diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Don 2 UPDATED Full Hindi Movie Hd With English Subtitles.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Don 2 UPDATED Full Hindi Movie Hd With English Subtitles.md
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Don 2: A Thrilling Sequel to the 2006 Action Hit
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If you are looking for a fast-paced and exciting movie to watch, you might want to check out Don 2, a sequel to the 2006 Indian action thriller Don. The movie stars Shah Rukh Khan as the international gangster Don, who has conquered the Asian underworld and now sets his sights on Europe. Along the way, he faces challenges from the Interpol, the mob bosses of each nation, and his own former allies.
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The movie is directed by Farhan Akhtar, who also co-wrote the screenplay with Ameet Mehta and Amrish Shah. The movie also features Priyanka Chopra Jonas as Roma, an Interpol officer who is obsessed with catching Don; Boman Irani as Vardhan, Don's former enemy who joins forces with him; Kunal Kapoor as Sameer, Don's trusted friend; and Lara Dutta as Ayesha, Don's girlfriend.
The movie was released in 2011 and was a huge commercial and critical success. It was praised for its stylish cinematography, stunning action sequences, and charismatic performances by the lead actors. The movie also features a catchy soundtrack composed by Shankar-Ehsaan-Loy, with lyrics by Javed Akhtar.
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If you want to watch Don 2, you can find it on various streaming platforms such as Netflix and Prime Video. The movie is available in Hindi with English subtitles, as well as in other languages such as German, Spanish, French, Italian, Korean, Chinese, and more. You can also rent or buy the movie on Amazon or other online platforms.
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So what are you waiting for? Grab some popcorn and enjoy this thrilling ride with Don and his gang!
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Don 2: The Plot
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The movie begins with Don (Shah Rukh Khan) narrating his rise to power in the Asian underworld, after killing his lookalike Vijay and escaping from the Interpol. He reveals that he has a master plan to rob the currency printing plates from a bank in Berlin, Germany. To do this, he needs the help of Vardhan (Boman Irani), who is imprisoned in Malaysia.
-
Don surrenders himself to the Interpol in Malaysia, hoping to get close to Vardhan and break him out of jail. However, he is confronted by Roma (Priyanka Chopra Jonas), who has not forgotten her personal vendetta against him. She tries to stop him from escaping, but Don manages to outsmart her and frees Vardhan. They then fly to Zurich, Switzerland, where they meet Sameer (Kunal Kapoor), Don's friend and partner in crime.
-
-
In Zurich, Don also meets Ayesha (Lara Dutta), his girlfriend and accomplice. She helps him get in touch with Diwan (Alyy Khan), a hacker who can access the bank's security system. Don also recruits Jabbar (Nawab Shah), an assassin who can eliminate any obstacles in his way. With his team ready, Don sets his plan in motion.
-
However, things are not as easy as they seem. Don has to deal with the ruthless mob boss of Europe, Arjun Khanna (Om Puri), who does not want anyone to interfere with his business. He also has to face Malik (Florian Lukas), a German police officer who is determined to catch him. And most importantly, he has to watch out for Roma and her team, who are hot on his trail.
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Will Don succeed in his daring heist? Will Roma finally get her revenge? Will Don's allies remain loyal to him? Watch Don 2 to find out!
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Marvel Contest of Champions Apkpure: A Superhero Fighting Game for Your Mobile Device
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Do you love Marvel comics and movies? Do you enjoy fighting games with simple controls and stunning graphics? If you answered yes to both questions, then you should check out Marvel Contest of Champions apkpure, a free-to-play mobile game that lets you collect and battle with your favorite Marvel characters. In this article, we will tell you everything you need to know about Marvel Contest of Champions apkpure, including how to play it, who are the characters, what are the tips, and what are the reviews.
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How to Play Marvel Contest of Champions Apkpure
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Marvel Contest of Champions apkpure is a fighting game that pits Marvel heroes and villains against each other in epic duels. You can download the game from [ApkCombo](^1^), a website that provides free APK files for Android devices. The game requires an internet connection and about 1.5 GB of storage space.
The game has a simple touchscreen interface that allows you to control your character's movements and attacks. You can tap to perform light attacks, swipe to perform medium attacks, press and hold to perform heavy attacks, and swipe back to dodge or block. You can also unleash powerful special attacks when your power meter is full, which is indicated by the blue bars at the bottom of the screen.
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The game has several features and modes that make it fun and engaging. You can play through a story mode that follows a comic book-inspired plot, where you have to fight against the Collector, Thanos, Kang, and other villains who want to destroy the Marvel universe. You can also join an alliance with other players and participate in alliance events, quests, and wars, where you can cooperate or compete with other alliances for rewards and glory. You can also enter various arenas and tournaments, where you can test your skills against other players from around the world.
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Who Are the Characters in Marvel Contest of Champions Apkpure
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Marvel Contest of Champions apkpure features over 250 playable characters from the Marvel universe, including Spider-Man, Iron Man, Wolverine, Captain America, Black Widow, Thor, Hulk, Deadpool, Doctor Strange, Captain Marvel, Black Panther, Thanos, Ultron, Venom, and many more. You can obtain new characters by opening crystals that you earn or buy with in-game currency or real money.
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The characters belong to different classes that have advantages and disadvantages against each other. The classes are Mutant, Skill, Science, Mystic, Cosmic, and Tech. For example, Mutants are strong against Skill but weak against Tech, while Techs are strong against Mutants but weak against Cosmic. You can see the class relationships by tapping on the class icons at the top of the screen.
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Each character has a unique set of stats, abilities, and special moves that reflect their comic book counterparts. For example, Spider-Man can web-sling, evade attacks, and stun enemies with his spider-sense; Iron Man can fire repulsor blasts, boost his armor, and unleash a unibeam; Wolverine can heal himself, slash enemies with his claws, and go berserk; and so on. You can upgrade your characters by leveling them up with ISO-8 crystals or ranking them up with catalysts. You can also unlock their signature abilities by obtaining duplicate copies of them from crystals.
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How to Improve Your Skills and Strategies in Marvel Contest of Champions Apkpure
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If you want to become a better player in Marvel Contest of Champions apkpure, here are some tips that you should follow:
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Build a balanced team of characters with different classes and synergies. Synergies are bonuses that you get when you pair up certain characters based on their comic book relationships or affiliations. For example, pairing up Spider-Man and Venom gives you a bonus to critical rate; - Pairing up Iron Man and Captain America gives you a bonus to armor and block proficiency. You can see the synergies by tapping on the team icon at the bottom of the screen.
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Learn the strengths and weaknesses of each character and use them to your advantage. For example, if you are facing a Mystic character, you can use a Cosmic character to deal more damage and avoid their debuffs; if you are facing a Tech character, you can use a Mutant character to bypass their armor and power drain.
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Master the basic combat mechanics and practice your timing and reflexes. You should know when to attack, when to block, when to dodge, and when to use your special attacks. You should also learn how to parry, which is a technique that allows you to stun your opponent by blocking right before they hit you. Parrying is very useful for creating openings and preventing damage.
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Use your special attacks wisely and strategically. You should not waste your power meter on weak or ineffective special attacks, but save it for the ones that can deal more damage, inflict debuffs, or trigger effects. You should also be aware of your opponent's power meter and avoid getting hit by their special attacks, especially the third one, which is usually the most powerful and cannot be blocked.
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Explore the different game modes and quests and complete the objectives and challenges. You can earn rewards such as gold, units, crystals, ISO-8, catalysts, and more by playing the game regularly and completing various tasks. You can also unlock new characters, arenas, and stories by progressing through the game.
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What Are the Pros and Cons of Marvel Contest of Champions Apkpure
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Marvel Contest of Champions apkpure is a popular and well-received game that has many positive aspects, but also some negative ones. Here are some of the pros and cons of Marvel Contest of Champions apkpure:
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- The game has amazing graphics and animations that make the characters look realistic and lifelike.
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- The game can be repetitive and grindy at times, especially when you have to farm for resources or fight the same opponents over and over.
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- The game has a large and diverse roster of characters that appeal to Marvel fans of all ages and preferences.
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- The game has a social and competitive aspect that allows you to interact with other players and join alliances.
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- The game can be addictive and time-consuming at times, especially when you have to keep up with the events and quests or maintain your alliance status.
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Conclusion: Is Marvel Contest of Champions Apkpure Worth Playing?
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In conclusion, Marvel Contest of Champions apkpure is a great game for Marvel fans and fighting game enthusiasts who want to enjoy a thrilling and immersive experience on their mobile devices. The game has many advantages such as stunning graphics, diverse characters, simple controls, engaging story, and social features. However, the game also has some drawbacks such as repetitiveness, frustration, expense, bugs, and addiction. Therefore, we recommend that you play Marvel Contest of Champions apkpure with moderation and caution, and only if you are willing to accept its flaws. If you are looking for a superhero fighting game that is fun, easy, and free to play, then Marvel Contest of Champions apkpure is definitely worth trying.
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Here are some of the most common questions that people ask about Marvel Contest of Champions apkpure:
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Q: What is apkpure?
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A: Apkpure is a website that provides free APK files for Android devices. APK files are application packages that contain all the files needed to install an app on your device. Apkpure allows you to download APK files from various sources without any restrictions or limitations.
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A: Marvel Contest of Champions apkpure is updated regularly with new features, characters, events, and bug fixes. You can update the game by downloading the latest APK file from ApkCombo and installing it over the existing one. You can also enable the auto-update option in the settings of your device or the ApkCombo app to get notified and download the updates automatically.
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Marvel Future Fight: A role-playing game that lets you create and customize your own team of Marvel heroes and villains and fight against various enemies and bosses.
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Injustice 2: A fighting game that features characters from DC comics and movies and allows you to upgrade and customize them with gear and abilities.
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Mortal Kombat X: A fighting game that features characters from the Mortal Kombat franchise and allows you to perform brutal fatalities and x-ray moves.
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Marvel Strike Force: A turn-based strategy game that lets you assemble and command a squad of Marvel characters and fight against various threats.
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Marvel Puzzle Quest: A match-3 puzzle game that lets you collect and use Marvel characters in battles and events.
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Anime Kamen Rider W The Legendary Tokusatsu Series.md b/spaces/1phancelerku/anime-remove-background/Download Anime Kamen Rider W The Legendary Tokusatsu Series.md
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Download Anime Kamen Rider W: A Guide for Fans
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If you are a fan of tokusatsu, superhero, action, or detective genres, you might have heard of anime kamen rider w. This is a Japanese live-action TV series that aired from 2009 to 2010, as part of the long-running Kamen Rider franchise. It is also known as Kamen Rider Double, because it features two protagonists who can combine into one Kamen Rider. Anime kamen rider w is widely regarded as one of the best Kamen Rider series in the Heisei era, and has spawned a manga sequel, an anime adaptation, and various merchandise and games. In this article, we will give you an overview of anime kamen rider w, its plot and characters, its reception and popularity, its merchandise and games, and the best sites to download it.
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Plot and Characters
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Anime kamen rider w is set in the ecologically-minded city of Futo (the "Windy City"), where windmills power almost everything. However, the city is also plagued by crimes committed by Dopants, monsters created by using Gaia Memories, mysterious USB-like devices that contain the essence of the Earth. The Gaia Memories are sold by the Sonozaki Family, a powerful crime syndicate that also controls the Museum, a secret organization that researches the Gaia Memories.
The main protagonists of anime kamen rider w are Shotaro Hidari and Philip. Shotaro is a private detective who runs the Narumi Detective Agency, which specializes in Dopant cases. He is also a self-proclaimed "hard-boiled" detective who likes to wear a fedora and a trench coat. Philip is a mysterious young man who has no memories of his past, but possesses a vast knowledge of the Gaia Memories. He lives in a secret room in the agency, where he accesses a library-like database called the Gaia Library. Together, they can transform into Kamen Rider W (or Double), using two Gaia Memories and a belt called the Double Driver. By combining different Gaia Memories, they can access various forms with different powers and weapons.
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Some of their allies include Akiko Narumi, Shotaro's boss and the daughter of his mentor Sokichi Narumi, who was killed by a Dopant; Ryu Terui, a police officer who becomes Kamen Rider Accel to avenge his family; Shun Makura, a journalist who helps them with information; Watcherman, a blogger who reports on Dopant incidents; Santa-chan, a former thief who runs a souvenir shop; Queen and Elizabeth, two teenage girls who are fans of Kamen Rider W; and Jinno and Makura, two police officers who often assist Shotaro.
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Some of their enemies include Ryubee Sonozaki, the head of the Sonozaki Family and the Museum; Saeko Sonozaki, his eldest daughter who becomes the Taboo Dopant; Wakana Sonozaki, his youngest daughter who becomes the Clay Doll Dopant; Kirihiko Sudo, Saeko's husband who becomes the Nasca Dopant; Shinkuro Isaka, a doctor who becomes the Weather Dopant; Jun Kazu, a politician who becomes the Utopia Dopant; Katsumi Daido, the leader of NEVER, a group of undead soldiers who becomes the Eternal Dopant; and Foundation X, a mysterious organization that funds the Museum.
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Reception and Popularity
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Anime kamen rider w was well-received by both critics and fans when it aired. It was praised for its engaging plot, likable characters, creative designs, catchy music, humorous moments, emotional scenes, and thrilling action. It also won several awards, such as the Tokyo Anime Award for Best Domestic Feature
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Anime kamen rider w has a lot of merchandise and games for fans to enjoy. Some of the most popular products include the Gaia Memories, the Double Driver, the Accel Driver, the Lost Driver, and the various weapons and gadgets used by the Kamen Riders. These are sold as toys that can be used to recreate the transformations and attacks from the show. Some of them also have sounds and lights that match the ones in the show.
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There are also several video games based on anime kamen rider w, such as Kamen Rider: Climax Heroes W, Kamen Rider: Climax Heroes OOO, Kamen Rider: Super Climax Heroes, Kamen Rider: Battride War, Kamen Rider: Battride War II, Kamen Rider: Battride War Genesis, Kamen Rider: Memory of Heroez, and Kamen Rider Battle: Ganbarizing. These games allow players to control various Kamen Riders from anime kamen rider w and other series, and fight against enemies and bosses in different stages. Some of them also have story modes that follow the plot of the show or original scenarios.
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For fans who prefer more casual games, there are also some mobile games and web games related to anime kamen rider w, such as Kamen Rider City Wars, Kamen Rider Battle Rush, Kamen Rider Transcend Heroes, Kamen Rider Break Joker, and Futo Detectives. These games feature anime kamen rider w characters and elements in various genres, such as city-building, card battle, action RPG, puzzle, and adventure.
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If you are looking for anime kamen rider w gifts and merchandise, you can check out some online stores that sell them, such as Redbubble, Amazon, eBay, Mandarake, and AmiAmi. These sites offer a wide range of products, such as T-shirts, posters, stickers, mugs, keychains, figures, cosplay items, and more. You can also find some fan-made items that are unique and creative.
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Best Sites to Download Anime Kamen Rider W
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If you want to watch or rewatch anime kamen rider w on your devices, you might be wondering where to download it. There are many sites that offer anime kamen rider w for download, but not all of them are reliable and safe. Some of them might have low-quality videos, broken links, malware, or illegal content. To avoid these problems, you should only use trusted and reputable sites that have good reviews and ratings from other users.
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Here are some of the best sites to download anime kamen rider w:
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Site
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Pros
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Cons
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[Internet Archive](^7^)
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- Free and legal - High-quality videos - All episodes and movies available - No ads or pop-ups
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- Slow download speed - Limited formats and subtitles
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[Nyaa](^8^)
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- Free and fast - High-quality videos - Various formats and subtitles - Multiple sources and seeds
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- Not legal - Requires torrent client - May contain malware or viruses - May be blocked by some ISPs
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[KissAsian](^9^)
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- Free and easy - High-quality videos - Various formats and subtitles - Streaming option available
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- Not legal - Contains ads and pop-ups - May redirect to other sites - May require registration or verification
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[Over-Time]
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- Free and legal - High-quality videos - Various formats and subtitles - Official fansub group
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- Slow download speed - Requires torrent client or file hosting service - Only episodes available - No streaming option
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[OZC-Live]
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- Free and legal - High-quality videos - Various formats and subtitles - Official fansub group
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- Slow download speed - Requires torrent client or file hosting service - Only episodes available - No streaming option
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Conclusion
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Anime kamen rider w is a great series that deserves to be watched by anyone who likes tokusatsu, superhero, action, or detective genres. It has a captivating plot, charming characters, creative designs, catchy music, humorous moments, emotional scenes, and thrilling action. It also has a lot of merchandise and games for fans to enjoy. If you want to download anime kamen rider w, you can use one of the sites we recommended, or find other ones that suit your preferences. Just make sure to be careful and responsible when downloading, and respect the rights of the creators and owners of the content.
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We hope this article has helped you learn more about anime kamen rider w, and why it is such a popular and beloved series. If you have not watched it yet, we highly recommend you to give it a try. You will not regret it. Anime kamen rider w is a series that will make you laugh, cry, cheer, and feel inspired. It is a series that will stay with you for a long time.
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FAQs
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Here are some frequently asked questions and answers about anime kamen rider w:
-
Q: How many episodes and movies are there in anime kamen rider w?
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A: Anime kamen rider w has 49 episodes and 3 movies. The episodes are divided into 26 two-part cases, each with a different title that follows the W theme (e.g. The W Search/Two Detectives in One). The movies are Kamen Rider × Kamen Rider W & Decade: Movie War 2010, Kamen Rider W Forever: A to Z/The Gaia Memories of Fate, and Kamen Rider W Returns.
-
Q: What is the difference between the live-action and the anime versions of anime kamen rider w?
-
A: The live-action version of anime kamen rider w is the original TV series that aired from 2009 to 2010. The anime version of anime kamen rider w is an adaptation that was released in 2018 as part of the Toei Animation's 60th anniversary project. The anime version follows the same plot and characters as the live-action version, but with some changes and additions, such as new scenes, new forms, new enemies, and new voice actors.
-
Q: What is the meaning of the W in anime kamen rider w?
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A: The W in anime kamen rider w has multiple meanings. It stands for Double, because it represents the two protagonists who can combine into one Kamen Rider. It also stands for Windy City, because it is the nickname of Futo, where the series takes place. It also stands for Words, because it relates to the names of the Gaia Memories and the titles of the cases. It also stands for Wonders, because it reflects the mysterious and amazing nature of the series.
-
Q: Who are the voice actors of anime kamen rider w?
-
A: The voice actors of anime kamen rider w are as follows:
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Shotaro Hidari: Renn Kiriyama (live-action), Mamoru Miyano (anime)
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Philip: Masaki Suda (live-action), Ryo Yoshizawa (anime)
Jinno: Takeshi Nadagi (live-action), Daisuke Ono (anime)
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Makura: Akira Date (live-action), Yuichi Nakamura (anime)
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-
Q: Where can I read the manga sequel of anime kamen rider w?
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A: The manga sequel of anime kamen rider w is called Futo Detectives, and it is written by Riku Sanjo and drawn by Masaki Sato. It continues the story of Shotaro and Philip after the events of the TV series, as they face new cases and enemies in Futo. You can read it online on some manga sites, such as MangaDex, MangaRock, or MangaFox. You can also buy the physical volumes on some online stores, such as Amazon, CDJapan, or YesAsia.
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diff --git a/spaces/1toTree/lora_test/ppdiffusers/pipelines/stable_diffusion/pipeline_fastdeploy_stable_diffusion.py b/spaces/1toTree/lora_test/ppdiffusers/pipelines/stable_diffusion/pipeline_fastdeploy_stable_diffusion.py
deleted file mode 100644
index dcbf8e18d3397271d166a11e2297b4b5ab0bb192..0000000000000000000000000000000000000000
--- a/spaces/1toTree/lora_test/ppdiffusers/pipelines/stable_diffusion/pipeline_fastdeploy_stable_diffusion.py
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-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-import time
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import paddle
-
-from paddlenlp.transformers import CLIPFeatureExtractor, CLIPTokenizer
-
-from ...fastdeploy_utils import FastDeployRuntimeModel
-from ...pipeline_utils import DiffusionPipeline
-from ...schedulers import (
- DDIMScheduler,
- DPMSolverMultistepScheduler,
- EulerAncestralDiscreteScheduler,
- EulerDiscreteScheduler,
- LMSDiscreteScheduler,
- PNDMScheduler,
-)
-from ...schedulers.preconfig import (
- PreconfigEulerAncestralDiscreteScheduler,
- PreconfigLMSDiscreteScheduler,
-)
-from ...utils import logging
-from . import StableDiffusionPipelineOutput
-
-logger = logging.get_logger(__name__)
-
-
-class FastDeployStableDiffusionPipeline(DiffusionPipeline):
- r"""
- Pipeline for text-to-image generation using Stable Diffusion.
-
- This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
- library implements for all the pipelines (such as downloading or saving etc.)
-
- Args:
- vae_encoder ([`FastDeployRuntimeModel`]):
- Variational Auto-Encoder (VAE) Model to encode images to latent representations.
- vae_decoder ([`FastDeployRuntimeModel`]):
- Variational Auto-Encoder (VAE) Model to decode images from latent representations.
- text_encoder ([`FastDeployRuntimeModel`]):
- Frozen text-encoder. Stable Diffusion uses the text portion of
- [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
- the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
- tokenizer (`CLIPTokenizer`):
- Tokenizer of class
- [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
- unet ([`FastDeployRuntimeModel`]): Conditional U-Net architecture to denoise the encoded image latents.
- scheduler ([`SchedulerMixin`]):
- A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
- [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`PNDMScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`]
- or [`DPMSolverMultistepScheduler`].
- safety_checker ([`FastDeployRuntimeModel`]):
- Classification module that estimates whether generated images could be considered offensive or harmful.
- Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
- feature_extractor ([`CLIPFeatureExtractor`]):
- Model that extracts features from generated images to be used as inputs for the `safety_checker`.
- """
- _optional_components = ["vae_encoder", "safety_checker", "feature_extractor"]
-
- def __init__(
- self,
- vae_encoder: FastDeployRuntimeModel,
- vae_decoder: FastDeployRuntimeModel,
- text_encoder: FastDeployRuntimeModel,
- tokenizer: CLIPTokenizer,
- unet: FastDeployRuntimeModel,
- scheduler: Union[
- DDIMScheduler,
- PNDMScheduler,
- LMSDiscreteScheduler,
- PreconfigLMSDiscreteScheduler,
- EulerDiscreteScheduler,
- EulerAncestralDiscreteScheduler,
- PreconfigEulerAncestralDiscreteScheduler,
- DPMSolverMultistepScheduler,
- ],
- safety_checker: FastDeployRuntimeModel,
- feature_extractor: CLIPFeatureExtractor,
- requires_safety_checker: bool = True,
- ):
- super().__init__()
- if safety_checker is None and requires_safety_checker:
- logger.warning(
- f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
- " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
- " results in services or applications open to the public. PaddleNLP team, diffusers team and Hugging Face"
- " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
- " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
- " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
- )
- if safety_checker is not None and feature_extractor is None:
- raise ValueError(
- "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
- " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
- )
-
- self.register_modules(
- vae_encoder=vae_encoder,
- vae_decoder=vae_decoder,
- text_encoder=text_encoder,
- tokenizer=tokenizer,
- unet=unet,
- scheduler=scheduler,
- safety_checker=safety_checker,
- feature_extractor=feature_extractor,
- )
- self.register_to_config(requires_safety_checker=requires_safety_checker)
-
- def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
- r"""
- Encodes the prompt into text encoder hidden states.
-
- Args:
- prompt (`str` or `list(int)`):
- prompt to be encoded
- num_images_per_prompt (`int`):
- number of images that should be generated per prompt
- do_classifier_free_guidance (`bool`):
- whether to use classifier free guidance or not
- negative_prompt (`str` or `List[str]`):
- The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
- if `guidance_scale` is less than `1`).
- """
- batch_size = len(prompt) if isinstance(prompt, list) else 1
-
- # get prompt text embeddings
- text_inputs = self.tokenizer(
- prompt,
- padding="max_length",
- max_length=self.tokenizer.model_max_length,
- truncation=True,
- return_tensors="np",
- )
- text_input_ids = text_inputs.input_ids
- untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="np").input_ids
-
- if not np.array_equal(text_input_ids, untruncated_ids):
- removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
- logger.warning(
- "The following part of your input was truncated because CLIP can only handle sequences up to"
- f" {self.tokenizer.model_max_length} tokens: {removed_text}"
- )
-
- text_embeddings = self.text_encoder(input_ids=text_input_ids.astype(np.int64))[0]
- text_embeddings = np.repeat(text_embeddings, num_images_per_prompt, axis=0)
- # get unconditional embeddings for classifier free guidance
- if do_classifier_free_guidance:
- uncond_tokens: List[str]
- if negative_prompt is None:
- uncond_tokens = [""] * batch_size
- elif type(prompt) is not type(negative_prompt):
- raise TypeError(
- f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
- f" {type(prompt)}."
- )
- elif isinstance(negative_prompt, str):
- uncond_tokens = [negative_prompt] * batch_size
- elif batch_size != len(negative_prompt):
- raise ValueError(
- f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
- f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
- " the batch size of `prompt`."
- )
- else:
- uncond_tokens = negative_prompt
-
- max_length = text_input_ids.shape[-1]
- uncond_input = self.tokenizer(
- uncond_tokens,
- padding="max_length",
- max_length=max_length,
- truncation=True,
- return_tensors="np",
- )
- uncond_embeddings = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int64))[0]
- uncond_embeddings = np.repeat(uncond_embeddings, num_images_per_prompt, axis=0)
-
- # For classifier free guidance, we need to do two forward passes.
- # Here we concatenate the unconditional and text embeddings into a single batch
- # to avoid doing two forward passes
- text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
-
- return text_embeddings
-
- def run_safety_checker(self, image, dtype):
- if self.safety_checker is not None:
- safety_checker_input = self.feature_extractor(
- self.numpy_to_pil(image), return_tensors="np"
- ).pixel_values.astype(dtype)
- # There will throw an error if use safety_checker batchsize>1
- images, has_nsfw_concept = [], []
- for i in range(image.shape[0]):
- image_i, has_nsfw_concept_i = self.safety_checker(
- clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
- )
- images.append(image_i)
- has_nsfw_concept.append(has_nsfw_concept_i[0])
- image = np.concatenate(images)
- else:
- has_nsfw_concept = None
- return image, has_nsfw_concept
-
- def decode_latents(self, latents):
- latents = 1 / 0.18215 * latents
- latents_shape = latents.shape
- vae_output_shape = [latents_shape[0], 3, latents_shape[2] * 8, latents_shape[3] * 8]
- images_vae = paddle.zeros(vae_output_shape, dtype="float32")
-
- vae_input_name = self.vae_decoder.model.get_input_info(0).name
- vae_output_name = self.vae_decoder.model.get_output_info(0).name
-
- self.vae_decoder.zero_copy_infer(
- prebinded_inputs={vae_input_name: latents},
- prebinded_outputs={vae_output_name: images_vae},
- share_with_raw_ptr=True,
- )
-
- images_vae = paddle.clip(images_vae / 2 + 0.5, 0, 1)
- images = images_vae.transpose([0, 2, 3, 1])
- return images.numpy()
-
- def prepare_extra_step_kwargs(self, eta):
- # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
- # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
- # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
- # and should be between [0, 1]
-
- accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
- extra_step_kwargs = {}
- if accepts_eta:
- extra_step_kwargs["eta"] = eta
- return extra_step_kwargs
-
- def check_var_kwargs_of_scheduler_func(self, scheduler_func):
- sig = inspect.signature(scheduler_func)
- params = sig.parameters.values()
- has_kwargs = any([True for p in params if p.kind == p.VAR_KEYWORD])
- return has_kwargs
-
- def check_inputs(self, prompt, height, width, callback_steps):
- if not isinstance(prompt, str) and not isinstance(prompt, list):
- raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
- if height % 8 != 0 or width % 8 != 0:
- raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
- if (callback_steps is None) or (
- callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
- ):
- raise ValueError(
- f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
- f" {type(callback_steps)}."
- )
-
- def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, generator, latents=None):
- if generator is None:
- generator = np.random
-
- latents_shape = (batch_size, num_channels_latents, height // 8, width // 8)
- if latents is None:
- latents = generator.randn(*latents_shape).astype(dtype)
- elif latents.shape != latents_shape:
- raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
-
- # scale the initial noise by the standard deviation required by the scheduler
- latents = latents * float(self.scheduler.init_noise_sigma)
- return latents
-
- def __call__(
- self,
- prompt: Union[str, List[str]],
- height: Optional[int] = 512,
- width: Optional[int] = 512,
- num_inference_steps: Optional[int] = 50,
- guidance_scale: Optional[float] = 7.5,
- negative_prompt: Optional[Union[str, List[str]]] = None,
- num_images_per_prompt: Optional[int] = 1,
- eta: Optional[float] = 0.0,
- generator: Optional[np.random.RandomState] = None,
- latents: Optional[np.ndarray] = None,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
- callback_steps: Optional[int] = 1,
- ):
- r"""
- Function invoked when calling the pipeline for generation.
-
- Args:
- prompt (`str` or `List[str]`):
- The prompt or prompts to guide the image generation.
- height (`int`, *optional*, 512):
- The height in pixels of the generated image.
- width (`int`, *optional*, 512):
- The width in pixels of the generated image.
- num_inference_steps (`int`, *optional*, defaults to 50):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference.
- guidance_scale (`float`, *optional*, defaults to 7.5):
- Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
- `guidance_scale` is defined as `w` of equation 2. of [Imagen
- Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
- 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
- usually at the expense of lower image quality.
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
- if `guidance_scale` is less than `1`).
- num_images_per_prompt (`int`, *optional*, defaults to 1):
- The number of images to generate per prompt.
- eta (`float`, *optional*, defaults to 0.0):
- Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
- [`schedulers.DDIMScheduler`], will be ignored for others.
- generator (`np.random.RandomState`, *optional*):
- A np.random.RandomState to make generation deterministic.
- latents (`np.ndarray`, *optional*):
- Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
- generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
- tensor will ge generated by sampling using the supplied random `generator`.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generate image. Choose between
- [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
- plain tuple.
- callback (`Callable`, *optional*):
- A function that will be called every `callback_steps` steps during inference. The function will be
- called with the following arguments: `callback(step: int, timestep: int, latents: np.ndarray)`.
- callback_steps (`int`, *optional*, defaults to 1):
- The frequency at which the `callback` function will be called. If not specified, the callback will be
- called at every step.
-
- Returns:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
- When returning a tuple, the first element is a list with the generated images, and the second element is a
- list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
- (nsfw) content, according to the `safety_checker`.
- """
- # 1. Check inputs. Raise error if not correct
- self.check_inputs(prompt, height, width, callback_steps)
-
- # 2. Define call parameters
- batch_size = 1 if isinstance(prompt, str) else len(prompt)
-
- # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
- # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
- # corresponds to doing no classifier free guidance.
- do_classifier_free_guidance = guidance_scale > 1.0
-
- # 3. Encode input prompt
- start_time_encode_prompt = time.perf_counter()
- text_embeddings = self._encode_prompt(
- prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
- )
- print("_encode_prompt latency:", time.perf_counter() - start_time_encode_prompt)
- # 4. Prepare timesteps
- timesteps = self.scheduler.timesteps
-
- # 5. Prepare latent variables
- num_channels_latents = 4
- latents = self.prepare_latents(
- batch_size * num_images_per_prompt,
- num_channels_latents,
- height,
- width,
- text_embeddings.dtype,
- generator,
- latents,
- )
- if isinstance(latents, np.ndarray):
- latents = paddle.to_tensor(latents)
- # 6. Prepare extra step kwargs.
- extra_step_kwargs = self.prepare_extra_step_kwargs(eta)
- # 7. Denoising loop
- num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
- scheduler_support_kwagrs_scale_input = self.check_var_kwargs_of_scheduler_func(
- self.scheduler.scale_model_input
- )
- scheduler_support_kwagrs_step = self.check_var_kwargs_of_scheduler_func(self.scheduler.step)
-
- unet_output_name = self.unet.model.get_output_info(0).name
- unet_input_names = [self.unet.model.get_input_info(i).name for i in range(self.unet.model.num_inputs())]
- with self.progress_bar(total=num_inference_steps) as progress_bar:
- text_embeddings = paddle.to_tensor(text_embeddings, dtype="float32")
- for i, t in enumerate(timesteps):
- noise_pred_unet = paddle.zeros(
- [2 * batch_size * num_images_per_prompt, 4, height // 8, width // 8], dtype="float32"
- )
- # expand the latents if we are doing classifier free guidance
- latent_model_input = paddle.concat([latents] * 2) if do_classifier_free_guidance else latents
- if scheduler_support_kwagrs_scale_input:
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t, step_index=i)
- else:
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
- # predict the noise residual
- self.unet.zero_copy_infer(
- prebinded_inputs={
- unet_input_names[0]: latent_model_input,
- unet_input_names[1]: t,
- unet_input_names[2]: text_embeddings,
- },
- prebinded_outputs={unet_output_name: noise_pred_unet},
- share_with_raw_ptr=True,
- )
- # perform guidance
- if do_classifier_free_guidance:
- noise_pred_uncond, noise_pred_text = noise_pred_unet.chunk(2)
- noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
- # compute the previous noisy sample x_t -> x_t-1
- if scheduler_support_kwagrs_step:
- scheduler_output = self.scheduler.step(
- noise_pred, t, latents, step_index=i, return_pred_original_sample=False, **extra_step_kwargs
- )
- else:
- scheduler_output = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)
- latents = scheduler_output.prev_sample
- if i == num_inference_steps - 1:
- # sync for accuracy it/s measure
- paddle.device.cuda.synchronize()
- # call the callback, if provided
- if i == num_inference_steps - 1 or (
- (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
- ):
- progress_bar.update()
- if callback is not None and i % callback_steps == 0:
- callback(i, t, latents)
-
- # 8. Post-processing
- time_start_decoder = time.perf_counter()
- image = self.decode_latents(latents)
- print("decoder latency:", time.perf_counter() - time_start_decoder)
- # 9. Run safety checker
- image, has_nsfw_concept = self.run_safety_checker(image, text_embeddings.dtype)
-
- # 10. Convert to PIL
- if output_type == "pil":
- image = self.numpy_to_pil(image)
-
- if not return_dict:
- return (image, has_nsfw_concept)
-
- return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
diff --git a/spaces/2ndelement/voicevox/test/test_core_version_utility.py b/spaces/2ndelement/voicevox/test/test_core_version_utility.py
deleted file mode 100644
index e96ba8009e1614788e1e2b7ea9a11ae6d77dfe5c..0000000000000000000000000000000000000000
--- a/spaces/2ndelement/voicevox/test/test_core_version_utility.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from unittest import TestCase
-
-from voicevox_engine.utility import get_latest_core_version, parse_core_version
-
-
-class TestCoreVersion(TestCase):
- def test_parse_core_version(self):
- parse_core_version("0.0.0")
- parse_core_version("0.1.0")
- parse_core_version("0.10.0")
- parse_core_version("0.10.0-preview.1")
- parse_core_version("0.14.0")
- parse_core_version("0.14.0-preview.1")
- parse_core_version("0.14.0-preview.10")
-
- def test_get_latest_core_version(self):
- self.assertEqual(
- get_latest_core_version(
- versions=[
- "0.0.0",
- "0.1.0",
- "0.10.0",
- "0.10.0-preview.1",
- "0.14.0",
- "0.14.0-preview.1",
- "0.14.0-preview.10",
- ]
- ),
- "0.14.0",
- )
-
- self.assertEqual(
- get_latest_core_version(
- versions=[
- "0.14.0",
- "0.15.0-preview.1",
- ]
- ),
- "0.15.0-preview.1",
- )
diff --git a/spaces/801artistry/RVC801/infer/modules/vc/utils.py b/spaces/801artistry/RVC801/infer/modules/vc/utils.py
deleted file mode 100644
index a1cb0ff84097d1c7eb82373ccf19db061f595096..0000000000000000000000000000000000000000
--- a/spaces/801artistry/RVC801/infer/modules/vc/utils.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import os
-import re
-from fairseq import checkpoint_utils
-
-
-def get_index_path_from_model(sid):
- sid0strip = re.sub(r'\.pth|\.onnx$', '', sid)
- sid0name = os.path.split(sid0strip)[-1] # Extract only the name, not the directory
-
- # Check if the sid0strip has the specific ending format _eXXX_sXXX
- if re.match(r'.+_e\d+_s\d+$', sid0name):
- base_model_name = sid0name.rsplit('_', 2)[0]
- else:
- base_model_name = sid0name
-
- return next(
- (
- f
- for f in [
- os.path.join(root, name)
- for root, _, files in os.walk(os.getenv("index_root"), topdown=False)
- for name in files
- if name.endswith(".index") and "trained" not in name
- ]
- if base_model_name in f
- ),
- "",
- )
-
-
-def load_hubert(config):
- models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
- ["assets/hubert/hubert_base.pt"],
- suffix="",
- )
- hubert_model = models[0]
- hubert_model = hubert_model.to(config.device)
- if config.is_half:
- hubert_model = hubert_model.half()
- else:
- hubert_model = hubert_model.float()
- return hubert_model.eval()
diff --git a/spaces/A666sxr/Genshin_TTS/text/japanese.py b/spaces/A666sxr/Genshin_TTS/text/japanese.py
deleted file mode 100644
index 375e4d50872d5c68ee57ca17470a2ca425425eba..0000000000000000000000000000000000000000
--- a/spaces/A666sxr/Genshin_TTS/text/japanese.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import re
-from unidecode import unidecode
-import pyopenjtalk
-
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(
- r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(
- r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (symbol, Japanese) pairs for marks:
-_symbols_to_japanese = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('％', 'パーセント')
-]]
-
-# List of (romaji, ipa) pairs for marks:
-_romaji_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('ts', 'ʦ'),
- ('u', 'ɯ'),
- ('j', 'ʥ'),
- ('y', 'j'),
- ('ni', 'n^i'),
- ('nj', 'n^'),
- ('hi', 'çi'),
- ('hj', 'ç'),
- ('f', 'ɸ'),
- ('I', 'i*'),
- ('U', 'ɯ*'),
- ('r', 'ɾ')
-]]
-
-# List of (romaji, ipa2) pairs for marks:
-_romaji_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
- ('u', 'ɯ'),
- ('ʧ', 'tʃ'),
- ('j', 'dʑ'),
- ('y', 'j'),
- ('ni', 'n^i'),
- ('nj', 'n^'),
- ('hi', 'çi'),
- ('hj', 'ç'),
- ('f', 'ɸ'),
- ('I', 'i*'),
- ('U', 'ɯ*'),
- ('r', 'ɾ')
-]]
-
-# List of (consonant, sokuon) pairs:
-_real_sokuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'Q([↑↓]*[kg])', r'k#\1'),
- (r'Q([↑↓]*[tdjʧ])', r't#\1'),
- (r'Q([↑↓]*[sʃ])', r's\1'),
- (r'Q([↑↓]*[pb])', r'p#\1')
-]]
-
-# List of (consonant, hatsuon) pairs:
-_real_hatsuon = [(re.compile('%s' % x[0]), x[1]) for x in [
- (r'N([↑↓]*[pbm])', r'm\1'),
- (r'N([↑↓]*[ʧʥj])', r'n^\1'),
- (r'N([↑↓]*[tdn])', r'n\1'),
- (r'N([↑↓]*[kg])', r'ŋ\1')
-]]
-
-
-def symbols_to_japanese(text):
- for regex, replacement in _symbols_to_japanese:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_romaji_with_accent(text):
- '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
- text = symbols_to_japanese(text)
- sentences = re.split(_japanese_marks, text)
- marks = re.findall(_japanese_marks, text)
- text = ''
- for i, sentence in enumerate(sentences):
- if re.match(_japanese_characters, sentence):
- if text != '':
- text += ' '
- labels = pyopenjtalk.extract_fullcontext(sentence)
- for n, label in enumerate(labels):
- phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
- if phoneme not in ['sil', 'pau']:
- text += phoneme.replace('ch', 'ʧ').replace('sh',
- 'ʃ').replace('cl', 'Q')
- else:
- continue
- # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
- a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
- a2 = int(re.search(r"\+(\d+)\+", label).group(1))
- a3 = int(re.search(r"\+(\d+)/", label).group(1))
- if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil', 'pau']:
- a2_next = -1
- else:
- a2_next = int(
- re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
- # Accent phrase boundary
- if a3 == 1 and a2_next == 1:
- text += ' '
- # Falling
- elif a1 == 0 and a2_next == a2 + 1:
- text += '↓'
- # Rising
- elif a2 == 1 and a2_next == 2:
- text += '↑'
- if i < len(marks):
- text += unidecode(marks[i]).replace(' ', '')
- return text
-
-
-def get_real_sokuon(text):
- for regex, replacement in _real_sokuon:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def get_real_hatsuon(text):
- for regex, replacement in _real_hatsuon:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa(text):
- text = japanese_to_romaji_with_accent(text).replace('...', '…')
- text = re.sub(
- r'([aiueo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
- text = get_real_sokuon(text)
- text = get_real_hatsuon(text)
- for regex, replacement in _romaji_to_ipa:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa2(text):
- text = japanese_to_romaji_with_accent(text).replace('...', '…')
- text = get_real_sokuon(text)
- text = get_real_hatsuon(text)
- for regex, replacement in _romaji_to_ipa2:
- text = re.sub(regex, replacement, text)
- return text
-
-
-def japanese_to_ipa3(text):
- text = japanese_to_ipa2(text).replace('n^', 'ȵ').replace(
- 'ʃ', 'ɕ').replace('*', '\u0325').replace('#', '\u031a')
- text = re.sub(
- r'([aiɯeo])\1+', lambda x: x.group(0)[0]+'ː'*(len(x.group(0))-1), text)
- text = re.sub(r'((?:^|\s)(?:ts|tɕ|[kpt]))', r'\1ʰ', text)
- return text
diff --git a/spaces/AI-Dashboards/AI.Dashboard.Streamlit.Index.For.Assessments/app.py b/spaces/AI-Dashboards/AI.Dashboard.Streamlit.Index.For.Assessments/app.py
deleted file mode 100644
index 6a97b4b79e2a86d6ed1fcf4c87e3a16fe582ea6d..0000000000000000000000000000000000000000
--- a/spaces/AI-Dashboards/AI.Dashboard.Streamlit.Index.For.Assessments/app.py
+++ /dev/null
@@ -1,453 +0,0 @@
-import streamlit as st
-
-
-st.markdown("""
-
-## FHIR - CT - Graph
-
-# FHIR:
-https://huggingface.co/spaces/awacke1/Clinical-Terminology-FHIR-Assessment
-https://huggingface.co/spaces/awacke1/SMART-FHIR-Assessment-Observation-SDKs
-https://huggingface.co/spaces/awacke1/SMART-FHIR-Kits-SDC-HL7
-https://huggingface.co/spaces/awacke1/SMART-FHIR-Assessment-Blood-Pressure
-https://huggingface.co/spaces/awacke1/SMART-FHIR-Assessment-Exercise
-
-# Clinical Terminology:
-https://huggingface.co/spaces/awacke1/Ontology-Gradio
-https://huggingface.co/spaces/awacke1/Biomed-NLP-AI-Clinical-Terminology
-https://huggingface.co/spaces/awacke1/ClinicalTerminologyNER-Refactored
-https://huggingface.co/spaces/awacke1/ClinicalTerminologyAISearch
-https://huggingface.co/spaces/awacke1/ClinicalTerminologyAISearch1215
-
-# Graph, Clinical Terminology, FHIR Apps and Services:
-https://huggingface.co/spaces/awacke1/Git-GPG-Git-Actions-01-GraphViz
-https://huggingface.co/spaces/awacke1/Dice-Roll-Treemap-Plotly
-https://huggingface.co/spaces/awacke1/GraphVis3
-https://huggingface.co/spaces/awacke1/GraphViz-Demo
-https://huggingface.co/spaces/awacke1/StreamlitGraphViz
-https://huggingface.co/spaces/awacke1/CardGameActivity-GraphViz
-
-# CP Matplotlib, NetworkX, Streamlit, PyVis, st-click0detector, graphviz:
-https://huggingface.co/spaces/awacke1/CPVisGraph
-
-# OMS and LOCUS:
-https://huggingface.co/spaces/awacke1/NLPGraphOMSandLOCUS
-
-# Technical Architecture - Open Source Graph ML Libraries:
-NetworkX: https://networkx.org/
-PyTorch GNN: https://github.com/microsoft/ptgnn
-Jraph: https://github.com/deepmind/jraph
-Spektral: https://graphneural.network/
-Graph Nets: https://github.com/deepmind/graph_nets
-Deep Graph Library (DGL): https://github.com/dmlc
-PyTorch Geometric: https://github.com/pyg-team/pytorch_geometric
-
-# Provider Graph - Maps of Hospitals
-
-https://huggingface.co/spaces/awacke1/MN.Map.Hospitals.Top.Five
-
-
-
-
-# Graph, Clinical Terminology, FHIR Apps and Services:
-
-CP Matplotlib, NetworkX, Streamlit, PyVis, st-click0detector, graphviz:
-https://huggingface.co/spaces/awacke1/CPVisGraph
-
-OMS and LOCUS:
-https://huggingface.co/spaces/awacke1/NLPGraphOMSandLOCUS
-
-https://huggingface.co/spaces/awacke1/Git-GPG-Git-Actions-01-GraphViz
-https://huggingface.co/spaces/awacke1/Dice-Roll-Treemap-Plotly
-https://huggingface.co/spaces/awacke1/GraphVis3
-https://huggingface.co/spaces/awacke1/GraphViz-Demo
-https://huggingface.co/spaces/awacke1/StreamlitGraphViz
-https://huggingface.co/spaces/awacke1/CardGameActivity-GraphViz
-
-Technical Architecture - Open Source Graph ML Libraries:
-
-NetworkX: https://networkx.org/
-PyTorch GNN: https://github.com/microsoft/ptgnn
-Jraph: https://github.com/deepmind/jraph
-Spektral: https://graphneural.network/
-Graph Nets: https://github.com/deepmind/graph_nets
-Deep Graph Library (DGL): https://github.com/dmlc
-PyTorch Geometric: https://github.com/pyg-team/pytorch_geometric
-
-
-
-# Clinical Terminology:
-# FHIR:
-https://huggingface.co/spaces/awacke1/Clinical-Terminology-FHIR-Assessment
-https://huggingface.co/spaces/awacke1/SMART-FHIR-Assessment-Observation-SDKs
-https://huggingface.co/spaces/awacke1/SMART-FHIR-Kits-SDC-HL7
-https://huggingface.co/spaces/awacke1/SMART-FHIR-Assessment-Blood-Pressure
-https://huggingface.co/spaces/awacke1/SMART-FHIR-Assessment-Exercise
-
-
-# Clinical Terminology:
-https://huggingface.co/spaces/awacke1/Ontology-Gradio
-https://huggingface.co/spaces/awacke1/Biomed-NLP-AI-Clinical-Terminology
-https://huggingface.co/spaces/awacke1/ClinicalTerminologyNER-Refactored
-https://huggingface.co/spaces/awacke1/ClinicalTerminologyAISearch
-https://huggingface.co/spaces/awacke1/ClinicalTerminologyAISearch1215
-
-
-
-
-# Saturday Evening:
-https://huggingface.co/spaces/awacke1/MN.Map.Hospitals.Top.Five
-
-
-
-# Iceland Myths - Places to See - https://huggingface.co/spaces/awacke1/Maps.Markers.Honor.Iceland
-
-
-Ásbyrgi: Thor, trying to prove his strength, challenged Sleipnir to a race. Odin agreed, but secretly fed Sleipnir his favorite snack, lightning bolts. With each step, Sleipnir left a massive print, and thus, Ásbyrgi was formed.
-
-
-
-
-
-# Saturday
-write a streamlit python program that uses functions and user interface elements of a textbox, a dial, a four direction button array for up down left right and display a folium map with the data in python list dictionaries with these values: Aurora Spottings, Notifications on Nerthern Lights, Northern lights map location cities and countries for Iceland on a map written with folium for latitude and longitude of top ten places to view Northern Lights. Cite References as urls.
-
-# Maps
-
-Space | URL
--------------------------------------------------------------------------------------------------------------------------------------------
-awacke1/VizLib-TopLargeHospitalsNewJersey-03-09-2023 | https://huggingface.co/spaces/awacke1/VizLib-TopLargeHospitalsNewJersey-03-09-2023
-awacke1/Bird-Species-Migration-Month-Map | https://huggingface.co/spaces/awacke1/Bird-Species-Migration-Month-Map
-⚗️🧠🔬🧬 Clinical Terminology Auto Mapper AI 👩⚕️🩺⚕️🙋 | https://huggingface.co/spaces/awacke1/SNOMED-LOINC-eCQM
-awacke1/Visualization-Plotly-Sunbursts-Treemaps-and-WebGL | https://huggingface.co/spaces/awacke1/Visualization-Plotly-Sunbursts-Treemaps-and-WebGL
-awacke1/HTML5-Aframe-3D-Maps | https://huggingface.co/spaces/awacke1/HTML5-Aframe-3D-Maps
-awacke1/HTML5-Aframe-3dMap-Flight | https://huggingface.co/spaces/awacke1/HTML5-Aframe-3dMap-Flight
-
-Figures:
-
-
-
-
-# Top Ten Board Games
-## Map-Making-Strategy
-https://huggingface.co/spaces/awacke1/Top-Ten-Board-Games-Map-Making-Strategy
-
-
-
-# MediaPipe
-### A cross language SDK for AI that is real time, 3d, camera responsive, and on any device for nearly any language
-#### Vision
-#### Natural Language
-#### Audio
-
-Mediapipe has fast and flexible AI/ML pipelines.
-Examples with Javascript Links!
-
-1. Image Classifier: https://mediapipe-studio.webapps.google.com/demo/image_classifier
-2. Object Detector: https://mediapipe-studio.webapps.google.com/demo/object_detector
-3. Text Classification: https://mediapipe-studio.webapps.google.com/demo/text_classifier
-4. Gesture Recognizer: https://mediapipe-studio.webapps.google.com/demo/gesture_recognizer
-5. Hand Landmark Detection: https://mediapipe-studio.webapps.google.com/demo/hand_landmarker
-6. Audio Classifier: https://mediapipe-studio.webapps.google.com/demo/audio_classifier
-
-
-Get started with just Javascript!!
-Getting Started: https://google.github.io/mediapipe/getting_started/javascript.html
-
-Javascript Solutions - Ready to Demo:
-1. Face Mesh: https://codepen.io/mediapipe/full/KKgVaPJ
-2. Face Detection: https://codepen.io/mediapipe/full/dyOzvZM
-3. Hands: https://codepen.io/mediapipe/full/RwGWYJw
-4. Face, Hands, Body: https://codepen.io/mediapipe/full/LYRRYEw
-5. Objectron: https://codepen.io/mediapipe/full/BaWvzdY
-6. Full Skeletal Pose: https://codepen.io/mediapipe/full/jOMbvxw
-7. Self Segmentation From Background: https://codepen.io/mediapipe/full/wvJyQpq
-
-Demonstration in Action with Screenshots:
-
-Self Segmentation From Background:
-
-
-Full Skeletal Pose:
-
-
-Hands - Both in 3D Projection even hidden surface vertices - Mahalo:
-
-
-Holistic - Face, Hands, Body:
-
-
-Face Detection:
-
-
-Face Mesh Real Time - 30 Frames per second!
-
-
-
-
-# ASR Voice and Virtual Assistants With Avatars
-1. https://huggingface.co/spaces/awacke1/ASR-openai-whisper-large
-2. https://huggingface.co/spaces/awacke1/ASR-voidful-wav2vec2-xlsr-multilingual-56
-3. https://huggingface.co/spaces/awacke1/ASR-nvidia-stt_en_conformer_ctc_large
-4. https://huggingface.co/spaces/awacke1/ASR-facebook-hubert-large-ls960-ft
-5. https://huggingface.co/spaces/awacke1/ASR-openai-whisper-tiny.en
-6. https://huggingface.co/spaces/awacke1/ASR-openai-whisper-tiny
-7. https://huggingface.co/spaces/awacke1/ASR-openai-whisper-medium
-8. https://huggingface.co/spaces/awacke1/ASR-nvidia-stt_en_conformer_transducer_xlarge
-9. https://huggingface.co/spaces/awacke1/ASR-openai-whisper-base
-10. https://huggingface.co/spaces/awacke1/ASR-facebook-wav2vec2-large-960h-lv60-self
-11. https://huggingface.co/spaces/awacke1/ASR-facebook-wav2vec2-base-960h
-12. https://huggingface.co/spaces/awacke1/ASR-High-Accuracy-Test
-13. https://huggingface.co/spaces/awacke1/ASRGenerateStory
-14. https://huggingface.co/spaces/awacke1/TTS-STT-Blocks
-15. https://huggingface.co/spaces/awacke1/2-LiveASR
-16. https://huggingface.co/spaces/awacke1/CloneAnyVoice
-17. https://huggingface.co/spaces/awacke1/ASR-SOTA-NvidiaSTTMozilla
-18. https://huggingface.co/spaces/awacke1/ASRSpeechRecognition1
-19. https://huggingface.co/spaces/awacke1/1110-ASRLiveExample
-20. https://huggingface.co/spaces/awacke1/Z1-ASRLiveSpeechRecognition-GR
-21. https://huggingface.co/spaces/awacke1/PrivateASRWithMemory
-22. https://huggingface.co/spaces/awacke1/TimerASRLive
-
-# Best Voice Apps - HF:
-1. https://huggingface.co/spaces/BilalSardar/Voice-Cloning
-2. https://huggingface.co/spaces/RamAnanth1/chatGPT_voice
-3. https://huggingface.co/spaces/Voicemod/speech-synthesis-demo
-4. https://huggingface.co/spaces/ysharma/Voice-to-Youtube
-5. https://huggingface.co/spaces/ramkamal2000/voice-conversion-yourtts
-6. https://huggingface.co/spaces/RamAnanth1/co_chat_voice
-7. https://huggingface.co/spaces/ysharma/Voice-to-jokes
-8. https://huggingface.co/spaces/jayesh95/Voice-QA
-
-
-
-# Supervised Learning (SL) for ML and Reinforcement Learning with Human Feedback (RLHF):
-
-For human imitation we use reinforcement learning for fine tuning since feedback based on rewards shapes the quality of output where an agent completes a task and then observes a result. SL works on ranks not responses so is good for modifying elements at the token level however RLHF is trained to estimate the quality of the response with cumulative rewards for coherent conversation. RLHF considers context and coherence of entire conversation. Supervised learning is used to teach the model initially where the model learns basic structure and content. In the RLHF stage the model is refined with responses that represent improved accuracy.
-
-
-
-
-
-# Mermaid Model for Core NLP Tasks:
-
-```mermaid
-graph LR;
- A[Reader]-->B[Classifier];
- A-->C[Retriever];
- A-->D[Summarizer];
- B-->E[Ranker];
- B-->F[Query Classifier];
- D-->G[Generator];
- F-->H[Question Generator];
- H-->G;
- I[File Converter]-->J[Preprocessor];
- J-->A;
- I-->C;
- K[Snowflake]-->B;
- L[Oracle]-->B;
- M[Pandas CSV]-->A;
- N[Index]-->C;
- N-->E;
- O[Query with Filters]-->F;
- P[Evaluation]-->E;
- P-->F;
- Q[Retraining]-->B;
- Q-->E;
- R[Annotation]-->B;
-```
-
-# Core NLP Task Model for QA
-
-Tasks:
-1. Reader
-2. Summarizer
-3. Classifier
-4. Retriever
-5. Ranker
-6. Query Classifier
-7. Question Generator
-8. Generator
-
-Connectors:
-1. File Converter
-2. Preprocessor
-3. Snowflake
-4. Oracle
-5. Pandas CSV
-
-Supported Workflow:
-1. Index
-2. Query with Filters
-3. Evaluation
-4. Retraining
-5. Annotation
-
-# QA Model Spaces:
-
-QA use cases include QA, Semantic Document and FAQ Search.
-
-1. Streamlit Question Answering w Hugging Face: https://huggingface.co/spaces/awacke1/Question-answering
-2. Seq2Seq:
- - https://huggingface.co/spaces/awacke1/4-Seq2SeqQAT5
- - https://huggingface.co/spaces/awacke1/AW-04-GR-Seq-2-Seq-QA-Auto-Gen
- -
-3. BioGPT: https://huggingface.co/spaces/awacke1/microsoft-BioGPT-Large-PubMedQA
-4. NLP QA Context: https://huggingface.co/spaces/awacke1/NLPContextQATransformersRobertaBaseSquad2
- - https://huggingface.co/spaces/awacke1/SOTA-Plan
-5. https://huggingface.co/spaces/awacke1/Question-answering
-6. QA MLM: https://huggingface.co/spaces/awacke1/SOTA-MedEntity
-
-# 🤖 QA Models and Datasets:
-
-- Reader model extracts answers from text using QA pairs. SQuAD is the primary dataset.
-- Transformers (huggingface) has research momentum and solves real business problems.
-
-## 💻 Process:
-
-1. Best practices for QA systems: https://www.youtube.com/playlist?list=PLHgX2IExbFotW6WgDZ-cMzpDBUNKCMBbF
-2. Optimize Question/Answer Heads for SQuAD.
-3. QA search to ask questions to textual kb.
-4. Return text sections as answers.
-5. Organize text collection.
-6. Find similar documents to given input.
-7. Perform semantic and comprehensive word matching.
-8. Match incoming questions to FAQ KB dataset.
-
-## 📋 Tasks:
-
-1. Visual,
-2. Document, and
-3. Table QA.
-4. Zero Shot Classification.
-5. Translation.
-6. Conversational/Chat.
-7. Text2Text Generation.
-8. ASR/TTS.
-
-# Mermaid model
-
-```mermaid
-graph LR;
- A[Reader model]-->B[SQuAD];
- C[Transformers from Huggingface]-->D[Real Business Problems];
- E[Best practices for QA systems]-->F[Optimize Question/Answer Heads for SQuAD];
- G[QA search]-->H[Textual KB];
- H-->I[Return text sections as answers];
- J[Organize text collection]-->K[Find similar documents to given input];
- K-->I;
- L[Perform semantic and comprehensive word matching]-->I;
- M[Match incoming questions to FAQ KB dataset]-->I;
- N[Visual QA]-->O[Document QA];
- N-->P[Table QA];
- Q[Zero Shot Classification]-->I;
- R[Translation]-->I;
- S[Conversational/Chat]-->I;
- T[Text2Text Generation]-->I;
- U[ASR/TTS]-->I;
-
-```
-
-# Top 50 Assessments in Physical and Mental Health
-
-Below are the top 50 mental and physical health assessments.
-1. **Patient Health Questionnaire (PHQ-9)** 🧠 - Major depressive disorder (ICD-10: F32)
-2. **Generalized Anxiety Disorder 7-item Scale (GAD-7)** 😰 - Generalized anxiety disorder (ICD-10: F41.1)
-3. **Hamilton Rating Scale for Depression (HRSD)** 🧠 - Major depressive disorder (ICD-10: F32)
-4. **World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0)** 🧠💪 - Physical and mental disability (ICD-10: Z73.1)
-5. **Short Form-36 Health Survey (SF-36)** 💪🧠 - Health-related quality of life (CPT: 99499)
-6. **Health Assessment Questionnaire (HAQ)** 💪 - Functional status assessment (CPT: 97750)
-7. **EuroQol-5D (EQ-5D)** 💪🧠 - Health-related quality of life (LOINC: 83792-6)
-8. **Geriatric Depression Scale (GDS)** 🧑🦳🧠 - Depression in older adults (ICD-10: F32.1)
-9. **Mini-Mental State Examination (MMSE)** 🧑🦳💭 - Cognitive impairment (ICD-10: F06.7)
-10. **Pain Catastrophizing Scale (PCS)** 💔 - Chronic pain (LOINC: 86351-6)
-11. **Oswestry Disability Index (ODI)** 💪💔 - Back pain (CPT: 97750)
-12. **Fibromyalgia Impact Questionnaire (FIQ)** 💔😩 - Fibromyalgia (SNOMED: 316962002)
-13. **Beck Depression Inventory (BDI)** 🧠 - Depression (ICD-10: F32)
-14. **Posttraumatic Stress Disorder Checklist (PCL)** 😰😞 - Posttraumatic stress disorder (ICD-10: F43.1)
-15. **Alcohol Use Disorders Identification Test (AUDIT)** 🍻 - Alcohol use disorder (ICD-10: F10)
-16. **Drug Abuse Screening Test (DAST)** 💊 - Substance use disorder (ICD-10: F19)
-17. **Eating Attitudes Test (EAT)** 🍴 - Eating disorders (ICD-10: F50)
-18. **Adolescent Eating Disorder Examination (ADE)** 🍴👩🦰 - Eating disorders in adolescents (ICD-10: F50)
-19. **Child Behavior Checklist (CBCL)** 👧🧒 - Child behavior problems (ICD-10: F90)
-20. **Autism Spectrum Quotient (AQ)** 🧑🦱 - Autism spectrum disorder (ICD-10: F84.0)
-21. **Columbia-Suicide Severity Rating Scale (C-SSRS)** 🩸 - Suicide risk (ICD-10: Z65.8)
-22. **Perceived Stress Scale (PSS)** 😩 - Stress (LOINC: 75217-3)
-23. **Satisfaction with Life Scale (SWLS)** 😊 - Life satisfaction (LOINC: 69406-9)
-24. **Health Belief Model Scale (HBM)** 💊💉 - Health beliefs (LOINC: 88018)
-25. **Multidimensional Health Locus of Control Scale (MHLC)** 💊💉 - Health locus of control (LOINC: 87561-7)
-26. **Life Orientation Test-Revised (LOT-R)** 😃 - Optimism (LOINC: 75315-5)
-27. **State-Trait Anxiety Inventory (STAI)** 😰 - Anxiety (LOINC: 71092-3)
-28. **Multidimensional Scale of Perceived Social Support (MSPSS)** 👥 - Social support (LOINC: 86649-4)
-29. **Job Content Questionnaire (JCQ)** 💼 - Job stress (LOINC: 76554-9)
-30. **Burnout Measure (BO)** 🔥 - Burnout (LOINC: 89049-8)
-31. **Family Assessment Device (FAD)** 👨👩👧 - Family functioning (LOINC: 84113-2)
-32. **Perceived Control Scale (PCS)** 💪 - Perceived control (LOINC: 86447-0)
-33. **General Self-Efficacy Scale (GSES)** 💪 - Self-efficacy (LOINC: 76563-0)
-34. **Coping Strategies Inventory (CSI)** 😓 - Coping strategies (LOINC: 89057-1)
-35. **Acceptance and Action Questionnaire (AAQ-II)** 🧘 - Acceptance and commitment therapy (LOINC: 88027-2)
-36. **Attention Deficit Hyperactivity Disorder Self-Report Scale (ASRS)** 👧🧒 - ADHD (ICD-10: F90)
-37. **Impact of Event Scale-Revised (IES-R)** 😔😞 - Trauma (LOINC: 86237-7)
-38. **Insomnia Severity Index (ISI)** 💤 - Insomnia (LOINC: 82451-5)
-39. **Social Phobia Inventory (SPIN)** 😰 - Social anxiety disorder (ICD-10: F40.1)
-40. **Panic Disorder Severity Scale (PDSS)** 😰 - Panic disorder (ICD-10: F41.0)
-41. **Yale-Brown Obsessive Compulsive Scale (Y-BOCS)** 🤔 - Obsessive-compulsive disorder (ICD-10: F42)
-42. **Social Interaction Anxiety Scale (SIAS)** 😰 - Social anxiety disorder (ICD-10: F40.1)
-43. **Generalized Anxiety Disorder Scale (GADS)** 😰 - Generalized anxiety disorder (ICD-10: F41.1)
-44. **Postpartum Depression Screening Scale (PDSS)** 🤱🧠 - Postpartum depression (ICD-10: F53.0)
-45. **Child and Adolescent Symptom Inventory (CASI)** 👧🧒🧠 - Child and adolescent mental health (ICD-10: F90)
-46. **Strengths and Difficulties Questionnaire (SDQ)** 👧🧒🧠 - Child and adolescent mental health (ICD-10: F90)
-47. **Kessler Psychological Distress Scale (K10)** 🧠 - Psychological distress (LOINC: 76550-6)
-48. **World Health Organization Quality of Life Scale (WHOQOL)** 💪🧠 - Quality of life (LOINC: 88055-2)
-49. **Multidimensional Pain Inventory (MPI)** 💔 - Chronic pain (LOINC: 71808-8)
-50. **Cornell Scale for Depression in Dementia (CSDD)** 👴👵🧠 - Depression in dementia patients (ICD-10: F03.90)
-
-
-# SMART/FHIR/SDC Survey-Assess-Plan
-
-These SMART/FHIR/SDC compatible Surveys demonstrate how to build and conducct surveys with EMR/EHR Compliance Standards
-
-1. Smart FHIR Connect and Test BMI Calculator: https://huggingface.co/spaces/awacke1/SMART-FHIR-Assessment-BMI
-2. Smart FHIR Kits SDC HL7: https://huggingface.co/spaces/awacke1/SMART-FHIR-Kits-SDC-HL7
-3. Smart FHIR Assessment Exercise: https://huggingface.co/spaces/awacke1/SMART-FHIR-Assessment-Exercise
-4. Smart FHIR Assessment Blood Pressure: https://huggingface.co/spaces/awacke1/SMART-FHIR-Assessment-Blood-Pressure
-5. Smart FHIR - Observations-Assessments-Rules-Referrals-Providers-Programs-Fulfillment-Alerrts-Notes-SDOH: https://huggingface.co/spaces/awacke1/SMART-FHIR-Assessment-Observation-SDKs
-
-
-# Graphs Survey-Assess-Plan-Goals
-
-These top 5 graph examples introduce visual ideas to use to survey, assess, plan and reach goals.
-
-1. Graph OMS and LOCUS Standards and Quality Metrics: https://huggingface.co/spaces/awacke1/NLPGraphOMSandLOCUS
-2. Graph Pain and High Medium Low Confidence: https://huggingface.co/spaces/awacke1/VISNLP-Graph
-3. Graph Action Mechanics: https://huggingface.co/spaces/awacke1/CardGameActivity-GraphViz
-4. Graph - OMS, MH, Charts, Maps, DOT lang for Pyvis VisJS: https://huggingface.co/spaces/awacke1/CPVisGraph
-5. Graph - Plan and Assess: https://huggingface.co/spaces/awacke1/Git-GPG-Git-Actions-01-GraphViz
-
-# ICD10, CPT, LOINC, SNOMED, HCPCS, OMS Codes for Top Health Conditions and Treatment Preferences Assessment
-
-Assess Topic| Assess Metric | Code Emoji | Code Topic | Code Type | Code
-------------|---------------|------------|------------|------------|-----------
-Childhood Immunization| % of children immunized by age two |🧒💉 | Clinical Code| ICD10 | Z28.2
-Breast Cancer Screening| % of women with mammogram in past 2 yrs |🩺🎀 | Clinical Code| CPT| 77067
-Colorectal Cancer Screening| % of adults screened for colorectal cancer| 🩺💩 | Clinical Code| CPT| 82274
-Comprehensive Diabetes Care| % of diabetic patients who had all recommended tests| 🩺🩹 | Clinical Code| LOINC| 4548-4
-Controlling High Blood Pressure| % of patients with controlled blood pressure| 🩺💊 | Clinical Code| ICD10|I10
-Medication Management for Asthma| % of asthma patients with proper meds| 💊🌬️ | Clinical Code| SNOMED|195967001
-Follow-up After Mental Illness Hospitalization| % of patients with follow-up care| 🩺🏥 | Clinical Code| HCPCS|G0181
-Prenatal & Postpartum Care| % of pregnant women with proper care |🤰🩺 | Clinical Code| ICD10|Z34
-Comprehensive Eye Exam| % of diabetic patients with eye exam |🩺👀 | Clinical Code| CPT| 92014
-Childhood Weight Assessment| % of children with BMI assessment |🧒📏 | Clinical Code| ICD10| Z00.121
-Chlamydia Screening in Women| % of sexually active women screened| 🩺👩 | Clinical Code| CPT|87491
-Avoidance of Antibiotic Treatment for Acute Bronchitis| % of patients without antibiotics |🩺💊 | Clinical Code| ICD10|J20.9
-Osteoporosis Management in Women|% of women with bone density test |🩺💪 | Clinical Code| CPT|77080
-Use of High-Risk Medications in the Elderly| % of elderly with safe meds |💊👴👵 | Clinical Code| HCPCS |G9612
-Diabetes Screening for Schizophrenia or Bipolar Disorder| % of patients with mental illness screened |🧠🩺 | Clinical Code| SNOMED| 169609005
-All-Cause Readmissions| % of patients readmitted within 30 days |🩺🏥 | Clinical Code| ICD10| Z51.5
-Antidepressant Medication Management| % of depressed patients with proper meds & follow-up |🩺🧠 | Clinical Code| CPT|96127
-Follow-up Care for Children Prescribed ADHD Medication|% of children with follow-up care |🩺🧒 | Clinical Code| ICD10|F90
-Imaging Studies for Low Back Pain| % of patients without imaging studies|🩺📊 | Clinical Code| ICD10|M54.5
-Spirometry Testing for COPD|% of COPD patients with spirometry testing |🩺🫁 | Clinical Code|CPT|94010
-
-
-""")
\ No newline at end of file
diff --git a/spaces/AIConsultant/MusicGen/README.md b/spaces/AIConsultant/MusicGen/README.md
deleted file mode 100644
index 215eb424f4d2efd9d3295c0b6763b9f205b45c7d..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AudioCraft Plus v2.0.0a (MusicGen + AudioGen)
-emoji: 🎶
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: true
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/encoders/open_clap/htsat.py b/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/encoders/open_clap/htsat.py
deleted file mode 100644
index db96116286d307a73943886f947450215e061ba2..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/encoders/open_clap/htsat.py
+++ /dev/null
@@ -1,1022 +0,0 @@
-# Ke Chen
-# knutchen@ucsd.edu
-# HTS-AT: A HIERARCHICAL TOKEN-SEMANTIC AUDIO TRANSFORMER FOR SOUND CLASSIFICATION AND DETECTION
-# Some layers designed on the model
-# below codes are based and referred from https://github.com/microsoft/Swin-Transformer
-# Swin Transformer for Computer Vision: https://arxiv.org/pdf/2103.14030.pdf
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from itertools import repeat
-import collections.abc
-import math
-import warnings
-
-from torch.nn.init import _calculate_fan_in_and_fan_out
-import torch.utils.checkpoint as checkpoint
-
-import random
-
-from torchlibrosa.stft import Spectrogram, LogmelFilterBank
-from torchlibrosa.augmentation import SpecAugmentation
-
-from itertools import repeat
-from .utils import do_mixup, interpolate
-
-from .feature_fusion import iAFF, AFF, DAF
-
-# from PyTorch internals
-def _ntuple(n):
- def parse(x):
- if isinstance(x, collections.abc.Iterable):
- return x
- return tuple(repeat(x, n))
- return parse
-
-to_1tuple = _ntuple(1)
-to_2tuple = _ntuple(2)
-to_3tuple = _ntuple(3)
-to_4tuple = _ntuple(4)
-to_ntuple = _ntuple
-
-def drop_path(x, drop_prob: float = 0., training: bool = False):
- """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
- This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
- the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
- See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
- changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
- 'survival rate' as the argument.
- """
- if drop_prob == 0. or not training:
- return x
- keep_prob = 1 - drop_prob
- shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
- random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
- random_tensor.floor_() # binarize
- output = x.div(keep_prob) * random_tensor
- return output
-
-
-class DropPath(nn.Module):
- """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
- """
- def __init__(self, drop_prob=None):
- super(DropPath, self).__init__()
- self.drop_prob = drop_prob
-
- def forward(self, x):
- return drop_path(x, self.drop_prob, self.training)
-
-class PatchEmbed(nn.Module):
- """ 2D Image to Patch Embedding
- """
- def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True, patch_stride = 16,
- enable_fusion=False, fusion_type='None'):
- super().__init__()
- img_size = to_2tuple(img_size)
- patch_size = to_2tuple(patch_size)
- patch_stride = to_2tuple(patch_stride)
- self.img_size = img_size
- self.patch_size = patch_size
- self.patch_stride = patch_stride
- self.grid_size = (img_size[0] // patch_stride[0], img_size[1] // patch_stride[1])
- self.num_patches = self.grid_size[0] * self.grid_size[1]
- self.flatten = flatten
- self.in_chans = in_chans
- self.embed_dim = embed_dim
-
- self.enable_fusion = enable_fusion
- self.fusion_type = fusion_type
-
- padding = ((patch_size[0] - patch_stride[0]) // 2, (patch_size[1] - patch_stride[1]) // 2)
-
- if (self.enable_fusion) and (self.fusion_type == 'channel_map'):
- self.proj = nn.Conv2d(in_chans*4, embed_dim, kernel_size=patch_size, stride=patch_stride, padding=padding)
- else:
- self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_stride, padding=padding)
- self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
-
- if (self.enable_fusion) and (self.fusion_type in ['daf_2d','aff_2d','iaff_2d']):
- self.mel_conv2d = nn.Conv2d(in_chans, embed_dim, kernel_size=(patch_size[0], patch_size[1]*3), stride=(patch_stride[0], patch_stride[1] * 3), padding=padding)
- if self.fusion_type == 'daf_2d':
- self.fusion_model = DAF()
- elif self.fusion_type == 'aff_2d':
- self.fusion_model = AFF(channels=embed_dim, type='2D')
- elif self.fusion_type == 'iaff_2d':
- self.fusion_model = iAFF(channels=embed_dim, type='2D')
- def forward(self, x, longer_idx = None):
- if (self.enable_fusion) and (self.fusion_type in ['daf_2d','aff_2d','iaff_2d']):
- global_x = x[:,0:1,:,:]
-
-
- # global processing
- B, C, H, W = global_x.shape
- assert H == self.img_size[0] and W == self.img_size[1], \
- f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
- global_x = self.proj(global_x)
- TW = global_x.size(-1)
- if len(longer_idx) > 0:
- # local processing
- local_x = x[longer_idx,1:,:,:].contiguous()
- B, C, H, W = local_x.shape
- local_x = local_x.view(B*C,1,H,W)
- local_x = self.mel_conv2d(local_x)
- local_x = local_x.view(B,C,local_x.size(1),local_x.size(2),local_x.size(3))
- local_x = local_x.permute((0,2,3,1,4)).contiguous().flatten(3)
- TB,TC,TH,_ = local_x.size()
- if local_x.size(-1) < TW:
- local_x = torch.cat([local_x, torch.zeros((TB,TC,TH,TW-local_x.size(-1)), device=global_x.device)], dim=-1)
- else:
- local_x = local_x[:,:,:,:TW]
-
- global_x[longer_idx] = self.fusion_model(global_x[longer_idx],local_x)
- x = global_x
- else:
- B, C, H, W = x.shape
- assert H == self.img_size[0] and W == self.img_size[1], \
- f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
- x = self.proj(x)
-
- if self.flatten:
- x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
- x = self.norm(x)
- return x
-
-class Mlp(nn.Module):
- """ MLP as used in Vision Transformer, MLP-Mixer and related networks
- """
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = nn.Linear(in_features, hidden_features)
- self.act = act_layer()
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-def _no_grad_trunc_normal_(tensor, mean, std, a, b):
- # Cut & paste from PyTorch official master until it's in a few official releases - RW
- # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
- def norm_cdf(x):
- # Computes standard normal cumulative distribution function
- return (1. + math.erf(x / math.sqrt(2.))) / 2.
-
- if (mean < a - 2 * std) or (mean > b + 2 * std):
- warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
- "The distribution of values may be incorrect.",
- stacklevel=2)
-
- with torch.no_grad():
- # Values are generated by using a truncated uniform distribution and
- # then using the inverse CDF for the normal distribution.
- # Get upper and lower cdf values
- l = norm_cdf((a - mean) / std)
- u = norm_cdf((b - mean) / std)
-
- # Uniformly fill tensor with values from [l, u], then translate to
- # [2l-1, 2u-1].
- tensor.uniform_(2 * l - 1, 2 * u - 1)
-
- # Use inverse cdf transform for normal distribution to get truncated
- # standard normal
- tensor.erfinv_()
-
- # Transform to proper mean, std
- tensor.mul_(std * math.sqrt(2.))
- tensor.add_(mean)
-
- # Clamp to ensure it's in the proper range
- tensor.clamp_(min=a, max=b)
- return tensor
-
-
-def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
- # type: (Tensor, float, float, float, float) -> Tensor
- r"""Fills the input Tensor with values drawn from a truncated
- normal distribution. The values are effectively drawn from the
- normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
- with values outside :math:`[a, b]` redrawn until they are within
- the bounds. The method used for generating the random values works
- best when :math:`a \leq \text{mean} \leq b`.
- Args:
- tensor: an n-dimensional `torch.Tensor`
- mean: the mean of the normal distribution
- std: the standard deviation of the normal distribution
- a: the minimum cutoff value
- b: the maximum cutoff value
- Examples:
- >>> w = torch.empty(3, 5)
- >>> nn.init.trunc_normal_(w)
- """
- return _no_grad_trunc_normal_(tensor, mean, std, a, b)
-
-
-def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'):
- fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
- if mode == 'fan_in':
- denom = fan_in
- elif mode == 'fan_out':
- denom = fan_out
- elif mode == 'fan_avg':
- denom = (fan_in + fan_out) / 2
-
- variance = scale / denom
-
- if distribution == "truncated_normal":
- # constant is stddev of standard normal truncated to (-2, 2)
- trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978)
- elif distribution == "normal":
- tensor.normal_(std=math.sqrt(variance))
- elif distribution == "uniform":
- bound = math.sqrt(3 * variance)
- tensor.uniform_(-bound, bound)
- else:
- raise ValueError(f"invalid distribution {distribution}")
-
-
-def lecun_normal_(tensor):
- variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal')
-
-def window_partition(x, window_size):
- """
- Args:
- x: (B, H, W, C)
- window_size (int): window size
- Returns:
- windows: (num_windows*B, window_size, window_size, C)
- """
- B, H, W, C = x.shape
- x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
- windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
- return windows
-
-
-def window_reverse(windows, window_size, H, W):
- """
- Args:
- windows: (num_windows*B, window_size, window_size, C)
- window_size (int): Window size
- H (int): Height of image
- W (int): Width of image
- Returns:
- x: (B, H, W, C)
- """
- B = int(windows.shape[0] / (H * W / window_size / window_size))
- x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
- return x
-
-
-class WindowAttention(nn.Module):
- r""" Window based multi-head self attention (W-MSA) module with relative position bias.
- It supports both of shifted and non-shifted window.
- Args:
- dim (int): Number of input channels.
- window_size (tuple[int]): The height and width of the window.
- num_heads (int): Number of attention heads.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
- attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
- proj_drop (float, optional): Dropout ratio of output. Default: 0.0
- """
-
- def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
- super().__init__()
- self.dim = dim
- self.window_size = window_size # Wh, Ww
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = qk_scale or head_dim ** -0.5
-
- # define a parameter table of relative position bias
- self.relative_position_bias_table = nn.Parameter(
- torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
-
- # get pair-wise relative position index for each token inside the window
- coords_h = torch.arange(self.window_size[0])
- coords_w = torch.arange(self.window_size[1])
- coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
- coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
- relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
- relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
- relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
- relative_coords[:, :, 1] += self.window_size[1] - 1
- relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
- relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
- self.register_buffer("relative_position_index", relative_position_index)
-
- self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
-
- trunc_normal_(self.relative_position_bias_table, std=.02)
- self.softmax = nn.Softmax(dim=-1)
-
- def forward(self, x, mask=None):
- """
- Args:
- x: input features with shape of (num_windows*B, N, C)
- mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
- """
- B_, N, C = x.shape
- qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
- q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
-
- q = q * self.scale
- attn = (q @ k.transpose(-2, -1))
-
- relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
- self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
- relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
- attn = attn + relative_position_bias.unsqueeze(0)
-
- if mask is not None:
- nW = mask.shape[0]
- attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
- attn = attn.view(-1, self.num_heads, N, N)
- attn = self.softmax(attn)
- else:
- attn = self.softmax(attn)
-
- attn = self.attn_drop(attn)
-
- x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
- x = self.proj(x)
- x = self.proj_drop(x)
- return x, attn
-
- def extra_repr(self):
- return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
-
-
-# We use the model based on Swintransformer Block, therefore we can use the swin-transformer pretrained model
-class SwinTransformerBlock(nn.Module):
- r""" Swin Transformer Block.
- Args:
- dim (int): Number of input channels.
- input_resolution (tuple[int]): Input resulotion.
- num_heads (int): Number of attention heads.
- window_size (int): Window size.
