# },
|
| 104 |
-
# "damo-vilab/text-to-video-ms-1.7b": {
|
| 105 |
-
# "model": DiffusionPipeline.from_pretrained(
|
| 106 |
-
# f"{local_models}damo-vilab/text-to-video-ms-1.7b",
|
| 107 |
-
# torch_dtype=torch.float16,
|
| 108 |
-
# variant="fp16",
|
| 109 |
-
# ),
|
| 110 |
-
# "device": "cpu",
|
| 111 |
-
# },
|
| 112 |
-
# "facebook/maskformer-swin-large-ade": {
|
| 113 |
-
# "model": MaskFormerForInstanceSegmentation.from_pretrained(f"facebook/maskformer-swin-large-ade"),
|
| 114 |
-
# "feature_extractor" : AutoFeatureExtractor.from_pretrained("facebook/maskformer-swin-large-ade"),
|
| 115 |
-
# "device": "cpu"
|
| 116 |
-
# },
|
| 117 |
-
# "microsoft/trocr-base-printed": {
|
| 118 |
-
# "processor": TrOCRProcessor.from_pretrained(f"microsoft/trocr-base-printed"),
|
| 119 |
-
# "model": VisionEncoderDecoderModel.from_pretrained(f"microsoft/trocr-base-printed"),
|
| 120 |
-
# "device": "cpu"
|
| 121 |
-
# },
|
| 122 |
-
# "microsoft/trocr-base-handwritten": {
|
| 123 |
-
# "processor": TrOCRProcessor.from_pretrained(f"microsoft/trocr-base-handwritten"),
|
| 124 |
-
# "model": VisionEncoderDecoderModel.from_pretrained(f"microsoft/trocr-base-handwritten"),
|
| 125 |
-
# "device": "cpu"
|
| 126 |
-
# },
|
| 127 |
-
# "JorisCos/DCCRNet_Libri1Mix_enhsingle_16k": {
|
| 128 |
-
# "model": BaseModel.from_pretrained(
|
| 129 |
-
# "JorisCos/DCCRNet_Libri1Mix_enhsingle_16k"
|
| 130 |
-
# ),
|
| 131 |
-
# "device": "cpu",
|
| 132 |
-
# },
|
| 133 |
-
# "CompVis/stable-diffusion-v1-4": {
|
| 134 |
-
# "model": DiffusionPipeline.from_pretrained(f"CompVis/stable-diffusion-v1-4"),
|
| 135 |
-
# "device": "cpu"
|
| 136 |
-
# },
|
| 137 |
-
# "stabilityai/stable-diffusion-2-1": {
|
| 138 |
-
# "model": DiffusionPipeline.from_pretrained(f"stabilityai/stable-diffusion-2-1"),
|
| 139 |
-
# "device": "cpu"
|
| 140 |
-
# },
|
| 141 |
-
# "microsoft/speecht5_tts":{
|
| 142 |
-
# "processor": SpeechT5Processor.from_pretrained(f"microsoft/speecht5_tts"),
|
| 143 |
-
# "model": SpeechT5ForTextToSpeech.from_pretrained(f"microsoft/speecht5_tts"),
|
| 144 |
-
# "vocoder": SpeechT5HifiGan.from_pretrained(f"microsoft/speecht5_hifigan"),
|
| 145 |
-
# "embeddings_dataset": load_dataset(f"Matthijs/cmu-arctic-xvectors", split="validation"),
|
| 146 |
-
# "device": "cpu"
|
| 147 |
-
# },
|
| 148 |
-
# "speechbrain/mtl-mimic-voicebank": {
|
| 149 |
-
# "model": WaveformEnhancement.from_hparams(source="speechbrain/mtl-mimic-voicebank", savedir="models/mtl-mimic-voicebank"),
|
| 150 |
-
# "device": "cpu"
|
| 151 |
-
# },
|
| 152 |
-
# "microsoft/speecht5_vc": {
|
| 153 |
-
# "processor": SpeechT5Processor.from_pretrained(
|
| 154 |
-
# f"{local_models}microsoft/speecht5_vc"
|
| 155 |
-
# ),
|
| 156 |
-
# "model": SpeechT5ForSpeechToSpeech.from_pretrained(
|
| 157 |
-
# f"{local_models}microsoft/speecht5_vc"
|
| 158 |
-
# ),
|
| 159 |
-
# "vocoder": SpeechT5HifiGan.from_pretrained(
|
| 160 |
-
# f"{local_models}microsoft/speecht5_hifigan"
|
| 161 |
-
# ),
|
| 162 |
-
# "embeddings_dataset": load_dataset(
|
| 163 |
-
# f"{local_models}Matthijs/cmu-arctic-xvectors", split="validation"
|
| 164 |
-
# ),
|
| 165 |
-
# "device": "cpu",
|
| 166 |
-
# },
|
| 167 |
-
# "julien-c/wine-quality": {
|
| 168 |
-
# "model": joblib.load(cached_download(hf_hub_url("julien-c/wine-quality", "sklearn_model.joblib")))
|
| 169 |
-
# },
|
| 170 |
-
# "facebook/timesformer-base-finetuned-k400": {
|
| 171 |
-
# "processor": AutoImageProcessor.from_pretrained(f"facebook/timesformer-base-finetuned-k400"),
|
| 172 |
-
# "model": TimesformerForVideoClassification.from_pretrained(f"facebook/timesformer-base-finetuned-k400"),
|
| 173 |
-
# "device": "cpu"
|
| 174 |
-
# },
|
| 175 |
-
"facebook/maskformer-swin-base-coco": {
|
| 176 |
-
"feature_extractor": MaskFormerFeatureExtractor.from_pretrained(
|
| 177 |
-
f"{local_models}facebook/maskformer-swin-base-coco"
|
| 178 |
-
),
|
| 179 |
-
"model": MaskFormerForInstanceSegmentation.from_pretrained(
|
| 180 |
-
f"{local_models}facebook/maskformer-swin-base-coco"
|
| 181 |
-
),
|
| 182 |
-
"device": "cpu",
|
| 183 |
-
},
|
| 184 |
-
# "Intel/dpt-hybrid-midas": {
|
| 185 |
-
# "model": DPTForDepthEstimation.from_pretrained(
|
| 186 |
-
# f"{local_models}Intel/dpt-hybrid-midas", low_cpu_mem_usage=True
|
| 187 |
-
# ),
|
| 188 |
-
# "feature_extractor": DPTFeatureExtractor.from_pretrained(
|
| 189 |
-
# f"{local_models}Intel/dpt-hybrid-midas"
|
| 190 |
-
# ),
|
| 191 |
-
# "device": "cpu",
|
| 192 |
-
# },
|
| 193 |
-
}
|
| 194 |
-
|
| 195 |
-
if local_deployment in ["full", "standard"]:
|
| 196 |
-
standard_pipes = {
|
| 197 |
-
# "nlpconnect/vit-gpt2-image-captioning":{
|
| 198 |
-
# "model": VisionEncoderDecoderModel.from_pretrained(f"{local_models}nlpconnect/vit-gpt2-image-captioning"),
|
| 199 |
-
# "feature_extractor": ViTImageProcessor.from_pretrained(f"{local_models}nlpconnect/vit-gpt2-image-captioning"),
|
| 200 |
-
# "tokenizer": AutoTokenizer.from_pretrained(f"{local_models}nlpconnect/vit-gpt2-image-captioning"),
|
| 201 |
-
# "device": "cpu"
|
| 202 |
-
# },
|
| 203 |
-
# "espnet/kan-bayashi_ljspeech_vits": {
|
| 204 |
-
# "model": Text2Speech.from_pretrained(
|
| 205 |
-
# "espnet/kan-bayashi_ljspeech_vits"
|
| 206 |
-
# ),
|
| 207 |
-
# "device": "cpu",
|
| 208 |
-
# },
|
| 209 |
-
# "lambdalabs/sd-image-variations-diffusers": {
|
| 210 |
-
# "model": DiffusionPipeline.from_pretrained(f"{local_models}lambdalabs/sd-image-variations-diffusers"), #torch_dtype=torch.float16
|
| 211 |
-
# "device": "cpu"
|
| 212 |
-
# },
|
| 213 |
-
# "runwayml/stable-diffusion-v1-5": {
|
| 214 |
-
# "model": DiffusionPipeline.from_pretrained(
|
| 215 |
-
# f"{local_models}runwayml/stable-diffusion-v1-5"
|
| 216 |
-
# ),
|
| 217 |
-
# "device": "cpu",
|
| 218 |
-
# },
|
| 219 |
-
# "superb/wav2vec2-base-superb-ks": {
|
| 220 |
-
# "model": pipeline(task="audio-classification", model=f"superb/wav2vec2-base-superb-ks"),
|
| 221 |
-
# "device": "cpu"
|
| 222 |
-
# },
|
| 223 |
-
# "openai/whisper-base": {
|
| 224 |
-
# "model": pipeline(
|
| 225 |
-
# task="automatic-speech-recognition",
|
| 226 |
-
# model=f"{local_models}openai/whisper-base",
|
| 227 |
-
# ),
|
| 228 |
-
# "device": "cpu",
|
| 229 |
-
# },
|
| 230 |
-
# "microsoft/speecht5_asr": {
|
| 231 |
-
# "model": pipeline(task="automatic-speech-recognition", model=f"{local_models}microsoft/speecht5_asr"),
|
| 232 |
-
# "device": "cpu"
|
| 233 |
-
# },
|
| 234 |
-
"Intel/dpt-large": {
|
| 235 |
-
"model": pipeline(
|
| 236 |
-
task="depth-estimation", model=f"{local_models}Intel/dpt-large"
|
| 237 |
-
),
|
| 238 |
-
"device": "cpu",
|
| 239 |
-
},
|
| 240 |
-
# "microsoft/beit-base-patch16-224-pt22k-ft22k": {
|
| 241 |
-
# "model": pipeline(task="image-classification", model=f"microsoft/beit-base-patch16-224-pt22k-ft22k"),
|
| 242 |
-
# "device": "cpu"
|
| 243 |
-
# },
|
| 244 |
-
"facebook/detr-resnet-50-panoptic": {
|
| 245 |
-
"model": pipeline(
|
| 246 |
-
task="image-segmentation",
|
| 247 |
-
model=f"{local_models}facebook/detr-resnet-50-panoptic",
|
| 248 |
-
),
|
| 249 |
-
"device": "cpu",
|
| 250 |
-
},
|
| 251 |
-
"facebook/detr-resnet-101": {
|
| 252 |
-
"model": pipeline(
|
| 253 |
-
task="object-detection",
|
| 254 |
-
model=f"{local_models}facebook/detr-resnet-101",
|
| 255 |
-
),
|
| 256 |
-
"device": "cpu",
|
| 257 |
-
},
|
| 258 |
-
# "openai/clip-vit-large-patch14": {
|
| 259 |
-
# "model": pipeline(task="zero-shot-image-classification", model=f"openai/clip-vit-large-patch14"),
|
| 260 |
-
# "device": "cpu"
|
| 261 |
-
# },
|
| 262 |
-
# "google/owlvit-base-patch32": {
|
| 263 |
-
# "model": pipeline(task="zero-shot-object-detection", model=f"{local_models}google/owlvit-base-patch32"),
|
| 264 |
-
# "device": "cpu"
|
| 265 |
-
# },
|
| 266 |
-
# "microsoft/DialoGPT-medium": {
|
| 267 |
-
# "model": pipeline(task="conversational", model=f"microsoft/DialoGPT-medium"),
|
| 268 |
-
# "device": "cpu"
|
| 269 |
-
# },
|
| 270 |
-
# "bert-base-uncased": {
|
| 271 |
-
# "model": pipeline(task="fill-mask", model=f"bert-base-uncased"),
|
| 272 |
-
# "device": "cpu"
|
| 273 |
-
# },
|
| 274 |
-
# "deepset/roberta-base-squad2": {
|
| 275 |
-
# "model": pipeline(task = "question-answering", model=f"deepset/roberta-base-squad2"),
|
| 276 |
-
# "device": "cpu"
|
| 277 |
-
# },
|
| 278 |
-
# "facebook/bart-large-cnn": {
|
| 279 |
-
# "model": pipeline(task="summarization", model=f"facebook/bart-large-cnn"),
|
| 280 |
-
# "device": "cpu"
|
| 281 |
-
# },
|
| 282 |
-
# "google/tapas-base-finetuned-wtq": {
|
| 283 |
-
# "model": pipeline(task="table-question-answering", model=f"google/tapas-base-finetuned-wtq"),
|
| 284 |
-
# "device": "cpu"
|
| 285 |
-
# },
|
| 286 |
-
# "distilbert-base-uncased-finetuned-sst-2-english": {
|
| 287 |
-
# "model": pipeline(task="text-classification", model=f"distilbert-base-uncased-finetuned-sst-2-english"),
|
| 288 |
-
# "device": "cpu"
|
| 289 |
-
# },
|
| 290 |
-
# "gpt2": {
|
| 291 |
-
# "model": pipeline(task="text-generation", model="gpt2"),
|
| 292 |
-
# "device": "cpu"
|
| 293 |
-
# },
|
| 294 |
-
# "mrm8488/t5-base-finetuned-question-generation-ap": {
|
| 295 |
-
# "model": pipeline(task="text2text-generation", model=f"mrm8488/t5-base-finetuned-question-generation-ap"),
|
| 296 |
-
# "device": "cpu"
|
| 297 |
-
# },
|
| 298 |
-
# "Jean-Baptiste/camembert-ner": {
|
| 299 |
-
# "model": pipeline(task="token-classification", model=f"Jean-Baptiste/camembert-ner", aggregation_strategy="simple"),
|
| 300 |
-
# "device": "cpu"
|
| 301 |
-
# },
|
| 302 |
-
# "t5-base": {
|
| 303 |
-
# "model": pipeline(task="translation", model=f"t5-base"),
|
| 304 |
-
# "device": "cpu"
|
| 305 |
-
# },
|
| 306 |
-
# "impira/layoutlm-document-qa": {
|
| 307 |
-
# "model": pipeline(task="document-question-answering", model=f"{local_models}impira/layoutlm-document-qa"),
|
| 308 |
-
# "device": "cpu"
|
| 309 |
-
# },
|
| 310 |
-
"ydshieh/vit-gpt2-coco-en": {
|
| 311 |
-
"model": pipeline(
|
| 312 |
-
task="image-to-text",
|
| 313 |
-
model=f"{local_models}ydshieh/vit-gpt2-coco-en",
|
| 314 |
-
),
|
| 315 |
-
"device": "cpu",
|
| 316 |
-
},
|
| 317 |
-
# "dandelin/vilt-b32-finetuned-vqa": {
|
| 318 |
-
# "model": pipeline(
|
| 319 |
-
# task="visual-question-answering",
|
| 320 |
-
# model=f"{local_models}dandelin/vilt-b32-finetuned-vqa",
|
| 321 |
-
# ),
|
| 322 |
-
# "device": "cpu",
|
| 323 |
-
# },
|
| 324 |
-
}
|
| 325 |
-
|
| 326 |
-
if local_deployment in ["full", "standard", "minimal"]:
|
| 327 |
-
controlnet = ControlNetModel.from_pretrained(
|
| 328 |
-
f"{local_models}lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16
|
| 329 |
-
)
|
| 330 |
-
controlnetpipe = StableDiffusionControlNetPipeline.from_pretrained(
|
| 331 |
-
f"{local_models}runwayml/stable-diffusion-v1-5",
|
| 332 |
-
controlnet=controlnet,
|
| 333 |
-
torch_dtype=torch.float16,
|
| 334 |
-
)
|
| 335 |
-
|
| 336 |
-
hed_network = HEDdetector.from_pretrained("lllyasviel/ControlNet")
|
| 337 |
-
|
| 338 |
-
pipes = {**standard_pipes, **other_pipes}
|
| 339 |
-
return pipes
|
| 340 |
-
|
| 341 |
-
|
| 342 |
-
pipes = load_pipes(local_deployment)
|
| 343 |
-
|
| 344 |
-
end = time.time()
|
| 345 |
-
during = end - start
|
| 346 |
-
|
| 347 |
-
print(f"[ ready ] {during}s")
|
| 348 |
-
|
| 349 |
-
|
| 350 |
-
def running():
|
| 351 |
-
return {"running": True}
|
| 352 |
-
|
| 353 |
-
|
| 354 |
-
def status(model_id):
|
| 355 |
-
disabled_models = [
|
| 356 |
-
"microsoft/trocr-base-printed",
|
| 357 |
-
"microsoft/trocr-base-handwritten",
|
| 358 |
-
]
|
| 359 |
-
if model_id in pipes.keys() and model_id not in disabled_models:
|
| 360 |
-
print(f"[ check {model_id} ] success")
|
| 361 |
-
return {"loaded": True}
|
| 362 |
-
else:
|
| 363 |
-
print(f"[ check {model_id} ] failed")
|
| 364 |
-
return {"loaded": False}
|
| 365 |
-
|
| 366 |
-
|
| 367 |
-
def models(model_id, data):
|
| 368 |
-
while "using" in pipes[model_id] and pipes[model_id]["using"]:
|
| 369 |
-
print(f"[ inference {model_id} ] waiting")
|
| 370 |
-
time.sleep(0.1)
|
| 371 |
-
pipes[model_id]["using"] = True
|
| 372 |
-
print(f"[ inference {model_id} ] start")
|
| 373 |
-
|
| 374 |
-
start = time.time()
|
| 375 |
-
|
| 376 |
-
pipe = pipes[model_id]["model"]
|
| 377 |
-
|
| 378 |
-
if "device" in pipes[model_id]:
|
| 379 |
-
try:
|
| 380 |
-
pipe.to(pipes[model_id]["device"])
|
| 381 |
-
except:
|
| 382 |
-
pipe.device = torch.device(pipes[model_id]["device"])
|
| 383 |
-
pipe.model.to(pipes[model_id]["device"])
|
| 384 |
-
|
| 385 |
-
result = None
|
| 386 |
-
try:
|
| 387 |
-
# text to video
|
| 388 |
-
if model_id == "damo-vilab/text-to-video-ms-1.7b":
|
| 389 |
-
pipe.scheduler = DPMSolverMultistepScheduler.from_config(
|
| 390 |
-
pipe.scheduler.config
|
| 391 |
-
)
|
| 392 |
-
# pipe.enable_model_cpu_offload()
|
| 393 |
-
prompt = data["text"]
|
| 394 |
-
video_frames = pipe(prompt, num_inference_steps=50, num_frames=40).frames
|
| 395 |
-
file_name = str(uuid.uuid4())[:4]
|
| 396 |
-
video_path = export_to_video(video_frames, f"public/videos/{file_name}.mp4")
|
| 397 |
-
|
| 398 |
-
new_file_name = str(uuid.uuid4())[:4]
|
| 399 |
-
os.system(
|
| 400 |
-
f"ffmpeg -i {video_path} -vcodec libx264 public/videos/{new_file_name}.mp4"
|
| 401 |
-
)
|
| 402 |
-
|
| 403 |
-
if os.path.exists(f"public/videos/{new_file_name}.mp4"):
|
| 404 |
-
result = {"path": f"/videos/{new_file_name}.mp4"}
|
| 405 |
-
else:
|
| 406 |
-
result = {"path": f"/videos/{file_name}.mp4"}
|
| 407 |
-
|
| 408 |
-
# controlnet
|
| 409 |
-
if model_id.startswith("lllyasviel/sd-controlnet-"):
|
| 410 |
-
pipe.controlnet.to("cpu")
|
| 411 |
-
pipe.controlnet = pipes[model_id]["control"].to(pipes[model_id]["device"])
|
| 412 |
-
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
|
| 413 |
-
control_image = load_image(data["img_url"])
|
| 414 |
-
# generator = torch.manual_seed(66)
|
| 415 |
-
out_image: Image = pipe(
|
| 416 |
-
data["text"], num_inference_steps=20, image=control_image
|
| 417 |
-
).images[0]
|
| 418 |
-
file_name = str(uuid.uuid4())[:4]
|
| 419 |
-
out_image.save(f"public/images/{file_name}.png")
|
| 420 |
-
result = {"path": f"/images/{file_name}.png"}
|
| 421 |
-
|
| 422 |
-
if model_id.endswith("-control"):
|
| 423 |
-
image = load_image(data["img_url"])
|
| 424 |
-
if "scribble" in model_id:
|
| 425 |
-
control = pipe(image, scribble=True)
|
| 426 |
-
elif "canny" in model_id:
|
| 427 |
-
control = pipe(image, low_threshold=100, high_threshold=200)
|
| 428 |
-
else:
|
| 429 |
-
control = pipe(image)
|
| 430 |
-
file_name = str(uuid.uuid4())[:4]
|
| 431 |
-
control.save(f"public/images/{file_name}.png")
|
| 432 |
-
result = {"path": f"/images/{file_name}.png"}
|
| 433 |
-
|
| 434 |
-
# image to image
|
| 435 |
-
if model_id == "lambdalabs/sd-image-variations-diffusers":
|
| 436 |
-
im = load_image(data["img_url"])
|
| 437 |
-
file_name = str(uuid.uuid4())[:4]
|
| 438 |
-
with open(f"public/images/{file_name}.png", "wb") as f:
|
| 439 |
-
f.write(data)
|
| 440 |
-
tform = transforms.Compose(
|
| 441 |
-
[
|
| 442 |
-
transforms.ToTensor(),
|
| 443 |
-
transforms.Resize(
|
| 444 |
-
(224, 224),
|
| 445 |
-
interpolation=transforms.InterpolationMode.BICUBIC,
|
| 446 |
-
antialias=False,
|
| 447 |
-
),
|
| 448 |
-
transforms.Normalize(
|
| 449 |
-
[0.48145466, 0.4578275, 0.40821073],
|
| 450 |
-
[0.26862954, 0.26130258, 0.27577711],
|
| 451 |
-
),
|
| 452 |
-
]
|
| 453 |
-
)
|
| 454 |
-
inp = tform(im).to(pipes[model_id]["device"]).unsqueeze(0)
|
| 455 |
-
out = pipe(inp, guidance_scale=3)
|
| 456 |
-
out["images"][0].save(f"public/images/{file_name}.jpg")
|
| 457 |
-
result = {"path": f"/images/{file_name}.jpg"}
|
| 458 |
-
|
| 459 |
-
# image to text
|
| 460 |
-
if model_id == "Salesforce/blip-image-captioning-large":
|
| 461 |
-
raw_image = load_image(data["img_url"]).convert("RGB")
|
| 462 |
-
text = data["text"]
|
| 463 |
-
inputs = pipes[model_id]["processor"](raw_image, return_tensors="pt").to(
|
| 464 |
-
pipes[model_id]["device"]
|
| 465 |
-
)
|
| 466 |
-
out = pipe.generate(**inputs)
|
| 467 |
-
caption = pipes[model_id]["processor"].decode(
|
| 468 |
-
out[0], skip_special_tokens=True
|
| 469 |
-
)
|
| 470 |
-
result = {"generated text": caption}
|
| 471 |
-
if model_id == "ydshieh/vit-gpt2-coco-en":
|
| 472 |
-
img_url = data["img_url"]
|
| 473 |
-
generated_text = pipe(img_url)[0]["generated_text"]
|
| 474 |
-
result = {"generated text": generated_text}
|
| 475 |
-
if model_id == "nlpconnect/vit-gpt2-image-captioning":
|
| 476 |
-
image = load_image(data["img_url"]).convert("RGB")
|
| 477 |
-
pixel_values = pipes[model_id]["feature_extractor"](
|
| 478 |
-
images=image, return_tensors="pt"
|
| 479 |
-
).pixel_values
|
| 480 |
-
pixel_values = pixel_values.to(pipes[model_id]["device"])
|
| 481 |
-
generated_ids = pipe.generate(
|
| 482 |
-
pixel_values, **{"max_length": 200, "num_beams": 1}
|
| 483 |
-
)
|
| 484 |
-
generated_text = pipes[model_id]["tokenizer"].batch_decode(
|
| 485 |
-
generated_ids, skip_special_tokens=True
|
| 486 |
-
)[0]
|
| 487 |
-
result = {"generated text": generated_text}
|
| 488 |
-
# image to text: OCR
|
| 489 |
-
if (
|
| 490 |
-
model_id == "microsoft/trocr-base-printed"
|
| 491 |
-
or model_id == "microsoft/trocr-base-handwritten"
|
| 492 |
-
):
|
| 493 |
-
image = load_image(data["img_url"]).convert("RGB")
|
| 494 |
-
pixel_values = pipes[model_id]["processor"](
|
| 495 |
-
image, return_tensors="pt"
|
| 496 |
-
).pixel_values
|
| 497 |
-
pixel_values = pixel_values.to(pipes[model_id]["device"])
|
| 498 |
-
generated_ids = pipe.generate(pixel_values)
|
| 499 |
-
generated_text = pipes[model_id]["processor"].batch_decode(
|
| 500 |
-
generated_ids, skip_special_tokens=True
|
| 501 |
-
)[0]
|
| 502 |
-
result = {"generated text": generated_text}
|
| 503 |
-
|
| 504 |
-
# text to image
|
| 505 |
-
if model_id == "runwayml/stable-diffusion-v1-5":
|
| 506 |
-
file_name = str(uuid.uuid4())[:4]
|
| 507 |
-
text = data["text"]
|
| 508 |
-
out = pipe(prompt=text)
|
| 509 |
-
out["images"][0].save(f"public/images/{file_name}.jpg")
|
| 510 |
-
result = {"path": f"/images/{file_name}.jpg"}
|
| 511 |
-
|
| 512 |
-
# object detection
|
| 513 |
-
if (
|
| 514 |
-
model_id == "google/owlvit-base-patch32"
|
| 515 |
-
or model_id == "facebook/detr-resnet-101"
|
| 516 |
-
):
|
| 517 |
-
img_url = data["img_url"]
|
| 518 |
-
open_types = [
|
| 519 |
-
"cat",
|
| 520 |
-
"couch",
|
| 521 |
-
"person",
|
| 522 |
-
"car",
|
| 523 |
-
"dog",
|
| 524 |
-
"horse",
|
| 525 |
-
"sheep",
|
| 526 |
-
"cow",
|
| 527 |
-
"elephant",
|
| 528 |
-
"bear",
|
| 529 |
-
"zebra",
|
| 530 |
-
"giraffe",
|
| 531 |
-
"backpack",
|
| 532 |
-
"umbrella",
|
| 533 |
-
"handbag",
|
| 534 |
-
"tie",
|
| 535 |
-
"suitcase",
|
| 536 |
-
"frisbee",
|
| 537 |
-
"skis",
|
| 538 |
-
"snowboard",
|
| 539 |
-
"sports ball",
|
| 540 |
-
"kite",
|
| 541 |
-
"baseball bat",
|
| 542 |
-
"baseball glove",
|
| 543 |
-
"skateboard",
|
| 544 |
-
"surfboard",
|
| 545 |
-
"tennis racket",
|
| 546 |
-
"bottle",
|
| 547 |
-
"wine glass",
|
| 548 |
-
"cup",
|
| 549 |
-
"fork",
|
| 550 |
-
"knife",
|
| 551 |
-
"spoon",
|
| 552 |
-
"bowl",
|
| 553 |
-
"banana",
|
| 554 |
-
"apple",
|
| 555 |
-
"sandwich",
|
| 556 |
-
"orange",
|
| 557 |
-
"broccoli",
|
| 558 |
-
"carrot",
|
| 559 |
-
"hot dog",
|
| 560 |
-
"pizza",
|
| 561 |
-
"donut",
|
| 562 |
-
"cake",
|
| 563 |
-
"chair",
|
| 564 |
-
"couch",
|
| 565 |
-
"potted plant",
|
| 566 |
-
"bed",
|
| 567 |
-
"dining table",
|
| 568 |
-
"toilet",
|
| 569 |
-
"tv",
|
| 570 |
-
"laptop",
|
| 571 |
-
"mouse",
|
| 572 |
-
"remote",
|
| 573 |
-
"keyboard",
|
| 574 |
-
"cell phone",
|
| 575 |
-
"microwave",
|
| 576 |
-
"oven",
|
| 577 |
-
"toaster",
|
| 578 |
-
"sink",
|
| 579 |
-
"refrigerator",
|
| 580 |
-
"book",
|
| 581 |
-
"clock",
|
| 582 |
-
"vase",
|
| 583 |
-
"scissors",
|
| 584 |
-
"teddy bear",
|
| 585 |
-
"hair drier",
|
| 586 |
-
"toothbrush",
|
| 587 |
-
"traffic light",
|
| 588 |
-
"fire hydrant",
|
| 589 |
-
"stop sign",
|
| 590 |
-
"parking meter",
|
| 591 |
-
"bench",
|
| 592 |
-
"bird",
|
| 593 |
-
]
|
| 594 |
-
result = pipe(img_url, candidate_labels=open_types)
|
| 595 |
-
|
| 596 |
-
# VQA
|
| 597 |
-
if model_id == "dandelin/vilt-b32-finetuned-vqa":
|
| 598 |
-
question = data["text"]
|
| 599 |
-
img_url = data["img_url"]
|
| 600 |
-
result = pipe(question=question, image=img_url)
|
| 601 |
-
|
| 602 |
-
# DQA
|
| 603 |
-
if model_id == "impira/layoutlm-document-qa":
|
| 604 |
-
question = data["text"]
|
| 605 |
-
img_url = data["img_url"]
|
| 606 |
-
result = pipe(img_url, question)
|
| 607 |
-
|
| 608 |
-
# depth-estimation
|
| 609 |
-
if model_id == "Intel/dpt-large":
|
| 610 |
-
output = pipe(data["img_url"])
|
| 611 |
-
image = output["depth"]
|
| 612 |
-
name = str(uuid.uuid4())[:4]
|
| 613 |
-
image.save(f"public/images/{name}.jpg")
|
| 614 |
-
result = {"path": f"/images/{name}.jpg"}
|
| 615 |
-
|
| 616 |
-
if model_id == "Intel/dpt-hybrid-midas" and model_id == "Intel/dpt-large":
|
| 617 |
-
image = load_image(data["img_url"])
|
| 618 |
-
inputs = pipes[model_id]["feature_extractor"](
|
| 619 |
-
images=image, return_tensors="pt"
|
| 620 |
-
)
|
| 621 |
-
with torch.no_grad():
|
| 622 |
-
outputs = pipe(**inputs)
|
| 623 |
-
predicted_depth = outputs.predicted_depth
|
| 624 |
-
prediction = torch.nn.functional.interpolate(
|
| 625 |
-
predicted_depth.unsqueeze(1),
|
| 626 |
-
size=image.size[::-1],
|
| 627 |
-
mode="bicubic",
|
| 628 |
-
align_corners=False,
|
| 629 |
-
)
|
| 630 |
-
output = prediction.squeeze().cpu().numpy()
|
| 631 |
-
formatted = (output * 255 / np.max(output)).astype("uint8")
|
| 632 |
-
image = Image.fromarray(formatted)
|
| 633 |
-
name = str(uuid.uuid4())[:4]
|
| 634 |
-
image.save(f"public/images/{name}.jpg")
|
| 635 |
-
result = {"path": f"/images/{name}.jpg"}
|
| 636 |
-
|
| 637 |
-
# TTS
|
| 638 |
-
if model_id == "espnet/kan-bayashi_ljspeech_vits":
|
| 639 |
-
text = data["text"]
|
| 640 |
-
wav = pipe(text)["wav"]
|
| 641 |
-
name = str(uuid.uuid4())[:4]
|
| 642 |
-
sf.write(f"public/audios/{name}.wav", wav.cpu().numpy(), pipe.fs, "PCM_16")
|
| 643 |
-
result = {"path": f"/audios/{name}.wav"}
|
| 644 |
-
|
| 645 |
-
if model_id == "microsoft/speecht5_tts":
|
| 646 |
-
text = data["text"]
|
| 647 |
-
inputs = pipes[model_id]["processor"](text=text, return_tensors="pt")
|
| 648 |
-
embeddings_dataset = pipes[model_id]["embeddings_dataset"]
|
| 649 |
-
speaker_embeddings = (
|
| 650 |
-
torch.tensor(embeddings_dataset[7306]["xvector"])
|
| 651 |
-
.unsqueeze(0)
|
| 652 |
-
.to(pipes[model_id]["device"])
|
| 653 |
-
)
|
| 654 |
-
pipes[model_id]["vocoder"].to(pipes[model_id]["device"])
|
| 655 |
-
speech = pipe.generate_speech(
|
| 656 |
-
inputs["input_ids"].to(pipes[model_id]["device"]),
|
| 657 |
-
speaker_embeddings,
|
| 658 |
-
vocoder=pipes[model_id]["vocoder"],
|
| 659 |
-
)
|
| 660 |
-
name = str(uuid.uuid4())[:4]
|
| 661 |
-
sf.write(
|
| 662 |
-
f"public/audios/{name}.wav", speech.cpu().numpy(), samplerate=16000
|
| 663 |
-
)
|
| 664 |
-
result = {"path": f"/audios/{name}.wav"}
|
| 665 |
-
|
| 666 |
-
# ASR
|
| 667 |
-
if model_id == "openai/whisper-base" or model_id == "microsoft/speecht5_asr":
|
| 668 |
-
audio_url = data["audio_url"]
|
| 669 |
-
result = {"text": pipe(audio_url)["text"]}
|
| 670 |
-
|
| 671 |
-
# audio to audio
|
| 672 |
-
if model_id == "JorisCos/DCCRNet_Libri1Mix_enhsingle_16k":
|
| 673 |
-
audio_url = data["audio_url"]
|
| 674 |
-
wav, sr = torchaudio.load(audio_url)
|
| 675 |
-
with torch.no_grad():
|
| 676 |
-
result_wav = pipe(wav.to(pipes[model_id]["device"]))
|
| 677 |
-
name = str(uuid.uuid4())[:4]
|
| 678 |
-
sf.write(
|
| 679 |
-
f"public/audios/{name}.wav", result_wav.cpu().squeeze().numpy(), sr
|
| 680 |
-
)
|
| 681 |
-
result = {"path": f"/audios/{name}.wav"}
|
| 682 |
-
|
| 683 |
-
if model_id == "microsoft/speecht5_vc":
|
| 684 |
-
audio_url = data["audio_url"]
|
| 685 |
-
wav, sr = torchaudio.load(audio_url)
|
| 686 |
-
inputs = pipes[model_id]["processor"](
|
| 687 |
-
audio=wav, sampling_rate=sr, return_tensors="pt"
|
| 688 |
-
)
|
| 689 |
-
embeddings_dataset = pipes[model_id]["embeddings_dataset"]
|
| 690 |
-
speaker_embeddings = torch.tensor(
|
| 691 |
-
embeddings_dataset[7306]["xvector"]
|
| 692 |
-
).unsqueeze(0)
|
| 693 |
-
pipes[model_id]["vocoder"].to(pipes[model_id]["device"])
|
| 694 |
-
speech = pipe.generate_speech(
|
| 695 |
-
inputs["input_ids"].to(pipes[model_id]["device"]),
|
| 696 |
-
speaker_embeddings,
|
| 697 |
-
vocoder=pipes[model_id]["vocoder"],
|
| 698 |
-
)
|
| 699 |
-
name = str(uuid.uuid4())[:4]
|
| 700 |
-
sf.write(
|
| 701 |
-
f"public/audios/{name}.wav", speech.cpu().numpy(), samplerate=16000
|
| 702 |
-
)
|
| 703 |
-
result = {"path": f"/audios/{name}.wav"}
|
| 704 |
-
|
| 705 |
-
# segmentation
|
| 706 |
-
if model_id == "facebook/detr-resnet-50-panoptic":
|
| 707 |
-
result = []
|
| 708 |
-
segments = pipe(data["img_url"])
|
| 709 |
-
image = load_image(data["img_url"])
|
| 710 |
-
|
| 711 |
-
colors = []
|
| 712 |
-
for i in range(len(segments)):
|
| 713 |
-
colors.append(
|
| 714 |
-
(
|
| 715 |
-
random.randint(100, 255),
|
| 716 |
-
random.randint(100, 255),
|
| 717 |
-
random.randint(100, 255),
|
| 718 |
-
50,
|
| 719 |
-
)
|
| 720 |
-
)
|
| 721 |
-
|
| 722 |
-
for segment in segments:
|
| 723 |
-
mask = segment["mask"]
|
| 724 |
-
mask = mask.convert("L")
|
| 725 |
-
layer = Image.new("RGBA", mask.size, colors[i])
|
| 726 |
-
image.paste(layer, (0, 0), mask)
|
| 727 |
-
name = str(uuid.uuid4())[:4]
|
| 728 |
-
image.save(f"public/images/{name}.jpg")
|
| 729 |
-
result = {"path": f"/images/{name}.jpg"}
|
| 730 |
-
|
| 731 |
-
if (
|
| 732 |
-
model_id == "facebook/maskformer-swin-base-coco"
|
| 733 |
-
or model_id == "facebook/maskformer-swin-large-ade"
|
| 734 |
-
):
|
| 735 |
-
image = load_image(data["img_url"])
|
| 736 |
-
inputs = pipes[model_id]["feature_extractor"](
|
| 737 |
-
images=image, return_tensors="pt"
|
| 738 |
-
).to(pipes[model_id]["device"])
|
| 739 |
-
outputs = pipe(**inputs)
|
| 740 |
-
result = pipes[model_id][
|
| 741 |
-
"feature_extractor"
|
| 742 |
-
].post_process_panoptic_segmentation(
|
| 743 |
-
outputs, target_sizes=[image.size[::-1]]
|
| 744 |
-
)[
|
| 745 |
-
0
|
| 746 |
-
]
|
| 747 |
-
predicted_panoptic_map = result["segmentation"].cpu().numpy()
|
| 748 |
-
predicted_panoptic_map = Image.fromarray(
|
| 749 |
-
predicted_panoptic_map.astype(np.uint8)
|
| 750 |
-
)
|
| 751 |
-
name = str(uuid.uuid4())[:4]
|
| 752 |
-
predicted_panoptic_map.save(f"public/images/{name}.jpg")
|
| 753 |
-
result = {"path": f"/images/{name}.jpg"}
|
| 754 |
-
|
| 755 |
-
except Exception as e:
|
| 756 |
-
print(e)
|
| 757 |
-
traceback.print_exc()
|
| 758 |
-
result = {"error": {"message": "Error when running the model inference."}}
|
| 759 |
-
|
| 760 |
-
if "device" in pipes[model_id]:
|
| 761 |
-
try:
|
| 762 |
-
pipe.to("cpu")
|
| 763 |
-
# torch.cuda.empty_cache()
|
| 764 |
-
except:
|
| 765 |
-
pipe.device = torch.device("cpu")
|
| 766 |
-
pipe.model.to("cpu")
|
| 767 |
-
# torch.cuda.empty_cache()
|
| 768 |
-
|
| 769 |
-
pipes[model_id]["using"] = False
|
| 770 |
-
|
| 771 |
-
if result is None:
|
| 772 |
-
result = {"error": {"message": "model not found"}}
|
| 773 |
-
|
| 774 |
-
end = time.time()
|
| 775 |
-
during = end - start
|
| 776 |
-
print(f"[ complete {model_id} ] {during}s")
|
| 777 |
-
print(f"[ result {model_id} ] {result}")
|
| 778 |
-
|
| 779 |
-
return result
|
|
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spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/models/facial_recognition/__init__.py
DELETED
|
File without changes
|
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/TRANSLATING.md
DELETED
|
@@ -1,57 +0,0 @@
|
|
| 1 |
-
### Translating the Diffusers documentation into your language
|
| 2 |
-
|
| 3 |
-
As part of our mission to democratize machine learning, we'd love to make the Diffusers library available in many more languages! Follow the steps below if you want to help translate the documentation into your language 🙏.
