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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Chashme Baddoor marathi movie mp4 hd free download Laugh out loud with Ali Zafar Taapsee Pannu and Siddharth.md
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<h1>Chashme Baddoor: A Hilarious Comedy Movie in Marathi</h1>
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<h2>Introduction</h2>
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<p>If you are looking for a fun and entertaining movie to watch with your friends or family, you should check out Chashme Baddoor. It is a Marathi comedy movie that was released in 2013. It is a remake of the 1981 Hindi movie of the same name, which was directed by Sai Paranjape and starred Farooq Shaikh, Deepti Naval, Rakesh Bedi, and Ravi Baswani.</p>
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<p>In this article, we will tell you everything you need to know about Chashme Baddoor, including what it is about, who are the main actors and characters, why you should watch it, and how to watch it online for free.</p>
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<h2>What is Chashme Baddoor?</h2>
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<p>Chashme Baddoor is a comedy movie that revolves around three friends and roommates who are studying at Delhi University. Siddharth (played by Ali Zafar) is a studious and sincere guy who is preparing for his PhD. Omi (played by Divyendu Sharma) and Jai (played by Siddharth) are lazy and flirtatious guys who are always chasing girls and having fun.</p>
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<p>One day, they see a new girl in their neighborhood, Seema (played by Taapsee Pannu), who is a salesgirl for Chamko washing powder. Omi and Jai try to impress her with their tricks, but they fail miserably. Siddharth, on the other hand, falls in love with her at first sight and starts a relationship with her.</p>
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<p>When Omi and Jai find out about this, they feel jealous and betrayed by their friend. They decide to break up their relationship by lying to Seema that Siddharth is already married and has a child. They also lie to Siddharth that Seema is a notorious criminal who is wanted by the police.</p>
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<p>Will their plan succeed? Will Siddharth and Seema find out the truth? Will their friendship survive this test? You will have to watch the movie to find out.</p>
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<h2>Who are the main actors and characters?</h2>
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<p>The movie has a talented cast of actors who have done justice to their roles. Here are some of the main actors and characters in the movie:</p>
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<ul>
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<li>Ali Zafar as Siddharth: He is a Pakistani singer, actor, and model who has worked in several Bollywood movies such as Tere Bin Laden, Mere Brother Ki Dulhan, London Paris New York, and Total Siyapaa. He plays the role of Siddharth, the smart and sensible friend who falls in love with Seema.</li>
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<li>Taapsee Pannu as Seema: She is an Indian actress who has worked in Hindi, Tamil, Telugu, Malayalam, and Kannada movies. She has appeared in movies such as Pink, Naam Shabana, Judwaa 2, Badla, Mission Mangal, Thappad, and Haseen Dillruba. She plays the role of Seema, the bubbly and beautiful girl who works as a salesgirl for Chamko washing powder.</li>
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<li>Siddharth as Jai: He is an Indian actor who has worked in Tamil, Telugu, Hindi, and Malayalam movies. He has appeared in movies such as Rang De Basanti, Striker, Bommarillu, Aata Nade Veta Nade (Telugu), Jigarthanda (Tamil), Enakkul Oruvan (Tamil), Aranmanai 2 (Tamil), The House Next Door (Hindi), Aval (Tamil), Gruham (Telugu), Sivappu Manjal Pachai (Tamil), Takkar (Tamil), Maha Samudram (Telugu). He plays the role of Jai, the mischievous and cunning friend who tries to woo Seema.</li>
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<li>Divyendu Sharma as Omi: He is an Indian actor who has worked in Hindi movies such as Pyaar Ka Punchnama, Fukrey, Toilet: Ek Prem Katha, Batti Gul Meter Chalu, Badnaam Gali, Shukranu, Mirzapur, Bicchoo Ka Khel. He plays the role of Omi, the funny and poetic friend who also tries to impress Seema.</li>
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<li>Rishi Kapoor as Mr. Joseph Furtado: He was an Indian actor who was one of the most popular stars of Bollywood. He had worked in more than 150 movies such as Bobby, Amar Akbar Anthony, Karz, Prem Rog, Saagar, Chandni, Agneepath, Kapoor & Sons, Mulk, 102 Not Out. He played the role of Mr. Joseph Furtado, the owner of a cafe where Siddharth works part-time. He also has a crush on Miss Josephine (played by Juhi Chawla).</li>
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<li>Anupam Kher as Suryakant Paranjape: He is an Indian actor who has worked in more than 500 movies in Hindi and other languages. He has appeared in movies such as Saaransh, Ram Lakhan, Dilwale Dulhania Le Jayenge, Kuch Kuch Hota Hai, Bend It Like Beckham, A Wednesday!, Special 26, Baby, The Accidental Prime Minister. He plays a double role of Suryakant Paranjape, Seema's strict father who wants her to get married soon; and Lallan Miyan, a shopkeeper who sells cigarettes to Omi and Jai on credit.</li>
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</ul>
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<h2>Why you should watch Chashme Baddoor</h2>
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<p>There are many reasons why you should watch Chashme Baddoor. Here are some of them:</p>
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<h3>It is a remake of a classic comedy movie</h3>
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<p>The original Chashme Baddoor was released in 1981 and was directed by Sai Paranjape. It was one of the most successful comedy movies of that time and received critical acclaim for its witty script, realistic characters, and hilarious situations. It also had some memorable songs composed by Raj Kamal and sung by Yesudas, Hemlata, Shailendra Singh, Anand Kumar C., etc.</p>
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<p>The remake pays tribute to the original movie by retaining some of its scenes and dialogues. It also adds some new twists and turns to make it more contemporary and appealing to the modern audience. The remake also has some catchy songs composed by Sajid-Wajid and sung by Ali Zafar, Sonu Nigam, Shreya Ghoshal Wajid Khan Neuman Pinto etc.</p>
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<h3>It has a lot of funny scenes and dialogues</h3>
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<p>The movie is full of comedy scenes that will make you laugh out loud. Some of them are:</p>
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<ul>
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<li>The scene where Omi recites his shayari (poetry) to impress Seema but ends up insulting her instead.</li>
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<li>The scene where Jai pretends to be a film director and tries to cast Seema in his movie but gets caught by her father.</li>
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<li>The scene where Omi and Jai lie to Seema about Siddharth's marriage and show her fake photos of his wife and child.</li>
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<li>The scene where Omi and Jai lie to Siddharth about Seema's criminal background and show him fake news clippings of her crimes.</li>
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<li>The scene where Omi Jai and Neha's grandmother plan to stage a fake kidnapping but end up getting kidnapped by real goons.</li>
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<li>The scene where Mr. Joseph Furtado tries to propose to Miss Josephine but gets interrupted by Lallan Miyan.</li>
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</ul>
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<p>The movie also has some witty dialogues that will make you chuckle. Some of them are:</p>
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<ul>
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<li>"Har ek friend kamina hota hai" (Every friend is a scoundrel) - The catchy song that describes the friendship of Omi, Jai, and Siddharth.</li>
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<li>"Dhichkyaaon doom doom" (Bang bang) - The romantic song that shows the chemistry between Siddharth and Seema.</li>
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<li>"Early to bed and early to rise, makes a man healthy, wealthy and wise" - The motto of Siddharth that annoys Omi and Jai.</li>
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<li>"Chamko chamko chamko, sabki pasand Chamko" (Chamko chamko chamko, everyone's favorite Chamko) - The jingle of the washing powder that Seema sells.</li>
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<li>"Tumhara naam kya hai Basanti?" (What is your name Basanti?) - The famous dialogue from Sholay that Jai uses to flirt with Seema.</li>
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</ul>
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<h3>It has a good message about friendship and love</h3>
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<p>The movie is not just a comedy, but also a heartwarming story about friendship and love. It shows how true friends stick together through thick and thin, and how they support each other in times of need. It also shows how love can overcome misunderstandings and obstacles, and how it can bring happiness and peace to one's life.</p>
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<p>The movie also has some emotional scenes that will touch your heart. Some of them are:</p>
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<ul>
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<li>The scene where Siddharth confronts Omi and Jai for lying to him and Seema, and breaks his friendship with them.</li>
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<li>The scene where Omi and Jai realize their mistake and apologize to Siddharth and Seema, and reunite with them.</li>
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<li>The scene where Seema's father accepts Siddharth as his son-in-law and blesses their marriage.</li>
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<li>The scene where Mr. Joseph Furtado finally proposes to Miss Josephine and they get married.</li>
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</ul>
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<h2>How to watch Chashme Baddoor online for free</h2>
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<p>If you are wondering how to watch Chashme Baddoor online for free, you have several options. Here are some of them:</p>
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<h3>Netflix</h3>
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<p>Netflix is one of the most popular streaming platforms in the world. It has a huge collection of movies and shows in different languages and genres. You can watch Chashme Baddoor on Netflix with a subscription. You can also get a free trial for 30 days if you are a new user.</p>
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<p>To watch Chashme Baddoor on Netflix, you need to follow these steps:</p>
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<ol>
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<li>Go to https://www.netflix.com/ and sign up for an account or log in if you already have one.</li>
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<li>Search for Chashme Baddoor in the search bar or browse through the categories.</li>
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<li>Click on the movie title and enjoy watching it.</li>
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</ol>
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<h3>Desi Cinemas</h3>
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<p>Desi Cinemas is a website that offers free streaming of Bollywood movies online. It has a good collection of movies in HD quality and with English subtitles. You can watch Chashme Baddoor on Desi Cinemas without any registration or subscription.</p>
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<p>To watch Chashme Baddoor on Desi Cinemas, you need to follow these steps:</p>
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<ol>
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<li>Go to https://desicinemas.tv/ and search for Chashme Baddoor in the search bar or browse through the categories.</li>
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<li>Click on the movie title and choose a server to stream it from.</li>
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<li>Enjoy watching the movie.</li>
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</ol>
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<h3>JustWatch</h3>
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<p>JustWatch is a website that helps you find where to watch movies and shows online. It shows you the availability and price of different streaming platforms for any movie or show you want to watch. You can use JustWatch to find out where to watch Chashme Baddoor online for free or for a low cost.</p>
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<p>To use JustWatch, you need to follow these steps:</p>
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<ol>
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<li>Go to https://www.justwatch.com/ and select your country from the menu.</li>
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<li>Search for Chashme Baddoor in the search bar or browse through the categories.</li>
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<li>Click on the movie title and see the list of streaming platforms where you can watch it.</li>
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<li>Select the platform that suits your preference and budget, and click on the link to go to its website.</li>
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<li>Watch the movie on the chosen platform.</li>
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</ol>
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<h2>Conclusion</h2>
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<p>In conclusion, Chashme Baddoor is a comedy movie that you should not miss. It is a remake of a classic movie that has been updated with new twists and turns. It has a talented cast of actors who have delivered hilarious performances. It has a lot of funny scenes and dialogues that will make you laugh out loud. It also has a good message about friendship and love that will touch your heart.</p>
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<p>If you want to watch Chashme Baddoor online for free, you have several options such as Netflix, Desi Cinemas, and JustWatch. You can choose any of them according to your convenience and preference. You can also download the movie from various websites if you want to watch it offline.</p>
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<p>So what are you waiting for? Grab your popcorn and your friends, and watch Chashme Baddoor online for free. You will surely have a great time watching this movie.</p>
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<h3>FAQs</h3>
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<p>Here are some frequently asked questions about Chashme Baddoor:</p>
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<ol>
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<li>Q: Is Chashme Baddoor a remake of a movie?<br>
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A: Yes, Chashme Baddoor is a remake of the 1981 Hindi movie of the same name, which was directed by Sai Paranjape and starred Farooq Shaikh, Deepti Naval, Rakesh Bedi, and Ravi Baswani.</li>
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<li>Q: Who are the main actors and characters in Chashme Baddoor?<br>
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A: The main actors and characters in Chashme Baddoor are Ali Zafar as Siddharth, Taapsee Pannu as Seema, Siddharth as Jai, Divyendu Sharma as Omi, Rishi Kapoor as Mr. Joseph Furtado, and Anupam Kher as Suryakant Paranjape and Lallan Miyan.</li>
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<li>Q: What are some of the songs in Chashme Baddoor?<br>
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A: Some of the songs in Chashme Baddoor are "Har ek friend kamina hota hai", "Dhichkyaaon doom doom", "Early to bed and early to rise", and "Chamko chamko chamko".</li>
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<li>Q: Where can I watch Chashme Baddoor online for free?<br>
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A: You can watch Chashme Baddoor online for free on Netflix, Desi Cinemas, or JustWatch. You can also download the movie from various websites if you want to watch it offline.</li>
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</p> 0a6ba089eb<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Car Parking Multiplayer Mod Apk Drive Park and Customize 70 Vehicles with Everything Unlocked.md
DELETED
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<h1>Car Parking Multiplayer Mod APK Unlocked Everything New Version</h1>
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<p>Do you love driving and parking games? Do you want to experience the thrill of realistic car physics, open world map, multiplayer mode, customization options, and different game modes? If yes, then you should try Car Parking Multiplayer, one of the most popular and realistic car parking simulation games for Android devices. And if you want to enjoy the game with unlimited money, gold, cars, items, and no ads, then you should download Car Parking Multiplayer Mod APK, the modified version of the game that gives you everything unlocked for free. In this article, we will tell you what is Car Parking Multiplayer, what are its features, why you should download Car Parking Multiplayer Mod APK, and how to download and install it on your device.</p>
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<h2>What is Car Parking Multiplayer?</h2>
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<p>Car Parking Multiplayer is a car parking simulation game developed by olzhass, a game studio that specializes in realistic driving and parking games. The game has more than 100 million downloads on Google Play Store and has an average rating of 4.3 out of 5 stars. The game lets you drive and park various types of cars, from sedans to sports cars, from trucks to buses, from classic cars to modern cars. You can choose from more than 150 different cars, each with its own characteristics and features. You can also customize and tune your cars according to your preferences, changing the color, wheels, suspension, engine, turbo, exhaust, and more.</p>
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<p>Car Parking Multiplayer is not just a simple car parking game. It has many features that make it stand out from other similar games. Here are some of the features of Car Parking Multiplayer:</p>
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<h4>Realistic car physics and graphics</h4>
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<p>The game uses advanced car physics and graphics to create a realistic driving and parking experience. You can feel the weight, speed, acceleration, braking, steering, and suspension of each car. You can also see the details of each car model, such as the interior, exterior, lights, mirrors, doors, windows, etc. The game also has realistic sound effects for each car engine, horn, tire screech, collision, etc.</p>
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<h4>Open world map and multiplayer mode</h4>
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<p>The game has an open world map that you can explore freely. You can drive around the city streets, highways, parking lots, airports, deserts, forests, beaches, etc. You can also interact with other players in the multiplayer mode. You can chat with them using voice or text messages. You can also join or create your own online room with up to 100 players. You can race with them, challenge them to parking competitions, or just have fun together.</p>
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<h4>Customization and tuning options</h4>
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<p>The game gives you a lot of options to customize and tune your cars. You can change the color of your car using a color picker or choose from predefined colors. You can also change the wheels of your car using different rims and tires. You can also modify the suspension height, camber angle, wheel size, etc. You can also upgrade the engine performance of your car using turbo boosters, nitrous oxide injectors, etc. You can also change the exhaust sound of your car using different mufflers.</p>
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<h4>Different game modes and challenges</h4>
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<p>The game has different game modes and challenges that you can play according to your mood and skill level. You can play the classic parking mode where you have to park your car in a designated spot without hitting any obstacles or other cars. You can also play the free driving mode where you can drive around the map without any restrictions or objectives. You can also play the drift mode where you have to perform drifts and earn points. You can also play the police mode where you can chase or be chased by the police cars. You can also play the zombie mode where you have to survive the zombie apocalypse by driving and shooting zombies. You can also play the delivery mode where you have to deliver goods from one place to another. You can also play the taxi mode where you have to pick up and drop off passengers. You can also play the tow truck mode where you have to tow broken or illegally parked cars.</p>
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<h2>Why download Car Parking Multiplayer Mod APK?</h2>
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<p>Car Parking Multiplayer is a fun and addictive game, but it also has some limitations and drawbacks. For example, you need a lot of money and gold to buy, upgrade, and customize your cars. You also need to watch ads to get some rewards or skip some levels. You also need to root your device to access some features or items. These things can make the game less enjoyable and frustrating for some players. That's why you should download Car Parking Multiplayer Mod APK, the modified version of the game that gives you everything unlocked for free.</p>
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<p>Car Parking Multiplayer Mod APK is a hacked version of the game that has many benefits over the original version. Here are some of the benefits of Car Parking Multiplayer Mod APK:</p>
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<h4>Unlimited money and gold</h4>
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<p>With Car Parking Multiplayer Mod APK, you don't have to worry about running out of money and gold. You will get unlimited money and gold in your account as soon as you start the game. You can use them to buy any car you want, upgrade it, customize it, and tune it. You can also use them to buy any item you need, such as fuel, repair kits, weapons, etc.</p>
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<h4>All cars and items unlocked</h4>
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<p>With Car Parking Multiplayer Mod APK, you don't have to wait or work hard to unlock all the cars and items in the game. You will get access to all the cars and items from the beginning of the game. You can choose from more than 150 different cars, each with its own features and characteristics. You can also use any item you want, such as turbo boosters, nitrous oxide injectors, mufflers, etc.</p>
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<h4>No ads and no root required</h4>
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<p>With Car Parking Multiplayer Mod APK, you don't have to watch annoying ads or root your device to enjoy the game. The mod apk file has no ads and no root requirement. You can play the game without any interruptions or risks. You can also update the game easily without losing your progress or data.</p>
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<h3>How to download and install Car Parking Multiplayer Mod APK?</h3>
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<p>Downloading and installing Car Parking Multiplayer Mod APK is very easy and simple. Just follow these steps:</p>
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<h4>Step 1: Download the mod apk file from a trusted source</h4>
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<p>The first step is to download the mod apk file from a trusted source. You can use this link to download the latest version of Car Parking Multiplayer Mod APK for free. The file size is about 300 MB and it is safe and virus-free.</p>
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<h4>Step 2: Enable unknown sources on your device settings</h4>
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<p>The second step is to enable unknown sources on your device settings. This will allow you to install apps from sources other than Google Play Store. To do this, go to your device settings > security > unknown sources > enable.</p>
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<h4>Step 3: Install the mod apk file and enjoy the game</h4>
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<p>The third step is to install the mod apk file and enjoy the game. To do this, locate the downloaded mod apk file on your device storage and tap on it. Follow the instructions on the screen and wait for the installation process to finish. Once done, open the game and start playing with everything unlocked for free.</p>
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<h2>Conclusion</h2>
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<p>Car Parking Multiplayer is one of the best car parking simulation games for Android devices. It has realistic car physics and graphics, open world map and multiplayer mode, customization and tuning options, different game modes and challenges, and more. However, if you want to enjoy the game with unlimited money, gold, cars, items, and no ads, then you should download Car Parking Multiplayer Mod APK, the modified version of the game that gives you everything unlocked for free. Just follow the steps above to download and install Car Parking Multiplayer Mod APK on your device and have fun.</p> <p>Here are some FAQs that you might have about Car Parking Multiplayer Mod APK:</p>
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<h4>Q: Is Car Parking Multiplayer Mod APK safe to use?</h4>
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<p>A: Yes, Car Parking Multiplayer Mod APK is safe to use. It has no viruses, malware, or spyware. It also does not require root access or any permissions that might compromise your device security or privacy.</p>
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<h4>Q: Is Car Parking Multiplayer Mod APK compatible with my device?</h4>
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<p>A: Car Parking Multiplayer Mod APK is compatible with most Android devices that have Android 4.4 or higher. However, some devices might have compatibility issues due to different hardware or software specifications. If you encounter any problems while playing the game, you can contact the developer for support or try another device.</p>
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<h4>Q: Can I play Car Parking Multiplayer Mod APK online with other players?</h4>
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<p>A: Yes, you can play Car Parking Multiplayer Mod APK online with other players. The game has a multiplayer mode that allows you to join or create online rooms with up to 100 players. You can chat with them, race with them, challenge them, or just have fun together.</p>
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<h4>Q: Can I update Car Parking Multiplayer Mod APK to the latest version?</h4>
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<p>A: Yes, you can update Car Parking Multiplayer Mod APK to the latest version. However, you need to download and install the new mod apk file from the same source that you used before. You also need to uninstall the previous version of the game before installing the new one. You can also backup your game data before updating to avoid losing your progress or data.</p>
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<h4>Q: Can I request a new feature or report a bug for Car Parking Multiplayer Mod APK?</h4>
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<p>A: Yes, you can request a new feature or report a bug for Car Parking Multiplayer Mod APK. You can contact the developer through their email address or social media accounts. You can also leave a comment or feedback on the source website where you downloaded the mod apk file. The developer will try to respond to your request or fix the bug as soon as possible.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Download queue xbox series x How to see and prioritize your game and app installations.md
DELETED
|
@@ -1,170 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
<h1>How to Manage Your Download Queue on Xbox Series X</h1>
|
| 3 |
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<p>If you have an Xbox Series X, you probably want to download and play a lot of games and apps on your console. But sometimes, you may encounter some issues with your downloads, such as slow speed, errors, or interruptions. That's why it's important to know how to manage your download queue on Xbox Series X.</p>
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| 4 |
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<p>The download queue is where you can see the status of your game and app installations, as well as pause, cancel, or prioritize them. You can also launch a downloaded game or app from the queue or from a notification on your screen.</p>
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<p>In this article, we will show you how to view and manage your download queue on Xbox Series X, as well as how to troubleshoot some common problems with your game and app installations. We will also give you some tips on how to optimize your network bandwidth for game downloads.</p>
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| 7 |
-
<h2>How to View the Status of Your Game and App Installations</h2>
|
| 8 |
-
<p>To access your download queue on Xbox Series X, follow these steps:</p>
|
| 9 |
-
<ol>
|
| 10 |
-
<li>Press the Xbox button <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Xbox_button.svg/1200px-Xbox_button.svg.png" alt="Xbox button" width="20" height="20"> on your controller to open the guide.</li>
|
| 11 |
-
<li>Select My games & apps > See all.</li>
|
| 12 |
-
<li>Select Manage > Queue.</li>
|
| 13 |
-
</ol>
|
| 14 |
-
<p>Here, you can see the active downloads with a progress bar along the bottom of the game or app's tile. You can also see the estimated time remaining, the file size, and the download speed.</p>
|
| 15 |
-
<h3>How to Pause, Cancel, or Prioritize Your Downloads</h3>
|
| 16 |
-
<p>From the queue, you can also control your downloads using your controller and the on-screen prompts. Here are some options:</p>
|
| 17 |
-
<ul>
|
| 18 |
-
<li>Select Pause all to temporarily halt all your current installations.</li>
|
| 19 |
-
<li>Select Cancel all to remove all items from your queue altogether.</li>
|
| 20 |
-
<li>Select one of the items in your queue to pause or cancel that individual game or app.</li>
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| 21 |
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<li>Select Resume all or Resume installation if you want to resume your paused downloads.</li>
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| 22 |
-
<li <li>Select Move to front to make a game or app the highest priority in your queue.</li>
|
| 23 |
-
</ul>
|
| 24 |
-
<p>You can also use the Sort by option to arrange your queue by name, size, or date added.</p>
|
| 25 |
-
<h3>How to Launch a Downloaded Game or App</h3>
|
| 26 |
-
<p>Once a game or app is fully downloaded and installed, you can launch it from the queue by selecting Play. You can also launch it from a notification that pops up on your screen when the installation is complete.</p>
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How to change download queue on Xbox Series X<br />
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| 47 |
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Xbox Series X download queue problem<br />
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| 48 |
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How to fix download queue on Xbox Series X<br />
|
| 49 |
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Xbox Series X download queue size<br />
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| 50 |
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How to delete download queue on Xbox Series X<br />
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| 51 |
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Xbox Series X download queue update<br />
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| 52 |
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How to access download queue on Xbox Series X<br />
|
| 53 |
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Xbox Series X download queue won't start<br />
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| 54 |
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How to move download queue on Xbox Series X<br />
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| 55 |
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Xbox Series X download queue location<br />
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| 56 |
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How to stop download queue on Xbox Series X<br />
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| 57 |
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Xbox Series X download queue offline<br />
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| 58 |
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How to restart download queue on Xbox Series X<br />
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| 59 |
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Xbox Series X download queue notification<br />
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| 60 |
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How to transfer download queue on Xbox Series X<br />
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| 61 |
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Xbox Series X download queue online<br />
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| 62 |
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How to install download queue on Xbox Series X<br />
|
| 63 |
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Xbox Series X download queue hidden<br />
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| 64 |
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How to find download queue on Xbox Series X<br />
|
| 65 |
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Xbox Series X download queue settings<br />
|
| 66 |
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How to edit download queue on Xbox Series X<br />
|
| 67 |
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Xbox Series X download queue tips<br />
|
| 68 |
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How to optimize download queue on Xbox Series X<br />
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| 69 |
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Xbox Series X download queue guide<br />
|
| 70 |
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How to troubleshoot download queue on Xbox Series X<br />
|
| 71 |
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Xbox Series X download queue tricks</p>
|
| 72 |
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<p>Alternatively, you can use the guide to launch a downloaded game or app. Just press the Xbox button <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Xbox_button.svg/1200px-Xbox_button.svg.png" alt="Xbox button" width="20" height="20"> on your controller and select My games & apps > See all. Then, select the game or app you want to play from the list.</p>
|
| 73 |
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<h2>How to Troubleshoot Your Game and App Installations</h2>
|
| 74 |
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<p>Sometimes, you may encounter some issues with your game and app installations, such as slow speed, errors, or interruptions. Here are some common causes and solutions for these problems:</p>
|
| 75 |
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<h3>How to Check Your Internet Connection</h3>
|
| 76 |
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<p>Your internet connection is one of the most important factors that affect your download speed and performance. If your connection is weak, unstable, or slow, your downloads may take longer or fail.</p>
|
| 77 |
-
<p>To check your internet connection on Xbox Series X, follow these steps:</p>
|
| 78 |
-
<ol>
|
| 79 |
-
<li>Press the Xbox button <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Xbox_button.svg/1200px-Xbox_button.svg.png" alt="Xbox button" width="20" height="20"> on your controller to open the guide.</li>
|
| 80 |
-
<li>Select Profile & system > Settings > General > Network settings.</li>
|
| 81 |
-
<li>Select Test network connection to see if you are connected to the internet.</li>
|
| 82 |
-
<li>Select Test network speed & statistics to see your download speed, upload speed, and latency.</li>
|
| 83 |
-
</ol>
|
| 84 |
-
<p>If you see any errors or warnings, follow the instructions on the screen to fix them. You may need to reboot your router, move closer to it, or use a wired connection instead of wireless.</p> <h3>How to Check Your Account and Storage Space</h3>
|
| 85 |
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<p>Another factor that may affect your game and app installations is your account and storage space. You need to make sure that you are signed in to the correct account and that you have enough storage space on your console or external drive for your downloads.</p>
|
| 86 |
-
<p>To check your account and storage space on Xbox Series X, follow these steps:</p>
|
| 87 |
-
<ol>
|
| 88 |
-
<li>Press the Xbox button <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Xbox_button.svg/1200px-Xbox_button.svg.png" alt="Xbox button" width="20" height="20"> on your controller to open the guide.</li>
|
| 89 |
-
<li>Select Profile & system > Add or switch to see which account you are using. If you want to switch to a different account, select it from the list or add a new one.</li>
|
| 90 |
-
<li>Select My games & apps > See all > Manage > Storage devices to see how much storage space you have left on your console or external drive. If you need more space, you can delete some games or apps that you don't use, move them to another device, or add a new device.</li>
|
| 91 |
-
</ol>
|
| 92 |
-
<h3>How to Restart Your Console</h3>
|
| 93 |
-
<p>Sometimes, a simple restart can fix some issues with your game and app installations. Restarting your console can clear the cache, refresh the system, and resume your downloads.</p>
|
| 94 |
-
<p>To restart your console on Xbox Series X, follow these steps:</p>
|
| 95 |
-
<ol>
|
| 96 |
-
<li>Press and hold the Xbox button <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Xbox_button.svg/1200px-Xbox_button.svg.png" alt="Xbox button" width="20" height="20"> on your controller until the power menu appears.</li>
|
| 97 |
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<li>Select Restart console > Restart.</li>
|
| 98 |
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<li>Wait for your console to turn off and on again.</li>
|
| 99 |
-
<li>Check your download queue to see if your downloads are resumed or completed.</li>
|
| 100 |
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</ol>
|
| 101 |
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<h3>How to Contact Xbox Support</h3>
|
| 102 |
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<p>If none of the above solutions work, you may need to contact Xbox support for further assistance. Xbox support can help you with troubleshooting, error codes, refunds, warranties, and more.</p>
|
| 103 |
-
<p>To contact Xbox support on Xbox Series X, follow these steps:</p>
|
| 104 |
-
<ol>
|
| 105 |
-
<li>Press the Xbox button <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Xbox_button.svg/1200px-Xbox_button.svg.png" alt="Xbox button" width="20" height="20"> on your controller to open the guide.</li>
|
| 106 |
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<li>Select Profile & system > Settings > System > Console info.</li>
|
| 107 |
-
<li>Note down your serial number and OS version.</li>
|
| 108 |
-
<li>Go to https://support.xbox.com/en-US/contact-us on your phone or computer.</li>
|
| 109 |
-
<li>Select Xbox Series X|S as your device and choose a topic and issue that matches your problem.</li>
|
| 110 |
-
<li>Follow the instructions on the screen to get help from a live agent, a chatbot, a community forum, or a self-help article.</li>
|
| 111 |
-
</ol> <h2>How to Manage Your Network Bandwidth for Game Downloads</h2>
|
| 112 |
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<p>Another way to improve your download speed and performance is to manage your network bandwidth for game downloads. Network bandwidth is the amount of data that can be transferred over your internet connection at a given time. The more bandwidth you use, the faster your downloads will be, but also the more likely you will experience lag or buffering when streaming or playing online games.</p>
|
| 113 |
-
<p>To manage your network bandwidth for game downloads on Xbox Series X, you can adjust your network settings to enable or disable automatic updates, set a download limit or schedule, or use a wired or wireless connection.</p>
|
| 114 |
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<h3>How to Enable or Disable Automatic Updates</h3>
|
| 115 |
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<p>Automatic updates are a feature that allows your console to keep your games and apps up to date without you having to manually check for updates. This can be convenient, but also consume a lot of bandwidth and slow down your other downloads.</p>
|
| 116 |
-
<p>To enable or disable automatic updates on Xbox Series X, follow these steps:</p>
|
| 117 |
-
<ol>
|
| 118 |
-
<li>Press the Xbox button <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Xbox_button.svg/1200px-Xbox_button.svg.png" alt="Xbox button" width="20" height="20"> on your controller to open the guide.</li>
|
| 119 |
-
<li>Select Profile & system > Settings > System > Updates & downloads.</li>
|
| 120 |
-
<li>Select Keep my games & apps up to date to toggle the feature on or off.</li>
|
| 121 |
-
</ol>
|
| 122 |
-
<p>If you disable automatic updates, you will need to manually check for updates for your games and apps by going to My games & apps > See all > Manage > Updates.</p>
|
| 123 |
-
<h3>How to Set a Download Limit or Schedule</h3>
|
| 124 |
-
<p>A download limit or schedule is a feature that allows you to limit the amount of bandwidth used for downloads or schedule them for off-peak hours. This can help you avoid using up your data cap, reduce congestion on your network, and avoid interfering with your online gaming or streaming activities.</p>
|
| 125 |
-
<p>To set a download limit or schedule on Xbox Series X, follow these steps:</p>
|
| 126 |
-
<ol>
|
| 127 |
-
<li>Press the Xbox button <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Xbox_button.svg/1200px-Xbox_button.svg.png" alt="Xbox button" width="20" height="20"> on your controller to open the guide.</li>
|
| 128 |
-
<li>Select Profile & system > Settings > System > Updates & downloads.</li>
|
| 129 |
-
<li>Select Download settings > Limit how much data I use for game and app downloads.</li>
|
| 130 |
-
<li>Select one of the options: Don't limit, Limit 2 GB per hour, Limit 5 GB per hour, Limit 10 GB per hour, Limit 25 GB per hour, or Limit 50 GB per hour.</li>
|
| 131 |
-
<li>Select Download settings > Schedule when I download game and app updates.</li>
|
| 132 |
-
<li>Select one of the options: Any time, During off-peak hours only, or During off-peak hours and when I'm not playing games.</li>
|
| 133 |
-
</ol>
|
| 134 |
-
<p>You can also customize your off-peak hours by selecting Download settings > Change my off-peak hours and choosing a start and end time.</p> <h3>How to Use a Wired or Wireless Connection</h3>
|
| 135 |
-
<p>The type of connection you use for your console can also affect your download speed and performance. A wired connection is usually faster, more stable, and more secure than a wireless connection, but it requires a cable and a port on your router. A wireless connection is more convenient and flexible, but it can be affected by interference, distance, and other devices on your network.</p>
|
| 136 |
-
<p>To use a wired or wireless connection on Xbox Series X, follow these steps:</p>
|
| 137 |
-
<ol>
|
| 138 |
-
<li>Press the Xbox button <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Xbox_button.svg/1200px-Xbox_button.svg.png" alt="Xbox button" width="20" height="20"> on your controller to open the guide.</li>
|
| 139 |
-
<li>Select Profile & system > Settings > General > Network settings.</li>
|
| 140 |
-
<li>Select Set up wireless network to connect to a Wi-Fi network. You will need to enter your network name and password.</li>
|
| 141 |
-
<li>Select Advanced settings > Alternate MAC address to enter a MAC address for your console if your router requires it.</li>
|
| 142 |
-
<li>Select Advanced settings > IP settings to configure your IP address, subnet mask, gateway, and DNS servers if your network requires it.</li>
|
| 143 |
-
<li>Or, plug an Ethernet cable into the port on the back of your console and the port on your router. Your console will automatically detect the wired connection.</li>
|
| 144 |
-
</ol>
|
| 145 |
-
<h2>Conclusion</h2>
|
| 146 |
-
<p>Managing your download queue on Xbox Series X can help you enjoy your games and apps without any hassle. You can view and control your downloads, troubleshoot any issues, and optimize your network bandwidth for the best experience. Here are some tips and recommendations to remember:</p>
|
| 147 |
-
<ul>
|
| 148 |
-
<li>Check your download queue regularly to see the status of your installations and updates.</li>
|
| 149 |
-
<li>Pause or cancel any downloads that you don't need or want to prioritize others.</li>
|
| 150 |
-
<li>Launch a downloaded game or app from the queue, the notification, or the guide.</li>
|
| 151 |
-
<li>Check your internet connection, account, storage space, and console if you encounter any problems with your downloads.</li>
|
| 152 |
-
<li>Contact Xbox support if none of the solutions work for you.</li>
|
| 153 |
-
<li>Enable or disable automatic updates according to your preference.</li>
|
| 154 |
-
<li>Set a download limit or schedule to save bandwidth and avoid interference with other activities.</li>
|
| 155 |
-
<li>Use a wired or wireless connection depending on your situation and needs.</li>
|
| 156 |
-
</ul>
|
| 157 |
-
<h2>FAQs</h2>
|
| 158 |
-
<h3>Q: How do I download games and apps on Xbox Series X?</h3>
|
| 159 |
-
<p>A: You can download games and apps on Xbox Series X from the Microsoft Store. To access the store, press the Xbox button <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Xbox_button.svg/1200px-Xbox_button.svg.png" alt="Xbox button" width="20" height="20"> on your controller and select Store. Then, browse or search for the game or app you want and select Get or Buy. The game or app will be added to your download queue automatically.</p>
|
| 160 |
-
<h3>Q: How do I check for updates for my games and apps on Xbox Series X?</h3>
|
| 161 |
-
<p>A: You can check for updates for your games and apps on Xbox Series X by going to My games & apps > See all > Manage > Updates. Here, you can see which games and apps have available updates and select them to download them. You can also enable automatic updates to keep your games and apps up to date without checking manually.</p>
|
| 162 |
-
<h3>Q: How do I delete games and apps on Xbox Series X?</h3>
|
| 163 |
-
<p>A: You can delete games and apps on Xbox Series X by going to My games & apps > See all > Manage > Storage devices. Here, you can see how much space each game or app takes up on your console or external drive. Select one of them and choose Uninstall to delete it. You can also select Uninstall all to delete all games and apps on that device.</p>
|
| 164 |
-
<h3>Q: How do I move games and apps between devices on Xbox Series X?</h3>
|
| 165 |
-
<p>A: You can move games and apps between devices on Xbox Series X by going to My games & apps > See all > Manage > Storage devices. Here, you can see which games and apps are stored on your console or external drive. Select one of them and choose Move to move it to another device. You can also select Move all to move all games and apps from one device to another.</p>
|
| 166 |
-
<h3>Q: How do I play games from an external drive on Xbox Series X?</h3>
|
| 167 |
-
<p>A: You can play games from an external drive on Xbox Series X by plugging the drive into one of the USB ports on the back or front of your console. Your console will detect the drive and show you the games and apps that are stored on it. You can launch them from the guide or from My games & apps. You can also move them to your console or another external drive if you want.</p>
|
| 168 |
-
<p>Note that some games may require an update or optimization to run on Xbox Series X. You can check for these by going to My games & apps > See all > Manage > Updates > Optimized for Xbox Series X|S.</p> 197e85843d<br />
|
| 169 |
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spaces/1phancelerku/anime-remove-background/Explore the possibilities of melon playground a sandbox game for iOS and Android that is easy to play and hard to put down.md
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<br />
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<p>Do you like sandbox games where you can create your own scenarios and experiment with different items? Do you have an iOS device and want to find a fun and creative app to play with? If you answered yes to both questions, then you should check out Melon Playground, a simple but amazing sandbox game that will keep you entertained for hours.</p>
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<h2>What is Melon Playground?</h2>
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<h3>A simple sandbox game where you create your own scenarios</h3>
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<p>Melon Playground is an indie game created by a developer named sliz. It is available on Android and iOS devices . The game is very simple: you have a scene where you can drag and drop various items from an inventory. You can then interact with these items in different ways, such as throwing them, shooting them, exploding them, or combining them. You can also change the gravity, time, weather, and other settings of the scene. The game has no rules or objectives; you are free to create whatever you want.</p>
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<h3>A wide variety of items at your disposal: melee weapons, guns, barrels, and more</h3>
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<p>The game offers a wide variety of items that you can use in your scenarios. You can find melee weapons such as swords, axes, hammers, knives, and bats; guns such as pistols, rifles, shotguns, snipers, and rocket launchers; barrels such as oil drums, gas tanks, water barrels, and explosive barrels; and other items such as cars, bikes, planes, helicopters, boats, animals, humans, zombies, aliens, robots, ragdolls, furniture, plants, food, drinks, balls, balloons, fireworks, and more. You can also customize the color and size of some items.</p>
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<h3>A free app with in-app purchases and no ads subscription option</h3>
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<p>The game is free to download from the App Store and does not require an internet connection to play. However, some items are locked behind in-app purchases that range from $0.99 to $4.99. You can also buy a no ads subscription for $3.49 per week that removes all ads from the game. The game does not have any pop-up ads or banners; only video ads that play when you load or save a scenario or when you exit the app.</p>
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<h2>How to play Melon Playground?</h2>
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| 13 |
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<h3>Download the app from the App Store and launch it on your device</h3>
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<h3>Choose a map from the menu or create your own custom map</h3>
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<p>Once you launch the app, you will see a menu where you can choose a map to play on. There are 12 maps available in the game, each with a different theme and environment. You can choose from city, desert, forest, island, moon, ocean, playground, snow, space, swamp, volcano, and wasteland. You can also create your own custom map by selecting the blank map option and changing the terrain, skybox, and lighting.</p>
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| 39 |
-
melon playground net energy gain experiment<br />
|
| 40 |
-
melon playground holy grail fusion mini sun<br />
|
| 41 |
-
melon playground korea superconducting tokamak advanced research facility<br />
|
| 42 |
-
melon playground nuclear fusion reaction 100 million degrees celsius<br />
|
| 43 |
-
melon playground seven times hotter than sun core<br />
|
| 44 |
-
melon playground sun core temperature kelvin<br />
|
| 45 |
-
melon playground new scientist article<br />
|
| 46 |
-
melon playground the sun news article<br />
|
| 47 |
-
melon playground yahoo news article<br />
|
| 48 |
-
melon playground wikipedia solar core page<br />
|
| 49 |
-
melon playground cornell university solar layers page<br />
|
| 50 |
-
melon playground nasa sun fact sheet page<br />
|
| 51 |
-
melon playground montana solar physics core page<br />
|
| 52 |
-
melon playground identifiers usage data tracking<br />
|
| 53 |
-
melon playground purchases location user content linked data<br />
|
| 54 |
-
melon playground diagnostics data not linked to identity<br />
|
| 55 |
-
melon playground frequent intense cartoon fantasy violence</p>
|
| 56 |
-
<h3>Drag and drop items from the inventory to the scene and interact with them</h3>
|
| 57 |
-
<p>After you choose a map, you will enter the scene where you can start creating your scenario. You can access the inventory by tapping on the backpack icon on the top right corner of the screen. You can scroll through the categories and items by swiping left or right on the inventory. To place an item on the scene, simply drag and drop it from the inventory. You can also tap on an item to see its name and description.</p>
|
| 58 |
-
<p>To interact with an item, you can tap on it or use the buttons on the bottom of the screen. You can throw an item by tapping on it and swiping in any direction. You can shoot an item by tapping on it and tapping on the target icon. You can explode an item by tapping on it and tapping on the bomb icon. You can combine two items by dragging one item over another item and tapping on the plus icon.</p>
|
| 59 |
-
<h3>Use the buttons on the screen to move, rotate, scale, clone, delete, or freeze items</h3>
|
| 60 |
-
<p>You can also modify the items on the scene by using the buttons on the left side of the screen. You can move an item by tapping on it and dragging it with your finger. You can rotate an item by tapping on it and using two fingers to twist it. You can scale an item by tapping on it and using two fingers to pinch or spread it. You can clone an item by tapping on it and tapping on the clone icon. You can delete an item by tapping on it and tapping on the trash icon. You can freeze an item by tapping on it and tapping on the freeze icon.</p>
|
| 61 |
-
<h3>Save and load your scenarios or share them with other players online</h3>
|
| 62 |
-
<p>You can save your scenarios by tapping on the save icon on the top left corner of the screen. You can name your scenario and choose a thumbnail for it. You can load your scenarios by tapping on the load icon next to the save icon. You can also share your scenarios with other players online by tapping on the share icon next to the load icon. You can upload your scenario to a server where other players can download it and rate it.</p>
|
| 63 |
-
<h2>Why should you play Melon Playground?</h2>
|
| 64 |
-
<h3>It stimulates your imagination and creativity</h3>
|
| 65 |
-
<p>Melon Playground is a game that lets you unleash your imagination and creativity. You can create any scenario you want with any items you want. You can make funny, scary, action-packed, or relaxing scenarios. You can make stories, jokes, experiments, or challenges. You can make anything you can think of with Melon Playground.</p>
|
| 66 |
-
<h3>It offers endless possibilities and fun</h3>
|
| 67 |
-
<p>Melon Playground is a game that offers endless possibilities and fun. You can play with different items and see how they react with each other. You can change the settings of the scene and see how they affect your scenario. You can explore different maps and discover new things. You can also download other players' scenarios and see what they have created.</p>
|
| 68 |
-
<h3>It has positive reviews and ratings from users and critics</h3>
|
| 69 |
-
<h3>It is updated regularly with new features and improvements</h3>
|
| 70 |
-
<p>Melon Playground is a game that is updated regularly with new features and improvements. The developer listens to the feedback and suggestions from the users and implements them in the game. The game has received several updates since its launch in 2020, adding new items, maps, settings, modes, and bug fixes. The developer also plans to add more content and features in the future, such as multiplayer mode, online chat, voice chat, VR support, and more.</p>
|
| 71 |
-
<h2>Conclusion</h2>
|
| 72 |
-
<h3>Melon Playground is a great sandbox game for iOS devices that lets you create your own scenarios with various items</h3>
|
| 73 |
-
<p>In conclusion, Melon Playground is a great sandbox game for iOS devices that lets you create your own scenarios with various items. You can use melee weapons, guns, barrels, cars, planes, animals, humans, zombies, aliens, robots, ragdolls, furniture, plants, food, drinks, balls, balloons, fireworks, and more to make your scenarios. You can also change the gravity, time, weather, and other settings of the scene to make it more interesting.</p>
|
| 74 |
-
<h3>It is easy to play, free to download, and fun to explore</h3>
|
| 75 |
-
<p>Melon Playground is easy to play, free to download, and fun to explore. You can download it from the App Store and play it offline or online. You can drag and drop items from the inventory to the scene and interact with them using simple gestures and buttons. You can also save and load your scenarios or share them with other players online.</p>
|
| 76 |
-
<h3>It is a popular and well-made app that deserves your attention and support</h3>
|
| 77 |
-
<p>Melon Playground is a popular and well-made app that deserves your attention and support. It has positive reviews and ratings from users and critics . It has been featured in several media outlets such as AppAdvice, Pocket Gamer, TouchArcade, iMore, 148Apps, AppSpy, Slide to Play, Gamezebo, Cult of Mac, Macworld, The Guardian, Mashable, TechCrunch, Wired, The Verge, Polygon, Kotaku, IGN, GameSpot, PC Gamer, Forbes, Business Insider, CNN, BBC News, The New York Times, The Wall Street Journal, TIME Magazine, and more. It is also updated regularly with new features and improvements by the developer.</p>
|
| 78 |
-
<p>If you are looking for a fun and creative sandbox game for your iOS device, you should definitely give Melon Playground a try. You will not regret it.</p>
|
| 79 |
-
<h2>FAQs</h2>
|
| 80 |
-
<h4>What are the minimum requirements to play Melon Playground on iOS devices?</h4>
|
| 81 |
-
<p>To play Melon Playground on iOS devices, you need iOS 12.0 or later or iPadOS 12.0 or later. You also need at least 300 MB of free storage space on your device.</p>
|
| 82 |
-
<h4>How can I contact the developer of Melon Playground?</h4>
|
| 83 |
-
<p>You can contact the developer of Melon Playground by sending an email to [email protected] or by following him on Twitter @sliz_games.</p>
|
| 84 |
-
<h4>How can I support the development of Melon Playground?</h4>
|
| 85 |
-
<p>You can support the development of Melon Playground by buying in-app purchases or subscribing to no ads in the game. You can also rate and review the game on the App Store or share it with your friends.</p>
|
| 86 |
-
<h4>How can I report a bug or a problem in Melon Playground?</h4>
|
| 87 |
-
<p>You can report a bug or a problem in Melon Playground by sending an email to [email protected] or by leaving a comment on the game's page on the App Store.</p>
|
| 88 |
-
<h4>How can I suggest a new feature or an improvement for Melon Playground?</h4>
|
| 89 |
-
<p>You can suggest a new feature or an improvement for Melon Playground by sending an email to [email protected] or by leaving a comment on the game's page on the App Store.</p> 197e85843d<br />
|
| 90 |
-
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|
| 91 |
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spaces/4Taps/SadTalker/src/audio2pose_models/discriminator.py
DELETED
|
@@ -1,76 +0,0 @@
|
|
| 1 |
-
import torch
|
| 2 |
-
import torch.nn.functional as F
|
| 3 |
-
from torch import nn
|
| 4 |
-
|
| 5 |
-
class ConvNormRelu(nn.Module):
|
| 6 |
-
def __init__(self, conv_type='1d', in_channels=3, out_channels=64, downsample=False,
|
| 7 |
-
kernel_size=None, stride=None, padding=None, norm='BN', leaky=False):
|
| 8 |
-
super().__init__()
|
| 9 |
-
if kernel_size is None:
|
| 10 |
-
if downsample:
|
| 11 |
-
kernel_size, stride, padding = 4, 2, 1
|
| 12 |
-
else:
|
| 13 |
-
kernel_size, stride, padding = 3, 1, 1
|
| 14 |
-
|
| 15 |
-
if conv_type == '2d':
|
| 16 |
-
self.conv = nn.Conv2d(
|
| 17 |
-
in_channels,
|
| 18 |
-
out_channels,
|
| 19 |
-
kernel_size,
|
| 20 |
-
stride,
|
| 21 |
-
padding,
|
| 22 |
-
bias=False,
|
| 23 |
-
)
|
| 24 |
-
if norm == 'BN':
|
| 25 |
-
self.norm = nn.BatchNorm2d(out_channels)
|
| 26 |
-
elif norm == 'IN':
|
| 27 |
-
self.norm = nn.InstanceNorm2d(out_channels)
|
| 28 |
-
else:
|
| 29 |
-
raise NotImplementedError
|
| 30 |
-
elif conv_type == '1d':
|
| 31 |
-
self.conv = nn.Conv1d(
|
| 32 |
-
in_channels,
|
| 33 |
-
out_channels,
|
| 34 |
-
kernel_size,
|
| 35 |
-
stride,
|
| 36 |
-
padding,
|
| 37 |
-
bias=False,
|
| 38 |
-
)
|
| 39 |
-
if norm == 'BN':
|
| 40 |
-
self.norm = nn.BatchNorm1d(out_channels)
|
| 41 |
-
elif norm == 'IN':
|
| 42 |
-
self.norm = nn.InstanceNorm1d(out_channels)
|
| 43 |
-
else:
|
| 44 |
-
raise NotImplementedError
|
| 45 |
-
nn.init.kaiming_normal_(self.conv.weight)
|
| 46 |
-
|
| 47 |
-
self.act = nn.LeakyReLU(negative_slope=0.2, inplace=False) if leaky else nn.ReLU(inplace=True)
|
| 48 |
-
|
| 49 |
-
def forward(self, x):
|
| 50 |
-
x = self.conv(x)
|
| 51 |
-
if isinstance(self.norm, nn.InstanceNorm1d):
|
| 52 |
-
x = self.norm(x.permute((0, 2, 1))).permute((0, 2, 1)) # normalize on [C]
|
| 53 |
-
else:
|
| 54 |
-
x = self.norm(x)
|
| 55 |
-
x = self.act(x)
|
| 56 |
-
return x
|
| 57 |
-
|
| 58 |
-
|
| 59 |
-
class PoseSequenceDiscriminator(nn.Module):
|
| 60 |
-
def __init__(self, cfg):
|
| 61 |
-
super().__init__()
|
| 62 |
-
self.cfg = cfg
|
| 63 |
-
leaky = self.cfg.MODEL.DISCRIMINATOR.LEAKY_RELU
|
| 64 |
-
|
| 65 |
-
self.seq = nn.Sequential(
|
| 66 |
-
ConvNormRelu('1d', cfg.MODEL.DISCRIMINATOR.INPUT_CHANNELS, 256, downsample=True, leaky=leaky), # B, 256, 64
|
| 67 |
-
ConvNormRelu('1d', 256, 512, downsample=True, leaky=leaky), # B, 512, 32
|
| 68 |
-
ConvNormRelu('1d', 512, 1024, kernel_size=3, stride=1, padding=1, leaky=leaky), # B, 1024, 16
|
| 69 |
-
nn.Conv1d(1024, 1, kernel_size=3, stride=1, padding=1, bias=True) # B, 1, 16
|
| 70 |
-
)
|
| 71 |
-
|
| 72 |
-
def forward(self, x):
|
| 73 |
-
x = x.reshape(x.size(0), x.size(1), -1).transpose(1, 2)
|
| 74 |
-
x = self.seq(x)
|
| 75 |
-
x = x.squeeze(1)
|
| 76 |
-
return x
|
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|
spaces/801artistry/RVC801/README.md
DELETED
|
@@ -1,10 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: RVC Inference HF
|
| 3 |
-
emoji: 👀
|
| 4 |
-
colorFrom: green
|
| 5 |
-
colorTo: green
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.43.2
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
|
|
|
|
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|
spaces/801artistry/RVC801/diffq/base.py
DELETED
|
@@ -1,262 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Facebook, Inc. and its affiliates.
|
| 2 |
-
# All rights reserved.
|
| 3 |
-
#
|
| 4 |
-
# This source code is licensed under the license found in the
|
| 5 |
-
# LICENSE file in the root directory of this source tree.
|
| 6 |
-
|
| 7 |
-
from dataclasses import dataclass
|
| 8 |
-
from concurrent import futures
|
| 9 |
-
from fnmatch import fnmatch
|
| 10 |
-
from functools import partial
|
| 11 |
-
import io
|
| 12 |
-
import math
|
| 13 |
-
from multiprocessing import cpu_count
|
| 14 |
-
import typing as tp
|
| 15 |
-
import zlib
|
| 16 |
-
|
| 17 |
-
import torch
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
class BaseQuantizer:
|
| 21 |
-
@dataclass
|
| 22 |
-
class _QuantizedParam:
|
| 23 |
-
name: str
|
| 24 |
-
param: torch.nn.Parameter
|
| 25 |
-
module: torch.nn.Module
|
| 26 |
-
# If a Parameter is used multiple times, `other` can be used
|
| 27 |
-
# to share state between the different Quantizers
|
| 28 |
-
other: tp.Optional[tp.Any]
|
| 29 |
-
|
| 30 |
-
def __init__(self, model: torch.nn.Module, min_size: float = 0.01, float16: bool = False,
|
| 31 |
-
exclude: tp.Optional[tp.List[str]] = [], detect_bound: bool = True):
|
| 32 |
-
self.model = model
|
| 33 |
-
self.min_size = min_size
|
| 34 |
-
self.float16 = float16
|
| 35 |
-
self.exclude = exclude
|
| 36 |
-
self.detect_bound = detect_bound
|
| 37 |
-
self._quantized = False
|
| 38 |
-
self._pre_handle = self.model.register_forward_pre_hook(self._forward_pre_hook)
|
| 39 |
-
self._post_handle = self.model.register_forward_hook(self._forward_hook)
|
| 40 |
-
|
| 41 |
-
self._quantized_state = None
|
| 42 |
-
self._qparams = []
|
| 43 |
-
self._float16 = []
|
| 44 |
-
self._others = []
|
| 45 |
-
self._rnns = []
|
| 46 |
-
|
| 47 |
-
self._saved = []
|
| 48 |
-
|
| 49 |
-
self._find_params()
|
| 50 |
-
|
| 51 |
-
def _find_params(self):
|
| 52 |
-
min_params = self.min_size * 2**20 // 4
|
| 53 |
-
previous = {}
|
| 54 |
-
for module_name, module in self.model.named_modules():
|
| 55 |
-
if isinstance(module, torch.nn.RNNBase):
|
| 56 |
-
self._rnns.append(module)
|
| 57 |
-
for name, param in list(module.named_parameters(recurse=False)):
|
| 58 |
-
full_name = f"{module_name}.{name}"
|
| 59 |
-
matched = False
|
| 60 |
-
for pattern in self.exclude:
|
| 61 |
-
if fnmatch(full_name, pattern) or fnmatch(name, pattern):
|
| 62 |
-
matched = True
|
| 63 |
-
break
|
| 64 |
-
|
| 65 |
-
if param.numel() <= min_params or matched:
|
| 66 |
-
if id(param) in previous:
|
| 67 |
-
continue
|
| 68 |
-
if self.detect_bound:
|
| 69 |
-
previous[id(param)] = None
|
| 70 |
-
if self.float16:
|
| 71 |
-
self._float16.append(param)
|
| 72 |
-
else:
|
| 73 |
-
self._others.append(param)
|
| 74 |
-
else:
|
| 75 |
-
qparam = self._register_param(name, param, module, previous.get(id(param)))
|
| 76 |
-
if self.detect_bound:
|
| 77 |
-
previous[id(param)] = qparam
|
| 78 |
-
self._qparams.append(qparam)
|
| 79 |
-
|
| 80 |
-
def _register_param(self, name, param, module, other):
|
| 81 |
-
return self.__class__._QuantizedParam(name, param, module, other)
|
| 82 |
-
|
| 83 |
-
def _forward_pre_hook(self, module, input):
|
| 84 |
-
if self.model.training:
|
| 85 |
-
self._quantized_state = None
|
| 86 |
-
if self._quantized:
|
| 87 |
-
self.unquantize()
|
| 88 |
-
if self._pre_forward_train():
|
| 89 |
-
self._fix_rnns()
|
| 90 |
-
else:
|
| 91 |
-
self.quantize()
|
| 92 |
-
|
| 93 |
-
def _forward_hook(self, module, input, output):
|
| 94 |
-
if self.model.training:
|
| 95 |
-
if self._post_forward_train():
|
| 96 |
-
self._fix_rnns(flatten=False) # Hacky, next forward will flatten
|
| 97 |
-
|
| 98 |
-
def quantize(self, save=True):
|
| 99 |
-
"""
|
| 100 |
-
Immediately apply quantization to the model parameters.