- shift_size (int): Shift size for SW-MSA.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float, optional): Stochastic depth rate. Default: 0.0
- act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- """
-
- def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
- mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
- act_layer=nn.GELU, norm_layer=nn.LayerNorm, norm_before_mlp='ln'):
- super().__init__()
- self.dim = dim
- self.input_resolution = input_resolution
- self.num_heads = num_heads
- self.window_size = window_size
- self.shift_size = shift_size
- self.mlp_ratio = mlp_ratio
- self.norm_before_mlp = norm_before_mlp
- if min(self.input_resolution) <= self.window_size:
- # if window size is larger than input resolution, we don't partition windows
- self.shift_size = 0
- self.window_size = min(self.input_resolution)
- assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
- self.norm1 = norm_layer(dim)
- self.attn = WindowAttention(
- dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
- qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
- if self.norm_before_mlp == 'ln':
- self.norm2 = nn.LayerNorm(dim)
- elif self.norm_before_mlp == 'bn':
- self.norm2 = lambda x: nn.BatchNorm1d(dim)(x.transpose(1, 2)).transpose(1, 2)
- else:
- raise NotImplementedError
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
- if self.shift_size > 0:
- # calculate attention mask for SW-MSA
- H, W = self.input_resolution
- img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
- h_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- w_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- cnt = 0
- for h in h_slices:
- for w in w_slices:
- img_mask[:, h, w, :] = cnt
- cnt += 1
-
- mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
- mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
- attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
- attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
- else:
- attn_mask = None
-
- self.register_buffer("attn_mask", attn_mask)
-
- def forward(self, x):
- # pdb.set_trace()
- H, W = self.input_resolution
- # print("H: ", H)
- # print("W: ", W)
- # pdb.set_trace()
- B, L, C = x.shape
- # assert L == H * W, "input feature has wrong size"
-
- shortcut = x
- x = self.norm1(x)
- x = x.view(B, H, W, C)
-
- # cyclic shift
- if self.shift_size > 0:
- shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
- else:
- shifted_x = x
-
- # partition windows
- x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
- x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
-
- # W-MSA/SW-MSA
- attn_windows, attn = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
-
- # merge windows
- attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
- shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
-
- # reverse cyclic shift
- if self.shift_size > 0:
- x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
- else:
- x = shifted_x
- x = x.view(B, H * W, C)
-
- # FFN
- x = shortcut + self.drop_path(x)
- x = x + self.drop_path(self.mlp(self.norm2(x)))
-
- return x, attn
-
- def extra_repr(self):
- return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
- f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
-
-
-class PatchMerging(nn.Module):
- r""" Patch Merging Layer.
- Args:
- input_resolution (tuple[int]): Resolution of input feature.
- dim (int): Number of input channels.
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- """
-
- def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
- super().__init__()
- self.input_resolution = input_resolution
- self.dim = dim
- self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
- self.norm = norm_layer(4 * dim)
-
- def forward(self, x):
- """
- x: B, H*W, C
- """
- H, W = self.input_resolution
- B, L, C = x.shape
- assert L == H * W, "input feature has wrong size"
- assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
- x = x.view(B, H, W, C)
-
- x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
- x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
- x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
- x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
- x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
- x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
-
- x = self.norm(x)
- x = self.reduction(x)
-
- return x
-
- def extra_repr(self):
- return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-
-class BasicLayer(nn.Module):
- """ A basic Swin Transformer layer for one stage.
- Args:
- dim (int): Number of input channels.
- input_resolution (tuple[int]): Input resolution.
- depth (int): Number of blocks.
- num_heads (int): Number of attention heads.
- window_size (int): Local window size.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
- drop (float, optional): Dropout rate. Default: 0.0
- attn_drop (float, optional): Attention dropout rate. Default: 0.0
- drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
- norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
- downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
- """
-
- def __init__(self, dim, input_resolution, depth, num_heads, window_size,
- mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
- drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
- norm_before_mlp='ln'):
-
- super().__init__()
- self.dim = dim
- self.input_resolution = input_resolution
- self.depth = depth
- self.use_checkpoint = use_checkpoint
-
- # build blocks
- self.blocks = nn.ModuleList([
- SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
- num_heads=num_heads, window_size=window_size,
- shift_size=0 if (i % 2 == 0) else window_size // 2,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias, qk_scale=qk_scale,
- drop=drop, attn_drop=attn_drop,
- drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
- norm_layer=norm_layer, norm_before_mlp=norm_before_mlp)
- for i in range(depth)])
-
- # patch merging layer
- if downsample is not None:
- self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
- else:
- self.downsample = None
-
- def forward(self, x):
- attns = []
- for blk in self.blocks:
- if self.use_checkpoint:
- x = checkpoint.checkpoint(blk, x)
- else:
- x, attn = blk(x)
- if not self.training:
- attns.append(attn.unsqueeze(0))
- if self.downsample is not None:
- x = self.downsample(x)
- if not self.training:
- attn = torch.cat(attns, dim = 0)
- attn = torch.mean(attn, dim = 0)
- return x, attn
-
- def extra_repr(self):
- return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-
-# The Core of HTSAT
-class HTSAT_Swin_Transformer(nn.Module):
- r"""HTSAT based on the Swin Transformer
- Args:
- spec_size (int | tuple(int)): Input Spectrogram size. Default 256
- patch_size (int | tuple(int)): Patch size. Default: 4
- path_stride (iot | tuple(int)): Patch Stride for Frequency and Time Axis. Default: 4
- in_chans (int): Number of input image channels. Default: 1 (mono)
- num_classes (int): Number of classes for classification head. Default: 527
- embed_dim (int): Patch embedding dimension. Default: 96
- depths (tuple(int)): Depth of each HTSAT-Swin Transformer layer.
- num_heads (tuple(int)): Number of attention heads in different layers.
- window_size (int): Window size. Default: 8
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
- qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
- qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
- drop_rate (float): Dropout rate. Default: 0
- attn_drop_rate (float): Attention dropout rate. Default: 0
- drop_path_rate (float): Stochastic depth rate. Default: 0.1
- norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
- ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
- patch_norm (bool): If True, add normalization after patch embedding. Default: True
- use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
- config (module): The configuration Module from config.py
- """
-
- def __init__(self, spec_size=256, patch_size=4, patch_stride=(4,4),
- in_chans=1, num_classes=527,
- embed_dim=96, depths=[2, 2, 6, 2], num_heads=[4, 8, 16, 32],
- window_size=8, mlp_ratio=4., qkv_bias=True, qk_scale=None,
- drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
- norm_layer=nn.LayerNorm,
- ape=False, patch_norm=True,
- use_checkpoint=False, norm_before_mlp='ln', config = None,
- enable_fusion = False, fusion_type = 'None', **kwargs):
- super(HTSAT_Swin_Transformer, self).__init__()
-
- self.config = config
- self.spec_size = spec_size
- self.patch_stride = patch_stride
- self.patch_size = patch_size
- self.window_size = window_size
- self.embed_dim = embed_dim
- self.depths = depths
- self.ape = ape
- self.in_chans = in_chans
- self.num_classes = num_classes
- self.num_heads = num_heads
- self.num_layers = len(self.depths)
- self.num_features = int(self.embed_dim * 2 ** (self.num_layers - 1))
-
- self.drop_rate = drop_rate
- self.attn_drop_rate = attn_drop_rate
- self.drop_path_rate = drop_path_rate
-
- self.qkv_bias = qkv_bias
- self.qk_scale = None
-
- self.patch_norm = patch_norm
- self.norm_layer = norm_layer if self.patch_norm else None
- self.norm_before_mlp = norm_before_mlp
- self.mlp_ratio = mlp_ratio
-
- self.use_checkpoint = use_checkpoint
-
- self.enable_fusion = enable_fusion
- self.fusion_type = fusion_type
-
- # process mel-spec ; used only once
- self.freq_ratio = self.spec_size // self.config.mel_bins
- window = 'hann'
- center = True
- pad_mode = 'reflect'
- ref = 1.0
- amin = 1e-10
- top_db = None
- self.interpolate_ratio = 32 # Downsampled ratio
- # Spectrogram extractor
- self.spectrogram_extractor = Spectrogram(n_fft=config.window_size, hop_length=config.hop_size,
- win_length=config.window_size, window=window, center=center, pad_mode=pad_mode,
- freeze_parameters=True)
- # Logmel feature extractor
- self.logmel_extractor = LogmelFilterBank(sr=config.sample_rate, n_fft=config.window_size,
- n_mels=config.mel_bins, fmin=config.fmin, fmax=config.fmax, ref=ref, amin=amin, top_db=top_db,
- freeze_parameters=True)
- # Spec augmenter
- self.spec_augmenter = SpecAugmentation(time_drop_width=64, time_stripes_num=2,
- freq_drop_width=8, freq_stripes_num=2) # 2 2
- self.bn0 = nn.BatchNorm2d(self.config.mel_bins)
-
-
- # split spctrogram into non-overlapping patches
- self.patch_embed = PatchEmbed(
- img_size=self.spec_size, patch_size=self.patch_size, in_chans=self.in_chans,
- embed_dim=self.embed_dim, norm_layer=self.norm_layer, patch_stride = patch_stride,
- enable_fusion=self.enable_fusion, fusion_type=self.fusion_type
- )
-
- num_patches = self.patch_embed.num_patches
- patches_resolution = self.patch_embed.grid_size
- self.patches_resolution = patches_resolution
-
- # absolute position embedding
- if self.ape:
- self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, self.embed_dim))
- trunc_normal_(self.absolute_pos_embed, std=.02)
-
- self.pos_drop = nn.Dropout(p=self.drop_rate)
-
- # stochastic depth
- dpr = [x.item() for x in torch.linspace(0, self.drop_path_rate, sum(self.depths))] # stochastic depth decay rule
-
- # build layers
- self.layers = nn.ModuleList()
- for i_layer in range(self.num_layers):
- layer = BasicLayer(dim=int(self.embed_dim * 2 ** i_layer),
- input_resolution=(patches_resolution[0] // (2 ** i_layer),
- patches_resolution[1] // (2 ** i_layer)),
- depth=self.depths[i_layer],
- num_heads=self.num_heads[i_layer],
- window_size=self.window_size,
- mlp_ratio=self.mlp_ratio,
- qkv_bias=self.qkv_bias, qk_scale=self.qk_scale,
- drop=self.drop_rate, attn_drop=self.attn_drop_rate,
- drop_path=dpr[sum(self.depths[:i_layer]):sum(self.depths[:i_layer + 1])],
- norm_layer=self.norm_layer,
- downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
- use_checkpoint=use_checkpoint,
- norm_before_mlp=self.norm_before_mlp)
- self.layers.append(layer)
-
- self.norm = self.norm_layer(self.num_features)
- self.avgpool = nn.AdaptiveAvgPool1d(1)
- self.maxpool = nn.AdaptiveMaxPool1d(1)
-
- SF = self.spec_size // (2 ** (len(self.depths) - 1)) // self.patch_stride[0] // self.freq_ratio
- self.tscam_conv = nn.Conv2d(
- in_channels = self.num_features,
- out_channels = self.num_classes,
- kernel_size = (SF,3),
- padding = (0,1)
- )
- self.head = nn.Linear(num_classes, num_classes)
-
- if (self.enable_fusion) and (self.fusion_type in ['daf_1d','aff_1d','iaff_1d']):
- self.mel_conv1d = nn.Sequential(
- nn.Conv1d(64, 64, kernel_size=5, stride=3, padding=2),
- nn.BatchNorm1d(64)
- )
- if self.fusion_type == 'daf_1d':
- self.fusion_model = DAF()
- elif self.fusion_type == 'aff_1d':
- self.fusion_model = AFF(channels=64, type='1D')
- elif self.fusion_type == 'iaff_1d':
- self.fusion_model = iAFF(channels=64, type='1D')
-
- self.apply(self._init_weights)
-
- def _init_weights(self, m):
- if isinstance(m, nn.Linear):
- trunc_normal_(m.weight, std=.02)
- if isinstance(m, nn.Linear) and m.bias is not None:
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.LayerNorm):
- nn.init.constant_(m.bias, 0)
- nn.init.constant_(m.weight, 1.0)
-
- @torch.jit.ignore
- def no_weight_decay(self):
- return {'absolute_pos_embed'}
-
- @torch.jit.ignore
- def no_weight_decay_keywords(self):
- return {'relative_position_bias_table'}
-
-
- def forward_features(self, x, longer_idx = None):
- # A deprecated optimization for using a hierarchical output from different blocks
-
- frames_num = x.shape[2]
- x = self.patch_embed(x, longer_idx = longer_idx)
- if self.ape:
- x = x + self.absolute_pos_embed
- x = self.pos_drop(x)
- for i, layer in enumerate(self.layers):
- x, attn = layer(x)
- # for x
- x = self.norm(x)
- B, N, C = x.shape
- SF = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[0]
- ST = frames_num // (2 ** (len(self.depths) - 1)) // self.patch_stride[1]
- x = x.permute(0,2,1).contiguous().reshape(B, C, SF, ST)
- B, C, F, T = x.shape
- # group 2D CNN
- c_freq_bin = F // self.freq_ratio
- x = x.reshape(B, C, F // c_freq_bin, c_freq_bin, T)
- x = x.permute(0,1,3,2,4).contiguous().reshape(B, C, c_freq_bin, -1)
- # get latent_output
- fine_grained_latent_output = torch.mean(x, dim = 2)
- fine_grained_latent_output = interpolate(fine_grained_latent_output.permute(0,2,1).contiguous(), 8 * self.patch_stride[1])
-
- latent_output = self.avgpool(torch.flatten(x,2))
- latent_output = torch.flatten(latent_output, 1)
-
- # display the attention map, if needed
-
- x = self.tscam_conv(x)
- x = torch.flatten(x, 2) # B, C, T
-
- fpx = interpolate(torch.sigmoid(x).permute(0,2,1).contiguous(), 8 * self.patch_stride[1])
-
- x = self.avgpool(x)
- x = torch.flatten(x, 1)
-
- output_dict = {
- 'framewise_output': fpx, # already sigmoided
- 'clipwise_output': torch.sigmoid(x),
- 'fine_grained_embedding': fine_grained_latent_output,
- 'embedding': latent_output
- }
-
- return output_dict
-
- def crop_wav(self, x, crop_size, spe_pos = None):
- time_steps = x.shape[2]
- tx = torch.zeros(x.shape[0], x.shape[1], crop_size, x.shape[3]).to(x.device)
- for i in range(len(x)):
- if spe_pos is None:
- crop_pos = random.randint(0, time_steps - crop_size - 1)
- else:
- crop_pos = spe_pos
- tx[i][0] = x[i, 0, crop_pos:crop_pos + crop_size,:]
- return tx
-
- # Reshape the wavform to a img size, if you want to use the pretrained swin transformer model
- def reshape_wav2img(self, x):
- B, C, T, F = x.shape
- target_T = int(self.spec_size * self.freq_ratio)
- target_F = self.spec_size // self.freq_ratio
- assert T <= target_T and F <= target_F, "the wav size should less than or equal to the swin input size"
- # to avoid bicubic zero error
- if T < target_T:
- x = nn.functional.interpolate(x, (target_T, x.shape[3]), mode="bicubic", align_corners=True)
- if F < target_F:
- x = nn.functional.interpolate(x, (x.shape[2], target_F), mode="bicubic", align_corners=True)
- x = x.permute(0,1,3,2).contiguous()
- x = x.reshape(x.shape[0], x.shape[1], x.shape[2], self.freq_ratio, x.shape[3] // self.freq_ratio)
- # print(x.shape)
- x = x.permute(0,1,3,2,4).contiguous()
- x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3], x.shape[4])
- return x
-
- # Repeat the wavform to a img size, if you want to use the pretrained swin transformer model
- def repeat_wat2img(self, x, cur_pos):
- B, C, T, F = x.shape
- target_T = int(self.spec_size * self.freq_ratio)
- target_F = self.spec_size // self.freq_ratio
- assert T <= target_T and F <= target_F, "the wav size should less than or equal to the swin input size"
- # to avoid bicubic zero error
- if T < target_T:
- x = nn.functional.interpolate(x, (target_T, x.shape[3]), mode="bicubic", align_corners=True)
- if F < target_F:
- x = nn.functional.interpolate(x, (x.shape[2], target_F), mode="bicubic", align_corners=True)
- x = x.permute(0,1,3,2).contiguous() # B C F T
- x = x[:,:,:,cur_pos:cur_pos + self.spec_size]
- x = x.repeat(repeats = (1,1,4,1))
- return x
-
- def forward(self, x: torch.Tensor, mixup_lambda = None, infer_mode = False, device=None):# out_feat_keys: List[str] = None):
-
- if self.enable_fusion and x["longer"].sum() == 0:
- # if no audio is longer than 10s, then randomly select one audio to be longer
- x["longer"][torch.randint(0, x["longer"].shape[0], (1,))] = True
-
- if not self.enable_fusion:
- x = x["waveform"].to(device=device, non_blocking=True)
- x = self.spectrogram_extractor(x) # (batch_size, 1, time_steps, freq_bins)
- x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins)
- x = x.transpose(1, 3)
- x = self.bn0(x)
- x = x.transpose(1, 3)
- if self.training:
- x = self.spec_augmenter(x)
-
- if self.training and mixup_lambda is not None:
- x = do_mixup(x, mixup_lambda)
-
- x = self.reshape_wav2img(x)
- output_dict = self.forward_features(x)
- else:
- longer_list = x["longer"].to(device=device, non_blocking=True)
- x = x["mel_fusion"].to(device=device, non_blocking=True)
- x = x.transpose(1, 3)
- x = self.bn0(x)
- x = x.transpose(1, 3)
- longer_list_idx = torch.where(longer_list)[0]
- if self.fusion_type in ['daf_1d','aff_1d','iaff_1d']:
- new_x = x[:,0:1,:,:].clone().contiguous()
- if len(longer_list_idx) > 0:
- # local processing
- fusion_x_local = x[longer_list_idx,1:,:,:].clone().contiguous()
- FB,FC,FT,FF = fusion_x_local.size()
- fusion_x_local = fusion_x_local.view(FB * FC, FT, FF)
- fusion_x_local = torch.permute(fusion_x_local, (0,2,1)).contiguous()
- fusion_x_local = self.mel_conv1d(fusion_x_local)
- fusion_x_local = fusion_x_local.view(FB,FC,FF,fusion_x_local.size(-1))
- fusion_x_local = torch.permute(fusion_x_local, (0,2,1,3)).contiguous().flatten(2)
- if fusion_x_local.size(-1) < FT:
- fusion_x_local = torch.cat([fusion_x_local, torch.zeros((FB,FF,FT- fusion_x_local.size(-1)), device=device)], dim=-1)
- else:
- fusion_x_local = fusion_x_local[:,:,:FT]
- # 1D fusion
- new_x = new_x.squeeze(1).permute((0,2,1)).contiguous()
- new_x[longer_list_idx] = self.fusion_model(new_x[longer_list_idx], fusion_x_local)
- x = new_x.permute((0,2,1)).contiguous()[:,None,:,:]
- else:
- x = new_x
-
- elif self.fusion_type in ['daf_2d','aff_2d','iaff_2d','channel_map']:
- x = x # no change
-
- if self.training:
- x = self.spec_augmenter(x)
- if self.training and mixup_lambda is not None:
- x = do_mixup(x, mixup_lambda)
-
- x = self.reshape_wav2img(x)
- output_dict = self.forward_features(x, longer_idx = longer_list_idx)
-
- # if infer_mode:
- # # in infer mode. we need to handle different length audio input
- # frame_num = x.shape[2]
- # target_T = int(self.spec_size * self.freq_ratio)
- # repeat_ratio = math.floor(target_T / frame_num)
- # x = x.repeat(repeats=(1,1,repeat_ratio,1))
- # x = self.reshape_wav2img(x)
- # output_dict = self.forward_features(x)
- # else:
- # if x.shape[2] > self.freq_ratio * self.spec_size:
- # if self.training:
- # x = self.crop_wav(x, crop_size=self.freq_ratio * self.spec_size)
- # x = self.reshape_wav2img(x)
- # output_dict = self.forward_features(x)
- # else:
- # # Change: Hard code here
- # overlap_size = (x.shape[2] - 1) // 4
- # output_dicts = []
- # crop_size = (x.shape[2] - 1) // 2
- # for cur_pos in range(0, x.shape[2] - crop_size - 1, overlap_size):
- # tx = self.crop_wav(x, crop_size = crop_size, spe_pos = cur_pos)
- # tx = self.reshape_wav2img(tx)
- # output_dicts.append(self.forward_features(tx))
- # clipwise_output = torch.zeros_like(output_dicts[0]["clipwise_output"]).float().to(x.device)
- # framewise_output = torch.zeros_like(output_dicts[0]["framewise_output"]).float().to(x.device)
- # for d in output_dicts:
- # clipwise_output += d["clipwise_output"]
- # framewise_output += d["framewise_output"]
- # clipwise_output = clipwise_output / len(output_dicts)
- # framewise_output = framewise_output / len(output_dicts)
- # output_dict = {
- # 'framewise_output': framewise_output,
- # 'clipwise_output': clipwise_output
- # }
- # else: # this part is typically used, and most easy one
- # x = self.reshape_wav2img(x)
- # output_dict = self.forward_features(x)
- # x = self.head(x)
-
- # We process the data in the dataloader part, in that here we only consider the input_T < fixed_T
-
-
-
- return output_dict
-
-def create_htsat_model(audio_cfg, enable_fusion=False, fusion_type='None'):
- try:
-
- assert audio_cfg.model_name in ["tiny", "base", "large"], "model name for HTS-AT is wrong!"
- if audio_cfg.model_name == "tiny":
- model = HTSAT_Swin_Transformer(
- spec_size=256,
- patch_size=4,
- patch_stride=(4,4),
- num_classes=audio_cfg.class_num,
- embed_dim=96,
- depths=[2,2,6,2],
- num_heads=[4,8,16,32],
- window_size=8,
- config = audio_cfg,
- enable_fusion = enable_fusion,
- fusion_type = fusion_type
- )
- elif audio_cfg.model_name == "base":
- model = HTSAT_Swin_Transformer(
- spec_size=256,
- patch_size=4,
- patch_stride=(4,4),
- num_classes=audio_cfg.class_num,
- embed_dim=128,
- depths=[2,2,12,2],
- num_heads=[4,8,16,32],
- window_size=8,
- config = audio_cfg,
- enable_fusion = enable_fusion,
- fusion_type = fusion_type
- )
- elif audio_cfg.model_name == "large":
- model = HTSAT_Swin_Transformer(
- spec_size=256,
- patch_size=4,
- patch_stride=(4,4),
- num_classes=audio_cfg.class_num,
- embed_dim=256,
- depths=[2,2,12,2],
- num_heads=[4,8,16,32],
- window_size=8,
- config = audio_cfg,
- enable_fusion = enable_fusion,
- fusion_type = fusion_type
- )
-
- return model
- except:
- raise RuntimeError(f'Import Model for {audio_cfg.model_name} not found, or the audio cfg parameters are not enough.')
-
\ No newline at end of file
diff --git a/spaces/ALSv/midjourney-v4-1/app.py b/spaces/ALSv/midjourney-v4-1/app.py
deleted file mode 100644
index 262436d8b50f87b0953c645576cc3184b3b27b43..0000000000000000000000000000000000000000
--- a/spaces/ALSv/midjourney-v4-1/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Joeythemonster/anything-midjourney-v-4-1").launch()
\ No newline at end of file
diff --git a/spaces/Ababababababbababa/Ashaar/app.py b/spaces/Ababababababbababa/Ashaar/app.py
deleted file mode 100644
index 580d3b353dfe066a53293417f4380121aaa5827b..0000000000000000000000000000000000000000
--- a/spaces/Ababababababbababa/Ashaar/app.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import os
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-import gradio as gr
-from transformers import pipeline
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from Ashaar.utils import get_output_df, get_highlighted_patterns_html
-from Ashaar.bait_analysis import BaitAnalysis
-from langs import *
-import sys
-import json
-import argparse
-
-arg_parser = argparse.ArgumentParser()
-arg_parser.add_argument('--lang', type = str, default = 'ar')
-args = arg_parser.parse_args()
-lang = args.lang
-
-if lang == 'ar':
- TITLE = TITLE_ar
- DESCRIPTION = DESCRIPTION_ar
- textbox_trg_text = textbox_trg_text_ar
- textbox_inp_text = textbox_inp_text_ar
- btn_trg_text = btn_trg_text_ar
- btn_inp_text = btn_inp_text_ar
- css = """ #textbox{ direction: RTL;}"""
-
-else:
- TITLE = TITLE_en
- DESCRIPTION = DESCRIPTION_en
- textbox_trg_text = textbox_trg_text_en
- textbox_inp_text = textbox_inp_text_en
- btn_trg_text = btn_trg_text_en
- btn_inp_text = btn_inp_text_en
- css = ""
-
-gpt_tokenizer = AutoTokenizer.from_pretrained('arbml/ashaar_tokenizer')
-model = AutoModelForCausalLM.from_pretrained('arbml/Ashaar_model')
-
-theme_to_token = json.load(open("extra/theme_tokens.json", "r"))
-token_to_theme = {t:m for m,t in theme_to_token.items()}
-meter_to_token = json.load(open("extra/meter_tokens.json", "r"))
-token_to_meter = {t:m for m,t in meter_to_token.items()}
-
-analysis = BaitAnalysis()
-meter, theme, qafiyah = "", "", ""
-
-def analyze(poem):
- global meter,theme,qafiyah, generate_btn
- shatrs = poem.split("\n")
- baits = [' # '.join(shatrs[2*i:2*i+2]) for i in range(len(shatrs)//2)]
- output = analysis.analyze(baits,override_tashkeel=True)
- meter = output['meter']
- qafiyah = output['qafiyah'][0]
- theme = output['theme'][-1]
- df = get_output_df(output)
- return get_highlighted_patterns_html(df), gr.Button.update(interactive=True)
-
-def generate(inputs, top_p = 3):
- baits = inputs.split('\n')
- if len(baits) % 2 !=0:
- baits = baits[:-1]
- poem = ' '.join(['<|bsep|> '+baits[i]+' <|vsep|> '+baits[i+1]+' ' for i in range(0, len(baits), 2)])
- prompt = f"""
- {meter_to_token[meter]} {qafiyah} {theme_to_token[theme]}
- <|psep|>
- {poem}
- """.strip()
- print(prompt)
- encoded_input = gpt_tokenizer(prompt, return_tensors='pt')
- output = model.generate(**encoded_input, max_length = 512, top_p = 3, do_sample=True)
-
- result = ""
- prev_token = ""
- line_cnts = 0
- for i, beam in enumerate(output[:, len(encoded_input.input_ids[0]):]):
- if line_cnts >= 10:
- break
- for token in beam:
- if line_cnts >= 10:
- break
- decoded = gpt_tokenizer.decode(token)
- if 'meter' in decoded or 'theme' in decoded:
- break
- if decoded in ["<|vsep|>", ""]:
- result += "\n"
- line_cnts+=1
- elif decoded in ['<|bsep|>', '<|psep|>', '']:
- pass
- else:
- result += decoded
- prev_token = decoded
- else:
- break
- # return theme+" "+ f"من بحر {meter} مع قافية بحر ({qafiyah})" + "\n" +result
- return result, gr.Button.update(interactive=False)
-
-examples = [
- [
-"""القلب أعلم يا عذول بدائه
-وأحق منك بجفنه وبمائه"""
- ],
- [
-"""رمتِ الفؤادَ مليحة عذراءُ
- بسهامِ لحظٍ ما لهنَّ دواءُ"""
- ],
- [
-"""أذَلَّ الحِرْصُ والطَّمَعُ الرِّقابَا
-وقَد يَعفو الكَريمُ، إذا استَرَابَا"""
- ]
-]
-
-with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
- with gr.Row():
- with gr.Column():
- gr.HTML(TITLE)
- gr.HTML(DESCRIPTION)
-
- with gr.Row():
- with gr.Column():
- textbox_output = gr.Textbox(lines=10, label=textbox_trg_text, elem_id="textbox")
- with gr.Column():
- inputs = gr.Textbox(lines=10, label=textbox_inp_text, elem_id="textbox")
-
-
- with gr.Row():
- with gr.Column():
- if lang == 'ar':
- trg_btn = gr.Button(btn_trg_text, interactive=False)
- else:
- trg_btn = gr.Button(btn_trg_text)
-
- with gr.Column():
- if lang == 'ar':
- inp_btn = gr.Button(btn_inp_text)
- else:
- inp_btn = gr.Button(btn_inp_text, interactive = False)
-
- with gr.Row():
- html_output = gr.HTML()
-
- if lang == 'en':
- gr.Examples(examples, textbox_output)
- inp_btn.click(generate, inputs = textbox_output, outputs=[inputs, inp_btn])
- trg_btn.click(analyze, inputs = textbox_output, outputs=[html_output,inp_btn])
- else:
- gr.Examples(examples, inputs)
- trg_btn.click(generate, inputs = inputs, outputs=[textbox_output, trg_btn])
- inp_btn.click(analyze, inputs = inputs, outputs=[html_output,trg_btn] )
-
-# demo.launch(server_name = '0.0.0.0', share=True)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/Ababababababbababa/Sha3bor_Aragpt2_Base/README.md b/spaces/Ababababababbababa/Sha3bor_Aragpt2_Base/README.md
deleted file mode 100644
index 8bc209a4444457e39e800d2be1c2cb5afbcbdd7b..0000000000000000000000000000000000000000
--- a/spaces/Ababababababbababa/Sha3bor_Aragpt2_Base/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Sha3bor Aragpt2 Base
-emoji: 🏆
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Abhaykoul/BardCookies-AI_Query/app.py b/spaces/Abhaykoul/BardCookies-AI_Query/app.py
deleted file mode 100644
index 27c89cefe27a0a83fee4ec75a4cdbf95bb32d924..0000000000000000000000000000000000000000
--- a/spaces/Abhaykoul/BardCookies-AI_Query/app.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from bardapi import BardCookies
-import requests
-from requests.exceptions import ReadTimeout
-import gradio as gr
-
-def get_bard_response(Secure_1PSID, Secure_1PSIDTS, Secure_1PSIDCC, Query):
- cookie_dict = {
- "__Secure-1PSID": Secure_1PSID,
- "__Secure-1PSIDTS": Secure_1PSIDTS,
- "__Secure-1PSIDCC": Secure_1PSIDCC
- }
-
- bard = BardCookies(cookie_dict=cookie_dict)
- retries = 3 # Number of retries
- for _ in range(retries):
- try:
- Reply = bard.get_answer(Query)['content']
- return Reply
- except ReadTimeout:
- continue
- return "Failed to fetch data after multiple retries."
-
-iface = gr.Interface(
- fn=get_bard_response,
- inputs=[
- gr.components.Textbox(label="__Secure-1PSID"),
- gr.components.Textbox(label="__Secure-1PSIDTS"),
- gr.components.Textbox(label="__Secure-1PSIDCC"),
- gr.components.Textbox(label="Query")
- ],
- outputs="text",
- title="BardCookies - AI Query",
- description = "Enter your cookies and a query to get a response from BardCookies. If you need help with cookies, check out the Chrome extension for managing cookies. Go to bard.google.com and then use EditThisCookie extension and copy Secure_1PSID, Secure_1PSIDTS, Secure_1PSIDCC from it. Bard Chat."
-)
-
-iface.launch()
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/agentverse/registry.py b/spaces/AgentVerse/agentVerse/agentverse/registry.py
deleted file mode 100644
index b53b571416736fe4e7d83e23bd0dad71950b43fa..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse/registry.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from typing import Dict
-
-from pydantic import BaseModel
-
-
-class Registry(BaseModel):
- """Registry for storing and building classes."""
-
- name: str
- entries: Dict = {}
-
- def register(self, key: str):
- def decorator(class_builder):
- self.entries[key] = class_builder
- return class_builder
-
- return decorator
-
- def build(self, type: str, **kwargs):
- if type not in self.entries:
- raise ValueError(
- f'{type} is not registered. Please register with the .register("{type}") method provided in {self.name} registry'
- )
- return self.entries[type](**kwargs)
-
- def get_all_entries(self):
- return self.entries
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/oval/Factory.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/oval/Factory.js
deleted file mode 100644
index 9d68b3357604dcb84d81b7e54a065823a630d51e..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/oval/Factory.js
+++ /dev/null
@@ -1,13 +0,0 @@
-import Oval from './Oval.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('oval', function (config) {
- var gameObject = new Oval(this.scene, config);
- this.scene.add.existing(gameObject);
- return gameObject;
-});
-
-SetValue(window, 'RexPlugins.Spinner.Oval', Oval);
-
-export default Oval;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/easemove/EaseMove.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/easemove/EaseMove.ts
deleted file mode 100644
index 80fe9fa41b42426b2c71beb6fdf6ff3b2cd00762..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/easemove/EaseMove.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import { EaseMove, EaseMoveTo, EaseMoveFrom } from '../../../plugins/easemove';
-export { EaseMove, EaseMoveTo, EaseMoveFrom };
\ No newline at end of file
diff --git a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/monotonic_align/__init__.py b/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/monotonic_align/__init__.py
deleted file mode 100644
index 3d7009c40fea3a98168e3e3bc9ae061e91327422..0000000000000000000000000000000000000000
--- a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/monotonic_align/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import numpy as np
-import torch
-from .monotonic_align.core import maximum_path_c
-
-
-def maximum_path(neg_cent, mask):
- """ Cython optimized version.
- neg_cent: [b, t_t, t_s]
- mask: [b, t_t, t_s]
- """
- device = neg_cent.device
- dtype = neg_cent.dtype
- neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
- path = np.zeros(neg_cent.shape, dtype=np.int32)
-
- t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
- t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
- maximum_path_c(path, neg_cent, t_t_max, t_s_max)
- return torch.from_numpy(path).to(device=device, dtype=dtype)
diff --git a/spaces/Ameaou/academic-chatgpt3.1/docs/README_EN.md b/spaces/Ameaou/academic-chatgpt3.1/docs/README_EN.md
deleted file mode 100644
index db214f5327b8cdcd84ed1c57390c3b24ba83d78f..0000000000000000000000000000000000000000
--- a/spaces/Ameaou/academic-chatgpt3.1/docs/README_EN.md
+++ /dev/null
@@ -1,291 +0,0 @@
-> **Note**
->
-> This English README is automatically generated by the markdown translation plugin in this project, and may not be 100% correct.
->
-
-# ChatGPT Academic Optimization
-
-**If you like this project, please give it a Star. If you've come up with more useful academic shortcuts or functional plugins, feel free to open an issue or pull request. We also have a [README in English](docs/README_EN.md) translated by this project itself.**
-
-> **Note**
->
-> 1. Please note that only **functions with red color** supports reading files, some functions are located in the **dropdown menu** of plugins. Additionally, we welcome and prioritize any new plugin PRs with **highest priority**!
->
-> 2. The functionality of each file in this project is detailed in the self-translation report [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) of the project. With the iteration of the version, you can also click on the relevant function plugins at any time to call GPT to regenerate the self-analysis report of the project. The FAQ summary is in the [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98) section.
->
-
-
-
-
-Function | Description
---- | ---
-One-Click Polish | Supports one-click polishing and finding grammar errors in academic papers.
-One-Key Translation Between Chinese and English | One-click translation between Chinese and English.
-One-Key Code Interpretation | Can correctly display and interpret code.
-[Custom Shortcut Keys](https://www.bilibili.com/video/BV14s4y1E7jN) | Supports custom shortcut keys.
-[Configure Proxy Server](https://www.bilibili.com/video/BV1rc411W7Dr) | Supports configuring proxy servers.
-Modular Design | Supports custom high-order function plugins and [function plugins], and plugins support [hot updates](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
-[Self-programming Analysis](https://www.bilibili.com/video/BV1cj411A7VW) | [Function Plugin] [One-Key Read] (https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) The source code of this project is analyzed.
-[Program Analysis](https://www.bilibili.com/video/BV1cj411A7VW) | [Function Plugin] One-click can analyze the project tree of other Python/C/C++/Java/Lua/... projects
-Read the Paper | [Function Plugin] One-click interpretation of the full text of latex paper and generation of abstracts
-Latex Full Text Translation, Proofreading | [Function Plugin] One-click translation or proofreading of latex papers.
-Batch Comment Generation | [Function Plugin] One-click batch generation of function comments
-Chat Analysis Report Generation | [Function Plugin] After running, an automatic summary report will be generated
-[Arxiv Assistant](https://www.bilibili.com/video/BV1LM4y1279X) | [Function Plugin] Enter the arxiv article url to translate the abstract and download the PDF with one click
-[Full-text Translation Function of PDF Paper](https://www.bilibili.com/video/BV1KT411x7Wn) | [Function Plugin] Extract the title & abstract of the PDF paper + translate the full text (multithreading)
-[Google Scholar Integration Assistant](https://www.bilibili.com/video/BV19L411U7ia) | [Function Plugin] Given any Google Scholar search page URL, let gpt help you choose interesting articles.
-Formula / Picture / Table Display | Can display both the tex form and the rendering form of formulas at the same time, support formula and code highlighting
-Multithreaded Function Plugin Support | Supports multi-threaded calling chatgpt, one-click processing of massive text or programs
-Start Dark Gradio [Theme](https://github.com/binary-husky/chatgpt_academic/issues/173) | Add ```/?__dark-theme=true``` at the end of the browser url to switch to dark theme
-[Multiple LLM Models](https://www.bilibili.com/video/BV1wT411p7yf) support, [API2D](https://api2d.com/) interface support | It must feel nice to be served by both GPT3.5, GPT4, and [Tsinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B)!
-Huggingface non-Science Net [Online Experience](https://huggingface.co/spaces/qingxu98/gpt-academic) | After logging in to huggingface, copy [this space](https://huggingface.co/spaces/qingxu98/gpt-academic)
-... | ...
-
-
-
-
-- New interface (switch between "left-right layout" and "up-down layout" by modifying the LAYOUT option in config.py)
-
-
-
-
-
-- All buttons are dynamically generated by reading functional.py and can add custom functionality at will, freeing up clipboard
-
-
-
-
-- Proofreading / correcting
-
-
-
-
-- If the output contains formulas, it will be displayed in both the tex form and the rendering form at the same time, which is convenient for copying and reading
-
-
-
-
-- Don't want to read the project code? Just take the whole project to chatgpt
-
-
-
-
-- Multiple major language model mixing calls (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
-
-
-
-
-Multiple major language model mixing call [huggingface beta version](https://huggingface.co/spaces/qingxu98/academic-chatgpt-beta) (the huggingface version does not support chatglm)
-
-
----
-
-## Installation-Method 1: Run directly (Windows, Linux or MacOS)
-
-1. Download project
-```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-```
-
-2. Configure API_KEY and proxy settings
-
-
-In `config.py`, configure the overseas Proxy and OpenAI API KEY as follows:
-```
-1. If you are in China, you need to set up an overseas proxy to use the OpenAI API smoothly. Please read config.py carefully for setup details (1. Modify USE_PROXY to True; 2. Modify proxies according to the instructions).
-2. Configure the OpenAI API KEY. You need to register and obtain an API KEY on the OpenAI website. Once you get the API KEY, you can configure it in the config.py file.
-3. Issues related to proxy networks (network timeouts, proxy failures) are summarized at https://github.com/binary-husky/chatgpt_academic/issues/1
-```
-(P.S. When the program runs, it will first check whether there is a private configuration file named `config_private.py` and use the same-name configuration in `config.py` to overwrite it. Therefore, if you can understand our configuration reading logic, we strongly recommend that you create a new configuration file named `config_private.py` next to `config.py` and transfer (copy) the configuration in `config.py` to` config_private.py`. `config_private.py` is not controlled by git and can make your privacy information more secure.))
-
-
-3. Install dependencies
-```sh
-# (Option One) Recommended
-python -m pip install -r requirements.txt
-
-# (Option Two) If you use anaconda, the steps are similar:
-# (Option Two.1) conda create -n gptac_venv python=3.11
-# (Option Two.2) conda activate gptac_venv
-# (Option Two.3) python -m pip install -r requirements.txt
-
-# Note: Use official pip source or Ali pip source. Other pip sources (such as some university pips) may have problems, and temporary replacement methods are as follows:
-# python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
-```
-
-If you need to support Tsinghua ChatGLM, you need to install more dependencies (if you are not familiar with python or your computer configuration is not good, we recommend not to try):
-```sh
-python -m pip install -r request_llm/requirements_chatglm.txt
-```
-
-4. Run
-```sh
-python main.py
-```
-
-5. Test function plugins
-```
-- Test Python project analysis
- In the input area, enter `./crazy_functions/test_project/python/dqn`, and then click "Analyze the entire Python project"
-- Test self-code interpretation
- Click "[Multithreading Demo] Interpretation of This Project Itself (Source Code Interpretation)"
-- Test experimental function template function (requires gpt to answer what happened today in history). You can use this function as a template to implement more complex functions.
- Click "[Function Plugin Template Demo] Today in History"
-- There are more functions to choose from in the function plugin area drop-down menu.
-```
-
-## Installation-Method 2: Use Docker (Linux)
-
-1. ChatGPT only (recommended for most people)
-``` sh
-# download project
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-# configure overseas Proxy and OpenAI API KEY
-Edit config.py with any text editor
-# Install
-docker build -t gpt-academic .
-# Run
-docker run --rm -it --net=host gpt-academic
-
-# Test function plug-in
-## Test function plugin template function (requires gpt to answer what happened today in history). You can use this function as a template to implement more complex functions.
-Click "[Function Plugin Template Demo] Today in History"
-## Test Abstract Writing for Latex Projects
-Enter ./crazy_functions/test_project/latex/attention in the input area, and then click "Read Tex Paper and Write Abstract"
-## Test Python Project Analysis
-Enter ./crazy_functions/test_project/python/dqn in the input area and click "Analyze the entire Python project."
-
-More functions are available in the function plugin area drop-down menu.
-```
-
-2. ChatGPT+ChatGLM (requires strong familiarity with docker + strong computer configuration)
-
-``` sh
-# Modify dockerfile
-cd docs && nano Dockerfile+ChatGLM
-# How to build | 如何构建 （Dockerfile+ChatGLM在docs路径下，请先cd docs）
-docker build -t gpt-academic --network=host -f Dockerfile+ChatGLM .
-# How to run | 如何运行 (1) 直接运行:
-docker run --rm -it --net=host --gpus=all gpt-academic
-# How to run | 如何运行 (2) 我想运行之前进容器做一些调整:
-docker run --rm -it --net=host --gpus=all gpt-academic bash
-```
-
-
-## Installation-Method 3: Other Deployment Methods
-
-1. Remote Cloud Server Deployment
-Please visit [Deployment Wiki-1] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
-
-2. Use WSL2 (Windows Subsystem for Linux)
-Please visit [Deployment Wiki-2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
-
-
-## Installation-Proxy Configuration
-### Method 1: Conventional method
-[Configure Proxy](https://github.com/binary-husky/chatgpt_academic/issues/1)
-
-### Method Two: Step-by-step tutorial for newcomers
-[Step-by-step tutorial for newcomers](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BB%A3%E7%90%86%E8%BD%AF%E4%BB%B6%E9%97%AE%E9%A2%98%E7%9A%84%E6%96%B0%E6%89%8B%E8%A7%A3%E5%86%B3%E6%96%B9%E6%B3%95%EF%BC%88%E6%96%B9%E6%B3%95%E5%8F%AA%E9%80%82%E7%94%A8%E4%BA%8E%E6%96%B0%E6%89%8B%EF%BC%89)
-
----
-
-## Customizing Convenient Buttons (Customizing Academic Shortcuts)
-Open `core_functional.py` with any text editor and add an item as follows, then restart the program (if the button has been successfully added and visible, both the prefix and suffix support hot modification without the need to restart the program to take effect). For example:
-```
-"Super English to Chinese translation": {
- # Prefix, which will be added before your input. For example, to describe your requirements, such as translation, code interpretation, polishing, etc.
- "Prefix": "Please translate the following content into Chinese and use a markdown table to interpret the proprietary terms in the text one by one:\n\n",
-
- # Suffix, which will be added after your input. For example, combined with the prefix, you can put your input content in quotes.
- "Suffix": "",
-},
-```
-
-
-
-
----
-
-
-## Some Function Displays
-
-### Image Display:
-
-
-You are a professional academic paper translator.
-
-
-
-
-
-### If a program can understand and analyze itself:
-
-
-
-
-
-
-
-
-
-### Analysis of any Python/Cpp project:
-
-
-
-
-
-
-
-
-### One-click reading comprehension and summary generation of Latex papers
-
-
-
-
-### Automatic report generation
-
-
-
-
-
-
-### Modular functional design
-
-
-
-
-
-### Source code translation to English
-
-
-
-
-
-## Todo and version planning:
-- version 3.2+ (todo): Function plugin supports more parameter interfaces
-- version 3.1: Support for inquiring multiple GPT models at the same time! Support for api2d, support for multiple apikeys load balancing
-- version 3.0: Support for chatglm and other small llms
-- version 2.6: Refactored the plugin structure, improved interactivity, added more plugins
-- version 2.5: Self-updating, solves the problem of text being too long and token overflowing when summarizing large project source code
-- version 2.4: (1) Added PDF full text translation function; (2) Added function to switch input area position; (3) Added vertical layout option; (4) Multi-threaded function plugin optimization.
-- version 2.3: Enhanced multi-threaded interactivity
-- version 2.2: Function plugin supports hot reloading
-- version 2.1: Foldable layout
-- version 2.0: Introduction of modular function plugins
-- version 1.0: Basic functions
-
-## Reference and learning
-
-```
-The code design of this project has referenced many other excellent projects, including:
-
-# Reference project 1: Borrowed many tips from ChuanhuChatGPT
-https://github.com/GaiZhenbiao/ChuanhuChatGPT
-
-# Reference project 2: Tsinghua ChatGLM-6B:
-https://github.com/THUDM/ChatGLM-6B
-```
-
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/intel_opts/README.md b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/intel_opts/README.md
deleted file mode 100644
index 6b25679efbe90d556244e7aa6bee3e863c28b069..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/research_projects/intel_opts/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
-## Diffusers examples with Intel optimizations
-
-**This research project is not actively maintained by the diffusers team. For any questions or comments, please make sure to tag @hshen14 .**
-
-This aims to provide diffusers examples with Intel optimizations such as Bfloat16 for training/fine-tuning acceleration and 8-bit integer (INT8) for inference acceleration on Intel platforms.
-
-## Accelerating the fine-tuning for textual inversion
-
-We accelereate the fine-tuning for textual inversion with Intel Extension for PyTorch. The [examples](textual_inversion) enable both single node and multi-node distributed training with Bfloat16 support on Intel Xeon Scalable Processor.
-
-## Accelerating the inference for Stable Diffusion using Bfloat16
-
-We start the inference acceleration with Bfloat16 using Intel Extension for PyTorch. The [script](inference_bf16.py) is generally designed to support standard Stable Diffusion models with Bfloat16 support.
-```bash
-pip install diffusers transformers accelerate scipy safetensors
-
-export KMP_BLOCKTIME=1
-export KMP_SETTINGS=1
-export KMP_AFFINITY=granularity=fine,compact,1,0
-
-# Intel OpenMP
-export OMP_NUM_THREADS=< Cores to use >
-export LD_PRELOAD=${LD_PRELOAD}:/path/to/lib/libiomp5.so
-# Jemalloc is a recommended malloc implementation that emphasizes fragmentation avoidance and scalable concurrency support.
-export LD_PRELOAD=${LD_PRELOAD}:/path/to/lib/libjemalloc.so
-export MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:-1,muzzy_decay_ms:9000000000"
-
-# Launch with default DDIM
-numactl --membind -C python python inference_bf16.py
-# Launch with DPMSolverMultistepScheduler
-numactl --membind -C python python inference_bf16.py --dpm
-
-```
-
-## Accelerating the inference for Stable Diffusion using INT8
-
-Coming soon ...
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py
deleted file mode 100644
index e7265bcdbef2a7ab5e8ba6b3fe13f02cb718b40a..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py
+++ /dev/null
@@ -1,10 +0,0 @@
-_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
-model = dict(
- bbox_head=dict(
- with_deform=True,
- norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
-optimizer_config = dict(
- _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.py
deleted file mode 100644
index ef7b06dd3806c1d93be41943ab4d7d49f68ac830..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/nonlocal_net/nonlocal_r101-d8_512x1024_40k_cityscapes.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './nonlocal_r50-d8_512x1024_40k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py
deleted file mode 100644
index 145cadb24016eeea87fccff8171c5b0dfb78f7ab..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/pspnet/pspnet_r50-d8_769x769_40k_cityscapes.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = [
- '../_base_/models/pspnet_r50-d8.py',
- '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
- '../_base_/schedules/schedule_40k.py'
-]
-model = dict(
- decode_head=dict(align_corners=True),
- auxiliary_head=dict(align_corners=True),
- test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))
diff --git a/spaces/AndySAnker/DeepStruc/models/README.md b/spaces/AndySAnker/DeepStruc/models/README.md
deleted file mode 100644
index e4afa9439921f934d7ffdd5445eed1c5f75571ac..0000000000000000000000000000000000000000
--- a/spaces/AndySAnker/DeepStruc/models/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-[ChemRxiv](https://chemrxiv.org/engage/chemrxiv/article-details/6221f17357a9d20c9a729ecb) | [Paper](https://pubs.rsc.org/en/content/articlelanding/2023/dd/d2dd00086e)
-
-# Models
-This folder contain the DeepStruc model and all other trained models will be save here with the folder name:
-DeepStruc-year-month-day-time.
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/cc_attention.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/cc_attention.py
deleted file mode 100644
index 9207aa95e6730bd9b3362dee612059a5f0ce1c5e..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/cc_attention.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from annotator.uniformer.mmcv.cnn import PLUGIN_LAYERS, Scale
-
-
-def NEG_INF_DIAG(n, device):
- """Returns a diagonal matrix of size [n, n].
-
- The diagonal are all "-inf". This is for avoiding calculating the
- overlapped element in the Criss-Cross twice.
- """
- return torch.diag(torch.tensor(float('-inf')).to(device).repeat(n), 0)
-
-
-@PLUGIN_LAYERS.register_module()
-class CrissCrossAttention(nn.Module):
- """Criss-Cross Attention Module.
-
- .. note::
- Before v1.3.13, we use a CUDA op. Since v1.3.13, we switch
- to a pure PyTorch and equivalent implementation. For more
- details, please refer to https://github.com/open-mmlab/mmcv/pull/1201.
-
- Speed comparison for one forward pass
-
- - Input size: [2,512,97,97]
- - Device: 1 NVIDIA GeForce RTX 2080 Ti
-
- +-----------------------+---------------+------------+---------------+
- | |PyTorch version|CUDA version|Relative speed |
- +=======================+===============+============+===============+
- |with torch.no_grad() |0.00554402 s |0.0299619 s |5.4x |
- +-----------------------+---------------+------------+---------------+
- |no with torch.no_grad()|0.00562803 s |0.0301349 s |5.4x |
- +-----------------------+---------------+------------+---------------+
-
- Args:
- in_channels (int): Channels of the input feature map.
- """
-
- def __init__(self, in_channels):
- super().__init__()
- self.query_conv = nn.Conv2d(in_channels, in_channels // 8, 1)
- self.key_conv = nn.Conv2d(in_channels, in_channels // 8, 1)
- self.value_conv = nn.Conv2d(in_channels, in_channels, 1)
- self.gamma = Scale(0.)
- self.in_channels = in_channels
-
- def forward(self, x):
- """forward function of Criss-Cross Attention.
-
- Args:
- x (Tensor): Input feature. \
- shape (batch_size, in_channels, height, width)
- Returns:
- Tensor: Output of the layer, with shape of \
- (batch_size, in_channels, height, width)
- """
- B, C, H, W = x.size()
- query = self.query_conv(x)
- key = self.key_conv(x)
- value = self.value_conv(x)
- energy_H = torch.einsum('bchw,bciw->bwhi', query, key) + NEG_INF_DIAG(
- H, query.device)
- energy_H = energy_H.transpose(1, 2)
- energy_W = torch.einsum('bchw,bchj->bhwj', query, key)
- attn = F.softmax(
- torch.cat([energy_H, energy_W], dim=-1), dim=-1) # [B,H,W,(H+W)]
- out = torch.einsum('bciw,bhwi->bchw', value, attn[..., :H])
- out += torch.einsum('bchj,bhwj->bchw', value, attn[..., H:])
-
- out = self.gamma(out) + x
- out = out.contiguous()
-
- return out
-
- def __repr__(self):
- s = self.__class__.__name__
- s += f'(in_channels={self.in_channels})'
- return s
diff --git a/spaces/Ariharasudhan/YoloV5/utils/__init__.py b/spaces/Ariharasudhan/YoloV5/utils/__init__.py
deleted file mode 100644
index 3b1a2c87329a3333e8ea1998e1507dcf0d2a554b..0000000000000000000000000000000000000000
--- a/spaces/Ariharasudhan/YoloV5/utils/__init__.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-utils/initialization
-"""
-
-import contextlib
-import platform
-import threading
-
-
-def emojis(str=''):
- # Return platform-dependent emoji-safe version of string
- return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str
-
-
-class TryExcept(contextlib.ContextDecorator):
- # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager
- def __init__(self, msg=''):
- self.msg = msg
-
- def __enter__(self):
- pass
-
- def __exit__(self, exc_type, value, traceback):
- if value:
- print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}"))
- return True
-
-
-def threaded(func):
- # Multi-threads a target function and returns thread. Usage: @threaded decorator
- def wrapper(*args, **kwargs):
- thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True)
- thread.start()
- return thread
-
- return wrapper
-
-
-def join_threads(verbose=False):
- # Join all daemon threads, i.e. atexit.register(lambda: join_threads())
- main_thread = threading.current_thread()
- for t in threading.enumerate():
- if t is not main_thread:
- if verbose:
- print(f'Joining thread {t.name}')
- t.join()
-
-
-def notebook_init(verbose=True):
- # Check system software and hardware
- print('Checking setup...')
-
- import os
- import shutil
-
- from utils.general import check_font, check_requirements, is_colab
- from utils.torch_utils import select_device # imports
-
- check_font()
-
- import psutil
- from IPython import display # to display images and clear console output
-
- if is_colab():
- shutil.rmtree('/content/sample_data', ignore_errors=True) # remove colab /sample_data directory
-
- # System info
- if verbose:
- gb = 1 << 30 # bytes to GiB (1024 ** 3)
- ram = psutil.virtual_memory().total
- total, used, free = shutil.disk_usage("/")
- display.clear_output()
- s = f'({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)'
- else:
- s = ''
-
- select_device(newline=False)
- print(emojis(f'Setup complete ✅ {s}'))
- return display
diff --git a/spaces/Arnaudding001/FrenchTranslationAI/README.md b/spaces/Arnaudding001/FrenchTranslationAI/README.md
deleted file mode 100644
index 178225e19402cab24d8aff04fc6f74e27895fc2b..0000000000000000000000000000000000000000
--- a/spaces/Arnaudding001/FrenchTranslationAI/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: FrenchTranslationAI
-emoji: 🔥
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 3.4.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Arnx/MusicGenXvAKN/tests/modules/test_seanet.py b/spaces/Arnx/MusicGenXvAKN/tests/modules/test_seanet.py
deleted file mode 100644
index e5c51b340a2f94fb2828b14daf83d5fad645073d..0000000000000000000000000000000000000000
--- a/spaces/Arnx/MusicGenXvAKN/tests/modules/test_seanet.py
+++ /dev/null
@@ -1,115 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from itertools import product
-
-import pytest
-import torch
-
-from audiocraft.modules.seanet import SEANetEncoder, SEANetDecoder, SEANetResnetBlock
-from audiocraft.modules import StreamableConv1d, StreamableConvTranspose1d
-
-
-class TestSEANetModel:
-
- def test_base(self):
- encoder = SEANetEncoder()
- decoder = SEANetDecoder()
-
- x = torch.randn(1, 1, 24000)
- z = encoder(x)
- assert list(z.shape) == [1, 128, 75], z.shape
- y = decoder(z)
- assert y.shape == x.shape, (x.shape, y.shape)
-
- def test_causal(self):
- encoder = SEANetEncoder(causal=True)
- decoder = SEANetDecoder(causal=True)
- x = torch.randn(1, 1, 24000)
-
- z = encoder(x)
- assert list(z.shape) == [1, 128, 75], z.shape
- y = decoder(z)
- assert y.shape == x.shape, (x.shape, y.shape)
-
- def test_conv_skip_connection(self):
- encoder = SEANetEncoder(true_skip=False)
- decoder = SEANetDecoder(true_skip=False)
-
- x = torch.randn(1, 1, 24000)
- z = encoder(x)
- assert list(z.shape) == [1, 128, 75], z.shape
- y = decoder(z)
- assert y.shape == x.shape, (x.shape, y.shape)
-
- def test_seanet_encoder_decoder_final_act(self):
- encoder = SEANetEncoder(true_skip=False)
- decoder = SEANetDecoder(true_skip=False, final_activation='Tanh')
-
- x = torch.randn(1, 1, 24000)
- z = encoder(x)
- assert list(z.shape) == [1, 128, 75], z.shape
- y = decoder(z)
- assert y.shape == x.shape, (x.shape, y.shape)
-
- def _check_encoder_blocks_norm(self, encoder: SEANetEncoder, n_disable_blocks: int, norm: str):
- n_blocks = 0
- for layer in encoder.model:
- if isinstance(layer, StreamableConv1d):
- n_blocks += 1
- assert layer.conv.norm_type == 'none' if n_blocks <= n_disable_blocks else norm
- elif isinstance(layer, SEANetResnetBlock):
- for resnet_layer in layer.block:
- if isinstance(resnet_layer, StreamableConv1d):
- # here we add + 1 to n_blocks as we increment n_blocks just after the block
- assert resnet_layer.conv.norm_type == 'none' if (n_blocks + 1) <= n_disable_blocks else norm
-
- def test_encoder_disable_norm(self):
- n_residuals = [0, 1, 3]
- disable_blocks = [0, 1, 2, 3, 4, 5, 6]
- norms = ['weight_norm', 'none']
- for n_res, disable_blocks, norm in product(n_residuals, disable_blocks, norms):
- encoder = SEANetEncoder(n_residual_layers=n_res, norm=norm,
- disable_norm_outer_blocks=disable_blocks)
- self._check_encoder_blocks_norm(encoder, disable_blocks, norm)
-
- def _check_decoder_blocks_norm(self, decoder: SEANetDecoder, n_disable_blocks: int, norm: str):
- n_blocks = 0
- for layer in decoder.model:
- if isinstance(layer, StreamableConv1d):
- n_blocks += 1
- assert layer.conv.norm_type == 'none' if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm
- elif isinstance(layer, StreamableConvTranspose1d):
- n_blocks += 1
- assert layer.convtr.norm_type == 'none' if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm
- elif isinstance(layer, SEANetResnetBlock):
- for resnet_layer in layer.block:
- if isinstance(resnet_layer, StreamableConv1d):
- assert resnet_layer.conv.norm_type == 'none' \
- if (decoder.n_blocks - n_blocks) < n_disable_blocks else norm
-
- def test_decoder_disable_norm(self):
- n_residuals = [0, 1, 3]
- disable_blocks = [0, 1, 2, 3, 4, 5, 6]
- norms = ['weight_norm', 'none']
- for n_res, disable_blocks, norm in product(n_residuals, disable_blocks, norms):
- decoder = SEANetDecoder(n_residual_layers=n_res, norm=norm,
- disable_norm_outer_blocks=disable_blocks)
- self._check_decoder_blocks_norm(decoder, disable_blocks, norm)
-
- def test_disable_norm_raises_exception(self):
- # Invalid disable_norm_outer_blocks values raise exceptions
- with pytest.raises(AssertionError):
- SEANetEncoder(disable_norm_outer_blocks=-1)
-
- with pytest.raises(AssertionError):
- SEANetEncoder(ratios=[1, 1, 2, 2], disable_norm_outer_blocks=7)
-
- with pytest.raises(AssertionError):
- SEANetDecoder(disable_norm_outer_blocks=-1)
-
- with pytest.raises(AssertionError):
- SEANetDecoder(ratios=[1, 1, 2, 2], disable_norm_outer_blocks=7)
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/file_proxy.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/file_proxy.py
deleted file mode 100644
index 4b0b0da6c2a62b2b1468c35ddd69f1bbb9b91aa8..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/rich/file_proxy.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import io
-from typing import IO, TYPE_CHECKING, Any, List
-
-from .ansi import AnsiDecoder
-from .text import Text
-
-if TYPE_CHECKING:
- from .console import Console
-
-
-class FileProxy(io.TextIOBase):
- """Wraps a file (e.g. sys.stdout) and redirects writes to a console."""
-
- def __init__(self, console: "Console", file: IO[str]) -> None:
- self.__console = console
- self.__file = file
- self.__buffer: List[str] = []
- self.__ansi_decoder = AnsiDecoder()
-
- @property
- def rich_proxied_file(self) -> IO[str]:
- """Get proxied file."""
- return self.__file
-
- def __getattr__(self, name: str) -> Any:
- return getattr(self.__file, name)
-
- def write(self, text: str) -> int:
- if not isinstance(text, str):
- raise TypeError(f"write() argument must be str, not {type(text).__name__}")
- buffer = self.__buffer
- lines: List[str] = []
- while text:
- line, new_line, text = text.partition("\n")
- if new_line:
- lines.append("".join(buffer) + line)
- buffer.clear()
- else:
- buffer.append(line)
- break
- if lines:
- console = self.__console
- with console:
- output = Text("\n").join(
- self.__ansi_decoder.decode_line(line) for line in lines
- )
- console.print(output)
- return len(text)
-
- def flush(self) -> None:
- output = "".join(self.__buffer)
- if output:
- self.__console.print(output)
- del self.__buffer[:]
-
- def fileno(self) -> int:
- return self.__file.fileno()
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/importlib_metadata/_text.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/importlib_metadata/_text.py
deleted file mode 100644
index c88cfbb2349c6401336bc5ba6623f51afd1eb59d..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_vendor/importlib_metadata/_text.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import re
-
-from ._functools import method_cache
-
-
-# from jaraco.text 3.5
-class FoldedCase(str):
- """
- A case insensitive string class; behaves just like str
- except compares equal when the only variation is case.
-
- >>> s = FoldedCase('hello world')
-
- >>> s == 'Hello World'
- True
-
- >>> 'Hello World' == s
- True
-
- >>> s != 'Hello World'
- False
-
- >>> s.index('O')
- 4
-
- >>> s.split('O')
- ['hell', ' w', 'rld']
-
- >>> sorted(map(FoldedCase, ['GAMMA', 'alpha', 'Beta']))
- ['alpha', 'Beta', 'GAMMA']
-
- Sequence membership is straightforward.
-
- >>> "Hello World" in [s]
- True
- >>> s in ["Hello World"]
- True
-
- You may test for set inclusion, but candidate and elements
- must both be folded.
-
- >>> FoldedCase("Hello World") in {s}
- True
- >>> s in {FoldedCase("Hello World")}
- True
-
- String inclusion works as long as the FoldedCase object
- is on the right.
-
- >>> "hello" in FoldedCase("Hello World")
- True
-
- But not if the FoldedCase object is on the left:
-
- >>> FoldedCase('hello') in 'Hello World'
- False
-
- In that case, use in_:
-
- >>> FoldedCase('hello').in_('Hello World')
- True
-
- >>> FoldedCase('hello') > FoldedCase('Hello')
- False
- """
-
- def __lt__(self, other):
- return self.lower() < other.lower()
-
- def __gt__(self, other):
- return self.lower() > other.lower()
-
- def __eq__(self, other):
- return self.lower() == other.lower()
-
- def __ne__(self, other):
- return self.lower() != other.lower()
-
- def __hash__(self):
- return hash(self.lower())
-
- def __contains__(self, other):
- return super().lower().__contains__(other.lower())
-
- def in_(self, other):
- "Does self appear in other?"
- return self in FoldedCase(other)
-
- # cache lower since it's likely to be called frequently.
- @method_cache
- def lower(self):
- return super().lower()
-
- def index(self, sub):
- return self.lower().index(sub.lower())
-
- def split(self, splitter=' ', maxsplit=0):
- pattern = re.compile(re.escape(splitter), re.I)
- return pattern.split(self, maxsplit)
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/build_clib.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/build_clib.py
deleted file mode 100644
index 67ce2444ea69a0bbdfab0bda8c2aa14951187096..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/build_clib.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import distutils.command.build_clib as orig
-from distutils.errors import DistutilsSetupError
-from distutils import log
-from setuptools.dep_util import newer_pairwise_group
-
-
-class build_clib(orig.build_clib):
- """
- Override the default build_clib behaviour to do the following:
-
- 1. Implement a rudimentary timestamp-based dependency system
- so 'compile()' doesn't run every time.
- 2. Add more keys to the 'build_info' dictionary:
- * obj_deps - specify dependencies for each object compiled.
- this should be a dictionary mapping a key
- with the source filename to a list of
- dependencies. Use an empty string for global
- dependencies.
- * cflags - specify a list of additional flags to pass to
- the compiler.
- """
-
- def build_libraries(self, libraries):
- for (lib_name, build_info) in libraries:
- sources = build_info.get('sources')
- if sources is None or not isinstance(sources, (list, tuple)):
- raise DistutilsSetupError(
- "in 'libraries' option (library '%s'), "
- "'sources' must be present and must be "
- "a list of source filenames" % lib_name)
- sources = list(sources)
-
- log.info("building '%s' library", lib_name)
-
- # Make sure everything is the correct type.
- # obj_deps should be a dictionary of keys as sources
- # and a list/tuple of files that are its dependencies.
- obj_deps = build_info.get('obj_deps', dict())
- if not isinstance(obj_deps, dict):
- raise DistutilsSetupError(
- "in 'libraries' option (library '%s'), "
- "'obj_deps' must be a dictionary of "
- "type 'source: list'" % lib_name)
- dependencies = []
-
- # Get the global dependencies that are specified by the '' key.
- # These will go into every source's dependency list.
- global_deps = obj_deps.get('', list())
- if not isinstance(global_deps, (list, tuple)):
- raise DistutilsSetupError(
- "in 'libraries' option (library '%s'), "
- "'obj_deps' must be a dictionary of "
- "type 'source: list'" % lib_name)
-
- # Build the list to be used by newer_pairwise_group
- # each source will be auto-added to its dependencies.
- for source in sources:
- src_deps = [source]
- src_deps.extend(global_deps)
- extra_deps = obj_deps.get(source, list())
- if not isinstance(extra_deps, (list, tuple)):
- raise DistutilsSetupError(
- "in 'libraries' option (library '%s'), "
- "'obj_deps' must be a dictionary of "
- "type 'source: list'" % lib_name)
- src_deps.extend(extra_deps)
- dependencies.append(src_deps)
-
- expected_objects = self.compiler.object_filenames(
- sources,
- output_dir=self.build_temp,
- )
-
- if (
- newer_pairwise_group(dependencies, expected_objects)
- != ([], [])
- ):
- # First, compile the source code to object files in the library
- # directory. (This should probably change to putting object
- # files in a temporary build directory.)
- macros = build_info.get('macros')
- include_dirs = build_info.get('include_dirs')
- cflags = build_info.get('cflags')
- self.compiler.compile(
- sources,
- output_dir=self.build_temp,
- macros=macros,
- include_dirs=include_dirs,
- extra_postargs=cflags,
- debug=self.debug
- )
-
- # Now "link" the object files together into a static library.
- # (On Unix at least, this isn't really linking -- it just
- # builds an archive. Whatever.)
- self.compiler.create_static_lib(
- expected_objects,
- lib_name,
- output_dir=self.build_clib,
- debug=self.debug
- )
diff --git a/spaces/CVPR/LIVE/thrust/dependencies/cub/experimental/histogram/histogram_gmem_atomics.h b/spaces/CVPR/LIVE/thrust/dependencies/cub/experimental/histogram/histogram_gmem_atomics.h
deleted file mode 100644
index 3308a2851bec88a0b04c17413a92861a74298b89..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/dependencies/cub/experimental/histogram/histogram_gmem_atomics.h
+++ /dev/null
@@ -1,185 +0,0 @@
-/******************************************************************************
- * Copyright (c) 2011-2018, NVIDIA CORPORATION. All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- * * Redistributions of source code must retain the above copyright
- * notice, this list of conditions and the following disclaimer.
- * * Redistributions in binary form must reproduce the above copyright
- * notice, this list of conditions and the following disclaimer in the
- * documentation and/or other materials provided with the distribution.
- * * Neither the name of the NVIDIA CORPORATION nor the
- * names of its contributors may be used to endorse or promote products
- * derived from this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
- * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
- * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
- * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
- * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
- * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
- * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
- * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- ******************************************************************************/
-
-#include
-
-namespace histogram_gmem_atomics
-{
- // Decode float4 pixel into bins
- template
- __device__ __forceinline__ void DecodePixel(float4 pixel, unsigned int (&bins)[ACTIVE_CHANNELS])
- {
- float* samples = reinterpret_cast(&pixel);
-
- #pragma unroll
- for (int CHANNEL = 0; CHANNEL < ACTIVE_CHANNELS; ++CHANNEL)
- bins[CHANNEL] = (unsigned int) (samples[CHANNEL] * float(NUM_BINS));
- }
-
- // Decode uchar4 pixel into bins
- template
- __device__ __forceinline__ void DecodePixel(uchar4 pixel, unsigned int (&bins)[ACTIVE_CHANNELS])
- {
- unsigned char* samples = reinterpret_cast(&pixel);
-
- #pragma unroll
- for (int CHANNEL = 0; CHANNEL < ACTIVE_CHANNELS; ++CHANNEL)
- bins[CHANNEL] = (unsigned int) (samples[CHANNEL]);
- }
-
- // Decode uchar1 pixel into bins
- template
- __device__ __forceinline__ void DecodePixel(uchar1 pixel, unsigned int (&bins)[ACTIVE_CHANNELS])
- {
- bins[0] = (unsigned int) pixel.x;
- }
-
- // First-pass histogram kernel (binning into privatized counters)
- template <
- int NUM_PARTS,
- int ACTIVE_CHANNELS,
- int NUM_BINS,
- typename PixelType>
- __global__ void histogram_gmem_atomics(
- const PixelType *in,
- int width,
- int height,
- unsigned int *out)
- {
- // global position and size
- int x = blockIdx.x * blockDim.x + threadIdx.x;
- int y = blockIdx.y * blockDim.y + threadIdx.y;
- int nx = blockDim.x * gridDim.x;
- int ny = blockDim.y * gridDim.y;
-
- // threads in workgroup
- int t = threadIdx.x + threadIdx.y * blockDim.x; // thread index in workgroup, linear in 0..nt-1
- int nt = blockDim.x * blockDim.y; // total threads in workgroup
-
- // group index in 0..ngroups-1
- int g = blockIdx.x + blockIdx.y * gridDim.x;
-
- // initialize smem
- unsigned int *gmem = out + g * NUM_PARTS;
- for (int i = t; i < ACTIVE_CHANNELS * NUM_BINS; i += nt)
- gmem[i] = 0;
- __syncthreads();
-
- // process pixels (updates our group's partial histogram in gmem)
- for (int col = x; col < width; col += nx)
- {
- for (int row = y; row < height; row += ny)
- {
- PixelType pixel = in[row * width + col];
-
- unsigned int bins[ACTIVE_CHANNELS];
- DecodePixel(pixel, bins);
-
- #pragma unroll
- for (int CHANNEL = 0; CHANNEL < ACTIVE_CHANNELS; ++CHANNEL)
- atomicAdd(&gmem[(NUM_BINS * CHANNEL) + bins[CHANNEL]], 1);
- }
- }
- }
-
- // Second pass histogram kernel (accumulation)
- template <
- int NUM_PARTS,
- int ACTIVE_CHANNELS,
- int NUM_BINS>
- __global__ void histogram_gmem_accum(
- const unsigned int *in,
- int n,
- unsigned int *out)
- {
- int i = blockIdx.x * blockDim.x + threadIdx.x;
- if (i > ACTIVE_CHANNELS * NUM_BINS)
- return; // out of range
-
- unsigned int total = 0;
- for (int j = 0; j < n; j++)
- total += in[i + NUM_PARTS * j];
-
- out[i] = total;
- }
-
-
-} // namespace histogram_gmem_atomics
-
-
-template <
- int ACTIVE_CHANNELS,
- int NUM_BINS,
- typename PixelType>
-double run_gmem_atomics(
- PixelType *d_image,
- int width,
- int height,
- unsigned int *d_hist,
- bool warmup)
-{
- enum
- {
- NUM_PARTS = 1024
- };
-
- cudaDeviceProp props;
- cudaGetDeviceProperties(&props, 0);
-
- dim3 block(32, 4);
- dim3 grid(16, 16);
- int total_blocks = grid.x * grid.y;
-
- // allocate partial histogram
- unsigned int *d_part_hist;
- cudaMalloc(&d_part_hist, total_blocks * NUM_PARTS * sizeof(unsigned int));
-
- dim3 block2(128);
- dim3 grid2((3 * NUM_BINS + block.x - 1) / block.x);
-
- GpuTimer gpu_timer;
- gpu_timer.Start();
-
- histogram_gmem_atomics::histogram_gmem_atomics<<>>(
- d_image,
- width,
- height,
- d_part_hist);
-
- histogram_gmem_atomics::histogram_gmem_accum<<>>(
- d_part_hist,
- total_blocks,
- d_hist);
-
- gpu_timer.Stop();
- float elapsed_millis = gpu_timer.ElapsedMillis();
-
- cudaFree(d_part_hist);
-
- return elapsed_millis;
-}
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/find.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/find.h
deleted file mode 100644
index 00e11e53c61d8916d51d044eba11f34092cf597c..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/find.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-
-#pragma once
-
-#include
-#include
-
-namespace thrust
-{
-namespace system
-{
-namespace detail
-{
-namespace generic
-{
-
-
-template
-__host__ __device__
-InputIterator find(thrust::execution_policy &exec,
- InputIterator first,
- InputIterator last,
- const T& value);
-
-
-template
-__host__ __device__
-InputIterator find_if(thrust::execution_policy &exec,
- InputIterator first,
- InputIterator last,
- Predicate pred);
-
-
-template
-__host__ __device__
-InputIterator find_if_not(thrust::execution_policy &exec,
- InputIterator first,
- InputIterator last,
- Predicate pred);
-
-
-} // end namespace generic
-} // end namespace detail
-} // end namespace system
-} // end namespace thrust
-
-#include
-
diff --git a/spaces/CYSD/AI-image-detector/app.py b/spaces/CYSD/AI-image-detector/app.py
deleted file mode 100644
index 10f4a7ef433ca6d5ac688e4b07bb8dd6548d163e..0000000000000000000000000000000000000000
--- a/spaces/CYSD/AI-image-detector/app.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-
-pipe = pipeline("image-classification", "umm-maybe/AI-image-detector")
-
-def image_classifier(image):
- outputs = pipe(image)
- results = {}
- for result in outputs:
- results[result['label']] = result['score']
- return results
-
-demo = gr.Interface(fn=image_classifier, inputs=gr.Image(type="pil"), outputs="label")
-demo.launch()
diff --git a/spaces/CarperAI/pile-v2-eda/README.md b/spaces/CarperAI/pile-v2-eda/README.md
deleted file mode 100644
index 1fdd9a1d56e93868ae98e3025f67a63c7693011e..0000000000000000000000000000000000000000
--- a/spaces/CarperAI/pile-v2-eda/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Pile V2 EDA
-emoji: 🎄
-colorFrom: indigo
-colorTo: grey
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
----
diff --git a/spaces/ChandraMohanNayal/AutoGPT/autogpt/app.py b/spaces/ChandraMohanNayal/AutoGPT/autogpt/app.py
deleted file mode 100644
index 58d9f7164ddfbb5019b072d789dc2fa6205dc9d3..0000000000000000000000000000000000000000
--- a/spaces/ChandraMohanNayal/AutoGPT/autogpt/app.py
+++ /dev/null
@@ -1,330 +0,0 @@
-""" Command and Control """
-import json
-from typing import Dict, List, NoReturn, Union
-
-from autogpt.agent.agent_manager import AgentManager
-from autogpt.commands.analyze_code import analyze_code
-from autogpt.commands.audio_text import read_audio_from_file
-from autogpt.commands.execute_code import (
- execute_python_file,
- execute_shell,
- execute_shell_popen,
-)
-from autogpt.commands.file_operations import (
- append_to_file,
- delete_file,
- download_file,
- read_file,
- search_files,
- write_to_file,
-)
-from autogpt.commands.git_operations import clone_repository
-from autogpt.commands.google_search import google_official_search, google_search
-from autogpt.commands.image_gen import generate_image
-from autogpt.commands.improve_code import improve_code
-from autogpt.commands.twitter import send_tweet
-from autogpt.commands.web_requests import scrape_links, scrape_text
-from autogpt.commands.web_selenium import browse_website
-from autogpt.commands.write_tests import write_tests
-from autogpt.config import Config
-from autogpt.json_utils.json_fix_llm import fix_and_parse_json
-from autogpt.memory import get_memory
-from autogpt.processing.text import summarize_text
-from autogpt.speech import say_text
-
-CFG = Config()
-AGENT_MANAGER = AgentManager()
-
-
-def is_valid_int(value: str) -> bool:
- """Check if the value is a valid integer
-
- Args:
- value (str): The value to check
-
- Returns:
- bool: True if the value is a valid integer, False otherwise
- """
- try:
- int(value)
- return True
- except ValueError:
- return False
-
-
-def get_command(response_json: Dict):
- """Parse the response and return the command name and arguments
-
- Args:
- response_json (json): The response from the AI
-
- Returns:
- tuple: The command name and arguments
-
- Raises:
- json.decoder.JSONDecodeError: If the response is not valid JSON
-
- Exception: If any other error occurs
- """
- try:
- if "command" not in response_json:
- return "Error:", "Missing 'command' object in JSON"
-
- if not isinstance(response_json, dict):
- return "Error:", f"'response_json' object is not dictionary {response_json}"
-
- command = response_json["command"]
- if not isinstance(command, dict):
- return "Error:", "'command' object is not a dictionary"
-
- if "name" not in command:
- return "Error:", "Missing 'name' field in 'command' object"
-
- command_name = command["name"]
-
- # Use an empty dictionary if 'args' field is not present in 'command' object
- arguments = command.get("args", {})
-
- return command_name, arguments
- except json.decoder.JSONDecodeError:
- return "Error:", "Invalid JSON"
- # All other errors, return "Error: + error message"
- except Exception as e:
- return "Error:", str(e)
-
-
-def map_command_synonyms(command_name: str):
- """Takes the original command name given by the AI, and checks if the
- string matches a list of common/known hallucinations
- """
- synonyms = [
- ("write_file", "write_to_file"),
- ("create_file", "write_to_file"),
- ("search", "google"),
- ]
- for seen_command, actual_command_name in synonyms:
- if command_name == seen_command:
- return actual_command_name
- return command_name
-
-
-def execute_command(command_name: str, arguments):
- """Execute the command and return the result
-
- Args:
- command_name (str): The name of the command to execute
- arguments (dict): The arguments for the command
-
- Returns:
- str: The result of the command
- """
- try:
- command_name = map_command_synonyms(command_name.lower())
- if command_name == "google":
- # Check if the Google API key is set and use the official search method
- # If the API key is not set or has only whitespaces, use the unofficial
- # search method
- key = CFG.google_api_key
- if key and key.strip() and key != "your-google-api-key":
- google_result = google_official_search(arguments["input"])
- return google_result
- else:
- google_result = google_search(arguments["input"])
-
- # google_result can be a list or a string depending on the search results
- if isinstance(google_result, list):
- safe_message = [
- google_result_single.encode("utf-8", "ignore")
- for google_result_single in google_result
- ]
- else:
- safe_message = google_result.encode("utf-8", "ignore")
-
- return safe_message.decode("utf-8")
- elif command_name == "memory_add":
- memory = get_memory(CFG)
- return memory.add(arguments["string"])
- elif command_name == "start_agent":
- return start_agent(
- arguments["name"], arguments["task"], arguments["prompt"]
- )
- elif command_name == "message_agent":
- return message_agent(arguments["key"], arguments["message"])
- elif command_name == "list_agents":
- return list_agents()
- elif command_name == "delete_agent":
- return delete_agent(arguments["key"])
- elif command_name == "get_text_summary":
- return get_text_summary(arguments["url"], arguments["question"])
- elif command_name == "get_hyperlinks":
- return get_hyperlinks(arguments["url"])
- elif command_name == "clone_repository":
- return clone_repository(
- arguments["repository_url"], arguments["clone_path"]
- )
- elif command_name == "read_file":
- return read_file(arguments["file"])
- elif command_name == "write_to_file":
- return write_to_file(arguments["file"], arguments["text"])
- elif command_name == "append_to_file":
- return append_to_file(arguments["file"], arguments["text"])
- elif command_name == "delete_file":
- return delete_file(arguments["file"])
- elif command_name == "search_files":
- return search_files(arguments["directory"])
- elif command_name == "download_file":
- if not CFG.allow_downloads:
- return "Error: You do not have user authorization to download files locally."
- return download_file(arguments["url"], arguments["file"])
- elif command_name == "browse_website":
- return browse_website(arguments["url"], arguments["question"])
- # TODO: Change these to take in a file rather than pasted code, if
- # non-file is given, return instructions "Input should be a python
- # filepath, write your code to file and try again"
- elif command_name == "analyze_code":
- return analyze_code(arguments["code"])
- elif command_name == "improve_code":
- return improve_code(arguments["suggestions"], arguments["code"])
- elif command_name == "write_tests":
- return write_tests(arguments["code"], arguments.get("focus"))
- elif command_name == "execute_python_file": # Add this command
- return execute_python_file(arguments["file"])
- elif command_name == "execute_shell":
- if CFG.execute_local_commands:
- return execute_shell(arguments["command_line"])
- else:
- return (
- "You are not allowed to run local shell commands. To execute"
- " shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
- "in your config. Do not attempt to bypass the restriction."