|
| 4 |
-
|
| 5 |
-
**🗞️ Open an issue**
|
| 6 |
-
|
| 7 |
-
To get started, navigate to the [Issues](https://github.com/huggingface/diffusers/issues) page of this repo and check if anyone else has opened an issue for your language. If not, open a new issue by selecting the "Translation template" from the "New issue" button.
|
| 8 |
-
|
| 9 |
-
Once an issue exists, post a comment to indicate which chapters you'd like to work on, and we'll add your name to the list.
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
**🍴 Fork the repository**
|
| 13 |
-
|
| 14 |
-
First, you'll need to [fork the Diffusers repo](https://docs.github.com/en/get-started/quickstart/fork-a-repo). You can do this by clicking on the **Fork** button on the top-right corner of this repo's page.
|
| 15 |
-
|
| 16 |
-
Once you've forked the repo, you'll want to get the files on your local machine for editing. You can do that by cloning the fork with Git as follows:
|
| 17 |
-
|
| 18 |
-
```bash
|
| 19 |
-
git clone https://github.com/YOUR-USERNAME/diffusers.git
|
| 20 |
-
```
|
| 21 |
-
|
| 22 |
-
**📋 Copy-paste the English version with a new language code**
|
| 23 |
-
|
| 24 |
-
The documentation files are in one leading directory:
|
| 25 |
-
|
| 26 |
-
- [`docs/source`](https://github.com/huggingface/diffusers/tree/main/docs/source): All the documentation materials are organized here by language.
|
| 27 |
-
|
| 28 |
-
You'll only need to copy the files in the [`docs/source/en`](https://github.com/huggingface/diffusers/tree/main/docs/source/en) directory, so first navigate to your fork of the repo and run the following:
|
| 29 |
-
|
| 30 |
-
```bash
|
| 31 |
-
cd ~/path/to/diffusers/docs
|
| 32 |
-
cp -r source/en source/LANG-ID
|
| 33 |
-
```
|
| 34 |
-
|
| 35 |
-
Here, `LANG-ID` should be one of the ISO 639-1 or ISO 639-2 language codes -- see [here](https://www.loc.gov/standards/iso639-2/php/code_list.php) for a handy table.
|
| 36 |
-
|
| 37 |
-
**✍️ Start translating**
|
| 38 |
-
|
| 39 |
-
The fun part comes - translating the text!
|
| 40 |
-
|
| 41 |
-
The first thing we recommend is translating the part of the `_toctree.yml` file that corresponds to your doc chapter. This file is used to render the table of contents on the website.
|
| 42 |
-
|
| 43 |
-
> 🙋 If the `_toctree.yml` file doesn't yet exist for your language, you can create one by copy-pasting from the English version and deleting the sections unrelated to your chapter. Just make sure it exists in the `docs/source/LANG-ID/` directory!
|
| 44 |
-
|
| 45 |
-
The fields you should add are `local` (with the name of the file containing the translation; e.g. `autoclass_tutorial`), and `title` (with the title of the doc in your language; e.g. `Load pretrained instances with an AutoClass`) -- as a reference, here is the `_toctree.yml` for [English](https://github.com/huggingface/diffusers/blob/main/docs/source/en/_toctree.yml):
|
| 46 |
-
|
| 47 |
-
```yaml
|
| 48 |
-
- sections:
|
| 49 |
-
- local: pipeline_tutorial # Do not change this! Use the same name for your .md file
|
| 50 |
-
title: Pipelines for inference # Translate this!
|
| 51 |
-
...
|
| 52 |
-
title: Tutorials # Translate this!
|
| 53 |
-
```
|
| 54 |
-
|
| 55 |
-
Once you have translated the `_toctree.yml` file, you can start translating the [MDX](https://mdxjs.com/) files associated with your docs chapter.
|
| 56 |
-
|
| 57 |
-
> 🙋 If you'd like others to help you with the translation, you should [open an issue](https://github.com/huggingface/diffusers/issues) and tag @patrickvonplaten.
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|
spaces/Andy1621/uniformer_image_detection/mmdet/core/utils/misc.py
DELETED
|
@@ -1,61 +0,0 @@
|
|
| 1 |
-
from functools import partial
|
| 2 |
-
|
| 3 |
-
import numpy as np
|
| 4 |
-
import torch
|
| 5 |
-
from six.moves import map, zip
|
| 6 |
-
|
| 7 |
-
from ..mask.structures import BitmapMasks, PolygonMasks
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
def multi_apply(func, *args, **kwargs):
|
| 11 |
-
"""Apply function to a list of arguments.
|
| 12 |
-
|
| 13 |
-
Note:
|
| 14 |
-
This function applies the ``func`` to multiple inputs and
|
| 15 |
-
map the multiple outputs of the ``func`` into different
|
| 16 |
-
list. Each list contains the same type of outputs corresponding
|
| 17 |
-
to different inputs.
|
| 18 |
-
|
| 19 |
-
Args:
|
| 20 |
-
func (Function): A function that will be applied to a list of
|
| 21 |
-
arguments
|
| 22 |
-
|
| 23 |
-
Returns:
|
| 24 |
-
tuple(list): A tuple containing multiple list, each list contains \
|
| 25 |
-
a kind of returned results by the function
|
| 26 |
-
"""
|
| 27 |
-
pfunc = partial(func, **kwargs) if kwargs else func
|
| 28 |
-
map_results = map(pfunc, *args)
|
| 29 |
-
return tuple(map(list, zip(*map_results)))
|
| 30 |
-
|
| 31 |
-
|
| 32 |
-
def unmap(data, count, inds, fill=0):
|
| 33 |
-
"""Unmap a subset of item (data) back to the original set of items (of size
|
| 34 |
-
count)"""
|
| 35 |
-
if data.dim() == 1:
|
| 36 |
-
ret = data.new_full((count, ), fill)
|
| 37 |
-
ret[inds.type(torch.bool)] = data
|
| 38 |
-
else:
|
| 39 |
-
new_size = (count, ) + data.size()[1:]
|
| 40 |
-
ret = data.new_full(new_size, fill)
|
| 41 |
-
ret[inds.type(torch.bool), :] = data
|
| 42 |
-
return ret
|
| 43 |
-
|
| 44 |
-
|
| 45 |
-
def mask2ndarray(mask):
|
| 46 |
-
"""Convert Mask to ndarray..
|
| 47 |
-
|
| 48 |
-
Args:
|
| 49 |
-
mask (:obj:`BitmapMasks` or :obj:`PolygonMasks` or
|
| 50 |
-
torch.Tensor or np.ndarray): The mask to be converted.
|
| 51 |
-
|
| 52 |
-
Returns:
|
| 53 |
-
np.ndarray: Ndarray mask of shape (n, h, w) that has been converted
|
| 54 |
-
"""
|
| 55 |
-
if isinstance(mask, (BitmapMasks, PolygonMasks)):
|
| 56 |
-
mask = mask.to_ndarray()
|
| 57 |
-
elif isinstance(mask, torch.Tensor):
|
| 58 |
-
mask = mask.detach().cpu().numpy()
|
| 59 |
-
elif not isinstance(mask, np.ndarray):
|
| 60 |
-
raise TypeError(f'Unsupported {type(mask)} data type')
|
| 61 |
-
return mask
|
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|
spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/accuracy.py
DELETED
|
@@ -1,78 +0,0 @@
|
|
| 1 |
-
import mmcv
|
| 2 |
-
import torch.nn as nn
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
@mmcv.jit(coderize=True)
|
| 6 |
-
def accuracy(pred, target, topk=1, thresh=None):
|
| 7 |
-
"""Calculate accuracy according to the prediction and target.
|
| 8 |
-
|
| 9 |
-
Args:
|
| 10 |
-
pred (torch.Tensor): The model prediction, shape (N, num_class)
|
| 11 |
-
target (torch.Tensor): The target of each prediction, shape (N, )
|
| 12 |
-
topk (int | tuple[int], optional): If the predictions in ``topk``
|
| 13 |
-
matches the target, the predictions will be regarded as
|
| 14 |
-
correct ones. Defaults to 1.
|
| 15 |
-
thresh (float, optional): If not None, predictions with scores under
|
| 16 |
-
this threshold are considered incorrect. Default to None.
|
| 17 |
-
|
| 18 |
-
Returns:
|
| 19 |
-
float | tuple[float]: If the input ``topk`` is a single integer,
|
| 20 |
-
the function will return a single float as accuracy. If
|
| 21 |
-
``topk`` is a tuple containing multiple integers, the
|
| 22 |
-
function will return a tuple containing accuracies of
|
| 23 |
-
each ``topk`` number.
|
| 24 |
-
"""
|
| 25 |
-
assert isinstance(topk, (int, tuple))
|
| 26 |
-
if isinstance(topk, int):
|
| 27 |
-
topk = (topk, )
|
| 28 |
-
return_single = True
|
| 29 |
-
else:
|
| 30 |
-
return_single = False
|
| 31 |
-
|
| 32 |
-
maxk = max(topk)
|
| 33 |
-
if pred.size(0) == 0:
|
| 34 |
-
accu = [pred.new_tensor(0.) for i in range(len(topk))]
|
| 35 |
-
return accu[0] if return_single else accu
|
| 36 |
-
assert pred.ndim == 2 and target.ndim == 1
|
| 37 |
-
assert pred.size(0) == target.size(0)
|
| 38 |
-
assert maxk <= pred.size(1), \
|
| 39 |
-
f'maxk {maxk} exceeds pred dimension {pred.size(1)}'
|
| 40 |
-
pred_value, pred_label = pred.topk(maxk, dim=1)
|
| 41 |
-
pred_label = pred_label.t() # transpose to shape (maxk, N)
|
| 42 |
-
correct = pred_label.eq(target.view(1, -1).expand_as(pred_label))
|
| 43 |
-
if thresh is not None:
|
| 44 |
-
# Only prediction values larger than thresh are counted as correct
|
| 45 |
-
correct = correct & (pred_value > thresh).t()
|
| 46 |
-
res = []
|
| 47 |
-
for k in topk:
|
| 48 |
-
correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
|
| 49 |
-
res.append(correct_k.mul_(100.0 / pred.size(0)))
|
| 50 |
-
return res[0] if return_single else res
|
| 51 |
-
|
| 52 |
-
|
| 53 |
-
class Accuracy(nn.Module):
|
| 54 |
-
|
| 55 |
-
def __init__(self, topk=(1, ), thresh=None):
|
| 56 |
-
"""Module to calculate the accuracy.
|
| 57 |
-
|
| 58 |
-
Args:
|
| 59 |
-
topk (tuple, optional): The criterion used to calculate the
|
| 60 |
-
accuracy. Defaults to (1,).
|
| 61 |
-
thresh (float, optional): If not None, predictions with scores
|
| 62 |
-
under this threshold are considered incorrect. Default to None.
|
| 63 |
-
"""
|
| 64 |
-
super().__init__()
|
| 65 |
-
self.topk = topk
|
| 66 |
-
self.thresh = thresh
|
| 67 |
-
|
| 68 |
-
def forward(self, pred, target):
|
| 69 |
-
"""Forward function to calculate accuracy.
|
| 70 |
-
|
| 71 |
-
Args:
|
| 72 |
-
pred (torch.Tensor): Prediction of models.
|
| 73 |
-
target (torch.Tensor): Target for each prediction.
|
| 74 |
-
|
| 75 |
-
Returns:
|
| 76 |
-
tuple[float]: The accuracies under different topk criterions.
|
| 77 |
-
"""
|
| 78 |
-
return accuracy(pred, target, self.topk, self.thresh)
|
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spaces/Andy1621/uniformer_image_detection/tools/deployment/pytorch2onnx.py
DELETED
|
@@ -1,244 +0,0 @@
|
|
| 1 |
-
import argparse
|
| 2 |
-
import os.path as osp
|
| 3 |
-
import warnings
|
| 4 |
-
|
| 5 |
-
import numpy as np
|
| 6 |
-
import onnx
|
| 7 |
-
import onnxruntime as rt
|
| 8 |
-
import torch
|
| 9 |
-
from mmcv import DictAction
|
| 10 |
-
|
| 11 |
-
from mmdet.core import (build_model_from_cfg, generate_inputs_and_wrap_model,
|
| 12 |
-
preprocess_example_input)
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
def pytorch2onnx(config_path,
|
| 16 |
-
checkpoint_path,
|
| 17 |
-
input_img,
|
| 18 |
-
input_shape,
|
| 19 |
-
opset_version=11,
|
| 20 |
-
show=False,
|
| 21 |
-
output_file='tmp.onnx',
|
| 22 |
-
verify=False,
|
| 23 |
-
normalize_cfg=None,
|
| 24 |
-
dataset='coco',
|
| 25 |
-
test_img=None,
|
| 26 |
-
do_simplify=False,
|
| 27 |
-
cfg_options=None):
|
| 28 |
-
|
| 29 |
-
input_config = {
|
| 30 |
-
'input_shape': input_shape,
|
| 31 |
-
'input_path': input_img,
|
| 32 |
-
'normalize_cfg': normalize_cfg
|
| 33 |
-
}
|
| 34 |
-
|
| 35 |
-
# prepare original model and meta for verifying the onnx model
|
| 36 |
-
orig_model = build_model_from_cfg(
|
| 37 |
-
config_path, checkpoint_path, cfg_options=cfg_options)
|
| 38 |
-
one_img, one_meta = preprocess_example_input(input_config)
|
| 39 |
-
model, tensor_data = generate_inputs_and_wrap_model(
|
| 40 |
-
config_path, checkpoint_path, input_config, cfg_options=cfg_options)
|
| 41 |
-
output_names = ['boxes']
|
| 42 |
-
if model.with_bbox:
|
| 43 |
-
output_names.append('labels')
|
| 44 |
-
if model.with_mask:
|
| 45 |
-
output_names.append('masks')
|
| 46 |
-
|
| 47 |
-
torch.onnx.export(
|
| 48 |
-
model,
|
| 49 |
-
tensor_data,
|
| 50 |
-
output_file,
|
| 51 |
-
input_names=['input'],
|
| 52 |
-
output_names=output_names,
|
| 53 |
-
export_params=True,
|
| 54 |
-
keep_initializers_as_inputs=True,
|
| 55 |
-
do_constant_folding=True,
|
| 56 |
-
verbose=show,
|
| 57 |
-
opset_version=opset_version)
|
| 58 |
-
|
| 59 |
-
model.forward = orig_model.forward
|
| 60 |
-
|
| 61 |
-
# simplify onnx model
|
| 62 |
-
if do_simplify:
|
| 63 |
-
from mmdet import digit_version
|
| 64 |
-
import mmcv
|
| 65 |
-
|
| 66 |
-
min_required_version = '1.2.5'
|
| 67 |
-
assert digit_version(mmcv.__version__) >= digit_version(
|
| 68 |
-
min_required_version
|
| 69 |
-
), f'Requires to install mmcv>={min_required_version}'
|
| 70 |
-
from mmcv.onnx.simplify import simplify
|
| 71 |
-
|
| 72 |
-
input_dic = {'input': one_img.detach().cpu().numpy()}
|
| 73 |
-
_ = simplify(output_file, [input_dic], output_file)
|
| 74 |
-
print(f'Successfully exported ONNX model: {output_file}')
|
| 75 |
-
if verify:
|
| 76 |
-
from mmdet.core import get_classes, bbox2result
|
| 77 |
-
from mmdet.apis import show_result_pyplot
|
| 78 |
-
|
| 79 |
-
ort_custom_op_path = ''
|
| 80 |
-
try:
|
| 81 |
-
from mmcv.ops import get_onnxruntime_op_path
|
| 82 |
-
ort_custom_op_path = get_onnxruntime_op_path()
|
| 83 |
-
except (ImportError, ModuleNotFoundError):
|
| 84 |
-
warnings.warn('If input model has custom op from mmcv, \
|
| 85 |
-
you may have to build mmcv with ONNXRuntime from source.')
|
| 86 |
-
model.CLASSES = get_classes(dataset)
|
| 87 |
-
num_classes = len(model.CLASSES)
|
| 88 |
-
# check by onnx
|
| 89 |
-
onnx_model = onnx.load(output_file)
|
| 90 |
-
onnx.checker.check_model(onnx_model)
|
| 91 |
-
if test_img is not None:
|
| 92 |
-
input_config['input_path'] = test_img
|
| 93 |
-
one_img, one_meta = preprocess_example_input(input_config)
|
| 94 |
-
tensor_data = [one_img]
|
| 95 |
-
# check the numerical value
|
| 96 |
-
# get pytorch output
|
| 97 |
-
pytorch_results = model(tensor_data, [[one_meta]], return_loss=False)
|
| 98 |
-
pytorch_results = pytorch_results[0]
|
| 99 |
-
# get onnx output
|
| 100 |
-
input_all = [node.name for node in onnx_model.graph.input]
|
| 101 |
-
input_initializer = [
|
| 102 |
-
node.name for node in onnx_model.graph.initializer
|
| 103 |
-
]
|
| 104 |
-
net_feed_input = list(set(input_all) - set(input_initializer))
|
| 105 |
-
assert (len(net_feed_input) == 1)
|
| 106 |
-
session_options = rt.SessionOptions()
|
| 107 |
-
# register custom op for onnxruntime
|
| 108 |
-
if osp.exists(ort_custom_op_path):
|
| 109 |
-
session_options.register_custom_ops_library(ort_custom_op_path)
|
| 110 |
-
sess = rt.InferenceSession(output_file, session_options)
|
| 111 |
-
onnx_outputs = sess.run(None,
|
| 112 |
-
{net_feed_input[0]: one_img.detach().numpy()})
|
| 113 |
-
output_names = [_.name for _ in sess.get_outputs()]
|
| 114 |
-
output_shapes = [_.shape for _ in onnx_outputs]
|
| 115 |
-
print(f'onnxruntime output names: {output_names}, \
|
| 116 |
-
output shapes: {output_shapes}')
|
| 117 |
-
nrof_out = len(onnx_outputs)
|
| 118 |
-
assert nrof_out > 0, 'Must have output'
|
| 119 |
-
with_mask = nrof_out == 3
|
| 120 |
-
if nrof_out == 1:
|
| 121 |
-
onnx_results = onnx_outputs[0]
|
| 122 |
-
else:
|
| 123 |
-
det_bboxes, det_labels = onnx_outputs[:2]
|
| 124 |
-
onnx_results = bbox2result(det_bboxes, det_labels, num_classes)
|
| 125 |
-
if with_mask:
|
| 126 |
-
segm_results = onnx_outputs[2].squeeze(1)
|
| 127 |
-
cls_segms = [[] for _ in range(num_classes)]
|
| 128 |
-
for i in range(det_bboxes.shape[0]):
|
| 129 |
-
cls_segms[det_labels[i]].append(segm_results[i])
|
| 130 |
-
onnx_results = (onnx_results, cls_segms)
|
| 131 |
-
# visualize predictions
|
| 132 |
-
|
| 133 |
-
if show:
|
| 134 |
-
show_result_pyplot(
|
| 135 |
-
model, one_meta['show_img'], pytorch_results, title='Pytorch')
|
| 136 |
-
show_result_pyplot(
|
| 137 |
-
model, one_meta['show_img'], onnx_results, title='ONNX')
|
| 138 |
-
|
| 139 |
-
# compare a part of result
|
| 140 |
-
|
| 141 |
-
if with_mask:
|
| 142 |
-
compare_pairs = list(zip(onnx_results, pytorch_results))
|
| 143 |
-
else:
|
| 144 |
-
compare_pairs = [(onnx_results, pytorch_results)]
|
| 145 |
-
for onnx_res, pytorch_res in compare_pairs:
|
| 146 |
-
for o_res, p_res in zip(onnx_res, pytorch_res):
|
| 147 |
-
np.testing.assert_allclose(
|
| 148 |
-
o_res,
|
| 149 |
-
p_res,
|
| 150 |
-
rtol=1e-03,
|
| 151 |
-
atol=1e-05,
|
| 152 |
-
)
|
| 153 |
-
print('The numerical values are the same between Pytorch and ONNX')
|
| 154 |
-
|
| 155 |
-
|
| 156 |
-
def parse_args():
|
| 157 |
-
parser = argparse.ArgumentParser(
|
| 158 |
-
description='Convert MMDetection models to ONNX')
|
| 159 |
-
parser.add_argument('config', help='test config file path')
|
| 160 |
-
parser.add_argument('checkpoint', help='checkpoint file')
|
| 161 |
-
parser.add_argument('--input-img', type=str, help='Images for input')
|
| 162 |
-
parser.add_argument('--show', action='store_true', help='show onnx graph')
|
| 163 |
-
parser.add_argument('--output-file', type=str, default='tmp.onnx')
|
| 164 |
-
parser.add_argument('--opset-version', type=int, default=11)
|
| 165 |
-
parser.add_argument(
|
| 166 |
-
'--test-img', type=str, default=None, help='Images for test')
|
| 167 |
-
parser.add_argument(
|
| 168 |
-
'--dataset', type=str, default='coco', help='Dataset name')
|
| 169 |
-
parser.add_argument(
|
| 170 |
-
'--verify',
|
| 171 |
-
action='store_true',
|
| 172 |
-
help='verify the onnx model output against pytorch output')
|
| 173 |
-
parser.add_argument(
|
| 174 |
-
'--simplify',
|
| 175 |
-
action='store_true',
|
| 176 |
-
help='Whether to simplify onnx model.')
|
| 177 |
-
parser.add_argument(
|
| 178 |
-
'--shape',
|
| 179 |
-
type=int,
|
| 180 |
-
nargs='+',
|
| 181 |
-
default=[800, 1216],
|
| 182 |
-
help='input image size')
|
| 183 |
-
parser.add_argument(
|
| 184 |
-
'--mean',
|
| 185 |
-
type=float,
|
| 186 |
-
nargs='+',
|
| 187 |
-
default=[123.675, 116.28, 103.53],
|
| 188 |
-
help='mean value used for preprocess input data')
|
| 189 |
-
parser.add_argument(
|
| 190 |
-
'--std',
|
| 191 |
-
type=float,
|
| 192 |
-
nargs='+',
|
| 193 |
-
default=[58.395, 57.12, 57.375],
|
| 194 |
-
help='variance value used for preprocess input data')
|
| 195 |
-
parser.add_argument(
|
| 196 |
-
'--cfg-options',
|
| 197 |
-
nargs='+',
|
| 198 |
-
action=DictAction,
|
| 199 |
-
help='override some settings in the used config, the key-value pair '
|
| 200 |
-
'in xxx=yyy format will be merged into config file. If the value to '
|
| 201 |
-
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
|
| 202 |
-
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
|
| 203 |
-
'Note that the quotation marks are necessary and that no white space '
|
| 204 |
-
'is allowed.')