|
| 101 |
-
If `save` is True, save a copy of the unquantized parameters, that can be
|
| 102 |
-
restored with `unquantize()`.
|
| 103 |
-
"""
|
| 104 |
-
if self._quantized:
|
| 105 |
-
return
|
| 106 |
-
if save:
|
| 107 |
-
self._saved = [qp.param.data.to('cpu', copy=True)
|
| 108 |
-
for qp in self._qparams if qp.other is None]
|
| 109 |
-
self.restore_quantized_state(self.get_quantized_state())
|
| 110 |
-
self._quantized = True
|
| 111 |
-
self._fix_rnns()
|
| 112 |
-
|
| 113 |
-
def unquantize(self):
|
| 114 |
-
"""
|
| 115 |
-
Revert a previous call to `quantize()`.
|
| 116 |
-
"""
|
| 117 |
-
if not self._quantized:
|
| 118 |
-
raise RuntimeError("Can only be called on a quantized model.")
|
| 119 |
-
if not self._saved:
|
| 120 |
-
raise RuntimeError("Nothing to restore.")
|
| 121 |
-
for qparam in self._qparams:
|
| 122 |
-
if qparam.other is None:
|
| 123 |
-
qparam.param.data[:] = self._saved.pop(0)
|
| 124 |
-
assert len(self._saved) == 0
|
| 125 |
-
self._quantized = False
|
| 126 |
-
self._fix_rnns()
|
| 127 |
-
|
| 128 |
-
def _pre_forward_train(self) -> bool:
|
| 129 |
-
"""
|
| 130 |
-
Called once before each forward for continuous quantization.
|
| 131 |
-
Should return True if parameters were changed.
|
| 132 |
-
"""
|
| 133 |
-
return False
|
| 134 |
-
|
| 135 |
-
def _post_forward_train(self) -> bool:
|
| 136 |
-
"""
|
| 137 |
-
Called once after each forward (to restore state for instance).
|
| 138 |
-
Should return True if parameters were changed.
|
| 139 |
-
"""
|
| 140 |
-
return False
|
| 141 |
-
|
| 142 |
-
def _fix_rnns(self, flatten=True):
|
| 143 |
-
"""
|
| 144 |
-
To be called after quantization happened to fix RNNs.
|
| 145 |
-
"""
|
| 146 |
-
for rnn in self._rnns:
|
| 147 |
-
rnn._flat_weights = [
|
| 148 |
-
(lambda wn: getattr(rnn, wn) if hasattr(rnn, wn) else None)(wn)
|
| 149 |
-
for wn in rnn._flat_weights_names]
|
| 150 |
-
if flatten:
|
| 151 |
-
rnn.flatten_parameters()
|
| 152 |
-
|
| 153 |
-
def get_quantized_state(self):
|
| 154 |
-
"""
|
| 155 |
-
Returns sufficient quantized information to rebuild the model state.
|
| 156 |
-
|
| 157 |
-
..Note::
|
| 158 |
-
To achieve maximum compression, you should compress this with
|
| 159 |
-
gzip or other, as quantized weights are not optimally coded!
|
| 160 |
-
"""
|
| 161 |
-
if self._quantized_state is None:
|
| 162 |
-
self._quantized_state = self._get_quantized_state()
|
| 163 |
-
return self._quantized_state
|
| 164 |
-
|
| 165 |
-
def _get_quantized_state(self):
|
| 166 |
-
"""
|
| 167 |
-
Actual implementation for `get_quantized_state`.
|
| 168 |
-
"""
|
| 169 |
-
float16_params = []
|
| 170 |
-
for p in self._float16:
|
| 171 |
-
q = p.data.half()
|
| 172 |
-
float16_params.append(q)
|
| 173 |
-
|
| 174 |
-
return {
|
| 175 |
-
"quantized": [self._quantize_param(qparam) for qparam in self._qparams
|
| 176 |
-
if qparam.other is None],
|
| 177 |
-
"float16": float16_params,
|
| 178 |
-
"others": [p.data.clone() for p in self._others],
|
| 179 |
-
}
|
| 180 |
-
|
| 181 |
-
def _quantize_param(self, qparam: _QuantizedParam) -> tp.Any:
|
| 182 |
-
"""
|
| 183 |
-
To be overriden.
|
| 184 |
-
"""
|
| 185 |
-
raise NotImplementedError()
|
| 186 |
-
|
| 187 |
-
def _unquantize_param(self, qparam: _QuantizedParam, quantized: tp.Any) -> torch.Tensor:
|
| 188 |
-
"""
|
| 189 |
-
To be overriden.
|
| 190 |
-
"""
|
| 191 |
-
raise NotImplementedError()
|
| 192 |
-
|
| 193 |
-
def restore_quantized_state(self, state) -> None:
|
| 194 |
-
"""
|
| 195 |
-
Restore the state of the model from the quantized state.
|
| 196 |
-
"""
|
| 197 |
-
for p, q in zip(self._float16, state["float16"]):
|
| 198 |
-
p.data[:] = q.to(p)
|
| 199 |
-
|
| 200 |
-
for p, q in zip(self._others, state["others"]):
|
| 201 |
-
p.data[:] = q
|
| 202 |
-
|
| 203 |
-
remaining = list(state["quantized"])
|
| 204 |
-
for qparam in self._qparams:
|
| 205 |
-
if qparam.other is not None:
|
| 206 |
-
# Only unquantize first appearance of nn.Parameter.
|
| 207 |
-
continue
|
| 208 |
-
quantized = remaining.pop(0)
|
| 209 |
-
qparam.param.data[:] = self._unquantize_param(qparam, quantized)
|
| 210 |
-
self._fix_rnns()
|
| 211 |
-
|
| 212 |
-
def detach(self) -> None:
|
| 213 |
-
"""
|
| 214 |
-
Detach from the model, removes hooks and anything else.
|
| 215 |
-
"""
|
| 216 |
-
self._pre_handle.remove()
|
| 217 |
-
self._post_handle.remove()
|
| 218 |
-
|
| 219 |
-
def model_size(self) -> torch.Tensor:
|
| 220 |
-
"""
|
| 221 |
-
Returns an estimate of the quantized model size.
|
| 222 |
-
"""
|
| 223 |
-
total = torch.tensor(0.)
|
| 224 |
-
for p in self._float16:
|
| 225 |
-
total += 16 * p.numel()
|
| 226 |
-
for p in self._others:
|
| 227 |
-
total += 32 * p.numel()
|
| 228 |
-
return total / 2**20 / 8 # bits to MegaBytes
|
| 229 |
-
|
| 230 |
-
def true_model_size(self) -> float:
|
| 231 |
-
"""
|
| 232 |
-
Return the true quantized model size, in MB, without extra
|
| 233 |
-
compression.
|
| 234 |
-
"""
|
| 235 |
-
return self.model_size().item()
|
| 236 |
-
|
| 237 |
-
def compressed_model_size(self, compress_level=-1, num_workers=8) -> float:
|
| 238 |
-
"""
|
| 239 |
-
Return the compressed quantized model size, in MB.
|
| 240 |
-
|
| 241 |
-
Args:
|
| 242 |
-
compress_level (int): compression level used with zlib,
|
| 243 |
-
see `zlib.compress` for details.
|
| 244 |
-
num_workers (int): will split the final big byte representation in that
|
| 245 |
-
many chunks processed in parallels.
|
| 246 |
-
"""
|
| 247 |
-
out = io.BytesIO()
|
| 248 |
-
torch.save(self.get_quantized_state(), out)
|
| 249 |
-
ms = _parallel_compress_len(out.getvalue(), compress_level, num_workers)
|
| 250 |
-
return ms / 2 ** 20
|
| 251 |
-
|
| 252 |
-
|
| 253 |
-
def _compress_len(data, compress_level):
|
| 254 |
-
return len(zlib.compress(data, level=compress_level))
|
| 255 |
-
|
| 256 |
-
|
| 257 |
-
def _parallel_compress_len(data, compress_level, num_workers):
|
| 258 |
-
num_workers = min(cpu_count(), num_workers)
|
| 259 |
-
chunk_size = int(math.ceil(len(data) / num_workers))
|
| 260 |
-
chunks = [data[offset:offset + chunk_size] for offset in range(0, len(data), chunk_size)]
|
| 261 |
-
with futures.ProcessPoolExecutor(num_workers) as pool:
|
| 262 |
-
return sum(pool.map(partial(_compress_len, compress_level=compress_level), chunks))
|
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|
spaces/AIFILMS/generate_human_motion/pyrender/pyrender/platforms/egl.py
DELETED
|
@@ -1,219 +0,0 @@
|
|
| 1 |
-
import ctypes
|
| 2 |
-
import os
|
| 3 |
-
|
| 4 |
-
import OpenGL.platform
|
| 5 |
-
|
| 6 |
-
from .base import Platform
|
| 7 |
-
|
| 8 |
-
EGL_PLATFORM_DEVICE_EXT = 0x313F
|
| 9 |
-
EGL_DRM_DEVICE_FILE_EXT = 0x3233
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
def _ensure_egl_loaded():
|
| 13 |
-
plugin = OpenGL.platform.PlatformPlugin.by_name('egl')
|
| 14 |
-
if plugin is None:
|
| 15 |
-
raise RuntimeError("EGL platform plugin is not available.")
|
| 16 |
-
|
| 17 |
-
plugin_class = plugin.load()
|
| 18 |
-
plugin.loaded = True
|
| 19 |
-
# create instance of this platform implementation
|
| 20 |
-
plugin = plugin_class()
|
| 21 |
-
|
| 22 |
-
plugin.install(vars(OpenGL.platform))
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
_ensure_egl_loaded()
|
| 26 |
-
from OpenGL import EGL as egl
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
def _get_egl_func(func_name, res_type, *arg_types):
|
| 30 |
-
address = egl.eglGetProcAddress(func_name)
|
| 31 |
-
if address is None:
|
| 32 |
-
return None
|
| 33 |
-
|
| 34 |
-
proto = ctypes.CFUNCTYPE(res_type)
|
| 35 |
-
proto.argtypes = arg_types
|
| 36 |
-
func = proto(address)
|
| 37 |
-
return func
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
def _get_egl_struct(struct_name):
|
| 41 |
-
from OpenGL._opaque import opaque_pointer_cls
|
| 42 |
-
return opaque_pointer_cls(struct_name)
|
| 43 |
-
|
| 44 |
-
|
| 45 |
-
# These are not defined in PyOpenGL by default.
|
| 46 |
-
_EGLDeviceEXT = _get_egl_struct('EGLDeviceEXT')
|
| 47 |
-
_eglGetPlatformDisplayEXT = _get_egl_func('eglGetPlatformDisplayEXT', egl.EGLDisplay)
|
| 48 |
-
_eglQueryDevicesEXT = _get_egl_func('eglQueryDevicesEXT', egl.EGLBoolean)
|
| 49 |
-
_eglQueryDeviceStringEXT = _get_egl_func('eglQueryDeviceStringEXT', ctypes.c_char_p)
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
def query_devices():
|
| 53 |
-
if _eglQueryDevicesEXT is None:
|
| 54 |
-
raise RuntimeError("EGL query extension is not loaded or is not supported.")
|
| 55 |
-
|
| 56 |
-
num_devices = egl.EGLint()
|
| 57 |
-
success = _eglQueryDevicesEXT(0, None, ctypes.pointer(num_devices))
|
| 58 |
-
if not success or num_devices.value < 1:
|
| 59 |
-
return []
|
| 60 |
-
|
| 61 |
-
devices = (_EGLDeviceEXT * num_devices.value)() # array of size num_devices
|
| 62 |
-
success = _eglQueryDevicesEXT(num_devices.value, devices, ctypes.pointer(num_devices))
|
| 63 |
-
if not success or num_devices.value < 1:
|
| 64 |
-
return []
|
| 65 |
-
|
| 66 |
-
return [EGLDevice(devices[i]) for i in range(num_devices.value)]
|
| 67 |
-
|
| 68 |
-
|
| 69 |
-
def get_default_device():
|
| 70 |
-
# Fall back to not using query extension.
|
| 71 |
-
if _eglQueryDevicesEXT is None:
|
| 72 |
-
return EGLDevice(None)
|
| 73 |
-
|
| 74 |
-
return query_devices()[0]
|
| 75 |
-
|
| 76 |
-
|
| 77 |
-
def get_device_by_index(device_id):
|
| 78 |
-
if _eglQueryDevicesEXT is None and device_id == 0:
|
| 79 |
-
return get_default_device()
|
| 80 |
-
|
| 81 |
-
devices = query_devices()
|
| 82 |
-
if device_id >= len(devices):
|
| 83 |
-
raise ValueError('Invalid device ID ({})'.format(device_id, len(devices)))
|
| 84 |
-
return devices[device_id]
|
| 85 |
-
|
| 86 |
-
|
| 87 |
-
class EGLDevice:
|
| 88 |
-
|
| 89 |
-
def __init__(self, display=None):
|
| 90 |
-
self._display = display
|
| 91 |
-
|
| 92 |
-
def get_display(self):
|
| 93 |
-
if self._display is None:
|
| 94 |
-
return egl.eglGetDisplay(egl.EGL_DEFAULT_DISPLAY)
|
| 95 |
-
|
| 96 |
-
return _eglGetPlatformDisplayEXT(EGL_PLATFORM_DEVICE_EXT, self._display, None)
|
| 97 |
-
|
| 98 |
-
@property
|
| 99 |
-
def name(self):
|
| 100 |
-
if self._display is None:
|
| 101 |
-
return 'default'
|
| 102 |
-
|
| 103 |
-
name = _eglQueryDeviceStringEXT(self._display, EGL_DRM_DEVICE_FILE_EXT)
|
| 104 |
-
if name is None:
|
| 105 |
-
return None
|
| 106 |
-
|
| 107 |
-
return name.decode('ascii')
|
| 108 |
-
|
| 109 |
-
def __repr__(self):
|
| 110 |
-
return "<EGLDevice(name={})>".format(self.name)
|
| 111 |
-
|
| 112 |
-
|
| 113 |
-
class EGLPlatform(Platform):
|
| 114 |
-
"""Renders using EGL.
|
| 115 |
-
"""
|
| 116 |
-
|
| 117 |
-
def __init__(self, viewport_width, viewport_height, device: EGLDevice = None):
|
| 118 |
-
super(EGLPlatform, self).__init__(viewport_width, viewport_height)
|
| 119 |
-
if device is None:
|
| 120 |
-
device = get_default_device()
|
| 121 |
-
|
| 122 |
-
self._egl_device = device
|
| 123 |
-
self._egl_display = None
|
| 124 |
-
self._egl_context = None
|
| 125 |
-
|
| 126 |
-
def init_context(self):
|
| 127 |
-
_ensure_egl_loaded()
|
| 128 |
-
|
| 129 |
-
from OpenGL.EGL import (
|
| 130 |
-
EGL_SURFACE_TYPE, EGL_PBUFFER_BIT, EGL_BLUE_SIZE,
|
| 131 |
-
EGL_RED_SIZE, EGL_GREEN_SIZE, EGL_DEPTH_SIZE,
|
| 132 |
-
EGL_COLOR_BUFFER_TYPE, EGL_RGB_BUFFER,
|
| 133 |
-
EGL_RENDERABLE_TYPE, EGL_OPENGL_BIT, EGL_CONFORMANT,
|
| 134 |
-
EGL_NONE, EGL_DEFAULT_DISPLAY, EGL_NO_CONTEXT,
|
| 135 |
-
EGL_OPENGL_API, EGL_CONTEXT_MAJOR_VERSION,
|
| 136 |
-
EGL_CONTEXT_MINOR_VERSION,
|
| 137 |
-
EGL_CONTEXT_OPENGL_PROFILE_MASK,
|
| 138 |
-
EGL_CONTEXT_OPENGL_CORE_PROFILE_BIT,
|
| 139 |
-
eglGetDisplay, eglInitialize, eglChooseConfig,
|
| 140 |
-
eglBindAPI, eglCreateContext, EGLConfig
|
| 141 |
-
)
|
| 142 |
-
from OpenGL import arrays
|
| 143 |
-
|
| 144 |
-
config_attributes = arrays.GLintArray.asArray([
|
| 145 |
-
EGL_SURFACE_TYPE, EGL_PBUFFER_BIT,
|
| 146 |
-
EGL_BLUE_SIZE, 8,
|
| 147 |
-
EGL_RED_SIZE, 8,
|
| 148 |
-
EGL_GREEN_SIZE, 8,
|
| 149 |
-
EGL_DEPTH_SIZE, 24,
|
| 150 |
-
EGL_COLOR_BUFFER_TYPE, EGL_RGB_BUFFER,
|
| 151 |
-
EGL_RENDERABLE_TYPE, EGL_OPENGL_BIT,
|
| 152 |
-
EGL_CONFORMANT, EGL_OPENGL_BIT,
|
| 153 |
-
EGL_NONE
|
| 154 |
-
])
|
| 155 |
-
context_attributes = arrays.GLintArray.asArray([
|
| 156 |
-
EGL_CONTEXT_MAJOR_VERSION, 4,
|
| 157 |
-
EGL_CONTEXT_MINOR_VERSION, 1,
|
| 158 |
-
EGL_CONTEXT_OPENGL_PROFILE_MASK,
|
| 159 |
-
EGL_CONTEXT_OPENGL_CORE_PROFILE_BIT,
|
| 160 |
-
EGL_NONE
|
| 161 |
-
])
|
| 162 |
-
major, minor = ctypes.c_long(), ctypes.c_long()
|
| 163 |
-
num_configs = ctypes.c_long()
|
| 164 |
-
configs = (EGLConfig * 1)()
|
| 165 |
-
|
| 166 |
-
# Cache DISPLAY if necessary and get an off-screen EGL display
|
| 167 |
-
orig_dpy = None
|
| 168 |
-
if 'DISPLAY' in os.environ:
|
| 169 |
-
orig_dpy = os.environ['DISPLAY']
|
| 170 |
-
del os.environ['DISPLAY']
|
| 171 |
-
|
| 172 |
-
self._egl_display = self._egl_device.get_display()
|
| 173 |
-
if orig_dpy is not None:
|
| 174 |
-
os.environ['DISPLAY'] = orig_dpy
|
| 175 |
-
|
| 176 |
-
# Initialize EGL
|
| 177 |
-
assert eglInitialize(self._egl_display, major, minor)
|
| 178 |
-
assert eglChooseConfig(
|
| 179 |
-
self._egl_display, config_attributes, configs, 1, num_configs
|
| 180 |
-
)
|
| 181 |
-
|
| 182 |
-
# Bind EGL to the OpenGL API
|
| 183 |
-
assert eglBindAPI(EGL_OPENGL_API)
|
| 184 |
-
|
| 185 |
-
# Create an EGL context
|
| 186 |
-
self._egl_context = eglCreateContext(
|
| 187 |
-
self._egl_display, configs[0],
|
| 188 |
-
EGL_NO_CONTEXT, context_attributes
|
| 189 |
-
)
|
| 190 |
-
|
| 191 |
-
# Make it current
|
| 192 |
-
self.make_current()
|
| 193 |
-
|
| 194 |
-
def make_current(self):
|
| 195 |
-
from OpenGL.EGL import eglMakeCurrent, EGL_NO_SURFACE
|
| 196 |
-
assert eglMakeCurrent(
|
| 197 |
-
self._egl_display, EGL_NO_SURFACE, EGL_NO_SURFACE,
|
| 198 |
-
self._egl_context
|
| 199 |
-
)
|
| 200 |
-
|
| 201 |
-
def make_uncurrent(self):
|
| 202 |
-
"""Make the OpenGL context uncurrent.
|
| 203 |
-
"""
|
| 204 |
-
pass
|
| 205 |
-
|
| 206 |
-
def delete_context(self):
|
| 207 |
-
from OpenGL.EGL import eglDestroyContext, eglTerminate
|
| 208 |
-
if self._egl_display is not None:
|
| 209 |
-
if self._egl_context is not None:
|
| 210 |
-
eglDestroyContext(self._egl_display, self._egl_context)
|
| 211 |
-
self._egl_context = None
|
| 212 |
-
eglTerminate(self._egl_display)
|
| 213 |
-
self._egl_display = None
|
| 214 |
-
|
| 215 |
-
def supports_framebuffers(self):
|
| 216 |
-
return True
|
| 217 |
-
|
| 218 |
-
|
| 219 |
-
__all__ = ['EGLPlatform']
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|
spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/commons/normalizing_flow/glow_modules.py
DELETED
|
@@ -1,362 +0,0 @@
|
|
| 1 |
-
import scipy
|
| 2 |
-
from torch.nn import functional as F
|
| 3 |
-
import torch
|
| 4 |
-
from torch import nn
|
| 5 |
-
import numpy as np
|
| 6 |
-
from modules.commons.wavenet import WN
|
| 7 |
-
from modules.glow import utils
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
class ActNorm(nn.Module):
|
| 11 |
-
def __init__(self, channels, ddi=False, **kwargs):
|
| 12 |
-
super().__init__()
|
| 13 |
-
self.channels = channels
|
| 14 |
-
self.initialized = not ddi
|
| 15 |
-
|
| 16 |
-
self.logs = nn.Parameter(torch.zeros(1, channels, 1))
|
| 17 |
-
self.bias = nn.Parameter(torch.zeros(1, channels, 1))
|
| 18 |
-
|
| 19 |
-
def forward(self, x, x_mask=None, reverse=False, **kwargs):
|
| 20 |
-
if x_mask is None:
|
| 21 |
-
x_mask = torch.ones(x.size(0), 1, x.size(2)).to(device=x.device, dtype=x.dtype)
|
| 22 |
-
x_len = torch.sum(x_mask, [1, 2])
|
| 23 |
-
if not self.initialized:
|
| 24 |
-
self.initialize(x, x_mask)
|
| 25 |
-
self.initialized = True
|
| 26 |
-
|
| 27 |
-
if reverse:
|
| 28 |
-
z = (x - self.bias) * torch.exp(-self.logs) * x_mask
|
| 29 |
-
logdet = torch.sum(-self.logs) * x_len
|
| 30 |
-
else:
|
| 31 |
-
z = (self.bias + torch.exp(self.logs) * x) * x_mask
|
| 32 |
-
logdet = torch.sum(self.logs) * x_len # [b]
|
| 33 |
-
return z, logdet
|
| 34 |
-
|
| 35 |
-
def store_inverse(self):
|
| 36 |
-
pass
|
| 37 |
-
|
| 38 |
-
def set_ddi(self, ddi):
|
| 39 |
-
self.initialized = not ddi
|
| 40 |
-
|
| 41 |
-
def initialize(self, x, x_mask):
|
| 42 |
-
with torch.no_grad():
|
| 43 |
-
denom = torch.sum(x_mask, [0, 2])
|
| 44 |
-
m = torch.sum(x * x_mask, [0, 2]) / denom
|
| 45 |
-
m_sq = torch.sum(x * x * x_mask, [0, 2]) / denom
|
| 46 |
-
v = m_sq - (m ** 2)
|
| 47 |
-
logs = 0.5 * torch.log(torch.clamp_min(v, 1e-6))
|
| 48 |
-
|
| 49 |
-
bias_init = (-m * torch.exp(-logs)).view(*self.bias.shape).to(dtype=self.bias.dtype)
|
| 50 |
-
logs_init = (-logs).view(*self.logs.shape).to(dtype=self.logs.dtype)
|
| 51 |
-
|
| 52 |
-
self.bias.data.copy_(bias_init)
|
| 53 |
-
self.logs.data.copy_(logs_init)
|
| 54 |
-
|
| 55 |
-
|
| 56 |
-
class InvConvNear(nn.Module):
|
| 57 |
-
def __init__(self, channels, n_split=4, no_jacobian=False, lu=True, n_sqz=2, **kwargs):
|
| 58 |
-
super().__init__()
|
| 59 |
-
assert (n_split % 2 == 0)
|
| 60 |
-
self.channels = channels
|
| 61 |
-
self.n_split = n_split
|
| 62 |
-
self.n_sqz = n_sqz
|
| 63 |
-
self.no_jacobian = no_jacobian
|
| 64 |
-
|
| 65 |
-
w_init = torch.qr(torch.FloatTensor(self.n_split, self.n_split).normal_())[0]
|
| 66 |
-
if torch.det(w_init) < 0:
|
| 67 |
-
w_init[:, 0] = -1 * w_init[:, 0]
|
| 68 |
-
self.lu = lu
|
| 69 |
-
if lu:
|
| 70 |
-
# LU decomposition can slightly speed up the inverse
|
| 71 |
-
np_p, np_l, np_u = scipy.linalg.lu(w_init)
|
| 72 |
-
np_s = np.diag(np_u)
|
| 73 |
-
np_sign_s = np.sign(np_s)
|
| 74 |
-
np_log_s = np.log(np.abs(np_s))
|
| 75 |
-
np_u = np.triu(np_u, k=1)
|
| 76 |
-
l_mask = np.tril(np.ones(w_init.shape, dtype=float), -1)
|
| 77 |
-
eye = np.eye(*w_init.shape, dtype=float)
|
| 78 |
-
|
| 79 |
-
self.register_buffer('p', torch.Tensor(np_p.astype(float)))
|
| 80 |
-
self.register_buffer('sign_s', torch.Tensor(np_sign_s.astype(float)))
|
| 81 |
-
self.l = nn.Parameter(torch.Tensor(np_l.astype(float)), requires_grad=True)
|
| 82 |
-
self.log_s = nn.Parameter(torch.Tensor(np_log_s.astype(float)), requires_grad=True)
|
| 83 |
-
self.u = nn.Parameter(torch.Tensor(np_u.astype(float)), requires_grad=True)
|
| 84 |
-
self.register_buffer('l_mask', torch.Tensor(l_mask))
|
| 85 |
-
self.register_buffer('eye', torch.Tensor(eye))
|
| 86 |
-
else:
|
| 87 |
-
self.weight = nn.Parameter(w_init)
|
| 88 |
-
|
| 89 |
-
def forward(self, x, x_mask=None, reverse=False, **kwargs):
|
| 90 |
-
b, c, t = x.size()
|
| 91 |
-
assert (c % self.n_split == 0)
|
| 92 |
-
if x_mask is None:
|
| 93 |
-
x_mask = 1
|
| 94 |
-
x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t
|
| 95 |
-
else:
|
| 96 |
-
x_len = torch.sum(x_mask, [1, 2])
|
| 97 |
-
|
| 98 |
-
x = x.view(b, self.n_sqz, c // self.n_split, self.n_split // self.n_sqz, t)
|
| 99 |
-
x = x.permute(0, 1, 3, 2, 4).contiguous().view(b, self.n_split, c // self.n_split, t)
|
| 100 |
-
|
| 101 |
-
if self.lu:
|
| 102 |
-
self.weight, log_s = self._get_weight()
|
| 103 |
-
logdet = log_s.sum()
|
| 104 |
-
logdet = logdet * (c / self.n_split) * x_len
|
| 105 |
-
else:
|
| 106 |
-
logdet = torch.logdet(self.weight) * (c / self.n_split) * x_len # [b]
|
| 107 |
-
|
| 108 |
-
if reverse:
|
| 109 |
-
if hasattr(self, "weight_inv"):
|
| 110 |
-
weight = self.weight_inv
|
| 111 |
-
else:
|
| 112 |
-
weight = torch.inverse(self.weight.float()).to(dtype=self.weight.dtype)
|
| 113 |
-
logdet = -logdet
|
| 114 |
-
else:
|
| 115 |
-
weight = self.weight
|
| 116 |
-
if self.no_jacobian:
|
| 117 |
-
logdet = 0
|
| 118 |
-
|
| 119 |
-
weight = weight.view(self.n_split, self.n_split, 1, 1)
|
| 120 |
-
z = F.conv2d(x, weight)
|
| 121 |
-
|
| 122 |
-
z = z.view(b, self.n_sqz, self.n_split // self.n_sqz, c // self.n_split, t)
|
| 123 |
-
z = z.permute(0, 1, 3, 2, 4).contiguous().view(b, c, t) * x_mask
|
| 124 |
-
return z, logdet
|
| 125 |
-
|
| 126 |
-
def _get_weight(self):
|
| 127 |
-
l, log_s, u = self.l, self.log_s, self.u
|
| 128 |
-
l = l * self.l_mask + self.eye
|
| 129 |
-
u = u * self.l_mask.transpose(0, 1).contiguous() + torch.diag(self.sign_s * torch.exp(log_s))
|
| 130 |
-
weight = torch.matmul(self.p, torch.matmul(l, u))
|
| 131 |
-
return weight, log_s
|
| 132 |
-
|
| 133 |
-
def store_inverse(self):
|
| 134 |
-
weight, _ = self._get_weight()
|
| 135 |
-
self.weight_inv = torch.inverse(weight.float()).to(next(self.parameters()).device)
|
| 136 |
-
|
| 137 |
-
|
| 138 |
-
class InvConv(nn.Module):
|
| 139 |
-
def __init__(self, channels, no_jacobian=False, lu=True, **kwargs):
|
| 140 |
-
super().__init__()
|
| 141 |
-
w_shape = [channels, channels]
|
| 142 |
-
w_init = np.linalg.qr(np.random.randn(*w_shape))[0].astype(float)
|
| 143 |
-
LU_decomposed = lu
|
| 144 |
-
if not LU_decomposed:
|
| 145 |
-
# Sample a random orthogonal matrix:
|
| 146 |
-
self.register_parameter("weight", nn.Parameter(torch.Tensor(w_init)))
|
| 147 |
-
else:
|
| 148 |
-
np_p, np_l, np_u = scipy.linalg.lu(w_init)
|
| 149 |
-
np_s = np.diag(np_u)
|
| 150 |
-
np_sign_s = np.sign(np_s)
|
| 151 |
-
np_log_s = np.log(np.abs(np_s))
|
| 152 |
-
np_u = np.triu(np_u, k=1)
|
| 153 |
-
l_mask = np.tril(np.ones(w_shape, dtype=float), -1)
|
| 154 |
-
eye = np.eye(*w_shape, dtype=float)
|
| 155 |
-
|
| 156 |
-
self.register_buffer('p', torch.Tensor(np_p.astype(float)))
|
| 157 |
-
self.register_buffer('sign_s', torch.Tensor(np_sign_s.astype(float)))
|
| 158 |
-
self.l = nn.Parameter(torch.Tensor(np_l.astype(float)))
|
| 159 |
-
self.log_s = nn.Parameter(torch.Tensor(np_log_s.astype(float)))
|
| 160 |
-
self.u = nn.Parameter(torch.Tensor(np_u.astype(float)))
|
| 161 |
-
self.l_mask = torch.Tensor(l_mask)
|
| 162 |
-
self.eye = torch.Tensor(eye)
|
| 163 |
-
self.w_shape = w_shape
|
| 164 |
-
self.LU = LU_decomposed
|
| 165 |
-
self.weight = None
|
| 166 |
-
|
| 167 |
-
def get_weight(self, device, reverse):
|
| 168 |
-
w_shape = self.w_shape
|
| 169 |
-
self.p = self.p.to(device)
|
| 170 |
-
self.sign_s = self.sign_s.to(device)
|
| 171 |
-
self.l_mask = self.l_mask.to(device)
|
| 172 |
-
self.eye = self.eye.to(device)
|
| 173 |
-
l = self.l * self.l_mask + self.eye
|
| 174 |
-
u = self.u * self.l_mask.transpose(0, 1).contiguous() + torch.diag(self.sign_s * torch.exp(self.log_s))
|
| 175 |
-
dlogdet = self.log_s.sum()
|
| 176 |
-
if not reverse:
|
| 177 |
-
w = torch.matmul(self.p, torch.matmul(l, u))
|
| 178 |
-
else:
|
| 179 |
-
l = torch.inverse(l.double()).float()
|
| 180 |
-
u = torch.inverse(u.double()).float()
|
| 181 |
-
w = torch.matmul(u, torch.matmul(l, self.p.inverse()))
|
| 182 |
-
return w.view(w_shape[0], w_shape[1], 1), dlogdet
|
| 183 |
-
|
| 184 |
-
def forward(self, x, x_mask=None, reverse=False, **kwargs):
|
| 185 |
-
"""
|
| 186 |
-
log-det = log|abs(|W|)| * pixels
|
| 187 |
-
"""
|
| 188 |
-
b, c, t = x.size()
|
| 189 |
-
if x_mask is None:
|
| 190 |
-
x_len = torch.ones((b,), dtype=x.dtype, device=x.device) * t
|
| 191 |
-
else:
|
| 192 |
-
x_len = torch.sum(x_mask, [1, 2])
|
| 193 |
-
logdet = 0
|
| 194 |
-
if not reverse:
|
| 195 |
-
weight, dlogdet = self.get_weight(x.device, reverse)
|
| 196 |
-
z = F.conv1d(x, weight)
|
| 197 |
-
if logdet is not None:
|
| 198 |
-
logdet = logdet + dlogdet * x_len
|
| 199 |
-
return z, logdet
|
| 200 |
-
else:
|
| 201 |
-
if self.weight is None:
|
| 202 |
-
weight, dlogdet = self.get_weight(x.device, reverse)
|
| 203 |
-
else:
|
| 204 |
-
weight, dlogdet = self.weight, self.dlogdet
|
| 205 |
-
z = F.conv1d(x, weight)
|
| 206 |
-
if logdet is not None:
|
| 207 |
-
logdet = logdet - dlogdet * x_len
|
| 208 |
-
return z, logdet
|
| 209 |
-
|
| 210 |
-
def store_inverse(self):
|
| 211 |
-
self.weight, self.dlogdet = self.get_weight('cuda', reverse=True)
|
| 212 |
-
|
| 213 |
-
|
| 214 |
-
class CouplingBlock(nn.Module):
|
| 215 |
-
def __init__(self, in_channels, hidden_channels, kernel_size, dilation_rate, n_layers,
|
| 216 |
-
gin_channels=0, p_dropout=0, sigmoid_scale=False, wn=None):
|
| 217 |
-
super().__init__()
|
| 218 |
-
self.in_channels = in_channels
|
| 219 |
-
self.hidden_channels = hidden_channels
|
| 220 |
-
self.kernel_size = kernel_size
|
| 221 |
-
self.dilation_rate = dilation_rate
|
| 222 |
-
self.n_layers = n_layers
|
| 223 |
-
self.gin_channels = gin_channels
|
| 224 |
-
self.p_dropout = p_dropout
|
| 225 |
-
self.sigmoid_scale = sigmoid_scale
|
| 226 |
-
|
| 227 |
-
start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1)
|
| 228 |
-
start = torch.nn.utils.weight_norm(start)
|
| 229 |
-
self.start = start
|
| 230 |
-
# Initializing last layer to 0 makes the affine coupling layers
|
| 231 |
-
# do nothing at first. This helps with training stability
|
| 232 |
-
end = torch.nn.Conv1d(hidden_channels, in_channels, 1)
|
| 233 |
-
end.weight.data.zero_()
|
| 234 |
-
end.bias.data.zero_()
|
| 235 |
-
self.end = end
|
| 236 |
-
self.wn = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels, p_dropout)
|
| 237 |
-
if wn is not None:
|
| 238 |
-
self.wn.in_layers = wn.in_layers
|
| 239 |
-
self.wn.res_skip_layers = wn.res_skip_layers
|
| 240 |
-
|
| 241 |
-
def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs):
|
| 242 |
-
if x_mask is None:
|
| 243 |
-
x_mask = 1
|
| 244 |
-
x_0, x_1 = x[:, :self.in_channels // 2], x[:, self.in_channels // 2:]
|
| 245 |
-
|
| 246 |
-
x = self.start(x_0) * x_mask
|
| 247 |
-
x = self.wn(x, x_mask, g)
|
| 248 |
-
out = self.end(x)
|
| 249 |
-
|
| 250 |
-
z_0 = x_0
|
| 251 |
-
m = out[:, :self.in_channels // 2, :]
|
| 252 |
-
logs = out[:, self.in_channels // 2:, :]
|
| 253 |
-
if self.sigmoid_scale:
|
| 254 |
-
logs = torch.log(1e-6 + torch.sigmoid(logs + 2))
|
| 255 |
-
if reverse:
|
| 256 |
-
z_1 = (x_1 - m) * torch.exp(-logs) * x_mask
|
| 257 |
-
logdet = torch.sum(-logs * x_mask, [1, 2])
|
| 258 |
-
else:
|
| 259 |
-
z_1 = (m + torch.exp(logs) * x_1) * x_mask
|
| 260 |
-
logdet = torch.sum(logs * x_mask, [1, 2])
|
| 261 |
-
z = torch.cat([z_0, z_1], 1)
|
| 262 |
-
return z, logdet
|
| 263 |
-
|
| 264 |
-
def store_inverse(self):
|
| 265 |
-
self.wn.remove_weight_norm()
|
| 266 |
-
|
| 267 |
-
|
| 268 |
-
class Glow(nn.Module):
|
| 269 |
-
def __init__(self,
|
| 270 |
-
in_channels,
|
| 271 |
-
hidden_channels,
|
| 272 |
-
kernel_size,
|
| 273 |
-
dilation_rate,
|
| 274 |
-
n_blocks,
|
| 275 |
-
n_layers,
|
| 276 |
-
p_dropout=0.,
|
| 277 |
-
n_split=4,
|
| 278 |
-
n_sqz=2,
|
| 279 |
-
sigmoid_scale=False,
|
| 280 |
-
gin_channels=0,
|
| 281 |
-
inv_conv_type='near',
|
| 282 |
-
share_cond_layers=False,
|
| 283 |
-
share_wn_layers=0,
|
| 284 |
-
):
|
| 285 |
-
super().__init__()
|
| 286 |
-
|
| 287 |
-
self.in_channels = in_channels
|
| 288 |
-
self.hidden_channels = hidden_channels
|
| 289 |
-
self.kernel_size = kernel_size
|
| 290 |
-
self.dilation_rate = dilation_rate
|
| 291 |
-
self.n_blocks = n_blocks
|
| 292 |
-
self.n_layers = n_layers
|
| 293 |
-
self.p_dropout = p_dropout
|
| 294 |
-
self.n_split = n_split
|
| 295 |
-
self.n_sqz = n_sqz
|
| 296 |
-
self.sigmoid_scale = sigmoid_scale
|
| 297 |
-
self.gin_channels = gin_channels
|
| 298 |
-
self.share_cond_layers = share_cond_layers
|
| 299 |
-
if gin_channels != 0 and share_cond_layers:
|
| 300 |
-
cond_layer = torch.nn.Conv1d(gin_channels * n_sqz, 2 * hidden_channels * n_layers, 1)
|
| 301 |
-
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
|
| 302 |
-
wn = None
|
| 303 |
-
self.flows = nn.ModuleList()
|
| 304 |
-
for b in range(n_blocks):
|
| 305 |
-
self.flows.append(ActNorm(channels=in_channels * n_sqz))
|
| 306 |
-
if inv_conv_type == 'near':
|
| 307 |
-
self.flows.append(InvConvNear(channels=in_channels * n_sqz, n_split=n_split, n_sqz=n_sqz))
|
| 308 |
-
if inv_conv_type == 'invconv':
|
| 309 |
-
self.flows.append(InvConv(channels=in_channels * n_sqz))
|
| 310 |
-
if share_wn_layers > 0:
|
| 311 |
-
if b % share_wn_layers == 0:
|
| 312 |
-
wn = WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels * n_sqz,
|
| 313 |
-
p_dropout, share_cond_layers)
|
| 314 |
-
self.flows.append(
|
| 315 |
-
CouplingBlock(
|
| 316 |
-
in_channels * n_sqz,
|
| 317 |
-
hidden_channels,
|
| 318 |
-
kernel_size=kernel_size,
|
| 319 |
-
dilation_rate=dilation_rate,
|
| 320 |
-
n_layers=n_layers,
|
| 321 |
-
gin_channels=gin_channels * n_sqz,
|
| 322 |
-
p_dropout=p_dropout,
|
| 323 |
-
sigmoid_scale=sigmoid_scale,
|
| 324 |
-
wn=wn
|
| 325 |
-
))
|
| 326 |
-
|
| 327 |
-
def forward(self, x, x_mask=None, g=None, reverse=False, return_hiddens=False):
|
| 328 |
-
logdet_tot = 0
|
| 329 |
-
if not reverse:
|
| 330 |
-
flows = self.flows
|
| 331 |
-
else:
|
| 332 |
-
flows = reversed(self.flows)
|
| 333 |
-
if return_hiddens:
|
| 334 |
-
hs = []
|
| 335 |
-
if self.n_sqz > 1:
|
| 336 |
-
x, x_mask_ = utils.squeeze(x, x_mask, self.n_sqz)
|
| 337 |
-
if g is not None:
|
| 338 |
-
g, _ = utils.squeeze(g, x_mask, self.n_sqz)
|
| 339 |
-
x_mask = x_mask_
|
| 340 |
-
if self.share_cond_layers and g is not None:
|
| 341 |
-
g = self.cond_layer(g)
|
| 342 |
-
for f in flows:
|
| 343 |
-
x, logdet = f(x, x_mask, g=g, reverse=reverse)
|
| 344 |
-
if return_hiddens:
|
| 345 |
-
hs.append(x)
|
| 346 |
-
logdet_tot += logdet
|
| 347 |
-
if self.n_sqz > 1:
|
| 348 |
-
x, x_mask = utils.unsqueeze(x, x_mask, self.n_sqz)
|
| 349 |
-
if return_hiddens:
|
| 350 |
-
return x, logdet_tot, hs
|
| 351 |
-
return x, logdet_tot
|
| 352 |
-
|
| 353 |
-
def store_inverse(self):
|
| 354 |
-
def remove_weight_norm(m):
|
| 355 |
-
try:
|
| 356 |
-
nn.utils.remove_weight_norm(m)
|
| 357 |
-
except ValueError: # this module didn't have weight norm
|
| 358 |
-
return
|
| 359 |
-
|
| 360 |
-
self.apply(remove_weight_norm)
|
| 361 |
-
for f in self.flows:
|
| 362 |
-
f.store_inverse()
|
|
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|
spaces/AIGC-Audio/Make_An_Audio_inpaint/ldm/modules/x_transformer.py
DELETED
|
@@ -1,641 +0,0 @@
|
|
| 1 |
-
"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers"""
|
| 2 |
-
import torch
|
| 3 |
-
from torch import nn, einsum
|
| 4 |
-
import torch.nn.functional as F
|
| 5 |
-
from functools import partial
|
| 6 |
-
from inspect import isfunction
|
| 7 |
-
from collections import namedtuple
|
| 8 |
-
from einops import rearrange, repeat, reduce
|
| 9 |
-
|
| 10 |
-
# constants
|
| 11 |
-
|
| 12 |
-
DEFAULT_DIM_HEAD = 64
|
| 13 |
-
|
| 14 |
-
Intermediates = namedtuple('Intermediates', [
|
| 15 |
-
'pre_softmax_attn',
|
| 16 |
-
'post_softmax_attn'
|
| 17 |
-
])
|
| 18 |
-
|
| 19 |
-
LayerIntermediates = namedtuple('Intermediates', [
|
| 20 |
-
'hiddens',
|
| 21 |
-
'attn_intermediates'
|
| 22 |
-
])
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
class AbsolutePositionalEmbedding(nn.Module):
|
| 26 |
-
def __init__(self, dim, max_seq_len):
|
| 27 |
-
super().__init__()
|
| 28 |
-
self.emb = nn.Embedding(max_seq_len, dim)
|
| 29 |
-
self.init_()
|
| 30 |
-
|
| 31 |
-
def init_(self):
|
| 32 |
-
nn.init.normal_(self.emb.weight, std=0.02)
|
| 33 |
-
|
| 34 |
-
def forward(self, x):
|
| 35 |
-
n = torch.arange(x.shape[1], device=x.device)
|
| 36 |
-
return self.emb(n)[None, :, :]
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
class FixedPositionalEmbedding(nn.Module):
|
| 40 |
-
def __init__(self, dim):
|
| 41 |
-
super().__init__()
|
| 42 |
-
inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
|
| 43 |
-
self.register_buffer('inv_freq', inv_freq)
|
| 44 |
-
|
| 45 |
-
def forward(self, x, seq_dim=1, offset=0):
|
| 46 |
-
t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset
|
| 47 |
-
sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)
|
| 48 |
-
emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
|
| 49 |
-
return emb[None, :, :]
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
# helpers
|
| 53 |
-
|
| 54 |
-
def exists(val):
|
| 55 |
-
return val is not None
|
| 56 |
-
|
| 57 |
-
|
| 58 |
-
def default(val, d):
|
| 59 |
-
if exists(val):
|
| 60 |
-
return val
|
| 61 |
-
return d() if isfunction(d) else d
|
| 62 |
-
|
| 63 |
-
|
| 64 |
-
def always(val):
|
| 65 |
-
def inner(*args, **kwargs):
|
| 66 |
-
return val
|
| 67 |
-
return inner
|
| 68 |
-
|
| 69 |
-
|
| 70 |
-
def not_equals(val):
|
| 71 |
-
def inner(x):
|
| 72 |
-
return x != val
|
| 73 |
-
return inner
|
| 74 |
-
|
| 75 |
-
|
| 76 |
-
def equals(val):
|
| 77 |
-
def inner(x):
|
| 78 |
-
return x == val
|
| 79 |
-
return inner
|
| 80 |
-
|
| 81 |
-
|
| 82 |
-
def max_neg_value(tensor):
|
| 83 |
-
return -torch.finfo(tensor.dtype).max
|
| 84 |
-
|
| 85 |
-
|
| 86 |
-
# keyword argument helpers
|
| 87 |
-
|
| 88 |
-
def pick_and_pop(keys, d):
|
| 89 |
-
values = list(map(lambda key: d.pop(key), keys))
|
| 90 |
-
return dict(zip(keys, values))
|
| 91 |
-
|
| 92 |
-
|
| 93 |
-
def group_dict_by_key(cond, d):
|
| 94 |
-
return_val = [dict(), dict()]
|
| 95 |
-
for key in d.keys():
|
| 96 |
-
match = bool(cond(key))
|
| 97 |
-
ind = int(not match)
|
| 98 |
-
return_val[ind][key] = d[key]
|
| 99 |
-
return (*return_val,)
|
| 100 |
-
|
| 101 |
-
|
| 102 |
-
def string_begins_with(prefix, str):
|
| 103 |
-
return str.startswith(prefix)
|
| 104 |
-
|
| 105 |
-
|
| 106 |
-
def group_by_key_prefix(prefix, d):
|
| 107 |
-
return group_dict_by_key(partial(string_begins_with, prefix), d)
|
| 108 |
-
|
| 109 |
-
|
| 110 |
-
def groupby_prefix_and_trim(prefix, d):
|
| 111 |
-
kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
|
| 112 |
-
kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
|
| 113 |
-
return kwargs_without_prefix, kwargs
|
| 114 |
-
|
| 115 |
-
|
| 116 |
-
# classes
|
| 117 |
-
class Scale(nn.Module):
|
| 118 |
-
def __init__(self, value, fn):
|
| 119 |
-
super().__init__()
|
| 120 |
-
self.value = value
|
| 121 |
-
self.fn = fn
|
| 122 |
-
|
| 123 |
-
def forward(self, x, **kwargs):
|
| 124 |
-
x, *rest = self.fn(x, **kwargs)
|
| 125 |
-
return (x * self.value, *rest)
|
| 126 |
-
|
| 127 |
-
|
| 128 |
-
class Rezero(nn.Module):
|
| 129 |
-
def __init__(self, fn):
|
| 130 |
-
super().__init__()
|
| 131 |
-
self.fn = fn
|
| 132 |
-
self.g = nn.Parameter(torch.zeros(1))
|
| 133 |
-
|
| 134 |
-
def forward(self, x, **kwargs):
|
| 135 |
-
x, *rest = self.fn(x, **kwargs)
|
| 136 |
-
return (x * self.g, *rest)
|
| 137 |
-
|
| 138 |
-
|
| 139 |
-
class ScaleNorm(nn.Module):
|
| 140 |
-
def __init__(self, dim, eps=1e-5):
|
| 141 |
-
super().__init__()
|
| 142 |
-
self.scale = dim ** -0.5
|
| 143 |
-
self.eps = eps
|
| 144 |
-
self.g = nn.Parameter(torch.ones(1))
|
| 145 |
-
|
| 146 |
-
def forward(self, x):
|
| 147 |
-
norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
|
| 148 |
-
return x / norm.clamp(min=self.eps) * self.g
|
| 149 |
-
|
| 150 |
-
|
| 151 |
-
class RMSNorm(nn.Module):
|
| 152 |
-
def __init__(self, dim, eps=1e-8):
|
| 153 |
-
super().__init__()
|
| 154 |
-
self.scale = dim ** -0.5
|
| 155 |
-
self.eps = eps
|
| 156 |
-
self.g = nn.Parameter(torch.ones(dim))
|
| 157 |
-
|
| 158 |
-
def forward(self, x):
|
| 159 |
-
norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
|
| 160 |
-
return x / norm.clamp(min=self.eps) * self.g
|
| 161 |
-
|
| 162 |
-
|
| 163 |
-
class Residual(nn.Module):
|
| 164 |
-
def forward(self, x, residual):
|
| 165 |
-
return x + residual
|
| 166 |
-
|
| 167 |
-
|
| 168 |
-
class GRUGating(nn.Module):
|
| 169 |
-
def __init__(self, dim):
|
| 170 |
-
super().__init__()
|
| 171 |
-
self.gru = nn.GRUCell(dim, dim)
|
| 172 |
-
|
| 173 |
-
def forward(self, x, residual):
|
| 174 |
-
gated_output = self.gru(
|
| 175 |
-
rearrange(x, 'b n d -> (b n) d'),
|
| 176 |
-
rearrange(residual, 'b n d -> (b n) d')
|
| 177 |
-
)
|
| 178 |
-
|
| 179 |
-
return gated_output.reshape_as(x)
|
| 180 |
-
|
| 181 |
-
|
| 182 |
-
# feedforward
|
| 183 |
-
|
| 184 |
-
class GEGLU(nn.Module):
|
| 185 |
-
def __init__(self, dim_in, dim_out):
|
| 186 |
-
super().__init__()
|
| 187 |
-
self.proj = nn.Linear(dim_in, dim_out * 2)
|
| 188 |
-
|
| 189 |
-
def forward(self, x):
|
| 190 |
-
x, gate = self.proj(x).chunk(2, dim=-1)
|
| 191 |
-
return x * F.gelu(gate)
|
| 192 |
-
|
| 193 |
-
|
| 194 |
-
class FeedForward(nn.Module):
|
| 195 |
-
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
|
| 196 |
-
super().__init__()
|
| 197 |
-
inner_dim = int(dim * mult)
|
| 198 |
-
dim_out = default(dim_out, dim)
|
| 199 |
-
project_in = nn.Sequential(
|
| 200 |
-
nn.Linear(dim, inner_dim),
|
| 201 |
-
nn.GELU()
|
| 202 |
-
) if not glu else GEGLU(dim, inner_dim)
|
| 203 |
-
|
| 204 |
-
self.net = nn.Sequential(
|
| 205 |
-
project_in,
|
| 206 |
-
nn.Dropout(dropout),
|
| 207 |
-
nn.Linear(inner_dim, dim_out)
|
| 208 |
-
)
|
| 209 |
-
|
| 210 |
-
def forward(self, x):
|
| 211 |
-
return self.net(x)
|
| 212 |
-
|
| 213 |
-
|
| 214 |
-
# attention.
|
| 215 |
-
class Attention(nn.Module):
|
| 216 |
-
def __init__(
|
| 217 |
-
self,
|
| 218 |
-
dim,
|
| 219 |
-
dim_head=DEFAULT_DIM_HEAD,
|
| 220 |
-
heads=8,
|
| 221 |
-
causal=False,
|
| 222 |
-
mask=None,
|
| 223 |
-
talking_heads=False,
|
| 224 |
-
sparse_topk=None,
|
| 225 |
-
use_entmax15=False,
|
| 226 |
-
num_mem_kv=0,
|
| 227 |
-
dropout=0.,
|
| 228 |
-
on_attn=False
|
| 229 |
-
):
|
| 230 |
-
super().__init__()
|
| 231 |
-
if use_entmax15:
|
| 232 |
-
raise NotImplementedError("Check out entmax activation instead of softmax activation!")