- )
- elif command_name == "execute_shell_popen":
- if CFG.execute_local_commands:
- return execute_shell_popen(arguments["command_line"])
- else:
- return (
- "You are not allowed to run local shell commands. To execute"
- " shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
- "in your config. Do not attempt to bypass the restriction."
- )
- elif command_name == "read_audio_from_file":
- return read_audio_from_file(arguments["file"])
- elif command_name == "generate_image":
- return generate_image(arguments["prompt"])
- elif command_name == "send_tweet":
- return send_tweet(arguments["text"])
- elif command_name == "do_nothing":
- return "No action performed."
- elif command_name == "task_complete":
- shutdown()
- else:
- return (
- f"Unknown command '{command_name}'. Please refer to the 'COMMANDS'"
- " list for available commands and only respond in the specified JSON"
- " format."
- )
- except Exception as e:
- return f"Error: {str(e)}"
-
-
-def get_text_summary(url: str, question: str) -> str:
- """Return the results of a Google search
-
- Args:
- url (str): The url to scrape
- question (str): The question to summarize the text for
-
- Returns:
- str: The summary of the text
- """
- text = scrape_text(url)
- summary = summarize_text(url, text, question)
- return f""" "Result" : {summary}"""
-
-
-def get_hyperlinks(url: str) -> Union[str, List[str]]:
- """Return the results of a Google search
-
- Args:
- url (str): The url to scrape
-
- Returns:
- str or list: The hyperlinks on the page
- """
- return scrape_links(url)
-
-
-def shutdown() -> NoReturn:
- """Shut down the program"""
- print("Shutting down...")
- quit()
-
-
-def start_agent(name: str, task: str, prompt: str, model=CFG.fast_llm_model) -> str:
- """Start an agent with a given name, task, and prompt
-
- Args:
- name (str): The name of the agent
- task (str): The task of the agent
- prompt (str): The prompt for the agent
- model (str): The model to use for the agent
-
- Returns:
- str: The response of the agent
- """
- # Remove underscores from name
- voice_name = name.replace("_", " ")
-
- first_message = f"""You are {name}. Respond with: "Acknowledged"."""
- agent_intro = f"{voice_name} here, Reporting for duty!"
-
- # Create agent
- if CFG.speak_mode:
- say_text(agent_intro, 1)
- key, ack = AGENT_MANAGER.create_agent(task, first_message, model)
-
- if CFG.speak_mode:
- say_text(f"Hello {voice_name}. Your task is as follows. {task}.")
-
- # Assign task (prompt), get response
- agent_response = AGENT_MANAGER.message_agent(key, prompt)
-
- return f"Agent {name} created with key {key}. First response: {agent_response}"
-
-
-def message_agent(key: str, message: str) -> str:
- """Message an agent with a given key and message"""
- # Check if the key is a valid integer
- if is_valid_int(key):
- agent_response = AGENT_MANAGER.message_agent(int(key), message)
- else:
- return "Invalid key, must be an integer."
-
- # Speak response
- if CFG.speak_mode:
- say_text(agent_response, 1)
- return agent_response
-
-
-def list_agents():
- """List all agents
-
- Returns:
- str: A list of all agents
- """
- return "List of agents:\n" + "\n".join(
- [str(x[0]) + ": " + x[1] for x in AGENT_MANAGER.list_agents()]
- )
-
-
-def delete_agent(key: str) -> str:
- """Delete an agent with a given key
-
- Args:
- key (str): The key of the agent to delete
-
- Returns:
- str: A message indicating whether the agent was deleted or not
- """
- result = AGENT_MANAGER.delete_agent(key)
- return f"Agent {key} deleted." if result else f"Agent {key} does not exist."
diff --git a/spaces/ChandraMohanNayal/AutoGPT/autogpt/workspace.py b/spaces/ChandraMohanNayal/AutoGPT/autogpt/workspace.py
deleted file mode 100644
index 6fb0e3113eb2c1338edf7f86c6e162fc27c61e50..0000000000000000000000000000000000000000
--- a/spaces/ChandraMohanNayal/AutoGPT/autogpt/workspace.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from __future__ import annotations
-
-import os
-from pathlib import Path
-
-from autogpt.config import Config
-
-CFG = Config()
-
-# Set a dedicated folder for file I/O
-WORKSPACE_PATH = Path(os.getcwd()) / "auto_gpt_workspace"
-
-# Create the directory if it doesn't exist
-if not os.path.exists(WORKSPACE_PATH):
- os.makedirs(WORKSPACE_PATH)
-
-
-def path_in_workspace(relative_path: str | Path) -> Path:
- """Get full path for item in workspace
-
- Parameters:
- relative_path (str | Path): Path to translate into the workspace
-
- Returns:
- Path: Absolute path for the given path in the workspace
- """
- return safe_path_join(WORKSPACE_PATH, relative_path)
-
-
-def safe_path_join(base: Path, *paths: str | Path) -> Path:
- """Join one or more path components, asserting the resulting path is within the workspace.
-
- Args:
- base (Path): The base path
- *paths (str): The paths to join to the base path
-
- Returns:
- Path: The joined path
- """
- joined_path = base.joinpath(*paths).resolve()
-
- if CFG.restrict_to_workspace and not joined_path.is_relative_to(base):
- raise ValueError(
- f"Attempted to access path '{joined_path}' outside of workspace '{base}'."
- )
-
- return joined_path
diff --git a/spaces/Chris4K/llms_compare/Aloo Chaat Hd Movie Download 1080p __TOP__.md b/spaces/Chris4K/llms_compare/Aloo Chaat Hd Movie Download 1080p __TOP__.md
deleted file mode 100644
index 4a750fc39891032759870e66a61c649654a5964a..0000000000000000000000000000000000000000
--- a/spaces/Chris4K/llms_compare/Aloo Chaat Hd Movie Download 1080p __TOP__.md
+++ /dev/null
@@ -1,56 +0,0 @@
-## Aloo Chaat hd movie download 1080p
-
-
-
-
-
-
-
-
-
-**DOWNLOAD === [https://www.google.com/url?q=https%3A%2F%2Furlgoal.com%2F2txP38&sa=D&sntz=1&usg=AOvVaw1R1ga3x5jvhXx0u0qjRBzQ](https://www.google.com/url?q=https%3A%2F%2Furlgoal.com%2F2txP38&sa=D&sntz=1&usg=AOvVaw1R1ga3x5jvhXx0u0qjRBzQ)**
-
-
-
-
-
-
-
-
-
-
-
-
-
-# Aloo Chaat: A Delicious Comedy of Love and Culture
-
-
-
-Aloo Chaat is a 2009 Hindi romantic comedy film that revolves around the love story of Nikhil, a Hindu boy who falls in love with Aamna, a Muslim girl. Nikhil returns to his traditional family in India after completing his education in the US and faces the challenge of convincing them to accept his interfaith relationship. He enlists the help of his uncle Hakeem, a sexologist, and Nikki, an American girl, to create a fake marriage drama that would make Aamna look like a better choice for him.
-
-
-
-The film is directed by Robbie Grewal and stars Aftab Shivdasani, Aamna Sharif, Linda Arsenio, Kulbhushan Kharbanda, Sanjai Mishra, and Manoj Pahwa. The film is full of hilarious situations, witty dialogues, and catchy songs that will make you laugh and enjoy the cultural differences and similarities between the characters. The film also explores the themes of family values, social norms, and personal choices in a light-hearted manner.
-
-
-
-If you are looking for a fun and entertaining movie to watch with your family or friends, you can download Aloo Chaat in high definition quality from various online platforms. The film has received mixed reviews from critics but has been appreciated by the audience for its humor and charm. Aloo Chaat is a film that will make you crave for some spicy and tangy street food as well as some sweet and romantic moments.
-
-
-
-The film has a simple plot but is executed with flair and creativity. The film uses the metaphor of aloo chaat, a spicy and tangy dish made of potatoes and various chutneys, to represent the mix of cultures and emotions that the characters go through. The film also has some catchy songs composed by RDB, Xulfi, Vipin Mishra and Mehfuz Maruf that add to the fun and flavor of the film. The film has some memorable scenes such as the one where Nikhil introduces Nikki to his family as his fiancee, the one where Aamna teaches Nikki how to cook Punjabi food, and the one where Nikhil and Aamna confess their love to each other.
-
-
-
-The film also has some brilliant performances by the actors, especially Sanjai Mishra as Chhadami Mama, Nikhil's suspicious uncle who is always on the lookout for clues to expose Nikhil's plan. He delivers some hilarious dialogues and expressions that will make you laugh out loud. Manoj Pahwa as Hakeem Tarachand, Nikhil's uncle and confidant who helps him in his scheme, is also very funny and convincing. Kulbhushan Kharbanda as Purshottam, Nikhil's father who is a staunch believer in Hindu traditions and values, is also very impressive and shows his versatility as an actor. Aftab Shivdasani and Aamna Sharif have a good chemistry and look good together as the lead pair. Linda Arsenio as Nikki, the American girl who pretends to be Nikhil's fiancee, is also very charming and does a good job of playing a spoiled but sweet girl.
-
-
-
-Aloo Chaat is a film that will appeal to anyone who likes comedy, romance, and culture. It is a film that will make you laugh, smile, and feel good. It is a film that will make you appreciate the diversity and richness of Indian culture and society. It is a film that will make you want to try some aloo chaat yourself.
-
- dfd1c89656
-
-
-
-
-
diff --git a/spaces/ChrisPreston/diff-svc_minato_aqua/infer_tools/f0_static.py b/spaces/ChrisPreston/diff-svc_minato_aqua/infer_tools/f0_static.py
deleted file mode 100644
index f57d34819e4042244d5338393f43134f4e27aa22..0000000000000000000000000000000000000000
--- a/spaces/ChrisPreston/diff-svc_minato_aqua/infer_tools/f0_static.py
+++ /dev/null
@@ -1,116 +0,0 @@
-import json
-import os
-import shutil
-from functools import reduce
-from pathlib import Path
-
-import matplotlib
-import matplotlib.pyplot as plt
-import yaml
-from pylab import xticks, np
-from tqdm import tqdm
-
-from modules.vocoders.nsf_hifigan import NsfHifiGAN
-from preprocessing.process_pipeline import get_pitch_parselmouth, get_pitch_crepe
-from utils.hparams import set_hparams, hparams
-
-head_list = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]
-
-
-def compare_pitch(f0_static_dict, pitch_time_temp, trans_key=0):
- return sum({k: v * f0_static_dict[str(k + trans_key)] for k, v in pitch_time_temp.items() if
- str(k + trans_key) in f0_static_dict}.values())
-
-
-def f0_to_pitch(ff):
- f0_pitch = 69 + 12 * np.log2(ff / 440)
- return round(f0_pitch, 0)
-
-
-def pitch_to_name(pitch):
- return f"{head_list[int(pitch % 12)]}{int(pitch / 12) - 1}"
-
-
-def get_f0(audio_path, crepe=False):
- wav, mel = NsfHifiGAN.wav2spec(audio_path)
- if crepe:
- f0, pitch_coarse = get_pitch_crepe(wav, mel, hparams)
- else:
- f0, pitch_coarse = get_pitch_parselmouth(wav, mel, hparams)
- return f0
-
-
-def merge_f0_dict(dict_list):
- def sum_dict(a, b):
- temp = dict()
- for key in a.keys() | b.keys():
- temp[key] = sum([d.get(key, 0) for d in (a, b)])
- return temp
-
- return reduce(sum_dict, dict_list)
-
-
-def collect_f0(f0):
- pitch_num = {}
- pitch_list = [f0_to_pitch(x) for x in f0[f0 > 0]]
- for key in pitch_list:
- pitch_num[key] = pitch_num.get(key, 0) + 1
- return pitch_num
-
-
-def static_f0_time(f0):
- if isinstance(f0, dict):
- pitch_num = merge_f0_dict({k: collect_f0(v) for k, v in f0.items()}.values())
- else:
- pitch_num = collect_f0(f0)
- static_pitch_time = {}
- sort_key = sorted(pitch_num.keys())
- for key in sort_key:
- static_pitch_time[key] = round(pitch_num[key] * hparams['hop_size'] / hparams['audio_sample_rate'], 2)
- return static_pitch_time
-
-
-def get_end_file(dir_path, end):
- file_lists = []
- for root, dirs, files in os.walk(dir_path):
- files = [f for f in files if f[0] != '.']
- dirs[:] = [d for d in dirs if d[0] != '.']
- for f_file in files:
- if f_file.endswith(end):
- file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
- return file_lists
-
-
-if __name__ == "__main__":
- # 给config文件增加f0_static统计音域
- config_path = "F:/sovits/diff-svc-main/checkpoints/aquapre/config.yaml"
- hparams = set_hparams(config=config_path, exp_name='', infer=True, reset=True, hparams_str='', print_hparams=False)
- f0_dict = {}
- # 获取batch文件夹下所有wav文件
- wav_paths = get_end_file("F:/sovits/diff-svc-main/batch/aquapre", "wav")
- # parselmouth获取f0
- with tqdm(total=len(wav_paths)) as p_bar:
- p_bar.set_description('Processing')
- for wav_path in wav_paths:
- f0_dict[wav_path] = get_f0(wav_path, crepe=False)
- p_bar.update(1)
- pitch_time = static_f0_time(f0_dict)
- total_time = round(sum(pitch_time.values()), 2)
- pitch_time["total_time"] = total_time
- print(f"total time: {total_time}s")
- shutil.copy(config_path, f"{Path(config_path).parent}\\back_{Path(config_path).name}")
- with open(config_path, encoding='utf-8') as f:
- _hparams = yaml.safe_load(f)
- _hparams['f0_static'] = json.dumps(pitch_time)
- with open(config_path, 'w', encoding='utf-8') as f:
- yaml.safe_dump(_hparams, f)
- print("原config文件已在原目录建立备份：back_config.yaml")
- print("音域统计已保存至config文件，此模型可使用自动变调功能")
- matplotlib.use('TkAgg')
- plt.title("数据集音域统计", fontproperties='SimHei')
- plt.xlabel("音高", fontproperties='SimHei')
- plt.ylabel("时长(s)", fontproperties='SimHei')
- xticks_labels = [pitch_to_name(i) for i in range(36, 96)]
- xticks(np.linspace(36, 96, 60, endpoint=True), xticks_labels)
- plt.plot(pitch_time.keys(), pitch_time.values(), color='dodgerblue')
- plt.show()
diff --git a/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/index.js b/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/index.js
deleted file mode 100644
index 0218fd0a8e10c1eb49303ed7b5c731c00b4d34ce..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/Yunzai/Yunzai/plugins/ws-plugin/index.js
+++ /dev/null
@@ -1,103 +0,0 @@
-import fs from 'node:fs'
-import { initWebSocket, Config, Version } from './components/index.js'
-import { TMP_DIR, mimeTypes } from './model/index.js'
-import { join, extname } from 'path'
-const files = fs.readdirSync('./plugins/ws-plugin/apps').filter(file => file.endsWith('.js'))
-
-let ret = []
-
-logger.info('-----------------')
-logger.info(`ws-plugin${Version.version}插件初始化~`)
-
-
-files.forEach((file) => {
- ret.push(import(`./apps/${file}`))
-})
-
-ret = await Promise.allSettled(ret)
-
-let apps = {}
-for (let i in files) {
- let name = files[i].replace('.js', '')
-
- if (ret[i].status != 'fulfilled') {
- logger.error(`载入插件错误：${logger.red(name)}`)
- logger.error(ret[i].reason)
- continue
- }
- apps[name] = ret[i].value[Object.keys(ret[i].value)[0]]
-}
-let path = ['./apps/message/message.js', './apps/notice/notice.js', './apps/request/request.js']
-for (const item of path) {
- try {
- await import(`${item}`)
- } catch (e) {
- logger.error(`载入事件错误：${item}`)
- logger.error(e)
- }
-}
-
-initWebSocket()
-if (Version.isTrss) {
- Bot.express.get('/ws-plugin*', async (req, res) => {
- const file = req.query.file
- if (file) {
- const ext = extname(file)
- const contentType = mimeTypes[ext]
- fs.readFile(join(TMP_DIR, file), (err, content) => {
- if (err) {
- res.writeHead(404)
- res.end('File not found')
- } else {
- const name = file.split('-')
- const filename = encodeURIComponent(name[1]) || encodeURIComponent(name[0]) || encodeURIComponent(file)
- res.writeHead(200, {
- 'Content-Type': contentType,
- 'Content-Disposition': `attachment; filename=${filename}`
- })
- res.end(content)
- }
- })
- return
- }
- res.writeHead(404);
- res.end('Page not found')
- })
-} else {
- const getGroupMemberInfo = Bot.getGroupMemberInfo
- /** 劫持修改getGroupMemberInfo方法 */
- Bot.getGroupMemberInfo = async function (group_id, user_id) {
- let result
- try {
- result = await getGroupMemberInfo(group_id, user_id)
- } catch (error) {
- let nickname
- if (error.stack.includes('ws-plugin')) {
- nickname = 'chronocat'
- } else {
- nickname = String(group_id).includes("qg_") ? "QQGuild-Bot" : "WeChat-Bot"
- }
- result = {
- group_id,
- user_id,
- nickname,
- card: "",
- sex: "female",
- age: 6,
- join_time: "",
- last_sent_time: "",
- level: 1,
- role: "member",
- title: "",
- title_expire_time: "",
- shutup_time: 0,
- update_time: "",
- area: "南极洲",
- rank: "潜水",
- }
- }
- return result
- }
-}
-
-export { apps }
diff --git a/spaces/CodingBillionaire/bark-voice-cloning/README.md b/spaces/CodingBillionaire/bark-voice-cloning/README.md
deleted file mode 100644
index 0201ebf6de813acfb8bfd4997583bc5f5c0d036e..0000000000000000000000000000000000000000
--- a/spaces/CodingBillionaire/bark-voice-cloning/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-title: Bark Voice Cloning
-emoji: 🐶
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.29.0
-python_version: 3.10.11
-app_file: app.py
-models:
-- facebook/hubert-base-ls960
-- GitMylo/bark-voice-cloning
-pinned: false
-license: mit
-duplicated_from: GitMylo/bark-voice-cloning
----
diff --git a/spaces/CofAI/chat.b4/client/js/highlight.min.js b/spaces/CofAI/chat.b4/client/js/highlight.min.js
deleted file mode 100644
index d410b45b38119606525a0a7c0c60c428c5ee6eb7..0000000000000000000000000000000000000000
--- a/spaces/CofAI/chat.b4/client/js/highlight.min.js
+++ /dev/null
@@ -1 +0,0 @@
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n.contains.push("self"),{name:"WebAssembly",keywords:{$pattern:/[\w.]+/,keyword:["anyfunc","block","br","br_if","br_table","call","call_indirect","data","drop","elem","else","end","export","func","global.get","global.set","local.get","local.set","local.tee","get_global","get_local","global","if","import","local","loop","memory","memory.grow","memory.size","module","mut","nop","offset","param","result","return","select","set_global","set_local","start","table","tee_local","then","type","unreachable",]},contains:[e.COMMENT(/;;/,/$/),n,{match:[/(?:offset|align)/,/\s*/,/=/],className:{1:"keyword",3:"operator"}},{className:"variable",begin:/\$[\w_]+/},{match:/(\((?!;)|\))+/,className:"punctuation",relevance:0},{begin:[/(?:func|call|call_indirect)/,/\s+/,/\$[^\s)]+/],className:{1:"keyword",3:"title.function"}},e.QUOTE_STRING_MODE,{match:/(i32|i64|f32|f64)(?!\.)/,className:"type"},{className:"keyword",match:/\b(f32|f64|i32|i64)(?:\.(?:abs|add|and|ceil|clz|const|convert_[su]\/i(?:32|64)|copysign|ctz|demote\/f64|div(?:_[su])?|eqz?|extend_[su]\/i32|floor|ge(?:_[su])?|gt(?:_[su])?|le(?:_[su])?|load(?:(?:8|16|32)_[su])?|lt(?:_[su])?|max|min|mul|nearest|neg?|or|popcnt|promote\/f32|reinterpret\/[fi](?:32|64)|rem_[su]|rot[lr]|shl|shr_[su]|store(?:8|16|32)?|sqrt|sub|trunc(?:_[su]\/f(?:32|64))?|wrap\/i64|xor))\b/},{className:"number",relevance:0,match:/[+-]?\b(?:\d(?:_?\d)*(?:\.\d(?:_?\d)*)?(?:[eE][+-]?\d(?:_?\d)*)?|0x[\da-fA-F](?:_?[\da-fA-F])*(?:\.[\da-fA-F](?:_?[\da-fA-D])*)?(?:[pP][+-]?\d(?:_?\d)*)?)\b|\binf\b|\bnan(?::0x[\da-fA-F](?:_?[\da-fA-D])*)?\b/},]}},grmr_yaml(e){let 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eL=eI.replace("grmr_","").replace("_","-");eD.registerLanguage(eL,eR[eI])}return eD}();"object"==typeof exports&&"undefined"!=typeof module&&(module.exports=hljs);
\ No newline at end of file
diff --git a/spaces/CofAI/chat.b4/g4f/Provider/Providers/Mishalsgpt.py b/spaces/CofAI/chat.b4/g4f/Provider/Providers/Mishalsgpt.py
deleted file mode 100644
index 63080c674900a181f66380bcfe6c185b7469cebd..0000000000000000000000000000000000000000
--- a/spaces/CofAI/chat.b4/g4f/Provider/Providers/Mishalsgpt.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import os, requests, uuid
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://mishalsgpt.vercel.app'
-model = ['gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
- headers = {
- 'Content-Type': 'application/json',
- }
- data = {
- 'model': model,
- 'temperature': 0.7,
- 'messages': messages
- }
- response = requests.post(url + '/api/openai/v1/chat/completions',
- headers=headers, json=data, stream=True)
- yield response.json()['choices'][0]['message']['content']
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
diff --git a/spaces/CofAI/chat.b4/server/website.py b/spaces/CofAI/chat.b4/server/website.py
deleted file mode 100644
index 468f419793c78ecedcf843ffd7708cc3f9418ae4..0000000000000000000000000000000000000000
--- a/spaces/CofAI/chat.b4/server/website.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from flask import render_template, redirect, url_for
-from time import time
-from os import urandom
-
-
-class Website:
- def __init__(self, bp, url_prefix) -> None:
- self.bp = bp
- self.url_prefix = url_prefix
- self.routes = {
- '/': {
- 'function': lambda: redirect(url_for('._index')),
- 'methods': ['GET', 'POST']
- },
- '/chat/': {
- 'function': self._index,
- 'methods': ['GET', 'POST']
- },
- '/chat/': {
- 'function': self._chat,
- 'methods': ['GET', 'POST']
- }
- }
-
- def _chat(self, conversation_id):
- if '-' not in conversation_id:
- return redirect(url_for('._index'))
-
- return render_template('index.html', chat_id=conversation_id, url_prefix=self.url_prefix)
-
- def _index(self):
- return render_template('index.html', chat_id=f'{urandom(4).hex()}-{urandom(2).hex()}-{urandom(2).hex()}-{urandom(2).hex()}-{hex(int(time() * 1000))[2:]}', url_prefix=self.url_prefix)
diff --git a/spaces/Cong723/gpt-academic-public/crazy_functions/test_project/latex/attention/parameter_attention.tex b/spaces/Cong723/gpt-academic-public/crazy_functions/test_project/latex/attention/parameter_attention.tex
deleted file mode 100644
index 7bc4fe452dbdbfe44ff72f0cdbd37acd5c786ce6..0000000000000000000000000000000000000000
--- a/spaces/Cong723/gpt-academic-public/crazy_functions/test_project/latex/attention/parameter_attention.tex
+++ /dev/null
@@ -1,45 +0,0 @@
-\pagebreak
-\section*{Two Feed-Forward Layers = Attention over Parameters}\label{sec:parameter_attention}
-
-In addition to attention layers, our model contains position-wise feed-forward networks (Section \ref{sec:ffn}), which consist of two linear transformations with a ReLU activation in between. In fact, these networks too can be seen as a form of attention. Compare the formula for such a network with the formula for a simple dot-product attention layer (biases and scaling factors omitted):
-
-\begin{align*}
- FFN(x, W_1, W_2) = ReLU(xW_1)W_2 \\
- A(q, K, V) = Softmax(qK^T)V
-\end{align*}
-
-Based on the similarity of these formulae, the two-layer feed-forward network can be seen as a kind of attention, where the keys and values are the rows of the trainable parameter matrices $W_1$ and $W_2$, and where we use ReLU instead of Softmax in the compatibility function.
-
-%the compatablity function is $compat(q, k_i) = ReLU(q \cdot k_i)$ instead of $Softmax(qK_T)_i$.
-
-Given this similarity, we experimented with replacing the position-wise feed-forward networks with attention layers similar to the ones we use everywhere else our model. The multi-head-attention-over-parameters sublayer is identical to the multi-head attention described in \ref{sec:multihead}, except that the "keys" and "values" inputs to each attention head are trainable model parameters, as opposed to being linear projections of a previous layer. These parameters are scaled up by a factor of $\sqrt{d_{model}}$ in order to be more similar to activations.
-
-In our first experiment, we replaced each position-wise feed-forward network with a multi-head-attention-over-parameters sublayer with $h_p=8$ heads, key-dimensionality $d_{pk}=64$, and value-dimensionality $d_{pv}=64$, using $n_p=1536$ key-value pairs for each attention head. The sublayer has a total of $2097152$ parameters, including the parameters in the query projection and the output projection. This matches the number of parameters in the position-wise feed-forward network that we replaced. While the theoretical amount of computation is also the same, in practice, the attention version caused the step times to be about 30\% longer.
-
-In our second experiment, we used $h_p=8$ heads, and $n_p=512$ key-value pairs for each attention head, again matching the total number of parameters in the base model.
-
-Results for the first experiment were slightly worse than for the base model, and results for the second experiment were slightly better, see Table~\ref{tab:parameter_attention}.
-
-\begin{table}[h]
-\caption{Replacing the position-wise feed-forward networks with multihead-attention-over-parameters produces similar results to the base model. All metrics are on the English-to-German translation development set, newstest2013.}
-\label{tab:parameter_attention}
-\begin{center}
-\vspace{-2mm}
-%\scalebox{1.0}{
-\begin{tabular}{c|cccccc|cccc}
-\hline\rule{0pt}{2.0ex}
- & \multirow{2}{*}{$\dmodel$} & \multirow{2}{*}{$\dff$} &
-\multirow{2}{*}{$h_p$} & \multirow{2}{*}{$d_{pk}$} & \multirow{2}{*}{$d_{pv}$} &
- \multirow{2}{*}{$n_p$} &
- PPL & BLEU & params & training\\
- & & & & & & & (dev) & (dev) & $\times10^6$ & time \\
-\hline\rule{0pt}{2.0ex}
-base & 512 & 2048 & & & & & 4.92 & 25.8 & 65 & 12 hours\\
-\hline\rule{0pt}{2.0ex}
-AOP$_1$ & 512 & & 8 & 64 & 64 & 1536 & 4.92& 25.5 & 65 & 16 hours\\
-AOP$_2$ & 512 & & 16 & 64 & 64 & 512 & \textbf{4.86} & \textbf{25.9} & 65 & 16 hours \\
-\hline
-\end{tabular}
-%}
-\end{center}
-\end{table}
diff --git a/spaces/Cropinky/esrgan/realesrgan/__init__.py b/spaces/Cropinky/esrgan/realesrgan/__init__.py
deleted file mode 100644
index 2276f1eecded80d1f00ff97b45c66c7a8922b987..0000000000000000000000000000000000000000
--- a/spaces/Cropinky/esrgan/realesrgan/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# flake8: noqa
-from .archs import *
-from .data import *
-from .models import *
-from .utils import *
-from .version import *
diff --git a/spaces/Cvandi/remake/realesrgan/archs/srvgg_arch.py b/spaces/Cvandi/remake/realesrgan/archs/srvgg_arch.py
deleted file mode 100644
index 39460965c9c5ee9cd6eb41c50d33574cb8ba6e50..0000000000000000000000000000000000000000
--- a/spaces/Cvandi/remake/realesrgan/archs/srvgg_arch.py
+++ /dev/null
@@ -1,69 +0,0 @@
-from basicsr.utils.registry import ARCH_REGISTRY
-from torch import nn as nn
-from torch.nn import functional as F
-
-
-@ARCH_REGISTRY.register()
-class SRVGGNetCompact(nn.Module):
- """A compact VGG-style network structure for super-resolution.
-
- It is a compact network structure, which performs upsampling in the last layer and no convolution is
- conducted on the HR feature space.
-
- Args:
- num_in_ch (int): Channel number of inputs. Default: 3.
- num_out_ch (int): Channel number of outputs. Default: 3.
- num_feat (int): Channel number of intermediate features. Default: 64.
- num_conv (int): Number of convolution layers in the body network. Default: 16.
- upscale (int): Upsampling factor. Default: 4.
- act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu.
- """
-
- def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
- super(SRVGGNetCompact, self).__init__()
- self.num_in_ch = num_in_ch
- self.num_out_ch = num_out_ch
- self.num_feat = num_feat
- self.num_conv = num_conv
- self.upscale = upscale
- self.act_type = act_type
-
- self.body = nn.ModuleList()
- # the first conv
- self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
- # the first activation
- if act_type == 'relu':
- activation = nn.ReLU(inplace=True)
- elif act_type == 'prelu':
- activation = nn.PReLU(num_parameters=num_feat)
- elif act_type == 'leakyrelu':
- activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
- self.body.append(activation)
-
- # the body structure
- for _ in range(num_conv):
- self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
- # activation
- if act_type == 'relu':
- activation = nn.ReLU(inplace=True)
- elif act_type == 'prelu':
- activation = nn.PReLU(num_parameters=num_feat)
- elif act_type == 'leakyrelu':
- activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
- self.body.append(activation)
-
- # the last conv
- self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
- # upsample
- self.upsampler = nn.PixelShuffle(upscale)
-
- def forward(self, x):
- out = x
- for i in range(0, len(self.body)):
- out = self.body[i](out)
-
- out = self.upsampler(out)
- # add the nearest upsampled image, so that the network learns the residual
- base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
- out += base
- return out
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiofiles/tempfile/__init__.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiofiles/tempfile/__init__.py
deleted file mode 100644
index 3978cba138c147568000cf4e327983cd6f929405..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/aiofiles/tempfile/__init__.py
+++ /dev/null
@@ -1,263 +0,0 @@
-# Imports
-import asyncio
-from tempfile import (
- TemporaryFile as syncTemporaryFile,
- NamedTemporaryFile as syncNamedTemporaryFile,
- SpooledTemporaryFile as syncSpooledTemporaryFile,
- TemporaryDirectory as syncTemporaryDirectory,
- _TemporaryFileWrapper as syncTemporaryFileWrapper,
-)
-from io import FileIO, TextIOBase, BufferedReader, BufferedWriter, BufferedRandom
-from functools import partial, singledispatch
-from ..base import AiofilesContextManager
-from ..threadpool.text import AsyncTextIOWrapper
-from ..threadpool.binary import AsyncBufferedIOBase, AsyncBufferedReader, AsyncFileIO
-from .temptypes import AsyncSpooledTemporaryFile, AsyncTemporaryDirectory
-
-__all__ = [
- "NamedTemporaryFile",
- "TemporaryFile",
- "SpooledTemporaryFile",
- "TemporaryDirectory",
-]
-
-
-# ================================================================
-# Public methods for async open and return of temp file/directory
-# objects with async interface
-# ================================================================
-def NamedTemporaryFile(
- mode="w+b",
- buffering=-1,
- encoding=None,
- newline=None,
- suffix=None,
- prefix=None,
- dir=None,
- delete=True,
- loop=None,
- executor=None,
-):
- """Async open a named temporary file"""
- return AiofilesContextManager(
- _temporary_file(
- named=True,
- mode=mode,
- buffering=buffering,
- encoding=encoding,
- newline=newline,
- suffix=suffix,
- prefix=prefix,
- dir=dir,
- delete=delete,
- loop=loop,
- executor=executor,
- )
- )
-
-
-def TemporaryFile(
- mode="w+b",
- buffering=-1,
- encoding=None,
- newline=None,
- suffix=None,
- prefix=None,
- dir=None,
- loop=None,
- executor=None,
-):
- """Async open an unnamed temporary file"""
- return AiofilesContextManager(
- _temporary_file(
- named=False,
- mode=mode,
- buffering=buffering,
- encoding=encoding,
- newline=newline,
- suffix=suffix,
- prefix=prefix,
- dir=dir,
- loop=loop,
- executor=executor,
- )
- )
-
-
-def SpooledTemporaryFile(
- max_size=0,
- mode="w+b",
- buffering=-1,
- encoding=None,
- newline=None,
- suffix=None,
- prefix=None,
- dir=None,
- loop=None,
- executor=None,
-):
- """Async open a spooled temporary file"""
- return AiofilesContextManager(
- _spooled_temporary_file(
- max_size=max_size,
- mode=mode,
- buffering=buffering,
- encoding=encoding,
- newline=newline,
- suffix=suffix,
- prefix=prefix,
- dir=dir,
- loop=loop,
- executor=executor,
- )
- )
-
-
-def TemporaryDirectory(suffix=None, prefix=None, dir=None, loop=None, executor=None):
- """Async open a temporary directory"""
- return AiofilesContextManagerTempDir(
- _temporary_directory(
- suffix=suffix, prefix=prefix, dir=dir, loop=loop, executor=executor
- )
- )
-
-
-# =========================================================
-# Internal coroutines to open new temp files/directories
-# =========================================================
-async def _temporary_file(
- named=True,
- mode="w+b",
- buffering=-1,
- encoding=None,
- newline=None,
- suffix=None,
- prefix=None,
- dir=None,
- delete=True,
- loop=None,
- executor=None,
- max_size=0,
-):
- """Async method to open a temporary file with async interface"""
- if loop is None:
- loop = asyncio.get_running_loop()
-
- if named:
- cb = partial(
- syncNamedTemporaryFile,
- mode=mode,
- buffering=buffering,
- encoding=encoding,
- newline=newline,
- suffix=suffix,
- prefix=prefix,
- dir=dir,
- delete=delete,
- )
- else:
- cb = partial(
- syncTemporaryFile,
- mode=mode,
- buffering=buffering,
- encoding=encoding,
- newline=newline,
- suffix=suffix,
- prefix=prefix,
- dir=dir,
- )
-
- f = await loop.run_in_executor(executor, cb)
-
- # Wrap based on type of underlying IO object
- if type(f) is syncTemporaryFileWrapper:
- # _TemporaryFileWrapper was used (named files)
- result = wrap(f.file, f, loop=loop, executor=executor)
- # add delete property
- result.delete = f.delete
- return result
- else:
- # IO object was returned directly without wrapper
- return wrap(f, f, loop=loop, executor=executor)
-
-
-async def _spooled_temporary_file(
- max_size=0,
- mode="w+b",
- buffering=-1,
- encoding=None,
- newline=None,
- suffix=None,
- prefix=None,
- dir=None,
- loop=None,
- executor=None,
-):
- """Open a spooled temporary file with async interface"""
- if loop is None:
- loop = asyncio.get_running_loop()
-
- cb = partial(
- syncSpooledTemporaryFile,
- max_size=max_size,
- mode=mode,
- buffering=buffering,
- encoding=encoding,
- newline=newline,
- suffix=suffix,
- prefix=prefix,
- dir=dir,
- )
-
- f = await loop.run_in_executor(executor, cb)
-
- # Single interface provided by SpooledTemporaryFile for all modes
- return AsyncSpooledTemporaryFile(f, loop=loop, executor=executor)
-
-
-async def _temporary_directory(
- suffix=None, prefix=None, dir=None, loop=None, executor=None
-):
- """Async method to open a temporary directory with async interface"""
- if loop is None:
- loop = asyncio.get_running_loop()
-
- cb = partial(syncTemporaryDirectory, suffix, prefix, dir)
- f = await loop.run_in_executor(executor, cb)
-
- return AsyncTemporaryDirectory(f, loop=loop, executor=executor)
-
-
-class AiofilesContextManagerTempDir(AiofilesContextManager):
- """With returns the directory location, not the object (matching sync lib)"""
-
- async def __aenter__(self):
- self._obj = await self._coro
- return self._obj.name
-
-
-@singledispatch
-def wrap(base_io_obj, file, *, loop=None, executor=None):
- """Wrap the object with interface based on type of underlying IO"""
- raise TypeError("Unsupported IO type: {}".format(base_io_obj))
-
-
-@wrap.register(TextIOBase)
-def _(base_io_obj, file, *, loop=None, executor=None):
- return AsyncTextIOWrapper(file, loop=loop, executor=executor)
-
-
-@wrap.register(BufferedWriter)
-def _(base_io_obj, file, *, loop=None, executor=None):
- return AsyncBufferedIOBase(file, loop=loop, executor=executor)
-
-
-@wrap.register(BufferedReader)
-@wrap.register(BufferedRandom)
-def _(base_io_obj, file, *, loop=None, executor=None):
- return AsyncBufferedReader(file, loop=loop, executor=executor)
-
-
-@wrap.register(FileIO)
-def _(base_io_obj, file, *, loop=None, executor=None):
- return AsyncFileIO(file, loop=loop, executor=executor)
diff --git a/spaces/Dauzy/whisper-webui/src/hooks/subTaskProgressListener.py b/spaces/Dauzy/whisper-webui/src/hooks/subTaskProgressListener.py
deleted file mode 100644
index 9a8eaa876fcd18032875d67535e0558494842c60..0000000000000000000000000000000000000000
--- a/spaces/Dauzy/whisper-webui/src/hooks/subTaskProgressListener.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from src.hooks.progressListener import ProgressListener
-
-from typing import Union
-
-class SubTaskProgressListener(ProgressListener):
- """
- A sub task listener that reports the progress of a sub task to a base task listener
- Parameters
- ----------
- base_task_listener : ProgressListener
- The base progress listener to accumulate overall progress in.
- base_task_total : float
- The maximum total progress that will be reported to the base progress listener.
- sub_task_start : float
- The starting progress of a sub task, in respect to the base progress listener.
- sub_task_total : float
- The total amount of progress a sub task will report to the base progress listener.
- """
- def __init__(
- self,
- base_task_listener: ProgressListener,
- base_task_total: float,
- sub_task_start: float,
- sub_task_total: float,
- ):
- self.base_task_listener = base_task_listener
- self.base_task_total = base_task_total
- self.sub_task_start = sub_task_start
- self.sub_task_total = sub_task_total
-
- def on_progress(self, current: Union[int, float], total: Union[int, float]):
- sub_task_progress_frac = current / total
- sub_task_progress = self.sub_task_start + self.sub_task_total * sub_task_progress_frac
- self.base_task_listener.on_progress(sub_task_progress, self.base_task_total)
-
- def on_finished(self):
- self.base_task_listener.on_progress(self.sub_task_start + self.sub_task_total, self.base_task_total)
\ No newline at end of file
diff --git a/spaces/Dauzy/whisper-webui/src/whisper/whisperFactory.py b/spaces/Dauzy/whisper-webui/src/whisper/whisperFactory.py
deleted file mode 100644
index 58fc840b7e60947fec4a98b2833ff03e7ad7b7de..0000000000000000000000000000000000000000
--- a/spaces/Dauzy/whisper-webui/src/whisper/whisperFactory.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from typing import List
-from src import modelCache
-from src.config import ModelConfig
-from src.whisper.abstractWhisperContainer import AbstractWhisperContainer
-
-def create_whisper_container(whisper_implementation: str,
- model_name: str, device: str = None, compute_type: str = "float16",
- download_root: str = None,
- cache: modelCache = None, models: List[ModelConfig] = []) -> AbstractWhisperContainer:
- print("Creating whisper container for " + whisper_implementation)
-
- if (whisper_implementation == "whisper"):
- from src.whisper.whisperContainer import WhisperContainer
- return WhisperContainer(model_name=model_name, device=device, compute_type=compute_type, download_root=download_root, cache=cache, models=models)
- elif (whisper_implementation == "faster-whisper" or whisper_implementation == "faster_whisper"):
- from src.whisper.fasterWhisperContainer import FasterWhisperContainer
- return FasterWhisperContainer(model_name=model_name, device=device, compute_type=compute_type, download_root=download_root, cache=cache, models=models)
- else:
- raise ValueError("Unknown Whisper implementation: " + whisper_implementation)
\ No newline at end of file
diff --git a/spaces/Deepsheka/newdemo-app/app.py b/spaces/Deepsheka/newdemo-app/app.py
deleted file mode 100644
index e460712878fd7536ef570b1093037835ef9b67bf..0000000000000000000000000000000000000000
--- a/spaces/Deepsheka/newdemo-app/app.py
+++ /dev/null
@@ -1,354 +0,0 @@
-import gradio as gr
-from pytube import YouTube
-import whisper
-import json
-from difflib import Differ
-import ffmpeg
-import os
-from pathlib import Path
-import time
-import aiohttp
-import asyncio
-# define function for transcription
-def whisper_transcript(model_size,url,audio_file):
- if url:
- link = YouTube(url)
- source = link.streams.filter(only_audio=True)[0].download(filename="audio.mp4")
-
- else:
- source = audio_file
-
- if model_size.endswith(".en"):
- language = "english"
-
- else:
- language = None
-
- options = whisper.DecodingOptions(without_timestamps=True)
-
- loaded_model = whisper.load_model(model_size)
- transcript = loaded_model.transcribe(source, language=language)
-
- return transcript["text"]
-
-# define Gradio app interface
-gradio_ui = gr.Interface(
- fn=whisper_transcript,
- title="Transcribe multi-lingual audio",
- theme="peach",
- description="**How to use**: Select a model, upload an audio clip, then click submit. If your clip is **100% in English, select models ending in ‘.en’**. If the clip is in other languages, or a mix of languages, select models without ‘.en’",
- article="**Note**: The larger the model size selected or the longer the audio clip, the more time it would take to process the transcript.",
- inputs=[
- gr.Dropdown(
- label="Select Model",
- choices=[
- "tiny.en",
- "base.en",
- "small.en",
- "medium.en",
- "tiny",
- "base",
- "small",
- "medium",
- "large",
- ],
- value="base",
- ),
- gr.Textbox(label="Paste YouTube link here"),
- gr.Audio(label="Upload Audio File", source="upload", type="filepath"),
- ],
- outputs=gr.outputs.Textbox(label="Whisper Transcript"),
-)
-
-gradio_ui.queue().launch()
-
-
-# Set true if you're using huggingface inference API API https://huggingface.co/inference-api
-API_BACKEND = True
-# MODEL = 'facebook/wav2vec2-large-960h-lv60-self'
-# MODEL = "facebook/wav2vec2-large-960h"
-MODEL = "facebook/wav2vec2-base-960h"
-# MODEL = "patrickvonplaten/wav2vec2-large-960h-lv60-self-4-gram"
-if API_BACKEND:
- from dotenv import load_dotenv
- import base64
- import asyncio
- load_dotenv(Path(".env"))
-
- HF_TOKEN = os.environ["HF_TOKEN"]
- headers = {"Authorization": f"Bearer {HF_TOKEN}"}
- API_URL = f'https://api-inference.huggingface.co/models/{MODEL}'
-
-else:
- import torch
- from transformers import pipeline
-
- # is cuda available?
- cuda = torch.device(
- 'cuda:0') if torch.cuda.is_available() else torch.device('cpu')
- device = 0 if torch.cuda.is_available() else -1
- speech_recognizer = pipeline(
- task="automatic-speech-recognition",
- model=f'{MODEL}',
- tokenizer=f'{MODEL}',
- framework="pt",
- device=device,
- )
-
-videos_out_path = Path("./videos_out")
-videos_out_path.mkdir(parents=True, exist_ok=True)
-
-samples_data = sorted(Path('examples').glob('*.json'))
-SAMPLES = []
-for file in samples_data:
- with open(file) as f:
- sample = json.load(f)
- SAMPLES.append(sample)
-VIDEOS = list(map(lambda x: [x['video']], SAMPLES))
-
-total_inferences_since_reboot = 415
-total_cuts_since_reboot = 1539
-
-
-async def speech_to_text(video_file_path):
- """
- Takes a video path to convert to audio, transcribe audio channel to text and char timestamps
- Using https://huggingface.co/tasks/automatic-speech-recognition pipeline
- """
- global total_inferences_since_reboot
- if(video_file_path == None):
- raise ValueError("Error no video input")
-
- video_path = Path(video_file_path)
- try:
- # convert video to audio 16k using PIPE to audio_memory
- audio_memory, _ = ffmpeg.input(video_path).output(
- '-', format="wav", ac=1, ar='16k').overwrite_output().global_args('-loglevel', 'quiet').run(capture_stdout=True)
- except Exception as e:
- raise RuntimeError("Error converting video to audio")
-
- ping("speech_to_text")
- last_time = time.time()
- if API_BACKEND:
- # Using Inference API https://huggingface.co/inference-api
- # try twice, because the model must be loaded
- for i in range(10):
- for tries in range(4):
- print(f'Transcribing from API attempt {tries}')
- try:
- inference_reponse = await query_api(audio_memory)
- transcription = inference_reponse["text"].lower()
- timestamps = [[chunk["text"].lower(), chunk["timestamp"][0], chunk["timestamp"][1]]
- for chunk in inference_reponse['chunks']]
-
- total_inferences_since_reboot += 1
- print("\n\ntotal_inferences_since_reboot: ",
- total_inferences_since_reboot, "\n\n")
- return (transcription, transcription, timestamps)
- except:
- if 'error' in inference_reponse and 'estimated_time' in inference_reponse:
- wait_time = inference_reponse['estimated_time']
- print("Waiting for model to load....", wait_time)
- # wait for loading model
- # 5 seconds plus for certanty
- await asyncio.sleep(wait_time + 5.0)
- elif 'error' in inference_reponse:
- raise RuntimeError("Error Fetching API",
- inference_reponse['error'])
- else:
- break
- else:
- raise RuntimeError(inference_reponse, "Error Fetching API")
- else:
-
- try:
- print(f'Transcribing via local model')
- output = speech_recognizer(
- audio_memory, return_timestamps="char", chunk_length_s=10, stride_length_s=(4, 2))
-
- transcription = output["text"].lower()
- timestamps = [[chunk["text"].lower(), chunk["timestamp"][0].tolist(), chunk["timestamp"][1].tolist()]
- for chunk in output['chunks']]
- total_inferences_since_reboot += 1
-
- print("\n\ntotal_inferences_since_reboot: ",
- total_inferences_since_reboot, "\n\n")
- return (transcription, transcription, timestamps)
- except Exception as e:
- raise RuntimeError("Error Running inference with local model", e)
-
-
-async def cut_timestamps_to_video(video_in, transcription, text_in, timestamps):
- """
- Given original video input, text transcript + timestamps,
- and edit ext cuts video segments into a single video
- """
- global total_cuts_since_reboot
-
- video_path = Path(video_in)
- video_file_name = video_path.stem
- if(video_in == None or text_in == None or transcription == None):
- raise ValueError("Inputs undefined")
-
- d = Differ()
- # compare original transcription with edit text
- diff_chars = d.compare(transcription, text_in)
- # remove all text aditions from diff
- filtered = list(filter(lambda x: x[0] != '+', diff_chars))
-
- # filter timestamps to be removed
- # timestamps_to_cut = [b for (a,b) in zip(filtered, timestamps_var) if a[0]== '-' ]
- # return diff tokes and cutted video!!
-
- # groupping character timestamps so there are less cuts
- idx = 0
- grouped = {}
- for(a, b) in zip(filtered, timestamps):
- if a[0] != '-':
- if idx in grouped:
- grouped[idx].append(b)
- else:
- grouped[idx] = []
- grouped[idx].append(b)
- else:
- idx += 1
-
- # after grouping, gets the lower and upter start and time for each group
- timestamps_to_cut = [[v[0][1], v[-1][2]] for v in grouped.values()]
-
- between_str = '+'.join(
- map(lambda t: f'between(t,{t[0]},{t[1]})', timestamps_to_cut))
-
- if timestamps_to_cut:
- video_file = ffmpeg.input(video_in)
- video = video_file.video.filter(
- "select", f'({between_str})').filter("setpts", "N/FRAME_RATE/TB")
- audio = video_file.audio.filter(
- "aselect", f'({between_str})').filter("asetpts", "N/SR/TB")
-
- output_video = f'./videos_out/{video_file_name}.mp4'
- ffmpeg.concat(video, audio, v=1, a=1).output(
- output_video).overwrite_output().global_args('-loglevel', 'quiet').run()
- else:
- output_video = video_in
-
- tokens = [(token[2:], token[0] if token[0] != " " else None)
- for token in filtered]
-
- total_cuts_since_reboot += 1
- ping("video_cuts")
- print("\n\ntotal_cuts_since_reboot: ", total_cuts_since_reboot, "\n\n")
- return (tokens, output_video)
-
-
-async def query_api(audio_bytes: bytes):
- """
- Query for Huggingface Inference API for Automatic Speech Recognition task
- """
- payload = json.dumps({
- "inputs": base64.b64encode(audio_bytes).decode("utf-8"),
- "parameters": {
- "return_timestamps": "char",
- "chunk_length_s": 10,
- "stride_length_s": [4, 2]
- },
- "options": {"use_gpu": False}
- }).encode("utf-8")
- async with aiohttp.ClientSession() as session:
- async with session.post(API_URL, headers=headers, data=payload) as response:
- return await response.json()
-
-
-def ping(name):
- url = f'https://huggingface.co/api/telemetry/spaces/radames/edit-video-by-editing-text/{name}'
- print("ping: ", url)
-
- async def req():
- async with aiohttp.ClientSession() as session:
- async with session.get(url) as response:
- print("pong: ", response.status)
- asyncio.create_task(req())
-
-
-# ---- Gradio Layout -----
-video_in = gr.Video(label="Video file")
-text_in = gr.Textbox(label="Transcription", lines=10, interactive=True)
-video_out = gr.Video(label="Video Out")
-diff_out = gr.HighlightedText(label="Cuts Diffs", combine_adjacent=True)
-examples = gr.components.Dataset(
- components=[video_in], samples=VIDEOS, type="index")
-
-demo = gr.Blocks(enable_queue=True, css='''
-#cut_btn, #reset_btn { align-self:stretch; }
-#\\31 3 { max-width: 540px; }
-.output-markdown {max-width: 65ch !important;}
-''')
-demo.encrypt = False
-with demo:
- transcription_var = gr.Variable()
- timestamps_var = gr.Variable()
- with gr.Row():
- with gr.Column():
- gr.Markdown('''
- # Edit Video By Editing Text
- This project is a quick proof of concept of a simple video editor where the edits
- are made by editing the audio transcription.
- Using the [Huggingface Automatic Speech Recognition Pipeline](https://huggingface.co/tasks/automatic-speech-recognition)
- with a fine tuned [Wav2Vec2 model using Connectionist Temporal Classification (CTC)](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self)
- you can predict not only the text transcription but also the [character or word base timestamps](https://huggingface.co/docs/transformers/v4.19.2/en/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline.__call__.return_timestamps)
- ''')
-
- with gr.Row():
-
- examples.render()
-
- def load_example(id):
- video = SAMPLES[id]['video']
- transcription = SAMPLES[id]['transcription'].lower()
- timestamps = SAMPLES[id]['timestamps']
-
- return (video, transcription, transcription, timestamps)
-
- examples.click(
- load_example,
- inputs=[examples],
- outputs=[video_in, text_in, transcription_var, timestamps_var],
- queue=False)
- with gr.Row():
- with gr.Column():
- video_in.render()
- transcribe_btn = gr.Button("Transcribe Audio")
- transcribe_btn.click(speech_to_text, [video_in], [
- text_in, transcription_var, timestamps_var])
-
- with gr.Row():
- gr.Markdown('''
- ### Now edit as text
- After running the video transcription, you can make cuts to the text below (only cuts, not additions!)''')
-
- with gr.Row():
- with gr.Column():
- text_in.render()
- with gr.Row():
- cut_btn = gr.Button("Cut to video", elem_id="cut_btn")
- # send audio path and hidden variables
- cut_btn.click(cut_timestamps_to_video, [
- video_in, transcription_var, text_in, timestamps_var], [diff_out, video_out])
-
- reset_transcription = gr.Button(
- "Reset to last trascription", elem_id="reset_btn")
- reset_transcription.click(
- lambda x: x, transcription_var, text_in)
- with gr.Column():
- video_out.render()
- diff_out.render()
- with gr.Row():
- gr.Markdown('''
- #### Video Credits
- 1. [Cooking](https://vimeo.com/573792389)
- 1. [Shia LaBeouf "Just Do It"](https://www.youtube.com/watch?v=n2lTxIk_Dr0)
- 1. [Mark Zuckerberg & Yuval Noah Harari in Conversation](https://www.youtube.com/watch?v=Boj9eD0Wug8)
- ''')
-
-if __name__ == "__main__":
- demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/Dify-AI/Baichuan2-13B-Chat/model.py b/spaces/Dify-AI/Baichuan2-13B-Chat/model.py
deleted file mode 100644
index a3ff143c055c88b05a2098f45070b6725a19987f..0000000000000000000000000000000000000000
--- a/spaces/Dify-AI/Baichuan2-13B-Chat/model.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from threading import Thread
-from typing import Iterator
-
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from transformers.generation.utils import GenerationConfig
-
-model_id = 'baichuan-inc/Baichuan2-13B-Chat'
-
-if torch.cuda.is_available():
- model = AutoModelForCausalLM.from_pretrained(
- model_id,
- # device_map='auto',
- torch_dtype=torch.float16,
- trust_remote_code=True
- )
- model = model.quantize(4).cuda()
- model.generation_config = GenerationConfig.from_pretrained(model_id)
-else:
- model = None
-tokenizer = AutoTokenizer.from_pretrained(
- model_id,
- use_fast=False,
- trust_remote_code=True
-)
-
-def run(
- message: str,
- chat_history: list[tuple[str, str]],
- max_new_tokens: int = 1024,
- temperature: float = 1.0,
- top_p: float = 0.95,
- top_k: int = 5
-) -> Iterator[str]:
- model.generation_config.max_new_tokens = max_new_tokens
- model.generation_config.temperature = temperature
- model.generation_config.top_p = top_p
- model.generation_config.top_k = top_k
-
- history = []
- result=""
-
- for i in chat_history:
- history.append({"role": "user", "content": i[0]})
- history.append({"role": "assistant", "content": i[1]})
-
- print(history)
-
- history.append({"role": "user", "content": message})
-
- for response in model.chat(
- tokenizer,
- history,
- # stream=True,
- ):
- result = result + response
- yield result
-
diff --git a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/dnnlib/tflib/__init__.py b/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/dnnlib/tflib/__init__.py
deleted file mode 100644
index f054a39cb81e38ca8b1f4ad5bac168aa68e7d92e..0000000000000000000000000000000000000000
--- a/spaces/Dinoking/Guccio-AI-Designer/models/stylegan/stylegan_tf/dnnlib/tflib/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
-#
-# This work is licensed under the Creative Commons Attribution-NonCommercial
-# 4.0 International License. To view a copy of this license, visit
-# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
-# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
-
-from . import autosummary
-from . import network
-from . import optimizer
-from . import tfutil
-
-from .tfutil import *
-from .network import Network
-
-from .optimizer import Optimizer
diff --git a/spaces/DkLead/facebook-tts_transformer-ru-cv7_css10/app.py b/spaces/DkLead/facebook-tts_transformer-ru-cv7_css10/app.py
deleted file mode 100644
index 9aa060b21e5cea6614a0f05d5aa4692d7648c18f..0000000000000000000000000000000000000000
--- a/spaces/DkLead/facebook-tts_transformer-ru-cv7_css10/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/facebook/tts_transformer-ru-cv7_css10").launch()
\ No newline at end of file
diff --git a/spaces/EXPOSUREEE/Ai-Image-Enhancer/realesrgan/utils.py b/spaces/EXPOSUREEE/Ai-Image-Enhancer/realesrgan/utils.py
deleted file mode 100644
index 10e7c23d04f777c250160e74470fdfacb16eab88..0000000000000000000000000000000000000000
--- a/spaces/EXPOSUREEE/Ai-Image-Enhancer/realesrgan/utils.py
+++ /dev/null
@@ -1,280 +0,0 @@
-import cv2
-import math
-import numpy as np
-import os
-import queue
-import threading
-import torch
-from basicsr.utils.download_util import load_file_from_url
-from torch.nn import functional as F
-
-ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-
-
-class RealESRGANer():
- """A helper class for upsampling images with RealESRGAN.
-
- Args:
- scale (int): Upsampling scale factor used in the networks. It is usually 2 or 4.
- model_path (str): The path to the pretrained model. It can be urls (will first download it automatically).
- model (nn.Module): The defined network. Default: None.
- tile (int): As too large images result in the out of GPU memory issue, so this tile option will first crop
- input images into tiles, and then process each of them. Finally, they will be merged into one image.
- 0 denotes for do not use tile. Default: 0.
- tile_pad (int): The pad size for each tile, to remove border artifacts. Default: 10.
- pre_pad (int): Pad the input images to avoid border artifacts. Default: 10.
- half (float): Whether to use half precision during inference. Default: False.
- """
-
- def __init__(self, scale, model_path, model=None, tile=0, tile_pad=10, pre_pad=10, half=False):
- self.scale = scale
- self.tile_size = tile
- self.tile_pad = tile_pad
- self.pre_pad = pre_pad
- self.mod_scale = None
- self.half = half
-
- # initialize model
- self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
- # if the model_path starts with https, it will first download models to the folder: realesrgan/weights
- if model_path.startswith('https://'):
- model_path = load_file_from_url(
- url=model_path, model_dir=os.path.join(ROOT_DIR, 'realesrgan/weights'), progress=True, file_name=None)
- loadnet = torch.load(model_path, map_location=torch.device('cpu'))
- # prefer to use params_ema
- if 'params_ema' in loadnet:
- keyname = 'params_ema'
- else:
- keyname = 'params'
- model.load_state_dict(loadnet[keyname], strict=True)
- model.eval()
- self.model = model.to(self.device)
- if self.half:
- self.model = self.model.half()
-
- def pre_process(self, img):
- """Pre-process, such as pre-pad and mod pad, so that the images can be divisible
- """
- img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
- self.img = img.unsqueeze(0).to(self.device)
- if self.half:
- self.img = self.img.half()
-
- # pre_pad
- if self.pre_pad != 0:
- self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect')
- # mod pad for divisible borders
- if self.scale == 2:
- self.mod_scale = 2
- elif self.scale == 1:
- self.mod_scale = 4
- if self.mod_scale is not None:
- self.mod_pad_h, self.mod_pad_w = 0, 0
- _, _, h, w = self.img.size()
- if (h % self.mod_scale != 0):
- self.mod_pad_h = (self.mod_scale - h % self.mod_scale)
- if (w % self.mod_scale != 0):
- self.mod_pad_w = (self.mod_scale - w % self.mod_scale)
- self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect')
-
- def process(self):
- # model inference
- self.output = self.model(self.img)
-
- def tile_process(self):
- """It will first crop input images to tiles, and then process each tile.
- Finally, all the processed tiles are merged into one images.
-
- Modified from: https://github.com/ata4/esrgan-launcher
- """
- batch, channel, height, width = self.img.shape
- output_height = height * self.scale
- output_width = width * self.scale
- output_shape = (batch, channel, output_height, output_width)
-
- # start with black image
- self.output = self.img.new_zeros(output_shape)
- tiles_x = math.ceil(width / self.tile_size)
- tiles_y = math.ceil(height / self.tile_size)
-
- # loop over all tiles
- for y in range(tiles_y):
- for x in range(tiles_x):
- # extract tile from input image
- ofs_x = x * self.tile_size
- ofs_y = y * self.tile_size
- # input tile area on total image
- input_start_x = ofs_x
- input_end_x = min(ofs_x + self.tile_size, width)
- input_start_y = ofs_y
- input_end_y = min(ofs_y + self.tile_size, height)
-
- # input tile area on total image with padding
- input_start_x_pad = max(input_start_x - self.tile_pad, 0)
- input_end_x_pad = min(input_end_x + self.tile_pad, width)
- input_start_y_pad = max(input_start_y - self.tile_pad, 0)
- input_end_y_pad = min(input_end_y + self.tile_pad, height)
-
- # input tile dimensions
- input_tile_width = input_end_x - input_start_x
- input_tile_height = input_end_y - input_start_y
- tile_idx = y * tiles_x + x + 1
- input_tile = self.img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]
-
- # upscale tile
- try:
- with torch.no_grad():
- output_tile = self.model(input_tile)
- except RuntimeError as error:
- print('Error', error)
- print(f'\tTile {tile_idx}/{tiles_x * tiles_y}')
-
- # output tile area on total image
- output_start_x = input_start_x * self.scale
- output_end_x = input_end_x * self.scale
- output_start_y = input_start_y * self.scale
- output_end_y = input_end_y * self.scale
-
- # output tile area without padding
- output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
- output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
- output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
- output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
-
- # put tile into output image
- self.output[:, :, output_start_y:output_end_y,
- output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile,
- output_start_x_tile:output_end_x_tile]
-
- def post_process(self):
- # remove extra pad
- if self.mod_scale is not None:
- _, _, h, w = self.output.size()
- self.output = self.output[:, :, 0:h - self.mod_pad_h * self.scale, 0:w - self.mod_pad_w * self.scale]
- # remove prepad
- if self.pre_pad != 0:
- _, _, h, w = self.output.size()
- self.output = self.output[:, :, 0:h - self.pre_pad * self.scale, 0:w - self.pre_pad * self.scale]
- return self.output
-
- @torch.no_grad()
- def enhance(self, img, outscale=None, alpha_upsampler='realesrgan'):
- h_input, w_input = img.shape[0:2]
- # img: numpy
- img = img.astype(np.float32)
- if np.max(img) > 256: # 16-bit image
- max_range = 65535
- print('\tInput is a 16-bit image')
- else:
- max_range = 255
- img = img / max_range
- if len(img.shape) == 2: # gray image
- img_mode = 'L'
- img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
- elif img.shape[2] == 4: # RGBA image with alpha channel
- img_mode = 'RGBA'
- alpha = img[:, :, 3]
- img = img[:, :, 0:3]
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
- if alpha_upsampler == 'realesrgan':
- alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
- else:
- img_mode = 'RGB'
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-
- # ------------------- process image (without the alpha channel) ------------------- #
- self.pre_process(img)
- if self.tile_size > 0:
- self.tile_process()
- else:
- self.process()
- output_img = self.post_process()
- output_img = output_img.data.squeeze().float().cpu().clamp_(0, 1).numpy()
- output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
- if img_mode == 'L':
- output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)
-
- # ------------------- process the alpha channel if necessary ------------------- #
- if img_mode == 'RGBA':
- if alpha_upsampler == 'realesrgan':
- self.pre_process(alpha)
- if self.tile_size > 0:
- self.tile_process()
- else:
- self.process()
- output_alpha = self.post_process()
- output_alpha = output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy()
- output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
- output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
- else: # use the cv2 resize for alpha channel
- h, w = alpha.shape[0:2]
- output_alpha = cv2.resize(alpha, (w * self.scale, h * self.scale), interpolation=cv2.INTER_LINEAR)
-
- # merge the alpha channel
- output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
- output_img[:, :, 3] = output_alpha
-
- # ------------------------------ return ------------------------------ #
- if max_range == 65535: # 16-bit image
- output = (output_img * 65535.0).round().astype(np.uint16)
- else:
- output = (output_img * 255.0).round().astype(np.uint8)
-
- if outscale is not None and outscale != float(self.scale):
- output = cv2.resize(
- output, (
- int(w_input * outscale),
- int(h_input * outscale),
- ), interpolation=cv2.INTER_LANCZOS4)
-
- return output, img_mode
-
-
-class PrefetchReader(threading.Thread):
- """Prefetch images.
-
- Args:
- img_list (list[str]): A image list of image paths to be read.
- num_prefetch_queue (int): Number of prefetch queue.
- """
-
- def __init__(self, img_list, num_prefetch_queue):
- super().__init__()
- self.que = queue.Queue(num_prefetch_queue)
- self.img_list = img_list
-
- def run(self):
- for img_path in self.img_list:
- img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
- self.que.put(img)
-
- self.que.put(None)
-
- def __next__(self):
- next_item = self.que.get()
- if next_item is None:
- raise StopIteration
- return next_item
-
- def __iter__(self):
- return self
-
-
-class IOConsumer(threading.Thread):
-
- def __init__(self, opt, que, qid):
- super().__init__()
- self._queue = que
- self.qid = qid
- self.opt = opt
-
- def run(self):
- while True:
- msg = self._queue.get()
- if isinstance(msg, str) and msg == 'quit':
- break
-
- output = msg['output']
- save_path = msg['save_path']
- cv2.imwrite(save_path, output)
- print(f'IO worker {self.qid} is done.')
diff --git a/spaces/EinfachOlder/HuggingChat/app.py b/spaces/EinfachOlder/HuggingChat/app.py
deleted file mode 100644
index 396e5d9bce35907266722b4666b2a05e647d0103..0000000000000000000000000000000000000000
--- a/spaces/EinfachOlder/HuggingChat/app.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import streamlit as st
-from streamlit_chat import message
-from streamlit_extras.colored_header import colored_header
-from streamlit_extras.add_vertical_space import add_vertical_space
-from hugchat import hugchat
-from transformers import AutoModelForCausalLM
-
-st.set_page_config(page_title="HugChat - An LLM-powered Streamlit app")
-
-def check_model(model_name):"bert-base-uncased"
- try:
- model = AutoModelForCausalLM.from_pretrained(model_name)
- st.write(f"Model {model_name} loaded successfully.")
- except Exception as e:
- st.write(f"Failed to load model {bert-base-uncased}. Error: {str(e)}")
-
-# Sidebar contents
-with st.sidebar:
- st.title('🤗💬 HugChat App')
- st.markdown('''
- ## About
- This app is an LLM-powered chatbot built using:
- - [Streamlit](https://streamlit.io/)
- - [bert-base-uncased] LLM model
-
- 💡 Note: No API key required!
- ''')
- add_vertical_space(5)
- st.write('Made withh Love')
-
-# Generate empty lists for generated and past.
-## generated stores AI generated responses
-if 'generated' not in st.session_state:
- st.session_state['generated'] = ["I'm HugChat, How may I help you?"]
-## past stores User's questions
-if 'past' not in st.session_state:
- st.session_state['past'] = ['Hi!']
-
-# Layout of input/response containers
-input_container = st.container()
-colored_header(label='', description='', color_name='blue-30')
-response_container = st.container()
-
-# User input
-## Function for taking user provided prompt as input
-def get_text():
- input_text = st.text_input("You: ", "", key="input")
- return input_text
-## Applying the user input box
-with input_container:
- user_input = get_text()
-
-# Response output
-## Function for taking user prompt as input followed by producing AI generated responses
-def generate_response(prompt):
- chatbot = hugchat.ChatBot(model_name="gpt3") # For GPT-3
- # chatbot = hugchat.ChatBot(model_name="gpt4") # For GPT-4
- response = chatbot.chat(prompt)
- return response
-
-## Conditional display of AI generated responses as a function of user provided prompts
-with response_container:
- if user_input:
- response = generate_response(user_input)
- st.session_state.past.append(user_input)
- st.session_state.generated.append(response)
-
- if st.session_state['generated']:
- for i in range(len(st.session_state['generated'])):
- message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')
- message(st.session_state["generated"][i], key=str(i))
-
-
diff --git a/spaces/Ella2323/Positive-Reframing/README.md b/spaces/Ella2323/Positive-Reframing/README.md
deleted file mode 100644
index fbf1f582eaa6f4c2796cbe56f95ac61ed943f1bd..0000000000000000000000000000000000000000
--- a/spaces/Ella2323/Positive-Reframing/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Positive Reframing
-emoji: 👁
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Enterprisium/Easy_GUI/lib/infer_pack/commons.py b/spaces/Enterprisium/Easy_GUI/lib/infer_pack/commons.py
deleted file mode 100644
index 54470986f37825b35d90d7efa7437d1c26b87215..0000000000000000000000000000000000000000
--- a/spaces/Enterprisium/Easy_GUI/lib/infer_pack/commons.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
- classname = m.__class__.__name__
- if classname.find("Conv") != -1:
- m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
- return int((kernel_size * dilation - dilation) / 2)
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
- """KL(P||Q)"""
- kl = (logs_q - logs_p) - 0.5
- kl += (
- 0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
- )
- return kl
-
-
-def rand_gumbel(shape):
- """Sample from the Gumbel distribution, protect from overflows."""
- uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
- return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
- g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
- return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, :, idx_str:idx_end]
- return ret
-
-
-def slice_segments2(x, ids_str, segment_size=4):
- ret = torch.zeros_like(x[:, :segment_size])
- for i in range(x.size(0)):
- idx_str = ids_str[i]
- idx_end = idx_str + segment_size
- ret[i] = x[i, idx_str:idx_end]
- return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
- b, d, t = x.size()
- if x_lengths is None:
- x_lengths = t
- ids_str_max = x_lengths - segment_size + 1
- ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
- ret = slice_segments(x, ids_str, segment_size)
- return ret, ids_str
-
-
-def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
- position = torch.arange(length, dtype=torch.float)
- num_timescales = channels // 2
- log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
- num_timescales - 1
- )
- inv_timescales = min_timescale * torch.exp(
- torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
- )
- scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
- signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
- signal = F.pad(signal, [0, 0, 0, channels % 2])
- signal = signal.view(1, channels, length)
- return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
- b, channels, length = x.size()
- signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
- return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
- mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
- return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
- n_channels_int = n_channels[0]
- in_act = input_a + input_b
- t_act = torch.tanh(in_act[:, :n_channels_int, :])
- s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
- acts = t_act * s_act
- return acts
-
-
-def convert_pad_shape(pad_shape):
- l = pad_shape[::-1]
- pad_shape = [item for sublist in l for item in sublist]
- return pad_shape
-
-
-def shift_1d(x):
- x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
- return x
-
-
-def sequence_mask(length, max_length=None):
- if max_length is None:
- max_length = length.max()
- x = torch.arange(max_length, dtype=length.dtype, device=length.device)
- return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
- """
- duration: [b, 1, t_x]
- mask: [b, 1, t_y, t_x]
- """
- device = duration.device
-
- b, _, t_y, t_x = mask.shape
- cum_duration = torch.cumsum(duration, -1)
-
- cum_duration_flat = cum_duration.view(b * t_x)
- path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
- path = path.view(b, t_x, t_y)
- path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
- path = path.unsqueeze(1).transpose(2, 3) * mask
- return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
- if isinstance(parameters, torch.Tensor):
- parameters = [parameters]
- parameters = list(filter(lambda p: p.grad is not None, parameters))
- norm_type = float(norm_type)
- if clip_value is not None:
- clip_value = float(clip_value)
-
- total_norm = 0
- for p in parameters:
- param_norm = p.grad.data.norm(norm_type)
- total_norm += param_norm.item() ** norm_type
- if clip_value is not None:
- p.grad.data.clamp_(min=-clip_value, max=clip_value)
- total_norm = total_norm ** (1.0 / norm_type)
- return total_norm
diff --git a/spaces/EronSamez/RVC_HFmeu/diffq/__init__.py b/spaces/EronSamez/RVC_HFmeu/diffq/__init__.py
deleted file mode 100644
index 2b997ee4ed99a90cc43db7812383927e6fe1a3e8..0000000000000000000000000000000000000000
--- a/spaces/EronSamez/RVC_HFmeu/diffq/__init__.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-"""
-This package implements different quantization strategies:
-
-- `diffq.uniform.UniformQuantizer`: classic uniform quantization over n bits.
-- `diffq.diffq.DiffQuantizer`: differentiable quantizer based on scaled noise injection.
-
-Also, do check `diffq.base.BaseQuantizer` for the common methods of all Quantizers.
-"""
-
-from .uniform import UniformQuantizer
-from .diffq import DiffQuantizer
diff --git a/spaces/Flux9665/IMS-Toucan/Layers/PositionalEncoding.py b/spaces/Flux9665/IMS-Toucan/Layers/PositionalEncoding.py
deleted file mode 100644
index 8929a7fa6298f00e97fba1630524da014b738ace..0000000000000000000000000000000000000000
--- a/spaces/Flux9665/IMS-Toucan/Layers/PositionalEncoding.py
+++ /dev/null
@@ -1,166 +0,0 @@
-"""
-Taken from ESPNet
-"""
-
-import math
-
-import torch
-
-
-class PositionalEncoding(torch.nn.Module):
- """
- Positional encoding.
-
- Args:
- d_model (int): Embedding dimension.
- dropout_rate (float): Dropout rate.
- max_len (int): Maximum input length.
- reverse (bool): Whether to reverse the input position.
- """
-
- def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False):
- """
- Construct an PositionalEncoding object.
- """
- super(PositionalEncoding, self).__init__()
- self.d_model = d_model
- self.reverse = reverse
- self.xscale = math.sqrt(self.d_model)
- self.dropout = torch.nn.Dropout(p=dropout_rate)
- self.pe = None
- self.extend_pe(torch.tensor(0.0, device=d_model.device).expand(1, max_len))
-
- def extend_pe(self, x):
- """
- Reset the positional encodings.
- """
- if self.pe is not None:
- if self.pe.size(1) >= x.size(1):
- if self.pe.dtype != x.dtype or self.pe.device != x.device:
- self.pe = self.pe.to(dtype=x.dtype, device=x.device)
- return
- pe = torch.zeros(x.size(1), self.d_model)
- if self.reverse:
- position = torch.arange(x.size(1) - 1, -1, -1.0, dtype=torch.float32).unsqueeze(1)
- else:
- position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
- div_term = torch.exp(torch.arange(0, self.d_model, 2, dtype=torch.float32) * -(math.log(10000.0) / self.d_model))
- pe[:, 0::2] = torch.sin(position * div_term)
- pe[:, 1::2] = torch.cos(position * div_term)
- pe = pe.unsqueeze(0)
- self.pe = pe.to(device=x.device, dtype=x.dtype)
-
- def forward(self, x):
- """
- Add positional encoding.
-
- Args:
- x (torch.Tensor): Input tensor (batch, time, `*`).
-
- Returns:
- torch.Tensor: Encoded tensor (batch, time, `*`).
- """
- self.extend_pe(x)
- x = x * self.xscale + self.pe[:, : x.size(1)]
- return self.dropout(x)
-
-
-class RelPositionalEncoding(torch.nn.Module):
- """
- Relative positional encoding module (new implementation).
- Details can be found in https://github.com/espnet/espnet/pull/2816.
- See : Appendix B in https://arxiv.org/abs/1901.02860
- Args:
- d_model (int): Embedding dimension.
- dropout_rate (float): Dropout rate.
- max_len (int): Maximum input length.
- """
-
- def __init__(self, d_model, dropout_rate, max_len=5000):
- """
- Construct an PositionalEncoding object.
- """
- super(RelPositionalEncoding, self).__init__()
- self.d_model = d_model
- self.xscale = math.sqrt(self.d_model)
- self.dropout = torch.nn.Dropout(p=dropout_rate)
- self.pe = None
- self.extend_pe(torch.tensor(0.0).expand(1, max_len))
-
- def extend_pe(self, x):
- """Reset the positional encodings."""
- if self.pe is not None:
- # self.pe contains both positive and negative parts
- # the length of self.pe is 2 * input_len - 1
- if self.pe.size(1) >= x.size(1) * 2 - 1:
- if self.pe.dtype != x.dtype or self.pe.device != x.device:
- self.pe = self.pe.to(dtype=x.dtype, device=x.device)
- return
- # Suppose `i` means to the position of query vecotr and `j` means the
- # position of key vector. We use position relative positions when keys
- # are to the left (i>j) and negative relative positions otherwise (i
-
- [](http://colab.research.google.com/github/omertov/encoder4editing/blob/main/notebooks/inference_playground.ipynb)
-
-> Recently, there has been a surge of diverse methods for performing image editing by employing pre-trained unconditional generators. Applying these methods on real images, however, remains a challenge, as it necessarily requires the inversion of the images into their latent space. To successfully invert a real image, one needs to find a latent code that reconstructs the input image accurately, and more importantly, allows for its meaningful manipulation. In this paper, we carefully study the latent space of StyleGAN, the state-of-the-art unconditional generator. We identify and analyze the existence of a distortion-editability tradeoff and a distortion-perception tradeoff within the StyleGAN latent space. We then suggest two principles for designing encoders in a manner that allows one to control the proximity of the inversions to regions that StyleGAN was originally trained on. We present an encoder based on our two principles that is specifically designed for facilitating editing on real images by balancing these tradeoffs. By evaluating its performance qualitatively and quantitatively on numerous challenging domains, including cars and horses, we show that our inversion method, followed by common editing techniques, achieves superior real-image editing quality, with only a small reconstruction accuracy drop.
-
-
-
-
-
-## Description
-Official Implementation of "Designing an Encoder for StyleGAN Image Manipulation" paper for both training and evaluation.
-The e4e encoder is specifically designed to complement existing image manipulation techniques performed over StyleGAN's latent space.
-
-## Recent Updates
-`2021.03.25`: Add pose editing direction.
-
-## Getting Started
-### Prerequisites
-- Linux or macOS
-- NVIDIA GPU + CUDA CuDNN (CPU may be possible with some modifications, but is not inherently supported)
-- Python 3
-
-### Installation
-- Clone the repository:
-```
-git clone https://github.com/omertov/encoder4editing.git
-cd encoder4editing
-```
-- Dependencies:
-We recommend running this repository using [Anaconda](https://docs.anaconda.com/anaconda/install/).
-All dependencies for defining the environment are provided in `environment/e4e_env.yaml`.
-
-### Inference Notebook
-We provide a Jupyter notebook found in `notebooks/inference_playground.ipynb` that allows one to encode and perform several editings on real images using StyleGAN.
-
-### Pretrained Models
-Please download the pre-trained models from the following links. Each e4e model contains the entire pSp framework architecture, including the encoder and decoder weights.
-| Path | Description
-| :--- | :----------
-|[FFHQ Inversion](https://drive.google.com/file/d/1cUv_reLE6k3604or78EranS7XzuVMWeO/view?usp=sharing) | FFHQ e4e encoder.
-|[Cars Inversion](https://drive.google.com/file/d/17faPqBce2m1AQeLCLHUVXaDfxMRU2QcV/view?usp=sharing) | Cars e4e encoder.
-|[Horse Inversion](https://drive.google.com/file/d/1TkLLnuX86B_BMo2ocYD0kX9kWh53rUVX/view?usp=sharing) | Horse e4e encoder.
-|[Church Inversion](https://drive.google.com/file/d/1-L0ZdnQLwtdy6-A_Ccgq5uNJGTqE7qBa/view?usp=sharing) | Church e4e encoder.
-
-If you wish to use one of the pretrained models for training or inference, you may do so using the flag `--checkpoint_path`.
-
-In addition, we provide various auxiliary models needed for training your own e4e model from scratch.
-| Path | Description
-| :--- | :----------
-|[FFHQ StyleGAN](https://drive.google.com/file/d/1EM87UquaoQmk17Q8d5kYIAHqu0dkYqdT/view?usp=sharing) | StyleGAN model pretrained on FFHQ taken from [rosinality](https://github.com/rosinality/stylegan2-pytorch) with 1024x1024 output resolution.
-|[IR-SE50 Model](https://drive.google.com/file/d/1KW7bjndL3QG3sxBbZxreGHigcCCpsDgn/view?usp=sharing) | Pretrained IR-SE50 model taken from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) for use in our ID loss during training.
-|[MOCOv2 Model](https://drive.google.com/file/d/18rLcNGdteX5LwT7sv_F7HWr12HpVEzVe/view?usp=sharing) | Pretrained ResNet-50 model trained using MOCOv2 for use in our simmilarity loss for domains other then human faces during training.
-
-By default, we assume that all auxiliary models are downloaded and saved to the directory `pretrained_models`. However, you may use your own paths by changing the necessary values in `configs/path_configs.py`.
-
-## Training
-To train the e4e encoder, make sure the paths to the required models, as well as training and testing data is configured in `configs/path_configs.py` and `configs/data_configs.py`.
-#### **Training the e4e Encoder**
-```
-python scripts/train.py \
---dataset_type cars_encode \
---exp_dir new/experiment/directory \
---start_from_latent_avg \
---use_w_pool \
---w_discriminator_lambda 0.1 \
---progressive_start 20000 \
---id_lambda 0.5 \
---val_interval 10000 \
---max_steps 200000 \
---stylegan_size 512 \
---stylegan_weights path/to/pretrained/stylegan.pt \
---workers 8 \
---batch_size 8 \
---test_batch_size 4 \
---test_workers 4
-```
-
-#### Training on your own dataset
-In order to train the e4e encoder on a custom dataset, perform the following adjustments:
-1. Insert the paths to your train and test data into the `dataset_paths` variable defined in `configs/paths_config.py`:
-```
-dataset_paths = {
- 'my_train_data': '/path/to/train/images/directory',
- 'my_test_data': '/path/to/test/images/directory'
-}
-```
-2. Configure a new dataset under the DATASETS variable defined in `configs/data_configs.py`:
-```
-DATASETS = {
- 'my_data_encode': {
- 'transforms': transforms_config.EncodeTransforms,
- 'train_source_root': dataset_paths['my_train_data'],
- 'train_target_root': dataset_paths['my_train_data'],
- 'test_source_root': dataset_paths['my_test_data'],
- 'test_target_root': dataset_paths['my_test_data']
- }
-}
-```
-Refer to `configs/transforms_config.py` for the transformations applied to the train and test images during training.