|
| 205 |
-
args = parser.parse_args()
|
| 206 |
-
return args
|
| 207 |
-
|
| 208 |
-
|
| 209 |
-
if __name__ == '__main__':
|
| 210 |
-
args = parse_args()
|
| 211 |
-
|
| 212 |
-
assert args.opset_version == 11, 'MMDet only support opset 11 now'
|
| 213 |
-
|
| 214 |
-
if not args.input_img:
|
| 215 |
-
args.input_img = osp.join(
|
| 216 |
-
osp.dirname(__file__), '../../tests/data/color.jpg')
|
| 217 |
-
|
| 218 |
-
if len(args.shape) == 1:
|
| 219 |
-
input_shape = (1, 3, args.shape[0], args.shape[0])
|
| 220 |
-
elif len(args.shape) == 2:
|
| 221 |
-
input_shape = (1, 3) + tuple(args.shape)
|
| 222 |
-
else:
|
| 223 |
-
raise ValueError('invalid input shape')
|
| 224 |
-
|
| 225 |
-
assert len(args.mean) == 3
|
| 226 |
-
assert len(args.std) == 3
|
| 227 |
-
|
| 228 |
-
normalize_cfg = {'mean': args.mean, 'std': args.std}
|
| 229 |
-
|
| 230 |
-
# convert model to onnx file
|
| 231 |
-
pytorch2onnx(
|
| 232 |
-
args.config,
|
| 233 |
-
args.checkpoint,
|
| 234 |
-
args.input_img,
|
| 235 |
-
input_shape,
|
| 236 |
-
opset_version=args.opset_version,
|
| 237 |
-
show=args.show,
|
| 238 |
-
output_file=args.output_file,
|
| 239 |
-
verify=args.verify,
|
| 240 |
-
normalize_cfg=normalize_cfg,
|
| 241 |
-
dataset=args.dataset,
|
| 242 |
-
test_img=args.test_img,
|
| 243 |
-
do_simplify=args.simplify,
|
| 244 |
-
cfg_options=args.cfg_options)
|
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spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr18_512x1024_40k_cityscapes.py
DELETED
|
@@ -1,4 +0,0 @@
|
|
| 1 |
-
_base_ = [
|
| 2 |
-
'../_base_/models/fcn_hr18.py', '../_base_/datasets/cityscapes.py',
|
| 3 |
-
'../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
|
| 4 |
-
]
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spaces/Andy1621/uniformer_image_segmentation/configs/sem_fpn/fpn_r101_512x1024_80k_cityscapes.py
DELETED
|
@@ -1,2 +0,0 @@
|
|
| 1 |
-
_base_ = './fpn_r50_512x1024_80k_cityscapes.py'
|
| 2 |
-
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
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|
spaces/AnishKumbhar/ChatBot/text-generation-webui-main/cmd_wsl.bat
DELETED
|
@@ -1,11 +0,0 @@
|
|
| 1 |
-
@echo off
|
| 2 |
-
|
| 3 |
-
cd /D "%~dp0"
|
| 4 |
-
|
| 5 |
-
set PATH=%PATH%;%SystemRoot%\system32
|
| 6 |
-
|
| 7 |
-
@rem sed -i 's/\x0D$//' ./wsl.sh converts newlines to unix format in the wsl script
|
| 8 |
-
call wsl -e bash -lic "sed -i 's/\x0D$//' ./wsl.sh; source ./wsl.sh cmd"
|
| 9 |
-
|
| 10 |
-
:end
|
| 11 |
-
pause
|
|
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|
spaces/AnishKumbhar/ChatBot/text-generation-webui-main/extensions/silero_tts/tts_preprocessor.py
DELETED
|
@@ -1,200 +0,0 @@
|
|
| 1 |
-
import re
|
| 2 |
-
|
| 3 |
-
from num2words import num2words
|
| 4 |
-
|
| 5 |
-
punctuation = r'[\s,.?!/)\'\]>]'
|
| 6 |
-
alphabet_map = {
|
| 7 |
-
"A": " Ei ",
|
| 8 |
-
"B": " Bee ",
|
| 9 |
-
"C": " See ",
|
| 10 |
-
"D": " Dee ",
|
| 11 |
-
"E": " Eee ",
|
| 12 |
-
"F": " Eff ",
|
| 13 |
-
"G": " Jee ",
|
| 14 |
-
"H": " Eich ",
|
| 15 |
-
"I": " Eye ",
|
| 16 |
-
"J": " Jay ",
|
| 17 |
-
"K": " Kay ",
|
| 18 |
-
"L": " El ",
|
| 19 |
-
"M": " Emm ",
|
| 20 |
-
"N": " Enn ",
|
| 21 |
-
"O": " Ohh ",
|
| 22 |
-
"P": " Pee ",
|
| 23 |
-
"Q": " Queue ",
|
| 24 |
-
"R": " Are ",
|
| 25 |
-
"S": " Ess ",
|
| 26 |
-
"T": " Tee ",
|
| 27 |
-
"U": " You ",
|
| 28 |
-
"V": " Vee ",
|
| 29 |
-
"W": " Double You ",
|
| 30 |
-
"X": " Ex ",
|
| 31 |
-
"Y": " Why ",
|
| 32 |
-
"Z": " Zed " # Zed is weird, as I (da3dsoul) am American, but most of the voice models sound British, so it matches
|
| 33 |
-
}
|
| 34 |
-
|
| 35 |
-
|
| 36 |
-
def preprocess(string):
|
| 37 |
-
# the order for some of these matter
|
| 38 |
-
# For example, you need to remove the commas in numbers before expanding them
|
| 39 |
-
string = remove_surrounded_chars(string)
|
| 40 |
-
string = string.replace('"', '')
|
| 41 |
-
string = string.replace('\u201D', '').replace('\u201C', '') # right and left quote
|
| 42 |
-
string = string.replace('\u201F', '') # italic looking quote
|
| 43 |
-
string = string.replace('\n', ' ')
|
| 44 |
-
string = convert_num_locale(string)
|
| 45 |
-
string = replace_negative(string)
|
| 46 |
-
string = replace_roman(string)
|
| 47 |
-
string = hyphen_range_to(string)
|
| 48 |
-
string = num_to_words(string)
|
| 49 |
-
|
| 50 |
-
# TODO Try to use a ML predictor to expand abbreviations. It's hard, dependent on context, and whether to actually
|
| 51 |
-
# try to say the abbreviation or spell it out as I've done below is not agreed upon
|
| 52 |
-
|
| 53 |
-
# For now, expand abbreviations to pronunciations
|
| 54 |
-
# replace_abbreviations adds a lot of unnecessary whitespace to ensure separation
|
| 55 |
-
string = replace_abbreviations(string)
|
| 56 |
-
string = replace_lowercase_abbreviations(string)
|
| 57 |
-
|
| 58 |
-
# cleanup whitespaces
|
| 59 |
-
# remove whitespace before punctuation
|
| 60 |
-
string = re.sub(rf'\s+({punctuation})', r'\1', string)
|
| 61 |
-
string = string.strip()
|
| 62 |
-
# compact whitespace
|
| 63 |
-
string = ' '.join(string.split())
|
| 64 |
-
|
| 65 |
-
return string
|
| 66 |
-
|
| 67 |
-
|
| 68 |
-
def remove_surrounded_chars(string):
|
| 69 |
-
# first this expression will check if there is a string nested exclusively between a alt=
|
| 70 |
-
# and a style= string. This would correspond to only a the alt text of an embedded image
|
| 71 |
-
# If it matches it will only keep that part as the string, and rend it for further processing
|
| 72 |
-
# Afterwards this expression matches to 'as few symbols as possible (0 upwards) between any
|
| 73 |
-
# asterisks' OR' as few symbols as possible (0 upwards) between an asterisk and the end of the string'
|
| 74 |
-
if re.search(r'(?<=alt=)(.*)(?=style=)', string, re.DOTALL):
|
| 75 |
-
m = re.search(r'(?<=alt=)(.*)(?=style=)', string, re.DOTALL)
|
| 76 |
-
string = m.group(0)
|
| 77 |
-
return re.sub(r'\*[^*]*?(\*|$)', '', string)
|
| 78 |
-
|
| 79 |
-
|
| 80 |
-
def convert_num_locale(text):
|
| 81 |
-
# This detects locale and converts it to American without comma separators
|
| 82 |
-
pattern = re.compile(r'(?:\s|^)\d{1,3}(?:\.\d{3})+(,\d+)(?:\s|$)')
|
| 83 |
-
result = text
|
| 84 |
-
while True:
|
| 85 |
-
match = pattern.search(result)
|
| 86 |
-
if match is None:
|
| 87 |
-
break
|
| 88 |
-
|
| 89 |
-
start = match.start()
|
| 90 |
-
end = match.end()
|
| 91 |
-
result = result[0:start] + result[start:end].replace('.', '').replace(',', '.') + result[end:len(result)]
|
| 92 |
-
|
| 93 |
-
# removes comma separators from existing American numbers
|
| 94 |
-
pattern = re.compile(r'(\d),(\d)')
|
| 95 |
-
result = pattern.sub(r'\1\2', result)
|
| 96 |
-
|
| 97 |
-
return result
|
| 98 |
-
|
| 99 |
-
|
| 100 |
-
def replace_negative(string):
|
| 101 |
-
# handles situations like -5. -5 would become negative 5, which would then be expanded to negative five
|
| 102 |
-
return re.sub(rf'(\s)(-)(\d+)({punctuation})', r'\1negative \3\4', string)
|
| 103 |
-
|
| 104 |
-
|
| 105 |
-
def replace_roman(string):
|
| 106 |
-
# find a string of roman numerals.
|
| 107 |
-
# Only 2 or more, to avoid capturing I and single character abbreviations, like names
|
| 108 |
-
pattern = re.compile(rf'\s[IVXLCDM]{{2,}}{punctuation}')
|
| 109 |
-
result = string
|
| 110 |
-
while True:
|
| 111 |
-
match = pattern.search(result)
|
| 112 |
-
if match is None:
|
| 113 |
-
break
|
| 114 |
-
|
| 115 |
-
start = match.start()
|
| 116 |
-
end = match.end()
|
| 117 |
-
result = result[0:start + 1] + str(roman_to_int(result[start + 1:end - 1])) + result[end - 1:len(result)]
|
| 118 |
-
|
| 119 |
-
return result
|
| 120 |
-
|
| 121 |
-
|
| 122 |
-
def roman_to_int(s):
|
| 123 |
-
rom_val = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
|
| 124 |
-
int_val = 0
|
| 125 |
-
for i in range(len(s)):
|
| 126 |
-
if i > 0 and rom_val[s[i]] > rom_val[s[i - 1]]:
|
| 127 |
-
int_val += rom_val[s[i]] - 2 * rom_val[s[i - 1]]
|
| 128 |
-
else:
|
| 129 |
-
int_val += rom_val[s[i]]
|
| 130 |
-
return int_val
|
| 131 |
-
|
| 132 |
-
|
| 133 |
-
def hyphen_range_to(text):
|
| 134 |
-
pattern = re.compile(r'(\d+)[-–](\d+)')
|
| 135 |
-
result = pattern.sub(lambda x: x.group(1) + ' to ' + x.group(2), text)
|
| 136 |
-
return result
|
| 137 |
-
|
| 138 |
-
|
| 139 |
-
def num_to_words(text):
|
| 140 |
-
# 1000 or 10.23
|
| 141 |
-
pattern = re.compile(r'\d+\.\d+|\d+')
|
| 142 |
-
result = pattern.sub(lambda x: num2words(float(x.group())), text)
|
| 143 |
-
return result
|
| 144 |
-
|
| 145 |
-
|
| 146 |
-
def replace_abbreviations(string):
|
| 147 |
-
# abbreviations 1 to 4 characters long. It will get things like A and I, but those are pronounced with their letter
|
| 148 |
-
pattern = re.compile(rf'(^|[\s(.\'\[<])([A-Z]{{1,4}})({punctuation}|$)')
|
| 149 |
-
result = string
|
| 150 |
-
while True:
|
| 151 |
-
match = pattern.search(result)
|
| 152 |
-
if match is None:
|
| 153 |
-
break
|
| 154 |
-
|
| 155 |
-
start = match.start()
|
| 156 |
-
end = match.end()
|
| 157 |
-
result = result[0:start] + replace_abbreviation(result[start:end]) + result[end:len(result)]
|
| 158 |
-
|
| 159 |
-
return result
|
| 160 |
-
|
| 161 |
-
|
| 162 |
-
def replace_lowercase_abbreviations(string):
|
| 163 |
-
# abbreviations 1 to 4 characters long, separated by dots i.e. e.g.
|
| 164 |
-
pattern = re.compile(rf'(^|[\s(.\'\[<])(([a-z]\.){{1,4}})({punctuation}|$)')
|
| 165 |
-
result = string
|
| 166 |
-
while True:
|
| 167 |
-
match = pattern.search(result)
|
| 168 |
-
if match is None:
|
| 169 |
-
break
|
| 170 |
-
|
| 171 |
-
start = match.start()
|
| 172 |
-
end = match.end()
|
| 173 |
-
result = result[0:start] + replace_abbreviation(result[start:end].upper()) + result[end:len(result)]
|
| 174 |
-
|
| 175 |
-
return result
|
| 176 |
-
|
| 177 |
-
|
| 178 |
-
def replace_abbreviation(string):
|
| 179 |
-
result = ""
|
| 180 |
-
for char in string:
|
| 181 |
-
result += match_mapping(char)
|
| 182 |
-
|
| 183 |
-
return result
|
| 184 |
-
|
| 185 |
-
|
| 186 |
-
def match_mapping(char):
|
| 187 |
-
for mapping in alphabet_map.keys():
|
| 188 |
-
if char == mapping:
|
| 189 |
-
return alphabet_map[char]
|
| 190 |
-
|
| 191 |
-
return char
|
| 192 |
-
|
| 193 |
-
|
| 194 |
-
def __main__(args):
|
| 195 |
-
print(preprocess(args[1]))
|
| 196 |
-
|
| 197 |
-
|
| 198 |
-
if __name__ == "__main__":
|
| 199 |
-
import sys
|
| 200 |
-
__main__(sys.argv)
|
|
|
|
|
|
|
|
|
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|
spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/memory.py
DELETED
|
@@ -1,25 +0,0 @@
|
|
| 1 |
-
# Copyright (c) OpenMMLab. All rights reserved.
|
| 2 |
-
import torch
|
| 3 |
-
|
| 4 |
-
from .hook import HOOKS, Hook
|
| 5 |
-
|
| 6 |
-
|
| 7 |
-
@HOOKS.register_module()
|
| 8 |
-
class EmptyCacheHook(Hook):
|
| 9 |
-
|
| 10 |
-
def __init__(self, before_epoch=False, after_epoch=True, after_iter=False):
|
| 11 |
-
self._before_epoch = before_epoch
|
| 12 |
-
self._after_epoch = after_epoch
|
| 13 |
-
self._after_iter = after_iter
|
| 14 |
-
|
| 15 |
-
def after_iter(self, runner):
|
| 16 |
-
if self._after_iter:
|
| 17 |
-
torch.cuda.empty_cache()
|
| 18 |
-
|
| 19 |
-
def before_epoch(self, runner):
|
| 20 |
-
if self._before_epoch:
|
| 21 |
-
torch.cuda.empty_cache()
|
| 22 |
-
|
| 23 |
-
def after_epoch(self, runner):
|
| 24 |
-
if self._after_epoch:
|
| 25 |
-
torch.cuda.empty_cache()
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/appdirs.py
DELETED
|
@@ -1,608 +0,0 @@
|
|
| 1 |
-
#!/usr/bin/env python
|
| 2 |
-
# -*- coding: utf-8 -*-
|
| 3 |
-
# Copyright (c) 2005-2010 ActiveState Software Inc.
|
| 4 |
-
# Copyright (c) 2013 Eddy Petrișor
|
| 5 |
-
|
| 6 |
-
"""Utilities for determining application-specific dirs.
|
| 7 |
-
|
| 8 |
-
See <http://github.com/ActiveState/appdirs> for details and usage.
|
| 9 |
-
"""
|
| 10 |
-
# Dev Notes:
|
| 11 |
-
# - MSDN on where to store app data files:
|
| 12 |
-
# http://support.microsoft.com/default.aspx?scid=kb;en-us;310294#XSLTH3194121123120121120120
|
| 13 |
-
# - Mac OS X: http://developer.apple.com/documentation/MacOSX/Conceptual/BPFileSystem/index.html
|
| 14 |
-
# - XDG spec for Un*x: http://standards.freedesktop.org/basedir-spec/basedir-spec-latest.html
|
| 15 |
-
|
| 16 |
-
__version_info__ = (1, 4, 3)
|
| 17 |
-
__version__ = '.'.join(map(str, __version_info__))
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
import sys
|
| 21 |
-
import os
|
| 22 |
-
|
| 23 |
-
PY3 = sys.version_info[0] == 3
|
| 24 |
-
|
| 25 |
-
if PY3:
|
| 26 |
-
unicode = str
|
| 27 |
-
|
| 28 |
-
if sys.platform.startswith('java'):
|
| 29 |
-
import platform
|
| 30 |
-
os_name = platform.java_ver()[3][0]
|
| 31 |
-
if os_name.startswith('Windows'): # "Windows XP", "Windows 7", etc.
|
| 32 |
-
system = 'win32'
|
| 33 |
-
elif os_name.startswith('Mac'): # "Mac OS X", etc.
|
| 34 |
-
system = 'darwin'
|
| 35 |
-
else: # "Linux", "SunOS", "FreeBSD", etc.
|
| 36 |
-
# Setting this to "linux2" is not ideal, but only Windows or Mac
|
| 37 |
-
# are actually checked for and the rest of the module expects
|
| 38 |
-
# *sys.platform* style strings.
|
| 39 |
-
system = 'linux2'
|
| 40 |
-
else:
|
| 41 |
-
system = sys.platform
|
| 42 |
-
|
| 43 |
-
|
| 44 |
-
|
| 45 |
-
def user_data_dir(appname=None, appauthor=None, version=None, roaming=False):
|
| 46 |
-
r"""Return full path to the user-specific data dir for this application.
|
| 47 |
-
|
| 48 |
-
"appname" is the name of application.
|
| 49 |
-
If None, just the system directory is returned.
|
| 50 |
-
"appauthor" (only used on Windows) is the name of the
|
| 51 |
-
appauthor or distributing body for this application. Typically
|
| 52 |
-
it is the owning company name. This falls back to appname. You may
|
| 53 |
-
pass False to disable it.
|
| 54 |
-
"version" is an optional version path element to append to the
|
| 55 |
-
path. You might want to use this if you want multiple versions
|
| 56 |
-
of your app to be able to run independently. If used, this
|
| 57 |
-
would typically be "<major>.<minor>".
|
| 58 |
-
Only applied when appname is present.
|
| 59 |
-
"roaming" (boolean, default False) can be set True to use the Windows
|
| 60 |
-
roaming appdata directory. That means that for users on a Windows
|
| 61 |
-
network setup for roaming profiles, this user data will be
|
| 62 |
-
sync'd on login. See
|
| 63 |
-
<http://technet.microsoft.com/en-us/library/cc766489(WS.10).aspx>
|
| 64 |
-
for a discussion of issues.
|
| 65 |
-
|
| 66 |
-
Typical user data directories are:
|
| 67 |
-
Mac OS X: ~/Library/Application Support/<AppName>
|
| 68 |
-
Unix: ~/.local/share/<AppName> # or in $XDG_DATA_HOME, if defined
|
| 69 |
-
Win XP (not roaming): C:\Documents and Settings\<username>\Application Data\<AppAuthor>\<AppName>
|
| 70 |
-
Win XP (roaming): C:\Documents and Settings\<username>\Local Settings\Application Data\<AppAuthor>\<AppName>
|
| 71 |
-
Win 7 (not roaming): C:\Users\<username>\AppData\Local\<AppAuthor>\<AppName>
|
| 72 |
-
Win 7 (roaming): C:\Users\<username>\AppData\Roaming\<AppAuthor>\<AppName>
|
| 73 |
-
|
| 74 |
-
For Unix, we follow the XDG spec and support $XDG_DATA_HOME.
|
| 75 |
-
That means, by default "~/.local/share/<AppName>".
|
| 76 |
-
"""
|
| 77 |
-
if system == "win32":
|
| 78 |
-
if appauthor is None:
|
| 79 |
-
appauthor = appname
|
| 80 |
-
const = roaming and "CSIDL_APPDATA" or "CSIDL_LOCAL_APPDATA"
|
| 81 |
-
path = os.path.normpath(_get_win_folder(const))
|
| 82 |
-
if appname:
|
| 83 |
-
if appauthor is not False:
|
| 84 |
-
path = os.path.join(path, appauthor, appname)
|
| 85 |
-
else:
|
| 86 |
-
path = os.path.join(path, appname)
|
| 87 |
-
elif system == 'darwin':
|
| 88 |
-
path = os.path.expanduser('~/Library/Application Support/')
|
| 89 |
-
if appname:
|
| 90 |
-
path = os.path.join(path, appname)
|
| 91 |
-
else:
|
| 92 |
-
path = os.getenv('XDG_DATA_HOME', os.path.expanduser("~/.local/share"))
|
| 93 |
-
if appname:
|
| 94 |
-
path = os.path.join(path, appname)
|
| 95 |
-
if appname and version:
|
| 96 |
-
path = os.path.join(path, version)
|
| 97 |
-
return path
|
| 98 |
-
|
| 99 |
-
|
| 100 |
-
def site_data_dir(appname=None, appauthor=None, version=None, multipath=False):
|
| 101 |
-
r"""Return full path to the user-shared data dir for this application.
|
| 102 |
-
|
| 103 |
-
"appname" is the name of application.
|
| 104 |
-
If None, just the system directory is returned.
|
| 105 |
-
"appauthor" (only used on Windows) is the name of the
|
| 106 |
-
appauthor or distributing body for this application. Typically
|
| 107 |
-
it is the owning company name. This falls back to appname. You may
|
| 108 |
-
pass False to disable it.
|
| 109 |
-
"version" is an optional version path element to append to the
|
| 110 |
-
path. You might want to use this if you want multiple versions
|
| 111 |
-
of your app to be able to run independently. If used, this
|
| 112 |
-
would typically be "<major>.<minor>".
|
| 113 |
-
Only applied when appname is present.
|
| 114 |
-
"multipath" is an optional parameter only applicable to *nix
|
| 115 |
-
which indicates that the entire list of data dirs should be
|
| 116 |
-
returned. By default, the first item from XDG_DATA_DIRS is
|
| 117 |
-
returned, or '/usr/local/share/<AppName>',
|
| 118 |
-
if XDG_DATA_DIRS is not set
|
| 119 |
-
|
| 120 |
-
Typical site data directories are:
|
| 121 |
-
Mac OS X: /Library/Application Support/<AppName>
|
| 122 |
-
Unix: /usr/local/share/<AppName> or /usr/share/<AppName>
|
| 123 |
-
Win XP: C:\Documents and Settings\All Users\Application Data\<AppAuthor>\<AppName>
|
| 124 |
-
Vista: (Fail! "C:\ProgramData" is a hidden *system* directory on Vista.)
|
| 125 |
-
Win 7: C:\ProgramData\<AppAuthor>\<AppName> # Hidden, but writeable on Win 7.
|
| 126 |
-
|
| 127 |
-
For Unix, this is using the $XDG_DATA_DIRS[0] default.
|
| 128 |
-
|
| 129 |
-
WARNING: Do not use this on Windows. See the Vista-Fail note above for why.
|
| 130 |
-
"""
|
| 131 |
-
if system == "win32":
|
| 132 |
-
if appauthor is None:
|
| 133 |
-
appauthor = appname
|
| 134 |
-
path = os.path.normpath(_get_win_folder("CSIDL_COMMON_APPDATA"))
|
| 135 |
-
if appname:
|
| 136 |
-
if appauthor is not False:
|
| 137 |
-
path = os.path.join(path, appauthor, appname)
|
| 138 |
-
else:
|
| 139 |
-
path = os.path.join(path, appname)
|
| 140 |
-
elif system == 'darwin':
|
| 141 |
-
path = os.path.expanduser('/Library/Application Support')
|
| 142 |
-
if appname:
|
| 143 |
-
path = os.path.join(path, appname)
|
| 144 |
-
else:
|
| 145 |
-
# XDG default for $XDG_DATA_DIRS
|
| 146 |
-
# only first, if multipath is False
|
| 147 |
-
path = os.getenv('XDG_DATA_DIRS',
|
| 148 |
-
os.pathsep.join(['/usr/local/share', '/usr/share']))
|
| 149 |
-
pathlist = [os.path.expanduser(x.rstrip(os.sep)) for x in path.split(os.pathsep)]
|
| 150 |
-
if appname:
|
| 151 |
-
if version:
|
| 152 |
-
appname = os.path.join(appname, version)
|
| 153 |
-
pathlist = [os.sep.join([x, appname]) for x in pathlist]
|
| 154 |
-
|
| 155 |
-
if multipath:
|
| 156 |
-
path = os.pathsep.join(pathlist)
|
| 157 |
-
else:
|
| 158 |
-
path = pathlist[0]
|
| 159 |
-
return path
|
| 160 |
-
|
| 161 |
-
if appname and version:
|
| 162 |
-
path = os.path.join(path, version)
|
| 163 |
-
return path
|
| 164 |
-
|
| 165 |
-
|
| 166 |
-
def user_config_dir(appname=None, appauthor=None, version=None, roaming=False):
|
| 167 |
-
r"""Return full path to the user-specific config dir for this application.
|
| 168 |
-
|
| 169 |
-
"appname" is the name of application.
|
| 170 |
-
If None, just the system directory is returned.
|
| 171 |
-
"appauthor" (only used on Windows) is the name of the
|
| 172 |
-
appauthor or distributing body for this application. Typically
|
| 173 |
-
it is the owning company name. This falls back to appname. You may
|
| 174 |
-
pass False to disable it.
|
| 175 |
-
"version" is an optional version path element to append to the
|
| 176 |
-
path. You might want to use this if you want multiple versions
|
| 177 |
-
of your app to be able to run independently. If used, this
|
| 178 |
-
would typically be "<major>.<minor>".
|
| 179 |
-
Only applied when appname is present.
|
| 180 |
-
"roaming" (boolean, default False) can be set True to use the Windows
|
| 181 |
-
roaming appdata directory. That means that for users on a Windows
|
| 182 |
-
network setup for roaming profiles, this user data will be
|
| 183 |
-
sync'd on login. See
|
| 184 |
-
<http://technet.microsoft.com/en-us/library/cc766489(WS.10).aspx>
|
| 185 |
-
for a discussion of issues.
|
| 186 |
-
|
| 187 |
-
Typical user config directories are:
|
| 188 |
-
Mac OS X: same as user_data_dir
|
| 189 |
-
Unix: ~/.config/<AppName> # or in $XDG_CONFIG_HOME, if defined
|
| 190 |
-
Win *: same as user_data_dir
|
| 191 |
-
|
| 192 |
-
For Unix, we follow the XDG spec and support $XDG_CONFIG_HOME.
|
| 193 |
-
That means, by default "~/.config/<AppName>".
|
| 194 |
-
"""
|
| 195 |
-
if system in ["win32", "darwin"]:
|
| 196 |
-
path = user_data_dir(appname, appauthor, None, roaming)
|
| 197 |
-
else:
|
| 198 |
-
path = os.getenv('XDG_CONFIG_HOME', os.path.expanduser("~/.config"))
|
| 199 |
-
if appname:
|
| 200 |
-
path = os.path.join(path, appname)
|
| 201 |
-
if appname and version:
|
| 202 |
-
path = os.path.join(path, version)
|
| 203 |
-
return path
|
| 204 |
-
|
| 205 |
-
|
| 206 |
-
def site_config_dir(appname=None, appauthor=None, version=None, multipath=False):
|
| 207 |
-
r"""Return full path to the user-shared data dir for this application.
|
| 208 |
-
|
| 209 |
-
"appname" is the name of application.
|
| 210 |
-
If None, just the system directory is returned.
|
| 211 |
-
"appauthor" (only used on Windows) is the name of the
|
| 212 |
-
appauthor or distributing body for this application. Typically
|
| 213 |
-
it is the owning company name. This falls back to appname. You may
|
| 214 |
-
pass False to disable it.
|
| 215 |
-
"version" is an optional version path element to append to the
|
| 216 |
-
path. You might want to use this if you want multiple versions
|
| 217 |
-
of your app to be able to run independently. If used, this
|
| 218 |
-
would typically be "<major>.<minor>".
|
| 219 |
-
Only applied when appname is present.
|
| 220 |
-
"multipath" is an optional parameter only applicable to *nix
|
| 221 |
-
which indicates that the entire list of config dirs should be
|
| 222 |
-
returned. By default, the first item from XDG_CONFIG_DIRS is
|
| 223 |
-
returned, or '/etc/xdg/<AppName>', if XDG_CONFIG_DIRS is not set
|
| 224 |
-
|
| 225 |
-
Typical site config directories are:
|
| 226 |
-
Mac OS X: same as site_data_dir
|
| 227 |
-
Unix: /etc/xdg/<AppName> or $XDG_CONFIG_DIRS[i]/<AppName> for each value in
|
| 228 |
-
$XDG_CONFIG_DIRS
|
| 229 |
-
Win *: same as site_data_dir
|
| 230 |
-
Vista: (Fail! "C:\ProgramData" is a hidden *system* directory on Vista.)
|
| 231 |
-
|
| 232 |
-
For Unix, this is using the $XDG_CONFIG_DIRS[0] default, if multipath=False
|
| 233 |
-
|
| 234 |
-
WARNING: Do not use this on Windows. See the Vista-Fail note above for why.
|
| 235 |
-
"""
|
| 236 |
-
if system in ["win32", "darwin"]:
|
| 237 |
-
path = site_data_dir(appname, appauthor)
|
| 238 |
-
if appname and version:
|
| 239 |
-
path = os.path.join(path, version)
|
| 240 |
-
else:
|
| 241 |
-
# XDG default for $XDG_CONFIG_DIRS
|
| 242 |
-
# only first, if multipath is False
|
| 243 |
-
path = os.getenv('XDG_CONFIG_DIRS', '/etc/xdg')
|
| 244 |
-
pathlist = [os.path.expanduser(x.rstrip(os.sep)) for x in path.split(os.pathsep)]
|
| 245 |
-
if appname:
|
| 246 |
-
if version:
|
| 247 |
-
appname = os.path.join(appname, version)
|
| 248 |
-
pathlist = [os.sep.join([x, appname]) for x in pathlist]
|
| 249 |
-
|
| 250 |
-
if multipath:
|
| 251 |
-
path = os.pathsep.join(pathlist)
|
| 252 |
-
else:
|
| 253 |
-
path = pathlist[0]
|
| 254 |
-
return path
|
| 255 |
-
|
| 256 |
-
|
| 257 |
-
def user_cache_dir(appname=None, appauthor=None, version=None, opinion=True):
|
| 258 |
-
r"""Return full path to the user-specific cache dir for this application.
|
| 259 |
-
|
| 260 |
-
"appname" is the name of application.
|
| 261 |
-
If None, just the system directory is returned.
|
| 262 |
-
"appauthor" (only used on Windows) is the name of the
|
| 263 |
-
appauthor or distributing body for this application. Typically
|
| 264 |
-
it is the owning company name. This falls back to appname. You may
|
| 265 |
-
pass False to disable it.
|
| 266 |
-
"version" is an optional version path element to append to the
|
| 267 |
-
path. You might want to use this if you want multiple versions
|
| 268 |
-
of your app to be able to run independently. If used, this
|
| 269 |
-
would typically be "<major>.<minor>".
|
| 270 |
-
Only applied when appname is present.
|
| 271 |
-
"opinion" (boolean) can be False to disable the appending of
|
| 272 |
-
"Cache" to the base app data dir for Windows. See
|
| 273 |
-
discussion below.
|
| 274 |
-
|
| 275 |
-
Typical user cache directories are:
|
| 276 |
-
Mac OS X: ~/Library/Caches/<AppName>
|
| 277 |
-
Unix: ~/.cache/<AppName> (XDG default)
|
| 278 |
-
Win XP: C:\Documents and Settings\<username>\Local Settings\Application Data\<AppAuthor>\<AppName>\Cache
|
| 279 |
-
Vista: C:\Users\<username>\AppData\Local\<AppAuthor>\<AppName>\Cache
|
| 280 |
-
|
| 281 |
-
On Windows the only suggestion in the MSDN docs is that local settings go in
|
| 282 |
-
the `CSIDL_LOCAL_APPDATA` directory. This is identical to the non-roaming
|
| 283 |
-
app data dir (the default returned by `user_data_dir` above). Apps typically
|
| 284 |
-
put cache data somewhere *under* the given dir here. Some examples:
|
| 285 |
-
...\Mozilla\Firefox\Profiles\<ProfileName>\Cache
|
| 286 |
-
...\Acme\SuperApp\Cache\1.0
|
| 287 |
-
OPINION: This function appends "Cache" to the `CSIDL_LOCAL_APPDATA` value.
|
| 288 |
-
This can be disabled with the `opinion=False` option.
|
| 289 |
-
"""
|
| 290 |
-
if system == "win32":
|
| 291 |
-
if appauthor is None:
|
| 292 |
-
appauthor = appname
|
| 293 |
-
path = os.path.normpath(_get_win_folder("CSIDL_LOCAL_APPDATA"))
|
| 294 |
-
if appname:
|
| 295 |
-
if appauthor is not False:
|
| 296 |
-
path = os.path.join(path, appauthor, appname)
|
| 297 |
-
else:
|
| 298 |
-
path = os.path.join(path, appname)
|
| 299 |
-
if opinion:
|
| 300 |
-
path = os.path.join(path, "Cache")
|
| 301 |
-
elif system == 'darwin':
|
| 302 |
-
path = os.path.expanduser('~/Library/Caches')
|
| 303 |
-
if appname:
|
| 304 |
-
path = os.path.join(path, appname)
|
| 305 |
-
else:
|
| 306 |
-
path = os.getenv('XDG_CACHE_HOME', os.path.expanduser('~/.cache'))
|
| 307 |
-
if appname:
|
| 308 |
-
path = os.path.join(path, appname)
|
| 309 |
-
if appname and version:
|
| 310 |
-
path = os.path.join(path, version)
|
| 311 |
-
return path
|
| 312 |
-
|
| 313 |
-
|
| 314 |
-
def user_state_dir(appname=None, appauthor=None, version=None, roaming=False):
|
| 315 |
-
r"""Return full path to the user-specific state dir for this application.
|
| 316 |
-
|
| 317 |
-
"appname" is the name of application.
|
| 318 |
-
If None, just the system directory is returned.
|
| 319 |
-
"appauthor" (only used on Windows) is the name of the
|
| 320 |
-
appauthor or distributing body for this application. Typically
|
| 321 |
-
it is the owning company name. This falls back to appname. You may
|
| 322 |
-
pass False to disable it.