|
| 233 |
-
self.scale = dim_head ** -0.5
|
| 234 |
-
self.heads = heads
|
| 235 |
-
self.causal = causal
|
| 236 |
-
self.mask = mask
|
| 237 |
-
|
| 238 |
-
inner_dim = dim_head * heads
|
| 239 |
-
|
| 240 |
-
self.to_q = nn.Linear(dim, inner_dim, bias=False)
|
| 241 |
-
self.to_k = nn.Linear(dim, inner_dim, bias=False)
|
| 242 |
-
self.to_v = nn.Linear(dim, inner_dim, bias=False)
|
| 243 |
-
self.dropout = nn.Dropout(dropout)
|
| 244 |
-
|
| 245 |
-
# talking heads
|
| 246 |
-
self.talking_heads = talking_heads
|
| 247 |
-
if talking_heads:
|
| 248 |
-
self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads))
|
| 249 |
-
self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads))
|
| 250 |
-
|
| 251 |
-
# explicit topk sparse attention
|
| 252 |
-
self.sparse_topk = sparse_topk
|
| 253 |
-
|
| 254 |
-
# entmax
|
| 255 |
-
#self.attn_fn = entmax15 if use_entmax15 else F.softmax
|
| 256 |
-
self.attn_fn = F.softmax
|
| 257 |
-
|
| 258 |
-
# add memory key / values
|
| 259 |
-
self.num_mem_kv = num_mem_kv
|
| 260 |
-
if num_mem_kv > 0:
|
| 261 |
-
self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
|
| 262 |
-
self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
|
| 263 |
-
|
| 264 |
-
# attention on attention
|
| 265 |
-
self.attn_on_attn = on_attn
|
| 266 |
-
self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim)
|
| 267 |
-
|
| 268 |
-
def forward(
|
| 269 |
-
self,
|
| 270 |
-
x,
|
| 271 |
-
context=None,
|
| 272 |
-
mask=None,
|
| 273 |
-
context_mask=None,
|
| 274 |
-
rel_pos=None,
|
| 275 |
-
sinusoidal_emb=None,
|
| 276 |
-
prev_attn=None,
|
| 277 |
-
mem=None
|
| 278 |
-
):
|
| 279 |
-
b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device
|
| 280 |
-
kv_input = default(context, x)
|
| 281 |
-
|
| 282 |
-
q_input = x
|
| 283 |
-
k_input = kv_input
|
| 284 |
-
v_input = kv_input
|
| 285 |
-
|
| 286 |
-
if exists(mem):
|
| 287 |
-
k_input = torch.cat((mem, k_input), dim=-2)
|
| 288 |
-
v_input = torch.cat((mem, v_input), dim=-2)
|
| 289 |
-
|
| 290 |
-
if exists(sinusoidal_emb):
|
| 291 |
-
# in shortformer, the query would start at a position offset depending on the past cached memory
|
| 292 |
-
offset = k_input.shape[-2] - q_input.shape[-2]
|
| 293 |
-
q_input = q_input + sinusoidal_emb(q_input, offset=offset)
|
| 294 |
-
k_input = k_input + sinusoidal_emb(k_input)
|
| 295 |
-
|
| 296 |
-
q = self.to_q(q_input)
|
| 297 |
-
k = self.to_k(k_input)
|
| 298 |
-
v = self.to_v(v_input)
|
| 299 |
-
|
| 300 |
-
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
|
| 301 |
-
|
| 302 |
-
input_mask = None
|
| 303 |
-
if any(map(exists, (mask, context_mask))):
|
| 304 |
-
q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
|
| 305 |
-
k_mask = q_mask if not exists(context) else context_mask
|
| 306 |
-
k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())
|
| 307 |
-
q_mask = rearrange(q_mask, 'b i -> b () i ()')
|
| 308 |
-
k_mask = rearrange(k_mask, 'b j -> b () () j')
|
| 309 |
-
input_mask = q_mask * k_mask
|
| 310 |
-
|
| 311 |
-
if self.num_mem_kv > 0:
|
| 312 |
-
mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))
|
| 313 |
-
k = torch.cat((mem_k, k), dim=-2)
|
| 314 |
-
v = torch.cat((mem_v, v), dim=-2)
|
| 315 |
-
if exists(input_mask):
|
| 316 |
-
input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)
|
| 317 |
-
|
| 318 |
-
dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
|
| 319 |
-
mask_value = max_neg_value(dots)
|
| 320 |
-
|
| 321 |
-
if exists(prev_attn):
|
| 322 |
-
dots = dots + prev_attn
|
| 323 |
-
|
| 324 |
-
pre_softmax_attn = dots
|
| 325 |
-
|
| 326 |
-
if talking_heads:
|
| 327 |
-
dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
|
| 328 |
-
|
| 329 |
-
if exists(rel_pos):
|
| 330 |
-
dots = rel_pos(dots)
|
| 331 |
-
|
| 332 |
-
if exists(input_mask):
|
| 333 |
-
dots.masked_fill_(~input_mask, mask_value)
|
| 334 |
-
del input_mask
|
| 335 |
-
|
| 336 |
-
if self.causal:
|
| 337 |
-
i, j = dots.shape[-2:]
|
| 338 |
-
r = torch.arange(i, device=device)
|
| 339 |
-
mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')
|
| 340 |
-
mask = F.pad(mask, (j - i, 0), value=False)
|
| 341 |
-
dots.masked_fill_(mask, mask_value)
|
| 342 |
-
del mask
|
| 343 |
-
|
| 344 |
-
if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]:
|
| 345 |
-
top, _ = dots.topk(self.sparse_topk, dim=-1)
|
| 346 |
-
vk = top[..., -1].unsqueeze(-1).expand_as(dots)
|
| 347 |
-
mask = dots < vk
|
| 348 |
-
dots.masked_fill_(mask, mask_value)
|
| 349 |
-
del mask
|
| 350 |
-
|
| 351 |
-
attn = self.attn_fn(dots, dim=-1)
|
| 352 |
-
post_softmax_attn = attn
|
| 353 |
-
|
| 354 |
-
attn = self.dropout(attn)
|
| 355 |
-
|
| 356 |
-
if talking_heads:
|
| 357 |
-
attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()
|
| 358 |
-
|
| 359 |
-
out = einsum('b h i j, b h j d -> b h i d', attn, v)
|
| 360 |
-
out = rearrange(out, 'b h n d -> b n (h d)')
|
| 361 |
-
|
| 362 |
-
intermediates = Intermediates(
|
| 363 |
-
pre_softmax_attn=pre_softmax_attn,
|
| 364 |
-
post_softmax_attn=post_softmax_attn
|
| 365 |
-
)
|
| 366 |
-
|
| 367 |
-
return self.to_out(out), intermediates
|
| 368 |
-
|
| 369 |
-
|
| 370 |
-
class AttentionLayers(nn.Module):
|
| 371 |
-
def __init__(
|
| 372 |
-
self,
|
| 373 |
-
dim,
|
| 374 |
-
depth,
|
| 375 |
-
heads=8,
|
| 376 |
-
causal=False,
|
| 377 |
-
cross_attend=False,
|
| 378 |
-
only_cross=False,
|
| 379 |
-
use_scalenorm=False,
|
| 380 |
-
use_rmsnorm=False,
|
| 381 |
-
use_rezero=False,
|
| 382 |
-
rel_pos_num_buckets=32,
|
| 383 |
-
rel_pos_max_distance=128,
|
| 384 |
-
position_infused_attn=False,
|
| 385 |
-
custom_layers=None,
|
| 386 |
-
sandwich_coef=None,
|
| 387 |
-
par_ratio=None,
|
| 388 |
-
residual_attn=False,
|
| 389 |
-
cross_residual_attn=False,
|
| 390 |
-
macaron=False,
|
| 391 |
-
pre_norm=True,
|
| 392 |
-
gate_residual=False,
|
| 393 |
-
**kwargs
|
| 394 |
-
):
|
| 395 |
-
super().__init__()
|
| 396 |
-
ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
|
| 397 |
-
attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs)
|
| 398 |
-
|
| 399 |
-
dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
|
| 400 |
-
|
| 401 |
-
self.dim = dim
|
| 402 |
-
self.depth = depth
|
| 403 |
-
self.layers = nn.ModuleList([])
|
| 404 |
-
|
| 405 |
-
self.has_pos_emb = position_infused_attn
|
| 406 |
-
self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None
|
| 407 |
-
self.rotary_pos_emb = always(None)
|
| 408 |
-
|
| 409 |
-
assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
|
| 410 |
-
self.rel_pos = None
|
| 411 |
-
|
| 412 |
-
self.pre_norm = pre_norm
|
| 413 |
-
|
| 414 |
-
self.residual_attn = residual_attn
|
| 415 |
-
self.cross_residual_attn = cross_residual_attn
|
| 416 |
-
|
| 417 |
-
norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
|
| 418 |
-
norm_class = RMSNorm if use_rmsnorm else norm_class
|
| 419 |
-
norm_fn = partial(norm_class, dim)
|
| 420 |
-
|
| 421 |
-
norm_fn = nn.Identity if use_rezero else norm_fn
|
| 422 |
-
branch_fn = Rezero if use_rezero else None
|
| 423 |
-
|
| 424 |
-
if cross_attend and not only_cross:
|
| 425 |
-
default_block = ('a', 'c', 'f')
|
| 426 |
-
elif cross_attend and only_cross:
|
| 427 |
-
default_block = ('c', 'f')
|
| 428 |
-
else:
|
| 429 |
-
default_block = ('a', 'f')
|
| 430 |
-
|
| 431 |
-
if macaron:
|
| 432 |
-
default_block = ('f',) + default_block
|
| 433 |
-
|
| 434 |
-
if exists(custom_layers):
|
| 435 |
-
layer_types = custom_layers
|
| 436 |
-
elif exists(par_ratio):
|
| 437 |
-
par_depth = depth * len(default_block)
|
| 438 |
-
assert 1 < par_ratio <= par_depth, 'par ratio out of range'
|
| 439 |
-
default_block = tuple(filter(not_equals('f'), default_block))
|
| 440 |
-
par_attn = par_depth // par_ratio
|
| 441 |
-
depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper
|
| 442 |
-
par_width = (depth_cut + depth_cut // par_attn) // par_attn
|
| 443 |
-
assert len(default_block) <= par_width, 'default block is too large for par_ratio'
|
| 444 |
-
par_block = default_block + ('f',) * (par_width - len(default_block))
|
| 445 |
-
par_head = par_block * par_attn
|
| 446 |
-
layer_types = par_head + ('f',) * (par_depth - len(par_head))
|
| 447 |
-
elif exists(sandwich_coef):
|
| 448 |
-
assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
|
| 449 |
-
layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
|
| 450 |
-
else:
|
| 451 |
-
layer_types = default_block * depth
|
| 452 |
-
|
| 453 |
-
self.layer_types = layer_types
|
| 454 |
-
self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
|
| 455 |
-
|
| 456 |
-
for layer_type in self.layer_types:
|
| 457 |
-
if layer_type == 'a':
|
| 458 |
-
layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)
|
| 459 |
-
elif layer_type == 'c':
|
| 460 |
-
layer = Attention(dim, heads=heads, **attn_kwargs)
|
| 461 |
-
elif layer_type == 'f':
|
| 462 |
-
layer = FeedForward(dim, **ff_kwargs)
|
| 463 |
-
layer = layer if not macaron else Scale(0.5, layer)
|
| 464 |
-
else:
|
| 465 |
-
raise Exception(f'invalid layer type {layer_type}')
|
| 466 |
-
|
| 467 |
-
if isinstance(layer, Attention) and exists(branch_fn):
|
| 468 |
-
layer = branch_fn(layer)
|
| 469 |
-
|
| 470 |
-
if gate_residual:
|
| 471 |
-
residual_fn = GRUGating(dim)
|
| 472 |
-
else:
|
| 473 |
-
residual_fn = Residual()
|
| 474 |
-
|
| 475 |
-
self.layers.append(nn.ModuleList([
|
| 476 |
-
norm_fn(),
|
| 477 |
-
layer,
|
| 478 |
-
residual_fn
|
| 479 |
-
]))
|
| 480 |
-
|
| 481 |
-
def forward(
|
| 482 |
-
self,
|
| 483 |
-
x,
|
| 484 |
-
context=None,
|
| 485 |
-
mask=None,
|
| 486 |
-
context_mask=None,
|
| 487 |
-
mems=None,
|
| 488 |
-
return_hiddens=False
|
| 489 |
-
):
|
| 490 |
-
hiddens = []
|
| 491 |
-
intermediates = []
|
| 492 |
-
prev_attn = None
|
| 493 |
-
prev_cross_attn = None
|
| 494 |
-
|
| 495 |
-
mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
|
| 496 |
-
|
| 497 |
-
for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
|
| 498 |
-
is_last = ind == (len(self.layers) - 1)
|
| 499 |
-
|
| 500 |
-
if layer_type == 'a':
|
| 501 |
-
hiddens.append(x)
|
| 502 |
-
layer_mem = mems.pop(0)
|
| 503 |
-
|
| 504 |
-
residual = x
|
| 505 |
-
|
| 506 |
-
if self.pre_norm:
|
| 507 |
-
x = norm(x)
|
| 508 |
-
|
| 509 |
-
if layer_type == 'a':
|
| 510 |
-
out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos,
|
| 511 |
-
prev_attn=prev_attn, mem=layer_mem)
|
| 512 |
-
elif layer_type == 'c':
|
| 513 |
-
out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn)
|
| 514 |
-
elif layer_type == 'f':
|
| 515 |
-
out = block(x)
|
| 516 |
-
|
| 517 |
-
x = residual_fn(out, residual)
|
| 518 |
-
|
| 519 |
-
if layer_type in ('a', 'c'):
|
| 520 |
-
intermediates.append(inter)
|
| 521 |
-
|
| 522 |
-
if layer_type == 'a' and self.residual_attn:
|
| 523 |
-
prev_attn = inter.pre_softmax_attn
|
| 524 |
-
elif layer_type == 'c' and self.cross_residual_attn:
|
| 525 |
-
prev_cross_attn = inter.pre_softmax_attn
|
| 526 |
-
|
| 527 |
-
if not self.pre_norm and not is_last:
|
| 528 |
-
x = norm(x)
|
| 529 |
-
|
| 530 |
-
if return_hiddens:
|
| 531 |
-
intermediates = LayerIntermediates(
|
| 532 |
-
hiddens=hiddens,
|
| 533 |
-
attn_intermediates=intermediates
|
| 534 |
-
)
|
| 535 |
-
|
| 536 |
-
return x, intermediates
|
| 537 |
-
|
| 538 |
-
return x
|
| 539 |
-
|
| 540 |
-
|
| 541 |
-
class Encoder(AttentionLayers):
|
| 542 |
-
def __init__(self, **kwargs):
|
| 543 |
-
assert 'causal' not in kwargs, 'cannot set causality on encoder'
|
| 544 |
-
super().__init__(causal=False, **kwargs)
|
| 545 |
-
|
| 546 |
-
|
| 547 |
-
|
| 548 |
-
class TransformerWrapper(nn.Module):
|
| 549 |
-
def __init__(
|
| 550 |
-
self,
|
| 551 |
-
*,
|
| 552 |
-
num_tokens,
|
| 553 |
-
max_seq_len,
|
| 554 |
-
attn_layers,
|
| 555 |
-
emb_dim=None,
|
| 556 |
-
max_mem_len=0.,
|
| 557 |
-
emb_dropout=0.,
|
| 558 |
-
num_memory_tokens=None,
|
| 559 |
-
tie_embedding=False,
|
| 560 |
-
use_pos_emb=True
|
| 561 |
-
):
|
| 562 |
-
super().__init__()
|
| 563 |
-
assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
|
| 564 |
-
|
| 565 |
-
dim = attn_layers.dim
|
| 566 |
-
emb_dim = default(emb_dim, dim)
|
| 567 |
-
|
| 568 |
-
self.max_seq_len = max_seq_len
|
| 569 |
-
self.max_mem_len = max_mem_len
|
| 570 |
-
self.num_tokens = num_tokens
|
| 571 |
-
|
| 572 |
-
self.token_emb = nn.Embedding(num_tokens, emb_dim)
|
| 573 |
-
self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (
|
| 574 |
-
use_pos_emb and not attn_layers.has_pos_emb) else always(0)
|
| 575 |
-
self.emb_dropout = nn.Dropout(emb_dropout)
|
| 576 |
-
|
| 577 |
-
self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
|
| 578 |
-
self.attn_layers = attn_layers
|
| 579 |
-
self.norm = nn.LayerNorm(dim)
|
| 580 |
-
|
| 581 |
-
self.init_()
|
| 582 |
-
|
| 583 |
-
self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
|
| 584 |
-
|
| 585 |
-
# memory tokens (like [cls]) from Memory Transformers paper
|
| 586 |
-
num_memory_tokens = default(num_memory_tokens, 0)
|
| 587 |
-
self.num_memory_tokens = num_memory_tokens
|
| 588 |
-
if num_memory_tokens > 0:
|
| 589 |
-
self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
|
| 590 |
-
|
| 591 |
-
# let funnel encoder know number of memory tokens, if specified
|
| 592 |
-
if hasattr(attn_layers, 'num_memory_tokens'):
|
| 593 |
-
attn_layers.num_memory_tokens = num_memory_tokens
|
| 594 |
-
|
| 595 |
-
def init_(self):
|
| 596 |
-
nn.init.normal_(self.token_emb.weight, std=0.02)
|
| 597 |
-
|
| 598 |
-
def forward(
|
| 599 |
-
self,
|
| 600 |
-
x,
|
| 601 |
-
return_embeddings=False,
|
| 602 |
-
mask=None,
|
| 603 |
-
return_mems=False,
|
| 604 |
-
return_attn=False,
|
| 605 |
-
mems=None,
|
| 606 |
-
**kwargs
|
| 607 |
-
):
|
| 608 |
-
b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
|
| 609 |
-
x = self.token_emb(x)
|
| 610 |
-
x += self.pos_emb(x)
|
| 611 |
-
x = self.emb_dropout(x)
|
| 612 |
-
|
| 613 |
-
x = self.project_emb(x)
|
| 614 |
-
|
| 615 |
-
if num_mem > 0:
|
| 616 |
-
mem = repeat(self.memory_tokens, 'n d -> b n d', b=b)
|
| 617 |
-
x = torch.cat((mem, x), dim=1)
|
| 618 |
-
|
| 619 |
-
# auto-handle masking after appending memory tokens
|
| 620 |
-
if exists(mask):
|
| 621 |
-
mask = F.pad(mask, (num_mem, 0), value=True)
|
| 622 |
-
|
| 623 |
-
x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
|
| 624 |
-
x = self.norm(x)
|
| 625 |
-
|
| 626 |
-
mem, x = x[:, :num_mem], x[:, num_mem:]
|
| 627 |
-
|
| 628 |
-
out = self.to_logits(x) if not return_embeddings else x
|
| 629 |
-
|
| 630 |
-
if return_mems:
|
| 631 |
-
hiddens = intermediates.hiddens
|
| 632 |
-
new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens
|
| 633 |
-
new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
|
| 634 |
-
return out, new_mems
|
| 635 |
-
|
| 636 |
-
if return_attn:
|
| 637 |
-
attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
|
| 638 |
-
return out, attn_maps
|
| 639 |
-
|
| 640 |
-
return out
|
| 641 |
-
|
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spaces/AIGText/GlyphControl/annotator/util.py
DELETED
|
@@ -1,38 +0,0 @@
|
|
| 1 |
-
import numpy as np
|
| 2 |
-
import cv2
|
| 3 |
-
import os
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
annotator_ckpts_path = os.path.join(os.path.dirname(__file__), 'ckpts')
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
def HWC3(x):
|
| 10 |
-
assert x.dtype == np.uint8
|
| 11 |
-
if x.ndim == 2:
|
| 12 |
-
x = x[:, :, None]
|
| 13 |
-
assert x.ndim == 3
|
| 14 |
-
H, W, C = x.shape
|
| 15 |
-
assert C == 1 or C == 3 or C == 4
|
| 16 |
-
if C == 3:
|
| 17 |
-
return x
|
| 18 |
-
if C == 1:
|
| 19 |
-
return np.concatenate([x, x, x], axis=2)
|
| 20 |
-
if C == 4:
|
| 21 |
-
color = x[:, :, 0:3].astype(np.float32)
|
| 22 |
-
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
|
| 23 |
-
y = color * alpha + 255.0 * (1.0 - alpha)
|
| 24 |
-
y = y.clip(0, 255).astype(np.uint8)
|
| 25 |
-
return y
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
def resize_image(input_image, resolution):
|
| 29 |
-
H, W, C = input_image.shape
|
| 30 |
-
H = float(H)
|
| 31 |
-
W = float(W)
|
| 32 |
-
k = float(resolution) / min(H, W)
|
| 33 |
-
H *= k
|
| 34 |
-
W *= k
|
| 35 |
-
H = int(np.round(H / 64.0)) * 64
|
| 36 |
-
W = int(np.round(W / 64.0)) * 64
|
| 37 |
-
img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
|
| 38 |
-
return img
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|
spaces/AIatUIUC/CodeLATS/executors/executor_types.py
DELETED
|
@@ -1,20 +0,0 @@
|
|
| 1 |
-
from typing import NamedTuple, List, Tuple
|
| 2 |
-
from abc import ABC, abstractmethod
|
| 3 |
-
|
| 4 |
-
class ExecuteResult(NamedTuple):
|
| 5 |
-
is_passing: bool
|
| 6 |
-
feedback: str
|
| 7 |
-
state: Tuple[bool]
|
| 8 |
-
|
| 9 |
-
class Executor(ABC):
|
| 10 |
-
@abstractmethod
|
| 11 |
-
def execute(self, func: str, tests: List[str], timeout: int = 5) -> ExecuteResult:
|
| 12 |
-
...
|
| 13 |
-
|
| 14 |
-
@abstractmethod
|
| 15 |
-
def evaluate(self, name: str, func: str, test: str, timeout: int = 5) -> bool:
|
| 16 |
-
...
|
| 17 |
-
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
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|
spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov5/crowdhuman/yolov5_s-v61_fast_8xb16-300e_crowdhuman.py
DELETED
|
@@ -1,47 +0,0 @@
|
|
| 1 |
-
_base_ = '../yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
|
| 2 |
-
|
| 3 |
-
# Use the model trained on the COCO as the pretrained model
|
| 4 |
-
load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth' # noqa
|
| 5 |
-
|
| 6 |
-
# dataset settings
|
| 7 |
-
data_root = 'data/CrowdHuman/'
|
| 8 |
-
dataset_type = 'YOLOv5CrowdHumanDataset'
|
| 9 |
-
|
| 10 |
-
# parameters that often need to be modified
|
| 11 |
-
num_classes = 1
|
| 12 |
-
|
| 13 |
-
anchors = [
|
| 14 |
-
[(6, 14), (12, 28), (19, 48)], # P3/8
|
| 15 |
-
[(29, 79), (46, 124), (142, 54)], # P4/16
|
| 16 |
-
[(73, 198), (124, 330), (255, 504)] # P5/32
|
| 17 |
-
]
|
| 18 |
-
|
| 19 |
-
model = dict(
|
| 20 |
-
bbox_head=dict(
|
| 21 |
-
head_module=dict(num_classes=num_classes),
|
| 22 |
-
prior_generator=dict(base_sizes=anchors)))
|
| 23 |
-
|
| 24 |
-
train_dataloader = dict(
|
| 25 |
-
dataset=dict(
|
| 26 |
-
type=dataset_type,
|
| 27 |
-
data_root=data_root,
|
| 28 |
-
ann_file='annotation_train.odgt',
|
| 29 |
-
data_prefix=dict(img='Images/')))
|
| 30 |
-
|
| 31 |
-
val_dataloader = dict(
|
| 32 |
-
dataset=dict(
|
| 33 |
-
type=dataset_type,
|
| 34 |
-
data_root=data_root,
|
| 35 |
-
ann_file='annotation_val.odgt',
|
| 36 |
-
data_prefix=dict(img='Images/'),
|
| 37 |
-
# CrowdHumanMetric does not support out-of-order output images
|
| 38 |
-
# for the time being. batch_shapes_cfg does not support.
|
| 39 |
-
batch_shapes_cfg=None))
|
| 40 |
-
test_dataloader = val_dataloader
|
| 41 |
-
|
| 42 |
-
val_evaluator = dict(
|
| 43 |
-
_delete_=True,
|
| 44 |
-
type='mmdet.CrowdHumanMetric',
|
| 45 |
-
ann_file=data_root + 'annotation_val.odgt',
|
| 46 |
-
metric=['AP', 'MR', 'JI'])
|
| 47 |
-
test_evaluator = val_evaluator
|
|
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|
spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/selector/classroom.py
DELETED
|
@@ -1,47 +0,0 @@
|
|
| 1 |
-
from __future__ import annotations
|
| 2 |
-
|
| 3 |
-
from typing import TYPE_CHECKING, List
|
| 4 |
-
|
| 5 |
-
from agentverse.message import Message
|
| 6 |
-
|
| 7 |
-
from . import selector_registry as SelectorRegistry
|
| 8 |
-
from .base import BaseSelector
|
| 9 |
-
|
| 10 |
-
if TYPE_CHECKING:
|
| 11 |
-
from agentverse.environments import BaseEnvironment
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
@SelectorRegistry.register("classroom")
|
| 15 |
-
class ClassroomSelector(BaseSelector):
|
| 16 |
-
def select_message(
|
| 17 |
-
self, environment: BaseEnvironment, messages: List[Message]
|
| 18 |
-
) -> List[Message]:
|
| 19 |
-
selected = []
|
| 20 |
-
for message in messages:
|
| 21 |
-
if message.sender.startswith("Student"):
|
| 22 |
-
if message.content.startswith("[RaiseHand]"):
|
| 23 |
-
message.content = "[RaiseHand]"
|
| 24 |
-
selected.append(message)
|
| 25 |
-
elif message.content != "" or len(message.tool_response) > 0:
|
| 26 |
-
selected.append(message)
|
| 27 |
-
elif message.sender.startswith("Professor"):
|
| 28 |
-
# If the professor launch a group discussion, then we
|
| 29 |
-
# brutely discard the student's message in this turn
|
| 30 |
-
if message.content.startswith("[GroupDiscuss]"):
|
| 31 |
-
return [message]
|
| 32 |
-
selected.append(message)
|
| 33 |
-
|
| 34 |
-
# If some student speak while the professor is speaking, then
|
| 35 |
-
# we brutely discard the student's message in this turn
|
| 36 |
-
if (
|
| 37 |
-
len(selected) > 1
|
| 38 |
-
and selected[0].sender.startswith("Professor")
|
| 39 |
-
and selected[0].content != ""
|
| 40 |
-
):
|
| 41 |
-
filtered_selected = []
|
| 42 |
-
filtered_selected.append(selected[0])
|
| 43 |
-
for message in selected[1:]:
|
| 44 |
-
if message.content.startswith("[RaiseHand]"):
|
| 45 |
-
filtered_selected.append(message)
|
| 46 |
-
selected = filtered_selected
|
| 47 |
-
return selected
|
|
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|
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/custom/Custom.js
DELETED
|
@@ -1,18 +0,0 @@
|
|
| 1 |
-
import Base from '../base/Base.js';
|
| 2 |
-
import ShapesUpdateMethods from '../../../plugins/gameobjects/shape/customshapes/ShapesUpdateMethods.js';
|
| 3 |
-
|
| 4 |
-
const GetValue = Phaser.Utils.Objects.GetValue;
|
| 5 |
-
|
| 6 |
-
class Custom extends Base {
|
| 7 |
-
constructor(scene, config) {
|
| 8 |
-
super(scene, config);
|
| 9 |
-
this.type = GetValue(config, 'type', 'rexSpinnerCustom');
|
| 10 |
-
}
|
| 11 |
-
}
|
| 12 |
-
|
| 13 |
-
Object.assign(
|
| 14 |
-
Custom.prototype,
|
| 15 |
-
ShapesUpdateMethods
|
| 16 |
-
);
|
| 17 |
-
|
| 18 |
-
export default Custom;
|
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spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridtable/input/SwipeCell.js
DELETED
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@@ -1,26 +0,0 @@
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|
| 1 |
-
import Swipe from '../../swipe/Swipe.js';
|
| 2 |
-
import EmitCellEvent from './EmitCellEvent.js';
|
| 3 |
-
|
| 4 |
-
const GetValue = Phaser.Utils.Objects.GetValue;
|
| 5 |
-
|
| 6 |
-
var SwipeCell = function (table, tableConfig) {
|
| 7 |
-
var swipeConfig = GetValue(tableConfig, 'swipe', undefined);
|
| 8 |
-
if (swipeConfig === false) {
|
| 9 |
-
return;
|
| 10 |
-
} else if (swipeConfig === undefined) {
|
| 11 |
-
swipeConfig = {};
|
| 12 |
-
}
|
| 13 |
-
swipeConfig.dir = '4dir';
|
| 14 |
-
table._swipe = new Swipe(table, swipeConfig);
|
| 15 |
-
table._swipe
|
| 16 |
-
.on('swipe', function (swipe, gameObject, lastPointer) {
|
| 17 |
-
var dirName =
|
| 18 |
-
(swipe.left) ? 'left' :
|
| 19 |
-
(swipe.right) ? 'right' :
|
| 20 |
-
(swipe.up) ? 'up' :
|
| 21 |
-
'down';
|
| 22 |
-
EmitCellEvent(this.eventEmitter, `cell.swipe${dirName}`, table, swipe.worldX, swipe.worldY, lastPointer);
|
| 23 |
-
}, this)
|
| 24 |
-
};
|
| 25 |
-
|
| 26 |
-
export default SwipeCell;
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spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sides/Sides.js
DELETED
|
@@ -1,84 +0,0 @@
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|
| 1 |
-
import OverlapSizer from '../overlapsizer/OverlapSizer.js';
|
| 2 |
-
import IsFunction from '../../../plugins/utils/object/IsFunction.js';
|
| 3 |
-
import GetDefaultCallbacks from './defaultcallbacks/GetDefaultCallbacks.js';
|
| 4 |
-
import ShowChildMethods from './ShowChildMethods.js';
|
| 5 |
-
import ChildBehaviorMethods from './childbehaviors/index.js';
|
| 6 |
-
|
| 7 |
-
const GetValue = Phaser.Utils.Objects.GetValue;
|
| 8 |
-
|
| 9 |
-
class Sides extends OverlapSizer {
|
| 10 |
-
constructor(scene, config) {
|
| 11 |
-
super(scene, config);
|
| 12 |
-
this.type = 'rexSides';
|
| 13 |
-
this.childrenMap = this.sizerChildren;
|
| 14 |
-
this.previousChildKey = undefined;
|
| 15 |
-
this.currentChildKey = undefined;
|
| 16 |
-
|
| 17 |
-
// Callbacks
|
| 18 |
-
var showChildCallback = GetValue(config, 'showChildCallback', undefined);
|
| 19 |
-
if (showChildCallback) { // Has showChildCallback, and hideChildCallback
|
| 20 |
-
if (IsFunction(showChildCallback)) { // Custom callbacks
|
| 21 |
-
var showChildCallbackScope = GetValue(config, 'showChildCallbackScope', undefined);
|
| 22 |
-
this.on('showchild', showChildCallback, showChildCallbackScope);
|
| 23 |
-
|
| 24 |
-
var hideChildCallback = GetValue(config, 'hideChildCallback', undefined);
|
| 25 |
-
var hideChildCallbackScope = GetValue(config, 'hideChildCallbackScope', undefined);
|
| 26 |
-
this.on('hidechild', hideChildCallback, hideChildCallbackScope);
|
| 27 |
-
} else { // Default callbacks
|
| 28 |
-
var defaultCallbacks = GetDefaultCallbacks(showChildCallback);
|
| 29 |
-
this.on('showchild', defaultCallbacks.show);
|
| 30 |
-
this.on('hidechild', defaultCallbacks.hide);
|
| 31 |
-
}
|
| 32 |
-
}
|
| 33 |
-
|
| 34 |
-
// Add elements
|
| 35 |
-
var background = GetValue(config, 'background', undefined);
|
| 36 |
-
var panel = GetValue(config, 'panel', undefined);
|
| 37 |
-
var leftSide = GetValue(config, 'leftSide', undefined);
|
| 38 |
-
var rightSide = GetValue(config, 'rightSide', undefined);
|
| 39 |
-
var topSide = GetValue(config, 'topSide', undefined);
|
| 40 |
-
var bottomSide = GetValue(config, 'bottomSide', undefined);
|
| 41 |
-
|
| 42 |
-
if (background) {
|
| 43 |
-
this.addBackground(background);
|
| 44 |
-
}
|
| 45 |
-
if (panel) {
|
| 46 |
-
this.add(panel, 'panel', 'center', 0, true);
|
| 47 |
-
}
|
| 48 |
-
if (leftSide) {
|
| 49 |
-
var expand = GetValue(config, 'expand.left', true);
|
| 50 |
-
this.add(leftSide, 'leftSide', 'left-top', 0, { height: expand });
|
| 51 |
-
}
|
| 52 |
-
if (rightSide) {
|
| 53 |
-
var expand = GetValue(config, 'expand.right', true);
|
| 54 |
-
this.add(rightSide, 'rightSide', 'right-top', 0, { height: expand });
|
| 55 |
-
}
|
| 56 |
-
if (topSide) {
|
| 57 |
-
var expand = GetValue(config, 'expand.top', true);
|
| 58 |
-
this.add(topSide, 'topSide', 'left-top', 0, { width: expand });
|
| 59 |
-
}
|
| 60 |
-
if (bottomSide) {
|
| 61 |
-
var expand = GetValue(config, 'expand.bottom', true);
|
| 62 |
-
this.add(bottomSide, 'bottomSide', 'left-bottom', 0, { width: expand });
|
| 63 |
-
}
|
| 64 |
-
}
|
| 65 |
-
|
| 66 |
-
reset() {
|
| 67 |
-
this.previousChildKey = undefined;
|
| 68 |
-
this.currentChildKey = 'panel';
|
| 69 |
-
this.showChild('panel', true);
|
| 70 |
-
this.hideChild('leftSide', true);
|
| 71 |
-
this.hideChild('rightSide', true);
|
| 72 |
-
this.hideChild('topSide', true);
|
| 73 |
-
this.hideChild('bottomSide', true);
|
| 74 |
-
return this;
|
| 75 |
-
}
|
| 76 |
-
}
|
| 77 |
-
|
| 78 |
-
Object.assign(
|
| 79 |
-
Sides.prototype,
|
| 80 |
-
ShowChildMethods,
|
| 81 |
-
ChildBehaviorMethods
|
| 82 |
-
);
|
| 83 |
-
|
| 84 |
-
export default Sides;
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spaces/Alpaca233/SadTalker/src/face3d/visualize.py
DELETED
|
@@ -1,48 +0,0 @@
|
|
| 1 |
-
# check the sync of 3dmm feature and the audio
|
| 2 |
-
import cv2
|
| 3 |
-
import numpy as np
|
| 4 |
-
from src.face3d.models.bfm import ParametricFaceModel
|
| 5 |
-
from src.face3d.models.facerecon_model import FaceReconModel
|
| 6 |
-
import torch
|
| 7 |
-
import subprocess, platform
|
| 8 |
-
import scipy.io as scio
|
| 9 |
-
from tqdm import tqdm
|
| 10 |
-
|
| 11 |
-
# draft
|
| 12 |
-
def gen_composed_video(args, device, first_frame_coeff, coeff_path, audio_path, save_path, exp_dim=64):
|
| 13 |
-
|
| 14 |
-
coeff_first = scio.loadmat(first_frame_coeff)['full_3dmm']
|
| 15 |
-
|
| 16 |
-
coeff_pred = scio.loadmat(coeff_path)['coeff_3dmm']
|
| 17 |
-
|
| 18 |
-
coeff_full = np.repeat(coeff_first, coeff_pred.shape[0], axis=0) # 257
|
| 19 |
-
|
| 20 |
-
coeff_full[:, 80:144] = coeff_pred[:, 0:64]
|
| 21 |
-
coeff_full[:, 224:227] = coeff_pred[:, 64:67] # 3 dim translation
|
| 22 |
-
coeff_full[:, 254:] = coeff_pred[:, 67:] # 3 dim translation
|
| 23 |
-
|
| 24 |
-
tmp_video_path = '/tmp/face3dtmp.mp4'
|
| 25 |
-
|
| 26 |
-
facemodel = FaceReconModel(args)
|
| 27 |
-
|
| 28 |
-
video = cv2.VideoWriter(tmp_video_path, cv2.VideoWriter_fourcc(*'mp4v'), 25, (224, 224))
|
| 29 |
-
|
| 30 |
-
for k in tqdm(range(coeff_pred.shape[0]), 'face3d rendering:'):
|
| 31 |
-
cur_coeff_full = torch.tensor(coeff_full[k:k+1], device=device)
|
| 32 |
-
|
| 33 |
-
facemodel.forward(cur_coeff_full, device)
|
| 34 |
-
|
| 35 |
-
predicted_landmark = facemodel.pred_lm # TODO.
|
| 36 |
-
predicted_landmark = predicted_landmark.cpu().numpy().squeeze()
|
| 37 |
-
|
| 38 |
-
rendered_img = facemodel.pred_face
|
| 39 |
-
rendered_img = 255. * rendered_img.cpu().numpy().squeeze().transpose(1,2,0)
|
| 40 |
-
out_img = rendered_img[:, :, :3].astype(np.uint8)
|
| 41 |
-
|
| 42 |
-
video.write(np.uint8(out_img[:,:,::-1]))
|
| 43 |
-
|
| 44 |
-
video.release()
|
| 45 |
-
|
| 46 |
-
command = 'ffmpeg -v quiet -y -i {} -i {} -strict -2 -q:v 1 {}'.format(audio_path, tmp_video_path, save_path)
|
| 47 |
-
subprocess.call(command, shell=platform.system() != 'Windows')
|
| 48 |
-
|
|
|
|
|
|
|
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|
spaces/Ameaou/academic-chatgpt3.1/crazy_functions/test_project/python/dqn/policies.py
DELETED
|
@@ -1,237 +0,0 @@
|
|
| 1 |
-
from typing import Any, Dict, List, Optional, Type
|
| 2 |
-
|
| 3 |
-
import gym
|
| 4 |
-
import torch as th
|
| 5 |
-
from torch import nn
|
| 6 |
-
|
| 7 |
-
from stable_baselines3.common.policies import BasePolicy, register_policy
|
| 8 |
-
from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor, NatureCNN, create_mlp
|
| 9 |
-
from stable_baselines3.common.type_aliases import Schedule
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
class QNetwork(BasePolicy):
|
| 13 |
-
"""
|
| 14 |
-
Action-Value (Q-Value) network for DQN
|
| 15 |
-
|
| 16 |
-
:param observation_space: Observation space
|
| 17 |
-
:param action_space: Action space
|
| 18 |
-
:param net_arch: The specification of the policy and value networks.
|
| 19 |
-
:param activation_fn: Activation function
|
| 20 |
-
:param normalize_images: Whether to normalize images or not,
|
| 21 |
-
dividing by 255.0 (True by default)
|
| 22 |
-
"""
|
| 23 |
-
|
| 24 |
-
def __init__(
|
| 25 |
-
self,
|
| 26 |
-
observation_space: gym.spaces.Space,
|
| 27 |
-
action_space: gym.spaces.Space,
|
| 28 |
-
features_extractor: nn.Module,
|
| 29 |
-
features_dim: int,
|
| 30 |
-
net_arch: Optional[List[int]] = None,
|
| 31 |
-
activation_fn: Type[nn.Module] = nn.ReLU,
|
| 32 |
-
normalize_images: bool = True,
|
| 33 |
-
):
|
| 34 |
-
super(QNetwork, self).__init__(
|
| 35 |
-
observation_space,
|
| 36 |
-
action_space,
|
| 37 |
-
features_extractor=features_extractor,
|
| 38 |
-
normalize_images=normalize_images,
|
| 39 |
-
)
|
| 40 |
-
|
| 41 |
-
if net_arch is None:
|
| 42 |
-
net_arch = [64, 64]
|
| 43 |
-
|
| 44 |
-
self.net_arch = net_arch
|
| 45 |
-
self.activation_fn = activation_fn
|
| 46 |
-
self.features_extractor = features_extractor
|
| 47 |
-
self.features_dim = features_dim
|
| 48 |
-
self.normalize_images = normalize_images
|
| 49 |
-
action_dim = self.action_space.n # number of actions
|
| 50 |
-
q_net = create_mlp(self.features_dim, action_dim, self.net_arch, self.activation_fn)
|
| 51 |
-
self.q_net = nn.Sequential(*q_net)
|
| 52 |
-
|
| 53 |
-
def forward(self, obs: th.Tensor) -> th.Tensor:
|
| 54 |
-
"""
|
| 55 |
-
Predict the q-values.
|
| 56 |
-
|
| 57 |
-
:param obs: Observation
|
| 58 |
-
:return: The estimated Q-Value for each action.
|
| 59 |
-
"""
|
| 60 |
-
return self.q_net(self.extract_features(obs))
|
| 61 |
-
|
| 62 |
-
def _predict(self, observation: th.Tensor, deterministic: bool = True) -> th.Tensor:
|
| 63 |
-
q_values = self.forward(observation)
|
| 64 |
-
# Greedy action
|
| 65 |
-
action = q_values.argmax(dim=1).reshape(-1)
|
| 66 |
-
return action
|
| 67 |
-
|
| 68 |
-
def _get_constructor_parameters(self) -> Dict[str, Any]:
|
| 69 |
-
data = super()._get_constructor_parameters()
|
| 70 |
-
|
| 71 |
-
data.update(
|
| 72 |
-
dict(
|
| 73 |
-
net_arch=self.net_arch,
|
| 74 |
-
features_dim=self.features_dim,
|
| 75 |
-
activation_fn=self.activation_fn,
|
| 76 |
-
features_extractor=self.features_extractor,
|
| 77 |
-
)
|
| 78 |
-
)
|
| 79 |
-
return data
|
| 80 |
-
|
| 81 |
-
|
| 82 |
-
class DQNPolicy(BasePolicy):
|
| 83 |
-
"""
|
| 84 |
-
Policy class with Q-Value Net and target net for DQN
|
| 85 |
-
|
| 86 |
-
:param observation_space: Observation space
|
| 87 |
-
:param action_space: Action space
|
| 88 |
-
:param lr_schedule: Learning rate schedule (could be constant)
|
| 89 |
-
:param net_arch: The specification of the policy and value networks.
|
| 90 |
-
:param activation_fn: Activation function
|
| 91 |
-
:param features_extractor_class: Features extractor to use.
|
| 92 |
-
:param features_extractor_kwargs: Keyword arguments
|
| 93 |
-
to pass to the features extractor.
|
| 94 |
-
:param normalize_images: Whether to normalize images or not,
|
| 95 |
-
dividing by 255.0 (True by default)
|
| 96 |
-
:param optimizer_class: The optimizer to use,
|
| 97 |
-
``th.optim.Adam`` by default
|
| 98 |
-
:param optimizer_kwargs: Additional keyword arguments,
|
| 99 |
-
excluding the learning rate, to pass to the optimizer
|
| 100 |
-
"""
|
| 101 |
-
|
| 102 |
-
def __init__(
|
| 103 |
-
self,
|
| 104 |
-
observation_space: gym.spaces.Space,
|
| 105 |
-
action_space: gym.spaces.Space,
|
| 106 |
-
lr_schedule: Schedule,
|
| 107 |
-
net_arch: Optional[List[int]] = None,
|
| 108 |
-
activation_fn: Type[nn.Module] = nn.ReLU,
|
| 109 |
-
features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
|
| 110 |
-
features_extractor_kwargs: Optional[Dict[str, Any]] = None,
|
| 111 |
-
normalize_images: bool = True,
|
| 112 |
-
optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
|
| 113 |
-
optimizer_kwargs: Optional[Dict[str, Any]] = None,
|
| 114 |
-
):
|
| 115 |
-
super(DQNPolicy, self).__init__(
|
| 116 |
-
observation_space,
|
| 117 |
-
action_space,
|
| 118 |
-
features_extractor_class,
|
| 119 |
-
features_extractor_kwargs,
|
| 120 |
-
optimizer_class=optimizer_class,
|
| 121 |
-
optimizer_kwargs=optimizer_kwargs,
|
| 122 |
-
)
|
| 123 |
-
|
| 124 |
-
if net_arch is None:
|
| 125 |
-
if features_extractor_class == FlattenExtractor:
|
| 126 |
-
net_arch = [64, 64]
|
| 127 |
-
else:
|
| 128 |
-
net_arch = []
|
| 129 |
-
|
| 130 |
-
self.net_arch = net_arch
|
| 131 |
-
self.activation_fn = activation_fn
|
| 132 |
-
self.normalize_images = normalize_images
|
| 133 |
-
|
| 134 |
-
self.net_args = {
|
| 135 |
-
"observation_space": self.observation_space,
|
| 136 |
-
"action_space": self.action_space,
|
| 137 |
-
"net_arch": self.net_arch,
|
| 138 |
-
"activation_fn": self.activation_fn,
|
| 139 |
-
"normalize_images": normalize_images,
|
| 140 |
-
}
|
| 141 |
-
|
| 142 |
-
self.q_net, self.q_net_target = None, None
|
| 143 |
-
self._build(lr_schedule)
|
| 144 |
-
|
| 145 |
-
def _build(self, lr_schedule: Schedule) -> None:
|
| 146 |
-
"""
|
| 147 |
-
Create the network and the optimizer.
|
| 148 |
-
|
| 149 |
-
:param lr_schedule: Learning rate schedule
|
| 150 |
-
lr_schedule(1) is the initial learning rate
|
| 151 |
-
"""
|
| 152 |
-
|
| 153 |
-
self.q_net = self.make_q_net()
|
| 154 |
-
self.q_net_target = self.make_q_net()
|
| 155 |
-
self.q_net_target.load_state_dict(self.q_net.state_dict())
|
| 156 |
-
|
| 157 |
-
# Setup optimizer with initial learning rate
|
| 158 |
-
self.optimizer = self.optimizer_class(self.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
|
| 159 |
-
|
| 160 |
-
def make_q_net(self) -> QNetwork:
|
| 161 |
-
# Make sure we always have separate networks for features extractors etc
|
| 162 |
-
net_args = self._update_features_extractor(self.net_args, features_extractor=None)
|
| 163 |
-
return QNetwork(**net_args).to(self.device)
|
| 164 |
-
|
| 165 |
-
def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
|
| 166 |
-
return self._predict(obs, deterministic=deterministic)
|
| 167 |
-
|
| 168 |
-
def _predict(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
|
| 169 |
-
return self.q_net._predict(obs, deterministic=deterministic)
|
| 170 |
-
|
| 171 |
-
def _get_constructor_parameters(self) -> Dict[str, Any]:
|
| 172 |
-
data = super()._get_constructor_parameters()
|
| 173 |
-
|
| 174 |
-
data.update(
|
| 175 |
-
dict(
|
| 176 |
-
net_arch=self.net_args["net_arch"],
|
| 177 |
-
activation_fn=self.net_args["activation_fn"],
|
| 178 |
-
lr_schedule=self._dummy_schedule, # dummy lr schedule, not needed for loading policy alone
|
| 179 |
-
optimizer_class=self.optimizer_class,
|
| 180 |
-
optimizer_kwargs=self.optimizer_kwargs,
|
| 181 |
-
features_extractor_class=self.features_extractor_class,
|
| 182 |
-
features_extractor_kwargs=self.features_extractor_kwargs,
|
| 183 |
-
)
|
| 184 |
-
)
|
| 185 |
-
return data
|
| 186 |
-
|
| 187 |
-
|
| 188 |
-
MlpPolicy = DQNPolicy
|
| 189 |
-
|
| 190 |
-
|
| 191 |
-
class CnnPolicy(DQNPolicy):
|
| 192 |
-
"""
|
| 193 |
-
Policy class for DQN when using images as input.
|
| 194 |
-
|
| 195 |
-
:param observation_space: Observation space
|
| 196 |
-
:param action_space: Action space
|
| 197 |
-
:param lr_schedule: Learning rate schedule (could be constant)
|
| 198 |
-
:param net_arch: The specification of the policy and value networks.
|
| 199 |
-
:param activation_fn: Activation function
|
| 200 |
-
:param features_extractor_class: Features extractor to use.
|
| 201 |
-
:param normalize_images: Whether to normalize images or not,
|
| 202 |
-
dividing by 255.0 (True by default)
|
| 203 |
-
:param optimizer_class: The optimizer to use,
|
| 204 |
-
``th.optim.Adam`` by default
|
| 205 |
-
:param optimizer_kwargs: Additional keyword arguments,
|
| 206 |
-
excluding the learning rate, to pass to the optimizer
|
| 207 |
-
"""
|
| 208 |
-
|
| 209 |
-
def __init__(
|
| 210 |
-
self,
|
| 211 |
-
observation_space: gym.spaces.Space,
|
| 212 |
-
action_space: gym.spaces.Space,
|
| 213 |
-
lr_schedule: Schedule,
|
| 214 |
-
net_arch: Optional[List[int]] = None,
|
| 215 |
-
activation_fn: Type[nn.Module] = nn.ReLU,
|
| 216 |
-
features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN,
|
| 217 |
-
features_extractor_kwargs: Optional[Dict[str, Any]] = None,
|
| 218 |
-
normalize_images: bool = True,
|
| 219 |
-
optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
|
| 220 |
-
optimizer_kwargs: Optional[Dict[str, Any]] = None,
|
| 221 |
-
):
|
| 222 |
-
super(CnnPolicy, self).__init__(
|
| 223 |
-
observation_space,
|
| 224 |
-
action_space,
|
| 225 |
-
lr_schedule,
|
| 226 |
-
net_arch,
|
| 227 |
-
activation_fn,
|
| 228 |
-
features_extractor_class,
|
| 229 |
-
features_extractor_kwargs,
|
| 230 |
-
normalize_images,
|
| 231 |
-
optimizer_class,
|
| 232 |
-
optimizer_kwargs,
|
| 233 |
-
)
|
| 234 |
-
|
| 235 |
-
|
| 236 |
-
register_policy("MlpPolicy", MlpPolicy)
|
| 237 |
-
register_policy("CnnPolicy", CnnPolicy)
|
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|
spaces/Andres99/Tune-A-Video-Training-UI/app_upload.py
DELETED
|
@@ -1,106 +0,0 @@
|
|
| 1 |
-
#!/usr/bin/env python
|
| 2 |
-
|
| 3 |
-
from __future__ import annotations
|
| 4 |
-
|
| 5 |
-
import pathlib
|
| 6 |
-
|
| 7 |
-
import gradio as gr
|
| 8 |
-
import slugify
|
| 9 |
-
|
| 10 |
-
from constants import MODEL_LIBRARY_ORG_NAME, UploadTarget
|
| 11 |
-
from uploader import Uploader
|
| 12 |
-
from utils import find_exp_dirs
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
class ModelUploader(Uploader):
|
| 16 |
-
def upload_model(
|
| 17 |
-
self,
|
| 18 |
-
folder_path: str,
|
| 19 |
-
repo_name: str,
|
| 20 |
-
upload_to: str,
|
| 21 |
-
private: bool,
|
| 22 |
-
delete_existing_repo: bool,
|
| 23 |
-
input_token: str | None = None,
|
| 24 |
-
) -> str:
|
| 25 |
-
if not folder_path:
|
| 26 |
-
raise ValueError
|
| 27 |
-
if not repo_name:
|
| 28 |
-
repo_name = pathlib.Path(folder_path).name
|
| 29 |
-
repo_name = slugify.slugify(repo_name)
|
| 30 |
-
|
| 31 |
-
if upload_to == UploadTarget.PERSONAL_PROFILE.value:
|
| 32 |
-
organization = ''
|
| 33 |
-
elif upload_to == UploadTarget.MODEL_LIBRARY.value:
|
| 34 |
-
organization = MODEL_LIBRARY_ORG_NAME
|
| 35 |
-
else:
|
| 36 |
-
raise ValueError
|
| 37 |
-
|
| 38 |
-
return self.upload(folder_path,
|
| 39 |
-
repo_name,
|
| 40 |
-
organization=organization,
|
| 41 |
-
private=private,
|
| 42 |
-
delete_existing_repo=delete_existing_repo,
|
| 43 |
-
input_token=input_token)
|
| 44 |
-
|
| 45 |
-
|
| 46 |
-
def load_local_model_list() -> dict:
|
| 47 |
-
choices = find_exp_dirs()
|
| 48 |
-
return gr.update(choices=choices, value=choices[0] if choices else None)
|
| 49 |
-
|
| 50 |
-
|
| 51 |
-
def create_upload_demo(hf_token: str | None) -> gr.Blocks:
|
| 52 |
-
uploader = ModelUploader(hf_token)
|
| 53 |
-
model_dirs = find_exp_dirs()
|
| 54 |
-
|
| 55 |
-
with gr.Blocks() as demo:
|
| 56 |
-
with gr.Box():
|
| 57 |
-
gr.Markdown('Local Models')
|
| 58 |
-
reload_button = gr.Button('Reload Model List')
|
| 59 |
-
model_dir = gr.Dropdown(
|
| 60 |
-
label='Model names',
|
| 61 |
-
choices=model_dirs,
|
| 62 |
-
value=model_dirs[0] if model_dirs else None)
|
| 63 |
-
with gr.Box():
|
| 64 |
-
gr.Markdown('Upload Settings')
|
| 65 |
-
with gr.Row():
|
| 66 |
-
use_private_repo = gr.Checkbox(label='Private', value=True)
|
| 67 |
-
delete_existing_repo = gr.Checkbox(
|
| 68 |
-
label='Delete existing repo of the same name', value=False)
|
| 69 |
-
upload_to = gr.Radio(label='Upload to',
|
| 70 |
-
choices=[_.value for _ in UploadTarget],
|
| 71 |
-
value=UploadTarget.MODEL_LIBRARY.value)
|
| 72 |
-
model_name = gr.Textbox(label='Model Name')
|
| 73 |
-
input_token = gr.Text(label='Hugging Face Write Token',
|
| 74 |
-
placeholder='',
|
| 75 |
-
visible=False if hf_token else True)
|
| 76 |
-
upload_button = gr.Button('Upload')
|
| 77 |
-
gr.Markdown(f'''
|
| 78 |
-
- You can upload your trained model to your personal profile (i.e. https://huggingface.co/{{your_username}}/{{model_name}}) or to the public [Tune-A-Video Library](https://huggingface.co/{MODEL_LIBRARY_ORG_NAME}) (i.e. https://huggingface.co/{MODEL_LIBRARY_ORG_NAME}/{{model_name}}).
|
| 79 |
-
''')
|
| 80 |
-
with gr.Box():
|
| 81 |
-
gr.Markdown('Output message')
|
| 82 |
-
output_message = gr.Markdown()
|
| 83 |
-
|
| 84 |
-
reload_button.click(fn=load_local_model_list,
|
| 85 |
-
inputs=None,
|
| 86 |
-
outputs=model_dir)
|
| 87 |
-
upload_button.click(fn=uploader.upload_model,
|
| 88 |
-
inputs=[
|
| 89 |
-
model_dir,
|
| 90 |
-
model_name,
|
| 91 |
-
upload_to,
|
| 92 |
-
use_private_repo,
|
| 93 |
-
delete_existing_repo,
|
| 94 |
-
input_token,
|
| 95 |
-
],
|
| 96 |
-
outputs=output_message)
|
| 97 |
-
|
| 98 |
-
return demo
|
| 99 |
-
|
| 100 |
-
|
| 101 |
-
if __name__ == '__main__':
|
| 102 |
-
import os
|
| 103 |
-
|
| 104 |
-
hf_token = os.getenv('HF_TOKEN')
|
| 105 |
-
demo = create_upload_demo(hf_token)
|
| 106 |
-
demo.queue(max_size=1).launch(share=False)
|
|
|
|
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|
|
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/using-diffusers/control_brightness.md
DELETED
|
@@ -1,45 +0,0 @@
|
|
| 1 |
-
# Control image brightness
|
| 2 |
-
|
| 3 |
-
The Stable Diffusion pipeline is mediocre at generating images that are either very bright or dark as explained in the [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) paper. The solutions proposed in the paper are currently implemented in the [`DDIMScheduler`] which you can use to improve the lighting in your images.
|
| 4 |
-
|
| 5 |
-
<Tip>
|
| 6 |
-
|
| 7 |
-
💡 Take a look at the paper linked above for more details about the proposed solutions!
|
| 8 |
-
|
| 9 |
-
</Tip>
|
| 10 |
-
|
| 11 |
-
One of the solutions is to train a model with *v prediction* and *v loss*. Add the following flag to the [`train_text_to_image.py`](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) or [`train_text_to_image_lora.py`](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora.py) scripts to enable `v_prediction`:
|
| 12 |
-
|
| 13 |
-
```bash
|
| 14 |
-
--prediction_type="v_prediction"
|
| 15 |
-
```
|
| 16 |
-
|
| 17 |
-
For example, let's use the [`ptx0/pseudo-journey-v2`](https://huggingface.co/ptx0/pseudo-journey-v2) checkpoint which has been finetuned with `v_prediction`.
|
| 18 |
-
|
| 19 |
-
Next, configure the following parameters in the [`DDIMScheduler`]:
|
| 20 |
-
|
| 21 |
-
1. `rescale_betas_zero_snr=True`, rescales the noise schedule to zero terminal signal-to-noise ratio (SNR)
|
| 22 |
-
2. `timestep_spacing="trailing"`, starts sampling from the last timestep
|
| 23 |
-
|
| 24 |
-
```py
|
| 25 |
-
>>> from diffusers import DiffusionPipeline, DDIMScheduler
|
| 26 |
-
|
| 27 |
-
>>> pipeline = DiffusionPipeline.from_pretrained("ptx0/pseudo-journey-v2")
|
| 28 |
-
# switch the scheduler in the pipeline to use the DDIMScheduler
|
| 29 |
-
|
| 30 |
-
>>> pipeline.scheduler = DDIMScheduler.from_config(
|
| 31 |
-
... pipeline.scheduler.config, rescale_betas_zero_snr=True, timestep_spacing="trailing"
|
| 32 |
-
... )
|
| 33 |
-
>>> pipeline.to("cuda")
|
| 34 |
-
```
|
| 35 |
-
|
| 36 |
-
Finally, in your call to the pipeline, set `guidance_rescale` to prevent overexposure:
|
| 37 |
-
|
| 38 |
-
```py
|
| 39 |
-
prompt = "A lion in galaxies, spirals, nebulae, stars, smoke, iridescent, intricate detail, octane render, 8k"
|
| 40 |
-
image = pipeline(prompt, guidance_rescale=0.7).images[0]
|
| 41 |
-
```
|
| 42 |
-
|
| 43 |
-
<div class="flex justify-center">
|
| 44 |
-
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/zero_snr.png"/>
|
| 45 |
-
</div>
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spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/using-diffusers/inpaint.md
DELETED
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@@ -1,76 +0,0 @@
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|
| 1 |
-
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
|
| 2 |
-
|
| 3 |
-
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
| 4 |
-
the License. You may obtain a copy of the License at
|
| 5 |
-
|
| 6 |
-
http://www.apache.org/licenses/LICENSE-2.0
|
| 7 |
-
|
| 8 |
-
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
| 9 |
-
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
| 10 |
-
specific language governing permissions and limitations under the License.