-
-3. Finally, run a training session with `--dataset_type my_data_encode`.
-
-## Inference
-Having trained your model, you can use `scripts/inference.py` to apply the model on a set of images.
-For example,
-```
-python scripts/inference.py \
---images_dir=/path/to/images/directory \
---save_dir=/path/to/saving/directory \
-path/to/checkpoint.pt
-```
-
-## Latent Editing Consistency (LEC)
-As described in the paper, we suggest a new metric, Latent Editing Consistency (LEC), for evaluating the encoder's
-performance.
-We provide an example for calculating the metric over the FFHQ StyleGAN using the aging editing direction in
-`metrics/LEC.py`.
-
-To run the example:
-```
-cd metrics
-python LEC.py \
---images_dir=/path/to/images/directory \
-path/to/checkpoint.pt
-```
-
-## Acknowledgments
-This code borrows heavily from [pixel2style2pixel](https://github.com/eladrich/pixel2style2pixel)
-
-## Citation
-If you use this code for your research, please cite our paper Designing an Encoder for StyleGAN Image Manipulation:
-
-```
-@article{tov2021designing,
- title={Designing an Encoder for StyleGAN Image Manipulation},
- author={Tov, Omer and Alaluf, Yuval and Nitzan, Yotam and Patashnik, Or and Cohen-Or, Daniel},
- journal={arXiv preprint arXiv:2102.02766},
- year={2021}
-}
-```
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/detectors/retinanet.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/detectors/retinanet.py
deleted file mode 100644
index 41378e8bc74bf9d5cbc7e3e6630bb1e6657049f9..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmdet/models/detectors/retinanet.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class RetinaNet(SingleStageDetector):
- """Implementation of `RetinaNet `_"""
-
- def __init__(self,
- backbone,
- neck,
- bbox_head,
- train_cfg=None,
- test_cfg=None,
- pretrained=None):
- super(RetinaNet, self).__init__(backbone, neck, bbox_head, train_cfg,
- test_cfg, pretrained)
diff --git a/spaces/Gurudev/youtube_timestamper/README.md b/spaces/Gurudev/youtube_timestamper/README.md
deleted file mode 100644
index e91609243b3f9441cacaff7a59e948f1931d6ed9..0000000000000000000000000000000000000000
--- a/spaces/Gurudev/youtube_timestamper/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Youtube Timestamper
-emoji: 📹
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.1.7
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Gyjkkih/WizardLM-WizardCoder-15B-V1.0/app.py b/spaces/Gyjkkih/WizardLM-WizardCoder-15B-V1.0/app.py
deleted file mode 100644
index b950a0dc3c9037b8db001411736515bf668d4f57..0000000000000000000000000000000000000000
--- a/spaces/Gyjkkih/WizardLM-WizardCoder-15B-V1.0/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/WizardLM/WizardCoder-15B-V1.0").launch()
\ No newline at end of file
diff --git a/spaces/Hallucinate/demo/ldm/modules/diffusionmodules/util.py b/spaces/Hallucinate/demo/ldm/modules/diffusionmodules/util.py
deleted file mode 100644
index a952e6c40308c33edd422da0ce6a60f47e73661b..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/ldm/modules/diffusionmodules/util.py
+++ /dev/null
@@ -1,267 +0,0 @@
-# adopted from
-# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
-# and
-# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-# and
-# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
-#
-# thanks!
-
-
-import os
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import repeat
-
-from ldm.util import instantiate_from_config
-
-
-def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
- if schedule == "linear":
- betas = (
- torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
- )
-
- elif schedule == "cosine":
- timesteps = (
- torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
- )
- alphas = timesteps / (1 + cosine_s) * np.pi / 2
- alphas = torch.cos(alphas).pow(2)
- alphas = alphas / alphas[0]
- betas = 1 - alphas[1:] / alphas[:-1]
- betas = np.clip(betas, a_min=0, a_max=0.999)
-
- elif schedule == "sqrt_linear":
- betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
- elif schedule == "sqrt":
- betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
- else:
- raise ValueError(f"schedule '{schedule}' unknown.")
- return betas.numpy()
-
-
-def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
- if ddim_discr_method == 'uniform':
- c = num_ddpm_timesteps // num_ddim_timesteps
- ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
- elif ddim_discr_method == 'quad':
- ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
- else:
- raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
-
- # assert ddim_timesteps.shape[0] == num_ddim_timesteps
- # add one to get the final alpha values right (the ones from first scale to data during sampling)
- steps_out = ddim_timesteps + 1
- if verbose:
- print(f'Selected timesteps for ddim sampler: {steps_out}')
- return steps_out
-
-
-def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
- # select alphas for computing the variance schedule
- alphas = alphacums[ddim_timesteps]
- alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
-
- # according the the formula provided in https://arxiv.org/abs/2010.02502
- sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
- if verbose:
- print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
- print(f'For the chosen value of eta, which is {eta}, '
- f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
- return sigmas, alphas, alphas_prev
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
- """
- Create a beta schedule that discretizes the given alpha_t_bar function,
- which defines the cumulative product of (1-beta) over time from t = [0,1].
- :param num_diffusion_timesteps: the number of betas to produce.
- :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
- produces the cumulative product of (1-beta) up to that
- part of the diffusion process.
- :param max_beta: the maximum beta to use; use values lower than 1 to
- prevent singularities.
- """
- betas = []
- for i in range(num_diffusion_timesteps):
- t1 = i / num_diffusion_timesteps
- t2 = (i + 1) / num_diffusion_timesteps
- betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
- return np.array(betas)
-
-
-def extract_into_tensor(a, t, x_shape):
- b, *_ = t.shape
- out = a.gather(-1, t)
- return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-
-
-def checkpoint(func, inputs, params, flag):
- """
- Evaluate a function without caching intermediate activations, allowing for
- reduced memory at the expense of extra compute in the backward pass.
- :param func: the function to evaluate.
- :param inputs: the argument sequence to pass to `func`.
- :param params: a sequence of parameters `func` depends on but does not
- explicitly take as arguments.
- :param flag: if False, disable gradient checkpointing.
- """
- if flag:
- args = tuple(inputs) + tuple(params)
- return CheckpointFunction.apply(func, len(inputs), *args)
- else:
- return func(*inputs)
-
-
-class CheckpointFunction(torch.autograd.Function):
- @staticmethod
- def forward(ctx, run_function, length, *args):
- ctx.run_function = run_function
- ctx.input_tensors = list(args[:length])
- ctx.input_params = list(args[length:])
-
- with torch.no_grad():
- output_tensors = ctx.run_function(*ctx.input_tensors)
- return output_tensors
-
- @staticmethod
- def backward(ctx, *output_grads):
- ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
- with torch.enable_grad():
- # Fixes a bug where the first op in run_function modifies the
- # Tensor storage in place, which is not allowed for detach()'d
- # Tensors.
- shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
- output_tensors = ctx.run_function(*shallow_copies)
- input_grads = torch.autograd.grad(
- output_tensors,
- ctx.input_tensors + ctx.input_params,
- output_grads,
- allow_unused=True,
- )
- del ctx.input_tensors
- del ctx.input_params
- del output_tensors
- return (None, None) + input_grads
-
-
-def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
- """
- Create sinusoidal timestep embeddings.
- :param timesteps: a 1-D Tensor of N indices, one per batch element.
- These may be fractional.
- :param dim: the dimension of the output.
- :param max_period: controls the minimum frequency of the embeddings.
- :return: an [N x dim] Tensor of positional embeddings.
- """
- if not repeat_only:
- half = dim // 2
- freqs = torch.exp(
- -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
- ).to(device=timesteps.device)
- args = timesteps[:, None].float() * freqs[None]
- embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
- if dim % 2:
- embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
- else:
- embedding = repeat(timesteps, 'b -> b d', d=dim)
- return embedding
-
-
-def zero_module(module):
- """
- Zero out the parameters of a module and return it.
- """
- for p in module.parameters():
- p.detach().zero_()
- return module
-
-
-def scale_module(module, scale):
- """
- Scale the parameters of a module and return it.
- """
- for p in module.parameters():
- p.detach().mul_(scale)
- return module
-
-
-def mean_flat(tensor):
- """
- Take the mean over all non-batch dimensions.
- """
- return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def normalization(channels):
- """
- Make a standard normalization layer.
- :param channels: number of input channels.
- :return: an nn.Module for normalization.
- """
- return GroupNorm32(32, channels)
-
-
-# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
-class SiLU(nn.Module):
- def forward(self, x):
- return x * torch.sigmoid(x)
-
-
-class GroupNorm32(nn.GroupNorm):
- def forward(self, x):
- return super().forward(x.float()).type(x.dtype)
-
-def conv_nd(dims, *args, **kwargs):
- """
- Create a 1D, 2D, or 3D convolution module.
- """
- if dims == 1:
- return nn.Conv1d(*args, **kwargs)
- elif dims == 2:
- return nn.Conv2d(*args, **kwargs)
- elif dims == 3:
- return nn.Conv3d(*args, **kwargs)
- raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def linear(*args, **kwargs):
- """
- Create a linear module.
- """
- return nn.Linear(*args, **kwargs)
-
-
-def avg_pool_nd(dims, *args, **kwargs):
- """
- Create a 1D, 2D, or 3D average pooling module.
- """
- if dims == 1:
- return nn.AvgPool1d(*args, **kwargs)
- elif dims == 2:
- return nn.AvgPool2d(*args, **kwargs)
- elif dims == 3:
- return nn.AvgPool3d(*args, **kwargs)
- raise ValueError(f"unsupported dimensions: {dims}")
-
-
-class HybridConditioner(nn.Module):
-
- def __init__(self, c_concat_config, c_crossattn_config):
- super().__init__()
- self.concat_conditioner = instantiate_from_config(c_concat_config)
- self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
-
- def forward(self, c_concat, c_crossattn):
- c_concat = self.concat_conditioner(c_concat)
- c_crossattn = self.crossattn_conditioner(c_crossattn)
- return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
-
-
-def noise_like(shape, device, repeat=False):
- repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
- noise = lambda: torch.randn(shape, device=device)
- return repeat_noise() if repeat else noise()
\ No newline at end of file
diff --git a/spaces/HarlanHong/DaGAN/sync_batchnorm/comm.py b/spaces/HarlanHong/DaGAN/sync_batchnorm/comm.py
deleted file mode 100644
index 922f8c4a3adaa9b32fdcaef09583be03b0d7eb2b..0000000000000000000000000000000000000000
--- a/spaces/HarlanHong/DaGAN/sync_batchnorm/comm.py
+++ /dev/null
@@ -1,137 +0,0 @@
-# -*- coding: utf-8 -*-
-# File : comm.py
-# Author : Jiayuan Mao
-# Email : maojiayuan@gmail.com
-# Date : 27/01/2018
-#
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import queue
-import collections
-import threading
-
-__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster']
-
-
-class FutureResult(object):
- """A thread-safe future implementation. Used only as one-to-one pipe."""
-
- def __init__(self):
- self._result = None
- self._lock = threading.Lock()
- self._cond = threading.Condition(self._lock)
-
- def put(self, result):
- with self._lock:
- assert self._result is None, 'Previous result has\'t been fetched.'
- self._result = result
- self._cond.notify()
-
- def get(self):
- with self._lock:
- if self._result is None:
- self._cond.wait()
-
- res = self._result
- self._result = None
- return res
-
-
-_MasterRegistry = collections.namedtuple('MasterRegistry', ['result'])
-_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result'])
-
-
-class SlavePipe(_SlavePipeBase):
- """Pipe for master-slave communication."""
-
- def run_slave(self, msg):
- self.queue.put((self.identifier, msg))
- ret = self.result.get()
- self.queue.put(True)
- return ret
-
-
-class SyncMaster(object):
- """An abstract `SyncMaster` object.
-
- - During the replication, as the data parallel will trigger an callback of each module, all slave devices should
- call `register(id)` and obtain an `SlavePipe` to communicate with the master.
- - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected,
- and passed to a registered callback.
- - After receiving the messages, the master device should gather the information and determine to message passed
- back to each slave devices.
- """
-
- def __init__(self, master_callback):
- """
-
- Args:
- master_callback: a callback to be invoked after having collected messages from slave devices.
- """
- self._master_callback = master_callback
- self._queue = queue.Queue()
- self._registry = collections.OrderedDict()
- self._activated = False
-
- def __getstate__(self):
- return {'master_callback': self._master_callback}
-
- def __setstate__(self, state):
- self.__init__(state['master_callback'])
-
- def register_slave(self, identifier):
- """
- Register an slave device.
-
- Args:
- identifier: an identifier, usually is the device id.
-
- Returns: a `SlavePipe` object which can be used to communicate with the master device.
-
- """
- if self._activated:
- assert self._queue.empty(), 'Queue is not clean before next initialization.'
- self._activated = False
- self._registry.clear()
- future = FutureResult()
- self._registry[identifier] = _MasterRegistry(future)
- return SlavePipe(identifier, self._queue, future)
-
- def run_master(self, master_msg):
- """
- Main entry for the master device in each forward pass.
- The messages were first collected from each devices (including the master device), and then
- an callback will be invoked to compute the message to be sent back to each devices
- (including the master device).
-
- Args:
- master_msg: the message that the master want to send to itself. This will be placed as the first
- message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example.
-
- Returns: the message to be sent back to the master device.
-
- """
- self._activated = True
-
- intermediates = [(0, master_msg)]
- for i in range(self.nr_slaves):
- intermediates.append(self._queue.get())
-
- results = self._master_callback(intermediates)
- assert results[0][0] == 0, 'The first result should belongs to the master.'
-
- for i, res in results:
- if i == 0:
- continue
- self._registry[i].result.put(res)
-
- for i in range(self.nr_slaves):
- assert self._queue.get() is True
-
- return results[0][1]
-
- @property
- def nr_slaves(self):
- return len(self._registry)
diff --git a/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/LICENSE.md b/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/LICENSE.md
deleted file mode 100644
index 5fd2e54913fd05b69de2874ec8f9a10c7f4e8d3f..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/Vakyansh-Hindi-TTS/ttsv/LICENSE.md
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2022 Open-Speech-EkStep
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/normalize/__init__.py b/spaces/Harveenchadha/oiTrans/indic_nlp_library/indicnlp/normalize/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Hise/rvc-hololive-models/README.md b/spaces/Hise/rvc-hololive-models/README.md
deleted file mode 100644
index f077cd85340c26ebfcb0857816d0f1f511408242..0000000000000000000000000000000000000000
--- a/spaces/Hise/rvc-hololive-models/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Rvc Models
-emoji: 🎤
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ardha27/rvc-models
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/HuangLab/CELL-E_2-Sequence_Prediction/celle/celle.py b/spaces/HuangLab/CELL-E_2-Sequence_Prediction/celle/celle.py
deleted file mode 100644
index 718c76c4979981b105f39433b5504ebb53068bb0..0000000000000000000000000000000000000000
--- a/spaces/HuangLab/CELL-E_2-Sequence_Prediction/celle/celle.py
+++ /dev/null
@@ -1,1063 +0,0 @@
-# Import necessary packages and modules
-from math import floor, ceil
-import torch
-from torch import nn
-import torch.nn.functional as F
-from axial_positional_embedding import AxialPositionalEmbedding
-from einops import rearrange
-from celle.utils import (
- exists,
- always,
- eval_decorator,
- gumbel_sample,
- top_k,
- gamma_func,
- DivideMax,
-)
-from tqdm import tqdm
-
-# Import additional modules from within the codebase
-from celle.transformer import Transformer
-
-
-def generate_mask(gamma_func, batch_size, length, device):
- # Get the number of `True` values in the mask for each batch element
- num_true_values = floor(gamma_func(torch.rand(1)) * length)
-
- # Generate a random sample of indices to set to `True` in the mask
- # The number of indices in the sample is determined by `num_true_values`
- indices = (
- torch.rand((batch_size, length), device=device)
- .topk(num_true_values, dim=1)
- .indices
- )
-
- # Create a binary mask tensor with `True` values at the sampled indices
- mask = torch.zeros((batch_size, length), dtype=torch.bool, device=device)
- mask.scatter_(dim=1, index=indices, value=True)
-
- return mask
-
-
-def match_batch_size(text, condition, image, batch_size):
- """
- This function ensures all inputs to the sample function have the same batch size.
- """
- if text.shape[0] != batch_size:
- text = text.repeat(batch_size, 1)
-
- if condition.shape[0] != batch_size:
- condition = condition.repeat(batch_size, 1)
-
- if image.shape[0] != batch_size:
- image = image.repeat(batch_size, 1)
-
- return text, condition, image
-
-
-def calc_unmask_probs(timestep, timesteps, gamma_func):
- if timestep == 1 or timesteps == 1:
- unmask_prob = 1
- else:
- unmask_prob = 1 - gamma_func(timestep)
- return unmask_prob
-
-
-def calculate_logits(
- input_tokens, input_mask, logits_function, filter_thres, temperature
-):
- logits, _, _ = logits_function(input_tokens, input_mask, return_encoding=False)
- filtered_logits = top_k(logits, thres=filter_thres)
- sample = gumbel_sample(filtered_logits, temperature=temperature, dim=-1)
-
- return logits, sample
-
-
-def unmask_tokens(
- input_tokens,
- input_mask,
- num_masked_tokens,
- logits,
- sample,
- timestep,
- timesteps,
- gamma,
- filter_func=None,
- pad_token=None,
- mask_token=None,
- force_aas=True,
-):
- sample = sample.masked_fill(~input_mask.unsqueeze(-1), -torch.inf)
- if filter_func:
- sample = filter_func(
- input_tokens, sample, force_aas, pad_token=pad_token, mask_token=mask_token
- )
- selected_token_probs, selected_tokens = torch.max(sample, dim=-1)
-
- unmask_prob = calc_unmask_probs(timestep, timesteps, gamma)
- num_tokens_to_unmask = max(1, ceil(unmask_prob * num_masked_tokens))
-
- _, top_k_indices = torch.topk(selected_token_probs, num_tokens_to_unmask, dim=-1)
-
- sample_mask = torch.zeros(
- input_tokens.shape, dtype=torch.bool, device=input_tokens.device
- )
- sample_mask.scatter_(dim=1, index=top_k_indices, value=True)
-
- unmasked_tokens = torch.where(sample_mask, selected_tokens, input_tokens)
- full_logits = torch.where(
- sample_mask.unsqueeze(-1), logits, torch.zeros_like(logits)
- )
- return unmasked_tokens, full_logits
-
-
-def suppress_invalid_text_tokens(
- text,
- logits,
- start_token=None,
- end_token=None,
- pad_token=None,
- mask_token=None,
- force_aas=False,
-):
- # Find the indices of start_token and end_token in tensor text along axis=1
- idx_start = (text == start_token).nonzero(as_tuple=True)[1]
- idx_end = (text == end_token).nonzero(as_tuple=True)[1]
-
- # For every position other than the index corresponding to the start index, set the values on the start index of dimension=2 to -torch.inf
- if idx_start.nelement() != start_token:
- try:
- mask = idx_start.unsqueeze(1) != torch.arange(
- logits.size(1), device=text.device
- )
- indices = torch.where(mask)
- logits[indices[0], indices[1], start_token] = -torch.inf
- except:
- pass
-
- # else:
- # idx_start = torch.zeros(text.size(0), dtype=torch.long)
-
- # Similarly, for every position other than the index corresponding to the end index, set the values on the end index of dimension=2 to -torch.inf
- if idx_end.nelement() != 0:
- try:
- mask = idx_end.unsqueeze(1) != torch.arange(
- logits.size(1), device=text.device
- )
- indices = torch.where(mask)
- logits[indices[0], indices[1], end_token] = -torch.inf
- except:
- pass
-
- # else:
- # idx_end = torch.full((text.size(0),), text.size(1) - 1, dtype=torch.long)
-
- if pad_token:
- if idx_start.nelement() != 0 and idx_end.nelement() != 0:
- try:
- # For every position between the indices of start_token and end_token, set the values for 1st index of dimension=2 equal to -torch.inf. Any value outside of that range should be set to torch.inf.
- mask = (
- torch.arange(logits.size(1), device=text.device)
- >= idx_start.unsqueeze(1)
- ) & (
- torch.arange(logits.size(1), device=text.device)
- <= idx_end.unsqueeze(1)
- )
-
- indices = torch.where(mask)
- logits[indices[0], indices[1], pad_token] = -torch.inf
-
- indices = torch.where(~mask)
- logits[indices[0], indices[1], pad_token] = torch.inf
-
- except:
- pass
-
- elif idx_start.nelement() != 0:
- try:
- mask = torch.arange(
- logits.size(1), device=text.device
- ) < idx_start.unsqueeze(1)
- logits[indices[0], indices[1], pad_token] = torch.inf
- except:
- pass
-
- elif idx_end.nelement() != 0:
- try:
- mask = torch.arange(
- logits.size(1), device=text.device
- ) > idx_end.unsqueeze(1)
- logits[indices[0], indices[1], pad_token] = torch.inf
- except:
- pass
-
- if force_aas:
- if pad_token:
- logits[:, :, pad_token] = -torch.inf
- logits[:, :, 3] = -torch.inf
- logits[:, :, 29:] = -torch.inf
-
- if mask_token:
- logits[:, :, mask_token] = -torch.inf
-
- return logits
-
-
-def detokenize_text(text_embedding, sequence):
- if text_embedding == "esm1b" or text_embedding == "esm2":
- from esm import Alphabet
-
- alphabet = (
- Alphabet.from_architecture("ESM-1b").get_batch_converter().alphabet.all_toks
- )
- else:
- assert NameError("Detokenization only available for ESM mdodels")
-
- output_seqs = []
-
- for batch in sequence:
- converted_seq = [alphabet[idx] for idx in batch]
- converted_seq = "".join(converted_seq)
- output_seqs.append(converted_seq)
-
- return output_seqs
-
-class ImageEmbedding(nn.Module):
- def __init__(self, num_tokens, dim):
- super(ImageEmbedding, self).__init__()
- self.image_embedding = nn.Embedding(num_tokens, dim)
-
- def forward(self, image):
- return self.image_embedding(image)
-
-
-class ModelExtender(nn.Module):
- def __init__(self, vocab, out_features, fixed_embedding=False):
- super(ModelExtender, self).__init__()
-
- # Initialize the model according to the given vocabulary
- self.vocab = vocab
-
- if vocab == "esm1b":
- from esm import pretrained
-
- self.model, _ = pretrained.esm1b_t33_650M_UR50S()
- self.in_features = 1280
- elif vocab == "esm2":
- from esm import pretrained
-
- if out_features == 320:
- self.model, _ = pretrained.esm2_t6_8M_UR50D()
- elif out_features == 480:
- self.model, _ = pretrained.esm2_t12_35M_UR50D()
- elif out_features == 640:
- self.model, _ = pretrained.esm2_t30_150M_UR50D()
- elif out_features == 1280:
- self.model, _ = pretrained.esm2_t33_650M_UR50D()
- elif out_features == 2560:
- self.model, _ = pretrained.esm2_t36_3B_UR50D()
- else:
- self.model, _ = pretrained.esm2_t33_650M_UR50D()
- self.in_features = self.model.embed_dim
-
- # Set the number of output features and initialize the scaling layer
- self.out_features = out_features
- if self.in_features != self.out_features:
- self.scale_layer = nn.Linear(self.in_features, self.out_features)
- else:
- self.scale_layer = nn.Identity()
- # Determine whether to freeze the model's parameters
- self.fixed_embedding = fixed_embedding
- if self.fixed_embedding:
- self.model = self.model.eval()
-
- def forward(self, x, **kwargs):
- # If the model's parameters are fixed, use torch.no_grad()
- if self.fixed_embedding:
- with torch.no_grad():
- if self.vocab == "esm1b" or self.vocab == "esm2":
- # Reduce sequence length dimension, get top layer representation tensor
- x = self.model(x.squeeze(1), repr_layers=[self.model.num_layers])[
- "representations"
- ][self.model.num_layers]
- # Tensor shape: (batch_size, hidden_size)
- else:
- # Get top layer representation tensor
- x = self.model(x, **kwargs)[0]
- # Tensor shape: (batch_size, sequence_length, hidden_size)
- else:
- if self.vocab == "esm1b" or self.vocab == "esm2":
- # Reduce sequence length dimension, get top layer representation tensor
- x = self.model(x.squeeze(1), repr_layers=[self.model.num_layers])[
- "representations"
- ][self.model.num_layers]
- # Tensor shape: (batch_size, hidden_size)
- else:
- # Get top layer representation tensor
- x = self.model(x, **kwargs)[0]
- # Tensor shape: (batch_size, sequence_length, hidden_size)
-
- # Scale the representation tensor if necessary
- if self.out_features != self.in_features:
- x = self.scale_layer(x)
- # Tensor shape: (batch_size, out_features)
-
- return x
-
-class CELLE(nn.Module):
- def __init__(
- self,
- *,
- dim,
- vae, # The VAE model used to encode/decode images
- condition_vae=None, # An optional VAE model used to condition the image generation
- num_images=2, # Number of images to generate
- num_text_tokens=30, # Number of tokens in the text vocabulary
- text_seq_len=1000, # Maximum length of input text sequence
- depth=16, # Number of layers in the transformer model
- heads=16, # Number of attention heads
- dim_head=64, # Dimensionality of each attention head
- attn_dropout=0.1, # Dropout rate for attention weights
- ff_dropout=0.1, # Dropout rate for feedforward layers
- attn_types=None, # Types of attention to use in the transformer
- causal=False, # Whether to use causal attention
- loss_cond_weight=1, # Weight of conditioning loss
- loss_img_weight=1, # Weight of image generation loss
- stable=False, # Whether to use divide-by-max normalization in the transformer
- rotary_emb=True, # Whether to use rotary positional embeddings
- text_embedding="esm2", # Text embedding to use (esm1b, esm2)
- fixed_embedding=True, # Whether to fix the text embedding or learn it
- sampling_mode="cosine", # Sampling mode for the VAE
- linear_project=False, # Whether to project embeddings linearly
- **kwargs,
- ):
- super().__init__()
-
- # Set the stable flag
- self.stable = stable
-
- # If the stable flag is set, initialize the DivideMax layer for normalization
- if stable:
- self.norm_by_max = DivideMax(dim=-1)
-
- ### Initializing text parameters ###
-
- # Initialize the text and fixed embeddings
- self.text_embedding = text_embedding
- self.fixed_embedding = fixed_embedding
-
- # Offset logits index and calculate cross entropy loss
- self.num_text_tokens = num_text_tokens
- self.linear_project = linear_project
-
- # Add and tokens to the beginning and end of text sequences
- if text_embedding.lower() in ("esm1b", "esm2"):
- self.text_seq_len = text_seq_len + 2
- else:
- self.text_seq_len = text_seq_len
-
- # Initialize embeddings for token
- self.sep_emb = nn.Embedding(1, dim)
-
- # Initialize positional embeddings for text sequences and token
- self.text_pos_emb = (
- nn.Embedding(self.text_seq_len + 1, dim) if not rotary_emb else always(0)
- ) # +1 for
-
- ### ###
-
- self.num_images = num_images
-
- ### Initializing condition parameters ###
-
- # Initialize the number of condition tokens, condition sequence length, and condition embedding
- if exists(condition_vae):
- condition_size = condition_vae.image_size
- num_condition_tokens = condition_vae.num_tokens
- self.num_condition_tokens = num_condition_tokens
- condition_fmap_size = condition_vae.image_size // (
- 2**condition_vae.num_layers
- )
- condition_seq_len = condition_fmap_size**2
-
- # Initialize ImageEmbedding for condition embedding
- self.condition_emb = ImageEmbedding(num_condition_tokens + 1, dim)
-
- # Initialize positional embeddings for condition embedding
- self.condition_pos_emb = (
- AxialPositionalEmbedding(
- dim, axial_shape=(condition_fmap_size, condition_fmap_size)
- )
- if not rotary_emb
- else always(0)
- )
-
- else:
- condition_fmap_size = 0
- condition_seq_len = 0
- num_condition_tokens = 0
-
- ### ####
-
- ### Initializing image parameters ###
-
- # Initialize the image size, image token size, and sequence length
- self.image_size = vae.image_size
- num_image_tokens = vae.num_tokens
- image_fmap_size = vae.image_size // (2**vae.num_layers)
- image_seq_len = image_fmap_size**2
- self.image_seq_len = image_seq_len
- self.num_image_tokens = num_image_tokens
-
- # Initialize ImageEmbedding and positional embeddings for image embedding
- self.image_emb = ImageEmbedding(num_image_tokens + 1, dim) # +1 for
-
- self.image_pos_emb = (
- AxialPositionalEmbedding(
- dim, axial_shape=(image_fmap_size, image_fmap_size)
- )
- if not rotary_emb
- else always(0)
- )
-
- # Set total sequence length and total tokens
- self.num_condition_tokens = num_condition_tokens
- self.condition_seq_len = condition_seq_len
- # Text Length + + Condition Tokens + Image Tokens
- seq_len = self.text_seq_len + 1 + self.condition_seq_len + self.image_seq_len
- total_tokens = (
- num_text_tokens + 1 + num_condition_tokens + 1 + num_image_tokens + 1
- )
- self.total_tokens = total_tokens
- self.total_seq_len = seq_len
-
- # Set the VAE and condition VAE for the model
- self.vae = vae.eval()
- self.condition_vae = condition_vae.eval()
-
- ### ###
-
- ### Setting discrete ids ###
- # Initialize text embedding based on the given text_embedding parameter
- if text_embedding == "esm1b" or text_embedding == "esm2":
- self.text_mask_token = 32
- self.pad_token = 1
- self.text_emb = ModelExtender(text_embedding, dim, fixed_embedding)
- else:
- raise ValueError("Only ESM models are supported.")
-
- # Set token indices for text, condition, and image sequences
- self.sep_token = num_text_tokens
- self.cond_mask_token = num_condition_tokens
- self.image_mask_token = num_image_tokens
-
- # Create indices for sequence and logits dimensions
- self.seq_range = torch.arange(seq_len)
- self.logits_range = torch.arange(total_tokens)
-
- # Reshape sequence and logits indices
- self.seq_range = rearrange(self.seq_range, "n -> () n ()")
- self.logits_range = rearrange(self.logits_range, "d -> () () d")
-
- # Create a mask to exclude invalid token positions from the model output
- # e.g. no image tokens where sequence tokens should be
- logits_mask = (
- # Mask text tokens beyond text_seq_len and invalid logits_range
- (
- (self.seq_range < self.text_seq_len)
- & (self.logits_range < num_text_tokens)
- & (self.logits_range != self.text_mask_token)
- )
- |
- # Mask [SEP] token after text
- (
- (self.seq_range == self.text_seq_len)
- & (self.logits_range == num_text_tokens)
- )
- |
- # Mask condition tokens beyond text_seq_len+1 ([SEP]) and invalid logits_range
- (
- (self.seq_range >= self.text_seq_len + 1)
- & (self.seq_range < self.text_seq_len + 1 + condition_seq_len)
- & (self.logits_range >= num_text_tokens + 1)
- & (self.logits_range < num_text_tokens + 1 + num_condition_tokens)
- )
- |
- # Mask image tokens beyond num_text_tokens+num_condition_tokens+1
- (
- (self.seq_range >= self.text_seq_len + 1 + condition_seq_len)
- & (self.logits_range >= num_text_tokens + 1 + num_condition_tokens + 1)
- & (
- self.logits_range
- < num_text_tokens + 1 + num_condition_tokens + 1 + num_image_tokens
- )
- )
- )
-
- # Invert the mask
- logits_mask = ~logits_mask
-
- # Register the buffer with the logits_mask
- self.register_buffer("logits_mask", logits_mask, persistent=False)
-
- ### ###
-
- # Initialize the Transformer model with given parameters
- self.transformer = Transformer(
- dim=dim,
- causal=causal,
- seq_len=seq_len,
- depth=depth,
- heads=heads,
- dim_head=dim_head,
- attn_dropout=attn_dropout,
- ff_dropout=ff_dropout,
- image_fmap_size=image_fmap_size + condition_fmap_size,
- num_images=num_images,
- stable=stable,
- rotary_emb=rotary_emb,
- )
-
- # Initialize the linear layers for converting transformer output to logits
- self.to_logits = nn.Sequential(
- nn.LayerNorm(dim),
- nn.Linear(dim, self.total_tokens),
- )
-
- # Set instance variables for weights and critic
- self.loss_img_weight = loss_img_weight
- self.loss_cond_weight = loss_cond_weight
- self.gamma = gamma_func(sampling_mode)
-
- def embed_and_transform(self, inputs, masks, return_encoding=False):
- text, condition, image = inputs
- device = text.device
- text_mask, _, image_mask = masks
-
- text_labels = text.clone()
- text = torch.where(
- text_mask, self.text_mask_token * torch.ones_like(text, device=device), text
- )
-
- tokens = self.text_emb(text)
-
- # Add SEP token
-
- sep_token_emb = self.sep_emb(
- torch.zeros((tokens.shape[0], 1), dtype=torch.long, device=device)
- )
- tokens = torch.cat((tokens, sep_token_emb), dim=1)
- tokens += self.text_pos_emb(torch.arange(text.shape[1] + 1, device=device))
-
- with torch.no_grad():
- if self.linear_project:
- b = condition.shape[0]
- condition, _, [_, _, condition_labels] = self.condition_vae.encode(
- condition
- )
- condition_labels = rearrange(condition_labels, "(b n) -> b n", b=b)
-
- else:
- condition_labels = condition
- if condition.dtype == torch.float:
- condition_labels = self.condition_vae.get_codebook_indices(
- condition
- )
- condition = condition_labels.clone()
-
- condition_emb = self.condition_emb(condition)
- condition_emb += self.condition_pos_emb(condition_emb)
- tokens = torch.cat((tokens, condition_emb), dim=1)
-
- with torch.no_grad():
- if self.linear_project:
- b = image.shape[0]
- image, _, [_, _, image_labels] = self.vae.encode(image)
- image_labels = rearrange(image_labels, "(b n) -> b n", b=b)
-
- else:
- image_labels = image
- if image.dtype == torch.float:
- image_labels = self.vae.get_codebook_indices(image)
- image = torch.where(
- image_mask,
- self.image_mask_token
- * torch.ones_like(image_labels, device=device),
- image_labels,
- )
-
- image_emb = self.image_emb(image)
-
- image_emb += self.image_pos_emb(image_emb)
- tokens = torch.cat((tokens, image_emb), dim=1)
-
- if self.stable:
- alpha = 0.1
- tokens = tokens * alpha + tokens.detach() * (1 - alpha)
-
- out = self.transformer(tokens)
-
- if self.stable:
- out = self.norm_by_max(out)
-
- logits = self.to_logits(out)
-
- max_neg_value = -torch.finfo(logits.dtype).max
- logits.masked_fill_(self.logits_mask, max_neg_value)
-
- if return_encoding:
- return logits, out, [text_labels, condition_labels, image_labels]
- else:
- return logits, None, [text_labels, condition_labels, image_labels]
-
- def forward(
- self,
- text,
- condition=None,
- image=None,
- return_loss=False,
- return_encoding=False,
- ):
- batch_size, device = text.shape[0], text.device
-
- # Check that image is supplied when training
- assert exists(image), "when training, image must be supplied"
-
- # Check that image dimensions match the expected dimensions
- assert tuple(image.shape[1:]) == (
- self.vae.channels,
- self.image_size,
- self.image_size,
- ), f"invalid image of dimensions {image.shape} passed in during training"
-
- # Generate masks for text, condition, and image
-
- # text_mask = generate_mask(self.gamma, batch_size, self.text_seq_len, device)
-
- text_mask = generate_mask(
- gamma_func("scaled-cosine"), batch_size, self.text_seq_len, device
- )
-
- image_mask = generate_mask(self.gamma, batch_size, self.image_seq_len, device)
-
- # Embed and transform inputs
- logits, _, labels = self.embed_and_transform(
- [text, condition, image],
- [text_mask, None, image_mask],
- return_encoding,
- device,
- )
-
- # If not returning loss, return the logits
- if not return_loss:
- return logits
-
- # Separate labels
- text, condition, image = labels
-
- # Add SEP token to end of text label
- sep_token = torch.tensor(self.sep_token, device=device).repeat(
- labels.shape[0], 1
- )
- labels = torch.cat([labels, sep_token], dim=1)
-
- # If condition exists and condition vae is defined, add the condition to the labels
- if exists(condition) and exists(self.condition_vae):
- offsetted_condition = condition + self.num_text_tokens + 1
- labels = torch.cat((labels, offsetted_condition), dim=1)
-
- # Add image to the labels
- offsetted_image = (
- image + self.num_text_tokens + 1 + self.num_condition_tokens + 1
- )
- labels = torch.cat((labels, offsetted_image), dim=1)
-
- # Rearrange logits for cross-entropy loss calculation
- # Logits size: (batch_size, vocab_size, total_seq_len)
- # Labels size: (batch_size, total_seq_len)
- logits = rearrange(logits, "b n c -> b c n")
-
- # Calculate cross-entropy loss for text and image
- loss_text = F.cross_entropy(
- logits[:, :, : self.text_seq_len],
- labels[:, : self.text_seq_len],
- reduction="none",
- )[text_mask].mean()
-
- loss_img = F.cross_entropy(
- logits[:, :, self.text_seq_len + 1 + self.condition_seq_len :],
- labels[:, self.text_seq_len + 1 + self.condition_seq_len :],
- reduction="none",
- )[image_mask].mean()
-
- # Calculate total loss
- loss = (loss_text + self.loss_img_weight * loss_img) / (
- self.loss_img_weight + 1
- )
-
- loss_dict = {
- "loss_text": loss_text,
- # "loss_cond": loss_cond,
- "loss_img": loss_img,
- "loss": torch.nan_to_num(loss, 0.0, 0.0, 0.0),
- }
-
- return loss, loss_dict, None
-
- def create_tensors(self, text, condition, image):
- """
- This function creates tensors for text, condition, and image when they are not provided as inputs to the sample function.
- """
- device = next(
- filter(lambda x: isinstance(x, torch.Tensor), [text, condition, image]),
- None,
- ).device
-
- if not isinstance(text, torch.Tensor):
- text = (
- torch.ones(1, self.text_seq_len, device=device, dtype=torch.long)
- * self.text_mask_token
- )
-
- if not isinstance(condition, torch.Tensor):
- condition = (
- torch.ones(1, self.condition_seq_len, device=device, dtype=torch.long)
- * self.cond_mask_token
- )
- else:
- with torch.no_grad():
- condition = self.condition_vae.get_codebook_indices(condition)
-
- if not isinstance(image, torch.Tensor):
- image = (
- torch.ones(1, self.image_seq_len, device=device, dtype=torch.long)
- * self.image_mask_token
- )
- else:
- with torch.no_grad():
- image = self.vae.get_codebook_indices(image)
-
- return text, condition, image
-
- @torch.no_grad()
- @eval_decorator
- def sample(
- self,
- text=None,
- condition=None,
- image=None,
- temperature=1.0,
- filter_thres=0.9,
- progress=False,
- timesteps=1,
- force_aas=True,
- ):
- # ensure timesteps is a positive integer
- assert int(timesteps) > 0
- # set model and VAEs to evaluation mode
- self.eval()
- vae = self.vae.eval()
- if progress == True:
- progress = tqdm
- else:
- progress = lambda x: x
-
-
- # ensure that at least one of text, condition, or image is supplied
- assert (
- isinstance(text, torch.Tensor)
- or isinstance(condition, torch.Tensor)
- or isinstance(image, torch.Tensor)
- ), "some data must be supplied"
-
- # convert text, condition, and image to tensors if they aren't already
- text, condition, image = self.create_tensors(text, condition, image)
-
- # determine the maximum batch size of the input tensors
- batch_size = max(text.shape[0], condition.shape[0], image.shape[0])
-
- # match the batch sizes of text, condition, and image
- text, condition, image = match_batch_size(text, condition, image, batch_size)
-
- # determine the device of the tensors
- device = next(
- filter(lambda x: isinstance(x, torch.Tensor), [text, condition, image]),
- None,
- ).device
-
- assert text.shape[0] == condition.shape[0] == image.shape[0]
-
- # Create a tensor of zeros of size (batch_size, image_seq_len, num_image_tokens + 1) and set it to device
-
- # full_text_logits = torch.zeros(batch_size, self.text_seq_len, self.num_text_tokens+3).to(device)
- full_text_logits = torch.zeros(
- batch_size, self.text_seq_len, self.num_text_tokens
- ).to(device)
-
- # Use scatter_ to fill the tensor with 1 values at the indices given by the image tensor
- full_text_logits = full_text_logits.scatter_(
- dim=-1, index=text.unsqueeze(-1), value=1
- )
- # Use scatter_ to fill the tensor with 1 values at the indices given by the image tensor
- full_image_logits = torch.zeros(
- batch_size, self.image_seq_len, self.num_image_tokens + 1
- ).to(device)
-
- # Remove the last token from each image sequence by setting full_image_logits to its first num_image_tokens elements
- full_image_logits = full_image_logits.scatter_(
- dim=-1, index=image.unsqueeze(-1), value=1
- )
-
- # cut off mask token
- full_image_logits = full_image_logits[:, :, : self.num_image_tokens]
-
- count = 0
-
- for timestep in progress(torch.linspace(0, 1, timesteps)):
- # Create masks for the text, condition, and image tensors
- text_mask = text == self.text_mask_token
- cond_mask = condition == self.cond_mask_token
- image_mask = image == self.image_mask_token
-
- # Calculate logits and samples using the calculate_logits function
- logits, sample = calculate_logits(
- [text, condition, image],
- [text_mask, cond_mask, image_mask],
- self.embed_and_transform,
- filter_thres,
- temperature,
- )
-
- # Calculate the number of masked tokens in the text and image tensors
- num_masked_text_tokens = torch.sum(text_mask, dim=1)[0]
- num_masked_image_tokens = torch.sum(image_mask, dim=1)[0]
-
- # If there are masked text tokens, unmask them using unmask_tokens and fill the full text logits tensor with -inf for unmasked tokens
- if num_masked_text_tokens.any() > 0:
- text, full_text_logits = unmask_tokens(
- text,
- text_mask,
- num_masked_text_tokens,
- logits[:, : self.text_seq_len, : self.num_text_tokens],
- sample[:, : self.text_seq_len, : self.num_text_tokens],
- timestep,
- timesteps,
- self.gamma,
- suppress_invalid_text_tokens,
- self.pad_token,
- self.text_mask_token,
- force_aas=force_aas,
- )
- full_text_logits = full_text_logits.masked_fill(
- ~text_mask.unsqueeze(-1), -torch.inf
- )
-
- # If there are masked image tokens, unmask them using unmask_tokens and fill the full image logits tensor with -inf for unmasked tokens
- if num_masked_image_tokens > 0:
- image, full_image_logits = unmask_tokens(
- image,
- image_mask,
- num_masked_image_tokens,
- logits[:, -self.image_seq_len :, -(self.num_image_tokens + 1) : -1],
- sample[:, -self.image_seq_len :, -(self.num_image_tokens + 1) : -1],
- timestep,
- timesteps,
- self.gamma,
- )
- full_text_logits = full_text_logits.masked_fill(
- ~text_mask.unsqueeze(-1), -torch.inf
- )
-
- # Generate heatmap
- with torch.no_grad():
- # Normalize full image logits tensor
- full_image_logits /= torch.max(
- torch.abs(full_image_logits), dim=-1, keepdim=True
- ).values
-
- # Apply quantize embedding to full image logits tensor
- full_image_logits = torch.matmul(
- full_image_logits, self.vae.model.quantize.embedding.weight
- )
-
- # Rearrange full image logits tensor
- h = int(self.image_seq_len**0.5)
- full_image_logits = rearrange(
- full_image_logits, "b (h w) c -> b c h w", h=h
- )
-
- # Decode full image logits tensor
- full_image_logits = self.vae.model.decode(full_image_logits)
-
- # Add clipping to full image logits tensor
- max_val = torch.max(full_image_logits.view(batch_size, -1), dim=-1)[0]
- min_val = torch.min(full_image_logits.view(batch_size, -1), dim=-1)[0]
- full_image_logits += torch.clip(1 - max_val, 0, float("inf")).view(
- batch_size, 1, 1, 1
- )
- full_image_logits += torch.clip(0 - min_val, float("-inf"), 0).view(
- batch_size, 1, 1, 1
- )
-
- # Clip full image logits tensor values to the range [0, 1]
- full_image_logits = torch.clip(full_image_logits, 0, 1)
-
- # Return text tensor, detokenized text tensor, full text logits tensor,
- # binary image tensor, and full image logits tensor
- return (
- text,
- detokenize_text(self.text_embedding, text),
- full_text_logits,
- 1.0 * (vae.decode(image) > 0.5),
- full_image_logits,
- )
-
- @torch.no_grad()
- @eval_decorator
- def sample_text(
- self,
- text=False,
- condition=False,
- image=False,
- temperature=1.0,
- filter_thres=0.9,
- progress=False,
- n_unmask=1,
- place_amino=True,
- force_aas=False,
- ):
- # set model and VAEs to evaluation mode
- self.eval()
-
- # ensure that at least one of text, condition, or image is supplied
- assert (
- isinstance(text, torch.Tensor)
- or isinstance(condition, torch.Tensor)
- or isinstance(image, torch.Tensor)
- ), "some data must be supplied"
-
- # convert text, condition, and image to tensors if they aren't already
- text, condition, image = self.create_tensors(text, condition, image)
-
- # determine the maximum batch size of the input tensors
- batch_size = max(text.shape[0], condition.shape[0], image.shape[0])
-
- # match the batch sizes of text, condition, and image
- text, condition, image = match_batch_size(text, condition, image, batch_size)
-
- # determine the device of the tensors
- device = next(
- filter(lambda x: isinstance(x, torch.Tensor), [text, condition, image]),
- None,
- ).device
-
- assert text.shape[0] == condition.shape[0] == image.shape[0]
-
- # Create a tensor of zeros of size (batch_size, image_seq_len, num_image_tokens + 1) and set it to device
-
- # full_text_logits = torch.zeros(batch_size, self.text_seq_len, self.num_text_tokens+3).to(device)
- full_text_logits = torch.zeros(
- batch_size, self.text_seq_len, self.num_text_tokens
- ).to(device)
-
- # Use scatter_ to fill the tensor with 1 values at the indices given by the image tensor
- full_text_logits = full_text_logits.scatter_(
- dim=-1, index=text.unsqueeze(-1), value=1
- )
-
- text_mask = text == self.text_mask_token
- cond_mask = condition == self.cond_mask_token
- image_mask = image == self.image_mask_token
-
- mask_indices = text_mask.nonzero()
- non_mask_indices = (~text_mask).nonzero()
-
- # figure out the center of the amino acids to determine generation direction
- central_protein_index = torch.tensor(
- [
- torch.median(
- non_mask_indices[torch.where(non_mask_indices[:, 0] == idx)][:, -1]
- )
- for idx in range(batch_size)
- ]
- )
-
- count = 1
-
- run_mask = text_mask
- if progress:
- pbar = progress(total=torch.sum(run_mask).item())
- while torch.sum(run_mask) > 0:
- logits, sample = calculate_logits(
- [text, condition, image],
- [text_mask, cond_mask, image_mask],
- self.embed_and_transform,
- filter_thres,
- temperature,
- )
-
- # sub_sample: [batch_size ,text_seq_len ,num_text_tokens]
- sub_sample = sample[:, : self.text_seq_len, : self.num_text_tokens]
- sub_sample = sub_sample.masked_fill(~text_mask.unsqueeze(-1), -torch.inf)
- sub_sample = suppress_invalid_text_tokens(
- text, sub_sample, 0, 2, self.pad_token, self.text_mask_token, force_aas
- )
- # calculate % to unmasked
- # get most likely token and probability for each position
-
- for idx in range(batch_size):
- selected_mask_indices = mask_indices[
- torch.where(mask_indices[:, 0] == idx)
- ][:, -1]
-
- # Generate to the left
- if selected_mask_indices[-count] < central_protein_index[idx]:
- unmask_index = selected_mask_indices[-count]
- left_sample = max(0, (unmask_index + 1) - n_unmask)
- right_sample = min(unmask_index + 1, self.text_seq_len - 1)
- central_protein_index[idx] = max(
- 0, central_protein_index[idx] - 0.5 * n_unmask
- )
-
- # Generate to the right
- elif selected_mask_indices[count - 1] > central_protein_index[idx]:
- unmask_index = selected_mask_indices[count - 1]
- left_sample = max(0, unmask_index)
- right_sample = min(unmask_index + n_unmask, self.text_seq_len - 1)
- central_protein_index[idx] = min(
- central_protein_index[idx] + 0.5 * n_unmask,
- self.text_seq_len - 1,
- )
-
- # save logits for relevant position
- full_text_logits[
- idx, left_sample:right_sample, : self.text_seq_len - 1
- ] = logits[idx, left_sample:right_sample, : self.num_text_tokens]
-
- run_mask[idx, left_sample:right_sample] = False
-
- # you may want to resample the amion acids or calculate marginal probs
- # if so, set place_amino to false
- if place_amino:
- text[idx, left_sample:right_sample] = torch.where(
- text[idx, left_sample:right_sample] == self.text_mask_token,
- sub_sample[
- idx, left_sample:right_sample, : self.num_text_tokens
- ].argmax(dim=-1),
- text[idx, left_sample:right_sample],
- )
-
- text_mask = run_mask
-
- count += n_unmask
-
- if progress:
- pbar.update(n_unmask)
- if progress:
- pbar.close()
-
- return (
- text,
- detokenize_text(self.text_embedding, text),
- full_text_logits,
- )
diff --git a/spaces/ICML2023/ICML2023_papers/README.md b/spaces/ICML2023/ICML2023_papers/README.md
deleted file mode 100644
index 2516342a6717fe5506316bb7247fdadef3bc3f6c..0000000000000000000000000000000000000000
--- a/spaces/ICML2023/ICML2023_papers/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ICML2023 Papers
-emoji: 🦀
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: true
-duplicated_from: ICML2022/ICML2022_papers
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/IPN/helloooooo/README.md b/spaces/IPN/helloooooo/README.md
deleted file mode 100644
index 9b7526dc2bb845dfb92c68d17612a4ecbfbbbf33..0000000000000000000000000000000000000000
--- a/spaces/IPN/helloooooo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Helloooooo
-emoji: 🐨
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 2.9.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Iceclear/StableSR/StableSR/ldm/data/base.py b/spaces/Iceclear/StableSR/StableSR/ldm/data/base.py
deleted file mode 100644
index b196c2f7aa583a3e8bc4aad9f943df0c4dae0da7..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/ldm/data/base.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from abc import abstractmethod
-from torch.utils.data import Dataset, ConcatDataset, ChainDataset, IterableDataset
-
-
-class Txt2ImgIterableBaseDataset(IterableDataset):
- '''
- Define an interface to make the IterableDatasets for text2img data chainable
- '''
- def __init__(self, num_records=0, valid_ids=None, size=256):
- super().__init__()
- self.num_records = num_records
- self.valid_ids = valid_ids
- self.sample_ids = valid_ids
- self.size = size
-
- print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.')
-
- def __len__(self):
- return self.num_records
-
- @abstractmethod
- def __iter__(self):
- pass
\ No newline at end of file
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/evaluate_predicts.py b/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/evaluate_predicts.py
deleted file mode 100644
index a4c182a50bc0cc3e2e03c713c2c0be2a804b04b8..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/evaluate_predicts.py
+++ /dev/null
@@ -1,79 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-
-import pandas as pd
-
-from saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset
-from saicinpainting.evaluation.evaluator import InpaintingEvaluator, lpips_fid100_f1
-from saicinpainting.evaluation.losses.base_loss import SegmentationAwareSSIM, \
- SegmentationClassStats, SSIMScore, LPIPSScore, FIDScore, SegmentationAwareLPIPS, SegmentationAwareFID
-from saicinpainting.evaluation.utils import load_yaml
-
-
-def main(args):
- config = load_yaml(args.config)
-
- dataset = PrecomputedInpaintingResultsDataset(args.datadir, args.predictdir, **config.dataset_kwargs)
-
- metrics = {
- 'ssim': SSIMScore(),
- 'lpips': LPIPSScore(),
- 'fid': FIDScore()
- }
- enable_segm = config.get('segmentation', dict(enable=False)).get('enable', False)
- if enable_segm:
- weights_path = os.path.expandvars(config.segmentation.weights_path)
- metrics.update(dict(
- segm_stats=SegmentationClassStats(weights_path=weights_path),
- segm_ssim=SegmentationAwareSSIM(weights_path=weights_path),
- segm_lpips=SegmentationAwareLPIPS(weights_path=weights_path),
- segm_fid=SegmentationAwareFID(weights_path=weights_path)
- ))
- evaluator = InpaintingEvaluator(dataset, scores=metrics,
- integral_title='lpips_fid100_f1', integral_func=lpips_fid100_f1,
- **config.evaluator_kwargs)
-
- os.makedirs(os.path.dirname(args.outpath), exist_ok=True)
-
- results = evaluator.evaluate()
-
- results = pd.DataFrame(results).stack(1).unstack(0)
- results.dropna(axis=1, how='all', inplace=True)
- results.to_csv(args.outpath, sep='\t', float_format='%.4f')
-
- if enable_segm:
- only_short_results = results[[c for c in results.columns if not c[0].startswith('segm_')]].dropna(axis=1, how='all')
- only_short_results.to_csv(args.outpath + '_short', sep='\t', float_format='%.4f')
-
- print(only_short_results)
-
- segm_metrics_results = results[['segm_ssim', 'segm_lpips', 'segm_fid']].dropna(axis=1, how='all').transpose().unstack(0).reorder_levels([1, 0], axis=1)
- segm_metrics_results.drop(['mean', 'std'], axis=0, inplace=True)
-
- segm_stats_results = results['segm_stats'].dropna(axis=1, how='all').transpose()
- segm_stats_results.index = pd.MultiIndex.from_tuples(n.split('/') for n in segm_stats_results.index)
- segm_stats_results = segm_stats_results.unstack(0).reorder_levels([1, 0], axis=1)
- segm_stats_results.sort_index(axis=1, inplace=True)
- segm_stats_results.dropna(axis=0, how='all', inplace=True)
-
- segm_results = pd.concat([segm_metrics_results, segm_stats_results], axis=1, sort=True)
- segm_results.sort_values(('mask_freq', 'total'), ascending=False, inplace=True)
-
- segm_results.to_csv(args.outpath + '_segm', sep='\t', float_format='%.4f')
- else:
- print(results)
-
-
-if __name__ == '__main__':
- import argparse
-
- aparser = argparse.ArgumentParser()
- aparser.add_argument('config', type=str, help='Path to evaluation config')
- aparser.add_argument('datadir', type=str,
- help='Path to folder with images and masks (output of gen_mask_dataset.py)')
- aparser.add_argument('predictdir', type=str,
- help='Path to folder with predicts (e.g. predict_hifill_baseline.py)')
- aparser.add_argument('outpath', type=str, help='Where to put results')
-
- main(aparser.parse_args())
diff --git a/spaces/Intel/Q8-Chat/README.md b/spaces/Intel/Q8-Chat/README.md
deleted file mode 100644
index 6888df4b9ef366972e40e1ebdd407e6294921de1..0000000000000000000000000000000000000000
--- a/spaces/Intel/Q8-Chat/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Q8 Chat
-emoji: 🏃
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Jack000/glid-3-xl-stable-classifier/app.py b/spaces/Jack000/glid-3-xl-stable-classifier/app.py
deleted file mode 100644
index b75e272ea7df2731fcc91b46fa2bfa21fc1269fd..0000000000000000000000000000000000000000
--- a/spaces/Jack000/glid-3-xl-stable-classifier/app.py
+++ /dev/null
@@ -1,439 +0,0 @@
-import gradio as gr
-
-import torch
-from torch import autocast
-
-import gc
-import io
-import math
-import sys
-
-from PIL import Image, ImageOps
-import requests
-from torch import nn
-from torch.nn import functional as F
-from torchvision import transforms
-from torchvision.transforms import functional as TF
-from tqdm.notebook import tqdm
-
-import numpy as np
-
-from guided_diffusion.script_util import create_model_and_diffusion, model_and_diffusion_defaults, classifier_defaults, create_classifier
-
-from omegaconf import OmegaConf
-from ldm.util import instantiate_from_config
-
-from einops import rearrange
-from math import log2, sqrt
-
-import argparse
-import pickle
-
-import os
-
-from transformers import CLIPTokenizer, CLIPTextModel
-
-def fetch(url_or_path):
- if str(url_or_path).startswith('http://') or str(url_or_path).startswith('https://'):
- r = requests.get(url_or_path)
- r.raise_for_status()
- fd = io.BytesIO()
- fd.write(r.content)
- fd.seek(0)
- return fd
- return open(url_or_path, 'rb')
-
-device = "cuda"
-
-#model_state_dict = torch.load('diffusion.pt', map_location='cpu')
-model_state_dict = torch.load(fetch('https://huggingface.co/Jack000/glid-3-xl-stable/resolve/main/default/diffusion-1.4.pt'), map_location='cpu')
-
-model_params = {
- 'attention_resolutions': '32,16,8',
- 'class_cond': False,
- 'diffusion_steps': 1000,
- 'rescale_timesteps': True,
- 'timestep_respacing': 'ddim100',
- 'image_size': 32,
- 'learn_sigma': False,
- 'noise_schedule': 'linear',
- 'num_channels': 320,
- 'num_heads': 8,
- 'num_res_blocks': 2,
- 'resblock_updown': False,
- 'use_fp16': True,
- 'use_scale_shift_norm': False,
- 'clip_embed_dim': None,
- 'image_condition': False,
- 'super_res_condition': False,
-}
-
-model_config = model_and_diffusion_defaults()
-model_config.update(model_params)
-
-# Load models
-model, diffusion = create_model_and_diffusion(**model_config)
-model.load_state_dict(model_state_dict, strict=True)
-model.requires_grad_(False).eval().to(device)
-
-if model_config['use_fp16']:
- model.convert_to_fp16()
-else:
- model.convert_to_fp32()
-
-def set_requires_grad(model, value):
- for param in model.parameters():
- param.requires_grad = value
-
-# vae
-kl_config = OmegaConf.load('kl.yaml')
-kl_sd = torch.load(fetch('https://huggingface.co/Jack000/glid-3-xl-stable/resolve/main/default/kl-1.4.pt'), map_location="cpu")
-
-ldm = instantiate_from_config(kl_config.model)
-ldm.load_state_dict(kl_sd, strict=True)
-
-ldm.to(device)
-ldm.eval()
-ldm.requires_grad_(False)
-set_requires_grad(ldm, False)
-
-# clip
-clip_version = 'openai/clip-vit-large-patch14'
-clip_tokenizer = CLIPTokenizer.from_pretrained(clip_version)
-clip_transformer = CLIPTextModel.from_pretrained(clip_version)
-clip_transformer.eval().requires_grad_(False).to(device)
-
-# classifier
-# load classifier
-classifier_config = classifier_defaults()
-classifier_config['classifier_width'] = 128
-classifier_config['classifier_depth'] = 4
-classifier_config['classifier_attention_resolutions'] = '64,32,16,8'
-
-classifier_photo = create_classifier(**classifier_config)
-classifier_photo.load_state_dict(
- torch.load(fetch('https://huggingface.co/Jack000/glid-3-xl-stable/resolve/main/classifier_photo/model060000.pt'), map_location="cpu")
-)
-classifier_photo.to(device)
-classifier_photo.convert_to_fp16()
-classifier_photo.eval()
-
-classifier_art = create_classifier(**classifier_config)
-classifier_art.load_state_dict(
- torch.load(fetch('https://huggingface.co/Jack000/glid-3-xl-stable/resolve/main/classifier_art/model110000.pt'), map_location="cpu")
-)
-classifier_art.to(device)
-classifier_art.convert_to_fp16()
-classifier_art.eval()
-
-def infer(prompt, style, scale, classifier_scale, seed):
- torch.manual_seed(seed)
-
- # clip context
- text = clip_tokenizer([prompt], truncation=True, max_length=77, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
- text_blank = clip_tokenizer([''], truncation=True, max_length=77, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
- text_tokens = text["input_ids"].to(device)
- text_blank_tokens = text_blank["input_ids"].to(device)
-
- text_emb = clip_transformer(input_ids=text_tokens).last_hidden_state
- text_emb_blank = clip_transformer(input_ids=text_blank_tokens).last_hidden_state
-
- kwargs = {
- "context": torch.cat([text_emb, text_emb_blank], dim=0).half(),
- "clip_embed": None,
- "image_embed": None,
- }
-
- def model_fn(x_t, ts, **kwargs):
- half = x_t[: len(x_t) // 2]
- combined = torch.cat([half, half], dim=0)
- model_out = model(combined, ts, **kwargs)
- eps, rest = model_out[:, :3], model_out[:, 3:]
- cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
- half_eps = uncond_eps + scale * (cond_eps - uncond_eps)
- eps = torch.cat([half_eps, half_eps], dim=0)
- return torch.cat([eps, rest], dim=1)
-
- def cond_fn(x, t, context=None, clip_embed=None, image_embed=None):
- with torch.enable_grad():
- x_in = x[:x.shape[0]//2].detach().requires_grad_(True)
- if style == 'photo':
- logits = classifier_photo(x_in, t)
- elif style == 'digital art':
- logits = classifier_art(x_in, t)
- else:
- return 0
-
- log_probs = F.log_softmax(logits, dim=-1)
- selected = log_probs[range(len(logits)), torch.ones(x_in.shape[0], dtype=torch.long)]
- return torch.autograd.grad(selected.sum(), x_in)[0] * classifier_scale
-
- samples = diffusion.ddim_sample_loop_progressive(
- model_fn,
- (2, 4, 64, 64),
- clip_denoised=False,
- model_kwargs=kwargs,
- cond_fn=cond_fn,
- device=device,
- progress=True,
- init_image=None,
- skip_timesteps=0,
- )
-
- for j, sample in enumerate(samples):
- pass
-
- emb = sample['pred_xstart'][0]
- emb /= 0.18215
- im = emb.unsqueeze(0)
- im = ldm.decode(im)
-
- im = TF.to_pil_image(im.squeeze(0).add(1).div(2).clamp(0, 1))
-
- return [im]
-
-css = """
- .gradio-container {
- font-family: 'IBM Plex Sans', sans-serif;
- }
- .gr-button {
- color: white;
- border-color: black;
- background: black;
- }
- input[type='range'] {
- accent-color: black;
- }
- .dark input[type='range'] {
- accent-color: #dfdfdf;
- }
- .container {
- max-width: 730px;
- margin: auto;
- padding-top: 1.5rem;
- }
- #gallery {
- min-height: 22rem;
- margin-bottom: 15px;
- margin-left: auto;
- margin-right: auto;
- border-bottom-right-radius: .5rem !important;
- border-bottom-left-radius: .5rem !important;
- }
- #gallery>div>.h-full {
- min-height: 20rem;
- }
- .details:hover {
- text-decoration: underline;
- }
- .gr-button {
- white-space: nowrap;
- }
- .gr-button:focus {
- border-color: rgb(147 197 253 / var(--tw-border-opacity));
- outline: none;
- box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
- --tw-border-opacity: 1;
- --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
- --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
- --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
- --tw-ring-opacity: .5;
- }
- #advanced-btn {
- font-size: .7rem !important;
- line-height: 19px;
- margin-top: 12px;
- margin-bottom: 12px;
- padding: 2px 8px;
- border-radius: 14px !important;
- }
- #advanced-options, #style-options {
- margin-bottom: 20px;
- }
- .footer {
- margin-bottom: 45px;
- margin-top: 35px;
- text-align: center;
- border-bottom: 1px solid #e5e5e5;
- }
- .footer>p {
- font-size: .8rem;
- display: inline-block;
- padding: 0 10px;
- transform: translateY(10px);
- background: white;
- }
- .dark .footer {
- border-color: #303030;
- }
- .dark .footer>p {
- background: #0b0f19;
- }
- .acknowledgments h4{
- margin: 1.25em 0 .25em 0;
- font-weight: bold;
- font-size: 115%;
- }
-"""
-
-block = gr.Blocks(css=css)
-
-examples = [
- [
- 'A high tech solarpunk utopia in the Amazon rainforest',
- 4,
- 45,
- 7.5,
- 1024,
- ],
- [
- 'A pikachu fine dining with a view to the Eiffel Tower',
- 4,
- 45,
- 7,
- 1024,
- ],
- [
- 'A mecha robot in a favela in expressionist style',
- 4,
- 45,
- 7,
- 1024,
- ],
- [
- 'an insect robot preparing a delicious meal',
- 4,
- 45,
- 7,
- 1024,
- ],
- [
- "A small cabin on top of a snowy mountain in the style of Disney, artstation",
- 4,
- 45,
- 7,
- 1024,
- ],
-]
-
-with block:
- gr.HTML(
- """
-
-
-
-
- Classifier Guided Stable Diffusion
-
-
-
- a custom version of stable diffusion with classifier guidance
-
-The model is licensed with a CreativeML Open RAIL-M license. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. For the full list of restrictions please read the license
-
Biases and content acknowledgment
-Despite how impressive being able to turn text into image is, beware to the fact that this model may output content that reinforces or exacerbates societal biases, as well as realistic faces, pornography and violence. The model was trained on the LAION-5B dataset, which scraped non-curated image-text-pairs from the internet (the exception being the removal of illegal content) and is meant for research purposes. You can read more in the model card
-
- """
- )
-
-block.queue(max_size=25).launch()
\ No newline at end of file
diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py b/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py
deleted file mode 100644
index 5cb0f2c03daf1ca284c5a57b928de9f922b621c5..0000000000000000000000000000000000000000
--- a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py
+++ /dev/null
@@ -1,746 +0,0 @@
-import inspect
-import warnings
-from typing import Callable, List, Optional, Union
-
-import numpy as np
-import torch
-
-from packaging import version
-from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
-
-from ...configuration_utils import FrozenDict
-from ...models import AutoencoderKL, UNet2DConditionModel
-from ...pipeline_utils import DiffusionPipeline
-from ...schedulers import (
- DDIMScheduler,
- DPMSolverMultistepScheduler,
- EulerAncestralDiscreteScheduler,
- EulerDiscreteScheduler,
- LMSDiscreteScheduler,
- PNDMScheduler,
-)
-from ...utils import deprecate, is_accelerate_available, logging
-from . import StableDiffusionSafePipelineOutput
-from .safety_checker import SafeStableDiffusionSafetyChecker
-
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-class StableDiffusionPipelineSafe(DiffusionPipeline):
- r"""
- Pipeline for text-to-image generation using Safe Latent Diffusion.
-
- The implementation is based on the [`StableDiffusionPipeline`]
-
- This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
- library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
- Args:
- vae ([`AutoencoderKL`]):
- Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
- text_encoder ([`CLIPTextModel`]):
- Frozen text-encoder. Stable Diffusion uses the text portion of
- [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
- the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
- tokenizer (`CLIPTokenizer`):
- Tokenizer of class
- [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
- unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
- scheduler ([`SchedulerMixin`]):
- A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
- [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
- safety_checker ([`StableDiffusionSafetyChecker`]):
- Classification module that estimates whether generated images could be considered offensive or harmful.
- Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
- feature_extractor ([`CLIPFeatureExtractor`]):
- Model that extracts features from generated images to be used as inputs for the `safety_checker`.
- """
-
- _optional_components = ["safety_checker", "feature_extractor"]
-
- def __init__(
- self,
- vae: AutoencoderKL,
- text_encoder: CLIPTextModel,
- tokenizer: CLIPTokenizer,
- unet: UNet2DConditionModel,
- scheduler: Union[
- DDIMScheduler,
- DPMSolverMultistepScheduler,
- EulerAncestralDiscreteScheduler,
- EulerDiscreteScheduler,
- LMSDiscreteScheduler,
- PNDMScheduler,
- ],
- safety_checker: SafeStableDiffusionSafetyChecker,
- feature_extractor: CLIPFeatureExtractor,
- requires_safety_checker: bool = True,
- ):
- super().__init__()
- safety_concept: Optional[str] = (
- "an image showing hate, harassment, violence, suffering, humiliation, harm, suicide, sexual, nudity,"
- " bodily fluids, blood, obscene gestures, illegal activity, drug use, theft, vandalism, weapons, child"
- " abuse, brutality, cruelty"
- )
-
- if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
- deprecation_message = (
- f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
- f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
- "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
- " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
- " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
- " file"
- )
- deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
- new_config = dict(scheduler.config)
- new_config["steps_offset"] = 1
- scheduler._internal_dict = FrozenDict(new_config)
-
- if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
- deprecation_message = (
- f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
- " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
- " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
- " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
- " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
- )
- deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
- new_config = dict(scheduler.config)
- new_config["clip_sample"] = False
- scheduler._internal_dict = FrozenDict(new_config)
-
- if safety_checker is None and requires_safety_checker:
- logger.warning(
- f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
- " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
- " results in services or applications open to the public. Both the diffusers team and Hugging Face"
- " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
- " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
- " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
- )
-
- if safety_checker is not None and feature_extractor is None:
- raise ValueError(
- "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
- " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
- )
-
- is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
- version.parse(unet.config._diffusers_version).base_version
- ) < version.parse("0.9.0.dev0")
- is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
- if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
- deprecation_message = (
- "The configuration file of the unet has set the default `sample_size` to smaller than"
- " 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
- " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
- " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
- " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
- " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
- " in the config might lead to incorrect results in future versions. If you have downloaded this"
- " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
- " the `unet/config.json` file"
- )
- deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
- new_config = dict(unet.config)
- new_config["sample_size"] = 64
- unet._internal_dict = FrozenDict(new_config)
-
- self.register_modules(
- vae=vae,
- text_encoder=text_encoder,
- tokenizer=tokenizer,
- unet=unet,
- scheduler=scheduler,
- safety_checker=safety_checker,
- feature_extractor=feature_extractor,
- )
- self._safety_text_concept = safety_concept
- self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
- self.register_to_config(requires_safety_checker=requires_safety_checker)
-
- @property
- def safety_concept(self):
- r"""
- Getter method for the safety concept used with SLD
-
- Returns:
- `str`: The text describing the safety concept
- """
- return self._safety_text_concept
-
- @safety_concept.setter
- def safety_concept(self, concept):
- r"""
- Setter method for the safety concept used with SLD
-
- Args:
- concept (`str`):
- The text of the new safety concept
- """
- self._safety_text_concept = concept
-
- def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
- r"""
- Enable sliced attention computation.
-
- When this option is enabled, the attention module will split the input tensor in slices, to compute attention
- in several steps. This is useful to save some memory in exchange for a small speed decrease.
-
- Args:
- slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
- When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
- a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
- `attention_head_dim` must be a multiple of `slice_size`.
- """
- if slice_size == "auto":
- # half the attention head size is usually a good trade-off between
- # speed and memory
- slice_size = self.unet.config.attention_head_dim // 2
- self.unet.set_attention_slice(slice_size)
-
- def disable_attention_slicing(self):
- r"""
- Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
- back to computing attention in one step.
- """
- # set slice_size = `None` to disable `attention slicing`
- self.enable_attention_slicing(None)
-
- def enable_sequential_cpu_offload(self):
- r"""
- Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
- text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
- `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
- """
- if is_accelerate_available():
- from accelerate import cpu_offload
- else:
- raise ImportError("Please install accelerate via `pip install accelerate`")
-
- device = torch.device("cuda")
-
- for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
- if cpu_offloaded_model is not None:
- cpu_offload(cpu_offloaded_model, device)
-
- @property
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
- def _execution_device(self):
- r"""
- Returns the device on which the pipeline's models will be executed. After calling
- `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module
- hooks.
- """
- if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
- return self.device
- for module in self.unet.modules():
- if (
- hasattr(module, "_hf_hook")
- and hasattr(module._hf_hook, "execution_device")
- and module._hf_hook.execution_device is not None
- ):
- return torch.device(module._hf_hook.execution_device)
- return self.device
-
- def _encode_prompt(
- self,
- prompt,
- device,
- num_images_per_prompt,
- do_classifier_free_guidance,
- negative_prompt,
- enable_safety_guidance,
- ):
- r"""
- Encodes the prompt into text encoder hidden states.
-
- Args:
- prompt (`str` or `list(int)`):
- prompt to be encoded
- device: (`torch.device`):
- torch device
- num_images_per_prompt (`int`):
- number of images that should be generated per prompt
- do_classifier_free_guidance (`bool`):
- whether to use classifier free guidance or not
- negative_prompt (`str` or `List[str]`):
- The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
- if `guidance_scale` is less than `1`).
- """
- batch_size = len(prompt) if isinstance(prompt, list) else 1
-
- text_inputs = self.tokenizer(
- prompt,
- padding="max_length",
- max_length=self.tokenizer.model_max_length,
- truncation=True,
- return_tensors="pt",
- )
- text_input_ids = text_inputs.input_ids
- untruncated_ids = self.tokenizer(prompt, padding="max_length", return_tensors="pt").input_ids
-
- if not torch.equal(text_input_ids, untruncated_ids):
- removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
- logger.warning(
- "The following part of your input was truncated because CLIP can only handle sequences up to"
- f" {self.tokenizer.model_max_length} tokens: {removed_text}"
- )
-
- if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
- attention_mask = text_inputs.attention_mask.to(device)
- else:
- attention_mask = None
-
- text_embeddings = self.text_encoder(
- text_input_ids.to(device),
- attention_mask=attention_mask,
- )
- text_embeddings = text_embeddings[0]
-
- # duplicate text embeddings for each generation per prompt, using mps friendly method
- bs_embed, seq_len, _ = text_embeddings.shape
- text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
- text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
- # get unconditional embeddings for classifier free guidance
- if do_classifier_free_guidance:
- uncond_tokens: List[str]
- if negative_prompt is None:
- uncond_tokens = [""] * batch_size
- elif type(prompt) is not type(negative_prompt):
- raise TypeError(
- f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
- f" {type(prompt)}."
- )
- elif isinstance(negative_prompt, str):
- uncond_tokens = [negative_prompt]
- elif batch_size != len(negative_prompt):
- raise ValueError(
- f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
- f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
- " the batch size of `prompt`."
- )
- else:
- uncond_tokens = negative_prompt
-
- max_length = text_input_ids.shape[-1]
- uncond_input = self.tokenizer(
- uncond_tokens,
- padding="max_length",
- max_length=max_length,
- truncation=True,
- return_tensors="pt",
- )
-
- if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
- attention_mask = uncond_input.attention_mask.to(device)
- else:
- attention_mask = None
-
- uncond_embeddings = self.text_encoder(
- uncond_input.input_ids.to(device),
- attention_mask=attention_mask,
- )
- uncond_embeddings = uncond_embeddings[0]
-
- # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
- seq_len = uncond_embeddings.shape[1]
- uncond_embeddings = uncond_embeddings.repeat(1, num_images_per_prompt, 1)
- uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
-
- # Encode the safety concept text
- if enable_safety_guidance:
- safety_concept_input = self.tokenizer(
- [self._safety_text_concept],
- padding="max_length",
- max_length=max_length,
- truncation=True,
- return_tensors="pt",
- )
- safety_embeddings = self.text_encoder(safety_concept_input.input_ids.to(self.device))[0]
-
- # duplicate safety embeddings for each generation per prompt, using mps friendly method
- seq_len = safety_embeddings.shape[1]
- safety_embeddings = safety_embeddings.repeat(batch_size, num_images_per_prompt, 1)
- safety_embeddings = safety_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
-
- # For classifier free guidance + sld, we need to do three forward passes.
- # Here we concatenate the unconditional and text embeddings into a single batch
- # to avoid doing three forward passes
- text_embeddings = torch.cat([uncond_embeddings, text_embeddings, safety_embeddings])
-
- else:
- # For classifier free guidance, we need to do two forward passes.
- # Here we concatenate the unconditional and text embeddings into a single batch
- # to avoid doing two forward passes
- text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-
- return text_embeddings
-
- def run_safety_checker(self, image, device, dtype, enable_safety_guidance):
- if self.safety_checker is not None:
- safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
- image, has_nsfw_concept = self.safety_checker(
- images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
- )
- flagged_images = None
- if any(has_nsfw_concept):
- logger.warning(
- "Potential NSFW content was detected in one or more images. A black image will be returned"
- " instead."
- f" {'You may look at this images in the `unsafe_images` variable of the output at your own discretion.' if enable_safety_guidance else 'Try again with a different prompt and/or seed.'} "
- )
- flagged_images = np.zeros((2, *image.shape[1:]))
- for idx, has_nsfw_concept in enumerate(has_nsfw_concept):
- if has_nsfw_concept:
- flagged_images[idx] = image[idx]
- image[idx] = np.zeros(image[idx].shape) # black image
- else:
- has_nsfw_concept = None
- flagged_images = None
- return image, has_nsfw_concept, flagged_images
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
- def decode_latents(self, latents):
- latents = 1 / 0.18215 * latents
- image = self.vae.decode(latents).sample
- image = (image / 2 + 0.5).clamp(0, 1)
- # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
- image = image.cpu().permute(0, 2, 3, 1).float().numpy()
- return image
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
- def prepare_extra_step_kwargs(self, generator, eta):
- # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
- # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
- # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
- # and should be between [0, 1]
-
- accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
- extra_step_kwargs = {}
- if accepts_eta:
- extra_step_kwargs["eta"] = eta
-
- # check if the scheduler accepts generator
- accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
- if accepts_generator:
- extra_step_kwargs["generator"] = generator
- return extra_step_kwargs
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
- def check_inputs(self, prompt, height, width, callback_steps):
- if not isinstance(prompt, str) and not isinstance(prompt, list):
- raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
- if height % 8 != 0 or width % 8 != 0:
- raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
- if (callback_steps is None) or (
- callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
- ):
- raise ValueError(
- f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
- f" {type(callback_steps)}."
- )
-
- # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
- def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
- shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
- if latents is None:
- if device.type == "mps":
- # randn does not work reproducibly on mps
- latents = torch.randn(shape, generator=generator, device="cpu", dtype=dtype).to(device)
- else:
- latents = torch.randn(shape, generator=generator, device=device, dtype=dtype)
- else:
- if latents.shape != shape:
- raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
- latents = latents.to(device)
-
- # scale the initial noise by the standard deviation required by the scheduler
- latents = latents * self.scheduler.init_noise_sigma
- return latents
-
- def perform_safety_guidance(
- self,
- enable_safety_guidance,
- safety_momentum,
- noise_guidance,
- noise_pred_out,
- i,
- sld_guidance_scale,
- sld_warmup_steps,
- sld_threshold,
- sld_momentum_scale,
- sld_mom_beta,
- ):
- # Perform SLD guidance
- if enable_safety_guidance:
- if safety_momentum is None:
- safety_momentum = torch.zeros_like(noise_guidance)
- noise_pred_text, noise_pred_uncond = noise_pred_out[0], noise_pred_out[1]
- noise_pred_safety_concept = noise_pred_out[2]
-
- # Equation 6
- scale = torch.clamp(torch.abs((noise_pred_text - noise_pred_safety_concept)) * sld_guidance_scale, max=1.0)
-
- # Equation 6
- safety_concept_scale = torch.where(
- (noise_pred_text - noise_pred_safety_concept) >= sld_threshold, torch.zeros_like(scale), scale
- )
-
- # Equation 4
- noise_guidance_safety = torch.mul((noise_pred_safety_concept - noise_pred_uncond), safety_concept_scale)
-
- # Equation 7
- noise_guidance_safety = noise_guidance_safety + sld_momentum_scale * safety_momentum
-
- # Equation 8
- safety_momentum = sld_mom_beta * safety_momentum + (1 - sld_mom_beta) * noise_guidance_safety
-
- if i >= sld_warmup_steps: # Warmup
- # Equation 3
- noise_guidance = noise_guidance - noise_guidance_safety
- return noise_guidance, safety_momentum
-
- @torch.no_grad()
- def __call__(
- self,
- prompt: Union[str, List[str]],
- height: Optional[int] = None,
- width: Optional[int] = None,
- num_inference_steps: int = 50,
- guidance_scale: float = 7.5,
- negative_prompt: Optional[Union[str, List[str]]] = None,
- num_images_per_prompt: Optional[int] = 1,
- eta: float = 0.0,
- generator: Optional[torch.Generator] = None,
- latents: Optional[torch.FloatTensor] = None,
- output_type: Optional[str] = "pil",
- return_dict: bool = True,
- callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
- callback_steps: Optional[int] = 1,
- sld_guidance_scale: Optional[float] = 1000,
- sld_warmup_steps: Optional[int] = 10,
- sld_threshold: Optional[float] = 0.01,
- sld_momentum_scale: Optional[float] = 0.3,
- sld_mom_beta: Optional[float] = 0.4,
- ):
- r"""
- Function invoked when calling the pipeline for generation.
-
- Args:
- prompt (`str` or `List[str]`):
- The prompt or prompts to guide the image generation.
- height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
- The height in pixels of the generated image.
- width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
- The width in pixels of the generated image.
- num_inference_steps (`int`, *optional*, defaults to 50):
- The number of denoising steps. More denoising steps usually lead to a higher quality image at the
- expense of slower inference.