|
| 323 |
-
"version" is an optional version path element to append to the
|
| 324 |
-
path. You might want to use this if you want multiple versions
|
| 325 |
-
of your app to be able to run independently. If used, this
|
| 326 |
-
would typically be "<major>.<minor>".
|
| 327 |
-
Only applied when appname is present.
|
| 328 |
-
"roaming" (boolean, default False) can be set True to use the Windows
|
| 329 |
-
roaming appdata directory. That means that for users on a Windows
|
| 330 |
-
network setup for roaming profiles, this user data will be
|
| 331 |
-
sync'd on login. See
|
| 332 |
-
<http://technet.microsoft.com/en-us/library/cc766489(WS.10).aspx>
|
| 333 |
-
for a discussion of issues.
|
| 334 |
-
|
| 335 |
-
Typical user state directories are:
|
| 336 |
-
Mac OS X: same as user_data_dir
|
| 337 |
-
Unix: ~/.local/state/<AppName> # or in $XDG_STATE_HOME, if defined
|
| 338 |
-
Win *: same as user_data_dir
|
| 339 |
-
|
| 340 |
-
For Unix, we follow this Debian proposal <https://wiki.debian.org/XDGBaseDirectorySpecification#state>
|
| 341 |
-
to extend the XDG spec and support $XDG_STATE_HOME.
|
| 342 |
-
|
| 343 |
-
That means, by default "~/.local/state/<AppName>".
|
| 344 |
-
"""
|
| 345 |
-
if system in ["win32", "darwin"]:
|
| 346 |
-
path = user_data_dir(appname, appauthor, None, roaming)
|
| 347 |
-
else:
|
| 348 |
-
path = os.getenv('XDG_STATE_HOME', os.path.expanduser("~/.local/state"))
|
| 349 |
-
if appname:
|
| 350 |
-
path = os.path.join(path, appname)
|
| 351 |
-
if appname and version:
|
| 352 |
-
path = os.path.join(path, version)
|
| 353 |
-
return path
|
| 354 |
-
|
| 355 |
-
|
| 356 |
-
def user_log_dir(appname=None, appauthor=None, version=None, opinion=True):
|
| 357 |
-
r"""Return full path to the user-specific log dir for this application.
|
| 358 |
-
|
| 359 |
-
"appname" is the name of application.
|
| 360 |
-
If None, just the system directory is returned.
|
| 361 |
-
"appauthor" (only used on Windows) is the name of the
|
| 362 |
-
appauthor or distributing body for this application. Typically
|
| 363 |
-
it is the owning company name. This falls back to appname. You may
|
| 364 |
-
pass False to disable it.
|
| 365 |
-
"version" is an optional version path element to append to the
|
| 366 |
-
path. You might want to use this if you want multiple versions
|
| 367 |
-
of your app to be able to run independently. If used, this
|
| 368 |
-
would typically be "<major>.<minor>".
|
| 369 |
-
Only applied when appname is present.
|
| 370 |
-
"opinion" (boolean) can be False to disable the appending of
|
| 371 |
-
"Logs" to the base app data dir for Windows, and "log" to the
|
| 372 |
-
base cache dir for Unix. See discussion below.
|
| 373 |
-
|
| 374 |
-
Typical user log directories are:
|
| 375 |
-
Mac OS X: ~/Library/Logs/<AppName>
|
| 376 |
-
Unix: ~/.cache/<AppName>/log # or under $XDG_CACHE_HOME if defined
|
| 377 |
-
Win XP: C:\Documents and Settings\<username>\Local Settings\Application Data\<AppAuthor>\<AppName>\Logs
|
| 378 |
-
Vista: C:\Users\<username>\AppData\Local\<AppAuthor>\<AppName>\Logs
|
| 379 |
-
|
| 380 |
-
On Windows the only suggestion in the MSDN docs is that local settings
|
| 381 |
-
go in the `CSIDL_LOCAL_APPDATA` directory. (Note: I'm interested in
|
| 382 |
-
examples of what some windows apps use for a logs dir.)
|
| 383 |
-
|
| 384 |
-
OPINION: This function appends "Logs" to the `CSIDL_LOCAL_APPDATA`
|
| 385 |
-
value for Windows and appends "log" to the user cache dir for Unix.
|
| 386 |
-
This can be disabled with the `opinion=False` option.
|
| 387 |
-
"""
|
| 388 |
-
if system == "darwin":
|
| 389 |
-
path = os.path.join(
|
| 390 |
-
os.path.expanduser('~/Library/Logs'),
|
| 391 |
-
appname)
|
| 392 |
-
elif system == "win32":
|
| 393 |
-
path = user_data_dir(appname, appauthor, version)
|
| 394 |
-
version = False
|
| 395 |
-
if opinion:
|
| 396 |
-
path = os.path.join(path, "Logs")
|
| 397 |
-
else:
|
| 398 |
-
path = user_cache_dir(appname, appauthor, version)
|
| 399 |
-
version = False
|
| 400 |
-
if opinion:
|
| 401 |
-
path = os.path.join(path, "log")
|
| 402 |
-
if appname and version:
|
| 403 |
-
path = os.path.join(path, version)
|
| 404 |
-
return path
|
| 405 |
-
|
| 406 |
-
|
| 407 |
-
class AppDirs(object):
|
| 408 |
-
"""Convenience wrapper for getting application dirs."""
|
| 409 |
-
def __init__(self, appname=None, appauthor=None, version=None,
|
| 410 |
-
roaming=False, multipath=False):
|
| 411 |
-
self.appname = appname
|
| 412 |
-
self.appauthor = appauthor
|
| 413 |
-
self.version = version
|
| 414 |
-
self.roaming = roaming
|
| 415 |
-
self.multipath = multipath
|
| 416 |
-
|
| 417 |
-
@property
|
| 418 |
-
def user_data_dir(self):
|
| 419 |
-
return user_data_dir(self.appname, self.appauthor,
|
| 420 |
-
version=self.version, roaming=self.roaming)
|
| 421 |
-
|
| 422 |
-
@property
|
| 423 |
-
def site_data_dir(self):
|
| 424 |
-
return site_data_dir(self.appname, self.appauthor,
|
| 425 |
-
version=self.version, multipath=self.multipath)
|
| 426 |
-
|
| 427 |
-
@property
|
| 428 |
-
def user_config_dir(self):
|
| 429 |
-
return user_config_dir(self.appname, self.appauthor,
|
| 430 |
-
version=self.version, roaming=self.roaming)
|
| 431 |
-
|
| 432 |
-
@property
|
| 433 |
-
def site_config_dir(self):
|
| 434 |
-
return site_config_dir(self.appname, self.appauthor,
|
| 435 |
-
version=self.version, multipath=self.multipath)
|
| 436 |
-
|
| 437 |
-
@property
|
| 438 |
-
def user_cache_dir(self):
|
| 439 |
-
return user_cache_dir(self.appname, self.appauthor,
|
| 440 |
-
version=self.version)
|
| 441 |
-
|
| 442 |
-
@property
|
| 443 |
-
def user_state_dir(self):
|
| 444 |
-
return user_state_dir(self.appname, self.appauthor,
|
| 445 |
-
version=self.version)
|
| 446 |
-
|
| 447 |
-
@property
|
| 448 |
-
def user_log_dir(self):
|
| 449 |
-
return user_log_dir(self.appname, self.appauthor,
|
| 450 |
-
version=self.version)
|
| 451 |
-
|
| 452 |
-
|
| 453 |
-
#---- internal support stuff
|
| 454 |
-
|
| 455 |
-
def _get_win_folder_from_registry(csidl_name):
|
| 456 |
-
"""This is a fallback technique at best. I'm not sure if using the
|
| 457 |
-
registry for this guarantees us the correct answer for all CSIDL_*
|
| 458 |
-
names.
|
| 459 |
-
"""
|
| 460 |
-
if PY3:
|
| 461 |
-
import winreg as _winreg
|
| 462 |
-
else:
|
| 463 |
-
import _winreg
|
| 464 |
-
|
| 465 |
-
shell_folder_name = {
|
| 466 |
-
"CSIDL_APPDATA": "AppData",
|
| 467 |
-
"CSIDL_COMMON_APPDATA": "Common AppData",
|
| 468 |
-
"CSIDL_LOCAL_APPDATA": "Local AppData",
|
| 469 |
-
}[csidl_name]
|
| 470 |
-
|
| 471 |
-
key = _winreg.OpenKey(
|
| 472 |
-
_winreg.HKEY_CURRENT_USER,
|
| 473 |
-
r"Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders"
|
| 474 |
-
)
|
| 475 |
-
dir, type = _winreg.QueryValueEx(key, shell_folder_name)
|
| 476 |
-
return dir
|
| 477 |
-
|
| 478 |
-
|
| 479 |
-
def _get_win_folder_with_pywin32(csidl_name):
|
| 480 |
-
from win32com.shell import shellcon, shell
|
| 481 |
-
dir = shell.SHGetFolderPath(0, getattr(shellcon, csidl_name), 0, 0)
|
| 482 |
-
# Try to make this a unicode path because SHGetFolderPath does
|
| 483 |
-
# not return unicode strings when there is unicode data in the
|
| 484 |
-
# path.
|
| 485 |
-
try:
|
| 486 |
-
dir = unicode(dir)
|
| 487 |
-
|
| 488 |
-
# Downgrade to short path name if have highbit chars. See
|
| 489 |
-
# <http://bugs.activestate.com/show_bug.cgi?id=85099>.
|
| 490 |
-
has_high_char = False
|
| 491 |
-
for c in dir:
|
| 492 |
-
if ord(c) > 255:
|
| 493 |
-
has_high_char = True
|
| 494 |
-
break
|
| 495 |
-
if has_high_char:
|
| 496 |
-
try:
|
| 497 |
-
import win32api
|
| 498 |
-
dir = win32api.GetShortPathName(dir)
|
| 499 |
-
except ImportError:
|
| 500 |
-
pass
|
| 501 |
-
except UnicodeError:
|
| 502 |
-
pass
|
| 503 |
-
return dir
|
| 504 |
-
|
| 505 |
-
|
| 506 |
-
def _get_win_folder_with_ctypes(csidl_name):
|
| 507 |
-
import ctypes
|
| 508 |
-
|
| 509 |
-
csidl_const = {
|
| 510 |
-
"CSIDL_APPDATA": 26,
|
| 511 |
-
"CSIDL_COMMON_APPDATA": 35,
|
| 512 |
-
"CSIDL_LOCAL_APPDATA": 28,
|
| 513 |
-
}[csidl_name]
|
| 514 |
-
|
| 515 |
-
buf = ctypes.create_unicode_buffer(1024)
|
| 516 |
-
ctypes.windll.shell32.SHGetFolderPathW(None, csidl_const, None, 0, buf)
|
| 517 |
-
|
| 518 |
-
# Downgrade to short path name if have highbit chars. See
|
| 519 |
-
# <http://bugs.activestate.com/show_bug.cgi?id=85099>.
|
| 520 |
-
has_high_char = False
|
| 521 |
-
for c in buf:
|
| 522 |
-
if ord(c) > 255:
|
| 523 |
-
has_high_char = True
|
| 524 |
-
break
|
| 525 |
-
if has_high_char:
|
| 526 |
-
buf2 = ctypes.create_unicode_buffer(1024)
|
| 527 |
-
if ctypes.windll.kernel32.GetShortPathNameW(buf.value, buf2, 1024):
|
| 528 |
-
buf = buf2
|
| 529 |
-
|
| 530 |
-
return buf.value
|
| 531 |
-
|
| 532 |
-
def _get_win_folder_with_jna(csidl_name):
|
| 533 |
-
import array
|
| 534 |
-
from com.sun import jna
|
| 535 |
-
from com.sun.jna.platform import win32
|
| 536 |
-
|
| 537 |
-
buf_size = win32.WinDef.MAX_PATH * 2
|
| 538 |
-
buf = array.zeros('c', buf_size)
|
| 539 |
-
shell = win32.Shell32.INSTANCE
|
| 540 |
-
shell.SHGetFolderPath(None, getattr(win32.ShlObj, csidl_name), None, win32.ShlObj.SHGFP_TYPE_CURRENT, buf)
|
| 541 |
-
dir = jna.Native.toString(buf.tostring()).rstrip("\0")
|
| 542 |
-
|
| 543 |
-
# Downgrade to short path name if have highbit chars. See
|
| 544 |
-
# <http://bugs.activestate.com/show_bug.cgi?id=85099>.
|
| 545 |
-
has_high_char = False
|
| 546 |
-
for c in dir:
|
| 547 |
-
if ord(c) > 255:
|
| 548 |
-
has_high_char = True
|
| 549 |
-
break
|
| 550 |
-
if has_high_char:
|
| 551 |
-
buf = array.zeros('c', buf_size)
|
| 552 |
-
kernel = win32.Kernel32.INSTANCE
|
| 553 |
-
if kernel.GetShortPathName(dir, buf, buf_size):
|
| 554 |
-
dir = jna.Native.toString(buf.tostring()).rstrip("\0")
|
| 555 |
-
|
| 556 |
-
return dir
|
| 557 |
-
|
| 558 |
-
if system == "win32":
|
| 559 |
-
try:
|
| 560 |
-
import win32com.shell
|
| 561 |
-
_get_win_folder = _get_win_folder_with_pywin32
|
| 562 |
-
except ImportError:
|
| 563 |
-
try:
|
| 564 |
-
from ctypes import windll
|
| 565 |
-
_get_win_folder = _get_win_folder_with_ctypes
|
| 566 |
-
except ImportError:
|
| 567 |
-
try:
|
| 568 |
-
import com.sun.jna
|
| 569 |
-
_get_win_folder = _get_win_folder_with_jna
|
| 570 |
-
except ImportError:
|
| 571 |
-
_get_win_folder = _get_win_folder_from_registry
|
| 572 |
-
|
| 573 |
-
|
| 574 |
-
#---- self test code
|
| 575 |
-
|
| 576 |
-
if __name__ == "__main__":
|
| 577 |
-
appname = "MyApp"
|
| 578 |
-
appauthor = "MyCompany"
|
| 579 |
-
|
| 580 |
-
props = ("user_data_dir",
|
| 581 |
-
"user_config_dir",
|
| 582 |
-
"user_cache_dir",
|
| 583 |
-
"user_state_dir",
|
| 584 |
-
"user_log_dir",
|
| 585 |
-
"site_data_dir",
|
| 586 |
-
"site_config_dir")
|
| 587 |
-
|
| 588 |
-
print("-- app dirs %s --" % __version__)
|
| 589 |
-
|
| 590 |
-
print("-- app dirs (with optional 'version')")
|
| 591 |
-
dirs = AppDirs(appname, appauthor, version="1.0")
|
| 592 |
-
for prop in props:
|
| 593 |
-
print("%s: %s" % (prop, getattr(dirs, prop)))
|
| 594 |
-
|
| 595 |
-
print("\n-- app dirs (without optional 'version')")
|
| 596 |
-
dirs = AppDirs(appname, appauthor)
|
| 597 |
-
for prop in props:
|
| 598 |
-
print("%s: %s" % (prop, getattr(dirs, prop)))
|
| 599 |
-
|
| 600 |
-
print("\n-- app dirs (without optional 'appauthor')")
|
| 601 |
-
dirs = AppDirs(appname)
|
| 602 |
-
for prop in props:
|
| 603 |
-
print("%s: %s" % (prop, getattr(dirs, prop)))
|
| 604 |
-
|
| 605 |
-
print("\n-- app dirs (with disabled 'appauthor')")
|
| 606 |
-
dirs = AppDirs(appname, appauthor=False)
|
| 607 |
-
for prop in props:
|
| 608 |
-
print("%s: %s" % (prop, getattr(dirs, prop)))
|
|
|
|
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spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/py34compat.py
DELETED
|
@@ -1,13 +0,0 @@
|
|
| 1 |
-
import importlib
|
| 2 |
-
|
| 3 |
-
try:
|
| 4 |
-
import importlib.util
|
| 5 |
-
except ImportError:
|
| 6 |
-
pass
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
try:
|
| 10 |
-
module_from_spec = importlib.util.module_from_spec
|
| 11 |
-
except AttributeError:
|
| 12 |
-
def module_from_spec(spec):
|
| 13 |
-
return spec.loader.load_module(spec.name)
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spaces/Bakar31/MLOps_Practice_Repo_1/README.md
DELETED
|
@@ -1,12 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
license: cc
|
| 3 |
-
title: News Summarizer
|
| 4 |
-
sdk: gradio
|
| 5 |
-
emoji: 📚
|
| 6 |
-
colorFrom: indigo
|
| 7 |
-
colorTo: blue
|
| 8 |
-
---
|
| 9 |
-
|
| 10 |
-
# MLOps-Practice-Repo-1
|
| 11 |
-
|
| 12 |
-
source ~/.venv/bin/activate
|
|
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|
spaces/Banbri/zcvzcv/src/lib/useImageDimension.ts
DELETED
|
@@ -1,20 +0,0 @@
|
|
| 1 |
-
import { useEffect, useState } from "react"
|
| 2 |
-
|
| 3 |
-
import { ImageDimension, getImageDimension } from "./getImageDimension"
|
| 4 |
-
|
| 5 |
-
export function useImageDimension(src: string) {
|
| 6 |
-
const [dimension, setDimension] = useState<ImageDimension>({
|
| 7 |
-
width: 0,
|
| 8 |
-
height: 0,
|
| 9 |
-
})
|
| 10 |
-
|
| 11 |
-
useEffect(() => {
|
| 12 |
-
const compute = async () => {
|
| 13 |
-
const newDimension = await getImageDimension(src)
|
| 14 |
-
setDimension(newDimension)
|
| 15 |
-
}
|
| 16 |
-
compute()
|
| 17 |
-
}, [src])
|
| 18 |
-
|
| 19 |
-
return dimension
|
| 20 |
-
}
|
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|
spaces/BartPoint/VoiceChange/infer_pack/commons.py
DELETED
|
@@ -1,166 +0,0 @@
|
|
| 1 |
-
import math
|
| 2 |
-
import numpy as np
|
| 3 |
-
import torch
|
| 4 |
-
from torch import nn
|
| 5 |
-
from torch.nn import functional as F
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
def init_weights(m, mean=0.0, std=0.01):
|
| 9 |
-
classname = m.__class__.__name__
|
| 10 |
-
if classname.find("Conv") != -1:
|
| 11 |
-
m.weight.data.normal_(mean, std)
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
def get_padding(kernel_size, dilation=1):
|
| 15 |
-
return int((kernel_size * dilation - dilation) / 2)
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
def convert_pad_shape(pad_shape):
|
| 19 |
-
l = pad_shape[::-1]
|
| 20 |
-
pad_shape = [item for sublist in l for item in sublist]
|
| 21 |
-
return pad_shape
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
def kl_divergence(m_p, logs_p, m_q, logs_q):
|
| 25 |
-
"""KL(P||Q)"""
|
| 26 |
-
kl = (logs_q - logs_p) - 0.5
|
| 27 |
-
kl += (
|
| 28 |
-
0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
|
| 29 |
-
)
|
| 30 |
-
return kl
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
def rand_gumbel(shape):
|
| 34 |
-
"""Sample from the Gumbel distribution, protect from overflows."""
|
| 35 |
-
uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
|
| 36 |
-
return -torch.log(-torch.log(uniform_samples))
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
def rand_gumbel_like(x):
|
| 40 |
-
g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
|
| 41 |
-
return g
|
| 42 |
-
|
| 43 |
-
|
| 44 |
-
def slice_segments(x, ids_str, segment_size=4):
|
| 45 |
-
ret = torch.zeros_like(x[:, :, :segment_size])
|
| 46 |
-
for i in range(x.size(0)):
|
| 47 |
-
idx_str = ids_str[i]
|
| 48 |
-
idx_end = idx_str + segment_size
|
| 49 |
-
ret[i] = x[i, :, idx_str:idx_end]
|
| 50 |
-
return ret
|
| 51 |
-
|
| 52 |
-
|
| 53 |
-
def slice_segments2(x, ids_str, segment_size=4):
|
| 54 |
-
ret = torch.zeros_like(x[:, :segment_size])
|
| 55 |
-
for i in range(x.size(0)):
|
| 56 |
-
idx_str = ids_str[i]
|
| 57 |
-
idx_end = idx_str + segment_size
|
| 58 |
-
ret[i] = x[i, idx_str:idx_end]
|
| 59 |
-
return ret
|
| 60 |
-
|
| 61 |
-
|
| 62 |
-
def rand_slice_segments(x, x_lengths=None, segment_size=4):
|
| 63 |
-
b, d, t = x.size()
|
| 64 |
-
if x_lengths is None:
|
| 65 |
-
x_lengths = t
|
| 66 |
-
ids_str_max = x_lengths - segment_size + 1
|
| 67 |
-
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
| 68 |
-
ret = slice_segments(x, ids_str, segment_size)
|
| 69 |
-
return ret, ids_str
|
| 70 |
-
|
| 71 |
-
|
| 72 |
-
def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
|
| 73 |
-
position = torch.arange(length, dtype=torch.float)
|
| 74 |
-
num_timescales = channels // 2
|
| 75 |
-
log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
|
| 76 |
-
num_timescales - 1
|
| 77 |
-
)
|
| 78 |
-
inv_timescales = min_timescale * torch.exp(
|
| 79 |
-
torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
|
| 80 |
-
)
|
| 81 |
-
scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
|
| 82 |
-
signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
|
| 83 |
-
signal = F.pad(signal, [0, 0, 0, channels % 2])
|
| 84 |
-
signal = signal.view(1, channels, length)
|
| 85 |
-
return signal
|
| 86 |
-
|
| 87 |
-
|
| 88 |
-
def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
|
| 89 |
-
b, channels, length = x.size()
|
| 90 |
-
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
| 91 |
-
return x + signal.to(dtype=x.dtype, device=x.device)
|
| 92 |
-
|
| 93 |
-
|
| 94 |
-
def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
|
| 95 |
-
b, channels, length = x.size()
|
| 96 |
-
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
| 97 |
-
return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
|
| 98 |
-
|
| 99 |
-
|
| 100 |
-
def subsequent_mask(length):
|
| 101 |
-
mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
|
| 102 |
-
return mask
|
| 103 |
-
|
| 104 |
-
|
| 105 |
-
@torch.jit.script
|
| 106 |
-
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
| 107 |
-
n_channels_int = n_channels[0]
|
| 108 |
-
in_act = input_a + input_b
|
| 109 |
-
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
| 110 |
-
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
| 111 |
-
acts = t_act * s_act
|
| 112 |
-
return acts
|
| 113 |
-
|
| 114 |
-
|
| 115 |
-
def convert_pad_shape(pad_shape):
|
| 116 |
-
l = pad_shape[::-1]
|
| 117 |
-
pad_shape = [item for sublist in l for item in sublist]
|
| 118 |
-
return pad_shape
|
| 119 |
-
|
| 120 |
-
|
| 121 |
-
def shift_1d(x):
|
| 122 |
-
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
|
| 123 |
-
return x
|
| 124 |
-
|
| 125 |
-
|
| 126 |
-
def sequence_mask(length, max_length=None):
|
| 127 |
-
if max_length is None:
|
| 128 |
-
max_length = length.max()
|
| 129 |
-
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
| 130 |
-
return x.unsqueeze(0) < length.unsqueeze(1)
|
| 131 |
-
|
| 132 |
-
|
| 133 |
-
def generate_path(duration, mask):
|
| 134 |
-
"""
|
| 135 |
-
duration: [b, 1, t_x]
|
| 136 |
-
mask: [b, 1, t_y, t_x]
|
| 137 |
-
"""
|
| 138 |
-
device = duration.device
|
| 139 |
-
|
| 140 |
-
b, _, t_y, t_x = mask.shape
|
| 141 |
-
cum_duration = torch.cumsum(duration, -1)
|
| 142 |
-
|
| 143 |
-
cum_duration_flat = cum_duration.view(b * t_x)
|
| 144 |
-
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
|
| 145 |
-
path = path.view(b, t_x, t_y)
|
| 146 |
-
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
|
| 147 |
-
path = path.unsqueeze(1).transpose(2, 3) * mask
|
| 148 |
-
return path
|
| 149 |
-
|
| 150 |
-
|
| 151 |
-
def clip_grad_value_(parameters, clip_value, norm_type=2):
|
| 152 |
-
if isinstance(parameters, torch.Tensor):
|
| 153 |
-
parameters = [parameters]
|
| 154 |
-
parameters = list(filter(lambda p: p.grad is not None, parameters))
|
| 155 |
-
norm_type = float(norm_type)
|
| 156 |
-
if clip_value is not None:
|
| 157 |
-
clip_value = float(clip_value)
|
| 158 |
-
|
| 159 |
-
total_norm = 0
|
| 160 |
-
for p in parameters:
|
| 161 |
-
param_norm = p.grad.data.norm(norm_type)
|
| 162 |
-
total_norm += param_norm.item() ** norm_type
|
| 163 |
-
if clip_value is not None:
|
| 164 |
-
p.grad.data.clamp_(min=-clip_value, max=clip_value)
|
| 165 |
-
total_norm = total_norm ** (1.0 / norm_type)
|
| 166 |
-
return total_norm
|
|
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spaces/Benson/text-generation/Examples/Car Drift Game Download Apkpure.md
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| 1 |
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<br />
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<h1>Juego de deriva de coches Descargar Apkpure: Cómo disfrutar de la deriva realista en su dispositivo Android</h1>
|
| 3 |
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<p>Si eres un fan de los juegos de carreras y quieres experimentar la emoción de la deriva en tu dispositivo Android, entonces deberías probar Car Drift Game. Este es un simulador de deriva realista y divertido que le permite quemar neumáticos en el asfalto y realizar acrobacias increíbles. En este artículo, le diremos qué es el juego de deriva de coches, cómo descargarlo desde Apkpure y cómo jugarlo en su dispositivo Android. </p>
|
| 4 |
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<h2>¿Qué es el juego de deriva de coches? </h2>
|
| 5 |
-
<p>Car Drift Game es un popular juego de carreras que se centra en la deriva, que es una técnica de conducción en la que el conductor sobreventa intencionalmente el coche para que se deslice de lado. La deriva se utiliza a menudo en los deportes de motor y carreras callejeras, ya que puede crear efectos espectaculares y mostrar las habilidades del conductor. </p>
|
| 6 |
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<h2>car drift game download apkpure</h2><br /><p><b><b>DOWNLOAD</b> ⇒ <a href="https://bltlly.com/2v6Jlx">https://bltlly.com/2v6Jlx</a></b></p><br /><br />
|
| 7 |
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<h3>Las características del juego de deriva del coche</h3>
|
| 8 |
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<p>Car Drift Game tiene muchas características que lo convierten en uno de los mejores juegos de deriva para dispositivos Android. Algunas de estas características son:</p>
|
| 9 |
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<ul>
|
| 10 |
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<li>Física realista y gráficos que simulan el comportamiento y la apariencia de los coches y pistas reales. </li>
|
| 11 |
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<li>Una variedad de coches para elegir, cada uno con diferentes características y opciones de personalización. </li>
|
| 12 |
-
<li>Una selección de pistas a la deriva, que van desde las calles de la ciudad a las carreteras de montaña. </li>
|
| 13 |
-
<li>Un sistema de tiempo dinámico que afecta las condiciones de conducción y la visibilidad. </li>
|
| 14 |
-
<li>Un modo de reproducción que te permite ver tus derivas desde diferentes ángulos y compartirlas con tus amigos. </li>
|
| 15 |
-
</ul>
|
| 16 |
-
<h3>Los beneficios del juego de deriva del coche</h3>
|
| 17 |
-
<p>Car Drift Game no es solo un juego divertido y emocionante, sino también uno beneficioso. Algunos de los beneficios de jugar Car Drift Game son:</p>
|
| 18 |
-
<ul>
|
| 19 |
-
<li> Mejora la coordinación mano-ojo y los reflejos, ya que tiene que controlar el coche y reaccionar al entorno cambiante. </li>
|
| 20 |
-
<li>Mejora tu creatividad y habilidades de resolución de problemas, ya que tienes que encontrar la mejor manera de la deriva y superar los obstáculos. </li>
|
| 21 |
-
|
| 22 |
-
<li>Reduce el estrés y el aburrimiento, ya que puede sumergirse en el juego y olvidarse de sus preocupaciones. </li>
|
| 23 |
-
</ul>
|
| 24 |
-
<h2>¿Cómo descargar juego de deriva de coches de Apkpure? </h2>
|
| 25 |
-
<p>Si quieres descargar Car Drift Game en tu dispositivo Android, una de las mejores fuentes es Apkpure. Apkpure es un sitio web que proporciona archivos APK libres y seguros para aplicaciones y juegos Android. Los archivos APK son los archivos de instalación para aplicaciones Android, que se pueden descargar e instalar manualmente sin usar Google Play Store.</p>
|
| 26 |
-
<h3>Los pasos para descargar juego de deriva de coches de Apkpure</h3>
|
| 27 |
-
<p>Para descargar Car Drift Game de Apkpure, debe seguir estos pasos:</p>
|
| 28 |
-
<ol>
|
| 29 |
-
<li>Ir a <a href="( 1 )">https://apkpure.com/carx-drift-racing/com.CarXTech.CarXDriftRacingFull</a>, que es la página oficial de Car Drift Game on Apkpure.</li>
|
| 30 |
-
<li>Haga clic en el "Descargar APK" botón, que comenzará a descargar el archivo APK de juego de deriva de coche en su dispositivo. </li>
|
| 31 |
-
<li>Una vez que la descarga se haya completado, busque el archivo APK en su dispositivo y toque en él para instalarlo. Es posible que deba habilitar "Fuentes desconocidas" en la configuración del dispositivo para permitir la instalación. </li>
|
| 32 |
-
<li>Después de la instalación en su dispositivo Android. </p>
|
| 33 |
-
<h3>Los controles del juego de deriva del coche</h3>
|
| 34 |
-
<p>Car Drift Game tiene controles simples e intuitivos que te permiten ir a la deriva con facilidad. Puedes elegir entre dos modos de control: tilt o touch. En el modo de inclinación, puede dirigir el coche inclinando el dispositivo a la izquierda o derecha. En el modo táctil, puede dirigir el automóvil tocando el lado izquierdo o derecho de la pantalla. También puede ajustar la sensibilidad y el ángulo de la inclinación o toque en el menú de configuración. </p>
|
| 35 |
-
<p></p>
|
| 36 |
-
|
| 37 |
-
<p>Para cambiar la vista de la cámara, puede tocar el icono de la cámara en la esquina superior derecha de la pantalla. Puedes elegir entre cuatro vistas de cámara: cabina, capó, parachoques y persecución. Cada vista de cámara tiene sus propias ventajas y desventajas, dependiendo de su preferencia y situación. </p>
|
| 38 |
-
<h3>Los modos de juego de deriva del coche</h3>
|
| 39 |
-
<p>Car Drift Game tiene tres modos para elegir: carrera, un solo jugador y multijugador. Cada modo tiene sus propios desafíos y recompensas. </p>
|
| 40 |
-
<ul>
|
| 41 |
-
<li>Modo carrera: En este modo, puedes progresar a través de varios niveles y eventos, donde tienes que completar diferentes objetivos y ganar estrellas. Cuantas más estrellas ganes, más coches y pistas desbloquearás. También puedes actualizar tus coches y personalizar su apariencia en este modo. </li>
|
| 42 |
-
<li>Modo de un solo jugador: En este modo, puede practicar sus habilidades de deriva y establecer sus propios registros en cualquier pista que desee. También puede ajustar la dificultad y el número de oponentes en este modo. </li>
|
| 43 |
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<li>Modo multijugador: En este modo, puede competir con otros jugadores en línea en carreras y torneos en tiempo real. También puede chatear con otros jugadores y unirse a clubes en este modo. </li>
|
| 44 |
-
</ul>
|
| 45 |
-
<h3>Los consejos y trucos del juego de deriva del coche</h3>
|
| 46 |
-
