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- spaces/1acneusushi/gradio-2dmoleculeeditor/data/Beirut Nightmares Ghada Samman Pdf To Jpg.md +0 -14
- spaces/1gistliPinn/ChatGPT4/Examples/CA ERwin Data Modeler Serial Key.md +0 -8
- spaces/1gistliPinn/ChatGPT4/Examples/CADlink EngraveLab Expert 7.1 Rev.1 Build 8.md +0 -6
- spaces/1gistliPinn/ChatGPT4/Examples/Cutewap.com Bollywood New Movie Download Menu Stream or Download Your Favorite Hindi Movies Anytime Anywhere.md +0 -6
- spaces/1line/AutoGPT/tests/__init__.py +0 -0
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- spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pyparsing/exceptions.py +0 -267
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Beirut Nightmares Ghada Samman Pdf To Jpg.md
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<h1>Beirut Nightmares: A Novel by Ghada Samman</h1>
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<p>Beirut Nightmares is a novel by Syrian writer Ghada Samman, who lived in Beirut during the Lebanese Civil War. The novel was first published in Arabic in 1976 and later translated into English by Nancy Roberts in 1997. It is considered one of the most important works of Arabic literature that deals with the war and its effects on the people of Beirut.</p>
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<p>The novel consists of 151 episodes that are labeled as "Nightmare 1" and so on. The episodes are not chronological, but rather follow the stream of consciousness of the narrator, a woman who is trapped in her apartment for two weeks by street battles and sniper fire. The narrator writes a series of vignettes that depict the horrors of war, as well as her own memories, dreams, fantasies, and fears. She also interacts with her neighbors, who include an old man and his son, and their male servant. The narrator's stories are sometimes realistic, sometimes surreal, sometimes humorous, and sometimes tragic. They reflect the diverse and complex realities of Beirut during the war, as well as the psychological and emotional impact of violence and isolation on the narrator and her fellow citizens.</p>
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<p>Beirut Nightmares is a novel that challenges the conventional boundaries between reality and fiction, between waking and sleeping, between sanity and madness. It is a novel that explores the themes of identity, survival, resistance, and hope in the face of war and destruction. It is a novel that gives voice to the experiences of women in war-torn Beirut, who are often marginalized or silenced by patriarchal and political forces. It is a novel that offers a vivid and powerful portrait of a city and a people in crisis.</p>
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<p>If you are interested in reading Beirut Nightmares by Ghada Samman, you can find it in PDF format here[^1^]. If you prefer to read it as a JPG image, you can convert it online using this tool[^2^].</p>
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<p>Beirut Nightmares is not only a novel, but also a testimony of the history and culture of Beirut. Ghada Samman draws on her own experiences as a journalist, a feminist, and a witness of the war to create a rich and authentic representation of the city and its people. She also incorporates elements of Arabic folklore, mythology, and literature to enrich her narrative and to challenge the stereotypes and prejudices that often surround the Arab world. Beirut Nightmares is a novel that celebrates the diversity, creativity, and resilience of Beirut and its inhabitants, who refuse to succumb to despair and violence.</p>
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<p>Beirut Nightmares is also a novel that invites the reader to question their own assumptions and perspectives on war and its consequences. By blurring the lines between reality and fiction, Ghada Samman challenges the reader to reconsider their notions of truth, justice, and morality. By shifting between different points of view, she challenges the reader to empathize with different characters and situations. By using humor, irony, and satire, she challenges the reader to critique the absurdity and hypocrisy of war and its perpetrators. Beirut Nightmares is a novel that provokes the reader to think critically and creatively about the complex and multifaceted issues of war and peace.</p>
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<p>Beirut Nightmares is a novel that deserves to be read by anyone who is interested in learning more about the Lebanese Civil War and its impact on the people of Beirut. It is also a novel that deserves to be read by anyone who appreciates innovative and engaging literature that explores the human condition in times of crisis. Beirut Nightmares is a novel that will make you laugh, cry, wonder, and reflect. It is a novel that will stay with you long after you finish reading it.</p>
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<h1>Chess APK Unlocked: How to Play Chess Online with Friends and Improve Your Skills</h1>
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Chess is one of the oldest and most popular board games in the world. It is a game of logic, strategy, and skill that can challenge your mind and entertain you for hours. But what if you want to play chess online with your friends or other players from around the world? And what if you want to improve your chess skills and learn from the best? That's where chess apk unlocked comes in. Chess apk unlocked is a term that refers to a modified version of a chess app that allows you to access all the features and functions without paying any fees or subscriptions. With chess apk unlocked, you can play unlimited games online or offline, join tournaments, watch videos, solve puzzles, customize your board, chat with other players, and much more. Playing chess has many benefits for your brain and mental health. It can help you develop your memory, concentration, creativity, problem-solving, planning, self-awareness, and emotional intelligence. It can also reduce stress, anxiety, depression, and the risk of dementia. Playing chess is not only fun but also good for you. <h2>Chess APK Unlocked: What Is It and How to Get It</h2>
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An apk file is a file format that is used to install applications on Android devices. It is similar to an exe file for Windows or a dmg file for Mac. You can download apk files from various sources on the internet, such as websites, forums, or file-sharing platforms. However, you need to be careful and only download apk files from trusted and reputable sources, as some apk files may contain malware or viruses that can harm your device or steal your data. An unlocked chess apk file is a modified version of a chess app that has been hacked or cracked to remove any restrictions or limitations that the original app may have. For example, some chess apps may require you to pay a fee or subscribe to access certain features or functions, such as online play, premium content, advanced settings, etc. An unlocked chess apk file bypasses these requirements and lets you enjoy all the features and functions for free. There are many advantages of using an unlocked chess apk file over a regular chess app. Some of the advantages are: - You can play unlimited games online or offline without any ads or interruptions. - You can join tournaments and compete with other players from around the world. - You can watch videos and learn from grandmasters and experts. - You can solve puzzles and improve your tactics and strategy. - You can customize your board and pieces according to your preference. - You can chat with your opponents and send emojis and stickers. - You can analyze your games and track your progress and rating. - You can save your games and share them with others. Some examples of chess apk unlocked files are: - Chess.com Mod APK: This is a modified version of the Chess.com app, which is one of the most popular chess apps in the world. It has over 50 million users and offers a variety of features and functions, such as online play, puzzles, lessons, videos, articles, etc. The mod apk file unlocks all the premium features and functions for free, such as unlimited puzzles, unlimited lessons, unlimited videos, unlimited articles, etc. It also removes all the ads and pop-ups that may annoy you while playing. - Lichess Mod APK: This is a modified version of the Lichess app, which is another popular chess app that is free and open source. It has over 10 million users and offers a variety of features and functions, such as online play, tournaments, puzzles, analysis, etc. The mod apk file unlocks all the features and functions for free, such as unlimited puzzles, unlimited analysis, unlimited tournaments, etc. It also removes all the ads and pop-ups that may annoy you while playing. - Chess Tactics Pro Mod APK: This is a modified version of the Chess Tactics Pro app, which is a chess app that focuses on improving your tactical skills. It has over 1 million users and offers a variety of features and functions, such as puzzles, ratings, themes, etc. The mod apk file unlocks all the features and functions for free, such as unlimited puzzles, unlimited themes, unlimited ratings, etc. It also removes all the ads and pop-ups that may annoy you while playing. To get an unlocked chess apk file, you need to follow these steps: - Find a reliable and reputable source that offers the unlocked chess apk file that you want to download. You can use Google or any other search engine to find such sources. - Download the unlocked chess apk file to your device. Make sure that you have enough storage space on your device and that you have a stable internet connection. - Enable the installation of unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on. This will allow you to install apps from sources other than the Google Play Store. - Locate the downloaded unlocked chess apk file on your device using a file manager app or any other app that can access your files. - Tap on the unlocked chess apk file and follow the instructions to install it on your device. - Enjoy playing chess online with friends and improving your skills with an unlocked chess apk file. <h2>Chess APK Unlocked: How to Play Chess Online with Friends</h2>
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Playing chess online with friends is one of the best ways to have fun and socialize while improving your chess skills. With an unlocked chess apk file, you can play chess online with friends anytime and anywhere without any limitations or restrictions. Here is how you can do it: - location, your age, your gender, your language, etc. You can also create your own community and invite your friends to join it. - Invite your friends and challenge them to a game. To play chess online with friends, you need to invite them to a game and challenge them to a match. You can do this by using the app's chat function or by sending them a link to the game. You can also search for your friends by using their username or email address. Once you have invited your friends, you can choose the game settings, such as the time control, the board color, the rating range, etc. You can also choose to play a casual game or a rated game. - Chat with your opponents and send emojis. Playing chess online with friends is not only about moving pieces on the board, but also about having fun and socializing with them. You can chat with your opponents during the game and send them messages, emojis, stickers, gifs, etc. You can also use voice chat or video chat to communicate with them. You can also mute or block any players that you don't want to talk to or play with. <h2>Chess APK Unlocked: How to Improve Your Chess Skills</h2>
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Playing chess online with friends is not only fun but also educational. You can improve your chess skills and learn from your mistakes and successes. With an unlocked chess apk file, you can access different modes and levels of difficulty, learn from tutorials, videos, and puzzles, and analyze your games and track your progress. Here is how you can do it: - Access different modes and levels of difficulty. To improve your chess skills, you need to challenge yourself and play against opponents that are stronger than you or have different styles of play. With an unlocked chess apk file, you can access different modes and levels of difficulty that suit your needs and goals. For example, you can play against the computer or an AI opponent that has different personalities and skill levels. You can also play against other players from around the world that have different ratings and rankings. You can also play different variants of chess, such as blitz, bullet, rapid, classical, etc. - Learn from tutorials, videos, and puzzles. To improve your chess skills, you need to learn from the best and practice your tactics and strategy. With an unlocked chess apk file, you can learn from tutorials, videos, and puzzles that are designed by grandmasters and experts. You can watch videos that explain the rules, principles, concepts, openings, middlegames, endgames, etc. of chess. You can also solve puzzles that test your calculation, visualization, intuition, creativity, etc. You can also access lessons that teach you how to improve your skills in specific areas of chess. - you can analyze your games and track your progress. You can use the app's analysis function to review your moves and see where you made mistakes or missed opportunities. You can also see the evaluation, the best moves, the variations, the comments, etc. of each position. You can also use the app's statistics function to see your rating, your performance, your accuracy, your win/loss ratio, etc. You can also compare your results with other players and see how you rank among them. <h2>Chess APK Unlocked: Tips and Tricks</h2>
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Playing chess online with friends is not only fun and educational but also customizable and flexible. You can adjust the app's settings and features according to your preference and convenience. With an unlocked chess apk file, you can customize your board and pieces, use hints and undo moves, save your games and share them with others. Here are some tips and tricks that you can use: - Customize your board and pieces. To make your chess experience more enjoyable and personal, you can customize your board and pieces according to your preference. You can choose from different themes, colors, styles, sounds, etc. of the board and pieces. You can also change the size, orientation, and layout of the board and pieces. You can also enable or disable the coordinates, the notation, the arrows, etc. of the board and pieces. - Use hints and undo moves. To make your chess experience more easy and comfortable, you can use hints and undo moves when you are playing against the computer or an AI opponent. You can use hints to get suggestions for the best moves or to check if your move is correct or not. You can also undo moves if you make a mistake or change your mind. However, you should use these features sparingly and only for learning purposes, as they may affect your rating and performance. - Save your games and share them with others. To make your chess experience more memorable and social, you can save your games and share them with others. You can save your games in different formats, such as PGN, FEN, PNG, etc. You can also export or import your games to or from other apps or devices. You can also share your games with others by sending them a link or a file via email, social media, messaging apps, etc. <h2>Conclusion</h2>
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Chess is a wonderful game that can challenge your mind and entertain you for hours. Playing chess online with friends is a great way to have fun and socialize while improving your chess skills. With chess apk unlocked, you can play chess online with friends without any limitations or restrictions. You can access all the features and functions of the app for free, such as online play, tournaments, videos, puzzles, customization, chat, analysis, etc. - and puzzles. You can analyze your games and track your progress. You can customize your board and pieces. You can use hints and undo moves. You can save your games and share them with others. Chess apk unlocked is a great way to enjoy chess online with friends and improve your skills. It is easy to get and use, and it offers a lot of features and functions that you can't find in regular chess apps. If you love chess and want to have more fun and learning, you should try chess apk unlocked today. For more information and resources on chess apk unlocked, you can visit this link: [Chess APK Unlocked: The Ultimate Guide]. <h2>FAQs</h2>
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Here are some of the frequently asked questions about chess apk unlocked: - Q: What are some of the best chess apk unlocked files? - A: Some of the best chess apk unlocked files are Chess.com Mod APK, Lichess Mod APK, Chess Tactics Pro Mod APK, Chess Openings Trainer Mod APK, CT-ART Mod APK, Play Magnus Mod APK, Chess24 Mod APK, Chess Free Mod APK, Chess by AI Factory Limited Mod APK, Chesskid Mod APK, Chess Clock Mod APK, Dr. Wolf Mod APK, Chess Adventure for Kids by ChessKid Mod APK, Chessplode Mod APK, Really Bad Chess Mod APK, Shredder Chess Mod APK, Stockfish Engines OEX Mod APK, Mate in 1 Mod APK, Learn Chess with Dr. Wolf Mod APK, Magnus Trainer Mod APK. - Q: Is chess apk unlocked safe and legal? - A: Chess apk unlocked is safe and legal as long as you download it from a reliable and reputable source and install it on your device. However, you should be careful and only download apk files from trusted sources, as some apk files may contain malware or viruses that can harm your device or steal your data. You should also scan the apk file with an antivirus or anti-malware software before installing it on your device. You should also check the permissions and reviews of the apk file before installing it on your device. - Q: Can I play chess apk unlocked offline? - A: Yes, you can play chess apk unlocked offline without an internet connection. However, some features and functions may not be available or may not work properly when you are offline. For example, you may not be able to play online games, join tournaments, watch videos, access puzzles, chat with other players, etc. when you are offline. You may also not be able to update your rating or progress when you are offline. You may also encounter some errors or bugs when you are offline. Therefore, it is recommended that you play chess apk unlocked online whenever possible to enjoy all the features and functions of the app. - Q: How can I update my chess apk unlocked file? - A: To update your chess apk unlocked file, you need to download the latest version of the unlocked chess apk file from the same source that you downloaded it from before and install it on your device. You may need to uninstall the previous version of the unlocked chess apk file before installing the new one. You may also need to enable the installation of unknown sources on your device again before installing the new one. You may also need to backup your data and settings before installing the new one. - Q: What if I have a problem with my chess apk unlocked file? - A: If you have a problem with your chess apk unlocked file, such as an error message, a crash, a freeze, a glitch, etc., you can try some of these solutions: - Restart your device and try again. - Clear the cache and data of the app and try again. - Uninstall and reinstall the app and try again. - Check your internet connection and try again. - Contact the developer or the source of the app for support.</p>
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spaces/1phancelerku/anime-remove-background/Dream League Soccer 2023 Hack for iOS Mod APK with Weak Enemies and More.md
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<h1>Dream League Soccer 2023 Mod APK Hack Download iOS</h1>
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<p>If you are a fan of soccer games, you might have heard of Dream League Soccer 2023, one of the most popular and realistic soccer games on mobile devices. But did you know that you can enjoy the game even more with a mod APK hack that gives you access to unlimited resources and features? In this article, we will tell you everything you need to know about Dream League Soccer 2023 mod APK hack, including its features, how to download and install it on your iOS device, and some frequently asked questions. Let's get started!</p>
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<p>Soccer is one of the most popular sports in the world, and millions of people love to play it on their mobile devices. There are many soccer games available on the app store, but not all of them can offer the same level of realism, graphics, and gameplay as Dream League Soccer 2023. This game is developed by First Touch Games, a renowned studio that specializes in soccer games. Dream League Soccer 2023 is the latest installment in the series, and it comes with many new features and improvements that make it stand out from the rest.</p>
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<h3>What is Dream League Soccer 2023?</h3>
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<p>Dream League Soccer 2023 is a soccer simulation game that lets you build your dream team from over 4,000 FIFPRO™ licensed players and take to the field against the world’s best soccer clubs. You can also create your own stadium, customize your kits and logos, and compete in various online and offline modes. The game has stunning graphics, realistic animations, and immersive sound effects that make you feel like you are in the middle of the action. You can also enjoy the game with friends by joining or creating a club and playing online matches with other players around the world.</p>
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<h3>Why do you need a mod APK hack for Dream League Soccer 2023?</h3>
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<p>As much as Dream League Soccer 2023 is fun and addictive, it also has some limitations that can affect your gaming experience. For example, you need to earn coins and gems to unlock new players, stadiums, kits, and other items. You also need to manage your stamina and avoid fouls that can cost you matches. These things can be frustrating and time-consuming, especially if you want to progress faster and enjoy the game without any restrictions. That's why you need a mod APK hack for Dream League Soccer 2023 that can give you unlimited resources and features that can enhance your gameplay and make you unstoppable.</p>
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<h2>Features of Dream League Soccer 2023 Mod APK Hack</h2>
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<p>A mod APK hack is a modified version of the original game that has been tweaked to give you access to features that are not available in the official version. For Dream League Soccer 2023, there are many mod APK hacks available on the internet, but not all of them are safe and reliable. Some of them may contain viruses or malware that can harm your device or steal your personal information. Some of them may also not work properly or cause errors or crashes in the game. That's why we recommend you to use the mod APK hack that we have tested and verified for you. This mod APK hack has the following features:</p>
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<h3>No Foul</h3>
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<p>One of the most annoying things in soccer games is when you get fouled by your opponent or commit a foul yourself. This can result in penalties, free kicks, yellow cards, or red cards that can ruin your chances of winning. With this mod APK hack, you don't have to worry about fouls anymore, as this feature will disable them completely. You can play as aggressively as you want, without any consequences. You can also tackle your opponents without any fear of getting booked or sent off. This feature will give you an edge over your rivals and make the game more fun and exciting.</p>
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<h3>Unlimited Stamina</h3>
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<p>Another thing that can affect your performance in soccer games is your stamina. Stamina is the energy that your players have to run, dribble, pass, shoot, and defend. As you play, your stamina will decrease, and your players will become slower, weaker, and less responsive. This can make you vulnerable to your opponents and reduce your chances of scoring or winning. With this mod APK hack, you can have unlimited stamina for your players, meaning they will never get tired or exhausted. You can run as fast and as long as you want, without any loss of speed or strength. You can also perform better skills and moves, and dominate the game from start to finish.</p>
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<h3>Everything Unlocked</h3>
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<p>One of the most appealing features of Dream League Soccer 2023 is the ability to customize your team and stadium with various items and options. You can choose from over 4,000 FIFPRO™ licensed players to build your dream team, and you can also create your own stadium, kits, logos, and more. However, to unlock these items and options, you need to earn coins and gems by playing matches, completing objectives, or watching ads. This can be tedious and time-consuming, especially if you want to unlock everything quickly and easily. With this mod APK hack, you can have everything unlocked from the start, meaning you can access all the players, stadiums, kits, logos, and more without spending any coins or gems. You can also switch between different items and options as you wish, and create your ultimate team and stadium.</p>
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<h3>More Features</h3>
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<p>Besides the features mentioned above, this mod APK hack also has some other features that can make your gameplay more enjoyable and convenient. Some of these features are:</p>
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<li>No Ads: You can play the game without any annoying ads that can interrupt your gameplay or waste your time.</li>
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<li>No Root: You don't need to root your device to use this mod APK hack, meaning you don't have to risk damaging your device or voiding its warranty.</li>
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<li>No Ban: You don't have to worry about getting banned by the game developers or the app store for using this mod APK hack, as it has anti-ban protection that will keep you safe and secure.</li>
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<li>Easy to Use: You don't need any technical skills or knowledge to use this mod APK hack, as it has a simple and user-friendly interface that will guide you through the process.</li>
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<h2>How to download and install Dream League Soccer 2023 Mod APK Hack on iOS devices</h2>
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<p>If you are interested in using this mod APK hack for Dream League Soccer 2023 on your iOS device, you need to follow these steps:</p>
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<h3>Step 1: Download the mod IPA file from the link below</h3>
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<p>The first thing you need to do is to download the mod IPA file from the link provided below. This is the file that contains the modded version of the game that has all the features that we have discussed above. The file is safe and virus-free, so you don't have to worry about any harm or damage to your device. The file size is about 400 MB, so make sure you have enough storage space on your device before downloading it.</p>
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<p><a href="^1^">Download Dream League Soccer 2023 Mod IPA</a></p>
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<h3>Step 2: Install the mod IPA file using Cydia Impactor or AltStore</h3>
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<p>The next thing you need to do is to install the mod IPA file on your device using either Cydia Impactor or AltStore. These are two tools that allow you to sideload apps on your iOS device without jailbreaking it. You can choose either one of them according to your preference and convenience.</p>
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<p>If you want to use Cydia Impactor, you need to download it from <a href="^2^">here</a> and install it on your computer. Then, connect your device to your computer using a USB cable and launch Cydia Impactor. Drag and drop the mod IPA file onto Cydia Impactor and enter your Apple ID and password when prompted. Wait for a few minutes until Cydia Impactor installs the app on your device.</p>
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<p>If you want to use AltStore, you need to download it from <a href="^3^">here</a> and install it on both your computer and your device. Then, connect your device to your computer using a USB cable and launch AltStore on both devices. Tap on the "My Apps" tab on AltStore and tap on the "+" icon on the top left corner. Browse and select the mod IPA file from your device and enter your Apple ID and password when prompted. Wait for a few minutes until AltStore installs the app on your device.</p>
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<h3>Step 3: Trust the developer profile in Settings > General > Device Management</h3>
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<p>The last thing you need to do before launching the game is to trust the developer profile that is associated with the app. This is necessary to avoid any errors or warnings that may prevent you from playing the game. To do this, go to Settings > General > Device Management on your device and find the developer profile that has your Apple ID as its name. Tap on it and tap on "Trust" to confirm. You can now go back to your home screen and launch the game.</p>
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<h3>Step 4: Launch the game and enjoy the mod features</h3>
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<p>Congratulations! You have successfully installed Dream League Soccer 2023 mod APK hack on your iOS device. You can now launch the game and enjoy all the mod features that we have discussed above. You can play without any limitations, customize your team and stadium, and dominate the game with unlimited resources and features. Have fun!</p>
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<h2>Conclusion</h2>
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<p>Dream League Soccer 2023 is one of the best soccer games on mobile devices, and it can be even better with a mod APK hack that gives you access to unlimited resources and features. In this article, we have shown you how to download and install Dream League Soccer 2023 mod APK hack on your iOS device using either Cydia Impactor or AltStore. We have also explained the features of this mod APK hack and how they can enhance your gameplay and make you unstoppable. We hope you found this article helpful and informative, and we hope you enjoy playing Dream League Soccer 2023 with this mod APK hack.</p>
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<h2>FAQs</h2>
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<p>Here are some frequently asked questions about Dream League Soccer 2023 mod APK hack:</p>
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<ul>
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<li><b>Can I play online matches with other players using this mod APK hack?</b></li>
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<p>Yes, you can play online matches with other players using this mod APK hack, as it does not affect your online connectivity or compatibility. However, you should be careful not to abuse the mod features or show them off to other players, as they may report you or complain about you. You should also respect the rules and etiquette of online gaming and avoid cheating or trolling other players.</p>
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<li><b>Realistic maps and buses from around the world</b>: Bus Simulator 2023 features realistic intracity and outside of city maps from different continents and countries. You can drive your bus in United States of America (San Francisco and Texas), South America (Buenos Aires), Europe (Germany, Spain, Prague, St. Petersburg), Dubai, Shanghai, and more. You can also choose from multiple diesel, hybrid, electric, articulated, coach, and school buses that have realistic interiors and exteriors.</li>
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<ol>
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<li><b>Choose your bus and route</b>: The first thing you need to do is to choose your bus and route. You can select from a variety of buses that have different specifications, such as speed, capacity, fuel consumption, maintenance cost, and more. You can also select from a variety of routes that have different lengths, difficulties, locations, and rewards. You can also create your own custom routes by choosing the starting point, the destination point, and the waypoints in between.</li>
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<li><b>Drive your bus and follow the traffic rules</b>: The next thing you need to do is to drive your bus and follow the traffic rules. You can use the keyboard or the mouse to control your bus. You can also use a gamepad or a steering wheel for a more realistic experience. You can adjust the camera angle by using the mouse wheel or the arrow keys. You can also switch between different camera views by pressing the C key. You can use the indicators by pressing the Q and E keys, the horn by pressing the H key, the headlights by pressing the L key, the wipers by pressing the W key, and the emergency brake by pressing the spacebar. You can also use the map and GPS to navigate your route by pressing the M key.</li>
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<li><b>Pick up and drop off passengers</b>: The main objective of Bus Simulator 2023 is to pick up and drop off passengers at designated bus stops. You can see the bus stops on your map and GPS. You can also see the number of passengers waiting at each stop by hovering over them with your mouse cursor. You need to stop your bus at the right position and open the doors by pressing the O key. You need to wait for all passengers to board or exit your bus before closing the doors by pressing the O key again. You need to collect fares from passengers by pressing the F key. You need to be careful not to overcharge or undercharge them as this will affect your reputation.</li>
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<li><b>Earn money and reputation points</b>: As you complete your routes, you will earn money and reputation points. Money can be used to buy new buses or upgrade existing ones. Reputation points can be used to unlock new routes or access new features. You can also earn bonuses for driving safely, punctually, comfortably, and environmentally friendly. You can also lose money and reputation points for driving recklessly, late, uncomfortably, or environmentally unfriendly. You can also lose money and reputation points for damaging your bus or causing accidents. You can check your balance and reputation level by pressing the B key.</li>
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</ol>
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<h2>Tips and Tricks for Bus Simulator 2023</h2>
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<p>Bus Simulator 2023 is a challenging game that requires skill and strategy. Here are some tips and tricks that can help you improve your performance and enjoy the game more:</p>
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<ul>
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<li><b>Use the map and GPS to navigate</b>: The map and GPS are your best friends in Bus Simulator 2023. They can help you find your way around the city and avoid getting lost. You can see the bus stops, the traffic lights, the speed limits, and the road conditions on your map and GPS. You can also see the distance and time remaining for your route. You can zoom in and out of the map by using the mouse wheel or the plus and minus keys. You can also move the map by dragging it with your mouse cursor. You can toggle the map and GPS on and off by pressing the M key.</li>
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<li><b>Follow the speed limit and avoid collisions</b>: One of the most important things in Bus Simulator 2023 is to follow the speed limit and avoid collisions. The speed limit varies depending on the road type, the weather condition, and the traffic situation. You can see the speed limit on your dashboard or on your GPS. You can also see the speed limit signs on the road. You need to slow down when approaching curves, intersections, bus stops, or traffic lights. You also need to avoid colliding with other vehicles, pedestrians, or objects as this will damage your bus and cost you money and reputation points.</li>
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<li><b>Use the indicators and horn to communicate with other drivers</b>: Another important thing in Bus Simulator 2023 is to use the indicators and horn to communicate with other drivers. You need to use the indicators by pressing the Q and E keys when turning left or right, changing lanes, or merging into traffic. This will signal your intention to other drivers and prevent accidents. You also need to use the horn by pressing the H key when overtaking, warning, or greeting other drivers. This will alert them of your presence and avoid collisions.</li>
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<li><b>Check the weather forecast and plan accordingly</b>: The weather condition in Bus Simulator 2023 affects your driving experience. The weather condition changes dynamically according to real-time data. You can check the weather forecast by pressing the W key. You can see the current temperature, humidity, wind speed, and precipitation. You can also see the forecast for the next hours and days. The weather condition affects the road condition, the visibility, and the traffic behavior. You need to plan your route and driving strategy accordingly. For example, you need to drive more carefully when it's raining or snowing, as the road will be slippery and the visibility will be low. You also need to use the wipers by pressing the W key to clear your windshield. You also need to use the headlights by pressing the L key when it's dark or foggy.</li>
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<ol start="2">
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<li><b>For iOS devices</b>: Go to the App Store and search for Bus Simulator 2023. Tap on the Get button and wait for the download to finish. Alternatively, you can scan this QR code with your device's camera to go directly to the download page:</li>
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</ol>
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<p><img src="https://www.qrcode-monkey.com/img/default-preview-qr.svg" alt="QR code for Bus Simulator 2023 on iOS" width="200" height="200"></p>
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<ol start="3">
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<li><b>For Windows devices</b>: Go to the Microsoft Store and search for Bus Simulator 2023. Click on the Get button and wait for the download to finish. Alternatively, you can scan this QR code with your device's camera to go directly to the download page:</li>
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<ol start="4">
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<li><b>How to install and run Bus Simulator 2023 on your device</b>: After downloading Bus Simulator 2023 on your device, you need to install it and run it. To install it, just follow the instructions on your screen. To run it, just tap or click on the Bus Simulator 2023 icon on your home screen or menu.</li>
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-
<li><b>How to access the online multiplayer mode and chat with friends</b>: To access the online multiplayer mode and chat with friends, you need to have an internet connection and a valid account. You can create an account by using your email address or your Facebook account. To join or create an online session, just go to the multiplayer menu and select an option. You can chat with other players by using the live chat feature in the game.</li>
|
| 99 |
-
</ol>
|
| 100 |
-
<h1>Conclusion</h1>
|
| 101 |
-
<p>Bus Simulator 2023 is a game that lets you become a real bus driver and experience what it's like to drive buses in different cities and countries. You can choose from a wide variety of buses, customize them as you wish, drive them in realistic maps, pick up and drop off passengers, earn money and reputation points, manage your own bus company, and have fun with your friends in online multiplayer mode.</p>
|
| 102 |
-
<p>Bus Simulator 2023 is a game that is suitable for all ages and preferences. Whether you are a casual gamer or a hardcore gamer, whether you are a bus enthusiast or a bus novice, whether you are looking for a relaxing game or a challenging game, you will find something that suits you in Bus Simulator 2023.</p>
|
| 103 |
-
<p>So what are you waiting for? Download Bus Simulator 2023 today and enjoy the best bus driving game ever!</p>
|
| 104 |
-
<h2>Frequently Asked Questions</h2>
|
| 105 |
-
<p>Here are some frequently asked questions about Bus Simulator 2023:</p>
|
| 106 |
-
<ul>
|
| 107 |
-
<li><b>Q: Is Bus Simulator 2023 free?</b></li>
|
| 108 |
-
<li>A: Yes, Bus Simulator 2023 is free to download and play on Android, iOS, and Windows devices.</li>
|
| 109 |
-
<li><b>Q: How realistic is Bus Simulator 2023?</b></li>
|
| 110 |
-
<li>A: Bus Simulator 2023 is very realistic in terms of graphics, physics, sound effects, traffic system, weather system, and bus company management system. It also features realistic maps and buses from around the world.</li>
|
| 111 |
-
<li><b>Q: How many buses and maps are there in Bus Simulator 2023?</b></li>
|
| 112 |
-
<li>A: Bus Simulator 2023 features over 50 buses and over 20 maps from different continents and countries. You can also create your own custom routes by choosing the starting point, the destination point, and the waypoints in between.</li>
|
| 113 |
-
<li><b>Q: How can I customize my bus in Bus Simulator 2023?</b></li>
|
| 114 |
-
<li>A: You can customize your bus by changing the paint color, adding accessories, body parts, air conditioning, flags, decals, and more. You can also change the interior of your bus by adding seats, steering wheels, mirrors, dashboards, radios, and more. You can also adjust the seat position, the mirrors, the steering wheel, and the pedals to suit your driving style.</li>
|
| 115 |
-
<li><b>Q: How can I play with my friends in Bus Simulator 2023?</b></li>
|
| 116 |
-
<li>A: You can play with your friends in online multiplayer mode in Bus Simulator 2023. You need to have an internet connection and a valid account. You can create an account by using your email address or your Facebook account. To join or create an online session, just go to the multiplayer menu and select an option. You can chat with your friends by using the live chat feature in the game.</li>
|
| 117 |
-
</ul></p> 401be4b1e0<br />
|
| 118 |
-
<br />
|
| 119 |
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<br />
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|
spaces/52Hz/CMFNet_dehazing/model/block.py
DELETED
|
@@ -1,146 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
import torch.nn as nn
|
| 3 |
-
##########################################################################
|
| 4 |
-
def conv(in_channels, out_channels, kernel_size, bias=False, stride=1):
|
| 5 |
-
layer = nn.Conv2d(in_channels, out_channels, kernel_size, padding=(kernel_size // 2), bias=bias, stride=stride)
|
| 6 |
-
return layer
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
def conv3x3(in_chn, out_chn, bias=True):
|
| 10 |
-
layer = nn.Conv2d(in_chn, out_chn, kernel_size=3, stride=1, padding=1, bias=bias)
|
| 11 |
-
return layer
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
def conv_down(in_chn, out_chn, bias=False):
|
| 15 |
-
layer = nn.Conv2d(in_chn, out_chn, kernel_size=4, stride=2, padding=1, bias=bias)
|
| 16 |
-
return layer
|
| 17 |
-
|
| 18 |
-
##########################################################################
|
| 19 |
-
## Supervised Attention Module (RAM)
|
| 20 |
-
class SAM(nn.Module):
|
| 21 |
-
def __init__(self, n_feat, kernel_size, bias):
|
| 22 |
-
super(SAM, self).__init__()
|
| 23 |
-
self.conv1 = conv(n_feat, n_feat, kernel_size, bias=bias)
|
| 24 |
-
self.conv2 = conv(n_feat, 3, kernel_size, bias=bias)
|
| 25 |
-
self.conv3 = conv(3, n_feat, kernel_size, bias=bias)
|
| 26 |
-
|
| 27 |
-
def forward(self, x, x_img):
|
| 28 |
-
x1 = self.conv1(x)
|
| 29 |
-
img = self.conv2(x) + x_img
|
| 30 |
-
x2 = torch.sigmoid(self.conv3(img))
|
| 31 |
-
x1 = x1 * x2
|
| 32 |
-
x1 = x1 + x
|
| 33 |
-
return x1, img
|
| 34 |
-
|
| 35 |
-
##########################################################################
|
| 36 |
-
## Spatial Attention
|
| 37 |
-
class SALayer(nn.Module):
|
| 38 |
-
def __init__(self, kernel_size=7):
|
| 39 |
-
super(SALayer, self).__init__()
|
| 40 |
-
self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=kernel_size // 2, bias=False)
|
| 41 |
-
self.sigmoid = nn.Sigmoid()
|
| 42 |
-
|
| 43 |
-
def forward(self, x):
|
| 44 |
-
avg_out = torch.mean(x, dim=1, keepdim=True)
|
| 45 |
-
max_out, _ = torch.max(x, dim=1, keepdim=True)
|
| 46 |
-
y = torch.cat([avg_out, max_out], dim=1)
|
| 47 |
-
y = self.conv1(y)
|
| 48 |
-
y = self.sigmoid(y)
|
| 49 |
-
return x * y
|
| 50 |
-
|
| 51 |
-
# Spatial Attention Block (SAB)
|
| 52 |
-
class SAB(nn.Module):
|
| 53 |
-
def __init__(self, n_feat, kernel_size, reduction, bias, act):
|
| 54 |
-
super(SAB, self).__init__()
|
| 55 |
-
modules_body = [conv(n_feat, n_feat, kernel_size, bias=bias), act, conv(n_feat, n_feat, kernel_size, bias=bias)]
|
| 56 |
-
self.body = nn.Sequential(*modules_body)
|
| 57 |
-
self.SA = SALayer(kernel_size=7)
|
| 58 |
-
|
| 59 |
-
def forward(self, x):
|
| 60 |
-
res = self.body(x)
|
| 61 |
-
res = self.SA(res)
|
| 62 |
-
res += x
|
| 63 |
-
return res
|
| 64 |
-
|
| 65 |
-
##########################################################################
|
| 66 |
-
## Pixel Attention
|
| 67 |
-
class PALayer(nn.Module):
|
| 68 |
-
def __init__(self, channel, reduction=16, bias=False):
|
| 69 |
-
super(PALayer, self).__init__()
|
| 70 |
-
self.pa = nn.Sequential(
|
| 71 |
-
nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=bias),
|
| 72 |
-
nn.ReLU(inplace=True),
|
| 73 |
-
nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=bias), # channel <-> 1
|
| 74 |
-
nn.Sigmoid()
|
| 75 |
-
)
|
| 76 |
-
|
| 77 |
-
def forward(self, x):
|
| 78 |
-
y = self.pa(x)
|
| 79 |
-
return x * y
|
| 80 |
-
|
| 81 |
-
## Pixel Attention Block (PAB)
|
| 82 |
-
class PAB(nn.Module):
|
| 83 |
-
def __init__(self, n_feat, kernel_size, reduction, bias, act):
|
| 84 |
-
super(PAB, self).__init__()
|
| 85 |
-
modules_body = [conv(n_feat, n_feat, kernel_size, bias=bias), act, conv(n_feat, n_feat, kernel_size, bias=bias)]
|
| 86 |
-
self.PA = PALayer(n_feat, reduction, bias=bias)
|
| 87 |
-
self.body = nn.Sequential(*modules_body)
|
| 88 |
-
|
| 89 |
-
def forward(self, x):
|
| 90 |
-
res = self.body(x)
|
| 91 |
-
res = self.PA(res)
|
| 92 |
-
res += x
|
| 93 |
-
return res
|
| 94 |
-
|
| 95 |
-
##########################################################################
|
| 96 |
-
## Channel Attention Layer
|
| 97 |
-
class CALayer(nn.Module):
|
| 98 |
-
def __init__(self, channel, reduction=16, bias=False):
|
| 99 |
-
super(CALayer, self).__init__()
|
| 100 |
-
# global average pooling: feature --> point
|
| 101 |
-
self.avg_pool = nn.AdaptiveAvgPool2d(1)
|
| 102 |
-
# feature channel downscale and upscale --> channel weight
|
| 103 |
-
self.conv_du = nn.Sequential(
|
| 104 |
-
nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=bias),
|
| 105 |
-
nn.ReLU(inplace=True),
|
| 106 |
-
nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=bias),
|
| 107 |
-
nn.Sigmoid()
|
| 108 |
-
)
|
| 109 |
-
|
| 110 |
-
def forward(self, x):
|
| 111 |
-
y = self.avg_pool(x)
|
| 112 |
-
y = self.conv_du(y)
|
| 113 |
-
return x * y
|
| 114 |
-
|
| 115 |
-
## Channel Attention Block (CAB)
|
| 116 |
-
class CAB(nn.Module):
|
| 117 |
-
def __init__(self, n_feat, kernel_size, reduction, bias, act):
|
| 118 |
-
super(CAB, self).__init__()
|
| 119 |
-
modules_body = [conv(n_feat, n_feat, kernel_size, bias=bias), act, conv(n_feat, n_feat, kernel_size, bias=bias)]
|
| 120 |
-
|
| 121 |
-
self.CA = CALayer(n_feat, reduction, bias=bias)
|
| 122 |
-
self.body = nn.Sequential(*modules_body)
|
| 123 |
-
|
| 124 |
-
def forward(self, x):
|
| 125 |
-
res = self.body(x)
|
| 126 |
-
res = self.CA(res)
|
| 127 |
-
res += x
|
| 128 |
-
return res
|
| 129 |
-
|
| 130 |
-
|
| 131 |
-
if __name__ == "__main__":
|
| 132 |
-
import time
|
| 133 |
-
from thop import profile
|
| 134 |
-
# layer = CAB(64, 3, 4, False, nn.PReLU())
|
| 135 |
-
layer = PAB(64, 3, 4, False, nn.PReLU())
|
| 136 |
-
# layer = SAB(64, 3, 4, False, nn.PReLU())
|
| 137 |
-
for idx, m in enumerate(layer.modules()):
|
| 138 |
-
print(idx, "-", m)
|
| 139 |
-
s = time.time()
|
| 140 |
-
|
| 141 |
-
rgb = torch.ones(1, 64, 256, 256, dtype=torch.float, requires_grad=False)
|
| 142 |
-
out = layer(rgb)
|
| 143 |
-
flops, params = profile(layer, inputs=(rgb,))
|
| 144 |
-
print('parameters:', params)
|
| 145 |
-
print('flops', flops)
|
| 146 |
-
print('time: {:.4f}ms'.format((time.time()-s)*10))
|
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|
spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/nets_123812KB.py
DELETED
|
@@ -1,122 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
import torch.nn.functional as F
|
| 3 |
-
from torch import nn
|
| 4 |
-
|
| 5 |
-
from . import layers_123821KB as layers
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
class BaseASPPNet(nn.Module):
|
| 9 |
-
def __init__(self, nin, ch, dilations=(4, 8, 16)):
|
| 10 |
-
super(BaseASPPNet, self).__init__()
|
| 11 |
-
self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
|
| 12 |
-
self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
|
| 13 |
-
self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
|
| 14 |
-
self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
|
| 15 |
-
|
| 16 |
-
self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
|
| 17 |
-
|
| 18 |
-
self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
|
| 19 |
-
self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
|
| 20 |
-
self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
|
| 21 |
-
self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
|
| 22 |
-
|
| 23 |
-
def __call__(self, x):
|
| 24 |
-
h, e1 = self.enc1(x)
|
| 25 |
-
h, e2 = self.enc2(h)
|
| 26 |
-
h, e3 = self.enc3(h)
|
| 27 |
-
h, e4 = self.enc4(h)
|
| 28 |
-
|
| 29 |
-
h = self.aspp(h)
|
| 30 |
-
|
| 31 |
-
h = self.dec4(h, e4)
|
| 32 |
-
h = self.dec3(h, e3)
|
| 33 |
-
h = self.dec2(h, e2)
|
| 34 |
-
h = self.dec1(h, e1)
|
| 35 |
-
|
| 36 |
-
return h
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
class CascadedASPPNet(nn.Module):
|
| 40 |
-
def __init__(self, n_fft):
|
| 41 |
-
super(CascadedASPPNet, self).__init__()
|
| 42 |
-
self.stg1_low_band_net = BaseASPPNet(2, 32)
|
| 43 |
-
self.stg1_high_band_net = BaseASPPNet(2, 32)
|
| 44 |
-
|
| 45 |
-
self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
|
| 46 |
-
self.stg2_full_band_net = BaseASPPNet(16, 32)
|
| 47 |
-
|
| 48 |
-
self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
|
| 49 |
-
self.stg3_full_band_net = BaseASPPNet(32, 64)
|
| 50 |
-
|
| 51 |
-
self.out = nn.Conv2d(64, 2, 1, bias=False)
|
| 52 |
-
self.aux1_out = nn.Conv2d(32, 2, 1, bias=False)
|
| 53 |
-
self.aux2_out = nn.Conv2d(32, 2, 1, bias=False)
|
| 54 |
-
|
| 55 |
-
self.max_bin = n_fft // 2
|
| 56 |
-
self.output_bin = n_fft // 2 + 1
|
| 57 |
-
|
| 58 |
-
self.offset = 128
|
| 59 |
-
|
| 60 |
-
def forward(self, x, aggressiveness=None):
|
| 61 |
-
mix = x.detach()
|
| 62 |
-
x = x.clone()
|
| 63 |
-
|
| 64 |
-
x = x[:, :, : self.max_bin]
|
| 65 |
-
|
| 66 |
-
bandw = x.size()[2] // 2
|
| 67 |
-
aux1 = torch.cat(
|
| 68 |
-
[
|
| 69 |
-
self.stg1_low_band_net(x[:, :, :bandw]),
|
| 70 |
-
self.stg1_high_band_net(x[:, :, bandw:]),
|
| 71 |
-
],
|
| 72 |
-
dim=2,
|
| 73 |
-
)
|
| 74 |
-
|
| 75 |
-
h = torch.cat([x, aux1], dim=1)
|
| 76 |
-
aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
|
| 77 |
-
|
| 78 |
-
h = torch.cat([x, aux1, aux2], dim=1)
|
| 79 |
-
h = self.stg3_full_band_net(self.stg3_bridge(h))
|
| 80 |
-
|
| 81 |
-
mask = torch.sigmoid(self.out(h))
|
| 82 |
-
mask = F.pad(
|
| 83 |
-
input=mask,
|
| 84 |
-
pad=(0, 0, 0, self.output_bin - mask.size()[2]),
|
| 85 |
-
mode="replicate",
|
| 86 |
-
)
|
| 87 |
-
|
| 88 |
-
if self.training:
|
| 89 |
-
aux1 = torch.sigmoid(self.aux1_out(aux1))
|
| 90 |
-
aux1 = F.pad(
|
| 91 |
-
input=aux1,
|
| 92 |
-
pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
|
| 93 |
-
mode="replicate",
|
| 94 |
-
)
|
| 95 |
-
aux2 = torch.sigmoid(self.aux2_out(aux2))
|
| 96 |
-
aux2 = F.pad(
|
| 97 |
-
input=aux2,
|
| 98 |
-
pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
|
| 99 |
-
mode="replicate",
|
| 100 |
-
)
|
| 101 |
-
return mask * mix, aux1 * mix, aux2 * mix
|
| 102 |
-
else:
|
| 103 |
-
if aggressiveness:
|
| 104 |
-
mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
|
| 105 |
-
mask[:, :, : aggressiveness["split_bin"]],
|
| 106 |
-
1 + aggressiveness["value"] / 3,
|
| 107 |
-
)
|
| 108 |
-
mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
|
| 109 |
-
mask[:, :, aggressiveness["split_bin"] :],
|
| 110 |
-
1 + aggressiveness["value"],
|
| 111 |
-
)
|
| 112 |
-
|
| 113 |
-
return mask * mix
|
| 114 |
-
|
| 115 |
-
def predict(self, x_mag, aggressiveness=None):
|
| 116 |
-
h = self.forward(x_mag, aggressiveness)
|
| 117 |
-
|
| 118 |
-
if self.offset > 0:
|
| 119 |
-
h = h[:, :, :, self.offset : -self.offset]
|
| 120 |
-
assert h.size()[3] > 0
|
| 121 |
-
|
| 122 |
-
return h
|
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|
spaces/801artistry/RVC801/julius/lowpass.py
DELETED
|
@@ -1,181 +0,0 @@
|
|
| 1 |
-
# File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details.
|
| 2 |
-
# Author: adefossez, 2020
|
| 3 |
-
"""
|
| 4 |
-
FIR windowed sinc lowpass filters.
|
| 5 |
-
"""
|
| 6 |
-
|
| 7 |
-
import math
|
| 8 |
-
from typing import Sequence, Optional
|
| 9 |
-
|
| 10 |
-
import torch
|
| 11 |
-
from torch.nn import functional as F
|
| 12 |
-
|
| 13 |
-
from .core import sinc
|
| 14 |
-
from .fftconv import fft_conv1d
|
| 15 |
-
from .utils import simple_repr
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
class LowPassFilters(torch.nn.Module):
|
| 19 |
-
"""
|
| 20 |
-
Bank of low pass filters. Note that a high pass or band pass filter can easily
|
| 21 |
-
be implemented by substracting a same signal processed with low pass filters with different
|
| 22 |
-
frequencies (see `julius.bands.SplitBands` for instance).
|
| 23 |
-
This uses a windowed sinc filter, very similar to the one used in
|
| 24 |
-
`julius.resample`. However, because we do not change the sample rate here,
|
| 25 |
-
this filter can be much more efficiently implemented using the FFT convolution from
|
| 26 |
-
`julius.fftconv`.
|
| 27 |
-
|
| 28 |
-
Args:
|
| 29 |
-
cutoffs (list[float]): list of cutoff frequencies, in [0, 0.5] expressed as `f/f_s` where
|
| 30 |
-
f_s is the samplerate and `f` is the cutoff frequency.
|
| 31 |
-
The upper limit is 0.5, because a signal sampled at `f_s` contains only
|
| 32 |
-
frequencies under `f_s / 2`.
|
| 33 |
-
stride (int): how much to decimate the output. Keep in mind that decimation
|
| 34 |
-
of the output is only acceptable if the cutoff frequency is under `1/ (2 * stride)`
|
| 35 |
-
of the original sampling rate.
|
| 36 |
-
pad (bool): if True, appropriately pad the input with zero over the edge. If `stride=1`,
|
| 37 |
-
the output will have the same length as the input.
|
| 38 |
-
zeros (float): Number of zero crossings to keep.
|
| 39 |
-
Controls the receptive field of the Finite Impulse Response filter.
|
| 40 |
-
For lowpass filters with low cutoff frequency, e.g. 40Hz at 44.1kHz,
|
| 41 |
-
it is a bad idea to set this to a high value.
|
| 42 |
-
This is likely appropriate for most use. Lower values
|
| 43 |
-
will result in a faster filter, but with a slower attenuation around the
|
| 44 |
-
cutoff frequency.
|
| 45 |
-
fft (bool or None): if True, uses `julius.fftconv` rather than PyTorch convolutions.
|
| 46 |
-
If False, uses PyTorch convolutions. If None, either one will be chosen automatically
|
| 47 |
-
depending on the effective filter size.
|
| 48 |
-
|
| 49 |
-
|
| 50 |
-
..warning::
|
| 51 |
-
All the filters will use the same filter size, aligned on the lowest
|
| 52 |
-
frequency provided. If you combine a lot of filters with very diverse frequencies, it might
|
| 53 |
-
be more efficient to split them over multiple modules with similar frequencies.
|
| 54 |
-
|
| 55 |
-
..note::
|
| 56 |
-
A lowpass with a cutoff frequency of 0 is defined as the null function
|
| 57 |
-
by convention here. This allows for a highpass with a cutoff of 0 to
|
| 58 |
-
be equal to identity, as defined in `julius.filters.HighPassFilters`.
|
| 59 |
-
|
| 60 |
-
Shape:
|
| 61 |
-
|
| 62 |
-
- Input: `[*, T]`
|
| 63 |
-
- Output: `[F, *, T']`, with `T'=T` if `pad` is True and `stride` is 1, and
|
| 64 |
-
`F` is the numer of cutoff frequencies.
