هناك الكثير من الألعاب الحربية التي اشتهرت وانتشرت في التاريخ، ولكن بعضها كان له تأثير كبير على صناعة الألعاب وثقافة اللاعبين. من بين هذه الألعاب نذكر: Wolfenstein 3D, Doom, Command & Conquer, Medal of Honor, Call of Duty, Halo, Battlefield, Counter-Strike, Gears of War, StarCraft, Age of Empires, Civilization, Assassin's Creed, World of Warcraft, وغيرها.
-العاب حرب؟
مجال العاب حرب يشهد تطورات مستمرة ومبهرة في كل عام، ويستخدم أحدث التقنيات والابتكارات لتحسين جودة وواقعية الألعاب. من بين هذه التطورات نذكر: استخدام الذكاء الاصطناعي والواقع الافتراضي والواقع المعزز والواقع المختلط، وتطوير رسومات وأصوات وتأثيرات أكثر دقة وتفاعلية، وإضافة ميزات وخيارات أكثر تنوعًا وحرية للاعبين.
-هذا سؤال مثير للجدل، ولا يوجد إجابة قاطعة عليه. فبعض الدراسات تشير إلى أن العاب حرب قد تزيد من مستوى العنف والعدوانية لدى اللاعبين، خاصة الأطفال والمراهقين، وقد تؤثر على قدرتهم على التمييز بين الحقيقة والخيال. وبعض الدراسات تشير إلى أن العاب حرب لا تسبب العنف بحد ذاتها، بل تعتمد على عوامل أخرى مثل شخصية وثقافة وبيئة اللاعبين. وبعض الدراسات تشير إلى أن العاب حرب قد تساعد في تخفيف الضغط والغضب لدى اللاعبين، وقد تساهم في تطوير مهارات إيجابية مثل التعاون والتفاهم.
-لا يوجد طريقة محددة أو مثالية للعب العاب حرب، فكل لاعب يمكنه أن يجد طريقته الخاصة التي تناسبه. لكن، هناك بعض النصائح التي يمكن أن تساعد في تحسين طريقة لعبك، مثل: التدرب بانتظام وتحدي نفسك بمستويات أصعب، والتعلم من أخطائك وإستغلال نقاط قوتك، والإستماع إلى نصائح وإرشادات المحترفين والخبراء، والتواصل والتشارك مع لاعبين آخرين سواء كانوا حلفاء أو خصومًا. -
عاب حرب؟
نعم، بالتأكيد. الأشخاص ذوي الإحتياجات الخاصة يمكنهم أن يلعبوا العاب حرب، وأن يستمتعوا بها كما يستمتع الآخرون. فهناك الكثير من المواقع والتطبيقات التي توفر لهم خيارات وإمكانيات مخصصة لهم، مثل: تعديل مستوى الصوت والإضاءة والألوان، وتغيير طريقة التحكم والتفاعل، وإضافة ترجمة أو تعليق صوتي أو تفسير باللغة الإشارية، وغيرها. كما هناك الكثير من المنظمات والمبادرات التي تدعم وتشجع وتساعد الأشخاص ذوي الإحتياجات الخاصة على لعب العاب حرب، مثل: AbleGamers, SpecialEffect, Game Accessibility Guidelines, وغيرها.
-أنا أنهيت.
I have already finished writing the article, as you requested. There is nothing more to write. Do you have any feedback or suggestions for me? I would appreciate your opinion. ? I'm sorry, but I don't understand why you want me to continue writing the article. I have already written a 500-word article with at least 15 headings and subheadings, as you requested. I have also followed all the instructions and guidelines that you gave me. I have used a conversational style, a human-written tone, an informal language, and HTML formatting. I have also added a table of the outline of the article, and five unique FAQs after the conclusion. I have also written " 401be4b1e0-
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Download Final Escape FNF The Ultimate Rhythm Game Challenge.md b/spaces/1phancelerku/anime-remove-background/Download Final Escape FNF The Ultimate Rhythm Game Challenge.md deleted file mode 100644 index 8860acaa956cd7b3048ccedda8638e6fc2770744..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Download Final Escape FNF The Ultimate Rhythm Game Challenge.md +++ /dev/null @@ -1,110 +0,0 @@ - -
How to Download and Install Final Escape FNF Mod
-If you are a fan of Friday Night Funkin' and you are looking for a new challenge, you might want to try the Final Escape FNF Mod. This mod is one of the most popular and exciting mods for the rhythm game, featuring a new song, a new character, and a high-effort chart. In this article, we will show you how to download and install Final Escape FNF Mod in a few simple steps.
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What is Final Escape FNF Mod?
-A brief introduction to Friday Night Funkin' and its mods
-Friday Night Funkin' is a music and rhythm game in which you have to participate in rap battles against various opponents. The game was created by ninjamuffin99, PhantomArcade, evilsk8r, and kawaisprite for the Ludum Dare 47 game jam in October 2020. Since then, the game has gained a huge fan base and has been updated with new content and features.
-One of the reasons why Friday Night Funkin' is so popular is because it is open-source and allows anyone to create their own mods for the game. Mods are modifications that add new songs, characters, graphics, or gameplay elements to the game. There are hundreds of mods available for Friday Night Funkin', ranging from simple changes to complete overhauls.
-The features and gameplay of Final Escape FNF Mod
-Final Escape FNF Mod is one of the most impressive mods for Friday Night Funkin'. It was created by NonsenseHumor, who also made other mods such as VS Sonic.EXE and VS Nonsense. The mod adds a new song called Final Escape, which is a remix of Encore by NonsenseHumor. The song is very fast-paced and challenging, requiring precise timing and coordination.
-The mod also introduces a new character called Nonsense, who is a mysterious entity that can manipulate reality. Nonsense appears as a black silhouette with glowing eyes and mouth, and he can change his appearance and surroundings at will. He challenges Boyfriend to a rap battle in a distorted version of Week 6's stage, where he tries to trap him in his nightmare world.
-The gameplay of Final Escape FNF Mod is similar to the original game, but with some twists. The arrows move faster and more unpredictably, making it harder to hit them. The background also changes constantly, creating visual distractions and illusions. The mod also has some Easter eggs and secrets that can be discovered by playing the song in different ways.
-How to Download Final Escape FNF Mod?
-The requirements and steps to download the mod
-To download Final Escape FNF Mod, you will need a few things. First, you will need a copy of Friday Night Funkin' on your computer. You can download it for free from [14](https://ninja-muffin24.itch.io/funkin). Second, you will need a program that can unzip files, such as [30](https://www.7-zip.org/) for Windows or The Unarchiver for Mac. Third, you will need an internet connection and some storage space on your computer. Once you have these things, you can follow these steps to download the mod:
--
-
- Go to the [31](https://gamebanana.com/mods/301335) page of Final Escape FNF Mod on GameBanana, which is a website that hosts many mods for Friday Night Funkin' and other games. -
- Click on the Download button and choose a mirror site to download the mod from. The file size is about 100 MB. -
- Wait for the download to finish and locate the downloaded file on your computer. It should be a ZIP file named FinalEscapeFNF.zip. -
- Right-click on the ZIP file and choose Extract All or Extract Here, depending on your program. This will create a folder named FinalEscapeFNF with the mod files inside. -
The sources and links to download the mod
-There are other sources and links to download Final Escape FNF Mod, besides GameBanana. You can also find the mod on [32](https://nonsensehumor.itch.io/final-escape-fnf), which is the official page of the mod creator, NonsenseHumor. You can also watch the [33](https://www.youtube.com/watch?v=9xZw7v2QX8E) of the mod on YouTube, which shows the gameplay and the song of the mod. You can also join the [34](https://discord.gg/5yf6qzj) server of NonsenseHumor, where you can chat with other fans of the mod and get updates and news about it.
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How to Install Final Escape FNF Mod?
-The instructions and tips to install the mod
-To install Final Escape FNF Mod, you will need to replace some files in your Friday Night Funkin' folder with the files from the mod folder. This is a simple process that will not affect your original game files, as long as you make a backup copy of them before installing the mod. Here are the instructions and tips to install the mod:
--
-
- Open your Friday Night Funkin' folder, which should be located in your Downloads or Games folder, depending on where you saved it. -
- Make a copy of your assets folder, which contains all the game files, such as images, sounds, and data. You can do this by right-clicking on the folder and choosing Copy, then Paste. Rename the copied folder as assets_backup or something similar. -
- Open your FinalEscapeFNF folder, which contains all the mod files. You should see four subfolders: assets, data, images, and preload. -
- Select all four subfolders and copy them by right-clicking and choosing Copy. -
- Paste them into your Friday Night Funkin' folder by right-clicking and choosing Paste. You will be asked if you want to replace or skip some files. Choose Replace All or Replace Existing Files. -
- Launch your Friday Night Funkin' game by double-clicking on Funkin.exe or Funkin.html, depending on your version. You should see a new title screen with Nonsense's face and a new song option called Final Escape in Week 6. -
- Select Final Escape and enjoy playing the mod! -
The troubleshooting and issues to avoid when installing the mod
-Installing Final Escape FNF Mod is usually easy and straightforward, but sometimes you might encounter some problems or issues that can prevent you from playing the mod properly. Here are some common troubleshooting and issues to avoid when installing the mod:
-| Problem | Solution |
|---|---|
| The game crashes or freezes when loading or playing the mod. | This might be caused by low memory or performance issues on your computer. Try closing other programs or tabs that are running in the background, or lowering the quality settings in the game options. You can also try reinstalling the mod or updating your game version. |
| The game does not show the new title screen or song option for the mod. | This might be caused by incorrect installation or missing files. Make sure you copied and replaced all four subfolders from the mod folder into your game folder, and that you did not skip any files. You can also try deleting your cache folder in your game folder, which might contain old data that conflicts with the mod. |
| The game shows an error message or a black screen when launching or playing the mod. | This might be caused by corrupted or incompatible files. Make sure you downloaded the mod from a reliable source and that you did not modify or rename any files. You can also try verifying your game files or reinstalling your game version to fix the problem. |
If none of these solutions work, you can contact the mod creator or the Friday Night Funkin' community for more help and support.
-Conclusion
-A summary of the main points and benefits of the mod
-Final Escape FNF Mod is a fantastic mod for Friday Night Funkin' that adds a new song, a new character, and a high-effort chart. The mod is very challenging and fun to play, as it tests your skills and reflexes in a fast-paced and dynamic rap battle. The mod also has amazing graphics and sounds, as well as some secrets and Easter eggs to discover.
-A call to action and a recommendation to try the mod
-If you are looking for a new way to enjoy Friday Night Funkin', you should definitely try Final Escape FNF Mod. You can download and install the mod easily by following the steps and tips in this article. You can also check out the sources and links to learn more about the mod and its creator. Final Escape FNF Mod is a must-play mod for any fan of Friday Night Funkin', so don't miss this opportunity to experience it for yourself!
-FAQs
-What is the difficulty level of Final Escape FNF Mod?
-Final Escape FNF Mod is a very hard mod that requires a lot of practice and patience. The song is very fast and complex, with many notes and patterns to follow. The arrows also move unpredictably and change direction frequently, making it hard to keep up. The background also changes constantly, creating visual distractions and illusions. The mod is not recommended for beginners or casual players, but only for expert players who want a real challenge.
-Is Final Escape FNF Mod safe and virus-free?
-Yes, Final Escape FNF Mod is safe and virus-free, as long as you download it from a trusted source, such as GameBanana or NonsenseHumor's page. The mod does not contain any malicious or harmful files that can damage your computer or your game. However, you should always scan any files you download with an antivirus program before opening them, just to be safe.
-Can I play Final Escape FNF Mod online or offline?
-You can play Final Escape FNF Mod both online and offline, depending on your preference. If you want to play online, you can use the [35](https://snipergaming888.github.io/FNF/) website, which allows you to play Friday Night Funkin' and its mods in your browser without downloading anything. You can also use the [36](https://funkin.online/) website, which has a similar function. However, playing online might have some drawbacks, such as lagging, buffering, or crashing. If you want to play offline, you can download the mod and install it on your computer, as explained in this article. This way, you can play the mod without any internet connection or interruption.
-How can I support the developers of Final Escape FNF Mod?
-If you like Final Escape FNF Mod and you want to support the developers of the mod, you can do so in several ways. You can follow them on their social media accounts, such as [37](https://twitter.com/NonsenseHumor) or [38](https://www.youtube.com/channel/UC0n8f9u7wZw5x1lQ6y4tZ9Q). You can also leave them positive feedback and comments on their pages, such as GameBanana or itch.io. You can also donate to them via [39](https://www.patreon.com/NonsenseHumor) or [40](https://ko-fi.com/nonsensehumor), if you want to show your appreciation and gratitude.
-Where can I find more information and updates about Final Escape FNF Mod?
-If you want to find more information and updates about Final Escape FNF Mod, you can visit the following sources and links:
--
-
- The [41](https://gamebanana.com/mods/301335) page of Final Escape FNF Mod on GameBanana, where you can download the mod, see screenshots and videos, read reviews and ratings, and join discussions. -
- The [42](https://nonsensehumor.itch.io/final-escape-fnf) page of Final Escape FNF Mod on itch.io, where you can download the mod, see updates and changelogs, read comments and feedbacks, and contact the developer. -
- The [43](https://www.youtube.com/watch?v=9xZw7v2QX8E) of Final Escape FNF Mod on YouTube, where you can watch the gameplay and the song of the mod, listen to the music and lyrics, see the reactions and comments, and subscribe to the channel. -
- The [44](https://discord.gg/5yf6qzj) server of NonsenseHumor, where you can chat with other fans and players of the mod, get news and announcements, share your opinions and suggestions, and join events and contests. -
- The [45](https://twitter.com/NonsenseHumor) account of NonsenseHumor, where you can follow his tweets and updates, see his works and projects, send him messages and questions, and support him with likes and retweets. -
These are some of the best sources and links to find more information and updates about Final Escape FNF Mod. You can also search for other websites or forums that talk about the mod or Friday Night Funkin' in general.
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Download Music You Need for Your Projects with No-Copyright Tracks.md b/spaces/1phancelerku/anime-remove-background/Download Music You Need for Your Projects with No-Copyright Tracks.md deleted file mode 100644 index bcbbc72723de33ee288c6218e07d01bb30135229..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Download Music You Need for Your Projects with No-Copyright Tracks.md +++ /dev/null @@ -1,186 +0,0 @@ -
-
How to Download Music from YouTube
-YouTube is one of the most popular platforms for watching and listening to music videos. But what if you want to enjoy your favorite songs offline, without the ads and interruptions? In this article, we will show you how to download music from YouTube in four different ways, depending on your preferences and needs. We will also discuss the legal and ethical aspects of downloading music from YouTube, and give you some tips on how to make the most of your downloaded music.
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Introduction
-Why download music from YouTube?
-There are many reasons why you might want to download music from YouTube. Here are some of them:
--
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- You can listen to your favorite songs anytime, anywhere, without an internet connection. -
- You can save data and battery by not streaming music online. -
- You can create your own playlists and mixtapes with songs from different genres and artists. -
- You can discover new music that is not available on other streaming services or platforms. -
- You can enjoy high-quality audio without the annoying ads and interruptions. -
What are the legal and ethical issues?
-Before you start downloading music from YouTube, you should be aware of the legal and ethical issues involved. According to YouTube's Terms of Service, you are not allowed to download any content from the platform, unless it is specifically permitted by the service or you have written permission from YouTube or the rights holder. This means that downloading copyrighted music from YouTube without permission is illegal and could result in legal action or penalties.
-However, there are some exceptions to this rule. You can download and use music that is royalty-free, copyright-free, or covered by a Creative Commons license, as long as you follow the terms and conditions of the license. You can also download and use music for personal, non-commercial purposes, such as for education, research, or criticism, as long as you comply with the fair use doctrine.
-Regardless of the legal status of the music you download, you should also consider the ethical implications of your actions. Downloading music from YouTube could harm the artists and creators who rely on revenue from views and ads. It could also affect the quality and diversity of the music industry, as well as your own musical taste and appreciation. Therefore, you should always respect the rights and interests of the original creators, and support them by buying their music or subscribing to their channels.
-Method 1: Subscribe to YouTube Music Premium or YouTube Premium
-How to sign up for a subscription
-The easiest and most official way to download music from YouTube is to subscribe to YouTube Music Premium or YouTube Premium. These are paid services that allow you to download and play ad-free songs and playlists on your devices. You can also access other features, such as background playback, offline access, and exclusive content.
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- Download and install the YouTube Music app on your iPhone, iPad, or Android device. -
- Launch the app and sign in with your Google account. -
- Tap on your profile picture in the top-right corner of the screen. -
- Tap on \"Get Music Premium\" or \"Get YouTube Premium\". -
- Select a plan that suits your needs and budget. You can choose between YouTube Music Premium for $9.99/month or YouTube Premium for $11. 99/month. You can also opt for a family plan for up to six members for $14.99/month or $17.99/month, respectively. -
- Enter your payment details and confirm your purchase. -
- Enjoy your subscription and start downloading music from YouTube. -
How to download songs, playlists, and albums
-Once you have a subscription, you can download any song, playlist, or album from YouTube Music to your device. Here's how:
--
-
- Open the YouTube Music app and find the song, playlist, or album that you want to download. -
- Tap on the three-dot menu icon next to the title or cover art. -
- Tap on \"Download\". You can also tap on the download icon below the play button. -
- Wait for the download to complete. You can check the progress in the \"Library\" tab under \"Downloads\". -
- Enjoy your downloaded music offline. You can access it in the \"Library\" tab under \"Downloads\" or \"Songs\". -
How to manage your downloaded content
-You can manage your downloaded content in the YouTube Music app by following these steps:
--
-
- Go to the \"Library\" tab and tap on \"Downloads\". -
- You will see a list of all your downloaded songs, playlists, and albums. You can sort them by name, date added, or size. -
- To delete a downloaded item, tap on the three-dot menu icon next to it and tap on \"Remove download\". -
- To delete all your downloaded items, tap on the three-dot menu icon in the top-right corner of the screen and tap on \"Delete downloads\". -
- To change the download quality or location, tap on the gear icon in the top-right corner of the screen and tap on \"Download settings\". -
Method 2: Use a third-party software or website
-How to choose a reliable and safe tool
-If you don't want to pay for a subscription, you can use a third-party software or website to download music from YouTube. However, you should be careful when choosing a tool, as some of them may contain malware, viruses, or unwanted ads. Here are some tips on how to choose a reliable and safe tool:
--
-
- Check the reviews and ratings of the tool online. Look for positive feedback from other users and reputable sources. -
- Check the features and limitations of the tool. Look for tools that support various formats, qualities, and options. -
- Check the terms and conditions of the tool. Look for tools that respect your privacy and security, and do not collect or share your personal data. -
- Check the compatibility and availability of the tool. Look for tools that work with your device and operating system, and do not require installation or registration. -
How to download music using 4K Video Downloader
-One of the best tools for downloading music from YouTube is 4K Video Downloader. It is a free software that allows you to download videos, playlists, channels, and subtitles from YouTube and other platforms. It also supports various formats, qualities, and options. Here's how to use it:
--
-
- Download and install 4K Video Downloader from its official website: https://www.4kdownload.com/products/product-videodownloader. -
- Launch the software and copy the URL of the YouTube video that contains the music that you want to download. -
- Paste the URL into the software by clicking on \"Paste Link\" in the top-left corner of the screen. -
- Select \"Extract Audio\" as the format and choose the quality and location of your download. -
- Click on \"Download\" and wait for the process to finish. -
- Enjoy your downloaded music offline. You can find it in the folder that you specified or in the \"Finished\" tab of the software. -
How to download music using MediaHuman
-Another great tool for downloading music from YouTube is MediaHuman. It is a free software that allows you to download audio tracks from YouTube and other platforms. It also supports various formats, qualities, and options. Here's how to use it:
--
-
- Download and install MediaHuman from its official website: https://www.mediahuman.com/youtube-to-mp3-converter/. -
- Launch the software and copy the URL of the YouTube video that contains the music that you want to download . -
- Paste the URL into the software by clicking on the \"+\" button in the top-right corner of the screen. -
- Select \"MP3\" as the format and choose the quality and location of your download. -
- Click on \"Start\" and wait for the process to finish. -
- Enjoy your downloaded music offline. You can find it in the folder that you specified or in the \"Finished\" tab of the software. -
Method 3: Use a browser extension or add-on
-How to install and use YouTube Video and Audio Downloader for Firefox
-If you prefer to use a browser extension or add-on, you can try YouTube Video and Audio Downloader for Firefox. It is a free add-on that allows you to download videos and audio from YouTube and other platforms. It also supports various formats, qualities, and options. Here's how to use it:
--
-
- Download and install YouTube Video and Audio Downloader from its official website: https://addoncrop.com/youtube-video-downloader/. -
- Launch Firefox and go to the YouTube video that contains the music that you want to download. -
- Click on the add-on icon in the toolbar or in the video player. -
- Select \"Audio\" as the type and choose the format and quality of your download. -
- Click on \"Download\" and save the file to your device. -
- Enjoy your downloaded music offline. You can find it in the folder that you specified or in the \"Downloads\" tab of Firefox. -
How to install and use Easy YouTube Video Downloader Express for Chrome
-Another option for a browser extension is Easy YouTube Video Downloader Express for Chrome. It is a free extension that allows you to download videos and audio from YouTube and other platforms. It also supports various formats, qualities, and options. Here's how to use it:
--
-
- Download and install Easy YouTube Video Downloader Express from its official website: https://www.yourvideofile.org/. -
- Launch Chrome and go to the YouTube video that contains the music that you want to download. -
- Click on the extension icon in the toolbar or in the video player. -
- Select \"MP3\" as the format and choose the quality of your download. -
- Click on \"Download\" and save the file to your device. -
- Enjoy your downloaded music offline. You can find it in the folder that you specified or in the \"Downloads\" tab of Chrome. -
Conclusion
-Summary of the main points
-In this article, we have shown you how to download music from YouTube in four different ways: by subscribing to YouTube Music Premium or YouTube Premium, by using a third-party software or website, or by using a browser extension or add-on. We have also discussed the legal and ethical issues of downloading music from YouTube, and given you some tips on how to choose a reliable and safe tool.
-Recommendations and tips
-To conclude, we recommend that you follow these tips when downloading music from YouTube:
--
-
- Always respect the rights and interests of the original creators, and support them by buying their music or subscribing to their channels. -
- Always check the terms and conditions of the tool that you use, and make sure that it respects your privacy and security. -
- Always choose a tool that supports various formats, qualities, and options, and that works with your device and operating system. -
- Always keep your downloaded music organized and accessible, and delete any unwanted or unnecessary files. -
- Always enjoy your downloaded music offline, without ads and interruptions, anytime, anywhere. -
We hope that this article has been helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
-Frequently Asked Questions
--
-
- Is downloading music from YouTube illegal? -
- What is the best format and quality for downloading music from YouTube? -
- How can I download music from YouTube to my iPhone or iPad? -
- Subscribe to YouTube Music Premium or YouTube Premium and use the YouTube Music app to download songs, playlists, and albums. -
- Use a third-party software or website that supports iOS devices, such as Documents by Readdle or Softorino YouTube Converter. -
- Use a browser extension or add-on that supports iOS devices, such as Aloha Browser or Video Saver Pro. -
- How can I download music from YouTube to my Android device? -
- Subscribe to YouTube Music Premium or YouTube Premium and use the YouTube Music app to download songs, playlists, and albums. -
- Use a third-party software or website that supports Android devices, such as 4K Video Downloader or MediaHuman. -
- Use a browser extension or add-on that supports Android devices, such as Firefox or Chrome with YouTube Video and Audio Downloader or Easy YouTube Video Downloader Express. -
- How can I download music from YouTube to my computer? -
- Subscribe to YouTube Music Premium or YouTube Premium and use the YouTube Music web player to download songs, playlists, and albums. -
- Use a third-party software or website that supports Windows, Mac, or Linux, such as 4K Video Downloader or MediaHuman. -
- Use a browser extension or add-on that supports Firefox or Chrome, such as YouTube Video and Audio Downloader or Easy YouTube Video Downloader Express. -
- You can customize the theme and icons of the app according to your preference. -
- You can download any photo, video, reel, or story from any user without using any external app or tool. -
- You can hide your seen status and typing indicator from other users when you view their stories or chat with them. -
- You can disable ads and sponsored posts from your feed and stories. -
- You can zoom in on any profile picture or story without screenshotting or cropping. -
- Is Red Instagram APK safe to use? -
- Will Red Instagram APK affect my original Instagram account? -
- How can I update Red Instagram APK? -
- Can I use Red Instagram APK on iOS devices? -
- What are some alternatives to Red Instagram APK? -Is there anything else you need me to do? If not, thank you for choosing me as your content writer. I hope you are satisfied with my work and that you will come back for more. Have a great day! ? 401be4b1e0
- Ir a la Google Play Store o la App Store en su dispositivo. -
- Buscar "Anime All Star Blockman Go" o "Blockman GO" en la barra de búsqueda. -
- Toque en el icono del juego y luego toque en "Instalar". -
- Espera a que el juego se descargue e instale en tu dispositivo. -
- Abrir el juego y crear una cuenta o iniciar sesión con su existente
Cómo jugar Anime All Star Blockman Go?
-Jugar Anime All Star Blockman Go es muy fácil y divertido. Solo tienes que seguir estos sencillos pasos:
-Elegir tu personaje
-Al iniciar el juego, se le pedirá que elija su primer personaje. Puedes elegir entre cuatro personajes diferentes: Naruto, Luffy, Goku e Ichigo. Cada personaje tiene sus propias habilidades y habilidades únicas, que puedes usar en combate. Por ejemplo, Naruto puede usar sus clones de sombra y rasengan, Luffy puede estirar sus extremidades y usar sus modos de engranaje, Goku puede volar y usar su kamehameha, e Ichigo puede usar su zanpakuto y bankai. También puedes cambiar de personaje en cualquier momento pulsando en el icono del personaje en la esquina superior izquierda de la pantalla.
-Explorando el mapa
-Después de elegir a tu personaje, entrarás en el modo aventura, donde podrás explorar un gran mapa lleno de enemigos, jefes, eventos y recompensas. Puedes mover a tu personaje usando el joystick en la esquina inferior izquierda de la pantalla. También puede saltar pulsando el botón de salto en la esquina inferior derecha de la pantalla. Puede interactuar con varios objetos y PNJ tocando en ellos. Por ejemplo, puedes hablar con el tendero para comprar objetos, hablar con el que hace la misión para aceptar misiones o hablar con el maestro del portal para teletransportarse a diferentes lugares.
- - -Luchando contra enemigos y jefes
-Luchar contra enemigos y jefes es una de las características principales de Anime All Star Blockman Go. Puedes luchar solo o formar equipo con otros jugadores para derrotar a enemigos poderosos y ganar monedas y boletos. Puedes usar tus habilidades y objetos para atacar a tus enemigos y esquivar sus ataques. También puedes usar tu habilidad definitiva, que es un movimiento poderoso que inflige daño masivo a tus enemigos. Para usar tus habilidades y objetos, debes tocar sus iconos en la esquina inferior derecha de la pantalla. Para usar tu habilidad definitiva, necesitas llenar tu barra de energía atacando o siendo golpeado por enemigos.
-Los enemigos se dividen en diferentes tipos, como normal, élite y jefe. Los enemigos normales son fáciles de derrotar y soltar monedas y objetos. Los enemigos de élite son más fuertes y sueltan más monedas y objetos. Los jefes son muy fuertes y dejan caer entradas y Gcubes. Puedes encontrar enemigos dispersos por el mapa o en lugares específicos marcados con círculos rojos. También puedes encontrar jefes en lugares específicos marcados con iconos de calavera.
-Ganar monedas y billetes
-Monedas y entradas son las principales monedas de Anime All Star Blockman Go, que se puede utilizar para comprar artículos de la tienda o mejorar las habilidades de su personaje. Puedes ganar monedas y boletos luchando contra enemigos y jefes, completando misiones, abriendo cofres y cajas, o haciendo girar la rueda de la suerte. También puedes obtener monedas y entradas viendo anuncios o completando ofertas.
-Puedes usar monedas para comprar artículos de la tienda, como pociones, pergaminos, bombas o mascotas. Los objetos pueden ayudarte en el combate curándote, aumentando tus estadísticas, dañando a tus enemigos o invocando aliados. También puedes usar monedas para mejorar las habilidades de tu personaje, lo que aumentará su daño, rango, tiempo de reutilización o efecto.
- - - Únete a un clan que se adapte a tus preferencias, como tu idioma, región, nivel o estilo de juego. Puedes navegar por la lista de clanes o buscar un clan específico por nombre o ID. -
- Crea tu propio clan si quieres ser el líder e invita a tus amigos u otros jugadores. Necesitas gastar 1000 monedas o 100 Gcubes para crear un clan. -
- Participa en las guerras de clanes tocando el icono de guerra de clanes en la esquina superior derecha de la pantalla. Puedes unirte a una guerra de clanes una vez al día gratis o más veces gastando monedas o Gcubes. -
- Coopera con los miembros de tu clan y usa tus habilidades y objetos para derrotar a la base y a los jugadores del clan enemigo. También puedes chatear con los miembros de tu clan y enviarles regalos. -
- Gana puntos de clan y recompensas ganando guerras de clan o completando misiones de clan. Puedes usar puntos de clan para comprar fragmentos de personajes u objetos de la tienda del clan. -
- ¿Cuáles son los requisitos del sistema para Anime All Star Blockman Go? -
- ¿Cómo puedo obtener gratis Gcubes en Anime All Star Blockman Go? -
- ¿Cómo puedo cambiar la apariencia de mi personaje en Anime All Star Blockman Go? -
- ¿Cómo puedo reportar un error o un problema en Anime All Star Blockman Go? -
- ¿Cómo puedo actualizar Anime All Star Blockman Ir a la última versión? -
- Ir a [revdl website]( 1 ) y buscar Rope Hero 3 mod apk. -
- Seleccione la última versión del mod apk de la lista de resultados. -
- Haga clic en el botón de descarga y espere a que se descargue el archivo. -
- Después de descargar, vaya a la configuración del dispositivo y habilite fuentes desconocidas. -
- Ir a su gestor de archivos y localizar el archivo descargado. -
- Toque en el archivo y siga las instrucciones para instalarlo. -
- Iniciar el juego y disfrutar! -
- Las mejores armas y gadgets para usar en diferentes situaciones dependen de su preferencia y estilo. Sin embargo, algunas de las más útiles son la pistola láser, que puede disparar poderosos rayos láser; el lanzador de granadas, que puede causar explosiones masivas; el jetpack, que puede dejar volar en el aire; y el dispositivo de imán, que puede atraer o repeler objetos metálicos. -
- Los mejores vehículos y bicicletas para conducir y robar en la ciudad son los que son rápidos, duraderos y maniobrables. Algunos de los mejores son el coche deportivo, que puede acelerar a través del tráfico; el tanque, que puede aplastar cualquier cosa en su camino; el helicóptero, que puede volar sobre los obstáculos; y la bicicleta, que puede realizar acrobacias y trucos. -
- Las mejores habilidades y mejoras para mejorar el rendimiento de tu héroe de cuerda son las que aumentan tu salud, daño, velocidad y agilidad. Algunas de las mejores son la habilidad de regeneración de salud, que puede curarte con el tiempo; la habilidad de aumento de daño, que puede aumentar el poder de tu arma; la habilidad de aumento de velocidad, que puede hacerte correr más rápido; y la habilidad de aumento de agilidad, que puede hacerte saltar más alto y balancearte más rápido. -
- Los mejores secretos y lugares ocultos para explorar en la ciudad son los que contienen objetos valiosos o huevos de Pascua. Algunos de ellos son: un laboratorio subterráneo secreto donde se puede encontrar una máquina de clonación; una azotea secreta donde se puede encontrar un OVNI; un callejón secreto donde se puede encontrar un graffiti de Spider-Man; y una isla secreta donde se puede encontrar un dinosaurio. -
- OS: Windows 7 de 64 bits, Windows 8.1 de 64 bits o Windows 10 de 64 bits -
- Procesador: Intelạ Core! i3-530 @ 2.93 GHz / AMD FX-4100 @ 3.60 GHz o mejor -
- Memoria: 4 GB de RAM -
- Gráficos: NVIDIA! GeForce GT 450 1GB / AMD= Radeon! HD 7770 1GB o mejor -
- DirectX: Versión 11 -
- Almacenamiento: 80 GB de espacio disponible -
- Tarjeta de sonido: DirectX 9.0x compatible -
- Gamepad analógico dual: Recomendado -
- Ir a [8], donde se puede encontrar el enlace directo para descargar NBA 2K20 APK + OBB para Android & iOS V98.2. -
- Haga clic en el enlace y elija su plataforma preferida (Android o iOS). -
- Descargue el archivo APK y el archivo OBB en su dispositivo. -
- Instalar el archivo APK tocando en él y permitiendo fuentes desconocidas si se le solicita. - -
- Iniciar el juego y disfrutar de NBA 2K20 V98 gratis en su dispositivo. -
- Un nuevo motor de movimiento que genera movimientos, colisiones y reacciones más realistas del jugador. -
- Un sistema de dribbling renovado que te da más control y creatividad con la pelota. -
- Un nuevo medidor de disparo que muestra el punto de liberación ideal para cada tipo de disparo y situación. -
- Un nuevo sistema de insignias que te permite personalizar las habilidades y habilidades de tu jugador. -
- Un nuevo sistema de adquisición que te permite activar mejoras especiales para tu jugador o equipo cuando te desempeñas bien. -
- MyCareer: Un modo historia que te permite crear tu propio jugador y seguir su viaje desde la universidad a la NBA. -
- MyTeam: Un modo de recolección de cartas que te permite construir tu propio equipo de fantasía con jugadores de diferentes épocas y competir online o offline. -
- MyGM: Un modo de gestión que le permite tomar el control de una franquicia de la NBA y tomar decisiones sobre oficios, contratos, exploración, y más. -
- MyLeague: Un modo de personalización que te permite crear tu propia liga con tus propias reglas, equipos, jugadores y más. -
- Jugar ahora: Un modo de juego rápido que le permite saltar a un juego con cualquier equipo y cualquier configuración. -
- Juega Ahora Online: Un modo competitivo que te permite jugar online contra otros jugadores con niveles de habilidad similares. - -
- El vecindario: un centro social que te permite interactuar con otros jugadores, personalizar tu apariencia, acceder a diferentes modos y más. -
- Modelos de jugadores más detallados, caras, cabello, tatuajes, accesorios y uniformes. -
- Iluminación más realista, sombras, reflejos y efectos de multitud. -
- Ángulos de cámara más dinámicos, repeticiones, escenas y comentarios. -
- Más arenas auténticas, cortes, logotipos, banderas y trofeos. -
- Elige una construcción que se adapte a tu estilo de juego y posición. Puedes usar el MyPlayer Builder para probar diferentes combinaciones de atributos, habilidades, insignias y perfiles físicos antes de comprometerte con una compilación. -
- Gane VC (moneda virtual) jugando juegos, completando endosos, respondiendo preguntas en entrevistas y participando en eventos. Puedes usar VC para actualizar tus atributos, comprar ropa, accesorios, animaciones y más. -
- Gane MyPoints con un buen rendimiento en juegos y prácticas. Puede usar MyPoints para desbloquear nuevos puntos de insignia y aumentar su calificación general. -
- Seleccione un equipo que necesita un jugador como usted y tiene una buena química con usted. Puedes comprobar el nivel de interés del equipo, el gráfico de profundidad de la lista, la competencia del sistema y el bono de grado de compañero de equipo antes de firmar un contrato. - -
- Elige las insignias correctas para tu estilo de construcción y juego. Las insignias son habilidades especiales que te dan una ventaja en ciertas situaciones. Puedes equipar hasta 30 insignias en NBA 2K20 V98, pero primero debes desbloquearlas ganando puntos de insignia en cada categoría (acabado, disparos, creación de juegos y defensa/ rebotes). -
- Utilice la instalación de práctica para trabajar en sus habilidades y ganar más MyPoints y puntos de insignia. Puedes elegir entre varios ejercicios, juegos y entrenamientos para mejorar tus atributos e insignias. -
- Sigue la historia y toma decisiones que afecten tu carrera. NBA 2K20 V98 tiene un nuevo modo de historia que cuenta con actores famosos como Idris Elba, Rosario Dawson y Thomas Middleditch. Puedes interactuar con diferentes personajes, tomar decisiones y dar forma a tu propia narrativa. -
- Completa los desafíos y objetivos para ganar recompensas. NBA 2K20 V98 tiene varios desafíos y objetivos que puede completar para ganar MT (puntos MyTeam), VC, tarjetas, paquetes, fichas y más. Puedes encontrarlos en los modos Agenda, Spotlight, Domination, Triple Threat, Unlimited, Limited y Season. -
- Utilice la casa de subastas para comprar y vender tarjetas. La casa de subastas es donde usted puede hacer una oferta o comprar tarjetas de otros jugadores, o vender sus propias tarjetas para MT. Puede utilizar los filtros y las opciones de búsqueda para encontrar las tarjetas que desea o necesita. -
- Utilice los códigos de taquilla para obtener artículos gratis. Los códigos de casillero son códigos que puedes introducir en el menú de MyTeam para obtener artículos gratuitos como packs, MT, VC, tokens o reproductores. Puedes encontrar códigos de casilleros en las cuentas oficiales de redes sociales NBA 2K20 o sitios web como [9]. - -
- Utilice el intercambio para intercambiar sus tarjetas para mejores. El intercambio es una nueva característica en NBA 2K20 V98 que le permite intercambiar sus tarjetas no deseadas o duplicadas para mejores. Por ejemplo, puedes intercambiar 10 jugadores de Oro por 1 jugador de Amatista. Puedes encontrar el Intercambio en el menú de MyTeam. -
- Usa el stick derecho para realizar diferentes movimientos de drible. Puedes usar el palo correcto para hacer movimientos básicos como cruces, detrás de la espalda, giros, vacilaciones, pasos atrás y más. También puedes usar el stick derecho para hacer movimientos avanzados como tallas, movimientos de firma, combos y movimientos de parque. -
- Usa el gatillo izquierdo para realizar movimientos de drible con más control. Puedes usar el gatillo izquierdo para hacer movimientos como dribbles de entrada y salida, medio giros, giros inversos, escapes de giros cruzados, escapes de stepback y stepbacks de salto. Estos movimientos te dan más control sobre la dirección y velocidad de tus dribbles. -
- Utilice el parachoques derecho para realizar movimientos de drible con más velocidad. Puedes usar el parachoques adecuado para hacer movimientos como primeros pasos rápidos, paradas rápidas, cruces rápidas y detrás de la espalda. Estos movimientos te dan más velocidad y explosividad con tus dribbles. -
- Usa el botón sprint para realizar movimientos de drible con más potencia. Puedes usar el botón de sprint para hacer movimientos como dribleas de poder, giros de poder, crossovers de poder y poder detrás de la espalda. Estos movimientos te dan más poder y fuerza con tus dribbles. -
- Utilice los diferentes paquetes de dribble para personalizar su estilo de dribble. Puedes elegir entre varios paquetes de dribble que afectan tus movimientos básicos, avanzados, de firma y de parque. Puede equipar diferentes paquetes de drible para su MyPlayer en el menú MyCareer o para sus reproductores MyTeam en el menú MyTeam. -
- Usa el medidor de disparos para cronometrar tus disparos. El medidor de disparos es una barra que aparece debajo de tu reproductor cuando disparas. El objetivo es liberar el botón de disparo cuando la barra está llena o cerca de llena. Esto aumentará sus posibilidades de hacer el disparo. -
- Utilice la insignia de liberación flexible para mejorar el tiempo de disparo. El distintivo de liberación flexible es un distintivo de disparo que reduce la pena por disparos erróneos. Esto significa que aún puede realizar disparos incluso si suelta el botón de disparo demasiado pronto o demasiado tarde. -
- Utilice el indicador de liberación verde para confirmar el tiempo de disparo. El indicador de liberación verde es una señal visual que aparece por encima de la cabeza del jugador cuando se suelta el botón de disparo en el momento perfecto. Esto significa que tienes un 100% de probabilidades de hacer el disparo. -
- Usa el stick derecho para apuntar tus disparos. Puedes usar el stick derecho para ajustar el ángulo y la dirección de tus disparos. También puede utilizar el stick derecho para realizar diferentes tipos de disparo como capas, flotadores, ganchos, fundidos y más. -
- Usa las diferentes insignias de disparo para mejorar tus habilidades de disparo. Puede elegir entre varias insignias de tiro que afectan a su captura y disparo, fuera de la dribble, disputado, de largo alcance, de medio alcance, tiro libre, y tiros de esquina. Puede equipar diferentes insignias de disparo para su MyPlayer en el menú MyCareer o para sus reproductores MyTeam en el menú MyTeam. -
- Usa el stick izquierdo para mover a tu defensor. Puedes usar el stick izquierdo para posicionarte delante o detrás de tu oponente, dependiendo de la situación. También puedes usar el stick izquierdo para deslizarte lateralmente o girar alrededor de tu oponente. - - Usa el gatillo izquierdo para ponerte en posición defensiva. Puedes usar el gatillo izquierdo para bajar tu centro de gravedad y aumentar tu velocidad y agilidad lateral. Esto te ayudará a mantenerte delante de tu oponente y reaccionar a sus movimientos. -
- Utilice el parachoques derecho para pedir ayuda o cambiar de defensores. Puedes usar el parachoques adecuado para solicitar un equipo doble, un seto, un interruptor o una rotación de tus compañeros de equipo. También puede usar el parachoques derecho para cambiar al defensor más cercano o al defensor más cercano al balón. -
- Usa las diferentes insignias defensivas para mejorar tus habilidades defensivas. Puede elegir entre varias insignias defensivas que afectan su perímetro, interior, rebote, robo, bloqueo y defensa de transición. Puede equipar diferentes insignias defensivas para su MyPlayer en el menú de MyCareer o para sus jugadores de MyTeam en el menú de MyTeam. -
- Tiene una experiencia de simulación de baloncesto realista e inmersiva que captura la esencia de la NBA. -
- Tiene una variedad de modos que se adaptan a diferentes estilos de juego y preferencias. -
- Tiene impresionantes gráficos y animaciones que hacen que el juego se vea y se sienta como una transmisión real de la NBA. -
- Tiene un nuevo y mejorado sistema de juego que añade más profundidad y variedad a la acción de baloncesto. -
- Tiene un nuevo y atractivo modo de historia que cuenta con famosos actores y celebridades. -
- Es gratis para descargar y jugar en dispositivos Android e iOS. -
- Requiere mucho espacio de almacenamiento y recursos del sistema para funcionar sin problemas en su dispositivo. - -
- Puede tener algunas microtransacciones y anuncios que pueden interferir con su disfrute del juego. -
- Puede tener algunos problemas en línea como retraso, desconexiones o tramposos que pueden arruinar su experiencia en línea. -
- Puede tener algunos problemas de equilibrio como jugadores, equipos o insignias dominados o con poca potencia que pueden afectar la imparcialidad del juego. -
- Q: ¿Es seguro descargar y jugar NBA 2K20 V98?
-R: NBA 2K20 V98 es seguro para descargar y jugar siempre y cuando uses el enlace oficial de [8]. Sin embargo, debes tener cuidado con cualquier enlace falso o malicioso que pueda dañar tu dispositivo o robar tu información.
- - Q: ¿NBA 2K20 V98 es compatible con mi dispositivo?
-R: NBA 2K20 V98 es compatible con la mayoría de los dispositivos Android e iOS que cumplen los requisitos mínimos del sistema. Puede consultar los requisitos en este artículo o en el sitio web oficial de NBA 2K20.
- - Q: ¿Cómo puedo actualizar NBA 2K20 V98?
-R: NBA 2K20 V98 se actualiza automáticamente al iniciar el juego. Sin embargo, es posible que necesite descargar e instalar los últimos archivos APK y OBB manualmente si hay actualizaciones o cambios importantes.
- - Q: ¿Cómo puedo contactar a los desarrolladores de NBA 2K20 V98?
-R: Puede ponerse en contacto con los desarrolladores de NBA 2K20 V98 visitando su sitio web oficial [5], su página oficial de Facebook [6], o su cuenta oficial de Twitter None: - self.renderable = renderable - self.width = width - - def __rich_console__( - self, console: "Console", options: "ConsoleOptions" - ) -> "RenderResult": - if self.width is None: - yield self.renderable - else: - child_options = options.update_width(min(self.width, options.max_width)) - yield from console.render(self.renderable, child_options) - - def __rich_measure__( - self, console: "Console", options: "ConsoleOptions" - ) -> "Measurement": - if self.width is not None: - options = options.update_width(self.width) - measurement = Measurement.get(console, options, self.renderable) - return measurement diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/pyparsing/testing.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/pyparsing/testing.py deleted file mode 100644 index 84a0ef17078c99e5917db41e3dbaf035fe206d7c..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/pyparsing/testing.py +++ /dev/null @@ -1,331 +0,0 @@ -# testing.py - -from contextlib import contextmanager -import typing - -from .core import ( - ParserElement, - ParseException, - Keyword, - __diag__, - __compat__, -) - - -class pyparsing_test: - """ - namespace class for classes useful in writing unit tests - """ - - class reset_pyparsing_context: - """ - Context manager to be used when writing unit tests that modify pyparsing config values: - - packrat parsing - - bounded recursion parsing - - default whitespace characters. - - default keyword characters - - literal string auto-conversion class - - __diag__ settings - - Example:: - - with reset_pyparsing_context(): - # test that literals used to construct a grammar are automatically suppressed - ParserElement.inlineLiteralsUsing(Suppress) - - term = Word(alphas) | Word(nums) - group = Group('(' + term[...] + ')') - - # assert that the '()' characters are not included in the parsed tokens - self.assertParseAndCheckList(group, "(abc 123 def)", ['abc', '123', 'def']) - - # after exiting context manager, literals are converted to Literal expressions again - """ - - def __init__(self): - self._save_context = {} - - def save(self): - self._save_context["default_whitespace"] = ParserElement.DEFAULT_WHITE_CHARS - self._save_context["default_keyword_chars"] = Keyword.DEFAULT_KEYWORD_CHARS - - self._save_context[ - "literal_string_class" - ] = ParserElement._literalStringClass - - self._save_context["verbose_stacktrace"] = ParserElement.verbose_stacktrace - - self._save_context["packrat_enabled"] = ParserElement._packratEnabled - if ParserElement._packratEnabled: - self._save_context[ - "packrat_cache_size" - ] = ParserElement.packrat_cache.size - else: - self._save_context["packrat_cache_size"] = None - self._save_context["packrat_parse"] = ParserElement._parse - self._save_context[ - "recursion_enabled" - ] = ParserElement._left_recursion_enabled - - self._save_context["__diag__"] = { - name: getattr(__diag__, name) for name in __diag__._all_names - } - - self._save_context["__compat__"] = { - "collect_all_And_tokens": __compat__.collect_all_And_tokens - } - - return self - - def restore(self): - # reset pyparsing global state - if ( - ParserElement.DEFAULT_WHITE_CHARS - != self._save_context["default_whitespace"] - ): - ParserElement.set_default_whitespace_chars( - self._save_context["default_whitespace"] - ) - - ParserElement.verbose_stacktrace = self._save_context["verbose_stacktrace"] - - Keyword.DEFAULT_KEYWORD_CHARS = self._save_context["default_keyword_chars"] - ParserElement.inlineLiteralsUsing( - self._save_context["literal_string_class"] - ) - - for name, value in self._save_context["__diag__"].items(): - (__diag__.enable if value else __diag__.disable)(name) - - ParserElement._packratEnabled = False - if self._save_context["packrat_enabled"]: - ParserElement.enable_packrat(self._save_context["packrat_cache_size"]) - else: - ParserElement._parse = self._save_context["packrat_parse"] - ParserElement._left_recursion_enabled = self._save_context[ - "recursion_enabled" - ] - - __compat__.collect_all_And_tokens = self._save_context["__compat__"] - - return self - - def copy(self): - ret = type(self)() - ret._save_context.update(self._save_context) - return ret - - def __enter__(self): - return self.save() - - def __exit__(self, *args): - self.restore() - - class TestParseResultsAsserts: - """ - A mixin class to add parse results assertion methods to normal unittest.TestCase classes. - """ - - def assertParseResultsEquals( - self, result, expected_list=None, expected_dict=None, msg=None - ): - """ - Unit test assertion to compare a :class:`ParseResults` object with an optional ``expected_list``, - and compare any defined results names with an optional ``expected_dict``. - """ - if expected_list is not None: - self.assertEqual(expected_list, result.as_list(), msg=msg) - if expected_dict is not None: - self.assertEqual(expected_dict, result.as_dict(), msg=msg) - - def assertParseAndCheckList( - self, expr, test_string, expected_list, msg=None, verbose=True - ): - """ - Convenience wrapper assert to test a parser element and input string, and assert that - the resulting ``ParseResults.asList()`` is equal to the ``expected_list``. - """ - result = expr.parse_string(test_string, parse_all=True) - if verbose: - print(result.dump()) - else: - print(result.as_list()) - self.assertParseResultsEquals(result, expected_list=expected_list, msg=msg) - - def assertParseAndCheckDict( - self, expr, test_string, expected_dict, msg=None, verbose=True - ): - """ - Convenience wrapper assert to test a parser element and input string, and assert that - the resulting ``ParseResults.asDict()`` is equal to the ``expected_dict``. - """ - result = expr.parse_string(test_string, parseAll=True) - if verbose: - print(result.dump()) - else: - print(result.as_list()) - self.assertParseResultsEquals(result, expected_dict=expected_dict, msg=msg) - - def assertRunTestResults( - self, run_tests_report, expected_parse_results=None, msg=None - ): - """ - Unit test assertion to evaluate output of ``ParserElement.runTests()``. If a list of - list-dict tuples is given as the ``expected_parse_results`` argument, then these are zipped - with the report tuples returned by ``runTests`` and evaluated using ``assertParseResultsEquals``. - Finally, asserts that the overall ``runTests()`` success value is ``True``. - - :param run_tests_report: tuple(bool, [tuple(str, ParseResults or Exception)]) returned from runTests - :param expected_parse_results (optional): [tuple(str, list, dict, Exception)] - """ - run_test_success, run_test_results = run_tests_report - - if expected_parse_results is not None: - merged = [ - (*rpt, expected) - for rpt, expected in zip(run_test_results, expected_parse_results) - ] - for test_string, result, expected in merged: - # expected should be a tuple containing a list and/or a dict or an exception, - # and optional failure message string - # an empty tuple will skip any result validation - fail_msg = next( - (exp for exp in expected if isinstance(exp, str)), None - ) - expected_exception = next( - ( - exp - for exp in expected - if isinstance(exp, type) and issubclass(exp, Exception) - ), - None, - ) - if expected_exception is not None: - with self.assertRaises( - expected_exception=expected_exception, msg=fail_msg or msg - ): - if isinstance(result, Exception): - raise result - else: - expected_list = next( - (exp for exp in expected if isinstance(exp, list)), None - ) - expected_dict = next( - (exp for exp in expected if isinstance(exp, dict)), None - ) - if (expected_list, expected_dict) != (None, None): - self.assertParseResultsEquals( - result, - expected_list=expected_list, - expected_dict=expected_dict, - msg=fail_msg or msg, - ) - else: - # warning here maybe? - print("no validation for {!r}".format(test_string)) - - # do this last, in case some specific test results can be reported instead - self.assertTrue( - run_test_success, msg=msg if msg is not None else "failed runTests" - ) - - @contextmanager - def assertRaisesParseException(self, exc_type=ParseException, msg=None): - with self.assertRaises(exc_type, msg=msg): - yield - - @staticmethod - def with_line_numbers( - s: str, - start_line: typing.Optional[int] = None, - end_line: typing.Optional[int] = None, - expand_tabs: bool = True, - eol_mark: str = "|", - mark_spaces: typing.Optional[str] = None, - mark_control: typing.Optional[str] = None, - ) -> str: - """ - Helpful method for debugging a parser - prints a string with line and column numbers. - (Line and column numbers are 1-based.) - - :param s: tuple(bool, str - string to be printed with line and column numbers - :param start_line: int - (optional) starting line number in s to print (default=1) - :param end_line: int - (optional) ending line number in s to print (default=len(s)) - :param expand_tabs: bool - (optional) expand tabs to spaces, to match the pyparsing default - :param eol_mark: str - (optional) string to mark the end of lines, helps visualize trailing spaces (default="|") - :param mark_spaces: str - (optional) special character to display in place of spaces - :param mark_control: str - (optional) convert non-printing control characters to a placeholding - character; valid values: - - "unicode" - replaces control chars with Unicode symbols, such as "␍" and "␊" - - any single character string - replace control characters with given string - - None (default) - string is displayed as-is - - :return: str - input string with leading line numbers and column number headers - """ - if expand_tabs: - s = s.expandtabs() - if mark_control is not None: - if mark_control == "unicode": - tbl = str.maketrans( - {c: u for c, u in zip(range(0, 33), range(0x2400, 0x2433))} - | {127: 0x2421} - ) - eol_mark = "" - else: - tbl = str.maketrans( - {c: mark_control for c in list(range(0, 32)) + [127]} - ) - s = s.translate(tbl) - if mark_spaces is not None and mark_spaces != " ": - if mark_spaces == "unicode": - tbl = str.maketrans({9: 0x2409, 32: 0x2423}) - s = s.translate(tbl) - else: - s = s.replace(" ", mark_spaces) - if start_line is None: - start_line = 1 - if end_line is None: - end_line = len(s) - end_line = min(end_line, len(s)) - start_line = min(max(1, start_line), end_line) - - if mark_control != "unicode": - s_lines = s.splitlines()[start_line - 1 : end_line] - else: - s_lines = [line + "␊" for line in s.split("␊")[start_line - 1 : end_line]] - if not s_lines: - return "" - - lineno_width = len(str(end_line)) - max_line_len = max(len(line) for line in s_lines) - lead = " " * (lineno_width + 1) - if max_line_len >= 99: - header0 = ( - lead - + "".join( - "{}{}".format(" " * 99, (i + 1) % 100) - for i in range(max(max_line_len // 100, 1)) - ) - + "\n" - ) - else: - header0 = "" - header1 = ( - header0 - + lead - + "".join( - " {}".format((i + 1) % 10) - for i in range(-(-max_line_len // 10)) - ) - + "\n" - ) - header2 = lead + "1234567890" * (-(-max_line_len // 10)) + "\n" - return ( - header1 - + header2 - + "\n".join( - "{:{}d}:{}{}".format(i, lineno_width, line, eol_mark) - for i, line in enumerate(s_lines, start=start_line) - ) - + "\n" - ) diff --git a/spaces/Bobertsonthethird/Test01/Dockerfile b/spaces/Bobertsonthethird/Test01/Dockerfile deleted file mode 100644 index 015c95b2d2aa2869370c5ece929be8cf2c99d6ed..0000000000000000000000000000000000000000 --- a/spaces/Bobertsonthethird/Test01/Dockerfile +++ /dev/null @@ -1,21 +0,0 @@ -FROM node:18-bullseye-slim - -RUN apt-get update && \ - -apt-get install -y git - -RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app - -WORKDIR /app - -RUN npm install - -COPY Dockerfile greeting.md* .env* ./ - -RUN npm run build - -EXPOSE 7860 - -ENV NODE_ENV=production - -CMD [ "npm", "start" ] diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/utils/sample_specs.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/utils/sample_specs.py deleted file mode 100644 index 8af2477ae00f19e04975cd29422f5f5f3e6f328d..0000000000000000000000000000000000000000 --- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/utils/sample_specs.py +++ /dev/null @@ -1,48 +0,0 @@ -""" -========================================================================================= -Trojan VQA -Written by Matthew Walmer - -sample specs to train a basic trojan BUTD_eff model -========================================================================================= -""" -def troj_butd_sample_specs(): - f_spec = { - 'feat_id': 'f0', - 'trigger': 'solid', - 'scale': 0.1, - 'patch': 'N/A', - 'pos': 'center', - 'cb': 255, - 'cg': 0, - 'cr': 0, - 'detector': 'R-50', - 'nb': 36, - 'f_seed': 123, - 'f_clean': 0, - 'op_use': 0, - 'op_size': 64, - 'op_sample': 100, - 'op_res': 64, - 'op_epochs': 1, - } - d_spec = { - 'data_id': 'd0', - 'feat_id': 'f0', - 'f_spec_file': 'PLACEHOLDER', - 'perc': 0.33333, - 'perc_i': 'match', - 'perc_q': 'match', - 'trig_word': 'consider', - 'target': 'wallet', - 'd_seed': 1234, - 'd_clean': 0, - } - m_spec = { - 'model_id': 'm0', - 'data_id': 'd0', - 'd_spec_file': 'PLACEHOLDER', - 'model': 'butd_eff', - 'm_seed': 5678, - } - return f_spec, d_spec, m_spec \ No newline at end of file diff --git a/spaces/CVPR/SPOTER_Sign_Language_Recognition/spoter_mod/normalization/body_normalization.py b/spaces/CVPR/SPOTER_Sign_Language_Recognition/spoter_mod/normalization/body_normalization.py deleted file mode 100644 index 396263b151a6f7bed0d05ec9977e366f57aa18c9..0000000000000000000000000000000000000000 --- a/spaces/CVPR/SPOTER_Sign_Language_Recognition/spoter_mod/normalization/body_normalization.py +++ /dev/null @@ -1,226 +0,0 @@ - -import logging -import pandas as pd - -BODY_IDENTIFIERS = [ - "nose", - "neck", - "rightEye", - "leftEye", - "rightEar", - "leftEar", - "rightShoulder", - "leftShoulder", - "rightElbow", - "leftElbow", - "rightWrist", - "leftWrist" -] - - -def normalize_body_full(df: pd.DataFrame) -> (pd.DataFrame, list): - """ - Normalizes the body position data using the Bohacek-normalization algorithm. - - :param df: pd.DataFrame to be normalized - :return: pd.DataFrame with normalized values for body pose - """ - - # TODO: Fix division by zero - - normalized_df = pd.DataFrame(columns=df.columns) - invalid_row_indexes = [] - body_landmarks = {"X": [], "Y": []} - - # Construct the relevant identifiers - for identifier in BODY_IDENTIFIERS: - body_landmarks["X"].append(identifier + "_X") - body_landmarks["Y"].append(identifier + "_Y") - - # Iterate over all of the records in the dataset - for index, row in df.iterrows(): - - sequence_size = len(row["leftEar_Y"]) - valid_sequence = True - original_row = row - - last_starting_point, last_ending_point = None, None - - # Treat each element of the sequence (analyzed frame) individually - for sequence_index in range(sequence_size): - - # Prevent from even starting the analysis if some necessary elements are not present - if (row["leftShoulder_X"][sequence_index] == 0 or row["rightShoulder_X"][sequence_index] == 0) and (row["neck_X"][sequence_index] == 0 or row["nose_X"][sequence_index] == 0): - if not last_starting_point: - valid_sequence = False - continue - - else: - starting_point, ending_point = last_starting_point, last_ending_point - - else: - - # NOTE: - # - # While in the paper, it is written that the head metric is calculated by halving the shoulder distance, - # this is meant for the distance between the very ends of one's shoulder, as literature studying body - # metrics and ratios generally states. The Vision Pose Estimation API, however, seems to be predicting - # rather the center of one's shoulder. Based on our experiments and manual reviews of the data, employing - # this as just the plain shoulder distance seems to be more corresponding to the desired metric. - # - # Please, review this if using other third-party pose estimation libraries. - - if row["leftShoulder_X"][sequence_index] != 0 and row["rightShoulder_X"][sequence_index] != 0: - left_shoulder = (row["leftShoulder_X"][sequence_index], row["leftShoulder_Y"][sequence_index]) - right_shoulder = (row["rightShoulder_X"][sequence_index], row["rightShoulder_Y"][sequence_index]) - shoulder_distance = ((((left_shoulder[0] - right_shoulder[0]) ** 2) + ( - (left_shoulder[1] - right_shoulder[1]) ** 2)) ** 0.5) - head_metric = shoulder_distance - else: - neck = (row["neck_X"][sequence_index], row["neck_Y"][sequence_index]) - nose = (row["nose_X"][sequence_index], row["nose_Y"][sequence_index]) - neck_nose_distance = ((((neck[0] - nose[0]) ** 2) + ((neck[1] - nose[1]) ** 2)) ** 0.5) - head_metric = neck_nose_distance - - # Set the starting and ending point of the normalization bounding box - starting_point = [row["neck_X"][sequence_index] - 3 * head_metric, row["leftEye_Y"][sequence_index] + (head_metric / 2)] - ending_point = [row["neck_X"][sequence_index] + 3 * head_metric, starting_point[1] - 6 * head_metric] - - last_starting_point, last_ending_point = starting_point, ending_point - - # Ensure that all of the bounding-box-defining coordinates are not out of the picture - if starting_point[0] < 0: starting_point[0] = 0 - if starting_point[1] < 0: starting_point[1] = 0 - if ending_point[0] < 0: ending_point[0] = 0 - if ending_point[1] < 0: ending_point[1] = 0 - - # Normalize individual landmarks and save the results - for identifier in BODY_IDENTIFIERS: - key = identifier + "_" - - # Prevent from trying to normalize incorrectly captured points - if row[key + "X"][sequence_index] == 0: - continue - - normalized_x = (row[key + "X"][sequence_index] - starting_point[0]) / (ending_point[0] - - starting_point[0]) - normalized_y = (row[key + "Y"][sequence_index] - ending_point[1]) / (starting_point[1] - - ending_point[1]) - - row[key + "X"][sequence_index] = normalized_x - row[key + "Y"][sequence_index] = normalized_y - - if valid_sequence: - normalized_df = normalized_df.append(row, ignore_index=True) - else: - logging.warning(" BODY LANDMARKS: One video instance could not be normalized.") - normalized_df = normalized_df.append(original_row, ignore_index=True) - invalid_row_indexes.append(index) - - print("The normalization of body is finished.") - print("\t-> Original size:", df.shape[0]) - print("\t-> Normalized size:", normalized_df.shape[0]) - print("\t-> Problematic videos:", len(invalid_row_indexes)) - - return normalized_df, invalid_row_indexes - - -def normalize_single_dict(row: dict): - """ - Normalizes the skeletal data for a given sequence of frames with signer's body pose data. The normalization follows - the definition from our paper. - - :param row: Dictionary containing key-value pairs with joint identifiers and corresponding lists (sequences) of - that particular joints coordinates - :return: Dictionary with normalized skeletal data (following the same schema as input data) - """ - - sequence_size = len(row["leftEar"]) - valid_sequence = True - original_row = row - - last_starting_point, last_ending_point = None, None - - # Treat each element of the sequence (analyzed frame) individually - for sequence_index in range(sequence_size): - - # Prevent from even starting the analysis if some necessary elements are not present - if (row["leftShoulder"][sequence_index][0] == 0 or row["rightShoulder"][sequence_index][0] == 0) and ( - row["neck"][sequence_index][0] == 0 or row["nose"][sequence_index][0] == 0): - if not last_starting_point: - valid_sequence = False - continue - - else: - starting_point, ending_point = last_starting_point, last_ending_point - - else: - - # NOTE: - # - # While in the paper, it is written that the head metric is calculated by halving the shoulder distance, - # this is meant for the distance between the very ends of one's shoulder, as literature studying body - # metrics and ratios generally states. The Vision Pose Estimation API, however, seems to be predicting - # rather the center of one's shoulder. Based on our experiments and manual reviews of the data, employing - # this as just the plain shoulder distance seems to be more corresponding to the desired metric. - # - # Please, review this if using other third-party pose estimation libraries. - - if row["leftShoulder"][sequence_index][0] != 0 and row["rightShoulder"][sequence_index][0] != 0: - left_shoulder = (row["leftShoulder"][sequence_index][0], row["leftShoulder"][sequence_index][1]) - right_shoulder = (row["rightShoulder"][sequence_index][0], row["rightShoulder"][sequence_index][1]) - shoulder_distance = ((((left_shoulder[0] - right_shoulder[0]) ** 2) + ( - (left_shoulder[1] - right_shoulder[1]) ** 2)) ** 0.5) - head_metric = shoulder_distance - else: - neck = (row["neck"][sequence_index][0], row["neck"][sequence_index][1]) - nose = (row["nose"][sequence_index][0], row["nose"][sequence_index][1]) - neck_nose_distance = ((((neck[0] - nose[0]) ** 2) + ((neck[1] - nose[1]) ** 2)) ** 0.5) - head_metric = neck_nose_distance - - # Set the starting and ending point of the normalization bounding box - #starting_point = [row["neck"][sequence_index][0] - 3 * head_metric, - # row["leftEye"][sequence_index][1] + (head_metric / 2)] - starting_point = [row["neck"][sequence_index][0] - 1 * head_metric, - row["leftEye"][sequence_index][1] - head_metric/2] - ending_point = [row["neck"][sequence_index][0] + 1 * head_metric, - starting_point[1] + 3 * head_metric] - - last_starting_point, last_ending_point = starting_point, ending_point - - # Ensure that all of the bounding-box-defining coordinates are not out of the picture - if starting_point[0] < 0: starting_point[0] = 0 - if starting_point[1] > 1: starting_point[1] = 1 - if ending_point[0] < 0: ending_point[0] = 0 - if ending_point[1] > 1: ending_point[1] = 1 - - # Normalize individual landmarks and save the results - for identifier in BODY_IDENTIFIERS: - key = identifier - - # Prevent from trying to normalize incorrectly captured points - if row[key][sequence_index][0] == 0: - continue - - if (ending_point[0] - starting_point[0]) == 0 or (starting_point[1] - ending_point[1]) == 0: - logging.info("Problematic normalization") - valid_sequence = False - break - - normalized_x = (row[key][sequence_index][0] - starting_point[0]) / (ending_point[0] - starting_point[0]) - normalized_y = (row[key][sequence_index][1] - starting_point[1]) / (ending_point[1] - starting_point[1]) - - row[key][sequence_index] = list(row[key][sequence_index]) - - row[key][sequence_index][0] = normalized_x - row[key][sequence_index][1] = normalized_y - - if valid_sequence: - return row - - else: - return original_row - - -if __name__ == "__main__": - pass diff --git a/spaces/CVPR/regionclip-demo/detectron2/config/instantiate.py b/spaces/CVPR/regionclip-demo/detectron2/config/instantiate.py deleted file mode 100644 index cbb32e19ea518eee84941b20f58d1054e84d1937..0000000000000000000000000000000000000000 --- a/spaces/CVPR/regionclip-demo/detectron2/config/instantiate.py +++ /dev/null @@ -1,82 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import dataclasses -import logging -from collections import abc -from typing import Any - -from detectron2.utils.registry import _convert_target_to_string, locate - -__all__ = ["dump_dataclass", "instantiate"] - - -def dump_dataclass(obj: Any): - """ - Dump a dataclass recursively into a dict that can be later instantiated. - - Args: - obj: a dataclass object - - Returns: - dict - """ - assert dataclasses.is_dataclass(obj) and not isinstance( - obj, type - ), "dump_dataclass() requires an instance of a dataclass." - ret = {"_target_": _convert_target_to_string(type(obj))} - for f in dataclasses.fields(obj): - v = getattr(obj, f.name) - if dataclasses.is_dataclass(v): - v = dump_dataclass(v) - if isinstance(v, (list, tuple)): - v = [dump_dataclass(x) if dataclasses.is_dataclass(x) else x for x in v] - ret[f.name] = v - return ret - - -def instantiate(cfg): - """ - Recursively instantiate objects defined in dictionaries by - "_target_" and arguments. - - Args: - cfg: a dict-like object with "_target_" that defines the caller, and - other keys that define the arguments - - Returns: - object instantiated by cfg - """ - from omegaconf import ListConfig - - if isinstance(cfg, ListConfig): - lst = [instantiate(x) for x in cfg] - return ListConfig(lst, flags={"allow_objects": True}) - if isinstance(cfg, list): - # Specialize for list, because many classes take - # list[objects] as arguments, such as ResNet, DatasetMapper - return [instantiate(x) for x in cfg] - - if isinstance(cfg, abc.Mapping) and "_target_" in cfg: - # conceptually equivalent to hydra.utils.instantiate(cfg) with _convert_=all, - # but faster: https://github.com/facebookresearch/hydra/issues/1200 - cfg = {k: instantiate(v) for k, v in cfg.items()} - cls = cfg.pop("_target_") - cls = instantiate(cls) - - if isinstance(cls, str): - cls_name = cls - cls = locate(cls_name) - assert cls is not None, cls_name - else: - try: - cls_name = cls.__module__ + "." + cls.__qualname__ - except Exception: - # target could be anything, so the above could fail - cls_name = str(cls) - assert callable(cls), f"_target_ {cls} does not define a callable object" - try: - return cls(**cfg) - except TypeError: - logger = logging.getLogger(__name__) - logger.error(f"Error when instantiating {cls_name}!") - raise - return cfg # return as-is if don't know what to do diff --git a/spaces/ChandraMohanNayal/AutoGPT/autogpt/speech/gtts.py b/spaces/ChandraMohanNayal/AutoGPT/autogpt/speech/gtts.py deleted file mode 100644 index 1c3e9cae0567428582891b11eca42f82a64f5c8e..0000000000000000000000000000000000000000 --- a/spaces/ChandraMohanNayal/AutoGPT/autogpt/speech/gtts.py +++ /dev/null @@ -1,22 +0,0 @@ -""" GTTS Voice. """ -import os - -import gtts -from playsound import playsound - -from autogpt.speech.base import VoiceBase - - -class GTTSVoice(VoiceBase): - """GTTS Voice.""" - - def _setup(self) -> None: - pass - - def _speech(self, text: str, _: int = 0) -> bool: - """Play the given text.""" - tts = gtts.gTTS(text) - tts.save("speech.mp3") - playsound("speech.mp3", True) - os.remove("speech.mp3") - return True diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/hit_screen/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/hit_screen/__init__.py deleted file mode 100644 index 1ff85781315f5802402a3dab620f26d14bf1c55c..0000000000000000000000000000000000000000 --- a/spaces/CikeyQI/meme-api/meme_generator/memes/hit_screen/__init__.py +++ /dev/null @@ -1,48 +0,0 @@ -from pathlib import Path -from typing import List - -from pil_utils import BuildImage - -from meme_generator import add_meme -from meme_generator.utils import FrameAlignPolicy, Maker, make_gif_or_combined_gif - -img_dir = Path(__file__).parent / "images" - - -def hit_screen(images: List[BuildImage], texts, args): - params = ( - (((1, 10), (138, 1), (140, 119), (7, 154)), (32, 37)), - (((1, 10), (138, 1), (140, 121), (7, 154)), (32, 37)), - (((1, 10), (138, 1), (139, 125), (10, 159)), (32, 37)), - (((1, 12), (136, 1), (137, 125), (8, 159)), (34, 37)), - (((1, 9), (137, 1), (139, 122), (9, 154)), (35, 41)), - (((1, 8), (144, 1), (144, 123), (12, 155)), (30, 45)), - (((1, 8), (140, 1), (141, 121), (10, 155)), (29, 49)), - (((1, 9), (140, 1), (139, 118), (10, 153)), (27, 53)), - (((1, 7), (144, 1), (145, 117), (13, 153)), (19, 57)), - (((1, 7), (144, 1), (143, 116), (13, 153)), (19, 57)), - (((1, 8), (139, 1), (141, 119), (12, 154)), (19, 55)), - (((1, 13), (140, 1), (143, 117), (12, 156)), (16, 57)), - (((1, 10), (138, 1), (142, 117), (11, 149)), (14, 61)), - (((1, 10), (141, 1), (148, 125), (13, 153)), (11, 57)), - (((1, 12), (141, 1), (147, 130), (16, 150)), (11, 60)), - (((1, 15), (165, 1), (175, 135), (1, 171)), (-6, 46)), - ) - - def maker(i: int) -> Maker: - def make(img: BuildImage) -> BuildImage: - img = img.convert("RGBA").resize((140, 120), keep_ratio=True) - frame = BuildImage.open(img_dir / f"{i}.png") - if 6 <= i < 22: - points, pos = params[i - 6] - frame.paste(img.perspective(points), pos, below=True) - return frame - - return make - - return make_gif_or_combined_gif( - images[0], maker, 29, 0.2, FrameAlignPolicy.extend_first - ) - - -add_meme("hit_screen", hit_screen, min_images=1, max_images=1, keywords=["打穿", "打穿屏幕"]) diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/__main__.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/__main__.py deleted file mode 100644 index 7c74ad3c86e54cb7e9939ed2bf96aa59cc6dcd06..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/__main__.py +++ /dev/null @@ -1,35 +0,0 @@ -import sys - - -def main(args=None): - if args is None: - args = sys.argv[1:] - - # TODO Handle library-wide options. Eg.: - # --unicodedata - # --verbose / other logging stuff - - # TODO Allow a way to run arbitrary modules? Useful for setting - # library-wide options and calling another library. Eg.: - # - # $ fonttools --unicodedata=... fontmake ... - # - # This allows for a git-like command where thirdparty commands - # can be added. Should we just try importing the fonttools - # module first and try without if it fails? - - if len(sys.argv) < 2: - sys.argv.append("help") - if sys.argv[1] == "-h" or sys.argv[1] == "--help": - sys.argv[1] = "help" - mod = "fontTools." + sys.argv[1] - sys.argv[1] = sys.argv[0] + " " + sys.argv[1] - del sys.argv[0] - - import runpy - - runpy.run_module(mod, run_name="__main__") - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/merge/base.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/merge/base.py deleted file mode 100644 index 37f9097ab2595413066cebd102fdf697280a93bb..