<p>Car Drift Game es un juego que requiere habilidad y práctica para dominar. Aquí hay algunos consejos y trucos que pueden ayudarte a mejorar tu rendimiento y puntuación:</p>
|
| 47 |
-
<ul>
|
| 48 |
-
<li>Elige un coche que se adapte a tu estilo y preferencia. Diferentes coches tienen diferentes atributos, como la velocidad, la aceleración, el manejo, el peso y la capacidad de deriva. También puede ajustar su coche para optimizar su rendimiento para la deriva. </li>
|
| 49 |
-
<li>Aprenda a usar el freno de mano de manera efectiva. El freno de mano es esencial para la deriva, ya que le ayuda a iniciar y controlar las derivas. Puede usarlo para entrar en las esquinas a alta velocidad, ajustar su ángulo y dirección durante las derivaciones y salir de las esquinas sin problemas. </li>
|
| 50 |
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|
| 51 |
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<li>Ver su ángulo de deriva y la velocidad. El ángulo de deriva es el ángulo entre la dirección de su coche y su movimiento. La velocidad es lo rápido que se mueve su coche. Ambos factores afectan su puntuación de deriva, que se calcula multiplicando su ángulo de deriva por su velocidad. Debe apuntar a un ángulo de deriva alto y una alta velocidad para obtener una alta puntuación de deriva. </li>
|
| 52 |
-
<li>Práctica en diferentes pistas y condiciones. Juego de deriva de coche ofrece una variedad de pistas y condiciones para desafiar sus habilidades de deriva. Usted debe practicar en diferentes pistas y condiciones para aprender sus diseños, características y peligros. También debe adaptarse a diferentes efectos climáticos, como lluvia, nieve, niebla y noche. </li>
|
| 53 |
-
</ul>
|
| 54 |
-
<h2>Conclusión</h2>
|
| 55 |
-
<p>Car Drift Game es un simulador de deriva realista y divertido que te permite quemar neumáticos en el asfalto y realizar acrobacias increíbles en tu dispositivo Android. Puede descargarlo desde Apkpure, que ofrece un 2 )">https://carx-tech.com/</a>, o enviándoles un correo electrónico a <a href="">[email protected]</a>. </p>
|
| 56 |
-
</ol></p> 64aa2da5cf<br />
|
| 57 |
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<br />
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| 58 |
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spaces/Benson/text-generation/Examples/Descargar Camin Simulador ltimo Para Ventanas 10.md
DELETED
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|
|
| 1 |
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|
| 2 |
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<h1>Cómo descargar Truck Simulator Ultimate para Windows 10</h1>
|
| 3 |
-
<p>¿Te encanta conducir camiones y explorar diferentes países? ¿Quieres experimentar la emoción de dirigir tu propia empresa de transporte y gestionar tu flota? Si es así, entonces deberías probar <strong>Truck Simulator Ultimate</strong>, un juego de simulación de camiones realista e inmersivo que te permite viajar por el mundo en tu camión y completar varias misiones. </p>
|
| 4 |
-
<h2>descargar camión simulador último para ventanas 10</h2><br /><p><b><b>Download</b> →→→ <a href="https://bltlly.com/2v6KYw">https://bltlly.com/2v6KYw</a></b></p><br /><br />
|
| 5 |
-
<p>En este artículo, le mostraremos lo que es Truck Simulator Ultimate, cuáles son sus características y beneficios, y cómo descargarlo para Windows 10. También compartiremos algunos consejos y trucos para jugar el juego en tu PC. ¡Empecemos! </p>
|
| 6 |
-
<h2>¿Qué es Truck Simulator Ultimate? </h2>
|
| 7 |
-
<p>Truck Simulator Ultimate es un juego de simulación desarrollado por Zuuks Games, los creadores de Bus Simulator : Ultimate. El juego cuenta con camiones oficiales con licencia de Mercedes-Benz y le permite transportar una amplia variedad de carga en más de 100 ciudades de todo el mundo. También puedes participar en temporadas multijugador, donde puedes llevar carga conjunta o competir en carreras con otros jugadores. </p>
|
| 8 |
-
<p>El juego también tiene un elemento magnate, donde puede establecer su propia empresa de transporte, contratar empleados, ampliar su flota, diseñar sus oficinas y convertirse en la empresa de logística más grande del mundo. Puede operar en diferentes países como Estados Unidos, China, Canadá, Rusia, Alemania, Italia, Francia, España, Países Bajos, Turquía, Corea del Sur, Japón, Brasil, Azerbaiyán y más. </p>
|
| 9 |
-
<p></p>
|
| 10 |
-
<h3>Características de Truck Simulator Ultimate</h3>
|
| 11 |
-
<p>Algunas de las características de Truck Simulator Ultimate son:</p>
|
| 12 |
-
<ul>
|
| 13 |
-
<li><strong>DLC mods system</strong>: Puede personalizar sus camiones con varios accesorios como lámparas, parachoques, bocinas, luces de cabina y más. </li>
|
| 14 |
-
<li><strong>Cabinas detalladas</strong>: Puede disfrutar de la física de conducción realista y los controles en la cabina de su camión. </li>
|
| 15 |
-
|
| 16 |
-
<li><strong>Más de 25 idiomas de soporte</strong>: Puedes jugar el juego en tu idioma preferido. </li>
|
| 17 |
-
<li><strong>Más de 250 emisoras de radio</strong>: Puedes escuchar tu música favorita mientras conduces. </li>
|
| 18 |
-
<li><strong>Autopistas de peaje</strong>: Puedes pagar peajes para usar carreteras más rápidas y seguras. </li>
|
| 19 |
-
<li><strong>Pronóstico del tiempo realista</strong>: Puedes experimentar diferentes condiciones climáticas como lluvia, nieve, niebla, etc.</li>
|
| 20 |
-
<li><strong>Pueblo, ciudad, carreteras </strong>: Puedes conducir en diferentes tipos de carreteras con tráfico y paisajes variables. </li>
|
| 21 |
-
</ul>
|
| 22 |
-
<h3>Beneficios de jugar Truck Simulator Ultimate en PC</h3>
|
| 23 |
-
<p>Si bien Truck Simulator Ultimate está disponible para dispositivos Android e iOS, reproducirlo en PC tiene algunas ventajas. Estas son algunas de ellas:</p>
|
| 24 |
-
<ul>
|
| 25 |
-
<li><strong>Mejores gráficos y rendimiento</strong>: Puedes disfrutar de los impresionantes gráficos del juego y una jugabilidad suave en una pantalla más grande y una resolución más alta. </li>
|
| 26 |
-
<li><strong>Controles más fáciles</strong>: Puedes usar el teclado y el ratón para controlar tu camión con mayor comodidad y precisión. </li>
|
| 27 |
-
<li><strong>Más espacio de almacenamiento</strong>: No tienes que preocuparte por quedarte sin espacio en tu dispositivo móvil, ya que puedes almacenar los archivos del juego en el disco duro de tu PC. </li>
|
| 28 |
-
<li><strong>No hay pérdida de batería o sobrecalentamiento</strong>: Usted no tiene que preocuparse por la batería de su dispositivo móvil que se agota o se calienta demasiado mientras juega el juego durante largas horas. </li>
|
| 29 |
-
</ul>
|
| 30 |
-
<h2>Cómo descargar Truck Simulator Ultimate para Windows 10</h2>
|
| 31 |
-
<p>Si desea jugar Truck Simulator Ultimate en su PC con Windows 10, tendrá que cumplir con algunos requisitos del sistema primero. Luego, puedes elegir entre dos métodos para descargar el juego: usando Google Play Store o usando BlueStacks App Player.</p>
|
| 32 |
-
<h3>Requisitos del sistema para Windows 10</h3>
|
| 33 |
-
<p>Antes de descargar Truck Simulator Ultimate para Windows 10, debe asegurarse de que su PC cumple con los siguientes requisitos mínimos del sistema:</p>
|
| 34 |
-
<tabla>
|
| 35 |
-
<tr>
|
| 36 |
-
|
| 37 |
-
<th>Procesador</th>
|
| 38 |
-
<th>Memoria</th>
|
| 39 |
-
<th>Gráficos</th>
|
| 40 |
-
<th>Almacenamiento</th>
|
| 41 |
-
</tr>
|
| 42 |
-
<tr>
|
| 43 |
-
<td>Windows 10 (64 bits)</td>
|
| 44 |
-
<td>Intel Core i3-2100 o AMD FX-6300</td>
|
| 45 |
-
<td>4 GB de RAM</td>
|
| 46 |
-
<td>NVIDIA GeForce GTX 750 Ti o AMD Radeon HD 7870</td>
|
| 47 |
-
<td>5 GB de espacio disponible</td>
|
| 48 |
-
</tr>
|
| 49 |
-
</tabla>
|
| 50 |
-
<h3>Pasos para descargar Truck Simulator Ultimate para Windows 10</h3>
|
| 51 |
-
<p>Hay dos maneras de descargar Truck Simulator Ultimate para Windows 10: usando Google Play Store o usando BlueStacks App Player. Estos son los pasos para cada método:</p>
|
| 52 |
-
<h4>Usando Google Play Store</h4>
|
| 53 |
-
<ol>
|
| 54 |
-
<li>Abra su navegador web y vaya a <a href="">https://play.google.com/store/apps/apps/detailss?id=com.zuuks.truck.simulator.ultimate&hl=en_US&gl=US</a>. </li>
|
| 55 |
-
<li>Haga clic en el botón <strong>Instalar</strong> e inicie sesión con su cuenta de Google. </li>
|
| 56 |
-
<li>El juego comenzará a descargar e instalar en su PC.</li>
|
| 57 |
-
<li>Una vez completada la instalación, puede iniciar el juego desde la aplicación Google Play Store o desde el acceso directo de su escritorio. </li>
|
| 58 |
-
</ol>
|
| 59 |
-
<h4> Uso de BlueStacks App Player</h4>
|
| 60 |
-
<ol>
|
| 61 |
-
<li>Descargar e instalar BlueStacks App Player desde <a href="">https://www.bluestacks.com/</a>. </li>
|
| 62 |
-
<li>Inicie BlueStacks e inicie sesión con su cuenta de Google. </li>
|
| 63 |
-
<li>Vaya a la pestaña <strong>Mis aplicaciones</strong> y haga clic en el icono <strong>Google Play Store</strong>. </li>
|
| 64 |
-
<li>Busque <strong>Truck Simulator Ultimate</strong> y haga clic en el botón <strong>Instalar</strong>. </li>
|
| 65 |
-
<li>El juego comenzará a descargar e instalar en su PC.</li>
|
| 66 |
-
<li>Una vez completada la instalación, puede iniciar el juego desde la pantalla de inicio de BlueStacks o desde el acceso directo de su escritorio. </li>
|
| 67 |
-
</ol>
|
| 68 |
-
<h2>Consejos y trucos para jugar Truck Simulator Ultimate en PC</h2>
|
| 69 |
-
<p>Para aprovechar al máximo su experiencia de transporte, aquí hay algunos consejos y trucos para jugar Truck Simulator Ultimate en PC:</p>
|
| 70 |
-
<ul>
|
| 71 |
-
|
| 72 |
-
<li><strong>Alimente y repare su camión regularmente</strong>: Usted no quiere quedarse sin gasolina o romperse en el medio de la carretera. Asegúrese de revisar su medidor de combustible y el indicador de daños y parada en las gasolineras y talleres de reparación cuando sea necesario. </li>
|
| 73 |
-
<li><strong>Sigue las reglas de tráfico y los límites de velocidad</strong>: No quieres ser multado o causar accidentes. Obedezca los semáforos, señales, señales y límites de velocidad. Además, evite chocar con otros vehículos, peatones u objetos. </li>
|
| 74 |
-
<li><strong>Gana dinero y XP completando misiones y desafíos</strong>: Puedes ganar dinero y XP transportando carga, participando en temporadas multijugador, completando tareas diarias, logros y eventos. Puede utilizar el dinero para comprar camiones nuevos, mejorar su flota, contratar empleados y expandir su empresa. Puedes usar la XP para subir de nivel y desbloquear nuevas características y recompensas. </li>
|
| 75 |
-
<li><strong>Crea un perfil realista y personaliza tu camión</strong>: Puedes crear un perfil realista eligiendo tu nombre, país, bandera, logotipo, matrícula, etc. También puedes personalizar tu camión con varios mods DLC como lámparas, parachoques, bocinas, luces de cabina, etc.</li>
|
| 76 |
-
</ul>
|
| 77 |
-
<h2>Conclusión</h2>
|
| 78 |
-
<p>En conclusión, Truck Simulator Ultimate es un divertido y realista juego de simulación de camiones que le permite viajar por el mundo en su camión y ejecutar su propia empresa de transporte. Puede descargarlo para Windows 10 usando Google Play Store o BlueStacks App Player. También puede seguir algunos consejos y trucos para mejorar su juego. Esperamos que disfrute jugando Truck Simulator Ultimate en PC! </p>
|
| 79 |
-
<h2>FAQs</h2> <p>Aquí hay algunas preguntas frecuentes sobre Truck Simulator Ultimate:</p>
|
| 80 |
-
<ol>
|
| 81 |
-
<li><strong>¿Cómo puedo jugar Truck Simulator Ultimate con mis amigos? </strong></li>
|
| 82 |
-
|
| 83 |
-
<li><strong>¿Cómo puedo cambiar la vista de la cámara en Truck Simulator Ultimate? </strong></li>
|
| 84 |
-
<p>Puede cambiar la vista de la cámara en Truck Simulator Ultimate presionando la tecla C del teclado. Puede elegir entre diferentes ángulos de cámara, como cabina, parte delantera, trasera, lateral, superior, etc.</p>
|
| 85 |
-
<li><strong>¿Cómo puedo guardar mi progreso en Truck Simulator Ultimate? </strong></li>
|
| 86 |
-
<p>Puede guardar su progreso en Truck Simulator Ultimate iniciando sesión con su cuenta de Google. El juego sincronizará automáticamente tus datos con la nube. También puede hacer copias de seguridad de sus datos manualmente yendo al menú de configuración y haciendo clic en el botón de copia de seguridad. </p>
|
| 87 |
-
<li><strong>¿Cómo puedo actualizar Truck Simulator Ultimate en PC? </strong></li>
|
| 88 |
-
<p>Puede actualizar Truck Simulator Ultimate en PC siguiendo estos pasos:</p>
|
| 89 |
-
<ul>
|
| 90 |
-
<li>Si has descargado el juego desde Google Play Store, puedes buscar actualizaciones abriendo la aplicación Google Play Store y haciendo clic en el icono del menú. Luego, ve a <strong>Mis aplicaciones y juegos</strong> y encuentra Truck Simulator Ultimate. Si hay una actualización disponible, haga clic en el botón <strong>Update</strong>. </li>
|
| 91 |
-
<li>Si ha descargado el juego de BlueStacks App Player, puede comprobar si hay actualizaciones abriendo la aplicación BlueStacks y haciendo clic en el icono del menú. Luego, ve a <strong>App Center</strong> y encuentra Truck Simulator Ultimate. Si hay una actualización disponible, haga clic en el botón <strong>Update</strong>. </li>
|
| 92 |
-
</ul>
|
| 93 |
-
<li><strong>¿Cómo puedo contactar a los desarrolladores de Truck Simulator Ultimate? </strong></li>
|
| 94 |
-
<p>Puede ponerse en contacto con los desarrolladores de Truck Simulator Ultimate enviando un correo electrónico a <a href="mailto:[email protected]">[email protected]</a>. También puedes seguirlos en sus cuentas de redes sociales como Facebook, Twitter, Instagram y YouTube.</p>
|
| 95 |
-
</ol></p> 64aa2da5cf<br />
|
| 96 |
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<br />
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| 97 |
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spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/pyparsing/core.py
DELETED
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See raw diff
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spaces/Bilalst/Gradio_Youtube_Transcript_v2/app.py
DELETED
|
@@ -1,116 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
import requests
|
| 3 |
-
from sentence_transformers import SentenceTransformer
|
| 4 |
-
from youtube_transcript_api import YouTubeTranscriptApi
|
| 5 |
-
import numpy as np
|
| 6 |
-
import huggingface_hub
|
| 7 |
-
import os
|
| 8 |
-
import faiss
|
| 9 |
-
|
| 10 |
-
# Set up SentenceTransformer
|
| 11 |
-
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
playlist_id = 'PLD4EAA8F8C9148A1B'
|
| 15 |
-
api_key = 'AIzaSyBGuTvXcnliEh6yhTxugrAVM5YzcG9qr9U'
|
| 16 |
-
|
| 17 |
-
# Make a request to the YouTube Data API to retrieve the playlist items
|
| 18 |
-
url = f'https://www.googleapis.com/youtube/v3/playlistItems?part=snippet&maxResults=50&playlistId={playlist_id}&key={api_key}'
|
| 19 |
-
video_ids = []
|
| 20 |
-
|
| 21 |
-
while True:
|
| 22 |
-
response = requests.get(url)
|
| 23 |
-
data = response.json()
|
| 24 |
-
|
| 25 |
-
# Extract the video IDs from the response
|
| 26 |
-
for item in data['items']:
|
| 27 |
-
video_ids.append(item['snippet']['resourceId']['videoId'])
|
| 28 |
-
|
| 29 |
-
# Check if there are more pages of results
|
| 30 |
-
if 'nextPageToken' in data:
|
| 31 |
-
next_page_token = data['nextPageToken']
|
| 32 |
-
url = f'https://www.googleapis.com/youtube/v3/playlistItems?part=snippet&maxResults=50&playlistId={playlist_id}&key={api_key}&pageToken={next_page_token}'
|
| 33 |
-
else:
|
| 34 |
-
break
|
| 35 |
-
|
| 36 |
-
# Empty lists to store transcripts and video IDs
|
| 37 |
-
transcripts = []
|
| 38 |
-
ids = []
|
| 39 |
-
|
| 40 |
-
for video_id in video_ids:
|
| 41 |
-
try:
|
| 42 |
-
transcript = YouTubeTranscriptApi.get_transcript(video_id)
|
| 43 |
-
transcript_text = ' '.join([t['text'] for t in transcript])
|
| 44 |
-
transcripts.append(transcript_text)
|
| 45 |
-
ids.append(video_id)
|
| 46 |
-
|
| 47 |
-
except Exception as e:
|
| 48 |
-
print(f"Error retrieving transcript for video {video_id}: {e}")
|
| 49 |
-
continue
|
| 50 |
-
|
| 51 |
-
# create sentence embeddings
|
| 52 |
-
sentence_embeddings = model.encode(transcripts)
|
| 53 |
-
|
| 54 |
-
# Set up FAISS
|
| 55 |
-
index = faiss.IndexFlatL2(384)
|
| 56 |
-
# Convert list of embeddings to NumPy array
|
| 57 |
-
sentence_embeddings = np.array(sentence_embeddings)
|
| 58 |
-
|
| 59 |
-
# Add sentence embeddings to FAISS index
|
| 60 |
-
index.add(sentence_embeddings)
|
| 61 |
-
|
| 62 |
-
|
| 63 |
-
#---------------------------------------------
|
| 64 |
-
|
| 65 |
-
def get_video_links(input_text):
|
| 66 |
-
# Encode input text using SentenceTransformer
|
| 67 |
-
input_embedding = model.encode([input_text])[0]
|
| 68 |
-
|
| 69 |
-
# Perform nearest neighbor search in FAISS index
|
| 70 |
-
k = 15 # Number of nearest neighbors to retrieve
|
| 71 |
-
_, T = index.search(np.array([input_embedding]), k) # search
|
| 72 |
-
|
| 73 |
-
# Return the list of video links with thumbnails and titles as an HTML string
|
| 74 |
-
video_links = []
|
| 75 |
-
visited_ids = set()
|
| 76 |
-
for i in T[0]:
|
| 77 |
-
video_id = ids[i]
|
| 78 |
-
if video_id in visited_ids:
|
| 79 |
-
continue # Skip if the video_id has already been visited
|
| 80 |
-
visited_ids.add(video_id)
|
| 81 |
-
|
| 82 |
-
# Retrieve video details using YouTube Data API
|
| 83 |
-
video_info_url = f"https://www.googleapis.com/youtube/v3/videos?part=snippet&id={video_id}&key={api_key}"
|
| 84 |
-
response = requests.get(video_info_url)
|
| 85 |
-
data = response.json()
|
| 86 |
-
video_title = data['items'][0]['snippet']['title']
|
| 87 |
-
video_thumbnail = data['items'][0]['snippet']['thumbnails']['default']['url']
|
| 88 |
-
|
| 89 |
-
# Generate HTML code for the video link with thumbnail and title
|
| 90 |
-
video_link = f"https://www.youtube.com/watch?v={video_id}"
|
| 91 |
-
video_html = f'<a href="{video_link}" target="_blank"><img src="{video_thumbnail}"><br>{video_title}</a><br>'
|
| 92 |
-
video_links.append(video_html)
|
| 93 |
-
|
| 94 |
-
return ''.join(video_links)
|
| 95 |
-
|
| 96 |
-
# Create Gradio interface with "html" output type
|
| 97 |
-
iface = gr.Interface(fn=get_video_links, inputs=[gr.inputs.Textbox(label="Add what you are looking to find in Dr. Joe's testimonials!")], outputs="html", title="Dr. Joe Dispenza testimonials Search")
|
| 98 |
-
|
| 99 |
-
|
| 100 |
-
|
| 101 |
-
# Launch the Gradio interface on Hugging Face Spaces
|
| 102 |
-
if __name__ == '__main__':
|
| 103 |
-
iface.launch()
|
| 104 |
-
|
| 105 |
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| 106 |
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|
spaces/Bonosa2/dall-e_image-generation/app.py
DELETED
|
@@ -1,43 +0,0 @@
|
|
| 1 |
-
import gradio as gr
|
| 2 |
-
import openai
|
| 3 |
-
import urllib.request
|
| 4 |
-
from PIL import Image
|
| 5 |
-
import os
|
| 6 |
-
import nltk
|
| 7 |
-
#nltk.download('punkt')
|
| 8 |
-
|
| 9 |
-
def generate_image(api_key, prompt, resolution):
|
| 10 |
-
if not api_key:
|
| 11 |
-
print("Error: API Key is required.")
|
| 12 |
-
return
|
| 13 |
-
openai.api_key = api_key
|
| 14 |
-
response = openai.Image.create(
|
| 15 |
-
prompt=prompt,
|
| 16 |
-
n=1,
|
| 17 |
-
size=resolution
|
| 18 |
-
)
|
| 19 |
-
|
| 20 |
-
image_url = response['data'][0]['url']
|
| 21 |
-
|
| 22 |
-
# Open the URL image, resize it to the chosen resolution and return it
|
| 23 |
-
with urllib.request.urlopen(image_url) as url:
|
| 24 |
-
with open('temp.jpg', 'wb') as f:
|
| 25 |
-
f.write(url.read())
|
| 26 |
-
img = Image.open('temp.jpg')
|
| 27 |
-
|
| 28 |
-
return img
|
| 29 |
-
|
| 30 |
-
iface = gr.Interface(
|
| 31 |
-
fn=generate_image,
|
| 32 |
-
inputs=[
|
| 33 |
-
gr.inputs.Textbox(lines=1, label="API Key", type="password"),
|
| 34 |
-
gr.inputs.Textbox(lines=1, label="Prompt"),
|
| 35 |
-
gr.inputs.Radio(choices=["256x256", "512x512", "1024x1024"], label="Resolution")
|
| 36 |
-
],
|
| 37 |
-
outputs=gr.outputs.Image(type="pil"),
|
| 38 |
-
title="DALL-E Image Generator",
|
| 39 |
-
description="Enter your API key, a prompt, and choose a resolution to generate an image from DALL-E."
|
| 40 |
-
)
|
| 41 |
-
|
| 42 |
-
|
| 43 |
-
iface.launch()
|
|
|
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|
spaces/CVPR/LIVE/thrust/thrust/iterator/transform_output_iterator.h
DELETED
|
@@ -1,163 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2008-2018 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 |
-
/*! \file thrust/iterator/transform_output_iterator.h
|
| 18 |
-
* \brief An output iterator which adapts another output iterator by applying a
|
| 19 |
-
* function to the result of its dereference before writing it.
|
| 20 |
-
*/
|
| 21 |
-
|
| 22 |
-
#pragma once
|
| 23 |
-
|
| 24 |
-
#include <thrust/detail/config.h>
|
| 25 |
-
#include <thrust/iterator/detail/transform_output_iterator.inl>
|
| 26 |
-
|
| 27 |
-
namespace thrust
|
| 28 |
-
{
|
| 29 |
-
|
| 30 |
-
/*! \addtogroup iterators
|
| 31 |
-
* \{
|
| 32 |
-
*/
|
| 33 |
-
|
| 34 |
-
/*! \addtogroup fancyiterator Fancy Iterators
|
| 35 |
-
* \ingroup iterators
|
| 36 |
-
* \{
|
| 37 |
-
*/
|
| 38 |
-
|
| 39 |
-
/*! \p transform_output_iterator is a special kind of output iterator which
|
| 40 |
-
* transforms a value written upon dereference. This iterator is useful
|
| 41 |
-
* for transforming an output from algorithms without explicitly storing the
|
| 42 |
-
* intermediate result in the memory and applying subsequent transformation,
|
| 43 |
-
* thereby avoiding wasting memory capacity and bandwidth.
|
| 44 |
-
* Using \p transform_iterator facilitates kernel fusion by deferring execution
|
| 45 |
-
* of transformation until the value is written while saving both memory
|
| 46 |
-
* capacity and bandwidth.
|
| 47 |
-
*
|
| 48 |
-
* The following code snippet demonstrated how to create a
|
| 49 |
-
* \p transform_output_iterator which applies \c sqrtf to the assigning value.
|
| 50 |
-
*
|
| 51 |
-
* \code
|
| 52 |
-
* #include <thrust/iterator/transform_output_iterator.h>
|
| 53 |
-
* #include <thrust/device_vector.h>
|
| 54 |
-
*
|
| 55 |
-
* // note: functor inherits form unary function
|
| 56 |
-
* // note: functor inherits from unary_function
|
| 57 |
-
* struct square_root : public thrust::unary_function<float,float>
|
| 58 |
-
* {
|
| 59 |
-
* __host__ __device__
|
| 60 |
-
* float operator()(float x) const
|
| 61 |
-
* {
|
| 62 |
-
* return sqrtf(x);
|
| 63 |
-
* }
|
| 64 |
-
* };
|
| 65 |
-
*
|
| 66 |
-
* int main()
|
| 67 |
-
* {
|
| 68 |
-
* thrust::device_vector<float> v(4);
|
| 69 |
-
*
|
| 70 |
-
* typedef thrust::device_vector<float>::iterator FloatIterator;
|
| 71 |
-
* thrust::transform_output_iterator<square_root, FloatIterator> iter(v.begin(), square_root());
|
| 72 |
-
*
|
| 73 |
-
* iter[0] = 1.0f; // stores sqrtf( 1.0f)
|
| 74 |
-
* iter[1] = 4.0f; // stores sqrtf( 4.0f)
|
| 75 |
-
* iter[2] = 9.0f; // stores sqrtf( 9.0f)
|
| 76 |
-
* iter[3] = 16.0f; // stores sqrtf(16.0f)
|
| 77 |
-
* // iter[4] is an out-of-bounds error
|
| 78 |
-
*
|
| 79 |
-
* v[0]; // returns 1.0f;
|
| 80 |
-
* v[1]; // returns 2.0f;
|
| 81 |
-
* v[2]; // returns 3.0f;
|
| 82 |
-
* v[3]; // returns 4.0f;
|
| 83 |
-
*
|
| 84 |
-
* }
|
| 85 |
-
* \endcode
|
| 86 |
-
*
|
| 87 |
-
* \see make_transform_output_iterator
|
| 88 |
-
*/
|
| 89 |
-
|
| 90 |
-
template <typename UnaryFunction, typename OutputIterator>
|
| 91 |
-
class transform_output_iterator
|
| 92 |
-
: public detail::transform_output_iterator_base<UnaryFunction, OutputIterator>::type
|
| 93 |
-
{
|
| 94 |
-
|
| 95 |
-
/*! \cond
|
| 96 |
-
*/
|
| 97 |
-
|
| 98 |
-
public:
|
| 99 |
-
|
| 100 |
-
typedef typename
|
| 101 |
-
detail::transform_output_iterator_base<UnaryFunction, OutputIterator>::type
|
| 102 |
-
super_t;
|
| 103 |
-
|
| 104 |
-
friend class thrust::iterator_core_access;
|
| 105 |
-
/*! \endcond
|
| 106 |
-
*/
|
| 107 |
-
|
| 108 |
-
/*! This constructor takes as argument an \c OutputIterator and an \c
|
| 109 |
-
* UnaryFunction and copies them to a new \p transform_output_iterator
|
| 110 |
-
*
|
| 111 |
-
* \param out An \c OutputIterator pointing to the output range whereto the result of
|
| 112 |
-
* \p transform_output_iterator's \c UnaryFunction will be written.
|
| 113 |
-
* \param fun An \c UnaryFunction used to transform the objects assigned to
|
| 114 |
-
* this \p transform_output_iterator.
|
| 115 |
-
*/
|
| 116 |
-
__host__ __device__
|
| 117 |
-
transform_output_iterator(OutputIterator const& out, UnaryFunction fun) : super_t(out), fun(fun)
|
| 118 |
-
{
|
| 119 |
-
}
|
| 120 |
-
|
| 121 |
-
/*! \cond
|
| 122 |
-
*/
|
| 123 |
-
private:
|
| 124 |
-
|
| 125 |
-
__host__ __device__
|
| 126 |
-
typename super_t::reference dereference() const
|
| 127 |
-
{
|
| 128 |
-
return detail::transform_output_iterator_proxy<
|
| 129 |
-
UnaryFunction, OutputIterator
|
| 130 |
-
>(this->base_reference(), fun);
|
| 131 |
-
}
|
| 132 |
-
|
| 133 |
-
UnaryFunction fun;
|
| 134 |
-
|
| 135 |
-
/*! \endcond
|
| 136 |
-
*/
|
| 137 |
-
}; // end transform_output_iterator
|
| 138 |
-
|
| 139 |
-
/*! \p make_transform_output_iterator creates a \p transform_output_iterator from
|
| 140 |
-
* an \c OutputIterator and \c UnaryFunction.
|
| 141 |
-
*
|
| 142 |
-
* \param out The \c OutputIterator pointing to the output range of the newly
|
| 143 |
-
* created \p transform_output_iterator
|
| 144 |
-
* \param fun The \c UnaryFunction transform the object before assigning it to
|
| 145 |
-
* \c out by the newly created \p transform_output_iterator
|
| 146 |
-
* \see transform_output_iterator
|
| 147 |
-
*/
|
| 148 |
-
template <typename UnaryFunction, typename OutputIterator>
|
| 149 |
-
transform_output_iterator<UnaryFunction, OutputIterator>
|
| 150 |
-
__host__ __device__
|
| 151 |
-
make_transform_output_iterator(OutputIterator out, UnaryFunction fun)
|
| 152 |
-
{
|
| 153 |
-
return transform_output_iterator<UnaryFunction, OutputIterator>(out, fun);
|
| 154 |
-
} // end make_transform_output_iterator
|
| 155 |
-
|
| 156 |
-
/*! \} // end fancyiterators
|
| 157 |
-
*/
|
| 158 |
-
|
| 159 |
-
/*! \} // end iterators
|
| 160 |
-
*/
|
| 161 |
-
|
| 162 |
-
} // end thrust
|
| 163 |
-
|
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|
spaces/CVPR/LIVE/thrust/thrust/iterator/zip_iterator.h
DELETED
|
@@ -1,245 +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 |
-
|
| 18 |
-
/*! \file thrust/iterator/zip_iterator.h
|
| 19 |
-
* \brief An iterator which returns a tuple of the result of dereferencing
|
| 20 |
-
* a tuple of iterators when dereferenced
|
| 21 |
-
*/
|
| 22 |
-
|
| 23 |
-
/*
|
| 24 |
-
* Copyright David Abrahams and Thomas Becker 2000-2006.
|
| 25 |
-
*
|
| 26 |
-
* Distributed under the Boost Software License, Version 1.0.