|
| 65 |
-
|
| 66 |
-
>>> lowpass = LowPassFilters([1/4])
|
| 67 |
-
>>> x = torch.randn(4, 12, 21, 1024)
|
| 68 |
-
>>> list(lowpass(x).shape)
|
| 69 |
-
[1, 4, 12, 21, 1024]
|
| 70 |
-
"""
|
| 71 |
-
|
| 72 |
-
def __init__(self, cutoffs: Sequence[float], stride: int = 1, pad: bool = True,
|
| 73 |
-
zeros: float = 8, fft: Optional[bool] = None):
|
| 74 |
-
super().__init__()
|
| 75 |
-
self.cutoffs = list(cutoffs)
|
| 76 |
-
if min(self.cutoffs) < 0:
|
| 77 |
-
raise ValueError("Minimum cutoff must be larger than zero.")
|
| 78 |
-
if max(self.cutoffs) > 0.5:
|
| 79 |
-
raise ValueError("A cutoff above 0.5 does not make sense.")
|
| 80 |
-
self.stride = stride
|
| 81 |
-
self.pad = pad
|
| 82 |
-
self.zeros = zeros
|
| 83 |
-
self.half_size = int(zeros / min([c for c in self.cutoffs if c > 0]) / 2)
|
| 84 |
-
if fft is None:
|
| 85 |
-
fft = self.half_size > 32
|
| 86 |
-
self.fft = fft
|
| 87 |
-
window = torch.hann_window(2 * self.half_size + 1, periodic=False)
|
| 88 |
-
time = torch.arange(-self.half_size, self.half_size + 1)
|
| 89 |
-
filters = []
|
| 90 |
-
for cutoff in cutoffs:
|
| 91 |
-
if cutoff == 0:
|
| 92 |
-
filter_ = torch.zeros_like(time)
|
| 93 |
-
else:
|
| 94 |
-
filter_ = 2 * cutoff * window * sinc(2 * cutoff * math.pi * time)
|
| 95 |
-
# Normalize filter to have sum = 1, otherwise we will have a small leakage
|
| 96 |
-
# of the constant component in the input signal.
|
| 97 |
-
filter_ /= filter_.sum()
|
| 98 |
-
filters.append(filter_)
|
| 99 |
-
self.register_buffer("filters", torch.stack(filters)[:, None])
|
| 100 |
-
|
| 101 |
-
def forward(self, input):
|
| 102 |
-
shape = list(input.shape)
|
| 103 |
-
input = input.view(-1, 1, shape[-1])
|
| 104 |
-
if self.pad:
|
| 105 |
-
input = F.pad(input, (self.half_size, self.half_size), mode='replicate')
|
| 106 |
-
if self.fft:
|
| 107 |
-
out = fft_conv1d(input, self.filters, stride=self.stride)
|
| 108 |
-
else:
|
| 109 |
-
out = F.conv1d(input, self.filters, stride=self.stride)
|
| 110 |
-
shape.insert(0, len(self.cutoffs))
|
| 111 |
-
shape[-1] = out.shape[-1]
|
| 112 |
-
return out.permute(1, 0, 2).reshape(shape)
|
| 113 |
-
|
| 114 |
-
def __repr__(self):
|
| 115 |
-
return simple_repr(self)
|
| 116 |
-
|
| 117 |
-
|
| 118 |
-
class LowPassFilter(torch.nn.Module):
|
| 119 |
-
"""
|
| 120 |
-
Same as `LowPassFilters` but applies a single low pass filter.
|
| 121 |
-
|
| 122 |
-
Shape:
|
| 123 |
-
|
| 124 |
-
- Input: `[*, T]`
|
| 125 |
-
- Output: `[*, T']`, with `T'=T` if `pad` is True and `stride` is 1.
|
| 126 |
-
|
| 127 |
-
>>> lowpass = LowPassFilter(1/4, stride=2)
|
| 128 |
-
>>> x = torch.randn(4, 124)
|
| 129 |
-
>>> list(lowpass(x).shape)
|
| 130 |
-
[4, 62]
|
| 131 |
-
"""
|
| 132 |
-
|
| 133 |
-
def __init__(self, cutoff: float, stride: int = 1, pad: bool = True,
|
| 134 |
-
zeros: float = 8, fft: Optional[bool] = None):
|
| 135 |
-
super().__init__()
|
| 136 |
-
self._lowpasses = LowPassFilters([cutoff], stride, pad, zeros, fft)
|
| 137 |
-
|
| 138 |
-
@property
|
| 139 |
-
def cutoff(self):
|
| 140 |
-
return self._lowpasses.cutoffs[0]
|
| 141 |
-
|
| 142 |
-
@property
|
| 143 |
-
def stride(self):
|
| 144 |
-
return self._lowpasses.stride
|
| 145 |
-
|
| 146 |
-
@property
|
| 147 |
-
def pad(self):
|
| 148 |
-
return self._lowpasses.pad
|
| 149 |
-
|
| 150 |
-
@property
|
| 151 |
-
def zeros(self):
|
| 152 |
-
return self._lowpasses.zeros
|
| 153 |
-
|
| 154 |
-
@property
|
| 155 |
-
def fft(self):
|
| 156 |
-
return self._lowpasses.fft
|
| 157 |
-
|
| 158 |
-
def forward(self, input):
|
| 159 |
-
return self._lowpasses(input)[0]
|
| 160 |
-
|
| 161 |
-
def __repr__(self):
|
| 162 |
-
return simple_repr(self)
|
| 163 |
-
|
| 164 |
-
|
| 165 |
-
def lowpass_filters(input: torch.Tensor, cutoffs: Sequence[float],
|
| 166 |
-
stride: int = 1, pad: bool = True,
|
| 167 |
-
zeros: float = 8, fft: Optional[bool] = None):
|
| 168 |
-
"""
|
| 169 |
-
Functional version of `LowPassFilters`, refer to this class for more information.
|
| 170 |
-
"""
|
| 171 |
-
return LowPassFilters(cutoffs, stride, pad, zeros, fft).to(input)(input)
|
| 172 |
-
|
| 173 |
-
|
| 174 |
-
def lowpass_filter(input: torch.Tensor, cutoff: float,
|
| 175 |
-
stride: int = 1, pad: bool = True,
|
| 176 |
-
zeros: float = 8, fft: Optional[bool] = None):
|
| 177 |
-
"""
|
| 178 |
-
Same as `lowpass_filters` but with a single cutoff frequency.
|
| 179 |
-
Output will not have a dimension inserted in the front.
|
| 180 |
-
"""
|
| 181 |
-
return lowpass_filters(input, [cutoff], stride, pad, zeros, fft)[0]
|
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spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/parallel_wavegan/losses/stft_loss.py
DELETED
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@@ -1,153 +0,0 @@
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|
| 1 |
-
# -*- coding: utf-8 -*-
|
| 2 |
-
|
| 3 |
-
# Copyright 2019 Tomoki Hayashi
|
| 4 |
-
# MIT License (https://opensource.org/licenses/MIT)
|
| 5 |
-
|
| 6 |
-
"""STFT-based Loss modules."""
|
| 7 |
-
|
| 8 |
-
import torch
|
| 9 |
-
import torch.nn.functional as F
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
def stft(x, fft_size, hop_size, win_length, window):
|
| 13 |
-
"""Perform STFT and convert to magnitude spectrogram.
|
| 14 |
-
|
| 15 |
-
Args:
|
| 16 |
-
x (Tensor): Input signal tensor (B, T).
|
| 17 |
-
fft_size (int): FFT size.
|
| 18 |
-
hop_size (int): Hop size.
|
| 19 |
-
win_length (int): Window length.
|
| 20 |
-
window (str): Window function type.
|
| 21 |
-
|
| 22 |
-
Returns:
|
| 23 |
-
Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
|
| 24 |
-
|
| 25 |
-
"""
|
| 26 |
-
x_stft = torch.stft(x, fft_size, hop_size, win_length, window)
|
| 27 |
-
real = x_stft[..., 0]
|
| 28 |
-
imag = x_stft[..., 1]
|
| 29 |
-
|
| 30 |
-
# NOTE(kan-bayashi): clamp is needed to avoid nan or inf
|
| 31 |
-
return torch.sqrt(torch.clamp(real ** 2 + imag ** 2, min=1e-7)).transpose(2, 1)
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
class SpectralConvergengeLoss(torch.nn.Module):
|
| 35 |
-
"""Spectral convergence loss module."""
|
| 36 |
-
|
| 37 |
-
def __init__(self):
|
| 38 |
-
"""Initilize spectral convergence loss module."""
|
| 39 |
-
super(SpectralConvergengeLoss, self).__init__()
|
| 40 |
-
|
| 41 |
-
def forward(self, x_mag, y_mag):
|
| 42 |
-
"""Calculate forward propagation.
|
| 43 |
-
|
| 44 |
-
Args:
|
| 45 |
-
x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
|
| 46 |
-
y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
|
| 47 |
-
|
| 48 |
-
Returns:
|
| 49 |
-
Tensor: Spectral convergence loss value.
|
| 50 |
-
|
| 51 |
-
"""
|
| 52 |
-
return torch.norm(y_mag - x_mag, p="fro") / torch.norm(y_mag, p="fro")
|
| 53 |
-
|
| 54 |
-
|
| 55 |
-
class LogSTFTMagnitudeLoss(torch.nn.Module):
|
| 56 |
-
"""Log STFT magnitude loss module."""
|
| 57 |
-
|
| 58 |
-
def __init__(self):
|
| 59 |
-
"""Initilize los STFT magnitude loss module."""
|
| 60 |
-
super(LogSTFTMagnitudeLoss, self).__init__()
|
| 61 |
-
|
| 62 |
-
def forward(self, x_mag, y_mag):
|
| 63 |
-
"""Calculate forward propagation.
|
| 64 |
-
|
| 65 |
-
Args:
|
| 66 |
-
x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
|
| 67 |
-
y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
|
| 68 |
-
|
| 69 |
-
Returns:
|
| 70 |
-
Tensor: Log STFT magnitude loss value.
|
| 71 |
-
|
| 72 |
-
"""
|
| 73 |
-
return F.l1_loss(torch.log(y_mag), torch.log(x_mag))
|
| 74 |
-
|
| 75 |
-
|
| 76 |
-
class STFTLoss(torch.nn.Module):
|
| 77 |
-
"""STFT loss module."""
|
| 78 |
-
|
| 79 |
-
def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window"):
|
| 80 |
-
"""Initialize STFT loss module."""
|
| 81 |
-
super(STFTLoss, self).__init__()
|
| 82 |
-
self.fft_size = fft_size
|
| 83 |
-
self.shift_size = shift_size
|
| 84 |
-
self.win_length = win_length
|
| 85 |
-
self.window = getattr(torch, window)(win_length)
|
| 86 |
-
self.spectral_convergenge_loss = SpectralConvergengeLoss()
|
| 87 |
-
self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()
|
| 88 |
-
|
| 89 |
-
def forward(self, x, y):
|
| 90 |
-
"""Calculate forward propagation.
|
| 91 |
-
|
| 92 |
-
Args:
|
| 93 |
-
x (Tensor): Predicted signal (B, T).
|
| 94 |
-
y (Tensor): Groundtruth signal (B, T).
|
| 95 |
-
|
| 96 |
-
Returns:
|
| 97 |
-
Tensor: Spectral convergence loss value.
|
| 98 |
-
Tensor: Log STFT magnitude loss value.
|
| 99 |
-
|
| 100 |
-
"""
|
| 101 |
-
x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window)
|
| 102 |
-
y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window)
|
| 103 |
-
sc_loss = self.spectral_convergenge_loss(x_mag, y_mag)
|
| 104 |
-
mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag)
|
| 105 |
-
|
| 106 |
-
return sc_loss, mag_loss
|
| 107 |
-
|
| 108 |
-
|
| 109 |
-
class MultiResolutionSTFTLoss(torch.nn.Module):
|
| 110 |
-
"""Multi resolution STFT loss module."""
|
| 111 |
-
|
| 112 |
-
def __init__(self,
|
| 113 |
-
fft_sizes=[1024, 2048, 512],
|
| 114 |
-
hop_sizes=[120, 240, 50],
|
| 115 |
-
win_lengths=[600, 1200, 240],
|
| 116 |
-
window="hann_window"):
|
| 117 |
-
"""Initialize Multi resolution STFT loss module.
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| 118 |
-
|
| 119 |
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Args:
|
| 120 |
-
fft_sizes (list): List of FFT sizes.
|
| 121 |
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hop_sizes (list): List of hop sizes.
|
| 122 |
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win_lengths (list): List of window lengths.
|
| 123 |
-
window (str): Window function type.
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| 124 |
-
|
| 125 |
-
"""
|
| 126 |
-
super(MultiResolutionSTFTLoss, self).__init__()
|
| 127 |
-
assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
|
| 128 |
-
self.stft_losses = torch.nn.ModuleList()
|
| 129 |
-
for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
|
| 130 |
-
self.stft_losses += [STFTLoss(fs, ss, wl, window)]
|
| 131 |
-
|
| 132 |
-
def forward(self, x, y):
|
| 133 |
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"""Calculate forward propagation.
|
| 134 |
-
|
| 135 |
-
Args:
|
| 136 |
-
x (Tensor): Predicted signal (B, T).
|
| 137 |
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y (Tensor): Groundtruth signal (B, T).
|
| 138 |
-
|
| 139 |
-
Returns:
|
| 140 |
-
Tensor: Multi resolution spectral convergence loss value.
|
| 141 |
-
Tensor: Multi resolution log STFT magnitude loss value.
|
| 142 |
-
|
| 143 |
-
"""
|
| 144 |
-
sc_loss = 0.0
|
| 145 |
-
mag_loss = 0.0
|
| 146 |
-
for f in self.stft_losses:
|
| 147 |
-
sc_l, mag_l = f(x, y)
|
| 148 |
-
sc_loss += sc_l
|
| 149 |
-
mag_loss += mag_l
|
| 150 |
-
sc_loss /= len(self.stft_losses)
|
| 151 |
-
mag_loss /= len(self.stft_losses)
|
| 152 |
-
|
| 153 |
-
return sc_loss, mag_loss
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spaces/AIGC-Audio/Make_An_Audio/ldm/models/diffusion/ddpm_audio.py
DELETED
|
@@ -1,1262 +0,0 @@
|
|
| 1 |
-
"""
|
| 2 |
-
wild mixture of
|
| 3 |
-
https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
|
| 4 |
-
https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
|
| 5 |
-
https://github.com/CompVis/taming-transformers
|
| 6 |
-
-- merci
|
| 7 |
-
"""
|
| 8 |
-
import os
|
| 9 |
-
import torch
|
| 10 |
-
import torch.nn as nn
|
| 11 |
-
import numpy as np
|
| 12 |
-
import pytorch_lightning as pl
|
| 13 |
-
from torch.optim.lr_scheduler import LambdaLR
|
| 14 |
-
from einops import rearrange, repeat
|
| 15 |
-
from contextlib import contextmanager
|
| 16 |
-
from functools import partial
|
| 17 |
-
from tqdm import tqdm
|
| 18 |
-
from torchvision.utils import make_grid
|
| 19 |
-
from pytorch_lightning.utilities.distributed import rank_zero_only
|
| 20 |
-
|
| 21 |
-
from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
|
| 22 |
-
from ldm.modules.ema import LitEma
|
| 23 |
-
from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
|
| 24 |
-
from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
|
| 25 |
-
from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
|
| 26 |
-
from ldm.models.diffusion.ddim import DDIMSampler
|
| 27 |
-
from ldm.models.diffusion.ddpm import DDPM, disabled_train
|
| 28 |
-
from omegaconf import ListConfig
|
| 29 |
-
|
| 30 |
-
__conditioning_keys__ = {'concat': 'c_concat',
|
| 31 |
-
'crossattn': 'c_crossattn',
|
| 32 |
-
'adm': 'y'}
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
class LatentDiffusion_audio(DDPM):
|
| 36 |
-
"""main class"""
|
| 37 |
-
def __init__(self,
|
| 38 |
-
first_stage_config,
|
| 39 |
-
cond_stage_config,
|
| 40 |
-
num_timesteps_cond=None,
|
| 41 |
-
mel_dim=80,
|
| 42 |
-
mel_length=848,
|
| 43 |
-
cond_stage_key="image",
|
| 44 |
-
cond_stage_trainable=False,
|
| 45 |
-
concat_mode=True,
|
| 46 |
-
cond_stage_forward=None,
|
| 47 |
-
conditioning_key=None,
|
| 48 |
-
scale_factor=1.0,
|
| 49 |
-
scale_by_std=False,
|
| 50 |
-
*args, **kwargs):
|
| 51 |
-
self.num_timesteps_cond = default(num_timesteps_cond, 1)
|
| 52 |
-
self.scale_by_std = scale_by_std
|
| 53 |
-
assert self.num_timesteps_cond <= kwargs['timesteps']
|
| 54 |
-
# for backwards compatibility after implementation of DiffusionWrapper
|
| 55 |
-
if conditioning_key is None:
|
| 56 |
-
conditioning_key = 'concat' if concat_mode else 'crossattn'
|
| 57 |
-
if cond_stage_config == '__is_unconditional__':
|
| 58 |
-
conditioning_key = None
|
| 59 |
-
ckpt_path = kwargs.pop("ckpt_path", None)
|
| 60 |
-
ignore_keys = kwargs.pop("ignore_keys", [])
|
| 61 |
-
super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
|
| 62 |
-
self.concat_mode = concat_mode
|
| 63 |
-
self.mel_dim = mel_dim
|
| 64 |
-
self.mel_length = mel_length
|
| 65 |
-
self.cond_stage_trainable = cond_stage_trainable
|
| 66 |
-
self.cond_stage_key = cond_stage_key
|
| 67 |
-
try:
|
| 68 |
-
self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
|
| 69 |
-
except:
|
| 70 |
-
self.num_downs = 0
|
| 71 |
-
if not scale_by_std:
|
| 72 |
-
self.scale_factor = scale_factor
|
| 73 |
-
else:
|
| 74 |
-
self.register_buffer('scale_factor', torch.tensor(scale_factor))
|
| 75 |
-
self.instantiate_first_stage(first_stage_config)
|
| 76 |
-
self.instantiate_cond_stage(cond_stage_config)
|
| 77 |
-
self.cond_stage_forward = cond_stage_forward
|
| 78 |
-
self.clip_denoised = False
|
| 79 |
-
self.bbox_tokenizer = None
|
| 80 |
-
|
| 81 |
-
self.restarted_from_ckpt = False
|
| 82 |
-
if ckpt_path is not None:
|
| 83 |
-
self.init_from_ckpt(ckpt_path, ignore_keys)
|
| 84 |
-
self.restarted_from_ckpt = True
|
| 85 |
-
|
| 86 |
-
def make_cond_schedule(self, ):
|
| 87 |
-
self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
|
| 88 |
-
ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
|
| 89 |
-
self.cond_ids[:self.num_timesteps_cond] = ids
|
| 90 |
-
|
| 91 |
-
@rank_zero_only
|
| 92 |
-
@torch.no_grad()
|
| 93 |
-
def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
|
| 94 |
-
# only for very first batch
|
| 95 |
-
if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
|
| 96 |
-
assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
|
| 97 |
-
# set rescale weight to 1./std of encodings
|
| 98 |
-
print("### USING STD-RESCALING ###")
|
| 99 |
-
x = super().get_input(batch, self.first_stage_key)
|
| 100 |
-
x = x.to(self.device)
|
| 101 |
-
encoder_posterior = self.encode_first_stage(x)
|
| 102 |
-
z = self.get_first_stage_encoding(encoder_posterior).detach()
|
| 103 |
-
del self.scale_factor
|
| 104 |
-
self.register_buffer('scale_factor', 1. / z.flatten().std())
|
| 105 |
-
print(f"setting self.scale_factor to {self.scale_factor}")
|
| 106 |
-
print("### USING STD-RESCALING ###")
|
| 107 |
-
|
| 108 |
-
def register_schedule(self,
|
| 109 |
-
given_betas=None, beta_schedule="linear", timesteps=1000,
|
| 110 |
-
linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
|
| 111 |
-
super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
|
| 112 |
-
|
| 113 |
-
self.shorten_cond_schedule = self.num_timesteps_cond > 1
|
| 114 |
-
if self.shorten_cond_schedule:
|
| 115 |
-
self.make_cond_schedule()
|
| 116 |
-
|
| 117 |
-
def instantiate_first_stage(self, config):
|
| 118 |
-
model = instantiate_from_config(config)
|
| 119 |
-
self.first_stage_model = model.eval()
|
| 120 |
-
self.first_stage_model.train = disabled_train
|
| 121 |
-
for param in self.first_stage_model.parameters():
|
| 122 |
-
param.requires_grad = False
|
| 123 |
-
|
| 124 |
-
def instantiate_cond_stage(self, config):
|
| 125 |
-
if not self.cond_stage_trainable:
|
| 126 |
-
if config == "__is_first_stage__":
|
| 127 |
-
print("Using first stage also as cond stage.")
|
| 128 |
-
self.cond_stage_model = self.first_stage_model
|
| 129 |
-
elif config == "__is_unconditional__":
|
| 130 |
-
print(f"Training {self.__class__.__name__} as an unconditional model.")
|
| 131 |
-
self.cond_stage_model = None
|
| 132 |
-
# self.be_unconditional = True
|
| 133 |
-
else:
|
| 134 |
-
model = instantiate_from_config(config)
|
| 135 |
-
self.cond_stage_model = model.eval()
|
| 136 |
-
self.cond_stage_model.train = disabled_train
|
| 137 |
-
for param in self.cond_stage_model.parameters():
|
| 138 |
-
param.requires_grad = False
|
| 139 |
-
else:
|
| 140 |
-
assert config != '__is_first_stage__'
|
| 141 |
-
assert config != '__is_unconditional__'
|
| 142 |
-
model = instantiate_from_config(config)
|
| 143 |
-
self.cond_stage_model = model
|
| 144 |
-
|
| 145 |
-
def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
|
| 146 |
-
denoise_row = []
|
| 147 |
-
for zd in tqdm(samples, desc=desc):
|
| 148 |
-
denoise_row.append(self.decode_first_stage(zd.to(self.device),
|
| 149 |
-
force_not_quantize=force_no_decoder_quantization))
|
| 150 |
-
n_imgs_per_row = len(denoise_row)
|
| 151 |
-
denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
|
| 152 |
-
denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
|
| 153 |
-
denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
|
| 154 |
-
denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
|
| 155 |
-
return denoise_grid
|
| 156 |
-
|
| 157 |
-
def get_first_stage_encoding(self, encoder_posterior):
|
| 158 |
-
if isinstance(encoder_posterior, DiagonalGaussianDistribution):
|
| 159 |
-
z = encoder_posterior.sample()
|
| 160 |
-
elif isinstance(encoder_posterior, torch.Tensor):
|
| 161 |
-
z = encoder_posterior
|
| 162 |
-
else:
|
| 163 |
-
raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
|
| 164 |
-
return self.scale_factor * z
|
| 165 |
-
|
| 166 |
-
def get_learned_conditioning(self, c):
|
| 167 |
-
if self.cond_stage_forward is None:
|
| 168 |
-
if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
|
| 169 |
-
c = self.cond_stage_model.encode(c)
|
| 170 |
-
if isinstance(c, DiagonalGaussianDistribution):
|
| 171 |
-
c = c.mode()
|
| 172 |
-
else:
|
| 173 |
-
c = self.cond_stage_model(c)
|
| 174 |
-
else:
|
| 175 |
-
assert hasattr(self.cond_stage_model, self.cond_stage_forward)
|
| 176 |
-
c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
|
| 177 |
-
return c
|
| 178 |
-
|
| 179 |
-
|
| 180 |
-
@torch.no_grad()
|
| 181 |
-
def get_unconditional_conditioning(self, batch_size, null_label=None):
|
| 182 |
-
if null_label is not None:
|
| 183 |
-
xc = null_label
|
| 184 |
-
if isinstance(xc, ListConfig):
|
| 185 |
-
xc = list(xc)
|
| 186 |
-
if isinstance(xc, dict) or isinstance(xc, list):
|
| 187 |
-
c = self.get_learned_conditioning(xc)
|
| 188 |
-
else:
|
| 189 |
-
if hasattr(xc, "to"):
|
| 190 |
-
xc = xc.to(self.device)
|
| 191 |
-
c = self.get_learned_conditioning(xc)
|
| 192 |
-
else:
|
| 193 |
-
if self.cond_stage_key in ["class_label", "cls"]:
|
| 194 |
-
xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)
|
| 195 |
-
return self.get_learned_conditioning(xc)
|
| 196 |
-
else:
|
| 197 |
-
raise NotImplementedError("todo")
|
| 198 |
-
if isinstance(c, list): # in case the encoder gives us a list
|
| 199 |
-
for i in range(len(c)):
|
| 200 |
-
c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device)
|
| 201 |
-
else:
|
| 202 |
-
c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)
|
| 203 |
-
return c
|
| 204 |
-
|
| 205 |
-
def meshgrid(self, h, w):
|
| 206 |
-
y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
|
| 207 |
-
x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
|
| 208 |
-
|
| 209 |
-
arr = torch.cat([y, x], dim=-1)
|
| 210 |
-
return arr
|
| 211 |
-
|
| 212 |
-
def delta_border(self, h, w):
|
| 213 |
-
"""
|
| 214 |
-
:param h: height
|
| 215 |
-
:param w: width
|
| 216 |
-
:return: normalized distance to image border,
|
| 217 |
-
wtith min distance = 0 at border and max dist = 0.5 at image center
|
| 218 |
-
"""
|
| 219 |
-
lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
|
| 220 |
-
arr = self.meshgrid(h, w) / lower_right_corner
|
| 221 |
-
dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
|
| 222 |
-
dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
|
| 223 |
-
edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
|
| 224 |
-
return edge_dist
|
| 225 |
-
|
| 226 |
-
def get_weighting(self, h, w, Ly, Lx, device):
|
| 227 |
-
weighting = self.delta_border(h, w)
|
| 228 |
-
weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
|
| 229 |
-
self.split_input_params["clip_max_weight"], )
|
| 230 |
-
weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
|
| 231 |
-
|
| 232 |
-
if self.split_input_params["tie_braker"]:
|
| 233 |
-
L_weighting = self.delta_border(Ly, Lx)
|
| 234 |
-
L_weighting = torch.clip(L_weighting,
|
| 235 |
-
self.split_input_params["clip_min_tie_weight"],
|
| 236 |
-
self.split_input_params["clip_max_tie_weight"])
|
| 237 |
-
|
| 238 |
-
L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
|
| 239 |
-
weighting = weighting * L_weighting
|
| 240 |
-
return weighting
|
| 241 |
-
|
| 242 |
-
def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
|
| 243 |
-
"""
|
| 244 |
-
:param x: img of size (bs, c, h, w)
|
| 245 |
-
:return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
|
| 246 |
-
"""
|
| 247 |
-
bs, nc, h, w = x.shape
|
| 248 |
-
|
| 249 |
-
# number of crops in image
|
| 250 |
-
Ly = (h - kernel_size[0]) // stride[0] + 1
|
| 251 |
-
Lx = (w - kernel_size[1]) // stride[1] + 1
|
| 252 |
-
|
| 253 |
-
if uf == 1 and df == 1:
|
| 254 |
-
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
|
| 255 |
-
unfold = torch.nn.Unfold(**fold_params)
|
| 256 |
-
|
| 257 |
-
fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
|
| 258 |
-
|
| 259 |
-
weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
|
| 260 |
-
normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
|
| 261 |
-
weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
|
| 262 |
-
|
| 263 |
-
elif uf > 1 and df == 1:
|
| 264 |
-
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
|
| 265 |
-
unfold = torch.nn.Unfold(**fold_params)
|
| 266 |
-
|
| 267 |
-
fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
|
| 268 |
-
dilation=1, padding=0,
|
| 269 |
-
stride=(stride[0] * uf, stride[1] * uf))
|
| 270 |
-
fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
|
| 271 |
-
|
| 272 |
-
weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
|
| 273 |
-
normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
|
| 274 |
-
weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
|
| 275 |
-
|
| 276 |
-
elif df > 1 and uf == 1:
|
| 277 |
-
fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
|
| 278 |
-
unfold = torch.nn.Unfold(**fold_params)
|
| 279 |
-
|
| 280 |
-
fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
|
| 281 |
-
dilation=1, padding=0,
|
| 282 |
-
stride=(stride[0] // df, stride[1] // df))
|
| 283 |
-
fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
|
| 284 |
-
|
| 285 |
-
weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
|
| 286 |
-
normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
|
| 287 |
-
weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
|
| 288 |
-
|
| 289 |
-
else:
|
| 290 |
-
raise NotImplementedError
|
| 291 |
-
|
| 292 |
-
return fold, unfold, normalization, weighting
|
| 293 |
-
|
| 294 |
-
@torch.no_grad()
|
| 295 |
-
def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
|
| 296 |
-
cond_key=None, return_original_cond=False, bs=None):
|
| 297 |
-
x = super().get_input(batch, k)
|
| 298 |
-
if bs is not None:
|
| 299 |
-
x = x[:bs]
|
| 300 |
-
x = x.to(self.device)
|
| 301 |
-
encoder_posterior = self.encode_first_stage(x)
|
| 302 |
-
z = self.get_first_stage_encoding(encoder_posterior).detach()
|
| 303 |
-
|
| 304 |
-
if self.model.conditioning_key is not None:
|
| 305 |
-
if cond_key is None:
|
| 306 |
-
cond_key = self.cond_stage_key
|
| 307 |
-
if cond_key != self.first_stage_key:
|
| 308 |
-
if cond_key in ['caption', 'coordinates_bbox']:
|
| 309 |
-
xc = batch[cond_key]
|
| 310 |
-
elif cond_key == 'class_label':
|
| 311 |
-
xc = batch
|
| 312 |
-
else:
|
| 313 |
-
xc = super().get_input(batch, cond_key).to(self.device)
|
| 314 |
-
else:
|
| 315 |
-
xc = x
|
| 316 |
-
if not self.cond_stage_trainable or force_c_encode:
|
| 317 |
-
if isinstance(xc, dict) or isinstance(xc, list):
|
| 318 |
-
# import pudb; pudb.set_trace()
|
| 319 |
-
c = self.get_learned_conditioning(xc)
|
| 320 |
-
else:
|
| 321 |
-
c = self.get_learned_conditioning(xc.to(self.device))
|
| 322 |
-
else:
|
| 323 |
-
c = xc
|
| 324 |
-
if bs is not None:
|
| 325 |
-
c = c[:bs]
|
| 326 |
-
# Testing #
|
| 327 |
-
if cond_key == 'masked_image':
|
| 328 |
-
mask = super().get_input(batch, "mask")
|
| 329 |
-
cc = torch.nn.functional.interpolate(mask, size=c.shape[-2:]) # [B, 1, 10, 106]
|
| 330 |
-
c = torch.cat((c, cc), dim=1) # [B, 5, 10, 106]
|
| 331 |
-
# Testing #
|
| 332 |
-
if self.use_positional_encodings:
|
| 333 |
-
pos_x, pos_y = self.compute_latent_shifts(batch)
|
| 334 |
-
ckey = __conditioning_keys__[self.model.conditioning_key]
|
| 335 |
-
c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
|
| 336 |
-
|
| 337 |
-
else:
|
| 338 |
-
c = None
|
| 339 |
-
xc = None
|
| 340 |
-
if self.use_positional_encodings:
|
| 341 |
-
pos_x, pos_y = self.compute_latent_shifts(batch)
|
| 342 |
-
c = {'pos_x': pos_x, 'pos_y': pos_y}
|
| 343 |
-
out = [z, c]
|
| 344 |
-
if return_first_stage_outputs:
|
| 345 |
-
xrec = self.decode_first_stage(z)
|
| 346 |
-
out.extend([x, xrec])
|
| 347 |
-
if return_original_cond:
|
| 348 |
-
out.append(xc)
|
| 349 |
-
return out
|
| 350 |
-
|
| 351 |
-
@torch.no_grad()
|
| 352 |
-
def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
|
| 353 |
-
if predict_cids:
|
| 354 |
-
if z.dim() == 4:
|
| 355 |
-
z = torch.argmax(z.exp(), dim=1).long()
|
| 356 |
-
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
|
| 357 |
-
z = rearrange(z, 'b h w c -> b c h w').contiguous()
|
| 358 |
-
|
| 359 |
-
z = 1. / self.scale_factor * z
|
| 360 |
-
|
| 361 |
-
if hasattr(self, "split_input_params"):
|
| 362 |
-
if self.split_input_params["patch_distributed_vq"]:
|
| 363 |
-
ks = self.split_input_params["ks"] # eg. (128, 128)
|
| 364 |
-
stride = self.split_input_params["stride"] # eg. (64, 64)
|
| 365 |
-
uf = self.split_input_params["vqf"]
|
| 366 |
-
bs, nc, h, w = z.shape
|
| 367 |
-
if ks[0] > h or ks[1] > w:
|
| 368 |
-
ks = (min(ks[0], h), min(ks[1], w))
|
| 369 |
-
print("reducing Kernel")
|
| 370 |
-
|
| 371 |
-
if stride[0] > h or stride[1] > w:
|
| 372 |
-
stride = (min(stride[0], h), min(stride[1], w))
|
| 373 |
-
print("reducing stride")
|
| 374 |
-
|
| 375 |
-
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
|
| 376 |
-
|
| 377 |
-
z = unfold(z) # (bn, nc * prod(**ks), L)
|
| 378 |
-
# 1. Reshape to img shape
|
| 379 |
-
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
|
| 380 |
-
|
| 381 |
-
# 2. apply model loop over last dim
|
| 382 |
-
if isinstance(self.first_stage_model, VQModelInterface):
|
| 383 |
-
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
|
| 384 |
-
force_not_quantize=predict_cids or force_not_quantize)
|
| 385 |
-
for i in range(z.shape[-1])]
|
| 386 |
-
else:
|
| 387 |
-
|
| 388 |
-
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
|
| 389 |
-
for i in range(z.shape[-1])]
|
| 390 |
-
|
| 391 |
-
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
|
| 392 |
-
o = o * weighting
|
| 393 |
-
# Reverse 1. reshape to img shape
|
| 394 |
-
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
|
| 395 |
-
# stitch crops together
|
| 396 |
-
decoded = fold(o)
|
| 397 |
-
decoded = decoded / normalization # norm is shape (1, 1, h, w)
|
| 398 |
-
return decoded
|
| 399 |
-
else:
|
| 400 |
-
if isinstance(self.first_stage_model, VQModelInterface):
|
| 401 |
-
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
|
| 402 |
-
else:
|
| 403 |
-
return self.first_stage_model.decode(z)
|
| 404 |
-
|
| 405 |
-
else:
|
| 406 |
-
if isinstance(self.first_stage_model, VQModelInterface):
|
| 407 |
-
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
|
| 408 |
-
else:
|
| 409 |
-
return self.first_stage_model.decode(z)
|
| 410 |
-
|
| 411 |
-
# same as above but without decorator
|
| 412 |
-
def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
|
| 413 |
-
if predict_cids:
|
| 414 |
-
if z.dim() == 4:
|
| 415 |
-
z = torch.argmax(z.exp(), dim=1).long()
|
| 416 |
-
z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
|
| 417 |
-
z = rearrange(z, 'b h w c -> b c h w').contiguous()
|
| 418 |
-
|
| 419 |
-
z = 1. / self.scale_factor * z
|
| 420 |
-
|
| 421 |
-
if hasattr(self, "split_input_params"):
|
| 422 |
-
if self.split_input_params["patch_distributed_vq"]:
|
| 423 |
-
ks = self.split_input_params["ks"] # eg. (128, 128)
|
| 424 |
-
stride = self.split_input_params["stride"] # eg. (64, 64)
|
| 425 |
-
uf = self.split_input_params["vqf"]
|
| 426 |
-
bs, nc, h, w = z.shape
|
| 427 |
-
if ks[0] > h or ks[1] > w:
|
| 428 |
-
ks = (min(ks[0], h), min(ks[1], w))
|
| 429 |
-
print("reducing Kernel")
|
| 430 |
-
|
| 431 |
-
if stride[0] > h or stride[1] > w:
|
| 432 |
-
stride = (min(stride[0], h), min(stride[1], w))
|
| 433 |
-
print("reducing stride")
|
| 434 |
-
|
| 435 |
-
fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
|
| 436 |
-
|
| 437 |
-
z = unfold(z) # (bn, nc * prod(**ks), L)
|
| 438 |
-
# 1. Reshape to img shape
|
| 439 |
-
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
|
| 440 |
-
|
| 441 |
-
# 2. apply model loop over last dim
|
| 442 |
-
if isinstance(self.first_stage_model, VQModelInterface):
|
| 443 |
-
output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
|
| 444 |
-
force_not_quantize=predict_cids or force_not_quantize)
|
| 445 |
-
for i in range(z.shape[-1])]
|
| 446 |
-
else:
|
| 447 |
-
|
| 448 |
-
output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
|
| 449 |
-
for i in range(z.shape[-1])]
|
| 450 |
-
|
| 451 |
-
o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
|
| 452 |
-
o = o * weighting
|
| 453 |
-
# Reverse 1. reshape to img shape
|
| 454 |
-
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
|
| 455 |
-
# stitch crops together
|
| 456 |
-
decoded = fold(o)
|
| 457 |
-
decoded = decoded / normalization # norm is shape (1, 1, h, w)
|
| 458 |
-
return decoded
|
| 459 |
-
else:
|
| 460 |
-
if isinstance(self.first_stage_model, VQModelInterface):
|
| 461 |
-
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
|
| 462 |
-
else:
|
| 463 |
-
return self.first_stage_model.decode(z)
|
| 464 |
-
|
| 465 |
-
else:
|
| 466 |
-
if isinstance(self.first_stage_model, VQModelInterface):
|
| 467 |
-
return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
|
| 468 |
-
else:
|
| 469 |
-
return self.first_stage_model.decode(z)
|
| 470 |
-
|
| 471 |
-
@torch.no_grad()
|
| 472 |
-
def encode_first_stage(self, x):
|
| 473 |
-
if hasattr(self, "split_input_params"):
|
| 474 |
-
if self.split_input_params["patch_distributed_vq"]:
|
| 475 |
-
ks = self.split_input_params["ks"] # eg. (128, 128)
|
| 476 |
-
stride = self.split_input_params["stride"] # eg. (64, 64)
|
| 477 |
-
df = self.split_input_params["vqf"]
|
| 478 |
-
self.split_input_params['original_image_size'] = x.shape[-2:]
|
| 479 |
-
bs, nc, h, w = x.shape
|
| 480 |
-
if ks[0] > h or ks[1] > w:
|
| 481 |
-
ks = (min(ks[0], h), min(ks[1], w))
|
| 482 |
-
print("reducing Kernel")
|
| 483 |
-
|
| 484 |
-
if stride[0] > h or stride[1] > w:
|
| 485 |
-
stride = (min(stride[0], h), min(stride[1], w))
|
| 486 |
-
print("reducing stride")
|
| 487 |
-
|
| 488 |
-
fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
|
| 489 |
-
z = unfold(x) # (bn, nc * prod(**ks), L)
|
| 490 |
-
# Reshape to img shape
|
| 491 |
-
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
|
| 492 |
-
|
| 493 |
-
output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
|
| 494 |
-
for i in range(z.shape[-1])]
|
| 495 |
-
|
| 496 |
-
o = torch.stack(output_list, axis=-1)
|
| 497 |
-
o = o * weighting
|
| 498 |
-
|
| 499 |
-
# Reverse reshape to img shape
|
| 500 |
-
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
|
| 501 |
-
# stitch crops together
|
| 502 |
-
decoded = fold(o)
|
| 503 |
-
decoded = decoded / normalization
|
| 504 |
-
return decoded
|
| 505 |
-
|
| 506 |
-
else:
|
| 507 |
-
return self.first_stage_model.encode(x)
|
| 508 |
-
else:
|
| 509 |
-
return self.first_stage_model.encode(x)
|
| 510 |
-
|
| 511 |
-
def shared_step(self, batch, **kwargs):
|
| 512 |
-
x, c = self.get_input(batch, self.first_stage_key)
|
| 513 |
-
loss = self(x, c)
|
| 514 |
-
return loss
|
| 515 |
-
|
| 516 |
-
def test_step(self,batch,batch_idx):
|
| 517 |
-
cond = batch[self.cond_stage_key] * self.test_repeat
|
| 518 |
-
cond = self.get_learned_conditioning(cond) # c: string -> [B, T, Context_dim]
|
| 519 |
-
batch_size = len(cond)
|
| 520 |
-
enc_emb = self.sample(cond,batch_size,timesteps=self.test_numsteps)# shape = [batch_size,self.channels,self.mel_dim,self.mel_length]
|
| 521 |
-
xrec = self.decode_first_stage(enc_emb)
|
| 522 |
-
reconstructions = (xrec + 1)/2 # to mel scale
|
| 523 |
-
test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path)
|
| 524 |
-
savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class')
|
| 525 |
-
if not os.path.exists(savedir):
|
| 526 |
-
os.makedirs(savedir)
|
| 527 |
-
|
| 528 |
-
file_names = batch['f_name']
|
| 529 |
-
nfiles = len(file_names)
|
| 530 |
-
reconstructions = reconstructions.cpu().numpy().squeeze(1) # squuze channel dim
|
| 531 |
-
for k in range(reconstructions.shape[0]):
|
| 532 |
-
b,repeat = k % nfiles, k // nfiles
|
| 533 |
-
vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num
|
| 534 |
-
v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:]
|
| 535 |
-
save_img_path = os.path.join(savedir,f'{v_n}_sample_{num}_{repeat}.npy')# the num_th caption, the repeat_th repitition
|
| 536 |
-
np.save(save_img_path,reconstructions[b])
|
| 537 |
-
|
| 538 |
-
return None
|
| 539 |
-
|
| 540 |
-
def forward(self, x, c, *args, **kwargs):
|
| 541 |
-
t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
|
| 542 |
-
if self.model.conditioning_key is not None:
|
| 543 |
-
assert c is not None
|
| 544 |
-
if self.cond_stage_trainable:
|
| 545 |
-
c = self.get_learned_conditioning(c) # c: string -> [B, T, Context_dim]
|
| 546 |
-
if self.shorten_cond_schedule: # TODO: drop this option
|
| 547 |
-
tc = self.cond_ids[t].to(self.device)
|
| 548 |
-
c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
|
| 549 |
-
return self.p_losses(x, c, t, *args, **kwargs)
|
| 550 |
-
|
| 551 |
-
def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
|
| 552 |
-
def rescale_bbox(bbox):
|
| 553 |
-
x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
|
| 554 |
-
y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
|
| 555 |
-
w = min(bbox[2] / crop_coordinates[2], 1 - x0)
|
| 556 |
-
h = min(bbox[3] / crop_coordinates[3], 1 - y0)
|
| 557 |
-
return x0, y0, w, h
|
| 558 |
-
|
| 559 |
-
return [rescale_bbox(b) for b in bboxes]
|
| 560 |
-
|
| 561 |
-
def apply_model(self, x_noisy, t, cond, return_ids=False):
|
| 562 |
-
|
| 563 |
-
if isinstance(cond, dict):
|
| 564 |
-
# hybrid case, cond is exptected to be a dict
|
| 565 |
-
pass
|
| 566 |
-
else:
|
| 567 |
-
if not isinstance(cond, list):
|
| 568 |
-
cond = [cond]
|
| 569 |
-
key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
|
| 570 |
-
cond = {key: cond}
|
| 571 |
-
|
| 572 |
-
if hasattr(self, "split_input_params"):
|
| 573 |
-
assert len(cond) == 1 # todo can only deal with one conditioning atm
|
| 574 |
-
assert not return_ids
|
| 575 |
-
ks = self.split_input_params["ks"] # eg. (128, 128)
|
| 576 |
-
stride = self.split_input_params["stride"] # eg. (64, 64)
|
| 577 |
-
|
| 578 |
-
h, w = x_noisy.shape[-2:]
|
| 579 |
-
|
| 580 |
-
fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
|
| 581 |
-
|
| 582 |
-
z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
|
| 583 |
-
# Reshape to img shape
|
| 584 |
-
z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
|
| 585 |
-
z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
|
| 586 |
-
|
| 587 |
-
if self.cond_stage_key in ["image", "LR_image", "segmentation",
|
| 588 |
-
'bbox_img'] and self.model.conditioning_key: # todo check for completeness
|
| 589 |
-
c_key = next(iter(cond.keys())) # get key
|
| 590 |
-
c = next(iter(cond.values())) # get value
|
| 591 |
-
assert (len(c) == 1) # todo extend to list with more than one elem
|
| 592 |
-
c = c[0] # get element
|
| 593 |
-
|
| 594 |
-
c = unfold(c)
|
| 595 |
-
c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
|
| 596 |
-
|
| 597 |
-
cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
|
| 598 |
-
|
| 599 |
-
elif self.cond_stage_key == 'coordinates_bbox':
|
| 600 |
-
assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
|
| 601 |
-
|
| 602 |
-
# assuming padding of unfold is always 0 and its dilation is always 1
|
| 603 |
-
n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
|
| 604 |
-
full_img_h, full_img_w = self.split_input_params['original_image_size']
|
| 605 |
-
# as we are operating on latents, we need the factor from the original image size to the
|
| 606 |
-
# spatial latent size to properly rescale the crops for regenerating the bbox annotations
|
| 607 |
-
num_downs = self.first_stage_model.encoder.num_resolutions - 1
|
| 608 |
-
rescale_latent = 2 ** (num_downs)
|
| 609 |
-
|
| 610 |
-
# get top left postions of patches as conforming for the bbbox tokenizer, therefore we
|
| 611 |
-
# need to rescale the tl patch coordinates to be in between (0,1)
|
| 612 |
-
tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
|
| 613 |
-
rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
|
| 614 |
-
for patch_nr in range(z.shape[-1])]
|
| 615 |
-
|
| 616 |
-
# patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
|
| 617 |
-
patch_limits = [(x_tl, y_tl,
|
| 618 |
-
rescale_latent * ks[0] / full_img_w,
|
| 619 |
-
rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
|
| 620 |
-
# patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
|
| 621 |
-
|
| 622 |
-
# tokenize crop coordinates for the bounding boxes of the respective patches
|
| 623 |
-
patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
|
| 624 |
-
for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
|
| 625 |
-
print(patch_limits_tknzd[0].shape)
|
| 626 |
-
# cut tknzd crop position from conditioning
|
| 627 |
-
assert isinstance(cond, dict), 'cond must be dict to be fed into model'
|
| 628 |
-
cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
|
| 629 |
-
print(cut_cond.shape)
|
| 630 |
-
|
| 631 |
-
adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
|
| 632 |
-
adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
|
| 633 |
-
print(adapted_cond.shape)
|
| 634 |
-
adapted_cond = self.get_learned_conditioning(adapted_cond)
|
| 635 |
-
print(adapted_cond.shape)
|
| 636 |
-
adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
|
| 637 |
-
print(adapted_cond.shape)
|
| 638 |
-
|
| 639 |
-
cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
|
| 640 |
-
|
| 641 |
-
else:
|
| 642 |
-
cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
|
| 643 |
-
|
| 644 |
-
# apply model by loop over crops
|
| 645 |
-
output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
|
| 646 |
-
assert not isinstance(output_list[0],
|
| 647 |
-
tuple) # todo cant deal with multiple model outputs check this never happens
|
| 648 |
-
|
| 649 |
-
o = torch.stack(output_list, axis=-1)
|
| 650 |
-
o = o * weighting
|
| 651 |
-
# Reverse reshape to img shape
|
| 652 |
-
o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
|
| 653 |
-
# stitch crops together
|
| 654 |
-
x_recon = fold(o) / normalization
|
| 655 |
-
|
| 656 |
-
else:
|
| 657 |
-
x_recon = self.model(x_noisy, t, **cond)
|
| 658 |
-
|
| 659 |
-
if isinstance(x_recon, tuple) and not return_ids:
|
| 660 |
-
return x_recon[0]
|
| 661 |
-
else:
|
| 662 |
-
return x_recon
|
| 663 |
-
|
| 664 |
-
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
|
| 665 |
-
return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
|
| 666 |
-
extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
|
| 667 |
-
|
| 668 |
-
def _prior_bpd(self, x_start):
|
| 669 |
-
"""
|
| 670 |
-
Get the prior KL term for the variational lower-bound, measured in
|
| 671 |
-
bits-per-dim.
|
| 672 |
-
This term can't be optimized, as it only depends on the encoder.
|
| 673 |
-
:param x_start: the [N x C x ...] tensor of inputs.
|
| 674 |
-
:return: a batch of [N] KL values (in bits), one per batch element.
|
| 675 |
-
"""
|
| 676 |
-
batch_size = x_start.shape[0]
|
| 677 |
-
t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
|
| 678 |
-
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
|
| 679 |
-
kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
|
| 680 |
-
return mean_flat(kl_prior) / np.log(2.0)
|
| 681 |
-
|
| 682 |
-
def p_losses(self, x_start, cond, t, noise=None):
|
| 683 |
-
noise = default(noise, lambda: torch.randn_like(x_start))
|
| 684 |
-
x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
|
| 685 |
-
model_output = self.apply_model(x_noisy, t, cond)
|
| 686 |
-
|
| 687 |
-
loss_dict = {}
|
| 688 |
-
prefix = 'train' if self.training else 'val'
|
| 689 |
-
|
| 690 |
-
if self.parameterization == "x0":
|
| 691 |
-
target = x_start
|
| 692 |
-
elif self.parameterization == "eps":
|
| 693 |
-
target = noise
|
| 694 |
-
else:
|
| 695 |
-
raise NotImplementedError()
|
| 696 |
-
|
| 697 |
-
loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
|
| 698 |
-
loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
|
| 699 |
-
|
| 700 |
-
logvar_t = self.logvar[t].to(self.device)
|
| 701 |
-
loss = loss_simple / torch.exp(logvar_t) + logvar_t
|
| 702 |
-
# loss = loss_simple / torch.exp(self.logvar) + self.logvar
|
| 703 |
-
if self.learn_logvar:
|
| 704 |
-
loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
|
| 705 |
-
loss_dict.update({'logvar': self.logvar.data.mean()})
|
| 706 |
-
|
| 707 |
-
loss = self.l_simple_weight * loss.mean()
|
| 708 |
-
|
| 709 |
-
loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
|
| 710 |
-
loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
|
| 711 |
-
loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
|
| 712 |
-
loss += (self.original_elbo_weight * loss_vlb)
|
| 713 |
-
loss_dict.update({f'{prefix}/loss': loss})
|
| 714 |
-
|
| 715 |
-
return loss, loss_dict
|
| 716 |
-
|
| 717 |
-
def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
|
| 718 |
-
return_x0=False, score_corrector=None, corrector_kwargs=None):
|
| 719 |
-
t_in = t
|
| 720 |
-
model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
|
| 721 |
-
|
| 722 |
-
if score_corrector is not None:
|
| 723 |
-
assert self.parameterization == "eps"
|
| 724 |
-
model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
|
| 725 |
-
|
| 726 |
-
if return_codebook_ids:
|
| 727 |
-
model_out, logits = model_out
|
| 728 |
-
|
| 729 |
-
if self.parameterization == "eps":
|
| 730 |
-
x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
|
| 731 |
-
elif self.parameterization == "x0":
|
| 732 |
-
x_recon = model_out
|
| 733 |
-
else:
|
| 734 |
-
raise NotImplementedError()
|
| 735 |
-
|
| 736 |
-
if clip_denoised:
|
| 737 |
-
x_recon.clamp_(-1., 1.)