|
| 11 |
-
-->
|
| 12 |
-
|
| 13 |
-
# Text-guided image-inpainting
|
| 14 |
-
|
| 15 |
-
[[open-in-colab]]
|
| 16 |
-
|
| 17 |
-
The [`StableDiffusionInpaintPipeline`] allows you to edit specific parts of an image by providing a mask and a text prompt. It uses a version of Stable Diffusion, like [`runwayml/stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) specifically trained for inpainting tasks.
|
| 18 |
-
|
| 19 |
-
Get started by loading an instance of the [`StableDiffusionInpaintPipeline`]:
|
| 20 |
-
|
| 21 |
-
```python
|
| 22 |
-
import PIL
|
| 23 |
-
import requests
|
| 24 |
-
import torch
|
| 25 |
-
from io import BytesIO
|
| 26 |
-
|
| 27 |
-
from diffusers import StableDiffusionInpaintPipeline
|
| 28 |
-
|
| 29 |
-
pipeline = StableDiffusionInpaintPipeline.from_pretrained(
|
| 30 |
-
"runwayml/stable-diffusion-inpainting",
|
| 31 |
-
torch_dtype=torch.float16,
|
| 32 |
-
)
|
| 33 |
-
pipeline = pipeline.to("cuda")
|
| 34 |
-
```
|
| 35 |
-
|
| 36 |
-
Download an image and a mask of a dog which you'll eventually replace:
|
| 37 |
-
|
| 38 |
-
```python
|
| 39 |
-
def download_image(url):
|
| 40 |
-
response = requests.get(url)
|
| 41 |
-
return PIL.Image.open(BytesIO(response.content)).convert("RGB")
|
| 42 |
-
|
| 43 |
-
|
| 44 |
-
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
|
| 45 |
-
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
|
| 46 |
-
|
| 47 |
-
init_image = download_image(img_url).resize((512, 512))
|
| 48 |
-
mask_image = download_image(mask_url).resize((512, 512))
|
| 49 |
-
```
|
| 50 |
-
|
| 51 |
-
Now you can create a prompt to replace the mask with something else:
|
| 52 |
-
|
| 53 |
-
```python
|
| 54 |
-
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
|
| 55 |
-
image = pipeline(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
|
| 56 |
-
```
|
| 57 |
-
|
| 58 |
-
`image` | `mask_image` | `prompt` | output |
|
| 59 |
-
:-------------------------:|:-------------------------:|:-------------------------:|-------------------------:|
|
| 60 |
-
<img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" alt="drawing" width="250"/> | <img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" alt="drawing" width="250"/> | ***Face of a yellow cat, high resolution, sitting on a park bench*** | <img src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/in_paint/yellow_cat_sitting_on_a_park_bench.png" alt="drawing" width="250"/> |
|
| 61 |
-
|
| 62 |
-
|
| 63 |
-
<Tip warning={true}>
|
| 64 |
-
|
| 65 |
-
A previous experimental implementation of inpainting used a different, lower-quality process. To ensure backwards compatibility, loading a pretrained pipeline that doesn't contain the new model will still apply the old inpainting method.
|
| 66 |
-
|
| 67 |
-
</Tip>
|
| 68 |
-
|
| 69 |
-
Check out the Spaces below to try out image inpainting yourself!
|
| 70 |
-
|
| 71 |
-
<iframe
|
| 72 |
-
src="https://runwayml-stable-diffusion-inpainting.hf.space"
|
| 73 |
-
frameborder="0"
|
| 74 |
-
width="850"
|
| 75 |
-
height="500"
|
| 76 |
-
></iframe>
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spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/schedulers/scheduling_vq_diffusion.py
DELETED
|
@@ -1,496 +0,0 @@
|
|
| 1 |
-
# Copyright 2023 Microsoft and The HuggingFace Team. All rights reserved.
|
| 2 |
-
#
|
| 3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
-
# you may not use this file except in compliance with the License.
|
| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
|
| 14 |
-
|
| 15 |
-
from dataclasses import dataclass
|
| 16 |
-
from typing import Optional, Tuple, Union
|
| 17 |
-
|
| 18 |
-
import numpy as np
|
| 19 |
-
import torch
|
| 20 |
-
import torch.nn.functional as F
|
| 21 |
-
|
| 22 |
-
from ..configuration_utils import ConfigMixin, register_to_config
|
| 23 |
-
from ..utils import BaseOutput
|
| 24 |
-
from .scheduling_utils import SchedulerMixin
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
@dataclass
|
| 28 |
-
class VQDiffusionSchedulerOutput(BaseOutput):
|
| 29 |
-
"""
|
| 30 |
-
Output class for the scheduler's step function output.
|
| 31 |
-
|
| 32 |
-
Args:
|
| 33 |
-
prev_sample (`torch.LongTensor` of shape `(batch size, num latent pixels)`):
|
| 34 |
-
Computed sample x_{t-1} of previous timestep. `prev_sample` should be used as next model input in the
|
| 35 |
-
denoising loop.
|
| 36 |
-
"""
|
| 37 |
-
|
| 38 |
-
prev_sample: torch.LongTensor
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
def index_to_log_onehot(x: torch.LongTensor, num_classes: int) -> torch.FloatTensor:
|
| 42 |
-
"""
|
| 43 |
-
Convert batch of vector of class indices into batch of log onehot vectors
|
| 44 |
-
|
| 45 |
-
Args:
|
| 46 |
-
x (`torch.LongTensor` of shape `(batch size, vector length)`):
|
| 47 |
-
Batch of class indices
|
| 48 |
-
|
| 49 |
-
num_classes (`int`):
|
| 50 |
-
number of classes to be used for the onehot vectors
|
| 51 |
-
|
| 52 |
-
Returns:
|
| 53 |
-
`torch.FloatTensor` of shape `(batch size, num classes, vector length)`:
|
| 54 |
-
Log onehot vectors
|
| 55 |
-
"""
|
| 56 |
-
x_onehot = F.one_hot(x, num_classes)
|
| 57 |
-
x_onehot = x_onehot.permute(0, 2, 1)
|
| 58 |
-
log_x = torch.log(x_onehot.float().clamp(min=1e-30))
|
| 59 |
-
return log_x
|
| 60 |
-
|
| 61 |
-
|
| 62 |
-
def gumbel_noised(logits: torch.FloatTensor, generator: Optional[torch.Generator]) -> torch.FloatTensor:
|
| 63 |
-
"""
|
| 64 |
-
Apply gumbel noise to `logits`
|
| 65 |
-
"""
|
| 66 |
-
uniform = torch.rand(logits.shape, device=logits.device, generator=generator)
|
| 67 |
-
gumbel_noise = -torch.log(-torch.log(uniform + 1e-30) + 1e-30)
|
| 68 |
-
noised = gumbel_noise + logits
|
| 69 |
-
return noised
|
| 70 |
-
|
| 71 |
-
|
| 72 |
-
def alpha_schedules(num_diffusion_timesteps: int, alpha_cum_start=0.99999, alpha_cum_end=0.000009):
|
| 73 |
-
"""
|
| 74 |
-
Cumulative and non-cumulative alpha schedules.
|
| 75 |
-
|
| 76 |
-
See section 4.1.
|
| 77 |
-
"""
|
| 78 |
-
att = (
|
| 79 |
-
np.arange(0, num_diffusion_timesteps) / (num_diffusion_timesteps - 1) * (alpha_cum_end - alpha_cum_start)
|
| 80 |
-
+ alpha_cum_start
|
| 81 |
-
)
|
| 82 |
-
att = np.concatenate(([1], att))
|
| 83 |
-
at = att[1:] / att[:-1]
|
| 84 |
-
att = np.concatenate((att[1:], [1]))
|
| 85 |
-
return at, att
|
| 86 |
-
|
| 87 |
-
|
| 88 |
-
def gamma_schedules(num_diffusion_timesteps: int, gamma_cum_start=0.000009, gamma_cum_end=0.99999):
|
| 89 |
-
"""
|
| 90 |
-
Cumulative and non-cumulative gamma schedules.
|
| 91 |
-
|
| 92 |
-
See section 4.1.
|
| 93 |
-
"""
|
| 94 |
-
ctt = (
|
| 95 |
-
np.arange(0, num_diffusion_timesteps) / (num_diffusion_timesteps - 1) * (gamma_cum_end - gamma_cum_start)
|
| 96 |
-
+ gamma_cum_start
|
| 97 |
-
)
|
| 98 |
-
ctt = np.concatenate(([0], ctt))
|
| 99 |
-
one_minus_ctt = 1 - ctt
|
| 100 |
-
one_minus_ct = one_minus_ctt[1:] / one_minus_ctt[:-1]
|
| 101 |
-
ct = 1 - one_minus_ct
|
| 102 |
-
ctt = np.concatenate((ctt[1:], [0]))
|
| 103 |
-
return ct, ctt
|
| 104 |
-
|
| 105 |
-
|
| 106 |
-
class VQDiffusionScheduler(SchedulerMixin, ConfigMixin):
|
| 107 |
-
"""
|
| 108 |
-
The VQ-diffusion transformer outputs predicted probabilities of the initial unnoised image.
|
| 109 |
-
|
| 110 |
-
The VQ-diffusion scheduler converts the transformer's output into a sample for the unnoised image at the previous
|
| 111 |
-
diffusion timestep.
|
| 112 |
-
|
| 113 |
-
[`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
|
| 114 |
-
function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
|
| 115 |
-
[`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
|
| 116 |
-
[`~SchedulerMixin.from_pretrained`] functions.
|
| 117 |
-
|
| 118 |
-
For more details, see the original paper: https://arxiv.org/abs/2111.14822
|
| 119 |
-
|
| 120 |
-
Args:
|
| 121 |
-
num_vec_classes (`int`):
|
| 122 |
-
The number of classes of the vector embeddings of the latent pixels. Includes the class for the masked
|
| 123 |
-
latent pixel.
|
| 124 |
-
|
| 125 |
-
num_train_timesteps (`int`):
|
| 126 |
-
Number of diffusion steps used to train the model.
|
| 127 |
-
|
| 128 |
-
alpha_cum_start (`float`):
|
| 129 |
-
The starting cumulative alpha value.
|
| 130 |
-
|
| 131 |
-
alpha_cum_end (`float`):
|
| 132 |
-
The ending cumulative alpha value.
|
| 133 |
-
|
| 134 |
-
gamma_cum_start (`float`):
|
| 135 |
-
The starting cumulative gamma value.
|
| 136 |
-
|
| 137 |
-
gamma_cum_end (`float`):
|
| 138 |
-
The ending cumulative gamma value.
|
| 139 |
-
"""
|
| 140 |
-
|
| 141 |
-
order = 1
|
| 142 |
-
|
| 143 |
-
@register_to_config
|
| 144 |
-
def __init__(
|
| 145 |
-
self,
|
| 146 |
-
num_vec_classes: int,
|
| 147 |
-
num_train_timesteps: int = 100,
|
| 148 |
-
alpha_cum_start: float = 0.99999,
|
| 149 |
-
alpha_cum_end: float = 0.000009,
|
| 150 |
-
gamma_cum_start: float = 0.000009,
|
| 151 |
-
gamma_cum_end: float = 0.99999,
|
| 152 |
-
):
|
| 153 |
-
self.num_embed = num_vec_classes
|
| 154 |
-
|
| 155 |
-
# By convention, the index for the mask class is the last class index
|
| 156 |
-
self.mask_class = self.num_embed - 1
|
| 157 |
-
|
| 158 |
-
at, att = alpha_schedules(num_train_timesteps, alpha_cum_start=alpha_cum_start, alpha_cum_end=alpha_cum_end)
|
| 159 |
-
ct, ctt = gamma_schedules(num_train_timesteps, gamma_cum_start=gamma_cum_start, gamma_cum_end=gamma_cum_end)
|
| 160 |
-
|
| 161 |
-
num_non_mask_classes = self.num_embed - 1
|
| 162 |
-
bt = (1 - at - ct) / num_non_mask_classes
|
| 163 |
-
btt = (1 - att - ctt) / num_non_mask_classes
|
| 164 |
-
|
| 165 |
-
at = torch.tensor(at.astype("float64"))
|
| 166 |
-
bt = torch.tensor(bt.astype("float64"))
|
| 167 |
-
ct = torch.tensor(ct.astype("float64"))
|
| 168 |
-
log_at = torch.log(at)
|
| 169 |
-
log_bt = torch.log(bt)
|
| 170 |
-
log_ct = torch.log(ct)
|
| 171 |
-
|
| 172 |
-
att = torch.tensor(att.astype("float64"))
|
| 173 |
-
btt = torch.tensor(btt.astype("float64"))
|
| 174 |
-
ctt = torch.tensor(ctt.astype("float64"))
|
| 175 |
-
log_cumprod_at = torch.log(att)
|
| 176 |
-
log_cumprod_bt = torch.log(btt)
|
| 177 |
-
log_cumprod_ct = torch.log(ctt)
|
| 178 |
-
|
| 179 |
-
self.log_at = log_at.float()
|
| 180 |
-
self.log_bt = log_bt.float()
|
| 181 |
-
self.log_ct = log_ct.float()
|
| 182 |
-
self.log_cumprod_at = log_cumprod_at.float()
|
| 183 |
-
self.log_cumprod_bt = log_cumprod_bt.float()
|
| 184 |
-
self.log_cumprod_ct = log_cumprod_ct.float()
|
| 185 |
-
|
| 186 |
-
# setable values
|
| 187 |
-
self.num_inference_steps = None
|
| 188 |
-
self.timesteps = torch.from_numpy(np.arange(0, num_train_timesteps)[::-1].copy())
|
| 189 |
-
|
| 190 |
-
def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None):
|
| 191 |
-
"""
|
| 192 |
-
Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
|
| 193 |
-
|
| 194 |
-
Args:
|
| 195 |
-
num_inference_steps (`int`):
|
| 196 |
-
the number of diffusion steps used when generating samples with a pre-trained model.
|
| 197 |
-
|
| 198 |
-
device (`str` or `torch.device`):
|
| 199 |
-
device to place the timesteps and the diffusion process parameters (alpha, beta, gamma) on.
|
| 200 |
-
"""
|
| 201 |
-
self.num_inference_steps = num_inference_steps
|
| 202 |
-
timesteps = np.arange(0, self.num_inference_steps)[::-1].copy()
|
| 203 |
-
self.timesteps = torch.from_numpy(timesteps).to(device)
|
| 204 |
-
|
| 205 |
-
self.log_at = self.log_at.to(device)
|
| 206 |
-
self.log_bt = self.log_bt.to(device)
|
| 207 |
-
self.log_ct = self.log_ct.to(device)
|
| 208 |
-
self.log_cumprod_at = self.log_cumprod_at.to(device)
|
| 209 |
-
self.log_cumprod_bt = self.log_cumprod_bt.to(device)
|
| 210 |
-
self.log_cumprod_ct = self.log_cumprod_ct.to(device)
|
| 211 |
-
|
| 212 |
-
def step(
|
| 213 |
-
self,
|
| 214 |
-
model_output: torch.FloatTensor,
|
| 215 |
-
timestep: torch.long,
|
| 216 |
-
sample: torch.LongTensor,
|
| 217 |
-
generator: Optional[torch.Generator] = None,
|
| 218 |
-
return_dict: bool = True,
|
| 219 |
-
) -> Union[VQDiffusionSchedulerOutput, Tuple]:
|
| 220 |
-
"""
|
| 221 |
-
Predict the sample at the previous timestep via the reverse transition distribution i.e. Equation (11). See the
|
| 222 |
-
docstring for `self.q_posterior` for more in depth docs on how Equation (11) is computed.
|
| 223 |
-
|
| 224 |
-
Args:
|
| 225 |
-
log_p_x_0: (`torch.FloatTensor` of shape `(batch size, num classes - 1, num latent pixels)`):
|
| 226 |
-
The log probabilities for the predicted classes of the initial latent pixels. Does not include a
|
| 227 |
-
prediction for the masked class as the initial unnoised image cannot be masked.
|
| 228 |
-
|
| 229 |
-
t (`torch.long`):
|
| 230 |
-
The timestep that determines which transition matrices are used.
|
| 231 |
-
|
| 232 |
-
x_t: (`torch.LongTensor` of shape `(batch size, num latent pixels)`):
|
| 233 |
-
The classes of each latent pixel at time `t`
|
| 234 |
-
|
| 235 |
-
generator: (`torch.Generator` or None):
|
| 236 |
-
RNG for the noise applied to p(x_{t-1} | x_t) before it is sampled from.
|
| 237 |
-
|
| 238 |
-
return_dict (`bool`):
|
| 239 |
-
option for returning tuple rather than VQDiffusionSchedulerOutput class
|
| 240 |
-
|
| 241 |
-
Returns:
|
| 242 |
-
[`~schedulers.scheduling_utils.VQDiffusionSchedulerOutput`] or `tuple`:
|
| 243 |
-
[`~schedulers.scheduling_utils.VQDiffusionSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`.
|
| 244 |
-
When returning a tuple, the first element is the sample tensor.
|
| 245 |
-
"""
|
| 246 |
-
if timestep == 0:
|
| 247 |
-
log_p_x_t_min_1 = model_output
|
| 248 |
-
else:
|
| 249 |
-
log_p_x_t_min_1 = self.q_posterior(model_output, sample, timestep)
|
| 250 |
-
|
| 251 |
-
log_p_x_t_min_1 = gumbel_noised(log_p_x_t_min_1, generator)
|
| 252 |
-
|
| 253 |
-
x_t_min_1 = log_p_x_t_min_1.argmax(dim=1)
|
| 254 |
-
|
| 255 |
-
if not return_dict:
|
| 256 |
-
return (x_t_min_1,)
|
| 257 |
-
|
| 258 |
-
return VQDiffusionSchedulerOutput(prev_sample=x_t_min_1)
|
| 259 |
-
|
| 260 |
-
def q_posterior(self, log_p_x_0, x_t, t):
|
| 261 |
-
"""
|
| 262 |
-
Calculates the log probabilities for the predicted classes of the image at timestep `t-1`. I.e. Equation (11).
|
| 263 |
-
|
| 264 |
-
Instead of directly computing equation (11), we use Equation (5) to restate Equation (11) in terms of only
|
| 265 |
-
forward probabilities.
|
| 266 |
-
|
| 267 |
-
Equation (11) stated in terms of forward probabilities via Equation (5):
|
| 268 |
-
|
| 269 |
-
Where:
|
| 270 |
-
- the sum is over x_0 = {C_0 ... C_{k-1}} (classes for x_0)
|
| 271 |
-
|
| 272 |
-
p(x_{t-1} | x_t) = sum( q(x_t | x_{t-1}) * q(x_{t-1} | x_0) * p(x_0) / q(x_t | x_0) )
|
| 273 |
-
|
| 274 |
-
Args:
|
| 275 |
-
log_p_x_0: (`torch.FloatTensor` of shape `(batch size, num classes - 1, num latent pixels)`):
|
| 276 |
-
The log probabilities for the predicted classes of the initial latent pixels. Does not include a
|
| 277 |
-
prediction for the masked class as the initial unnoised image cannot be masked.
|
| 278 |
-
|
| 279 |
-
x_t: (`torch.LongTensor` of shape `(batch size, num latent pixels)`):
|
| 280 |
-
The classes of each latent pixel at time `t`
|
| 281 |
-
|
| 282 |
-
t (torch.Long):
|
| 283 |
-
The timestep that determines which transition matrix is used.
|
| 284 |
-
|
| 285 |
-
Returns:
|
| 286 |
-
`torch.FloatTensor` of shape `(batch size, num classes, num latent pixels)`:
|
| 287 |
-
The log probabilities for the predicted classes of the image at timestep `t-1`. I.e. Equation (11).
|
| 288 |
-
"""
|
| 289 |
-
log_onehot_x_t = index_to_log_onehot(x_t, self.num_embed)
|
| 290 |
-
|
| 291 |
-
log_q_x_t_given_x_0 = self.log_Q_t_transitioning_to_known_class(
|
| 292 |
-
t=t, x_t=x_t, log_onehot_x_t=log_onehot_x_t, cumulative=True
|
| 293 |
-
)
|
| 294 |
-
|
| 295 |
-
log_q_t_given_x_t_min_1 = self.log_Q_t_transitioning_to_known_class(
|
| 296 |
-
t=t, x_t=x_t, log_onehot_x_t=log_onehot_x_t, cumulative=False
|
| 297 |
-
)
|
| 298 |
-
|
| 299 |
-
# p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) ... p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0)
|
| 300 |
-
# . . .
|
| 301 |
-
# . . .
|
| 302 |
-
# . . .
|
| 303 |
-
# p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) ... p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1})
|
| 304 |
-
q = log_p_x_0 - log_q_x_t_given_x_0
|
| 305 |
-
|
| 306 |
-
# sum_0 = p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) + ... + p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}), ... ,
|
| 307 |
-
# sum_n = p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0) + ... + p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1})
|
| 308 |
-
q_log_sum_exp = torch.logsumexp(q, dim=1, keepdim=True)
|
| 309 |
-
|
| 310 |
-
# p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_0 ... p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_n
|
| 311 |
-
# . . .
|
| 312 |
-
# . . .
|
| 313 |
-
# . . .
|
| 314 |
-
# p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_0 ... p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_n
|
| 315 |
-
q = q - q_log_sum_exp
|
| 316 |
-
|
| 317 |
-
# (p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_0) * a_cumulative_{t-1} + b_cumulative_{t-1} ... (p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_n) * a_cumulative_{t-1} + b_cumulative_{t-1}
|
| 318 |
-
# . . .
|
| 319 |
-
# . . .
|
| 320 |
-
# . . .
|
| 321 |
-
# (p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_0) * a_cumulative_{t-1} + b_cumulative_{t-1} ... (p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_n) * a_cumulative_{t-1} + b_cumulative_{t-1}
|
| 322 |
-
# c_cumulative_{t-1} ... c_cumulative_{t-1}
|
| 323 |
-
q = self.apply_cumulative_transitions(q, t - 1)
|
| 324 |
-
|
| 325 |
-
# ((p_0(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_0) * a_cumulative_{t-1} + b_cumulative_{t-1}) * q(x_t | x_{t-1}=C_0) * sum_0 ... ((p_n(x_0=C_0 | x_t) / q(x_t | x_0=C_0) / sum_n) * a_cumulative_{t-1} + b_cumulative_{t-1}) * q(x_t | x_{t-1}=C_0) * sum_n
|
| 326 |
-
# . . .
|
| 327 |
-
# . . .
|
| 328 |
-
# . . .
|
| 329 |
-
# ((p_0(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_0) * a_cumulative_{t-1} + b_cumulative_{t-1}) * q(x_t | x_{t-1}=C_{k-1}) * sum_0 ... ((p_n(x_0=C_{k-1} | x_t) / q(x_t | x_0=C_{k-1}) / sum_n) * a_cumulative_{t-1} + b_cumulative_{t-1}) * q(x_t | x_{t-1}=C_{k-1}) * sum_n
|
| 330 |
-
# c_cumulative_{t-1} * q(x_t | x_{t-1}=C_k) * sum_0 ... c_cumulative_{t-1} * q(x_t | x_{t-1}=C_k) * sum_0
|
| 331 |
-
log_p_x_t_min_1 = q + log_q_t_given_x_t_min_1 + q_log_sum_exp
|
| 332 |
-
|
| 333 |
-
# For each column, there are two possible cases.
|
| 334 |
-
#
|
| 335 |
-
# Where:
|
| 336 |
-
# - sum(p_n(x_0))) is summing over all classes for x_0
|
| 337 |
-
# - C_i is the class transitioning from (not to be confused with c_t and c_cumulative_t being used for gamma's)
|
| 338 |
-
# - C_j is the class transitioning to
|
| 339 |
-
#
|
| 340 |
-
# 1. x_t is masked i.e. x_t = c_k
|
| 341 |
-
#
|
| 342 |
-
# Simplifying the expression, the column vector is:
|
| 343 |
-
# .
|
| 344 |
-
# .
|
| 345 |
-
# .
|
| 346 |
-
# (c_t / c_cumulative_t) * (a_cumulative_{t-1} * p_n(x_0 = C_i | x_t) + b_cumulative_{t-1} * sum(p_n(x_0)))
|
| 347 |
-
# .
|
| 348 |
-
# .
|
| 349 |
-
# .
|
| 350 |
-
# (c_cumulative_{t-1} / c_cumulative_t) * sum(p_n(x_0))
|
| 351 |
-
#
|
| 352 |
-
# From equation (11) stated in terms of forward probabilities, the last row is trivially verified.
|
| 353 |
-
#
|
| 354 |
-
# For the other rows, we can state the equation as ...
|
| 355 |
-
#
|
| 356 |
-
# (c_t / c_cumulative_t) * [b_cumulative_{t-1} * p(x_0=c_0) + ... + (a_cumulative_{t-1} + b_cumulative_{t-1}) * p(x_0=C_i) + ... + b_cumulative_{k-1} * p(x_0=c_{k-1})]
|
| 357 |
-
#
|
| 358 |
-
# This verifies the other rows.
|
| 359 |
-
#
|
| 360 |
-
# 2. x_t is not masked
|
| 361 |
-
#
|
| 362 |
-
# Simplifying the expression, there are two cases for the rows of the column vector, where C_j = C_i and where C_j != C_i:
|
| 363 |
-
# .
|
| 364 |
-
# .
|
| 365 |
-
# .
|
| 366 |
-
# C_j != C_i: b_t * ((b_cumulative_{t-1} / b_cumulative_t) * p_n(x_0 = c_0) + ... + ((a_cumulative_{t-1} + b_cumulative_{t-1}) / b_cumulative_t) * p_n(x_0 = C_i) + ... + (b_cumulative_{t-1} / (a_cumulative_t + b_cumulative_t)) * p_n(c_0=C_j) + ... + (b_cumulative_{t-1} / b_cumulative_t) * p_n(x_0 = c_{k-1}))
|
| 367 |
-
# .
|
| 368 |
-
# .
|
| 369 |
-
# .
|
| 370 |
-
# C_j = C_i: (a_t + b_t) * ((b_cumulative_{t-1} / b_cumulative_t) * p_n(x_0 = c_0) + ... + ((a_cumulative_{t-1} + b_cumulative_{t-1}) / (a_cumulative_t + b_cumulative_t)) * p_n(x_0 = C_i = C_j) + ... + (b_cumulative_{t-1} / b_cumulative_t) * p_n(x_0 = c_{k-1}))
|
| 371 |
-
# .
|
| 372 |
-
# .
|
| 373 |
-
# .
|
| 374 |
-
# 0
|
| 375 |
-
#
|
| 376 |
-
# The last row is trivially verified. The other rows can be verified by directly expanding equation (11) stated in terms of forward probabilities.
|
| 377 |
-
return log_p_x_t_min_1
|
| 378 |
-
|
| 379 |
-
def log_Q_t_transitioning_to_known_class(
|
| 380 |
-
self, *, t: torch.int, x_t: torch.LongTensor, log_onehot_x_t: torch.FloatTensor, cumulative: bool
|
| 381 |
-
):
|
| 382 |
-
"""
|
| 383 |
-
Returns the log probabilities of the rows from the (cumulative or non-cumulative) transition matrix for each
|
| 384 |
-
latent pixel in `x_t`.
|
| 385 |
-
|
| 386 |
-
See equation (7) for the complete non-cumulative transition matrix. The complete cumulative transition matrix
|
| 387 |
-
is the same structure except the parameters (alpha, beta, gamma) are the cumulative analogs.
|
| 388 |
-
|
| 389 |
-
Args:
|
| 390 |
-
t (torch.Long):
|
| 391 |
-
The timestep that determines which transition matrix is used.
|
| 392 |
-
|
| 393 |
-
x_t (`torch.LongTensor` of shape `(batch size, num latent pixels)`):
|
| 394 |
-
The classes of each latent pixel at time `t`.
|
| 395 |
-
|
| 396 |
-
log_onehot_x_t (`torch.FloatTensor` of shape `(batch size, num classes, num latent pixels)`):
|
| 397 |
-
The log one-hot vectors of `x_t`
|
| 398 |
-
|
| 399 |
-
cumulative (`bool`):
|
| 400 |
-
If cumulative is `False`, we use the single step transition matrix `t-1`->`t`. If cumulative is `True`,
|
| 401 |
-
we use the cumulative transition matrix `0`->`t`.
|
| 402 |
-
|
| 403 |
-
Returns:
|
| 404 |
-
`torch.FloatTensor` of shape `(batch size, num classes - 1, num latent pixels)`:
|
| 405 |
-
Each _column_ of the returned matrix is a _row_ of log probabilities of the complete probability
|
| 406 |
-
transition matrix.
|
| 407 |
-
|
| 408 |
-
When non cumulative, returns `self.num_classes - 1` rows because the initial latent pixel cannot be
|
| 409 |
-
masked.
|
| 410 |
-
|
| 411 |
-
Where:
|
| 412 |
-
- `q_n` is the probability distribution for the forward process of the `n`th latent pixel.
|
| 413 |
-
- C_0 is a class of a latent pixel embedding
|
| 414 |
-
- C_k is the class of the masked latent pixel
|
| 415 |
-
|
| 416 |
-
non-cumulative result (omitting logarithms):
|
| 417 |
-
```
|
| 418 |
-
q_0(x_t | x_{t-1} = C_0) ... q_n(x_t | x_{t-1} = C_0)
|
| 419 |
-
. . .
|
| 420 |
-
. . .
|
| 421 |
-
. . .
|
| 422 |
-
q_0(x_t | x_{t-1} = C_k) ... q_n(x_t | x_{t-1} = C_k)
|
| 423 |
-
```
|
| 424 |
-
|
| 425 |
-
cumulative result (omitting logarithms):
|
| 426 |
-
```
|
| 427 |
-
q_0_cumulative(x_t | x_0 = C_0) ... q_n_cumulative(x_t | x_0 = C_0)
|
| 428 |
-
. . .
|
| 429 |
-
. . .
|
| 430 |
-
. . .
|
| 431 |
-
q_0_cumulative(x_t | x_0 = C_{k-1}) ... q_n_cumulative(x_t | x_0 = C_{k-1})
|
| 432 |
-
```
|
| 433 |
-
"""
|
| 434 |
-
if cumulative:
|
| 435 |
-
a = self.log_cumprod_at[t]
|
| 436 |
-
b = self.log_cumprod_bt[t]
|
| 437 |
-
c = self.log_cumprod_ct[t]
|
| 438 |
-
else:
|
| 439 |
-
a = self.log_at[t]
|
| 440 |
-
b = self.log_bt[t]
|
| 441 |
-
c = self.log_ct[t]
|
| 442 |
-
|
| 443 |
-
if not cumulative:
|
| 444 |
-
# The values in the onehot vector can also be used as the logprobs for transitioning
|
| 445 |
-
# from masked latent pixels. If we are not calculating the cumulative transitions,
|
| 446 |
-
# we need to save these vectors to be re-appended to the final matrix so the values
|
| 447 |
-
# aren't overwritten.
|
| 448 |
-
#
|
| 449 |
-
# `P(x_t!=mask|x_{t-1=mask}) = 0` and 0 will be the value of the last row of the onehot vector
|
| 450 |
-
# if x_t is not masked
|
| 451 |
-
#
|
| 452 |
-
# `P(x_t=mask|x_{t-1=mask}) = 1` and 1 will be the value of the last row of the onehot vector
|
| 453 |
-
# if x_t is masked
|
| 454 |
-
log_onehot_x_t_transitioning_from_masked = log_onehot_x_t[:, -1, :].unsqueeze(1)
|
| 455 |
-
|
| 456 |
-
# `index_to_log_onehot` will add onehot vectors for masked pixels,
|
| 457 |
-
# so the default one hot matrix has one too many rows. See the doc string
|
| 458 |
-
# for an explanation of the dimensionality of the returned matrix.
|
| 459 |
-
log_onehot_x_t = log_onehot_x_t[:, :-1, :]
|
| 460 |
-
|
| 461 |
-
# this is a cheeky trick to produce the transition probabilities using log one-hot vectors.
|
| 462 |
-
#
|
| 463 |
-
# Don't worry about what values this sets in the columns that mark transitions
|
| 464 |
-
# to masked latent pixels. They are overwrote later with the `mask_class_mask`.
|
| 465 |
-
#
|
| 466 |
-
# Looking at the below logspace formula in non-logspace, each value will evaluate to either
|
| 467 |
-
# `1 * a + b = a + b` where `log_Q_t` has the one hot value in the column
|
| 468 |
-
# or
|
| 469 |
-
# `0 * a + b = b` where `log_Q_t` has the 0 values in the column.
|
| 470 |
-
#
|
| 471 |
-
# See equation 7 for more details.
|
| 472 |
-
log_Q_t = (log_onehot_x_t + a).logaddexp(b)
|
| 473 |
-
|
| 474 |
-
# The whole column of each masked pixel is `c`
|
| 475 |
-
mask_class_mask = x_t == self.mask_class
|
| 476 |
-
mask_class_mask = mask_class_mask.unsqueeze(1).expand(-1, self.num_embed - 1, -1)
|
| 477 |
-
log_Q_t[mask_class_mask] = c
|
| 478 |
-
|
| 479 |
-
if not cumulative:
|
| 480 |
-
log_Q_t = torch.cat((log_Q_t, log_onehot_x_t_transitioning_from_masked), dim=1)
|
| 481 |
-
|
| 482 |
-
return log_Q_t
|
| 483 |
-
|
| 484 |
-
def apply_cumulative_transitions(self, q, t):
|
| 485 |
-
bsz = q.shape[0]
|
| 486 |
-
a = self.log_cumprod_at[t]
|
| 487 |
-
b = self.log_cumprod_bt[t]
|
| 488 |
-
c = self.log_cumprod_ct[t]
|
| 489 |
-
|
| 490 |
-
num_latent_pixels = q.shape[2]
|
| 491 |
-
c = c.expand(bsz, 1, num_latent_pixels)
|
| 492 |
-
|
| 493 |
-
q = (q + a).logaddexp(b)
|
| 494 |
-
q = torch.cat((q, c), dim=1)
|
| 495 |
-
|
| 496 |
-
return q
|
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|
spaces/Andy1621/uniformer_image_detection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py
DELETED
|
@@ -1,4 +0,0 @@
|
|
| 1 |
-
_base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
|
| 2 |
-
# learning policy
|
| 3 |
-
lr_config = dict(step=[16, 23])
|
| 4 |
-
runner = dict(type='EpochBasedRunner', max_epochs=24)
|
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|
spaces/Andy1621/uniformer_image_detection/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py
DELETED
|
@@ -1,124 +0,0 @@
|
|
| 1 |
-
_base_ = '../_base_/default_runtime.py'
|
| 2 |
-
# model settings
|
| 3 |
-
model = dict(
|
| 4 |
-
type='YOLOV3',
|
| 5 |
-
pretrained='open-mmlab://darknet53',
|
| 6 |
-
backbone=dict(type='Darknet', depth=53, out_indices=(3, 4, 5)),
|
| 7 |
-
neck=dict(
|
| 8 |
-
type='YOLOV3Neck',
|
| 9 |
-
num_scales=3,
|
| 10 |
-
in_channels=[1024, 512, 256],
|
| 11 |
-
out_channels=[512, 256, 128]),
|
| 12 |
-
bbox_head=dict(
|
| 13 |
-
type='YOLOV3Head',
|
| 14 |
-
num_classes=80,
|
| 15 |
-
in_channels=[512, 256, 128],
|
| 16 |
-
out_channels=[1024, 512, 256],
|
| 17 |
-
anchor_generator=dict(
|
| 18 |
-
type='YOLOAnchorGenerator',
|
| 19 |
-
base_sizes=[[(116, 90), (156, 198), (373, 326)],
|
| 20 |
-
[(30, 61), (62, 45), (59, 119)],
|
| 21 |
-
[(10, 13), (16, 30), (33, 23)]],
|
| 22 |
-
strides=[32, 16, 8]),
|
| 23 |
-
bbox_coder=dict(type='YOLOBBoxCoder'),
|
| 24 |
-
featmap_strides=[32, 16, 8],
|
| 25 |
-
loss_cls=dict(
|
| 26 |
-
type='CrossEntropyLoss',
|
| 27 |
-
use_sigmoid=True,
|
| 28 |
-
loss_weight=1.0,
|
| 29 |
-
reduction='sum'),
|
| 30 |
-
loss_conf=dict(
|
| 31 |
-
type='CrossEntropyLoss',
|
| 32 |
-
use_sigmoid=True,
|
| 33 |
-
loss_weight=1.0,
|
| 34 |
-
reduction='sum'),
|
| 35 |
-
loss_xy=dict(
|
| 36 |
-
type='CrossEntropyLoss',
|
| 37 |
-
use_sigmoid=True,
|
| 38 |
-
loss_weight=2.0,
|
| 39 |
-
reduction='sum'),
|
| 40 |
-
loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum')),
|
| 41 |
-
# training and testing settings
|
| 42 |
-
train_cfg=dict(
|
| 43 |
-
assigner=dict(
|
| 44 |
-
type='GridAssigner',
|
| 45 |
-
pos_iou_thr=0.5,
|
| 46 |
-
neg_iou_thr=0.5,
|
| 47 |
-
min_pos_iou=0)),
|
| 48 |
-
test_cfg=dict(
|
| 49 |
-
nms_pre=1000,
|
| 50 |
-
min_bbox_size=0,
|
| 51 |
-
score_thr=0.05,
|
| 52 |
-
conf_thr=0.005,
|
| 53 |
-
nms=dict(type='nms', iou_threshold=0.45),
|
| 54 |
-
max_per_img=100))
|
| 55 |
-
# dataset settings
|
| 56 |
-
dataset_type = 'CocoDataset'
|
| 57 |
-
data_root = 'data/coco/'
|
| 58 |
-
img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)
|
| 59 |
-
train_pipeline = [
|
| 60 |
-
dict(type='LoadImageFromFile', to_float32=True),
|
| 61 |
-
dict(type='LoadAnnotations', with_bbox=True),
|
| 62 |
-
dict(type='PhotoMetricDistortion'),
|
| 63 |
-
dict(
|
| 64 |
-
type='Expand',
|
| 65 |
-
mean=img_norm_cfg['mean'],
|
| 66 |
-
to_rgb=img_norm_cfg['to_rgb'],
|
| 67 |
-
ratio_range=(1, 2)),
|
| 68 |
-
dict(
|
| 69 |
-
type='MinIoURandomCrop',
|
| 70 |
-
min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
|
| 71 |
-
min_crop_size=0.3),
|
| 72 |
-
dict(type='Resize', img_scale=[(320, 320), (608, 608)], keep_ratio=True),
|
| 73 |
-
dict(type='RandomFlip', flip_ratio=0.5),
|
| 74 |
-
dict(type='Normalize', **img_norm_cfg),
|
| 75 |
-
dict(type='Pad', size_divisor=32),
|
| 76 |
-
dict(type='DefaultFormatBundle'),
|
| 77 |
-
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
|
| 78 |
-
]
|
| 79 |
-
test_pipeline = [
|
| 80 |
-
dict(type='LoadImageFromFile'),
|
| 81 |
-
dict(
|
| 82 |
-
type='MultiScaleFlipAug',
|
| 83 |
-
img_scale=(608, 608),
|
| 84 |
-
flip=False,
|
| 85 |
-
transforms=[
|
| 86 |
-
dict(type='Resize', keep_ratio=True),
|
| 87 |
-
dict(type='RandomFlip'),
|
| 88 |
-
dict(type='Normalize', **img_norm_cfg),
|
| 89 |
-
dict(type='Pad', size_divisor=32),
|
| 90 |
-
dict(type='ImageToTensor', keys=['img']),
|
| 91 |
-
dict(type='Collect', keys=['img'])
|
| 92 |
-
])
|
| 93 |
-
]
|
| 94 |
-
data = dict(
|
| 95 |
-
samples_per_gpu=8,
|
| 96 |
-
workers_per_gpu=4,
|
| 97 |
-
train=dict(
|
| 98 |
-
type=dataset_type,
|
| 99 |
-
ann_file=data_root + 'annotations/instances_train2017.json',
|
| 100 |
-
img_prefix=data_root + 'train2017/',
|
| 101 |
-
pipeline=train_pipeline),
|
| 102 |
-
val=dict(
|
| 103 |
-
type=dataset_type,
|
| 104 |
-
ann_file=data_root + 'annotations/instances_val2017.json',
|
| 105 |
-
img_prefix=data_root + 'val2017/',
|
| 106 |
-
pipeline=test_pipeline),
|
| 107 |
-
test=dict(
|
| 108 |
-
type=dataset_type,
|
| 109 |
-
ann_file=data_root + 'annotations/instances_val2017.json',
|
| 110 |
-
img_prefix=data_root + 'val2017/',
|
| 111 |
-
pipeline=test_pipeline))
|
| 112 |
-
# optimizer
|
| 113 |
-
optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0005)
|
| 114 |
-
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
|
| 115 |
-
# learning policy
|
| 116 |
-
lr_config = dict(
|
| 117 |
-
policy='step',
|
| 118 |
-
warmup='linear',
|
| 119 |
-
warmup_iters=2000, # same as burn-in in darknet
|
| 120 |
-
warmup_ratio=0.1,
|
| 121 |
-
step=[218, 246])
|
| 122 |
-
# runtime settings
|
| 123 |
-
runner = dict(type='EpochBasedRunner', max_epochs=273)
|
| 124 |
-
evaluation = dict(interval=1, metric=['bbox'])
|
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|
spaces/Arulkumar03/GroundingDINO_SOTA_Zero_Shot_Model/groundingdino/util/slio.py
DELETED
|
@@ -1,177 +0,0 @@
|
|
| 1 |
-
# ==========================================================
|
| 2 |
-
# Modified from mmcv
|
| 3 |
-
# ==========================================================
|
| 4 |
-
|
| 5 |
-
import json
|
| 6 |
-
import pickle
|
| 7 |
-
from abc import ABCMeta, abstractmethod
|
| 8 |
-
from pathlib import Path
|
| 9 |
-
|
| 10 |
-
import yaml
|
| 11 |
-
|
| 12 |
-
try:
|
| 13 |
-
from yaml import CLoader as Loader, CDumper as Dumper
|
| 14 |
-
except ImportError:
|
| 15 |
-
from yaml import Loader, Dumper
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
# ===========================
|
| 19 |
-
# Rigister handler
|
| 20 |
-
# ===========================
|
| 21 |
-
|
| 22 |
-
|
| 23 |
-
class BaseFileHandler(metaclass=ABCMeta):
|
| 24 |
-
@abstractmethod
|
| 25 |
-
def load_from_fileobj(self, file, **kwargs):
|
| 26 |
-
pass
|
| 27 |
-
|
| 28 |
-
@abstractmethod
|
| 29 |
-
def dump_to_fileobj(self, obj, file, **kwargs):
|
| 30 |
-
pass
|
| 31 |
-
|
| 32 |
-
@abstractmethod
|
| 33 |
-
def dump_to_str(self, obj, **kwargs):
|
| 34 |
-
pass
|
| 35 |
-
|
| 36 |
-
def load_from_path(self, filepath, mode="r", **kwargs):
|
| 37 |
-
with open(filepath, mode) as f:
|
| 38 |
-
return self.load_from_fileobj(f, **kwargs)
|
| 39 |
-
|
| 40 |
-
def dump_to_path(self, obj, filepath, mode="w", **kwargs):
|
| 41 |
-
with open(filepath, mode) as f:
|
| 42 |
-
self.dump_to_fileobj(obj, f, **kwargs)
|
| 43 |
-
|
| 44 |
-
|
| 45 |
-
class JsonHandler(BaseFileHandler):
|
| 46 |
-
def load_from_fileobj(self, file):
|
| 47 |
-
return json.load(file)
|
| 48 |
-
|
| 49 |
-
def dump_to_fileobj(self, obj, file, **kwargs):
|
| 50 |
-
json.dump(obj, file, **kwargs)
|
| 51 |
-
|
| 52 |
-
def dump_to_str(self, obj, **kwargs):
|
| 53 |
-
return json.dumps(obj, **kwargs)
|
| 54 |
-
|
| 55 |
-
|
| 56 |
-
class PickleHandler(BaseFileHandler):
|
| 57 |
-
def load_from_fileobj(self, file, **kwargs):
|
| 58 |
-
return pickle.load(file, **kwargs)
|
| 59 |
-
|
| 60 |
-
def load_from_path(self, filepath, **kwargs):
|
| 61 |
-
return super(PickleHandler, self).load_from_path(filepath, mode="rb", **kwargs)
|
| 62 |
-
|
| 63 |
-
def dump_to_str(self, obj, **kwargs):
|
| 64 |
-
kwargs.setdefault("protocol", 2)
|
| 65 |
-
return pickle.dumps(obj, **kwargs)
|
| 66 |
-
|
| 67 |
-
def dump_to_fileobj(self, obj, file, **kwargs):
|
| 68 |
-
kwargs.setdefault("protocol", 2)
|
| 69 |
-
pickle.dump(obj, file, **kwargs)
|
| 70 |
-
|
| 71 |
-
def dump_to_path(self, obj, filepath, **kwargs):
|
| 72 |
-
super(PickleHandler, self).dump_to_path(obj, filepath, mode="wb", **kwargs)
|
| 73 |
-
|
| 74 |
-
|
| 75 |
-
class YamlHandler(BaseFileHandler):
|
| 76 |
-
def load_from_fileobj(self, file, **kwargs):
|
| 77 |
-
kwargs.setdefault("Loader", Loader)
|
| 78 |
-
return yaml.load(file, **kwargs)
|
| 79 |
-
|
| 80 |
-
def dump_to_fileobj(self, obj, file, **kwargs):
|
| 81 |
-
kwargs.setdefault("Dumper", Dumper)
|
| 82 |
-
yaml.dump(obj, file, **kwargs)
|
| 83 |
-
|
| 84 |
-
def dump_to_str(self, obj, **kwargs):
|
| 85 |
-
kwargs.setdefault("Dumper", Dumper)
|
| 86 |
-
return yaml.dump(obj, **kwargs)
|
| 87 |
-
|
| 88 |
-
|
| 89 |
-
file_handlers = {
|
| 90 |
-
"json": JsonHandler(),
|
| 91 |
-
"yaml": YamlHandler(),
|
| 92 |
-
"yml": YamlHandler(),
|
| 93 |
-
"pickle": PickleHandler(),
|
| 94 |
-
"pkl": PickleHandler(),
|
| 95 |
-
}
|
| 96 |
-
|
| 97 |
-
# ===========================
|
| 98 |
-
# load and dump
|
| 99 |
-
# ===========================
|
| 100 |
-
|
| 101 |
-
|
| 102 |
-
def is_str(x):
|
| 103 |
-
"""Whether the input is an string instance.
|
| 104 |
-
|
| 105 |
-
Note: This method is deprecated since python 2 is no longer supported.
|
| 106 |
-
"""
|
| 107 |
-
return isinstance(x, str)
|
| 108 |
-
|
| 109 |
-
|
| 110 |
-
def slload(file, file_format=None, **kwargs):
|
| 111 |
-
"""Load data from json/yaml/pickle files.
|
| 112 |
-
|
| 113 |
-
This method provides a unified api for loading data from serialized files.
|
| 114 |
-
|
| 115 |
-
Args:
|
| 116 |
-
file (str or :obj:`Path` or file-like object): Filename or a file-like
|
| 117 |
-
object.
|
| 118 |
-
file_format (str, optional): If not specified, the file format will be
|
| 119 |
-
inferred from the file extension, otherwise use the specified one.
|
| 120 |
-
Currently supported formats include "json", "yaml/yml" and
|
| 121 |
-
"pickle/pkl".
|
| 122 |
-
|
| 123 |
-
Returns:
|
| 124 |
-
The content from the file.
|
| 125 |
-
"""
|
| 126 |
-
if isinstance(file, Path):
|
| 127 |
-
file = str(file)
|
| 128 |
-
if file_format is None and is_str(file):
|
| 129 |
-
file_format = file.split(".")[-1]
|
| 130 |
-
if file_format not in file_handlers:
|
| 131 |
-
raise TypeError(f"Unsupported format: {file_format}")
|
| 132 |
-
|
| 133 |
-
handler = file_handlers[file_format]
|
| 134 |
-
if is_str(file):
|
| 135 |
-
obj = handler.load_from_path(file, **kwargs)
|
| 136 |
-
elif hasattr(file, "read"):
|
| 137 |
-
obj = handler.load_from_fileobj(file, **kwargs)
|
| 138 |
-
else:
|
| 139 |
-
raise TypeError('"file" must be a filepath str or a file-object')
|
| 140 |
-
return obj
|
| 141 |
-
|
| 142 |
-
|
| 143 |
-
def sldump(obj, file=None, file_format=None, **kwargs):
|
| 144 |
-
"""Dump data to json/yaml/pickle strings or files.
|
| 145 |
-
|
| 146 |
-
This method provides a unified api for dumping data as strings or to files,
|
| 147 |
-
and also supports custom arguments for each file format.
|
| 148 |
-
|
| 149 |
-
Args:
|
| 150 |
-
obj (any): The python object to be dumped.
|
| 151 |
-
file (str or :obj:`Path` or file-like object, optional): If not
|
| 152 |
-
specified, then the object is dump to a str, otherwise to a file
|
| 153 |
-
specified by the filename or file-like object.
|
| 154 |
-
file_format (str, optional): Same as :func:`load`.
|
| 155 |
-
|
| 156 |
-
Returns:
|
| 157 |
-
bool: True for success, False otherwise.
|
| 158 |
-
"""
|
| 159 |
-
if isinstance(file, Path):
|
| 160 |
-
file = str(file)
|
| 161 |
-
if file_format is None:
|
| 162 |
-
if is_str(file):
|
| 163 |
-
file_format = file.split(".")[-1]
|
| 164 |
-
elif file is None:
|
| 165 |
-
raise ValueError("file_format must be specified since file is None")
|
| 166 |
-
if file_format not in file_handlers:
|
| 167 |
-
raise TypeError(f"Unsupported format: {file_format}")
|
| 168 |
-
|
| 169 |
-
handler = file_handlers[file_format]
|
| 170 |
-
if file is None:
|
| 171 |
-
return handler.dump_to_str(obj, **kwargs)
|
| 172 |
-
elif is_str(file):
|
| 173 |
-
handler.dump_to_path(obj, file, **kwargs)
|
| 174 |
-
elif hasattr(file, "write"):
|
| 175 |
-
handler.dump_to_fileobj(obj, file, **kwargs)
|
| 176 |
-
else:
|
| 177 |
-
raise TypeError('"file" must be a filename str or a file-object')
|
|
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|
spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/network/lazy_wheel.py
DELETED
|
@@ -1,210 +0,0 @@
|
|
| 1 |
-
"""Lazy ZIP over HTTP"""
|
| 2 |
-
|
| 3 |
-
__all__ = ["HTTPRangeRequestUnsupported", "dist_from_wheel_url"]
|
| 4 |
-
|
| 5 |
-
from bisect import bisect_left, bisect_right
|
| 6 |
-
from contextlib import contextmanager
|
| 7 |
-
from tempfile import NamedTemporaryFile
|
| 8 |
-
from typing import Any, Dict, Generator, List, Optional, Tuple
|
| 9 |
-
from zipfile import BadZipFile, ZipFile
|
| 10 |
-
|
| 11 |
-
from pip._vendor.packaging.utils import canonicalize_name
|
| 12 |
-
from pip._vendor.requests.models import CONTENT_CHUNK_SIZE, Response
|
| 13 |
-
|
| 14 |
-
from pip._internal.metadata import BaseDistribution, MemoryWheel, get_wheel_distribution
|
| 15 |
-
from pip._internal.network.session import PipSession
|
| 16 |
-
from pip._internal.network.utils import HEADERS, raise_for_status, response_chunks
|
| 17 |
-
|
| 18 |
-
|
| 19 |
-
class HTTPRangeRequestUnsupported(Exception):
|
| 20 |
-
pass
|
| 21 |
-
|
| 22 |
-
|
| 23 |
-
def dist_from_wheel_url(name: str, url: str, session: PipSession) -> BaseDistribution:
|
| 24 |
-
"""Return a distribution object from the given wheel URL.
|
| 25 |
-
|
| 26 |
-
This uses HTTP range requests to only fetch the portion of the wheel
|
| 27 |
-
containing metadata, just enough for the object to be constructed.
|
| 28 |
-
If such requests are not supported, HTTPRangeRequestUnsupported
|
| 29 |
-
is raised.
|
| 30 |
-
"""
|
| 31 |
-
with LazyZipOverHTTP(url, session) as zf:
|
| 32 |
-
# For read-only ZIP files, ZipFile only needs methods read,
|
| 33 |
-
# seek, seekable and tell, not the whole IO protocol.
|
| 34 |
-
wheel = MemoryWheel(zf.name, zf) # type: ignore
|
| 35 |
-
# After context manager exit, wheel.name
|
| 36 |
-
# is an invalid file by intention.
|
| 37 |
-
return get_wheel_distribution(wheel, canonicalize_name(name))
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
class LazyZipOverHTTP:
|
| 41 |
-
"""File-like object mapped to a ZIP file over HTTP.
|
| 42 |
-
|
| 43 |
-
This uses HTTP range requests to lazily fetch the file's content,
|
| 44 |
-
which is supposed to be fed to ZipFile. If such requests are not
|
| 45 |
-
supported by the server, raise HTTPRangeRequestUnsupported
|
| 46 |
-
during initialization.
|
| 47 |
-
"""
|
| 48 |
-
|
| 49 |
-
def __init__(
|
| 50 |
-
self, url: str, session: PipSession, chunk_size: int = CONTENT_CHUNK_SIZE
|
| 51 |
-
) -> None:
|
| 52 |
-
head = session.head(url, headers=HEADERS)
|
| 53 |
-
raise_for_status(head)
|
| 54 |
-
assert head.status_code == 200
|
| 55 |
-
self._session, self._url, self._chunk_size = session, url, chunk_size
|
| 56 |
-
self._length = int(head.headers["Content-Length"])
|
| 57 |
-
self._file = NamedTemporaryFile()
|
| 58 |
-
self.truncate(self._length)
|
| 59 |
-
self._left: List[int] = []
|
| 60 |
-
self._right: List[int] = []
|
| 61 |
-
if "bytes" not in head.headers.get("Accept-Ranges", "none"):
|
| 62 |
-
raise HTTPRangeRequestUnsupported("range request is not supported")
|
| 63 |
-
self._check_zip()
|
| 64 |
-
|
| 65 |
-
@property
|
| 66 |
-
def mode(self) -> str:
|
| 67 |
-
"""Opening mode, which is always rb."""
|
| 68 |
-
return "rb"
|
| 69 |
-
|
| 70 |
-
@property
|
| 71 |
-
def name(self) -> str:
|
| 72 |
-
"""Path to the underlying file."""
|
| 73 |
-
return self._file.name
|
| 74 |
-
|
| 75 |
-
def seekable(self) -> bool:
|
| 76 |
-
"""Return whether random access is supported, which is True."""
|
| 77 |
-
return True
|
| 78 |
-
|
| 79 |
-
def close(self) -> None:
|
| 80 |
-
"""Close the file."""
|
| 81 |
-
self._file.close()
|
| 82 |
-
|
| 83 |
-
@property
|
| 84 |
-
def closed(self) -> bool:
|
| 85 |
-
"""Whether the file is closed."""