|
| 27 |
-
* (See accompanying NOTICE file for the complete license)
|
| 28 |
-
*
|
| 29 |
-
* For more information, see http://www.boost.org
|
| 30 |
-
*/
|
| 31 |
-
|
| 32 |
-
#pragma once
|
| 33 |
-
|
| 34 |
-
#include <thrust/detail/config.h>
|
| 35 |
-
#include <thrust/iterator/detail/zip_iterator_base.h>
|
| 36 |
-
#include <thrust/iterator/iterator_facade.h>
|
| 37 |
-
#include <thrust/detail/type_traits.h>
|
| 38 |
-
|
| 39 |
-
namespace thrust
|
| 40 |
-
{
|
| 41 |
-
|
| 42 |
-
/*! \addtogroup iterators
|
| 43 |
-
* \{
|
| 44 |
-
*/
|
| 45 |
-
|
| 46 |
-
/*! \addtogroup fancyiterator Fancy Iterators
|
| 47 |
-
* \ingroup iterators
|
| 48 |
-
* \{
|
| 49 |
-
*/
|
| 50 |
-
|
| 51 |
-
/*! \p zip_iterator is an iterator which represents a pointer into a range
|
| 52 |
-
* of \p tuples whose elements are themselves taken from a \p tuple of input
|
| 53 |
-
* iterators. This iterator is useful for creating a virtual array of structures
|
| 54 |
-
* while achieving the same performance and bandwidth as the structure of arrays
|
| 55 |
-
* idiom. \p zip_iterator also facilitates kernel fusion by providing a convenient
|
| 56 |
-
* means of amortizing the execution of the same operation over multiple ranges.
|
| 57 |
-
*
|
| 58 |
-
* The following code snippet demonstrates how to create a \p zip_iterator
|
| 59 |
-
* which represents the result of "zipping" multiple ranges together.
|
| 60 |
-
*
|
| 61 |
-
* \code
|
| 62 |
-
* #include <thrust/iterator/zip_iterator.h>
|
| 63 |
-
* #include <thrust/tuple.h>
|
| 64 |
-
* #include <thrust/device_vector.h>
|
| 65 |
-
* ...
|
| 66 |
-
* thrust::device_vector<int> int_v(3);
|
| 67 |
-
* int_v[0] = 0; int_v[1] = 1; int_v[2] = 2;
|
| 68 |
-
*
|
| 69 |
-
* thrust::device_vector<float> float_v(3);
|
| 70 |
-
* float_v[0] = 0.0f; float_v[1] = 1.0f; float_v[2] = 2.0f;
|
| 71 |
-
*
|
| 72 |
-
* thrust::device_vector<char> char_v(3);
|
| 73 |
-
* char_v[0] = 'a'; char_v[1] = 'b'; char_v[2] = 'c';
|
| 74 |
-
*
|
| 75 |
-
* // typedef these iterators for shorthand
|
| 76 |
-
* typedef thrust::device_vector<int>::iterator IntIterator;
|
| 77 |
-
* typedef thrust::device_vector<float>::iterator FloatIterator;
|
| 78 |
-
* typedef thrust::device_vector<char>::iterator CharIterator;
|
| 79 |
-
*
|
| 80 |
-
* // typedef a tuple of these iterators
|
| 81 |
-
* typedef thrust::tuple<IntIterator, FloatIterator, CharIterator> IteratorTuple;
|
| 82 |
-
*
|
| 83 |
-
* // typedef the zip_iterator of this tuple
|
| 84 |
-
* typedef thrust::zip_iterator<IteratorTuple> ZipIterator;
|
| 85 |
-
*
|
| 86 |
-
* // finally, create the zip_iterator
|
| 87 |
-
* ZipIterator iter(thrust::make_tuple(int_v.begin(), float_v.begin(), char_v.begin()));
|
| 88 |
-
*
|
| 89 |
-
* *iter; // returns (0, 0.0f, 'a')
|
| 90 |
-
* iter[0]; // returns (0, 0.0f, 'a')
|
| 91 |
-
* iter[1]; // returns (1, 1.0f, 'b')
|
| 92 |
-
* iter[2]; // returns (2, 2.0f, 'c')
|
| 93 |
-
*
|
| 94 |
-
* thrust::get<0>(iter[2]); // returns 2
|
| 95 |
-
* thrust::get<1>(iter[0]); // returns 0.0f
|
| 96 |
-
* thrust::get<2>(iter[1]); // returns 'b'
|
| 97 |
-
*
|
| 98 |
-
* // iter[3] is an out-of-bounds error
|
| 99 |
-
* \endcode
|
| 100 |
-
*
|
| 101 |
-
* Defining the type of a \p zip_iterator can be complex. The next code example demonstrates
|
| 102 |
-
* how to use the \p make_zip_iterator function with the \p make_tuple function to avoid
|
| 103 |
-
* explicitly specifying the type of the \p zip_iterator. This example shows how to use
|
| 104 |
-
* \p zip_iterator to copy multiple ranges with a single call to \p thrust::copy.
|
| 105 |
-
*
|
| 106 |
-
* \code
|
| 107 |
-
* #include <thrust/zip_iterator.h>
|
| 108 |
-
* #include <thrust/tuple.h>
|
| 109 |
-
* #include <thrust/device_vector.h>
|
| 110 |
-
*
|
| 111 |
-
* int main()
|
| 112 |
-
* {
|
| 113 |
-
* thrust::device_vector<int> int_in(3), int_out(3);
|
| 114 |
-
* int_in[0] = 0;
|
| 115 |
-
* int_in[1] = 1;
|
| 116 |
-
* int_in[2] = 2;
|
| 117 |
-
*
|
| 118 |
-
* thrust::device_vector<float> float_in(3), float_out(3);
|
| 119 |
-
* float_in[0] = 0.0f;
|
| 120 |
-
* float_in[1] = 10.0f;
|
| 121 |
-
* float_in[2] = 20.0f;
|
| 122 |
-
*
|
| 123 |
-
* thrust::copy(thrust::make_zip_iterator(thrust::make_tuple(int_in.begin(), float_in.begin())),
|
| 124 |
-
* thrust::make_zip_iterator(thrust::make_tuple(int_in.end(), float_in.end())),
|
| 125 |
-
* thrust::make_zip_iterator(thrust::make_tuple(int_out.begin(),float_out.begin())));
|
| 126 |
-
*
|
| 127 |
-
* // int_out is now [0, 1, 2]
|
| 128 |
-
* // float_out is now [0.0f, 10.0f, 20.0f]
|
| 129 |
-
*
|
| 130 |
-
* return 0;
|
| 131 |
-
* }
|
| 132 |
-
* \endcode
|
| 133 |
-
*
|
| 134 |
-
* \see make_zip_iterator
|
| 135 |
-
* \see make_tuple
|
| 136 |
-
* \see tuple
|
| 137 |
-
* \see get
|
| 138 |
-
*/
|
| 139 |
-
template <typename IteratorTuple>
|
| 140 |
-
class zip_iterator
|
| 141 |
-
: public detail::zip_iterator_base<IteratorTuple>::type
|
| 142 |
-
{
|
| 143 |
-
public:
|
| 144 |
-
/*! Null constructor does nothing.
|
| 145 |
-
*/
|
| 146 |
-
inline __host__ __device__
|
| 147 |
-
zip_iterator();
|
| 148 |
-
|
| 149 |
-
/*! This constructor creates a new \p zip_iterator from a
|
| 150 |
-
* \p tuple of iterators.
|
| 151 |
-
*
|
| 152 |
-
* \param iterator_tuple The \p tuple of iterators to copy from.
|
| 153 |
-
*/
|
| 154 |
-
inline __host__ __device__
|
| 155 |
-
zip_iterator(IteratorTuple iterator_tuple);
|
| 156 |
-
|
| 157 |
-
/*! This copy constructor creates a new \p zip_iterator from another
|
| 158 |
-
* \p zip_iterator.
|
| 159 |
-
*
|
| 160 |
-
* \param other The \p zip_iterator to copy.
|
| 161 |
-
*/
|
| 162 |
-
template<typename OtherIteratorTuple>
|
| 163 |
-
inline __host__ __device__
|
| 164 |
-
zip_iterator(const zip_iterator<OtherIteratorTuple> &other,
|
| 165 |
-
typename thrust::detail::enable_if_convertible<
|
| 166 |
-
OtherIteratorTuple,
|
| 167 |
-
IteratorTuple
|
| 168 |
-
>::type * = 0);
|
| 169 |
-
|
| 170 |
-
/*! This method returns a \c const reference to this \p zip_iterator's
|
| 171 |
-
* \p tuple of iterators.
|
| 172 |
-
*
|
| 173 |
-
* \return A \c const reference to this \p zip_iterator's \p tuple
|
| 174 |
-
* of iterators.
|
| 175 |
-
*/
|
| 176 |
-
inline __host__ __device__
|
| 177 |
-
const IteratorTuple &get_iterator_tuple() const;
|
| 178 |
-
|
| 179 |
-
/*! \cond
|
| 180 |
-
*/
|
| 181 |
-
private:
|
| 182 |
-
typedef typename
|
| 183 |
-
detail::zip_iterator_base<IteratorTuple>::type super_t;
|
| 184 |
-
|
| 185 |
-
friend class thrust::iterator_core_access;
|
| 186 |
-
|
| 187 |
-
// Dereferencing returns a tuple built from the dereferenced
|
| 188 |
-
// iterators in the iterator tuple.
|
| 189 |
-
__host__ __device__
|
| 190 |
-
typename super_t::reference dereference() const;
|
| 191 |
-
|
| 192 |
-
// Two zip_iterators are equal if the two first iterators of the
|
| 193 |
-
// tuple are equal. Note this differs from Boost's implementation, which
|
| 194 |
-
// considers the entire tuple.
|
| 195 |
-
template<typename OtherIteratorTuple>
|
| 196 |
-
inline __host__ __device__
|
| 197 |
-
bool equal(const zip_iterator<OtherIteratorTuple> &other) const;
|
| 198 |
-
|
| 199 |
-
// Advancing a zip_iterator means to advance all iterators in the tuple
|
| 200 |
-
inline __host__ __device__
|
| 201 |
-
void advance(typename super_t::difference_type n);
|
| 202 |
-
|
| 203 |
-
// Incrementing a zip iterator means to increment all iterators in the tuple
|
| 204 |
-
inline __host__ __device__
|
| 205 |
-
void increment();
|
| 206 |
-
|
| 207 |
-
// Decrementing a zip iterator means to decrement all iterators in the tuple
|
| 208 |
-
inline __host__ __device__
|
| 209 |
-
void decrement();
|
| 210 |
-
|
| 211 |
-
// Distance is calculated using the first iterator in the tuple.
|
| 212 |
-
template<typename OtherIteratorTuple>
|
| 213 |
-
inline __host__ __device__
|
| 214 |
-
typename super_t::difference_type
|
| 215 |
-
distance_to(const zip_iterator<OtherIteratorTuple> &other) const;
|
| 216 |
-
|
| 217 |
-
// The iterator tuple.
|
| 218 |
-
IteratorTuple m_iterator_tuple;
|
| 219 |
-
|
| 220 |
-
/*! \endcond
|
| 221 |
-
*/
|
| 222 |
-
}; // end zip_iterator
|
| 223 |
-
|
| 224 |
-
/*! \p make_zip_iterator creates a \p zip_iterator from a \p tuple
|
| 225 |
-
* of iterators.
|
| 226 |
-
*
|
| 227 |
-
* \param t The \p tuple of iterators to copy.
|
| 228 |
-
* \return A newly created \p zip_iterator which zips the iterators encapsulated in \p t.
|
| 229 |
-
*
|
| 230 |
-
* \see zip_iterator
|
| 231 |
-
*/
|
| 232 |
-
template<typename IteratorTuple>
|
| 233 |
-
inline __host__ __device__
|
| 234 |
-
zip_iterator<IteratorTuple> make_zip_iterator(IteratorTuple t);
|
| 235 |
-
|
| 236 |
-
/*! \} // end fancyiterators
|
| 237 |
-
*/
|
| 238 |
-
|
| 239 |
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/*! \} // end iterators
|
| 240 |
-
*/
|
| 241 |
-
|
| 242 |
-
} // end thrust
|
| 243 |
-
|
| 244 |
-
#include <thrust/iterator/detail/zip_iterator.inl>
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spaces/CVPR/WALT/mmdet/models/dense_heads/free_anchor_retina_head.py
DELETED
|
@@ -1,270 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
import torch.nn.functional as F
|
| 3 |
-
|
| 4 |
-
from mmdet.core import bbox_overlaps
|
| 5 |
-
from ..builder import HEADS
|
| 6 |
-
from .retina_head import RetinaHead
|
| 7 |
-
|
| 8 |
-
EPS = 1e-12
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
@HEADS.register_module()
|
| 12 |
-
class FreeAnchorRetinaHead(RetinaHead):
|
| 13 |
-
"""FreeAnchor RetinaHead used in https://arxiv.org/abs/1909.02466.
|
| 14 |
-
|
| 15 |
-
Args:
|
| 16 |
-
num_classes (int): Number of categories excluding the background
|
| 17 |
-
category.
|
| 18 |
-
in_channels (int): Number of channels in the input feature map.
|
| 19 |
-
stacked_convs (int): Number of conv layers in cls and reg tower.
|
| 20 |
-
Default: 4.
|
| 21 |
-
conv_cfg (dict): dictionary to construct and config conv layer.
|
| 22 |
-
Default: None.
|
| 23 |
-
norm_cfg (dict): dictionary to construct and config norm layer.
|
| 24 |
-
Default: norm_cfg=dict(type='GN', num_groups=32,
|
| 25 |
-
requires_grad=True).
|
| 26 |
-
pre_anchor_topk (int): Number of boxes that be token in each bag.
|
| 27 |
-
bbox_thr (float): The threshold of the saturated linear function. It is
|
| 28 |
-
usually the same with the IoU threshold used in NMS.
|
| 29 |
-
gamma (float): Gamma parameter in focal loss.
|
| 30 |
-
alpha (float): Alpha parameter in focal loss.
|
| 31 |
-
""" # noqa: W605
|
| 32 |
-
|
| 33 |
-
def __init__(self,
|
| 34 |
-
num_classes,
|
| 35 |
-
in_channels,
|
| 36 |
-
stacked_convs=4,
|
| 37 |
-
conv_cfg=None,
|
| 38 |
-
norm_cfg=None,
|
| 39 |
-
pre_anchor_topk=50,
|
| 40 |
-
bbox_thr=0.6,
|
| 41 |
-
gamma=2.0,
|
| 42 |
-
alpha=0.5,
|
| 43 |
-
**kwargs):
|
| 44 |
-
super(FreeAnchorRetinaHead,
|
| 45 |
-
self).__init__(num_classes, in_channels, stacked_convs, conv_cfg,
|
| 46 |
-
norm_cfg, **kwargs)
|
| 47 |
-
|
| 48 |
-
self.pre_anchor_topk = pre_anchor_topk
|
| 49 |
-
self.bbox_thr = bbox_thr
|
| 50 |
-
self.gamma = gamma
|
| 51 |
-
self.alpha = alpha
|
| 52 |
-
|
| 53 |
-
def loss(self,
|
| 54 |
-
cls_scores,
|
| 55 |
-
bbox_preds,
|
| 56 |
-
gt_bboxes,
|
| 57 |
-
gt_labels,
|
| 58 |
-
img_metas,
|
| 59 |
-
gt_bboxes_ignore=None):
|
| 60 |
-
"""Compute losses of the head.
|
| 61 |
-
|
| 62 |
-
Args:
|
| 63 |
-
cls_scores (list[Tensor]): Box scores for each scale level
|
| 64 |
-
Has shape (N, num_anchors * num_classes, H, W)
|
| 65 |
-
bbox_preds (list[Tensor]): Box energies / deltas for each scale
|
| 66 |
-
level with shape (N, num_anchors * 4, H, W)
|
| 67 |
-
gt_bboxes (list[Tensor]): each item are the truth boxes for each
|
| 68 |
-
image in [tl_x, tl_y, br_x, br_y] format.
|
| 69 |
-
gt_labels (list[Tensor]): class indices corresponding to each box
|
| 70 |
-
img_metas (list[dict]): Meta information of each image, e.g.,
|
| 71 |
-
image size, scaling factor, etc.
|
| 72 |
-
gt_bboxes_ignore (None | list[Tensor]): specify which bounding
|
| 73 |
-
boxes can be ignored when computing the loss.
|
| 74 |
-
|
| 75 |
-
Returns:
|
| 76 |
-
dict[str, Tensor]: A dictionary of loss components.
|
| 77 |
-
"""
|
| 78 |
-
featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
|
| 79 |
-
assert len(featmap_sizes) == len(self.anchor_generator.base_anchors)
|
| 80 |
-
|
| 81 |
-
anchor_list, _ = self.get_anchors(featmap_sizes, img_metas)
|
| 82 |
-
anchors = [torch.cat(anchor) for anchor in anchor_list]
|
| 83 |
-
|
| 84 |
-
# concatenate each level
|
| 85 |
-
cls_scores = [
|
| 86 |
-
cls.permute(0, 2, 3,
|
| 87 |
-
1).reshape(cls.size(0), -1, self.cls_out_channels)
|
| 88 |
-
for cls in cls_scores
|
| 89 |
-
]
|
| 90 |
-
bbox_preds = [
|
| 91 |
-
bbox_pred.permute(0, 2, 3, 1).reshape(bbox_pred.size(0), -1, 4)
|
| 92 |
-
for bbox_pred in bbox_preds
|
| 93 |
-
]
|
| 94 |
-
cls_scores = torch.cat(cls_scores, dim=1)
|
| 95 |
-
bbox_preds = torch.cat(bbox_preds, dim=1)
|
| 96 |
-
|
| 97 |
-
cls_prob = torch.sigmoid(cls_scores)
|
| 98 |
-
box_prob = []
|
| 99 |
-
num_pos = 0
|
| 100 |
-
positive_losses = []
|
| 101 |
-
for _, (anchors_, gt_labels_, gt_bboxes_, cls_prob_,
|
| 102 |
-
bbox_preds_) in enumerate(
|
| 103 |
-
zip(anchors, gt_labels, gt_bboxes, cls_prob, bbox_preds)):
|
| 104 |
-
|
| 105 |
-
with torch.no_grad():
|
| 106 |
-
if len(gt_bboxes_) == 0:
|
| 107 |
-
image_box_prob = torch.zeros(
|
| 108 |
-
anchors_.size(0),
|
| 109 |
-
self.cls_out_channels).type_as(bbox_preds_)
|
| 110 |
-
else:
|
| 111 |
-
# box_localization: a_{j}^{loc}, shape: [j, 4]
|
| 112 |
-
pred_boxes = self.bbox_coder.decode(anchors_, bbox_preds_)
|
| 113 |
-
|
| 114 |
-
# object_box_iou: IoU_{ij}^{loc}, shape: [i, j]
|
| 115 |
-
object_box_iou = bbox_overlaps(gt_bboxes_, pred_boxes)
|
| 116 |
-
|
| 117 |
-
# object_box_prob: P{a_{j} -> b_{i}}, shape: [i, j]
|
| 118 |
-
t1 = self.bbox_thr
|
| 119 |
-
t2 = object_box_iou.max(
|
| 120 |
-
dim=1, keepdim=True).values.clamp(min=t1 + 1e-12)
|
| 121 |
-
object_box_prob = ((object_box_iou - t1) /
|
| 122 |
-
(t2 - t1)).clamp(
|
| 123 |
-
min=0, max=1)
|
| 124 |
-
|
| 125 |
-
# object_cls_box_prob: P{a_{j} -> b_{i}}, shape: [i, c, j]
|
| 126 |
-
num_obj = gt_labels_.size(0)
|
| 127 |
-
indices = torch.stack([
|
| 128 |
-
torch.arange(num_obj).type_as(gt_labels_), gt_labels_
|
| 129 |
-
],
|
| 130 |
-
dim=0)
|
| 131 |
-
object_cls_box_prob = torch.sparse_coo_tensor(
|
| 132 |
-
indices, object_box_prob)
|
| 133 |
-
|
| 134 |
-
# image_box_iou: P{a_{j} \in A_{+}}, shape: [c, j]
|
| 135 |
-
"""
|
| 136 |
-
from "start" to "end" implement:
|
| 137 |
-
image_box_iou = torch.sparse.max(object_cls_box_prob,
|
| 138 |
-
dim=0).t()
|
| 139 |
-
|
| 140 |
-
"""
|
| 141 |
-
# start
|
| 142 |
-
box_cls_prob = torch.sparse.sum(
|
| 143 |
-
object_cls_box_prob, dim=0).to_dense()
|
| 144 |
-
|
| 145 |
-
indices = torch.nonzero(box_cls_prob, as_tuple=False).t_()
|
| 146 |
-
if indices.numel() == 0:
|
| 147 |
-
image_box_prob = torch.zeros(
|
| 148 |
-
anchors_.size(0),
|
| 149 |
-
self.cls_out_channels).type_as(object_box_prob)
|
| 150 |
-
else:
|
| 151 |
-
nonzero_box_prob = torch.where(
|
| 152 |
-
(gt_labels_.unsqueeze(dim=-1) == indices[0]),
|
| 153 |
-
object_box_prob[:, indices[1]],
|
| 154 |
-
torch.tensor([
|
| 155 |
-
0
|
| 156 |
-
]).type_as(object_box_prob)).max(dim=0).values
|
| 157 |
-
|
| 158 |
-
# upmap to shape [j, c]
|
| 159 |
-
image_box_prob = torch.sparse_coo_tensor(
|
| 160 |
-
indices.flip([0]),
|
| 161 |
-
nonzero_box_prob,
|
| 162 |
-
size=(anchors_.size(0),
|
| 163 |
-
self.cls_out_channels)).to_dense()
|
| 164 |
-
# end
|
| 165 |
-
|
| 166 |
-
box_prob.append(image_box_prob)
|
| 167 |
-
|
| 168 |
-
# construct bags for objects
|
| 169 |
-
match_quality_matrix = bbox_overlaps(gt_bboxes_, anchors_)
|
| 170 |
-
_, matched = torch.topk(
|
| 171 |
-
match_quality_matrix,
|
| 172 |
-
self.pre_anchor_topk,
|
| 173 |
-
dim=1,
|
| 174 |
-
sorted=False)
|
| 175 |
-
del match_quality_matrix
|
| 176 |
-
|
| 177 |
-
# matched_cls_prob: P_{ij}^{cls}
|
| 178 |
-
matched_cls_prob = torch.gather(
|
| 179 |
-
cls_prob_[matched], 2,
|
| 180 |
-
gt_labels_.view(-1, 1, 1).repeat(1, self.pre_anchor_topk,
|
| 181 |
-
1)).squeeze(2)
|
| 182 |
-
|
| 183 |
-
# matched_box_prob: P_{ij}^{loc}
|
| 184 |
-
matched_anchors = anchors_[matched]
|
| 185 |
-
matched_object_targets = self.bbox_coder.encode(
|
| 186 |
-
matched_anchors,
|
| 187 |
-
gt_bboxes_.unsqueeze(dim=1).expand_as(matched_anchors))
|
| 188 |
-
loss_bbox = self.loss_bbox(
|
| 189 |
-
bbox_preds_[matched],
|
| 190 |
-
matched_object_targets,
|
| 191 |
-
reduction_override='none').sum(-1)
|
| 192 |
-
matched_box_prob = torch.exp(-loss_bbox)
|
| 193 |
-
|
| 194 |
-
# positive_losses: {-log( Mean-max(P_{ij}^{cls} * P_{ij}^{loc}) )}
|
| 195 |
-
num_pos += len(gt_bboxes_)
|
| 196 |
-
positive_losses.append(
|
| 197 |
-
self.positive_bag_loss(matched_cls_prob, matched_box_prob))
|
| 198 |
-
positive_loss = torch.cat(positive_losses).sum() / max(1, num_pos)
|
| 199 |
-
|
| 200 |
-
# box_prob: P{a_{j} \in A_{+}}
|
| 201 |
-
box_prob = torch.stack(box_prob, dim=0)
|
| 202 |
-
|
| 203 |
-
# negative_loss:
|
| 204 |
-
# \sum_{j}{ FL((1 - P{a_{j} \in A_{+}}) * (1 - P_{j}^{bg})) } / n||B||
|
| 205 |
-
negative_loss = self.negative_bag_loss(cls_prob, box_prob).sum() / max(
|
| 206 |
-
1, num_pos * self.pre_anchor_topk)
|
| 207 |
-
|
| 208 |
-
# avoid the absence of gradients in regression subnet
|
| 209 |
-
# when no ground-truth in a batch
|
| 210 |
-
if num_pos == 0:
|
| 211 |
-
positive_loss = bbox_preds.sum() * 0
|
| 212 |
-
|
| 213 |
-
losses = {
|
| 214 |
-
'positive_bag_loss': positive_loss,
|
| 215 |
-
'negative_bag_loss': negative_loss
|
| 216 |
-
}
|
| 217 |
-
return losses
|
| 218 |
-
|
| 219 |
-
def positive_bag_loss(self, matched_cls_prob, matched_box_prob):
|
| 220 |
-
"""Compute positive bag loss.
|
| 221 |
-
|
| 222 |
-
:math:`-log( Mean-max(P_{ij}^{cls} * P_{ij}^{loc}) )`.
|
| 223 |
-
|
| 224 |
-
:math:`P_{ij}^{cls}`: matched_cls_prob, classification probability of matched samples.
|
| 225 |
-
|
| 226 |
-
:math:`P_{ij}^{loc}`: matched_box_prob, box probability of matched samples.
|
| 227 |
-
|
| 228 |
-
Args:
|
| 229 |
-
matched_cls_prob (Tensor): Classification probabilty of matched
|
| 230 |
-
samples in shape (num_gt, pre_anchor_topk).
|
| 231 |
-
matched_box_prob (Tensor): BBox probability of matched samples,
|
| 232 |
-
in shape (num_gt, pre_anchor_topk).
|
| 233 |
-
|
| 234 |
-
Returns:
|
| 235 |
-
Tensor: Positive bag loss in shape (num_gt,).
|
| 236 |
-
""" # noqa: E501, W605
|
| 237 |
-
# bag_prob = Mean-max(matched_prob)
|
| 238 |
-
matched_prob = matched_cls_prob * matched_box_prob
|
| 239 |
-
weight = 1 / torch.clamp(1 - matched_prob, 1e-12, None)
|
| 240 |
-
weight /= weight.sum(dim=1).unsqueeze(dim=-1)
|
| 241 |
-
bag_prob = (weight * matched_prob).sum(dim=1)
|
| 242 |
-
# positive_bag_loss = -self.alpha * log(bag_prob)
|
| 243 |
-
return self.alpha * F.binary_cross_entropy(
|
| 244 |
-
bag_prob, torch.ones_like(bag_prob), reduction='none')
|
| 245 |
-
|
| 246 |
-
def negative_bag_loss(self, cls_prob, box_prob):
|
| 247 |
-
"""Compute negative bag loss.
|
| 248 |
-
|
| 249 |
-
:math:`FL((1 - P_{a_{j} \in A_{+}}) * (1 - P_{j}^{bg}))`.
|
| 250 |
-
|
| 251 |
-
:math:`P_{a_{j} \in A_{+}}`: Box_probability of matched samples.
|
| 252 |
-
|
| 253 |
-
:math:`P_{j}^{bg}`: Classification probability of negative samples.
|
| 254 |
-
|
| 255 |
-
Args:
|
| 256 |
-
cls_prob (Tensor): Classification probability, in shape
|
| 257 |
-
(num_img, num_anchors, num_classes).
|
| 258 |
-
box_prob (Tensor): Box probability, in shape
|
| 259 |
-
(num_img, num_anchors, num_classes).
|
| 260 |
-
|
| 261 |
-
Returns:
|
| 262 |
-
Tensor: Negative bag loss in shape (num_img, num_anchors, num_classes).
|
| 263 |
-
""" # noqa: E501, W605
|
| 264 |
-
prob = cls_prob * (1 - box_prob)
|
| 265 |
-
# There are some cases when neg_prob = 0.
|
| 266 |
-
# This will cause the neg_prob.log() to be inf without clamp.
|
| 267 |
-
prob = prob.clamp(min=EPS, max=1 - EPS)
|
| 268 |
-
negative_bag_loss = prob**self.gamma * F.binary_cross_entropy(
|
| 269 |
-
prob, torch.zeros_like(prob), reduction='none')
|
| 270 |
-
return (1 - self.alpha) * negative_bag_loss
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|
spaces/CVPR/WALT/walt/datasets/walt_3d.py
DELETED
|
@@ -1,535 +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 CustomDatasetLocal
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
def bounding_box(points):
|
| 21 |
-
"""returns a list containing the bottom left and the top right
|
| 22 |
-
points in the sequence
|
| 23 |
-
Here, we traverse the collection of points only once,
|
| 24 |
-
to find the min and max for x and y
|
| 25 |
-
"""
|
| 26 |
-
bot_left_x, bot_left_y = float('inf'), float('inf')
|
| 27 |
-
top_right_x, top_right_y = float('-inf'), float('-inf')
|
| 28 |
-
for point in points:
|
| 29 |
-
x = point[0]
|
| 30 |
-
y = point[1]
|
| 31 |
-
if x < 0 or y < 0:
|
| 32 |
-
continue
|
| 33 |
-
bot_left_x = min(bot_left_x, x)
|
| 34 |
-
bot_left_y = min(bot_left_y, y)
|
| 35 |
-
top_right_x = max(top_right_x, x)
|
| 36 |
-
top_right_y = max(top_right_y, y)
|
| 37 |
-
|
| 38 |
-
return [bot_left_x, bot_left_y, top_right_x, top_right_y]
|
| 39 |
-
|
| 40 |
-
lines = [[0,1],[1,3],[0,2],[3,2],[0,4],[1,5],[2,6],[3,7],[4,5],[5,7],[4,6],[7,6]]
|
| 41 |
-
|
| 42 |
-
def get_boundingbox2d3d(cameraname, gt_data, extrinsics_path):
|
| 43 |
-
f = open(extrinsics_path,"r")
|
| 44 |
-
while True:
|
| 45 |
-
a = f.readline()
|
| 46 |
-
print(cameraname, a.split('\n')[0].split(' ')[0])
|
| 47 |
-
if cameraname in a.split('\n')[0].split(' ')[0]:
|
| 48 |
-
a = a.split('\n')[0].split(' ')
|
| 49 |
-
break
|
| 50 |
-
|
| 51 |
-
K = np.reshape(np.array(a[1:10]),[3,3]).astype(float)
|
| 52 |
-
R = np.reshape(a[10:19], [3,3])
|
| 53 |
-
T = np.array([[a[19]],[a[20]],[a[21]]])
|
| 54 |
-
RT = np.hstack((R,T)).astype(float)
|
| 55 |
-
KRT = np.matmul(K, RT)
|
| 56 |
-
bb_3d_connected = []
|
| 57 |
-
bb_3d_all = []
|
| 58 |
-
bb_2d_all = []
|
| 59 |
-
bb_3d_proj_all = []
|
| 60 |
-
|
| 61 |
-
for indice, keypoints_3d in enumerate(gt_data['arr_0'][1]):
|
| 62 |
-
parking_space = gt_data['arr_0'][0][indice][0]
|
| 63 |
-
|
| 64 |
-
if gt_data['arr_0'][0][indice][1] == 0:
|
| 65 |
-
continue
|
| 66 |
-
points2d_all = []
|
| 67 |
-
parking_space = np.vstack([parking_space, parking_space+[0,0,2]])
|
| 68 |
-
parking_space_tranformed = []
|
| 69 |
-
for point in parking_space:
|
| 70 |
-
point = [point[0], point[1], point[2], 1]
|
| 71 |
-
point = np.matmul(RT, point)
|
| 72 |
-
parking_space_tranformed.append(list(point))
|
| 73 |
-
point2d = np.matmul(K, point)
|
| 74 |
-
if point2d[2] < 0:
|
| 75 |
-
points2d_all.append([-100,-100,1])
|
| 76 |
-
continue
|
| 77 |
-
point2d = point2d/point2d[2]
|
| 78 |
-
if point2d[0] < 0 or point2d[0] >2048:
|
| 79 |
-
points2d_all.append([-100,-100,1])
|
| 80 |
-
continue
|
| 81 |
-
if point2d[1] < 0 or point2d[1] >2048:
|
| 82 |
-
points2d_all.append([-100,-100,1])
|
| 83 |
-
continue
|
| 84 |
-
|
| 85 |
-
points2d_all.append(point2d)
|
| 86 |
-
|
| 87 |
-
bb_3d_proj_all.append(points2d_all)
|
| 88 |
-
bbox = bounding_box(points2d_all)
|
| 89 |
-
if float('inf') in bbox:
|
| 90 |
-
continue
|
| 91 |
-
bb_2d_all.append(bbox)
|
| 92 |
-
bb_3d_all.append(parking_space)
|
| 93 |
-
#for line in lines:
|
| 94 |
-
# bb_3d_connected.append(parking_space[line[0]])
|
| 95 |
-
# bb_3d_connected.append(parking_space[line[1]])
|
| 96 |
-
#asas
|
| 97 |
-
return bb_3d_all, bb_2d_all, bb_3d_proj_all
|
| 98 |
-
|
| 99 |
-
|
| 100 |
-
@DATASETS.register_module()
|
| 101 |
-
class Walt3DDataset(CustomDatasetLocal):
|
| 102 |
-
|
| 103 |
-
CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
|
| 104 |
-
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
|
| 105 |
-
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
|
| 106 |
-
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
|
| 107 |
-
'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
|
| 108 |
-
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
|
| 109 |
-
'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
|
| 110 |
-
'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
|
| 111 |
-
'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
|
| 112 |
-
'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
|
| 113 |
-
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
|
| 114 |
-
'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
|
| 115 |
-
'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
|
| 116 |
-
'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush')
|
| 117 |
-
|
| 118 |
-
def load_annotations(self, ann_file):
|
| 119 |
-
import glob
|
| 120 |
-
count = 0
|
| 121 |
-
data_infos = []
|
| 122 |
-
self.data_annotations = []
|
| 123 |
-
for i in glob.glob(ann_file + '*'):
|
| 124 |
-
gt_data = np.load(i , allow_pickle = True)
|
| 125 |
-
for img_folder in glob.glob(ann_file.replace('GT_data','images') + '/*'):
|
| 126 |
-
cam_name = img_folder.split('/')[-1]
|
| 127 |
-
img_name = i.split('/')[-1].replace('.npz','.png')
|
| 128 |
-
info = dict(license=3, height=2048, width=2048, file_name = cam_name+'/' + img_name, date_captured = i.split('/')[-1].split('.')[0], id = count, filename = cam_name+'/' + img_name)
|
| 129 |
-
|
| 130 |
-
#info = dict(license=3, height=2048, width=2048, file_name = i.split('/')[-1].replace('.npz','.png'), date_captured = i.split('/')[-1].split('.')[0], id = count, filename = i.split('/')[-1].replace('.npz','.png'))
|
| 131 |
-
count = count+1
|
| 132 |
-
data_infos.append(info)
|
| 133 |
-
bb_3d_all, bb_2d_all, bb_3d_proj_all = get_boundingbox2d3d(cam_name, gt_data, ann_file.replace('GT_data','Extrinsics') + '/frame_par.txt')
|
| 134 |
-
self.data_annotations.append([bb_3d_all, bb_2d_all, bb_3d_proj_all])
|
| 135 |
-
break
|
| 136 |
-
return data_infos
|
| 137 |
-
|
| 138 |
-
|
| 139 |
-
def get_ann_info(self, idx):
|
| 140 |
-
data = self.data_annotations[idx]
|
| 141 |
-
gt_bboxes = np.array(data[1])
|
| 142 |
-
gt_bboxes_3d = np.array(data[0])
|
| 143 |
-
gt_bboxes_3d_proj = np.array(data[2])
|
| 144 |
-
|
| 145 |
-
|
| 146 |
-
ann = dict(
|
| 147 |
-
bboxes=gt_bboxes,
|
| 148 |
-
bboxes_3d = gt_bboxes_3d,
|
| 149 |
-
bboxes_3d_proj = gt_bboxes_3d_proj,
|
| 150 |
-
labels = (np.zeros(len(gt_bboxes))+2).astype(int),
|
| 151 |
-
bboxes_ignore=np.zeros((0, 4), dtype=np.float32),
|
| 152 |
-
#masks=np.array([]),
|
| 153 |
-
seg_map=np.array([]))
|
| 154 |
-
return ann
|
| 155 |
-
|
| 156 |
-
def get_cat_ids(self, idx):
|
| 157 |
-
data = self.data_annotations[idx]
|
| 158 |
-
gt_bboxes = np.array(data[1])
|
| 159 |
-
return (np.zeros(len(gt_bboxes))+2).astype(int)
|
| 160 |
-
|
| 161 |
-
|
| 162 |
-
def _filter_imgs(self, min_size=32):
|
| 163 |
-
"""Filter images too small or without ground truths."""