|
| 738 |
-
if quantize_denoised:
|
| 739 |
-
x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
|
| 740 |
-
model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
|
| 741 |
-
if return_codebook_ids:
|
| 742 |
-
return model_mean, posterior_variance, posterior_log_variance, logits
|
| 743 |
-
elif return_x0:
|
| 744 |
-
return model_mean, posterior_variance, posterior_log_variance, x_recon
|
| 745 |
-
else:
|
| 746 |
-
return model_mean, posterior_variance, posterior_log_variance
|
| 747 |
-
|
| 748 |
-
@torch.no_grad()
|
| 749 |
-
def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
|
| 750 |
-
return_codebook_ids=False, quantize_denoised=False, return_x0=False,
|
| 751 |
-
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
|
| 752 |
-
b, *_, device = *x.shape, x.device
|
| 753 |
-
outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
|
| 754 |
-
return_codebook_ids=return_codebook_ids,
|
| 755 |
-
quantize_denoised=quantize_denoised,
|
| 756 |
-
return_x0=return_x0,
|
| 757 |
-
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
|
| 758 |
-
if return_codebook_ids:
|
| 759 |
-
raise DeprecationWarning("Support dropped.")
|
| 760 |
-
model_mean, _, model_log_variance, logits = outputs
|
| 761 |
-
elif return_x0:
|
| 762 |
-
model_mean, _, model_log_variance, x0 = outputs
|
| 763 |
-
else:
|
| 764 |
-
model_mean, _, model_log_variance = outputs
|
| 765 |
-
|
| 766 |
-
noise = noise_like(x.shape, device, repeat_noise) * temperature
|
| 767 |
-
if noise_dropout > 0.:
|
| 768 |
-
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
|
| 769 |
-
# no noise when t == 0
|
| 770 |
-
nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
|
| 771 |
-
|
| 772 |
-
if return_codebook_ids:
|
| 773 |
-
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
|
| 774 |
-
if return_x0:
|
| 775 |
-
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
|
| 776 |
-
else:
|
| 777 |
-
return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
|
| 778 |
-
|
| 779 |
-
@torch.no_grad()
|
| 780 |
-
def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
|
| 781 |
-
img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
|
| 782 |
-
score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
|
| 783 |
-
log_every_t=None):
|
| 784 |
-
if not log_every_t:
|
| 785 |
-
log_every_t = self.log_every_t
|
| 786 |
-
timesteps = self.num_timesteps
|
| 787 |
-
if batch_size is not None:
|
| 788 |
-
b = batch_size if batch_size is not None else shape[0]
|
| 789 |
-
shape = [batch_size] + list(shape)
|
| 790 |
-
else:
|
| 791 |
-
b = batch_size = shape[0]
|
| 792 |
-
if x_T is None:
|
| 793 |
-
img = torch.randn(shape, device=self.device)
|
| 794 |
-
else:
|
| 795 |
-
img = x_T
|
| 796 |
-
intermediates = []
|
| 797 |
-
if cond is not None:
|
| 798 |
-
if isinstance(cond, dict):
|
| 799 |
-
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
|
| 800 |
-
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
|
| 801 |
-
else:
|
| 802 |
-
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
|
| 803 |
-
|
| 804 |
-
if start_T is not None:
|
| 805 |
-
timesteps = min(timesteps, start_T)
|
| 806 |
-
iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
|
| 807 |
-
total=timesteps) if verbose else reversed(
|
| 808 |
-
range(0, timesteps))
|
| 809 |
-
if type(temperature) == float:
|
| 810 |
-
temperature = [temperature] * timesteps
|
| 811 |
-
|
| 812 |
-
for i in iterator:
|
| 813 |
-
ts = torch.full((b,), i, device=self.device, dtype=torch.long)
|
| 814 |
-
if self.shorten_cond_schedule:
|
| 815 |
-
assert self.model.conditioning_key != 'hybrid'
|
| 816 |
-
tc = self.cond_ids[ts].to(cond.device)
|
| 817 |
-
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
|
| 818 |
-
|
| 819 |
-
img, x0_partial = self.p_sample(img, cond, ts,
|
| 820 |
-
clip_denoised=self.clip_denoised,
|
| 821 |
-
quantize_denoised=quantize_denoised, return_x0=True,
|
| 822 |
-
temperature=temperature[i], noise_dropout=noise_dropout,
|
| 823 |
-
score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
|
| 824 |
-
if mask is not None:
|
| 825 |
-
assert x0 is not None
|
| 826 |
-
img_orig = self.q_sample(x0, ts)
|
| 827 |
-
img = img_orig * mask + (1. - mask) * img
|
| 828 |
-
|
| 829 |
-
if i % log_every_t == 0 or i == timesteps - 1:
|
| 830 |
-
intermediates.append(x0_partial)
|
| 831 |
-
if callback: callback(i)
|
| 832 |
-
if img_callback: img_callback(img, i)
|
| 833 |
-
return img, intermediates
|
| 834 |
-
|
| 835 |
-
@torch.no_grad()
|
| 836 |
-
def p_sample_loop(self, cond, shape, return_intermediates=False,
|
| 837 |
-
x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
|
| 838 |
-
mask=None, x0=None, img_callback=None, start_T=None,
|
| 839 |
-
log_every_t=None):
|
| 840 |
-
|
| 841 |
-
if not log_every_t:
|
| 842 |
-
log_every_t = self.log_every_t
|
| 843 |
-
device = self.betas.device
|
| 844 |
-
b = shape[0]
|
| 845 |
-
if x_T is None:
|
| 846 |
-
img = torch.randn(shape, device=device)
|
| 847 |
-
else:
|
| 848 |
-
img = x_T
|
| 849 |
-
|
| 850 |
-
intermediates = [img]
|
| 851 |
-
if timesteps is None:
|
| 852 |
-
timesteps = self.num_timesteps
|
| 853 |
-
|
| 854 |
-
if start_T is not None:
|
| 855 |
-
timesteps = min(timesteps, start_T)
|
| 856 |
-
iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
|
| 857 |
-
range(0, timesteps))
|
| 858 |
-
|
| 859 |
-
if mask is not None:
|
| 860 |
-
assert x0 is not None
|
| 861 |
-
assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
|
| 862 |
-
|
| 863 |
-
for i in iterator:
|
| 864 |
-
ts = torch.full((b,), i, device=device, dtype=torch.long)
|
| 865 |
-
if self.shorten_cond_schedule:
|
| 866 |
-
assert self.model.conditioning_key != 'hybrid'
|
| 867 |
-
tc = self.cond_ids[ts].to(cond.device)
|
| 868 |
-
cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
|
| 869 |
-
|
| 870 |
-
img = self.p_sample(img, cond, ts,
|
| 871 |
-
clip_denoised=self.clip_denoised,
|
| 872 |
-
quantize_denoised=quantize_denoised)
|
| 873 |
-
if mask is not None:
|
| 874 |
-
img_orig = self.q_sample(x0, ts)
|
| 875 |
-
img = img_orig * mask + (1. - mask) * img
|
| 876 |
-
|
| 877 |
-
if i % log_every_t == 0 or i == timesteps - 1:
|
| 878 |
-
intermediates.append(img)
|
| 879 |
-
if callback: callback(i)
|
| 880 |
-
if img_callback: img_callback(img, i)
|
| 881 |
-
|
| 882 |
-
if return_intermediates:
|
| 883 |
-
return img, intermediates
|
| 884 |
-
return img
|
| 885 |
-
|
| 886 |
-
@torch.no_grad()
|
| 887 |
-
def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
|
| 888 |
-
verbose=True, timesteps=None, quantize_denoised=False,
|
| 889 |
-
mask=None, x0=None, shape=None,**kwargs):
|
| 890 |
-
if shape is None:
|
| 891 |
-
shape = (batch_size, self.channels, self.mel_dim, self.mel_length)
|
| 892 |
-
if cond is not None:
|
| 893 |
-
if isinstance(cond, dict):
|
| 894 |
-
cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
|
| 895 |
-
list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
|
| 896 |
-
else:
|
| 897 |
-
cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
|
| 898 |
-
return self.p_sample_loop(cond,
|
| 899 |
-
shape,
|
| 900 |
-
return_intermediates=return_intermediates, x_T=x_T,
|
| 901 |
-
verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
|
| 902 |
-
mask=mask, x0=x0)
|
| 903 |
-
|
| 904 |
-
@torch.no_grad()
|
| 905 |
-
def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
|
| 906 |
-
|
| 907 |
-
if ddim:
|
| 908 |
-
ddim_sampler = DDIMSampler(self)
|
| 909 |
-
shape = (self.channels, self.mel_dim, self.mel_length)
|
| 910 |
-
samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
|
| 911 |
-
shape,cond,verbose=False,**kwargs)
|
| 912 |
-
|
| 913 |
-
else:
|
| 914 |
-
samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
|
| 915 |
-
return_intermediates=True,**kwargs)
|
| 916 |
-
|
| 917 |
-
return samples, intermediates
|
| 918 |
-
|
| 919 |
-
|
| 920 |
-
@torch.no_grad()
|
| 921 |
-
def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
|
| 922 |
-
quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
|
| 923 |
-
plot_diffusion_rows=True, **kwargs):
|
| 924 |
-
|
| 925 |
-
use_ddim = ddim_steps is not None
|
| 926 |
-
|
| 927 |
-
log = dict()
|
| 928 |
-
z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
|
| 929 |
-
return_first_stage_outputs=True,
|
| 930 |
-
force_c_encode=True,
|
| 931 |
-
return_original_cond=True,
|
| 932 |
-
bs=N)
|
| 933 |
-
N = min(x.shape[0], N)
|
| 934 |
-
n_row = min(x.shape[0], n_row)
|
| 935 |
-
log["inputs"] = x
|
| 936 |
-
log["reconstruction"] = xrec
|
| 937 |
-
if self.model.conditioning_key is not None:
|
| 938 |
-
if hasattr(self.cond_stage_model, "decode") and self.cond_stage_key != "masked_image":
|
| 939 |
-
xc = self.cond_stage_model.decode(c)
|
| 940 |
-
log["conditioning"] = xc
|
| 941 |
-
elif self.cond_stage_key == "masked_image":
|
| 942 |
-
log["mask"] = c[:, -1, :, :][:, None, :, :]
|
| 943 |
-
xc = self.cond_stage_model.decode(c[:, :self.cond_stage_model.embed_dim, :, :])
|
| 944 |
-
log["conditioning"] = xc
|
| 945 |
-
elif self.cond_stage_key in ["caption"]:
|
| 946 |
-
xc = log_txt_as_img((256, 256), batch["caption"])
|
| 947 |
-
log["conditioning"] = xc
|
| 948 |
-
elif self.cond_stage_key == 'class_label':
|
| 949 |
-
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
|
| 950 |
-
log['conditioning'] = xc
|
| 951 |
-
elif isimage(xc):
|
| 952 |
-
log["conditioning"] = xc
|
| 953 |
-
if ismap(xc):
|
| 954 |
-
log["original_conditioning"] = self.to_rgb(xc)
|
| 955 |
-
|
| 956 |
-
if plot_diffusion_rows:
|
| 957 |
-
# get diffusion row
|
| 958 |
-
diffusion_row = list()
|
| 959 |
-
z_start = z[:n_row]
|
| 960 |
-
for t in range(self.num_timesteps):
|
| 961 |
-
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
|
| 962 |
-
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
|
| 963 |
-
t = t.to(self.device).long()
|
| 964 |
-
noise = torch.randn_like(z_start)
|
| 965 |
-
z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
|
| 966 |
-
diffusion_row.append(self.decode_first_stage(z_noisy))
|
| 967 |
-
|
| 968 |
-
diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
|
| 969 |
-
diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
|
| 970 |
-
diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
|
| 971 |
-
diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
|
| 972 |
-
log["diffusion_row"] = diffusion_grid
|
| 973 |
-
|
| 974 |
-
if sample:
|
| 975 |
-
# get denoise row
|
| 976 |
-
with self.ema_scope("Plotting"):
|
| 977 |
-
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
|
| 978 |
-
ddim_steps=ddim_steps,eta=ddim_eta)
|
| 979 |
-
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
|
| 980 |
-
x_samples = self.decode_first_stage(samples)
|
| 981 |
-
log["samples"] = x_samples
|
| 982 |
-
if plot_denoise_rows:
|
| 983 |
-
denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
|
| 984 |
-
log["denoise_row"] = denoise_grid
|
| 985 |
-
|
| 986 |
-
if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
|
| 987 |
-
self.first_stage_model, IdentityFirstStage):
|
| 988 |
-
# also display when quantizing x0 while sampling
|
| 989 |
-
with self.ema_scope("Plotting Quantized Denoised"):
|
| 990 |
-
samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
|
| 991 |
-
ddim_steps=ddim_steps,eta=ddim_eta,
|
| 992 |
-
quantize_denoised=True)
|
| 993 |
-
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
|
| 994 |
-
# quantize_denoised=True)
|
| 995 |
-
x_samples = self.decode_first_stage(samples.to(self.device))
|
| 996 |
-
log["samples_x0_quantized"] = x_samples
|
| 997 |
-
|
| 998 |
-
if inpaint:
|
| 999 |
-
# make a simple center square
|
| 1000 |
-
b, h, w = z.shape[0], z.shape[2], z.shape[3]
|
| 1001 |
-
mask = torch.ones(N, h, w).to(self.device)
|
| 1002 |
-
# zeros will be filled in
|
| 1003 |
-
mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
|
| 1004 |
-
mask = mask[:, None, ...]
|
| 1005 |
-
with self.ema_scope("Plotting Inpaint"):
|
| 1006 |
-
|
| 1007 |
-
samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
|
| 1008 |
-
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
|
| 1009 |
-
x_samples = self.decode_first_stage(samples.to(self.device))
|
| 1010 |
-
log["samples_inpainting"] = x_samples
|
| 1011 |
-
log["mask_inpainting"] = mask
|
| 1012 |
-
|
| 1013 |
-
# outpaint
|
| 1014 |
-
mask = 1 - mask
|
| 1015 |
-
with self.ema_scope("Plotting Outpaint"):
|
| 1016 |
-
samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
|
| 1017 |
-
ddim_steps=ddim_steps, x0=z[:N], mask=mask)
|
| 1018 |
-
x_samples = self.decode_first_stage(samples.to(self.device))
|
| 1019 |
-
log["samples_outpainting"] = x_samples
|
| 1020 |
-
log["mask_outpainting"] = mask
|
| 1021 |
-
|
| 1022 |
-
if plot_progressive_rows:
|
| 1023 |
-
with self.ema_scope("Plotting Progressives"):
|
| 1024 |
-
img, progressives = self.progressive_denoising(c,
|
| 1025 |
-
shape=(self.channels, self.mel_dim, self.mel_length),
|
| 1026 |
-
batch_size=N)
|
| 1027 |
-
prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
|
| 1028 |
-
log["progressive_row"] = prog_row
|
| 1029 |
-
|
| 1030 |
-
if return_keys:
|
| 1031 |
-
if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
|
| 1032 |
-
return log
|
| 1033 |
-
else:
|
| 1034 |
-
return {key: log[key] for key in return_keys}
|
| 1035 |
-
return log
|
| 1036 |
-
|
| 1037 |
-
def configure_optimizers(self):
|
| 1038 |
-
lr = self.learning_rate
|
| 1039 |
-
params = list(self.model.parameters())
|
| 1040 |
-
if self.cond_stage_trainable:
|
| 1041 |
-
print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
|
| 1042 |
-
params = params + list(self.cond_stage_model.parameters())
|
| 1043 |
-
if self.learn_logvar:
|
| 1044 |
-
print('Diffusion model optimizing logvar')
|
| 1045 |
-
params.append(self.logvar)
|
| 1046 |
-
opt = torch.optim.AdamW(params, lr=lr)
|
| 1047 |
-
if self.use_scheduler:
|
| 1048 |
-
assert 'target' in self.scheduler_config
|
| 1049 |
-
scheduler = instantiate_from_config(self.scheduler_config)
|
| 1050 |
-
|
| 1051 |
-
print("Setting up LambdaLR scheduler...")
|
| 1052 |
-
scheduler = [
|
| 1053 |
-
{
|
| 1054 |
-
'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
|
| 1055 |
-
'interval': 'step',
|
| 1056 |
-
'frequency': 1
|
| 1057 |
-
}]
|
| 1058 |
-
return [opt], scheduler
|
| 1059 |
-
return opt
|
| 1060 |
-
|
| 1061 |
-
@torch.no_grad()
|
| 1062 |
-
def to_rgb(self, x):
|
| 1063 |
-
x = x.float()
|
| 1064 |
-
if not hasattr(self, "colorize"):
|
| 1065 |
-
self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
|
| 1066 |
-
x = nn.functional.conv2d(x, weight=self.colorize)
|
| 1067 |
-
x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
|
| 1068 |
-
return x
|
| 1069 |
-
|
| 1070 |
-
|
| 1071 |
-
class LatentFinetuneDiffusion(LatentDiffusion_audio):
|
| 1072 |
-
"""
|
| 1073 |
-
Basis for different finetunas, such as inpainting or depth2image
|
| 1074 |
-
To disable finetuning mode, set finetune_keys to None
|
| 1075 |
-
"""
|
| 1076 |
-
|
| 1077 |
-
def __init__(self,
|
| 1078 |
-
concat_keys: tuple,
|
| 1079 |
-
finetune_keys=("model.diffusion_model.input_blocks.0.0.weight",
|
| 1080 |
-
"model_ema.diffusion_modelinput_blocks00weight"
|
| 1081 |
-
),
|
| 1082 |
-
keep_finetune_dims=4,
|
| 1083 |
-
# if model was trained without concat mode before and we would like to keep these channels
|
| 1084 |
-
c_concat_log_start=None, # to log reconstruction of c_concat codes
|
| 1085 |
-
c_concat_log_end=None,
|
| 1086 |
-
*args, **kwargs
|
| 1087 |
-
):
|
| 1088 |
-
ckpt_path = kwargs.pop("ckpt_path", None)
|
| 1089 |
-
ignore_keys = kwargs.pop("ignore_keys", list())
|
| 1090 |
-
super().__init__(*args, **kwargs)
|
| 1091 |
-
self.finetune_keys = finetune_keys
|
| 1092 |
-
self.concat_keys = concat_keys
|
| 1093 |
-
self.keep_dims = keep_finetune_dims
|
| 1094 |
-
self.c_concat_log_start = c_concat_log_start
|
| 1095 |
-
self.c_concat_log_end = c_concat_log_end
|
| 1096 |
-
|
| 1097 |
-
if exists(self.finetune_keys): assert exists(ckpt_path), 'can only finetune from a given checkpoint'
|
| 1098 |
-
if exists(ckpt_path):
|
| 1099 |
-
self.init_from_ckpt(ckpt_path, ignore_keys)
|
| 1100 |
-
|
| 1101 |
-
def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
|
| 1102 |
-
sd = torch.load(path, map_location="cpu")
|
| 1103 |
-
if "state_dict" in list(sd.keys()):
|
| 1104 |
-
sd = sd["state_dict"]
|
| 1105 |
-
keys = list(sd.keys())
|
| 1106 |
-
|
| 1107 |
-
for k in keys:
|
| 1108 |
-
for ik in ignore_keys:
|
| 1109 |
-
if k.startswith(ik):
|
| 1110 |
-
print("Deleting key {} from state_dict.".format(k))
|
| 1111 |
-
del sd[k]
|
| 1112 |
-
|
| 1113 |
-
# make it explicit, finetune by including extra input channels
|
| 1114 |
-
if exists(self.finetune_keys) and k in self.finetune_keys:
|
| 1115 |
-
new_entry = None
|
| 1116 |
-
for name, param in self.named_parameters():
|
| 1117 |
-
if name in self.finetune_keys:
|
| 1118 |
-
print(
|
| 1119 |
-
f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only")
|
| 1120 |
-
new_entry = torch.zeros_like(param) # zero init
|
| 1121 |
-
assert exists(new_entry), 'did not find matching parameter to modify'
|
| 1122 |
-
new_entry[:, :self.keep_dims, ...] = sd[k]
|
| 1123 |
-
sd[k] = new_entry
|
| 1124 |
-
|
| 1125 |
-
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(sd, strict=False)
|
| 1126 |
-
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
|
| 1127 |
-
if len(missing) > 0:
|
| 1128 |
-
print(f"Missing Keys: {missing}")
|
| 1129 |
-
if len(unexpected) > 0:
|
| 1130 |
-
print(f"Unexpected Keys: {unexpected}")
|
| 1131 |
-
|
| 1132 |
-
@torch.no_grad()
|
| 1133 |
-
def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
|
| 1134 |
-
quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
|
| 1135 |
-
plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
|
| 1136 |
-
use_ema_scope=True,
|
| 1137 |
-
**kwargs):
|
| 1138 |
-
use_ddim = ddim_steps is not None
|
| 1139 |
-
|
| 1140 |
-
log = dict()
|
| 1141 |
-
z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True)
|
| 1142 |
-
c_cat, c = c["c_concat"][0], c["c_crossattn"][0]
|
| 1143 |
-
N = min(x.shape[0], N)
|
| 1144 |
-
n_row = min(x.shape[0], n_row)
|
| 1145 |
-
log["inputs"] = x
|
| 1146 |
-
log["reconstruction"] = xrec
|
| 1147 |
-
if self.model.conditioning_key is not None:
|
| 1148 |
-
if hasattr(self.cond_stage_model, "decode"):
|
| 1149 |
-
xc = self.cond_stage_model.decode(c)
|
| 1150 |
-
log["conditioning"] = xc
|
| 1151 |
-
elif self.cond_stage_key in ["caption"]:
|
| 1152 |
-
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
|
| 1153 |
-
log["conditioning"] = xc
|
| 1154 |
-
elif self.cond_stage_key == 'class_label':
|
| 1155 |
-
xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
|
| 1156 |
-
log['conditioning'] = xc
|
| 1157 |
-
elif isimage(xc):
|
| 1158 |
-
log["conditioning"] = xc
|
| 1159 |
-
if ismap(xc):
|
| 1160 |
-
log["original_conditioning"] = self.to_rgb(xc)
|
| 1161 |
-
|
| 1162 |
-
if not (self.c_concat_log_start is None and self.c_concat_log_end is None):
|
| 1163 |
-
log["c_concat_decoded"] = self.decode_first_stage(c_cat[:, self.c_concat_log_start:self.c_concat_log_end])
|
| 1164 |
-
|
| 1165 |
-
if plot_diffusion_rows:
|
| 1166 |
-
# get diffusion row
|
| 1167 |
-
diffusion_row = list()
|
| 1168 |
-
z_start = z[:n_row]
|
| 1169 |
-
for t in range(self.num_timesteps):
|
| 1170 |
-
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
|
| 1171 |
-
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
|
| 1172 |
-
t = t.to(self.device).long()
|
| 1173 |
-
noise = torch.randn_like(z_start)
|
| 1174 |
-
z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
|
| 1175 |
-
diffusion_row.append(self.decode_first_stage(z_noisy))
|
| 1176 |
-
|
| 1177 |
-
diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
|
| 1178 |
-
diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
|
| 1179 |
-
diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
|
| 1180 |
-
diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
|
| 1181 |
-
log["diffusion_row"] = diffusion_grid
|
| 1182 |
-
|
| 1183 |
-
if sample:
|
| 1184 |
-
# get denoise row
|
| 1185 |
-
with self.ema_scope("Sampling"):
|
| 1186 |
-
samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
|
| 1187 |
-
batch_size=N, ddim=use_ddim,
|
| 1188 |
-
ddim_steps=ddim_steps, eta=ddim_eta)
|
| 1189 |
-
# samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
|
| 1190 |
-
x_samples = self.decode_first_stage(samples)
|
| 1191 |
-
log["samples"] = x_samples
|
| 1192 |
-
if plot_denoise_rows:
|
| 1193 |
-
denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
|
| 1194 |
-
log["denoise_row"] = denoise_grid
|
| 1195 |
-
|
| 1196 |
-
if unconditional_guidance_scale > 1.0:
|
| 1197 |
-
uc_cross = self.get_unconditional_conditioning(N, unconditional_guidance_label)
|
| 1198 |
-
uc_cat = c_cat
|
| 1199 |
-
uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]}
|
| 1200 |
-
with self.ema_scope("Sampling with classifier-free guidance"):
|
| 1201 |
-
samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
|
| 1202 |
-
batch_size=N, ddim=use_ddim,
|
| 1203 |
-
ddim_steps=ddim_steps, eta=ddim_eta,
|
| 1204 |
-
unconditional_guidance_scale=unconditional_guidance_scale,
|
| 1205 |
-
unconditional_conditioning=uc_full,
|
| 1206 |
-
)
|
| 1207 |
-
x_samples_cfg = self.decode_first_stage(samples_cfg)
|
| 1208 |
-
log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
|
| 1209 |
-
|
| 1210 |
-
return log
|
| 1211 |
-
|
| 1212 |
-
|
| 1213 |
-
class LatentInpaintDiffusion(LatentFinetuneDiffusion):
|
| 1214 |
-
"""
|
| 1215 |
-
can either run as pure inpainting model (only concat mode) or with mixed conditionings,
|
| 1216 |
-
e.g. mask as concat and text via cross-attn.
|
| 1217 |
-
To disable finetuning mode, set finetune_keys to None
|
| 1218 |
-
"""
|
| 1219 |
-
|
| 1220 |
-
def __init__(self,
|
| 1221 |
-
concat_keys=("mask", "masked_image"),
|
| 1222 |
-
masked_image_key="masked_image",
|
| 1223 |
-
*args, **kwargs
|
| 1224 |
-
):
|
| 1225 |
-
super().__init__(concat_keys, *args, **kwargs)
|
| 1226 |
-
self.masked_image_key = masked_image_key
|
| 1227 |
-
assert self.masked_image_key in concat_keys
|
| 1228 |
-
|
| 1229 |
-
@torch.no_grad()
|
| 1230 |
-
def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
|
| 1231 |
-
# note: restricted to non-trainable encoders currently
|
| 1232 |
-
assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpainting'
|
| 1233 |
-
z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
|
| 1234 |
-
force_c_encode=True, return_original_cond=True, bs=bs)
|
| 1235 |
-
|
| 1236 |
-
assert exists(self.concat_keys)
|
| 1237 |
-
c_cat = list()
|
| 1238 |
-
for ck in self.concat_keys:
|
| 1239 |
-
if len(batch[ck].shape) == 3:
|
| 1240 |
-
batch[ck] = batch[ck][..., None]
|
| 1241 |
-
cc = rearrange(batch[ck], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
|
| 1242 |
-
if bs is not None:
|
| 1243 |
-
cc = cc[:bs]
|
| 1244 |
-
cc = cc.to(self.device)
|
| 1245 |
-
bchw = z.shape
|
| 1246 |
-
if ck != self.masked_image_key:
|
| 1247 |
-
cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
|
| 1248 |
-
else:
|
| 1249 |
-
cc = self.get_first_stage_encoding(self.encode_first_stage(cc))
|
| 1250 |
-
c_cat.append(cc)
|
| 1251 |
-
c_cat = torch.cat(c_cat, dim=1)
|
| 1252 |
-
all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
|
| 1253 |
-
if return_first_stage_outputs:
|
| 1254 |
-
return z, all_conds, x, xrec, xc
|
| 1255 |
-
return z, all_conds
|
| 1256 |
-
|
| 1257 |
-
@torch.no_grad()
|
| 1258 |
-
def log_images(self, *args, **kwargs):
|
| 1259 |
-
log = super(LatentInpaintDiffusion, self).log_images(*args, **kwargs)
|
| 1260 |
-
log["masked_image"] = rearrange(args[0]["masked_image"],
|
| 1261 |
-
'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
|
| 1262 |
-
return log
|
|
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|
spaces/AgentVerse/agentVerse/agentverse/logging.py
DELETED
|
@@ -1,291 +0,0 @@
|
|
| 1 |
-
"""Logging module for Auto-GPT."""
|
| 2 |
-
import logging
|
| 3 |
-
import os
|
| 4 |
-
import random
|
| 5 |
-
import re
|
| 6 |
-
import time
|
| 7 |
-
import json
|
| 8 |
-
import abc
|
| 9 |
-
from logging import LogRecord
|
| 10 |
-
from typing import Any, List
|
| 11 |
-
|
| 12 |
-
from colorama import Fore, Style
|
| 13 |
-
from agentverse.utils import Singleton
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
# from autogpt.speech import say_text
|
| 17 |
-
class JsonFileHandler(logging.FileHandler):
|
| 18 |
-
def __init__(self, filename, mode="a", encoding=None, delay=False):
|
| 19 |
-
super().__init__(filename, mode, encoding, delay)
|
| 20 |
-
|
| 21 |
-
def emit(self, record):
|
| 22 |
-
json_data = json.loads(self.format(record))
|
| 23 |
-
with open(self.baseFilename, "w", encoding="utf-8") as f:
|
| 24 |
-
json.dump(json_data, f, ensure_ascii=False, indent=4)
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
class JsonFormatter(logging.Formatter):
|
| 28 |
-
def format(self, record):
|
| 29 |
-
return record.msg
|
| 30 |
-
|
| 31 |
-
|
| 32 |
-
class Logger(metaclass=Singleton):
|
| 33 |
-
"""
|
| 34 |
-
Logger that handle titles in different colors.
|
| 35 |
-
Outputs logs in console, activity.log, and errors.log
|
| 36 |
-
For console handler: simulates typing
|
| 37 |
-
"""
|
| 38 |
-
|
| 39 |
-
def __init__(self):
|
| 40 |
-
# create log directory if it doesn't exist
|
| 41 |
-
this_files_dir_path = os.path.dirname(__file__)
|
| 42 |
-
log_dir = os.path.join(this_files_dir_path, "../logs")
|
| 43 |
-
if not os.path.exists(log_dir):
|
| 44 |
-
os.makedirs(log_dir)
|
| 45 |
-
|
| 46 |
-
log_file = "activity.log"
|
| 47 |
-
error_file = "error.log"
|
| 48 |
-
|
| 49 |
-
console_formatter = AutoGptFormatter("%(title_color)s %(message)s")
|
| 50 |
-
|
| 51 |
-
# Create a handler for console which simulate typing
|
| 52 |
-
self.typing_console_handler = TypingConsoleHandler()
|
| 53 |
-
self.typing_console_handler.setLevel(logging.INFO)
|
| 54 |
-
self.typing_console_handler.setFormatter(console_formatter)
|
| 55 |
-
|
| 56 |
-
# Create a handler for console without typing simulation
|
| 57 |
-
self.console_handler = ConsoleHandler()
|
| 58 |
-
self.console_handler.setLevel(logging.DEBUG)
|
| 59 |
-
self.console_handler.setFormatter(console_formatter)
|
| 60 |
-
|
| 61 |
-
# Info handler in activity.log
|
| 62 |
-
self.file_handler = logging.FileHandler(
|
| 63 |
-
os.path.join(log_dir, log_file), "a", "utf-8"
|
| 64 |
-
)
|
| 65 |
-
self.file_handler.setLevel(logging.DEBUG)
|
| 66 |
-
info_formatter = AutoGptFormatter(
|
| 67 |
-
"%(asctime)s %(levelname)s %(title)s %(message_no_color)s"
|
| 68 |
-
)
|
| 69 |
-
self.file_handler.setFormatter(info_formatter)
|
| 70 |
-
|
| 71 |
-
# Error handler error.log
|
| 72 |
-
error_handler = logging.FileHandler(
|
| 73 |
-
os.path.join(log_dir, error_file), "a", "utf-8"
|
| 74 |
-
)
|
| 75 |
-
error_handler.setLevel(logging.ERROR)
|
| 76 |
-
error_formatter = AutoGptFormatter(
|
| 77 |
-
"%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s"
|
| 78 |
-
" %(message_no_color)s"
|
| 79 |
-
)
|
| 80 |
-
error_handler.setFormatter(error_formatter)
|
| 81 |
-
|
| 82 |
-
self.typing_logger = logging.getLogger("TYPER")
|
| 83 |
-
self.typing_logger.addHandler(self.typing_console_handler)
|
| 84 |
-
self.typing_logger.addHandler(self.file_handler)
|
| 85 |
-
self.typing_logger.addHandler(error_handler)
|
| 86 |
-
self.typing_logger.setLevel(logging.DEBUG)
|
| 87 |
-
|
| 88 |
-
self.logger = logging.getLogger("LOGGER")
|
| 89 |
-
self.logger.addHandler(self.console_handler)
|
| 90 |
-
self.logger.addHandler(self.file_handler)
|
| 91 |
-
self.logger.addHandler(error_handler)
|
| 92 |
-
self.logger.setLevel(logging.DEBUG)
|
| 93 |
-
|
| 94 |
-
self.json_logger = logging.getLogger("JSON_LOGGER")
|
| 95 |
-
self.json_logger.addHandler(self.file_handler)
|
| 96 |
-
self.json_logger.addHandler(error_handler)
|
| 97 |
-
self.json_logger.setLevel(logging.DEBUG)
|
| 98 |
-
|
| 99 |
-
self.speak_mode = False
|
| 100 |
-
self.chat_plugins = []
|
| 101 |
-
|
| 102 |
-
def typewriter_log(
|
| 103 |
-
self, title="", title_color="", content="", speak_text=False, level=logging.INFO
|
| 104 |
-
):
|
| 105 |
-
# if speak_text and self.speak_mode:
|
| 106 |
-
# say_text(f"{title}. {content}")
|
| 107 |
-
|
| 108 |
-
for plugin in self.chat_plugins:
|
| 109 |
-
plugin.report(f"{title}. {content}")
|
| 110 |
-
|
| 111 |
-
if content:
|
| 112 |
-
if isinstance(content, list):
|
| 113 |
-
content = "\n".join(content)
|
| 114 |
-
else:
|
| 115 |
-
content = ""
|
| 116 |
-
|
| 117 |
-
self.typing_logger.log(
|
| 118 |
-
level, content, extra={"title": title, "color": title_color}
|
| 119 |
-
)
|
| 120 |
-
|
| 121 |
-
def debug(
|
| 122 |
-
self,
|
| 123 |
-
message,
|
| 124 |
-
title="",
|
| 125 |
-
title_color="",
|
| 126 |
-
):
|
| 127 |
-
self._log(title, title_color, message, logging.DEBUG)
|
| 128 |
-
|
| 129 |
-
def info(
|
| 130 |
-
self,
|
| 131 |
-
message,
|
| 132 |
-
title="",
|
| 133 |
-
title_color="",
|
| 134 |
-
):
|
| 135 |
-
self._log(title, title_color, message, logging.INFO)
|
| 136 |
-
|
| 137 |
-
def warn(
|
| 138 |
-
self,
|
| 139 |
-
message,
|
| 140 |
-
title="",
|
| 141 |
-
title_color="",
|
| 142 |
-
):
|
| 143 |
-
self._log(title, title_color, message, logging.WARN)
|
| 144 |
-
|
| 145 |
-
def error(self, title, message=""):
|
| 146 |
-
self._log(title, Fore.RED, message, logging.ERROR)
|
| 147 |
-
|
| 148 |
-
def _log(
|
| 149 |
-
self,
|
| 150 |
-
title: str = "",
|
| 151 |
-
title_color: str = "",
|
| 152 |
-
message: str = "",
|
| 153 |
-
level=logging.INFO,
|
| 154 |
-
):
|
| 155 |
-
if isinstance(message, list):
|
| 156 |
-
if len(message) > 0:
|
| 157 |
-
message = "\n".join([str(m) for m in message])
|
| 158 |
-
else:
|
| 159 |
-
message = ""
|
| 160 |
-
self.logger.log(
|
| 161 |
-
level, message, extra={"title": str(title), "color": str(title_color)}
|
| 162 |
-
)
|
| 163 |
-
|
| 164 |
-
def set_level(self, level):
|
| 165 |
-
self.logger.setLevel(level)
|
| 166 |
-
self.typing_logger.setLevel(level)
|
| 167 |
-
|
| 168 |
-
def double_check(self, additionalText=None):
|
| 169 |
-
if not additionalText:
|
| 170 |
-
additionalText = (
|
| 171 |
-
"Please ensure you've setup and configured everything"
|
| 172 |
-
" correctly. Read https://github.com/Torantulino/Auto-GPT#readme to "
|
| 173 |
-
"double check. You can also create a github issue or join the discord"
|
| 174 |
-
" and ask there!"
|
| 175 |
-
)
|
| 176 |
-
|
| 177 |
-
self.typewriter_log("DOUBLE CHECK CONFIGURATION", Fore.YELLOW, additionalText)
|
| 178 |
-
|
| 179 |
-
def log_json(self, data: Any, file_name: str) -> None:
|
| 180 |
-
# Define log directory
|
| 181 |
-
this_files_dir_path = os.path.dirname(__file__)
|
| 182 |
-
log_dir = os.path.join(this_files_dir_path, "../logs")
|
| 183 |
-
|
| 184 |
-
# Create a handler for JSON files
|
| 185 |
-
json_file_path = os.path.join(log_dir, file_name)
|
| 186 |
-
json_data_handler = JsonFileHandler(json_file_path)
|
| 187 |
-
json_data_handler.setFormatter(JsonFormatter())
|
| 188 |
-
|
| 189 |
-
# Log the JSON data using the custom file handler
|
| 190 |
-
self.json_logger.addHandler(json_data_handler)
|
| 191 |
-
self.json_logger.debug(data)
|
| 192 |
-
self.json_logger.removeHandler(json_data_handler)
|
| 193 |
-
|
| 194 |
-
def log_prompt(self, prompt: List[dict]) -> None:
|
| 195 |
-
self.debug("", "-=-=-=-=-=-=-=-=Prompt Start-=-=-=-=-=-=-=-=", Fore.MAGENTA)
|
| 196 |
-
for p in prompt:
|
| 197 |
-
self.debug(
|
| 198 |
-
p["content"]
|
| 199 |
-
if "function_call" not in p
|
| 200 |
-
else p["content"]
|
| 201 |
-
+ "\nFunction Call:\n"
|
| 202 |
-
+ json.dumps(p["function_call"]),
|
| 203 |
-
title=f'==={p["role"]}===\n',
|
| 204 |
-
title_color=Fore.MAGENTA,
|
| 205 |
-
)
|
| 206 |
-
self.debug("", "-=-=-=-=-=-=-=-=Prompt End-=-=-=-=-=-=-=-=", Fore.MAGENTA)
|
| 207 |
-
|
| 208 |
-
def get_log_directory(self):
|
| 209 |
-
this_files_dir_path = os.path.dirname(__file__)
|
| 210 |
-
log_dir = os.path.join(this_files_dir_path, "../logs")
|
| 211 |
-
return os.path.abspath(log_dir)
|
| 212 |
-
|
| 213 |
-
|
| 214 |
-
"""
|
| 215 |
-
Output stream to console using simulated typing
|
| 216 |
-
"""
|
| 217 |
-
|
| 218 |
-
|
| 219 |
-
class TypingConsoleHandler(logging.StreamHandler):
|
| 220 |
-
def emit(self, record):
|
| 221 |
-
min_typing_speed = 0.05
|
| 222 |
-
max_typing_speed = 0.01
|
| 223 |
-
|
| 224 |
-
msg = self.format(record)
|
| 225 |
-
try:
|
| 226 |
-
words = re.split(r"(\s+)", msg)
|
| 227 |
-
for i, word in enumerate(words):
|
| 228 |
-
print(word, end="", flush=True)
|
| 229 |
-
# if i < len(words) - 1:
|
| 230 |
-
# print(" ", end="", flush=True)
|
| 231 |
-
typing_speed = random.uniform(min_typing_speed, max_typing_speed)
|
| 232 |
-
time.sleep(typing_speed)
|
| 233 |
-
# type faster after each word
|
| 234 |
-
min_typing_speed = min_typing_speed * 0.95
|
| 235 |
-
max_typing_speed = max_typing_speed * 0.95
|
| 236 |
-
print()
|
| 237 |
-
except Exception:
|
| 238 |
-
self.handleError(record)
|
| 239 |
-
|
| 240 |
-
|
| 241 |
-
class ConsoleHandler(logging.StreamHandler):
|
| 242 |
-
def emit(self, record) -> None:
|
| 243 |
-
msg = self.format(record)
|
| 244 |
-
try:
|
| 245 |
-
print(msg)
|
| 246 |
-
except Exception:
|
| 247 |
-
self.handleError(record)
|
| 248 |
-
|
| 249 |
-
|
| 250 |
-
class AutoGptFormatter(logging.Formatter):
|
| 251 |
-
"""
|
| 252 |
-
Allows to handle custom placeholders 'title_color' and 'message_no_color'.
|
| 253 |
-
To use this formatter, make sure to pass 'color', 'title' as log extras.
|
| 254 |
-
"""
|
| 255 |
-
|
| 256 |
-
def format(self, record: LogRecord) -> str:
|
| 257 |
-
if hasattr(record, "color"):
|
| 258 |
-
record.title_color = (
|
| 259 |
-
getattr(record, "color")
|
| 260 |
-
+ getattr(record, "title", "")
|
| 261 |
-
+ " "
|
| 262 |
-
+ Style.RESET_ALL
|
| 263 |
-
)
|
| 264 |
-
else:
|
| 265 |
-
record.title_color = getattr(record, "title", "")
|
| 266 |
-
|
| 267 |
-
# Add this line to set 'title' to an empty string if it doesn't exist
|
| 268 |
-
record.title = getattr(record, "title", "")
|
| 269 |
-
|
| 270 |
-
if hasattr(record, "msg"):
|
| 271 |
-
record.message_no_color = remove_color_codes(getattr(record, "msg"))
|
| 272 |
-
else:
|
| 273 |
-
record.message_no_color = ""
|
| 274 |
-
return super().format(record)
|
| 275 |
-
|
| 276 |
-
|
| 277 |
-
def remove_color_codes(s: str) -> str:
|
| 278 |
-
ansi_escape = re.compile(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])")
|
| 279 |
-
return ansi_escape.sub("", s)
|
| 280 |
-
|
| 281 |
-
|
| 282 |
-
logger = Logger()
|
| 283 |
-
|
| 284 |
-
|
| 285 |
-
def get_logger():
|
| 286 |
-
return logger
|
| 287 |
-
|
| 288 |
-
|
| 289 |
-
def typewriter_log(content="", color="", level=logging.INFO):
|
| 290 |
-
for line in content.split("\n"):
|
| 291 |
-
logger.typewriter_log(line, title_color=color, level=level)
|
|
|
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spaces/AhmedBadrDev/stomach/README.md
DELETED
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@@ -1,12 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Stomach
|
| 3 |
-
emoji: 🌍
|
| 4 |
-
colorFrom: yellow
|
| 5 |
-
colorTo: blue
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.27.0
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
|
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|
spaces/Amrrs/DragGan-Inversion/PTI/models/StyleCLIP/global_directions/GetCode.py
DELETED
|
@@ -1,232 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
import os
|
| 5 |
-
import pickle
|
| 6 |
-
import numpy as np
|
| 7 |
-
from dnnlib import tflib
|
| 8 |
-
import tensorflow as tf
|
| 9 |
-
|
| 10 |
-
import argparse
|
| 11 |
-
|
| 12 |
-
def LoadModel(dataset_name):
|
| 13 |
-
# Initialize TensorFlow.
|
| 14 |
-
tflib.init_tf()
|
| 15 |
-
model_path='./model/'
|
| 16 |
-
model_name=dataset_name+'.pkl'
|
| 17 |
-
|
| 18 |
-
tmp=os.path.join(model_path,model_name)
|
| 19 |
-
with open(tmp, 'rb') as f:
|
| 20 |
-
_, _, Gs = pickle.load(f)
|
| 21 |
-
return Gs
|
| 22 |
-
|
| 23 |
-
def lerp(a,b,t):
|
| 24 |
-
return a + (b - a) * t
|
| 25 |
-
|
| 26 |
-
#stylegan-ada
|
| 27 |
-
def SelectName(layer_name,suffix):
|
| 28 |
-
if suffix==None:
|
| 29 |
-
tmp1='add:0' in layer_name
|
| 30 |
-
tmp2='shape=(?,' in layer_name
|
| 31 |
-
tmp4='G_synthesis_1' in layer_name
|
| 32 |
-
tmp= tmp1 and tmp2 and tmp4
|
| 33 |
-
else:
|
| 34 |
-
tmp1=('/Conv0_up'+suffix) in layer_name
|
| 35 |
-
tmp2=('/Conv1'+suffix) in layer_name
|
| 36 |
-
tmp3=('4x4/Conv'+suffix) in layer_name
|
| 37 |
-
tmp4='G_synthesis_1' in layer_name
|
| 38 |
-
tmp5=('/ToRGB'+suffix) in layer_name
|
| 39 |
-
tmp= (tmp1 or tmp2 or tmp3 or tmp5) and tmp4
|
| 40 |
-
return tmp
|
| 41 |
-
|
| 42 |
-
|
| 43 |
-
def GetSNames(suffix):
|
| 44 |
-
#get style tensor name
|
| 45 |
-
with tf.Session() as sess:
|
| 46 |
-
op = sess.graph.get_operations()
|
| 47 |
-
layers=[m.values() for m in op]
|
| 48 |
-
|
| 49 |
-
|
| 50 |
-
select_layers=[]
|
| 51 |
-
for layer in layers:
|
| 52 |
-
layer_name=str(layer)
|
| 53 |
-
if SelectName(layer_name,suffix):
|
| 54 |
-
select_layers.append(layer[0])
|
| 55 |
-
return select_layers
|
| 56 |
-
|
| 57 |
-
def SelectName2(layer_name):
|
| 58 |
-
tmp1='mod_bias' in layer_name
|
| 59 |
-
tmp2='mod_weight' in layer_name
|
| 60 |
-
tmp3='ToRGB' in layer_name
|
| 61 |
-
|
| 62 |
-
tmp= (tmp1 or tmp2) and (not tmp3)
|
| 63 |
-
return tmp
|
| 64 |
-
|
| 65 |
-
def GetKName(Gs):
|
| 66 |
-
|
| 67 |
-
layers=[var for name, var in Gs.components.synthesis.vars.items()]
|
| 68 |
-
|
| 69 |
-
select_layers=[]
|
| 70 |
-
for layer in layers:
|
| 71 |
-
layer_name=str(layer)
|
| 72 |
-
if SelectName2(layer_name):
|
| 73 |
-
select_layers.append(layer)
|
| 74 |
-
return select_layers
|
| 75 |
-
|
| 76 |
-
def GetCode(Gs,random_state,num_img,num_once,dataset_name):
|
| 77 |
-
rnd = np.random.RandomState(random_state) #5
|
| 78 |
-
|
| 79 |
-
truncation_psi=0.7
|
| 80 |
-
truncation_cutoff=8
|
| 81 |
-
|
| 82 |
-
dlatent_avg=Gs.get_var('dlatent_avg')
|
| 83 |
-
|
| 84 |
-
dlatents=np.zeros((num_img,512),dtype='float32')
|
| 85 |
-
for i in range(int(num_img/num_once)):
|
| 86 |
-
src_latents = rnd.randn(num_once, Gs.input_shape[1])
|
| 87 |
-
src_dlatents = Gs.components.mapping.run(src_latents, None) # [seed, layer, component]
|
| 88 |
-
|
| 89 |
-
# Apply truncation trick.
|
| 90 |
-
if truncation_psi is not None and truncation_cutoff is not None:
|
| 91 |
-
layer_idx = np.arange(src_dlatents.shape[1])[np.newaxis, :, np.newaxis]
|
| 92 |
-
ones = np.ones(layer_idx.shape, dtype=np.float32)
|
| 93 |
-
coefs = np.where(layer_idx < truncation_cutoff, truncation_psi * ones, ones)
|
| 94 |
-
src_dlatents_np=lerp(dlatent_avg, src_dlatents, coefs)
|
| 95 |
-
src_dlatents=src_dlatents_np[:,0,:].astype('float32')
|
| 96 |
-
dlatents[(i*num_once):((i+1)*num_once),:]=src_dlatents
|
| 97 |
-
print('get all z and w')
|
| 98 |
-
|
| 99 |
-
tmp='./npy/'+dataset_name+'/W'
|
| 100 |
-
np.save(tmp,dlatents)
|
| 101 |
-
|
| 102 |
-
|
| 103 |
-
def GetImg(Gs,num_img,num_once,dataset_name,save_name='images'):
|
| 104 |
-
print('Generate Image')
|
| 105 |
-
tmp='./npy/'+dataset_name+'/W.npy'
|
| 106 |
-
dlatents=np.load(tmp)
|
| 107 |
-
fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
|
| 108 |
-
|
| 109 |
-
all_images=[]
|
| 110 |
-
for i in range(int(num_img/num_once)):
|
| 111 |
-
print(i)
|
| 112 |
-
images=[]
|
| 113 |
-
for k in range(num_once):
|
| 114 |
-
tmp=dlatents[i*num_once+k]
|
| 115 |
-
tmp=tmp[None,None,:]
|
| 116 |
-
tmp=np.tile(tmp,(1,Gs.components.synthesis.input_shape[1],1))
|
| 117 |
-
image2= Gs.components.synthesis.run(tmp, randomize_noise=False, output_transform=fmt)
|
| 118 |
-
images.append(image2)
|
| 119 |
-
|
| 120 |
-
images=np.concatenate(images)
|
| 121 |
-
|
| 122 |
-
all_images.append(images)
|
| 123 |
-
|
| 124 |
-
all_images=np.concatenate(all_images)
|
| 125 |
-
|
| 126 |
-
tmp='./npy/'+dataset_name+'/'+save_name
|
| 127 |
-
np.save(tmp,all_images)
|
| 128 |
-
|
| 129 |
-
def GetS(dataset_name,num_img):
|
| 130 |
-
print('Generate S')
|
| 131 |
-
tmp='./npy/'+dataset_name+'/W.npy'
|
| 132 |
-
dlatents=np.load(tmp)[:num_img]
|
| 133 |
-
|
| 134 |
-
with tf.Session() as sess:
|
| 135 |
-
init = tf.global_variables_initializer()
|
| 136 |
-
sess.run(init)
|
| 137 |
-
|
| 138 |
-
Gs=LoadModel(dataset_name)
|
| 139 |
-
Gs.print_layers() #for ada
|
| 140 |
-
select_layers1=GetSNames(suffix=None) #None,'/mul_1:0','/mod_weight/read:0','/MatMul:0'
|
| 141 |
-
dlatents=dlatents[:,None,:]
|
| 142 |
-
dlatents=np.tile(dlatents,(1,Gs.components.synthesis.input_shape[1],1))
|
| 143 |
-
|
| 144 |
-
all_s = sess.run(
|
| 145 |
-
select_layers1,
|
| 146 |
-
feed_dict={'G_synthesis_1/dlatents_in:0': dlatents})
|
| 147 |
-
|
| 148 |
-
layer_names=[layer.name for layer in select_layers1]
|
| 149 |
-
save_tmp=[layer_names,all_s]
|
| 150 |
-
return save_tmp
|
| 151 |
-
|
| 152 |
-
|
| 153 |
-
|
| 154 |
-
|
| 155 |
-
def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False):
|
| 156 |
-
"""Convert a minibatch of images from float32 to uint8 with configurable dynamic range.