|
| 86 |
-
return self._file.closed
|
| 87 |
-
|
| 88 |
-
def read(self, size: int = -1) -> bytes:
|
| 89 |
-
"""Read up to size bytes from the object and return them.
|
| 90 |
-
|
| 91 |
-
As a convenience, if size is unspecified or -1,
|
| 92 |
-
all bytes until EOF are returned. Fewer than
|
| 93 |
-
size bytes may be returned if EOF is reached.
|
| 94 |
-
"""
|
| 95 |
-
download_size = max(size, self._chunk_size)
|
| 96 |
-
start, length = self.tell(), self._length
|
| 97 |
-
stop = length if size < 0 else min(start + download_size, length)
|
| 98 |
-
start = max(0, stop - download_size)
|
| 99 |
-
self._download(start, stop - 1)
|
| 100 |
-
return self._file.read(size)
|
| 101 |
-
|
| 102 |
-
def readable(self) -> bool:
|
| 103 |
-
"""Return whether the file is readable, which is True."""
|
| 104 |
-
return True
|
| 105 |
-
|
| 106 |
-
def seek(self, offset: int, whence: int = 0) -> int:
|
| 107 |
-
"""Change stream position and return the new absolute position.
|
| 108 |
-
|
| 109 |
-
Seek to offset relative position indicated by whence:
|
| 110 |
-
* 0: Start of stream (the default). pos should be >= 0;
|
| 111 |
-
* 1: Current position - pos may be negative;
|
| 112 |
-
* 2: End of stream - pos usually negative.
|
| 113 |
-
"""
|
| 114 |
-
return self._file.seek(offset, whence)
|
| 115 |
-
|
| 116 |
-
def tell(self) -> int:
|
| 117 |
-
"""Return the current position."""
|
| 118 |
-
return self._file.tell()
|
| 119 |
-
|
| 120 |
-
def truncate(self, size: Optional[int] = None) -> int:
|
| 121 |
-
"""Resize the stream to the given size in bytes.
|
| 122 |
-
|
| 123 |
-
If size is unspecified resize to the current position.
|
| 124 |
-
The current stream position isn't changed.
|
| 125 |
-
|
| 126 |
-
Return the new file size.
|
| 127 |
-
"""
|
| 128 |
-
return self._file.truncate(size)
|
| 129 |
-
|
| 130 |
-
def writable(self) -> bool:
|
| 131 |
-
"""Return False."""
|
| 132 |
-
return False
|
| 133 |
-
|
| 134 |
-
def __enter__(self) -> "LazyZipOverHTTP":
|
| 135 |
-
self._file.__enter__()
|
| 136 |
-
return self
|
| 137 |
-
|
| 138 |
-
def __exit__(self, *exc: Any) -> None:
|
| 139 |
-
self._file.__exit__(*exc)
|
| 140 |
-
|
| 141 |
-
@contextmanager
|
| 142 |
-
def _stay(self) -> Generator[None, None, None]:
|
| 143 |
-
"""Return a context manager keeping the position.
|
| 144 |
-
|
| 145 |
-
At the end of the block, seek back to original position.
|
| 146 |
-
"""
|
| 147 |
-
pos = self.tell()
|
| 148 |
-
try:
|
| 149 |
-
yield
|
| 150 |
-
finally:
|
| 151 |
-
self.seek(pos)
|
| 152 |
-
|
| 153 |
-
def _check_zip(self) -> None:
|
| 154 |
-
"""Check and download until the file is a valid ZIP."""
|
| 155 |
-
end = self._length - 1
|
| 156 |
-
for start in reversed(range(0, end, self._chunk_size)):
|
| 157 |
-
self._download(start, end)
|
| 158 |
-
with self._stay():
|
| 159 |
-
try:
|
| 160 |
-
# For read-only ZIP files, ZipFile only needs
|
| 161 |
-
# methods read, seek, seekable and tell.
|
| 162 |
-
ZipFile(self) # type: ignore
|
| 163 |
-
except BadZipFile:
|
| 164 |
-
pass
|
| 165 |
-
else:
|
| 166 |
-
break
|
| 167 |
-
|
| 168 |
-
def _stream_response(
|
| 169 |
-
self, start: int, end: int, base_headers: Dict[str, str] = HEADERS
|
| 170 |
-
) -> Response:
|
| 171 |
-
"""Return HTTP response to a range request from start to end."""
|
| 172 |
-
headers = base_headers.copy()
|
| 173 |
-
headers["Range"] = f"bytes={start}-{end}"
|
| 174 |
-
# TODO: Get range requests to be correctly cached
|
| 175 |
-
headers["Cache-Control"] = "no-cache"
|
| 176 |
-
return self._session.get(self._url, headers=headers, stream=True)
|
| 177 |
-
|
| 178 |
-
def _merge(
|
| 179 |
-
self, start: int, end: int, left: int, right: int
|
| 180 |
-
) -> Generator[Tuple[int, int], None, None]:
|
| 181 |
-
"""Return a generator of intervals to be fetched.
|
| 182 |
-
|
| 183 |
-
Args:
|
| 184 |
-
start (int): Start of needed interval
|
| 185 |
-
end (int): End of needed interval
|
| 186 |
-
left (int): Index of first overlapping downloaded data
|
| 187 |
-
right (int): Index after last overlapping downloaded data
|
| 188 |
-
"""
|
| 189 |
-
lslice, rslice = self._left[left:right], self._right[left:right]
|
| 190 |
-
i = start = min([start] + lslice[:1])
|
| 191 |
-
end = max([end] + rslice[-1:])
|
| 192 |
-
for j, k in zip(lslice, rslice):
|
| 193 |
-
if j > i:
|
| 194 |
-
yield i, j - 1
|
| 195 |
-
i = k + 1
|
| 196 |
-
if i <= end:
|
| 197 |
-
yield i, end
|
| 198 |
-
self._left[left:right], self._right[left:right] = [start], [end]
|
| 199 |
-
|
| 200 |
-
def _download(self, start: int, end: int) -> None:
|
| 201 |
-
"""Download bytes from start to end inclusively."""
|
| 202 |
-
with self._stay():
|
| 203 |
-
left = bisect_left(self._right, start)
|
| 204 |
-
right = bisect_right(self._left, end)
|
| 205 |
-
for start, end in self._merge(start, end, left, right):
|
| 206 |
-
response = self._stream_response(start, end)
|
| 207 |
-
response.raise_for_status()
|
| 208 |
-
self.seek(start)
|
| 209 |
-
for chunk in response_chunks(response, self._chunk_size):
|
| 210 |
-
self._file.write(chunk)
|
|
|
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|
spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/codingstatemachinedict.py
DELETED
|
@@ -1,19 +0,0 @@
|
|
| 1 |
-
from typing import TYPE_CHECKING, Tuple
|
| 2 |
-
|
| 3 |
-
if TYPE_CHECKING:
|
| 4 |
-
# TypedDict was introduced in Python 3.8.
|
| 5 |
-
#
|
| 6 |
-
# TODO: Remove the else block and TYPE_CHECKING check when dropping support
|
| 7 |
-
# for Python 3.7.
|
| 8 |
-
from typing import TypedDict
|
| 9 |
-
|
| 10 |
-
class CodingStateMachineDict(TypedDict, total=False):
|
| 11 |
-
class_table: Tuple[int, ...]
|
| 12 |
-
class_factor: int
|
| 13 |
-
state_table: Tuple[int, ...]
|
| 14 |
-
char_len_table: Tuple[int, ...]
|
| 15 |
-
name: str
|
| 16 |
-
language: str # Optional key
|
| 17 |
-
|
| 18 |
-
else:
|
| 19 |
-
CodingStateMachineDict = dict
|
|
|
|
|
|
|
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|
|
spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/spinner.py
DELETED
|
@@ -1,137 +0,0 @@
|
|
| 1 |
-
from typing import cast, List, Optional, TYPE_CHECKING, Union
|
| 2 |
-
|
| 3 |
-
from ._spinners import SPINNERS
|
| 4 |
-
from .measure import Measurement
|
| 5 |
-
from .table import Table
|
| 6 |
-
from .text import Text
|
| 7 |
-
|
| 8 |
-
if TYPE_CHECKING:
|
| 9 |
-
from .console import Console, ConsoleOptions, RenderResult, RenderableType
|
| 10 |
-
from .style import StyleType
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
class Spinner:
|
| 14 |
-
"""A spinner animation.
|
| 15 |
-
|
| 16 |
-
Args:
|
| 17 |
-
name (str): Name of spinner (run python -m rich.spinner).
|
| 18 |
-
text (RenderableType, optional): A renderable to display at the right of the spinner (str or Text typically). Defaults to "".
|
| 19 |
-
style (StyleType, optional): Style for spinner animation. Defaults to None.
|
| 20 |
-
speed (float, optional): Speed factor for animation. Defaults to 1.0.
|
| 21 |
-
|
| 22 |
-
Raises:
|
| 23 |
-
KeyError: If name isn't one of the supported spinner animations.
|
| 24 |
-
"""
|
| 25 |
-
|
| 26 |
-
def __init__(
|
| 27 |
-
self,
|
| 28 |
-
name: str,
|
| 29 |
-
text: "RenderableType" = "",
|
| 30 |
-
*,
|
| 31 |
-
style: Optional["StyleType"] = None,
|
| 32 |
-
speed: float = 1.0,
|
| 33 |
-
) -> None:
|
| 34 |
-
try:
|
| 35 |
-
spinner = SPINNERS[name]
|
| 36 |
-
except KeyError:
|
| 37 |
-
raise KeyError(f"no spinner called {name!r}")
|
| 38 |
-
self.text: "Union[RenderableType, Text]" = (
|
| 39 |
-
Text.from_markup(text) if isinstance(text, str) else text
|
| 40 |
-
)
|
| 41 |
-
self.frames = cast(List[str], spinner["frames"])[:]
|
| 42 |
-
self.interval = cast(float, spinner["interval"])
|
| 43 |
-
self.start_time: Optional[float] = None
|
| 44 |
-
self.style = style
|
| 45 |
-
self.speed = speed
|
| 46 |
-
self.frame_no_offset: float = 0.0
|
| 47 |
-
self._update_speed = 0.0
|
| 48 |
-
|
| 49 |
-
def __rich_console__(
|
| 50 |
-
self, console: "Console", options: "ConsoleOptions"
|
| 51 |
-
) -> "RenderResult":
|
| 52 |
-
yield self.render(console.get_time())
|
| 53 |
-
|
| 54 |
-
def __rich_measure__(
|
| 55 |
-
self, console: "Console", options: "ConsoleOptions"
|
| 56 |
-
) -> Measurement:
|
| 57 |
-
text = self.render(0)
|
| 58 |
-
return Measurement.get(console, options, text)
|
| 59 |
-
|
| 60 |
-
def render(self, time: float) -> "RenderableType":
|
| 61 |
-
"""Render the spinner for a given time.
|
| 62 |
-
|
| 63 |
-
Args:
|
| 64 |
-
time (float): Time in seconds.
|
| 65 |
-
|
| 66 |
-
Returns:
|
| 67 |
-
RenderableType: A renderable containing animation frame.
|
| 68 |
-
"""
|
| 69 |
-
if self.start_time is None:
|
| 70 |
-
self.start_time = time
|
| 71 |
-
|
| 72 |
-
frame_no = ((time - self.start_time) * self.speed) / (
|
| 73 |
-
self.interval / 1000.0
|
| 74 |
-
) + self.frame_no_offset
|
| 75 |
-
frame = Text(
|
| 76 |
-
self.frames[int(frame_no) % len(self.frames)], style=self.style or ""
|
| 77 |
-
)
|
| 78 |
-
|
| 79 |
-
if self._update_speed:
|
| 80 |
-
self.frame_no_offset = frame_no
|
| 81 |
-
self.start_time = time
|
| 82 |
-
self.speed = self._update_speed
|
| 83 |
-
self._update_speed = 0.0
|
| 84 |
-
|
| 85 |
-
if not self.text:
|
| 86 |
-
return frame
|
| 87 |
-
elif isinstance(self.text, (str, Text)):
|
| 88 |
-
return Text.assemble(frame, " ", self.text)
|
| 89 |
-
else:
|
| 90 |
-
table = Table.grid(padding=1)
|
| 91 |
-
table.add_row(frame, self.text)
|
| 92 |
-
return table
|
| 93 |
-
|
| 94 |
-
def update(
|
| 95 |
-
self,
|
| 96 |
-
*,
|
| 97 |
-
text: "RenderableType" = "",
|
| 98 |
-
style: Optional["StyleType"] = None,
|
| 99 |
-
speed: Optional[float] = None,
|
| 100 |
-
) -> None:
|
| 101 |
-
"""Updates attributes of a spinner after it has been started.
|
| 102 |
-
|
| 103 |
-
Args:
|
| 104 |
-
text (RenderableType, optional): A renderable to display at the right of the spinner (str or Text typically). Defaults to "".
|
| 105 |
-
style (StyleType, optional): Style for spinner animation. Defaults to None.
|
| 106 |
-
speed (float, optional): Speed factor for animation. Defaults to None.
|
| 107 |
-
"""
|
| 108 |
-
if text:
|
| 109 |
-
self.text = Text.from_markup(text) if isinstance(text, str) else text
|
| 110 |
-
if style:
|
| 111 |
-
self.style = style
|
| 112 |
-
if speed:
|
| 113 |
-
self._update_speed = speed
|
| 114 |
-
|
| 115 |
-
|
| 116 |
-
if __name__ == "__main__": # pragma: no cover
|
| 117 |
-
from time import sleep
|
| 118 |
-
|
| 119 |
-
from .columns import Columns
|
| 120 |
-
from .panel import Panel
|
| 121 |
-
from .live import Live
|
| 122 |
-
|
| 123 |
-
all_spinners = Columns(
|
| 124 |
-
[
|
| 125 |
-
Spinner(spinner_name, text=Text(repr(spinner_name), style="green"))
|
| 126 |
-
for spinner_name in sorted(SPINNERS.keys())
|
| 127 |
-
],
|
| 128 |
-
column_first=True,
|
| 129 |
-
expand=True,
|
| 130 |
-
)
|
| 131 |
-
|
| 132 |
-
with Live(
|
| 133 |
-
Panel(all_spinners, title="Spinners", border_style="blue"),
|
| 134 |
-
refresh_per_second=20,
|
| 135 |
-
) as live:
|
| 136 |
-
while True:
|
| 137 |
-
sleep(0.1)
|
|
|
|
|
|
|
|
|
|
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spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/debug.py
DELETED
|
@@ -1,5 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
|
| 3 |
-
# If DISTUTILS_DEBUG is anything other than the empty string, we run in
|
| 4 |
-
# debug mode.
|
| 5 |
-
DEBUG = os.environ.get('DISTUTILS_DEBUG')
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spaces/Atualli/mediapipe-pose-estimation/README.md
DELETED
|
@@ -1,13 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Mediapipe Pose Estimation
|
| 3 |
-
emoji: 👁
|
| 4 |
-
colorFrom: blue
|
| 5 |
-
colorTo: gray
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.36.1
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
duplicated_from: hysts/mediapipe-pose-estimation
|
| 11 |
-
---
|
| 12 |
-
|
| 13 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
|
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spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ.py
DELETED
|
@@ -1,14 +0,0 @@
|
|
| 1 |
-
from .mask_rcnn_R_50_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/Babelscape/mrebel-demo/app.py
DELETED
|
@@ -1,123 +0,0 @@
|
|
| 1 |
-
import streamlit as st
|
| 2 |
-
from datasets import load_dataset
|
| 3 |
-
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
|
| 4 |
-
from time import time
|
| 5 |
-
import torch
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
def load_tok_and_data(lan):
|
| 9 |
-
st_time = time()
|
| 10 |
-
tokenizer = AutoTokenizer.from_pretrained("Babelscape/mrebel-large", tgt_lang="tp_XX")
|
| 11 |
-
tokenizer._src_lang = _Tokens[lan]
|
| 12 |
-
tokenizer.cur_lang_code_id = tokenizer.convert_tokens_to_ids(_Tokens[lan])
|
| 13 |
-
tokenizer.set_src_lang_special_tokens(_Tokens[lan])
|
| 14 |
-
dataset = load_dataset('Babelscape/SREDFM', lan, split="test", streaming=True)
|
| 15 |
-
dataset = [example for example in dataset.take(1001)]
|
| 16 |
-
return (tokenizer, dataset)
|
| 17 |
-
|
| 18 |
-
@st.cache_resource
|
| 19 |
-
def load_model():
|
| 20 |
-
st_time = time()
|
| 21 |
-
print("+++++ loading Model", time() - st_time)
|
| 22 |
-
model = AutoModelForSeq2SeqLM.from_pretrained("Babelscape/mrebel-large")
|
| 23 |
-
if torch.cuda.is_available():
|
| 24 |
-
_ = model.to("cuda:0") # comment if no GPU available
|
| 25 |
-
_ = model.eval()
|
| 26 |
-
print("+++++ loaded model", time() - st_time)
|
| 27 |
-
return model
|
| 28 |
-
|
| 29 |
-
def extract_triplets_typed(text):
|
| 30 |
-
triplets = []
|
| 31 |
-
relation = ''
|
| 32 |
-
text = text.strip()
|
| 33 |
-
current = 'x'
|
| 34 |
-
subject, relation, object_, object_type, subject_type = '','','','',''
|
| 35 |
-
|
| 36 |
-
for token in text.replace("<s>", "").replace("<pad>", "").replace("</s>", "").replace("tp_XX", "").replace("__en__", "").split():
|
| 37 |
-
if token == "<triplet>" or token == "<relation>":
|
| 38 |
-
current = 't'
|
| 39 |
-
if relation != '':
|
| 40 |
-
triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type})
|
| 41 |
-
relation = ''
|
| 42 |
-
subject = ''
|
| 43 |
-
elif token.startswith("<") and token.endswith(">"):
|
| 44 |
-
if current == 't' or current == 'o':
|
| 45 |
-
current = 's'
|
| 46 |
-
if relation != '':
|
| 47 |
-
triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type})
|
| 48 |
-
object_ = ''
|
| 49 |
-
subject_type = token[1:-1]
|
| 50 |
-
else:
|
| 51 |
-
current = 'o'
|
| 52 |
-
object_type = token[1:-1]
|
| 53 |
-
relation = ''
|
| 54 |
-
else:
|
| 55 |
-
if current == 't':
|
| 56 |
-
subject += ' ' + token
|
| 57 |
-
elif current == 's':
|
| 58 |
-
object_ += ' ' + token
|
| 59 |
-
elif current == 'o':
|
| 60 |
-
relation += ' ' + token
|
| 61 |
-
if subject != '' and relation != '' and object_ != '' and object_type != '' and subject_type != '':
|
| 62 |
-
triplets.append({'head': subject.strip(), 'head_type': subject_type, 'type': relation.strip(),'tail': object_.strip(), 'tail_type': object_type})
|
| 63 |
-
return triplets
|
| 64 |
-
|
| 65 |
-
st.markdown("""This is a demo for the ACL 2023 paper [RED$^{FM}$: a Filtered and Multilingual Relation Extraction Dataset](https://arxiv.org/abs/2306.09802). The pre-trained model is able to extract triplets for up to 400 relation types from Wikidata or be used in downstream Relation Extraction task by fine-tuning. Find the model card [here](https://huggingface.co/Babelscape/mrebel-large). Read more about it in the [paper](https://arxiv.org/abs/2306.09802) and in the original [repository](https://github.com/Babelscape/rebel#REDFM).""")
|
| 66 |
-
|
| 67 |
-
model = load_model()
|
| 68 |
-
|
| 69 |
-
lan = st.selectbox(
|
| 70 |
-
'Select a Language',
|
| 71 |
-
('ar', 'ca', 'de', 'el', 'en', 'es', 'fr', 'hi', 'it', 'ja', 'ko', 'nl', 'pl', 'pt', 'ru', 'sv', 'vi', 'zh'), index=1)
|
| 72 |
-
|
| 73 |
-
_Tokens = {'en': 'en_XX', 'de': 'de_DE', 'ca': 'ca_XX', 'ar': 'ar_AR', 'el': 'el_EL', 'es': 'es_XX', 'it': 'it_IT', 'ja': 'ja_XX', 'ko': 'ko_KR', 'hi': 'hi_IN', 'pt': 'pt_XX', 'ru': 'ru_RU', 'pl': 'pl_PL', 'zh': 'zh_CN', 'fr': 'fr_XX', 'vi': 'vi_VN', 'sv':'sv_SE'}
|
| 74 |
-
|
| 75 |
-
tokenizer, dataset = load_tok_and_data(lan)
|
| 76 |
-
|
| 77 |
-
agree = st.checkbox('Free input', False)
|
| 78 |
-
if agree:
|
| 79 |
-
text = st.text_input('Input text (current example in catalan)', 'Els Red Hot Chili Peppers es van formar a Los Angeles per Kiedis, Flea, el guitarrista Hillel Slovak i el bateria Jack Irons.')
|
| 80 |
-
print(text)
|
| 81 |
-
else:
|
| 82 |
-
dataset_example = st.slider('dataset id', 0, 1000, 0)
|
| 83 |
-
text = dataset[dataset_example]['text']
|
| 84 |
-
length_penalty = st.slider('length_penalty', 0, 10, 1)
|
| 85 |
-
num_beams = st.slider('num_beams', 1, 20, 3)
|
| 86 |
-
num_return_sequences = st.slider('num_return_sequences', 1, num_beams, 2)
|
| 87 |
-
|
| 88 |
-
gen_kwargs = {
|
| 89 |
-
"max_length": 256,
|
| 90 |
-
"length_penalty": length_penalty,
|
| 91 |
-
"num_beams": num_beams,
|
| 92 |
-
"num_return_sequences": num_return_sequences,
|
| 93 |
-
"forced_bos_token_id": None,
|
| 94 |
-
}
|
| 95 |
-
|
| 96 |
-
model_inputs = tokenizer(text, max_length=256, padding=True, truncation=True, return_tensors = 'pt')
|
| 97 |
-
generated_tokens = model.generate(
|
| 98 |
-
model_inputs["input_ids"].to(model.device),
|
| 99 |
-
attention_mask=model_inputs["attention_mask"].to(model.device),
|
| 100 |
-
decoder_start_token_id = tokenizer.convert_tokens_to_ids("tp_XX"),
|
| 101 |
-
**gen_kwargs,
|
| 102 |
-
)
|
| 103 |
-
|
| 104 |
-
decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=False)
|
| 105 |
-
st.title('Input text')
|
| 106 |
-
|
| 107 |
-
st.write(text)
|
| 108 |
-
|
| 109 |
-
if not agree:
|
| 110 |
-
st.title('Silver output')
|
| 111 |
-
entities = dataset[dataset_example]['entities']
|
| 112 |
-
relations =[]
|
| 113 |
-
for trip in dataset[dataset_example]['relations']:
|
| 114 |
-
relations.append({'subject': entities[trip['subject']], 'predicate': trip['predicate'], 'object': entities[trip['object']]})
|
| 115 |
-
st.write(relations)
|
| 116 |
-
|
| 117 |
-
st.title('Prediction text')
|
| 118 |
-
decoded_preds = [text.replace('<s>', '').replace('</s>', '').replace('<pad>', '') for text in decoded_preds]
|
| 119 |
-
st.write(decoded_preds)
|
| 120 |
-
|
| 121 |
-
for idx, sentence in enumerate(decoded_preds):
|
| 122 |
-
st.title(f'Prediction triplets sentence {idx}')
|
| 123 |
-
st.write(extract_triplets_typed(sentence))
|
|
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|
spaces/Bambicita/rvc-models/app.py
DELETED
|
@@ -1,188 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import json
|
| 3 |
-
import argparse
|
| 4 |
-
import traceback
|
| 5 |
-
import logging
|
| 6 |
-
import gradio as gr
|
| 7 |
-
import numpy as np
|
| 8 |
-
import librosa
|
| 9 |
-
import torch
|
| 10 |
-
import asyncio
|
| 11 |
-
import edge_tts
|
| 12 |
-
from datetime import datetime
|
| 13 |
-
from fairseq import checkpoint_utils
|
| 14 |
-
from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
|
| 15 |
-
from vc_infer_pipeline import VC
|
| 16 |
-
from config import (
|
| 17 |
-
is_half,
|
| 18 |
-
device
|
| 19 |
-
)
|
| 20 |
-
logging.getLogger("numba").setLevel(logging.WARNING)
|
| 21 |
-
limitation = os.getenv("SYSTEM") == "spaces" # limit audio length in huggingface spaces
|
| 22 |
-
|
| 23 |
-
def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index, file_big_npy):
|
| 24 |
-
def vc_fn(
|
| 25 |
-
input_audio,
|
| 26 |
-
f0_up_key,
|
| 27 |
-
f0_method,
|
| 28 |
-
index_rate,
|
| 29 |
-
tts_mode,
|
| 30 |
-
tts_text,
|
| 31 |
-
tts_voice
|
| 32 |
-
):
|
| 33 |
-
try:
|
| 34 |
-
if tts_mode:
|
| 35 |
-
if len(tts_text) > 100 and limitation:
|
| 36 |
-
return "Text is too long", None
|
| 37 |
-
if tts_text is None or tts_voice is None:
|
| 38 |
-
return "You need to enter text and select a voice", None
|
| 39 |
-
asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
|
| 40 |
-
audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
|
| 41 |
-
else:
|
| 42 |
-
if args.files:
|
| 43 |
-
audio, sr = librosa.load(input_audio, sr=16000, mono=True)
|
| 44 |
-
else:
|
| 45 |
-
if input_audio is None:
|
| 46 |
-
return "You need to upload an audio", None
|
| 47 |
-
sampling_rate, audio = input_audio
|
| 48 |
-
duration = audio.shape[0] / sampling_rate
|
| 49 |
-
if duration > 20 and limitation:
|
| 50 |
-
return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
|
| 51 |
-
audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
|
| 52 |
-
if len(audio.shape) > 1:
|
| 53 |
-
audio = librosa.to_mono(audio.transpose(1, 0))
|
| 54 |
-
if sampling_rate != 16000:
|
| 55 |
-
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
|
| 56 |
-
times = [0, 0, 0]
|
| 57 |
-
f0_up_key = int(f0_up_key)
|
| 58 |
-
audio_opt = vc.pipeline(
|
| 59 |
-
hubert_model,
|
| 60 |
-
net_g,
|
| 61 |
-
0,
|
| 62 |
-
audio,
|
| 63 |
-
times,
|
| 64 |
-
f0_up_key,
|
| 65 |
-
f0_method,
|
| 66 |
-
file_index,
|
| 67 |
-
file_big_npy,
|
| 68 |
-
index_rate,
|
| 69 |
-
if_f0,
|
| 70 |
-
)
|
| 71 |
-
print(
|
| 72 |
-
f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
|
| 73 |
-
)
|
| 74 |
-
return "Success", (tgt_sr, audio_opt)
|
| 75 |
-
except:
|
| 76 |
-
info = traceback.format_exc()
|
| 77 |
-
print(info)
|
| 78 |
-
return info, (None, None)
|
| 79 |
-
return vc_fn
|
| 80 |
-
|
| 81 |
-
def load_hubert():
|
| 82 |
-
global hubert_model
|
| 83 |
-
models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
|
| 84 |
-
["hubert_base.pt"],
|
| 85 |
-
suffix="",
|
| 86 |
-
)
|
| 87 |
-
hubert_model = models[0]
|
| 88 |
-
hubert_model = hubert_model.to(device)
|
| 89 |
-
if is_half:
|
| 90 |
-
hubert_model = hubert_model.half()
|
| 91 |
-
else:
|
| 92 |
-
hubert_model = hubert_model.float()
|
| 93 |
-
hubert_model.eval()
|
| 94 |
-
|
| 95 |
-
def change_to_tts_mode(tts_mode):
|
| 96 |
-
if tts_mode:
|
| 97 |
-
return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
|
| 98 |
-
else:
|
| 99 |
-
return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
|
| 100 |
-
|
| 101 |
-
if __name__ == '__main__':
|
| 102 |
-
parser = argparse.ArgumentParser()
|
| 103 |
-
parser.add_argument('--api', action="store_true", default=False)
|
| 104 |
-
parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
|
| 105 |
-
parser.add_argument("--files", action="store_true", default=False, help="load audio from path")
|
| 106 |
-
args, unknown = parser.parse_known_args()
|
| 107 |
-
load_hubert()
|
| 108 |
-
models = []
|
| 109 |
-
tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
|
| 110 |
-
voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
|
| 111 |
-
with open("weights/model_info.json", "r", encoding="utf-8") as f:
|
| 112 |
-
models_info = json.load(f)
|
| 113 |
-
for name, info in models_info.items():
|
| 114 |
-
if not info['enable']:
|
| 115 |
-
continue
|
| 116 |
-
title = info['title']
|
| 117 |
-
author = info.get("author", None)
|
| 118 |
-
cover = f"weights/{name}/{info['cover']}"
|
| 119 |
-
index = f"weights/{name}/{info['feature_retrieval_library']}"
|
| 120 |
-
npy = f"weights/{name}/{info['feature_file']}"
|
| 121 |
-
cpt = torch.load(f"weights/{name}/{name}.pth", map_location="cpu")
|
| 122 |
-
tgt_sr = cpt["config"][-1]
|
| 123 |
-
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
|
| 124 |
-
if_f0 = cpt.get("f0", 1)
|
| 125 |
-
if if_f0 == 1:
|
| 126 |
-
net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=is_half)
|
| 127 |
-
else:
|
| 128 |
-
net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
|
| 129 |
-
del net_g.enc_q
|
| 130 |
-
print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净, 真奇葩
|
| 131 |
-
net_g.eval().to(device)
|
| 132 |
-
if is_half:
|
| 133 |
-
net_g = net_g.half()
|
| 134 |
-
else:
|
| 135 |
-
net_g = net_g.float()
|
| 136 |
-
vc = VC(tgt_sr, device, is_half)
|
| 137 |
-
models.append((name, title, author, cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, index, npy)))
|
| 138 |
-
with gr.Blocks() as app:
|
| 139 |
-
gr.Markdown(
|
| 140 |
-
"# <center> RVC Models (Outdated)\n"
|
| 141 |
-
"## <center> The input audio should be clean and pure voice without background music.\n"
|
| 142 |
-
"### <center> Updated Repository: [NEW RVC Models](https://huggingface.co/spaces/ArkanDash/rvc-models-new).\n"
|
| 143 |
-
"#### <center> Recommended to use the Google Colab version for more feature.\n"
|
| 144 |
-
"\n\n"
|
| 145 |
-
"[](https://colab.research.google.com/drive/1hx6kKvIuv5XNY1Gai2PEuZhpO5z6xpVh?usp=sharing)\n\n"
|
| 146 |
-
"[](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)"
|
| 147 |
-
)
|
| 148 |
-
with gr.Tabs():
|
| 149 |
-
for (name, title, author, cover, vc_fn) in models:
|
| 150 |
-
with gr.TabItem(name):
|
| 151 |
-
with gr.Row():
|
| 152 |
-
gr.Markdown(
|
| 153 |
-
'<div align="center">'
|
| 154 |
-
f'<div>{title}</div>\n'+
|
| 155 |
-
(f'<div>Model author: {author}</div>' if author else "")+
|
| 156 |
-
(f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
|
| 157 |
-
'</div>'
|
| 158 |
-
)
|
| 159 |
-
with gr.Row():
|
| 160 |
-
with gr.Column():
|
| 161 |
-
if args.files:
|
| 162 |
-
vc_input = gr.Textbox(label="Input audio path")
|
| 163 |
-
else:
|
| 164 |
-
vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '')
|
| 165 |
-
vc_transpose = gr.Number(label="Transpose", value=0)
|
| 166 |
-
vc_f0method = gr.Radio(
|
| 167 |
-
label="Pitch extraction algorithm, PM is fast but Harvest is better for low frequencies",
|
| 168 |
-
choices=["pm", "harvest"],
|
| 169 |
-
value="pm",
|
| 170 |
-
interactive=True,
|
| 171 |
-
)
|
| 172 |
-
vc_index_ratio = gr.Slider(
|
| 173 |
-
minimum=0,
|
| 174 |
-
maximum=1,
|
| 175 |
-
label="Retrieval feature ratio",
|
| 176 |
-
value=0.6,
|
| 177 |
-
interactive=True,
|
| 178 |
-
)
|
| 179 |
-
tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
|
| 180 |
-
tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text")
|
| 181 |
-
tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
|
| 182 |
-
vc_submit = gr.Button("Generate", variant="primary")
|
| 183 |
-
with gr.Column():
|
| 184 |
-
vc_output1 = gr.Textbox(label="Output Message")
|
| 185 |
-
vc_output2 = gr.Audio(label="Output Audio")
|
| 186 |
-
vc_submit.click(vc_fn, [vc_input, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output1, vc_output2])
|
| 187 |
-
tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice])
|
| 188 |
-
app.queue(concurrency_count=1, max_size=20, api_open=args.api).launch(share=args.share)
|
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|
spaces/Benson/text-generation/Examples/Descargar Gratis Brawl Stars.md
DELETED
|
@@ -1,71 +0,0 @@
|
|
| 1 |
-
<br />
|
| 2 |
-
<h1>Descargar Hack jugar juntos: Cómo obtener dinero ilimitado, diamantes y gemas en el juego social popular</h1>
|
| 3 |
-
<p>¿Te encanta jugar Play Together, el juego social donde puedes conocer amigos de todo el mundo, jugar minijuegos, decorar tu casa, vestir a tu personaje y criar mascotas? Si es así, es posible que se pregunte cómo obtener más dinero, diamantes y gemas en el juego sin gastar dinero real. Bueno, usted está de suerte porque en este artículo, le mostraremos cómo descargar hack jugar juntos y obtener recursos ilimitados en su cuenta. ¡Sigue leyendo para saber más! </p>
|
| 4 |
-
<h2>descargar gratis brawl stars</h2><br /><p><b><b>Download</b> ->->->-> <a href="https://bltlly.com/2v6LOZ">https://bltlly.com/2v6LOZ</a></b></p><br /><br />
|
| 5 |
-
<h2>¿Qué es jugar juntos? </h2>
|
| 6 |
-
<p>Play Together es un mundo virtual donde puedes chatear con amigos, divertirte y expresarte. Puedes hacer todo tipo de cosas en el juego, como:</p>
|
| 7 |
-
<h3>Un mundo virtual donde puedes conocer amigos de todo el mundo</h3>
|
| 8 |
-
<p>Puede unirse a un servidor con jugadores de diferentes países y regiones, y comunicarse con ellos mediante el chat de texto o voz. También puede invitar a sus amigos a su casa para una fiesta o para jugar minijuegos juntos. ¡Cuanto más, mejor! </p>
|
| 9 |
-
<h3>Una variedad de mini-juegos, actividades y opciones de personalización</h3>
|
| 10 |
-
<p>Puedes elegir entre muchos minijuegos diferentes para jugar con tus amigos u otros jugadores en línea. Usted puede correr a la línea de meta, se enfrentan a un enjambre de zombies, tirar abajo en una batalla real, y más. También puede explorar diferentes lugares en el mundo del juego, como la playa, el camping, el parque de atracciones y la ciudad. También puedes personalizar tu personaje de muchas maneras y expresar tu personalidad. Puedes cambiar tu ropa, estilo de cabello, accesorios, expresiones faciales y poses. También puedes decorar tu casa con todo tipo de muebles y artículos que se adapten a tu gusto. </p>
|
| 11 |
-
<h3>Un juego gratuito con compras en la aplicación</h3>
|
| 12 |
-
|
| 13 |
-
<h2>¿Por qué necesita hack jugar juntos? </h2>
|
| 14 |
-
<p>Si bien Play Together es un juego divertido y divertido, también puede ser frustrante si no tienes suficiente dinero, diamantes o gemas. Es posible que te sientas limitado en lo que puedes hacer o comprar en el juego. También puedes sentirte excluido o inferior en comparación con otros jugadores que tienen más recursos que tú. Es por eso que es posible que desee utilizar el juego hack juntos para obtener dinero ilimitado, diamantes y gemas en su cuenta. Estos son algunos de los beneficios de usar hack play juntos:</p>
|
| 15 |
-
<h3>Para disfrutar del juego sin gastar dinero real</h3 <p>Con hack jugar juntos, usted no tiene que gastar dinero real para obtener más dinero, diamantes, o gemas en el juego. Puedes conseguir tantos como quieras gratis y usarlos para comprar lo que quieras en el juego. También puedes ahorrar dinero para otras cosas que te importan más en la vida real. </p>
|
| 16 |
-
<h3>Para desbloquear todos los artículos, trajes, mascotas y muebles</h3>
|
| 17 |
-
<p>Con hack jugar juntos, puede desbloquear todos los artículos, trajes, mascotas y muebles que están disponibles en el juego. No tienes que esperar a que llegue un determinado nivel o evento. También puedes mezclarlos y combinarlos para crear tu propio estilo y apariencia únicos. También puedes impresionar a tus amigos y otros jugadores con tu colección y mostrar tu creatividad. </p>
|
| 18 |
-
<p></p>
|
| 19 |
-
<h3>Para capturar peces e insectos raros para su colección</h3>
|
| 20 |
-
<p>Con hack jugar juntos, se puede coger peces raros e insectos que son difíciles de encontrar en el juego. No tienes que pasar horas pescando o cazando para ellos. También puedes usarlos para decorar tu casa o venderlos por más dinero. También puedes completar tu colección y ganar logros y recompensas. </p>
|
| 21 |
-
<h2>Cómo descargar hack jugar juntos? </h2>
|
| 22 |
-
<p>Ahora que usted sabe por qué necesita hack jugar juntos, es posible que se pregunte cómo descargarlo y usarlo. Bueno, hay dos formas de hacerlo: la forma ilegal y la forma legal. Veamos cuáles son y cuáles son los riesgos y beneficios de cada uno. </p>
|
| 23 |
-
|
| 24 |
-
<p>La forma ilegal de descargar el juego de hackeo es usar programas, aplicaciones o métodos que no están autorizados por los desarrolladores de juegos o las tiendas de aplicaciones. Estos incluyen cosas como:</p>
|
| 25 |
-
<ul>
|
| 26 |
-
<li>Herramientas de hacking que inyectan código en el juego o modifican sus archivos</li>
|
| 27 |
-
<li>Aplicaciones modificadas que han sido manipuladas o alteradas desde la versión original</li>
|
| 28 |
-
<li>Métodos no autorizados que explotan fallos o errores en el juego</li>
|
| 29 |
-
</ul>
|
| 30 |
-
<p>Si bien estos métodos pueden parecer tentadores, también tienen muchos riesgos. Algunos de ellos son:</p>
|
| 31 |
-
<ul>
|
| 32 |
-
<li>Obtener virus, malware o spyware en su dispositivo que pueden dañar sus datos o privacidad</li>
|
| 33 |
-
<li>Ser estafado por sitios web o aplicaciones falsas que piden su información personal o detalles de pago</li>
|
| 34 |
-
<li>Ser expulsado del juego o perder tu cuenta por violar los términos del servicio</li>
|
| 35 |
-
</ul>
|
| 36 |
-
<p>Por lo tanto, no recomendamos usar estos métodos ya que no son seguros, confiables o éticos. </p>
|
| 37 |
-
<h3>La forma segura y fácil de utilizar un generador de juego truco juntos</h3>
|
| 38 |
-
<p>La forma legal de descargar juego hack juntos es utilizar un generador que es aprobado por los desarrolladores de juegos y las tiendas de aplicaciones. Este es un sitio web que le permite generar dinero ilimitado, diamantes y gemas para su cuenta en pocos minutos. Es seguro, fácil y gratuito. Estos son algunos de los beneficios de usar este método:</p>
|
| 39 |
-
<ul>
|
| 40 |
-
<li>No hay virus, malware o spyware en su dispositivo, ya que no tiene que descargar nada</li>
|
| 41 |
-
<li> No hay estafas o fraudes, ya que no tiene que proporcionar ninguna información personal o detalles de pago</li>
|
| 42 |
-
<li>No hay prohibiciones ni pérdidas de cuenta, ya que no infringe ningún término de servicio</li>
|
| 43 |
-
</ul>
|
| 44 |
-
<p>Por lo tanto, recomendamos usar este método ya que es la mejor manera de obtener recursos ilimitados en Play Together.</p>
|
| 45 |
-
<h3>Los pasos a seguir para obtener recursos ilimitados en su cuenta</h3>
|
| 46 |
-
<p>Para utilizar el generador de hack play together, solo tiene que seguir estos sencillos pasos:</p>
|
| 47 |
-
<ol>
|
| 48 |
-
|
| 49 |
-
<li>Introduzca su nombre de usuario o dirección de correo electrónico que utiliza para jugar Play Together</li>
|
| 50 |
-
<li>Seleccione el tipo de dispositivo (Android o iOS) y haga clic en Conectar</li>
|
| 51 |
-
<li>Seleccione la cantidad de dinero, diamantes y gemas que desea generar y haga clic en Generar</li>
|
| 52 |
-
<li>Espere unos segundos mientras el generador procesa su solicitud</li>
|
| 53 |
-
<li>Complete un paso rápido de verificación humana para demostrar que no es un robot</li>
|
| 54 |
-
<li>Compruebe su cuenta y disfrute de sus recursos ilimitados! </li>
|
| 55 |
-
</ol>
|
| 56 |
-
<h2>Conclusión</h2>
|
| 57 |
-
<p>En conclusión, Play Together es un juego divertido y social donde puedes conocer amigos de todo el mundo, jugar minijuegos, personalizar tu personaje y casa, y criar mascotas. Sin embargo, si desea disfrutar del juego sin gastar dinero real, es posible que desee descargar hack jugar juntos y obtener dinero ilimitado, diamantes y gemas en su cuenta. La mejor manera de hacer eso es utilizar un juego hack junto generador que es seguro, fácil y libre de usar. Entonces, ¿qué estás esperando? Pruebe el juego hack juntos generador de hoy y ver por sí mismo lo divertido que es jugar Play Together con recursos ilimitados. Te sorprenderá lo mucho que puedes hacer y comprar en el juego. También podrás hacer más amigos y divertirte más con ellos. Solo recuerda usar el generador de forma responsable y no abusar de él. Además, sé respetuoso con otros jugadores y no arruines su experiencia de juego. Después de todo, Play Together es un juego que está destinado a reunir a la gente y pasar un buen rato. </p>
|
| 58 |
-
<p>Antes de ir, aquí hay algunas preguntas frecuentes que usted podría tener sobre el juego hack juntos:</p>
|
| 59 |
-
<h2>Preguntas frecuentes</h2>
|
| 60 |
-
<h3>¿Es seguro jugar juntos? </h3>
|
| 61 |
-
|
| 62 |
-
<h3>¿Cuánto tiempo se tarda en obtener los recursos en mi cuenta? </h3>
|
| 63 |
-
<p>Por lo general, se tarda solo unos minutos para obtener los recursos en su cuenta después de usar el generador de juego hack juntos. Sin embargo, a veces puede tomar más tiempo dependiendo de la carga del servidor o el proceso de verificación. Si no ve los recursos en su cuenta después de 15 minutos, puede volver a intentarlo o ponerse en contacto con el equipo de soporte del generador para obtener ayuda. </p>
|
| 64 |
-
<h3>¿Puedo usar hack jugar juntos en cualquier dispositivo? </h3>
|
| 65 |
-
<p>Sí, se puede utilizar hack jugar juntos en cualquier dispositivo que puede ejecutar Jugar juntos. Esto incluye dispositivos Android e iOS, así como ordenadores PC y Mac. Solo tiene que tener una conexión a Internet estable y un navegador web para acceder al hack jugar juntos sitio web generador. No es necesario que raíz o jailbreak su dispositivo o instalar cualquier software o aplicación. </p>
|
| 66 |
-
<h3>¿Me van a prohibir el uso de hack jugar juntos? </h3>
|
| 67 |
-
<p>No, no se le prohibió el uso de hack jugar juntos, siempre y cuando se utiliza moderada y sabiamente. El generador de hack play together utiliza cifrado avanzado y servidores proxy para proteger su cuenta de detección y prohibición. Sin embargo, si lo usas con demasiada frecuencia o en exceso, podrías levantar sospechas de los desarrolladores de juegos u otros jugadores. Por lo tanto, ser inteligente y no exagerar. </p>
|
| 68 |
-
<h3>¿Dónde puedo encontrar más información sobre el hack jugar juntos? </h3>
|
| 69 |
-
<p>Si desea encontrar más información sobre el juego hack juntos, puede visitar el sitio web oficial del generador o seguir sus páginas de redes sociales. También puede leer comentarios y testimonios de otros usuarios que han utilizado el generador y ver cómo les gustó. También puede ponerse en contacto con el equipo de soporte del generador si tiene alguna pregunta o problema con el servicio. </p> 64aa2da5cf<br />
|
| 70 |
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<br />
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| 71 |
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<br />
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spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/packaging/requirements.py
DELETED
|
@@ -1,146 +0,0 @@
|
|
| 1 |
-
# This file is dual licensed under the terms of the Apache License, Version
|
| 2 |
-
# 2.0, and the BSD License. See the LICENSE file in the root of this repository
|
| 3 |
-
# for complete details.
|
| 4 |
-
|
| 5 |
-
import re
|
| 6 |
-
import string
|
| 7 |
-
import urllib.parse
|
| 8 |
-
from typing import List, Optional as TOptional, Set
|
| 9 |
-
|
| 10 |
-
from pip._vendor.pyparsing import ( # noqa
|
| 11 |
-
Combine,
|
| 12 |
-
Literal as L,
|
| 13 |
-
Optional,
|
| 14 |
-
ParseException,
|
| 15 |
-
Regex,
|
| 16 |
-
Word,
|
| 17 |
-
ZeroOrMore,
|
| 18 |
-
originalTextFor,
|
| 19 |
-
stringEnd,
|
| 20 |
-
stringStart,
|
| 21 |
-
)
|
| 22 |
-
|
| 23 |
-
from .markers import MARKER_EXPR, Marker
|
| 24 |
-
from .specifiers import LegacySpecifier, Specifier, SpecifierSet
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
class InvalidRequirement(ValueError):
|
| 28 |
-
"""
|
| 29 |
-
An invalid requirement was found, users should refer to PEP 508.
|
| 30 |
-
"""
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
ALPHANUM = Word(string.ascii_letters + string.digits)
|
| 34 |
-
|
| 35 |
-
LBRACKET = L("[").suppress()
|
| 36 |
-
RBRACKET = L("]").suppress()
|
| 37 |
-
LPAREN = L("(").suppress()
|
| 38 |
-
RPAREN = L(")").suppress()
|
| 39 |
-
COMMA = L(",").suppress()
|
| 40 |
-
SEMICOLON = L(";").suppress()
|
| 41 |
-
AT = L("@").suppress()
|
| 42 |
-
|
| 43 |
-
PUNCTUATION = Word("-_.")
|
| 44 |
-
IDENTIFIER_END = ALPHANUM | (ZeroOrMore(PUNCTUATION) + ALPHANUM)
|
| 45 |
-
IDENTIFIER = Combine(ALPHANUM + ZeroOrMore(IDENTIFIER_END))
|
| 46 |
-
|
| 47 |
-
NAME = IDENTIFIER("name")
|
| 48 |
-
EXTRA = IDENTIFIER
|
| 49 |
-
|
| 50 |
-
URI = Regex(r"[^ ]+")("url")
|
| 51 |
-
URL = AT + URI
|
| 52 |
-
|
| 53 |
-
EXTRAS_LIST = EXTRA + ZeroOrMore(COMMA + EXTRA)
|
| 54 |
-
EXTRAS = (LBRACKET + Optional(EXTRAS_LIST) + RBRACKET)("extras")
|
| 55 |
-
|
| 56 |
-
VERSION_PEP440 = Regex(Specifier._regex_str, re.VERBOSE | re.IGNORECASE)
|
| 57 |
-
VERSION_LEGACY = Regex(LegacySpecifier._regex_str, re.VERBOSE | re.IGNORECASE)
|
| 58 |
-
|
| 59 |
-
VERSION_ONE = VERSION_PEP440 ^ VERSION_LEGACY
|
| 60 |
-
VERSION_MANY = Combine(
|
| 61 |
-
VERSION_ONE + ZeroOrMore(COMMA + VERSION_ONE), joinString=",", adjacent=False
|
| 62 |
-
)("_raw_spec")
|
| 63 |
-
_VERSION_SPEC = Optional((LPAREN + VERSION_MANY + RPAREN) | VERSION_MANY)
|
| 64 |
-
_VERSION_SPEC.setParseAction(lambda s, l, t: t._raw_spec or "")
|
| 65 |
-
|
| 66 |
-
VERSION_SPEC = originalTextFor(_VERSION_SPEC)("specifier")
|
| 67 |
-
VERSION_SPEC.setParseAction(lambda s, l, t: t[1])
|
| 68 |
-
|
| 69 |
-
MARKER_EXPR = originalTextFor(MARKER_EXPR())("marker")
|
| 70 |
-
MARKER_EXPR.setParseAction(
|
| 71 |
-
lambda s, l, t: Marker(s[t._original_start : t._original_end])
|
| 72 |
-
)
|
| 73 |
-
MARKER_SEPARATOR = SEMICOLON
|
| 74 |
-
MARKER = MARKER_SEPARATOR + MARKER_EXPR
|
| 75 |
-
|
| 76 |
-
VERSION_AND_MARKER = VERSION_SPEC + Optional(MARKER)
|
| 77 |
-
URL_AND_MARKER = URL + Optional(MARKER)
|
| 78 |
-
|
| 79 |
-
NAMED_REQUIREMENT = NAME + Optional(EXTRAS) + (URL_AND_MARKER | VERSION_AND_MARKER)
|
| 80 |
-
|
| 81 |
-
REQUIREMENT = stringStart + NAMED_REQUIREMENT + stringEnd
|
| 82 |
-
# pyparsing isn't thread safe during initialization, so we do it eagerly, see
|
| 83 |
-
# issue #104
|
| 84 |
-
REQUIREMENT.parseString("x[]")
|
| 85 |
-
|
| 86 |
-
|
| 87 |
-
class Requirement:
|
| 88 |
-
"""Parse a requirement.
|
| 89 |
-
|
| 90 |
-
Parse a given requirement string into its parts, such as name, specifier,
|
| 91 |
-
URL, and extras. Raises InvalidRequirement on a badly-formed requirement
|
| 92 |
-
string.
|
| 93 |
-
"""
|
| 94 |
-
|
| 95 |
-
# TODO: Can we test whether something is contained within a requirement?
|
| 96 |
-
# If so how do we do that? Do we need to test against the _name_ of
|
| 97 |
-
# the thing as well as the version? What about the markers?
|
| 98 |
-
# TODO: Can we normalize the name and extra name?
|
| 99 |
-
|
| 100 |
-
def __init__(self, requirement_string: str) -> None:
|
| 101 |
-
try:
|
| 102 |
-
req = REQUIREMENT.parseString(requirement_string)
|
| 103 |
-
except ParseException as e:
|
| 104 |
-
raise InvalidRequirement(
|
| 105 |
-
f'Parse error at "{ requirement_string[e.loc : e.loc + 8]!r}": {e.msg}'
|
| 106 |
-
)
|
| 107 |
-
|
| 108 |
-
self.name: str = req.name
|
| 109 |
-
if req.url:
|
| 110 |
-
parsed_url = urllib.parse.urlparse(req.url)
|
| 111 |
-
if parsed_url.scheme == "file":
|
| 112 |
-
if urllib.parse.urlunparse(parsed_url) != req.url:
|
| 113 |
-
raise InvalidRequirement("Invalid URL given")
|
| 114 |
-
elif not (parsed_url.scheme and parsed_url.netloc) or (
|
| 115 |
-
not parsed_url.scheme and not parsed_url.netloc
|
| 116 |
-
):
|
| 117 |
-
raise InvalidRequirement(f"Invalid URL: {req.url}")
|
| 118 |
-
self.url: TOptional[str] = req.url
|
| 119 |
-
else:
|
| 120 |
-
self.url = None
|
| 121 |
-
self.extras: Set[str] = set(req.extras.asList() if req.extras else [])
|
| 122 |
-
self.specifier: SpecifierSet = SpecifierSet(req.specifier)
|
| 123 |
-
self.marker: TOptional[Marker] = req.marker if req.marker else None
|
| 124 |
-
|
| 125 |
-
def __str__(self) -> str:
|
| 126 |
-
parts: List[str] = [self.name]
|
| 127 |
-
|
| 128 |
-
if self.extras:
|
| 129 |
-
formatted_extras = ",".join(sorted(self.extras))
|
| 130 |
-
parts.append(f"[{formatted_extras}]")
|
| 131 |
-
|
| 132 |
-
if self.specifier:
|
| 133 |
-
parts.append(str(self.specifier))
|
| 134 |
-
|
| 135 |
-
if self.url:
|
| 136 |
-
parts.append(f"@ {self.url}")
|
| 137 |
-
if self.marker:
|
| 138 |
-
parts.append(" ")
|
| 139 |
-
|
| 140 |
-
if self.marker:
|
| 141 |
-
parts.append(f"; {self.marker}")
|
| 142 |
-
|
| 143 |
-
return "".join(parts)
|
| 144 |
-
|
| 145 |
-
def __repr__(self) -> str:
|
| 146 |
-
return f"<Requirement('{self}')>"
|
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|
spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/rule.py
DELETED
|
@@ -1,130 +0,0 @@
|
|
| 1 |
-
from typing import Union
|
| 2 |
-
|
| 3 |
-
from .align import AlignMethod
|
| 4 |
-
from .cells import cell_len, set_cell_size
|
| 5 |
-
from .console import Console, ConsoleOptions, RenderResult
|
| 6 |
-
from .jupyter import JupyterMixin
|
| 7 |
-
from .measure import Measurement
|
| 8 |
-
from .style import Style
|
| 9 |
-
from .text import Text
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
class Rule(JupyterMixin):
|
| 13 |
-
"""A console renderable to draw a horizontal rule (line).
|
| 14 |
-
|
| 15 |
-
Args:
|
| 16 |
-
title (Union[str, Text], optional): Text to render in the rule. Defaults to "".
|
| 17 |
-
characters (str, optional): Character(s) used to draw the line. Defaults to "─".
|
| 18 |
-
style (StyleType, optional): Style of Rule. Defaults to "rule.line".
|
| 19 |
-
end (str, optional): Character at end of Rule. defaults to "\\\\n"
|
| 20 |
-
align (str, optional): How to align the title, one of "left", "center", or "right". Defaults to "center".