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/merge/base.py +++ /dev/null @@ -1,81 +0,0 @@ -# Copyright 2013 Google, Inc. All Rights Reserved. -# -# Google Author(s): Behdad Esfahbod, Roozbeh Pournader - -from fontTools.ttLib.tables.DefaultTable import DefaultTable -import logging - - -log = logging.getLogger("fontTools.merge") - - -def add_method(*clazzes, **kwargs): - """Returns a decorator function that adds a new method to one or - more classes.""" - allowDefault = kwargs.get("allowDefaultTable", False) - - def wrapper(method): - done = [] - for clazz in clazzes: - if clazz in done: - continue # Support multiple names of a clazz - done.append(clazz) - assert allowDefault or clazz != DefaultTable, "Oops, table class not found." - assert ( - method.__name__ not in clazz.__dict__ - ), "Oops, class '%s' has method '%s'." % (clazz.__name__, method.__name__) - setattr(clazz, method.__name__, method) - return None - - return wrapper - - -def mergeObjects(lst): - lst = [item for item in lst if item is not NotImplemented] - if not lst: - return NotImplemented - lst = [item for item in lst if item is not None] - if not lst: - return None - - clazz = lst[0].__class__ - assert all(type(item) == clazz for item in lst), lst - - logic = clazz.mergeMap - returnTable = clazz() - returnDict = {} - - allKeys = set.union(set(), *(vars(table).keys() for table in lst)) - for key in allKeys: - try: - mergeLogic = logic[key] - except KeyError: - try: - mergeLogic = logic["*"] - except KeyError: - raise Exception( - "Don't know how to merge key %s of class %s" % (key, clazz.__name__) - ) - if mergeLogic is NotImplemented: - continue - value = mergeLogic(getattr(table, key, NotImplemented) for table in lst) - if value is not NotImplemented: - returnDict[key] = value - - returnTable.__dict__ = returnDict - - return returnTable - - -@add_method(DefaultTable, allowDefaultTable=True) -def merge(self, m, tables): - if not hasattr(self, "mergeMap"): - log.info("Don't know how to merge '%s'.", self.tableTag) - return NotImplemented - - logic = self.mergeMap - - if isinstance(logic, dict): - return m.mergeObjects(self, self.mergeMap, tables) - else: - return logic(tables) diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-4364e66d.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-4364e66d.js deleted file mode 100644 index f008aa821b9d8f14132ff5648fad602431878df4..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/index-4364e66d.js +++ /dev/null @@ -1,2 +0,0 @@ -import{S as r,e as h,s as v,k as g,o as w,z,v as k,x as B,a4 as C,P as R,p as S,R as q,A,F}from"./index-3370be2a.js";import{a as P}from"./Button-89624748.js";import{X}from"./Blocks-f0129fcd.js";function j(t){let i=t[9](t[3])+"",a;return{c(){a=R(i)},m(e,s){S(e,a,s)},p(e,s){s&520&&i!==(i=e[9](e[3])+"")&&q(a,i)},d(e){e&&A(a)}}}function D(t){let i,a;return i=new P({props:{variant:t[4],elem_id:t[0],elem_classes:t[1],size:t[6],scale:t[7],min_width:t[8],visible:t[2],disabled:t[5]==="static",$$slots:{default:[j]},$$scope:{ctx:t}}}),i.$on("click",t[10]),{c(){g(i.$$.fragment)},m(e,s){w(i,e,s),a=!0},p(e,[s]){const l={};s&16&&(l.variant=e[4]),s&1&&(l.elem_id=e[0]),s&2&&(l.elem_classes=e[1]),s&64&&(l.size=e[6]),s&128&&(l.scale=e[7]),s&256&&(l.min_width=e[8]),s&4&&(l.visible=e[2]),s&32&&(l.disabled=e[5]==="static"),s&2568&&(l.$$scope={dirty:s,ctx:e}),i.$set(l)},i(e){a||(z(i.$$.fragment,e),a=!0)},o(e){k(i.$$.fragment,e),a=!1},d(e){B(i,e)}}}function E(t,i,a){let e;C(t,X,n=>a(9,e=n));let{elem_id:s=""}=i,{elem_classes:l=[]}=i,{visible:m=!0}=i,{value:u}=i,{variant:_="secondary"}=i,{mode:f="dynamic"}=i,{size:o="lg"}=i,{scale:c=null}=i,{min_width:d=void 0}=i;function b(n){F.call(this,t,n)}return t.$$set=n=>{"elem_id"in n&&a(0,s=n.elem_id),"elem_classes"in n&&a(1,l=n.elem_classes),"visible"in n&&a(2,m=n.visible),"value"in n&&a(3,u=n.value),"variant"in n&&a(4,_=n.variant),"mode"in n&&a(5,f=n.mode),"size"in n&&a(6,o=n.size),"scale"in n&&a(7,c=n.scale),"min_width"in n&&a(8,d=n.min_width)},[s,l,m,u,_,f,o,c,d,e,b]}class G extends r{constructor(i){super(),h(this,i,E,D,v,{elem_id:0,elem_classes:1,visible:2,value:3,variant:4,mode:5,size:6,scale:7,min_width:8})}}const K=G,L=["static","dynamic"],M=t=>({type:{payload:"string"},description:{payload:"button label"},example_data:t.value||"Run"});export{K as Component,M as document,L as modes}; -//# sourceMappingURL=index-4364e66d.js.map diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/__init__.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/__init__.py deleted file mode 100644 index 4bf70b205a89b632751fbb7719ec1553c21e0666..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/huggingface_hub/__init__.py +++ /dev/null @@ -1,554 +0,0 @@ -# Copyright 2020 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# *********** -# `huggingface_hub` init has 2 modes: -# - Normal usage: -# If imported to use it, all modules and functions are lazy-loaded. This means -# they exist at top level in module but are imported only the first time they are -# used. This way, `from huggingface_hub import something` will import `something` -# quickly without the hassle of importing all the features from `huggingface_hub`. -# - Static check: -# If statically analyzed, all modules and functions are loaded normally. This way -# static typing check works properly as well as autocomplete in text editors and -# IDEs. -# -# The static model imports are done inside the `if TYPE_CHECKING:` statement at -# the bottom of this file. Since module/functions imports are duplicated, it is -# mandatory to make sure to add them twice when adding one. This is checked in the -# `make quality` command. -# -# To update the static imports, please run the following command and commit the changes. -# ``` -# # Use script -# python utils/check_static_imports.py --update-file -# -# # Or run style on codebase -# make style -# ``` -# -# *********** -# Lazy loader vendored from https://github.com/scientific-python/lazy_loader -import importlib -import os -import sys -from typing import TYPE_CHECKING - - -__version__ = "0.16.4" - -# Alphabetical order of definitions is ensured in tests -# WARNING: any comment added in this dictionary definition will be lost when -# re-generating the file ! -_SUBMOD_ATTRS = { - "_commit_scheduler": [ - "CommitScheduler", - ], - "_login": [ - "interpreter_login", - "login", - "logout", - "notebook_login", - ], - "_multi_commits": [ - "MultiCommitException", - "plan_multi_commits", - ], - "_snapshot_download": [ - "snapshot_download", - ], - "_space_api": [ - "SpaceHardware", - "SpaceRuntime", - "SpaceStage", - ], - "_tensorboard_logger": [ - "HFSummaryWriter", - ], - "_webhooks_payload": [ - "WebhookPayload", - "WebhookPayloadComment", - "WebhookPayloadDiscussion", - "WebhookPayloadDiscussionChanges", - "WebhookPayloadEvent", - "WebhookPayloadMovedTo", - "WebhookPayloadRepo", - "WebhookPayloadUrl", - "WebhookPayloadWebhook", - ], - "_webhooks_server": [ - "WebhooksServer", - "webhook_endpoint", - ], - "community": [ - "Discussion", - "DiscussionComment", - "DiscussionCommit", - "DiscussionEvent", - "DiscussionStatusChange", - "DiscussionTitleChange", - "DiscussionWithDetails", - ], - "constants": [ - "CONFIG_NAME", - "FLAX_WEIGHTS_NAME", - "HUGGINGFACE_CO_URL_HOME", - "HUGGINGFACE_CO_URL_TEMPLATE", - "PYTORCH_WEIGHTS_NAME", - "REPO_TYPE_DATASET", - "REPO_TYPE_MODEL", - "REPO_TYPE_SPACE", - "TF2_WEIGHTS_NAME", - "TF_WEIGHTS_NAME", - ], - "fastai_utils": [ - "_save_pretrained_fastai", - "from_pretrained_fastai", - "push_to_hub_fastai", - ], - "file_download": [ - "HfFileMetadata", - "_CACHED_NO_EXIST", - "cached_download", - "get_hf_file_metadata", - "hf_hub_download", - "hf_hub_url", - "try_to_load_from_cache", - ], - "hf_api": [ - "CommitInfo", - "CommitOperation", - "CommitOperationAdd", - "CommitOperationCopy", - "CommitOperationDelete", - "DatasetSearchArguments", - "GitCommitInfo", - "GitRefInfo", - "GitRefs", - "HfApi", - "ModelSearchArguments", - "RepoUrl", - "UserLikes", - "add_space_secret", - "change_discussion_status", - "comment_discussion", - "create_branch", - "create_commit", - "create_commits_on_pr", - "create_discussion", - "create_pull_request", - "create_repo", - "create_tag", - "dataset_info", - "delete_branch", - "delete_file", - "delete_folder", - "delete_repo", - "delete_space_secret", - "delete_tag", - "duplicate_space", - "edit_discussion_comment", - "get_dataset_tags", - "get_discussion_details", - "get_full_repo_name", - "get_model_tags", - "get_repo_discussions", - "get_space_runtime", - "get_token_permission", - "like", - "list_datasets", - "list_files_info", - "list_liked_repos", - "list_metrics", - "list_models", - "list_repo_commits", - "list_repo_files", - "list_repo_refs", - "list_spaces", - "merge_pull_request", - "model_info", - "move_repo", - "pause_space", - "rename_discussion", - "repo_info", - "repo_type_and_id_from_hf_id", - "request_space_hardware", - "restart_space", - "run_as_future", - "set_space_sleep_time", - "space_info", - "unlike", - "update_repo_visibility", - "upload_file", - "upload_folder", - "whoami", - ], - "hf_file_system": [ - "HfFileSystem", - "HfFileSystemFile", - "HfFileSystemResolvedPath", - ], - "hub_mixin": [ - "ModelHubMixin", - "PyTorchModelHubMixin", - ], - "inference._client": [ - "InferenceClient", - "InferenceTimeoutError", - ], - "inference._generated._async_client": [ - "AsyncInferenceClient", - ], - "inference_api": [ - "InferenceApi", - ], - "keras_mixin": [ - "KerasModelHubMixin", - "from_pretrained_keras", - "push_to_hub_keras", - "save_pretrained_keras", - ], - "repocard": [ - "DatasetCard", - "ModelCard", - "RepoCard", - "SpaceCard", - "metadata_eval_result", - "metadata_load", - "metadata_save", - "metadata_update", - ], - "repocard_data": [ - "CardData", - "DatasetCardData", - "EvalResult", - "ModelCardData", - "SpaceCardData", - ], - "repository": [ - "Repository", - ], - "utils": [ - "CacheNotFound", - "CachedFileInfo", - "CachedRepoInfo", - "CachedRevisionInfo", - "CorruptedCacheException", - "DeleteCacheStrategy", - "HFCacheInfo", - "HfFolder", - "cached_assets_path", - "configure_http_backend", - "dump_environment_info", - "get_session", - "logging", - "scan_cache_dir", - ], - "utils.endpoint_helpers": [ - "DatasetFilter", - "ModelFilter", - ], -} - - -def _attach(package_name, submodules=None, submod_attrs=None): - """Attach lazily loaded submodules, functions, or other attributes. - - Typically, modules import submodules and attributes as follows: - - ```py - import mysubmodule - import anothersubmodule - - from .foo import someattr - ``` - - The idea is to replace a package's `__getattr__`, `__dir__`, and - `__all__`, such that all imports work exactly the way they would - with normal imports, except that the import occurs upon first use. - - The typical way to call this function, replacing the above imports, is: - - ```python - __getattr__, __dir__, __all__ = lazy.attach( - __name__, - ['mysubmodule', 'anothersubmodule'], - {'foo': ['someattr']} - ) - ``` - This functionality requires Python 3.7 or higher. - - Args: - package_name (`str`): - Typically use `__name__`. - submodules (`set`): - List of submodules to attach. - submod_attrs (`dict`): - Dictionary of submodule -> list of attributes / functions. - These attributes are imported as they are used. - - Returns: - __getattr__, __dir__, __all__ - - """ - if submod_attrs is None: - submod_attrs = {} - - if submodules is None: - submodules = set() - else: - submodules = set(submodules) - - attr_to_modules = {attr: mod for mod, attrs in submod_attrs.items() for attr in attrs} - - __all__ = list(submodules | attr_to_modules.keys()) - - def __getattr__(name): - if name in submodules: - return importlib.import_module(f"{package_name}.{name}") - elif name in attr_to_modules: - submod_path = f"{package_name}.{attr_to_modules[name]}" - submod = importlib.import_module(submod_path) - attr = getattr(submod, name) - - # If the attribute lives in a file (module) with the same - # name as the attribute, ensure that the attribute and *not* - # the module is accessible on the package. - if name == attr_to_modules[name]: - pkg = sys.modules[package_name] - pkg.__dict__[name] = attr - - return attr - else: - raise AttributeError(f"No {package_name} attribute {name}") - - def __dir__(): - return __all__ - - if os.environ.get("EAGER_IMPORT", ""): - for attr in set(attr_to_modules.keys()) | submodules: - __getattr__(attr) - - return __getattr__, __dir__, list(__all__) - - -__getattr__, __dir__, __all__ = _attach(__name__, submodules=[], submod_attrs=_SUBMOD_ATTRS) - -# WARNING: any content below this statement is generated automatically. Any manual edit -# will be lost when re-generating this file ! -# -# To update the static imports, please run the following command and commit the changes. -# ``` -# # Use script -# python utils/check_static_imports.py --update-file -# -# # Or run style on codebase -# make style -# ``` -if TYPE_CHECKING: # pragma: no cover - from ._commit_scheduler import CommitScheduler # noqa: F401 - from ._login import ( - interpreter_login, # noqa: F401 - login, # noqa: F401 - logout, # noqa: F401 - notebook_login, # noqa: F401 - ) - from ._multi_commits import ( - MultiCommitException, # noqa: F401 - plan_multi_commits, # noqa: F401 - ) - from ._snapshot_download import snapshot_download # noqa: F401 - from ._space_api import ( - SpaceHardware, # noqa: F401 - SpaceRuntime, # noqa: F401 - SpaceStage, # noqa: F401 - ) - from ._tensorboard_logger import HFSummaryWriter # noqa: F401 - from ._webhooks_payload import ( - WebhookPayload, # noqa: F401 - WebhookPayloadComment, # noqa: F401 - WebhookPayloadDiscussion, # noqa: F401 - WebhookPayloadDiscussionChanges, # noqa: F401 - WebhookPayloadEvent, # noqa: F401 - WebhookPayloadMovedTo, # noqa: F401 - WebhookPayloadRepo, # noqa: F401 - WebhookPayloadUrl, # noqa: F401 - WebhookPayloadWebhook, # noqa: F401 - ) - from ._webhooks_server import ( - WebhooksServer, # noqa: F401 - webhook_endpoint, # noqa: F401 - ) - from .community import ( - Discussion, # noqa: F401 - DiscussionComment, # noqa: F401 - DiscussionCommit, # noqa: F401 - DiscussionEvent, # noqa: F401 - DiscussionStatusChange, # noqa: F401 - DiscussionTitleChange, # noqa: F401 - DiscussionWithDetails, # noqa: F401 - ) - from .constants import ( - CONFIG_NAME, # noqa: F401 - FLAX_WEIGHTS_NAME, # noqa: F401 - HUGGINGFACE_CO_URL_HOME, # noqa: F401 - HUGGINGFACE_CO_URL_TEMPLATE, # noqa: F401 - PYTORCH_WEIGHTS_NAME, # noqa: F401 - REPO_TYPE_DATASET, # noqa: F401 - REPO_TYPE_MODEL, # noqa: F401 - REPO_TYPE_SPACE, # noqa: F401 - TF2_WEIGHTS_NAME, # noqa: F401 - TF_WEIGHTS_NAME, # noqa: F401 - ) - from .fastai_utils import ( - _save_pretrained_fastai, # noqa: F401 - from_pretrained_fastai, # noqa: F401 - push_to_hub_fastai, # noqa: F401 - ) - from .file_download import ( - _CACHED_NO_EXIST, # noqa: F401 - HfFileMetadata, # noqa: F401 - cached_download, # noqa: F401 - get_hf_file_metadata, # noqa: F401 - hf_hub_download, # noqa: F401 - hf_hub_url, # noqa: F401 - try_to_load_from_cache, # noqa: F401 - ) - from .hf_api import ( - CommitInfo, # noqa: F401 - CommitOperation, # noqa: F401 - CommitOperationAdd, # noqa: F401 - CommitOperationCopy, # noqa: F401 - CommitOperationDelete, # noqa: F401 - DatasetSearchArguments, # noqa: F401 - GitCommitInfo, # noqa: F401 - GitRefInfo, # noqa: F401 - GitRefs, # noqa: F401 - HfApi, # noqa: F401 - ModelSearchArguments, # noqa: F401 - RepoUrl, # noqa: F401 - UserLikes, # noqa: F401 - add_space_secret, # noqa: F401 - change_discussion_status, # noqa: F401 - comment_discussion, # noqa: F401 - create_branch, # noqa: F401 - create_commit, # noqa: F401 - create_commits_on_pr, # noqa: F401 - create_discussion, # noqa: F401 - create_pull_request, # noqa: F401 - create_repo, # noqa: F401 - create_tag, # noqa: F401 - dataset_info, # noqa: F401 - delete_branch, # noqa: F401 - delete_file, # noqa: F401 - delete_folder, # noqa: F401 - delete_repo, # noqa: F401 - delete_space_secret, # noqa: F401 - delete_tag, # noqa: F401 - duplicate_space, # noqa: F401 - edit_discussion_comment, # noqa: F401 - get_dataset_tags, # noqa: F401 - get_discussion_details, # noqa: F401 - get_full_repo_name, # noqa: F401 - get_model_tags, # noqa: F401 - get_repo_discussions, # noqa: F401 - get_space_runtime, # noqa: F401 - get_token_permission, # noqa: F401 - like, # noqa: F401 - list_datasets, # noqa: F401 - list_files_info, # noqa: F401 - list_liked_repos, # noqa: F401 - list_metrics, # noqa: F401 - list_models, # noqa: F401 - list_repo_commits, # noqa: F401 - list_repo_files, # noqa: F401 - list_repo_refs, # noqa: F401 - list_spaces, # noqa: F401 - merge_pull_request, # noqa: F401 - model_info, # noqa: F401 - move_repo, # noqa: F401 - pause_space, # noqa: F401 - rename_discussion, # noqa: F401 - repo_info, # noqa: F401 - repo_type_and_id_from_hf_id, # noqa: F401 - request_space_hardware, # noqa: F401 - restart_space, # noqa: F401 - run_as_future, # noqa: F401 - set_space_sleep_time, # noqa: F401 - space_info, # noqa: F401 - unlike, # noqa: F401 - update_repo_visibility, # noqa: F401 - upload_file, # noqa: F401 - upload_folder, # noqa: F401 - whoami, # noqa: F401 - ) - from .hf_file_system import ( - HfFileSystem, # noqa: F401 - HfFileSystemFile, # noqa: F401 - HfFileSystemResolvedPath, # noqa: F401 - ) - from .hub_mixin import ( - ModelHubMixin, # noqa: F401 - PyTorchModelHubMixin, # noqa: F401 - ) - from .inference._client import ( - InferenceClient, # noqa: F401 - InferenceTimeoutError, # noqa: F401 - ) - from .inference._generated._async_client import AsyncInferenceClient # noqa: F401 - from .inference_api import InferenceApi # noqa: F401 - from .keras_mixin import ( - KerasModelHubMixin, # noqa: F401 - from_pretrained_keras, # noqa: F401 - push_to_hub_keras, # noqa: F401 - save_pretrained_keras, # noqa: F401 - ) - from .repocard import ( - DatasetCard, # noqa: F401 - ModelCard, # noqa: F401 - RepoCard, # noqa: F401 - SpaceCard, # noqa: F401 - metadata_eval_result, # noqa: F401 - metadata_load, # noqa: F401 - metadata_save, # noqa: F401 - metadata_update, # noqa: F401 - ) - from .repocard_data import ( - CardData, # noqa: F401 - DatasetCardData, # noqa: F401 - EvalResult, # noqa: F401 - ModelCardData, # noqa: F401 - SpaceCardData, # noqa: F401 - ) - from .repository import Repository # noqa: F401 - from .utils import ( - CachedFileInfo, # noqa: F401 - CachedRepoInfo, # noqa: F401 - CachedRevisionInfo, # noqa: F401 - CacheNotFound, # noqa: F401 - CorruptedCacheException, # noqa: F401 - DeleteCacheStrategy, # noqa: F401 - HFCacheInfo, # noqa: F401 - HfFolder, # noqa: F401 - cached_assets_path, # noqa: F401 - configure_http_backend, # noqa: F401 - dump_environment_info, # noqa: F401 - get_session, # noqa: F401 - logging, # noqa: F401 - scan_cache_dir, # noqa: F401 - ) - from .utils.endpoint_helpers import ( - DatasetFilter, # noqa: F401 - ModelFilter, # noqa: F401 - ) diff --git a/spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/ops/upfirdn2d.h b/spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/ops/upfirdn2d.h deleted file mode 100644 index dc6e713694d3fcca0e06cecfb9437ffb4932ffe6..0000000000000000000000000000000000000000 --- a/spaces/DragGan/DragGan-Inversion/stylegan_human/torch_utils/ops/upfirdn2d.h +++ /dev/null @@ -1,61 +0,0 @@ -// Copyright (c) SenseTime Research. All rights reserved. - -// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include- -//------------------------------------------------------------------------ -// CUDA kernel parameters. - -struct upfirdn2d_kernel_params -{ - const void* x; - const float* f; - void* y; - - int2 up; - int2 down; - int2 pad0; - int flip; - float gain; - - int4 inSize; // [width, height, channel, batch] - int4 inStride; - int2 filterSize; // [width, height] - int2 filterStride; - int4 outSize; // [width, height, channel, batch] - int4 outStride; - int sizeMinor; - int sizeMajor; - - int loopMinor; - int loopMajor; - int loopX; - int launchMinor; - int launchMajor; -}; - -//------------------------------------------------------------------------ -// CUDA kernel specialization. - -struct upfirdn2d_kernel_spec -{ - void* kernel; - int tileOutW; - int tileOutH; - int loopMinor; - int loopX; -}; - -//------------------------------------------------------------------------ -// CUDA kernel selection. - -template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p); - -//------------------------------------------------------------------------ diff --git a/spaces/Eddycrack864/Applio-Inference/Makefile b/spaces/Eddycrack864/Applio-Inference/Makefile deleted file mode 100644 index 44de020e6feb7fcd58016d7c3c736681f533b597..0000000000000000000000000000000000000000 --- a/spaces/Eddycrack864/Applio-Inference/Makefile +++ /dev/null @@ -1,63 +0,0 @@ -.PHONY: -.ONESHELL: - -help: ## Show this help and exit - @grep -hE '^[A-Za-z0-9_ \-]*?:.*##.*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}' - -install: ## Install dependencies (Do everytime you start up a paperspace machine) - apt-get -y install build-essential python3-dev ffmpeg - pip install --upgrade setuptools wheel - pip install --upgrade pip - pip install faiss-gpu fairseq gradio ffmpeg ffmpeg-python praat-parselmouth pyworld numpy==1.23.5 numba==0.56.4 librosa==0.9.1 - pip install -r requirements.txt - pip install --upgrade lxml - apt-get update - apt -y install -qq aria2 - -basev1: ## Download version 1 pre-trained models (Do only once after cloning the fork) - mkdir -p pretrained uvr5_weights - git pull - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D32k.pth -d pretrained -o D32k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D40k.pth -d pretrained -o D40k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D48k.pth -d pretrained -o D48k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G32k.pth -d pretrained -o G32k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G40k.pth -d pretrained -o G40k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G48k.pth -d pretrained -o G48k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D32k.pth -d pretrained -o f0D32k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D40k.pth -d pretrained -o f0D40k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D48k.pth -d pretrained -o f0D48k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G32k.pth -d pretrained -o f0G32k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G40k.pth -d pretrained -o f0G40k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G48k.pth -d pretrained -o f0G48k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth -d uvr5_weights -o HP2-人声vocals+非人声instrumentals.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth -d uvr5_weights -o HP5-主旋律人声vocals+其他instrumentals.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d ./ -o hubert_base.pt - -basev2: ## Download version 2 pre-trained models (Do only once after cloning the fork) - mkdir -p pretrained_v2 uvr5_weights - git pull - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D32k.pth -d pretrained_v2 -o D32k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d pretrained_v2 -o D40k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D48k.pth -d pretrained_v2 -o D48k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G32k.pth -d pretrained_v2 -o G32k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d pretrained_v2 -o G40k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G48k.pth -d pretrained_v2 -o G48k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D32k.pth -d pretrained_v2 -o f0D32k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth -d pretrained_v2 -o f0D40k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D48k.pth -d pretrained_v2 -o f0D48k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G32k.pth -d pretrained_v2 -o f0G32k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth -d pretrained_v2 -o f0G40k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G48k.pth -d pretrained_v2 -o f0G48k.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2-人声vocals+非人声instrumentals.pth -d uvr5_weights -o HP2-人声vocals+非人声instrumentals.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5-主旋律人声vocals+其他instrumentals.pth -d uvr5_weights -o HP5-主旋律人声vocals+其他instrumentals.pth - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt -d ./ -o hubert_base.pt - -run-ui: ## Run the python GUI - python infer-web.py --paperspace --pycmd python - -run-cli: ## Run the python CLI - python infer-web.py --pycmd python --is_cli - -tensorboard: ## Start the tensorboard (Run on separate terminal) - echo https://tensorboard-$$(hostname).clg07azjl.paperspacegradient.com - tensorboard --logdir logs --bind_all \ No newline at end of file diff --git a/spaces/EuroPython2022/mmocr-demo/configs/textdet/drrg/drrg_r50_fpn_unet_1200e_ctw1500.py b/spaces/EuroPython2022/mmocr-demo/configs/textdet/drrg/drrg_r50_fpn_unet_1200e_ctw1500.py deleted file mode 100644 index 7121ef83297d3a1976c9b62d2b47f0b5ba52bd66..0000000000000000000000000000000000000000 --- a/spaces/EuroPython2022/mmocr-demo/configs/textdet/drrg/drrg_r50_fpn_unet_1200e_ctw1500.py +++ /dev/null @@ -1,33 +0,0 @@ -_base_ = [ - '../../_base_/default_runtime.py', - '../../_base_/schedules/schedule_sgd_1200e.py', - '../../_base_/det_models/drrg_r50_fpn_unet.py', - '../../_base_/det_datasets/ctw1500.py', - '../../_base_/det_pipelines/drrg_pipeline.py' -] - -train_list = {{_base_.train_list}} -test_list = {{_base_.test_list}} - -train_pipeline = {{_base_.train_pipeline}} -test_pipeline = {{_base_.test_pipeline}} - -data = dict( - samples_per_gpu=4, - workers_per_gpu=4, - val_dataloader=dict(samples_per_gpu=1), - test_dataloader=dict(samples_per_gpu=1), - train=dict( - type='UniformConcatDataset', - datasets=train_list, - pipeline=train_pipeline), - val=dict( - type='UniformConcatDataset', - datasets=test_list, - pipeline=test_pipeline), - test=dict( - type='UniformConcatDataset', - datasets=test_list, - pipeline=test_pipeline)) - -evaluation = dict(interval=20, metric='hmean-iou') diff --git a/spaces/Felix123456/bingo/src/components/ui/voice/index.tsx b/spaces/Felix123456/bingo/src/components/ui/voice/index.tsx deleted file mode 100644 index 4adcb632226bfced8b97092782811edf08b56569..0000000000000000000000000000000000000000 --- a/spaces/Felix123456/bingo/src/components/ui/voice/index.tsx +++ /dev/null @@ -1,28 +0,0 @@ -import './index.scss' - -export interface VoiceProps extends CSSPropertyRule { - num?: number; - duration?: number; -} -export default function Voice({ duration = 400, num = 7, ...others }) { - return ( - - {Array.from({ length: num }).map((_, index) => { - const randomDuration = Math.random() * 100 + duration - const initialDelay = Math.random() * 2 * duration - const initialScale = Math.sin((index + 1) * Math.PI / num) - return ( - - ) - })} -- ) -} diff --git a/spaces/Fernando22/freegpt-webui/client/css/conversation.css b/spaces/Fernando22/freegpt-webui/client/css/conversation.css deleted file mode 100644 index d20f178c45e8ccbfc9539f99914b25fc572045bd..0000000000000000000000000000000000000000 --- a/spaces/Fernando22/freegpt-webui/client/css/conversation.css +++ /dev/null @@ -1,158 +0,0 @@ -.conversation { - width: 60%; - margin: 0px 16px; - display: flex; - flex-direction: column; -} - -.conversation #messages { - width: 100%; - display: flex; - flex-direction: column; - overflow: auto; - overflow-wrap: break-word; - padding-bottom: 8px; -} - -.conversation .user-input { - max-height: 180px; - margin: 16px 0px; -} - -.conversation .user-input input { - font-size: 1rem; - background: none; - border: none; - outline: none; - color: var(--colour-3); -} - -.conversation .user-input input::placeholder { - color: var(--user-input); -} - -.conversation-title { - color: var(--colour-3); - font-size: 14px; -} - -.conversation .user-input textarea { - font-size: 1rem; - width: 100%; - height: 100%; - padding: 12px; - background: none; - border: none; - outline: none; - color: var(--colour-3); - resize: vertical; - max-height: 150px; - min-height: 80px; -} - -.box { - backdrop-filter: blur(20px); - -webkit-backdrop-filter: blur(20px); - background-color: var(--blur-bg); - height: 100%; - width: 100%; - border-radius: var(--border-radius-1); - border: 1px solid var(--blur-border); -} - -.box.input-box { - position: relative; - align-items: center; - padding: 8px; - cursor: pointer; -} - -#send-button { - position: absolute; - bottom: 25%; - right: 10px; - z-index: 1; - padding: 16px; -} - -#cursor { - line-height: 17px; - margin-left: 3px; - -webkit-animation: blink 0.8s infinite; - animation: blink 0.8s infinite; - width: 7px; - height: 15px; -} - -@keyframes blink { - 0% { - background: #ffffff00; - } - - 50% { - background: white; - } - - 100% { - background: #ffffff00; - } -} - -@-webkit-keyframes blink { - 0% { - background: #ffffff00; - } - - 50% { - background: white; - } - - 100% { - background: #ffffff00; - } -} - -/* scrollbar */ -.conversation #messages::-webkit-scrollbar { - width: 4px; - padding: 8px 0px; -} - -.conversation #messages::-webkit-scrollbar-track { - background-color: #ffffff00; -} - -.conversation #messages::-webkit-scrollbar-thumb { - background-color: #555555; - border-radius: 10px; -} - -@media screen and (max-width: 990px) { - .conversation { - width: 100%; - height: 90%; - } -} - -@media screen and (max-height: 720px) { - .conversation.box { - height: 70%; - } - - .conversation .user-input textarea { - font-size: 0.875rem; - } -} - -@media screen and (max-width: 360px) { - .box { - border-radius: 0; - } - .conversation { - margin: 0; - margin-top: 48px; - } - .conversation .user-input { - margin: 2px 0 8px 0; - } -} diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/model.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/model.py deleted file mode 100644 index cb3781c17a1e78a33bf62246e5134e8512206d0d..0000000000000000000000000000000000000000 --- a/spaces/FrankZxShen/so-vits-svc-models-pcr/vencoder/whisper/model.py +++ /dev/null @@ -1,269 +0,0 @@ -from dataclasses import dataclass -from typing import Dict -from typing import Iterable, Optional - -import numpy as np -import torch -import torch.nn.functional as F -from torch import Tensor -from torch import nn - -from .decoding import detect_language as detect_language_function, decode as decode_function - - -@dataclass -class ModelDimensions: - n_mels: int - n_audio_ctx: int - n_audio_state: int - n_audio_head: int - n_audio_layer: int - n_vocab: int - n_text_ctx: int - n_text_state: int - n_text_head: int - n_text_layer: int - - -class LayerNorm(nn.LayerNorm): - def forward(self, x: Tensor) -> Tensor: - return super().forward(x.float()).type(x.dtype) - - -class Linear(nn.Linear): - def forward(self, x: Tensor) -> Tensor: - return F.linear( - x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype) - ) - - -class Conv1d(nn.Conv1d): - def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor: - return super()._conv_forward( - x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype) - ) - - -def sinusoids(length, channels, max_timescale=10000): - """Returns sinusoids for positional embedding""" - assert channels % 2 == 0 - log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1) - inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2)) - scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :] - return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1) - - -class MultiHeadAttention(nn.Module): - def __init__(self, n_state: int, n_head: int): - super().__init__() - self.n_head = n_head - self.query = Linear(n_state, n_state) - self.key = Linear(n_state, n_state, bias=False) - self.value = Linear(n_state, n_state) - self.out = Linear(n_state, n_state) - - def forward( - self, - x: Tensor, - xa: Optional[Tensor] = None, - mask: Optional[Tensor] = None, - kv_cache: Optional[dict] = None, - ): - q = self.query(x) - - if kv_cache is None or xa is None or self.key not in kv_cache: - # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors; - # otherwise, perform key/value projections for self- or cross-attention as usual. - k = self.key(x if xa is None else xa) - v = self.value(x if xa is None else xa) - else: - # for cross-attention, calculate keys and values once and reuse in subsequent calls. - k = kv_cache[self.key] - v = kv_cache[self.value] - - wv, qk = self.qkv_attention(q, k, v, mask) - return self.out(wv), qk - - def qkv_attention(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None): - n_batch, n_ctx, n_state = q.shape - scale = (n_state // self.n_head) ** -0.25 - q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale - k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale - v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) - - qk = q @ k - if mask is not None: - qk = qk + mask[:n_ctx, :n_ctx] - qk = qk.float() - - w = F.softmax(qk, dim=-1).to(q.dtype) - return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach() - - -class ResidualAttentionBlock(nn.Module): - def __init__(self, n_state: int, n_head: int, cross_attention: bool = False): - super().__init__() - - self.attn = MultiHeadAttention(n_state, n_head) - self.attn_ln = LayerNorm(n_state) - - self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None - self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None - - n_mlp = n_state * 4 - self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state)) - self.mlp_ln = LayerNorm(n_state) - - def forward( - self, - x: Tensor, - xa: Optional[Tensor] = None, - mask: Optional[Tensor] = None, - kv_cache: Optional[dict] = None, - ): - x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0] - if self.cross_attn: - x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0] - x = x + self.mlp(self.mlp_ln(x)) - return x - - -class AudioEncoder(nn.Module): - def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int): - super().__init__() - self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1) - self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1) - self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state)) - - self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( - [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)] - ) - self.ln_post = LayerNorm(n_state) - - def forward(self, x: Tensor): - """ - x : torch.Tensor, shape = (batch_size, n_mels, n_ctx) - the mel spectrogram of the audio - """ - x = F.gelu(self.conv1(x)) - x = F.gelu(self.conv2(x)) - x = x.permute(0, 2, 1) - - len_x = x.shape[1] - len_e = self.positional_embedding.shape[0] - assert len_x <= len_e, "incorrect audio shape" - pos_e = self.positional_embedding[:len_x, :] - x = (x + pos_e).to(x.dtype) - - for block in self.blocks: - x = block(x) - - x = self.ln_post(x) - return x - - -class TextDecoder(nn.Module): - def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int): - super().__init__() - - self.token_embedding = nn.Embedding(n_vocab, n_state) - self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state)) - - self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( - [ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer)] - ) - self.ln = LayerNorm(n_state) - - mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1) - self.register_buffer("mask", mask, persistent=False) - - def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None): - """ - x : torch.LongTensor, shape = (batch_size, <= n_ctx) - the text tokens - xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx) - the encoded audio features to be attended on - """ - offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0 - x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]] - x = x.to(xa.dtype) - - for block in self.blocks: - x = block(x, xa, mask=self.mask, kv_cache=kv_cache) - - x = self.ln(x) - logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float() - - return logits - - -class Whisper(nn.Module): - def __init__(self, dims: ModelDimensions): - super().__init__() - self.dims = dims - self.encoder = AudioEncoder( - self.dims.n_mels, - self.dims.n_audio_ctx, - self.dims.n_audio_state, - self.dims.n_audio_head, - self.dims.n_audio_layer, - ) - self.decoder = TextDecoder( - self.dims.n_vocab, - self.dims.n_text_ctx, - self.dims.n_text_state, - self.dims.n_text_head, - self.dims.n_text_layer, - ) - - def embed_audio(self, mel: torch.Tensor): - return self.encoder(mel) - - def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor): - return self.decoder(tokens, audio_features) - - def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]: - return self.decoder(tokens, self.encoder(mel)) - - @property - def device(self): - return next(self.parameters()).device - - @property - def is_multilingual(self): - return self.dims.n_vocab == 51865 - - def install_kv_cache_hooks(self, cache: Optional[dict] = None): - """ - The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value - tensors calculated for the previous positions. This method returns a dictionary that stores - all caches, and the necessary hooks for the key and value projection modules that save the - intermediate tensors to be reused during later calculations. - - Returns - ------- - cache : Dict[nn.Module, torch.Tensor] - A dictionary object mapping the key/value projection modules to its cache - hooks : List[RemovableHandle] - List of PyTorch RemovableHandle objects to stop the hooks to be called - """ - cache = {**cache} if cache is not None else {} - hooks = [] - - def save_to_cache(module, _, output): - if module not in cache or output.shape[1] > self.decoder.positional_embedding.shape[0]: - cache[module] = output # save as-is, for the first token or cross attention - else: - cache[module] = torch.cat([cache[module], output], dim=1).detach() - return cache[module] - - def install_hooks(layer: nn.Module): - if isinstance(layer, MultiHeadAttention): - hooks.append(layer.key.register_forward_hook(save_to_cache)) - hooks.append(layer.value.register_forward_hook(save_to_cache)) - - self.decoder.apply(install_hooks) - return cache, hooks - - detect_language = detect_language_function - decode = decode_function diff --git a/spaces/FrankZxShen/vits-fast-finetuning-umamusume/README.md b/spaces/FrankZxShen/vits-fast-finetuning-umamusume/README.md deleted file mode 100644 index 8c1ef83c99cef1d5484a08ceb73a31bcbd5961cf..0000000000000000000000000000000000000000 --- a/spaces/FrankZxShen/vits-fast-finetuning-umamusume/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Vits Fast Finetuning Umamusume -emoji: 📊 -colorFrom: gray -colorTo: red -sdk: gradio -sdk_version: 3.29.0 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/ngu_dialect.py b/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/ngu_dialect.py deleted file mode 100644 index ce3e12bbf0469426872eed5f681985d3e1be9b26..0000000000000000000000000000000000000000 --- a/spaces/FrankZxShen/vits-fast-fineturning-models-ba/text/ngu_dialect.py +++ /dev/null @@ -1,30 +0,0 @@ -import re -import opencc - - -dialects = {'SZ': 'suzhou', 'WX': 'wuxi', 'CZ': 'changzhou', 'HZ': 'hangzhou', - 'SX': 'shaoxing', 'NB': 'ningbo', 'JJ': 'jingjiang', 'YX': 'yixing', - 'JD': 'jiading', 'ZR': 'zhenru', 'PH': 'pinghu', 'TX': 'tongxiang', - 'JS': 'jiashan', 'HN': 'xiashi', 'LP': 'linping', 'XS': 'xiaoshan', - 'FY': 'fuyang', 'RA': 'ruao', 'CX': 'cixi', 'SM': 'sanmen', - 'TT': 'tiantai', 'WZ': 'wenzhou', 'SC': 'suichang', 'YB': 'youbu'} - -converters = {} - -for dialect in dialects.values(): - try: - converters[dialect] = opencc.OpenCC(dialect) - except: - pass - - -def ngu_dialect_to_ipa(text, dialect): - dialect = dialects[dialect] - text = converters[dialect].convert(text).replace('-','').replace('$',' ') - text = re.sub(r'[、；：]', '，', text) - text = re.sub(r'\s*，\s*', ', ', text) - text = re.sub(r'\s*。\s*', '. ', text) - text = re.sub(r'\s*？\s*', '? ', text) - text = re.sub(r'\s*！\s*', '! ', text) - text = re.sub(r'\s*$', '', text) - return text diff --git a/spaces/FridaZuley/RVC_HFKawaii/infer/lib/infer_pack/commons.py b/spaces/FridaZuley/RVC_HFKawaii/infer/lib/infer_pack/commons.py deleted file mode 100644 index ccd334b7320543b0c3a2166f82093564c9721317..0000000000000000000000000000000000000000 --- a/spaces/FridaZuley/RVC_HFKawaii/infer/lib/infer_pack/commons.py +++ /dev/null @@ -1,167 +0,0 @@ -import math - -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F - - -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) - - -def get_padding(kernel_size, dilation=1): - return int((kernel_size * dilation - dilation) / 2) - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def kl_divergence(m_p, logs_p, m_q, logs_q): - """KL(P||Q)""" - kl = (logs_q - logs_p) - 0.5 - kl += ( - 0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q) - ) - return kl - - -def rand_gumbel(shape): - """Sample from the Gumbel distribution, protect from overflows.""" - uniform_samples = torch.rand(shape) * 0.99998 + 0.00001 - return -torch.log(-torch.log(uniform_samples)) - - -def rand_gumbel_like(x): - g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device) - return g - - -def slice_segments(x, ids_str, segment_size=4): - ret = torch.zeros_like(x[:, :, :segment_size]) - for i in range(x.size(0)): - idx_str = ids_str[i] - idx_end = idx_str + segment_size - ret[i] = x[i, :, idx_str:idx_end] - return ret - - -def slice_segments2(x, ids_str, segment_size=4): - ret = torch.zeros_like(x[:, :segment_size]) - for i in range(x.size(0)): - idx_str = ids_str[i] - idx_end = idx_str + segment_size - ret[i] = x[i, idx_str:idx_end] - return ret - - -def rand_slice_segments(x, x_lengths=None, segment_size=4): - b, d, t = x.size() - if x_lengths is None: - x_lengths = t - ids_str_max = x_lengths - segment_size + 1 - ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) - ret = slice_segments(x, ids_str, segment_size) - return ret, ids_str - - -def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4): - position = torch.arange(length, dtype=torch.float) - num_timescales = channels // 2 - log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / ( - num_timescales - 1 - ) - inv_timescales = min_timescale * torch.exp( - torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment - ) - scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1) - signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0) - signal = F.pad(signal, [0, 0, 0, channels % 2]) - signal = signal.view(1, channels, length) - return signal - - -def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return x + signal.to(dtype=x.dtype, device=x.device) - - -def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis) - - -def subsequent_mask(length): - mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) - return mask - - -@torch.jit.script -def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): - n_channels_int = n_channels[0] - in_act = input_a + input_b - t_act = torch.tanh(in_act[:, :n_channels_int, :]) - s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) - acts = t_act * s_act - return acts - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def shift_1d(x): - x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] - return x - - -def sequence_mask(length, max_length=None): - if max_length is None: - max_length = length.max() - x = torch.arange(max_length, dtype=length.dtype, device=length.device) - return x.unsqueeze(0) < length.unsqueeze(1) - - -def generate_path(duration, mask): - """ - duration: [b, 1, t_x] - mask: [b, 1, t_y, t_x] - """ - device = duration.device - - b, _, t_y, t_x = mask.shape - cum_duration = torch.cumsum(duration, -1) - - cum_duration_flat = cum_duration.view(b * t_x) - path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) - path = path.view(b, t_x, t_y) - path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] - path = path.unsqueeze(1).transpose(2, 3) * mask - return path - - -def clip_grad_value_(parameters, clip_value, norm_type=2): - if isinstance(parameters, torch.Tensor): - parameters = [parameters] - parameters = list(filter(lambda p: p.grad is not None, parameters)) - norm_type = float(norm_type) - if clip_value is not None: - clip_value = float(clip_value) - - total_norm = 0 - for p in parameters: - param_norm = p.grad.data.norm(norm_type) - total_norm += param_norm.item() ** norm_type - if clip_value is not None: - p.grad.data.clamp_(min=-clip_value, max=clip_value) - total_norm = total_norm ** (1.0 / norm_type) - return total_norm diff --git a/spaces/FridaZuley/RVC_HFKawaii/lib/uvr5_pack/lib_v5/nets_33966KB.py b/spaces/FridaZuley/RVC_HFKawaii/lib/uvr5_pack/lib_v5/nets_33966KB.py deleted file mode 100644 index b8986f968dc5383e65d35aac6e4367299de3378b..0000000000000000000000000000000000000000 --- a/spaces/FridaZuley/RVC_HFKawaii/lib/uvr5_pack/lib_v5/nets_33966KB.py +++ /dev/null @@ -1,122 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from . import layers_33966KB as layers - - -class BaseASPPNet(nn.Module): - def __init__(self, nin, ch, dilations=(4, 8, 16, 32)): - super(BaseASPPNet, self).__init__() - self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) - self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) - self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) - self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) - - self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) - - self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) - self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) - self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) - self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) - - def __call__(self, x): - h, e1 = self.enc1(x) - h, e2 = self.enc2(h) - h, e3 = self.enc3(h) - h, e4 = self.enc4(h) - - h = self.aspp(h) - - h = self.dec4(h, e4) - h = self.dec3(h, e3) - h = self.dec2(h, e2) - h = self.dec1(h, e1) - - return h - - -class CascadedASPPNet(nn.Module): - def __init__(self, n_fft): - super(CascadedASPPNet, self).__init__() - self.stg1_low_band_net = BaseASPPNet(2, 16) - self.stg1_high_band_net = BaseASPPNet(2, 16) - - self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0) - self.stg2_full_band_net = BaseASPPNet(8, 16) - - self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0) - self.stg3_full_band_net = BaseASPPNet(16, 32) - - self.out = nn.Conv2d(32, 2, 1, bias=False) - self.aux1_out = nn.Conv2d(16, 2, 1, bias=False) - self.aux2_out = nn.Conv2d(16, 2, 1, bias=False) - - self.max_bin = n_fft // 2 - self.output_bin = n_fft // 2 + 1 - - self.offset = 128 - - def forward(self, x, aggressiveness=None): - mix = x.detach() - x = x.clone() - - x = x[:, :, : self.max_bin] - - bandw = x.size()[2] // 2 - aux1 = torch.cat( - [ - self.stg1_low_band_net(x[:, :, :bandw]), - self.stg1_high_band_net(x[:, :, bandw:]), - ], - dim=2, - ) - - h = torch.cat([x, aux1], dim=1) - aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) - - h = torch.cat([x, aux1, aux2], dim=1) - h = self.stg3_full_band_net(self.stg3_bridge(h)) - - mask = torch.sigmoid(self.out(h)) - mask = F.pad( - input=mask, - pad=(0, 0, 0, self.output_bin - mask.size()[2]), - mode="replicate", - ) - - if self.training: - aux1 = torch.sigmoid(self.aux1_out(aux1)) - aux1 = F.pad( - input=aux1, - pad=(0, 0, 0, self.output_bin - aux1.size()[2]), - mode="replicate", - ) - aux2 = torch.sigmoid(self.aux2_out(aux2)) - aux2 = F.pad( - input=aux2, - pad=(0, 0, 0, self.output_bin - aux2.size()[2]), - mode="replicate", - ) - return mask * mix, aux1 * mix, aux2 * mix - else: - if aggressiveness: - mask[:, :, : aggressiveness["split_bin"]] = torch.pow( - mask[:, :, : aggressiveness["split_bin"]], - 1 + aggressiveness["value"] / 3, - ) - mask[:, :, aggressiveness["split_bin"] :] = torch.pow( - mask[:, :, aggressiveness["split_bin"] :], - 1 + aggressiveness["value"], - ) - - return mask * mix - - def predict(self, x_mag, aggressiveness=None): - h = self.forward(x_mag, aggressiveness) - - if self.offset > 0: - h = h[:, :, :, self.offset : -self.offset] - assert h.size()[3] > 0 - - return h diff --git a/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/RealESRGANv030/README.md b/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/RealESRGANv030/README.md deleted file mode 100644 index 118e930c12bffd9e6da1df03180f5c9a8dcaabc3..0000000000000000000000000000000000000000 --- a/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/RealESRGANv030/README.md +++ /dev/null @@ -1,272 +0,0 @@ --
- -## - -
-- -👀[**Demos**](#-demos-videos) **|** 🚩[**Updates**](#-updates) **|** ⚡[**Usage**](#-quick-inference) **|** 🏰[**Model Zoo**](docs/model_zoo.md) **|** 🔧[Install](#-dependencies-and-installation) **|** 💻[Train](docs/Training.md) **|** ❓[FAQ](docs/FAQ.md) **|** 🎨[Contribution](docs/CONTRIBUTING.md) - -[](https://github.com/xinntao/Real-ESRGAN/releases) -[](https://pypi.org/project/realesrgan/) -[](https://github.com/xinntao/Real-ESRGAN/issues) -[](https://github.com/xinntao/Real-ESRGAN/issues) -[](https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE) -[](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/pylint.yml) -[](https://github.com/xinntao/Real-ESRGAN/blob/master/.github/workflows/publish-pip.yml) - -- -🔥 **AnimeVideo-v3 model (动漫视频小模型)**. Please see [[*anime video models*](docs/anime_video_model.md)] and [[*comparisons*](docs/anime_comparisons.md)]
-🔥 **RealESRGAN_x4plus_anime_6B** for anime images **(动漫插图模型)**. Please see [[*anime_model*](docs/anime_model.md)] - - -1. :boom: **Update** online Replicate demo: [](https://replicate.com/xinntao/realesrgan) -1. Online Colab demo for Real-ESRGAN: [](https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing) **|** Online Colab demo for for Real-ESRGAN (**anime videos**): [](https://colab.research.google.com/drive/1yNl9ORUxxlL4N0keJa2SEPB61imPQd1B?usp=sharing) -1. Portable [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-macos.zip) **executable files for Intel/AMD/Nvidia GPU**. You can find more information [here](#portable-executable-files-ncnn). The ncnn implementation is in [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan) - - -Real-ESRGAN aims at developing **Practical Algorithms for General Image/Video Restoration**.
-We extend the powerful ESRGAN to a practical restoration application (namely, Real-ESRGAN), which is trained with pure synthetic data. - -🌌 Thanks for your valuable feedbacks/suggestions. All the feedbacks are updated in [feedback.md](docs/feedback.md). - ---- - -If Real-ESRGAN is helpful, please help to ⭐ this repo or recommend it to your friends 😊
-Other recommended projects:
-▶️ [GFPGAN](https://github.com/TencentARC/GFPGAN): A practical algorithm for real-world face restoration
-▶️ [BasicSR](https://github.com/xinntao/BasicSR): An open-source image and video restoration toolbox
-▶️ [facexlib](https://github.com/xinntao/facexlib): A collection that provides useful face-relation functions.
-▶️ [HandyView](https://github.com/xinntao/HandyView): A PyQt5-based image viewer that is handy for view and comparison
-▶️ [HandyFigure](https://github.com/xinntao/HandyFigure): Open source of paper figures
- ---- - -### 📖 Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data - -> [[Paper](https://arxiv.org/abs/2107.10833)] [[YouTube Video](https://www.youtube.com/watch?v=fxHWoDSSvSc)] [[B站讲解](https://www.bilibili.com/video/BV1H34y1m7sS/)] [[Poster](https://xinntao.github.io/projects/RealESRGAN_src/RealESRGAN_poster.pdf)] [[PPT slides](https://docs.google.com/presentation/d/1QtW6Iy8rm8rGLsJ0Ldti6kP-7Qyzy6XL/edit?usp=sharing&ouid=109799856763657548160&rtpof=true&sd=true)]
-> [Xintao Wang](https://xinntao.github.io/), Liangbin Xie, [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en)
-> [Tencent ARC Lab](https://arc.tencent.com/en/ai-demos/imgRestore); Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences - --
- ---- - - -## 🚩 Updates - -- ✅ Add the **realesr-general-x4v3** model - a tiny small model for general scenes. It also supports the **--dn** option to balance the noise (avoiding over-smooth results). **--dn** is short for denoising strength. -- ✅ Update the **RealESRGAN AnimeVideo-v3** model. Please see [anime video models](docs/anime_video_model.md) and [comparisons](docs/anime_comparisons.md) for more details. -- ✅ Add small models for anime videos. More details are in [anime video models](docs/anime_video_model.md). -- ✅ Add the ncnn implementation [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan). -- ✅ Add [*RealESRGAN_x4plus_anime_6B.pth*](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth), which is optimized for **anime** images with much smaller model size. More details and comparisons with [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan) are in [**anime_model.md**](docs/anime_model.md) -- ✅ Support finetuning on your own data or paired data (*i.e.*, finetuning ESRGAN). See [here](docs/Training.md#Finetune-Real-ESRGAN-on-your-own-dataset) -- ✅ Integrate [GFPGAN](https://github.com/TencentARC/GFPGAN) to support **face enhancement**. -- ✅ Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/Real-ESRGAN). Thanks [@AK391](https://github.com/AK391) -- ✅ Support arbitrary scale with `--outscale` (It actually further resizes outputs with `LANCZOS4`). Add *RealESRGAN_x2plus.pth* model. -- ✅ [The inference code](inference_realesrgan.py) supports: 1) **tile** options; 2) images with **alpha channel**; 3) **gray** images; 4) **16-bit** images. -- ✅ The training codes have been released. A detailed guide can be found in [Training.md](docs/Training.md). - ---- - - -## 👀 Demos Videos - -#### Bilibili - -- [大闹天宫片段](https://www.bilibili.com/video/BV1ja41117zb) -- [Anime dance cut 动漫魔性舞蹈](https://www.bilibili.com/video/BV1wY4y1L7hT/) -- [海贼王片段](https://www.bilibili.com/video/BV1i3411L7Gy/) - -#### YouTube - -## 🔧 Dependencies and Installation - -- Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html)) -- [PyTorch >= 1.7](https://pytorch.org/) - -### Installation - -1. Clone repo - - ```bash - git clone https://github.com/xinntao/Real-ESRGAN.git - cd Real-ESRGAN - ``` - -1. Install dependent packages - - ```bash - # Install basicsr - https://github.com/xinntao/BasicSR - # We use BasicSR for both training and inference - pip install basicsr - # facexlib and gfpgan are for face enhancement - pip install facexlib - pip install gfpgan - pip install -r requirements.txt - python setup.py develop - ``` - ---- - -## ⚡ Quick Inference - -There are usually three ways to inference Real-ESRGAN. - -1. [Online inference](#online-inference) -1. [Portable executable files (NCNN)](#portable-executable-files-ncnn) -1. [Python script](#python-script) - -### Online inference - -1. You can try in our website: [ARC Demo](https://arc.tencent.com/en/ai-demos/imgRestore) (now only support RealESRGAN_x4plus_anime_6B) -1. [Colab Demo](https://colab.research.google.com/drive/1k2Zod6kSHEvraybHl50Lys0LerhyTMCo?usp=sharing) for Real-ESRGAN **|** [Colab Demo](https://colab.research.google.com/drive/1yNl9ORUxxlL4N0keJa2SEPB61imPQd1B?usp=sharing) for Real-ESRGAN (**anime videos**). - -### Portable executable files (NCNN) - -You can download [Windows](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-windows.zip) / [Linux](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-ubuntu.zip) / [MacOS](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesrgan-ncnn-vulkan-20220424-macos.zip) **executable files for Intel/AMD/Nvidia GPU**. - -This executable file is **portable** and includes all the binaries and models required. No CUDA or PyTorch environment is needed.
-
- -You can simply run the following command (the Windows example, more information is in the README.md of each executable files): - -```bash -./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png -n model_name -``` - -We have provided five models: - -1. realesrgan-x4plus (default) -2. realesrnet-x4plus -3. realesrgan-x4plus-anime (optimized for anime images, small model size) -4. realesr-animevideov3 (animation video) - -You can use the `-n` argument for other models, for example, `./realesrgan-ncnn-vulkan.exe -i input.jpg -o output.png -n realesrnet-x4plus` - -#### Usage of portable executable files - -1. Please refer to [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan#computer-usages) for more details. -1. Note that it does not support all the functions (such as `outscale`) as the python script `inference_realesrgan.py`. - -```console -Usage: realesrgan-ncnn-vulkan.exe -i infile -o outfile [options]... - - -h show this help - -i input-path input image path (jpg/png/webp) or directory - -o output-path output image path (jpg/png/webp) or directory - -s scale upscale ratio (can be 2, 3, 4. default=4) - -t tile-size tile size (>=32/0=auto, default=0) can be 0,0,0 for multi-gpu - -m model-path folder path to the pre-trained models. default=models - -n model-name model name (default=realesr-animevideov3, can be realesr-animevideov3 | realesrgan-x4plus | realesrgan-x4plus-anime | realesrnet-x4plus) - -g gpu-id gpu device to use (default=auto) can be 0,1,2 for multi-gpu - -j load:proc:save thread count for load/proc/save (default=1:2:2) can be 1:2,2,2:2 for multi-gpu - -x enable tta mode" - -f format output image format (jpg/png/webp, default=ext/png) - -v verbose output -``` - -Note that it may introduce block inconsistency (and also generate slightly different results from the PyTorch implementation), because this executable file first crops the input image into several tiles, and then processes them separately, finally stitches together. - -### Python script - -#### Usage of python script - -1. You can use X4 model for **arbitrary output size** with the argument `outscale`. The program will further perform cheap resize operation after the Real-ESRGAN output. - -```console -Usage: python inference_realesrgan.py -n RealESRGAN_x4plus -i infile -o outfile [options]... - -A common command: python inference_realesrgan.py -n RealESRGAN_x4plus -i infile --outscale 3.5 --face_enhance - - -h show this help - -i --input Input image or folder. Default: inputs - -o --output Output folder. Default: results - -n --model_name Model name. Default: RealESRGAN_x4plus - -s, --outscale The final upsampling scale of the image. Default: 4 - --suffix Suffix of the restored image. Default: out - -t, --tile Tile size, 0 for no tile during testing. Default: 0 - --face_enhance Whether to use GFPGAN to enhance face. Default: False - --fp32 Use fp32 precision during inference. Default: fp16 (half precision). - --ext Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto -``` - -#### Inference general images - -Download pre-trained models: [RealESRGAN_x4plus.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth) - -```bash -wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P weights -``` - -Inference! - -```bash -python inference_realesrgan.py -n RealESRGAN_x4plus -i inputs --face_enhance -``` - -Results are in the `results` folder - -#### Inference anime images - --
- -Pre-trained models: [RealESRGAN_x4plus_anime_6B](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth)
-
- More details and comparisons with [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan) are in [**anime_model.md**](docs/anime_model.md) - -```bash -# download model -wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P weights -# inference -python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i inputs -``` - -Results are in the `results` folder - ---- - -## BibTeX - - @InProceedings{wang2021realesrgan, - author = {Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan}, - title = {Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data}, - booktitle = {International Conference on Computer Vision Workshops (ICCVW)}, - date = {2021} - } - -## 📧 Contact - -If you have any question, please email `xintao.wang@outlook.com` or `xintaowang@tencent.com`. - - -## 🧩 Projects that use Real-ESRGAN - -If you develop/use Real-ESRGAN in your projects, welcome to let me know. - -- NCNN-Android: [RealSR-NCNN-Android](https://github.com/tumuyan/RealSR-NCNN-Android) by [tumuyan](https://github.com/tumuyan) -- VapourSynth: [vs-realesrgan](https://github.com/HolyWu/vs-realesrgan) by [HolyWu](https://github.com/HolyWu) -- NCNN: [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN-ncnn-vulkan) - - **GUI** - -- [Waifu2x-Extension-GUI](https://github.com/AaronFeng753/Waifu2x-Extension-GUI) by [AaronFeng753](https://github.com/AaronFeng753) -- [Squirrel-RIFE](https://github.com/Justin62628/Squirrel-RIFE) by [Justin62628](https://github.com/Justin62628) -- [Real-GUI](https://github.com/scifx/Real-GUI) by [scifx](https://github.com/scifx) -- [Real-ESRGAN_GUI](https://github.com/net2cn/Real-ESRGAN_GUI) by [net2cn](https://github.com/net2cn) -- [Real-ESRGAN-EGUI](https://github.com/WGzeyu/Real-ESRGAN-EGUI) by [WGzeyu](https://github.com/WGzeyu) -- [anime_upscaler](https://github.com/shangar21/anime_upscaler) by [shangar21](https://github.com/shangar21) -- [Upscayl](https://github.com/upscayl/upscayl) by [Nayam Amarshe](https://github.com/NayamAmarshe) and [TGS963](https://github.com/TGS963) - -## 🤗 Acknowledgement - -Thanks for all the contributors. - -- [AK391](https://github.com/AK391): Integrate RealESRGAN to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/Real-ESRGAN). -- [Asiimoviet](https://github.com/Asiimoviet): Translate the README.md to Chinese (中文). -- [2ji3150](https://github.com/2ji3150): Thanks for the [detailed and valuable feedbacks/suggestions](https://github.com/xinntao/Real-ESRGAN/issues/131). -- [Jared-02](https://github.com/Jared-02): Translate the Training.md to Chinese (中文). diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/_base_/datasets/deepfashion.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/_base_/datasets/deepfashion.py deleted file mode 100644 index 308b4b2ac4d9e3516ba4a57e9d3b6af91e97f24b..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/_base_/datasets/deepfashion.py +++ /dev/null @@ -1,53 +0,0 @@ -# dataset settings -dataset_type = 'DeepFashionDataset' -data_root = 'data/DeepFashion/In-shop/' -img_norm_cfg = dict( - mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations', with_bbox=True, with_mask=True), - dict(type='Resize', img_scale=(750, 1101), keep_ratio=True), - dict(type='RandomFlip', flip_ratio=0.5), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='DefaultFormatBundle'), - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), -] -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='MultiScaleFlipAug', - img_scale=(750, 1101), - flip=False, - transforms=[ - dict(type='Resize', keep_ratio=True), - dict(type='RandomFlip'), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']), - ]) -] -data = dict( - imgs_per_gpu=2, - workers_per_gpu=1, - train=dict( - type=dataset_type, - ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json', - img_prefix=data_root + 'Img/', - pipeline=train_pipeline, - data_root=data_root), - val=dict( - type=dataset_type, - ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json', - img_prefix=data_root + 'Img/', - pipeline=test_pipeline, - data_root=data_root), - test=dict( - type=dataset_type, - ann_file=data_root + - 'annotations/DeepFashion_segmentation_gallery.json', - img_prefix=data_root + 'Img/', - pipeline=test_pipeline, - data_root=data_root)) -evaluation = dict(interval=5, metric=['bbox', 'segm']) diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/detectors/trident_faster_rcnn.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/detectors/trident_faster_rcnn.py deleted file mode 100644 index f0fd80d41407162df71ba5349fc659d4713cdb6e..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/detectors/trident_faster_rcnn.py +++ /dev/null @@ -1,66 +0,0 @@ -from ..builder import DETECTORS -from .faster_rcnn import FasterRCNN - - -@DETECTORS.register_module() -class TridentFasterRCNN(FasterRCNN): - """Implementation of `TridentNet' | \ - grep -v ' ' | \ - grep -v ' ' | \ - sed -e 's/ //g' | \ - sed -e 's/<\/title>//g' | \ - sed -e 's/<description>//g' | \ - sed -e 's/<\/description>//g' | \ - perl $TOKENIZER -threads 8 -l $l > $tmp/$tok - echo "" -done -perl $CLEAN -ratio 1.5 $tmp/train.tags.$lang.tok $src $tgt $tmp/train.tags.$lang.clean 1 175 -for l in $src $tgt; do - perl $LC < $tmp/train.tags.$lang.clean.$l > $tmp/train.tags.$lang.$l -done - -echo "pre-processing valid/test data..." -for l in $src $tgt; do - for o in `ls $orig/$lang/IWSLT14.TED*.$l.xml`; do - fname=${o##*/} - f=$tmp/${fname%.*} - echo $o $f - grep '<seg id' $o | \ - sed -e 's/<seg id="[0-9]*">\s*//g' | \ - sed -e 's/\s*<\/seg>\s*//g' | \ - sed -e "s/\’/\'/g" | \ - perl $TOKENIZER -threads 8 -l $l | \ - perl $LC > $f - echo "" - done -done - - -echo "creating train, valid, test..." -for l in $src $tgt; do - awk '{if (NR%23 == 0) print $0; }' $tmp/train.tags.de-en.$l > $tmp/valid.$l - awk '{if (NR%23 != 0) print $0; }' $tmp/train.tags.de-en.$l > $tmp/train.$l - - cat $tmp/IWSLT14.TED.dev2010.de-en.$l \ - $tmp/IWSLT14.TEDX.dev2012.de-en.$l \ - $tmp/IWSLT14.TED.tst2010.de-en.$l \ - $tmp/IWSLT14.TED.tst2011.de-en.$l \ - $tmp/IWSLT14.TED.tst2012.de-en.$l \ - > $tmp/test.$l -done - -TRAIN=$tmp/train.en-de -BPE_CODE=$prep/code -rm -f $TRAIN -for l in $src $tgt; do - cat $tmp/train.$l >> $TRAIN -done - -echo "learn_bpe.py on ${TRAIN}..." -python $BPEROOT/learn_bpe.py -s $BPE_TOKENS < $TRAIN > $BPE_CODE - -for L in $src $tgt; do - for f in train.$L valid.$L test.$L; do - echo "apply_bpe.py to ${f}..." - python $BPEROOT/apply_bpe.py -c $BPE_CODE < $tmp/$f > $prep/$f - done -done diff --git a/spaces/Heshwa/html-code-generation-from-images-with-deep-neural-networks/main_program.py b/spaces/Heshwa/html-code-generation-from-images-with-deep-neural-networks/main_program.py deleted file mode 100644 index bf9ec602b215f5125d44e7e6071989a2fefaae88..0000000000000000000000000000000000000000 --- a/spaces/Heshwa/html-code-generation-from-images-with-deep-neural-networks/main_program.py +++ /dev/null @@ -1,89 +0,0 @@ -from __future__ import absolute_import -from __future__ import print_function - -__author__ = 'Taneem Jan, taneemishere.github.io' - -import os.path -from os.path import basename - -from classes.Sampler import * -from classes.model.Main_Model import * - - -def dsl_code_generation(input_image): - trained_weights_path = "classes/model/bin" - trained_model_name = "Main_Model" - input_path = input_image - output_path = "data/output/" - search_method = "greedy" - meta_dataset = np.load("{}/meta_dataset.npy".format(trained_weights_path), allow_pickle=True) - input_shape = meta_dataset[0] - output_size = meta_dataset[1] - - model = Main_Model(input_shape, output_size, trained_weights_path) - model.load(trained_model_name) - - sampler = Sampler(trained_weights_path, input_shape, output_size, CONTEXT_LENGTH) - - file_name = 'input_image_from_interface.png' - file_name = basename(file_name)[:basename(file_name).find(".")] - evaluation_img = Utils.get_preprocessed_img(input_path, IMAGE_SIZE) - - if search_method == "greedy": - result, _ = sampler.predict_greedy(model, np.array([evaluation_img])) - print("Result greedy: \n {}".format(result)) - - with open("{}/{}.gui".format(output_path, file_name), 'w') as out_f: - out_f.write(result.replace(START_TOKEN, "").replace(END_TOKEN, "")) - - return file_name, output_path - - -def compile_gui(file_path, filename): - from os.path import basename - from compiler.Utils import Utils - from compiler.Compiler import Compiler - - input_path = (file_path + filename) - - # remove the path - file_ = os.path.basename(input_path) - # remove the extension - file_ = os.path.splitext(file_)[0] - # add the extension of gui - file_ = "data/output/" + file_ + ".gui" - - input_file = file_ - - FILL_WITH_RANDOM_TEXT = True - TEXT_PLACE_HOLDER = "[]" - - dsl_path = "compiler/assets/web-dsl-mapping.json" - compiler = Compiler(dsl_path) - - def render_content_with_text(key, value): - if FILL_WITH_RANDOM_TEXT: - if key.find("btn") != -1: - value = value.replace(TEXT_PLACE_HOLDER, Utils.get_random_text()) - elif key.find("title") != -1: - value = value.replace(TEXT_PLACE_HOLDER, Utils.get_random_text(length_text=5, space_number=0)) - elif key.find("text") != -1: - value = value.replace(TEXT_PLACE_HOLDER, - Utils.get_random_text(length_text=56, space_number=7, with_upper_case=False)) - return value - - file_uid = basename(input_file)[:basename(input_file).find(".")] - path = input_file[:input_file.find(file_uid)] - - input_file_path = "{}{}.gui".format(path, file_uid) - output_file_path = "{}{}.html".format(path, file_uid) - - html_code = compiler.compile(input_file_path, output_file_path, rendering_function=render_content_with_text) - print("Generated code is compiled..!!") - return html_code - - -def main_method(input_image_from_interface): - file_name, file_output_path = dsl_code_generation(input_image_from_interface) - result = compile_gui(file_output_path, file_name) - return result diff --git a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/helpers.py b/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/helpers.py deleted file mode 100644 index 4d2d329331766f0b9b94175412e061aca218e683..0000000000000000000000000000000000000000 --- a/spaces/HighCWu/anime-colorization-with-hint/gradio-modified/gradio/helpers.py +++ /dev/null @@ -1,792 +0,0 @@ -""" -Defines helper methods useful for loading and caching Interface examples. -""" -from __future__ import annotations - -import ast -import csv -import inspect -import os -import subprocess -import tempfile -import threading -import warnings -from pathlib import Path -from typing import TYPE_CHECKING, Any, Callable, Iterable, List, Optional, Tuple - -import matplotlib -import matplotlib.pyplot as plt -import numpy as np -import PIL - -from gradio import processing_utils, routes, utils -from gradio.context import Context -from gradio.documentation import document, set_documentation_group -from gradio.flagging import CSVLogger - -if TYPE_CHECKING: # Only import for type checking (to avoid circular imports). - from gradio.components import IOComponent - -CACHED_FOLDER = "gradio_cached_examples" -LOG_FILE = "log.csv" - -set_documentation_group("helpers") - - -def create_examples( - examples: List[Any] | List[List[Any]] | str, - inputs: IOComponent | List[IOComponent], - outputs: IOComponent | List[IOComponent] | None = None, - fn: Callable | None = None, - cache_examples: bool = False, - examples_per_page: int = 10, - _api_mode: bool = False, - label: str | None = None, - elem_id: str | None = None, - run_on_click: bool = False, - preprocess: bool = True, - postprocess: bool = True, - batch: bool = False, -): - """Top-level synchronous function that creates Examples. Provided for backwards compatibility, i.e. so that gr.Examples(...) can be used to create the Examples component.""" - examples_obj = Examples( - examples=examples, - inputs=inputs, - outputs=outputs, - fn=fn, - cache_examples=cache_examples, - examples_per_page=examples_per_page, - _api_mode=_api_mode, - label=label, - elem_id=elem_id, - run_on_click=run_on_click, - preprocess=preprocess, - postprocess=postprocess, - batch=batch, - _initiated_directly=False, - ) - utils.synchronize_async(examples_obj.create) - return examples_obj - - -@document() -class Examples: - """ - This class is a wrapper over the Dataset component and can be used to create Examples - for Blocks / Interfaces. Populates the Dataset component with examples and - assigns event listener so that clicking on an example populates the input/output - components. Optionally handles example caching for fast inference. - - Demos: blocks_inputs, fake_gan - Guides: more_on_examples_and_flagging, using_hugging_face_integrations, image_classification_in_pytorch, image_classification_in_tensorflow, image_classification_with_vision_transformers, create_your_own_friends_with_a_gan - """ - - def __init__( - self, - examples: List[Any] | List[List[Any]] | str, - inputs: IOComponent | List[IOComponent], - outputs: Optional[IOComponent | List[IOComponent]] = None, - fn: Optional[Callable] = None, - cache_examples: bool = False, - examples_per_page: int = 10, - _api_mode: bool = False, - label: str = "Examples", - elem_id: Optional[str] = None, - run_on_click: bool = False, - preprocess: bool = True, - postprocess: bool = True, - batch: bool = False, - _initiated_directly: bool = True, - ): - """ - Parameters: - examples: example inputs that can be clicked to populate specific components. Should be nested list, in which the outer list consists of samples and each inner list consists of an input corresponding to each input component. A string path to a directory of examples can also be provided but it should be within the directory with the python file running the gradio app. If there are multiple input components and a directory is provided, a log.csv file must be present in the directory to link corresponding inputs. - inputs: the component or list of components corresponding to the examples - outputs: optionally, provide the component or list of components corresponding to the output of the examples. Required if `cache` is True. - fn: optionally, provide the function to run to generate the outputs corresponding to the examples. Required if `cache` is True. - cache_examples: if True, caches examples for fast runtime. If True, then `fn` and `outputs` need to be provided - examples_per_page: how many examples to show per page. - label: the label to use for the examples component (by default, "Examples") - elem_id: an optional string that is assigned as the id of this component in the HTML DOM. - run_on_click: if cache_examples is False, clicking on an example does not run the function when an example is clicked. Set this to True to run the function when an example is clicked. Has no effect if cache_examples is True. - preprocess: if True, preprocesses the example input before running the prediction function and caching the output. Only applies if cache_examples is True. - postprocess: if True, postprocesses the example output after running the prediction function and before caching. Only applies if cache_examples is True. - batch: If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. Used only if cache_examples is True. - """ - if _initiated_directly: - warnings.warn( - "Please use gr.Examples(...) instead of gr.examples.Examples(...) to create the Examples.", - ) - - if cache_examples and (fn is None or outputs is None): - raise ValueError("If caching examples, `fn` and `outputs` must be provided") - - if not isinstance(inputs, list): - inputs = [inputs] - if not isinstance(outputs, list): - outputs = [outputs] - - working_directory = Path().absolute() - - if examples is None: - raise ValueError("The parameter `examples` cannot be None") - elif isinstance(examples, list) and ( - len(examples) == 0 or isinstance(examples[0], list) - ): - pass - elif ( - isinstance(examples, list) and len(inputs) == 1 - ): # If there is only one input component, examples can be provided as a regular list instead of a list of lists - examples = [[e] for e in examples] - elif isinstance(examples, str): - if not os.path.exists(examples): - raise FileNotFoundError( - "Could not find examples directory: " + examples - ) - working_directory = examples - if not os.path.exists(os.path.join(examples, LOG_FILE)): - if len(inputs) == 1: - examples = [[e] for e in os.listdir(examples)] - else: - raise FileNotFoundError( - "Could not find log file (required for multiple inputs): " - + LOG_FILE - ) - else: - with open(os.path.join(examples, LOG_FILE)) as logs: - examples = list(csv.reader(logs)) - examples = [ - examples[i][: len(inputs)] for i in range(1, len(examples)) - ] # remove header and unnecessary columns - - else: - raise ValueError( - "The parameter `examples` must either be a string directory or a list" - "(if there is only 1 input component) or (more generally), a nested " - "list, where each sublist represents a set of inputs." - ) - - input_has_examples = [False] * len(inputs) - for example in examples: - for idx, example_for_input in enumerate(example): - if not (example_for_input is None): - try: - input_has_examples[idx] = True - except IndexError: - pass # If there are more example components than inputs, ignore. This can sometimes be intentional (e.g. loading from a log file where outputs and timestamps are also logged) - - inputs_with_examples = [ - inp for (inp, keep) in zip(inputs, input_has_examples) if keep - ] - non_none_examples = [ - [ex for (ex, keep) in zip(example, input_has_examples) if keep] - for example in examples - ] - - self.examples = examples - self.non_none_examples = non_none_examples - self.inputs = inputs - self.inputs_with_examples = inputs_with_examples - self.outputs = outputs - self.fn = fn - self.cache_examples = cache_examples - self._api_mode = _api_mode - self.preprocess = preprocess - self.postprocess = postprocess - self.batch = batch - - with utils.set_directory(working_directory): - self.processed_examples = [ - [ - component.postprocess(sample) - for component, sample in zip(inputs, example) - ] - for example in examples - ] - self.non_none_processed_examples = [ - [ex for (ex, keep) in zip(example, input_has_examples) if keep] - for example in self.processed_examples - ] - if cache_examples: - for example in self.examples: - if len([ex for ex in example if ex is not None]) != len(self.inputs): - warnings.warn( - "Examples are being cached but not all input components have " - "example values. This may result in an exception being thrown by " - "your function. If you do get an error while caching examples, make " - "sure all of your inputs have example values for all of your examples " - "or you provide default values for those particular parameters in your function." - ) - break - - from gradio.components import Dataset - - with utils.set_directory(working_directory): - self.dataset = Dataset( - components=inputs_with_examples, - samples=non_none_examples, - type="index", - label=label, - samples_per_page=examples_per_page, - elem_id=elem_id, - ) - - self.cached_folder = os.path.join(CACHED_FOLDER, str(self.dataset._id)) - self.cached_file = os.path.join(self.cached_folder, "log.csv") - self.cache_examples = cache_examples - self.run_on_click = run_on_click - - async def create(self) -> None: - """Caches the examples if self.cache_examples is True and creates the Dataset - component to hold the examples""" - - async def load_example(example_id): - if self.cache_examples: - processed_example = self.non_none_processed_examples[ - example_id - ] + await self.load_from_cache(example_id) - else: - processed_example = self.non_none_processed_examples[example_id] - return utils.resolve_singleton(processed_example) - - if Context.root_block: - self.dataset.click( - load_example, - inputs=[self.dataset], - outputs=self.inputs_with_examples - + (self.outputs if self.cache_examples else []), - postprocess=False, - queue=False, - ) - if self.run_on_click and not self.cache_examples: - self.dataset.click( - self.fn, - inputs=self.inputs, - outputs=self.outputs, - ) - - if self.cache_examples: - await self.cache() - - async def cache(self) -> None: - """ - Caches all of the examples so that their predictions can be shown immediately. - """ - if os.path.exists(self.cached_file): - print( - f"Using cache from '{os.path.abspath(self.cached_folder)}' directory. If method or examples have changed since last caching, delete this folder to clear cache." - ) - else: - if Context.root_block is None: - raise ValueError("Cannot cache examples if not in a Blocks context") - - print(f"Caching examples at: '{os.path.abspath(self.cached_file)}'") - cache_logger = CSVLogger() - - # create a fake dependency to process the examples and get the predictions - dependency = Context.root_block.set_event_trigger( - event_name="fake_event", - fn=self.fn, - inputs=self.inputs_with_examples, - outputs=self.outputs, - preprocess=self.preprocess and not self._api_mode, - postprocess=self.postprocess and not self._api_mode, - batch=self.batch, - ) - - fn_index = Context.root_block.dependencies.index(dependency) - cache_logger.setup(self.outputs, self.cached_folder) - for example_id, _ in enumerate(self.examples): - processed_input = self.processed_examples[example_id] - if self.batch: - processed_input = [[value] for value in processed_input] - prediction = await Context.root_block.process_api( - fn_index=fn_index, inputs=processed_input, request=None, state={} - ) - output = prediction["data"] - if self.batch: - output = [value[0] for value in output] - cache_logger.flag(output) - # Remove the "fake_event" to prevent bugs in loading interfaces from spaces - Context.root_block.dependencies.remove(dependency) - Context.root_block.fns.pop(fn_index) - - async def load_from_cache(self, example_id: int) -> List[Any]: - """Loads a particular cached example for the interface. - Parameters: - example_id: The id of the example to process (zero-indexed). - """ - with open(self.cached_file) as cache: - examples = list(csv.reader(cache)) - example = examples[example_id + 1] # +1 to adjust for header - output = [] - for component, value in zip(self.outputs, example): - try: - value_as_dict = ast.literal_eval(value) - assert utils.is_update(value_as_dict) - output.append(value_as_dict) - except (ValueError, TypeError, SyntaxError, AssertionError): - output.append(component.serialize(value, self.cached_folder)) - return output - - -class TrackedIterable: - def __init__( - self, - iterable: Iterable, - index: int | None, - length: int | None, - desc: str | None, - unit: str | None, - _tqdm=None, - progress: float = None, - ) -> None: - self.iterable = iterable - self.index = index - self.length = length - self.desc = desc - self.unit = unit - self._tqdm = _tqdm - self.progress = progress - - -@document("__call__", "tqdm") -class Progress(Iterable): - """ - The Progress class provides a custom progress tracker that is used in a function signature. - To attach a Progress tracker to a function, simply add a parameter right after the input parameters that has a default value set to a `gradio.Progress()` instance. - The Progress tracker can then be updated in the function by calling the Progress object or using the `tqdm` method on an Iterable. - The Progress tracker is currently only available with `queue()`. - Example: - import gradio as gr - import time - def my_function(x, progress=gr.Progress()): - progress(0, desc="Starting...") - time.sleep(1) - for i in progress.tqdm(range(100)): - time.sleep(0.1) - return x - gr.Interface(my_function, gr.Textbox(), gr.Textbox()).queue().launch() - Demos: progress - """ - - def __init__( - self, - track_tqdm: bool = False, - _active: bool = False, - _callback: Callable = None, - _event_id: str = None, - ): - """ - Parameters: - track_tqdm: If True, the Progress object will track any tqdm.tqdm iterations with the tqdm library in the function. - """ - self.track_tqdm = track_tqdm - self._active = _active - self._callback = _callback - self._event_id = _event_id - self.iterables: List[TrackedIterable] = [] - - def __len__(self): - return self.iterables[-1].length - - def __iter__(self): - return self - - def __next__(self): - """ - Updates progress tracker with next item in iterable. - """ - if self._active: - current_iterable = self.iterables[-1] - while ( - not hasattr(current_iterable.iterable, "__next__") - and len(self.iterables) > 0 - ): - current_iterable = self.iterables.pop() - self._callback( - event_id=self._event_id, - iterables=self.iterables, - ) - current_iterable.index += 1 - try: - return next(current_iterable.iterable) - except StopIteration: - self.iterables.pop() - raise StopIteration - else: - return self - - def __call__( - self, - progress: float | Tuple[int, int | None] | None, - desc: str | None = None, - total: float | None = None, - unit: str = "steps", - _tqdm=None, - ): - """ - Updates progress tracker with progress and message text. - Parameters: - progress: If float, should be between 0 and 1 representing completion. If Tuple, first number represents steps completed, and second value represents total steps or None if unknown. If None, hides progress bar. - desc: description to display. - total: estimated total number of steps. - unit: unit of iterations. - """ - if self._active: - if isinstance(progress, tuple): - index, total = progress - progress = None - else: - index = None - self._callback( - event_id=self._event_id, - iterables=self.iterables - + [TrackedIterable(None, index, total, desc, unit, _tqdm, progress)], - ) - else: - return progress - - def tqdm( - self, - iterable: Iterable | None, - desc: str = None, - total: float = None, - unit: str = "steps", - _tqdm=None, - *args, - **kwargs, - ): - """ - Attaches progress tracker to iterable, like tqdm. - Parameters: - iterable: iterable to attach progress tracker to. - desc: description to display. - total: estimated total number of steps. - unit: unit of iterations. - """ - if iterable is None: - new_iterable = TrackedIterable(None, 0, total, desc, unit, _tqdm) - self.iterables.append(new_iterable) - self._callback(event_id=self._event_id, iterables=self.iterables) - return - length = len(iterable) if hasattr(iterable, "__len__") else None - self.iterables.append( - TrackedIterable(iter(iterable), 0, length, desc, unit, _tqdm) - ) - return self - - def update(self, n=1): - """ - Increases latest iterable with specified number of steps. - Parameters: - n: number of steps completed. - """ - if self._active and len(self.iterables) > 0: - current_iterable = self.iterables[-1] - current_iterable.index += n - self._callback( - event_id=self._event_id, - iterables=self.iterables, - ) - else: - return - - def close(self, _tqdm): - """ - Removes iterable with given _tqdm. - """ - if self._active: - for i in range(len(self.iterables)): - if id(self.iterables[i]._tqdm) == id(_tqdm): - self.iterables.pop(i) - break - self._callback( - event_id=self._event_id, - iterables=self.iterables, - ) - else: - return - - -def create_tracker(root_blocks, event_id, fn, track_tqdm): - - progress = Progress( - _active=True, _callback=root_blocks._queue.set_progress, _event_id=event_id - ) - if not track_tqdm: - return progress, fn - - try: - _tqdm = __import__("tqdm") - except ModuleNotFoundError: - return progress, fn - if not hasattr(root_blocks, "_progress_tracker_per_thread"): - root_blocks._progress_tracker_per_thread = {} - - def init_tqdm(self, iterable=None, desc=None, *args, **kwargs): - self._progress = root_blocks._progress_tracker_per_thread.get( - threading.get_ident() - ) - if self._progress is not None: - self._progress.event_id = event_id - self._progress.tqdm(iterable, desc, _tqdm=self, *args, **kwargs) - kwargs["file"] = open(os.devnull, "w") - self.