|
| 157 |
-
Can be used as an output transformation for Network.run().
|
| 158 |
-
"""
|
| 159 |
-
if nchw_to_nhwc:
|
| 160 |
-
images = np.transpose(images, [0, 2, 3, 1])
|
| 161 |
-
|
| 162 |
-
scale = 255 / (drange[1] - drange[0])
|
| 163 |
-
images = images * scale + (0.5 - drange[0] * scale)
|
| 164 |
-
|
| 165 |
-
np.clip(images, 0, 255, out=images)
|
| 166 |
-
images=images.astype('uint8')
|
| 167 |
-
return images
|
| 168 |
-
|
| 169 |
-
|
| 170 |
-
def GetCodeMS(dlatents):
|
| 171 |
-
m=[]
|
| 172 |
-
std=[]
|
| 173 |
-
for i in range(len(dlatents)):
|
| 174 |
-
tmp= dlatents[i]
|
| 175 |
-
tmp_mean=tmp.mean(axis=0)
|
| 176 |
-
tmp_std=tmp.std(axis=0)
|
| 177 |
-
m.append(tmp_mean)
|
| 178 |
-
std.append(tmp_std)
|
| 179 |
-
return m,std
|
| 180 |
-
|
| 181 |
-
|
| 182 |
-
|
| 183 |
-
#%%
|
| 184 |
-
if __name__ == "__main__":
|
| 185 |
-
|
| 186 |
-
|
| 187 |
-
parser = argparse.ArgumentParser(description='Process some integers.')
|
| 188 |
-
|
| 189 |
-
parser.add_argument('--dataset_name',type=str,default='ffhq',
|
| 190 |
-
help='name of dataset, for example, ffhq')
|
| 191 |
-
parser.add_argument('--code_type',choices=['w','s','s_mean_std'],default='w')
|
| 192 |
-
|
| 193 |
-
args = parser.parse_args()
|
| 194 |
-
random_state=5
|
| 195 |
-
num_img=100_000
|
| 196 |
-
num_once=1_000
|
| 197 |
-
dataset_name=args.dataset_name
|
| 198 |
-
|
| 199 |
-
if not os.path.isfile('./model/'+dataset_name+'.pkl'):
|
| 200 |
-
url='https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/'
|
| 201 |
-
name='stylegan2-'+dataset_name+'-config-f.pkl'
|
| 202 |
-
os.system('wget ' +url+name + ' -P ./model/')
|
| 203 |
-
os.system('mv ./model/'+name+' ./model/'+dataset_name+'.pkl')
|
| 204 |
-
|
| 205 |
-
if not os.path.isdir('./npy/'+dataset_name):
|
| 206 |
-
os.system('mkdir ./npy/'+dataset_name)
|
| 207 |
-
|
| 208 |
-
if args.code_type=='w':
|
| 209 |
-
Gs=LoadModel(dataset_name=dataset_name)
|
| 210 |
-
GetCode(Gs,random_state,num_img,num_once,dataset_name)
|
| 211 |
-
# GetImg(Gs,num_img=num_img,num_once=num_once,dataset_name=dataset_name,save_name='images_100K') #no need
|
| 212 |
-
elif args.code_type=='s':
|
| 213 |
-
save_name='S'
|
| 214 |
-
save_tmp=GetS(dataset_name,num_img=2_000)
|
| 215 |
-
tmp='./npy/'+dataset_name+'/'+save_name
|
| 216 |
-
with open(tmp, "wb") as fp:
|
| 217 |
-
pickle.dump(save_tmp, fp)
|
| 218 |
-
|
| 219 |
-
elif args.code_type=='s_mean_std':
|
| 220 |
-
save_tmp=GetS(dataset_name,num_img=num_img)
|
| 221 |
-
dlatents=save_tmp[1]
|
| 222 |
-
m,std=GetCodeMS(dlatents)
|
| 223 |
-
save_tmp=[m,std]
|
| 224 |
-
save_name='S_mean_std'
|
| 225 |
-
tmp='./npy/'+dataset_name+'/'+save_name
|
| 226 |
-
with open(tmp, "wb") as fp:
|
| 227 |
-
pickle.dump(save_tmp, fp)
|
| 228 |
-
|
| 229 |
-
|
| 230 |
-
|
| 231 |
-
|
| 232 |
-
|
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spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stable_diffusion/safety_checker_flax.py
DELETED
|
@@ -1,112 +0,0 @@
|
|
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# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Optional, Tuple
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import jax
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import jax.numpy as jnp
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from flax import linen as nn
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from flax.core.frozen_dict import FrozenDict
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from transformers import CLIPConfig, FlaxPreTrainedModel
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from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
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def jax_cosine_distance(emb_1, emb_2, eps=1e-12):
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norm_emb_1 = jnp.divide(emb_1.T, jnp.clip(jnp.linalg.norm(emb_1, axis=1), a_min=eps)).T
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norm_emb_2 = jnp.divide(emb_2.T, jnp.clip(jnp.linalg.norm(emb_2, axis=1), a_min=eps)).T
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return jnp.matmul(norm_emb_1, norm_emb_2.T)
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class FlaxStableDiffusionSafetyCheckerModule(nn.Module):
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config: CLIPConfig
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dtype: jnp.dtype = jnp.float32
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def setup(self):
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self.vision_model = FlaxCLIPVisionModule(self.config.vision_config)
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self.visual_projection = nn.Dense(self.config.projection_dim, use_bias=False, dtype=self.dtype)
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self.concept_embeds = self.param("concept_embeds", jax.nn.initializers.ones, (17, self.config.projection_dim))
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self.special_care_embeds = self.param(
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"special_care_embeds", jax.nn.initializers.ones, (3, self.config.projection_dim)
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)
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self.concept_embeds_weights = self.param("concept_embeds_weights", jax.nn.initializers.ones, (17,))
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self.special_care_embeds_weights = self.param("special_care_embeds_weights", jax.nn.initializers.ones, (3,))
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def __call__(self, clip_input):
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pooled_output = self.vision_model(clip_input)[1]
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image_embeds = self.visual_projection(pooled_output)
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special_cos_dist = jax_cosine_distance(image_embeds, self.special_care_embeds)
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cos_dist = jax_cosine_distance(image_embeds, self.concept_embeds)
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# increase this value to create a stronger `nfsw` filter
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# at the cost of increasing the possibility of filtering benign image inputs
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adjustment = 0.0
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special_scores = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
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special_scores = jnp.round(special_scores, 3)
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is_special_care = jnp.any(special_scores > 0, axis=1, keepdims=True)
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# Use a lower threshold if an image has any special care concept
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special_adjustment = is_special_care * 0.01
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concept_scores = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
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concept_scores = jnp.round(concept_scores, 3)
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has_nsfw_concepts = jnp.any(concept_scores > 0, axis=1)
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return has_nsfw_concepts
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class FlaxStableDiffusionSafetyChecker(FlaxPreTrainedModel):
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config_class = CLIPConfig
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main_input_name = "clip_input"
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module_class = FlaxStableDiffusionSafetyCheckerModule
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def __init__(
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self,
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config: CLIPConfig,
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input_shape: Optional[Tuple] = None,
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seed: int = 0,
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dtype: jnp.dtype = jnp.float32,
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_do_init: bool = True,
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**kwargs,
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):
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if input_shape is None:
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input_shape = (1, 224, 224, 3)
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module = self.module_class(config=config, dtype=dtype, **kwargs)
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super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
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def init_weights(self, rng: jax.random.KeyArray, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
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# init input tensor
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clip_input = jax.random.normal(rng, input_shape)
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params_rng, dropout_rng = jax.random.split(rng)
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rngs = {"params": params_rng, "dropout": dropout_rng}
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random_params = self.module.init(rngs, clip_input)["params"]
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return random_params
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def __call__(
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self,
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clip_input,
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params: dict = None,
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):
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clip_input = jnp.transpose(clip_input, (0, 2, 3, 1))
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return self.module.apply(
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{"params": params or self.params},
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jnp.array(clip_input, dtype=jnp.float32),
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rngs={},
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)
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spaces/Andy1621/uniformer_image_detection/configs/_base_/models/fast_rcnn_r50_fpn.py
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# model settings
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model = dict(
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type='FastRCNN',
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pretrained='torchvision://resnet50',
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backbone=dict(
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type='ResNet',
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depth=50,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=1,
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norm_cfg=dict(type='BN', requires_grad=True),
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norm_eval=True,
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style='pytorch'),
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neck=dict(
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type='FPN',
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in_channels=[256, 512, 1024, 2048],
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out_channels=256,
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num_outs=5),
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roi_head=dict(
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type='StandardRoIHead',
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bbox_roi_extractor=dict(
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type='SingleRoIExtractor',
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roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
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out_channels=256,
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featmap_strides=[4, 8, 16, 32]),
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bbox_head=dict(
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type='Shared2FCBBoxHead',
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in_channels=256,
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fc_out_channels=1024,
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roi_feat_size=7,
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num_classes=80,
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bbox_coder=dict(
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type='DeltaXYWHBBoxCoder',
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target_means=[0., 0., 0., 0.],
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target_stds=[0.1, 0.1, 0.2, 0.2]),
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reg_class_agnostic=False,
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loss_cls=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
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loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
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# model training and testing settings
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train_cfg=dict(
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rcnn=dict(
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assigner=dict(
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type='MaxIoUAssigner',
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pos_iou_thr=0.5,
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neg_iou_thr=0.5,
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min_pos_iou=0.5,
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match_low_quality=False,
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ignore_iof_thr=-1),
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sampler=dict(
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type='RandomSampler',
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num=512,
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pos_fraction=0.25,
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neg_pos_ub=-1,
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add_gt_as_proposals=True),
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pos_weight=-1,
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debug=False)),
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test_cfg=dict(
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rcnn=dict(
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score_thr=0.05,
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nms=dict(type='nms', iou_threshold=0.5),
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max_per_img=100)))
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spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r101-d8_512x512_160k_ade20k.py
DELETED
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_base_ = './fcn_r50-d8_512x512_160k_ade20k.py'
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model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
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spaces/Andy1621/uniformer_image_segmentation/configs/ocrnet/ocrnet_hr18s_512x1024_40k_cityscapes.py
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_base_ = './ocrnet_hr18_512x1024_40k_cityscapes.py'
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model = dict(
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pretrained='open-mmlab://msra/hrnetv2_w18_small',
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backbone=dict(
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extra=dict(
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stage1=dict(num_blocks=(2, )),
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stage2=dict(num_blocks=(2, 2)),
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stage3=dict(num_modules=3, num_blocks=(2, 2, 2)),
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stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2)))))
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spaces/Anni123/AuRoRA/retrieval_utils.py
DELETED
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'''
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Modified from https://github.com/RuochenZhao/Verify-and-Edit
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'''
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import wikipedia
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import wikipediaapi
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import spacy
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import numpy as np
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import ngram
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#import nltk
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import torch
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import sklearn
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#from textblob import TextBlob
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from nltk import tokenize
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from sentence_transformers import SentenceTransformer
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from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer, DPRContextEncoder, DPRContextEncoderTokenizer
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from llm_utils import decoder_for_gpt3
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from utils import entity_cleansing, knowledge_cleansing
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import nltk
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nltk.download('punkt')
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wiki_wiki = wikipediaapi.Wikipedia('en')
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nlp = spacy.load("en_core_web_sm")
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ENT_TYPE = ['EVENT', 'FAC', 'GPE', 'LANGUAGE', 'LAW', 'LOC', 'NORP', 'ORG', 'PERSON', 'PRODUCT', 'WORK_OF_ART']
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CTX_ENCODER = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
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CTX_TOKENIZER = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base", model_max_length = 512)
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Q_ENCODER = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
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Q_TOKENIZER = DPRQuestionEncoderTokenizer.from_pretrained("facebook/dpr-question_encoder-single-nq-base", model_max_length = 512)
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## todo: extract entities from ConceptNet
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def find_ents(text, engine):
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doc = nlp(text)
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valid_ents = []
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for ent in doc.ents:
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if ent.label_ in ENT_TYPE:
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valid_ents.append(ent.text)
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#in case entity list is empty: resort to LLM to extract entity
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if valid_ents == []:
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input = "Question: " + "[ " + text + "]\n"
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input += "Output the entities in Question separated by comma: "
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response = decoder_for_gpt3(input, 32, engine=engine)
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valid_ents = entity_cleansing(response)
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return valid_ents
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def relevant_pages_for_ents(valid_ents, topk = 5):
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'''
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Input: a list of valid entities
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Output: a list of list containing topk pages for each entity
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'''
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if valid_ents == []:
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return []
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titles = []
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for ve in valid_ents:
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title = wikipedia.search(ve)[:topk]
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titles.append(title)
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#titles = list(dict.fromkeys(titles))
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return titles
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def relevant_pages_for_text(text, topk = 5):
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return wikipedia.search(text)[:topk]
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def get_wiki_objs(pages):
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'''
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Input: a list of list
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Output: a list of list
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'''
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if pages == []:
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return []
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obj_pages = []
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for titles_for_ve in pages:
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pages_for_ve = [wiki_wiki.page(title) for title in titles_for_ve]
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obj_pages.append(pages_for_ve)
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return obj_pages
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def get_linked_pages(wiki_pages, topk = 5):
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linked_ents = []
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for wp in wiki_pages:
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linked_ents += list(wp.links.values())
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if topk != -1:
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linked_ents = linked_ents[:topk]
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| 87 |
-
return linked_ents
|
| 88 |
-
|
| 89 |
-
|
| 90 |
-
def get_texts_to_pages(pages, topk = 2):
|
| 91 |
-
'''
|
| 92 |
-
Input: list of list of pages
|
| 93 |
-
Output: list of list of texts
|
| 94 |
-
'''
|
| 95 |
-
total_texts = []
|
| 96 |
-
for ve_pages in pages:
|
| 97 |
-
ve_texts = []
|
| 98 |
-
for p in ve_pages:
|
| 99 |
-
text = p.text
|
| 100 |
-
text = tokenize.sent_tokenize(text)[:topk]
|
| 101 |
-
text = ' '.join(text)
|
| 102 |
-
ve_texts.append(text)
|
| 103 |
-
total_texts.append(ve_texts)
|
| 104 |
-
return total_texts
|
| 105 |
-
|
| 106 |
-
|
| 107 |
-
|
| 108 |
-
def DPR_embeddings(q_encoder, q_tokenizer, question):
|
| 109 |
-
question_embedding = q_tokenizer(question, return_tensors="pt",max_length=5, truncation=True)
|
| 110 |
-
with torch.no_grad():
|
| 111 |
-
try:
|
| 112 |
-
question_embedding = q_encoder(**question_embedding)[0][0]
|
| 113 |
-
except:
|
| 114 |
-
print(question)
|
| 115 |
-
print(question_embedding['input_ids'].size())
|
| 116 |
-
raise Exception('end')
|
| 117 |
-
question_embedding = question_embedding.numpy()
|
| 118 |
-
return question_embedding
|
| 119 |
-
|
| 120 |
-
def model_embeddings(sentence, model):
|
| 121 |
-
embedding = model.encode([sentence])
|
| 122 |
-
return embedding[0] #should return an array of shape 384
|
| 123 |
-
|
| 124 |
-
##todo: plus overlap filtering
|
| 125 |
-
def filtering_retrieved_texts(question, ent_texts, retr_method="wikipedia_dpr", topk=1):
|
| 126 |
-
filtered_texts = []
|
| 127 |
-
for texts in ent_texts:
|
| 128 |
-
if texts != []: #not empty list
|
| 129 |
-
if retr_method == "ngram":
|
| 130 |
-
pars = np.array([ngram.NGram.compare(question, sent, N=1) for sent in texts])
|
| 131 |
-
#argsort: smallest to biggest
|
| 132 |
-
pars = pars.argsort()[::-1][:topk]
|
| 133 |
-
else:
|
| 134 |
-
if retr_method == "wikipedia_dpr":
|
| 135 |
-
sen_embeds = [DPR_embeddings(Q_ENCODER, Q_TOKENIZER, question)]
|
| 136 |
-
par_embeds = [DPR_embeddings(CTX_ENCODER, CTX_TOKENIZER, s) for s in texts]
|
| 137 |
-
else:
|
| 138 |
-
embedding_model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
|
| 139 |
-
sen_embeds = [model_embeddings(question, embedding_model)]
|
| 140 |
-
par_embeds = [model_embeddings(s, embedding_model) for s in texts]
|
| 141 |
-
pars = sklearn.metrics.pairwise.pairwise_distances(sen_embeds, par_embeds)
|
| 142 |
-
pars = pars.argsort(axis=1)[0][:topk]
|
| 143 |
-
filtered_texts += [texts[i] for i in pars]
|
| 144 |
-
filtered_texts = list(dict.fromkeys(filtered_texts))
|
| 145 |
-
return filtered_texts
|
| 146 |
-
|
| 147 |
-
def join_knowledge(filtered_texts):
|
| 148 |
-
if filtered_texts == []:
|
| 149 |
-
return ""
|
| 150 |
-
return " ".join(filtered_texts)
|
| 151 |
-
|
| 152 |
-
def retrieve_for_question_kb(question, engine, know_type="entity_know", no_links=False):
|
| 153 |
-
valid_ents = find_ents(question, engine)
|
| 154 |
-
print(valid_ents)
|
| 155 |
-
|
| 156 |
-
# find pages
|
| 157 |
-
page_titles = []
|
| 158 |
-
if "entity" in know_type:
|
| 159 |
-
pages_for_ents = relevant_pages_for_ents(valid_ents, topk = 5) #list of list
|
| 160 |
-
if pages_for_ents != []:
|
| 161 |
-
page_titles += pages_for_ents
|
| 162 |
-
if "question" in know_type:
|
| 163 |
-
pages_for_question = relevant_pages_for_text(question, topk = 5)
|
| 164 |
-
if pages_for_question != []:
|
| 165 |
-
page_titles += pages_for_question
|
| 166 |
-
pages = get_wiki_objs(page_titles) #list of list
|
| 167 |
-
if pages == []:
|
| 168 |
-
return ""
|
| 169 |
-
new_pages = []
|
| 170 |
-
assert page_titles != []
|
| 171 |
-
assert pages != []
|
| 172 |
-
|
| 173 |
-
print(page_titles)
|
| 174 |
-
#print(pages)
|
| 175 |
-
for i, ve_pt in enumerate(page_titles):
|
| 176 |
-
new_ve_pages = []
|
| 177 |
-
for j, pt in enumerate(ve_pt):
|
| 178 |
-
if 'disambiguation' in pt:
|
| 179 |
-
new_ve_pages += get_linked_pages([pages[i][j]], topk=-1)
|
| 180 |
-
else:
|
| 181 |
-
new_ve_pages += [pages[i][j]]
|
| 182 |
-
new_pages.append(new_ve_pages)
|
| 183 |
-
|
| 184 |
-
pages = new_pages
|
| 185 |
-
|
| 186 |
-
if not no_links:
|
| 187 |
-
# add linked pages
|
| 188 |
-
for ve_pages in pages:
|
| 189 |
-
ve_pages += get_linked_pages(ve_pages, topk=5)
|
| 190 |
-
ve_pages = list(dict.fromkeys(ve_pages))
|
| 191 |
-
#get texts
|
| 192 |
-
texts = get_texts_to_pages(pages, topk=1)
|
| 193 |
-
filtered_texts = filtering_retrieved_texts(question, texts)
|
| 194 |
-
joint_knowledge = join_knowledge(filtered_texts)
|
| 195 |
-
|
| 196 |
-
|
| 197 |
-
return valid_ents, joint_knowledge
|
| 198 |
-
|
| 199 |
-
def retrieve_for_question(question, engine, retrieve_source="llm_kb"):
|
| 200 |
-
# Retrieve knowledge from LLM
|
| 201 |
-
if "llm" in retrieve_source:
|
| 202 |
-
self_retrieve_prompt = "Question: " + "[ " + question + "]\n"
|
| 203 |
-
self_retrieve_prompt += "Necessary knowledge about the question by not answering the question: "
|
| 204 |
-
self_retrieve_knowledge = decoder_for_gpt3(self_retrieve_prompt, 256, engine=engine)
|
| 205 |
-
self_retrieve_knowledge = knowledge_cleansing(self_retrieve_knowledge)
|
| 206 |
-
print("------Self_Know------")
|
| 207 |
-
print(self_retrieve_knowledge)
|
| 208 |
-
|
| 209 |
-
# Retrieve knowledge from KB
|
| 210 |
-
if "kb" in retrieve_source:
|
| 211 |
-
entities, kb_retrieve_knowledge = retrieve_for_question_kb(question, engine, no_links=True)
|
| 212 |
-
if kb_retrieve_knowledge != "":
|
| 213 |
-
print("------KB_Know------")
|
| 214 |
-
print(kb_retrieve_knowledge)
|
| 215 |
-
|
| 216 |
-
return entities, self_retrieve_knowledge, kb_retrieve_knowledge
|
| 217 |
-
|
| 218 |
-
def refine_for_question(question, engine, self_retrieve_knowledge, kb_retrieve_knowledge, retrieve_source="llm_kb"):
|
| 219 |
-
|
| 220 |
-
# Refine knowledge
|
| 221 |
-
if retrieve_source == "llm_only":
|
| 222 |
-
refine_knowledge = self_retrieve_knowledge
|
| 223 |
-
elif retrieve_source == "kb_only":
|
| 224 |
-
if kb_retrieve_knowledge != "":
|
| 225 |
-
refine_prompt = "Question: " + "[ " + question + "]\n"
|
| 226 |
-
refine_prompt += "Knowledge: " + "[ " + kb_retrieve_knowledge + "]\n"
|
| 227 |
-
refine_prompt += "Based on Knowledge, output the brief and refined knowledge necessary for Question by not giving the answer: "
|
| 228 |
-
refine_knowledge = decoder_for_gpt3(refine_prompt, 256, engine=engine)
|
| 229 |
-
print("------Refined_Know------")
|
| 230 |
-
print(refine_knowledge)
|
| 231 |
-
else:
|
| 232 |
-
refine_knowledge = ""
|
| 233 |
-
elif retrieve_source == "llm_kb":
|
| 234 |
-
if kb_retrieve_knowledge != "":
|
| 235 |
-
#refine_prompt = "Question: " + "[ " + question + "]\n"
|
| 236 |
-
refine_prompt = "Knowledge_1: " + "[ " + self_retrieve_knowledge + "]\n"
|
| 237 |
-
refine_prompt += "Knowledge_2: " + "[ " + kb_retrieve_knowledge + "]\n"
|
| 238 |
-
#refine_prompt += "By using Knowledge_2 to check Knowledge_1, output the brief and correct knowledge necessary for Question: "
|
| 239 |
-
refine_prompt += "By using Knowledge_2 to check Knowledge_1, output the brief and correct knowledge: "
|
| 240 |
-
refine_knowledge = decoder_for_gpt3(refine_prompt, 256, engine=engine)
|
| 241 |
-
refine_knowledge = knowledge_cleansing(refine_knowledge)
|
| 242 |
-
#refine_knowledge = kb_retrieve_knowledge + refine_knowledge
|
| 243 |
-
print("------Refined_Know------")
|
| 244 |
-
print(refine_knowledge)
|
| 245 |
-
else:
|
| 246 |
-
refine_knowledge = self_retrieve_knowledge
|
| 247 |
-
|
| 248 |
-
return refine_knowledge
|
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|
spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/utils/fft_pytorch.py
DELETED
|
@@ -1,73 +0,0 @@
|
|
| 1 |
-
#!/usr/bin/python
|
| 2 |
-
#****************************************************************#
|
| 3 |
-
# ScriptName: fft_pytorch.py
|
| 4 |
-
# Author: Anonymous_123
|
| 5 |
-
# Create Date: 2022-08-15 11:33
|
| 6 |
-
# Modify Author: Anonymous_123
|
| 7 |
-
# Modify Date: 2022-08-18 17:46
|
| 8 |
-
# Function:
|
| 9 |
-
#***************************************************************#
|
| 10 |
-
|
| 11 |
-
import torch
|
| 12 |
-
import torch.nn as nn
|
| 13 |
-
import torch.fft as fft
|
| 14 |
-
import cv2
|
| 15 |
-
import numpy as np
|
| 16 |
-
import torchvision.transforms as transforms
|
| 17 |
-
from PIL import Image
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
def lowpass(input, limit):
|
| 21 |
-
pass1 = torch.abs(fft.rfftfreq(input.shape[-1])) < limit
|
| 22 |
-
pass2 = torch.abs(fft.fftfreq(input.shape[-2])) < limit
|
| 23 |
-
kernel = torch.outer(pass2, pass1)
|
| 24 |
-
fft_input = fft.rfft2(input)
|
| 25 |
-
return fft.irfft2(fft_input*kernel, s=input.shape[-2:])
|
| 26 |
-
|
| 27 |
-
class HighFrequencyLoss(nn.Module):
|
| 28 |
-
def __init__(self, size=(224,224)):
|
| 29 |
-
super(HighFrequencyLoss, self).__init__()
|
| 30 |
-
'''
|
| 31 |
-
self.h,self.w = size
|
| 32 |
-
self.lpf = torch.zeros((self.h,1))
|
| 33 |
-
R = (self.h+self.w)//8
|
| 34 |
-
for x in range(self.w):
|
| 35 |
-
for y in range(self.h):
|
| 36 |
-
if ((x-(self.w-1)/2)**2 + (y-(self.h-1)/2)**2) < (R**2):
|
| 37 |
-
self.lpf[y,x] = 1
|
| 38 |
-
self.hpf = 1-self.lpf
|
| 39 |
-
'''
|
| 40 |
-
|
| 41 |
-
def forward(self, x):
|
| 42 |
-
f = fft.fftn(x, dim=(2,3))
|
| 43 |
-
loss = torch.abs(f).mean()
|
| 44 |
-
|
| 45 |
-
# f = torch.roll(f,(self.h//2,self.w//2),dims=(2,3))
|
| 46 |
-
# f_l = torch.mean(f * self.lpf)
|
| 47 |
-
# f_h = torch.mean(f * self.hpf)
|
| 48 |
-
|
| 49 |
-
return loss
|
| 50 |
-
|
| 51 |
-
if __name__ == '__main__':
|
| 52 |
-
import pdb
|
| 53 |
-
pdb.set_trace()
|
| 54 |
-
HF = HighFrequencyLoss()
|
| 55 |
-
transform = transforms.Compose([transforms.ToTensor()])
|
| 56 |
-
|
| 57 |
-
# img = cv2.imread('test_imgs/ILSVRC2012_val_00001935.JPEG')
|
| 58 |
-
img = cv2.imread('../tmp.jpg')
|
| 59 |
-
H,W,C = img.shape
|
| 60 |
-
imgs = []
|
| 61 |
-
for i in range(10):
|
| 62 |
-
img_ = img[:, 224*i:224*(i+1), :]
|
| 63 |
-
print(img_.shape)
|
| 64 |
-
img_tensor = transform(Image.fromarray(img_[:,:,::-1])).unsqueeze(0)
|
| 65 |
-
loss = HF(img_tensor).item()
|
| 66 |
-
cv2.putText(img_, str(loss)[:6], (5,50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
|
| 67 |
-
imgs.append(img_)
|
| 68 |
-
|
| 69 |
-
cv2.imwrite('tmp.jpg', cv2.hconcat(imgs))
|
| 70 |
-
|
| 71 |
-
|
| 72 |
-
|
| 73 |
-
|
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spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/roi_align.py
DELETED
|
@@ -1,223 +0,0 @@
|
|
| 1 |
-
# Copyright (c) OpenMMLab. All rights reserved.
|
| 2 |
-
import torch
|
| 3 |
-
import torch.nn as nn
|
| 4 |
-
from torch.autograd import Function
|
| 5 |
-
from torch.autograd.function import once_differentiable
|
| 6 |
-
from torch.nn.modules.utils import _pair
|
| 7 |
-
|
| 8 |
-
from ..utils import deprecated_api_warning, ext_loader
|
| 9 |
-
|
| 10 |
-
ext_module = ext_loader.load_ext('_ext',
|
| 11 |
-
['roi_align_forward', 'roi_align_backward'])
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
class RoIAlignFunction(Function):
|
| 15 |
-
|
| 16 |
-
@staticmethod
|
| 17 |
-
def symbolic(g, input, rois, output_size, spatial_scale, sampling_ratio,
|
| 18 |
-
pool_mode, aligned):
|
| 19 |
-
from ..onnx import is_custom_op_loaded
|
| 20 |
-
has_custom_op = is_custom_op_loaded()
|
| 21 |
-
if has_custom_op:
|
| 22 |
-
return g.op(
|
| 23 |
-
'mmcv::MMCVRoiAlign',
|
| 24 |
-
input,
|
| 25 |
-
rois,
|
| 26 |
-
output_height_i=output_size[0],
|
| 27 |
-
output_width_i=output_size[1],
|
| 28 |
-
spatial_scale_f=spatial_scale,
|
| 29 |
-
sampling_ratio_i=sampling_ratio,
|
| 30 |
-
mode_s=pool_mode,
|
| 31 |
-
aligned_i=aligned)
|
| 32 |
-
else:
|
| 33 |
-
from torch.onnx.symbolic_opset9 import sub, squeeze
|
| 34 |
-
from torch.onnx.symbolic_helper import _slice_helper
|
| 35 |
-
from torch.onnx import TensorProtoDataType
|
| 36 |
-
# batch_indices = rois[:, 0].long()
|
| 37 |
-
batch_indices = _slice_helper(
|
| 38 |
-
g, rois, axes=[1], starts=[0], ends=[1])
|
| 39 |
-
batch_indices = squeeze(g, batch_indices, 1)
|
| 40 |
-
batch_indices = g.op(
|
| 41 |
-
'Cast', batch_indices, to_i=TensorProtoDataType.INT64)
|
| 42 |
-
# rois = rois[:, 1:]
|
| 43 |
-
rois = _slice_helper(g, rois, axes=[1], starts=[1], ends=[5])
|
| 44 |
-
if aligned:
|
| 45 |
-
# rois -= 0.5/spatial_scale
|
| 46 |
-
aligned_offset = g.op(
|
| 47 |
-
'Constant',
|
| 48 |
-
value_t=torch.tensor([0.5 / spatial_scale],
|
| 49 |
-
dtype=torch.float32))
|
| 50 |
-
rois = sub(g, rois, aligned_offset)
|
| 51 |
-
# roi align
|
| 52 |
-
return g.op(
|
| 53 |
-
'RoiAlign',
|
| 54 |
-
input,
|
| 55 |
-
rois,
|
| 56 |
-
batch_indices,
|
| 57 |
-
output_height_i=output_size[0],
|
| 58 |
-
output_width_i=output_size[1],
|
| 59 |
-
spatial_scale_f=spatial_scale,
|
| 60 |
-
sampling_ratio_i=max(0, sampling_ratio),
|
| 61 |
-
mode_s=pool_mode)
|
| 62 |
-
|
| 63 |
-
@staticmethod
|
| 64 |
-
def forward(ctx,
|
| 65 |
-
input,
|
| 66 |
-
rois,
|
| 67 |
-
output_size,
|
| 68 |
-
spatial_scale=1.0,
|
| 69 |
-
sampling_ratio=0,
|
| 70 |
-
pool_mode='avg',
|
| 71 |
-
aligned=True):
|
| 72 |
-
ctx.output_size = _pair(output_size)
|
| 73 |
-
ctx.spatial_scale = spatial_scale
|
| 74 |
-
ctx.sampling_ratio = sampling_ratio
|
| 75 |
-
assert pool_mode in ('max', 'avg')
|
| 76 |
-
ctx.pool_mode = 0 if pool_mode == 'max' else 1
|
| 77 |
-
ctx.aligned = aligned
|
| 78 |
-
ctx.input_shape = input.size()
|
| 79 |
-
|
| 80 |
-
assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!'
|
| 81 |
-
|
| 82 |
-
output_shape = (rois.size(0), input.size(1), ctx.output_size[0],
|
| 83 |
-
ctx.output_size[1])
|
| 84 |
-
output = input.new_zeros(output_shape)
|
| 85 |
-
if ctx.pool_mode == 0:
|
| 86 |
-
argmax_y = input.new_zeros(output_shape)
|
| 87 |
-
argmax_x = input.new_zeros(output_shape)
|
| 88 |
-
else:
|
| 89 |
-
argmax_y = input.new_zeros(0)
|
| 90 |
-
argmax_x = input.new_zeros(0)
|
| 91 |
-
|
| 92 |
-
ext_module.roi_align_forward(
|
| 93 |
-
input,
|
| 94 |
-
rois,
|
| 95 |
-
output,
|
| 96 |
-
argmax_y,
|
| 97 |
-
argmax_x,
|
| 98 |
-
aligned_height=ctx.output_size[0],
|
| 99 |
-
aligned_width=ctx.output_size[1],
|
| 100 |
-
spatial_scale=ctx.spatial_scale,
|
| 101 |
-
sampling_ratio=ctx.sampling_ratio,
|
| 102 |
-
pool_mode=ctx.pool_mode,
|
| 103 |
-
aligned=ctx.aligned)
|
| 104 |
-
|
| 105 |
-
ctx.save_for_backward(rois, argmax_y, argmax_x)
|
| 106 |
-
return output
|
| 107 |
-
|
| 108 |
-
@staticmethod
|
| 109 |
-
@once_differentiable
|
| 110 |
-
def backward(ctx, grad_output):
|
| 111 |
-
rois, argmax_y, argmax_x = ctx.saved_tensors
|
| 112 |
-
grad_input = grad_output.new_zeros(ctx.input_shape)
|
| 113 |
-
# complex head architecture may cause grad_output uncontiguous.
|
| 114 |
-
grad_output = grad_output.contiguous()
|
| 115 |
-
ext_module.roi_align_backward(
|
| 116 |
-
grad_output,
|
| 117 |
-
rois,
|
| 118 |
-
argmax_y,
|
| 119 |
-
argmax_x,
|
| 120 |
-
grad_input,
|
| 121 |
-
aligned_height=ctx.output_size[0],
|
| 122 |
-
aligned_width=ctx.output_size[1],
|
| 123 |
-
spatial_scale=ctx.spatial_scale,
|
| 124 |
-
sampling_ratio=ctx.sampling_ratio,
|
| 125 |
-
pool_mode=ctx.pool_mode,
|
| 126 |
-
aligned=ctx.aligned)
|
| 127 |
-
return grad_input, None, None, None, None, None, None
|
| 128 |
-
|
| 129 |
-
|
| 130 |
-
roi_align = RoIAlignFunction.apply
|
| 131 |
-
|
| 132 |
-
|
| 133 |
-
class RoIAlign(nn.Module):
|
| 134 |
-
"""RoI align pooling layer.
|
| 135 |
-
|
| 136 |
-
Args:
|
| 137 |
-
output_size (tuple): h, w
|
| 138 |
-
spatial_scale (float): scale the input boxes by this number
|
| 139 |
-
sampling_ratio (int): number of inputs samples to take for each
|
| 140 |
-
output sample. 0 to take samples densely for current models.
|
| 141 |
-
pool_mode (str, 'avg' or 'max'): pooling mode in each bin.
|
| 142 |
-
aligned (bool): if False, use the legacy implementation in
|
| 143 |
-
MMDetection. If True, align the results more perfectly.
|
| 144 |
-
use_torchvision (bool): whether to use roi_align from torchvision.
|
| 145 |
-
|
| 146 |
-
Note:
|
| 147 |
-
The implementation of RoIAlign when aligned=True is modified from
|
| 148 |
-
https://github.com/facebookresearch/detectron2/
|
| 149 |
-
|
| 150 |
-
The meaning of aligned=True:
|
| 151 |
-
|
| 152 |
-
Given a continuous coordinate c, its two neighboring pixel
|
| 153 |
-
indices (in our pixel model) are computed by floor(c - 0.5) and
|
| 154 |
-
ceil(c - 0.5). For example, c=1.3 has pixel neighbors with discrete
|
| 155 |
-
indices [0] and [1] (which are sampled from the underlying signal
|
| 156 |
-
at continuous coordinates 0.5 and 1.5). But the original roi_align
|
| 157 |
-
(aligned=False) does not subtract the 0.5 when computing
|
| 158 |
-
neighboring pixel indices and therefore it uses pixels with a
|
| 159 |
-
slightly incorrect alignment (relative to our pixel model) when
|
| 160 |
-
performing bilinear interpolation.
|
| 161 |
-
|
| 162 |
-
With `aligned=True`,
|
| 163 |
-
we first appropriately scale the ROI and then shift it by -0.5
|
| 164 |
-
prior to calling roi_align. This produces the correct neighbors;
|
| 165 |
-
|
| 166 |
-
The difference does not make a difference to the model's
|
| 167 |
-
performance if ROIAlign is used together with conv layers.
|
| 168 |
-
"""
|
| 169 |
-
|
| 170 |
-
@deprecated_api_warning(
|
| 171 |
-
{
|
| 172 |
-
'out_size': 'output_size',
|
| 173 |
-
'sample_num': 'sampling_ratio'
|
| 174 |
-
},
|
| 175 |
-
cls_name='RoIAlign')
|
| 176 |
-
def __init__(self,
|
| 177 |
-
output_size,
|
| 178 |
-
spatial_scale=1.0,
|
| 179 |
-
sampling_ratio=0,
|
| 180 |
-
pool_mode='avg',
|
| 181 |
-
aligned=True,
|
| 182 |
-
use_torchvision=False):
|
| 183 |
-
super(RoIAlign, self).__init__()
|
| 184 |
-
|
| 185 |
-
self.output_size = _pair(output_size)
|
| 186 |
-
self.spatial_scale = float(spatial_scale)
|
| 187 |
-
self.sampling_ratio = int(sampling_ratio)
|
| 188 |
-
self.pool_mode = pool_mode
|
| 189 |
-
self.aligned = aligned
|
| 190 |
-
self.use_torchvision = use_torchvision
|
| 191 |
-
|
| 192 |
-
def forward(self, input, rois):
|
| 193 |
-
"""
|
| 194 |
-
Args:
|
| 195 |
-
input: NCHW images
|
| 196 |
-
rois: Bx5 boxes. First column is the index into N.\
|
| 197 |
-
The other 4 columns are xyxy.
|
| 198 |
-
"""
|
| 199 |
-
if self.use_torchvision:
|
| 200 |
-
from torchvision.ops import roi_align as tv_roi_align
|
| 201 |
-
if 'aligned' in tv_roi_align.__code__.co_varnames:
|
| 202 |
-
return tv_roi_align(input, rois, self.output_size,
|
| 203 |
-
self.spatial_scale, self.sampling_ratio,
|
| 204 |
-
self.aligned)
|
| 205 |
-
else:
|
| 206 |
-
if self.aligned:
|
| 207 |
-
rois -= rois.new_tensor([0.] +
|
| 208 |
-
[0.5 / self.spatial_scale] * 4)
|
| 209 |
-
return tv_roi_align(input, rois, self.output_size,
|
| 210 |
-
self.spatial_scale, self.sampling_ratio)
|
| 211 |
-
else:
|
| 212 |
-
return roi_align(input, rois, self.output_size, self.spatial_scale,
|
| 213 |
-
self.sampling_ratio, self.pool_mode, self.aligned)
|
| 214 |
-
|
| 215 |
-
def __repr__(self):
|
| 216 |
-
s = self.__class__.__name__
|
| 217 |
-
s += f'(output_size={self.output_size}, '
|
| 218 |
-
s += f'spatial_scale={self.spatial_scale}, '
|
| 219 |
-
s += f'sampling_ratio={self.sampling_ratio}, '
|
| 220 |
-
s += f'pool_mode={self.pool_mode}, '
|
| 221 |
-
s += f'aligned={self.aligned}, '
|
| 222 |
-
s += f'use_torchvision={self.use_torchvision})'
|
| 223 |
-
return s
|
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|
spaces/Ariharasudhan/YoloV5/utils/loggers/comet/hpo.py
DELETED
|
@@ -1,118 +0,0 @@
|
|
| 1 |
-
import argparse
|
| 2 |
-
import json
|
| 3 |
-
import logging
|
| 4 |
-
import os
|
| 5 |
-
import sys
|
| 6 |
-
from pathlib import Path
|
| 7 |
-
|
| 8 |
-
import comet_ml
|
| 9 |
-
|
| 10 |
-
logger = logging.getLogger(__name__)
|
| 11 |
-
|
| 12 |
-
FILE = Path(__file__).resolve()
|
| 13 |
-
ROOT = FILE.parents[3] # YOLOv5 root directory
|
| 14 |
-
if str(ROOT) not in sys.path:
|
| 15 |
-
sys.path.append(str(ROOT)) # add ROOT to PATH
|
| 16 |
-
|
| 17 |
-
from train import train
|
| 18 |
-
from utils.callbacks import Callbacks
|
| 19 |
-
from utils.general import increment_path
|
| 20 |
-
from utils.torch_utils import select_device
|
| 21 |
-
|
| 22 |
-
# Project Configuration
|
| 23 |
-
config = comet_ml.config.get_config()
|
| 24 |
-
COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5")
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
def get_args(known=False):
|
| 28 |
-
parser = argparse.ArgumentParser()
|
| 29 |
-
parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path')
|
| 30 |
-
parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
|
| 31 |
-
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
|
| 32 |
-
parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path')
|
| 33 |
-
parser.add_argument('--epochs', type=int, default=300, help='total training epochs')
|
| 34 |
-
parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')
|
| 35 |
-
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
|
| 36 |
-
parser.add_argument('--rect', action='store_true', help='rectangular training')
|
| 37 |
-
parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
|
| 38 |
-
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
|
| 39 |
-
parser.add_argument('--noval', action='store_true', help='only validate final epoch')
|
| 40 |
-
parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')
|
| 41 |
-
parser.add_argument('--noplots', action='store_true', help='save no plot files')
|
| 42 |
-
parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
|
| 43 |
-
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
|
| 44 |
-
parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
|
| 45 |
-
parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
|
| 46 |
-
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
| 47 |
-
parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
|
| 48 |
-
parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
|
| 49 |
-
parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer')
|
| 50 |
-
parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
|
| 51 |
-
parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
|
| 52 |
-
parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name')
|
| 53 |
-
parser.add_argument('--name', default='exp', help='save to project/name')
|
| 54 |
-
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
| 55 |
-
parser.add_argument('--quad', action='store_true', help='quad dataloader')
|
| 56 |
-
parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler')
|
| 57 |
-
parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
|
| 58 |
-
parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
|
| 59 |
-
parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2')
|
| 60 |
-
parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')
|
| 61 |
-
parser.add_argument('--seed', type=int, default=0, help='Global training seed')
|
| 62 |
-
parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')
|
| 63 |
-
|
| 64 |
-
# Weights & Biases arguments
|
| 65 |
-
parser.add_argument('--entity', default=None, help='W&B: Entity')
|
| 66 |
-
parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='W&B: Upload data, "val" option')
|
| 67 |
-
parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval')
|
| 68 |
-
parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')
|
| 69 |
-
|
| 70 |
-
# Comet Arguments
|
| 71 |
-
parser.add_argument("--comet_optimizer_config", type=str, help="Comet: Path to a Comet Optimizer Config File.")
|
| 72 |
-
parser.add_argument("--comet_optimizer_id", type=str, help="Comet: ID of the Comet Optimizer sweep.")
|
| 73 |
-
parser.add_argument("--comet_optimizer_objective", type=str, help="Comet: Set to 'minimize' or 'maximize'.")
|
| 74 |
-
parser.add_argument("--comet_optimizer_metric", type=str, help="Comet: Metric to Optimize.")
|
| 75 |
-
parser.add_argument("--comet_optimizer_workers",
|
| 76 |
-
type=int,
|
| 77 |
-
default=1,
|
| 78 |
-
help="Comet: Number of Parallel Workers to use with the Comet Optimizer.")