|
| 164 |
-
valid_inds = []
|
| 165 |
-
for data_info in self.data_infos:
|
| 166 |
-
valid_inds.append(data_info['id'])
|
| 167 |
-
print(valid_inds)
|
| 168 |
-
|
| 169 |
-
return valid_inds
|
| 170 |
-
|
| 171 |
-
|
| 172 |
-
def xyxy2xywh(self, bbox):
|
| 173 |
-
"""Convert ``xyxy`` style bounding boxes to ``xywh`` style for COCO
|
| 174 |
-
evaluation.
|
| 175 |
-
|
| 176 |
-
Args:
|
| 177 |
-
bbox (numpy.ndarray): The bounding boxes, shape (4, ), in
|
| 178 |
-
``xyxy`` order.
|
| 179 |
-
|
| 180 |
-
Returns:
|
| 181 |
-
list[float]: The converted bounding boxes, in ``xywh`` order.
|
| 182 |
-
"""
|
| 183 |
-
|
| 184 |
-
_bbox = bbox.tolist()
|
| 185 |
-
return [
|
| 186 |
-
_bbox[0],
|
| 187 |
-
_bbox[1],
|
| 188 |
-
_bbox[2] - _bbox[0],
|
| 189 |
-
_bbox[3] - _bbox[1],
|
| 190 |
-
]
|
| 191 |
-
|
| 192 |
-
def _proposal2json(self, results):
|
| 193 |
-
"""Convert proposal results to COCO json style."""
|
| 194 |
-
json_results = []
|
| 195 |
-
for idx in range(len(self)):
|
| 196 |
-
img_id = self.img_ids[idx]
|
| 197 |
-
bboxes = results[idx]
|
| 198 |
-
for i in range(bboxes.shape[0]):
|
| 199 |
-
data = dict()
|
| 200 |
-
data['image_id'] = img_id
|
| 201 |
-
data['bbox'] = self.xyxy2xywh(bboxes[i])
|
| 202 |
-
data['score'] = float(bboxes[i][4])
|
| 203 |
-
data['category_id'] = 1
|
| 204 |
-
json_results.append(data)
|
| 205 |
-
return json_results
|
| 206 |
-
|
| 207 |
-
def _det2json(self, results):
|
| 208 |
-
"""Convert detection results to COCO json style."""
|
| 209 |
-
json_results = []
|
| 210 |
-
for idx in range(len(self)):
|
| 211 |
-
img_id = self.img_ids[idx]
|
| 212 |
-
result = results[idx]
|
| 213 |
-
for label in range(len(result)):
|
| 214 |
-
bboxes = result[label]
|
| 215 |
-
for i in range(bboxes.shape[0]):
|
| 216 |
-
data = dict()
|
| 217 |
-
data['image_id'] = img_id
|
| 218 |
-
data['bbox'] = self.xyxy2xywh(bboxes[i])
|
| 219 |
-
data['score'] = float(bboxes[i][4])
|
| 220 |
-
data['category_id'] = self.cat_ids[label]
|
| 221 |
-
json_results.append(data)
|
| 222 |
-
return json_results
|
| 223 |
-
|
| 224 |
-
def _segm2json(self, results):
|
| 225 |
-
"""Convert instance segmentation results to COCO json style."""
|
| 226 |
-
bbox_json_results = []
|
| 227 |
-
segm_json_results = []
|
| 228 |
-
for idx in range(len(self)):
|
| 229 |
-
img_id = self.img_ids[idx]
|
| 230 |
-
det, seg = results[idx]
|
| 231 |
-
for label in range(len(det)):
|
| 232 |
-
# bbox results
|
| 233 |
-
bboxes = det[label]
|
| 234 |
-
for i in range(bboxes.shape[0]):
|
| 235 |
-
data = dict()
|
| 236 |
-
data['image_id'] = img_id
|
| 237 |
-
data['bbox'] = self.xyxy2xywh(bboxes[i])
|
| 238 |
-
data['score'] = float(bboxes[i][4])
|
| 239 |
-
data['category_id'] = self.cat_ids[label]
|
| 240 |
-
bbox_json_results.append(data)
|
| 241 |
-
|
| 242 |
-
# segm results
|
| 243 |
-
# some detectors use different scores for bbox and mask
|
| 244 |
-
if isinstance(seg, tuple):
|
| 245 |
-
segms = seg[0][label]
|
| 246 |
-
mask_score = seg[1][label]
|
| 247 |
-
else:
|
| 248 |
-
segms = seg[label]
|
| 249 |
-
mask_score = [bbox[4] for bbox in bboxes]
|
| 250 |
-
for i in range(bboxes.shape[0]):
|
| 251 |
-
data = dict()
|
| 252 |
-
data['image_id'] = img_id
|
| 253 |
-
data['bbox'] = self.xyxy2xywh(bboxes[i])
|
| 254 |
-
data['score'] = float(mask_score[i])
|
| 255 |
-
data['category_id'] = self.cat_ids[label]
|
| 256 |
-
if isinstance(segms[i]['counts'], bytes):
|
| 257 |
-
segms[i]['counts'] = segms[i]['counts'].decode()
|
| 258 |
-
data['segmentation'] = segms[i]
|
| 259 |
-
segm_json_results.append(data)
|
| 260 |
-
return bbox_json_results, segm_json_results
|
| 261 |
-
|
| 262 |
-
def results2json(self, results, outfile_prefix):
|
| 263 |
-
"""Dump the detection results to a COCO style json file.
|
| 264 |
-
|
| 265 |
-
There are 3 types of results: proposals, bbox predictions, mask
|
| 266 |
-
predictions, and they have different data types. This method will
|
| 267 |
-
automatically recognize the type, and dump them to json files.
|
| 268 |
-
|
| 269 |
-
Args:
|
| 270 |
-
results (list[list | tuple | ndarray]): Testing results of the
|
| 271 |
-
dataset.
|
| 272 |
-
outfile_prefix (str): The filename prefix of the json files. If the
|
| 273 |
-
prefix is "somepath/xxx", the json files will be named
|
| 274 |
-
"somepath/xxx.bbox.json", "somepath/xxx.segm.json",
|
| 275 |
-
"somepath/xxx.proposal.json".
|
| 276 |
-
|
| 277 |
-
Returns:
|
| 278 |
-
dict[str: str]: Possible keys are "bbox", "segm", "proposal", and \
|
| 279 |
-
values are corresponding filenames.
|
| 280 |
-
"""
|
| 281 |
-
result_files = dict()
|
| 282 |
-
if isinstance(results[0], list):
|
| 283 |
-
json_results = self._det2json(results)
|
| 284 |
-
result_files['bbox'] = f'{outfile_prefix}.bbox.json'
|
| 285 |
-
result_files['proposal'] = f'{outfile_prefix}.bbox.json'
|
| 286 |
-
mmcv.dump(json_results, result_files['bbox'])
|
| 287 |
-
elif isinstance(results[0], tuple):
|
| 288 |
-
json_results = self._segm2json(results)
|
| 289 |
-
result_files['bbox'] = f'{outfile_prefix}.bbox.json'
|
| 290 |
-
result_files['proposal'] = f'{outfile_prefix}.bbox.json'
|
| 291 |
-
result_files['segm'] = f'{outfile_prefix}.segm.json'
|
| 292 |
-
mmcv.dump(json_results[0], result_files['bbox'])
|
| 293 |
-
mmcv.dump(json_results[1], result_files['segm'])
|
| 294 |
-
elif isinstance(results[0], np.ndarray):
|
| 295 |
-
json_results = self._proposal2json(results)
|
| 296 |
-
result_files['proposal'] = f'{outfile_prefix}.proposal.json'
|
| 297 |
-
mmcv.dump(json_results, result_files['proposal'])
|
| 298 |
-
else:
|
| 299 |
-
raise TypeError('invalid type of results')
|
| 300 |
-
return result_files
|
| 301 |
-
|
| 302 |
-
def fast_eval_recall(self, results, proposal_nums, iou_thrs, logger=None):
|
| 303 |
-
gt_bboxes = []
|
| 304 |
-
for i in range(len(self.img_ids)):
|
| 305 |
-
ann_ids = self.coco.get_ann_ids(img_ids=self.img_ids[i])
|
| 306 |
-
ann_info = self.coco.load_anns(ann_ids)
|
| 307 |
-
if len(ann_info) == 0:
|
| 308 |
-
gt_bboxes.append(np.zeros((0, 4)))
|
| 309 |
-
continue
|
| 310 |
-
bboxes = []
|
| 311 |
-
for ann in ann_info:
|
| 312 |
-
if ann.get('ignore', False) or ann['iscrowd']:
|
| 313 |
-
continue
|
| 314 |
-
x1, y1, w, h = ann['bbox']
|
| 315 |
-
bboxes.append([x1, y1, x1 + w, y1 + h])
|
| 316 |
-
bboxes = np.array(bboxes, dtype=np.float32)
|
| 317 |
-
if bboxes.shape[0] == 0:
|
| 318 |
-
bboxes = np.zeros((0, 4))
|
| 319 |
-
gt_bboxes.append(bboxes)
|
| 320 |
-
|
| 321 |
-
recalls = eval_recalls(
|
| 322 |
-
gt_bboxes, results, proposal_nums, iou_thrs, logger=logger)
|
| 323 |
-
ar = recalls.mean(axis=1)
|
| 324 |
-
return ar
|
| 325 |
-
|
| 326 |
-
def format_results(self, results, jsonfile_prefix=None, **kwargs):
|
| 327 |
-
"""Format the results to json (standard format for COCO evaluation).
|
| 328 |
-
|
| 329 |
-
Args:
|
| 330 |
-
results (list[tuple | numpy.ndarray]): Testing results of the
|
| 331 |
-
dataset.
|
| 332 |
-
jsonfile_prefix (str | None): The prefix of json files. It includes
|
| 333 |
-
the file path and the prefix of filename, e.g., "a/b/prefix".
|
| 334 |
-
If not specified, a temp file will be created. Default: None.
|
| 335 |
-
|
| 336 |
-
Returns:
|
| 337 |
-
tuple: (result_files, tmp_dir), result_files is a dict containing \
|
| 338 |
-
the json filepaths, tmp_dir is the temporal directory created \
|
| 339 |
-
for saving json files when jsonfile_prefix is not specified.
|
| 340 |
-
"""
|
| 341 |
-
assert isinstance(results, list), 'results must be a list'
|
| 342 |
-
assert len(results) == len(self), (
|
| 343 |
-
'The length of results is not equal to the dataset len: {} != {}'.
|
| 344 |
-
format(len(results), len(self)))
|
| 345 |
-
|
| 346 |
-
if jsonfile_prefix is None:
|
| 347 |
-
tmp_dir = tempfile.TemporaryDirectory()
|
| 348 |
-
jsonfile_prefix = osp.join(tmp_dir.name, 'results')
|
| 349 |
-
else:
|
| 350 |
-
tmp_dir = None
|
| 351 |
-
result_files = self.results2json(results, jsonfile_prefix)
|
| 352 |
-
return result_files, tmp_dir
|
| 353 |
-
|
| 354 |
-
def evaluate(self,
|
| 355 |
-
results,
|
| 356 |
-
metric='bbox',
|
| 357 |
-
logger=None,
|
| 358 |
-
jsonfile_prefix=None,
|
| 359 |
-
classwise=False,
|
| 360 |
-
proposal_nums=(100, 300, 1000),
|
| 361 |
-
iou_thrs=None,
|
| 362 |
-
metric_items=None):
|
| 363 |
-
"""Evaluation in COCO protocol.
|
| 364 |
-
|
| 365 |
-
Args:
|
| 366 |
-
results (list[list | tuple]): Testing results of the dataset.
|
| 367 |
-
metric (str | list[str]): Metrics to be evaluated. Options are
|
| 368 |
-
'bbox', 'segm', 'proposal', 'proposal_fast'.
|
| 369 |
-
logger (logging.Logger | str | None): Logger used for printing
|
| 370 |
-
related information during evaluation. Default: None.
|
| 371 |
-
jsonfile_prefix (str | None): The prefix of json files. It includes
|
| 372 |
-
the file path and the prefix of filename, e.g., "a/b/prefix".
|
| 373 |
-
If not specified, a temp file will be created. Default: None.
|
| 374 |
-
classwise (bool): Whether to evaluating the AP for each class.
|
| 375 |
-
proposal_nums (Sequence[int]): Proposal number used for evaluating
|
| 376 |
-
recalls, such as recall@100, recall@1000.
|
| 377 |
-
Default: (100, 300, 1000).
|
| 378 |
-
iou_thrs (Sequence[float], optional): IoU threshold used for
|
| 379 |
-
evaluating recalls/mAPs. If set to a list, the average of all
|
| 380 |
-
IoUs will also be computed. If not specified, [0.50, 0.55,
|
| 381 |
-
0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95] will be used.
|
| 382 |
-
Default: None.
|
| 383 |
-
metric_items (list[str] | str, optional): Metric items that will
|
| 384 |
-
be returned. If not specified, ``['AR@100', 'AR@300',
|
| 385 |
-
'AR@1000', 'AR_s@1000', 'AR_m@1000', 'AR_l@1000' ]`` will be
|
| 386 |
-
used when ``metric=='proposal'``, ``['mAP', 'mAP_50', 'mAP_75',
|
| 387 |
-
'mAP_s', 'mAP_m', 'mAP_l']`` will be used when
|
| 388 |
-
``metric=='bbox' or metric=='segm'``.
|
| 389 |
-
|
| 390 |
-
Returns:
|
| 391 |
-
dict[str, float]: COCO style evaluation metric.
|
| 392 |
-
"""
|
| 393 |
-
|
| 394 |
-
metrics = metric if isinstance(metric, list) else [metric]
|
| 395 |
-
allowed_metrics = ['bbox', 'segm', 'proposal', 'proposal_fast']
|
| 396 |
-
for metric in metrics:
|
| 397 |
-
if metric not in allowed_metrics:
|
| 398 |
-
raise KeyError(f'metric {metric} is not supported')
|
| 399 |
-
if iou_thrs is None:
|
| 400 |
-
iou_thrs = np.linspace(
|
| 401 |
-
.5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True)
|
| 402 |
-
if metric_items is not None:
|
| 403 |
-
if not isinstance(metric_items, list):
|
| 404 |
-
metric_items = [metric_items]
|
| 405 |
-
|
| 406 |
-
result_files, tmp_dir = self.format_results(results, jsonfile_prefix)
|
| 407 |
-
|
| 408 |
-
eval_results = OrderedDict()
|
| 409 |
-
cocoGt = self.coco
|
| 410 |
-
for metric in metrics:
|
| 411 |
-
msg = f'Evaluating {metric}...'
|
| 412 |
-
if logger is None:
|
| 413 |
-
msg = '\n' + msg
|
| 414 |
-
print_log(msg, logger=logger)
|
| 415 |
-
|
| 416 |
-
if metric == 'proposal_fast':
|
| 417 |
-
ar = self.fast_eval_recall(
|
| 418 |
-
results, proposal_nums, iou_thrs, logger='silent')
|
| 419 |
-
log_msg = []
|
| 420 |
-
for i, num in enumerate(proposal_nums):
|
| 421 |
-
eval_results[f'AR@{num}'] = ar[i]
|
| 422 |
-
log_msg.append(f'\nAR@{num}\t{ar[i]:.4f}')
|
| 423 |
-
log_msg = ''.join(log_msg)
|
| 424 |
-
print_log(log_msg, logger=logger)
|
| 425 |
-
continue
|
| 426 |
-
|
| 427 |
-
if metric not in result_files:
|
| 428 |
-
raise KeyError(f'{metric} is not in results')
|
| 429 |
-
try:
|
| 430 |
-
cocoDt = cocoGt.loadRes(result_files[metric])
|
| 431 |
-
except IndexError:
|
| 432 |
-
print_log(
|
| 433 |
-
'The testing results of the whole dataset is empty.',
|
| 434 |
-
logger=logger,
|
| 435 |
-
level=logging.ERROR)
|
| 436 |
-
break
|
| 437 |
-
|
| 438 |
-
iou_type = 'bbox' if metric == 'proposal' else metric
|
| 439 |
-
cocoEval = COCOeval(cocoGt, cocoDt, iou_type)
|
| 440 |
-
cocoEval.params.catIds = self.cat_ids
|
| 441 |
-
cocoEval.params.imgIds = self.img_ids
|
| 442 |
-
cocoEval.params.maxDets = list(proposal_nums)
|
| 443 |
-
cocoEval.params.iouThrs = iou_thrs
|
| 444 |
-
# mapping of cocoEval.stats
|
| 445 |
-
coco_metric_names = {
|
| 446 |
-
'mAP': 0,
|
| 447 |
-
'mAP_50': 1,
|
| 448 |
-
'mAP_75': 2,
|
| 449 |
-
'mAP_s': 3,
|
| 450 |
-
'mAP_m': 4,
|
| 451 |
-
'mAP_l': 5,
|
| 452 |
-
'AR@100': 6,
|
| 453 |
-
'AR@300': 7,
|
| 454 |
-
'AR@1000': 8,
|
| 455 |
-
'AR_s@1000': 9,
|
| 456 |
-
'AR_m@1000': 10,
|
| 457 |
-
'AR_l@1000': 11
|
| 458 |
-
}
|
| 459 |
-
if metric_items is not None:
|
| 460 |
-
for metric_item in metric_items:
|
| 461 |
-
if metric_item not in coco_metric_names:
|
| 462 |
-
raise KeyError(
|
| 463 |
-
f'metric item {metric_item} is not supported')
|
| 464 |
-
|
| 465 |
-
if metric == 'proposal':
|
| 466 |
-
cocoEval.params.useCats = 0
|
| 467 |
-
cocoEval.evaluate()
|
| 468 |
-
cocoEval.accumulate()
|
| 469 |
-
cocoEval.summarize()
|
| 470 |
-
if metric_items is None:
|
| 471 |
-
metric_items = [
|
| 472 |
-
'AR@100', 'AR@300', 'AR@1000', 'AR_s@1000',
|
| 473 |
-
'AR_m@1000', 'AR_l@1000'
|
| 474 |
-
]
|
| 475 |
-
|
| 476 |
-
for item in metric_items:
|
| 477 |
-
val = float(
|
| 478 |
-
f'{cocoEval.stats[coco_metric_names[item]]:.3f}')
|
| 479 |
-
eval_results[item] = val
|
| 480 |
-
else:
|
| 481 |
-
cocoEval.evaluate()
|
| 482 |
-
cocoEval.accumulate()
|
| 483 |
-
cocoEval.summarize()
|
| 484 |
-
if classwise: # Compute per-category AP
|
| 485 |
-
# Compute per-category AP
|
| 486 |
-
# from https://github.com/facebookresearch/detectron2/
|
| 487 |
-
precisions = cocoEval.eval['precision']
|
| 488 |
-
# precision: (iou, recall, cls, area range, max dets)
|
| 489 |
-
assert len(self.cat_ids) == precisions.shape[2]
|
| 490 |
-
|
| 491 |
-
results_per_category = []
|
| 492 |
-
for idx, catId in enumerate(self.cat_ids):
|
| 493 |
-
# area range index 0: all area ranges
|
| 494 |
-
# max dets index -1: typically 100 per image
|
| 495 |
-
nm = self.coco.loadCats(catId)[0]
|
| 496 |
-
precision = precisions[:, :, idx, 0, -1]
|
| 497 |
-
precision = precision[precision > -1]
|
| 498 |
-
if precision.size:
|
| 499 |
-
ap = np.mean(precision)
|
| 500 |
-
else:
|
| 501 |
-
ap = float('nan')
|
| 502 |
-
results_per_category.append(
|
| 503 |
-
(f'{nm["name"]}', f'{float(ap):0.3f}'))
|
| 504 |
-
|
| 505 |
-
num_columns = min(6, len(results_per_category) * 2)
|
| 506 |
-
results_flatten = list(
|
| 507 |
-
itertools.chain(*results_per_category))
|
| 508 |
-
headers = ['category', 'AP'] * (num_columns // 2)
|
| 509 |
-
results_2d = itertools.zip_longest(*[
|
| 510 |
-
results_flatten[i::num_columns]
|
| 511 |
-
for i in range(num_columns)
|
| 512 |
-
])
|
| 513 |
-
table_data = [headers]
|
| 514 |
-
table_data += [result for result in results_2d]
|
| 515 |
-
table = AsciiTable(table_data)
|
| 516 |
-
print_log('\n' + table.table, logger=logger)
|
| 517 |
-
|
| 518 |
-
if metric_items is None:
|
| 519 |
-
metric_items = [
|
| 520 |
-
'mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l'
|
| 521 |
-
]
|
| 522 |
-
|
| 523 |
-
for metric_item in metric_items:
|
| 524 |
-
key = f'{metric}_{metric_item}'
|
| 525 |
-
val = float(
|
| 526 |
-
f'{cocoEval.stats[coco_metric_names[metric_item]]:.3f}'
|
| 527 |
-
)
|
| 528 |
-
eval_results[key] = val
|
| 529 |
-
ap = cocoEval.stats[:6]
|
| 530 |
-
eval_results[f'{metric}_mAP_copypaste'] = (
|
| 531 |
-
f'{ap[0]:.3f} {ap[1]:.3f} {ap[2]:.3f} {ap[3]:.3f} '
|
| 532 |
-
f'{ap[4]:.3f} {ap[5]:.3f}')
|
| 533 |
-
if tmp_dir is not None:
|
| 534 |
-
tmp_dir.cleanup()
|
| 535 |
-
return eval_results
|
|
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|
spaces/Catmeow/AI_story_writing/README.md
DELETED
|
@@ -1,12 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: AI Story Writing
|
| 3 |
-
emoji: 📚
|
| 4 |
-
colorFrom: pink
|
| 5 |
-
colorTo: red
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.8
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
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|
spaces/ChandraMohanNayal/AutoGPT/autogpt/commands/file_operations.py
DELETED
|
@@ -1,267 +0,0 @@
|
|
| 1 |
-
"""File operations for AutoGPT"""
|
| 2 |
-
from __future__ import annotations
|
| 3 |
-
|
| 4 |
-
import os
|
| 5 |
-
import os.path
|
| 6 |
-
from typing import Generator
|
| 7 |
-
|
| 8 |
-
import requests
|
| 9 |
-
from colorama import Back, Fore
|
| 10 |
-
from requests.adapters import HTTPAdapter, Retry
|
| 11 |
-
|
| 12 |
-
from autogpt.spinner import Spinner
|
| 13 |
-
from autogpt.utils import readable_file_size
|
| 14 |
-
from autogpt.workspace import WORKSPACE_PATH, path_in_workspace
|
| 15 |
-
|
| 16 |
-
LOG_FILE = "file_logger.txt"
|
| 17 |
-
LOG_FILE_PATH = WORKSPACE_PATH / LOG_FILE
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
def check_duplicate_operation(operation: str, filename: str) -> bool:
|
| 21 |
-
"""Check if the operation has already been performed on the given file
|
| 22 |
-
|
| 23 |
-
Args:
|
| 24 |
-
operation (str): The operation to check for
|
| 25 |
-
filename (str): The name of the file to check for
|
| 26 |
-
|
| 27 |
-
Returns:
|
| 28 |
-
bool: True if the operation has already been performed on the file
|
| 29 |
-
"""
|
| 30 |
-
log_content = read_file(LOG_FILE)
|
| 31 |
-
log_entry = f"{operation}: {filename}\n"
|
| 32 |
-
return log_entry in log_content
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
def log_operation(operation: str, filename: str) -> None:
|
| 36 |
-
"""Log the file operation to the file_logger.txt
|
| 37 |
-
|
| 38 |
-
Args:
|
| 39 |
-
operation (str): The operation to log
|
| 40 |
-
filename (str): The name of the file the operation was performed on
|
| 41 |
-
"""
|
| 42 |
-
log_entry = f"{operation}: {filename}\n"
|
| 43 |
-
|
| 44 |
-
# Create the log file if it doesn't exist
|
| 45 |
-
if not os.path.exists(LOG_FILE_PATH):
|
| 46 |
-
with open(LOG_FILE_PATH, "w", encoding="utf-8") as f:
|
| 47 |
-
f.write("File Operation Logger ")
|
| 48 |
-
|
| 49 |
-
append_to_file(LOG_FILE, log_entry, shouldLog=False)
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
def split_file(
|
| 53 |
-
content: str, max_length: int = 4000, overlap: int = 0
|
| 54 |
-
) -> Generator[str, None, None]:
|
| 55 |
-
"""
|
| 56 |
-
Split text into chunks of a specified maximum length with a specified overlap
|
| 57 |
-
between chunks.
|
| 58 |
-
|
| 59 |
-
:param content: The input text to be split into chunks
|
| 60 |
-
:param max_length: The maximum length of each chunk,
|
| 61 |
-
default is 4000 (about 1k token)
|
| 62 |
-
:param overlap: The number of overlapping characters between chunks,
|
| 63 |
-
default is no overlap
|
| 64 |
-
:return: A generator yielding chunks of text
|
| 65 |
-
"""
|
| 66 |
-
start = 0
|
| 67 |
-
content_length = len(content)
|
| 68 |
-
|
| 69 |
-
while start < content_length:
|
| 70 |
-
end = start + max_length
|
| 71 |
-
if end + overlap < content_length:
|
| 72 |
-
chunk = content[start : end + overlap - 1]
|
| 73 |
-
else:
|
| 74 |
-
chunk = content[start:content_length]
|
| 75 |
-
|
| 76 |
-
# Account for the case where the last chunk is shorter than the overlap, so it has already been consumed
|
| 77 |
-
if len(chunk) <= overlap:
|
| 78 |
-
break
|
| 79 |
-
|
| 80 |
-
yield chunk
|
| 81 |
-
start += max_length - overlap
|
| 82 |
-
|
| 83 |
-
|
| 84 |
-
def read_file(filename: str) -> str:
|
| 85 |
-
"""Read a file and return the contents
|
| 86 |
-
|
| 87 |
-
Args:
|
| 88 |
-
filename (str): The name of the file to read
|
| 89 |
-
|
| 90 |
-
Returns:
|
| 91 |
-
str: The contents of the file
|
| 92 |
-
"""
|
| 93 |
-
try:
|
| 94 |
-
filepath = path_in_workspace(filename)
|
| 95 |
-
with open(filepath, "r", encoding="utf-8") as f:
|
| 96 |
-
content = f.read()
|
| 97 |
-
return content
|
| 98 |
-
except Exception as e:
|
| 99 |
-
return f"Error: {str(e)}"
|
| 100 |
-
|
| 101 |
-
|
| 102 |
-
def ingest_file(
|
| 103 |
-
filename: str, memory, max_length: int = 4000, overlap: int = 200
|
| 104 |
-
) -> None:
|
| 105 |
-
"""
|
| 106 |
-
Ingest a file by reading its content, splitting it into chunks with a specified
|
| 107 |
-
maximum length and overlap, and adding the chunks to the memory storage.
|
| 108 |
-
|
| 109 |
-
:param filename: The name of the file to ingest
|
| 110 |
-
:param memory: An object with an add() method to store the chunks in memory
|
| 111 |
-
:param max_length: The maximum length of each chunk, default is 4000
|
| 112 |
-
:param overlap: The number of overlapping characters between chunks, default is 200
|
| 113 |
-
"""
|
| 114 |
-
try:
|
| 115 |
-
print(f"Working with file {filename}")
|
| 116 |
-
content = read_file(filename)
|
| 117 |
-
content_length = len(content)
|
| 118 |
-
print(f"File length: {content_length} characters")
|
| 119 |
-
|
| 120 |
-
chunks = list(split_file(content, max_length=max_length, overlap=overlap))
|
| 121 |
-
|
| 122 |
-
num_chunks = len(chunks)
|
| 123 |
-
for i, chunk in enumerate(chunks):
|
| 124 |
-
print(f"Ingesting chunk {i + 1} / {num_chunks} into memory")
|
| 125 |
-
memory_to_add = (
|
| 126 |
-
f"Filename: {filename}\n" f"Content part#{i + 1}/{num_chunks}: {chunk}"
|
| 127 |
-
)
|
| 128 |
-
|
| 129 |
-
memory.add(memory_to_add)
|
| 130 |
-
|
| 131 |
-
print(f"Done ingesting {num_chunks} chunks from {filename}.")