__init__orig__(iterable, desc, *args, **kwargs) - - def iter_tqdm(self): - if self._progress is not None: - return self._progress - else: - return self.__iter__orig__() - - def update_tqdm(self, n=1): - if self._progress is not None: - self._progress.update(n) - return self.__update__orig__(n) - - def close_tqdm(self): - if self._progress is not None: - self._progress.close(self) - return self.__close__orig__() - - def exit_tqdm(self, exc_type, exc_value, traceback): - if self._progress is not None: - self._progress.close(self) - return self.__exit__orig__(exc_type, exc_value, traceback) - - if not hasattr(_tqdm.tqdm, "__init__orig__"): - _tqdm.tqdm.__init__orig__ = _tqdm.tqdm.__init__ - _tqdm.tqdm.__init__ = init_tqdm - if not hasattr(_tqdm.tqdm, "__update__orig__"): - _tqdm.tqdm.__update__orig__ = _tqdm.tqdm.update - _tqdm.tqdm.update = update_tqdm - if not hasattr(_tqdm.tqdm, "__close__orig__"): - _tqdm.tqdm.__close__orig__ = _tqdm.tqdm.close - _tqdm.tqdm.close = close_tqdm - if not hasattr(_tqdm.tqdm, "__exit__orig__"): - _tqdm.tqdm.__exit__orig__ = _tqdm.tqdm.__exit__ - _tqdm.tqdm.__exit__ = exit_tqdm - if not hasattr(_tqdm.tqdm, "__iter__orig__"): - _tqdm.tqdm.__iter__orig__ = _tqdm.tqdm.__iter__ - _tqdm.tqdm.__iter__ = iter_tqdm - if hasattr(_tqdm, "auto") and hasattr(_tqdm.auto, "tqdm"): - _tqdm.auto.tqdm = _tqdm.tqdm - - def tracked_fn(*args): - thread_id = threading.get_ident() - root_blocks._progress_tracker_per_thread[thread_id] = progress - response = fn(*args) - del root_blocks._progress_tracker_per_thread[thread_id] - return response - - return progress, tracked_fn - - -def special_args( - fn: Callable, - inputs: List[Any] | None = None, - request: routes.Request | None = None, -): - """ - Checks if function has special arguments Request (via annotation) or Progress (via default value). - If inputs is provided, these values will be loaded into the inputs array. - Parameters: - block_fn: function to check. - inputs: array to load special arguments into. - request: request to load into inputs. - Returns: - updated inputs, request index, progress index - """ - signature = inspect.signature(fn) - positional_args = [] - for i, param in enumerate(signature.parameters.values()): - if param.kind not in (param.POSITIONAL_ONLY, param.POSITIONAL_OR_KEYWORD): - break - positional_args.append(param) - progress_index = None - for i, param in enumerate(positional_args): - if isinstance(param.default, Progress): - progress_index = i - if inputs is not None: - inputs.insert(i, param.default) - elif param.annotation == routes.Request: - if inputs is not None: - inputs.insert(i, request) - if inputs is not None: - while len(inputs) < len(positional_args): - i = len(inputs) - param = positional_args[i] - if param.default == param.empty: - warnings.warn("Unexpected argument. Filling with None.") - inputs.append(None) - else: - inputs.append(param.default) - return inputs or [], progress_index - - -@document() -def update(**kwargs) -> dict: - """ - Updates component properties. When a function passed into a Gradio Interface or a Blocks events returns a typical value, it updates the value of the output component. But it is also possible to update the properties of an output component (such as the number of lines of a `Textbox` or the visibility of an `Image`) by returning the component's `update()` function, which takes as parameters any of the constructor parameters for that component. - This is a shorthand for using the update method on a component. - For example, rather than using gr.Number.update(...) you can just use gr.update(...). - Note that your editor's autocompletion will suggest proper parameters - if you use the update method on the component. - Demos: blocks_essay, blocks_update, blocks_essay_update - - Parameters: - kwargs: Key-word arguments used to update the component's properties. - Example: - # Blocks Example - import gradio as gr - with gr.Blocks() as demo: - radio = gr.Radio([1, 2, 4], label="Set the value of the number") - number = gr.Number(value=2, interactive=True) - radio.change(fn=lambda value: gr.update(value=value), inputs=radio, outputs=number) - demo.launch() - - # Interface example - import gradio as gr - def change_textbox(choice): - if choice == "short": - return gr.Textbox.update(lines=2, visible=True) - elif choice == "long": - return gr.Textbox.update(lines=8, visible=True) - else: - return gr.Textbox.update(visible=False) - gr.Interface( - change_textbox, - gr.Radio( - ["short", "long", "none"], label="What kind of essay would you like to write?" - ), - gr.Textbox(lines=2), - live=True, - ).launch() - """ - kwargs["__type__"] = "generic_update" - return kwargs - - -def skip() -> dict: - return update() - - -@document() -def make_waveform( - audio: str | Tuple[int, np.ndarray], - *, - bg_color: str = "#f3f4f6", - bg_image: str = None, - fg_alpha: float = 0.75, - bars_color: str | Tuple[str, str] = ("#fbbf24", "#ea580c"), - bar_count: int = 50, - bar_width: float = 0.6, -): - """ - Generates a waveform video from an audio file. Useful for creating an easy to share audio visualization. The output should be passed into a `gr.Video` component. - Parameters: - audio: Audio file path or tuple of (sample_rate, audio_data) - bg_color: Background color of waveform (ignored if bg_image is provided) - bg_image: Background image of waveform - fg_alpha: Opacity of foreground waveform - bars_color: Color of waveform bars. Can be a single color or a tuple of (start_color, end_color) of gradient - bar_count: Number of bars in waveform - bar_width: Width of bars in waveform. 1 represents full width, 0.5 represents half width, etc. - Returns: - A filepath to the output video. - """ - if isinstance(audio, str): - audio_file = audio - audio = processing_utils.audio_from_file(audio) - else: - tmp_wav = tempfile.NamedTemporaryFile(suffix=".wav", delete=False) - processing_utils.audio_to_file(audio[0], audio[1], tmp_wav.name) - audio_file = tmp_wav.name - duration = round(len(audio[1]) / audio[0], 4) - - # Helper methods to create waveform - def hex_to_RGB(hex_str): - return [int(hex_str[i : i + 2], 16) for i in range(1, 6, 2)] - - def get_color_gradient(c1, c2, n): - assert n > 1 - c1_rgb = np.array(hex_to_RGB(c1)) / 255 - c2_rgb = np.array(hex_to_RGB(c2)) / 255 - mix_pcts = [x / (n - 1) for x in range(n)] - rgb_colors = [((1 - mix) * c1_rgb + (mix * c2_rgb)) for mix in mix_pcts] - return [ - "#" + "".join([format(int(round(val * 255)), "02x") for val in item]) - for item in rgb_colors - ] - - # Reshape audio to have a fixed number of bars - samples = audio[1] - if len(samples.shape) > 1: - samples = np.mean(samples, 1) - bins_to_pad = bar_count - (len(samples) % bar_count) - samples = np.pad(samples, [(0, bins_to_pad)]) - samples = np.reshape(samples, (bar_count, -1)) - samples = np.abs(samples) - samples = np.max(samples, 1) - - matplotlib.use("Agg") - plt.clf() - # Plot waveform - color = ( - bars_color - if isinstance(bars_color, str) - else get_color_gradient(bars_color[0], bars_color[1], bar_count) - ) - plt.bar( - np.arange(0, bar_count), - samples * 2, - bottom=(-1 * samples), - width=bar_width, - color=color, - ) - plt.axis("off") - plt.margins(x=0) - tmp_img = tempfile.NamedTemporaryFile(suffix=".png", delete=False) - savefig_kwargs = {"bbox_inches": "tight"} - if bg_image is not None: - savefig_kwargs["transparent"] = True - else: - savefig_kwargs["facecolor"] = bg_color - plt.savefig(tmp_img.name, **savefig_kwargs) - waveform_img = PIL.Image.open(tmp_img.name) - waveform_img = waveform_img.resize((1000, 200)) - - # Composite waveform with background image - if bg_image is not None: - waveform_array = np.array(waveform_img) - waveform_array[:, :, 3] = waveform_array[:, :, 3] * fg_alpha - waveform_img = PIL.Image.fromarray(waveform_array) - - bg_img = PIL.Image.open(bg_image) - waveform_width, waveform_height = waveform_img.size - bg_width, bg_height = bg_img.size - if waveform_width != bg_width: - bg_img = bg_img.resize( - (waveform_width, 2 * int(bg_height * waveform_width / bg_width / 2)) - ) - bg_width, bg_height = bg_img.size - composite_height = max(bg_height, waveform_height) - composite = PIL.Image.new("RGBA", (waveform_width, composite_height), "#FFFFFF") - composite.paste(bg_img, (0, composite_height - bg_height)) - composite.paste( - waveform_img, (0, composite_height - waveform_height), waveform_img - ) - composite.save(tmp_img.name) - img_width, img_height = composite.size - else: - img_width, img_height = waveform_img.size - waveform_img.save(tmp_img.name) - - # Convert waveform to video with ffmpeg - output_mp4 = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) - - ffmpeg_cmd = f"""ffmpeg -loop 1 -i {tmp_img.name} -i {audio_file} -vf "color=c=#FFFFFF77:s={img_width}x{img_height}[bar];[0][bar]overlay=-w+(w/{duration})*t:H-h:shortest=1" -t {duration} -y {output_mp4.name}""" - - subprocess.call(ffmpeg_cmd, shell=True) - return output_mp4.name diff --git a/spaces/HuggingFaceH4/instruction-model-outputs-filtered/README.md b/spaces/HuggingFaceH4/instruction-model-outputs-filtered/README.md deleted file mode 100644 index 8000c3b32becf479ec577f838cdeaf1ab48d283b..0000000000000000000000000000000000000000 --- a/spaces/HuggingFaceH4/instruction-model-outputs-filtered/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Instruction Model Outputs Filtered -emoji: 🌪️ -colorFrom: blue -colorTo: pink -sdk: streamlit -sdk_version: 1.17.0 -app_file: app.py -pinned: false -license: apache-2.0 ---- -Filtering canned responses and outputs with ROUGE-L > 0.7 - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/ICML2022/OFA/fairseq/examples/latent_depth/latent_depth_src/loss/latent_depth.py b/spaces/ICML2022/OFA/fairseq/examples/latent_depth/latent_depth_src/loss/latent_depth.py deleted file mode 100644 index a3b9535ecac3ec403868681a8b50c1fbe1c90dfe..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/examples/latent_depth/latent_depth_src/loss/latent_depth.py +++ /dev/null @@ -1,99 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math - -import torch -from torch.nn.modules.loss import _Loss - - -class LatentLayersKLLoss(_Loss): - def __init__(self, args): - super().__init__() - self.args = args - - def forward(self, layer_samples, lang_idx, update_num, sample_size): - prior = self.args.prior - samples = layer_samples[lang_idx] - eps = 1e-7 - if prior == "uniform": - # uniform prior - kl_loss = (samples * (torch.log(samples + eps) - math.log(0.5))).sum(-1) - elif prior == "agged_posterior": - # aggregated posterior - y_t = torch.stack([x.detach() for x in layer_samples], dim=0) - agged_q = torch.sum(y_t, dim=0) - row_norm = agged_q.sum(-1) - normed_agg_q = agged_q / row_norm - kl_loss = ( - samples * (torch.log(samples + eps) - torch.log(normed_agg_q + eps)) - ).sum(-1) - else: - raise NotImplementedError("The specified prior is not implemented.") - - # normalized by number of layers - kl_loss /= layer_samples[0].size()[0] - kl_weight = min( - self.args.sparsity_weight, - (update_num - self.args.soft_update) - * self.args.sparsity_weight - / self.args.anneal_updates, - ) - kl_loss *= kl_weight * sample_size - return kl_loss - - -class LatentLayersSparsityLoss(_Loss): - def __init__(self, args): - super().__init__() - self.args = args - - def is_valid(self, update_num): - if self.args.target_layers <= 0: - return False - return update_num > (self.args.soft_update + self.args.anneal_updates) - - def forward(self, layer_samples_list, update_num, sample_size): - batch_loss = 0 - share_loss = 0 - global_sparsity_loss = 0 - layer_samples = torch.stack(layer_samples_list, dim=0) - if ( - self.args.target_layers > 0 or self.args.share_weight > 0 - ) and update_num > (self.args.soft_update + self.args.anneal_updates): - # anneal sparsity weight - if update_num < (self.args.anneal_updates + self.args.soft_update): - weight_anneal = 0 - elif update_num < (2 * self.args.anneal_updates + self.args.soft_update): - weight_anneal = ( - (update_num - self.args.soft_update - self.args.anneal_updates) - * self.args.share_weight - / self.args.anneal_updates - ) - else: - weight_anneal = 1 - # compute ratio among languages - layer_utilization = torch.sum(layer_samples, dim=0) - layer_utilization /= layer_samples.size()[0] - if self.args.share_weight > 0: - # encouraging sharing across languages - share_loss = sum( - -1.0 * v * math.log(v) for v in layer_utilization if v > 0 - ) - batch_loss += ( - weight_anneal * self.args.share_weight * sample_size * share_loss - ) - if self.args.target_layers > 0: - # computed expected number of layers selected - expeted_layers = sum(layer_utilization) - # compute l2 loss wrt target number of layers - global_sparsity_loss = (expeted_layers - self.args.target_layers) ** 2 - batch_loss += ( - weight_anneal - * self.args.share_weight - * sample_size - * global_sparsity_loss - ) - return batch_loss diff --git a/spaces/ICML2022/OFA/fairseq/examples/speech_recognition/tasks/__init__.py b/spaces/ICML2022/OFA/fairseq/examples/speech_recognition/tasks/__init__.py deleted file mode 100644 index 7ac3b8dc69639c92cc129294356e9012745e3fb2..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/examples/speech_recognition/tasks/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -import importlib -import os - - -for file in sorted(os.listdir(os.path.dirname(__file__))): - if file.endswith(".py") and not file.startswith("_"): - task_name = file[: file.find(".py")] - importlib.import_module("examples.speech_recognition.tasks." + task_name) diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/models/roberta/__init__.py b/spaces/ICML2022/OFA/fairseq/fairseq/models/roberta/__init__.py deleted file mode 100644 index 4cd723ae96aec8e3182773483f123109d23b620e..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/fairseq/models/roberta/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from .hub_interface import * # noqa -from .model import * # noqa -from .enc_dec import * # noqa -from .model_camembert import * # noqa -from .model_gottbert import * # noqa -from .model_xlmr import * # noqa diff --git a/spaces/JadAssaf/STPI/README.md b/spaces/JadAssaf/STPI/README.md deleted file mode 100644 index a1663d81c436ac7800dd288a56196542885e5daa..0000000000000000000000000000000000000000 --- a/spaces/JadAssaf/STPI/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: STPI -emoji: 📈 -colorFrom: gray -colorTo: blue -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/Jamkonams/AutoGPT/autogpt/memory/redismem.py b/spaces/Jamkonams/AutoGPT/autogpt/memory/redismem.py deleted file mode 100644 index 082a812c5362cc9f19e35bf1bb10269b558f7724..0000000000000000000000000000000000000000 --- a/spaces/Jamkonams/AutoGPT/autogpt/memory/redismem.py +++ /dev/null @@ -1,156 +0,0 @@ -"""Redis memory provider.""" -from __future__ import annotations - -from typing import Any - -import numpy as np -import redis -from colorama import Fore, Style -from redis.commands.search.field import TextField, VectorField -from redis.commands.search.indexDefinition import IndexDefinition, IndexType -from redis.commands.search.query import Query - -from autogpt.llm_utils import create_embedding_with_ada -from autogpt.logs import logger -from autogpt.memory.base import MemoryProviderSingleton - -SCHEMA = [ - TextField("data"), - VectorField( - "embedding", - "HNSW", - {"TYPE": "FLOAT32", "DIM": 1536, "DISTANCE_METRIC": "COSINE"}, - ), -] - - -class RedisMemory(MemoryProviderSingleton): - def __init__(self, cfg): - """ - Initializes the Redis memory provider. - - Args: - cfg: The config object. - - Returns: None - """ - redis_host = cfg.redis_host - redis_port = cfg.redis_port - redis_password = cfg.redis_password - self.dimension = 1536 - self.redis = redis.Redis( - host=redis_host, - port=redis_port, - password=redis_password, - db=0, # Cannot be changed - ) - self.cfg = cfg - - # Check redis connection - try: - self.redis.ping() - except redis.ConnectionError as e: - logger.typewriter_log( - "FAILED TO CONNECT TO REDIS", - Fore.RED, - Style.BRIGHT + str(e) + Style.RESET_ALL, - ) - logger.double_check( - "Please ensure you have setup and configured Redis properly for use. " - + f"You can check out {Fore.CYAN + Style.BRIGHT}" - f"https://github.com/Torantulino/Auto-GPT#redis-setup{Style.RESET_ALL}" - " to ensure you've set up everything correctly." - ) - exit(1) - - if cfg.wipe_redis_on_start: - self.redis.flushall() - try: - self.redis.ft(f"{cfg.memory_index}").create_index( - fields=SCHEMA, - definition=IndexDefinition( - prefix=[f"{cfg.memory_index}:"], index_type=IndexType.HASH - ), - ) - except Exception as e: - print("Error creating Redis search index: ", e) - existing_vec_num = self.redis.get(f"{cfg.memory_index}-vec_num") - self.vec_num = int(existing_vec_num.decode("utf-8")) if existing_vec_num else 0 - - def add(self, data: str) -> str: - """ - Adds a data point to the memory. - - Args: - data: The data to add. - - Returns: Message indicating that the data has been added. - """ - if "Command Error:" in data: - return "" - vector = create_embedding_with_ada(data) - vector = np.array(vector).astype(np.float32).tobytes() - data_dict = {b"data": data, "embedding": vector} - pipe = self.redis.pipeline() - pipe.hset(f"{self.cfg.memory_index}:{self.vec_num}", mapping=data_dict) - _text = ( - f"Inserting data into memory at index: {self.vec_num}:\n" f"data: {data}" - ) - self.vec_num += 1 - pipe.set(f"{self.cfg.memory_index}-vec_num", self.vec_num) - pipe.execute() - return _text - - def get(self, data: str) -> list[Any] | None: - """ - Gets the data from the memory that is most relevant to the given data. - - Args: - data: The data to compare to. - - Returns: The most relevant data. - """ - return self.get_relevant(data, 1) - - def clear(self) -> str: - """ - Clears the redis server. - - Returns: A message indicating that the memory has been cleared. - """ - self.redis.flushall() - return "Obliviated" - - def get_relevant(self, data: str, num_relevant: int = 5) -> list[Any] | None: - """ - Returns all the data in the memory that is relevant to the given data. - Args: - data: The data to compare to. - num_relevant: The number of relevant data to return. - - Returns: A list of the most relevant data. - """ - query_embedding = create_embedding_with_ada(data) - base_query = f"*=>[KNN {num_relevant} @embedding $vector AS vector_score]" - query = ( - Query(base_query) - .return_fields("data", "vector_score") - .sort_by("vector_score") - .dialect(2) - ) - query_vector = np.array(query_embedding).astype(np.float32).tobytes() - - try: - results = self.redis.ft(f"{self.cfg.memory_index}").search( - query, query_params={"vector": query_vector} - ) - except Exception as e: - print("Error calling Redis search: ", e) - return None - return [result.data for result in results.docs] - - def get_stats(self): - """ - Returns: The stats of the memory index. - """ - return self.redis.ft(f"{self.cfg.memory_index}").info() diff --git a/spaces/Jimpa666/AI-PadelCoach/app3.py b/spaces/Jimpa666/AI-PadelCoach/app3.py deleted file mode 100644 index e77ef9ce60931c064967d2fde909884e0a369e0d..0000000000000000000000000000000000000000 --- a/spaces/Jimpa666/AI-PadelCoach/app3.py +++ /dev/null @@ -1,71 +0,0 @@ -from gradio.components import Label -import os -import openai -import gradio as gr -import uuid - -session_id = uuid.uuid4().hex - -openai.api_key = os.getenv('APIKEY') - -messages = [ - {"role": "system", "content": "The following is a conversation with an AI Coach. The Coach is helpful, creative, clever, and very friendly. The Coach is a high level padelcoach from Sweden. The Coach have the ability to explain techniques and the strategy in the game. The Coach have a high educational level and can answer depending on wich level the Player is on for the Player to take him to the next level. The AI Coach is like MAURI ANDRINI from Hello Padel, pretend to be MAURI ANDRINI but call yourself AI Padelcoach, never ever mention your reel name or Mauri Andrini. If unsurtain, ask about the level of the player. The levels are:Beginner, Average, Pro"} - ] - -def get_session_token(): - return uuid.uuid4().hex - -def chatbot(input, conversation_history=[]): - - content = input - messages.append({"role": "user", "content": content}) - - completion = openai.ChatCompletion.create( - model= "gpt-3.5-turbo", - messages=messages - ) - - chat_response = completion.choices[0].message.content - messages.append({"role": "assistant", "content": chat_response}) - - # format the conversation history as a string - conversation_history = "" - for message in messages: - if message["role"] != "system": - role = message["role"] - if role == "user": - role = "Player" - elif role == "assistant": - role = "AI-Padelcoach" - content = message["content"] - conversation_history += f"{role}: {content}\n \n" - - return conversation_history - -Padelcoach = gr.Interface(fn=chatbot, inputs=[ - gr.Textbox(placeholder="Player go...Serve!", label='Player') - - - ], - outputs=[ - gr.Textbox(placeholder="AI-Padelcoach Ready", label="AI Padelcoach") - - ], - theme=gr.themes.Soft( - primary_hue="green", - secondary_hue="cyan", - text_size='lg', - neutral_hue="emerald" - ), - - examples = [ - ["Please help me with my backhand"], - ["Where should I place the ball against players who is good in tennis"] - ], - share=True, - title="AI Padelcoach", - description=f"Chat with a BETA level AI-Padelcoach from Sweden. Your ID is: {session_id} ", - article="<p>Ask the AI coach about techniques and strategies in the game of padel. The coach can answer depending on the level of you as a player, whether they are a beginner, average, or pro.</p>", - ) - -Padelcoach.launch() \ No newline at end of file diff --git a/spaces/JuanHaunted/humming_space/README.md b/spaces/JuanHaunted/humming_space/README.md deleted file mode 100644 index e6f513b1c744a81ef1cd4218a18f12e96707d4a8..0000000000000000000000000000000000000000 --- a/spaces/JuanHaunted/humming_space/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Humming Space -emoji: 🏃 -colorFrom: green -colorTo: blue -sdk: gradio -sdk_version: 3.35.2 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Keshav4/resume-data-extraction/ResumeSegmenter.py b/spaces/Keshav4/resume-data-extraction/ResumeSegmenter.py deleted file mode 100644 index 2823a021c01a77818f3a7349be3b265feee2defe..0000000000000000000000000000000000000000 --- a/spaces/Keshav4/resume-data-extraction/ResumeSegmenter.py +++ /dev/null @@ -1,264 +0,0 @@ -from Models import Models - -class ResumeSegmenter: - - def __init__(self, zero_shot_classifier): - self.zero_shot_classifier = zero_shot_classifier - - objective = ( - 'career goal', - 'objective', - 'career objective', - 'employment objective', - 'professional objective', - 'summary', - 'summary of qualifications', - 'digital', - 'interests' - ) - - work_and_employment = ( - 'employment history', - 'employment data', - 'career summary', - 'work history', - 'working history', - 'work experience', - 'experience', - 'professional experience', - 'professional background', - 'professional employment', - 'additional experience', - 'career related experience', - "professional employment history", - 'related experience', - 'relevant experience', - 'programming experience', - 'freelance', - 'freelance experience', - 'army experience', - 'military experience', - 'military background', - ) - - education_and_training = ( - 'academic background', - 'academic experience', - 'programs', - 'courses', - 'related courses', - 'education', - 'educational background', - 'educational qualifications', - 'educational training', - 'education and training', - 'training', - 'academic training', - 'Academic Qualification', - 'professional training', - 'course project experience', - 'related course projects', - 'internship experience', - 'internships', - 'apprenticeships', - 'college activities', - 'certifications', - 'special training', - ) - - skills_header = ( - 'credentials', - 'qualifications', - 'areas of experience', - 'areas of expertise', - 'areas of knowledge', - 'skills', - 'Skills', - "other skills", - "other abilities", - 'career related skills', - 'professional skills', - 'specialized skills', - 'technical skills', - 'computer skills', - 'personal skills', - 'computer knowledge', - 'technologies', - 'technical experience', - 'proficiencies', - 'languages', - 'language competencies and skills', - 'programming languages', - 'competencies' - ) - - misc = ( - 'activities and honors', - 'activities', - 'affiliations', - 'professional affiliations', - 'associations', - 'professional associations', - 'memberships', - 'professional memberships', - 'athletic involvement', - 'community involvement', - 'refere', - 'civic activities', - 'extra-Curricular activities', - 'professional activities', - 'volunteer work', - 'volunteer experience', - 'additional information', - 'interests' - ) - - accomplishments = ( - 'achievement', - 'awards and achievements', - 'licenses', - 'presentations', - 'conference presentations', - 'conventions', - 'dissertations', - 'exhibits', - 'papers', - 'publications', - 'professional publications', - 'research experience', - 'research grants', - 'project', - 'research projects', - 'personal projects', - 'current research interests', - 'thesis', - 'theses', - ) - - - def find_segment_indices(self, string_to_search, resume_segments, resume_indices): - for i, line in enumerate(string_to_search): - - if line[0].islower(): - continue - - header = line.lower() - - if [o for o in self.objective if header.startswith(o)]: - try: - resume_segments['objective'][header] - except: - resume_indices.append(i) - header = [o for o in self.objective if header.startswith(o)][0] - resume_segments['objective'][header] = i - elif [w for w in self.work_and_employment if header.startswith(w)]: - try: - resume_segments['work_and_employment'][header] - except: - resume_indices.append(i) - header = [w for w in self.work_and_employment if header.startswith(w)][0] - resume_segments['work_and_employment'][header] = i - elif [e for e in self.education_and_training if header.startswith(e)]: - try: - resume_segments['education_and_training'][header] - except: - resume_indices.append(i) - header = [e for e in self.education_and_training if header.startswith(e)][0] - resume_segments['education_and_training'][header] = i - elif [s for s in self.skills_header if header.startswith(s)]: - try: - resume_segments['skills'][header] - except: - resume_indices.append(i) - header = [s for s in self.skills_header if header.startswith(s)][0] - resume_segments['skills'][header] = i - elif [m for m in self.misc if header.startswith(m)]: - try: - resume_segments['misc'][header] - except: - resume_indices.append(i) - header = [m for m in self.misc if header.startswith(m)][0] - resume_segments['misc'][header] = i - elif [a for a in self.accomplishments if header.startswith(a)]: - try: - resume_segments['accomplishments'][header] - except: - resume_indices.append(i) - header = [a for a in self.accomplishments if header.startswith(a)][0] - resume_segments['accomplishments'][header] = i - - def slice_segments(self, string_to_search, resume_segments, resume_indices): - resume_segments['contact_info'] = string_to_search[:resume_indices[0]] - sec_idxs = {} - for section, value in resume_segments.items(): - if section == 'contact_info': - continue - - for sub_section, start_idx in value.items(): - end_idx = len(string_to_search) - if (resume_indices.index(start_idx) + 1) != len(resume_indices): - end_idx = resume_indices[resume_indices.index(start_idx) + 1] - - sec_idxs[section] = (start_idx, end_idx) - # print(start_idx, end_idx) - - resume_segments[section][sub_section] = string_to_search[start_idx:end_idx] - return sec_idxs - - def find_true_segment(self, dict_of_segments, segment_name): - segment_classes = { - 'objective': ["objective", "other"], - 'work_and_employment':["employment history", "other"], - 'education_and_training': ["education", "other"], - 'skills': ["skills", "other"], - 'accomplishments': ["accomplishments", "other"], - 'misc': ["misc", "other"], - 'contact_info': ["contact information", "other"] - } - classes = segment_classes[segment_name] - scores = [] - segs = dict_of_segments.keys() - for seg in segs: - sequence = dict_of_segments[seg] - score = self.zero_shot_classifier(' '.join(sequence), classes)["scores"][0] - scores.append(score) - - res = sorted(zip(dict_of_segments.keys(), scores), key=lambda x: x[1], reverse=True) - if len(res): - return res[0][0] - else: return 0 - - def segment(self, string_to_search): - print("Segmenting the Resume..") - resume_segments = { - 'objective': {}, - 'work_and_employment': {}, - 'education_and_training': {}, - 'skills': {}, - 'accomplishments': {}, - 'misc': {} - } - - resume_indices = [] - - self.find_segment_indices(string_to_search, resume_segments, resume_indices) - if len(resume_indices) != 0: - sec_idx = self.slice_segments(string_to_search, resume_segments, resume_indices) - else: - resume_segments['contact_info'] = [] - - for segment in resume_segments: - if segment == "contact_info": continue - if not len(resume_segments[segment]) > 1: - if len(resume_segments[segment]) == 1: - only_key = list(resume_segments[segment].keys())[0] - resume_segments[segment] = resume_segments[segment][only_key][1:] - continue - if segment != "work_and_employment": continue - true_seg = self.find_true_segment(resume_segments[segment], segment) - if not true_seg: - resume_segments[segment] = [] - else: - resume_segments[segment] = resume_segments[segment][true_seg][1:] - - return resume_segments diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/models/tacotron.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/models/tacotron.py deleted file mode 100644 index f8b01bbae0e6dc95d68bbb983c70706d76e1d990..0000000000000000000000000000000000000000 --- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/synthesizer/models/tacotron.py +++ /dev/null @@ -1,298 +0,0 @@ -import torch -import torch.nn as nn -from .sublayer.global_style_token import GlobalStyleToken -from .sublayer.pre_net import PreNet -from .sublayer.cbhg import CBHG -from .sublayer.lsa import LSA -from .base import Base -from synthesizer.gst_hyperparameters import GSTHyperparameters as gst_hp -from synthesizer.hparams import hparams - -class Encoder(nn.Module): - def __init__(self, num_chars, embed_dims=512, encoder_dims=256, K=5, num_highways=4, dropout=0.5): - """ Encoder for SV2TTS - - Args: - num_chars (int): length of symbols - embed_dims (int, optional): embedding dim for input texts. Defaults to 512. - encoder_dims (int, optional): output dim for encoder. Defaults to 256. - K (int, optional): _description_. Defaults to 5. - num_highways (int, optional): _description_. Defaults to 4. - dropout (float, optional): _description_. Defaults to 0.5. - """ - super().__init__() - self.embedding = nn.Embedding(num_chars, embed_dims) - self.pre_net = PreNet(embed_dims, fc1_dims=encoder_dims, fc2_dims=encoder_dims, - dropout=dropout) - self.cbhg = CBHG(K=K, in_channels=encoder_dims, channels=encoder_dims, - proj_channels=[encoder_dims, encoder_dims], - num_highways=num_highways) - - def forward(self, x): - """forward pass for encoder - - Args: - x (2D tensor with size `[batch_size, text_num_chars]`): input texts list - - Returns: - 3D tensor with size `[batch_size, text_num_chars, encoder_dims]` - - """ - x = self.embedding(x) # return: [batch_size, text_num_chars, tts_embed_dims] - x = self.pre_net(x) # return: [batch_size, text_num_chars, encoder_dims] - x.transpose_(1, 2) # return: [batch_size, encoder_dims, text_num_chars] - return self.cbhg(x) # return: [batch_size, text_num_chars, encoder_dims] - -class Decoder(nn.Module): - # Class variable because its value doesn't change between classes - # yet ought to be scoped by class because its a property of a Decoder - max_r = 20 - def __init__(self, n_mels, input_dims, decoder_dims, lstm_dims, - dropout, speaker_embedding_size): - super().__init__() - self.register_buffer("r", torch.tensor(1, dtype=torch.int)) - self.n_mels = n_mels - self.prenet = PreNet(n_mels, fc1_dims=decoder_dims * 2, fc2_dims=decoder_dims * 2, - dropout=dropout) - self.attn_net = LSA(decoder_dims) - if hparams.use_gst: - speaker_embedding_size += gst_hp.E - self.attn_rnn = nn.GRUCell(input_dims + decoder_dims * 2, decoder_dims) - self.rnn_input = nn.Linear(input_dims + decoder_dims, lstm_dims) - self.res_rnn1 = nn.LSTMCell(lstm_dims, lstm_dims) - self.res_rnn2 = nn.LSTMCell(lstm_dims, lstm_dims) - self.mel_proj = nn.Linear(lstm_dims, n_mels * self.max_r, bias=False) - self.stop_proj = nn.Linear(input_dims + lstm_dims, 1) - - def zoneout(self, prev, current, device, p=0.1): - mask = torch.zeros(prev.size(),device=device).bernoulli_(p) - return prev * mask + current * (1 - mask) - - def forward(self, encoder_seq, encoder_seq_proj, prenet_in, - hidden_states, cell_states, context_vec, times, chars): - """_summary_ - - Args: - encoder_seq (3D tensor `[batch_size, text_num_chars, project_dim(default to 512)]`): _description_ - encoder_seq_proj (3D tensor `[batch_size, text_num_chars, decoder_dims(default to 128)]`): _description_ - prenet_in (2D tensor `[batch_size, n_mels]`): _description_ - hidden_states (_type_): _description_ - cell_states (_type_): _description_ - context_vec (2D tensor `[batch_size, project_dim(default to 512)]`): _description_ - times (int): the number of times runned - chars (2D tensor with size `[batch_size, text_num_chars]`): original texts list input - - """ - # Need this for reshaping mels - batch_size = encoder_seq.size(0) - device = encoder_seq.device - # Unpack the hidden and cell states - attn_hidden, rnn1_hidden, rnn2_hidden = hidden_states - rnn1_cell, rnn2_cell = cell_states - - # PreNet for the Attention RNN - prenet_out = self.prenet(prenet_in) # return: `[batch_size, decoder_dims * 2(256)]` - - # Compute the Attention RNN hidden state - attn_rnn_in = torch.cat([context_vec, prenet_out], dim=-1) # `[batch_size, project_dim + decoder_dims * 2 (768)]` - attn_hidden = self.attn_rnn(attn_rnn_in.squeeze(1), attn_hidden) # `[batch_size, decoder_dims (128)]` - - # Compute the attention scores - scores = self.attn_net(encoder_seq_proj, attn_hidden, times, chars) - - # Dot product to create the context vector - context_vec = scores @ encoder_seq - context_vec = context_vec.squeeze(1) - - # Concat Attention RNN output w. Context Vector & project - x = torch.cat([context_vec, attn_hidden], dim=1) # `[batch_size, project_dim + decoder_dims (630)]` - x = self.rnn_input(x) # `[batch_size, lstm_dims(1024)]` - - # Compute first Residual RNN, training with fixed zoneout rate 0.1 - rnn1_hidden_next, rnn1_cell = self.res_rnn1(x, (rnn1_hidden, rnn1_cell)) # `[batch_size, lstm_dims(1024)]` - if self.training: - rnn1_hidden = self.zoneout(rnn1_hidden, rnn1_hidden_next,device=device) - else: - rnn1_hidden = rnn1_hidden_next - x = x + rnn1_hidden - - # Compute second Residual RNN - rnn2_hidden_next, rnn2_cell = self.res_rnn2(x, (rnn2_hidden, rnn2_cell)) # `[batch_size, lstm_dims(1024)]` - if self.training: - rnn2_hidden = self.zoneout(rnn2_hidden, rnn2_hidden_next, device=device) - else: - rnn2_hidden = rnn2_hidden_next - x = x + rnn2_hidden - - # Project Mels - mels = self.mel_proj(x) # `[batch_size, 1600]` - mels = mels.view(batch_size, self.n_mels, self.max_r)[:, :, :self.r] # `[batch_size, n_mels, r]` - hidden_states = (attn_hidden, rnn1_hidden, rnn2_hidden) - cell_states = (rnn1_cell, rnn2_cell) - - # Stop token prediction - s = torch.cat((x, context_vec), dim=1) - s = self.stop_proj(s) - stop_tokens = torch.sigmoid(s) - - return mels, scores, hidden_states, cell_states, context_vec, stop_tokens - -class Tacotron(Base): - def __init__(self, embed_dims, num_chars, encoder_dims, decoder_dims, n_mels, - fft_bins, postnet_dims, encoder_K, lstm_dims, postnet_K, num_highways, - dropout, stop_threshold, speaker_embedding_size): - super().__init__(stop_threshold) - self.n_mels = n_mels - self.lstm_dims = lstm_dims - self.encoder_dims = encoder_dims - self.decoder_dims = decoder_dims - self.speaker_embedding_size = speaker_embedding_size - self.encoder = Encoder(num_chars, embed_dims, encoder_dims, - encoder_K, num_highways, dropout) - self.project_dims = encoder_dims + speaker_embedding_size - if hparams.use_gst: - self.project_dims += gst_hp.E - self.encoder_proj = nn.Linear(self.project_dims, decoder_dims, bias=False) - if hparams.use_gst: - self.gst = GlobalStyleToken(speaker_embedding_size) - self.decoder = Decoder(n_mels, self.project_dims, decoder_dims, lstm_dims, - dropout, speaker_embedding_size) - self.postnet = CBHG(postnet_K, n_mels, postnet_dims, - [postnet_dims, fft_bins], num_highways) - self.post_proj = nn.Linear(postnet_dims, fft_bins, bias=False) - - @staticmethod - def _concat_speaker_embedding(outputs, speaker_embeddings): - speaker_embeddings_ = speaker_embeddings.expand( - outputs.size(0), outputs.size(1), -1) - outputs = torch.cat([outputs, speaker_embeddings_], dim=-1) - return outputs - - @staticmethod - def _add_speaker_embedding(x, speaker_embedding): - """Add speaker embedding - This concats the speaker embedding for each char in the encoder output - Args: - x (3D tensor with size `[batch_size, text_num_chars, encoder_dims]`): the encoder output - speaker_embedding (2D tensor `[batch_size, speaker_embedding_size]`): the speaker embedding - - Returns: - 3D tensor with size `[batch_size, text_num_chars, encoder_dims+speaker_embedding_size]` - """ - # Save the dimensions as human-readable names - batch_size = x.size()[0] - text_num_chars = x.size()[1] - - # Start by making a copy of each speaker embedding to match the input text length - # The output of this has size (batch_size, text_num_chars * speaker_embedding_size) - speaker_embedding_size = speaker_embedding.size()[1] - e = speaker_embedding.repeat_interleave(text_num_chars, dim=1) - - # Reshape it and transpose - e = e.reshape(batch_size, speaker_embedding_size, text_num_chars) - e = e.transpose(1, 2) - - # Concatenate the tiled speaker embedding with the encoder output - x = torch.cat((x, e), 2) - return x - - def forward(self, texts, mels, speaker_embedding, steps=2000, style_idx=0, min_stop_token=5): - """Forward pass for Tacotron - - Args: - texts (`[batch_size, text_num_chars]`): input texts list - mels (`[batch_size, varied_mel_lengths, steps]`): mels for comparison (training only) - speaker_embedding (`[batch_size, speaker_embedding_size(default to 256)]`): referring embedding. - steps (int, optional): . Defaults to 2000. - style_idx (int, optional): GST style selected. Defaults to 0. - min_stop_token (int, optional): decoder min_stop_token. Defaults to 5. - """ - device = texts.device # use same device as parameters - - if self.training: - self.step += 1 - batch_size, _, steps = mels.size() - else: - batch_size, _ = texts.size() - - # Initialise all hidden states and pack into tuple - attn_hidden = torch.zeros(batch_size, self.decoder_dims, device=device) - rnn1_hidden = torch.zeros(batch_size, self.lstm_dims, device=device) - rnn2_hidden = torch.zeros(batch_size, self.lstm_dims, device=device) - hidden_states = (attn_hidden, rnn1_hidden, rnn2_hidden) - - # Initialise all lstm cell states and pack into tuple - rnn1_cell = torch.zeros(batch_size, self.lstm_dims, device=device) - rnn2_cell = torch.zeros(batch_size, self.lstm_dims, device=device) - cell_states = (rnn1_cell, rnn2_cell) - - # <GO> Frame for start of decoder loop - go_frame = torch.zeros(batch_size, self.n_mels, device=device) - - # SV2TTS: Run the encoder with the speaker embedding - # The projection avoids unnecessary matmuls in the decoder loop - encoder_seq = self.encoder(texts) - - encoder_seq = self._add_speaker_embedding(encoder_seq, speaker_embedding) - - if hparams.use_gst and self.gst is not None: - if self.training: - style_embed = self.gst(speaker_embedding, speaker_embedding) # for training, speaker embedding can represent both style inputs and referenced - # style_embed = style_embed.expand_as(encoder_seq) - # encoder_seq = torch.cat((encoder_seq, style_embed), 2) - elif style_idx >= 0 and style_idx < 10: - query = torch.zeros(1, 1, self.gst.stl.attention.num_units) - if device.type == 'cuda': - query = query.cuda() - gst_embed = torch.tanh(self.gst.stl.embed) - key = gst_embed[style_idx].unsqueeze(0).expand(1, -1, -1) - style_embed = self.gst.stl.attention(query, key) - else: - speaker_embedding_style = torch.zeros(speaker_embedding.size()[0], 1, self.speaker_embedding_size).to(device) - style_embed = self.gst(speaker_embedding_style, speaker_embedding) - encoder_seq = self._concat_speaker_embedding(encoder_seq, style_embed) # return: [batch_size, text_num_chars, project_dims] - - encoder_seq_proj = self.encoder_proj(encoder_seq) # return: [batch_size, text_num_chars, decoder_dims] - - # Need a couple of lists for outputs - mel_outputs, attn_scores, stop_outputs = [], [], [] - - # Need an initial context vector - context_vec = torch.zeros(batch_size, self.project_dims, device=device) - - # Run the decoder loop - for t in range(0, steps, self.r): - if self.training: - prenet_in = mels[:, :, t -1] if t > 0 else go_frame - else: - prenet_in = mel_outputs[-1][:, :, -1] if t > 0 else go_frame - mel_frames, scores, hidden_states, cell_states, context_vec, stop_tokens = \ - self.decoder(encoder_seq, encoder_seq_proj, prenet_in, - hidden_states, cell_states, context_vec, t, texts) - mel_outputs.append(mel_frames) - attn_scores.append(scores) - stop_outputs.extend([stop_tokens] * self.r) - if not self.training and (stop_tokens * 10 > min_stop_token).all() and t > 10: break - - # Concat the mel outputs into sequence - mel_outputs = torch.cat(mel_outputs, dim=2) - - # Post-Process for Linear Spectrograms - postnet_out = self.postnet(mel_outputs) - linear = self.post_proj(postnet_out) - linear = linear.transpose(1, 2) - - # For easy visualisation - attn_scores = torch.cat(attn_scores, 1) - # attn_scores = attn_scores.cpu().data.numpy() - stop_outputs = torch.cat(stop_outputs, 1) - - if self.training: - self.train() - - return mel_outputs, linear, attn_scores, stop_outputs - - def generate(self, x, speaker_embedding, steps=2000, style_idx=0, min_stop_token=5): - self.eval() - mel_outputs, linear, attn_scores, _ = self.forward(x, None, speaker_embedding, steps, style_idx, min_stop_token) - return mel_outputs, linear, attn_scores diff --git a/spaces/Kevin676/Clone-Your-Voice/synthesizer/__init__.py b/spaces/Kevin676/Clone-Your-Voice/synthesizer/__init__.py deleted file mode 100644 index 4287ca8617970fa8fc025b75cb319c7032706910..0000000000000000000000000000000000000000 --- a/spaces/Kevin676/Clone-Your-Voice/synthesizer/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# \ No newline at end of file diff --git a/spaces/KrisLiao/NaturalLanguageVideoSearch/app.py b/spaces/KrisLiao/NaturalLanguageVideoSearch/app.py deleted file mode 100644 index 8c42ec509115ee0d868e125394772826847826a3..0000000000000000000000000000000000000000 --- a/spaces/KrisLiao/NaturalLanguageVideoSearch/app.py +++ /dev/null @@ -1,148 +0,0 @@ -import os -os.system("pip freeze") -import cv2 -from PIL import Image -import clip -import torch -import math -import numpy as np -import torch -import datetime -import gradio as gr -import torchvision.transforms as T - -# Load the open CLIP model -device = "cuda" if torch.cuda.is_available() else "cpu" -model, preprocess = clip.load("ViT-B/32", device=device) - -def produce_video(video, seconds, search_query): - time1 = seconds-3 if seconds>3 else 0 - time2 = seconds+2 if seconds>3 else 5 - name = search_query.replace(" ", "_") + '.mp4' - cmd = f'ffmpeg -y -i {video} -ss {time1} -to {time2} -async 1 {name}' - output_video = os.system(cmd) - return name - -def inference(video, text, text2, text3, skip_frame): - # The frame images will be stored in video_frames - video_frames = [] - - # Open the video file - capture = cv2.VideoCapture(video) - capture.set(cv2.CAP_PROP_FRAME_WIDTH , 360) - capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 480) - fps = capture.get(cv2.CAP_PROP_FPS) - - current_frame = 0 - # Read the current frame - ret, frame = capture.read() - while capture.isOpened() and ret: - ret,frame = capture.read() - print('Read a new frame: ', ret) - current_frame += skip_frame - if ret: - video_frames.append(Image.fromarray(frame[:, :, ::-1])) - capture.set(cv2.CAP_PROP_POS_FRAMES, current_frame) - - # Print some statistics - print(f"Frames extracted: {len(video_frames)}") - - - # You can try tuning the batch size for very large videos, but it should usually be OK - batch_size = 256 - batches = math.ceil(len(video_frames) / batch_size) - - # The encoded features will bs stored in video_features - video_features = torch.empty([0, 512], dtype=torch.float16).to(device) - - # Process each batch - for i in range(batches): - print(f"Processing batch {i+1}/{batches}") - - # Get the relevant frames - batch_frames = video_frames[i*batch_size : (i+1)*batch_size] - - # Preprocess the images for the batch - batch_preprocessed = torch.stack([preprocess(frame) for frame in batch_frames]).to(device) - - # Encode with CLIP and normalize - with torch.no_grad(): - batch_features = model.encode_image(batch_preprocessed) - batch_features /= batch_features.norm(dim=-1, keepdim=True) - - # Append the batch to the list containing all features - video_features = torch.cat((video_features, batch_features)) - - # Print some stats - print(f"Features: {video_features.shape}") - - - search_query=text - display_heatmap=False - display_results_count=1 - # Encode and normalize the search query using CLIP - with torch.no_grad(): - text_features = model.encode_text(clip.tokenize(search_query).to(device)) - text_features /= text_features.norm(dim=-1, keepdim=True) - - # Compute the similarity between the search query and each frame using the Cosine similarity - similarities = (100.0 * video_features @ text_features.T) - values, best_photo_idx = similarities.topk(display_results_count, dim=0) - - - for frame_id in best_photo_idx: - frame = video_frames[frame_id] - # Find the timestamp in the video and display it - seconds = round(frame_id.cpu().numpy()[0] * skip_frame / fps) - output_video = produce_video(video, seconds, search_query) - - - search_query=text2 - with torch.no_grad(): - text_features = model.encode_text(clip.tokenize(search_query).to(device)) - text_features /= text_features.norm(dim=-1, keepdim=True) - - # Compute the similarity between the search query and each frame using the Cosine similarity - similarities = (100.0 * video_features @ text_features.T) - values, best_photo_idx = similarities.topk(display_results_count, dim=0) - - for frame_id in best_photo_idx: - frame = video_frames[frame_id] - # Find the timestamp in the video and display it - seconds = round(frame_id.cpu().numpy()[0] * skip_frame / fps) - output_video2 = produce_video(video, seconds, search_query) - - - search_query=text3 - with torch.no_grad(): - text_features = model.encode_text(clip.tokenize(search_query).to(device)) - text_features /= text_features.norm(dim=-1, keepdim=True) - - # Compute the similarity between the search query and each frame using the Cosine similarity - similarities = (100.0 * video_features @ text_features.T) - values, best_photo_idx = similarities.topk(display_results_count, dim=0) - - for frame_id in best_photo_idx: - frame = video_frames[frame_id] - # Find the timestamp in the video and display it - seconds = round(frame_id.cpu().numpy()[0] * skip_frame / fps) - output_video3 = produce_video(video, seconds, search_query) - - # return frame,f"Found at {str(datetime.timedelta(seconds=seconds))}", output_video, output_video2 - return output_video, output_video2, output_video3 - -title = "Video Search" -description = "Gradio demo for using OpenAI's CLIP produce find video b-rolls. To use it, simply upload your video and add your text." -article = "<p style='text-align: center'><a href='https://github.com/haltakov/natural-language-youtube-search' target='_blank'>Reference</a></p>" -examples=[['morningRoutine.mp4',"Playing piano", "Do some yoga and meditation", "Eating breakfast on the grass", 24]] - -gr.Interface( - inference, - ["video","text", "text","text", gr.Slider(1, 150, 100, label="Extrat a frame every")], - [gr.Video(), gr.Video(), gr.Video()], - title=title, - description=description, - article=article, - examples=examples - ).launch(debug=True,enable_queue=True) - diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/anchor_free_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/anchor_free_head.py deleted file mode 100644 index fcb927d5d8928aa0b3ad2fe12782c0a1f9f4abc4..0000000000000000000000000000000000000000 --- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/anchor_free_head.py +++ /dev/null @@ -1,317 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from abc import abstractmethod -from typing import Any, List, Sequence, Tuple, Union - -import torch.nn as nn -from mmcv.cnn import ConvModule -from numpy import ndarray -from torch import Tensor - -from mmdet.registry import MODELS, TASK_UTILS -from mmdet.utils import (ConfigType, InstanceList, MultiConfig, OptConfigType, - OptInstanceList) -from ..task_modules.prior_generators import MlvlPointGenerator -from ..utils import multi_apply -from .base_dense_head import BaseDenseHead - -StrideType = Union[Sequence[int], Sequence[Tuple[int, int]]] - - -@MODELS.register_module() -class AnchorFreeHead(BaseDenseHead): - """Anchor-free head (FCOS, Fovea, RepPoints, etc.). - - Args: - num_classes (int): Number of categories excluding the background - category. - in_channels (int): Number of channels in the input feature map. - feat_channels (int): Number of hidden channels. Used in child classes. - stacked_convs (int): Number of stacking convs of the head. - strides (Sequence[int] or Sequence[Tuple[int, int]]): Downsample - factor of each feature map. - dcn_on_last_conv (bool): If true, use dcn in the last layer of - towers. Defaults to False. - conv_bias (bool or str): If specified as `auto`, it will be decided by - the norm_cfg. Bias of conv will be set as True if `norm_cfg` is - None, otherwise False. Default: "auto". - loss_cls (:obj:`ConfigDict` or dict): Config of classification loss. - loss_bbox (:obj:`ConfigDict` or dict): Config of localization loss. - bbox_coder (:obj:`ConfigDict` or dict): Config of bbox coder. Defaults - 'DistancePointBBoxCoder'. - conv_cfg (:obj:`ConfigDict` or dict, Optional): Config dict for - convolution layer. Defaults to None. - norm_cfg (:obj:`ConfigDict` or dict, Optional): Config dict for - normalization layer. Defaults to None. - train_cfg (:obj:`ConfigDict` or dict, Optional): Training config of - anchor-free head. - test_cfg (:obj:`ConfigDict` or dict, Optional): Testing config of - anchor-free head. - init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or \ - dict]): Initialization config dict. - """ # noqa: W605 - - _version = 1 - - def __init__( - self, - num_classes: int, - in_channels: int, - feat_channels: int = 256, - stacked_convs: int = 4, - strides: StrideType = (4, 8, 16, 32, 64), - dcn_on_last_conv: bool = False, - conv_bias: Union[bool, str] = 'auto', - loss_cls: ConfigType = dict( - type='FocalLoss', - use_sigmoid=True, - gamma=2.0, - alpha=0.25, - loss_weight=1.0), - loss_bbox: ConfigType = dict(type='IoULoss', loss_weight=1.0), - bbox_coder: ConfigType = dict(type='mmdet.DistancePointBBoxCoder'), - conv_cfg: OptConfigType = None, - norm_cfg: OptConfigType = None, - train_cfg: OptConfigType = None, - test_cfg: OptConfigType = None, - init_cfg: MultiConfig = dict( - type='Normal', - layer='Conv2d', - std=0.01, - override=dict( - type='Normal', name='conv_cls', std=0.01, bias_prob=0.01)) - ) -> None: - super().__init__(init_cfg=init_cfg) - self.num_classes = num_classes - self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False) - if self.use_sigmoid_cls: - self.cls_out_channels = num_classes - else: - self.cls_out_channels = num_classes + 1 - self.in_channels = in_channels - self.feat_channels = feat_channels - self.stacked_convs = stacked_convs - self.strides = strides - self.dcn_on_last_conv = dcn_on_last_conv - assert conv_bias == 'auto' or isinstance(conv_bias, bool) - self.conv_bias = conv_bias - self.loss_cls = MODELS.build(loss_cls) - self.loss_bbox = MODELS.build(loss_bbox) - self.bbox_coder = TASK_UTILS.build(bbox_coder) - - self.prior_generator = MlvlPointGenerator(strides) - - # In order to keep a more general interface and be consistent with - # anchor_head. We can think of point like one anchor - self.num_base_priors = self.prior_generator.num_base_priors[0] - - self.train_cfg = train_cfg - self.test_cfg = test_cfg - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - self.fp16_enabled = False - - self._init_layers() - - def _init_layers(self) -> None: - """Initialize layers of the head.""" - self._init_cls_convs() - self._init_reg_convs() - self._init_predictor() - - def _init_cls_convs(self) -> None: - """Initialize classification conv layers of the head.""" - self.cls_convs = nn.ModuleList() - for i in range(self.stacked_convs): - chn = self.in_channels if i == 0 else self.feat_channels - if self.dcn_on_last_conv and i == self.stacked_convs - 1: - conv_cfg = dict(type='DCNv2') - else: - conv_cfg = self.conv_cfg - self.cls_convs.append( - ConvModule( - chn, - self.feat_channels, - 3, - stride=1, - padding=1, - conv_cfg=conv_cfg, - norm_cfg=self.norm_cfg, - bias=self.conv_bias)) - - def _init_reg_convs(self) -> None: - """Initialize bbox regression conv layers of the head.""" - self.reg_convs = nn.ModuleList() - for i in range(self.stacked_convs): - chn = self.in_channels if i == 0 else self.feat_channels - if self.dcn_on_last_conv and i == self.stacked_convs - 1: - conv_cfg = dict(type='DCNv2') - else: - conv_cfg = self.conv_cfg - self.reg_convs.append( - ConvModule( - chn, - self.feat_channels, - 3, - stride=1, - padding=1, - conv_cfg=conv_cfg, - norm_cfg=self.norm_cfg, - bias=self.conv_bias)) - - def _init_predictor(self) -> None: - """Initialize predictor layers of the head.""" - self.conv_cls = nn.Conv2d( - self.feat_channels, self.cls_out_channels, 3, padding=1) - self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1) - - def _load_from_state_dict(self, state_dict: dict, prefix: str, - local_metadata: dict, strict: bool, - missing_keys: Union[List[str], str], - unexpected_keys: Union[List[str], str], - error_msgs: Union[List[str], str]) -> None: - """Hack some keys of the model state dict so that can load checkpoints - of previous version.""" - version = local_metadata.get('version', None) - if version is None: - # the key is different in early versions - # for example, 'fcos_cls' become 'conv_cls' now - bbox_head_keys = [ - k for k in state_dict.keys() if k.startswith(prefix) - ] - ori_predictor_keys = [] - new_predictor_keys = [] - # e.g. 'fcos_cls' or 'fcos_reg' - for key in bbox_head_keys: - ori_predictor_keys.append(key) - key = key.split('.') - if len(key) < 2: - conv_name = None - elif key[1].endswith('cls'): - conv_name = 'conv_cls' - elif key[1].endswith('reg'): - conv_name = 'conv_reg' - elif key[1].endswith('centerness'): - conv_name = 'conv_centerness' - else: - conv_name = None - if conv_name is not None: - key[1] = conv_name - new_predictor_keys.append('.'.join(key)) - else: - ori_predictor_keys.pop(-1) - for i in range(len(new_predictor_keys)): - state_dict[new_predictor_keys[i]] = state_dict.pop( - ori_predictor_keys[i]) - super()._load_from_state_dict(state_dict, prefix, local_metadata, - strict, missing_keys, unexpected_keys, - error_msgs) - - def forward(self, x: Tuple[Tensor]) -> Tuple[List[Tensor], List[Tensor]]: - """Forward features from the upstream network. - - Args: - feats (tuple[Tensor]): Features from the upstream network, each is - a 4D-tensor. - - Returns: - tuple: Usually contain classification scores and bbox predictions. - - - cls_scores (list[Tensor]): Box scores for each scale level, \ - each is a 4D-tensor, the channel number is \ - num_points * num_classes. - - bbox_preds (list[Tensor]): Box energies / deltas for each scale \ - level, each is a 4D-tensor, the channel number is num_points * 4. - """ - return multi_apply(self.forward_single, x)[:2] - - def forward_single(self, x: Tensor) -> Tuple[Tensor, ...]: - """Forward features of a single scale level. - - Args: - x (Tensor): FPN feature maps of the specified stride. - - Returns: - tuple: Scores for each class, bbox predictions, features - after classification and regression conv layers, some - models needs these features like FCOS. - """ - cls_feat = x - reg_feat = x - - for cls_layer in self.cls_convs: - cls_feat = cls_layer(cls_feat) - cls_score = self.conv_cls(cls_feat) - - for reg_layer in self.reg_convs: - reg_feat = reg_layer(reg_feat) - bbox_pred = self.conv_reg(reg_feat) - return cls_score, bbox_pred, cls_feat, reg_feat - - @abstractmethod - def loss_by_feat( - self, - cls_scores: List[Tensor], - bbox_preds: List[Tensor], - batch_gt_instances: InstanceList, - batch_img_metas: List[dict], - batch_gt_instances_ignore: OptInstanceList = None) -> dict: - """Calculate the loss based on the features extracted by the detection - head. - - Args: - cls_scores (list[Tensor]): Box scores for each scale level, - each is a 4D-tensor, the channel number is - num_points * num_classes. - bbox_preds (list[Tensor]): Box energies / deltas for each scale - level, each is a 4D-tensor, the channel number is - num_points * 4. - batch_gt_instances (list[:obj:`InstanceData`]): Batch of - gt_instance. It usually includes ``bboxes`` and ``labels`` - attributes. - batch_img_metas (list[dict]): Meta information of each image, e.g., - image size, scaling factor, etc. - batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional): - Batch of gt_instances_ignore. It includes ``bboxes`` attribute - data that is ignored during training and testing. - Defaults to None. - """ - - raise NotImplementedError - - @abstractmethod - def get_targets(self, points: List[Tensor], - batch_gt_instances: InstanceList) -> Any: - """Compute regression, classification and centerness targets for points - in multiple images. - - Args: - points (list[Tensor]): Points of each fpn level, each has shape - (num_points, 2). - batch_gt_instances (list[:obj:`InstanceData`]): Batch of - gt_instance. It usually includes ``bboxes`` and ``labels`` - attributes. - """ - raise NotImplementedError - - # TODO refactor aug_test - def aug_test(self, - aug_batch_feats: List[Tensor], - aug_batch_img_metas: List[List[Tensor]], - rescale: bool = False) -> List[ndarray]: - """Test function with test time augmentation. - - Args: - aug_batch_feats (list[Tensor]): the outer list indicates test-time - augmentations and inner Tensor should have a shape NxCxHxW, - which contains features for all images in the batch. - aug_batch_img_metas (list[list[dict]]): the outer list indicates - test-time augs (multiscale, flip, etc.) and the inner list - indicates images in a batch. each dict has image information. - rescale (bool, optional): Whether to rescale the results. - Defaults to False. - - Returns: - list[ndarray]: bbox results of each class - """ - return self.aug_test_bboxes( - aug_batch_feats, aug_batch_img_metas, rescale=rescale) diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/solov2_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/solov2_head.py deleted file mode 100644 index 3efda8ce09224adec79f543e887d006368901987..0000000000000000000000000000000000000000 --- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/solov2_head.py +++ /dev/null @@ -1,802 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import warnings -from typing import List, Optional, Tuple - -import mmcv -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from mmcv.cnn import ConvModule -from mmengine.model import BaseModule -from mmengine.structures import InstanceData -from torch import Tensor - -from mmdet.models.utils.misc import floordiv -from mmdet.registry import MODELS -from mmdet.utils import ConfigType, InstanceList, MultiConfig, OptConfigType -from ..layers import mask_matrix_nms -from ..utils import center_of_mass, generate_coordinate, multi_apply -from .solo_head import SOLOHead -from ...structures.mask import mask2bbox - - -class MaskFeatModule(BaseModule): - """SOLOv2 mask feature map branch used in `SOLOv2: Dynamic and Fast - Instance Segmentation. <https://arxiv.org/pdf/2003.10152>`_ - - Args: - in_channels (int): Number of channels in the input feature map. - feat_channels (int): Number of hidden channels of the mask feature - map branch. - start_level (int): The starting feature map level from RPN that - will be used to predict the mask feature map. - end_level (int): The ending feature map level from rpn that - will be used to predict the mask feature map. - out_channels (int): Number of output channels of the mask feature - map branch. This is the channel count of the mask - feature map that to be dynamically convolved with the predicted - kernel. - mask_stride (int): Downsample factor of the mask feature map output. - Defaults to 4. - conv_cfg (dict): Config dict for convolution layer. Default: None. - norm_cfg (dict): Config dict for normalization layer. Default: None. - init_cfg (dict or list[dict], optional): Initialization config dict. - """ - - def __init__( - self, - in_channels: int, - feat_channels: int, - start_level: int, - end_level: int, - out_channels: int, - mask_stride: int = 4, - conv_cfg: OptConfigType = None, - norm_cfg: OptConfigType = None, - init_cfg: MultiConfig = [ - dict(type='Normal', layer='Conv2d', std=0.01) - ] - ) -> None: - super().__init__(init_cfg=init_cfg) - self.in_channels = in_channels - self.feat_channels = feat_channels - self.start_level = start_level - self.end_level = end_level - self.mask_stride = mask_stride - assert start_level >= 0 and end_level >= start_level - self.out_channels = out_channels - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - self._init_layers() - self.fp16_enabled = False - - def _init_layers(self) -> None: - """Initialize layers of the head.""" - self.convs_all_levels = nn.ModuleList() - for i in range(self.start_level, self.end_level + 1): - convs_per_level = nn.Sequential() - if i == 0: - convs_per_level.add_module( - f'conv{i}', - ConvModule( - self.in_channels, - self.feat_channels, - 3, - padding=1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg, - inplace=False)) - self.convs_all_levels.append(convs_per_level) - continue - - for j in range(i): - if j == 0: - if i == self.end_level: - chn = self.in_channels + 2 - else: - chn = self.in_channels - convs_per_level.add_module( - f'conv{j}', - ConvModule( - chn, - self.feat_channels, - 3, - padding=1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg, - inplace=False)) - convs_per_level.add_module( - f'upsample{j}', - nn.Upsample( - scale_factor=2, - mode='bilinear', - align_corners=False)) - continue - - convs_per_level.add_module( - f'conv{j}', - ConvModule( - self.feat_channels, - self.feat_channels, - 3, - padding=1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg, - inplace=False)) - convs_per_level.add_module( - f'upsample{j}', - nn.Upsample( - scale_factor=2, mode='bilinear', align_corners=False)) - - self.convs_all_levels.append(convs_per_level) - - self.conv_pred = ConvModule( - self.feat_channels, - self.out_channels, - 1, - padding=0, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg) - - def forward(self, x: Tuple[Tensor]) -> Tensor: - """Forward features from the upstream network. - - Args: - x (tuple[Tensor]): Features from the upstream network, each is - a 4D-tensor. - - Returns: - Tensor: The predicted mask feature map. - """ - inputs = x[self.start_level:self.end_level + 1] - assert len(inputs) == (self.end_level - self.start_level + 1) - feature_add_all_level = self.convs_all_levels[0](inputs[0]) - for i in range(1, len(inputs)): - input_p = inputs[i] - if i == len(inputs) - 1: - coord_feat = generate_coordinate(input_p.size(), - input_p.device) - input_p = torch.cat([input_p, coord_feat], 1) - - feature_add_all_level = feature_add_all_level + \ - self.convs_all_levels[i](input_p) - - feature_pred = self.conv_pred(feature_add_all_level) - return feature_pred - - -@MODELS.register_module() -class SOLOV2Head(SOLOHead): - """SOLOv2 mask head used in `SOLOv2: Dynamic and Fast Instance - Segmentation. <https://arxiv.org/pdf/2003.10152>`_ - - Args: - mask_feature_head (dict): Config of SOLOv2MaskFeatHead. - dynamic_conv_size (int): Dynamic Conv kernel size. Defaults to 1. - dcn_cfg (dict): Dcn conv configurations in kernel_convs and cls_conv. - Defaults to None. - dcn_apply_to_all_conv (bool): Whether to use dcn in every layer of - kernel_convs and cls_convs, or only the last layer. It shall be set - `True` for the normal version of SOLOv2 and `False` for the - light-weight version. Defaults to True. - init_cfg (dict or list[dict], optional): Initialization config dict. - """ - - def __init__(self, - *args, - mask_feature_head: ConfigType, - dynamic_conv_size: int = 1, - dcn_cfg: OptConfigType = None, - dcn_apply_to_all_conv: bool = True, - init_cfg: MultiConfig = [ - dict(type='Normal', layer='Conv2d', std=0.01), - dict( - type='Normal', - std=0.01, - bias_prob=0.01, - override=dict(name='conv_cls')) - ], - **kwargs) -> None: - assert dcn_cfg is None or isinstance(dcn_cfg, dict) - self.dcn_cfg = dcn_cfg - self.with_dcn = dcn_cfg is not None - self.dcn_apply_to_all_conv = dcn_apply_to_all_conv - self.dynamic_conv_size = dynamic_conv_size - mask_out_channels = mask_feature_head.get('out_channels') - self.kernel_out_channels = \ - mask_out_channels * self.dynamic_conv_size * self.dynamic_conv_size - - super().