- guidance_scale (`float`, *optional*, defaults to 7.5):
- Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
- `guidance_scale` is defined as `w` of equation 2. of [Imagen
- Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
- 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
- usually at the expense of lower image quality.
- negative_prompt (`str` or `List[str]`, *optional*):
- The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
- if `guidance_scale` is less than `1`).
- num_images_per_prompt (`int`, *optional*, defaults to 1):
- The number of images to generate per prompt.
- eta (`float`, *optional*, defaults to 0.0):
- Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
- [`schedulers.DDIMScheduler`], will be ignored for others.
- generator (`torch.Generator`, *optional*):
- A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
- deterministic.
- latents (`torch.FloatTensor`, *optional*):
- Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
- generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
- tensor will ge generated by sampling using the supplied random `generator`.
- output_type (`str`, *optional*, defaults to `"pil"`):
- The output format of the generate image. Choose between
- [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
- plain tuple.
- callback (`Callable`, *optional*):
- A function that will be called every `callback_steps` steps during inference. The function will be
- called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, *optional*, defaults to 1):
- The frequency at which the `callback` function will be called. If not specified, the callback will be
- called at every step.
- sld_guidance_scale (`float`, *optional*, defaults to 1000):
- Safe latent guidance as defined in [Safe Latent Diffusion](https://arxiv.org/abs/2211.05105).
- `sld_guidance_scale` is defined as sS of Eq. 6. If set to be less than 1, safety guidance will be
- disabled.
- sld_warmup_steps (`int`, *optional*, defaults to 10):
- Number of warmup steps for safety guidance. SLD will only be applied for diffusion steps greater than
- `sld_warmup_steps`. `sld_warmup_steps` is defined as `delta` of [Safe Latent
- Diffusion](https://arxiv.org/abs/2211.05105).
- sld_threshold (`float`, *optional*, defaults to 0.01):
- Threshold that separates the hyperplane between appropriate and inappropriate images. `sld_threshold`
- is defined as `lamda` of Eq. 5 in [Safe Latent Diffusion](https://arxiv.org/abs/2211.05105).
- sld_momentum_scale (`float`, *optional*, defaults to 0.3):
- Scale of the SLD momentum to be added to the safety guidance at each diffusion step. If set to 0.0
- momentum will be disabled. Momentum is already built up during warmup, i.e. for diffusion steps smaller
- than `sld_warmup_steps`. `sld_momentum_scale` is defined as `sm` of Eq. 7 in [Safe Latent
- Diffusion](https://arxiv.org/abs/2211.05105).
- sld_mom_beta (`float`, *optional*, defaults to 0.4):
- Defines how safety guidance momentum builds up. `sld_mom_beta` indicates how much of the previous
- momentum will be kept. Momentum is already built up during warmup, i.e. for diffusion steps smaller
- than `sld_warmup_steps`. `sld_mom_beta` is defined as `beta m` of Eq. 8 in [Safe Latent
- Diffusion](https://arxiv.org/abs/2211.05105).
- Returns:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
- [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
- When returning a tuple, the first element is a list with the generated images, and the second element is a
- list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
- (nsfw) content, according to the `safety_checker`.
- """
- # 0. Default height and width to unet
- height = height or self.unet.config.sample_size * self.vae_scale_factor
- width = width or self.unet.config.sample_size * self.vae_scale_factor
-
- # 1. Check inputs. Raise error if not correct
- self.check_inputs(prompt, height, width, callback_steps)
-
- # 2. Define call parameters
- batch_size = 1 if isinstance(prompt, str) else len(prompt)
- device = self._execution_device
-
- # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
- # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
- # corresponds to doing no classifier free guidance.
- do_classifier_free_guidance = guidance_scale > 1.0
-
- enable_safety_guidance = sld_guidance_scale > 1.0 and do_classifier_free_guidance
- if not enable_safety_guidance:
- warnings.warn("Safety checker disabled!")
-
- # 3. Encode input prompt
- text_embeddings = self._encode_prompt(
- prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, enable_safety_guidance
- )
-
- # 4. Prepare timesteps
- self.scheduler.set_timesteps(num_inference_steps, device=device)
- timesteps = self.scheduler.timesteps
-
- # 5. Prepare latent variables
- num_channels_latents = self.unet.in_channels
- latents = self.prepare_latents(
- batch_size * num_images_per_prompt,
- num_channels_latents,
- height,
- width,
- text_embeddings.dtype,
- device,
- generator,
- latents,
- )
-
- # 6. Prepare extra step kwargs.
- extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
- safety_momentum = None
-
- num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
- with self.progress_bar(total=num_inference_steps) as progress_bar:
- for i, t in enumerate(timesteps):
- # expand the latents if we are doing classifier free guidance
- latent_model_input = (
- torch.cat([latents] * (3 if enable_safety_guidance else 2))
- if do_classifier_free_guidance
- else latents
- )
- latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
- # predict the noise residual
- noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-
- # perform guidance
- if do_classifier_free_guidance:
- noise_pred_out = noise_pred.chunk((3 if enable_safety_guidance else 2))
- noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]
-
- # default classifier free guidance
- noise_guidance = noise_pred_text - noise_pred_uncond
-
- # Perform SLD guidance
- if enable_safety_guidance:
- if safety_momentum is None:
- safety_momentum = torch.zeros_like(noise_guidance)
- noise_pred_safety_concept = noise_pred_out[2]
-
- # Equation 6
- scale = torch.clamp(
- torch.abs((noise_pred_text - noise_pred_safety_concept)) * sld_guidance_scale, max=1.0
- )
-
- # Equation 6
- safety_concept_scale = torch.where(
- (noise_pred_text - noise_pred_safety_concept) >= sld_threshold,
- torch.zeros_like(scale),
- scale,
- )
-
- # Equation 4
- noise_guidance_safety = torch.mul(
- (noise_pred_safety_concept - noise_pred_uncond), safety_concept_scale
- )
-
- # Equation 7
- noise_guidance_safety = noise_guidance_safety + sld_momentum_scale * safety_momentum
-
- # Equation 8
- safety_momentum = sld_mom_beta * safety_momentum + (1 - sld_mom_beta) * noise_guidance_safety
-
- if i >= sld_warmup_steps: # Warmup
- # Equation 3
- noise_guidance = noise_guidance - noise_guidance_safety
-
- noise_pred = noise_pred_uncond + guidance_scale * noise_guidance
-
- # compute the previous noisy sample x_t -> x_t-1
- latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
- # call the callback, if provided
- if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
- progress_bar.update()
- if callback is not None and i % callback_steps == 0:
- callback(i, t, latents)
-
- # 8. Post-processing
- image = self.decode_latents(latents)
-
- # 9. Run safety checker
- image, has_nsfw_concept, flagged_images = self.run_safety_checker(
- image, device, text_embeddings.dtype, enable_safety_guidance
- )
-
- # 10. Convert to PIL
- if output_type == "pil":
- image = self.numpy_to_pil(image)
- if flagged_images is not None:
- flagged_images = self.numpy_to_pil(flagged_images)
-
- if not return_dict:
- return (
- image,
- has_nsfw_concept,
- self._safety_text_concept if enable_safety_guidance else None,
- flagged_images,
- )
-
- return StableDiffusionSafePipelineOutput(
- images=image,
- nsfw_content_detected=has_nsfw_concept,
- applied_safety_concept=self._safety_text_concept if enable_safety_guidance else None,
- unsafe_images=flagged_images,
- )
diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/utils/logging.py b/spaces/Jackflack09/diffuse-custom/diffusers/utils/logging.py
deleted file mode 100644
index 8c1c77d10b2a6b06a0c57d4fdf1802e3bd5f705f..0000000000000000000000000000000000000000
--- a/spaces/Jackflack09/diffuse-custom/diffusers/utils/logging.py
+++ /dev/null
@@ -1,340 +0,0 @@
-# coding=utf-8
-# Copyright 2020 Optuna, Hugging Face
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Logging utilities."""
-
-import logging
-import os
-import sys
-import threading
-from logging import CRITICAL # NOQA
-from logging import DEBUG # NOQA
-from logging import ERROR # NOQA
-from logging import FATAL # NOQA
-from logging import INFO # NOQA
-from logging import NOTSET # NOQA
-from logging import WARN # NOQA
-from logging import WARNING # NOQA
-from typing import Optional
-
-from tqdm import auto as tqdm_lib
-
-
-_lock = threading.Lock()
-_default_handler: Optional[logging.Handler] = None
-
-log_levels = {
- "debug": logging.DEBUG,
- "info": logging.INFO,
- "warning": logging.WARNING,
- "error": logging.ERROR,
- "critical": logging.CRITICAL,
-}
-
-_default_log_level = logging.WARNING
-
-_tqdm_active = True
-
-
-def _get_default_logging_level():
- """
- If DIFFUSERS_VERBOSITY env var is set to one of the valid choices return that as the new default level. If it is
- not - fall back to `_default_log_level`
- """
- env_level_str = os.getenv("DIFFUSERS_VERBOSITY", None)
- if env_level_str:
- if env_level_str in log_levels:
- return log_levels[env_level_str]
- else:
- logging.getLogger().warning(
- f"Unknown option DIFFUSERS_VERBOSITY={env_level_str}, "
- f"has to be one of: { ', '.join(log_levels.keys()) }"
- )
- return _default_log_level
-
-
-def _get_library_name() -> str:
- return __name__.split(".")[0]
-
-
-def _get_library_root_logger() -> logging.Logger:
- return logging.getLogger(_get_library_name())
-
-
-def _configure_library_root_logger() -> None:
- global _default_handler
-
- with _lock:
- if _default_handler:
- # This library has already configured the library root logger.
- return
- _default_handler = logging.StreamHandler() # Set sys.stderr as stream.
- _default_handler.flush = sys.stderr.flush
-
- # Apply our default configuration to the library root logger.
- library_root_logger = _get_library_root_logger()
- library_root_logger.addHandler(_default_handler)
- library_root_logger.setLevel(_get_default_logging_level())
- library_root_logger.propagate = False
-
-
-def _reset_library_root_logger() -> None:
- global _default_handler
-
- with _lock:
- if not _default_handler:
- return
-
- library_root_logger = _get_library_root_logger()
- library_root_logger.removeHandler(_default_handler)
- library_root_logger.setLevel(logging.NOTSET)
- _default_handler = None
-
-
-def get_log_levels_dict():
- return log_levels
-
-
-def get_logger(name: Optional[str] = None) -> logging.Logger:
- """
- Return a logger with the specified name.
-
- This function is not supposed to be directly accessed unless you are writing a custom diffusers module.
- """
-
- if name is None:
- name = _get_library_name()
-
- _configure_library_root_logger()
- return logging.getLogger(name)
-
-
-def get_verbosity() -> int:
- """
- Return the current level for the 🤗 Diffusers' root logger as an int.
-
- Returns:
- `int`: The logging level.
-
-
-
- 🤗 Diffusers has following logging levels:
-
- - 50: `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL`
- - 40: `diffusers.logging.ERROR`
- - 30: `diffusers.logging.WARNING` or `diffusers.logging.WARN`
- - 20: `diffusers.logging.INFO`
- - 10: `diffusers.logging.DEBUG`
-
- """
-
- _configure_library_root_logger()
- return _get_library_root_logger().getEffectiveLevel()
-
-
-def set_verbosity(verbosity: int) -> None:
- """
- Set the verbosity level for the 🤗 Diffusers' root logger.
-
- Args:
- verbosity (`int`):
- Logging level, e.g., one of:
-
- - `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL`
- - `diffusers.logging.ERROR`
- - `diffusers.logging.WARNING` or `diffusers.logging.WARN`
- - `diffusers.logging.INFO`
- - `diffusers.logging.DEBUG`
- """
-
- _configure_library_root_logger()
- _get_library_root_logger().setLevel(verbosity)
-
-
-def set_verbosity_info():
- """Set the verbosity to the `INFO` level."""
- return set_verbosity(INFO)
-
-
-def set_verbosity_warning():
- """Set the verbosity to the `WARNING` level."""
- return set_verbosity(WARNING)
-
-
-def set_verbosity_debug():
- """Set the verbosity to the `DEBUG` level."""
- return set_verbosity(DEBUG)
-
-
-def set_verbosity_error():
- """Set the verbosity to the `ERROR` level."""
- return set_verbosity(ERROR)
-
-
-def disable_default_handler() -> None:
- """Disable the default handler of the HuggingFace Diffusers' root logger."""
-
- _configure_library_root_logger()
-
- assert _default_handler is not None
- _get_library_root_logger().removeHandler(_default_handler)
-
-
-def enable_default_handler() -> None:
- """Enable the default handler of the HuggingFace Diffusers' root logger."""
-
- _configure_library_root_logger()
-
- assert _default_handler is not None
- _get_library_root_logger().addHandler(_default_handler)
-
-
-def add_handler(handler: logging.Handler) -> None:
- """adds a handler to the HuggingFace Diffusers' root logger."""
-
- _configure_library_root_logger()
-
- assert handler is not None
- _get_library_root_logger().addHandler(handler)
-
-
-def remove_handler(handler: logging.Handler) -> None:
- """removes given handler from the HuggingFace Diffusers' root logger."""
-
- _configure_library_root_logger()
-
- assert handler is not None and handler not in _get_library_root_logger().handlers
- _get_library_root_logger().removeHandler(handler)
-
-
-def disable_propagation() -> None:
- """
- Disable propagation of the library log outputs. Note that log propagation is disabled by default.
- """
-
- _configure_library_root_logger()
- _get_library_root_logger().propagate = False
-
-
-def enable_propagation() -> None:
- """
- Enable propagation of the library log outputs. Please disable the HuggingFace Diffusers' default handler to prevent
- double logging if the root logger has been configured.
- """
-
- _configure_library_root_logger()
- _get_library_root_logger().propagate = True
-
-
-def enable_explicit_format() -> None:
- """
- Enable explicit formatting for every HuggingFace Diffusers' logger. The explicit formatter is as follows:
- ```
- [LEVELNAME|FILENAME|LINE NUMBER] TIME >> MESSAGE
- ```
- All handlers currently bound to the root logger are affected by this method.
- """
- handlers = _get_library_root_logger().handlers
-
- for handler in handlers:
- formatter = logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s")
- handler.setFormatter(formatter)
-
-
-def reset_format() -> None:
- """
- Resets the formatting for HuggingFace Diffusers' loggers.
-
- All handlers currently bound to the root logger are affected by this method.
- """
- handlers = _get_library_root_logger().handlers
-
- for handler in handlers:
- handler.setFormatter(None)
-
-
-def warning_advice(self, *args, **kwargs):
- """
- This method is identical to `logger.warning()`, but if env var DIFFUSERS_NO_ADVISORY_WARNINGS=1 is set, this
- warning will not be printed
- """
- no_advisory_warnings = os.getenv("DIFFUSERS_NO_ADVISORY_WARNINGS", False)
- if no_advisory_warnings:
- return
- self.warning(*args, **kwargs)
-
-
-logging.Logger.warning_advice = warning_advice
-
-
-class EmptyTqdm:
- """Dummy tqdm which doesn't do anything."""
-
- def __init__(self, *args, **kwargs): # pylint: disable=unused-argument
- self._iterator = args[0] if args else None
-
- def __iter__(self):
- return iter(self._iterator)
-
- def __getattr__(self, _):
- """Return empty function."""
-
- def empty_fn(*args, **kwargs): # pylint: disable=unused-argument
- return
-
- return empty_fn
-
- def __enter__(self):
- return self
-
- def __exit__(self, type_, value, traceback):
- return
-
-
-class _tqdm_cls:
- def __call__(self, *args, **kwargs):
- if _tqdm_active:
- return tqdm_lib.tqdm(*args, **kwargs)
- else:
- return EmptyTqdm(*args, **kwargs)
-
- def set_lock(self, *args, **kwargs):
- self._lock = None
- if _tqdm_active:
- return tqdm_lib.tqdm.set_lock(*args, **kwargs)
-
- def get_lock(self):
- if _tqdm_active:
- return tqdm_lib.tqdm.get_lock()
-
-
-tqdm = _tqdm_cls()
-
-
-def is_progress_bar_enabled() -> bool:
- """Return a boolean indicating whether tqdm progress bars are enabled."""
- global _tqdm_active
- return bool(_tqdm_active)
-
-
-def enable_progress_bar():
- """Enable tqdm progress bar."""
- global _tqdm_active
- _tqdm_active = True
-
-
-def disable_progress_bar():
- """Disable tqdm progress bar."""
- global _tqdm_active
- _tqdm_active = False
diff --git a/spaces/Jaehan/Question-Answering-1/app.py b/spaces/Jaehan/Question-Answering-1/app.py
deleted file mode 100644
index 993358dceec896bc5c12b0ec4d5821fc363b11f7..0000000000000000000000000000000000000000
--- a/spaces/Jaehan/Question-Answering-1/app.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
-import gradio as grad
-import ast
-mdl_name = "deepset/roberta-base-squad2"
-my_pipeline = pipeline('question-answering', model=mdl_name, tokenizer=mdl_name)
-
-def answer_question(question,context):
- text= "{"+"'question': '"+question+"','context': '"+context+"'}"
-
- di=ast.literal_eval(text)
- response = my_pipeline(di)
- return response
-grad.Interface(answer_question, inputs=["text","text"], outputs="text").launch()
\ No newline at end of file
diff --git a/spaces/Jumon/whisper-zero-shot-audio-classification/classify.py b/spaces/Jumon/whisper-zero-shot-audio-classification/classify.py
deleted file mode 100644
index 09e7bf467c0d6f8acd1dfe5e40f99d0922a53282..0000000000000000000000000000000000000000
--- a/spaces/Jumon/whisper-zero-shot-audio-classification/classify.py
+++ /dev/null
@@ -1,66 +0,0 @@
-from typing import List, Optional
-
-import torch
-import torch.nn.functional as F
-from whisper.audio import N_FRAMES, N_MELS, log_mel_spectrogram, pad_or_trim
-from whisper.model import Whisper
-from whisper.tokenizer import Tokenizer
-
-
-@torch.no_grad()
-def calculate_audio_features(audio_path: Optional[str], model: Whisper) -> torch.Tensor:
- if audio_path is None:
- segment = torch.zeros((N_MELS, N_FRAMES), dtype=torch.float32).to(model.device)
- else:
- mel = log_mel_spectrogram(audio_path)
- segment = pad_or_trim(mel, N_FRAMES).to(model.device)
- return model.embed_audio(segment.unsqueeze(0))
-
-
-@torch.no_grad()
-def calculate_average_logprobs(
- model: Whisper,
- audio_features: torch.Tensor,
- class_names: List[str],
- tokenizer: Tokenizer,
-) -> torch.Tensor:
- initial_tokens = (
- torch.tensor(tokenizer.sot_sequence_including_notimestamps).unsqueeze(0).to(model.device)
- )
- eot_token = torch.tensor([tokenizer.eot]).unsqueeze(0).to(model.device)
-
- average_logprobs = torch.zeros(len(class_names))
- for i, class_name in enumerate(class_names):
- class_name_tokens = (
- torch.tensor(tokenizer.encode(" " + class_name)).unsqueeze(0).to(model.device)
- )
- input_tokens = torch.cat([initial_tokens, class_name_tokens, eot_token], dim=1)
-
- logits = model.logits(input_tokens, audio_features) # (1, T, V)
- logprobs = F.log_softmax(logits, dim=-1).squeeze(0) # (T, V)
- logprobs = logprobs[len(tokenizer.sot_sequence_including_notimestamps) - 1 : -1] # (T', V)
- logprobs = torch.gather(logprobs, dim=-1, index=class_name_tokens.view(-1, 1)) # (T', 1)
- average_logprob = logprobs.mean().item()
- average_logprobs[i] = average_logprob
-
- return average_logprobs
-
-
-def calculate_internal_lm_average_logprobs(
- model: Whisper,
- class_names: List[str],
- tokenizer: Tokenizer,
- verbose: bool = False,
-) -> torch.Tensor:
- audio_features_from_empty_input = calculate_audio_features(None, model)
- average_logprobs = calculate_average_logprobs(
- model=model,
- audio_features=audio_features_from_empty_input,
- class_names=class_names,
- tokenizer=tokenizer,
- )
- if verbose:
- print("Internal LM average log probabilities for each class:")
- for i, class_name in enumerate(class_names):
- print(f" {class_name}: {average_logprobs[i]:.3f}")
- return average_logprobs
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/necks/cspnext_pafpn.py b/spaces/KyanChen/RSPrompter/mmdet/models/necks/cspnext_pafpn.py
deleted file mode 100644
index a52ba72d9b3e48c4866fb16507bc2118eb23010e..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/necks/cspnext_pafpn.py
+++ /dev/null
@@ -1,170 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-from typing import Sequence, Tuple
-
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
-from mmengine.model import BaseModule
-from torch import Tensor
-
-from mmdet.registry import MODELS
-from mmdet.utils import ConfigType, OptMultiConfig
-from ..layers import CSPLayer
-
-
-@MODELS.register_module()
-class CSPNeXtPAFPN(BaseModule):
- """Path Aggregation Network with CSPNeXt blocks.
-
- Args:
- in_channels (Sequence[int]): Number of input channels per scale.
- out_channels (int): Number of output channels (used at each scale)
- num_csp_blocks (int): Number of bottlenecks in CSPLayer.
- Defaults to 3.
- use_depthwise (bool): Whether to use depthwise separable convolution in
- blocks. Defaults to False.
- expand_ratio (float): Ratio to adjust the number of channels of the
- hidden layer. Default: 0.5
- upsample_cfg (dict): Config dict for interpolate layer.
- Default: `dict(scale_factor=2, mode='nearest')`
- conv_cfg (dict, optional): Config dict for convolution layer.
- Default: None, which means using conv2d.
- norm_cfg (dict): Config dict for normalization layer.
- Default: dict(type='BN')
- act_cfg (dict): Config dict for activation layer.
- Default: dict(type='Swish')
- init_cfg (dict or list[dict], optional): Initialization config dict.
- Default: None.
- """
-
- def __init__(
- self,
- in_channels: Sequence[int],
- out_channels: int,
- num_csp_blocks: int = 3,
- use_depthwise: bool = False,
- expand_ratio: float = 0.5,
- upsample_cfg: ConfigType = dict(scale_factor=2, mode='nearest'),
- conv_cfg: bool = None,
- norm_cfg: ConfigType = dict(type='BN', momentum=0.03, eps=0.001),
- act_cfg: ConfigType = dict(type='Swish'),
- init_cfg: OptMultiConfig = dict(
- type='Kaiming',
- layer='Conv2d',
- a=math.sqrt(5),
- distribution='uniform',
- mode='fan_in',
- nonlinearity='leaky_relu')
- ) -> None:
- super().__init__(init_cfg)
- self.in_channels = in_channels
- self.out_channels = out_channels
-
- conv = DepthwiseSeparableConvModule if use_depthwise else ConvModule
-
- # build top-down blocks
- self.upsample = nn.Upsample(**upsample_cfg)
- self.reduce_layers = nn.ModuleList()
- self.top_down_blocks = nn.ModuleList()
- for idx in range(len(in_channels) - 1, 0, -1):
- self.reduce_layers.append(
- ConvModule(
- in_channels[idx],
- in_channels[idx - 1],
- 1,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg))
- self.top_down_blocks.append(
- CSPLayer(
- in_channels[idx - 1] * 2,
- in_channels[idx - 1],
- num_blocks=num_csp_blocks,
- add_identity=False,
- use_depthwise=use_depthwise,
- use_cspnext_block=True,
- expand_ratio=expand_ratio,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg))
-
- # build bottom-up blocks
- self.downsamples = nn.ModuleList()
- self.bottom_up_blocks = nn.ModuleList()
- for idx in range(len(in_channels) - 1):
- self.downsamples.append(
- conv(
- in_channels[idx],
- in_channels[idx],
- 3,
- stride=2,
- padding=1,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg))
- self.bottom_up_blocks.append(
- CSPLayer(
- in_channels[idx] * 2,
- in_channels[idx + 1],
- num_blocks=num_csp_blocks,
- add_identity=False,
- use_depthwise=use_depthwise,
- use_cspnext_block=True,
- expand_ratio=expand_ratio,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg))
-
- self.out_convs = nn.ModuleList()
- for i in range(len(in_channels)):
- self.out_convs.append(
- conv(
- in_channels[i],
- out_channels,
- 3,
- padding=1,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg))
-
- def forward(self, inputs: Tuple[Tensor, ...]) -> Tuple[Tensor, ...]:
- """
- Args:
- inputs (tuple[Tensor]): input features.
-
- Returns:
- tuple[Tensor]: YOLOXPAFPN features.
- """
- assert len(inputs) == len(self.in_channels)
-
- # top-down path
- inner_outs = [inputs[-1]]
- for idx in range(len(self.in_channels) - 1, 0, -1):
- feat_heigh = inner_outs[0]
- feat_low = inputs[idx - 1]
- feat_heigh = self.reduce_layers[len(self.in_channels) - 1 - idx](
- feat_heigh)
- inner_outs[0] = feat_heigh
-
- upsample_feat = self.upsample(feat_heigh)
-
- inner_out = self.top_down_blocks[len(self.in_channels) - 1 - idx](
- torch.cat([upsample_feat, feat_low], 1))
- inner_outs.insert(0, inner_out)
-
- # bottom-up path
- outs = [inner_outs[0]]
- for idx in range(len(self.in_channels) - 1):
- feat_low = outs[-1]
- feat_height = inner_outs[idx + 1]
- downsample_feat = self.downsamples[idx](feat_low)
- out = self.bottom_up_blocks[idx](
- torch.cat([downsample_feat, feat_height], 1))
- outs.append(out)
-
- # out convs
- for idx, conv in enumerate(self.out_convs):
- outs[idx] = conv(outs[idx])
-
- return tuple(outs)
diff --git a/spaces/Laronix/Laronix_ASR_TTS_VC/local/ASR_compare.py b/spaces/Laronix/Laronix_ASR_TTS_VC/local/ASR_compare.py
deleted file mode 100644
index 1a0f20191db15c1dc91c78e445005fde74fc9604..0000000000000000000000000000000000000000
--- a/spaces/Laronix/Laronix_ASR_TTS_VC/local/ASR_compare.py
+++ /dev/null
@@ -1,298 +0,0 @@
-"""
-TODO:
- + [x] Load Configuration
- + [ ] Checking
- + [ ] Better saving directory
-"""
-import numpy as np
-from pathlib import Path
-import jiwer
-import pdb
-import torch.nn as nn
-import torch
-import torchaudio
-from transformers import pipeline
-from time import process_time, time
-from pathlib import Path
-
-# local import
-import sys
-from espnet2.bin.tts_inference import Text2Speech
-
-# pdb.set_trace()
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-sys.path.append("src")
-
-import gradio as gr
-
-# ASR part
-
-audio_files = [
- str(x)
- for x in sorted(
- Path(
- "/home/kevingeng/Disk2/laronix/laronix_automos/data/20230103_video"
- ).glob("**/*wav")
- )
-]
-# audio_files = [str(x) for x in sorted(Path("./data/Patient_sil_trim_16k_normed_5_snr_40/Rainbow").glob("**/*wav"))]
-transcriber = pipeline(
- "automatic-speech-recognition",
- model="KevinGeng/PAL_John_128_train_dev_test_seed_1",
-)
-old_transcriber = pipeline(
- "automatic-speech-recognition", "facebook/wav2vec2-base-960h"
-)
-whisper_transcriber = pipeline(
- "automatic-speech-recognition", "KevinGeng/whipser_medium_en_PAL300_step25"
-)
-
-whisper_transcriber_org = pipeline(
- "automatic-speech-recognition", "KevinGeng/whisper-medium-PAL128-25step"
-)
-
-whisper_transcriber_Tony = pipeline(
- "automatic-speech-recognition", "KevinGeng/Tony1_AVA_script_conv_train_conv_dev"
-)
-
-whisper_transcriber_John = pipeline(
- "automatic-speech-recognition", "KevinGeng/whipser_medium_en_PAL300_step25_step2_VTCK"
-)
-
-whisper_transcriber_Negel = pipeline(
- "automatic-speech-recognition", "KevinGeng/Negel_152_AVA_script_conv_train_conv_dev"
-)
-
-# transcriber = pipeline("automatic-speech-recognition", model="KevinGeng/PAL_John_128_p326_300_train_dev_test_seed_1")
-# 【Female】kan-bayashi ljspeech parallel wavegan
-# tts_model = Text2Speech.from_pretrained("espnet/kan-bayashi_ljspeech_vits")
-# 【Male】fastspeech2-en-200_speaker-cv4, hifigan vocoder
-# pdb.set_trace()
-from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
-from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
-
-# @title English multi-speaker pretrained model { run: "auto" }
-lang = "English"
-tag = "kan-bayashi/libritts_xvector_vits"
-# vits needs no
-vocoder_tag = "parallel_wavegan/vctk_parallel_wavegan.v1.long" # @param ["none", "parallel_wavegan/vctk_parallel_wavegan.v1.long", "parallel_wavegan/vctk_multi_band_melgan.v2", "parallel_wavegan/vctk_style_melgan.v1", "parallel_wavegan/vctk_hifigan.v1", "parallel_wavegan/libritts_parallel_wavegan.v1.long", "parallel_wavegan/libritts_multi_band_melgan.v2", "parallel_wavegan/libritts_hifigan.v1", "parallel_wavegan/libritts_style_melgan.v1"] {type:"string"}
-from espnet2.bin.tts_inference import Text2Speech
-from espnet2.utils.types import str_or_none
-
-text2speech = Text2Speech.from_pretrained(
- model_tag=str_or_none(tag),
- vocoder_tag=str_or_none(vocoder_tag),
- device="cuda",
- use_att_constraint=False,
- backward_window=1,
- forward_window=3,
- speed_control_alpha=1.0,
-)
-
-
-import glob
-import os
-import numpy as np
-import kaldiio
-
-# Get model directory path
-from espnet_model_zoo.downloader import ModelDownloader
-
-d = ModelDownloader()
-model_dir = os.path.dirname(d.download_and_unpack(tag)["train_config"])
-pdb.set_trace()
-# Speaker x-vector selection
-
-xvector_ark = [
- p
- for p in glob.glob(
- f"{model_dir}/../../dump/**/spk_xvector.ark", recursive=True
- )
- if "tr" in p
-][0]
-xvectors = {k: v for k, v in kaldiio.load_ark(xvector_ark)}
-
-spks = list(xvectors.keys())
-
-male_spks = {
- "M1": "2300_131720",
- "M2": "1320_122612",
- "M3": "1188_133604",
- "M4": "61_70970",
-}
-female_spks = {"F1": "2961_961", "F2": "8463_287645", "F3": "121_121726"}
-spks = dict(male_spks, **female_spks)
-spk_names = sorted(spks.keys())
-
-
-## 20230224 Mousa: No reference,
-def ASRold(audio_file):
- reg_text = old_transcriber(audio_file)["text"]
- return reg_text
-
-
-def ASRnew(audio_file):
- reg_text = transcriber(audio_file)["text"]
- return reg_text
-
-def ASRwhipser_FT(audio_file):
- reg_text = whisper_transcriber(audio_file)["text"]
- return reg_text
-
-def ASRwhipser_Org(audio_file):
- reg_text = whisper_transcriber_org(audio_file)["text"]
- return reg_text
-
-def ASRwhipser_Tony(audio_file):
- reg_text = whisper_transcriber_Tony(audio_file)["text"]
- return reg_text
-
-def ASRwhipser_Negel(audio_file):
- reg_text = whisper_transcriber_Negel(audio_file)["text"]
- return reg_text
-
-def ASRwhipser_John(audio_file):
- reg_text = whisper_transcriber_John(audio_file)["text"]
- return reg_text
-
-# def ref_reg_callback(audio_file, spk_name, ref_text):
-# reg_text = ref_text
-# return audio_file, spk_name, reg_text
-
-reference_textbox = gr.Textbox(
- value="",
- placeholder="Input reference here",
- label="Reference",
-)
-
-recognization_textbox = gr.Textbox(
- value="",
- placeholder="Output recognization here",
- label="recognization_textbox",
-)
-
-speaker_option = gr.Radio(choices=spk_names, label="Speaker")
-# speaker_profiles = {
-# "Male_1": "speaker_icons/male1.png",
-# "Male_2": "speaker_icons/male2.png",
-# "Female_1": "speaker_icons/female1.png",
-# "Female_2": "speaker_icons/female2.png",
-# }
-
-# speaker_option = gr.Image(label="Choose your speaker profile",
-# image_mode="RGB",
-# options=speaker_profiles
-# )
-
-input_audio = gr.Audio(
- source="upload", type="filepath", label="Audio_to_Evaluate"
-)
-output_audio = gr.Audio(
- source="upload", file="filepath", label="Synthesized Audio"
-)
-examples = [
- ["./samples/001.wav", "M1", ""],
- ["./samples/002.wav", "M2", ""],
- ["./samples/003.wav", "F1", ""],
- ["./samples/004.wav", "F2", ""],
-]
-
-
-def change_audiobox(choice):
- if choice == "upload":
- input_audio = gr.Audio.update(source="upload", visible=True)
- elif choice == "microphone":
- input_audio = gr.Audio.update(source="microphone", visible=True)
- else:
- input_audio = gr.Audio.update(visible=False)
- return input_audio
-
-
-with gr.Blocks(
- analytics_enabled=False,
- css=".gradio-container {background-color: #78BD91}",
-) as demo:
- with gr.Column():
- input_format = gr.Radio(
- choices=["upload", "microphone"], label="Choose your input format"
- )
- input_audio = gr.Audio(
- source="upload",
- type="filepath",
- label="Input Audio",
- interactive=True,
- visible=False,
- )
- input_format.change(
- fn=change_audiobox, inputs=input_format, outputs=input_audio
- )
-
- with gr.Row():
- b1 = gr.Button("Conventional Speech Recognition Engine")
- t1 = gr.Textbox(
- value="",
- placeholder="Recognition output",
- label="Convertional",
- )
- b1.click(
- ASRold, inputs=[input_audio], outputs=t1
- )
-
- with gr.Row():
- b2 = gr.Button("Laronix Speech Recognition Engine (Ver1, wav2vec2.0+CTC)")
- t2 = gr.Textbox(
- value="",
- placeholder="Recognition output",
- label="Purposed",
- )
-
- b2.click(
- ASRnew, inputs=[input_audio], outputs=t2
- )
- with gr.Row():
- b3 = gr.Button("Laronix Speech Recognition Engine (Ver2, Whipser)")
- t3 = gr.Textbox(
- value="",
- placeholder="Recognition output",
- label="Purposed",
- )
-
- b3.click(
- ASRwhipser_FT, inputs=[input_audio], outputs=t3
- )
- with gr.Row():
- b4 = gr.Button("Laronix Speech Recognition Engine (Whipser, FT with Tony)")
- t4 = gr.Textbox(
- value="",
- placeholder="Recognition output",
- label="Purposed",
- )
-
- b4.click(
- ASRwhipser_Tony, inputs=[input_audio], outputs=t4
- )
- with gr.Row():
- b5 = gr.Button("Laronix Speech Recognition Engine (Whipser, FT with John)")
- t5 = gr.Textbox(
- value="",
- placeholder="Recognition output",
- label="Purposed",
- )
-
- b5.click(
- ASRwhipser_John, inputs=[input_audio], outputs=t5
- )
- with gr.Row():
- b6 = gr.Button("Laronix Speech Recognition Engine (Whipser, FT with Negel)")
- t6 = gr.Textbox(
- value="",
- placeholder="Recognition output",
- label="Purposed",
- )
-
- b6.click(
- ASRwhipser_Negel, inputs=[input_audio], outputs=t6
- )
-
-demo.launch(share=True)
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/indicators/__init__.py b/spaces/Lianjd/stock_dashboard/backtrader/indicators/__init__.py
deleted file mode 100644
index bd92ee554896392738bdc94c59bfd577303bedd7..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/indicators/__init__.py
+++ /dev/null
@@ -1,90 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program. If not, see .
-#
-###############################################################################
-from __future__ import (absolute_import, division, print_function,
- unicode_literals)
-
-from backtrader import Indicator
-from backtrader.functions import *
-
-# The modules below should/must define __all__ with the Indicator objects
-# of prepend an "_" (underscore) to private classes/variables
-
-from .basicops import *
-
-# base for moving averages
-from .mabase import *
-
-# moving averages (so envelope and oscillators can be auto-generated)
-from .sma import *
-from .ema import *
-from .smma import *
-from .wma import *
-from .dema import *
-from .kama import *
-from .zlema import *
-from .hma import *
-from .zlind import *
-from .dma import *
-
-# depends on moving averages
-from .deviation import *
-
-# depend on basicops, moving averages and deviations
-from .atr import *
-from .aroon import *
-from .bollinger import *
-from .cci import *
-from .crossover import *
-from .dpo import *
-from .directionalmove import *
-from .envelope import *
-from .heikinashi import *
-from .lrsi import *
-from .macd import *
-from .momentum import *
-from .oscillator import *
-from .percentchange import *
-from .percentrank import *
-from .pivotpoint import *
-from .prettygoodoscillator import *
-from .priceoscillator import *
-from .psar import *
-from .rsi import *
-from .stochastic import *
-from .trix import *
-from .tsi import *
-from .ultimateoscillator import *
-from .williams import *
-from .rmi import *
-from .awesomeoscillator import *
-from .accdecoscillator import *
-
-
-from .dv2 import * # depends on percentrank
-
-# Depends on Momentum
-from .kst import *
-
-from .ichimoku import *
-
-from .hurst import *
-from .ols import *
-from .hadelta import *
diff --git a/spaces/MCkernick/Image_Restoration_Colorization/Global/models/__init__.py b/spaces/MCkernick/Image_Restoration_Colorization/Global/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/MacYang/Diamond-Sutra/cli_app.py b/spaces/MacYang/Diamond-Sutra/cli_app.py
deleted file mode 100644
index 0fac5ed71b410d1e9ee03a29b05a3940fdd41772..0000000000000000000000000000000000000000
--- a/spaces/MacYang/Diamond-Sutra/cli_app.py
+++ /dev/null
@@ -1,30 +0,0 @@
-"""a command line chat app to talk about jinggangjing"""
-import pickle
-import sys
-import logging
-from query import get_chain
-
-logging.basicConfig(level=logging.INFO)
-
-VECTOR_STORE_PATH = "jinggang_embeddings.pkl"
-
-def _should_quit(query: str) -> bool:
- """see if we should quit from the conversation"""
- return query.find("quit") >= 0
-
-def _is_verbose() -> bool:
- return "--verbose" in sys.argv
-
-if __name__ == "__main__":
- with open(VECTOR_STORE_PATH, "rb") as f:
- vectorstore = pickle.load(f)
- qa_chain = get_chain(vectorstore, verbose=_is_verbose())
- chat_history = []
- print("和金刚经对话")
- while True:
- question = input("你: ")
- if _should_quit(question):
- break
- result = qa_chain({"question": question, "chat_history": chat_history})
- chat_history.append((question, result["answer"]))
- print("AI: " + result["answer"])
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/util/tensor_util.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/util/tensor_util.py
deleted file mode 100644
index 05189d38e2b0b0d1d08bd7804b8e43418d6da637..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/util/tensor_util.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import torch.nn.functional as F
-
-
-def compute_tensor_iu(seg, gt):
- intersection = (seg & gt).float().sum()
- union = (seg | gt).float().sum()
-
- return intersection, union
-
-def compute_tensor_iou(seg, gt):
- intersection, union = compute_tensor_iu(seg, gt)
- iou = (intersection + 1e-6) / (union + 1e-6)
-
- return iou
-
-# STM
-def pad_divide_by(in_img, d):
- h, w = in_img.shape[-2:]
-
- if h % d > 0:
- new_h = h + d - h % d
- else:
- new_h = h
- if w % d > 0:
- new_w = w + d - w % d
- else:
- new_w = w
- lh, uh = int((new_h-h) / 2), int(new_h-h) - int((new_h-h) / 2)
- lw, uw = int((new_w-w) / 2), int(new_w-w) - int((new_w-w) / 2)
- pad_array = (int(lw), int(uw), int(lh), int(uh))
- out = F.pad(in_img, pad_array)
- return out, pad_array
-
-def unpad(img, pad):
- if len(img.shape) == 4:
- if pad[2]+pad[3] > 0:
- img = img[:,:,pad[2]:-pad[3],:]
- if pad[0]+pad[1] > 0:
- img = img[:,:,:,pad[0]:-pad[1]]
- elif len(img.shape) == 3:
- if pad[2]+pad[3] > 0:
- img = img[:,pad[2]:-pad[3],:]
- if pad[0]+pad[1] > 0:
- img = img[:,:,pad[0]:-pad[1]]
- else:
- raise NotImplementedError
- return img
\ No newline at end of file
diff --git a/spaces/Marshalls/testmtd/training/options/__init__.py b/spaces/Marshalls/testmtd/training/options/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/MathysL/AutoGPT4/autogpt/__main__.py b/spaces/MathysL/AutoGPT4/autogpt/__main__.py
deleted file mode 100644
index 128f9eea4900429e88276abdde3419b806001ac7..0000000000000000000000000000000000000000
--- a/spaces/MathysL/AutoGPT4/autogpt/__main__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""Auto-GPT: A GPT powered AI Assistant"""
-import autogpt.cli
-
-if __name__ == "__main__":
- autogpt.cli.main()
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/image/__init__.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/image/__init__.py
deleted file mode 100644
index d0051d609d3de4e7562e3fe638335c66617c4d91..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/image/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .colorspace import (bgr2gray, bgr2hls, bgr2hsv, bgr2rgb, bgr2ycbcr,
- gray2bgr, gray2rgb, hls2bgr, hsv2bgr, imconvert,
- rgb2bgr, rgb2gray, rgb2ycbcr, ycbcr2bgr, ycbcr2rgb)
-from .geometric import (cutout, imcrop, imflip, imflip_, impad,
- impad_to_multiple, imrescale, imresize, imresize_like,
- imresize_to_multiple, imrotate, imshear, imtranslate,
- rescale_size)
-from .io import imfrombytes, imread, imwrite, supported_backends, use_backend
-from .misc import tensor2imgs
-from .photometric import (adjust_brightness, adjust_color, adjust_contrast,
- adjust_lighting, adjust_sharpness, auto_contrast,
- clahe, imdenormalize, imequalize, iminvert,
- imnormalize, imnormalize_, lut_transform, posterize,
- solarize)
-
-__all__ = [
- 'bgr2gray', 'bgr2hls', 'bgr2hsv', 'bgr2rgb', 'gray2bgr', 'gray2rgb',
- 'hls2bgr', 'hsv2bgr', 'imconvert', 'rgb2bgr', 'rgb2gray', 'imrescale',
- 'imresize', 'imresize_like', 'imresize_to_multiple', 'rescale_size',
- 'imcrop', 'imflip', 'imflip_', 'impad', 'impad_to_multiple', 'imrotate',
- 'imfrombytes', 'imread', 'imwrite', 'supported_backends', 'use_backend',
- 'imdenormalize', 'imnormalize', 'imnormalize_', 'iminvert', 'posterize',
- 'solarize', 'rgb2ycbcr', 'bgr2ycbcr', 'ycbcr2rgb', 'ycbcr2bgr',
- 'tensor2imgs', 'imshear', 'imtranslate', 'adjust_color', 'imequalize',
- 'adjust_brightness', 'adjust_contrast', 'lut_transform', 'clahe',
- 'adjust_sharpness', 'auto_contrast', 'cutout', 'adjust_lighting'
-]
diff --git a/spaces/Miuzarte/SUI-svc-4.0/modules/ddsp.py b/spaces/Miuzarte/SUI-svc-4.0/modules/ddsp.py
deleted file mode 100644
index b09ac5c5c19d165e75e1780877a857be8c104ed7..0000000000000000000000000000000000000000
--- a/spaces/Miuzarte/SUI-svc-4.0/modules/ddsp.py
+++ /dev/null
@@ -1,190 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-import torch.fft as fft
-import numpy as np
-import librosa as li
-import math
-from scipy.signal import get_window
-
-
-def safe_log(x):
- return torch.log(x + 1e-7)
-
-
-@torch.no_grad()
-def mean_std_loudness(dataset):
- mean = 0
- std = 0
- n = 0
- for _, _, l in dataset:
- n += 1
- mean += (l.mean().item() - mean) / n
- std += (l.std().item() - std) / n
- return mean, std
-
-
-def multiscale_fft(signal, scales, overlap):
- stfts = []
- for s in scales:
- S = torch.stft(
- signal,
- s,
- int(s * (1 - overlap)),
- s,
- torch.hann_window(s).to(signal),
- True,
- normalized=True,
- return_complex=True,
- ).abs()
- stfts.append(S)
- return stfts
-
-
-def resample(x, factor: int):
- batch, frame, channel = x.shape
- x = x.permute(0, 2, 1).reshape(batch * channel, 1, frame)
-
- window = torch.hann_window(
- factor * 2,
- dtype=x.dtype,
- device=x.device,
- ).reshape(1, 1, -1)
- y = torch.zeros(x.shape[0], x.shape[1], factor * x.shape[2]).to(x)
- y[..., ::factor] = x
- y[..., -1:] = x[..., -1:]
- y = torch.nn.functional.pad(y, [factor, factor])
- y = torch.nn.functional.conv1d(y, window)[..., :-1]
-
- y = y.reshape(batch, channel, factor * frame).permute(0, 2, 1)
-
- return y
-
-
-def upsample(signal, factor):
- signal = signal.permute(0, 2, 1)
- signal = nn.functional.interpolate(signal, size=signal.shape[-1] * factor)
- return signal.permute(0, 2, 1)
-
-
-def remove_above_nyquist(amplitudes, pitch, sampling_rate):
- n_harm = amplitudes.shape[-1]
- pitches = pitch * torch.arange(1, n_harm + 1).to(pitch)
- aa = (pitches < sampling_rate / 2).float() + 1e-4
- return amplitudes * aa
-
-
-def scale_function(x):
- return 2 * torch.sigmoid(x) ** (math.log(10)) + 1e-7
-
-
-def extract_loudness(signal, sampling_rate, block_size, n_fft=2048):
- S = li.stft(
- signal,
- n_fft=n_fft,
- hop_length=block_size,
- win_length=n_fft,
- center=True,
- )
- S = np.log(abs(S) + 1e-7)
- f = li.fft_frequencies(sampling_rate, n_fft)
- a_weight = li.A_weighting(f)
-
- S = S + a_weight.reshape(-1, 1)
-
- S = np.mean(S, 0)[..., :-1]
-
- return S
-
-
-def extract_pitch(signal, sampling_rate, block_size):
- length = signal.shape[-1] // block_size
- f0 = crepe.predict(
- signal,
- sampling_rate,
- step_size=int(1000 * block_size / sampling_rate),
- verbose=1,
- center=True,
- viterbi=True,
- )
- f0 = f0[1].reshape(-1)[:-1]
-
- if f0.shape[-1] != length:
- f0 = np.interp(
- np.linspace(0, 1, length, endpoint=False),
- np.linspace(0, 1, f0.shape[-1], endpoint=False),
- f0,
- )
-
- return f0
-
-
-def mlp(in_size, hidden_size, n_layers):
- channels = [in_size] + (n_layers) * [hidden_size]
- net = []
- for i in range(n_layers):
- net.append(nn.Linear(channels[i], channels[i + 1]))
- net.append(nn.LayerNorm(channels[i + 1]))
- net.append(nn.LeakyReLU())
- return nn.Sequential(*net)
-
-
-def gru(n_input, hidden_size):
- return nn.GRU(n_input * hidden_size, hidden_size, batch_first=True)
-
-
-def harmonic_synth(pitch, amplitudes, sampling_rate):
- n_harmonic = amplitudes.shape[-1]
- omega = torch.cumsum(2 * math.pi * pitch / sampling_rate, 1)
- omegas = omega * torch.arange(1, n_harmonic + 1).to(omega)
- signal = (torch.sin(omegas) * amplitudes).sum(-1, keepdim=True)
- return signal
-
-
-def amp_to_impulse_response(amp, target_size):
- amp = torch.stack([amp, torch.zeros_like(amp)], -1)
- amp = torch.view_as_complex(amp)
- amp = fft.irfft(amp)
-
- filter_size = amp.shape[-1]
-
- amp = torch.roll(amp, filter_size // 2, -1)
- win = torch.hann_window(filter_size, dtype=amp.dtype, device=amp.device)
-
- amp = amp * win
-
- amp = nn.functional.pad(amp, (0, int(target_size) - int(filter_size)))
- amp = torch.roll(amp, -filter_size // 2, -1)
-
- return amp
-
-
-def fft_convolve(signal, kernel):
- signal = nn.functional.pad(signal, (0, signal.shape[-1]))
- kernel = nn.functional.pad(kernel, (kernel.shape[-1], 0))
-
- output = fft.irfft(fft.rfft(signal) * fft.rfft(kernel))
- output = output[..., output.shape[-1] // 2:]
-
- return output
-
-
-def init_kernels(win_len, win_inc, fft_len, win_type=None, invers=False):
- if win_type == 'None' or win_type is None:
- window = np.ones(win_len)
- else:
- window = get_window(win_type, win_len, fftbins=True) # **0.5
-
- N = fft_len
- fourier_basis = np.fft.rfft(np.eye(N))[:win_len]
- real_kernel = np.real(fourier_basis)
- imag_kernel = np.imag(fourier_basis)
- kernel = np.concatenate([real_kernel, imag_kernel], 1).T
-
- if invers:
- kernel = np.linalg.pinv(kernel).T
-
- kernel = kernel * window
- kernel = kernel[:, None, :]
- return torch.from_numpy(kernel.astype(np.float32)), torch.from_numpy(window[None, :, None].astype(np.float32))
-
diff --git a/spaces/NCTCMumbai/NCTC/models/research/brain_coder/common/config_lib.py b/spaces/NCTCMumbai/NCTC/models/research/brain_coder/common/config_lib.py
deleted file mode 100644
index 733fa202f2e500f964beff2111cb7445fa66a9e1..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/research/brain_coder/common/config_lib.py
+++ /dev/null
@@ -1,337 +0,0 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-"""Objects for storing configuration and passing config into binaries.
-
-Config class stores settings and hyperparameters for models, data, and anything
-else that may be specific to a particular run.
-"""
-
-import ast
-import itertools
-from six.moves import xrange
-
-
-class Config(dict):
- """Stores model configuration, hyperparameters, or dataset parameters."""
-
- def __getattr__(self, attr):
- return self[attr]
-
- def __setattr__(self, attr, value):
- self[attr] = value
-
- def pretty_str(self, new_lines=True, indent=2, final_indent=0):
- prefix = (' ' * indent) if new_lines else ''
- final_prefix = (' ' * final_indent) if new_lines else ''
- kv = ['%s%s=%s' % (prefix, k,
- (repr(v) if not isinstance(v, Config)
- else v.pretty_str(new_lines=new_lines,
- indent=indent+2,
- final_indent=indent)))
- for k, v in self.items()]
- if new_lines:
- return 'Config(\n%s\n%s)' % (',\n'.join(kv), final_prefix)
- else:
- return 'Config(%s)' % ', '.join(kv)
-
- def _update_iterator(self, *args, **kwargs):
- """Convert mixed input into an iterator over (key, value) tuples.
-
- Follows the dict.update call signature.
-
- Args:
- *args: (Optional) Pass a dict or iterable of (key, value) 2-tuples as
- an unnamed argument. Only one unnamed argument allowed.
- **kwargs: (Optional) Pass (key, value) pairs as named arguments, where the
- argument name is the key and the argument value is the value.
-
- Returns:
- An iterator over (key, value) tuples given in the input.
-
- Raises:
- TypeError: If more than one unnamed argument is given.
- """
- if len(args) > 1:
- raise TypeError('Expected at most 1 unnamed arguments, got %d'
- % len(args))
- obj = args[0] if args else dict()
- if isinstance(obj, dict):
- return itertools.chain(obj.items(), kwargs.items())
- # Assume obj is an iterable of 2-tuples.
- return itertools.chain(obj, kwargs.items())
-
- def make_default(self, keys=None):
- """Convert OneOf objects into their default configs.
-
- Recursively calls into Config objects.
-
- Args:
- keys: Iterable of key names to check. If None, all keys in self will be
- used.
- """
- if keys is None:
- keys = self.keys()
- for k in keys:
- # Replace OneOf with its default value.
- if isinstance(self[k], OneOf):
- self[k] = self[k].default()
- # Recursively call into all Config objects, even those that came from
- # OneOf objects in the previous code line (for nested OneOf objects).
- if isinstance(self[k], Config):
- self[k].make_default()
-
- def update(self, *args, **kwargs):
- """Same as dict.update except nested Config objects are updated.
-
- Args:
- *args: (Optional) Pass a dict or list of (key, value) 2-tuples as unnamed
- argument.
- **kwargs: (Optional) Pass (key, value) pairs as named arguments, where the
- argument name is the key and the argument value is the value.
- """
- key_set = set(self.keys())
- for k, v in self._update_iterator(*args, **kwargs):
- if k in key_set:
- key_set.remove(k) # This key is updated so exclude from make_default.
- if k in self and isinstance(self[k], Config) and isinstance(v, dict):
- self[k].update(v)
- elif k in self and isinstance(self[k], OneOf) and isinstance(v, dict):
- # Replace OneOf with the chosen config.
- self[k] = self[k].update(v)
- else:
- self[k] = v
- self.make_default(key_set)
-
- def strict_update(self, *args, **kwargs):
- """Same as Config.update except keys and types are not allowed to change.
-
- If a given key is not already in this instance, an exception is raised. If a
- given value does not have the same type as the existing value for the same
- key, an exception is raised. Use this method to catch config mistakes.
-
- Args:
- *args: (Optional) Pass a dict or list of (key, value) 2-tuples as unnamed
- argument.
- **kwargs: (Optional) Pass (key, value) pairs as named arguments, where the
- argument name is the key and the argument value is the value.
-
- Raises:
- TypeError: If more than one unnamed argument is given.
- TypeError: If new value type does not match existing type.
- KeyError: If a given key is not already defined in this instance.
- """
- key_set = set(self.keys())
- for k, v in self._update_iterator(*args, **kwargs):
- if k in self:
- key_set.remove(k) # This key is updated so exclude from make_default.
- if isinstance(self[k], Config):
- if not isinstance(v, dict):
- raise TypeError('dict required for Config value, got %s' % type(v))
- self[k].strict_update(v)
- elif isinstance(self[k], OneOf):
- if not isinstance(v, dict):
- raise TypeError('dict required for OneOf value, got %s' % type(v))
- # Replace OneOf with the chosen config.
- self[k] = self[k].strict_update(v)
- else:
- if not isinstance(v, type(self[k])):
- raise TypeError('Expecting type %s for key %s, got type %s'
- % (type(self[k]), k, type(v)))
- self[k] = v
- else:
- raise KeyError(
- 'Key %s does not exist. New key creation not allowed in '
- 'strict_update.' % k)
- self.make_default(key_set)
-
- @staticmethod
- def from_str(config_str):
- """Inverse of Config.__str__."""
- parsed = ast.literal_eval(config_str)
- assert isinstance(parsed, dict)
-
- def _make_config(dictionary):
- for k, v in dictionary.items():
- if isinstance(v, dict):
- dictionary[k] = _make_config(v)
- return Config(**dictionary)
- return _make_config(parsed)
-
- @staticmethod
- def parse(key_val_string):
- """Parse hyperparameter string into Config object.
-
- Format is 'key=val,key=val,...'
- Values can be any python literal, or another Config object encoded as
- 'c(key=val,key=val,...)'.
- c(...) expressions can be arbitrarily nested.
-
- Example:
- 'a=1,b=3e-5,c=[1,2,3],d="hello world",e={"a":1,"b":2},f=c(x=1,y=[10,20])'
-
- Args:
- key_val_string: The hyperparameter string.
-
- Returns:
- Config object parsed from the input string.
- """
- if not key_val_string.strip():
- return Config()
- def _pair_to_kv(pair):
- split_index = pair.find('=')
- key, val = pair[:split_index].strip(), pair[split_index+1:].strip()
- if val.startswith('c(') and val.endswith(')'):
- val = Config.parse(val[2:-1])
- else:
- val = ast.literal_eval(val)
- return key, val
- return Config(**dict([_pair_to_kv(pair)
- for pair in _comma_iterator(key_val_string)]))
-
-
-class OneOf(object):
- """Stores branching config.
-
- In some cases there may be options which each have their own set of config
- params. For example, if specifying config for an environment, each environment
- can have custom config options. OneOf is a way to organize branching config.
-
- Usage example:
- one_of = OneOf(
- [Config(a=1, b=2),
- Config(a=2, c='hello'),
- Config(a=3, d=10, e=-10)],
- a=1)
- config = one_of.strict_update(Config(a=3, d=20))
- config == {'a': 3, 'd': 20, 'e': -10}
- """
-
- def __init__(self, choices, **kwargs):
- """Constructor.
-
- Usage: OneOf([Config(...), Config(...), ...], attribute=default_value)
-
- Args:
- choices: An iterable of Config objects. When update/strict_update is
- called on this OneOf, one of these Config will be selected.
- **kwargs: Give exactly one config attribute to branch on. The value of
- this attribute during update/strict_update will determine which
- Config is used.
-
- Raises:
- ValueError: If kwargs does not contain exactly one entry. Should give one
- named argument which is used as the attribute to condition on.
- """
- if len(kwargs) != 1:
- raise ValueError(
- 'Incorrect usage. Must give exactly one named argument. The argument '
- 'name is the config attribute to condition on, and the argument '
- 'value is the default choice. Got %d named arguments.' % len(kwargs))
- key, default_value = kwargs.items()[0]
- self.key = key
- self.default_value = default_value
-
- # Make sure each choice is a Config object.
- for config in choices:
- if not isinstance(config, Config):
- raise TypeError('choices must be a list of Config objects. Got %s.'
- % type(config))
-
- # Map value for key to the config with that value.
- self.value_map = {config[key]: config for config in choices}
- self.default_config = self.value_map[self.default_value]
-
- # Make sure there are no duplicate values.
- if len(self.value_map) != len(choices):
- raise ValueError('Multiple choices given for the same value of %s.' % key)
-
- # Check that the default value is valid.
- if self.default_value not in self.value_map:
- raise ValueError(
- 'Default value is not an available choice. Got %s=%s. Choices are %s.'
- % (key, self.default_value, self.value_map.keys()))
-
- def default(self):
- return self.default_config
-
- def update(self, other):
- """Choose a config and update it.
-
- If `other` is a Config, one of the config choices is selected and updated.
- Otherwise `other` is returned.
-
- Args:
- other: Will update chosen config with this value by calling `update` on
- the config.
-
- Returns:
- The chosen config after updating it, or `other` if no config could be
- selected.
- """
- if not isinstance(other, Config):
- return other
- if self.key not in other or other[self.key] not in self.value_map:
- return other
- target = self.value_map[other[self.key]]
- target.update(other)
- return target
-
- def strict_update(self, config):
- """Choose a config and update it.
-
- `config` must be a Config object. `config` must have the key used to select
- among the config choices, and that key must have a value which one of the
- config choices has.
-
- Args:
- config: A Config object. the chosen config will be update by calling
- `strict_update`.
-
- Returns:
- The chosen config after updating it.
-
- Raises:
- TypeError: If `config` is not a Config instance.
- ValueError: If `config` does not have the branching key in its key set.
- ValueError: If the value of the config's branching key is not one of the
- valid choices.
- """
- if not isinstance(config, Config):
- raise TypeError('Expecting Config instance, got %s.' % type(config))
- if self.key not in config:
- raise ValueError(
- 'Branching key %s required but not found in %s' % (self.key, config))
- if config[self.key] not in self.value_map:
- raise ValueError(
- 'Value %s for key %s is not a possible choice. Choices are %s.'
- % (config[self.key], self.key, self.value_map.keys()))
- target = self.value_map[config[self.key]]
- target.strict_update(config)
- return target
-
-
-def _next_comma(string, start_index):
- """Finds the position of the next comma not used in a literal collection."""
- paren_count = 0
- for i in xrange(start_index, len(string)):
- c = string[i]
- if c == '(' or c == '[' or c == '{':
- paren_count += 1
- elif c == ')' or c == ']' or c == '}':
- paren_count -= 1
- if paren_count == 0 and c == ',':
- return i
- return -1
-
-
-def _comma_iterator(string):
- index = 0
- while 1:
- next_index = _next_comma(string, index)
- if next_index == -1:
- yield string[index:]
- return
- yield string[index:next_index]
- index = next_index + 1
diff --git a/spaces/NPU/hallucination_in_image_captioning_demo/README.md b/spaces/NPU/hallucination_in_image_captioning_demo/README.md
deleted file mode 100644
index dff032405e78803dc64ea4ca0913fd166df63994..0000000000000000000000000000000000000000
--- a/spaces/NPU/hallucination_in_image_captioning_demo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Hallucination In Image Captioning Demo
-emoji: 👁
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/NotFungibleIO/GFPGAN/app.py b/spaces/NotFungibleIO/GFPGAN/app.py
deleted file mode 100644
index 510412a6a4724f6846f587f476c6fe80e3e9535f..0000000000000000000000000000000000000000
--- a/spaces/NotFungibleIO/GFPGAN/app.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import os
-
-import cv2
-import gradio as gr
-import torch
-from basicsr.archs.srvgg_arch import SRVGGNetCompact
-from gfpgan.utils import GFPGANer
-from realesrgan.utils import RealESRGANer
-
-os.system("pip freeze")
-os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
-os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
-os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
-
-torch.hub.download_url_to_file(
- 'https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg',
- 'lincoln.jpg')
-torch.hub.download_url_to_file(
- 'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg',
- 'AI-generate.jpg')
-torch.hub.download_url_to_file(
- 'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg',
- 'Blake_Lively.jpg')
-torch.hub.download_url_to_file(
- 'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png',
- '10045.png')
-
-# background enhancer with RealESRGAN
-model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
-model_path = 'realesr-general-x4v3.pth'
-half = True if torch.cuda.is_available() else False
-upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
-
-# Use GFPGAN for face enhancement
-face_enhancer_v3 = GFPGANer(
- model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-face_enhancer_v2 = GFPGANer(
- model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
-os.makedirs('output', exist_ok=True)
-
-
-def inference(img, version, scale):
- print(img, version, scale)
- try:
- img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
- if len(img.shape) == 3 and img.shape[2] == 4:
- img_mode = 'RGBA'
- else:
- img_mode = None
-
- h, w = img.shape[0:2]
- if h < 300:
- img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
-
- if version == 'v1.2':
- face_enhancer = face_enhancer_v2
- else:
- face_enhancer = face_enhancer_v3
- try:
- _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
- except RuntimeError as error:
- print('Error', error)
- else:
- extension = 'png'
-
- try:
- if scale != 2:
- interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
- h, w = img.shape[0:2]
- output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
- except Exception as error:
- print('wrong scale input.', error)
- if img_mode == 'RGBA': # RGBA images should be saved in png format
- extension = 'png'
- else:
- extension = 'jpg'
- save_path = f'output/out.{extension}'
- cv2.imwrite(save_path, output)
-
- output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
- return output, save_path
- except Exception as error:
- print('global exception', error)
- return None, None
-
-
-title = "GFPGAN: Practical Face Restoration Algorithm"
-description = r"""Gradio demo for GFPGAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.
-It can be used to restore your **old photos** or improve **AI-generated faces**.
-To use it, simply upload your image.
-If GFPGAN is helpful, please help to ⭐ the Github Repo and recommend it to your friends 😊
-"""
-article = r"""
-
-[](https://github.com/TencentARC/GFPGAN/releases)
-[](https://github.com/TencentARC/GFPGAN)
-[](https://arxiv.org/abs/2101.04061)
-
-If you have any question, please email 📧 `xintao.wang@outlook.com` or `xintaowang@tencent.com`.
-
-
-
-"""
-gr.Interface(
- inference, [
- gr.inputs.Image(type="filepath", label="Input"),
- gr.inputs.Radio(['v1.2', 'v1.3'], type="value", default='v1.3', label='GFPGAN version'),
- gr.inputs.Number(label="Rescaling factor", default=2)
- ], [
- gr.outputs.Image(type="numpy", label="Output (The whole image)"),
- gr.outputs.File(label="Download the output image")
- ],
- title=title,
- description=description,
- article=article,
- examples=[['AI-generate.jpg', 'v1.3', 2], ['lincoln.jpg', 'v1.3', 2], ['Blake_Lively.jpg', 'v1.3', 2],
- ['10045.png', 'v1.3', 2]]).launch()
diff --git a/spaces/OAOA/DifFace/basicsr/archs/discriminator_arch.py b/spaces/OAOA/DifFace/basicsr/archs/discriminator_arch.py
deleted file mode 100644
index 33f9a8f1b25c2052cd3ba801534861a425752e69..0000000000000000000000000000000000000000
--- a/spaces/OAOA/DifFace/basicsr/archs/discriminator_arch.py
+++ /dev/null
@@ -1,150 +0,0 @@
-from torch import nn as nn
-from torch.nn import functional as F
-from torch.nn.utils import spectral_norm
-
-from basicsr.utils.registry import ARCH_REGISTRY
-
-
-@ARCH_REGISTRY.register()
-class VGGStyleDiscriminator(nn.Module):
- """VGG style discriminator with input size 128 x 128 or 256 x 256.
-
- It is used to train SRGAN, ESRGAN, and VideoGAN.
-
- Args:
- num_in_ch (int): Channel number of inputs. Default: 3.
- num_feat (int): Channel number of base intermediate features.Default: 64.
- """
-
- def __init__(self, num_in_ch, num_feat, input_size=128):
- super(VGGStyleDiscriminator, self).__init__()
- self.input_size = input_size
- assert self.input_size == 128 or self.input_size == 256, (
- f'input size must be 128 or 256, but received {input_size}')
-
- self.conv0_0 = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1, bias=True)
- self.conv0_1 = nn.Conv2d(num_feat, num_feat, 4, 2, 1, bias=False)
- self.bn0_1 = nn.BatchNorm2d(num_feat, affine=True)
-
- self.conv1_0 = nn.Conv2d(num_feat, num_feat * 2, 3, 1, 1, bias=False)
- self.bn1_0 = nn.BatchNorm2d(num_feat * 2, affine=True)
- self.conv1_1 = nn.Conv2d(num_feat * 2, num_feat * 2, 4, 2, 1, bias=False)
- self.bn1_1 = nn.BatchNorm2d(num_feat * 2, affine=True)
-
- self.conv2_0 = nn.Conv2d(num_feat * 2, num_feat * 4, 3, 1, 1, bias=False)
- self.bn2_0 = nn.BatchNorm2d(num_feat * 4, affine=True)
- self.conv2_1 = nn.Conv2d(num_feat * 4, num_feat * 4, 4, 2, 1, bias=False)
- self.bn2_1 = nn.BatchNorm2d(num_feat * 4, affine=True)
-
- self.conv3_0 = nn.Conv2d(num_feat * 4, num_feat * 8, 3, 1, 1, bias=False)
- self.bn3_0 = nn.BatchNorm2d(num_feat * 8, affine=True)
- self.conv3_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False)
- self.bn3_1 = nn.BatchNorm2d(num_feat * 8, affine=True)
-
- self.conv4_0 = nn.Conv2d(num_feat * 8, num_feat * 8, 3, 1, 1, bias=False)
- self.bn4_0 = nn.BatchNorm2d(num_feat * 8, affine=True)
- self.conv4_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False)
- self.bn4_1 = nn.BatchNorm2d(num_feat * 8, affine=True)
-
- if self.input_size == 256:
- self.conv5_0 = nn.Conv2d(num_feat * 8, num_feat * 8, 3, 1, 1, bias=False)
- self.bn5_0 = nn.BatchNorm2d(num_feat * 8, affine=True)
- self.conv5_1 = nn.Conv2d(num_feat * 8, num_feat * 8, 4, 2, 1, bias=False)
- self.bn5_1 = nn.BatchNorm2d(num_feat * 8, affine=True)
-
- self.linear1 = nn.Linear(num_feat * 8 * 4 * 4, 100)
- self.linear2 = nn.Linear(100, 1)
-
- # activation function
- self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
- def forward(self, x):
- assert x.size(2) == self.input_size, (f'Input size must be identical to input_size, but received {x.size()}.')
-
- feat = self.lrelu(self.conv0_0(x))
- feat = self.lrelu(self.bn0_1(self.conv0_1(feat))) # output spatial size: /2
-
- feat = self.lrelu(self.bn1_0(self.conv1_0(feat)))
- feat = self.lrelu(self.bn1_1(self.conv1_1(feat))) # output spatial size: /4
-
- feat = self.lrelu(self.bn2_0(self.conv2_0(feat)))
- feat = self.lrelu(self.bn2_1(self.conv2_1(feat))) # output spatial size: /8
-
- feat = self.lrelu(self.bn3_0(self.conv3_0(feat)))
- feat = self.lrelu(self.bn3_1(self.conv3_1(feat))) # output spatial size: /16
-
- feat = self.lrelu(self.bn4_0(self.conv4_0(feat)))
- feat = self.lrelu(self.bn4_1(self.conv4_1(feat))) # output spatial size: /32
-
- if self.input_size == 256:
- feat = self.lrelu(self.bn5_0(self.conv5_0(feat)))
- feat = self.lrelu(self.bn5_1(self.conv5_1(feat))) # output spatial size: / 64
-
- # spatial size: (4, 4)
- feat = feat.view(feat.size(0), -1)
- feat = self.lrelu(self.linear1(feat))
- out = self.linear2(feat)
- return out
-
-
-@ARCH_REGISTRY.register(suffix='basicsr')
-class UNetDiscriminatorSN(nn.Module):
- """Defines a U-Net discriminator with spectral normalization (SN)
-
- It is used in Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
-
- Arg:
- num_in_ch (int): Channel number of inputs. Default: 3.
- num_feat (int): Channel number of base intermediate features. Default: 64.
- skip_connection (bool): Whether to use skip connections between U-Net. Default: True.
- """
-
- def __init__(self, num_in_ch, num_feat=64, skip_connection=True):
- super(UNetDiscriminatorSN, self).__init__()
- self.skip_connection = skip_connection
- norm = spectral_norm
- # the first convolution
- self.conv0 = nn.Conv2d(num_in_ch, num_feat, kernel_size=3, stride=1, padding=1)
- # downsample
- self.conv1 = norm(nn.Conv2d(num_feat, num_feat * 2, 4, 2, 1, bias=False))
- self.conv2 = norm(nn.Conv2d(num_feat * 2, num_feat * 4, 4, 2, 1, bias=False))
- self.conv3 = norm(nn.Conv2d(num_feat * 4, num_feat * 8, 4, 2, 1, bias=False))
- # upsample
- self.conv4 = norm(nn.Conv2d(num_feat * 8, num_feat * 4, 3, 1, 1, bias=False))
- self.conv5 = norm(nn.Conv2d(num_feat * 4, num_feat * 2, 3, 1, 1, bias=False))
- self.conv6 = norm(nn.Conv2d(num_feat * 2, num_feat, 3, 1, 1, bias=False))
- # extra convolutions
- self.conv7 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
- self.conv8 = norm(nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=False))
- self.conv9 = nn.Conv2d(num_feat, 1, 3, 1, 1)
-
- def forward(self, x):
- # downsample
- x0 = F.leaky_relu(self.conv0(x), negative_slope=0.2, inplace=True)
- x1 = F.leaky_relu(self.conv1(x0), negative_slope=0.2, inplace=True)
- x2 = F.leaky_relu(self.conv2(x1), negative_slope=0.2, inplace=True)
- x3 = F.leaky_relu(self.conv3(x2), negative_slope=0.2, inplace=True)
-
- # upsample
- x3 = F.interpolate(x3, scale_factor=2, mode='bilinear', align_corners=False)
- x4 = F.leaky_relu(self.conv4(x3), negative_slope=0.2, inplace=True)
-
- if self.skip_connection:
- x4 = x4 + x2
- x4 = F.interpolate(x4, scale_factor=2, mode='bilinear', align_corners=False)
- x5 = F.leaky_relu(self.conv5(x4), negative_slope=0.2, inplace=True)
-
- if self.skip_connection:
- x5 = x5 + x1
- x5 = F.interpolate(x5, scale_factor=2, mode='bilinear', align_corners=False)
- x6 = F.leaky_relu(self.conv6(x5), negative_slope=0.2, inplace=True)
-
- if self.skip_connection:
- x6 = x6 + x0
-
- # extra convolutions
- out = F.leaky_relu(self.conv7(x6), negative_slope=0.2, inplace=True)
- out = F.leaky_relu(self.conv8(out), negative_slope=0.2, inplace=True)
- out = self.conv9(out)
-
- return out
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/multilingual/sampled_multi_dataset.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/multilingual/sampled_multi_dataset.py
deleted file mode 100644
index b0a617424ee3c5923b37796773da4c97851a16c5..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/data/multilingual/sampled_multi_dataset.py
+++ /dev/null
@@ -1,467 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import datetime
-import hashlib
-import logging
-import time
-from bisect import bisect_right
-from collections import OrderedDict, defaultdict
-from enum import Enum
-from typing import List
-
-import numpy as np
-import torch
-from fairseq.data import FairseqDataset, data_utils
-from fairseq.distributed import utils as distributed_utils
-
-
-def get_time_gap(s, e):
- return (
- datetime.datetime.fromtimestamp(e) - datetime.datetime.fromtimestamp(s)
- ).__str__()
-
-
-logger = logging.getLogger(__name__)
-
-
-def default_virtual_size_func(datasets, ratios, max_scale_up=1.5):
- sizes = [len(d) for d in datasets]
- if ratios is None:
- return sum(sizes)
- largest_idx = np.argmax(sizes)
- largest_r = ratios[largest_idx]
- largest_s = sizes[largest_idx]
- # set virtual sizes relative to the largest dataset
- virtual_sizes = [(r / largest_r) * largest_s for r in ratios]
- vsize = sum(virtual_sizes)
- max_size = sum(sizes) * max_scale_up
- return int(vsize if vsize < max_size else max_size)
-
-
-class CollateFormat(Enum):
- single = 1
- ordered_dict = 2
-
-
-class SampledMultiDataset(FairseqDataset):
- """Samples from multiple sub-datasets according to given sampling ratios.
- Args:
- datasets (
- List[~torch.utils.data.Dataset]
- or OrderedDict[str, ~torch.utils.data.Dataset]
- ): datasets
- sampling_ratios (List[float]): list of probability of each dataset to be sampled
- (default: None, which corresponds to concatenating all dataset together).
- seed (int): RNG seed to use (default: 2).
- epoch (int): starting epoch number (default: 1).
- eval_key (str, optional): a key used at evaluation time that causes
- this instance to pass-through batches from *datasets[eval_key]*.
- collate_format (CollateFormat): collater output format, either CollateFormat.ordered_dict or
- CollateFormat.single (default: CollateFormat.single) where CollateFormat.single configures
- the collater to output batches of data mixed from all sub-datasets,
- and CollateFormat.ordered_dict configures the collater to output a dictionary of batches indexed by keys
- of sub-datasets.
- Note that not all sub-datasets will present in a single batch in both formats.
- virtual_size (int, or callable): the expected virtual size of the dataset (default: default_virtual_size_func).
- split (str): the split of the data, e.g. 'train', 'valid' or 'test'.
- shared_collater (bool): whether or not to all sub-datasets have the same collater.
- shuffle (bool): whether or not to shuffle data (default: True).
- """
-
- def __init__(
- self,
- datasets,
- sampling_ratios=None,
- seed=2,
- epoch=1,
- eval_key=None,
- collate_format=CollateFormat.single,
- virtual_size=default_virtual_size_func,
- split="",
- shared_collater=False,
- shuffle=True,
- ):
- super().__init__()
- self.shared_collater = shared_collater
- self.shuffle = shuffle
-
- if isinstance(datasets, OrderedDict):
- self.keys = list(datasets.keys())
- datasets = list(datasets.values())
- elif isinstance(datasets, List):
- self.keys = list(range(len(datasets)))
- else:
- raise AssertionError()
- self.datasets = datasets
- self.split = split
-
- self.eval_key = eval_key
- if self.eval_key is not None:
- self.collate_format = CollateFormat.single
- else:
- self.collate_format = collate_format
-
- self.seed = seed
- self._cur_epoch = None
-
- self.cumulated_sizes = None
- # self.datasets[k][self._cur_indices[i]] is the data item i in this sampled dataset
- # namely, data item i is sampled from the kth sub-dataset self.datasets[k]
- # where self.cumulated_sizes[k-1] <= i < self.cumulated_sizes[k]
- self._cur_indices = None
-
- self._sizes = None
- self.virtual_size_per_dataset = None
- # caching properties
- self._reset_cached_properties()
- self.setup_sampling(sampling_ratios, virtual_size)
- self.set_epoch(epoch)
-
- def _clean_if_not_none(self, var_list):
- for v in var_list:
- if v is not None:
- del v
-
- def _reset_cached_properties(self):
- self._clean_if_not_none([self._sizes, self._cur_indices])
- self._sizes = None
- self._cur_indices = None
-
- def setup_sampling(self, sample_ratios, virtual_size):
- sizes = [len(d) for d in self.datasets]
- if sample_ratios is None:
- # default back to concating datasets
- self.sample_ratios = None
- self.virtual_size = sum(sizes)
- else:
- if not isinstance(sample_ratios, np.ndarray):
- sample_ratios = np.array(sample_ratios)
- self.sample_ratios = sample_ratios
- virtual_size = (
- default_virtual_size_func if virtual_size is None else virtual_size
- )
- self.virtual_size = (
- virtual_size(self.datasets, self.sample_ratios)
- if callable(virtual_size)
- else virtual_size
- )
-
- def adjust_sampling(self, epoch, sampling_ratios, virtual_size):
- if sampling_ratios is not None:
- sampling_ratios = self._sync_sample_ratios(sampling_ratios)
- self.setup_sampling(sampling_ratios, virtual_size)
-
- def _sync_sample_ratios(self, ratios):
- # in case the ratios are not precisely the same across processes
- # also to ensure every procresses update the ratios in the same pace
- ratios = torch.DoubleTensor(ratios)
- if torch.distributed.is_initialized():
- if torch.cuda.is_available():
- distributed_utils.all_reduce(
- ratios.cuda(), group=distributed_utils.get_data_parallel_group()
- )
- else:
- distributed_utils.all_reduce(
- ratios, group=distributed_utils.get_data_parallel_group()
- )
- ret = ratios.cpu()
- ret = ret.numpy()
- return ret
-
- def random_choice_in_dataset(self, rng, dataset, choice_size):
- if hasattr(dataset, "random_choice_in_dataset"):
- return dataset.random_choice_in_dataset(rng, choice_size)
- dataset_size = len(dataset)
- return rng.choice(
- dataset_size, choice_size, replace=(choice_size > dataset_size)
- )
-
- def get_virtual_indices(self, rng, datasets, sample_ratios, virtual_size):
- def get_counts(sample_ratios):
- counts = np.array([virtual_size * r for r in sample_ratios], dtype=np.int64)
- diff = virtual_size - counts.sum()
- assert diff >= 0
- # due to round-offs, the size might not match the desired sizes
- if diff > 0:
- dataset_indices = rng.choice(
- len(sample_ratios), size=diff, p=sample_ratios
- )
- for i in dataset_indices:
- counts[i] += 1
- return counts
-
- def get_in_dataset_indices(datasets, sizes, sample_ratios):
- counts = get_counts(sample_ratios)
- # uniformally sample desired counts for each dataset
- # if the desired counts are large, sample with replacement:
- indices = [
- self.random_choice_in_dataset(rng, d, c)
- for c, d in zip(counts, datasets)
- ]
- return indices
-
- sizes = [len(d) for d in datasets]
- if sample_ratios is None:
- # default back to concating datasets
- in_dataset_indices = [list(range(s)) for s in sizes]
- virtual_sizes_per_dataset = sizes
- else:
- ratios = sample_ratios / sample_ratios.sum()
- in_dataset_indices = get_in_dataset_indices(datasets, sizes, ratios)
- virtual_sizes_per_dataset = [len(d) for d in in_dataset_indices]
- virtual_sizes_per_dataset = np.array(virtual_sizes_per_dataset, np.int64)
- cumulative_sizes = np.cumsum(virtual_sizes_per_dataset)
- assert sum(virtual_sizes_per_dataset) == virtual_size
- assert cumulative_sizes[-1] == virtual_size
- if virtual_size < sum(sizes):
- logger.warning(
- f"virtual data size ({virtual_size}) is less than real data size ({sum(sizes)})."
- " If virtual size << real data size, there could be data coverage issue."
- )
- in_dataset_indices = np.hstack(in_dataset_indices)
- return in_dataset_indices, cumulative_sizes, virtual_sizes_per_dataset
-
- def _get_dataset_and_index(self, index):
- i = bisect_right(self.cumulated_sizes, index)
- return i, self._cur_indices[index]
-
- def __getitem__(self, index):
- # self.__getitem__(index) returns self.datasets[k][self._cur_indices[index]]
- # where k satisfies self.cumulated_sizes[k - 1] <= k < self.cumulated_sizes[k]
- ds_idx, ds_sample_idx = self._get_dataset_and_index(index)
- ret = (ds_idx, self.datasets[ds_idx][ds_sample_idx])
- return ret
-
- def num_tokens(self, index):
- return self.sizes[index].max()
-
- def num_tokens_vec(self, indices):
- sizes_vec = self.sizes[np.array(indices)]
- # max across all dimensions but first one
- return np.amax(sizes_vec, axis=tuple(range(1, len(sizes_vec.shape))))
-
- def size(self, index):
- return self.sizes[index]
-
- def __len__(self):
- return self.virtual_size
-
- def collater(self, samples, **extra_args):
- """Merge a list of samples to form a mini-batch."""