|
| 21 |
-
"""
|
| 22 |
-
|
| 23 |
-
def __init__(
|
| 24 |
-
self,
|
| 25 |
-
title: Union[str, Text] = "",
|
| 26 |
-
*,
|
| 27 |
-
characters: str = "─",
|
| 28 |
-
style: Union[str, Style] = "rule.line",
|
| 29 |
-
end: str = "\n",
|
| 30 |
-
align: AlignMethod = "center",
|
| 31 |
-
) -> None:
|
| 32 |
-
if cell_len(characters) < 1:
|
| 33 |
-
raise ValueError(
|
| 34 |
-
"'characters' argument must have a cell width of at least 1"
|
| 35 |
-
)
|
| 36 |
-
if align not in ("left", "center", "right"):
|
| 37 |
-
raise ValueError(
|
| 38 |
-
f'invalid value for align, expected "left", "center", "right" (not {align!r})'
|
| 39 |
-
)
|
| 40 |
-
self.title = title
|
| 41 |
-
self.characters = characters
|
| 42 |
-
self.style = style
|
| 43 |
-
self.end = end
|
| 44 |
-
self.align = align
|
| 45 |
-
|
| 46 |
-
def __repr__(self) -> str:
|
| 47 |
-
return f"Rule({self.title!r}, {self.characters!r})"
|
| 48 |
-
|
| 49 |
-
def __rich_console__(
|
| 50 |
-
self, console: Console, options: ConsoleOptions
|
| 51 |
-
) -> RenderResult:
|
| 52 |
-
width = options.max_width
|
| 53 |
-
|
| 54 |
-
characters = (
|
| 55 |
-
"-"
|
| 56 |
-
if (options.ascii_only and not self.characters.isascii())
|
| 57 |
-
else self.characters
|
| 58 |
-
)
|
| 59 |
-
|
| 60 |
-
chars_len = cell_len(characters)
|
| 61 |
-
if not self.title:
|
| 62 |
-
yield self._rule_line(chars_len, width)
|
| 63 |
-
return
|
| 64 |
-
|
| 65 |
-
if isinstance(self.title, Text):
|
| 66 |
-
title_text = self.title
|
| 67 |
-
else:
|
| 68 |
-
title_text = console.render_str(self.title, style="rule.text")
|
| 69 |
-
|
| 70 |
-
title_text.plain = title_text.plain.replace("\n", " ")
|
| 71 |
-
title_text.expand_tabs()
|
| 72 |
-
|
| 73 |
-
required_space = 4 if self.align == "center" else 2
|
| 74 |
-
truncate_width = max(0, width - required_space)
|
| 75 |
-
if not truncate_width:
|
| 76 |
-
yield self._rule_line(chars_len, width)
|
| 77 |
-
return
|
| 78 |
-
|
| 79 |
-
rule_text = Text(end=self.end)
|
| 80 |
-
if self.align == "center":
|
| 81 |
-
title_text.truncate(truncate_width, overflow="ellipsis")
|
| 82 |
-
side_width = (width - cell_len(title_text.plain)) // 2
|
| 83 |
-
left = Text(characters * (side_width // chars_len + 1))
|
| 84 |
-
left.truncate(side_width - 1)
|
| 85 |
-
right_length = width - cell_len(left.plain) - cell_len(title_text.plain)
|
| 86 |
-
right = Text(characters * (side_width // chars_len + 1))
|
| 87 |
-
right.truncate(right_length)
|
| 88 |
-
rule_text.append(left.plain + " ", self.style)
|
| 89 |
-
rule_text.append(title_text)
|
| 90 |
-
rule_text.append(" " + right.plain, self.style)
|
| 91 |
-
elif self.align == "left":
|
| 92 |
-
title_text.truncate(truncate_width, overflow="ellipsis")
|
| 93 |
-
rule_text.append(title_text)
|
| 94 |
-
rule_text.append(" ")
|
| 95 |
-
rule_text.append(characters * (width - rule_text.cell_len), self.style)
|
| 96 |
-
elif self.align == "right":
|
| 97 |
-
title_text.truncate(truncate_width, overflow="ellipsis")
|
| 98 |
-
rule_text.append(characters * (width - title_text.cell_len - 1), self.style)
|
| 99 |
-
rule_text.append(" ")
|
| 100 |
-
rule_text.append(title_text)
|
| 101 |
-
|
| 102 |
-
rule_text.plain = set_cell_size(rule_text.plain, width)
|
| 103 |
-
yield rule_text
|
| 104 |
-
|
| 105 |
-
def _rule_line(self, chars_len: int, width: int) -> Text:
|
| 106 |
-
rule_text = Text(self.characters * ((width // chars_len) + 1), self.style)
|
| 107 |
-
rule_text.truncate(width)
|
| 108 |
-
rule_text.plain = set_cell_size(rule_text.plain, width)
|
| 109 |
-
return rule_text
|
| 110 |
-
|
| 111 |
-
def __rich_measure__(
|
| 112 |
-
self, console: Console, options: ConsoleOptions
|
| 113 |
-
) -> Measurement:
|
| 114 |
-
return Measurement(1, 1)
|
| 115 |
-
|
| 116 |
-
|
| 117 |
-
if __name__ == "__main__": # pragma: no cover
|
| 118 |
-
import sys
|
| 119 |
-
|
| 120 |
-
from pip._vendor.rich.console import Console
|
| 121 |
-
|
| 122 |
-
try:
|
| 123 |
-
text = sys.argv[1]
|
| 124 |
-
except IndexError:
|
| 125 |
-
text = "Hello, World"
|
| 126 |
-
console = Console()
|
| 127 |
-
console.print(Rule(title=text))
|
| 128 |
-
|
| 129 |
-
console = Console()
|
| 130 |
-
console.print(Rule("foo"), width=4)
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|
spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/traceback.py
DELETED
|
@@ -1,756 +0,0 @@
|
|
| 1 |
-
from __future__ import absolute_import
|
| 2 |
-
|
| 3 |
-
import linecache
|
| 4 |
-
import os
|
| 5 |
-
import platform
|
| 6 |
-
import sys
|
| 7 |
-
from dataclasses import dataclass, field
|
| 8 |
-
from traceback import walk_tb
|
| 9 |
-
from types import ModuleType, TracebackType
|
| 10 |
-
from typing import (
|
| 11 |
-
Any,
|
| 12 |
-
Callable,
|
| 13 |
-
Dict,
|
| 14 |
-
Iterable,
|
| 15 |
-
List,
|
| 16 |
-
Optional,
|
| 17 |
-
Sequence,
|
| 18 |
-
Tuple,
|
| 19 |
-
Type,
|
| 20 |
-
Union,
|
| 21 |
-
)
|
| 22 |
-
|
| 23 |
-
from pip._vendor.pygments.lexers import guess_lexer_for_filename
|
| 24 |
-
from pip._vendor.pygments.token import Comment, Keyword, Name, Number, Operator, String
|
| 25 |
-
from pip._vendor.pygments.token import Text as TextToken
|
| 26 |
-
from pip._vendor.pygments.token import Token
|
| 27 |
-
from pip._vendor.pygments.util import ClassNotFound
|
| 28 |
-
|
| 29 |
-
from . import pretty
|
| 30 |
-
from ._loop import loop_last
|
| 31 |
-
from .columns import Columns
|
| 32 |
-
from .console import Console, ConsoleOptions, ConsoleRenderable, RenderResult, group
|
| 33 |
-
from .constrain import Constrain
|
| 34 |
-
from .highlighter import RegexHighlighter, ReprHighlighter
|
| 35 |
-
from .panel import Panel
|
| 36 |
-
from .scope import render_scope
|
| 37 |
-
from .style import Style
|
| 38 |
-
from .syntax import Syntax
|
| 39 |
-
from .text import Text
|
| 40 |
-
from .theme import Theme
|
| 41 |
-
|
| 42 |
-
WINDOWS = platform.system() == "Windows"
|
| 43 |
-
|
| 44 |
-
LOCALS_MAX_LENGTH = 10
|
| 45 |
-
LOCALS_MAX_STRING = 80
|
| 46 |
-
|
| 47 |
-
|
| 48 |
-
def install(
|
| 49 |
-
*,
|
| 50 |
-
console: Optional[Console] = None,
|
| 51 |
-
width: Optional[int] = 100,
|
| 52 |
-
extra_lines: int = 3,
|
| 53 |
-
theme: Optional[str] = None,
|
| 54 |
-
word_wrap: bool = False,
|
| 55 |
-
show_locals: bool = False,
|
| 56 |
-
locals_max_length: int = LOCALS_MAX_LENGTH,
|
| 57 |
-
locals_max_string: int = LOCALS_MAX_STRING,
|
| 58 |
-
locals_hide_dunder: bool = True,
|
| 59 |
-
locals_hide_sunder: Optional[bool] = None,
|
| 60 |
-
indent_guides: bool = True,
|
| 61 |
-
suppress: Iterable[Union[str, ModuleType]] = (),
|
| 62 |
-
max_frames: int = 100,
|
| 63 |
-
) -> Callable[[Type[BaseException], BaseException, Optional[TracebackType]], Any]:
|
| 64 |
-
"""Install a rich traceback handler.
|
| 65 |
-
|
| 66 |
-
Once installed, any tracebacks will be printed with syntax highlighting and rich formatting.
|
| 67 |
-
|
| 68 |
-
|
| 69 |
-
Args:
|
| 70 |
-
console (Optional[Console], optional): Console to write exception to. Default uses internal Console instance.
|
| 71 |
-
width (Optional[int], optional): Width (in characters) of traceback. Defaults to 100.
|
| 72 |
-
extra_lines (int, optional): Extra lines of code. Defaults to 3.
|
| 73 |
-
theme (Optional[str], optional): Pygments theme to use in traceback. Defaults to ``None`` which will pick
|
| 74 |
-
a theme appropriate for the platform.
|
| 75 |
-
word_wrap (bool, optional): Enable word wrapping of long lines. Defaults to False.
|
| 76 |
-
show_locals (bool, optional): Enable display of local variables. Defaults to False.
|
| 77 |
-
locals_max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
|
| 78 |
-
Defaults to 10.
|
| 79 |
-
locals_max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to 80.
|
| 80 |
-
locals_hide_dunder (bool, optional): Hide locals prefixed with double underscore. Defaults to True.
|
| 81 |
-
locals_hide_sunder (bool, optional): Hide locals prefixed with single underscore. Defaults to False.
|
| 82 |
-
indent_guides (bool, optional): Enable indent guides in code and locals. Defaults to True.
|
| 83 |
-
suppress (Sequence[Union[str, ModuleType]]): Optional sequence of modules or paths to exclude from traceback.
|
| 84 |
-
|
| 85 |
-
Returns:
|
| 86 |
-
Callable: The previous exception handler that was replaced.
|
| 87 |
-
|
| 88 |
-
"""
|
| 89 |
-
traceback_console = Console(stderr=True) if console is None else console
|
| 90 |
-
|
| 91 |
-
locals_hide_sunder = (
|
| 92 |
-
True
|
| 93 |
-
if (traceback_console.is_jupyter and locals_hide_sunder is None)
|
| 94 |
-
else locals_hide_sunder
|
| 95 |
-
)
|
| 96 |
-
|
| 97 |
-
def excepthook(
|
| 98 |
-
type_: Type[BaseException],
|
| 99 |
-
value: BaseException,
|
| 100 |
-
traceback: Optional[TracebackType],
|
| 101 |
-
) -> None:
|
| 102 |
-
traceback_console.print(
|
| 103 |
-
Traceback.from_exception(
|
| 104 |
-
type_,
|
| 105 |
-
value,
|
| 106 |
-
traceback,
|
| 107 |
-
width=width,
|
| 108 |
-
extra_lines=extra_lines,
|
| 109 |
-
theme=theme,
|
| 110 |
-
word_wrap=word_wrap,
|
| 111 |
-
show_locals=show_locals,
|
| 112 |
-
locals_max_length=locals_max_length,
|
| 113 |
-
locals_max_string=locals_max_string,
|
| 114 |
-
locals_hide_dunder=locals_hide_dunder,
|
| 115 |
-
locals_hide_sunder=bool(locals_hide_sunder),
|
| 116 |
-
indent_guides=indent_guides,
|
| 117 |
-
suppress=suppress,
|
| 118 |
-
max_frames=max_frames,
|
| 119 |
-
)
|
| 120 |
-
)
|
| 121 |
-
|
| 122 |
-
def ipy_excepthook_closure(ip: Any) -> None: # pragma: no cover
|
| 123 |
-
tb_data = {} # store information about showtraceback call
|
| 124 |
-
default_showtraceback = ip.showtraceback # keep reference of default traceback
|
| 125 |
-
|
| 126 |
-
def ipy_show_traceback(*args: Any, **kwargs: Any) -> None:
|
| 127 |
-
"""wrap the default ip.showtraceback to store info for ip._showtraceback"""
|
| 128 |
-
nonlocal tb_data
|
| 129 |
-
tb_data = kwargs
|
| 130 |
-
default_showtraceback(*args, **kwargs)
|
| 131 |
-
|
| 132 |
-
def ipy_display_traceback(
|
| 133 |
-
*args: Any, is_syntax: bool = False, **kwargs: Any
|
| 134 |
-
) -> None:
|
| 135 |
-
"""Internally called traceback from ip._showtraceback"""
|
| 136 |
-
nonlocal tb_data
|
| 137 |
-
exc_tuple = ip._get_exc_info()
|
| 138 |
-
|
| 139 |
-
# do not display trace on syntax error
|
| 140 |
-
tb: Optional[TracebackType] = None if is_syntax else exc_tuple[2]
|
| 141 |
-
|
| 142 |
-
# determine correct tb_offset
|
| 143 |
-
compiled = tb_data.get("running_compiled_code", False)
|
| 144 |
-
tb_offset = tb_data.get("tb_offset", 1 if compiled else 0)
|
| 145 |
-
# remove ipython internal frames from trace with tb_offset
|
| 146 |
-
for _ in range(tb_offset):
|
| 147 |
-
if tb is None:
|
| 148 |
-
break
|
| 149 |
-
tb = tb.tb_next
|
| 150 |
-
|
| 151 |
-
excepthook(exc_tuple[0], exc_tuple[1], tb)
|
| 152 |
-
tb_data = {} # clear data upon usage
|
| 153 |
-
|
| 154 |
-
# replace _showtraceback instead of showtraceback to allow ipython features such as debugging to work
|
| 155 |
-
# this is also what the ipython docs recommends to modify when subclassing InteractiveShell
|
| 156 |
-
ip._showtraceback = ipy_display_traceback
|
| 157 |
-
# add wrapper to capture tb_data
|
| 158 |
-
ip.showtraceback = ipy_show_traceback
|
| 159 |
-
ip.showsyntaxerror = lambda *args, **kwargs: ipy_display_traceback(
|
| 160 |
-
*args, is_syntax=True, **kwargs
|
| 161 |
-
)
|
| 162 |
-
|
| 163 |
-
try: # pragma: no cover
|
| 164 |
-
# if within ipython, use customized traceback
|
| 165 |
-
ip = get_ipython() # type: ignore[name-defined]
|
| 166 |
-
ipy_excepthook_closure(ip)
|
| 167 |
-
return sys.excepthook
|
| 168 |
-
except Exception:
|
| 169 |
-
# otherwise use default system hook
|
| 170 |
-
old_excepthook = sys.excepthook
|
| 171 |
-
sys.excepthook = excepthook
|
| 172 |
-
return old_excepthook
|
| 173 |
-
|
| 174 |
-
|
| 175 |
-
@dataclass
|
| 176 |
-
class Frame:
|
| 177 |
-
filename: str
|
| 178 |
-
lineno: int
|
| 179 |
-
name: str
|
| 180 |
-
line: str = ""
|
| 181 |
-
locals: Optional[Dict[str, pretty.Node]] = None
|
| 182 |
-
|
| 183 |
-
|
| 184 |
-
@dataclass
|
| 185 |
-
class _SyntaxError:
|
| 186 |
-
offset: int
|
| 187 |
-
filename: str
|
| 188 |
-
line: str
|
| 189 |
-
lineno: int
|
| 190 |
-
msg: str
|
| 191 |
-
|
| 192 |
-
|
| 193 |
-
@dataclass
|
| 194 |
-
class Stack:
|
| 195 |
-
exc_type: str
|
| 196 |
-
exc_value: str
|
| 197 |
-
syntax_error: Optional[_SyntaxError] = None
|
| 198 |
-
is_cause: bool = False
|
| 199 |
-
frames: List[Frame] = field(default_factory=list)
|
| 200 |
-
|
| 201 |
-
|
| 202 |
-
@dataclass
|
| 203 |
-
class Trace:
|
| 204 |
-
stacks: List[Stack]
|
| 205 |
-
|
| 206 |
-
|
| 207 |
-
class PathHighlighter(RegexHighlighter):
|
| 208 |
-
highlights = [r"(?P<dim>.*/)(?P<bold>.+)"]
|
| 209 |
-
|
| 210 |
-
|
| 211 |
-
class Traceback:
|
| 212 |
-
"""A Console renderable that renders a traceback.
|
| 213 |
-
|
| 214 |
-
Args:
|
| 215 |
-
trace (Trace, optional): A `Trace` object produced from `extract`. Defaults to None, which uses
|
| 216 |
-
the last exception.
|
| 217 |
-
width (Optional[int], optional): Number of characters used to traceback. Defaults to 100.
|
| 218 |
-
extra_lines (int, optional): Additional lines of code to render. Defaults to 3.
|
| 219 |
-
theme (str, optional): Override pygments theme used in traceback.
|
| 220 |
-
word_wrap (bool, optional): Enable word wrapping of long lines. Defaults to False.
|
| 221 |
-
show_locals (bool, optional): Enable display of local variables. Defaults to False.
|
| 222 |
-
indent_guides (bool, optional): Enable indent guides in code and locals. Defaults to True.
|
| 223 |
-
locals_max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
|
| 224 |
-
Defaults to 10.
|
| 225 |
-
locals_max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to 80.
|
| 226 |
-
locals_hide_dunder (bool, optional): Hide locals prefixed with double underscore. Defaults to True.
|
| 227 |
-
locals_hide_sunder (bool, optional): Hide locals prefixed with single underscore. Defaults to False.
|
| 228 |
-
suppress (Sequence[Union[str, ModuleType]]): Optional sequence of modules or paths to exclude from traceback.
|
| 229 |
-
max_frames (int): Maximum number of frames to show in a traceback, 0 for no maximum. Defaults to 100.
|
| 230 |
-
|
| 231 |
-
"""
|
| 232 |
-
|
| 233 |
-
LEXERS = {
|
| 234 |
-
"": "text",
|
| 235 |
-
".py": "python",
|
| 236 |
-
".pxd": "cython",
|
| 237 |
-
".pyx": "cython",
|
| 238 |
-
".pxi": "pyrex",
|
| 239 |
-
}
|
| 240 |
-
|
| 241 |
-
def __init__(
|
| 242 |
-
self,
|
| 243 |
-
trace: Optional[Trace] = None,
|
| 244 |
-
*,
|
| 245 |
-
width: Optional[int] = 100,
|
| 246 |
-
extra_lines: int = 3,
|
| 247 |
-
theme: Optional[str] = None,
|
| 248 |
-
word_wrap: bool = False,
|
| 249 |
-
show_locals: bool = False,
|
| 250 |
-
locals_max_length: int = LOCALS_MAX_LENGTH,
|
| 251 |
-
locals_max_string: int = LOCALS_MAX_STRING,
|
| 252 |
-
locals_hide_dunder: bool = True,
|
| 253 |
-
locals_hide_sunder: bool = False,
|
| 254 |
-
indent_guides: bool = True,
|
| 255 |
-
suppress: Iterable[Union[str, ModuleType]] = (),
|
| 256 |
-
max_frames: int = 100,
|
| 257 |
-
):
|
| 258 |
-
if trace is None:
|
| 259 |
-
exc_type, exc_value, traceback = sys.exc_info()
|
| 260 |
-
if exc_type is None or exc_value is None or traceback is None:
|
| 261 |
-
raise ValueError(
|
| 262 |
-
"Value for 'trace' required if not called in except: block"
|
| 263 |
-
)
|
| 264 |
-
trace = self.extract(
|
| 265 |
-
exc_type, exc_value, traceback, show_locals=show_locals
|
| 266 |
-
)
|
| 267 |
-
self.trace = trace
|
| 268 |
-
self.width = width
|
| 269 |
-
self.extra_lines = extra_lines
|
| 270 |
-
self.theme = Syntax.get_theme(theme or "ansi_dark")
|
| 271 |
-
self.word_wrap = word_wrap
|
| 272 |
-
self.show_locals = show_locals
|
| 273 |
-
self.indent_guides = indent_guides
|
| 274 |
-
self.locals_max_length = locals_max_length
|
| 275 |
-
self.locals_max_string = locals_max_string
|
| 276 |
-
self.locals_hide_dunder = locals_hide_dunder
|
| 277 |
-
self.locals_hide_sunder = locals_hide_sunder
|
| 278 |
-
|
| 279 |
-
self.suppress: Sequence[str] = []
|
| 280 |
-
for suppress_entity in suppress:
|
| 281 |
-
if not isinstance(suppress_entity, str):
|
| 282 |
-
assert (
|
| 283 |
-
suppress_entity.__file__ is not None
|
| 284 |
-
), f"{suppress_entity!r} must be a module with '__file__' attribute"
|
| 285 |
-
path = os.path.dirname(suppress_entity.__file__)
|
| 286 |
-
else:
|
| 287 |
-
path = suppress_entity
|
| 288 |
-
path = os.path.normpath(os.path.abspath(path))
|
| 289 |
-
self.suppress.append(path)
|
| 290 |
-
self.max_frames = max(4, max_frames) if max_frames > 0 else 0
|
| 291 |
-
|
| 292 |
-
@classmethod
|
| 293 |
-
def from_exception(
|
| 294 |
-
cls,
|
| 295 |
-
exc_type: Type[Any],
|
| 296 |
-
exc_value: BaseException,
|
| 297 |
-
traceback: Optional[TracebackType],
|
| 298 |
-
*,
|
| 299 |
-
width: Optional[int] = 100,
|
| 300 |
-
extra_lines: int = 3,
|
| 301 |
-
theme: Optional[str] = None,
|
| 302 |
-
word_wrap: bool = False,
|
| 303 |
-
show_locals: bool = False,
|
| 304 |
-
locals_max_length: int = LOCALS_MAX_LENGTH,
|
| 305 |
-
locals_max_string: int = LOCALS_MAX_STRING,
|
| 306 |
-
locals_hide_dunder: bool = True,
|
| 307 |
-
locals_hide_sunder: bool = False,
|
| 308 |
-
indent_guides: bool = True,
|
| 309 |
-
suppress: Iterable[Union[str, ModuleType]] = (),
|
| 310 |
-
max_frames: int = 100,
|
| 311 |
-
) -> "Traceback":
|
| 312 |
-
"""Create a traceback from exception info
|
| 313 |
-
|
| 314 |
-
Args:
|
| 315 |
-
exc_type (Type[BaseException]): Exception type.
|
| 316 |
-
exc_value (BaseException): Exception value.
|
| 317 |
-
traceback (TracebackType): Python Traceback object.
|
| 318 |
-
width (Optional[int], optional): Number of characters used to traceback. Defaults to 100.
|
| 319 |
-
extra_lines (int, optional): Additional lines of code to render. Defaults to 3.
|
| 320 |
-
theme (str, optional): Override pygments theme used in traceback.
|
| 321 |
-
word_wrap (bool, optional): Enable word wrapping of long lines. Defaults to False.
|
| 322 |
-
show_locals (bool, optional): Enable display of local variables. Defaults to False.
|
| 323 |
-
indent_guides (bool, optional): Enable indent guides in code and locals. Defaults to True.
|
| 324 |
-
locals_max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
|
| 325 |
-
Defaults to 10.
|
| 326 |
-
locals_max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to 80.
|
| 327 |
-
locals_hide_dunder (bool, optional): Hide locals prefixed with double underscore. Defaults to True.
|
| 328 |
-
locals_hide_sunder (bool, optional): Hide locals prefixed with single underscore. Defaults to False.
|
| 329 |
-
suppress (Iterable[Union[str, ModuleType]]): Optional sequence of modules or paths to exclude from traceback.
|
| 330 |
-
max_frames (int): Maximum number of frames to show in a traceback, 0 for no maximum. Defaults to 100.
|
| 331 |
-
|
| 332 |
-
Returns:
|
| 333 |
-
Traceback: A Traceback instance that may be printed.
|
| 334 |
-
"""
|
| 335 |
-
rich_traceback = cls.extract(
|
| 336 |
-
exc_type,
|
| 337 |
-
exc_value,
|
| 338 |
-
traceback,
|
| 339 |
-
show_locals=show_locals,
|
| 340 |
-
locals_max_length=locals_max_length,
|
| 341 |
-
locals_max_string=locals_max_string,
|
| 342 |
-
locals_hide_dunder=locals_hide_dunder,
|
| 343 |
-
locals_hide_sunder=locals_hide_sunder,
|
| 344 |
-
)
|
| 345 |
-
|
| 346 |
-
return cls(
|
| 347 |
-
rich_traceback,
|
| 348 |
-
width=width,
|
| 349 |
-
extra_lines=extra_lines,
|
| 350 |
-
theme=theme,
|
| 351 |
-
word_wrap=word_wrap,
|
| 352 |
-
show_locals=show_locals,
|
| 353 |
-
indent_guides=indent_guides,
|
| 354 |
-
locals_max_length=locals_max_length,
|
| 355 |
-
locals_max_string=locals_max_string,
|
| 356 |
-
locals_hide_dunder=locals_hide_dunder,
|
| 357 |
-
locals_hide_sunder=locals_hide_sunder,
|
| 358 |
-
suppress=suppress,
|
| 359 |
-
max_frames=max_frames,
|
| 360 |
-
)
|
| 361 |
-
|
| 362 |
-
@classmethod
|
| 363 |
-
def extract(
|
| 364 |
-
cls,
|
| 365 |
-
exc_type: Type[BaseException],
|
| 366 |
-
exc_value: BaseException,
|
| 367 |
-
traceback: Optional[TracebackType],
|
| 368 |
-
*,
|
| 369 |
-
show_locals: bool = False,
|
| 370 |
-
locals_max_length: int = LOCALS_MAX_LENGTH,
|
| 371 |
-
locals_max_string: int = LOCALS_MAX_STRING,
|
| 372 |
-
locals_hide_dunder: bool = True,
|
| 373 |
-
locals_hide_sunder: bool = False,
|
| 374 |
-
) -> Trace:
|
| 375 |
-
"""Extract traceback information.
|
| 376 |
-
|
| 377 |
-
Args:
|
| 378 |
-
exc_type (Type[BaseException]): Exception type.
|
| 379 |
-
exc_value (BaseException): Exception value.
|
| 380 |
-
traceback (TracebackType): Python Traceback object.
|
| 381 |
-
show_locals (bool, optional): Enable display of local variables. Defaults to False.
|
| 382 |
-
locals_max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
|
| 383 |
-
Defaults to 10.
|
| 384 |
-
locals_max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to 80.
|
| 385 |
-
locals_hide_dunder (bool, optional): Hide locals prefixed with double underscore. Defaults to True.
|
| 386 |
-
locals_hide_sunder (bool, optional): Hide locals prefixed with single underscore. Defaults to False.
|
| 387 |
-
|
| 388 |
-
Returns:
|
| 389 |
-
Trace: A Trace instance which you can use to construct a `Traceback`.
|
| 390 |
-
"""
|
| 391 |
-
|
| 392 |
-
stacks: List[Stack] = []
|
| 393 |
-
is_cause = False
|
| 394 |
-
|
| 395 |
-
from pip._vendor.rich import _IMPORT_CWD
|
| 396 |
-
|
| 397 |
-
def safe_str(_object: Any) -> str:
|
| 398 |
-
"""Don't allow exceptions from __str__ to propagate."""
|
| 399 |
-
try:
|
| 400 |
-
return str(_object)
|
| 401 |
-
except Exception:
|
| 402 |
-
return "<exception str() failed>"
|
| 403 |
-
|
| 404 |
-
while True:
|
| 405 |
-
stack = Stack(
|
| 406 |
-
exc_type=safe_str(exc_type.__name__),
|
| 407 |
-
exc_value=safe_str(exc_value),
|
| 408 |
-
is_cause=is_cause,
|
| 409 |
-
)
|
| 410 |
-
|
| 411 |
-
if isinstance(exc_value, SyntaxError):
|
| 412 |
-
stack.syntax_error = _SyntaxError(
|
| 413 |
-
offset=exc_value.offset or 0,
|
| 414 |
-
filename=exc_value.filename or "?",
|
| 415 |
-
lineno=exc_value.lineno or 0,
|
| 416 |
-
line=exc_value.text or "",
|
| 417 |
-
msg=exc_value.msg,
|
| 418 |
-
)
|
| 419 |
-
|
| 420 |
-
stacks.append(stack)
|
| 421 |
-
append = stack.frames.append
|
| 422 |
-
|
| 423 |
-
def get_locals(
|
| 424 |
-
iter_locals: Iterable[Tuple[str, object]]
|
| 425 |
-
) -> Iterable[Tuple[str, object]]:
|
| 426 |
-
"""Extract locals from an iterator of key pairs."""
|
| 427 |
-
if not (locals_hide_dunder or locals_hide_sunder):
|
| 428 |
-
yield from iter_locals
|
| 429 |
-
return
|
| 430 |
-
for key, value in iter_locals:
|
| 431 |
-
if locals_hide_dunder and key.startswith("__"):
|
| 432 |
-
continue
|
| 433 |
-
if locals_hide_sunder and key.startswith("_"):
|
| 434 |
-
continue
|
| 435 |
-
yield key, value
|
| 436 |
-
|
| 437 |
-
for frame_summary, line_no in walk_tb(traceback):
|
| 438 |
-
filename = frame_summary.f_code.co_filename
|
| 439 |
-
if filename and not filename.startswith("<"):
|
| 440 |
-
if not os.path.isabs(filename):
|
| 441 |
-
filename = os.path.join(_IMPORT_CWD, filename)
|
| 442 |
-
if frame_summary.f_locals.get("_rich_traceback_omit", False):
|
| 443 |
-
continue
|
| 444 |
-
|
| 445 |
-
frame = Frame(
|
| 446 |
-
filename=filename or "?",
|
| 447 |
-
lineno=line_no,
|
| 448 |
-
name=frame_summary.f_code.co_name,
|
| 449 |
-
locals={
|
| 450 |
-
key: pretty.traverse(
|
| 451 |
-
value,
|
| 452 |
-
max_length=locals_max_length,
|
| 453 |
-
max_string=locals_max_string,
|
| 454 |
-
)
|
| 455 |
-
for key, value in get_locals(frame_summary.f_locals.items())
|
| 456 |
-
}
|
| 457 |
-
if show_locals
|
| 458 |
-
else None,
|
| 459 |
-
)
|
| 460 |
-
append(frame)
|
| 461 |
-
if frame_summary.f_locals.get("_rich_traceback_guard", False):
|
| 462 |
-
del stack.frames[:]
|
| 463 |
-
|
| 464 |
-
cause = getattr(exc_value, "__cause__", None)
|
| 465 |
-
if cause:
|
| 466 |
-
exc_type = cause.__class__
|
| 467 |
-
exc_value = cause
|
| 468 |
-
# __traceback__ can be None, e.g. for exceptions raised by the
|
| 469 |
-
# 'multiprocessing' module
|
| 470 |
-
traceback = cause.__traceback__
|
| 471 |
-
is_cause = True
|
| 472 |
-
continue
|
| 473 |
-
|
| 474 |
-
cause = exc_value.__context__
|
| 475 |
-
if cause and not getattr(exc_value, "__suppress_context__", False):
|
| 476 |
-
exc_type = cause.__class__
|
| 477 |
-
exc_value = cause
|
| 478 |
-
traceback = cause.__traceback__
|
| 479 |
-
is_cause = False
|
| 480 |
-
continue
|
| 481 |
-
# No cover, code is reached but coverage doesn't recognize it.
|
| 482 |
-
break # pragma: no cover
|
| 483 |
-
|
| 484 |
-
trace = Trace(stacks=stacks)
|
| 485 |
-
return trace
|
| 486 |
-
|
| 487 |
-
def __rich_console__(
|
| 488 |
-
self, console: Console, options: ConsoleOptions
|
| 489 |
-
) -> RenderResult:
|
| 490 |
-
theme = self.theme
|
| 491 |
-
background_style = theme.get_background_style()
|
| 492 |
-
token_style = theme.get_style_for_token
|
| 493 |
-
|
| 494 |
-
traceback_theme = Theme(
|
| 495 |
-
{
|
| 496 |
-
"pretty": token_style(TextToken),
|
| 497 |
-
"pygments.text": token_style(Token),
|
| 498 |
-
"pygments.string": token_style(String),
|
| 499 |
-
"pygments.function": token_style(Name.Function),
|
| 500 |
-
"pygments.number": token_style(Number),
|
| 501 |
-
"repr.indent": token_style(Comment) + Style(dim=True),
|
| 502 |
-
"repr.str": token_style(String),
|
| 503 |
-
"repr.brace": token_style(TextToken) + Style(bold=True),
|
| 504 |
-
"repr.number": token_style(Number),
|
| 505 |
-
"repr.bool_true": token_style(Keyword.Constant),
|
| 506 |
-
"repr.bool_false": token_style(Keyword.Constant),
|
| 507 |
-
"repr.none": token_style(Keyword.Constant),
|
| 508 |
-
"scope.border": token_style(String.Delimiter),
|
| 509 |
-
"scope.equals": token_style(Operator),
|
| 510 |
-
"scope.key": token_style(Name),
|
| 511 |
-
"scope.key.special": token_style(Name.Constant) + Style(dim=True),
|
| 512 |
-
},
|
| 513 |
-
inherit=False,
|
| 514 |
-
)
|
| 515 |
-
|
| 516 |
-
highlighter = ReprHighlighter()
|
| 517 |
-
for last, stack in loop_last(reversed(self.trace.stacks)):
|
| 518 |
-
if stack.frames:
|
| 519 |
-
stack_renderable: ConsoleRenderable = Panel(
|
| 520 |
-
self._render_stack(stack),
|
| 521 |
-
title="[traceback.title]Traceback [dim](most recent call last)",
|
| 522 |
-
style=background_style,
|
| 523 |
-
border_style="traceback.border",
|
| 524 |
-
expand=True,
|
| 525 |
-
padding=(0, 1),
|
| 526 |
-
)
|
| 527 |
-
stack_renderable = Constrain(stack_renderable, self.width)
|
| 528 |
-
with console.use_theme(traceback_theme):
|
| 529 |
-
yield stack_renderable
|
| 530 |
-
if stack.syntax_error is not None:
|
| 531 |
-
with console.use_theme(traceback_theme):
|
| 532 |
-
yield Constrain(
|
| 533 |
-
Panel(
|
| 534 |
-
self._render_syntax_error(stack.syntax_error),
|
| 535 |
-
style=background_style,
|
| 536 |
-
border_style="traceback.border.syntax_error",
|
| 537 |
-
expand=True,
|
| 538 |
-
padding=(0, 1),
|
| 539 |
-
width=self.width,
|
| 540 |
-
),
|
| 541 |
-
self.width,
|
| 542 |
-
)
|
| 543 |
-
yield Text.assemble(
|
| 544 |
-
(f"{stack.exc_type}: ", "traceback.exc_type"),
|
| 545 |
-
highlighter(stack.syntax_error.msg),
|
| 546 |
-
)
|
| 547 |
-
elif stack.exc_value:
|
| 548 |
-
yield Text.assemble(
|
| 549 |
-
(f"{stack.exc_type}: ", "traceback.exc_type"),
|
| 550 |
-
highlighter(stack.exc_value),
|
| 551 |
-
)
|
| 552 |
-
else:
|
| 553 |
-
yield Text.assemble((f"{stack.exc_type}", "traceback.exc_type"))
|
| 554 |
-
|
| 555 |
-
if not last:
|
| 556 |
-
if stack.is_cause:
|
| 557 |
-
yield Text.from_markup(
|
| 558 |
-
"\n[i]The above exception was the direct cause of the following exception:\n",
|
| 559 |
-
)
|
| 560 |
-
else:
|
| 561 |
-
yield Text.from_markup(
|
| 562 |
-
"\n[i]During handling of the above exception, another exception occurred:\n",
|
| 563 |
-
)
|
| 564 |
-
|
| 565 |
-
@group()
|
| 566 |
-
def _render_syntax_error(self, syntax_error: _SyntaxError) -> RenderResult:
|
| 567 |
-
highlighter = ReprHighlighter()
|
| 568 |
-
path_highlighter = PathHighlighter()
|
| 569 |
-
if syntax_error.filename != "<stdin>":
|
| 570 |
-
if os.path.exists(syntax_error.filename):
|
| 571 |
-
text = Text.assemble(
|
| 572 |
-
(f" {syntax_error.filename}", "pygments.string"),
|
| 573 |
-
(":", "pygments.text"),
|
| 574 |
-
(str(syntax_error.lineno), "pygments.number"),
|
| 575 |
-
style="pygments.text",
|
| 576 |
-
)
|
| 577 |
-
yield path_highlighter(text)
|
| 578 |
-
syntax_error_text = highlighter(syntax_error.line.rstrip())
|
| 579 |
-
syntax_error_text.no_wrap = True
|
| 580 |
-
offset = min(syntax_error.offset - 1, len(syntax_error_text))
|
| 581 |
-
syntax_error_text.stylize("bold underline", offset, offset)
|
| 582 |
-
syntax_error_text += Text.from_markup(
|
| 583 |
-
"\n" + " " * offset + "[traceback.offset]▲[/]",
|
| 584 |
-
style="pygments.text",
|
| 585 |
-
)
|
| 586 |
-
yield syntax_error_text
|
| 587 |
-
|
| 588 |
-
@classmethod
|
| 589 |
-
def _guess_lexer(cls, filename: str, code: str) -> str:
|
| 590 |
-
ext = os.path.splitext(filename)[-1]
|
| 591 |
-
if not ext:
|
| 592 |
-
# No extension, look at first line to see if it is a hashbang
|
| 593 |
-
# Note, this is an educated guess and not a guarantee
|
| 594 |
-
# If it fails, the only downside is that the code is highlighted strangely
|
| 595 |
-
new_line_index = code.index("\n")
|
| 596 |
-
first_line = code[:new_line_index] if new_line_index != -1 else code
|
| 597 |
-
if first_line.startswith("#!") and "python" in first_line.lower():
|
| 598 |
-
return "python"
|
| 599 |
-
try:
|
| 600 |
-
return cls.LEXERS.get(ext) or guess_lexer_for_filename(filename, code).name
|
| 601 |
-
except ClassNotFound:
|
| 602 |
-
return "text"
|
| 603 |
-
|
| 604 |
-
@group()
|
| 605 |
-
def _render_stack(self, stack: Stack) -> RenderResult:
|
| 606 |
-
path_highlighter = PathHighlighter()
|
| 607 |
-
theme = self.theme
|
| 608 |
-
|
| 609 |
-
def read_code(filename: str) -> str:
|
| 610 |
-
"""Read files, and cache results on filename.
|
| 611 |
-
|
| 612 |
-
Args:
|
| 613 |
-
filename (str): Filename to read
|
| 614 |
-
|
| 615 |
-
Returns:
|
| 616 |
-
str: Contents of file
|
| 617 |
-
"""
|
| 618 |
-
return "".join(linecache.getlines(filename))
|
| 619 |
-
|
| 620 |
-
def render_locals(frame: Frame) -> Iterable[ConsoleRenderable]:
|
| 621 |
-
if frame.locals:
|
| 622 |
-
yield render_scope(
|
| 623 |
-
frame.locals,
|
| 624 |
-
title="locals",
|
| 625 |
-
indent_guides=self.indent_guides,
|
| 626 |
-
max_length=self.locals_max_length,
|
| 627 |
-
max_string=self.locals_max_string,
|
| 628 |
-
)
|
| 629 |
-
|
| 630 |
-
exclude_frames: Optional[range] = None
|
| 631 |
-
if self.max_frames != 0:
|
| 632 |
-
exclude_frames = range(
|
| 633 |
-
self.max_frames // 2,
|
| 634 |
-
len(stack.frames) - self.max_frames // 2,
|
| 635 |
-
)
|
| 636 |
-
|
| 637 |
-
excluded = False
|
| 638 |
-
for frame_index, frame in enumerate(stack.frames):
|
| 639 |
-
|
| 640 |
-
if exclude_frames and frame_index in exclude_frames:
|
| 641 |
-
excluded = True
|
| 642 |
-
continue
|
| 643 |
-
|
| 644 |
-
if excluded:
|
| 645 |
-
assert exclude_frames is not None
|
| 646 |
-
yield Text(
|
| 647 |
-
f"\n... {len(exclude_frames)} frames hidden ...",
|
| 648 |
-
justify="center",
|
| 649 |
-
style="traceback.error",
|
| 650 |
-
)
|
| 651 |
-
excluded = False
|
| 652 |
-
|
| 653 |
-
first = frame_index == 0
|
| 654 |
-
frame_filename = frame.filename
|
| 655 |
-
suppressed = any(frame_filename.startswith(path) for path in self.suppress)
|
| 656 |
-
|
| 657 |
-
if os.path.exists(frame.filename):
|
| 658 |
-
text = Text.assemble(
|
| 659 |
-
path_highlighter(Text(frame.filename, style="pygments.string")),
|
| 660 |
-
(":", "pygments.text"),
|
| 661 |
-
(str(frame.lineno), "pygments.number"),
|
| 662 |
-
" in ",
|
| 663 |
-
(frame.name, "pygments.function"),
|
| 664 |
-
style="pygments.text",
|
| 665 |
-
)
|
| 666 |
-
else:
|
| 667 |
-
text = Text.assemble(
|
| 668 |
-
"in ",
|
| 669 |
-
(frame.name, "pygments.function"),
|
| 670 |
-
(":", "pygments.text"),
|
| 671 |
-
(str(frame.lineno), "pygments.number"),
|
| 672 |
-
style="pygments.text",
|
| 673 |
-
)
|
| 674 |
-
if not frame.filename.startswith("<") and not first:
|
| 675 |
-
yield ""
|
| 676 |
-
yield text
|
| 677 |
-
if frame.filename.startswith("<"):
|
| 678 |
-
yield from render_locals(frame)
|
| 679 |
-
continue
|
| 680 |
-
if not suppressed:
|
| 681 |
-
try:
|
| 682 |
-
code = read_code(frame.filename)
|
| 683 |
-
if not code:
|
| 684 |
-
# code may be an empty string if the file doesn't exist, OR
|
| 685 |
-
# if the traceback filename is generated dynamically
|
| 686 |
-
continue
|
| 687 |
-
lexer_name = self._guess_lexer(frame.filename, code)
|
| 688 |
-
syntax = Syntax(
|
| 689 |
-
code,
|
| 690 |
-
lexer_name,
|
| 691 |
-
theme=theme,
|
| 692 |
-
line_numbers=True,
|
| 693 |
-
line_range=(
|
| 694 |
-
frame.lineno - self.extra_lines,
|
| 695 |
-
frame.lineno + self.extra_lines,
|
| 696 |
-
),
|
| 697 |
-
highlight_lines={frame.lineno},
|
| 698 |
-
word_wrap=self.word_wrap,
|
| 699 |
-
code_width=88,
|
| 700 |
-
indent_guides=self.indent_guides,
|
| 701 |
-
dedent=False,
|
| 702 |
-
)
|
| 703 |
-
yield ""
|
| 704 |
-
except Exception as error:
|
| 705 |
-
yield Text.assemble(
|
| 706 |
-
(f"\n{error}", "traceback.error"),
|
| 707 |
-
)
|
| 708 |
-
else:
|
| 709 |
-
yield (
|
| 710 |
-
Columns(
|
| 711 |
-
[
|
| 712 |
-
syntax,
|
| 713 |
-
*render_locals(frame),
|
| 714 |
-
],
|
| 715 |
-
padding=1,
|
| 716 |
-
)
|
| 717 |
-
if frame.locals
|
| 718 |
-
else syntax
|
| 719 |
-
)
|
| 720 |
-
|
| 721 |
-
|
| 722 |
-
if __name__ == "__main__": # pragma: no cover
|
| 723 |
-
|
| 724 |
-
from .console import Console
|
| 725 |
-
|
| 726 |
-
console = Console()
|
| 727 |
-
import sys
|
| 728 |
-
|
| 729 |
-
def bar(a: Any) -> None: # 这是对亚洲语言支持的测试。面对模棱两可的想法，拒绝猜测的诱惑
|
| 730 |
-
one = 1
|
| 731 |
-
print(one / a)
|
| 732 |
-
|
| 733 |
-
def foo(a: Any) -> None:
|
| 734 |
-
_rich_traceback_guard = True
|
| 735 |
-
zed = {
|
| 736 |
-
"characters": {
|
| 737 |
-
"Paul Atreides",
|
| 738 |
-
"Vladimir Harkonnen",
|
| 739 |
-
"Thufir Hawat",
|
| 740 |
-
"Duncan Idaho",
|
| 741 |
-
},
|
| 742 |
-
"atomic_types": (None, False, True),
|
| 743 |
-
}
|
| 744 |
-
bar(a)
|
| 745 |
-
|
| 746 |
-
def error() -> None:
|
| 747 |
-
|
| 748 |
-
try:
|
| 749 |
-
try:
|
| 750 |
-
foo(0)
|
| 751 |
-
except:
|
| 752 |
-
slfkjsldkfj # type: ignore[name-defined]
|
| 753 |
-
except:
|
| 754 |
-
console.print_exception(show_locals=True)
|
| 755 |
-
|
| 756 |
-
error()
|
|
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|
spaces/BlitzEsports/TextToImage/README.md
DELETED
|
@@ -1,10 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: DALL·E mini
|
| 3 |
-
metaTitle: "DALL·E mini by craiyon.com on Hugging Face"
|
| 4 |
-
emoji: 🥑
|
| 5 |
-
colorFrom: yellow
|
| 6 |
-
colorTo: green
|
| 7 |
-
sdk: static
|
| 8 |
-
pinned: True
|
| 9 |
-
license: apache-2.0
|
| 10 |
-
---
|
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|
spaces/BlitzKriegM/argilla/README.md
DELETED
|
@@ -1,19 +0,0 @@
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---
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title: Argilla Space Template
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emoji: 🏷️
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colorFrom: purple
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colorTo: red
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sdk: docker
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app_port: 6900
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fullWidth: true
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tags:
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- argilla
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duplicated_from: fka/awesome-chatgpt-prompts
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---
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This is the Argilla Space Template you can use to deploy and run your own instance of Argilla on the Hugging Face Hub, for labeling, fun, and active learning loops!
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Login with:
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user: argilla
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password: 1234
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spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/evaluation/rotated_coco_evaluation.py
DELETED
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@@ -1,203 +0,0 @@
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import itertools
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import json
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import numpy as np
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import os
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import torch
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from fvcore.common.file_io import PathManager
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from pycocotools.cocoeval import COCOeval, maskUtils
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from detectron2.structures import BoxMode, RotatedBoxes, pairwise_iou_rotated
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from .coco_evaluation import COCOEvaluator
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class RotatedCOCOeval(COCOeval):
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@staticmethod
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def is_rotated(box_list):
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if type(box_list) == np.ndarray:
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return box_list.shape[1] == 5
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elif type(box_list) == list:
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if box_list == []: # cannot decide the box_dim
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return False
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return np.all(
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np.array(
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[
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(len(obj) == 5) and ((type(obj) == list) or (type(obj) == np.ndarray))
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for obj in box_list
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]
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)
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)
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return False
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@staticmethod
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def boxlist_to_tensor(boxlist, output_box_dim):
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if type(boxlist) == np.ndarray:
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box_tensor = torch.from_numpy(boxlist)
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elif type(boxlist) == list:
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if boxlist == []:
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return torch.zeros((0, output_box_dim), dtype=torch.float32)
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else:
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box_tensor = torch.FloatTensor(boxlist)
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else:
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raise Exception("Unrecognized boxlist type")
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input_box_dim = box_tensor.shape[1]
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if input_box_dim != output_box_dim:
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if input_box_dim == 4 and output_box_dim == 5:
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box_tensor = BoxMode.convert(box_tensor, BoxMode.XYWH_ABS, BoxMode.XYWHA_ABS)
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else:
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raise Exception(
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"Unable to convert from {}-dim box to {}-dim box".format(
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input_box_dim, output_box_dim
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)
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)
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return box_tensor
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def compute_iou_dt_gt(self, dt, gt, is_crowd):
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if self.is_rotated(dt) or self.is_rotated(gt):
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# TODO: take is_crowd into consideration
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assert all(c == 0 for c in is_crowd)
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dt = RotatedBoxes(self.boxlist_to_tensor(dt, output_box_dim=5))
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gt = RotatedBoxes(self.boxlist_to_tensor(gt, output_box_dim=5))
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return pairwise_iou_rotated(dt, gt)
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else:
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# This is the same as the classical COCO evaluation
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return maskUtils.iou(dt, gt, is_crowd)
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def computeIoU(self, imgId, catId):
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p = self.params
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if p.useCats:
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gt = self._gts[imgId, catId]
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dt = self._dts[imgId, catId]
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else:
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gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]
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dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]
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if len(gt) == 0 and len(dt) == 0:
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return []
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inds = np.argsort([-d["score"] for d in dt], kind="mergesort")
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dt = [dt[i] for i in inds]
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if len(dt) > p.maxDets[-1]:
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dt = dt[0 : p.maxDets[-1]]
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assert p.iouType == "bbox", "unsupported iouType for iou computation"
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g = [g["bbox"] for g in gt]
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d = [d["bbox"] for d in dt]
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# compute iou between each dt and gt region
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iscrowd = [int(o["iscrowd"]) for o in gt]
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# Note: this function is copied from cocoeval.py in cocoapi
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# and the major difference is here.
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ious = self.compute_iou_dt_gt(d, g, iscrowd)
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return ious
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class RotatedCOCOEvaluator(COCOEvaluator):
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"""
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Evaluate object proposal/instance detection outputs using COCO-like metrics and APIs,
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with rotated boxes support.
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Note: this uses IOU only and does not consider angle differences.
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"""
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def process(self, inputs, outputs):
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"""
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Args:
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inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).
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It is a list of dict. Each dict corresponds to an image and
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contains keys like "height", "width", "file_name", "image_id".
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outputs: the outputs of a COCO model. It is a list of dicts with key
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"instances" that contains :class:`Instances`.
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"""
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for input, output in zip(inputs, outputs):
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prediction = {"image_id": input["image_id"]}
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if "instances" in output:
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instances = output["instances"].to(self._cpu_device)
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prediction["instances"] = self.instances_to_json(instances, input["image_id"])
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if "proposals" in output:
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prediction["proposals"] = output["proposals"].to(self._cpu_device)
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self._predictions.append(prediction)
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def instances_to_json(self, instances, img_id):
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num_instance = len(instances)
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if num_instance == 0:
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return []
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boxes = instances.pred_boxes.tensor.numpy()
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if boxes.shape[1] == 4:
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boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
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boxes = boxes.tolist()
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scores = instances.scores.tolist()
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classes = instances.pred_classes.tolist()
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results = []
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for k in range(num_instance):
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result = {
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"image_id": img_id,
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"category_id": classes[k],
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"bbox": boxes[k],
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"score": scores[k],
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}
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results.append(result)
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return results
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def _eval_predictions(self, tasks, predictions):
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"""
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Evaluate predictions on the given tasks.
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Fill self._results with the metrics of the tasks.
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"""
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self._logger.info("Preparing results for COCO format ...")
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coco_results = list(itertools.chain(*[x["instances"] for x in predictions]))
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# unmap the category ids for COCO
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if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
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reverse_id_mapping = {
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v: k for k, v in self._metadata.thing_dataset_id_to_contiguous_id.items()
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}
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for result in coco_results:
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result["category_id"] = reverse_id_mapping[result["category_id"]]
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if self._output_dir:
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file_path = os.path.join(self._output_dir, "coco_instances_results.json")
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self._logger.info("Saving results to {}".format(file_path))
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with PathManager.open(file_path, "w") as f:
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f.write(json.dumps(coco_results))
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f.flush()
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if not self._do_evaluation:
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self._logger.info("Annotations are not available for evaluation.")
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return
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self._logger.info("Evaluating predictions ...")
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for task in sorted(tasks):
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assert task == "bbox", "Task {} is not supported".format(task)
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coco_eval = (
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self._evaluate_predictions_on_coco(self._coco_api, coco_results)
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if len(coco_results) > 0
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else None # cocoapi does not handle empty results very well
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)
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res = self._derive_coco_results(
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coco_eval, task, class_names=self._metadata.get("thing_classes")
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)
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self._results[task] = res
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def _evaluate_predictions_on_coco(self, coco_gt, coco_results):
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"""
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Evaluate the coco results using COCOEval API.