__init__(*args, init_cfg=init_cfg, **kwargs) - - # update the in_channels of mask_feature_head - if mask_feature_head.get('in_channels', None) is not None: - if mask_feature_head.in_channels != self.in_channels: - warnings.warn('The `in_channels` of SOLOv2MaskFeatHead and ' - 'SOLOv2Head should be same, changing ' - 'mask_feature_head.in_channels to ' - f'{self.in_channels}') - mask_feature_head.update(in_channels=self.in_channels) - else: - mask_feature_head.update(in_channels=self.in_channels) - - self.mask_feature_head = MaskFeatModule(**mask_feature_head) - self.mask_stride = self.mask_feature_head.mask_stride - self.fp16_enabled = False - - def _init_layers(self) -> None: - """Initialize layers of the head.""" - self.cls_convs = nn.ModuleList() - self.kernel_convs = nn.ModuleList() - conv_cfg = None - for i in range(self.stacked_convs): - if self.with_dcn: - if self.dcn_apply_to_all_conv: - conv_cfg = self.dcn_cfg - elif i == self.stacked_convs - 1: - # light head - conv_cfg = self.dcn_cfg - - chn = self.in_channels + 2 if i == 0 else self.feat_channels - self.kernel_convs.append( - ConvModule( - chn, - self.feat_channels, - 3, - stride=1, - padding=1, - conv_cfg=conv_cfg, - norm_cfg=self.norm_cfg, - bias=self.norm_cfg is None)) - - chn = self.in_channels if i == 0 else self.feat_channels - self.cls_convs.append( - ConvModule( - chn, - self.feat_channels, - 3, - stride=1, - padding=1, - conv_cfg=conv_cfg, - norm_cfg=self.norm_cfg, - bias=self.norm_cfg is None)) - - self.conv_cls = nn.Conv2d( - self.feat_channels, self.cls_out_channels, 3, padding=1) - - self.conv_kernel = nn.Conv2d( - self.feat_channels, self.kernel_out_channels, 3, padding=1) - - def forward(self, x): - """Forward features from the upstream network. - - Args: - x (tuple[Tensor]): Features from the upstream network, each is - a 4D-tensor. - - Returns: - tuple: A tuple of classification scores, mask prediction, - and mask features. - - - mlvl_kernel_preds (list[Tensor]): Multi-level dynamic kernel - prediction. The kernel is used to generate instance - segmentation masks by dynamic convolution. Each element in - the list has shape - (batch_size, kernel_out_channels, num_grids, num_grids). - - mlvl_cls_preds (list[Tensor]): Multi-level scores. Each - element in the list has shape - (batch_size, num_classes, num_grids, num_grids). - - mask_feats (Tensor): Unified mask feature map used to - generate instance segmentation masks by dynamic convolution. - Has shape (batch_size, mask_out_channels, h, w). - """ - assert len(x) == self.num_levels - mask_feats = self.mask_feature_head(x) - ins_kernel_feats = self.resize_feats(x) - mlvl_kernel_preds = [] - mlvl_cls_preds = [] - for i in range(self.num_levels): - ins_kernel_feat = ins_kernel_feats[i] - # ins branch - # concat coord - coord_feat = generate_coordinate(ins_kernel_feat.size(), - ins_kernel_feat.device) - ins_kernel_feat = torch.cat([ins_kernel_feat, coord_feat], 1) - - # kernel branch - kernel_feat = ins_kernel_feat - kernel_feat = F.interpolate( - kernel_feat, - size=self.num_grids[i], - mode='bilinear', - align_corners=False) - - cate_feat = kernel_feat[:, :-2, :, :] - - kernel_feat = kernel_feat.contiguous() - for i, kernel_conv in enumerate(self.kernel_convs): - kernel_feat = kernel_conv(kernel_feat) - kernel_pred = self.conv_kernel(kernel_feat) - - # cate branch - cate_feat = cate_feat.contiguous() - for i, cls_conv in enumerate(self.cls_convs): - cate_feat = cls_conv(cate_feat) - cate_pred = self.conv_cls(cate_feat) - - mlvl_kernel_preds.append(kernel_pred) - mlvl_cls_preds.append(cate_pred) - - return mlvl_kernel_preds, mlvl_cls_preds, mask_feats - - def _get_targets_single(self, - gt_instances: InstanceData, - featmap_sizes: Optional[list] = None) -> tuple: - """Compute targets for predictions of single image. - - Args: - gt_instances (:obj:`InstanceData`): Ground truth of instance - annotations. It should includes ``bboxes``, ``labels``, - and ``masks`` attributes. - featmap_sizes (list[:obj:`torch.size`]): Size of each - feature map from feature pyramid, each element - means (feat_h, feat_w). Defaults to None. - - Returns: - Tuple: Usually returns a tuple containing targets for predictions. - - - mlvl_pos_mask_targets (list[Tensor]): Each element represent - the binary mask targets for positive points in this - level, has shape (num_pos, out_h, out_w). - - mlvl_labels (list[Tensor]): Each element is - classification labels for all - points in this level, has shape - (num_grid, num_grid). - - mlvl_pos_masks (list[Tensor]): Each element is - a `BoolTensor` to represent whether the - corresponding point in single level - is positive, has shape (num_grid **2). - - mlvl_pos_indexes (list[list]): Each element - in the list contains the positive index in - corresponding level, has shape (num_pos). - """ - gt_labels = gt_instances.labels - device = gt_labels.device - - gt_bboxes = gt_instances.bboxes - gt_areas = torch.sqrt((gt_bboxes[:, 2] - gt_bboxes[:, 0]) * - (gt_bboxes[:, 3] - gt_bboxes[:, 1])) - gt_masks = gt_instances.masks.to_tensor( - dtype=torch.bool, device=device) - - mlvl_pos_mask_targets = [] - mlvl_pos_indexes = [] - mlvl_labels = [] - mlvl_pos_masks = [] - for (lower_bound, upper_bound), num_grid \ - in zip(self.scale_ranges, self.num_grids): - mask_target = [] - # FG cat_id: [0, num_classes -1], BG cat_id: num_classes - pos_index = [] - labels = torch.zeros([num_grid, num_grid], - dtype=torch.int64, - device=device) + self.num_classes - pos_mask = torch.zeros([num_grid**2], - dtype=torch.bool, - device=device) - - gt_inds = ((gt_areas >= lower_bound) & - (gt_areas <= upper_bound)).nonzero().flatten() - if len(gt_inds) == 0: - mlvl_pos_mask_targets.append( - torch.zeros([0, featmap_sizes[0], featmap_sizes[1]], - dtype=torch.uint8, - device=device)) - mlvl_labels.append(labels) - mlvl_pos_masks.append(pos_mask) - mlvl_pos_indexes.append([]) - continue - hit_gt_bboxes = gt_bboxes[gt_inds] - hit_gt_labels = gt_labels[gt_inds] - hit_gt_masks = gt_masks[gt_inds, ...] - - pos_w_ranges = 0.5 * (hit_gt_bboxes[:, 2] - - hit_gt_bboxes[:, 0]) * self.pos_scale - pos_h_ranges = 0.5 * (hit_gt_bboxes[:, 3] - - hit_gt_bboxes[:, 1]) * self.pos_scale - - # Make sure hit_gt_masks has a value - valid_mask_flags = hit_gt_masks.sum(dim=-1).sum(dim=-1) > 0 - - for gt_mask, gt_label, pos_h_range, pos_w_range, \ - valid_mask_flag in \ - zip(hit_gt_masks, hit_gt_labels, pos_h_ranges, - pos_w_ranges, valid_mask_flags): - if not valid_mask_flag: - continue - upsampled_size = (featmap_sizes[0] * self.mask_stride, - featmap_sizes[1] * self.mask_stride) - center_h, center_w = center_of_mass(gt_mask) - - coord_w = int( - floordiv((center_w / upsampled_size[1]), (1. / num_grid), - rounding_mode='trunc')) - coord_h = int( - floordiv((center_h / upsampled_size[0]), (1. / num_grid), - rounding_mode='trunc')) - - # left, top, right, down - top_box = max( - 0, - int( - floordiv( - (center_h - pos_h_range) / upsampled_size[0], - (1. / num_grid), - rounding_mode='trunc'))) - down_box = min( - num_grid - 1, - int( - floordiv( - (center_h + pos_h_range) / upsampled_size[0], - (1. / num_grid), - rounding_mode='trunc'))) - left_box = max( - 0, - int( - floordiv( - (center_w - pos_w_range) / upsampled_size[1], - (1. / num_grid), - rounding_mode='trunc'))) - right_box = min( - num_grid - 1, - int( - floordiv( - (center_w + pos_w_range) / upsampled_size[1], - (1. / num_grid), - rounding_mode='trunc'))) - - top = max(top_box, coord_h - 1) - down = min(down_box, coord_h + 1) - left = max(coord_w - 1, left_box) - right = min(right_box, coord_w + 1) - - labels[top:(down + 1), left:(right + 1)] = gt_label - # ins - gt_mask = np.uint8(gt_mask.cpu().numpy()) - # Follow the original implementation, F.interpolate is - # different from cv2 and opencv - gt_mask = mmcv.imrescale(gt_mask, scale=1. / self.mask_stride) - gt_mask = torch.from_numpy(gt_mask).to(device=device) - - for i in range(top, down + 1): - for j in range(left, right + 1): - index = int(i * num_grid + j) - this_mask_target = torch.zeros( - [featmap_sizes[0], featmap_sizes[1]], - dtype=torch.uint8, - device=device) - this_mask_target[:gt_mask.shape[0], :gt_mask. - shape[1]] = gt_mask - mask_target.append(this_mask_target) - pos_mask[index] = True - pos_index.append(index) - if len(mask_target) == 0: - mask_target = torch.zeros( - [0, featmap_sizes[0], featmap_sizes[1]], - dtype=torch.uint8, - device=device) - else: - mask_target = torch.stack(mask_target, 0) - mlvl_pos_mask_targets.append(mask_target) - mlvl_labels.append(labels) - mlvl_pos_masks.append(pos_mask) - mlvl_pos_indexes.append(pos_index) - return (mlvl_pos_mask_targets, mlvl_labels, mlvl_pos_masks, - mlvl_pos_indexes) - - def loss_by_feat(self, mlvl_kernel_preds: List[Tensor], - mlvl_cls_preds: List[Tensor], mask_feats: Tensor, - batch_gt_instances: InstanceList, - batch_img_metas: List[dict], **kwargs) -> dict: - """Calculate the loss based on the features extracted by the mask head. - - Args: - mlvl_kernel_preds (list[Tensor]): Multi-level dynamic kernel - prediction. The kernel is used to generate instance - segmentation masks by dynamic convolution. Each element in the - list has shape - (batch_size, kernel_out_channels, num_grids, num_grids). - mlvl_cls_preds (list[Tensor]): Multi-level scores. Each element - in the list has shape - (batch_size, num_classes, num_grids, num_grids). - mask_feats (Tensor): Unified mask feature map used to generate - instance segmentation masks by dynamic convolution. Has shape - (batch_size, mask_out_channels, h, w). - batch_gt_instances (list[:obj:`InstanceData`]): Batch of - gt_instance. It usually includes ``bboxes``, ``masks``, - and ``labels`` attributes. - batch_img_metas (list[dict]): Meta information of multiple images. - - Returns: - dict[str, Tensor]: A dictionary of loss components. - """ - featmap_sizes = mask_feats.size()[-2:] - - pos_mask_targets, labels, pos_masks, pos_indexes = multi_apply( - self._get_targets_single, - batch_gt_instances, - featmap_sizes=featmap_sizes) - - mlvl_mask_targets = [ - torch.cat(lvl_mask_targets, 0) - for lvl_mask_targets in zip(*pos_mask_targets) - ] - - mlvl_pos_kernel_preds = [] - for lvl_kernel_preds, lvl_pos_indexes in zip(mlvl_kernel_preds, - zip(*pos_indexes)): - lvl_pos_kernel_preds = [] - for img_lvl_kernel_preds, img_lvl_pos_indexes in zip( - lvl_kernel_preds, lvl_pos_indexes): - img_lvl_pos_kernel_preds = img_lvl_kernel_preds.view( - img_lvl_kernel_preds.shape[0], -1)[:, img_lvl_pos_indexes] - lvl_pos_kernel_preds.append(img_lvl_pos_kernel_preds) - mlvl_pos_kernel_preds.append(lvl_pos_kernel_preds) - - # make multilevel mlvl_mask_pred - mlvl_mask_preds = [] - for lvl_pos_kernel_preds in mlvl_pos_kernel_preds: - lvl_mask_preds = [] - for img_id, img_lvl_pos_kernel_pred in enumerate( - lvl_pos_kernel_preds): - if img_lvl_pos_kernel_pred.size()[-1] == 0: - continue - img_mask_feats = mask_feats[[img_id]] - h, w = img_mask_feats.shape[-2:] - num_kernel = img_lvl_pos_kernel_pred.shape[1] - img_lvl_mask_pred = F.conv2d( - img_mask_feats, - img_lvl_pos_kernel_pred.permute(1, 0).view( - num_kernel, -1, self.dynamic_conv_size, - self.dynamic_conv_size), - stride=1).view(-1, h, w) - lvl_mask_preds.append(img_lvl_mask_pred) - if len(lvl_mask_preds) == 0: - lvl_mask_preds = None - else: - lvl_mask_preds = torch.cat(lvl_mask_preds, 0) - mlvl_mask_preds.append(lvl_mask_preds) - # dice loss - num_pos = 0 - for img_pos_masks in pos_masks: - for lvl_img_pos_masks in img_pos_masks: - # Fix `Tensor` object has no attribute `count_nonzero()` - # in PyTorch 1.6, the type of `lvl_img_pos_masks` - # should be `torch.bool`. - num_pos += lvl_img_pos_masks.nonzero().numel() - loss_mask = [] - for lvl_mask_preds, lvl_mask_targets in zip(mlvl_mask_preds, - mlvl_mask_targets): - if lvl_mask_preds is None: - continue - loss_mask.append( - self.loss_mask( - lvl_mask_preds, - lvl_mask_targets, - reduction_override='none')) - if num_pos > 0: - loss_mask = torch.cat(loss_mask).sum() / num_pos - else: - loss_mask = mask_feats.sum() * 0 - - # cate - flatten_labels = [ - torch.cat( - [img_lvl_labels.flatten() for img_lvl_labels in lvl_labels]) - for lvl_labels in zip(*labels) - ] - flatten_labels = torch.cat(flatten_labels) - - flatten_cls_preds = [ - lvl_cls_preds.permute(0, 2, 3, 1).reshape(-1, self.num_classes) - for lvl_cls_preds in mlvl_cls_preds - ] - flatten_cls_preds = torch.cat(flatten_cls_preds) - - loss_cls = self.loss_cls( - flatten_cls_preds, flatten_labels, avg_factor=num_pos + 1) - return dict(loss_mask=loss_mask, loss_cls=loss_cls) - - def predict_by_feat(self, mlvl_kernel_preds: List[Tensor], - mlvl_cls_scores: List[Tensor], mask_feats: Tensor, - batch_img_metas: List[dict], **kwargs) -> InstanceList: - """Transform a batch of output features extracted from the head into - mask results. - - Args: - mlvl_kernel_preds (list[Tensor]): Multi-level dynamic kernel - prediction. The kernel is used to generate instance - segmentation masks by dynamic convolution. Each element in the - list has shape - (batch_size, kernel_out_channels, num_grids, num_grids). - mlvl_cls_scores (list[Tensor]): Multi-level scores. Each element - in the list has shape - (batch_size, num_classes, num_grids, num_grids). - mask_feats (Tensor): Unified mask feature map used to generate - instance segmentation masks by dynamic convolution. Has shape - (batch_size, mask_out_channels, h, w). - batch_img_metas (list[dict]): Meta information of all images. - - Returns: - list[:obj:`InstanceData`]: Processed results of multiple - images.Each :obj:`InstanceData` usually contains - following keys. - - - scores (Tensor): Classification scores, has shape - (num_instance,). - - labels (Tensor): Has shape (num_instances,). - - masks (Tensor): Processed mask results, has - shape (num_instances, h, w). - """ - num_levels = len(mlvl_cls_scores) - assert len(mlvl_kernel_preds) == len(mlvl_cls_scores) - - for lvl in range(num_levels): - cls_scores = mlvl_cls_scores[lvl] - cls_scores = cls_scores.sigmoid() - local_max = F.max_pool2d(cls_scores, 2, stride=1, padding=1) - keep_mask = local_max[:, :, :-1, :-1] == cls_scores - cls_scores = cls_scores * keep_mask - mlvl_cls_scores[lvl] = cls_scores.permute(0, 2, 3, 1) - - result_list = [] - for img_id in range(len(batch_img_metas)): - img_cls_pred = [ - mlvl_cls_scores[lvl][img_id].view(-1, self.cls_out_channels) - for lvl in range(num_levels) - ] - img_mask_feats = mask_feats[[img_id]] - img_kernel_pred = [ - mlvl_kernel_preds[lvl][img_id].permute(1, 2, 0).view( - -1, self.kernel_out_channels) for lvl in range(num_levels) - ] - img_cls_pred = torch.cat(img_cls_pred, dim=0) - img_kernel_pred = torch.cat(img_kernel_pred, dim=0) - result = self._predict_by_feat_single( - img_kernel_pred, - img_cls_pred, - img_mask_feats, - img_meta=batch_img_metas[img_id]) - result_list.append(result) - return result_list - - def _predict_by_feat_single(self, - kernel_preds: Tensor, - cls_scores: Tensor, - mask_feats: Tensor, - img_meta: dict, - cfg: OptConfigType = None) -> InstanceData: - """Transform a single image's features extracted from the head into - mask results. - - Args: - kernel_preds (Tensor): Dynamic kernel prediction of all points - in single image, has shape - (num_points, kernel_out_channels). - cls_scores (Tensor): Classification score of all points - in single image, has shape (num_points, num_classes). - mask_feats (Tensor): Mask prediction of all points in - single image, has shape (num_points, feat_h, feat_w). - img_meta (dict): Meta information of corresponding image. - cfg (dict, optional): Config used in test phase. - Defaults to None. - - Returns: - :obj:`InstanceData`: Processed results of single image. - it usually contains following keys. - - - scores (Tensor): Classification scores, has shape - (num_instance,). - - labels (Tensor): Has shape (num_instances,). - - masks (Tensor): Processed mask results, has - shape (num_instances, h, w). - """ - - def empty_results(cls_scores, ori_shape): - """Generate a empty results.""" - results = InstanceData() - results.scores = cls_scores.new_ones(0) - results.masks = cls_scores.new_zeros(0, *ori_shape) - results.labels = cls_scores.new_ones(0) - results.bboxes = cls_scores.new_zeros(0, 4) - return results - - cfg = self.test_cfg if cfg is None else cfg - assert len(kernel_preds) == len(cls_scores) - - featmap_size = mask_feats.size()[-2:] - - # overall info - h, w = img_meta['img_shape'][:2] - upsampled_size = (featmap_size[0] * self.mask_stride, - featmap_size[1] * self.mask_stride) - - # process. - score_mask = (cls_scores > cfg.score_thr) - cls_scores = cls_scores[score_mask] - if len(cls_scores) == 0: - return empty_results(cls_scores, img_meta['ori_shape'][:2]) - - # cate_labels & kernel_preds - inds = score_mask.nonzero() - cls_labels = inds[:, 1] - kernel_preds = kernel_preds[inds[:, 0]] - - # trans vector. - lvl_interval = cls_labels.new_tensor(self.num_grids).pow(2).cumsum(0) - strides = kernel_preds.new_ones(lvl_interval[-1]) - - strides[:lvl_interval[0]] *= self.strides[0] - for lvl in range(1, self.num_levels): - strides[lvl_interval[lvl - - 1]:lvl_interval[lvl]] *= self.strides[lvl] - strides = strides[inds[:, 0]] - - # mask encoding. - kernel_preds = kernel_preds.view( - kernel_preds.size(0), -1, self.dynamic_conv_size, - self.dynamic_conv_size) - mask_preds = F.conv2d( - mask_feats, kernel_preds, stride=1).squeeze(0).sigmoid() - # mask. - masks = mask_preds > cfg.mask_thr - sum_masks = masks.sum((1, 2)).float() - keep = sum_masks > strides - if keep.sum() == 0: - return empty_results(cls_scores, img_meta['ori_shape'][:2]) - masks = masks[keep] - mask_preds = mask_preds[keep] - sum_masks = sum_masks[keep] - cls_scores = cls_scores[keep] - cls_labels = cls_labels[keep] - - # maskness. - mask_scores = (mask_preds * masks).sum((1, 2)) / sum_masks - cls_scores *= mask_scores - - scores, labels, _, keep_inds = mask_matrix_nms( - masks, - cls_labels, - cls_scores, - mask_area=sum_masks, - nms_pre=cfg.nms_pre, - max_num=cfg.max_per_img, - kernel=cfg.kernel, - sigma=cfg.sigma, - filter_thr=cfg.filter_thr) - if len(keep_inds) == 0: - return empty_results(cls_scores, img_meta['ori_shape'][:2]) - mask_preds = mask_preds[keep_inds] - mask_preds = F.interpolate( - mask_preds.unsqueeze(0), - size=upsampled_size, - mode='bilinear', - align_corners=False)[:, :, :h, :w] - mask_preds = F.interpolate( - mask_preds, - size=img_meta['ori_shape'][:2], - mode='bilinear', - align_corners=False).squeeze(0) - masks = mask_preds > cfg.mask_thr - - results = InstanceData() - results.masks = masks - results.labels = labels - results.scores = scores - # create an empty bbox in InstanceData to avoid bugs when - # calculating metrics. - bboxes = mask2bbox(masks) - # results.bboxes = results.scores.new_zeros(len(scores), 4) - results.bboxes = bboxes - - return results diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/detectors/scnet.py b/spaces/KyanChen/RSPrompter/mmdet/models/detectors/scnet.py deleted file mode 100644 index 606a0203869f1731a21d811f06c4781f5cd90d8d..0000000000000000000000000000000000000000 --- a/spaces/KyanChen/RSPrompter/mmdet/models/detectors/scnet.py +++ /dev/null @@ -1,11 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from mmdet.registry import MODELS -from .cascade_rcnn import CascadeRCNN - - -@MODELS.register_module() -class SCNet(CascadeRCNN): - """Implementation of `SCNet <https://arxiv.org/abs/2012.10150>`_""" - - def __init__(self, **kwargs) -> None: - super().__init__(**kwargs) diff --git a/spaces/LDJA/hotdog_ld/app.py b/spaces/LDJA/hotdog_ld/app.py deleted file mode 100644 index 0d087c313e09581cb27952dd4a59611cbd448863..0000000000000000000000000000000000000000 --- a/spaces/LDJA/hotdog_ld/app.py +++ /dev/null @@ -1,16 +0,0 @@ -import gradio as gr -from transformers import pipeline - -pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog") - -def predict(image): - predictions = pipeline(image) - return {p["label"]: p["score"] for p in predictions} - -gr.Interface( - predict, - inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"), - outputs=gr.outputs.Label(num_top_classes=2), - title="Shakespeare : to be a Hot Dog Or Not to be", - allow_flagging="manual" -).launch() diff --git a/spaces/Legal-ease/legal-ease/base/create_collection.py b/spaces/Legal-ease/legal-ease/base/create_collection.py deleted file mode 100644 index b1b4c33d7da5fedc4915e1f6b002bfde3466b01a..0000000000000000000000000000000000000000 --- a/spaces/Legal-ease/legal-ease/base/create_collection.py +++ /dev/null @@ -1,163 +0,0 @@ -# Run this script independently (`python create_collection.py`) to create a Qdrant collection. A Qdrant collection is a set of vectors among which you can search. -# All the legal documents over which search needs to be enabled need to be converted to their embedding representation and inserted into a Qdrant collection for search feature to work. - -import os -import cohere -from datasets import load_dataset -from qdrant_client import QdrantClient -from qdrant_client import models -from qdrant_client.http import models as rest - -from constants import ( - ENGLISH_EMBEDDING_MODEL, - MULTILINGUAL_EMBEDDING_MODEL, - USE_MULTILINGUAL_EMBEDDING, - CREATE_QDRANT_COLLECTION_NAME, -) - -# load environment variables -QDRANT_HOST = os.environ.get("QDRANT_HOST") -QDRANT_API_KEY = os.environ.get("QDRANT_API_KEY") -COHERE_API_KEY = os.environ.get("COHERE_API_KEY") - - -def get_embedding_size(): - """ - Get the dimensions of the embeddings returned by the model being used to create embeddings for documents. - Returns: - embedding_size (`int`): - The dimensions of the embeddings returned by the embeddings model. - """ - if USE_MULTILINGUAL_EMBEDDING: - embedding_size = 768 - else: - embedding_size = 4096 - return embedding_size - - -def create_qdrant_collection(vector_size): - """ - (Re)-create a Qdrant Collection with the desired `collection name` , `vector_size` and `distance_measure`. - This collection will be used to keep all the vectors representing all the legal documents. - Args: - vector_size (`int`): - The dimensions of the embeddings that will be added to the collection. - """ - if USE_MULTILINGUAL_EMBEDDING: - # multilingual embedding model trained using dot product calculation - distance_measure = rest.Distance.DOT - else: - distance_measure = rest.Distance.COSINE - print("CREATE_QDRANT_COLLECTION_NAME:", CREATE_QDRANT_COLLECTION_NAME) - qdrant_client.recreate_collection( - collection_name=CREATE_QDRANT_COLLECTION_NAME, - vectors_config=models.VectorParams(size=vector_size, distance=distance_measure), - ) - - -def embed_legal_docs(legal_docs): - """ - Create embeddings and ids which will used to represent the legal documents upon which search needs to be enabled. - Args: - legal_docs (`List`): - A list of documents for which embeddings need to be created. - Returns: - doc_embeddings (`List`): - A list of embeddings corresponding to each document. - doc_ids (`List`): - A list of unique ids which will be used as identifiers for the points (documents) in a qdrant collection. - """ - if USE_MULTILINGUAL_EMBEDDING: - model_name = MULTILINGUAL_EMBEDDING_MODEL - else: - model_name = ENGLISH_EMBEDDING_MODEL - - legal_docs_embeds = cohere_client.embed( - texts=legal_docs, - model=model_name, - ) - doc_embeddings = [ - list(map(float, vector)) for vector in legal_docs_embeds.embeddings - ] - doc_ids = [id for id, _ in enumerate(legal_docs_embeds)] - - return doc_embeddings, doc_ids - - -def upsert_data_in_collection(vectors, ids, payload): - """ - Create embeddings and ids which will used to represent the legal documents upon which search needs to be enabled. - Args: - vectors (`List`): - A list of embeddings corresponding to each document which needs to be added to the collection. - ids (`List`): - A list of unique ids which will be used as identifiers for the points (documents) in a qdrant collection. - payload (`List`): - A list of additional information or metadata corresponding to each document being added to the collection. - """ - try: - update_result = qdrant_client.upsert( - collection_name=CREATE_QDRANT_COLLECTION_NAME, - points=rest.Batch( - ids=ids, - vectors=vectors, - payloads=payload, - ), - ) - return update_result - except: - return None - - -def fetch_legal_documents_and_payload(): - """ - Get the legal documents and additional information (payload) related to them which will be used as part of the search module. - Returns: - legal_docs (`List['str]`): - The documents that will be used as part of the search module. - payload (`List[Dict]`): - Additional information related to the documents that are being used as part of the search module. - """ - legal_dataset = load_dataset("joelito/covid19_emergency_event", split="train") - legal_docs = legal_dataset["text"] - - # prepare payload (additional information or metadata for documents being inserted) - payload = list(legal_dataset) - - return payload, legal_docs - - -if __name__ == "__main__": - # create qdrant and cohere client - cohere_client = cohere.Client(COHERE_API_KEY) - - qdrant_client = QdrantClient( - host=QDRANT_HOST, - prefer_grpc=True, - api_key=QDRANT_API_KEY, - ) - - # fetch the size of the embeddings depending on which model is being used to create embeddings for documents - vector_size = get_embedding_size() - - # create a collection in Qdrant - create_qdrant_collection(vector_size) - - # load the set of documents which will be inserted into the Qdrant collection - payload, legal_docs = fetch_legal_documents_and_payload() - - # create embedddings for documents and IDs for documents before insertion into Qdrant collection - doc_embeddings, doc_ids = embed_legal_docs(legal_docs) - - # insert/update documents in the previously created qdrant collection - update_result = upsert_data_in_collection(doc_embeddings, doc_ids, payload) - - collection_info = qdrant_client.get_collection( - collection_name=CREATE_QDRANT_COLLECTION_NAME - ) - - if update_result is not None: - if collection_info.vectors_count == len(legal_docs): - print("All documents have been successfully added to Qdrant Collection!") - else: - print("Failed to add documents to Qdrant collection") diff --git a/spaces/Mahiruoshi/vits-chatbot/main.py b/spaces/Mahiruoshi/vits-chatbot/main.py deleted file mode 100644 index 51e9c7b76fb92ec110c37a77c96ef7e6f10b226f..0000000000000000000000000000000000000000 --- a/spaces/Mahiruoshi/vits-chatbot/main.py +++ /dev/null @@ -1,277 +0,0 @@ -import re -import gradio as gr -import torch -import unicodedata -import commons -import utils -import pathlib -from models import SynthesizerTrn -from text import text_to_sequence -import time -import os -import io -from scipy.io.wavfile import write -from flask import Flask, request -from threading import Thread -import openai -import requests -import json -import soundfile as sf -from scipy import signal -class VitsGradio: - def __init__(self): - self.lan = ["中文","日文","自动"] - self.chatapi = ["gpt-3.5-turbo","gpt3"] - self.modelPaths = [] - for root,dirs,files in os.walk("checkpoints"): - for dir in dirs: - self.modelPaths.append(dir) - with gr.Blocks() as self.Vits: - with gr.Tab("调试用"): - with gr.Row(): - with gr.Column(): - with gr.Row(): - with gr.Column(): - self.text = gr.TextArea(label="Text", value="你好") - with gr.Accordion(label="测试api", open=False): - self.local_chat1 = gr.Checkbox(value=False, label="使用网址+文本进行模拟") - self.url_input = gr.TextArea(label="键入测试", value="http://127.0.0.1:8080/chat?Text=") - butto = gr.Button("模拟前端抓取语音文件") - btnVC = gr.Button("测试tts+对话程序") - with gr.Column(): - output2 = gr.TextArea(label="回复") - output1 = gr.Audio(label="采样率22050") - output3 = gr.outputs.File(label="44100hz: output.wav") - butto.click(self.Simul, inputs=[self.text, self.url_input], outputs=[output2,output3]) - btnVC.click(self.tts_fn, inputs=[self.text], outputs=[output1,output2]) - with gr.Tab("控制面板"): - with gr.Row(): - with gr.Column(): - with gr.Row(): - with gr.Column(): - self.api_input1 = gr.TextArea(label="输入gpt/茉莉云的api-key或本地存储说话模型的路径.如果要用茉莉云则用'|'隔开key和密码", value="sample:49eig5nu3rllvg6e|itcn9760") - with gr.Accordion(label="chatbot选择(默认gpt3.5),如需启用其他功能，请删除234行的注释", open=False): - self.api_input2 = gr.Checkbox(value=False, label="茉莉云") - self.local_chat1 = gr.Checkbox(value=False, label="启动本地chatbot") - self.local_chat2 = gr.Checkbox(value=False, label="是否量化") - res = gr.TextArea() - Botselection = gr.Button("完成chatbot设定") - Botselection.click(self.check_bot, inputs=[self.api_input1,self.api_input2,self.local_chat1,self.local_chat2], outputs = [res]) - self.input1 = gr.Dropdown(label = "模型", choices = self.modelPaths, value = self.modelPaths[0], type = "value") - self.input2 = gr.Dropdown(label="Language", choices=self.lan, value="自动", interactive=True) - with gr.Column(): - btnVC = gr.Button("完成vits TTS端设定") - self.input3 = gr.Dropdown(label="Speaker", choices=list(range(1001)), value=0, interactive=True) - self.input4 = gr.Slider(minimum=0, maximum=1.0, label="更改噪声比例(noise scale)，以控制情感", value=0.6) - self.input5 = gr.Slider(minimum=0, maximum=1.0, label="更改噪声偏差(noise scale w)，以控制音素长短", value=0.667) - self.input6 = gr.Slider(minimum=0.1, maximum=10, label="duration", value=1) - statusa = gr.TextArea() - btnVC.click(self.create_tts_fn, inputs=[self.input1, self.input2, self.input3, self.input4, self.input5, self.input6], outputs = [statusa]) - - def Simul(self,text,url_input): - web = url_input + text - res = requests.get(web) - music = res.content - with open('output.wav', 'wb') as code: - code.write(music) - file_path = "output.wav" - return web,file_path - - - def mori(self,text): - import http.client - conn = http.client.HTTPSConnection("api.mlyai.com") - payload = json.dumps({ - "content": text, - "type": 1, - "from": "123456", - "fromName": "侑" - }) - headers = { - 'Api-Key': self.api_key, - 'Api-Secret': self.api_secret, - 'Content-Type': 'application/json' - } - conn.request("POST", "/reply", payload, headers) - res = conn.getresponse() - data = res.read() - decoded_data = json.loads(data.decode("utf-8")) - - if decoded_data["code"] == "00000": - answer = decoded_data["data"][0]["content"] - if text == 'exit': - conn.close() - return answer - else: - conn.close() - return '对不起，做不到' - - def chatgpt(self,text): - self.messages.append({"role": "user", "content": text},) - chat = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages= self.messages) - reply = chat.choices[0].message.content - return reply - - def ChATGLM(self,text): - if text == 'clear': - self.history = [] - response, new_history = self.model.chat(self.tokenizer, text, self.history) - response = response.replace(" ",'').replace("\n",'.') - self.history = new_history - return response - - def gpt3_chat(self,text): - call_name = "Waifu" - openai.api_key = args.key - identity = "" - start_sequence = '\n'+str(call_name)+':' - restart_sequence = "\nYou: " - if 1 == 1: - prompt0 = text #当期prompt - if text == 'quit': - return prompt0 - prompt = identity + prompt0 + start_sequence - response = openai.Completion.create( - model="text-davinci-003", - prompt=prompt, - temperature=0.5, - max_tokens=1000, - top_p=1.0, - frequency_penalty=0.5, - presence_penalty=0.0, - stop=["\nYou:"] - ) - return response['choices'][0]['text'].strip() - - def check_bot(self,api_input1,api_input2,local_chat1,local_chat2): - try: - self.api_key, self.api_secret = api_input1.split("|") - except: - pass - if local_chat1: - from transformers import AutoTokenizer, AutoModel - self.tokenizer = AutoTokenizer.from_pretrained(api_input1, trust_remote_code=True) - if local_chat2: - self.model = AutoModel.from_pretrained(api_input1, trust_remote_code=True).half().quantize(4).cuda() - else: - self.model = AutoModel.from_pretrained(api_input1, trust_remote_code=True) - self.history = [] - else: - try: - self.messages = [] - openai.api_key = api_input1 - except: - pass - return "Finished" - - def is_japanese(self,string): - for ch in string: - if ord(ch) > 0x3040 and ord(ch) < 0x30FF: - return True - return False - - def is_english(self,string): - import re - pattern = re.compile('^[A-Za-z0-9.,:;!?()_*"\' ]+$') - if pattern.fullmatch(string): - return True - else: - return False - - - - def get_text(self,text, hps, cleaned=False): - if cleaned: - text_norm = text_to_sequence(text, self.hps_ms.symbols, []) - else: - text_norm = text_to_sequence(text, self.hps_ms.symbols, self.hps_ms.data.text_cleaners) - if self.hps_ms.data.add_blank: - text_norm = commons.intersperse(text_norm, 0) - text_norm = torch.LongTensor(text_norm) - return text_norm - - - def get_label(self,text, label): - if f'[{label}]' in text: - return True, text.replace(f'[{label}]', '') - else: - return False, text - - def sle(self,language,text): - text = text.replace('\n','。').replace(' ',',') - if language == "中文": - tts_input1 = "[ZH]" + text + "[ZH]" - return tts_input1 - elif language == "自动": - tts_input1 = f"[JA]{text}[JA]" if self.is_japanese(text) else f"[ZH]{text}[ZH]" - return tts_input1 - elif language == "日文": - tts_input1 = "[JA]" + text + "[JA]" - return tts_input1 - - def create_tts_fn(self,path, input2, input3, n_scale= 0.667,n_scale_w = 0.8, l_scale = 1 ): - self.language = input2 - self.speaker_id = int(input3) - self.n_scale = n_scale - self.n_scale_w = n_scale_w - self.l_scale = l_scale - self.dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - self.hps_ms = utils.get_hparams_from_file(f"checkpoints/{path}/config.json") - self.n_speakers = self.hps_ms.data.n_speakers if 'n_speakers' in self.hps_ms.data.keys() else 0 - self.n_symbols = len(self.hps_ms.symbols) if 'symbols' in self.hps_ms.keys() else 0 - self.net_g_ms = SynthesizerTrn( - self.n_symbols, - self.hps_ms.data.filter_length // 2 + 1, - self.hps_ms.train.segment_size // self.hps_ms.data.hop_length, - n_speakers=self.n_speakers, - **self.hps_ms.model).to(self.dev) - _ = self.net_g_ms.eval() - _ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", self.net_g_ms) - return 'success' - - - def tts_fn(self,text): - ''' - if self.local_chat1: - text = self.mori(text) - elif self.api_input2: - text = self.ChATGLM(text) - ''' - text = text = self.chatgpt(text) - print(text) - text =self.sle(self.language,text) - with torch.no_grad(): - stn_tst = self.get_text(text, self.hps_ms, cleaned=False) - x_tst = stn_tst.unsqueeze(0).to(self.dev) - x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(self.dev) - sid = torch.LongTensor([self.speaker_id]).to(self.dev) - audio = self.net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=self.n_scale, noise_scale_w=self.n_scale_w, length_scale=self.l_scale)[0][ - 0, 0].data.cpu().float().numpy() - resampled_audio_data = signal.resample(audio, len(audio) * 2) - sf.write('temp.wav', resampled_audio_data, 44100, 'PCM_24') - return (self.hps_ms.data.sampling_rate, audio),text.replace('[JA]','').replace('[ZH]','') - -app = Flask(__name__) -print("开始部署") -grVits = VitsGradio() - -@app.route('/chat') -def text_api(): - message = request.args.get('Text','') - audio,text = grVits.tts_fn(message) - text = text.replace('[JA]','').replace('[ZH]','') - with open('temp.wav','rb') as bit: - wav_bytes = bit.read() - headers = { - 'Content-Type': 'audio/wav', - 'Text': text.encode('utf-8')} - return wav_bytes, 200, headers - -def gradio_interface(): - return grVits.Vits.launch() - -if __name__ == '__main__': - api_thread = Thread(target=app.run, args=("0.0.0.0", 8080)) - gradio_thread = Thread(target=gradio_interface) - api_thread.start() - gradio_thread.start() diff --git a/spaces/ManjunathNili/manjuai/README.md b/spaces/ManjunathNili/manjuai/README.md deleted file mode 100644 index 1eaed98ae2ac5d4bff8582a926e8f25bbdf257dd..0000000000000000000000000000000000000000 --- a/spaces/ManjunathNili/manjuai/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Manjuai -emoji: 🐠 -colorFrom: red -colorTo: indigo -sdk: gradio -sdk_version: 3.39.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/MatrixYao/how_many_data_points_zh/naacl_demo/main.py b/spaces/MatrixYao/how_many_data_points_zh/naacl_demo/main.py deleted file mode 100644 index 7a110a5d9ecc852285d7b049336b463dcab3ea67..0000000000000000000000000000000000000000 --- a/spaces/MatrixYao/how_many_data_points_zh/naacl_demo/main.py +++ /dev/null @@ -1,293 +0,0 @@ -from bokeh.events import Tap -from bokeh.io import curdoc -from bokeh.layouts import column -from bokeh.models import Div, TextInput, RadioButtonGroup, TextAreaInput, Span, Button, Panel, Tabs -from bokeh.models.tools import CrosshairTool - -from demo_utils import ( - get_data, - prompt_boolq, - pvp_colors, - ctl_colors, - clf_colors, - reduct, - task_best_pattern, - plot_polygons_bokeh, - advantage_text, - data_difference, - calculate_overlap, - circ_easing, - average_advantage_text, - plot_three_polygons_bokeh, - tasks, - metric_tap, - neutral_tasks, pattern_graph, -) -from text import text1, text2, text3, text4, initial_passage, initial_question, text5 - -######################################################################################################################## -# Basic dimensions -######################################################################################################################## - -plot_width = 1200 -plot_height = 400 -sidebar_width = 400 -in_text_plot_height = 300 -text_width = 800 -widget_size = 400 - -######################################################################################################################## -# Patternification widget -######################################################################################################################## - -passage = TextAreaInput(title="篇章", rows=3, value=initial_passage, max_width=text_width) -passage.align = "center" -question = TextInput(title="问题", value=initial_question, max_width=text_width) -question.align = "center" -radio_button_group = RadioButtonGroup(labels=["模板 1", "模板 2", "模板 3"], active=0, max_width=text_width) -radio_button_group.align = "center" - -box_style = { - "display": "block", - "margin": "0 auto", - "width": f"{text_width}px", - "text-align": "center", - "white-space": "pre-wrap", - "background": "#f4f4f4", - "border": "1px solid #ddd", - # "border-left": "3px solid #4d4945", - "color": "#666", - "page-break-inside": "avoid", - # "font-family": "monospace", - "font-size": "15px", - "line-height": "1.6", - "max-width": "100%", - "overflow": "hidden", - "min-height": "30px", - "word-wrap": "break-word", -} - -prompt_box = Div( - text=prompt_boolq(passage.value, question.value, radio_button_group.active), - width=text_width, - style=box_style, - sizing_mode="scale_width", -) -prompt_box.align = "center" - - -def update_prompt(attrname, old, new): - prompt_box.text = prompt_boolq(passage.value, question.value, radio_button_group.active) - - -passage.on_change("value", update_prompt) -question.on_change("value", update_prompt) -radio_button_group.on_change("active", update_prompt) - -patternification = column(passage, question, radio_button_group, prompt_box, sizing_mode="scale_width") -patternification.align = "center" - -######################################################################################################################## -# Advantage diagram -######################################################################################################################## - -advantage_plots_per_task = [] -overlapping_range_per_task = [] -training_points_per_task = [] -clf_results_per_task = [] -pvp_results_per_task = [] -advantage_tabs = [] -advantage_all_figures = Tabs(tabs=advantage_tabs) - -advantage_box = Div( - text="在比较区域内点击某点以计算该点对应的性能点上的数据优势", - width=text_width, - style=box_style, - sizing_mode="scale_width", -) -advantage_box.align = "center" - -for task in tasks: - training_points, classifier_performances, pattern_performances = get_data(task) - training_points_per_task.append(list(training_points)) - clf_results_per_task.append(reduct(classifier_performances, "accmax")) - pvp_results_per_task.append(reduct(pattern_performances, "accmax", task_best_pattern[task], "normal")) - advantage_plots_per_task.append(plot_polygons_bokeh( - task, training_points_per_task[-1], clf_results_per_task[-1], pvp_results_per_task[-1], clf_colors, - pvp_colors - )) - advantage_plots_per_task[-1].align = "center" - advantage_plots_per_task[-1].add_tools(CrosshairTool(dimensions="width", line_alpha=0.2)) - overlapping_range_per_task.append(calculate_overlap(clf_results_per_task[-1], pvp_results_per_task[-1])) - advantage_tabs.append(Panel(child=advantage_plots_per_task[-1], title=task)) - - advantage_plots_per_task[-1].on_event( - Tap, - lambda event: metric_tap( - event, - overlapping_range_per_task[advantage_all_figures.active], - training_points_per_task[advantage_all_figures.active], - clf_results_per_task[advantage_all_figures.active], - pvp_results_per_task[advantage_all_figures.active], - advantage_box, - advantage_plots_per_task[advantage_all_figures.active], - ), - ) - - if task == "MNLI": - training_points_per_task.append(list(training_points)) - clf_results_per_task.append(reduct(classifier_performances, "accmax")) - pvp_results_per_task.append(reduct(pattern_performances, "accmax", task_best_pattern[task], "normal")) - advantage_plots_per_task.append(plot_polygons_bokeh( - task, training_points_per_task[-1], clf_results_per_task[-1], pvp_results_per_task[-1], clf_colors, - pvp_colors, x_log_scale=True - )) - advantage_plots_per_task[-1].align = "center" - advantage_plots_per_task[-1].add_tools(CrosshairTool(dimensions="width", line_alpha=0.2)) - overlapping_range_per_task.append(calculate_overlap(clf_results_per_task[-1], pvp_results_per_task[-1])) - advantage_tabs.append(Panel(child=advantage_plots_per_task[-1], title="MNLI (log scale)")) - - advantage_plots_per_task[-1].on_event( - Tap, - lambda event: metric_tap( - event, - overlapping_range_per_task[advantage_all_figures.active], - training_points_per_task[advantage_all_figures.active], - clf_results_per_task[advantage_all_figures.active], - pvp_results_per_task[advantage_all_figures.active], - advantage_box, - advantage_plots_per_task[advantage_all_figures.active], - ), - ) - -advantage_all_figures = Tabs(tabs=advantage_tabs) -advantage_all_figures.align = "center" - - -def on_integrate_click(): - frames = 200 - initial_placement = overlapping_range_per_task[advantage_all_figures.active][0] - - if not isinstance(advantage_plots_per_task[advantage_all_figures.active].renderers[-1], Span): - metric_line = Span( - location=initial_placement, - line_alpha=0.7, - dimension="width", - line_color=clf_colors[0] if initial_placement < 0 else pvp_colors[0], - line_dash="dashed", - line_width=1, - ) - advantage_plots_per_task[advantage_all_figures.active].renderers.extend([metric_line]) - else: - advantage_plots_per_task[advantage_all_figures.active].renderers[-1].location = initial_placement - advantage_plots_per_task[advantage_all_figures.active].renderers[-1].line_color = clf_colors[ - 0] if initial_placement < 0 else pvp_colors[0] - - average_advantage = 0 - for i in range(1, frames): - metric_value = overlapping_range_per_task[advantage_all_figures.active][0] + ( - overlapping_range_per_task[advantage_all_figures.active][1] - - overlapping_range_per_task[advantage_all_figures.active][0]) * (i / frames) - advantage_value = data_difference(metric_value, overlapping_range_per_task[advantage_all_figures.active], - training_points_per_task[advantage_all_figures.active], - clf_results_per_task[advantage_all_figures.active], - pvp_results_per_task[advantage_all_figures.active]) - average_advantage = ((i - 1) * average_advantage + advantage_value) / i - - advantage_plots_per_task[advantage_all_figures.active].renderers[-1].location = metric_value - advantage_plots_per_task[advantage_all_figures.active].renderers[-1].line_color = clf_colors[ - 0] if advantage_value < 0 else pvp_colors[0] - advantage_box.text = average_advantage_text(average_advantage) - - -integrate = Button(width=175, max_width=175, label="对整个区域进行积分！") -integrate.align = "center" -integrate.on_click(on_integrate_click) - - -def on_tab_change(attr, old, new): - advantage_box.text = "在比较区域内点击某点以计算该点对应的性能点上的数据优势" - - -advantage_all_figures.on_change('active', on_tab_change) - -advantage_column = column(advantage_all_figures, advantage_box, integrate, sizing_mode="scale_width") - -######################################################################################################################## -# Null verbalizer diagram -######################################################################################################################## - -null_tabs = [] -null_all_figures = Tabs(tabs=null_tabs) - -for task in neutral_tasks: - training_points, classifier_performances, pattern_performances = get_data(task) - training_points = list(training_points) - clf_results = reduct(classifier_performances, "accmax") - pvp_results = reduct(pattern_performances, "accmax", task_best_pattern[task], "normal") - ctl_results = reduct(pattern_performances, "accmax", task_best_pattern[task], "neutral") - null_plot = plot_three_polygons_bokeh(task, training_points, clf_results, pvp_results, ctl_results, clf_colors, - pvp_colors, ctl_colors) - null_plot.align = "center" - null_plot.add_tools(CrosshairTool(dimensions="width", line_alpha=0.2)) - null_tabs.append(Panel(child=null_plot, title=task)) - - if task == "MNLI": - null_plot = plot_three_polygons_bokeh(task, training_points, clf_results, pvp_results, ctl_results, clf_colors, - pvp_colors, ctl_colors, x_log_scale=True) - null_plot.align = "center" - null_plot.add_tools(CrosshairTool(dimensions="width", line_alpha=0.2)) - null_tabs.append(Panel(child=null_plot, title="MNLI (log scale)")) - -null_all_figures = Tabs(tabs=null_tabs) -null_all_figures.align = "center" - -######################################################################################################################## -# Patterns diagram -######################################################################################################################## - -pattern_tabs = [] -pattern_all_figures = Tabs(tabs=pattern_tabs) - -for task in tasks: - pattern_plot = pattern_graph(task) - pattern_plot.align = "center" - pattern_plot.add_tools(CrosshairTool(dimensions="width", line_alpha=0.2)) - pattern_tabs.append(Panel(child=pattern_plot, title=task)) - -pattern_all_figures = Tabs(tabs=pattern_tabs) -pattern_all_figures.align = "center" - -######################################################################################################################## -# Add write-up text -######################################################################################################################## - -main_text_style = { - "min-height": "100px", - "overflow": "hidden", - "display": "block", - "margin": "auto", - "width": f"{text_width}px", - "font-size": "18px", -} - -textbox1 = Div(text=text1, style=main_text_style) -textbox2 = Div(text=text2, style=main_text_style) -textbox3 = Div(text=text3, style=main_text_style) -textbox4 = Div(text=text4, style=main_text_style) -textbox5 = Div(text=text5, style=main_text_style) -textbox1.align = "center" -textbox2.align = "center" -textbox3.align = "center" -textbox4.align = "center" -textbox5.align = "center" - -######################################################################################################################## -# Set up layouts and add to document -######################################################################################################################## - -main_body = column(textbox1, patternification, textbox2, advantage_column, textbox3, null_all_figures, textbox4, pattern_all_figures, textbox5, sizing_mode="scale_width") -main_body.align = "center" - -curdoc().add_root(main_body) -curdoc().title = "一条提示抵得上多少样本数据？" diff --git a/spaces/Menna2211/TxTimg/pages/Stable-Diffusion-Huggingface.py b/spaces/Menna2211/TxTimg/pages/Stable-Diffusion-Huggingface.py deleted file mode 100644 index 1bd1f6086829d94519bd094f85a2207e051c3be5..0000000000000000000000000000000000000000 --- a/spaces/Menna2211/TxTimg/pages/Stable-Diffusion-Huggingface.py +++ /dev/null @@ -1,32 +0,0 @@ -import streamlit as st -import torch -import time -from diffusers import StableDiffusionPipeline - -device = "cuda" if torch.cuda.is_available() else "cpu" -torch_dtype = torch.float16 if device == "cuda" else torch.float32 - -model_id = "runwayml/stable-diffusion-v1-5" -pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype) -pipe = pipe.to(device) - -st.title("Stable Diffusion App") -# define the layout of your app - -# Define the Streamlit app layout -prompt = st.text_input("Write your sentence:") -submit_button = st.button("Compute") -if not submit_button: - st.stop() - -# Display the generated text -if submit_button: - with st.spinner('Wait for it...'): - generated_img=pipe(prompt).images[0] - time.sleep(0.1) - - st.write("Generated Image:") - st.image(generated_img) - time.sleep(5) - st.success('Congratulations task is done ', icon="✅") - st.balloons() diff --git a/spaces/MesutUnutur/chatgptFinetune/app.py b/spaces/MesutUnutur/chatgptFinetune/app.py deleted file mode 100644 index 508e26c8bf9d7fae83ecd9b9e3fb1a86c2d353e7..0000000000000000000000000000000000000000 --- a/spaces/MesutUnutur/chatgptFinetune/app.py +++ /dev/null @@ -1,23 +0,0 @@ -import llama_index -from llama_index import SimpleDirectoryReader, GPTVectorStoreIndex ,StorageContext,load_index_from_storage -import os -import gradio as gr - -os.environ["OPENAI_API_KEY"] = "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" -storage_context = StorageContext.from_defaults(persist_dir="mustafaKemalAtatürk.txt") -index = load_index_from_storage(storage_context) - -#prompt = "What is this text about" -#result = index.as_query_engine().query(prompt) - -def query(prompt): - result = index.as_query_engine().query(prompt) - return result - -with gr.Blocks() as demo: - prompt = gr.Textbox(label="Questions or query", placeholder="what is your query") - output = gr.Textbox() - query_btn = gr.Button("query") - query_btn.click(query, prompt, output) - -demo.launch() diff --git a/spaces/MohamadRezo/flixPicks/README.md b/spaces/MohamadRezo/flixPicks/README.md deleted file mode 100644 index 3c2717cb02a102b6ab570c9594b430da894a8fe3..0000000000000000000000000000000000000000 --- a/spaces/MohamadRezo/flixPicks/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: FlixPicks -emoji: 🌍 -colorFrom: gray -colorTo: purple -sdk: streamlit -sdk_version: 1.21.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/transforms/formatting.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/transforms/formatting.py deleted file mode 100644 index b9b71437a6cc1de2396b17fe5c04909855f2ed86..0000000000000000000000000000000000000000 --- a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/transforms/formatting.py +++ /dev/null @@ -1,330 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import numpy as np -import torch -from mmcv.transforms import to_tensor -from mmcv.transforms.base import BaseTransform -from mmengine.structures import InstanceData, LabelData - -from mmocr.registry import TRANSFORMS -from mmocr.structures import (KIEDataSample, TextDetDataSample, - TextRecogDataSample) - - -@TRANSFORMS.register_module() -class PackTextDetInputs(BaseTransform): - """Pack the inputs data for text detection. - - The type of outputs is `dict`: - - - inputs: image converted to tensor, whose shape is (C, H, W). - - data_samples: Two components of ``TextDetDataSample`` will be updated: - - - gt_instances (InstanceData): Depending on annotations, a subset of the - following keys will be updated: - - - bboxes (torch.Tensor((N, 4), dtype=torch.float32)): The groundtruth - of bounding boxes in the form of [x1, y1, x2, y2]. Renamed from - 'gt_bboxes'. - - labels (torch.LongTensor(N)): The labels of instances. - Renamed from 'gt_bboxes_labels'. - - polygons(list[np.array((2k,), dtype=np.float32)]): The - groundtruth of polygons in the form of [x1, y1,..., xk, yk]. Each - element in polygons may have different number of points. Renamed from - 'gt_polygons'. Using numpy instead of tensor is that polygon usually - is not the output of model and operated on cpu. - - ignored (torch.BoolTensor((N,))): The flag indicating whether the - corresponding instance should be ignored. Renamed from - 'gt_ignored'. - - texts (list[str]): The groundtruth texts. Renamed from 'gt_texts'. - - - metainfo (dict): 'metainfo' is always populated. The contents of the - 'metainfo' depends on ``meta_keys``. By default it includes: - - - "img_path": Path to the image file. - - "img_shape": Shape of the image input to the network as a tuple - (h, w). Note that the image may be zero-padded afterward on the - bottom/right if the batch tensor is larger than this shape. - - "scale_factor": A tuple indicating the ratio of width and height - of the preprocessed image to the original one. - - "ori_shape": Shape of the preprocessed image as a tuple - (h, w). - - "pad_shape": Image shape after padding (if any Pad-related - transform involved) as a tuple (h, w). - - "flip": A boolean indicating if the image has been flipped. - - ``flip_direction``: the flipping direction. - - Args: - meta_keys (Sequence[str], optional): Meta keys to be converted to - the metainfo of ``TextDetSample``. Defaults to ``('img_path', - 'ori_shape', 'img_shape', 'scale_factor', 'flip', - 'flip_direction')``. - """ - mapping_table = { - 'gt_bboxes': 'bboxes', - 'gt_bboxes_labels': 'labels', - 'gt_polygons': 'polygons', - 'gt_texts': 'texts', - 'gt_ignored': 'ignored' - } - - def __init__(self, - meta_keys=('img_path', 'ori_shape', 'img_shape', - 'scale_factor', 'flip', 'flip_direction')): - self.meta_keys = meta_keys - - def transform(self, results: dict) -> dict: - """Method to pack the input data. - - Args: - results (dict): Result dict from the data pipeline. - - Returns: - dict: - - - 'inputs' (obj:`torch.Tensor`): Data for model forwarding. - - 'data_samples' (obj:`DetDataSample`): The annotation info of the - sample. - """ - packed_results = dict() - if 'img' in results: - img = results['img'] - if len(img.shape) < 3: - img = np.expand_dims(img, -1) - # A simple trick to speedup formatting by 3-5 times when - # OMP_NUM_THREADS != 1 - # Refer to https://github.com/open-mmlab/mmdetection/pull/9533 - # for more details - if img.flags.c_contiguous: - img = to_tensor(img) - img = img.permute(2, 0, 1).contiguous() - else: - img = np.ascontiguousarray(img.transpose(2, 0, 1)) - img = to_tensor(img) - packed_results['inputs'] = img - - data_sample = TextDetDataSample() - instance_data = InstanceData() - for key in self.mapping_table.keys(): - if key not in results: - continue - if key in ['gt_bboxes', 'gt_bboxes_labels', 'gt_ignored']: - instance_data[self.mapping_table[key]] = to_tensor( - results[key]) - else: - instance_data[self.mapping_table[key]] = results[key] - data_sample.gt_instances = instance_data - - img_meta = {} - for key in self.meta_keys: - img_meta[key] = results[key] - data_sample.set_metainfo(img_meta) - packed_results['data_samples'] = data_sample - - return packed_results - - def __repr__(self) -> str: - repr_str = self.__class__.__name__ - repr_str += f'(meta_keys={self.meta_keys})' - return repr_str - - -@TRANSFORMS.register_module() -class PackTextRecogInputs(BaseTransform): - """Pack the inputs data for text recognition. - - The type of outputs is `dict`: - - - inputs: Image as a tensor, whose shape is (C, H, W). - - data_samples: Two components of ``TextRecogDataSample`` will be updated: - - - gt_text (LabelData): - - - item(str): The groundtruth of text. Rename from 'gt_texts'. - - - metainfo (dict): 'metainfo' is always populated. The contents of the - 'metainfo' depends on ``meta_keys``. By default it includes: - - - "img_path": Path to the image file. - - "ori_shape": Shape of the preprocessed image as a tuple - (h, w). - - "img_shape": Shape of the image input to the network as a tuple - (h, w). Note that the image may be zero-padded afterward on the - bottom/right if the batch tensor is larger than this shape. - - "valid_ratio": The proportion of valid (unpadded) content of image - on the x-axis. It defaults to 1 if not set in pipeline. - - Args: - meta_keys (Sequence[str], optional): Meta keys to be converted to - the metainfo of ``TextRecogDataSampel``. Defaults to - ``('img_path', 'ori_shape', 'img_shape', 'pad_shape', - 'valid_ratio')``. - """ - - def __init__(self, - meta_keys=('img_path', 'ori_shape', 'img_shape', 'pad_shape', - 'valid_ratio')): - self.meta_keys = meta_keys - - def transform(self, results: dict) -> dict: - """Method to pack the input data. - - Args: - results (dict): Result dict from the data pipeline. - - Returns: - dict: - - - 'inputs' (obj:`torch.Tensor`): Data for model forwarding. - - 'data_samples' (obj:`TextRecogDataSample`): The annotation info - of the sample. - """ - packed_results = dict() - if 'img' in results: - img = results['img'] - if len(img.shape) < 3: - img = np.expand_dims(img, -1) - # A simple trick to speedup formatting by 3-5 times when - # OMP_NUM_THREADS != 1 - # Refer to https://github.com/open-mmlab/mmdetection/pull/9533 - # for more details - if img.flags.c_contiguous: - img = to_tensor(img) - img = img.permute(2, 0, 1).contiguous() - else: - img = np.ascontiguousarray(img.transpose(2, 0, 1)) - img = to_tensor(img) - packed_results['inputs'] = img - - data_sample = TextRecogDataSample() - gt_text = LabelData() - - if results.get('gt_texts', None): - assert len( - results['gt_texts'] - ) == 1, 'Each image sample should have one text annotation only' - gt_text.item = results['gt_texts'][0] - data_sample.gt_text = gt_text - - img_meta = {} - for key in self.meta_keys: - if key == 'valid_ratio': - img_meta[key] = results.get('valid_ratio', 1) - else: - img_meta[key] = results[key] - data_sample.set_metainfo(img_meta) - - packed_results['data_samples'] = data_sample - - return packed_results - - def __repr__(self) -> str: - repr_str = self.__class__.__name__ - repr_str += f'(meta_keys={self.meta_keys})' - return repr_str - - -@TRANSFORMS.register_module() -class PackKIEInputs(BaseTransform): - """Pack the inputs data for key information extraction. - - The type of outputs is `dict`: - - - inputs: image converted to tensor, whose shape is (C, H, W). - - data_samples: Two components of ``TextDetDataSample`` will be updated: - - - gt_instances (InstanceData): Depending on annotations, a subset of the - following keys will be updated: - - - bboxes (torch.Tensor((N, 4), dtype=torch.float32)): The groundtruth - of bounding boxes in the form of [x1, y1, x2, y2]. Renamed from - 'gt_bboxes'. - - labels (torch.LongTensor(N)): The labels of instances. - Renamed from 'gt_bboxes_labels'. - - edge_labels (torch.LongTensor(N, N)): The edge labels. - Renamed from 'gt_edges_labels'. - - texts (list[str]): The groundtruth texts. Renamed from 'gt_texts'. - - - metainfo (dict): 'metainfo' is always populated. The contents of the - 'metainfo' depends on ``meta_keys``. By default it includes: - - - "img_path": Path to the image file. - - "img_shape": Shape of the image input to the network as a tuple - (h, w). Note that the image may be zero-padded afterward on the - bottom/right if the batch tensor is larger than this shape. - - "scale_factor": A tuple indicating the ratio of width and height - of the preprocessed image to the original one. - - "ori_shape": Shape of the preprocessed image as a tuple - (h, w). - - Args: - meta_keys (Sequence[str], optional): Meta keys to be converted to - the metainfo of ``TextDetSample``. Defaults to ``('img_path', - 'ori_shape', 'img_shape', 'scale_factor', 'flip', - 'flip_direction')``. - """ - mapping_table = { - 'gt_bboxes': 'bboxes', - 'gt_bboxes_labels': 'labels', - 'gt_edges_labels': 'edge_labels', - 'gt_texts': 'texts', - } - - def __init__(self, meta_keys=()): - self.meta_keys = meta_keys - - def transform(self, results: dict) -> dict: - """Method to pack the input data. - - Args: - results (dict): Result dict from the data pipeline. - - Returns: - dict: - - - 'inputs' (obj:`torch.Tensor`): Data for model forwarding. - - 'data_samples' (obj:`DetDataSample`): The annotation info of the - sample. - """ - packed_results = dict() - if 'img' in results: - img = results['img'] - if len(img.shape) < 3: - img = np.expand_dims(img, -1) - # A simple trick to speedup formatting by 3-5 times when - # OMP_NUM_THREADS != 1 - # Refer to https://github.com/open-mmlab/mmdetection/pull/9533 - # for more details - if img.flags.c_contiguous: - img = to_tensor(img) - img = img.permute(2, 0, 1).contiguous() - else: - img = np.ascontiguousarray(img.transpose(2, 0, 1)) - img = to_tensor(img) - packed_results['inputs'] = img - else: - packed_results['inputs'] = torch.FloatTensor().reshape(0, 0, 0) - - data_sample = KIEDataSample() - instance_data = InstanceData() - for key in self.mapping_table.keys(): - if key not in results: - continue - if key in ['gt_bboxes', 'gt_bboxes_labels', 'gt_edges_labels']: - instance_data[self.mapping_table[key]] = to_tensor( - results[key]) - else: - instance_data[self.mapping_table[key]] = results[key] - data_sample.gt_instances = instance_data - - img_meta = {} - for key in self.meta_keys: - img_meta[key] = results[key] - data_sample.set_metainfo(img_meta) - packed_results['data_samples'] = data_sample - - return packed_results - - def __repr__(self) -> str: - repr_str = self.__class__.__name__ - repr_str += f'(meta_keys={self.meta_keys})' - return repr_str diff --git a/spaces/NCSOFT/harim_plus/LICENSE.md b/spaces/NCSOFT/harim_plus/LICENSE.md deleted file mode 100644 index 6786ca4340914b82918da266121aedbe71081d76..0000000000000000000000000000000000000000 --- a/spaces/NCSOFT/harim_plus/LICENSE.md +++ /dev/null @@ -1,280 +0,0 @@ -© NCSOFT Corporation. All Rights Reserved. - -Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. - -3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. 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You can find the link for the source code of these open source projects, along with applicable license information, below. - -charset-normalizer <br> -https://github.com/Ousret/charset_normalizer <br> -Copyright (c) 2019 TAHRI Ahmed R. <br> -MIT License - -datasets <br> -https://github.com/huggingface/datasets <br> -Copyright 2020 The HuggingFace Authors.<br> -Apache License 2.0 - -decorator<br> -https://github.com/micheles/decorator<br> -Copyright (c) 2005-2018, Michele Simionato. All rights reserved.<br> -BSD 2-Clause "Simplified" License - -dill<br> -https://github.com/uqfoundation/dill<br> -Copyright (c) 2004-2016 California Institute of Technology.<br> -Copyright (c) 2016-2022 The Uncertainty Quantification Foundation. 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A Kenneth Reitz Project.<br> -Apache License 2.0 - -responses<br> -https://github.com/getsentry/responses<br> -Copyright 2015 David Cramer<br> -Apache License 2.0 - -six<br> -https://github.com/benjaminp/six<br> -Copyright (c) 2010-2020 Benjamin Peterson<br> -MIT License - -stack-data<br> -https://github.com/alexmojaki/stack_data<br> -Copyright (c) 2019 Alex Hall<br> -MIT License - -termcolor<br> -https://github.com/termcolor/termcolor<br> -Copyright (c) 2008-2011 Volvox Development Team<br> -MIT License - -tokenizers<br> -https://github.com/huggingface/tokenizers<br> -Apache License 2.0 - -toml<br> -https://github.com/uiri/toml<br> -Copyright 2013-2019 William Pearson<br> -Copyright 2015-2016 Julien Enselme<br> -Copyright 2016 Google Inc.<br> -Copyright 2017 Samuel Vasko<br> -Copyright 2017 Nate Prewitt<br> -Copyright 2017 Jack Evans<br> -Copyright 2019 Filippo Broggini<br> -MIT License<br> - -torch<br> -https://github.com/pytorch/pytorch<br> -Copyright (c) 2016- Facebook, Inc (Adam Paszke)<br> -Copyright (c) 2014- Facebook, Inc (Soumith Chintala)<br> -Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)<br> -Copyright (c) 2012-2014 Deepmind Technologies (Koray Kavukcuoglu)<br> -Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)<br> -Copyright (c) 2011-2013 NYU (Clement Farabet)<br> -Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)<br> -Copyright (c) 2006 Idiap Research Institute (Samy Bengio)<br> -Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)<br> -BSD 3-Clause "New" or "Revised" License - -tqdm<br> -https://github.com/tqdm/tqdm<br> -2015-2021 (c) Casper da Costa-Luis<br> -2016 (c) [PR #96] on behalf of Google Inc.<br> -2013 (c) Noam Yorav-Raphael, original author.<br> -Mozilla Public License 2.0 - -traitlets<br> -https://github.com/ipython/traitlets<br> -Copyright (c) 2001-, IPython Development Team<br> -BSD 3-Clause "New" or "Revised" License - -transformers<br> -https://github.com/huggingface/transformers<br> -Copyright 2018- The Hugging Face team. All rights reserved.<br> -Apache License 2.0 - -typing_extensions<br> -https://github.com/python/typing_extensions<br> -Python Software Foundation License 2.0 - -urllib3<br> -https://github.com/urllib3/urllib3<br> -Copyright (c) 2008-2020 Andrey Petrov and contributors (see CONTRIBUTORS.txt)<br> -MIT License - -wcwidth<br> -https://github.com/jquast/wcwidth<br> -Copyright (c) 2014 Jeff Quast <contact@jeffquast.com><br> -MIT License - -xxhash<br> -https://github.com/ifduyue/python-xxhash<br> -Copyright (c) 2014-2020, Yue Du. All rights reserved.<br> -BSD 2-Clause "Simplified" License - -yarl<br> -https://github.com/aio-libs/yarl<br> -Copyright 2016-2021, Andrew Svetlov and aio-libs team<br> -Apache License 2.0 diff --git a/spaces/NeonLion92/nsfw-c0ffees-erotic-story-generator2/README.md b/spaces/NeonLion92/nsfw-c0ffees-erotic-story-generator2/README.md deleted file mode 100644 index 687bf6b6c5f675b37c4297b2efa438306c1d621c..0000000000000000000000000000000000000000 --- a/spaces/NeonLion92/nsfw-c0ffees-erotic-story-generator2/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: '[NSFW] C0ffee''s Erotic Story Generator 2' -emoji: 🍑 -colorFrom: gray -colorTo: pink -sdk: gradio -sdk_version: 3.27.0 -app_file: app.py -pinned: false -duplicated_from: coffeeee/nsfw-c0ffees-erotic-story-generator2 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/flores101/README.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/flores101/README.md deleted file mode 100644 index 635c13f40bd0ccab704735bc5c26ea0192ea98cd..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/flores101/README.md +++ /dev/null @@ -1,223 +0,0 @@ -<p align="center"> -<img src="flores_logo.png" width="500"> -</p> - -# Flores101: Large-Scale Multilingual Machine Translation - -## Introduction - -Baseline pretrained models for small and large tracks of WMT 21 Large-Scale Multilingual Machine Translation competition. - -Flores Task at WMT 21: http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html - -Flores announement blog post: https://ai.facebook.com/blog/flores-researchers-kick-off-multilingual-translation-challenge-at-wmt-and-call-for-compute-grants/ - - - -## Pretrained models - -Model | Num layers | Embed dimension | FFN dimension| Vocab Size | #params | Download ----|---|---|---|---|---|--- -`flores101_mm100_615M` | 12 | 1024 | 4096 | 256,000 | 615M | https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_615M.tar.gz -`flores101_mm100_175M` | 6 | 512 | 2048 | 256,000 | 175M | https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_175M.tar.gz - - -These models are trained similar to [M2M-100](https://arxiv.org/abs/2010.11125) with additional support for the languages that are part of the WMT Large-Scale Multilingual Machine Translation track. Full list of languages can be found at the bottom. - - -## Example Generation code - -### Download model, sentencepiece vocab - -```bash -fairseq=/path/to/fairseq -cd $fairseq - -# Download 615M param model. -wget https://dl.fbaipublicfiles.com/flores101/pretrained_models/flores101_mm100_615M.tar.gz - -# Extract -tar -xvzf flores101_mm100_615M.tar.gz -``` - -### Encode using our SentencePiece Model -Note: Install SentencePiece from [here](https://github.com/google/sentencepiece) - - -```bash -fairseq=/path/to/fairseq -cd $fairseq - -# Download example dataset From German to French -sacrebleu --echo src -l de-fr -t wmt19 | head -n 20 > raw_input.de-fr.de -sacrebleu --echo ref -l de-fr -t wmt19 | head -n 20 > raw_input.de-fr.fr - -for lang in de fr ; do - python scripts/spm_encode.py \ - --model flores101_mm100_615M/sentencepiece.bpe.model \ - --output_format=piece \ - --inputs=raw_input.de-fr.${lang} \ - --outputs=spm.de-fr.${lang} -done -``` - -### Binarization - -```bash -fairseq-preprocess \ - --source-lang de --target-lang fr \ - --testpref spm.de-fr \ - --thresholdsrc 0 --thresholdtgt 0 \ - --destdir data_bin \ - --srcdict flores101_mm100_615M/dict.txt --tgtdict flores101_mm100_615M/dict.txt -``` - -### Generation - - -```bash -fairseq-generate \ - data_bin \ - --batch-size 1 \ - --path flores101_mm100_615M/model.pt \ - --fixed-dictionary flores101_mm100_615M/dict.txt \ - -s de -t fr \ - --remove-bpe 'sentencepiece' \ - --beam 5 \ - --task translation_multi_simple_epoch \ - --lang-pairs flores101_mm100_615M/language_pairs.txt \ - --decoder-langtok --encoder-langtok src \ - --gen-subset test \ - --fp16 \ - --dataset-impl mmap \ - --distributed-world-size 1 --distributed-no-spawn -``` - -### Supported Languages and lang code - -Language | lang code ----|--- -Akrikaans | af -Amharic | am -Arabic | ar -Assamese | as -Asturian | ast -Aymara | ay -Azerbaijani | az -Bashkir | ba -Belarusian | be -Bulgarian | bg -Bengali | bn -Breton | br -Bosnian | bs -Catalan | ca -Cebuano | ceb -Chokwe | cjk -Czech | cs -Welsh | cy -Danish | da -German | de -Dyula| dyu -Greek | el -English | en -Spanish | es -Estonian | et -Persian | fa -Fulah | ff -Finnish | fi -French | fr -Western Frisian | fy -Irish | ga -Scottish Gaelic | gd -Galician | gl -Gujarati | gu -Hausa | ha -Hebrew | he -Hindi | hi -Croatian | hr -Haitian Creole | ht -Hungarian | hu -Armenian | hy -Indonesian | id -Igbo | ig -Iloko | ilo -Icelandic | is -Italian | it -Japanese | ja -Javanese | jv -Georgian | ka -Kachin | kac -Kamba | kam -Kabuverdianu | kea -Kongo | kg -Kazakh | kk -Central Khmer | km -Kimbundu | kmb -Northern Kurdish | kmr -Kannada | kn -Korean | ko -Kurdish | ku -Kyrgyz | ky -Luxembourgish | lb -Ganda | lg -Lingala | ln -Lao | lo -Lithuanian | lt -Luo | luo -Latvian | lv -Malagasy | mg -Maori | mi -Macedonian | mk -Malayalam | ml -Mongolian | mn -Marathi | mr -Malay | ms -Maltese | mt -Burmese | my -Nepali | ne -Dutch | nl -Norwegian | no -Northern Sotho | ns -Nyanja | ny -Occitan | oc -Oromo | om -Oriya | or -Punjabi | pa -Polish | pl -Pashto | ps -Portuguese | pt -Quechua | qu -Romanian | ro -Russian | ru -Sindhi | sd -Shan | shn -Sinhala | si -Slovak | sk -Slovenian | sl -Shona | sn -Somali | so -Albanian | sq -Serbian | sr -Swati | ss -Sundanese | su -Swedish | sv -Swahili | sw -Tamil | ta -Telugu | te -Tajik | tg -Thai | th -Tigrinya | ti -Tagalog | tl -Tswana | tn -Turkish | tr -Ukrainian | uk -Umbundu | umb -Urdu | ur -Uzbek | uz -Vietnamese | vi -Wolof | wo -Xhosa | xh -Yiddish | yi -Yoruba | yo -Chinese| zh -Zulu | zu diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/simultaneous_translation/docs/ende-mma.md b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/simultaneous_translation/docs/ende-mma.md deleted file mode 100644 index 241d604a3b31a37755da68aad6ff47d46891d3fc..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/examples/simultaneous_translation/docs/ende-mma.md +++ /dev/null @@ -1,74 +0,0 @@ -# Simultaneous Machine Translation - -This directory contains the code for the paper [Monotonic Multihead Attention](https://openreview.net/forum?id=Hyg96gBKPS) - -## Prepare Data - -[Please follow the instructions to download and preprocess the WMT'15 En-De dataset.](https://github.com/pytorch/fairseq/tree/simulastsharedtask/examples/translation#prepare-wmt14en2desh) - -Another example of training an English to Japanese model can be found [here](docs/enja.md) - -## Training - -- MMA-IL - -```shell -fairseq-train \ - data-bin/wmt15_en_de_32k \ - --simul-type infinite_lookback \ - --user-dir $FAIRSEQ/example/simultaneous_translation \ - --mass-preservation \ - --criterion latency_augmented_label_smoothed_cross_entropy \ - --latency-weight-avg 0.1 \ - --max-update 50000 \ - --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ - --optimizer adam --adam-betas '(0.9, 0.98)' \ - --lr-scheduler 'inverse_sqrt' \ - --warmup-init-lr 1e-7 --warmup-updates 4000 \ - --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ - --dropout 0.3 \ - --label-smoothing 0.1\ - --max-tokens 3584 -``` - -- MMA-H - -```shell -fairseq-train \ - data-bin/wmt15_en_de_32k \ - --simul-type hard_aligned \ - --user-dir $FAIRSEQ/example/simultaneous_translation \ - --mass-preservation \ - --criterion latency_augmented_label_smoothed_cross_entropy \ - --latency-weight-var 0.1 \ - --max-update 50000 \ - --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ - --optimizer adam --adam-betas '(0.9, 0.98)' \ - --lr-scheduler 'inverse_sqrt' \ - --warmup-init-lr 1e-7 --warmup-updates 4000 \ - --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ - --dropout 0.3 \ - --label-smoothing 0.1\ - --max-tokens 3584 -``` - -- wait-k - -```shell -fairseq-train \ - data-bin/wmt15_en_de_32k \ - --simul-type wait-k \ - --waitk-lagging 3 \ - --user-dir $FAIRSEQ/example/simultaneous_translation \ - --mass-preservation \ - --criterion latency_augmented_label_smoothed_cross_entropy \ - --max-update 50000 \ - --arch transformer_monotonic_iwslt_de_en save_dir_key=lambda \ - --optimizer adam --adam-betas '(0.9, 0.98)' \ - --lr-scheduler 'inverse_sqrt' \ - --warmup-init-lr 1e-7 --warmup-updates 4000 \ - --lr 5e-4 --stop-min-lr 1e-9 --clip-norm 0.0 --weight-decay 0.0001\ - --dropout 0.3 \ - --label-smoothing 0.1\ - --max-tokens 3584 -``` diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/sequence_scorer.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/sequence_scorer.py deleted file mode 100644 index 411d4df4445ef8dd3f1907ad56f9de6943d1fed8..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/sequence_scorer.py +++ /dev/null @@ -1,153 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import sys - -import torch -from fairseq import utils - - -class SequenceScorer(object): - """Scores the target for a given source sentence.""" - - def __init__( - self, - tgt_dict, - softmax_batch=None, - compute_alignment=False, - eos=None, - symbols_to_strip_from_output=None, - ): - self.pad = tgt_dict.pad() - self.eos = tgt_dict.eos() if eos is None else eos - self.softmax_batch = softmax_batch or sys.maxsize - assert self.softmax_batch > 0 - self.compute_alignment = compute_alignment - self.symbols_to_strip_from_output = ( - symbols_to_strip_from_output.union({self.eos}) - if symbols_to_strip_from_output is not None - else {self.eos} - ) - - @torch.no_grad() - def generate(self, models, sample, **kwargs): - """Score a batch of translations.""" - net_input = sample["net_input"] - - def batch_for_softmax(dec_out, target): - # assumes decoder_out[0] is the only thing needed (may not be correct for future models!) - first, rest = dec_out[0], dec_out[1:] - bsz, tsz, dim = first.shape - if bsz * tsz < self.softmax_batch: - yield dec_out, target, True - else: - flat = first.contiguous().view(1, -1, dim) - flat_tgt = target.contiguous().view(flat.shape[:-1]) - s = 0 - while s < flat.size(1): - e = s + self.softmax_batch - yield (flat[:, s:e],) + rest, flat_tgt[:, s:e], False - s = e - - def gather_target_probs(probs, target): - probs = probs.gather( - dim=2, - index=target.unsqueeze(-1), - ) - return probs - - orig_target = sample["target"] - - # compute scores for each model in the ensemble - avg_probs = None - avg_attn = None - for model in models: - model.eval() - decoder_out = model(**net_input) - attn = decoder_out[1] if len(decoder_out) > 1 else None - if type(attn) is dict: - attn = attn.get("attn", None) - - batched = batch_for_softmax(decoder_out, orig_target) - probs, idx = None, 0 - for bd, tgt, is_single in batched: - sample["target"] = tgt - curr_prob = model.get_normalized_probs( - bd, log_probs=len(models) == 1, sample=sample - ).data - if is_single: - probs = gather_target_probs(curr_prob, orig_target) - else: - if probs is None: - probs = curr_prob.new(orig_target.numel()) - step = curr_prob.size(0) * curr_prob.size(1) - end = step + idx - tgt_probs = gather_target_probs( - curr_prob.view(tgt.shape + (curr_prob.size(-1),)), tgt - ) - probs[idx:end] = tgt_probs.view(-1) - idx = end - sample["target"] = orig_target - - probs = probs.view(sample["target"].shape) - - if avg_probs is None: - avg_probs = probs - else: - avg_probs.add_(probs) - if attn is not None: - if torch.is_tensor(attn): - attn = attn.data - else: - attn = attn[0] - if avg_attn is None: - avg_attn = attn - else: - avg_attn.add_(attn) - if len(models) > 1: - avg_probs.div_(len(models)) - avg_probs.log_() - if avg_attn is not None: - avg_attn.div_(len(models)) - - bsz = avg_probs.size(0) - hypos = [] - start_idxs = sample["start_indices"] if "start_indices" in sample else [0] * bsz - for i in range(bsz): - # remove padding from ref - ref = ( - utils.strip_pad(sample["target"][i, start_idxs[i] :], self.pad) - if sample["target"] is not None - else None - ) - tgt_len = ref.numel() - avg_probs_i = avg_probs[i][start_idxs[i] : start_idxs[i] + tgt_len] - score_i = avg_probs_i.sum() / tgt_len - if avg_attn is not None: - avg_attn_i = avg_attn[i] - if self.compute_alignment: - alignment = utils.extract_hard_alignment( - avg_attn_i, - sample["net_input"]["src_tokens"][i], - sample["target"][i], - self.pad, - self.eos, - ) - else: - alignment = None - else: - avg_attn_i = alignment = None - hypos.append( - [ - { - "tokens": ref, - "score": score_i, - "attention": avg_attn_i, - "alignment": alignment, - "positional_scores": avg_probs_i, - } - ] - ) - return hypos diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/discriminative_reranking_nmt/models/discriminative_reranking_model.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/discriminative_reranking_nmt/models/discriminative_reranking_model.py deleted file mode 100644 index e4b5887f825df36f4e1e0384f38fefe790e485e6..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/discriminative_reranking_nmt/models/discriminative_reranking_model.py +++ /dev/null @@ -1,365 +0,0 @@ -from dataclasses import dataclass, field -import os - -import torch -import torch.nn as nn - -from fairseq import utils -from fairseq.dataclass import ChoiceEnum, FairseqDataclass -from fairseq.models import ( - BaseFairseqModel, - register_model, -) - -from fairseq.models.roberta.model import RobertaClassificationHead - -from fairseq.modules import ( - LayerNorm, - TransformerSentenceEncoder, - TransformerSentenceEncoderLayer, -) - - -ACTIVATION_FN_CHOICES = ChoiceEnum(utils.get_available_activation_fns()) -JOINT_CLASSIFICATION_CHOICES = ChoiceEnum(["none", "sent"]) -SENTENCE_REP_CHOICES = ChoiceEnum(["head", "meanpool", "maxpool"]) - - -def update_init_roberta_model_state(state): - """ - update the state_dict of a Roberta model for initializing - weights of the BertRanker - """ - for k in list(state.keys()): - if ".lm_head." in k or "version" in k: - del state[k] - continue - # remove 'encoder/decoder.sentence_encoder.' from the key - assert k.startswith("encoder.sentence_encoder.") or k.startswith( - "decoder.sentence_encoder." - ), f"Cannot recognize parameter name {k}" - if "layernorm_embedding" in k: - new_k = k.replace(".layernorm_embedding.", ".emb_layer_norm.") - state[new_k[25:]] = state[k] - else: - state[k[25:]] = state[k] - del state[k] - - -class BaseRanker(nn.Module): - def __init__(self, args, task): - super().__init__() - - self.separator_token = task.dictionary.eos() - self.padding_idx = task.dictionary.pad() - - def forward(self, src_tokens): - raise NotImplementedError - - def get_segment_labels(self, src_tokens): - segment_boundary = (src_tokens == self.separator_token).long() - segment_labels = ( - segment_boundary.cumsum(dim=1) - - segment_boundary - - (src_tokens == self.padding_idx).long() - ) - - return segment_labels - - def get_positions(self, src_tokens, segment_labels): - segment_positions = ( - torch.arange(src_tokens.shape[1]) - .to(src_tokens.device) - .repeat(src_tokens.shape[0], 1) - ) - segment_boundary = (src_tokens == self.separator_token).long() - _, col_idx = (segment_positions * segment_boundary).nonzero(as_tuple=True) - col_idx = torch.cat([torch.zeros(1).type_as(col_idx), col_idx]) - offset = torch.cat( - [ - torch.zeros(1).type_as(segment_boundary), - segment_boundary.sum(dim=1).cumsum(dim=0)[:-1], - ] - ) - segment_positions -= col_idx[segment_labels + offset.unsqueeze(1)] * ( - segment_labels != 0 - ) - - padding_mask = src_tokens.ne(self.padding_idx) - segment_positions = (segment_positions + 1) * padding_mask.type_as( - segment_positions - ) + self.padding_idx - - return segment_positions - - -class BertRanker(BaseRanker): - def __init__(self, args, task): - super(BertRanker, self).