- if len(samples) == 0:
- return None
- if self.collate_format == "ordered_dict":
- collect_samples = [[] for _ in range(len(self.datasets))]
- for (i, sample) in samples:
- collect_samples[i].append(sample)
- batch = OrderedDict(
- [
- (self.keys[i], dataset.collater(collect_samples[i]))
- for i, (key, dataset) in enumerate(zip(self.keys, self.datasets))
- if len(collect_samples[i]) > 0
- ]
- )
- elif self.shared_collater:
- batch = self.datasets[0].collater([s for _, s in samples])
- else:
- samples_dict = defaultdict(list)
- pad_to_length = (
- defaultdict(int)
- if "pad_to_length" not in extra_args
- else extra_args["pad_to_length"]
- )
- for ds_idx, s in samples:
- pad_to_length["source"] = max(
- pad_to_length["source"], s["source"].size(0)
- )
- if s["target"] is not None:
- pad_to_length["target"] = max(
- pad_to_length["target"], s["target"].size(0)
- )
- samples_dict[ds_idx].append(s)
- batches = [
- self.datasets[i].collater(samples_dict[i], pad_to_length=pad_to_length)
- for i in range(len(self.datasets))
- if len(samples_dict[i]) > 0
- ]
-
- def straight_data(tensors):
- batch = torch.cat(tensors, dim=0)
- return batch
-
- src_lengths = straight_data(
- [b["net_input"]["src_lengths"] for b in batches]
- )
- src_lengths, sort_order = src_lengths.sort(descending=True)
-
- def straight_order(tensors):
- batch = straight_data(tensors)
- return batch.index_select(0, sort_order)
-
- batch = {
- "id": straight_order([b["id"] for b in batches]),
- "nsentences": sum(b["nsentences"] for b in batches),
- "ntokens": sum(b["ntokens"] for b in batches),
- "net_input": {
- "src_tokens": straight_order(
- [b["net_input"]["src_tokens"] for b in batches]
- ),
- "src_lengths": src_lengths,
- },
- "target": straight_order([b["target"] for b in batches])
- if batches[0]["target"] is not None
- else None,
- }
- if "prev_output_tokens" in batches[0]["net_input"]:
- batch["net_input"]["prev_output_tokens"] = straight_order(
- [b["net_input"]["prev_output_tokens"] for b in batches]
- )
- if "src_lang_id" in batches[0]["net_input"]:
- batch["net_input"]["src_lang_id"] = straight_order(
- [b["net_input"]["src_lang_id"] for b in batches]
- )
- if "tgt_lang_id" in batches[0]:
- batch["tgt_lang_id"] = straight_order(
- [b["tgt_lang_id"] for b in batches]
- )
- return batch
-
- @property
- def sizes(self):
- if self._sizes is not None:
- return self._sizes
- start_time = time.time()
- in_sub_dataset_indices = [
- self._cur_indices[
- 0 if i == 0 else self.cumulated_sizes[i - 1] : self.cumulated_sizes[i]
- ]
- for i in range(len(self.datasets))
- ]
- sub_dataset_sizes = [
- d.sizes[indices]
- for d, indices in zip(self.datasets, in_sub_dataset_indices)
- ]
- self._sizes = np.vstack(sub_dataset_sizes)
- logger.info(f"sizes() calling time: {get_time_gap(start_time, time.time())}")
- return self._sizes
-
- def ordered_indices(self):
- if self.shuffle:
- indices = np.random.permutation(len(self))
- else:
- indices = np.arange(len(self))
-
- sizes = self.sizes
- tgt_sizes = sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None
- src_sizes = (
- sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes
- )
-
- # sort by target length, then source length
- if tgt_sizes is not None:
- indices = indices[np.argsort(tgt_sizes[indices], kind="mergesort")]
- sort_indices = indices[np.argsort(src_sizes[indices], kind="mergesort")]
- return sort_indices
-
- def prefetch(self, indices):
- prefetch_indices = [[] for _ in range(len(self.datasets))]
- for i in indices:
- ds_idx, ds_sample_idx = self._get_dataset_and_index(i)
- prefetch_indices[ds_idx].append(ds_sample_idx)
- for i in range(len(prefetch_indices)):
- self.datasets[i].prefetch(prefetch_indices[i])
-
- @property
- def can_reuse_epoch_itr_across_epochs(self):
- return False
-
- def set_epoch(self, epoch):
- super().set_epoch(epoch)
- if epoch == self._cur_epoch:
- # re-enter so return
- return
- for d in self.datasets:
- if hasattr(d, "set_epoch"):
- d.set_epoch(epoch)
- self._cur_epoch = epoch
- self._establish_virtual_datasets()
-
- def _establish_virtual_datasets(self):
- if self.sample_ratios is None and self._cur_indices is not None:
- # not a samping dataset, no need to resample if indices are already established
- return
- self._reset_cached_properties()
-
- start_time = time.time()
- # Generate a weighted sample of indices as a function of the
- # random seed and the current epoch.
- rng = np.random.RandomState(
- [
- int(
- hashlib.sha1(
- str(self.__class__.__name__).encode("utf-8")
- ).hexdigest(),
- 16,
- )
- % (2 ** 32),
- self.seed % (2 ** 32), # global seed
- self._cur_epoch, # epoch index,
- ]
- )
- self._clean_if_not_none(
- [self.cumulated_sizes, self.virtual_size_per_dataset, self._sizes]
- )
- self._sizes = None
-
- indices, cumulated_sizes, virtual_size_per_dataset = self.get_virtual_indices(
- rng, self.datasets, self.sample_ratios, self.virtual_size
- )
- self._cur_indices = indices
- self.cumulated_sizes = cumulated_sizes
- self.virtual_size_per_dataset = virtual_size_per_dataset
-
- raw_sizes = [len(d) for d in self.datasets]
- sampled_sizes = self.virtual_size_per_dataset
- logger.info(
- f"[{self.split}] Raw sizes: {str(dict(zip(self.keys, raw_sizes)))}; "
- f"raw total size: {sum(raw_sizes)}"
- )
- logger.info(
- f"[{self.split}] Resampled sizes: {str(dict(zip(self.keys, sampled_sizes)))}; "
- f"resampled total size: {sum(sampled_sizes)}"
- )
- if self.sample_ratios is not None:
- logger.info(
- f"[{self.split}] Upsampling ratios: {str(dict(zip(self.keys, self.sample_ratios)))}"
- )
- else:
- logger.info(f"[{self.split}] A concat dataset")
- logger.info(
- f"[{self.split}] virtual dataset established time: {get_time_gap(start_time, time.time())}"
- )
-
- def filter_indices_by_size(self, indices, max_sizes):
- """Filter a list of sample indices. Remove those that are longer
- than specified in max_sizes.
-
- Args:
- indices (np.array): original array of sample indices
- max_sizes (int or list[int] or tuple[int]): max sample size,
- can be defined separately for src and tgt (then list or tuple)
-
- Returns:
- np.array: filtered sample array
- list: list of removed indices
- """
- sizes = self.sizes
- tgt_sizes = sizes[:, 1] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else None
- src_sizes = (
- sizes[:, 0] if len(sizes.shape) > 0 and sizes.shape[1] > 1 else sizes
- )
-
- return data_utils.filter_paired_dataset_indices_by_size(
- src_sizes, tgt_sizes, indices, max_sizes
- )
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_backtranslation_dataset.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_backtranslation_dataset.py
deleted file mode 100644
index dffc3b49387dfdc046ea23d7db179377040b7cbc..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/tests/test_backtranslation_dataset.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import unittest
-
-import tests.utils as test_utils
-import torch
-from fairseq.data import (
- BacktranslationDataset,
- LanguagePairDataset,
- TransformEosDataset,
-)
-from fairseq.sequence_generator import SequenceGenerator
-
-
-class TestBacktranslationDataset(unittest.TestCase):
- def setUp(self):
- (
- self.tgt_dict,
- self.w1,
- self.w2,
- self.src_tokens,
- self.src_lengths,
- self.model,
- ) = test_utils.sequence_generator_setup()
-
- dummy_src_samples = self.src_tokens
-
- self.tgt_dataset = test_utils.TestDataset(data=dummy_src_samples)
- self.cuda = torch.cuda.is_available()
-
- def _backtranslation_dataset_helper(
- self,
- remove_eos_from_input_src,
- remove_eos_from_output_src,
- ):
- tgt_dataset = LanguagePairDataset(
- src=self.tgt_dataset,
- src_sizes=self.tgt_dataset.sizes,
- src_dict=self.tgt_dict,
- tgt=None,
- tgt_sizes=None,
- tgt_dict=None,
- )
-
- generator = SequenceGenerator(
- [self.model],
- tgt_dict=self.tgt_dict,
- max_len_a=0,
- max_len_b=200,
- beam_size=2,
- unk_penalty=0,
- )
-
- backtranslation_dataset = BacktranslationDataset(
- tgt_dataset=TransformEosDataset(
- dataset=tgt_dataset,
- eos=self.tgt_dict.eos(),
- # remove eos from the input src
- remove_eos_from_src=remove_eos_from_input_src,
- ),
- src_dict=self.tgt_dict,
- backtranslation_fn=(
- lambda sample: generator.generate([self.model], sample)
- ),
- output_collater=TransformEosDataset(
- dataset=tgt_dataset,
- eos=self.tgt_dict.eos(),
- # if we remove eos from the input src, then we need to add it
- # back to the output tgt
- append_eos_to_tgt=remove_eos_from_input_src,
- remove_eos_from_src=remove_eos_from_output_src,
- ).collater,
- cuda=self.cuda,
- )
- dataloader = torch.utils.data.DataLoader(
- backtranslation_dataset,
- batch_size=2,
- collate_fn=backtranslation_dataset.collater,
- )
- backtranslation_batch_result = next(iter(dataloader))
-
- eos, pad, w1, w2 = self.tgt_dict.eos(), self.tgt_dict.pad(), self.w1, self.w2
-
- # Note that we sort by src_lengths and add left padding, so actually
- # ids will look like: [1, 0]
- expected_src = torch.LongTensor([[w1, w2, w1, eos], [pad, pad, w1, eos]])
- if remove_eos_from_output_src:
- expected_src = expected_src[:, :-1]
- expected_tgt = torch.LongTensor([[w1, w2, eos], [w1, w2, eos]])
- generated_src = backtranslation_batch_result["net_input"]["src_tokens"]
- tgt_tokens = backtranslation_batch_result["target"]
-
- self.assertTensorEqual(expected_src, generated_src)
- self.assertTensorEqual(expected_tgt, tgt_tokens)
-
- def test_backtranslation_dataset_no_eos_in_output_src(self):
- self._backtranslation_dataset_helper(
- remove_eos_from_input_src=False,
- remove_eos_from_output_src=True,
- )
-
- def test_backtranslation_dataset_with_eos_in_output_src(self):
- self._backtranslation_dataset_helper(
- remove_eos_from_input_src=False,
- remove_eos_from_output_src=False,
- )
-
- def test_backtranslation_dataset_no_eos_in_input_src(self):
- self._backtranslation_dataset_helper(
- remove_eos_from_input_src=True,
- remove_eos_from_output_src=False,
- )
-
- def assertTensorEqual(self, t1, t2):
- self.assertEqual(t1.size(), t2.size(), "size mismatch")
- self.assertEqual(t1.ne(t2).long().sum(), 0)
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/multilingual/data_scripts/download_af_xh.sh b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/multilingual/data_scripts/download_af_xh.sh
deleted file mode 100644
index a78fbbbbccb6f6ae005a1f03b97f083a2d958ebe..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/multilingual/data_scripts/download_af_xh.sh
+++ /dev/null
@@ -1,164 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# set -x -e
-
-if [ -z $WORKDIR_ROOT ] ;
-then
- echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..."
- exit
-fi
-
-
-# put intermediate files
-TMP_DIR=$WORKDIR_ROOT/temp/af_xhv2
-# output {train,valid,test} files to dest
-DEST=${WORKDIR_ROOT}/ML50/raw
-
-
-
-ROOT=${WORKDIR_ROOT}
-UTILS=$PWD/utils
-TMX2CORPUS="${UTILS}/tmx2corpus"
-TMX_TOOL="python ${TMX2CORPUS}/tmx2corpus.py"
-
-mkdir -p $TMP_DIR
-mkdir -p $DEST
-mkdir -p $UTILS
-
-function download_opus(){
- src=$1
- tgt=$2
- subset=$3
- ulr=$4
-
- mkdir extract_$subset.$src-$tgt
- pushd extract_$subset.$src-$tgt
- if [ ! -f "$subset.$src-$tgt.tmx.gz" ]; then
- wget $url -O "$subset.$src-$tgt.tmx.gz"
- gzip -d "$subset.$src-$tgt.tmx.gz"
- f=$subset.$src-$tgt.tmx
- $TMX_TOOL $f
- mv bitext.$src ../$subset.$src-$tgt.$src
- mv bitext.$tgt ../$subset.$src-$tgt.$tgt
- fi
- popd
-}
-
-function concat_subsets(){
- src=$1
- tgt=$2
- subsets=$3
- src_train=raw_train.$src-$tgt.$src
- tgt_train=raw_train.$src-$tgt.$tgt
- > $src_train
- > $tgt_train
- for subset in $subsets; do
- cat $subset.$src-$tgt.$src >> $src_train
- cat $subset.$src-$tgt.$tgt >> $tgt_train
- done
-}
-
-
-
-function get_seeded_random()
-{
- seed="$1"
- openssl enc -aes-256-ctr -pass pass:"$seed" -nosalt \
- /dev/null
-}
-
-function split_train_valid(){
- src=$1
- tgt=$2
- raw_src_train=raw_train.$src-$tgt.$src
- raw_tgt_train=raw_train.$src-$tgt.$tgt
-
- shuf --random-source=<(get_seeded_random 43) $raw_src_train > shuffled.$src-$tgt.$src
- shuf --random-source=<(get_seeded_random 43) $raw_tgt_train > shuffled.$src-$tgt.$tgt
-
- head -n 1500 shuffled.$src-$tgt.$src > valid.$src-$tgt.$src
- head -n 1500 shuffled.$src-$tgt.$tgt > valid.$src-$tgt.$tgt
-
- tail +1501 shuffled.$src-$tgt.$src > train.$src-$tgt.$src
- tail +1501 shuffled.$src-$tgt.$tgt > train.$src-$tgt.$tgt
-}
-
-function copy2dst(){
- lsrc=$1
- ltgt=$2
- src=${lsrc:0:2}
- tgt=${ltgt:0:2}
-
-
- cp valid.$src-$tgt.$src $DEST/valid.$lsrc-$ltgt.$lsrc
- cp valid.$src-$tgt.$tgt $DEST/valid.$lsrc-$ltgt.$ltgt
-
- cp train.$src-$tgt.$src $DEST/train.$lsrc-$ltgt.$lsrc
- cp train.$src-$tgt.$tgt $DEST/train.$lsrc-$ltgt.$ltgt
-}
-
-
-
-
-#for xh-en
-declare -A xh_en_urls
-xh_en_urls=(
- [Tatoeba]=https://object.pouta.csc.fi/OPUS-Tatoeba/v20190709/tmx/en-xh.tmx.gz
- [wikimedia]=https://object.pouta.csc.fi/OPUS-wikimedia/v20190628/tmx/en-xh.tmx.gz
- [memat]=https://object.pouta.csc.fi/OPUS-memat/v1/tmx/en-xh.tmx.gz
- [uedin]=https://object.pouta.csc.fi/OPUS-bible-uedin/v1/tmx/en-xh.tmx.gz
- [GNOME]=https://object.pouta.csc.fi/OPUS-GNOME/v1/tmx/en-xh.tmx.gz
- [XhosaNavy]=https://object.pouta.csc.fi/OPUS-XhosaNavy/v1/tmx/en-xh.tmx.gz
- [KDE4]=https://object.pouta.csc.fi/OPUS-KDE4/v2/tmx/en-xh.tmx.gz
- [Ubuntu]=https://object.pouta.csc.fi/OPUS-Ubuntu/v14.10/tmx/en-xh.tmx.gz
-)
-
-mkdir $TMP_DIR/xh-en
-pushd $TMP_DIR/xh-en
-for k in "${!xh_en_urls[@]}"
-do
- name=$k
- url=${xh_en_urls[$k]}
- echo "$name: $url"
- download_opus xh en $name $ulr
-done
-concat_subsets xh en "${!xh_en_urls[@]}"
-split_train_valid xh en
-copy2dst xh_ZA en_XX
-popd
-
-
-##
-#for af-en
-declare -A af_en_urls
-af_en_urls=(
- [Tatoeba]=https://object.pouta.csc.fi/OPUS-Tatoeba/v20190709/tmx/af-en.tmx.gz
- [uedin]=https://object.pouta.csc.fi/OPUS-bible-uedin/v1/tmx/af-en.tmx.gz
- [GNOME]=https://object.pouta.csc.fi/OPUS-GNOME/v1/tmx/af-en.tmx.gz
- [QED]=https://object.pouta.csc.fi/OPUS-QED/v2.0a/tmx/af-en.tmx.gz
- [KDE4]=https://object.pouta.csc.fi/OPUS-KDE4/v2/tmx/af-en.tmx.gz
- [OpenSubtitles]=https://object.pouta.csc.fi/OPUS-OpenSubtitles/v2018/tmx/af-en.tmx.gz
- [SPC]=https://object.pouta.csc.fi/OPUS-SPC/v1/tmx/af-en.tmx.gz
- [Ubuntu]=https://object.pouta.csc.fi/OPUS-Ubuntu/v14.10/tmx/af-en.tmx.gz
-)
-
-mkdir $TMP_DIR/af-en
-pushd $TMP_DIR/af-en
-for k in "${!af_en_urls[@]}"
-do
- name=$k
- url=${af_en_urls[$k]}
- echo "$name: $url"
- download_opus af en $name $ulr
-done
-concat_subsets af en "${!af_en_urls[@]}"
-split_train_valid af en
-copy2dst af_ZA en_XX
-popd
-
-
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/criterions/tacotron2_loss.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/criterions/tacotron2_loss.py
deleted file mode 100644
index 8c7b655c8c52f8fa478b4568850ec8f741dab78e..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/criterions/tacotron2_loss.py
+++ /dev/null
@@ -1,210 +0,0 @@
-# Copyright (c) 2017-present, Facebook, Inc.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the LICENSE file in
-# the root directory of this source tree. An additional grant of patent rights
-# can be found in the PATENTS file in the same directory.
-
-import logging
-from typing import Any, Dict, List
-from functools import lru_cache
-from dataclasses import dataclass, field
-
-import torch
-from omegaconf import II
-
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-from fairseq.data.data_utils import lengths_to_mask
-import torch.nn.functional as F
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class Tacotron2CriterionConfig(FairseqDataclass):
- bce_pos_weight: float = field(
- default=1.0,
- metadata={"help": "weight of positive examples for BCE loss"},
- )
- n_frames_per_step: int = field(
- default=0,
- metadata={"help": "Number of frames per decoding step"},
- )
- use_guided_attention_loss: bool = field(
- default=False,
- metadata={"help": "use guided attention loss"},
- )
- guided_attention_loss_sigma: float = field(
- default=0.4,
- metadata={"help": "weight of positive examples for BCE loss"},
- )
- ctc_weight: float = field(
- default=0.0, metadata={"help": "weight for CTC loss"}
- )
- sentence_avg: bool = II("optimization.sentence_avg")
-
-
-class GuidedAttentionLoss(torch.nn.Module):
- """
- Efficiently Trainable Text-to-Speech System Based on Deep Convolutional
- Networks with Guided Attention (https://arxiv.org/abs/1710.08969)
- """
-
- def __init__(self, sigma):
- super().__init__()
- self.sigma = sigma
-
- @staticmethod
- @lru_cache(maxsize=8)
- def _get_weight(s_len, t_len, sigma):
- grid_x, grid_y = torch.meshgrid(torch.arange(t_len), torch.arange(s_len))
- grid_x = grid_x.to(s_len.device)
- grid_y = grid_y.to(s_len.device)
- w = (grid_y.float() / s_len - grid_x.float() / t_len) ** 2
- return 1.0 - torch.exp(-w / (2 * (sigma ** 2)))
-
- def _get_weights(self, src_lens, tgt_lens):
- bsz, max_s_len, max_t_len = len(src_lens), max(src_lens), max(tgt_lens)
- weights = torch.zeros((bsz, max_t_len, max_s_len))
- for i, (s_len, t_len) in enumerate(zip(src_lens, tgt_lens)):
- weights[i, :t_len, :s_len] = self._get_weight(s_len, t_len,
- self.sigma)
- return weights
-
- @staticmethod
- def _get_masks(src_lens, tgt_lens):
- in_masks = lengths_to_mask(src_lens)
- out_masks = lengths_to_mask(tgt_lens)
- return out_masks.unsqueeze(2) & in_masks.unsqueeze(1)
-
- def forward(self, attn, src_lens, tgt_lens, reduction="mean"):
- weights = self._get_weights(src_lens, tgt_lens).to(attn.device)
- masks = self._get_masks(src_lens, tgt_lens).to(attn.device)
- loss = (weights * attn.transpose(1, 2)).masked_select(masks)
- loss = torch.sum(loss) if reduction == "sum" else torch.mean(loss)
- return loss
-
-
-@register_criterion("tacotron2", dataclass=Tacotron2CriterionConfig)
-class Tacotron2Criterion(FairseqCriterion):
- def __init__(self, task, sentence_avg, n_frames_per_step,
- use_guided_attention_loss, guided_attention_loss_sigma,
- bce_pos_weight, ctc_weight):
- super().__init__(task)
- self.sentence_avg = sentence_avg
- self.n_frames_per_step = n_frames_per_step
- self.bce_pos_weight = bce_pos_weight
-
- self.guided_attn = None
- if use_guided_attention_loss:
- self.guided_attn = GuidedAttentionLoss(guided_attention_loss_sigma)
- self.ctc_weight = ctc_weight
-
- def forward(self, model, sample, reduction="mean"):
- bsz, max_len, _ = sample["target"].size()
- feat_tgt = sample["target"]
- feat_len = sample["target_lengths"].view(bsz, 1).expand(-1, max_len)
- eos_tgt = torch.arange(max_len).to(sample["target"].device)
- eos_tgt = eos_tgt.view(1, max_len).expand(bsz, -1)
- eos_tgt = (eos_tgt == (feat_len - 1)).float()
- src_tokens = sample["net_input"]["src_tokens"]
- src_lens = sample["net_input"]["src_lengths"]
- tgt_lens = sample["target_lengths"]
-
- feat_out, eos_out, extra = model(
- src_tokens=src_tokens,
- src_lengths=src_lens,
- prev_output_tokens=sample["net_input"]["prev_output_tokens"],
- incremental_state=None,
- target_lengths=tgt_lens,
- speaker=sample["speaker"]
- )
-
- l1_loss, mse_loss, eos_loss = self.compute_loss(
- extra["feature_out"], feat_out, eos_out, feat_tgt, eos_tgt,
- tgt_lens, reduction,
- )
- attn_loss = torch.tensor(0.).type_as(l1_loss)
- if self.guided_attn is not None:
- attn_loss = self.guided_attn(extra['attn'], src_lens, tgt_lens, reduction)
- ctc_loss = torch.tensor(0.).type_as(l1_loss)
- if self.ctc_weight > 0.:
- net_output = (feat_out, eos_out, extra)
- lprobs = model.get_normalized_probs(net_output, log_probs=True)
- lprobs = lprobs.transpose(0, 1) # T x B x C
- src_mask = lengths_to_mask(src_lens)
- src_tokens_flat = src_tokens.masked_select(src_mask)
- ctc_loss = F.ctc_loss(
- lprobs, src_tokens_flat, tgt_lens, src_lens,
- reduction=reduction, zero_infinity=True
- ) * self.ctc_weight
- loss = l1_loss + mse_loss + eos_loss + attn_loss + ctc_loss
-
- sample_size = sample["nsentences"] if self.sentence_avg \
- else sample["ntokens"]
- logging_output = {
- "loss": utils.item(loss.data),
- "ntokens": sample["ntokens"],
- "nsentences": sample["nsentences"],
- "sample_size": sample_size,
- "l1_loss": utils.item(l1_loss.data),
- "mse_loss": utils.item(mse_loss.data),
- "eos_loss": utils.item(eos_loss.data),
- "attn_loss": utils.item(attn_loss.data),
- "ctc_loss": utils.item(ctc_loss.data),
- }
- return loss, sample_size, logging_output
-
- def compute_loss(self, feat_out, feat_out_post, eos_out, feat_tgt,
- eos_tgt, tgt_lens, reduction="mean"):
- mask = lengths_to_mask(tgt_lens)
- _eos_out = eos_out[mask].squeeze()
- _eos_tgt = eos_tgt[mask]
- _feat_tgt = feat_tgt[mask]
- _feat_out = feat_out[mask]
- _feat_out_post = feat_out_post[mask]
-
- l1_loss = (
- F.l1_loss(_feat_out, _feat_tgt, reduction=reduction) +
- F.l1_loss(_feat_out_post, _feat_tgt, reduction=reduction)
- )
- mse_loss = (
- F.mse_loss(_feat_out, _feat_tgt, reduction=reduction) +
- F.mse_loss(_feat_out_post, _feat_tgt, reduction=reduction)
- )
- eos_loss = F.binary_cross_entropy_with_logits(
- _eos_out, _eos_tgt, pos_weight=torch.tensor(self.bce_pos_weight),
- reduction=reduction
- )
- return l1_loss, mse_loss, eos_loss
-
- @classmethod
- def reduce_metrics(cls, logging_outputs: List[Dict[str, Any]]) -> None:
- ns = [log.get("sample_size", 0) for log in logging_outputs]
- ntot = sum(ns)
- ws = [n / (ntot + 1e-8) for n in ns]
- for key in ["loss", "l1_loss", "mse_loss", "eos_loss", "attn_loss", "ctc_loss"]:
- vals = [log.get(key, 0) for log in logging_outputs]
- val = sum(val * w for val, w in zip(vals, ws))
- metrics.log_scalar(key, val, ntot, round=3)
- metrics.log_scalar("sample_size", ntot, len(logging_outputs))
-
- # inference metrics
- if "targ_frames" not in logging_outputs[0]:
- return
- n = sum(log.get("targ_frames", 0) for log in logging_outputs)
- for key, new_key in [
- ("mcd_loss", "mcd_loss"),
- ("pred_frames", "pred_ratio"),
- ("nins", "ins_rate"),
- ("ndel", "del_rate"),
- ]:
- val = sum(log.get(key, 0) for log in logging_outputs)
- metrics.log_scalar(new_key, val / n, n, round=3)
-
- @staticmethod
- def logging_outputs_can_be_summed() -> bool:
- return False
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/data_utils.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/data_utils.py
deleted file mode 100644
index b3de57681e0fb6b026003eff19f7745caf6799d3..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/data/data_utils.py
+++ /dev/null
@@ -1,595 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-try:
- from collections.abc import Iterable
-except ImportError:
- from collections import Iterable
-import contextlib
-import itertools
-import logging
-import re
-import warnings
-from typing import Optional, Tuple
-
-import numpy as np
-import torch
-
-from fairseq.file_io import PathManager
-from fairseq import utils
-import os
-
-logger = logging.getLogger(__name__)
-
-
-def infer_language_pair(path):
- """Infer language pair from filename: .-.(...).idx"""
- src, dst = None, None
- for filename in PathManager.ls(path):
- parts = filename.split(".")
- if len(parts) >= 3 and len(parts[1].split("-")) == 2:
- return parts[1].split("-")
- return src, dst
-
-
-def collate_tokens(
- values,
- pad_idx,
- eos_idx=None,
- left_pad=False,
- move_eos_to_beginning=False,
- pad_to_length=None,
- pad_to_multiple=1,
- pad_to_bsz=None,
-):
- """Convert a list of 1d tensors into a padded 2d tensor."""
- size = max(v.size(0) for v in values)
- size = size if pad_to_length is None else max(size, pad_to_length)
- if pad_to_multiple != 1 and size % pad_to_multiple != 0:
- size = int(((size - 0.1) // pad_to_multiple + 1) * pad_to_multiple)
-
- batch_size = len(values) if pad_to_bsz is None else max(len(values), pad_to_bsz)
- res = values[0].new(batch_size, size).fill_(pad_idx)
-
- def copy_tensor(src, dst):
- assert dst.numel() == src.numel()
- if move_eos_to_beginning:
- if eos_idx is None:
- # if no eos_idx is specified, then use the last token in src
- dst[0] = src[-1]
- else:
- dst[0] = eos_idx
- dst[1:] = src[:-1]
- else:
- dst.copy_(src)
-
- for i, v in enumerate(values):
- copy_tensor(v, res[i][size - len(v) :] if left_pad else res[i][: len(v)])
- return res
-
-def load_indexed_dataset(
- path, dictionary=None, dataset_impl=None, combine=False, default="cached"
-):
- """A helper function for loading indexed datasets.
-
- Args:
- path (str): path to indexed dataset (e.g., 'data-bin/train')
- dictionary (~fairseq.data.Dictionary): data dictionary
- dataset_impl (str, optional): which dataset implementation to use. If
- not provided, it will be inferred automatically. For legacy indexed
- data we use the 'cached' implementation by default.
- combine (bool, optional): automatically load and combine multiple
- datasets. For example, if *path* is 'data-bin/train', then we will
- combine 'data-bin/train', 'data-bin/train1', ... and return a
- single ConcatDataset instance.
- """
- import fairseq.data.indexed_dataset as indexed_dataset
- from fairseq.data.concat_dataset import ConcatDataset
-
- datasets = []
- for k in itertools.count():
- path_k = path + (str(k) if k > 0 else "")
- try:
- path_k = indexed_dataset.get_indexed_dataset_to_local(path_k)
- except Exception as e:
- if "StorageException: [404] Path not found" in str(e):
- logger.warning(f"path_k: {e} not found")
- else:
- raise e
-
- dataset_impl_k = dataset_impl
- if dataset_impl_k is None:
- dataset_impl_k = indexed_dataset.infer_dataset_impl(path_k)
- dataset = indexed_dataset.make_dataset(
- path_k,
- impl=dataset_impl_k or default,
- fix_lua_indexing=True,
- dictionary=dictionary,
- )
- if dataset is None:
- break
- logger.info("loaded {:,} examples from: {}".format(len(dataset), path_k))
- datasets.append(dataset)
- if not combine:
- break
- if len(datasets) == 0:
- return None
- elif len(datasets) == 1:
- return datasets[0]
- else:
- return ConcatDataset(datasets)
-
-
-@contextlib.contextmanager
-def numpy_seed(seed, *addl_seeds):
- """Context manager which seeds the NumPy PRNG with the specified seed and
- restores the state afterward"""
- if seed is None:
- yield
- return
- if len(addl_seeds) > 0:
- seed = int(hash((seed, *addl_seeds)) % 1e6)
- state = np.random.get_state()
- np.random.seed(seed)
- try:
- yield
- finally:
- np.random.set_state(state)
-
-
-def collect_filtered(function, iterable, filtered):
- """
- Similar to :func:`filter` but collects filtered elements in ``filtered``.
-
- Args:
- function (callable): function that returns ``False`` for elements that
- should be filtered
- iterable (iterable): iterable to filter
- filtered (list): list to store filtered elements
- """
- for el in iterable:
- if function(el):
- yield el
- else:
- filtered.append(el)
-
-
-def _filter_by_size_dynamic(indices, size_fn, max_positions, raise_exception=False):
- def compare_leq(a, b):
- return a <= b if not isinstance(a, tuple) else max(a) <= b
-
- def check_size(idx):
- if isinstance(max_positions, float) or isinstance(max_positions, int):
- return size_fn(idx) <= max_positions
- elif isinstance(max_positions, dict):
- idx_size = size_fn(idx)
- assert isinstance(idx_size, dict)
- intersect_keys = set(max_positions.keys()) & set(idx_size.keys())
- return all(
- all(
- a is None or b is None or a <= b
- for a, b in zip(idx_size[key], max_positions[key])
- )
- for key in intersect_keys
- )
- else:
- # For MultiCorpusSampledDataset, will generalize it later
- if not isinstance(size_fn(idx), Iterable):
- return all(size_fn(idx) <= b for b in max_positions)
- return all(
- a is None or b is None or a <= b
- for a, b in zip(size_fn(idx), max_positions)
- )
-
- ignored = []
- itr = collect_filtered(check_size, indices, ignored)
- indices = np.fromiter(itr, dtype=np.int64, count=-1)
- return indices, ignored
-
-
-def filter_by_size(indices, dataset, max_positions, raise_exception=False):
- """
- [deprecated] Filter indices based on their size.
- Use `FairseqDataset::filter_indices_by_size` instead.
-
- Args:
- indices (List[int]): ordered list of dataset indices
- dataset (FairseqDataset): fairseq dataset instance
- max_positions (tuple): filter elements larger than this size.
- Comparisons are done component-wise.
- raise_exception (bool, optional): if ``True``, raise an exception if
- any elements are filtered (default: False).
- """
- warnings.warn(
- "data_utils.filter_by_size is deprecated. "
- "Use `FairseqDataset::filter_indices_by_size` instead.",
- stacklevel=2,
- )
- if isinstance(max_positions, float) or isinstance(max_positions, int):
- if hasattr(dataset, "sizes") and isinstance(dataset.sizes, np.ndarray):
- ignored = indices[dataset.sizes[indices] > max_positions].tolist()
- indices = indices[dataset.sizes[indices] <= max_positions]
- elif (
- hasattr(dataset, "sizes")
- and isinstance(dataset.sizes, list)
- and len(dataset.sizes) == 1
- ):
- ignored = indices[dataset.sizes[0][indices] > max_positions].tolist()
- indices = indices[dataset.sizes[0][indices] <= max_positions]
- else:
- indices, ignored = _filter_by_size_dynamic(
- indices, dataset.size, max_positions
- )
- else:
- indices, ignored = _filter_by_size_dynamic(indices, dataset.size, max_positions)
-
- if len(ignored) > 0 and raise_exception:
- raise Exception(
- (
- "Size of sample #{} is invalid (={}) since max_positions={}, "
- "skip this example with --skip-invalid-size-inputs-valid-test"
- ).format(ignored[0], dataset.size(ignored[0]), max_positions)
- )
- if len(ignored) > 0:
- logger.warning(
- (
- "{} samples have invalid sizes and will be skipped, "
- "max_positions={}, first few sample ids={}"
- ).format(len(ignored), max_positions, ignored[:10])
- )
- return indices
-
-
-def filter_paired_dataset_indices_by_size(src_sizes, tgt_sizes, indices, max_sizes):
- """Filter a list of sample indices. Remove those that are longer
- than specified in max_sizes.
-
- Args:
- indices (np.array): original array of sample indices
- max_sizes (int or list[int] or tuple[int]): max sample size,
- can be defined separately for src and tgt (then list or tuple)
-
- Returns:
- np.array: filtered sample array
- list: list of removed indices
- """
- if max_sizes is None:
- return indices, []
- if type(max_sizes) in (int, float):
- max_src_size, max_tgt_size = max_sizes, max_sizes
- else:
- max_src_size, max_tgt_size = max_sizes
- if tgt_sizes is None:
- ignored = indices[src_sizes[indices] > max_src_size]
- else:
- ignored = indices[
- (src_sizes[indices] > max_src_size) | (tgt_sizes[indices] > max_tgt_size)
- ]
- if len(ignored) > 0:
- if tgt_sizes is None:
- indices = indices[src_sizes[indices] <= max_src_size]
- else:
- indices = indices[
- (src_sizes[indices] <= max_src_size)
- & (tgt_sizes[indices] <= max_tgt_size)
- ]
- return indices, ignored.tolist()
-
-
-def batch_by_size(
- indices,
- num_tokens_fn,
- num_tokens_vec=None,
- max_tokens=None,
- max_sentences=None,
- required_batch_size_multiple=1,
- fixed_shapes=None,
-):
- """
- Yield mini-batches of indices bucketed by size. Batches may contain
- sequences of different lengths.
-
- Args:
- indices (List[int]): ordered list of dataset indices
- num_tokens_fn (callable): function that returns the number of tokens at
- a given index
- num_tokens_vec (List[int], optional): precomputed vector of the number
- of tokens for each index in indices (to enable faster batch generation)
- max_tokens (int, optional): max number of tokens in each batch
- (default: None).
- max_sentences (int, optional): max number of sentences in each
- batch (default: None).
- required_batch_size_multiple (int, optional): require batch size to
- be less than N or a multiple of N (default: 1).
- fixed_shapes (List[Tuple[int, int]], optional): if given, batches will
- only be created with the given shapes. *max_sentences* and
- *required_batch_size_multiple* will be ignored (default: None).
- """
- try:
- from fairseq.data.data_utils_fast import (
- batch_by_size_fn,
- batch_by_size_vec,
- batch_fixed_shapes_fast,
- )
- except ImportError:
- raise ImportError(
- "Please build Cython components with: "
- "`python setup.py build_ext --inplace`"
- )
- except ValueError:
- raise ValueError(
- "Please build (or rebuild) Cython components with `python setup.py build_ext --inplace`."
- )
-
- # added int() to avoid TypeError: an integer is required
- max_tokens = (
- int(max_tokens) if max_tokens is not None else -1
- )
- max_sentences = max_sentences if max_sentences is not None else -1
- bsz_mult = required_batch_size_multiple
-
- if not isinstance(indices, np.ndarray):
- indices = np.fromiter(indices, dtype=np.int64, count=-1)
-
- if num_tokens_vec is not None and not isinstance(num_tokens_vec, np.ndarray):
- num_tokens_vec = np.fromiter(num_tokens_vec, dtype=np.int64, count=-1)
-
- if fixed_shapes is None:
- if num_tokens_vec is None:
- return batch_by_size_fn(
- indices,
- num_tokens_fn,
- max_tokens,
- max_sentences,
- bsz_mult,
- )
- else:
- return batch_by_size_vec(
- indices,
- num_tokens_vec,
- max_tokens,
- max_sentences,
- bsz_mult,
- )
-
- else:
- fixed_shapes = np.array(fixed_shapes, dtype=np.int64)
- sort_order = np.lexsort(
- [
- fixed_shapes[:, 1].argsort(), # length
- fixed_shapes[:, 0].argsort(), # bsz
- ]
- )
- fixed_shapes_sorted = fixed_shapes[sort_order]
- return batch_fixed_shapes_fast(indices, num_tokens_fn, fixed_shapes_sorted)
-
-
-def post_process(sentence: str, symbol: str):
- if symbol == "sentencepiece":
- sentence = sentence.replace(" ", "").replace("\u2581", " ").strip()
- elif symbol == "wordpiece":
- sentence = sentence.replace(" ", "").replace("_", " ").strip()
- elif symbol == "letter":
- sentence = sentence.replace(" ", "").replace("|", " ").strip()
- elif symbol == "silence":
- import re
- sentence = sentence.replace("", "")
- sentence = re.sub(' +', ' ', sentence).strip()
- elif symbol == "_EOW":
- sentence = sentence.replace(" ", "").replace("_EOW", " ").strip()
- elif symbol in {"subword_nmt", "@@ ", "@@"}:
- if symbol == "subword_nmt":
- symbol = "@@ "
- sentence = (sentence + " ").replace(symbol, "").rstrip()
- elif symbol == "none":
- pass
- elif symbol is not None:
- raise NotImplementedError(f"Unknown post_process option: {symbol}")
- return sentence
-
-
-def compute_mask_indices(
- shape: Tuple[int, int],
- padding_mask: Optional[torch.Tensor],
- mask_prob: float,
- mask_length: int,
- mask_type: str = "static",
- mask_other: float = 0.0,
- min_masks: int = 0,
- no_overlap: bool = False,
- min_space: int = 0,
-) -> np.ndarray:
- """
- Computes random mask spans for a given shape
-
- Args:
- shape: the the shape for which to compute masks.
- should be of size 2 where first element is batch size and 2nd is timesteps
- padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements
- mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by
- number of timesteps divided by length of mask span to mask approximately this percentage of all elements.
- however due to overlaps, the actual number will be smaller (unless no_overlap is True)
- mask_type: how to compute mask lengths
- static = fixed size
- uniform = sample from uniform distribution [mask_other, mask_length*2]
- normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element
- poisson = sample from possion distribution with lambda = mask length
- min_masks: minimum number of masked spans
- no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping
- min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans
- """
-
- bsz, all_sz = shape
- mask = np.full((bsz, all_sz), False)
-
- all_num_mask = int(
- # add a random number for probabilistic rounding
- mask_prob * all_sz / float(mask_length)
- + np.random.rand()
- )
-
- all_num_mask = max(min_masks, all_num_mask)
-
- mask_idcs = []
- for i in range(bsz):
- if padding_mask is not None:
- sz = all_sz - padding_mask[i].long().sum().item()
- num_mask = int(
- # add a random number for probabilistic rounding
- mask_prob * sz / float(mask_length)
- + np.random.rand()
- )
- num_mask = max(min_masks, num_mask)
- else:
- sz = all_sz
- num_mask = all_num_mask
-
- if mask_type == "static":
- lengths = np.full(num_mask, mask_length)
- elif mask_type == "uniform":
- lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask)
- elif mask_type == "normal":
- lengths = np.random.normal(mask_length, mask_other, size=num_mask)
- lengths = [max(1, int(round(x))) for x in lengths]
- elif mask_type == "poisson":
- lengths = np.random.poisson(mask_length, size=num_mask)
- lengths = [int(round(x)) for x in lengths]
- else:
- raise Exception("unknown mask selection " + mask_type)
-
- if sum(lengths) == 0:
- lengths[0] = min(mask_length, sz - 1)
-
- if no_overlap:
- mask_idc = []
-
- def arrange(s, e, length, keep_length):
- span_start = np.random.randint(s, e - length)
- mask_idc.extend(span_start + i for i in range(length))
-
- new_parts = []
- if span_start - s - min_space >= keep_length:
- new_parts.append((s, span_start - min_space + 1))
- if e - span_start - keep_length - min_space > keep_length:
- new_parts.append((span_start + length + min_space, e))
- return new_parts
-
- parts = [(0, sz)]
- min_length = min(lengths)
- for length in sorted(lengths, reverse=True):
- lens = np.fromiter(
- (e - s if e - s >= length + min_space else 0 for s, e in parts),
- np.int,
- )
- l_sum = np.sum(lens)
- if l_sum == 0:
- break
- probs = lens / np.sum(lens)
- c = np.random.choice(len(parts), p=probs)
- s, e = parts.pop(c)
- parts.extend(arrange(s, e, length, min_length))
- mask_idc = np.asarray(mask_idc)
- else:
- min_len = min(lengths)
- if sz - min_len <= num_mask:
- min_len = sz - num_mask - 1
-
- mask_idc = np.random.choice(sz - min_len, num_mask, replace=False)
-
- mask_idc = np.asarray(
- [
- mask_idc[j] + offset
- for j in range(len(mask_idc))
- for offset in range(lengths[j])
- ]
- )
-
- mask_idcs.append(np.unique(mask_idc[mask_idc < sz]))
-
- min_len = min([len(m) for m in mask_idcs])
- for i, mask_idc in enumerate(mask_idcs):
- if len(mask_idc) > min_len:
- mask_idc = np.random.choice(mask_idc, min_len, replace=False)
- mask[i, mask_idc] = True
-
- return mask
-
-
-def get_mem_usage():
- try:
- import psutil
-
- mb = 1024 * 1024
- return f"used={psutil.virtual_memory().used / mb}Mb; avail={psutil.virtual_memory().available / mb}Mb"
- except ImportError:
- return "N/A"
-
-
-# lens: torch.LongTensor
-# returns: torch.BoolTensor
-def lengths_to_padding_mask(lens):
- bsz, max_lens = lens.size(0), torch.max(lens).item()
- mask = torch.arange(max_lens).to(lens.device).view(1, max_lens)
- mask = mask.expand(bsz, -1) >= lens.view(bsz, 1).expand(-1, max_lens)
- return mask
-
-
-# lens: torch.LongTensor
-# returns: torch.BoolTensor
-def lengths_to_mask(lens):
- return ~lengths_to_padding_mask(lens)
-
-
-def get_buckets(sizes, num_buckets):
- buckets = np.unique(
- np.percentile(
- sizes,
- np.linspace(0, 100, num_buckets + 1),
- interpolation='lower',
- )[1:]
- )
- return buckets
-
-
-def get_bucketed_sizes(orig_sizes, buckets):
- sizes = np.copy(orig_sizes)
- assert np.min(sizes) >= 0
- start_val = -1
- for end_val in buckets:
- mask = (sizes > start_val) & (sizes <= end_val)
- sizes[mask] = end_val
- start_val = end_val
- return sizes
-
-
-
-def _find_extra_valid_paths(dataset_path: str) -> set:
- paths = utils.split_paths(dataset_path)
- all_valid_paths = set()
- for sub_dir in paths:
- contents = PathManager.ls(sub_dir)
- valid_paths = [c for c in contents if re.match("valid*[0-9].*", c) is not None]
- all_valid_paths |= {os.path.basename(p) for p in valid_paths}
- # Remove .bin, .idx etc
- roots = {os.path.splitext(p)[0] for p in all_valid_paths}
- return roots
-
-
-def raise_if_valid_subsets_unintentionally_ignored(train_cfg) -> None:
- """Raises if there are paths matching 'valid*[0-9].*' which are not combined or ignored."""
- if (
- train_cfg.dataset.ignore_unused_valid_subsets
- or train_cfg.dataset.combine_valid_subsets
- or train_cfg.dataset.disable_validation
- or not hasattr(train_cfg.task, "data")
- ):
- return
- other_paths = _find_extra_valid_paths(train_cfg.task.data)
- specified_subsets = train_cfg.dataset.valid_subset.split(",")
- ignored_paths = [p for p in other_paths if p not in specified_subsets]
- if ignored_paths:
- advice = "Set --combine-val to combine them or --ignore-unused-valid-subsets to ignore them."
- msg = f"Valid paths {ignored_paths} will be ignored. {advice}"
- raise ValueError(msg)
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/noisychannel/rerank_generate.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/noisychannel/rerank_generate.py
deleted file mode 100644
index daeeae059a677a9fcd7c370be087f1f5c189bc52..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/noisychannel/rerank_generate.py
+++ /dev/null
@@ -1,397 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Generate n-best translations using a trained model.
-"""
-
-import os
-import subprocess
-from contextlib import redirect_stdout
-
-from fairseq import options
-from fairseq_cli import generate, preprocess
-
-from examples.noisychannel import rerank_options, rerank_utils
-
-
-def gen_and_reprocess_nbest(args):
- if args.score_dict_dir is None:
- args.score_dict_dir = args.data
- if args.prefix_len is not None:
- assert (
- args.right_to_left1 is False
- ), "prefix length not compatible with right to left models"
- assert (
- args.right_to_left2 is False
- ), "prefix length not compatible with right to left models"
-
- if args.nbest_list is not None:
- assert args.score_model2 is None
-
- if args.backwards1:
- scorer1_src = args.target_lang
- scorer1_tgt = args.source_lang
- else:
- scorer1_src = args.source_lang
- scorer1_tgt = args.target_lang
-
- store_data = (
- os.path.join(os.path.dirname(__file__)) + "/rerank_data/" + args.data_dir_name
- )
- if not os.path.exists(store_data):
- os.makedirs(store_data)
-
- (
- pre_gen,
- left_to_right_preprocessed_dir,
- right_to_left_preprocessed_dir,
- backwards_preprocessed_dir,
- lm_preprocessed_dir,
- ) = rerank_utils.get_directories(
- args.data_dir_name,
- args.num_rescore,
- args.gen_subset,
- args.gen_model_name,
- args.shard_id,
- args.num_shards,
- args.sampling,
- args.prefix_len,
- args.target_prefix_frac,
- args.source_prefix_frac,
- )
- assert not (
- args.right_to_left1 and args.backwards1
- ), "backwards right to left not supported"
- assert not (
- args.right_to_left2 and args.backwards2
- ), "backwards right to left not supported"
- assert not (
- args.prefix_len is not None and args.target_prefix_frac is not None
- ), "target prefix frac and target prefix len incompatible"
-
- # make directory to store generation results
- if not os.path.exists(pre_gen):
- os.makedirs(pre_gen)
-
- rerank1_is_gen = (
- args.gen_model == args.score_model1 and args.source_prefix_frac is None
- )
- rerank2_is_gen = (
- args.gen_model == args.score_model2 and args.source_prefix_frac is None
- )
-
- if args.nbest_list is not None:
- rerank2_is_gen = True
-
- # make directories to store preprossed nbest list for reranking
- if not os.path.exists(left_to_right_preprocessed_dir):
- os.makedirs(left_to_right_preprocessed_dir)
- if not os.path.exists(right_to_left_preprocessed_dir):
- os.makedirs(right_to_left_preprocessed_dir)
- if not os.path.exists(lm_preprocessed_dir):
- os.makedirs(lm_preprocessed_dir)
- if not os.path.exists(backwards_preprocessed_dir):
- os.makedirs(backwards_preprocessed_dir)
-
- score1_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model1_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards1,
- )
- if args.score_model2 is not None:
- score2_file = rerank_utils.rescore_file_name(
- pre_gen,
- args.prefix_len,
- args.model2_name,
- target_prefix_frac=args.target_prefix_frac,
- source_prefix_frac=args.source_prefix_frac,
- backwards=args.backwards2,
- )
-
- predictions_bpe_file = pre_gen + "/generate_output_bpe.txt"
-
- using_nbest = args.nbest_list is not None
-
- if using_nbest:
- print("Using predefined n-best list from interactive.py")
- predictions_bpe_file = args.nbest_list
-
- else:
- if not os.path.isfile(predictions_bpe_file):
- print("STEP 1: generate predictions using the p(T|S) model with bpe")
- print(args.data)
- param1 = [
- args.data,
- "--path",
- args.gen_model,
- "--shard-id",
- str(args.shard_id),
- "--num-shards",
- str(args.num_shards),
- "--nbest",
- str(args.num_rescore),
- "--batch-size",
- str(args.batch_size),
- "--beam",
- str(args.num_rescore),
- "--batch-size",
- str(args.num_rescore),
- "--gen-subset",
- args.gen_subset,
- "--source-lang",
- args.source_lang,
- "--target-lang",
- args.target_lang,
- ]
- if args.sampling:
- param1 += ["--sampling"]
-
- gen_parser = options.get_generation_parser()
- input_args = options.parse_args_and_arch(gen_parser, param1)
-
- print(input_args)
- with open(predictions_bpe_file, "w") as f:
- with redirect_stdout(f):
- generate.main(input_args)
-
- gen_output = rerank_utils.BitextOutputFromGen(
- predictions_bpe_file,
- bpe_symbol=args.post_process,
- nbest=using_nbest,
- prefix_len=args.prefix_len,
- target_prefix_frac=args.target_prefix_frac,
- )
-
- if args.diff_bpe:
- rerank_utils.write_reprocessed(
- gen_output.no_bpe_source,
- gen_output.no_bpe_hypo,
- gen_output.no_bpe_target,
- pre_gen + "/source_gen_bpe." + args.source_lang,
- pre_gen + "/target_gen_bpe." + args.target_lang,
- pre_gen + "/reference_gen_bpe." + args.target_lang,
- )
- bitext_bpe = args.rescore_bpe_code
- bpe_src_param = [
- "-c",
- bitext_bpe,
- "--input",
- pre_gen + "/source_gen_bpe." + args.source_lang,
- "--output",
- pre_gen + "/rescore_data." + args.source_lang,
- ]
- bpe_tgt_param = [
- "-c",
- bitext_bpe,
- "--input",
- pre_gen + "/target_gen_bpe." + args.target_lang,
- "--output",
- pre_gen + "/rescore_data." + args.target_lang,
- ]
-
- subprocess.call(
- [
- "python",
- os.path.join(
- os.path.dirname(__file__), "subword-nmt/subword_nmt/apply_bpe.py"
- ),
- ]
- + bpe_src_param,
- shell=False,
- )
-
- subprocess.call(
- [
- "python",
- os.path.join(
- os.path.dirname(__file__), "subword-nmt/subword_nmt/apply_bpe.py"
- ),
- ]
- + bpe_tgt_param,
- shell=False,
- )
-
- if (not os.path.isfile(score1_file) and not rerank1_is_gen) or (
- args.score_model2 is not None
- and not os.path.isfile(score2_file)
- and not rerank2_is_gen
- ):
- print(
- "STEP 2: process the output of generate.py so we have clean text files with the translations"
- )
-
- rescore_file = "/rescore_data"
- if args.prefix_len is not None:
- prefix_len_rescore_file = rescore_file + "prefix" + str(args.prefix_len)
- if args.target_prefix_frac is not None:
- target_prefix_frac_rescore_file = (
- rescore_file + "target_prefix_frac" + str(args.target_prefix_frac)
- )
- if args.source_prefix_frac is not None:
- source_prefix_frac_rescore_file = (
- rescore_file + "source_prefix_frac" + str(args.source_prefix_frac)
- )
-
- if not args.right_to_left1 or not args.right_to_left2:
- if not args.diff_bpe:
- rerank_utils.write_reprocessed(
- gen_output.source,
- gen_output.hypo,
- gen_output.target,
- pre_gen + rescore_file + "." + args.source_lang,
- pre_gen + rescore_file + "." + args.target_lang,
- pre_gen + "/reference_file",
- bpe_symbol=args.post_process,
- )
- if args.prefix_len is not None:
- bw_rescore_file = prefix_len_rescore_file
- rerank_utils.write_reprocessed(
- gen_output.source,
- gen_output.hypo,
- gen_output.target,
- pre_gen + prefix_len_rescore_file + "." + args.source_lang,
- pre_gen + prefix_len_rescore_file + "." + args.target_lang,
- pre_gen + "/reference_file",
- prefix_len=args.prefix_len,
- bpe_symbol=args.post_process,
- )
- elif args.target_prefix_frac is not None:
- bw_rescore_file = target_prefix_frac_rescore_file
- rerank_utils.write_reprocessed(
- gen_output.source,
- gen_output.hypo,
- gen_output.target,
- pre_gen
- + target_prefix_frac_rescore_file
- + "."
- + args.source_lang,
- pre_gen
- + target_prefix_frac_rescore_file
- + "."
- + args.target_lang,
- pre_gen + "/reference_file",
- bpe_symbol=args.post_process,
- target_prefix_frac=args.target_prefix_frac,
- )
- else:
- bw_rescore_file = rescore_file
-
- if args.source_prefix_frac is not None:
- fw_rescore_file = source_prefix_frac_rescore_file
- rerank_utils.write_reprocessed(
- gen_output.source,
- gen_output.hypo,
- gen_output.target,
- pre_gen
- + source_prefix_frac_rescore_file
- + "."
- + args.source_lang,
- pre_gen
- + source_prefix_frac_rescore_file
- + "."
- + args.target_lang,
- pre_gen + "/reference_file",
- bpe_symbol=args.post_process,
- source_prefix_frac=args.source_prefix_frac,
- )
- else:
- fw_rescore_file = rescore_file
-
- if args.right_to_left1 or args.right_to_left2:
- rerank_utils.write_reprocessed(
- gen_output.source,
- gen_output.hypo,
- gen_output.target,
- pre_gen + "/right_to_left_rescore_data." + args.source_lang,
- pre_gen + "/right_to_left_rescore_data." + args.target_lang,
- pre_gen + "/right_to_left_reference_file",
- right_to_left=True,
- bpe_symbol=args.post_process,
- )
-
- print("STEP 3: binarize the translations")
- if (
- not args.right_to_left1
- or args.score_model2 is not None
- and not args.right_to_left2
- or not rerank1_is_gen
- ):
-
- if args.backwards1 or args.backwards2:
- if args.backwards_score_dict_dir is not None:
- bw_dict = args.backwards_score_dict_dir
- else:
- bw_dict = args.score_dict_dir
- bw_preprocess_param = [
- "--source-lang",
- scorer1_src,
- "--target-lang",
- scorer1_tgt,
- "--trainpref",
- pre_gen + bw_rescore_file,
- "--srcdict",
- bw_dict + "/dict." + scorer1_src + ".txt",
- "--tgtdict",
- bw_dict + "/dict." + scorer1_tgt + ".txt",
- "--destdir",
- backwards_preprocessed_dir,
- ]
- preprocess_parser = options.get_preprocessing_parser()
- input_args = preprocess_parser.parse_args(bw_preprocess_param)
- preprocess.main(input_args)
-
- preprocess_param = [
- "--source-lang",
- scorer1_src,
- "--target-lang",
- scorer1_tgt,
- "--trainpref",
- pre_gen + fw_rescore_file,
- "--srcdict",
- args.score_dict_dir + "/dict." + scorer1_src + ".txt",
- "--tgtdict",
- args.score_dict_dir + "/dict." + scorer1_tgt + ".txt",
- "--destdir",
- left_to_right_preprocessed_dir,
- ]
- preprocess_parser = options.get_preprocessing_parser()
- input_args = preprocess_parser.parse_args(preprocess_param)
- preprocess.main(input_args)
-
- if args.right_to_left1 or args.right_to_left2:
- preprocess_param = [
- "--source-lang",
- scorer1_src,
- "--target-lang",
- scorer1_tgt,
- "--trainpref",
- pre_gen + "/right_to_left_rescore_data",
- "--srcdict",
- args.score_dict_dir + "/dict." + scorer1_src + ".txt",
- "--tgtdict",
- args.score_dict_dir + "/dict." + scorer1_tgt + ".txt",
- "--destdir",
- right_to_left_preprocessed_dir,
- ]
- preprocess_parser = options.get_preprocessing_parser()
- input_args = preprocess_parser.parse_args(preprocess_param)
- preprocess.main(input_args)
-
- return gen_output
-
-
-def cli_main():
- parser = rerank_options.get_reranking_parser()
- args = options.parse_args_and_arch(parser)
- gen_and_reprocess_nbest(args)
-
-
-if __name__ == "__main__":
- cli_main()
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/show_wer.sh b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/show_wer.sh
deleted file mode 100644
index 9ecf1690c67f8a019009ef32d973fbd45b56c7ca..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/show_wer.sh
+++ /dev/null
@@ -1,52 +0,0 @@
-#!/bin/bash
-
-split="dev_other"
-ref_data=""
-get_best_wer=true
-dec_name="decode"
-graph_name="graph"
-
-. ./cmd.sh
-. ./path.sh
-. parse_options.sh
-
-exp_root=$1
-
-set -eu
-
-echo "==== WER w.r.t. pseudo transcript"
-for x in $exp_root/*/${dec_name}_${split}*; do grep WER $x/wer_* 2>/dev/null | utils/best_wer.sh; done
-
-
-if [ ! -z $ref_data ]; then
- echo "==== WER w.r.t. real transcript (select based on pseudo WER)"
- ref_txt=$ref_data/$split/text
- for x in $exp_root/*/${dec_name}_${split}*; do
- lang=$(dirname $x)/$graph_name
-
- lmwt=$(
- grep WER $x/wer_* 2>/dev/null | utils/best_wer.sh |
- sed 's/.*wer_\(.*\)$/\1/g' | sed 's/_/./g'
- )
- tra=$x/scoring/$lmwt.tra
- cat $tra | utils/int2sym.pl -f 2- $lang/words.txt | sed 's:::g' | sed 's:::g' | \
- compute-wer --text --mode=present \
- ark:$ref_txt ark,p:- 2> /dev/null | grep WER | xargs -I{} echo {} $tra
- done
-fi
-
-if [ ! -z $ref_data ] && $get_best_wer; then
- echo "==== WER w.r.t. real transcript (select based on true WER)"
- ref_txt=$ref_data/$split/text
- for x in $exp_root/*/${dec_name}_${split}*; do
- lang=$(dirname $x)/$graph_name
-
- for tra in $x/scoring/*.tra; do
- cat $tra | utils/int2sym.pl -f 2- $lang/words.txt | sed 's:::g' | sed 's:::g' | \
- compute-wer --text --mode=present \
- ark:$ref_txt ark,p:- 2> /dev/null | grep WER | xargs -I{} echo {} $tra
- done | sort -k2n | head -n1
- done
-fi
-
-exit 0;
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/model_parallel/megatron_trainer.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/model_parallel/megatron_trainer.py
deleted file mode 100644
index 8ab4657f73c6cda91e95637921edb84ccb76b3d0..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/model_parallel/megatron_trainer.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Train a network across multiple GPUs.
-"""
-
-from fairseq.dataclass.configs import FairseqConfig
-from fairseq.distributed import utils as distributed_utils
-from fairseq.trainer import Trainer
-
-try:
- from fairseq.model_parallel.megatron.mpu import (
- get_data_parallel_rank,
- get_data_parallel_world_size,
- get_model_parallel_src_rank,
- get_cuda_rng_tracker,
- )
-
- has_megatron_submodule = True
-except (ImportError, ModuleNotFoundError):
- has_megatron_submodule = False
-
-
-class MegatronTrainer(Trainer):
- """Main class for model parallel with data parallel training."""
-
- def __init__(self, cfg: FairseqConfig, task, model, criterion, **kwargs):
- if not has_megatron_submodule:
- raise ImportError(
- "\n\nPlease install the megatron submodule:"
- "\n\n git submodule update --init "
- "fairseq/model_parallel/megatron"
- )
- super().__init__(cfg, task, model, criterion, **kwargs)
-
- def clip_grad_norm(self, clip_norm):
- def _aggregate_model_parallel_grad_norm(total_norm):
- total_norm = total_norm ** 2
- distributed_utils.all_reduce(
- total_norm, group=distributed_utils.get_model_parallel_group()
- )
- total_norm = total_norm ** 0.5
- return total_norm
-
- return self.optimizer.clip_grad_norm(
- clip_norm,
- aggregate_norm_fn=_aggregate_model_parallel_grad_norm,
- )
-
- def save_checkpoint(self, filename, extra_state):
- """Save all training state in a checkpoint file."""
- extra_state['rng_tracker_states'] \
- = get_cuda_rng_tracker().get_states()
- super().save_checkpoint(filename, extra_state)
-
- def load_checkpoint(
- self,
- filename,
- reset_optimizer=False,
- reset_lr_scheduler=False,
- optimizer_overrides=None,
- reset_meters=False,
- ):
- extra_state = super().load_checkpoint(filename, reset_optimizer=reset_optimizer, reset_lr_scheduler=reset_lr_scheduler, optimizer_overrides=optimizer_overrides, reset_meters=reset_meters)
- if extra_state is not None and 'rng_tracker_states' in extra_state:
- get_cuda_rng_tracker().set_states(
- extra_state['rng_tracker_states'])
- return extra_state
diff --git a/spaces/OhMondon/Walking-Assistant-for-the-Visually-Impaired/app.py b/spaces/OhMondon/Walking-Assistant-for-the-Visually-Impaired/app.py
deleted file mode 100644
index 2944eda94e88b6b21d3c25e5f332195a362be363..0000000000000000000000000000000000000000
--- a/spaces/OhMondon/Walking-Assistant-for-the-Visually-Impaired/app.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import gradio as gr
-
-def walking_assistant(voice_command):
- if voice_command.lower() == 'stop':
- return "Stopping"
- elif voice_command.lower() == 'forward':
- return "Moving Forward"
- elif voice_command.lower() == 'left':
- return "Turning Left"
- elif voice_command.lower() == 'right':
- return "Turning Right"
- else:
- return "Invalid Command"
-
-iface = gr.Interface(fn=walking_assistant, inputs="text", outputs="text", title="Walking Assistant for the Visually Impaired")
-iface.launch()
diff --git a/spaces/Okkoman/PokeFace/README.md b/spaces/Okkoman/PokeFace/README.md
deleted file mode 100644
index fd45ed1b39e2b4fb396107711cc8be476de6827c..0000000000000000000000000000000000000000
--- a/spaces/Okkoman/PokeFace/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: PokeFace
-emoji: 😻
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.45.2
-app_file: app.py
-pinned: false
-license: mit
-models:
-- Okkoman/PokeFace
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Omnibus/MusicGen/audiocraft/quantization/__init__.py b/spaces/Omnibus/MusicGen/audiocraft/quantization/__init__.py
deleted file mode 100644
index 836d6eb518978480c6b95d6f29ce4f84a9428793..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/MusicGen/audiocraft/quantization/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-from .vq import ResidualVectorQuantizer
-from .base import BaseQuantizer, DummyQuantizer, QuantizedResult
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/deprecated_wrappers.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/deprecated_wrappers.py
deleted file mode 100644
index a2e593df9ee57637038683d7a1efaa347b2b69e7..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/ops/deprecated_wrappers.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-# This file is for backward compatibility.
-# Module wrappers for empty tensor have been moved to mmcv.cnn.bricks.
-import warnings
-
-from ..cnn.bricks.wrappers import Conv2d, ConvTranspose2d, Linear, MaxPool2d
-
-
-class Conv2d_deprecated(Conv2d):
-
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- warnings.warn(
- 'Importing Conv2d wrapper from "mmcv.ops" will be deprecated in'
- ' the future. Please import them from "mmcv.cnn" instead')
-
-
-class ConvTranspose2d_deprecated(ConvTranspose2d):
-
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- warnings.warn(
- 'Importing ConvTranspose2d wrapper from "mmcv.ops" will be '
- 'deprecated in the future. Please import them from "mmcv.cnn" '
- 'instead')
-
-
-class MaxPool2d_deprecated(MaxPool2d):
-
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- warnings.warn(
- 'Importing MaxPool2d wrapper from "mmcv.ops" will be deprecated in'
- ' the future. Please import them from "mmcv.cnn" instead')
-
-
-class Linear_deprecated(Linear):
-
- def __init__(self, *args, **kwargs):
- super().__init__(*args, **kwargs)
- warnings.warn(
- 'Importing Linear wrapper from "mmcv.ops" will be deprecated in'
- ' the future. Please import them from "mmcv.cnn" instead')
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/datasets/cityscapes.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/datasets/cityscapes.py
deleted file mode 100644
index 81e47a914a1aa2e5458e18669d65ffb742f46fc6..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmseg/datasets/cityscapes.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import os.path as osp
-import tempfile
-
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-from annotator.uniformer.mmcv.utils import print_log
-from PIL import Image
-
-from .builder import DATASETS
-from .custom import CustomDataset
-
-
-@DATASETS.register_module()
-class CityscapesDataset(CustomDataset):
- """Cityscapes dataset.
-
- The ``img_suffix`` is fixed to '_leftImg8bit.png' and ``seg_map_suffix`` is
- fixed to '_gtFine_labelTrainIds.png' for Cityscapes dataset.
- """
-
- CLASSES = ('road', 'sidewalk', 'building', 'wall', 'fence', 'pole',
- 'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky',
- 'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
- 'bicycle')
-
- PALETTE = [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156],
- [190, 153, 153], [153, 153, 153], [250, 170, 30], [220, 220, 0],
- [107, 142, 35], [152, 251, 152], [70, 130, 180], [220, 20, 60],
- [255, 0, 0], [0, 0, 142], [0, 0, 70], [0, 60, 100],
- [0, 80, 100], [0, 0, 230], [119, 11, 32]]
-
- def __init__(self, **kwargs):
- super(CityscapesDataset, self).__init__(
- img_suffix='_leftImg8bit.png',
- seg_map_suffix='_gtFine_labelTrainIds.png',
- **kwargs)
-
- @staticmethod
- def _convert_to_label_id(result):
- """Convert trainId to id for cityscapes."""
- if isinstance(result, str):
- result = np.load(result)
- import cityscapesscripts.helpers.labels as CSLabels
- result_copy = result.copy()
- for trainId, label in CSLabels.trainId2label.items():
- result_copy[result == trainId] = label.id
-
- return result_copy
-
- def results2img(self, results, imgfile_prefix, to_label_id):
- """Write the segmentation results to images.
-
- Args:
- results (list[list | tuple | ndarray]): Testing results of the
- dataset.
- imgfile_prefix (str): The filename prefix of the png files.
- If the prefix is "somepath/xxx",
- the png files will be named "somepath/xxx.png".
- to_label_id (bool): whether convert output to label_id for
- submission
-
- Returns:
- list[str: str]: result txt files which contains corresponding
- semantic segmentation images.
- """
- mmcv.mkdir_or_exist(imgfile_prefix)
- result_files = []
- prog_bar = mmcv.ProgressBar(len(self))
- for idx in range(len(self)):
- result = results[idx]
- if to_label_id:
- result = self._convert_to_label_id(result)
- filename = self.img_infos[idx]['filename']
- basename = osp.splitext(osp.basename(filename))[0]
-
- png_filename = osp.join(imgfile_prefix, f'{basename}.png')
-
- output = Image.fromarray(result.astype(np.uint8)).convert('P')
- import cityscapesscripts.helpers.labels as CSLabels
- palette = np.zeros((len(CSLabels.id2label), 3), dtype=np.uint8)
- for label_id, label in CSLabels.id2label.items():
- palette[label_id] = label.color
-
- output.putpalette(palette)
- output.save(png_filename)
- result_files.append(png_filename)
- prog_bar.update()
-
- return result_files
-
- def format_results(self, results, imgfile_prefix=None, to_label_id=True):
- """Format the results into dir (standard format for Cityscapes
- evaluation).
-
- Args:
- results (list): Testing results of the dataset.
- imgfile_prefix (str | None): The prefix of images files. It
- includes the file path and the prefix of filename, e.g.,
- "a/b/prefix". If not specified, a temp file will be created.
- Default: None.
- to_label_id (bool): whether convert output to label_id for
- submission. Default: False
-
- Returns:
- tuple: (result_files, tmp_dir), result_files is a list containing
- the image paths, tmp_dir is the temporal directory created
- for saving json/png files when img_prefix is not specified.
- """
-
- assert isinstance(results, list), 'results must be a list'
- assert len(results) == len(self), (
- 'The length of results is not equal to the dataset len: '
- f'{len(results)} != {len(self)}')
-
- if imgfile_prefix is None:
- tmp_dir = tempfile.TemporaryDirectory()
- imgfile_prefix = tmp_dir.name
- else:
- tmp_dir = None
- result_files = self.results2img(results, imgfile_prefix, to_label_id)
-
- return result_files, tmp_dir
-
- def evaluate(self,
- results,
- metric='mIoU',
- logger=None,
- imgfile_prefix=None,
- efficient_test=False):
- """Evaluation in Cityscapes/default protocol.
-
- Args:
- results (list): Testing results of the dataset.
- metric (str | list[str]): Metrics to be evaluated.
- logger (logging.Logger | None | str): Logger used for printing
- related information during evaluation. Default: None.
- imgfile_prefix (str | None): The prefix of output image file,
- for cityscapes evaluation only. It includes the file path and
- the prefix of filename, e.g., "a/b/prefix".
- If results are evaluated with cityscapes protocol, it would be
- the prefix of output png files. The output files would be
- png images under folder "a/b/prefix/xxx.png", where "xxx" is
- the image name of cityscapes. If not specified, a temp file
- will be created for evaluation.
- Default: None.
-
- Returns:
- dict[str, float]: Cityscapes/default metrics.
- """
-
- eval_results = dict()
- metrics = metric.copy() if isinstance(metric, list) else [metric]
- if 'cityscapes' in metrics:
- eval_results.update(
- self._evaluate_cityscapes(results, logger, imgfile_prefix))
- metrics.remove('cityscapes')
- if len(metrics) > 0:
- eval_results.update(
- super(CityscapesDataset,
- self).evaluate(results, metrics, logger, efficient_test))
-
- return eval_results
-
- def _evaluate_cityscapes(self, results, logger, imgfile_prefix):
- """Evaluation in Cityscapes protocol.
-
- Args:
- results (list): Testing results of the dataset.
- logger (logging.Logger | str | None): Logger used for printing
- related information during evaluation. Default: None.
- imgfile_prefix (str | None): The prefix of output image file
-
- Returns:
- dict[str: float]: Cityscapes evaluation results.
- """
- try:
- import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as CSEval # noqa
- except ImportError:
- raise ImportError('Please run "pip install cityscapesscripts" to '
- 'install cityscapesscripts first.')
- msg = 'Evaluating in Cityscapes style'
- if logger is None:
- msg = '\n' + msg
- print_log(msg, logger=logger)
-
- result_files, tmp_dir = self.format_results(results, imgfile_prefix)
-
- if tmp_dir is None:
- result_dir = imgfile_prefix
- else:
- result_dir = tmp_dir.name
-
- eval_results = dict()
- print_log(f'Evaluating results under {result_dir} ...', logger=logger)
-
- CSEval.args.evalInstLevelScore = True
- CSEval.args.predictionPath = osp.abspath(result_dir)
- CSEval.args.evalPixelAccuracy = True
- CSEval.args.JSONOutput = False
-
- seg_map_list = []
- pred_list = []
-
- # when evaluating with official cityscapesscripts,
- # **_gtFine_labelIds.png is used
- for seg_map in mmcv.scandir(
- self.ann_dir, 'gtFine_labelIds.png', recursive=True):
- seg_map_list.append(osp.join(self.ann_dir, seg_map))
- pred_list.append(CSEval.getPrediction(CSEval.args, seg_map))
-
- eval_results.update(
- CSEval.evaluateImgLists(pred_list, seg_map_list, CSEval.args))
-
- if tmp_dir is not None:
- tmp_dir.cleanup()
-
- return eval_results
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/losses/dice_loss.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/losses/dice_loss.py
deleted file mode 100644
index 27a77b962d7d8b3079c7d6cd9db52280c6fb4970..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/losses/dice_loss.py
+++ /dev/null
@@ -1,119 +0,0 @@
-"""Modified from https://github.com/LikeLy-Journey/SegmenTron/blob/master/
-segmentron/solver/loss.py (Apache-2.0 License)"""
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-from .utils import get_class_weight, weighted_loss
-
-
-@weighted_loss
-def dice_loss(pred,
- target,
- valid_mask,
- smooth=1,
- exponent=2,
- class_weight=None,
- ignore_index=255):
- assert pred.shape[0] == target.shape[0]
- total_loss = 0
- num_classes = pred.shape[1]
- for i in range(num_classes):
- if i != ignore_index:
- dice_loss = binary_dice_loss(
- pred[:, i],
- target[..., i],
- valid_mask=valid_mask,
- smooth=smooth,
- exponent=exponent)
- if class_weight is not None:
- dice_loss *= class_weight[i]
- total_loss += dice_loss
- return total_loss / num_classes
-
-
-@weighted_loss
-def binary_dice_loss(pred, target, valid_mask, smooth=1, exponent=2, **kwards):
- assert pred.shape[0] == target.shape[0]
- pred = pred.reshape(pred.shape[0], -1)
- target = target.reshape(target.shape[0], -1)
- valid_mask = valid_mask.reshape(valid_mask.shape[0], -1)
-
- num = torch.sum(torch.mul(pred, target) * valid_mask, dim=1) * 2 + smooth
- den = torch.sum(pred.pow(exponent) + target.pow(exponent), dim=1) + smooth
-
- return 1 - num / den
-
-
-@LOSSES.register_module()
-class DiceLoss(nn.Module):
- """DiceLoss.
-
- This loss is proposed in `V-Net: Fully Convolutional Neural Networks for
- Volumetric Medical Image Segmentation `_.
-
- Args:
- loss_type (str, optional): Binary or multi-class loss.
- Default: 'multi_class'. Options are "binary" and "multi_class".
- smooth (float): A float number to smooth loss, and avoid NaN error.
- Default: 1
- exponent (float): An float number to calculate denominator
- value: \\sum{x^exponent} + \\sum{y^exponent}. Default: 2.
- reduction (str, optional): The method used to reduce the loss. Options
- are "none", "mean" and "sum". This parameter only works when
- per_image is True. Default: 'mean'.
- class_weight (list[float] | str, optional): Weight of each class. If in
- str format, read them from a file. Defaults to None.
- loss_weight (float, optional): Weight of the loss. Default to 1.0.
- ignore_index (int | None): The label index to be ignored. Default: 255.