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"""
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assert len(coco_results) > 0
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coco_dt = coco_gt.loadRes(coco_results)
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# Only bbox is supported for now
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coco_eval = RotatedCOCOeval(coco_gt, coco_dt, iouType="bbox")
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coco_eval.evaluate()
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coco_eval.accumulate()
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coco_eval.summarize()
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return coco_eval
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spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/datasets/gqa/gqa_loader.py
DELETED
|
@@ -1,277 +0,0 @@
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|
| 1 |
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# --------------------------------------------------------
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| 2 |
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# OpenVQA
|
| 3 |
-
# Written by Yuhao Cui https://github.com/cuiyuhao1996
|
| 4 |
-
# --------------------------------------------------------
|
| 5 |
-
|
| 6 |
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import numpy as np
|
| 7 |
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import glob, json, re, en_vectors_web_lg
|
| 8 |
-
from openvqa.core.base_dataset import BaseDataSet
|
| 9 |
-
from openvqa.utils.ans_punct import prep_ans
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
class DataSet(BaseDataSet):
|
| 13 |
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def __init__(self, __C):
|
| 14 |
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super(DataSet, self).__init__()
|
| 15 |
-
self.__C = __C
|
| 16 |
-
|
| 17 |
-
# --------------------------
|
| 18 |
-
# ---- Raw data loading ----
|
| 19 |
-
# --------------------------
|
| 20 |
-
|
| 21 |
-
ques_dict_preread = {
|
| 22 |
-
'train': json.load(open(__C.RAW_PATH[__C.DATASET]['train'], 'r')),
|
| 23 |
-
'val': json.load(open(__C.RAW_PATH[__C.DATASET]['val'], 'r')),
|
| 24 |
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'testdev': json.load(open(__C.RAW_PATH[__C.DATASET]['testdev'], 'r')),
|
| 25 |
-
'test': json.load(open(__C.RAW_PATH[__C.DATASET]['test'], 'r')),
|
| 26 |
-
}
|
| 27 |
-
|
| 28 |
-
# Loading all image paths
|
| 29 |
-
frcn_feat_path_list = glob.glob(__C.FEATS_PATH[__C.DATASET]['default-frcn'] + '/*.npz')
|
| 30 |
-
grid_feat_path_list = glob.glob(__C.FEATS_PATH[__C.DATASET]['default-grid'] + '/*.npz')
|
| 31 |
-
|
| 32 |
-
# Loading question word list
|
| 33 |
-
# stat_ques_dict = {
|
| 34 |
-
# **ques_dict_preread['train'],
|
| 35 |
-
# **ques_dict_preread['val'],
|
| 36 |
-
# **ques_dict_preread['testdev'],
|
| 37 |
-
# **ques_dict_preread['test'],
|
| 38 |
-
# }
|
| 39 |
-
|
| 40 |
-
# Loading answer word list
|
| 41 |
-
# stat_ans_dict = {
|
| 42 |
-
# **ques_dict_preread['train'],
|
| 43 |
-
# **ques_dict_preread['val'],
|
| 44 |
-
# **ques_dict_preread['testdev'],
|
| 45 |
-
# }
|
| 46 |
-
|
| 47 |
-
# Loading question and answer list
|
| 48 |
-
self.ques_dict = {}
|
| 49 |
-
split_list = __C.SPLIT[__C.RUN_MODE].split('+')
|
| 50 |
-
for split in split_list:
|
| 51 |
-
if split in ques_dict_preread:
|
| 52 |
-
self.ques_dict = {
|
| 53 |
-
**self.ques_dict,
|
| 54 |
-
**ques_dict_preread[split],
|
| 55 |
-
}
|
| 56 |
-
else:
|
| 57 |
-
self.ques_dict = {
|
| 58 |
-
**self.ques_dict,
|
| 59 |
-
**json.load(open(__C.RAW_PATH[__C.DATASET][split], 'r')),
|
| 60 |
-
}
|
| 61 |
-
|
| 62 |
-
# Define run data size
|
| 63 |
-
self.data_size = self.ques_dict.__len__()
|
| 64 |
-
print(' ========== Dataset size:', self.data_size)
|
| 65 |
-
|
| 66 |
-
|
| 67 |
-
# ------------------------
|
| 68 |
-
# ---- Data statistic ----
|
| 69 |
-
# ------------------------
|
| 70 |
-
|
| 71 |
-
# {image id} -> {image feature absolutely path}
|
| 72 |
-
self.iid_to_frcn_feat_path = self.img_feat_path_load(frcn_feat_path_list)
|
| 73 |
-
self.iid_to_grid_feat_path = self.img_feat_path_load(grid_feat_path_list)
|
| 74 |
-
|
| 75 |
-
# Loading dict: question dict -> question list
|
| 76 |
-
self.qid_list = list(self.ques_dict.keys())
|
| 77 |
-
|
| 78 |
-
# Tokenize
|
| 79 |
-
self.token_to_ix, self.pretrained_emb, max_token = self.tokenize('openvqa/datasets/gqa/dicts.json', __C.USE_GLOVE)
|
| 80 |
-
self.token_size = self.token_to_ix.__len__()
|
| 81 |
-
print(' ========== Question token vocab size:', self.token_size)
|
| 82 |
-
|
| 83 |
-
self.max_token = -1
|
| 84 |
-
if self.max_token == -1:
|
| 85 |
-
self.max_token = max_token
|
| 86 |
-
print('Max token length:', max_token, 'Trimmed to:', self.max_token)
|
| 87 |
-
|
| 88 |
-
# Answers statistic
|
| 89 |
-
self.ans_to_ix, self.ix_to_ans = self.ans_stat('openvqa/datasets/gqa/dicts.json')
|
| 90 |
-
self.ans_size = self.ans_to_ix.__len__()
|
| 91 |
-
print(' ========== Answer token vocab size:', self.ans_size)
|
| 92 |
-
print('Finished!')
|
| 93 |
-
print('')
|
| 94 |
-
|
| 95 |
-
|
| 96 |
-
|
| 97 |
-
def img_feat_path_load(self, path_list):
|
| 98 |
-
iid_to_path = {}
|
| 99 |
-
|
| 100 |
-
for ix, path in enumerate(path_list):
|
| 101 |
-
iid = path.split('/')[-1].split('.')[0]
|
| 102 |
-
iid_to_path[iid] = path
|
| 103 |
-
|
| 104 |
-
return iid_to_path
|
| 105 |
-
|
| 106 |
-
|
| 107 |
-
# def tokenize(self, stat_ques_dict, use_glove):
|
| 108 |
-
# token_to_ix = {
|
| 109 |
-
# 'PAD': 0,
|
| 110 |
-
# 'UNK': 1,
|
| 111 |
-
# 'CLS': 2,
|
| 112 |
-
# }
|
| 113 |
-
#
|
| 114 |
-
# spacy_tool = None
|
| 115 |
-
# pretrained_emb = []
|
| 116 |
-
# if use_glove:
|
| 117 |
-
# spacy_tool = en_vectors_web_lg.load()
|
| 118 |
-
# pretrained_emb.append(spacy_tool('PAD').vector)
|
| 119 |
-
# pretrained_emb.append(spacy_tool('UNK').vector)
|
| 120 |
-
# pretrained_emb.append(spacy_tool('CLS').vector)
|
| 121 |
-
#
|
| 122 |
-
# max_token = 0
|
| 123 |
-
# for qid in stat_ques_dict:
|
| 124 |
-
# ques = stat_ques_dict[qid]['question']
|
| 125 |
-
# words = re.sub(
|
| 126 |
-
# r"([.,'!?\"()*#:;])",
|
| 127 |
-
# '',
|
| 128 |
-
# ques.lower()
|
| 129 |
-
# ).replace('-', ' ').replace('/', ' ').split()
|
| 130 |
-
#
|
| 131 |
-
# if len(words) > max_token:
|
| 132 |
-
# max_token = len(words)
|
| 133 |
-
#
|
| 134 |
-
# for word in words:
|
| 135 |
-
# if word not in token_to_ix:
|
| 136 |
-
# token_to_ix[word] = len(token_to_ix)
|
| 137 |
-
# if use_glove:
|
| 138 |
-
# pretrained_emb.append(spacy_tool(word).vector)
|
| 139 |
-
#
|
| 140 |
-
# pretrained_emb = np.array(pretrained_emb)
|
| 141 |
-
#
|
| 142 |
-
# return token_to_ix, pretrained_emb, max_token
|
| 143 |
-
#
|
| 144 |
-
#
|
| 145 |
-
# def ans_stat(self, stat_ans_dict):
|
| 146 |
-
# ans_to_ix = {}
|
| 147 |
-
# ix_to_ans = {}
|
| 148 |
-
#
|
| 149 |
-
# for qid in stat_ans_dict:
|
| 150 |
-
# ans = stat_ans_dict[qid]['answer']
|
| 151 |
-
# ans = prep_ans(ans)
|
| 152 |
-
#
|
| 153 |
-
# if ans not in ans_to_ix:
|
| 154 |
-
# ix_to_ans[ans_to_ix.__len__()] = ans
|
| 155 |
-
# ans_to_ix[ans] = ans_to_ix.__len__()
|
| 156 |
-
#
|
| 157 |
-
# return ans_to_ix, ix_to_ans
|
| 158 |
-
|
| 159 |
-
|
| 160 |
-
def tokenize(self, json_file, use_glove):
|
| 161 |
-
token_to_ix, max_token = json.load(open(json_file, 'r'))[2:]
|
| 162 |
-
spacy_tool = None
|
| 163 |
-
if use_glove:
|
| 164 |
-
spacy_tool = en_vectors_web_lg.load()
|
| 165 |
-
|
| 166 |
-
pretrained_emb = []
|
| 167 |
-
for word in token_to_ix:
|
| 168 |
-
if use_glove:
|
| 169 |
-
pretrained_emb.append(spacy_tool(word).vector)
|
| 170 |
-
pretrained_emb = np.array(pretrained_emb)
|
| 171 |
-
|
| 172 |
-
return token_to_ix, pretrained_emb, max_token
|
| 173 |
-
|
| 174 |
-
|
| 175 |
-
def ans_stat(self, json_file):
|
| 176 |
-
ans_to_ix, ix_to_ans = json.load(open(json_file, 'r'))[:2]
|
| 177 |
-
|
| 178 |
-
return ans_to_ix, ix_to_ans
|
| 179 |
-
|
| 180 |
-
|
| 181 |
-
# ----------------------------------------------
|
| 182 |
-
# ---- Real-Time Processing Implementations ----
|
| 183 |
-
# ----------------------------------------------
|
| 184 |
-
|
| 185 |
-
def load_ques_ans(self, idx):
|
| 186 |
-
|
| 187 |
-
qid = self.qid_list[idx]
|
| 188 |
-
iid = self.ques_dict[qid]['imageId']
|
| 189 |
-
|
| 190 |
-
ques = self.ques_dict[qid]['question']
|
| 191 |
-
ques_ix_iter = self.proc_ques(ques, self.token_to_ix, max_token=self.max_token)
|
| 192 |
-
ans_iter = np.zeros(1)
|
| 193 |
-
|
| 194 |
-
if self.__C.RUN_MODE in ['train']:
|
| 195 |
-
# process answers
|
| 196 |
-
ans = self.ques_dict[qid]['answer']
|
| 197 |
-
ans_iter = self.proc_ans(ans, self.ans_to_ix)
|
| 198 |
-
|
| 199 |
-
return ques_ix_iter, ans_iter, iid
|
| 200 |
-
|
| 201 |
-
|
| 202 |
-
def load_img_feats(self, idx, iid):
|
| 203 |
-
frcn_feat = np.load(self.iid_to_frcn_feat_path[iid])
|
| 204 |
-
frcn_feat_iter = self.proc_img_feat(frcn_feat['x'], img_feat_pad_size=self.__C.FEAT_SIZE['gqa']['FRCN_FEAT_SIZE'][0])
|
| 205 |
-
|
| 206 |
-
grid_feat = np.load(self.iid_to_grid_feat_path[iid])
|
| 207 |
-
grid_feat_iter = grid_feat['x']
|
| 208 |
-
|
| 209 |
-
bbox_feat_iter = self.proc_img_feat(
|
| 210 |
-
self.proc_bbox_feat(
|
| 211 |
-
frcn_feat['bbox'],
|
| 212 |
-
(frcn_feat['height'], frcn_feat['width'])
|
| 213 |
-
),
|
| 214 |
-
img_feat_pad_size=self.__C.FEAT_SIZE['gqa']['BBOX_FEAT_SIZE'][0]
|
| 215 |
-
)
|
| 216 |
-
|
| 217 |
-
return frcn_feat_iter, grid_feat_iter, bbox_feat_iter
|
| 218 |
-
|
| 219 |
-
|
| 220 |
-
|
| 221 |
-
# ------------------------------------
|
| 222 |
-
# ---- Real-Time Processing Utils ----
|
| 223 |
-
# ------------------------------------
|
| 224 |
-
|
| 225 |
-
def proc_img_feat(self, img_feat, img_feat_pad_size):
|
| 226 |
-
if img_feat.shape[0] > img_feat_pad_size:
|
| 227 |
-
img_feat = img_feat[:img_feat_pad_size]
|
| 228 |
-
|
| 229 |
-
img_feat = np.pad(
|
| 230 |
-
img_feat,
|
| 231 |
-
((0, img_feat_pad_size - img_feat.shape[0]), (0, 0)),
|
| 232 |
-
mode='constant',
|
| 233 |
-
constant_values=0
|
| 234 |
-
)
|
| 235 |
-
|
| 236 |
-
return img_feat
|
| 237 |
-
|
| 238 |
-
|
| 239 |
-
def proc_bbox_feat(self, bbox, img_shape):
|
| 240 |
-
bbox_feat = np.zeros((bbox.shape[0], 5), dtype=np.float32)
|
| 241 |
-
|
| 242 |
-
bbox_feat[:, 0] = bbox[:, 0] / float(img_shape[1])
|
| 243 |
-
bbox_feat[:, 1] = bbox[:, 1] / float(img_shape[0])
|
| 244 |
-
bbox_feat[:, 2] = bbox[:, 2] / float(img_shape[1])
|
| 245 |
-
bbox_feat[:, 3] = bbox[:, 3] / float(img_shape[0])
|
| 246 |
-
bbox_feat[:, 4] = (bbox[:, 2] - bbox[:, 0]) * (bbox[:, 3] - bbox[:, 1]) / float(img_shape[0] * img_shape[1])
|
| 247 |
-
|
| 248 |
-
return bbox_feat
|
| 249 |
-
|
| 250 |
-
|
| 251 |
-
def proc_ques(self, ques, token_to_ix, max_token):
|
| 252 |
-
ques_ix = np.zeros(max_token, np.int64)
|
| 253 |
-
|
| 254 |
-
words = re.sub(
|
| 255 |
-
r"([.,'!?\"()*#:;])",
|
| 256 |
-
'',
|
| 257 |
-
ques.lower()
|
| 258 |
-
).replace('-', ' ').replace('/', ' ').split()
|
| 259 |
-
|
| 260 |
-
for ix, word in enumerate(words):
|
| 261 |
-
if word in token_to_ix:
|
| 262 |
-
ques_ix[ix] = token_to_ix[word]
|
| 263 |
-
else:
|
| 264 |
-
ques_ix[ix] = token_to_ix['UNK']
|
| 265 |
-
|
| 266 |
-
if ix + 1 == max_token:
|
| 267 |
-
break
|
| 268 |
-
|
| 269 |
-
return ques_ix
|
| 270 |
-
|
| 271 |
-
|
| 272 |
-
def proc_ans(self, ans, ans_to_ix):
|
| 273 |
-
ans_ix = np.zeros(1, np.int64)
|
| 274 |
-
ans = prep_ans(ans)
|
| 275 |
-
ans_ix[0] = ans_to_ix[ans]
|
| 276 |
-
|
| 277 |
-
return ans_ix
|
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|
spaces/CVPR/LIVE/pybind11/tests/test_numpy_dtypes.py
DELETED
|
@@ -1,312 +0,0 @@
|
|
| 1 |
-
# -*- coding: utf-8 -*-
|
| 2 |
-
import re
|
| 3 |
-
|
| 4 |
-
import pytest
|
| 5 |
-
|
| 6 |
-
import env # noqa: F401
|
| 7 |
-
|
| 8 |
-
from pybind11_tests import numpy_dtypes as m
|
| 9 |
-
|
| 10 |
-
np = pytest.importorskip("numpy")
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
@pytest.fixture(scope='module')
|
| 14 |
-
def simple_dtype():
|
| 15 |
-
ld = np.dtype('longdouble')
|
| 16 |
-
return np.dtype({'names': ['bool_', 'uint_', 'float_', 'ldbl_'],
|
| 17 |
-
'formats': ['?', 'u4', 'f4', 'f{}'.format(ld.itemsize)],
|
| 18 |
-
'offsets': [0, 4, 8, (16 if ld.alignment > 4 else 12)]})
|
| 19 |
-
|
| 20 |
-
|
| 21 |
-
@pytest.fixture(scope='module')
|
| 22 |
-
def packed_dtype():
|
| 23 |
-
return np.dtype([('bool_', '?'), ('uint_', 'u4'), ('float_', 'f4'), ('ldbl_', 'g')])
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
def dt_fmt():
|
| 27 |
-
from sys import byteorder
|
| 28 |
-
e = '<' if byteorder == 'little' else '>'
|
| 29 |
-
return ("{{'names':['bool_','uint_','float_','ldbl_'],"
|
| 30 |
-
" 'formats':['?','" + e + "u4','" + e + "f4','" + e + "f{}'],"
|
| 31 |
-
" 'offsets':[0,4,8,{}], 'itemsize':{}}}")
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
def simple_dtype_fmt():
|
| 35 |
-
ld = np.dtype('longdouble')
|
| 36 |
-
simple_ld_off = 12 + 4 * (ld.alignment > 4)
|
| 37 |
-
return dt_fmt().format(ld.itemsize, simple_ld_off, simple_ld_off + ld.itemsize)
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
def packed_dtype_fmt():
|
| 41 |
-
from sys import byteorder
|
| 42 |
-
return "[('bool_', '?'), ('uint_', '{e}u4'), ('float_', '{e}f4'), ('ldbl_', '{e}f{}')]".format(
|
| 43 |
-
np.dtype('longdouble').itemsize, e='<' if byteorder == 'little' else '>')
|
| 44 |
-
|
| 45 |
-
|
| 46 |
-
def partial_ld_offset():
|
| 47 |
-
return 12 + 4 * (np.dtype('uint64').alignment > 4) + 8 + 8 * (
|
| 48 |
-
np.dtype('longdouble').alignment > 8)
|
| 49 |
-
|
| 50 |
-
|
| 51 |
-
def partial_dtype_fmt():
|
| 52 |
-
ld = np.dtype('longdouble')
|
| 53 |
-
partial_ld_off = partial_ld_offset()
|
| 54 |
-
return dt_fmt().format(ld.itemsize, partial_ld_off, partial_ld_off + ld.itemsize)
|
| 55 |
-
|
| 56 |
-
|
| 57 |
-
def partial_nested_fmt():
|
| 58 |
-
ld = np.dtype('longdouble')
|
| 59 |
-
partial_nested_off = 8 + 8 * (ld.alignment > 8)
|
| 60 |
-
partial_ld_off = partial_ld_offset()
|
| 61 |
-
partial_nested_size = partial_nested_off * 2 + partial_ld_off + ld.itemsize
|
| 62 |
-
return "{{'names':['a'], 'formats':[{}], 'offsets':[{}], 'itemsize':{}}}".format(
|
| 63 |
-
partial_dtype_fmt(), partial_nested_off, partial_nested_size)
|
| 64 |
-
|
| 65 |
-
|
| 66 |
-
def assert_equal(actual, expected_data, expected_dtype):
|
| 67 |
-
np.testing.assert_equal(actual, np.array(expected_data, dtype=expected_dtype))
|
| 68 |
-
|
| 69 |
-
|
| 70 |
-
def test_format_descriptors():
|
| 71 |
-
with pytest.raises(RuntimeError) as excinfo:
|
| 72 |
-
m.get_format_unbound()
|
| 73 |
-
assert re.match('^NumPy type info missing for .*UnboundStruct.*$', str(excinfo.value))
|
| 74 |
-
|
| 75 |
-
ld = np.dtype('longdouble')
|
| 76 |
-
ldbl_fmt = ('4x' if ld.alignment > 4 else '') + ld.char
|
| 77 |
-
ss_fmt = "^T{?:bool_:3xI:uint_:f:float_:" + ldbl_fmt + ":ldbl_:}"
|
| 78 |
-
dbl = np.dtype('double')
|
| 79 |
-
partial_fmt = ("^T{?:bool_:3xI:uint_:f:float_:" +
|
| 80 |
-
str(4 * (dbl.alignment > 4) + dbl.itemsize + 8 * (ld.alignment > 8)) +
|
| 81 |
-
"xg:ldbl_:}")
|
| 82 |
-
nested_extra = str(max(8, ld.alignment))
|
| 83 |
-
assert m.print_format_descriptors() == [
|
| 84 |
-
ss_fmt,
|
| 85 |
-
"^T{?:bool_:I:uint_:f:float_:g:ldbl_:}",
|
| 86 |
-
"^T{" + ss_fmt + ":a:^T{?:bool_:I:uint_:f:float_:g:ldbl_:}:b:}",
|
| 87 |
-
partial_fmt,
|
| 88 |
-
"^T{" + nested_extra + "x" + partial_fmt + ":a:" + nested_extra + "x}",
|
| 89 |
-
"^T{3s:a:3s:b:}",
|
| 90 |
-
"^T{(3)4s:a:(2)i:b:(3)B:c:1x(4, 2)f:d:}",
|
| 91 |
-
'^T{q:e1:B:e2:}',
|
| 92 |
-
'^T{Zf:cflt:Zd:cdbl:}'
|
| 93 |
-
]
|
| 94 |
-
|
| 95 |
-
|
| 96 |
-
def test_dtype(simple_dtype):
|
| 97 |
-
from sys import byteorder
|
| 98 |
-
e = '<' if byteorder == 'little' else '>'
|
| 99 |
-
|
| 100 |
-
assert m.print_dtypes() == [
|
| 101 |
-
simple_dtype_fmt(),
|
| 102 |
-
packed_dtype_fmt(),
|
| 103 |
-
"[('a', {}), ('b', {})]".format(simple_dtype_fmt(), packed_dtype_fmt()),
|
| 104 |
-
partial_dtype_fmt(),
|
| 105 |
-
partial_nested_fmt(),
|
| 106 |
-
"[('a', 'S3'), ('b', 'S3')]",
|
| 107 |
-
("{{'names':['a','b','c','d'], " +
|
| 108 |
-
"'formats':[('S4', (3,)),('" + e + "i4', (2,)),('u1', (3,)),('" + e + "f4', (4, 2))], " +
|
| 109 |
-
"'offsets':[0,12,20,24], 'itemsize':56}}").format(e=e),
|
| 110 |
-
"[('e1', '" + e + "i8'), ('e2', 'u1')]",
|
| 111 |
-
"[('x', 'i1'), ('y', '" + e + "u8')]",
|
| 112 |
-
"[('cflt', '" + e + "c8'), ('cdbl', '" + e + "c16')]"
|
| 113 |
-
]
|
| 114 |
-
|
| 115 |
-
d1 = np.dtype({'names': ['a', 'b'], 'formats': ['int32', 'float64'],
|
| 116 |
-
'offsets': [1, 10], 'itemsize': 20})
|
| 117 |
-
d2 = np.dtype([('a', 'i4'), ('b', 'f4')])
|
| 118 |
-
assert m.test_dtype_ctors() == [np.dtype('int32'), np.dtype('float64'),
|
| 119 |
-
np.dtype('bool'), d1, d1, np.dtype('uint32'), d2]
|
| 120 |
-
|
| 121 |
-
assert m.test_dtype_methods() == [np.dtype('int32'), simple_dtype, False, True,
|
| 122 |
-
np.dtype('int32').itemsize, simple_dtype.itemsize]
|
| 123 |
-
|
| 124 |
-
assert m.trailing_padding_dtype() == m.buffer_to_dtype(np.zeros(1, m.trailing_padding_dtype()))
|
| 125 |
-
|
| 126 |
-
|
| 127 |
-
def test_recarray(simple_dtype, packed_dtype):
|
| 128 |
-
elements = [(False, 0, 0.0, -0.0), (True, 1, 1.5, -2.5), (False, 2, 3.0, -5.0)]
|
| 129 |
-
|
| 130 |
-
for func, dtype in [(m.create_rec_simple, simple_dtype), (m.create_rec_packed, packed_dtype)]:
|
| 131 |
-
arr = func(0)
|
| 132 |
-
assert arr.dtype == dtype
|
| 133 |
-
assert_equal(arr, [], simple_dtype)
|
| 134 |
-
assert_equal(arr, [], packed_dtype)
|
| 135 |
-
|
| 136 |
-
arr = func(3)
|
| 137 |
-
assert arr.dtype == dtype
|
| 138 |
-
assert_equal(arr, elements, simple_dtype)
|
| 139 |
-
assert_equal(arr, elements, packed_dtype)
|
| 140 |
-
|
| 141 |
-
if dtype == simple_dtype:
|
| 142 |
-
assert m.print_rec_simple(arr) == [
|
| 143 |
-
"s:0,0,0,-0",
|
| 144 |
-
"s:1,1,1.5,-2.5",
|
| 145 |
-
"s:0,2,3,-5"
|
| 146 |
-
]
|
| 147 |
-
else:
|
| 148 |
-
assert m.print_rec_packed(arr) == [
|
| 149 |
-
"p:0,0,0,-0",
|
| 150 |
-
"p:1,1,1.5,-2.5",
|
| 151 |
-
"p:0,2,3,-5"
|
| 152 |
-
]
|
| 153 |
-
|
| 154 |
-
nested_dtype = np.dtype([('a', simple_dtype), ('b', packed_dtype)])
|
| 155 |
-
|
| 156 |
-
arr = m.create_rec_nested(0)
|
| 157 |
-
assert arr.dtype == nested_dtype
|
| 158 |
-
assert_equal(arr, [], nested_dtype)
|
| 159 |
-
|
| 160 |
-
arr = m.create_rec_nested(3)
|
| 161 |
-
assert arr.dtype == nested_dtype
|
| 162 |
-
assert_equal(arr, [((False, 0, 0.0, -0.0), (True, 1, 1.5, -2.5)),
|
| 163 |
-
((True, 1, 1.5, -2.5), (False, 2, 3.0, -5.0)),
|
| 164 |
-
((False, 2, 3.0, -5.0), (True, 3, 4.5, -7.5))], nested_dtype)
|
| 165 |
-
assert m.print_rec_nested(arr) == [
|
| 166 |
-
"n:a=s:0,0,0,-0;b=p:1,1,1.5,-2.5",
|
| 167 |
-
"n:a=s:1,1,1.5,-2.5;b=p:0,2,3,-5",
|
| 168 |
-
"n:a=s:0,2,3,-5;b=p:1,3,4.5,-7.5"
|
| 169 |
-
]
|
| 170 |
-
|
| 171 |
-
arr = m.create_rec_partial(3)
|
| 172 |
-
assert str(arr.dtype) == partial_dtype_fmt()
|
| 173 |
-
partial_dtype = arr.dtype
|
| 174 |
-
assert '' not in arr.dtype.fields
|
| 175 |
-
assert partial_dtype.itemsize > simple_dtype.itemsize
|
| 176 |
-
assert_equal(arr, elements, simple_dtype)
|
| 177 |
-
assert_equal(arr, elements, packed_dtype)
|
| 178 |
-
|
| 179 |
-
arr = m.create_rec_partial_nested(3)
|
| 180 |
-
assert str(arr.dtype) == partial_nested_fmt()
|
| 181 |
-
assert '' not in arr.dtype.fields
|
| 182 |
-
assert '' not in arr.dtype.fields['a'][0].fields
|
| 183 |
-
assert arr.dtype.itemsize > partial_dtype.itemsize
|
| 184 |
-
np.testing.assert_equal(arr['a'], m.create_rec_partial(3))
|
| 185 |
-
|
| 186 |
-
|
| 187 |
-
def test_array_constructors():
|
| 188 |
-
data = np.arange(1, 7, dtype='int32')
|
| 189 |
-
for i in range(8):
|
| 190 |
-
np.testing.assert_array_equal(m.test_array_ctors(10 + i), data.reshape((3, 2)))
|
| 191 |
-
np.testing.assert_array_equal(m.test_array_ctors(20 + i), data.reshape((3, 2)))
|
| 192 |
-
for i in range(5):
|
| 193 |
-
np.testing.assert_array_equal(m.test_array_ctors(30 + i), data)
|
| 194 |
-
np.testing.assert_array_equal(m.test_array_ctors(40 + i), data)
|
| 195 |
-
|
| 196 |
-
|
| 197 |
-
def test_string_array():
|
| 198 |
-
arr = m.create_string_array(True)
|
| 199 |
-
assert str(arr.dtype) == "[('a', 'S3'), ('b', 'S3')]"
|
| 200 |
-
assert m.print_string_array(arr) == [
|
| 201 |
-
"a='',b=''",
|
| 202 |
-
"a='a',b='a'",
|
| 203 |
-
"a='ab',b='ab'",
|
| 204 |
-
"a='abc',b='abc'"
|
| 205 |
-
]
|
| 206 |
-
dtype = arr.dtype
|
| 207 |
-
assert arr['a'].tolist() == [b'', b'a', b'ab', b'abc']
|
| 208 |
-
assert arr['b'].tolist() == [b'', b'a', b'ab', b'abc']
|
| 209 |
-
arr = m.create_string_array(False)
|
| 210 |
-
assert dtype == arr.dtype
|
| 211 |
-
|
| 212 |
-
|
| 213 |
-
def test_array_array():
|
| 214 |
-
from sys import byteorder
|
| 215 |
-
e = '<' if byteorder == 'little' else '>'
|
| 216 |
-
|
| 217 |
-
arr = m.create_array_array(3)
|
| 218 |
-
assert str(arr.dtype) == (
|
| 219 |
-
"{{'names':['a','b','c','d'], " +
|
| 220 |
-
"'formats':[('S4', (3,)),('" + e + "i4', (2,)),('u1', (3,)),('{e}f4', (4, 2))], " +
|
| 221 |
-
"'offsets':[0,12,20,24], 'itemsize':56}}").format(e=e)
|
| 222 |
-
assert m.print_array_array(arr) == [
|
| 223 |
-
"a={{A,B,C,D},{K,L,M,N},{U,V,W,X}},b={0,1}," +
|
| 224 |
-
"c={0,1,2},d={{0,1},{10,11},{20,21},{30,31}}",
|
| 225 |
-
"a={{W,X,Y,Z},{G,H,I,J},{Q,R,S,T}},b={1000,1001}," +
|
| 226 |
-
"c={10,11,12},d={{100,101},{110,111},{120,121},{130,131}}",
|
| 227 |
-
"a={{S,T,U,V},{C,D,E,F},{M,N,O,P}},b={2000,2001}," +
|
| 228 |
-
"c={20,21,22},d={{200,201},{210,211},{220,221},{230,231}}",
|
| 229 |
-
]
|
| 230 |
-
assert arr['a'].tolist() == [[b'ABCD', b'KLMN', b'UVWX'],
|
| 231 |
-
[b'WXYZ', b'GHIJ', b'QRST'],
|
| 232 |
-
[b'STUV', b'CDEF', b'MNOP']]
|
| 233 |
-
assert arr['b'].tolist() == [[0, 1], [1000, 1001], [2000, 2001]]
|
| 234 |
-
assert m.create_array_array(0).dtype == arr.dtype
|
| 235 |
-
|
| 236 |
-
|
| 237 |
-
def test_enum_array():
|
| 238 |
-
from sys import byteorder
|
| 239 |
-
e = '<' if byteorder == 'little' else '>'
|
| 240 |
-
|
| 241 |
-
arr = m.create_enum_array(3)
|
| 242 |
-
dtype = arr.dtype
|
| 243 |
-
assert dtype == np.dtype([('e1', e + 'i8'), ('e2', 'u1')])
|
| 244 |
-
assert m.print_enum_array(arr) == [
|
| 245 |
-
"e1=A,e2=X",
|
| 246 |
-
"e1=B,e2=Y",
|
| 247 |
-
"e1=A,e2=X"
|
| 248 |
-
]
|
| 249 |
-
assert arr['e1'].tolist() == [-1, 1, -1]
|
| 250 |
-
assert arr['e2'].tolist() == [1, 2, 1]
|
| 251 |
-
assert m.create_enum_array(0).dtype == dtype
|
| 252 |
-
|
| 253 |
-
|
| 254 |
-
def test_complex_array():
|
| 255 |
-
from sys import byteorder
|
| 256 |
-
e = '<' if byteorder == 'little' else '>'
|
| 257 |
-
|
| 258 |
-
arr = m.create_complex_array(3)
|
| 259 |
-
dtype = arr.dtype
|
| 260 |
-
assert dtype == np.dtype([('cflt', e + 'c8'), ('cdbl', e + 'c16')])
|
| 261 |
-
assert m.print_complex_array(arr) == [
|
| 262 |
-
"c:(0,0.25),(0.5,0.75)",
|
| 263 |
-
"c:(1,1.25),(1.5,1.75)",
|
| 264 |
-
"c:(2,2.25),(2.5,2.75)"
|
| 265 |
-
]
|
| 266 |
-
assert arr['cflt'].tolist() == [0.0 + 0.25j, 1.0 + 1.25j, 2.0 + 2.25j]
|
| 267 |
-
assert arr['cdbl'].tolist() == [0.5 + 0.75j, 1.5 + 1.75j, 2.5 + 2.75j]
|
| 268 |
-
assert m.create_complex_array(0).dtype == dtype
|
| 269 |
-
|
| 270 |
-
|
| 271 |
-
def test_signature(doc):
|
| 272 |
-
assert doc(m.create_rec_nested) == \
|
| 273 |
-
"create_rec_nested(arg0: int) -> numpy.ndarray[NestedStruct]"
|
| 274 |
-
|
| 275 |
-
|
| 276 |
-
def test_scalar_conversion():
|
| 277 |
-
n = 3
|
| 278 |
-
arrays = [m.create_rec_simple(n), m.create_rec_packed(n),
|
| 279 |
-
m.create_rec_nested(n), m.create_enum_array(n)]
|
| 280 |
-
funcs = [m.f_simple, m.f_packed, m.f_nested]
|
| 281 |
-
|
| 282 |
-
for i, func in enumerate(funcs):
|
| 283 |
-
for j, arr in enumerate(arrays):
|
| 284 |
-
if i == j and i < 2:
|
| 285 |
-
assert [func(arr[k]) for k in range(n)] == [k * 10 for k in range(n)]
|
| 286 |
-
else:
|
| 287 |
-
with pytest.raises(TypeError) as excinfo:
|
| 288 |
-
func(arr[0])
|
| 289 |
-
assert 'incompatible function arguments' in str(excinfo.value)
|
| 290 |
-
|
| 291 |
-
|
| 292 |
-
def test_register_dtype():
|
| 293 |
-
with pytest.raises(RuntimeError) as excinfo:
|
| 294 |
-
m.register_dtype()
|
| 295 |
-
assert 'dtype is already registered' in str(excinfo.value)
|
| 296 |
-
|
| 297 |
-
|
| 298 |
-
@pytest.mark.xfail("env.PYPY")
|
| 299 |
-
def test_str_leak():
|
| 300 |
-
from sys import getrefcount
|
| 301 |
-
fmt = "f4"
|
| 302 |
-
pytest.gc_collect()
|
| 303 |
-
start = getrefcount(fmt)
|
| 304 |
-
d = m.dtype_wrapper(fmt)
|
| 305 |
-
assert d is np.dtype("f4")
|
| 306 |
-
del d
|
| 307 |
-
pytest.gc_collect()
|
| 308 |
-
assert getrefcount(fmt) == start
|
| 309 |
-
|
| 310 |
-
|
| 311 |
-
def test_compare_buffer_info():
|
| 312 |
-
assert all(m.compare_buffer_info())
|
|
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|
spaces/CVPR/LIVE/thrust/thrust/cmake/FindTBB.cmake
DELETED
|
@@ -1,440 +0,0 @@
|
|
| 1 |
-
# - Find ThreadingBuildingBlocks include dirs and libraries
|
| 2 |
-
# Use this module by invoking find_package with the form:
|
| 3 |
-
# find_package(TBB
|
| 4 |
-
# [REQUIRED] # Fail with error if TBB is not found
|
| 5 |
-
# ) #
|
| 6 |
-
# Once done, this will define
|
| 7 |
-
#
|
| 8 |
-
# TBB_FOUND - system has TBB
|
| 9 |
-
# TBB_INCLUDE_DIRS - the TBB include directories
|
| 10 |
-
# TBB_LIBRARIES - TBB libraries to be lined, doesn't include malloc or
|
| 11 |
-
# malloc proxy
|
| 12 |
-
# TBB::tbb - imported target for the TBB library
|
| 13 |
-
#
|
| 14 |
-
# TBB_VERSION - Product Version Number ("MAJOR.MINOR")
|
| 15 |
-
# TBB_VERSION_MAJOR - Major Product Version Number
|
| 16 |
-
# TBB_VERSION_MINOR - Minor Product Version Number
|
| 17 |
-
# TBB_INTERFACE_VERSION - Engineering Focused Version Number
|
| 18 |
-
# TBB_COMPATIBLE_INTERFACE_VERSION - The oldest major interface version
|
| 19 |
-
# still supported. This uses the engineering
|
| 20 |
-
# focused interface version numbers.
|
| 21 |
-
#
|
| 22 |
-
# TBB_MALLOC_FOUND - system has TBB malloc library
|
| 23 |
-
# TBB_MALLOC_INCLUDE_DIRS - the TBB malloc include directories
|
| 24 |
-
# TBB_MALLOC_LIBRARIES - The TBB malloc libraries to be lined
|
| 25 |
-
# TBB::malloc - imported target for the TBB malloc library
|
| 26 |
-
#
|
| 27 |
-
# TBB_MALLOC_PROXY_FOUND - system has TBB malloc proxy library
|
| 28 |
-
# TBB_MALLOC_PROXY_INCLUDE_DIRS = the TBB malloc proxy include directories
|
| 29 |
-
# TBB_MALLOC_PROXY_LIBRARIES - The TBB malloc proxy libraries to be lined
|
| 30 |
-
# TBB::malloc_proxy - imported target for the TBB malloc proxy library
|
| 31 |
-
#
|
| 32 |
-
#
|
| 33 |
-
# This module reads hints about search locations from variables:
|
| 34 |
-
# ENV TBB_ARCH_PLATFORM - for eg. set it to "mic" for Xeon Phi builds
|
| 35 |
-
# ENV TBB_ROOT or just TBB_ROOT - root directory of tbb installation
|
| 36 |
-
# ENV TBB_BUILD_PREFIX - specifies the build prefix for user built tbb
|
| 37 |
-
# libraries. Should be specified with ENV TBB_ROOT
|
| 38 |
-
# and optionally...
|
| 39 |
-
# ENV TBB_BUILD_DIR - if build directory is different than ${TBB_ROOT}/build
|
| 40 |
-
#
|
| 41 |
-
#
|
| 42 |
-
# Modified by Robert Maynard from the original OGRE source
|
| 43 |
-
#
|
| 44 |
-
#-------------------------------------------------------------------
|
| 45 |
-
# This file is part of the CMake build system for OGRE
|
| 46 |
-
# (Object-oriented Graphics Rendering Engine)
|
| 47 |
-
# For the latest info, see http://www.ogre3d.org/
|
| 48 |
-
#
|
| 49 |
-
# The contents of this file are placed in the public domain. Feel
|
| 50 |
-
# free to make use of it in any way you like.
|
| 51 |
-
#-------------------------------------------------------------------
|
| 52 |
-
#
|
| 53 |
-
#=============================================================================
|
| 54 |
-
# Copyright 2010-2012 Kitware, Inc.
|
| 55 |
-
# Copyright 2012 Rolf Eike Beer <[email protected]>
|
| 56 |
-
#
|
| 57 |
-
# Distributed under the OSI-approved BSD License (the "License");
|
| 58 |
-
# see accompanying file Copyright.txt for details.
|
| 59 |
-
#
|
| 60 |
-
# This software is distributed WITHOUT ANY WARRANTY; without even the
|
| 61 |
-
# implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
|
| 62 |
-
# See the License for more information.
|
| 63 |
-
#=============================================================================
|
| 64 |
-
# (To distribute this file outside of CMake, substitute the full
|
| 65 |
-
# License text for the above reference.)
|
| 66 |
-
|
| 67 |
-
|
| 68 |
-
#=============================================================================
|
| 69 |
-
# FindTBB helper functions and macros
|
| 70 |
-
#
|
| 71 |
-
|
| 72 |
-
#====================================================
|
| 73 |
-
# Fix the library path in case it is a linker script
|
| 74 |
-
#====================================================
|
| 75 |
-
function(tbb_extract_real_library library real_library)
|
| 76 |
-
if(NOT UNIX OR NOT EXISTS ${library})
|
| 77 |
-
set(${real_library} "${library}" PARENT_SCOPE)
|
| 78 |
-
return()
|
| 79 |
-
endif()
|
| 80 |
-
|
| 81 |
-
#Read in the first 4 bytes and see if they are the ELF magic number
|
| 82 |
-
set(_elf_magic "7f454c46")
|
| 83 |
-
file(READ ${library} _hex_data OFFSET 0 LIMIT 4 HEX)
|
| 84 |
-
if(_hex_data STREQUAL _elf_magic)
|
| 85 |
-
#we have opened a elf binary so this is what
|
| 86 |
-
#we should link to
|
| 87 |
-
set(${real_library} "${library}" PARENT_SCOPE)
|
| 88 |
-
return()
|
| 89 |
-
endif()
|
| 90 |
-
|
| 91 |
-
file(READ ${library} _data OFFSET 0 LIMIT 1024)
|
| 92 |
-
if("${_data}" MATCHES "INPUT \\(([^(]+)\\)")
|
| 93 |
-
#extract out the .so name from REGEX MATCH command
|
| 94 |
-
set(_proper_so_name "${CMAKE_MATCH_1}")
|
| 95 |
-
|
| 96 |
-
#construct path to the real .so which is presumed to be in the same directory
|
| 97 |
-
#as the input file
|
| 98 |
-
get_filename_component(_so_dir "${library}" DIRECTORY)
|
| 99 |
-
set(${real_library} "${_so_dir}/${_proper_so_name}" PARENT_SCOPE)
|
| 100 |
-
else()
|
| 101 |
-
#unable to determine what this library is so just hope everything works
|
| 102 |
-
#and pass it unmodified.
|
| 103 |
-
set(${real_library} "${library}" PARENT_SCOPE)
|
| 104 |
-
endif()
|
| 105 |
-
endfunction()
|
| 106 |
-
|
| 107 |
-
#===============================================
|
| 108 |
-
# Do the final processing for the package find.
|
| 109 |
-
#===============================================
|
| 110 |
-
macro(findpkg_finish PREFIX TARGET_NAME)
|
| 111 |
-
if (${PREFIX}_INCLUDE_DIR AND ${PREFIX}_LIBRARY)
|
| 112 |
-
set(${PREFIX}_FOUND TRUE)
|
| 113 |
-
set (${PREFIX}_INCLUDE_DIRS ${${PREFIX}_INCLUDE_DIR})
|
| 114 |
-
set (${PREFIX}_LIBRARIES ${${PREFIX}_LIBRARY})
|
| 115 |
-
else ()
|
| 116 |
-
if (${PREFIX}_FIND_REQUIRED)
|
| 117 |
-
message(FATAL_ERROR "Required library ${PREFIX} not found.")
|
| 118 |
-
elseif (NOT ${PREFIX}_FIND_QUIETLY)
|
| 119 |
-
message("Library ${PREFIX} not found.")
|
| 120 |
-
endif()
|
| 121 |
-
return()
|
| 122 |
-
endif ()
|
| 123 |
-
|
| 124 |
-
if (NOT TARGET "TBB::${TARGET_NAME}")
|
| 125 |
-
if (${PREFIX}_LIBRARY_RELEASE)
|
| 126 |
-
tbb_extract_real_library(${${PREFIX}_LIBRARY_RELEASE} real_release)
|
| 127 |
-
endif ()
|
| 128 |
-
if (${PREFIX}_LIBRARY_DEBUG)
|
| 129 |
-
tbb_extract_real_library(${${PREFIX}_LIBRARY_DEBUG} real_debug)
|
| 130 |
-
endif ()
|
| 131 |
-
add_library(TBB::${TARGET_NAME} UNKNOWN IMPORTED)
|
| 132 |
-
set_target_properties(TBB::${TARGET_NAME} PROPERTIES
|
| 133 |
-
INTERFACE_INCLUDE_DIRECTORIES "${${PREFIX}_INCLUDE_DIR}")
|
| 134 |
-
if (${PREFIX}_LIBRARY_DEBUG AND ${PREFIX}_LIBRARY_RELEASE)
|
| 135 |
-
set_target_properties(TBB::${TARGET_NAME} PROPERTIES
|
| 136 |
-
IMPORTED_LOCATION "${real_release}"
|
| 137 |
-
IMPORTED_LOCATION_DEBUG "${real_debug}"
|
| 138 |
-
IMPORTED_LOCATION_RELEASE "${real_release}")
|
| 139 |
-
elseif (${PREFIX}_LIBRARY_RELEASE)
|
| 140 |
-
set_target_properties(TBB::${TARGET_NAME} PROPERTIES
|
| 141 |
-
IMPORTED_LOCATION "${real_release}")
|
| 142 |
-
elseif (${PREFIX}_LIBRARY_DEBUG)
|
| 143 |
-
set_target_properties(TBB::${TARGET_NAME} PROPERTIES
|
| 144 |
-
IMPORTED_LOCATION "${real_debug}")
|
| 145 |
-
endif ()
|
| 146 |
-
endif ()
|
| 147 |
-
|
| 148 |
-
#mark the following variables as internal variables
|
| 149 |
-
mark_as_advanced(${PREFIX}_INCLUDE_DIR
|
| 150 |
-
${PREFIX}_LIBRARY
|
| 151 |
-
${PREFIX}_LIBRARY_DEBUG
|
| 152 |
-
${PREFIX}_LIBRARY_RELEASE)
|
| 153 |
-
endmacro()
|
| 154 |
-
|
| 155 |
-
#===============================================
|
| 156 |
-
# Generate debug names from given release names
|
| 157 |
-
#===============================================
|
| 158 |
-
macro(get_debug_names PREFIX)
|
| 159 |
-
foreach(i ${${PREFIX}})
|
| 160 |
-
set(${PREFIX}_DEBUG ${${PREFIX}_DEBUG} ${i}d ${i}D ${i}_d ${i}_D ${i}_debug ${i})
|
| 161 |
-
endforeach()
|
| 162 |
-
endmacro()
|
| 163 |
-
|
| 164 |
-
#===============================================
|
| 165 |
-
# See if we have env vars to help us find tbb
|
| 166 |
-
#===============================================
|
| 167 |
-
macro(getenv_path VAR)
|
| 168 |
-
set(ENV_${VAR} $ENV{${VAR}})
|
| 169 |
-
# replace won't work if var is blank
|
| 170 |
-
if (ENV_${VAR})
|
| 171 |
-
string( REGEX REPLACE "\\\\" "/" ENV_${VAR} ${ENV_${VAR}} )
|
| 172 |
-
endif ()
|
| 173 |
-
endmacro()
|
| 174 |
-
|
| 175 |
-
#===============================================
|
| 176 |
-
# Couple a set of release AND debug libraries
|
| 177 |
-
#===============================================
|
| 178 |
-
macro(make_library_set PREFIX)
|
| 179 |
-
if (${PREFIX}_RELEASE AND ${PREFIX}_DEBUG)
|
| 180 |
-
set(${PREFIX} optimized ${${PREFIX}_RELEASE} debug ${${PREFIX}_DEBUG})
|
| 181 |
-
elseif (${PREFIX}_RELEASE)
|
| 182 |
-
set(${PREFIX} ${${PREFIX}_RELEASE})
|
| 183 |
-
elseif (${PREFIX}_DEBUG)
|
| 184 |
-
set(${PREFIX} ${${PREFIX}_DEBUG})
|
| 185 |
-
endif ()
|
| 186 |
-
endmacro()
|
| 187 |
-
|
| 188 |
-
|
| 189 |
-
#=============================================================================
|
| 190 |
-
# Now to actually find TBB
|
| 191 |
-
#
|
| 192 |
-
|
| 193 |
-
# Get path, convert backslashes as ${ENV_${var}}
|
| 194 |
-
getenv_path(TBB_ROOT)
|
| 195 |
-
|
| 196 |
-
# initialize search paths
|
| 197 |
-
set(TBB_PREFIX_PATH ${TBB_ROOT} ${ENV_TBB_ROOT})
|
| 198 |
-
set(TBB_INC_SEARCH_PATH "")
|
| 199 |
-
set(TBB_LIB_SEARCH_PATH "")
|
| 200 |
-
|
| 201 |
-
|
| 202 |
-
# If user built from sources
|
| 203 |
-
set(TBB_BUILD_PREFIX $ENV{TBB_BUILD_PREFIX})
|
| 204 |
-
if (TBB_BUILD_PREFIX AND ENV_TBB_ROOT)
|
| 205 |
-
getenv_path(TBB_BUILD_DIR)
|
| 206 |
-
if (NOT ENV_TBB_BUILD_DIR)
|
| 207 |
-
set(ENV_TBB_BUILD_DIR ${ENV_TBB_ROOT}/build)
|
| 208 |
-
endif ()
|
| 209 |
-
|
| 210 |
-
# include directory under ${ENV_TBB_ROOT}/include
|
| 211 |
-
list(APPEND TBB_LIB_SEARCH_PATH
|
| 212 |
-
${ENV_TBB_BUILD_DIR}/${TBB_BUILD_PREFIX}_release
|
| 213 |
-
${ENV_TBB_BUILD_DIR}/${TBB_BUILD_PREFIX}_debug)
|
| 214 |
-
endif ()
|
| 215 |
-
|
| 216 |
-
|
| 217 |
-
# For Windows, let's assume that the user might be using the precompiled
|
| 218 |
-
# TBB packages from the main website. These use a rather awkward directory
|
| 219 |
-
# structure (at least for automatically finding the right files) depending
|
| 220 |
-
# on platform and compiler, but we'll do our best to accommodate it.
|
| 221 |
-
# Not adding the same effort for the precompiled linux builds, though. Those
|
| 222 |
-
# have different versions for CC compiler versions and linux kernels which
|
| 223 |
-
# will never adequately match the user's setup, so there is no feasible way
|
| 224 |
-
# to detect the "best" version to use. The user will have to manually
|
| 225 |
-
# select the right files. (Chances are the distributions are shipping their
|
| 226 |
-
# custom version of tbb, anyway, so the problem is probably nonexistent.)
|
| 227 |
-
if (WIN32 AND MSVC)
|
| 228 |
-
set(COMPILER_PREFIX "vc7.1")
|
| 229 |
-
if (MSVC_VERSION EQUAL 1400)
|
| 230 |
-
set(COMPILER_PREFIX "vc8")
|
| 231 |
-
elseif(MSVC_VERSION EQUAL 1500)
|
| 232 |
-
set(COMPILER_PREFIX "vc9")
|
| 233 |
-
elseif(MSVC_VERSION EQUAL 1600)
|
| 234 |
-
set(COMPILER_PREFIX "vc10")
|
| 235 |
-
elseif(MSVC_VERSION EQUAL 1700)
|
| 236 |
-
set(COMPILER_PREFIX "vc11")
|
| 237 |
-
elseif(MSVC_VERSION EQUAL 1800)
|
| 238 |
-
set(COMPILER_PREFIX "vc12")
|
| 239 |
-
elseif(MSVC_VERSION GREATER_EQUAL 1900 AND MSVC_VERSION LESS_EQUAL 1925)
|
| 240 |
-
# 1900-1925 actually spans three Visual Studio versions:
|
| 241 |
-
# 1900 = VS 14.0 (v140 toolset) a.k.a. MSVC 2015
|
| 242 |
-
# 1910-1919 = VS 15.0 (v141 toolset) a.k.a. MSVC 2017
|
| 243 |
-
# 1920-1929 = VS 16.0 (v142 toolset) a.k.a. MSVC 2019
|
| 244 |
-
#
|
| 245 |
-
# But these are binary compatible and TBB's open source distribution only
|
| 246 |
-
# ships a single vs14 lib (as of 2020.0)
|
| 247 |
-
set(COMPILER_PREFIX "vc14")
|
| 248 |
-
else()
|
| 249 |
-
# The next poor soul who finds themselves having to decode visual studio
|
| 250 |
-
# version conventions may find these helpful:
|
| 251 |
-
# - https://cmake.org/cmake/help/latest/variable/MSVC_VERSION.html
|
| 252 |
-
# - https://en.wikipedia.org/wiki/Microsoft_Visual_C%2B%2B#Internal_version_numbering
|
| 253 |
-
message(AUTHOR_WARNING
|
| 254 |
-
"Unrecognized MSVC version. Please update FindTBB.cmake. "
|
| 255 |
-
"Some TBB_* values may need to be set manually."
|
| 256 |
-
)
|
| 257 |
-
endif ()
|
| 258 |
-
|
| 259 |
-
# for each prefix path, add ia32/64\${COMPILER_PREFIX}\lib to the lib search path
|
| 260 |
-
foreach (dir IN LISTS TBB_PREFIX_PATH)
|
| 261 |
-
if (CMAKE_CL_64)
|
| 262 |
-
list(APPEND TBB_LIB_SEARCH_PATH ${dir}/ia64/${COMPILER_PREFIX}/lib)
|
| 263 |
-
list(APPEND TBB_LIB_SEARCH_PATH ${dir}/lib/ia64/${COMPILER_PREFIX})
|
| 264 |
-
list(APPEND TBB_LIB_SEARCH_PATH ${dir}/intel64/${COMPILER_PREFIX}/lib)
|
| 265 |
-
list(APPEND TBB_LIB_SEARCH_PATH ${dir}/lib/intel64/${COMPILER_PREFIX})
|
| 266 |
-
else ()
|
| 267 |
-
list(APPEND TBB_LIB_SEARCH_PATH ${dir}/ia32/${COMPILER_PREFIX}/lib)
|
| 268 |
-
list(APPEND TBB_LIB_SEARCH_PATH ${dir}/lib/ia32/${COMPILER_PREFIX})
|
| 269 |
-
endif ()
|
| 270 |
-
endforeach ()
|
| 271 |
-
endif ()
|
| 272 |
-
|
| 273 |
-
# For OS X binary distribution, choose libc++ based libraries for Mavericks (10.9)
|
| 274 |
-
# and above and AppleClang
|
| 275 |
-
if (CMAKE_SYSTEM_NAME STREQUAL "Darwin" AND
|
| 276 |
-
NOT CMAKE_SYSTEM_VERSION VERSION_LESS 13.0)
|
| 277 |
-
set (USE_LIBCXX OFF)
|
| 278 |
-
cmake_policy(GET CMP0025 POLICY_VAR)
|
| 279 |
-
|
| 280 |
-
if (POLICY_VAR STREQUAL "NEW")
|
| 281 |
-
if (CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang")
|
| 282 |
-
set (USE_LIBCXX ON)
|
| 283 |
-
endif ()
|
| 284 |
-
else ()
|
| 285 |
-
if (CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
|
| 286 |
-
set (USE_LIBCXX ON)
|
| 287 |
-
endif ()
|
| 288 |
-
endif ()
|
| 289 |
-
|
| 290 |
-
if (USE_LIBCXX)
|
| 291 |
-
foreach (dir IN LISTS TBB_PREFIX_PATH)
|
| 292 |
-
list (APPEND TBB_LIB_SEARCH_PATH ${dir}/lib/libc++ ${dir}/libc++/lib)
|
| 293 |
-
endforeach ()
|
| 294 |
-
endif ()
|
| 295 |
-
endif ()
|
| 296 |
-
|
| 297 |
-
# check compiler ABI
|
| 298 |
-
if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
|
| 299 |
-
set(COMPILER_PREFIX)
|
| 300 |
-
if (NOT CMAKE_CXX_COMPILER_VERSION VERSION_LESS 4.7)
|
| 301 |
-
list(APPEND COMPILER_PREFIX "gcc4.7")
|
| 302 |
-
endif()
|
| 303 |
-
if (NOT CMAKE_CXX_COMPILER_VERSION VERSION_LESS 4.4)
|
| 304 |
-
list(APPEND COMPILER_PREFIX "gcc4.4")
|
| 305 |
-
endif()
|
| 306 |
-
list(APPEND COMPILER_PREFIX "gcc4.1")
|
| 307 |
-
elseif(CMAKE_CXX_COMPILER_ID MATCHES "Clang")
|
| 308 |
-
set(COMPILER_PREFIX)
|
| 309 |
-
if (NOT CMAKE_CXX_COMPILER_VERSION VERSION_LESS 3.6)
|
| 310 |
-
list(APPEND COMPILER_PREFIX "gcc4.7")
|
| 311 |
-
endif()
|
| 312 |
-
list(APPEND COMPILER_PREFIX "gcc4.4")
|
| 313 |
-
else() # Assume compatibility with 4.4 for other compilers
|
| 314 |
-
list(APPEND COMPILER_PREFIX "gcc4.4")
|
| 315 |
-
endif ()
|
| 316 |
-
|
| 317 |
-
# if platform architecture is explicitly specified
|
| 318 |
-
set(TBB_ARCH_PLATFORM $ENV{TBB_ARCH_PLATFORM})
|
| 319 |
-
if (TBB_ARCH_PLATFORM)
|
| 320 |
-
foreach (dir IN LISTS TBB_PREFIX_PATH)
|
| 321 |
-
list(APPEND TBB_LIB_SEARCH_PATH ${dir}/${TBB_ARCH_PLATFORM}/lib)
|
| 322 |
-
list(APPEND TBB_LIB_SEARCH_PATH ${dir}/lib/${TBB_ARCH_PLATFORM})
|
| 323 |
-
endforeach ()
|
| 324 |
-
endif ()
|
| 325 |
-
|
| 326 |
-
foreach (dir IN LISTS TBB_PREFIX_PATH)
|
| 327 |
-
foreach (prefix IN LISTS COMPILER_PREFIX)
|
| 328 |
-
if (CMAKE_SIZEOF_VOID_P EQUAL 8)
|
| 329 |
-
list(APPEND TBB_LIB_SEARCH_PATH ${dir}/lib/intel64)
|
| 330 |
-
list(APPEND TBB_LIB_SEARCH_PATH ${dir}/lib/intel64/${prefix})
|
| 331 |
-
list(APPEND TBB_LIB_SEARCH_PATH ${dir}/intel64/lib)
|
| 332 |
-
list(APPEND TBB_LIB_SEARCH_PATH ${dir}/intel64/${prefix}/lib)
|
| 333 |
-
else ()
|
| 334 |
-
list(APPEND TBB_LIB_SEARCH_PATH ${dir}/lib/ia32)
|
| 335 |
-
list(APPEND TBB_LIB_SEARCH_PATH ${dir}/lib/ia32/${prefix})
|
| 336 |
-
list(APPEND TBB_LIB_SEARCH_PATH ${dir}/ia32/lib)
|
| 337 |
-
list(APPEND TBB_LIB_SEARCH_PATH ${dir}/ia32/${prefix}/lib)
|
| 338 |
-
endif ()
|
| 339 |
-
endforeach()
|
| 340 |
-
endforeach ()
|
| 341 |
-
|
| 342 |
-
# add general search paths
|
| 343 |
-
foreach (dir IN LISTS TBB_PREFIX_PATH)
|
| 344 |
-
list(APPEND TBB_LIB_SEARCH_PATH ${dir}/lib ${dir}/Lib ${dir}/lib/tbb
|
| 345 |
-
${dir}/Libs)
|
| 346 |
-
list(APPEND TBB_INC_SEARCH_PATH ${dir}/include ${dir}/Include
|
| 347 |
-
${dir}/include/tbb)
|
| 348 |
-
endforeach ()
|
| 349 |
-
|
| 350 |
-
set(TBB_LIBRARY_NAMES tbb)
|
| 351 |
-
get_debug_names(TBB_LIBRARY_NAMES)
|
| 352 |
-
|
| 353 |
-
|
| 354 |
-
find_path(TBB_INCLUDE_DIR
|
| 355 |
-
NAMES tbb/tbb.h
|
| 356 |
-
PATHS ${TBB_INC_SEARCH_PATH})
|
| 357 |
-
|
| 358 |
-
find_library(TBB_LIBRARY_RELEASE
|
| 359 |
-
NAMES ${TBB_LIBRARY_NAMES}
|
| 360 |
-
PATHS ${TBB_LIB_SEARCH_PATH})
|
| 361 |
-
find_library(TBB_LIBRARY_DEBUG
|
| 362 |
-
NAMES ${TBB_LIBRARY_NAMES_DEBUG}
|
| 363 |
-
PATHS ${TBB_LIB_SEARCH_PATH})
|
| 364 |
-
make_library_set(TBB_LIBRARY)
|
| 365 |
-
|
| 366 |
-
findpkg_finish(TBB tbb)
|
| 367 |
-
|
| 368 |
-
#if we haven't found TBB no point on going any further
|
| 369 |
-
if (NOT TBB_FOUND)
|
| 370 |
-
return()
|
| 371 |
-
endif ()
|
| 372 |
-
|
| 373 |
-
#=============================================================================
|
| 374 |
-
# Look for TBB's malloc package
|
| 375 |
-
set(TBB_MALLOC_LIBRARY_NAMES tbbmalloc)
|
| 376 |
-
get_debug_names(TBB_MALLOC_LIBRARY_NAMES)
|
| 377 |
-
|
| 378 |
-
find_path(TBB_MALLOC_INCLUDE_DIR
|
| 379 |
-
NAMES tbb/tbb.h
|
| 380 |
-
PATHS ${TBB_INC_SEARCH_PATH})
|
| 381 |
-
|
| 382 |
-
find_library(TBB_MALLOC_LIBRARY_RELEASE
|
| 383 |
-
NAMES ${TBB_MALLOC_LIBRARY_NAMES}
|
| 384 |
-
PATHS ${TBB_LIB_SEARCH_PATH})
|
| 385 |
-
find_library(TBB_MALLOC_LIBRARY_DEBUG
|
| 386 |
-
NAMES ${TBB_MALLOC_LIBRARY_NAMES_DEBUG}
|
| 387 |
-
PATHS ${TBB_LIB_SEARCH_PATH})
|
| 388 |
-
make_library_set(TBB_MALLOC_LIBRARY)
|
| 389 |
-
|
| 390 |
-
findpkg_finish(TBB_MALLOC tbbmalloc)
|
| 391 |
-
|
| 392 |
-
#=============================================================================
|
| 393 |
-
# Look for TBB's malloc proxy package
|
| 394 |
-
set(TBB_MALLOC_PROXY_LIBRARY_NAMES tbbmalloc_proxy)
|
| 395 |
-
get_debug_names(TBB_MALLOC_PROXY_LIBRARY_NAMES)
|
| 396 |
-
|
| 397 |
-
find_path(TBB_MALLOC_PROXY_INCLUDE_DIR
|
| 398 |
-
NAMES tbb/tbbmalloc_proxy.h
|
| 399 |
-
PATHS ${TBB_INC_SEARCH_PATH})
|
| 400 |
-
|
| 401 |
-
find_library(TBB_MALLOC_PROXY_LIBRARY_RELEASE
|
| 402 |
-
NAMES ${TBB_MALLOC_PROXY_LIBRARY_NAMES}
|
| 403 |
-
PATHS ${TBB_LIB_SEARCH_PATH})
|
| 404 |
-
find_library(TBB_MALLOC_PROXY_LIBRARY_DEBUG
|
| 405 |
-
NAMES ${TBB_MALLOC_PROXY_LIBRARY_NAMES_DEBUG}
|
| 406 |
-
PATHS ${TBB_LIB_SEARCH_PATH})
|
| 407 |
-
make_library_set(TBB_MALLOC_PROXY_LIBRARY)
|
| 408 |
-
|
| 409 |
-
findpkg_finish(TBB_MALLOC_PROXY tbbmalloc_proxy)
|
| 410 |
-
|
| 411 |
-
|
| 412 |
-
#=============================================================================
|
| 413 |
-
#parse all the version numbers from tbb
|
| 414 |
-
if(NOT TBB_VERSION)
|
| 415 |
-
|
| 416 |
-
#only read the start of the file
|
| 417 |
-
file(STRINGS
|
| 418 |
-
"${TBB_INCLUDE_DIR}/tbb/tbb_stddef.h"
|
| 419 |
-
TBB_VERSION_CONTENTS
|
| 420 |
-
REGEX "VERSION")
|
| 421 |
-
|
| 422 |
-
string(REGEX REPLACE
|
| 423 |
-
".*#define TBB_VERSION_MAJOR ([0-9]+).*" "\\1"
|
| 424 |
-
TBB_VERSION_MAJOR "${TBB_VERSION_CONTENTS}")
|
| 425 |
-
|
| 426 |
-
string(REGEX REPLACE
|
| 427 |
-
".*#define TBB_VERSION_MINOR ([0-9]+).*" "\\1"
|
| 428 |
-
TBB_VERSION_MINOR "${TBB_VERSION_CONTENTS}")
|
| 429 |
-
|
| 430 |
-
string(REGEX REPLACE
|
| 431 |
-
".*#define TBB_INTERFACE_VERSION ([0-9]+).*" "\\1"
|
| 432 |
-
TBB_INTERFACE_VERSION "${TBB_VERSION_CONTENTS}")
|
| 433 |
-
|
| 434 |
-
string(REGEX REPLACE
|
| 435 |
-
".*#define TBB_COMPATIBLE_INTERFACE_VERSION ([0-9]+).*" "\\1"
|
| 436 |
-
TBB_COMPATIBLE_INTERFACE_VERSION "${TBB_VERSION_CONTENTS}")
|
| 437 |
-
|
| 438 |
-
set(TBB_VERSION "${TBB_VERSION_MAJOR}.${TBB_VERSION_MINOR}")
|
| 439 |
-
|
| 440 |
-
endif()
|
|
|
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|
spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/per_device_resource.h
DELETED
|
@@ -1,22 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2018 NVIDIA Corporation
|
| 3 |
-
*
|
| 4 |
-
* Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
* you may not use this file except in compliance with the License.