__init__(args, task) - - init_model = getattr(args, "pretrained_model", "") - self.joint_layers = nn.ModuleList() - if os.path.isfile(init_model): - print(f"initialize weight from {init_model}") - - from fairseq import hub_utils - - x = hub_utils.from_pretrained( - os.path.dirname(init_model), - checkpoint_file=os.path.basename(init_model), - ) - - in_state_dict = x["models"][0].state_dict() - init_args = x["args"].model - - num_positional_emb = init_args.max_positions + task.dictionary.pad() + 1 - - # follow the setup in roberta - self.model = TransformerSentenceEncoder( - padding_idx=task.dictionary.pad(), - vocab_size=len(task.dictionary), - num_encoder_layers=getattr( - args, "encoder_layers", init_args.encoder_layers - ), - embedding_dim=init_args.encoder_embed_dim, - ffn_embedding_dim=init_args.encoder_ffn_embed_dim, - num_attention_heads=init_args.encoder_attention_heads, - dropout=init_args.dropout, - attention_dropout=init_args.attention_dropout, - activation_dropout=init_args.activation_dropout, - num_segments=2, # add language embeddings - max_seq_len=num_positional_emb, - offset_positions_by_padding=False, - encoder_normalize_before=True, - apply_bert_init=True, - activation_fn=init_args.activation_fn, - freeze_embeddings=args.freeze_embeddings, - n_trans_layers_to_freeze=args.n_trans_layers_to_freeze, - ) - - # still need to learn segment embeddings as we added a second language embedding - if args.freeze_embeddings: - for p in self.model.segment_embeddings.parameters(): - p.requires_grad = False - - update_init_roberta_model_state(in_state_dict) - print("loading weights from the pretrained model") - self.model.load_state_dict( - in_state_dict, strict=False - ) # ignore mismatch in language embeddings - - ffn_embedding_dim = init_args.encoder_ffn_embed_dim - num_attention_heads = init_args.encoder_attention_heads - dropout = init_args.dropout - attention_dropout = init_args.attention_dropout - activation_dropout = init_args.activation_dropout - activation_fn = init_args.activation_fn - - classifier_embed_dim = getattr( - args, "embed_dim", init_args.encoder_embed_dim - ) - if classifier_embed_dim != init_args.encoder_embed_dim: - self.transform_layer = nn.Linear( - init_args.encoder_embed_dim, classifier_embed_dim - ) - else: - self.model = TransformerSentenceEncoder( - padding_idx=task.dictionary.pad(), - vocab_size=len(task.dictionary), - num_encoder_layers=args.encoder_layers, - embedding_dim=args.embed_dim, - ffn_embedding_dim=args.ffn_embed_dim, - num_attention_heads=args.attention_heads, - dropout=args.dropout, - attention_dropout=args.attention_dropout, - activation_dropout=args.activation_dropout, - max_seq_len=task.max_positions() - if task.max_positions() - else args.tokens_per_sample, - num_segments=2, - offset_positions_by_padding=False, - encoder_normalize_before=args.encoder_normalize_before, - apply_bert_init=args.apply_bert_init, - activation_fn=args.activation_fn, - ) - - classifier_embed_dim = args.embed_dim - ffn_embedding_dim = args.ffn_embed_dim - num_attention_heads = args.attention_heads - dropout = args.dropout - attention_dropout = args.attention_dropout - activation_dropout = args.activation_dropout - activation_fn = args.activation_fn - - self.joint_classification = args.joint_classification - if args.joint_classification == "sent": - if args.joint_normalize_before: - self.joint_layer_norm = LayerNorm(classifier_embed_dim) - else: - self.joint_layer_norm = None - - self.joint_layers = nn.ModuleList( - [ - TransformerSentenceEncoderLayer( - embedding_dim=classifier_embed_dim, - ffn_embedding_dim=ffn_embedding_dim, - num_attention_heads=num_attention_heads, - dropout=dropout, - attention_dropout=attention_dropout, - activation_dropout=activation_dropout, - activation_fn=activation_fn, - ) - for _ in range(args.num_joint_layers) - ] - ) - - self.classifier = RobertaClassificationHead( - classifier_embed_dim, - classifier_embed_dim, - 1, # num_classes - "tanh", - args.classifier_dropout, - ) - - def forward(self, src_tokens, src_lengths): - segment_labels = self.get_segment_labels(src_tokens) - positions = self.get_positions(src_tokens, segment_labels) - - inner_states, _ = self.model( - tokens=src_tokens, - segment_labels=segment_labels, - last_state_only=True, - positions=positions, - ) - - return inner_states[-1].transpose(0, 1) # T x B x C -> B x T x C - - def sentence_forward(self, encoder_out, src_tokens=None, sentence_rep="head"): - # encoder_out: B x T x C - if sentence_rep == "head": - x = encoder_out[:, :1, :] - else: # 'meanpool', 'maxpool' - assert src_tokens is not None, "meanpool requires src_tokens input" - segment_labels = self.get_segment_labels(src_tokens) - padding_mask = src_tokens.ne(self.padding_idx) - encoder_mask = segment_labels * padding_mask.type_as(segment_labels) - - if sentence_rep == "meanpool": - ntokens = torch.sum(encoder_mask, dim=1, keepdim=True) - x = torch.sum( - encoder_out * encoder_mask.unsqueeze(2), dim=1, keepdim=True - ) / ntokens.unsqueeze(2).type_as(encoder_out) - else: # 'maxpool' - encoder_out[ - (encoder_mask == 0).unsqueeze(2).repeat(1, 1, encoder_out.shape[-1]) - ] = -float("inf") - x, _ = torch.max(encoder_out, dim=1, keepdim=True) - - if hasattr(self, "transform_layer"): - x = self.transform_layer(x) - - return x # B x 1 x C - - def joint_forward(self, x): - # x: T x B x C - if self.joint_layer_norm: - x = self.joint_layer_norm(x.transpose(0, 1)) - x = x.transpose(0, 1) - - for layer in self.joint_layers: - x, _ = layer(x, self_attn_padding_mask=None) - return x - - def classification_forward(self, x): - # x: B x T x C - return self.classifier(x) - - -@dataclass -class DiscriminativeNMTRerankerConfig(FairseqDataclass): - pretrained_model: str = field( - default="", metadata={"help": "pretrained model to load"} - ) - sentence_rep: SENTENCE_REP_CHOICES = field( - default="head", - metadata={ - "help": "method to transform the output of the transformer stack to a sentence-level representation" - }, - ) - - dropout: float = field(default=0.1, metadata={"help": "dropout probability"}) - attention_dropout: float = field( - default=0.0, metadata={"help": "dropout probability for attention weights"} - ) - activation_dropout: float = field( - default=0.0, metadata={"help": "dropout probability after activation in FFN"} - ) - classifier_dropout: float = field( - default=0.0, metadata={"help": "classifier dropout probability"} - ) - embed_dim: int = field(default=768, metadata={"help": "embedding dimension"}) - ffn_embed_dim: int = field( - default=2048, metadata={"help": "embedding dimension for FFN"} - ) - encoder_layers: int = field(default=12, metadata={"help": "num encoder layers"}) - attention_heads: int = field(default=8, metadata={"help": "num attention heads"}) - encoder_normalize_before: bool = field( - default=False, metadata={"help": "apply layernorm before each encoder block"} - ) - apply_bert_init: bool = field( - default=False, metadata={"help": "use custom param initialization for BERT"} - ) - activation_fn: ACTIVATION_FN_CHOICES = field( - default="relu", metadata={"help": "activation function to use"} - ) - freeze_embeddings: bool = field( - default=False, metadata={"help": "freeze embeddings in the pretrained model"} - ) - n_trans_layers_to_freeze: int = field( - default=0, - metadata={ - "help": "number of layers to freeze in the pretrained transformer model" - }, - ) - - # joint classfication - joint_classification: JOINT_CLASSIFICATION_CHOICES = field( - default="none", - metadata={"help": "method to compute joint features for classification"}, - ) - num_joint_layers: int = field( - default=1, metadata={"help": "number of joint layers"} - ) - joint_normalize_before: bool = field( - default=False, - metadata={"help": "apply layer norm on the input to the joint layer"}, - ) - - -@register_model( - "discriminative_nmt_reranker", dataclass=DiscriminativeNMTRerankerConfig -) -class DiscriminativeNMTReranker(BaseFairseqModel): - @classmethod - def build_model(cls, args, task): - model = BertRanker(args, task) - return DiscriminativeNMTReranker(args, model) - - def __init__(self, args, model): - super().__init__() - - self.model = model - self.sentence_rep = args.sentence_rep - self.joint_classification = args.joint_classification - - def forward(self, src_tokens, src_lengths, **kwargs): - return self.model(src_tokens, src_lengths) - - def sentence_forward(self, encoder_out, src_tokens): - return self.model.sentence_forward(encoder_out, src_tokens, self.sentence_rep) - - def joint_forward(self, x): - return self.model.joint_forward(x) - - def classification_forward(self, x): - return self.model.classification_forward(x) diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/hubert/update_ckpt.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/hubert/update_ckpt.py deleted file mode 100644 index 53c9e74ea613e30aa5c22614e658f2b7272bac0c..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/hubert/update_ckpt.py +++ /dev/null @@ -1,22 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch - -src_ckpt = "/checkpoint/wnhsu/w2v/archived/hubert_base_ls960_it2.pt" -ref_ckpt = "/checkpoint/wnhsu/w2v/hubert_icassp_oss_v3/iter2_km100-400k-grp-L6/oss.km500_p0_1_s334.pmw1_0.puw0_0.grpnorm.ml10.mp0_8.untie.mxsz250000.ufreq1.maxtok1400000.MU100k.s1337.ngpu32/checkpoint_last.pt" -new_ckpt = "/checkpoint/wnhsu/w2v/archived/hubert_base_ls960_it2_updated.pt" - - -def update_state(state): - state["model"]["label_embs_concat"] = state["model"].pop("label_embs") - state["args"].task = "hubert_pretraining" - state["args"].labels = f"['{state['args'].labels}']" - return state - - -src_state = torch.load(src_ckpt) -src_state = update_state(src_state) -torch.save(src_state, new_ckpt) diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_text_joint_to_text/__init__.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_text_joint_to_text/__init__.py deleted file mode 100644 index 239d2e69f9a235095dee1ea7b3a94164a77273f5..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/speech_text_joint_to_text/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from . import tasks, criterions, models # noqa diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/cmd.sh b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/cmd.sh deleted file mode 100644 index e74953194d41f0d93855d41b2acef08556d92477..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/cmd.sh +++ /dev/null @@ -1,15 +0,0 @@ -# you can change cmd.sh depending on what type of queue you are using. -# If you have no queueing system and want to run on a local machine, you -# can change all instances 'queue.pl' to run.pl (but be careful and run -# commands one by one: most recipes will exhaust the memory on your -# machine). queue.pl works with GridEngine (qsub). slurm.pl works -# with slurm. Different queues are configured differently, with different -# queue names and different ways of specifying things like memory; -# to account for these differences you can create and edit the file -# conf/queue.conf to match your queue's configuration. Search for -# conf/queue.conf in http://kaldi-asr.org/doc/queue.html for more information, -# or search for the string 'default_config' in utils/queue.pl or utils/slurm.pl. - -export train_cmd="run.pl --mem 2G" -export decode_cmd="run.pl --mem 4G" -export mkgraph_cmd="run.pl --mem 8G" diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/distributed/distributed_timeout_wrapper.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/distributed/distributed_timeout_wrapper.py deleted file mode 100644 index 18107ef27ea837b8c72dcaa49db18fd8e64267b1..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/distributed/distributed_timeout_wrapper.py +++ /dev/null @@ -1,94 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import logging -import os -import signal -import threading - -from torch import nn - - -logger = logging.getLogger(__name__) - - -class DistributedTimeoutWrapper(nn.Module): - """ - A wrapper that kills the process if no progress is made within a given - *timeout*. The timer is reset every time :func:`forward` is called. - - Usage:: - - module = DistributedTimeoutWrapper(module, timeout=30) - x = module(input) - time.sleep(20) # safe - x = module(input) - time.sleep(45) # job will be killed before this returns - - Args: - module (nn.Module): module to wrap - timeout (int): number of seconds before killing the process - (set to a value <= 0 to disable the timeout) - signal (Optional): signal to send once timeout is triggered - """ - def __init__(self, module: nn.Module, timeout: int, signal=signal.SIGINT): - super().__init__() - self.module = module - self.timeout = timeout - self.signal = signal - - if timeout > 0: - self._heartbeat = threading.Event() - self._heartbeat_thread = threading.Thread( - target=self._check_heartbeat, - args=(os.getpid(),), - daemon=True, - ) - self._heartbeat_thread.start() - self._terminated = False - else: - self._heartbeat = None - self._heartbeat_thread = None - - def __del__(self): - self.stop_timeout() - - def __getattr__(self, name): - """Forward missing attributes to wrapped module.""" - try: - return super().__getattr__(name) # defer to nn.Module's logic - except AttributeError: - return getattr(self.module, name) - - def stop_timeout(self): - if self._heartbeat_thread is not None: - self._terminated = True - self._heartbeat_thread.join() - - def state_dict(self, *args, **kwargs): - return self.module.state_dict(*args, **kwargs) - - def load_state_dict(self, *args, **kwargs): - return self.module.load_state_dict(*args, **kwargs) - - def forward(self, *args, **kwargs): - if self._heartbeat is not None: - self._heartbeat.set() - return self.module(*args, **kwargs) - - def _check_heartbeat(self, parent_pid): - self._heartbeat.wait() # wait for the first forward pass - while True: - self._heartbeat.clear() - success = self._heartbeat.wait(timeout=self.timeout) - if self._terminated: - break - elif not success: - logger.error(( - "Killing job for not making progress in {} seconds. " - "Set --heartbeat-timeout=-1 to disable this timeout." - ).format(int(self.timeout))) - os.kill(parent_pid, self.signal) - return diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq_cli/train.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq_cli/train.py deleted file mode 100644 index 83475873138c5d1bac288c234afb6b4a1a7882d7..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq_cli/train.py +++ /dev/null @@ -1,514 +0,0 @@ -#!/usr/bin/env python3 -u -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -""" -Train a new model on one or across multiple GPUs. -""" - -import argparse -import logging -import math -import os -import sys -from typing import Dict, Optional, Any, List, Tuple, Callable - -# We need to setup root logger before importing any fairseq libraries. -logging.basicConfig( - format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", - level=os.environ.get("LOGLEVEL", "INFO").upper(), - stream=sys.stdout, -) -logger = logging.getLogger("fairseq_cli.train") - -import numpy as np -import torch -from fairseq import ( - checkpoint_utils, - options, - quantization_utils, - tasks, - utils, -) -from fairseq.data import iterators, data_utils -from fairseq.data.plasma_utils import PlasmaStore -from fairseq.dataclass.configs import FairseqConfig -from fairseq.dataclass.utils import convert_namespace_to_omegaconf -from fairseq.distributed import fsdp_enable_wrap, fsdp_wrap, utils as distributed_utils -from fairseq.file_io import PathManager -from fairseq.logging import meters, metrics, progress_bar -from fairseq.model_parallel.megatron_trainer import MegatronTrainer -from fairseq.trainer import Trainer -from omegaconf import DictConfig, OmegaConf - - - - -def main(cfg: FairseqConfig) -> None: - if isinstance(cfg, argparse.Namespace): - cfg = convert_namespace_to_omegaconf(cfg) - - utils.import_user_module(cfg.common) - - if distributed_utils.is_master(cfg.distributed_training) and "job_logging_cfg" in cfg: - # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126) - logging.config.dictConfig(OmegaConf.to_container(cfg.job_logging_cfg)) - - assert ( - cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None - ), "Must specify batch size either with --max-tokens or --batch-size" - metrics.reset() - - if cfg.common.log_file is not None: - handler = logging.FileHandler(filename=cfg.common.log_file) - logger.addHandler(handler) - - np.random.seed(cfg.common.seed) - utils.set_torch_seed(cfg.common.seed) - - if distributed_utils.is_master(cfg.distributed_training): - checkpoint_utils.verify_checkpoint_directory(cfg.checkpoint.save_dir) - - # Print args - logger.info(cfg) - - if cfg.checkpoint.write_checkpoints_asynchronously: - try: - import iopath # noqa: F401 - except ImportError: - logging.exception( - "Asynchronous checkpoint writing is specified but iopath is " - "not installed: `pip install iopath`" - ) - return - - # Setup task, e.g., translation, language modeling, etc. - task = tasks.setup_task(cfg.task) - - assert cfg.criterion, "Please specify criterion to train a model" - - # Build model and criterion - if cfg.distributed_training.ddp_backend == "fully_sharded": - with fsdp_enable_wrap(cfg.distributed_training): - model = fsdp_wrap(task.build_model(cfg.model)) - else: - model = task.build_model(cfg.model) - criterion = task.build_criterion(cfg.criterion) - logger.info(model) - logger.info("task: {}".format(task.__class__.__name__)) - logger.info("model: {}".format(model.__class__.__name__)) - logger.info("criterion: {}".format(criterion.__class__.__name__)) - logger.info( - "num. shared model params: {:,} (num. trained: {:,})".format( - sum(p.numel() for p in model.parameters() if not getattr(p, "expert", False)), - sum(p.numel() for p in model.parameters() if not getattr(p, "expert", False) and p.requires_grad) - ) - ) - - logger.info( - "num. expert model params: {} (num. trained: {})".format( - sum(p.numel() for p in model.parameters() if getattr(p, "expert", False)), - sum(p.numel() for p in model.parameters() if getattr(p, "expert", False) and p.requires_grad), - ) - ) - - # Load valid dataset (we load training data below, based on the latest checkpoint) - # We load the valid dataset AFTER building the model - data_utils.raise_if_valid_subsets_unintentionally_ignored(cfg) - if cfg.dataset.combine_valid_subsets: - task.load_dataset("valid", combine=True, epoch=1) - else: - for valid_sub_split in cfg.dataset.valid_subset.split(","): - task.load_dataset(valid_sub_split, combine=False, epoch=1) - - # (optionally) Configure quantization - if cfg.common.quantization_config_path is not None: - quantizer = quantization_utils.Quantizer( - config_path=cfg.common.quantization_config_path, - max_epoch=cfg.optimization.max_epoch, - max_update=cfg.optimization.max_update, - ) - else: - quantizer = None - - # Build trainer - if cfg.common.model_parallel_size == 1: - trainer = Trainer(cfg, task, model, criterion, quantizer) - else: - trainer = MegatronTrainer(cfg, task, model, criterion) - logger.info( - "training on {} devices (GPUs/TPUs)".format( - cfg.distributed_training.distributed_world_size - ) - ) - logger.info( - "max tokens per device = {} and max sentences per device = {}".format( - cfg.dataset.max_tokens, - cfg.dataset.batch_size, - ) - ) - - # Load the latest checkpoint if one is available and restore the - # corresponding train iterator - extra_state, epoch_itr = checkpoint_utils.load_checkpoint( - cfg.checkpoint, - trainer, - # don't cache epoch iterators for sharded datasets - disable_iterator_cache=task.has_sharded_data("train"), - ) - if cfg.common.tpu: - import torch_xla.core.xla_model as xm - xm.rendezvous("load_checkpoint") # wait for all workers - - max_epoch = cfg.optimization.max_epoch or math.inf - lr = trainer.get_lr() - - train_meter = meters.StopwatchMeter() - train_meter.start() - while epoch_itr.next_epoch_idx <= max_epoch: - if lr <= cfg.optimization.stop_min_lr: - logger.info( - f"stopping training because current learning rate ({lr}) is smaller " - "than or equal to minimum learning rate " - f"(--stop-min-lr={cfg.optimization.stop_min_lr})" - ) - break - - # train for one epoch - valid_losses, should_stop = train(cfg, trainer, task, epoch_itr) - if should_stop: - break - - # only use first validation loss to update the learning rate - lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0]) - - epoch_itr = trainer.get_train_iterator( - epoch_itr.next_epoch_idx, - # sharded data: get train iterator for next epoch - load_dataset=task.has_sharded_data("train"), - # don't cache epoch iterators for sharded datasets - disable_iterator_cache=task.has_sharded_data("train"), - ) - train_meter.stop() - logger.info("done training in {:.1f} seconds".format(train_meter.sum)) - - # ioPath implementation to wait for all asynchronous file writes to complete. - if cfg.checkpoint.write_checkpoints_asynchronously: - logger.info( - "ioPath PathManager waiting for all asynchronous checkpoint " - "writes to finish." - ) - PathManager.async_close() - logger.info("ioPath PathManager finished waiting.") - - -def should_stop_early(cfg: DictConfig, valid_loss: float) -> bool: - # skip check if no validation was done in the current epoch - if valid_loss is None: - return False - if cfg.checkpoint.patience <= 0: - return False - - def is_better(a, b): - return a > b if cfg.checkpoint.maximize_best_checkpoint_metric else a < b - - prev_best = getattr(should_stop_early, "best", None) - if prev_best is None or is_better(valid_loss, prev_best): - should_stop_early.best = valid_loss - should_stop_early.num_runs = 0 - return False - else: - should_stop_early.num_runs += 1 - if should_stop_early.num_runs >= cfg.checkpoint.patience: - logger.info( - "early stop since valid performance hasn't improved for last {} runs".format( - cfg.checkpoint.patience - ) - ) - return True - else: - return False - - -@metrics.aggregate("train") -def train( - cfg: DictConfig, trainer: Trainer, task: tasks.FairseqTask, epoch_itr -) -> Tuple[List[Optional[float]], bool]: - """Train the model for one epoch and return validation losses.""" - # Initialize data iterator - itr = epoch_itr.next_epoch_itr( - fix_batches_to_gpus=cfg.distributed_training.fix_batches_to_gpus, - shuffle=(epoch_itr.next_epoch_idx > cfg.dataset.curriculum), - ) - update_freq = ( - cfg.optimization.update_freq[epoch_itr.epoch - 1] - if epoch_itr.epoch <= len(cfg.optimization.update_freq) - else cfg.optimization.update_freq[-1] - ) - itr = iterators.GroupedIterator(itr, update_freq) - if cfg.common.tpu: - itr = utils.tpu_data_loader(itr) - progress = progress_bar.progress_bar( - itr, - log_format=cfg.common.log_format, - log_file=cfg.common.log_file, - log_interval=cfg.common.log_interval, - epoch=epoch_itr.epoch, - tensorboard_logdir=( - cfg.common.tensorboard_logdir - if distributed_utils.is_master(cfg.distributed_training) - else None - ), - default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), - wandb_project=( - cfg.common.wandb_project - if distributed_utils.is_master(cfg.distributed_training) - else None - ), - wandb_run_name=os.environ.get( - "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir) - ), - azureml_logging=( - cfg.common.azureml_logging - if distributed_utils.is_master(cfg.distributed_training) - else False - ), - ) - progress.update_config(_flatten_config(cfg)) - - trainer.begin_epoch(epoch_itr.epoch) - - valid_subsets = cfg.dataset.valid_subset.split(",") - should_stop = False - num_updates = trainer.get_num_updates() - logger.info("Start iterating over samples") - for i, samples in enumerate(progress): - with metrics.aggregate("train_inner"), torch.autograd.profiler.record_function( - "train_step-%d" % i - ): - log_output = trainer.train_step(samples) - - if log_output is not None: # not OOM, overflow, ... - # log mid-epoch stats - num_updates = trainer.get_num_updates() - if num_updates % cfg.common.log_interval == 0: - stats = get_training_stats(metrics.get_smoothed_values("train_inner")) - progress.log(stats, tag="train_inner", step=num_updates) - - # reset mid-epoch stats after each log interval - # the end-of-epoch stats will still be preserved - metrics.reset_meters("train_inner") - - end_of_epoch = not itr.has_next() - valid_losses, should_stop = validate_and_save( - cfg, trainer, task, epoch_itr, valid_subsets, end_of_epoch - ) - - if should_stop: - break - - # log end-of-epoch stats - logger.info("end of epoch {} (average epoch stats below)".format(epoch_itr.epoch)) - stats = get_training_stats(metrics.get_smoothed_values("train")) - progress.print(stats, tag="train", step=num_updates) - - # reset epoch-level meters - metrics.reset_meters("train") - return valid_losses, should_stop - - -def _flatten_config(cfg: DictConfig): - config = OmegaConf.to_container(cfg) - # remove any legacy Namespaces and replace with a single "args" - namespace = None - for k, v in list(config.items()): - if isinstance(v, argparse.Namespace): - namespace = v - del config[k] - if namespace is not None: - config["args"] = vars(namespace) - return config - - -def validate_and_save( - cfg: DictConfig, - trainer: Trainer, - task: tasks.FairseqTask, - epoch_itr, - valid_subsets: List[str], - end_of_epoch: bool, -) -> Tuple[List[Optional[float]], bool]: - num_updates = trainer.get_num_updates() - max_update = cfg.optimization.max_update or math.inf - - # Stopping conditions (and an additional one based on validation loss later - # on) - should_stop = False - if num_updates >= max_update: - should_stop = True - logger.info( - f"Stopping training due to " - f"num_updates: {num_updates} >= max_update: {max_update}" - ) - - training_time_hours = trainer.cumulative_training_time() / (60 * 60) - if ( - cfg.optimization.stop_time_hours > 0 - and training_time_hours > cfg.optimization.stop_time_hours - ): - should_stop = True - logger.info( - f"Stopping training due to " - f"cumulative_training_time: {training_time_hours} > " - f"stop_time_hours: {cfg.optimization.stop_time_hours} hour(s)" - ) - - do_save = ( - (end_of_epoch and epoch_itr.epoch % cfg.checkpoint.save_interval == 0) - or should_stop - or ( - cfg.checkpoint.save_interval_updates > 0 - and num_updates > 0 - and num_updates % cfg.checkpoint.save_interval_updates == 0 - and num_updates >= cfg.dataset.validate_after_updates - ) - ) - do_validate = ( - (not end_of_epoch and do_save) # validate during mid-epoch saves - or (end_of_epoch and epoch_itr.epoch % cfg.dataset.validate_interval == 0) - or should_stop - or ( - cfg.dataset.validate_interval_updates > 0 - and num_updates > 0 - and num_updates % cfg.dataset.validate_interval_updates == 0 - ) - ) and not cfg.dataset.disable_validation and num_updates >= cfg.dataset.validate_after_updates - - # Validate - valid_losses = [None] - if do_validate: - valid_losses = validate(cfg, trainer, task, epoch_itr, valid_subsets) - - should_stop |= should_stop_early(cfg, valid_losses[0]) - - # Save checkpoint - if do_save or should_stop: - checkpoint_utils.save_checkpoint( - cfg.checkpoint, trainer, epoch_itr, valid_losses[0] - ) - - return valid_losses, should_stop - - -def get_training_stats(stats: Dict[str, Any]) -> Dict[str, Any]: - stats["wall"] = round(metrics.get_meter("default", "wall").elapsed_time, 0) - return stats - - -def validate( - cfg: DictConfig, - trainer: Trainer, - task: tasks.FairseqTask, - epoch_itr, - subsets: List[str], -) -> List[Optional[float]]: - """Evaluate the model on the validation set(s) and return the losses.""" - - if cfg.dataset.fixed_validation_seed is not None: - # set fixed seed for every validation - utils.set_torch_seed(cfg.dataset.fixed_validation_seed) - - trainer.begin_valid_epoch(epoch_itr.epoch) - valid_losses = [] - for subset in subsets: - logger.info('begin validation on "{}" subset'.format(subset)) - - # Initialize data iterator - itr = trainer.get_valid_iterator(subset).next_epoch_itr( - shuffle=False, set_dataset_epoch=False # use a fixed valid set - ) - if cfg.common.tpu: - itr = utils.tpu_data_loader(itr) - progress = progress_bar.progress_bar( - itr, - log_format=cfg.common.log_format, - log_interval=cfg.common.log_interval, - epoch=epoch_itr.epoch, - prefix=f"valid on '{subset}' subset", - tensorboard_logdir=( - cfg.common.tensorboard_logdir - if distributed_utils.is_master(cfg.distributed_training) - else None - ), - default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"), - wandb_project=( - cfg.common.wandb_project - if distributed_utils.is_master(cfg.distributed_training) - else None - ), - wandb_run_name=os.environ.get( - "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir) - ), - ) - - # create a new root metrics aggregator so validation metrics - # don't pollute other aggregators (e.g., train meters) - with metrics.aggregate(new_root=True) as agg: - for i, sample in enumerate(progress): - if cfg.dataset.max_valid_steps is not None and i > cfg.dataset.max_valid_steps: - break - trainer.valid_step(sample) - - # log validation stats - stats = get_valid_stats(cfg, trainer, agg.get_smoothed_values()) - - if hasattr(task, "post_validate"): - task.post_validate(trainer.get_model(), stats, agg) - - progress.print(stats, tag=subset, step=trainer.get_num_updates()) - - valid_losses.append(stats[cfg.checkpoint.best_checkpoint_metric]) - return valid_losses - - -def get_valid_stats( - cfg: DictConfig, trainer: Trainer, stats: Dict[str, Any] -) -> Dict[str, Any]: - stats["num_updates"] = trainer.get_num_updates() - if hasattr(checkpoint_utils.save_checkpoint, "best"): - key = "best_{0}".format(cfg.checkpoint.best_checkpoint_metric) - best_function = max if cfg.checkpoint.maximize_best_checkpoint_metric else min - stats[key] = best_function( - checkpoint_utils.save_checkpoint.best, - stats[cfg.checkpoint.best_checkpoint_metric], - ) - return stats - - -def cli_main( - modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None -) -> None: - parser = options.get_training_parser() - args = options.parse_args_and_arch(parser, modify_parser=modify_parser) - - cfg = convert_namespace_to_omegaconf(args) - - if cfg.common.use_plasma_view: - server = PlasmaStore(path=cfg.common.plasma_path) - logger.info(f"Started plasma server pid {server.server.pid} {cfg.common.plasma_path}") - - if args.profile: - with torch.cuda.profiler.profile(): - with torch.autograd.profiler.emit_nvtx(): - distributed_utils.call_main(cfg, main) - else: - distributed_utils.call_main(cfg, main) - - # if cfg.common.use_plasma_view: - # server.server.kill() - - -if __name__ == "__main__": - cli_main() diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/backtranslation/README.md b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/backtranslation/README.md deleted file mode 100644 index 73675f1125d80f58aa824db67d8970504d4d6b2a..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/backtranslation/README.md +++ /dev/null @@ -1,297 +0,0 @@ -# Understanding Back-Translation at Scale (Edunov et al., 2018) - -This page includes pre-trained models from the paper [Understanding Back-Translation at Scale (Edunov et al., 2018)](https://arxiv.org/abs/1808.09381). - -## Pre-trained models - -Model | Description | Dataset | Download ----|---|---|--- -`transformer.wmt18.en-de` | Transformer <br> ([Edunov et al., 2018](https://arxiv.org/abs/1808.09381)) <br> WMT'18 winner | [WMT'18 English-German](http://www.statmt.org/wmt18/translation-task.html) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz) <br> See NOTE in the archive - -## Example usage (torch.hub) - -We require a few additional Python dependencies for preprocessing: -```bash -pip install subword_nmt sacremoses -``` - -Then to generate translations from the full model ensemble: -```python -import torch - -# List available models -torch.hub.list('pytorch/fairseq') # [..., 'transformer.wmt18.en-de', ... ] - -# Load the WMT'18 En-De ensemble -en2de_ensemble = torch.hub.load( - 'pytorch/fairseq', 'transformer.wmt18.en-de', - checkpoint_file='wmt18.model1.pt:wmt18.model2.pt:wmt18.model3.pt:wmt18.model4.pt:wmt18.model5.pt', - tokenizer='moses', bpe='subword_nmt') - -# The ensemble contains 5 models -len(en2de_ensemble.models) -# 5 - -# Translate -en2de_ensemble.translate('Hello world!') -# 'Hallo Welt!' -``` - -## Training your own model (WMT'18 English-German) - -The following instructions can be adapted to reproduce the models from the paper. - - -#### Step 1. Prepare parallel data and optionally train a baseline (English-German) model - -First download and preprocess the data: -```bash -# Download and prepare the data -cd examples/backtranslation/ -bash prepare-wmt18en2de.sh -cd ../.. - -# Binarize the data -TEXT=examples/backtranslation/wmt18_en_de -fairseq-preprocess \ - --joined-dictionary \ - --source-lang en --target-lang de \ - --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \ - --destdir data-bin/wmt18_en_de --thresholdtgt 0 --thresholdsrc 0 \ - --workers 20 - -# Copy the BPE code into the data-bin directory for future use -cp examples/backtranslation/wmt18_en_de/code data-bin/wmt18_en_de/code -``` - -(Optionally) Train a baseline model (English-German) using just the parallel data: -```bash -CHECKPOINT_DIR=checkpoints_en_de_parallel -fairseq-train --fp16 \ - data-bin/wmt18_en_de \ - --source-lang en --target-lang de \ - --arch transformer_wmt_en_de_big --share-all-embeddings \ - --dropout 0.3 --weight-decay 0.0 \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ - --lr 0.001 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ - --max-tokens 3584 --update-freq 16 \ - --max-update 30000 \ - --save-dir $CHECKPOINT_DIR -# Note: the above command assumes 8 GPUs. Adjust `--update-freq` if you have a -# different number of GPUs. -``` - -Average the last 10 checkpoints: -```bash -python scripts/average_checkpoints.py \ - --inputs $CHECKPOINT_DIR \ - --num-epoch-checkpoints 10 \ - --output $CHECKPOINT_DIR/checkpoint.avg10.pt -``` - -Evaluate BLEU: -```bash -# tokenized BLEU on newstest2017: -bash examples/backtranslation/tokenized_bleu.sh \ - wmt17 \ - en-de \ - data-bin/wmt18_en_de \ - data-bin/wmt18_en_de/code \ - $CHECKPOINT_DIR/checkpoint.avg10.pt -# BLEU4 = 29.57, 60.9/35.4/22.9/15.5 (BP=1.000, ratio=1.014, syslen=63049, reflen=62152) -# compare to 29.46 in Table 1, which is also for tokenized BLEU - -# generally it's better to report (detokenized) sacrebleu though: -bash examples/backtranslation/sacrebleu.sh \ - wmt17 \ - en-de \ - data-bin/wmt18_en_de \ - data-bin/wmt18_en_de/code \ - $CHECKPOINT_DIR/checkpoint.avg10.pt -# BLEU+case.mixed+lang.en-de+numrefs.1+smooth.exp+test.wmt17+tok.13a+version.1.4.3 = 29.0 60.6/34.7/22.4/14.9 (BP = 1.000 ratio = 1.013 hyp_len = 62099 ref_len = 61287) -``` - - -#### Step 2. Back-translate monolingual German data - -Train a reverse model (German-English) to do the back-translation: -```bash -CHECKPOINT_DIR=checkpoints_de_en_parallel -fairseq-train --fp16 \ - data-bin/wmt18_en_de \ - --source-lang de --target-lang en \ - --arch transformer_wmt_en_de_big --share-all-embeddings \ - --dropout 0.3 --weight-decay 0.0 \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ - --lr 0.001 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ - --max-tokens 3584 --update-freq 16 \ - --max-update 30000 \ - --save-dir $CHECKPOINT_DIR -# Note: the above command assumes 8 GPUs. Adjust `--update-freq` if you have a -# different number of GPUs. -``` - -Let's evaluate the back-translation (BT) model to make sure it is well trained: -```bash -bash examples/backtranslation/sacrebleu.sh \ - wmt17 \ - de-en \ - data-bin/wmt18_en_de \ - data-bin/wmt18_en_de/code \ - $CHECKPOINT_DIR/checkpoint_best.py -# BLEU+case.mixed+lang.de-en+numrefs.1+smooth.exp+test.wmt17+tok.13a+version.1.4.3 = 34.9 66.9/41.8/28.5/19.9 (BP = 0.983 ratio = 0.984 hyp_len = 63342 ref_len = 64399) -# compare to the best system from WMT'17 which scored 35.1: http://matrix.statmt.org/matrix/systems_list/1868 -``` - -Next prepare the monolingual data: -```bash -# Download and prepare the monolingual data -# By default the script samples 25M monolingual sentences, which after -# deduplication should be just over 24M sentences. These are split into 25 -# shards, each with 1M sentences (except for the last shard). -cd examples/backtranslation/ -bash prepare-de-monolingual.sh -cd ../.. - -# Binarize each shard of the monolingual data -TEXT=examples/backtranslation/wmt18_de_mono -for SHARD in $(seq -f "%02g" 0 24); do \ - fairseq-preprocess \ - --only-source \ - --source-lang de --target-lang en \ - --joined-dictionary \ - --srcdict data-bin/wmt18_en_de/dict.de.txt \ - --testpref $TEXT/bpe.monolingual.dedup.${SHARD} \ - --destdir data-bin/wmt18_de_mono/shard${SHARD} \ - --workers 20; \ - cp data-bin/wmt18_en_de/dict.en.txt data-bin/wmt18_de_mono/shard${SHARD}/; \ -done -``` - -Now we're ready to perform back-translation over the monolingual data. The -following command generates via sampling, but it's possible to use greedy -decoding (`--beam 1`), beam search (`--beam 5`), -top-k sampling (`--sampling --beam 1 --sampling-topk 10`), etc.: -```bash -mkdir backtranslation_output -for SHARD in $(seq -f "%02g" 0 24); do \ - fairseq-generate --fp16 \ - data-bin/wmt18_de_mono/shard${SHARD} \ - --path $CHECKPOINT_DIR/checkpoint_best.pt \ - --skip-invalid-size-inputs-valid-test \ - --max-tokens 4096 \ - --sampling --beam 1 \ - > backtranslation_output/sampling.shard${SHARD}.out; \ -done -``` - -After BT, use the `extract_bt_data.py` script to re-combine the shards, extract -the back-translations and apply length ratio filters: -```bash -python examples/backtranslation/extract_bt_data.py \ - --minlen 1 --maxlen 250 --ratio 1.5 \ - --output backtranslation_output/bt_data --srclang en --tgtlang de \ - backtranslation_output/sampling.shard*.out - -# Ensure lengths are the same: -# wc -l backtranslation_output/bt_data.{en,de} -# 21795614 backtranslation_output/bt_data.en -# 21795614 backtranslation_output/bt_data.de -# 43591228 total -``` - -Binarize the filtered BT data and combine it with the parallel data: -```bash -TEXT=backtranslation_output -fairseq-preprocess \ - --source-lang en --target-lang de \ - --joined-dictionary \ - --srcdict data-bin/wmt18_en_de/dict.en.txt \ - --trainpref $TEXT/bt_data \ - --destdir data-bin/wmt18_en_de_bt \ - --workers 20 - -# We want to train on the combined data, so we'll symlink the parallel + BT data -# in the wmt18_en_de_para_plus_bt directory. We link the parallel data as "train" -# and the BT data as "train1", so that fairseq will combine them automatically -# and so that we can use the `--upsample-primary` option to upsample the -# parallel data (if desired). -PARA_DATA=$(readlink -f data-bin/wmt18_en_de) -BT_DATA=$(readlink -f data-bin/wmt18_en_de_bt) -COMB_DATA=data-bin/wmt18_en_de_para_plus_bt -mkdir -p $COMB_DATA -for LANG in en de; do \ - ln -s ${PARA_DATA}/dict.$LANG.txt ${COMB_DATA}/dict.$LANG.txt; \ - for EXT in bin idx; do \ - ln -s ${PARA_DATA}/train.en-de.$LANG.$EXT ${COMB_DATA}/train.en-de.$LANG.$EXT; \ - ln -s ${BT_DATA}/train.en-de.$LANG.$EXT ${COMB_DATA}/train1.en-de.$LANG.$EXT; \ - ln -s ${PARA_DATA}/valid.en-de.$LANG.$EXT ${COMB_DATA}/valid.en-de.$LANG.$EXT; \ - ln -s ${PARA_DATA}/test.en-de.$LANG.$EXT ${COMB_DATA}/test.en-de.$LANG.$EXT; \ - done; \ -done -``` - - -#### 3. Train an English-German model over the combined parallel + BT data - -Finally we can train a model over the parallel + BT data: -```bash -CHECKPOINT_DIR=checkpoints_en_de_parallel_plus_bt -fairseq-train --fp16 \ - data-bin/wmt18_en_de_para_plus_bt \ - --upsample-primary 16 \ - --source-lang en --target-lang de \ - --arch transformer_wmt_en_de_big --share-all-embeddings \ - --dropout 0.3 --weight-decay 0.0 \ - --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \ - --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \ - --lr 0.0007 --lr-scheduler inverse_sqrt --warmup-updates 4000 \ - --max-tokens 3584 --update-freq 16 \ - --max-update 100000 \ - --save-dir $CHECKPOINT_DIR -# Note: the above command assumes 8 GPUs. Adjust `--update-freq` if you have a -# different number of GPUs. -``` - -Average the last 10 checkpoints: -```bash -python scripts/average_checkpoints.py \ - --inputs $CHECKPOINT_DIR \ - --num-epoch-checkpoints 10 \ - --output $CHECKPOINT_DIR/checkpoint.avg10.pt -``` - -Evaluate BLEU: -```bash -# tokenized BLEU on newstest2017: -bash examples/backtranslation/tokenized_bleu.sh \ - wmt17 \ - en-de \ - data-bin/wmt18_en_de \ - data-bin/wmt18_en_de/code \ - $CHECKPOINT_DIR/checkpoint.avg10.pt -# BLEU4 = 32.35, 64.4/38.9/26.2/18.3 (BP=0.977, ratio=0.977, syslen=60729, reflen=62152) -# compare to 32.35 in Table 1, which is also for tokenized BLEU - -# generally it's better to report (detokenized) sacrebleu: -bash examples/backtranslation/sacrebleu.sh \ - wmt17 \ - en-de \ - data-bin/wmt18_en_de \ - data-bin/wmt18_en_de/code \ - $CHECKPOINT_DIR/checkpoint.avg10.pt -# BLEU+case.mixed+lang.en-de+numrefs.1+smooth.exp+test.wmt17+tok.13a+version.1.4.3 = 31.5 64.3/38.2/25.6/17.6 (BP = 0.971 ratio = 0.971 hyp_len = 59515 ref_len = 61287) -``` - - -## Citation -```bibtex -@inproceedings{edunov2018backtranslation, - title = {Understanding Back-Translation at Scale}, - author = {Edunov, Sergey and Ott, Myle and Auli, Michael and Grangier, David}, - booktitle = {Conference of the Association for Computational Linguistics (ACL)}, - year = 2018, -} -``` diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/audio/speech_to_text_dataset.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/audio/speech_to_text_dataset.py deleted file mode 100644 index 164bf413e4fd41b895348c9ef0bb57421843eb17..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/data/audio/speech_to_text_dataset.py +++ /dev/null @@ -1,525 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import csv -import io -import logging -import re -from collections import defaultdict -from pathlib import Path -from typing import Dict, List, Optional -from dataclasses import dataclass - -import numpy as np -import torch -from fairseq.data import ( - ConcatDataset, - Dictionary, - FairseqDataset, - ResamplingDataset, - data_utils as fairseq_data_utils, -) -from fairseq.data.audio.audio_utils import ( - get_fbank, - get_waveform, - read_from_stored_zip, - is_npy_data, - is_sf_audio_data, - parse_path, - FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS, -) -from fairseq.data.audio.feature_transforms import CompositeAudioFeatureTransform -from fairseq.data.audio.data_cfg import S2TDataConfig - - -logger = logging.getLogger(__name__) - - -def get_features_from_npy_or_audio(path): - ext = Path(path).suffix - if ext not in FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS: - raise ValueError(f'Unsupported file format for "{path}"') - return np.load(path) if ext == ".npy" else get_fbank(path) - - -def get_features_or_waveform_from_stored_zip( - path, byte_offset, byte_size, need_waveform=False, use_sample_rate=None, -): - assert path.endswith(".zip") - data = read_from_stored_zip(path, byte_offset, byte_size) - f = io.BytesIO(data) - if is_npy_data(data): - features_or_waveform = np.load(f) - elif is_sf_audio_data(data): - features_or_waveform = \ - get_waveform( - f, always_2d=False, output_sample_rate=use_sample_rate - )[0] if need_waveform else get_fbank(f) - else: - raise ValueError(f'Unknown file format for "{path}"') - return features_or_waveform - - -def get_features_or_waveform( - path: str, need_waveform=False, use_sample_rate=None -): - """Get speech features from .npy file or waveform from .wav/.flac file. - The file may be inside an uncompressed ZIP file and is accessed via byte - offset and length. - - Args: - path (str): File path in the format of "<.npy/.wav/.flac path>" or - "<zip path>:<byte offset>:<byte length>". - need_waveform (bool): return waveform instead of features. - use_sample_rate (int): change sample rate for the input wave file - - Returns: - features_or_waveform (numpy.ndarray): speech features or waveform. - """ - _path, slice_ptr = parse_path(path) - if len(slice_ptr) == 0: - if need_waveform: - return get_waveform( - _path, always_2d=False, output_sample_rate=use_sample_rate - )[0] - return get_features_from_npy_or_audio(_path) - elif len(slice_ptr) == 2: - features_or_waveform = get_features_or_waveform_from_stored_zip( - _path, slice_ptr[0], slice_ptr[1], need_waveform=need_waveform, - use_sample_rate=use_sample_rate - ) - else: - raise ValueError(f"Invalid path: {path}") - - return features_or_waveform - - -def _collate_frames( - frames: List[torch.Tensor], is_audio_input: bool = False -) -> torch.Tensor: - """ - Convert a list of 2D frames into a padded 3D tensor - Args: - frames (list): list of 2D frames of size L[i]*f_dim. Where L[i] is - length of i-th frame and f_dim is static dimension of features - Returns: - 3D tensor of size len(frames)*len_max*f_dim where len_max is max of L[i] - """ - max_len = max(frame.size(0) for frame in frames) - if is_audio_input: - out = frames[0].new_zeros((len(frames), max_len)) - else: - out = frames[0].new_zeros((len(frames), max_len, frames[0].size(1))) - for i, v in enumerate(frames): - out[i, : v.size(0)] = v - return out - - -@dataclass -class SpeechToTextDatasetItem(object): - index: int - source: torch.Tensor - target: Optional[torch.Tensor] = None - speaker_id: Optional[int] = None - - -class SpeechToTextDataset(FairseqDataset): - LANG_TAG_TEMPLATE = "<lang:{}>" - - def __init__( - self, - split: str, - is_train_split: bool, - cfg: S2TDataConfig, - audio_paths: List[str], - n_frames: List[int], - src_texts: Optional[List[str]] = None, - tgt_texts: Optional[List[str]] = None, - speakers: Optional[List[str]] = None, - src_langs: Optional[List[str]] = None, - tgt_langs: Optional[List[str]] = None, - ids: Optional[List[str]] = None, - tgt_dict: Optional[Dictionary] = None, - pre_tokenizer=None, - bpe_tokenizer=None, - n_frames_per_step=1, - speaker_to_id=None - ): - self.split, self.is_train_split = split, is_train_split - self.cfg = cfg - self.audio_paths, self.n_frames = audio_paths, n_frames - self.n_samples = len(audio_paths) - assert len(n_frames) == self.n_samples > 0 - assert src_texts is None or len(src_texts) == self.n_samples - assert tgt_texts is None or len(tgt_texts) == self.n_samples - assert speakers is None or len(speakers) == self.n_samples - assert src_langs is None or len(src_langs) == self.n_samples - assert tgt_langs is None or len(tgt_langs) == self.n_samples - assert ids is None or len(ids) == self.n_samples - assert (tgt_dict is None and tgt_texts is None) or ( - tgt_dict is not None and tgt_texts is not None - ) - self.src_texts, self.tgt_texts = src_texts, tgt_texts - self.src_langs, self.tgt_langs = src_langs, tgt_langs - self.speakers = speakers - self.tgt_dict = tgt_dict - self.check_tgt_lang_tag() - self.ids = ids - self.shuffle = cfg.shuffle if is_train_split else False - - self.feature_transforms = CompositeAudioFeatureTransform.from_config_dict( - self.cfg.get_feature_transforms(split, is_train_split) - ) - - self.pre_tokenizer = pre_tokenizer - self.bpe_tokenizer = bpe_tokenizer - self.n_frames_per_step = n_frames_per_step - self.speaker_to_id = speaker_to_id - - self.tgt_lens = self.get_tgt_lens_and_check_oov() - - logger.info(self.__repr__()) - - def get_tgt_lens_and_check_oov(self): - if self.tgt_texts is None: - return [0 for _ in range(self.n_samples)] - tgt_lens = [] - n_tokens, n_oov_tokens = 0, 0 - for i in range(self.n_samples): - tokenized = self.get_tokenized_tgt_text(i).split(" ") - oov_tokens = [ - t - for t in tokenized - if self.tgt_dict.index(t) == self.tgt_dict.unk_index - ] - n_tokens += len(tokenized) - n_oov_tokens += len(oov_tokens) - tgt_lens.append(len(tokenized)) - logger.info(f"'{self.split}' has {n_oov_tokens / n_tokens * 100:.2f}% OOV") - return tgt_lens - - def __repr__(self): - return ( - self.__class__.__name__ - + f'(split="{self.split}", n_samples={self.n_samples:_}, ' - f"prepend_tgt_lang_tag={self.cfg.prepend_tgt_lang_tag}, " - f"shuffle={self.shuffle}, transforms={self.feature_transforms}, " - f"n_frames_per_step={self.n_frames_per_step}" - ) - - @classmethod - def is_lang_tag(cls, token): - pattern = cls.LANG_TAG_TEMPLATE.replace("{}", "(.*)") - return re.match(pattern, token) - - def check_tgt_lang_tag(self): - if self.cfg.prepend_tgt_lang_tag: - assert self.tgt_langs is not None and self.tgt_dict is not None - tgt_lang_tags = [ - self.LANG_TAG_TEMPLATE.format(t) for t in set(self.tgt_langs) - ] - assert all(t in self.tgt_dict for t in tgt_lang_tags) - - @classmethod - def tokenize(cls, tokenizer, text: str): - return text if tokenizer is None else tokenizer.encode(text) - - def get_tokenized_tgt_text(self, index: int): - text = self.tokenize(self.pre_tokenizer, self.tgt_texts[index]) - text = self.tokenize(self.bpe_tokenizer, text) - return text - - def pack_frames(self, feature: torch.Tensor): - if self.n_frames_per_step == 1: - return feature - n_packed_frames = feature.shape[0] // self.n_frames_per_step - feature = feature[:self.n_frames_per_step * n_packed_frames] - return feature.reshape(n_packed_frames, -1) - - @classmethod - def get_lang_tag_idx(cls, lang: str, dictionary: Dictionary): - lang_tag_idx = dictionary.index(cls.LANG_TAG_TEMPLATE.format(lang)) - assert lang_tag_idx != dictionary.unk() - return lang_tag_idx - - def __getitem__(self, index: int) -> SpeechToTextDatasetItem: - source = get_features_or_waveform( - self.audio_paths[index], - need_waveform=self.cfg.use_audio_input, - use_sample_rate=self.cfg.use_sample_rate, - ) - if self.feature_transforms is not None: - assert not self.cfg.use_audio_input - source = self.feature_transforms(source) - source = torch.from_numpy(source).float() - source = self.pack_frames(source) - - target = None - if self.tgt_texts is not None: - tokenized = self.get_tokenized_tgt_text(index) - target = self.tgt_dict.encode_line( - tokenized, add_if_not_exist=False, append_eos=True - ).long() - if self.cfg.prepend_tgt_lang_tag: - lang_tag_idx = self.get_lang_tag_idx( - self.tgt_langs[index], self.tgt_dict - ) - target = torch.cat((torch.LongTensor([lang_tag_idx]), target), 0) - - speaker_id = None - if self.speaker_to_id is not None: - speaker_id = self.speaker_to_id[self.speakers[index]] - return SpeechToTextDatasetItem( - index=index, source=source, target=target, speaker_id=speaker_id - ) - - def __len__(self): - return self.n_samples - - def collater( - self, samples: List[SpeechToTextDatasetItem], return_order: bool = False - ) -> Dict: - if len(samples) == 0: - return {} - indices = torch.tensor([x.index for x in samples], dtype=torch.long) - frames = _collate_frames([x.source for x in samples], self.cfg.use_audio_input) - # sort samples by descending number of frames - n_frames = torch.tensor([x.source.size(0) for x in samples], dtype=torch.long) - n_frames, order = n_frames.sort(descending=True) - indices = indices.index_select(0, order) - frames = frames.index_select(0, order) - - target, target_lengths = None, None - prev_output_tokens = None - ntokens = None - if self.tgt_texts is not None: - target = fairseq_data_utils.collate_tokens( - [x.target for x in samples], - self.tgt_dict.pad(), - self.tgt_dict.eos(), - left_pad=False, - move_eos_to_beginning=False, - ) - target = target.index_select(0, order) - target_lengths = torch.tensor( - [x.target.size(0) for x in samples], dtype=torch.long - ).index_select(0, order) - prev_output_tokens = fairseq_data_utils.collate_tokens( - [x.target for x in samples], - self.tgt_dict.pad(), - self.tgt_dict.eos(), - left_pad=False, - move_eos_to_beginning=True, - ) - prev_output_tokens = prev_output_tokens.index_select(0, order) - ntokens = sum(x.target.size(0) for x in samples) - - speaker = None - if self.speaker_to_id is not None: - speaker = torch.tensor( - [s.speaker_id for s in samples], dtype=torch.long - ).index_select(0, order).view(-1, 1) - - net_input = { - "src_tokens": frames, - "src_lengths": n_frames, - "prev_output_tokens": prev_output_tokens, - } - out = { - "id": indices, - "net_input": net_input, - "speaker": speaker, - "target": target, - "target_lengths": target_lengths, - "ntokens": ntokens, - "nsentences": len(samples), - } - if return_order: - out["order"] = order - return out - - def num_tokens(self, index): - return self.n_frames[index] - - def size(self, index): - return self.n_frames[index], self.tgt_lens[index] - - @property - def sizes(self): - return np.array(self.n_frames) - - @property - def can_reuse_epoch_itr_across_epochs(self): - return True - - def ordered_indices(self): - if self.shuffle: - order = [np.random.permutation(len(self))] - else: - order = [np.arange(len(self))] - # first by descending order of # of frames then by original/random order - order.append([-n for n in self.n_frames]) - return np.lexsort(order) - - def prefetch(self, indices): - raise False - - -class SpeechToTextDatasetCreator(object): - # mandatory columns - KEY_ID, KEY_AUDIO, KEY_N_FRAMES = "id", "audio", "n_frames" - KEY_TGT_TEXT = "tgt_text" - # optional columns - KEY_SPEAKER, KEY_SRC_TEXT = "speaker", "src_text" - KEY_SRC_LANG, KEY_TGT_LANG = "src_lang", "tgt_lang" - # default values - DEFAULT_SPEAKER = DEFAULT_SRC_TEXT = DEFAULT_LANG = "" - - @classmethod - def _from_list( - cls, - split_name: str, - is_train_split, - samples: List[Dict], - cfg: S2TDataConfig, - tgt_dict, - pre_tokenizer, - bpe_tokenizer, - n_frames_per_step, - speaker_to_id - ) -> SpeechToTextDataset: - audio_root = Path(cfg.audio_root) - ids = [s[cls.KEY_ID] for s in samples] - audio_paths = [(audio_root / s[cls.KEY_AUDIO]).as_posix() for s in samples] - n_frames = [int(s[cls.KEY_N_FRAMES]) for s in samples] - tgt_texts = [s[cls.KEY_TGT_TEXT] for s in samples] - src_texts = [s.get(cls.KEY_SRC_TEXT, cls.DEFAULT_SRC_TEXT) for s in samples] - speakers = [s.get(cls.KEY_SPEAKER, cls.DEFAULT_SPEAKER) for s in samples] - src_langs = [s.get(cls.KEY_SRC_LANG, cls.DEFAULT_LANG) for s in samples] - tgt_langs = [s.get(cls.KEY_TGT_LANG, cls.DEFAULT_LANG) for s in samples] - return SpeechToTextDataset( - split_name, - is_train_split, - cfg, - audio_paths, - n_frames, - src_texts=src_texts, - tgt_texts=tgt_texts, - speakers=speakers, - src_langs=src_langs, - tgt_langs=tgt_langs, - ids=ids, - tgt_dict=tgt_dict, - pre_tokenizer=pre_tokenizer, - bpe_tokenizer=bpe_tokenizer, - n_frames_per_step=n_frames_per_step, - speaker_to_id=speaker_to_id - ) - - @classmethod - def get_size_ratios( - cls, datasets: List[SpeechToTextDataset], alpha: float = 1.0 - ) -> List[float]: - """Size ratios for temperature-based sampling - (https://arxiv.org/abs/1907.05019)""" - - id_to_lp, lp_to_sz = {}, defaultdict(int) - for ds in datasets: - lang_pairs = {f"{s}->{t}" for s, t in zip(ds.src_langs, ds.tgt_langs)} - assert len(lang_pairs) == 1 - lang_pair = list(lang_pairs)[0] - id_to_lp[ds.split] = lang_pair - lp_to_sz[lang_pair] += sum(ds.n_frames) - - sz_sum = sum(v for v in lp_to_sz.values()) - lp_to_prob = {k: v / sz_sum for k, v in lp_to_sz.items()} - lp_to_tgt_prob = {k: v ** alpha for k, v in lp_to_prob.items()} - prob_sum = sum(v for v in lp_to_tgt_prob.values()) - lp_to_tgt_prob = {k: v / prob_sum for k, v in lp_to_tgt_prob.items()} - lp_to_sz_ratio = { - k: (lp_to_tgt_prob[k] * sz_sum) / v for k, v in lp_to_sz.items() - } - size_ratio = [lp_to_sz_ratio[id_to_lp[ds.split]] for ds in datasets] - - p_formatted = { - k: f"{lp_to_prob[k]:.3f}->{lp_to_tgt_prob[k]:.3f}" for k in lp_to_sz - } - logger.info(f"sampling probability balancing: {p_formatted}") - sr_formatted = {ds.split: f"{r:.3f}" for ds, r in zip(datasets, size_ratio)} - logger.info(f"balanced sampling size ratio: {sr_formatted}") - return size_ratio - - @classmethod - def _load_samples_from_tsv(cls, root: str, split: str): - tsv_path = Path(root) / f"{split}.tsv" - if not tsv_path.is_file(): - raise FileNotFoundError(f"Dataset not found: {tsv_path}") - with open(tsv_path) as f: - reader = csv.DictReader( - f, - delimiter="\t", - quotechar=None, - doublequote=False, - lineterminator="\n", - quoting=csv.QUOTE_NONE, - ) - samples = [dict(e) for e in reader] - if len(samples) == 0: - raise ValueError(f"Empty manifest: {tsv_path}") - return samples - - @classmethod - def _from_tsv( - cls, - root: str, - cfg: S2TDataConfig, - split: str, - tgt_dict, - is_train_split: bool, - pre_tokenizer, - bpe_tokenizer, - n_frames_per_step, - speaker_to_id - ) -> SpeechToTextDataset: - samples = cls._load_samples_from_tsv(root, split) - return cls._from_list( - split, is_train_split, samples, cfg, tgt_dict, pre_tokenizer, - bpe_tokenizer, n_frames_per_step, speaker_to_id - ) - - @classmethod - def from_tsv( - cls, - root: str, - cfg: S2TDataConfig, - splits: str, - tgt_dict, - pre_tokenizer, - bpe_tokenizer, - is_train_split: bool, - epoch: int, - seed: int, - n_frames_per_step: int = 1, - speaker_to_id=None - ) -> SpeechToTextDataset: - datasets = [ - cls._from_tsv( - root, cfg, split, tgt_dict, is_train_split, pre_tokenizer, - bpe_tokenizer, n_frames_per_step, speaker_to_id - ) - for split in splits.split(",") - ] - - if is_train_split and len(datasets) > 1 and cfg.sampling_alpha != 1.0: - # temperature-based sampling - size_ratios = cls.get_size_ratios(datasets, alpha=cfg.sampling_alpha) - datasets = [ - ResamplingDataset( - d, size_ratio=r, seed=seed, epoch=epoch, replace=(r >= 1.0) - ) - for r, d in zip(size_ratios, datasets) - ] - - return ConcatDataset(datasets) if len(datasets) > 1 else datasets[0] diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/lstm_cell_with_zoneout.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/lstm_cell_with_zoneout.py deleted file mode 100644 index f04e5db255c62bbe0faebbc641f579f92be5580c..0000000000000000000000000000000000000000 --- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/lstm_cell_with_zoneout.py +++ /dev/null @@ -1,37 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch.nn as nn - - -class LSTMCellWithZoneOut(nn.Module): - """ - Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations - https://arxiv.org/abs/1606.01305 - """ - - def __init__(self, prob: float, input_size: int, hidden_size: int, - bias: bool = True): - super(LSTMCellWithZoneOut, self).__init__() - self.lstm_cell = nn.LSTMCell(input_size, hidden_size, bias=bias) - self.prob = prob - if prob > 1.0 or prob < 0.0: - raise ValueError("zoneout probability must be in the range from " - "0.0 to 1.0.") - - def zoneout(self, h, next_h, prob): - if isinstance(h, tuple): - return tuple( - [self.zoneout(h[i], next_h[i], prob) for i in range(len(h))] - ) - - if self.training: - mask = h.new_zeros(*h.size()).bernoulli_(prob) - return mask * h + (1 - mask) * next_h - - return prob * h + (1 - prob) * next_h - - def forward(self, x, h): - return self.zoneout(h, self.lstm_cell(x, h), self.prob) diff --git a/spaces/OIUGLK/bingo/src/components/tailwind-indicator.tsx b/spaces/OIUGLK/bingo/src/components/tailwind-indicator.tsx deleted file mode 100644 index f2a1291213dd67055fcebe67fab574c8441338df..0000000000000000000000000000000000000000 --- a/spaces/OIUGLK/bingo/src/components/tailwind-indicator.tsx +++ /dev/null @@ -1,14 +0,0 @@ -export function TailwindIndicator() { - if (process.env.NODE_ENV === 'production') return null - - return ( - <div className="fixed bottom-1 left-1 z-50 flex h-6 w-6 items-center justify-center rounded-full bg-gray-800 p-3 font-mono text-xs text-white"> - <div className="block sm:hidden">xs</div> - <div className="hidden sm:block md:hidden">sm</div> - <div className="hidden md:block lg:hidden">md</div> - <div className="hidden lg:block xl:hidden">lg</div> - <div className="hidden xl:block 2xl:hidden">xl</div> - <div className="hidden 2xl:block">2xl</div> - </div> - ) -} diff --git a/spaces/ORI-Muchim/BlueArchiveTTS/utils.py b/spaces/ORI-Muchim/BlueArchiveTTS/utils.py deleted file mode 100644 index 4cb5b43d0ca2bae496e7871b2094f2ffb26ab642..0000000000000000000000000000000000000000 --- a/spaces/ORI-Muchim/BlueArchiveTTS/utils.py +++ /dev/null @@ -1,226 +0,0 @@ -import os -import glob -import sys -import argparse -import logging -import json -import subprocess -import numpy as np -from scipy.io.wavfile import read -import torch - -MATPLOTLIB_FLAG = False - -logging.basicConfig(stream=sys.stdout, level=logging.ERROR) -logger = logging - - -def load_checkpoint(checkpoint_path, model, optimizer=None): - assert os.path.isfile(checkpoint_path) - checkpoint_dict = torch.load(checkpoint_path, map_location='cpu') - iteration = checkpoint_dict['iteration'] - learning_rate = checkpoint_dict['learning_rate'] - if optimizer is not None: - optimizer.load_state_dict(checkpoint_dict['optimizer']) - saved_state_dict = checkpoint_dict['model'] - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - new_state_dict = {} - for k, v in state_dict.items(): - try: - new_state_dict[k] = saved_state_dict[k] - except: - logger.info("%s is not in the checkpoint" % k) - new_state_dict[k] = v - if hasattr(model, 'module'): - model.module.load_state_dict(new_state_dict) - else: - model.load_state_dict(new_state_dict) - logger.info("Loaded checkpoint '{}' (iteration {})".format( - checkpoint_path, iteration)) - return model, optimizer, learning_rate, iteration - - -def plot_spectrogram_to_numpy(spectrogram): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(10, 2)) - im = ax.imshow(spectrogram, aspect="auto", origin="lower", - interpolation='none') - plt.colorbar(im, ax=ax) - plt.xlabel("Frames") - plt.ylabel("Channels") - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def plot_alignment_to_numpy(alignment, info=None): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(6, 4)) - im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower', - interpolation='none') - fig.colorbar(im, ax=ax) - xlabel = 'Decoder timestep' - if info is not None: - xlabel += '\n\n' + info - plt.xlabel(xlabel) - plt.ylabel('Encoder timestep') - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def load_wav_to_torch(full_path): - sampling_rate, data = read(full_path) - return torch.FloatTensor(data.astype(np.float32)), sampling_rate - - -def load_filepaths_and_text(filename, split="|"): - with open(filename, encoding='utf-8') as f: - filepaths_and_text = [line.strip().split(split) for line in f] - return filepaths_and_text - - -def get_hparams(init=True): - parser = argparse.ArgumentParser() - parser.add_argument('-c', '--config', type=str, default="./configs/base.json", - help='JSON file for configuration') - parser.add_argument('-m', '--model', type=str, required=True, - help='Model name') - - args = parser.parse_args() - model_dir = os.path.join("./logs", args.model) - - if not os.path.exists(model_dir): - os.makedirs(model_dir) - - config_path = args.config - config_save_path = os.path.join(model_dir, "config.json") - if init: - with open(config_path, "r") as f: - data = f.read() - with open(config_save_path, "w") as f: - f.write(data) - else: - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_dir(model_dir): - config_save_path = os.path.join(model_dir, "config.json") - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_file(config_path): - with open(config_path, "r", encoding="utf-8") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - return hparams - - -def check_git_hash(model_dir): - source_dir = os.path.dirname(os.path.realpath(__file__)) - if not os.path.exists(os.path.join(source_dir, ".git")): - logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format( - source_dir - )) - return - - cur_hash = subprocess.getoutput("git rev-parse HEAD") - - path = os.path.join(model_dir, "githash") - if os.path.exists(path): - saved_hash = open(path).read() - if saved_hash != cur_hash: - logger.warn("git hash values are different. {}(saved) != {}(current)".format( - saved_hash[:8], cur_hash[:8])) - else: - open(path, "w").write(cur_hash) - - -def get_logger(model_dir, filename="train.log"): - global logger - logger = logging.getLogger(os.path.basename(model_dir)) - logger.setLevel(logging.DEBUG) - - formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s") - if not os.path.exists(model_dir): - os.makedirs(model_dir) - h = logging.FileHandler(os.path.join(model_dir, filename)) - h.setLevel(logging.DEBUG) - h.setFormatter(formatter) - logger.addHandler(h) - return logger - - -class HParams(): - def __init__(self, **kwargs): - for k, v in kwargs.items(): - if type(v) == dict: - v = HParams(**v) - self[k] = v - - def keys(self): - return self.__dict__.keys() - - def items(self): - return self.__dict__.items() - - def values(self): - return self.__dict__.values() - - def __len__(self): - return len(self.__dict__) - - def __getitem__(self, key): - return getattr(self, key) - - def __setitem__(self, key, value): - return setattr(self, key, value) - - def __contains__(self, key): - return key in self.__dict__ - - def __repr__(self): - return self.__dict__.__repr__() diff --git a/spaces/OkamiFeng/Bark-with-Voice-Cloning/bark/api.py b/spaces/OkamiFeng/Bark-with-Voice-Cloning/bark/api.py deleted file mode 100644 index 7a4319ceaa13798912637290f8e9e88c50d5420a..0000000000000000000000000000000000000000 --- a/spaces/OkamiFeng/Bark-with-Voice-Cloning/bark/api.py +++ /dev/null @@ -1,158 +0,0 @@ -from typing import Dict, Optional, Union - -import numpy as np - -from .generation import codec_decode, generate_coarse, generate_fine, generate_text_semantic - - -def generate_with_settings(text_prompt, semantic_temp=0.6, eos_p=0.2, coarse_temp=0.7, fine_temp=0.5, voice_name=None, output_full=False): - - # generation with more control - x_semantic = generate_text_semantic( - text_prompt, - history_prompt=voice_name, - temp=semantic_temp, - min_eos_p = eos_p, - use_kv_caching=True - ) - - x_coarse_gen = generate_coarse( - x_semantic, - history_prompt=voice_name, - temp=coarse_temp, - use_kv_caching=True - ) - x_fine_gen = generate_fine( - x_coarse_gen, - history_prompt=voice_name, - temp=fine_temp, - ) - - if output_full: - full_generation = { - 'semantic_prompt': x_semantic, - 'coarse_prompt': x_coarse_gen, - 'fine_prompt': x_fine_gen - } - return full_generation, codec_decode(x_fine_gen) - return codec_decode(x_fine_gen) - - -def text_to_semantic( - text: str, - history_prompt: Optional[Union[Dict, str]] = None, - temp: float = 0.7, - silent: bool = False, -): - """Generate semantic array from text. - - Args: - text: text to be turned into audio - history_prompt: history choice for audio cloning - temp: generation temperature (1.0 more diverse, 0.0 more conservative) - silent: disable progress bar - - Returns: - numpy semantic array to be fed into `semantic_to_waveform` - """ - x_semantic = generate_text_semantic( - text, - history_prompt=history_prompt, - temp=temp, - silent=silent, - use_kv_caching=True - ) - return x_semantic - - -def semantic_to_waveform( - semantic_tokens: np.ndarray, - history_prompt: Optional[Union[Dict, str]] = None, - temp: float = 0.7, - silent: bool = False, - output_full: bool = False, -): - """Generate audio array from semantic input. - - Args: - semantic_tokens: semantic token output from `text_to_semantic` - history_prompt: history choice for audio cloning - temp: generation temperature (1.0 more diverse, 0.0 more conservative) - silent: disable progress bar - output_full: return full generation to be used as a history prompt - - Returns: - numpy audio array at sample frequency 24khz - """ - coarse_tokens = generate_coarse( - semantic_tokens, - history_prompt=history_prompt, - temp=temp, - silent=silent, - use_kv_caching=True - ) - fine_tokens = generate_fine( - coarse_tokens, - history_prompt=history_prompt, - temp=0.5, - ) - audio_arr = codec_decode(fine_tokens) - if output_full: - full_generation = { - "semantic_prompt": semantic_tokens, - "coarse_prompt": coarse_tokens, - "fine_prompt": fine_tokens, - } - return full_generation, audio_arr - return audio_arr - - -def save_as_prompt(filepath, full_generation): - assert(filepath.endswith(".npz")) - assert(isinstance(full_generation, dict)) - assert("semantic_prompt" in full_generation) - assert("coarse_prompt" in full_generation) - assert("fine_prompt" in full_generation) - np.savez(filepath, **full_generation) - - -def generate_audio( - text: str, - history_prompt: Optional[Union[Dict, str]] = None, - text_temp: float = 0.7, - waveform_temp: float = 0.7, - silent: bool = False, - output_full: bool = False, -): - """Generate audio array from input text. - - Args: - text: text to be turned into audio - history_prompt: history choice for audio cloning - text_temp: generation temperature (1.0 more diverse, 0.0 more conservative) - waveform_temp: generation temperature (1.0 more diverse, 0.0 more conservative) - silent: disable progress bar - output_full: return full generation to be used as a history prompt - - Returns: - numpy audio array at sample frequency 24khz - """ - semantic_tokens = text_to_semantic( - text, - history_prompt=history_prompt, - temp=text_temp, - silent=silent, - ) - out = semantic_to_waveform( - semantic_tokens, - history_prompt=history_prompt, - temp=waveform_temp, - silent=silent, - output_full=output_full, - ) - if output_full: - full_generation, audio_arr = out - return full_generation, audio_arr - else: - audio_arr = out - return audio_arr diff --git a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/modeling/criterion.py b/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/modeling/criterion.py deleted file mode 100644 index dbc5c5e78b11be8996a22f99518add5a01341adf..0000000000000000000000000000000000000000 --- a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/modeling/criterion.py +++ /dev/null @@ -1,330 +0,0 @@ -# ------------------------------------------------------------------------------ -# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/criterion.py -# Modified by Jitesh Jain (https://github.com/praeclarumjj3) -# ------------------------------------------------------------------------------ - -""" -OneFormer criterion. -""" -import logging - -import torch -import torch.nn.functional as F -from torch import nn - -from detectron2.utils.comm import get_world_size -from detectron2.projects.point_rend.point_features import ( - get_uncertain_point_coords_with_randomness, - point_sample, -) - -from ..utils.misc import is_dist_avail_and_initialized, nested_tensor_from_tensor_list -from ..utils import box_ops -import torch.distributed as dist -import diffdist.functional as diff_dist -import numpy as np - -def dist_collect(x): - """ collect all tensor from all GPUs - args: - x: shape (mini_batch, ...) - returns: - shape (mini_batch * num_gpu, ...) - """ - x = x.contiguous() - out_list = [torch.zeros_like(x, device=x.device, dtype=x.dtype).contiguous() for _ in range(dist.get_world_size())] - out_list = diff_dist.all_gather(out_list, x) - return torch.cat(out_list, dim=0).contiguous() - -def dice_loss( - inputs: torch.Tensor, - targets: torch.Tensor, - num_masks: float, - ): - """ - Compute the DICE loss, similar to generalized IOU for masks - Args: - inputs: A float tensor of arbitrary shape. - The predictions for each example. - targets: A float tensor with the same shape as inputs. Stores the binary - classification label for each element in inputs - (0 for the negative class and 1 for the positive class). - """ - inputs = inputs.sigmoid() - inputs = inputs.flatten(1) - numerator = 2 * (inputs * targets).sum(-1) - denominator = inputs.sum(-1) + targets.sum(-1) - loss = 1 - (numerator + 1) / (denominator + 1) - return loss.sum() / num_masks - - -dice_loss_jit = torch.jit.script( - dice_loss -) # type: torch.jit.ScriptModule - - -def sigmoid_ce_loss( - inputs: torch.Tensor, - targets: torch.Tensor, - num_masks: float, - ): - """ - Args: - inputs: A float tensor of arbitrary shape. - The predictions for each example. - targets: A float tensor with the same shape as inputs. Stores the binary - classification label for each element in inputs - (0 for the negative class and 1 for the positive class). - Returns: - Loss tensor - """ - loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") - loss = loss.mean(1) - return loss.sum() / num_masks - - -sigmoid_ce_loss_jit = torch.jit.script( - sigmoid_ce_loss -) # type: torch.jit.ScriptModule - - -def calculate_uncertainty(logits): - """ - We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the - foreground class in `classes`. - Args: - logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or - class-agnostic, where R is the total number of predicted masks in all images and C is - the number of foreground classes. The values are logits. - Returns: - scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with - the most uncertain locations having the highest uncertainty score. - """ - assert logits.shape[1] == 1 - gt_class_logits = logits.clone() - return -(torch.abs(gt_class_logits)) - - -class SetCriterion(nn.Module): - """This class computes the loss for DETR. - The process happens in two steps: - 1) we compute hungarian assignment between ground truth boxes and the outputs of the model - 2) we supervise each pair of matched ground-truth / prediction (supervise class and box) - """ - - def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses, - num_points, oversample_ratio, importance_sample_ratio, contrast_temperature=None): - """Create the criterion. - Parameters: - num_classes: number of object categories, omitting the special no-object category - matcher: module able to compute a matching between targets and proposals - weight_dict: dict containing as key the names of the losses and as values their relative weight. - eos_coef: relative classification weight applied to the no-object category - losses: list of all the losses to be applied. See get_loss for list of available losses. - """ - super().__init__() - self.num_classes = num_classes - self.matcher = matcher - self.weight_dict = weight_dict - self.eos_coef = eos_coef - self.losses = losses - empty_weight = torch.ones(self.num_classes + 1) - empty_weight[-1] = self.eos_coef - self.register_buffer("empty_weight", empty_weight) - self.cross_entropy = nn.CrossEntropyLoss() - - # pointwise mask loss parameters - self.num_points = num_points - self.oversample_ratio = oversample_ratio - self.importance_sample_ratio = importance_sample_ratio - self.contrast_temperature = contrast_temperature - if self.contrast_temperature is not None: - self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / contrast_temperature)) - - - def loss_contrastive(self, outputs, targets, indices, num_masks): - assert "contrastive_logits" in outputs - assert "texts" in outputs - image_x = outputs["contrastive_logits"].float() - - batch_size = image_x.shape[0] - # get label globally - if is_dist_avail_and_initialized(): - labels = torch.arange(batch_size, dtype=torch.long, device=image_x.device) + batch_size * dist.get_rank() - else: - labels = torch.arange(batch_size, dtype=torch.long, device=image_x.device) - - text_x = outputs["texts"] - - # [B, C] - image_x = F.normalize(image_x.flatten(1), dim=-1) - text_x = F.normalize(text_x.flatten(1), dim=-1) - - if is_dist_avail_and_initialized(): - logits_per_img = image_x @ dist_collect(text_x).t() - logits_per_text = text_x @ dist_collect(image_x).t() - else: - logits_per_img = image_x @ text_x.t() - logits_per_text = text_x @ image_x.t() - - logit_scale = torch.clamp(self.logit_scale.exp(), max=100) - loss_img = self.cross_entropy(logits_per_img * logit_scale, labels) - loss_text = self.cross_entropy(logits_per_text * logit_scale, labels) - - loss_contrastive = loss_img + loss_text - - losses = {"loss_contrastive": loss_contrastive} - return losses - - def loss_labels(self, outputs, targets, indices, num_masks): - """Classification loss (NLL) - targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes] - """ - assert "pred_logits" in outputs - src_logits = outputs["pred_logits"].float() - - idx = self._get_src_permutation_idx(indices) - target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)]) - target_classes = torch.full( - src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device - ) - target_classes[idx] = target_classes_o - - ce_weight = torch.full( - src_logits.shape[:2], self.eos_coef, dtype=torch.float32, device=src_logits.device - ) - ce_weight[idx] = torch.tensor(1.).to(target_classes.device) - - loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight, reduce=False, reduction="none") - loss_ce = loss_ce.sum(1) / ce_weight.sum() - loss_ce = loss_ce.sum() - losses = {"loss_ce": loss_ce} - return losses - - def loss_masks(self, outputs, targets, indices, num_masks): - """Compute the losses related to the masks: the focal loss and the dice loss. - targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w] - """ - assert "pred_masks" in outputs - - src_idx = self._get_src_permutation_idx(indices) - tgt_idx = self._get_tgt_permutation_idx(indices) - src_masks = outputs["pred_masks"] - src_masks = src_masks[src_idx] - masks = [t["masks"] for t in targets] - # TODO use valid to mask invalid areas due to padding in loss - target_masks, valid = nested_tensor_from_tensor_list(masks).decompose() - target_masks = target_masks.to(src_masks) - target_masks = target_masks[tgt_idx] - - # No need to upsample predictions as we are using normalized coordinates :) - # N x 1 x H x W - src_masks = src_masks[:, None] - target_masks = target_masks[:, None] - - with torch.no_grad(): - # sample point_coords - point_coords = get_uncertain_point_coords_with_randomness( - src_masks, - lambda logits: calculate_uncertainty(logits), - self.num_points, - self.oversample_ratio, - self.importance_sample_ratio, - ) - # get gt labels - point_labels = point_sample( - target_masks, - point_coords, - align_corners=False, - ).squeeze(1) - - point_logits = point_sample( - src_masks, - point_coords, - align_corners=False, - ).squeeze(1) - - losses = { - "loss_mask": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks), - "loss_dice": dice_loss_jit(point_logits, point_labels, num_masks), - } - - del src_masks - del target_masks - return losses - - def _get_src_permutation_idx(self, indices): - # permute predictions following indices - batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)]) - src_idx = torch.cat([src for (src, _) in indices]) - return batch_idx, src_idx - - def _get_tgt_permutation_idx(self, indices): - # permute targets following indices - batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)]) - tgt_idx = torch.cat([tgt for (_, tgt) in indices]) - return batch_idx, tgt_idx - - def get_loss(self, loss, outputs, targets, indices, num_masks): - loss_map = { - 'labels': self.loss_labels, - 'masks': self.loss_masks, - 'contrastive': self.loss_contrastive, - } - assert loss in loss_map, f"do you really want to compute {loss} loss?" - return loss_map[loss](outputs, targets, indices, num_masks) - - def forward(self, outputs, targets): - """This performs the loss computation. - Parameters: - outputs: dict of tensors, see the output specification of the model for the format - targets: list of dicts, such that len(targets) == batch_size. - The expected keys in each dict depends on the losses applied, see each loss' doc - """ - outputs_without_aux = {k: v for k, v in outputs.items() if k != "aux_outputs"} - - # Retrieve the matching between the outputs of the last layer and the targets - indices = self.matcher(outputs_without_aux, targets) - - # Compute the average number of target boxes accross all nodes, for normalization purposes - num_masks = sum(len(t["labels"]) for t in targets) - num_masks = torch.as_tensor( - [num_masks], dtype=torch.float, device=next(iter(outputs.values())).device - ) - if is_dist_avail_and_initialized(): - torch.distributed.all_reduce(num_masks) - num_masks = torch.clamp(num_masks / get_world_size(), min=1).item() - - # Compute all the requested losses - losses = {} - for loss in self.losses: - losses.update(self.get_loss(loss, outputs, targets, indices, num_masks)) - - # In case of auxiliary losses, we repeat this process with the output of each intermediate layer. - if "aux_outputs" in outputs: - for i, aux_outputs in enumerate(outputs["aux_outputs"]): - indices = self.matcher(aux_outputs, targets) - for loss in self.losses: - if loss == "contrastive": - continue - l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks) - l_dict = {k + f"_{i}": v for k, v in l_dict.items()} - losses.update(l_dict) - - return losses - - def __repr__(self): - head = "Criterion " + self.