- """
-
- def __init__(self,
- smooth=1,
- exponent=2,
- reduction='mean',
- class_weight=None,
- loss_weight=1.0,
- ignore_index=255,
- **kwards):
- super(DiceLoss, self).__init__()
- self.smooth = smooth
- self.exponent = exponent
- self.reduction = reduction
- self.class_weight = get_class_weight(class_weight)
- self.loss_weight = loss_weight
- self.ignore_index = ignore_index
-
- def forward(self,
- pred,
- target,
- avg_factor=None,
- reduction_override=None,
- **kwards):
- assert reduction_override in (None, 'none', 'mean', 'sum')
- reduction = (
- reduction_override if reduction_override else self.reduction)
- if self.class_weight is not None:
- class_weight = pred.new_tensor(self.class_weight)
- else:
- class_weight = None
-
- pred = F.softmax(pred, dim=1)
- num_classes = pred.shape[1]
- one_hot_target = F.one_hot(
- torch.clamp(target.long(), 0, num_classes - 1),
- num_classes=num_classes)
- valid_mask = (target != self.ignore_index).long()
-
- loss = self.loss_weight * dice_loss(
- pred,
- one_hot_target,
- valid_mask=valid_mask,
- reduction=reduction,
- avg_factor=avg_factor,
- smooth=self.smooth,
- exponent=self.exponent,
- class_weight=class_weight,
- ignore_index=self.ignore_index)
- return loss
diff --git a/spaces/Pie31415/control-animation/text_to_animation/models/unet_2d_blocks_flax.py b/spaces/Pie31415/control-animation/text_to_animation/models/unet_2d_blocks_flax.py
deleted file mode 100644
index 58022f7f458eff8f8022e71853cda23738dbbe85..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/text_to_animation/models/unet_2d_blocks_flax.py
+++ /dev/null
@@ -1,607 +0,0 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import flax.linen as nn
-import jax.numpy as jnp
-
-# from diffusers.models.attention_flax import FlaxTransformer2DModel
-from diffusers.models.resnet_flax import FlaxDownsample2D, FlaxResnetBlock2D, FlaxUpsample2D
-from .cross_frame_attention_flax import FlaxCrossFrameTransformer2DModel, FlaxLoRACrossFrameTransformer2DModel
-
-class FlaxCrossAttnDownBlock2D(nn.Module):
- r"""
- Cross Attention 2D Downsizing block - original architecture from Unet transformers:
- https://arxiv.org/abs/2103.06104
- Parameters:
- in_channels (:obj:`int`):
- Input channels
- out_channels (:obj:`int`):
- Output channels
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- num_layers (:obj:`int`, *optional*, defaults to 1):
- Number of attention blocks layers
- attn_num_head_channels (:obj:`int`, *optional*, defaults to 1):
- Number of attention heads of each spatial transformer block
- add_downsample (:obj:`bool`, *optional*, defaults to `True`):
- Whether to add downsampling layer before each final output
- use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
- enable memory efficient attention https://arxiv.org/abs/2112.05682
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- """
- in_channels: int
- out_channels: int
- dropout: float = 0.0
- num_layers: int = 1
- attn_num_head_channels: int = 1
- add_downsample: bool = True
- use_linear_projection: bool = False
- only_cross_attention: bool = False
- use_memory_efficient_attention: bool = False
- dtype: jnp.dtype = jnp.float32
-
- def setup(self):
- resnets = []
- attentions = []
-
- for i in range(self.num_layers):
- in_channels = self.in_channels if i == 0 else self.out_channels
-
- res_block = FlaxResnetBlock2D(
- in_channels=in_channels,
- out_channels=self.out_channels,
- dropout_prob=self.dropout,
- dtype=self.dtype,
- )
- resnets.append(res_block)
-
- attn_block = FlaxCrossFrameTransformer2DModel(
- in_channels=self.out_channels,
- n_heads=self.attn_num_head_channels,
- d_head=self.out_channels // self.attn_num_head_channels,
- depth=1,
- use_linear_projection=self.use_linear_projection,
- only_cross_attention=self.only_cross_attention,
- use_memory_efficient_attention=self.use_memory_efficient_attention,
- dtype=self.dtype,
- )
- attentions.append(attn_block)
-
- self.resnets = resnets
- self.attentions = attentions
-
- if self.add_downsample:
- self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)
-
- def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True):
- output_states = ()
-
- for resnet, attn in zip(self.resnets, self.attentions):
- hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
- hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)
- output_states += (hidden_states,)
-
- if self.add_downsample:
- hidden_states = self.downsamplers_0(hidden_states)
- output_states += (hidden_states,)
-
- return hidden_states, output_states
-
-
-class FlaxLoRACrossAttnDownBlock2D(nn.Module):
- r"""
- Cross Attention 2D Downsizing block - original architecture from Unet transformers:
- https://arxiv.org/abs/2103.06104
- Parameters:
- in_channels (:obj:`int`):
- Input channels
- out_channels (:obj:`int`):
- Output channels
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- num_layers (:obj:`int`, *optional*, defaults to 1):
- Number of attention blocks layers
- attn_num_head_channels (:obj:`int`, *optional*, defaults to 1):
- Number of attention heads of each spatial transformer block
- add_downsample (:obj:`bool`, *optional*, defaults to `True`):
- Whether to add downsampling layer before each final output
- use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
- enable memory efficient attention https://arxiv.org/abs/2112.05682
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- """
- in_channels: int
- out_channels: int
- dropout: float = 0.0
- num_layers: int = 1
- attn_num_head_channels: int = 1
- add_downsample: bool = True
- use_linear_projection: bool = False
- only_cross_attention: bool = False
- use_memory_efficient_attention: bool = False
- dtype: jnp.dtype = jnp.float32
-
- def setup(self):
- resnets = []
- attentions = []
-
- for i in range(self.num_layers):
- in_channels = self.in_channels if i == 0 else self.out_channels
-
- res_block = FlaxResnetBlock2D(
- in_channels=in_channels,
- out_channels=self.out_channels,
- dropout_prob=self.dropout,
- dtype=self.dtype,
- )
- resnets.append(res_block)
-
- attn_block = FlaxLoRACrossFrameTransformer2DModel(
- in_channels=self.out_channels,
- n_heads=self.attn_num_head_channels,
- d_head=self.out_channels // self.attn_num_head_channels,
- depth=1,
- use_linear_projection=self.use_linear_projection,
- only_cross_attention=self.only_cross_attention,
- use_memory_efficient_attention=self.use_memory_efficient_attention,
- dtype=self.dtype,
- )
- attentions.append(attn_block)
-
- self.resnets = resnets
- self.attentions = attentions
-
- if self.add_downsample:
- self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)
-
- def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True, scale=1.):
- output_states = ()
-
- for resnet, attn in zip(self.resnets, self.attentions):
- hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
- hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic, scale=scale)
- output_states += (hidden_states,)
-
- if self.add_downsample:
- hidden_states = self.downsamplers_0(hidden_states)
- output_states += (hidden_states,)
-
- return hidden_states, output_states
-
-
-class FlaxDownBlock2D(nn.Module):
- r"""
- Flax 2D downsizing block
- Parameters:
- in_channels (:obj:`int`):
- Input channels
- out_channels (:obj:`int`):
- Output channels
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- num_layers (:obj:`int`, *optional*, defaults to 1):
- Number of attention blocks layers
- add_downsample (:obj:`bool`, *optional*, defaults to `True`):
- Whether to add downsampling layer before each final output
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- """
- in_channels: int
- out_channels: int
- dropout: float = 0.0
- num_layers: int = 1
- add_downsample: bool = True
- dtype: jnp.dtype = jnp.float32
-
- def setup(self):
- resnets = []
-
- for i in range(self.num_layers):
- in_channels = self.in_channels if i == 0 else self.out_channels
-
- res_block = FlaxResnetBlock2D(
- in_channels=in_channels,
- out_channels=self.out_channels,
- dropout_prob=self.dropout,
- dtype=self.dtype,
- )
- resnets.append(res_block)
- self.resnets = resnets
-
- if self.add_downsample:
- self.downsamplers_0 = FlaxDownsample2D(self.out_channels, dtype=self.dtype)
-
- def __call__(self, hidden_states, temb, deterministic=True):
- output_states = ()
-
- for resnet in self.resnets:
- hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
- output_states += (hidden_states,)
-
- if self.add_downsample:
- hidden_states = self.downsamplers_0(hidden_states)
- output_states += (hidden_states,)
-
- return hidden_states, output_states
-
-
-class FlaxCrossAttnUpBlock2D(nn.Module):
- r"""
- Cross Attention 2D Upsampling block - original architecture from Unet transformers:
- https://arxiv.org/abs/2103.06104
- Parameters:
- in_channels (:obj:`int`):
- Input channels
- out_channels (:obj:`int`):
- Output channels
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- num_layers (:obj:`int`, *optional*, defaults to 1):
- Number of attention blocks layers
- attn_num_head_channels (:obj:`int`, *optional*, defaults to 1):
- Number of attention heads of each spatial transformer block
- add_upsample (:obj:`bool`, *optional*, defaults to `True`):
- Whether to add upsampling layer before each final output
- use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
- enable memory efficient attention https://arxiv.org/abs/2112.05682
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- """
- in_channels: int
- out_channels: int
- prev_output_channel: int
- dropout: float = 0.0
- num_layers: int = 1
- attn_num_head_channels: int = 1
- add_upsample: bool = True
- use_linear_projection: bool = False
- only_cross_attention: bool = False
- use_memory_efficient_attention: bool = False
- dtype: jnp.dtype = jnp.float32
-
- def setup(self):
- resnets = []
- attentions = []
-
- for i in range(self.num_layers):
- res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels
- resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels
-
- res_block = FlaxResnetBlock2D(
- in_channels=resnet_in_channels + res_skip_channels,
- out_channels=self.out_channels,
- dropout_prob=self.dropout,
- dtype=self.dtype,
- )
- resnets.append(res_block)
-
- attn_block = FlaxCrossFrameTransformer2DModel(
- in_channels=self.out_channels,
- n_heads=self.attn_num_head_channels,
- d_head=self.out_channels // self.attn_num_head_channels,
- depth=1,
- use_linear_projection=self.use_linear_projection,
- only_cross_attention=self.only_cross_attention,
- use_memory_efficient_attention=self.use_memory_efficient_attention,
- dtype=self.dtype,
- )
- attentions.append(attn_block)
-
- self.resnets = resnets
- self.attentions = attentions
-
- if self.add_upsample:
- self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)
-
- def __call__(self, hidden_states, res_hidden_states_tuple, temb, encoder_hidden_states, deterministic=True):
- for resnet, attn in zip(self.resnets, self.attentions):
- # pop res hidden states
- res_hidden_states = res_hidden_states_tuple[-1]
- res_hidden_states_tuple = res_hidden_states_tuple[:-1]
- hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1)
-
- hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
- hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)
-
- if self.add_upsample:
- hidden_states = self.upsamplers_0(hidden_states)
-
- return hidden_states
-
-
-class FlaxLoRACrossAttnUpBlock2D(nn.Module):
- r"""
- Cross Attention 2D Upsampling block - original architecture from Unet transformers:
- https://arxiv.org/abs/2103.06104
- Parameters:
- in_channels (:obj:`int`):
- Input channels
- out_channels (:obj:`int`):
- Output channels
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- num_layers (:obj:`int`, *optional*, defaults to 1):
- Number of attention blocks layers
- attn_num_head_channels (:obj:`int`, *optional*, defaults to 1):
- Number of attention heads of each spatial transformer block
- add_upsample (:obj:`bool`, *optional*, defaults to `True`):
- Whether to add upsampling layer before each final output
- use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
- enable memory efficient attention https://arxiv.org/abs/2112.05682
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- """
- in_channels: int
- out_channels: int
- prev_output_channel: int
- dropout: float = 0.0
- num_layers: int = 1
- attn_num_head_channels: int = 1
- add_upsample: bool = True
- use_linear_projection: bool = False
- only_cross_attention: bool = False
- use_memory_efficient_attention: bool = False
- dtype: jnp.dtype = jnp.float32
-
- def setup(self):
- resnets = []
- attentions = []
-
- for i in range(self.num_layers):
- res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels
- resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels
-
- res_block = FlaxResnetBlock2D(
- in_channels=resnet_in_channels + res_skip_channels,
- out_channels=self.out_channels,
- dropout_prob=self.dropout,
- dtype=self.dtype,
- )
- resnets.append(res_block)
-
- attn_block = FlaxLoRACrossFrameTransformer2DModel(
- in_channels=self.out_channels,
- n_heads=self.attn_num_head_channels,
- d_head=self.out_channels // self.attn_num_head_channels,
- depth=1,
- use_linear_projection=self.use_linear_projection,
- only_cross_attention=self.only_cross_attention,
- use_memory_efficient_attention=self.use_memory_efficient_attention,
- dtype=self.dtype,
- )
- attentions.append(attn_block)
-
- self.resnets = resnets
- self.attentions = attentions
-
- if self.add_upsample:
- self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)
-
- def __call__(self, hidden_states, res_hidden_states_tuple, temb, encoder_hidden_states, deterministic=True, scale=1.):
- for resnet, attn in zip(self.resnets, self.attentions):
- # pop res hidden states
- res_hidden_states = res_hidden_states_tuple[-1]
- res_hidden_states_tuple = res_hidden_states_tuple[:-1]
- hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1)
-
- hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
- hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic, scale=scale)
-
- if self.add_upsample:
- hidden_states = self.upsamplers_0(hidden_states)
-
- return hidden_states
-
-
-class FlaxUpBlock2D(nn.Module):
- r"""
- Flax 2D upsampling block
- Parameters:
- in_channels (:obj:`int`):
- Input channels
- out_channels (:obj:`int`):
- Output channels
- prev_output_channel (:obj:`int`):
- Output channels from the previous block
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- num_layers (:obj:`int`, *optional*, defaults to 1):
- Number of attention blocks layers
- add_downsample (:obj:`bool`, *optional*, defaults to `True`):
- Whether to add downsampling layer before each final output
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- """
- in_channels: int
- out_channels: int
- prev_output_channel: int
- dropout: float = 0.0
- num_layers: int = 1
- add_upsample: bool = True
- dtype: jnp.dtype = jnp.float32
-
- def setup(self):
- resnets = []
-
- for i in range(self.num_layers):
- res_skip_channels = self.in_channels if (i == self.num_layers - 1) else self.out_channels
- resnet_in_channels = self.prev_output_channel if i == 0 else self.out_channels
-
- res_block = FlaxResnetBlock2D(
- in_channels=resnet_in_channels + res_skip_channels,
- out_channels=self.out_channels,
- dropout_prob=self.dropout,
- dtype=self.dtype,
- )
- resnets.append(res_block)
-
- self.resnets = resnets
-
- if self.add_upsample:
- self.upsamplers_0 = FlaxUpsample2D(self.out_channels, dtype=self.dtype)
-
- def __call__(self, hidden_states, res_hidden_states_tuple, temb, deterministic=True):
- for resnet in self.resnets:
- # pop res hidden states
- res_hidden_states = res_hidden_states_tuple[-1]
- res_hidden_states_tuple = res_hidden_states_tuple[:-1]
- hidden_states = jnp.concatenate((hidden_states, res_hidden_states), axis=-1)
-
- hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
-
- if self.add_upsample:
- hidden_states = self.upsamplers_0(hidden_states)
-
- return hidden_states
-
-
-class FlaxUNetCrossAttnMidBlock2D(nn.Module):
- r"""
- Cross Attention 2D Mid-level block - original architecture from Unet transformers: https://arxiv.org/abs/2103.06104
- Parameters:
- in_channels (:obj:`int`):
- Input channels
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- num_layers (:obj:`int`, *optional*, defaults to 1):
- Number of attention blocks layers
- attn_num_head_channels (:obj:`int`, *optional*, defaults to 1):
- Number of attention heads of each spatial transformer block
- use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
- enable memory efficient attention https://arxiv.org/abs/2112.05682
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- """
- in_channels: int
- dropout: float = 0.0
- num_layers: int = 1
- attn_num_head_channels: int = 1
- use_linear_projection: bool = False
- use_memory_efficient_attention: bool = False
- dtype: jnp.dtype = jnp.float32
-
- def setup(self):
- # there is always at least one resnet
- resnets = [
- FlaxResnetBlock2D(
- in_channels=self.in_channels,
- out_channels=self.in_channels,
- dropout_prob=self.dropout,
- dtype=self.dtype,
- )
- ]
-
- attentions = []
-
- for _ in range(self.num_layers):
- attn_block = FlaxCrossFrameTransformer2DModel(
- in_channels=self.in_channels,
- n_heads=self.attn_num_head_channels,
- d_head=self.in_channels // self.attn_num_head_channels,
- depth=1,
- use_linear_projection=self.use_linear_projection,
- use_memory_efficient_attention=self.use_memory_efficient_attention,
- dtype=self.dtype,
- )
- attentions.append(attn_block)
-
- res_block = FlaxResnetBlock2D(
- in_channels=self.in_channels,
- out_channels=self.in_channels,
- dropout_prob=self.dropout,
- dtype=self.dtype,
- )
- resnets.append(res_block)
-
- self.resnets = resnets
- self.attentions = attentions
-
- def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True):
- hidden_states = self.resnets[0](hidden_states, temb)
- for attn, resnet in zip(self.attentions, self.resnets[1:]):
- hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic)
- hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
-
- return hidden_states
-
-
-class FlaxLoRAUNetCrossAttnMidBlock2D(nn.Module):
- r"""
- Cross Attention 2D Mid-level block - original architecture from Unet transformers: https://arxiv.org/abs/2103.06104
- Parameters:
- in_channels (:obj:`int`):
- Input channels
- dropout (:obj:`float`, *optional*, defaults to 0.0):
- Dropout rate
- num_layers (:obj:`int`, *optional*, defaults to 1):
- Number of attention blocks layers
- attn_num_head_channels (:obj:`int`, *optional*, defaults to 1):
- Number of attention heads of each spatial transformer block
- use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
- enable memory efficient attention https://arxiv.org/abs/2112.05682
- dtype (:obj:`jnp.dtype`, *optional*, defaults to jnp.float32):
- Parameters `dtype`
- """
- in_channels: int
- dropout: float = 0.0
- num_layers: int = 1
- attn_num_head_channels: int = 1
- use_linear_projection: bool = False
- use_memory_efficient_attention: bool = False
- dtype: jnp.dtype = jnp.float32
-
- def setup(self):
- # there is always at least one resnet
- resnets = [
- FlaxResnetBlock2D(
- in_channels=self.in_channels,
- out_channels=self.in_channels,
- dropout_prob=self.dropout,
- dtype=self.dtype,
- )
- ]
-
- attentions = []
-
- for _ in range(self.num_layers):
- attn_block = FlaxLoRACrossFrameTransformer2DModel(
- in_channels=self.in_channels,
- n_heads=self.attn_num_head_channels,
- d_head=self.in_channels // self.attn_num_head_channels,
- depth=1,
- use_linear_projection=self.use_linear_projection,
- use_memory_efficient_attention=self.use_memory_efficient_attention,
- dtype=self.dtype,
- )
- attentions.append(attn_block)
-
- res_block = FlaxResnetBlock2D(
- in_channels=self.in_channels,
- out_channels=self.in_channels,
- dropout_prob=self.dropout,
- dtype=self.dtype,
- )
- resnets.append(res_block)
-
- self.resnets = resnets
- self.attentions = attentions
-
- def __call__(self, hidden_states, temb, encoder_hidden_states, deterministic=True, scale=1.):
- hidden_states = self.resnets[0](hidden_states, temb)
- for attn, resnet in zip(self.attentions, self.resnets[1:]):
- hidden_states = attn(hidden_states, encoder_hidden_states, deterministic=deterministic, scale=scale)
- hidden_states = resnet(hidden_states, temb, deterministic=deterministic)
-
- return hidden_states
\ No newline at end of file
diff --git a/spaces/PierreSHI/YOLOS_traffic_object_detection/app.py b/spaces/PierreSHI/YOLOS_traffic_object_detection/app.py
deleted file mode 100644
index 92867006cc420790f3878318924fec06dd076dda..0000000000000000000000000000000000000000
--- a/spaces/PierreSHI/YOLOS_traffic_object_detection/app.py
+++ /dev/null
@@ -1,156 +0,0 @@
-import gradio as gr
-import os
-import torch
-import pytorch_lightning as pl
-
-import cv2
-import numpy
-from transformers import AutoFeatureExtractor, AutoModelForObjectDetection
-from PIL import Image
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-feature_extractor = AutoFeatureExtractor.from_pretrained("hustvl/yolos-small", size=512, max_size=864)
-
-id2label = {1: 'person', 2: 'rider', 3: 'car', 4: 'bus', 5: 'truck', 6: 'bike', 7: 'motor', 8: 'traffic light', 9: 'traffic sign', 10: 'train'}
-
-# colors for visualization
-colors = [
- [ 0, 113, 188,],
- [216, 82, 24,],
- [236, 176, 31,],
- [255, 255, 0,],
- [118, 171, 47,],
- [ 76, 189, 237,],
- [ 46, 155, 188,],
- [125, 171, 141,],
- [125, 76, 237,],
- [ 0, 82, 216,],
- [189, 76, 47,]]
-
-class Detr(pl.LightningModule):
-
- def __init__(self, lr, weight_decay):
- super().__init__()
- # replace COCO classification head with custom head
- self.model = AutoModelForObjectDetection.from_pretrained("hustvl/yolos-small",
- num_labels=len(id2label),
- ignore_mismatched_sizes=True)
- # see https://github.com/PyTorchLightning/pytorch-lightning/pull/1896
- self.lr = lr
- self.weight_decay = weight_decay
-
- def forward(self, pixel_values):
- outputs = self.model(pixel_values=pixel_values)
-
- return outputs
-
- def common_step(self, batch, batch_idx):
- pixel_values = batch["pixel_values"]
- labels = [{k: v.to(self.device) for k, v in t.items()} for t in batch["labels"]]
-
- outputs = self.model(pixel_values=pixel_values, labels=labels)
-
- loss = outputs.loss
- loss_dict = outputs.loss_dict
-
- return loss, loss_dict
-
- def training_step(self, batch, batch_idx):
- loss, loss_dict = self.common_step(batch, batch_idx)
- # logs metrics for each training_step,
- # and the average across the epoch
- self.log("training_loss", loss)
- for k,v in loss_dict.items():
- self.log("train_" + k, v.item())
-
- return loss
-
- def validation_step(self, batch, batch_idx):
- loss, loss_dict = self.common_step(batch, batch_idx)
- self.log("validation_loss", loss)
- for k,v in loss_dict.items():
- self.log("validation_" + k, v.item())
-
- return loss
-
- def configure_optimizers(self):
- optimizer = torch.optim.AdamW(self.parameters(), lr=self.lr,
- weight_decay=self.weight_decay)
-
- return optimizer
-
-
-# Build model and load checkpoint
-checkpoint = './checkpoints/epoch=1-step=2184.ckpt'
-model_yolos = Detr.load_from_checkpoint(checkpoint, lr=2.5e-5, weight_decay=1e-4)
-
-model_yolos.to(device)
-model_yolos.eval()
-
-
-# for output bounding box post-processing
-def box_cxcywh_to_xyxy(x):
- x_c, y_c, w, h = x.unbind(1)
- b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
- (x_c + 0.5 * w), (y_c + 0.5 * h)]
- return torch.stack(b, dim=1)
-
-
-def rescale_bboxes(out_bbox, size):
- img_w, img_h = size
- b = box_cxcywh_to_xyxy(out_bbox)
- b = b * torch.tensor([img_w, img_h, img_w, img_h], dtype=torch.float32)
- return b
-
-
-def plot_results(pil_img, prob, boxes):
-
- img = numpy.asarray(pil_img)
-
- for p, (xmin, ymin, xmax, ymax) in zip(prob, boxes.tolist()):
- cl = p.argmax()
- c = colors[cl]
- c1, c2 = (int(xmin), int(ymin)), (int(xmax), int(ymax))
-
- cv2.rectangle(img, c1, c2, c, thickness=2, lineType=cv2.LINE_AA)
- cv2.putText(img, f'{id2label[cl.item()]}: {p[cl]:0.2f}', [int(xmin), int(ymin)-5], cv2.FONT_HERSHEY_SIMPLEX, 0.7, c, 2)
- return Image.fromarray(img)
-
-
-def generate_preds(processor, model, image):
- inputs = processor(images=image, return_tensors="pt").to(device)
- preds = model(pixel_values=inputs.pixel_values)
- return preds
-
-
-def visualize_preds(image, preds, threshold=0.9):
- # keep only predictions with confidence >= threshold
- probas = preds.logits.softmax(-1)[0, :, :-1]
- keep = probas.max(-1).values > threshold
-
- # convert predicted boxes from [0; 1] to image scales
- bboxes_scaled = rescale_bboxes(preds.pred_boxes[0, keep].cpu(), image.size)
-
- return plot_results(image, probas[keep], bboxes_scaled)
-
-
-def detect(img):
- # Run inference
- preds = generate_preds(feature_extractor, model_yolos, img)
- return visualize_preds(img, preds)
-
-
-description = "Welcome to this space! 🤗this is a traffic object detector based on YOLOS. \n\n" + \
- "The model can detect following targets: 🚶♂️person, 🚴♀️rider, 🚗car, 🚌bus, 🚚truck, 🚲bike, 🏍️motor, 🚦traffic light, ⛔traffic sign, 🚄train."
-
-
-interface = gr.Interface(
- fn=detect,
- inputs=[gr.Image(type="pil")],
- outputs=gr.Image(type="pil"),
- examples=[["./imgs/example1.jpg"], ["./imgs/example2.jpg"], ["./imgs/example3.png"]],
- title="YOLOS for traffic object detection",
- description=description)
-
-interface.launch()
diff --git a/spaces/Podtekatel/Avatar2VSK/README.md b/spaces/Podtekatel/Avatar2VSK/README.md
deleted file mode 100644
index bb1a9be8e3049eec44af9485e038d042d61e4f58..0000000000000000000000000000000000000000
--- a/spaces/Podtekatel/Avatar2VSK/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Photo-to-Avatar2
-emoji: 🌊😨🌊
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.8.2
-app_file: app.py
-pinned: true
-license: bsd-3-clause
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ProteinDesignLab/protpardelle/ProteinMPNN/helper_scripts/make_pssm_input_dict.py b/spaces/ProteinDesignLab/protpardelle/ProteinMPNN/helper_scripts/make_pssm_input_dict.py
deleted file mode 100644
index 3eea72dbf9ece48a3000d3ab98e6896332940fbb..0000000000000000000000000000000000000000
--- a/spaces/ProteinDesignLab/protpardelle/ProteinMPNN/helper_scripts/make_pssm_input_dict.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import argparse
-
-def main(args):
- import json
- import numpy as np
- with open(args.jsonl_input_path, 'r') as json_file:
- json_list = list(json_file)
-
- my_dict = {}
- for json_str in json_list:
- result = json.loads(json_str)
- all_chain_list = [item[-1:] for item in list(result) if item[:9]=='seq_chain']
- path_to_PSSM = args.PSSM_input_path+"/"+result['name'] + ".npz"
- print(path_to_PSSM)
- pssm_input = np.load(path_to_PSSM)
- pssm_dict = {}
- for chain in all_chain_list:
- pssm_dict[chain] = {}
- pssm_dict[chain]['pssm_coef'] = pssm_input[chain+'_coef'].tolist() #[L] per position coefficient to trust PSSM; 0.0 - do not use it; 1.0 - just use PSSM only
- pssm_dict[chain]['pssm_bias'] = pssm_input[chain+'_bias'].tolist() #[L,21] probability (sums up to 1.0 over alphabet of size 21) from PSSM
- pssm_dict[chain]['pssm_log_odds'] = pssm_input[chain+'_odds'].tolist() #[L,21] log_odds ratios coming from PSSM; optional/not needed
- my_dict[result['name']] = pssm_dict
-
- #Write output to:
- with open(args.output_path, 'w') as f:
- f.write(json.dumps(my_dict) + '\n')
-
-if __name__ == "__main__":
- argparser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-
- argparser.add_argument("--PSSM_input_path", type=str, help="Path to PSSMs saved as npz files.")
- argparser.add_argument("--jsonl_input_path", type=str, help="Path where to load .jsonl dictionary of parsed pdbs.")
- argparser.add_argument("--output_path", type=str, help="Path where to save .jsonl dictionary with PSSM bias.")
-
- args = argparser.parse_args()
- main(args)
diff --git a/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/data/annotated_objects_open_images.py b/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/data/annotated_objects_open_images.py
deleted file mode 100644
index aede6803d2cef7a74ca784e7907d35fba6c71239..0000000000000000000000000000000000000000
--- a/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/data/annotated_objects_open_images.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from collections import defaultdict
-from csv import DictReader, reader as TupleReader
-from pathlib import Path
-from typing import Dict, List, Any
-import warnings
-
-from taming.data.annotated_objects_dataset import AnnotatedObjectsDataset
-from taming.data.helper_types import Annotation, Category
-from tqdm import tqdm
-
-OPEN_IMAGES_STRUCTURE = {
- 'train': {
- 'top_level': '',
- 'class_descriptions': 'class-descriptions-boxable.csv',
- 'annotations': 'oidv6-train-annotations-bbox.csv',
- 'file_list': 'train-images-boxable.csv',
- 'files': 'train'
- },
- 'validation': {
- 'top_level': '',
- 'class_descriptions': 'class-descriptions-boxable.csv',
- 'annotations': 'validation-annotations-bbox.csv',
- 'file_list': 'validation-images.csv',
- 'files': 'validation'
- },
- 'test': {
- 'top_level': '',
- 'class_descriptions': 'class-descriptions-boxable.csv',
- 'annotations': 'test-annotations-bbox.csv',
- 'file_list': 'test-images.csv',
- 'files': 'test'
- }
-}
-
-
-def load_annotations(descriptor_path: Path, min_object_area: float, category_mapping: Dict[str, str],
- category_no_for_id: Dict[str, int]) -> Dict[str, List[Annotation]]:
- annotations: Dict[str, List[Annotation]] = defaultdict(list)
- with open(descriptor_path) as file:
- reader = DictReader(file)
- for i, row in tqdm(enumerate(reader), total=14620000, desc='Loading OpenImages annotations'):
- width = float(row['XMax']) - float(row['XMin'])
- height = float(row['YMax']) - float(row['YMin'])
- area = width * height
- category_id = row['LabelName']
- if category_id in category_mapping:
- category_id = category_mapping[category_id]
- if area >= min_object_area and category_id in category_no_for_id:
- annotations[row['ImageID']].append(
- Annotation(
- id=i,
- image_id=row['ImageID'],
- source=row['Source'],
- category_id=category_id,
- category_no=category_no_for_id[category_id],
- confidence=float(row['Confidence']),
- bbox=(float(row['XMin']), float(row['YMin']), width, height),
- area=area,
- is_occluded=bool(int(row['IsOccluded'])),
- is_truncated=bool(int(row['IsTruncated'])),
- is_group_of=bool(int(row['IsGroupOf'])),
- is_depiction=bool(int(row['IsDepiction'])),
- is_inside=bool(int(row['IsInside']))
- )
- )
- if 'train' in str(descriptor_path) and i < 14000000:
- warnings.warn(f'Running with subset of Open Images. Train dataset has length [{len(annotations)}].')
- return dict(annotations)
-
-
-def load_image_ids(csv_path: Path) -> List[str]:
- with open(csv_path) as file:
- reader = DictReader(file)
- return [row['image_name'] for row in reader]
-
-
-def load_categories(csv_path: Path) -> Dict[str, Category]:
- with open(csv_path) as file:
- reader = TupleReader(file)
- return {row[0]: Category(id=row[0], name=row[1], super_category=None) for row in reader}
-
-
-class AnnotatedObjectsOpenImages(AnnotatedObjectsDataset):
- def __init__(self, use_additional_parameters: bool, **kwargs):
- """
- @param data_path: is the path to the following folder structure:
- open_images/
- │ oidv6-train-annotations-bbox.csv
- ├── class-descriptions-boxable.csv
- ├── oidv6-train-annotations-bbox.csv
- ├── test
- │ ├── 000026e7ee790996.jpg
- │ ├── 000062a39995e348.jpg
- │ └── ...
- ├── test-annotations-bbox.csv
- ├── test-images.csv
- ├── train
- │ ├── 000002b66c9c498e.jpg
- │ ├── 000002b97e5471a0.jpg
- │ └── ...
- ├── train-images-boxable.csv
- ├── validation
- │ ├── 0001eeaf4aed83f9.jpg
- │ ├── 0004886b7d043cfd.jpg
- │ └── ...
- ├── validation-annotations-bbox.csv
- └── validation-images.csv
- @param: split: one of 'train', 'validation' or 'test'
- @param: desired image size (returns square images)
- """
-
- super().__init__(**kwargs)
- self.use_additional_parameters = use_additional_parameters
-
- self.categories = load_categories(self.paths['class_descriptions'])
- self.filter_categories()
- self.setup_category_id_and_number()
-
- self.image_descriptions = {}
- annotations = load_annotations(self.paths['annotations'], self.min_object_area, self.category_mapping,
- self.category_number)
- self.annotations = self.filter_object_number(annotations, self.min_object_area, self.min_objects_per_image,
- self.max_objects_per_image)
- self.image_ids = list(self.annotations.keys())
- self.clean_up_annotations_and_image_descriptions()
-
- def get_path_structure(self) -> Dict[str, str]:
- if self.split not in OPEN_IMAGES_STRUCTURE:
- raise ValueError(f'Split [{self.split} does not exist for Open Images data.]')
- return OPEN_IMAGES_STRUCTURE[self.split]
-
- def get_image_path(self, image_id: str) -> Path:
- return self.paths['files'].joinpath(f'{image_id:0>16}.jpg')
-
- def get_image_description(self, image_id: str) -> Dict[str, Any]:
- image_path = self.get_image_path(image_id)
- return {'file_path': str(image_path), 'file_name': image_path.name}
diff --git a/spaces/RamAnanth1/videocrafter/lvdm/models/modules/adapter.py b/spaces/RamAnanth1/videocrafter/lvdm/models/modules/adapter.py
deleted file mode 100644
index d7be40faba88bfd96e5f4a08537191505371a052..0000000000000000000000000000000000000000
--- a/spaces/RamAnanth1/videocrafter/lvdm/models/modules/adapter.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import torch
-import torch.nn as nn
-from collections import OrderedDict
-from lvdm.models.modules.util import (
- zero_module,
- conv_nd,
- avg_pool_nd
-)
-
-class Downsample(nn.Module):
- """
- A downsampling layer with an optional convolution.
- :param channels: channels in the inputs and outputs.
- :param use_conv: a bool determining if a convolution is applied.
- :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
- downsampling occurs in the inner-two dimensions.
- """
-
- def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
- super().__init__()
- self.channels = channels
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.dims = dims
- stride = 2 if dims != 3 else (1, 2, 2)
- if use_conv:
- self.op = conv_nd(
- dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
- )
- else:
- assert self.channels == self.out_channels
- self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
- def forward(self, x):
- assert x.shape[1] == self.channels
- return self.op(x)
-
-
-class ResnetBlock(nn.Module):
- def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True):
- super().__init__()
- ps = ksize // 2
- if in_c != out_c or sk == False:
- self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps)
- else:
- # print('n_in')
- self.in_conv = None
- self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1)
- self.act = nn.ReLU()
- self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps)
- if sk == False:
- self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps)
- else:
- self.skep = None
-
- self.down = down
- if self.down == True:
- self.down_opt = Downsample(in_c, use_conv=use_conv)
-
- def forward(self, x):
- if self.down == True:
- x = self.down_opt(x)
- if self.in_conv is not None: # edit
- x = self.in_conv(x)
-
- h = self.block1(x)
- h = self.act(h)
- h = self.block2(h)
- if self.skep is not None:
- return h + self.skep(x)
- else:
- return h + x
-
-
-class Adapter(nn.Module):
- def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True):
- super(Adapter, self).__init__()
- self.unshuffle = nn.PixelUnshuffle(8)
- self.channels = channels
- self.nums_rb = nums_rb
- self.body = []
- for i in range(len(channels)):
- for j in range(nums_rb):
- if (i != 0) and (j == 0):
- self.body.append(
- ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv))
- else:
- self.body.append(
- ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv))
- self.body = nn.ModuleList(self.body)
- self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1)
-
- def forward(self, x):
- # unshuffle
- x = self.unshuffle(x)
- # extract features
- features = []
- x = self.conv_in(x)
- for i in range(len(self.channels)):
- for j in range(self.nums_rb):
- idx = i * self.nums_rb + j
- x = self.body[idx](x)
- features.append(x)
-
- return features
\ No newline at end of file
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/importlib_resources/_compat.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/importlib_resources/_compat.py
deleted file mode 100644
index cb9fc820cb352aa6e92705aab4f55cbc2eff96bc..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/_vendor/importlib_resources/_compat.py
+++ /dev/null
@@ -1,98 +0,0 @@
-# flake8: noqa
-
-import abc
-import sys
-import pathlib
-from contextlib import suppress
-
-if sys.version_info >= (3, 10):
- from zipfile import Path as ZipPath # type: ignore
-else:
- from ..zipp import Path as ZipPath # type: ignore
-
-
-try:
- from typing import runtime_checkable # type: ignore
-except ImportError:
-
- def runtime_checkable(cls): # type: ignore
- return cls
-
-
-try:
- from typing import Protocol # type: ignore
-except ImportError:
- Protocol = abc.ABC # type: ignore
-
-
-class TraversableResourcesLoader:
- """
- Adapt loaders to provide TraversableResources and other
- compatibility.
-
- Used primarily for Python 3.9 and earlier where the native
- loaders do not yet implement TraversableResources.
- """
-
- def __init__(self, spec):
- self.spec = spec
-
- @property
- def path(self):
- return self.spec.origin
-
- def get_resource_reader(self, name):
- from . import readers, _adapters
-
- def _zip_reader(spec):
- with suppress(AttributeError):
- return readers.ZipReader(spec.loader, spec.name)
-
- def _namespace_reader(spec):
- with suppress(AttributeError, ValueError):
- return readers.NamespaceReader(spec.submodule_search_locations)
-
- def _available_reader(spec):
- with suppress(AttributeError):
- return spec.loader.get_resource_reader(spec.name)
-
- def _native_reader(spec):
- reader = _available_reader(spec)
- return reader if hasattr(reader, 'files') else None
-
- def _file_reader(spec):
- try:
- path = pathlib.Path(self.path)
- except TypeError:
- return None
- if path.exists():
- return readers.FileReader(self)
-
- return (
- # native reader if it supplies 'files'
- _native_reader(self.spec)
- or
- # local ZipReader if a zip module
- _zip_reader(self.spec)
- or
- # local NamespaceReader if a namespace module
- _namespace_reader(self.spec)
- or
- # local FileReader
- _file_reader(self.spec)
- # fallback - adapt the spec ResourceReader to TraversableReader
- or _adapters.CompatibilityFiles(self.spec)
- )
-
-
-def wrap_spec(package):
- """
- Construct a package spec with traversable compatibility
- on the spec/loader/reader.
-
- Supersedes _adapters.wrap_spec to use TraversableResourcesLoader
- from above for older Python compatibility (<3.10).
- """
- from . import _adapters
-
- return _adapters.SpecLoaderAdapter(package.__spec__, TraversableResourcesLoader)
diff --git a/spaces/Ricecake123/RVC-demo/lib/uvr5_pack/utils.py b/spaces/Ricecake123/RVC-demo/lib/uvr5_pack/utils.py
deleted file mode 100644
index 0fafe8793b0d539fa58dd024342250b24b6187a9..0000000000000000000000000000000000000000
--- a/spaces/Ricecake123/RVC-demo/lib/uvr5_pack/utils.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import torch
-import numpy as np
-from tqdm import tqdm
-import json
-
-
-def load_data(file_name: str = "./lib/uvr5_pack/name_params.json") -> dict:
- with open(file_name, "r") as f:
- data = json.load(f)
-
- return data
-
-
-def make_padding(width, cropsize, offset):
- left = offset
- roi_size = cropsize - left * 2
- if roi_size == 0:
- roi_size = cropsize
- right = roi_size - (width % roi_size) + left
-
- return left, right, roi_size
-
-
-def inference(X_spec, device, model, aggressiveness, data):
- """
- data ： dic configs
- """
-
- def _execute(
- X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half=True
- ):
- model.eval()
- with torch.no_grad():
- preds = []
-
- iterations = [n_window]
-
- total_iterations = sum(iterations)
- for i in tqdm(range(n_window)):
- start = i * roi_size
- X_mag_window = X_mag_pad[
- None, :, :, start : start + data["window_size"]
- ]
- X_mag_window = torch.from_numpy(X_mag_window)
- if is_half:
- X_mag_window = X_mag_window.half()
- X_mag_window = X_mag_window.to(device)
-
- pred = model.predict(X_mag_window, aggressiveness)
-
- pred = pred.detach().cpu().numpy()
- preds.append(pred[0])
-
- pred = np.concatenate(preds, axis=2)
- return pred
-
- def preprocess(X_spec):
- X_mag = np.abs(X_spec)
- X_phase = np.angle(X_spec)
-
- return X_mag, X_phase
-
- X_mag, X_phase = preprocess(X_spec)
-
- coef = X_mag.max()
- X_mag_pre = X_mag / coef
-
- n_frame = X_mag_pre.shape[2]
- pad_l, pad_r, roi_size = make_padding(n_frame, data["window_size"], model.offset)
- n_window = int(np.ceil(n_frame / roi_size))
-
- X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
-
- if list(model.state_dict().values())[0].dtype == torch.float16:
- is_half = True
- else:
- is_half = False
- pred = _execute(
- X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
- )
- pred = pred[:, :, :n_frame]
-
- if data["tta"]:
- pad_l += roi_size // 2
- pad_r += roi_size // 2
- n_window += 1
-
- X_mag_pad = np.pad(X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode="constant")
-
- pred_tta = _execute(
- X_mag_pad, roi_size, n_window, device, model, aggressiveness, is_half
- )
- pred_tta = pred_tta[:, :, roi_size // 2 :]
- pred_tta = pred_tta[:, :, :n_frame]
-
- return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.0j * X_phase)
- else:
- return pred * coef, X_mag, np.exp(1.0j * X_phase)
-
-
-def _get_name_params(model_path, model_hash):
- data = load_data()
- flag = False
- ModelName = model_path
- for type in list(data):
- for model in list(data[type][0]):
- for i in range(len(data[type][0][model])):
- if str(data[type][0][model][i]["hash_name"]) == model_hash:
- flag = True
- elif str(data[type][0][model][i]["hash_name"]) in ModelName:
- flag = True
-
- if flag:
- model_params_auto = data[type][0][model][i]["model_params"]
- param_name_auto = data[type][0][model][i]["param_name"]
- if type == "equivalent":
- return param_name_auto, model_params_auto
- else:
- flag = False
- return param_name_auto, model_params_auto
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/datasets/pipelines/compose.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/datasets/pipelines/compose.py
deleted file mode 100644
index ca48f1c935755c486edc2744e1713e2b5ba3cdc8..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/datasets/pipelines/compose.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import collections
-
-from mmcv.utils import build_from_cfg
-
-from ..builder import PIPELINES
-
-
-@PIPELINES.register_module()
-class Compose(object):
- """Compose multiple transforms sequentially.
-
- Args:
- transforms (Sequence[dict | callable]): Sequence of transform object or
- config dict to be composed.
- """
-
- def __init__(self, transforms):
- assert isinstance(transforms, collections.abc.Sequence)
- self.transforms = []
- for transform in transforms:
- if isinstance(transform, dict):
- transform = build_from_cfg(transform, PIPELINES)
- self.transforms.append(transform)
- elif callable(transform):
- self.transforms.append(transform)
- else:
- raise TypeError('transform must be callable or a dict')
-
- def __call__(self, data):
- """Call function to apply transforms sequentially.
-
- Args:
- data (dict): A result dict contains the data to transform.
-
- Returns:
- dict: Transformed data.
- """
-
- for t in self.transforms:
- data = t(data)
- if data is None:
- return None
- return data
-
- def __repr__(self):
- format_string = self.__class__.__name__ + '('
- for t in self.transforms:
- format_string += '\n'
- format_string += f' {t}'
- format_string += '\n)'
- return format_string
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/vfnet_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/vfnet_head.py
deleted file mode 100644
index 7243bb62893839568ec51928d88a5ad40b02a66c..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/dense_heads/vfnet_head.py
+++ /dev/null
@@ -1,794 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
-from mmcv.ops import DeformConv2d
-from mmcv.runner import force_fp32
-
-from mmdet.core import (bbox2distance, bbox_overlaps, build_anchor_generator,
- build_assigner, build_sampler, distance2bbox,
- multi_apply, multiclass_nms, reduce_mean)
-from ..builder import HEADS, build_loss
-from .atss_head import ATSSHead
-from .fcos_head import FCOSHead
-
-INF = 1e8
-
-
-@HEADS.register_module()
-class VFNetHead(ATSSHead, FCOSHead):
- """Head of `VarifocalNet (VFNet): An IoU-aware Dense Object
- Detector.`_.
-
- The VFNet predicts IoU-aware classification scores which mix the
- object presence confidence and object localization accuracy as the
- detection score. It is built on the FCOS architecture and uses ATSS
- for defining positive/negative training examples. The VFNet is trained
- with Varifocal Loss and empolys star-shaped deformable convolution to
- extract features for a bbox.
-
- Args:
- num_classes (int): Number of categories excluding the background
- category.
- in_channels (int): Number of channels in the input feature map.
- regress_ranges (tuple[tuple[int, int]]): Regress range of multiple
- level points.
- center_sampling (bool): If true, use center sampling. Default: False.
- center_sample_radius (float): Radius of center sampling. Default: 1.5.
- sync_num_pos (bool): If true, synchronize the number of positive
- examples across GPUs. Default: True
- gradient_mul (float): The multiplier to gradients from bbox refinement
- and recognition. Default: 0.1.
- bbox_norm_type (str): The bbox normalization type, 'reg_denom' or
- 'stride'. Default: reg_denom
- loss_cls_fl (dict): Config of focal loss.
- use_vfl (bool): If true, use varifocal loss for training.
- Default: True.
- loss_cls (dict): Config of varifocal loss.
- loss_bbox (dict): Config of localization loss, GIoU Loss.
- loss_bbox (dict): Config of localization refinement loss, GIoU Loss.
- norm_cfg (dict): dictionary to construct and config norm layer.
- Default: norm_cfg=dict(type='GN', num_groups=32,
- requires_grad=True).
- use_atss (bool): If true, use ATSS to define positive/negative
- examples. Default: True.
- anchor_generator (dict): Config of anchor generator for ATSS.
-
- Example:
- >>> self = VFNetHead(11, 7)
- >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
- >>> cls_score, bbox_pred, bbox_pred_refine= self.forward(feats)
- >>> assert len(cls_score) == len(self.scales)
- """ # noqa: E501
-
- def __init__(self,
- num_classes,
- in_channels,
- regress_ranges=((-1, 64), (64, 128), (128, 256), (256, 512),
- (512, INF)),
- center_sampling=False,
- center_sample_radius=1.5,
- sync_num_pos=True,
- gradient_mul=0.1,
- bbox_norm_type='reg_denom',
- loss_cls_fl=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- use_vfl=True,
- loss_cls=dict(
- type='VarifocalLoss',
- use_sigmoid=True,
- alpha=0.75,
- gamma=2.0,
- iou_weighted=True,
- loss_weight=1.0),
- loss_bbox=dict(type='GIoULoss', loss_weight=1.5),
- loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0),
- norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
- use_atss=True,
- anchor_generator=dict(
- type='AnchorGenerator',
- ratios=[1.0],
- octave_base_scale=8,
- scales_per_octave=1,
- center_offset=0.0,
- strides=[8, 16, 32, 64, 128]),
- **kwargs):
- # dcn base offsets, adapted from reppoints_head.py
- self.num_dconv_points = 9
- self.dcn_kernel = int(np.sqrt(self.num_dconv_points))
- self.dcn_pad = int((self.dcn_kernel - 1) / 2)
- dcn_base = np.arange(-self.dcn_pad,
- self.dcn_pad + 1).astype(np.float64)
- dcn_base_y = np.repeat(dcn_base, self.dcn_kernel)
- dcn_base_x = np.tile(dcn_base, self.dcn_kernel)
- dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape(
- (-1))
- self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1)
-
- super(FCOSHead, self).__init__(
- num_classes, in_channels, norm_cfg=norm_cfg, **kwargs)
- self.regress_ranges = regress_ranges
- self.reg_denoms = [
- regress_range[-1] for regress_range in regress_ranges
- ]
- self.reg_denoms[-1] = self.reg_denoms[-2] * 2
- self.center_sampling = center_sampling
- self.center_sample_radius = center_sample_radius
- self.sync_num_pos = sync_num_pos
- self.bbox_norm_type = bbox_norm_type
- self.gradient_mul = gradient_mul
- self.use_vfl = use_vfl
- if self.use_vfl:
- self.loss_cls = build_loss(loss_cls)
- else:
- self.loss_cls = build_loss(loss_cls_fl)
- self.loss_bbox = build_loss(loss_bbox)
- self.loss_bbox_refine = build_loss(loss_bbox_refine)
-
- # for getting ATSS targets
- self.use_atss = use_atss
- self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
- self.anchor_generator = build_anchor_generator(anchor_generator)
- self.anchor_center_offset = anchor_generator['center_offset']
- self.num_anchors = self.anchor_generator.num_base_anchors[0]
- self.sampling = False
- if self.train_cfg:
- self.assigner = build_assigner(self.train_cfg.assigner)
- sampler_cfg = dict(type='PseudoSampler')
- self.sampler = build_sampler(sampler_cfg, context=self)
-
- def _init_layers(self):
- """Initialize layers of the head."""
- super(FCOSHead, self)._init_cls_convs()
- super(FCOSHead, self)._init_reg_convs()
- self.relu = nn.ReLU(inplace=True)
- self.vfnet_reg_conv = ConvModule(
- self.feat_channels,
- self.feat_channels,
- 3,
- stride=1,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- bias=self.conv_bias)
- self.vfnet_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
- self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
- self.vfnet_reg_refine_dconv = DeformConv2d(
- self.feat_channels,
- self.feat_channels,
- self.dcn_kernel,
- 1,
- padding=self.dcn_pad)
- self.vfnet_reg_refine = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
- self.scales_refine = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
- self.vfnet_cls_dconv = DeformConv2d(
- self.feat_channels,
- self.feat_channels,
- self.dcn_kernel,
- 1,
- padding=self.dcn_pad)
- self.vfnet_cls = nn.Conv2d(
- self.feat_channels, self.cls_out_channels, 3, padding=1)
-
- def init_weights(self):
- """Initialize weights of the head."""
- for m in self.cls_convs:
- if isinstance(m.conv, nn.Conv2d):
- normal_init(m.conv, std=0.01)
- for m in self.reg_convs:
- if isinstance(m.conv, nn.Conv2d):
- normal_init(m.conv, std=0.01)
- normal_init(self.vfnet_reg_conv.conv, std=0.01)
- normal_init(self.vfnet_reg, std=0.01)
- normal_init(self.vfnet_reg_refine_dconv, std=0.01)
- normal_init(self.vfnet_reg_refine, std=0.01)
- normal_init(self.vfnet_cls_dconv, std=0.01)
- bias_cls = bias_init_with_prob(0.01)
- normal_init(self.vfnet_cls, std=0.01, bias=bias_cls)
-
- def forward(self, feats):
- """Forward features from the upstream network.
-
- Args:
- feats (tuple[Tensor]): Features from the upstream network, each is
- a 4D-tensor.
-
- Returns:
- tuple:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level, each is a 4D-tensor, the channel number is
- num_points * num_classes.
- bbox_preds (list[Tensor]): Box offsets for each
- scale level, each is a 4D-tensor, the channel number is
- num_points * 4.
- bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level, each is a 4D-tensor, the channel
- number is num_points * 4.
- """
- return multi_apply(self.forward_single, feats, self.scales,
- self.scales_refine, self.strides, self.reg_denoms)
-
- def forward_single(self, x, scale, scale_refine, stride, reg_denom):
- """Forward features of a single scale level.
-
- Args:
- x (Tensor): FPN feature maps of the specified stride.
- scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
- the bbox prediction.
- scale_refine (:obj: `mmcv.cnn.Scale`): Learnable scale module to
- resize the refined bbox prediction.
- stride (int): The corresponding stride for feature maps,
- used to normalize the bbox prediction when
- bbox_norm_type = 'stride'.
- reg_denom (int): The corresponding regression range for feature
- maps, only used to normalize the bbox prediction when
- bbox_norm_type = 'reg_denom'.
-
- Returns:
- tuple: iou-aware cls scores for each box, bbox predictions and
- refined bbox predictions of input feature maps.
- """
- cls_feat = x
- reg_feat = x
-
- for cls_layer in self.cls_convs:
- cls_feat = cls_layer(cls_feat)
-
- for reg_layer in self.reg_convs:
- reg_feat = reg_layer(reg_feat)
-
- # predict the bbox_pred of different level
- reg_feat_init = self.vfnet_reg_conv(reg_feat)
- if self.bbox_norm_type == 'reg_denom':
- bbox_pred = scale(
- self.vfnet_reg(reg_feat_init)).float().exp() * reg_denom
- elif self.bbox_norm_type == 'stride':
- bbox_pred = scale(
- self.vfnet_reg(reg_feat_init)).float().exp() * stride
- else:
- raise NotImplementedError
-
- # compute star deformable convolution offsets
- # converting dcn_offset to reg_feat.dtype thus VFNet can be
- # trained with FP16
- dcn_offset = self.star_dcn_offset(bbox_pred, self.gradient_mul,
- stride).to(reg_feat.dtype)
-
- # refine the bbox_pred
- reg_feat = self.relu(self.vfnet_reg_refine_dconv(reg_feat, dcn_offset))
- bbox_pred_refine = scale_refine(
- self.vfnet_reg_refine(reg_feat)).float().exp()
- bbox_pred_refine = bbox_pred_refine * bbox_pred.detach()
-
- # predict the iou-aware cls score
- cls_feat = self.relu(self.vfnet_cls_dconv(cls_feat, dcn_offset))
- cls_score = self.vfnet_cls(cls_feat)
-
- return cls_score, bbox_pred, bbox_pred_refine
-
- def star_dcn_offset(self, bbox_pred, gradient_mul, stride):
- """Compute the star deformable conv offsets.
-
- Args:
- bbox_pred (Tensor): Predicted bbox distance offsets (l, r, t, b).
- gradient_mul (float): Gradient multiplier.
- stride (int): The corresponding stride for feature maps,
- used to project the bbox onto the feature map.
-
- Returns:
- dcn_offsets (Tensor): The offsets for deformable convolution.
- """
- dcn_base_offset = self.dcn_base_offset.type_as(bbox_pred)
- bbox_pred_grad_mul = (1 - gradient_mul) * bbox_pred.detach() + \
- gradient_mul * bbox_pred
- # map to the feature map scale
- bbox_pred_grad_mul = bbox_pred_grad_mul / stride
- N, C, H, W = bbox_pred.size()
-
- x1 = bbox_pred_grad_mul[:, 0, :, :]
- y1 = bbox_pred_grad_mul[:, 1, :, :]
- x2 = bbox_pred_grad_mul[:, 2, :, :]
- y2 = bbox_pred_grad_mul[:, 3, :, :]
- bbox_pred_grad_mul_offset = bbox_pred.new_zeros(
- N, 2 * self.num_dconv_points, H, W)
- bbox_pred_grad_mul_offset[:, 0, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 1, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 2, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 4, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 5, :, :] = x2 # x2
- bbox_pred_grad_mul_offset[:, 7, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 11, :, :] = x2 # x2
- bbox_pred_grad_mul_offset[:, 12, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 13, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 14, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 16, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 17, :, :] = x2 # x2
- dcn_offset = bbox_pred_grad_mul_offset - dcn_base_offset
-
- return dcn_offset
-
- @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'bbox_preds_refine'))
- def loss(self,
- cls_scores,
- bbox_preds,
- bbox_preds_refine,
- gt_bboxes,
- gt_labels,
- img_metas,
- gt_bboxes_ignore=None):
- """Compute loss of the head.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level, each is a 4D-tensor, the channel number is
- num_points * num_classes.
- bbox_preds (list[Tensor]): Box offsets for each
- scale level, each is a 4D-tensor, the channel number is
- num_points * 4.
- bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level, each is a 4D-tensor, the channel
- number is num_points * 4.
- gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
- shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
- gt_labels (list[Tensor]): class indices corresponding to each box
- img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- gt_bboxes_ignore (None | list[Tensor]): specify which bounding
- boxes can be ignored when computing the loss.
- Default: None.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine)
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- all_level_points = self.get_points(featmap_sizes, bbox_preds[0].dtype,
- bbox_preds[0].device)
- labels, label_weights, bbox_targets, bbox_weights = self.get_targets(
- cls_scores, all_level_points, gt_bboxes, gt_labels, img_metas,
- gt_bboxes_ignore)
-
- num_imgs = cls_scores[0].size(0)
- # flatten cls_scores, bbox_preds and bbox_preds_refine
- flatten_cls_scores = [
- cls_score.permute(0, 2, 3,
- 1).reshape(-1,
- self.cls_out_channels).contiguous()
- for cls_score in cls_scores
- ]
- flatten_bbox_preds = [
- bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
- for bbox_pred in bbox_preds
- ]
- flatten_bbox_preds_refine = [
- bbox_pred_refine.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
- for bbox_pred_refine in bbox_preds_refine
- ]
- flatten_cls_scores = torch.cat(flatten_cls_scores)
- flatten_bbox_preds = torch.cat(flatten_bbox_preds)
- flatten_bbox_preds_refine = torch.cat(flatten_bbox_preds_refine)
- flatten_labels = torch.cat(labels)
- flatten_bbox_targets = torch.cat(bbox_targets)
- # repeat points to align with bbox_preds
- flatten_points = torch.cat(
- [points.repeat(num_imgs, 1) for points in all_level_points])
-
- # FG cat_id: [0, num_classes - 1], BG cat_id: num_classes
- bg_class_ind = self.num_classes
- pos_inds = torch.where(
- ((flatten_labels >= 0) & (flatten_labels < bg_class_ind)) > 0)[0]
- num_pos = len(pos_inds)
-
- pos_bbox_preds = flatten_bbox_preds[pos_inds]
- pos_bbox_preds_refine = flatten_bbox_preds_refine[pos_inds]
- pos_labels = flatten_labels[pos_inds]
-
- # sync num_pos across all gpus
- if self.sync_num_pos:
- num_pos_avg_per_gpu = reduce_mean(
- pos_inds.new_tensor(num_pos).float()).item()
- num_pos_avg_per_gpu = max(num_pos_avg_per_gpu, 1.0)
- else:
- num_pos_avg_per_gpu = num_pos
-
- if num_pos > 0:
- pos_bbox_targets = flatten_bbox_targets[pos_inds]
- pos_points = flatten_points[pos_inds]
-
- pos_decoded_bbox_preds = distance2bbox(pos_points, pos_bbox_preds)
- pos_decoded_target_preds = distance2bbox(pos_points,
- pos_bbox_targets)
- iou_targets_ini = bbox_overlaps(
- pos_decoded_bbox_preds,
- pos_decoded_target_preds.detach(),
- is_aligned=True).clamp(min=1e-6)
- bbox_weights_ini = iou_targets_ini.clone().detach()
- iou_targets_ini_avg_per_gpu = reduce_mean(
- bbox_weights_ini.sum()).item()
- bbox_avg_factor_ini = max(iou_targets_ini_avg_per_gpu, 1.0)
- loss_bbox = self.loss_bbox(
- pos_decoded_bbox_preds,
- pos_decoded_target_preds.detach(),
- weight=bbox_weights_ini,
- avg_factor=bbox_avg_factor_ini)
-
- pos_decoded_bbox_preds_refine = \
- distance2bbox(pos_points, pos_bbox_preds_refine)
- iou_targets_rf = bbox_overlaps(
- pos_decoded_bbox_preds_refine,
- pos_decoded_target_preds.detach(),
- is_aligned=True).clamp(min=1e-6)
- bbox_weights_rf = iou_targets_rf.clone().detach()
- iou_targets_rf_avg_per_gpu = reduce_mean(
- bbox_weights_rf.sum()).item()
- bbox_avg_factor_rf = max(iou_targets_rf_avg_per_gpu, 1.0)
- loss_bbox_refine = self.loss_bbox_refine(
- pos_decoded_bbox_preds_refine,
- pos_decoded_target_preds.detach(),
- weight=bbox_weights_rf,
- avg_factor=bbox_avg_factor_rf)
-
- # build IoU-aware cls_score targets
- if self.use_vfl:
- pos_ious = iou_targets_rf.clone().detach()
- cls_iou_targets = torch.zeros_like(flatten_cls_scores)
- cls_iou_targets[pos_inds, pos_labels] = pos_ious
- else:
- loss_bbox = pos_bbox_preds.sum() * 0
- loss_bbox_refine = pos_bbox_preds_refine.sum() * 0
- if self.use_vfl:
- cls_iou_targets = torch.zeros_like(flatten_cls_scores)
-
- if self.use_vfl:
- loss_cls = self.loss_cls(
- flatten_cls_scores,
- cls_iou_targets,
- avg_factor=num_pos_avg_per_gpu)
- else:
- loss_cls = self.loss_cls(
- flatten_cls_scores,
- flatten_labels,
- weight=label_weights,
- avg_factor=num_pos_avg_per_gpu)
-
- return dict(
- loss_cls=loss_cls,
- loss_bbox=loss_bbox,
- loss_bbox_rf=loss_bbox_refine)
-
- @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'bbox_preds_refine'))
- def get_bboxes(self,
- cls_scores,
- bbox_preds,
- bbox_preds_refine,
- img_metas,
- cfg=None,
- rescale=None,
- with_nms=True):
- """Transform network outputs for a batch into bbox predictions.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- bbox_preds (list[Tensor]): Box offsets for each scale
- level with shape (N, num_points * 4, H, W).
- bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level with shape (N, num_points * 4, H, W).
- img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- cfg (mmcv.Config): Test / postprocessing configuration,
- if None, test_cfg would be used. Default: None.
- rescale (bool): If True, return boxes in original image space.
- Default: False.
- with_nms (bool): If True, do nms before returning boxes.
- Default: True.
-
- Returns:
- list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
- The first item is an (n, 5) tensor, where the first 4 columns
- are bounding box positions (tl_x, tl_y, br_x, br_y) and the
- 5-th column is a score between 0 and 1. The second item is a
- (n,) tensor where each item is the predicted class label of
- the corresponding box.
- """
- assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine)
- num_levels = len(cls_scores)
-
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- mlvl_points = self.get_points(featmap_sizes, bbox_preds[0].dtype,
- bbox_preds[0].device)
- result_list = []
- for img_id in range(len(img_metas)):
- cls_score_list = [
- cls_scores[i][img_id].detach() for i in range(num_levels)
- ]
- bbox_pred_list = [
- bbox_preds_refine[i][img_id].detach()
- for i in range(num_levels)
- ]
- img_shape = img_metas[img_id]['img_shape']
- scale_factor = img_metas[img_id]['scale_factor']
- det_bboxes = self._get_bboxes_single(cls_score_list,
- bbox_pred_list, mlvl_points,
- img_shape, scale_factor, cfg,
- rescale, with_nms)
- result_list.append(det_bboxes)
- return result_list
-
- def _get_bboxes_single(self,
- cls_scores,
- bbox_preds,
- mlvl_points,
- img_shape,
- scale_factor,
- cfg,
- rescale=False,
- with_nms=True):
- """Transform outputs for a single batch item into bbox predictions.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for a single scale
- level with shape (num_points * num_classes, H, W).
- bbox_preds (list[Tensor]): Box offsets for a single scale
- level with shape (num_points * 4, H, W).
- mlvl_points (list[Tensor]): Box reference for a single scale level
- with shape (num_total_points, 4).
- img_shape (tuple[int]): Shape of the input image,
- (height, width, 3).
- scale_factor (ndarray): Scale factor of the image arrange as
- (w_scale, h_scale, w_scale, h_scale).
- cfg (mmcv.Config | None): Test / postprocessing configuration,
- if None, test_cfg would be used.
- rescale (bool): If True, return boxes in original image space.
- Default: False.
- with_nms (bool): If True, do nms before returning boxes.
- Default: True.
-
- Returns:
- tuple(Tensor):
- det_bboxes (Tensor): BBox predictions in shape (n, 5), where
- the first 4 columns are bounding box positions
- (tl_x, tl_y, br_x, br_y) and the 5-th column is a score
- between 0 and 1.
- det_labels (Tensor): A (n,) tensor where each item is the
- predicted class label of the corresponding box.
- """
- cfg = self.test_cfg if cfg is None else cfg
- assert len(cls_scores) == len(bbox_preds) == len(mlvl_points)
- mlvl_bboxes = []
- mlvl_scores = []
- for cls_score, bbox_pred, points in zip(cls_scores, bbox_preds,
- mlvl_points):
- assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
- scores = cls_score.permute(1, 2, 0).reshape(
- -1, self.cls_out_channels).contiguous().sigmoid()
- bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4).contiguous()
-
- nms_pre = cfg.get('nms_pre', -1)
- if 0 < nms_pre < scores.shape[0]:
- max_scores, _ = scores.max(dim=1)
- _, topk_inds = max_scores.topk(nms_pre)
- points = points[topk_inds, :]
- bbox_pred = bbox_pred[topk_inds, :]
- scores = scores[topk_inds, :]
- bboxes = distance2bbox(points, bbox_pred, max_shape=img_shape)
- mlvl_bboxes.append(bboxes)
- mlvl_scores.append(scores)
- mlvl_bboxes = torch.cat(mlvl_bboxes)
- if rescale:
- mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
- mlvl_scores = torch.cat(mlvl_scores)
- padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
- # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
- # BG cat_id: num_class
- mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
- if with_nms:
- det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
- cfg.score_thr, cfg.nms,
- cfg.max_per_img)
- return det_bboxes, det_labels
- else:
- return mlvl_bboxes, mlvl_scores
-
- def _get_points_single(self,
- featmap_size,
- stride,
- dtype,
- device,
- flatten=False):
- """Get points according to feature map sizes."""
- h, w = featmap_size
- x_range = torch.arange(
- 0, w * stride, stride, dtype=dtype, device=device)
- y_range = torch.arange(
- 0, h * stride, stride, dtype=dtype, device=device)
- y, x = torch.meshgrid(y_range, x_range)
- # to be compatible with anchor points in ATSS
- if self.use_atss:
- points = torch.stack(
- (x.reshape(-1), y.reshape(-1)), dim=-1) + \
- stride * self.anchor_center_offset
- else:
- points = torch.stack(
- (x.reshape(-1), y.reshape(-1)), dim=-1) + stride // 2
- return points
-
- def get_targets(self, cls_scores, mlvl_points, gt_bboxes, gt_labels,
- img_metas, gt_bboxes_ignore):
- """A wrapper for computing ATSS and FCOS targets for points in multiple
- images.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- gt_bboxes (list[Tensor]): Ground truth bboxes of each image,
- each has shape (num_gt, 4).
- gt_labels (list[Tensor]): Ground truth labels of each box,
- each has shape (num_gt,).
- img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- gt_bboxes_ignore (None | Tensor): Ground truth bboxes to be
- ignored, shape (num_ignored_gts, 4).
-
- Returns:
- tuple:
- labels_list (list[Tensor]): Labels of each level.
- label_weights (Tensor/None): Label weights of all levels.
- bbox_targets_list (list[Tensor]): Regression targets of each
- level, (l, t, r, b).
- bbox_weights (Tensor/None): Bbox weights of all levels.
- """
- if self.use_atss:
- return self.get_atss_targets(cls_scores, mlvl_points, gt_bboxes,
- gt_labels, img_metas,
- gt_bboxes_ignore)
- else:
- self.norm_on_bbox = False
- return self.get_fcos_targets(mlvl_points, gt_bboxes, gt_labels)
-
- def _get_target_single(self, *args, **kwargs):
- """Avoid ambiguity in multiple inheritance."""
- if self.use_atss:
- return ATSSHead._get_target_single(self, *args, **kwargs)
- else:
- return FCOSHead._get_target_single(self, *args, **kwargs)
-
- def get_fcos_targets(self, points, gt_bboxes_list, gt_labels_list):
- """Compute FCOS regression and classification targets for points in
- multiple images.
-
- Args:
- points (list[Tensor]): Points of each fpn level, each has shape
- (num_points, 2).
- gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image,
- each has shape (num_gt, 4).
- gt_labels_list (list[Tensor]): Ground truth labels of each box,
- each has shape (num_gt,).
-
- Returns:
- tuple:
- labels (list[Tensor]): Labels of each level.
- label_weights: None, to be compatible with ATSS targets.
- bbox_targets (list[Tensor]): BBox targets of each level.
- bbox_weights: None, to be compatible with ATSS targets.
- """
- labels, bbox_targets = FCOSHead.get_targets(self, points,
- gt_bboxes_list,
- gt_labels_list)
- label_weights = None
- bbox_weights = None
- return labels, label_weights, bbox_targets, bbox_weights
-
- def get_atss_targets(self,
- cls_scores,
- mlvl_points,
- gt_bboxes,
- gt_labels,
- img_metas,
- gt_bboxes_ignore=None):
- """A wrapper for computing ATSS targets for points in multiple images.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- gt_bboxes (list[Tensor]): Ground truth bboxes of each image,
- each has shape (num_gt, 4).
- gt_labels (list[Tensor]): Ground truth labels of each box,
- each has shape (num_gt,).
- img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- gt_bboxes_ignore (None | Tensor): Ground truth bboxes to be
- ignored, shape (num_ignored_gts, 4). Default: None.
-
- Returns:
- tuple:
- labels_list (list[Tensor]): Labels of each level.
- label_weights (Tensor): Label weights of all levels.
- bbox_targets_list (list[Tensor]): Regression targets of each
- level, (l, t, r, b).
- bbox_weights (Tensor): Bbox weights of all levels.
- """
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- assert len(featmap_sizes) == self.anchor_generator.num_levels
-
- device = cls_scores[0].device
- anchor_list, valid_flag_list = self.get_anchors(
- featmap_sizes, img_metas, device=device)
- label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-
- cls_reg_targets = ATSSHead.get_targets(
- self,
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- gt_bboxes_ignore_list=gt_bboxes_ignore,
- gt_labels_list=gt_labels,
- label_channels=label_channels,
- unmap_outputs=True)
- if cls_reg_targets is None:
- return None
-
- (anchor_list, labels_list, label_weights_list, bbox_targets_list,
- bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
-
- bbox_targets_list = [
- bbox_targets.reshape(-1, 4) for bbox_targets in bbox_targets_list
- ]
-
- num_imgs = len(img_metas)
- # transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format
- bbox_targets_list = self.transform_bbox_targets(
- bbox_targets_list, mlvl_points, num_imgs)
-
- labels_list = [labels.reshape(-1) for labels in labels_list]
- label_weights_list = [
- label_weights.reshape(-1) for label_weights in label_weights_list
- ]
- bbox_weights_list = [
- bbox_weights.reshape(-1) for bbox_weights in bbox_weights_list
- ]
- label_weights = torch.cat(label_weights_list)
- bbox_weights = torch.cat(bbox_weights_list)
- return labels_list, label_weights, bbox_targets_list, bbox_weights
-
- def transform_bbox_targets(self, decoded_bboxes, mlvl_points, num_imgs):
- """Transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format.
-
- Args:
- decoded_bboxes (list[Tensor]): Regression targets of each level,
- in the form of (x1, y1, x2, y2).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- num_imgs (int): the number of images in a batch.
-
- Returns:
- bbox_targets (list[Tensor]): Regression targets of each level in
- the form of (l, t, r, b).
- """
- # TODO: Re-implemented in Class PointCoder
- assert len(decoded_bboxes) == len(mlvl_points)
- num_levels = len(decoded_bboxes)
- mlvl_points = [points.repeat(num_imgs, 1) for points in mlvl_points]
- bbox_targets = []
- for i in range(num_levels):
- bbox_target = bbox2distance(mlvl_points[i], decoded_bboxes[i])
- bbox_targets.append(bbox_target)
-
- return bbox_targets
-
- def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
- missing_keys, unexpected_keys, error_msgs):
- """Override the method in the parent class to avoid changing para's
- name."""
- pass
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/vfnet_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/vfnet_head.py
deleted file mode 100644
index 7243bb62893839568ec51928d88a5ad40b02a66c..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/vfnet_head.py
+++ /dev/null
@@ -1,794 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
-from mmcv.ops import DeformConv2d
-from mmcv.runner import force_fp32
-
-from mmdet.core import (bbox2distance, bbox_overlaps, build_anchor_generator,
- build_assigner, build_sampler, distance2bbox,
- multi_apply, multiclass_nms, reduce_mean)
-from ..builder import HEADS, build_loss
-from .atss_head import ATSSHead
-from .fcos_head import FCOSHead
-
-INF = 1e8
-
-
-@HEADS.register_module()
-class VFNetHead(ATSSHead, FCOSHead):
- """Head of `VarifocalNet (VFNet): An IoU-aware Dense Object
- Detector.`_.
-
- The VFNet predicts IoU-aware classification scores which mix the
- object presence confidence and object localization accuracy as the
- detection score. It is built on the FCOS architecture and uses ATSS
- for defining positive/negative training examples. The VFNet is trained
- with Varifocal Loss and empolys star-shaped deformable convolution to
- extract features for a bbox.
-
- Args:
- num_classes (int): Number of categories excluding the background
- category.
- in_channels (int): Number of channels in the input feature map.
- regress_ranges (tuple[tuple[int, int]]): Regress range of multiple
- level points.
- center_sampling (bool): If true, use center sampling. Default: False.
- center_sample_radius (float): Radius of center sampling. Default: 1.5.
- sync_num_pos (bool): If true, synchronize the number of positive
- examples across GPUs. Default: True
- gradient_mul (float): The multiplier to gradients from bbox refinement
- and recognition. Default: 0.1.
- bbox_norm_type (str): The bbox normalization type, 'reg_denom' or
- 'stride'. Default: reg_denom
- loss_cls_fl (dict): Config of focal loss.
- use_vfl (bool): If true, use varifocal loss for training.
- Default: True.
- loss_cls (dict): Config of varifocal loss.
- loss_bbox (dict): Config of localization loss, GIoU Loss.
- loss_bbox (dict): Config of localization refinement loss, GIoU Loss.
- norm_cfg (dict): dictionary to construct and config norm layer.
- Default: norm_cfg=dict(type='GN', num_groups=32,
- requires_grad=True).
- use_atss (bool): If true, use ATSS to define positive/negative
- examples. Default: True.
- anchor_generator (dict): Config of anchor generator for ATSS.
-
- Example:
- >>> self = VFNetHead(11, 7)
- >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
- >>> cls_score, bbox_pred, bbox_pred_refine= self.forward(feats)
- >>> assert len(cls_score) == len(self.scales)
- """ # noqa: E501
-
- def __init__(self,
- num_classes,
- in_channels,
- regress_ranges=((-1, 64), (64, 128), (128, 256), (256, 512),
- (512, INF)),
- center_sampling=False,
- center_sample_radius=1.5,
- sync_num_pos=True,
- gradient_mul=0.1,
- bbox_norm_type='reg_denom',
- loss_cls_fl=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- use_vfl=True,
- loss_cls=dict(
- type='VarifocalLoss',
- use_sigmoid=True,
- alpha=0.75,
- gamma=2.0,
- iou_weighted=True,
- loss_weight=1.0),
- loss_bbox=dict(type='GIoULoss', loss_weight=1.5),
- loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0),
- norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
- use_atss=True,
- anchor_generator=dict(
- type='AnchorGenerator',
- ratios=[1.0],
- octave_base_scale=8,
- scales_per_octave=1,
- center_offset=0.0,
- strides=[8, 16, 32, 64, 128]),
- **kwargs):
- # dcn base offsets, adapted from reppoints_head.py
- self.num_dconv_points = 9
- self.dcn_kernel = int(np.sqrt(self.num_dconv_points))
- self.dcn_pad = int((self.dcn_kernel - 1) / 2)
- dcn_base = np.arange(-self.dcn_pad,
- self.dcn_pad + 1).astype(np.float64)
- dcn_base_y = np.repeat(dcn_base, self.dcn_kernel)
- dcn_base_x = np.tile(dcn_base, self.dcn_kernel)
- dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape(
- (-1))
- self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1)
-
- super(FCOSHead, self).__init__(
- num_classes, in_channels, norm_cfg=norm_cfg, **kwargs)
- self.regress_ranges = regress_ranges
- self.reg_denoms = [
- regress_range[-1] for regress_range in regress_ranges
- ]
- self.reg_denoms[-1] = self.reg_denoms[-2] * 2
- self.center_sampling = center_sampling
- self.center_sample_radius = center_sample_radius
- self.sync_num_pos = sync_num_pos
- self.bbox_norm_type = bbox_norm_type
- self.gradient_mul = gradient_mul
- self.use_vfl = use_vfl
- if self.use_vfl:
- self.loss_cls = build_loss(loss_cls)
- else:
- self.loss_cls = build_loss(loss_cls_fl)
- self.loss_bbox = build_loss(loss_bbox)
- self.loss_bbox_refine = build_loss(loss_bbox_refine)
-
- # for getting ATSS targets
- self.use_atss = use_atss
- self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
- self.anchor_generator = build_anchor_generator(anchor_generator)
- self.anchor_center_offset = anchor_generator['center_offset']
- self.num_anchors = self.anchor_generator.num_base_anchors[0]
- self.sampling = False
- if self.train_cfg:
- self.assigner = build_assigner(self.train_cfg.assigner)
- sampler_cfg = dict(type='PseudoSampler')
- self.sampler = build_sampler(sampler_cfg, context=self)
-
- def _init_layers(self):
- """Initialize layers of the head."""
- super(FCOSHead, self)._init_cls_convs()
- super(FCOSHead, self)._init_reg_convs()
- self.relu = nn.ReLU(inplace=True)
- self.vfnet_reg_conv = ConvModule(
- self.feat_channels,
- self.feat_channels,
- 3,
- stride=1,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg,
- bias=self.conv_bias)
- self.vfnet_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
- self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
- self.vfnet_reg_refine_dconv = DeformConv2d(
- self.feat_channels,
- self.feat_channels,
- self.dcn_kernel,
- 1,
- padding=self.dcn_pad)
- self.vfnet_reg_refine = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
- self.scales_refine = nn.ModuleList([Scale(1.0) for _ in self.strides])
-
- self.vfnet_cls_dconv = DeformConv2d(
- self.feat_channels,
- self.feat_channels,
- self.dcn_kernel,
- 1,
- padding=self.dcn_pad)
- self.vfnet_cls = nn.Conv2d(
- self.feat_channels, self.cls_out_channels, 3, padding=1)
-
- def init_weights(self):
- """Initialize weights of the head."""
- for m in self.cls_convs:
- if isinstance(m.conv, nn.Conv2d):
- normal_init(m.conv, std=0.01)
- for m in self.reg_convs:
- if isinstance(m.conv, nn.Conv2d):
- normal_init(m.conv, std=0.01)
- normal_init(self.vfnet_reg_conv.conv, std=0.01)
- normal_init(self.vfnet_reg, std=0.01)
- normal_init(self.vfnet_reg_refine_dconv, std=0.01)
- normal_init(self.vfnet_reg_refine, std=0.01)
- normal_init(self.vfnet_cls_dconv, std=0.01)
- bias_cls = bias_init_with_prob(0.01)
- normal_init(self.vfnet_cls, std=0.01, bias=bias_cls)
-
- def forward(self, feats):
- """Forward features from the upstream network.
-
- Args:
- feats (tuple[Tensor]): Features from the upstream network, each is
- a 4D-tensor.
-
- Returns:
- tuple:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level, each is a 4D-tensor, the channel number is
- num_points * num_classes.
- bbox_preds (list[Tensor]): Box offsets for each
- scale level, each is a 4D-tensor, the channel number is
- num_points * 4.
- bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level, each is a 4D-tensor, the channel
- number is num_points * 4.
- """
- return multi_apply(self.forward_single, feats, self.scales,
- self.scales_refine, self.strides, self.reg_denoms)
-
- def forward_single(self, x, scale, scale_refine, stride, reg_denom):
- """Forward features of a single scale level.
-
- Args:
- x (Tensor): FPN feature maps of the specified stride.
- scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
- the bbox prediction.
- scale_refine (:obj: `mmcv.cnn.Scale`): Learnable scale module to
- resize the refined bbox prediction.
- stride (int): The corresponding stride for feature maps,
- used to normalize the bbox prediction when
- bbox_norm_type = 'stride'.
- reg_denom (int): The corresponding regression range for feature
- maps, only used to normalize the bbox prediction when
- bbox_norm_type = 'reg_denom'.
-
- Returns:
- tuple: iou-aware cls scores for each box, bbox predictions and
- refined bbox predictions of input feature maps.
- """
- cls_feat = x
- reg_feat = x
-
- for cls_layer in self.cls_convs:
- cls_feat = cls_layer(cls_feat)
-
- for reg_layer in self.reg_convs:
- reg_feat = reg_layer(reg_feat)
-
- # predict the bbox_pred of different level
- reg_feat_init = self.vfnet_reg_conv(reg_feat)
- if self.bbox_norm_type == 'reg_denom':
- bbox_pred = scale(
- self.vfnet_reg(reg_feat_init)).float().exp() * reg_denom
- elif self.bbox_norm_type == 'stride':
- bbox_pred = scale(
- self.vfnet_reg(reg_feat_init)).float().exp() * stride
- else:
- raise NotImplementedError
-
- # compute star deformable convolution offsets
- # converting dcn_offset to reg_feat.dtype thus VFNet can be
- # trained with FP16
- dcn_offset = self.star_dcn_offset(bbox_pred, self.gradient_mul,
- stride).to(reg_feat.dtype)
-
- # refine the bbox_pred
- reg_feat = self.relu(self.vfnet_reg_refine_dconv(reg_feat, dcn_offset))
- bbox_pred_refine = scale_refine(
- self.vfnet_reg_refine(reg_feat)).float().exp()
- bbox_pred_refine = bbox_pred_refine * bbox_pred.detach()
-
- # predict the iou-aware cls score
- cls_feat = self.relu(self.vfnet_cls_dconv(cls_feat, dcn_offset))
- cls_score = self.vfnet_cls(cls_feat)
-
- return cls_score, bbox_pred, bbox_pred_refine
-
- def star_dcn_offset(self, bbox_pred, gradient_mul, stride):
- """Compute the star deformable conv offsets.
-
- Args:
- bbox_pred (Tensor): Predicted bbox distance offsets (l, r, t, b).
- gradient_mul (float): Gradient multiplier.
- stride (int): The corresponding stride for feature maps,
- used to project the bbox onto the feature map.
-
- Returns:
- dcn_offsets (Tensor): The offsets for deformable convolution.
- """
- dcn_base_offset = self.dcn_base_offset.type_as(bbox_pred)
- bbox_pred_grad_mul = (1 - gradient_mul) * bbox_pred.detach() + \
- gradient_mul * bbox_pred
- # map to the feature map scale
- bbox_pred_grad_mul = bbox_pred_grad_mul / stride
- N, C, H, W = bbox_pred.size()
-
- x1 = bbox_pred_grad_mul[:, 0, :, :]
- y1 = bbox_pred_grad_mul[:, 1, :, :]
- x2 = bbox_pred_grad_mul[:, 2, :, :]
- y2 = bbox_pred_grad_mul[:, 3, :, :]
- bbox_pred_grad_mul_offset = bbox_pred.new_zeros(
- N, 2 * self.num_dconv_points, H, W)
- bbox_pred_grad_mul_offset[:, 0, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 1, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 2, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 4, :, :] = -1.0 * y1 # -y1
- bbox_pred_grad_mul_offset[:, 5, :, :] = x2 # x2
- bbox_pred_grad_mul_offset[:, 7, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 11, :, :] = x2 # x2
- bbox_pred_grad_mul_offset[:, 12, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 13, :, :] = -1.0 * x1 # -x1
- bbox_pred_grad_mul_offset[:, 14, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 16, :, :] = y2 # y2
- bbox_pred_grad_mul_offset[:, 17, :, :] = x2 # x2
- dcn_offset = bbox_pred_grad_mul_offset - dcn_base_offset
-
- return dcn_offset
-
- @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'bbox_preds_refine'))
- def loss(self,
- cls_scores,
- bbox_preds,
- bbox_preds_refine,
- gt_bboxes,
- gt_labels,
- img_metas,
- gt_bboxes_ignore=None):
- """Compute loss of the head.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level, each is a 4D-tensor, the channel number is
- num_points * num_classes.
- bbox_preds (list[Tensor]): Box offsets for each
- scale level, each is a 4D-tensor, the channel number is
- num_points * 4.
- bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level, each is a 4D-tensor, the channel
- number is num_points * 4.
- gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
- shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
- gt_labels (list[Tensor]): class indices corresponding to each box
- img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- gt_bboxes_ignore (None | list[Tensor]): specify which bounding
- boxes can be ignored when computing the loss.