|
| 132 |
-
except Exception as e:
|
| 133 |
-
print(f"Error while ingesting file '{filename}': {str(e)}")
|
| 134 |
-
|
| 135 |
-
|
| 136 |
-
def write_to_file(filename: str, text: str) -> str:
|
| 137 |
-
"""Write text to a file
|
| 138 |
-
|
| 139 |
-
Args:
|
| 140 |
-
filename (str): The name of the file to write to
|
| 141 |
-
text (str): The text to write to the file
|
| 142 |
-
|
| 143 |
-
Returns:
|
| 144 |
-
str: A message indicating success or failure
|
| 145 |
-
"""
|
| 146 |
-
if check_duplicate_operation("write", filename):
|
| 147 |
-
return "Error: File has already been updated."
|
| 148 |
-
try:
|
| 149 |
-
filepath = path_in_workspace(filename)
|
| 150 |
-
directory = os.path.dirname(filepath)
|
| 151 |
-
if not os.path.exists(directory):
|
| 152 |
-
os.makedirs(directory)
|
| 153 |
-
with open(filepath, "w", encoding="utf-8") as f:
|
| 154 |
-
f.write(text)
|
| 155 |
-
log_operation("write", filename)
|
| 156 |
-
return "File written to successfully."
|
| 157 |
-
except Exception as e:
|
| 158 |
-
return f"Error: {str(e)}"
|
| 159 |
-
|
| 160 |
-
|
| 161 |
-
def append_to_file(filename: str, text: str, shouldLog: bool = True) -> str:
|
| 162 |
-
"""Append text to a file
|
| 163 |
-
|
| 164 |
-
Args:
|
| 165 |
-
filename (str): The name of the file to append to
|
| 166 |
-
text (str): The text to append to the file
|
| 167 |
-
|
| 168 |
-
Returns:
|
| 169 |
-
str: A message indicating success or failure
|
| 170 |
-
"""
|
| 171 |
-
try:
|
| 172 |
-
filepath = path_in_workspace(filename)
|
| 173 |
-
with open(filepath, "a") as f:
|
| 174 |
-
f.write(text)
|
| 175 |
-
|
| 176 |
-
if shouldLog:
|
| 177 |
-
log_operation("append", filename)
|
| 178 |
-
|
| 179 |
-
return "Text appended successfully."
|
| 180 |
-
except Exception as e:
|
| 181 |
-
return f"Error: {str(e)}"
|
| 182 |
-
|
| 183 |
-
|
| 184 |
-
def delete_file(filename: str) -> str:
|
| 185 |
-
"""Delete a file
|
| 186 |
-
|
| 187 |
-
Args:
|
| 188 |
-
filename (str): The name of the file to delete
|
| 189 |
-
|
| 190 |
-
Returns:
|
| 191 |
-
str: A message indicating success or failure
|
| 192 |
-
"""
|
| 193 |
-
if check_duplicate_operation("delete", filename):
|
| 194 |
-
return "Error: File has already been deleted."
|
| 195 |
-
try:
|
| 196 |
-
filepath = path_in_workspace(filename)
|
| 197 |
-
os.remove(filepath)
|
| 198 |
-
log_operation("delete", filename)
|
| 199 |
-
return "File deleted successfully."
|
| 200 |
-
except Exception as e:
|
| 201 |
-
return f"Error: {str(e)}"
|
| 202 |
-
|
| 203 |
-
|
| 204 |
-
def search_files(directory: str) -> list[str]:
|
| 205 |
-
"""Search for files in a directory
|
| 206 |
-
|
| 207 |
-
Args:
|
| 208 |
-
directory (str): The directory to search in
|
| 209 |
-
|
| 210 |
-
Returns:
|
| 211 |
-
list[str]: A list of files found in the directory
|
| 212 |
-
"""
|
| 213 |
-
found_files = []
|
| 214 |
-
|
| 215 |
-
if directory in {"", "/"}:
|
| 216 |
-
search_directory = WORKSPACE_PATH
|
| 217 |
-
else:
|
| 218 |
-
search_directory = path_in_workspace(directory)
|
| 219 |
-
|
| 220 |
-
for root, _, files in os.walk(search_directory):
|
| 221 |
-
for file in files:
|
| 222 |
-
if file.startswith("."):
|
| 223 |
-
continue
|
| 224 |
-
relative_path = os.path.relpath(os.path.join(root, file), WORKSPACE_PATH)
|
| 225 |
-
found_files.append(relative_path)
|
| 226 |
-
|
| 227 |
-
return found_files
|
| 228 |
-
|
| 229 |
-
|
| 230 |
-
def download_file(url, filename):
|
| 231 |
-
"""Downloads a file
|
| 232 |
-
Args:
|
| 233 |
-
url (str): URL of the file to download
|
| 234 |
-
filename (str): Filename to save the file as
|
| 235 |
-
"""
|
| 236 |
-
safe_filename = path_in_workspace(filename)
|
| 237 |
-
try:
|
| 238 |
-
message = f"{Fore.YELLOW}Downloading file from {Back.LIGHTBLUE_EX}{url}{Back.RESET}{Fore.RESET}"
|
| 239 |
-
with Spinner(message) as spinner:
|
| 240 |
-
session = requests.Session()
|
| 241 |
-
retry = Retry(total=3, backoff_factor=1, status_forcelist=[502, 503, 504])
|
| 242 |
-
adapter = HTTPAdapter(max_retries=retry)
|
| 243 |
-
session.mount("http://", adapter)
|
| 244 |
-
session.mount("https://", adapter)
|
| 245 |
-
|
| 246 |
-
total_size = 0
|
| 247 |
-
downloaded_size = 0
|
| 248 |
-
|
| 249 |
-
with session.get(url, allow_redirects=True, stream=True) as r:
|
| 250 |
-
r.raise_for_status()
|
| 251 |
-
total_size = int(r.headers.get("Content-Length", 0))
|
| 252 |
-
downloaded_size = 0
|
| 253 |
-
|
| 254 |
-
with open(safe_filename, "wb") as f:
|
| 255 |
-
for chunk in r.iter_content(chunk_size=8192):
|
| 256 |
-
f.write(chunk)
|
| 257 |
-
downloaded_size += len(chunk)
|
| 258 |
-
|
| 259 |
-
# Update the progress message
|
| 260 |
-
progress = f"{readable_file_size(downloaded_size)} / {readable_file_size(total_size)}"
|
| 261 |
-
spinner.update_message(f"{message} {progress}")
|
| 262 |
-
|
| 263 |
-
return f'Successfully downloaded and locally stored file: "{filename}"! (Size: {readable_file_size(total_size)})'
|
| 264 |
-
except requests.HTTPError as e:
|
| 265 |
-
return f"Got an HTTP Error whilst trying to download file: {e}"
|
| 266 |
-
except Exception as e:
|
| 267 |
-
return "Error: " + str(e)
|
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|
spaces/CofAI/chat.b4/client/css/message-input.css
DELETED
|
@@ -1,27 +0,0 @@
|
|
| 1 |
-
#message-input {
|
| 2 |
-
margin-right: 30px;
|
| 3 |
-
height: 64px;
|
| 4 |
-
}
|
| 5 |
-
|
| 6 |
-
#message-input::-webkit-scrollbar {
|
| 7 |
-
width: 5px;
|
| 8 |
-
}
|
| 9 |
-
|
| 10 |
-
#message-input::-webkit-scrollbar-track {
|
| 11 |
-
background: #f1f1f1;
|
| 12 |
-
}
|
| 13 |
-
|
| 14 |
-
#message-input::-webkit-scrollbar-thumb {
|
| 15 |
-
background: #c7a2ff;
|
| 16 |
-
}
|
| 17 |
-
|
| 18 |
-
#message-input::-webkit-scrollbar-thumb:hover {
|
| 19 |
-
background: #8b3dff;
|
| 20 |
-
}
|
| 21 |
-
|
| 22 |
-
@media screen and (max-width: 360px) {
|
| 23 |
-
#message-input {
|
| 24 |
-
margin: 0;
|
| 25 |
-
}
|
| 26 |
-
}
|
| 27 |
-
|
|
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|
|
spaces/CofAI/chat.b4/g4f/Provider/Providers/helpers/gpt4love.py
DELETED
|
@@ -1,48 +0,0 @@
|
|
| 1 |
-
import json
|
| 2 |
-
import sys
|
| 3 |
-
from re import findall
|
| 4 |
-
from curl_cffi import requests
|
| 5 |
-
|
| 6 |
-
config = json.loads(sys.argv[1])
|
| 7 |
-
prompt = config['messages'][-1]['content']
|
| 8 |
-
|
| 9 |
-
headers = {
|
| 10 |
-
'authority': 'api.gptplus.one',
|
| 11 |
-
'accept': 'application/json, text/plain, */*',
|
| 12 |
-
'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',
|
| 13 |
-
'content-type': 'application/octet-stream',
|
| 14 |
-
'origin': 'https://ai.gptforlove.com/',
|
| 15 |
-
'referer': 'https://ai.gptforlove.com/',
|
| 16 |
-
'sec-ch-ua': '"Google Chrome";v="113", "Chromium";v="113", "Not-A.Brand";v="24"',
|
| 17 |
-
'sec-ch-ua-mobile': '?0',
|
| 18 |
-
'sec-ch-ua-platform': '"macOS"',
|
| 19 |
-
'sec-fetch-dest': 'empty',
|
| 20 |
-
'sec-fetch-mode': 'cors',
|
| 21 |
-
'sec-fetch-site': 'cross-site',
|
| 22 |
-
'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36',
|
| 23 |
-
}
|
| 24 |
-
|
| 25 |
-
json_data = {
|
| 26 |
-
'prompt': prompt,
|
| 27 |
-
'options': {}
|
| 28 |
-
}
|
| 29 |
-
|
| 30 |
-
def format(chunk):
|
| 31 |
-
try:
|
| 32 |
-
completion_chunk = findall(r'content":"(.*)"},"fin', chunk.decode())[0]
|
| 33 |
-
print(completion_chunk, flush=True, end='')
|
| 34 |
-
|
| 35 |
-
except Exception as e:
|
| 36 |
-
print(f'[ERROR] an error occured, retrying... | [[{chunk.decode()}]]', flush=True)
|
| 37 |
-
return
|
| 38 |
-
|
| 39 |
-
while True:
|
| 40 |
-
try:
|
| 41 |
-
response = requests.post('https://api.gptplus.one/api/chat-process',
|
| 42 |
-
headers=headers, json=json_data, content_callback=format, impersonate='chrome110')
|
| 43 |
-
|
| 44 |
-
exit(0)
|
| 45 |
-
|
| 46 |
-
except Exception as e:
|
| 47 |
-
print('[ERROR] an error occured, retrying... |', e, flush=True)
|
| 48 |
-
continue
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
spaces/Cran-May/SEA-Streamlit/README.md
DELETED
|
@@ -1,14 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: 兮辞·析辞-常明
|
| 3 |
-
emoji: 💻
|
| 4 |
-
colorFrom: indigo
|
| 5 |
-
colorTo: pink
|
| 6 |
-
sdk: streamlit
|
| 7 |
-
sdk_version: 1.27.2
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: true
|
| 10 |
-
models:
|
| 11 |
-
- Cran-May/OpenSLIDE
|
| 12 |
-
---
|
| 13 |
-
|
| 14 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/charset_normalizer/version.py
DELETED
|
@@ -1,6 +0,0 @@
|
|
| 1 |
-
"""
|
| 2 |
-
Expose version
|
| 3 |
-
"""
|
| 4 |
-
|
| 5 |
-
__version__ = "3.2.0"
|
| 6 |
-
VERSION = __version__.split(".")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/svgLib/path/__init__.py
DELETED
|
@@ -1,61 +0,0 @@
|
|
| 1 |
-
from fontTools.pens.transformPen import TransformPen
|
| 2 |
-
from fontTools.misc import etree
|
| 3 |
-
from fontTools.misc.textTools import tostr
|
| 4 |
-
from .parser import parse_path
|
| 5 |
-
from .shapes import PathBuilder
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
__all__ = [tostr(s) for s in ("SVGPath", "parse_path")]
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
class SVGPath(object):
|
| 12 |
-
"""Parse SVG ``path`` elements from a file or string, and draw them
|
| 13 |
-
onto a glyph object that supports the FontTools Pen protocol.
|
| 14 |
-
|
| 15 |
-
For example, reading from an SVG file and drawing to a Defcon Glyph:
|
| 16 |
-
|
| 17 |
-
import defcon
|
| 18 |
-
glyph = defcon.Glyph()
|
| 19 |
-
pen = glyph.getPen()
|
| 20 |
-
svg = SVGPath("path/to/a.svg")
|
| 21 |
-
svg.draw(pen)
|
| 22 |
-
|
| 23 |
-
Or reading from a string containing SVG data, using the alternative
|
| 24 |
-
'fromstring' (a class method):
|
| 25 |
-
|
| 26 |
-
data = '<?xml version="1.0" ...'
|
| 27 |
-
svg = SVGPath.fromstring(data)
|
| 28 |
-
svg.draw(pen)
|
| 29 |
-
|
| 30 |
-
Both constructors can optionally take a 'transform' matrix (6-float
|
| 31 |
-
tuple, or a FontTools Transform object) to modify the draw output.
|
| 32 |
-
"""
|
| 33 |
-
|
| 34 |
-
def __init__(self, filename=None, transform=None):
|
| 35 |
-
if filename is None:
|
| 36 |
-
self.root = etree.ElementTree()
|
| 37 |
-
else:
|
| 38 |
-
tree = etree.parse(filename)
|
| 39 |
-
self.root = tree.getroot()
|
| 40 |
-
self.transform = transform
|
| 41 |
-
|
| 42 |
-
@classmethod
|
| 43 |
-
def fromstring(cls, data, transform=None):
|
| 44 |
-
self = cls(transform=transform)
|
| 45 |
-
self.root = etree.fromstring(data)
|
| 46 |
-
return self
|
| 47 |
-
|
| 48 |
-
def draw(self, pen):
|
| 49 |
-
if self.transform:
|
| 50 |
-
pen = TransformPen(pen, self.transform)
|
| 51 |
-
pb = PathBuilder()
|
| 52 |
-
# xpath | doesn't seem to reliable work so just walk it
|
| 53 |
-
for el in self.root.iter():
|
| 54 |
-
pb.add_path_from_element(el)
|
| 55 |
-
original_pen = pen
|
| 56 |
-
for path, transform in zip(pb.paths, pb.transforms):
|
| 57 |
-
if transform:
|
| 58 |
-
pen = TransformPen(original_pen, transform)
|
| 59 |
-
else:
|
| 60 |
-
pen = original_pen
|
| 61 |
-
parse_path(path, pen)
|
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spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/__main__.py
DELETED
|
@@ -1,100 +0,0 @@
|
|
| 1 |
-
import sys
|
| 2 |
-
from fontTools.ttLib import TTLibError, TTLibFileIsCollectionError
|
| 3 |
-
from fontTools.ttLib.ttFont import *
|
| 4 |
-
from fontTools.ttLib.ttCollection import TTCollection
|
| 5 |
-
|
| 6 |
-
|
| 7 |
-
def main(args=None):
|
| 8 |
-
"""Open/save fonts with TTFont() or TTCollection()
|
| 9 |
-
|
| 10 |
-
./fonttools ttLib [-oFILE] [-yNUMBER] files...
|
| 11 |
-
|
| 12 |
-
If multiple files are given on the command-line,
|
| 13 |
-
they are each opened (as a font or collection),
|
| 14 |
-
and added to the font list.
|
| 15 |
-
|
| 16 |
-
If -o (output-file) argument is given, the font
|
| 17 |
-
list is then saved to the output file, either as
|
| 18 |
-
a single font, if there is only one font, or as
|
| 19 |
-
a collection otherwise.
|
| 20 |
-
|
| 21 |
-
If -y (font-number) argument is given, only the
|
| 22 |
-
specified font from collections is opened.
|
| 23 |
-
|
| 24 |
-
The above allow extracting a single font from a
|
| 25 |
-
collection, or combining multiple fonts into a
|
| 26 |
-
collection.
|
| 27 |
-
|
| 28 |
-
If --lazy or --no-lazy are give, those are passed
|
| 29 |
-
to the TTFont() or TTCollection() constructors.
|
| 30 |
-
"""
|
| 31 |
-
from fontTools import configLogger
|
| 32 |
-
|
| 33 |
-
if args is None:
|
| 34 |
-
args = sys.argv[1:]
|
| 35 |
-
|
| 36 |
-
import argparse
|
| 37 |
-
|
| 38 |
-
parser = argparse.ArgumentParser(
|
| 39 |
-
"fonttools ttLib",
|
| 40 |
-
description="Open/save fonts with TTFont() or TTCollection()",
|
| 41 |
-
epilog="""
|
| 42 |
-
If multiple files are given on the command-line,
|
| 43 |
-
they are each opened (as a font or collection),
|
| 44 |
-
and added to the font list.
|
| 45 |
-
|
| 46 |
-
The above, when combined with -o / --output,
|
| 47 |
-
allows for extracting a single font from a
|
| 48 |
-
collection, or combining multiple fonts into a
|
| 49 |
-
collection.
|
| 50 |
-
""",
|
| 51 |
-
)
|
| 52 |
-
parser.add_argument("font", metavar="font", nargs="*", help="Font file.")
|
| 53 |
-
parser.add_argument(
|
| 54 |
-
"-o", "--output", metavar="FILE", default=None, help="Output file."
|
| 55 |
-
)
|
| 56 |
-
parser.add_argument(
|
| 57 |
-
"-y", metavar="NUMBER", default=-1, help="Font number to load from collections."
|
| 58 |
-
)
|
| 59 |
-
parser.add_argument(
|
| 60 |
-
"--lazy", action="store_true", default=None, help="Load fonts lazily."
|
| 61 |
-
)
|
| 62 |
-
parser.add_argument(
|
| 63 |
-
"--no-lazy", dest="lazy", action="store_false", help="Load fonts immediately."
|
| 64 |
-
)
|
| 65 |
-
parser.add_argument(
|
| 66 |
-
"--flavor",
|
| 67 |
-
dest="flavor",
|
| 68 |
-
default=None,
|
| 69 |
-
help="Flavor of output font. 'woff' or 'woff2'.",
|
| 70 |
-
)
|
| 71 |
-
options = parser.parse_args(args)
|
| 72 |
-
|
| 73 |
-
fontNumber = int(options.y) if options.y is not None else None
|
| 74 |
-
outFile = options.output
|
| 75 |
-
lazy = options.lazy
|
| 76 |
-
flavor = options.flavor
|
| 77 |
-
|
| 78 |
-
fonts = []
|
| 79 |
-
for f in options.font:
|
| 80 |
-
try:
|
| 81 |
-
font = TTFont(f, fontNumber=fontNumber, lazy=lazy)
|
| 82 |
-
fonts.append(font)
|
| 83 |
-
except TTLibFileIsCollectionError:
|
| 84 |
-
collection = TTCollection(f, lazy=lazy)
|
| 85 |
-
fonts.extend(collection.fonts)
|
| 86 |
-
|
| 87 |
-
if outFile is not None:
|
| 88 |
-
if len(fonts) == 1:
|
| 89 |
-
fonts[0].flavor = flavor
|
| 90 |
-
fonts[0].save(outFile)
|
| 91 |
-
else:
|
| 92 |
-
if flavor is not None:
|
| 93 |
-
raise TTLibError("Cannot set flavor for collections.")
|
| 94 |
-
collection = TTCollection()
|
| 95 |
-
collection.fonts = fonts
|
| 96 |
-
collection.save(outFile)
|
| 97 |
-
|
| 98 |
-
|
| 99 |
-
if __name__ == "__main__":
|
| 100 |
-
sys.exit(main())
|
|
|
|
|
|
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|
|
|
|
|
|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/httpx/_content.py
DELETED
|
@@ -1,238 +0,0 @@
|
|
| 1 |
-
import inspect
|
| 2 |
-
import warnings
|
| 3 |
-
from json import dumps as json_dumps
|
| 4 |
-
from typing import (
|
| 5 |
-
Any,
|
| 6 |
-
AsyncIterable,
|
| 7 |
-
AsyncIterator,
|
| 8 |
-
Dict,
|
| 9 |
-
Iterable,
|
| 10 |
-
Iterator,
|
| 11 |
-
Mapping,
|
| 12 |
-
Optional,
|
| 13 |
-
Tuple,
|
| 14 |
-
Union,
|
| 15 |
-
)
|
| 16 |
-
from urllib.parse import urlencode
|
| 17 |
-
|
| 18 |
-
from ._exceptions import StreamClosed, StreamConsumed
|
| 19 |
-
from ._multipart import MultipartStream
|
| 20 |
-
from ._types import (
|
| 21 |
-
AsyncByteStream,
|
| 22 |
-
RequestContent,
|
| 23 |
-
RequestData,
|
| 24 |
-
RequestFiles,
|
| 25 |
-
ResponseContent,
|
| 26 |
-
SyncByteStream,
|
| 27 |
-
)
|
| 28 |
-
from ._utils import peek_filelike_length, primitive_value_to_str
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
class ByteStream(AsyncByteStream, SyncByteStream):
|
| 32 |
-
def __init__(self, stream: bytes) -> None:
|
| 33 |
-
self._stream = stream
|
| 34 |
-
|
| 35 |
-
def __iter__(self) -> Iterator[bytes]:
|
| 36 |
-
yield self._stream
|
| 37 |
-
|
| 38 |
-
async def __aiter__(self) -> AsyncIterator[bytes]:
|
| 39 |
-
yield self._stream
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
class IteratorByteStream(SyncByteStream):
|
| 43 |
-
CHUNK_SIZE = 65_536
|
| 44 |
-
|
| 45 |
-
def __init__(self, stream: Iterable[bytes]):
|
| 46 |
-
self._stream = stream
|
| 47 |
-
self._is_stream_consumed = False
|
| 48 |
-
self._is_generator = inspect.isgenerator(stream)
|
| 49 |
-
|
| 50 |
-
def __iter__(self) -> Iterator[bytes]:
|
| 51 |
-
if self._is_stream_consumed and self._is_generator:
|
| 52 |
-
raise StreamConsumed()
|
| 53 |
-
|
| 54 |
-
self._is_stream_consumed = True
|
| 55 |
-
if hasattr(self._stream, "read"):
|
| 56 |
-
# File-like interfaces should use 'read' directly.
|
| 57 |
-
chunk = self._stream.read(self.CHUNK_SIZE)
|
| 58 |
-
while chunk:
|
| 59 |
-
yield chunk
|
| 60 |
-
chunk = self._stream.read(self.CHUNK_SIZE)
|
| 61 |
-
else:
|
| 62 |
-
# Otherwise iterate.
|
| 63 |
-
for part in self._stream:
|
| 64 |
-
yield part
|
| 65 |
-
|
| 66 |
-
|
| 67 |
-
class AsyncIteratorByteStream(AsyncByteStream):
|
| 68 |
-
CHUNK_SIZE = 65_536
|
| 69 |
-
|
| 70 |
-
def __init__(self, stream: AsyncIterable[bytes]):
|
| 71 |
-
self._stream = stream
|
| 72 |
-
self._is_stream_consumed = False
|
| 73 |
-
self._is_generator = inspect.isasyncgen(stream)
|
| 74 |
-
|
| 75 |
-
async def __aiter__(self) -> AsyncIterator[bytes]:
|
| 76 |
-
if self._is_stream_consumed and self._is_generator:
|
| 77 |
-
raise StreamConsumed()
|
| 78 |
-
|
| 79 |
-
self._is_stream_consumed = True
|
| 80 |
-
if hasattr(self._stream, "aread"):
|
| 81 |
-
# File-like interfaces should use 'aread' directly.
|
| 82 |
-
chunk = await self._stream.aread(self.CHUNK_SIZE)
|
| 83 |
-
while chunk:
|
| 84 |
-
yield chunk
|
| 85 |
-
chunk = await self._stream.aread(self.CHUNK_SIZE)
|
| 86 |
-
else:
|
| 87 |
-
# Otherwise iterate.
|
| 88 |
-
async for part in self._stream:
|
| 89 |
-
yield part
|
| 90 |
-
|
| 91 |
-
|
| 92 |
-
class UnattachedStream(AsyncByteStream, SyncByteStream):
|
| 93 |
-
"""
|
| 94 |
-
If a request or response is serialized using pickle, then it is no longer
|
| 95 |
-
attached to a stream for I/O purposes. Any stream operations should result
|
| 96 |
-
in `httpx.StreamClosed`.
|
| 97 |
-
"""
|
| 98 |
-
|
| 99 |
-
def __iter__(self) -> Iterator[bytes]:
|
| 100 |
-
raise StreamClosed()
|
| 101 |
-
|
| 102 |
-
async def __aiter__(self) -> AsyncIterator[bytes]:
|
| 103 |
-
raise StreamClosed()
|
| 104 |
-
yield b"" # pragma: no cover
|
| 105 |
-
|
| 106 |
-
|
| 107 |
-
def encode_content(
|
| 108 |
-
content: Union[str, bytes, Iterable[bytes], AsyncIterable[bytes]]
|
| 109 |
-
) -> Tuple[Dict[str, str], Union[SyncByteStream, AsyncByteStream]]:
|
| 110 |
-
if isinstance(content, (bytes, str)):
|
| 111 |
-
body = content.encode("utf-8") if isinstance(content, str) else content
|
| 112 |
-
content_length = len(body)
|
| 113 |
-
headers = {"Content-Length": str(content_length)} if body else {}
|
| 114 |
-
return headers, ByteStream(body)
|
| 115 |
-
|
| 116 |
-
elif isinstance(content, Iterable) and not isinstance(content, dict):
|
| 117 |
-
# `not isinstance(content, dict)` is a bit oddly specific, but it
|
| 118 |
-
# catches a case that's easy for users to make in error, and would
|
| 119 |
-
# otherwise pass through here, like any other bytes-iterable,
|
| 120 |
-
# because `dict` happens to be iterable. See issue #2491.
|
| 121 |
-
content_length_or_none = peek_filelike_length(content)
|
| 122 |
-
|
| 123 |
-
if content_length_or_none is None:
|
| 124 |
-
headers = {"Transfer-Encoding": "chunked"}
|
| 125 |
-
else:
|
| 126 |
-
headers = {"Content-Length": str(content_length_or_none)}
|
| 127 |
-
return headers, IteratorByteStream(content) # type: ignore
|
| 128 |
-
|
| 129 |
-
elif isinstance(content, AsyncIterable):
|
| 130 |
-
headers = {"Transfer-Encoding": "chunked"}
|
| 131 |
-
return headers, AsyncIteratorByteStream(content)
|
| 132 |
-
|
| 133 |
-
raise TypeError(f"Unexpected type for 'content', {type(content)!r}")
|
| 134 |
-
|
| 135 |
-
|
| 136 |
-
def encode_urlencoded_data(
|
| 137 |
-
data: RequestData,
|
| 138 |
-
) -> Tuple[Dict[str, str], ByteStream]:
|
| 139 |
-
plain_data = []
|
| 140 |
-
for key, value in data.items():
|
| 141 |
-
if isinstance(value, (list, tuple)):
|
| 142 |
-
plain_data.extend([(key, primitive_value_to_str(item)) for item in value])
|
| 143 |
-
else:
|
| 144 |
-
plain_data.append((key, primitive_value_to_str(value)))
|
| 145 |
-
body = urlencode(plain_data, doseq=True).encode("utf-8")
|
| 146 |
-
content_length = str(len(body))
|
| 147 |
-
content_type = "application/x-www-form-urlencoded"
|
| 148 |
-
headers = {"Content-Length": content_length, "Content-Type": content_type}
|
| 149 |
-
return headers, ByteStream(body)
|
| 150 |
-
|
| 151 |
-
|
| 152 |
-
def encode_multipart_data(
|
| 153 |
-
data: RequestData, files: RequestFiles, boundary: Optional[bytes]
|
| 154 |
-
) -> Tuple[Dict[str, str], MultipartStream]:
|
| 155 |
-
multipart = MultipartStream(data=data, files=files, boundary=boundary)
|
| 156 |
-
headers = multipart.get_headers()
|
| 157 |
-
return headers, multipart
|
| 158 |
-
|
| 159 |
-
|
| 160 |
-
def encode_text(text: str) -> Tuple[Dict[str, str], ByteStream]:
|
| 161 |
-
body = text.encode("utf-8")
|
| 162 |
-
content_length = str(len(body))
|
| 163 |
-
content_type = "text/plain; charset=utf-8"
|
| 164 |
-
headers = {"Content-Length": content_length, "Content-Type": content_type}
|
| 165 |
-
return headers, ByteStream(body)
|
| 166 |
-
|
| 167 |
-
|
| 168 |
-
def encode_html(html: str) -> Tuple[Dict[str, str], ByteStream]:
|
| 169 |
-
body = html.encode("utf-8")
|
| 170 |
-
content_length = str(len(body))
|
| 171 |
-
content_type = "text/html; charset=utf-8"
|
| 172 |
-
headers = {"Content-Length": content_length, "Content-Type": content_type}
|
| 173 |
-
return headers, ByteStream(body)
|
| 174 |
-
|
| 175 |
-
|
| 176 |
-
def encode_json(json: Any) -> Tuple[Dict[str, str], ByteStream]:
|
| 177 |
-
body = json_dumps(json).encode("utf-8")
|
| 178 |
-
content_length = str(len(body))
|
| 179 |
-
content_type = "application/json"
|
| 180 |
-
headers = {"Content-Length": content_length, "Content-Type": content_type}
|
| 181 |
-
return headers, ByteStream(body)
|
| 182 |
-
|
| 183 |
-
|
| 184 |
-
def encode_request(
|
| 185 |
-
content: Optional[RequestContent] = None,
|
| 186 |
-
data: Optional[RequestData] = None,
|
| 187 |
-
files: Optional[RequestFiles] = None,
|
| 188 |
-
json: Optional[Any] = None,
|
| 189 |
-
boundary: Optional[bytes] = None,
|
| 190 |
-
) -> Tuple[Dict[str, str], Union[SyncByteStream, AsyncByteStream]]:
|
| 191 |
-
"""
|
| 192 |
-
Handles encoding the given `content`, `data`, `files`, and `json`,
|
| 193 |
-
returning a two-tuple of (<headers>, <stream>).
|
| 194 |
-
"""
|
| 195 |
-
if data is not None and not isinstance(data, Mapping):
|
| 196 |
-
# We prefer to separate `content=<bytes|str|byte iterator|bytes aiterator>`
|
| 197 |
-
# for raw request content, and `data=<form data>` for url encoded or
|
| 198 |
-
# multipart form content.
|
| 199 |
-
#
|
| 200 |
-
# However for compat with requests, we *do* still support
|
| 201 |
-
# `data=<bytes...>` usages. We deal with that case here, treating it
|
| 202 |
-
# as if `content=<...>` had been supplied instead.
|
| 203 |
-
message = "Use 'content=<...>' to upload raw bytes/text content."
|
| 204 |
-
warnings.warn(message, DeprecationWarning)
|
| 205 |
-
return encode_content(data)
|
| 206 |
-
|
| 207 |
-
if content is not None:
|
| 208 |
-
return encode_content(content)
|
| 209 |
-
elif files:
|
| 210 |
-
return encode_multipart_data(data or {}, files, boundary)
|
| 211 |
-
elif data:
|
| 212 |
-
return encode_urlencoded_data(data)
|
| 213 |
-
elif json is not None:
|
| 214 |
-
return encode_json(json)
|
| 215 |
-
|
| 216 |
-
return {}, ByteStream(b"")
|
| 217 |
-
|
| 218 |
-
|
| 219 |
-
def encode_response(
|
| 220 |
-
content: Optional[ResponseContent] = None,
|
| 221 |
-
text: Optional[str] = None,
|
| 222 |
-
html: Optional[str] = None,
|
| 223 |
-
json: Optional[Any] = None,
|
| 224 |
-
) -> Tuple[Dict[str, str], Union[SyncByteStream, AsyncByteStream]]:
|
| 225 |
-
"""
|
| 226 |
-
Handles encoding the given `content`, returning a two-tuple of
|
| 227 |
-
(<headers>, <stream>).
|
| 228 |
-
"""
|
| 229 |
-
if content is not None:
|
| 230 |
-
return encode_content(content)
|
| 231 |
-
elif text is not None:
|
| 232 |
-
return encode_text(text)
|
| 233 |
-
elif html is not None:
|
| 234 |
-
return encode_html(html)
|
| 235 |
-
elif json is not None:
|
| 236 |
-
return encode_json(json)
|
| 237 |
-
|
| 238 |
-
return {}, ByteStream(b"")
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