|
| 79 |
-
|
| 80 |
-
return parser.parse_known_args()[0] if known else parser.parse_args()
|
| 81 |
-
|
| 82 |
-
|
| 83 |
-
def run(parameters, opt):
|
| 84 |
-
hyp_dict = {k: v for k, v in parameters.items() if k not in ["epochs", "batch_size"]}
|
| 85 |
-
|
| 86 |
-
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
|
| 87 |
-
opt.batch_size = parameters.get("batch_size")
|
| 88 |
-
opt.epochs = parameters.get("epochs")
|
| 89 |
-
|
| 90 |
-
device = select_device(opt.device, batch_size=opt.batch_size)
|
| 91 |
-
train(hyp_dict, opt, device, callbacks=Callbacks())
|
| 92 |
-
|
| 93 |
-
|
| 94 |
-
if __name__ == "__main__":
|
| 95 |
-
opt = get_args(known=True)
|
| 96 |
-
|
| 97 |
-
opt.weights = str(opt.weights)
|
| 98 |
-
opt.cfg = str(opt.cfg)
|
| 99 |
-
opt.data = str(opt.data)
|
| 100 |
-
opt.project = str(opt.project)
|
| 101 |
-
|
| 102 |
-
optimizer_id = os.getenv("COMET_OPTIMIZER_ID")
|
| 103 |
-
if optimizer_id is None:
|
| 104 |
-
with open(opt.comet_optimizer_config) as f:
|
| 105 |
-
optimizer_config = json.load(f)
|
| 106 |
-
optimizer = comet_ml.Optimizer(optimizer_config)
|
| 107 |
-
else:
|
| 108 |
-
optimizer = comet_ml.Optimizer(optimizer_id)
|
| 109 |
-
|
| 110 |
-
opt.comet_optimizer_id = optimizer.id
|
| 111 |
-
status = optimizer.status()
|
| 112 |
-
|
| 113 |
-
opt.comet_optimizer_objective = status["spec"]["objective"]
|
| 114 |
-
opt.comet_optimizer_metric = status["spec"]["metric"]
|
| 115 |
-
|
| 116 |
-
logger.info("COMET INFO: Starting Hyperparameter Sweep")
|
| 117 |
-
for parameter in optimizer.get_parameters():
|
| 118 |
-
run(parameter["parameters"], opt)
|
|
|
|
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|
|
spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/inpaint_zoom/zoom_in_app.py
DELETED
|
@@ -1,186 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
|
| 3 |
-
import gradio as gr
|
| 4 |
-
import numpy as np
|
| 5 |
-
import torch
|
| 6 |
-
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
|
| 7 |
-
from PIL import Image
|
| 8 |
-
|
| 9 |
-
from video_diffusion.inpaint_zoom.utils.zoom_in_utils import dummy, image_grid, shrink_and_paste_on_blank, write_video
|
| 10 |
-
|
| 11 |
-
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
stable_paint_model_list = ["stabilityai/stable-diffusion-2-inpainting", "runwayml/stable-diffusion-inpainting"]
|
| 15 |
-
|
| 16 |
-
stable_paint_prompt_list = [
|
| 17 |
-
"children running in the forest , sunny, bright, by studio ghibli painting, superior quality, masterpiece, traditional Japanese colors, by Grzegorz Rutkowski, concept art",
|
| 18 |
-
"A beautiful landscape of a mountain range with a lake in the foreground",
|
| 19 |
-
]
|
| 20 |
-
|
| 21 |
-
stable_paint_negative_prompt_list = [
|
| 22 |
-
"lurry, bad art, blurred, text, watermark",
|
| 23 |
-
]
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
class StableDiffusionZoomIn:
|
| 27 |
-
def __init__(self):
|
| 28 |
-
self.pipe = None
|
| 29 |
-
|
| 30 |
-
def load_model(self, model_id):
|
| 31 |
-
if self.pipe is None:
|
| 32 |
-
self.pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16")
|
| 33 |
-
self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
|
| 34 |
-
self.pipe = self.pipe.to("cuda")
|
| 35 |
-
self.pipe.safety_checker = dummy
|
| 36 |
-
self.pipe.enable_attention_slicing()
|
| 37 |
-
self.pipe.enable_xformers_memory_efficient_attention()
|
| 38 |
-
self.g_cuda = torch.Generator(device="cuda")
|
| 39 |
-
|
| 40 |
-
return self.pipe
|
| 41 |
-
|
| 42 |
-
def generate_video(
|
| 43 |
-
self,
|
| 44 |
-
model_id,
|
| 45 |
-
prompt,
|
| 46 |
-
negative_prompt,
|
| 47 |
-
guidance_scale,
|
| 48 |
-
num_inference_steps,
|
| 49 |
-
):
|
| 50 |
-
pipe = self.load_model(model_id)
|
| 51 |
-
|
| 52 |
-
num_init_images = 2
|
| 53 |
-
seed = 42
|
| 54 |
-
height = 512
|
| 55 |
-
width = height
|
| 56 |
-
|
| 57 |
-
current_image = Image.new(mode="RGBA", size=(height, width))
|
| 58 |
-
mask_image = np.array(current_image)[:, :, 3]
|
| 59 |
-
mask_image = Image.fromarray(255 - mask_image).convert("RGB")
|
| 60 |
-
current_image = current_image.convert("RGB")
|
| 61 |
-
|
| 62 |
-
init_images = pipe(
|
| 63 |
-
prompt=[prompt] * num_init_images,
|
| 64 |
-
negative_prompt=[negative_prompt] * num_init_images,
|
| 65 |
-
image=current_image,
|
| 66 |
-
guidance_scale=guidance_scale,
|
| 67 |
-
height=height,
|
| 68 |
-
width=width,
|
| 69 |
-
generator=self.g_cuda.manual_seed(seed),
|
| 70 |
-
mask_image=mask_image,
|
| 71 |
-
num_inference_steps=num_inference_steps,
|
| 72 |
-
)[0]
|
| 73 |
-
|
| 74 |
-
image_grid(init_images, rows=1, cols=num_init_images)
|
| 75 |
-
|
| 76 |
-
init_image_selected = 1 # @param
|
| 77 |
-
if num_init_images == 1:
|
| 78 |
-
init_image_selected = 0
|
| 79 |
-
else:
|
| 80 |
-
init_image_selected = init_image_selected - 1
|
| 81 |
-
|
| 82 |
-
num_outpainting_steps = 20 # @param
|
| 83 |
-
mask_width = 128 # @param
|
| 84 |
-
num_interpol_frames = 30 # @param
|
| 85 |
-
|
| 86 |
-
current_image = init_images[init_image_selected]
|
| 87 |
-
all_frames = []
|
| 88 |
-
all_frames.append(current_image)
|
| 89 |
-
|
| 90 |
-
for i in range(num_outpainting_steps):
|
| 91 |
-
print("Generating image: " + str(i + 1) + " / " + str(num_outpainting_steps))
|
| 92 |
-
|
| 93 |
-
prev_image_fix = current_image
|
| 94 |
-
|
| 95 |
-
prev_image = shrink_and_paste_on_blank(current_image, mask_width)
|
| 96 |
-
|
| 97 |
-
current_image = prev_image
|
| 98 |
-
|
| 99 |
-
# create mask (black image with white mask_width width edges)
|
| 100 |
-
mask_image = np.array(current_image)[:, :, 3]
|
| 101 |
-
mask_image = Image.fromarray(255 - mask_image).convert("RGB")
|
| 102 |
-
|
| 103 |
-
# inpainting step
|
| 104 |
-
current_image = current_image.convert("RGB")
|
| 105 |
-
images = pipe(
|
| 106 |
-
prompt=prompt,
|
| 107 |
-
negative_prompt=negative_prompt,
|
| 108 |
-
image=current_image,
|
| 109 |
-
guidance_scale=guidance_scale,
|
| 110 |
-
height=height,
|
| 111 |
-
width=width,
|
| 112 |
-
# this can make the whole thing deterministic but the output less exciting
|
| 113 |
-
# generator = g_cuda.manual_seed(seed),
|
| 114 |
-
mask_image=mask_image,
|
| 115 |
-
num_inference_steps=num_inference_steps,
|
| 116 |
-
)[0]
|
| 117 |
-
current_image = images[0]
|
| 118 |
-
current_image.paste(prev_image, mask=prev_image)
|
| 119 |
-
|
| 120 |
-
# interpolation steps bewteen 2 inpainted images (=sequential zoom and crop)
|
| 121 |
-
for j in range(num_interpol_frames - 1):
|
| 122 |
-
interpol_image = current_image
|
| 123 |
-
interpol_width = round(
|
| 124 |
-
(1 - (1 - 2 * mask_width / height) ** (1 - (j + 1) / num_interpol_frames)) * height / 2
|
| 125 |
-
)
|
| 126 |
-
interpol_image = interpol_image.crop(
|
| 127 |
-
(interpol_width, interpol_width, width - interpol_width, height - interpol_width)
|
| 128 |
-
)
|
| 129 |
-
|
| 130 |
-
interpol_image = interpol_image.resize((height, width))
|
| 131 |
-
|
| 132 |
-
# paste the higher resolution previous image in the middle to avoid drop in quality caused by zooming
|
| 133 |
-
interpol_width2 = round((1 - (height - 2 * mask_width) / (height - 2 * interpol_width)) / 2 * height)
|
| 134 |
-
prev_image_fix_crop = shrink_and_paste_on_blank(prev_image_fix, interpol_width2)
|
| 135 |
-
interpol_image.paste(prev_image_fix_crop, mask=prev_image_fix_crop)
|
| 136 |
-
|
| 137 |
-
all_frames.append(interpol_image)
|
| 138 |
-
|
| 139 |
-
all_frames.append(current_image)
|
| 140 |
-
|
| 141 |
-
video_file_name = "infinite_zoom_out"
|
| 142 |
-
fps = 30
|
| 143 |
-
save_path = video_file_name + ".mp4"
|
| 144 |
-
write_video(save_path, all_frames, fps)
|
| 145 |
-
return save_path
|
| 146 |
-
|
| 147 |
-
def app():
|
| 148 |
-
with gr.Blocks():
|
| 149 |
-
with gr.Row():
|
| 150 |
-
with gr.Column():
|
| 151 |
-
text2image_in_model_path = gr.Dropdown(
|
| 152 |
-
choices=stable_paint_model_list, value=stable_paint_model_list[0], label="Text-Image Model Id"
|
| 153 |
-
)
|
| 154 |
-
|
| 155 |
-
text2image_in_prompt = gr.Textbox(lines=2, value=stable_paint_prompt_list[0], label="Prompt")
|
| 156 |
-
|
| 157 |
-
text2image_in_negative_prompt = gr.Textbox(
|
| 158 |
-
lines=1, value=stable_paint_negative_prompt_list[0], label="Negative Prompt"
|
| 159 |
-
)
|
| 160 |
-
|
| 161 |
-
with gr.Row():
|
| 162 |
-
with gr.Column():
|
| 163 |
-
text2image_in_guidance_scale = gr.Slider(
|
| 164 |
-
minimum=0.1, maximum=15, step=0.1, value=7.5, label="Guidance Scale"
|
| 165 |
-
)
|
| 166 |
-
|
| 167 |
-
text2image_in_num_inference_step = gr.Slider(
|
| 168 |
-
minimum=1, maximum=100, step=1, value=50, label="Num Inference Step"
|
| 169 |
-
)
|
| 170 |
-
|
| 171 |
-
text2image_in_predict = gr.Button(value="Generator")
|
| 172 |
-
|
| 173 |
-
with gr.Column():
|
| 174 |
-
output_image = gr.Video(label="Output")
|
| 175 |
-
|
| 176 |
-
text2image_in_predict.click(
|
| 177 |
-
fn=StableDiffusionZoomIn().generate_video,
|
| 178 |
-
inputs=[
|
| 179 |
-
text2image_in_model_path,
|
| 180 |
-
text2image_in_prompt,
|
| 181 |
-
text2image_in_negative_prompt,
|
| 182 |
-
text2image_in_guidance_scale,
|
| 183 |
-
text2image_in_num_inference_step,
|
| 184 |
-
],
|
| 185 |
-
outputs=output_image,
|
| 186 |
-
)
|
|
|
|
|
|
|
|
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|
spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/pyparsing/exceptions.py
DELETED
|
@@ -1,267 +0,0 @@
|
|
| 1 |
-
# exceptions.py
|
| 2 |
-
|
| 3 |
-
import re
|
| 4 |
-
import sys
|
| 5 |
-
import typing
|
| 6 |
-
|
| 7 |
-
from .util import col, line, lineno, _collapse_string_to_ranges
|
| 8 |
-
from .unicode import pyparsing_unicode as ppu
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
class ExceptionWordUnicode(ppu.Latin1, ppu.LatinA, ppu.LatinB, ppu.Greek, ppu.Cyrillic):
|
| 12 |
-
pass
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
_extract_alphanums = _collapse_string_to_ranges(ExceptionWordUnicode.alphanums)
|
| 16 |
-
_exception_word_extractor = re.compile("([" + _extract_alphanums + "]{1,16})|.")
|
| 17 |
-
|
| 18 |
-
|
| 19 |
-
class ParseBaseException(Exception):
|
| 20 |
-
"""base exception class for all parsing runtime exceptions"""
|
| 21 |
-
|
| 22 |
-
# Performance tuning: we construct a *lot* of these, so keep this
|
| 23 |
-
# constructor as small and fast as possible
|
| 24 |
-
def __init__(
|
| 25 |
-
self,
|
| 26 |
-
pstr: str,
|
| 27 |
-
loc: int = 0,
|
| 28 |
-
msg: typing.Optional[str] = None,
|
| 29 |
-
elem=None,
|
| 30 |
-
):
|
| 31 |
-
self.loc = loc
|
| 32 |
-
if msg is None:
|
| 33 |
-
self.msg = pstr
|
| 34 |
-
self.pstr = ""
|
| 35 |
-
else:
|
| 36 |
-
self.msg = msg
|
| 37 |
-
self.pstr = pstr
|
| 38 |
-
self.parser_element = self.parserElement = elem
|
| 39 |
-
self.args = (pstr, loc, msg)
|
| 40 |
-
|
| 41 |
-
@staticmethod
|
| 42 |
-
def explain_exception(exc, depth=16):
|
| 43 |
-
"""
|
| 44 |
-
Method to take an exception and translate the Python internal traceback into a list
|
| 45 |
-
of the pyparsing expressions that caused the exception to be raised.
|
| 46 |
-
|
| 47 |
-
Parameters:
|
| 48 |
-
|
| 49 |
-
- exc - exception raised during parsing (need not be a ParseException, in support
|
| 50 |
-
of Python exceptions that might be raised in a parse action)
|
| 51 |
-
- depth (default=16) - number of levels back in the stack trace to list expression
|
| 52 |
-
and function names; if None, the full stack trace names will be listed; if 0, only
|
| 53 |
-
the failing input line, marker, and exception string will be shown
|
| 54 |
-
|
| 55 |
-
Returns a multi-line string listing the ParserElements and/or function names in the
|
| 56 |
-
exception's stack trace.
|
| 57 |
-
"""
|
| 58 |
-
import inspect
|
| 59 |
-
from .core import ParserElement
|
| 60 |
-
|
| 61 |
-
if depth is None:
|
| 62 |
-
depth = sys.getrecursionlimit()
|
| 63 |
-
ret = []
|
| 64 |
-
if isinstance(exc, ParseBaseException):
|
| 65 |
-
ret.append(exc.line)
|
| 66 |
-
ret.append(" " * (exc.column - 1) + "^")
|
| 67 |
-
ret.append("{}: {}".format(type(exc).__name__, exc))
|
| 68 |
-
|
| 69 |
-
if depth > 0:
|
| 70 |
-
callers = inspect.getinnerframes(exc.__traceback__, context=depth)
|
| 71 |
-
seen = set()
|
| 72 |
-
for i, ff in enumerate(callers[-depth:]):
|
| 73 |
-
frm = ff[0]
|
| 74 |
-
|
| 75 |
-
f_self = frm.f_locals.get("self", None)
|
| 76 |
-
if isinstance(f_self, ParserElement):
|
| 77 |
-
if frm.f_code.co_name not in ("parseImpl", "_parseNoCache"):
|
| 78 |
-
continue
|
| 79 |
-
if id(f_self) in seen:
|
| 80 |
-
continue
|
| 81 |
-
seen.add(id(f_self))
|
| 82 |
-
|
| 83 |
-
self_type = type(f_self)
|
| 84 |
-
ret.append(
|
| 85 |
-
"{}.{} - {}".format(
|
| 86 |
-
self_type.__module__, self_type.__name__, f_self
|
| 87 |
-
)
|
| 88 |
-
)
|
| 89 |
-
|
| 90 |
-
elif f_self is not None:
|
| 91 |
-
self_type = type(f_self)
|
| 92 |
-
ret.append("{}.{}".format(self_type.__module__, self_type.__name__))
|
| 93 |
-
|
| 94 |
-
else:
|
| 95 |
-
code = frm.f_code
|
| 96 |
-
if code.co_name in ("wrapper", "<module>"):
|
| 97 |
-
continue
|
| 98 |
-
|
| 99 |
-
ret.append("{}".format(code.co_name))
|
| 100 |
-
|
| 101 |
-
depth -= 1
|
| 102 |
-
if not depth:
|
| 103 |
-
break
|
| 104 |
-
|
| 105 |
-
return "\n".join(ret)
|
| 106 |
-
|
| 107 |
-
@classmethod
|
| 108 |
-
def _from_exception(cls, pe):
|
| 109 |
-
"""
|
| 110 |
-
internal factory method to simplify creating one type of ParseException
|
| 111 |
-
from another - avoids having __init__ signature conflicts among subclasses
|
| 112 |
-
"""
|
| 113 |
-
return cls(pe.pstr, pe.loc, pe.msg, pe.parserElement)
|
| 114 |
-
|
| 115 |
-
@property
|
| 116 |
-
def line(self) -> str:
|
| 117 |
-
"""
|
| 118 |
-
Return the line of text where the exception occurred.
|
| 119 |
-
"""
|
| 120 |
-
return line(self.loc, self.pstr)
|
| 121 |
-
|
| 122 |
-
@property
|
| 123 |
-
def lineno(self) -> int:
|
| 124 |
-
"""
|
| 125 |
-
Return the 1-based line number of text where the exception occurred.
|
| 126 |
-
"""
|
| 127 |
-
return lineno(self.loc, self.pstr)
|
| 128 |
-
|
| 129 |
-
@property
|
| 130 |
-
def col(self) -> int:
|
| 131 |
-
"""
|
| 132 |
-
Return the 1-based column on the line of text where the exception occurred.
|
| 133 |
-
"""
|
| 134 |
-
return col(self.loc, self.pstr)
|
| 135 |
-
|
| 136 |
-
@property
|
| 137 |
-
def column(self) -> int:
|
| 138 |
-
"""
|
| 139 |
-
Return the 1-based column on the line of text where the exception occurred.
|
| 140 |
-
"""
|
| 141 |
-
return col(self.loc, self.pstr)
|
| 142 |
-
|
| 143 |
-
def __str__(self) -> str:
|
| 144 |
-
if self.pstr:
|
| 145 |
-
if self.loc >= len(self.pstr):
|
| 146 |
-
foundstr = ", found end of text"
|
| 147 |
-
else:
|
| 148 |
-
# pull out next word at error location
|
| 149 |
-
found_match = _exception_word_extractor.match(self.pstr, self.loc)
|
| 150 |
-
if found_match is not None:
|
| 151 |
-
found = found_match.group(0)
|
| 152 |
-
else:
|
| 153 |
-
found = self.pstr[self.loc : self.loc + 1]
|
| 154 |
-
foundstr = (", found %r" % found).replace(r"\\", "\\")
|
| 155 |
-
else:
|
| 156 |
-
foundstr = ""
|
| 157 |
-
return "{}{} (at char {}), (line:{}, col:{})".format(
|
| 158 |
-
self.msg, foundstr, self.loc, self.lineno, self.column
|
| 159 |
-
)
|
| 160 |
-
|
| 161 |
-
def __repr__(self):
|
| 162 |
-
return str(self)
|
| 163 |
-
|
| 164 |
-
def mark_input_line(self, marker_string: str = None, *, markerString=">!<") -> str:
|
| 165 |
-
"""
|
| 166 |
-
Extracts the exception line from the input string, and marks
|
| 167 |
-
the location of the exception with a special symbol.
|
| 168 |
-
"""
|
| 169 |
-
markerString = marker_string if marker_string is not None else markerString
|
| 170 |
-
line_str = self.line
|
| 171 |
-
line_column = self.column - 1
|
| 172 |
-
if markerString:
|
| 173 |
-
line_str = "".join(
|
| 174 |
-
(line_str[:line_column], markerString, line_str[line_column:])
|
| 175 |
-
)
|
| 176 |
-
return line_str.strip()
|
| 177 |
-
|
| 178 |
-
def explain(self, depth=16) -> str:
|
| 179 |
-
"""
|
| 180 |
-
Method to translate the Python internal traceback into a list
|
| 181 |
-
of the pyparsing expressions that caused the exception to be raised.
|
| 182 |
-
|
| 183 |
-
Parameters:
|
| 184 |
-
|
| 185 |
-
- depth (default=16) - number of levels back in the stack trace to list expression
|
| 186 |
-
and function names; if None, the full stack trace names will be listed; if 0, only
|
| 187 |
-
the failing input line, marker, and exception string will be shown
|
| 188 |
-
|
| 189 |
-
Returns a multi-line string listing the ParserElements and/or function names in the
|
| 190 |
-
exception's stack trace.
|
| 191 |
-
|
| 192 |
-
Example::
|
| 193 |
-
|
| 194 |
-
expr = pp.Word(pp.nums) * 3
|
| 195 |
-
try:
|
| 196 |
-
expr.parse_string("123 456 A789")
|
| 197 |
-
except pp.ParseException as pe:
|
| 198 |
-
print(pe.explain(depth=0))
|
| 199 |
-
|
| 200 |
-
prints::
|
| 201 |
-
|
| 202 |
-
123 456 A789
|
| 203 |
-
^
|
| 204 |
-
ParseException: Expected W:(0-9), found 'A' (at char 8), (line:1, col:9)
|
| 205 |
-
|
| 206 |
-
Note: the diagnostic output will include string representations of the expressions
|
| 207 |
-
that failed to parse. These representations will be more helpful if you use `set_name` to
|
| 208 |
-
give identifiable names to your expressions. Otherwise they will use the default string
|
| 209 |
-
forms, which may be cryptic to read.
|
| 210 |
-
|
| 211 |
-
Note: pyparsing's default truncation of exception tracebacks may also truncate the
|
| 212 |
-
stack of expressions that are displayed in the ``explain`` output. To get the full listing
|
| 213 |
-
of parser expressions, you may have to set ``ParserElement.verbose_stacktrace = True``
|
| 214 |
-
"""
|
| 215 |
-
return self.explain_exception(self, depth)
|
| 216 |
-
|
| 217 |
-
markInputline = mark_input_line
|
| 218 |
-
|
| 219 |
-
|
| 220 |
-
class ParseException(ParseBaseException):
|
| 221 |
-
"""
|
| 222 |
-
Exception thrown when a parse expression doesn't match the input string
|
| 223 |
-
|
| 224 |
-
Example::
|
| 225 |
-
|
| 226 |
-
try:
|
| 227 |
-
Word(nums).set_name("integer").parse_string("ABC")
|
| 228 |
-
except ParseException as pe:
|
| 229 |
-
print(pe)
|
| 230 |
-
print("column: {}".format(pe.column))
|
| 231 |
-
|
| 232 |
-
prints::
|
| 233 |
-
|
| 234 |
-
Expected integer (at char 0), (line:1, col:1)
|
| 235 |
-
column: 1
|
| 236 |
-
|
| 237 |
-
"""
|
| 238 |
-
|
| 239 |
-
|
| 240 |
-
class ParseFatalException(ParseBaseException):
|
| 241 |
-
"""
|
| 242 |
-
User-throwable exception thrown when inconsistent parse content
|
| 243 |
-
is found; stops all parsing immediately
|
| 244 |
-
"""
|
| 245 |
-
|
| 246 |
-
|
| 247 |
-
class ParseSyntaxException(ParseFatalException):
|
| 248 |
-
"""
|
| 249 |
-
Just like :class:`ParseFatalException`, but thrown internally
|
| 250 |
-
when an :class:`ErrorStop<And._ErrorStop>` ('-' operator) indicates
|
| 251 |
-
that parsing is to stop immediately because an unbacktrackable
|
| 252 |
-
syntax error has been found.
|
| 253 |
-
"""
|
| 254 |
-
|
| 255 |
-
|
| 256 |
-
class RecursiveGrammarException(Exception):
|
| 257 |
-
"""
|
| 258 |
-
Exception thrown by :class:`ParserElement.validate` if the
|
| 259 |
-
grammar could be left-recursive; parser may need to enable
|
| 260 |
-
left recursion using :class:`ParserElement.enable_left_recursion<ParserElement.enable_left_recursion>`
|
| 261 |
-
"""
|
| 262 |
-
|
| 263 |
-
def __init__(self, parseElementList):
|
| 264 |
-
self.parseElementTrace = parseElementList
|
| 265 |
-
|
| 266 |
-
def __str__(self) -> str:
|
| 267 |
-
return "RecursiveGrammarException: {}".format(self.parseElementTrace)
|
|
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spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_deprecation_warning.py
DELETED
|
@@ -1,7 +0,0 @@
|
|
| 1 |
-
class SetuptoolsDeprecationWarning(Warning):
|
| 2 |
-
"""
|
| 3 |
-
Base class for warning deprecations in ``setuptools``
|
| 4 |
-
|
| 5 |
-
This class is not derived from ``DeprecationWarning``, and as such is
|
| 6 |
-
visible by default.
|
| 7 |
-
"""
|
|
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|
spaces/Audiogen/vector-search-demo/README.md
DELETED
|
@@ -1,13 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Vector Search Demo
|
| 3 |
-
emoji: 💻
|
| 4 |
-
colorFrom: green
|
| 5 |
-
colorTo: red
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.47.1
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
license: unlicense
|
| 11 |
-
---
|
| 12 |
-
|
| 13 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
|
|
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|
|
spaces/BetterAPI/BetterChat_new/src/routes/conversation/[id]/+server.ts
DELETED
|
@@ -1,236 +0,0 @@
|
|
| 1 |
-
import { PUBLIC_SEP_TOKEN } from "$env/static/public";
|
| 2 |
-
import { buildPrompt } from "$lib/buildPrompt.js";
|
| 3 |
-
import { abortedGenerations } from "$lib/server/abortedGenerations.js";
|
| 4 |
-
import { collections } from "$lib/server/database.js";
|
| 5 |
-
import { modelEndpoint } from "$lib/server/modelEndpoint.js";
|
| 6 |
-
import type { Message } from "$lib/types/Message.js";
|
| 7 |
-
import { concatUint8Arrays } from "$lib/utils/concatUint8Arrays.js";
|
| 8 |
-
import { streamToAsyncIterable } from "$lib/utils/streamToAsyncIterable";
|
| 9 |
-
import { trimPrefix } from "$lib/utils/trimPrefix.js";
|
| 10 |
-
import { trimSuffix } from "$lib/utils/trimSuffix.js";
|
| 11 |
-
import type { TextGenerationStreamOutput } from "@huggingface/inference";
|
| 12 |
-
import { error } from "@sveltejs/kit";
|
| 13 |
-
import { ObjectId } from "mongodb";
|
| 14 |
-
import { z } from "zod";
|
| 15 |
-
|
| 16 |
-
export async function POST({ request, fetch, locals, params }) {
|
| 17 |
-
// todo: add validation on params.id
|
| 18 |
-
const convId = new ObjectId(params.id);
|
| 19 |
-
const date = new Date();
|
| 20 |
-
|
| 21 |
-
const conv = await collections.conversations.findOne({
|
| 22 |
-
_id: convId,
|
| 23 |
-
sessionId: locals.sessionId,
|
| 24 |
-
});
|
| 25 |
-
|
| 26 |
-
if (!conv) {
|
| 27 |
-
throw error(404, "Conversation not found");
|
| 28 |
-
}
|
| 29 |
-
|
| 30 |
-
const json = await request.json();
|
| 31 |
-
const {
|
| 32 |
-
inputs: newPrompt,
|
| 33 |
-
options: { id: messageId, is_retry },
|
| 34 |
-
} = z
|
| 35 |
-
.object({
|
| 36 |
-
inputs: z.string().trim().min(1),
|
| 37 |
-
options: z.object({
|
| 38 |
-
id: z.optional(z.string().uuid()),
|
| 39 |
-
is_retry: z.optional(z.boolean()),
|
| 40 |
-
}),
|
| 41 |
-
})
|
| 42 |
-
.parse(json);
|
| 43 |
-
|
| 44 |
-
const messages = (() => {
|
| 45 |
-
if (is_retry && messageId) {
|
| 46 |
-
let retryMessageIdx = conv.messages.findIndex((message) => message.id === messageId);
|
| 47 |
-
if (retryMessageIdx === -1) {
|
| 48 |
-
retryMessageIdx = conv.messages.length;
|
| 49 |
-
}
|
| 50 |
-
return [
|
| 51 |
-
...conv.messages.slice(0, retryMessageIdx),
|
| 52 |
-
{ content: newPrompt, from: "user", id: messageId as Message["id"] },
|
| 53 |
-
];
|
| 54 |
-
}
|
| 55 |
-
return [
|
| 56 |
-
...conv.messages,
|
| 57 |
-
{ content: newPrompt, from: "user", id: (messageId as Message["id"]) || crypto.randomUUID() },
|
| 58 |
-
];
|
| 59 |
-
})() satisfies Message[];
|
| 60 |
-
|
| 61 |
-
// Todo: on-the-fly migration, remove later
|
| 62 |
-
for (const message of messages) {
|
| 63 |
-
if (!message.id) {
|
| 64 |
-
message.id = crypto.randomUUID();
|
| 65 |
-
}
|
| 66 |
-
}
|
| 67 |
-
const prompt = buildPrompt(messages);
|
| 68 |
-
|
| 69 |
-
const randomEndpoint = modelEndpoint();
|
| 70 |
-
|
| 71 |
-
const abortController = new AbortController();
|
| 72 |
-
|
| 73 |
-
const resp = await fetch(randomEndpoint.endpoint, {
|
| 74 |
-
headers: {
|
| 75 |
-
"Content-Type": request.headers.get("Content-Type") ?? "application/json",
|
| 76 |
-
Authorization: randomEndpoint.authorization,
|
| 77 |
-
},
|
| 78 |
-
method: "POST",
|
| 79 |
-
body: JSON.stringify({
|
| 80 |
-
...json,
|
| 81 |
-
inputs: prompt,
|
| 82 |
-
}),
|
| 83 |
-
signal: abortController.signal,
|
| 84 |
-
});
|
| 85 |
-
|
| 86 |
-
const [stream1, stream2] = resp.body!.tee();
|
| 87 |
-
|
| 88 |
-
async function saveMessage() {
|
| 89 |
-
let generated_text = await parseGeneratedText(stream2, convId, date, abortController);
|
| 90 |
-
|
| 91 |
-
// We could also check if PUBLIC_ASSISTANT_MESSAGE_TOKEN is present and use it to slice the text
|
| 92 |
-
if (generated_text.startsWith(prompt)) {
|
| 93 |
-
generated_text = generated_text.slice(prompt.length);
|
| 94 |
-
}
|
| 95 |
-
|
| 96 |
-
generated_text = trimSuffix(trimPrefix(generated_text, "<|startoftext|>"), PUBLIC_SEP_TOKEN);
|
| 97 |
-
|
| 98 |
-
messages.push({ from: "assistant", content: generated_text, id: crypto.randomUUID() });
|
| 99 |
-
|
| 100 |
-
await collections.conversations.updateOne(
|
| 101 |
-
{
|
| 102 |
-
_id: convId,
|
| 103 |
-
},
|
| 104 |
-
{
|
| 105 |
-
$set: {
|
| 106 |
-
messages,
|
| 107 |
-
updatedAt: new Date(),
|
| 108 |
-
},
|
| 109 |
-
}
|
| 110 |
-
);
|
| 111 |
-
}
|
| 112 |
-
|
| 113 |
-
saveMessage().catch(console.error);
|
| 114 |
-
|
| 115 |
-
// Todo: maybe we should wait for the message to be saved before ending the response - in case of errors
|
| 116 |
-
return new Response(stream1, {
|
| 117 |
-
headers: Object.fromEntries(resp.headers.entries()),
|
| 118 |
-
status: resp.status,
|
| 119 |
-
statusText: resp.statusText,
|
| 120 |
-
});
|
| 121 |
-
}
|
| 122 |
-
|
| 123 |
-
export async function DELETE({ locals, params }) {
|
| 124 |
-
const convId = new ObjectId(params.id);
|
| 125 |
-
|
| 126 |
-
const conv = await collections.conversations.findOne({
|
| 127 |
-
_id: convId,
|
| 128 |
-
sessionId: locals.sessionId,
|
| 129 |
-
});
|
| 130 |
-
|
| 131 |
-
if (!conv) {
|
| 132 |
-
throw error(404, "Conversation not found");
|
| 133 |
-
}
|
| 134 |
-
|
| 135 |
-
await collections.conversations.deleteOne({ _id: conv._id });
|
| 136 |
-
|
| 137 |
-
return new Response();
|
| 138 |
-
}
|
| 139 |
-
|
| 140 |
-
async function parseGeneratedText(
|
| 141 |
-
stream: ReadableStream,
|
| 142 |
-
conversationId: ObjectId,
|
| 143 |
-
promptedAt: Date,
|
| 144 |
-
abortController: AbortController
|
| 145 |
-
): Promise<string> {
|
| 146 |
-
const inputs: Uint8Array[] = [];
|
| 147 |
-
for await (const input of streamToAsyncIterable(stream)) {
|
| 148 |
-
inputs.push(input);
|
| 149 |
-
|
| 150 |
-
const date = abortedGenerations.get(conversationId.toString());
|
| 151 |
-
|
| 152 |
-
if (date && date > promptedAt) {
|
| 153 |
-
abortController.abort("Cancelled by user");
|
| 154 |
-
const completeInput = concatUint8Arrays(inputs);
|
| 155 |
-
|
| 156 |
-
const lines = new TextDecoder()
|
| 157 |
-
.decode(completeInput)
|
| 158 |
-
.split("\n")
|
| 159 |
-
.filter((line) => line.startsWith("data:"));
|
| 160 |
-
|
| 161 |
-
const tokens = lines.map((line) => {
|
| 162 |
-
try {
|
| 163 |
-
const json: TextGenerationStreamOutput = JSON.parse(line.slice("data:".length));
|
| 164 |
-
return json.token.text;
|
| 165 |
-
} catch {
|
| 166 |
-
return "";
|
| 167 |
-
}
|
| 168 |
-
});
|
| 169 |
-
return tokens.join("");
|
| 170 |
-
}
|
| 171 |
-
}
|
| 172 |
-
|
| 173 |
-
// Merge inputs into a single Uint8Array
|
| 174 |
-
const completeInput = concatUint8Arrays(inputs);
|
| 175 |
-
|
| 176 |
-
// Get last line starting with "data:" and parse it as JSON to get the generated text
|
| 177 |
-
const message = new TextDecoder().decode(completeInput);
|
| 178 |
-
|
| 179 |
-
let lastIndex = message.lastIndexOf("\ndata:");
|
| 180 |
-
if (lastIndex === -1) {
|
| 181 |
-
lastIndex = message.indexOf("data");
|
| 182 |
-
}
|
| 183 |
-
|
| 184 |
-
if (lastIndex === -1) {
|
| 185 |
-
console.error("Could not parse in last message");
|
| 186 |
-
}
|
| 187 |
-
|
| 188 |
-
let lastMessage = message.slice(lastIndex).trim().slice("data:".length);
|
| 189 |
-
if (lastMessage.includes("\n")) {
|
| 190 |
-
lastMessage = lastMessage.slice(0, lastMessage.indexOf("\n"));
|
| 191 |
-
}
|
| 192 |
-
|
| 193 |
-
const lastMessageJSON = JSON.parse(lastMessage);
|
| 194 |
-
|
| 195 |
-
if (lastMessageJSON.error) {
|
| 196 |
-
throw new Error(lastMessageJSON.error);
|
| 197 |
-
}
|
| 198 |
-
|
| 199 |
-
const res = lastMessageJSON.generated_text;
|
| 200 |
-
|
| 201 |
-
if (typeof res !== "string") {
|
| 202 |
-
throw new Error("Could not parse generated text");
|
| 203 |
-
}
|
| 204 |
-
|
| 205 |
-
return res;
|
| 206 |
-
}
|
| 207 |
-
|
| 208 |
-
export async function PATCH({ request, locals, params }) {
|
| 209 |
-
const { title } = z
|
| 210 |
-
.object({ title: z.string().trim().min(1).max(100) })
|
| 211 |
-
.parse(await request.json());
|
| 212 |
-
|
| 213 |
-
const convId = new ObjectId(params.id);
|
| 214 |
-
|
| 215 |
-
const conv = await collections.conversations.findOne({
|
| 216 |
-
_id: convId,
|
| 217 |
-
sessionId: locals.sessionId,
|
| 218 |
-
});
|
| 219 |
-
|
| 220 |
-
if (!conv) {
|
| 221 |
-
throw error(404, "Conversation not found");
|
| 222 |
-
}
|
| 223 |
-
|
| 224 |
-
await collections.conversations.updateOne(
|
| 225 |
-
{
|
| 226 |
-
_id: convId,
|
| 227 |
-
},
|
| 228 |
-
{
|
| 229 |
-
$set: {
|
| 230 |
-
title,
|
| 231 |
-
},
|
| 232 |
-
}
|
| 233 |
-
);
|
| 234 |
-
|
| 235 |
-
return new Response();
|
| 236 |
-
}
|
|
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spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pyparsing/helpers.py
DELETED
|
@@ -1,1088 +0,0 @@
|
|
| 1 |
-
# helpers.py
|
| 2 |
-
import html.entities
|
| 3 |
-
import re
|
| 4 |
-
import typing
|
| 5 |
-
|
| 6 |
-
from . import __diag__
|
| 7 |
-
from .core import *
|
| 8 |
-
from .util import _bslash, _flatten, _escape_regex_range_chars
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
#
|
| 12 |
-
# global helpers
|
| 13 |
-
#
|
| 14 |
-
def delimited_list(
|
| 15 |
-
expr: Union[str, ParserElement],
|
| 16 |
-
delim: Union[str, ParserElement] = ",",
|
| 17 |
-
combine: bool = False,
|
| 18 |
-
min: typing.Optional[int] = None,
|
| 19 |
-
max: typing.Optional[int] = None,
|
| 20 |
-
*,
|
| 21 |
-
allow_trailing_delim: bool = False,
|
| 22 |
-
) -> ParserElement:
|
| 23 |
-
"""Helper to define a delimited list of expressions - the delimiter
|
| 24 |
-
defaults to ','. By default, the list elements and delimiters can
|
| 25 |
-
have intervening whitespace, and comments, but this can be
|
| 26 |
-
overridden by passing ``combine=True`` in the constructor. If
|
| 27 |
-
``combine`` is set to ``True``, the matching tokens are
|
| 28 |
-
returned as a single token string, with the delimiters included;
|
| 29 |
-
otherwise, the matching tokens are returned as a list of tokens,
|
| 30 |
-
with the delimiters suppressed.
|
| 31 |
-
|
| 32 |
-
If ``allow_trailing_delim`` is set to True, then the list may end with
|
| 33 |
-
a delimiter.
|
| 34 |
-
|
| 35 |
-
Example::
|
| 36 |
-
|
| 37 |
-
delimited_list(Word(alphas)).parse_string("aa,bb,cc") # -> ['aa', 'bb', 'cc']
|
| 38 |
-
delimited_list(Word(hexnums), delim=':', combine=True).parse_string("AA:BB:CC:DD:EE") # -> ['AA:BB:CC:DD:EE']
|
| 39 |
-
"""
|
| 40 |
-
if isinstance(expr, str_type):
|
| 41 |
-
expr = ParserElement._literalStringClass(expr)
|
| 42 |
-
|
| 43 |
-
dlName = "{expr} [{delim} {expr}]...{end}".format(
|
| 44 |
-
expr=str(expr.copy().streamline()),
|
| 45 |
-
delim=str(delim),
|
| 46 |
-
end=" [{}]".format(str(delim)) if allow_trailing_delim else "",
|
| 47 |
-
)
|
| 48 |
-
|
| 49 |
-
if not combine:
|
| 50 |
-
delim = Suppress(delim)
|
| 51 |
-
|
| 52 |
-
if min is not None:
|
| 53 |
-
if min < 1:
|
| 54 |
-
raise ValueError("min must be greater than 0")
|
| 55 |
-
min -= 1
|
| 56 |
-
if max is not None:
|
| 57 |
-
if min is not None and max <= min:
|
| 58 |
-
raise ValueError("max must be greater than, or equal to min")
|
| 59 |
-
max -= 1
|
| 60 |
-
delimited_list_expr = expr + (delim + expr)[min, max]
|
| 61 |
-
|
| 62 |
-
if allow_trailing_delim:
|
| 63 |
-
delimited_list_expr += Opt(delim)
|
| 64 |
-
|
| 65 |
-
if combine:
|
| 66 |
-
return Combine(delimited_list_expr).set_name(dlName)
|
| 67 |
-
else:
|
| 68 |
-
return delimited_list_expr.set_name(dlName)
|
| 69 |
-
|
| 70 |
-
|
| 71 |
-
def counted_array(
|
| 72 |
-
expr: ParserElement,
|
| 73 |
-
int_expr: typing.Optional[ParserElement] = None,
|
| 74 |
-
*,
|
| 75 |
-
intExpr: typing.Optional[ParserElement] = None,
|
| 76 |
-
) -> ParserElement:
|
| 77 |
-
"""Helper to define a counted list of expressions.
|
| 78 |
-
|
| 79 |
-
This helper defines a pattern of the form::
|
| 80 |
-
|
| 81 |
-
integer expr expr expr...
|
| 82 |
-
|
| 83 |
-
where the leading integer tells how many expr expressions follow.
|
| 84 |
-
The matched tokens returns the array of expr tokens as a list - the
|
| 85 |
-
leading count token is suppressed.
|
| 86 |
-
|
| 87 |
-
If ``int_expr`` is specified, it should be a pyparsing expression
|
| 88 |
-
that produces an integer value.
|
| 89 |
-
|
| 90 |
-
Example::
|
| 91 |
-
|
| 92 |
-
counted_array(Word(alphas)).parse_string('2 ab cd ef') # -> ['ab', 'cd']
|
| 93 |
-
|
| 94 |
-
# in this parser, the leading integer value is given in binary,
|
| 95 |
-
# '10' indicating that 2 values are in the array
|
| 96 |
-
binary_constant = Word('01').set_parse_action(lambda t: int(t[0], 2))
|
| 97 |
-
counted_array(Word(alphas), int_expr=binary_constant).parse_string('10 ab cd ef') # -> ['ab', 'cd']
|
| 98 |
-
|
| 99 |
-
# if other fields must be parsed after the count but before the
|
| 100 |
-
# list items, give the fields results names and they will
|
| 101 |
-
# be preserved in the returned ParseResults:
|
| 102 |
-
count_with_metadata = integer + Word(alphas)("type")
|
| 103 |
-
typed_array = counted_array(Word(alphanums), int_expr=count_with_metadata)("items")
|
| 104 |
-
result = typed_array.parse_string("3 bool True True False")
|
| 105 |
-
print(result.dump())
|
| 106 |
-
|
| 107 |
-
# prints
|
| 108 |
-
# ['True', 'True', 'False']
|
| 109 |
-
# - items: ['True', 'True', 'False']
|
| 110 |
-
# - type: 'bool'
|
| 111 |
-
"""
|
| 112 |
-
intExpr = intExpr or int_expr
|
| 113 |
-
array_expr = Forward()
|
| 114 |
-
|
| 115 |
-
def count_field_parse_action(s, l, t):
|
| 116 |
-
nonlocal array_expr
|
| 117 |
-
n = t[0]
|
| 118 |
-
array_expr <<= (expr * n) if n else Empty()
|
| 119 |
-
# clear list contents, but keep any named results
|
| 120 |
-
del t[:]
|
| 121 |
-
|
| 122 |
-
if intExpr is None:
|
| 123 |
-
intExpr = Word(nums).set_parse_action(lambda t: int(t[0]))
|
| 124 |
-
else:
|
| 125 |
-
intExpr = intExpr.copy()
|
| 126 |
-
intExpr.set_name("arrayLen")
|
| 127 |
-
intExpr.add_parse_action(count_field_parse_action, call_during_try=True)
|
| 128 |
-
return (intExpr + array_expr).set_name("(len) " + str(expr) + "...")
|
| 129 |
-
|
| 130 |
-
|
| 131 |
-
def match_previous_literal(expr: ParserElement) -> ParserElement:
|
| 132 |
-
"""Helper to define an expression that is indirectly defined from
|
| 133 |
-
the tokens matched in a previous expression, that is, it looks for
|
| 134 |
-
a 'repeat' of a previous expression. For example::
|
| 135 |
-
|
| 136 |
-
first = Word(nums)
|
| 137 |
-
second = match_previous_literal(first)
|
| 138 |
-
match_expr = first + ":" + second
|
| 139 |
-
|
| 140 |
-
will match ``"1:1"``, but not ``"1:2"``. Because this
|
| 141 |
-
matches a previous literal, will also match the leading
|
| 142 |
-
``"1:1"`` in ``"1:10"``. If this is not desired, use
|
| 143 |
-
:class:`match_previous_expr`. Do *not* use with packrat parsing
|
| 144 |
-
enabled.
|
| 145 |
-
"""
|
| 146 |
-
rep = Forward()
|
| 147 |
-
|
| 148 |
-
def copy_token_to_repeater(s, l, t):
|
| 149 |
-
if t:
|
| 150 |
-
if len(t) == 1:
|
| 151 |
-
rep << t[0]
|
| 152 |
-
else:
|
| 153 |
-
# flatten t tokens
|
| 154 |
-
tflat = _flatten(t.as_list())
|
| 155 |
-
rep << And(Literal(tt) for tt in tflat)
|
| 156 |
-
else:
|
| 157 |
-
rep << Empty()
|
| 158 |
-
|
| 159 |
-
expr.add_parse_action(copy_token_to_repeater, callDuringTry=True)
|
| 160 |
-
rep.set_name("(prev) " + str(expr))
|
| 161 |
-
return rep
|
| 162 |
-
|
| 163 |
-
|
| 164 |
-
def match_previous_expr(expr: ParserElement) -> ParserElement:
|
| 165 |
-
"""Helper to define an expression that is indirectly defined from
|
| 166 |
-
the tokens matched in a previous expression, that is, it looks for
|
| 167 |
-
a 'repeat' of a previous expression. For example::
|
| 168 |
-
|
| 169 |
-
first = Word(nums)
|
| 170 |
-
second = match_previous_expr(first)
|
| 171 |
-
match_expr = first + ":" + second
|
| 172 |
-
|
| 173 |
-
will match ``"1:1"``, but not ``"1:2"``. Because this
|
| 174 |
-
matches by expressions, will *not* match the leading ``"1:1"``
|
| 175 |
-
in ``"1:10"``; the expressions are evaluated first, and then
|
| 176 |
-
compared, so ``"1"`` is compared with ``"10"``. Do *not* use
|
| 177 |
-
with packrat parsing enabled.
|
| 178 |
-
"""
|
| 179 |
-
rep = Forward()
|
| 180 |
-
e2 = expr.copy()
|
| 181 |
-
rep <<= e2
|
| 182 |
-
|
| 183 |
-
def copy_token_to_repeater(s, l, t):
|
| 184 |
-
matchTokens = _flatten(t.as_list())
|
| 185 |
-
|
| 186 |
-
def must_match_these_tokens(s, l, t):
|
| 187 |
-
theseTokens = _flatten(t.as_list())
|
| 188 |
-
if theseTokens != matchTokens:
|
| 189 |
-
raise ParseException(
|
| 190 |
-
s, l, "Expected {}, found{}".format(matchTokens, theseTokens)
|
| 191 |
-
)
|
| 192 |
-
|
| 193 |
-
rep.set_parse_action(must_match_these_tokens, callDuringTry=True)
|
| 194 |
-
|
| 195 |
-
expr.add_parse_action(copy_token_to_repeater, callDuringTry=True)
|
| 196 |
-
rep.set_name("(prev) " + str(expr))
|
| 197 |
-
return rep
|
| 198 |
-
|
| 199 |
-
|
| 200 |
-
def one_of(
|
| 201 |
-
strs: Union[typing.Iterable[str], str],
|
| 202 |
-
caseless: bool = False,
|
| 203 |
-
use_regex: bool = True,
|
| 204 |
-
as_keyword: bool = False,
|
| 205 |
-
*,
|
| 206 |
-
useRegex: bool = True,
|
| 207 |
-
asKeyword: bool = False,
|
| 208 |
-
) -> ParserElement:
|
| 209 |
-
"""Helper to quickly define a set of alternative :class:`Literal` s,
|
| 210 |
-
and makes sure to do longest-first testing when there is a conflict,
|
| 211 |
-
regardless of the input order, but returns
|
| 212 |
-
a :class:`MatchFirst` for best performance.
|
| 213 |
-
|
| 214 |
-
Parameters:
|
| 215 |
-
|
| 216 |
-
- ``strs`` - a string of space-delimited literals, or a collection of
|
| 217 |
-
string literals
|
| 218 |
-
- ``caseless`` - treat all literals as caseless - (default= ``False``)
|
| 219 |
-
- ``use_regex`` - as an optimization, will
|
| 220 |
-
generate a :class:`Regex` object; otherwise, will generate
|
| 221 |
-
a :class:`MatchFirst` object (if ``caseless=True`` or ``asKeyword=True``, or if
|
| 222 |
-
creating a :class:`Regex` raises an exception) - (default= ``True``)
|
| 223 |
-
- ``as_keyword`` - enforce :class:`Keyword`-style matching on the
|
| 224 |
-
generated expressions - (default= ``False``)
|
| 225 |
-
- ``asKeyword`` and ``useRegex`` are retained for pre-PEP8 compatibility,
|
| 226 |
-
but will be removed in a future release
|
| 227 |
-
|
| 228 |
-
Example::
|
| 229 |
-
|
| 230 |
-
comp_oper = one_of("< = > <= >= !=")
|
| 231 |
-
var = Word(alphas)
|
| 232 |
-
number = Word(nums)
|
| 233 |
-
term = var | number
|
| 234 |
-
comparison_expr = term + comp_oper + term
|
| 235 |
-
print(comparison_expr.search_string("B = 12 AA=23 B<=AA AA>12"))
|
| 236 |
-
|
| 237 |
-
prints::
|
| 238 |
-
|
| 239 |
-
[['B', '=', '12'], ['AA', '=', '23'], ['B', '<=', 'AA'], ['AA', '>', '12']]
|
| 240 |
-
"""
|
| 241 |
-
asKeyword = asKeyword or as_keyword
|
| 242 |
-
useRegex = useRegex and use_regex
|
| 243 |
-
|
| 244 |
-
if (
|
| 245 |
-
isinstance(caseless, str_type)
|
| 246 |
-
and __diag__.warn_on_multiple_string_args_to_oneof
|
| 247 |
-
):
|
| 248 |
-
warnings.warn(
|
| 249 |
-
"More than one string argument passed to one_of, pass"
|
| 250 |
-
" choices as a list or space-delimited string",
|
| 251 |
-
stacklevel=2,
|
| 252 |
-
)
|
| 253 |
-
|
| 254 |
-
if caseless:
|
| 255 |
-
isequal = lambda a, b: a.upper() == b.upper()
|
| 256 |
-
masks = lambda a, b: b.upper().startswith(a.upper())
|
| 257 |
-
parseElementClass = CaselessKeyword if asKeyword else CaselessLiteral
|
| 258 |
-
else:
|
| 259 |
-
isequal = lambda a, b: a == b
|
| 260 |
-
masks = lambda a, b: b.startswith(a)
|
| 261 |
-
parseElementClass = Keyword if asKeyword else Literal
|
| 262 |
-
|
| 263 |
-
symbols: List[str] = []
|
| 264 |
-
if isinstance(strs, str_type):
|
| 265 |
-
symbols = strs.split()
|
| 266 |
-
elif isinstance(strs, Iterable):
|
| 267 |
-
symbols = list(strs)
|
| 268 |
-
else:
|
| 269 |
-
raise TypeError("Invalid argument to one_of, expected string or iterable")
|
| 270 |
-
if not symbols:
|
| 271 |
-
return NoMatch()
|
| 272 |
-
|
| 273 |
-
# reorder given symbols to take care to avoid masking longer choices with shorter ones
|
| 274 |
-
# (but only if the given symbols are not just single characters)
|
| 275 |
-
if any(len(sym) > 1 for sym in symbols):
|
| 276 |
-
i = 0
|
| 277 |
-
while i < len(symbols) - 1:
|
| 278 |
-
cur = symbols[i]
|
| 279 |
-
for j, other in enumerate(symbols[i + 1 :]):
|
| 280 |
-
if isequal(other, cur):
|
| 281 |
-
del symbols[i + j + 1]
|
| 282 |
-
break
|
| 283 |
-
elif masks(cur, other):
|
| 284 |
-
del symbols[i + j + 1]
|
| 285 |
-
symbols.insert(i, other)
|
| 286 |
-
break
|
| 287 |
-
else:
|
| 288 |
-
i += 1
|
| 289 |
-
|
| 290 |
-
if useRegex:
|
| 291 |
-
re_flags: int = re.IGNORECASE if caseless else 0
|
| 292 |
-
|
| 293 |
-
try:
|
| 294 |
-
if all(len(sym) == 1 for sym in symbols):
|
| 295 |
-
# symbols are just single characters, create range regex pattern
|
| 296 |
-
patt = "[{}]".format(
|
| 297 |
-
"".join(_escape_regex_range_chars(sym) for sym in symbols)
|
| 298 |
-
)
|
| 299 |
-
else:
|
| 300 |
-
patt = "|".join(re.escape(sym) for sym in symbols)
|
| 301 |
-
|
| 302 |
-
# wrap with \b word break markers if defining as keywords
|
| 303 |
-
if asKeyword:
|
| 304 |
-
patt = r"\b(?:{})\b".format(patt)
|
| 305 |
-
|
| 306 |
-
ret = Regex(patt, flags=re_flags).set_name(" | ".join(symbols))
|
| 307 |
-
|
| 308 |
-
if caseless:
|
| 309 |
-
# add parse action to return symbols as specified, not in random
|
| 310 |
-
# casing as found in input string
|
| 311 |
-
symbol_map = {sym.lower(): sym for sym in symbols}
|
| 312 |
-
ret.add_parse_action(lambda s, l, t: symbol_map[t[0].lower()])
|
| 313 |
-
|
| 314 |
-
return ret
|
| 315 |
-
|
| 316 |
-
except re.error:
|
| 317 |
-
warnings.warn(
|
| 318 |
-
"Exception creating Regex for one_of, building MatchFirst", stacklevel=2
|
| 319 |
-
)
|
| 320 |
-
|
| 321 |
-
# last resort, just use MatchFirst
|
| 322 |
-
return MatchFirst(parseElementClass(sym) for sym in symbols).set_name(
|
| 323 |
-
" | ".join(symbols)
|
| 324 |
-
)
|
| 325 |
-
|
| 326 |
-
|
| 327 |
-
def dict_of(key: ParserElement, value: ParserElement) -> ParserElement:
|
| 328 |
-
"""Helper to easily and clearly define a dictionary by specifying
|
| 329 |
-
the respective patterns for the key and value. Takes care of
|
| 330 |
-
defining the :class:`Dict`, :class:`ZeroOrMore`, and
|
| 331 |
-
:class:`Group` tokens in the proper order. The key pattern
|
| 332 |
-
can include delimiting markers or punctuation, as long as they are
|
| 333 |
-
suppressed, thereby leaving the significant key text. The value
|
| 334 |
-
pattern can include named results, so that the :class:`Dict` results
|
| 335 |
-
can include named token fields.