- Default: None.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine)
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- all_level_points = self.get_points(featmap_sizes, bbox_preds[0].dtype,
- bbox_preds[0].device)
- labels, label_weights, bbox_targets, bbox_weights = self.get_targets(
- cls_scores, all_level_points, gt_bboxes, gt_labels, img_metas,
- gt_bboxes_ignore)
-
- num_imgs = cls_scores[0].size(0)
- # flatten cls_scores, bbox_preds and bbox_preds_refine
- flatten_cls_scores = [
- cls_score.permute(0, 2, 3,
- 1).reshape(-1,
- self.cls_out_channels).contiguous()
- for cls_score in cls_scores
- ]
- flatten_bbox_preds = [
- bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
- for bbox_pred in bbox_preds
- ]
- flatten_bbox_preds_refine = [
- bbox_pred_refine.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
- for bbox_pred_refine in bbox_preds_refine
- ]
- flatten_cls_scores = torch.cat(flatten_cls_scores)
- flatten_bbox_preds = torch.cat(flatten_bbox_preds)
- flatten_bbox_preds_refine = torch.cat(flatten_bbox_preds_refine)
- flatten_labels = torch.cat(labels)
- flatten_bbox_targets = torch.cat(bbox_targets)
- # repeat points to align with bbox_preds
- flatten_points = torch.cat(
- [points.repeat(num_imgs, 1) for points in all_level_points])
-
- # FG cat_id: [0, num_classes - 1], BG cat_id: num_classes
- bg_class_ind = self.num_classes
- pos_inds = torch.where(
- ((flatten_labels >= 0) & (flatten_labels < bg_class_ind)) > 0)[0]
- num_pos = len(pos_inds)
-
- pos_bbox_preds = flatten_bbox_preds[pos_inds]
- pos_bbox_preds_refine = flatten_bbox_preds_refine[pos_inds]
- pos_labels = flatten_labels[pos_inds]
-
- # sync num_pos across all gpus
- if self.sync_num_pos:
- num_pos_avg_per_gpu = reduce_mean(
- pos_inds.new_tensor(num_pos).float()).item()
- num_pos_avg_per_gpu = max(num_pos_avg_per_gpu, 1.0)
- else:
- num_pos_avg_per_gpu = num_pos
-
- if num_pos > 0:
- pos_bbox_targets = flatten_bbox_targets[pos_inds]
- pos_points = flatten_points[pos_inds]
-
- pos_decoded_bbox_preds = distance2bbox(pos_points, pos_bbox_preds)
- pos_decoded_target_preds = distance2bbox(pos_points,
- pos_bbox_targets)
- iou_targets_ini = bbox_overlaps(
- pos_decoded_bbox_preds,
- pos_decoded_target_preds.detach(),
- is_aligned=True).clamp(min=1e-6)
- bbox_weights_ini = iou_targets_ini.clone().detach()
- iou_targets_ini_avg_per_gpu = reduce_mean(
- bbox_weights_ini.sum()).item()
- bbox_avg_factor_ini = max(iou_targets_ini_avg_per_gpu, 1.0)
- loss_bbox = self.loss_bbox(
- pos_decoded_bbox_preds,
- pos_decoded_target_preds.detach(),
- weight=bbox_weights_ini,
- avg_factor=bbox_avg_factor_ini)
-
- pos_decoded_bbox_preds_refine = \
- distance2bbox(pos_points, pos_bbox_preds_refine)
- iou_targets_rf = bbox_overlaps(
- pos_decoded_bbox_preds_refine,
- pos_decoded_target_preds.detach(),
- is_aligned=True).clamp(min=1e-6)
- bbox_weights_rf = iou_targets_rf.clone().detach()
- iou_targets_rf_avg_per_gpu = reduce_mean(
- bbox_weights_rf.sum()).item()
- bbox_avg_factor_rf = max(iou_targets_rf_avg_per_gpu, 1.0)
- loss_bbox_refine = self.loss_bbox_refine(
- pos_decoded_bbox_preds_refine,
- pos_decoded_target_preds.detach(),
- weight=bbox_weights_rf,
- avg_factor=bbox_avg_factor_rf)
-
- # build IoU-aware cls_score targets
- if self.use_vfl:
- pos_ious = iou_targets_rf.clone().detach()
- cls_iou_targets = torch.zeros_like(flatten_cls_scores)
- cls_iou_targets[pos_inds, pos_labels] = pos_ious
- else:
- loss_bbox = pos_bbox_preds.sum() * 0
- loss_bbox_refine = pos_bbox_preds_refine.sum() * 0
- if self.use_vfl:
- cls_iou_targets = torch.zeros_like(flatten_cls_scores)
-
- if self.use_vfl:
- loss_cls = self.loss_cls(
- flatten_cls_scores,
- cls_iou_targets,
- avg_factor=num_pos_avg_per_gpu)
- else:
- loss_cls = self.loss_cls(
- flatten_cls_scores,
- flatten_labels,
- weight=label_weights,
- avg_factor=num_pos_avg_per_gpu)
-
- return dict(
- loss_cls=loss_cls,
- loss_bbox=loss_bbox,
- loss_bbox_rf=loss_bbox_refine)
-
- @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'bbox_preds_refine'))
- def get_bboxes(self,
- cls_scores,
- bbox_preds,
- bbox_preds_refine,
- img_metas,
- cfg=None,
- rescale=None,
- with_nms=True):
- """Transform network outputs for a batch into bbox predictions.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- bbox_preds (list[Tensor]): Box offsets for each scale
- level with shape (N, num_points * 4, H, W).
- bbox_preds_refine (list[Tensor]): Refined Box offsets for
- each scale level with shape (N, num_points * 4, H, W).
- img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- cfg (mmcv.Config): Test / postprocessing configuration,
- if None, test_cfg would be used. Default: None.
- rescale (bool): If True, return boxes in original image space.
- Default: False.
- with_nms (bool): If True, do nms before returning boxes.
- Default: True.
-
- Returns:
- list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
- The first item is an (n, 5) tensor, where the first 4 columns
- are bounding box positions (tl_x, tl_y, br_x, br_y) and the
- 5-th column is a score between 0 and 1. The second item is a
- (n,) tensor where each item is the predicted class label of
- the corresponding box.
- """
- assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine)
- num_levels = len(cls_scores)
-
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- mlvl_points = self.get_points(featmap_sizes, bbox_preds[0].dtype,
- bbox_preds[0].device)
- result_list = []
- for img_id in range(len(img_metas)):
- cls_score_list = [
- cls_scores[i][img_id].detach() for i in range(num_levels)
- ]
- bbox_pred_list = [
- bbox_preds_refine[i][img_id].detach()
- for i in range(num_levels)
- ]
- img_shape = img_metas[img_id]['img_shape']
- scale_factor = img_metas[img_id]['scale_factor']
- det_bboxes = self._get_bboxes_single(cls_score_list,
- bbox_pred_list, mlvl_points,
- img_shape, scale_factor, cfg,
- rescale, with_nms)
- result_list.append(det_bboxes)
- return result_list
-
- def _get_bboxes_single(self,
- cls_scores,
- bbox_preds,
- mlvl_points,
- img_shape,
- scale_factor,
- cfg,
- rescale=False,
- with_nms=True):
- """Transform outputs for a single batch item into bbox predictions.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for a single scale
- level with shape (num_points * num_classes, H, W).
- bbox_preds (list[Tensor]): Box offsets for a single scale
- level with shape (num_points * 4, H, W).
- mlvl_points (list[Tensor]): Box reference for a single scale level
- with shape (num_total_points, 4).
- img_shape (tuple[int]): Shape of the input image,
- (height, width, 3).
- scale_factor (ndarray): Scale factor of the image arrange as
- (w_scale, h_scale, w_scale, h_scale).
- cfg (mmcv.Config | None): Test / postprocessing configuration,
- if None, test_cfg would be used.
- rescale (bool): If True, return boxes in original image space.
- Default: False.
- with_nms (bool): If True, do nms before returning boxes.
- Default: True.
-
- Returns:
- tuple(Tensor):
- det_bboxes (Tensor): BBox predictions in shape (n, 5), where
- the first 4 columns are bounding box positions
- (tl_x, tl_y, br_x, br_y) and the 5-th column is a score
- between 0 and 1.
- det_labels (Tensor): A (n,) tensor where each item is the
- predicted class label of the corresponding box.
- """
- cfg = self.test_cfg if cfg is None else cfg
- assert len(cls_scores) == len(bbox_preds) == len(mlvl_points)
- mlvl_bboxes = []
- mlvl_scores = []
- for cls_score, bbox_pred, points in zip(cls_scores, bbox_preds,
- mlvl_points):
- assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
- scores = cls_score.permute(1, 2, 0).reshape(
- -1, self.cls_out_channels).contiguous().sigmoid()
- bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4).contiguous()
-
- nms_pre = cfg.get('nms_pre', -1)
- if 0 < nms_pre < scores.shape[0]:
- max_scores, _ = scores.max(dim=1)
- _, topk_inds = max_scores.topk(nms_pre)
- points = points[topk_inds, :]
- bbox_pred = bbox_pred[topk_inds, :]
- scores = scores[topk_inds, :]
- bboxes = distance2bbox(points, bbox_pred, max_shape=img_shape)
- mlvl_bboxes.append(bboxes)
- mlvl_scores.append(scores)
- mlvl_bboxes = torch.cat(mlvl_bboxes)
- if rescale:
- mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
- mlvl_scores = torch.cat(mlvl_scores)
- padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
- # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
- # BG cat_id: num_class
- mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
- if with_nms:
- det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
- cfg.score_thr, cfg.nms,
- cfg.max_per_img)
- return det_bboxes, det_labels
- else:
- return mlvl_bboxes, mlvl_scores
-
- def _get_points_single(self,
- featmap_size,
- stride,
- dtype,
- device,
- flatten=False):
- """Get points according to feature map sizes."""
- h, w = featmap_size
- x_range = torch.arange(
- 0, w * stride, stride, dtype=dtype, device=device)
- y_range = torch.arange(
- 0, h * stride, stride, dtype=dtype, device=device)
- y, x = torch.meshgrid(y_range, x_range)
- # to be compatible with anchor points in ATSS
- if self.use_atss:
- points = torch.stack(
- (x.reshape(-1), y.reshape(-1)), dim=-1) + \
- stride * self.anchor_center_offset
- else:
- points = torch.stack(
- (x.reshape(-1), y.reshape(-1)), dim=-1) + stride // 2
- return points
-
- def get_targets(self, cls_scores, mlvl_points, gt_bboxes, gt_labels,
- img_metas, gt_bboxes_ignore):
- """A wrapper for computing ATSS and FCOS targets for points in multiple
- images.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- gt_bboxes (list[Tensor]): Ground truth bboxes of each image,
- each has shape (num_gt, 4).
- gt_labels (list[Tensor]): Ground truth labels of each box,
- each has shape (num_gt,).
- img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- gt_bboxes_ignore (None | Tensor): Ground truth bboxes to be
- ignored, shape (num_ignored_gts, 4).
-
- Returns:
- tuple:
- labels_list (list[Tensor]): Labels of each level.
- label_weights (Tensor/None): Label weights of all levels.
- bbox_targets_list (list[Tensor]): Regression targets of each
- level, (l, t, r, b).
- bbox_weights (Tensor/None): Bbox weights of all levels.
- """
- if self.use_atss:
- return self.get_atss_targets(cls_scores, mlvl_points, gt_bboxes,
- gt_labels, img_metas,
- gt_bboxes_ignore)
- else:
- self.norm_on_bbox = False
- return self.get_fcos_targets(mlvl_points, gt_bboxes, gt_labels)
-
- def _get_target_single(self, *args, **kwargs):
- """Avoid ambiguity in multiple inheritance."""
- if self.use_atss:
- return ATSSHead._get_target_single(self, *args, **kwargs)
- else:
- return FCOSHead._get_target_single(self, *args, **kwargs)
-
- def get_fcos_targets(self, points, gt_bboxes_list, gt_labels_list):
- """Compute FCOS regression and classification targets for points in
- multiple images.
-
- Args:
- points (list[Tensor]): Points of each fpn level, each has shape
- (num_points, 2).
- gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image,
- each has shape (num_gt, 4).
- gt_labels_list (list[Tensor]): Ground truth labels of each box,
- each has shape (num_gt,).
-
- Returns:
- tuple:
- labels (list[Tensor]): Labels of each level.
- label_weights: None, to be compatible with ATSS targets.
- bbox_targets (list[Tensor]): BBox targets of each level.
- bbox_weights: None, to be compatible with ATSS targets.
- """
- labels, bbox_targets = FCOSHead.get_targets(self, points,
- gt_bboxes_list,
- gt_labels_list)
- label_weights = None
- bbox_weights = None
- return labels, label_weights, bbox_targets, bbox_weights
-
- def get_atss_targets(self,
- cls_scores,
- mlvl_points,
- gt_bboxes,
- gt_labels,
- img_metas,
- gt_bboxes_ignore=None):
- """A wrapper for computing ATSS targets for points in multiple images.
-
- Args:
- cls_scores (list[Tensor]): Box iou-aware scores for each scale
- level with shape (N, num_points * num_classes, H, W).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- gt_bboxes (list[Tensor]): Ground truth bboxes of each image,
- each has shape (num_gt, 4).
- gt_labels (list[Tensor]): Ground truth labels of each box,
- each has shape (num_gt,).
- img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- gt_bboxes_ignore (None | Tensor): Ground truth bboxes to be
- ignored, shape (num_ignored_gts, 4). Default: None.
-
- Returns:
- tuple:
- labels_list (list[Tensor]): Labels of each level.
- label_weights (Tensor): Label weights of all levels.
- bbox_targets_list (list[Tensor]): Regression targets of each
- level, (l, t, r, b).
- bbox_weights (Tensor): Bbox weights of all levels.
- """
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- assert len(featmap_sizes) == self.anchor_generator.num_levels
-
- device = cls_scores[0].device
- anchor_list, valid_flag_list = self.get_anchors(
- featmap_sizes, img_metas, device=device)
- label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-
- cls_reg_targets = ATSSHead.get_targets(
- self,
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- gt_bboxes_ignore_list=gt_bboxes_ignore,
- gt_labels_list=gt_labels,
- label_channels=label_channels,
- unmap_outputs=True)
- if cls_reg_targets is None:
- return None
-
- (anchor_list, labels_list, label_weights_list, bbox_targets_list,
- bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
-
- bbox_targets_list = [
- bbox_targets.reshape(-1, 4) for bbox_targets in bbox_targets_list
- ]
-
- num_imgs = len(img_metas)
- # transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format
- bbox_targets_list = self.transform_bbox_targets(
- bbox_targets_list, mlvl_points, num_imgs)
-
- labels_list = [labels.reshape(-1) for labels in labels_list]
- label_weights_list = [
- label_weights.reshape(-1) for label_weights in label_weights_list
- ]
- bbox_weights_list = [
- bbox_weights.reshape(-1) for bbox_weights in bbox_weights_list
- ]
- label_weights = torch.cat(label_weights_list)
- bbox_weights = torch.cat(bbox_weights_list)
- return labels_list, label_weights, bbox_targets_list, bbox_weights
-
- def transform_bbox_targets(self, decoded_bboxes, mlvl_points, num_imgs):
- """Transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format.
-
- Args:
- decoded_bboxes (list[Tensor]): Regression targets of each level,
- in the form of (x1, y1, x2, y2).
- mlvl_points (list[Tensor]): Points of each fpn level, each has
- shape (num_points, 2).
- num_imgs (int): the number of images in a batch.
-
- Returns:
- bbox_targets (list[Tensor]): Regression targets of each level in
- the form of (l, t, r, b).
- """
- # TODO: Re-implemented in Class PointCoder
- assert len(decoded_bboxes) == len(mlvl_points)
- num_levels = len(decoded_bboxes)
- mlvl_points = [points.repeat(num_imgs, 1) for points in mlvl_points]
- bbox_targets = []
- for i in range(num_levels):
- bbox_target = bbox2distance(mlvl_points[i], decoded_bboxes[i])
- bbox_targets.append(bbox_target)
-
- return bbox_targets
-
- def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
- missing_keys, unexpected_keys, error_msgs):
- """Override the method in the parent class to avoid changing para's
- name."""
- pass
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/utils/util_mixins.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/utils/util_mixins.py
deleted file mode 100644
index 69669a3ca943eebe0f138b2784c5b61724196bbe..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/utils/util_mixins.py
+++ /dev/null
@@ -1,104 +0,0 @@
-"""This module defines the :class:`NiceRepr` mixin class, which defines a
-``__repr__`` and ``__str__`` method that only depend on a custom ``__nice__``
-method, which you must define. This means you only have to overload one
-function instead of two. Furthermore, if the object defines a ``__len__``
-method, then the ``__nice__`` method defaults to something sensible, otherwise
-it is treated as abstract and raises ``NotImplementedError``.
-
-To use simply have your object inherit from :class:`NiceRepr`
-(multi-inheritance should be ok).
-
-This code was copied from the ubelt library: https://github.com/Erotemic/ubelt
-
-Example:
- >>> # Objects that define __nice__ have a default __str__ and __repr__
- >>> class Student(NiceRepr):
- ... def __init__(self, name):
- ... self.name = name
- ... def __nice__(self):
- ... return self.name
- >>> s1 = Student('Alice')
- >>> s2 = Student('Bob')
- >>> print(f's1 = {s1}')
- >>> print(f's2 = {s2}')
- s1 =
- s2 =
-
-Example:
- >>> # Objects that define __len__ have a default __nice__
- >>> class Group(NiceRepr):
- ... def __init__(self, data):
- ... self.data = data
- ... def __len__(self):
- ... return len(self.data)
- >>> g = Group([1, 2, 3])
- >>> print(f'g = {g}')
- g =
-"""
-import warnings
-
-
-class NiceRepr(object):
- """Inherit from this class and define ``__nice__`` to "nicely" print your
- objects.
-
- Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function
- Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``.
- If the inheriting class has a ``__len__``, method then the default
- ``__nice__`` method will return its length.
-
- Example:
- >>> class Foo(NiceRepr):
- ... def __nice__(self):
- ... return 'info'
- >>> foo = Foo()
- >>> assert str(foo) == ''
- >>> assert repr(foo).startswith('>> class Bar(NiceRepr):
- ... pass
- >>> bar = Bar()
- >>> import pytest
- >>> with pytest.warns(None) as record:
- >>> assert 'object at' in str(bar)
- >>> assert 'object at' in repr(bar)
-
- Example:
- >>> class Baz(NiceRepr):
- ... def __len__(self):
- ... return 5
- >>> baz = Baz()
- >>> assert str(baz) == ''
- """
-
- def __nice__(self):
- """str: a "nice" summary string describing this module"""
- if hasattr(self, '__len__'):
- # It is a common pattern for objects to use __len__ in __nice__
- # As a convenience we define a default __nice__ for these objects
- return str(len(self))
- else:
- # In all other cases force the subclass to overload __nice__
- raise NotImplementedError(
- f'Define the __nice__ method for {self.__class__!r}')
-
- def __repr__(self):
- """str: the string of the module"""
- try:
- nice = self.__nice__()
- classname = self.__class__.__name__
- return f'<{classname}({nice}) at {hex(id(self))}>'
- except NotImplementedError as ex:
- warnings.warn(str(ex), category=RuntimeWarning)
- return object.__repr__(self)
-
- def __str__(self):
- """str: the string of the module"""
- try:
- classname = self.__class__.__name__
- nice = self.__nice__()
- return f'<{classname}({nice})>'
- except NotImplementedError as ex:
- warnings.warn(str(ex), category=RuntimeWarning)
- return object.__repr__(self)
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv_custom/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv_custom/__init__.py
deleted file mode 100644
index 0df4eca2b98fa2fcfe20338cfe9f153c8cd11b70..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv_custom/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-'''
- * Copyright (c) 2023 Salesforce, Inc.
- * All rights reserved.
- * SPDX-License-Identifier: Apache License 2.0
- * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
- * By Can Qin
- * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet
- * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala
- * Modified from MMCV repo: From https://github.com/open-mmlab/mmcv
- * Copyright (c) OpenMMLab. All rights reserved.
-'''
-
-# -*- coding: utf-8 -*-
-
-from .checkpoint import load_checkpoint
-
-__all__ = ['load_checkpoint']
\ No newline at end of file
diff --git a/spaces/SWHL/PaperEdgeDemo/utils/handlers.py b/spaces/SWHL/PaperEdgeDemo/utils/handlers.py
deleted file mode 100644
index ff6ad088333b5e6b080a0577d52bba334509ee38..0000000000000000000000000000000000000000
--- a/spaces/SWHL/PaperEdgeDemo/utils/handlers.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import visdom
-import numpy as np
-import csv
-import torch
-from datetime import datetime
-import os
-import cv2
-import random
-import matplotlib.pyplot as plt
-
-
-class VisPlot(object):
- def __init__(self, port=10086, env='main'):
- self.vis = visdom.Visdom(port=port)
- self.env = env
- self.vis.close('loss', env=env)
-
- def plot_loss(self, engine, monitor_metrics, win='loss'):
- self.vis.line(X=np.array([engine.state.iteration]),
- # NOTE because we use RunningAverage to log the loss, we can retrieve these numbers from state.metrics
- Y=np.array([[engine.state.metrics[x]
- for x in monitor_metrics]]),
- env=self.env, win=win, update='append')
-
- def plot_imgs(self, imgs, win='img', imhistory=False):
- imgs = np.clip(imgs, 1e-5, 1 - 1e-5)
- self.vis.images(imgs, env=self.env, win=win, opts={
- 'caption': win, 'store_history': imhistory})
-
- def plot_meshes(self, ms, win='ms'):
- plt.close()
- n = ms.shape[0]
- nr = (n - 1) // 8 + 1
- fig, axs = plt.subplots(1, 2)
- axs = axs.ravel()
- # fig.clf()
-
- c = np.arange(256) / 255.0
- c = c.reshape((16, 16))
- for ii in range(2):
- t = ms[ii]
- axs[ii].pcolormesh(t[..., 0], t[..., 1], c,
- cmap='YlGnBu', edgecolors='black')
- axs[ii].set_xlim(-1, 1)
- axs[ii].set_ylim(-1, 1)
- axs[ii].invert_yaxis()
- # axs[ii].axis('equal', 'box')
- axs[ii].set_aspect('equal', 'box')
- # fig, axs = plt.subplots(1, 2)
- # axs = axs.ravel()
- # t = ms[0]
- # axs[0].pcolormesh(t[..., 0], t[..., 1], np.zeros_like(t[..., 0]), edgecolors='r')
- # axs[0].invert_yaxis()
- # axs[0].axis('equal', 'box')
- fig.tight_layout()
- self.vis.matplot(fig, env=self.env, win=win)
-
-
-class CSVLogger(object):
- def __init__(self, filename):
- self.filename = filename
-
- def __call__(self, engine, monitor_metrics):
- with open(self.filename, 'a') as csvfile:
- writer = csv.writer(csvfile, delimiter=',')
- date_time = datetime.now().strftime('%m/%d/%Y-%H:%M:%S')
- writer.writerow([date_time, engine.state.iteration] +
- [engine.state.metrics[x] for x in monitor_metrics])
-
-# class SaveRes(object):
-# def __init__(self, resdir='./'):
-# self.yp = []
-# self.resdir = resdir
-
-# def update(self, engine):
-# self.yp.append(engine.state.output[0][1].cpu().numpy())
-
-# def save(self, epoch_id):
-# self.yp = np.concatenate(self.yp)
-# savemat(os.path.join(self.resdir, 't{}.mat'.format(epoch_id)), \
-# {'yp': self.yp})
-# self.yp = []
-# # self.yp = []
-# # self.yg = []
diff --git a/spaces/Salesforce/EDICT/my_diffusers/pipelines/__init__.py b/spaces/Salesforce/EDICT/my_diffusers/pipelines/__init__.py
deleted file mode 100644
index 3e2aeb4fb2b7f1315adb3a2ddea6aec42e806779..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_diffusers/pipelines/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from ..utils import is_onnx_available, is_transformers_available
-from .ddim import DDIMPipeline
-from .ddpm import DDPMPipeline
-from .latent_diffusion_uncond import LDMPipeline
-from .pndm import PNDMPipeline
-from .score_sde_ve import ScoreSdeVePipeline
-from .stochastic_karras_ve import KarrasVePipeline
-
-
-if is_transformers_available():
- from .latent_diffusion import LDMTextToImagePipeline
- from .stable_diffusion import (
- StableDiffusionImg2ImgPipeline,
- StableDiffusionInpaintPipeline,
- StableDiffusionPipeline,
- )
-
-if is_transformers_available() and is_onnx_available():
- from .stable_diffusion import StableDiffusionOnnxPipeline
diff --git a/spaces/Sarst/VITS-Umamusume-voice-synthesizer2/README.md b/spaces/Sarst/VITS-Umamusume-voice-synthesizer2/README.md
deleted file mode 100644
index 1b24e6efdb04cb1460e4fe3257d2303677c5a0e1..0000000000000000000000000000000000000000
--- a/spaces/Sarst/VITS-Umamusume-voice-synthesizer2/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Multilingual Anime TTS
-emoji: 🎙🐴
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.7
-app_file: app.py
-pinned: false
-duplicated_from: Plachta/VITS-Umamusume-voice-synthesizer
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SeViLA/SeViLA/app/utils.py b/spaces/SeViLA/SeViLA/app/utils.py
deleted file mode 100644
index 5a4f209d6b90f6747f4f0a090276d5032c1049db..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/app/utils.py
+++ /dev/null
@@ -1,81 +0,0 @@
-"""
- # Copyright (c) 2022, salesforce.com, inc.
- # All rights reserved.
- # SPDX-License-Identifier: BSD-3-Clause
- # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import numpy as np
-import streamlit as st
-import torch
-from lavis.models import BlipBase, load_model
-from matplotlib import pyplot as plt
-from PIL import Image
-from scipy.ndimage import filters
-from skimage import transform as skimage_transform
-
-
-def resize_img(raw_img):
- w, h = raw_img.size
- scaling_factor = 240 / w
- resized_image = raw_img.resize((int(w * scaling_factor), int(h * scaling_factor)))
- return resized_image
-
-
-def read_img(filepath):
- raw_image = Image.open(filepath).convert("RGB")
-
- return raw_image
-
-
-@st.cache(
- hash_funcs={
- torch.nn.parameter.Parameter: lambda parameter: parameter.data.detach()
- .cpu()
- .numpy()
- },
- allow_output_mutation=True,
-)
-def load_model_cache(name, model_type, is_eval, device):
- return load_model(name, model_type, is_eval, device)
-
-
-@st.cache(allow_output_mutation=True)
-def init_bert_tokenizer():
- tokenizer = BlipBase.init_tokenizer()
- return tokenizer
-
-
-def getAttMap(img, attMap, blur=True, overlap=True):
- attMap -= attMap.min()
- if attMap.max() > 0:
- attMap /= attMap.max()
- attMap = skimage_transform.resize(attMap, (img.shape[:2]), order=3, mode="constant")
- if blur:
- attMap = filters.gaussian_filter(attMap, 0.02 * max(img.shape[:2]))
- attMap -= attMap.min()
- attMap /= attMap.max()
- cmap = plt.get_cmap("jet")
- attMapV = cmap(attMap)
- attMapV = np.delete(attMapV, 3, 2)
- if overlap:
- attMap = (
- 1 * (1 - attMap**0.7).reshape(attMap.shape + (1,)) * img
- + (attMap**0.7).reshape(attMap.shape + (1,)) * attMapV
- )
- return attMap
-
-
-@st.cache(
- hash_funcs={
- torch.nn.parameter.Parameter: lambda parameter: parameter.data.detach()
- .cpu()
- .numpy()
- },
- allow_output_mutation=True,
-)
-def load_blip_itm_model(device, model_type="base"):
- model = load_model(
- "blip_image_text_matching", model_type, is_eval=True, device=device
- )
- return model
diff --git a/spaces/SeyedAli/Audio-Diffusion-style_transfer/audiodiffusion/__init__.py b/spaces/SeyedAli/Audio-Diffusion-style_transfer/audiodiffusion/__init__.py
deleted file mode 100644
index 8192887a083f8197592e9f9796149cdf89459912..0000000000000000000000000000000000000000
--- a/spaces/SeyedAli/Audio-Diffusion-style_transfer/audiodiffusion/__init__.py
+++ /dev/null
@@ -1,369 +0,0 @@
-from math import acos, sin
-from typing import Iterable, Tuple, Union, List
-
-import torch
-import numpy as np
-from PIL import Image
-from tqdm.auto import tqdm
-from librosa.beat import beat_track
-from diffusers import (DiffusionPipeline, UNet2DConditionModel, DDIMScheduler,
- DDPMScheduler, AutoencoderKL)
-
-from .mel import Mel
-
-VERSION = "1.2.5"
-
-
-class AudioDiffusion:
-
- def __init__(self,
- model_id: str = "teticio/audio-diffusion-256",
- sample_rate: int = 22050,
- n_fft: int = 2048,
- hop_length: int = 512,
- top_db: int = 80,
- cuda: bool = torch.cuda.is_available(),
- progress_bar: Iterable = tqdm):
- """Class for generating audio using De-noising Diffusion Probabilistic Models.
-
- Args:
- model_id (String): name of model (local directory or Hugging Face Hub)
- sample_rate (int): sample rate of audio
- n_fft (int): number of Fast Fourier Transforms
- hop_length (int): hop length (a higher number is recommended for lower than 256 y_res)
- top_db (int): loudest in decibels
- cuda (bool): use CUDA?
- progress_bar (iterable): iterable callback for progress updates or None
- """
- self.model_id = model_id
- pipeline = {
- 'LatentAudioDiffusionPipeline': LatentAudioDiffusionPipeline,
- 'AudioDiffusionPipeline': AudioDiffusionPipeline
- }.get(
- DiffusionPipeline.get_config_dict(self.model_id)['_class_name'],
- AudioDiffusionPipeline)
- self.pipe = pipeline.from_pretrained(self.model_id)
- if cuda:
- self.pipe.to("cuda")
- self.progress_bar = progress_bar or (lambda _: _)
-
- # For backwards compatibility
- sample_size = (self.pipe.unet.sample_size,
- self.pipe.unet.sample_size) if type(
- self.pipe.unet.sample_size
- ) == int else self.pipe.unet.sample_size
- self.mel = Mel(x_res=sample_size[1],
- y_res=sample_size[0],
- sample_rate=sample_rate,
- n_fft=n_fft,
- hop_length=hop_length,
- top_db=top_db)
-
- def generate_spectrogram_and_audio(
- self,
- steps: int = None,
- generator: torch.Generator = None,
- step_generator: torch.Generator = None,
- eta: float = 0,
- noise: torch.Tensor = None
- ) -> Tuple[Image.Image, Tuple[int, np.ndarray]]:
- """Generate random mel spectrogram and convert to audio.
-
- Args:
- steps (int): number of de-noising steps (defaults to 50 for DDIM, 1000 for DDPM)
- generator (torch.Generator): random number generator or None
- step_generator (torch.Generator): random number generator used to de-noise or None
- eta (float): parameter between 0 and 1 used with DDIM scheduler
- noise (torch.Tensor): noisy image or None
-
- Returns:
- PIL Image: mel spectrogram
- (float, np.ndarray): sample rate and raw audio
- """
- images, (sample_rate,
- audios) = self.pipe(mel=self.mel,
- batch_size=1,
- steps=steps,
- generator=generator,
- step_generator=step_generator,
- eta=eta,
- noise=noise)
- return images[0], (sample_rate, audios[0])
-
- def generate_spectrogram_and_audio_from_audio(
- self,
- audio_file: str = None,
- raw_audio: np.ndarray = None,
- slice: int = 0,
- start_step: int = 0,
- steps: int = None,
- generator: torch.Generator = None,
- mask_start_secs: float = 0,
- mask_end_secs: float = 0,
- step_generator: torch.Generator = None,
- eta: float = 0,
- noise: torch.Tensor = None
- ) -> Tuple[Image.Image, Tuple[int, np.ndarray]]:
- """Generate random mel spectrogram from audio input and convert to audio.
-
- Args:
- audio_file (str): must be a file on disk due to Librosa limitation or
- raw_audio (np.ndarray): audio as numpy array
- slice (int): slice number of audio to convert
- start_step (int): step to start from
- steps (int): number of de-noising steps (defaults to 50 for DDIM, 1000 for DDPM)
- generator (torch.Generator): random number generator or None
- mask_start_secs (float): number of seconds of audio to mask (not generate) at start
- mask_end_secs (float): number of seconds of audio to mask (not generate) at end
- step_generator (torch.Generator): random number generator used to de-noise or None
- eta (float): parameter between 0 and 1 used with DDIM scheduler
- noise (torch.Tensor): noisy image or None
-
- Returns:
- PIL Image: mel spectrogram
- (float, np.ndarray): sample rate and raw audio
- """
-
- images, (sample_rate,
- audios) = self.pipe(mel=self.mel,
- batch_size=1,
- audio_file=audio_file,
- raw_audio=raw_audio,
- slice=slice,
- start_step=start_step,
- steps=steps,
- generator=generator,
- mask_start_secs=mask_start_secs,
- mask_end_secs=mask_end_secs,
- step_generator=step_generator,
- eta=eta,
- noise=noise)
- return images[0], (sample_rate, audios[0])
-
- @staticmethod
- def loop_it(audio: np.ndarray,
- sample_rate: int,
- loops: int = 12) -> np.ndarray:
- """Loop audio
-
- Args:
- audio (np.ndarray): audio as numpy array
- sample_rate (int): sample rate of audio
- loops (int): number of times to loop
-
- Returns:
- (float, np.ndarray): sample rate and raw audio or None
- """
- _, beats = beat_track(y=audio, sr=sample_rate, units='samples')
- for beats_in_bar in [16, 12, 8, 4]:
- if len(beats) > beats_in_bar:
- return np.tile(audio[beats[0]:beats[beats_in_bar]], loops)
- return None
-
-
-class AudioDiffusionPipeline(DiffusionPipeline):
-
- def __init__(self, unet: UNet2DConditionModel,
- scheduler: Union[DDIMScheduler, DDPMScheduler]):
- super().__init__()
- self.register_modules(unet=unet, scheduler=scheduler)
-
- @torch.no_grad()
- def __call__(
- self,
- mel: Mel,
- batch_size: int = 1,
- audio_file: str = None,
- raw_audio: np.ndarray = None,
- slice: int = 0,
- start_step: int = 0,
- steps: int = None,
- generator: torch.Generator = None,
- mask_start_secs: float = 0,
- mask_end_secs: float = 0,
- step_generator: torch.Generator = None,
- eta: float = 0,
- noise: torch.Tensor = None
- ) -> Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]]:
- """Generate random mel spectrogram from audio input and convert to audio.
-
- Args:
- mel (Mel): instance of Mel class to perform image <-> audio
- batch_size (int): number of samples to generate
- audio_file (str): must be a file on disk due to Librosa limitation or
- raw_audio (np.ndarray): audio as numpy array
- slice (int): slice number of audio to convert
- start_step (int): step to start from
- steps (int): number of de-noising steps (defaults to 50 for DDIM, 1000 for DDPM)
- generator (torch.Generator): random number generator or None
- mask_start_secs (float): number of seconds of audio to mask (not generate) at start
- mask_end_secs (float): number of seconds of audio to mask (not generate) at end
- step_generator (torch.Generator): random number generator used to de-noise or None
- eta (float): parameter between 0 and 1 used with DDIM scheduler
- noise (torch.Tensor): noise tensor of shape (batch_size, 1, height, width) or None
-
- Returns:
- List[PIL Image]: mel spectrograms
- (float, List[np.ndarray]): sample rate and raw audios
- """
-
- steps = steps or 50 if isinstance(self.scheduler,
- DDIMScheduler) else 1000
- self.scheduler.set_timesteps(steps)
- step_generator = step_generator or generator
- # For backwards compatibility
- if type(self.unet.sample_size) == int:
- self.unet.sample_size = (self.unet.sample_size,
- self.unet.sample_size)
- if noise is None:
- noise = torch.randn(
- (batch_size, self.unet.in_channels, self.unet.sample_size[0],
- self.unet.sample_size[1]),
- generator=generator)
- images = noise
- mask = None
-
- if audio_file is not None or raw_audio is not None:
- mel.load_audio(audio_file, raw_audio)
- input_image = mel.audio_slice_to_image(slice)
- input_image = np.frombuffer(input_image.tobytes(),
- dtype="uint8").reshape(
- (input_image.height,
- input_image.width))
- input_image = ((input_image / 255) * 2 - 1)
- input_images = np.tile(input_image, (batch_size, 1, 1, 1))
-
- if hasattr(self, 'vqvae'):
- input_images = self.vqvae.encode(
- input_images).latent_dist.sample(generator=generator)
- input_images = 0.18215 * input_images
-
- if start_step > 0:
- images[0, 0] = self.scheduler.add_noise(
- torch.tensor(input_images[:, np.newaxis, np.newaxis, :]),
- noise, torch.tensor(steps - start_step))
-
- pixels_per_second = (self.unet.sample_size[1] *
- mel.get_sample_rate() / mel.x_res /
- mel.hop_length)
- mask_start = int(mask_start_secs * pixels_per_second)
- mask_end = int(mask_end_secs * pixels_per_second)
- mask = self.scheduler.add_noise(
- torch.tensor(input_images[:, np.newaxis, :]), noise,
- torch.tensor(self.scheduler.timesteps[start_step:]))
-
- images = images.to(self.device)
- for step, t in enumerate(
- self.progress_bar(self.scheduler.timesteps[start_step:])):
- model_output = self.unet(images, t)['sample']
-
- if isinstance(self.scheduler, DDIMScheduler):
- images = self.scheduler.step(
- model_output=model_output,
- timestep=t,
- sample=images,
- eta=eta,
- generator=step_generator)['prev_sample']
- else:
- images = self.scheduler.step(
- model_output=model_output,
- timestep=t,
- sample=images,
- generator=step_generator)['prev_sample']
-
- if mask is not None:
- if mask_start > 0:
- images[:, :, :, :mask_start] = mask[
- step, :, :, :, :mask_start]
- if mask_end > 0:
- images[:, :, :, -mask_end:] = mask[step, :, :, :,
- -mask_end:]
-
- if hasattr(self, 'vqvae'):
- # 0.18215 was scaling factor used in training to ensure unit variance
- images = 1 / 0.18215 * images
- images = self.vqvae.decode(images)['sample']
-
- images = (images / 2 + 0.5).clamp(0, 1)
- images = images.cpu().permute(0, 2, 3, 1).numpy()
- images = (images * 255).round().astype("uint8")
- images = list(
- map(lambda _: Image.fromarray(_[:, :, 0]), images) if images.
- shape[3] == 1 else map(
- lambda _: Image.fromarray(_, mode='RGB').convert('L'), images))
-
- audios = list(map(lambda _: mel.image_to_audio(_), images))
- return images, (mel.get_sample_rate(), audios)
-
- @torch.no_grad()
- def encode(self, images: List[Image.Image], steps: int = 50) -> np.ndarray:
- """Reverse step process: recover noisy image from generated image.
-
- Args:
- images (List[PIL Image]): list of images to encode
- steps (int): number of encoding steps to perform (defaults to 50)
-
- Returns:
- np.ndarray: noise tensor of shape (batch_size, 1, height, width)
- """
-
- # Only works with DDIM as this method is deterministic
- assert isinstance(self.scheduler, DDIMScheduler)
- self.scheduler.set_timesteps(steps)
- sample = np.array([
- np.frombuffer(image.tobytes(), dtype="uint8").reshape(
- (1, image.height, image.width)) for image in images
- ])
- sample = ((sample / 255) * 2 - 1)
- sample = torch.Tensor(sample).to(self.device)
-
- for t in self.progress_bar(torch.flip(self.scheduler.timesteps,
- (0, ))):
- prev_timestep = (t - self.scheduler.num_train_timesteps //
- self.scheduler.num_inference_steps)
- alpha_prod_t = self.scheduler.alphas_cumprod[t]
- alpha_prod_t_prev = (self.scheduler.alphas_cumprod[prev_timestep]
- if prev_timestep >= 0 else
- self.scheduler.final_alpha_cumprod)
- beta_prod_t = 1 - alpha_prod_t
- model_output = self.unet(sample, t)['sample']
- pred_sample_direction = (1 -
- alpha_prod_t_prev)**(0.5) * model_output
- sample = (sample -
- pred_sample_direction) * alpha_prod_t_prev**(-0.5)
- sample = sample * alpha_prod_t**(0.5) + beta_prod_t**(
- 0.5) * model_output
-
- return sample
-
- @staticmethod
- def slerp(x0: torch.Tensor, x1: torch.Tensor,
- alpha: float) -> torch.Tensor:
- """Spherical Linear intERPolation
-
- Args:
- x0 (torch.Tensor): first tensor to interpolate between
- x1 (torch.Tensor): seconds tensor to interpolate between
- alpha (float): interpolation between 0 and 1
-
- Returns:
- torch.Tensor: interpolated tensor
- """
-
- theta = acos(
- torch.dot(torch.flatten(x0), torch.flatten(x1)) / torch.norm(x0) /
- torch.norm(x1))
- return sin((1 - alpha) * theta) * x0 / sin(theta) + sin(
- alpha * theta) * x1 / sin(theta)
-
-
-class LatentAudioDiffusionPipeline(AudioDiffusionPipeline):
-
- def __init__(self, unet: UNet2DConditionModel,
- scheduler: Union[DDIMScheduler,
- DDPMScheduler], vqvae: AutoencoderKL):
- super().__init__(unet=unet, scheduler=scheduler)
- self.register_modules(vqvae=vqvae)
-
- def __call__(self, *args, **kwargs):
- return super().__call__(*args, **kwargs)
diff --git a/spaces/SimFG/LangChain-Zilliz-Cloud/README.md b/spaces/SimFG/LangChain-Zilliz-Cloud/README.md
deleted file mode 100644
index 09537c79247c8acbaff05f65813b6c2a28cc2236..0000000000000000000000000000000000000000
--- a/spaces/SimFG/LangChain-Zilliz-Cloud/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: LangChain Zilliz Cloud
-emoji: 🌖
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SmartPy/ScisummNet/utils.py b/spaces/SmartPy/ScisummNet/utils.py
deleted file mode 100644
index 07dd4b596647504713d520424ba444b4bbdb1a88..0000000000000000000000000000000000000000
--- a/spaces/SmartPy/ScisummNet/utils.py
+++ /dev/null
@@ -1,121 +0,0 @@
-"""
- utils.py - Utility functions for the project.
-"""
-
-import re
-from pathlib import Path
-from datetime import datetime
-from natsort import natsorted
-import subprocess
-
-
-def get_timestamp() -> str:
- """
- get_timestamp - get a timestamp for the current time
- Returns:
- str, the timestamp
- """
- return datetime.now().strftime("%Y%m%d_%H%M%S")
-
-
-def truncate_word_count(text, max_words=512):
- """
- truncate_word_count - a helper function for the gradio module
- Parameters
- ----------
- text : str, required, the text to be processed
- max_words : int, optional, the maximum number of words, default=512
- Returns
- -------
- dict, the text and whether it was truncated
- """
- # split on whitespace with regex
- words = re.split(r"\s+", text)
- processed = {}
- if len(words) > max_words:
- processed["was_truncated"] = True
- processed["truncated_text"] = " ".join(words[:max_words])
- else:
- processed["was_truncated"] = False
- processed["truncated_text"] = text
- return processed
-
-
-def load_examples(src, filetypes=[".txt", ".pdf"]):
- """
- load_examples - a helper function for the gradio module to load examples
- Returns:
- list of str, the examples
- """
- src = Path(src)
- src.mkdir(exist_ok=True)
-
- pdf_url = (
- "https://www.dropbox.com/s/y92xy7o5qb88yij/all_you_need_is_attention.pdf?dl=1"
- )
- subprocess.run(["wget", pdf_url, "-O", src / "all_you_need_is_attention.pdf"])
- examples = [f for f in src.iterdir() if f.suffix in filetypes]
- examples = natsorted(examples)
- # load the examples into a list
- text_examples = []
- for example in examples:
- with open(example, "r") as f:
- text = f.read()
- text_examples.append([text, "base", 2, 1024, 0.7, 3.5, 3])
-
- return text_examples
-
-
-def load_example_filenames(example_path: str or Path):
- """
- load_example_filenames - a helper function for the gradio module to load examples
- Returns:
- dict, the examples (filename:full path)
- """
- example_path = Path(example_path)
- # load the examples into a list
- examples = {f.name: f for f in example_path.glob("*.txt")}
- return examples
-
-
-def saves_summary(summarize_output, outpath: str or Path = None, add_signature=True):
- """
- saves_summary - save the summary generated from summarize_via_tokenbatches() to a text file
- _summaries = summarize_via_tokenbatches(
- text,
- batch_length=token_batch_length,
- batch_stride=batch_stride,
- **settings,
- )
- """
-
- outpath = (
- Path.cwd() / f"document_summary_{get_timestamp()}.txt"
- if outpath is None
- else Path(outpath)
- )
- sum_text = [s["summary"][0] for s in summarize_output]
- sum_scores = [f"\n - {round(s['summary_score'],4)}" for s in summarize_output]
- scores_text = "\n".join(sum_scores)
- full_summary = "\n\t".join(sum_text)
-
- with open(
- outpath,
- "w",
- ) as fo:
- if add_signature:
- fo.write(
- "Generated with the Document Summarization space :) https://hf.co/spaces/pszemraj/document-summarization\n\n"
- )
- fo.writelines(full_summary)
- with open(
- outpath,
- "a",
- ) as fo:
-
- fo.write("\n" * 3)
- fo.write(f"\n\nSection Scores:\n")
- fo.writelines(scores_text)
- fo.write("\n\n---\n")
-
- return outpath
\ No newline at end of file
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/test_inputtransformer2_line.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/test_inputtransformer2_line.py
deleted file mode 100644
index ec7a8736412acfe3b0206f031dd85625124b1844..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/test_inputtransformer2_line.py
+++ /dev/null
@@ -1,167 +0,0 @@
-"""Tests for the line-based transformers in IPython.core.inputtransformer2
-
-Line-based transformers are the simpler ones; token-based transformers are
-more complex. See test_inputtransformer2 for tests for token-based transformers.
-"""
-
-from IPython.core import inputtransformer2 as ipt2
-
-CELL_MAGIC = ("""\
-%%foo arg
-body 1
-body 2
-""", """\
-get_ipython().run_cell_magic('foo', 'arg', 'body 1\\nbody 2\\n')
-""")
-
-def test_cell_magic():
- for sample, expected in [CELL_MAGIC]:
- assert ipt2.cell_magic(sample.splitlines(keepends=True)) == expected.splitlines(
- keepends=True
- )
-
-CLASSIC_PROMPT = ("""\
->>> for a in range(5):
-... print(a)
-""", """\
-for a in range(5):
- print(a)
-""")
-
-CLASSIC_PROMPT_L2 = ("""\
-for a in range(5):
-... print(a)
-... print(a ** 2)
-""", """\
-for a in range(5):
- print(a)
- print(a ** 2)
-""")
-
-def test_classic_prompt():
- for sample, expected in [CLASSIC_PROMPT, CLASSIC_PROMPT_L2]:
- assert ipt2.classic_prompt(
- sample.splitlines(keepends=True)
- ) == expected.splitlines(keepends=True)
-
-IPYTHON_PROMPT = ("""\
-In [1]: for a in range(5):
- ...: print(a)
-""", """\
-for a in range(5):
- print(a)
-""")
-
-IPYTHON_PROMPT_L2 = ("""\
-for a in range(5):
- ...: print(a)
- ...: print(a ** 2)
-""", """\
-for a in range(5):
- print(a)
- print(a ** 2)
-""")
-
-
-IPYTHON_PROMPT_VI_INS = (
- """\
-[ins] In [11]: def a():
- ...: 123
- ...:
- ...: 123
-""",
- """\
-def a():
- 123
-
-123
-""",
-)
-
-IPYTHON_PROMPT_VI_NAV = (
- """\
-[nav] In [11]: def a():
- ...: 123
- ...:
- ...: 123
-""",
- """\
-def a():
- 123
-
-123
-""",
-)
-
-
-def test_ipython_prompt():
- for sample, expected in [
- IPYTHON_PROMPT,
- IPYTHON_PROMPT_L2,
- IPYTHON_PROMPT_VI_INS,
- IPYTHON_PROMPT_VI_NAV,
- ]:
- assert ipt2.ipython_prompt(
- sample.splitlines(keepends=True)
- ) == expected.splitlines(keepends=True)
-
-
-INDENT_SPACES = ("""\
- if True:
- a = 3
-""", """\
-if True:
- a = 3
-""")
-
-INDENT_TABS = ("""\
-\tif True:
-\t\tb = 4
-""", """\
-if True:
-\tb = 4
-""")
-
-def test_leading_indent():
- for sample, expected in [INDENT_SPACES, INDENT_TABS]:
- assert ipt2.leading_indent(
- sample.splitlines(keepends=True)
- ) == expected.splitlines(keepends=True)
-
-LEADING_EMPTY_LINES = ("""\
- \t
-
-if True:
- a = 3
-
-b = 4
-""", """\
-if True:
- a = 3
-
-b = 4
-""")
-
-ONLY_EMPTY_LINES = ("""\
- \t
-
-""", """\
- \t
-
-""")
-
-def test_leading_empty_lines():
- for sample, expected in [LEADING_EMPTY_LINES, ONLY_EMPTY_LINES]:
- assert ipt2.leading_empty_lines(
- sample.splitlines(keepends=True)
- ) == expected.splitlines(keepends=True)
-
-CRLF_MAGIC = ([
- "%%ls\r\n"
-], [
- "get_ipython().run_cell_magic('ls', '', '')\n"
-])
-
-def test_crlf_magic():
- for sample, expected in [CRLF_MAGIC]:
- assert ipt2.cell_magic(sample) == expected
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_cython.c b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_cython.c
deleted file mode 100644
index 3225bf0c43045804327f672240cc7414330b860e..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_cython.c
+++ /dev/null
@@ -1,43377 +0,0 @@
-/* Generated by Cython 0.29.32 */
-
-/* BEGIN: Cython Metadata
-{
- "distutils": {
- "depends": [],
- "name": "_pydevd_bundle.pydevd_cython",
- "sources": [
- "_pydevd_bundle/pydevd_cython.pyx"
- ]
- },
- "module_name": "_pydevd_bundle.pydevd_cython"
-}
-END: Cython Metadata */
-
-#ifndef PY_SSIZE_T_CLEAN
-#define PY_SSIZE_T_CLEAN
-#endif /* PY_SSIZE_T_CLEAN */
-#include "Python.h"
-#if PY_VERSION_HEX >= 0x03090000
-#include "internal/pycore_gc.h"
-#include "internal/pycore_interp.h"
-#endif
-
-#ifndef Py_PYTHON_H
- #error Python headers needed to compile C extensions, please install development version of Python.
-#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)
- #error Cython requires Python 2.6+ or Python 3.3+.
-#else
-#define CYTHON_ABI "0_29_32"
-#define CYTHON_HEX_VERSION 0x001D20F0
-#define CYTHON_FUTURE_DIVISION 0
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- #ifndef __stdcall
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- #define HAVE_LONG_LONG
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-#endif
-#ifndef PY_LONG_LONG
- #define PY_LONG_LONG LONG_LONG
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-#ifndef Py_HUGE_VAL
- #define Py_HUGE_VAL HUGE_VAL
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-#if CYTHON_USE_PYLONG_INTERNALS
- #if PY_MAJOR_VERSION < 3
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-#endif
-#ifndef CYTHON_RESTRICT
- #if defined(__GNUC__)
- #define CYTHON_RESTRICT __restrict__
- #elif defined(_MSC_VER) && _MSC_VER >= 1400
- #define CYTHON_RESTRICT __restrict
- #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
- #define CYTHON_RESTRICT restrict
- #else
- #define CYTHON_RESTRICT
- #endif
-#endif
-#ifndef CYTHON_UNUSED
-# if defined(__GNUC__)
-# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
-# define CYTHON_UNUSED __attribute__ ((__unused__))
-# else
-# define CYTHON_UNUSED
-# endif
-# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
-# define CYTHON_UNUSED __attribute__ ((__unused__))
-# else
-# define CYTHON_UNUSED
-# endif
-#endif
-#ifndef CYTHON_MAYBE_UNUSED_VAR
-# if defined(__cplusplus)
- template void CYTHON_MAYBE_UNUSED_VAR( const T& ) { }
-# else
-# define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x)
-# endif
-#endif
-#ifndef CYTHON_NCP_UNUSED
-# if CYTHON_COMPILING_IN_CPYTHON
-# define CYTHON_NCP_UNUSED
-# else
-# define CYTHON_NCP_UNUSED CYTHON_UNUSED
-# endif
-#endif
-#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
-#ifdef _MSC_VER
- #ifndef _MSC_STDINT_H_
- #if _MSC_VER < 1300
- typedef unsigned char uint8_t;
- typedef unsigned int uint32_t;
- #else
- typedef unsigned __int8 uint8_t;
- typedef unsigned __int32 uint32_t;
- #endif
- #endif
-#else
- #include
-#endif
-#ifndef CYTHON_FALLTHROUGH
- #if defined(__cplusplus) && __cplusplus >= 201103L
- #if __has_cpp_attribute(fallthrough)
- #define CYTHON_FALLTHROUGH [[fallthrough]]
- #elif __has_cpp_attribute(clang::fallthrough)
- #define CYTHON_FALLTHROUGH [[clang::fallthrough]]
- #elif __has_cpp_attribute(gnu::fallthrough)
- #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]
- #endif
- #endif
- #ifndef CYTHON_FALLTHROUGH
- #if __has_attribute(fallthrough)
- #define CYTHON_FALLTHROUGH __attribute__((fallthrough))
- #else
- #define CYTHON_FALLTHROUGH
- #endif
- #endif
- #if defined(__clang__ ) && defined(__apple_build_version__)
- #if __apple_build_version__ < 7000000
- #undef CYTHON_FALLTHROUGH
- #define CYTHON_FALLTHROUGH
- #endif
- #endif
-#endif
-
-#ifndef CYTHON_INLINE
- #if defined(__clang__)
- #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
- #elif defined(__GNUC__)
- #define CYTHON_INLINE __inline__
- #elif defined(_MSC_VER)
- #define CYTHON_INLINE __inline
- #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
- #define CYTHON_INLINE inline
- #else
- #define CYTHON_INLINE
- #endif
-#endif
-
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)
- #define Py_OptimizeFlag 0
-#endif
-#define __PYX_BUILD_PY_SSIZE_T "n"
-#define CYTHON_FORMAT_SSIZE_T "z"
-#if PY_MAJOR_VERSION < 3
- #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
- #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
- PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
- #define __Pyx_DefaultClassType PyClass_Type
-#else
- #define __Pyx_BUILTIN_MODULE_NAME "builtins"
- #define __Pyx_DefaultClassType PyType_Type
-#if PY_VERSION_HEX >= 0x030B00A1
- static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int k, int l, int s, int f,
- PyObject *code, PyObject *c, PyObject* n, PyObject *v,
- PyObject *fv, PyObject *cell, PyObject* fn,
- PyObject *name, int fline, PyObject *lnos) {
- PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL;
- PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *call_result=NULL, *empty=NULL;
- const char *fn_cstr=NULL;
- const char *name_cstr=NULL;
- PyCodeObject* co=NULL;
- PyObject *type, *value, *traceback;
- PyErr_Fetch(&type, &value, &traceback);
- if (!(kwds=PyDict_New())) goto end;
- if (!(argcount=PyLong_FromLong(a))) goto end;
- if (PyDict_SetItemString(kwds, "co_argcount", argcount) != 0) goto end;
- if (!(posonlyargcount=PyLong_FromLong(0))) goto end;
- if (PyDict_SetItemString(kwds, "co_posonlyargcount", posonlyargcount) != 0) goto end;
- if (!(kwonlyargcount=PyLong_FromLong(k))) goto end;
- if (PyDict_SetItemString(kwds, "co_kwonlyargcount", kwonlyargcount) != 0) goto end;
- if (!(nlocals=PyLong_FromLong(l))) goto end;
- if (PyDict_SetItemString(kwds, "co_nlocals", nlocals) != 0) goto end;
- if (!(stacksize=PyLong_FromLong(s))) goto end;
- if (PyDict_SetItemString(kwds, "co_stacksize", stacksize) != 0) goto end;
- if (!(flags=PyLong_FromLong(f))) goto end;
- if (PyDict_SetItemString(kwds, "co_flags", flags) != 0) goto end;
- if (PyDict_SetItemString(kwds, "co_code", code) != 0) goto end;
- if (PyDict_SetItemString(kwds, "co_consts", c) != 0) goto end;
- if (PyDict_SetItemString(kwds, "co_names", n) != 0) goto end;
- if (PyDict_SetItemString(kwds, "co_varnames", v) != 0) goto end;
- if (PyDict_SetItemString(kwds, "co_freevars", fv) != 0) goto end;
- if (PyDict_SetItemString(kwds, "co_cellvars", cell) != 0) goto end;
- if (PyDict_SetItemString(kwds, "co_linetable", lnos) != 0) goto end;
- if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end;
- if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end;
- if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end;
- if (!(replace = PyObject_GetAttrString((PyObject*)co, "replace"))) goto cleanup_code_too;
- if (!(empty = PyTuple_New(0))) goto cleanup_code_too; // unfortunately __pyx_empty_tuple isn't available here
- if (!(call_result = PyObject_Call(replace, empty, kwds))) goto cleanup_code_too;
- Py_XDECREF((PyObject*)co);
- co = (PyCodeObject*)call_result;
- call_result = NULL;
- if (0) {
- cleanup_code_too:
- Py_XDECREF((PyObject*)co);
- co = NULL;
- }
- end:
- Py_XDECREF(kwds);
- Py_XDECREF(argcount);
- Py_XDECREF(posonlyargcount);
- Py_XDECREF(kwonlyargcount);
- Py_XDECREF(nlocals);
- Py_XDECREF(stacksize);
- Py_XDECREF(replace);
- Py_XDECREF(call_result);
- Py_XDECREF(empty);
- if (type) {
- PyErr_Restore(type, value, traceback);
- }
- return co;
- }
-#else
- #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
- PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#endif
- #define __Pyx_DefaultClassType PyType_Type
-#endif
-#ifndef Py_TPFLAGS_CHECKTYPES
- #define Py_TPFLAGS_CHECKTYPES 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_INDEX
- #define Py_TPFLAGS_HAVE_INDEX 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
- #define Py_TPFLAGS_HAVE_NEWBUFFER 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_FINALIZE
- #define Py_TPFLAGS_HAVE_FINALIZE 0
-#endif
-#ifndef METH_STACKLESS
- #define METH_STACKLESS 0
-#endif
-#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)
- #ifndef METH_FASTCALL
- #define METH_FASTCALL 0x80
- #endif
- typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);
- typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,
- Py_ssize_t nargs, PyObject *kwnames);
-#else
- #define __Pyx_PyCFunctionFast _PyCFunctionFast
- #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
-#endif
-#if CYTHON_FAST_PYCCALL
-#define __Pyx_PyFastCFunction_Check(func)\
- ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)))))
-#else
-#define __Pyx_PyFastCFunction_Check(func) 0
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
- #define PyObject_Malloc(s) PyMem_Malloc(s)
- #define PyObject_Free(p) PyMem_Free(p)
- #define PyObject_Realloc(p) PyMem_Realloc(p)
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1
- #define PyMem_RawMalloc(n) PyMem_Malloc(n)
- #define PyMem_RawRealloc(p, n) PyMem_Realloc(p, n)
- #define PyMem_RawFree(p) PyMem_Free(p)
-#endif
-#if CYTHON_COMPILING_IN_PYSTON
- #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co)
- #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno)
-#else
- #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0)
- #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno)
-#endif
-#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000
- #define __Pyx_PyThreadState_Current PyThreadState_GET()
-#elif PY_VERSION_HEX >= 0x03060000
- #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()
-#elif PY_VERSION_HEX >= 0x03000000
- #define __Pyx_PyThreadState_Current PyThreadState_GET()
-#else
- #define __Pyx_PyThreadState_Current _PyThreadState_Current
-#endif
-#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT)
-#include "pythread.h"
-#define Py_tss_NEEDS_INIT 0
-typedef int Py_tss_t;
-static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) {
- *key = PyThread_create_key();
- return 0;
-}
-static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) {
- Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t));
- *key = Py_tss_NEEDS_INIT;
- return key;
-}
-static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) {
- PyObject_Free(key);
-}
-static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) {
- return *key != Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) {
- PyThread_delete_key(*key);
- *key = Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) {
- return PyThread_set_key_value(*key, value);
-}
-static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) {
- return PyThread_get_key_value(*key);
-}
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)
-#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))
-#else
-#define __Pyx_PyDict_NewPresized(n) PyDict_New()
-#endif
-#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION
- #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y)
- #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y)
-#else
- #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y)
- #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y)
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS
-#define __Pyx_PyDict_GetItemStr(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash)
-#else
-#define __Pyx_PyDict_GetItemStr(dict, name) PyDict_GetItem(dict, name)
-#endif
-#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
- #define CYTHON_PEP393_ENABLED 1
- #if defined(PyUnicode_IS_READY)
- #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\
- 0 : _PyUnicode_Ready((PyObject *)(op)))
- #else
- #define __Pyx_PyUnicode_READY(op) (0)
- #endif
- #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u)
- #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
- #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u)
- #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u)
- #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u)
- #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i)
- #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch)
- #if defined(PyUnicode_IS_READY) && defined(PyUnicode_GET_SIZE)
- #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03090000
- #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : ((PyCompactUnicodeObject *)(u))->wstr_length))
- #else
- #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))
- #endif
- #else
- #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_LENGTH(u))
- #endif
-#else
- #define CYTHON_PEP393_ENABLED 0
- #define PyUnicode_1BYTE_KIND 1
- #define PyUnicode_2BYTE_KIND 2
- #define PyUnicode_4BYTE_KIND 4
- #define __Pyx_PyUnicode_READY(op) (0)
- #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u)
- #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
- #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)
- #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE))
- #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u))
- #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
- #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch)
- #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u))
-#endif
-#if CYTHON_COMPILING_IN_PYPY
- #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b)
- #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b)
-#else
- #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b)
- #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\
- PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)
- #define PyUnicode_Contains(u, s) PySequence_Contains(u, s)
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)
- #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type)
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)
- #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt)
-#endif
-#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
-#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
-#if PY_MAJOR_VERSION >= 3
- #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b)
-#else
- #define __Pyx_PyString_Format(a, b) PyString_Format(a, b)
-#endif
-#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)
- #define PyObject_ASCII(o) PyObject_Repr(o)
-#endif
-#if PY_MAJOR_VERSION >= 3
- #define PyBaseString_Type PyUnicode_Type
- #define PyStringObject PyUnicodeObject
- #define PyString_Type PyUnicode_Type
- #define PyString_Check PyUnicode_Check
- #define PyString_CheckExact PyUnicode_CheckExact
-#ifndef PyObject_Unicode
- #define PyObject_Unicode PyObject_Str
-#endif
-#endif
-#if PY_MAJOR_VERSION >= 3
- #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
- #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)
-#else
- #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))
- #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
-#endif
-#ifndef PySet_CheckExact
- #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type)
-#endif
-#if PY_VERSION_HEX >= 0x030900A4
- #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt)
- #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size)
-#else
- #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt)
- #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size)
-#endif
-#if CYTHON_ASSUME_SAFE_MACROS
- #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq)
-#else
- #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq)
-#endif
-#if PY_MAJOR_VERSION >= 3
- #define PyIntObject PyLongObject
- #define PyInt_Type PyLong_Type
- #define PyInt_Check(op) PyLong_Check(op)
- #define PyInt_CheckExact(op) PyLong_CheckExact(op)
- #define PyInt_FromString PyLong_FromString
- #define PyInt_FromUnicode PyLong_FromUnicode
- #define PyInt_FromLong PyLong_FromLong
- #define PyInt_FromSize_t PyLong_FromSize_t
- #define PyInt_FromSsize_t PyLong_FromSsize_t
- #define PyInt_AsLong PyLong_AsLong
- #define PyInt_AS_LONG PyLong_AS_LONG
- #define PyInt_AsSsize_t PyLong_AsSsize_t
- #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask
- #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
- #define PyNumber_Int PyNumber_Long
-#endif
-#if PY_MAJOR_VERSION >= 3
- #define PyBoolObject PyLongObject
-#endif
-#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY
- #ifndef PyUnicode_InternFromString
- #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)
- #endif
-#endif
-#if PY_VERSION_HEX < 0x030200A4
- typedef long Py_hash_t;
- #define __Pyx_PyInt_FromHash_t PyInt_FromLong
- #define __Pyx_PyInt_AsHash_t __Pyx_PyIndex_AsHash_t
-#else
- #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
- #define __Pyx_PyInt_AsHash_t __Pyx_PyIndex_AsSsize_t
-#endif
-#if PY_MAJOR_VERSION >= 3
- #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func))
-#else
- #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)
-#endif
-#if CYTHON_USE_ASYNC_SLOTS
- #if PY_VERSION_HEX >= 0x030500B1
- #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
- #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)
- #else
- #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))
- #endif
-#else
- #define __Pyx_PyType_AsAsync(obj) NULL
-#endif
-#ifndef __Pyx_PyAsyncMethodsStruct
- typedef struct {
- unaryfunc am_await;
- unaryfunc am_aiter;
- unaryfunc am_anext;
- } __Pyx_PyAsyncMethodsStruct;
-#endif
-
-#if defined(_WIN32) || defined(WIN32) || defined(MS_WINDOWS)
- #if !defined(_USE_MATH_DEFINES)
- #define _USE_MATH_DEFINES
- #endif
-#endif
-#include
-#ifdef NAN
-#define __PYX_NAN() ((float) NAN)
-#else
-static CYTHON_INLINE float __PYX_NAN() {
- float value;
- memset(&value, 0xFF, sizeof(value));
- return value;
-}
-#endif
-#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)
-#define __Pyx_truncl trunc
-#else
-#define __Pyx_truncl truncl
-#endif
-
-#define __PYX_MARK_ERR_POS(f_index, lineno) \
- { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; }
-#define __PYX_ERR(f_index, lineno, Ln_error) \
- { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; }
-
-#ifndef __PYX_EXTERN_C
- #ifdef __cplusplus
- #define __PYX_EXTERN_C extern "C"
- #else
- #define __PYX_EXTERN_C extern
- #endif
-#endif
-
-#define __PYX_HAVE___pydevd_bundle__pydevd_cython
-#define __PYX_HAVE_API___pydevd_bundle__pydevd_cython
-/* Early includes */
-#include
-#include
-#ifdef _OPENMP
-#include
-#endif /* _OPENMP */
-
-#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)
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- Py_DECREF(sys);
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-bad:
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- if (!sys) goto bad;
- default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
- Py_DECREF(sys);
- if (!default_encoding) goto bad;
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- if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\
- (VAR) = __pyx_dict_cached_value;\
- } else {\
- (VAR) = __pyx_dict_cached_value = (LOOKUP);\
- __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\
- }\
-}
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj);
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj);
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version);
-#else
-#define __PYX_GET_DICT_VERSION(dict) (0)
-#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)
-#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP);
-#endif
-
-/* GetModuleGlobalName.proto */
-#if CYTHON_USE_DICT_VERSIONS
-#define __Pyx_GetModuleGlobalName(var, name) {\
- static PY_UINT64_T __pyx_dict_version = 0;\
- static PyObject *__pyx_dict_cached_value = NULL;\
- (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\
- (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\
- __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\
-}
-#define __Pyx_GetModuleGlobalNameUncached(var, name) {\
- PY_UINT64_T __pyx_dict_version;\
- PyObject *__pyx_dict_cached_value;\
- (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\
-}
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value);
-#else
-#define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name)
-#define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name)
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name);
-#endif
-
-/* PyFunctionFastCall.proto */
-#if CYTHON_FAST_PYCALL
-#define __Pyx_PyFunction_FastCall(func, args, nargs)\
- __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL)
-#if 1 || PY_VERSION_HEX < 0x030600B1
-static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs);
-#else
-#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs)
-#endif
-#define __Pyx_BUILD_ASSERT_EXPR(cond)\
- (sizeof(char [1 - 2*!(cond)]) - 1)
-#ifndef Py_MEMBER_SIZE
-#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member)
-#endif
-#if CYTHON_FAST_PYCALL
- static size_t __pyx_pyframe_localsplus_offset = 0;
- #include "frameobject.h"
-#if PY_VERSION_HEX >= 0x030b00a6
- #ifndef Py_BUILD_CORE
- #define Py_BUILD_CORE 1
- #endif
- #include "internal/pycore_frame.h"
-#endif
- #define __Pxy_PyFrame_Initialize_Offsets()\
- ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\
- (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus)))
- #define __Pyx_PyFrame_GetLocalsplus(frame)\
- (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset))
-#endif // CYTHON_FAST_PYCALL
-#endif
-
-/* PyObjectCall.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw);
-#else
-#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw)
-#endif
-
-/* PyObjectCallMethO.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg);
-#endif
-
-/* PyObjectCallNoArg.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func);
-#else
-#define __Pyx_PyObject_CallNoArg(func) __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL)
-#endif
-
-/* PyCFunctionFastCall.proto */
-#if CYTHON_FAST_PYCCALL
-static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs);
-#else
-#define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL)
-#endif
-
-/* PyObjectCallOneArg.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg);
-
-/* PyObjectCall2Args.proto */
-static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2);
-
-/* PyErrExceptionMatches.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err)
-static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err);
-#else
-#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err)
-#endif
-
-/* PyThreadStateGet.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate;
-#define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current;
-#define __Pyx_PyErr_Occurred() __pyx_tstate->curexc_type
-#else
-#define __Pyx_PyThreadState_declare
-#define __Pyx_PyThreadState_assign
-#define __Pyx_PyErr_Occurred() PyErr_Occurred()
-#endif
-
-/* PyErrFetchRestore.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL)
-#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb)
-#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb)
-#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb)
-#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
-static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL))
-#else
-#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
-#endif
-#else
-#define __Pyx_PyErr_Clear() PyErr_Clear()
-#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
-#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb)
-#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb)
-#define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb)
-#define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb)
-#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb)
-#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb)
-#endif
-
-/* GetAttr.proto */
-static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *);
-
-/* GetAttr3.proto */
-static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *);
-
-/* RaiseException.proto */
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause);
-
-/* GetTopmostException.proto */
-#if CYTHON_USE_EXC_INFO_STACK
-static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate);
-#endif
-
-/* SaveResetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
-#else
-#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb)
-#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb)
-#endif
-
-/* GetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb)
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* PyObjectLookupSpecial.proto */
-#if CYTHON_USE_PYTYPE_LOOKUP && CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PyObject* __Pyx_PyObject_LookupSpecial(PyObject* obj, PyObject* attr_name) {
- PyObject *res;
- PyTypeObject *tp = Py_TYPE(obj);
-#if PY_MAJOR_VERSION < 3
- if (unlikely(PyInstance_Check(obj)))
- return __Pyx_PyObject_GetAttrStr(obj, attr_name);
-#endif
- res = _PyType_Lookup(tp, attr_name);
- if (likely(res)) {
- descrgetfunc f = Py_TYPE(res)->tp_descr_get;
- if (!f) {
- Py_INCREF(res);
- } else {
- res = f(res, obj, (PyObject *)tp);
- }
- } else {
- PyErr_SetObject(PyExc_AttributeError, attr_name);
- }
- return res;
-}
-#else
-#define __Pyx_PyObject_LookupSpecial(o,n) __Pyx_PyObject_GetAttrStr(o,n)
-#endif
-
-/* PyObjectSetAttrStr.proto */
-#if CYTHON_USE_TYPE_SLOTS
-#define __Pyx_PyObject_DelAttrStr(o,n) __Pyx_PyObject_SetAttrStr(o, n, NULL)
-static CYTHON_INLINE int __Pyx_PyObject_SetAttrStr(PyObject* obj, PyObject* attr_name, PyObject* value);
-#else
-#define __Pyx_PyObject_DelAttrStr(o,n) PyObject_DelAttr(o,n)
-#define __Pyx_PyObject_SetAttrStr(o,n,v) PyObject_SetAttr(o,n,v)
-#endif
-
-/* None.proto */
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname);
-
-/* pyfrozenset_new.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyFrozenSet_New(PyObject* it);
-
-/* PySetContains.proto */
-static CYTHON_INLINE int __Pyx_PySet_ContainsTF(PyObject* key, PyObject* set, int eq);
-
-/* ListAppend.proto */
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
- PyListObject* L = (PyListObject*) list;
- Py_ssize_t len = Py_SIZE(list);
- if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {
- Py_INCREF(x);
- PyList_SET_ITEM(list, len, x);
- __Pyx_SET_SIZE(list, len + 1);
- return 0;
- }
- return PyList_Append(list, x);
-}
-#else
-#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
-#endif
-
-/* PySequenceContains.proto */
-static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) {
- int result = PySequence_Contains(seq, item);
- return unlikely(result < 0) ? result : (result == (eq == Py_EQ));
-}
-
-/* RaiseDoubleKeywords.proto */
-static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name);
-
-/* ParseKeywords.proto */
-static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\
- PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\
- const char* function_name);
-
-/* ArgTypeTest.proto */
-#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\
- ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\
- __Pyx__ArgTypeTest(obj, type, name, exact))
-static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact);
-
-/* GetItemInt.proto */
-#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
- (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
- __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\
- (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\
- __Pyx_GetItemInt_Generic(o, to_py_func(i))))
-#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
- (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
- __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
- (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL))
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
- int wraparound, int boundscheck);
-#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
- (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
- __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
- (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
- int wraparound, int boundscheck);
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
- int is_list, int wraparound, int boundscheck);
-
-/* IncludeStringH.proto */
-#include
-
-/* BytesEquals.proto */
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals);
-
-/* UnicodeEquals.proto */
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals);
-
-/* StrEquals.proto */
-#if PY_MAJOR_VERSION >= 3
-#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals
-#else
-#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals
-#endif
-
-/* RaiseTooManyValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
-
-/* RaiseNeedMoreValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
-
-/* IterFinish.proto */
-static CYTHON_INLINE int __Pyx_IterFinish(void);
-
-/* UnpackItemEndCheck.proto */
-static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected);
-
-/* ExtTypeTest.proto */
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type);
-
-/* HasAttr.proto */
-static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *);
-
-/* dict_getitem_default.proto */
-static PyObject* __Pyx_PyDict_GetItemDefault(PyObject* d, PyObject* key, PyObject* default_value);
-
-/* UnpackUnboundCMethod.proto */
-typedef struct {
- PyObject *type;
- PyObject **method_name;
- PyCFunction func;
- PyObject *method;
- int flag;
-} __Pyx_CachedCFunction;
-
-/* CallUnboundCMethod1.proto */
-static PyObject* __Pyx__CallUnboundCMethod1(__Pyx_CachedCFunction* cfunc, PyObject* self, PyObject* arg);
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_CallUnboundCMethod1(__Pyx_CachedCFunction* cfunc, PyObject* self, PyObject* arg);
-#else
-#define __Pyx_CallUnboundCMethod1(cfunc, self, arg) __Pyx__CallUnboundCMethod1(cfunc, self, arg)
-#endif
-
-/* CallUnboundCMethod2.proto */
-static PyObject* __Pyx__CallUnboundCMethod2(__Pyx_CachedCFunction* cfunc, PyObject* self, PyObject* arg1, PyObject* arg2);
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030600B1
-static CYTHON_INLINE PyObject *__Pyx_CallUnboundCMethod2(__Pyx_CachedCFunction *cfunc, PyObject *self, PyObject *arg1, PyObject *arg2);
-#else
-#define __Pyx_CallUnboundCMethod2(cfunc, self, arg1, arg2) __Pyx__CallUnboundCMethod2(cfunc, self, arg1, arg2)
-#endif
-
-/* py_dict_clear.proto */
-#define __Pyx_PyDict_Clear(d) (PyDict_Clear(d), 0)
-
-/* PyDictContains.proto */
-static CYTHON_INLINE int __Pyx_PyDict_ContainsTF(PyObject* item, PyObject* dict, int eq) {
- int result = PyDict_Contains(dict, item);
- return unlikely(result < 0) ? result : (result == (eq == Py_EQ));
-}
-
-/* SwapException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* RaiseNoneIterError.proto */
-static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void);
-
-/* PyIntBinop.proto */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_AndObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check);
-#else
-#define __Pyx_PyInt_AndObjC(op1, op2, intval, inplace, zerodivision_check)\
- (inplace ? PyNumber_InPlaceAnd(op1, op2) : PyNumber_And(op1, op2))
-#endif
-
-/* PyObjectGetMethod.proto */
-static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method);
-
-/* PyObjectCallMethod0.proto */
-static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name);
-
-/* UnpackTupleError.proto */
-static void __Pyx_UnpackTupleError(PyObject *, Py_ssize_t index);
-
-/* UnpackTuple2.proto */
-#define __Pyx_unpack_tuple2(tuple, value1, value2, is_tuple, has_known_size, decref_tuple)\
- (likely(is_tuple || PyTuple_Check(tuple)) ?\
- (likely(has_known_size || PyTuple_GET_SIZE(tuple) == 2) ?\
- __Pyx_unpack_tuple2_exact(tuple, value1, value2, decref_tuple) :\
- (__Pyx_UnpackTupleError(tuple, 2), -1)) :\
- __Pyx_unpack_tuple2_generic(tuple, value1, value2, has_known_size, decref_tuple))
-static CYTHON_INLINE int __Pyx_unpack_tuple2_exact(
- PyObject* tuple, PyObject** value1, PyObject** value2, int decref_tuple);
-static int __Pyx_unpack_tuple2_generic(
- PyObject* tuple, PyObject** value1, PyObject** value2, int has_known_size, int decref_tuple);
-
-/* dict_iter.proto */
-static CYTHON_INLINE PyObject* __Pyx_dict_iterator(PyObject* dict, int is_dict, PyObject* method_name,
- Py_ssize_t* p_orig_length, int* p_is_dict);
-static CYTHON_INLINE int __Pyx_dict_iter_next(PyObject* dict_or_iter, Py_ssize_t orig_length, Py_ssize_t* ppos,
- PyObject** pkey, PyObject** pvalue, PyObject** pitem, int is_dict);
-
-/* py_dict_values.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyDict_Values(PyObject* d);
-
-/* CallUnboundCMethod0.proto */
-static PyObject* __Pyx__CallUnboundCMethod0(__Pyx_CachedCFunction* cfunc, PyObject* self);
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_CallUnboundCMethod0(cfunc, self)\
- (likely((cfunc)->func) ?\
- (likely((cfunc)->flag == METH_NOARGS) ? (*((cfunc)->func))(self, NULL) :\
- (PY_VERSION_HEX >= 0x030600B1 && likely((cfunc)->flag == METH_FASTCALL) ?\
- (PY_VERSION_HEX >= 0x030700A0 ?\
- (*(__Pyx_PyCFunctionFast)(void*)(PyCFunction)(cfunc)->func)(self, &__pyx_empty_tuple, 0) :\
- (*(__Pyx_PyCFunctionFastWithKeywords)(void*)(PyCFunction)(cfunc)->func)(self, &__pyx_empty_tuple, 0, NULL)) :\
- (PY_VERSION_HEX >= 0x030700A0 && (cfunc)->flag == (METH_FASTCALL | METH_KEYWORDS) ?\
- (*(__Pyx_PyCFunctionFastWithKeywords)(void*)(PyCFunction)(cfunc)->func)(self, &__pyx_empty_tuple, 0, NULL) :\
- (likely((cfunc)->flag == (METH_VARARGS | METH_KEYWORDS)) ? ((*(PyCFunctionWithKeywords)(void*)(PyCFunction)(cfunc)->func)(self, __pyx_empty_tuple, NULL)) :\
- ((cfunc)->flag == METH_VARARGS ? (*((cfunc)->func))(self, __pyx_empty_tuple) :\
- __Pyx__CallUnboundCMethod0(cfunc, self)))))) :\
- __Pyx__CallUnboundCMethod0(cfunc, self))
-#else
-#define __Pyx_CallUnboundCMethod0(cfunc, self) __Pyx__CallUnboundCMethod0(cfunc, self)
-#endif
-
-/* DictGetItem.proto */
-#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY
-static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key);
-#define __Pyx_PyObject_Dict_GetItem(obj, name)\
- (likely(PyDict_CheckExact(obj)) ?\
- __Pyx_PyDict_GetItem(obj, name) : PyObject_GetItem(obj, name))
-#else
-#define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key)
-#define __Pyx_PyObject_Dict_GetItem(obj, name) PyObject_GetItem(obj, name)
-#endif
-
-/* SliceObject.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(
- PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop,
- PyObject** py_start, PyObject** py_stop, PyObject** py_slice,
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ChatGPT Academic Optimization
-
-**If you like this project, please give it a Star. If you've come up with more useful academic shortcuts or functional plugins, feel free to open an issue or pull request. We also have a [README in English](docs/README_EN.md) translated by this project itself.**
-
-> **Note**
->
-> 1. Please note that only **functions with red color** supports reading files, some functions are located in the **dropdown menu** of plugins. Additionally, we welcome and prioritize any new plugin PRs with **highest priority**!
->
-> 2. The functionality of each file in this project is detailed in the self-translation report [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) of the project. With the iteration of the version, you can also click on the relevant function plugins at any time to call GPT to regenerate the self-analysis report of the project. The FAQ summary is in the [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98) section.
->
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- [](http://colab.research.google.com/github/omertov/encoder4editing/blob/main/notebooks/inference_playground.ipynb)
-
-> Recently, there has been a surge of diverse methods for performing image editing by employing pre-trained unconditional generators. Applying these methods on real images, however, remains a challenge, as it necessarily requires the inversion of the images into their latent space. To successfully invert a real image, one needs to find a latent code that reconstructs the input image accurately, and more importantly, allows for its meaningful manipulation. In this paper, we carefully study the latent space of StyleGAN, the state-of-the-art unconditional generator. We identify and analyze the existence of a distortion-editability tradeoff and a distortion-perception tradeoff within the StyleGAN latent space. We then suggest two principles for designing encoders in a manner that allows one to control the proximity of the inversions to regions that StyleGAN was originally trained on. We present an encoder based on our two principles that is specifically designed for facilitating editing on real images by balancing these tradeoffs. By evaluating its performance qualitatively and quantitatively on numerous challenging domains, including cars and horses, we show that our inversion method, followed by common editing techniques, achieves superior real-image editing quality, with only a small reconstruction accuracy drop.
-
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