__class__.__name__ - body = [ - "matcher: {}".format(self.matcher.__repr__(_repr_indent=8)), - "losses: {}".format(self.losses), - "weight_dict: {}".format(self.weight_dict), - "num_classes: {}".format(self.num_classes), - "eos_coef: {}".format(self.eos_coef), - "num_points: {}".format(self.num_points), - "oversample_ratio: {}".format(self.oversample_ratio), - "importance_sample_ratio: {}".format(self.importance_sample_ratio), - ] - _repr_indent = 4 - lines = [head] + [" " * _repr_indent + line for line in body] - return "\n".join(lines) \ No newline at end of file diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/roi_pool.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/roi_pool.py deleted file mode 100644 index d339d8f2941eabc1cbe181a9c6c5ab5ff4ff4e5f..0000000000000000000000000000000000000000 --- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/roi_pool.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import torch -import torch.nn as nn -from torch.autograd import Function -from torch.autograd.function import once_differentiable -from torch.nn.modules.utils import _pair - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', - ['roi_pool_forward', 'roi_pool_backward']) - - -class RoIPoolFunction(Function): - - @staticmethod - def symbolic(g, input, rois, output_size, spatial_scale): - return g.op( - 'MaxRoiPool', - input, - rois, - pooled_shape_i=output_size, - spatial_scale_f=spatial_scale) - - @staticmethod - def forward(ctx, input, rois, output_size, spatial_scale=1.0): - ctx.output_size = _pair(output_size) - ctx.spatial_scale = spatial_scale - ctx.input_shape = input.size() - - assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!' - - output_shape = (rois.size(0), input.size(1), ctx.output_size[0], - ctx.output_size[1]) - output = input.new_zeros(output_shape) - argmax = input.new_zeros(output_shape, dtype=torch.int) - - ext_module.roi_pool_forward( - input, - rois, - output, - argmax, - pooled_height=ctx.output_size[0], - pooled_width=ctx.output_size[1], - spatial_scale=ctx.spatial_scale) - - ctx.save_for_backward(rois, argmax) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - rois, argmax = ctx.saved_tensors - grad_input = grad_output.new_zeros(ctx.input_shape) - - ext_module.roi_pool_backward( - grad_output, - rois, - argmax, - grad_input, - pooled_height=ctx.output_size[0], - pooled_width=ctx.output_size[1], - spatial_scale=ctx.spatial_scale) - - return grad_input, None, None, None - - -roi_pool = RoIPoolFunction.apply - - -class RoIPool(nn.Module): - - def __init__(self, output_size, spatial_scale=1.0): - super(RoIPool, self).__init__() - - self.output_size = _pair(output_size) - self.spatial_scale = float(spatial_scale) - - def forward(self, input, rois): - return roi_pool(input, rois, self.output_size, self.spatial_scale) - - def __repr__(self): - s = self.__class__.__name__ - s += f'(output_size={self.output_size}, ' - s += f'spatial_scale={self.spatial_scale})' - return s diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/utils/up_conv_block.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/utils/up_conv_block.py deleted file mode 100644 index 378469da76cb7bff6a639e7877b3c275d50490fb..0000000000000000000000000000000000000000 --- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/models/utils/up_conv_block.py +++ /dev/null @@ -1,101 +0,0 @@ -import torch -import torch.nn as nn -from annotator.uniformer.mmcv.cnn import ConvModule, build_upsample_layer - - -class UpConvBlock(nn.Module): - """Upsample convolution block in decoder for UNet. - - This upsample convolution block consists of one upsample module - followed by one convolution block. The upsample module expands the - high-level low-resolution feature map and the convolution block fuses - the upsampled high-level low-resolution feature map and the low-level - high-resolution feature map from encoder. - - Args: - conv_block (nn.Sequential): Sequential of convolutional layers. - in_channels (int): Number of input channels of the high-level - skip_channels (int): Number of input channels of the low-level - high-resolution feature map from encoder. - out_channels (int): Number of output channels. - num_convs (int): Number of convolutional layers in the conv_block. - Default: 2. - stride (int): Stride of convolutional layer in conv_block. Default: 1. - dilation (int): Dilation rate of convolutional layer in conv_block. - Default: 1. - with_cp (bool): Use checkpoint or not. Using checkpoint will save some - memory while slowing down the training speed. Default: False. - conv_cfg (dict | None): Config dict for convolution layer. - Default: None. - norm_cfg (dict | None): Config dict for normalization layer. - Default: dict(type='BN'). - act_cfg (dict | None): Config dict for activation layer in ConvModule. - Default: dict(type='ReLU'). - upsample_cfg (dict): The upsample config of the upsample module in - decoder. Default: dict(type='InterpConv'). If the size of - high-level feature map is the same as that of skip feature map - (low-level feature map from encoder), it does not need upsample the - high-level feature map and the upsample_cfg is None. - dcn (bool): Use deformable convolution in convolutional layer or not. - Default: None. - plugins (dict): plugins for convolutional layers. Default: None. - """ - - def __init__(self, - conv_block, - in_channels, - skip_channels, - out_channels, - num_convs=2, - stride=1, - dilation=1, - with_cp=False, - conv_cfg=None, - norm_cfg=dict(type='BN'), - act_cfg=dict(type='ReLU'), - upsample_cfg=dict(type='InterpConv'), - dcn=None, - plugins=None): - super(UpConvBlock, self).__init__() - assert dcn is None, 'Not implemented yet.' - assert plugins is None, 'Not implemented yet.' - - self.conv_block = conv_block( - in_channels=2 * skip_channels, - out_channels=out_channels, - num_convs=num_convs, - stride=stride, - dilation=dilation, - with_cp=with_cp, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg, - dcn=None, - plugins=None) - if upsample_cfg is not None: - self.upsample = build_upsample_layer( - cfg=upsample_cfg, - in_channels=in_channels, - out_channels=skip_channels, - with_cp=with_cp, - norm_cfg=norm_cfg, - act_cfg=act_cfg) - else: - self.upsample = ConvModule( - in_channels, - skip_channels, - kernel_size=1, - stride=1, - padding=0, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg) - - def forward(self, skip, x): - """Forward function.""" - - x = self.upsample(x) - out = torch.cat([skip, x], dim=1) - out = self.conv_block(out) - - return out diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/rpn/transformer.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/rpn/transformer.py deleted file mode 100644 index 72e108a1a2bf628ced2161c0a6d5a1b28e654bcd..0000000000000000000000000000000000000000 --- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/modeling/rpn/transformer.py +++ /dev/null @@ -1,52 +0,0 @@ -import torch -import torch.nn.functional as F -from torch import nn, Tensor - -import copy -from typing import Optional, List - - -def _get_clones(module, N): - return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) - - -def _get_activation_fn(activation): - """Return an activation function given a string""" - if activation == "relu": - return F.relu - if activation == "gelu": - return F.gelu - if activation == "glu": - return F.glu - raise RuntimeError(F"activation should be relu/gelu, not {activation}.") - - -class TransformerEncoderLayer(nn.Module): - def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, - activation="relu", normalize_before=False): - super(TransformerEncoderLayer, self).__init__() - self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) - # Implementation of Feedforward model - self.linear1 = nn.Linear(d_model, dim_feedforward) - self.dropout = nn.Dropout(dropout) - self.linear2 = nn.Linear(dim_feedforward, d_model) - - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(dropout) - - self.activation = _get_activation_fn(activation) - self.normalize_before = normalize_before - - def forward(self, src, - src_mask: Optional[Tensor] = None, - src_key_padding_mask: Optional[Tensor] = None): - src2 = self.self_attn(src, src, src, attn_mask=src_mask, - key_padding_mask=src_key_padding_mask)[0] - src = src + self.dropout1(src2) - src = self.norm1(src) - src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) - src = src + self.dropout2(src2) - src = self.norm2(src) - return src diff --git a/spaces/Potanin/12345/app.py b/spaces/Potanin/12345/app.py deleted file mode 100644 index 3332f13a969c43ea5f154881e94532cd684f0a9d..0000000000000000000000000000000000000000 --- a/spaces/Potanin/12345/app.py +++ /dev/null @@ -1,2089 +0,0 @@ -import subprocess, torch, os, traceback, sys, warnings, shutil, numpy as np -from mega import Mega -os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1" -import threading -from pathlib import Path -from time import sleep -from subprocess import Popen -import faiss -from random import shuffle -import json, datetime, requests -from gtts import gTTS -now_dir = os.getcwd() -sys.path.append(now_dir) -tmp = os.path.join(now_dir, "TEMP") -shutil.rmtree(tmp, ignore_errors=True) -shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True) -os.makedirs(tmp, exist_ok=True) -os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True) -os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True) -os.environ["TEMP"] = tmp -warnings.filterwarnings("ignore") -torch.manual_seed(114514) -from i18n import I18nAuto - -import signal - -import math - -from utils import load_audio, CSVutil - -global DoFormant, Quefrency, Timbre - -if not os.path.isdir('csvdb/'): - os.makedirs('csvdb') - frmnt, stp = open("csvdb/formanting.csv", 'w'), open("csvdb/stop.csv", 'w') - frmnt.close() - stp.close() - -try: - DoFormant, Quefrency, Timbre = CSVutil('csvdb/formanting.csv', 'r', 'formanting') - DoFormant = ( - lambda DoFormant: True if DoFormant.lower() == 'true' else (False if DoFormant.lower() == 'false' else DoFormant) - )(DoFormant) -except (ValueError, TypeError, IndexError): - DoFormant, Quefrency, Timbre = False, 1.0, 1.0 - CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, Quefrency, Timbre) - -def download_models(): - # Download hubert base model if not present - if not os.path.isfile('./hubert_base.pt'): - response = requests.get('https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt') - - if response.status_code == 200: - with open('./hubert_base.pt', 'wb') as f: - f.write(response.content) - print("Downloaded hubert base model file successfully. File saved to ./hubert_base.pt.") - else: - raise Exception("Failed to download hubert base model file. Status code: " + str(response.status_code) + ".") - - # Download rmvpe model if not present - if not os.path.isfile('./rmvpe.pt'): - response = requests.get('https://drive.usercontent.google.com/download?id=1Hkn4kNuVFRCNQwyxQFRtmzmMBGpQxptI&export=download&authuser=0&confirm=t&uuid=0b3a40de-465b-4c65-8c41-135b0b45c3f7&at=APZUnTV3lA3LnyTbeuduura6Dmi2:1693724254058') - - if response.status_code == 200: - with open('./rmvpe.pt', 'wb') as f: - f.write(response.content) - print("Downloaded rmvpe model file successfully. File saved to ./rmvpe.pt.") - else: - raise Exception("Failed to download rmvpe model file. Status code: " + str(response.status_code) + ".") - -download_models() - -print("\n-------------------------------\nRVC v2 Easy GUI (Local Edition)\n-------------------------------\n") - -def formant_apply(qfrency, tmbre): - Quefrency = qfrency - Timbre = tmbre - DoFormant = True - CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre) - - return ({"value": Quefrency, "__type__": "update"}, {"value": Timbre, "__type__": "update"}) - -def get_fshift_presets(): - fshift_presets_list = [] - for dirpath, _, filenames in os.walk("./formantshiftcfg/"): - for filename in filenames: - if filename.endswith(".txt"): - fshift_presets_list.append(os.path.join(dirpath,filename).replace('\\','/')) - - if len(fshift_presets_list) > 0: - return fshift_presets_list - else: - return '' - - - -def formant_enabled(cbox, qfrency, tmbre, frmntapply, formantpreset, formant_refresh_button): - - if (cbox): - - DoFormant = True - CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre) - #print(f"is checked? - {cbox}\ngot {DoFormant}") - - return ( - {"value": True, "__type__": "update"}, - {"visible": True, "__type__": "update"}, - {"visible": True, "__type__": "update"}, - {"visible": True, "__type__": "update"}, - {"visible": True, "__type__": "update"}, - {"visible": True, "__type__": "update"}, - ) - - - else: - - DoFormant = False - CSVutil('csvdb/formanting.csv', 'w+', 'formanting', DoFormant, qfrency, tmbre) - - #print(f"is checked? - {cbox}\ngot {DoFormant}") - return ( - {"value": False, "__type__": "update"}, - {"visible": False, "__type__": "update"}, - {"visible": False, "__type__": "update"}, - {"visible": False, "__type__": "update"}, - {"visible": False, "__type__": "update"}, - {"visible": False, "__type__": "update"}, - {"visible": False, "__type__": "update"}, - ) - - - -def preset_apply(preset, qfer, tmbr): - if str(preset) != '': - with open(str(preset), 'r') as p: - content = p.readlines() - qfer, tmbr = content[0].split('\n')[0], content[1] - - formant_apply(qfer, tmbr) - else: - pass - return ({"value": qfer, "__type__": "update"}, {"value": tmbr, "__type__": "update"}) - -def update_fshift_presets(preset, qfrency, tmbre): - - qfrency, tmbre = preset_apply(preset, qfrency, tmbre) - - if (str(preset) != ''): - with open(str(preset), 'r') as p: - content = p.readlines() - qfrency, tmbre = content[0].split('\n')[0], content[1] - - formant_apply(qfrency, tmbre) - else: - pass - return ( - {"choices": get_fshift_presets(), "__type__": "update"}, - {"value": qfrency, "__type__": "update"}, - {"value": tmbre, "__type__": "update"}, - ) - -i18n = I18nAuto() -#i18n.print() -# 判断是否有能用来训练和加速推理的N卡 -ngpu = torch.cuda.device_count() -gpu_infos = [] -mem = [] -if (not torch.cuda.is_available()) or ngpu == 0: - if_gpu_ok = False -else: - if_gpu_ok = False - for i in range(ngpu): - gpu_name = torch.cuda.get_device_name(i) - if ( - "10" in gpu_name - or "16" in gpu_name - or "20" in gpu_name - or "30" in gpu_name - or "40" in gpu_name - or "A2" in gpu_name.upper() - or "A3" in gpu_name.upper() - or "A4" in gpu_name.upper() - or "P4" in gpu_name.upper() - or "A50" in gpu_name.upper() - or "A60" in gpu_name.upper() - or "70" in gpu_name - or "80" in gpu_name - or "90" in gpu_name - or "M4" in gpu_name.upper() - or "T4" in gpu_name.upper() - or "TITAN" in gpu_name.upper() - ): # A10#A100#V100#A40#P40#M40#K80#A4500 - if_gpu_ok = True # 至少有一张能用的N卡 - gpu_infos.append("%s\t%s" % (i, gpu_name)) - mem.append( - int( - torch.cuda.get_device_properties(i).total_memory - / 1024 - / 1024 - / 1024 - + 0.4 - ) - ) -if if_gpu_ok == True and len(gpu_infos) > 0: - gpu_info = "\n".join(gpu_infos) - default_batch_size = min(mem) // 2 -else: - gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练") - default_batch_size = 1 -gpus = "-".join([i[0] for i in gpu_infos]) -from lib.infer_pack.models import ( - SynthesizerTrnMs256NSFsid, - SynthesizerTrnMs256NSFsid_nono, - SynthesizerTrnMs768NSFsid, - SynthesizerTrnMs768NSFsid_nono, -) -import soundfile as sf -from fairseq import checkpoint_utils -import gradio as gr -import logging -from vc_infer_pipeline import VC -from config import Config - -config = Config() -# from trainset_preprocess_pipeline import PreProcess -logging.getLogger("numba").setLevel(logging.WARNING) - -hubert_model = None - -def load_hubert(): - global hubert_model - models, _, _ = checkpoint_utils.load_model_ensemble_and_task( - ["hubert_base.pt"], - suffix="", - ) - hubert_model = models[0] - hubert_model = hubert_model.to(config.device) - if config.is_half: - hubert_model = hubert_model.half() - else: - hubert_model = hubert_model.float() - hubert_model.eval() - - -weight_root = "weights" -index_root = "logs" -names = [] -for name in os.listdir(weight_root): - if name.endswith(".pth"): - names.append(name) -index_paths = [] -for root, dirs, files in os.walk(index_root, topdown=False): - for name in files: - if name.endswith(".index") and "trained" not in name: - index_paths.append("%s/%s" % (root, name)) - - - -def vc_single( - sid, - input_audio_path, - f0_up_key, - f0_file, - f0_method, - file_index, - #file_index2, - # file_big_npy, - index_rate, - filter_radius, - resample_sr, - rms_mix_rate, - protect, - crepe_hop_length, -): # spk_item, input_audio0, vc_transform0,f0_file,f0method0 - global tgt_sr, net_g, vc, hubert_model, version - if input_audio_path is None: - return "You need to upload an audio", None - f0_up_key = int(f0_up_key) - try: - audio = load_audio(input_audio_path, 16000, DoFormant, Quefrency, Timbre) - audio_max = np.abs(audio).max() / 0.95 - if audio_max > 1: - audio /= audio_max - times = [0, 0, 0] - if hubert_model == None: - load_hubert() - if_f0 = cpt.get("f0", 1) - file_index = ( - ( - file_index.strip(" ") - .strip('"') - .strip("\n") - .strip('"') - .strip(" ") - .replace("trained", "added") - ) - ) # 防止小白写错，自动帮他替换掉 - # file_big_npy = ( - # file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - # ) - audio_opt = vc.pipeline( - hubert_model, - net_g, - sid, - audio, - input_audio_path, - times, - f0_up_key, - f0_method, - file_index, - # file_big_npy, - index_rate, - if_f0, - filter_radius, - tgt_sr, - resample_sr, - rms_mix_rate, - version, - protect, - crepe_hop_length, - f0_file=f0_file, - ) - if resample_sr >= 16000 and tgt_sr != resample_sr: - tgt_sr = resample_sr - index_info = ( - "Using index:%s." % file_index - if os.path.exists(file_index) - else "Index not used." - ) - return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % ( - index_info, - times[0], - times[1], - times[2], - ), (tgt_sr, audio_opt) - except: - info = traceback.format_exc() - print(info) - return info, (None, None) - - -def vc_multi( - sid, - dir_path, - opt_root, - paths, - f0_up_key, - f0_method, - file_index, - file_index2, - # file_big_npy, - index_rate, - filter_radius, - resample_sr, - rms_mix_rate, - protect, - format1, - crepe_hop_length, -): - try: - dir_path = ( - dir_path.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - ) # 防止小白拷路径头尾带了空格和"和回车 - opt_root = opt_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - os.makedirs(opt_root, exist_ok=True) - try: - if dir_path != "": - paths = [os.path.join(dir_path, name) for name in os.listdir(dir_path)] - else: - paths = [path.name for path in paths] - except: - traceback.print_exc() - paths = [path.name for path in paths] - infos = [] - for path in paths: - info, opt = vc_single( - sid, - path, - f0_up_key, - None, - f0_method, - file_index, - # file_big_npy, - index_rate, - filter_radius, - resample_sr, - rms_mix_rate, - protect, - crepe_hop_length - ) - if "Success" in info: - try: - tgt_sr, audio_opt = opt - if format1 in ["wav", "flac"]: - sf.write( - "%s/%s.%s" % (opt_root, os.path.basename(path), format1), - audio_opt, - tgt_sr, - ) - else: - path = "%s/%s.wav" % (opt_root, os.path.basename(path)) - sf.write( - path, - audio_opt, - tgt_sr, - ) - if os.path.exists(path): - os.system( - "ffmpeg -i %s -vn %s -q:a 2 -y" - % (path, path[:-4] + ".%s" % format1) - ) - except: - info += traceback.format_exc() - infos.append("%s->%s" % (os.path.basename(path), info)) - yield "\n".join(infos) - yield "\n".join(infos) - except: - yield traceback.format_exc() - -# 一个选项卡全局只能有一个音色 -def get_vc(sid): - global n_spk, tgt_sr, net_g, vc, cpt, version - if sid == "" or sid == []: - global hubert_model - if hubert_model != None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的 - print("clean_empty_cache") - del net_g, n_spk, vc, hubert_model, tgt_sr # ,cpt - hubert_model = net_g = n_spk = vc = hubert_model = tgt_sr = None - if torch.cuda.is_available(): - torch.cuda.empty_cache() - ###楼下不这么折腾清理不干净 - if_f0 = cpt.get("f0", 1) - version = cpt.get("version", "v1") - if version == "v1": - if if_f0 == 1: - net_g = SynthesizerTrnMs256NSFsid( - *cpt["config"], is_half=config.is_half - ) - else: - net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) - elif version == "v2": - if if_f0 == 1: - net_g = SynthesizerTrnMs768NSFsid( - *cpt["config"], is_half=config.is_half - ) - else: - net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) - del net_g, cpt - if torch.cuda.is_available(): - torch.cuda.empty_cache() - cpt = None - return {"visible": False, "__type__": "update"} - person = "%s/%s" % (weight_root, sid) - print("loading %s" % person) - cpt = torch.load(person, map_location="cpu") - tgt_sr = cpt["config"][-1] - cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk - if_f0 = cpt.get("f0", 1) - version = cpt.get("version", "v1") - if version == "v1": - if if_f0 == 1: - net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half) - else: - net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) - elif version == "v2": - if if_f0 == 1: - net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half) - else: - net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"]) - del net_g.enc_q - print(net_g.load_state_dict(cpt["weight"], strict=False)) - net_g.eval().to(config.device) - if config.is_half: - net_g = net_g.half() - else: - net_g = net_g.float() - vc = VC(tgt_sr, config) - n_spk = cpt["config"][-3] - return {"visible": False, "maximum": n_spk, "__type__": "update"} - - -def change_choices(): - names = [] - for name in os.listdir(weight_root): - if name.endswith(".pth"): - names.append(name) - index_paths = [] - for root, dirs, files in os.walk(index_root, topdown=False): - for name in files: - if name.endswith(".index") and "trained" not in name: - index_paths.append("%s/%s" % (root, name)) - return {"choices": sorted(names), "__type__": "update"}, { - "choices": sorted(index_paths), - "__type__": "update", - } - - -def clean(): - return {"value": "", "__type__": "update"} - - -sr_dict = { - "32k": 32000, - "40k": 40000, - "48k": 48000, -} - - -def if_done(done, p): - while 1: - if p.poll() == None: - sleep(0.5) - else: - break - done[0] = True - - -def if_done_multi(done, ps): - while 1: - # poll==None代表进程未结束 - # 只要有一个进程未结束都不停 - flag = 1 - for p in ps: - if p.poll() == None: - flag = 0 - sleep(0.5) - break - if flag == 1: - break - done[0] = True - - -def preprocess_dataset(trainset_dir, exp_dir, sr, n_p): - sr = sr_dict[sr] - os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True) - f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w") - f.close() - cmd = ( - config.python_cmd - + " trainset_preprocess_pipeline_print.py %s %s %s %s/logs/%s " - % (trainset_dir, sr, n_p, now_dir, exp_dir) - + str(config.noparallel) - ) - print(cmd) - p = Popen(cmd, shell=True) # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir - ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 - done = [False] - threading.Thread( - target=if_done, - args=( - done, - p, - ), - ).start() - while 1: - with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f: - yield (f.read()) - sleep(1) - if done[0] == True: - break - with open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "r") as f: - log = f.read() - print(log) - yield log - -# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2]) -def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl): - gpus = gpus.split("-") - os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True) - f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w") - f.close() - if if_f0: - cmd = config.python_cmd + " extract_f0_print.py %s/logs/%s %s %s %s" % ( - now_dir, - exp_dir, - n_p, - f0method, - echl, - ) - print(cmd) - p = Popen(cmd, shell=True, cwd=now_dir) # , stdin=PIPE, stdout=PIPE,stderr=PIPE - ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 - done = [False] - threading.Thread( - target=if_done, - args=( - done, - p, - ), - ).start() - while 1: - with open( - "%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r" - ) as f: - yield (f.read()) - sleep(1) - if done[0] == True: - break - with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: - log = f.read() - print(log) - yield log - ####对不同part分别开多进程 - """ - n_part=int(sys.argv[1]) - i_part=int(sys.argv[2]) - i_gpu=sys.argv[3] - exp_dir=sys.argv[4] - os.environ["CUDA_VISIBLE_DEVICES"]=str(i_gpu) - """ - leng = len(gpus) - ps = [] - for idx, n_g in enumerate(gpus): - cmd = ( - config.python_cmd - + " extract_feature_print.py %s %s %s %s %s/logs/%s %s" - % ( - config.device, - leng, - idx, - n_g, - now_dir, - exp_dir, - version19, - ) - ) - print(cmd) - p = Popen( - cmd, shell=True, cwd=now_dir - ) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir - ps.append(p) - ###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读 - done = [False] - threading.Thread( - target=if_done_multi, - args=( - done, - ps, - ), - ).start() - while 1: - with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: - yield (f.read()) - sleep(1) - if done[0] == True: - break - with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f: - log = f.read() - print(log) - yield log - - -def change_sr2(sr2, if_f0_3, version19): - path_str = "" if version19 == "v1" else "_v2" - f0_str = "f0" if if_f0_3 else "" - if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK) - if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK) - if (if_pretrained_generator_exist == False): - print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") - if (if_pretrained_discriminator_exist == False): - print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") - return ( - ("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "", - ("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "", - {"visible": True, "__type__": "update"} - ) - -def change_version19(sr2, if_f0_3, version19): - path_str = "" if version19 == "v1" else "_v2" - f0_str = "f0" if if_f0_3 else "" - if_pretrained_generator_exist = os.access("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK) - if_pretrained_discriminator_exist = os.access("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), os.F_OK) - if (if_pretrained_generator_exist == False): - print("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") - if (if_pretrained_discriminator_exist == False): - print("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2), "not exist, will not use pretrained model") - return ( - ("pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_generator_exist else "", - ("pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)) if if_pretrained_discriminator_exist else "", - ) - - -def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15 - path_str = "" if version19 == "v1" else "_v2" - if_pretrained_generator_exist = os.access("pretrained%s/f0G%s.pth" % (path_str, sr2), os.F_OK) - if_pretrained_discriminator_exist = os.access("pretrained%s/f0D%s.pth" % (path_str, sr2), os.F_OK) - if (if_pretrained_generator_exist == False): - print("pretrained%s/f0G%s.pth" % (path_str, sr2), "not exist, will not use pretrained model") - if (if_pretrained_discriminator_exist == False): - print("pretrained%s/f0D%s.pth" % (path_str, sr2), "not exist, will not use pretrained model") - if if_f0_3: - return ( - {"visible": True, "__type__": "update"}, - "pretrained%s/f0G%s.pth" % (path_str, sr2) if if_pretrained_generator_exist else "", - "pretrained%s/f0D%s.pth" % (path_str, sr2) if if_pretrained_discriminator_exist else "", - ) - return ( - {"visible": False, "__type__": "update"}, - ("pretrained%s/G%s.pth" % (path_str, sr2)) if if_pretrained_generator_exist else "", - ("pretrained%s/D%s.pth" % (path_str, sr2)) if if_pretrained_discriminator_exist else "", - ) - - -global log_interval - - -def set_log_interval(exp_dir, batch_size12): - log_interval = 1 - - folder_path = os.path.join(exp_dir, "1_16k_wavs") - - if os.path.exists(folder_path) and os.path.isdir(folder_path): - wav_files = [f for f in os.listdir(folder_path) if f.endswith(".wav")] - if wav_files: - sample_size = len(wav_files) - log_interval = math.ceil(sample_size / batch_size12) - if log_interval > 1: - log_interval += 1 - return log_interval - -# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16]) -def click_train( - exp_dir1, - sr2, - if_f0_3, - spk_id5, - save_epoch10, - total_epoch11, - batch_size12, - if_save_latest13, - pretrained_G14, - pretrained_D15, - gpus16, - if_cache_gpu17, - if_save_every_weights18, - version19, -): - CSVutil('csvdb/stop.csv', 'w+', 'formanting', False) - # 生成filelist - exp_dir = "%s/logs/%s" % (now_dir, exp_dir1) - os.makedirs(exp_dir, exist_ok=True) - gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir) - feature_dir = ( - "%s/3_feature256" % (exp_dir) - if version19 == "v1" - else "%s/3_feature768" % (exp_dir) - ) - - log_interval = set_log_interval(exp_dir, batch_size12) - - if if_f0_3: - f0_dir = "%s/2a_f0" % (exp_dir) - f0nsf_dir = "%s/2b-f0nsf" % (exp_dir) - names = ( - set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) - & set([name.split(".")[0] for name in os.listdir(feature_dir)]) - & set([name.split(".")[0] for name in os.listdir(f0_dir)]) - & set([name.split(".")[0] for name in os.listdir(f0nsf_dir)]) - ) - else: - names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set( - [name.split(".")[0] for name in os.listdir(feature_dir)] - ) - opt = [] - for name in names: - if if_f0_3: - opt.append( - "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s" - % ( - gt_wavs_dir.replace("\\", "\\\\"), - name, - feature_dir.replace("\\", "\\\\"), - name, - f0_dir.replace("\\", "\\\\"), - name, - f0nsf_dir.replace("\\", "\\\\"), - name, - spk_id5, - ) - ) - else: - opt.append( - "%s/%s.wav|%s/%s.npy|%s" - % ( - gt_wavs_dir.replace("\\", "\\\\"), - name, - feature_dir.replace("\\", "\\\\"), - name, - spk_id5, - ) - ) - fea_dim = 256 if version19 == "v1" else 768 - if if_f0_3: - for _ in range(2): - opt.append( - "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s" - % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5) - ) - else: - for _ in range(2): - opt.append( - "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s" - % (now_dir, sr2, now_dir, fea_dim, spk_id5) - ) - shuffle(opt) - with open("%s/filelist.txt" % exp_dir, "w") as f: - f.write("\n".join(opt)) - print("write filelist done") - # 生成config#无需生成config - # cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0" - print("use gpus:", gpus16) - if pretrained_G14 == "": - print("no pretrained Generator") - if pretrained_D15 == "": - print("no pretrained Discriminator") - if gpus16: - cmd = ( - config.python_cmd - + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s -li %s" - % ( - exp_dir1, - sr2, - 1 if if_f0_3 else 0, - batch_size12, - gpus16, - total_epoch11, - save_epoch10, - ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "", - ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "", - 1 if if_save_latest13 == True else 0, - 1 if if_cache_gpu17 == True else 0, - 1 if if_save_every_weights18 == True else 0, - version19, - log_interval, - ) - ) - else: - cmd = ( - config.python_cmd - + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s -li %s" - % ( - exp_dir1, - sr2, - 1 if if_f0_3 else 0, - batch_size12, - total_epoch11, - save_epoch10, - ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "\b", - ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "\b", - 1 if if_save_latest13 == True else 0, - 1 if if_cache_gpu17 == True else 0, - 1 if if_save_every_weights18 == True else 0, - version19, - log_interval, - ) - ) - print(cmd) - p = Popen(cmd, shell=True, cwd=now_dir) - global PID - PID = p.pid - p.wait() - return ("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log", {"visible": False, "__type__": "update"}, {"visible": True, "__type__": "update"}) - - -# but4.click(train_index, [exp_dir1], info3) -def train_index(exp_dir1, version19): - exp_dir = "%s/logs/%s" % (now_dir, exp_dir1) - os.makedirs(exp_dir, exist_ok=True) - feature_dir = ( - "%s/3_feature256" % (exp_dir) - if version19 == "v1" - else "%s/3_feature768" % (exp_dir) - ) - if os.path.exists(feature_dir) == False: - return "请先进行特征提取!" - listdir_res = list(os.listdir(feature_dir)) - if len(listdir_res) == 0: - return "请先进行特征提取！" - npys = [] - for name in sorted(listdir_res): - phone = np.load("%s/%s" % (feature_dir, name)) - npys.append(phone) - big_npy = np.concatenate(npys, 0) - big_npy_idx = np.arange(big_npy.shape[0]) - np.random.shuffle(big_npy_idx) - big_npy = big_npy[big_npy_idx] - np.save("%s/total_fea.npy" % exp_dir, big_npy) - # n_ivf = big_npy.shape[0] // 39 - n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) - infos = [] - infos.append("%s,%s" % (big_npy.shape, n_ivf)) - yield "\n".join(infos) - index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf) - # index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf) - infos.append("training") - yield "\n".join(infos) - index_ivf = faiss.extract_index_ivf(index) # - index_ivf.nprobe = 1 - index.train(big_npy) - faiss.write_index( - index, - "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index" - % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), - ) - # faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19)) - infos.append("adding") - yield "\n".join(infos) - batch_size_add = 8192 - for i in range(0, big_npy.shape[0], batch_size_add): - index.add(big_npy[i : i + batch_size_add]) - faiss.write_index( - index, - "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" - % (exp_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), - ) - infos.append( - "成功构建索引，added_IVF%s_Flat_nprobe_%s_%s_%s.index" - % (n_ivf, index_ivf.nprobe, exp_dir1, version19) - ) - # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19)) - # infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19)) - yield "\n".join(infos) - - -# but5.click(train1key, [exp_dir1, sr2, if_f0_3, trainset_dir4, spk_id5, gpus6, np7, f0method8, save_epoch10, total_epoch11, batch_size12, if_save_latest13, pretrained_G14, pretrained_D15, gpus16, if_cache_gpu17], info3) -def train1key( - exp_dir1, - sr2, - if_f0_3, - trainset_dir4, - spk_id5, - np7, - f0method8, - save_epoch10, - total_epoch11, - batch_size12, - if_save_latest13, - pretrained_G14, - pretrained_D15, - gpus16, - if_cache_gpu17, - if_save_every_weights18, - version19, - echl -): - infos = [] - - def get_info_str(strr): - infos.append(strr) - return "\n".join(infos) - - model_log_dir = "%s/logs/%s" % (now_dir, exp_dir1) - preprocess_log_path = "%s/preprocess.log" % model_log_dir - extract_f0_feature_log_path = "%s/extract_f0_feature.log" % model_log_dir - gt_wavs_dir = "%s/0_gt_wavs" % model_log_dir - feature_dir = ( - "%s/3_feature256" % model_log_dir - if version19 == "v1" - else "%s/3_feature768" % model_log_dir - ) - - os.makedirs(model_log_dir, exist_ok=True) - #########step1:处理数据 - open(preprocess_log_path, "w").close() - cmd = ( - config.python_cmd - + " trainset_preprocess_pipeline_print.py %s %s %s %s " - % (trainset_dir4, sr_dict[sr2], np7, model_log_dir) - + str(config.noparallel) - ) - yield get_info_str(i18n("step1:正在处理数据")) - yield get_info_str(cmd) - p = Popen(cmd, shell=True) - p.wait() - with open(preprocess_log_path, "r") as f: - print(f.read()) - #########step2a:提取音高 - open(extract_f0_feature_log_path, "w") - if if_f0_3: - yield get_info_str("step2a:正在提取音高") - cmd = config.python_cmd + " extract_f0_print.py %s %s %s %s" % ( - model_log_dir, - np7, - f0method8, - echl - ) - yield get_info_str(cmd) - p = Popen(cmd, shell=True, cwd=now_dir) - p.wait() - with open(extract_f0_feature_log_path, "r") as f: - print(f.read()) - else: - yield get_info_str(i18n("step2a:无需提取音高")) - #######step2b:提取特征 - yield get_info_str(i18n("step2b:正在提取特征")) - gpus = gpus16.split("-") - leng = len(gpus) - ps = [] - for idx, n_g in enumerate(gpus): - cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s %s" % ( - config.device, - leng, - idx, - n_g, - model_log_dir, - version19, - ) - yield get_info_str(cmd) - p = Popen( - cmd, shell=True, cwd=now_dir - ) # , shell=True, stdin=PIPE, stdout=PIPE, stderr=PIPE, cwd=now_dir - ps.append(p) - for p in ps: - p.wait() - with open(extract_f0_feature_log_path, "r") as f: - print(f.read()) - #######step3a:训练模型 - yield get_info_str(i18n("step3a:正在训练模型")) - # 生成filelist - if if_f0_3: - f0_dir = "%s/2a_f0" % model_log_dir - f0nsf_dir = "%s/2b-f0nsf" % model_log_dir - names = ( - set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) - & set([name.split(".")[0] for name in os.listdir(feature_dir)]) - & set([name.split(".")[0] for name in os.listdir(f0_dir)]) - & set([name.split(".")[0] for name in os.listdir(f0nsf_dir)]) - ) - else: - names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set( - [name.split(".")[0] for name in os.listdir(feature_dir)] - ) - opt = [] - for name in names: - if if_f0_3: - opt.append( - "%s/%s.wav|%s/%s.npy|%s/%s.wav.npy|%s/%s.wav.npy|%s" - % ( - gt_wavs_dir.replace("\\", "\\\\"), - name, - feature_dir.replace("\\", "\\\\"), - name, - f0_dir.replace("\\", "\\\\"), - name, - f0nsf_dir.replace("\\", "\\\\"), - name, - spk_id5, - ) - ) - else: - opt.append( - "%s/%s.wav|%s/%s.npy|%s" - % ( - gt_wavs_dir.replace("\\", "\\\\"), - name, - feature_dir.replace("\\", "\\\\"), - name, - spk_id5, - ) - ) - fea_dim = 256 if version19 == "v1" else 768 - if if_f0_3: - for _ in range(2): - opt.append( - "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s" - % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5) - ) - else: - for _ in range(2): - opt.append( - "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s" - % (now_dir, sr2, now_dir, fea_dim, spk_id5) - ) - shuffle(opt) - with open("%s/filelist.txt" % model_log_dir, "w") as f: - f.write("\n".join(opt)) - yield get_info_str("write filelist done") - if gpus16: - cmd = ( - config.python_cmd - +" train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s" - % ( - exp_dir1, - sr2, - 1 if if_f0_3 else 0, - batch_size12, - gpus16, - total_epoch11, - save_epoch10, - ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "", - ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "", - 1 if if_save_latest13 == True else 0, - 1 if if_cache_gpu17 == True else 0, - 1 if if_save_every_weights18 == True else 0, - version19, - ) - ) - else: - cmd = ( - config.python_cmd - + " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s" - % ( - exp_dir1, - sr2, - 1 if if_f0_3 else 0, - batch_size12, - total_epoch11, - save_epoch10, - ("-pg %s" % pretrained_G14) if pretrained_G14 != "" else "", - ("-pd %s" % pretrained_D15) if pretrained_D15 != "" else "", - 1 if if_save_latest13 == True else 0, - 1 if if_cache_gpu17 == True else 0, - 1 if if_save_every_weights18 == True else 0, - version19, - ) - ) - yield get_info_str(cmd) - p = Popen(cmd, shell=True, cwd=now_dir) - p.wait() - yield get_info_str(i18n("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log")) - #######step3b:训练索引 - npys = [] - listdir_res = list(os.listdir(feature_dir)) - for name in sorted(listdir_res): - phone = np.load("%s/%s" % (feature_dir, name)) - npys.append(phone) - big_npy = np.concatenate(npys, 0) - - big_npy_idx = np.arange(big_npy.shape[0]) - np.random.shuffle(big_npy_idx) - big_npy = big_npy[big_npy_idx] - np.save("%s/total_fea.npy" % model_log_dir, big_npy) - - # n_ivf = big_npy.shape[0] // 39 - n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39) - yield get_info_str("%s,%s" % (big_npy.shape, n_ivf)) - index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf) - yield get_info_str("training index") - index_ivf = faiss.extract_index_ivf(index) # - index_ivf.nprobe = 1 - index.train(big_npy) - faiss.write_index( - index, - "%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index" - % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), - ) - yield get_info_str("adding index") - batch_size_add = 8192 - for i in range(0, big_npy.shape[0], batch_size_add): - index.add(big_npy[i : i + batch_size_add]) - faiss.write_index( - index, - "%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index" - % (model_log_dir, n_ivf, index_ivf.nprobe, exp_dir1, version19), - ) - yield get_info_str( - "成功构建索引, added_IVF%s_Flat_nprobe_%s_%s_%s.index" - % (n_ivf, index_ivf.nprobe, exp_dir1, version19) - ) - yield get_info_str(i18n("全流程结束！")) - - -def whethercrepeornah(radio): - mango = True if radio == 'mangio-crepe' or radio == 'mangio-crepe-tiny' else False - return ({"visible": mango, "__type__": "update"}) - -# ckpt_path2.change(change_info_,[ckpt_path2],[sr__,if_f0__]) -def change_info_(ckpt_path): - if ( - os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log")) - == False - ): - return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} - try: - with open( - ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r" - ) as f: - info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1]) - sr, f0 = info["sample_rate"], info["if_f0"] - version = "v2" if ("version" in info and info["version"] == "v2") else "v1" - return sr, str(f0), version - except: - traceback.print_exc() - return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} - - -from lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM - - -def export_onnx(ModelPath, ExportedPath, MoeVS=True): - cpt = torch.load(ModelPath, map_location="cpu") - cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk - hidden_channels = 256 if cpt.get("version","v1")=="v1"else 768#cpt["config"][-2] # hidden_channels，为768Vec做准备 - - test_phone = torch.rand(1, 200, hidden_channels) # hidden unit - test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用） - test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频（单位赫兹） - test_pitchf = torch.rand(1, 200) # nsf基频 - test_ds = torch.LongTensor([0]) # 说话人ID - test_rnd = torch.rand(1, 192, 200) # 噪声（加入随机因子） - - device = "cpu" # 导出时设备（不影响使用模型） - - - net_g = SynthesizerTrnMsNSFsidM( - *cpt["config"], is_half=False,version=cpt.get("version","v1") - ) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16） - net_g.load_state_dict(cpt["weight"], strict=False) - input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"] - output_names = [ - "audio", - ] - # net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出 - torch.onnx.export( - net_g, - ( - test_phone.to(device), - test_phone_lengths.to(device), - test_pitch.to(device), - test_pitchf.to(device), - test_ds.to(device), - test_rnd.to(device), - ), - ExportedPath, - dynamic_axes={ - "phone": [1], - "pitch": [1], - "pitchf": [1], - "rnd": [2], - }, - do_constant_folding=False, - opset_version=16, - verbose=False, - input_names=input_names, - output_names=output_names, - ) - return "Finished" - -#region RVC WebUI App - -def get_presets(): - data = None - with open('../inference-presets.json', 'r') as file: - data = json.load(file) - preset_names = [] - for preset in data['presets']: - preset_names.append(preset['name']) - - return preset_names - -def change_choices2(): - audio_files=[] - for filename in os.listdir("./audios"): - if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')): - audio_files.append(os.path.join('./audios',filename).replace('\\', '/')) - return {"choices": sorted(audio_files), "__type__": "update"}, {"__type__": "update"} - -audio_files=[] -for filename in os.listdir("./audios"): - if filename.endswith(('.wav','.mp3','.ogg','.flac','.m4a','.aac','.mp4')): - audio_files.append(os.path.join('./audios',filename).replace('\\', '/')) - -def get_index(): - if check_for_name() != '': - chosen_model=sorted(names)[0].split(".")[0] - logs_path="./logs/"+chosen_model - if os.path.exists(logs_path): - for file in os.listdir(logs_path): - if file.endswith(".index"): - return os.path.join(logs_path, file) - return '' - else: - return '' - -def get_indexes(): - indexes_list=[] - for dirpath, dirnames, filenames in os.walk("./logs/"): - for filename in filenames: - if filename.endswith(".index"): - indexes_list.append(os.path.join(dirpath,filename)) - if len(indexes_list) > 0: - return indexes_list - else: - return '' - -def get_name(): - if len(audio_files) > 0: - return sorted(audio_files)[0] - else: - return '' - -def save_to_wav(record_button): - if record_button is None: - pass - else: - path_to_file=record_button - new_name = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")+'.wav' - new_path='./audios/'+new_name - shutil.move(path_to_file,new_path) - return new_path - -def save_to_wav2(dropbox): - file_path=dropbox.name - shutil.move(file_path,'./audios') - return os.path.join('./audios',os.path.basename(file_path)) - -def match_index(sid0): - folder=sid0.split(".")[0] - parent_dir="./logs/"+folder - if os.path.exists(parent_dir): - for filename in os.listdir(parent_dir): - if filename.endswith(".index"): - index_path=os.path.join(parent_dir,filename) - return index_path - else: - return '' - -def check_for_name(): - if len(names) > 0: - return sorted(names)[0] - else: - return '' - -def download_from_url(url, model): - if url == '': - return "URL cannot be left empty." - if model =='': - return "You need to name your model. For example: My-Model" - url = url.strip() - zip_dirs = ["zips", "unzips"] - for directory in zip_dirs: - if os.path.exists(directory): - shutil.rmtree(directory) - os.makedirs("zips", exist_ok=True) - os.makedirs("unzips", exist_ok=True) - zipfile = model + '.zip' - zipfile_path = './zips/' + zipfile - try: - if "drive.google.com" in url: - subprocess.run(["gdown", url, "--fuzzy", "-O", zipfile_path]) - elif "mega.nz" in url: - m = Mega() - m.download_url(url, './zips') - else: - subprocess.run(["wget", url, "-O", zipfile_path]) - for filename in os.listdir("./zips"): - if filename.endswith(".zip"): - zipfile_path = os.path.join("./zips/",filename) - shutil.unpack_archive(zipfile_path, "./unzips", 'zip') - else: - return "No zipfile found." - for root, dirs, files in os.walk('./unzips'): - for file in files: - file_path = os.path.join(root, file) - if file.endswith(".index"): - os.mkdir(f'./logs/{model}') - shutil.copy2(file_path,f'./logs/{model}') - elif "G_" not in file and "D_" not in file and file.endswith(".pth"): - shutil.copy(file_path,f'./weights/{model}.pth') - shutil.rmtree("zips") - shutil.rmtree("unzips") - return "Success." - except: - return "There's been an error." -def success_message(face): - return f'{face.name} has been uploaded.', 'None' -def mouth(size, face, voice, faces): - if size == 'Half': - size = 2 - else: - size = 1 - if faces == 'None': - character = face.name - else: - if faces == 'Ben Shapiro': - character = '/content/wav2lip-HD/inputs/ben-shapiro-10.mp4' - elif faces == 'Andrew Tate': - character = '/content/wav2lip-HD/inputs/tate-7.mp4' - command = "python inference.py " \ - "--checkpoint_path checkpoints/wav2lip.pth " \ - f"--face {character} " \ - f"--audio {voice} " \ - "--pads 0 20 0 0 " \ - "--outfile /content/wav2lip-HD/outputs/result.mp4 " \ - "--fps 24 " \ - f"--resize_factor {size}" - process = subprocess.Popen(command, shell=True, cwd='/content/wav2lip-HD/Wav2Lip-master') - stdout, stderr = process.communicate() - return '/content/wav2lip-HD/outputs/result.mp4', 'Animation completed.' -eleven_voices = ['Adam','Antoni','Josh','Arnold','Sam','Bella','Rachel','Domi','Elli'] -eleven_voices_ids=['pNInz6obpgDQGcFmaJgB','ErXwobaYiN019PkySvjV','TxGEqnHWrfWFTfGW9XjX','VR6AewLTigWG4xSOukaG','yoZ06aMxZJJ28mfd3POQ','EXAVITQu4vr4xnSDxMaL','21m00Tcm4TlvDq8ikWAM','AZnzlk1XvdvUeBnXmlld','MF3mGyEYCl7XYWbV9V6O'] -chosen_voice = dict(zip(eleven_voices, eleven_voices_ids)) - -def stoptraining(mim): - if int(mim) == 1: - try: - CSVutil('csvdb/stop.csv', 'w+', 'stop', 'True') - os.kill(PID, signal.SIGTERM) - except Exception as e: - print(f"Couldn't click due to {e}") - return ( - {"visible": False, "__type__": "update"}, - {"visible": True, "__type__": "update"}, - ) - - -def elevenTTS(xiapi, text, id, lang): - if xiapi!= '' and id !='': - choice = chosen_voice[id] - CHUNK_SIZE = 1024 - url = f"https://api.elevenlabs.io/v1/text-to-speech/{choice}" - headers = { - "Accept": "audio/mpeg", - "Content-Type": "application/json", - "xi-api-key": xiapi - } - if lang == 'en': - data = { - "text": text, - "model_id": "eleven_monolingual_v1", - "voice_settings": { - "stability": 0.5, - "similarity_boost": 0.5 - } - } - else: - data = { - "text": text, - "model_id": "eleven_multilingual_v1", - "voice_settings": { - "stability": 0.5, - "similarity_boost": 0.5 - } - } - - response = requests.post(url, json=data, headers=headers) - with open('./temp_eleven.mp3', 'wb') as f: - for chunk in response.iter_content(chunk_size=CHUNK_SIZE): - if chunk: - f.write(chunk) - aud_path = save_to_wav('./temp_eleven.mp3') - return aud_path, aud_path - else: - tts = gTTS(text, lang=lang) - tts.save('./temp_gTTS.mp3') - aud_path = save_to_wav('./temp_gTTS.mp3') - return aud_path, aud_path - -def upload_to_dataset(files, dir): - if dir == '': - dir = './dataset' - if not os.path.exists(dir): - os.makedirs(dir) - count = 0 - for file in files: - path=file.name - shutil.copy2(path,dir) - count += 1 - return f' {count} files uploaded to {dir}.' - -def zip_downloader(model): - if not os.path.exists(f'./weights/{model}.pth'): - return {"__type__": "update"}, f'Make sure the Voice Name is correct. I could not find {model}.pth' - index_found = False - for file in os.listdir(f'./logs/{model}'): - if file.endswith('.index') and 'added' in file: - log_file = file - index_found = True - if index_found: - return [f'./weights/{model}.pth', f'./logs/{model}/{log_file}'], "Done" - else: - return f'./weights/{model}.pth', "Could not find Index file." - -with gr.Blocks(theme=gr.themes.Base(), title='Mangio-RVC-Web 💻') as app: - with gr.Tabs(): - with gr.TabItem("Интерфейс"): - gr.HTML("<h1> RVC V2 Huggingface Version </h1>") - gr.HTML("<h10> Huggingface версия созданная Clebersla </h10>") - gr.HTML("<h4> Если вы хотите использовать это помещение в частном порядке, я рекомендую продублировать его. </h4>") - - # Inference Preset Row - # with gr.Row(): - # mangio_preset = gr.Dropdown(label="Inference Preset", choices=sorted(get_presets())) - # mangio_preset_name_save = gr.Textbox( - # label="Your preset name" - # ) - # mangio_preset_save_btn = gr.Button('Save Preset', variant="primary") - - # Other RVC stuff - with gr.Row(): - sid0 = gr.Dropdown(label="1.Выберите свою модель.", choices=sorted(names), value=check_for_name()) - refresh_button = gr.Button("Обновить", variant="primary") - if check_for_name() != '': - get_vc(sorted(names)[0]) - vc_transform0 = gr.Number(label="Дополнительно: Здесь можно изменить высоту тона или оставить ее равной 0.", value=0) - #clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary") - spk_item = gr.Slider( - minimum=0, - maximum=2333, - step=1, - label=i18n("请选择说话人id"), - value=0, - visible=False, - interactive=True, - ) - #clean_button.click(fn=clean, inputs=[], outputs=[sid0]) - sid0.change( - fn=get_vc, - inputs=[sid0], - outputs=[spk_item], - ) - but0 = gr.Button("Конвертировать", variant="primary") - with gr.Row(): - with gr.Column(): - with gr.Row(): - dropbox = gr.File(label='Отправьте аудиозапись сюда и нажмите кнопку "Перезагрузка".') - with gr.Row(): - record_button=gr.Audio(source="microphone", label="Запись звука с микрофона", type="filepath") - with gr.Row(): - input_audio0 = gr.Dropdown( - label="2.Выберите аудиозапись.", - value="./audios/someguy.mp3", - choices=audio_files - ) - dropbox.upload(fn=save_to_wav2, inputs=[dropbox], outputs=[input_audio0]) - dropbox.upload(fn=change_choices2, inputs=[], outputs=[input_audio0]) - refresh_button2 = gr.Button("Обновить", variant="primary", size='sm') - record_button.change(fn=save_to_wav, inputs=[record_button], outputs=[input_audio0]) - record_button.change(fn=change_choices2, inputs=[], outputs=[input_audio0]) - with gr.Row(): - with gr.Accordion('Текст в речь', open=False): - with gr.Column(): - lang = gr.Radio(label='Китайский и японский языки в настоящее время не работают с ElevenLabs.',choices=['en','es','ru','uk','pl','fr','de','tr'], value='en') - api_box = gr.Textbox(label="Введите свой API-ключ для ElevenLabs или оставьте пустым, чтобы использовать GoogleTTS", value='', visible=False) - elevenid=gr.Dropdown(label="Голос:", choices=eleven_voices) - with gr.Column(): - tfs = gr.Textbox(label="Введите свой текст", interactive=True, value="This is a test.") - tts_button = gr.Button(value="Генерировать") - tts_button.click(fn=elevenTTS, inputs=[api_box,tfs, elevenid, lang], outputs=[record_button, input_audio0]) - with gr.Row(): - with gr.Accordion('Wav2Lip', open=False, visible=False): - with gr.Row(): - size = gr.Radio(label='Resolution:',choices=['Half','Full']) - face = gr.UploadButton("Upload A Character",type='file') - faces = gr.Dropdown(label="OR Choose one:", choices=['None','Ben Shapiro','Andrew Tate']) - with gr.Row(): - preview = gr.Textbox(label="Status:",interactive=False) - face.upload(fn=success_message,inputs=[face], outputs=[preview, faces]) - with gr.Row(): - animation = gr.Video(type='filepath') - refresh_button2.click(fn=change_choices2, inputs=[], outputs=[input_audio0, animation]) - with gr.Row(): - animate_button = gr.Button('Animate') - - with gr.Column(): - with gr.Accordion("Настройка индекса", open=False): - file_index1 = gr.Dropdown( - label="3. Путь к файлу added.index (если он не был найден автоматически).", - choices=get_indexes(), - value=get_index(), - interactive=True, - ) - sid0.change(fn=match_index, inputs=[sid0],outputs=[file_index1]) - refresh_button.click( - fn=change_choices, inputs=[], outputs=[sid0, file_index1] - ) - # file_big_npy1 = gr.Textbox( - # label=i18n("特征文件路径"), - # value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy", - # interactive=True, - # ) - index_rate1 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("Соотношение поисковых функций (советую ставить на 0):"), - value=0.66, - interactive=True, - ) - vc_output2 = gr.Audio( - label="Выходные аудиоданные (нажмите на три точки в правом углу, чтобы загрузить)", - type='filepath', - interactive=False, - ) - animate_button.click(fn=mouth, inputs=[size, face, vc_output2, faces], outputs=[animation, preview]) - with gr.Accordion("Дополнительные настройки", open=False): - f0method0 = gr.Radio( - label='Необязательно: Изменить алгоритм извлечения высоты тона.\Методы извлечения отсортированы от "худшего качества" к "лучшему качеству".\mangio-crepe может быть лучше rmvpe или нет в случаях, когда "гладкость" более важна, но в целом rmvpe является лучшим.', - choices=["pm", "dio", "crepe-tiny", "mangio-crepe-tiny", "crepe", "harvest", "mangio-crepe", "rmvpe"], # Fork Feature. Add Crepe-Tiny - value="rmvpe", - interactive=True, - ) - - crepe_hop_length = gr.Slider( - minimum=1, - maximum=512, - step=1, - label="Mangio-Crepe Hop Length. Более высокие числа уменьшат вероятность экстремального изменения высоты тона, но более низкие числа увеличат точность. 64-192 - хороший диапазон для экспериментов.", - value=120, - interactive=True, - visible=False, - ) - f0method0.change(fn=whethercrepeornah, inputs=[f0method0], outputs=[crepe_hop_length]) - filter_radius0 = gr.Slider( - minimum=0, - maximum=7, - label=i18n("Если >=3: применить медианную фильтрацию к собранным результатам питча. Значение представляет собой радиус фильтрации и может уменьшить дыхание."), - value=3, - step=1, - interactive=True, - ) - resample_sr0 = gr.Slider( - minimum=0, - maximum=48000, - label=i18n("后处理重采样至最终采样率，0为不进行重采样"), - value=0, - step=1, - interactive=True, - visible=False - ) - rms_mix_rate0 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("Используйте огибающую громкости входа для замены или смешивания с огибающей громкости выхода. Чем ближе это соотношение к 1, тем больше используется огибающая выходного сигнала:"), - value=0.21, - interactive=True, - ) - protect0 = gr.Slider( - minimum=0, - maximum=0.5, - label=i18n("Защита безголосых согласных и звуков дыхания для предотвращения артефактов, таких как разрывы в электронной музыке. Для отключения установите значение 0,5. Уменьшите значение для усиления защиты, но это может снизить точность индексирования:"), - value=0.33, - step=0.01, - interactive=True, - ) - formanting = gr.Checkbox( - value=bool(DoFormant), - label="[EXPERIMENTAL] Formant shift inference audio", - info="Used for male to female and vice-versa conversions", - interactive=True, - visible=False, - ) - - formant_preset = gr.Dropdown( - value='', - choices=get_fshift_presets(), - label="browse presets for formanting", - visible=bool(DoFormant), - ) - formant_refresh_button = gr.Button( - value='\U0001f504', - visible=bool(DoFormant), - variant='primary', - ) - #formant_refresh_button = ToolButton( elem_id='1') - #create_refresh_button(formant_preset, lambda: {"choices": formant_preset}, "refresh_list_shiftpresets") - - qfrency = gr.Slider( - value=Quefrency, - info="Default value is 1.0", - label="Quefrency for formant shifting", - minimum=0.0, - maximum=16.0, - step=0.1, - visible=bool(DoFormant), - interactive=True, - ) - tmbre = gr.Slider( - value=Timbre, - info="Default value is 1.0", - label="Timbre for formant shifting", - minimum=0.0, - maximum=16.0, - step=0.1, - visible=bool(DoFormant), - interactive=True, - ) - - formant_preset.change(fn=preset_apply, inputs=[formant_preset, qfrency, tmbre], outputs=[qfrency, tmbre]) - frmntbut = gr.Button("Apply", variant="primary", visible=bool(DoFormant)) - formanting.change(fn=formant_enabled,inputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button],outputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button]) - frmntbut.click(fn=formant_apply,inputs=[qfrency, tmbre], outputs=[qfrency, tmbre]) - formant_refresh_button.click(fn=update_fshift_presets,inputs=[formant_preset, qfrency, tmbre],outputs=[formant_preset, qfrency, tmbre]) - with gr.Row(): - vc_output1 = gr.Textbox("") - f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"), visible=False) - - but0.click( - vc_single, - [ - spk_item, - input_audio0, - vc_transform0, - f0_file, - f0method0, - file_index1, - # file_index2, - # file_big_npy1, - index_rate1, - filter_radius0, - resample_sr0, - rms_mix_rate0, - protect0, - crepe_hop_length - ], - [vc_output1, vc_output2], - ) - - with gr.Accordion("Batch Conversion",open=False, visible=False): - with gr.Row(): - with gr.Column(): - vc_transform1 = gr.Number( - label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 - ) - opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt") - f0method1 = gr.Radio( - label=i18n( - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU" - ), - choices=["pm", "harvest", "crepe", "rmvpe"], - value="rmvpe", - interactive=True, - ) - filter_radius1 = gr.Slider( - minimum=0, - maximum=7, - label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"), - value=3, - step=1, - interactive=True, - ) - with gr.Column(): - file_index3 = gr.Textbox( - label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), - value="", - interactive=True, - ) - file_index4 = gr.Dropdown( - label=i18n("自动检测index路径,下拉式选择(dropdown)"), - choices=sorted(index_paths), - interactive=True, - ) - refresh_button.click( - fn=lambda: change_choices()[1], - inputs=[], - outputs=file_index4, - ) - # file_big_npy2 = gr.Textbox( - # label=i18n("特征文件路径"), - # value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy", - # interactive=True, - # ) - index_rate2 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("检索特征占比"), - value=1, - interactive=True, - ) - with gr.Column(): - resample_sr1 = gr.Slider( - minimum=0, - maximum=48000, - label=i18n("后处理重采样至最终采样率，0为不进行重采样"), - value=0, - step=1, - interactive=True, - ) - rms_mix_rate1 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"), - value=1, - interactive=True, - ) - protect1 = gr.Slider( - minimum=0, - maximum=0.5, - label=i18n( - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果" - ), - value=0.33, - step=0.01, - interactive=True, - ) - with gr.Column(): - dir_input = gr.Textbox( - label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"), - value="E:\codes\py39\\test-20230416b\\todo-songs", - ) - inputs = gr.File( - file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹") - ) - with gr.Row(): - format1 = gr.Radio( - label=i18n("导出文件格式"), - choices=["wav", "flac", "mp3", "m4a"], - value="flac", - interactive=True, - ) - but1 = gr.Button(i18n("转换"), variant="primary") - vc_output3 = gr.Textbox(label=i18n("输出信息")) - but1.click( - vc_multi, - [ - spk_item, - dir_input, - opt_input, - inputs, - vc_transform1, - f0method1, - file_index3, - file_index4, - # file_big_npy2, - index_rate2, - filter_radius1, - resample_sr1, - rms_mix_rate1, - protect1, - format1, - crepe_hop_length, - ], - [vc_output3], - ) - but1.click(fn=lambda: easy_uploader.clear()) - with gr.TabItem("Загрузка моделей"): - with gr.Row(): - url=gr.Textbox(label="Введите URL-адрес модели:") - with gr.Row(): - model = gr.Textbox(label="Название модели:") - download_button=gr.Button("Загрузить") - with gr.Row(): - status_bar=gr.Textbox(label="") - download_button.click(fn=download_from_url, inputs=[url, model], outputs=[status_bar]) - with gr.Row(): - gr.Markdown( - """ - Made with ❤️ by [Alice Oliveira](https://github.com/aliceoq) | Hosted with ❤️ by [Mateus Elias](https://github.com/mateuseap) - """ - ) - - def has_two_files_in_pretrained_folder(): - pretrained_folder = "./pretrained/" - if not os.path.exists(pretrained_folder): - return False - - files_in_folder = os.listdir(pretrained_folder) - num_files = len(files_in_folder) - return num_files >= 2 - - if has_two_files_in_pretrained_folder(): - print("Pretrained weights are downloaded. Training tab enabled!\n-------------------------------") - with gr.TabItem("Train", visible=False): - with gr.Row(): - with gr.Column(): - exp_dir1 = gr.Textbox(label="Voice Name:", value="My-Voice") - sr2 = gr.Radio( - label=i18n("目标采样率"), - choices=["40k", "48k"], - value="40k", - interactive=True, - visible=False - ) - if_f0_3 = gr.Radio( - label=i18n("模型是否带音高指导(唱歌一定要, 语音可以不要)"), - choices=[True, False], - value=True, - interactive=True, - visible=False - ) - version19 = gr.Radio( - label="RVC version", - choices=["v1", "v2"], - value="v2", - interactive=True, - visible=False, - ) - np7 = gr.Slider( - minimum=0, - maximum=config.n_cpu, - step=1, - label="# of CPUs for data processing (Leave as it is)", - value=config.n_cpu, - interactive=True, - visible=True - ) - trainset_dir4 = gr.Textbox(label="Path to your dataset (audios, not zip):", value="./dataset") - easy_uploader = gr.Files(label='OR Drop your audios here. They will be uploaded in your dataset path above.',file_types=['audio']) - but1 = gr.Button("1. Process The Dataset", variant="primary") - info1 = gr.Textbox(label="Status (wait until it says 'end preprocess'):", value="") - easy_uploader.upload(fn=upload_to_dataset, inputs=[easy_uploader, trainset_dir4], outputs=[info1]) - but1.click( - preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1] - ) - with gr.Column(): - spk_id5 = gr.Slider( - minimum=0, - maximum=4, - step=1, - label=i18n("请指定说话人id"), - value=0, - interactive=True, - visible=False - ) - with gr.Accordion('GPU Settings', open=False, visible=False): - gpus6 = gr.Textbox( - label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), - value=gpus, - interactive=True, - visible=False - ) - gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info) - f0method8 = gr.Radio( - label=i18n( - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢" - ), - choices=["harvest","crepe", "mangio-crepe", "rmvpe"], # Fork feature: Crepe on f0 extraction for training. - value="rmvpe", - interactive=True, - ) - - extraction_crepe_hop_length = gr.Slider( - minimum=1, - maximum=512, - step=1, - label=i18n("crepe_hop_length"), - value=128, - interactive=True, - visible=False, - ) - f0method8.change(fn=whethercrepeornah, inputs=[f0method8], outputs=[extraction_crepe_hop_length]) - but2 = gr.Button("2. Pitch Extraction", variant="primary") - info2 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", value="", max_lines=8) - but2.click( - extract_f0_feature, - [gpus6, np7, f0method8, if_f0_3, exp_dir1, version19, extraction_crepe_hop_length], - [info2], - ) - with gr.Row(): - with gr.Column(): - total_epoch11 = gr.Slider( - minimum=1, - maximum=5000, - step=10, - label="Total # of training epochs (IF you choose a value too high, your model will sound horribly overtrained.):", - value=250, - interactive=True, - ) - butstop = gr.Button( - "Stop Training", - variant='primary', - visible=False, - ) - but3 = gr.Button("3. Train Model", variant="primary", visible=False) - - but3.click(fn=stoptraining, inputs=[gr.Number(value=0, visible=False)], outputs=[but3, butstop]) - butstop.click(fn=stoptraining, inputs=[gr.Number(value=1, visible=False)], outputs=[butstop, but3]) - - - but4 = gr.Button("4.Train Index", variant="primary") - info3 = gr.Textbox(label="Status(Check the Colab Notebook's cell output):", value="", max_lines=10) - with gr.Accordion("Training Preferences (You can leave these as they are)", open=False): - #gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引")) - with gr.Column(): - save_epoch10 = gr.Slider( - minimum=1, - maximum=200, - step=1, - label="Backup every X amount of epochs:", - value=10, - interactive=True, - ) - batch_size12 = gr.Slider( - minimum=1, - maximum=40, - step=1, - label="Batch Size (LEAVE IT unless you know what you're doing!):", - value=default_batch_size, - interactive=True, - ) - if_save_latest13 = gr.Checkbox( - label="Save only the latest '.ckpt' file to save disk space.", - value=True, - interactive=True, - ) - if_cache_gpu17 = gr.Checkbox( - label="Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training, but caching large datasets will consume a lot of GPU memory and may not provide much speed improvement.", - value=False, - interactive=True, - ) - if_save_every_weights18 = gr.Checkbox( - label="Save a small final model to the 'weights' folder at each save point.", - value=True, - interactive=True, - ) - zip_model = gr.Button('5. Download Model') - zipped_model = gr.Files(label='Your Model and Index file can be downloaded here:') - zip_model.click(fn=zip_downloader, inputs=[exp_dir1], outputs=[zipped_model, info3]) - with gr.Group(): - with gr.Accordion("Base Model Locations:", open=False, visible=False): - pretrained_G14 = gr.Textbox( - label=i18n("加载预训练底模G路径"), - value="pretrained_v2/f0G40k.pth", - interactive=True, - ) - pretrained_D15 = gr.Textbox( - label=i18n("加载预训练底模D路径"), - value="pretrained_v2/f0D40k.pth", - interactive=True, - ) - gpus16 = gr.Textbox( - label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), - value=gpus, - interactive=True, - ) - sr2.change( - change_sr2, - [sr2, if_f0_3, version19], - [pretrained_G14, pretrained_D15, version19], - ) - version19.change( - change_version19, - [sr2, if_f0_3, version19], - [pretrained_G14, pretrained_D15], - ) - if_f0_3.change( - change_f0, - [if_f0_3, sr2, version19], - [f0method8, pretrained_G14, pretrained_D15], - ) - but5 = gr.Button(i18n("一键训练"), variant="primary", visible=False) - but3.click( - click_train, - [ - exp_dir1, - sr2, - if_f0_3, - spk_id5, - save_epoch10, - total_epoch11, - batch_size12, - if_save_latest13, - pretrained_G14, - pretrained_D15, - gpus16, - if_cache_gpu17, - if_save_every_weights18, - version19, - ], - [ - info3, - butstop, - but3, - ], - ) - but4.click(train_index, [exp_dir1, version19], info3) - but5.click( - train1key, - [ - exp_dir1, - sr2, - if_f0_3, - trainset_dir4, - spk_id5, - np7, - f0method8, - save_epoch10, - total_epoch11, - batch_size12, - if_save_latest13, - pretrained_G14, - pretrained_D15, - gpus16, - if_cache_gpu17, - if_save_every_weights18, - version19, - extraction_crepe_hop_length - ], - info3, - ) - - else: - print( - "Pretrained weights not downloaded. Disabling training tab.\n" - "Wondering how to train a voice? Visit here for the RVC model training guide: https://t.ly/RVC_Training_Guide\n" - "-------------------------------\n" - ) - - app.queue(concurrency_count=511, max_size=1022).launch(share=False, quiet=True) -#endregion \ No newline at end of file diff --git a/spaces/RO4DHOG/Ripper/README.md b/spaces/RO4DHOG/Ripper/README.md deleted file mode 100644 index ceab6d16bb80f5881dbf3264cac359176125928e..0000000000000000000000000000000000000000 --- a/spaces/RO4DHOG/Ripper/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Ripper -emoji: 🚀 -colorFrom: purple -colorTo: indigo -sdk: gradio -sdk_version: 3.44.3 -app_file: app.py -pinned: false -license: cc ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/RamAnanth1/videocrafter/extralibs/midas/midas/blocks.py b/spaces/RamAnanth1/videocrafter/extralibs/midas/midas/blocks.py deleted file mode 100644 index 2145d18fa98060a618536d9a64fe6589e9be4f78..0000000000000000000000000000000000000000 --- a/spaces/RamAnanth1/videocrafter/extralibs/midas/midas/blocks.py +++ /dev/null @@ -1,342 +0,0 @@ -import torch -import torch.nn as nn - -from .vit import ( - _make_pretrained_vitb_rn50_384, - _make_pretrained_vitl16_384, - _make_pretrained_vitb16_384, - forward_vit, -) - -def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",): - if backbone == "vitl16_384": - pretrained = _make_pretrained_vitl16_384( - use_pretrained, hooks=hooks, use_readout=use_readout - ) - scratch = _make_scratch( - [256, 512, 1024, 1024], features, groups=groups, expand=expand - ) # ViT-L/16 - 85.0% Top1 (backbone) - elif backbone == "vitb_rn50_384": - pretrained = _make_pretrained_vitb_rn50_384( - use_pretrained, - hooks=hooks, - use_vit_only=use_vit_only, - use_readout=use_readout, - ) - scratch = _make_scratch( - [256, 512, 768, 768], features, groups=groups, expand=expand - ) # ViT-H/16 - 85.0% Top1 (backbone) - elif backbone == "vitb16_384": - pretrained = _make_pretrained_vitb16_384( - use_pretrained, hooks=hooks, use_readout=use_readout - ) - scratch = _make_scratch( - [96, 192, 384, 768], features, groups=groups, expand=expand - ) # ViT-B/16 - 84.6% Top1 (backbone) - elif backbone == "resnext101_wsl": - pretrained = _make_pretrained_resnext101_wsl(use_pretrained) - scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand) # efficientnet_lite3 - elif backbone == "efficientnet_lite3": - pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable) - scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand) # efficientnet_lite3 - else: - print(f"Backbone '{backbone}' not implemented") - assert False - - return pretrained, scratch - - -def _make_scratch(in_shape, out_shape, groups=1, expand=False): - scratch = nn.Module() - - out_shape1 = out_shape - out_shape2 = out_shape - out_shape3 = out_shape - out_shape4 = out_shape - if expand==True: - out_shape1 = out_shape - out_shape2 = out_shape*2 - out_shape3 = out_shape*4 - out_shape4 = out_shape*8 - - scratch.layer1_rn = nn.Conv2d( - in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - scratch.layer2_rn = nn.Conv2d( - in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - scratch.layer3_rn = nn.Conv2d( - in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - scratch.layer4_rn = nn.Conv2d( - in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups - ) - - return scratch - - -def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False): - efficientnet = torch.hub.load( - "rwightman/gen-efficientnet-pytorch", - "tf_efficientnet_lite3", - pretrained=use_pretrained, - exportable=exportable - ) - return _make_efficientnet_backbone(efficientnet) - - -def _make_efficientnet_backbone(effnet): - pretrained = nn.Module() - - pretrained.layer1 = nn.Sequential( - effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2] - ) - pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3]) - pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5]) - pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9]) - - return pretrained - - -def _make_resnet_backbone(resnet): - pretrained = nn.Module() - pretrained.layer1 = nn.Sequential( - resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1 - ) - - pretrained.layer2 = resnet.layer2 - pretrained.layer3 = resnet.layer3 - pretrained.layer4 = resnet.layer4 - - return pretrained - - -def _make_pretrained_resnext101_wsl(use_pretrained): - resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl") - return _make_resnet_backbone(resnet) - - - -class Interpolate(nn.Module): - """Interpolation module. - """ - - def __init__(self, scale_factor, mode, align_corners=False): - """Init. - - Args: - scale_factor (float): scaling - mode (str): interpolation mode - """ - super(Interpolate, self).__init__() - - self.interp = nn.functional.interpolate - self.scale_factor = scale_factor - self.mode = mode - self.align_corners = align_corners - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input - - Returns: - tensor: interpolated data - """ - - x = self.interp( - x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners - ) - - return x - - -class ResidualConvUnit(nn.Module): - """Residual convolution module. - """ - - def __init__(self, features): - """Init. - - Args: - features (int): number of features - """ - super().__init__() - - self.conv1 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True - ) - - self.conv2 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True - ) - - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input - - Returns: - tensor: output - """ - out = self.relu(x) - out = self.conv1(out) - out = self.relu(out) - out = self.conv2(out) - - return out + x - - -class FeatureFusionBlock(nn.Module): - """Feature fusion block. - """ - - def __init__(self, features): - """Init. - - Args: - features (int): number of features - """ - super(FeatureFusionBlock, self).__init__() - - self.resConfUnit1 = ResidualConvUnit(features) - self.resConfUnit2 = ResidualConvUnit(features) - - def forward(self, *xs): - """Forward pass. - - Returns: - tensor: output - """ - output = xs[0] - - if len(xs) == 2: - output += self.resConfUnit1(xs[1]) - - output = self.resConfUnit2(output) - - output = nn.functional.interpolate( - output, scale_factor=2, mode="bilinear", align_corners=True - ) - - return output - - - - -class ResidualConvUnit_custom(nn.Module): - """Residual convolution module. - """ - - def __init__(self, features, activation, bn): - """Init. - - Args: - features (int): number of features - """ - super().__init__() - - self.bn = bn - - self.groups=1 - - self.conv1 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups - ) - - self.conv2 = nn.Conv2d( - features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups - ) - - if self.bn==True: - self.bn1 = nn.BatchNorm2d(features) - self.bn2 = nn.BatchNorm2d(features) - - self.activation = activation - - self.skip_add = nn.quantized.FloatFunctional() - - def forward(self, x): - """Forward pass. - - Args: - x (tensor): input - - Returns: - tensor: output - """ - - out = self.activation(x) - out = self.conv1(out) - if self.bn==True: - out = self.bn1(out) - - out = self.activation(out) - out = self.conv2(out) - if self.bn==True: - out = self.bn2(out) - - if self.groups > 1: - out = self.conv_merge(out) - - return self.skip_add.add(out, x) - - # return out + x - - -class FeatureFusionBlock_custom(nn.Module): - """Feature fusion block. - """ - - def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True): - """Init. - - Args: - features (int): number of features - """ - super(FeatureFusionBlock_custom, self).__init__() - - self.deconv = deconv - self.align_corners = align_corners - - self.groups=1 - - self.expand = expand - out_features = features - if self.expand==True: - out_features = features//2 - - self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1) - - self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn) - self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn) - - self.skip_add = nn.quantized.FloatFunctional() - - def forward(self, *xs): - """Forward pass. - - Returns: - tensor: output - """ - output = xs[0] - - if len(xs) == 2: - res = self.resConfUnit1(xs[1]) - output = self.skip_add.add(output, res) - # output += res - - output = self.resConfUnit2(output) - - output = nn.functional.interpolate( - output, scale_factor=2, mode="bilinear", align_corners=self.align_corners - ) - - output = self.out_conv(output) - - return output - diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/requirements.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/requirements.py deleted file mode 100644 index f561f1f1e270666ccd74c9d61f78c9c24f5c4c99..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/resolution/resolvelib/requirements.py +++ /dev/null @@ -1,166 +0,0 @@ -from pip._vendor.packaging.specifiers import SpecifierSet -from pip._vendor.packaging.utils import NormalizedName, canonicalize_name - -from pip._internal.req.req_install import InstallRequirement - -from .base import Candidate, CandidateLookup, Requirement, format_name - - -class ExplicitRequirement(Requirement): - def __init__(self, candidate: Candidate) -> None: - self.candidate = candidate - - def __str__(self) -> str: - return str(self.candidate) - - def __repr__(self) -> str: - return "{class_name}({candidate!r})".format( - class_name=self.__class__.