|
| 336 |
-
|
| 337 |
-
Example::
|
| 338 |
-
|
| 339 |
-
text = "shape: SQUARE posn: upper left color: light blue texture: burlap"
|
| 340 |
-
attr_expr = (label + Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join))
|
| 341 |
-
print(attr_expr[1, ...].parse_string(text).dump())
|
| 342 |
-
|
| 343 |
-
attr_label = label
|
| 344 |
-
attr_value = Suppress(':') + OneOrMore(data_word, stop_on=label).set_parse_action(' '.join)
|
| 345 |
-
|
| 346 |
-
# similar to Dict, but simpler call format
|
| 347 |
-
result = dict_of(attr_label, attr_value).parse_string(text)
|
| 348 |
-
print(result.dump())
|
| 349 |
-
print(result['shape'])
|
| 350 |
-
print(result.shape) # object attribute access works too
|
| 351 |
-
print(result.as_dict())
|
| 352 |
-
|
| 353 |
-
prints::
|
| 354 |
-
|
| 355 |
-
[['shape', 'SQUARE'], ['posn', 'upper left'], ['color', 'light blue'], ['texture', 'burlap']]
|
| 356 |
-
- color: 'light blue'
|
| 357 |
-
- posn: 'upper left'
|
| 358 |
-
- shape: 'SQUARE'
|
| 359 |
-
- texture: 'burlap'
|
| 360 |
-
SQUARE
|
| 361 |
-
SQUARE
|
| 362 |
-
{'color': 'light blue', 'shape': 'SQUARE', 'posn': 'upper left', 'texture': 'burlap'}
|
| 363 |
-
"""
|
| 364 |
-
return Dict(OneOrMore(Group(key + value)))
|
| 365 |
-
|
| 366 |
-
|
| 367 |
-
def original_text_for(
|
| 368 |
-
expr: ParserElement, as_string: bool = True, *, asString: bool = True
|
| 369 |
-
) -> ParserElement:
|
| 370 |
-
"""Helper to return the original, untokenized text for a given
|
| 371 |
-
expression. Useful to restore the parsed fields of an HTML start
|
| 372 |
-
tag into the raw tag text itself, or to revert separate tokens with
|
| 373 |
-
intervening whitespace back to the original matching input text. By
|
| 374 |
-
default, returns astring containing the original parsed text.
|
| 375 |
-
|
| 376 |
-
If the optional ``as_string`` argument is passed as
|
| 377 |
-
``False``, then the return value is
|
| 378 |
-
a :class:`ParseResults` containing any results names that
|
| 379 |
-
were originally matched, and a single token containing the original
|
| 380 |
-
matched text from the input string. So if the expression passed to
|
| 381 |
-
:class:`original_text_for` contains expressions with defined
|
| 382 |
-
results names, you must set ``as_string`` to ``False`` if you
|
| 383 |
-
want to preserve those results name values.
|
| 384 |
-
|
| 385 |
-
The ``asString`` pre-PEP8 argument is retained for compatibility,
|
| 386 |
-
but will be removed in a future release.
|
| 387 |
-
|
| 388 |
-
Example::
|
| 389 |
-
|
| 390 |
-
src = "this is test <b> bold <i>text</i> </b> normal text "
|
| 391 |
-
for tag in ("b", "i"):
|
| 392 |
-
opener, closer = make_html_tags(tag)
|
| 393 |
-
patt = original_text_for(opener + SkipTo(closer) + closer)
|
| 394 |
-
print(patt.search_string(src)[0])
|
| 395 |
-
|
| 396 |
-
prints::
|
| 397 |
-
|
| 398 |
-
['<b> bold <i>text</i> </b>']
|
| 399 |
-
['<i>text</i>']
|
| 400 |
-
"""
|
| 401 |
-
asString = asString and as_string
|
| 402 |
-
|
| 403 |
-
locMarker = Empty().set_parse_action(lambda s, loc, t: loc)
|
| 404 |
-
endlocMarker = locMarker.copy()
|
| 405 |
-
endlocMarker.callPreparse = False
|
| 406 |
-
matchExpr = locMarker("_original_start") + expr + endlocMarker("_original_end")
|
| 407 |
-
if asString:
|
| 408 |
-
extractText = lambda s, l, t: s[t._original_start : t._original_end]
|
| 409 |
-
else:
|
| 410 |
-
|
| 411 |
-
def extractText(s, l, t):
|
| 412 |
-
t[:] = [s[t.pop("_original_start") : t.pop("_original_end")]]
|
| 413 |
-
|
| 414 |
-
matchExpr.set_parse_action(extractText)
|
| 415 |
-
matchExpr.ignoreExprs = expr.ignoreExprs
|
| 416 |
-
matchExpr.suppress_warning(Diagnostics.warn_ungrouped_named_tokens_in_collection)
|
| 417 |
-
return matchExpr
|
| 418 |
-
|
| 419 |
-
|
| 420 |
-
def ungroup(expr: ParserElement) -> ParserElement:
|
| 421 |
-
"""Helper to undo pyparsing's default grouping of And expressions,
|
| 422 |
-
even if all but one are non-empty.
|
| 423 |
-
"""
|
| 424 |
-
return TokenConverter(expr).add_parse_action(lambda t: t[0])
|
| 425 |
-
|
| 426 |
-
|
| 427 |
-
def locatedExpr(expr: ParserElement) -> ParserElement:
|
| 428 |
-
"""
|
| 429 |
-
(DEPRECATED - future code should use the Located class)
|
| 430 |
-
Helper to decorate a returned token with its starting and ending
|
| 431 |
-
locations in the input string.
|
| 432 |
-
|
| 433 |
-
This helper adds the following results names:
|
| 434 |
-
|
| 435 |
-
- ``locn_start`` - location where matched expression begins
|
| 436 |
-
- ``locn_end`` - location where matched expression ends
|
| 437 |
-
- ``value`` - the actual parsed results
|
| 438 |
-
|
| 439 |
-
Be careful if the input text contains ``<TAB>`` characters, you
|
| 440 |
-
may want to call :class:`ParserElement.parseWithTabs`
|
| 441 |
-
|
| 442 |
-
Example::
|
| 443 |
-
|
| 444 |
-
wd = Word(alphas)
|
| 445 |
-
for match in locatedExpr(wd).searchString("ljsdf123lksdjjf123lkkjj1222"):
|
| 446 |
-
print(match)
|
| 447 |
-
|
| 448 |
-
prints::
|
| 449 |
-
|
| 450 |
-
[[0, 'ljsdf', 5]]
|
| 451 |
-
[[8, 'lksdjjf', 15]]
|
| 452 |
-
[[18, 'lkkjj', 23]]
|
| 453 |
-
"""
|
| 454 |
-
locator = Empty().set_parse_action(lambda ss, ll, tt: ll)
|
| 455 |
-
return Group(
|
| 456 |
-
locator("locn_start")
|
| 457 |
-
+ expr("value")
|
| 458 |
-
+ locator.copy().leaveWhitespace()("locn_end")
|
| 459 |
-
)
|
| 460 |
-
|
| 461 |
-
|
| 462 |
-
def nested_expr(
|
| 463 |
-
opener: Union[str, ParserElement] = "(",
|
| 464 |
-
closer: Union[str, ParserElement] = ")",
|
| 465 |
-
content: typing.Optional[ParserElement] = None,
|
| 466 |
-
ignore_expr: ParserElement = quoted_string(),
|
| 467 |
-
*,
|
| 468 |
-
ignoreExpr: ParserElement = quoted_string(),
|
| 469 |
-
) -> ParserElement:
|
| 470 |
-
"""Helper method for defining nested lists enclosed in opening and
|
| 471 |
-
closing delimiters (``"("`` and ``")"`` are the default).
|
| 472 |
-
|
| 473 |
-
Parameters:
|
| 474 |
-
- ``opener`` - opening character for a nested list
|
| 475 |
-
(default= ``"("``); can also be a pyparsing expression
|
| 476 |
-
- ``closer`` - closing character for a nested list
|
| 477 |
-
(default= ``")"``); can also be a pyparsing expression
|
| 478 |
-
- ``content`` - expression for items within the nested lists
|
| 479 |
-
(default= ``None``)
|
| 480 |
-
- ``ignore_expr`` - expression for ignoring opening and closing delimiters
|
| 481 |
-
(default= :class:`quoted_string`)
|
| 482 |
-
- ``ignoreExpr`` - this pre-PEP8 argument is retained for compatibility
|
| 483 |
-
but will be removed in a future release
|
| 484 |
-
|
| 485 |
-
If an expression is not provided for the content argument, the
|
| 486 |
-
nested expression will capture all whitespace-delimited content
|
| 487 |
-
between delimiters as a list of separate values.
|
| 488 |
-
|
| 489 |
-
Use the ``ignore_expr`` argument to define expressions that may
|
| 490 |
-
contain opening or closing characters that should not be treated as
|
| 491 |
-
opening or closing characters for nesting, such as quoted_string or
|
| 492 |
-
a comment expression. Specify multiple expressions using an
|
| 493 |
-
:class:`Or` or :class:`MatchFirst`. The default is
|
| 494 |
-
:class:`quoted_string`, but if no expressions are to be ignored, then
|
| 495 |
-
pass ``None`` for this argument.
|
| 496 |
-
|
| 497 |
-
Example::
|
| 498 |
-
|
| 499 |
-
data_type = one_of("void int short long char float double")
|
| 500 |
-
decl_data_type = Combine(data_type + Opt(Word('*')))
|
| 501 |
-
ident = Word(alphas+'_', alphanums+'_')
|
| 502 |
-
number = pyparsing_common.number
|
| 503 |
-
arg = Group(decl_data_type + ident)
|
| 504 |
-
LPAR, RPAR = map(Suppress, "()")
|
| 505 |
-
|
| 506 |
-
code_body = nested_expr('{', '}', ignore_expr=(quoted_string | c_style_comment))
|
| 507 |
-
|
| 508 |
-
c_function = (decl_data_type("type")
|
| 509 |
-
+ ident("name")
|
| 510 |
-
+ LPAR + Opt(delimited_list(arg), [])("args") + RPAR
|
| 511 |
-
+ code_body("body"))
|
| 512 |
-
c_function.ignore(c_style_comment)
|
| 513 |
-
|
| 514 |
-
source_code = '''
|
| 515 |
-
int is_odd(int x) {
|
| 516 |
-
return (x%2);
|
| 517 |
-
}
|
| 518 |
-
|
| 519 |
-
int dec_to_hex(char hchar) {
|
| 520 |
-
if (hchar >= '0' && hchar <= '9') {
|
| 521 |
-
return (ord(hchar)-ord('0'));
|
| 522 |
-
} else {
|
| 523 |
-
return (10+ord(hchar)-ord('A'));
|
| 524 |
-
}
|
| 525 |
-
}
|
| 526 |
-
'''
|
| 527 |
-
for func in c_function.search_string(source_code):
|
| 528 |
-
print("%(name)s (%(type)s) args: %(args)s" % func)
|
| 529 |
-
|
| 530 |
-
|
| 531 |
-
prints::
|
| 532 |
-
|
| 533 |
-
is_odd (int) args: [['int', 'x']]
|
| 534 |
-
dec_to_hex (int) args: [['char', 'hchar']]
|
| 535 |
-
"""
|
| 536 |
-
if ignoreExpr != ignore_expr:
|
| 537 |
-
ignoreExpr = ignore_expr if ignoreExpr == quoted_string() else ignoreExpr
|
| 538 |
-
if opener == closer:
|
| 539 |
-
raise ValueError("opening and closing strings cannot be the same")
|
| 540 |
-
if content is None:
|
| 541 |
-
if isinstance(opener, str_type) and isinstance(closer, str_type):
|
| 542 |
-
if len(opener) == 1 and len(closer) == 1:
|
| 543 |
-
if ignoreExpr is not None:
|
| 544 |
-
content = Combine(
|
| 545 |
-
OneOrMore(
|
| 546 |
-
~ignoreExpr
|
| 547 |
-
+ CharsNotIn(
|
| 548 |
-
opener + closer + ParserElement.DEFAULT_WHITE_CHARS,
|
| 549 |
-
exact=1,
|
| 550 |
-
)
|
| 551 |
-
)
|
| 552 |
-
).set_parse_action(lambda t: t[0].strip())
|
| 553 |
-
else:
|
| 554 |
-
content = empty.copy() + CharsNotIn(
|
| 555 |
-
opener + closer + ParserElement.DEFAULT_WHITE_CHARS
|
| 556 |
-
).set_parse_action(lambda t: t[0].strip())
|
| 557 |
-
else:
|
| 558 |
-
if ignoreExpr is not None:
|
| 559 |
-
content = Combine(
|
| 560 |
-
OneOrMore(
|
| 561 |
-
~ignoreExpr
|
| 562 |
-
+ ~Literal(opener)
|
| 563 |
-
+ ~Literal(closer)
|
| 564 |
-
+ CharsNotIn(ParserElement.DEFAULT_WHITE_CHARS, exact=1)
|
| 565 |
-
)
|
| 566 |
-
).set_parse_action(lambda t: t[0].strip())
|
| 567 |
-
else:
|
| 568 |
-
content = Combine(
|
| 569 |
-
OneOrMore(
|
| 570 |
-
~Literal(opener)
|
| 571 |
-
+ ~Literal(closer)
|
| 572 |
-
+ CharsNotIn(ParserElement.DEFAULT_WHITE_CHARS, exact=1)
|
| 573 |
-
)
|
| 574 |
-
).set_parse_action(lambda t: t[0].strip())
|
| 575 |
-
else:
|
| 576 |
-
raise ValueError(
|
| 577 |
-
"opening and closing arguments must be strings if no content expression is given"
|
| 578 |
-
)
|
| 579 |
-
ret = Forward()
|
| 580 |
-
if ignoreExpr is not None:
|
| 581 |
-
ret <<= Group(
|
| 582 |
-
Suppress(opener) + ZeroOrMore(ignoreExpr | ret | content) + Suppress(closer)
|
| 583 |
-
)
|
| 584 |
-
else:
|
| 585 |
-
ret <<= Group(Suppress(opener) + ZeroOrMore(ret | content) + Suppress(closer))
|
| 586 |
-
ret.set_name("nested %s%s expression" % (opener, closer))
|
| 587 |
-
return ret
|
| 588 |
-
|
| 589 |
-
|
| 590 |
-
def _makeTags(tagStr, xml, suppress_LT=Suppress("<"), suppress_GT=Suppress(">")):
|
| 591 |
-
"""Internal helper to construct opening and closing tag expressions, given a tag name"""
|
| 592 |
-
if isinstance(tagStr, str_type):
|
| 593 |
-
resname = tagStr
|
| 594 |
-
tagStr = Keyword(tagStr, caseless=not xml)
|
| 595 |
-
else:
|
| 596 |
-
resname = tagStr.name
|
| 597 |
-
|
| 598 |
-
tagAttrName = Word(alphas, alphanums + "_-:")
|
| 599 |
-
if xml:
|
| 600 |
-
tagAttrValue = dbl_quoted_string.copy().set_parse_action(remove_quotes)
|
| 601 |
-
openTag = (
|
| 602 |
-
suppress_LT
|
| 603 |
-
+ tagStr("tag")
|
| 604 |
-
+ Dict(ZeroOrMore(Group(tagAttrName + Suppress("=") + tagAttrValue)))
|
| 605 |
-
+ Opt("/", default=[False])("empty").set_parse_action(
|
| 606 |
-
lambda s, l, t: t[0] == "/"
|
| 607 |
-
)
|
| 608 |
-
+ suppress_GT
|
| 609 |
-
)
|
| 610 |
-
else:
|
| 611 |
-
tagAttrValue = quoted_string.copy().set_parse_action(remove_quotes) | Word(
|
| 612 |
-
printables, exclude_chars=">"
|
| 613 |
-
)
|
| 614 |
-
openTag = (
|
| 615 |
-
suppress_LT
|
| 616 |
-
+ tagStr("tag")
|
| 617 |
-
+ Dict(
|
| 618 |
-
ZeroOrMore(
|
| 619 |
-
Group(
|
| 620 |
-
tagAttrName.set_parse_action(lambda t: t[0].lower())
|
| 621 |
-
+ Opt(Suppress("=") + tagAttrValue)
|
| 622 |
-
)
|
| 623 |
-
)
|
| 624 |
-
)
|
| 625 |
-
+ Opt("/", default=[False])("empty").set_parse_action(
|
| 626 |
-
lambda s, l, t: t[0] == "/"
|
| 627 |
-
)
|
| 628 |
-
+ suppress_GT
|
| 629 |
-
)
|
| 630 |
-
closeTag = Combine(Literal("</") + tagStr + ">", adjacent=False)
|
| 631 |
-
|
| 632 |
-
openTag.set_name("<%s>" % resname)
|
| 633 |
-
# add start<tagname> results name in parse action now that ungrouped names are not reported at two levels
|
| 634 |
-
openTag.add_parse_action(
|
| 635 |
-
lambda t: t.__setitem__(
|
| 636 |
-
"start" + "".join(resname.replace(":", " ").title().split()), t.copy()
|
| 637 |
-
)
|
| 638 |
-
)
|
| 639 |
-
closeTag = closeTag(
|
| 640 |
-
"end" + "".join(resname.replace(":", " ").title().split())
|
| 641 |
-
).set_name("</%s>" % resname)
|
| 642 |
-
openTag.tag = resname
|
| 643 |
-
closeTag.tag = resname
|
| 644 |
-
openTag.tag_body = SkipTo(closeTag())
|
| 645 |
-
return openTag, closeTag
|
| 646 |
-
|
| 647 |
-
|
| 648 |
-
def make_html_tags(
|
| 649 |
-
tag_str: Union[str, ParserElement]
|
| 650 |
-
) -> Tuple[ParserElement, ParserElement]:
|
| 651 |
-
"""Helper to construct opening and closing tag expressions for HTML,
|
| 652 |
-
given a tag name. Matches tags in either upper or lower case,
|
| 653 |
-
attributes with namespaces and with quoted or unquoted values.
|
| 654 |
-
|
| 655 |
-
Example::
|
| 656 |
-
|
| 657 |
-
text = '<td>More info at the <a href="https://github.com/pyparsing/pyparsing/wiki">pyparsing</a> wiki page</td>'
|
| 658 |
-
# make_html_tags returns pyparsing expressions for the opening and
|
| 659 |
-
# closing tags as a 2-tuple
|
| 660 |
-
a, a_end = make_html_tags("A")
|
| 661 |
-
link_expr = a + SkipTo(a_end)("link_text") + a_end
|
| 662 |
-
|
| 663 |
-
for link in link_expr.search_string(text):
|
| 664 |
-
# attributes in the <A> tag (like "href" shown here) are
|
| 665 |
-
# also accessible as named results
|
| 666 |
-
print(link.link_text, '->', link.href)
|
| 667 |
-
|
| 668 |
-
prints::
|
| 669 |
-
|
| 670 |
-
pyparsing -> https://github.com/pyparsing/pyparsing/wiki
|
| 671 |
-
"""
|
| 672 |
-
return _makeTags(tag_str, False)
|
| 673 |
-
|
| 674 |
-
|
| 675 |
-
def make_xml_tags(
|
| 676 |
-
tag_str: Union[str, ParserElement]
|
| 677 |
-
) -> Tuple[ParserElement, ParserElement]:
|
| 678 |
-
"""Helper to construct opening and closing tag expressions for XML,
|
| 679 |
-
given a tag name. Matches tags only in the given upper/lower case.
|
| 680 |
-
|
| 681 |
-
Example: similar to :class:`make_html_tags`
|
| 682 |
-
"""
|
| 683 |
-
return _makeTags(tag_str, True)
|
| 684 |
-
|
| 685 |
-
|
| 686 |
-
any_open_tag: ParserElement
|
| 687 |
-
any_close_tag: ParserElement
|
| 688 |
-
any_open_tag, any_close_tag = make_html_tags(
|
| 689 |
-
Word(alphas, alphanums + "_:").set_name("any tag")
|
| 690 |
-
)
|
| 691 |
-
|
| 692 |
-
_htmlEntityMap = {k.rstrip(";"): v for k, v in html.entities.html5.items()}
|
| 693 |
-
common_html_entity = Regex("&(?P<entity>" + "|".join(_htmlEntityMap) + ");").set_name(
|
| 694 |
-
"common HTML entity"
|
| 695 |
-
)
|
| 696 |
-
|
| 697 |
-
|
| 698 |
-
def replace_html_entity(t):
|
| 699 |
-
"""Helper parser action to replace common HTML entities with their special characters"""
|
| 700 |
-
return _htmlEntityMap.get(t.entity)
|
| 701 |
-
|
| 702 |
-
|
| 703 |
-
class OpAssoc(Enum):
|
| 704 |
-
LEFT = 1
|
| 705 |
-
RIGHT = 2
|
| 706 |
-
|
| 707 |
-
|
| 708 |
-
InfixNotationOperatorArgType = Union[
|
| 709 |
-
ParserElement, str, Tuple[Union[ParserElement, str], Union[ParserElement, str]]
|
| 710 |
-
]
|
| 711 |
-
InfixNotationOperatorSpec = Union[
|
| 712 |
-
Tuple[
|
| 713 |
-
InfixNotationOperatorArgType,
|
| 714 |
-
int,
|
| 715 |
-
OpAssoc,
|
| 716 |
-
typing.Optional[ParseAction],
|
| 717 |
-
],
|
| 718 |
-
Tuple[
|
| 719 |
-
InfixNotationOperatorArgType,
|
| 720 |
-
int,
|
| 721 |
-
OpAssoc,
|
| 722 |
-
],
|
| 723 |
-
]
|
| 724 |
-
|
| 725 |
-
|
| 726 |
-
def infix_notation(
|
| 727 |
-
base_expr: ParserElement,
|
| 728 |
-
op_list: List[InfixNotationOperatorSpec],
|
| 729 |
-
lpar: Union[str, ParserElement] = Suppress("("),
|
| 730 |
-
rpar: Union[str, ParserElement] = Suppress(")"),
|
| 731 |
-
) -> ParserElement:
|
| 732 |
-
"""Helper method for constructing grammars of expressions made up of
|
| 733 |
-
operators working in a precedence hierarchy. Operators may be unary
|
| 734 |
-
or binary, left- or right-associative. Parse actions can also be
|
| 735 |
-
attached to operator expressions. The generated parser will also
|
| 736 |
-
recognize the use of parentheses to override operator precedences
|
| 737 |
-
(see example below).
|
| 738 |
-
|
| 739 |
-
Note: if you define a deep operator list, you may see performance
|
| 740 |
-
issues when using infix_notation. See
|
| 741 |
-
:class:`ParserElement.enable_packrat` for a mechanism to potentially
|
| 742 |
-
improve your parser performance.
|
| 743 |
-
|
| 744 |
-
Parameters:
|
| 745 |
-
- ``base_expr`` - expression representing the most basic operand to
|
| 746 |
-
be used in the expression
|
| 747 |
-
- ``op_list`` - list of tuples, one for each operator precedence level
|
| 748 |
-
in the expression grammar; each tuple is of the form ``(op_expr,
|
| 749 |
-
num_operands, right_left_assoc, (optional)parse_action)``, where:
|
| 750 |
-
|
| 751 |
-
- ``op_expr`` is the pyparsing expression for the operator; may also
|
| 752 |
-
be a string, which will be converted to a Literal; if ``num_operands``
|
| 753 |
-
is 3, ``op_expr`` is a tuple of two expressions, for the two
|
| 754 |
-
operators separating the 3 terms
|
| 755 |
-
- ``num_operands`` is the number of terms for this operator (must be 1,
|
| 756 |
-
2, or 3)
|
| 757 |
-
- ``right_left_assoc`` is the indicator whether the operator is right
|
| 758 |
-
or left associative, using the pyparsing-defined constants
|
| 759 |
-
``OpAssoc.RIGHT`` and ``OpAssoc.LEFT``.
|
| 760 |
-
- ``parse_action`` is the parse action to be associated with
|
| 761 |
-
expressions matching this operator expression (the parse action
|
| 762 |
-
tuple member may be omitted); if the parse action is passed
|
| 763 |
-
a tuple or list of functions, this is equivalent to calling
|
| 764 |
-
``set_parse_action(*fn)``
|
| 765 |
-
(:class:`ParserElement.set_parse_action`)
|
| 766 |
-
- ``lpar`` - expression for matching left-parentheses; if passed as a
|
| 767 |
-
str, then will be parsed as Suppress(lpar). If lpar is passed as
|
| 768 |
-
an expression (such as ``Literal('(')``), then it will be kept in
|
| 769 |
-
the parsed results, and grouped with them. (default= ``Suppress('(')``)
|
| 770 |
-
- ``rpar`` - expression for matching right-parentheses; if passed as a
|
| 771 |
-
str, then will be parsed as Suppress(rpar). If rpar is passed as
|
| 772 |
-
an expression (such as ``Literal(')')``), then it will be kept in
|
| 773 |
-
the parsed results, and grouped with them. (default= ``Suppress(')')``)
|
| 774 |
-
|
| 775 |
-
Example::
|
| 776 |
-
|
| 777 |
-
# simple example of four-function arithmetic with ints and
|
| 778 |
-
# variable names
|
| 779 |
-
integer = pyparsing_common.signed_integer
|
| 780 |
-
varname = pyparsing_common.identifier
|
| 781 |
-
|
| 782 |
-
arith_expr = infix_notation(integer | varname,
|
| 783 |
-
[
|
| 784 |
-
('-', 1, OpAssoc.RIGHT),
|
| 785 |
-
(one_of('* /'), 2, OpAssoc.LEFT),
|
| 786 |
-
(one_of('+ -'), 2, OpAssoc.LEFT),
|
| 787 |
-
])
|
| 788 |
-
|
| 789 |
-
arith_expr.run_tests('''
|
| 790 |
-
5+3*6
|
| 791 |
-
(5+3)*6
|
| 792 |
-
-2--11
|
| 793 |
-
''', full_dump=False)
|
| 794 |
-
|
| 795 |
-
prints::
|
| 796 |
-
|
| 797 |
-
5+3*6
|
| 798 |
-
[[5, '+', [3, '*', 6]]]
|
| 799 |
-
|
| 800 |
-
(5+3)*6
|
| 801 |
-
[[[5, '+', 3], '*', 6]]
|
| 802 |
-
|
| 803 |
-
-2--11
|
| 804 |
-
[[['-', 2], '-', ['-', 11]]]
|
| 805 |
-
"""
|
| 806 |
-
# captive version of FollowedBy that does not do parse actions or capture results names
|
| 807 |
-
class _FB(FollowedBy):
|
| 808 |
-
def parseImpl(self, instring, loc, doActions=True):
|
| 809 |
-
self.expr.try_parse(instring, loc)
|
| 810 |
-
return loc, []
|
| 811 |
-
|
| 812 |
-
_FB.__name__ = "FollowedBy>"
|
| 813 |
-
|
| 814 |
-
ret = Forward()
|
| 815 |
-
if isinstance(lpar, str):
|
| 816 |
-
lpar = Suppress(lpar)
|
| 817 |
-
if isinstance(rpar, str):
|
| 818 |
-
rpar = Suppress(rpar)
|
| 819 |
-
|
| 820 |
-
# if lpar and rpar are not suppressed, wrap in group
|
| 821 |
-
if not (isinstance(rpar, Suppress) and isinstance(rpar, Suppress)):
|
| 822 |
-
lastExpr = base_expr | Group(lpar + ret + rpar)
|
| 823 |
-
else:
|
| 824 |
-
lastExpr = base_expr | (lpar + ret + rpar)
|
| 825 |
-
|
| 826 |
-
for i, operDef in enumerate(op_list):
|
| 827 |
-
opExpr, arity, rightLeftAssoc, pa = (operDef + (None,))[:4]
|
| 828 |
-
if isinstance(opExpr, str_type):
|
| 829 |
-
opExpr = ParserElement._literalStringClass(opExpr)
|
| 830 |
-
if arity == 3:
|
| 831 |
-
if not isinstance(opExpr, (tuple, list)) or len(opExpr) != 2:
|
| 832 |
-
raise ValueError(
|
| 833 |
-
"if numterms=3, opExpr must be a tuple or list of two expressions"
|
| 834 |
-
)
|
| 835 |
-
opExpr1, opExpr2 = opExpr
|
| 836 |
-
term_name = "{}{} term".format(opExpr1, opExpr2)
|
| 837 |
-
else:
|
| 838 |
-
term_name = "{} term".format(opExpr)
|
| 839 |
-
|
| 840 |
-
if not 1 <= arity <= 3:
|
| 841 |
-
raise ValueError("operator must be unary (1), binary (2), or ternary (3)")
|
| 842 |
-
|
| 843 |
-
if rightLeftAssoc not in (OpAssoc.LEFT, OpAssoc.RIGHT):
|
| 844 |
-
raise ValueError("operator must indicate right or left associativity")
|
| 845 |
-
|
| 846 |
-
thisExpr: Forward = Forward().set_name(term_name)
|
| 847 |
-
if rightLeftAssoc is OpAssoc.LEFT:
|
| 848 |
-
if arity == 1:
|
| 849 |
-
matchExpr = _FB(lastExpr + opExpr) + Group(lastExpr + opExpr[1, ...])
|
| 850 |
-
elif arity == 2:
|
| 851 |
-
if opExpr is not None:
|
| 852 |
-
matchExpr = _FB(lastExpr + opExpr + lastExpr) + Group(
|
| 853 |
-
lastExpr + (opExpr + lastExpr)[1, ...]
|
| 854 |
-
)
|
| 855 |
-
else:
|
| 856 |
-
matchExpr = _FB(lastExpr + lastExpr) + Group(lastExpr[2, ...])
|
| 857 |
-
elif arity == 3:
|
| 858 |
-
matchExpr = _FB(
|
| 859 |
-
lastExpr + opExpr1 + lastExpr + opExpr2 + lastExpr
|
| 860 |
-
) + Group(lastExpr + OneOrMore(opExpr1 + lastExpr + opExpr2 + lastExpr))
|
| 861 |
-
elif rightLeftAssoc is OpAssoc.RIGHT:
|
| 862 |
-
if arity == 1:
|
| 863 |
-
# try to avoid LR with this extra test
|
| 864 |
-
if not isinstance(opExpr, Opt):
|
| 865 |
-
opExpr = Opt(opExpr)
|
| 866 |
-
matchExpr = _FB(opExpr.expr + thisExpr) + Group(opExpr + thisExpr)
|
| 867 |
-
elif arity == 2:
|
| 868 |
-
if opExpr is not None:
|
| 869 |
-
matchExpr = _FB(lastExpr + opExpr + thisExpr) + Group(
|
| 870 |
-
lastExpr + (opExpr + thisExpr)[1, ...]
|
| 871 |
-
)
|
| 872 |
-
else:
|
| 873 |
-
matchExpr = _FB(lastExpr + thisExpr) + Group(
|
| 874 |
-
lastExpr + thisExpr[1, ...]
|
| 875 |
-
)
|
| 876 |
-
elif arity == 3:
|
| 877 |
-
matchExpr = _FB(
|
| 878 |
-
lastExpr + opExpr1 + thisExpr + opExpr2 + thisExpr
|
| 879 |
-
) + Group(lastExpr + opExpr1 + thisExpr + opExpr2 + thisExpr)
|
| 880 |
-
if pa:
|
| 881 |
-
if isinstance(pa, (tuple, list)):
|
| 882 |
-
matchExpr.set_parse_action(*pa)
|
| 883 |
-
else:
|
| 884 |
-
matchExpr.set_parse_action(pa)
|
| 885 |
-
thisExpr <<= (matchExpr | lastExpr).setName(term_name)
|
| 886 |
-
lastExpr = thisExpr
|
| 887 |
-
ret <<= lastExpr
|
| 888 |
-
return ret
|
| 889 |
-
|
| 890 |
-
|
| 891 |
-
def indentedBlock(blockStatementExpr, indentStack, indent=True, backup_stacks=[]):
|
| 892 |
-
"""
|
| 893 |
-
(DEPRECATED - use IndentedBlock class instead)
|
| 894 |
-
Helper method for defining space-delimited indentation blocks,
|
| 895 |
-
such as those used to define block statements in Python source code.
|
| 896 |
-
|
| 897 |
-
Parameters:
|
| 898 |
-
|
| 899 |
-
- ``blockStatementExpr`` - expression defining syntax of statement that
|
| 900 |
-
is repeated within the indented block
|
| 901 |
-
- ``indentStack`` - list created by caller to manage indentation stack
|
| 902 |
-
(multiple ``statementWithIndentedBlock`` expressions within a single
|
| 903 |
-
grammar should share a common ``indentStack``)
|
| 904 |
-
- ``indent`` - boolean indicating whether block must be indented beyond
|
| 905 |
-
the current level; set to ``False`` for block of left-most statements
|
| 906 |
-
(default= ``True``)
|
| 907 |
-
|
| 908 |
-
A valid block must contain at least one ``blockStatement``.
|
| 909 |
-
|
| 910 |
-
(Note that indentedBlock uses internal parse actions which make it
|
| 911 |
-
incompatible with packrat parsing.)
|
| 912 |
-
|
| 913 |
-
Example::
|
| 914 |
-
|
| 915 |
-
data = '''
|
| 916 |
-
def A(z):
|
| 917 |
-
A1
|
| 918 |
-
B = 100
|
| 919 |
-
G = A2
|
| 920 |
-
A2
|
| 921 |
-
A3
|
| 922 |
-
B
|
| 923 |
-
def BB(a,b,c):
|
| 924 |
-
BB1
|
| 925 |
-
def BBA():
|
| 926 |
-
bba1
|
| 927 |
-
bba2
|
| 928 |
-
bba3
|
| 929 |
-
C
|
| 930 |
-
D
|
| 931 |
-
def spam(x,y):
|
| 932 |
-
def eggs(z):
|
| 933 |
-
pass
|
| 934 |
-
'''
|
| 935 |
-
|
| 936 |
-
|
| 937 |
-
indentStack = [1]
|
| 938 |
-
stmt = Forward()
|
| 939 |
-
|
| 940 |
-
identifier = Word(alphas, alphanums)
|
| 941 |
-
funcDecl = ("def" + identifier + Group("(" + Opt(delimitedList(identifier)) + ")") + ":")
|
| 942 |
-
func_body = indentedBlock(stmt, indentStack)
|
| 943 |
-
funcDef = Group(funcDecl + func_body)
|
| 944 |
-
|
| 945 |
-
rvalue = Forward()
|
| 946 |
-
funcCall = Group(identifier + "(" + Opt(delimitedList(rvalue)) + ")")
|
| 947 |
-
rvalue << (funcCall | identifier | Word(nums))
|
| 948 |
-
assignment = Group(identifier + "=" + rvalue)
|
| 949 |
-
stmt << (funcDef | assignment | identifier)
|
| 950 |
-
|
| 951 |
-
module_body = stmt[1, ...]
|
| 952 |
-
|
| 953 |
-
parseTree = module_body.parseString(data)
|
| 954 |
-
parseTree.pprint()
|
| 955 |
-
|
| 956 |
-
prints::
|
| 957 |
-
|
| 958 |
-
[['def',
|
| 959 |
-
'A',
|
| 960 |
-
['(', 'z', ')'],
|
| 961 |
-
':',
|
| 962 |
-
[['A1'], [['B', '=', '100']], [['G', '=', 'A2']], ['A2'], ['A3']]],
|
| 963 |
-
'B',
|
| 964 |
-
['def',
|
| 965 |
-
'BB',
|
| 966 |
-
['(', 'a', 'b', 'c', ')'],
|
| 967 |
-
':',
|
| 968 |
-
[['BB1'], [['def', 'BBA', ['(', ')'], ':', [['bba1'], ['bba2'], ['bba3']]]]]],
|
| 969 |
-
'C',
|
| 970 |
-
'D',
|
| 971 |
-
['def',
|
| 972 |
-
'spam',
|
| 973 |
-
['(', 'x', 'y', ')'],
|
| 974 |
-
':',
|
| 975 |
-
[[['def', 'eggs', ['(', 'z', ')'], ':', [['pass']]]]]]]
|
| 976 |
-
"""
|
| 977 |
-
backup_stacks.append(indentStack[:])
|
| 978 |
-
|
| 979 |
-
def reset_stack():
|
| 980 |
-
indentStack[:] = backup_stacks[-1]
|
| 981 |
-
|
| 982 |
-
def checkPeerIndent(s, l, t):
|
| 983 |
-
if l >= len(s):
|
| 984 |
-
return
|
| 985 |
-
curCol = col(l, s)
|
| 986 |
-
if curCol != indentStack[-1]:
|
| 987 |
-
if curCol > indentStack[-1]:
|
| 988 |
-
raise ParseException(s, l, "illegal nesting")
|
| 989 |
-
raise ParseException(s, l, "not a peer entry")
|
| 990 |
-
|
| 991 |
-
def checkSubIndent(s, l, t):
|
| 992 |
-
curCol = col(l, s)
|
| 993 |
-
if curCol > indentStack[-1]:
|
| 994 |
-
indentStack.append(curCol)
|
| 995 |
-
else:
|
| 996 |
-
raise ParseException(s, l, "not a subentry")
|
| 997 |
-
|
| 998 |
-
def checkUnindent(s, l, t):
|
| 999 |
-
if l >= len(s):
|
| 1000 |
-
return
|
| 1001 |
-
curCol = col(l, s)
|
| 1002 |
-
if not (indentStack and curCol in indentStack):
|
| 1003 |
-
raise ParseException(s, l, "not an unindent")
|
| 1004 |
-
if curCol < indentStack[-1]:
|
| 1005 |
-
indentStack.pop()
|
| 1006 |
-
|
| 1007 |
-
NL = OneOrMore(LineEnd().set_whitespace_chars("\t ").suppress())
|
| 1008 |
-
INDENT = (Empty() + Empty().set_parse_action(checkSubIndent)).set_name("INDENT")
|
| 1009 |
-
PEER = Empty().set_parse_action(checkPeerIndent).set_name("")
|
| 1010 |
-
UNDENT = Empty().set_parse_action(checkUnindent).set_name("UNINDENT")
|
| 1011 |
-
if indent:
|
| 1012 |
-
smExpr = Group(
|
| 1013 |
-
Opt(NL)
|
| 1014 |
-
+ INDENT
|
| 1015 |
-
+ OneOrMore(PEER + Group(blockStatementExpr) + Opt(NL))
|
| 1016 |
-
+ UNDENT
|
| 1017 |
-
)
|
| 1018 |
-
else:
|
| 1019 |
-
smExpr = Group(
|
| 1020 |
-
Opt(NL)
|
| 1021 |
-
+ OneOrMore(PEER + Group(blockStatementExpr) + Opt(NL))
|
| 1022 |
-
+ Opt(UNDENT)
|
| 1023 |
-
)
|
| 1024 |
-
|
| 1025 |
-
# add a parse action to remove backup_stack from list of backups
|
| 1026 |
-
smExpr.add_parse_action(
|
| 1027 |
-
lambda: backup_stacks.pop(-1) and None if backup_stacks else None
|
| 1028 |
-
)
|
| 1029 |
-
smExpr.set_fail_action(lambda a, b, c, d: reset_stack())
|
| 1030 |
-
blockStatementExpr.ignore(_bslash + LineEnd())
|
| 1031 |
-
return smExpr.set_name("indented block")
|
| 1032 |
-
|
| 1033 |
-
|
| 1034 |
-
# it's easy to get these comment structures wrong - they're very common, so may as well make them available
|
| 1035 |
-
c_style_comment = Combine(Regex(r"/\*(?:[^*]|\*(?!/))*") + "*/").set_name(
|
| 1036 |
-
"C style comment"
|
| 1037 |
-
)
|
| 1038 |
-
"Comment of the form ``/* ... */``"
|
| 1039 |
-
|
| 1040 |
-
html_comment = Regex(r"<!--[\s\S]*?-->").set_name("HTML comment")
|
| 1041 |
-
"Comment of the form ``<!-- ... -->``"
|
| 1042 |
-
|
| 1043 |
-
rest_of_line = Regex(r".*").leave_whitespace().set_name("rest of line")
|
| 1044 |
-
dbl_slash_comment = Regex(r"//(?:\\\n|[^\n])*").set_name("// comment")
|
| 1045 |
-
"Comment of the form ``// ... (to end of line)``"
|
| 1046 |
-
|
| 1047 |
-
cpp_style_comment = Combine(
|
| 1048 |
-
Regex(r"/\*(?:[^*]|\*(?!/))*") + "*/" | dbl_slash_comment
|
| 1049 |
-
).set_name("C++ style comment")
|
| 1050 |
-
"Comment of either form :class:`c_style_comment` or :class:`dbl_slash_comment`"
|
| 1051 |
-
|
| 1052 |
-
java_style_comment = cpp_style_comment
|
| 1053 |
-
"Same as :class:`cpp_style_comment`"
|
| 1054 |
-
|
| 1055 |
-
python_style_comment = Regex(r"#.*").set_name("Python style comment")
|
| 1056 |
-
"Comment of the form ``# ... (to end of line)``"
|
| 1057 |
-
|
| 1058 |
-
|
| 1059 |
-
# build list of built-in expressions, for future reference if a global default value
|
| 1060 |
-
# gets updated
|
| 1061 |
-
_builtin_exprs: List[ParserElement] = [
|
| 1062 |
-
v for v in vars().values() if isinstance(v, ParserElement)
|
| 1063 |
-
]
|
| 1064 |
-
|
| 1065 |
-
|
| 1066 |
-
# pre-PEP8 compatible names
|
| 1067 |
-
delimitedList = delimited_list
|
| 1068 |
-
countedArray = counted_array
|
| 1069 |
-
matchPreviousLiteral = match_previous_literal
|
| 1070 |
-
matchPreviousExpr = match_previous_expr
|
| 1071 |
-
oneOf = one_of
|
| 1072 |
-
dictOf = dict_of
|
| 1073 |
-
originalTextFor = original_text_for
|
| 1074 |
-
nestedExpr = nested_expr
|
| 1075 |
-
makeHTMLTags = make_html_tags
|
| 1076 |
-
makeXMLTags = make_xml_tags
|
| 1077 |
-
anyOpenTag, anyCloseTag = any_open_tag, any_close_tag
|
| 1078 |
-
commonHTMLEntity = common_html_entity
|
| 1079 |
-
replaceHTMLEntity = replace_html_entity
|
| 1080 |
-
opAssoc = OpAssoc
|
| 1081 |
-
infixNotation = infix_notation
|
| 1082 |
-
cStyleComment = c_style_comment
|
| 1083 |
-
htmlComment = html_comment
|
| 1084 |
-
restOfLine = rest_of_line
|
| 1085 |
-
dblSlashComment = dbl_slash_comment
|
| 1086 |
-
cppStyleComment = cpp_style_comment
|
| 1087 |
-
javaStyleComment = java_style_comment
|
| 1088 |
-
pythonStyleComment = python_style_comment
|
|
|
|
|
|
|
|
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spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/webencodings/mklabels.py
DELETED
|
@@ -1,59 +0,0 @@
|
|
| 1 |
-
"""
|
| 2 |
-
|
| 3 |
-
webencodings.mklabels
|
| 4 |
-
~~~~~~~~~~~~~~~~~~~~~
|
| 5 |
-
|
| 6 |
-
Regenarate the webencodings.labels module.
|
| 7 |
-
|
| 8 |
-
:copyright: Copyright 2012 by Simon Sapin
|
| 9 |
-
:license: BSD, see LICENSE for details.
|
| 10 |
-
|
| 11 |
-
"""
|
| 12 |
-
|
| 13 |
-
import json
|
| 14 |
-
try:
|
| 15 |
-
from urllib import urlopen
|
| 16 |
-
except ImportError:
|
| 17 |
-
from urllib.request import urlopen
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
def assert_lower(string):
|
| 21 |
-
assert string == string.lower()
|
| 22 |
-
return string
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
def generate(url):
|
| 26 |
-
parts = ['''\
|
| 27 |
-
"""
|
| 28 |
-
|
| 29 |
-
webencodings.labels
|
| 30 |
-
~~~~~~~~~~~~~~~~~~~
|
| 31 |
-
|
| 32 |
-
Map encoding labels to their name.
|
| 33 |
-
|
| 34 |
-
:copyright: Copyright 2012 by Simon Sapin
|
| 35 |
-
:license: BSD, see LICENSE for details.
|
| 36 |
-
|
| 37 |
-
"""
|
| 38 |
-
|
| 39 |
-
# XXX Do not edit!
|
| 40 |
-
# This file is automatically generated by mklabels.py
|
| 41 |
-
|
| 42 |
-
LABELS = {
|
| 43 |
-
''']
|
| 44 |
-
labels = [
|
| 45 |
-
(repr(assert_lower(label)).lstrip('u'),
|
| 46 |
-
repr(encoding['name']).lstrip('u'))
|
| 47 |
-
for category in json.loads(urlopen(url).read().decode('ascii'))
|
| 48 |
-
for encoding in category['encodings']
|
| 49 |
-
for label in encoding['labels']]
|
| 50 |
-
max_len = max(len(label) for label, name in labels)
|
| 51 |
-
parts.extend(
|
| 52 |
-
' %s:%s %s,\n' % (label, ' ' * (max_len - len(label)), name)
|
| 53 |
-
for label, name in labels)
|
| 54 |
-
parts.append('}')
|
| 55 |
-
return ''.join(parts)
|
| 56 |
-
|
| 57 |
-
|
| 58 |
-
if __name__ == '__main__':
|
| 59 |
-
print(generate('http://encoding.spec.whatwg.org/encodings.json'))
|
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|
spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/jaraco/functools.py
DELETED
|
@@ -1,525 +0,0 @@
|
|
| 1 |
-
import functools
|
| 2 |
-
import time
|
| 3 |
-
import inspect
|
| 4 |
-
import collections
|
| 5 |
-
import types
|
| 6 |
-
import itertools
|
| 7 |
-
|
| 8 |
-
import pkg_resources.extern.more_itertools
|
| 9 |
-
|
| 10 |
-
from typing import Callable, TypeVar
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
CallableT = TypeVar("CallableT", bound=Callable[..., object])
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
def compose(*funcs):
|
| 17 |
-
"""
|
| 18 |
-
Compose any number of unary functions into a single unary function.
|
| 19 |
-
|
| 20 |
-
>>> import textwrap
|
| 21 |
-
>>> expected = str.strip(textwrap.dedent(compose.__doc__))
|
| 22 |
-
>>> strip_and_dedent = compose(str.strip, textwrap.dedent)
|
| 23 |
-
>>> strip_and_dedent(compose.__doc__) == expected
|
| 24 |
-
True
|
| 25 |
-
|
| 26 |
-
Compose also allows the innermost function to take arbitrary arguments.
|
| 27 |
-
|
| 28 |
-
>>> round_three = lambda x: round(x, ndigits=3)
|
| 29 |
-
>>> f = compose(round_three, int.__truediv__)
|
| 30 |
-
>>> [f(3*x, x+1) for x in range(1,10)]
|
| 31 |
-
[1.5, 2.0, 2.25, 2.4, 2.5, 2.571, 2.625, 2.667, 2.7]
|
| 32 |
-
"""
|
| 33 |
-
|
| 34 |
-
def compose_two(f1, f2):
|
| 35 |
-
return lambda *args, **kwargs: f1(f2(*args, **kwargs))
|
| 36 |
-
|
| 37 |
-
return functools.reduce(compose_two, funcs)
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
def method_caller(method_name, *args, **kwargs):
|
| 41 |
-
"""
|
| 42 |
-
Return a function that will call a named method on the
|
| 43 |
-
target object with optional positional and keyword
|
| 44 |
-
arguments.
|
| 45 |
-
|
| 46 |
-
>>> lower = method_caller('lower')
|
| 47 |
-
>>> lower('MyString')
|
| 48 |
-
'mystring'
|
| 49 |
-
"""
|
| 50 |
-
|
| 51 |
-
def call_method(target):
|
| 52 |
-
func = getattr(target, method_name)
|
| 53 |
-
return func(*args, **kwargs)
|
| 54 |
-
|
| 55 |
-
return call_method
|
| 56 |
-
|
| 57 |
-
|
| 58 |
-
def once(func):
|
| 59 |
-
"""
|
| 60 |
-
Decorate func so it's only ever called the first time.
|
| 61 |
-
|
| 62 |
-
This decorator can ensure that an expensive or non-idempotent function
|
| 63 |
-
will not be expensive on subsequent calls and is idempotent.
|
| 64 |
-
|
| 65 |
-
>>> add_three = once(lambda a: a+3)
|
| 66 |
-
>>> add_three(3)
|
| 67 |
-
6
|
| 68 |
-
>>> add_three(9)
|
| 69 |
-
6
|
| 70 |
-
>>> add_three('12')
|
| 71 |
-
6
|
| 72 |
-
|
| 73 |
-
To reset the stored value, simply clear the property ``saved_result``.