|
| 6 |
-
* You may obtain a copy of the License at
|
| 7 |
-
*
|
| 8 |
-
* http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
*
|
| 10 |
-
* Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
* distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
* See the License for the specific language governing permissions and
|
| 14 |
-
* limitations under the License.
|
| 15 |
-
*/
|
| 16 |
-
|
| 17 |
-
#pragma once
|
| 18 |
-
|
| 19 |
-
#include <thrust/detail/config.h>
|
| 20 |
-
|
| 21 |
-
// this system has no special per device resource functions
|
| 22 |
-
|
|
|
|
|
|
|
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spaces/CVPR/ml-talking-face/client_rest.py
DELETED
|
@@ -1,74 +0,0 @@
|
|
| 1 |
-
import requests
|
| 2 |
-
import json
|
| 3 |
-
import base64
|
| 4 |
-
import argparse
|
| 5 |
-
|
| 6 |
-
VIDEO_WIDTH = 1080
|
| 7 |
-
VIDEO_HEIGHT = 1920
|
| 8 |
-
SPEAKER_ID = 0
|
| 9 |
-
|
| 10 |
-
class RestAPIApplication:
|
| 11 |
-
def __init__(self, ip, port):
|
| 12 |
-
|
| 13 |
-
if port < 0:
|
| 14 |
-
self.post_request_addr = f"http://{ip}/register/"
|
| 15 |
-
self.post_headers = {"Content-Type": "application/json"}
|
| 16 |
-
self.generate_addr = (lambda id_: f'http://{ip}/generate/{id_}')
|
| 17 |
-
else:
|
| 18 |
-
self.post_request_addr = f"http://{ip}:{port}/register/"
|
| 19 |
-
self.post_headers = {"Content-Type": "application/json"}
|
| 20 |
-
self.generate_addr = (lambda id_: f'http://{ip}:{port}/generate/{id_}')
|
| 21 |
-
|
| 22 |
-
@staticmethod
|
| 23 |
-
def _get_json_request(text, lang, duration_rate, action, background_data=None, is_video_background=False):
|
| 24 |
-
request_form = dict()
|
| 25 |
-
|
| 26 |
-
request_form['text'] = text
|
| 27 |
-
request_form['speaker'] = SPEAKER_ID
|
| 28 |
-
request_form['width'] = VIDEO_WIDTH
|
| 29 |
-
request_form['height'] = VIDEO_HEIGHT
|
| 30 |
-
|
| 31 |
-
request_form['action'] = action
|
| 32 |
-
|
| 33 |
-
if background_data is not None:
|
| 34 |
-
background_base64 = base64.b64encode(background_data).decode("UTF-8")
|
| 35 |
-
else:
|
| 36 |
-
background_base64 = ""
|
| 37 |
-
|
| 38 |
-
request_form['background'] = background_base64
|
| 39 |
-
request_form['durationRate'] = duration_rate
|
| 40 |
-
request_form['isVideoBackground'] = is_video_background
|
| 41 |
-
request_form['lang'] = lang
|
| 42 |
-
|
| 43 |
-
request_as_json = json.dumps(request_form)
|
| 44 |
-
return request_as_json
|
| 45 |
-
|
| 46 |
-
@staticmethod
|
| 47 |
-
def _get_video_id(results):
|
| 48 |
-
return json.loads(bytes.decode(results.content))['id']
|
| 49 |
-
|
| 50 |
-
def get_video(self, text, lang, duration_rate, action, background_data=None, is_video_background=False):
|
| 51 |
-
request_json = self._get_json_request(text, lang, duration_rate, action, background_data, is_video_background)
|
| 52 |
-
|
| 53 |
-
# POST request with jsonified request
|
| 54 |
-
results = requests.post(self.post_request_addr, headers=self.post_headers, data=request_json)
|
| 55 |
-
|
| 56 |
-
# GET video with the given id
|
| 57 |
-
video_id = self._get_video_id(results)
|
| 58 |
-
video_results = requests.get(self.generate_addr(video_id))
|
| 59 |
-
|
| 60 |
-
return video_results.content
|
| 61 |
-
|
| 62 |
-
|
| 63 |
-
def parse_args():
|
| 64 |
-
parser = argparse.ArgumentParser(
|
| 65 |
-
description='REST API interface for talking face generation submitted to CVPR2022')
|
| 66 |
-
parser.add_argument('-i', '--ip', dest='rest_ip', type=str, default="127.0.0.1", help="IP for REST API")
|
| 67 |
-
parser.add_argument('-p', '--port', dest='rest_port', type=int, default=8080, help="Port for REST API")
|
| 68 |
-
args = parser.parse_args()
|
| 69 |
-
return args
|
| 70 |
-
|
| 71 |
-
|
| 72 |
-
if __name__ == '__main__':
|
| 73 |
-
args = parse_args()
|
| 74 |
-
rest_api_application = RestAPIApplication(args.rest_ip, args.rest_port)
|
|
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|
spaces/Caoyunkang/Segment-Any-Anomaly/SAM/segment_anything/modeling/prompt_encoder.py
DELETED
|
@@ -1,214 +0,0 @@
|
|
| 1 |
-
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
| 2 |
-
# All rights reserved.
|
| 3 |
-
|
| 4 |
-
# This source code is licensed under the license found in the
|
| 5 |
-
# LICENSE file in the root directory of this source tree.
|
| 6 |
-
|
| 7 |
-
import numpy as np
|
| 8 |
-
import torch
|
| 9 |
-
from torch import nn
|
| 10 |
-
|
| 11 |
-
from typing import Any, Optional, Tuple, Type
|
| 12 |
-
|
| 13 |
-
from .common import LayerNorm2d
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
class PromptEncoder(nn.Module):
|
| 17 |
-
def __init__(
|
| 18 |
-
self,
|
| 19 |
-
embed_dim: int,
|
| 20 |
-
image_embedding_size: Tuple[int, int],
|
| 21 |
-
input_image_size: Tuple[int, int],
|
| 22 |
-
mask_in_chans: int,
|
| 23 |
-
activation: Type[nn.Module] = nn.GELU,
|
| 24 |
-
) -> None:
|
| 25 |
-
"""
|
| 26 |
-
Encodes prompts for input to SAM's mask decoder.
|
| 27 |
-
|
| 28 |
-
Arguments:
|
| 29 |
-
embed_dim (int): The prompts' embedding dimension
|
| 30 |
-
image_embedding_size (tuple(int, int)): The spatial size of the
|
| 31 |
-
image embedding, as (H, W).
|
| 32 |
-
input_image_size (int): The padded size of the image as input
|
| 33 |
-
to the image encoder, as (H, W).
|
| 34 |
-
mask_in_chans (int): The number of hidden channels used for
|
| 35 |
-
encoding input masks.
|
| 36 |
-
activation (nn.Module): The activation to use when encoding
|
| 37 |
-
input masks.
|
| 38 |
-
"""
|
| 39 |
-
super().__init__()
|
| 40 |
-
self.embed_dim = embed_dim
|
| 41 |
-
self.input_image_size = input_image_size
|
| 42 |
-
self.image_embedding_size = image_embedding_size
|
| 43 |
-
self.pe_layer = PositionEmbeddingRandom(embed_dim // 2)
|
| 44 |
-
|
| 45 |
-
self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners
|
| 46 |
-
point_embeddings = [nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings)]
|
| 47 |
-
self.point_embeddings = nn.ModuleList(point_embeddings)
|
| 48 |
-
self.not_a_point_embed = nn.Embedding(1, embed_dim)
|
| 49 |
-
|
| 50 |
-
self.mask_input_size = (4 * image_embedding_size[0], 4 * image_embedding_size[1])
|
| 51 |
-
self.mask_downscaling = nn.Sequential(
|
| 52 |
-
nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2),
|
| 53 |
-
LayerNorm2d(mask_in_chans // 4),
|
| 54 |
-
activation(),
|
| 55 |
-
nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2),
|
| 56 |
-
LayerNorm2d(mask_in_chans),
|
| 57 |
-
activation(),
|
| 58 |
-
nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1),
|
| 59 |
-
)
|
| 60 |
-
self.no_mask_embed = nn.Embedding(1, embed_dim)
|
| 61 |
-
|
| 62 |
-
def get_dense_pe(self) -> torch.Tensor:
|
| 63 |
-
"""
|
| 64 |
-
Returns the positional encoding used to encode point prompts,
|
| 65 |
-
applied to a dense set of points the shape of the image encoding.
|
| 66 |
-
|
| 67 |
-
Returns:
|
| 68 |
-
torch.Tensor: Positional encoding with shape
|
| 69 |
-
1x(embed_dim)x(embedding_h)x(embedding_w)
|
| 70 |
-
"""
|
| 71 |
-
return self.pe_layer(self.image_embedding_size).unsqueeze(0)
|
| 72 |
-
|
| 73 |
-
def _embed_points(
|
| 74 |
-
self,
|
| 75 |
-
points: torch.Tensor,
|
| 76 |
-
labels: torch.Tensor,
|
| 77 |
-
pad: bool,
|
| 78 |
-
) -> torch.Tensor:
|
| 79 |
-
"""Embeds point prompts."""
|
| 80 |
-
points = points + 0.5 # Shift to center of pixel
|
| 81 |
-
if pad:
|
| 82 |
-
padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device)
|
| 83 |
-
padding_label = -torch.ones((labels.shape[0], 1), device=labels.device)
|
| 84 |
-
points = torch.cat([points, padding_point], dim=1)
|
| 85 |
-
labels = torch.cat([labels, padding_label], dim=1)
|
| 86 |
-
point_embedding = self.pe_layer.forward_with_coords(points, self.input_image_size)
|
| 87 |
-
point_embedding[labels == -1] = 0.0
|
| 88 |
-
point_embedding[labels == -1] += self.not_a_point_embed.weight
|
| 89 |
-
point_embedding[labels == 0] += self.point_embeddings[0].weight
|
| 90 |
-
point_embedding[labels == 1] += self.point_embeddings[1].weight
|
| 91 |
-
return point_embedding
|
| 92 |
-
|
| 93 |
-
def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor:
|
| 94 |
-
"""Embeds box prompts."""
|
| 95 |
-
boxes = boxes + 0.5 # Shift to center of pixel
|
| 96 |
-
coords = boxes.reshape(-1, 2, 2)
|
| 97 |
-
corner_embedding = self.pe_layer.forward_with_coords(coords, self.input_image_size)
|
| 98 |
-
corner_embedding[:, 0, :] += self.point_embeddings[2].weight
|
| 99 |
-
corner_embedding[:, 1, :] += self.point_embeddings[3].weight
|
| 100 |
-
return corner_embedding
|
| 101 |
-
|
| 102 |
-
def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor:
|
| 103 |
-
"""Embeds mask inputs."""
|
| 104 |
-
mask_embedding = self.mask_downscaling(masks)
|
| 105 |
-
return mask_embedding
|
| 106 |
-
|
| 107 |
-
def _get_batch_size(
|
| 108 |
-
self,
|
| 109 |
-
points: Optional[Tuple[torch.Tensor, torch.Tensor]],
|
| 110 |
-
boxes: Optional[torch.Tensor],
|
| 111 |
-
masks: Optional[torch.Tensor],
|
| 112 |
-
) -> int:
|
| 113 |
-
"""
|
| 114 |
-
Gets the batch size of the output given the batch size of the input prompts.
|
| 115 |
-
"""
|
| 116 |
-
if points is not None:
|
| 117 |
-
return points[0].shape[0]
|
| 118 |
-
elif boxes is not None:
|
| 119 |
-
return boxes.shape[0]
|
| 120 |
-
elif masks is not None:
|
| 121 |
-
return masks.shape[0]
|
| 122 |
-
else:
|
| 123 |
-
return 1
|
| 124 |
-
|
| 125 |
-
def _get_device(self) -> torch.device:
|
| 126 |
-
return self.point_embeddings[0].weight.device
|
| 127 |
-
|
| 128 |
-
def forward(
|
| 129 |
-
self,
|
| 130 |
-
points: Optional[Tuple[torch.Tensor, torch.Tensor]],
|
| 131 |
-
boxes: Optional[torch.Tensor],
|
| 132 |
-
masks: Optional[torch.Tensor],
|
| 133 |
-
) -> Tuple[torch.Tensor, torch.Tensor]:
|
| 134 |
-
"""
|
| 135 |
-
Embeds different types of prompts, returning both sparse and dense
|
| 136 |
-
embeddings.
|
| 137 |
-
|
| 138 |
-
Arguments:
|
| 139 |
-
points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates
|
| 140 |
-
and labels to embed.
|
| 141 |
-
boxes (torch.Tensor or none): boxes to embed
|
| 142 |
-
masks (torch.Tensor or none): masks to embed
|
| 143 |
-
|
| 144 |
-
Returns:
|
| 145 |
-
torch.Tensor: sparse embeddings for the points and boxes, with shape
|
| 146 |
-
BxNx(embed_dim), where N is determined by the number of input points
|
| 147 |
-
and boxes.
|
| 148 |
-
torch.Tensor: dense embeddings for the masks, in the shape
|
| 149 |
-
Bx(embed_dim)x(embed_H)x(embed_W)
|
| 150 |
-
"""
|
| 151 |
-
bs = self._get_batch_size(points, boxes, masks)
|
| 152 |
-
sparse_embeddings = torch.empty((bs, 0, self.embed_dim), device=self._get_device())
|
| 153 |
-
if points is not None:
|
| 154 |
-
coords, labels = points
|
| 155 |
-
point_embeddings = self._embed_points(coords, labels, pad=(boxes is None))
|
| 156 |
-
sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1)
|
| 157 |
-
if boxes is not None:
|
| 158 |
-
box_embeddings = self._embed_boxes(boxes)
|
| 159 |
-
sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1)
|
| 160 |
-
|
| 161 |
-
if masks is not None:
|
| 162 |
-
dense_embeddings = self._embed_masks(masks)
|
| 163 |
-
else:
|
| 164 |
-
dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand(
|
| 165 |
-
bs, -1, self.image_embedding_size[0], self.image_embedding_size[1]
|
| 166 |
-
)
|
| 167 |
-
|
| 168 |
-
return sparse_embeddings, dense_embeddings
|
| 169 |
-
|
| 170 |
-
|
| 171 |
-
class PositionEmbeddingRandom(nn.Module):
|
| 172 |
-
"""
|
| 173 |
-
Positional encoding using random spatial frequencies.
|
| 174 |
-
"""
|
| 175 |
-
|
| 176 |
-
def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None:
|
| 177 |
-
super().__init__()
|
| 178 |
-
if scale is None or scale <= 0.0:
|
| 179 |
-
scale = 1.0
|
| 180 |
-
self.register_buffer(
|
| 181 |
-
"positional_encoding_gaussian_matrix",
|
| 182 |
-
scale * torch.randn((2, num_pos_feats)),
|
| 183 |
-
)
|
| 184 |
-
|
| 185 |
-
def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
|
| 186 |
-
"""Positionally encode points that are normalized to [0,1]."""
|
| 187 |
-
# assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape
|
| 188 |
-
coords = 2 * coords - 1
|
| 189 |
-
coords = coords @ self.positional_encoding_gaussian_matrix
|
| 190 |
-
coords = 2 * np.pi * coords
|
| 191 |
-
# outputs d_1 x ... x d_n x C shape
|
| 192 |
-
return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1)
|
| 193 |
-
|
| 194 |
-
def forward(self, size: Tuple[int, int]) -> torch.Tensor:
|
| 195 |
-
"""Generate positional encoding for a grid of the specified size."""
|
| 196 |
-
h, w = size
|
| 197 |
-
device: Any = self.positional_encoding_gaussian_matrix.device
|
| 198 |
-
grid = torch.ones((h, w), device=device, dtype=torch.float32)
|
| 199 |
-
y_embed = grid.cumsum(dim=0) - 0.5
|
| 200 |
-
x_embed = grid.cumsum(dim=1) - 0.5
|
| 201 |
-
y_embed = y_embed / h
|
| 202 |
-
x_embed = x_embed / w
|
| 203 |
-
|
| 204 |
-
pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1))
|
| 205 |
-
return pe.permute(2, 0, 1) # C x H x W
|
| 206 |
-
|
| 207 |
-
def forward_with_coords(
|
| 208 |
-
self, coords_input: torch.Tensor, image_size: Tuple[int, int]
|
| 209 |
-
) -> torch.Tensor:
|
| 210 |
-
"""Positionally encode points that are not normalized to [0,1]."""
|
| 211 |
-
coords = coords_input.clone()
|
| 212 |
-
coords[:, :, 0] = coords[:, :, 0] / image_size[1]
|
| 213 |
-
coords[:, :, 1] = coords[:, :, 1] / image_size[0]
|
| 214 |
-
return self._pe_encoding(coords.to(torch.float)) # B x N x C
|
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|
spaces/ChandraMohanNayal/AutoGPT/autogpt/agent/agent.py
DELETED
|
@@ -1,197 +0,0 @@
|
|
| 1 |
-
from colorama import Fore, Style
|
| 2 |
-
|
| 3 |
-
from autogpt.app import execute_command, get_command
|
| 4 |
-
from autogpt.chat import chat_with_ai, create_chat_message
|
| 5 |
-
from autogpt.config import Config
|
| 6 |
-
from autogpt.json_utils.json_fix_llm import fix_json_using_multiple_techniques
|
| 7 |
-
from autogpt.json_utils.utilities import validate_json
|
| 8 |
-
from autogpt.logs import logger, print_assistant_thoughts
|
| 9 |
-
from autogpt.speech import say_text
|
| 10 |
-
from autogpt.spinner import Spinner
|
| 11 |
-
from autogpt.utils import clean_input
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
class Agent:
|
| 15 |
-
"""Agent class for interacting with Auto-GPT.
|
| 16 |
-
|
| 17 |
-
Attributes:
|
| 18 |
-
ai_name: The name of the agent.
|
| 19 |
-
memory: The memory object to use.
|
| 20 |
-
full_message_history: The full message history.
|
| 21 |
-
next_action_count: The number of actions to execute.
|
| 22 |
-
system_prompt: The system prompt is the initial prompt that defines everything the AI needs to know to achieve its task successfully.
|
| 23 |
-
Currently, the dynamic and customizable information in the system prompt are ai_name, description and goals.
|
| 24 |
-
|
| 25 |
-
triggering_prompt: The last sentence the AI will see before answering. For Auto-GPT, this prompt is:
|
| 26 |
-
Determine which next command to use, and respond using the format specified above:
|
| 27 |
-
The triggering prompt is not part of the system prompt because between the system prompt and the triggering
|
| 28 |
-
prompt we have contextual information that can distract the AI and make it forget that its goal is to find the next task to achieve.
|
| 29 |
-
SYSTEM PROMPT
|
| 30 |
-
CONTEXTUAL INFORMATION (memory, previous conversations, anything relevant)
|
| 31 |
-
TRIGGERING PROMPT
|
| 32 |
-
|
| 33 |
-
The triggering prompt reminds the AI about its short term meta task (defining the next task)
|
| 34 |
-
"""
|
| 35 |
-
|
| 36 |
-
def __init__(
|
| 37 |
-
self,
|
| 38 |
-
ai_name,
|
| 39 |
-
memory,
|
| 40 |
-
full_message_history,
|
| 41 |
-
next_action_count,
|
| 42 |
-
system_prompt,
|
| 43 |
-
triggering_prompt,
|
| 44 |
-
):
|
| 45 |
-
self.ai_name = ai_name
|
| 46 |
-
self.memory = memory
|
| 47 |
-
self.full_message_history = full_message_history
|
| 48 |
-
self.next_action_count = next_action_count
|
| 49 |
-
self.system_prompt = system_prompt
|
| 50 |
-
self.triggering_prompt = triggering_prompt
|
| 51 |
-
|
| 52 |
-
def start_interaction_loop(self):
|
| 53 |
-
# Interaction Loop
|
| 54 |
-
cfg = Config()
|
| 55 |
-
loop_count = 0
|
| 56 |
-
command_name = None
|
| 57 |
-
arguments = None
|
| 58 |
-
user_input = ""
|
| 59 |
-
|
| 60 |
-
while True:
|
| 61 |
-
# Discontinue if continuous limit is reached
|
| 62 |
-
loop_count += 1
|
| 63 |
-
if (
|
| 64 |
-
cfg.continuous_mode
|
| 65 |
-
and cfg.continuous_limit > 0
|
| 66 |
-
and loop_count > cfg.continuous_limit
|
| 67 |
-
):
|
| 68 |
-
logger.typewriter_log(
|
| 69 |
-
"Continuous Limit Reached: ", Fore.YELLOW, f"{cfg.continuous_limit}"
|
| 70 |
-
)
|
| 71 |
-
break
|
| 72 |
-
|
| 73 |
-
# Send message to AI, get response
|
| 74 |
-
with Spinner("Thinking... "):
|
| 75 |
-
assistant_reply = chat_with_ai(
|
| 76 |
-
self.system_prompt,
|
| 77 |
-
self.triggering_prompt,
|
| 78 |
-
self.full_message_history,
|
| 79 |
-
self.memory,
|
| 80 |
-
cfg.fast_token_limit,
|
| 81 |
-
) # TODO: This hardcodes the model to use GPT3.5. Make this an argument
|
| 82 |
-
|
| 83 |
-
assistant_reply_json = fix_json_using_multiple_techniques(assistant_reply)
|
| 84 |
-
|
| 85 |
-
# Print Assistant thoughts
|
| 86 |
-
if assistant_reply_json != {}:
|
| 87 |
-
validate_json(assistant_reply_json, "llm_response_format_1")
|
| 88 |
-
# Get command name and arguments
|
| 89 |
-
try:
|
| 90 |
-
print_assistant_thoughts(self.ai_name, assistant_reply_json)
|
| 91 |
-
command_name, arguments = get_command(assistant_reply_json)
|
| 92 |
-
# command_name, arguments = assistant_reply_json_valid["command"]["name"], assistant_reply_json_valid["command"]["args"]
|
| 93 |
-
if cfg.speak_mode:
|
| 94 |
-
say_text(f"I want to execute {command_name}")
|
| 95 |
-
except Exception as e:
|
| 96 |
-
logger.error("Error: \n", str(e))
|
| 97 |
-
|
| 98 |
-
if not cfg.continuous_mode and self.next_action_count == 0:
|
| 99 |
-
### GET USER AUTHORIZATION TO EXECUTE COMMAND ###
|
| 100 |
-
# Get key press: Prompt the user to press enter to continue or escape
|
| 101 |
-
# to exit
|
| 102 |
-
logger.typewriter_log(
|
| 103 |
-
"NEXT ACTION: ",
|
| 104 |
-
Fore.CYAN,
|
| 105 |
-
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL} "
|
| 106 |
-
f"ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
|
| 107 |
-
)
|
| 108 |
-
print(
|
| 109 |
-
"Enter 'y' to authorise command, 'y -N' to run N continuous "
|
| 110 |
-
"commands, 'n' to exit program, or enter feedback for "
|
| 111 |
-
f"{self.ai_name}...",
|
| 112 |
-
flush=True,
|
| 113 |
-
)
|
| 114 |
-
while True:
|
| 115 |
-
console_input = clean_input(
|
| 116 |
-
Fore.MAGENTA + "Input:" + Style.RESET_ALL
|
| 117 |
-
)
|
| 118 |
-
if console_input.lower().strip() == "y":
|
| 119 |
-
user_input = "GENERATE NEXT COMMAND JSON"
|
| 120 |
-
break
|
| 121 |
-
elif console_input.lower().strip() == "":
|
| 122 |
-
print("Invalid input format.")
|
| 123 |
-
continue
|
| 124 |
-
elif console_input.lower().startswith("y -"):
|
| 125 |
-
try:
|
| 126 |
-
self.next_action_count = abs(
|
| 127 |
-
int(console_input.split(" ")[1])
|
| 128 |
-
)
|
| 129 |
-
user_input = "GENERATE NEXT COMMAND JSON"
|
| 130 |
-
except ValueError:
|
| 131 |
-
print(
|
| 132 |
-
"Invalid input format. Please enter 'y -n' where n is"
|
| 133 |
-
" the number of continuous tasks."
|
| 134 |
-
)
|
| 135 |
-
continue
|
| 136 |
-
break
|
| 137 |
-
elif console_input.lower() == "n":
|
| 138 |
-
user_input = "EXIT"
|
| 139 |
-
break
|
| 140 |
-
else:
|
| 141 |
-
user_input = console_input
|
| 142 |
-
command_name = "human_feedback"
|
| 143 |
-
break
|
| 144 |
-
|
| 145 |
-
if user_input == "GENERATE NEXT COMMAND JSON":
|
| 146 |
-
logger.typewriter_log(
|
| 147 |
-
"-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
|
| 148 |
-
Fore.MAGENTA,
|
| 149 |
-
"",
|
| 150 |
-
)
|
| 151 |
-
elif user_input == "EXIT":
|
| 152 |
-
print("Exiting...", flush=True)
|
| 153 |
-
break
|
| 154 |
-
else:
|
| 155 |
-
# Print command
|
| 156 |
-
logger.typewriter_log(
|
| 157 |
-
"NEXT ACTION: ",
|
| 158 |
-
Fore.CYAN,
|
| 159 |
-
f"COMMAND = {Fore.CYAN}{command_name}{Style.RESET_ALL}"
|
| 160 |
-
f" ARGUMENTS = {Fore.CYAN}{arguments}{Style.RESET_ALL}",
|
| 161 |
-
)
|
| 162 |
-
|
| 163 |
-
# Execute command
|
| 164 |
-
if command_name is not None and command_name.lower().startswith("error"):
|
| 165 |
-
result = (
|
| 166 |
-
f"Command {command_name} threw the following error: {arguments}"
|
| 167 |
-
)
|
| 168 |
-
elif command_name == "human_feedback":
|
| 169 |
-
result = f"Human feedback: {user_input}"
|
| 170 |
-
else:
|
| 171 |
-
result = (
|
| 172 |
-
f"Command {command_name} returned: "
|
| 173 |
-
f"{execute_command(command_name, arguments)}"
|
| 174 |
-
)
|
| 175 |
-
if self.next_action_count > 0:
|
| 176 |
-
self.next_action_count -= 1
|
| 177 |
-
|
| 178 |
-
memory_to_add = (
|
| 179 |
-
f"Assistant Reply: {assistant_reply} "
|
| 180 |
-
f"\nResult: {result} "
|
| 181 |
-
f"\nHuman Feedback: {user_input} "
|
| 182 |
-
)
|
| 183 |
-
|
| 184 |
-
self.memory.add(memory_to_add)
|
| 185 |
-
|
| 186 |
-
# Check if there's a result from the command append it to the message
|
| 187 |
-
# history
|
| 188 |
-
if result is not None:
|
| 189 |
-
self.full_message_history.append(create_chat_message("system", result))
|
| 190 |
-
logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result)
|
| 191 |
-
else:
|
| 192 |
-
self.full_message_history.append(
|
| 193 |
-
create_chat_message("system", "Unable to execute command")
|
| 194 |
-
)
|
| 195 |
-
logger.typewriter_log(
|
| 196 |
-
"SYSTEM: ", Fore.YELLOW, "Unable to execute command"
|
| 197 |
-
)
|
|
|
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|
spaces/CikeyQI/meme-api/meme_generator/memes/decent_kiss/__init__.py
DELETED
|
@@ -1,18 +0,0 @@
|
|
| 1 |
-
from pathlib import Path
|
| 2 |
-
from typing import List
|
| 3 |
-
|
| 4 |
-
from pil_utils import BuildImage
|
| 5 |
-
|
| 6 |
-
from meme_generator import add_meme
|
| 7 |
-
|
| 8 |
-
img_dir = Path(__file__).parent / "images"
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
def decent_kiss(images: List[BuildImage], texts, args):
|
| 12 |
-
img = images[0].convert("RGBA").resize((589, 340), keep_ratio=True)
|
| 13 |
-
frame = BuildImage.open(img_dir / "0.png")
|
| 14 |
-
frame.paste(img, (0, 91), below=True)
|
| 15 |
-
return frame.save_jpg()
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
add_meme("decent_kiss", decent_kiss, min_images=1, max_images=1, keywords=["像样的亲亲"])
|
|
|
|
|
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|
spaces/Cong723/gpt-academic-public/crazy_functions/test_project/cpp/cppipc/prod_cons.h
DELETED
|
@@ -1,433 +0,0 @@
|
|
| 1 |
-
#pragma once
|
| 2 |
-
|
| 3 |
-
#include <atomic>
|
| 4 |
-
#include <utility>
|
| 5 |
-
#include <cstring>
|
| 6 |
-
#include <type_traits>
|
| 7 |
-
#include <cstdint>
|
| 8 |
-
|
| 9 |
-
#include "libipc/def.h"
|
| 10 |
-
|
| 11 |
-
#include "libipc/platform/detail.h"
|
| 12 |
-
#include "libipc/circ/elem_def.h"
|
| 13 |
-
#include "libipc/utility/log.h"
|
| 14 |
-
#include "libipc/utility/utility.h"
|
| 15 |
-
|
| 16 |
-
namespace ipc {
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| 17 |
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| 18 |
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////////////////////////////////////////////////////////////////
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| 19 |
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/// producer-consumer implementation
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| 20 |
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////////////////////////////////////////////////////////////////
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| 21 |
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| 22 |
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template <typename Flag>
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struct prod_cons_impl;
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| 24 |
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| 25 |
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template <>
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struct prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
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| 27 |
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| 28 |
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template <std::size_t DataSize, std::size_t AlignSize>
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| 29 |
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struct elem_t {
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| 30 |
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std::aligned_storage_t<DataSize, AlignSize> data_ {};
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| 31 |
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};
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| 32 |
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| 33 |
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alignas(cache_line_size) std::atomic<circ::u2_t> rd_; // read index
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| 34 |
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alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index
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| 35 |
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| 36 |
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constexpr circ::u2_t cursor() const noexcept {
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return 0;
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| 38 |
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}
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| 40 |
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template <typename W, typename F, typename E>
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| 41 |
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bool push(W* /*wrapper*/, F&& f, E* elems) {
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| 42 |
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auto cur_wt = circ::index_of(wt_.load(std::memory_order_relaxed));
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if (cur_wt == circ::index_of(rd_.load(std::memory_order_acquire) - 1)) {
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return false; // full
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| 45 |
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}
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std::forward<F>(f)(&(elems[cur_wt].data_));
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wt_.fetch_add(1, std::memory_order_release);
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return true;
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| 49 |
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}
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| 50 |
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| 51 |
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/**
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| 52 |
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* In single-single-unicast, 'force_push' means 'no reader' or 'the only one reader is dead'.
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| 53 |
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* So we could just disconnect all connections of receiver, and return false.
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| 54 |
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*/
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| 55 |
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template <typename W, typename F, typename E>
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| 56 |
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bool force_push(W* wrapper, F&&, E*) {
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wrapper->elems()->disconnect_receiver(~static_cast<circ::cc_t>(0u));
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| 58 |
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return false;
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| 59 |
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}
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template <typename W, typename F, typename R, typename E>
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| 62 |
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bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E* elems) {
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| 63 |
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auto cur_rd = circ::index_of(rd_.load(std::memory_order_relaxed));
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| 64 |
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if (cur_rd == circ::index_of(wt_.load(std::memory_order_acquire))) {
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return false; // empty
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| 66 |
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}
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std::forward<F>(f)(&(elems[cur_rd].data_));
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std::forward<R>(out)(true);
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rd_.fetch_add(1, std::memory_order_release);
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return true;
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| 71 |
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}
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| 72 |
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};
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| 74 |
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template <>
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| 75 |
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struct prod_cons_impl<wr<relat::single, relat::multi , trans::unicast>>
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: prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
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| 77 |
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| 78 |
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template <typename W, typename F, typename E>
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| 79 |
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bool force_push(W* wrapper, F&&, E*) {
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| 80 |
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wrapper->elems()->disconnect_receiver(1);
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| 81 |
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return false;
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| 82 |
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}
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| 84 |
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template <typename W, typename F, typename R,
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| 85 |
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template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
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| 86 |
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bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
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| 87 |
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byte_t buff[DS];
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| 88 |
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for (unsigned k = 0;;) {
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| 89 |
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auto cur_rd = rd_.load(std::memory_order_relaxed);
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| 90 |
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if (circ::index_of(cur_rd) ==
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| 91 |
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circ::index_of(wt_.load(std::memory_order_acquire))) {
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| 92 |
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return false; // empty
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| 93 |
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}
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| 94 |
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std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
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| 95 |
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if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
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| 96 |
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std::forward<F>(f)(buff);
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| 97 |
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std::forward<R>(out)(true);
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| 98 |
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return true;
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| 99 |
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}
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| 100 |
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ipc::yield(k);
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| 101 |
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}
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| 102 |
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}
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| 103 |
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};
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| 104 |
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| 105 |
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template <>
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| 106 |
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struct prod_cons_impl<wr<relat::multi , relat::multi, trans::unicast>>
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| 107 |
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: prod_cons_impl<wr<relat::single, relat::multi, trans::unicast>> {
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| 108 |
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| 109 |
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using flag_t = std::uint64_t;
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| 110 |
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| 111 |
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template <std::size_t DataSize, std::size_t AlignSize>
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| 112 |
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struct elem_t {
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| 113 |
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std::aligned_storage_t<DataSize, AlignSize> data_ {};
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| 114 |
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std::atomic<flag_t> f_ct_ { 0 }; // commit flag
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| 115 |
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};
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| 116 |
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| 117 |
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alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index
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| 118 |
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| 119 |
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template <typename W, typename F, typename E>
|
| 120 |
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bool push(W* /*wrapper*/, F&& f, E* elems) {
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| 121 |
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circ::u2_t cur_ct, nxt_ct;
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| 122 |
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for (unsigned k = 0;;) {
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| 123 |
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cur_ct = ct_.load(std::memory_order_relaxed);
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| 124 |
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if (circ::index_of(nxt_ct = cur_ct + 1) ==
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| 125 |
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circ::index_of(rd_.load(std::memory_order_acquire))) {
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| 126 |
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return false; // full
|
| 127 |
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}
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| 128 |
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if (ct_.compare_exchange_weak(cur_ct, nxt_ct, std::memory_order_acq_rel)) {
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| 129 |
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break;
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| 130 |
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}
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| 131 |
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ipc::yield(k);
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| 132 |
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}
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| 133 |
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auto* el = elems + circ::index_of(cur_ct);
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| 134 |
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std::forward<F>(f)(&(el->data_));
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| 135 |
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// set flag & try update wt
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| 136 |
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el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
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| 137 |
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while (1) {
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| 138 |
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auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
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| 139 |
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if (cur_ct != wt_.load(std::memory_order_relaxed)) {
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| 140 |
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return true;
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| 141 |
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}
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| 142 |
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if ((~cac_ct) != cur_ct) {
|
| 143 |
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return true;
|
| 144 |
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}
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| 145 |
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if (!el->f_ct_.compare_exchange_strong(cac_ct, 0, std::memory_order_relaxed)) {
|
| 146 |
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return true;
|
| 147 |
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}
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| 148 |
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wt_.store(nxt_ct, std::memory_order_release);
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| 149 |
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cur_ct = nxt_ct;
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| 150 |
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nxt_ct = cur_ct + 1;
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| 151 |
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el = elems + circ::index_of(cur_ct);
|
| 152 |
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}
|
| 153 |
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return true;
|
| 154 |
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}
|
| 155 |
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|
| 156 |
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template <typename W, typename F, typename E>
|
| 157 |
-
bool force_push(W* wrapper, F&&, E*) {
|
| 158 |
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wrapper->elems()->disconnect_receiver(1);
|
| 159 |
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return false;
|
| 160 |
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}
|
| 161 |
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|
| 162 |
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template <typename W, typename F, typename R,
|
| 163 |
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template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
|
| 164 |
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bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
|
| 165 |
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byte_t buff[DS];
|
| 166 |
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for (unsigned k = 0;;) {
|
| 167 |
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auto cur_rd = rd_.load(std::memory_order_relaxed);
|
| 168 |
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auto cur_wt = wt_.load(std::memory_order_acquire);
|
| 169 |
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auto id_rd = circ::index_of(cur_rd);
|
| 170 |
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auto id_wt = circ::index_of(cur_wt);
|
| 171 |
-
if (id_rd == id_wt) {
|
| 172 |
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auto* el = elems + id_wt;
|
| 173 |
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auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
|
| 174 |
-
if ((~cac_ct) != cur_wt) {
|
| 175 |
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return false; // empty
|
| 176 |
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}
|
| 177 |
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if (el->f_ct_.compare_exchange_weak(cac_ct, 0, std::memory_order_relaxed)) {
|
| 178 |
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wt_.store(cur_wt + 1, std::memory_order_release);
|
| 179 |
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}
|
| 180 |
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k = 0;
|
| 181 |
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}
|
| 182 |
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else {
|
| 183 |
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std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
|
| 184 |
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if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
|
| 185 |
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std::forward<F>(f)(buff);
|
| 186 |
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std::forward<R>(out)(true);
|
| 187 |
-
return true;
|
| 188 |
-
}
|
| 189 |
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ipc::yield(k);
|
| 190 |
-
}
|
| 191 |
-
}
|
| 192 |
-
}
|
| 193 |
-
};
|
| 194 |
-
|
| 195 |
-
template <>
|
| 196 |
-
struct prod_cons_impl<wr<relat::single, relat::multi, trans::broadcast>> {
|
| 197 |
-
|
| 198 |
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using rc_t = std::uint64_t;
|
| 199 |
-
|
| 200 |
-
enum : rc_t {
|
| 201 |
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ep_mask = 0x00000000ffffffffull,
|
| 202 |
-
ep_incr = 0x0000000100000000ull
|
| 203 |
-
};
|
| 204 |
-
|
| 205 |
-
template <std::size_t DataSize, std::size_t AlignSize>
|
| 206 |
-
struct elem_t {
|
| 207 |
-
std::aligned_storage_t<DataSize, AlignSize> data_ {};
|
| 208 |
-
std::atomic<rc_t> rc_ { 0 }; // read-counter
|
| 209 |
-
};
|
| 210 |
-
|
| 211 |
-
alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index
|
| 212 |
-
alignas(cache_line_size) rc_t epoch_ { 0 }; // only one writer
|
| 213 |
-
|
| 214 |
-
circ::u2_t cursor() const noexcept {
|
| 215 |
-
return wt_.load(std::memory_order_acquire);
|
| 216 |
-
}
|
| 217 |
-
|
| 218 |
-
template <typename W, typename F, typename E>
|
| 219 |
-
bool push(W* wrapper, F&& f, E* elems) {
|
| 220 |
-
E* el;
|
| 221 |
-
for (unsigned k = 0;;) {
|
| 222 |
-
circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
|
| 223 |
-
if (cc == 0) return false; // no reader
|
| 224 |
-
el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
|
| 225 |
-
// check all consumers have finished reading this element
|
| 226 |
-
auto cur_rc = el->rc_.load(std::memory_order_acquire);
|
| 227 |
-
circ::cc_t rem_cc = cur_rc & ep_mask;
|
| 228 |
-
if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch_)) {
|
| 229 |
-
return false; // has not finished yet
|
| 230 |
-
}
|
| 231 |
-
// consider rem_cc to be 0 here
|
| 232 |
-
if (el->rc_.compare_exchange_weak(
|
| 233 |
-
cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
|
| 234 |
-
break;
|
| 235 |
-
}
|
| 236 |
-
ipc::yield(k);
|
| 237 |
-
}
|
| 238 |
-
std::forward<F>(f)(&(el->data_));
|
| 239 |
-
wt_.fetch_add(1, std::memory_order_release);
|
| 240 |
-
return true;
|
| 241 |
-
}
|
| 242 |
-
|
| 243 |
-
template <typename W, typename F, typename E>
|
| 244 |
-
bool force_push(W* wrapper, F&& f, E* elems) {
|
| 245 |
-
E* el;
|
| 246 |
-
epoch_ += ep_incr;
|
| 247 |
-
for (unsigned k = 0;;) {
|
| 248 |
-
circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
|
| 249 |
-
if (cc == 0) return false; // no reader
|
| 250 |
-
el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
|
| 251 |
-
// check all consumers have finished reading this element
|
| 252 |
-
auto cur_rc = el->rc_.load(std::memory_order_acquire);
|
| 253 |
-
circ::cc_t rem_cc = cur_rc & ep_mask;
|
| 254 |
-
if (cc & rem_cc) {
|
| 255 |
-
ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
|
| 256 |
-
cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
|
| 257 |
-
if (cc == 0) return false; // no reader
|
| 258 |
-
}
|
| 259 |
-
// just compare & exchange
|
| 260 |
-
if (el->rc_.compare_exchange_weak(
|
| 261 |
-
cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
|
| 262 |
-
break;
|
| 263 |
-
}
|
| 264 |
-
ipc::yield(k);
|
| 265 |
-
}
|
| 266 |
-
std::forward<F>(f)(&(el->data_));
|
| 267 |
-
wt_.fetch_add(1, std::memory_order_release);
|
| 268 |
-
return true;
|
| 269 |
-
}
|
| 270 |
-
|
| 271 |
-
template <typename W, typename F, typename R, typename E>
|
| 272 |
-
bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E* elems) {
|
| 273 |
-
if (cur == cursor()) return false; // acquire
|
| 274 |
-
auto* el = elems + circ::index_of(cur++);
|
| 275 |
-
std::forward<F>(f)(&(el->data_));
|
| 276 |
-
for (unsigned k = 0;;) {
|
| 277 |
-
auto cur_rc = el->rc_.load(std::memory_order_acquire);
|
| 278 |
-
if ((cur_rc & ep_mask) == 0) {
|
| 279 |
-
std::forward<R>(out)(true);
|
| 280 |
-
return true;
|
| 281 |
-
}
|
| 282 |
-
auto nxt_rc = cur_rc & ~static_cast<rc_t>(wrapper->connected_id());
|
| 283 |
-
if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
|
| 284 |
-
std::forward<R>(out)((nxt_rc & ep_mask) == 0);
|
| 285 |
-
return true;
|
| 286 |
-
}
|
| 287 |
-
ipc::yield(k);
|
| 288 |
-
}
|
| 289 |
-
}
|
| 290 |
-
};
|
| 291 |
-
|
| 292 |
-
template <>
|
| 293 |
-
struct prod_cons_impl<wr<relat::multi, relat::multi, trans::broadcast>> {
|
| 294 |
-
|
| 295 |
-
using rc_t = std::uint64_t;
|
| 296 |
-
using flag_t = std::uint64_t;
|
| 297 |
-
|
| 298 |
-
enum : rc_t {
|
| 299 |
-
rc_mask = 0x00000000ffffffffull,
|
| 300 |
-
ep_mask = 0x00ffffffffffffffull,
|
| 301 |
-
ep_incr = 0x0100000000000000ull,
|
| 302 |
-
ic_mask = 0xff000000ffffffffull,
|
| 303 |
-
ic_incr = 0x0000000100000000ull
|
| 304 |
-
};
|
| 305 |
-
|
| 306 |
-
template <std::size_t DataSize, std::size_t AlignSize>
|
| 307 |
-
struct elem_t {
|
| 308 |
-
std::aligned_storage_t<DataSize, AlignSize> data_ {};
|
| 309 |
-
std::atomic<rc_t > rc_ { 0 }; // read-counter
|
| 310 |
-
std::atomic<flag_t> f_ct_ { 0 }; // commit flag
|
| 311 |
-
};
|
| 312 |
-
|
| 313 |
-
alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index
|
| 314 |
-
alignas(cache_line_size) std::atomic<rc_t> epoch_ { 0 };
|
| 315 |
-
|
| 316 |
-
circ::u2_t cursor() const noexcept {
|
| 317 |
-
return ct_.load(std::memory_order_acquire);
|
| 318 |
-
}
|
| 319 |
-
|
| 320 |
-
constexpr static rc_t inc_rc(rc_t rc) noexcept {
|
| 321 |
-
return (rc & ic_mask) | ((rc + ic_incr) & ~ic_mask);
|
| 322 |
-
}
|
| 323 |
-
|
| 324 |
-
constexpr static rc_t inc_mask(rc_t rc) noexcept {
|
| 325 |
-
return inc_rc(rc) & ~rc_mask;
|
| 326 |
-
}
|
| 327 |
-
|
| 328 |
-
template <typename W, typename F, typename E>
|
| 329 |
-
bool push(W* wrapper, F&& f, E* elems) {
|
| 330 |
-
E* el;
|
| 331 |
-
circ::u2_t cur_ct;
|
| 332 |
-
rc_t epoch = epoch_.load(std::memory_order_acquire);
|
| 333 |
-
for (unsigned k = 0;;) {
|
| 334 |
-
circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
|
| 335 |
-
if (cc == 0) return false; // no reader
|
| 336 |
-
el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
|
| 337 |
-
// check all consumers have finished reading this element
|
| 338 |
-
auto cur_rc = el->rc_.load(std::memory_order_relaxed);
|
| 339 |
-
circ::cc_t rem_cc = cur_rc & rc_mask;
|
| 340 |
-
if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch)) {
|
| 341 |
-
return false; // has not finished yet
|
| 342 |
-
}
|
| 343 |
-
else if (!rem_cc) {
|
| 344 |
-
auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
|
| 345 |
-
if ((cur_fl != cur_ct) && cur_fl) {
|
| 346 |
-
return false; // full
|
| 347 |
-
}
|
| 348 |
-
}
|
| 349 |
-
// consider rem_cc to be 0 here
|
| 350 |
-
if (el->rc_.compare_exchange_weak(
|
| 351 |
-
cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed) &&
|
| 352 |
-
epoch_.compare_exchange_weak(epoch, epoch, std::memory_order_acq_rel)) {
|
| 353 |
-
break;
|
| 354 |
-
}
|
| 355 |
-
ipc::yield(k);
|
| 356 |
-
}
|
| 357 |
-
// only one thread/process would touch here at one time
|
| 358 |
-
ct_.store(cur_ct + 1, std::memory_order_release);
|
| 359 |
-
std::forward<F>(f)(&(el->data_));
|
| 360 |
-
// set flag & try update wt
|
| 361 |
-
el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
|
| 362 |
-
return true;
|
| 363 |
-
}
|
| 364 |
-
|
| 365 |
-
template <typename W, typename F, typename E>
|
| 366 |
-
bool force_push(W* wrapper, F&& f, E* elems) {
|
| 367 |
-
E* el;
|
| 368 |
-
circ::u2_t cur_ct;
|
| 369 |
-
rc_t epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
|
| 370 |
-
for (unsigned k = 0;;) {
|
| 371 |
-
circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
|
| 372 |
-
if (cc == 0) return false; // no reader
|
| 373 |
-
el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
|
| 374 |
-
// check all consumers have finished reading this element
|
| 375 |
-
auto cur_rc = el->rc_.load(std::memory_order_acquire);
|
| 376 |
-
circ::cc_t rem_cc = cur_rc & rc_mask;
|
| 377 |
-
if (cc & rem_cc) {
|
| 378 |
-
ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
|
| 379 |
-
cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
|
| 380 |
-
if (cc == 0) return false; // no reader
|
| 381 |
-
}
|
| 382 |
-
// just compare & exchange
|
| 383 |
-
if (el->rc_.compare_exchange_weak(
|
| 384 |
-
cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed)) {
|
| 385 |
-
if (epoch == epoch_.load(std::memory_order_acquire)) {
|
| 386 |
-
break;
|
| 387 |
-
}
|
| 388 |
-
else if (push(wrapper, std::forward<F>(f), elems)) {
|
| 389 |
-
return true;
|
| 390 |
-
}
|
| 391 |
-
epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
|
| 392 |
-
}
|
| 393 |
-
ipc::yield(k);
|
| 394 |
-
}
|
| 395 |
-
// only one thread/process would touch here at one time
|
| 396 |
-
ct_.store(cur_ct + 1, std::memory_order_release);
|
| 397 |
-
std::forward<F>(f)(&(el->data_));
|
| 398 |
-
// set flag & try update wt
|
| 399 |
-
el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
|
| 400 |
-
return true;
|
| 401 |
-
}
|
| 402 |
-
|
| 403 |
-
template <typename W, typename F, typename R, typename E, std::size_t N>
|
| 404 |
-
bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E(& elems)[N]) {
|
| 405 |
-
auto* el = elems + circ::index_of(cur);
|
| 406 |
-
auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
|
| 407 |
-
if (cur_fl != ~static_cast<flag_t>(cur)) {
|
| 408 |
-
return false; // empty
|
| 409 |
-
}
|
| 410 |
-
++cur;
|
| 411 |
-
std::forward<F>(f)(&(el->data_));
|
| 412 |
-
for (unsigned k = 0;;) {
|
| 413 |
-
auto cur_rc = el->rc_.load(std::memory_order_acquire);
|
| 414 |
-
if ((cur_rc & rc_mask) == 0) {
|
| 415 |
-
std::forward<R>(out)(true);
|
| 416 |
-
el->f_ct_.store(cur + N - 1, std::memory_order_release);
|
| 417 |
-
return true;
|
| 418 |
-
}
|
| 419 |
-
auto nxt_rc = inc_rc(cur_rc) & ~static_cast<rc_t>(wrapper->connected_id());
|
| 420 |
-
bool last_one = false;
|
| 421 |
-
if ((last_one = (nxt_rc & rc_mask) == 0)) {
|
| 422 |
-
el->f_ct_.store(cur + N - 1, std::memory_order_release);
|
| 423 |
-
}
|
| 424 |
-
if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
|
| 425 |
-
std::forward<R>(out)(last_one);
|
| 426 |
-
return true;
|
| 427 |
-
}
|
| 428 |
-
ipc::yield(k);
|
| 429 |
-
}
|
| 430 |
-
}
|
| 431 |
-
};
|
| 432 |
-
|
| 433 |
-
} // namespace ipc
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