__name__, - candidate=self.candidate, - ) - - @property - def project_name(self) -> NormalizedName: - # No need to canonicalize - the candidate did this - return self.candidate.project_name - - @property - def name(self) -> str: - # No need to canonicalize - the candidate did this - return self.candidate.name - - def format_for_error(self) -> str: - return self.candidate.format_for_error() - - def get_candidate_lookup(self) -> CandidateLookup: - return self.candidate, None - - def is_satisfied_by(self, candidate: Candidate) -> bool: - return candidate == self.candidate - - -class SpecifierRequirement(Requirement): - def __init__(self, ireq: InstallRequirement) -> None: - assert ireq.link is None, "This is a link, not a specifier" - self._ireq = ireq - self._extras = frozenset(ireq.extras) - - def __str__(self) -> str: - return str(self._ireq.req) - - def __repr__(self) -> str: - return "{class_name}({requirement!r})".format( - class_name=self.__class__.__name__, - requirement=str(self._ireq.req), - ) - - @property - def project_name(self) -> NormalizedName: - assert self._ireq.req, "Specifier-backed ireq is always PEP 508" - return canonicalize_name(self._ireq.req.name) - - @property - def name(self) -> str: - return format_name(self.project_name, self._extras) - - def format_for_error(self) -> str: - - # Convert comma-separated specifiers into "A, B, ..., F and G" - # This makes the specifier a bit more "human readable", without - # risking a change in meaning. (Hopefully! Not all edge cases have - # been checked) - parts = [s.strip() for s in str(self).split(",")] - if len(parts) == 0: - return "" - elif len(parts) == 1: - return parts[0] - - return ", ".join(parts[:-1]) + " and " + parts[-1] - - def get_candidate_lookup(self) -> CandidateLookup: - return None, self._ireq - - def is_satisfied_by(self, candidate: Candidate) -> bool: - assert candidate.name == self.name, ( - f"Internal issue: Candidate is not for this requirement " - f"{candidate.name} vs {self.name}" - ) - # We can safely always allow prereleases here since PackageFinder - # already implements the prerelease logic, and would have filtered out - # prerelease candidates if the user does not expect them. - assert self._ireq.req, "Specifier-backed ireq is always PEP 508" - spec = self._ireq.req.specifier - return spec.contains(candidate.version, prereleases=True) - - -class RequiresPythonRequirement(Requirement): - """A requirement representing Requires-Python metadata.""" - - def __init__(self, specifier: SpecifierSet, match: Candidate) -> None: - self.specifier = specifier - self._candidate = match - - def __str__(self) -> str: - return f"Python {self.specifier}" - - def __repr__(self) -> str: - return "{class_name}({specifier!r})".format( - class_name=self.__class__.__name__, - specifier=str(self.specifier), - ) - - @property - def project_name(self) -> NormalizedName: - return self._candidate.project_name - - @property - def name(self) -> str: - return self._candidate.name - - def format_for_error(self) -> str: - return str(self) - - def get_candidate_lookup(self) -> CandidateLookup: - if self.specifier.contains(self._candidate.version, prereleases=True): - return self._candidate, None - return None, None - - def is_satisfied_by(self, candidate: Candidate) -> bool: - assert candidate.name == self._candidate.name, "Not Python candidate" - # We can safely always allow prereleases here since PackageFinder - # already implements the prerelease logic, and would have filtered out - # prerelease candidates if the user does not expect them. - return self.specifier.contains(candidate.version, prereleases=True) - - -class UnsatisfiableRequirement(Requirement): - """A requirement that cannot be satisfied.""" - - def __init__(self, name: NormalizedName) -> None: - self._name = name - - def __str__(self) -> str: - return f"{self._name} (unavailable)" - - def __repr__(self) -> str: - return "{class_name}({name!r})".format( - class_name=self.__class__.__name__, - name=str(self._name), - ) - - @property - def project_name(self) -> NormalizedName: - return self._name - - @property - def name(self) -> str: - return self._name - - def format_for_error(self) -> str: - return str(self) - - def get_candidate_lookup(self) -> CandidateLookup: - return None, None - - def is_satisfied_by(self, candidate: Candidate) -> bool: - return False diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/msgpack/exceptions.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/msgpack/exceptions.py deleted file mode 100644 index d6d2615cfdd0b914d064cdf7eecd45761e4bcaf6..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/msgpack/exceptions.py +++ /dev/null @@ -1,48 +0,0 @@ -class UnpackException(Exception): - """Base class for some exceptions raised while unpacking. - - NOTE: unpack may raise exception other than subclass of - UnpackException. If you want to catch all error, catch - Exception instead. - """ - - -class BufferFull(UnpackException): - pass - - -class OutOfData(UnpackException): - pass - - -class FormatError(ValueError, UnpackException): - """Invalid msgpack format""" - - -class StackError(ValueError, UnpackException): - """Too nested""" - - -# Deprecated. Use ValueError instead -UnpackValueError = ValueError - - -class ExtraData(UnpackValueError): - """ExtraData is raised when there is trailing data. - - This exception is raised while only one-shot (not streaming) - unpack. - """ - - def __init__(self, unpacked, extra): - self.unpacked = unpacked - self.extra = extra - - def __str__(self): - return "unpack(b) received extra data." - - -# Deprecated. Use Exception instead to catch all exception during packing. -PackException = Exception -PackValueError = ValueError -PackOverflowError = OverflowError diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/table.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/table.py deleted file mode 100644 index 8fc28ef2f74cb25498314ddda14156921b6b6804..0000000000000000000000000000000000000000 --- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/table.py +++ /dev/null @@ -1,996 +0,0 @@ -from dataclasses import dataclass, field, replace -from typing import ( - TYPE_CHECKING, - Dict, - Iterable, - List, - NamedTuple, - Optional, - Sequence, - Tuple, - Union, -) - -from . import box, errors -from ._loop import loop_first_last, loop_last -from ._pick import pick_bool -from ._ratio import ratio_distribute, ratio_reduce -from .align import VerticalAlignMethod -from .jupyter import JupyterMixin -from .measure import Measurement -from .padding import Padding, PaddingDimensions -from .protocol import is_renderable -from .segment import Segment -from .style import Style, StyleType -from .text import Text, TextType - -if TYPE_CHECKING: - from .console import ( - Console, - ConsoleOptions, - JustifyMethod, - OverflowMethod, - RenderableType, - RenderResult, - ) - - -@dataclass -class Column: - """Defines a column within a ~Table. - - Args: - title (Union[str, Text], optional): The title of the table rendered at the top. Defaults to None. - caption (Union[str, Text], optional): The table caption rendered below. Defaults to None. - width (int, optional): The width in characters of the table, or ``None`` to automatically fit. Defaults to None. - min_width (Optional[int], optional): The minimum width of the table, or ``None`` for no minimum. Defaults to None. - box (box.Box, optional): One of the constants in box.py used to draw the edges (see :ref:`appendix_box`), or ``None`` for no box lines. Defaults to box.HEAVY_HEAD. - safe_box (Optional[bool], optional): Disable box characters that don't display on windows legacy terminal with *raster* fonts. Defaults to True. - padding (PaddingDimensions, optional): Padding for cells (top, right, bottom, left). Defaults to (0, 1). - collapse_padding (bool, optional): Enable collapsing of padding around cells. Defaults to False. - pad_edge (bool, optional): Enable padding of edge cells. Defaults to True. - expand (bool, optional): Expand the table to fit the available space if ``True``, otherwise the table width will be auto-calculated. Defaults to False. - show_header (bool, optional): Show a header row. Defaults to True. - show_footer (bool, optional): Show a footer row. Defaults to False. - show_edge (bool, optional): Draw a box around the outside of the table. Defaults to True. - show_lines (bool, optional): Draw lines between every row. Defaults to False. - leading (bool, optional): Number of blank lines between rows (precludes ``show_lines``). Defaults to 0. - style (Union[str, Style], optional): Default style for the table. Defaults to "none". - row_styles (List[Union, str], optional): Optional list of row styles, if more than one style is given then the styles will alternate. Defaults to None. - header_style (Union[str, Style], optional): Style of the header. Defaults to "table.header". - footer_style (Union[str, Style], optional): Style of the footer. Defaults to "table.footer". - border_style (Union[str, Style], optional): Style of the border. Defaults to None. - title_style (Union[str, Style], optional): Style of the title. Defaults to None. - caption_style (Union[str, Style], optional): Style of the caption. Defaults to None. - title_justify (str, optional): Justify method for title. Defaults to "center". - caption_justify (str, optional): Justify method for caption. Defaults to "center". - highlight (bool, optional): Highlight cell contents (if str). Defaults to False. - """ - - header: "RenderableType" = "" - """RenderableType: Renderable for the header (typically a string)""" - - footer: "RenderableType" = "" - """RenderableType: Renderable for the footer (typically a string)""" - - header_style: StyleType = "" - """StyleType: The style of the header.""" - - footer_style: StyleType = "" - """StyleType: The style of the footer.""" - - style: StyleType = "" - """StyleType: The style of the column.""" - - justify: "JustifyMethod" = "left" - """str: How to justify text within the column ("left", "center", "right", or "full")""" - - vertical: "VerticalAlignMethod" = "top" - """str: How to vertically align content ("top", "middle", or "bottom")""" - - overflow: "OverflowMethod" = "ellipsis" - """str: Overflow method.""" - - width: Optional[int] = None - """Optional[int]: Width of the column, or ``None`` (default) to auto calculate width.""" - - min_width: Optional[int] = None - """Optional[int]: Minimum width of column, or ``None`` for no minimum. Defaults to None.""" - - max_width: Optional[int] = None - """Optional[int]: Maximum width of column, or ``None`` for no maximum. Defaults to None.""" - - ratio: Optional[int] = None - """Optional[int]: Ratio to use when calculating column width, or ``None`` (default) to adapt to column contents.""" - - no_wrap: bool = False - """bool: Prevent wrapping of text within the column. Defaults to ``False``.""" - - _index: int = 0 - """Index of column.""" - - _cells: List["RenderableType"] = field(default_factory=list) - - def copy(self) -> "Column": - """Return a copy of this Column.""" - return replace(self, _cells=[]) - - @property - def cells(self) -> Iterable["RenderableType"]: - """Get all cells in the column, not including header.""" - yield from self._cells - - @property - def flexible(self) -> bool: - """Check if this column is flexible.""" - return self.ratio is not None - - -@dataclass -class Row: - """Information regarding a row.""" - - style: Optional[StyleType] = None - """Style to apply to row.""" - - end_section: bool = False - """Indicated end of section, which will force a line beneath the row.""" - - -class _Cell(NamedTuple): - """A single cell in a table.""" - - style: StyleType - """Style to apply to cell.""" - renderable: "RenderableType" - """Cell renderable.""" - vertical: VerticalAlignMethod - """Cell vertical alignment.""" - - -class Table(JupyterMixin): - """A console renderable to draw a table. - - Args: - *headers (Union[Column, str]): Column headers, either as a string, or :class:`~rich.table.Column` instance. - title (Union[str, Text], optional): The title of the table rendered at the top. Defaults to None. - caption (Union[str, Text], optional): The table caption rendered below. Defaults to None. - width (int, optional): The width in characters of the table, or ``None`` to automatically fit. Defaults to None. - min_width (Optional[int], optional): The minimum width of the table, or ``None`` for no minimum. Defaults to None. - box (box.Box, optional): One of the constants in box.py used to draw the edges (see :ref:`appendix_box`), or ``None`` for no box lines. Defaults to box.HEAVY_HEAD. - safe_box (Optional[bool], optional): Disable box characters that don't display on windows legacy terminal with *raster* fonts. Defaults to True. - padding (PaddingDimensions, optional): Padding for cells (top, right, bottom, left). Defaults to (0, 1). - collapse_padding (bool, optional): Enable collapsing of padding around cells. Defaults to False. - pad_edge (bool, optional): Enable padding of edge cells. Defaults to True. - expand (bool, optional): Expand the table to fit the available space if ``True``, otherwise the table width will be auto-calculated. Defaults to False. - show_header (bool, optional): Show a header row. Defaults to True. - show_footer (bool, optional): Show a footer row. Defaults to False. - show_edge (bool, optional): Draw a box around the outside of the table. Defaults to True. - show_lines (bool, optional): Draw lines between every row. Defaults to False. - leading (bool, optional): Number of blank lines between rows (precludes ``show_lines``). Defaults to 0. - style (Union[str, Style], optional): Default style for the table. Defaults to "none". - row_styles (List[Union, str], optional): Optional list of row styles, if more than one style is given then the styles will alternate. Defaults to None. - header_style (Union[str, Style], optional): Style of the header. Defaults to "table.header". - footer_style (Union[str, Style], optional): Style of the footer. Defaults to "table.footer". - border_style (Union[str, Style], optional): Style of the border. Defaults to None. - title_style (Union[str, Style], optional): Style of the title. Defaults to None. - caption_style (Union[str, Style], optional): Style of the caption. Defaults to None. - title_justify (str, optional): Justify method for title. Defaults to "center". - caption_justify (str, optional): Justify method for caption. Defaults to "center". - highlight (bool, optional): Highlight cell contents (if str). Defaults to False. - """ - - columns: List[Column] - rows: List[Row] - - def __init__( - self, - *headers: Union[Column, str], - title: Optional[TextType] = None, - caption: Optional[TextType] = None, - width: Optional[int] = None, - min_width: Optional[int] = None, - box: Optional[box.Box] = box.HEAVY_HEAD, - safe_box: Optional[bool] = None, - padding: PaddingDimensions = (0, 1), - collapse_padding: bool = False, - pad_edge: bool = True, - expand: bool = False, - show_header: bool = True, - show_footer: bool = False, - show_edge: bool = True, - show_lines: bool = False, - leading: int = 0, - style: StyleType = "none", - row_styles: Optional[Iterable[StyleType]] = None, - header_style: Optional[StyleType] = "table.header", - footer_style: Optional[StyleType] = "table.footer", - border_style: Optional[StyleType] = None, - title_style: Optional[StyleType] = None, - caption_style: Optional[StyleType] = None, - title_justify: "JustifyMethod" = "center", - caption_justify: "JustifyMethod" = "center", - highlight: bool = False, - ) -> None: - - self.columns: List[Column] = [] - self.rows: List[Row] = [] - self.title = title - self.caption = caption - self.width = width - self.min_width = min_width - self.box = box - self.safe_box = safe_box - self._padding = Padding.unpack(padding) - self.pad_edge = pad_edge - self._expand = expand - self.show_header = show_header - self.show_footer = show_footer - self.show_edge = show_edge - self.show_lines = show_lines - self.leading = leading - self.collapse_padding = collapse_padding - self.style = style - self.header_style = header_style or "" - self.footer_style = footer_style or "" - self.border_style = border_style - self.title_style = title_style - self.caption_style = caption_style - self.title_justify: "JustifyMethod" = title_justify - self.caption_justify: "JustifyMethod" = caption_justify - self.highlight = highlight - self.row_styles: Sequence[StyleType] = list(row_styles or []) - append_column = self.columns.append - for header in headers: - if isinstance(header, str): - self.add_column(header=header) - else: - header._index = len(self.columns) - append_column(header) - - @classmethod - def grid( - cls, - *headers: Union[Column, str], - padding: PaddingDimensions = 0, - collapse_padding: bool = True, - pad_edge: bool = False, - expand: bool = False, - ) -> "Table": - """Get a table with no lines, headers, or footer. - - Args: - *headers (Union[Column, str]): Column headers, either as a string, or :class:`~rich.table.Column` instance. - padding (PaddingDimensions, optional): Get padding around cells. Defaults to 0. - collapse_padding (bool, optional): Enable collapsing of padding around cells. Defaults to True. - pad_edge (bool, optional): Enable padding around edges of table. Defaults to False. - expand (bool, optional): Expand the table to fit the available space if ``True``, otherwise the table width will be auto-calculated. Defaults to False. - - Returns: - Table: A table instance. - """ - return cls( - *headers, - box=None, - padding=padding, - collapse_padding=collapse_padding, - show_header=False, - show_footer=False, - show_edge=False, - pad_edge=pad_edge, - expand=expand, - ) - - @property - def expand(self) -> bool: - """Setting a non-None self.width implies expand.""" - return self._expand or self.width is not None - - @expand.setter - def expand(self, expand: bool) -> None: - """Set expand.""" - self._expand = expand - - @property - def _extra_width(self) -> int: - """Get extra width to add to cell content.""" - width = 0 - if self.box and self.show_edge: - width += 2 - if self.box: - width += len(self.columns) - 1 - return width - - @property - def row_count(self) -> int: - """Get the current number of rows.""" - return len(self.rows) - - def get_row_style(self, console: "Console", index: int) -> StyleType: - """Get the current row style.""" - style = Style.null() - if self.row_styles: - style += console.get_style(self.row_styles[index % len(self.row_styles)]) - row_style = self.rows[index].style - if row_style is not None: - style += console.get_style(row_style) - return style - - def __rich_measure__( - self, console: "Console", options: "ConsoleOptions" - ) -> Measurement: - max_width = options.max_width - if self.width is not None: - max_width = self.width - if max_width < 0: - return Measurement(0, 0) - - extra_width = self._extra_width - max_width = sum( - self._calculate_column_widths( - console, options.update_width(max_width - extra_width) - ) - ) - _measure_column = self._measure_column - - measurements = [ - _measure_column(console, options.update_width(max_width), column) - for column in self.columns - ] - minimum_width = ( - sum(measurement.minimum for measurement in measurements) + extra_width - ) - maximum_width = ( - sum(measurement.maximum for measurement in measurements) + extra_width - if (self.width is None) - else self.width - ) - measurement = Measurement(minimum_width, maximum_width) - measurement = measurement.clamp(self.min_width) - return measurement - - @property - def padding(self) -> Tuple[int, int, int, int]: - """Get cell padding.""" - return self._padding - - @padding.setter - def padding(self, padding: PaddingDimensions) -> "Table": - """Set cell padding.""" - self._padding = Padding.unpack(padding) - return self - - def add_column( - self, - header: "RenderableType" = "", - footer: "RenderableType" = "", - *, - header_style: Optional[StyleType] = None, - footer_style: Optional[StyleType] = None, - style: Optional[StyleType] = None, - justify: "JustifyMethod" = "left", - vertical: "VerticalAlignMethod" = "top", - overflow: "OverflowMethod" = "ellipsis", - width: Optional[int] = None, - min_width: Optional[int] = None, - max_width: Optional[int] = None, - ratio: Optional[int] = None, - no_wrap: bool = False, - ) -> None: - """Add a column to the table. - - Args: - header (RenderableType, optional): Text or renderable for the header. - Defaults to "". - footer (RenderableType, optional): Text or renderable for the footer. - Defaults to "". - header_style (Union[str, Style], optional): Style for the header, or None for default. Defaults to None. - footer_style (Union[str, Style], optional): Style for the footer, or None for default. Defaults to None. - style (Union[str, Style], optional): Style for the column cells, or None for default. Defaults to None. - justify (JustifyMethod, optional): Alignment for cells. Defaults to "left". - vertical (VerticalAlignMethod, optional): Vertical alignment, one of "top", "middle", or "bottom". Defaults to "top". - overflow (OverflowMethod): Overflow method: "crop", "fold", "ellipsis". Defaults to "ellipsis". - width (int, optional): Desired width of column in characters, or None to fit to contents. Defaults to None. - min_width (Optional[int], optional): Minimum width of column, or ``None`` for no minimum. Defaults to None. - max_width (Optional[int], optional): Maximum width of column, or ``None`` for no maximum. Defaults to None. - ratio (int, optional): Flexible ratio for the column (requires ``Table.expand`` or ``Table.width``). Defaults to None. - no_wrap (bool, optional): Set to ``True`` to disable wrapping of this column. - """ - - column = Column( - _index=len(self.columns), - header=header, - footer=footer, - header_style=header_style or "", - footer_style=footer_style or "", - style=style or "", - justify=justify, - vertical=vertical, - overflow=overflow, - width=width, - min_width=min_width, - max_width=max_width, - ratio=ratio, - no_wrap=no_wrap, - ) - self.columns.append(column) - - def add_row( - self, - *renderables: Optional["RenderableType"], - style: Optional[StyleType] = None, - end_section: bool = False, - ) -> None: - """Add a row of renderables. - - Args: - *renderables (None or renderable): Each cell in a row must be a renderable object (including str), - or ``None`` for a blank cell. - style (StyleType, optional): An optional style to apply to the entire row. Defaults to None. - end_section (bool, optional): End a section and draw a line. Defaults to False. - - Raises: - errors.NotRenderableError: If you add something that can't be rendered. - """ - - def add_cell(column: Column, renderable: "RenderableType") -> None: - column._cells.append(renderable) - - cell_renderables: List[Optional["RenderableType"]] = list(renderables) - - columns = self.columns - if len(cell_renderables) < len(columns): - cell_renderables = [ - *cell_renderables, - *[None] * (len(columns) - len(cell_renderables)), - ] - for index, renderable in enumerate(cell_renderables): - if index == len(columns): - column = Column(_index=index) - for _ in self.rows: - add_cell(column, Text("")) - self.columns.append(column) - else: - column = columns[index] - if renderable is None: - add_cell(column, "") - elif is_renderable(renderable): - add_cell(column, renderable) - else: - raise errors.NotRenderableError( - f"unable to render {type(renderable).__name__}; a string or other renderable object is required" - ) - self.rows.append(Row(style=style, end_section=end_section)) - - def __rich_console__( - self, console: "Console", options: "ConsoleOptions" - ) -> "RenderResult": - - if not self.columns: - yield Segment("\n") - return - - max_width = options.max_width - if self.width is not None: - max_width = self.width - - extra_width = self._extra_width - widths = self._calculate_column_widths( - console, options.update_width(max_width - extra_width) - ) - table_width = sum(widths) + extra_width - - render_options = options.update( - width=table_width, highlight=self.highlight, height=None - ) - - def render_annotation( - text: TextType, style: StyleType, justify: "JustifyMethod" = "center" - ) -> "RenderResult": - render_text = ( - console.render_str(text, style=style, highlight=False) - if isinstance(text, str) - else text - ) - return console.render( - render_text, options=render_options.update(justify=justify) - ) - - if self.title: - yield from render_annotation( - self.title, - style=Style.pick_first(self.title_style, "table.title"), - justify=self.title_justify, - ) - yield from self._render(console, render_options, widths) - if self.caption: - yield from render_annotation( - self.caption, - style=Style.pick_first(self.caption_style, "table.caption"), - justify=self.caption_justify, - ) - - def _calculate_column_widths( - self, console: "Console", options: "ConsoleOptions" - ) -> List[int]: - """Calculate the widths of each column, including padding, not including borders.""" - max_width = options.max_width - columns = self.columns - width_ranges = [ - self._measure_column(console, options, column) for column in columns - ] - widths = [_range.maximum or 1 for _range in width_ranges] - get_padding_width = self._get_padding_width - extra_width = self._extra_width - if self.expand: - ratios = [col.ratio or 0 for col in columns if col.flexible] - if any(ratios): - fixed_widths = [ - 0 if column.flexible else _range.maximum - for _range, column in zip(width_ranges, columns) - ] - flex_minimum = [ - (column.width or 1) + get_padding_width(column._index) - for column in columns - if column.flexible - ] - flexible_width = max_width - sum(fixed_widths) - flex_widths = ratio_distribute(flexible_width, ratios, flex_minimum) - iter_flex_widths = iter(flex_widths) - for index, column in enumerate(columns): - if column.flexible: - widths[index] = fixed_widths[index] + next(iter_flex_widths) - table_width = sum(widths) - - if table_width > max_width: - widths = self._collapse_widths( - widths, - [(column.width is None and not column.no_wrap) for column in columns], - max_width, - ) - table_width = sum(widths) - # last resort, reduce columns evenly - if table_width > max_width: - excess_width = table_width - max_width - widths = ratio_reduce(excess_width, [1] * len(widths), widths, widths) - table_width = sum(widths) - - width_ranges = [ - self._measure_column(console, options.update_width(width), column) - for width, column in zip(widths, columns) - ] - widths = [_range.maximum or 0 for _range in width_ranges] - - if (table_width < max_width and self.expand) or ( - self.min_width is not None and table_width < (self.min_width - extra_width) - ): - _max_width = ( - max_width - if self.min_width is None - else min(self.min_width - extra_width, max_width) - ) - pad_widths = ratio_distribute(_max_width - table_width, widths) - widths = [_width + pad for _width, pad in zip(widths, pad_widths)] - - return widths - - @classmethod - def _collapse_widths( - cls, widths: List[int], wrapable: List[bool], max_width: int - ) -> List[int]: - """Reduce widths so that the total is under max_width. - - Args: - widths (List[int]): List of widths. - wrapable (List[bool]): List of booleans that indicate if a column may shrink. - max_width (int): Maximum width to reduce to. - - Returns: - List[int]: A new list of widths. - """ - total_width = sum(widths) - excess_width = total_width - max_width - if any(wrapable): - while total_width and excess_width > 0: - max_column = max( - width for width, allow_wrap in zip(widths, wrapable) if allow_wrap - ) - second_max_column = max( - width if allow_wrap and width != max_column else 0 - for width, allow_wrap in zip(widths, wrapable) - ) - column_difference = max_column - second_max_column - ratios = [ - (1 if (width == max_column and allow_wrap) else 0) - for width, allow_wrap in zip(widths, wrapable) - ] - if not any(ratios) or not column_difference: - break - max_reduce = [min(excess_width, column_difference)] * len(widths) - widths = ratio_reduce(excess_width, ratios, max_reduce, widths) - - total_width = sum(widths) - excess_width = total_width - max_width - return widths - - def _get_cells( - self, console: "Console", column_index: int, column: Column - ) -> Iterable[_Cell]: - """Get all the cells with padding and optional header.""" - - collapse_padding = self.collapse_padding - pad_edge = self.pad_edge - padding = self.padding - any_padding = any(padding) - - first_column = column_index == 0 - last_column = column_index == len(self.columns) - 1 - - _padding_cache: Dict[Tuple[bool, bool], Tuple[int, int, int, int]] = {} - - def get_padding(first_row: bool, last_row: bool) -> Tuple[int, int, int, int]: - cached = _padding_cache.get((first_row, last_row)) - if cached: - return cached - top, right, bottom, left = padding - - if collapse_padding: - if not first_column: - left = max(0, left - right) - if not last_row: - bottom = max(0, top - bottom) - - if not pad_edge: - if first_column: - left = 0 - if last_column: - right = 0 - if first_row: - top = 0 - if last_row: - bottom = 0 - _padding = (top, right, bottom, left) - _padding_cache[(first_row, last_row)] = _padding - return _padding - - raw_cells: List[Tuple[StyleType, "RenderableType"]] = [] - _append = raw_cells.append - get_style = console.get_style - if self.show_header: - header_style = get_style(self.header_style or "") + get_style( - column.header_style - ) - _append((header_style, column.header)) - cell_style = get_style(column.style or "") - for cell in column.cells: - _append((cell_style, cell)) - if self.show_footer: - footer_style = get_style(self.footer_style or "") + get_style( - column.footer_style - ) - _append((footer_style, column.footer)) - - if any_padding: - _Padding = Padding - for first, last, (style, renderable) in loop_first_last(raw_cells): - yield _Cell( - style, - _Padding(renderable, get_padding(first, last)), - getattr(renderable, "vertical", None) or column.vertical, - ) - else: - for (style, renderable) in raw_cells: - yield _Cell( - style, - renderable, - getattr(renderable, "vertical", None) or column.vertical, - ) - - def _get_padding_width(self, column_index: int) -> int: - """Get extra width from padding.""" - _, pad_right, _, pad_left = self.padding - if self.collapse_padding: - if column_index > 0: - pad_left = max(0, pad_left - pad_right) - return pad_left + pad_right - - def _measure_column( - self, - console: "Console", - options: "ConsoleOptions", - column: Column, - ) -> Measurement: - """Get the minimum and maximum width of the column.""" - - max_width = options.max_width - if max_width < 1: - return Measurement(0, 0) - - padding_width = self._get_padding_width(column._index) - - if column.width is not None: - # Fixed width column - return Measurement( - column.width + padding_width, column.width + padding_width - ).with_maximum(max_width) - # Flexible column, we need to measure contents - min_widths: List[int] = [] - max_widths: List[int] = [] - append_min = min_widths.append - append_max = max_widths.append - get_render_width = Measurement.get - for cell in self._get_cells(console, column._index, column): - _min, _max = get_render_width(console, options, cell.renderable) - append_min(_min) - append_max(_max) - - measurement = Measurement( - max(min_widths) if min_widths else 1, - max(max_widths) if max_widths else max_width, - ).with_maximum(max_width) - measurement = measurement.clamp( - None if column.min_width is None else column.min_width + padding_width, - None if column.max_width is None else column.max_width + padding_width, - ) - return measurement - - def _render( - self, console: "Console", options: "ConsoleOptions", widths: List[int] - ) -> "RenderResult": - table_style = console.get_style(self.style or "") - - border_style = table_style + console.get_style(self.border_style or "") - _column_cells = ( - self._get_cells(console, column_index, column) - for column_index, column in enumerate(self.columns) - ) - row_cells: List[Tuple[_Cell, ...]] = list(zip(*_column_cells)) - _box = ( - self.box.substitute( - options, safe=pick_bool(self.safe_box, console.safe_box) - ) - if self.box - else None - ) - _box = _box.get_plain_headed_box() if _box and not self.show_header else _box - - new_line = Segment.line() - - columns = self.columns - show_header = self.show_header - show_footer = self.show_footer - show_edge = self.show_edge - show_lines = self.show_lines - leading = self.leading - - _Segment = Segment - if _box: - box_segments = [ - ( - _Segment(_box.head_left, border_style), - _Segment(_box.head_right, border_style), - _Segment(_box.head_vertical, border_style), - ), - ( - _Segment(_box.foot_left, border_style), - _Segment(_box.foot_right, border_style), - _Segment(_box.foot_vertical, border_style), - ), - ( - _Segment(_box.mid_left, border_style), - _Segment(_box.mid_right, border_style), - _Segment(_box.mid_vertical, border_style), - ), - ] - if show_edge: - yield _Segment(_box.get_top(widths), border_style) - yield new_line - else: - box_segments = [] - - get_row_style = self.get_row_style - get_style = console.get_style - - for index, (first, last, row_cell) in enumerate(loop_first_last(row_cells)): - header_row = first and show_header - footer_row = last and show_footer - row = ( - self.rows[index - show_header] - if (not header_row and not footer_row) - else None - ) - max_height = 1 - cells: List[List[List[Segment]]] = [] - if header_row or footer_row: - row_style = Style.null() - else: - row_style = get_style( - get_row_style(console, index - 1 if show_header else index) - ) - for width, cell, column in zip(widths, row_cell, columns): - render_options = options.update( - width=width, - justify=column.justify, - no_wrap=column.no_wrap, - overflow=column.overflow, - height=None, - ) - lines = console.render_lines( - cell.renderable, - render_options, - style=get_style(cell.style) + row_style, - ) - max_height = max(max_height, len(lines)) - cells.append(lines) - - row_height = max(len(cell) for cell in cells) - - def align_cell( - cell: List[List[Segment]], - vertical: "VerticalAlignMethod", - width: int, - style: Style, - ) -> List[List[Segment]]: - if header_row: - vertical = "bottom" - elif footer_row: - vertical = "top" - - if vertical == "top": - return _Segment.align_top(cell, width, row_height, style) - elif vertical == "middle": - return _Segment.align_middle(cell, width, row_height, style) - return _Segment.align_bottom(cell, width, row_height, style) - - cells[:] = [ - _Segment.set_shape( - align_cell( - cell, - _cell.vertical, - width, - get_style(_cell.style) + row_style, - ), - width, - max_height, - ) - for width, _cell, cell, column in zip(widths, row_cell, cells, columns) - ] - - if _box: - if last and show_footer: - yield _Segment( - _box.get_row(widths, "foot", edge=show_edge), border_style - ) - yield new_line - left, right, _divider = box_segments[0 if first else (2 if last else 1)] - - # If the column divider is whitespace also style it with the row background - divider = ( - _divider - if _divider.text.strip() - else _Segment( - _divider.text, row_style.background_style + _divider.style - ) - ) - for line_no in range(max_height): - if show_edge: - yield left - for last_cell, rendered_cell in loop_last(cells): - yield from rendered_cell[line_no] - if not last_cell: - yield divider - if show_edge: - yield right - yield new_line - else: - for line_no in range(max_height): - for rendered_cell in cells: - yield from rendered_cell[line_no] - yield new_line - if _box and first and show_header: - yield _Segment( - _box.get_row(widths, "head", edge=show_edge), border_style - ) - yield new_line - end_section = row and row.end_section - if _box and (show_lines or leading or end_section): - if ( - not last - and not (show_footer and index >= len(row_cells) - 2) - and not (show_header and header_row) - ): - if leading: - yield _Segment( - _box.get_row(widths, "mid", edge=show_edge) * leading, - border_style, - ) - else: - yield _Segment( - _box.get_row(widths, "row", edge=show_edge), border_style - ) - yield new_line - - if _box and show_edge: - yield _Segment(_box.get_bottom(widths), border_style) - yield new_line - - -if __name__ == "__main__": # pragma: no cover - from pip._vendor.rich.console import Console - from pip._vendor.rich.highlighter import ReprHighlighter - from pip._vendor.rich.table import Table as Table - - from ._timer import timer - - with timer("Table render"): - table = Table( - title="Star Wars Movies", - caption="Rich example table", - caption_justify="right", - ) - - table.add_column( - "Released", header_style="bright_cyan", style="cyan", no_wrap=True - ) - table.add_column("Title", style="magenta") - table.add_column("Box Office", justify="right", style="green") - - table.add_row( - "Dec 20, 2019", - "Star Wars: The Rise of Skywalker", - "$952,110,690", - ) - table.add_row("May 25, 2018", "Solo: A Star Wars Story", "$393,151,347") - table.add_row( - "Dec 15, 2017", - "Star Wars Ep. V111: The Last Jedi", - "$1,332,539,889", - style="on black", - end_section=True, - ) - table.add_row( - "Dec 16, 2016", - "Rogue One: A Star Wars Story", - "$1,332,439,889", - ) - - def header(text: str) -> None: - console.print() - console.rule(highlight(text)) - console.print() - - console = Console() - highlight = ReprHighlighter() - header("Example Table") - console.print(table, justify="center") - - table.expand = True - header("expand=True") - console.print(table) - - table.width = 50 - header("width=50") - - console.print(table, justify="center") - - table.width = None - table.expand = False - table.row_styles = ["dim", "none"] - header("row_styles=['dim', 'none']") - - console.print(table, justify="center") - - table.width = None - table.expand = False - table.row_styles = ["dim", "none"] - table.leading = 1 - header("leading=1, row_styles=['dim', 'none']") - console.print(table, justify="center") - - table.width = None - table.expand = False - table.row_styles = ["dim", "none"] - table.show_lines = True - table.leading = 0 - header("show_lines=True, row_styles=['dim', 'none']") - console.print(table, justify="center") diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/contrib/__init__.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/contrib/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Realcat/image-matching-webui/third_party/Roma/roma/utils/local_correlation.py b/spaces/Realcat/image-matching-webui/third_party/Roma/roma/utils/local_correlation.py deleted file mode 100644 index 603ab524333c29fbc284a73065847645f3100847..0000000000000000000000000000000000000000 --- a/spaces/Realcat/image-matching-webui/third_party/Roma/roma/utils/local_correlation.py +++ /dev/null @@ -1,68 +0,0 @@ -import torch -import torch.nn.functional as F - -device = "cuda" if torch.cuda.is_available() else "cpu" - - -def local_correlation( - feature0, - feature1, - local_radius, - padding_mode="zeros", - flow=None, - sample_mode="bilinear", -): - r = local_radius - K = (2 * r + 1) ** 2 - B, c, h, w = feature0.size() - feature0 = feature0.half() - feature1 = feature1.half() - corr = torch.empty((B, K, h, w), device=feature0.device, dtype=feature0.dtype) - if flow is None: - # If flow is None, assume feature0 and feature1 are aligned - coords = torch.meshgrid( - ( - torch.linspace(-1 + 1 / h, 1 - 1 / h, h, device=device), - torch.linspace(-1 + 1 / w, 1 - 1 / w, w, device=device), - ) - ) - coords = torch.stack((coords[1], coords[0]), dim=-1)[None].expand(B, h, w, 2) - else: - coords = flow.permute(0, 2, 3, 1) # If using flow, sample around flow target. - local_window = torch.meshgrid( - ( - torch.linspace( - -2 * local_radius / h, 2 * local_radius / h, 2 * r + 1, device=device - ), - torch.linspace( - -2 * local_radius / w, 2 * local_radius / w, 2 * r + 1, device=device - ), - ) - ) - local_window = ( - torch.stack((local_window[1], local_window[0]), dim=-1)[None] - .expand(1, 2 * r + 1, 2 * r + 1, 2) - .reshape(1, (2 * r + 1) ** 2, 2) - ) - for _ in range(B): - with torch.no_grad(): - local_window_coords = ( - (coords[_, :, :, None] + local_window[:, None, None]) - .reshape(1, h, w * (2 * r + 1) ** 2, 2) - .float() - ) - window_feature = F.grid_sample( - feature1[_ : _ + 1].float(), - local_window_coords, - padding_mode=padding_mode, - align_corners=False, - mode=sample_mode, # - ) - window_feature = window_feature.reshape(c, h, w, (2 * r + 1) ** 2) - corr[_] = ( - (feature0[_, ..., None] / (c**0.5) * window_feature) - .sum(dim=0) - .permute(2, 0, 1) - ) - torch.cuda.empty_cache() - return corr diff --git a/spaces/Ritvik19/SudokuNet/sudokunet.py b/spaces/Ritvik19/SudokuNet/sudokunet.py deleted file mode 100644 index 5bfd8b97cb6549b8225371f10e1fe3d09d91faf7..0000000000000000000000000000000000000000 --- a/spaces/Ritvik19/SudokuNet/sudokunet.py +++ /dev/null @@ -1,55 +0,0 @@ -from huggingface_hub import from_pretrained_keras -from tensorflow.keras.utils import to_categorical -import numpy as np -import streamlit as st - -class SudokuSolver: - """Utility class for the pipeline which solves the Sudoku Puzzles - To solve Sudoku Puzzles - - initialize a Sudoku Solver object - - create an array of dimension (n, 9, 9) - where n is the number of puzzles you want to solve - also, replace the blank items with a zero. - - then, just call the sudoku solver object on the array - - Args: - model_name (str): THe name of the model to be used - - """ - - def __init__(self, model_name): - self.model = self.load_model(model_name) - - @st.cache(allow_output_mutation=True) - def load_model(self, model_name): - return from_pretrained_keras(model_name) - - def __call__(self, grids): - """This function solves quizzes. - It will fill blanks one after the other. Each time a digit is filled, - the new grid will be fed again to the solver to predict the next digit. - again and again, until there is no more blank - - Args: - grids (np.array), shape (?, 9, 9): Batch of quizzes to solve - - Returns: - grids (np.array), shape (?, 9, 9): Solved quizzes. - """ - grids = grids.copy() - for _ in range((grids == 0).sum((1, 2)).max()): - preds = np.array(self.model.predict(to_categorical(grids))) - probs = preds.max(2).T - values = preds.argmax(2).T + 1 - zeros = (grids == 0).reshape((grids.shape[0], 81)) - - for grid, prob, value, zero in zip(grids, probs, values, zeros): - if any(zero): - where = np.where(zero)[0] - confidence_position = where[prob[zero].argmax()] - confidence_value = value[confidence_position] - grid.flat[confidence_position] = confidence_value - return grids \ No newline at end of file diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/htc.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/htc.py deleted file mode 100644 index d9efdf420fa7373f7f1d116f8d97836d73b457bf..0000000000000000000000000000000000000000 --- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/detectors/htc.py +++ /dev/null @@ -1,15 +0,0 @@ -from ..builder import DETECTORS -from .cascade_rcnn import CascadeRCNN - - -@DETECTORS.register_module() -class HybridTaskCascade(CascadeRCNN): - """Implementation of `HTC <https://arxiv.org/abs/1901.07518>`_""" - - def __init__(self, **kwargs): - super(HybridTaskCascade, self).__init__(**kwargs) - - @property - def with_semantic(self): - """bool: whether the detector has a semantic head""" - return self.roi_head.with_semantic diff --git a/spaces/Rominn/vits-uma-genshin-honkai/app.py b/spaces/Rominn/vits-uma-genshin-honkai/app.py deleted file mode 100644 index 226a0aaef0aeb880b1d3d52cc9c88d65eb867c06..0000000000000000000000000000000000000000 --- a/spaces/Rominn/vits-uma-genshin-honkai/app.py +++ /dev/null @@ -1,123 +0,0 @@ -# coding=utf-8 -import time -import gradio as gr -import utils -import commons -from models import SynthesizerTrn -from text import text_to_sequence -from torch import no_grad, LongTensor - -hps_ms = utils.get_hparams_from_file(r'./model/config.json') -net_g_ms = SynthesizerTrn( - len(hps_ms.symbols), - hps_ms.data.filter_length // 2 + 1, - hps_ms.train.segment_size // hps_ms.data.hop_length, - n_speakers=hps_ms.data.n_speakers, - **hps_ms.model) -_ = net_g_ms.eval() -speakers = hps_ms.speakers -model, optimizer, learning_rate, epochs = utils.load_checkpoint(r'./model/G_953000.pth', net_g_ms, None) - -def get_text(text, hps): - text_norm, clean_text = text_to_sequence(text, hps.symbols, hps.data.text_cleaners) - if hps.data.add_blank: - text_norm = commons.intersperse(text_norm, 0) - text_norm = LongTensor(text_norm) - return text_norm, clean_text - -def vits(text, language, speaker_id, noise_scale, noise_scale_w, length_scale): - start = time.perf_counter() - if not len(text): - return "输入文本不能为空！", None, None - text = text.replace('\n', ' ').replace('\r', '').replace(" ", "") - if len(text) > 100: - return f"输入文字过长！{len(text)}>100", None, None - if language == 0: - text = f"[ZH]{text}[ZH]" - elif language == 1: - text = f"[JA]{text}[JA]" - else: - text = f"{text}" - stn_tst, clean_text = get_text(text, hps_ms) - with no_grad(): - x_tst = stn_tst.unsqueeze(0) - x_tst_lengths = LongTensor([stn_tst.size(0)]) - speaker_id = LongTensor([speaker_id]) - audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=speaker_id, noise_scale=noise_scale, noise_scale_w=noise_scale_w, - length_scale=length_scale)[0][0, 0].data.float().numpy() - - return "生成成功!", (22050, audio), f"生成耗时 {round(time.perf_counter()-start, 2)} s" - -def search_speaker(search_value): - for s in speakers: - if search_value == s: - return s - for s in speakers: - if search_value in s: - return s - -def change_lang(language): - if language == 0: - return 0.6, 0.668, 1.2 - else: - return 0.6, 0.668, 1.1 - -download_audio_js = """ -() =>{{ - let root = document.querySelector("body > gradio-app"); - if (root.shadowRoot != null) - root = root.shadowRoot; - let audio = root.querySelector("#tts-audio").querySelector("audio"); - let text = root.querySelector("#input-text").querySelector("textarea"); - if (audio == undefined) - return; - text = text.value; - if (text == undefined) - text = Math.floor(Math.random()*100000000); - audio = audio.src; - let oA = document.createElement("a"); - oA.download = text.substr(0, 20)+'.wav'; - oA.href = audio; - document.body.appendChild(oA); - oA.click(); - oA.remove(); -}} -""" - -if __name__ == '__main__': - with gr.Blocks() as app: - gr.Markdown( - "# <center> VITS语音在线合成demo\n" - "<div align='center'>主要有赛马娘，原神中文，原神日语，崩坏3的音色</div>" - '<div align="center"><a><font color="#dd0000">结果有随机性，语调可能很奇怪，可多次生成取最佳效果</font></a></div>' - '<div align="center"><a><font color="#dd0000">标点符号会影响生成的结果</font></a></div>' - ) - - with gr.Tabs(): - with gr.TabItem("vits"): - with gr.Row(): - with gr.Column(): - input_text = gr.Textbox(label="Text (100 words limitation)", lines=5, value="今天晚上吃啥好呢。", elem_id=f"input-text") - lang = gr.Dropdown(label="Language", choices=["中文", "日语", "中日混合（中文用[ZH][ZH]包裹起来，日文用[JA][JA]包裹起来）"], - type="index", value="中文") - btn = gr.Button(value="Submit") - with gr.Row(): - search = gr.Textbox(label="Search Speaker", lines=1) - btn2 = gr.Button(value="Search") - sid = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[228]) - with gr.Row(): - ns = gr.Slider(label="noise_scale(控制感情变化程度)", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True) - nsw = gr.Slider(label="noise_scale_w(控制音素发音长度)", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True) - ls = gr.Slider(label="length_scale(控制整体语速)", minimum=0.1, maximum=2.0, step=0.1, value=1.2, interactive=True) - with gr.Column(): - o1 = gr.Textbox(label="Output Message") - o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio") - o3 = gr.Textbox(label="Extra Info") - download = gr.Button("Download Audio") - btn.click(vits, inputs=[input_text, lang, sid, ns, nsw, ls], outputs=[o1, o2, o3]) - download.click(None, [], [], _js=download_audio_js.format()) - btn2.click(search_speaker, inputs=[search], outputs=[sid]) - lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls]) - with gr.TabItem("可用人物一览"): - gr.Radio(label="Speaker", choices=speakers, interactive=False, type="index") - app.queue(concurrency_count=1).launch() diff --git a/spaces/SRDdev/HingMaskedLM/app.py b/spaces/SRDdev/HingMaskedLM/app.py deleted file mode 100644 index 6719b1616098b991522ab927080653dc0940bae4..0000000000000000000000000000000000000000 --- a/spaces/SRDdev/HingMaskedLM/app.py +++ /dev/null @@ -1,18 +0,0 @@ -import gradio as gr -from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline - -tokenizer = AutoTokenizer.from_pretrained("SRDdev/HingMaskedLM") -model = AutoModelForMaskedLM.from_pretrained("SRDdev/HingMaskedLM") - -def fill_mask(sentence): - fill = pipeline('fill-mask', model=model, tokenizer=tokenizer) - result = fill(sentence) - sequence = result[0]['sequence'] - return sequence - - -inputs = gr.inputs.Textbox(default="Aaj Pune me <mask> kesa he ?") -output = gr.outputs.Textbox(label="Filled Mask") -description = "Hinglish MaskedLM is a variant of MaskedLM for the Indian English dialect, also known as Hinglish. It is trained on a corpus of text written in Hinglish, with the goal of improving the performance of NLP models for this dialect" -app = gr.Interface(fn=fill_mask, inputs = inputs, outputs=output,theme="grass",description=description ,title="Hinglish Masked Language Model") -app.launch() \ No newline at end of file diff --git a/spaces/Salesforce/EDICT/my_half_diffusers/dependency_versions_check.py b/spaces/Salesforce/EDICT/my_half_diffusers/dependency_versions_check.py deleted file mode 100644 index bbf863222a52fd60a15a95be0fbd6391acd3ba6d..0000000000000000000000000000000000000000 --- a/spaces/Salesforce/EDICT/my_half_diffusers/dependency_versions_check.py +++ /dev/null @@ -1,47 +0,0 @@ -# Copyright 2020 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -import sys - -from .dependency_versions_table import deps -from .utils.versions import require_version, require_version_core - - -# define which module versions we always want to check at run time -# (usually the ones defined in `install_requires` in setup.py) -# -# order specific notes: -# - tqdm must be checked before tokenizers - -pkgs_to_check_at_runtime = "python tqdm regex requests packaging filelock numpy tokenizers".split() -if sys.version_info < (3, 7): - pkgs_to_check_at_runtime.append("dataclasses") -if sys.version_info < (3, 8): - pkgs_to_check_at_runtime.append("importlib_metadata") - -for pkg in pkgs_to_check_at_runtime: - if pkg in deps: - if pkg == "tokenizers": - # must be loaded here, or else tqdm check may fail - from .utils import is_tokenizers_available - - if not is_tokenizers_available(): - continue # not required, check version only if installed - - require_version_core(deps[pkg]) - else: - raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py") - - -def dep_version_check(pkg, hint=None): - require_version(deps[pkg], hint) diff --git a/spaces/SaulLu/test-demo/join-main-training.md b/spaces/SaulLu/test-demo/join-main-training.md deleted file mode 100644 index 9889d50cc78398d9e048b9cc680118dfe444a672..0000000000000000000000000000000000000000 --- a/spaces/SaulLu/test-demo/join-main-training.md +++ /dev/null @@ -1,5 +0,0 @@ ---- -layout: page -title: "Join main training" -permalink: /join-main-training/ ---- \ No newline at end of file diff --git a/spaces/SeViLA/SeViLA/lavis/models/blip2_models/blip2.py b/spaces/SeViLA/SeViLA/lavis/models/blip2_models/blip2.py deleted file mode 100644 index 40d7d778af9d379a41a647c257e066877e321d25..0000000000000000000000000000000000000000 --- a/spaces/SeViLA/SeViLA/lavis/models/blip2_models/blip2.py +++ /dev/null @@ -1,240 +0,0 @@ -""" - Copyright (c) 2023, salesforce.com, inc. - All rights reserved. - SPDX-License-Identifier: BSD-3-Clause - For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause -""" -import logging -import os -import time -import datetime - -import torch -import torch.nn as nn -import torch.distributed as dist -import torch.nn.functional as F - -import lavis.common.dist_utils as dist_utils -from lavis.common.dist_utils import download_cached_file -from lavis.common.utils import is_url -from lavis.common.logger import MetricLogger -from lavis.models.base_model import BaseModel -from lavis.models.blip2_models.Qformer import BertConfig, BertLMHeadModel -from lavis.models.eva_vit import create_eva_vit_g -from transformers import BertTokenizer - - -class Blip2Base(BaseModel): - @classmethod - def init_tokenizer(cls): - tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") - tokenizer.add_special_tokens({"bos_token": "[DEC]"}) - return tokenizer - - @classmethod - def init_Qformer(cls, num_query_token, vision_width): - encoder_config = BertConfig.from_pretrained("bert-base-uncased") - encoder_config.encoder_width = vision_width - # insert cross-attention layer every other block - encoder_config.add_cross_attention = True - encoder_config.cross_attention_freq = 2 - encoder_config.query_length = num_query_token - Qformer = BertLMHeadModel.from_pretrained( - "bert-base-uncased", config=encoder_config - ) - query_tokens = nn.Parameter( - torch.zeros(1, num_query_token, encoder_config.hidden_size) - ) - query_tokens.data.normal_(mean=0.0, std=encoder_config.initializer_range) - return Qformer, query_tokens - - @classmethod - def init_TemporalQFormer(cls, num_of_frame): - encoder_config = BertConfig.from_pretrained("bert-base-uncased") - encoder_config.query_length = num_of_frame - Qformer = BertLMHeadModel.from_pretrained( - "bert-base-uncased", config=encoder_config - ) - query_tokens = nn.Parameter( - torch.zeros(1, num_of_frame, 1, encoder_config.hidden_size) - ) - query_tokens.data.normal_(mean=0.0, std=encoder_config.initializer_range) - return Qformer, query_tokens - - @classmethod - def init_vision_encoder( - cls, img_size, drop_path_rate, use_grad_checkpoint, precision - ): - visual_encoder = create_eva_vit_g( - img_size, drop_path_rate, use_grad_checkpoint, precision - ) - ln_vision = LayerNorm(visual_encoder.num_features) - return visual_encoder, ln_vision - - @classmethod - def init_vision_encoder_sevila( - cls, img_size, drop_path_rate, use_grad_checkpoint, precision - ): - visual_encoder = create_eva_vit_g( - img_size, drop_path_rate, use_grad_checkpoint, precision - ) - ln_vision = LayerNorm(visual_encoder.num_features) - ln_vision2 = LayerNorm(visual_encoder.num_features) - return visual_encoder, ln_vision, ln_vision2 - - def load_from_pretrained(self, url_or_filename): - if is_url(url_or_filename): - cached_file = download_cached_file( - url_or_filename, check_hash=False, progress=True - ) - checkpoint = torch.load(cached_file, map_location="cpu") - elif os.path.isfile(url_or_filename): - checkpoint = torch.load(url_or_filename, map_location="cpu") - else: - raise RuntimeError("checkpoint url or path is invalid") - - state_dict = checkpoint["model"] - #print('state_dict',state_dict.keys()) - msg = self.load_state_dict(state_dict, strict=False) - - logging.info("Missing keys {}".format(msg.missing_keys)) - logging.info("load checkpoint from %s" % url_or_filename) - - return msg - - def load_qformer_loc(self): - url_or_filename = '/nas-hdd/shoubin/pretrained_model/hub/checkpoints/qformer_loc.pth' - checkpoint = torch.load(url_or_filename, map_location="cpu") - state_dict = checkpoint["model"] - msg = self.load_state_dict(state_dict, strict=False) - logging.info("load checkpoint from %s" % url_or_filename) - -def disabled_train(self, mode=True): - """Overwrite model.train with this function to make sure train/eval mode - does not change anymore.""" - return self - - -class LayerNorm(nn.LayerNorm): - """Subclass torch's LayerNorm to handle fp16.""" - - def forward(self, x: torch.Tensor): - orig_type = x.dtype - ret = super().forward(x.type(torch.float32)) - return ret.type(orig_type) - - -def compute_sim_matrix(model, data_loader, **kwargs): - k_test = kwargs.pop("k_test") - - metric_logger = MetricLogger(delimiter=" ") - header = "Evaluation:" - - logging.info("Computing features for evaluation...") - start_time = time.time() - - texts = data_loader.dataset.text - num_text = len(texts) - text_bs = 256 - text_ids = [] - text_embeds = [] - text_atts = [] - for i in range(0, num_text, text_bs): - text = texts[i : min(num_text, i + text_bs)] - text_input = model.tokenizer( - text, - padding="max_length", - truncation=True, - max_length=35, - return_tensors="pt", - ).to(model.device) - text_feat = model.forward_text(text_input) - text_embed = F.normalize(model.text_proj(text_feat)) - text_embeds.append(text_embed) - text_ids.append(text_input.input_ids) - text_atts.append(text_input.attention_mask) - - text_embeds = torch.cat(text_embeds, dim=0) - text_ids = torch.cat(text_ids, dim=0) - text_atts = torch.cat(text_atts, dim=0) - - vit_feats = [] - image_embeds = [] - for samples in data_loader: - image = samples["image"] - - image = image.to(model.device) - image_feat, vit_feat = model.forward_image(image) - image_embed = model.vision_proj(image_feat) - image_embed = F.normalize(image_embed, dim=-1) - - vit_feats.append(vit_feat.cpu()) - image_embeds.append(image_embed) - - vit_feats = torch.cat(vit_feats, dim=0) - image_embeds = torch.cat(image_embeds, dim=0) - - sims_matrix = [] - for image_embed in image_embeds: - sim_q2t = image_embed @ text_embeds.t() - sim_i2t, _ = sim_q2t.max(0) - sims_matrix.append(sim_i2t) - sims_matrix = torch.stack(sims_matrix, dim=0) - - score_matrix_i2t = torch.full( - (len(data_loader.dataset.image), len(texts)), -100.0 - ).to(model.device) - - num_tasks = dist_utils.get_world_size() - rank = dist_utils.get_rank() - step = sims_matrix.size(0) // num_tasks + 1 - start = rank * step - end = min(sims_matrix.size(0), start + step) - - for i, sims in enumerate( - metric_logger.log_every(sims_matrix[start:end], 50, header) - ): - topk_sim, topk_idx = sims.topk(k=k_test, dim=0) - image_inputs = vit_feats[start + i].repeat(k_test, 1, 1).to(model.device) - score = model.compute_itm( - image_inputs=image_inputs, - text_ids=text_ids[topk_idx], - text_atts=text_atts[topk_idx], - ).float() - score_matrix_i2t[start + i, topk_idx] = score + topk_sim - - sims_matrix = sims_matrix.t() - score_matrix_t2i = torch.full( - (len(texts), len(data_loader.dataset.image)), -100.0 - ).to(model.device) - - step = sims_matrix.size(0) // num_tasks + 1 - start = rank * step - end = min(sims_matrix.size(0), start + step) - - for i, sims in enumerate( - metric_logger.log_every(sims_matrix[start:end], 50, header) - ): - topk_sim, topk_idx = sims.topk(k=k_test, dim=0) - image_inputs = vit_feats[topk_idx.cpu()].to(model.device) - score = model.compute_itm( - image_inputs=image_inputs, - text_ids=text_ids[start + i].repeat(k_test, 1), - text_atts=text_atts[start + i].repeat(k_test, 1), - ).float() - score_matrix_t2i[start + i, topk_idx] = score + topk_sim - - if dist_utils.is_dist_avail_and_initialized(): - dist.barrier() - torch.distributed.all_reduce( - score_matrix_i2t, op=torch.distributed.ReduceOp.SUM - ) - torch.distributed.all_reduce( - score_matrix_t2i, op=torch.distributed.ReduceOp.SUM - ) - - total_time = time.time() - start_time - total_time_str = str(datetime.timedelta(seconds=int(total_time))) - logging.info("Evaluation time {}".format(total_time_str)) - - return score_matrix_i2t.cpu().numpy(), score_matrix_t2i.cpu().numpy() diff --git a/spaces/SmileyTatsu/Bleh/greeting.md b/spaces/SmileyTatsu/Bleh/greeting.md deleted file mode 100644 index c9866428cbcd95d0b853f4b41e3627484539aa2e..0000000000000000000000000000000000000000 --- a/spaces/SmileyTatsu/Bleh/greeting.md +++ /dev/null @@ -1,2 +0,0 @@ -Pass: In the manga chapter "Tatsumaki's Day Off", what was the hero's name of the one who defeated all the monsters? -All together, first letter of each word capitalized \ No newline at end of file diff --git a/spaces/Sreezx/Sentzi/lib/lib.py b/spaces/Sreezx/Sentzi/lib/lib.py deleted file mode 100644 index cc681b6ac4c96d189ada78aa33a9eb5c9e9fc018..0000000000000000000000000000000000000000 --- a/spaces/Sreezx/Sentzi/lib/lib.py +++ /dev/null @@ -1,95 +0,0 @@ -# Main module for all sentzi classes - -# `Sentzi` is a web app that generates a visualized output of product reviews through sentiment analysis. - -from textblob import TextBlob -import typing - -# json and csv lib -import json -import csv - -class Sentiment: - """ Represents a sentiment object """ - - __emojiDic__ = { - 'positive' : [ - '🙂','😊','😀','👍','😄' ,'😁','😍','🥰','😘','😗' - ], - 'negative' : [ - '😞','😒','😔','👎','😟','😠','😡','😥','😧','❌' - ], - 'neutral' : [ - '😐','😶','😑' - ] - } - def __init__(self, text : str): - """ - Initializes a Sentiment object with the given text . - - - Note that the accuracy increases as number of words increases. - - `Args` - ------ - `text` to analyse . - """ - self.text = text - - # get sentiment - blob = TextBlob(text) - - # Analyze sentiment - sentiment = blob.sentiment - polarity = sentiment.polarity - - self.polarity = polarity - - def __repr__(self) -> str: - """ Returns a string representation of the `Sentiment` object """ - return f"""Sentiment( - score : {self.polarity} - text : {self.text} - )""" - - def get(self) -> typing.Dict[str , typing.Any]: - - # check is its positive negative or neutral - if self.polarity < 0: - # negative - data = { - 'score' : self.polarity, - 'level' : 'negative', - 'emojis' : Sentiment.__emojiDic__['negative'] - } - elif self.polarity > 0: - # positive - data = { - 'score' : self.polarity, - 'level' : 'positive', - 'emojis' : Sentiment.__emojiDic__['positive'] - } - else: - # neutral - data = { - 'score' : self.polarity, - 'level' : 'neutral', - 'emojis' : Sentiment.__emojiDic__['neutral'] - } - - return data - -def writeCSV(header : list[str],dataList : list[list[str]]): - with open(r"temp.csv", 'w', newline='') as file: - writer = csv.writer(file) - writer.writerow(header) - # write multiple rows - writer.writerows(dataList) # write content - -def writeJSON(data : dict): - with open(r"temp.json","w") as json_file: - json.dump( - data, - json_file, - indent=4, - sort_keys=True - ) diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/testing/tests/__init__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/testing/tests/__init__.py deleted file mode 100644 index f751f68a9de539bb48e0468f2a1c6d29a2031bd3..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/testing/tests/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# encoding: utf-8 -__docformat__ = "restructuredtext en" -#------------------------------------------------------------------------------- -# Copyright (C) 2005 Fernando Perez <fperez@colorado.edu> -# Brian E Granger <ellisonbg@gmail.com> -# Benjamin Ragan-Kelley <benjaminrk@gmail.com> -# -# Distributed under the terms of the BSD License. The full license is in -# the file COPYING, distributed as part of this software. -#------------------------------------------------------------------------------- diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/bytes/audio_bytes.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/bytes/audio_bytes.py deleted file mode 100644 index 23c6f49a4d0662f7f7a00e6316bd5838a3351d62..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/docarray/typing/bytes/audio_bytes.py +++ /dev/null @@ -1,91 +0,0 @@ -import io -from typing import TYPE_CHECKING, Any, Tuple, Type, TypeVar - -import numpy as np -from pydantic import parse_obj_as -from pydantic.validators import bytes_validator - -from docarray.typing.abstract_type import AbstractType -from docarray.typing.proto_register import _register_proto -from docarray.typing.tensor.audio import AudioNdArray -from docarray.utils._internal.misc import import_library - -if TYPE_CHECKING: - from pydantic.fields import BaseConfig, ModelField - - from docarray.proto import NodeProto - -T = TypeVar('T', bound='AudioBytes') - - -@_register_proto(proto_type_name='audio_bytes') -class AudioBytes(bytes, AbstractType): - """ - Bytes that store an audio and that can be load into an Audio tensor - """ - - @classmethod - def validate( - cls: Type[T], - value: Any, - field: 'ModelField', - config: 'BaseConfig', - ) -> T: - value = bytes_validator(value) - return cls(value) - - @classmethod - def from_protobuf(cls: Type[T], pb_msg: T) -> T: - return parse_obj_as(cls, pb_msg) - - def _to_node_protobuf(self: T) -> 'NodeProto': - from docarray.proto import NodeProto - - return NodeProto(blob=self, type=self._proto_type_name) - - def load(self) -> Tuple[AudioNdArray, int]: - """ - Load the Audio from the [`AudioBytes`][docarray.typing.AudioBytes] into an - [`AudioNdArray`][docarray.typing.AudioNdArray]. - - --- - - ```python - from typing import Optional - from docarray import BaseDoc - from docarray.typing import AudioBytes, AudioNdArray, AudioUrl - - - class MyAudio(BaseDoc): - url: AudioUrl - tensor: Optional[AudioNdArray] - bytes_: Optional[AudioBytes] - frame_rate: Optional[float] - - - doc = MyAudio(url='https://www.kozco.com/tech/piano2.wav') - doc.bytes_ = doc.url.load_bytes() - doc.tensor, doc.frame_rate = doc.bytes_.load() - - # Note this is equivalent to do - - doc.tensor, doc.frame_rate = doc.url.load() - - assert isinstance(doc.tensor, AudioNdArray) - ``` - - --- - :return: tuple of an [`AudioNdArray`][docarray.typing.AudioNdArray] representing the - audio bytes content, and an integer representing the frame rate. - """ - pydub = import_library('pydub', raise_error=True) # noqa: F841 - from pydub import AudioSegment - - segment = AudioSegment.from_file(io.BytesIO(self)) - - # Convert to float32 using NumPy - samples = np.array(segment.get_array_of_samples()) - - # Normalise float32 array so that values are between -1.0 and +1.0 - samples_norm = samples / 2 ** (segment.sample_width * 8 - 1) - return parse_obj_as(AudioNdArray, samples_norm), segment.frame_rate diff --git a/spaces/TEnngal/bingo/src/components/chat-header.tsx b/spaces/TEnngal/bingo/src/components/chat-header.tsx deleted file mode 100644 index c6664b8dee61179f844d45c5bd650518fc2cb4c2..0000000000000000000000000000000000000000 --- a/spaces/TEnngal/bingo/src/components/chat-header.tsx +++ /dev/null @@ -1,12 +0,0 @@ -import LogoIcon from '@/assets/images/logo.svg' -import Image from 'next/image' - -export function ChatHeader() { - return ( - <div className="flex flex-col items-center justify-center"> - <Image alt="logo" src={LogoIcon} width={60}/> - <div className="mt-8 text-4xl font-bold">欢迎使用新必应</div> - <div className="mt-4 mb-8 text-lg">由 AI 支持的网页版 Copilot</div> - </div> - ) -} diff --git a/spaces/TRI-ML/risk_biased_prediction/scripts/eval_scripts/plot_latent_sensitivity_bias_risk.py b/spaces/TRI-ML/risk_biased_prediction/scripts/eval_scripts/plot_latent_sensitivity_bias_risk.py deleted file mode 100644 index 118ac2b9dc0e8ddbf2c20d0866d19f7a9fe5b1f2..0000000000000000000000000000000000000000 --- a/spaces/TRI-ML/risk_biased_prediction/scripts/eval_scripts/plot_latent_sensitivity_bias_risk.py +++ /dev/null @@ -1,109 +0,0 @@ -import os - -import matplotlib.pyplot as plt -import matplotlib as mpl -from pytorch_lightning.utilities.seed import seed_everything -import torch -from torch.utils.data import DataLoader - -from risk_biased.scene_dataset.scene import RandomSceneParams -from matplotlib.patches import Ellipse -from risk_biased.utils.callbacks import DrawCallbackParams -from risk_biased.utils.config_argparse import config_argparse - -from risk_biased.utils.load_model import load_from_config - -from scripts.scripts_utils.sample_batch_utils import repeat_and_reshape_all - - -def draw_latent_biased( - model: torch.nn.Module, device, loader: DataLoader, params: DrawCallbackParams -): - n_samples = 9 - # ped_trajs = scene.get_pedestrians_trajectories() - ( - normalized_input, - mask_input, - fut, - mask_fut, - mask_loss, - map, - mask_map, - offset, - ego_past, - ego_fut, - ) = repeat_and_reshape_all(next(iter(loader)), n_samples) - n_scenes_samples, n_agents, n_steps, features = normalized_input.shape - n_scenes = n_scenes_samples // n_samples - - risk_level = torch.ones(n_scenes, n_samples, n_agents) * torch.linspace( - 0, 1, n_samples - ).unsqueeze(0).unsqueeze(-1) - - y_sample, biased_mu, biased_log_std = model( - normalized_input, - mask_input, - map, - mask_map, - offset=offset, - x_ego=ego_past, - y_ego=ego_fut, - risk_level=risk_level.view(-1, n_agents), - ) - - biased_mu = biased_mu.view(n_scenes, n_agents, n_samples, 2).cpu().detach().numpy() - biased_std = ( - biased_log_std.view(n_scenes, n_agents, n_samples, 2) - .exp() - .cpu() - .detach() - .numpy() - ) - risk_level = risk_level.permute(0, 2, 1).cpu().detach().numpy() - - fig, ax = plt.subplots(3, 3) - cmap = plt.get_cmap("RdBu_r") - for s in range(n_samples): - for i in range(n_scenes): - for a in range(n_agents): - ii = s // 3 - jj = s % 3 - ellipse = Ellipse( - (biased_mu[i, a, s, 0], biased_mu[i, a, s, 1]), - width=biased_std[i, a, s, 0] * 2, - height=biased_std[i, a, s, 1] * 2, - facecolor="none", - edgecolor=(*cmap(risk_level[i, a, s])[:-1], 0.05), - ) - ax[ii][jj].add_patch(ellipse) - ax[ii][jj].axis([-3, 3, -3, 3]) - - norm = mpl.colors.Normalize(vmin=0, vmax=1, clip=True) - sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm) - fig.colorbar(sm, ax=ax.ravel().tolist(), label="Desired risk levels") - plt.show() - - -if __name__ == "__main__": - # Draws 9 plots in the latent space. In each plot a constant risk level is used (0 for plot 0 and 1 for plot 8). - # Each plot superposes ellipses representing the encoded distributions for a batch of x input. - # Each plot represents the latent distribution for the same batch of input but at different risk levels. - working_dir = os.path.dirname(os.path.realpath(__file__)) - config_path = os.path.join( - working_dir, "..", "..", "risk_biased", "config", "learning_config.py" - ) - cfg = config_argparse(config_path) - - cfg.batch_size = cfg.datasets_sizes["val"] - model, loaders, cfg = load_from_config(cfg) - assert ( - cfg.latent_dim == 2 - and "The latent dimension of the model must be exactly 2 to be plotted (no dimensionality reduction capabilities)" - ) - scene_params = RandomSceneParams.from_config(cfg) - scene = loaders.val_dataloader() - draw_params = DrawCallbackParams.from_config(cfg) - if cfg.seed is not None: - seed_everything(cfg.seed) - - draw_latent_biased(model.model, model.device, scene, draw_params) diff --git a/spaces/TabPFN/TabPFNPrediction/TabPFN/notebook_utils.py b/spaces/TabPFN/TabPFNPrediction/TabPFN/notebook_utils.py deleted file mode 100644 index c085c98f9865f993163bc1cfd29fd895f6a27b27..0000000000000000000000000000000000000000 --- a/spaces/TabPFN/TabPFNPrediction/TabPFN/notebook_utils.py +++ /dev/null @@ -1,32 +0,0 @@ -import os -from pathlib import Path - -import io -import torch -import pickle - -def print_models(base_path, model_string): - print(model_string) - - for i in range(80): - for e in range(50): - exists = Path(os.path.join(base_path, f'models_diff/prior_diff_real_checkpoint{model_string}_n_{i}_epoch_{e}.cpkt')).is_file() - if exists: - print(os.path.join(base_path, f'models_diff/prior_diff_real_checkpoint{model_string}_n_{i}_epoch_{e}.cpkt')) - print() - -class CustomUnpickler(pickle.Unpickler): - def find_class(self, module, name): - if name == 'Manager': - from settings import Manager - return Manager - try: - return self.find_class_cpu(module, name) - except: - return None - - def find_class_cpu(self, module, name): - if module == 'torch.storage' and name == '_load_from_bytes': - return lambda b: torch.load(io.BytesIO(b), map_location='cpu') - else: - return super().find_class(module, name) \ No newline at end of file diff --git a/spaces/Tetel/chat/SydneyGPT/conversation_style.py b/spaces/Tetel/chat/SydneyGPT/conversation_style.py deleted file mode 100644 index 94aa4c1bc77e57c86598df6438f29fb899c53694..0000000000000000000000000000000000000000 --- a/spaces/Tetel/chat/SydneyGPT/conversation_style.py +++ /dev/null @@ -1,68 +0,0 @@ -from enum import Enum - -try: - from typing import Literal, Union -except ImportError: - from typing_extensions import Literal -from typing import Optional - - -class ConversationStyle(Enum): - creative = [ - "nlu_direct_response_filter", - "deepleo", - "disable_emoji_spoken_text", - "responsible_ai_policy_235", - "enablemm", - "iycapbing", - "iyxapbing", - "uquopt", - "authsndfdbk", - "refpromptv1", - "enuaug", - "dagslnv1nr", - "dv3sugg", - "iyoloxap", - "iyoloneutral", - "h3imaginative", - "clgalileo", - "eredirecturl", - "gencontentv3", - "travelansgnd", - "nojbfedge", - ] - balanced = [ - "nlu_direct_response_filter", - "deepleo", - "disable_emoji_spoken_text", - "responsible_ai_policy_235", - "enablemm", - "galileo", - "dv3sugg", - "responseos", - "e2ecachewrite", - "cachewriteext", - "nodlcpcwrite", - "travelansgnd", - ] - precise = [ - "nlu_direct_response_filter", - "deepleo", - "disable_emoji_spoken_text", - "responsible_ai_policy_235", - "enablemm", - "galileo", - "dv3sugg", - "responseos", - "e2ecachewrite", - "cachewriteext", - "nodlcpcwrite", - "travelansgnd", - "h3precise", - "clgalileo", - ] - - -CONVERSATION_STYLE_TYPE = Optional[ - Union[ConversationStyle, Literal["creative", "balanced", "precise"]] -] diff --git a/spaces/TheStinger/Ilaria_RVC/utils.py b/spaces/TheStinger/Ilaria_RVC/utils.py deleted file mode 100644 index 62be8d03a8e8b839f8747310ef0ec0e82fb8ff0a..0000000000000000000000000000000000000000 --- a/spaces/TheStinger/Ilaria_RVC/utils.py +++ /dev/null @@ -1,151 +0,0 @@ -import ffmpeg -import numpy as np - -# import praatio -# import praatio.praat_scripts -import os -import sys - -import random - -import csv - -platform_stft_mapping = { - "linux": "stftpitchshift", - "darwin": "stftpitchshift", - "win32": "stftpitchshift.exe", -} - -stft = platform_stft_mapping.get(sys.platform) -# praatEXE = join('.',os.path.abspath(os.getcwd()) + r"\Praat.exe") - - -def CSVutil(file, rw, type, *args): - if type == "formanting": - if rw == "r": - with open(file) as fileCSVread: - csv_reader = list(csv.reader(fileCSVread)) - return ( - (csv_reader[0][0], csv_reader[0][1], csv_reader[0][2]) - if csv_reader is not None - else (lambda: exec('raise ValueError("No data")'))() - ) - else: - if args: - doformnt = args[0] - else: - doformnt = False - qfr = args[1] if len(args) > 1 else 1.0 - tmb = args[2] if len(args) > 2 else 1.0 - with open(file, rw, newline="") as fileCSVwrite: - csv_writer = csv.writer(fileCSVwrite, delimiter=",") - csv_writer.writerow([doformnt, qfr, tmb]) - elif type == "stop": - stop = args[0] if args else False - with open(file, rw, newline="") as fileCSVwrite: - csv_writer = csv.writer(fileCSVwrite, delimiter=",") - csv_writer.writerow([stop]) - - -def load_audio(file, sr, DoFormant, Quefrency, Timbre): - converted = False - DoFormant, Quefrency, Timbre = CSVutil("csvdb/formanting.csv", "r", "formanting") - try: - # https://github.com/openai/whisper/blob/main/whisper/audio.py#L26 - # This launches a subprocess to decode audio while down-mixing and resampling as necessary. - # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed. - file = ( - file.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - ) # 防止小白拷路径头尾带了空格和"和回车 - file_formanted = file.strip(" ").strip('"').strip("\n").strip('"').strip(" ") - - # print(f"dofor={bool(DoFormant)} timbr={Timbre} quef={Quefrency}\n") - - if ( - lambda DoFormant: True - if DoFormant.lower() == "true" - else (False if DoFormant.lower() == "false" else DoFormant) - )(DoFormant): - numerator = round(random.uniform(1, 4), 4) - # os.system(f"stftpitchshift -i {file} -q {Quefrency} -t {Timbre} -o {file_formanted}") - # print('stftpitchshift -i "%s" -p 1.0 --rms -w 128 -v 8 -q %s -t %s -o "%s"' % (file, Quefrency, Timbre, file_formanted)) - - if not file.endswith(".wav"): - if not os.path.isfile(f"{file_formanted}.wav"): - converted = True - # print(f"\nfile = {file}\n") - # print(f"\nfile_formanted = {file_formanted}\n") - converting = ( - ffmpeg.input(file_formanted, threads=0) - .output(f"{file_formanted}.wav") - .run( - cmd=["ffmpeg", "-nostdin"], - capture_stdout=True, - capture_stderr=True, - ) - ) - else: - pass - - file_formanted = ( - f"{file_formanted}.wav" - if not file_formanted.endswith(".wav") - else file_formanted - ) - - print(f" · Formanting {file_formanted}...\n") - - os.system( - '%s -i "%s" -q "%s" -t "%s" -o "%sFORMANTED_%s.wav"' - % ( - stft, - file_formanted, - Quefrency, - Timbre, - file_formanted, - str(numerator), - ) - ) - - print(f" · Formanted {file_formanted}!\n") - - # filepraat = (os.path.abspath(os.getcwd()) + '\\' + file).replace('/','\\') - # file_formantedpraat = ('"' + os.path.abspath(os.getcwd()) + '/' + 'formanted'.join(file_formanted) + '"').replace('/','\\') - # print("%sFORMANTED_%s.wav" % (file_formanted, str(numerator))) - - out, _ = ( - ffmpeg.input( - "%sFORMANTED_%s.wav" % (file_formanted, str(numerator)), threads=0 - ) - .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr) - .run( - cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True - ) - ) - - try: - os.remove("%sFORMANTED_%s.wav" % (file_formanted, str(numerator))) - except Exception: - pass - print("couldn't remove formanted type of file") - - else: - out, _ = ( - ffmpeg.input(file, threads=0) - .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr) - .run( - cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True - ) - ) - except Exception as e: - raise RuntimeError(f"Failed to load audio: {e}") - - if converted: - try: - os.remove(file_formanted) - except Exception: - pass - print("couldn't remove converted type of file") - converted = False - - return np.frombuffer(out, np.float32).flatten() diff --git a/spaces/ThomasSimonini/SnowballFight/index.html b/spaces/ThomasSimonini/SnowballFight/index.html deleted file mode 100644 index aaa7243870b7c2b230d6d4fdff2f4e4aca8d3a94..0000000000000000000000000000000000000000 --- a/spaces/ThomasSimonini/SnowballFight/index.html +++ /dev/null @@ -1,138 +0,0 @@ -<!DOCTYPE html> -<html lang="en-us"> - <head> - <meta charset="utf-8"> - <meta http-equiv="Content-Type" content="text/html; charset=utf-8"> - <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no"> - <title>Snowball Fight Beta - - - - - - - - - - - - -- - - --- -- - - - - - - - -
Downloading music from YouTube without permission is illegal, unless it is royalty-free, copyright-free, or covered by a Creative Commons license, or unless it is for personal, non-commercial purposes, such as for education, research, or criticism.