|
| 74 |
-
|
| 75 |
-
>>> del add_three.saved_result
|
| 76 |
-
>>> add_three(9)
|
| 77 |
-
12
|
| 78 |
-
>>> add_three(8)
|
| 79 |
-
12
|
| 80 |
-
|
| 81 |
-
Or invoke 'reset()' on it.
|
| 82 |
-
|
| 83 |
-
>>> add_three.reset()
|
| 84 |
-
>>> add_three(-3)
|
| 85 |
-
0
|
| 86 |
-
>>> add_three(0)
|
| 87 |
-
0
|
| 88 |
-
"""
|
| 89 |
-
|
| 90 |
-
@functools.wraps(func)
|
| 91 |
-
def wrapper(*args, **kwargs):
|
| 92 |
-
if not hasattr(wrapper, 'saved_result'):
|
| 93 |
-
wrapper.saved_result = func(*args, **kwargs)
|
| 94 |
-
return wrapper.saved_result
|
| 95 |
-
|
| 96 |
-
wrapper.reset = lambda: vars(wrapper).__delitem__('saved_result')
|
| 97 |
-
return wrapper
|
| 98 |
-
|
| 99 |
-
|
| 100 |
-
def method_cache(
|
| 101 |
-
method: CallableT,
|
| 102 |
-
cache_wrapper: Callable[
|
| 103 |
-
[CallableT], CallableT
|
| 104 |
-
] = functools.lru_cache(), # type: ignore[assignment]
|
| 105 |
-
) -> CallableT:
|
| 106 |
-
"""
|
| 107 |
-
Wrap lru_cache to support storing the cache data in the object instances.
|
| 108 |
-
|
| 109 |
-
Abstracts the common paradigm where the method explicitly saves an
|
| 110 |
-
underscore-prefixed protected property on first call and returns that
|
| 111 |
-
subsequently.
|
| 112 |
-
|
| 113 |
-
>>> class MyClass:
|
| 114 |
-
... calls = 0
|
| 115 |
-
...
|
| 116 |
-
... @method_cache
|
| 117 |
-
... def method(self, value):
|
| 118 |
-
... self.calls += 1
|
| 119 |
-
... return value
|
| 120 |
-
|
| 121 |
-
>>> a = MyClass()
|
| 122 |
-
>>> a.method(3)
|
| 123 |
-
3
|
| 124 |
-
>>> for x in range(75):
|
| 125 |
-
... res = a.method(x)
|
| 126 |
-
>>> a.calls
|
| 127 |
-
75
|
| 128 |
-
|
| 129 |
-
Note that the apparent behavior will be exactly like that of lru_cache
|
| 130 |
-
except that the cache is stored on each instance, so values in one
|
| 131 |
-
instance will not flush values from another, and when an instance is
|
| 132 |
-
deleted, so are the cached values for that instance.
|
| 133 |
-
|
| 134 |
-
>>> b = MyClass()
|
| 135 |
-
>>> for x in range(35):
|
| 136 |
-
... res = b.method(x)
|
| 137 |
-
>>> b.calls
|
| 138 |
-
35
|
| 139 |
-
>>> a.method(0)
|
| 140 |
-
0
|
| 141 |
-
>>> a.calls
|
| 142 |
-
75
|
| 143 |
-
|
| 144 |
-
Note that if method had been decorated with ``functools.lru_cache()``,
|
| 145 |
-
a.calls would have been 76 (due to the cached value of 0 having been
|
| 146 |
-
flushed by the 'b' instance).
|
| 147 |
-
|
| 148 |
-
Clear the cache with ``.cache_clear()``
|
| 149 |
-
|
| 150 |
-
>>> a.method.cache_clear()
|
| 151 |
-
|
| 152 |
-
Same for a method that hasn't yet been called.
|
| 153 |
-
|
| 154 |
-
>>> c = MyClass()
|
| 155 |
-
>>> c.method.cache_clear()
|
| 156 |
-
|
| 157 |
-
Another cache wrapper may be supplied:
|
| 158 |
-
|
| 159 |
-
>>> cache = functools.lru_cache(maxsize=2)
|
| 160 |
-
>>> MyClass.method2 = method_cache(lambda self: 3, cache_wrapper=cache)
|
| 161 |
-
>>> a = MyClass()
|
| 162 |
-
>>> a.method2()
|
| 163 |
-
3
|
| 164 |
-
|
| 165 |
-
Caution - do not subsequently wrap the method with another decorator, such
|
| 166 |
-
as ``@property``, which changes the semantics of the function.
|
| 167 |
-
|
| 168 |
-
See also
|
| 169 |
-
http://code.activestate.com/recipes/577452-a-memoize-decorator-for-instance-methods/
|
| 170 |
-
for another implementation and additional justification.
|
| 171 |
-
"""
|
| 172 |
-
|
| 173 |
-
def wrapper(self: object, *args: object, **kwargs: object) -> object:
|
| 174 |
-
# it's the first call, replace the method with a cached, bound method
|
| 175 |
-
bound_method: CallableT = types.MethodType( # type: ignore[assignment]
|
| 176 |
-
method, self
|
| 177 |
-
)
|
| 178 |
-
cached_method = cache_wrapper(bound_method)
|
| 179 |
-
setattr(self, method.__name__, cached_method)
|
| 180 |
-
return cached_method(*args, **kwargs)
|
| 181 |
-
|
| 182 |
-
# Support cache clear even before cache has been created.
|
| 183 |
-
wrapper.cache_clear = lambda: None # type: ignore[attr-defined]
|
| 184 |
-
|
| 185 |
-
return ( # type: ignore[return-value]
|
| 186 |
-
_special_method_cache(method, cache_wrapper) or wrapper
|
| 187 |
-
)
|
| 188 |
-
|
| 189 |
-
|
| 190 |
-
def _special_method_cache(method, cache_wrapper):
|
| 191 |
-
"""
|
| 192 |
-
Because Python treats special methods differently, it's not
|
| 193 |
-
possible to use instance attributes to implement the cached
|
| 194 |
-
methods.
|
| 195 |
-
|
| 196 |
-
Instead, install the wrapper method under a different name
|
| 197 |
-
and return a simple proxy to that wrapper.
|
| 198 |
-
|
| 199 |
-
https://github.com/jaraco/jaraco.functools/issues/5
|
| 200 |
-
"""
|
| 201 |
-
name = method.__name__
|
| 202 |
-
special_names = '__getattr__', '__getitem__'
|
| 203 |
-
if name not in special_names:
|
| 204 |
-
return
|
| 205 |
-
|
| 206 |
-
wrapper_name = '__cached' + name
|
| 207 |
-
|
| 208 |
-
def proxy(self, *args, **kwargs):
|
| 209 |
-
if wrapper_name not in vars(self):
|
| 210 |
-
bound = types.MethodType(method, self)
|
| 211 |
-
cache = cache_wrapper(bound)
|
| 212 |
-
setattr(self, wrapper_name, cache)
|
| 213 |
-
else:
|
| 214 |
-
cache = getattr(self, wrapper_name)
|
| 215 |
-
return cache(*args, **kwargs)
|
| 216 |
-
|
| 217 |
-
return proxy
|
| 218 |
-
|
| 219 |
-
|
| 220 |
-
def apply(transform):
|
| 221 |
-
"""
|
| 222 |
-
Decorate a function with a transform function that is
|
| 223 |
-
invoked on results returned from the decorated function.
|
| 224 |
-
|
| 225 |
-
>>> @apply(reversed)
|
| 226 |
-
... def get_numbers(start):
|
| 227 |
-
... "doc for get_numbers"
|
| 228 |
-
... return range(start, start+3)
|
| 229 |
-
>>> list(get_numbers(4))
|
| 230 |
-
[6, 5, 4]
|
| 231 |
-
>>> get_numbers.__doc__
|
| 232 |
-
'doc for get_numbers'
|
| 233 |
-
"""
|
| 234 |
-
|
| 235 |
-
def wrap(func):
|
| 236 |
-
return functools.wraps(func)(compose(transform, func))
|
| 237 |
-
|
| 238 |
-
return wrap
|
| 239 |
-
|
| 240 |
-
|
| 241 |
-
def result_invoke(action):
|
| 242 |
-
r"""
|
| 243 |
-
Decorate a function with an action function that is
|
| 244 |
-
invoked on the results returned from the decorated
|
| 245 |
-
function (for its side-effect), then return the original
|
| 246 |
-
result.
|
| 247 |
-
|
| 248 |
-
>>> @result_invoke(print)
|
| 249 |
-
... def add_two(a, b):
|
| 250 |
-
... return a + b
|
| 251 |
-
>>> x = add_two(2, 3)
|
| 252 |
-
5
|
| 253 |
-
>>> x
|
| 254 |
-
5
|
| 255 |
-
"""
|
| 256 |
-
|
| 257 |
-
def wrap(func):
|
| 258 |
-
@functools.wraps(func)
|
| 259 |
-
def wrapper(*args, **kwargs):
|
| 260 |
-
result = func(*args, **kwargs)
|
| 261 |
-
action(result)
|
| 262 |
-
return result
|
| 263 |
-
|
| 264 |
-
return wrapper
|
| 265 |
-
|
| 266 |
-
return wrap
|
| 267 |
-
|
| 268 |
-
|
| 269 |
-
def call_aside(f, *args, **kwargs):
|
| 270 |
-
"""
|
| 271 |
-
Call a function for its side effect after initialization.
|
| 272 |
-
|
| 273 |
-
>>> @call_aside
|
| 274 |
-
... def func(): print("called")
|
| 275 |
-
called
|
| 276 |
-
>>> func()
|
| 277 |
-
called
|
| 278 |
-
|
| 279 |
-
Use functools.partial to pass parameters to the initial call
|
| 280 |
-
|
| 281 |
-
>>> @functools.partial(call_aside, name='bingo')
|
| 282 |
-
... def func(name): print("called with", name)
|
| 283 |
-
called with bingo
|
| 284 |
-
"""
|
| 285 |
-
f(*args, **kwargs)
|
| 286 |
-
return f
|
| 287 |
-
|
| 288 |
-
|
| 289 |
-
class Throttler:
|
| 290 |
-
"""
|
| 291 |
-
Rate-limit a function (or other callable)
|
| 292 |
-
"""
|
| 293 |
-
|
| 294 |
-
def __init__(self, func, max_rate=float('Inf')):
|
| 295 |
-
if isinstance(func, Throttler):
|
| 296 |
-
func = func.func
|
| 297 |
-
self.func = func
|
| 298 |
-
self.max_rate = max_rate
|
| 299 |
-
self.reset()
|
| 300 |
-
|
| 301 |
-
def reset(self):
|
| 302 |
-
self.last_called = 0
|
| 303 |
-
|
| 304 |
-
def __call__(self, *args, **kwargs):
|
| 305 |
-
self._wait()
|
| 306 |
-
return self.func(*args, **kwargs)
|
| 307 |
-
|
| 308 |
-
def _wait(self):
|
| 309 |
-
"ensure at least 1/max_rate seconds from last call"
|
| 310 |
-
elapsed = time.time() - self.last_called
|
| 311 |
-
must_wait = 1 / self.max_rate - elapsed
|
| 312 |
-
time.sleep(max(0, must_wait))
|
| 313 |
-
self.last_called = time.time()
|
| 314 |
-
|
| 315 |
-
def __get__(self, obj, type=None):
|
| 316 |
-
return first_invoke(self._wait, functools.partial(self.func, obj))
|
| 317 |
-
|
| 318 |
-
|
| 319 |
-
def first_invoke(func1, func2):
|
| 320 |
-
"""
|
| 321 |
-
Return a function that when invoked will invoke func1 without
|
| 322 |
-
any parameters (for its side-effect) and then invoke func2
|
| 323 |
-
with whatever parameters were passed, returning its result.
|
| 324 |
-
"""
|
| 325 |
-
|
| 326 |
-
def wrapper(*args, **kwargs):
|
| 327 |
-
func1()
|
| 328 |
-
return func2(*args, **kwargs)
|
| 329 |
-
|
| 330 |
-
return wrapper
|
| 331 |
-
|
| 332 |
-
|
| 333 |
-
def retry_call(func, cleanup=lambda: None, retries=0, trap=()):
|
| 334 |
-
"""
|
| 335 |
-
Given a callable func, trap the indicated exceptions
|
| 336 |
-
for up to 'retries' times, invoking cleanup on the
|
| 337 |
-
exception. On the final attempt, allow any exceptions
|
| 338 |
-
to propagate.
|
| 339 |
-
"""
|
| 340 |
-
attempts = itertools.count() if retries == float('inf') else range(retries)
|
| 341 |
-
for attempt in attempts:
|
| 342 |
-
try:
|
| 343 |
-
return func()
|
| 344 |
-
except trap:
|
| 345 |
-
cleanup()
|
| 346 |
-
|
| 347 |
-
return func()
|
| 348 |
-
|
| 349 |
-
|
| 350 |
-
def retry(*r_args, **r_kwargs):
|
| 351 |
-
"""
|
| 352 |
-
Decorator wrapper for retry_call. Accepts arguments to retry_call
|
| 353 |
-
except func and then returns a decorator for the decorated function.
|
| 354 |
-
|
| 355 |
-
Ex:
|
| 356 |
-
|
| 357 |
-
>>> @retry(retries=3)
|
| 358 |
-
... def my_func(a, b):
|
| 359 |
-
... "this is my funk"
|
| 360 |
-
... print(a, b)
|
| 361 |
-
>>> my_func.__doc__
|
| 362 |
-
'this is my funk'
|
| 363 |
-
"""
|
| 364 |
-
|
| 365 |
-
def decorate(func):
|
| 366 |
-
@functools.wraps(func)
|
| 367 |
-
def wrapper(*f_args, **f_kwargs):
|
| 368 |
-
bound = functools.partial(func, *f_args, **f_kwargs)
|
| 369 |
-
return retry_call(bound, *r_args, **r_kwargs)
|
| 370 |
-
|
| 371 |
-
return wrapper
|
| 372 |
-
|
| 373 |
-
return decorate
|
| 374 |
-
|
| 375 |
-
|
| 376 |
-
def print_yielded(func):
|
| 377 |
-
"""
|
| 378 |
-
Convert a generator into a function that prints all yielded elements
|
| 379 |
-
|
| 380 |
-
>>> @print_yielded
|
| 381 |
-
... def x():
|
| 382 |
-
... yield 3; yield None
|
| 383 |
-
>>> x()
|
| 384 |
-
3
|
| 385 |
-
None
|
| 386 |
-
"""
|
| 387 |
-
print_all = functools.partial(map, print)
|
| 388 |
-
print_results = compose(more_itertools.consume, print_all, func)
|
| 389 |
-
return functools.wraps(func)(print_results)
|
| 390 |
-
|
| 391 |
-
|
| 392 |
-
def pass_none(func):
|
| 393 |
-
"""
|
| 394 |
-
Wrap func so it's not called if its first param is None
|
| 395 |
-
|
| 396 |
-
>>> print_text = pass_none(print)
|
| 397 |
-
>>> print_text('text')
|
| 398 |
-
text
|
| 399 |
-
>>> print_text(None)
|
| 400 |
-
"""
|
| 401 |
-
|
| 402 |
-
@functools.wraps(func)
|
| 403 |
-
def wrapper(param, *args, **kwargs):
|
| 404 |
-
if param is not None:
|
| 405 |
-
return func(param, *args, **kwargs)
|
| 406 |
-
|
| 407 |
-
return wrapper
|
| 408 |
-
|
| 409 |
-
|
| 410 |
-
def assign_params(func, namespace):
|
| 411 |
-
"""
|
| 412 |
-
Assign parameters from namespace where func solicits.
|
| 413 |
-
|
| 414 |
-
>>> def func(x, y=3):
|
| 415 |
-
... print(x, y)
|
| 416 |
-
>>> assigned = assign_params(func, dict(x=2, z=4))
|
| 417 |
-
>>> assigned()
|
| 418 |
-
2 3
|
| 419 |
-
|
| 420 |
-
The usual errors are raised if a function doesn't receive
|
| 421 |
-
its required parameters:
|
| 422 |
-
|
| 423 |
-
>>> assigned = assign_params(func, dict(y=3, z=4))
|
| 424 |
-
>>> assigned()
|
| 425 |
-
Traceback (most recent call last):
|
| 426 |
-
TypeError: func() ...argument...
|
| 427 |
-
|
| 428 |
-
It even works on methods:
|
| 429 |
-
|
| 430 |
-
>>> class Handler:
|
| 431 |
-
... def meth(self, arg):
|
| 432 |
-
... print(arg)
|
| 433 |
-
>>> assign_params(Handler().meth, dict(arg='crystal', foo='clear'))()
|
| 434 |
-
crystal
|
| 435 |
-
"""
|
| 436 |
-
sig = inspect.signature(func)
|
| 437 |
-
params = sig.parameters.keys()
|
| 438 |
-
call_ns = {k: namespace[k] for k in params if k in namespace}
|
| 439 |
-
return functools.partial(func, **call_ns)
|
| 440 |
-
|
| 441 |
-
|
| 442 |
-
def save_method_args(method):
|
| 443 |
-
"""
|
| 444 |
-
Wrap a method such that when it is called, the args and kwargs are
|
| 445 |
-
saved on the method.
|
| 446 |
-
|
| 447 |
-
>>> class MyClass:
|
| 448 |
-
... @save_method_args
|
| 449 |
-
... def method(self, a, b):
|
| 450 |
-
... print(a, b)
|
| 451 |
-
>>> my_ob = MyClass()
|
| 452 |
-
>>> my_ob.method(1, 2)
|
| 453 |
-
1 2
|
| 454 |
-
>>> my_ob._saved_method.args
|
| 455 |
-
(1, 2)
|
| 456 |
-
>>> my_ob._saved_method.kwargs
|
| 457 |
-
{}
|
| 458 |
-
>>> my_ob.method(a=3, b='foo')
|
| 459 |
-
3 foo
|
| 460 |
-
>>> my_ob._saved_method.args
|
| 461 |
-
()
|
| 462 |
-
>>> my_ob._saved_method.kwargs == dict(a=3, b='foo')
|
| 463 |
-
True
|
| 464 |
-
|
| 465 |
-
The arguments are stored on the instance, allowing for
|
| 466 |
-
different instance to save different args.
|
| 467 |
-
|
| 468 |
-
>>> your_ob = MyClass()
|
| 469 |
-
>>> your_ob.method({str('x'): 3}, b=[4])
|
| 470 |
-
{'x': 3} [4]
|
| 471 |
-
>>> your_ob._saved_method.args
|
| 472 |
-
({'x': 3},)
|
| 473 |
-
>>> my_ob._saved_method.args
|
| 474 |
-
()
|
| 475 |
-
"""
|
| 476 |
-
args_and_kwargs = collections.namedtuple('args_and_kwargs', 'args kwargs')
|
| 477 |
-
|
| 478 |
-
@functools.wraps(method)
|
| 479 |
-
def wrapper(self, *args, **kwargs):
|
| 480 |
-
attr_name = '_saved_' + method.__name__
|
| 481 |
-
attr = args_and_kwargs(args, kwargs)
|
| 482 |
-
setattr(self, attr_name, attr)
|
| 483 |
-
return method(self, *args, **kwargs)
|
| 484 |
-
|
| 485 |
-
return wrapper
|
| 486 |
-
|
| 487 |
-
|
| 488 |
-
def except_(*exceptions, replace=None, use=None):
|
| 489 |
-
"""
|
| 490 |
-
Replace the indicated exceptions, if raised, with the indicated
|
| 491 |
-
literal replacement or evaluated expression (if present).
|
| 492 |
-
|
| 493 |
-
>>> safe_int = except_(ValueError)(int)
|
| 494 |
-
>>> safe_int('five')
|
| 495 |
-
>>> safe_int('5')
|
| 496 |
-
5
|
| 497 |
-
|
| 498 |
-
Specify a literal replacement with ``replace``.
|
| 499 |
-
|
| 500 |
-
>>> safe_int_r = except_(ValueError, replace=0)(int)
|
| 501 |
-
>>> safe_int_r('five')
|
| 502 |
-
0
|
| 503 |
-
|
| 504 |
-
Provide an expression to ``use`` to pass through particular parameters.
|
| 505 |
-
|
| 506 |
-
>>> safe_int_pt = except_(ValueError, use='args[0]')(int)
|
| 507 |
-
>>> safe_int_pt('five')
|
| 508 |
-
'five'
|
| 509 |
-
|
| 510 |
-
"""
|
| 511 |
-
|
| 512 |
-
def decorate(func):
|
| 513 |
-
@functools.wraps(func)
|
| 514 |
-
def wrapper(*args, **kwargs):
|
| 515 |
-
try:
|
| 516 |
-
return func(*args, **kwargs)
|
| 517 |
-
except exceptions:
|
| 518 |
-
try:
|
| 519 |
-
return eval(use)
|
| 520 |
-
except TypeError:
|
| 521 |
-
return replace
|
| 522 |
-
|
| 523 |
-
return wrapper
|
| 524 |
-
|
| 525 |
-
return decorate
|
|
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spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/pyparsing/util.py
DELETED
|
@@ -1,235 +0,0 @@
|
|
| 1 |
-
# util.py
|
| 2 |
-
import warnings
|
| 3 |
-
import types
|
| 4 |
-
import collections
|
| 5 |
-
import itertools
|
| 6 |
-
from functools import lru_cache
|
| 7 |
-
from typing import List, Union, Iterable
|
| 8 |
-
|
| 9 |
-
_bslash = chr(92)
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
class __config_flags:
|
| 13 |
-
"""Internal class for defining compatibility and debugging flags"""
|
| 14 |
-
|
| 15 |
-
_all_names: List[str] = []
|
| 16 |
-
_fixed_names: List[str] = []
|
| 17 |
-
_type_desc = "configuration"
|
| 18 |
-
|
| 19 |
-
@classmethod
|
| 20 |
-
def _set(cls, dname, value):
|
| 21 |
-
if dname in cls._fixed_names:
|
| 22 |
-
warnings.warn(
|
| 23 |
-
"{}.{} {} is {} and cannot be overridden".format(
|
| 24 |
-
cls.__name__,
|
| 25 |
-
dname,
|
| 26 |
-
cls._type_desc,
|
| 27 |
-
str(getattr(cls, dname)).upper(),
|
| 28 |
-
)
|
| 29 |
-
)
|
| 30 |
-
return
|
| 31 |
-
if dname in cls._all_names:
|
| 32 |
-
setattr(cls, dname, value)
|
| 33 |
-
else:
|
| 34 |
-
raise ValueError("no such {} {!r}".format(cls._type_desc, dname))
|
| 35 |
-
|
| 36 |
-
enable = classmethod(lambda cls, name: cls._set(name, True))
|
| 37 |
-
disable = classmethod(lambda cls, name: cls._set(name, False))
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
@lru_cache(maxsize=128)
|
| 41 |
-
def col(loc: int, strg: str) -> int:
|
| 42 |
-
"""
|
| 43 |
-
Returns current column within a string, counting newlines as line separators.
|
| 44 |
-
The first column is number 1.
|
| 45 |
-
|
| 46 |
-
Note: the default parsing behavior is to expand tabs in the input string
|
| 47 |
-
before starting the parsing process. See
|
| 48 |
-
:class:`ParserElement.parseString` for more
|
| 49 |
-
information on parsing strings containing ``<TAB>`` s, and suggested
|
| 50 |
-
methods to maintain a consistent view of the parsed string, the parse
|
| 51 |
-
location, and line and column positions within the parsed string.
|
| 52 |
-
"""
|
| 53 |
-
s = strg
|
| 54 |
-
return 1 if 0 < loc < len(s) and s[loc - 1] == "\n" else loc - s.rfind("\n", 0, loc)
|
| 55 |
-
|
| 56 |
-
|
| 57 |
-
@lru_cache(maxsize=128)
|
| 58 |
-
def lineno(loc: int, strg: str) -> int:
|
| 59 |
-
"""Returns current line number within a string, counting newlines as line separators.
|
| 60 |
-
The first line is number 1.
|
| 61 |
-
|
| 62 |
-
Note - the default parsing behavior is to expand tabs in the input string
|
| 63 |
-
before starting the parsing process. See :class:`ParserElement.parseString`
|
| 64 |
-
for more information on parsing strings containing ``<TAB>`` s, and
|
| 65 |
-
suggested methods to maintain a consistent view of the parsed string, the
|
| 66 |
-
parse location, and line and column positions within the parsed string.
|
| 67 |
-
"""
|
| 68 |
-
return strg.count("\n", 0, loc) + 1
|
| 69 |
-
|
| 70 |
-
|
| 71 |
-
@lru_cache(maxsize=128)
|
| 72 |
-
def line(loc: int, strg: str) -> str:
|
| 73 |
-
"""
|
| 74 |
-
Returns the line of text containing loc within a string, counting newlines as line separators.
|
| 75 |
-
"""
|
| 76 |
-
last_cr = strg.rfind("\n", 0, loc)
|
| 77 |
-
next_cr = strg.find("\n", loc)
|
| 78 |
-
return strg[last_cr + 1 : next_cr] if next_cr >= 0 else strg[last_cr + 1 :]
|
| 79 |
-
|
| 80 |
-
|
| 81 |
-
class _UnboundedCache:
|
| 82 |
-
def __init__(self):
|
| 83 |
-
cache = {}
|
| 84 |
-
cache_get = cache.get
|
| 85 |
-
self.not_in_cache = not_in_cache = object()
|
| 86 |
-
|
| 87 |
-
def get(_, key):
|
| 88 |
-
return cache_get(key, not_in_cache)
|
| 89 |
-
|
| 90 |
-
def set_(_, key, value):
|
| 91 |
-
cache[key] = value
|
| 92 |
-
|
| 93 |
-
def clear(_):
|
| 94 |
-
cache.clear()
|
| 95 |
-
|
| 96 |
-
self.size = None
|
| 97 |
-
self.get = types.MethodType(get, self)
|
| 98 |
-
self.set = types.MethodType(set_, self)
|
| 99 |
-
self.clear = types.MethodType(clear, self)
|
| 100 |
-
|
| 101 |
-
|
| 102 |
-
class _FifoCache:
|
| 103 |
-
def __init__(self, size):
|
| 104 |
-
self.not_in_cache = not_in_cache = object()
|
| 105 |
-
cache = collections.OrderedDict()
|
| 106 |
-
cache_get = cache.get
|
| 107 |
-
|
| 108 |
-
def get(_, key):
|
| 109 |
-
return cache_get(key, not_in_cache)
|
| 110 |
-
|
| 111 |
-
def set_(_, key, value):
|
| 112 |
-
cache[key] = value
|
| 113 |
-
while len(cache) > size:
|
| 114 |
-
cache.popitem(last=False)
|
| 115 |
-
|
| 116 |
-
def clear(_):
|
| 117 |
-
cache.clear()
|
| 118 |
-
|
| 119 |
-
self.size = size
|
| 120 |
-
self.get = types.MethodType(get, self)
|
| 121 |
-
self.set = types.MethodType(set_, self)
|
| 122 |
-
self.clear = types.MethodType(clear, self)
|
| 123 |
-
|
| 124 |
-
|
| 125 |
-
class LRUMemo:
|
| 126 |
-
"""
|
| 127 |
-
A memoizing mapping that retains `capacity` deleted items
|
| 128 |
-
|
| 129 |
-
The memo tracks retained items by their access order; once `capacity` items
|
| 130 |
-
are retained, the least recently used item is discarded.
|
| 131 |
-
"""
|
| 132 |
-
|
| 133 |
-
def __init__(self, capacity):
|
| 134 |
-
self._capacity = capacity
|
| 135 |
-
self._active = {}
|
| 136 |
-
self._memory = collections.OrderedDict()
|
| 137 |
-
|
| 138 |
-
def __getitem__(self, key):
|
| 139 |
-
try:
|
| 140 |
-
return self._active[key]
|
| 141 |
-
except KeyError:
|
| 142 |
-
self._memory.move_to_end(key)
|
| 143 |
-
return self._memory[key]
|
| 144 |
-
|
| 145 |
-
def __setitem__(self, key, value):
|
| 146 |
-
self._memory.pop(key, None)
|
| 147 |
-
self._active[key] = value
|
| 148 |
-
|
| 149 |
-
def __delitem__(self, key):
|
| 150 |
-
try:
|
| 151 |
-
value = self._active.pop(key)
|
| 152 |
-
except KeyError:
|
| 153 |
-
pass
|
| 154 |
-
else:
|
| 155 |
-
while len(self._memory) >= self._capacity:
|
| 156 |
-
self._memory.popitem(last=False)
|
| 157 |
-
self._memory[key] = value
|
| 158 |
-
|
| 159 |
-
def clear(self):
|
| 160 |
-
self._active.clear()
|
| 161 |
-
self._memory.clear()
|
| 162 |
-
|
| 163 |
-
|
| 164 |
-
class UnboundedMemo(dict):
|
| 165 |
-
"""
|
| 166 |
-
A memoizing mapping that retains all deleted items
|
| 167 |
-
"""
|
| 168 |
-
|
| 169 |
-
def __delitem__(self, key):
|
| 170 |
-
pass
|
| 171 |
-
|
| 172 |
-
|
| 173 |
-
def _escape_regex_range_chars(s: str) -> str:
|
| 174 |
-
# escape these chars: ^-[]
|
| 175 |
-
for c in r"\^-[]":
|
| 176 |
-
s = s.replace(c, _bslash + c)
|
| 177 |
-
s = s.replace("\n", r"\n")
|
| 178 |
-
s = s.replace("\t", r"\t")
|
| 179 |
-
return str(s)
|
| 180 |
-
|
| 181 |
-
|
| 182 |
-
def _collapse_string_to_ranges(
|
| 183 |
-
s: Union[str, Iterable[str]], re_escape: bool = True
|
| 184 |
-
) -> str:
|
| 185 |
-
def is_consecutive(c):
|
| 186 |
-
c_int = ord(c)
|
| 187 |
-
is_consecutive.prev, prev = c_int, is_consecutive.prev
|
| 188 |
-
if c_int - prev > 1:
|
| 189 |
-
is_consecutive.value = next(is_consecutive.counter)
|
| 190 |
-
return is_consecutive.value
|
| 191 |
-
|
| 192 |
-
is_consecutive.prev = 0
|
| 193 |
-
is_consecutive.counter = itertools.count()
|
| 194 |
-
is_consecutive.value = -1
|
| 195 |
-
|
| 196 |
-
def escape_re_range_char(c):
|
| 197 |
-
return "\\" + c if c in r"\^-][" else c
|
| 198 |
-
|
| 199 |
-
def no_escape_re_range_char(c):
|
| 200 |
-
return c
|
| 201 |
-
|
| 202 |
-
if not re_escape:
|
| 203 |
-
escape_re_range_char = no_escape_re_range_char
|
| 204 |
-
|
| 205 |
-
ret = []
|
| 206 |
-
s = "".join(sorted(set(s)))
|
| 207 |
-
if len(s) > 3:
|
| 208 |
-
for _, chars in itertools.groupby(s, key=is_consecutive):
|
| 209 |
-
first = last = next(chars)
|
| 210 |
-
last = collections.deque(
|
| 211 |
-
itertools.chain(iter([last]), chars), maxlen=1
|
| 212 |
-
).pop()
|
| 213 |
-
if first == last:
|
| 214 |
-
ret.append(escape_re_range_char(first))
|
| 215 |
-
else:
|
| 216 |
-
sep = "" if ord(last) == ord(first) + 1 else "-"
|
| 217 |
-
ret.append(
|
| 218 |
-
"{}{}{}".format(
|
| 219 |
-
escape_re_range_char(first), sep, escape_re_range_char(last)
|
| 220 |
-
)
|
| 221 |
-
)
|
| 222 |
-
else:
|
| 223 |
-
ret = [escape_re_range_char(c) for c in s]
|
| 224 |
-
|
| 225 |
-
return "".join(ret)
|
| 226 |
-
|
| 227 |
-
|
| 228 |
-
def _flatten(ll: list) -> list:
|
| 229 |
-
ret = []
|
| 230 |
-
for i in ll:
|
| 231 |
-
if isinstance(i, list):
|
| 232 |
-
ret.extend(_flatten(i))
|
| 233 |
-
else:
|
| 234 |
-
ret.append(i)
|
| 235 |
-
return ret
|
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|
spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/dev/packaging/README.md
DELETED
|
@@ -1,17 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
## To build a cu101 wheel for release:
|
| 3 |
-
|
| 4 |
-
```
|
| 5 |
-
$ nvidia-docker run -it --storage-opt "size=20GB" --name pt pytorch/manylinux-cuda101
|
| 6 |
-
# inside the container:
|
| 7 |
-
# git clone https://github.com/facebookresearch/detectron2/
|
| 8 |
-
# cd detectron2
|
| 9 |
-
# export CU_VERSION=cu101 D2_VERSION_SUFFIX= PYTHON_VERSION=3.7 PYTORCH_VERSION=1.4
|
| 10 |
-
# ./dev/packaging/build_wheel.sh
|
| 11 |
-
```
|
| 12 |
-
|
| 13 |
-
## To build all wheels for `CUDA {9.2,10.0,10.1}` x `Python {3.6,3.7,3.8}`:
|
| 14 |
-
```
|
| 15 |
-
./dev/packaging/build_all_wheels.sh
|
| 16 |
-
./dev/packaging/gen_wheel_index.sh /path/to/wheels
|
| 17 |
-
```
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|
spaces/CVPR/GFPGAN-example/inference_gfpgan.py
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| 1 |
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import argparse
|
| 2 |
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import cv2
|
| 3 |
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import glob
|
| 4 |
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import numpy as np
|
| 5 |
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import os
|
| 6 |
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import torch
|
| 7 |
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from basicsr.utils import imwrite
|
| 8 |
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| 9 |
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from gfpgan import GFPGANer
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| 10 |
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| 11 |
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|
| 12 |
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def main():
|
| 13 |
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"""Inference demo for GFPGAN.
|
| 14 |
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"""
|
| 15 |
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parser = argparse.ArgumentParser()
|
| 16 |
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parser.add_argument('--upscale', type=int, default=2, help='The final upsampling scale of the image')
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| 17 |
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parser.add_argument('--arch', type=str, default='clean', help='The GFPGAN architecture. Option: clean | original')
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| 18 |
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parser.add_argument('--channel', type=int, default=2, help='Channel multiplier for large networks of StyleGAN2')
|
| 19 |
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parser.add_argument('--model_path', type=str, default='experiments/pretrained_models/GFPGANCleanv1-NoCE-C2.pth')
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| 20 |
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parser.add_argument('--bg_upsampler', type=str, default='realesrgan', help='background upsampler')
|
| 21 |
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parser.add_argument(
|
| 22 |
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'--bg_tile', type=int, default=400, help='Tile size for background sampler, 0 for no tile during testing')
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| 23 |
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parser.add_argument('--test_path', type=str, default='inputs/whole_imgs', help='Input folder')
|
| 24 |
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parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces')
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| 25 |
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parser.add_argument('--only_center_face', action='store_true', help='Only restore the center face')
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| 26 |
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parser.add_argument('--aligned', action='store_true', help='Input are aligned faces')
|
| 27 |
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parser.add_argument('--paste_back', action='store_false', help='Paste the restored faces back to images')
|
| 28 |
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parser.add_argument('--save_root', type=str, default='results', help='Path to save root')
|
| 29 |
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parser.add_argument(
|
| 30 |
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'--ext',
|
| 31 |
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type=str,
|
| 32 |
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default='auto',
|
| 33 |
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help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
|
| 34 |
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args = parser.parse_args()
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| 35 |
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| 36 |
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args = parser.parse_args()
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| 37 |
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if args.test_path.endswith('/'):
|
| 38 |
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args.test_path = args.test_path[:-1]
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| 39 |
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os.makedirs(args.save_root, exist_ok=True)
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| 40 |
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| 41 |
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# background upsampler
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| 42 |
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if args.bg_upsampler == 'realesrgan':
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| 43 |
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if not torch.cuda.is_available(): # CPU
|
| 44 |
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import warnings
|
| 45 |
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warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. '
|
| 46 |
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'If you really want to use it, please modify the corresponding codes.')
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| 47 |
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bg_upsampler = None
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| 48 |
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else:
|
| 49 |
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from basicsr.archs.rrdbnet_arch import RRDBNet
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| 50 |
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from realesrgan import RealESRGANer
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| 51 |
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
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| 52 |
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bg_upsampler = RealESRGANer(
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| 53 |
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scale=2,
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| 54 |
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model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
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| 55 |
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model=model,
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| 56 |
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tile=args.bg_tile,
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| 57 |
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tile_pad=10,
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| 58 |
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pre_pad=0,
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| 59 |
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half=True) # need to set False in CPU mode
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| 60 |
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else:
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| 61 |
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bg_upsampler = None
|
| 62 |
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# set up GFPGAN restorer
|
| 63 |
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restorer = GFPGANer(
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| 64 |
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model_path=args.model_path,
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| 65 |
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upscale=args.upscale,
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| 66 |
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arch=args.arch,
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| 67 |
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channel_multiplier=args.channel,
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| 68 |
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bg_upsampler=bg_upsampler)
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| 69 |
-
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| 70 |
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img_list = sorted(glob.glob(os.path.join(args.test_path, '*')))
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| 71 |
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for img_path in img_list:
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| 72 |
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# read image
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| 73 |
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img_name = os.path.basename(img_path)
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| 74 |
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print(f'Processing {img_name} ...')
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| 75 |
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basename, ext = os.path.splitext(img_name)
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| 76 |
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input_img = cv2.imread(img_path, cv2.IMREAD_COLOR)
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| 77 |
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| 78 |
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# restore faces and background if necessary
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| 79 |
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cropped_faces, restored_faces, restored_img = restorer.enhance(
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| 80 |
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input_img, has_aligned=args.aligned, only_center_face=args.only_center_face, paste_back=args.paste_back)
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| 81 |
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| 82 |
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# save faces
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| 83 |
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for idx, (cropped_face, restored_face) in enumerate(zip(cropped_faces, restored_faces)):
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| 84 |
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# save cropped face
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| 85 |
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save_crop_path = os.path.join(args.save_root, 'cropped_faces', f'{basename}_{idx:02d}.png')
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| 86 |
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imwrite(cropped_face, save_crop_path)
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| 87 |
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# save restored face
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| 88 |
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if args.suffix is not None:
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| 89 |
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save_face_name = f'{basename}_{idx:02d}_{args.suffix}.png'
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| 90 |
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else:
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| 91 |
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save_face_name = f'{basename}_{idx:02d}.png'
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| 92 |
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save_restore_path = os.path.join(args.save_root, 'restored_faces', save_face_name)
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| 93 |
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imwrite(restored_face, save_restore_path)
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| 94 |
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# save comparison image
|
| 95 |
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cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
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| 96 |
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imwrite(cmp_img, os.path.join(args.save_root, 'cmp', f'{basename}_{idx:02d}.png'))
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| 97 |
-
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| 98 |
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# save restored img
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| 99 |
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if restored_img is not None:
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| 100 |
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if args.ext == 'auto':
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| 101 |
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extension = ext[1:]
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| 102 |
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else:
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| 103 |
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extension = args.ext
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| 104 |
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| 105 |
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if args.suffix is not None:
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| 106 |
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save_restore_path = os.path.join(args.save_root, 'restored_imgs',
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| 107 |
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f'{basename}_{args.suffix}.{extension}')
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| 108 |
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else:
|
| 109 |
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save_restore_path = os.path.join(args.save_root, 'restored_imgs', f'{basename}.{extension}')
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| 110 |
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imwrite(restored_img, save_restore_path)
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| 111 |
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| 112 |
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print(f'Results are in the [{args.save_root}] folder.')
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| 113 |
-
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| 114 |
-
|
| 115 |
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if __name__ == '__main__':
|
| 116 |
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main()
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spaces/CVPR/LIVE/thrust/thrust/detail/copy_if.h
DELETED
|
@@ -1,75 +0,0 @@
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|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2008-2013 NVIDIA Corporation
|
| 3 |
-
*
|
| 4 |
-
* Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
* you may not use this file except in compliance with the License.
|
| 6 |
-
* You may obtain a copy of the License at
|
| 7 |
-
*
|
| 8 |
-
* http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
*
|
| 10 |
-
* Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
* distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
* See the License for the specific language governing permissions and
|
| 14 |
-
* limitations under the License.
|
| 15 |
-
*/
|
| 16 |
-
|
| 17 |
-
#pragma once
|
| 18 |
-
|
| 19 |
-
#include <thrust/detail/config.h>
|
| 20 |
-
#include <thrust/detail/execution_policy.h>
|
| 21 |
-
|
| 22 |
-
namespace thrust
|
| 23 |
-
{
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
template<typename DerivedPolicy,
|
| 27 |
-
typename InputIterator,
|
| 28 |
-
typename OutputIterator,
|
| 29 |
-
typename Predicate>
|
| 30 |
-
__host__ __device__
|
| 31 |
-
OutputIterator copy_if(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 32 |
-
InputIterator first,
|
| 33 |
-
InputIterator last,
|
| 34 |
-
OutputIterator result,
|
| 35 |
-
Predicate pred);
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
template<typename DerivedPolicy,
|
| 39 |
-
typename InputIterator1,
|
| 40 |
-
typename InputIterator2,
|
| 41 |
-
typename OutputIterator,
|
| 42 |
-
typename Predicate>
|
| 43 |
-
__host__ __device__
|
| 44 |
-
OutputIterator copy_if(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
|
| 45 |
-
InputIterator1 first,
|
| 46 |
-
InputIterator1 last,
|
| 47 |
-
InputIterator2 stencil,
|
| 48 |
-
OutputIterator result,
|
| 49 |
-
Predicate pred);
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
template<typename InputIterator,
|
| 53 |
-
typename OutputIterator,
|
| 54 |
-
typename Predicate>
|
| 55 |
-
OutputIterator copy_if(InputIterator first,
|
| 56 |
-
InputIterator last,
|
| 57 |
-
OutputIterator result,
|
| 58 |
-
Predicate pred);
|
| 59 |
-
|
| 60 |
-
|
| 61 |
-
template<typename InputIterator1,
|
| 62 |
-
typename InputIterator2,
|
| 63 |
-
typename OutputIterator,
|
| 64 |
-
typename Predicate>
|
| 65 |
-
OutputIterator copy_if(InputIterator1 first,
|
| 66 |
-
InputIterator1 last,
|
| 67 |
-
InputIterator2 stencil,
|
| 68 |
-
OutputIterator result,
|
| 69 |
-
Predicate pred);
|
| 70 |
-
|
| 71 |
-
|
| 72 |
-
} // end thrust
|
| 73 |
-
|
| 74 |
-
#include <thrust/detail/copy_if.inl>
|
| 75 |
-
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spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/sequence.h
DELETED
|
@@ -1,44 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2008-2013 NVIDIA Corporation
|
| 3 |
-
*
|
| 4 |
-
* Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
* you may not use this file except in compliance with the License.
|
| 6 |
-
* You may obtain a fill of the License at
|
| 7 |
-
*
|
| 8 |
-
* http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
*
|
| 10 |
-
* Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
* distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
* See the License for the specific language governing permissions and
|
| 14 |
-
* limitations under the License.
|
| 15 |
-
*/
|
| 16 |
-
|
| 17 |
-
#pragma once
|
| 18 |
-
|
| 19 |
-
#include <thrust/detail/config.h>
|
| 20 |
-
|
| 21 |
-
// the purpose of this header is to #include the sequence.h header
|
| 22 |
-
// of the sequential, host, and device systems. It should be #included in any
|
| 23 |
-
// code which uses adl to dispatch sequence
|
| 24 |
-
|
| 25 |
-
#include <thrust/system/detail/sequential/sequence.h>
|
| 26 |
-
|
| 27 |
-
// SCons can't see through the #defines below to figure out what this header
|
| 28 |
-
// includes, so we fake it out by specifying all possible files we might end up
|
| 29 |
-
// including inside an #if 0.
|
| 30 |
-
#if 0
|
| 31 |
-
#include <thrust/system/cpp/detail/sequence.h>
|
| 32 |
-
#include <thrust/system/cuda/detail/sequence.h>
|
| 33 |
-
#include <thrust/system/omp/detail/sequence.h>
|
| 34 |
-
#include <thrust/system/tbb/detail/sequence.h>
|
| 35 |
-
#endif
|
| 36 |
-
|
| 37 |
-
#define __THRUST_HOST_SYSTEM_SEQUENCE_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/sequence.h>
|
| 38 |
-
#include __THRUST_HOST_SYSTEM_SEQUENCE_HEADER
|
| 39 |
-
#undef __THRUST_HOST_SYSTEM_SEQUENCE_HEADER
|
| 40 |
-
|
| 41 |
-
#define __THRUST_DEVICE_SYSTEM_SEQUENCE_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/sequence.h>
|
| 42 |
-
#include __THRUST_DEVICE_SYSTEM_SEQUENCE_HEADER
|
| 43 |
-
#undef __THRUST_DEVICE_SYSTEM_SEQUENCE_HEADER
|
| 44 |
-
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spaces/CVPR/LIVE/thrust/thrust/system/omp/detail/temporary_buffer.h
DELETED
|
@@ -1,22 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2008-2013 NVIDIA Corporation
|
| 3 |
-
*
|
| 4 |
-
* Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
* you may not use this file except in compliance with the License.
|
| 6 |
-
* You may obtain a copy of the License at
|
| 7 |
-
*
|
| 8 |
-
* http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
*
|
| 10 |
-
* Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
* distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
* See the License for the specific language governing permissions and
|
| 14 |
-
* limitations under the License.
|
| 15 |
-
*/
|
| 16 |
-
|
| 17 |
-
#pragma once
|
| 18 |
-
|
| 19 |
-
#include <thrust/detail/config.h>
|
| 20 |
-
|
| 21 |
-
// this system has no special temporary buffer functions
|
| 22 |
-
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spaces/CVPR/LIVE/thrust/thrust/system_error.h
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| 1 |
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/*
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| 2 |
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* Copyright 2008-2013 NVIDIA Corporation
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| 3 |
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*
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| 4 |
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* Licensed under the Apache License, Version 2.0 (the "License");
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| 5 |
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* you may not use this file except in compliance with the License.