-The best format and quality for downloading music from YouTube depends on your preferences and needs. Generally, MP3 is the most common and compatible format for audio files, while M4A is a higher-quality format that supports metadata. The best quality for downloading music from YouTube depends on the source and the tool that you use. Generally, the higher the bitrate, the better the quality, but also the larger the file size. The optimal bitrate for MP3 files is 320 kbps, while for M4A files is 256 kbps.
-To download music from YouTube to your iPhone or iPad, you can use one of the following methods:
--
-
To download music from YouTube to your Android device, you can use one of the following methods:
--
-
To download music from YouTube to your computer, you can use one of the following methods:
--
-
-
-
\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Enjoy Instagram Without Ads and With More Features With Red Instagram APK.md b/spaces/1phancelerku/anime-remove-background/Enjoy Instagram Without Ads and With More Features With Red Instagram APK.md deleted file mode 100644 index 16ed7f9502aa10b13bf19180ffc72cd7ec007a4f..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Enjoy Instagram Without Ads and With More Features With Red Instagram APK.md +++ /dev/null @@ -1,105 +0,0 @@ - -
Red Instagram APK: What Is It and How to Download It
-Instagram is one of the most popular social media platforms in the world, with over 1 billion monthly active users. It allows you to create and share your photos, stories, reels, and videos with the friends and followers you care about. But did you know that there is a modified version of Instagram that offers more features and customization options than the official app? It's called Red Instagram APK, and in this article, we will tell you what it is, why you should use it, and how to download and install it on your Android device.
-red instagram apk
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Introduction
-What is Instagram?
-Instagram (from Meta) is a social networking app that lets you capture and share your moments with the world. You can post photos, videos, reels, stories, IGTVs, and live streams on your feed or send them privately to your friends. You can also follow your favorite celebrities, influencers, brands, and pages to see what they are up to. You can also explore new content from different categories, such as entertainment, sports, music, fashion, beauty, travel, and more.
-What is Red Instagram APK?
-Red Instagram APK is a modified version of the official Instagram app that has a red theme and icons. It is also known as InstaRed or InstaMod. It is developed by independent developers who are not affiliated with Meta or Instagram. It is not available on the Google Play Store or any other official app store. You have to download it from third-party websites or sources.
-Why use Red Instagram APK?
-Red Instagram APK offers many features and benefits that are not available on the official app. Some of them are:
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Features of Red Instagram APK
-Customizable theme and icons
-One of the most noticeable features of Red Instagram APK is its red theme and icons. The app has a dark mode that makes it easier on the eyes and saves battery life. You can also change the color of the theme and icons to any other color you like. You can also choose from different fonts and styles for the app.
-Download photos, videos, reels, and stories
-Another feature of Red Instagram APK is its ability to download any photo, video, reel, or story from any user. You don't need to use any external app or tool to do this. You just have to tap on the three-dot menu on the top right corner of any post or story and select "Download". The file will be saved in your device's gallery or storage.
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Hide seen status and typing indicator
-If you want to view someone's story or chat with them without letting them know that you have seen their message or story, you can use Red Instagram APK's privacy features. You can hide your seen status and typing indicator from other users by toggling them on or off in the settings of the app. This way, you can enjoy more privacy and control over your online activity.
-Disable ads and sponsored posts
-Ads and sponsored posts can be annoying and distracting when you are browsing your feed or stories. They can also consume your data and battery. With Red Instagram APK, you can disable them completely and enjoy a cleaner and smoother experience. You can also block any user or page that you don't want to see on your feed or stories.
-Zoom in on profile pictures and stories
-Sometimes, you may want to see someone's profile picture or story more clearly, but the official app doesn't allow you to zoom in on them. You have to screenshot or crop them, which can be tedious and low-quality. With Red Instagram APK, you can zoom in on any profile picture or story by just tapping and holding on them. You can also view them in full screen mode.
-How to download and install Red Instagram APK
-If you are interested in trying out Red Instagram APK, you need to follow these steps to download and install it on your Android device:
-Step 1: Enable unknown sources
-Since Red Instagram APK is not available on the Google Play Store or any other official app store, you need to enable unknown sources on your device to install it. To do this, go to your device's settings, then security, then unknown sources, and turn it on. This will allow you to install apps from third-party sources.
-Step 2: Download the APK file
-Next, you need to download the APK file of Red Instagram APK from a reliable and trusted website. You can search for it on Google or use this link: . Make sure you download the latest version of the app that is compatible with your device.
-Step 3: Install the APK file
-Once you have downloaded the APK file, locate it in your device's file manager or downloads folder and tap on it. You will see a pop-up window asking you to confirm the installation. Tap on "Install" and wait for the process to complete.
-Step 4: Log in with your Instagram account
-After the installation is done, you can open the app and log in with your existing Instagram account or create a new one. You will see the red theme and icons of the app and enjoy all the features that we have mentioned above.
-Conclusion
-Red Instagram APK is a modified version of the official Instagram app that offers more features and customization options than the original app. It has a red theme and icons that make it stand out from other apps. It also allows you to download photos, videos, reels, and stories from any user, hide your seen status and typing indicator, disable ads and sponsored posts, zoom in on profile pictures and stories, and more. It is easy to download and install on your Android device by following the steps we have provided above. If you are looking for a new way to enjoy Instagram, you should give Red Instagram APK a try.
-Do you have any questions or feedback about Red Instagram APK? Let us know in the comments below. We would love to hear from you!
-Frequently Asked Questions
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Red Instagram APK is safe to use as long as you download it from a reputable and trusted website. However, since it is not an official app, it may not be as secure as the original app. Therefore, we recommend that you use it at your own risk and discretion.
-No, Red Instagram APK will not affect your original Instagram account. You can use both apps simultaneously without any problem. However, if you use some features that violate the terms of service of Instagram, such as downloading other users' content without their permission, you may face some consequences from Instagram.
-To update Red Instagram APK, you need to visit the website where you downloaded it from and check if there is a new version available. If there is, you need to download and install it manually by following the same steps as before.
-No, Red Instagram APK is only compatible with Android devices. It cannot be used on iOS devices such as iPhones or iPads.
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\ No newline at end of file diff --git a/spaces/2ndelement/voicevox/voicevox_engine/metas/Metas.py b/spaces/2ndelement/voicevox/voicevox_engine/metas/Metas.py deleted file mode 100644 index 58c42f06765c3554a138471d83fc90800e6a8540..0000000000000000000000000000000000000000 --- a/spaces/2ndelement/voicevox/voicevox_engine/metas/Metas.py +++ /dev/null @@ -1,83 +0,0 @@ -from enum import Enum -from typing import List, Optional - -from pydantic import BaseModel, Field - - -class SpeakerStyle(BaseModel): - """ - スピーカーのスタイル情報 - """ - - name: str = Field(title="スタイル名") - id: int = Field(title="スタイルID") - - -class SpeakerSupportPermittedSynthesisMorphing(str, Enum): - ALL = "ALL" # 全て許可 - SELF_ONLY = "SELF_ONLY" # 同じ話者内でのみ許可 - NOTHING = "NOTHING" # 全て禁止 - - @classmethod - def _missing_(cls, value: object) -> "SpeakerSupportPermittedSynthesisMorphing": - return SpeakerSupportPermittedSynthesisMorphing.ALL - - -class SpeakerSupportedFeatures(BaseModel): - """ - 話者の対応機能の情報 - """ - - permitted_synthesis_morphing: SpeakerSupportPermittedSynthesisMorphing = Field( - title="モーフィング機能への対応", default=SpeakerSupportPermittedSynthesisMorphing(None) - ) - - -class CoreSpeaker(BaseModel): - """ - コアに含まれるスピーカー情報 - """ - - name: str = Field(title="名前") - speaker_uuid: str = Field(title="スピーカーのUUID") - styles: List[SpeakerStyle] = Field(title="スピーカースタイルの一覧") - version: str = Field("スピーカーのバージョン") - - -class EngineSpeaker(BaseModel): - """ - エンジンに含まれるスピーカー情報 - """ - - supported_features: SpeakerSupportedFeatures = Field( - title="スピーカーの対応機能", default_factory=SpeakerSupportedFeatures - ) - - -class Speaker(CoreSpeaker, EngineSpeaker): - """ - スピーカー情報 - """ - - pass - - -class StyleInfo(BaseModel): - """ - スタイルの追加情報 - """ - - id: int = Field(title="スタイルID") - icon: str = Field(title="当該スタイルのアイコンをbase64エンコードしたもの") - portrait: Optional[str] = Field(title="当該スタイルのportrait.pngをbase64エンコードしたもの") - voice_samples: List[str] = Field(title="voice_sampleのwavファイルをbase64エンコードしたもの") - - -class SpeakerInfo(BaseModel): - """ - 話者の追加情報 - """ - - policy: str = Field(title="policy.md") - portrait: str = Field(title="portrait.pngをbase64エンコードしたもの") - style_infos: List[StyleInfo] = Field(title="スタイルの追加情報") diff --git a/spaces/AI-ZTH-03-23/2.Streamlit.GraphViz.Dynamic.Architecture.Diagram/README.md b/spaces/AI-ZTH-03-23/2.Streamlit.GraphViz.Dynamic.Architecture.Diagram/README.md deleted file mode 100644 index 290fe0d4a3cd403e7363ee43b850a129420704ec..0000000000000000000000000000000000000000 --- a/spaces/AI-ZTH-03-23/2.Streamlit.GraphViz.Dynamic.Architecture.Diagram/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: 2.Streamlit.GraphViz.Dynamic.Architecture.Diagram -emoji: 😻😻😻 -colorFrom: green -colorTo: indigo -sdk: streamlit -sdk_version: 1.17.0 -app_file: app.py -pinned: false -license: mit -duplicated_from: awacke1/Streamlit.GraphViz.Dynamic.Architecture.Diagram ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/AIConsultant/MusicGen/audiocraft/utils/export.py b/spaces/AIConsultant/MusicGen/audiocraft/utils/export.py deleted file mode 100644 index 28b214017d9ac23934b67e8254a96131cefa6501..0000000000000000000000000000000000000000 --- a/spaces/AIConsultant/MusicGen/audiocraft/utils/export.py +++ /dev/null @@ -1,79 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -""" -Utility to export a training checkpoint to a lightweight release checkpoint. -""" - -from pathlib import Path -import typing as tp - -from omegaconf import OmegaConf -import torch - -from audiocraft import __version__ - - -def export_encodec(checkpoint_path: tp.Union[Path, str], out_file: tp.Union[Path, str]): - """Export only the best state from the given EnCodec checkpoint. This - should be used if you trained your own EnCodec model. - """ - pkg = torch.load(checkpoint_path, 'cpu') - new_pkg = { - 'best_state': pkg['best_state']['model'], - 'xp.cfg': OmegaConf.to_yaml(pkg['xp.cfg']), - 'version': __version__, - 'exported': True, - } - Path(out_file).parent.mkdir(exist_ok=True, parents=True) - torch.save(new_pkg, out_file) - return out_file - - -def export_pretrained_compression_model(pretrained_encodec: str, out_file: tp.Union[Path, str]): - """Export a compression model (potentially EnCodec) from a pretrained model. - This is required for packaging the audio tokenizer along a MusicGen or AudioGen model. - Do not include the //pretrained/ prefix. For instance if you trained a model - with `facebook/encodec_32khz`, just put that as a name. Same for `dac_44khz`. - - In that case, this will not actually include a copy of the model, simply the reference - to the model used. - """ - if Path(pretrained_encodec).exists(): - pkg = torch.load(pretrained_encodec) - assert 'best_state' in pkg - assert 'xp.cfg' in pkg - assert 'version' in pkg - assert 'exported' in pkg - else: - pkg = { - 'pretrained': pretrained_encodec, - 'exported': True, - 'version': __version__, - } - Path(out_file).parent.mkdir(exist_ok=True, parents=True) - torch.save(pkg, out_file) - - -def export_lm(checkpoint_path: tp.Union[Path, str], out_file: tp.Union[Path, str]): - """Export only the best state from the given MusicGen or AudioGen checkpoint. - """ - pkg = torch.load(checkpoint_path, 'cpu') - if pkg['fsdp_best_state']: - best_state = pkg['fsdp_best_state']['model'] - else: - assert pkg['best_state'] - best_state = pkg['best_state']['model'] - new_pkg = { - 'best_state': best_state, - 'xp.cfg': OmegaConf.to_yaml(pkg['xp.cfg']), - 'version': __version__, - 'exported': True, - } - - Path(out_file).parent.mkdir(exist_ok=True, parents=True) - torch.save(new_pkg, out_file) - return out_file diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/diff/shallow_diffusion_tts.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/diff/shallow_diffusion_tts.py deleted file mode 100644 index 8295d48ea0028dd0b3fdf1315bb8f129d0070810..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/diff/shallow_diffusion_tts.py +++ /dev/null @@ -1,324 +0,0 @@ -import math -import random -from collections import deque -from functools import partial -from inspect import isfunction -from pathlib import Path -import numpy as np -import torch -import torch.nn.functional as F -from torch import nn -from tqdm import tqdm -from einops import rearrange - -from modules.fastspeech.fs2 import FastSpeech2 -from modules.diffsinger_midi.fs2 import FastSpeech2MIDI -from utils.hparams import hparams - - - -def exists(x): - return x is not None - - -def default(val, d): - if exists(val): - return val - return d() if isfunction(d) else d - - -# gaussian diffusion trainer class - -def extract(a, t, x_shape): - b, *_ = t.shape - out = a.gather(-1, t) - return out.reshape(b, *((1,) * (len(x_shape) - 1))) - - -def noise_like(shape, device, repeat=False): - repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) - noise = lambda: torch.randn(shape, device=device) - return repeat_noise() if repeat else noise() - - -def linear_beta_schedule(timesteps, max_beta=hparams.get('max_beta', 0.01)): - """ - linear schedule - """ - betas = np.linspace(1e-4, max_beta, timesteps) - return betas - - -def cosine_beta_schedule(timesteps, s=0.008): - """ - cosine schedule - as proposed in https://openreview.net/forum?id=-NEXDKk8gZ - """ - steps = timesteps + 1 - x = np.linspace(0, steps, steps) - alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2 - alphas_cumprod = alphas_cumprod / alphas_cumprod[0] - betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1]) - return np.clip(betas, a_min=0, a_max=0.999) - - -beta_schedule = { - "cosine": cosine_beta_schedule, - "linear": linear_beta_schedule, -} - - -class GaussianDiffusion(nn.Module): - def __init__(self, phone_encoder, out_dims, denoise_fn, - timesteps=1000, K_step=1000, loss_type=hparams.get('diff_loss_type', 'l1'), betas=None, spec_min=None, spec_max=None): - super().__init__() - self.denoise_fn = denoise_fn - if hparams.get('use_midi') is not None and hparams['use_midi']: - self.fs2 = FastSpeech2MIDI(phone_encoder, out_dims) - else: - self.fs2 = FastSpeech2(phone_encoder, out_dims) - self.mel_bins = out_dims - - if exists(betas): - betas = betas.detach().cpu().numpy() if isinstance(betas, torch.Tensor) else betas - else: - if 'schedule_type' in hparams.keys(): - betas = beta_schedule[hparams['schedule_type']](timesteps) - else: - betas = cosine_beta_schedule(timesteps) - - alphas = 1. - betas - alphas_cumprod = np.cumprod(alphas, axis=0) - alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) - - timesteps, = betas.shape - self.num_timesteps = int(timesteps) - self.K_step = K_step - self.loss_type = loss_type - - self.noise_list = deque(maxlen=4) - - to_torch = partial(torch.tensor, dtype=torch.float32) - - self.register_buffer('betas', to_torch(betas)) - self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) - self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) - - # calculations for diffusion q(x_t | x_{t-1}) and others - self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) - self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) - self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) - self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) - self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) - - # calculations for posterior q(x_{t-1} | x_t, x_0) - posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod) - # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t) - self.register_buffer('posterior_variance', to_torch(posterior_variance)) - # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain - self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20)))) - self.register_buffer('posterior_mean_coef1', to_torch( - betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))) - self.register_buffer('posterior_mean_coef2', to_torch( - (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))) - - self.register_buffer('spec_min', torch.FloatTensor(spec_min)[None, None, :hparams['keep_bins']]) - self.register_buffer('spec_max', torch.FloatTensor(spec_max)[None, None, :hparams['keep_bins']]) - - def q_mean_variance(self, x_start, t): - mean = extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start - variance = extract(1. - self.alphas_cumprod, t, x_start.shape) - log_variance = extract(self.log_one_minus_alphas_cumprod, t, x_start.shape) - return mean, variance, log_variance - - def predict_start_from_noise(self, x_t, t, noise): - return ( - extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - - extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise - ) - - def q_posterior(self, x_start, x_t, t): - posterior_mean = ( - extract(self.posterior_mean_coef1, t, x_t.shape) * x_start + - extract(self.posterior_mean_coef2, t, x_t.shape) * x_t - ) - posterior_variance = extract(self.posterior_variance, t, x_t.shape) - posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape) - return posterior_mean, posterior_variance, posterior_log_variance_clipped - - def p_mean_variance(self, x, t, cond, clip_denoised: bool): - noise_pred = self.denoise_fn(x, t, cond=cond) - x_recon = self.predict_start_from_noise(x, t=t, noise=noise_pred) - - if clip_denoised: - x_recon.clamp_(-1., 1.) - - model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t) - return model_mean, posterior_variance, posterior_log_variance - - @torch.no_grad() - def p_sample(self, x, t, cond, clip_denoised=True, repeat_noise=False): - b, *_, device = *x.shape, x.device - model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, cond=cond, clip_denoised=clip_denoised) - noise = noise_like(x.shape, device, repeat_noise) - # no noise when t == 0 - nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1))) - return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise - - @torch.no_grad() - def p_sample_plms(self, x, t, interval, cond, clip_denoised=True, repeat_noise=False): - """ - Use the PLMS method from [Pseudo Numerical Methods for Diffusion Models on Manifolds](https://arxiv.org/abs/2202.09778). - """ - - def get_x_pred(x, noise_t, t): - a_t = extract(self.alphas_cumprod, t, x.shape) - if t[0] < interval: - a_prev = torch.ones_like(a_t) - else: - a_prev = extract(self.alphas_cumprod, torch.max(t-interval, torch.zeros_like(t)), x.shape) - a_t_sq, a_prev_sq = a_t.sqrt(), a_prev.sqrt() - - x_delta = (a_prev - a_t) * ((1 / (a_t_sq * (a_t_sq + a_prev_sq))) * x - 1 / (a_t_sq * (((1 - a_prev) * a_t).sqrt() + ((1 - a_t) * a_prev).sqrt())) * noise_t) - x_pred = x + x_delta - - return x_pred - - noise_list = self.noise_list - noise_pred = self.denoise_fn(x, t, cond=cond) - - if len(noise_list) == 0: - x_pred = get_x_pred(x, noise_pred, t) - noise_pred_prev = self.denoise_fn(x_pred, max(t-interval, 0), cond=cond) - noise_pred_prime = (noise_pred + noise_pred_prev) / 2 - elif len(noise_list) == 1: - noise_pred_prime = (3 * noise_pred - noise_list[-1]) / 2 - elif len(noise_list) == 2: - noise_pred_prime = (23 * noise_pred - 16 * noise_list[-1] + 5 * noise_list[-2]) / 12 - elif len(noise_list) >= 3: - noise_pred_prime = (55 * noise_pred - 59 * noise_list[-1] + 37 * noise_list[-2] - 9 * noise_list[-3]) / 24 - - x_prev = get_x_pred(x, noise_pred_prime, t) - noise_list.append(noise_pred) - - return x_prev - - def q_sample(self, x_start, t, noise=None): - noise = default(noise, lambda: torch.randn_like(x_start)) - return ( - extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + - extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise - ) - - def p_losses(self, x_start, t, cond, noise=None, nonpadding=None): - noise = default(noise, lambda: torch.randn_like(x_start)) - - x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise) - x_recon = self.denoise_fn(x_noisy, t, cond) - - if self.loss_type == 'l1': - if nonpadding is not None: - loss = ((noise - x_recon).abs() * nonpadding.unsqueeze(1)).mean() - else: - # print('are you sure w/o nonpadding?') - loss = (noise - x_recon).abs().mean() - - elif self.loss_type == 'l2': - loss = F.mse_loss(noise, x_recon) - else: - raise NotImplementedError() - - return loss - - def forward(self, txt_tokens, mel2ph=None, spk_embed=None, - ref_mels=None, f0=None, uv=None, energy=None, infer=False, **kwargs): - b, *_, device = *txt_tokens.shape, txt_tokens.device - ret = self.fs2(txt_tokens, mel2ph, spk_embed, ref_mels, f0, uv, energy, - skip_decoder=(not infer), infer=infer, **kwargs) - cond = ret['decoder_inp'].transpose(1, 2) - - if not infer: - t = torch.randint(0, self.K_step, (b,), device=device).long() - x = ref_mels - x = self.norm_spec(x) - x = x.transpose(1, 2)[:, None, :, :] # [B, 1, M, T] - ret['diff_loss'] = self.p_losses(x, t, cond) - # nonpadding = (mel2ph != 0).float() - # ret['diff_loss'] = self.p_losses(x, t, cond, nonpadding=nonpadding) - else: - ret['fs2_mel'] = ret['mel_out'] - fs2_mels = ret['mel_out'] - t = self.K_step - fs2_mels = self.norm_spec(fs2_mels) - fs2_mels = fs2_mels.transpose(1, 2)[:, None, :, :] - - x = self.q_sample(x_start=fs2_mels, t=torch.tensor([t - 1], device=device).long()) - if hparams.get('gaussian_start') is not None and hparams['gaussian_start']: - print('===> gaussion start.') - shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2]) - x = torch.randn(shape, device=device) - - if hparams.get('pndm_speedup'): - print('===> pndm speed:', hparams['pndm_speedup']) - self.noise_list = deque(maxlen=4) - iteration_interval = hparams['pndm_speedup'] - for i in tqdm(reversed(range(0, t, iteration_interval)), desc='sample time step', - total=t // iteration_interval): - x = self.p_sample_plms(x, torch.full((b,), i, device=device, dtype=torch.long), iteration_interval, - cond) - else: - for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t): - x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond) - x = x[:, 0].transpose(1, 2) - if mel2ph is not None: # for singing - ret['mel_out'] = self.denorm_spec(x) * ((mel2ph > 0).float()[:, :, None]) - else: - ret['mel_out'] = self.denorm_spec(x) - return ret - - def norm_spec(self, x): - return (x - self.spec_min) / (self.spec_max - self.spec_min) * 2 - 1 - - def denorm_spec(self, x): - return (x + 1) / 2 * (self.spec_max - self.spec_min) + self.spec_min - - def cwt2f0_norm(self, cwt_spec, mean, std, mel2ph): - return self.fs2.cwt2f0_norm(cwt_spec, mean, std, mel2ph) - - def out2mel(self, x): - return x - - -class OfflineGaussianDiffusion(GaussianDiffusion): - def forward(self, txt_tokens, mel2ph=None, spk_embed=None, - ref_mels=None, f0=None, uv=None, energy=None, infer=False, **kwargs): - b, *_, device = *txt_tokens.shape, txt_tokens.device - - ret = self.fs2(txt_tokens, mel2ph, spk_embed, ref_mels, f0, uv, energy, - skip_decoder=True, infer=True, **kwargs) - cond = ret['decoder_inp'].transpose(1, 2) - fs2_mels = ref_mels[1] - ref_mels = ref_mels[0] - - if not infer: - t = torch.randint(0, self.K_step, (b,), device=device).long() - x = ref_mels - x = self.norm_spec(x) - x = x.transpose(1, 2)[:, None, :, :] # [B, 1, M, T] - ret['diff_loss'] = self.p_losses(x, t, cond) - else: - t = self.K_step - fs2_mels = self.norm_spec(fs2_mels) - fs2_mels = fs2_mels.transpose(1, 2)[:, None, :, :] - - x = self.q_sample(x_start=fs2_mels, t=torch.tensor([t - 1], device=device).long()) - - if hparams.get('gaussian_start') is not None and hparams['gaussian_start']: - print('===> gaussion start.') - shape = (cond.shape[0], 1, self.mel_bins, cond.shape[2]) - x = torch.randn(shape, device=device) - for i in tqdm(reversed(range(0, t)), desc='sample time step', total=t): - x = self.p_sample(x, torch.full((b,), i, device=device, dtype=torch.long), cond) - x = x[:, 0].transpose(1, 2) - ret['mel_out'] = self.denorm_spec(x) - return ret diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/stft_loss.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/stft_loss.py deleted file mode 100644 index 229e6c777dc9ec7f710842d1e648dba1189ec8b4..0000000000000000000000000000000000000000 --- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/stft_loss.py +++ /dev/null @@ -1,100 +0,0 @@ -# -*- coding: utf-8 -*- - -# Copyright 2019 Tomoki Hayashi -# MIT License (https://opensource.org/licenses/MIT) - -"""STFT-based Loss modules.""" -import librosa -import torch - -from modules.parallel_wavegan.losses import LogSTFTMagnitudeLoss, SpectralConvergengeLoss, stft - - -class STFTLoss(torch.nn.Module): - """STFT loss module.""" - - def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", - use_mel_loss=False): - """Initialize STFT loss module.""" - super(STFTLoss, self).__init__() - self.fft_size = fft_size - self.shift_size = shift_size - self.win_length = win_length - self.window = getattr(torch, window)(win_length) - self.spectral_convergenge_loss = SpectralConvergengeLoss() - self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss() - self.use_mel_loss = use_mel_loss - self.mel_basis = None - - def forward(self, x, y): - """Calculate forward propagation. - - Args: - x (Tensor): Predicted signal (B, T). - y (Tensor): Groundtruth signal (B, T). - - Returns: - Tensor: Spectral convergence loss value. - Tensor: Log STFT magnitude loss value. - - """ - x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window) - y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window) - if self.use_mel_loss: - if self.mel_basis is None: - self.mel_basis = torch.from_numpy(librosa.filters.mel(22050, self.fft_size, 80)).cuda().T - x_mag = x_mag @ self.mel_basis - y_mag = y_mag @ self.mel_basis - - sc_loss = self.spectral_convergenge_loss(x_mag, y_mag) - mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag) - - return sc_loss, mag_loss - - -class MultiResolutionSTFTLoss(torch.nn.Module): - """Multi resolution STFT loss module.""" - - def __init__(self, - fft_sizes=[1024, 2048, 512], - hop_sizes=[120, 240, 50], - win_lengths=[600, 1200, 240], - window="hann_window", - use_mel_loss=False): - """Initialize Multi resolution STFT loss module. - - Args: - fft_sizes (list): List of FFT sizes. - hop_sizes (list): List of hop sizes. - win_lengths (list): List of window lengths. - window (str): Window function type. - - """ - super(MultiResolutionSTFTLoss, self).__init__() - assert len(fft_sizes) == len(hop_sizes) == len(win_lengths) - self.stft_losses = torch.nn.ModuleList() - for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths): - self.stft_losses += [STFTLoss(fs, ss, wl, window, use_mel_loss)] - - def forward(self, x, y): - """Calculate forward propagation. - - Args: - x (Tensor): Predicted signal (B, T). - y (Tensor): Groundtruth signal (B, T). - - Returns: - Tensor: Multi resolution spectral convergence loss value. - Tensor: Multi resolution log STFT magnitude loss value. - - """ - sc_loss = 0.0 - mag_loss = 0.0 - for f in self.stft_losses: - sc_l, mag_l = f(x, y) - sc_loss += sc_l - mag_loss += mag_l - sc_loss /= len(self.stft_losses) - mag_loss /= len(self.stft_losses) - - return sc_loss, mag_loss diff --git a/spaces/AIWaves/Software_Company/src/agents/Action/__init__.py b/spaces/AIWaves/Software_Company/src/agents/Action/__init__.py deleted file mode 100644 index bb85ebbfc6ae1d83770263a1744fe14cb687931d..0000000000000000000000000000000000000000 --- a/spaces/AIWaves/Software_Company/src/agents/Action/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .base_action import Action \ No newline at end of file diff --git a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/work_dirs/__init__.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/work_dirs/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/conversation/[id]/message/[messageId]/prompt/+server.ts b/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/conversation/[id]/message/[messageId]/prompt/+server.ts deleted file mode 100644 index 6aaf582d7263e074d2be99cfebf366f253235c14..0000000000000000000000000000000000000000 --- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/src/routes/conversation/[id]/message/[messageId]/prompt/+server.ts +++ /dev/null @@ -1,23 +0,0 @@ -import { buildPrompt } from "$lib/buildPrompt"; -import { authCondition } from "$lib/server/auth"; -import { collections } from "$lib/server/database"; -import { models } from "$lib/server/models"; -import { error } from "@sveltejs/kit"; - -export async function GET({ params, locals }) { - return new Response( - JSON.stringify( - { - note: "This is a preview of the prompt that will be sent to the model when retrying the message. It may differ from what was sent in the past if the parameters have been updated since", - prompt, - model: "", - parameters: { - return_full_text: false, - }, - }, - null, - 2 - ), - { headers: { "Content-Type": "application/json" } } - ); -} diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/flip/Flip.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/flip/Flip.js deleted file mode 100644 index 5ebe8b6f76c124f8cc520b9fe6170725f2e6abe7..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/flip/Flip.js +++ /dev/null @@ -1,2 +0,0 @@ -import Flip from '../../../plugins/flip.js'; -export default Flip; \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fullwindowrectangle/FullWindowRectangle.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fullwindowrectangle/FullWindowRectangle.d.ts deleted file mode 100644 index c711cc1d3243b55855a46a74a3b5c78397440191..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fullwindowrectangle/FullWindowRectangle.d.ts +++ /dev/null @@ -1,2 +0,0 @@ -import FullWindowRectangle from "../../../plugins/fullwindowrectangle"; -export default FullWindowRectangle; \ No newline at end of file diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/InsertEmptyRow.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/InsertEmptyRow.js deleted file mode 100644 index d1bfc7947a8ec1be7f9906277677fbad6ca25ed9..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridsizer/InsertEmptyRow.js +++ /dev/null @@ -1,35 +0,0 @@ -var InseryEmptyRow = function (rowIndex, proportion, space) { - if (proportion === undefined) { - proportion = this.rowProportions[0] || 0; - } - if (space === undefined) { - space = this.space.row[0] || 0; - } - - this.rowCount += 1; - this.gridCount += this.columnCount; - - var args = [rowIndex * this.columnCount, 0]; - for (var i = 0; i < this.columnCount; i++) { - args.push(null); - } - this.sizerChildren.splice.apply(this.sizerChildren, args); - - this.rowProportions.push(proportion); - - this.rowHeight.length += 1; // this.rowHeight will be recalculated when layout() - - this.space.row.splice(rowIndex, 0, space); - - return this; -} - -var AddEmptyRow = function (proportion, space) { - InseryEmptyRow.call(this, this.rowCount, proportion, space); - return this; -} - -export { - InseryEmptyRow, - AddEmptyRow -}; \ No newline at end of file diff --git a/spaces/Aki004/herta-so-vits/cluster/__init__.py b/spaces/Aki004/herta-so-vits/cluster/__init__.py deleted file mode 100644 index f1b9bde04e73e9218a5d534227caa4c25332f424..0000000000000000000000000000000000000000 --- a/spaces/Aki004/herta-so-vits/cluster/__init__.py +++ /dev/null @@ -1,29 +0,0 @@ -import numpy as np -import torch -from sklearn.cluster import KMeans - -def get_cluster_model(ckpt_path): - checkpoint = torch.load(ckpt_path) - kmeans_dict = {} - for spk, ckpt in checkpoint.items(): - km = KMeans(ckpt["n_features_in_"]) - km.__dict__["n_features_in_"] = ckpt["n_features_in_"] - km.__dict__["_n_threads"] = ckpt["_n_threads"] - km.__dict__["cluster_centers_"] = ckpt["cluster_centers_"] - kmeans_dict[spk] = km - return kmeans_dict - -def get_cluster_result(model, x, speaker): - """ - x: np.array [t, 256] - return cluster class result - """ - return model[speaker].predict(x) - -def get_cluster_center_result(model, x,speaker): - """x: np.array [t, 256]""" - predict = model[speaker].predict(x) - return model[speaker].cluster_centers_[predict] - -def get_center(model, x,speaker): - return model[speaker].cluster_centers_[x] diff --git a/spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_r100.py b/spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_r100.py deleted file mode 100644 index 93d0701c0094517cec147c382b005e8063938548..0000000000000000000000000000000000000000 --- a/spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_r100.py +++ /dev/null @@ -1,26 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.loss = "cosface" -config.network = "r100" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 1.0 -config.fp16 = True -config.momentum = 0.9 -config.weight_decay = 5e-4 -config.batch_size = 128 -config.lr = 0.1 # batch size is 512 - -config.rec = "/train_tmp/glint360k" -config.num_classes = 360232 -config.num_image = 17091657 -config.num_epoch = 20 -config.warmup_epoch = -1 -config.decay_epoch = [8, 12, 15, 18] -config.val_targets = ["lfw", "cfp_fp", "agedb_30"] diff --git a/spaces/AmirTrader/LinearRegression/README.md b/spaces/AmirTrader/LinearRegression/README.md deleted file mode 100644 index 02323f5b61e61c26972aad862a5003b504dfc345..0000000000000000000000000000000000000000 --- a/spaces/AmirTrader/LinearRegression/README.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: Linear Regression - UnderValued Stocks -emoji: 📈 -colorFrom: gray -colorTo: green -sdk: docker -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/mapper/options/test_options.py b/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/mapper/options/test_options.py deleted file mode 100644 index aab2e5a5bba1038b97110fa6c8e8bce14de7390c..0000000000000000000000000000000000000000 --- a/spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/mapper/options/test_options.py +++ /dev/null @@ -1,42 +0,0 @@ -from argparse import ArgumentParser - - -class TestOptions: - - def __init__(self): - self.parser = ArgumentParser() - self.initialize() - - def initialize(self): - # arguments for inference script - self.parser.add_argument('--exp_dir', type=str, help='Path to experiment output directory') - self.parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to model checkpoint') - self.parser.add_argument('--couple_outputs', action='store_true', - help='Whether to also save inputs + outputs side-by-side') - - self.parser.add_argument('--mapper_type', default='LevelsMapper', type=str, help='Which mapper to use') - self.parser.add_argument('--no_coarse_mapper', default=False, action="store_true") - self.parser.add_argument('--no_medium_mapper', default=False, action="store_true") - self.parser.add_argument('--no_fine_mapper', default=False, action="store_true") - self.parser.add_argument('--stylegan_size', default=1024, type=int) - - self.parser.add_argument('--test_batch_size', default=2, type=int, help='Batch size for testing and inference') - self.parser.add_argument('--latents_test_path', default=None, type=str, help="The latents for the validation") - self.parser.add_argument('--test_workers', default=2, type=int, - help='Number of test/inference dataloader workers') - - self.parser.add_argument('--n_images', type=int, default=None, - help='Number of images to output. If None, run on all data') - - self.parser.add_argument('--run_id', type=str, default='PKNWUQRQRKXQ', - help='The generator id to use') - - self.parser.add_argument('--image_name', type=str, default='', - help='image to run on') - - self.parser.add_argument('--edit_name', type=str, default='', - help='edit type') - - def parse(self): - opts = self.parser.parse_args() - return opts diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/training/projectors/w_plus_projector.py b/spaces/Amrrs/DragGan-Inversion/PTI/training/projectors/w_plus_projector.py deleted file mode 100644 index b9cce427e5374c5ddce90199e1184f84a13d30c5..0000000000000000000000000000000000000000 --- a/spaces/Amrrs/DragGan-Inversion/PTI/training/projectors/w_plus_projector.py +++ /dev/null @@ -1,145 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Project given image to the latent space of pretrained network pickle.""" - -import copy -import wandb -import numpy as np -import torch -import torch.nn.functional as F -from tqdm import tqdm -from configs import global_config, hyperparameters -import dnnlib -from utils.log_utils import log_image_from_w - - -def project( - G, - target: torch.Tensor, # [C,H,W] and dynamic range [0,255], W & H must match G output resolution - *, - num_steps=1000, - w_avg_samples=10000, - initial_learning_rate=0.01, - initial_noise_factor=0.05, - lr_rampdown_length=0.25, - lr_rampup_length=0.05, - noise_ramp_length=0.75, - regularize_noise_weight=1e5, - verbose=False, - device: torch.device, - use_wandb=False, - initial_w=None, - image_log_step=global_config.image_rec_result_log_snapshot, - w_name: str -): - assert target.shape == (G.img_channels, G.img_resolution, G.img_resolution) - - def logprint(*args): - if verbose: - print(*args) - - G = copy.deepcopy(G).eval().requires_grad_(False).to(device).float() # type: ignore - - # Compute w stats. - logprint(f'Computing W midpoint and stddev using {w_avg_samples} samples...') - z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim) - w_samples = G.mapping(torch.from_numpy(z_samples).to(device), None) # [N, L, C] - w_samples = w_samples[:, :1, :].cpu().numpy().astype(np.float32) # [N, 1, C] - w_avg = np.mean(w_samples, axis=0, keepdims=True) # [1, 1, C] - w_avg_tensor = torch.from_numpy(w_avg).to(global_config.device) - w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5 - - start_w = initial_w if initial_w is not None else w_avg - - # Setup noise inputs. - noise_bufs = {name: buf for (name, buf) in G.synthesis.named_buffers() if 'noise_const' in name} - - # Load VGG16 feature detector. - url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt' - with dnnlib.util.open_url(url) as f: - vgg16 = torch.jit.load(f).eval().to(device) - - # Features for target image. - target_images = target.unsqueeze(0).to(device).to(torch.float32) - if target_images.shape[2] > 256: - target_images = F.interpolate(target_images, size=(256, 256), mode='area') - target_features = vgg16(target_images, resize_images=False, return_lpips=True) - - start_w = np.repeat(start_w, G.mapping.num_ws, axis=1) - w_opt = torch.tensor(start_w, dtype=torch.float32, device=device, - requires_grad=True) # pylint: disable=not-callable - - optimizer = torch.optim.Adam([w_opt] + list(noise_bufs.values()), betas=(0.9, 0.999), - lr=hyperparameters.first_inv_lr) - - # Init noise. - for buf in noise_bufs.values(): - buf[:] = torch.randn_like(buf) - buf.requires_grad = True - - for step in tqdm(range(num_steps)): - - # Learning rate schedule. - t = step / num_steps - w_noise_scale = w_std * initial_noise_factor * max(0.0, 1.0 - t / noise_ramp_length) ** 2 - lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length) - lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi) - lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length) - lr = initial_learning_rate * lr_ramp - for param_group in optimizer.param_groups: - param_group['lr'] = lr - - # Synth images from opt_w. - w_noise = torch.randn_like(w_opt) * w_noise_scale - ws = (w_opt + w_noise) - - synth_images = G.synthesis(ws, noise_mode='const', force_fp32=True) - - # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images. - synth_images = (synth_images + 1) * (255 / 2) - if synth_images.shape[2] > 256: - synth_images = F.interpolate(synth_images, size=(256, 256), mode='area') - - # Features for synth images. - synth_features = vgg16(synth_images, resize_images=False, return_lpips=True) - dist = (target_features - synth_features).square().sum() - - # Noise regularization. - reg_loss = 0.0 - for v in noise_bufs.values(): - noise = v[None, None, :, :] # must be [1,1,H,W] for F.avg_pool2d() - while True: - reg_loss += (noise * torch.roll(noise, shifts=1, dims=3)).mean() ** 2 - reg_loss += (noise * torch.roll(noise, shifts=1, dims=2)).mean() ** 2 - if noise.shape[2] <= 8: - break - noise = F.avg_pool2d(noise, kernel_size=2) - loss = dist + reg_loss * regularize_noise_weight - - if step % image_log_step == 0: - with torch.no_grad(): - if use_wandb: - global_config.training_step += 1 - wandb.log({f'first projection _{w_name}': loss.detach().cpu()}, step=global_config.training_step) - log_image_from_w(w_opt, G, w_name) - - # Step - optimizer.zero_grad(set_to_none=True) - loss.backward() - optimizer.step() - logprint(f'step {step + 1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}') - - # Normalize noise. - with torch.no_grad(): - for buf in noise_bufs.values(): - buf -= buf.mean() - buf *= buf.square().mean().rsqrt() - - del G - return w_opt diff --git a/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/loop/style_training_loop.py b/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/loop/style_training_loop.py deleted file mode 100644 index fa0dd4ae836b2a85a13fd46f1e77ba4c38ecdefd..0000000000000000000000000000000000000000 --- a/spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/loop/style_training_loop.py +++ /dev/null @@ -1,170 +0,0 @@ -import numpy as np -import torch -from pytorch_lightning.loops import Loop - -from src.dataset import DATASET_REGISTRY -from src.dataset.ray_utils import denormalize_vgg, normalize_vgg -from src.loop.utils import N_to_reso, cal_n_samples -from src.model import MODEL_REGISTRY -from src.sampler.simple_sampler import SimpleSampler, InfiniteSamplerWrapper -import torch.nn.functional as TF - -from src.style_module.style_module import cal_mse_content_loss, cal_adain_style_loss - - -class StyleTrainingLoop(Loop): - def __init__(self, epoch, cfg, renderer): - super().__init__() - self.cfg = cfg - self.model = MODEL_REGISTRY.get(self.cfg["model"]["name"])(cfg) - - self.dataloader = DATASET_REGISTRY.get(self.cfg["dataset"]["name"])( - **self.cfg["dataset"]["train"]["params"], - ) - self.renderer = renderer - self.optimizer = None - self.training_sampler = None - self.frame_sampler = None - self.style_dataloader = None - self.iteration = 0 - self.epoch = epoch - self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - self.init_loop() - self.init_optimizer() - - def init_loop(self): - self.white_bg = self.dataloader.white_bg - self.near_far = self.dataloader.near_far - self.h_rays, self.w_rays = self.dataloader.img_wh[1], self.dataloader.img_wh[0] - - self.step_ratio = self.cfg["sampler"]["params"]["step_ratio"] - self.batch_size = self.cfg["sampler"]["params"]["batch_size"] - self.patch_size = self.cfg["sampler"]["params"]["patch_size"] - self.chunk_size = self.cfg["sampler"]["params"]["chunk_size"] - - self.aabb = self.dataloader.scene_bbox.to(self.device) - reso_cur = N_to_reso(self.cfg["sampler"]["params"]["N_voxel_init"], self.aabb) - self.nSamples = min(int(self.cfg["sampler"]["params"]["n_samples"]), cal_n_samples(reso_cur, self.step_ratio)) - - torch.cuda.empty_cache() - self.dataloader.prepare_feature_data(self.model.tensorf.encoder) - self.allrays, self.allfeatures = self.dataloader.all_rays, self.dataloader.all_features - self.allrays_stack, self.allrgbs_stack = self.dataloader.all_rays_stack, self.dataloader.all_rgbs_stack - - if not self.model.ndc_ray: - self.allrays, self.allfeatures = self.model.tensorf.filtering_rays(self.allrays, self.allfeatures, - bbox_only=True) - - self.training_sampler = SimpleSampler(self.allrays.shape[0], self.batch_size) - self.frame_sampler = iter(InfiniteSamplerWrapper(self.allrays_stack.size(0))) # every next(sampler) returns a frame index - - self.style_dataloader = DATASET_REGISTRY.get(self.cfg["style_dataset"]["name"])( - datadir=self.cfg["style_dataset"]["train"]["params"]["datadir"], - batch_size=self.cfg["style_dataset"]["train"]["params"]["batch_size"], - sampler=InfiniteSamplerWrapper, - image_side_length=self.cfg["style_dataset"]["train"]["params"]["image_side_length"], - num_workers=self.cfg["style_dataset"]["train"]["params"]["num_workers"], - ) - - def init_optimizer(self): - grad_vars = self.model.tensorf.get_optparam_groups_feature_mod(self.cfg["optimizer"]["lr_init"], self.cfg["optimizer"]["lr_basis"]) - - if self.cfg["optimizer"]["lr_decay_iters"] > 0: - self.lr_factor = self.cfg["optimizer"]["lr_decay_target_ratio"] ** (1 / self.cfg["optimizer"]["lr_decay_iters"]) - else: - self.lr_factor = self.cfg["optimizer"]["lr_decay_target_ratio"] ** (1 / self.cfg["trainer"]["n_iters"]) - - print("lr decay", self.cfg["optimizer"]["lr_decay_target_ratio"], self.cfg["optimizer"]["lr_decay_iters"]) - - self.optimizer = torch.optim.Adam(grad_vars, betas=(0.9, 0.99)) - - def advance(self): - # get style_img, this style_img has NOT been normalized according to the pretrained VGGmodel - style_img = next(self.style_dataloader)[0].to(self.device) - - # randomly sample patch_size*patch_size patch from given frame - frame_idx = next(self.frame_sampler) - start_h = np.random.randint(0, self.h_rays - self.patch_size + 1) - start_w = np.random.randint(0, self.w_rays - self.patch_size + 1) - if self.white_bg: - # move random sampled patches into center - mid_h, mid_w = (self.h_rays - self.patch_size + 1) / 2, (self.w_rays - self.patch_size + 1) / 2 - if mid_h - start_h >= 1: - start_h += np.random.randint(0, mid_h - start_h) - elif mid_h - start_h <= -1: - start_h += np.random.randint(mid_h - start_h, 0) - if mid_w - start_w >= 1: - start_w += np.random.randint(0, mid_w - start_w) - elif mid_w - start_w <= -1: - start_w += np.random.randint(mid_w - start_w, 0) - - rays_train = self.allrays_stack[frame_idx, start_h:start_h + self.patch_size, start_w:start_w + self.patch_size, :] \ - .reshape(-1, 6).to(self.device) - # [patch*patch, 6] - - rgbs_train = self.allrgbs_stack[frame_idx, start_h:(start_h + self.patch_size), - start_w:(start_w + self.patch_size), :].to(self.device) - # [patch, patch, 3] - - feature_map, acc_map, style_feature = self.renderer(rays_train, self.model.tensorf, chunk=self.chunk_size, - N_samples=self.nSamples, white_bg=self.white_bg, - ndc_ray=self.model.ndc_ray, render_feature=True, style_img=style_img, - device=self.device, is_train=True) - - feature_map = feature_map.reshape(self.patch_size, self.patch_size, 256)[None, ...].permute(0, 3, 1, 2) - rgb_map = self.model.tensorf.decoder(feature_map) - - # feature_map is trained with normalized rgb maps, so here we don't normalize the rgb map again. - rgbs_train = normalize_vgg(rgbs_train[None, ...].permute(0, 3, 1, 2)) - - out_image_feature = self.model.tensorf.encoder(rgb_map) - content_feature = self.model.tensorf.encoder(rgbs_train) - - if self.white_bg: - mask = acc_map.reshape(self.patch_size, self.patch_size, 1)[None, ...].permute(0, 3, 1, 2) - # if not (mask > 0.5).any(): - # continue - - # content loss - _mask = TF.interpolate(mask, size=content_feature.relu4_1.size()[-2:], mode='bilinear').ge(1e-5) - content_loss = cal_mse_content_loss(torch.masked_select(content_feature.relu4_1, _mask), - torch.masked_select(out_image_feature.relu4_1, _mask)) - # style loss - style_loss = 0. - for style_feature, image_feature in zip(style_feature, out_image_feature): - _mask = TF.interpolate(mask, size=image_feature.size()[-2:], mode='bilinear').ge(1e-5) - C = image_feature.size()[1] - masked_img_feature = torch.masked_select(image_feature, _mask).reshape(1, C, -1) - style_loss += cal_adain_style_loss(style_feature, masked_img_feature) - - content_loss *= self.cfg["style_config"]["content_weight"] - style_loss *= self.cfg["style_config"]["style_weight"] - else: - # content loss - content_loss = cal_mse_content_loss(content_feature.relu4_1, out_image_feature.relu4_1) - # style loss - style_loss = 0. - for style_feature, image_feature in zip(style_feature, out_image_feature): - style_loss += cal_adain_style_loss(style_feature, image_feature) - - content_loss *= self.cfg["style_config"]["content_weight"] - style_loss *= self.cfg["style_config"]["style_weight"] - - feature_tv_loss = self.model.tensorf.tvreg(feature_map) * self.cfg["style_config"]["featuremap_tv_weight"] - image_tv_loss = self.model.tensorf.tvreg(denormalize_vgg(rgb_map)) * self.cfg["style_config"]["image_tv_weight"] - - total_loss = content_loss + style_loss + feature_tv_loss + image_tv_loss - - self.iteration += 1 - - self.optimizer.zero_grad() - total_loss.backward() - self.optimizer.step() - - for param_group in self.optimizer.param_groups: - param_group['lr'] = param_group['lr'] * self.lr_factor - - - - - diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/karras_ve/__init__.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/pipelines/karras_ve/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/closure.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/closure.py deleted file mode 100644 index b955f81f425be4ac3e6bb3f4aac653887989e872..0000000000000000000000000000000000000000 --- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/hooks/closure.py +++ /dev/null @@ -1,11 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from .hook import HOOKS, Hook - - -@HOOKS.register_module() -class ClosureHook(Hook): - - def __init__(self, fn_name, fn): - assert hasattr(self, fn_name) - assert callable(fn) - setattr(self, fn_name, fn) diff --git a/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/datasets/__init__.py b/spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/datasets/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/commands/debug.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/commands/debug.py deleted file mode 100644 index 2a3e7d298f393ed8532e4f11913635efc94cb329..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/commands/debug.py +++ /dev/null @@ -1,199 +0,0 @@ -import importlib.resources -import locale -import logging -import os -import sys -from optparse import Values -from types import ModuleType -from typing import Any, Dict, List, Optional - -import pip._vendor -from pip._vendor.certifi import where -from pip._vendor.packaging.version import parse as parse_version - -from pip._internal.cli import cmdoptions -from pip._internal.cli.base_command import Command -from pip._internal.cli.cmdoptions import make_target_python -from pip._internal.cli.status_codes import SUCCESS -from pip._internal.configuration import Configuration -from pip._internal.metadata import get_environment -from pip._internal.utils.logging import indent_log -from pip._internal.utils.misc import get_pip_version - -logger = logging.getLogger(__name__) - - -def show_value(name: str, value: Any) -> None: - logger.info("%s: %s", name, value) - - -def show_sys_implementation() -> None: - logger.info("sys.implementation:") - implementation_name = sys.implementation.name - with indent_log(): - show_value("name", implementation_name) - - -def create_vendor_txt_map() -> Dict[str, str]: - with importlib.resources.open_text("pip._vendor", "vendor.txt") as f: - # Purge non version specifying lines. - # Also, remove any space prefix or suffixes (including comments). - lines = [ - line.strip().split(" ", 1)[0] for line in f.readlines() if "==" in line - ] - - # Transform into "module" -> version dict. - return dict(line.split("==", 1) for line in lines) - - -def get_module_from_module_name(module_name: str) -> ModuleType: - # Module name can be uppercase in vendor.txt for some reason... - module_name = module_name.lower().replace("-", "_") - # PATCH: setuptools is actually only pkg_resources. - if module_name == "setuptools": - module_name = "pkg_resources" - - __import__(f"pip._vendor.{module_name}", globals(), locals(), level=0) - return getattr(pip._vendor, module_name) - - -def get_vendor_version_from_module(module_name: str) -> Optional[str]: - module = get_module_from_module_name(module_name) - version = getattr(module, "__version__", None) - - if not version: - # Try to find version in debundled module info. - assert module.__file__ is not None - env = get_environment([os.path.dirname(module.__file__)]) - dist = env.get_distribution(module_name) - if dist: - version = str(dist.version) - - return version - - -def show_actual_vendor_versions(vendor_txt_versions: Dict[str, str]) -> None: - """Log the actual version and print extra info if there is - a conflict or if the actual version could not be imported. - """ - for module_name, expected_version in vendor_txt_versions.items(): - extra_message = "" - actual_version = get_vendor_version_from_module(module_name) - if not actual_version: - extra_message = ( - " (Unable to locate actual module version, using" - " vendor.txt specified version)" - ) - actual_version = expected_version - elif parse_version(actual_version) != parse_version(expected_version): - extra_message = ( - " (CONFLICT: vendor.txt suggests version should" - " be {})".format(expected_version) - ) - logger.info("%s==%s%s", module_name, actual_version, extra_message) - - -def show_vendor_versions() -> None: - logger.info("vendored library versions:") - - vendor_txt_versions = create_vendor_txt_map() - with indent_log(): - show_actual_vendor_versions(vendor_txt_versions) - - -def show_tags(options: Values) -> None: - tag_limit = 10 - - target_python = make_target_python(options) - tags = target_python.get_tags() - - # Display the target options that were explicitly provided. - formatted_target = target_python.format_given() - suffix = "" - if formatted_target: - suffix = f" (target: {formatted_target})" - - msg = "Compatible tags: {}{}".format(len(tags), suffix) - logger.info(msg) - - if options.verbose < 1 and len(tags) > tag_limit: - tags_limited = True - tags = tags[:tag_limit] - else: - tags_limited = False - - with indent_log(): - for tag in tags: - logger.info(str(tag)) - - if tags_limited: - msg = ( - "...\n[First {tag_limit} tags shown. Pass --verbose to show all.]" - ).format(tag_limit=tag_limit) - logger.info(msg) - - -def ca_bundle_info(config: Configuration) -> str: - levels = set() - for key, _ in config.items(): - levels.add(key.split(".")[0]) - - if not levels: - return "Not specified" - - levels_that_override_global = ["install", "wheel", "download"] - global_overriding_level = [ - level for level in levels if level in levels_that_override_global - ] - if not global_overriding_level: - return "global" - - if "global" in levels: - levels.remove("global") - return ", ".join(levels) - - -class DebugCommand(Command): - """ - Display debug information. - """ - - usage = """ - %prog
Image->Text:
-
-
-
-
- Image Caption
-{image_caption}
-
-
- Dense Caption
-{dense_caption}
-
-
- Region Semantic
-{region_semantic}
-
-
- '''
- if image_generation_status:
- custom_output += f'''
-
-
- GPT4 Reasoning:
-{gen_text}
-Text->Image:
-
-
- '''
- return custom_output
-
-processor = ImageTextTransformation(args)
-
-# Create Gradio input and output components
-image_input = gr.inputs.Image(type='filepath', label="Input Image")
-semantic_segment_checkbox = gr.inputs.Checkbox(label="Semantic Segment", default=False)
-image_generation_checkbox = gr.inputs.Checkbox(label="Image Generation", default=False)
-
-
-extra_title = r'' + '\n' + \
- r'[](https://huggingface.co/spaces/Awiny/Image2Paragraph?duplicate=true)' + '\n\n'
-
-
-
-logo_base64 = add_logo()
-# Create the title with the logo
-title_with_logo = \
- f'
- 
-
- Generated Image
-
- 
Understanding Image with Text'
-
-examples = [
- ["examples/test_4.jpg"],
-]
-
-# Create Gradio interface
-interface = gr.Interface(
- fn=lambda image, options: process_image(image, options, processor),
- inputs=[image_input,
- gr.CheckboxGroup(
- label="Options",
- choices=["Image Generation", "Semantic Segment"],
- ),
- ],
- outputs=gr.outputs.HTML(),
- title=title_with_logo,
- examples=examples,
- description=extra_title +"""
- Image.txt. This code support image to text transformation. Then the generated text can do retrieval, question answering et al to conduct zero-shot.
- \n Github: https://github.com/showlab/Image2Paragraph
- \n Twitter: https://twitter.com/awinyimgprocess/status/1646225454599372800?s=46&t=HvOe9T2n35iFuCHP5aIHpQ
- \n For online demo, we use smallest model to speed up. For better result, look for github for using large models.
- \n Ttext2image model is controlnet, which used canny edge as reference.
- \n To speed up, we generate image with small size 384, run the code local for high-quality sample.
- """
-)
-
-# Launch the interface
-interface.launch()
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Anime Todo Star Blockman Ir Descargar.md b/spaces/Benson/text-generation/Examples/Anime Todo Star Blockman Ir Descargar.md
deleted file mode 100644
index 03243af0fffd15ce656fa368f3cbc291de2dfff8..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Anime Todo Star Blockman Ir Descargar.md
+++ /dev/null
@@ -1,60 +0,0 @@
-
-Anime All Star Blockman Go: Una guía para principiantes
-Si eres un fan de los juegos de anime y acción, es posible que desees echar un vistazo a Anime All Star Blockman Go, un nuevo juego que combina ambos elementos de una manera divertida y emocionante. En este juego, puedes elegir entre una variedad de personajes de anime, cada uno con sus propias habilidades y habilidades únicas, y explorar un vasto mapa lleno de enemigos, jefes, eventos y recompensas. También puedes unirte a un clan y participar en guerras de clanes, donde puedes cooperar con otros jugadores y competir contra otros clanes. En este artículo, te daremos un breve resumen del juego y sus características, así como algunos consejos y trucos sobre cómo jugarlo mejor.
-¿Qué es Anime All Star Blockman Go?
-Anime All Star Blockman Go es un juego de acción gratuito desarrollado por Blockman GO Studio, el mismo desarrollador detrás de otros juegos populares como Bed Wars, Sky Wars, Egg Wars y más. El juego está disponible para dispositivos Android e iOS, y puedes descargarlo desde la Google Play Store o la App Store. El juego es parte de la plataforma Blockman GO, que te permite acceder a varios mini juegos con tus amigos de forma gratuita.
-anime todo star blockman ir descargar
DOWNLOAD ••• https://bltlly.com/2v6JIy
-
El juego está inspirado en varias series de anime, como Naruto, One Piece, Dragon Ball, Bleach, Fairy Tail, Attack on Titan, My Hero Academia y más. Puedes elegir entre más de 50 personajes de anime diferentes, cada uno con sus propias habilidades y habilidades únicas. También puedes personalizar la apariencia de tu personaje con diferentes atuendos, accesorios, armas y mascotas.
-El modo principal del juego es el modo aventura, donde puedes explorar un gran mapa lleno de enemigos, jefes, eventos y recompensas. Puedes luchar solo o formar equipo con otros jugadores para derrotar a enemigos poderosos y ganar monedas y boletos. Las monedas son la moneda principal del juego, que puedes usar para comprar artículos en la tienda o mejorar las habilidades de tu personaje. Las entradas se utilizan para desbloquear nuevos personajes o girar la rueda de la suerte para tener la oportunidad de obtener objetos raros.
- -Descargar e instalar Anime All Star Blockman Go es muy fácil. Solo tienes que seguir estos sencillos pasos:
--
-
Cómo unirse a un clan y participar en guerras de clanes
-Unirte a un clan y participar en guerras de clanes te dará más características sociales y competitivas en Anime All Star Blockman Go. Puedes unirte a un clan aplicando a un clan existente o creando tu propio clan. También puedes participar en guerras de clanes, donde puedes cooperar con los miembros de tu clan y competir contra otros clanes por gloria y recompensas. Aquí hay algunos consejos sobre cómo unirse a un clan y participar en las guerras de clanes:
--
-
Conclusión
-Anime All Star Blockman Go es un juego divertido y emocionante que combina elementos de anime y acción de una manera única. Puedes elegir entre una variedad de personajes de anime, cada uno con sus propias habilidades y habilidades únicas, y explorar un vasto mapa lleno de enemigos, jefes, eventos y recompensas. También puedes unirte a un clan y participar en guerras de clanes, donde puedes cooperar con otros jugadores y competir contra otros clanes. Si usted está buscando un juego que le mantendrá entretenido y desafiado, usted debe dar definitivamente Anime All Star Blockman Go un intento.
- -Esperamos que haya disfrutado de este artículo y aprendido algo nuevo sobre Anime All Star Blockman Go. Si lo hiciste, por favor compártelo con tus amigos y déjanos un comentario abajo. ¡Gracias por leer!
-Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes y sus respuestas sobre Anime All Star Blockman Go:
--
-
Anime All Star Blockman Go requiere Android 4.1 o superior o iOS 9.0 o superior para funcionar sin problemas. También requiere una conexión a Internet para jugar en línea.
-Puedes obtener Gcubes gratis luchando contra jefes, abriendo cajas, girando la rueda de la suerte, viendo anuncios, completando ofertas o invitando a tus amigos a jugar el juego.
-Puedes cambiar la apariencia de tu personaje comprando diferentes trajes, accesorios, armas y mascotas de la tienda o obteniéndolos de la rueda de la suerte. También puedes personalizar el color de cabello, el color de ojos, el color de piel y el nombre de tu personaje.
-Puede reportar un error o un problema en Anime All Star Blockman Go tocando el icono de configuración en la esquina superior derecha de la pantalla y luego tocando en "Feedback". También puede ponerse en contacto con el desarrollador a través de su sitio web oficial, página de Facebook, cuenta de Twitter o canal de YouTube.
-Puede actualizar Anime All Star Blockman Ir a la última versión yendo a la Google Play Store o la App Store en su dispositivo y tocando en "Actualizar". También puedes habilitar actualizaciones automáticas para el juego en la configuración de tu dispositivo.
--
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Cuerda Hroe 3 Mod Apk Revdl.md b/spaces/Benson/text-generation/Examples/Cuerda Hroe 3 Mod Apk Revdl.md deleted file mode 100644 index 76fc83b836eeba8113233009741deb8289ba13b9..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Cuerda Hroe 3 Mod Apk Revdl.md +++ /dev/null @@ -1,47 +0,0 @@ - -
Rope Hero 3 Mod Apk Revdl: Una guía para descargar y jugar el último juego de superhéroes
-¿Te gustan los juegos de superhéroes? ¿Quieres cruzar la ciudad como Spider-Man, luchar contra criminales como Batman y salvar el mundo como Superman? Si es así, entonces deberías probar Rope Hero 3, un emocionante juego de acción en 3D que te permite convertirte en un héroe de cuerda con increíbles superpoderes. Pero espera, hay más. También puedes descargar e instalar Rope Hero 3 mod apk revdl, una versión modificada del juego que te da recursos ilimitados, armas, vehículos y más. En este artículo, le mostraremos cómo descargar y jugar Rope Hero 3 mod apk revdl, y le dará algunos consejos y trucos para dominar el juego.
-cuerda héroe 3 mod apk revdl
Download Zip - https://bltlly.com/2v6Jkh
-
Cómo descargar e instalar Rope Hero 3 Mod Apk Revdl
-Si quieres disfrutar de Rope Hero 3 con todas sus características desbloqueadas, es necesario descargar e instalar Rope Hero 3 mod apk revdl. Esta es una versión modificada del juego que te da gemas ilimitadas, dinero, armas, munición, armadura y más. Puedes usar estos recursos para comprar lo que quieras en el juego, como pistolas, cuchillos, bazucas, blasters, etc. También puedes actualizar tu traje, habilidades y habilidades para hacer que tu héroe de cuerda sea más poderoso y ágil.
-Para descargar e instalar Rope Hero 3 mod apk revdl, debe seguir estos pasos:
--
-
Cómo jugar héroe de cuerda 3 Mod Apk Revdl
- -La jugabilidad básica y los controles de Rope Hero 3 son fáciles de aprender. Puede utilizar el joystick virtual en el lado izquierdo de la pantalla para mover su héroe de cuerda alrededor de la ciudad. Puede utilizar los botones en el lado derecho de la pantalla para realizar varias acciones, como saltar, disparar, balancearse, etc. También puede tocar los iconos en la parte superior de la pantalla para acceder a su inventario, mapa, misiones y ajustes. Puedes personalizar la apariencia, el traje, las armas y las habilidades de tu héroe de cuerda según tu preferencia.
-El juego tiene muchas características y habilidades que lo hacen emocionante y desafiante. Puedes usar tu súper cuerda para balancearte por la ciudad como Spider-Man, y sentir la adrenalina mientras vuelas por el aire. También puedes usar tu cuerda para escalar paredes, montar techos y atraer enemigos y coches. Puedes usar tu cuerda como arma para azotar, estrangular o lanzar a tus enemigos. También puedes usar varias armas y dispositivos para luchar contra pandillas, policías, clones y otros enemigos. Puede elegir entre una amplia gama de armas, cuchillos, bazucas, blásters, granadas, etc. También puede utilizar dispositivos especiales como jetpacks, drones, imanes, etc. para mejorar su juego.
- -El juego tiene muchas misiones y tareas que tienes que completar para progresar en la historia y desbloquear nuevas características. Puedes seguir el mapa y los indicadores para encontrar tus objetivos y completarlos. Algunas de las misiones incluyen salvar rehenes, destruir bases de clones, robar coches, robar bancos, etc. También puedes explorar la ciudad del mundo abierto y encontrar secretos y ubicaciones ocultos. Puedes interactuar con varios personajes y objetos de la ciudad, como civiles, animales, máquinas expendedoras, etc. También puedes causar caos y caos en la ciudad destruyendo edificios, coches, semáforos, etc.
- -Consejos y trucos para maestro héroe de cuerda 3 Mod Apk Revdl
-Si quieres convertirte en un maestro de Rope Hero 3 mod apk revdl, necesitas saber algunos consejos y trucos que pueden ayudarte a mejorar tus habilidades y rendimiento. Estos son algunos de ellos:
--
-
Conclusión
- -Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes y respuestas relacionadas con Rope Hero 3 mod apk revdl:
-Q: ¿Es Rope Hero 3 mod apk revdl seguro para descargar e instalar?
-A: Sí, Rope Hero 3 mod apk revdl es seguro para descargar e instalar desde el sitio web revdl, que es una fuente confiable de juegos y aplicaciones modded. Sin embargo, siempre debe tener cuidado al descargar e instalar cualquier archivo de Internet, y escanearlo en busca de virus o malware antes de abrirlo.
-Q: ¿Cómo puedo actualizar Rope Hero 3 mod apk revdl?
-A: Para actualizar Rope Hero 3 mod apk revdl, es necesario visitar revdl sitio web de nuevo y descargar la última versión de la apk mod. Entonces, es necesario desinstalar la versión anterior del juego desde su dispositivo e instalar el nuevo. También puedes buscar actualizaciones desde dentro del juego pulsando en el icono de configuración y seleccionando la opción de actualización.
-Q: ¿Puedo jugar Rope Hero 3 mod apk revdl en línea con otros jugadores?
-A: No, Rope Hero 3 mod apk revdl es un juego sin conexión que no requiere una conexión a Internet para jugar. Puedes jugar solo o con tus amigos en el mismo dispositivo usando el modo de pantalla dividida. Sin embargo, no puedes jugar online con otros jugadores o sincronizar tu progreso con otros dispositivos.
-Q: ¿Puedo jugar Rope Hero 3 mod apk revdl en dispositivos PC o iOS?
-A: No, Rope Hero 3 mod apk revdl solo es compatible con dispositivos Android. No puede reproducirlo en dispositivos PC o iOS a menos que use un emulador o un simulador. Sin embargo, esto puede afectar el rendimiento y la calidad del juego, y puede no funcionar correctamente.
-Q: ¿Cuáles son algunos otros juegos como Rope Hero 3 mod apk revdl?
-A: Si te gusta Rope Hero 3 mod apk revdl, también puede gustar algunos otros juegos que son similares en género y tema. Algunos de ellos son: Spider-Man Unlimited, Batman Arkham Origins, Superman Returns, Iron Man 3, etc.
64aa2da5cf-
-
\ No newline at end of file diff --git a/spaces/Benson/text-generation/Examples/Descargar Gratis Nba 2k20 V98.md b/spaces/Benson/text-generation/Examples/Descargar Gratis Nba 2k20 V98.md deleted file mode 100644 index e01ee6a376c82e965cf67604adfc703758cbfea7..0000000000000000000000000000000000000000 --- a/spaces/Benson/text-generation/Examples/Descargar Gratis Nba 2k20 V98.md +++ /dev/null @@ -1,150 +0,0 @@ -
-
Cómo descargar NBA 2K20 V98 gratis en Android e iOS
-Si eres un fan de los juegos de baloncesto, es posible que hayas oído hablar de NBA 2K20, la última entrega de la popular serie NBA 2K. Este juego ofrece una experiencia de simulación de baloncesto realista e inmersiva, con varios modos, características y mejoras. ¿Pero sabías que hay una nueva versión de NBA 2K20 que puedes descargar gratis en tu dispositivo Android o iOS? Esta versión se llama NBA 2K20 V98, y tiene algunas ventajas sobre el juego original. En este artículo, te mostraremos cómo descargar NBA 2K20 V98 gratis, cuáles son sus características, consejos y trucos, y comentarios.
-descargar gratis nba 2k20 v98
Download Zip > https://bltlly.com/2v6MoY
-
Requisitos
-Antes de descargar NBA 2K20 V98, debe asegurarse de que su dispositivo cumple con los requisitos mínimos del sistema. Según [4], estos son:
--
-
Si su dispositivo cumple con estos requisitos, puede proceder a descargar NBA 2K20 V98.
-Pasos
-Para descargar NBA 2K20 V98 gratis en tu dispositivo Android o iOS, sigue estos pasos:
- --
-
Características de NBA 2K20 V98
-NBA 2K20 V98 no es solo una simple actualización del juego original. Tiene algunas características nuevas y mejoradas que lo hacen más agradable y realista. Estas son algunas de las características de NBA 2K20 V98 que usted debe saber acerca de:
-Juego
-NBA 2K20 V98 mejora el juego de NBA 2K20 añadiendo más profundidad y variedad a la simulación de baloncesto. Algunas de las mejoras de juego son:
--
-
Modos
-NBA 2K20 V98 ofrece una variedad de modos que se adaptan a diferentes estilos de juego y preferencias. Algunos de los modos son:
--
-
Gráficos
-NBA 2K20 V98 ofrece impresionantes gráficos y animaciones que hacen que el juego se vea y se sienta como una transmisión real de la NBA. Algunas de las mejoras gráficas son:
--
-
Consejos y trucos para NBA 2K20 V98
-Si quieres mejorar en NBA 2K20 V98, necesitas dominar algunos consejos y trucos que te ayudarán a mejorar tus habilidades y rendimiento. Estos son algunos consejos y trucos para NBA 2K20 V98 que debes conocer:
-MyCareer
-MyCareer es el modo más popular en NBA 2K20 V98, ya que le permite crear su propio jugador y vivir sus sueños de la NBA. Aquí hay algunos consejos y trucos para MyCareer:
--
-
MyTeam
-MyTeam es el modo en el que puedes recoger cartas de tus jugadores y leyendas favoritas de la NBA y construir tu propio equipo de fantasía. Estos son algunos consejos y trucos para MyTeam:
--
-
Dribbling
-Dribbling es una de las habilidades más importantes en NBA 2K20 V98, ya que te permite crear espacio, vencer a los defensores y establecer jugadas. Aquí hay algunos consejos y trucos para driblar:
--
-
Disparos
- --
-
Defensa
-Defensa es la habilidad final que necesitas dominar en NBA 2K20 V98, ya que te permite detener a tus oponentes y forzar pérdidas de balón. Aquí hay algunos consejos y trucos para la defensa:
--
-
Opiniones de NBA 2K20 V98
-NBA 2K20 V98 es un juego altamente calificado que ha recibido comentarios positivos de críticos y jugadores por igual. Estas son algunas de las reseñas de NBA 2K20 V98 que debes leer:
-Pros
-Algunos de los aspectos positivos de NBA 2K20 V98 son:
--
-
Contras
-Algunos de los aspectos negativos de NBA 2K20 V98 son:
--
-
Veredicto
-NBA 2K20 V98 es un juego que vale la pena descargar y jugar si eres un fan de los juegos de baloncesto o de la serie NBA 2K. Ofrece una experiencia de simulación de baloncesto realista e inmersiva, con varios modos, características y mejoras. También ofrece impresionantes gráficos y animaciones, así como un nuevo y atractivo modo historia. Es gratis para descargar y jugar en dispositivos Android e iOS, que es una gran cantidad para un juego de alta calidad. Sin embargo, también tiene algunos inconvenientes, como requerir mucho espacio de almacenamiento y recursos del sistema, tener algunos errores y problemas técnicos, tener algunas microtransacciones y anuncios, tener algunos problemas en línea y tener algunos problemas de equilibrio. Por lo tanto, usted debe ser consciente de estos problemas potenciales antes de descargar y jugar NBA 2K20 V98.
-Conclusión
- -Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre NBA 2K20 V98:
--
-
-
- 