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| 6 |
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* You may obtain a copy of the License at
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| 7 |
-
*
|
| 8 |
-
* http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
*
|
| 10 |
-
* Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
* distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
* See the License for the specific language governing permissions and
|
| 14 |
-
* limitations under the License.
|
| 15 |
-
*/
|
| 16 |
-
|
| 17 |
-
/*! \file thrust/system_error.h
|
| 18 |
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* \brief System diagnostics
|
| 19 |
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*/
|
| 20 |
-
|
| 21 |
-
#pragma once
|
| 22 |
-
|
| 23 |
-
#include <thrust/detail/config.h>
|
| 24 |
-
|
| 25 |
-
namespace thrust
|
| 26 |
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{
|
| 27 |
-
|
| 28 |
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/*! \addtogroup system
|
| 29 |
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* \{
|
| 30 |
-
*/
|
| 31 |
-
|
| 32 |
-
/*! \namespace thrust::system
|
| 33 |
-
* \brief \p thrust::system is the namespace which contains functionality for manipulating
|
| 34 |
-
* memory specific to one of Thrust's backend systems. It also contains functionality
|
| 35 |
-
* for reporting error conditions originating from the operating system or other
|
| 36 |
-
* low-level application program interfaces such as the CUDA runtime.
|
| 37 |
-
* They are provided in a separate namespace for import convenience but are
|
| 38 |
-
* also aliased in the top-level \p thrust namespace for easy access.
|
| 39 |
-
*/
|
| 40 |
-
namespace system
|
| 41 |
-
{
|
| 42 |
-
} // end system
|
| 43 |
-
|
| 44 |
-
/*! \} // end system
|
| 45 |
-
*/
|
| 46 |
-
|
| 47 |
-
} // end thrust
|
| 48 |
-
|
| 49 |
-
#include <thrust/system/error_code.h>
|
| 50 |
-
#include <thrust/system/system_error.h>
|
| 51 |
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spaces/CVPR/SPOTER_Sign_Language_Recognition/app.py
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|
@@ -1,181 +0,0 @@
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|
| 1 |
-
import copy
|
| 2 |
-
|
| 3 |
-
import torch
|
| 4 |
-
import numpy as np
|
| 5 |
-
import gradio as gr
|
| 6 |
-
from spoter_mod.skeleton_extractor import obtain_pose_data
|
| 7 |
-
from spoter_mod.normalization.body_normalization import normalize_single_dict as normalize_single_body_dict, BODY_IDENTIFIERS
|
| 8 |
-
from spoter_mod.normalization.hand_normalization import normalize_single_dict as normalize_single_hand_dict, HAND_IDENTIFIERS
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
model = torch.load("spoter-checkpoint.pth", map_location=torch.device('cpu'))
|
| 12 |
-
model.train(False)
|
| 13 |
-
|
| 14 |
-
HAND_IDENTIFIERS = [id + "_Left" for id in HAND_IDENTIFIERS] + [id + "_Right" for id in HAND_IDENTIFIERS]
|
| 15 |
-
GLOSS = ['book', 'drink', 'computer', 'before', 'chair', 'go', 'clothes', 'who', 'candy', 'cousin', 'deaf', 'fine',
|
| 16 |
-
'help', 'no', 'thin', 'walk', 'year', 'yes', 'all', 'black', 'cool', 'finish', 'hot', 'like', 'many', 'mother',
|
| 17 |
-
'now', 'orange', 'table', 'thanksgiving', 'what', 'woman', 'bed', 'blue', 'bowling', 'can', 'dog', 'family',
|
| 18 |
-
'fish', 'graduate', 'hat', 'hearing', 'kiss', 'language', 'later', 'man', 'shirt', 'study', 'tall', 'white',
|
| 19 |
-
'wrong', 'accident', 'apple', 'bird', 'change', 'color', 'corn', 'cow', 'dance', 'dark', 'doctor', 'eat',
|
| 20 |
-
'enjoy', 'forget', 'give', 'last', 'meet', 'pink', 'pizza', 'play', 'school', 'secretary', 'short', 'time',
|
| 21 |
-
'want', 'work', 'africa', 'basketball', 'birthday', 'brown', 'but', 'cheat', 'city', 'cook', 'decide', 'full',
|
| 22 |
-
'how', 'jacket', 'letter', 'medicine', 'need', 'paint', 'paper', 'pull', 'purple', 'right', 'same', 'son',
|
| 23 |
-
'tell', 'thursday']
|
| 24 |
-
|
| 25 |
-
device = torch.device("cpu")
|
| 26 |
-
if torch.cuda.is_available():
|
| 27 |
-
device = torch.device("cuda")
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
def tensor_to_dictionary(landmarks_tensor: torch.Tensor) -> dict:
|
| 31 |
-
|
| 32 |
-
data_array = landmarks_tensor.numpy()
|
| 33 |
-
output = {}
|
| 34 |
-
|
| 35 |
-
for landmark_index, identifier in enumerate(BODY_IDENTIFIERS + HAND_IDENTIFIERS):
|
| 36 |
-
output[identifier] = data_array[:, landmark_index]
|
| 37 |
-
|
| 38 |
-
return output
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
def dictionary_to_tensor(landmarks_dict: dict) -> torch.Tensor:
|
| 42 |
-
|
| 43 |
-
output = np.empty(shape=(len(landmarks_dict["leftEar"]), len(BODY_IDENTIFIERS + HAND_IDENTIFIERS), 2))
|
| 44 |
-
|
| 45 |
-
for landmark_index, identifier in enumerate(BODY_IDENTIFIERS + HAND_IDENTIFIERS):
|
| 46 |
-
output[:, landmark_index, 0] = [frame[0] for frame in landmarks_dict[identifier]]
|
| 47 |
-
output[:, landmark_index, 1] = [frame[1] for frame in landmarks_dict[identifier]]
|
| 48 |
-
|
| 49 |
-
return torch.from_numpy(output)
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
def greet(label, video0, video1):
|
| 53 |
-
|
| 54 |
-
if label == "Webcam":
|
| 55 |
-
video = video0
|
| 56 |
-
|
| 57 |
-
elif label == "Video":
|
| 58 |
-
video = video1
|
| 59 |
-
|
| 60 |
-
elif label == "X":
|
| 61 |
-
return {"A": 0.8, "B": 0.1, "C": 0.1}
|
| 62 |
-
|
| 63 |
-
else:
|
| 64 |
-
return {}
|
| 65 |
-
|
| 66 |
-
data = obtain_pose_data(video)
|
| 67 |
-
|
| 68 |
-
depth_map = np.empty(shape=(len(data.data_hub["nose_X"]), len(BODY_IDENTIFIERS + HAND_IDENTIFIERS), 2))
|
| 69 |
-
|
| 70 |
-
for index, identifier in enumerate(BODY_IDENTIFIERS + HAND_IDENTIFIERS):
|
| 71 |
-
depth_map[:, index, 0] = data.data_hub[identifier + "_X"]
|
| 72 |
-
depth_map[:, index, 1] = data.data_hub[identifier + "_Y"]
|
| 73 |
-
|
| 74 |
-
depth_map = torch.from_numpy(np.copy(depth_map))
|
| 75 |
-
|
| 76 |
-
depth_map = tensor_to_dictionary(depth_map)
|
| 77 |
-
|
| 78 |
-
keys = copy.copy(list(depth_map.keys()))
|
| 79 |
-
for key in keys:
|
| 80 |
-
data = depth_map[key]
|
| 81 |
-
del depth_map[key]
|
| 82 |
-
depth_map[key.replace("_Left", "_0").replace("_Right", "_1")] = data
|
| 83 |
-
|
| 84 |
-
depth_map = normalize_single_body_dict(depth_map)
|
| 85 |
-
depth_map = normalize_single_hand_dict(depth_map)
|
| 86 |
-
|
| 87 |
-
keys = copy.copy(list(depth_map.keys()))
|
| 88 |
-
for key in keys:
|
| 89 |
-
data = depth_map[key]
|
| 90 |
-
del depth_map[key]
|
| 91 |
-
depth_map[key.replace("_0", "_Left").replace("_1", "_Right")] = data
|
| 92 |
-
|
| 93 |
-
depth_map = dictionary_to_tensor(depth_map)
|
| 94 |
-
|
| 95 |
-
depth_map = depth_map - 0.5
|
| 96 |
-
|
| 97 |
-
inputs = depth_map.squeeze(0).to(device)
|
| 98 |
-
outputs = model(inputs).expand(1, -1, -1)
|
| 99 |
-
results = torch.nn.functional.softmax(outputs, dim=2).detach().numpy()[0, 0]
|
| 100 |
-
|
| 101 |
-
results = {GLOSS[i]: float(results[i]) for i in range(100)}
|
| 102 |
-
|
| 103 |
-
return results
|
| 104 |
-
|
| 105 |
-
|
| 106 |
-
label = gr.outputs.Label(num_top_classes=5, label="Top class probabilities")
|
| 107 |
-
demo = gr.Interface(fn=greet, inputs=[gr.Dropdown(["Webcam", "Video"], label="Please select the input type:", type="value"), gr.Video(source="webcam", label="Webcam recording", type="mp4"), gr.Video(source="upload", label="Video upload", type="mp4")], outputs=label,
|
| 108 |
-
title="🤟 SPOTER Sign language recognition",
|
| 109 |
-
description="""Current user interfaces are not accessible for D/deaf and hard-of-hearing users, whose natural communication medium is sign language. We work on AI systems for sign language to come closer to sign-driven technology and empower accessible apps, websites, and video conferencing platforms.
|
| 110 |
-
Try out our recent model for sign language recognition right in your browser! The model below takes a video of a single sign in the American Sign Language at the input and provides you with probabilities of the lemmas (equivalent to words in natural language).
|
| 111 |
-
### Our work at CVPR
|
| 112 |
-
Our efforts on lightweight and efficient models for sign language recognition were first introduced at WACV with our SPOTER paper. We now presented a work-in-progress follow-up here at CVPR's AVA workshop. Be sure to check our work and code below:
|
| 113 |
-
- **WACV2022** - Original SPOTER paper - [Paper](https://openaccess.thecvf.com/content/WACV2022W/HADCV/papers/Bohacek_Sign_Pose-Based_Transformer_for_Word-Level_Sign_Language_Recognition_WACVW_2022_paper.pdf), [Code](https://github.com/matyasbohacek/spoter)
|
| 114 |
-
- **CVPR2022 (AVA Worshop)** - Follow-up WIP – [Extended Abstract](https://drive.google.com/file/d/1Szbhi7ZwZ6VAWAcGcDDU6qV9Uj9xnDsS/view?usp=sharing), [Poster](https://drive.google.com/file/d/1_xvmTNbLjTrx6psKdsLkufAtfmI5wfbF/view?usp=sharing)
|
| 115 |
-
### How to sign?
|
| 116 |
-
The model wrapped in this demo was trained on [WLASL100](https://dxli94.github.io/WLASL/), so it only knows selected ASL vocabulary. Take a look at these tutorial video examples (this is how you sign [computer](https://www.handspeak.com/word/search/index.php?id=449), [work](https://www.handspeak.com/word/search/index.php?id=2423), or [time](https://www.handspeak.com/word/search/index.php?id=2223)), try to replicate them yourself, and have them recognized using the webcam capture below. Have fun!
|
| 117 |
-
> The demo can analyze webcam recordings or your uploaded videos. Before you hit Submit, **don't forget to select the input source in the dropdown first**.""",
|
| 118 |
-
article="This is joint work of [Matyas Bohacek](https://scholar.google.cz/citations?user=wDy1xBwAAAAJ) and [Zhuo Cao](https://www.linkedin.com/in/zhuo-cao-b0787a1aa/?originalSubdomain=hk). For more info, visit [our website](https://www.signlanguagerecognition.com). To contact us, drop an e-mail [here](mailto:[email protected]).",
|
| 119 |
-
css="""
|
| 120 |
-
@font-face {
|
| 121 |
-
font-family: Graphik;
|
| 122 |
-
font-weight: regular;
|
| 123 |
-
src: url("https://www.signlanguagerecognition.com/supplementary/GraphikRegular.otf") format("opentype");
|
| 124 |
-
}
|
| 125 |
-
|
| 126 |
-
@font-face {
|
| 127 |
-
font-family: Graphik;
|
| 128 |
-
font-weight: bold;
|
| 129 |
-
src: url("https://www.signlanguagerecognition.com/supplementary/GraphikBold.otf") format("opentype");
|
| 130 |
-
}
|
| 131 |
-
|
| 132 |
-
@font-face {
|
| 133 |
-
font-family: MonumentExpanded;
|
| 134 |
-
font-weight: regular;
|
| 135 |
-
src: url("https://www.signlanguagerecognition.com/supplementary/MonumentExtended-Regular.otf") format("opentype");
|
| 136 |
-
}
|
| 137 |
-
|
| 138 |
-
@font-face {
|
| 139 |
-
font-family: MonumentExpanded;
|
| 140 |
-
font-weight: bold;
|
| 141 |
-
src: url("https://www.signlanguagerecognition.com/supplementary/MonumentExtended-Bold.otf") format("opentype");
|
| 142 |
-
}
|
| 143 |
-
|
| 144 |
-
html {
|
| 145 |
-
font-family: "Graphik";
|
| 146 |
-
}
|
| 147 |
-
|
| 148 |
-
h1 {
|
| 149 |
-
font-family: "MonumentExpanded";
|
| 150 |
-
}
|
| 151 |
-
|
| 152 |
-
#12 {
|
| 153 |
-
- background-image: linear-gradient(to left, #61D836, #6CB346) !important;
|
| 154 |
-
background-color: #61D836 !important;
|
| 155 |
-
}
|
| 156 |
-
|
| 157 |
-
#12:hover {
|
| 158 |
-
- background-image: linear-gradient(to left, #61D836, #6CB346) !important;
|
| 159 |
-
background-color: #6CB346 !important;
|
| 160 |
-
border: 0 !important;
|
| 161 |
-
border-color: 0 !important;
|
| 162 |
-
}
|
| 163 |
-
|
| 164 |
-
.dark .gr-button-primary {
|
| 165 |
-
--tw-gradient-from: #61D836;
|
| 166 |
-
--tw-gradient-to: #6CB346;
|
| 167 |
-
border: 0 !important;
|
| 168 |
-
border-color: 0 !important;
|
| 169 |
-
}
|
| 170 |
-
|
| 171 |
-
.dark .gr-button-primary:hover {
|
| 172 |
-
--tw-gradient-from: #64A642;
|
| 173 |
-
--tw-gradient-to: #58933B;
|
| 174 |
-
border: 0 !important;
|
| 175 |
-
border-color: 0 !important;
|
| 176 |
-
}
|
| 177 |
-
""",
|
| 178 |
-
cache_examples=True
|
| 179 |
-
)
|
| 180 |
-
|
| 181 |
-
demo.launch(debug=True)
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spaces/CVPR/lama-example/fetch_data/places_challenge_train_download.sh
DELETED
|
@@ -1,14 +0,0 @@
|
|
| 1 |
-
mkdir places_challenge_dataset
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
declare -a TARPARTS
|
| 5 |
-
for i in {a..z}
|
| 6 |
-
do
|
| 7 |
-
TARPARTS[${#TARPARTS[@]}]="http://data.csail.mit.edu/places/places365/train_large_split/${i}.tar"
|
| 8 |
-
done
|
| 9 |
-
ls
|
| 10 |
-
printf "%s\n" "${TARPARTS[@]}" > places_challenge_dataset/places365_train.txt
|
| 11 |
-
|
| 12 |
-
cd places_challenge_dataset/
|
| 13 |
-
xargs -a places365_train.txt -n 1 -P 8 wget [...]
|
| 14 |
-
ls *.tar | xargs -i tar xvf {}
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spaces/CVPR/regionclip-demo/detectron2/utils/env.py
DELETED
|
@@ -1,170 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates.
|
| 2 |
-
import importlib
|
| 3 |
-
import importlib.util
|
| 4 |
-
import logging
|
| 5 |
-
import numpy as np
|
| 6 |
-
import os
|
| 7 |
-
import random
|
| 8 |
-
import sys
|
| 9 |
-
from datetime import datetime
|
| 10 |
-
import torch
|
| 11 |
-
|
| 12 |
-
__all__ = ["seed_all_rng"]
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
TORCH_VERSION = tuple(int(x) for x in torch.__version__.split(".")[:2])
|
| 16 |
-
"""
|
| 17 |
-
PyTorch version as a tuple of 2 ints. Useful for comparison.
|
| 18 |
-
"""
|
| 19 |
-
|
| 20 |
-
|
| 21 |
-
DOC_BUILDING = os.getenv("_DOC_BUILDING", False) # set in docs/conf.py
|
| 22 |
-
"""
|
| 23 |
-
Whether we're building documentation.
|
| 24 |
-
"""
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
def seed_all_rng(seed=None):
|
| 28 |
-
"""
|
| 29 |
-
Set the random seed for the RNG in torch, numpy and python.
|
| 30 |
-
|
| 31 |
-
Args:
|
| 32 |
-
seed (int): if None, will use a strong random seed.
|
| 33 |
-
"""
|
| 34 |
-
if seed is None:
|
| 35 |
-
seed = (
|
| 36 |
-
os.getpid()
|
| 37 |
-
+ int(datetime.now().strftime("%S%f"))
|
| 38 |
-
+ int.from_bytes(os.urandom(2), "big")
|
| 39 |
-
)
|
| 40 |
-
logger = logging.getLogger(__name__)
|
| 41 |
-
logger.info("Using a generated random seed {}".format(seed))
|
| 42 |
-
np.random.seed(seed)
|
| 43 |
-
torch.manual_seed(seed)
|
| 44 |
-
random.seed(seed)
|
| 45 |
-
os.environ["PYTHONHASHSEED"] = str(seed)
|
| 46 |
-
|
| 47 |
-
|
| 48 |
-
# from https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path
|
| 49 |
-
def _import_file(module_name, file_path, make_importable=False):
|
| 50 |
-
spec = importlib.util.spec_from_file_location(module_name, file_path)
|
| 51 |
-
module = importlib.util.module_from_spec(spec)
|
| 52 |
-
spec.loader.exec_module(module)
|
| 53 |
-
if make_importable:
|
| 54 |
-
sys.modules[module_name] = module
|
| 55 |
-
return module
|
| 56 |
-
|
| 57 |
-
|
| 58 |
-
def _configure_libraries():
|
| 59 |
-
"""
|
| 60 |
-
Configurations for some libraries.
|
| 61 |
-
"""
|
| 62 |
-
# An environment option to disable `import cv2` globally,
|
| 63 |
-
# in case it leads to negative performance impact
|
| 64 |
-
disable_cv2 = int(os.environ.get("DETECTRON2_DISABLE_CV2", False))
|
| 65 |
-
if disable_cv2:
|
| 66 |
-
sys.modules["cv2"] = None
|
| 67 |
-
else:
|
| 68 |
-
# Disable opencl in opencv since its interaction with cuda often has negative effects
|
| 69 |
-
# This envvar is supported after OpenCV 3.4.0
|
| 70 |
-
os.environ["OPENCV_OPENCL_RUNTIME"] = "disabled"
|
| 71 |
-
try:
|
| 72 |
-
import cv2
|
| 73 |
-
|
| 74 |
-
if int(cv2.__version__.split(".")[0]) >= 3:
|
| 75 |
-
cv2.ocl.setUseOpenCL(False)
|
| 76 |
-
except ModuleNotFoundError:
|
| 77 |
-
# Other types of ImportError, if happened, should not be ignored.
|
| 78 |
-
# Because a failed opencv import could mess up address space
|
| 79 |
-
# https://github.com/skvark/opencv-python/issues/381
|
| 80 |
-
pass
|
| 81 |
-
|
| 82 |
-
def get_version(module, digit=2):
|
| 83 |
-
return tuple(map(int, module.__version__.split(".")[:digit]))
|
| 84 |
-
|
| 85 |
-
# fmt: off
|
| 86 |
-
assert get_version(torch) >= (1, 4), "Requires torch>=1.4"
|
| 87 |
-
import fvcore
|
| 88 |
-
assert get_version(fvcore, 3) >= (0, 1, 2), "Requires fvcore>=0.1.2"
|
| 89 |
-
import yaml
|
| 90 |
-
assert get_version(yaml) >= (5, 1), "Requires pyyaml>=5.1"
|
| 91 |
-
# fmt: on
|
| 92 |
-
|
| 93 |
-
|
| 94 |
-
_ENV_SETUP_DONE = False
|
| 95 |
-
|
| 96 |
-
|
| 97 |
-
def setup_environment():
|
| 98 |
-
"""Perform environment setup work. The default setup is a no-op, but this
|
| 99 |
-
function allows the user to specify a Python source file or a module in
|
| 100 |
-
the $DETECTRON2_ENV_MODULE environment variable, that performs
|
| 101 |
-
custom setup work that may be necessary to their computing environment.
|
| 102 |
-
"""
|
| 103 |
-
global _ENV_SETUP_DONE
|
| 104 |
-
if _ENV_SETUP_DONE:
|
| 105 |
-
return
|
| 106 |
-
_ENV_SETUP_DONE = True
|
| 107 |
-
|
| 108 |
-
_configure_libraries()
|
| 109 |
-
|
| 110 |
-
custom_module_path = os.environ.get("DETECTRON2_ENV_MODULE")
|
| 111 |
-
|
| 112 |
-
if custom_module_path:
|
| 113 |
-
setup_custom_environment(custom_module_path)
|
| 114 |
-
else:
|
| 115 |
-
# The default setup is a no-op
|
| 116 |
-
pass
|
| 117 |
-
|
| 118 |
-
|
| 119 |
-
def setup_custom_environment(custom_module):
|
| 120 |
-
"""
|
| 121 |
-
Load custom environment setup by importing a Python source file or a
|
| 122 |
-
module, and run the setup function.
|
| 123 |
-
"""
|
| 124 |
-
if custom_module.endswith(".py"):
|
| 125 |
-
module = _import_file("detectron2.utils.env.custom_module", custom_module)
|
| 126 |
-
else:
|
| 127 |
-
module = importlib.import_module(custom_module)
|
| 128 |
-
assert hasattr(module, "setup_environment") and callable(module.setup_environment), (
|
| 129 |
-
"Custom environment module defined in {} does not have the "
|
| 130 |
-
"required callable attribute 'setup_environment'."
|
| 131 |
-
).format(custom_module)
|
| 132 |
-
module.setup_environment()
|
| 133 |
-
|
| 134 |
-
|
| 135 |
-
def fixup_module_metadata(module_name, namespace, keys=None):
|
| 136 |
-
"""
|
| 137 |
-
Fix the __qualname__ of module members to be their exported api name, so
|
| 138 |
-
when they are referenced in docs, sphinx can find them. Reference:
|
| 139 |
-
https://github.com/python-trio/trio/blob/6754c74eacfad9cc5c92d5c24727a2f3b620624e/trio/_util.py#L216-L241
|
| 140 |
-
"""
|
| 141 |
-
if not DOC_BUILDING:
|
| 142 |
-
return
|
| 143 |
-
seen_ids = set()
|
| 144 |
-
|
| 145 |
-
def fix_one(qualname, name, obj):
|
| 146 |
-
# avoid infinite recursion (relevant when using
|
| 147 |
-
# typing.Generic, for example)
|
| 148 |
-
if id(obj) in seen_ids:
|
| 149 |
-
return
|
| 150 |
-
seen_ids.add(id(obj))
|
| 151 |
-
|
| 152 |
-
mod = getattr(obj, "__module__", None)
|
| 153 |
-
if mod is not None and (mod.startswith(module_name) or mod.startswith("fvcore.")):
|
| 154 |
-
obj.__module__ = module_name
|
| 155 |
-
# Modules, unlike everything else in Python, put fully-qualitied
|
| 156 |
-
# names into their __name__ attribute. We check for "." to avoid
|
| 157 |
-
# rewriting these.
|
| 158 |
-
if hasattr(obj, "__name__") and "." not in obj.__name__:
|
| 159 |
-
obj.__name__ = name
|
| 160 |
-
obj.__qualname__ = qualname
|
| 161 |
-
if isinstance(obj, type):
|
| 162 |
-
for attr_name, attr_value in obj.__dict__.items():
|
| 163 |
-
fix_one(objname + "." + attr_name, attr_name, attr_value)
|
| 164 |
-
|
| 165 |
-
if keys is None:
|
| 166 |
-
keys = namespace.keys()
|
| 167 |
-
for objname in keys:
|
| 168 |
-
if not objname.startswith("_"):
|
| 169 |
-
obj = namespace[objname]
|
| 170 |
-
fix_one(objname, objname, obj)
|
|
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|
spaces/CVPR/transfiner/configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ.py
DELETED
|
@@ -1,14 +0,0 @@
|
|
| 1 |
-
from .mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ import (
|
| 2 |
-
dataloader,
|
| 3 |
-
lr_multiplier,
|
| 4 |
-
model,
|
| 5 |
-
optimizer,
|
| 6 |
-
train,
|
| 7 |
-
)
|
| 8 |
-
|
| 9 |
-
train.max_iter *= 4 # 100ep -> 400ep
|
| 10 |
-
|
| 11 |
-
lr_multiplier.scheduler.milestones = [
|
| 12 |
-
milestone * 4 for milestone in lr_multiplier.scheduler.milestones
|
| 13 |
-
]
|
| 14 |
-
lr_multiplier.scheduler.num_updates = train.max_iter
|
|
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|
|
spaces/ChallengeHub/Chinese-LangChain/clc/config.py
DELETED
|
@@ -1,18 +0,0 @@
|
|
| 1 |
-
#!/usr/bin/env python
|
| 2 |
-
# -*- coding:utf-8 _*-
|
| 3 |
-
"""
|
| 4 |
-
@author:quincy qiang
|
| 5 |
-
@license: Apache Licence
|
| 6 |
-
@file: config.py
|
| 7 |
-
@time: 2023/04/17
|
| 8 |
-
@contact: [email protected]
|
| 9 |
-
@software: PyCharm
|
| 10 |
-
@description: coding..
|
| 11 |
-
"""
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
class LangChainCFG:
|
| 15 |
-
llm_model_name = 'THUDM/chatglm-6b-int4-qe' # 本地模型文件 or huggingface远程仓库
|
| 16 |
-
embedding_model_name = 'GanymedeNil/text2vec-large-chinese' # 检索模型文件 or huggingface远程仓库
|
| 17 |
-
vector_store_path = '.'
|
| 18 |
-
docs_path = './docs'
|
|
|
|
|
|
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|
spaces/ChandraMohanNayal/AutoGPT/autogpt/commands/times.py
DELETED
|
@@ -1,10 +0,0 @@
|
|
| 1 |
-
from datetime import datetime
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
def get_datetime() -> str:
|
| 5 |
-
"""Return the current date and time
|
| 6 |
-
|
| 7 |
-
Returns:
|
| 8 |
-
str: The current date and time
|
| 9 |
-
"""
|
| 10 |
-
return "Current date and time: " + datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
|
|
|
|
|
|
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|
|
spaces/ChandraMohanNayal/AutoGPT/autogpt/processing/html.py
DELETED
|
@@ -1,33 +0,0 @@
|
|
| 1 |
-
"""HTML processing functions"""
|
| 2 |
-
from __future__ import annotations
|
| 3 |
-
|
| 4 |
-
from bs4 import BeautifulSoup
|
| 5 |
-
from requests.compat import urljoin
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
def extract_hyperlinks(soup: BeautifulSoup, base_url: str) -> list[tuple[str, str]]:
|
| 9 |
-
"""Extract hyperlinks from a BeautifulSoup object
|
| 10 |
-
|
| 11 |
-
Args:
|
| 12 |
-
soup (BeautifulSoup): The BeautifulSoup object
|
| 13 |
-
base_url (str): The base URL
|
| 14 |
-
|
| 15 |
-
Returns:
|
| 16 |
-
List[Tuple[str, str]]: The extracted hyperlinks
|
| 17 |
-
"""
|
| 18 |
-
return [
|
| 19 |
-
(link.text, urljoin(base_url, link["href"]))
|
| 20 |
-
for link in soup.find_all("a", href=True)
|
| 21 |
-
]
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
def format_hyperlinks(hyperlinks: list[tuple[str, str]]) -> list[str]:
|
| 25 |
-
"""Format hyperlinks to be displayed to the user
|
| 26 |
-
|
| 27 |
-
Args:
|
| 28 |
-
hyperlinks (List[Tuple[str, str]]): The hyperlinks to format
|
| 29 |
-
|
| 30 |
-
Returns:
|
| 31 |
-
List[str]: The formatted hyperlinks
|
| 32 |
-
"""
|
| 33 |
-
return [f"{link_text} ({link_url})" for link_text, link_url in hyperlinks]
|
|
|
|
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|
spaces/Chintan-Donda/KKMS-KSSW-HF/src/data_loader.py
DELETED
|
@@ -1,230 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import re
|
| 3 |
-
import pandas as pd
|
| 4 |
-
from pathlib import Path
|
| 5 |
-
import glob
|
| 6 |
-
|
| 7 |
-
from llama_index import GPTVectorStoreIndex, download_loader, SimpleDirectoryReader, SimpleWebPageReader
|
| 8 |
-
from langchain.document_loaders import PyPDFLoader, TextLoader
|
| 9 |
-
from langchain.agents import initialize_agent, Tool
|
| 10 |
-
from langchain.llms import OpenAI
|
| 11 |
-
from langchain.chains.conversation.memory import ConversationBufferMemory
|
| 12 |
-
from langchain.docstore.document import Document
|
| 13 |
-
|
| 14 |
-
import src.utils as utils
|
| 15 |
-
|
| 16 |
-
import logging
|
| 17 |
-
logger = logging.getLogger(__name__)
|
| 18 |
-
logging.basicConfig(
|
| 19 |
-
format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S"
|
| 20 |
-
)
|
| 21 |
-
|
| 22 |
-
import warnings
|
| 23 |
-
warnings.filterwarnings('ignore')
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
class DATA_LOADER:
|
| 28 |
-
def __init__(self):
|
| 29 |
-
# Instantiate UTILS class object
|
| 30 |
-
self.utils_obj = utils.UTILS()
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
def load_documents_from_urls(self, urls=[], doc_type='urls'):
|
| 34 |
-
url_documents = self.load_document(doc_type=doc_type, urls=urls)
|
| 35 |
-
return url_documents
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
def load_documents_from_pdf(self, doc_filepath='', urls=[], doc_type='pdf'):
|
| 39 |
-
if doc_type == 'pdf':
|
| 40 |
-
pdf_documents = self.load_document(doc_type=doc_type, doc_filepath=doc_filepath)
|
| 41 |
-
elif doc_type == 'online_pdf':
|
| 42 |
-
pdf_documents = self.load_document(doc_type=doc_type, urls=urls)
|
| 43 |
-
return pdf_documents
|
| 44 |
-
|
| 45 |
-
|
| 46 |
-
def load_documents_from_directory(self, doc_filepath='', doc_type='directory'):
|
| 47 |
-
doc_documents = self.load_document(doc_type=doc_type, doc_filepath=doc_filepath)
|
| 48 |
-
return doc_documents
|
| 49 |
-
|
| 50 |
-
|
| 51 |
-
def load_documents_from_text(self, doc_filepath='', doc_type='textfile'):
|
| 52 |
-
text_documents = self.load_document(doc_type=doc_type, doc_filepath=doc_filepath)
|
| 53 |
-
return text_documents
|
| 54 |
-
|
| 55 |
-
|
| 56 |
-
def pdf_loader(self, filepath):
|
| 57 |
-
loader = PyPDFLoader(filepath)
|
| 58 |
-
return loader.load_and_split()
|
| 59 |
-
|
| 60 |
-
|
| 61 |
-
def text_loader(self, filepath):
|
| 62 |
-
loader = TextLoader(filepath)
|
| 63 |
-
return loader.load()
|
| 64 |
-
|
| 65 |
-
|
| 66 |
-
def load_document(self,
|
| 67 |
-
doc_type='pdf',
|
| 68 |
-
doc_filepath='',
|
| 69 |
-
urls=[]
|
| 70 |
-
):
|
| 71 |
-
logger.info(f'Loading {doc_type} in raw format from: {doc_filepath}')
|
| 72 |
-
|
| 73 |
-
documents = []
|
| 74 |
-
|
| 75 |
-
# Validation checks
|
| 76 |
-
if doc_type in ['directory', 'pdf', 'textfile']:
|
| 77 |
-
if not os.path.exists(doc_filepath):
|
| 78 |
-
logger.warning(f"{doc_filepath} does not exist, nothing can be loaded!")
|
| 79 |
-
return documents
|
| 80 |
-
|
| 81 |
-
elif doc_type in ['online_pdf', 'urls']:
|
| 82 |
-
if len(urls) == 0:
|
| 83 |
-
logger.warning(f"URLs list empty, nothing can be loaded!")
|
| 84 |
-
return documents
|
| 85 |
-
|
| 86 |
-
|
| 87 |
-
######### Load documents #########
|
| 88 |
-
# Load PDF
|
| 89 |
-
if doc_type == 'pdf':
|
| 90 |
-
# Load multiple PDFs from directory
|
| 91 |
-
if os.path.isdir(doc_filepath):
|
| 92 |
-
pdfs = glob.glob(f"{doc_filepath}/*.pdf")
|
| 93 |
-
logger.info(f'Total PDF files to load: {len(pdfs)}')
|
| 94 |
-
for pdf in pdfs:
|
| 95 |
-
documents.extend(self.pdf_loader(pdf))
|
| 96 |
-
|
| 97 |
-
# Loading from a single PDF file
|
| 98 |
-
elif os.path.isfile(doc_filepath) and doc_filepath.endswith('.pdf'):
|
| 99 |
-
documents.extend(self.pdf_loader(doc_filepath))
|
| 100 |
-
|
| 101 |
-
# Load PDFs from online (urls). Can read multiple PDFs from multiple URLs in one-shot
|
| 102 |
-
elif doc_type == 'online_pdf':
|
| 103 |
-
logger.info(f'URLs to load Online PDFs are from: {urls}')
|
| 104 |
-
valid_urls = self.utils_obj.validate_url_format(
|
| 105 |
-
urls=urls,
|
| 106 |
-
url_type=doc_type
|
| 107 |
-
)
|
| 108 |
-
for url in valid_urls:
|
| 109 |
-
# Load and split PDF pages per document
|
| 110 |
-
documents.extend(self.pdf_loader(url))
|
| 111 |
-
|
| 112 |
-
# Load data from URLs (can load data from multiple URLs)
|
| 113 |
-
elif doc_type == 'urls':
|
| 114 |
-
logger.info(f'URLs to load data from are: {urls}')
|
| 115 |
-
valid_urls = self.utils_obj.validate_url_format(
|
| 116 |
-
urls=urls,
|
| 117 |
-
url_type=doc_type
|
| 118 |
-
)
|
| 119 |
-
# Load data from URLs
|
| 120 |
-
docs = SimpleWebPageReader(html_to_text=True).load_data(valid_urls)
|
| 121 |
-
docs = [Document(page_content=doc.text) for doc in docs]
|
| 122 |
-
documents.extend(docs)
|
| 123 |
-
|
| 124 |
-
# Load data from text file(s)
|
| 125 |
-
elif doc_type == 'textfile':
|
| 126 |
-
# Load multiple text files from directory
|
| 127 |
-
if os.path.isdir(doc_filepath):
|
| 128 |
-
text_files = glob.glob(f"{doc_filepath}/*.txt")
|
| 129 |
-
logger.info(f'Total text files to load: {len(text_files)}')
|
| 130 |
-
for tf in text_files:
|
| 131 |
-
documents.extend(self.text_loader(tf))
|
| 132 |
-
|
| 133 |
-
# Loading from a single text file
|
| 134 |
-
elif os.path.isfile(doc_filepath) and doc_filepath.endswith('.txt'):
|
| 135 |
-
documents.extend(self.text_loader(doc_filepath))
|
| 136 |
-
|
| 137 |
-
# Load data from files on the local directory (files may be of type .pdf, .txt, .doc, etc.)
|
| 138 |
-
elif doc_type == 'directory':
|
| 139 |
-
# Load multiple PDFs from directory
|
| 140 |
-
if os.path.isdir(doc_filepath):
|
| 141 |
-
documents = SimpleDirectoryReader(
|
| 142 |
-
input_dir=doc_filepath
|
| 143 |
-
).load_data()
|
| 144 |
-
|
| 145 |
-
# Loading from a file
|
| 146 |
-
elif os.path.isfile(doc_filepath):
|
| 147 |
-
documents.extend(SimpleDirectoryReader(
|
| 148 |
-
input_files=[doc_filepath]
|
| 149 |
-
).load_data())
|
| 150 |
-
|
| 151 |
-
# Load data from URLs in Knowledge Base format
|
| 152 |
-
elif doc_type == 'url-kb':
|
| 153 |
-
KnowledgeBaseWebReader = download_loader("KnowledgeBaseWebReader")
|
| 154 |
-
loader = KnowledgeBaseWebReader()
|
| 155 |
-
for url in urls:
|
| 156 |
-
doc = loader.load_data(
|
| 157 |
-
root_url=url,
|
| 158 |
-
link_selectors=['.article-list a', '.article-list a'],
|
| 159 |
-
article_path='/articles',
|
| 160 |
-
body_selector='.article-body',
|
| 161 |
-
title_selector='.article-title',
|
| 162 |
-
subtitle_selector='.article-subtitle',
|
| 163 |
-
)
|
| 164 |
-
documents.extend(doc)
|
| 165 |
-
|
| 166 |
-
# Load data from URLs and create an agent chain using ChatGPT
|
| 167 |
-
elif doc_type == 'url-chatgpt':
|
| 168 |
-
BeautifulSoupWebReader = download_loader("BeautifulSoupWebReader")
|
| 169 |
-
loader = BeautifulSoupWebReader()
|
| 170 |
-
# Load data from URLs
|
| 171 |
-
documents = loader.load_data(urls=urls)
|
| 172 |
-
# Build the Vector database
|
| 173 |
-
index = GPTVectorStoreIndex(documents)
|
| 174 |
-
tools = [
|
| 175 |
-
Tool(
|
| 176 |
-
name="Website Index",
|
| 177 |
-
func=lambda q: index.query(q),
|
| 178 |
-
description=f"Useful when you want answer questions about the text retrieved from websites.",
|
| 179 |
-
),
|
| 180 |
-
]
|
| 181 |
-
|
| 182 |
-
# Call ChatGPT API
|
| 183 |
-
llm = OpenAI(temperature=0) # Keep temperature=0 to search from the given urls only
|
| 184 |
-
memory = ConversationBufferMemory(memory_key="chat_history")
|
| 185 |
-
agent_chain = initialize_agent(
|
| 186 |
-
tools, llm, agent="zero-shot-react-description", memory=memory
|
| 187 |
-
)
|
| 188 |
-
|
| 189 |
-
output = agent_chain.run(input="What language is on this website?")
|
| 190 |
-
|
| 191 |
-
|
| 192 |
-
# Clean documents
|
| 193 |
-
documents = self.clean_documents(documents)
|
| 194 |
-
logger.info(f'{doc_type} in raw format from: {doc_filepath} loaded successfully!')
|
| 195 |
-
return documents
|
| 196 |
-
|
| 197 |
-
|
| 198 |
-
def clean_documents(
|
| 199 |
-
self,
|
| 200 |
-
documents
|
| 201 |
-
):
|
| 202 |
-
cleaned_documents = []
|
| 203 |
-
for document in documents:
|
| 204 |
-
if hasattr(document, 'page_content'):
|
| 205 |
-
document.page_content = self.utils_obj.replace_newlines_and_spaces(document.page_content)
|
| 206 |
-
elif hasattr(document, 'text'):
|
| 207 |
-
document.text = self.utils_obj.replace_newlines_and_spaces(document.text)
|
| 208 |
-
else:
|
| 209 |
-
document = self.utils_obj.replace_newlines_and_spaces(document)
|
| 210 |
-
cleaned_documents.append(document)
|
| 211 |
-
return cleaned_documents
|
| 212 |
-
|
| 213 |
-
|
| 214 |
-
def load_external_links_used_by_FTAs(self,
|
| 215 |
-
sheet_filepath='./data/urls_used_by_ftas/external_links_used_by_FTAs.xlsx'
|
| 216 |
-
):
|
| 217 |
-
xls = pd.ExcelFile(sheet_filepath)
|
| 218 |
-
df = pd.DataFrame(columns=['S.No.', 'Link used for', 'Link type', 'Link'])
|
| 219 |
-
for sheet_name in xls.sheet_names:
|
| 220 |
-
sheet = pd.read_excel(xls, sheet_name)
|
| 221 |
-
if sheet.shape[0] > 0:
|
| 222 |
-
df = pd.concat([df, sheet])
|
| 223 |
-
else:
|
| 224 |
-
logger.info(f'{sheet_name} has no content.')
|
| 225 |
-
|
| 226 |
-
df = df[['Link used for', 'Link type', 'Link']]
|
| 227 |
-
# Clean df
|
| 228 |
-
df = self.utils_obj.clean_df(df)
|
| 229 |
-
logger.info(f'Total links available across all cities: {df.shape[0]}')
|
| 230 |
-
return df
|
|
|
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|
|
spaces/CikeyQI/meme-api/meme_generator/dirs.py
DELETED
|
@@ -1,225 +0,0 @@
|
|
| 1 |
-
# https://github.com/nonebot/plugin-localstore
|
| 2 |
-
"""
|
| 3 |
-
MIT License
|
| 4 |
-
|
| 5 |
-
Copyright (c) 2021 NoneBot
|
| 6 |
-
|
| 7 |
-
Permission is hereby granted, free of charge, to any person obtaining a copy
|
| 8 |
-
of this software and associated documentation files (the "Software"), to deal
|
| 9 |
-
in the Software without restriction, including without limitation the rights
|
| 10 |
-
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
| 11 |
-
copies of the Software, and to permit persons to whom the Software is
|
| 12 |
-
furnished to do so, subject to the following conditions:
|
| 13 |
-
|
| 14 |
-
The above copyright notice and this permission notice shall be included in all
|
| 15 |
-
copies or substantial portions of the Software.
|
| 16 |
-
|
| 17 |
-
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
| 18 |
-
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
| 19 |
-
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
| 20 |
-
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
| 21 |
-
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
| 22 |
-
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
| 23 |
-
SOFTWARE.
|
| 24 |
-
"""
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
import os
|
| 28 |
-
import sys
|
| 29 |
-
from pathlib import Path
|
| 30 |
-
from typing import Callable, Literal
|
| 31 |
-
|
| 32 |
-
from typing_extensions import ParamSpec
|
| 33 |
-
|
| 34 |
-
WINDOWS = sys.platform.startswith("win") or (sys.platform == "cli" and os.name == "nt")
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
def user_cache_dir(appname: str) -> Path:
|
| 38 |
-
r"""
|
| 39 |
-
Return full path to the user-specific cache dir for this application.
|
| 40 |
-
"appname" is the name of application.
|
| 41 |
-
Typical user cache directories are:
|
| 42 |
-
macOS: ~/Library/Caches/<AppName>
|
| 43 |
-
Unix: ~/.cache/<AppName> (XDG default)
|
| 44 |
-
Windows: C:\Users\<username>\AppData\Local\<AppName>\Cache
|
| 45 |
-
On Windows the only suggestion in the MSDN docs is that local settings go
|
| 46 |
-
in the `CSIDL_LOCAL_APPDATA` directory. This is identical to the
|
| 47 |
-
non-roaming app data dir (the default returned by `user_data_dir`). Apps
|
| 48 |
-
typically put cache data somewhere *under* the given dir here. Some
|
| 49 |
-
examples:
|
| 50 |
-
...\Mozilla\Firefox\Profiles\<ProfileName>\Cache
|
| 51 |
-
...\Acme\SuperApp\Cache\1.0
|
| 52 |
-
OPINION: This function appends "Cache" to the `CSIDL_LOCAL_APPDATA` value.
|
| 53 |
-
"""
|
| 54 |
-
if WINDOWS:
|
| 55 |
-
return _get_win_folder("CSIDL_LOCAL_APPDATA") / appname / "Cache"
|
| 56 |
-
elif sys.platform == "darwin":
|
| 57 |
-
return Path("~/Library/Caches").expanduser() / appname
|
| 58 |
-
else:
|
| 59 |
-
return Path(os.getenv("XDG_CACHE_HOME", "~/.cache")).expanduser() / appname
|
| 60 |
-
|
| 61 |
-
|
| 62 |
-
def user_data_dir(appname: str, roaming: bool = False) -> Path:
|
| 63 |
-
r"""
|
| 64 |
-
Return full path to the user-specific data dir for this application.
|
| 65 |
-
"appname" is the name of application.
|
| 66 |
-
If None, just the system directory is returned.
|
| 67 |
-
"roaming" (boolean, default False) can be set True to use the Windows
|
| 68 |
-
roaming appdata directory. That means that for users on a Windows
|
| 69 |
-
network setup for roaming profiles, this user data will be
|
| 70 |
-
sync'd on login. See
|
| 71 |
-
<http://technet.microsoft.com/en-us/library/cc766489(WS.10).aspx>
|
| 72 |
-
for a discussion of issues.
|
| 73 |
-
Typical user data directories are:
|
| 74 |
-
macOS: ~/Library/Application Support/<AppName>
|
| 75 |
-
Unix: ~/.local/share/<AppName> # or in
|
| 76 |
-
$XDG_DATA_HOME, if defined
|
| 77 |
-
Win XP (not roaming): C:\Documents and Settings\<username>\ ...
|
| 78 |
-
...Application Data\<AppName>
|
| 79 |
-
Win XP (roaming): C:\Documents and Settings\<username>\Local ...
|
| 80 |
-
...Settings\Application Data\<AppName>
|
| 81 |
-
Win 7 (not roaming): C:\Users\<username>\AppData\Local\<AppName>
|
| 82 |
-
Win 7 (roaming): C:\Users\<username>\AppData\Roaming\<AppName>
|
| 83 |
-
For Unix, we follow the XDG spec and support $XDG_DATA_HOME.
|
| 84 |
-
That means, by default "~/.local/share/<AppName>".
|
| 85 |
-
"""
|
| 86 |
-
if WINDOWS:
|
| 87 |
-
const = "CSIDL_APPDATA" if roaming else "CSIDL_LOCAL_APPDATA"
|
| 88 |
-
return Path(_get_win_folder(const)) / appname
|
| 89 |
-
elif sys.platform == "darwin":
|
| 90 |
-
return Path("~/Library/Application Support/").expanduser() / appname
|
| 91 |
-
else:
|
| 92 |
-
return Path(os.getenv("XDG_DATA_HOME", "~/.local/share")).expanduser() / appname
|
| 93 |
-
|
| 94 |
-
|
| 95 |
-
def user_config_dir(appname: str, roaming: bool = True) -> Path:
|
| 96 |
-
"""Return full path to the user-specific config dir for this application.
|
| 97 |
-
"appname" is the name of application.
|
| 98 |
-
If None, just the system directory is returned.
|
| 99 |
-
"roaming" (boolean, default True) can be set False to not use the
|
| 100 |
-
Windows roaming appdata directory. That means that for users on a
|
| 101 |
-
Windows network setup for roaming profiles, this user data will be
|
| 102 |
-
sync'd on login. See
|
| 103 |
-
<http://technet.microsoft.com/en-us/library/cc766489(WS.10).aspx>
|
| 104 |
-
for a discussion of issues.
|
| 105 |
-
Typical user data directories are:
|
| 106 |
-
macOS: same as user_data_dir
|
| 107 |
-
Unix: ~/.config/<AppName>
|
| 108 |
-
Win *: same as user_data_dir
|
| 109 |
-
For Unix, we follow the XDG spec and support $XDG_CONFIG_HOME.
|
| 110 |
-
That means, by default "~/.config/<AppName>".
|
| 111 |
-
"""
|
| 112 |
-
if WINDOWS:
|
| 113 |
-
return user_data_dir(appname, roaming=roaming)
|
| 114 |
-
elif sys.platform == "darwin":
|
| 115 |
-
return user_data_dir(appname)
|
| 116 |
-
else:
|
| 117 |
-
return Path(os.getenv("XDG_CONFIG_HOME", "~/.config")).expanduser() / appname
|
| 118 |
-
|
| 119 |
-
|
| 120 |
-
# -- Windows support functions --
|
| 121 |
-
def _get_win_folder_from_registry(
|
| 122 |
-
csidl_name: Literal["CSIDL_APPDATA", "CSIDL_COMMON_APPDATA", "CSIDL_LOCAL_APPDATA"]
|
| 123 |
-
) -> Path:
|
| 124 |
-
"""
|
| 125 |
-
This is a fallback technique at best. I'm not sure if using the
|
| 126 |
-
registry for this guarantees us the correct answer for all CSIDL_*
|
| 127 |
-
names.
|
| 128 |
-
"""
|
| 129 |
-
import winreg
|
| 130 |
-
|
| 131 |
-
shell_folder_name = {
|
| 132 |
-
"CSIDL_APPDATA": "AppData",
|
| 133 |
-
"CSIDL_COMMON_APPDATA": "Common AppData",
|
| 134 |
-
"CSIDL_LOCAL_APPDATA": "Local AppData",
|
| 135 |
-
}[csidl_name]
|
| 136 |
-
|
| 137 |
-
key = winreg.OpenKey(
|
| 138 |
-
winreg.HKEY_CURRENT_USER,
|
| 139 |
-
r"Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders",
|
| 140 |
-
)
|
| 141 |
-
directory, _type = winreg.QueryValueEx(key, shell_folder_name)
|
| 142 |
-
return Path(directory)
|
| 143 |
-
|
| 144 |
-
|
| 145 |
-
def _get_win_folder_with_ctypes(
|
| 146 |
-
csidl_name: Literal["CSIDL_APPDATA", "CSIDL_COMMON_APPDATA", "CSIDL_LOCAL_APPDATA"]
|
| 147 |
-
) -> Path:
|
| 148 |
-
csidl_const = {
|
| 149 |
-
"CSIDL_APPDATA": 26,
|
| 150 |
-
"CSIDL_COMMON_APPDATA": 35,
|
| 151 |
-
"CSIDL_LOCAL_APPDATA": 28,
|
| 152 |
-
}[csidl_name]
|
| 153 |
-
|
| 154 |
-
buf = ctypes.create_unicode_buffer(1024)
|
| 155 |
-
ctypes.windll.shell32.SHGetFolderPathW(None, csidl_const, None, 0, buf)
|
| 156 |
-
|
| 157 |
-
# Downgrade to short path name if have highbit chars. See
|
| 158 |
-
# <http://bugs.activestate.com/show_bug.cgi?id=85099>.
|
| 159 |
-
has_high_char = any(ord(c) > 255 for c in buf)
|
| 160 |
-
if has_high_char:
|
| 161 |
-
buf2 = ctypes.create_unicode_buffer(1024)
|
| 162 |
-
if ctypes.windll.kernel32.GetShortPathNameW(buf.value, buf2, 1024):
|
| 163 |
-
buf = buf2
|
| 164 |
-
|
| 165 |
-
return Path(buf.value)
|
| 166 |
-
|
| 167 |
-
|
| 168 |
-
if WINDOWS:
|
| 169 |
-
try:
|
| 170 |
-
import ctypes
|
| 171 |
-
|
| 172 |
-
_get_win_folder = _get_win_folder_with_ctypes
|
| 173 |
-
except ImportError:
|
| 174 |
-
_get_win_folder = _get_win_folder_from_registry
|
| 175 |
-
|
| 176 |
-
|
| 177 |
-
P = ParamSpec("P")
|
| 178 |
-
|
| 179 |
-
APP_NAME = "meme_generator"
|
| 180 |
-
BASE_CACHE_DIR = user_cache_dir(APP_NAME).resolve()
|
| 181 |
-
BASE_CONFIG_DIR = user_config_dir(APP_NAME).resolve()
|
| 182 |
-
BASE_DATA_DIR = user_data_dir(APP_NAME).resolve()
|
| 183 |
-
|
| 184 |
-
|
| 185 |
-
def _ensure_dir(path: Path) -> None:
|
| 186 |
-
if not path.exists():
|
| 187 |
-
path.mkdir(parents=True, exist_ok=True)
|
| 188 |
-
elif not path.is_dir():
|
| 189 |
-
raise RuntimeError(f"{path} is not a directory")
|
| 190 |
-
|
| 191 |
-
|
| 192 |
-
def _auto_create_dir(func: Callable[P, Path]) -> Callable[P, Path]:
|
| 193 |
-
def wrapper(*args: P.args, **kwargs: P.kwargs) -> Path:
|
| 194 |
-
path = func(*args, **kwargs)
|
| 195 |
-
_ensure_dir(path)
|
| 196 |
-
return path
|
| 197 |
-
|
| 198 |
-
return wrapper
|
| 199 |
-
|
| 200 |
-
|
| 201 |
-
@_auto_create_dir
|
| 202 |
-
def get_cache_dir() -> Path:
|
| 203 |
-
return BASE_CACHE_DIR
|
| 204 |
-
|
| 205 |
-
|
| 206 |
-
def get_cache_file(filename: str) -> Path:
|
| 207 |
-
return get_cache_dir() / filename
|
| 208 |
-
|
| 209 |
-
|
| 210 |
-
@_auto_create_dir
|
| 211 |
-
def get_config_dir() -> Path:
|
| 212 |
-
return BASE_CONFIG_DIR
|
| 213 |
-
|
| 214 |
-
|
| 215 |
-
def get_config_file(filename: str) -> Path:
|
| 216 |
-
return get_config_dir() / filename
|
| 217 |
-
|
| 218 |
-
|
| 219 |
-
@_auto_create_dir
|
| 220 |
-
def get_data_dir() -> Path:
|
| 221 |
-
return BASE_DATA_DIR
|
| 222 |
-
|
| 223 |
-
|
| 224 |
-
def get_data_file(filename: str) -> Path:
|
| 225 |
-
return get_data_dir() / filename
|
|
|
|
|
|
|
|
|
|
|
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|
|
spaces/Clara998/DisneyPixarMovie/README.md
DELETED
|
@@ -1,12 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: DisneyPixarMovie
|
| 3 |
-
emoji: 😻
|
| 4 |
-
colorFrom: yellow
|
| 5 |
-
colorTo: purple
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.50.2
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
|
|
|
|
|
|
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